Behavioral Finance: Limited Rationality in Financial Markets 3739831197, 9783739831190

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Dr. Rolf J. Daxhammer is professor for Financial Markets at ESB Business School, Reutlingen University. Máté Facsar is Vice President Sales, Global Account Manager at FactSet, a global provider of integrated financial information and analytical applications. Zsolt Papp, Managing Director, is a senior investment specialist in the Global Fixed Income, Currency and Commodities group of J.P.Morgan Asset Management, a global leader in asset management service.

ISBN 978-3-7398-3119-0

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3rd ed.

Behavioral Finance

For students and practitioners alike, our book aims at opening the door to another perspective on financial markets: a behavioral perspective based on a Homo Oeconomicus Humanus. This agent acts with limited rationality when making decisions. He/she uses heuristics and shortcuts and is prone to the influence of emotions. This sounds familiar in real life and can be transferred to what happens in financial markets, too.

Daxhammer / Facsar / Papp

Over the last 50 years, neoclassical financial theory has been dominating our perception of what is happening in financial markets. It has spurred numerous valuable theories and concepts all based on the concept of Homo Economicus, the strictly rational economic man. However, humans do not always act in a strictly rational manner.

Rolf J. Daxhammer / Máté Facsar / Zsolt Papp

Behavioral Finance Limited Rationality in Financial Markets 3 rd edition

Behavioral Finance

Dr. Rolf J. Daxhammer is professor for Financial Markets at ESB Business School, Reutlingen University. His teaching and research interests are International Financial Markets, Investment Banking and Behavioral Finance. In his consulting work he is engaged in projects in Private Wealth Management und Financial Nudging, amongst others.

Máté Facsar is Vice President Sales for Management Consulting & Professional Services Firms at FactSet, a global provider of integrated financial information and analytical applications. His close cooperation with Leaders in Asset and Wealth Management over the last decade enables him to monitor the application of Behavioral Finance and to address the challenges Portfolio Managers and Wealth Advisors face.

Zsolt Papp, Managing Director, is a senior investment specialist in Global Fixed Income, Currency and Commodities group of J.P. Morgan Asset Management, a global leader in asset management services. He has 30 years’ experience in the financial industry in the UK and Switzerland on the sellside and buy-side, with a special focus on emerging markets.

Rolf J. Daxhammer Máté Facsar Zsolt Papp

Behavioral Finance Limited Rationality in Financial Markets 3rd edition

UVK Verlag · München

Umschlagmotiv: © deli - Fotolia.com

Bibliografische Information der Deutschen Nationalbibliothek Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über http://dnb.dnb.de abrufbar.

3rd Edition 2023 DOI: https://doi.org/10.24053/9783739881195

© UVK Verlag 2023 – ein Unternehmen der Narr Francke Attempto Verlag GmbH + Co. KG, Dischingerweg 5 · D-72070 Tübingen Das Werk einschließlich aller seiner Teile ist urheberrechtlich geschützt. Jede Verwertung außerhalb der engen Grenzen des Urheberrechtsgesetzes ist ohne Zustimmung des Verlages unzulässig und strafbar. Das gilt insbesondere für Vervielfältigungen, Übersetzungen, Mikroverfilmungen und die Einspeicherung und Verarbeitung in elektronischen Systemen. Alle Informationen in diesem Buch wurden mit großer Sorgfalt erstellt. Fehler können dennoch nicht völlig ausgeschlossen werden. Weder Verlag noch Autor:innen oder Herausgeber:innen übernehmen deshalb eine Gewährleistung für die Korrektheit des Inhaltes und haften nicht für fehlerhafte Angaben und deren Folgen. Diese Publikation enthält gegebenenfalls Links zu externen Inhalten Dritter, auf die weder Verlag noch Autor:innen oder Herausgeber:innen Einfluss haben. Für die Inhalte der verlinkten Seiten sind stets die jeweiligen Anbieter:innen oder Betreibenden der Seiten verantwortlich. Internet: www.narr.de eMail: [email protected] CPI books GmbH, Leck

ISBN 978-3-7398-3119-0 (Print) ISBN 978-3-7398-8119-5 (ePDF) ISBN 978-3-7398-0586-3 (ePub)

Preface 3rd Edition The most obvious adjustment in the 3rd edition of “Behavioral Finance” is its language. This is the first time, that it is available in English, too. And with the additional language comes another author: Zsolt Papp. He brings the weight of thirty years in the financial industry to the team, adding even more “reality check” to the book’s blend of theoretical rigour and practical perspective. As for all good economists the prime motivation for offering a product variation is demand. Over the last ten years we have learnt that our approach to insights into Behavioral Finance is not only appreciated by a German speaking audience, but by an international one, too. So, the basic concept of the book remains unchanged. In addition, we have added some up-to-date information especially in chapter 5 on historic and recent speculative bubbles and in chapter 13 on latest developments. With all that we hope that the reader will enjoy this 3rd edition as much as the previous ones. December 2022: Rolf Daxhammer, Máté Facsar and Zsolt Papp

Online Knowledge Check available: https://files.narr.digital/9783739831190/Check.zip

Dedication To Gela Daxhammer as well to Josef Daxhammer and Katharina Daxhammer To Fanny Facsar and Gábor Facsar To Mária Erzsébet and Sándor Papp and Marcsi

Table of Content Preface 3rd Edition ................................................................................................................ 5 Dedication .............................................................................................................................. 7 Introduction ......................................................................................................................... 15 Section I − The Homo Economicus in the center of Traditional Finance ... 21

1

How Neoclassical Theory shaped rational economic behavior ..... 21

1.1

From Traditional Finance to Emotional Finance ........................................... 22

1.2

Classical theories of Traditional Finance ........................................................ 29

1.2.1

The rational economic market participant according to Smith ................. 29

1.2.2

Random Walk Theory according to Bachelier ............................................... 31

1.2.3

Expected Utility Theory according to von Neumann & Morgenstern ..... 36

1.2.4

Information processing according to Bayes ................................................... 40

1.2.5

Efficient Market Hypothesis according to Fama ........................................... 42

Summary Chapter 1 ........................................................................................................... 48 2

Limitations of Traditional Finance ........................................................... 51 Models of Neoclassical Capital Market Theory ............................................. 51 Portfolio Selection Theory ................................................................................. 51 Capital Asset Pricing Model (CAPM) .............................................................. 58 Arbitrage Pricing Theory as an alternative to CAPM.................................. 62

2.2

Valuation methods as a basis for financial decisions ................................... 64

2.2.1

Fundamental Analysis......................................................................................... 65

2.2.2

Technical Analysis ............................................................................................... 70

2.3

Old vs. new reality ‒ the Black Swan .............................................................. 75

Summary Chapter 2 ........................................................................................................... 78 Concluding remarks Section I ......................................................................................... 79 Section II ‒ Recurring speculative bubbles ‒ triggered by the Homo Economicus Humanus ........................................................................ 81

3

Investor behavior from the perspective of Behavioral Finance .... 81

3.1

Starting point and objective of Behavioral Finance...................................... 81

3.1.1

Evolving concept of rationality......................................................................... 84

3.1.2

Departure from the Expected Utility Theory ‒ Bounded Rationality ........ 88

3.2

Change of perspective within the framework of Behavioral Finance ...... 90

10

Table of Content

3.2.1

Comparison of neoclassical and behavioral capital market theory ...........90

3.2.2

Research methods of Behavioral Finance ........................................................94

3.2.3

The investor in the course of time ....................................................................97

Summary Chapter 3......................................................................................................... 100 4

Speculative bubbles as a sign of market anomalies.......................... 101

4.1

Causes of speculative bubbles and their intensification ........................... 101

4.1.1

Herding ................................................................................................................ 104

4.1.2

Limits of arbitrage ............................................................................................. 107

4.2

Anatomy of speculative bubbles according to Kindleberger & Minsky 111

4.3

Detailed review of bubbles and market anomalies..................................... 114

4.3.1

Significance of speculative bubbles for economies .................................... 115

4.3.2

Types of speculative bubbles ........................................................................... 116

4.3.3

Types of capital market anomalies ................................................................ 118

Summary Chapter 4......................................................................................................... 125 5

Speculative bubbles from the 17th to 21st century ............................. 127

5.1

Benoit Mandelbrot’s market characteristics ................................................ 128

5.2

Examples of significant speculative bubbles................................................ 130

5.2.1

The Tulip Mania of 1636 .................................................................................. 131

5.2.2

The Mississippi bubble of 1716 ....................................................................... 134

5.2.3

The stock market boom and crash of 1929................................................... 138

5.2.4

The dot-com speculative bubble of the late 1990s...................................... 141

5.2.5

The U.S. real-estate credit bubble between 2001 and 2006 ....................... 146

5.2.6

Speculative bubbles after the U.S. mortgage crisis ..................................... 153

5.3

Indications of speculative bubbles in Private Equity ................................. 162

Summary Chapter 5......................................................................................................... 168 Concluding remarks Section II...................................................................................... 169 Section III – The Homo Economicus Humanus within the information and decision-making process ......................................................... 171

6

Information and Decision-Making Process .................................... 171

6.1

Phases of the information and decision-making process.......................... 171

6.1.1

Information perception .................................................................................... 172

6.1.2

Information Processing/Evaluation ............................................................... 176

6.1.3

Investment Decision.......................................................................................... 178

Table of Content

11

6.2

Basis of decision-making from the perspective of Behavioral Finance .. 180

6.2.1

Decision-making based on Prospect Theory................................................ 180

6.2.2

Features of the valuation functions................................................................ 184

6.2.3

Valuation of securities based on the Prospect Theory ............................... 187

Summary Chapter 6 ......................................................................................................... 192 7

Limited rationality during information perception ........................ 193

7.1

Heuristics of cognitive origin .......................................................................... 195

7.1.1

Misperception of probabilities......................................................................... 195

7.1.2

Misinterpretation of information ................................................................... 202

7.2

Heuristics of emotional origin ........................................................................ 207

7.3

Assessment of the risk/return-harmfulness of reviewed heuristics ....... 210

Summary Chapter 7 ......................................................................................................... 212 8

Limited rationality during information processing ........................ 213

8.1

Heuristics of cognitive origin .......................................................................... 214

8.1.1

Misperception of probabilities......................................................................... 214

8.1.2

Misperception of information ......................................................................... 222

8.1.3

Misperception of objective reality .................................................................. 223

8.1.4

Misperception of one’s own abilities ............................................................. 228

8.2

Heuristics of emotional origin ....................................................................... 234

8.3

Assessment of the risk/return-harmfulness of the heuristics considered .................................................................................................................. 235

Summary Chapter 8 ......................................................................................................... 237 9

Limited rationality during decision-making...................................... 239

9.1

Heuristics of cognitive origin .......................................................................... 240

9.1.1

Misperception of objective reality .................................................................. 240

9.1.2

Misperception of own abilities ........................................................................ 242

9.2

Heuristics of emotional origin ........................................................................ 245

9.2.1

Misperception of objective reality .................................................................. 245

9.2.2

Misperception of one’s own abilities ............................................................. 254

9.3

Assessment of the risk-/return-harmfulness of the considered heuristics ......................................................................................................................... 260

9.4

Overview of the heuristics considered in the information and decisionmaking process ................................................................................................... 262

Summary Chapter 9 ......................................................................................................... 264 Concluding remarks Section III..................................................................................... 265

12

Table of Content

Section IV – Applications of Behavioral Finance and Recent Developments ................................................................................................. 267

10

Applications of Behavioral Finance in Wealth Management....... 267

10.1

Overview of limited rational behavior in investment advice ..................... 268

10.2

Dealing with heuristics in investment advice ............................................. 273

10.2.1 Applied heuristics during information perception..................................... 275 10.2.2 Applied heuristics during information processing..................................... 277 10.2.3 Applied heuristics during decision-making ................................................. 280 Summary Chapter 10 ...................................................................................................... 284 11

Application of Behavioral Finance in corporate governance....... 285

11.1

Overconfidence in entrepreneurial investment decisions ........................ 285

11.2

Dividend policy from the perspective of Behavioral Finance .................. 291

11.3

Initial Public Offerings from the perspective of Behavioral Finance ..... 296

11.4

Corporate Governance from the perspective of Behavioral Finance ..... 298

11.5

Equity Premium Puzzle .................................................................................... 303

Summary Chapter 11 ...................................................................................................... 304 12

Financial Nudging ‒ behavioral approaches for better financial decisions............................................................................................................. 307

12.1

Libertarian Paternalism .................................................................................... 307

12.1.1 Choice architecture ........................................................................................... 308 12.1.2 Freedom of choice and paternalism ............................................................... 309 12.1.3 Types and characteristics of nudging ............................................................ 310 12.1.4 Criticism of libertarian paternalism .............................................................. 315 12.2

Financial nudging approaches ........................................................................ 317

12.2.1 Behavioral science foundations of financial nudging ................................ 318 12.2.2 Personal Loans ................................................................................................... 320 12.2.3 Credit Cards ........................................................................................................ 321 12.2.4 Mortgages ............................................................................................................ 323 12.2.5 Pension provisions ............................................................................................ 324 12.2.6 Shares and bonds ............................................................................................... 328 Summary Chapter 12 ...................................................................................................... 331 13

Further development of Behavioral Finance ‒ a look into the future .................................................................................................................. 333

13.1

Limits of Behavioral Finance........................................................................... 333

Table of Content

13.2

13

Emergence of Neurofinance/Neuroeconomics ............................................ 335

13.2.1 Research on the human brain .......................................................................... 337 13.2.2 Decision processes from the perspective of Neurofinance ....................... 341 13.3

Origin of Emotional Finance ........................................................................... 347

13.3.1 Emotions as a basis for investment decisions .............................................. 349 13.3.2 Interpretation of market movements from an Emotional Finance perspective ........................................................................................................... 353 Summary Chapter 13 ....................................................................................................... 358 Concluding remarks Section IV .................................................................................... 359 Glossary ........................................................................................................................... 361 Literature ........................................................................................................................ 373 Books ............................................................................................................................... 373 Journals and Essays ...................................................................................................... 379 Websites ........................................................................................................................... 393 Biographies ..................................................................................................................... 399 Index ................................................................................................................................. 401

Introduction Thousands of business school students around the world are learning to assess the risks of investments and calculate expected returns using Harry Markowitz’ portfolio theory or William Sharpe’s capital asset pricing model. The Swedish Nobel Committee has awarded many prizes for the underlying scientific achievements and the concepts and models of neoclassical capital market theory are widely used in the practice of portfolio managers and CFOs. What are these models based on? To what extent are they able to reflect reality? Can market participants (primarily buyers and sellers in the financial markets) really be expected to follow the concepts and models and to include them in their financial decisions? The concepts and models of traditional economics illustrate what the majority of economists still assume: the existence of fundamentally efficient markets. According to this assumption, manias, panics, or crashes on the capital market cannot occur, at least not systematically, because markets react to new information efficiently and, thus, result in the best, pareto-efficient allocation of resources. This view is increasingly questioned with the analysis of speculative bubbles in the second section of this book. The cryptocurrency hype in 2020/2021 as the latest major example of speculative market developments is an example of the existence of fundamental limits to rational markets. Thus, over the course of the centuries, time and again speculative bubbles developed, because the market participants fell into “irrational exuberance” and bought, for example, even when they could already guess that the speculative objects were clearly overvalued. Over the past 40 years, Behavioral Finance research has produced numerous results according to which, when making financial decisions, we are guided by our emotions or simple rules of thumb rather than by strictly rational motives. Daniel Kahneman, one of the best-known researchers in the field of Behavioral Finance, received the Nobel Prize in 2002 for his insights into decisions under uncertainty. With the help of magnetic resonance tomographs, it was shown that the cerebellum is often the most active part of the brain when it comes to financial decisions ‒ it is linked to emotions and connects us evolutionarily, for example, with reptiles. It is therefore not surprising that our brain occasionally takes shortcuts in order to be able to make decisions more quickly more easily. Behavioral Finance is based on the insight that market participants are only capable of limited rational behavior due to psychological, mental, and neural limitations. This goes against the assumption of rationality in the theory of expected utility. The concept of widespread limited rationality is a central component and starting point of Behavioral Finance research. It also contradicts the assumption that even if there was limited rational behavior of a few individuals it would be neutralised due to the heterogeneity of market participants. Therefore, limited rationality should not be reflected in the market outcome. Rather, the proponents of

16

Introduction

Behavioral Finance expect a paradigm extension, which supplements the economic concepts and principles of neoclassical capital market theory with psychological, sociological, and neurological aspects. The first section of this book is devoted to the behaviors expected from market participants within the framework of neoclassical theory. The study of the assumptions on which the individual concepts and models of neoclassical capital market theory are based is crucial in order to be able to apply them in the subsequent sections to classify and interpret the actual behavior of market participants. The second section provides an overview of the development of Behavioral Finance as a new field of research in order to be able to interpret and explain the behavior of market participants (primarily investors on the financial markets). In the sense of the paradigm expansion mentioned above, doubts are growing as to whether the behavior of market participants can be explained using the traditional theory alone. In the third section it is to be clarified how the market participant, in the person of the investor from the viewpoint of asset management, simplifies decisions by using heuristics. In addition, it will be discussed which influences leading to suboptimal decisions can have an impact on the investor in the decision-making process. In this context, the limited rational behavior of market participants is examined from the perspective of wealth management (financial advice for highnet-worth individuals) and, where appropriate, from the perspective of the private equity investment process. The focus is on the phases of decision-making. It is shown which heuristics are used by investors and investment advisors in the different phases of the decision-making process. The aim of the explanations given is to point out limited rational behavior by findings which, according to the current state of research, are responsible for the observable behavior of market participants. It should be expressly pointed out that Behavioral Finance research in this area in particular is subject to ongoing development. The fourth and last section focuses on the application of the findings from Behavioral Finance in selected subject areas. The focus here is on investment advice in wealth management, the strategic decisions of corporate leaders and financial nudging. In addition, the fourth section will provide an outlook on future research directions and introduce new areas such as Neurofinance and Emotional Finance. These two research areas have already contributed to the investigation of the causes of limited rational behavior. They investigate amongst others processes that have so far been running unconsciously, such as emotions, fantasies, and fears. And they put them into the centre of financial market decisions. The book is divided into a total of thirteen chapters. The following information provides a first overview of the topics covered and the contents conveyed. In the first chapter, the decision theories and concepts of rational decision-making are at the forefront. After working through the chapter, you will learn about the development of economic perspectives, starting from classical economics to emotional finance. In the first subchapter you will be able to follow the everchanging integration of psychology into economics. In addition to looking at the

Introduction

17

individual perspectives, you will learn about the fundamental decision theories and concepts of neoclassical capital market theory. Here, the focus is on the concept of homo economicus as well as on the behavioral patterns postulated based on neoclassical capital market theory. When studying the decision theories and concepts, you will recognize clear deviations from the actual behavior of market participants, which can increasingly be viewed as a motivation for a paradigm expansion through Behavioral Finance. In the second chapter you will learn about the models of neoclassical capital market theory that are used to determine the expected return and the risk of securities. In addition, you will learn about the valuation approaches used in financial decisions based on fundamental and technical analysis. After working through this chapter, you will understand the increasing criticism of the listed models and you will also gain an insight into real market conditions that are difficult to reconcile with neoclassical capital market theory. The third chapter is devoted to the Homo Economicus Humanus ‒ the market participant who symbolizes the paradigm shift towards Behavioral Finance. As you work through this chapter, you will learn about the objectives and development of Behavioral Finance. On the other hand, you will get to know the market participant as an investor acting rationally only to a limited extent. The fourth chapter focuses on speculative bubbles as signs of recurring and persistent market anomalies. In addition to the origin and causes of the formation of speculative bubbles, you will learn about the different phases and types of speculative bubbles. Furthermore, you will be able to classify the role of the herd instinct as the driving force of speculative bubbles in the structure of recurring market anomalies. Finally, you will encounter other significant capital market anomalies, some of which are short-lived, while others are medium- to long-term capital market anomalies. The fifth chapter is devoted to historical speculative bubbles. After working through this chapter, you will know the most important speculative bubbles in the history of the financial markets and you will understand typical characteristics of the capital markets that can lead to turbulence. You will also be able to explain the development of historical bubbles based on the Kindleberger/Minsky five-phase model and you will apply it to current and future bubbles. In the sixth chapter, you will learn the basis of the information and decisionmaking process and you will understand which perceptual disturbances can prevent market participants from absorbing and processing information. You will also learn the basis of decision-making from the perspective of Behavioral Finance: The Prospect Theory as the alternative to traditional expected utility theory. You will understand how, on the one hand, the S-shaped value function is used to describe the market participant’s attitude to risk and, on the other hand, how the weighting function is used to transform objective probabilities into subjective ones. These two approaches will illustrate the valuation of securities based on Prospect Theory and show the cognitive limitations of market participants.

18

Introduction

The seventh chapter focuses on the behavior of market participants during information perception, the first phase within the information and decision-making process. You will learn about the cognitive and emotional heuristics that facilitate the perception of information, but make it difficult for market participants to gain an objective view of the capital market. In this and the following chapters 8 and 9 you will also be able to recognise the effects of the heuristics on the behavior of the market participant and you will classify the risk-/return damaging effect of each individual heuristic. The eighth chapter deals with the second process stage in the information and decision-making process: information processing. In this phase, too, market participants use certain heuristics which can lead to limited rational behaviour. In this chapter you will learn about the most important heuristics that facilitate but also distort information processing and evaluation for the Homo Economicus Humanus. In the ninth chapter you will explore the third and final stage of the information and decision-making process. You will learn about the essential heuristics used during decision-making and you will be able to understand the limited rational behavior of the Homo Economicus Humanus. In the tenth chapter, you will recognize the intensity to which both financial advisors and their clients can be influenced in their decision-making by the application of heuristics. You will identify possibilities to limit risk-/return damaging behavior depending on the wealth of the investor and the origin of the heuristics. In addition, this chapter will present measures for each individual heuristic that aim to increase the quality of advice (in the sense of a customer-oriented presentation of returns and risks). The eleventh chapter focuses on limited rational behavior in the context of corporate decision-making. You will get to know the drivers of limited rational behavior, such as overestimating the self-confidence of corporate leaders, and you will be able to classify their effects on the development of the overall profitability of corporates. In addition, you will look at certain entrepreneurial activities from the perspective of Behavioral Finance and thus recognize how strongly psychological influences can influence corporate decisions. In addition to dividend policy and the initial issue of shares, the impact of different remuneration concepts within the framework of corporate governance will also be considered. The chapter is rounded off with a discussion of the “Equity Premium Puzzle" from a Behavioral Finance perspective. In the twelfth chapter, a rather new application of the Behavioral Finance findings is presented. This involves identifying and presenting approaches to how, from an economic policy perspective, people can be persuaded to make better decisions about financial products and services. To this end, so-called nudges on loans, credit cards, mortgages, retirement provisions and shares/bonds are explained. “Libertarian paternalism" forms the theoretical framework for this and is therefore discussed in detail in the chapter.

Introduction

19

In the thirteenth and closing chapter, an outlook on new research directions within Behavioral Financial market research will be given. New ideas and correspondingly new research results have already shifted the existing boundaries of Behavioral Finance. In this sense, the chapter leads to the mentioned boundaries and then presents two new research directions within Behavioral Finance research. The focus is on Neurofinance, which aims to investigate the causes of limited rational behavior on the basis of brain research. In addition, Emotional Finance is presented as a new research area, in which unconscious mental processes can be explored. Note: Central core statements are framed in grey.

Section I − The Homo Economicus in the center of Traditional Finance

1

How Neoclassical Theory shaped rational economic behavior The first chapter explores the development of changing perspectives on market participants from the Traditional Finance to Behavioral Finance. It presents an overview on the fluctuating influence of psychology in economics on the one hand, and the normative decision theories and concepts of the Neoclassical Theory on the other hand. The focus of this first chapter lies on the concept of the Rational Economic Market Participant also called the Homo Economicus. When exploring the decision theories and concepts, you will recognize significant deviations from the expected behavior of market participants, which can increasingly be interpreted as an impulse for a paradigm shift through the rapidly expanding field of Behavioral Finance.

Let us imagine a theatre stage play for investment decisions in the financial markets. First, we see the proponents of traditional finance ‒ a group of rationally acting protagonists also referred to as Homo Economicus; the emotional market participant (also called Homo Economicus Humanus) does not appear in the stage play. Rather, the protagonists in this play make perfectly rational decisions, apply unlimited analytical capacities to any available information and align their preferences according to the →Expected Utility Theory. As such this play is likely to be met with a good dose of disbelief by the audience who might be looking for a script with more credible protagonists. Here, the proponents of Behavioral Finance enter, are replacing the Homo Economicus with a market participant who is more in line with reality, with observed decision-making, and who occasionally succumbs to speculative fever. In short, the proponents of Behavioral Finance are intending to put a more realistic play on stage. This involves characters who seemingly are prone to repeat past errors. For instance, some would compare the incredible rally in cryptocurrencies in 2020/21 to the infamous tulip mania in the 17th century in the Netherlands, when investors supposedly were willing to bet entire farms on rising tulip bulb prices (newest insights in chapter 5 will help to reflect on a more realistic view of the tulip mania). Cryptocurrencies will be reviewed in chapter 5 as well. Richard Thaler, one of the central protagonists of Behavioral Finance1, recorded the smouldering conflict about the real market participant at a conference of the 1

Also spelled Behavio(u)ral Finance in the literature

22

1 How Neoclassical Theory shaped rational economic behavior

National Bureau of Economic Research (NBER) with Robert Barro, advocate of the traditional view, as follows: „The difference between us is that you assume people are as smart as you are, while I assume people are as dumb as I am.“ (Thaler, quoted after Robert Bloomfield, 2010, p. 23) Following the above quote, the aim of the first two chapters is to guide you through the debate on the fundamental assumptions regarding the behavior of market participants and at the same time to suggest possible starting points for adjustments to the traditional framework of →Neoclassical Economics. 1.1

From Traditional Finance to Emotional Finance

The development of economic sciences and its fundamental assumptions about human behavior has been shaped by the views of leading scientists. Depending on the prevailing opinion, psychological influences on the decision-making of market participants were followed with varying intensity. They played an important role in the age of Classical Economics but were subsequently largely suppressed until the emergence of Behavioral Finance. It is therefore not surprising that the theoretical framework of rational behavior developed in the era of Neoclassical Economics is still reflected in the concepts and models applied today. Development of Economic Sciences

Fig. 1: Development of Economic Sciences

18th - 19th Century – The Age of Classical Economics In the middle of the 18th century, economists began to analyze human influences on decision-making. These beginnings formed the basis for the emergence of behavioral research in financial markets. One tried to combine the economic benefits

1.1 From Traditional Finance to Emotional Finance

23

of consumption with psychological approaches. Adam Smith2 was instrumental in the development of Classical Economics. In his much-acclaimed essay “The Theory of Moral Sentiments" in 1759, he used social psychology to describe the foundation of human morality, with the goal to moderate one’s behavior and preserve harmony. His book “An Inquiry into the Nature and Causes of the Wealth of Nations” in 1776 is perceived as one of the most influential books ever written and equated with the beginning of Classical Economics. It laid the intellectual foundation of the great 19th century era of free trade and economic expansion. Consequently, the common sense of free trade is accepted worldwide even though this could be questioned today considering the various global trade disputes. National wealth was defined in Smith’s days in terms of a country’s reserve of gold and silver that shall not be reduced through importing goods from other countries. Protectionism through taxes on imports and protection of domestic industries were common practice. Smith argued that markets were best kept free from governmental influence and are guided by an invisible hand. The selfregulation of market forces should almost automatically lead to equilibrium and full employment. The basis of this way of thinking was human action, which was based solely on economic motives and rational considerations. In addition, Smith was of the opinion that a nation’s wealth is not the quantity of precious metals but the total amount of its production and commerce – a term we call today GDP or Gross Domestic Product (see Adam Smith Institute, 2021). Psychology experienced its upswing in the 19th century, when science started to be applied to it. Hermann Ebbinghaus3 pioneered in the development of experimental methods and made outstanding contributions to the research of learning and memory. He showed that memories have different life cycles. Some are shortlived, others last for days or even weeks and remain stored in the long-term memory. His research showed that scientific methods could be applied to the study of the higher thought process (see Britannica, 2021). In the middle of the 19th century, the widespread observation of animal behavior followed, based on Charles Darwin’s4 assumption that mental characteristics of mammals are similar to each other. From the 20th Century on – The Age of Neoclassical Economics In the course of the 20th century, Classical Economics was replaced by Neoclassical Economics. The central assumption of neoclassical economics was the model of the Rational Economic Market Participant or better known as the Homo Economicus, which presents market participants as rational, benefit oriented and fully informed individuals (see chapter 1.2.1). As a result, the attempt to explain the investment behavior of market participants through psychology was largely suppressed. 2

Adam Smith | Scottish economist | 1723-1790

3

Hermann Ebbinghaus | German psychologist | 1850-1909

4

Charles Darwin | English biologist | 1809-1882

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1 How Neoclassical Theory shaped rational economic behavior

Initially, however, investment decisions were not considered from a scientific point of view, but rather as art. Even John M. Keynes5 saw investing in companies primarily as speculation and compared the stock market with a beauty contest. „It is not a case of choosing those [faces] that, to the best of one’s judgment, are really the prettiest, nor even those that average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practice the fourth, fifth and higher degrees.“ (Keynes, quoted after Montier, 2007, p. 91) The beginning of the development of the Neoclassical Economics is associated with the doctoral thesis “The Theory of Speculation” by Louis Bachelier6 in 1900. Bachelier is credited to be the first person to model the stochastic process under which equity prices evolve. His finding, that price movements follow a random process was the basis for the Random Walk Theory (see chapter 1.2.2) ‒ the theory according to which, stock prices move upwards or downwards without “memory", i.e., independently of historic prices (see Gehrig/Zimmermann, 1999. p. 5 and Mandelbrot/Hudson, 2004. p. 87). Most of the decision-making theories and concepts used as a basis for rational behavior had been developed during the Great Depression of 1929 followed by the burst of the speculative bubble of the golden twenties (see chapter 5.2.4). For example, the theory of efficient capital markets developed when Alfred Cowles7 first systematically analyzed the predictability of security prices in the 1930s. The hypothesis that security prices are not predictable according to the random walk theory was finally operationalized and empirically tested by Holbrook Working8 in the 1940s. Today, with the vast amount of data and the surge of artificial intelligence and machine learning, even the weakest signals are explored to predict future price developments. In 1936, another attempt to incorporate psychological influences into the decisionmaking of market participants became apparent. In his work “General Theory of Employment, Interest and Money", John M. Keynes9 argued that the economy is not dominated by rational market participants alone, who pursue economic advantages as if guided by an invisible hand. While acknowledging that economic action is largely determined by economic motives, he countered this by saying that it is often also influenced by instincts. These instincts, which he referred to

5

John Maynard Keynes | British economist | 1883-1946

6

Louis Bachelier | French mathematician | 1870-1946

7

Alfred Cowles | American economist | 1891-1984

8

Holbrook Working | American economist | 1895-1985

9 When assigning the protagonists mentioned, the chronological aspect is in the foreground

and not necessarily the content-related assignment, as is quite obviously the case with John M. Keynes.

1.1 From Traditional Finance to Emotional Finance

25

as animal spirits, were an important cause of economic fluctuations and involuntary unemployment. Keynes was convinced that economies, which are left to their own, were prone to excesses. Manias occur, which in turn lead to outbreaks of panic. He believed that the state should play an appropriate role in regulating the markets and counteract excesses caused by animal spirits. From the 1960s – The Age of Keynesian Economics Subsequently, and particularly in the 1960s and 1970s, Keynes’ General Theory was “post-Keynesianized” in that Animal Spirits were almost completely removed. The result was a theory that narrowed the differences between the General Theory and the standard statements of Neoclassical Economics to such an extent that there was hardly any room left for investment behavior based on instincts. The neoclassics of the 1960s believed that instincts should be completely removed from economic theory (see Shiller 2009, pp. 8). Based on the findings of Louis Bachelier, Eugene F. Fama10 developed the Efficient Market Hypothesis in the 1960s (see chapter 1.2.5). It describes a market as efficient if share prices reflect all available information. Thus, consistent generation of alpha (excess returns) is impossible. At the same time, however, the rationality of individuals was increasingly questioned by the experiments of Maurice Allais11 in 1953 and Daniel Ellsberg12 in 1961. The initial results of the experiments are as a matter of fact regarded as the basis for Behavioral Finance. The experiments made it clear that individuals violated the axioms developed earlier in the 1940s by John von Neumann13 and Oskar Morgenstern14 to underpin the Bernoulli Principle of rational investors (see chapter 1.2.3). In the area of collective rational behavior or collective rationality, the contribution of John F. Muth15 are to be highlighted. He developed the concept of rational expectations. Muth states in his article “Rational Expectations and the Theory of Price Movements (1961)” that market participants use all available information to form their expectations and learn from their expectation mistakes. Expectations are created by constantly updating and reinterpreting the available information. The Portfolio Selection Theory developed in 1952 by Harry M. Markowitz16 was acknowledged as a key milestone for the development of models in Neoclassical Economics (see chapter 2.1.1). The core idea of the theory is the development of efficient portfolios by considering the correlation of individual securities (see

10

Eugene Francis Fama | American economist | born 1939 Maurice Allais | French economist and Nobel Prize Winner 1988 | 1911-2010 12 Daniel Ellsberg | American economist | born 1931 13 John von Neumann | Hungarian-American mathematician | 1903-1957 14 Oskar Morgenstern | German economist | 1902-1977 15 John Fraser Muth | American economist | 1930-2005 16 Harry Max Markowitz | American economist and Nobel Prize Winner 1990 | born 1927 11

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1 How Neoclassical Theory shaped rational economic behavior

Karlen, 2004, p. 13). However, Markowitz’ theory was only the beginning of a development away from a purely descriptive to a normative capital market theory. Building on Markowitz’s portfolio theory, William F. Sharpe17, John V. Lintner18 and Jan Mossin19 independently developed the well-known Capital Asset Pricing Model (CAPM) in the 1960s (see chapter 2.1.2). The model became a fundamental tool in the modern portfolio theory as it allowed to track the different risks of investments back to an easily understandable, linear relationship (see Garz/Günther/Moriabadi, 2002, pp. 17). It is a mathematical model with the goal to describe how securities prices should be based on their relative riskiness compared to the return on risk-free assets (see Baker/Nofsinger, 2010, p. 136.). In 1976 the CAPM was challenged by the Arbitrage Pricing Theory (APT) developed by Stephen A. Ross20 (see chapter 2.1.3). In contrast to the CAPM, this theory considers multiple risk factors of systematic nature and therefore is closer to reality. According to its name, price information is derived from arbitrage opportunities (see Bank/Gerke, 2005, pp. 4). A further milestone was the work of Franco Modigliani21 and Merton H. Miller22 in the field of Corporate Finance Theory in 1958, which showed that, assuming an efficient and perfect capital market, the capital structure from equity and debt capitalization is irrelevant for the level of capital costs. The reason for the irrelevance lies in the constant total capital costs, which do not change regardless of the amount of debt in a perfect and efficient market. With a higher level of indebtedness, the cost of equity capitalization increases, but it only relates to a smaller share of capital. At the same time, the share of debt capitalization increases, and the lower and constant costs of debt financing compared to equity relate to a higher share of capital and thus fully compensate for the higher costs of equity capitalization. The respective costs of equity and debt capitalization as well as their proportions change exactly in such a way that the effects compensate each other and thus have no influence on the level of the total cost of capital in an efficient and perfect market. Finally, a ground-breaking innovation in the field of derivatives (options) valuation was made by Fischer S. Black23, Myron S. Scholes24 and Robert C. Merton25 in the early 1970s with the development of the option pricing formula. The three scientists based their findings on the research of Markowitz, Modigliani and 17

William Forsyth Sharpe | American economist | born 1934 John Virgil Lintner | American economist | 1916-1983 19 Jan Mossin | Norwegian economist | 1936-1987 20 Stephen Alan Ross | American economist | 1944-2017 21 Franco Modigliani | Italian-American economist & Nobel Prize Winner 1985 | 1918-2003 22 Merton Howard Miller | American economist & Nobel Prize Winner 1990 | 1923-2000 23 Fischer Sheffey Black | American economist | 1938-1995 24 Myron Samuel Scholes | Canadian-American economist & Nobel Prize Winner 1997 | born 1941 25 Robert Cox Merton | American economist & Nobel Prize Winner 1997 | born 1944 18

1.1 From Traditional Finance to Emotional Finance

27

Miller by constructing a risky portfolio consisting of a loan and the underlying security, mirroring the cash-flows associated with the option and thus ultimately opening the door to valuing derivatives. From the 1980s to present – The Age of Behavioral Finance/Economics From around 1980, Behavioral Economics developed as a sub-field of economics. This direction was instrumental in increasingly incorporating sociological and psychological aspects to economic sciences. Behavioral Economics examines behavioral patterns of market participants that are inconsistent with the concept of Homo Economicus ‒ for example, the rejection of utility maximisation. Although most of the findings from research on the actual observable behavior of market participants did not come to light until after 1980, two new fields of scientific research developed as early as 1950 and are considered the basis of Behavioral Finance. On the one hand, scientists in the field of Cognitive Psychology began to analyze mental processes that seemed to be responsible for human behavior (see chapters 6-9). The central component and starting point of Behavioral Finance is the Theory of Bounded Rationality by Herbert A. Simon26 from the mid1950s onwards. According to this theory, market participants are only capable of limited rational behavior. On the other hand, decision-making under uncertainty received considerable attention when Daniel Kahneman27 and Amos N. Tversky28 developed the →Prospect Theory (1979, 1992) which became the intellectual foundation for Behavioral Finance (see Pompian, 2006, pp. 20). With their experiments, the two Israeli psychologists attempted to classify the previously unexplainable deviations from the ideal image of the Homo Economicus. The increased focus on emotional and cognitive driven behavior of the market participants, almost simultaneously gave birth to the emergence of Behavioral Finance as a new specific field of research on the decision-making process of individuals. It attempts to explain what happens on the financial markets by taking human behavior into account. It examines which factors lead to a different evaluation of information and consequently to a deviating decision-making from the assumptions made by traditional finance. Based on these insights, Behavioral Finance attempts, among others, to make forecasts about the future behavior of market participants. Daniel Kahneman and Vernon L. Smith29 both share the Nobel Prize in Economic Sciences in 2002. Amos N. Tversky died in 1996 and could not receive the Nobel Prize posthumously (see Blechschmidt, 2007, pp. 11). Another important contributor in the field of Behavioral Finance is the American economist and Nobel Prize Winner of 2017 Richard H. Thaler30. His main inter-

26

Herbert Alexander Simon | American economist & Nobel Prize Winner 1978 | 1916-2001 Daniel Kahneman | Isreali psychologist and economist & Nobel Prize Winner 2002 | born 1934 28 Amos Nathan Tversky | Israeli psychologist | 1937-1996 29 Vernon Lomax Smith | American economist & Nobel Prize Winner 2002 | born 1927 30 Richard H. Thaler | American economist & Nobel Prize Winner 2017 | born 1945 27

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1 How Neoclassical Theory shaped rational economic behavior

est was the investigation of decision anomalies as systematic deviations from rational behavior (see Wahren, 2009, p. 45). Next, operant conditioning, in which the learning process is accomplished by trial and error, resulted from the research findings of Edward L. Thorndike31 and formed a further basis of Behavioral Financial market research. The psychology of learning based on these experiments developed over time into →Behaviorism. This allowed other approaches in the study of memory, as human and animal behavior could be investigated using scientific methods (see Schriek, 2009, pp. 20). From early 2000 – The Age of Neuroeconomics Increasing research using non-invasive computer tomography via fMRI – functional Magnetic Resonance Imaging ‒ and the subsequent collaboration between neuroscientists, psychologists and economists has led to the development of a new area in economic science ‒ Neuroeconomics and the specific direction of Neurofinance (see chapter 13.2). Technological developments in brain research are providing opportunities to examine neuronal activities to help explain the actual behavior of market participants. The goal is to determine how choices are reflected biologically and which neuronal processes are activated when decisions under uncertainty are taken. Analyzing decision-making would no longer rely on the traditional axiomatic approach only, but combined with brain imaging to better understand why and how we react to a specific situation (see Elger/Schwarz, 2009, p. 36 and Bossaerts/Murawski, 2010). From 2009 ‒ The Age of Emotional Finance First approaches to the exploration of unconscious processes became visible through the description of Keynes’ animal spirits. The research of unconscious processes did not really surface until 2009 with the development of Emotional Finance by Richard Taffler32 and David Tuckett33. Central elements of this specific area of research are the effects of illusion and the desire for wish-fulfillment (see chapter 13.3). The aim is to investigate the consequences of unconscious and highly complex processes that lead market participants to emotionally driven behavior. Consequently, unconscious processes are to be brought into consciousness in order to develop strategies for dealing with reoccurring emotional phenomena (see Baker/Nofsinger, 2010, p. 95). Traditional Finance presents the market participant as a rational individuum. Behavioral Finance, on the other hand, examines what happens on the financial markets by taking human behavior into account. Finally, Neurofinance uses findings to decipher the neuronal basis of decisions and human behavior based on the processes in the brain.

31

Edward Lee Thorndike | American psychologist | 1874-1949 Richard Taffler | Professor of Finance & Accounting, Warwick Business School 33 David Tuckett | Professor and Director of the Centre for the Study of Decision-Making Uncertainty in the Faculty of Brain Sciences at University College London (UCL) 32

1.2 Classical theories of Traditional Finance

1.2

29

Classical theories of Traditional Finance

The following chapter dives into the classical theories of traditional finance where normative assumptions play a key role. In other words, this chapter highlights how investors “should” make decisions. While this chapter might be somewhat challenging from a general interest point of view, we aim to give a good overview of the classical theories developed in traditional finance. Doing so, we reflect both on challenges and possibilities they offer to evaluate risk return profiles of investments. As such, the Neoclassical Capital Market Theory (or simply Neoclassical Theory) developed at the beginning of the 20th century from the old financial market theory, which focused on accounting and →Fundamental Analysis. It evolved around the premises of perfect rationality of market participants as well as perfect financial markets. The resulting equilibrium theories are based on rational and at the same time risk-averse market participants. In this sense, the processing of information according to the Bayes’ Theorem and decision-making within the framework of the Expected Utility Theory represent important core elements of the neoclassical theory. Besides these two theories, the neoclassical theory is decisively influenced by the Efficient Market Hypothesis. In the following subchapters, we will be focusing on the concept of the Rational Economic Market Participant according to Smith (see chapter 1.2.1), the Random Walk Theory of Bachelier (see chapter 1.2.2), the Expected Utility Theory of Morgenstern and von Neumann (see chapter 1.2.3), information processing according to Bayes (see chapter 1.2.4) and lastly the Efficient Market Hypothesis of Fama (see chapter 1.2.5). 1.2.1 The rational economic market participant according to Smith

The concept of the Rational Economic Market Participant34 forms the basis for the neoclassical theory. The origin of this concept dates to the 18th century, the time of classical economics. Scottish economist Adam Smith is regarded as its founder, who, with the following quotation, points out the perfect self-interest as one of three fundamental principles of the Rational Economic Market Participant also referred to as the Homo Economicus: "It is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own interest." (A. Smith, The Wealth of Nations, 1776) The term “Homo Economicus" may sound exaggerated, but the individual concepts and models of the classical theories of traditional finance hardly allow for a different view of the expected behavior of market participants. The concepts share the core assumption that market participants are “rational maximizers”. It implies a positive model of behavior with the aim of explaining and predicting economic 34

Occasionally referred to as Rational Economic Man (REM) in the literature

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1 How Neoclassical Theory shaped rational economic behavior

and social developments and is governed by three fundamental principles in any economic decision:

 Perfect Self-Interest, whereby one’s own goals and ideas are at the forefront of one’s actions.  Perfect Rationality, when making decisions: allowing optimal implementation of well-planned actions in which benefit-maximizing behavior with scarce goods is aimed at.

 Perfect Information, since neither information asymmetries nor transaction costs exist. Due to these simplifications and the extremely high abstraction of the models underlying these principles, the neoclassical theory can be represented very elegantly by mathematical equations (see Bank/Gerke, 2005, p. 2). It is the simplification of economic analysis, which is one of the two advantages why economists like to use the concept of the Homo Economicus. The second advantage is the possibility to quantify the findings (see Pompian, 2006, pp. 15). The above advantages imply that reality and the complexity of human beings (the way they take decisions, the implication of emotions etc.) is reduced to a minimum. This reduction or simplification causes most criticism. Psychologists argue that human behavior is less the product of perfect rationality but rather of subjective impulses such as greed & fear, love & hate or pleasure & pain, where any of the impulses can cause significant valuation errors in asset prices. Perfect self-interest stands in absolute contradiction to voluntary activities, to selflessness and to kindness religions stand for as much as over 2,000 years. Hence, social engagements in our communities show that we sometimes think far less only of ourselves than is assumed by the self-interest-oriented concept of the Homo Economicus. Furthermore, the rapidly expanding areas in investment management (e.g., introduction of Artificial Intelligence (AI) and Machine Learning (ML)) suggests that market participant cannot have perfect information or knowledge on every subject. Despite the simplification of reality, the concept is, in some areas, quite suitable for systematically analyzing reactions to changes in the environment. Notwithstanding far-reaching concerns about the assumptions of this concept, the behavior of market participants is to some extent similar to that of the Homo Economicus, in that they also react systematically to changes in their environment (see Mazanek, 2006, pp. 14). Later, we will examine the extent to which market participants deviate from the assumptions of the Homo Economicus. In chapter 3.2.3 the focus is explicitly on the observable differences, leading to recurring speculative bubbles (see chapter 5) and to limited rational decisions (see chapters 7 to 9). The concept of the Homo Economicus is a behavioral assumption with the aim of explaining and predicting economic and social developments.

1.2 Classical theories of Traditional Finance

1.2.2

31

Random Walk Theory according to Bachelier

The Random Walk Theory suggests that changes in security prices are independent of each other. In other words, yesterday’s price change has no effect on today’s price change and today’s price change has no effect on tomorrow’s price change. Based on the name of the theory, it proclaims that security prices follow a random and hence unpredictable path, which is why predicting their price movements is futile in the long run. Fundamental or technical analysis would be of no value, hence passive overactive portfolio management is to be favored. With the continued strong inflows into index-tracking funds (often packaged as Exchange Traded Funds - ETFs), where an index is simply “followed” without an active security selection, one could argue that the Random Walk Theory of security prices is leveraged by financial institutions. According to fund data provider Morningstar, assets in passive U.S. equity funds overtook those in active funds for the first time in Augst 2019 (see Skypala, 2020). In addition, the significant variation on the cost structure of passive versus active products can make ETFs more attractive to the investment community. Having said that, there are many actively managed funds that deliver excess returns (also called “alpha”), at least for a certain time. A quick glance into the history of this theory: it is based on the dissertation of French mathematician Louis Bachelier entitled “Théorie de la Spéculation” (1900). In his work, Bachelier claimed that futures quotes for government bonds on the Paris securities exchange in the 19th century followed a random pattern and therefore would not allow market participants to generate excess returns (see Schredelseker, 2002, pp. 407). At that time, →Fundamental Analysis and the growing importance of →Chart Analysis played a central role. The basic idea of the Random Walk Theory evolves around the assumption that security prices always change with the same probability ‒ analogous to the probability of a coin flip ("heads" or “tails") (see Mandelbrot/Hudson, 2004, pp. 9). The magnitude of the price change can be measured. According to the theory, most price changes of securities ‒ 68 percent ‒ are relatively small movements within one →Standard Deviation (σ) from the mean value. The standard deviation illustrates the →Volatility of an investment around its mean value, which is key for assessing the risk of an investment. Within +/- two standard deviations, 95 percent of all price changes take place, and within +/- three standard deviations, 99 percent of all price changes would be found. A few price changes however ‒ the remaining 1 percent ‒ represent particularly large deviations and are therefore, according to the theory, very unlikely. If the price movements are connected, a bell curve shaped distribution appears. The large number of small price movements are in the middle, the rare large price movements at the two ends of the bell curve. The distribution of price movements described here corresponds to the widely known normal distribution of Johann Carl Friedrich Gauss35 ‒ also called Gaussian distribution (see Fig. 2). 35

Johann Carl Friedrich Gauss | German mathematician and physicist | 1777-1855

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1 How Neoclassical Theory shaped rational economic behavior

Price movements according to the normal distribution

Fig. 2: Percentage of security price changes by standard deviation

We have become very aware of the importance of the normal distribution in the wake of the SARS-CoV-2 (Covid-19) Pandemic 2020/21. While in the financial world a flat bell curve is by no means preferred due to the higher volatility in the form of a broader distribution of returns, during the Corona pandemic a flat bell curve was very much desired and intended by social distancing and lock-downs in order to avoid the sudden influx of infected people into hospitals in the case of a tapered curve. Covid outbreak leading to fastest sell-off in financial history

Fig. 3: Meltdown in days at specific market crash events, FactSet

1.2 Classical theories of Traditional Finance

33

In the capital markets, however, contrary to the above description, sharp price drops of over 5 percent and more can be observed time and again. Apparently, such severe outliers as can be found at the outer ends of the bell curve occur more often than expected by theory. For example, the fastest price decline in the history of financial markets in the wake of the Corona pandemic from March 2020 is a perfect example of why a risk analysis based on a normal distribution is not able to simulate realistic results when rather a fat-tail approach would indicate the correct portfolio risk. The capital markets lost over 30 percent within a very short period of time (approx. 30 trading days). In comparison, such losses in the past, including the 1987 crash, lasted up to 180 trading days (see Fig. 3). Despite the evidence of outlier events at the outer ends of the distribution (also called fat-tail distribution) ‒ such as, for example, March 2020 when markets dropped over 30 percent from their respective peaks, faster than in any other stock market crash before in history ‒ the findings of Carl Friedrich Gauss gained great attention in the field of financial markets. As a matter of fact, the former ten deutsche mark banknotes of the Federal Republic of Germany showed the image of Gauss as well as the bell curve (see Fig. 4).

Fig. 4: Ten-DM-banknote with bell-curve and Carl Friedrich Gauss

The Random Walk Theory as a fundamental concept of the neoclassical theory already reveals one of the main problems of this economic approach, namely that the conclusions drawn from empirical tests may not be valid, since the assumptions made can be falsified from the outset (see example 1.1). March 2020 is another example why risk analysis based purely on normal distribution may not capture all potential outcomes. Applying a fat-tail approach could improve exante risk analysis helping to capture the correct outcome. Example 1.1: Lack of validity of empirical tests The following chart of the S&P 500 illustrates extreme price developments in the period from 2000 to 2021, which do not correspond to the assumptions of the Random Walk Theory (see chapter 2.3). Thus, market participants may face considerable losses if they rely on moderate price fluctuations within a standard deviation under the assumption of the random walk theory. Strong price fluctuations occur as a result of unexpected events and lead to higher frequency of price movements found at the outer

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1 How Neoclassical Theory shaped rational economic behavior

edges of the density function than would be expected under the assumption of the random walk theory. However, in addition to substantial losses, enormous gains can also be recorded if market participants can time entry/exit properly. Extreme price movements between 2000 and 2022 – S&P 500

Fig. 5: S&P 500 Price chart (2000-2022); FactSet

Biography of Louis Bachelier Louis Jean-Baptiste Alphonse Bachelier was born on March 11, 1870 in the French port of Le Havre. He began studying mathematics as a graduate student at the Sorbonne at the age of 22. His doctoral supervisor was Henri Poincaré, with whom Bachelier obtained his doctorate in 1900 with the thesis “Théorie de la Spéculation", in which he sought a probabilistic approach to securities price movements. Until the outbreak of the First World War, Bachelier financed his upkeep through scholarships and as a lecturer at the Sorbonne. In 1919, after the end of his army service, Bachelier found a position as an assistant professor in Besançon. Due to a misinterpretation of one of his papers by Paul Lévy in 1926, he was blackballed when he attempted to receive a permanent professorship in Dijon. Lévy, without having read his entire work, accused him of making serious mistakes, which he regretted later. Finally, in 1927 he was awarded a permanent position in Besançon. His work was hardly noticed by the economists of his time. Only after his death was the importance of his theory recognized. Bachelier is regarded as the founder of financial mathematics and one of the pioneers of the theory of stochastic processes in the field of financial markets. He died on 26 April 1946 in St-Servan-surMer, France (see Mandelbrot/Hudson, 2004, pp. 47).

1.2 Classical theories of Traditional Finance

35

Deep Dive Random-Walk Formally a random walk can be represented as: Pt+1 = Pt + εt oder E[Pt+1] = Pt P stands for the price of the security at times t or t+1. The expression εt represents a random term that determines the form of the random walk based on the assumptions made. The strictest form of random walk would result if the random term εt is subject to a normal distribution, independent of the past and has an expected value of zero (see Mandelbrot/Hudson, 2004, pp. 10). This would mean as stated in the beginning, that yesterday’s price change has no effect on today’s price change and today’s price change has no effect on tomorrow’s price change. Normal distribution as the basis of the Random Walk Theory Due to the central assumption that price changes and thus also the return of securities can be approximately described by means of the normal distribution (see Fig. 6), it is important to consider the characteristics of such a distribution, which can be listed as follows (see Mandelbrot/Hudson, 2004, pp. 35):  The area under the frequency function is always 100 percent.  The height of the bell curve illustrates the most frequently occurring return ‒ this return is also referred to as the mean of the returns or the average return.  The normal distribution is symmetrical, looks the same on the left and right of the mean value.  The probability of higher yields decreases more and more to the right of the mean value, as does the probability of lower yields to the left of the mean value.  The normal distribution is described by the mean value of the return μ and the standard deviation in the form of the volatility σ.

Fig. 6: Exemplary return development based on the normal distribution

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1 How Neoclassical Theory shaped rational economic behavior

Depending on the mean and the standard deviation36, the normal distribution can take on different forms, which simultaneously indicate the expected return and volatility (see Fig. 7).

Fig. 7: Forms of the normal distribution

Three distributions are shown in Fig. 7 (A, B and C). The distributions A and B have the same mean and are located at the same place, measured by the mean. The distribution C has a higher mean and is therefore located further to the right on the x-axis. In terms of volatility, distributions A and C are equally volatile. The distribution B, on the other hand, shows a higher volatility. This can be seen from the fact that the distribution is flatter than the other two. In distributions A and C, far more observations are close to the mean value, while in distribution B, more observations are at more extreme values. So, the flatter a distribution is, the higher is the risk ‒ measured as standard deviation. 1.2.3 Expected Utility Theory according to von Neumann & Morgenstern

The neoclassical capital market theory describes market participants as “rational” if they formulate realistic expectations and implement them according to the expected utility theory. In contrast to this view, the behaviorally biased market participant is prone to unrealistic expectations and consequently disregards the expected utility theory explained below. The Expected Utility Theory has the objective to analyze rational decisions, when the decision-maker is facing risky outcomes or in other words, faces different choices with respective probabilities of outcome (see Bank/Gerke, 2005, pp. 35). Together with the Bayes’ Theorem (see chapter 1.2.4) of information processing, the ex-

36 Standard Deviation is a statistic metric that measures the dispersion of a dataset relative to its mean. It is calculated as the square root of the variance (σ2), which measures how far each number in the set is with respect to the mean. The higher the deviation from the data set, the more spread out the data and thus the higher the standard deviation. It can be used to improve the portfolio construction and with that the overall asset allocation.

1.2 Classical theories of Traditional Finance

37

pected utility theory forms the basis for the Efficient Market Hypothesis. In the case of the expected utility theory, there are two types to differentiate:  Objective Expected Utility Theory by Oscar Morgenstern & John von Neumann (1947) – the distribution function of possible consequences is known.  Subjective Expected Utility Theory by Leonard J. Savage37 (1954) – the distribution function of the consequences is unknown; the decision-maker must determine the probability of the consequences through subjective estimation. This subchapter focuses on the Objective Expected Utility Theory. For Morgenstern and von Neumann, it was a normative model of how a rational person should make decisions when facing alternative outcomes and not a descriptive model about how decisions are really made. The theory is anchored in certain axioms which, however, are often violated when considering the actual behavior of market participants. These violations and doubts about the validity of the assumptions spurred the emergence of →Behavioral Finance, taking on the challenging task of uncovering why and how market participants choose as they do (see Forbes, 2009, p. 26). To do so, the Prospect Theory (see chapter 6.2) was developed as a descriptive and alternative theory to the Expected Utility Theory. It was developed by the psychologists Daniel Kahneman and Amos Tversky and assumes that market participants assess their investment results relative to a reference point rather than looking at their final assets. Therefore, depending on the reference point, the results can be positive (gains) or negative (losses). The objective of the expected utility theory is to analyze rational behavior under uncertainty. The central object of the investigation is the making of decisions without their results/consequences being known in advance.

Biographies of Morgenstern and von Neumann Oskar Morgenstern was born on January 24, 1902 in Görlitz/Germany. In 1925 he received his doctorate in political science from the University of Vienna. Shortly afterwards he received a scholarship from the Rockefeller Foundation. In 1929 he returned to Vienna from the U.S. and accepted a professorship at the University of Vienna. During his time at the university, he belonged to the so-called “Austrian Circle”, a group of Austrian economists. In 1938 he emigrated to the U.S. and became professor at Princeton University, where he developed the game theory together with von Neumann. In addition to game theory, they also developed the Expected Utility Theory as a method of evaluating decisions under uncertainty. 37

Leonard J. Savage | American mathematician | 1917-1971

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Morgenstern was appointed Distinguished Professor of Game Theory and Mathematical Economics by the New York University. He died in Princeton on July 26, 1977. John von Neumann was born in Budapest/Hungary on December 28, 1903. His high intelligence was already evident at the age of six, when he was able to divide eight-digit numbers. After graduating from high school, he attended various universities in Europe and obtained his diploma in chemical engineering at the ETH Zurich. In addition, he studied mathematics and obtained his doctorate from the University of Budapest in 1926. In 1928, he habilitated at the University of Berlin with his work Allgemeine Eigenwerttheorie symmetrischer Funktionaloperatoren. In 1933 he became professor of mathematics at the newly founded Institute for Advanced Study in Princeton, New Jersey. In 1933 von Neumann became co-editor of the Annals of Mathematics and in 1935 of Compositio Mathematica. Together with Oskar Morgenstern he wrote The Theory of Games and Economic Behavior in 1944, with which he became the founder of game theory. He also wrote a book on quantum mechanics and participated in the development of axiomatic set theory. During World War II von Neumann was an advisor to the U.S. Army. From 1943 he worked on the Manhattan Project in Los Alamos on the development of atomic bombs. Neumann received numerous honors for his scientific achievements, including the Medal of Merit, the Medal for Freedom, and the Albert Einstein Commemorative Award. In addition, the John von Neumann Institute for Computing in Jülich was named after him. He died on 8 February 1957 in Washington D.C. Deep Dive Expected Utility Theory Basic idea of the objective Expected Utility Theory The central element is a utility function u, whose expected value can be used to represent preferences. The determination of the expected value plays a special role in the calculation of the expected benefit EU. n

Formally, the utility function can be represented as follows: EU(a) = Σ pi * u(ai) i=1

The term u(ai) represents the respective benefit of state i of alternative a. pi is the corresponding probability of the occurrence of this state. The sum of the probabilities of all states Σ pi is 1. i=n1

In consequence, two alternatives a and b emerge. If a has a higher expected utility than b, alternative a is preferred over b, i.e., a > b, if EU(a) > EU(b). Based on the above, the expected utility of an alternative is the key element for a rational decision, whereby the market participant chooses the alternative that has the highest expected utility (see Kottke, 2005, p. 8).

1.2 Classical theories of Traditional Finance

39

Axioms for rational behavior For the respective preference statements (alternative a versus alternative b) to result in rational behavior, the preferences must fulfil three specific axioms listed below. Behavioral Finance research shows, that these axioms are not always fulfilled. Complete order The axiom “complete order” consists of two partial axioms ‒ completeness and transitivity. Both properties must be fulfilled within the “complete order” axiom. Completeness means that all alternatives are considered in a decision. For each alternative, a > b or b > a must apply accordingly. Transitivity is the property that exists when all alternatives satisfy the condition that if a > b and b > c, then in consequence a > c. The violation of this axiom and thus the departure from the rational behavior of the market participant can be explained by various →Biases (also called heuristics or rules of thumbs) identified through Behavioral Finance research. Although they reduce the degree of complexity of the decision when confronted with uncertainty, they may lead to biased or poor choices under uncertainty. Nonetheless market participants seem to consistently rely on these shortcuts to reduce the alternatives at hand (see Elton/Gruber/Brown and Goetzmann, 2007, p. 488). For example, the range of alternatives can be limited if the market participant is subject to home bias. In this case, domestic investments are preferred over foreign ones, as domestic investments are associated with higher security. Continuity The axiom “continuity” is fulfilled if the alternatives, b, c behave to each other in such a way that a > b > c and a probability p [p∈0,1] ensures that the term p * a + (1-p) * c equals b. The axiom of continuity thus requires that an indifference can be established between alternative b and a combination of a and c. In other words, an original preference between two alternatives should not change if both alternatives are extended by the same third alternative. In the context of Behavioral Finance research, we observe however, that market participants assign higher probability to an alternative over another alternative who is less available/familiar. This phenomenon, known as →Availability Bias, can lead to a biased subjective perception of the objective probability of the alternative in question. Independence The axiom “independence” formulates the condition that an original preference between two alternatives is not changed if further decision possibilities are brought into play. If one starts from a > b and the two alternatives are supplemented by the alternative c, then the axiom of independence is fulfilled if p [p∈0,1] applies to all probabilities:

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1 How Neoclassical Theory shaped rational economic behavior

p * a + (1-p) * c > p * b + (1-p) * c A further characteristic of the independence axiom is the possibility of substituting one alternative for another if the decision-maker is indifferent between the two alternatives. However, the substitution should not affect the preference of the decision-maker. If we now look at the decision-making process from a psychological point of view, it becomes clear that market participants may also feel emotional unease when choosing between two alternatives. This situation arises when the alternative not chosen or an additionally chosen alternative (in this case alternative c) has characteristics that are in contrast to the investor’s existing values and decisions. In this case, according to the →Theory of Cognitive Dissonance (see chapter 6.1.3) by Leon Festinger38, an emotional imbalance arises, which can be reduced by suppressing the contradicting and highlighting the confirming information to make an investment decision. In that aspect, market participants start to limit the amount of information available through →Selective Perception (see chapter 7.1.2). If a decision has already been made, an attempt is made to ensure the continuation of the investment decision through →Selective Decision-Making (see chapter 9.1.1). 1.2.4

Information processing according to Bayes

The theorem developed by Thomas Bayes and published by Richard Price after his death in 1763 is another important basic assumption of rational behavior. As in the expected utility theory, the alternatives for a decision and their prior or ex-ante probabilities of occurrence are also known in the Bayes’ Theorem. If there is a change with regards to the information at hand, the original probabilities of occurrence should ex-post adapt to the new situation. However, if the necessary adaptation is not made, decision-makers are not in a position to make the optimal (rational) decision. The following example illustrates the extent to which market participants should change their original assessment of the future development of a security based on analysts’ estimates. According to the Bayes’ theorem, market participants would be expected to reassess the future performance of their shares based on additional information. Example 1.2: Adjustment of the probability assessment based on the Bayes’ Theorem The types of possible recommendations (buy, hold or sell) stand for the uncertain states. The probability of a buy recommendation is assumed to be 60 percent of all recommendations, 30 percent for hold and 10 percent for sell recommendations. The ex-ante probability of holding a winner share, i.e., before processing a new recommendation, is assumed to be 50 percent. 38

Leon Festinger | American social psychologist | 1919-1989

1.2 Classical theories of Traditional Finance

41

Now the question is: What is the ex-post probability of each recommendation type associated with holding a winner share? A further assumption comes into play: the estimated probability that a recommendation will actually lead to rising prices. We assume that a buy recommendation foretells a winning share in 70 percent of the time [p(buy|winner) = 0.7], a hold recommendation foretells a winning share in 25 percent of the time [p(hold|winner) = 0.25] and a sell recommendation foretells a winning share in 5 percent of the time [p(sell|winner) = 0.05]. p(recomi|winner) =

p(buy|winner) =

p(hold|winner) =

p(sell|winner) =

p(winner|recomi) * p(recomi) Σi p(winner|recomi) * p(recomi) 0.7 x 0.6 (0.7 x 0.6) + (0.25 x 0.3) + (0.05 x 0.1) 0.25 x 0.3 (0.7 x 0.6) + (0.25 x 0.3) + (0.05 x 0.1) 0.05 x 0.1 (0.7 x 0.6) + (0.25 x 0.3) + (0.05 x 0.1)

=

=

=

0.42 0.5 0.075 0.5 0.005 0.5

= 0.84

= 0.15

= 0.01

The example just calculated illustrates a change in the probability assessment based on the recommendations by analysts. The original probability that the share will rise (50 percent) has risen to 84 percent due to a buy recommendation. However, if a hold recommendation is issued, the ex-post probability that the share will nevertheless rise falls to 15 percent. With a sell recommendation, this probability drops to just one percent. If the recommendations were now repeated, the probability that the share would rise with successive buy recommendations would reach even higher values. With six buy recommendations published in succession, market participants could count on almost 100 percent probability of rising prices. In the case of the hold or sell recommendations, however, the probability assessment of rising prices would continue to fall until it reaches almost 0 percent after just three published hold recommendations (see Forbes, 2009, pp. 70). Observations of market participants reveal a differentiated picture. Studies by Victor L. Bernard39 and Jacob K. Thomas40 (1990) show that analysts, for example, do not promptly adjust their estimations to the information provided. As a 39 40

Victor L. Bernard | American Professor of Accounting | 1952-1995

Jacob K. Thomas | American Professor of Accounting and Finance, Yale School of Management

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1 How Neoclassical Theory shaped rational economic behavior

result, their recommendations to the new information are adjusted with a delay. Consequently, market participants can generate excess returns if they take advantage. Bernard and Thomas confirmed this phenomenon as the post-earningsannouncement drift. First detected 1968 (see chapter 4.4.3) ‒ it explains that a security can experience further gains if research analysts start to upgrade recommendations and further losses if they start to downgrade recommendations after earnings announcements. The reason for this behavior can be seen in the fact that the previously communicated analysts’ recommendations are gradually adjusted based on the new information. The post-earnings announcement drift clearly shows once again that the Homo Economicus Humanus is not in a position to instantly incorporate all information into the valuation of the security prices. This is due to certain cognitive and emotional limitations, which are described in detail from chapter 6 onwards. Biography of Thomas Bayes Bayes was born in London in 1702. He was the oldest of seven children. In 1719 Bayes enrolled at the University of Edinburgh for Logic and Theology. After his studies he was ordained a Presbyterian clergyman like his father and initially worked with him. His theory of probability “Essay towards solving a problem in the doctrine of chances” was published posthumously in the Philosophical Transactions in 1763. His considerations were first adopted by Pierre Simon Laplace in 1781, rediscovered by Marie-Jean Condorcet and remained unchallenged until George Boole critically questioned them in his “Laws of Thought” and developed them into their present form. In 1742 Bayes was elected a fellow of the Royal Society, although he had no mathematic publication under his name. He died on April 17, 1761 at Tunbridge Wells, England. The Bayes’ Theorem illustrates how a market participant’s probability assessment should change when new information is received. This involves the adjustment from ex-ante probabilities to ex-post probabilities.

1.2.5 Efficient Market Hypothesis according to Fama

To complete the classical theories of traditional finance, we need to take a look at the Efficient Market Hypothesis (EMH). It is mainly based on the assumption of a rational information processing and decision-making behavior of market participants. The basis for the Efficient Market Hypothesis are decisions on the basis of the Expected Utility Theory (see chapter 1.2.3) and the processing of information in the sense of the Bayes’ Theorem (see chapter 1.2.4).

1.2 Classical theories of Traditional Finance

43

The starting point for the EMH is the consideration that if it were possible to predict future developments from past price developments, there would be the possibility of achieving an excess return. However, the excess return would be quickly neutralized, as many market participants would try to exploit this for their own benefit, as well. In essence, prices would quickly reach their “right" level. Consequently, new information would not only be reflected in future prices, but already in today’s prices. On the basis of this development, it would have to be assumed that the prices follow a random walk. This would be the case only if new information would have a direct impact on the price development. In line with Bachelier’s theory described above, no excess returns can be achieved by observing past prices (see Karlen, 2003, pp. 15). Eugene Fama was also inspired by this consideration when he developed the Efficient Market Hypothesis in 1970. The hypothesis describes a market as efficient when security prices fully reflect all available information: “A market in which prices always fully reflect available information is called efficient.” (Fama, 1970, p. 383) This finding implies that investors cannot gain an information processing advantage for themselves in order to generate excess returns (see Garz/Günther/Moriabadi, 2002, p. 82). An information-efficient market can be described by the following three characteristics (see Rau, 2010, p. 334): [1] All market participants are rational. They value a security on the basis of discounted future dividends or cash flows. They use all available information to determine the fundamental value of the security. If it is higher than the current price, the demand for the security increases. In the opposite case, market participants sell the securities or use the possibility of short selling. [2] Some market participants are irrational. Their uncorrelated false valuations neutralize each other. If an “optimist” feels that the security is undervalued and a “pessimist” feels that the security is overvalued, these two market participants will neutralize their mispricing among themselves. The price of the security would remain unchanged. [3] Arbitrage is unlimited. Arbitrageurs are market participants with “unlimited” liquidity. If a mispricing occurs in the short-term, they compensate for it by trading a large number of the corresponding securities. Even if some market participants are systematically irrational, a large number of arbitrageurs can thus achieve a return to fundamental valuation. As already indicated in point 3, the influence of irrational market participants on price formation can be compensated by two mechanisms (see Shleifer, 2000, p. 2):  Arbitrage: The concept of arbitrage is based on the simultaneous purchasing and selling of an identical security, commodity, or currency, across two different markets while exploiting price differences. The transactions aim to bring the valuations back in line with the fundamental values.

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Example 1.3: Arbitrage as a possibility to limit irrational market behavior Assuming that the common shares of BMW are traded at the same time in Munich at EUR 87.00 and on the Frankfurt Stock Exchange at EUR 87.26 (a difference of EUR 0.26 per share). If the simultaneous purchase and sell order of, for example, 1,000 shares is executed, the buyer makes a profit of EUR 260. So, the capital outlay for the buyer in this example is still EUR 87,000 plus the purchase fees and charges. This transaction would realign the two notations to each other and consequently offset the effects of irrational market behavior. In most cases, however, the order costs are so high that the profit from the arbitrage transaction cannot cover these costs. In practice, only large financial institutions or hedge funds can take advantage of arbitrage opportunities. In addition, the interest rate differences between credit and deposit interest rates can reduce excess return as well, so that credit-financed arbitrage is rare, in real life.  Natural selection by market forces. This refers to incurred losses in the course of irrational “purchase high, sell low” or, in other words, transactions when securities are overvalued and over time undergo a price correction. As a result, the returns of these investors are lower than normal and they are forced out of the market over time, according to Milton Friedman41: “They must become much less wealthy and eventually disappear from the market.” (Friedman, quoted after Shleifer, 2000, p. 4) Furthermore, Fama considers information that is already known as “old or useless” information. He defines three types of useless information, which then leads to the three known forms of the efficient market hypothesis (see Shleifer, 2000, p. 5). The extent to which arbitrageurs are actually able to compensate for the effects of “irrationally” acting market participants is discussed in chapter 4.1.2. Numerous obstacles make it difficult or impossible for arbitrageurs to intervene in the form described above. In addition to certain risks, the costs of arbitrage also mean that institutional investors do not always intervene. Moreover, in some cases investors are not interested in returning prices to fundamental valuation. Rather, they may be interested in profiting from the mispricing (see chapter 4.1.2). Three-step concept of the Efficient Market Hypothesis (EMH) In the three-step concept, Fama distinguishes between the weak, semi-strong and strong form of market efficiency (see Shleifer, 2003, pp. 6): The weak form of market efficiency characterizes a market in which the price histories of the traded securities are included in the current prices. In essence, this form of efficiency corresponds to Bachelier’s →Random Walk Theory. This means that the information on past prices do not lead to any excess returns and therefore the →Technical Analysis (see chapter 2.2.2) is of no use (see Schredelsker, 2002, p. 418):

41

Milton Friedman | American Economist and Nobel Prize winner (1976) | 1912-2006

1.2 Classical theories of Traditional Finance

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Example 1.4: Weak form of market efficiency If a technical analyst discovers that prices are rising in January, the analyst tries to profit from the price increases in January by buying in December. However, this phenomenon is also observed by other market participants, who also bet on rising prices in January. Thus, prices rise in December due to the demand of the technical analyst without additional increases in January – thus the discovery through chart analysis destroys the anticipated excess return by itself. Nevertheless, technical analysis is still used by a good portion of the market participants to make decisions. In this respect, the confidence in this form of financial market analysis illustrates limited rationality among market participants. Research results of Behavioral Finance illustrate the psychological reasons for the trust in technical analysis. For example, there are indications that certain →Biases implicate that the knowledge of past prices can certainly be used to achieve an excess return. For example, the economists Werner de Bondt and Richard Thaler (1985) have pointed out that the future, long-term development of securities can be partially predicted by looking at past developments. This phenomenon known as the →Winner-Loser Effect is described in more detail in chapter 4.3.3.  The semi-strong form of market efficiency is based on the idea that all other publicly available information is also priced into the valuation of securities. If a market is information-efficient in the semi-strong sense, fundamental analysis based on public information (newspaper reports, annual accounts, etc.) does not lead to excess returns. Example 1.5: Semi-strong form of market efficiency If an analyst discovers in the annual report of a company that the company’s debt is too high, he or she could achieve an excess return by recognizing this and by reacting accordingly. In an efficient market, however, the development of corporate debt is also noticed by other analysts ‒ the price falls as a result, since the information is priced into the security prices in the shortest possible time. So, it is only the fastest market participants, who can generate an excess return by reacting to that information. Besides the weak form, the semi-strong form of market efficiency is also empirically doubted. For example, an initially cautious attitude towards corporate earnings reports can empirically be observed. As already noted in chapter 1.2.4 (Information processing according to Bayes), this results in further, extended gains in the case of positive information and further losses in the case of negative information based on a gradual, cautious adjustment of the previous estimates. This postearnings-announcement drift illustrates the possibility of potentially achieving an excess return by knowing all public information (see Shefrin, 2000, p. 96).  The strong form of market efficiency includes the correct processing of all conceivable information into securities prices (in addition to price histories and

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1 How Neoclassical Theory shaped rational economic behavior

all publicly available information, also non-public insider information). If the market is information-efficient in the strict sense of the word, not even insiders are able to generate excess returns from their information advantage. Example 1.6: Strong form of market efficiency Does a board member of a company sell large blocks of shares because he or she is worried about increasing debt, in an efficient market, insider transactions are also noticed by other investors. As a result, the share price falls in a very short period of time and excess returns are not possible. The strong form of market efficiency is undoubtedly questioned by Behavioral Finance research as well. Nejat Seyhun’s42 (1986) research results showed that insiders achieve excess returns by selling their holdings only after positive information has been priced into the shares, and only acquiring them after negative information has been announced. In the context of the above examples, this behavior shows how insiders can reap benefits from their exclusive information with the consequence that they can exploit price movements to their advantage. In this context, the different meanings of the terms “information efficient capital market” and “perfect capital market” should be pointed out. This is necessary as they are two different concepts, and the two terms should not be confused with each other. A perfect market is characterized by the following criteria:  no transaction costs (fees, taxes, commissions) in the market,  securities are infinitely divisible,

 perfect competition (all market participants are price-takers),  market participants have equal, free access to information,  market participants are rationally acting individuals with the goal of maximizing the expected benefit. In contrast to a perfect market, an information efficient market can also be efficient if transaction costs exist, the securities are not divisible or if the market participants do not have the same information. Market efficiency refers only to the processing of information. The Efficient Market Hypothesis describes a market as efficient when security prices fully reflect all available information. Three types of information are distinguished, which lead to the three known forms of the efficient market hypothesis.

42 H. Nejat Seyhun, Professor of Finance at the University of Michigan; Journal of Financial Economics, vol. 16 Issue 2, 1986 “Insiders’ profits, costs of trading, and market efficiency”

1.2 Classical theories of Traditional Finance

47

Biography of Eugene Fama, Nobel Prize winner 2013 Eugene F. Fama was born on February 14, 1939 in Boston. He is known as a co-founder of neoclassical capital market theory and is strongly associated with the study of economic market mechanisms. Fama received worldwide recognition for the efficient market hypothesis, in which security prices always fully reflect the available information. Fama has received numerous awards for his scientific research: In 2013 he was awarded the Alfred Nobel Memorial Prize for Economics together with Robert Shiller and Lars Peter Hansen for the empirical analysis of security prizes. In particular, his analysis of the efficient market hypothesis from the 1960s were honored. Other awards include the Fred Arditti Innovation Award in 2007, the Deutsche Bank Prize in Financial Economics in 2005, the Morgan Stanley American Finance Association Award for Excellence in Finance in 2007 and the Onassis Prize in Finance in 2009. After completing his bachelor’s degree at Tufts University in 1960, he received his PhD from the University of Chicago Booth School of Business in 1964. His doctoral supervisor was Benoit Mandelbrot, who produced groundbreaking work on fractal geometry and chaos research. Fama’s doctoral thesis, which concluded that equity prices are unpredictable and follow a random walk, was published in the Journal of Finance in 1965. Since 1963 he has been a professor at the Chicago Booth School of Business. Deep Dive Efficient Market Hypothesis Conditions for efficient markets as an extension of market efficiency according to Fama The conditions for efficient markets listed below (see Garz/Günther/Moriabadi, 2002, pp. 80) illustrate at the macroeconomic level the steering function of prices, in which the efficient market hypothesis is embedded. In an efficient market, which does not necessarily have to be perfect, prices should send signals that ensure that capital is allocated to the best possible use in economic terms. Operational Efficiency Operational efficiency means that market prices must not be systematically distorted by the imperfections that exist in reality, such as taxes or transaction costs. Three factors play a significant role in the assessment of operational efficiency, all of which aim at increasing the willingness of all market participants to trade:  To ensure operational efficiency, market organization (e.g., brokers, stock exchanges) must ensure sufficient liquidity in the securities traded at all times.  However, the market structure must also be able to minimize transaction costs. These include direct transaction costs such as stock exchange turnover tax and commissions, as well as indirect transaction costs resulting from block trading

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1 How Neoclassical Theory shaped rational economic behavior

of large blocks of shares. The indirect costs mean that the share packages can only be sold or bought by accepting a significant price reduction/premium.  In a broader sense, the legal framework must also be considered. In this context, it is mainly a matter of protecting market participants from the negative effects of insider trading or other fraud. Information Efficiency In addition to operational efficiency, the discussion about information efficiency also dominates the definition of an allocation-efficient market. According to the basic assumption of the efficient market hypothesis a market is considered information efficient if all information concerning the value of an investment is immediately priced in its market value. However, this would mean that a market participant would have no incentive whatsoever to collect and analyze information in the strict sense of the information efficiency hypothesis. If this does not happen, however, the processing of all information in the securities prices is questionable. For this reason, allocation efficiency must also be questioned, because if all market participants were to behave according to the maxim of information efficiency, there would be no incentives to obtain information. If this were the case, no one would react to new information. Consequently, at least in the short-term, there must be an incentive to procure and evaluate information in order to generate an excess return through its exploitation. Valuation Efficiency As a hierarchical concept, the valuation efficiency of a capital market represents the top level in an efficiency concept. Both operational efficiency and information efficiency are necessary but not sufficient conditions for a valuation-efficient market. For prices in a market to ultimately assume a steering function, they must adapt quickly and without frictional losses to a changing situation ‒ they must process the information correctly. This means that the fundamental values of companies must be reflected properly in the prices paid on the capital markets. In this context, the models of neoclassical capital market theory (in particular CAPM and APT ‒ see chapters 2.1.2 and 2.1.3) offer possibilities to evaluate investments.

Summary Chapter 1 Today, neoclassical capital market theory is an independent and recognized discipline within economics. It developed in the course of the 20th century, and its intellectual-historical currents formed the concept of Homo Economicus. The starting point of neoclassical capital market theory is usually associated with the doctoral thesis of Louis Bachelier in 1900. His insight, that stock price movements can be modelled by means of stochastic processes and consequently have the statistical property of a pure random process,

Summary Chapter 1

formed the Random Walk Theory, according to which security prices move upwards or downwards without “memory”, i.e., independently of the previously realized prices. The concept of Homo Economicus is accepted as a positive behavioral model with the aim of explaining and predicting economic and social developments. The concept allows a model-theoretical simplification of the economic analysis of human behavior. According to the Random Walk Theory it is possible to classify the frequency of price changes of securities on the basis of a normal distribution. According to this, 68 percent of the price changes are within plus/minus one standard deviation around the mean and about 95 percent are within two standard deviations. The Expected Utility Theory aims to analyze rational behavior under uncertainty. The decision-maker plays an important role in this process. He has to choose between different actions, whose results/consequences are uncertain. Together with the Bayes’ Theorem of information processing, the Expected Utility Theory forms the basis for the efficient market hypothesis. The Bayes’ Theorem illustrates how the probability assessment of a market participant should change when new information is received. The Efficient Market Hypothesis is a further building block in the concept of rational behavior. It describes a market as efficient when security prices fully reflect all available information. The influence of irrational market participants is neutralized by the concept of arbitrage and natural selection.

49

2

Limitations of Traditional Finance In the second chapter you will learn about the models of neoclassical capital market theory which are applied to determine the expected return and risk of securities. In addition, you will learn about valuation approaches for investment decisions based on fundamental analysis and technical analysis. After working through this chapter, you will understand the increasing criticism of the traditional models. Through the description of “Black Swans" you will better understand real market conditions which are difficult to reconcile with neoclassical capital market theory.

Models of Neoclassical Capital Market Theory Modern finance as a matter of fact, is still based on the concepts of neoclassical capital market theory. It describes the functionality of capital markets with the help of models in which rationally acting market participants play the main role. The models listed below have the objective of presenting reality in a simplified form. They rely on abstract assumptions rather than on real conditions. When considering the listed models, the basic idea of neoclassical capital market theory, as a normative decision theory, is clearly evident: It shows how investors should behave and not necessarily how they behave in reality (see Pompian, 2006, p. 10). For this reason, the following subchapter will also illustrate how market participants, in contrast to the postulated assumptions, behave in the context of their decision-making. This basic idea plays a central role in the following chapters. Portfolio Selection Theory

The old familiar saying “Don’t put all your eggs into one basket” illustrates very well the idea of diversification, although expressed in a rather naive form. A rationally acting market participant who is only focused on returns would have to bet solely on the security that promises the highest return. The discrepancy between this expected and actually observed behavior inspired Harry M. Markowitz to develop the Portfolio Selection Theory43 in 1952. As a doctoral student, Markowitz came to the conclusion that in addition to the expected return on an investment, the associated risk must also be considered: „It seemed obvious that investors are concerned with risk and return, and that these should be measured for the portfolio as a whole.“ (Markowitz, 1991, p. 470) Markowitz introduced the so-called two-parameter approach, which uses the expected value and the standard deviation to describe the future return on investments. However, the portfolio theory was not only able to describe the expected 43

Later referred to as Portfolio Theory.

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return of certain investments, but also to determine the expected value and standard deviation of the return of a portfolio by considering the correlation between the securities in a specific portfolio. Basic idea of the Portfolio Selection Theory Markowitz assumed, based on the above quote, that investors would take into account the risk of the security in addition to the expected return when making their investment decisions. These considerations relate primarily to equity shares but are also applicable to other risk asset classes. Based on this assessment, the following theoretical model assumptions about market characteristics and the expected behavior of market participants can be derived:  It is assumed that the expected return is subject to a stochastic random process in the sense of normal distribution. In this way, risk can be defined as a deviation from the expected return and measured as a standard deviation.  Another assumption is the planning horizon of a specific period in which a security is bought in t0 an sold in t1. In this case, the capital amount is invested entirely in risky securities, assuming a perfect market with any divisibility of securities. The investors’ preference is based exclusively on the asset value at the final period.  The model is also characterized by risk-averse and rational market participants, who demand higher returns in order to take on greater risk or, for a given return, always choose the security with the lowest risk ‒ consequently, the only parameters in focus are the return and the standard deviation of the return (see Karlen, 2004, p. 13).  In addition, the model implies decision-making under uncertainty about future returns on an investment. The presentation of the decision problem based on expected return (µ) and risk in the form variance or standard deviation (σ) consists primarily in determining these values. However, since these values are not directly known, they must be estimated in a first step. For this purpose, both the mean value and the standard deviation are calculated from a sample of return values already realized in the past. Portfolio Selection Theory is based on the two-parameter approach, which is able to describe the future return on investments by means of the expected value and the standard deviation. Building efficient portfolios Market participants behave risk-averse according to the assumptions presented. Based on this attitude, they would select minimum variance portfolios (MVP) that are characterized by the following efficiency criteria:  For an efficient portfolio there is no other portfolio with lower risk for a given return.

2.1 Models of Neoclassical Capital Market Theory

53

 For an efficient portfolio there is no other portfolio with a higher expected return for a given risk.  For an efficient portfolio, there is no portfolio with lower risk and a higher expected return. The overall minimum risk portfolio M (see Fig. 8) separates the number of MVPs into efficient and inefficient portfolios. The portfolios that meet the efficiency criteria are on the outer edge (positive slope) of the efficiency curve. These portfolios are efficient and have an optimal risk/return profile. This means that the portfolio in question has a lower risk than the security with the lowest risk, while achieving a higher expected return. Uncorrelated security returns lead to the so-called →Diversification Effect: the portfolio variance falls below the arithmetic mean of the individual risks (see Garz/Günther/Moriabadi, 2002, p. 32). According to Markowitz, the main benefit of diversification into several investments is the reduction of risk (see Markowitz, 1991, p. 469). In that aspect, the expected value (µ) and the variance or standard deviation (σ) of the securities involved are the two key parameters. The theory uses these parameters because it assumes that the returns on the capital market ‒ as described by Bachelier ‒ follow the normal distribution, which can be described by these parameters. The advantage of diversification is based on the different nature of the companies. It is largely dependent on the correlation coefficient of the securities under consideration. The maximum diversification effect occurs when the business activities of the companies correlate negatively. Figure 8 shows diversification through three securities (A, B, C). The wrapping effect (AMC curve) illustrates all combinations that the investor can consider when taking a decision. The curves AB, AC and BC represent different combinations of the individual securities. The AMC curve can contain all three securities.

Fig. 8: Risk-Return-Diagram with wrapping effect

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Let’s examine the significance of different correlation coefficients as an important aspect for portfolio construction:  Perfectly positive correlation (correlation equal to 1): Two securities demonstrate a perfectly positive correlation if both of them are dependent on exactly the same systematic risk factors and none of them depend on specific or idiosyncratic risk factors (see below). This means that the returns of both securities always move in the same direction (e.g., dotted line between A and B in Fig. 8).  Positive correlation (correlation between 0 and 1): Two securities show a positive correlation between 0 and 1, if they have many common systematic risk factors but also a number of specific ones. The returns of both securities move generally in the same direction.  Independence (correlation equal to 0): If the securities in question do not have common systematic risk factors, but depend on specific ones only, their return fluctuates due to these specific influences. As a consequence, the securities show no correlation.  Negative correlation (correlation between -1 and 0): Negative correlation is given if the securities in question have common, systematic risk factors, which, however, are opposite in their direction of action. Due to the opposite direction, the returns do not correlate instead, they move in opposite directions.  Perfectly negative correlation (correlation equals -1): In this case, the securities in question would have the same systematic risk factors, but no specific ones. The systematic risk factors are affecting the return of the securities/asset classes in the opposite direction. A perfectly negative correlation is hardly to be expected in reality. This is due to systematic risk factors that affect all asset classes in a certain way, although with different degrees of intensity. These fluctuations are due to a large number of influencing factors that can be divided into two classes (see Gehrig/Zimmermann, 1999, p. 46): [1] Systematic risk factors (market risk, not diversifiable) Factors that influence all securities and cause a slightly positive correlation of returns. These factors cannot completely be diversified away even by portfolios with high number of constituents. Investors accepting higher risk via these systematic risk factors are compensated by a corresponding risk premium. Examples are interest rate levels, exchange rates, economic trends, global political events, natural disasters, etc. [2] Unsystematic risk factors (specific/idiosyncratic risk, diversifiable) They only affect a single security. These unsystematic risks are not compensated as they can be largely diversified through a large number of securities within the portfolio in question. Examples include the company’s product portfolio, management decisions, the company’s economic situation and analysts’ assessments of the company (e.g., Wirecard fraud scandal in fall 2020). The advantages of diversification were already known generations before Markowitz, as the well-known mathematician Daniel Bernoulli44 said:

44

Daniel Bernoulli | Swiss mathematician | 1700-1782

2.1 Models of Neoclassical Capital Market Theory

55

„... it is advisable to divide goods which are exposed to some small danger into several portions rather than to risk them all together.“ (Bernoulli, quoted after Chapados, 2011, p. 2). The possibility of reducing specific unsystematic risk is shown in Fig. 9. As the number of securities in the portfolio increases, the overall portfolio risk approaches the risk of the market portfolio, i.e., the portfolio that contains all assets on the market. Equity portfolios with 10 to 15 different, randomly selected securities already have a high diversification effect (see Garz/Günther/Moriabadi, 2002, p. 41).

Fig. 9: Reduction of specifc, unsystematic risk via diversifcation

The diversification effect is based on the uniqueness of companies, which as a result reduce the portfolio risk below the arithmetic mean of the individual risks (variance) as soon as a portfolio consists of several securities. The diversification effect is caused by the correlation of the securities considered and is described by the correlation coefficient. The portfolio selection theory replaced the one-dimensional view of investments with a two-dimensional view. As a result, not only the expected return but also the corresponding risk is taken into account. This system helps the rational investor to decide under uncertainty either for a given return with the lowest possible risk or for a given risk with maximum return (see Gehrig/Zimmermann, 1999, p. 39). In this context, let us look at the concept of lending and borrowing. It assumes that it is possible to lend or borrow capital for an unlimited period at a risk-free interest rate in order to adjust the return in one’s own portfolio. The lender does this in order to invest part of the capital in the risk-free investment and thereby reduce the overall risk of the investments. The borrower on the other side tries to increase the return of the investments through the borrowed capital in the sense

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of the leverage effect. This creates an incentive for all market participants to hold only the market portfolio and to adjust it to their risk preferences via lending and borrowing. The empirical confirmation of risk reduction through portfolio construction consisting of numerous securities has contributed to the strong dissemination and acceptance of the portfolio theory in practice. Nonetheless, the application of the portfolio theory in practice is questionable at least. Among other things, there needs to be sufficient and reliable data for the expected return and risk. Although historical data series suggest that these two parameters are valid, it remains to be seen, to what extent the data will still be valid in the future and thus an efficient portfolio can be constructed ex-ante. Furthermore, as noted above, the advantages of the portfolio theory can only be used if the portfolio in question contains a sufficiently large number of securities. A private investor will not necessarily have the possibility to diversify sufficiently to align with the specific requirements of the portfolio theory.45 Numerous studies confirmed concentrated portfolios held by retail investors (Barber and Odean, 2000; Goetzmann and Kumar, 2008 and Mitton and Vorkink, 2007). Consequently, the theory is likely to be considered primarily by institutional investors. Even Markowitz admitted in an interview 1997 that he was unable to escape the constraints of the emotional consequences of investment decisions and invested like the majority of investors when he first set the asset allocation for his portfolio in mid-1950s: “I should have computed the historical co-variances of the asset classes and drawn an efficient frontier. Instead, I visualized my grief if the stock market went way up and I wasn’t in it – or if it went way down and I was completely in it. My intention was to minimize my future regret. So I split my contributions 50/50 between bonds and equities.” (Markowitz, quoted after Jason Zweig 1997) Nowadays, even portfolios with a relatively small number of securities can be optimized. Market Data and Financial Software firms like FactSet offer asset allocation tools to help optimize the asset allocation within a portfolio. The asset allocation tool in figure 10 is using a mean-variance optimization model to align with the desired return expectation of the client in the Wealth Management space. In addition, it is possible to incorporate one’s own view of the expected returns of individual asset classes into the portfolio weighting. This is done via the BlackLitterman editor, which is based on the Bayes’ theorem from chapter 1.2.4. Different estimates are used (based on historical data or equilibrium assumptions) to obtain a revised estimate. The resulting “mixed estimation model” was developed by Henri Theil46 in 1960, but was not applied to the analysis of financial data

45

We will go into further detail on the possibility of investing in investment funds in chapters 10 and 12.

46

Henri Theil | Dutch econometrician | 1924-2000

2.1 Models of Neoclassical Capital Market Theory

57

until the early 1990s by Fischer S. Black47 and Robert B. Litterman.48 The following illustration (Fig. 10) shows the optimization result from the existing portfolio (dark grey x with annualized return of 10.18 percent and 5.40 percent risk) to the optimal portfolio (light grey x with annualized target return of 13.00 percent and 5.25 percent corresponding risk). In addition, the risk/return profiles of the asset classes and regions used are also listed (e.g., Developed Europe with an annualized return of 14.16 percent and 9.80 percent risk. Asset Allocation Optimazation – Efficient Frontier

Fig. 10: Portfolio Optimization via the Asset Allocation Optimization Tool; FactSet

Biography of Harry Markowitz, Nobel Prize in Economic Sciences 1990 Harry M. Markowitz was born on August 24, 1927 in Chicago, Illinois. While studying at the University of Chicago, he became a member of the renowned Cowles Commission for Research Economics, which, among other things, defined and shaped economic thinking. In 1952 Markowitz began working for the RAND Corporation. During this time, Markowitz, who was only 25 years old, met William F. Sharpe. Together with Sharpe, Markowitz started the development of portfolio theory. In 1952 the article “Portfolio Selection” was published in the renowned Journal of Finance, which is considered a milestone in neoclassical capital market theory. The article became one of the most important economic publications.49 For the classification of portfolios, Markowitz subsequently worked on the further development of computer-aided calculation methods. For this purpose, he developed the so-called SIMSCRIPT programming language.

47

Fischer S. Black | American economist | 1938-1995

48

Robert B. Litterman | American economist | born 1951

49

https://doi.org/10.2307/2975974

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2 Limitations of Traditional Finance

In 1990 Markowitz received the Alfred Nobel Memorial Prize in Economics together with William Sharpe and Merton Howard Miller for his work in the field of portfolio theory. Capital Asset Pricing Model (CAPM)

Building on the portfolio selection theory, William Sharpe, John Lintner and Jan Mossin independently developed the Capital Asset Pricing Model (CAPM) in the 1960s. The CAPM enables the quantification and assessment of individual risk factors. It translates the risk-return preferences defined by the portfolio selection theory for an efficient market portfolio into a clear prediction of the relationship between return and risk. The CAPM is a one-factor model which is based on the level of systematic, non-diversifiable risk a security is exposed to in the market it is trading. In consequence, the larger the systematic market risk, the larger the expected return for the security in question. In order to identify an efficient portfolio, the portfolio theory was extended by two further theoretical model assumptions (see Fama/French, 2004, p. 26):  Assumption that all market participants strive to hold efficient portfolios in the sense of the portfolio theory.  Assumption of a market in equilibrium, which leads to a complete market clearance. The starting point of the CAPM is the Security Market Line (SML ‒ see Figure 11). It describes the expected return of an individual security (y-axis) based on systematic, non-diversifiable risk (x-axis) within the market portfolio. The level of risk is determined by the beta of a security against the market or market portfolio. The security market line helps to evaluate if an investment product offers a favorable expected return compared to its level of risk. Since the beta factor or the systematic risk is the second determining factor besides the risk-free interest rate, all investments ‒ whether portfolio or individual securities ‒ must always be on the security market line. The CAPM defines the correct risk premium (Ri - Rf) for individual securities as observed in the market. In well-functioning financial markets, the expected/required risk premium (return) of an investment is calculated using the beta factor of an investment (see Jokisch/Mayer, 2002, p. 148). Main assumptions for generating the Security Market Line (see Garz/Günther/Moriabadi, 2002, p. 65):  Markets with perfect competition, no one can influence prices, no taxes or transaction costs.  All market participants are rational and have homogeneous expectations. All investment alternatives are taken into consideration resulting in decisions based on risk-return assessments (expected returns and standard deviations).

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59

 All market participants are equally well informed. The CAPM therefore does not allow any information leaps by individuals.  There is only one risk-free asset, but multiple risk assets. Market participants can lend or borrow capital at a risk-free rate (see Lending/Borrowing in chapter 2.1.1) Based on the above assumptions an identical market portfolio can be determined for all market participants. Moreover, the assumptions illustrate a strong modeltheoretical reference. Deviating behavior on the capital markets and thus recurring deviations from the fundamental value of the securities are therefore not surprising. CAPM allows a clear prediction of the relationship between risk and return. The expected return is divided into a risk-free interest rate and a market risk premium. The risks assumed by the market participant are expressed by the beta factor of the investment.

Limitations of CAPM Fundamental criticism of the applicability of the CAPM is based on its theoretical model assumptions. The model assumes a market in equilibrium. Reality suggests however that securities are not arbitrarily divisible and that taxes and transaction costs exist in the market. Furthermore, empirical verifiability is almost impossible, since it amounts to an ex-post view, which cannot be transferred to an ex-ante view. The significance of the beta factors is particularly dependent on the selected time interval of the historical return values. Due to the high impact of the time component on the beta factors, precise documentation of the determination procedure is essential for later interpretation (see Gehrig/Zimmermann, 1999, pp. 82). A further point of criticism lies in the one-dimensionality of the model. Only historical returns are considered to calculate the market beta. Additional factors such as the size of the company, the price (market) to book ratio of a security or the price earnings ratio could significantly increase the information content of the estimation (see Gehrig/Zimmermann, 1999, p. 92). Nevertheless, the application of CAPM by the CFOs around the world demonstrates the great importance of determining the cost of capital as a basis for investment decisions. In 1999, Duke University conducted a survey with about 400 CFOs. 73.5 percent of the respondents said they would include the CAPM in their decision. A further survey of European CFOs in 2001 came to a similar result with 77 percent (see Mandelbrot, 2004, pp. 60). This high usage rate was confirmed by a further survey among 412 CFOs in 2012, conducted by ESCP Europe, with 80 percent of the respondents using the CAPM. Despite the limitations, the high rate of usage illustrates that the CAPM provides an important initial indication of the risks and returns of securities. It illustrates

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the risk aversion of investors and distinguishes between systematic and unsystematic risks of an investment (see Brealey/Myers/Allen, 2005, p. 16). Biography of William Sharpe, Nobel Prize in Economic Sciences 1990 William F. Sharpe was born on Juni 16, 1934 in Boston. In 1952, he began studying business administration at the University of California in Los Angeles. After graduating in 1956, he began his professional career at the RAND Corporation, where he met Harry Markowitz. Markowitz served as an unofficial mentor during Sharpe’s doctoral thesis. In 1961, he received his doctorate for his work on a one-factor model for pricing securities. He completed the CAPM while teaching at the University of Washington. In 1964 the model was published in the Journal of Finance. At the same time John Lintner and Jack Treynor also developed the CAPM. Sharpe was a professor at Stanford University from 1970 to 1998. During this time, he advised numerous investment firms. He also developed, among other things, the Sharpe ratio for comparing portfolio management performance and the binominal method for option pricing. In 1986, Sharpe founded the Sharpe-Russell Research Company (today: William F. Sharpe Associates) with a focus on advising pension funds, endowments and foundations on asset allocations. In addition to numerous honorary doctorates, Sharpe received the Alfred Nobel Memorial Prize in Economics in 1990. Deep Dive – regression coefficient Beta ßi The beta factor provides information about the expected return of an investment compared to the market return the respective security trades in: − Beta > 1 means that the return of an investment increases (decreases) more than the market return increases (decreases) on average. − Beta < 1 means that the return of an investment reacts less strongly to market movements. It increases (decreases) less than the market return increases (decreases) on average. − Beta = 1 means, that the volatility of a security’s return equals the volatility of the market’s it is traded on. On average the return of the security is the same as of the overall market’s. − Beta = 0 means, that the return of a security is independent of market risk. It corresponds to the risk-free interest rate. The beta factor is calculated by evaluating historical returns. It is defined as the quotient of the covariance of the expected return of security i with the expected return of market portfolio M to the variance of market portfolio M. β=

COV (ri, rM) VAR (rM)

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61

The expected return is divided into a risk-free interest rate and a market risk premium, which compensates for the systematic, non-diversifiable market risk. From the portfolio theory, it is evident, that diversification reduces unsystematic risks of individual securities. The capital market risks assumed by the market participants are expressed by the regression coefficient Beta ßi of the investment. It does not show how close the relationship between the return on securities and the market return is, but merely indicates the direction of the dependency. It is therefore a measure of the sensitivity of the return on an investment to changes in the market return. The beta factor can be interpreted as follows (see Jokisch/Mayer, 2002, pp. 146): It indicates how much the return of a security deviates on average from its expected value if the market return deviates by one unit from its respective expected value. This behavior is known as reactivity. The beta measures the volatility of a security relative to the volatility of the market it is traded on:  A portfolio with a beta factor of 1.5 has an expected return whose risk premium is 1.5 times bigger than the market’s risk premium.  A portfolio whose securities are characterized by high beta factors is riskier than a portfolio with securities whose beta factors are low. The portfolio is nevertheless less risky than the individual securities in the portfolio.

 Accordingly, a security with a beta of 1.5 illustrates that if the return on the equity market rises by 1 percent, the return on the security rises by an average of 1.5 percent.  A market portfolio containing all tradable securities has a beta factor of 1.  The higher the beta factor, the higher the return an investor demands for taking on additional risk as the systematic market risk cannot be diversified. The following example illustrates that aspect: if the beta of a security is 2.0, the risk-free rate (Germany) is 0.8 percent and the market rate of return is 10 percent, then the market’s excess return is 9.2 percent (10-0.8). Consequently, the excess return of the security is 18.4 percent (Beta x market’s excess return being 2 x 9.2) and the total required return to invest in that particular security would be 19.2 percent (18.4+0.8). In addition to the risk premium, the investor can also expect the time value of money, being the risk-free rate in the CAPM formula. This risk-free interest rate is nothing more than compensation for the temporary renunciation of consumption. The linear relationship between the market risk assumed and the return expected for it is shown in Fig. 11 below. According to the CAPM, all investments (portfolios as well as individual securities) can be found on the security market line (chart).

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2 Limitations of Traditional Finance

Fig. 11: Security-Market-Linie with market portfolio

The expected return μ is displayed through Ri for a given security. Rf indicates the risk-free rate and RM the expected return of the market or the →Market Portfolio. RM - Rf 1

=

Ri - R f βi

(RM - Rf) * βi = Ri - Rf

|* βi |+ Rf

CAPM: Ri = Rf + βi * (RM - Rf)

Arbitrage Pricing Theory as an alternative to CAPM

The Arbitrage Pricing Theory (APT) was developed by Stephen A. Ross in 1976 as an alternative asset pricing model to →CAPM. The APT is an asset pricing model that aims to identify securities which might be temporarily mispriced. Using the APT, arbitrageurs intend to exploit any deviation from the fundamental or fair value of a security. The APT is mainly differentiating to the CAPM by being a multi-factor model that adapts better to real market conditions in such a way that, unlike the CAPM, it takes multiple macroeconomic variables, that capture systematic risk, into consideration. The expected return of a security is based on the linear relationship between the securities’ expected return and the above mentioned multiple systematic, non-diversifiable risk factors. The ATP is based solely on the argument of the freedom of arbitrage on the capital market, i.e., the capital market offers no further opportunities for risk-free profits once all arbitrage possibilities have been exploited (see Bank/Gerke, 2005, pp. 189): The model comes with three main assumptions:

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63

 Returns of a security or asset are explained by systematic, non-diversifiable factors.  Market participants are risk averse when trying to exploit arbitrage opportunities.  In addition, the APT assumes a perfect market in which market participants behave as price takers. Despite being more aligned to realistic market conditions, the APT’s ability to exploit systematic deviations from the fundamental valuation of a security, is limited by certain factors. These factors, referred to as the “limits of arbitrage", will be the focus in chapter 4.1.2. The multi-factor model of the APT can be illustrated in the following form: E(Ri,t) = αi + βi,1 * F1,t + βi,2 * F2,t + … + βi,K * FK,t + εi,t In contrast to the CAPM, the risk factors in APT are not only captured by means of a variable such as the beta factor, but are identified as separate risk factors Fj (j = 1, ... K). Although these factors have micro- and macroeconomic backgrounds, the theory does not provide any information about their concrete nature or about the interpretation of their content (see Jokisch/Mayer, 2002, p. 149). Only the limitation to systematic factors is emphasized. These factors influence the returns of all securities traded on the market. The unsystematic risks to be reduced via diversification, are expressed by the residual εi,t. αi is the return if the security does not have exposure to any systematic risk factors, which is the case if ß = 0. The Arbitrage Pricing Theory is distinguished from the CAPM by the consideration of multiple, systematic risk factors. However, information on the concrete nature of the risk factors is not provided. Advantages and Disadvantages of the APT compared to the CAPM Advantages:  APT is characterized by the multidimensionality of risk factors. This allows more flexibility in modelling and a more differentiated insight into the risk structures of investments.  Better economic interpretability of the results if the risk factors are specified in advance of the assessment. In the past, the following factors have proved to be suitable: − Spread between long-term and short-term interest rates − Change in expected inflation − Change in the level of industrial production − Credit Spread between bonds with a high and a low rating.

 Better empirical testability of the results (see Bessler/Opfer, 2003): In the APT model, the market portfolio does not play a role. Its non-observability in reality

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makes empirical testing of the CAPM difficult.  Moreover, the model provides better empirical test results overall due to the higher explanatory insights and is also able to explain return effects for which the CAPM had previously failed (see Garz/Günther/Moriabadi, 2002, p. 77). Disadvantages:  Uncertainty about the nature of risk factors. There is no precise definition about the risk factors used for asset pricing. Although this means a high degree of flexibility, the unspecified nature of the risk factors entails the risk of pseudointerrelationships.  The practical application of the APT can be impaired by a considerably higher estimation effort in determining the risk factors (see Rau, 2004, p. 58). Biography of Stephen A. Ross Stephen Alan Ross was born February 3, 1944 in Boston and died March 3, 2017 in Connecticut, U.S. Ross received his bachelor’s degree in physics from California Institute of Technology (CalTech) in 1965 and continued his doctoral studies in economics at Harvard Business School, where he received his doctorate in 1970. Ross served as the inaugural Franco Modigliani Professor of Financial Economics at the MIT Sloan School of Management. Before joining MIT, he was the Sterling Professor of Economics and Finance at Yale School of Management and at the University of Pennsylvania. In addition to developing the Arbitrage Pricing Theory, Ross also helped to develop the foundations of the Theory of Agency. Ross has received numerous awards for his contributions among others in 2015, the Deutsche Bank Prize for developing models used for assessing prices for options, in 2012 the Onassis Prize for Finance and the Graham and Dodd Award for financial writing. 2.2

Valuation methods as a basis for financial decisions

The objective of research in financial markets is not only to evaluate investments per se but also to understand the observable behavior of market participants. The oldest form of research is the Fundamental Analysis, which helps to determine the economic situation of a company by using financial ratios (so-called multiples such as the price-earnings ratio). Based on that analysis a decision is formed to either purchase or to sell a security. In the course of time, empirical research led to the previously discussed neoclassical models and theories of traditional finance. These are based on more or less realistic assumptions of the capital markets and backed by mathematical approaches. Moreover, they are based on the basic framework of the rationally thinking and acting Homo Economicus.

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65

In the previous remarks on the observable behavior of market participants it could already be deduced that they do not always behave in conformity with the assumptions of the neoclassical capital market theory. This assessment becomes increasingly important when the number and intensity of both historical and recently experienced speculative bubbles in financial markets are taken into account (see chapter 5). These speculative bubbles suggest that market participants are not acting entirely rationally. Another example is their confidence in the second oldest form of financial market research being the Technical Analysis, introduced first by Charles Dow in the late 1800s. It is based on the observation of price developments with the goal to identify trading opportunities in price trends and patterns seen on charts. Technical analysis sees its main value in determining the best possible time to buy and/or sell an investment. This type of risk analysis can also be explained by the means of psychology. Over the last 40 years Behavioral Finance emerged as a new field of financial market research. It no longer starts from a rationally acting Homo Economicus, but rather from a semi-rationally acting →Homo Economicus Humanus (see Weber, 2007, pp. 11). The third section of this book will focus on the insights gathered via Behavioral Finance to better understand the observable behavior of the market participants. 2.2.1 Fundamental Analysis

Since the beginning of financial market research, fundamental analysis has meant little more than explaining the reason for rising or falling of security prices. The word “because” is key in understanding the reason for price movements. A security, currency or bond rises or falls “because” a certain economic or geopolitical development has an influence on the price development. The price of wheat rises “because” an extreme heat wave over Kansas destroys crops. The USD falls “because” a military conflict in the Middle East is imminent. If you know the reason, you expect to know the possible impact on the financial markets. But this view is by no means as clear-cut as it is described here. In many cases, the background can be neither fully captured nor can it be clearly interpreted. The market mechanism, which on the one hand links the news with the price development and on the other hand the reason for a development with the actual effect, is often inconsistent. In addition, it is often not the news that has an impact on the price development, but the price developments that determine the news (see Mandelbrot, 2004, pp. 7). The automatic search for causal links can be illustrated by a story in Nassim Taleb’s “The Black Swan”: The prices of U.S. treasuries rose on the day Saddam Hussein was tracked down in his hiding place in Iraq in December 2013 and Bloomberg News ran the following headline at 13:01 to give the “reason” for the market move: “U.S. treasuries rise, Hussein capture may not curb terrorism”. Half an hour later, bond prices fell again, and the new headline read: “U.S. treasuries fall; Hussein capture boosts allure of risky assets”.

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2 Limitations of Traditional Finance

Obviously, Hussein’s arrest was the most important event of the day, and because the automatic search for causes influences thinking, this event was the preferred explanation for everything that happened in the market that day (see Kahneman, 2015, p. 100). However, fundamental analysis is not only concerned with explaining price developments on the basis of the news flow. It also has the objective of evaluating key financial figures of public but increasingly also of private companies to subsequently derive investment proposals. The most commonly used forms of fundamental analysis are the Dividend Discount Model (DDM), which is restricted to publicly traded securities, and the much more comprehensive Discounted Cash Flow Analysis (DCF). However, according to the →Efficient Market Hypothesis, these analyzes are only advantageous and promising if they incorporate information that is still largely ignored by the market. Such information would be, for example, the introduction of new products resulting in additional cash flow (e.g., drugs from biotech companies). Exemplary is the price development of the German biotech company Biontech during the covid pandemic as it rose notably when news about a promising vaccine to prevent the spread of the disease was released. Basic idea of fundamental analysis Fundamental analysis assumes that security prices will develop over the long term on the basis of the company’s key financial figures. These figures help to determine the fair market value of a security and to forecast the possible price development to formulate an investment proposal (see Kitzmann, 2009, p. 64). In the following, the most important key figures and financial ratios are listed and briefly explained:  Earnings per Share, EPS: is a company’s profit divided by its weighted average number of common shares over the reporting term. Earnings per Share =

Net Income Weighted Avg. Shares Outstanding

 Dividends per Share, DPS: is the sum of declared dividends issued by a company divided by the amount of common shares outstanding at the period of dividend payment. Dividends per Share =

Sum of Dividends Common Shares Outstanding

 Book Value per Share, BVPS: Calculated by dividing the shareholder’s equity (which is the difference between total assets and total liabilities) by the total number of outstanding shares. Book Value per Share =

Shareholder Equity Total Outstanding Shares

2.2 Valuation methods as a basis for financial decisions

67

These key figures can then be used for the relative valuation of companies. Relative, because an over- or undervaluation of a security can be identified by comparing it with a benchmark or the average/median value of a peer group of similar companies.  Price-to-Book Value Ratio, P/B: The Price to Book value ratio is the current price per share divided by the latest quarterly Book Value Per Share. Comparing two securities of the same sector, the security with a lower P/B is more attractive since less has to be paid for a certain book value (e.g., P/B value < 1). A low P/B can be an indication of an undervalued company but also for a company with fundamental problems. Price-to-Book Value Ratio =

Current Price per Share Book Value per Share

 Price-to-Earnings Ratio, P/E: It compares the market value of the company (measured by the current share price) with the estimated earnings per share (EPS). If you compare two securities with otherwise identical factors, the company with the lower price-to-earnings ratio is more attractive because less has to be paid for a certain profit. A high P/E ratio can indicate that the security is over-valued. A company with no earnings or making losses does not have a P/E. Price-to-Earnings Ratio =

Current Price per Share Estimated Earnings per Share

The central concept of fundamental analysis is that of an “intrinsic or fair value" which a homo economicus would attribute to the share on the basis of the availability of all information relevant to valuation. If it is possible to estimate the intrinsic value of a company by analysing all valuation-relevant factors (future dividends, their presumed growth rate and risk-adjusted discount rates), a direct recommendation can be generated from the comparison with the share price of the company:  If the intrinsic value of a company is above it’s current share price, it is considered undervalued and should be bought.  However, if the intrinsic value is below the current share price, it is considered overvalued and the security should be sold. Consequently, fundamental analysis does justice to the normative decision theory of the neoclassical view in the sense that the result of the analysis leads to an immediate recommendation for action. (see example 2.1). Here, research analysts work with a whole range of multiples (EPS, P/E ratio, PBC, etc.) to determine the “fair value” of a security and generate a recommendation for action. The following example 2.1 in Figure 12 shows a Street Takeaway from FactSet StreetAccount Market Intelligence, in which former portfolio managers, traders, analysts and economists, summarize broker comments following major news events such as earnings, M&A, analyst presentations, and other company events.

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2 Limitations of Traditional Finance

It lists selected analyst comments to illustrate the “market opinion”. The objective of these takeaways is to filter out the “noise” from the news and to limit the information overload by means of easily readable bullet points. Portfolio Managers and analysts can quickly find out about the recommendations of Research Analysts on the securities they are invested in or are monitoring for a potential investment. Similar condensed reports are available through other market providers. Example 2.1: Analyst recommendations based on Snap’s quarterly result in October 2021

Fig. 12: Street Takeaway Snap Inc., FactSet StreetAccount Market Intelligence

Fundamental Analysis assumptions  Share prices and their intrinsic values regularly do not match.  Share prices tend to return to their intrinsic value, thus correcting an over- or undervaluation.  It should be possible to estimate the intrinsic value with sufficient accuracy, otherwise fundamental analysis would be superfluous. However, the exact determination of intrinsic value remains difficult, as there is no objective knowledge of the factors that determine value and the criteria that tell us how these factors should be valued and weighted in each individual case (see Schredelseker, 2002, pp. 300). In order to be able to make meaningful statements about the “fair” value of a security or a company, valuation models that discount future earnings are a prerequisite. The value of a security from the investor’s point of view can be interpreted as the sum of the present values of all dividends expected in the future. However, this valuation approach based on the Dividend Discount Model (DDM) reflects only part of a broader approach to investment valuation. For this reason, cash flow

2.2 Valuation methods as a basis for financial decisions

69

discounting is also to be considered as a possibility for valuing the company within the framework of a Discounted Cash Flow (DCF) analysis. The DCF analysis is extended to all investors, including lenders. It thus makes it possible to value companies as a whole, even if they do not pay dividends and therefore cannot be valued by the DDM. Both valuation methods are described below in order to illustrate the reference to the application of the CAPM. Both the DDM and the DCF are determined by two parameters. Firstly, the cash flows necessary to determine the value of the company and secondly, the discount factor (CAPM), which is used to discount the cash flows or dividends (DDM). To derive the DDM formula, we assume a holding period of one year for the selected security. Since neither the future price of the security nor the dividend at the end of the year is known today, the return is described as the expected value E(R): E(d1)

E(R) =

+

P0

Expected Dividend Yield

E(P1) – P0 P0 Expected Return on Capital

The formula illustrates that the return of a security is derived from two sources: the dividend yield E(d1)/P0 and the return on capital gains (E(P1)-P0)/P0. In order to calculate the “intrinsic” value of a security, the above formula is resolved to P0: E(R) =

E(d1) + E(P1) – P0 P

=

E(d1) + E(P1) P

-1

or P0 =

E(d1) + E(P1) 1+E(R )

The current price of a security P0 is therefore the sum of the present value of the dividends and the share price at the end of year P1. Both are discounted using the discount factor relevant to the market participant. This is derived from the cost of equity, which represents the expected or required return in the sense of the CAPM or APT (see chapters 2.1.2 and 2.1.3). In the case of the DCF analysis, all capital providers are included, with the consequence that the company cash flows are now discounted with the total cost of capital. The discounting factor is determined by calculating the Weighted Average Cost of Capital (WACC). The weighted average cost of capital is determined on the basis of the valuation of the cost of debt capital and equity capital according to their shares in the balance sheet at market value. While profits are subject to corporate tax and this is calculated before the corresponding payment is distributed to shareholders, borrowing costs can be deducted before tax is calculated. As a result, the cost of debt is reduced because a certain amount of tax is saved in line with the corporate tax rate. If this tax advantage is taken into account and the corporate tax rate is referred to as tc, then the WACC is calculated as:

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2 Limitations of Traditional Finance

WACC =

Equity Equity + Debt

*

E(RDebt) * (1-tc) +

Equity Equity + Debt

* E(REquity)

Here, E(RDebt) corresponds to the expected interest on borrowed capital or debt, while E(REquity) corresponds to the cost of equity (CAPM) already used for dividend discounting. Debt and equity are each represented at market value, respectively. FCFFt

T

P0 = Pshort + Plong = Σ

t=1

(1

+WACC)t

+

1 (1 +WACC)t

*

FCFFt * (1+g) (WACC - g)

The enterprise value is determined on the basis of the short-term (e.g., five years) as well as long-term cash flows (g - perpetuity as a constant increase in future cash flows after the short-term calculation period) of the company. The cash flows are determined by the Free Cash Flow to the Firm (FCFF) and discounted to today’s level using the WACC. The FCFF is calculated on the basis of Earnings Before Interest, Taxes, Depreciation and Amortization (EBITDA). After deduction of depreciation and taxes, the operating cash flow is obtained. In the final step, the free cash flow of the company (FCFF) is calculated by adjusting for investments in current assets and fixed assets. By subtracting the market value of debt from the calculated enterprise value and dividing the resulting equity by the number of shares, the value of a share is obtained discounted to today’s level. The central starting point of fundamental analysis is the determination of the “intrinsic value” of a security. This can be interpreted as the sum of the present values of all dividends expected in the future or, in the context of DCF analysis, by discounting the total company cash flows and subsequently deducting the cost of debt.

2.2.2

Technical Analysis

Technical Analysis is the study of historic price movements with the goal to predict/anticipate future price trends. The sources of information for interpreting price movements are obtained from the prices of securities and from the trading volume of the respective security. This type of analysis involves a variety of rules and strategies with which market participants observe the historical price developments in order to draw conclusions about their potential future course of action. Technical Analysts are not primarily interested in the company itself, but solely in past price developments. Consequently, fundamental analysis plays no role in this form of analysis, as it would interfere with their view on market developments.

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Even though, technical analysis is applied within institutional investment, it is much less important to professional investors compared to fundamental analysis. It is estimated that up to 90 percent of professional investors apply fundamental analysis in the context of securities analysis. Technical analysis tends to be more important for private investors, as this group of market participants is largely excluded from time- and cost-intensive fundamental analysis. Company information reaches the technical analyst last in the chain of market participants, so to speak. However, they believe that they can use the signals that the better-informed leave behind through their actions in the market. While fundamental analysis asks “whether” an investment should be made, technical analysis focuses on the question of “when” the investment should be made. An additional difference in the forms of analysis is the time horizon. The fundamental analysis is addressed more to the long-term oriented investor, while the technical analysis is important for the short-term investor The central assumption of technical analysis is that securities follow typical chart patterns known as “trends” (see example 2.2). This core assumption is also based on behavioral patterns of investors analyzed through Behavioral Finance, which are identified, for example, as →Herding. It can often be observed that rising prices cause prices to continue to rise, as investors jump on the “bandwagon” in the hope of rising prices. “The bull market nourishes the bull market” is an often used saying and can likely be applied to the cryptocurrency rally of 2020 and 2021. This self-reinforcing mechanism can be observed in both rising and falling prices. Behavioral Finance examines the triggers that lead to the formation of speculative bubbles in the context of the herd instinct. The topic of speculative bubbles is the central theme of the second section of this book. Example 2.2: Chart pattern One of the most popular price chart patterns is the “shoulder-head-shoulder” formation. It is a reversal pattern that indicates that the price is likely to move against its current trend soon. It occurs in two forms:

The left-hand chart signals falling prices after the price has not been able to overcome the absolute high point (head). As soon as the support line (neckline)

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is undercut or broken, falling prices are usually the case. The right-hand chart on the other hand signals rising prices. This is the case if the price development crosses the resistance line (neckline) after several lows. Technical analysts use these patterns to analyze highs and lows and then act accordingly. Overview Chart Types Technical analysts mainly use graphical price information when analysing trends. Depending on the selected time horizon, short-term charts (intraday charts), medium-term charts of up to six months or long-term charts of up to ten years are used. The horizontal axis shows the time horizon, while the vertical axis shows the prices. There are four basic types of charts according to their formal presentation (see Murphy, 2001, pp. 51). These are explained in the following illustrations:  Line Charts are a graphical representation of closing prices of a specific frequency (e.g., daily, weekly) where the price action is connected with a continuous line. This chart form is the most basic type and often used to visualize long-term price developments. Some believe that this form of presentation is best suited for technical analysis, as the closing price is perceived to be the most important price of a trading day.

Fig. 13: Line chart of S&P 500 with recession period (grey); FactSet

 Bar Charts, illustrate the fluctuation range of a particular period through multiple vertical price bars. Each bar typically shows the open, high, low and closing price of the day – also called OHLC-charts. Whereas the closing price is represented by a small horizontal line to the right of the bar, the opening price, is represented by a line on the left side of the bar. This type of chart gives an impression of the extent of the price fluctuations in the selected period and the general price dynamics. It allows technical traders to analyze trends, spot potential reversals and monitor price volatility.

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Fig. 14: Bar chart Siemens (6 Month, 2021); FactSet

 Candlestick Charts are the Japanese form of the bar charts as it originates from Japanese rice merchants and traders in the 1700s before it first became popular in the U.S. and in the last 15-20 years in Europe, too. The only difference to the bar charts is the graphical presentation of the price movements. In the case of the candlestick chart, a thin line (shadow) shows the daily fluctuation range from the highest to the lowest price. The wide part of the candlestick is called “real body” and measures the distance between opening and closing price. If the closing price is higher than the opening price, the body is white or green, whereas for the opposite it is black or red. This type of display best highlights the relationship between opening and closing prices and offers a better overview of the daily price fluctuation then the simple line charts.

Fig. 15: Candlestick Chart of Siemens (6 months, 2021); FactSet

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 Point & Figure Charts, use a series of boxes containing X’s for an uptrend and O’s for a downtrend. A minimum price change (also called box size) needs to occur for an X or O to be added to the current column. In addition, a new column is plotted when the price reverses by the so called “reversal amount”. In practice the 3-point reversal amount is used more frequently, which means that a selected price change is multiplied by 3 (e.g., price change 0.45 * 3 = reversal amount 1.35). Contrary to the above listed charts, the P&F charts plot price movements without considering the passage of time. As depicted below, the months are usually shown for orientation. The P&F charts are usually used to confirm trends from other chart types such as the candlestick charts to avoid false breakouts. This type of charting was originated by Charles Dow at the end of the 19th century and is mainly used in the U.S.

Fig. 16: Point & Figure Chart of Apple; FactSet

Criticism of technical securities analysis There are numerous reasons why technical analysis is controversially debated in the market and securities analysis (see Schredelseker, 2002, p. 394):  It is criticized that this form of analysis is not based on scientific theory, but rather on a series of ad-hoc statements.

 Technical analysis, if used by a majority of investors, would be self-fulfilling. Behavioral Finance examines herding as the cause of mass movement, which influences investors in their decisions. If, for example, investors would buy a

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security because it is close to a resistance line and further gains are expected when it breaks through, the security would subsequently reach higher prices.  Trading rules based on technical analysis are sometimes very vaguely formulated, which makes their applicability questionable. In particular, it is only clear ex-post when a trend has actually become one or when a price formation has assumed the predicted development. There are no clear predictions as to how long a trend will actually last after generating corresponding price signals. The central assumption of technical analysis is that the prices of securities follow trends. This assumption is based on the behavior of investors, which is often characterized by herding from the point of view of technical analysts.

2.3

Old vs. new reality ‒ the Black Swan

Zoologically a curiosity, but from the perspective of risk management the worst case ‒ Black Swans. According to Nassim Taleb, they are characterized by three basic attributes: [1] They are outside the normal expectations, since nothing indicates their existence in the past. [2] They have enormous effects on the future, and they are retrospectively predictable. [3] The latter is based on explanations that are constructed in retrospect. Black Swans change traditional investment paradigms and establish new ones:  The “long-only” principle of investment no longer seems to be the only ultima ratio. Investments into securities of any asset type undergo periodic corrections of differing magnitude. In recurring occasions, corrections triggered by a particular event can erase gains of months or even years in a short period of time (see covid induced 30 percent correction within 30 trading days in March 2020). Long term secular bull markets are followed by secular bear markets.  Traditional risk models cannot adequately reflect risks. Although classical financial risk models and valuation tools such as the portfolio theory according to Markowitz help market participants to better understand interrelationships between securities, they do not adequately reflect the real risks. Extreme risks in the form of →Fat Tail Risks, are systematically underestimated due to the assumptions of neoclassical capital market theory.

 Diversification has shown weaknesses in the course of the financial crisis. An analysis by Allianz Global Investors ‒ Neue Zoologie des Risikomanagements der Kapitalanlage (2010) showed that diversification in times of crisis, when low or negative correlations would be required, does not bring the expected risk

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reduction. The desired →Diversification Effect decreases in times of crisis because correlations change unexpectedly. Not only with regard to index and securities price developments, e.g. Financial Crisis 2007-2009, it is evident that the →Random Walk Theory on the basis of normally distributed returns is subject to considerable doubt. Recurring financial market crises indicate the appearance of the Black Swan described at the beginning. It occurs more frequently than the normal distribution according to Gauss would suggest. Thus, the assumption of traditional risk models that price fluctuations are normally distributed cannot stand up to practical testing. In reality, fluctuations in the return of securities do not follow the pattern of a bell curve, but fluctuate unevenly. Consequently, the normal distribution according to Gauss largely ignores the so-called Fat Tail Risk (extreme price risks outside three standard deviations). As can be seen from past experience, the neglect of extreme price risks has proven to be particularly detrimental to portfolio performance. When classifying extreme market corrections, not only the price change must be taken into account, but also the increasing volatility. Volatility is the range of fluctuation of price movements. For example, the internationally popular VIX Index (ticker symbol for the Chicago Board Options Exchange CBOE Volatility Index) measures the expectation of volatility based on the S&P 500 index options. High volatility usually occurs for a short period of time and then flattens out just as quickly. The volatility of an equity index expresses the nervousness of market participants. If investors are nervous due to increased uncertainty about expected market movements, they sell their securities as soon as new information reaches the market. In this respect, increased volatility is also a sign for →Herding (see chapter 4.1.1) by the market participants who, due to a certain event, try to align their actions in order to reduce or avoid →Cognitive Dissonance (see chapter 6.1.3). Volatility will only decrease again once market participants have become accustomed to the new information or situation. From this point on, price fluctuations are less pronounced and market participants begin to carefully weigh up the new information and incorporate it into the security prices. The beginning of the Great Depression in 1929, Black Monday 1987, 9/11 2001 and of course the Covid Pandemic triggered market crash in March 2020 are classical examples of black swans. Example 2.3: Black Swan event ‒ Russian default in August 1998 During the 1990s, the prevailing “bull market” seemed to reach its limits with rising security prices. There were isolated concerns about the recession in Japan, a possible devaluation of the Chinese Yuan or the removal of the 42nd U.S. President Bill Clinton through impeachment. There were however no significant problems. Suddenly, worrying news emerged from Russia in 1998. A region that two years earlier had been considered an emerging market was now defaulting on its sovereign bonds. Western financial institutions were in danger of suffering heavy losses. In fact, former U.S. hedgefund LTCM was holding significant positions in Russian government bonds now loosing drastically in

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value and bound to be bailed out in consequence by a consortium of Wall Street Banks to avoid further spill-over effects to other market players. On August 4, 1998, the Dow Jones reacted to news from Russia with a loss of 3.5 percent. Three weeks later, as news from Moscow worsened, the Dow Jones index fell a further 4.4 percent, only to finally lose an additional 6.8 percent on 31 August 1998. Other markets, such as the bond markets, were also hit hard. Some corporate bonds of financial institutions lost a third of their value. For many market participants, this was difficult to understand. There was a seemingly irrational and unforeseen panic. As we now know, the Russian crisis was quickly absorbed by the International Monetary Fund (IMF). Wall Street calmed down after the U.S. Central Bank’s (FED) support measures, and the bull market continued into the spring of 2001. According to the neoclassical capital market theory, this event, as well as the fastest market dislocation surrounding the start of the covid pandemic in March 2020, should hardly have ever happened. The neoclassical theories, which are an integral part of the curriculum at today’s business schools, consider the probability of such an event as that of August 31, 1998 based on the normal distribution to be 1 in 20 million. An event that could be expected, even if one were to trade on the capital market every day, once in 100,000 years. This is not surprising, since, as mentioned above, these scientific approaches are based on the bell curve, in which extreme price fluctuations occur extremely rarely. In reality, however, these market reactions can be observed more often than one would expect. One year before the Russian crisis, in the Asian crisis of 1997, the Dow fell 7.7 percent in one day ‒ probability scenario: 1 in 500 billion. In July 2002, the Dow recorded three strong days of losses in seven trading days ‒ probability scenario: 1 in 4 trillion. The probability of an event such as the one on October 19, 1987, when the Dow fell by 29.2 percent, is so low that it has no relevance in the calculations: 1 in 1050. These events illustrate: The capital markets are risky, and they are not fully covered by the Gaussian normal distribution. If we look at the analysis by Nassim Taleb on the daily price development of the Dow Jones between 1916 and 2003, it is striking that the price developments at the two ends of the normal distribution are unexpectedly high. The theory states that during this period there should be 58 days with price changes of more than 3.4 percent. In reality, however, such changes occurred 1,001 times during the period under consideration. Price changes of more than 4.5 percent should have occurred on six days, in reality there were 366 days. Price changes of more than 7 percent should occur once in 300,000 years, but in reality, there were 48 such days between 1916 and 2003 (see Mandelbrot/Hudson, 2004, pp. 3). In autumn/winter 2008, at the hight of the U.S. mortgage credit crisis, there were five days on which the Dow Jones moved +/- 7 percent. During the fastest market crash in the financial history in March 2020, there were 4 out of 6 trading days in the Dow Jones Index with daily losses of more than 6 percent. March 16, 2020 marked the peak with -13 percent.

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The statistically difficult comprehensibility of the capital markets is further illustrated by the number of days accounted for half of all profits in the financial markets: 10 days in 50 years (see Taleb, 2008, p. 331). Economists have been trying for a century to analyze, assess, explain and ultimately profit from risk. However, within the framework of these attempts the real probabilities of extreme price changes have hardly ever been sufficiently taken into account. Black swans occur unpredictably, have an enormous impact on the future and, in retrospect, appear predictable.

Summary Chapter 2 Portfolio Selection Theory is based on a two-parameter approach in which the expected return of an investment is described on the basis of the expected value at a given level of risk (the variance). The expected value and variance of a portfolio are determined taking into account the correlation between several stocks. Capital Asset Pricing Model translates the investment allocations for an efficient portfolio, as defined by portfolio theory, into a clear prediction of the relationship between risk and return. The expected return is divided into a risk-free interest rate and a risk premium that compensates for the systematic, non-diversifiable market risk. The capital market risk assumed by the market participant is captured by the beta factor of the investment. Arbitrage Pricing Theory is regarded as an alternative valuation model to CAPM. In contrast to the CAPM, the APT takes multiple risk factors into account and thus more closely resembles the real world. Although the risk factors have a micro- and macroeconomic background, the theory does not convey any information about their concrete nature and interpretation of their content. The main areas of financial analysis include, in the order in which they arise, fundamental, technical and behavioral analysis. The starting point of fundamental analysis is to determine the “intrinsic value” of a security. This can be calculated as the sum of the present values of all expected future dividends or by discounting the total corporate cash flows and subsequently deducting the cost of debt capital as part of the DCF analysis. Technical analysis is based on the observation of share price developments in order to draw conclusions about future rising or falling prices. Through various chart types, direct recommendations for a purchase or a sale of a security can be articulated.

Concluding remarks Section I

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In contrast to fundamental analysis, where the question to be answered is “whether” an investment is lucrative, technical analysis poses the question “when” the investment should be made. Sudden and unexpected market corrections characterize the appearance of black swans. This phenomenon is an indication that the capital markets are not fully following the Gaussian normal distribution. Rather, it is clear, that significant price changes occur much more often at the outer ends of the normal distribution than is postulated by the theory (fat tail risk).

Concluding remarks Section I The first section focused on the behavior of the market participant according to the concept of the Homo Economicus, which is based on the neoclassical capital market concepts and models. These are examples of the way in which a market participant should behave according to the theoretical model assumptions. The question arises as to how these models, which do not appear to be capable of making realistic forecasts all the time, could have achieved such significance in the first place. An answer could be provided by the Swedish National Bank, which, in honor of Alfred Nobel donates the Nobel Prize for Economic Sciences. In the past, the prize was awarded not only to several economists such as Harry Markowitz and William Sharpe, who developed models based on normal distribution, which are now known as the Portfolio Selection Theory. In 1997, Myron Scholes and Robert Merton also received the highest academic accolade for an option valuation model. It is therefore at least conceivable that the increase in the importance of scientific models based on gaussian normal distribution could be a direct result of the Nobel Prize. In 2013, additional scientists who have rendered outstanding services to the analysis of asset prices were honored. Among others, Eugene Fama received the Nobel Prize for his findings on the Efficient Market Hypothesis. Remarkably, however, Robert Shiller was also honored as a scientist who doubts the Efficient Market Hypothesis. The normal distribution runs through economics like a red thread, burning terms such as sigma, variance, standard deviation and correlation into the memories of economics students and finance professionals alike. But to what extent do these models actually reflect the actions of the Homo Economicus Humanus? Despite the comprehensive documentation of their purely theoretical assumptions, the neoclassical models are nonetheless used by investment professionals. Although they were repeatedly adapted after the crash on October 19, 1987, to reduce the accusation of being unrealistic, they are still being applied in practice. However, the current application of the models is also due to the fact that there are no relevant alternatives with which financial market participants could quantify their expected return and the corresponding risks. In this

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context, it is argued that the models are at the least rather suitable for “normal” trading conditions. Risk provisioning at banks, based on the normal distribution, culminated in the 1998 Basel II capital adequacy directive ‒ now reformed as Basel III in response to the financial market crisis. According to this, banks must deposit more equity capital when granting loans than was required before the new regulation. This is in response to the fact that risk management approaches based on normal distribution (e.g., →Value at Risk) are subject to weaknesses. While this regulatory attempt may mitigate the consequences of future financial disasters, a measure that directly fights the causes of misjudgments, remains wishful thinking. Rather, the recurring speculative bubbles considered in the second section are an expression of the bounded rationality of the market participants ‒ actors who are far detached from the assumptions of the Homo Economicus.

Section II ‒ Recurring speculative bubbles ‒ triggered by the Homo Economicus Humanus

3

Investor behavior from the perspective of Behavioral Finance The third chapter is devoted to the Homo Economicus Humanus – a market participant whose behavior triggered the expansion of existing paradigms. While working through this chapter, you will learn about the objectives and development of Behavioral Finance and experience the market participant with bounded rationality.

The first section of this book focused on the neoclassical view of the expected behavior of the market participant in the form of the Homo Economicus. The focus was on the concepts and models that are still widely used, whereby the deviations from real market conditions were highlighted step by step. In the second section, we go a step further and examine how the change in perspective from a rational Homo Economicus to an emotional →Homo Economicus Humanus is taking place. In this sense, the development of Behavioral Finance comes into focus, which is explained in the context of an incipient paradigm shift within the economic sciences (see chapter 3). In addition, this section examines the emergence of speculative bubbles (see chapter 4) as a consequence of limited rational behavior and selected speculative bubbles (see chapter 5) are examined in detail. 3.1 Starting point and objective of Behavioral Finance Behavioral Finance is a rapidly expanding field of economic sciences, with the primary objective to provide explanations for the decisions of market participants that can be observed in real life. These explanatory approaches are based on the combination of behavioral/cognitive psychology with the well-known models and principles of traditional economics. The difficulty of interpreting empirical observations of the behavior of market participants based on →Expected Utility Theory within an efficient market led to the development of Behavioral Finance. It aims to explain observable inconsistencies by considering the individual and group behavior of market participants (see Baker/Nofsinger, 2010, p. 3). In general, this line of research can be defined as a behavioral science-based financial market theory. It attempts to combine capital market theoretical approaches with behavioral approaches. Behavioral Finance has the following objectives:  The aim is to clarify why apparently rational investors in the financial markets repeatedly make semi-rational decisions and generally tend to behave subsequently semi-rational.

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 It aims to explain actual, observable investor behavior and other phenomena on the capital markets. With the help of the findings, recurring errors are to be avoided where possible.  In addition, it aims to supplementing existing models in those areas where weaknesses have been identified; among other things, the assumptions of neoclassical capital market theory, which in reality are not observable (see chapter 1 & 2). The central component and starting point of Behavioral Finance is the Theory of Bounded Rationality by Herbert Simon from the mid-1950s onwards. According to this theory, market participants are only capable of limited rational behavior. While neoclassical capital market theory assumes a rationally acting market participant who is practically exclusively focused on maximizing returns, Behavioral Finance analyzes the emotional motives that guide market participants in their financial decisions. Practice has shown that the rational Homo Economicus, who makes cool and objective decisions, only exists in theory. Rather, a Homo Economicus Humanus is emerging, which is often influenced by cognitive and emotional distortions. Moreover, empirical studies (see Fama, 1991) have shown that by far not all activities on the capital market can be explained with the help of the concepts and models of neoclassical capital market theory alone. Thus, one of the basic assumptions, the normal distribution hypothesis, is subject to considerable doubt. Even Eugene Fama and Harry Markowitz come to the conclusion that normal distribution is not necessarily applicable. It became apparent that the normative decision theory should be extended to include descriptive components, i.e., the observation of actual investor behavior (see Westphal/Horstkotte, 2002, pp. 210). Based on these observations, Daniel Kahneman and Amos Tversky developed the Prospect Theory in 1978 as the most important descriptive decision theory in Behavioral Finance research. The theory was developed as an alternative to the Expected Utility Theory. The Prospect Theory, which will play a central role in the third section, reflects a decision-making process under uncertainty, in which a decision is made between two alternatives. These alternatives are linked to one another through probabilities (also called prospects) (see Blechschmidt, 2007, pp. 11). The real strength of Behavioral Finance is shown, among other things, by the empirical observation that the limited rationality of market participants can lead to substantial and long-lasting deviations from the fundamental value of securities. This is the subject of chapter 4 in the analysis of speculative bubbles. The approaches of Behavioral Finance thus lead to a better explanation of investment returns based on the knowledge about the behavior of investors (see Garz/Günter/Moriabadi, 2002, p. 103). Behavioral Finance can therefore draw its justification from the analysis of problems which, using models such as Prospect Theory, explain the apparently irrational behavior of market participants and its consequences.

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The progressive development of Behavioral Finance has led, for example, to the concrete differentiation of →Heuristics, which are used to cope with the information overflow (see Barberis/Thaler, 2005, pp. 1). Behavioral Finance is based on the understanding that market participants express limited rationality. A Homo Economicus Humanus emerges, which is often influenced by cognitive and emotional limitations.

Biography of Herbert A. Simon, Nobel Prize in Economic Sciences 1978 Herbert Alexander Simon was born on June 15, 1916 in Milwaukee, Wisconsin. He died on February 9, 2001 in Pittsburgh, Pennsylvania. In 1933 Simon entered the University of Chicago to study social sciences and mathematics. He contemplated to study biology but discarded the idea as he was color blind. Earlier he discovered that the external world is different to the perceived world. An undergraduate field study for a term paper developed into his main field of activity: organizational decision-making. In 1942, he began a second education in economics at the University of Chicago and established contacts with economists (including Milton Friedman). He was also a member of the commission responsible for coordinating the Marshall Plan. He coined the term “bounded rationality”, which emerged as a central component and starting point of Behavioral Finance. According to this concept, market participants accept satisfactory solutions and do not always strive to maximize their investment returns. In 1975 he was awarded the A.M. Turning Award (highest distinction in the field of computer sciences) for his contribution to artificial intelligence and the psychology of human decision-making. In 1986 he received the U.S. National Medal of Science and in 1988 the John-von-Neumann Theory Award. He received the highest academic honor in 1978 in the form of the Memorial Prize to Alfred Nobel for his research on decision-making processes within economic organisations. Biography of Daniel Kahneman, Nobel Prize in Economic Sciences 2002 Kahneman is an Israeli psychologist born on March 5, 1934 in Tel Aviv. Kahneman studied psychology and mathematics at the Hebrew University of Jerusalem and at the University of California, Berkeley. After graduation, he worked as a consultant to the Israeli Defence Forces. He lectured at various Universities as the Hebrew University of Jerusalem, the University of British Columbia or the University of California, Berkeley. Kahneman is a professor emeritus at the Princeton University of psychology and public affairs.

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In collaboration with Amos Tversky he developed the new decision theory known as Prospect Theory. Together they gained international recognition through their experimental research on judgment heuristics and cognitive biases. Among numerous awards, he received the Nobel Prize in Economic Sciences in 2002, alongside Vernon L. Smith. He received the prize for his contribution to decision-making under uncertainty, which he developed with Amos Tversky: Based on their findings, market participants do not act based on restrictive economic assumptions but rather repeatedly make mistakes that can be proven and predicted. In 2013 he received the Presidential Medal of Freedom from President Barack Obama. Biography of Amos Tversky Amos Tversky was born March 16, 1937 in Haifa, Palestine. He died June 2, 1996 in Stanford, California. Tversky developed the Prospect Theory with Kahneman to represent human decision-making under uncertainty. In 1965 he received his doctorate from the University of Michigan and lectured at the Hebrew University of Jerusalem before moving to Stanford University. He has been instrumental in the psychological study of heuristics, cognitive bias, and decision-making under uncertainty. His essay in Econometrica on decision-making under uncertainty (1979), published jointly with Kahneman, is one of the most cited documents in economics50. In 1980 he was accepted into the American Academy of Arts and Sciences. Five years later he was accepted into the National Academy of Sciences of the United States. 3.1.1 Evolving concept of rationality

Since the existence of classical economics, the existence of the Homo Economicus was omnipresent, too. Despite the ever-increasing number of objections, this notion is still part of the central assumptions. Thus, market participants formulate their preferences as required by the axioms of the Expected Utility Theory. This means that the market participant:  behaves in accordance with axiom 0 ‒ according to which the formation of capital receives highest priority („more money is not worse than less money“)  observes the complete order ‒ according to which all alternatives are taken into account when taking a decision, and  respects independence ‒ according to which an original preference between two alternatives is not changed when further decision-making options are brought into play (“only money matters”)

50

https://doi.org/10.2307/1914185

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The assumption of strictly rational behavior in the sense of the Expected Utility Theory has, however, gradually eased in the meantime to the extent that it is assumed that market participants do not act strictly rationally but rather in a limited way (see Wahren, 2009, p. 64). This conclusion evolved with the result of surveys, experiments, brain scans and simulations, which lead to the conclusion that people do not behave as expected by neoclassical capital market theory. More so, deviation from the guidelines of neoclassical economics is not by chance and therefore unpredictably, but rather systematically. It can be considered proven that market participants follow their own behavioral patterns triggered by respective situations. Do market participants therefore act irrationally, or can they simply not behave differently? Based on the complex interrelationships on the capital markets, it can rather be assumed that market participants do not act irrationally, but simply try to cope with the most diverse and complex conditions, often involving uncertainty. Hence, capital markets are characterized by the following conditions:    

Information overload Few and unclear signals that require quick decisions Numerous options to choose from Uncertainty regarding the expected returns/risks

The enormous complexity on the capital markets allows market participants not only to make their own decisions, but also to influence others and to let themselves be influenced by others without losing their own orientation. Doing so, they avoid complex calculations and instead use rules of thumb also called “heuristics” deeply anchored in the brain to cope with the abundance of information. These heuristics:  enable the market participant to take a decision even if only limited information can be processed, and  represent short cuts in the brain that are part of a very specific rationality and allow market participants to remain functional under constantly changing economic situations. The heuristics-based behavior of market participants cannot therefore be described as irrational per se. Rather, it is an efficient strategy for managing the information flow. The patterns underlying these behaviors are, so to speak, part of the very own rationality of every market participant (see Heuser, 2008, pp. 35). Forms of rationality according to Oswald Neuberger Oswald Neuberger51 developed the following three concepts of rationality triggered by the lack of consideration of irrational action in capital market theory (see Wahren, 2009, pp. 66):

51

Oswald Neuberger | German psychologist | born 1941

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 Technical-instrumental rationality – requires the following assumptions for the rational behavior of the market participant: − Goal or a preference given. − Overview of the situation and all alternative courses of action are known. − Knows which actions lead to which result with which probability. − Has a decision rule that allows to choose between different alternatives. − Is infinitely fast in his decision-making. − Has all necessary information at his disposal. The above assumptions suggest that market participants may not be able to act rationally. The human being underlying the above concept of rationality corresponds in the broadest sense to that of the Homo Economicus. Furthermore, Neuberger distinguishes between normative and reflexive rationality:  Normative Rationality – is concerned with the general evaluation of an action: − How are the results of an action to be perceived? − What is the benefit of an action? − Are results and benefits socially recognized?  Reflexive Rationality – is concerned with how the market participant evaluates his/her action: − How does the market participant judge what is achieved with respect to the very own expectations? − If the market participant finds the achieved result in line with expectation, he or she has acted rationally in a personal sense. The decisive factor is who judges rationality. Different observers would thus be able to arrive at different judgments when evaluating an action. Definition of the term “rationality vs. irrationality” At this point, it is appropriate to take a closer look at the term “irrationality”, which is used in the literature to classify market participants when making suboptimal decisions. When an action or a decision is deemed irrational, it is important to pay close attention to the perspective from which the characterization is made. In the following examples, individual facts should illustrate the meaning of rational vs. irrational:  Does an investor who refrains from investing in equities and thus accepts a lower return act irrationally ‒ or from the own subjective perspective rather rationally, because the goal is to avoid unknown risks?  Does a fund manager who increases the cash positions in anticipation of falling prices act irrationally ‒ or rather rationally in the sense of the investors, even if one can assume that share prices will develop positively in the long term?  Is a professor who teaches his students portfolio theory, but at the same time refrains from a correspondingly optimal diversification, acting irrationally ‒ or rather rationally, since he or she prefers to live out his need for speculation in this way?

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In the light of these examples, Niklas Luhmann’s52 statement captures the problem of the term in a particularly effective way: “When investing, is it at all rational to behave rationally”? (Luhmann, quoted after Wahren, 2007, p. 68) The above listed examples show the difficulty to draw the line between rationality and irrationality with regard to investments. The point of view of the observer plays a decisive role in the assessment. Gerd Gigerenzer53 criticizes in this context that even psychologists are risking to abandon psychology for the pure logic of economic-rational thinking: “Generations of social science students have listened to entertaining lectures on how stupid the rest of humanity is for constantly straying from the path of logic and getting lost in the fog of intuition. But logical norms are blind to content and culture, disregarding evolved skills and environmental structures. Often, what looks like a thinking error from a purely logical standpoint turns out to be an intelligent social judgment in the real world.” (Gigerenzer, quoted after Wahren, 2007, p. 68 – translated quote into english) To answer the question: “What is rational?” Herbert Simon offers the following definition (Simon, quoted after Wahren, 2007, p. 66): „It is best to use the word ‘rational’ only together with adverbs: A decision …  … can be objectively rational if it is indeed the right behavior to maximize given values in a given situation,  … can be subjectively rational if it maximizes the achievement of the goal in relation to the current knowledge of the person acting,  … is consciously rational to the extent that the adaptation of means is a conscious process,  … is deliberately rational to the extent that the adjustment of means has been deliberately brought about,  … is individually rational if it is determined according to the individual’s goals.“ The explanations just given show to what extent the determination of whether a certain conduct is rational depends on the perspective and intentions of the respective decision-maker. As a consequence, the term “irrational” can only be used in such cases where, with knowledge of the framework conditions under which the decision-maker reaches a decision, the decision is clearly directed against all reason.

52 Niklas Luhmann, German sociologist, philosopher and social theorist (1927-1998) He was

considered a co-founder of the sociological systems theory and a transdisciplinary social scientist. 53 Gerd Gigerenzer, German psychologist (born 1947) is Director of the Center for Adaptive Behavior and Cognition at the Max-Planck-Institut in Berlin.

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The behavior of market participants cannot per se be described as irrational. The patterns underlying these behaviors are regarded as part of the rationality of each market participant. The above listed examples illustrate that is very difficult to distinguish between “rational” and “irrational” in all sorts of money matters.

3.1.2 Departure from the Expected Utility Theory ‒ Bounded Rationality

The concept of Bounded Rationality, introduced by Herbert Simon in the mid1950s, marked the beginning of a gradual departure from the Expected Utility Theory. At this time, economists found themselves in a quandary. On the one hand, they realized that the models based on rational decisions were hardly compatible with reality. On the other hand, the psychological insights of the time were also hardly compatible with the economic approaches of bounded rationality. According to the concept, market participants make decisions that are worse than those that would be possible under theoretical conditions. According to the maxim of bounded rationality, however, they are sufficiently good to end the search for alternatives once a solution has been found that makes you feel at peace (see Elger/Schwarz, 2009, pp. 49). In the course of decision-making, market participants consequently deviate systematically from the axioms of the Expected Utility Theory. The reason for this can be seen in the nature of the capital markets. Under the above-mentioned conditions, they exceed the cognitive abilities of market participants to calculate the future expected utility of all alternatives. Consequently, they are not in a position to identify the alternative with the highest expected value. This restriction does not, however, imply from the outset that market participants make fundamentally incorrect decisions and behave incorrectly. Rather, as has already been explained in chapter 3.1.1, the assessment of rationality must take into account the environment in which the decision is taken. The concept also allows for subjectively rational decisions. From this point of view, therefore, the decision-making process is always to be regarded as rational if it is purposeful and deliberate. It must, so to speak, be in harmony with the limited individual level of knowledge and the limited cognitive processing capabilities of the market participant (see Blechschmidt, 2007, pp. 13). Within the framework of bounded rationality, the decision-making process is controlled by two interlocking and interacting components which are linked together in the sense of a key-lock principle and lead to sufficiently good decisions.  On the one hand, there are the internal limitations of the human brain.  On the other hand, there is the information structure of the external environmental conditions in which the decision-maker acts.

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Sufficiently good decisions are therefore facilitated through the following measures:  Recognition and reflection processes that reduce the need to search for information,  Heuristics that control the search for information and determine its end,

 Simple decision rules, which use the available, found information An example that illustrates the effects of limited rationality is the investment/borrowing process in the context of customer advisory services at credit institutions. In the following, the effects are explained using the example of a private customer (see Brost/Neske, 2008, pp. 72). Example 3.1: Bounded rationality within the investment process The investment process impressively illustrates both the effects of bounded rationality and limited capacity for perception and processing of information. Due to the complexity of the information and decision-making situations, investors experience a high information deficit when it comes to selecting the right investment product. In addition to the need for information, human decision-making behavior is characterized by limited rational behavior, which occurs in such a way that the methods mentioned for determining sufficiently good decisions are applied to situations to which they do not (or no longer) fit. Thus, some investors react to recently experienced profits with an increasing willingness to take risks. In combination with this behavior, →Control Illusion suggests to the investor that his or her abilities are suitable for a much better performance. This behavior may also apply to the investor if losses have been incurred. In this case, the willingness to take risks increases in order to compensate for the losses suffered. A third type of behavior also illustrates the harmful effects of an unfavorable application of bounded rationality. Investors are often subject to short-sightedness (myopia) regardless of their experience. However, this is not only evident in the premature profit-taking, but also in the premature realization of losses made with investments for retirement. Interim corrections are overestimated, leading to a change of the risk assessment of products suitable for long term investments. The concept of bounded rationality is characterized through the following attributes:  The decision-making process is no longer based on benefit-maximizing, but on satisfying behavior of the market participants.  The assumption of deciding according to stable, complete and consistent preferences based on the Expected Utility Theory is becoming increasingly questionable.  Rather, the concept assumes that not all, but only some of the possible alternatives are used for decision-making:

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−

This behavior enables the market participant to compare alternatives of different dimensions with each other ‒ as a result, the optimal alternative is no longer selected, but the one that first meets the set requirement.

−

Thus, the search is no longer for the global optimum, but for a local optimum.

 A further characteristic of the concept is the splitting of the decision process into sequences or manageable partial problems instead of the simulated evaluation of all possible alternatives. Based on this assumption ‒ also known as sequencing ‒ the market participant is given the opportunity to feed back the consequences of decisions in the decision-making process. This enables adaptive learning. With the introduction of the concept of bounded rationality, fully rational behavior was, thus, recognized by economists as impossible, and the concept allowed the decision-making processes to be described better than entirely based on the model of the Homo Economicus (see example 3.1). Within the framework of bounded rationality, market participants make decisions that are worse than would be possible under theoretical conditions. However, according to the maxim of bounded rationality, they are sufficiently good to end the search for alternatives once a satisfactory solution has been found. With this concept, subjectively rational decisions are possible, and completely rational behavior becomes extremely unlikely.

3.2

Change of perspective within the framework of Behavioral Finance

In the course of the development of Behavioral Finance, and particularly due to the financial market crisis from 2007 onwards, calls are being made for a revision of the previously postulated neoclassical capital market theory. The basis for the discussion about a paradigm extension around the findings of the Behavioral Finance is based on the way market participants make their decisions. In order to be able to evaluate decisions, a basic framework is necessary which implies certain assumptions about their behavior (see Shefrin, 2008, pp. 1). The discussion about a possible paradigm extension starts at this very point ‒ should the assumptions be made exclusively on the basis of neoclassical capital market theory or should they be extended by the findings of Behavioral Finance? 3.2.1 Comparison of neoclassical and behavioral capital market theory

Description of capital market theories Basically, the two capital market theories differ, firstly in the behavior that is assigned to market participants and, secondly, in the extent of the rationality of the market participants.

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The neoclassical capital market theory assumes that the Homo Economicus makes his decisions on the basis of restrictive assumptions (see chapter 1.2.1). The main characteristic here is the complete rationality in the decision-making of generally completely homogeneous market participants. Behavioral capital market theory deviates from the postulated assumptions of the Homo Economicus and analyzes the observable behavior of in part completely heterogenic investors with the help of psychology, sociology and increasingly neurology. These are characterized by bounded rationality, which is caused both by the cognitive limitations of humans and by the information structure in the capital market. Basis for decisions In neoclassical capital market theory, decisions are based on the Expected Utility Theory developed by Morgenstern and von Neumann (see chapter 1.2.3). Market participants act in accordance with the postulated axioms in view of the uncertainty regarding the expected return. In accordance with the Bayes’ Theorem, probabilities are repeatedly adjusted in the course of new information. Psychologists now argue that market participants neither adhere to the axioms of Morgenstern’s and von Neumann’s Expected Utility Theory nor do they adjust the probabilities of an outcome based on the Bayes’ Theorem. Rather, market participants make their decisions on the basis of the →Prospect Theory of Amos Tversky and Daniel Kahneman (see chapter 6.2) as a counterpart to the Expected Utility Theory. Decision-making under uncertainty on the basis of the Prospect Theory is regarded as the intellectual basis of Behavioral Finance (see Pompian, 2006, pp. 20). Among others, the American economists Richard Thaler and Robert Shiller dealt with the driving forces behind investors’ decisions. Numerous studies focused on the consequences of feedback models and the impairment of arbitrage. The feedback theory deals with the speculative bubble formation as a consequence of unbridled, mutually influencing trading by market participants (see Shiller, 2003, pp. 90). Research results show that market participants make their decisions on the basis of rules of thumb or heuristics (see chapters 7, 8 and 9) and emotions (see chapter 13.3) instead of the Bayes’ Theorem. Heuristics can be divided into two groups. There are heuristics of cognitive origin, which are characterized by the fact that they require a higher effort and that their use during the information and decision-making process is higher. Another type of heuristics are the ones of emotional origin that are used to make quick decisions. The number of these heuristics is lower compared to heuristics of cognitive origin. The ongoing insights gathered through Behavioral Finance research led not only to the differentiation of numerous heuristics but also to suggestions when their impact on financial decision-making should be controlled. Michael Pompian published important insights in “Behavioral Finance and Wealth Management” in the

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way client advisory services can be enhanced using Behavioral Finance (see Pompian, 2006, pp. 33). There it is argued, for example, that depending on the amount of assets, it may not be beneficial to correct certain heuristics. These should be tolerated, as they do not pose a threat to the investor’s overall wealth. Other heuristics must in turn be corrected so that the investor’s assets are not endangered over the years. A detailed description of these findings can be found in chapter 10. In addition to using heuristics, market participants also base their decisions on emotions. Emotions influence the decision-making process by helping to avoid postponing decisions. In addition, they support the decision-making process by causing a decision to be made at all, literally speeding up the process. It is assumed that emotions and basic reasoning together achieve better results than decisions based only on rational considerations (see Elster, 1998, p. 59). Market characteristics A further distinction between the two theoretical approaches is market pricing. The followers of neoclassical economics consider the distribution of returns along a normal distribution. In this sense, the probabilities of individual price developments are independently and identically distributed. This assumption is based on the →Random Walk Theory by Bachelier (see chapter 1.2.2). Behavioral Finance advocates reject this type of return distribution and argue in favour of a strong dependence of the pricing process on recent history (“prices have a memory”). Another area in which both theories differ is within information distribution. The Neoclassics follow the approach of the →Efficient Market Hypothesis, according to which all information is already priced into the valuations and available to all market participants to the same extent. Behavioral Economists, on the other hand, put clear information asymmetries at the forefront of their argumentation. The information is neither available to all market participants nor is it immediately priced into the valuation of the security. Finally, market behavior differs in its tendency to overreactions. Within the framework of neoclassical capital market theory, overreactions do not occur because market participants behave rationally. Market participants who may be inclined to move prices against the fair valuation would be neutralized by arbitrageurs instantly. From the perspective of Behavioral Finance, there is a strong tendency towards euphoric or panic-like price movements. This is a characteristic that Daniel Kahneman also emphasized in relation to market psychology: “… Market has a psychology. Indeed, it has a character. It has thoughts, beliefs, moods, and sometimes stormy emotions.“ (Kahneman, quoted after Shefrin, 2008, p. 215) Arbitrage opportunities The last point of comparison focuses on the possibility to correct price movements caused by semi-rational market participants through arbitrage. This approach to generate excess returns is discussed in more detail in chapter 4.1 when it comes to the causes which strengthen speculative bubbles.

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The Neoclassics regard the arbitrage principle for approximating fundamental values as given and functioning. Behavioral Economists, on the other hand, perceive limitations to arbitrage or the unequal struggle between “smart” and “ordinary” investors as a central approach to Behavioral Finance research (see Shiller, 2003, p. 96). The strength of Behavioral Finance is shown by the fact that irrationality in the markets can lead to substantial and long-lasting price distortions (see Barberis/Thaler, 2005, p. 2; Daxhammer, 2006). Behavioral Finance can thus see its justification, among other things, in answering problems through the use of alternative models (e.g., Prospect Theory) to explain the bounded rationality of market participants and its consequences (see Barberis/Thaler, 2005, p. 1). Comparison of capital market theories reveals significant differences in the underlying characteristics Criterias

Neoclassical Economics

Description

 Economic Theory which defines the expected behavior of homogeneous market participants based on restrictive assumptions  Unlimited Rationality of Market Participants “Rational Economic Actor”

 Economic Theory which interprets the observable behavior of heterogenous market participants based on Psychology  Limited Rationality of Market Participants “Behaviorally Biased Actor”

 Expected Utility Theory  Bayes-Theorem

 Prospect Theory  Heuristics & Biases

Basis for decisionmaking Market characteristics

Behavioral Economics

 Random-Walk Theory – normal distribution of security prices (independent of each other)  Efficient Market Hypothesis – all market participants are equally informed  Market Participants do not panic

 Interdependent distribution of security prices  Information asymmetries – imperfect distribution of information  Market Participants do panic

Possibility  Functioning arbitrage as basis for arbitrage for approximation to fundamental valuation

 Limited possibility for arbitrage due to collectively occurring irrational/semirational behaviour

Fig. 17: Comparison of neoclassical and behavioral economics

The discussion about a paradigm expansion through the findings of Behavioral Finance led to the clarification of numerous differences, e.g., in the decision-making of investors or the nature of the financial markets.

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3.2.2 Research methods of Behavioral Finance

Predominantly, the research results of Behavioral Finance are based on the findings of social, psychological and behavioral research. These are used to make statements about the capital markets and to explain market events. Due to the complexity of human behavior, it was also necessary to narrow down the scope of research in this area. Behavioral Finance is thus limited to incidents in individual decision-making behavior that:  evolve systematically and  are not cancelled out by the actions of many market participants or disappear due to market forces. Furthermore, three factors play a significant role in Behavioral Finance research:  Individuals (decision-makers in the broader sense)  Investors (decision-makers operating on capital markets)

 Market variables (such as prices and volumes) Since the group of individuals has been sufficiently surveyed by psychological research, the main focus of Behavioral Finance is on investors and market variables. The research methods used in Behavioral Finance have developed over time from pure surveys to simulations based on new technological possibilities. The individual research methods are explained in more detail below (see Heuser, 2008, pp. 35). Surveys Surveys were the starting point for scientists to identify certain patterns of economic behavior. As a result, shortcuts in the thought process have become visible, making it easier for market participants to deal with the abundance of information and stimuli on the capital market. For example, surveys showed how subjects’ probability assessments change when the corresponding event becomes more probable due to easily imaginable accompanying circumstances. Example 3.2: Assessment of probabilities Subjects evaluate the probability of the S&P 500 falling by half differently without additional information than if the S&P 500 were to fall due to an easily imaginable event. For example, the probability is rated higher if the stock market collapse is associated with a sharp rise in oil prices as a result of a war in the Middle East. Assessing probabilities in relation to the imaginability of the underlying events can not only lead to massive economic misjudgments, but also to shortening the decision-making process. If a scenario is in the consciousness of the decision-maker, the probability of its occurrence is perceived to be higher than is objectively the case.

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Apart from testing hypothetical questions, surveys also allow to test expressions of opinion and well-being. However, special caution is necessary in the interpretation, since the answers of an individual survey cannot be generalized without further consideration. More meaningful are long-term studies, such as the “German Socio-Economic Panel ‒ SOEP”. This is an annual survey since 1984, in which approximately 11,000 households with about 23,000 people are surveyed. The results of the panel make it possible to review theses and develop new questions. Experiments Experiments offer another possibility to investigate the actual behavior of humans. Experimental economic research heralded a new era in economic thinking. In experiments, certain games are replayed by the participants, often involving real money. Probably the most frequently conducted experiment is the ultimatum game. It not only shows a pattern of perception by market participants, but also gives an idea of what is important to them and what drives them. The ultimatum game is an example of how fairness and cooperation between two people can exist or be denied. The game is about sharing a certain amount of money received by player A with his opponent B. If player B does not accept the offer from player A because he or she feels that the amount offered is too little, then both go away empty-handed. Thus, it can often be seen that player B sacrifices the offered amount that he or she might receive because he or she feels that the division is unfair. However, the decision of the players varies considerably with regard to the amount of money wagered. The game can also be played in a simplified form as a dictator game. In this case, player B, as the recipient of the money, can no longer refuse A’s ("dictator") offer if he or she considers it unfair. In this case, however, it can also be observed that the dictators would give up a considerable part of the total amount, even if this is not strictly rational in the neoclassical sense. Even if the experiments reveal numerous insights into human behavior, the investigations still need to be much broader. This is necessary because the laboratory experiments are mostly carried out with students, who per se cannot be regarded as a fair representation of society. Brain Scans Modern brain research offers a further and extremely effective way of identifying the actual behavior of market participants. It helps to clarify which part of the brain actually becomes active when an individual makes a decision. The link between neuroscience and economics is also referred to as Neuroeconomics54 or, in a specific case, Neurofinance (see chapter 13.2).

54 In addition to Neuroeconomics, Neuromarketing has emerged as a further field of science involving research on the brain. Neuromarketing is a branch of marketing that uses neuroscientific technologies, for example functional magnetic resonance imaging. The goal of neuromarketing is to investigate the previously invisible states and processes that control

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The activity of individual brain areas is mapped by measuring the oxygen content. If an area is subject to greater stress during the decision-making process, the MRT ‒ Magnetic Resonance Tomograph ‒ registers the additional oxygen content in the blood and displays it in different colors. With this type of examination, economists have the possibility to recognize in which areas of the brain emotions such as anger, shame or joy are evoked and where conscious consideration and rational decisions can be located. Example 3.3: Activity of individual brain areas in dependence of perceived information Subjects were presented with several products while their brain waves were measured. First, only the product was shown, then the price was added. The subjects were asked to decide between two products and to purchase one of them. The test initially showed that different brain regions were activated depending on the perceived information (product vs. price). If only the product was visible, the part of the brain responsible for anticipation would dominate. If the respective price was displayed in the next step, the part of the brain responsible for the expectation of pain and loss of money was activated directly. The activity of this part supported the decision not to buy. This experiment illustrates how people compare the immediate pleasure of receiving the product with the immediate pain of spending money when making consumption decisions. The credit industry found a quick answer to these findings. Credit cards or consumer loans prevent the immediate pain, because the product does not have to be paid for immediately. This could also explain why in the U.S., but also in Germany, credit card debt has been increasing among the population in recent decades. The results from such brain scans are very promising. It can be assumed that new theories of human behavior could be developed on the basis of these findings. Simulations The fourth way of investigating the actually observable behavior of market participants results from simulations. This option enables theories to be checked and optimized with the help of computer simulations. They can provide an answer to how market participants interact under certain circumstances. It is also possible to check how the result changes if the settings are changed. Thomas C. Schelling55 is considered an expert in the field of simulations. In addition to the development of the nuclear deterrence strategy during the Cold War,

the decision of a potential consumer for or against a product and to relate them to visible behavior. In particular, it is investigated which brain areas are activated by different (product) stimuli. 55

Thomas Crombie Schelling | American economist & Nobel Prize winner 2005 | 1921-2016

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he has also studied everyday phenomena such as the disintegration of ethnically mixed districts. Example 3.4: Simulation of trading after the introduction of capital gains tax The introduction of a capital gains tax in a fictitious trading environment can be seen as an example of simulations on capital markets. In this case, the determining factor “taxation” changes, which can lead to a change in the trading volume of securities. The result of the simulation can represent both the increase in securities trading until the introduction of the additional tax and the increase/decrease in securities trading with the introduction of the capital gains tax. It is also possible to check the effect on securities trading of advertising messages sent before the introduction of the tax. Behavioral Finance provides insights into the behavior of market participants in order to make statements about the capital markets and explain market events. This is particularly necessary if the (unrealistic) assumptions of the neoclassical capital market theory cannot adequately explain real market movements. Behavioral Finance uses different methods to investigate the behavior of market participants. These include surveys, experiments, brain scans and simulations.

3.2.3 The investor in the course of time

For more than two centuries, economic science has built its foundations on the existence of the Homo Economicus. Adam Smith’s work “The Wealth of Nations” published in 1776 not only laid the foundation for a new science, but also created the image of the “desired” market participant in the form of the much-cited Homo Economicus. Management methods and leadership principles continue to be based on the premise that people actually behave as Adam Smith assumed. And while John M. Keynes introduced the term “animal spirits” to explain economic fluctuations, the neoclassical concept of the rational market participant continued to dominate capital market theory in the 20th century. With every model, with almost every new result, the contradictions between the theoretical guidelines and the observable behavior of market participants increased in the course of the 20th century. Accordingly, market participants seemed, e.g., to be less interested in maximizing self-interest than in aligning reactions with the fair or unfair actions of their surroundings (see Elger/Schwarz, 2009, p. 49). As a result of the increasing realization that market participants do not live up to the expectations, the foundation on which neoclassical capital market theory is based is increasingly questioned. The scientific community is striving to adapt the rational Homo Economicus in order to come closer to the modern economic world

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and align the theoretical foundations with the observable behavior of the market participants (see Fig. 18). The concept of the Homo Economicus Humanus has the advantage that market participants can identify themselves with the respective assumptions. Experiments with students and other subjects clearly show deviations from the ideal conception of an investor within the neoclassical capital market theory. The subjects were not able or did not want to maximize self-interest alone. They sometimes deliberately accepted losses in order to motivate others to be fair and cooperate or simply to comply with their own moral concepts. The considerations presented here paint a picture of a market participant who, guided by rules of thumb, often follows own ideas of fairness and rationality. The new behavior-oriented capital market theory is capable of freeing the individual from the rigid behavior of the Homo Economicus. The human being becomes an influencing, but also an influenceable being (see Heuser, 2008, p. 36). Changing perspectives lead to a revaluation of the market participant in the form of the behaviorally biased actor aka Homo Economicus Humanus Criterias

Rational Economic Actor

Behaviorally Biased Actor

Perspective of market participant

Market participant as a rationally calculating being that strives to maximize benefits by including relevant information

Market participant as semi rational being, who neglects information, and is influenced by feelings, intuitions/emotions

Foundation

Concept based on a decision-making system fueled with restrictive assumptions about investors and the market itself

Concept based on findings resulting from surveys, experiments, brain measurements and simulations

Use of information

Optimal information processing, whereby market participants maximize utility and make rational decisions

Limited information processing capabilities among market participants due to cognitive and emotional influences

Scientific Background

Normative decision-making framework based on classical economics (18. C.) “How should the investor behave?“

Descriptive decisionmaking framework based on Behavioral Finance (from 1980) “How does the investor behave?“

Fig. 18: The investor in the course of time

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The market participant in the sense of the Homo Economicus Humanus is less interested in maximizing self-interest than in orienting his reactions to the actions of other individuals. Neoclassical capital market theory reacted to the initial findings of Behavioral Finance with skepticism and rejection. Merton Miller56, the co-founder of the →Dividend Discount Model, expressed a clear criticism of Hersh Shefrin57 and Meir Statman’s58 finding that dividend payments would psychologically guide the investor in several respects: “They were distracting and diverted the attention of scholars away from identifying the fundamental forces that drive markets.” (Miller, quoted after Shefrin, 2000, p. 9) Shefrin and Statman argued that the dividend payment is intended to tempt the investor to use the dividend for consumption rather than selling the asset to finance consumer spending: “[…] only consume the dividend, but don’t touch the portfolio capital.” (Shefrin und Statman, quoted after Barberis/Thaler, 2005, p. 59) Dividend payments for consumption purposes would also prevent possible regrets if sold assets subsequently continued to rise (see Barberis/Thaler, 2005, p. 620). This phenomenon, also known as Regret Aversion, will be the focus of the discussion in chapter 9.2. In addition, Robert C. Merton59, Nobel Prize winner and once in charge of the failed LTCM60 hedge fund, has expressed his rejection of the recognition of the existence of inefficient markets: “[…] the evidence against market efficiency was premature.” (Merton, quoted after Shefrin, 2000, p. 10) It may be true that the findings in 1987 were not strong enough and could not be supported by sufficient market data. However, the fundamental rejection of the new findings was reckless and very costly in view of the crisis surrounding LTCM. The controversial consideration of efficient markets reached its peak with a publication by Fama, the founder of the Efficient Market Hypothesis. The title of the publication clearly shows his attitude towards the findings of Behavioral Finance: “Efficiency Survives the Attack of the Anomalies” (Fama, quoted after Shefrin, 2000, p. 10) 56

Merton Miller | American economist & Nobel Prize Winner 1990 | 1923-2000 Hersh Shefrin | Canadian economist 58 Meir Statman | Israeli economist | born 1947 59 Robert Cox Merton | American economist & Nobel Prize winner 1997 | born 1944 60 Long Term Capital Management – A hedge fund which went into bankruptcy in 1998 in the course of the Russian sovereign debt fall-out as irrational valuations lasted longer than LTCM management expected. 57

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Summary Chapter 3 Research into emotional and cognitive behavior led to the development of Behavioral Finance from 1980 onwards. This new field of research attempts to explain what happens in the financial markets by taking human behavior into account. Behavioral Finance is based on the insight that market participants are only capable of limited rational behavior. A Homo Economicus Humanus is emerging, who is regularly influenced by cognitive and emotional limitations. The Prospect Theory is one of the foundations of Behavioral Finance. It was developed in 1978 by Kahneman and Tversky as a descriptive decision theory. The theory was conceived as an alternative and generalization of the Expected Utility Theory. The behavior of market participants cannot be described as irrational per se. The patterns underlying these behaviors can be regarded as part of the subjective rationality of each market participant. The discussion about a paradigm extension by the findings of the behaviororiented capital market theory led to the clarification of numerous differences, e.g., in the decision-making of investors or the nature of the market. Behavioral Finance provides insights into the behavior of market participants in order to make statements about the capital markets and explain market events. There are various ways of researching behavior, including surveys, experiments, brain measurements and simulations. The market participant in the sense of the Homo Economicus Humanus is less interested in maximizing self-interest than in orienting his reactions to the actions of other individuals.

4

Speculative bubbles as a sign of market anomalies The fourth chapter focuses on speculative bubbles as signs of recurring and persistent market anomalies. In addition to the reasons for the formation of bubbles, you will learn about the different phases and types. Furthermore, you will be able to classify the herd instinct as the driving force of speculative bubbles within the structure of recurring market anomalies. Finally, you will get to know the most important capital market anomalies, some of which only last for a short time, while others can be observed on the capital markets in the medium to long term.

Speculative asset price bubbles ‒ which in the standard definition are described as a strong and long-lasting mispricing of the speculative object/asset class (see Brunnermeier/Oehmke, 2012) ‒ run like a red thread through the history of the financial markets. The first supposedly significant speculative bubble emerged in the 17th century in Holland as the widely known Tulip Mania. Others were to follow in the next centuries (see chapter 5). But how do these mostly euphoric and ultimately panicky market developments arise? This chapter explores the drivers for these recurring speculative bubbles. 4.1

Causes of speculative bubbles and their intensification

Speculative bubbles do not arise due to a sudden event. It is a chain of diverse circumstances that lead to a long-term exaggeration of the value of a speculative object or even the economy of a country. An important aspect to understand the development of bubbles lies within the term: “social contagion of boom thinking”. It is fueled by the public observation and media coverage of rapidly rising security prices, resulting in a narrative that strengthens the belief in the continuation of the boom and that lends it ever more credibility (see Shiller, 2008, pp. 56). This development on the capital markets, known as feedback theory, is one of the causes of speculative bubbles. Alan Greenspan, former chairman of the U.S. Federal Reserve (1987-2006), recognized in March 2008 ‒ after the mortgage bubble in the United States popped ‒ that there had indeed been “enthusiasm” and “speculative fever”. He wrote in the Financial Times: “The essential problem is that our models – both risk models and econometric models – as complex as they have become, are still too simple to capture the full array of governing variables that drive global economic reality. A model, of necessity, is an abstraction from the full detail of the real world.” (Greenspan, 2008)

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While Greenspan acknowledged the reality of speculative bubbles, he remained a proponent of the assumption that formal mathematical models are the only way to understand economic processes. The only limitation he sees is not the assumptions made, but the limitation of the amount and type of data and our ability to evaluate it. Greenspan’s statement further illustrates that the majority of economists do not necessarily consider the contagion of market participants by ideas/stories to be systematically relevant. Formation of asset price bubbles like epidemics As Greenspan has already admitted “speculative fever” as the cause for an asset price bubble, social contagion can be described by the example of an epidemic or pandemic – the latter referring to an epidemic spreading across multiple continents or even globally, a term which has been widely familiarized during Covid19. Like epidemics, speculative bubbles also break out from time to time. A theory has been developed for the spread of an epidemic that enables doctors to better understand the epidemic: Epidemiology. This theory is basically driven by two aspects. One is the rate of infection and the other is the rate of decay. The rate of infection indicates the rate at which the infection is transmitted from person to person. The rate of decay indicates the rate at which individuals recover or fall victim to the disease and are therefore no longer contagious. If the infection rate exceeds the decay rate, an epidemic breaks out. The infection rate develops based on certain factors ‒ such as weather conditions. For example, in winter the infection rate of the flu is higher because the virus can spread better due to the low temperatures. In society, the spread is similar to a speculative epidemic. Sooner or later the infection rate rises above the decay rate due to a certain factor and an overly optimistic market assessment starts to spread out. As arguments for further rising asset prices gain broader acceptance among investors, the epidemic eventually gets out of control. Example 4.1: Contagion through interpretation during the real estate boom of the mid-2000s Karl Case and Robert Shiller conducted in 1988 and in 2003 two practically identical surveys among home buyers in Los Angeles, San Francisco, Boston and Milwaukee (see Case/Shiller, 200461). The results showed impressively how strongly expectations about rising house prices impacted the decision to buy a house, which further fueled the real estate boom. The average price increase that home buyers expected for the next 10 years was between 11.7 percent p.a. and 15.7 percent p.a. (survey of 2003). Note, a rate of

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11.7 percent corresponds with a tripling of the value of the property in ten years. The survey responses showed that the interpretation of the already realized considerable price increases and stories by other buyers influenced the decision to also buy a house. This illustrates how strongly market participants’ assessment can be influenced by other investors’ opinions. Another central point that contributes to the intensification of a speculative bubble is the media coverage and more recently social media. The latter played a key role in the so-called “short squeeze” of GameStop, which triggered a rally of 1,600 percent in January 2021. In essence, a group of day traders on Reddit – an American social news and discussion website – had been arguing for some time that GameStop’s share price was undervalued. Their investment case was boosted by the large presence of short sellers in the stock. These day traders realized that they could push the share price to such a level that would force the short sellers to buy back shares, thus covering the short position and creating a huge profit for themselves. It is plausible to assume that without the usage of such a social platform ‒ which has several million users – the day traders may not have been able to force professional investment firms into a short squeeze (see Smith/Wigglesworth, 2021). The media typically intensifies its coverage with increasingly rising prices, which subsequently encourages the belief in the story of the boom, leading to further price increases. This phenomenon is also known as the price-story-price-loop and can be compared with the self-fulfilling prophecy. With both phenomena, market participants anticipate the respective development of the securities and act accordingly. The feedback loop described above can also occur in the form of price-economic activity-price loops. In this case, economic growth increases due to rising prices, which leads to further loops due to increasing economic optimism. This further increases the economic activity of a country. Speculative bubbles do not necessarily happen because of limited rational behavior. In the initial stage, the bubble can also be regarded as the result of rational behavior. This is the case when market participants obtain information by observing the behavior of other market participants, which they then disclose to others through their behavior. Rational market participants would base their decisions on the actions of other market participants, thus saving information costs through rational interpretation and consequently participate in the formation of the bubble. However, the initially as rationally perceived interpretation can later turn out to be erroneous. This is the case when rational market participants adopt overly optimistic/pessimistic view of others and thereby disregard their own independently collected information. This behavior, known as the information cascade, leads to a decline in the quality of information in the group. This loss of quality occurs

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as more and more members of a group disregard their own independently and personally collected information and focus on their general perception. As a result, they no longer appear in the collective judgment of the group, which means that the quality of the information in the group is increasingly deteriorating (see Shiller, 2008, pp. 56). Boom thinking is often promoted by general observation and media reporting. As a result, stories emerge that strengthen the belief that the boom will continue. This development on the capital markets, known as feedback theory, is one of the causes of speculative bubbles as a result of unbridled trading by market participants. In the early days of Behavioral Finance research, the dominant view was that the mistakes made by individual market participants were no longer significant in the overall market development. However, this view fails to recognize how systematically prices can be distorted when not only individual investors but masses of market participants leave or enter the market (see Kitzmann, 2009, p. 22). This is aptly illustrated by the event surrounding GameStop as described above. 4.1.1 Herding

As described in the previous subchapter, speculative bubbles are mostly based on boom stories, which generate more and more attention in the course of their distribution among investors and ultimately drive up asset prices. Market participants who observe each other and orient themselves accordingly, play a major role in inflating asset prices. People, as social beings, not only orient themselves towards others, they also look for leading figures and “swim with the current”. Shiller describes the significance of herd behavior as follows: „The meaning of herd behaviour is that investors tend to do as other investors do. They imitate the behaviour of others and disregard their own information.“ (Shiller, quoted after Redhead, 2008, p. 542) Charles-Marie Gustave Le Bon62 one of the founders of mass psychology outlined in his work La psychologie des foules (translated: The Crowd: A Study of the Popular Mind (1896)) the most important statements about herding:  Masses develop a collective soul – acting among participants is coordinated. There is a high degree of solidarity within the masses.  Overall interest infects individual interests ‒ emotional contagion in the context of feedback theory.  Simple feelings predominate ‒ individuals in the masses are characterized by impulsiveness and irritability.  Opinions and rumours sway high and lead to the formation of opinions on the basis of individual rumours or suppositions. 62

Charles-Marie Gustave Le Bon | French social psychologists | 1841-1931

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Even though the above statements were formulated over 120 years ago, they seem to be at the core in the current discussion on herding through social media. Mass phenomena are particularly evident when a bubble bursts or sudden price losses occur. In such moments, as was the case on 9/11, 2001 and in the days following the resumption of regular trading on the world’s stock exchanges or the covid induced market crash in March 2020, the masses act in panic when prices unexpectedly fall. Fear seizes almost all market participants causing securities to be sold at any price. As a result of this panic-like behavior, many more market participants are enticed to sell their securities, causing even greater losses. According to Le Bon, the mass is characterized by the following features (see Le Bon/Eisler, 2007, pp. 29):  Loss of conscious and predominance of the unconscious personality.  Orientation of thoughts and feelings by suggestion as well as contagion from other market participants.  Tendency for the immediate realization of the suggested ideas. Of decisive importance in the intensification of speculative bubbles is how the majority of market participants judge information. In this sense, the media coverage but even more so social media plays a major role. Whereas investment magazines or electronic media played an important role for retail traders in the past, social media channels offer a new environment to exchange ideas on potential trades. These channels are especially attracting the younger smart phone affine generation. If certain reports have a concerted effect on the masses, the attention paid to fundamental valuation becomes increasingly less important and the securities are traded according to the views expressed in those reports. The mood determines the market behavior of investors (see Kitzmann, 2009, pp. 20). Herding or herd behavior can be divided into four categories (see Graham, 1999, pp. 239 and Daxhammer/Hagenbuch, 2016):  Herding based on information cascades Market participants join the opinion of the masses and ignore their own private information. This is the case when the masses have already formed an opinion and their own opinion cannot change the opinion of the masses.  Herding based on reputation interests Out of concern for their own reputation, market participants neglect their own information and agree with the Group’s opinion.  Herding based on information sources Market participants use sources of information that they believe other market participants will also use.  Herding based on historical market movements Market participants analyze historical market movements and act accordingly in the assumption that others will do the same. Usually this implies technical analysis.

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An example of the effects herding can have on other market participants and on one’s own investment success is shown in Fig. 19. It shows the development of 128 technology funds between 1997 and 2006, in which the inflows and outflows from these funds are compared. From the beginning of 1999 onwards, investors invested more and more in technology funds, as the spectacular returns up to that point made the sector increasingly popular. Due to the impressive performance at the turn of the millennium, valuations were already very high. Despite this, the mutual funds’ prices continued to rise as more and more investors joined the herd movement – a bubble formed. The subsequent correction phase from 2001 to 2003 caused prices to fall and investors sold their technology funds, thus completing their unintentionally irrational “buy high, sell low” strategy. Effect of herding using the example of net inflows into technology funds during the dot-com speculative bubble

Fig. 19: Development of net inflows into technology funds 1997-2006 (see UBS Wealth Management Research, 2008, p. 22)

The masses can develop behavior patterns that can fuel a speculative bubble. Mass phenomena are particularly evident when a bubble bursts or sudden price losses occur. At such moments, the masses act in panic when prices are threatening to fall. Speculative bubbles are reinforced by the herd behavior of market participants.

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Special circumstances, which are explained below as “the limits of arbitrage”, prevent market participants who even recognize the limited rational behavior of other investors from correcting the exaggerations. Rather, they themselves have an interest in following developments in the given direction and earning a share of the profits from exaggerated price swings. This further intensifies speculative bubbles (see Heuser, 2008, pp. 122 and Daxhammer, 2006). Charles P. Kindleberger63 argued that it is rational to participate in speculation as long as it is clear to the participant that it is still at an early stage and as long as one believes that the other market participants think the same (see Kindleberger, 2001, p. 52). The question of how to recognize the respective stage of a speculative bubble is not answered by Kindleberger and remains to this day a central, unsolved question in which theory and practice are equally interested. Limited arbitrage plays a special role in the analysis of speculative bubbles. From the point of view of neoclassical capital market theory, arbitrage as a mechanism would promptly compensate for misjudgments. However, certain risks and costs prevent its application, which can reinforce existing market trends. In the following, the limits of arbitrage and the most significant market anomalies are explained as further causes for bubble formation. 4.1.2 Limits of arbitrage

In the →Neoclassical Capital Market Theory, arbitrage is an effective way of preventing systematic deviation of security prices from fundamental valuations. Deviations from fundamentally justified valuations of a security are balanced out by rationally acting market participants – the arbitrageurs. They would exploit price differences when buying and selling a security on two different exchanges at the same time64. In the neoclassical interpretation, the differences are caused by so-called “noise traders”, a group of market participants who make their investment decisions under the influence of emotions and through limited rationality. The investment decisions of this group are often influenced by rumours. The arbitrageurs, on the other hand, embody the image of Homo Economicus, who make decisions based on completely rational expectations on the basis of the →Expected Utility Theory. However, the assumption that arbitrageurs can correct mispricing is strongly challenged in literary discourse (see Shleifer/Vishny, 1997). Under the keyword “Limits of Arbitrage”, numerous reasons are given for the fact that rationally acting market participants are only able to correct mispricing under rather limiting conditions or not at all. The main arguments of the critics of arbitrage (Shleifer/Vishny, 1997; Barberis/Thaler, 2002) are based on the risks and costs 63 64

Charles Poor Kindleberger | American economic historian | 1910-2003

This basically refers to a situation in which the same security is traded at a higher price on exchange A than on exchange B. It would then be bought on B and sold on A at the same time. In the constellation above, arbitrage refers to the purchase/sale of an objectively recognisably undervalued/overvalued security.

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associated with arbitrage. Thus, persistent over- or undervaluation can be explained as the result of limited arbitrage (see Kottke, 2005, pp. 13). Risks of Arbitrage  Fundamental Risk Limits the possibility of arbitrage severely, as the current mispricing of a security may remain when new headlines hit the markets. For example, the valuation of a security originally considered too low may justify an even lower valuation, resulting in losses for the arbitrageur. Furthermore, the fundamental risk of a further deterioration of the corporate earnings can hardly be reduced by using derivative transactions.  Noise-Trader Risk The effectiveness of arbitrage can be limited by noise traders, whose investment decisions are based on “noise” in the market rather than on in-depth analysis. Noise-traders can continue to misprice an investment in the short-term, which in turn exposes the arbitrageur to additional, incalculable losses.  Time Risk This risk usually refers to the arbitrageur’s short time horizon. It is also referred to an agency constellation as a lot of know-how and capital is required to detect mispricings and to exploit them in an economically lucrative way. The arbitrageur’s capital providers are usually not in a position to understand the complexity of the arbitrage process and assess the skill of the agents, the investment specialists, in accordance with the return achieved. If, due to the risks mentioned above, the arbitrageur does not succeed in achieving the expected return in the expected time frame, capital providers may withdraw from their given commitment. As a result, severe losses are possible, which further limits the attractiveness of the arbitrage. This is exactly what happened to Melvin Capital and other hedge funds losing more than USD 5 billion in January 2021, when retail investors rallied GameStop by over 1.600 percent in the course of 4 weeks. The arbitrageur is therefore very sensitive to additional deviations from fundamental values due to the temporary nature of external liquid funds. In this context, the British economist John M. Keynes commented on the dangers of incalculable market movements: “Markets can remain irrational longer than you can remain solvent.” (Keynes, quoted from Bodie, Kane, Marcus, 2009, p. 265) Costs of Arbitrage  Transaction costs They lead to a limitation of the economic attractiveness of arbitrage transactions. Transaction costs are commission fees and bid-ask spreads. The bid price represents the price at which a security can be sold, whereas the higher ask price is used as the basis for the purchase transaction.

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 Securities Lending Fee If the short seller, for example a hedge fund, does not own the securities intended for short selling, additional fees for borrowing the securities may arise. The short seller borrows the corresponding securities and pays a certain fee to the lender of the securities. If the prices then fall, the security can be purchased at a lower price and returned to the lender. Example 4.2: Risks of Arbitrage ‒ Short Selling hitting VW common shares In addition to the fees that arise for short selling, the arbitrageur may also be exposed to price risks. This explains the spectacular rise in the price of the Volkswagen (VW) common shares to up to EUR 1,000 at the end of 2008, as short sellers had to buy back the shares at sharply rising prices. The need for the buy-back of the securities arose because Porsche had announced that it had acquired the vast majority of VW shares in an intent of a hostile takeover via option transactions. As a result, the number of shares outstanding on VW (the free float) reduced sharply, leading to a massive price increase. Other Restrictions  Legal restrictions For many market participants, short selling is not permitted, making securities lending completely impossible (see Barberis/Thaler, 2005, pp. 5). Chinese authorities issued a short-selling ban after the severe losses on the mainland stock exchange CSI 300 in summer 2015. This legal restriction meant that short sellers could not correct mispricings. The fact that these measures are not always effective was evident in the financial crisis of 2008. Both the U.S. and the UK banned the short selling of financial stocks after the collapse of Lehman Brothers. Prices rose briefly, but continued to fall until the end of the ban on October 8. Even the then head of the SEC, Chris Cox, regarded the enactment of the short-selling ban as the biggest mistake of his term of office (see Kalhammer, 2015).  Obligation to return the borrowed securities Arbitrageurs who engage in short selling may also be forced to return the borrowed securities to the lender if the latter wishes to sell the securities on the market prematurely. In this case, the arbitrageur must close his position early, which can result in high losses (see Shiller, 2003, pp. 97). In addition, arbitrage by institutional investors can be limited by the fact that they simply have no interest in reducing a mispricing. Rather, arbitrageurs often have the incentive to maintain or extend the mispricing through their actions instead of limiting it through their actions. This is the case if arbitrageurs can assume that the misjudgment will continue to grow in the short-term. Brunnermeier and Nagel (2004) documented this phenomenon shortly before the dot-com bubble burst at the turn of the millennium. In their research results, it is evident that hedge funds participated in the strong rally of the U.S. Technology Index NASDAQ until before the bursting of the bubble. This assessment is confirmed by the findings of Griffin et al. (2003). They conclude

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that institutional investors participated particularly in the rising trend of NASDAQ 100 securities (see Ramadorai, 2010). Example 4.3: Limited Arbitrage based on Royal Dutch/Shell Transport In 1999, the economists Froot/Debora studied the merger of Royal Dutch and Shell Transport in 1907. The two companies merged at a ratio of 60:40. The original companies were to remain independent entities and be listed independently on the stock exchange. The cash flows generated were also to be split 60:40. However, their research showed that the market value of Royal Dutch fluctuated between a valuation of up to 35 percent lower and 15 percent higher than the actual ratio of market values. The two scientists cite this development as clear evidence of limited arbitrage. Limited arbitrage can also be illustrated by the bankruptcy of the hedge fund LTCM. LTCM attempted to achieve excess returns on the basis of the arbitrage theory (see chapter 2.1.3). However, the erroneous assumption that incorrect valuations would be corrected within a short period of time led to the fund’s imbalance and ultimately to its liquidation. LTCM invested, among others, in the previously separately listed securities of today’s Royal Dutch Shell. According to the terms of the merger, the market value of Royal Dutch was to be one and a half times that of Shell Transport. However, the valuation of Shell Transport was 18 percent lower than expected. LTCM invested heavily in the valuation differences in the expectation that they would adjust to the expected level. Contrary to expectations, the valuation differences did not adjust, but instead increased, with the consequence that the assumptions of the Random Walk Theory did not prove true. The exorbitant losses of LTCM had to be covered by the intervention of the U.S. Federal Reserve System (FED). The hypothesis formulated in the initial consideration, according to which the miscalculations caused by noise traders are corrected by arbitrageurs back to the fundamental value, must be questioned against the background of the risks and costs associated with arbitrage. Thus, mispricings cannot be exploited by arbitrage without risk or cost as postulated in traditional financial market theory. On the contrary, it is possible that mispricings may persist in the long term. In this context, the two American economists Nicholas Barberis and Richard Thaler summarize the general considerations regarding the limited arbitrage possibilities on the markets as follows: “In contrast, then, to straightforward-sounding textbook arbitrage, real world arbitrage entails both costs and risks, which under some conditions will limit arbitrage and allow deviations from fundamental value to persist.” (Barberis and Thaler, quoted after Kottke, 2005, p. 266) In summary, it can be stated that limitations in arbitrage favors the formation of speculative asset bubbles. The behavior of noise traders, which contributes decisively to the formation of bubbles, cannot be reliably prevented by arbitrageurs.

4.2 Anatomy of speculative bubbles according to Kindleberger & Minsky

4.2

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In spite of individual differences, speculative bubbles can often be characterized in retrospect by identifiable phases they undergo between rising and collapsing. Each bubble tends to go through five phases: Displacement, Boom, Euphoria, Crisis and finally Revulsion. The five-phase model (see Fig. 20) was developed by the U.S. economists Charles Kindleberger and Hyman Minsky (see Montier, 2009, pp. 730): Phase 1 – Displacement In the first phase of a speculative bubble, prices in the respective asset class begin to rise. Furthermore, the first phase is often characterized by an exogenous shock, which leads to a shift of profit opportunities and consequently of investments from one sector to another. The increasing investments cause eventually a boom. The emergence of the Internet, for example, can be seen as an exogenous factor. The technology promised to revolutionize not only the way business is done, but also the way of life within societies. Phase 2 – Boom The second phase is usually characterized by endogenous factors, such as the expansion of available liquidity, which facilitate the investment in the speculative asset class. As such central banks lowered worldwide key interest rates in the wake of the financial and economic crisis from 2008 onwards, thereby providing access to seemingly limitless liquidity. Rising prices within the asset class in question are gradually attracting more attention ‒ a positive feedback loop is developing as described in subchapter 4.1 that reinforces the belief in the emergence and continuation of the boom. This leads to the “social contagion of boom thinking” as one of the two main drivers of a bubble (see Shiller, 2008, pp. 56). Phase 3 – Euphoria This is the stage when rational expectations morph into irrational exuberance as more and more investments flow in the speculative object. Adam Smith described such behavior by market participants as “excessive trading”. Kindleberger notes that the concept of excessive trading is merely speculative. Market participants overestimate the expected returns and increase their bets excessively. The belief to become wealthy in a short amount of time is characteristic of this phase. More so, the second driver of a speculative bubble becomes apparent: Herding: Market participants observe other investors and their actions and tend to imitate them as the price of the asset class in questions keeps rising. Another characteristic element of this phase is the mindset “This time is different”. Market participants often consider the valuation methods previously considered valid to be no longer meaningful because they do not correctly capture new market developments. Thus, new valuation methods are created in this phase to

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justify the enormously increased security prices. During the dot-com bubble between 1998 and early 2000, market participants preferred to use among other ratios, the price-turnover ratio, since the usual price-earnings ratio appeared too high or was not possible due to non-existent profits. One could even observe a valuation based on the visits of the company’s websites. The euphoria not only changes the valuation methods, but also the market participants themselves. The seemingly endless rise in share prices results in excessive optimism and overconfidence among investors. They overestimate their own knowledge, while investment risks are underestimated. Phase 4 – Crisis The crisis phase, also called the critical phase or phase of financial distress, is initiated by rising interest rates or newly introduced regulations making further investments less attractive. Corporate insiders start to liquidate their positions, resulting in significant insider selling. Corporates may be confronted with the fact that they cannot meet their financial obligations. Usually, there is a triggering event which illustrates the desolate financial situation of the company. Kindleberger comments on this phase as follows: “The specific signal that precipitates the crisis may be the failure of a bank or firm stretched too tight, the revelation of a swindle or defalcation by someone who sought to escape distress by dishonest means, or a fall in the price of the primary object of speculation as it, at first alone, is seen to be overpriced.” (Kindleberger, quoted after Montier, 2009, p. 735) The detection of fraud and swindles usually leads to a clear preference for liquidity. This development became apparent in the U.S. balance sheet fraud scandals surrounding Enron and Worldcom in 2001. The increase in debt poses a great danger if a speculative bubble deflates. If, subsequently, the economy goes into a recession but debt is still massive, companies will be forced to increase their cash flows in order to service their liabilities, which are rising in real terms. This can be done by “buying up sales” or “selling assets”. In the case of the former, the company’s market share can be increased by increasing sales through decreasing sales prices ‒ the sales are purchased by the company “at the expense of profit”. In the case of the “sale of assets”, the excess capacity can also only be sold at lower prices. In both cases, deflationary developments can intensify. Phase 5 – Revulsion The last phase is characterized by the strong aversion of market participants to the capital markets. In this last phase, market participants capitulate and sell remaining assets at any possible prices. Stock exchanges are characterized by low turnover following panic selling. The potential sellers have already sold and there are hardly any market participants left who could sell further assets and hardly any buyers who want to enter the market in this phase.

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September 2008 can be compared with this phase. In the course of the bankruptcy of Lehman Brothers, stock markets worldwide plummeted and marked striking index lows. Panic usually ends when one of the three subsequent events occurs:  Prices fall so low that market participants are tempted to repurchase securities.  Trading is interrupted by the introduction of limits to price declines (e.g., circuit breaker).  The so called “lender of last resort” acts as a buyer of shares ‒ e.g., U.S. Federal Reserve (FED). However, it is unlikely that the Fed will act as a buyer of private sector assets as the Federal Reserve Act of 1913 does not give explicit authorization to buy securities, corporate bonds, short-term debt, mortgages or agricultural land. In contrast, it may buy gold, foreign currencies, bank acceptances and bills of exchange. In exceptional cases, however, the FED may override this rule. For this, the Board of Governors must come to the conclusion that an unusual and urgent circumstance exists. In addition, at least five out of seven governors must vote in favour of the FED’s participation in the bond market e.g., such as, for example, through the FED’s Quantitative Easing (QE) programs between November 2008 and October 2014 or in the wake of the Covid crisis. In March 2020, the creation of two facilities was announced, the Primary Dealer Credit Facility (PDCF), offering short-term funding to primary dealers, and the Secondary Market Corporate Credit Facility (SMCCF). Under the latter, the Federal Reserve would go on to purchase corporate bonds for the first time in it its history. The U.S. Federal Reserve uses the adjustment of key interest rates, at which banks can borrow from the Fed, as the most important measure to regulate the economy in a severe economic downturn or overheating. After 9/11 2001 and the real estate crisis in 2008, key interest rates were lowered to below 1 percent. Between 2009 and the end of 2015, the key interest rate was at the lowest level since the central bank was established (between 0.00 percent and 0.25 percent). This period is historically the longest period in which the central bank did not raise interest rates. In March 2020, the key policy rate was again lowered to 0.00 percent due to the Covid crisis. In an effort to calm markets, regulatory measures on the part of policymakers are often seen. In 2002, for example, the Sarbanes & Oxley Act was passed in response to the U.S. accounting fraud scandal. With the help of this law, the Chairman of the Board of Management and the Chief Financial Officer are required to certify the business figures and are subject to far-reaching liability consequences. The Act is aimed at all companies that make use of the capital market in the U.S. The Market Abuse Regulation (MAR) has been in force since July 2016. This regulation aims to make the transparency of broker recommendations on securities more comprehensible. Brokerage houses are obliged to send the history of the assessments of a security in the disclaimer to the customers after issuing a written or telephonic recommendation. This is intended to avoid conflicts of interest or to make them transparent at the very least.

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A speculative bubble usually undergoes five phases: Displacement, Boom, Euphoria, Crisis and Revulsion. The five-phase model was developed by the U.S. economists Charles Kindleberger and Hyman Minsky.

Displacement

Boom

Euphoria

 Start of a  Growing op-  Rational exububble with a timism about berance displacement morphs into speculative leading to risirrational exobject leads ing prices to social con- uberance,  Displacement leading to tagion with via exogemore investboom-thinknous shock, ments into ing creates new the specific  Credit exopportunity pansion fuels asset class in a specific second stage  Recent price sector of the  New finanincreases are economy ‒ extrapolated cial institue.g., major into the futions are political ture, unrealisfounded, change, detic expectacredit standregulation, ards are loos- tions evolve technological ened to meet  Trade on or financial margin becredit deinnovation gins, new valmand but  Example: also to secure uation methLaunch of the ods are cremarket share Internet 1990s ated  Fraud common at this stage, not exposed until later

Distress

Revulsion

 External event causes decline in confidence and pause in explosive price increase  Insiders start to take profits  Investors / Corporations become distressed sellers as incomes / revenues drop below interest payments  Unravelling prices increase shift to liquidity

 Run-of-themill selling turns into outright panic  Levered Companies go bankrupt  Very restrictive credit approvals as banks start to distrust market participants  Panic continues until lender of last resort starts to restore confidence, value investors start buying  Increase in regulatory measures (e.g., Sarbanes & Oxley Act after Enron; Basel III)

Abb.20: Anatomy of speculative bubbles according to Kindleberger/Minsky

4.3

Detailed review of bubbles and market anomalies

Speculative bubbles have considerable effects on the economies involved (see next subchapter). They show both positive and negative effects on an economy. On the one hand, speculative bubbles often trigger technological upheavals, create a new infrastructure and form the basis for future growth. On the other hand, speculative bubbles can limit the information function in the markets and lead to a loss of purchasing power and confidence.

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Empirical studies also confirm the existence of capital market anomalies as the cause of long-term mispricing of equities, which can also trigger speculative bubbles. These market anomalies relate to securities prices that systematically deviate from their fundamental values. Some capital market anomalies disappear after they have been discovered and exploited by market participants, while others even become more pronounced over time and can be observed continuously. 4.3.1 Significance of speculative bubbles for economies

Contrary to the prevailing view that speculative bubbles have predominantly negative effects on economies (ECB, 2005 p. 53; Shiller, 2005b, p. 208; Hartcher, 2006, p. 147 and Mandelbrot/Hudson, 2004, p. 280), there is also evidence that they in fact can have positive effects. As shown in chapter 4.2, positive effects for economies can occur especially at the beginning of speculative bubbles (in Phase 1 and 2). As a consequence of rising asset prices, confidence in the market is strengthened. This makes it easier for start-up companies to go public or to obtain venture capital, which often facilitates the emergence of new technologies and industries leading to the creation of new jobs. In the following, the most important positive and negative effects are pointed out (see Eustermann, 2010, pp. 28). Positive effects of speculative bubbles  Income and Wealth effect Görgens, Ruckriegel and Seitz (2008) argue that the long-term increase in asset prices could lead to rising consumption opportunities with a correspondingly positive effect on the demand side in the real sector. This effect occurs both in the case of securities and real estate. A rising property value enables additional consumption through additional mortage possibilities.  Balance sheet effect Rising asset prices can influence investment activity via the balance sheet effect. The reason for this is better credit conditions that market participants (households and companies) receive due to rising asset values (see Bernanke and Gertler, 2000, p. 17). The improved balance sheet ratios thus have a positive effect on the credit rating for the corporations to obtain additional liquidity.  Transfer effect between stock markets and investment decisions Tobins q established a connection between investment activities and the exchange ratio of capital goods to other goods in the context of the →Portfolio Selection Theory. The formula q establishes the relationship between the market value of a firm and the replacement value of the firm’s assets. In other words, it helps to evaluate if a given business or market is overvalued or undervalued. According to Gilchrist, Himmelberg, Huberman (2005), new investments are profitable as long as q > 1, i.e., if high security prices increase the value of the firm, it has the opportunity to raise capital for investments by issuing new shares. If security prices fall, however, new investments decline.

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Negative Effects of speculative bubbles  Loss of the information function of prices A speculative bubble causes asset prices to decouple from their fundamental values. As a result, prices lose their information function, which in efficient markets serves as a signal to ensure the best possible allocation of resources. This can lead to mispricings and misguided actions on the part of all economic actors, which in turn can have a negative impact on the stability of the financial system.  Loss of purchasing power As shown in phase 5 (Revulsion) in chapter 4.2, extreme corrections on the capital markets do occur from time to time. These lead to a loss of purchasing power, which can subsequently lead to sharp declines in production. With the spill over to the real economy, unemployment and bankruptcies rise and credit defaults increase. Thus, the loss of purchasing power leads to a reduction in the active or asset side of the commercial banks, which makes lending even more difficult.  Loss of confidence among market participants Ultimately, the spiral of intensifying negative impulses causes a persistent loss of confidence among market participants. Investment and consumption activities decrease significantly. All in all, a macroeconomic assessment of the speculative bubble can only be made by offsetting the positive and negative effects, which is likely to be extremely difficult in practice. An example of speculative bubbles with a positive net effect is the dot-com bubble. Although this bubble destroyed a lot of capital in retrospect, it also created many sustainable jobs in a new industry. Companies such as Google and Amazon, for example, have become globally dominant technology firms and have more than made up for the share price losses in the wake of the bursting of the bubble. Apple’s market value has even increased more than a five hundred-fold since the end of 2002, more than fulfilling high investor expectations in the bubble year 2000. However, bubbles that relate only to certain specific speculative objects usually have a negative net effect. 4.3.2 Types of speculative bubbles

Speculative bubbles can usually be classified according to certain criteria. These criteria are based on behavioral and market influences (see Montier, 2009, pp. 783). Rational or semi-rational speculative bubbles This type of bubble is characterized by the fact that, in rational expectations, the price of an asset is a function of its expected value at the time of sale. These bubbles are also called “rational stochastic bubbles”. At any given point in time, there is a certain probability that the bubble will burst. Market participants know this, but do not know when the bubble will burst. If market participants buy the asset, despite a possible bursting, they expect to be able to resell the asset at a higher price. This behavior corresponds to the greater fool theory ‒ the other market

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participants being the greater fool who later pays an even higher price for the security. Behavioral and market related basis for the bubble: Lack of other investment options with a corresponding return. Intrinsic speculative bubbles Unlike rational bubbles, which are not necessarily related to fundamentals, intrinsic ones depend on them. The bubble inflates when the fundamentals of an investment improve. The fundamentals are generally of an economic nature, such as the demand for an asset (sales volume) or the level of production costs. These types of bubbles are characterized by an exaggerated reaction to news about fundamentals and the projection of past highs into the future. Market participants use the representativeness heuristic, which is applied in the context of information processing. In doing so, the facts are not judged by their statistical probability, but by their frequency of occurrence. With this heuristic, the market participant arrives at a distorted probability assessment of the development of an investment, since personal experience or observations make the occurrence of a development appear far more frequent than would be the case with a correct analysis of the actual probability. Behavioral and market related basis for the bubble: Representativeness/Excessive Optimism. Whims and fashions Speculative bubbles based on whims and fashions are caused by socio-psychological factors. The focus of this bubble formation is the psychology of the market participants in the euphoric stage. The market participants are strongly characterized by group behavior or herding. They agree with the majority opinion and thereby suppress their own views. There is a general faith in a “new age”, which is supported by the belief in an incessant rise, e.g., in stock prices, cryptocurrencies etc. In this environment another heuristic occurs ‒ overconfidence, where market participants are overly optimistic and come to a heightened self-assessment of their abilities. Returns are consequently overestimated, while risks are underestimated. In combination with overconfidence, the influences of the representativeness heuristic also become visible. The effects of these heuristics are discussed in more detail in chapter 8 in the third section. Behavioral and market related basis for the bubble: Group Behavior/Desire/Excess Optimism/Representativeness. Information-related speculative bubbles This type of speculative bubble is given when security prices do not contain all information and consequently the fundamental value deviates from the given price. Behavioral and market related basis for the bubble: Lack of information.

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Basically, four types of bubble are distinguished. They can either arise as a result of rational expectations and are therefore called rational bubbles; as intrinsic speculative bubbles that rely heavily on fundamental data; as a result of the whims and fashions of market participants; or as a result of a lack of essential information.

4.3.3 Types of capital market anomalies

Within the context of financial markets, market anomalies denote market developments that deviate from the capital market models of neoclassical capital market theory (see Barberis/Thaler, 2005, p. 31). Inadequate evaluation of information not only affects individuals but also the general information processing on the capital markets. As a result of the erroneous, incomplete, and delayed evaluation of information, capital market anomalies become visible which are mostly caused by the behavior of semi-rational market participants. Type of anomaly

Company Size Book/Market value

 Valuation anomaly

-

 Price reaction anomaly after earnings release

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Profit Announcement Drift Dividend change Share buy-back Splits IPOs/Capital Raise M&A Insider Trading

 Calendar anomaly

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January effect Weekend/Monday effect

 Autocorrelation anomaly in returns

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Momentum Mean Reversion

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Extreme Volatility

Short-term anomalies

Long-term anomalies

Related examples

 Volatility anomaly Fig. 21: Overview Capital Market Anomalies

The formation of speculative bubbles is one example of such capital market anomalies. Deviations from the rational behavior of the Homo Economicus form the basis for the explanation of investor behavior through Behavioral Finance (see Pompian, 2006, p. 9). Encouraged by, among other things, better data availability and increasing computing power of computers, empirical capital market research is gaining momentum in the context of financial market research. The result is an

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increasing number of research projects65 that identify market distortions shown in Fig. 21. Some capital market anomalies disappear after they have been discovered and exploited by market participants. These are short-term anomalies and can be interpreted as at least a long-term effect of the arbitrage mechanism from chapter 4.1. Others are even intensified over time and can be observed continuously in the form of medium to long-term anomalies. Short-term capital market anomalies Short-term anomalies have long been the subject of discussions about the →Efficient Market Hypothesis. Currently, however, these anomalies play a rather subordinate role, as their effects have become increasingly weaker or the anomaly has diminished after a short time. Valuation anomalies According to the →CAPM, differences in returns between securities are exclusively attributable to differences in risk. In that aspect, the deviating →Beta factors characterize the risk differences. However, in reality, return differences between securities are not entirely due to differing beta factors, but also to different characteristics of firm-specific financial ratios. These ratios can help predict the short-term return development of securities. As such, the value effect (book value/market value) is one of the short-term anomalies, which indicates the possibility to generate excess returns in securities with a low price/book value or price/earnings ratio. Similarly, excess returns can be generated with securities that have a high dividend yield. This effect is based on the value strategy, introduced by Benjamin Graham66. When evaluating a security, value investors are focused on the intrinsic value in the form of the aforementioned price/earnings ratio (PER). Other key ratios are the price/book value or the price/turnover value. The securities of companies that have a low PER and/or a high dividend yield are called value shares. On the other hand, the securities of companies that do not pay dividends and also have a high PER are called growth shares. Most of the results within the scope of this analysis are controversial, as an exact classification of the securities is often not possible. Price reaction anomalies In accordance with the efficient market hypothesis, new information is processed immediately after it is announced. In addition, the market participant is adjusting assumptions on future cash flows of the company in question. In a perfect capital

65

deBondt/Thaler, 1989, pp. 191; Daniel/Hirshleifer/Subrahmanyam, 1998, pp. 1867; Dimson/Mussavian, 2000, pp. 5; Barberis/Thaler, 2005, pp. 24; Hirshleifer, 2001, pp. 1555)

66

Benjamin Graham | British-American economist, professor & investor | 1894-1976

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market, this results in an immediate price adjustment of the security towards the fundamentally justified level. In practice, however, a delayed price adjustment after the announcement of pricerelevant information can be observed, which is contradicting the semi-strong form of the efficient market hypothesis. Based on the →Conservatism Bias (see chapter 9.1), there is a restrained attitude towards the corporate earnings release. This can lead to further gains in the event of positive information (and vice versa), resulting in a gradual adjustment of the previously conservative estimate. This post-earnings-announcement drift, first observed by Raymond J. Ball and Philipp Brown (1968), and later confirmed by Victor L. Bernard and Jacob K. Thomas in 1990 illustrates the possibility of generating an excess return by knowing all public information (see Shefrin, 2000, p. 96). The reason for the adjustment delay is seen in the fact that market participants derive future corporate profits predominantly from historical values and include information on the current profit situation of a company only gradually in the estimation of future profits. This clearly shows the deviation from the assumptions of information processing according to Bayes (see chapter 1.2.4). Market participants do not act in accordance with Bayesian premises and for this reason allow market anomalies to arise. Calendar anomalies Securities generate positive excess returns within certain recurring time periods. The most prominent example of this is the January effect. This effect is related to the small company effect, first documented by Rolf W. Banz (1981). Accordingly, companies with a relatively low market capitalization generate higher returns on average than companies with a higher capitalization. Based on these study results, Marc Reinganum (1983) documented the importance of the month of January for possible excess returns for small companies. Consequently, a large part of the excess returns that these companies achieve is generated in the first two weeks of January. One possible reason for this development may be that investors sell securities of smaller companies at the end of the year and claim possible losses for tax purposes. These companies are then repurchased in January, which causes their prices to rise (see Schredelseker, 2002, p. 451). Comparing the average index price development for the last 20 years between the STOXX Europe Small Caps index containing 200 small and medium-sized companies in Europe and the STOXX Europe 50 containing the 50 biggest European companies by market value, the January effect seems to persist. The comparison in Figure 22 shows an average outperformance of the STOXX Europe Small Cap for the month of January of 2.0 percent. The same comparison between the German SDAX, containing 70 small and medium-sized companies with the German DAX containing the largest 40, shows an outperformance of even 2.8 percent over the last 20 years.

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Fig. 22: January-Effect STOXX Europe SMALL vs. STOXX Europe 50 "Seasonality AnalysisReport”; FactSet

Medium- to long-term capital market anomalies Apart of capital market anomalies, which have lost prominence, there are also anomalies that have remained observable for many years (see Kottke, 2005, p. 18). Autocorrelation anomalies for security returns Another group of research activities is concerned with the predictability of future security price developments on the basis of historical price data. The focus here is not only on the return of individual securities, but also of market indices. The autocorrelation (temporal sequential dependence) of security returns contradicts the →Random Walk Theory and consequently breaks with Fama’s efficient market hypothesis. In the field of autocorrelation, the focus is on the momentum effect and the mean-reversion effect. The empirical observation that security prices have a tendency to return to their long-term “mean” values is called mean reversion. The mean value often corresponds with the fundamental value of a security. The deviation from fundamentally justified valuations can be explained by the heuristics used by market participants in the decision-making process. Werner de Bondt and Richard Thaler (1985) found indications that the future, long-term development of securities can be predicted by looking at historic prices. They allocated the constituents of the NYSE (New York Stock Exchange) into winner and loser portfolios and analyzed the development of the securities between 1926 and 1982. Their research showed that the portfolio consisting of the previously underperforming securities achieved a 25 percent higher return over a period of 36 months than the portfolio with previously outperforming securities (see Shleifer, 2000, p. 17). Figure 23 shows the above-average performance of former underperforming se-

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curities and the below-average performance of former outperforming securities over several years. This anomaly, known as the winner-loser effect, is exemplary for research into investor behavior through Behavioral Finance.

Fig. 23: Development of extreme winners and losers (see Shleifer, 2000, p. 17)

This market anomaly can be explained through the →Availability Bias (see chapter 7.1.1). Initially, market participants are too pessimistic about underperforming securities and too optimistic about outperforming ones. They over-react to information, which causes the securities to move away from their fundamental values as market participants decide to buy or sell based on the perceived development of a security. After a while, however, the exaggerations are slowly reduced (see Shefrin, 2007, pp. 270). The return to a fundamentally justified valuation is due to the mean reversion already mentioned (see Garz/Günther/Moriabadi, 2002, p. 113). Accordingly, the weak form of the efficient market hypothesis, in which excess returns would not be possible through knowledge of historic prices, cannot be confirmed. Consequently, it seems as if chances are good to generate excess returns with historically underperforming securities rather than with historically outperforming ones. A similar capital market anomaly documented on many stock exchanges over a long period of time is the so-called momentum effect. This was first documented by Narasimhan Jegadeesh and Sheridan Titman (1993) In contrast to the winner-loser effect, which considers the long-term development of former winners and losers, the momentum effect is based on the short-term development of securities. Accordingly, securities with a good performance in the short to mediumterm also develop positively in the near future of three to twelve months. The same applies to securities that have performed negatively in the short to mediumterm. They tend to continue to underperform in the near future. In consequence, the momentum effect appears to be clearly contradicting the efficient market hypothesis. It would be expected, that if the anomaly became

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known, arbitrageurs would take advantage of the mispricing, which is not the case (see Lewis, 2008, pp. 81). The reason for this and what role the decision-making process of market participants plays in this respect will be examined in more detail in Section 3. Price development anomalies The level of price volatility, which is observed on the capital markets, is often not compatible with Bachelier’s Random Walk Theory. The price developments bear no relation to the fluctuations of the underlying fundamental data. For example, in March 2020, equity indices around the world dropped harshly with the intensifying Covid-19 pandemic. Consequently, it is difficult to explain the observed excess volatility in terms of the efficient market hypothesis. Rather, such price developments show the effects of the herding, as already explained in chapter 2.3. Deep Dive Mean-Reversion How successful would a mean-reversion investment strategy be under current conditions in July 2021 where global indices again are separating notably from their 200-week moving averages and thus a mean reversion might be expected at some point in the future. To answer this, we have evaluated together with Robert Zielinski, VP, Portfolio Analytics at FactSet a similar approach to de Bondt and Thaler this time using the S&P 500 (see FactSet Insight, 2021). First, we divided the constituents of the S&P 500 into outperforming and underperforming portfolios as of January 1990. The winning portfolio contains the 50 securities with the highest 1-, 2-, 3- and 5-year return, whereas the loser portfolio contains the securities that have performed worst in the same time periods. The securities in the two portfolios were subsequently held over the same time periods before a respective rebalancing with again, the best and worst performing securities took place. The analysis runs till June 2021.

Fig. 24: Overview Risk/Return Profile of loser & winner portfolios versus benchmark (S&P 500), FactSet Alpha Testing

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The results show that a mean-reversion strategy can still generate impressive returns. One could even argue that rebalancing every 2 or 3 years would be very challenging to beat as the annual return of previously underperforming securities is between 13.56 and 14.53 percent (see column B, rows 9-18 of Fig. 24). The point of differentiation comes however, when we look at the associated risk needed to achieve respective portfolio returns. The portfolios with a 2-year rebalancing are among the best performing with annual portfolio returns of more than 14 percent. The annualized volatility in column H however shows that the risk associated with the slightly higher returns than the portfolios with a 3-year rebalancing is higher as well. This shows, again, that the higher the portfolio return, the more risk is to be accepted. This becomes evident when we look at the Sharpe Ratio, developed by William F. Sharpe to measure the return of an investment compared to its risk. With a value of 0.46 for the selected portfolio in row 12, it is among the highest ratios for all portfolios, even though the standard deviation is lower than for the portfolios with a 2-year rebalancing. Based on the above, the portfolio in row 12 generates a maximum annual excess return versus the winner portfolio of 3.73 percent. The previously outperforming securities now underperform by -0.77 percent against the benchmark (11.03 percent) as well as the competing loser portfolio (2.96 percent). In contrast, the portfolios rebalanced every 5 and 1 year generate only modest excess returns (rows 5, 6, 21-24). In fact, the loser portfolios rebalanced every year keep underperforming the winner portfolios especially if the selection of securities is based on a short-term performance horizon such as 1 or 2 years (row 23 and 24).

Fig. 25: Excess return of loser versus winner portfolio between 1990-2021, FactSet Alpha Testing

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Let´s have a closer look at the strategy in row 12 which rebalances the winner and loser portfolio every 3 years based on the returns of the last 12 months. Looking at the cumulative excess return versus the benchmark over 30 years, previously underperforming securities add up to 3,000 percent return, whereas the outperforming securities lose about 500 percent in the same time period compared to the benchmark (see Fig. 25). The reason for the underperformance is exactly the phenomenon discussed above – lo g-term reversion to the mean where outperforming securities experience strong drawdowns in the course of market corrections (see Fig. 26). The winner portfolio experiences two strong drawdowns (one after the burst of the dot-com bubble in 2000 and the second after the real estate mortgage crisis in 2008). Although loser portfolios experience notable drawdowns as well (see 2008 and covid pandemic induced market sell-off 2020), they tend to recover faster and drawdowns can be less intense as was the case after the dot-com bubble.

Fig. 26: Maximum Drawdown of Loser and Winner Portfolios between 1990-2021, FactSet Alpha Testing

Summary Chapter 4 Speculative asset price bubbles are created by the concatenation of various circumstances that lead to a price exaggeration of securities, assets (e.g., real estate) or even the economic activity of a country. The social contagion of boom thinking plays a decisive role. It develops from the general observation and media coverage of rapidly rising securities prices. The assumption that the boom will continue is further reinforced. As

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a result, further price increases are occurring. This development is also referred to as a price-story-price loop. Prices can be influenced by market participants who randomly exit or enter the market. This can reinforce a stock market trend such as a bull or a bear market. In the neoclassical capital market theory, arbitrage is an effective way to realign prices with fundamental valuations. However, the assumption that arbitrage can correct mispricings is strongly challenged in the literature because of its specific risks and costs. Every bubble tends to go through five phases: Displacement, Boom, Euphoria, Crisis and Revulsion. The five-phase model was developed by the U.S. economists Charles Kindleberger and Hyman Minsky. Speculative bubbles can have positive and negative effects on economies. If the positive effects, such as the sustainable creation of jobs, outweigh the negative effects, e.g., the reduction in purchasing power, the speculative bubble has a positive net effect; conversely there is a negative net effect. Speculative bubbles can also be divided into four types. The individual forms differ according to the psychological causes that lead to the formation of bubbles. A distinction is made between rational/almost rational speculative bubbles, intrinsic speculative bubbles, whims and fashions and informationrelated speculative bubbles. Empirical studies show the existence of market anomalies as the cause of long-lasting mispricing of securities. Capital market anomalies can be divided into two groups ‒ historical and persistent ones. Historical anomalies have largely lost their significance. Persistent anomalies, on the other hand, are still observable ‒ these include autocorrelation anomalies and price development anomalies.

5

Speculative bubbles from the 17th to 21st century The fifth chapter focuses on historical speculative bubbles. After working through this chapter, you will have a good understanding of the most important bubbles in the history of financial markets and you will understand typical characteristics of capital markets that can lead to increased volatility. You will also be able to explain the steps leading to the historical bubbles in question on the basis of the five-phase model and apply this to current speculative developments.

Speculative bubbles as a result of systematic mis-valuations have not only developed in the recent decades. Their existence dates back at least to the 17th century, when the tulip mania broke out in Holland and considerable funds were invested in the hoped-for increase in value of the tulips. The stock market crashes of 2000 and 2008 show how speculative bubbles are triggered from the perspective of the market participant behavior (decision-making between fear and greed). The objects of speculation can be variably substituted ‒ whether commodities, infrastructure projects such as the British railway mania, technological innovations during the dot-com bubble or new financial products prior to the U.S. mortage credit bubble or more recently with cryptocurrencies ‒ investors’ interest has been sparked, and will continue to be sparked by selected asset classes in the future. Although speculative exaggerations run like a red thread through the history of the financial markets, it is clear that investors do not learn from mistakes and, moreover, cannot remember their mistakes (see Garz/Günther/Moriabadi, 2002, p. 102). The Black Friday on the New York Stock Exchange in 1929 is usually perceived as the first globally relevant collapse of a speculative bubble. In addition, it is often argued that nothing could have been learned from it, as conditions in today’s world are fundamentally different from those in 1929. This statement illustrates how quickly people forget and how strongly the behavior of market participants can influence the markets. This view was also held by John Kenneth Galbraith67 in his book “The Great Crash of 1929”: “I am sure that the stock market crash of 1929 will happen again. All that is needed for a new crash is for the memory of that madness to fade away” (Galbraith, quoted after Elger/Schwarz, 2009, p. 17). Alan Greenspan, head of the Federal Reserve from 1987-2006, is of the opinion that speculative bubbles arise due to the unchanging human nature. In his view, the decisive factor is how they are financed. The higher the external debt, the more far-reaching the consequences of the bursting of the bubble for the economies involved (see Robb, 2014). 67

John Kenneth Galbraith | Canadian-American economist, diplomat, intellectual | 1908-2006

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In addition to the degree of debt financing, Karl-Heinz Thielmann, Managing Director of Long-Term Investing Research AG, states that frequent and systematic fraud makes a speculative bubble particularly dangerous. With regard to the dotcom bubble, this can be explained by some dubious business models: “Fantasy stories were used to take money out of the pockets of investors who no longer had much to do with the true possibilities of the Internet. Germany in particular, with its “Neuer Markt”, developed into a magnet for bogus companies” (see Thielmann quoted in Private Banking Magazin, 2014). The financial crisis of 2008 was also preceded by a clandestine softening of the quality standards of banks when granting loans ‒ up to and including systematic falsification of the information in loan applications (see Private Banking Magazin, 2014). Werner De Bondt68, an important proponent of Behavioral Finance, noted that speculative bubbles can be worrying for the economy. They not only distort the allocation function of the markets, but also lead to a loss of confidence in the integrity of the financial markets: “Asset market bubbles are worrisome because they misallocate scarce resources and because they lead to economic stagnation. Even if a bubble at first remains confined to one sector, contagion and spill-over effects can cause further damage. Bubbles also redistribute wealth. Sometimes good people get hurt. Financial earthquakes undermine the public’s trust in the integrity of the financial system” (de Bondt, quoted after Forbes, 2009, S. 90). 5.1

Benoit Mandelbrot’s market characteristics

In “The (Mis)Behavior of Markets”, Benoit Mandelbrot69 listed his “Ten Heresies of Finance” characterizing capital markets (see Mandelbrot/Hudson, 2004, pp. 227): [1] Markets are turbulent. [2] Markets are very, very risky – riskier than the standard theories imagine. [3] Market “timing” matters greatly. Big gains and losses concentrate into small packages of time. [4] Prices often leap, not glide. That adds to the risk. [5] In markets, time is flexible. [6] Markets in all places and ages work alike. [7] Markets are inherently uncertain, and bubbles are inevitable. [8] Markets are deceptive.

68

Werner de Bondt | Belgian Economist and Founding Director of the Richard H. Driehaus Center for Behavioral Finance at DePaul University in Chicago | Born 1954

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Benoit Mandelbrot | Polish-born French-American mathematician | 1924-2010

5.1 Benoit Mandelbrot’s market characteristics

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[9] Forecasting prices may be perilous, but you can estimate the odds of future volatility. [10] In financial markets, the idea of “value” has a limited role. Many of these characteristics are mainly responsible for the large number of speculative bubbles in the history of capital markets and are therefore important for the further understanding of speculative bubbles. In the following, six market characteristics will be discussed in more detail, as they can contribute significantly to the triggering of a bubble.  [1] Markets are turbulent Observed market development deviates significantly from the postulated →Random Walk Theory by Louis Bachelier. Furthermore, it can be observed that the business activities of companies influence capital markets in the long term. This susceptibility also leads to the observation that markets can react euphorically or panically from time to time.  [2] Markets are very very risk – riskier than the standard theories imagine According to neoclassical capital market theory, price movements such as the 8.5 percent plunge in the German DAX experienced on 9/11, 2001, or the 13 percent drop in the Dow Jones on March 16, 2020 in the course of the Covid Pandemic are almost impossible. The theory expects such movements with a probability of less than 1 in 20 million. This means that such movements would only be expected once, even if a market participant traded every day over a period of 100,000 years (see Mandelbrot/Hudson, 2004, pp. 3). In the crash year 2008 following the U.S. real estate credit crisis, there were five days with a price drop of 6.5 to 7.2 percent each; similarly in the aftermath of the Brexit decision in mid-2016.  [3] Market “timing” matters greatly. Big gains and losses concentrate into small packages of time Strong gains as well as losses are limited to short time intervals. For example, the Dow Jones Industrial Average (DJIA) returned on one single day – March 24, 2020 – 11.37 percent, which was more than the 9.72 percent for the entire calendar year. In 2019, five days were responsible for half of the annual performance of the DJIA.  [4] Prices often leap, not glide. That adds to the risk Continuity is a widely observed human assumption regarding market processes. The markets, however, record unexpected and sometimes sharp jumps. Example 5.2: Strong leaps instead of continuous movements In 1961, MIT professor Stanley Alexander published a study that showed that the markets could be systematically outperformed. His idea was based on the assumption that if the market rose by 5 percent, the investor would enter the market. However, if the market falls by 5 percent, the market participant should

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bet on further price deteriorations. In this way, an annual excess return of 36.8 percent was calculated if the market participant had used this tactic from 1929 to 1959. During the same period, an average annual market return of 3 percent was achieved. However, in his calculations Alexander assumed that price movements would be continuous and once the 5 percent mark was reached, the investor could enter/exit. In reality, however, prices are moving by strong and sometimes sudden leaps. If a security suddenly rises by 5.5 percent, the investor already loses 0.5 percentage points because he or she could not enter the security immediately. The same happens if the market suddenly falls. With this tactic, the investor would lose up to 90 percent of its capital in a year. Alexander published another article three years later, revising the wrong assumptions (see Mandelbrot/Hudson, 2004, p. 235).  [7] Markets are inherently uncertain, and bubbles are inevitable This statement illustrates the subjective perception of the market situation by market participants. Certain investors enter markets when prices have already risen sharply ‒ they are also referred to as ordinary investors ‒ and may thus reinforce the development of a speculative bubble.  [8] Markets are deceptive This statement refers to the attempt to determine future developments based on technical analysis. Price trends represent the past. However, they do not provide sufficient interpretation for the future. There are various factors that can cause market turbulence such as speculative bubbles. Benoit Mandelbrot names ten attributes that can characterize markets. According to this, markets are, among other things, misleading, volatile, risky and turbulent.

5.2

Examples of significant speculative bubbles

Numerous speculative bubbles in the past illustrate, as mentioned at the beginning of the chapter, that market participants are not learning from their mistakes. The reason for this may be that they rely on government institutions to monitor the regularity of transactions. On the other hand, most financial transactions are so complicated that even experienced market participants cannot always understand them. However, speculative bubbles also have their significance for the financing of companies. In some cases, start-ups receive at the beginning of a development necessary seed-capital to launch their operations and strengthen their market position (see Kitzmann, 2009, p. 10). In fact, stock exchanges were created with the goal to spread risks and finance large projects by offering securities in the respective companies. The financing function via offering of securities to investors was

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necessary because individual investors were usually not in a position to finance mining projects and discovery expeditions on their own. Securities soon became the object of speculation when the Dutch East India Company was founded in 1602. However, market participants at that time speculated not only on possible price increases, but rather on the dividends, which could amount to between 25 and 75 percent of the capital invested (see Elger/Schwarz, 2009, pp. 17). The term “stock exchange” was first used in 1409, when such an institution was founded in Bruges/Belgium. The first German stock exchanges were founded in Nuremberg and Augsburg in 1540. The Frankfurt Stock Exchange opened in 1585. In comparison, the New York Stock Exchange (NYSE) was founded 1792. 5.2.1 The Tulip Mania of 1636

The Dutch tulip speculation is one of the most famous and oldest speculative bubbles. It is rich in anecdotes and stories that are meant to illustrate the extent of the excesses during the tulip mania. Allegedly, a Dutch sailor invited to dinner ate a Semper Augustus tulip bulb (worth USD 1 million today, see Harford, 2020), mistaking it for an ordinary onion. We will encounter a similar anecdote again 370 years later during the Bitcoin bubble. Much of what is popularly believed to be known about the tulip mania, however, is based on satirical poetry in the Dutch tradition in the aftermath of the bursting of the bubble by Scottish journalist Charles Mackay70 and others 200 years later (see Boissoneault, 2017). Thus, even the widely respected economist John Kenneth Galbraith adopted excerpts from Mackay in his publications71. The research of Anne Goldgar, Professor of Early Modern History at King’s College in London, has explored the tulip mania in depth over 10 years studying Dutch archives in Amsterdam, Alkmaar, Enkhuizen and especially Haarlem as the center of the tulip trade and clearly refuted some of the prevailing views in her publication Tulipmania: Money, Honor, and Knowledge on the Dutch Golden Age, 2008 (see History. com, 2020). To the same conclusion come William Quinn and John D. Turner, academics at the Queen’s University Belfast. In Boom and Bust – A Global History of Financial Bubbles, 2020 they point out that the popular narrative we are familiar with is largely fictional as Charles Mackay’s account is unreliable and based on second-hand evidence. They point out that the mania was financially and economically trivial (see Plender, 2020). Contrary to popular understanding, the speculative bubble may not have been as irrational as has been assumed so far. The research results of Anne Goldgar show that mainly the wealthy merchant class was interested in the exotic flowers and that prices exceeded the 5,000-guilder level only in individual cases. Tulips with unique patterns and colors instead of simple bold-colored petals became a status symbol and were highly traded due to their exceptional beauty. They were difficult to grow, which is why they were considered valuable and rose in price accord70

1841 – Memoirs of Extraordinary Popular Delusions and the Madness of Crowds

71

1990 – A Short History of Financial Euphoria

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ingly. The price of ordinary tulips was much lower. Today, it is known that the mosaic pattern originates from a virus transmitted via aphids (see Friedmann, 2009). Phase 1 – Displacement The origin of the speculative bubble with tulip bulbs is said to be based on Carolus Clusius (Charles de l’Écluse). In 1573, he moved to Vienna to set up the imperial botanical garden at the request of Emperor Maximilan II. However, after Maximilan’s death three years later, the project ended as Rudolf II, Maximilian’s successor decided to establish a new garden in Prague without Clusius. He later returned to the Dutch University of Leiden, where, among other things, he cultivated tulips, which he did not intend to sell but to use as medicinal plants (see Valauskas, 2014; Scott, 2017). However, the tulip bulb still found its way into the gardens of the rich upper class. Following independence of Holland from Spain and the growing trade profits with other nations, the prosperity increased, especially among merchants, and thus created the opportunities to acquire exotic good. The trade in tulip bulbs flourished and produced high growth in value, especially for exceptional blossoms. The strong increase in demand, combined with the equally strong increase in prices, was caused by two aspects. Firstly, the demand for tulips increased due to the emerging fashion in France for decorating clothes with tulips. The subsequent increase in tulip prices attracted new investors who saw tulip bulbs as a lucrative investment. However, it should be noted that the merchants were connected to each other in various ways, being profession, family or religion (see History.com, 2020). Secondly, the rising demand and the sharply increasing prices were based on the expansion of tulip breeding from purely professional growers to all interested breeders in 1634. Phase 2 – Boom The prospect of quick wealth was tempting. No knowledge, no land and no hard work were necessary: the only thing needed was start-up capital. Thus, the speculative bubble was fed mainly by private loans from wealthy individuals who could afford luxury goods. It remained concentrated among the wealthy population and did not extend to all segments of the social stratification, as had been previously assumed. Tulip bulbs were recognized as a general means of payment alongside the national currency (guilder). Trading was largely orderly and organized in taverns being supervised by committees of experts. In addition, companies were founded for the sale of tulips. Anne Goldgar documented six foundations (see History.com, 2020). Phase 3 – Euphoria The most significant speculation began only after September 1636, when the tulip bulbs were already planted, so that they could blossom the next spring. The exotic shapes and colours, which were decisive for the valuation of the tulips, were last

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seen by the traders in the previous spring. Thus, bidding in November and December 1636 took place without flower specimens being available. Expectations and hopes for the extraordinary tulips to be seen in the spring drove up prices. Although, as mentioned above, certain types of tulips reached up to 5,000 guilders, only 37 buyers were identified in the archives who spent more than 300 guilders for one bulb – equivalent of the annual earnings of a craftsman. The highest amount was three times as much as Rembrandt’s asking price five years later for his famous work “The Night Watch” or equal to the price of a house in 1637 (see Harford, 2020). According to new research, the number of traders involved was far less than previously thought. In her research, Anne Goldgar never found longer trading chains with more than five traders. The assumption that on some days a tulip bulb was traded up to 100 times can therefore not be confirmed. Phase 4 & 5 – Crisis and Revulsion The transition from the fourth to the fifth phase is difficult to distinguish for the tulip mania. Therefore, the developments from the peak of the speculative bubble to the fall of tulip prices are considered together. In early 1637, after a five-week price spike, it became clear to some market participants that these prices could not be sustainable. The expectation of further rising prices and the possibility to sell at even higher prices to the next speculator in the sense of the “greater fool theory”72 toppled. This change in expectations is considered to be the decisive trigger for the beginning of the collapse of a speculative base. Prices started to fall; this abruptly changed the confidence of market participants in the speculative object. Sharply falling prices meant that the prices demanded could not be achieved at auctions. Those investors who had entered the market late were now suddenly incurring losses. More and more owners of tulip bulbs wanted to sell quickly; prices plummeted. The average tulip investor recorded a loss of 95 percent within weeks ‒ but only on paper, as Anne Goldgar’s findings show. Basically, it is evident today that only those who did not receive the high prices for the tulips called at the peak of the bubble suffered losses. The highly traded tulips should have been paid only when the bulb was delivered in May-June. Due to the decline in prices, this was no longer possible. Another reason for the extreme price drop can be that by now tulips were blooming in mass. The initial expectation to see individual special tulips were caught up by the reality of abundant shoots of flowers. The Semper Augustus was not as unique as was thought in winter 1636 (see Harford, 2020). According to the common trading rule, “buy the rumor, sell the news” the bubble imploded in the spring 1637 when the facts were visible to everyone.

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Greater Fool Theory: The theory argues that prices go up because people are able to sell overpriced securities to a “greater fool”, whether or not they are overvalued. That is, of course, until there are no greater fools left (Source: Investopedia)

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Anne Goldgar was unable to confirm the widespread assumption that bankruptcies doubled between 1635 and 1637. The Dutch economy did not seem to have been further affected by the bursting of the speculative bubble. Mentions of bankruptcies in the archives are documented not because of the bursting bubble, but for other reasons (see Goldgar, 2018). Furthermore, the trade in tulips was not banned, which is probably the reason why the Netherlands are still considered the land of tulips today, accounting for 35 percent of global flower and plant exports, worth EUR 6.2 billion (see Reuters, 2020). Rather, the loss of trust among merchants led to a cultural crisis, since now the given word was worth nothing: “In this case it was very difficult to deal with the fact that almost all of your relationships are based on trust, and people said, ‘I don’t care that I said I’m going to buy this thing, I don’t want it anymore and I’m not going to pay for it.’ There was really no mechanism to make people pay because the courts were unwilling to get involved” (Goldgar, quoted in Smithonianmag.com, 2017). 5.2.2 The Mississippi bubble of 1716

The speculative bubble around John Law – Scottish economist, banker, gambler and sentenced for murder – was based on the immense debt of France during the reign of Louis XIV between 1638 and 1715. The debt burden was so high that national bankruptcy seemed inevitable. After the death of Louis XIV in 1715, the precarious situation became obvious. In 1715, France had an annual income of 145 million livres and expenditures of 142 million livres, leaving the country with a mere 3 million livres to meet interest payments of 220 million livres on the debt it had piled up. Consequently, the debt was trading at a discount of 80 percent of face value (see Mises, 2018). As the heir to the throne infant Louis XV was only seven years old, the Duke of Orleans took over government and ruled as regent in the name of the Prince. The Duke was not distinguished by a profound interest in the affairs of France. He increasingly felt that the duties of his office were a burden (see The Tech Blog, 2020). John Law brought himself into play by presenting the Duke a system that would restore France’s prosperity by replacing the precious metal money with paper money already used in Britain and the Netherlands. He took the view that a currency based on precious metal money did not meet the needs of an export-oriented nation. Law saw precious metal money as the reason for France’s weak economy with a debt of about 3 billion livres, as this means of payment was only available in limited quantities. Law recognized early on the importance of corporate finance based on the granting of loans and advocated increasing the money supply in order to increase the production facilities and reduce dependence on metal money. His proposal to restore France’s creditworthiness by setting up its own bank was met with great approval from the regent. In 1716, Law obtained the right to create the Banque Générale, which had the right to issue its own banknotes, which previously did not exist in France. The paper money was not backed fully by precious metal, but instead by 50 precent with government bonds. A precious metal-based

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banking system was converted into a credit-based banking system, where the liabilities of the bank are counterbalanced with loans instead of precious metals (see Hanke, 2018). Law’s new paper money helped restore confidence in France’s economy as foreign trade recovered. Subsequently, the paper money traded at a premium of 15 percent to face value, in contrast to the previous precious metal money, which was constantly suffering depreciation. Phase 1 – Displacement The first phase of Law’s speculative bubble was based on his unexpected success in reviving France’s economy with the introduction of paper money. The regent was so convinced about Law’s abilities that he increasingly became of the opinion to replace precious metal with paper money. During this period of unbroken confidence in his abilities, Law proposed to the regent the creation of the Compagnie d’Occident also called the Mississippi Company. This company would maintain a monopoly on trade with the Mississippi province of Louisiana and French Canada and in addition had the right to collect taxes. The latter helped to increase the bank’s assets. Valuable precious metals were suspected to lie beneath the soil of this fertile land (see MacKay/de la Vega, 2010, p. 39). Convinced of the promising prospects, the company was founded in 1717. The share capital was divided into 200,000 shares and offered to the creditors of the Crown in exchange for its debt. In order to convince the creditors to participate in the exchange, the newly issued shares had to offer higher future returns than the Crown’s debt. The interest rate on the debt decreased from over 6 to below 2 percent while the share price rose sharply on the expectation that the area around today’s state of Louisiana would be transformed into a prosperous region. By subscribing to the share issue, creditors had become shareholders of a company whose main assets were Louisiana and the exchanged debt (see Chancellor, 2019; Velde, 2003). Law received more and more privileges on the basis of its initially successful business plans. His bank, which also held a monopoly on the tobacco trade, was elevated to the status of Banque Royale (Royal Bank of France) in December 1718. Law worked closely with the French state. He took over the debts of the state and converted them into shares of the Compagnie, with the aim of selling them to private investors. In return, he secured the rights to collect a large part of the taxes. Phase 2 – Boom The financing of the entrepreneurial expansion was based primarily on the opportunities provided by the Royal Bank. It grew rapidly expending with new branches in Lyons, Rochelle, Tours, Amiens and Orleans. The public accepted more and more the new paper money as they feared further debasement of the metal money they had. In contrast to the precious metal coins, the paper money traded at a premium of up to 15 percent on the nominal value (see Braunberger, 2008). In addition, and notable for the further development of the speculative bubble, there was no limitation on the total number of banknotes issued. Subsequently the

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money supply increased considerably from 40 million livres73 to over a billion. The increased circulation of money had a positive effect on the economy stimulating trade activities. Other private banks leveraged Law’s banknotes to extend credit facilities. The French economy received the much needed and hoped for confidence boost. France was in full prosperity mode – unemployment disappeared, and the sovereign debt crisis was wiped away (see Chancellor, 2019). The increased money supply positively affected the share price of the Mississippi Company as well as it allowed for continuous share buy backs. The subsequent price increase helped to attract even more investors buying into the social contagion of boom thinking (see Mises, 2018). Law was presumably driven by the regent to flood the country with paper money, the value of which sooner or later had to collapse. It therefore remains unclear what Law’s guilt is for violating the financial principles of sound financial management as a whole. Besides the influence of the government, Law’s view was probably also clouded by the (initially) extraordinary success of the Royal Bank (see MacKay/de la Vega, 2010, p. 40). Phase 3 – Euphoria Due to the considerable expansion of the trading monopoly 1719 into the countries east of the Indian subcontinent, China and Africa 50,000 new shares were issued in 1719. Law promised its investors an annual dividend of 200 Livre on each share. The public was enthusiastic about the company’s vision of the future. At least 300,000 investors wanted to buy the 50,000 shares. Due to the overburdening interest for the shares, Law authorized a much larger issue of 300,000 shares, whose proceeds would cover the whole national debt of France (see Velde, 2003). Law’s house was besieged daily by expectant investors. The hysteria surrounding the coveted shares went so far that some wealthy investors had bought an apartment in the immediate vicinity of Law’s house to find out immediately whether they had been selected as shareholders. Soon people of all ages and incomes were speculating on the rise of Mississippi shares (see MacKay/de la Vega, 2010, p. 42). The share price sometimes rose by 10 or 20 percent within a few hours, dazzling many people, even from modest backgrounds, with the prospect of quick wealth. A good comparison would be the crypto frenzy between August – December 2021 elevating the prices to unprecedented highs. In 1719, the share price for the company rose from 500 to over 10,000 Livres, an increase of 1,900 percent (see Chaparro, 2017). It reached its all time high on January 8, 1720 with nearly 11,000 Livres. Thousands of market participants flocked to the open-air stock markets in the rue Quincampoix. The lucky Mississippians were called by the new word “millionaires” – the equivalent of today’s “ultra-high net worth individuals” (see Chancellor, 2019). 73

French unit of silver currency; comparable to the British pound. The purchasing power of the livre today is difficult to determine. Monetary and economic systems are too different, the considerable baskets of goods have changed too much, and the value of the livre has already gradually declined in historical times to be able to make reliable statements. The often-assumed rate of one coined silver livre = 5 - 15 euros can probably only be used approximately for the 1760s to the late 1780s and even then should be used with caution.

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Phase 4 – Crisis The critical phase of the speculative bubble began when Prince de Conti, a prominent aristocrat and previously strong supporter of Law, did not receive an allocation of shares he expected. In his anger, he demanded all of his notes to be redeemed for coins of the Royal Bank. Fearing imitators, Law pushed the regent to convince Conti to reduce the size of his withdrawal as the initial request would have drained a large portion of the coins available. Rumours of this incident started to spread and the public started to wonder if their banknotes are backed by the coins of the Bank and would be redeemed as promised. A bank run started. Virtually the whole of Paris gathered at the Bank trying to convert their paper money back in coins, causing people to die by being crushed almost every day. Subsequently the livre became worthless by the end of 1720. Public started to panic in the face of the deteriorating livre and started to sell the shares of the company to change it into precious metals (see The Tech Blog, 2020). Law was surprised by the investors’ behavior, as he had not expected the shares to be sold. He assumed that the shares would be held long-term, similar to government bonds. The rush for the exit accelerated as it became known, that no wealth was to be found in Louisiana. The shares were now considered overvalued and no longer attractive. Apart from the tanking share prices, inflation surged notably. Food prices doubled by the end of 1719 as the excess liquidity found its way into the real economy. Law tried to fight the inflation by tightening monetary policy, which can be one of the reasons why investors began to sell their shares at high prices and to shift the proceeds to other forms of investment. In a State Council of Ministers, it was determined that banknotes for 2.6 billion livre were in circulation, while the country’s coin stocks did not even cover half of this amount (see MacKay/de la Vega, 2010, pp. 54). Law tried in vain to counteract both the fall in prices and inflation. He froze the share price and reduced the value of the banknotes by 50 percent (see Velde, 2003). Phase 5 – Revulsion The last phase of the speculative bubble was expressed in the despair and anger of many investors who lost their assets due to the collapsing share price to below 500 livre by September 1721. The bursting of the bubble in France foreshadowed the subsequent bursting of a very similar bubble in Britain with the same goal of financing the national debt ‒ the South Seath Bubble focusing on trade in South America. One of the most famous victims of the South Sea speculative bubble was the famous physicist Isaac Newton. Initially, he made strong gains with the shares of the South Sea Company, but lost them later in the crisis and finally suffered a loss of 20,000 pounds. In the wake of these heavy losses Newton formulated his famous statement: “I can calculate the movement of the stars, but not the madness of men.” (Newton, quoted after Arnold, 2010, p. 158) Eventually, Law was forced to flee France and he died impoverished in Venice in 1729. After the speculative bubble burst, paper money was again replaced by precious

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metal money. It took eighty years before France introduced again paper money (see Business Insider, 2017). As is the case with many bubbles – they have their positive effects on the economy as well. In this case, Louisiana was developed and New Orleans was founded under the Mississippi Company. In addition, major research efforts into monetary policy were conducted culminating in Adam Smith’s Wealth of Nations first published 1776. The positive effects were, however, clearly outweighed by the heavy burden this speculative bubble left on France and throughout Europe. The collapse of the bubble resulted in a widespread revulsion against the stock markets, the French capitalism and industry was thrown back by decades (see Velde, 2003). 5.2.3 The stock market boom and crash of 1929

Another important speculative bubble occurred in the U.S. at the beginning of the 20th century. After the post-war recession, the markets faced a major economic upswing. New production techniques, such as the assembly line and modern management strategies were introduced. The progress in industrialization caused an euphoria that gave society the belief in rapid wealth. The optimism and upswing of the “golden twenties” was followed by the crash of 1929. Numerous bank closures in the aftermath caused the Great Depression (see Mussler, 2008). Phase 1 – Displacement At the beginning of the 1920s, the economic environment was characterized by a strong corporate consolidation and expansion. Unemployment fell to an average of 3.7 percent, U.S. GDP grew at an annual rate of 4.7 percent over the time period of 1922 and 1929 (see Goldman Sachs, 2019). The positive economic environment led in conjunction to a strong rise in the stock markets. Today well-known companies such as General Motors were established in this period of growth. The growing optimism among market participants rose over the years with the accompanying positive corporate news. Henry Ford, for example, began production of the T-model in 1927. The New York Federal Reserve lowered its key interest rates from 4 percent to 3.5 percent, making it more attractive for market participants to credit-finance security purchases. Lionel Robbins, Professor at the London School of Economics, commented on this development as follows: “From that date, according to all evidence, the situation got completely out of control.” (Robbins, quoted by Forbes, 2009, p. 105) This first phase was also characterized by the exuberant comments by market participants. John Raskob, Director of General Motors, was particularly positive about the market development at that time: “Everybody ought to be rich.” (Forbes, 2009, p. 104) Phase 2 – Boom The strong expansion of credit-financed investments and consumption raised the standard of living within society but also contributed significantly to the emergence of the speculative bubble. As such, credit-financed consumption was not

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only prominent within the middle class, also investment firms leveraged up to 80 percent of their holdings with loans. As commercial banks were restricted from trading securities, numerous affiliates were set up to enter the investment banking and brokerage business. In addition, investment management firms, were established, which allowed broad sections of the population to participate in the rising share prices. In 1928, 186 new equity funds were launched, each of which met with strong demand. Two years later, the number of fund managers was as high as 750. The strong demand is also reflected in the volume of assets under management. In 1927, when investment companies started their activities, they collected up to USD 440 million for equity investments. By 1929, contributions to the funds had risen to an estimated USD 3 billion. Phase 3 – Euphoria The bubble became euphoric from 1928 onwards. The Dow Jones Industrial Index had already more than tripled from 63 points in August 1924 to 200 points in July 1928. However, between August 1928 and the peak in September 1929, it nearly doubled again to 381 points. The market rose in large swings instead of a steady increase. The shares of Radio Corporation of America (RCA), for example, rose from USD 0.94 in March 1928 to over USD 5.05 in September 1929. The rising prices were supported by strong volume. Market participants began to trade increasingly leveraged with now up to 90 percent of their investments financed with bank loans (see Richardson/Komai/Gou/Park, 2013). Consequently, even a small increase in the value of the investment could result in a huge profit for the investor. On the other hand, market participants had to provide liquidity when the investments started to decline, otherwise the positions were automatically sold by the bank. In the course of the excessive lending and to avoid forced selling, brokerage houses themselves began to provide loans for securities purchases. Outstanding loans rose from USD 3.5 billion at the end of 1927 to USD 7.0 billion by the summer of 1929. Initially, lenders received 5 percent interest p.a.; however, as a result of the high demand, up to 12 percent had to be paid in the meantime (see Forbes, 2009, p. 106). Phase 4 – Crisis The Federal Reserve (Fed) had successively raised its key policy rate, the discount rate, from 4 percent in April 1928 to 5 percent in August 1928. The critical phase started when the Fed further increased the discount rate to 6 percent in August 1929. The commercial banks were also urged to restrict their lending to stock market investors. However, lending continued for the time being, as foreign banks and private investors were still willing to provide loans to finance equity investments. It was only when the remaining lenders started to assume that investments in securities did not pose a secure return in the future that they increased lending rates and made it more difficult to raise credit.

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There is also the anecdote of a shoeshine boy giving Joseph Kennedy, father of former U.S. President John F. Kennedy stock tips, who then sold all of his positions as he realized it would be time to sell when everyone, even shoeshine boys, is affected by the hype offering stock tips (see Waxman, 2019). Phase 5 – Revulsion The crash occurred without an exact obvious reason or trigger event. Rather, the development over the following days illustrates an irrational overreaction, which was initiated when market participants began to sell their leveraged investment positions. Mass hysteria set in because there was no longer a buyer willing to purchase the shares. The stock market started to decline on September 4 and by October 23 it had already fallen from 381 points to 306 points. On October 24, also known as Black Thursday, the Dow fell immediately 11 percent at the opening bell. Despite support purchases by institutional investors on the next day, Friday, share prices fell sharply again on Monday (Black Monday), October 28. The Dow fell another 13.5 percent followed by the peak of selling on Tuesday (Black Tuesday), October 29, resulting in another 11.7 percent loss for the day (see Amadeo, 2020/I). On this one day, the entire profits of a year were wiped out. Up to 16 million shares were sold within that day. The edition of the TIME on November 4, 1929 put the mood of the traders in perspective: “For so many months so many people had saved money and borrowed money and borrowed on their borrowings to possess themselves of the little pieces of paper by virtue of which they became partners in U. S. Industry, now they were trying to get rid of them even more frantically than they had tried to get them.” (Quote Time, Nov 4, 1929) Some shares quoted previously in the triple-digits now found a buyer for one or two dollars. Remarkably for today’s digitalized and real-time world, stock tickers ran hours behind schedule as the machines could not cope with the tremendous trading volume. Three years later, 89 percent of the market capitalization, which had reached its peak in 1929, was evaporated (see History, 2021). In response to the crash, the American stock exchange supervisory authority, the Securities Exchange Commission (SEC), was founded and new regulations were implemented to make the market more resilient in case another crash would occur. As a result of the economic crisis that now set in, the economy collapsed. High unemployment and a bankruptcy wave through corporate America resulted in declining tax revenues. The U.S. economy decreased from USD 105 billion in 1929 to USD 57 billion in 1933 and global trade plummeted by 65 percent. Deflation set in with a rate of 10 percent a year between 1929 and 1933 (see Amadeo, 2020/I). Many think the crash led instantly into the Great Depression between 1929 and 1939, but that was not the case. The market actually recovered from its low of 198 points in mid-November to 293 points in April 1930, but thereafter resumed its extended decline until July 1932. The Great Depression began once the banks were failing

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on the bad loans in their books in 1930-1932 – as there was no deposit insurance bank runs began all over the country (see Time, 2019). Policy makers and central banks were focused on the preservation of the gold standard and balanced budgets, which in turn restricted them in using monetary or fiscal policies to stabilize the economy. As trust between banks evaporated due to ever increasing bankruptcies, fewer firms approached them to issue long-term capital in the 1930s. (see Goldman Sachs, 2019). As the crisis spread across the globe, populations opted to vote for political parties promising relief and ways out of the global depression. In Germany, Chancellor Heinrich Brüning attempted to present a balanced budget despite dwindling tax revenues. He cut social benefits and public contracts, which exacerbated the economic downturn, resulting in more than 6 million unemployed people in the winter of 1932/33 in Germany alone (see Petersdorff, 2008). In the subsequent parliamentary elections of November 1932 and its aftermath, Adolf Hitler rose to power. 5.2.4 The dot-com speculative bubble of the late 1990s

The last speculative bubble of the old millennium began with the commercialization of the Internet. The belief in unlimited profit possibilities through the Internet caused market participants to become euphoric. Companies that geared their business strategy to the Internet, mobile telecommunications and technology were henceforth referred to as the New Economy. Companies that had no contact with the Internet, such as Alcoa, producer of aluminum products, where assigned to the Old Economy. The dot-com speculative bubble was characterized by young companies without necessarily a profitable business model, which were, however, often valued higher on the market than old economy companies with proven profitable business models. The fact that most of these new economy companies made no profits did not per se make the dot-com bubble questionable. Rather, it was the fraudulent activities of certain companies that wanted to profit from the euphoria through fictitious or fraudulent sales figures or balance sheet practices (see Thielmann, 2014). Phase 1 – Displacement The first phase already began in the early to mid-1990s, when the Internet as a communication medium slowly became accessible to the general public. Companies recognized the promising sales opportunities on the Internet and began to expand the infrastructure. In this sense, huge investments were made in telecommunications technology, which meant that the speculative bubble was not only limited to companies with future Internet sales, but also spread to companies that developed the technology. Phase 2 – Boom The second phase was characterized by increasing media coverage of rising security prices. The social contagion of boom thinking began to spread as people started to grasp the transformational power of the Internet. Companies refinanced

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themselves at particularly favourable interest rates of about 3 percent (Federal Funds Rate Jan 1994 vs 9.75 percent in May 1989). These historically low-key interest rates again proved to be an important catalyser in the creation of a speculative bubble. Exchanges who did not yet have a specific index for technology companies, created one similar to the American technology index Nasdaq. As such, the Deutsche Börse launched the “Neuer Markt” index in 1997 for companies from the technology, media, telecommunications and biotech sectors (also called TMT sector). The second phase was also characterized by increasing investment activity of companies due to concerns about the Y2K bug. Y2K stood for the “year 2000” and the concern that the software in use at the time might not be able to switch correctly to the year 2000. In order to prevent the corresponding risks of a collapse of the systems, huge investments were initiated. The demand for corresponding software upgrades has also led to significant new spending, resulting in unexpected earnings surprises for the tech companies (see Amadeo, 2020/II). Against this backdrop, many companies saw their chances in the Internet offering unlimited earnings potential. The number of Initial Public Offerings (IPOs) by companies in the new economy increased to a new record of 457 in 1999. Investors were offered the prospect of huge increases in turnover with fantastic returns on sales. 117 of the 457 doubled in price on the first trading day (see Whitefoot, 2017). Amazon (IPO May 1997) and Ebay (IPO September 1998) also debuted on the Nasdaq in this period. Phase 3 – Euphoria Companies changed their names to the ending “.com” to take advantage of the euphoria among investors leading to sharp rises in their security prices. For example, France Telecom merely changed their logo to highlight the “com” ending as an affiliation to the Internet sector. Others either added the aforementioned ending or created completely new corporate names. An academic study by Cooper/Dimitrov/Rau, 2001 found that the 95 companies that added the endings “.com”, “.net” or “internet” to their names benefited of cumulative abnormal returns of 74 percent for the 10 days surrounding the announcement day of the name change. Excessive media coverage did not only pull in retail investors but also made stars from some of the celebrity analysts who seemed to have a good run. This in turn re-enforced the social contagion of boom thinking as Larry Puglia, Vice President at T. Rowe Price recalls: “Even as valuations became detached from any rational financial underpinning, commentators suggested these companies were re-writing the rules of business.” (Puglia, quoted in Citywire Selector, 2020) Investors were thrilled by the initial public offerings. Particularly in this phase, IPOs were seen as a guarantee for substantial price gains on the first trading day

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or the first few days after the issue (see chapter 11.3). Price increases by 100 percent on the day of the IPO was the rule rather than the exception. For example, the first quote of the first social media platform theGlobe.com in November 1998 was USD 87.00 with an initial offer price of USD 9.00. Investors lucky enough to receive some shares at the initial offer price had at the end of the day a first day return of 605 percent. This demonstrated the importance to participate in the initial allocation of shares at the offer price. In this phase of the boom, investors paid no attention whatsoever to review the business model or to engage in any efforts for a fundamental analysis. In some cases, they were only aware of the name of the security they wanted or had purchased. As a result, IPOs in Techs scooped up USD 145 billion between 1995 and the end of 2000, USD 45 billion alone in 2000, much of it to disappear shortly later (see Waters, 2021). The IPOs not only allowed the market participants to profit directly from the price increases, but also led indirectly to considerable trading volumes and commission gains for the banks. In the course of subscribing to securities, millions of retail investors opened a securities account at their local bank for the first time. The euphoria of investors for companies of the New Economy can be illustrated very well by the absurd levels of valuations it had reached. For example, according to FactSet, Cisco Systems had a P/E ratio of 156 on March 27, 2000. This ratio would require an astronomical rate of profit growth to justify the valuation. To put that into perspective, a P/E ratio of 156 means, that investors are willing to pay USD 156 for USD 1 of current earnings. From a behavioral perspective it seems that →Overconfidence in TMT companies, →Recency Bias extrapolating recent upward trend, →Confirmation Bias and →Herding had a great effect on driving valuations to skyrocketing levels. The biases mentioned will be of special focus in section III of this book. It is also evident, that rational market participants either had not the power to drive valuations back to realistic levels or simply had no interest in doing so, which raises hard questions about the validity of the market efficiency assumption. Phase 4 – Crisis The critical phase began in March 2000, when investors became increasingly doubtful of company valuations. Quarterly results for the first quarter ending March 31st showed that more and more companies were unable to meet their ambitious growth expectations. More so, the majority of the tech companies had billion-dollar valuations, but no revenues or earnings whatsoever. The first “death lists” of companies that might be threatened by insolvency were circulating (see Whitefoot, 2017). In addition, the Federal Reserve (Fed) raised the Fed Funds Rate from 4.75 percent in June 1999 to 6.5 percent in May 2000. Equity markets experienced first losses already in March with the Nasdaq Composite falling from 5,048 points on March 10 to 3,321 points on April 14. Very often a trend reversal can be observed when key interest rates rise. This is due to the fact that the higher cost of borrowing

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means that companies cannot request as much loans to finance their expansion plans as they would if rates were lower. By curbing economic activity, the central bank aims to counteract rising inflation. Investors now had a viable alternative to equities in the form of fixed-income investments via corporate or government bonds. Investors also increasingly lost interest in subscribing to new securities from upcoming IPOs, as they now often received full allocation of the securities they had subscribed for. This, however, proved to be problematic as most investors had generally subscribed to far more shares than they actually wanted to be allocated; this was based on the experience of previous hotly contested IPOs where usually only a fraction of the shares were allocated to a retail investor if at all. First trading day price increases flattened out and prices fell in the face of increasing sell orders immediately after the opening bell (see Mohr, 2008). Phase 5 – Revulsion The last phase of the dot-com speculative bubble was marked by sharp price drops following the revelation of manipulated financial figures by some companies. For example, German Comroad (navigation technology) sales figures turned out to be 97 percent fictitious (see Capital, 2020). The fall in stock prices was almost as fast as the price explosion at the turn of the millennium. The Nasdaq Composite fell by 80 percent from its high in March 2000 to its low in October 2002. Other technology indices were even discontinued such as the German Neuer Markt in March 2003 and completely replaced in December 2004 by the new TecDax index, consisting of 30 tech companies. Speculative bubble at the turn of the millennium – Nasdaq Composite

Fig. 27: Price chart Nasdaq Composite, 1996-2003; FactSet

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Remarkably and often overlooked, the crash of the dot-com bubble did not affect each and every index the same way – whereas large-cap growth and technology indices around the world plummeted, the rotation to value stocks turned out to be very profitable. While the Nasdaq Composite fell by 40 percent in the initial crash year of 2000, the Russell 2500 Value Index gained 17 percent, giving investors an opportunity to compensate for some of the losses. Rotation from Nasdaq Composite into Russell 2500 Value

Fig. 28: Nasdaq Composite vs. Russell 2500 (indexed), Year 2000; FactSet

Unsurprisingly, the crash had a notable impact in the culture of individual share ownership among retail investors. Due to loss of confidence, investment in equities dropped in favour of secure deposit accounts for years to come (see Forbes, 2021). Many companies went bankrupt (e.g., Pets.com, eToys.com, theGlobe.com) as they simply ran out of cash. Spending millions on marketing could not remain the only way to attract and retain investors. Others, however rebounded as they returned to profitability. For example, Amazon, fell sharply from the highs (split-adjusted) of USD 5.00 in December 1999 to below USD 0.30 in October 2001. At the time of writing (June 2022) the share price stood at USD 100 with a market capitalization of USD 1.1 trillion. Some companies tried to get rid of the affiliation to the dot-com boom by removing the .com ending or changing their corporate names altogether. A study by Cooper/ Khorana/Osobov/Patel/Rau in 2005 examined the return effect of a name change of 67 companies dropping the .com ending. The study revealed abnormal returns of up to 70 percent for the sixty days surrounding the announcement day of the name change. “Now that dot-com fever has turned into a plague, companies left and right are changing their names to disassociate themselves with the stigma of failure.

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IntraNet Solutions renamed itself Stellent Inc. on Wednesday, and Internet.com became INT Media Group in May. Industry officials say thriving dot-coms are trying to avoid being lumped in with the rotting corpses of failed dot-coms such as pets.com, garden.com, furniture.com and living.com. Companies are distancing themselves from that smell,” said Bridget Levin of Minneapolis-based Nametag International Inc. IntraNet Solutions said its name change was intended to reflect its expanded business. But Alan Meckler, chairman and CEO of Internet.com, was more pointed: “It’s window dressing for the financial community,” he said. It retains its coveted Internet.com domain name. “For those in the know, our customers, nothing ever changed” (Associated Press News Wire, August 30, 2001). 5.2.5 The U.S. real-estate credit bubble between 2001 and 2006

The first speculative bubble of the new millennium resulted from the surge in real estate prices in the U.S. and other parts of the World, caused, among other things, by the very low interest rates from central banks in the wake of the collapsed dotcom bubble. Often low-income and high-risk borrowers (sub-prime borrowers) are seen to be the main cause for bursting the housing bubble. However, more recent research suggests that mortgage defaults in the prime segment played a bigger role in the crisis than previously assumed (see Albanesi/De Giorgi/Nosal, 2017). The crisis first began as a banking crisis, it then developed into a global financial crisis, which particularly affected Europe’s peripheral countries through massive unemployment. Prior to the crisis, market participants assumed that progress in the development of financial products and the existing financial market regulation would prevent serious financial crises. Developments since 2007 seem to contradict this view. Signs of the approaching crisis were increasingly evident in the form of inflationary real estate prices, rising borrowing for real estate acquisitions and the growing foreign trade deficit as a sign of rising foreign debt, especially in the case of the U.S. (see Reinhart/Rogoff 2009, p. 200). Phase 1 – Displacement The legal foundation for the real estate credit bubble was laid in the 1980s. The adoption of the Depository Institutions Deregulation and Monetary Control Act (DIDMCA) and the Alternative Mortgage Transaction Parity Act (AMTPA) were the main reasons for granting subprime loans in the form of adjustable-rate mortgages to high-risk borrowers. These mortgages were enticing as they offered an initial interest rate well below market interest for the first two years subject to prolongation thereafter. Thus, the DIDMCA facilitated mortgages for borrowers with lower solvency. In addition, the AMTPA, which was passed in 1982, legalized granting mortgages with a variable interest rate and high processing fees (see Eustermann, 2010, p. 21).

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In addition, the attractiveness of mortgage loans increased through the drastically reduced key interest rate of the U.S. Federal Reserve. In the context of the collapsed dot-com bubble in March 2000 and especially after the terrorist attacks of 9/11, 2001, the key interest rate fell from as high as 6.5 percent to 1 percent by mid-2004 (see Fig. 29). Falling interest rate as a first measure after collapsing bubbles, but also the cause for new ones ‒ Overview of U.S. key interest rate Dot-com Bubble

Fig. 29: Development of the U.S. Federal Funds Rate between 1997 and 2004; FactSet

The low interest rate achieved the intended stimulation of the economy and credit lending. Real estate mortgage originations grew exponentially. Subprime mortgages grew from 8 percent of all mortgage originations in 2003 to up to 20 percent between 2005 and 2006. Mortgages to prime and middle-class creditors increased even from 20 percent in 2004 to 35 percent in 2007 (see Albanesi/De Giorgi/Nosal, 2017). Market participants simply looked for a new asset class to invest in once the dot-com bubble crashed. The real estate market, which had been characterized by rising prices since 1990 spiked the interest and created the new boom-thinking. In the course of the massive expansion of liquidity due to the lowered interest rate, investments into real estate picked up drastically. Phase 2 – Boom In the wake of the low interest rate, commercial banks were looking for new sources of interest income. Inadequate regulatory controls, lack of restrictions to lending and reduced quality standards in the lending process of the banks enabled a massive expansion of loans to borrowers with no equity at all for the intended real estate financing. Only the property to be financed was taken as collateral. With continuously rising property prices (124 percent between 1997 and 2006), the loan was expected to be repaid without any problems at a future sale of the property (see The Economist, 2007). The media coverage on the new possibilities

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to become wealthy through leveraged real estate investments increased the social contagion of boom thinking. As a result, U.S. household debt as a percentage of its income increased from 92 percent in 2000 to 130 percent at the end of 2007 (see Krugman, 2010). Faced with low returns on government bonds, institutional investors were also looking for new investment opportunities. Mortgage Banks created these new opportunities in the form securitized loan commitments. This gave birth to structured products in the form of MBS (Mortage Backed Securities)74 and CDO (Collateral Debt Obligations)75. These highly complex financial products contained loan receivables from debtors with different credit ratings. For a long time, the rating agencies gave many of these products the highest rating. These new innovative financial products enabled market participants around the world to invest in the booming U.S. real estate market. Phase 3 – Euphoria Market participants became even more euphoric about the appreciation prospects of their properties. They were increasingly acquired with the aim of selling them again within a very short period of time. This approach, also known as “flipping”, led to a massive increase in property transactions and prices. Not surprisingly, the euphoric mood was clearly visible on the S&P/Case-Shiller U.S. National Home Price Index (see Fig. 30). The cumulative price increase from 1996 to the peak in 2005 was 100 percent. The yearly increase in 2005 reached an impressive 13.5 percent ‒ four times the rate of economic growth in the U.S. in 2005 (middle chart). Putting this into perspective, the development prior to the credit bubble (left chart) between 1987 and 1996 shows a cumulative increase of roughly 30 percent. The right-hand chart illustrates the burst of the bubble and the subsequent rise in real estate prices to similar and higher levels in the course of the second decade of the new millennium. This development will be covered in the next subchapter. The “This time is different” syndrome continued to be characteristic of the third phase, with the then Federal Reserve Chairman Alan Greenspan also being among the supporters of securitized credit claims. In his opinion, they would not only promote risk allocation, but would also greatly reduce the illiquidity of the asset class in question ‒ real estate. As a result, rising prices of risky assets as well as rising profits of participating banks as a result of innovative products would be justified.

74

In general, the investor receives the interest and principal payments from the borrower/ real estate owner.

75 CDOs on the other hand can be seen as a promise to pay the investors from the cash flow collected through the pool of bonds or assets the CDO sponsor owns. They are sliced into tranches with varying interest levels based on the probability of default the tranches would have. In that aspect, the most senior tranches are the safest (and consequently with the lowest interest payment) and the last to lose from a default of the assets it holds.

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Development of U.S. real estate prices with recessive periods prior...

within the real estate bubble

after...

S&P/Case-Shiller U.S. National Home Price %-change vs. prior year S&P/Case-Shiller U.S. National Home Price Recession Periods – United States

Fig. 30: S&P / Case-Shiller U.S. National Home Price Index 1986-2021; FactSet

The increasing importance of the participating banks in the securitisation business is also reflected in the ratio of their earnings performance to GDP. In the 1970s, credit institutions contributed 4 percent to GDP, but by 2007 their share had already grown to 8 percent (see Reinhart/Rogoff, 2009, p. 207). Phase 4 – Crisis The Fed started tightening borrowing conditions in July 2004 and by June 2006 it had raised the Fed Funds Target Rate in 17 steps to a new cycle high at 5.25 percent. This had a notable effect on permits for new homes, which declined drastically as of 2006 by over 30 percent. Real estate prices based on the Case-Shiller Price Index showed a clear contraction as well (see Fig. 31). Research into participating borrower classes concludes that the main responsibility for the bubble and its subsequent burst lies mainly with affluent middle-class and prime-credit borrowers. As the mortgages of the prime borrowers were much bigger than the one of the subprime borrowers, the impact of a default was much worse. In fact, the chance of a prime-borrower defaulting actually increased from 0 percent to 5 percent by 2006, whereas the probability of default for sub-prime borrowers doubled from 6 to 12 percent. In addition, prime borrowers with multiple homes had less incentive to hold on to their homes than those who only had one. As such, the rate of delinquent mortgage debt rose for the top quartile of borrowers from 13 percent in 2003 to 23 percent by 2006. On the other hand, subprime default rates actually dropped from 22 percent in 2003 to 11 percent in 2006 (see Albanesi, De Giorgi and Nosal, 2017; Adelino, Schoar and Severino, 2016).

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Reaction of real estate prices and demand for housing permits following interest rate increases

Fig. 31: Interest rate vs. real estate prices and housing permits; FactSet

According to MIT Sloan Professor Antoinette Schoar, the willingness to default and foreclosure was much higher among the prime borrowers (see Church, 2016). “In our opinion, the facts don’t line up with this narrative … Calling this crisis a subprime crisis is a misnomer. In fact, it was a prime crisis.” (Antoinette Schoar, quoted by Church, 2016) With defaults mounting, two hedge funds by Bear Stearns Asset Management heavily invested in CDOs had to be closed in mid-2007 as they had lost nearly all of their value. Even government-backed home mortgage companies such as Fannie Mae and Freddie Mac were caught up in the turmoil and investment banks had to make massive write-downs on their balance sheets. Initially only U.S. institutions were affected by the crisis, but by 2008 it had spread to Europe. Several German financial institutions, including Hypo-Real Estate, IKB Mittelstandsbank and Landesbanken (Saxony, Baden-Württemberg) had to be rescued by the tax payer. Due to widening credit defaults, a crisis of confidence arose among institutional investors. With the insolvency of Lehman Brothers in autumn 2008, following the last-minute sale of Bear Stearns to JP Morgan, confidence among banks was considerably damaged. The interbank market increasingly dried up, with the result that banks hardly lent money to each other. The crisis of confidence on the interbank market is best illustrated by the TED spread (Treasury Bill Eurodollar Difference) (see Fig. 32). This indicates the difference between the interest rate of the three-month →LIBOR for interbank business and the three-month U.S. Treasury Bill. While the TED spread was still at 55 basis points or 0.55 percent in June 2006, it peaked at 4.64 percent in October 2008. The large difference resulted from the sharp rise in LIBOR to 4.75 percent and the sharp decline in Treasury Bills. The risk premium that banks demanded from each

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other thus reached almost 5 percent! The interbank market became very expensive in relation to the U.S. Treasury market, so that the majority of investments by financial institutions were made in government securities. Ted spread shows crisis of confidence in interbank trading in autumn 2008

Fig. 32: TED-Spread 1995-2021; FactSet

Phase 5 – Revulsion The mortgage-crisis peaked in the collapse of the fourth-largest U.S. investment bank Lehman Brothers on September 15, 2008, which subsequently led to what is known today as the Global Financial Crisis (GFC). Investors hoping to make up for the losses via Credit Default Swaps did not receive payment as the insurance companies themselves lacked the capital to cover CDS holders in the course of the imploding MBS market. American International Group (AIG) held more than USD 440 billion of CDS and had to be bailed out by the U.S. Government with over USD 85 billion to avoid bankruptcy (see Harrington/Moses, 2008). The crisis of confidence was not only felt in the interbank market between credit institutions, but also increasingly spread to lending to corporate and private customers. Due to the asymmetrical distribution of information, which made it increasingly difficult for banks to assess the actual situation of mortgage applicants, lending was drastically restricted. In addition, growing concern about bank customers’ savings led to the first bank runs. British bank Northern Rock experienced a bank run in September 2008, with customers of the bank withdrawing up to GBP 1 billion within a very short period of time. To contain the risks of further bank runs, governments around the globe tried to contain the panic by giving state guarantees for saving deposits as done by the German government in October 2008. The financial sector crisis also had a serious impact on global economic development. Global real GDP fell by 2.1 percent in 2009. European countries such as Finland (-8.1 percent), Germany (-5.6 percent) and Italy (-5.3 percent) were much

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harder hit than the U.S. with “only” -2.5 percent. According to Kenneth Rogoff and Carmen Reinhart, the last phase of a serious financial crisis, can be described by the three subsequent characteristics (see Reinhart/Rogoff 2009, p. 224):  Severe and persistent impairment of the affected assets – collapse of property prices by up to 35 percent over a period of six years (Jul ‘06 – March ‘12) and 60 percent decrease in global indices such as the S&P 500 between October 2007 and March 2009. On September 29, 2008 the Dow Jones Industrial Average dropped 777 points to 10,365 (7 percent) ‒ to that date, the biggest absolute drop in history. The recession low was struck on March 9, 2009 with 6,547 points (or another 37 percent loss).  Strongly increasing unemployment with an equally strong decline in productivity – The unemployment rate in the U.S. rose from 4.4 to 10 percent in October 2009, just slightly below the previous peak of 10.7 percent in December 1982. 7.7 million lost their jobs in the wake of the economic downturn. The U.S. industrial output decreased especially in the manufacturing sector by 18 percent between 2007 and 2009. The high of 2007 has not been reached since. As the real-estate crisis spread across the globe, high unemployment rates were especially affecting peripheral EU countries such as Spain (26.3 percent) and Greece (27.5 percent) in 2013.

 Strongly rising public debt – The analysis results of Reinhart and Rogoff revealed an increase in public debt in the affected countries by an average of 75 percent since 2007, close to the benchmark of an average 86 percent increase recorded in earlier deep financial crises after World War II. Not only the rescue attempts with tax money played a major role here, but also the lack of tax revenues due to declining industrial production. The rising refinancing costs of the countries affected have an aggravating effect here (see the example of peripheral EU countries). The mortgage crisis demonstrated that the causes can be both micro- and macroeconomic in nature. On the microeconomic side, the lack of transparency in the securitisation process, irresponsible lending practices and the high complexity of structured products, which made an adequate risk assessment impossible, are among the triggers of the crisis. On the macroeconomic side, the excessively expansive monetary policy following the bursting of the dot-com bubble and the attacks of 9/11 is one of the core causes of the crisis. Excess liquidity increasingly found its way into the equity and real estate markets. Global current account deficits and a negative U.S. savings rate in 2005 and 2006 signalled that the society was living beyond its means. In response and as an attempt to contain the crisis, central banks around the globe pumped billions into the financial markets by purchasing government debt and troubled private assets from banks worth USD 2.5 trillion, being the largest injection in financial market history (see Altman, 2009). In addition, key interest rates were reduced drastically. Within two years between 2007 and 2009, the ECB lowered its interest rates from 4 percent to 1 percent, the Bank of England from 5.75 percent to 0.5 percent, the Fed from 5.25 percent to 0.25 percent and the Reserve Bank of Australian from 7.25 percent to 3.00 percent (see Fig. 33).

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Policy Rates around the globe

Fig. 33: Global Policy Rates; FactSet

Furthermore, governments around the world set up massive financial packages to bailout and re-capitalise the financial sector and stimulate the economy. Perhaps best-known is the Troubled Asset Relief Program (TARP) set up by the U.S. government in 2008 to purchase or insure up to USD 700 billion of “toxic” assets, mainly residential and commercial mortgages and related obligations. Other European governments set up similar programs, including the UK (GBP 500 billion), France (EUR 360 billion), Germany (EUR 480 billion) and Switzerland (USD 60 billion). Common to these packages were the combination of debt and equity finance, the transfer of private liabilities to the state and unprecedented levels of public involvement in the financial sector in many of the countries. 5.2.6 Speculative bubbles after the U.S. mortgage crisis

Following the bursting of the real estate bubble in the U.S. in 2008 and the resulting instability in numerous countries, particularly in Europe, central banks around the world, as lender of last resort, declared the supply of liquidity to be their main task. While the objective immediately after the crisis and in subsequent years was to restore confidence among banks in order to lend to each other again, from around 2013 onwards it became the prevention of increasingly apparent deflation risks. In the past, the central banks were able to counter deflationary tendencies by keeping interest rates as low as possible. However, in recent years measures such as direct liquidity injections, quantitative easing programs (bond purchases by central banks in the secondary market) and negative deposit interest rates, which have hardly been practiced before, are being used to boost lending and inflation. Phase 1 – Displacement After the collapse of Lehman Brothers in 2008, central banks around the world drastically lowered key interest rates down to the 0 line (see Fig. 33 in previous

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subchapter). This stabilized global capital markets which subsequently embarked on the longest bull market in recent history interrupted in March 2020 by the outbreak of the SARS-CoV-2 (Covid-19) pandemic. The spreading financial crisis dragged peripheral European countries such as Greece, Spain, Portugal, but also Ireland into a downward spiral by 2010. There was not only the danger of government defaults but also the danger of a collapse of the Euro zone. In retrospect, the statement made by then ECB President Mario Draghi in July 2012 can be classified as a so-called game changer: “Within our mandate, the ECB is ready to do whatever it takes to preserve the Euro. And believe me, it will be enough.” (Mario Draghi, July 26 2012, London). With this announcement, the speculation against peripheral European countries, ended abruptly. Yields on 10-year bonds fell substantially and global equity markets recovered from the lows. Phase 2 – Boom In addition to the reduction of interest rates, the launch of various quantitative easing programs led to an artificial increase in the global money supply. The Federal Reserve (Fed) led the way with its first of several (five in total, three main programs with extensions) large-scale asset purchase programs in December 2008. The aim of these programs (often referred to as QE1, QE2, QE3) was to “…downward pressure on longer-term interest rates and thus supporting economic activity and job creation by making financial conditions more accommodative” (see Federal Reserve, Open market operations). Total security purchases amounted to USD 4.5 trillion between December 2008 and August 2014 (see Global QE Tracker Atlantic Council, 2020). In September 2012 the ECB entered the unexplored territory of buying up government bonds of distressed euro countries. While in September 2012 only the statement to buy up government bonds led to a noticeable reduction in yields, the ECB started to purchase bonds in the amount of EUR 60 billion per month beginning March 2015. Since then, the ECB has purchased bonds in varying monthly amounts between EUR 8-15 billion summing up to a net value of EUR 2.6 trillion by December 2018. In November 2019, the ECB Governing Council restarted monthly purchases of EUR 20 billion “…to run for as long as necessary to reinforce the accommodative impact of its policy rates and to end shortly before it starts raising the key ECB interest rates” (see ECB, 2021). In the course of this, government bond yields in the Euro zone decreased below 1 percent, including those of fiscal weaker countries such as Spain or Italy, and some turned even negative. The ECB was not the only central bank, along with the Fed and the Bank of England, to follow this path. For its part, the Bank of Japan, which had pioneered QE in the early 2000s, started its own programme of buying up government bonds in 2013, eventually accumulating assets worth of USD 3.6 trillion by the end of 2020. This, known as QQE (Quantitative and Qualitative Easing), was expanded by the

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negative interest rates in January 2016 (see FAZ, 2016) to “QQE with negative rates”. Thus, the Central Bank of Japan, along with Switzerland, Sweden and Denmark, had pushed their interest rates into negative territory and are still (June 2022) applying it except of Sweden. Central bank bond purchases led to falling yields on government bonds

Fig. 34: Interest rates on fixed-income bonds in Europe; FactSet

The liquidity created was not immediately passed on by the banks to consumers in the form of loans but was parked within the Federal Reserve and other Central Banks as excess liquidity. This excess liquidity was increasingly used to purchase securities, real estate and other assets due to the lack of investment opportunities in the real economy. The purchase of bonds and MBS by the Central Bank further reduced yields due to additional demand. This led to a reduction in the real interest rate (taking inflation into account), making consumer credit more affordable. On the flipside, it highlights the problem of falling interest rates on savings deposits. Savers are receiving less and less interest, while borrowers are being encouraged to borrow at more favourable rates. In the medium term, lower interest rates have the goal to promote economic development and thus job creation. Phase 3 ‒ Euphoria Phase 3 lasted until February 2020, when the combination of an oil price shock with the spread of the SARS-CoV-2 (Covid-19) virus triggered an even sharper market crash than in 2008. Euphoria manifested itself differently in regions and asset classes and also led to a bubble in a hitherto unknown investment vehicle, the cryptocurrency.

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Equities China’s equity market was the first to display signs of euphoria and crisis in this cycle. Between June 2014 and June 2015, the CSI 300 Information Technology Index rose by 186 percent, fueled by a mix of government encouragement to invest in equities, accommodative monetary policy and leveraged purchases by private investors. In comparison, the Nasdaq Composite gained a mere 13 percent in the same period, impressively illustrating the extent of the stock market exuberance in China. Strong rise of the CSI 300 Tech during the euphoria phase

Fig. 35: Comparison between CSI 300 Tech and Nasdaq Composite between 2009-2021; FactSet

On April 7, 2015, the Chinese news agency Xinhua reported that a robust stock market was important for China, and that the increases of the last few months were “rational”. However, on June 12 the market started plunging and – with intermittent pauses and temporary recoveries – in late January 2016 stabilized 45 percent lower. The exact causes of the sell-off are still being disputed, but it showed all the hallmarks of a leveraged rally being abruptly ended by the lack of additional lending. China’s stock market eventually recovered, helped by a combination of government measures, which included various regulatory measures such as a six-month lock-up period for major shareholders, corporate executives and company directors, short-selling ban and the installation of circuit breakers, although the latter may have been less effective than expected. Meanwhile, global stock markets continued their steady upward trend, only temporarily interrupted by concerns surrounding Fed rate hikes in 2018, to new historic heights. By end of January 2020, the Dow Jones Industrial Index had risen to 29,103 points or 110 percent higher than the peak before the crash starting in 2007 (see Fig. 36).

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Fig. 36: Dow Jones Industrial Average; FactSet

Bond markets also greatly expanded in the last decade, fueled by a combination of low borrowing costs, growing secular demand for fixed income securities by institutional investors such as insurance and pension funds, asset purchase programs and tax considerations (see Fig. 37).

Fig. 37: Bloomberg Global High Yield vs. Global Aggregate – Total Return (indexed), FactSet

An important reason to issue bonds are the high taxes that would have to be paid when American companies repatriate assets from overseas to the United States. Apple, for example, has issued bonds worth USD 158 billion since May 2013, as the interest rates (0.50 and 3 percent on maturities from 3 years to up to 40 years) are much lower than the current 21 percent tax rate (2021) that would be payable if capital was repatriated from abroad. As a result, companies do not primarily use

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debt capital to invest in additional growth, but rather to avoid the tax implications of repatriating capital from abroad (see Schaefer, 2016). Financing takeovers and asset purchase programs have spurred the issuance of corporate bonds in Europe. In October 2016, Bayer issued bonds for USD 56.9 billion with the clear objective to use the proceeds for the takeover of Monsanto. At the same time, the ECB announced that its bond purchase programme would also include corporate bonds. Did Bayer anticipate the newly launched ECB purchase program to aim for new acquisitions? At least, Bayer met two criteria that enabled it to procure debt capital to cover the acquisition costs through “ECB financing”. The bonds issued are not considered as bonds issued by financial institutions, and Bayer’s bonds are investment grade rated by the three major rating agencies. Demand for Bayer’s bonds from the ECB has correspondingly reduced the cost of the new debt to finance the acquisition. Crypto Currencies The last decade has also seen the rise of a new investment phenomena, cryptocurrencies fueled by technological innovation and waning trust in fiat money. A key argument for cryptocurrencies is that the technology behind them (such as blockchain) is meant to increase security for transactions, create more transparency and reduce dependency on government-controlled, centralized money. However, since the feasibility of blockchain based transactions is yet to be proven, investors tend to get excited early on and eventually succumb to the fear of missing out (see Daxhammer/Facsar, 2018). The rise of the cryptocurrency is spurred by technological innovations such as cloud computing, machine learning, artificial intelligence or 5G (Fifth-Generation wireless) creating completely new ways of interacting and servicing consumers and thereby changing the competitive marketplace. The excess liquidity created by central banks in all likelihood also fueled the rally in cryptocurrencies. At the beginning of 2017 the price of Bitcoin, one of the most popular cryptocurrencies, was at barely USD 1,000. By mid-December 2017 the price surged 20-fold to a new all-time high being an astonishing 260 percent away from its 200-day moving average. To put that into perspective, the Nasdaq Composite on the top of the dot-com bubble was 54 percent higher than the 200-day moving average. After a first collapse beginning 2018, cryptocurrencies started to recover notably as of mid 2020 to reach new record highs in November 2021. In fact, Bitcoin increased 240 percent above the former peak marked December 2017 (see Fig. 38). As investors were eager to jump on the crypto train believing in the narrative of instant wealth, the number of currencies exploded to about 2,000 by end of 2018 and to over 2,800 by end of 2021. These currencies were created via Initial Coin Offerings or ICO’s. These Initial Coin Offerings (ICOs) rapidly surged to new highs raising USD 6.5 billion in 2017 alone through as many as 700 crowdfunding initiatives. By 2020 the amount raised through ICOs had reached USD 20 billion.

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Fig. 38: Bitcoin with 200-day moving average, 2017-2021, Tradingview.com

ICOs became popular because they could crowdfund their fundraising campaigns with little or no regulatory requirements or proof of an actual product or company. Within the universe of more than 2,000 currencies or currencyblockchain-based tokens, satirical tokens such as the Dogecoin or UET – Useless Ethereum Token –, illustrated how low the barriers of entry were and the lack of regulatory scrutiny. UET reached a market cap of about USD 1 million in January 2018, even though the initiator was upfront transparent about the seriousness of the crowdfunding project: “You’re going to give some random person on the internet money, and they’re going to take it and go buy stuff with it. Seriously don’t buy these tokens.” (see https://uetoken.com/) Another similarity to the dot-com bubble emerged in the first phase of the crypto frenzy. The reader might recall how companies in the tech-bubble of 1999-2000 changed their names to the ending “.com” to take advantage of the euphoria among investors leading to sharp rises in their share prices (see subchapter 5.2.4). It wasn’t different this time. On December 21, 2017 Long Island Iced Tea Corp. announced a change of name to Long Blockchain Corp on December 21, 2017 producing abnormal intraday returns of close to 500 percent closing the day with a share price increase of 183 percent. The company claimed to focus on opportunities to leverage the benefits of the blockchain technology (see Daxhammer/Facsar, 2018).

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The hype around cryptocurrencies has created its own myth just like during the Tulip Mania. It is reported that Laszlo Hanyecz, crypto developer, paid in 2010 two pizzas with 10,000 Bitcoins (worth then USD 41, in December 2021 however USD 450 million) being the first person to use bitcoin in a commercial transaction. Phase 4 & 5 – Crisis/Distress The sell-off started on February 21, 2020 with a small correction of 0.38 percent for the S&P 500. Only five days later, the correction had turned into a slump of 12.42 percent and by 23 March into a crash of 33 percent. In fact, the crash, triggered by the global spread of the Covid-19 virus, was the fastest ever in financial history. The S&P 500 lost 33 precent within 23 trading days. The 1987 crash with a similar magnitude took 72 trading days. Bursting bubbles such as the dot-com needed 638 days to reach the bottom while losing 50 percent of value. The 2008 financial crisis took 356 days to lose 56 percent (see Fig. 39). Covid causing fastest 30 percent drop in history

Fig. 39: Meltdown in days of selected events – S&P 500; FactSet

Bond markets and cryptocurrencies also suffered a sharp correction. The Bloomberg Global Aggregate Index decreased by 54 percent and Bitcoin by 48 percent. Covid not only caused the biggest market crash since 1929, it also prompted politicians to implement wide ranging lockdown measures to stop the spread of the virus. The upshot of these measures was, however, the deepest recession since the great depression. The International Monetary Fund (IM) estimates that global GDP contracted by 3.3 percent in 2020. U.S. GDP declined by 3.5 percent and the Euro zone even by 6.6 percent (see IMF World Economic Outlook, April 2021).

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The World after Covid – New Displacement or Continued Euphoria? Governments and central banks applied the same remedy that seemed to have worked in the previous decade: aggressively expanding money supply and massive fiscal packages. The IMF estimates that the fiscal deficit in the so-called advanced economies ballooned to 11.7 percent of GDP in 2020 from 2.9 percent in 2019. Moreover, fiscal largesse is expected to continue in 2021 with an expected deficit of 10.4 percent of GDP. As a consequence, the debt burden is forecast to grow to 122.5 percent in 2021 from 103.8 percent in 2019 (see IMF Fiscal Monitor, April 2021). The policy of seemingly unlimited fiat money and fiscal spending most likely prevented a deeper and longer recession, possibly even a depression. Actually, at the time of writing (fall 2021) the global economy seems firmly embarked on an, albeit somewhat volatile, recovery. In addition, it also succeeded in creating the most dynamic financial market recovery.

Fig. 40: Nasdaq Composite 2009-2022; FactSet

The speed at which markets rebounded after the crash is indeed astonishing and can probably not entirely be explained by central bank and government support. Technological innovation, in particular the spread of social media as a platform for investment ideas also played a key role. Whereas previously dedicated news channels and information providers such as Bloomberg, for example, were limited to professional investors, social media enable more market participants than ever to interact and exchange investment ideas faster than ever. The number of stock investors has picked up notably among the younger generation since the Covid crash. The German Institute for Equity Investments (DAI – Deutsches Aktieninstitut) estimates that more than 600,000 new investors below the age of 30 entered the market, which represents an increase of 67 percent from a year ago (see n-tv, 2021).

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Initial Public Offerings by start-ups without or hardly any notable revenues were a main driver of the dot-com bubble keeping the boom-thinking narrative alive. Post-Covid, revenue-less start-ups are again entering the equity markets although this time in a slightly different form via SPACs ‒ Special Purpose Acquisition Companies, which have become the talk of the finance world in 2020. These SPACs, which are listed companies, gather funds with the goal to search and acquire privately held companies to then list them on the exchanges and use the proceeds to fund one or more mergers within a limited time of up to two years. In 2020 there were 105 announced SPAC mergers, in the first half of 2021, already 112. Until June 2021, there were 263 initial preliminary filings as a prerequisite for IPOs. 255 out of these are SPACs. According to FactSet, SPACs accounted for 55 percent of all IPOs in 2020 and in the first quarter of 2021 they represent almost 70 percent (see Potter, 2021). It cannot yet be determined if this is a new displacement starting a new bubble or just the continuation of the previous bubble, which, however, would imply that it didn’t burst in 2020, only deflated somewhat. Furthermore, in view of this, it is evident, that market participants are semi rational at most, susceptible to cognitive and emotional biases such as →Overconfidence, →Optimism and →Selective Perception during bubble phases. Unsophisticated market participants are caught up by the believe that this time, big profits will be made in a short amount of time. Professional investors on the other hand may or may not involve themselves to correct prices and/or solely participate in the evolvement of a bubble (see Daxhammer/Facsar, 2018). 5.3

Indications of speculative bubbles in Private Equity

While the previous subchapter focused on the speculative asset price bubbles on the public equity markets, the following subchapter will analyze to what extent speculative bubbles also occur within private equity. The prevailing opinion is that private equity is the generic term for investments in unlisted companies. This characteristic represents the main difference to public equity, where securities of listed companies are traded. The basis of existence for private market investments is created by the restriction of corporate financing via the publicly accessible capital markets or credit institutions. If a company needs financing outside ordinary bank loans, it can, for example, either acquire additional equity capital by issuing new shares on publicly organized capital markets (stock exchanges) or obtain loans by offering bonds to investors. In both cases, the company faces a number of requirements that must be met in order to obtain the desired capital. For example, new shares can only be issued on the capital markets if the company has decided to be listed in a specific market segment of a stock exchange. In the case of a company in Europe, listing can take place on the Regulated Market organized through the European Securities Trading Act or the Open Market, which is a regulated unofficial market by the Stock Exchange (in this case the Deutsche Börse AG). These market segments differ primarily in the terms of the admission criteria to be listed on an exchange.

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Going public is a time-consuming and costly process, whereby the marketability of the company in question must first be checked. The IPO is prepared and supported with the help of numerous investment banks, forming a syndicate to share the risk and to successfully distribute the new securities. During this time, the company is evaluated and marketed to investors as a possible investment object. In addition to these marketing requirements, further formal admission criteria must be fulfilled. These criteria (including disclosure requirements, minimum duration of the company’s existence or the minimum level of free float and market capitalization) result from the requirements of the Stock Exchange in question (see Daxhammer/Resch/Schacht, 2017 pp. 154). Just as the admission to the organized capital markets is restricted by a variety of criteria, raising capital by requesting a loan from a bank is subject to certain criteria as well. If a company cannot clearly prove its ability to pay interest and repayment, it can opt for another alternative of financing itself through other private lenders. For a young company, the use of funds from private sources can also mean intensive advertising expenditure for its own business model or the products/services to be manufactured. Due to the higher risk involved, the investors are careful to examine the potential target company in detail. Even Christopher Columbus needed seven years to obtain the necessary funds for his exploratory trip to the west from the Spanish royal couple Ferdinand II and Isabella I. Columbus may not have been aware in the 15th century that he had realized one of the first private equity-financed projects. Private equity investments differ primarily in the type of financing, which depends on the respective stage of development of the company to be financed. On the one hand, investors provide financial resources during the early phase of financing a start-up company (venture capital). On the other hand, we also speak of late phase financing when the company to be financed is already in an advanced stage of its development (growth capital). While venture capital is aimed to kickstart the operations of a start-up, growth capital often focuses on international expansion strategies as well (see Demaria, 2010, pp. 77). As the interface between investors as capital providers and the companies as capital seekers, Private Equity (PE) firms fulfil the following three main functions (see Eustermann, 2010, pp. 34):  Financing function ‒ Target companies are provided with capital for the purpose of an investment (including the creation of structures, internationalisation), which they would otherwise not receive via the organized capital market or credit institutions.  Investment function ‒ The PE firms invest the funds entrusted to them in the respective investment funds in which the target companies are located. The PE companies (general partners) select and evaluate adequate target companies for the investors (limited partners).

 Support function – The PE firms aim to increase the value in the target company through active investment management/involvement into operations. It

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is important to note that it is not the ultimate goal to fully exploit the opportunities within the target companies. There should be room to grow for the event of a resale to other PE companies (so-called secondary buyouts). The resale of a target company is particularly advisable as PE firms generally pursue a clearly defined strategy. Some specialise in start-ups, others in companies in the advanced life-cycle. Once the development goals of a start-up have been achieved, the company needs experts who can provide additional growth perspectives for its further development. After considering the fundamental function of private equity investments, the question arises whether exaggerations can also occur in these markets. Data for private equity transactions between 1996 and 2020 shows a remarkably high correlation with public equity markets, both at the global and regional level (see Fig. 41, transactions with a value higher than USD 1 million). Recurring speculative bubbles – both in Public and Private Equity

Fig. 41: Number of Transactions in Private Equity between 1996-2020; FactSet

Data from FactSet shows that not only the number but also the valuation of private equity transactions is fluctuating in line with public markets’ valuations. Based on this data, EV/EBITDA multiples of higher than 10 seem to signal elevated valuations, whilst multiples of higher than 12 have only occurred four times between 1996 and 2020, three of which in the Asia Pacific (APAC) region. The EV/EBITDA multiple in APAC is estimated to have increased to 17 in the first half of 2021. The multiple for all transactions worldwide for the first six month of 2021 is estimated at 14.56 – well above previous highs in 2020 (11.79) and 2007 (11.34), and 12 percent higher than the EV/EBITDA (next twelve months) of the MSCI World Index with 12.99 (June 2021).

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EBITDA Multiples nearing/reaching new highs

Fig. 42: EV/EBITDA Multiples in the private equity deals between 1996-2020; FactSet

Rising valuations and number of transactions are not surprising in the light of ultra-low interest rates and return seeking investors. According to McKinsey, PE Firms sat on uninvested capital or dry powder of over USD 2 trillion in 2019 (see Intuition, 2019). Another metric pointing towards a bubble building in Private Equity transactions is the increase in buyout deal value, which increased by 8 percent to USD 592 billion in 2020, 7 percent higher than the five-year average of USD 555 billion (see MacArthur/Burack/De Vusser/Yang/Dessard, 2020). With strong growing competition for available privately held target companies through the rise of SPACs, not only the likelihood for a further increase of valuation rises, but also of lower returns for investors when firms are sold again (see Saigol, 2018). The CEO of TorreyCove Capital Partners noted in 2018: “... rising interest rates could potentially be a problem, yet these concerns haven’t stopped private equity firms from racing to investors for more capital — indeed, the very recognition that bull markets don’t last forever may be fueling a rush to raise money.” (David Fann, quoted by Idzelis, 2018) The race for Assets under Management through newly launched funds is not only fueled by the low interest rate environment, but also by the need to compensate falling management fees with more assets. As such global fundraising increased from USD 414 billion in 2007 to USD 733 billion in 2021, topping the previous high of USD 719bn in 2019 (see Mendoza, 2022). Chapter four focused on the conditions for the emergence of bubbles on organized capital markets, the public equity markets. The following conditions in particular could be observed:

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 Boom-Thinking – The social contagion of boom-thinking leads to the perception among market participants that the rising prices of the asset class in question can be expected to be sustained and continuous. The social contagion of boom thinking allows the formation of herd movements, which additionally strengthen a developing financial market bubble. Market participants tend to form opinions on the basis of masses of people and thus tend to perceive information selectively.  Limited arbitrage – The concept of arbitrage should be to induce price-corrective measures through the actions of institutional investors. In this way, securities would approach their fundamental value. However, practice shows that arbitrage has its limits. These limits, in terms of both costs and risks, prevent arbitrageurs from opposing limited rational investors. Rather, arbitrageurs participate in bubble pricing as long as they consider it rational and can profit accordingly. As a consequence, the formation of bubbles may increase.  Market anomalies – Short-term as well as medium/long-term anomalies lead to the emergence as well as the intensification of speculative bubbles. Market participants systematically tend to misprice information, which is why securities move away from their fundamentals. In private equity, conditions can also be identified which can lead to the formation of speculative bubbles but also to the sudden end of an economically positive development in itself (see Eustermann, 2010, pp. 131).  Image of the PE industry – The image of the industry contributes to the financing of companies by PE firms in the long-term, but also leads to the obstruction of this investment opportunity. An example of this is the "locust debate" launched by former German SPD Chairman Franz Müntefering in 2005, calling private equity funds locusts (see Waleczek, 2021). Such designations can lead to medium-sized companies turning away from PE firms when it comes to an investment need or sale. This can slow down the corporate development of the company as without the existence of PE firms many medium-sized companies would be underfunded. Market participants consequently run the risk of being influenced in their judgments by such emotive language However, in the general rejection of private equity investments it is easily overlooked that PE firms often select poorly managed companies for their investment purposes, restructure them and only sell them again after a necessary restructuring.  Successful transactions – This condition aims to distort the perception of successful transactions. Market participants who have completed successful PE transactions over a certain period of time tend to be more and more inclined to →Overconfidence. In the course of this distortion of perception they lose risk awareness and assess objective probabilities as higher than they actually are (see Probability Weighting chapter 6). As a consequence, PE firms are increasingly involved in acquisitions and thus accelerate the formation of bubbles.

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 Public perception – Large PE firms in particular, which handle transactions worth billions of dollars, are strongly monitored by the public. This often leads them to overestimate their capabilities and tend to expand their transaction activities in order to demonstrate their successful position publicly. These firms are increasingly feeling pressure to act, which has a trend-setting effect on a developing speculative bubble.

 Reputation – Another condition very similar to public perception, which can lead to a strengthening speculative bubble in the private equity market, is the desire of a PE firm to carry out a transaction at all costs. The desire to acquire a desired reputation in the public perception through the transaction of a company leads to excessive transaction prices for the company to be acquired. In the bidding competition for German car manufacturer Opel in 2009, for example, the financial investor RHJ pushed its way into the bidding process alongside the automotive supplier Magna. According to Röder, Henze, Ludwig (2003), the Winner’s Curse effect plays a significant role in the tendency to wanting to buy or sell a company at any price. The Winner’s Curse effect occurs, for example, when a buyer has already mentally prepared for the takeover of the target company and is eager to complete the transaction. The Winner’s Curse effect can lead to excessive price offers in the bidding process on the part of the buyer. The term Winner’s Curse stems from the fact that the winner in a bidding competition has in all likelihood paid a price that is higher than a justified market price.  Competitive Pressure – Finally, competitive pressure within a PE firm can also lead to rushed transactions if only one general partner does not have a transaction to show at the end of a year, but the other general partners of the PE firm have already completed a transaction. Peer pressure can lead to the execution of the next best transaction, which can intensify the speculative bubble in the PE market. In addition, and especially in the current market environment, the competition between SPACs (see subchapter 5.2.6/Euphoria Phase) and PE Funds targeting the same private companies can lead to even higher investments. Conditions that can lead to speculative bubbles in public/private markets Condition

Public Equity

 Boom Thinking

 Limited Arbitrage

 Market Anomalies

Resulting effect

Source*

 Price increase through social conta-  Shiller (2000) gion with boom thinking, creates hearding and hinders perception of information  Arbitrageurs abstain from price cor-  Barberis/ rective measures because the risks Thaler (2005) and costs of arbitrage become unpredictable  Lead to incorrect valuation due to  Barberis/Thaerroneous processing of inforler (2005) mation by market participants

Private Equity

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 Image of a sector

 Influences investment activities due to selective perception as a result of public debates

 Goldberg/ Seitz (2009)

 Successful Transactions

 Increases confidence in own abilities (leads to overconfidence) and reduce risk awareness

 Leschke/ Seitz/Goldberg (2009)

 Public Awareness

 Leads to increased investment ac Pronk/Jepsen tivities and amplifies existing trend (2009) (herding) in case of lucrative transactions  Goldberg/  Leads to high willingness to exeDaham (2009) cute on a transaction which results in rising valuations (influenced by Overconfidence)  May lead to hasty transactions if a  Jepsen/ partner within a PE company is un- Leschke (2009) der pressure to act

 Striving for reputation  Peer pressure

*Conditions and effects for private equity resulting from expert interviews between Eustermann (2010) and the listed participants. Complete expert interviews listed in the appendix. Fig. 43: Conditions for speculative bubbles

Summary Chapter 5 Speculative bubbles as a result of systematic misjudgments have not only developed in the recent decades. Their existence dates back at least far into the 17th century. Although speculative exaggerations run like a red thread through the history of the financial markets, it is clear that investors do not learn from their mistakes and, moreover, do not remember their mistakes. On the one hand, market participants rely on state institutions, on the other hand, most financial transactions are so complicated that even experienced market participants cannot always understand them. Important scholars in the field of speculative bubbles include Benoit Mandelbrot, who lists ten properties according to which capital markets can be characterized; as well as Kenneth Rogoff, Carmen Reinhart (2008, This Time is different) and Charles Kindleberger (1978, Manias, Panics and Crashes), who have analyzed historical speculative bubbles in detail. According to Werner de Bondt speculative bubbles not only distort the allocation function of markets, but also lead to a loss of confidence in the integrity of financial markets. Speculative bubbles can arise for various reasons. For example, the view that the value of a particular good, such as a tulip bulb, will increase, can lead to the social contagion of boom thinking. In addition, rampant public debt, as

Concluding remarks Section II

169

in the case of the John Law bubble, can be the cause of a disproportionate increase in asset prices. Moreover, speculative bubbles do not only arise on organized capital markets (public equity) but also on the unregulated markets for private market investments (private equity). These speculative bubbles arise from the increase in transaction volumes, which are accompanied by rising acquisition prices. As in public equity, certain framework conditions are responsible for the strengthening of a speculative bubble in private equity (including the image of the industry, public perception, reputation).

Concluding remarks Section II A look at the speculative bubbles listed in chapter 5 clearly showed that market participants are inclined to be guided by emotions. It also became clear that speculative bubbles cannot always be limited by arbitrage. Arbitrageurs are not easily able to adjust the valuation of securities to the fundamental values due to the restrictions imposed on them. In addition, there are market anomalies which, in the course of information processing, can cause valuations to deviate from the fundamental value of the securities in the long term. The new behavioral capital market theory, Behavioral Finance, describes a market participant who is removed from the behavioral mould of the Homo Economicus. The market participant becomes an influencing, but also an influenceable being ‒ the Homo Economicus Humanus. Furthermore, the individual market participant reveals within the mass behaviors that can further fuel a speculative bubble. According to the framework of the feedback theory, market participants are infected with boom thinking. This arises from the general observation and media coverage of rapidly rising security prices. The assumption that the boom will continue is further reinforced. As a consequence, further price increases occur. With threatening losses, however, the opinion of the masses tilts again and panic breaks out. Due to the herd behavior of the market participants, speculative bubbles are strengthened in addition to the aforementioned causes, such as the limited arbitrage possibility. These findings clearly show the limits of neoclassical capital market theory. The initiated paradigm expansion towards a behavior-oriented approach is in full swing. The market participant must be analyzed according to the actually observable behavioral patterns. The development of hypotheses based on assumptions that are not justifiable under real conditions face serious limitations for a practical application. The third section analyzes the actual decision-making behavior of market participants. These are examined for limited rational behavior in the course of the perception, processing/evaluation of information and investment decisions. This makes it possible to classify the behavior of the market participant in the right context in view of information asymmetries.

Section III – The Homo Economicus Humanus within the information and decision-making process

6

Information and Decision-Making Process In this chapter, you will learn the basis of the information and decision-making process and understand which perceptual disturbances can prevent market participants from receiving and processing information. In addition, you will learn the basis of decision-making from the perspective of Behavioral Finance: being the Prospect Theory, as an alternative to traditional Expected Utility Theory. You will understand how market participants use the Sshaped value function to describe their attitude towards risk and the weighting function to transform objective probabilities according to their views. These two functions will help you understand how securities are valued on the basis of Prospect Theory and will show you the cognitive limitations of market participants.

6.1

Phases of the information and decision-making process

The information and decision-making process represents the entirety of the information gathering and processing procedures. It consists of three steps: (see Fig. 44) first, information perception (see chapter 6.1.1), then information processing or evaluation (see chapter 6.1.2) and finally the investment decision-making (see chapter 6.1.3). During this three-step process, market participants are not only confronted with an overwhelming amount of information, but also with a situation of uncertainty. Under these conditions, they make use of certain tools, the so-called heuristics and the subjective evaluation of probabilities. Heuristics are used as support for preparing decisions reducing the number of alternatives to choose from and thus increases the probability to make a decision (see Lewis, 2008, p. 43). While the heuristics applied can make the preparation of decisions very efficient, they may also lead to systematic distortions, which are discussed in detail in chapters 7 to 9. The assessment of probabilities also influences the way risk is dealt with in the decision-making process. Objective probabilities are over- or undervalued, with the consequence that the market participant’s risk assessment changes (see chapter 6.2.3).

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Information Perception

Information Processing

Decision-Making

 Market participants process relevant information

 Market participants implement decision based on the preceding process steps  Market participants face cognitive limitations in  Investment behavior is processing capacity and characterized by meas Active/passive process speed of processing the ures to reduce cognitive to reduce uncertainty information dissonance and involve Perceptual disorders: ment of numerous heu Application of biases  Selective perception of ristics: (heuristics): individual informa These measures hold  Accelerate decisiontion on to decisions made, making  Information Overload and  Lead to systematic (based on the quantity  Expand on existing distortions of information) commitments due to  Inclusion of information misinterpretation of from short-term and information long-term memory  Market participants create a visualization of their environment by using internal/external sources of information

 Result: Decision is formulated through information perception and processing Fig. 44: Phases of the information and decision-making process

6.1.1 Information perception

The information and decision-making process begins with the perception of information. Market participants register external and internal stimuli, which help to generate a picture of their environment. External stimuli can be understood as the information flow via media (especially social media), whereas internal stimuli are the emotional feelings (especially greed and fear). Information is generally understood to be a signal that is intended to help reduce uncertainty and prepare decisions. The perception of information can result from active research or be unintentional and voluntary, e.g., when leafing through a magazine (passive perception of information). The intensity of the information perception depends on the decision to take. In the case of simple decisions, information already available is retrieved from memory (internal information sources). Complex decisions, however, usually lead to the acquisition of information via active searches in publications, forums, discussions with other market participants, etc. (external sources of information). The relationship between internal and external sources of information depends essentially on three factors:  Participation of the market participant in the decision ‒ the higher this is, the more external information is required.

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 Stock of internal information ‒ the less information the market participant can retrieve from memory for the decision, the more external information is in demand.  Cost-benefit ratio of information searches ‒ the more favorable the ratio, the more open the market participant is to external information searches. In the perception of information, decision-relevant signals are taken from the set of all signals. According to Keith B. Payne76, the perception process includes two different perception disorders that are characteristic for the information intake (see Mazanek, 2006, p. 68):  Firstly, not all information and stimuli are perceived.  On the other hand, no distinction is made between relevant and non-relevant information. The expectations of market participants often seem to correlate with each other, which ultimately favours →Herding. The reason for correlating expectations lies in the limited perceptive capacity of market participants. These are probably also responsible for the application of heuristics such as the availability bias or the representativeness bias. The limited perceptive capacity is impressively demonstrated by the study results of Michael S. Rashes (2001). Between 1996 and 1997, he examined the pricing of MCI Communication (index ticker MCIC), a mobile phone operator with a market capitalization of USD 20 billion, and Massmutual Corporate Investors (index ticker MCI), a closed-end fund with USD 200 million in managed assets. MCIC was in merger negotiations during this period, with correspondingly volatile price swings in the company’s share price. Rashes tested the hypothesis of limited market participants’ perceptiveness, which showed that not only MCIC’s share price but also the confusingly similar MCI experienced similar price swings. Same phenomenon was observable in January 2021 when Elon Musk suggested his 42 million followers to use alternative messaging app Signal instead of Facebook and Twitter. Similarly named company Signal Advance Inc., a small medical devices company, surged in three trading days by 5,100 percent. Despite reports about the confusion, the share price continued to rally another 885 percent (see Economic Times, 2021). The limited perceptive capacity ultimately leads to the equal treatment of similar asset positions/securities with the consequence that they are valued equally when new information comes in. In this way, market participants endeavour to reduce complexity and accelerate information processing (see Rau 2010, pp. 333). If market participants wanted to process and correctly evaluate all existing information, they would need to have unlimited perceptual and cognitive capacities. However, scientific studies show that the human brain is subject to very severe limitations. The capacity to process complex information declines rapidly after a short time. Conscious interaction with the environment takes place in the pre76

Keith B. Payne | Professor of Psychology and Neuroscience, University of North Carolina

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frontal cortex, the frontal area of the brain. This area is responsible for active thinking. Amy Arnsten from Yale Medical School is researching the processes of this area of the brain and explains that the active perception and processing of information quickly fatigues due to the intensive consumption of metabolic energy such as glucose and oxygen. The intensive energy consumption of the prefrontal cortex is associated with its evolutionary history. In contrast to other brain regions, this area is still very young and will have to develop continuously to meet the demands of the ever-increasing flow of information (see Rock, 2011, pp. 25). Consequently, it is not surprising that in the process of perceiving and processing information people make use of certain aids in order to be able to make an acceptable decision. Selective perception This rule of conduct (see chapter 7.1.2) illustrates how the perception of market participants is influenced by their expectations:  Information that does not meet their own expectations is accorded less importance than information that confirms our expectations.  Experience or habitual behavior prevents the unlimited reception of information and results in suboptimal preparation for decision-making. The market participant refers, e.g., in the perception of information, to his previously acquired knowledge and thus overlooks new information.  Furthermore, market participants tend to overvalue so-called key information. The latter is information which is considered particularly important for decision-making and which can replace or neglect other information. This selective perception is particularly noticeable in the case of complex problems. In the following, three principles are presented which are observed in the context of selective information perception (see Kohlert, 2009, pp. 78):  Principle of completeness ‒ If the available information is not sufficient to enable a decision to be made, own information (e.g., from experience) is independently included in order to obtain a coherent overall impression.  Principle of similarity and equality ‒ According to this principle, market participants strive to condense similar information into a consistent overall picture. New information that contradicts this overall picture is neglected.  Figure-ground principle ‒ According to this principle, information that deviates from the previous perception is evaluated rationally to a limited extent. They are viewed from a preconceived point of view, which allows a reinterpretation of this new information. A classical example is the battle between Coca-Cola and Pepsi. In an open test, Coca-Cola scores better, in a blind test, however, Pepsi usually scores better (see Marketing10, 2020). The reason for this phenomenon is the automatically better as-sessment of individual product characteristics based on positive associations with the respective brand.

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Example 6.1: Selective perception After an investment in a company, a market participant would, in accordance with his positive expectations, look for and perceive predominantly positive news and thus be put in an overoptimistic mood. As a result, he or she weights the importance of positive news more heavily and underestimates negative news. In extreme cases, only positive news is sought to justify an investment and negative news that makes the investment appear unjustified is completely neglected. However, if the share price does not rise as expected, the market participant cannot understand this and, in view of his positive perception, holds on to the investment. If the bad news continues, his selective perception may change. In this case, the market participant will believe that the investment in the company was a mistake from the outset. However, in order not to admit the mistake, the market participant will hold on to the investment for a disproportionately long time in the hope of rising prices. This heuristic, also known as →Disposition Effect, is regarded as the cause of the below-average performance of many portfolios. Within the framework of selective perception, individual decision rules are used to reduce the cognitive load caused by the abundance of information. These cognitive loads represent the limitations of the human brain to process the multitude of information. The process of information perception can thus be regarded as a cognitive information processing process (see Mazanek, 2006, p. 68). Information Overload The second perceptual disorder, where market participants do not distinguish between relevant and irrelevant information, usually occurs when the amount of information is too great. Consequently, the market participant makes his decision on the basis of reduced information quality. This phenomenon is also known as information overload. According to this, too much information causes stress to the market participant, reduces his ability to set priorities and makes it difficult to remember earlier information (see Kohlert, 2009, p. 81). Example 6.2: Information Overload The effects of information overload have been extensively studied in experiments. The findings of the Sheena Iyengar, who tested the sales quota of jams in a supermarket, were ground-breaking. Her findings led to the conclusion that the fewer varieties of jam available, the higher the sales quota. For 24 varieties, only 3 percent of interested consumers bought, while for six varieties the sales quota rose to 30 percent. In a large-scale review of these findings, Benjamin Scheibehenne was able to put these results into perspective. He examined 50 studies which aimed to evaluate the effects of information overload. On average, hardly any other study led to similarly remarkable results. However, this is also due to the fact that the differences between the studies are very large. Studies that confirm

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Iyengar’s results still fail to answer the question under what rare circumstances this effect occurs. As a result of Scheibehenne’s research, three points are important: (1) If you know exactly what you want, you benefit from more choice. (2) When people are asked whether they would prefer more or less choice, they opt for more choice. (3) However, if you have to choose from many options, it is very difficult to choose between them (see Wirtschaftswoche, 2010). The activation potential of information can also be counted among this perception disorder. Accordingly, the decisive factor for information perception is whether the information can attract the attention of the market participant. Thus, a supporter of technical analysis will attach more importance to a presentation of the price development than a fundamentally oriented market participant. The perception of information is a cognitive information-processing action in which cognitive stress is eliminated by individual decision rules. The applied decision rules, such as selective perception or information prioritization in the case of information overload, are considered perceptual disorders.

6.1.2 Information Processing/Evaluation

The information processing/evaluation follows as the second step within the information and decision-making process These two phases, which are sequential in themselves, are considered together, as they are sometimes difficult to separate. The objective of this phase is first of all to prepare a decision. The information from the prior step (information perception) is combined with the information and/or strategies available in the memory. During information processing, the decisive difference between the real market participant and the Homo Economicus becomes apparent. It becomes clear that the market participant has a limited processing capacity and therefore cannot perceive and process all information at the same time. Rather, information processing is serial, i.e., recording and processing can only occur alternately. The reason for this approach is seen in the limited information processing capacity of the brain. Up to now, it was assumed that the human brain can process seven information units at once (no matter which kind of information). These frequently published study results by George Miller from 1956 were relativized by a broad study by the University of Missouri-Columbia from 2001. According to Nelson Cowan’s research, the human brain can process four units of information at once rather than seven (see Cambridge University Press, 2001). Even this number depends on the complexity of the individual factors. For example, four numbers are no problem, four long words are already more difficult to

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remember or process. Four sentences which are not known to us, i.e., which are not anchored in long-term memory (like an advertising slogan), are very difficult to remember according to Cowan’s study results (see Rock, 2011, p. 42). In line with this finding, it is not surprising that market participants use tools (heuristics) for decision-making under stress and lack of time, which speed up the decisionmaking process between several alternatives and at the same time reduce the cognitive burden. Although the use of heuristics and the associated erroneous decisions do not occur by chance, but rather appear systematic and predictable, it is important to consider the rational reasons for using heuristics (see Schwartz, 2010, pp. 58):  In some cases, market participants do not know the optimal solution to a given situation. In addition, the necessary costs/efforts for working out the optimal solution path appear to be too high.  Moreover, it is sometimes not possible to obtain all the information necessary for decision-making within a certain period of time.  The overwhelming flood of information can inhibit the market participant in the decision-making process. In addition, the emotional reference to the facts of the case can make it difficult to take an objective view.  The use of heuristics is certainly advisable if the results of a complete calculation show considerable sources of error.  The application of heuristics is particularly useful if the result corresponds or is similar to that of detailed calculations and therefore leads more quickly to a comparable decision. Markus Pasche of the Friedrich-Schiller-University Jena in Germany, points out that due to the limited information processing capacity, market participants commit decision-making errors and will make future decisions on this basis. As a consequence of the empirical studies Pasche calls market participants “imperfect”. In information processing, they are characterized by the behavioral patterns listed below (see Mazanek, 2006, p. 59):  There is an incentive for market participants to allow information processing errors which are considered acceptable in the course of increasing information costs.  Market participants use only a part of the available information.  There is an incentive for market participants ‒ in violation of the →Bayes’ Theorem ‒ to temporarily ignore current information. Expectations are adjusted to the changed situation with a time lag.  In addition, market participants tend to use simple, adaptive mechanisms or socalled simplification strategies to limit the cognitive effort. The Homo Economicus Humanus is therefore characterized by a limited information processing capacity on the one hand and a limited speed in information processing on the other hand. Finally, information processing can be impaired by the following factors (see Kohlert, 2009, p. 81):

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 Discrepancy between required and available information ‒ market participants usually do not have access to the entire range of information they need for decision-making in the sense of neoclassical capital market theory.  Novelty of the information or experience in a problem area ‒ market participants can process far less information if they are not very familiar with the confronted problem area and therefore need more information.  Ambiguity of information ‒ information can be interpreted differently: Information is available, but its meaning remains unclear.  Quality of information ‒ high-quality, decision-relevant information is usually difficult to distinguish from so-called data smog.  Presentation format ‒ information processing can be limited by the wide range of presentation options. This is particularly the case when the effort required to gain an overall understanding is very high. Information processing is followed by the evaluation of the collected information. The evaluation of the information finally leads to a decision on the investment transaction to be made. Information from both short-term and long-term memory is used for this purpose. The information evaluation leading to a decision can be influenced by a number of cognitive heuristics (see chapter 8). Within the information processing process, the effects of rules of thumb applied by the Homo Economicus Humanus become apparent due to the limited information processing capacity and the limited speed of information processing: They lead to recurring, systematic distortions in the information and decision-making process.

6.1.3 Investment Decision

The information and decision-making process concludes with a specific investment decision through the market participants. This last phase of the information and decision-making process is characterized, among other things, by the avoidance of cognitive dissonance. In 1957, Leon Festinger77 developed the theory of cognitive dissonance, which states that market participants may experience emotional discomfort after deciding between two options (see Cooper, 2007, p. 6). The reason for this is information that is in contrast to the existing values and decisions of the market participant. Cognitive dissonance describes the imbalance between individual psychological cognitions, such as attitude, emotions or belief in a decision made. Within the framework of cognitive dissonance, the market participant attempts to correct these imbalances by suppressing negative information on an investment decision 77

Leon Festinger | American social psychologist | 1919-1989

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and emphasising positive information (see Festinger, 1957, p. 144). The stronger the emotional attachment to the decision made, the higher the self-bond to the decision and thus the higher the possible dissonance (see Mazanek, 2006, p. 98). The attempt to avoid cognitive dissonance influences investment behavior in two ways (see Nofsinger, 2008, p. 39):  Firstly, the ability to monitor and review one’s own investment decisions is impaired by the selection of information.  Secondly, active investment activity is overshadowed by the uneasiness of the situation. Reasons for the self-commitment/determination of a decision The strength of the cognitive dissonance is influenced by the commitment to make an investment decision according to the reference values listed below (see Goldberg/von Nitzsch, 2000, pp. 121):  Freedom of decision ‒ in the capital markets, market participants can decide voluntarily, i.e., without compulsion, to make an investment, with the result that the commitment to this investment is particularly high.  Responsibility ‒ responsibility is only assumed if the development of the investment is already foreseeable and the market participant could not expect any surprising developments in the form of losses.  Irreversible costs ‒ which become irrecoverable in the event of a loss when the investment is sold.  Deviation from the norm ‒ if an investor’s decision differs from the opinion of the other market participants, the commitment will increase disproportionately. A deviation from the norm would be, for example, the decision to buy only long-term U.S. government bonds in a market situation of fears of inflation. If inflation rises these securities would lose a lot of value, as market participants would sell them and switch to other securities with higher interest/coupon rates. The attempts to avoid cognitive dissonance are all based on the goal of reducing possible regret for wrong decisions. Thus, market participants apply selective perception already known from information perception (see chapter 7.1.2). Possible regrets should also be reduced by selective decision-making (see chapter 9.1.1). While selective perception is used for reducing the quantity of information, an already made decision is to be maintained by selective decision-making. In doing so, the market participant attempts to evaluate the decision made as a success despite high costs (see Westphal/Horstkotte, 2001, p. 219). Accordingly, the theory of cognitive dissonance includes several phases of the information and decision-making process ‒ both the reception of information (via selective perception) and investment behavior (via selective decision-making).

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In the case of investment behavior, market participants experience cognitive dissonance when faced with information that contradicts a decision. This emotional unease is reduced by selective perception and selective decisionmaking. The result is that negative information to a potential decision is suppressed, while positive information is highlighted. In practice, this behavior is expressed by the disposition effect, among other things.

6.2

Basis of decision-making from the perspective of Behavioral Finance

The traditional basis for the evaluation of decisions by market participants is based on the Expected Utility Theory presented in chapter 1.2.3. However, it is becoming increasingly apparent that the Expected Utility Theory is not observed or its guidelines are violated when decisions are made under uncertainty and risk (de Bondt and Thaler 1995, Hirshleifer 2001, Barberis and Thaler 2003). As an alternative to the expected utility theory, Daniel Kahneman and Amos Tversky (1979, 1981) developed the Prospect Theory. It is a theory of the average expected behavior of market participants. It illustrates how an individual or group of individuals on average behave in an environment of uncertainty and risk. The Prospect Theory differs from the expected utility theory, among other things in that the decision-making process is influenced by →Loss Aversion. Decisions are made on the basis of losses or gains rather than on the absolute development of assets. It also takes into account the observation that the behavior of market participants systematically deviates from the postulated assumptions of neoclassical capital market theory (see Altman, 2010, pp. 191). Decision-making in the sense of the Prospect Theory is based on two functional correlations. On the one hand on the value function, which maps the loss aversion, and on the other hand on the weighting function, which takes into account the non-linear transformation of objective probabilities into subjective ones (see Levy 2010, pp. 211). 6.2.1 Decision-making based on Prospect Theory

The findings of →Behavioral Finance illustrate two central aspects according to which securities valuation can be distinguished between neoclassical and behavioral financial market research (see Shefrin, 2008, p. 391):  Sentiment – Proponents of Behavioral Finance often see the sentiment of market participants as a driving factor in the formation of securities prices. It is the result of systematically occurring errors in the information and decision-making process. Proponents of neoclassical finance, on the other hand, consider the influence of this factor to be rather small. They therefore assume that the market participants

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as a whole78 are free from distortions of perception when processing existing information. While the proponents of Behavioral Finance regard market anomalies as the result of changing sentiment, the neoclassics interpret the deviation of the valuation of a security from the fundamental value of the investment as merely temporary market anomalies.  Expected utility ‒ The followers of the neoclassical capital market theory assume that market participants want to maximize the expected utility of their investment. The proponents of Behavioral Finance, on the other hand, have a critical attitude towards the expected utility theory. They take the view that market participants repeatedly behave inconsistently with the expected utility theory. They hold the view that market participants behave in accordance with a psychologically oriented theory ‒ the Prospect Theory. The Prospect Theory thus represents a model of decision-making under uncertainty, in which from various alternatives for action one should be selected. This concept is also known as the “New Expectations Theory” and was further developed in 1981 into the cumulative prospect theory, which considers not only two alternatives but the totality of all alternatives. The prospect theory processes the view that economic decisions are influenced by a series of distortions of perception (→Biases) (see Elger/Schwarz, 2009, p. 102). As a result, neoclassical theories, such as the expected utility theory, appear inadequate for determining the actual behavior of market participants, since they take a normative view of the market participant. A normative view sets clear rules for rationally acting investors as to how they should behave, but thereby neglects the actual influences that can occur in reality when decisions are made under uncertainty and lead to limited rationality (see Barberis/Thaler, 2002, p. 16). In reality, market participants usually act differently from the rational specifications of the expected utility theory and thus arrive at “suboptimal" decisions. They use heuristics to reduce the complexity of the information and the influence of emotions when making decisions under risk and uncertainty. The aim of prospect theory is to explain decisions that are materially inconsistent with the rational assessment of probabilities and the expected utility theory. It allows to shed more light on the reasons for limited rational decisions, for example better explain to market participants the effects of their behavior (keeping underperforming securities, selling outperforming securities). Kahneman received the Nobel Prize in Economics in 2002 for his work, Amos Tversky died before the award (see Elton/Gruber/Brown/Goetzmann, 2007, p. 485). In contrast to the expected utility theory, the prospect theory distinguishes two phases of the decision-making process. The data preparation (editing), i.e., the selection of the alternatives to be evaluated, and the final evaluation of the selected alternative. In both phases the heuristics listed in chapters 7-9 are included (see Laux/Gillenkirch/Schenk-Mathes, 2012, p. 163):

78

Individual exceptions may be temporary

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Phase 1 ‒ Editing Consequences and probabilities of the available alternatives are transformed by certain operations (such as combination and simplification) in order to simplify the decision with regard to the cognitive limitations of the market participant. The first four operations (combination, simplification, coding and segregation) are performed separately for each alternative. The other two operations (removal of common elements, elimination of dominant alternatives) are overarching options affecting the entire set of alternatives. In the following the behavior of a limited rational market participant is considered when deciding between the omission or the selection of the stated alternatives from a given lottery {100; 0.4│100; 0.2│200.10; 0.399│50; 0.0001} (see Fig. 45). The lottery specified here represents the four available alternatives from which market participants can select the best possible alternative for oneself. The probabilities of all alternatives add up to 1. From the perspective of an investment, the present lottery is therefore about the alternative of winning an amount of EUR 100 with a probability of 40 percent. Further alternatives are to win EUR 100 with 20 percent probability, EUR 200.10 with 39.9 percent probability and EUR 50 with 0.01 percent probability.  Combination: The market participant recognizes that consequence 100 is achieved with both a probability of 0.4 and a probability of 0.2. In this case he or she simplifies the consideration of these two alternatives by adding the probabilities of these identical outcomes. Consequently, he or she obtains the result of achieving 100 with a probability of 0.6.  Simplification: The market participant also recognizes that he or she can simplify the third consequence by rounding up and down. In this way, he or she obtains the consequence 200 with a probability of 0.4.  Segregation: This involves separating safe components of an alternative from risky alternatives. The market participant segregates the amount of a lottery which he or she will certainly receive. Segregation depends on the reference point set. If the market participant sets the reference point at EUR 50, i.e., this is the amount he or she intends to invest, his prospects look as follows: {10050; 0.6│200-50; 0.4} = {50; 0.6│150; 0.4}. In this case the market participant can only win. This means that safe consequences are separated from uncertain ones, so that a profit of EUR 50 can be achieved “with certainty” and with a probability of 0.4 a profit of an additional EUR 100.  Cancellation: In our example, the market participant realizes that achieving consequence 50 is very unlikely. Consequently, he or she removes this consequence from the lottery. However, a cancellation can also be made if two alternatives are compared: If a market participant is to decide between the two alternatives {200; 0.2 │-50; 0.8} and {200; 0.2 │-100; 0.8}, then he or she will leave out identical components in the alternatives available for selection. The market participant would only compare the 80 percent probability of losing EUR 100 with the 80 percent probability of losing EUR 50.

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 Elimination of dominated alternatives (detection of dominance): This operation involves the elimination of stochastically dominated alternatives. This should prevent stochastically dominated alternatives from entering the evaluation phase in the first place. In this way, the neglect of other alternatives should be avoided. As an example, with the alternatives {200; 0.5 │0; 0.5} and {300; 0.5 │0; 0.5} the first alternative would not be considered. Consequently, this would have to be eliminated and the remaining one would have to be compared with a possible third alternative. After the editing in the above starting example the sequence of the alternatives is reduced to only {100; 0.6│200; 0.4}. Now the last operation, the coding, can be done.  Coding: A reference point r is defined. The entry price at purchase of a security or, as in our case, the investment sum can be set as a reference point. In our example, a reference point of EUR 150 leads to the following prospect: {100-150; 0.6│200-150; 0.4}={-50; 0.6│50; 0.4}. In this alternative, the market participant would reduce his assets to EUR 100 (i.e., suffer a loss of 50 EUR) with a 60 percent probability, or gain EUR 50 with a 40 percent probability, thereby increasing his or her assets to EUR 200. Selection of alternatives through the application of operations during the editing phase

Fig. 45: Data preparation within the framework of the Prospect Theory

Phase 2 ‒ Evaluation After completion of the first phase, the prepared alternatives, the so-called prospects, are evaluated. The evaluation is carried out by using two functions: the subjective value function and the weighting function (see chapter 6.2.2). By means of the S-shaped value function gains and losses are evaluated in relation

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to the reference point coded in the data preparation phase instead of the final value. Results that are above the reference point are perceived as relative gains, while results that are below are perceived as relative losses. Results exactly on the reference point are considered neutral (see Schriek, 2009, p. 49). Furthermore, in prospect theory not only the consequences but also the probabilities are subjectively assessed by transforming them into so-called decision weights π. These indicate the weight assigned to a probability in the decision. Thus, the weights reflect the subjective evaluation, but not the objective assessment of the underlying probabilities. The weighting function illustrates how objective probabilities are transformed by the market participant into non-objective, subjectively perceived probabilities (see Laux/Gillenkirch/Schenk-Mathes, 2012, p. 165). 6.2.2 Features of the valuation functions

In the following, the characteristic features of both the S-shaped value function and the weighting function will be presented. This is necessary in order to be able to correctly classify the explanations in chapters 7-9. The value function The attitude towards risk is described in Prospect Theory by the S-shaped value function (see Fig. 46). The value function has three characteristic features (see Hens/Bachmann, 2008, pp. 36):  The value function is concave (right-curved) in the profit range and convex (left-curved) in the loss range. This curvature behavior, also known as decreasing sensitivity, is measured with the decreasing gain in value of an additional unit of profit or decreasing loss in value of an additional unit of loss. It also indicates the change in the risk attitude of the market participant as a function of profit or loss. Market participants behave in the loss area in a risk-friendly manner, as there is hope that relative losses will be offset by a possible “recovery”. In the profit area, on the other hand, market participants are risk-averse because they are worried that the relative profits achieved could be lost. This phenomenon, already mentioned as the disposition effect, illustrates the behavior according to which future decisions are particularly dependent on whether the position is in the profit or loss range (see Kottke, 2005, p. 124). Consequently, profit and loss only arise through valuation, i.e., through the setting of a reference point.  The value function is steeper in the loss range than in the profit range. This characteristic illustrates the loss aversion of the market participant. Numerous studies have shown that market participants perceive losses to be about twice as strong as gains of the same amount (Kahneman/Tversky, 1979). Thus, in the prospect theory, a loss of EUR 1 and a subsequent profit of EUR 1 would result in a negative benefit. By contrast, in the expected utility theory the same constellation would lead to a neutral value of 0 (see Altman, 2010, pp. 201). The cause of this valuation difference can be seen in the theory of cognitive disso-

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nance (see chapters 1.3 & 6.1.3). According to this theory, losses generate dissonance among market participants as the security investment decision must subsequently be classified as incorrect. The extent of the loss aversion is strongly dependent on the commitment of the market participant for the respective security investment. If the commitment of the market participant is high, the slope in the loss range of the value function also increases (see Goldberg/von Nitzsch, 2000, p. 130).  The value function represents relative profits and losses, which are defined by a reference point. Their asymmetry depends on how a relative loss or gain behaves to a certain reference point. The individual reference point of the market participant determines whether a profit or loss is involved. The entry prices of the securities often serve as the reference point (however, many other reference points are also conceivable, such as the risk-free interest rate, an index, inflation rate, ...). This lies in the coordinate origin at the border between profit and loss. The reference point also has a special effect on the perception of profits/losses by the market participant. The sensitivity of the investor with regard to experienced relative profits or losses changes depending on the distance to the reference point. Near the reference point, the value function has an increased sensitivity. The amounts are perceived more strongly than those that lie farther away from it. Thus, the decreasing sensitivity is reflected in the valuation of rising profits in the concave area and rising losses in the convex area (see Garz/Günther/Moriabadi, 2002, pp. 120). The decreasing sensitivity is a counterpart to the decreasing marginal utility of traditional economic theory. It is an expression of Weber’s law of psychology, according to which an additional stimulus must be stronger the higher the basic stimulus is (see Blechschmidt, 2007, p. 33). Prospect Theory forms the basis for decision-making by market participants

Fig. 46: Value function of the Prospect Theory (1979)

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However, the evaluation of investment performance on the basis of reference points is not per se limited rational. If the reference points are realistic and not repeatedly changed, there is no contradiction with rational decision-making. However, if the reference points are repeatedly changed, limited rational decisions may be the result. In the Prospect Theory, the risk attitude is described by the S-shaped value function. Market participants define their risk attitude by reference points. With increasing distance from a reference point, the sensitivity with regard to experienced gains or losses changes. The weighting function Empirical studies show that market participants do not assess probabilities according to the objective probability of occurrence, but rather over- or underestimate them based on their subjective perception of the situation at hand. Thomas Langer and Martin Weber (2005) prove that the probability assessment has an effect on the risk assessment. They show that the behavior of market participants, according to which they are risk-averse in the profit range but risk-seeking in the loss range (see value function), can change drastically when probability weighting is taken into account79. Figure 47 shows the weighting function. Kahneman and Tversky found in experiments that the risk appetite of the test person changes depending on the objective probability. According to this, the market participants overvalue extremely low probabilities w(p) > p, whereas medium and high probabilities are undervalued w(p) < p (curved line). The objective probability (dotted line) is disregarded. If we now consider the S-shaped value function from Fig. 46, the following changes in the behavior of the market participant result (see Yazdipour, 2010, p. 143):  If the market participant observes the objective probability distribution (dashed line), his risk behavior is based solely on the S-shaped convex value function. In other words, the market participant is risk averse in the profit area, but is willing to take risks in the loss area.  If he or she does not observe the objective probability distribution, the market participant may even behave in the opposite direction to the value function: o If low probabilities are overestimated, the market participants are more willing to take risks. In the profit range of the value function, market participants are more willing to take risks because they estimate the probability of the event occurring to be higher. In the loss range, on the other hand, market participants behave more risk-averse, since the risk of loss is overestimated.

79 A detailed discussion of how economic entities perceive risk can be found, for example, in Daxhammer/Hanneke/Nisch, 2012

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o If medium and high probabilities are underestimated, the market participants are less willing to take risks (>0.4 in Fig. 47). In the profit range of the value function, they behave more risk-averse, since they estimate the probability of the event to be lower. In the loss range, on the other hand, market participants are more risk seeking, since here too the risk of loss is underestimated. If the probability assessment is included in the decision-making process, the observed behavior of the market participants may deviate from the expected behavior based on the value function. Overestimation of low probabilities vs. underestimation of high probabilities ‒ Probability Weighting

Fig. 47: Weighting function of the Prospect Theory (1992)

6.2.3 Valuation of securities based on the Prospect Theory

The Prospect Theory identifies five characteristic behavioral patterns based on which market participants “value” securities ‒ in the sense of assigning values to them (see Shefrin, 2008, pp. 392 and Altman 2010, pp. 203):  In the decision-making process, market participants are influenced by the way in which facts are presented. Kahneman and Tversky (1979) found in experiments that market participants choose the alternative that is highlighted by a positive presentation. This is also the case if the other alternative promises an equal or higher return. This phenomenon, known as framing, illustrates the market participant’s willingness to be influenced by the corresponding representation (see examples 7.4 and 8.4). Basically, the market participant should not be influenced by the framing, as it has no effect on the respective event. However, influenceability makes it clear that market participants are subject to a cognitive illusion. Therefore, different

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ways of representation in graphics or text can lead to different reactions, which can have a lasting influence on the market participant’s decision (see Altman 2010, pp. 203).  Market participants evaluate results using a specific reference point. If a market participant has bought a security for a share price of EUR 100, this purchase price can be chosen as a reference point for the valuation. The reference point may shift as the investment period increases. If, for example, after a longer holding period, the market participant actually realizes a profit which is lower than the profit already experienced but not yet realized, the investor may feel the pain of relative loss rather than the happiness of absolute profit. This change in awareness is a consequence of the shift in reference points during a longer holding period, which includes unrealized gains in the valuation of a position. Investors often focus on recently achieved valuation levels, which then represent the new reference points (see Nofsinger, 2008, p. 28). The decisive factor for the change in the reference point is the achievement of a new valuation level (which can happen after a longer holding period but also within hours after purchase depending on the investment product selected (e.g., highly leveraged options in a day-trading environment). Although reference points are often associated with initial prices or recently experienced highs, there are a number of other reference points. For example, the inflation rate can be chosen as a reference point for the expected yield development. It is also possible to measure the development of your own portfolio with a →Market Portfolio. Last but not least, the performance of other market participants is often chosen as a reference point for the development of one’s own returns. The consideration of reference points is one of the most important extensions of the Prospect Theory compared to the expected utility theory (see Eisenführ/ Weber, 2004, p. 376).  Market participants strive to avoid losses relative to their reference points. While in the profit zone the concave course (comparable to a utility function) is assumed, the value function is convex in the loss zone and is generally steeper than in the profit zone. The steeper course in the loss area reflects the phenomenon of loss aversion. Losses are valued twice as high as profits of the same magnitude. Losses are perceived as dissonant events and contradict the original decision of the investor to buy the security. Overall, the different valuations of profits and losses has the effect, among other things, that investors hold on to losses for too long while selling winning shares too quickly. This behavior of realising profits too early and letting losses run for too long is known as disposition effect (see chapter 9.2.1). Figure 48 shows the damaging effect of the disposition effect. Shares that are held longer in the investor’s portfolio have a lower return on average because market participants are reluctant to sell shares at a loss during a downward trend. This effect was analyzed by Terrance

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Odean80 in 1998. He examined the trading behavior of 10,000 customers of an American online broker. Odean also points out that the disposition effect seems to be less pronounced towards the end of the year. He attributes this phenomenon to the tax claim of realized losses (see Kaustia 2010, pp. 171).  Mark Grinblatt81 and Matti Keloharju82 also provided additional insights into the disposition effect, confirming Odean’s findings with Finnish investors in 2001 and at the same time concluding that experienced investors are less subject to the effect than inexperienced investors (see Blechschmidt, 2007, p. 40). Disposition effect leads to a deterioration of the portfolio return with increasing holding period of underperforming securities

Fig. 48: Illustration of disposition effect on the holding period of underperforming securities; Source: Schlarbaum, Lewllen & Lease (1978), Adjusted June 2008; UBS Wealth Management Research /Behavioral Finance June 2008

 Market participants change their risk attitude as soon as they are confronted with losses. This change in attitude, also known as the reflection effect (see chapter 8.2) results from the decreasing sensitivity in the area of losses. This means that risky alternatives are now preferred and thus market participants are more willing to take additional risk. In the profit area, however, market participants remain risk-averse. If there is a threat of loss, they tend to invest additional capital in order to avert the potential loss. The newly purchased securities lead, among other things, to a reduction in the average entry price (Beeler/Hunton, 1997) with the consequence that the next possible upward movement will bring the investment into the profit zone more quickly (see Shefrin, 2008, pp. 392). A 80 Terrance Odean | Chair of the Finance Group at the Haas School of Business, University of California 81 Mark 82

Grinblatt | Professor Emeritus of Finance Anderson School of Management

Matti Keloharju | Professor of Finance, Aalto Distinguished Professor, Aalto University

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well-known example of the devastating consequences of a change in risk attitude and the subsequent purchase of securities is the insolvency of Barings Bank caused by the derivatives trader Nick Leeson. When Leeson suffered the first losses with his transactions, he tried to compensate for them by increasing the level of risk. However, this led to an even greater loss, which now required even more extensive deals to cover. Leeson’s risky derivatives business had caused Barings Bank losses of EUR 1.3 billion in 1995. In the end, these losses became so great that Barings Bank was no longer able to cover its liabilities and eventually had to file for bankruptcy (see Boerse.de, 2002).  Market participants overestimate the probability of events that are highly unlikely. Kahneman and Tversky see this as the reason for many wrong decisions. Market participants often overestimate small probabilities and underestimate large probabilities. As a consequence, wrong priorities are set, with the consequence that disproportionately much time is spent on relatively insignificant decisions. The change in risk attitude is additionally reinforced by the tendency to overestimate the probability of rarely occurring events and underestimate the probability of frequently occurring events. This becomes clear when market participants invest in stock offerings (so-called IPOs ‒ Initial Public Offerings), exotic stocks or options that are only profitable if there is a drastic development within the next few months. As a consequence, this behavior leads to an unnecessary increase in portfolio risks. With regard to the framing effect, it should be mentioned that the market participant may be tempted to take a respective decision if possible gains or losses are highlighted. Once the market participant has decided to invest, future price developments are evaluated depending on the reference point set (including the entry price or investment amount). As already described, the reference point can shift. If the relative profit from the reference point rises by EUR 20 in the long term, the reference point can be EUR 170 in the future instead of EUR 150. However, if the investment turns out to be a wrong decision and relative losses are incurred, the market participant may be tempted to “ride it out". The reason for this is the high sensitivity to initial losses. As losses increase, sensitivity decreases due to the decreasing slope of the value function in the loss area, and the market participant is all the more willing to wait for a possible recovery of his investment. The strength of the sensitivity can be seen from the colored bars in Fig. 49. It decreases with increasing distance from the reference point. If a relative profit between EUR 10 and 20 is considered to be very valuable, the value of a profit between EUR 30 and 40 already decreases. In the loss range, in addition to the same decreasing sensitivity, the double value of losses is also apparent. This is where the loss aversion becomes apparent. A relative loss between EUR 10 and 20 is perceived almost twice as strongly as a relative gain of the same amount. Under these circumstances, the market participant is unwilling to revise a wrong decision and forfeits the →Dispositions Effect (see chapter 9.2.1). This effect also

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leads to initial gains being realized too quickly. This is due to the high sensitivity near the reference point. In addition to the disposition effect, the →Reflection Effect (see chapter 8.2) can also be seen in the behavior of the market participant. Due to the risk aversion, initial gains are realized as quickly as possible. If, however, underperforming securities are not sold in the course of further deteriorating prices, the market participant becomes risk seeking in the loss area. If the market participant now also includes the weighting function in the decisionmaking, the risk assessment will again be adjusted. The objective probability of 60 percent of achieving an additional EUR 50 is perceived less than 60 percent. As a result, the market participant is risk-averse when prices rise – in the consequence small initial profits are sold off. Market participants feel decreasing sensitivity ‒ both to profits and losses

Fig. 49: Value function of the Prospect Theory

The Prospect Theory is a model of decision-making under uncertainty, in which one should be selected from several alternatives for action. It aims at explaining decisions which show a serious inconsistency with the neoclassical-rational evaluation of probabilities and the theory of expected utility.

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Summary Chapter 6 Within the information and decision-making process, information perception has the objective of picking up signals to reduce uncertainty. In this phase of the information process, the market participant is influenced by two types of perception disorders. On the one hand, selective perception makes it more difficult to absorb information, and on the other hand, the quantity of information to be processed (information overflow) hampers comprehensive absorption of the information. In the second step of the process ‒ information processing/evaluation ‒ the market participants prepare for their decisions. This process step is characterized by limited processing capacity. In order to prepare a decision, e.g., under stress and lack of time, tools are used which accelerate the decisionmaking process and at the same time reduce cognitive stress. These tools (so-called heuristics) can on the one hand make decision-making very efficient, but on the other hand can lead to systematic distortions. In the last process step, decision-making, the actual investment behavior can be observed. Here, the effects of cognitive dissonance become visible. The strength of the cognitive dissonance is influenced by the commitment to an investment decision. Attempts to avoid cognitive dissonance aim to reduce possible regrets. Behavioral Finance characterizes two central aspects according to which securities pricing and valuation can be distinguished between neoclassical and behavioral financial market research ‒ the mood in the capital market and the behavior of market participants. They behave in accordance with a psychologically oriented expectation theory ‒ the Prospect Theory. The Prospect Theory was presented in 1979 by Daniel Kahneman and Amos Tversky as an alternative to the classical expected utility theory ‒ it is based on the view that economic decisions are influenced by subjective assessment of profits and losses. In contrast to the expected utility theory, the Prospect Theory distinguishes between two phases of the decision-making process. The data preparation and the final evaluation of the selected alternative. In the Prospect Theory, the risk attitude is described by the S-shaped value function. Market participants orientate their risk attitude towards reference points. With increasing distance from a reference point, the sensitivity with regard to experienced gains or losses changes. In addition to the value function, market participants also use the weighting function. Here, objective probabilities are transformed into subjective probabilities. Low probabilities are overestimated, while high probabilities are underestimated.

7

Limited rationality during information perception The seventh chapter focuses on the behavior of market participants during the information perception. As you work through this chapter, you will become familiar with the cognitive and emotional heuristics which, while facilitating the perception of information, make it difficult to gain an objective view of the capital market. In this and subsequent chapters 8 and 9 you will also be able to recognize the effect of the heuristics under consideration on the behavior of market participants and to classify the damaging effects of each individual heuristic on risk and return.

As pointed out in the previous chapter, the most significant findings in the research of limited rational behavior were contributed by Daniel Kahneman and Amos Tversky, the founding fathers of Behavioral Finance, Kahneman and Tversky analyzed how market participants apply heuristics to assess uncertain events. Although these help to speed up decision-making, they often lead to systematic distortions in the information and decision-making process. Figure 71 gives an overview of the heuristics identified over time. In addition, for some heuristics, certain sub-forms have already been researched as well e.g., narrow framing within the context of →Framing. These sub-forms will be discussed in detail whenever necessary. It should also be noted that these heuristics are not clearly defined. Their classification into individual phases of the information and decision-making process is based on published research results. The identified heuristics will be further evaluated and delimited from each other in the course of current and future research. In the following chapters 7 to 9, the heuristics are subject to additional structuring, which simplifies their study and promotes recognition in the future. The heuristics are categorized by their origin and by their misinterpretation of the underlying facts. First of all, a distinction is made as to whether the heuristics are of cognitive or emotional origin83. This first distinction is important so that in chapter 10 the correct measure against limited rational behavior can be suggested. In the next step, the heuristics are differentiated according to whether they result from a misjudgment of probabilities, information, objective reality or one’s own abilities. This categorization simplifies the differentiation of individual heuristics as more and more are identified in the course of research.

83 This distinction is closely related but not congruent with the distinction used by Kahneman (2011) in the decision process between System 1 (fast, automatic, always active, emotional, stereotyping, unconscious) and system 2 (slow, strenuous, rarely active, logical, calculating, conscious).

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Besides the categorization of heuristics, their harmfulness to portfolio risk and return is shown on a scale from 0 to 10 on the respective RRH-indicator (Risk/Return-Harmfulness indicator®). This indicator was developed to give readers the opportunity to link the heuristics to a value, which significantly increases the learning effect and the awareness of the degree of harmfulness they have (Daxhammer/Facsar, 2012). The RRH indicator is based on the effects of individual heuristics on the behavior of market participants. These effects therefore form the basis for the classification of heuristics on the RRH indicator (see Fig. 50). As such, the portfolio detriment of the availability bias is depicted below. Accordingly, the risk/return harmfulness is rated 6. The RRH indicator should however not be understood as an unambiguous, ordinal measure, but rather as an instrument of didactic preparation. A more detailed explanation of the RRH indicator is given in chapter 7.3. Furthermore, a detailed description of the value of each heuristic is given at the end of chapters 7, 8 and 9.

Fig. 50: RRH Indicator Availability Bias

Applied heuristics during information perception Information Perception  Availability Bias  Risk Perception Bias  Selective Perception  Framing Bias  Herding

Information Processing

Decision-Making

 Anchoring & Adjustment  Selective DecisionBias Making Bias  Representativeness Bias

 Self-Attribution Bias

 Ambiguity Aversion Bias  Hindsight Bias  Conservatism Bias

 Endowment Bias

 Mental Accounting Bias

 Optimism Bias

 Recency Bias

 Dispositions Effect

 Overconfidence Bias

 Status-Quo Bias

 Illusion of Control Bias

 Self-Control Bias

 Reflection Effect

 Regret Aversion Bias

Fig. 51: Applied heuristics during information perception

During information perception, market participants mainly use heuristics of cognitive origin. Here, the availability bias is used to determine the probability of occurrence of events depending on the imagination market participants have about it. Easily retrievable scenarios are perceived to be more probable than those that are more difficult to imagine or to understand. Furthermore, the risk perception of market participants is based on the misinterpretation of probabilities. This perception heuristic illustrates the changing risk perception in the course of profits or

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losses. Objective probabilities are not perceived as such. Rather, they are overweighted or under-weighted, which is why the risk taken with an investment changes subjectively. As a further heuristic of cognitive origin, selective perception leads to misinterpretation of information. It is a phenomenon in which market participants consciously or unconsciously neglect information. Consequently, they only perceive information that they want to perceive. Information that contradicts the decision to be made is ignored, thus preventing an objective assessment of the situation. Information can also be misinterpreted if it is presented in different forms. The framing bias refers to the phenomenon that the presentation of one and the same factual situation in a different way leads to different decisions. Finally, herding, as a heuristic of emotional origin, rounds off the heuristics applied during information perception. Market participants rely on their gut instinct and forego an in-depth analysis of their investment decision. They behave like the mass of market participants and run, for example, the risk of being the last investors in an overheating situation, who then incur the highest losses when a bubble bursts. When perceiving information, market participants make use of certain heuristics, which help to provide a quick overview of the available information, but also prevent all available information from being included in the decision-making process.

7.1

Heuristics of cognitive origin

The heuristics considered below are characterized by the fact that market participants misjudge probabilities or available information. The values assigned to the RRH index are presented in detail in chapter 7.3. Heuristics

Value RRH-Index

Underlying Misperception

Availability Bias

6

 Probability

Risk Perception Bias

2

 Probability

Selective Perception

4

 Information

Framing Bias

5

 Information

Fig. 52: Heuristics of cognitive origin during information perception

7.1.1 Misperception of probabilities

Availability Bias General description One of the most important perceptual heuristics is the availability bias. It describes the tendency of market participants to assign a certain significance to information subject to their imagination or the estimated probability of occurrence. Market

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participants who apply this heuristic in the course of information perception consider scenarios based on the retrievability of memories – the easier to imagine/to retrieve the more probable than those that are more difficult to imagine or to understand (see Pompian, 2006, p. 94). During information perception, the availability bias allows to fade out the less probable scenarios. The basis for the assessment of probability is the frequency of occurrence of certain events in the past. The more frequently an event has already occurred, the easier it is to imagine than a new occurrence. Events that have not yet occurred can therefore be less easily captured by market participants, which means that their probability of occurrence is assessed as rather low (see Hens/Bachmann, 2008, p. 68). The following example illustrates the availability bias (see Zweig, 2007, p. 171): Example 7.1: Availability Bias What answer would you give to the following questions? - Which is riskier: nuclear reactors or sunlight? - Which animal species is responsible for the most human deaths in the U.S.? Alligators, bears, deer, sharks or snakes? Match (on a yearly basis) the causes of death in (in the left column) to the respective number of deaths worldwide (in the right column): - War a) 310,000 - Suicide b) 815,000 - Murder c) 520,000 Experiments have shown that most people are subject to availability bias when their response is influenced by easily available memories. The nuclear disaster in Fukushima/Japan (2011) shows how much easily available information can influence risk assessment. Although American statistics show that the number of U.S. deaths caused by the worst nuclear accident in history (Chernobyl/Ukraine 1986) ‒ in contrast to the predictions of several tens of thousands of deaths at the time ‒ is around 100, and 8,000 U.S. citizens die every year of skin cancer caused by excessive exposure to solar radiation, nuclear technology is considered to be much more dangerous than exposure to solar radiation. With reference to the second question, in a typical year in the U.S., roe deer are responsible for about 130 deaths among humans ‒ seven times more than alligators, bears, sharks and snakes together. The increased deaths from roe deer can be explained less by their aggressiveness than by the animals running in front of moving cars causing fatal accidents. Finally, most respondents conclude that war claims more lives than suicide. In fact, in most years, fewer people die in wars than from murder and suicide. Suicide ranks first among the three causes of death listed. Availability bias is also one of the reasons why it is so difficult for innovative entrepreneurs to find venture capital. Investors have hardly heard of the company and therefore find it difficult to imagine its prospects of success.

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The bias can appear in several categories, the four most prominent ones when it comes to investment decisions are (see Pompian, 2006, p. 94): [1] Retrievability ‒ This refers to ideas/scenarios that can be retrieved most easily from memory and therefore are perceived most credible. Many market participants would choose investment funds that are heavily advertized in the media on the basis of retrievability. These investment companies focus in particular on their most successful products. Market participants who rely on media coverage would not consider so-called “hidden champions” who rely less on advertising. [2] Categorization ‒ This form of availability aims at the tendency of the human brain to compare new facts with given, already stored information and thus to categorize them. If less information is found for a certain subject, it is given a lower probability. For example, if a French person simultaneously tries to create a list of highquality French and U.S. vineyards, compiling the list of U.S. vineyards could be considerably more difficult. Consequently, the French person would predict that it is less likely to find high-quality vineyards in the U.S. ‒ even if this does not necessarily correspond to reality. [3] Narrow range of experience ‒ Availability bias can also occur when market participants try to formulate a probability on a very restrictive frame of reference. In this case, the probability of an event occurring based on the own range of experience would be estimated to be higher than it actually is. Thus, it is clear from the above example that the assessment of probabilities of occurrence by pure imagination cannot be the correct basis for a decision. A study by Brad Barber and Terrance Odean (2005) showed that out of thousands of securities, market participants select the securities of companies that mostly attract their attention, e.g., on the basis of heavy advertisement in investment magazines, which have recently experienced sharp price fluctuations or have undergone extreme daily movements. [4] Resonance ‒ The last form of availability bias aims at influencing the perceived probability of occurrence by one’s own personal preferences. It is therefore not surprising that a market participant who prefers to buy bargains also looks for undervalued companies on the capital markets. However, focusing exclusively on apparently undervalued companies would at the same time have the disadvantage of a potentially unbalanced portfolio. Risk/return-damaging behavior of market participants The investment behavior of market participants, especially private investors, can be influenced by availability bias in different ways: 

Investment selection based on easily retrievable information (e.g., advertising, recommendations) This approach makes it difficult or impossible for market participants to evaluate the target companies in which investments are to be made based on detailed →Fundamental Analysis.

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 Investment selection based on categorical attributes (e.g., industrial/geographical region) The attributes are based on the availability of the experiences anchored in the memory through personal experience. Securities from other industries or regions are less considered. As a result, investors may limit themselves to a certain country. This behavior can in turn lead to portfolios being unbalanced towards a region or an industry, whereby clustering risk, i.e., the risk of selecting securities from one region/industry. And this increases the portfolio’s vulnerability. This heuristic, also known as home bias or sector bias, has been investigated in numerous studies (French/Poterba, 1991, Chan/Covrig/Ng, 2005). Figure 53 below shows that the citizens of a country invest disproportionately higher in local companies compared to the weight of their countries within the worldwide equity markets (see Nofsinger, 2008, p. 67). Whereby the distinction between “domestic” securities is not as straight forward in today’s globalized economies as many companies derive a notable part of their revenues outside of their countries of domicile (e.g., Novartis, a Swiss pharmaceutical company, generates only 1.6 Percent of its revenues in Switzerland, first being the U.S. with 33.9 percent, followed by Germany with 9.3 percent and Japan with 5.8 percent). Home bias with considerable impact on the geographical distribution of the assets to be invested – in percent Country weight in global equity market Ownership of domestic securities by local nationals

Fig. 53: Impact of home bias on the geographical allocation of assets to be invested (2005)

In the area of active fund management by professional investors, the availability bias is limited due to complex processes for security selection and portfolio construction. Nevertheless, professional fund managers may still face exposure to considerable risk since the selected companies do not necessarily generate sales only in their home markets. They often operate worldwide and are therefore exposed to the opportunities and risks of the respective country in which they operate.

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In today’s investment environment, a portfolio manager is required to have detailed understanding of a company’s geographical distribution of sales, ideally using the information for decision-making. Although a glance at the annual balance sheets of the companies can help, the information may not be immediately apparent or differ greatly from the presentation of other companies and therefore takes a lot of time to analyze and to compare in-between portfolio holdings. Taking the German DAX as an example, it becomes apparent that the index constituents generate only approximately 1/4 of their turnover (26 percent Revenue Exposure by Country) in Germany ‒ an active/passive investment strategy with a focus on Germany cannot therefore be achieved with exclusively DAX index constituents. Figure 54 (upper right quadrant) shows that, although all index members are domiciled in Germany (inner circle), only 26 percent of their revenue is actually generated in Germany (outer ring)84:

Fig. 54: Geographical distribution of turnover using the example of the German DAX; FactSet

With the information available today, portfolio managers have the opportunity to select those companies that generate their sales mainly in Germany ‒ whether 84

FactSet Revere Geographic Revenue (“GeoRev”) Exposure data provides a highly structured and normalized display of companies’ revenues by geography. It helps to understand the geographic footprint of a company based on sources of revenue versus country of domicile, and analyze global revenue exposures at the company, index, or portfolio level. This makes it possible to determine the regional sales focus of a company.

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they are domiciled in the U.S., Asia or other European countries. Ultimately, portfolio managers have the ability to overview their regional weighting in the runup to regional crises and thus reduce the risk of portfolio losses in time. The availability heuristic describes the tendency of market participants to determine the significance of information upon the imagined or estimated probability of occurrence.

Risk perception bias General description This heuristic can be illustrated with a quotation from Kahneman and Tversky: “A person who has not made peace with his losses is likely to accept gambles that would be unacceptable to him otherwise.” (Kahneman/Tversky, quoted after Nofsinger, 2008, p. 32) With their quote, Kahneman and Tversky point out that market participants change their risk perception depending on profits or losses experienced. The change in risk perception is strongly dependent on whether the market participant is confronted with unrealized relative profits or losses, or whether profits or losses already realized require a further decision. Example 7.2: Change in risk appetite Consider the following situation and decide according to your preferences: A coin toss is offered where you win EUR 20 if the coin is tails or lose EUR 20 if the coin falls on heads. Would you accept the game? Now consider that you have already won EUR 100 in previous games. Would you play now? Has your preference for participating in the game changed once you have included a previous win in your consideration? What if you had already lost EUR 20 instead of winning EUR 100? Would this change your participation in the game? Results show that many respondents to the above questions would participate in the game, however after considering previous winnings or losses, they would decline. The probability of winning EUR 20 does not change in the different scenarios. The objectively expected probability remains at 50 percent. Despite this obvious fact, the respondents’ risk assessment changes considerably. Depending on whether the realized profit has already been booked as an asset increase or is only seen as additional “play money”, the risk appetite changes. Kahneman and Johnson carried out an experiment with 95 students in which the students were asked to decide whether they wanted to continue playing after a sudden profit. After the unexpected win, the students behaved as if they were

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playing with the “money of the casino”. 77 percent of the participants were prepared to take the risk again (house money effect). As soon as the participants “got used to” winning or were asked to stop gambling with “house money”, only 41 percent of the participants were prepared to risk a bet. The explanation for this behavior can be found in →Probability Weighting in chapter 6.2.2. If medium and high probabilities are underestimated, market participants are less willing to take risks. After booking a realized profit, market participants estimate the probability of winning again to be lower than would be correct from an objective probability assessment. They become increasingly risk-averse. This behavior could not be observed immediately after the first unexpected profit, which the participants had realized but not yet booked as their own money. The initial gains influenced the decision-making of the participants to such an extent that they behaved in a risk-averse manner, contrary to expectations. The situation is different when the respondents realized a loss. In this case, 60 percent of the participants were not prepared to play again. This behavior is astonishing in view of the intrinsically risk-taking behavior of the market participants in the negative area of the value function. In the experiment, the risk-taking behavior suddenly decreased as soon as an actual loss occurred and was realized (see Nofsinger, 2008, p. 32). The explanation for the change in risk attitude is therefore the positioning of the market participant. If an investment is to be continued considering an already incurred but not yet realized paper loss, the market participant continues to keep the holdings and has so far suffered an unrealized paper loss. The market participant is willing to take a risk in the negative area of the value function. If, on the other hand, a paper loss has been realized by selling the securities, the market participant appears to become risk-averse and wants to avoid the market. Consequently, the inclination to reinvest is influenced by the realized loss. However, risk appetite returns again if the market participant (e.g., in a horse betting) has lost several times in the course of a day. In this case, often at the end of the day everything is put on “one card”. The individual overestimates the objective probability of making a profit on a risky investment. Probability weighting provides again the explanation for the change in risk assessment. If low probabilities are overestimated, market participants are more willing to take risks. They are increasingly willing to take risks when it comes to losses, as they weight the unlikely event of rising prices with a higher probability. In the profit area, market participants are also less risk-averse. This can be observed in the context of a bull market (long-term rising security prices). As soon as security prices have risen sharply, other, inexperienced market participants start trading. In the sense of the social contagion of boom thinking (see chapter 4.1), these market participants rate the now declining probability of further increases in share prices higher than would be objectively justified. Their perception of risk has changed from initial risk aversion to risk seeking.

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Risk/return-damaging behavior of market participants Market participants behave in accordance with the above-mentioned circumstances depending on the under- or overvaluation of objective probabilities in a risk-averse or risk-seeking manner. The change in risk perception is more likely to occur among professionals in wealth management and also among institutional investors than among professionals in private equity. Due to the long-term nature of their investments, the latter are less focused on short-term market developments and therefore run less of a risk of changing their risk perception depending on the profits and losses they experience.  Risk perception on the basis of misjudgement of objective probabilities The tendency to misjudge objective probabilities can occur depending on whether the event is expected or unexpected: - After an unexpected profit, investors are willing to purchase risky securities. This phenomenon is based on the house money effect, whereby the investor has not yet booked the unexpected profit to his assets. The probability of rising profits is overestimated, which means that the investor acts less riskaverse than would be expected in the profit range of the value function. - After a profit has already been booked and could be used for an additional investment opportunity, investors are less willing to take risks again. This phenomenon is based on the fact that they consider relatively high probabilities to be lower than would be justified. They are increasingly risk-averse. As a result, they are becoming increasingly passive and are missing necessary adjustments to their portfolio. - After an unexpected loss, investors are unwilling to buy risky securities again. They behave in this way because they tend to underestimate objectively high probabilities and therefore their risk aversion increases. - After having experienced losses several times, investors are again willing to take risks. The reason for this may be the overestimation of objectively lower probabilities. As a result, the risk of the portfolio can increase with such behavior. The change in risk perception is based on the phenomenon that market participants change their risk perception and appetite depending on a loss or profit that has occurred. This change is based on non-objective probability assessments.

7.1.2 Misinterpretation of information

Selective perception General description Selective perception together with selective decision-making forms the basis of the theory of cognitive dissonance (see chapter 6.1.3). Both components have the

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objective of mitigating developments that run counter to one’s own ideas. Selective perception appears at the beginning of the information and decision process, whereas selective decision-making can be observed in the execution of decisions. Selective perception (see chapter 6.1.1) is an occurrence in which market participants consciously or unconsciously neglect information. It can occur both before and after a decision is taken. Before a decision is taken, it leads to market participants perceiving only the information they want to perceive. Information that contradicts the decision to be taken is ignored, thus preventing an objective assessment of the situation. However, selective perception also means that market participants, following decisions with a high commitment, only perceive what supports the decision taken, in particular if they find that the alternative chosen was the allegedly worse one. In this case, attention is drawn to the advantages of the choice made. The market participant hides information and only sees what ultimately confirms the decision made (see Schriek, 2009, p. 32). Selective perception can therefore lead to one and the same situation being interpreted completely differently by market participants. The reason for the differences in interpretation can lie in individual needs, personal experiences and the emotions brought into the situation (see Mazanek, 2006, p. 72). Example 7.3: Selective Perception In recent months, an investor has conducted intensive research into the growth prospects and economic conditions of companies that process credit card transactions. After intensive research and market observations, the investor believes that the next and possibly last acquisition of such a company (A) by one of the well-known credit card issuers (B) is very likely. Shortly before his or her decision to invest in the shares of company A, he or she reads on social media about the imminent possibility that company A could be taken over. In this case, the analysts forecast that the shares of takeover candidate A have a profit chance of up to 70 percent. The market participant then decides to invest EUR 5,000 in company A, in the expectation that he or she will be able to participate in the possible price increases. In the next few days after the completion of his or her investment, however, the share price of company A begins to fall. A short time later, the market participant reads the following headline in the business press: „Declining turnover in the credit card business burdens payment processors ‒ end of the boom?“ How does the market participant react to this? It is very likely, that the negative information is being devalued, perhaps even ignored to justify the decision already made. This is due to the fact, that the voluntary commitment taken by the market participant after weeks of research is so high that he or she no longer wants to take back his decision with a feeling of loss. As a result, the market participant sticks to the investment, as even the first book losses do not undermine the hope that the share price will still perform in line with the previous expectations.

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In the literature, the tendency to perceive confirmatory information can also be found under the term →Confirmation Bias. This is the same heuristic as selective perception. Risk/return-damaging behavior of market participants In practice, selective perception can influence both institutional and private investors in their perception of information:  Seeking out confirming information From the total amount of information, analysts often only consider information that meets their expectations and, moreover, information that does not contradict the forecasts made so far. The 1988 study by John E. Hunter and Daniel T. Coggin confirmed this finding. The analysts have extremely strong perceptual expectations which, as a result of selective perception, can lead to a false perception of the information. In 1997 the findings have been confirmed by Praveen Sinha, Lawrence D. Brown and Somnath. For example, an analyst with a positive attitude will look for rising security prices in the event of rising unemployment rates, as inflation will fall as a result of an improving labor market (due to falling wage costs). On the other hand, an analyst with a negative attitude will expect falling security prices, as a deteriorating labor market could have a negative impact on economic growth (see Goldberg/von Nitzsch, 2000, p. 61).  Risk of herding Selective perception can lead directly to herding (see chapter 7.2), when investors suppress information that argues against an investment and rely on the opinion of the group. This behavior was largely responsible for the emergence of the dot-com speculative bubble (see development of the Nasdaq Composite in Fig. 55) and leads to under-diversified investor portfolios. The same was observable in the course of the GameStop/AMC buying frenzy in January and May 2021 triggered by the social network Reddit (see chapter 5.2.6). Private investors, in particular, have a tendency to justify a decision already made by confirming information. This behavior is a result of limited rationality, as investors hold on to losing positions, although information would suggest an immediate divestiture. Herding can thus also indirectly lead to the disposition effect. As such, investors (both private and institutional) interpreted information on technology and telecommunications stocks received during the dotcom bubble in the belief that the price increases would continue (see Pompian, 2006, p. 88). Market participants were led to believe by the media coverage that a balanced portfolio can be created solely through widely known technology shares. Selective perception can lead to the view that “This time is different” (see chapter 4.2) and limit perception to information confirming the market development. This approach, which viewed the situation at the time in particular from the perspective of confirming information, greatly favoured the development of the

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dot-com speculative bubble, with the index for U.S. technology stocks Nasdaq Composite reaching a record value of 5,048 points on 10 March 2000. However, as a result of the bursting bubble, the index fell by over 70 percent to 1,114 points until a bottom was struck on October 9 2002. Speculative bubble at the turn of the millennium ‒ Nasdaq Composite

Fig. 55: Price development Nasdaq Composite, 1996-2002; FactSet

Selective perception is used by market participants in the context of cognitive dissonance. Information is consciously or unconsciously neglected with the aim of obtaining confirmation of a decision to be made or already made. Selective perception can lead directly to herding.

Framing Bias General description Framing bias refers to the phenomenon that the type of presentation of one and the same choice leads to different decisions. Accordingly, a framing bias is given when different decisions are caused by the different processing of an unambiguous fact. The sequence of the perceived information, the different graphic representation or the embedding of information in a certain context can cause a framing bias. In addition, different decisions can be evoked depending on whether the decision situation is presented in a positive (profit) or negative (loss) way (see Karlen, 2004, p.. 22). The f ming bias was identified by Kahneman and Tversky in 1981, in connection with the Prospect Theory (see chapter 6.2).

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Example 7.4: Framing Bias The effects of the framing bias can be illustrated as follows: Supermarket situation: You are offered one grapefruit for EUR 1, but three for EUR 2.50. Which offer do you choose? Hospital situation: Before a surgery a patient is informed about the application of a certain medication. The doctor can use two different explanations about the effect of the medication to be used: 75 percent of patients survive the operation with the use of the medication vs. 25 percent of patients treated with the medication die. Investment advice situation: The investment advisor can present the development of a security in different ways: Presentation of the last six months with steadily rising prices vs. presentation of the last two years with strong price losses in the first 18 months and subsequent recovery in the last six months. Experiments have shown that test persons in the above examples arrived at different decisions depending on the form of presentation. Thus, the effects of the framing bias are actively applied in influencing market participants. In supermarkets, an attempt is made to explain consumers that it is cheaper to buy three grapefruits than just one. The patient in the hospital will come to two completely different decisions for the same type of medication ‒ depending on whether survival or death is given priority. In the same way, the decision-making of the investors can be influenced. If information relevant to the decision presents a positive short-term outlook, the inclination to invest is higher than if the long-term development of the investment is shown. The presentation format plays therefore a large role for the risk perception of an investment. According to the research results of Elke Weber, Niklas Siebenmorgen and Martin Weber (2005), private investors arrive at completely different risk assessments of a certain type of investment, if these are presented in different formats. The scientists presented sixteen investment alternatives to a group of students in three different ways: [1] Verbal description of the development, [2] Development of annual returns over the past ten years in a bar chart, and [3] Distribution of annual returns according to the normal distribution curve. As a result of the study, it turned out that market participants assess the diversification of the investment and thus the actual risk completely differently depending on the subjectively perceived risk. If investors are familiar with one form of presentation, the risk of the investment is clearly underestimated (presentation forms [1] and [2]). However, if they are not familiar with the form of presentation (presentation form [3]), the risk is estimated to be higher than is actually the case (see Weber, 2007, pp. 156).

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Consequently, the form of presentation, but also the way in which the investment is assessed (positive vs. negative with regard to the development of returns), influences the willingness to make an investment. A further phenomenon can be described by narrow framing. Here, the market participant focuses on individual aspects of a situation. Other aspects are mostly hidden, which prevents an objective assessment of the probabilities. An example of this is the focus on the short-term price development by an investor with a long-term orientation. If only the short-term price development is taken into account, as media coverage may tempt to do so, the market participant will fade out other factors influencing risk and return. These include the general, longterm development of the industry to which the company belongs or the overall economic development. Risk/return-damaging behavior of market participants The framing bias mainly unfolds its return-damaging effect in the case of private investments.  Influencing risk perception through the way risks are presented Improperly formulated questions prior to or during an investment may cause the investor to be unjustifiably risk-seeking or risk-averse.  Influencing risk perception based on realized gains or losses Investors tend to base future investment decisions on realized profits or losses. If losses have been incurred, investors tend to take riskier positions in order to compensate for the losses. If profits have been made, investors tend to take lower-risk investments in order not to risk realized profits.  Influencing the frequency of transactions through perception of shortterm price developments Investors may be tempted by the sub-form of the representation effect, narrow framing, to react to short-term price movements and thus act more frequently than would be justified in the context of their investment planning. Investors reduce portfolio profitability through frequent trading solely due to transaction costs and may increase portfolio risk by changing portfolio diversification. The framing bias describes the phenomenon that the presentation of one and the same issue in different ways leads to different decisions.

7.2

Heuristics of emotional origin

During the reception of information, a heuristic with emotional origin stands out (see Fig. 56). This is the habit of market participants to observe the actions of other investors, which can lead to a social consensus of market behavior. This social consensus ultimately leads to herding (see chapter 4.1.1).

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Heuristics Herding

Value RRH-Index

Underlying Misperception

5

 Probability

Fig. 56: Heuristics of emotional origin during information perception

Herding General description Herding can be observed in particular during the reception of information. Some market participants keep a constant eye on the news pages or social media sites and regularly check their portfolio levels. If a movement starts on the stock market, the masses often move together in the corresponding direction. Nobody wants to be left behind or be on the wrong side. This was very observable in January and May 2021 when the short squeeze in GameStop and AMC was triggered through the concentrated actions of many retail investors communicating via Reddit.com. The problem with herding is mainly that market participants, at the expense of thorough analysis, tend to rely on their “gut feeling” by observing the behavior of others. If many act in the same way, one is not alone in making a wrong decision. The concentration of market participants on apparently irrelevant but possibly profitable information can be impressively illustrated by the following example from the dot-com speculation bubble at the beginning of 2000. Example 7.5: Herding During the dot-com bubble, companies started to adapt their company name to indicate their focus on the internet distribution channel. The more market participants bought shares of companies with Internet sales, the more companies planned to change their names to .com or .net. According to the findings of Cooper, Dimitrov and Rau (2001), 147 companies changed their company name between 1998 and 1999. Within the first three weeks after the change, the prices of the companies concerned rose by an average of 38 percent. Companies that were exclusively active on the internet experienced a 57 percent increase in the value of their shares during the three-week period in question. Other companies with limited internet activity saw their shares increase in value by 35 percent. Some companies that adjusted their names and had no internet activity to date achieved an increase in value of around 16 percent by adjusting their business strategy to internet sales. Even companies that neither had internet activities nor any significant experience with the distribution channel achieved a 48 percent increase in value of their shares through the name change. After the collapse of the dot-com bubble, the trend reversed and companies often decided to remove the aforementioned endings from their company names. 67 percent of companies deleted the internet reference from their company

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names. Even this change led to a corresponding premium from market participants. The return to traditional forms of distribution led to an average share price increase of 64 percent for the corresponding companies over the following two months (see Nofsinger, 2008, p. 80). As already described in chapter 4.1.1, herding can be divided into four categories:    

Herding based on information cascades Herding based on reputational interests Herding based on information sources Herding based on historical market movements

Risk/return-damaging behavior of market participants Herding leads to significant portfolio damage, especially for private investors. In the area of private equity investments, herding can lead to further investments in the “hip” industry in the wake of successful transactions by other general partners.  Impairment of portfolio diversification when overweighting bubbleforming investments Investors tend to overweight such securities due to media coverage, e.g., reports on frequently commented investments. In this way, portfolio diversification is considerably restricted and any losses incurred cannot be offset by other investments. In the area of private equity investments, reports of successful transactions can lead to further investments by other private equity houses. In this case, there is a risk that new investments will simultaneously increase the portfolio size if the general partner invests at excessive prices in the relevant industrial sector during the course of a bubble formation, which then cannot be realized if the company is subsequently sold to another private equity investor.  Tendency to disposition effect Market participants run the risk of misinterpreting media reports on the development of a possible speculative bubble. In this sense there is a danger that the investor, due to risk aversion in the profit area of the value function, will exit too early from a fundamentally justified price rally. This can be the case if rising prices are seen as a temporary price recovery after a correction. On the other hand, there is a danger that investors who only become aware of a possible speculative bubble relatively late will also misinterpret media coverage. There is a danger that these investors will use such positive reports to enter the market and, when the speculative bubble bursts, will not be willing to quickly realize the changing environment and thus contain any losses that arise. This phenomenon, also known as disposition effect, will be discussed in more detail in chapter 9. Herding behavior is a heuristic of emotional origin. Market participants are strongly fixated on behavior in their environment and are influenced by the opinion of the masses. The perception of information can be influenced by the opinion of the masses.

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7.3

Assessment of the risk/return-harmfulness of reviewed heuristics

In this section, the disclosure of portfolio impairment within the framework of the RRH indicator® for the reviewed →Heuristics is presented in detail. Here, the focus is on the justification for the defined categorisation of a heuristic in the RRH indicator®. Various aspects are used to determine the harmfulness of a heuristic. In addition to the origin of the heuristic, the decisive factor is how strong the impact of the heuristic is on the behavior of the market participants. Heuristics of cognitive origin vs. emotional origin. The origin of the heuristic should be included in the harmfulness analysis, since heuristics can be countered with different effectiveness depending on their origin. The harmfulness of heuristics of cognitive origin can be reduced by improved information. Heuristics of emotional origin, on the other hand, cannot be weakened by improved information (see Kahneman, 2011, pp. 166). In this case, the risk/return-damaging effect can only be illustrated to the market participant. During a panic-like market movement it can be assumed that heuristics of emotional origin are used despite clarification of their mode of action. For this reason, it is justified to apply a double weighting of heuristics of emotional origin whereas heuristics of cognitive origin are single weighted with regards to the expected portfolio damage. Heuristics that reinforce other limited rational behavior. Within the assessment of the return-damaging effect, it must also be examined whether a heuristic during its application by the market participant also directly causes another heuristic. If this is the case, the directly caused heuristic and thus the risk/returndamaging behavior implied by it is again double weighted. All other behaviors of the originally applied heuristic are single weighted. It should also be noted that the directly induced heuristics, in turn, can indirectly induce further heuristics themselves (e.g., selective perception, which can directly lead to herding behavior which itself can lead to the disposition effect). The links between the heuristics and the direct & indirect heuristics caused by them are shown in chapter 9.4 in Figure 71. In order to reduce complexity, the RRH indicator itself only includes the directly induced heuristics. For the applied heuristics during information perception, the following evaluations result: Bias

Risk/return damaging behavior of market participants  Investment se-  Investment se Investment  Investment  Availability lection based lection based selection selection  Type: Cognion narrow based on rebased on cate- on narrow tive range of experange of experitrievability of gorization  RRH-Index: 6 information rience (e.g., In- ence (e.g., In(e.g., resi5+(1 for being dustry) dustry) (e.g., heavy dency) cognitive) advertise Leads to Home ment) Bias RRH-Index

 +1

 +2

 +1

 +1

7.3 Assessment of the risk/return-harmfulness of reviewed heuristics

Bias

Risk/return damaging behavior of market participants

 Risk Percep Risk perception tion based on erroneous inter Type: Cognitive pretation of ob RRH-Index: 2 jective probabil1+1 (cognitive) ities

RRH-Index

 +1

Bias

Risk/return damaging behavior of market participants

 Selective Perception  Type: Cognitive  RRH-Index: 4 3+1 (cognitive)

 Information  Tendency for perception Herding dominated by confirming news

RRH-Index

 +1

Bias

Risk/return damaging behavior of market participants

 Framing Bias  Type: Cognitive  RRH-Index: 5 4+1 (cognitive)

 Altering per-  Increase of ception of transaction risk based on frequency the presentabased on tion of short-term risk/return price change

RRH-Index

 +1

Bias

Risk/return damaging behavior of market participants

 +2

 +1

 Tendency for the Dispositions-Effect

 +2

 Herding  Type: Emotional  RRH-Index: 5 3+2 (emotional)

 Focus on over-  Tendency for valued investDispositions-Efments deteriofect rates portfolio diversification

RRH-Index

 +1

 +2

211

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Summary Chapter 7 When perceiving information, market participants make use of certain heuristics, which help to obtain a quick overview of the available information, but also prevent all available information from being included in the decision-making process. Misinterpretation of probabilities The availability bias describes the tendency of market participants to determine the significance of information based on the imagined or estimated probability of occurrence. The change in risk perception and risk attitude is based on the phenomenon that market participants change their risk perception or risk attitude depending on a loss or profit that has occurred. This change is based on non-objective probability assessments. Herding is a heuristic of emotional origin. Market participants are strongly focused on the behavior in their environment and are influenced by the opinion of the masses. The perception of information can therefore be influenced by the opinion of the masses as well. Herding can also lead to the disposition effect if market participants buying late into a previously strong trending security are now confronted with losses in the course of falling prices. Misinterpretation of information Selective perception is applied by market participants in the context of cognitive dissonance. Information is consciously or unconsciously neglected with the aim of obtaining confirmation of a decision to be made or a decision already made. Selective perception can lead directly to herd behavior, which in turn can lead to disposition effects. The framing effect describes the phenomenon that the presentation of one and the same fact in different ways leads to different decisions. It can lead directly to the disposition effect.

8

Limited rationality during information processing The second stage in the information and decision-making process deals with information processing. Market participants also use certain heuristics in this phase, which can lead to limited rational behavior. In this chapter you will learn about the most important heuristics that distort information processing for the Homo Economicus Humanus.

Information Perception  Availability Bias  Risk Perception Bias  Selective Perzeption  Framing Bias  Herding

Information Processing

Decision-Making

 Anchoring & Adjustment Bias  Representativeness Bias  Ambiguity Aversion Bias  Conservatism Bias  Mental Accounting Bias  Recency Bias  Overconfidence Bias  Illusion of Control Bias  Reflection Effect

 Selective DecisionMaking Bias  Self-Attribution Bias  Hindsight Bias  Endowment Bias  Optimism Bias  Dispositions Effect  Status-Quo Bias  Self-Control Bias  Regret Aversion Bias

Fig. 57: Applied heuristics during information processing

The second phase of the information and decision process includes a series of heuristics used to simplify information processing. The vast majority of these heuristics are of cognitive origin. Among the heuristics of cognitive origin is the anchoring & adjustment bias. Semi-rational market participants use this heuristic to better assess the importance of a given situation and to evaluate future developments with respect to an anchor point. Another heuristic, the representativeness bias, enables the market participant to reduce the complexity of the information to be processed. For this purpose, information that appears to be similar to existing information is not analyzed further. Instead, an assessment is made on the basis of stereotypes, in which, for example, probabilities are assessed on the basis of similarities. Misjudgment of probabilities can also occur through the application of the ambiguity aversion bias. This heuristic occurs above all when a market participant has classified information as unknown and cannot accurately assess the degree of unfamiliarity. The subjectively perceived lack of information influences in particular the valuation of the expected return on domestic and foreign securities. In addition, conservatism bias unfolds its return-damaging effect during information processing as it prevents

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market participants from changing existing views when new information arrives. Information processing can also be made more difficult due to misinterpretation of objective profitability. In this category, mental accounting is used by market participants to assign their investments into mental accounts depending on the purpose or other selected criteria. Limited rationality becomes apparent by neglecting correlations between individual accounts, which can ultimately reduce portfolio profitability. The recency bias prevents the full consideration of all available information. It illustrates the tendency of market participants to remember recently experienced events better and to give them a higher weighting than events further back in the past. This tendency can, for example, affect the assessment of a fund manager’s performance. Two further heuristics of cognitive origin lead to a failure to correctly assess one’s own abilities. Market participants’ overconfidence of their own abilities leads them to misjudge their own prognostic capacity and competence. They underestimate the risk of an incorrect forecast and thus increase portfolio risk. In addition, the illusion of control bias gives market participants the feeling that they are able to forecast and control the markets. This is one of the consequences of overestimating own abilities and occurs when the market participant has been successful in the market at times. It distorts the formation of expectations and falsifies the learning processes. 8.1

Heuristics of cognitive origin

Heuristics

Value RRH-Index

Anchoring & Adjustment

5

 Probability

Representativeness

3

 Probability

Ambiguity Aversion

8

 Probability

Conservatism

2

 Information

Mental Accounting

7

 Objective Reality

Recency Bias

5

 Objective Reality

Overconfidence

4

 Own Abilities

Illusion of Control

5

 Own Abilities

Underlying Misperception

Fig. 58: Heuristics of cognitive origin during information processing

8.1.1 Misperception of probabilities

Anchoring & Adjustment Bias General description Anchoring & Adjustment bias is used by market participants as a kind of guideline, with the help of which they try to assess the significance of a problem or fact.

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This benchmark is often a random value that can be modified by further information until the final decision is made and is called an anchor. The actually limited rational behavior is expressed in the fact that the anchor is not sufficiently modified as soon as new information is processed. In contrast to limited-rational investors, the Homo Economicus would evaluate the new information objectively and separately from the current market situation on the basis of the Bayes’ theorem (see chapter 1.3.4). In addition, it is also conceivable that third parties (analysts, investment advisors, etc.) would intentionally set an anchor (e.g., certain index level at the end of the year, price target of a share, etc.) in order to influence market participants according to their opinion. The setting of an anchor is therefore subjective and leads to the market participant being mentally bound (see Lewis, 2008, p. 50). Example 8.1: Anchoring and Adjustment Bias In 1974, Kahneman and Tversky conducted an experiment that still impressively demonstrates the effectiveness of this heuristic. Two groups were shown a wheel of fortune with numbers from 1 to 100. After turning, the needle landed at 65 in the first group and at 10 in the second group. Afterwards the participants were asked to write down whether the number of African countries in the U.N. was larger or smaller than 65 and 10 respectively. Next, the participants were asked to write down the exact proportion of African countries. The group where the wheel of fortune stopped at 65 estimated this proportion to be 45 percent on average, while the other group estimated the proportion to be 25 percent on average. Although the participants were aware that the reference was completely random, this did not prevent them from using it as an anchor value (see UBS Wealth Management Research, 2008). According to Tversky and Kahneman: “In many situations, people make estimates by starting from an initial value that is adjusted to yield the final answer. The initial value, or starting point, may be suggested by the formulation of the problem, or it may be the result of a partial computation. In either case, adjustments are typically insufficient. That is, different starting points yield different estimates, which are biased toward the initial values. We call this phenomenon anchoring.“ (Tversky/Kahneman 1974, p. 128) Risk/return-damaging behavior of market participants The effects of this heuristic are often visible in daily market activity. This was demonstrated by the research results of Gregory B. Northcraft and Margaret A. Neale in 1987 from the University of Arizona. According to this, research analysts are also subject to the effects of this heuristic to a considerable extent:  Market expectations too close to the market development and return of the previous year This behavior can lead to investors aligning their expectations of the development of the observed securities or indices too strongly with the current devel-

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opment, disregarding the historical fluctuation margin. Similarly, there are limits to the rationality of forecasting the future development of the observed securities or indices on the basis of the return development of the past year. Accordingly, investors make probability assessments of future price developments that are too strongly based on current price developments. For example, yearend levels of equity indices are forecasted based on recently achieved price levels and not on historically established return potential. If information is evaluated on the basis of the anchoring & adjustment bias, the investor’s risk appetite may be unjustifiably too high or too low, with the result that portfolio risk and return characteristics deteriorate. Anchoring with the return development of previous observation periods can also impair the correct information valuation of private equity investors. If the return on the last investment is used as the basis for future investment decisions, false expectations and a correspondingly incorrect risk assessment of the investment can be the result.  Strong anchoring on the economic states of countries or companies Investors can become entrenched in the economic situation of a country or a company and subsequently be “blinded” by the development experienced so far. In the 1980s, Japan was considered the strongest economy based on the assumption that economic conditions would continue for decades to come. Nokia was also considered a pioneer in the development of telecommunications technology. At present, in 2022, both Japan and Nokia will no longer be able to continue with past developments. The reasons for the lack of recovery in both Japan and Nokia’s case may be many and varied. For example, Japanese credit institutions were not consistently and sustainably restructured in the wake of collapsing property prices in the early 1990s. This continues to have an impact to this day, with the result that investment in both the private and public sectors is very low and deflationary tendencies continue to be a problem. Nokia has meanwhile given up its independence by being acquired through Microsoft. A misguided product policy as a result of unrecognized customer needs is regarded as the reason for this.  Conservatism in the course of processing new information This heuristic, which is discussed in more detail in chapter 8.1.2, can significantly influence the evaluation of new information in the course of the anchoring & adjustment bias. Accordingly, professional market participants (including research analysts) tend to adjust their forecasts to new information with a delay. They publish their corresponding assessment shortly before the quarterly figures of the companies they monitor are published. It would be reasonable to assume that analysts reassess their estimates as new information becomes available. Due to the delayed adjustment, many securities are subject to a socalled →Post-Earnings-Announcement Drift (see chapter 4.3.3). This results in further price gains in the case of positive reports and further losses in the case of negative reports. The reason for this phenomenon is that the analysts are so influenced by their earlier forecasts that they only adjust them to

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the new information with a delay. As a result, companies often present surprisingly good results in upswings, while in downturns the forecasts often cannot be met. The reason for the adjustment delay is seen in the fact that market participants derive future corporate profits predominantly from historical values and include information on the current profit situation of a company only gradually in the estimation of future profits (see Pompian, 2006, p. 78). The anchoring & adjustment bias shows its effect in that the anchor set is not sufficiently adjusted as soon as new information is processed. This heuristic can also directly cause the conservatism bias.

Representativeness Bias General description Psychological research has shown that the brain uses shortcuts to reduce the complexity of the information to be analyzed. These abbreviations help the brain to estimate the final result of a full analysis of all information before all information is actually processed. One of these shortcut mechanisms is the representativeness heuristic, which allows the brain to quickly organize and process the available amount of information. Within this process, however, systematic judgment distortions can also occur, in which an object is linked to a certain group of objects. This means that the brain assumes that objects with similar characteristics correspond to each other and therefore do not need to be further analyzed. Representativeness is therefore an assessment based on stereotypes, in which probabilities are formed and assessed on the basis of similarities (see Nofsinger, 2008, p. 63). Example 8.2: Representativeness Bias Consider the following example to illustrate representativeness: Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a student, she has also been intensively engaged in issues of social justice and discrimination and participated in anti-nuclear demonstrations. What do you think is the probability of the following claims about Linda? - Linda is a bank employee - Linda is a bank employee and active in the women’s movement With this experiment in 1983, Tversky/Kahneman came to the conclusion that about 90 percent of the test subjects considered the second statement about Linda more likely. In terms of the representativeness bias, the test subjects quickly developed a pattern that the second statement about Linda was considered more plausible (i.e., more representative) than the first. However, if one considers the assessment of the two answer options on the basis of probability theory, answer 1 has the higher probability than answer 2, since answer 2 is a subset of answer 1.

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Connected to the representativeness bias is the information source effect. Here the quality of information is estimated the higher, the more sources report about the same information. The market participant dispenses with a detailed examination of the quality of the information due to the multitude of sources which reinforces the believe in the correctness of the information85 (see Mazanek, 2006, p. 83). The representativeness bias can be used in different ways by market participants. Amos Tversky and Daniel Kahneman (1974) describe them on the basis of the violation of statistical principles (see Taffler 2010, pp. 260):  Neglecting basic characteristics (Base-Rate Neglect) In doing so, a market participant would compare an investment in company A with a pre-formulated, familiar and easy-to-understand scheme. In this way, the market participant would reduce the time-consuming analysis of the company and would therefore ignore the effects of unnoticed parameters (e.g., product development, sales markets, competitive situation, etc.). However, disregarding these parameters can have a lasting effect on the development of an investment.  Neglecting the population (Sample-Size Neglect) Market participants derive future developments from a small sample. However, they do not take into account the representativeness of the individual values considered. Scientists refer to this behavior as the “law of small numbers”. It can happen that market participants, when assessing the skills of a fund manager, only look at a few quarters and not at the totality of the data available to them. This phenomenon can be illustrated by the forecast development of two fictitious foreign exchange analysts. A private investor observes the USD forecasts of the two analysts for one week. Analyst 1 is correct on all five days, whereas analyst 2 has made false forecasts on each day. The market participant now concludes that analyst 1 will continue to be better than analyst 2 for the next week. The market participant deduces a causal relationship from a very short-term empirical correlation. However, this procedure is not only observable among private investors. According to research results by Shefrin (2000), even financial experts tend to use the representativeness bias (see Goldberg/ von Nitzsch, 2000, p. 79).  Overestimating probabilities In this variant of representativeness, objective probabilities are subjectively often assessed differently (→Probability Weighting). This phenomenon is also called conjunction fallacy. Market participants overlook the fact that the probability of two events occurring simultaneously can never be greater than the probability of each individual event. They evaluate a common event with a higher representativeness than the individual events would have on their own. This phenomenon can be illustrated by the following example: If the probability for rain the next day is 40 percent and the announced visit of the in-laws is 80

85

For example, also in connection with social media

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percent, the event “in-laws come in the rain” can never have a higher probability than 40 percent. Another phenomenon in connection with overestimated probabilities is conditional probability fallacy. This refers to the tendency of market participants to confuse condition and event at high conditional probabilities. This phenomenon can be explained by the assessment of crash risks. As is well known, October is considered a crash month on the stock markets, as the probability of crash in this month is high on basis of historical observations. During 1929 and 2021, 11 of 38 crashes happened in October (formally written: p(October | crash) = 11:38 = 28 percent). It is therefore not surprising that the month of October is often considered a crash month. However, this overestimates the conditional probability p(October | crash) because the crash happened only in 11 of the 93 Octobers in 1929 to 2021. Accordingly, the correct probability corresponds to approximately 12 percent (= 11:93) as opposed to 28 percent.  Overestimating empirical correlations Here, an apparent correlation of events is assumed. Market participants tend to recognize correlations where there are none. For example, extreme price increases on the Nasdaq Composite were explained by excellent prospects for the companies listed, which would justify high price gains. Market participants formed a spurious correlation between “IPO on the Nasdaq Composite” and “high growth rates of profits”, which had been reflected in strongly rising prices at the time of the dot-com speculative bubble. However, these price gains (especially the first day gains on the initial listing day) were not only observed for high-quality companies, but also for fundamentally weaker companies. This empirical assumption of a causality proved to be a fallacy in the light of the subsequent price drops once the bubble ended (see Mazanek, 2006, p. 82). Risk/return-damaging behavior of market participants  Information evaluation based on insufficient data volume Investors run the risk of misjudging the skills of a fund manager due to insufficient data sets. The probability of achieving the same good results as in the short-term past is estimated to be higher than is actually the case. Investors therefore run the risk of taking more risk than would be justified (see weighting function prospect theory chapter 6.2). The evaluation of the development of returns on the basis of an insufficient amount of data can also exist in the case of private equity investments. This would be the case if the limited partner, like the investor in public equity, made the investment decision on the basis of few or short-term investment results.  Information evaluation based on insufficient attention to sector development Investors often neglect basic characteristics of an industrial sector when evaluating an investment opportunity. If a company is included in a specific industrial sector, past developments in this sector are often projected across the board onto the company being valued. Market participants therefore run the

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risk of rejecting or accepting an investment without having carried out a fundamental analysis. Representativeness bias is a shortcut mechanism that allows the brain to organize the available amount of information and assess it via stereotypes. The market participant often misjudges probabilities on the basis of this heuristic.

Ambiguity Aversion Bias General description Ambiguity aversion means that the market participant is afraid of the unknown and therefore prefers the known to the unknown. Ambiguity aversion describes the uncertainty about the uncertainty. If information is classified as unknown, and the market participant is unable to accurately assess the degree of ignorance, ambiguity aversion occurs. The perceived ambiguity is greater the less the market participant believes to know about an issue, the more information he or she lacks and the more ambiguous the information is. The subjectively perceived lack of information influences in particular the assessment of the return expectations of unknown values. Ultimately, ambiguity aversion leads the market participant to avoid investments that may be lucrative, but cannot be assessed precisely (see Karlen, 2004, p. 28). This behavior can be explained using the weighting function from →Prospect Theory. In the context of a complete →Fundamental Analysis, investing in a particular company would lead to the formulation of an objective probability via the expected security price. Market participants who tend to be ambiguity averse would underestimate this objective probability due to not knowing the company and hence unjustifiably overestimate the risk associated with the potential investment. Example 8.3: Ambiguity Aversion Bias As already mentioned, ambiguity aversion is expressed in the assessment of expected returns. For example, a market participant intends to invest in Argentinian bonds. Whether the investment is actually taking place, depends not only on the knowledge of the Argentinian economy. If the market participant knows the most important influencing factors and is at the same time able to forecast respective return development, the sensation that everything is under control will rise and therefore the investment will be made. However, if the decisive influencing factors are not known, the impression of a lack of control grows. In this case, the market participant avoids the uncontrollable situation and will possibly buy a domestic bond instead. Ambiguity aversion as a result of lack of control means that market participants could be unjustifiably skeptical about an engagement in Argentinian bonds.

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Risk/return-damaging behavior of market participants Ambiguity aversion is one of the most damaging heuristics used by market participants according to the RRH index (with a score of 8). Retail investors in particular tend to use this heuristic.  Focus on low-risk investments, as perceived risks are unjustifiably high Investors with a tendency towards ambiguity aversion have higher return expectations for unfamiliar securities as the investment risk is considered higher. Consequently, there is a danger that the focus will be on low-risk securities investments, with corresponding detrimental effects on the ability to achieve investment targets.  Tendency to invest in domestic securities (home bias) Retail investors avoid investments in securities where they feel they do not have enough competence. In this case, they orientate themselves strongly towards domestic securities that seem familiar. This behavior is illustrated by the already presented home bias, in which investors focus on domestic capital markets/securities. This orientation suggests a higher degree of control to investors, making them unintentionally risk seeking. Overweighting domestic securities lowers the geographic diversification of a portfolio, which can significantly increase portfolio risk.  Tendency to availability bias when assessing investment risks Accordingly, investors tend to invest in securities, with which they are more familiar. For example, an investment in the shares of one’s own employer appears to be well justified and thus more profitable and less risky in the eyes of an employee than an investment in unknown/foreign investments. This investment approach often considerably reduces portfolio diversification and profitability, as it creates a substantial cluster risk along with human capital. The latter is well understood in the wake of the bankruptcy of Wirecard in 2020, when many employees held Wirecard shares for retirement and suffered painful losses as a result of their concentrated exposure to securities of their own employer.  Tendency for overconfidence The considerable damage to portfolios caused by ambiguity aversion is also evident if the investor believes to be competent in a certain investment area, type of company etc. One would expect that an investor with a tendency towards ambiguity aversion would not make any investments that is considered risky. However, this observation does not apply if the investor is presented with an initially unknown investment opportunity where certain competency is attributed to one’s abilities. In this case the investor is prepared to take even more risk. Ambiguity aversion represents the uncertainty over uncertainty. The market participant cannot correctly assess the objective probability of facts and therefore refrains from making potentially lucrative or diversificationenhancing investments.

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8.1.2 Misperception of information

Conservatism Bias General description Conservatism bias is the failure to adjust existing views or expectations when new information is received. As a result, new information tends to receive little attention leading to a delay in reacting to it. Market participants can also use the representativeness bias if the new information corresponds to existing views and show an overreaction. Otherwise, the market participant often reacts to the new data with conservatism. David Hirshleifer of Ohio State University notes that this heuristic is used because of the time-consuming processing of information. If information is presented in a way that is cognitively difficult to grasp, such as statistics or abstract information, then the information is less weighted or considered as less important. In contrast, easily processable information such as examples or certain scenarios/ “anecdotes” are overweighted (see Pompian, 2006, p. 119). In daily market behavior, conservatism can be observed by the well-known phenomenon of post-earnings-announcement drift (see chapter 4.3.3). Positive announcements lead to additional price gains and negative announcements to additional losses as previous conservative assessments are gradually adjusted. This phenomenon illustrates the possibility to generate an excess return by knowing all public information (see Shefrin, 2000, p. 96). Here it becomes apparent that market participants predominantly base expectations on historical values and only gradually incorporate new information.

Fig. 59: Sharp Estimates (grey bordered) vs. Default Estimates (all Broker Estimates ‒ Mean 25 Estimates) in the Broker Outlook Report; FactSet

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Delayed adjustment to new company information can be observed very well within the Broker Estimates report in FactSet. In figure 59 the difference between the average analyst estimate (Mean 25 Estimates), which includes all estimates of the last 100 days, and the Sharp Consensus86 (framed in grey: with 22 estimates) is clearly visible. The yearly EPS estimate excluding 3 brokers for not adjusting their estimates is -0.26 U.S. cents, vs. -0.29 including the 3 brokers. Risk/return-damaging behavior of market participants Conservatism can occur during information processing both among retail and institutional investors.  Tendency to slow down the processing of new information In contrast to “anchoring & adjustment”, market participants do not stick to predetermined price levels, but rather to a previously formed opinion about the company. If new information arrives that should lead to a revision of the previous opinion, market participants tend to stick to outdated assessments. Investors react only slowly to the revision of analysts’ recommendations and therefore run the risk of incurring unnecessary losses. Conservatism is the attitude to not adjust existing views or expectations when new information is received. New information tends to receive too little attention and is only priced into securities prices after a delay.

8.1.3 Misperception of objective reality

Mental Accounting General description Mental accounting is based on the research results of Richard Thaler87 and describes the tendency of market participants to group their assets according to certain categories (e.g., purpose, investment period) and to book them in mental accounts. In general, mental accounting is understood to mean the totality of all cognitive procedures used by market participants to organize, evaluate and control financial activities (see Thaler, 1999, pp. 183). The market participant sets up independent mental accounts for each decision, each fact or even for each investment. This behavior is expressed, for example, by 86

Sharp consensus figures are calculated by FactSet Estimates to provide a more accurate consensus than the default consensus figure, where opportunities to do so exist. Used in conjunction with the default consensus items, the new Sharp Consensus items can be excellent indicators for identifying and predicting earnings surprises. 87 Richard Thaler | American economist and Professor of Behavioral Science and Economics at the University of Chicago Booth School of Business; Nobel Prize in Economics 2017

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recording investment sums separately from one another, depending on the type of income or intended use (see Pompian, 2006, p. 171). Mental accounts are significantly influenced by two concepts: on the one hand by the value function from the →Prospect Theory and on the other hand by the →Framing Bias. The latter has a strong impact on the decision-making of the market participant with regard to the respective mental account. This can be illustrated by the following example. Example 8.4: Mental Accounting in the light of the framing bias Richard Thaler conducted an experiment with two groups. The following conditions were suggested to one group. The participants receive EUR 30 and can then decide whether to keep the money and leave the game, or to participate in a coin game with further chances of winning or losing. If they win, they will receive an additional EUR 9 to their initial entry. If they lose, 9 EUR will be deducted from the initial amount. 70 percent of the participants decided to play the additional game, as they considered the initial EUR 30 as an unexpected profit not yet booked as their own assets. The second group was presented with the same game with the following conditions. They were asked whether they wanted to participate in a coin game in which they would either receive EUR 39 if they had chosen the right side of the coins or EUR 21 if they had bet on the wrong side. If they do not want to participate in the coin game, they could keep the sum of EUR 30. The difference to the first group was therefore that the second group did not have an unexpected win available from the beginning. Rather, they were shown the possible final amount in case of a win or loss. Although both groups had the same chances of winning, only 34 percent of the second group wanted to participate in the coin game. The presentation of the game conditions has a lasting effect on the participation in the game. The participants of the first group behaved in accordance with the house money effect (see chapter 7.2.1/example 7.2) as if they were playing with the “money of the casino”. It is also evident that the participants booked the amounts of money at their disposal in different accounts. In addition to the representativeness bias, the behavior in the management of mental accounts is also influenced by prospect theory. Each of these accounts has an S-shaped value function with a corresponding reference point. Within the framework of prospect theory, mental accounting leads to different treatment of profits and losses. For example, market participants form two mental accounts, one for realized gains/losses and one for unrealized gains/losses. They now make decisions that confirm their initial investment decision (sell winning shares, hold on to losing shares) instead of reassessing the initial investment decision according to the given situation. Subsequently, the market participant makes limited rational decisions that are not geared to the future development of the investment, but rather to confirming the decision made earlier (see Hens/ Bachmann, 2008, p. 84).

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The maximisation of one’s own benefit (in the sense of the prospect theory value function) pursued here is referred to in psychology as hedonistic valuation (hedonic framing). Accordingly, market participants act in different ways depending on profits or losses for comprehensible reasons:  Profits are separated (segregation) This behavior allows the market participant to experience the joy of the positive result of his investment over a longer period of time. This is due to the decreasing sensitivity in the profit range of the value function (see Fig. 60). The higher the profit of an investment, the less profit growth is perceived. Consequently, the high sensitivity for initial profits is used to sell them quickly. In addition, the separation of individual profit positions maintains the feeling of confirmation for a longer period of time. In this case, the increased sensitivity is exploited for each individual position. Mental accounting on the basis of Prospect Theory

Due to the decreasing sensitivity in the profit area of the value function, market participants tend to consider buy and sell positions separately. Fig. 60: Mental accounting on the basis of Prospect Theory

 Losses are summed up (integration) The sale of a loss position leads to emotional reactions in addition to the closure of the mental account. To reduce the feeling of regret, loss positions are sold in a single transaction. The reason for this is the decreasing sensitivity of the market participant, which in this case goes hand in hand with increasing overall losses.  Smaller losses are combined with larger profits As a result, the loss is less noticeable, because a reduced profit is less painful than being confronted with losses.  Smaller profits are separated from larger losses As a result, smaller gains are more enjoyable than reducing a large loss by this smaller amount of appreciation. In marketing, the effects of mental accounting are often used to give consumers the feeling that they are getting a service “for free”. A flat rate offer is nothing

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more than that the consumer pays for the corresponding service (internet, fitness studio, all-inclusive hotel) in advance. Since several small payments are combined into a larger advance payment, the decoupling of the payment from the actual consumption reduces the perceived costs. Every time the consumer makes use of the services, there is a good feeling of getting something “for free”. However, if the consumer had to pay for each service individually, most likely less consumption would be the result, since each individual payment would make the costs more obvious. Risk/return-damaging behavior of market participants Mental accounting can affect the investment behavior of market participants, especially retail investors, in many ways (see Nofsinger, 2008, p. 48):  Neglecting correlations by considering investments and returns separately Mental accounting can tempt particularly retail investors to look at investments separately. Correlations between individual investments are ignored, which can increase the damage to the portfolio if cluster risks are not noticed. In addition, there is the danger that by focusing on the expected dividend payment, the investor may refrain from selling the securities investment and thereby increase the portfolio risk if the position in question is not regularly adjusted in line with the portfolio allocation.  Unconscious increase in portfolio risk when investing in employer shares The unconscious increase of the portfolio risk is especially given if the investor has the possibility to invest in the shares of his own employer. This poses the risk of the decreasing →Diversification Effect if the employee invests in the employer’s securities independently of other investments. The investment is not considered together with investments in other securities, but rather separately. And, as already described above, a considerable cluster risk can arise.  Tendency to stick with the Status Quo Effect Mental accounting also proves its portfolio-damaging effect when investors book transaction costs and income taxes in a separate mental account and do not want to sell sharply increased shares because of the increased sensitivity to these stand-alone costs. This risk was particularly prevalent in Germany, before the introduction of withholding tax (2009) when investors only had to wait for one year to avoid paying tax on capital gains. If investors delay the decision regarding the sale of securities, it can reduce the generated book profits again. The status quo effect as an immediate heuristic can also lead to the use of other heuristics such as →Disposition Effect and →Ambiguity Aversion.  Tendency towards the house money effect Mental accounting can also tempt both retail and institutional investors to the so-called house money effect (see chapter 7.1.1. Risk perception). Affected investors do not view the income from an investment with the same value as the invested capital itself. They use the returns for riskier investments and thus

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increase portfolio risk. This behavior is also based on the interpretation of objective probabilities according to →Probability Weighting. Accordingly, low probabilities are rated higher, with the consequence that the investor is exposed to increasing risk (see Pompian, 2006, p. 174). Mental accounting stands for the limited rational inclination of market participants to book their assets in mental accounts depending on certain categories. The recorded investment sums are valued independently of each other according to the Prospect Theory.

Recency Bias General description Recency bias describes the tendency of market participants to remember recent events better and to weight them more heavily than events that occurred further in the past. This heuristic plays a major role, for example, when market participants want to assess the performance of a fund manager over a certain period of time. If only a few quarters are used as the basis for assessing the fund manager, investors run the risk of being blinded by a short-term positive performance in the context of generally rising equity prices. The same applies to the assessment of future earnings prospects/loss risks for securities based on recent price developments. The effect of the recency bias can be illustrated using the following example. Example 8.5: Recency Bias At the beginning of 2020, a retail investor invests into securities of an emerging biotechnology company and a pharmaceutical manufacturer. Both companies are quoted at EUR 100 at the beginning of the year. In the course of the year, the biotechnology company’s price falls continuously to EUR 75. The price of the pharmaceutical company remains stable during the same year, but in the last two trading weeks of the year it unexpectedly falls to EUR 80. Confronted with the decision which of the securities to keep for the following year, the investor is influenced by the emotional pain caused through the paper losses incurred over the year. Although the biotechnology company’s securities have lost more value over the year, the investor may feel a stronger emotional pain from the sudden drop in the pharmaceutical company’s share price as it is still vivid in his memory. As a result, the investor is more negative about the future performance of the pharmaceutical company and may tend to sell the shares ‒ even though the biotechnology company lost more value over the year. This assessment is based on the curvature of the value function from the Prospect Theory. The sensitivity to unexpected losses and profits is very high near the reference point, which is the case for the pharmaceutical company. In the case of the biotechnology

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company whose share price is further away from the reference point, the sensitivity is lower because the market participant has become accustomed to this gradual development over the past few months. Risk/return-damaging behavior of market participants The recency bias is a heuristic that can especially influence the behavior of retail investors (see Pompian, 2006, p. 220):  Tendency to neglect fundamental valuation Retail investors run the risk of neglecting not only the fundamental valuation of the securities but also established valuation principles due to short/mediumterm price movements. They may succumb to the view that "this time is different" (see chapter 4.2) and to pro-cyclical behavior reinforcing the bubble formation on capital markets. Investors contribute to the risk/return detriment through this behavior, since they can suffer significant losses if valuations revert to fundamentally justified levels (mean reversion effect, see chapter 4.3.3).  Tendency to neglect appropriate portfolio diversification The recency bias can lead investors to increasingly invest in securities that are currently for a short period in “fashion” and thus attract attention. Through this one-sided concentration, investors jeopardise portfolio diversification, which, according to portfolio theory, should lead to a portfolio with a large number of securities with as negative or at least as low correlation as possible (see chapter 2.1.1).  Tendency to representativeness bias Retail investors may tend to project short-term developments onto future longterm developments. Market participants run the risk of entering an investment, e.g., after short-term, conspicuous price gains and thereby significantly increase the portfolio risk. In this heuristic, the fundamental valuation of the corresponding security is neglected, which takes into account long-term prospects. The recency bias describes the tendency of market participants to remember events they have recently experienced better and to weight them more highly than events that occurred further in the past. This behavior distorts the objective reality and the market participant makes his decision based on recently published data.

8.1.4 Misperception of one’s own abilities

Overconfidence Bias General description Overconfidence bias can be described as unjustified belief in one’s own cognitive abilities. Market participants overestimate their level of knowledge, underestimate risks and tend to have an exaggerated belief that they can control market move-

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ments. Their assessment that they have sufficient information leads them to make investment decisions that result in a deviation from the optimally diversified portfolio according to Markowitz. In addition, studies (see example 8.6) have shown that market participants prone to overconfidence act much more frequently due to the conviction that they are making correct forecasts, and thus significantly decrease portfolio return due to the associated transaction costs (see Nofsinger, 2008, p. 11). In Behavioral Finance there is a distinction between two types of overconfidence (see Pompian, 2006, p. 52):  Overconfidence in investment predictions (prediction overconfidence) This form of overconfidence of one’s own capabilities stands for excessively narrowly defined forecasts of the expected returns on an investment that is to be made. Market participants therefore run the risk of underestimating the risk of loss of their investments and thus unconsciously increasing the portfolio risk.  Overconfidence in judgments (certainty overconfidence) This type of overconfidence of one’s own capabilities alludes to the insufficient consideration of factors that threaten expected returns, which in retrospect turn an apparently promising investment into a loss-making one. Market participants subject to this type of overconfidence trade too often and hold underdiversified portfolios. Example 8.6: Overconfidence in relation to gender In order to assess overconfidence of market participants, a survey was conducted of 35,000 U.S. households that had a trading account with a large direct broker (Barber/Odean, 2001). The study focused on security transactions between 1991 and 1997, using the frequency of transactions to measure overconfidence. Assumption: The more often an account holder trades securities, the more convinced is the trader of own abilities. Overconfidence causes the highest level of trading activity among single men 85.0 73.0

51.0

53.0

Single 0-30 Women

Married 31-182 Women

Married 183-365 Men

Single >365 Men

Fig. 61: Annual portfolio turnover by gender and marital status in percent

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The results were broken down by gender and marital status of the households studied. The survey results showed that single men traded most. They sold 85 percent of their securities within a year and purchased other securities in return (portfolio turnover). In comparison, married men had a portfolio turnover of 73 percent. Single women achieved a portfolio turnover of 53 percent compared to 51 percent for married women. In this context, the results show that men, especially single men, have a higher overconfidence and therefore act much more than women (see Fig. 61). In addition, the relationship between portfolio turnover and portfolio profitability after deduction of transaction costs was examined among 78,000 households in the period from 1991 to 1996. Two hypotheses were to be tested within the scope of the study: - Does a market participant trade a lot because the right information is available and therefore beats the market respectively a buy-and-hold strategy? - Does a market participant trade a lot because of overconfidence and, due to the high trading volume and the resulting transaction costs, beats neither the market nor respectively a buy-and-hold strategy? To investigate these two hypotheses, the households in question were divided into five groups. Group 1 included the 20 percent of households that traded least (on average 2.4 percent p.a.). Group 2 contained the next 20 percent of households that traded second least. Finally, group 5 was formed with an average annual turnover of 250 percent ‒ i.e., the group members sold and rebuilt the entire portfolio once, sold and rebuilt it again and finally sold half of the second tranche and bought new securities instead. It became clear that the net performance (return less transaction costs) in the first group was 18.7 percent; in group 5, however, which had the highest securities account turnover, it was only 11.4 percent (see Fig. 62). The results of the study suggest that overconfidence leads to high trading volumes and significantly worsens portfolio return (see Nofsinger, 2008, p. 13). High portfolio turnover due to overconfidence significantly lowers portfolio return – in percent Portfolio return excluding transaction cost Portfolio Turnover per month

Fig. 62: Portfolio return after transaction costs in relation to portfolio turnover

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The different levels of trading activity of women and men is also based on the expectations of society, according to Briony D. Pulford and Andrew M. Colman from the University of Leicester. Women are more likely to be exposed to social pressure to underestimate themselves than men. This would offer a possible explanation for the lower trading activity of women (see Glaser/Weber, 2010, pp. 241). In addition to overconfidence based on gender-specific characteristics, scientific results (March/Shapira, 1987; Koehler/Brenner/Griffin, 2002) show clear overconfidence even with increasing expertise. Thus, managers estimate their probability of success to be highest when they consider themselves to be knowledgeable (see chapter 11.1). Risk/return-damaging behavior of market participants Overconfidence is a phenomenon that can be observed among the vast majority of market participants. In the following, the damaging consequences that affect both retail and institutional investors will be highlighted (see Pompian, 2006, p. 54):  Tendency to overestimate own analytical capabilities Investors who are subject to overconfidence overestimate their ability to assess risk/reward characteristics of potential investments. There is a risk that investors may lose sight of negative information, which, if taken into account, could prevent an investment.  Tendency to frequent action in the course of heuristics Investors with a tendency to overconfidence are much more likely to act based on the assumption of having specialist knowledge than other market participants with little or no overconfidence.  Danger of underestimating the risk of losses Retail investors in particular underestimate the risk of losses, since they pursue or compile far less long-term investment statistics than institutional investors do. Therefore, retail investors run the risk of holding under-diversified portfolios with a higher risk. Overestimation of one’s own capabilities is shown to be an unjustified belief in one’s own cogitative abilities. Market participants overestimate their level of knowledge, underestimate risks and tend to exaggerate the assumption that they can control market movements.

Illusion of Control Bias General description The illusion of control gives market participants the feeling that they are better able to forecast or control the markets than is actually the case. It is thus a direct consequence of overconfidence and occurs above all when the market participant

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has been successful in the market at times. It distorts the formation of expectations and distorts learning processes. Ellen Langer from Harvard University describes the illusion of control as “Expectancy of a personal probability inappropriately higher than the objective probability would warrant.” (Langer, 1975) According to Langer, the illusion of control is exacerbated in particular if the following characteristics are found in the decision-making process of the market participants:    

Active choice with regard to the securities to be selected Apparently successful forecast of past share price developments Greatest possible familiarity with the underlying facts or investment form Intensive absorption of information

In another experiment, it was shown that the independent selection of lottery numbers encourages the participant to bet far more than was the case when the participant was supposed to play based on automatically selected numbers. The participant feels a much higher degree of control over the possible effects of his decision when he or she has active decision-making power than without the decision-making power. The →Limited Rationality of this approach is demonstrated by the fact that the objective probability is the same in every case (see Pompian, 2006, p. 111). Example 8.7: Illusion of Control among retail investors The execution of transactions has changed considerably since the establishment of online trading via direct brokerage apps. Retail investors, especially the younger generation, have the opportunity to select and trade securities autonomously without professional advice. The ability to make one’s own selections is one of the characteristics that lead to illusion of control. The market participant perceives an apparent control of the final result through the mere selection of securities. Furthermore, the correct prediction of possible events plays a major role in the formation of control illusion. This becomes evident if we look at the retail investor triggered short squeezes among AMC or Gamestop in 2021. Traders gathered on social network platforms like reddit.com and engaged in a concentrated action to push certain companies’ share prices by several hundred percent. The success of these trading frenzies surely enforced the illusion of control among involved traders – especially the young generation who have never experienced a market crash. In addition, familiarity with the underlying facts can increase the illusion of control. Retail investors have become accustomed to online trading very quickly. They have very quickly developed knowledge of the functionalities of the trading platforms and thus gained confidence in dealing with online trading. Ultimately, the illusion of control on the part of retail investors is further enhanced by the possibility of obtaining information especially over social media

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(e.g., YouTube, Instagram, Facebook and many more). These sources emerged over the last 10 years and have a tremendous impact not only the individual trader rapidly consuming information but on the market itself as it results in unexpected market movements as seen in 2021 with the companies mentioned above. Basically, the illusion of control can be explained by the probability weighting function. Market participants get the sensation that they can forecast the markets correctly and therefore control them better than is objectively the case. Moreover, market participants assess probabilities as higher than objectively justified, with the consequence that their risk aversion in the profit area is reduced or their riskseeking behavior in the loss area is increased. Risk/return-damaging behavior of market participants Illusion of control has far-reaching consequences for the behavior of market participants. It affects retail and institutional investors as well as corporate leaders (see Pompian, 2006, p. 115):  Damage to portfolio diversification when “favored” investments are overweighted Retail investors tend to have a one-sided bias in their portfolios due to illusion of control. Companies that they seem to know well have a strong attraction. Popular in-demand stocks are often found in their portfolios, which is why there is a risk of substantial losses if the market situation suddenly changes.  Increased portfolio damage if limit orders are used injudiciously Retail investors run the risk of premature transactions if they use automatic limit functions when purchasing/selling securities. These functions create a false sense of illusion of control, as they will feel that they can correctly predict markets according to the limits set. Limit orders, however, may be detrimental to returns, as short-term price swings unintentionally trigger a buy or sell order (more likely sell orders in the form of stop-loss orders once a purchase has taken place).88  Tendency to overestimate oneself with frequent trading The illusion of control is closely related to overconfidence, as the investor sees his competence confirmed by the perceived control over the capital markets. However, overconfidence also increases trading activity, with a corresponding increase in portfolio damage. Illusion of control gives market participants the feeling that they can forecast the markets better or control them more than is actually the case. It is closely linked to and exacerbates the overestimation of one’s own capabilities.

88

The danger of such „price jumps“ has gained further importance due to the increasing spread of high-frequency trading strategies.

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8.2 Heuristics of emotional origin Heuristic Reflection Effect

Value RRH-Index

Underlying Misperception

3

 Objective Reality

Fig. 63: Heuristics of emotional origin in the context of information processing

Reflection Effect General description The reflection effect (see chapter 6.2.3) represents a change in the attitude of the market participant, which can change the risk assessment for a given securities exposure. This change of attitude is based on the transition from a relative profit position to a relative loss position or vice versa. In a profit position, the market participant behaves in a risk-averse manner in order not to lose the profits achieved. If, however, an investment develops unexpectedly badly turning into a loss position, the risk attitude of the market participant changes from risk-averse to risk-seeking. This change of attitude is based on the shape of the value function. Reversal of the risk appetite is also linked to the presentation effect. A change in risk attitude is therefore also possible if a certain form of presentation is chosen, which changes the market participant’s reference point. Example 8.8: Reversal of risk appetite Participants in an experiment that can be considered a “typical” Behavioral Finance experiment were given the following conditions of participation: In the first round they could decide whether they wanted to receive either EUR 500 safely or play a lottery with a 50 percent probability of winning EUR 1,000 or 50 percent nothing. The conduct of this game showed that the vast majority of participants preferred the safe EUR 500 over the uncertain EUR 1,000, due to the decreasing sensitivity with increasing distance from the reference point. As expected, they behaved risk-averse due to the concave curvature of the value function in the winning range. In the second round the game was played with reversed signs. The participants now had to decide whether they would accept a safe loss of EUR 500 or, with a 50 percent probability, lose EUR 1,000 or 50 percent nothing. Under these conditions, the willingness to take risks changed and the participants became more willing to take risks. They decided against the certain loss and instead took part in the game, in which they would either lose nothing at all or would have to pay an even higher amount (see Goldberg/von Nitzsch, 2000, p. 90).

8.3 Assessment of the risk/return-harmfulness of the heuristics considered

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Risk/return-damaging behavior of market participants Reflection effect is a bias that can affect many market participants in different constellations.  Change in risk attitude depending on profits or losses Market participants are strongly influenced by the profit and loss situation and change their risk attitude accordingly. They are risk-averse in a profit situation and risk-seeking in a loss situation. This adversely affects portfolio performance as no consistent risk measure can be specified to which a portfolio optimisation could be fit. The reflection effect leads to a change in attitude, with market participants becoming risk seeking when switching from profits to losses, but risk averse when switching from losses to profits.

8.3 Assessment of the risk/return-harmfulness of the heuristics considered Heuristic

Risk/return damaging behavior of market participants

 Anchoring & Adjustment  Type: Cognitive  RRH-Index: 5 4 + 1 for being cognitive

 Expectations  Strong attach-  Can lead to too closely ment to ecoConservatism aligned to renomic situain the course cent market tion of a of processing country/ new infordevelopments company mation

RRH-Index

 +1

 +1

 +2

Heuristic

Risk/return damaging behavior of market participants

 Representativeness  Type: Cognitive  RRH-Index: 3 2+1 (cognitive)

 Evaluation of  Evaluation of ininformation formation based based on insuf- on insufficient ficient data analysis of spesample (Samcific sector (Baseple-Size neglect) Rate Neglect)

RRH-Index Heuristic  Ambiguity Aversion  Type: Cognitive  RRH-Index: 8 7+1 (cognitive) RRH-Index

 +1

 +1

Risk/return damaging behavior of market participants  Focus on con-  Tendency to  Tendency to  Tendency to Home Bias by Availability Overconfidence servative ininvesting in Bias when as- through pervestments, as domestic se- sessing inceived compeperceived risks vestment risk tency in similar are rated higher curities investment area

 +1

 +2

 +2

 +2

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Heuristic

Risk/return damaging behavior of market participants

 Conservatism  Tendency to delayed pro Type: Cognitive cessing of new  RRH-Index: 2 information 1+1 (cognitive) RRH-Index

 +1

Heuristic

Risk/return damaging behavior of market participants  Neglect invest-  Tendency to  Tendency to  Tendency to ments that off- increase in Status-Quo-Ef- House Money set or correlate portfolio risk fect when an Effect through over- investment across acinvestment in performed counts/ employer portfolios well in the stocks past

 Mental Accounting  Type: Cognitiv  RRH-Index: 7 6+1 (cognitiv) RRH-Index

 +1

 +1

 +2

 +2

Heuristic

Risk/return damaging behavior of market participants

 Recency Bias  Type: Cognitiv  RRH-Index: 5 4+1 (cognitiv)

 Tendency to  Tendency to  Tendency to Representativeneglect funda- neglect optimal portfolio ness Bias mental valuation „This time asset allocation is different“

RRH-Index

 +1

 +1

 +2

Heuristic

Risk/return damaging behavior of market participants

 Over Confidence  Type: Cognitiv  RRH-Index: 4 3+1 (cognitiv)

 Tendency to  Tendency to  Tendency to trade excessi- underestimate overestimate own ability to vely downside risks evaluate a company

RRH-Index

 +1

Heuristic

 +1

Risk/return damaging behavior of market participants

 Illusion of Con-  Tendency to trol maintain underdiversified  Type: Cognitiv portfolios  RRH-Index: 5 4+1 (cognitiv) RRH-Index

 +1

 +1

 Tendency to  Tendency to use limit orOverconfidence ders as a false which leads to sense of conexcessive tradtrol ing  +1

 +2

8.3 Assessment of the risk/return-harmfulness of the heuristics considered

Heuristic

237

Risk/return damaging behavior of market participants

 Reflection  Change in risk Effect attitude de Type: Emotional pending on profits/losses  RRH-Index: 3 1+2 (emotional) RRH-Index

 +1

Summary Chapter 8 Information processing and evaluation is affected by the use of heuristics. Here, heuristics of cognitive origin play the main role. The heuristics can be classified based on the underlying misjudgment or misinterpretation of respective factors. Misjudgment of probabilities In the anchoring & adjustment bias the set anchor is not sufficiently adapted as soon as new information is processed. This heuristic also reinforces the conservatism effect. The representativeness bias is a shortcut mechanism that allows the brain to organize the available amount of information and to judge it by stereotypes. On the basis of this bias, the market participant estimates e.g., probabilities incorrectly. Ambiguity aversion is based on “uncertainty about uncertainty”. The market participant cannot correctly assess the objective probability of facts and therefore refrains from potentially lucrative or diversification-enhancing investments. Ambiguity aversion can lead directly to home bias, availability heuristics and overconfidence. Misinterpretation of information Conservatism as a heuristic of cognitive origin is the attitude not to adjust existing views or expectations when new information arrives. New information tends to be given too little attention and is only priced into securities prices after a delay. Misinterpretation of objective reality Mental accounting stands for the limited rational inclination of market participants to book their assets in mental accounts depending on certain categories. The recorded investment sums and investment decisions are evaluated separately from each other. Mental accounting leads directly to the status quo and house money effect. The status quo effect causes further indirect heuristics such as the disposition effect and the ambiguity aversion.

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The recency bias describes the tendency of market participants to remember recently experienced events better and to weight them higher than events that occurred further in the past. This behavior distorts the objective reality and the market participant makes his decision based on recently published data. The recency effect leads directly to the representativeness bias. Reflection effect leads to a change in attitude in which market participants become risk-seeking at the transition from profits to losses and risk-averse at the transition from losses to profits. Misjudgment of own abilities Overconfidence illustrates unjustified belief in one’s own cognitive abilities. Market participants overestimate their level of knowledge, underestimate risks and tend to have an exaggerated view that they can control market movements. Illusion of control gives market participants the feeling that they are better able to predict or control the markets than they actually are. It is closely linked to overconfidence and increases it further.

9

Limited rationality during decision-making In the ninth chapter, you will explore the third and final stage of the information and decision-making process. You will learn the essential heuristics used during the decision-making process and you will understand the limited rational behavior of the Homo Economicus Humanus.

Information Perception  Availability Bias

Information Processing

Decision-Making

 Risk Perception Bias

 Anchoring & Adjustment  Selective DecisionBias Making Bias

 Selective Perception

 Representativeness Bias

 Framing Bias

 Ambiguity Aversion Bias  Hindsight Bias

 Herding

 Conservatism Bias

 Endowment Bias

 Mental Accounting Bias

 Optimism Bias

 Recency Bias

 Dispositions Effect

 Overconfidence Bias

 Status-Quo Bias

 Illusion of Control Bias

 Self-Control Bias

 Reflection Effect

 Regret Aversion Bias

 Self-Attribution Bias

Fig. 64: Applied heuristics during decision-making

Following the numerous limited rational behavior patterns identified in the previous steps of the information and decision-making process, the heuristics during the actual decision-making will be examined below. Within the heuristics of cognitive origin, the selective decision-making is one of the heuristics that has quite a return-damaging effect on investments. Similar to selective perception during information perception, the market participant tries to reduce cognitive dissonance via selective decision-making. Another heuristic of cognitive origin is self-attribution. The market participant considers himself responsible for a realized profit. Losses, on the other hand, are attributed to external circumstances. Finally, the hindsight bias completes the heuristics of cognitive origin. Accordingly, market participants do not learn from their mistakes, but rather assure themselves that they have already anticipated the events in advance. In addition to the heuristics of cognitive origin, there are numerous heuristics of emotional origin in this phase of the information and decision-making process. They help to explain why market participants suddenly tend to panic in certain situations depending on their positioning. In that aspect, the endowment bias,

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which, among other things, causes market participants holding on to an investment for an unjustifiably long period of time. The optimism effect stands for the tendency of market participants to see market developments in an infinitely positive light. Another heuristic, the loss aversion, unfolds its return-damaging effect within the framework of the disposition effect. Market participants hold on to loser shares, but sell the winning shares too early. Loss aversion can also be reinforced by the status-quo bias in which market participants remain passive. Another bias with a detrimental effect on returns is the self-control bias. Finally, the regret aversion bias leads to sustained damage to returns, since market participants prefer not to make any decisions at all out of fear of making the wrong ones. 9.1

Heuristics of cognitive origin

Heuristic

Value RRH-Index

Underlying Misperception

Selective Decision-Making

5

 Objective Reality

Self-Attribution

7

 Own Abilities

Hindsight BIas

3

 Own Abilities

Fig. 65: Heuristics of cognitive origin during decision-making

9.1.1 Misperception of objective reality

Selective Decision-Making General description The selective decision-making bias is one of the two consequences for the need to remove cognitive dissonance (see chapter 6.1.3). Through selective perception, the market participant attempts register information that appears to confirm a decision taken. Selective decision-making on the other hand occurs when a decision taken involved high commitment. The decision-maker is determent to achieve the desired success, even under exorbitant economic costs. Thus, for example, companies often continue to invest in ongoing but loss-making projects so that previous investments are not in vain (see Goldberg/von Nitzsch, 2000, p. 128). This behavior is comparable to the →Sunk Cost Effect, which means that the selective decision-maker does not sell an investment in the loss area, but rather injects additional capital in order to complete the investment profitably at a later date. Example 9.1: Selective decision-making Two examples are given below to illustrate the impact of the selective decisionmaking bias (in particular the sunk cost effect).

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Both differ by two factors: firstly, the investment and, secondly, the duration of the decision made In case 1, a tourist purchases tickets for the four hills ski jumping tournament in Innsbruck/Austria for EUR 50. The tourist has been looking forward to this event for a long time. However, on the day of the tournament there is a heavy snowstorm at the tourist’s place of residence. Although the journey to the event still could take place, the efforts to invest would be considerable, which could spoil the real pleasure. Would the tourist rather go to the event if the tickets had been paid or if the tickets had been received as a gift? In case 2, the changed component is the duration of the decision. As in case 1, the tourist would like to go to the ski jumping event, which is to take place next week. On the day of the event there is a snowstorm at his place of residence. How would the tourist decide if the tickets had been bought 6 months ago and how would the tourist decide if they had bought the day before? Both cases are influenced by the →Mental Accounting. In case 1, a mental account was opened and charged with EUR 50. If the tourist does not go to the event, the account is closed without the corresponding pleasure of watching the ski tournament. So, in order to avoid the emotional burden of buying the tickets without attending the event, the tourist would therefore go to the event and make any additional efforts to be able to close the event positively after all. In case 2, the time component plays a decisive role. If the tickets have already been purchased six months ago, the emotional burden that would arise when closing the mental account without attending the ski jumping event is reduced in accordance with the distance to the time of purchase of the tickets. The negative influence of the sunk-cost effect, i.e., the investment of additional capital, thus decreases over time (see Nofsinger, 2008, p. 47). The selective decision-making can be described using the value function from the →Prospect Theory (see chapter 6.2). Corresponding to the convex curvature in the loss range, the sensitivity for increasing losses decreases. Accordingly, the market participant is unwilling to sell a position that has already incurred an unrealized loss. Rather, the market participant would, by making a selective decision, seek to make a subsequent purchase in order to lower the average entry price through the additional investment and thus move more quickly into the profit zone in the event of a recovery in share prices. This widespread practice by mostly retail investors is also known as “average down”. Risk/return-damaging behavior of market participants In practice, the selective decision-making is particularly often perceptible as:  Tendency to loss aversion in the loss area of the value function Investors tend not to realize paper losses, as this could be interpreted as an acknowledgement of an incorrect decision. Rather, affected investors hold on to investments that would no longer be bought if the decision were to be made today. This behavior is also referred to as →Disposition Effect.

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 Tendency to average down although investment is no longer advisable/ rational (sunk cost effect) The portfolio-damaging effect of the selective decision-making is reinforced by adding to existing positions, which under current circumstance would no longer be advisable to be held – e.g., average down on Wirecard positions in the course of price melt down after fraud scandal became evident. As a result, investors increase portfolio risks and tie up additional capital that could be invested in other assets. Subsequent investments of new assets as part of a savings plan (regular investment in securities), e.g., in a broadly diversified fund or exchange traded fund (ETF), is not regarded as limited rationality. In this case, regular purchasing helps to achieve in the long-term a balanced purchase price without short-term over-reactions. The selective decision-making has the objective to lead a previous decision, which was made under high commitment, to the desired success in any case. This heuristic strengthens the disposition effect and the sunk cost effect.

9.1.2 Misperception of own abilities

Self-attribution Bias General description Human behavior is strongly influenced by the fact that we learn about our abilities by observing the effects of our actions. Many people, and in this sense also market participants, have a tendency to overestimate their own responsibility for success. This behavior, known as self-attribution bias, illustrates how market participants attribute success to their own abilities, but blame other, external circumstances for failure. In the case of attributing success to own abilities, we speak about the selfattribution bias which is applied to internalise the factors leading to a success. On the other hand, if the responsibility for failure is rejected, the self-protecting bias is applied. Self-attribution thus reinforces the overconfidence bias of the market participant, which is why the return-damaging effect of the self-attribution bias is particularly pronounced. While the self-enhancing bias has a cognitive link, the self-protecting bias is based on an emotional background. The market participant tries to make himself actively responsible for success, but in the event of failure self-confidence should automatically be protected (see Pompian, 2006, pp. 105). Risk/return-damaging behavior of market participants Both retail and institutional investors run the risk of sustainably damaging their risk/return performance by applying this heuristic.

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 Tendency towards increased risks, since random successes are attributed to own ability Investors often take a higher risk when they attribute success to their own abilities instead of to the mostly random market movements.  Tendency to neglect recommended portfolio composition This is particularly the case when investors acquire securities of which they assume that they themselves are jointly responsible for the success of the company. Corporate leaders in particular tend to behave in this way.  Tendency to trade frequently within the framework of →Overconfidence Self-attribution reinforces the impression of successful investment behavior and thus leads to overconfidence. For this reason, trading activity also increases with the familiar effect on portfolio detriment (see chapter 8.1.4).  Tendency to →Selective Perception Apart of becoming overconfident, market participants are inclined to use selective perception as they tend to give more weight to information that confirms decisions. Self-attribution leads market participants to ascribe success to their own abilities, but blame other, external circumstances for failure.

Hindsight Bias General description Hindsight bias is the heuristic that causes market participants to retrospectively assess the probability of an event occurring as higher than they did before it occurred. An event is classified as foreseeable with hindsight. This heuristic is therefore also known as the “I-knew-it-all-along effect” (see Yazdipour/Howard, 2010, pp. 39). This heuristic is based on the fact that market participants assess their ability to determine probabilities of occurrence as being higher than they actually are. Consequently, market participants assume that they will be able to predict events more accurately in the future. In the end, market participants do not learn from their mistakes and lull themselves into a false sense of security when making investment decisions (see Pompian, 2006, p. 199). Example 9.2: Hindsight Bias The development of the global financial crisis began with the escalating U.S. real estate credit crisis. The first signs of the impending collapse of the mort-

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gage market were already visible in the summer of 2007. However, market participants did not begin to act until summer/autumn 2008, when the crisis fully unfolded and several banks were placed under government supervision. In retrospect, the emergence of this crisis is portrayed from various sides as inevitable. Moreover, reasons are listed which have caused the bubble to burst (including interest rate developments, regulatory conditions, behavior of the rating agencies). Many market participants now agree that this crisis had to come. It is surprising, however, that market participants invested aggressively in securitized mortgage products before the crisis escalated. Their propensity for selective perception, selective decision-making (in the case of losses that have already occurred) and conservative reaction to new information severely limited their ability to react in the run-up to the crisis. The hindsight bias in the global financial debt crisis aptly illustrates the effects of this heuristic. In addition to the unjustified belief in the predictability of the crisis, there is also the lack of insight into how such developments could be avoided in the future. Risk/return-damaging behavior of market participants  Tendency to rewrite history/their own memories and thus increase portfolio risk The hindsight bias can lead market participants (private as well as institutional investors) taking more risk than they would do without this limited rational behavior. Market participants begin to present events as predictable with hindsight. As a result of this behavior, they believe they have better forecasting capabilities in retrospect. Moreover, they do not learn from their mistakes, with the consequence that they are also inclined to act in a manner that is harmful to risk/return in the future. They do not want to admit to themselves that a possible herd instinct, for example, has influenced their decision-making.  Tendency to misinterpret the performance of fund managers Retail investors may unjustifiably praise or fault their investment advisors when funds perform well or poorly. They thus withdraw their investments too early or invest more if security prices have been rising for some time. This behavior reinforces the prevailing market movement both when prices are falling and when they are rising. The hindsight bias leads market participants to believe in retrospect that they had assessed the probability of an event occurring more highly than they actually did. Therefore, they do not learn from their mistakes.

9.2 Heuristics of emotional origin

9.2

245

Heuristics of emotional origin

Heuristic

Value RRH-Index

Endowment Bias

5

 Objective Reality

Optimism Bias

8

 Objective Reality

Disposition Effect

5

 Objective Reality

Status-Quo Bias

7

 Objective Reality

Self-Control Bias

4

 Own Abilities

Regret Aversion Bias

8

 Own Abilities

Underlying Misperception

Fig. 66: Heuristics of emotional origin during decision-making

9.2.1 Misperception of objective reality

Endowment Bias General description The endowment bias describes the tendency of market participants to value an asset more highly when they have acquired it than when they have not yet acquired it. This behavior differs from the assumptions of traditional economics in that market participants judge the investment not only by its intrinsic value, but also by its attachment or “habituation” to the investment. The question therefore arises as to why market participants add a premium to the selling price compared to the price they would pay if they were buying under the same circumstances. Rui Zhu, Xinlei Chen and Srabana Dasgupta (2008) investigated this question and showed through their experiments that market participants desire compensation for the pain of losing the item by adding a premium to the sales price. As a consequence, the pain and the required premium increase the more positive aspects the investor associates with the item in question. This behavior can be explained well by the value function from prospect theory. Market participants perceive the pain of selling as a loss. However, they are reluctant to realize the losses, which is why market participants expect additional compensation from the buyer. Furthermore, experiments (List, 2004; Nicolosi, Peng and Zhu, 2009) have shown that the endowment bias is less strong if the market participant has sufficient experience and is correspondingly less emotional (see Dowling/Lucey, 2010, pp. 313). Example 9.3: Endowment Bias The endowment bias can be illustrated very well by initial public offerings (IPO). According to scientists Loughran and Ritter (2002), this heuristic can be used to explain the phenomenon of “leaving money on the table”.

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This phenomenon describes the significant gains that are often observed on the first day of the offering. Particularly during the dot-com bubble, enormous gains were observed in the initial issue of shares. At the end of the first day of trading, the shares of the semiconductor manufacturer Infineon were trading at around EUR 85, even though the issue price was EUR 35. The researchers argue that market participants consider the risk of leaving possible additional gains on the “table” in the first days of the initial issue, or missing them if they exit early, to be higher than the transaction costs and taxes that would be incurred if the securities were sold immediately (if an allocation was received at the time of issue). The phenomenon can also be linked to the probability valuation function. If objectively low probabilities are subjectively rated higher by market participants, they are more willing to take a risk for price gains ‒ in other words, they remain invested for the time being in the hope of making additional gains during the first few trading days. Behavioral finance addresses the IPO of securities not only in the context of the endowment bias. Chapter 11.3 takes a closer look at three phenomena that occur in the context of IPOs. These are the undervaluation of the issue price as a further explanatory approach to justify significant subscription gains, temporary IPO booms that additionally attract other companies, and the long-term negative performance following the IPOs. Risk/return-damaging behavior of market participants  Tendency to increase portfolio risks by holding on to “inherited” investments Market participants run the risk of incurring significant losses if they do not sell inherited securities or securities of the employer in a timely manner. An example of this is the considerable risk that can arise if, for example, pensioners, after leaving a company, do not want to sell the company shares they have acquired so far because of emotional ties. For example, in the case of the interim insolvency of General Motors in early 2009, affected investors suffered substantial or total losses as is the case with Wirecard.  Tendency to the →Status Quo Effect Market participants also avoid selling securities because they want to avoid or delay the costs of the transaction and the tax payments on the profits. As a result, necessary portfolio adjustments may not take place at all or too late. The endowment bias describes the tendency of market participants to estimate the value of their investment higher when they have acquired it than when they have not yet acquired the investment. They are reluctant to sell securities and thus risk a corresponding reduction in portfolio stability.

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Optimism Bias General description The optimism bias describes the behavior of market participants to assess positive market developments as more likely than negative ones. They assume that negative market events can only affect other market participants. It also shows that market participants consider themselves to be better informed, which is why they overestimate their capabilities. This behavior can be observed particularly among corporate leaders (see Gider/Hackbarth, 2010, p. 392 ff). Daniel Kahneman and Dan Lovallo attribute the optimism bias to the “inside view” of market participants. In the inside view, limited rational market participants focus on the current situation and put their own investment participation in the foreground. In contrast, market participants who follow the “outside view” compare the current situation with past market developments and are therefore better able to make an accurate forecast of expected developments (see Harvard Business Review, 2003). Example 9.4: Optimism bias Market participants who show a tendency towards the optimism bias run the risk of investing more in their employer’s securities. At the end of 2000, 62 percent of the pension funds of Enron employees were invested in Enron. This suggests that Enron’s former 22,000 employees were extremely optimistic about the future development of the company. Enron, once the seventh largest company in the USA, filed for bankruptcy on December 2, 2001 in the wake of a balance sheet manipulation scandal. In the U.S. in particular, companies are offering their employees the opportunity to invest their pension savings in their own shares. Procter & Gamble employees even invest up to 95 percent of their pension funds in their own company and thus run the risk of investing too one-sidedly. This approach may be understandable if investors do not have sufficient knowledge of other investment opportunities. In this case, investing in the employer’s shares appears to them to be a convenient alternative to other investment opportunities unknown to them. In addition to the optimism bias, however, the →Home Bias (see Fig. 53 in chapter 7.1.1) may also be responsible for the increased investment in companies in the home country (see Pompian, 2006, p. 165). Risk/return-damaging behavior of market participants The optimism bias particularly affects retail investors. In the area of private equity investments, the general partners may have a tendency towards the optimism effect.  Tendency to increased risks due to focus on employer’s shares Investors assume that it is safer to buy the employer’s shares, since they know the company well and therefore do not expect any negative surprises. This way

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of thinking proves to be very dangerous and leads to a risk concentration in the portfolio (cluster risk).  Tendency to overestimate oneself Investors think that they will achieve above-average investment results in contrast to other investors. In doing so, market participants often trade excessively, with a corresponding increase in portfolio damage solely on the basis of transaction costs.  Tendency towards home bias The optimism bias can also cause home bias, as investors are too optimistic about companies from their own home country.  Tendency towards increased risks, through focus on positive reporting Retail investors also tend to rely too much on positive reports and invest accordingly. The distorted view of the development of returns increases the willingness to take risks. The optimism bias characterizes the behavior of market participants to assess positive market developments as more likely than negative ones. This heuristic also leads to overestimation of one’s own capabilities and to investments in geographically or otherwise familiar areas.

Disposition Effect General description The disposition effect was identified in 1979 by Daniel Kahneman and Amos Tversky as part of the Prospect Theory. It was derived from the desire of market participants to avoid losses and to realize gains quickly. Hersh Shefrin and Meir Statman came in addition to the conclusion that the attempt to avoid losses and the associated regret and to seek “fame” through profits affects the investment behavior of market participants significantly. This psychological behavior is the basis for the disposition effect. It also occurs in practice in the form of the →Loss Aversion. Shefrin and Statman (1985) developed a conceptual framework based on the four causes of the disposition effect (being Prospect Theory, Mental Accounting, Regret Aversion and Self-Control) (see Kaustia, 2010, pp. 171).  Prospect Theory ‒ According to the preferences of the Prospect Theory, the market participant behaves in a risk-averse manner as soon as relative profits arise and, in a risk-seeking manner when an investment slips into loss. Gains are therefore realized early on, while losing positions are held because the market participant is now risk seeking. The following points can be mentioned as characteristic for the valuation of securities along the value function: −

In principle, the market participant shows varying degrees of sensitivity along the value function

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Near the reference point it is very high, but flattens out again with increasing distance. This finding is based on the curvature of the value function (see chapter 6.2). Accordingly, the right curvature in the profit range leads to a premature sale of securities. In the loss area, the flattening left curvature ensures that the existing paper loss is maintained, with the hope of being able to make up for it when prices rise. Contrary to expectations, risk aversion can also rise abruptly in the loss area of the value function (see Fig. 67). This is the case when the market participant is confronted with extremely high losses. Sensitivity rises again and the market participant sells the securities to relieve the ongoing psychological pain. −

Market participants change their risk attitude as soon as a position changes from a relative gain to a relative loss This effect, also known as “reversal of risk appetite”, is due to the curvature of the value function according to the Prospect Theory. According to the →Reflection Effect, the market participant behaves risk-averse in the profit area, but is willing to take risks when switching to the loss area.

−

The choice of the reference point can be influenced by the framing bias For example, statements by research analysts regarding target prices, interpretation of economic data by investment advisors, etc.

Increase in sensitivity to extreme losses based on the value function

Fig. 67: Increasing risk aversion in the loss range with extreme characteristics

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 →Mental Accounting ‒ As described above, mental accounting leads to the creation of mental accounts, which record financial resources either by origin or by use. The limited rational behavior is expressed in the fact that the market participant looks at each individual position on its own and, according to the hedonistic valuation (see chapter 8.1.3), tends partly to the →Segregation or →Integration of losses and profits.  →Regret Aversion ‒ According to this heuristic, the market participant regrets the investment in an asset if it is sold at a loss and an error must be admitted. The regret aversion thus also increases the disposition effect.  →Self-control – According to this heuristic, the market participant tends not to stick to predetermined price levels at which they would sell underperforming positions in order to protect their investment capital from further losses. Example 9.5: Loss aversion To illustrate loss aversion, it is advisable to take another look at the example of selective perception mentioned above in chapter 6.1. In the example, a market participant, after extensive research in the press and in the media, acquired 100 ordinary shares of a credit card transaction processor. Although the losses incurred following the investment triggered cognitive dissonance, the market participant could not make the decision to sell the company and realize the losses. Due to loss aversion, the market participant remained with this investment in the hope that the initial loss of 25 percent would soon be made up again. However, the hopes of the investor were not confirmed in the subsequent period, and the loss increased up to 50 percent of the investment. The market participant then decided to view the investment as a long-term investment, helping to partially reduce the cognitive dissonance that had arisen due to the paper loss. The result, however, was that the detrimental effects of the loss aversion proved to be true. The investor accepts the paper loss and thus refrains from a potentially more profitable new investment if remaining investment value had been secured and reallocated elsewhere. Risk/return-damaging behavior of market participants The loss aversion occurs with both private and institutional investors.  Tendency to hold on to underperforming and to prematurely sell outperforming investments Fig. 68 shows the average holding period of securities in the portfolios of investors analyzed in a study by UBS (2008). Falling securities remain in the portfolio for an average of 124 days before being sold, while rising securities are sold after 104 days on average. The disposition effect illustrates that for psychological reasons investors evaluate their investments depending on whether their value has risen or fallen after purchase (in relation to the subjective reference point). Consequently, losses are “let run”, while gains are “constrained” by the early sale (see Goldberg/von Nitzsch, 2000, p. 93). This behavior significantly increases the portfolio risk of investors and reduces the expected return.

9.2 Heuristics of emotional origin

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Average holding period in days

Holding period of losing stocks on average 3 weeks longer than of winning shares

Fig. 68: Average holding period of loser and winner investments (UBS Wealth Management Research, 2008)

 Tendency to frequently check portfolio development and therefore trading too frequently If investors with a tendency to loss aversion check their portfolio development too frequently, they may be tempted to trade frequently as well. This shortterm oriented investment behavior corresponds to the Myopic Loss Aversion described by Shlomo Benartzi and Richard Thaler. The term “myopic” stands for myopia in the investment horizon of the market participant (see Pompian, 2006, p. 243). If market participants review the securities in which they have invested too often and these securities are also highly volatile, then market participants are more often confronted with a potential loss than with gains, which may only be realized over a longer time horizon. The damaging effect of the disposition effect also occurs after the sale of outperforming securities. Fig. 69 shows the development of winning securities one year after their sale. It becomes clear that the average performance in the twelve months following the sale is considerably higher than that of the underperforming securities remaining in the portfolio. Thus, the outperforming securities no longer in the portfolio increased by a further 11.6 percent on average, while the underperforming securities still in the portfolio increased by only 5 percent on average in the following months. The following example further illustrates the price distorting effect of the disposition effect on the capital markets. Example 9.6: Price formation under the influence of the disposition effect The disposition effect can lead to deviations from the fundamental value of the securities. If many market participants have had unrealized gains on a particular security, they may tend to sell the security after positive news to realize their gains. As a result, the price of the security will fall, although good fundamentals are more likely to lead to rising prices.

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Performance of investments within 12 months after liquidation

Winning securities sold too early developed in the following period twice as good as losing securities

Fig. 69: Performance of losing and winning investments (Source: UBS Wealth Management Research, 2008)

On the other hand, a security that is already characterized by falling notations is no longer sold by market participants, since losses are not realized in accordance with the disposition effect. Consequently, the price of the security may be above its justified fundamental value. Mark Grinblatt and Bing Han (2002) have found that former winning securities tend to be undervalued on the short to mid-term, while former losing securities tend to be overvalued.  Tendency to panic in the event of extreme losses The risk attitude of investors may change as an unrealized loss position increases. The investor initially becomes risk seeking in the loss range. However, when extremely high losses are reached, the investor may become risk averse again leading to the abrupt liquidation of the position, in order to limit the existential threat and the feeling of remorse about a wrong decision (see Goldberg/von Nitzsch, 2000, p. 98). The disposition effect has been investigated in numerous empirical studies. Terrance Odean (1998) confirmed the effect for the U.S. equity markets, Zur Shapira and Itzhak Venezia (2001) have found evidence for the effect on the Israeli stock market. Many other scientists have also been able to find evidence the disposition effect independently of the stock exchange (see Hens/Bachmann, 2008, p. 84). The disposition effect increases with increasing self-commitment to the investment made. It can be seen in the slope of the value function in the loss range, which is significantly greater in absolute terms than in the profit range.

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Status-quo Bias General description Comparable to the endowment bias, the status-quo bias proves to be a heuristic that also tends to make the market participant adopt a passive attitude. Research into the endowment bias therefore provided a large part of the theoretical knowledge about the status-quo bias, which was mainly investigated by William Samuelson and Richard Zeckhauser (1988). Within the framework of this heuristic, market participants leave the composition of their portfolios unchanged, although an adjustment of the individual weights would be necessary as a result of market developments. This behavior can intensify the loss aversion of market participants if they hold on to increasing paper losses. The reason for loss aversion can be seen, as already described in chapter 6.2, in the double value of losses versus gains on the value function. For this reason, market participants hold on to losses instead of selling the securities in a timely manner. It has been shown that this heuristic is used in particular by market participants who prefer that given circumstances remain relatively unchanged. The status-quo bias is often used by the insurance industry to maintain the holding period of an insurance product by the policyholders for a longer period of time (see example 9.7 status-quo bias). The status-quo bias increases in particular the more alternatives are offered for selection ‒ this is also the case in the fund industry, where the investor tends to stay with an already selected product if he is offered a confusing number of alternatives (see Dowling/Lucey, 2010, pp. 313). Example 9.7: Status-quo bias In the early 1990s, New Jersey and Pennsylvania reformed the terms and conditions of their motor insurance policies. Two types of insurances were offered ‒ a more expensive insurance with additional benefits and a cheaper insurance without additional benefits. Policyholders were initially assigned one of the two types of insurance automatically, but had the option to change the insurance they had been initially assigned. As one can assume from the way the status-quo bias works, the majority of policyholders have remained in their assigned insurance. In fact, 70 percent of policyholders in New Jersey remained in the more expensive insurance. In Pennsylvania, 80 percent of policyholders remained in the cheaper insurance allocated to them (see Pompian, 2006, p. 248). This example shows very impressively how a heuristic can be used to increase product sales. Chapter 10 focuses on how heuristics can be used or dealt with in investment consulting to improve the quality of advice.

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Risk/return-damaging behavior of market participants  Tendency to increase portfolio damage through inactivity This leads to a deterioration in the risk/return ratio, as necessary adjustments to individual weights are not made, and an investment is retained to defer the payment of transaction costs or taxes.  Tendency to loss aversion (→Disposition Effect) Retail investors run the risk of holding on to investments that already show a paper loss. The status-quo bias goes hand in hand with the loss version, where investments with unrealized losses are not sold.  Tendency to →Ambiguity Aversion Retail investors can increasingly tend towards ambiguity aversion through this heuristic. As a result, they invest more heavily in investments that they believe they know. Since market participants can identify more easily with known investments, they will stick with them even if the investments only generate low returns. The status-quo bias means that market participants leave the composition of their portfolios unchanged, even though individual weightings would have to be adjusted in line with market changes. It reinforces the loss aversion, the ambiguity heuristic and also the endowment bias.

9.2.2 Misperception of one’s own abilities

Self-control Bias General description The self-control bias, identified by Richard Thaler and Hersh Shefrin, 1981, represents the weakness of market participants in not always consistently and without disruption pursuing an investment goal such as retirement planning. Market participants may need self-discipline and external support in order to achieve set goals. The self-control bias also illustrates the tendency of market participants to prefer investments that generate a dividend. In this way, they can resist the temptation to sell shares in order to satisfy their consumption needs. Dividend payments are appreciated by many market participants because they provide them with a continuous source of liquidity without having to use up their investment capital (see Ben-David, 2010, pp. 442). The self-control bias can best be illustrated by the life-cycle hypothesis. This is a theory of rational behavior that focuses on the savings activities of market participants in the individual life cycles. The basis of the life-cycle hypothesis is the decision to divide income between consumer spending and saving. The investment or saving represents future consumption at the expense of current consumption. The main motivation for a continuous and disciplined savings behavior is that market participants must expect a significant loss of income with retirement,

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which is to be compensated by the savings efforts. According to the life-cycle hypothesis of Franco Modigliani and Richard Brumberg (1954), the savings behavior of market participants is determined by two characteristics: [1] Most market participants prefer a higher standard of living over a lower one, i.e., they want to maximise their current consumption expenditures. [2] Most market participants prefer a relatively constant standard of living over their entire life course. They do not like fluctuations. Based on these findings, Hersh Shefrin and Richard Thaler developed the Behavioral Life-Cycle Theory (1998). This is a descriptive model which places the self-control of market participants in their saving efforts at the centre. By placing the emphasis on self-control, it is intended to prevent market participants from consuming their possible future income or assets today, at the expense of future consumption. The assumption of the model is based on the fact that, in contrast to the neoclassical view, market participants regard their asset positions (regular income vs. assets) as not interchangeable and divide them into three different →Mental Accounts ‒ current income, current assets and future income ‒ depending on the type and amount of income and the frequency of any cash flow. While mental accounts are usually set up anew for each problem, in Behavioral Life-Cycle Theory they are permanent structures. The division of payment flows into different mental accounts is intended to increase self-control. The temptation to consume is assumed to be greatest in current income and least in future income. Empirical studies have resulted in valuable findings that could only be explained by Behavioral Life-Cycle Theory. It was found that market participants have a much stronger preference for dividend payments at retirement age. The recurrent dividend payments are intended to secure current consumption from the “current income” account, whereby current assets as an investment sum should not be affected due to benefit deductions (see chapter 11.2). The benefit deduction stands for the mental effort that would have to be expended to violate behavior considered reasonable (see Pompian, 2006, p. 151). Example 9.8: Self-control bias Richard Thaler and Shlomo Benartzi developed the “Save More Tomorrow Program, 1988 (SMTP)” to help employees build a continuous and sustainable savings plan.89 The program is based on the following four pillars: 1. Employees are asked to increase their contributions long before the payment of the increased contributions begins. 2. Program participants’ contributions are automatically increased as soon as they start the program.

89 See

also chapter 12.

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3. Program participants’ contributions are automatically increased according to an agreed plan until the agreed contribution ceiling is reached. 4. Program participants may withdraw from the program at any time. The participation rates of employees in the SMTP can be impressively illustrated by comparing the participation rates with a conventional retirement benefit program. Prior to the introduction of SMTP, employee participation in the conventional company pension scheme was very low. The employer offered 315 employees the opportunity to consult with a pension consultant. 286 of the 315 agreed to this consultation. The pension consultant calculated the individual increase in the current savings rate for each employee. If an employee was not prepared to increase the savings rate significantly, it should be increased by a maximum of 5 percent. Only 28 percent of the 286 employees agreed to the savings plan on the basis of the consultations. The remaining 72 percent (162 employees) were now offered SMTP. This differed from the conventional pension plan in that the savings rate was not increased by 5 percent but by 3 percentage points p.a. In addition, the increase would only begin with the next pay rise and would gradually increase by a further 3 percentage points with each pay rise. Although the rate of increase was much more aggressive in this case, 78 percent of the remaining 162 employees agreed to it.90 The majority of the 162 employees remained in the program when, as agreed with the next wage increase, the previously set increase in the savings rate was activated. Only 4 employees left before the second pay rise and 29 before the third pay rise. The participation rate shows impressively that the vast majority of approximately 80 percent of the 162 employees remained in the program after three wage increases and the associated 9 percentage point increase in the savings rate. The employees who left the program did not reduce their payments, they simply did not participate in future increases in the savings rate (see Pompian, 2006, p. 153). This example illustrates how the lack of self-control among market participants can be improved by taking behavioral science findings into account. Risk/return-harmful behavior of market participants  Tendency to consume at the expense of future savings targets Market participants, especially private investors, run the risk of consuming too much at the present moment at the expense of future savings targets. The weakness of market participants to save in a disciplined manner may jeopardise the objective of a well-thought-out pension plan. If a market participant with weak90

To illustrate the increase of the savings rate by x percent vs. x percentage points: the current savings rate is 10 percent of disposable income. If it is increased by 5 percent, as in the example, the savings rate rises to 10.5 percent. If, on the other hand, the savings rate is increased by 3 percentage points, the savings rate rises to 13 percent.

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ness for self-control is about to retire, there is a danger that the risk appetite will increase sharply in order to achieve the necessary return after all.  Tendency to neglect recommended portfolio diversification Due to the self-control effect, private investors can build up insufficiently diversified portfolios and violate fundamental investment principles (including the compound interest effect). Some investors prefer investments that generate returns for consumption during the year. They overload their portfolios with interest-generating investments such as bonds and run the risk of jeopardising their real investment level due to inflation. The self-control bias stands for the weakness of the market participant in not always pursuing an investment goal such as old-age provision consistently and without interruption. It leads to unbalanced portfolio composition and disregard for fundamental investment principles such as the compound interest effect.

Regret Aversion Bias General description This anomaly in investment behavior represents the market participant’s endeavour to avoid making any wrong decisions that one might regret afterwards. This distortion can occur, on the one hand, if the market participant made a wrong decision and, on the other hand, if retrospectively the right decision was not made. In addition, it has been found that the regret aversion bias can gain strength with increasing observation by third parties (Shefrin/Statman, 1985). This regret highlights the responsibility for the losses incurred on the one hand or the profits missed on the other, if the investor is too hesitant to make an investment. Even Markowitz could not escape the constraints of the emotional consequences of investment decisions and invested his contributions in private pension provision subject to the regret aversion: “I should have computed the historical co-variances of the asset classes and drawn an efficient frontier. Instead, I visualized my grief if the stock market went way up and I wasn’t in it – or if it went way down and I was completely in it. My intention was to minimize my future regret. So I split my contributions 50/50 between bonds and equities.” (Markowitz, quoted after Jason Zweig, 1997) In order to avoid the regret of having missed out on rising prices, investments with dividend payments, for example, are favoured in order to finance the consumption wishes with the distributions and to be able to keep the investment (see Shefrin, 2000, p. 31). The effects of the regret aversion are also evident in terms of mental accounting. As a rule, the market participant keeps “cash-effective” accounts in which the profits and losses are booked. Occasionally, however, “non-cash” mental accounts

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are also kept, which register the fictitious payments that would have resulted if a certain decision (e.g., the early sale of an investment) had not been made.91 Example 9.9: Regret aversion bias To illustrate the regret aversion, we will use the behavior of the following market participant. A market participant decides to invest into Apple after intensive observation of the media coverage. As a result of good quarterly figures, the shares increase by 20 percent within a short time. After a while, however, it becomes apparent that the share price is stagnating at a high level, raising fears of an imminent price correction. The market participant therefore decides to sell the shares after they have already fallen by 5 percent. The expectation seems to be confirmed shortly after the sale, as the share price actually continues to fall. To this effect, the perception of the investor changes directly, with the consequence that now only negative news are taken into consideration to further confirm the previous decision to sell the securities (selective perception). Likewise, the market participant feels a deep inner satisfaction, since he/she sold the securities in the presence of third parties and was able to present the decision accordingly as his/her skill. Unexpectedly however, the share price begins to rise again. The regret is limited until the selling price is reached, but once it is exceeded, the regret grows stronger. The market participant has already opened a new “non-cash” mental account in which the “missed” profits are now booked. Risk/return-damaging behavior of market participants In practice, the example shown above is a very common pattern of behavior.  Increase in portfolio impairment due to a tendency towards low-risk investments Regret aversion can increase the risk aversion of market participants in their investment decisions. This is particularly the result of past losses that have made the risks of the investments clear to market participants. In the long term, this behavior leads to a limited portfolio return due to low-risk investments.  Increase in portfolio impairment due to inactivity with extreme gains →Status Quo Bias Portfolio impairment can also be increased if the investor holds on to “winning shares” for an excessive period of time without periodically analyzing the financial situation and earnings perspectives of the company. In this case, according to the market anomalies analyzed in chapter 4.3.3 (winner-loser-effect), the winning stocks could give up their profits in the long run if internal/external events affect the corporate development (e.g., bankruptcy of General Motors in 2009) On the other hand and taking Apple as an example, holding shares 91 This

can also happen in the course of the shift of reference points.

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over a very long time of companies with a healthy economic situation can be very profitable.  Tendency towards loss aversion →Disposition Effect Affected investors try to suppress a wrong investment decision. For this reason, they tend to hold on to losing shares because they do not want to admit to the wrong decision. The risk-damaging effect of this behavior has already been described in detail above.  Tendency to →Herding Regret aversion can also lead to herd behavior when investors seek the protection of other market participants’ opinions in order to make and execute an investment decision. By paying attention to the behavior of the masses, investors try to limit future regret, because in the case of a wrong decision not only they alone have made the incorrect decision. The resulting increase in portfolio risk is easily understandable in view of the bursting of the dotcom speculative bubble.  Tendency to →Cognitive Dissonance With regret aversion, investors evaluate not only how much profit they lose due to a premature exit, but also how much loss they might have saved because they have sold an investment in time. The prerequisite for keeping a non-cash account is therefore that the market participant can understand to what success or failure the other, unselected alternative would have led. Thus, a loss is replaced by the lost profit, while the relative profit is replaced by the saved loss. A cognitive dissonance now arises when the market participant has made a decision against an alternative which would have resulted in a profit afterwards. The intensity of regret aversion depends on the intensity of the voluntary commitment (see Goldberg/von Nitzsch, 2000, p. 147). The anomaly of regret aversion bias in investment behavior stands for the market participant’s endeavor to avoid making any wrong decisions that one might regret afterwards. This heuristic also causes conservatism and herding.

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9.3

Assessment of the risk-/return-harmfulness of the considered heuristics

Heuristic

Risk/return damaging behavior of market participants

 Selective Deci-  Tendency to  Tendency to Sunk-Cost EfLoss Aversion sion-Making fect in negative  Type: Cognitive area of value  RRH-Index: 5 4+(1 for being cog- function nitive) RRH-Index

Heuristic

 +2

 +2

Risk/return damaging behavior of market participants

 Self-Attribution  Tendency to  Tendency to  Tendency to Bias Overconfidence neglect recom- trade excesmended port- sively leading to  Type: Cognitive increased risk folio composi RRH-Index: 7 tion taking 6+1 (cognitive)  +1

 Tendency to Selective Perception

RRH-Index

 +2

Heuristic

Risk/return damaging behavior of market participants

 Hindsight Bias  Type: Cognitive  RRH-Index: 3 2+1 (cognitive)

 Tendency to  Tendency to excessive risk misinterpret taking when the perforinvestor remance of a writes own fund manmemories ager

RRH-Index

 +1

Heuristic

Risk/return damaging behavior of market participants

 Endowment Bias  Type: Emotional  RRH-Index: 5 3+2 (emotional)

 Tendency to  Tendency to increase risks Status-Quo by holding on Effect to inherited assets

RRH-Index

 +1

 +1

 +2

 +1

 +2

9.2 Heuristics of emotional origin

Heuristic

261

Risk/return damaging behavior of market participants

 Optimism Effect  Neglect invest-  Tendency to  Tendency to  Tendency to ments that off- increase in Status-Quo-Ef- House Money  Type: Emotional portfolio risk set or correlate fect when an Effect  RRH-Index: 8 through over- investment across ac6+2 (emotional) investment in performed counts/ employer well in the portfolios stocks past RRH-Index

Heuristic  Dispositions Effect  Type: Emotional  RRH-Index: 5 3+2 (emotional)

 +1

 +1

 +2

 +2

Risk/return damaging behavior of market participants  Tendency to  Tendency to  Tendency to increased trad- ad-hoc reacsell investing through ments with tions when exgains and reluc- frequent port- treme losses tance to sell in- folio check-ins are faced vest-ments that have lost value

RRH-Index

 +1

Heuristic

Risk/return damaging behavior of market participants

 Status-Quo Effect  Type: Emotional  RRH-Index: 7 5+2 (emotional)

 Tendency to  Tendency to  Tendency to increase port- loss aversion Ambiguity via the Dispofolio risk Aversion sition Effect through inactivity

RRH-Index

 +1

Heuristic

Risk/return damaging behavior of market participants

 Self-Control Bias  Type: Emotional  RRH-Index: 4 2+2 (emotional) RRH-Index

 +1

 +2

 Tendency to  Tendency to consume today neglect recomat the expense mended portfolio diversifiof saving for cation tomorrow  +1

 +1

 +1

 +2

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9 Limited rationality during decision-making

Heuristic

Risk/return damaging behavior of market participants

 Regret Aversion  Shy away from  Increase port-  Tendency to  Tendency to folio risk by markets that Disposition Cognitive DisBias holding too have recently Effect sonance  Type: Emotional long onto winexperienced  RRH-Index: 8 ning investcorrection 6+2 (emotional) ments RRH-Index

9.4

 +1

 +1

 +2

 +2

Overview of the heuristics considered in the information and decision-making process

In chapters 7 to 9, the most important heuristics in the information and decisionmaking process were analyzed in detail and their risk/return impact on the RRH index were displayed. In the following figure the RRH index values of the individual heuristics are presented in an overview.

Fig. 70: Overview of RRH index values of considered heuristics (own representation)

The RRH index values of the individual heuristics are significantly influenced by the additional direct heuristics. In the following illustration, the heuristics considered in the information and decision process are divided into four groups ‒ depending on how many direct subsequent heuristics are caused (0-1-2-3). It also shows that a number of heuristics have not only direct but also indirect subsequent heuristics. Thus, the endowment bias leads directly to the status-quo bias. However, the status-quo bias leads to two further heuristics ‒ the disposition effect and the ambiguity aversion. The risk/return damaging effect is further reinforced by the follow-up heuristics caused by the ambiguity aversion (home bias, availability and overconfidence).

9.2 Heuristics of emotional origin

Fig. 71: Overview of Heuristics (own presentation)

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Summary Chapter 9 As in the previous two chapters, the phase of the investment decision-making is also characterized by the application of heuristics. In contrast to the previous two phases, heuristics of emotional origin dominate. Misperception of the objective reality The aim of the selective decision-making is to ensure that an earlier decision, which was made under a high level of commitment, will in any case lead to the desired success. This heuristic leads to the disposition effect and the sunk-cost effect. In the category of heuristics of emotional origin, the endowment bias describes the tendency of market participants to estimate the value of an investment higher when they own it than when they have not yet acquired it. They are reluctant to sell securities and thus risk a corresponding reduction in portfolio profitability. The optimism bias characterizes the behavior of market participants to assess positive market developments as more likely than negative ones. This heuristic also leads to overconfidence and to home bias. The disposition effect increases with increasing commitment to the investment in question. Based on the slope of the value function, the disposition effect is significantly greater in the loss range, than in the profit range. The status- quo effect means that market participants leave the composition of their portfolios unchanged, even though an adjustment of the individual weights would be necessary in the course of market changes. It reinforces loss aversion, ambiguity heuristics and also the endowment bias. Misperception of own capabilities The misjudgment of one’s own abilities occurs with heuristics of both cognitive and emotional origin. Within the heuristics of cognitive origin, selfattribution leads to market participants attributing success to their own abilities, but blaming other, external circumstances for failure. In addition, the hindsight bias leads market participants to believe in retrospect that they had assessed the probability of the occurrence of an event as higher than was actually the case before the event occurred. They therefore do not learn from their mistakes. In the case of heuristics of emotional origin, the self-control effect stands for the weakness of the market participant in not always pursuing an investment goal such as retirement planning consistently and without interruption. It leads to an unbalanced portfolio composition and disregard for fundamental investment principles such as the compound interest effect. Furthermore, the regret aversion bias stands for the market participant’s efforts to avoid making any wrong decisions that one might regret in retrospect. This heuristic also evokes conservatism and herding.

Concluding remarks Section III

265

Concluding remarks Section III In the third section of this book, it became clear that the market participant is subject to cognitive and emotional limitations within the information and decision-making process. The Prospect Theory, a first framework for evaluating the behavior of market participants, resulted from Behavioral Finance research. It is now up to further research to substantiate the collected results and expand them with robust models that do not lose sight of the market participant92 ‒ the market participant who cannot eliminate certain emotional and cognitive influences. Due to the limitations of the information and decision-making process, market participants use rules of thumb to select the information they need for their decision-making in an efficient way in the light of limited resources. Systematic distortions can occur here due to the fact that the Homo Economicus Humanus has difficulty in evading heuristics depending on their origin when making decisions. Depending on the heuristics applied, the portfolio’s harmfulness may vary considerably. On the one hand, this is due to the fact that certain heuristics, such as the endowment bias, can cause further heuristics directly or indirectly. Another driver for the increasing portfolio damage is the emotional origin of individual heuristics. As a rule, these cannot be remedied by improved information and, as can be seen, for example, from recurring speculative bubbles, occur again and again, so to speak against better knowledge. Thus, in the information and decision-making process, ambiguity aversion with an RRH score of 8 proves to be the most risk/return-damaging heuristic. When applied, this heuristic causes three further heuristics (home bias, availability and overconfidence). Other heuristics with an RRH score of 8 that are particularly damaging to the portfolio are the optimism effect and the regret aversion bias. Although these do not cause three but only two immediate heuristics, they are difficult to counter because of their emotional nature. Section III was able to show that the observable behavior of market participants can be explained from a behavioral science perspective. However, the deeper cause for the application of heuristics, beyond pure resource optimization, has remained unexplained so far. This is where the fourth and final section of this book comes in connection with other extensions of Behavioral Finance. One of the objectives is to find out the reason for the use of heuristics. Research results show that neuronal processes in the human brain are significantly involved in portfolio-damaging behavior of market participants.

92 It

can be assumed that in future other players in financial markets will also be subjected to an analysis in line with the behavioral finance approach. One might think of securities traders and corporate finance specialists, among others.

Section IV – Applications of Behavioral Finance and Recent Developments

10 Applications of Behavioral Finance in Wealth Management The fourth section of this book is devoted to the application of the insights from Behavioral Finance on selected topics such as investment advice in the context of wealth management. After working through this chapter, you will see by way of example the intensity to which both advised and advising market participants can be influenced in their decision-making by the application of heuristics. In doing so, you will learn about possibilities to limit risk/return-damaging behavior depending on the investor’s financial situation and the origin of the heuristics. In addition, this chapter presents measures for each individual heuristic which aim to increase the quality of advice (in terms of a customer-oriented presentation of returns and risks). The insolvency of Lehman Brothers in 2008 and the subsequent turmoil on the international financial markets significantly undermined bank customers’ confidence in the business activities of banks. Banks were subsequently confronted with more stringent regulatory measures and changed customer needs. Additional costs resulting from these regulatory requirements and the preference for financial investment products that were in demand as a result of rising risk aversion led to a significant decline in income from advising investors. The success of wealth management is based to a large extent on the perceived quality of the services, which are, however, almost interchangeable due to identical basic concepts. The type of advice provided and the ability to understand the client and gain their trust are therefore decisive competitive factors. Although banking services are predominantly advertised with rational arguments, the client’s decision is often based on emotional feelings. As shown in chapter 5, emotions can lead to long-lasting speculative bubbles or have a significant influence on the information and decision-making process of the individual market participant (see section III). As a matter of fact, the application of Behavioral Finance in wealth management is increasing substantially. According to a study by Charles Schwab in 2020, 81 percent of advisors apply behavioral finance techniques while interacting with clients, up from 71 percent in 2019 (see GARP, 2021). The application not only helps to strengthen the relationship to the client but is also results in attracting new mandates. According to the BeFi Barometer 2020, 66 percent of wealth management advisors said they gained new client mandates in the first months of 2020 (first Covid lockdown) thanks to the application of behavioral finance, compared with 36 percent who did not use any behavioral tools (see WealthBriefing, 2020).

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The application of the findings of behavioral finance can be seen through its increased integration into customer advisory services. Credit Suisse, for example, already applies the findings systematically in its advisory process. The advisory process is divided into five stages ‒ stage 1: needs analysis, stage 2: financial concept, stage 3: client profile, stage 4: strategy and stage 5: implementation. The findings of behavioral finance are particularly effective in stages 2, 3 and 4 (see Hens/Meier, 2016). In stage 2, “the preparation of the financial concept”, the effects of →Mental Accounting is integrated. While market participants differentiate between realized losses and unrealized losses, among other things, Credit Suisse leverages this heuristic to distinguish the available fixed assets between those intended for specific causes and the freely available fixed assets. This guarantees that the servicing of liabilities is ensured by generating income from the earmarked fixed assets. In this way, the client can be sure that the earmarked fixed assets are not endangered when the investment strategy for the freely available fixed assets is determined. In stage 3, “Client Profile”, the risk capacity and risk tolerance of the investor are determined with the help of Behavioral Finance. Here, the use of the value function from the →Prospect Theory provides important insights into the client’s risk tolerance. Even if a client is financially capable of taking risks, the risk tolerance may still be a factor against a particular investment. If the client is not willing to emotionally tolerate a certain loss, the one-sided consideration of the purely monetary risk capacity can lead to subjectively incorrect investment advice. The findings on herding are applied in stage 4, “Strategy”. Here, the macro phenomena from chapter 4 are targeted: On the one hand, the momentum strategy is applied when outperforming securities are expected to continue to rise, whereby an investment period of approximately ten months is considered attractive. A further strategy is concerned with the longer-term investment horizon of up to four years with the approximation of the security valuation to its fundamental value. In doing so, the →Mean Reversion Effect of extreme winner and loser shares is supposedly exploited. This is intended to anticipate the incipient recovery of loser stocks and the incipient decline in the price of winner stocks.

10.1 Overview of limited rational behavior in investment advice The following subchapter provides an overview of the limited rational behavior in wealth management and private equity. The focus here is on the one hand on the distinction by which market participant the heuristics are applied (investment advisor vs. investor or general partner as investment manager vs. limited partner as investor) and on the other hand, the origin of the heuristics. Knowledge of the origin of the heuristics (cognitive or emotional) in particular is important in order to be able to mitigate the risk/return detrimental effects during the information and decision-making process. The knowledge of the origin of the heuristic can be helpful in two ways. On the one hand, it helps to decide in which way the risk/return detrimental effects on the behavior of the market participant can be reduced

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and on the other hand, it helps to weigh up whether a cognitive or emotional bias requires a behavior-correcting measure or whether the effects are more tolerable after case-specific consideration. This second aspect will be the focus of analysis in subchapter 10.2. In the following Figures 72 and 73, a four-field matrix shows the heuristics typically used by actors in wealth management (investment advisors/investors) and by actors in private equity (general partners/limited partners). In addition, the heuristics are classified according to the phase in the information and decisionmaking process in which they are used and according to their cognitive or emotional origin. A look at Fig. 72 shows that the majority of the heuristics used in wealth management are in the cognitive domain and are predominantly applied by investors. Despite the majority of investor’ use of limited rational behavior, it is clear that investment advisors can also be prone to such risk/return-damaging behavior and should be sensitized through appropriate training.

Fig. 72: Overview Heuristics in Wealth Management, own presentation

Fig. 73 shows that the general partners, i.e., the investment managers of the private equity companies, make greater use of heuristics than the limited partners (shareholders) of the corresponding PE funds. This fact can be interpreted in a way that the general partners are more closely involved in the concrete investment decision than the limited partners. Furthermore, the commitment to an investment is higher on the side of the general partner than on the side of the limited partner, which is why the tendency towards heuristics can be stronger.

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In addition to differentiating according to the origin of the heuristics, Pompian sees the investor’s wealth as a further criterion that should be used to limit the effects of heuristics.

Fig. 73: Overview Heuristics in Private Equity, own presentation

According to Pompian, the following two principles should be followed when dealing with limited rational behavior:  Principle I: Moderate Biases in less wealthy clients; adapt to biases in wealthier clients. First, the investment advisor should carefully moderate the risk-return impact of the biases used by less-wealthy clients, in order to avoid a reduction of the client’s standard of living if the advisor were to tolerate the biases in question. If, on the other hand, the investor has a high level of wealth, the investment advice should be adapted to the heuristics to some extent in order not to jeopardize the subjective well-being of the investor with the advice. One could say that those who have more assets can afford more “mistakes”.  Principle II: Moderate cognitive biases; adapt to emotional biases. Second, the investment advisor not only needs to take the level of wealth into consideration but also the origin of heuristics. A less-wealthy client exhibiting strong emotional biases should be both moderated and adapted to. Same approach would apply to a client with a strong level of wealth exhibiting cognitive biases. Fig. 74 shows how these two principles are to be interpreted on closer examination. The Adapt option stands for the tolerance to the biases exhibited by the client. This means that, due to the high level of wealth, the investor’s standard of

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living could only be endangered by an unusually sharp fall in prices, which is why a moderation of the biases would tend to lead to an increased uneasiness regarding portfolio design. The Moderate option, on the other hand, stands for a portfolio composition that takes into account the effects of the corresponding biases (e.g., broad diversification across several industries and investment regions). Basically, this option is the application of the neoclassical →Portfolio Theory (including consideration of possible speculative bubbles or “black swans”). Biases should be tempered in their effect on the behavior of the clients with a low level of wealth, as existential risks can arise if the risk/return-damaging effects are not considered. In those cases where Pompian proposes the Moderate & Adapt option, a compromise should be sought in portfolio design according to portfolio theory and portfolio design according to the client’s wishes (see Pompian, 2006, p. 44). Limiting the effects of heuristics depending on wealth status and origin of heuristic High Level of Wealth (Adapt) Moderate & Adapt

Adapt

Cognitive Biases (Moderate)

Emotional Biases (Adapt) Moderate

Moderate & Adapt

Low Level of Wealth (Moderate) Fig. 74: Principles for Moderate & Adapt approach to biases

Example 10.1: Moderate & Adapt The procedure just described for dealing with the effects of heuristics is illustrated below using the example of an investor in a private banking setting. The investor is 65 years old and has assets of around EUR 100,000. You, as the investment advisor, have known the investor for several years and know that the investment goal is to secure the financial needs for retirement. The investor is of the opinion that the capital markets, especially the stock market, are very risky and is afraid of losses. The fear of risky investments is based on her relatives’ stories about the Great Depression of 1929. You note that despite the call to diversify the investments, the investor continues to invest exclusively 100 percent in fixed-income securities (e.g., government bonds). Because of your fear that the investor might suffer a loss of purchasing power due to rising inflation, you decide to conduct a survey with her on the basis of Behavioral Finance in order to determine the type of heuristics to which the investor might be inclined.

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The survey leads to the conclusion that your investor is prone to the following heuristics: - Loss aversion ‒ tendency to exaggerated fear of loss - Anchoring & adjustment ‒ tendency to automatically focus on past price levels as a basis for estimating future developments - Status quo ‒ desire not to make changes Based on a well-balanced portfolio, the investor ought to have a portfolio composition consisting of 70 percent fixed-income securities, 25 percent equities and 5 percent cash. The recommended portfolio structure differs considerably from the current 100 percent fixed-income investments. You will notice that the investor is strongly influenced by the effects of the heuristics applied. The information now available on the investor’s financial situation and the type of heuristics (emotional ‒ loss aversion, status quo) allow an exact allocation to one of the four fields in Fig. 74. You therefore decide to counter the investor’s heuristics using the “Moderate & Adapt” option, since although the investor is threatened with a loss of purchasing power, the exclusive moderation of the heuristics would impair the investor’s subjective well-being. In accordance with the knowledge you have gained, you will work out a portfolio composition which does not exactly follow the required asset allocation according to the investor’s risk attitude but is based on a “compromise” – being the “moderate and adapt” approach. Fig. 75 below shows an example of how a behaviorally adjusted asset allocation could deviate from an ideal asset allocation based on a mean variance output. The risk/return detriment is contained, and the emotional situation of the client is taken into account. Behaviorally adjusted asset allocation ‒ example in percent

Asset Allocation based on mean variance output Recommendation

Behaviorally Adjusted Asset Allocation Recommendation

Variance (Absolute)

Fixed Income

70

75

5

Equities

25

15

-10

5

10

5

Cash

Fig. 75: Adjustment Asset Allocation based on investor behavior

10.2 Dealing with heuristics in investment advice

273

10.2 Dealing with heuristics in investment advice The third section (chapters 7-9) described the risk/return-damaging effects of heuristics to which the Homo Economicus Humanus can be inclined in the information and decision-making process. The objective of this subchapter is now to consider the approaches to dealing with the individual heuristics in investment advice. Here, a distinction is made between two views. One focus will be on dealing with behavior that is harmful to risk/return from the point of view of the market participants who receive advice, i.e., the investors in wealth management or the limited partners in private equity. Alternatively, the sides will be changed, whereby the focus will be on dealing with behavior that is harmful to risk/return from the perspective of the market participants who give advice, i.e., the investment advisors or the general partner in private equity. In addition to the first differentiation with regard to market participants, the objectives of the measures applied will play a special role. In this context, measures against risk/return-damaging behavior will be presented with the aim of increasing the quality of advice. In addition, these measures can offer the opportunity to strengthen the competitive advantage in investment consulting. At this point it should be noted that the pure promotion of product sales in the sense of exploiting risk/return-damaging behavior is not necessarily the primary objective of behavioral finance findings. In the following, the presented options for action are discussed on the basis of the following system: As in section three, a phase-dependent classification of heuristics is made along the information and decision-making process. In addition, the origin of the heuristics will influence the recommended measure for limiting the risk/return-damaging behavior. The differentiation according to the origin of the heuristic is necessary because not all heuristics can be limited in their effects in the same way. The majority of heuristics has a cognitive origin. They are therefore based on the erroneous interpretation of facts and can be limited in their effects by consulting and correcting the information. In contrast, other heuristics, such as the regret aversion, have an emotional origin. They are based on impulses that are often followed unconsciously by the market participant. They are therefore not caused by misinterpretation of information and hence cannot be reduced by correct information. Rather, it is up to the investment advisor to make the harmful effects of emotional heuristics clear to investors and, if necessary, to reduce them with the help of advice. A prerequisite for the measures listed is that the tendency to use a particular heuristic has been checked through a behavioral finance test. In this test, the respondent’s reactions are analyzed by means of case studies and an individual profile is created. Example 10.1 can serve as a simplified version of such a test (see Pompian, 2006, pp. 94):

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Example 10.1: Behavioral Finance Test Questions for Availability Bias Question 1 Suppose you have free liquidity to invest and hear about an investment tip from your neighbor, who is known for successful investments. Your neighbor recommends you to invest in company X, which produces lighter fluid for charcoal grills. How do you react in this situation? a) I will most likely follow the advice, as my neighbor usually has the right instinct. b) I will understand the investment idea as an inspiration and will start my own research before making a decision. Question 2 Suppose you would like to make an investment in the generic drug manufacturer X. Your friend has sent you a company report and as you like the story, you decide to buy 100 shares of the company. Shortly before you make the purchase, you hear in the business news that a competitor of generic drug manufacturer X has reported very good quarterly figures, leading to a 10 percent share price increase. How do you act in this situation? a) I would see this information as a confirmation of the profitability of generic drug companies and would carry out my intended purchase of company X. b) I would stop my intended share purchase of company X first, investigate the competitor and then execute my intended share purchase of company X. c) I will purchase the competitor’s shares, as they appear to promise higher returns than those of company X. Question 3 What claims more lives in the United States? a) Lightning b) Tornadoes Evaluation A tendency to availability bias is likely if answer option a) was chosen for question 1, option c) for question 2 and option b) for question 3. In the case of question 3, the choice of option b) is understandable, since media coverage of tornadoes causes them to be more memorable and therefore more readily available than coverage of lightning with fatal consequences. Statistically, however, more Americans die as a result of lightning than as a result of tornadoes.

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10.2.1 Applied heuristics during information perception

Training options to limit the effects of investor biases during information perception Training method

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Check Emphasis on Recommended Objecti- neg. conaction vity sequences

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Fig. 76: Training options to limit investor biases during information perception

 →Availability Bias (cognitive) Investment advisors should pay attention to the objective assessment of the investment situation by expanding the information base. They should be careful that investors do not overestimate the current development of an intended investment, but that they perceive the long-term perspective through the correspondingly broad information basis. With regard to the perception of information by the Limited Partner, the availability heuristic is to be regarded as less problematic, as PE investments are long-term decisions with an average investment horizon of seven years. Therefore, a Limited Partner has a strong incentive to gather all information, if possible.  →Risk Perception (cognitive) The investment advisor can again attenuate this heuristic with improved information. This involves clarifying the objective probabilities (see chapter 6.2.2). The investment advisor should ensure that the investor does not overestimate the objective probability of future profits and is therefore inclined to take more risk than would be justified. Likewise, the investment advisor should ensure that the investor does not underestimate the objective probability of rising prices after losses and thus unjustifiably reduces risk. The investment advisor can point out historical return developments to support his/her argumentation. These could, for example, illustrate to the investor the tendency for the security valuation to return to its long-term average value, if

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a mean-reversion process can be assumed. This extension of information should limit the over-/underestimation of objective probabilities.  →Selective Perception (cognitive) Due to the cognitive origin of this heuristic, investors with a tendency to selective perception can be helped by the investment advisor checking the information on which the decision is based for completeness. In addition, the relationship with the investor can possibly be strengthened by communicating realistic expectations for an investment. The client will appreciate the reference to the concrete argument covering all aspects of an investment potentially as recognizable differentiation in the advisory service by increasing confidence in the capabilities of the advisor. However, care must be taken to ensure that this approach does not lead to a reinforcement of the status quo bias, which is detrimental to risk/return relationship.  →Framing Bias (cognitive) Attention to this heuristic is extremely important for a long-term client relationship (both in wealth management and private equity). The Investment Advisor/General Partner can strengthen the client relationship right from the start by taking into account the influencing factors listed below. Thus, it is up to the advisor to determine the risk tolerance of the investor through a comprehensive risk assessment. The way the questions are asked plays a major role here. It should be considered that the temporarily short-sighted expectations of the investor have to be reconciled with the possibly long-term objective of wealth accumulation. Care should also be taken to ensure that the information used for decisionmaking (including statistics) is as neutral as possible. This reduces the risk that the investor will be tempted to make a certain decision based on the presentation of the information and regret it afterwards (in the sense of the regret aversion bias). Finally, it should be noted that the framing bias can cause loss aversion. Accordingly, the investment advisor should ensure that the investor does not make future investment decisions based on past profits or losses. A sudden loss can therefore change the risk tolerance, leading to a risk-averse behavior, contrary to what is known in the loss area of the value function. In accordance with the value function, an investor in the loss area would be risk-seeking in order to offset unrealized or realized losses with future profits. It is important to ensure that the investor is aware of the increased risks he/she is about to take. It is also detrimental to the portfolio if the investor is excessively riskaverse after sudden losses in the course of an unexpected market development and thus allows a possible investment opportunity to pass. This would be the case, for example, if after an overreaction to negative news or a panicky market reaction, securities are not purchased quickly enough. Portfolio-damaging behavior must also be considered in the context of profits. If these are initial profits, investors are increasingly risk-averse in line with the increased sensitivity

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in the profit area of the value function and sell the securities too early. However, if the profit occurs unexpectedly, it can happen that the investor is “dazzled” by the profits and, contrary to the findings of the value function, is tempted to take risk-seeking action (as if the unforeseen profit was an invitation to gamble) (see chapter 7.1.1/Risk perception). If the described behavior occurs, appropriate training on the advantages of diversification or the correct portfolio composition can help to minimise the negative effects of this heuristic.  →Herding (emotional) As one of the few heuristics with an emotional background during information perception, herding could be mitigated by making the dangers and negative consequences clearer. Due to the emotional background of this heuristic, the investment consultant can hardly influence the investor with improved information. Rather, it would be important to make the investor aware of the subsequent events when a bubble bursts, such as after the end of the dot-com bubble. In this way, the short-term investment pressure on the part of the investor could be moderated. The customer relationship would be further strengthened by avoiding unnecessary risks. However, the investment advisor must be aware that there may also be bubbles that could be exploited, which could be rational for the investor. 10.2.2 Applied heuristics during information processing

Training options to limit the effects of investor biases during information perception Training method  applicable

 Training method not applicable

Representativeness Anchor & Adjustment Ambiguity Aversion Conservatism Mental Accounting Recency Overconfidence Control Illusion Reflection Effect

Client training through … Analysis of Check Emphasis Recommended influencing Objecti- on neg. action factors vity consequences Review return   expectations  Emphasis on past/historic    portfolio performance Emphasis on advantages    of portfolio diversification Support in analyzing    new information Emphasis on a holistic    portfolio approach Review long term return    development Review long term return    development Increase sensitivity to    variation in risk Emphasis on recommen   dations for action

Fig. 77: Training options to limit investor biases during information processing

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 →Anchoring and Adjustment Bias (cognitive) This heuristic could be limited in its risk/return detrimental effect by an objective approach. The investment advisor can ask the investor the question whether he/she is assessing the current situation (e.g., expected return) rationally or whether he/she is relying more on an expectation that is too strongly oriented towards the return development of the immediate past (e.g., previous year).  →Representativeness Bias (cognitive) The representativeness bias could be improved by analyzing the risk/return influencing factors. For example, the return development of a fund should be examined over the course of several years, instead of merely using short-term performance as the basis for an investment. Vanguard Investments conducted a survey of the five best funds annually between 1994 and 2003. The results show impressively how effectively this heuristic could be limited by correct information (see Pompian, 2006, p. 73):

o Only 16 percent of the top five funds made it into the top five next year. o On average, top five funds from the previous year hardly managed to outperform the market next year. o 21 percent of the top five funds disappeared completely from the market within the next ten years, mostly due to lack of success.  →Ambiguity Aversion Bias (cognitive) Due to its cognitive origin, ambiguity aversion could be mitigated most effectively by analyzing the factors influencing risk and return. The investor should be able to understand the positive effects of diversification of a portfolio using a wide range of investments/regions. Consequently, a differentiation from the competition could be achieved through extensive training of the investors.  →Conservatism Bias (cognitive) Due to the cognitive background of this heuristic, its risk/return detrimental effects could be mitigated by pointing to the objective implications of new information. The investment advisor should make the investor aware of the importance of processing new information. In addition, investors should seek professional assistance in interpreting new information if the comprehensibility of the information is limited.  →Mental Accounting Bias (cognitive) The quality of advice can be improved by clarifying the factors influencing returns. It would thus be important to take a holistic view of an investor’s investments and help him to achieve this perspective. In this way, correlations could be identified that could help to reduce portfolio risk. For example, an investor’s propensity to invest in his own employer’s shares and their implications for the diversification effect should also be addressed. Mental accounting could also be used to optimize the financial planning, as shown by the example of Credit Suisse at the beginning of this chapter. In this

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way, the investor could structure his investments in accordance with the goals to be pursued and would not run the risk of opening a mental account for each individual investment.  →Recency Bias (cognitive) The recency bias could most easily be remedied by using meaningful information. Here it is important to put the current development, which can lead to an overhasty investment, into perspective by pointing out long-term return developments. In addition, the investor should be made aware of the advantages of investing in undervalued and currently “out-of-fashion” investments. These investments can be determined by fundamental analysis using certain indicators, such as a low price-earnings ratio (see chapter 2.2.1). In this way, portfolio diversification can be increased and the investor does not run the risk of favoring investments that have already reached the end of their current price development.  →Overconfidence Bias (cognitive) The overestimation of one’s own knowledge about the development of returns on investments could be countered by analyzing the development of returns to date. Investors who tend to overestimate their own abilities act too often and thus increase their transaction costs considerably (see chapter 8.1.3). The provision of accurate information on the return on previous investments could protect market participants from unrealistic expectations and thus reduce portfolio damage.  →Illusion of Control Bias (cognitive) The control illusion is a heuristic in which the investment advisor should sharpen the objective view of the client in order to limit its effects. The client should understand that an investment rarely produces 100 percent of the expected result and that it is therefore impossible to control the market or to forecast its development with absolute certainty. The client should aim to allow other opinions on market developments and to evaluate them objectively.  →Reflection Effect / Reversal of the willingness to take risks (emotional) With this heuristic, too, it would be useful to highlight the risk/return detrimental effect that could occur if the client changes his/her willingness to take risks in an unjustifiable way. Since this heuristic is of emotional origin, a complete limitation of the effects should not be expected. Rather, sensitivity should be increased to the negative consequences that may arise from an increase in portfolio risk if the client, in the event of a unrealized losses, purchases additional securities at a “discount”, thereby reducing the degree of portfolio diversification.

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10.2.3 Applied heuristics during decision-making

Training options to limit the effects of investor biases during decisionmaking Training method  applicable

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Check Objectivity

Emphasis on neg. conRecommended sequences action

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Fig. 78: Training options to limit investor biases during decision-making

 →Selective Decision-Making (cognitive) The selective decision-making as part of the cognitive dissonance could also be limited by an objective view of the investment situation. This could help the investor to objectively grasp the situation, to realize the paper loss, if necessary, and to choose another investment instead. The investment advisor should be careful that the client does not make his/her decision on the basis of the disposition effect and continues to keep holding onto underperforming securities.  →Self-Attribution Bias (cognitive) The negative effects of self-attribution could be limited by analyzing the factors influencing risk/return. In doing so, the investor should be encouraged to consider realized gains and losses objectively. By analyzing the causes retrospectively, the investor could identify errors made and avoid them in the future. The objective analysis of the results could also help to identify and reduce overconfidence.

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 →Hindsight Bias (cognitive) With this heuristic, the quality of advice could be improved by making the factors influencing risk and return clear to the investor. Here, the excessive forecasting ability should be taken into account, which market participants assume especially after the occurrence of forecasted market movements. The analysis of realized gains and losses should have a positive influence on future investment decisions, as the insufficient forecasting ability could be highlighted on the basis of the analysis. In addition, the investment advisor should put the return performance of fund managers into perspective or align the fund managers’ strategy with current market developments. This would limit unjustified criticism or praise by investors and they would make their decision on the basis of objective considerations.  →Endowment Bias (emotional) The handling of this emotional heuristic could be approached in different ways. However, it should be noted that, due to its emotional origin, it is not the corrected information, that plays a crucial role, but rather the understanding of the negative effects that the application of the heuristic entails. In the case of inherited securities, for example, the investment advisor could already guide the investor to the correct way of thinking or deciding by asking the right questions. If the investor is asked how high the portion of the inheritance would be, which he/she would invest in the available security, then already from the answer a starting point for handling the heuristic results, i.e., if necessary partial or full liquidation of the securities, in order to be able to pursue and reach thereby the individual investment goals. A further starting point would be the comparison of possible profits or avoidable losses with the transaction fees incurred. Investors often avoid selling their investments because they only have the transaction fees to be paid in mind.  →Optimism Bias (emotional) The risk/return-damaging effects of this heuristic could be countered relatively quickly by the following individual measures. The increase in the quality of advice could thus be realized effectively and quickly. The investment advisor should emphasize the importance of a well-structured savings plan to meet the investment objectives. Investors with a tendency towards the optimism effect regularly forego a disciplined savings plan and invest more in the heat of the moment. In this sense, the advantages of the compound interest effect should be emphasized, which have a considerable impact on asset accumulation in the course of a long-term savings plan. It is also important that investors understand the advantages of diversification. This would limit disproportionate investment in employer shares or in certain securities that the investor considers to be of particular personal interest.  →Disposition Effect (emotional) The loss aversion as one of the most harmful heuristics could be countered by clarifying the extremely negative effects of this heuristic. In this way, the in-

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vestor would be able to see the negative effects of holding on to underperforming securities. To counter the risk/return detrimental effects, the investment advisor should, for example, recommend the controlled use of stop-loss trading orders. These are trading instructions to limit the maximum loss by automatically initiating the sale of securities if the corresponding limits are exceeded. In addition, investment advice should also aim at the correct approach to unrealized profits. The client should let winning securities run as long as possible and not sell them immediately shortly after the purchase due to the increased sensitivity for unrealized gains in the profit area of the value function. An indication for the continuation of an investment can again be formulated through fundamental analysis. According to this, a moderate price-earnings ratio (P/E ratio) in comparison with securities of competitors in the respective sector can be a sign that the incipient price increase has not yet reached its end point. Positive company news is also a possible sign that the share price increase may still continue.  →Status Quo Bias (emotional) The status quo bias is a heuristic whose risk/return-damaging effects are probably the most difficult to reduce. The investment advisor should point out the negative consequences of this heuristic, which additionally causes further heuristics such as the disposition effect, the ambiguity aversion or the endowment bias. The investment advisor should also highlight the negative effects of insufficient diversification. This applies in particular to the considerable risks associated with falling security prices. In this case, an investor with a tendency towards this heuristic is particularly at risk, as the underperforming securities are not sold in time due to the disposition effect and consequently the portfolio’s detrimental effect increases significantly due to the possibly low diversification. In addition, the investment advisor should emphasize the advantages of selling underperforming securities. The client does not remain in an inactive position, but actively pursues the long-term investment goals that have been set. If expected transaction fees increase the tendency to this heuristic, the investment advisor should compare the actual costs with the return advantages that another investment can offer.  →Self-Control Bias (emotional) Acknowledging this heuristic, the quality of advice could be improved by prioritising issues such as expenditure control, planning discipline and portfolio diversification. As with any heuristic of emotional origin, the negative consequences of neglect as well as the positive aspects of a well-thought-out investment planning based on long-term asset growth should be emphasized (e.g., compound interest effect, closing the gap between current and aspired levels of retirement provisions).

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 →Regret Aversion Bias (emotional) The regret aversion bias, as the last heuristic of emotional origin, could be countered rather easily by showing the investor the harmful effects of inactivity with regard to necessary investment decisions. It should be noted here that this heuristic, like the status quo effect, causes additional harmful behavior (disposition effect, herding and cognitive dissonance). The investment advisor should emphasize the advantages of assuming a certain amount of risk to achieve a certain level of returns. Even if investors are increasingly risk-averse after the sale of securities and consequently through the realisation of losses (see chapter 7.1.1 Risk perception), the portfolio composition should not consist exclusively of investments whose risk classification can be regarded as low. In this case the achievement of set investment goals would be at risk. Furthermore, the risk/return-damaging behavior in the course of the disposition effect and herding should also be taken into account. Accordingly, the investment advisor should recommend the use of stop-loss trading instructions in order to protect the investment capital from expanding losses that may result from the inactivity of the investor in the event of existing losses. In addition, the investment advisor should limit the potentially short-term investment pressure that investors may feel as a result of herding by highlighting the negative consequences following the bursting of speculative bubbles. The measures just presented for limiting risk/return-damaging behavior all have the objective of strengthening the relationship with the investor and systematically advising him/her according to his/her wishes and needs. On the basis of the recommendations presented, it becomes clear that a long-term client relationship is only possible by increasing the quality of advice. Systematic training of investment advisors is also required to limit behavior that is detrimental to risk/return. By understanding research results, advisors can help their clients to achieve the desired investment goals by asking specific questions and providing corrective information. In addition, Behavioral Finance enables advisors to understand the expectations of investors and to interpret these expectations in an emotionally correct way by means of extended investigation possibilities (e.g., personality analysis). Knowledge of the psychological motives behind decisions makes it easier for the advisor to achieve financial goals. It is necessary to give investors insight into the portfolio composition so that they understand the reasons behind the specific selections. By tailoring the investments to the investor’s needs, hectic action in the event of turbulent markets can be avoided. Instead, the client should be able to identify with his portfolio at all times. In addition to the advantages for the investor, advisors are also supported in their work, as theycan better interpret the expectations and needs of the investor. Advisors can check their own actions and decisions for cognitive distortions, with the result that no false expectations about future returns are created in the investor’s mind (see Pompian, 2006, p. 17).

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In order to better meet the growing expectations of investors, in addition to pure client advice, the findings of Behavioral Finance are also incorporated into the development of products. Thus, it is in the human nature to avoid unknown investments that can lead to a loss, but to willingly enter into profitable bets. This basic pattern of human decision-making can be met by developing structured products that combine the investor’s desire for safety as well as the investor’s need for speculation. Such products are, for example, guarantee funds or guarantee certificates that guarantee a minimum return on the investment amount by sacrificing some of the investments’ upside potential (see Bank/Gerke, 2005, p. 467).

Summary Chapter 10 The tenth chapter of this book focused on the importance of Behavioral Finance in investment advice. The ever more tightening of regulatory requirements and the changing needs of investors require additional efforts from financial institutions to regain lost trust. The use of Behavioral Finance as a measure to differentiate themselves from the competition could help to achieve this. In order to effectively combat risk/return-damaging behavior, it is important to distinguish between heuristics of cognitive and emotional origin. Heuristics of cognitive origin can be limited in their harmfulness by analyzing the factors influencing returns and by improving information. Heuristics of emotional background, on the other hand, can only be counteracted to some extent by visualizing the risk/return-damaging behavior. In addition to this first distinction, the investor must also identify with the recommended measures. Here, the investor’s financial situation can be used as a point of reference for either far-reaching measures or for a moderate reduction of the risk/return-detrimental potential. If a client in his financial position is particularly vulnerable to the effects of the heuristics used, investment advice should be tailored to limit such behavior. However, if the investor is only harmed by extreme price losses and if the strict limitation of conduct would create emotional resistance on the part of the investor, a combination of “moderate & adapt” should be considered. Finally, the development of recommendations for action also made it clear that the findings of Behavioral Finance can be used most effectively to improve the quality of advice. The sheer exploitation of “limited rational” behaviors to increase product sales is not advisable in the sense of regaining lost trust as it can endanger the long-term relationship with the investor.

11 Application of Behavioral Finance in corporate governance The eleventh chapter of this book focuses on limited rational behavior in the context of corporate governance. After completion, you will have become familiar with the drivers of limited rational behavior, such as the overconfidence of corporate leaders (in the sense of the board of directors, executive management or senior management level) and you will be able to classify their effects on the development of the overall profitability of companies. In addition, you will look at certain entrepreneurial activities from the perspective of Behavioral Finance and thus recognize how psychological influences can impact corporate decisions. In addition to dividend policy and the initial issue of shares, the impact of different remuneration concepts within the framework of corporate governance will also be considered. The chapter is rounded off with a discussion of the Equity Premium Puzzle.

11.1 Overconfidence in entrepreneurial investment decisions Chapter 8 examined in detail the effects of →Overconfidence on the behavior of market participants. The objective of this chapter is to examine investment decisions under uncertainty from the corporate perspective. These investment decisions concern the funds to be invested in new acquisitions, the valuation of existing investments or the redistribution of investment funds between different divisions of a company. In summary, the budgeting of fixed capital is a defined process in which the composition and scope of the company’s assets are determined. This composition is ultimately responsible for generating cash flow and thus for the profitability, value and existence of the company. Due to the scope of investment decisions, it is extremely important to be aware of the limited rational decisions that are made as a result of the overconfidence of the company’s leaders, as these can have a significant impact on overall profitability. Investment decisions are basically made according to whether they can increase the value of the organization. To identify suitable investments or projects, new opportunities are usually analyzed by determining their present value. This form of analysis known as net present value (NPV) was introduced by Joel Dean93 in 1951 and has been used with increasing popularity ever since (see Gervais, 2010, p. 413). Although investment decisions appear transparent by applying the NPV calculation formula and can be made according to rational criteria, the decisions

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Joel Dean | American Economist | 1906-1979

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are not safe from the influence of →Homo Economicus Humanus. By estimating future cash flows and selecting the discount rate to be used, corporate decision-makers have a considerable influence on the result of an investment calculation and thus on the decision-making process. Research into limited rational behavior was initially mainly limited to the investment behavior of market participants on the capital markets. Analyzing investment decisions by corporate leaders was made possible by drawing on the seminal research findings of Herbert Simon (1955, 1959), Julius Margolis (1958) and Richard Cyert/James March (1963). They argued for the inclusion of human behavior in the analysis of corporate decisions, as this has an impact on the overall profitability of the organization and thus on the financial situation of all stakeholders. Numerous studies show the tendency of corporate leaders to overestimate the precision of their knowledge and information during the decision-making process (Fischoff/Slovic/Lichtenstein 1977; Alpert/Raiffa, 1982). The tendency to overconfidence is not limited to a specific professional group, but affects a wide range of people, from investment bankers, entrepreneurs, engineers to lawyers and the protagonists of this chapter ‒ the corporate leaders (Kidd 1970; Cooper/Woo/Dunkelberg, 1988; Russo/Schoemaker, 1992). On the basis of the above-mentioned research results, certain framework conditions can be identified which are the main reason for the tendency of corporate leaders to overconfidence (see Gervais, p. 413): [1] Capital budgeting decisions are very complex Factors that are considered uncertain at the time of the decision must be taken into account. Under these conditions, the tendency to overconfidence is particularly widespread. [2] Due to their one-time character, capital budgeting decisions do not allow for learning-effects in the narrow sense According to Kahneman/Lovallo (1993), a learning effect would be possible if the decisions were repeatedly made under similar conditions and the outcome of the decision could be identified in the short-term for feedback. Corporate leaders are confronted with complex investment decisions at irregular intervals and receive feedback on the decision taken very late and in varying degrees of quality. In addition, the lack of a learning effect is also due to the fact that many corporate leaders evaluate each decision situation as a completely new one and therefore do not rely on past results (Einhorn/Hogarth, 1978; Brehmer, 1980). [3] Successful corporate leaders are promoted, with the tendency to overconfidence This tendency exists because they ascribe the success to their own ability rather than to circumstances (→Self-Attribution). By contrast, unsuccessful corporate leaders are usually let go or are not promoted (Miller/Ross, 1975; Langer/Roth, 1975; Nisbett/Ross, 1980).

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[4] Corporate leaders seem to be more prone to overconfidence than the general population, since they apply for managerial positions and are accordingly convinced of their abilities. In addition, those candidates are subconsciously selected for positions who have had relevant success in the past (Gervais, Heaton and Odean, 2001). According to Shefrin, overconfidence therefore often leads to corporate leaders being surprised by unexpected changes. In doing so, they underestimate the risks that can arise in the course of an investment project. Shefrin (2007) furthermore identifies two main factors leading to overconfidence: [1] The apparent control over the influencing parameters, [2] The inadequate risk management. Psychological evidence shows that apparent control is associated with a reduced perception of risk. This correlation seems to be particularly true for people of white color. When making decisions about technological issues, they trust the opinion of engineers more than people of color (Flynn/Slovic/Mertz, 1994). The second driver, inadequate risk management, results from the use of the →Availability Bias. Here, corporate leaders seem to be less risk aware when they themselves have to name typical risk factors. The result is a list that is shorter than if the compilation of possible risk factors was done by outsiders. In the course of applying the availability bias, managers or those responsible for risk assessment capture aspects that are most familiar to them at the time. Factors with which they have less contact are not taken into account. As a result, the risk and the potential for surprises increase during the course of project implementation. This fact was confirmed by a survey of risk and financial managers, conducted by Financial Executives Research Foundation, FM Global and The National Association of Corporate Treasurers (2003). The results of the survey showed that risk managers consider risk factors that are quite different from those that financial managers would do. Risk managers cited insurable risks on buildings as the main risk; financial managers, on the other hand, risks that can result from incorrect investment activities, such as investments based on unrealistic NPV calculations (see Shefrin, 2007, p. 42). Overconfidence is not a phenomenon of today’s generation or is merely based on the increasing flood of information. Adam Smith already attempted an interpretation in 1776:

“The over-weening conceit which the greater part of men have of their own abilities, is an ancient evil remarked by the philosophers and moralists of all ages Their absurd presumption in their own good fortune has been less taken notice of. It is, however, if possible, still more universal ... The chance of gain is by every man more or less over-valued, and the chance of loss is by most men under-valued, and by scarce any man, who is in tolerable health and spirits, valued more than it is worth.” (Smith, 1776, cited after Gervais, 2010, p. 414)

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In this context, it is assumed that the heuristically clouded view is the result of the optimistic attitude of corporate leaders towards the company’s future return development. Thus, they underestimate the risks of changes along the way (see Gider/Hackbarth, 2010, p. 393). In the following, the effect of overconfidence can be illustrated using the example of Iridium, formerly a subsidiary of Motorola Inc: Example 11.1: Overconfidence among corporate leaders Iridium was created in the late 1980s as a subsidiary of Motorola to build a satellite telecommunications network. The realization of the project was calculated to take 11 years and cost nearly USD 5 billion. The subsidiary was founded shortly after a presentation of the senior engineers to Motorola’s board of directors. During this two-hour presentation, the board members were so convinced of the concept of the communication network that they ordered its realization right away. No financing calculations, no cash flow analyzes were requested. The board members based their decision on their personal and subjective assessment. This reaction illustrates the overconfidence in the course of inadequate risk management. Accordingly, the company’s management did not see the need to examine the risks of this project in detail. Iridium’s business model initially assumed up to 8 million business customers with heavy travel activity. After 1.5 million customer inquiries, the installation of the satellite telecommunications network resulted in a customer base of just 20,000 customers. This turnaround came as a profound surprise to the corporate leaders. In retrospect, four factors could be identified which caused the surprising deviation from the forecasts: - Service usability inside/outside of buildings ‒ The engineers found that their equipment only worked outdoors. They had neither expected nor considered this technical problem. - Competition in mobile telecommunications ‒ Although in the late 1980s mobile phones were only used by a few individuals with an affinity for technology, ten years later, when Iridium offered its service, mobile phones were widespread among the population. Motorola’s CEO, Robert Galvin, confirmed that he had not anticipated this market development. - Device features/size ‒ Iridium’s satellite phones did not meet the demands of the time or the mobile phones available at that time. Iridium’s devices were several times the size of standard mobile phones and had a long antenna. Mobile phone users regarded the devices as cumbersome to handle (a comparable device was used by Gordon Gekko in the 1987 film “Wall Street“). - Cost ‒ The purchase and operating costs for Iridium devices were several times higher than for mobile phones at the time. A satellite phone, for example, cost USD 3,000 compared to an average of USD 300 for competitors’ mobile phones. The operating costs for calls were also between 4 and 10 USD/minute.

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Iridium had to admit to itself that the business model was not sustainable under the conditions prevailing at the time. Within a year of entering the market, Iridium had to file for insolvency (see Shefrin, 2007, pp. 41). In the course of time, numerous empirical studies have been carried out, which have shown the tendency of company managers to overestimate their own abilities (Ben-David/Graham/Harvey, 2007; Hackbarth, 2008, 2009). In this context, the following behaviors can be seen as an indication of overconfidence (see Gider/ Hackbarth, 2010, pp. 408):  increased propensity to invest in acquisitions that require a high degree of external debt;  limited propensity to distribute corporate profits to shareholders in the form of dividend payments;  increased propensity to use share buyback programs. Numerous attempts have also been made to measure the degree of overconfidence. Among other things, the holdings of corporate shares or the communication of corporate leaders in media is examined (see Gervais, 2010, p. 418):  Investment ratio in corporate shares This way of measuring overconfidence was proposed and developed by Ulrike Malmendier and Geoffrey Tate (2005, 2008). They analyzed the degree of investment of corporate leaders and managers in corporate shares. Such an analysis, which is now widely used, provides information about voluntarily accepted under-diversification due to over-investment in the said securities. According to Malmendier and Tate, a corporate leader is characterized as overconfident if he/she continues to hold on to the company’s shares after the holding period has expired and even voluntarily increases the investment ratio.  Analysis of external communication This form of analysis (Malmendier and Tate, 2005, 2008) looks at the choice of words in newspaper articles (including The Economist, Business Week and The New York Times), which can then be classified as overconfident or conservative on the basis of semantic evaluations. Thus, an article was classified as overconfident if it described the corporate leader as “too confident” or “optimistic”. Whereas articles in which the business leader was described as “cautious”, “conservative”, “not confident” or “not optimistic” were classified as conservative. If the number of articles that can be classified as overconfident is higher than the number of articles with conservative wording, the corporate leader is considered overconfident in the opinion of the researchers. In addition, further studies have determined the effect of overconfidence on the profitability of the companies concerned. Three central points can be highlighted here:

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 Mergers & Acquisitions by corporate leaders with a tendency to overconfidence reduce the company value for the acquiring company (among others Jensen/Ruback, 1983; Eger, 1983; Andrade/Mitchell/Stafford, 2001) The value of the company, expressed by the company’s share price, fell by approximately 0.8 percent within a period of three days after the announcement of the takeover under the above-mentioned conditions. Between 1980 and 2001, USD 220 billion were destroyed in the course of 12,000 takeovers with a takeover volume of USD 3.4 trillion (Moeller/Schlingmann/Stulz, 2005). The reduction in the value of the company is considered a discount for the overconfidence of corporate leaders, which is then “punished” by market participants (see Shefrin, 2007, p. 162).  Because of overconfidence, corporate founders run the risk of making a bad investment more often than corporate leaders hired externally or developed internally In particular, overconfidence increases in small, private companies, as those responsible feel less compelled to justify their decisions to external observation by capital market investors. In this sense, the potential success of their own company is overestimated, with the consequence that the intended market entry through acquisitions can prove to be a considerable risk for the continued existence of the company.  Business leaders with a tendency to overestimate themselves run the risk of underestimating the costs of the projects they enter into and the time required for their implementation (Kidd, 1970; Hall, 1982; Lovallo and Kahneman, 2003) Due to overconfidence in their capabilities, corporate leaders tend to make misjudgments regarding the implementation time of the respective project. For this reason, the value of the company is reduced in two ways. On the one hand, the project costs increase, since the extended implementation time entails more personnel costs. On the other hand, the cash flow generation from the expected operating activities is delayed. After a detailed examination of the negative effects of overconfidence, a number of options are listed below to limit the effects thereof (see Gervais, 2010, p. 425):  Active checking of results and reaction to self-attribution Corporate leaders should be trained and sensitised to the risk/return-damaging effects of overconfidence. They can then better assess that their ability to process information is limited and they should, thus, make more realistic assessments. It is also important to adapt project monitoring to ensure that results can be measured at regular intervals and that comparability with other projects is ensured, so that a real learning effect can be achieved. In addition to improving project controlling, it is also necessary to take into account the effects of →Self-Attribution on the decision-making of the corporate leaders. This would allow the causes of successful and wrong decisions

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to be viewed objectively, acknowledging the role of chance, which would enable better decisions to be made in the future.  Calculation of future cash flows with higher discount factor This measure makes it possible to counter the negative effects of over-investment and to adjust the resulting expectations regarding the profitability of projects. In this way, projects are calculated cautiously so that even under adverse circumstances (e.g., change in refinancing costs, change in the competitive situation, change in the economic situation) the project can still be completed profitably.  Adjustment of contractual incentives for corporate leaders This approach, also known as contractual incentives, stands for the incentivebased adjustment of the remuneration paid to corporate managers for their work. In their work, they should always represent the interests of the owners and not pursue short-term goals based on a flawed remuneration policy that could possibly endanger the value of the company in the long term (see chapter 11.4). Overconfidence can cause corporate leaders to be taken by surprise in unexpected turns of events. They tend to underestimate risks that may arise in the course of an investment project.

11.2 Dividend policy from the perspective of Behavioral Finance The following section focuses on the question of why companies pay dividends to their shareholders in a certain amount and often according to a relatively stable pattern. The traditional view of corporate dividend policy has been influenced by the Modigliani-Miller Theorem (1961). One of the results of this theorem is that in the case of transaction costs or taxation of dividends, companies should waive the distribution. Merton Miller94 and Franco Modigliani95 shared this view, as they believed that market participants are immune to framing effects. If market participants wanted to receive dividends but did not receive them, they would still be able to earn the “dividend income” by selling shares. Despite this theoretically understandable view, the right dividend policy continues to occupy the economic debate. According to Hersh Shefrin, there are supporters for the Modigliani-Miller theorem as well as opponents who defend the dividend payment, since the distribution to shareholders can be seen as a compensation for possible entrepreneurial mistakes which could reduce the value of the company. This view could be considered 94

Merton Miller | American economist and Nobel Prize winner 1990 | 1923-2000

95 Franco Modigliani | Italian-American economist and Nobel Prize winner 1985 | 1918 -2003

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correct if the dividend payment would be paid continuously irrespective of possible short-term fluctuations in profits. Accordingly, the dividend payment would compensate for share price reductions that could occur in the course of entrepreneurial wrong decisions. In essence, dividends would divert funds from the hands of corporate leaders into the hands of shareholders, who might be able to put them to better use (see Shefrin, 2007, pp. 110). Behavioral Finance research has also been dealing with the question posed at the outset as to why companies pay dividends to their shareholders, but has not yet been able to find a completely satisfactory answer. This chapter therefore concentrates on presenting the hypotheses under discussion. The incomplete understanding of the motivation to pay dividends to shareholders is described by Fischer Black’s96 term “dividends puzzle”: “The harder we look at the dividend picture, the more it seems like a puzzle, with pieces that just don’t fit together.” (Black, 1976, quoted by Ben-David, 2010, p. 435) In the course of time, numerous economists have dealt with this question, with two hypotheses being in the air, although some of these have already been refuted in empirical studies (Allen/Michaely, 2003; Frankfurter/Wood, 2006; DeAngelo/ Skinner, 2009): [1] On the one hand, the distribution of dividends could serve as a means of resolving conflicts of interest. [2] On the other hand, it could be understood as a signal in response to the asymmetrical distribution of information. In order to better understand the following explanations of the research results and the ongoing discussions, it is helpful to summarize the empirically collected findings on dividends (see Ben-David, 2010, p. 436):  Dividends have been paid out to shareholders as primary profit sharing for about 400 years.  Dividends are mainly paid by companies that have already advanced in their life-cycle and have stable recurring earnings (for example, Apple introduced the first dividend payment in March 2012).  Although dividends are the most common method of distributing corporate profits, the importance of this type of profit distribution is declining in comparison to the buyback of own shares, which is becoming increasingly important. It should be noted that companies either actively pay out dividends to their shareholders or increase the share price of the company by buying back their own shares. Shareholders who wish to generate cash flows from the company can sell the shares at the increased price. The latter is becoming increasingly important 96

Fischer Black | American economist and author of the Black-Scholes options equation | 1938-1995

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because investors do not have to pay income tax in the event of an increase in the share price (without selling shares), which would be payable by investors in the event of dividends being paid out (Fama and French, 2001; Julio and Ikenberry, 2004).  Dividends are characterized by lower volatility than security prices.  Although the repurchase of own shares can often be advantageous for tax reasons, as shareholders do not have to pay tax in this case, dividend distributions, which are taxed via the final withholding tax, continue to be the most common form of profit sharing.  The announcement of a dividend pay-out or its increase usually leads to rising share prices, whereas the reduction of the dividends to be distributed or their suspension often leads to falling share prices.  Due to the long-term nature of the decision for or against a dividend payment, corporate leaders often delay the dividend payment to avoid price losses that would be feared due to falling or no dividend payments. In the context of research on the motivation for dividend payments (Allen/ Michaely, 2003), an attempt was made to view dividend payments from a rational perspective. Among other things, the distribution is also mentioned as compensation for transaction costs incurred in trading securities. Some research papers list general motives for the distribution of dividends, in which the distribution itself is the main focus. Other research focuses on behavioral motives. The aim of this research is to find out why dividends are actually paid. General Motives Below is a list of the most important findings from the wide range of research work on dividend policy (see Ben-David, 2010, pp. 438). Target group-specific motives Institutional investors, for example, might prefer dividend payments for regulatory or tax reasons; retail investors close to retirement age prefer dividend payments as they provide recurring income. Investors with relatively low salaries prefer dividends because of their lower tax rate (Graham/Kumar, 2006). However, the latter does not apply fully to EU countries, as the so-called final withholding tax on dividends or capital gains is independent of the personal tax rate of the investor. Life-cycle of the company Another research approach focuses on the life-cycle of companies. The research results of Grullon et al. (2002) indicate that companies that pay dividends show less volatility in their business/return development. This view is confirmed by J.B. Chay and Jungwon Suh (2008) in the sense that, in their view, dividends are paid by companies that are exposed to less uncertainty about future earnings performance (see Ben-David, 2010, p. 439).

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Continuity concept A further explanation for the dividend policy is based on the idea of continuity. Accordingly, companies pay dividends because they have done so in the past and because shareholders expect dividends to be paid out. Dividends have been paid out for almost four centuries. This form of cash disbursement is considered a generally valid and practiced form of profit sharing for shareholders. Consequently, the assumption that a certain social pressure for dividend distribution has developed cannot be dismissed (Frankfurter/Wood, 1997). Moreover, in the 19th century, many shareholders regarded dividend payments as a safeguard against fraudulent accounting practices. Shareholders were able to calculate the value of the share from the dividends paid out, and accordingly they were less concerned about the accounting by managers. The idea of continuity is also fuelled by the fear that if the dividends are reduced, the share price of the company concerned will also have to fall (Brav et al., 2005). This view is also confirmed by a survey of 300 CFOs conducted by Duke University in 2004. According to this survey, 75 percent of the respondents see dividend decisions as a signal for corporate well-being and 80 percent of the respondents expect negative price reactions if the dividends are reduced or suspended. In addition, 60 percent of those surveyed would take out debt capital to finance investment projects instead of waiving or reducing dividend payments (see Shefrin, 2007, p. 121). Valuation Market participants use a number of ratios for the fundamental valuation of securities (see chapter 2.2.1). These key figures, such as the dividend per share or the book value per share, are used to value a security for a possible over- or undervaluation. The valuation of a security based on the dividend yield (dividends expressed in relation to the share price) is often regarded as an indicator of the value of a company (Graham/Dodd/Cottle, 1934; Gordon, 1959; Baskin, 1988). Dividend payment is also considered a signal that emphasizes the quality of entrepreneurial activity (Miller/Modigliani, 1961; Bhattacharya, 1979; Miller and Rock, 1985; John and Williams, 1985). According to this view, organizations pay dividends in order to signal investors the positive outlook for the economic situation of the company. Behavioral Motives Research that examines behavioral motives for the distribution of dividends postulates that the companies concerned want to reduce the perceived transaction costs or increase the perceived value of the security through the payment of dividends (see Ben-David, 2010, pp. 440). Sentiment of the market participants The perception of the value of a security can change depending on the economic situation. For example, dividend-paying companies appear to enjoy the favor of

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market participants more in recessions than they do in an economic growth phase. Malcolm Baker and Jeffrey Wurgler (2004) argue that the demand for dividend payments changes depending on the risk appetite of market participants. If the risk appetite is low during recession phases and the sentiment of market participants is poor, market participants tend to purchase high-dividend securities, which compensate for share price reductions through the dividend pay-out. In times of economic expansion, however, when the mood of market participants is positive and the risk appetite is high, organizations that invest their profits in research and development (R&D) and therefore do not pay dividends seem to be preferred. Heuristics The preference of market participants for dividends can also be explained by considering self-control and mental accounting biases. According to →Self-Control, market participants prefer dividends as protection against the hasty sale of securities to satisfy their own consumer needs (Thaler and Shefrin, 1981; Shefrin and Statman, 1984). In this sense, only the dividends would be used for consumption, but the investment capital would remain untouched if possible. Hersh Shefrin and Meir Statman (1984) also make use of the →Mental Accounting Bias to explain the preference for dividends. According to this, market participants book income from capital gains and dividends in different accounts. The reason for this can be found in the different value (→Value Function) for smaller and larger profits. Several smaller gains are perceived as more valuable than one large gain if realized by the sale of the securities. This is due to the increased sensitivity caused by the steep slope of the value function near the reference point. Dividends are preferred by market participants, especially by older investors close to retirement age, if this regularly recurring income is to compensate for a loss of income/salary. Thus, dividend income is booked separately from investment capital in a corresponding consumer account. The investment capital is not used for consumption due to self-imposed rules. For this reason, the investment capital is also booked in another mental account, the investment account (see Shefrin, 2007, p. 115). Behavioral Finance research in the context of corporate dividend policy deals with the question of why dividends are paid in the first place. In addition to general motives, behavioral reasons, such as the separate consideration of dividends and price gains, are considered.

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11.3 Initial Public Offerings from the perspective of Behavioral Finance The initial public offering (IPO) of a company is not only from the investor’s point of view a very decisive event in the life-cycle of a company. It is also particularly exciting from the perspective of →Behavioral Finance, as it leads to three specific phenomena (see Derrien, 2010 pp. 475):  Undervaluation of issue price (Underpricing)  Temporary IPO Booms  Long-term negative performance after initial offering Underpricing As mentioned at the beginning of this chapter, this phenomenon involves relatively large price gains during the first few trading days. As a starting point, price gains of between 10 and 15 percent can be explained from a rational perspective. According to Tim Jenkinson and Alexander Ljungqvist (2001), information asymmetries between the actors involved (companies, investment banks, investors) lead to corresponding price gains during the first trading days. The term “information asymmetry” stands for the constellation in which the actors involved do not have access to the same company information. The price gains are therefore interpreted as compensation for the first subscribers on the basis of the risk taken and form an incentive to subscribe to the securities. Price gains, however, which are far above the aforementioned 10-15 percent and in boom times can go as far as a doubling of the issue price, suggest the behavioral science perspective to explain the possible causes. For example, the intentional reduction of the issue price may be related to the desire of the company (issuer) issuing shares to receive positive recommendations from analysts. According to Jay Ritter and Tim Loughran (2004), issuers “buy” positive analysts’ assessments by reducing the issue price. This is particularly the case in times of highly overvalued shares, as was the case during the dot-com speculative bubble 1999/2000. The supporting investment banks secure a share of the issue proceeds by being allotted shares prior to the initial listing (so-called “green shoe”), which can then be sold with high price gains during the first few trading days to stabilize the price, which is rising due to the positive assessments expressed prior to the issue. A further explanation for these so-called “subscription profits” are optimistic investors who would pay more for the subscription of the shares than was determined by the issuer during the pricing phase (Derrien, 2005). Pricing is based on feedback from institutional market participants who base their pricing on the fundamental value of the company, as well as from private investors who might pay a significantly higher price for the share due to media coverage. If the new shares are now issued below the price that private investors are prepared to pay, further price increases can follow, since private investors will enter the market after the

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initial listing of the securities if they were not allocated shares directly at the time of issue (see Derrien, 2010, pp. 477). Temporary IPO booms The second phenomenon in the context of equity issues is the significant accumulation of issues at times when the price gains are very high in the first few days. As shown in Fig. 79, the number of new issues increases when significant price gains are possible after the initial listing. These periods, such as during the dotcom bubble from the mid-1990s until the beginning of 2000, are characterized by a serious overvaluation of companies. It can therefore be assumed that companies will, if possible, choose a corresponding time window in which to place their shares. Significant subscription gains with increased issuance activity – during dot-com speculative bubble 1998-2000

Fig. 79: Number of IPOs and average underwriting profit between 1980 and 2015; Ritter (2020)

Research results indicate a number of considerations in the timing of an IPO. Some companies try to take advantage of the temporary overvaluation of companies already listed on the stock exchange and place a large proportion of their shares. According to Marco Pagano, Fabio Panetta and Luigi Zingales (1998) and Bharat A. Jain and Omesh Kini (1994), the overall profitability from the operating activities of listed companies declines in the course of two years after the issue. This indicates that companies use a temporary overvaluation window for the IPO. The overall profitability here is not to be understood as the profitability or return on investment from the investor’s perspective, but rather from the perspective of the company that determines the profitability of its business activities in the context of its internal accounting.

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In contrast, other companies intend to issue only a small proportion of shares in order to improve their competitive position through the proceeds. According to Paul Schultz and Mir Zaman (2001), some corporate leaders are not interested in the short-term high issue proceeds and thus in exploiting limited rational market participants, but rather in obtaining the greatest possible control over the company (see Derrien, 2010, pp. 481). Long-term negative performance of IPOs The third phenomenon, the long-term decline in the price of new issues, is still a relatively unexplored area. However, the fact that some prices of newly issued shares fall sharply over time reinforces the criticism of Fama’s →Efficient Market Hypothesis (see chapter 1.2.5). According to this, in an efficient market, incorrectly stated business figures would have to be noticed by the market participants. That should lead to a downward correction of the share price within a very short time. The explanation for the delayed response can be found in the second phenomenon ‒ IPO boom phases. During IPO boom phases, corporate leaders tend to go public, as their company is valued particularly attractively during these phases. Consequently, they have an interest in exploiting the valuation, even if it appears exaggerated in their eyes. If significant subscription profits are generated in the course of the issue, not only are other companies attracted, but also other market participants who will take advantage of the opportunity in future security issues. The overvaluation of the securities only becomes apparent to market participants with some delay. In line with the market anomalies described in chapter 4.3.3, new issues therefore have a →Mean Reversion Effect over time. The market participants, who have so far perceived only positive news due to the →Availability Bias, may increasingly notice that the valuation of the securities has moved away from the fundamental value. Accordingly, the prices of securities correct the overvaluation over time and are subject to negative price developments over a longer period of time. 11.4 Corporate Governance from the perspective of Behavioral Finance The next subchapter focuses on the remuneration of corporate leaders. It will be examined what the objectives of individual remuneration components are and how these affect the behavior of corporate leaders. According to the principalagent theory, the agents should safeguard the interests of the shareholders (principals) as legal owners of the company. In this constellation, the supervisory board, which is elected by the shareholders, should represent the shareholders’ interests and should monitor the decisions of the corporate leaders in order to prevent transactions that do not serve to increase the value of the firm. This traditional view has been challenged several times in recent years. Corporate scandals have become more frequent (e.g., Enron 2001, Volkswagen 2016 or Wirecard 2020), all

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of which have shown that some corporate leaders are turning away from the interests of shareholders and putting their own interests first. Behavioral Finance research is also increasingly concerned with the impact of remuneration components on the behavior of the actors involved. The focus here is on the level and type of remuneration, which should both reward performance and protect against overpayment. Within the research approaches, →Overconfidence and →Prospect Theory play an important role. The members of the supervisory board could, for example, underestimate possible conflicts of interest between company managers and shareholders due to their overestimation of their own capabilities. Furthermore, the supervisory board may tend to underestimate the risk of behavioral conflicts, which in turn may lead to conflicts of interest. This concerns, for example, the continuation of business projects which are not terminated in time due to →Selective Decision-making, but may even be continued with additional investment funds (Sunk Cost Effect). Due to overconfidence, the members of the supervisory board run the risk of granting too high compensation to the company’s management. According to Shefrin, overconfidence on the part of the supervisory board members leads to the adoption of compensation guidelines that grant unjustified advantages to the corporate leaders. The results of anonymous surveys conducted by the business magazine Forbes point to these difficulties of the supervisory board in negotiating correct, performance-related compensation with the executive management. In 2012, for example, 46 percent of 175 supervisory board members surveyed voted for an increase in executive compensation, even though the economic situation of the companies involved had deteriorated (see Forbes Insights, 2012). One supervisory board member, who is also the CEO of a company, acknowledged the problems of the supervisory board to put together incentive-compatible compensation packages. The supervisory board member sees overconfidence as the cause: “Compensation committee members are not malevolent. I’ve seen situations that are messed up, and yet the directors think they’re doing a hell of a job. They delude them-selves. They think things are being done right and fairly ‒ they don’t think they’re being had ‒ when actually the excesses they’re approving are just mind-boggling [...] It’s really amateurs vs. pros. I’m classing the directors, in most cases, as amateurs, and management, together with the compensation consultants they hire, as pros. You can have a very sophisticated board ‒ and it’ll still be amateurs vs. pros.” (Shefrin, 2007, p. 130) According to Shefrin (2007), an important reason for the supervisory boards’ approval of disproportionate compensation demands lies in the ignorance of behavioral distortions. According to this, overconfidence does not only cause an unjustified increase in risk in certain investment situations. The company’s management considers the compensation demands to be fair, as, according to the →SelfAttribution Bias, they see themselves as the cause of the company’s success.

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Business failures, on the other hand, are blamed on external, adverse circumstances, which is why they reject contractually agreed methods of sanction in the event of adverse developments. This view can be illustrated by the following quote from a corporate leader: “So my view of incentive compensation of any kind is that it’s fine if it isn’t just a giveaway program. The pendulum has to swing both ways ‒ and usually it doesn’t. A compensation committee also hears a lot about external factors, things that couldn’t have been anticipated when the budget was being made. People say, “We worked our butts off” [...]. And you have to answer, “Look, that was the deal. You agreed to work here for a year under that deal, and if the shareholders get dung, then you get dung.” (Shefrin, 2007, p. 131) Overconfidence is also evident among CFOs (Chief Financial Officers) with regard to their ability to forecast the development of future stock index levels. Duke University professors collected over 11,600 forecasts from CFOs and verified their accuracy. The results were both astonishing and clear: the correlation between their estimates and the actual performance of the S&P 500 was just below zero. When they predicted falling prices, they actually tended to rise (see Kahneman, 2014, pp. 323). Quarterly share buyback in USD million and number of participating companies rise/fall in line with the development of the stock market

Fig. 80: Share buyback of U.S. companies in relation to the index performance using the example of the S&P 500; FactSet Buyback Quarterly Report, June 2016.

Another limited rational behavior comes into light with regard to share buybacks. Corporate leaders decide to buy back shares when equity markets are booming (when they are in a so-called bull market). Conversely, share buybacks fall sharply when the markets are in a bear market. This fact, as shown in Fig. 80, is questionable insofar as corporate leaders buy back shares at a high price and as a result

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these funds can no longer flow into the development of the firm. In consequence, they are subject to →Herding, whereby an anti-cyclical buyback program would make more economic sense when share prices are falling. It should also be noted, however, that, due to the frequent linking of their bonuses to the share price, corporate leaders may be inclined to buy back shares in order to achieve the desired increase in the price of the company’s shares. In view of the Prospect Theory as a further research concept for the analysis of corporate governance conflicts of interest, the remuneration of company directors with stock options plays a significant role in the threat to the value of the company. Stock options are often granted to employees in management positions as part of the performance assessment. They can then be sold after a certain period, usually several years. When dealing with these options, certain limited rational behavior becomes apparent, which endanger the value of the company. This includes, in particular, the overvaluation of stock options, the overvaluation of objectively low probabilities and the tendency to manipulate company reports in order to obtain the corresponding options. Research results show that stock options are granted particularly at times when employees are particularly confident about the company’s prospects (Bergman/Jenter, 2003). If the security appears to be overvalued in the eyes of corporate leaders, they tend to grant stock options to the respective employees as part of their remuneration. However, this behavior is not in the interest of shareholders, since granting stock options in times of overvaluation limits the possibility of value-enhancing company takeovers, since some of the shares have already been allocated to employees and are therefore no longer available as acquisition capital. The granting of stock options also raises the expectation among the participating employees that they will be able to sell them on the derivatives market at a certain time in the best possible way. Consequently, they overestimate the prospects of rising share prices, which would generate significant earnings when selling the stock options due to the leverage effect of options. As an explanation for this behavior, the →Probability Weighting from the Prospect Theory (see chapter 6.2) can be used. According to this theory, market participants or, as in this case, employees with stock options tend to overestimate low probabilities of extreme events, such as sharply rising share prices at the time of the possible sale of the stock options. Consequently, it is possible that employees in management positions take more risks in their decision-making in order to achieve the highest possible share price increases. If this is the case, high-risk corporate decisions can be made which, in retrospect, may even endanger the existence of the firm. Many banks, for example, which prior to the financial crisis in 2008 invested in complex mortgage-backed securities, are exposed to this accusation. In order to align the interests of employees with those of shareholders, stock options can be granted to employees with management functions and thus also top

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managers. The supervisory board grants stock options in order to provide the relevant employees with an incentive to invest in projects with higher risks. However, suitable control instruments should be used to prevent the company’s managers from pursuing projects that are detrimental to the company’s long-term profitability due to the incentive structure that may have a short-term effect. This can be achieved by longer holding periods of the options or by loss sharing, which is also possible, though not widely used in the corporate world. As described above, it is therefore possible that the company’s management may make decisions that are not in the best interests of the shareholders, as the projects entered into could result in negative NPVs and thus lead to impairments. However, the company’s management may still be inclined to take these risks, as they are interested in a correspondingly high share price, which will be achieved if the decisions made are successful. It should also be mentioned that the corporate leaders often have no consequences to fear apart from jeopardizing their jobs (often cushioned with generous severance payments) and may therefore be inclined to take more risks than shareholders would wish to take in order to maintain and increase the value of their shares. An example of this is the merger between Daimler and Chrysler, which has since been reversed, under the leadership of Jürgen Schrempp, Chairman of the Board of Management of Daimler. In retrospect, the merger led to huge losses in the share price, which fell from EUR 101 to EUR 24 in 2003 within five years. Schrempp resigned 2005. Interestingly, the stock options he had received were designed in such a way that even if the share price fell, individual gains were still possible by dynamically adjusting the exercise price, even downwards. The increase in the share price is not only possible by increasing the risks taken, but also by fraudulent business transactions through balance sheet manipulations. Unethically behaving corporate leaders may even assume that the balance sheet manipulations committed by them to increase the share price will not be uncovered due to their baffling overconfidence. The collapse of Wirecard in 2020, a financial service provider based in Germany, is a typical example. Research results, such as those of David J. Denis, Paul Hanouna and Atulya Sarin (2005), show that stock options are used more frequently as a remuneration component in companies in which balance sheet manipulations have been committed. Furthermore, a certain composition of the supervisory board and a certain shareholder structure seems to encourage or limit balance sheet manipulation. For example, in companies with strong institutional investor participation among the shareholders, the danger is greater that corporate leaders commit balance sheet manipulations, as they run the risk of being dismissed more quickly if the set targets are not met. However, the risk of balance sheet manipulation is noticeably reduced if the supervisory board is made up of completely independent members. This is probably due to the fact that the supervisory board is less involved in the operational business of the company and can therefore check the processes in the company more independently. Accounting fraud scandals involving Enron, Worldcom, Tyco or most recently Wirecard are all attributable to over-motivated corporate leaders who, because of

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their potential loss aversion, took more risks and wanted to hide the mistakes of their decisions from the supervisory bodies. The loss aversion known from the →Prospect Theory is also applicable to investment decisions on a corporate level. Just as market participants on the capital markets keep underperforming securities due to their loss aversion, corporate leaders also tend to continue with loss-making projects. If the first signs of a negative profitability development of the investment project (negative NPV) appear, the corporate leader finds him-/herself in the negative area of the value function. In the hope of bringing the investment into the profit zone by means of additional budget allocations, corporate leaders also intend to leverage the sunk cost effect. An unethical aspect is added if the losses incurred due to the fear of being dismissed are covered up with fraudulent transactions (see Shefrin, 2007, pp. 127). From the perspective of Behavioral Finance, corporate leaders must be sensitized to the fact that losses incurred are accepted and communicated at a certain, previously agreed level. This basically corresponds to a “stop-loss order” among private investors. In this way, they can, for example, avoid the risk of life-long prison sentences to which the corporate leaders of the above-mentioned companies have been sentenced. As a result of the increasing number of accounting manipulations since the Enron accounting scandal became public, legislation in the U.S. has enacted the SarbanesOxley Act (2002). This act is intended to change the financial reporting of listed companies to such an extent that in the future the financial statements will be signed by the Chief Executive Officer and the Chief Financial Officer, who can be held criminally liable in the event of balance sheet manipulation (see Morck, 2010, p. 467). In the area of corporate governance, the focus is on the remuneration of the company’s management and the resulting risks to the overall profitability of the company. The behavior of corporate leaders can be influenced by overconfidence and loss aversion.

11.5 Equity Premium Puzzle As already discussed in chapter 2, the expected return on a security should depend (linearly) on the risk associated with the investment; at least this is the neoclassical view. Empirical studies (e.g., Dimson/Marsh/Staunton, 2002) suggest, however, that in the long run the risk of shares is hardly higher than that of bonds, while the return is almost twice as high (Equity Premium Puzzle). Either the measure of risk in neoclassical models is unsuitable or investors’ perception of risk does not correspond to the models (see Forbes, 2009, pp. 269). Before we look at possible explanations for this phenomenon from the perspective of Behavioral Finance, it should be briefly mentioned that it also has implications for the financing of companies. It can be concluded, for example, that equity fi-

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nancing becomes more “expensive” for a company the greater the excess return. This is another possible explanation for the fact that in practice, if possible, debt financing is often preferred to equity financing. A concise explanation for the Equity Premium Puzzle is provided by Behavioral Finance research starting with the work of Shlomo Benartzi and Richard Thaler (1995 and 2001). They draw on two well-known distortions to explain it: →Myopic Loss Aversion (with a short time horizon) and →Mental Accounting. Especially when investors who tend to review the returns on their investments at short intervals (e.g., annually or even sub-annually) are inclined to demand compensation for a potential loss that may arise in the short-term. In this case, the risk premium would therefore depend on a measure of risk that takes into account short-term fluctuations in returns. This effect is even more pronounced when investors consider their securities investments in separate accounts. This would not result in an average calculation in which losses of individual securities are offset against profits of others (see Forbes, 2009, pp. 272). Last but not least, of course, the question arises in this context, too, as to whether this investor behavior is irrational in the strict sense. Chapter 13 examines the neuro-biological roots of this behavior in more detail. Even without this reference, however, it should already be clear that the loss aversion has a justification in terms of evolutionary biology. Centuries, if not millennia ago, a relatively smaller profit from an economic activity was regrettable, but it was also manageable (e.g., lower crop yield). Whereas, on the other hand, a loss could well be accompanied by a threat to livelihood (loss of livestock). In this respect, it is easy to understand that our mental processes are still marked by this loss aversion, especially since our brain only develops in small steps in evolutionary-biological terms (see Forbes, 2009, p. 280). Equity returns are often higher than their risk profile in the long term would imply (Equity Premium Puzzle). This can be explained with the help of shortterm loss aversion and mental accounting

Summary Chapter 11 This chapter focused on investment (capital budgeting) decisions made under uncertainty from a corporate perspective. It became clear that overconfidence on behalf of corporate leaders represents a potentially grave danger to the firm. The overestimation of one’s own capabilities can be abetted by a number of factors. The complexity and heterogeneity of the decisions to be taken play a decisive role, as does the prospect of promotion in the course of successful projects. In addition, the basic disposition of the company’s management to overconfidence also plays a role.

Summary Chapter 11

305

Overconfidence leads corporate leaders to being surprised by unexpected changes. They underestimate risks that may arise in the course of an investment project. The effects of overconfidence could be limited by, among other things, actively reviewing the investment results and considering the effects of self-attribution. In addition, the prudent calculation of future cash flows may limit the tendency to over-invest due to overconfidence. Furthermore, this chapter focused on answering the question of why companies pay dividends to shareholders. Although Behavioral Finance research has not yet produced clear results, there is a hypothesis that dividends are paid out as backstop against investment decisions by corporate leaders that might be harmful to return and risk expectations of shareholders. Similarly, dividend payments could be understood as a signal function in the course of asymmetrical information distribution. In addition to general motives, behavioral factors are also worth mentioning as reasons for the dividend payment, such as the separation of the company’s dividends and share price gains in the context of mental accounting. The IPO of a company is also within the focus of Behavioral Finance research in the context of entrepreneurial decisions, focusing on three phenomena: Issue prices that are too low to allow significant underwriting profits; temporary IPO booms that additionally attract other companies; and the longterm negative performance following the IPO. Next, the impact of the remuneration of corporate leaders and managers on decision-making and thus on the overall profitability of a company was examined. It became apparent that the remuneration of corporate leaders can be distorted by the overconfidence of the corporate leaders themselves and that of the supervisory board. In addition, it became apparent that corporate leaders could be prone to balance sheet manipulation due to the loss aversion, if they wanted to achieve rising share prices with regard to their remuneration, partly through stock options. It has been shown that corporate governance should also take psychological influences into account in order to reduce risks and ensure the overall profitability of the firm. During the discussion of the Equity Premium Puzzle, it became clear why it can be explained from the perspective of Behavioral Finance that the returns on shares in the long term are higher than their risk profile would imply.

12 Financial Nudging ‒ behavioral approaches for better financial decisions In the following chapter you will learn how the economic policy approach of nudging can be applied to financial issues, especially in consumer policy. After a brief introduction to the concept of libertarian paternalism, you will learn which behavioral insights form the basis for nudging approaches. You will learn the basic principles of nudging and apply them to specific financial decisions.

12.1 Libertarian Paternalism Libertarian paternalism is based on a central question concerning the economic view of human beings: Does a person always make the best decision for him- or herself or at least better than someone else would make it for him or her? And does the person behave rationally, selfishly, and is he or she omniscient? If one affirms these questions, one is moving within the framework of the assumption of homo economicus, who always finds the optimal solution and claims not to be restricted in the freedom of choice. Richard Thaler and Cass R. Sunstein counter this assumption: “The false assumption is that almost all people, almost all of the time, make choices that are in their best interest or at the very least are better than the choices that would be made by someone else. We claim that this assumption is false ‒ indeed, obviously false. In fact, we do not think that anyone believes it on reflection.” (Thaler/Sunstein, 2009, p. 10) Empirical studies and a closer look at reality reveal that humans act with limited rationality. As described in chapter 3, the market participant tends to be guided by emotions and heuristics and makes decisions that cannot always be rationally justified and can therefore lead to disadvantages. Those who follow this assumption of the human nature arrive at the idea that it would be useful for individuals to receive some form of support in their decision-making behavior. This can be explained by the example of overweight: It can be assumed that not every person concerned behaves optimally in the medical sense in order to promote his or her health. The city of New York therefore tried to ban large drinking cups for sweet and savoury drinks in order to counteract the obesity and associated illnesses such as diabetes mellitus of many citizens from the metropolis. This kind of politics is called “hard” paternalism. The state restricts its citizens in their freedom of choice through regulations and prohibitions and thereby patronises

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them to a certain extent. It does this because it believes that it can thereby increase the level of benefit to its citizens.97 Thaler and Sunstein are promoting a “gentler” alternative on the same basis. They call this “libertarian paternalism”. It is characterized by two features. On the one hand, it should not interfere with people’s freedom of choice. They should be able to decide in freedom for the action they want. On the other hand, it should at the same time enable public and private institutions to influence consumers in their behavior in a certain direction that aims at their long-term well-being and is in line with their long-term goals.98 A cafeteria can be taken as an example, which wants to promote healthy eating habits of its guests. As such, the guests can be influenced in their behavior by making healthy meals easier to reach than fast food. However, if someone wants to eat fast food, the person should be able to do so as well.99 Thus, libertarian paternalism is sometimes called the “real third way” of state economic and regulatory policy in the 21st century. At first glance, however, it cannot be assigned to any political orientation, but is first and foremost committed to promoting efficient tools to help individuals follow their preferences (see Neumann, 2013, p. 3). In the following, the topics of decision-making architecture associated with libertarian paternalism, the importance of freedom of choice, the instruments of nudging and the limits of libertarian paternalism are explained. Libertarian paternalism is characterized by two features. Firstly, it should not interfere with the freedom of choice of individuals. They should be free to choose the action they want. On the other hand, it should make it easier for them to choose a socially and individually desirable alternative.

12.1.1 Choice architecture

The way in which a decision-making situation is designed and then presented is called decision architecture or choice architecture. In this respect, the considerations are closely linked to the →Framing Bias. The public urinals at Amsterdam Airport provide an impressive example of how decision architecture is handled. Inside the porcelain bowl, stickers of small black 97 See Lestch, Blain, 2014 and Horn, 2013a; it could also be argued that the state is reacting to negative external effects, which have to be borne by society as a whole as health care costs. 98 That such an approach gives rise to a whole range of fundamental criticisms is discussed in detail in chapter 12.1.4. 99 See

Thaler/Sunstein, 2003, pp. 175.; it is important to note, in this example, that it is clear from a nutritional science perspective which foods are healthy.

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flies have been placed at a point that minimizes the splashing effect. Visitors are influenced by the fly and aim at it. This reduced spillage/splash by 80 percent.100 The choice architecture is based on an essential idea: Decisions to act do not always optimally reflect people’s preferences. It is therefore not possible to reliably infer the preferences of an individual based on the action he or she has taken. Just because a person performs an action does not mean that he or she prefers that action over all other possible alternatives. Libertarian paternalism tries to present the different alternatives within the decision-making situation in such a way that the person may have the possibility to actually express his/her preferences (see Sunstein/Thaler, 2003, p. 1162). In addition, the systematic occurrence of anomalies and distortions should be counteracted in order to enable the individual to behave more rationally. In this way, the decision-maker can be presented with a situation that attempts to compensate for anomalies in order to enable him to act according to his individual preferences without distortion. It is important to note that there can never be a completely neutral design, as decision situations are always perceived individually. Depending on how the decision is presented, it always influences the person in his or her behavior. However, the choice architecture can be designed in such a way that certain alternatives are highlighted and influence the decision-maker in his behavior. The idea is that this should be done in accordance with the long-term preferences of the individual. The choice architect is thus responsible for the decision framework. He or she determines, organizes and forms the context in which the decision-making situation is presented to an individual. Both public and private institutions can do this. As a public institution, the state plays this role particularly often. In addition, its decisions regarding the choice architecture reaches large parts of the population and must therefore be carefully weighed up. In the same way, incidentally, many individuals are in the position of choice architects in their professional life or everyday life; e.g., a doctor who presents possible treatments to a patient, a car salesperson who wants to sell a car to a customer, a bank employee who presents different forms of financing to a customer or a parent who raises his/her child and shows him/her possible courses of action (see Neumann, 2013, p. 33 and Thaler/Sunstein, 2009, p. 3). 12.1.2 Freedom of choice and paternalism

One could conclude that libertarian paternalism is a contradiction in terms. Thaler and Sunstein deal extensively with the question of how to reconcile liberal choice with regulations and other paternalistic acts (Sunstein/Thaler, 2003, p. 1160).

100

See Vicente (2006), pp. 84; of course, one must again assume that both effects are desired individually or by society as a whole.

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Sunstein expresses the importance of freedom of choice as follows: “The general maxim of the libertarian paternalism we outline and advocate demands that people can simply avoid the option suggested by the planners.” (Sunstein, 2007, p. 291) A human being must not be denied the whole range of alternative actions. Thus, if a state does not exclude any alternatives and individuals retain free choice over their actions, it is in the spirit of libertarian paternalism.101 Many people are sceptical about paternalism. Thaler and Sunstein see the reason for this in two erroneous assumptions. On the one hand, the view is deceptive that one can avoid influencing people. In many situations decisions have to be made that influence others. Secondly, the assumption that paternalism must always be accompanied by coercion is misleading (see Thaler/Sunstein, 2009, p. 10). Libertarian paternalism does indeed aim to influence individuals towards a specific alternative that corresponds to their or society’s preferences. However, it leaves them all freedom in their decisions, so that they choose the alternative that they prefer. Through the decision architecture, economic subjects are given the opportunity to express their preferences.

12.1.3 Types and characteristics of nudging

This brings us to the question of how specific incentives can be designed for individuals which want to move a preferred alternative into the foreground. Thaler and Sunstein call this approach “nudging”. Literally, the person gets a small and gentle “nudge” in a certain direction. In today’s world we encounter nudges every day, e.g., a GPS that shows a “best” route on a map, an SMS that reminds us of an appointment or simply on the changed gate at an airport, information on food that lists various nutrients or the default settings on smartphones and laptops. In addition to freedom of choice and coercion, a libertarian paternalistic nudge should satisfy two other principles. Firstly, the nudge should be transparent. In particular, in the case of nudges designed by governments, it is important that they are made recognisable to consumers. The actions and policies of the government of a state are the subject of public discussion. No consumer should be manipulated or cheated by such a nudge. Rather, he or she should be fully aware that a nudge is present. The second principle is effectiveness. A nudge should be effective so that many consumers are guided by the incentive. 101 In

order to discuss the problem of freedom of choice, which leads to the expression of individual preferences, even more intensively, a more in-depth analysis is required, but this is not the subject of this book is. See also chapter 12.1.4.

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One of the advantages of a nudge is that they usually have relatively low costs and can still be very effective. A small nudge can have a large impact. With the help of cost-benefit analysis, it should therefore always be considered whether the nudge makes sense. The benefits should of course outweigh the costs. Otherwise, it may be possible to use other instruments that achieve better results. Whether a nudge is effective can only be predicted to a limited extent. Even if one has rational and conclusive expectations for a nudge’s result, it can have a completely different effect in reality. Therefore, it is important to test nudges in reality and look for evidence of their effectiveness. Empirical work is one of the keys to an effective nudge. Many approaches to nudges are therefore, in our context of financial nudges, a follow-up product of essential empirical findings from Behavioral Finance.102 Based on the above-mentioned findings, The Behavioral Insights Team103 developed the basic concept “EAST” for the design of nudges.

easy timely

attractive social

Fig. 81: The “EAST” concept of The Behavioral Insights Team104

The “EAST” concept provides decision architects with a simple and understandable guideline on how nudges are ideally structured to stimulate specific behavior. It consists of the initial letters of the four principles “easy, attractive, social and timely”. Make it easy The more information available and the more complex a decision situation is, the more difficult it is for a person to make a decision. Much of this information available is insignificant and complicates the decision-making process. Therefore, a decision architect should make sure that the information is presented in a simple and easily comprehensible way. An example of this is standard specifications or simplified messages. The use of simple language, concrete recommendations and the omission of irrelevant information are useful starting points.

102 Sunstein

(2014), pp. 583.; see also the results presented in Chapter 3.

103

The Behavioral Insights Team is a company in cooperation with the Government of Great Britain and the Nesta Foundation. It is considered one of the most important institutions dealing with the practical implementation of nudges; see also Halpern (2015).

104

Halpern et al. (2014).

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Make it attractive Individuals tend to opt for attention-grabbing alternatives. If, in addition, the decision situation is presented in an attractive way, this has a positive effect on the willingness to act. Nudges should therefore ideally attract attention and make the recommended decision attractive for the individual. Make it social Humans are social beings. They let themselves be influenced by other people through their behavior. This can also be used to emphasize a particular alternative. For example, one can emphasize the desired behavior by using other people as examples or one can have an individual make a recommendation to a third person. If an individual perceives how other individuals behave in the same situation, this has a noticeable influence on his/her own behavior. This also applies in the other direction. If someone tells a third person that they want to perform an action, it is more likely that the sender of the message will actually perform that action. Make it timely To encourage a certain behavior, it is important that the nudge comes at the right time. Ideally, the individual can act directly and immediately. This is less about forcing decisions under time pressure. However, a nudge should allow for a timely reaction before the relevant decision is lost in the crowd of further pending decisions (see Halpern et al., 2014, pp. 8). Based on the above principles, ten important characteristics of nudges are now explained, which can be found in almost all fields suitable for nudges. Not all of the characteristics for successful nudges need to be fulfilled in each individual case. Nudges should simplify the decision-making situation in an attractive way. They should start at the right time and take into account that people can be influenced by the actions of other people.

Ten important characteristics of nudges 1. Default rules/standard specifications

6. Warnings and graphics

2. Reduction of complexity

7. Commitment

3. Social norms

8. Reminders

4. Strengthening simplicity

9. Intended implementation

5. Communication of relevant information

10. Historical information

Fig. 82: Ten important characteristics of nudges105

105

Own presentation based on Sunstein (2014), pp. 585.

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1. Default rules/standard specifications Default rules are measures such as pre-settings, standard defaults or automatic memberships. If, for example, an employee is automatically included in a financial pension scheme, the individual savings volume can be increased significantly. The same applies to health insurance or technical equipment. For individuals, the time and effort required to select all contract components/settings themselves is often too great. Certain default settings are perceived by people as a relief and are only changed if necessary.106 Default rules are one of the most important features of nudges, since pre-settings and automatic memberships are familiar to people in many places in daily life. 2. Reducing complexity107 In many countries around the world, complexity in application and application procedures is a serious problem because it can increase costs for those involved and tends to discourage people from applying. If a program is too confusing, opaque, or simply too complex, fewer people enrol or do not apply at all. These programs can be in areas such as health, education, finance or employment. The realizable benefits of the programs are not exhausted. Ideal would be simple, intuitive programs to increase the effectiveness. 3. Social norms If an individual is shown how most people behaved in this situation when making a decision, this has a great influence on his or her actions.108 The more local and concrete the information is, the stronger the effect; for example, in the case of the electricity bill: by adding the information “most people in your area have used less energy”, the electricity consumer in question is encouraged to rethink his or her habits. In this way, possible goals such as reducing energy consumption can be supported. 4. Strengthening simplicity Often people choose simple things. When individuals easily understand what is at stake and quickly grasp what they can do, they tend to act earlier and faster. This nudge is closely related to reducing complexity. It can reduce possible barriers or inconsistencies in the choice of a socially desirable alternative to reduce the perceived difficulty of grasping the alternatives. In this sense, the possibility of keeping the telephone number when changing a mobile phone provider is a nudge in favour of the desired competition in this industry. 5. Communication of relevant information109 In many cases the information situation is opaque, incomplete or too complex. However, if a person were to be given relevant information in a prominent posi106

This also corresponds to the →Status-Quo Bias.

107 See

→Ambiguity Aversion.

108 See

also the description of →Herding.

109 This in turn is closely linked to „2. Reducing complexity“ and „4. Strengthening simplicity”.

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tion when making a decision, this could influence his or her actions. For example, many bank customers are not aware of the interest rate of their overdraft facility and therefore often make use of this comparatively more expensive loan. A simple indication of when a customer wants to take advantage of it associated with its costs could influence the decision. 6. Warnings and graphics If a decision involves considerable risks, it is a good idea to draw attention to these risks by means of warnings, graphics or other attention-grabbing displays. The images of secondary diseases of smoking on cigarette boxes are an impressive nudge based on this observation. Large, colored or bold words, sentences or symbols can also be instruments of warning. In the long-term, however, people can be conditioned to such warnings. There is therefore a danger that individuals will trivialise warnings and believe that the risks do not exist for them in particular.110 Here it is advisable to work with positive information, for example with motivating or rewarding measures. It has been shown that people are less inclined to trivialise a warning if concrete steps to reduce the risk are suggested. 7. Commitment Almost all individuals set goals in their lives. These include, for example, saving more money, achieving a desired weight, learning a language or achieving a very personal goal. It has been shown that people are more likely to achieve these goals if they commit themselves to as concrete actions or milestones as possible before they reach them.111 People are thus more motivated. Yale University scientists offer a homepage for this purpose (www.stickk.com). If someone wants to achieve any goal, he or she commits to a certain action in case of non-achievement. A referee, e.g., a friend, decides in the end whether the goal has been achieved or not. Whoever wants to can share the challenge with other people. It has proven to be very effective when people commit to a certain “penalty” in case of failure and other people follow the whole process of the challenge. It is this combination of commitment in case of failure in combination with the social level that makes people more likely to turn their planned actions into reality. 8. Reminders In today’s world, people have to take care of many things. So, it can happen that some matters are unconsciously forgotten. The reason for this lies in human nature, which is a combination of forgetfulness, postponement, prioritization, and inertia.112 To counteract this, reminders can influence individuals in their actions. The timing of the reminder plays a significant role and the possibility to react immediately to it. A simple SMS or e-mail can be sufficient to remind a patient of the expiring vaccination and at the same time recommend that he or she make an appointment with a doctor as soon as possible. 110

For instance, in the sense of →Overconfidence or the →Optimism Bias

111 In 112

a certain sense, this corresponds to opening a →Mental Account.

This refers directly to the → Availability Bias and to the →Recency Bias.

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9. Intended implementation A message such as "Do you intend to vote?" makes people more likely to become active than if their intention is not addressed. Simply asking whether individuals want to implement a certain planned action increases the probability of actual activity. Appealing to a person’s already documented characteristics, such as “Your previous actions lead one to expect you to vote”, has similar effects.113 10. Historical information Public institutions can have a lot of information about the behavior of people in the past. People tend to be forgetful. Therefore, reminding individuals of their own past actions can have a great impact.114 By doing so, they will be able to come to terms with them and possibly change their habits. For example, if current electricity bills are much higher than in the past. It is possible that the customer has changed his consumption pattern without being consciously aware of it (see Sunstein, 2014, pp. 585.). 12.1.4 Criticism of libertarian paternalism

The still young idea of libertarian paternalism is met with criticism from a scientific point of view and also from economic policy practice. Both are quite necessary for a constructive further development of the approach in order to ultimately develop concepts that can be applied in practice on a broad basis. The statement by Thaler/Sunstein that basically a form of paternalism is unavoidable in every decision-making situation can be doubted in its general validity. In fact, decisions are very often performed by other people or presented in a decision architecture. However, this is not always the case. At least there are situations in which the starting position is not consciously influenced by third parties in the sense of nudges. Thus, it is quite common in political controversies for different positions to be underlined by a conscious and targeted presentation of information. This is rather an expression of political competition and less the result of nudging efforts. Furthermore, the normative validity of rational decision theory is criticized. Irrational behavior at the micro level can result in different effects at the macro level. Thus, if an individual behaves consistently according to irrational aspects, this can lead to the provision of public goods or the reduction of externalities. An example of this is when an entrepreneur tends to take excessive risks out of a tendency to overconfidence. This may be detrimental to the individual, but society may benefit if the entrepreneur’s behavior drives innovation and provides development opportunities for other entrepreneurs, even if he or she fails.115 113

This procedure uses the →Selective Perception, as it specifically addresses certain plans or actions. 114 115

See also above 9. Intended implementation.

See Binder, 2014, p. 526; an example of this is the development of the railways in the U.S., where it was the ruinous competition between railway companies that led to a branched rail network.

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Jan Schnellenbach criticizes that a single uniform criterion is insufficient to determine the best interest of the decision-maker. Every person is measured by a one-size-fits-all, e.g., if only an average weight is applied in the case of overweight, instead of also including other criteria such as the genetic prerequisites. Furthermore, this approach ignores a principle of Behavioral Finance, namely the heterogeneity of people. Whether someone wants to save more money or lose weight depends on the life situation and personal preferences. This may not be sufficiently taken into account if only one single, uniform decision criterion is used (see Schnellenbach, 2012, p. 270). Robert Sugden points out that libertarian paternalism apparently assumes that the decision-maker acts rationally only to a limited extent and is influenced by distortions and heuristics, while the “choice architect” is acquitted of all this. The “choice architect” acts benevolently and has no problems in finding out what is rationally the best decision for the consumer. Mario Rizzo and Glen Whitman add that while limited rationality can be assumed for the individual from the perspective of Behavioral Finance, the assumption of the epistemic authority of the choice architect can be criticized. How is the choice architect supposed to know what the preferences of individuals are and what distortions they are subject to in order to shape the decision appropriately? The evaluation and assessment of what is most conducive to the well-being of the individual can diverge between the choice architect and the individual, even if perfect rationality is assumed. A uniform definition of well-being is difficult to find because of the individuality of people (see Sugden, 2011, pp. 6. and Rizzo/ Whitman, 2009, p. 737). Even if the preferences of the individual are known, this would not suffice to create an optimal decision architecture for the individual. One would have to know what concrete distortions the individual is subject to, to what extent he or she does react to these, and what interrelationships exist between the distortions. It is also not yet clear who should take on the role of “choice architect” in a particular situation. Is it the government, a panel of experts or a private company? Who is responsible, for example, in the case of financial retirement planning? Only the state or financial institutions, or both to varying degrees? There is also a discussion about whether the introduction of far-reaching nudges will deprive people of the opportunity to learn about their preferences in their own way and pace. By prescribing certain decision paths, people are not able to explore their own preferences themselves, which can lead to negative yet useful experiences, but may follow the preferences of the “choice architect”. Possibly this leads to controlled preferences and is even strengthened by repetitions. Therefore, it should also be considered how learning processes can be reconciled with libertarian paternalism (see Reisch, 2009, p. 39 and Binder, 2014, pp. 531). Thaler and Sunstein themselves address the so-called slippery slope criticism in their publication “Nudge”.116 This means that a government may initially act with libertarian paternalistic approaches, but then gradually moves away from them 116

Thaler/Sunstein (2009)

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and ends up working with hard paternalistic measures (see Thaler/Sunstein, 2009, pp. 235.). To prevent this, institutional structures must be established. For Riccardo Rebonato, the slippery slope issue therefore represents a subliminal possibility for manipulative advertising measures to influence the outcome of a decision-making process (see Rebonato, 2014, pp. 368). And although David Henderson does not fundamentally reject libertarian paternalism in this context, he does take up the issue that every human being, including the “choice architect”, is subject to distortions and heuristics and that the recommendation of such a person should therefore be critically questioned. Furthermore, he draws a connection to Hayek’s knowledge problem (see Henderson, 2014, p. 273.) Friedrich August von Hayek117 describes that knowledge cannot simply be added up. It is limited, subjective and locally scattered over economic subjects and society. Since the state, too, has only incomplete knowledge, Henderson (and Hayek) concludes that it should rather be ascribed a passive role in people’s lives, especially when it comes to dealing with individual preferences (see Horn, 2013b, and Henderson, 2014, p. 273). And finally, one problem should be pointed out that has so far received little attention in the scientific discussion. When discussing nudging approaches, a clear distinction should be made as to whether nudges attempt to compensate for distortions or whether nudges exploit distortions in order to achieve overriding political goals. This distinction is not always easy in individual cases. On the one hand, a nudge who brings all voters to the polls by means of a reminder SMS can be classified as democratically legitimized positive. It helps voters to express their political preferences. However, if the nudge primarily addresses voters of a certain political direction, it would be critical. As a consequence of all these points of criticism, we believe it can be said that transparency in dealing with nudging approaches is of central importance; transparency about which nudges are set by which “choice architects”. This is indispensable to open the political discourse and, if necessary, to take measures against possible manipulation. Both are necessary in turn to increase the social acceptance of the new political instrument of nudging and to establish it in the long-term. The discussion of the nudging approach refers to three problems, among others: First, who ensures that preferences are not manipulated by nudging? Secondly, how transparent must nudges be? And thirdly, are nudges allowed to exploit distortions and heuristics to achieve political goals?

12.2 Financial nudging approaches Nudges, as already mentioned, have many different fields of application. In some countries, attempts are already under way to translate behavioral insights118 into Friedrich August von Hayek | Austrian-British economist and Nobel Prize winner 1974 | 1899-1992

117

118

See e.g., Sousa Lourenço (2016)

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political practice, for example, with reminder text messages on health issues in Mozambique, on energy consumption in the UK or on the credit market in the U.S. and South Africa (see Nesterak, 2014). With the White House Social and Behavioral Sciences Team, The Behavioral Insights Team and MindLab, the governments of the U.S., UK and Denmark, respectively, have already established institutions that also deal with behavioral science applications. They are pioneers in that area in the international arena. Whereas Germany, for example, is several years behind in this development and only began to build up relevant structures in 2014. In mid-2014, the German government set up three consultant posts in the staff for Political Planning, Policy Issues and Special Tasks, which are dedicated to behavioral economics in political issues (see Plickert/Beck, 2014). In addition to institutions linked to the government, private companies such as Allianz119 or Barclays120 have also set up “Behavioral Finance Teams”, which attempt to develop company-specific strategies in the financial services environment leveraging behavioral science findings. The considerations that now follow focus on nudging approaches in financial products and services. We will use the term financial nudging for this. The following explanations are structured as follows: First, we will answer the questions of why behavioral economics is relevant in finance and which distortions and heuristics can occur in concrete terms. This is followed by an explanation of nudging approaches already used in various countries for various financial products and services: credit cards, mortgages, pensions and shares/bonds. 12.2.1 Behavioral science foundations of financial nudging

There are a number of reasons why Behavioral Finance can contribute to a better understanding of user/consumer behavior in financial services. Many financial products are not easily understood by the average consumer.121 For example, the fee and cost structure is often so complex that a simple interest rate on a loan, for example, usually does not reflect the total costs. A consumer can therefore not easily estimate how high the costs incurred can be in comparison to other products, e.g., in retail when products can be compared easily. When simplifying the complex issues through the use of heuristics, people can make wrong decisions.

119

http://befi.allianzgi.com/

120 https://www.investmentphilosophy.com/ 121

In this book, we have not gone into the whole subject of financial literacy in greater depth. At any rate, according to recent studies it is undisputed that users of financial services are usually hardly able to understand the complexity of financial products, whose benefits very often depend on a random process (e.g,. in the case of securities); see e.g., Aprea (2016).

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When it comes to financial issues, consumers often have to weigh up the future and the present. There is a tendency for individuals to opt for things that are advantageous in the short-term and to ignore long-term risks. An example of this is when a person takes out short-term, easily accessible loans at excessive cost and underestimates the risk of repayment in the long-term. Especially with insurance and long-term investments, decisions often have to be made under uncertainty. In addition, individuals tend not to record risks statistically correctly. This can lead to them taking out unfavorable insurance policies or making unprofitable investment decisions. Another possible reason for irrational behavior is emotion. Decisions are made under the influence of emotions.122 For example, in the case of insurance, fear can have a strong influence, while a careful cognitive assessment of the situation is much harder to come by. Some important financial decisions are made not very often in the course of a person’s life, especially when it comes to retirement planning or financing a home. It often takes a long time to see whether they have been made sensibly and advantageously. An individual can therefore learn little from past mistakes. If possible, he or she must make a good decision the first time around. Therefore, irrational distortions and wrong decisions weigh particularly heavily in such cases. The following chart shows various distortions and heuristics that can occur in the financial decision-making process, subdivided according to the process stages and type of origin in line with chapters 7 to 9:

Fig. 83: Distortions and heuristics in the financial decision-making process

For example, when making financial decisions, people may tend to selectively perceive only positive information about an investment alternative. Emotions such as fear and regret aversion are drivers for insurance products that an individual 122

See also chapter 13.

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might not have taken out if he or she had looked at the probabilities objectively. Investors also tend to overestimate themselves and their abilities, or they simply follow the opinion of a certain group. All this can lead to decisions that are not in the best interest of the individual from a purely financial perspective.123 In the following, we will discuss nudging approaches on various financial products and services that aim to induce better decisions for the individual’s benefit. 12.2.2 Personal Loans

Consumers can either invest their own assets, e.g., invest money or take out a loan. There are different problems in this respect. First of all, this is explained when investing money and then when taking out a loan. A study by the UK Financial Conduct Authority (FCA)124 examined the influence of reminders on the changing behavior of account holders in connection with falling interest rates. People who invest money at an attractive interest rate for a limited period of time, e.g., as part of an introductory offer, tend not to withdraw their money from this account when the interest rate falls again. Rationally speaking, these people could invest their money in another account with the same provider at a higher interest rate. One reason for this may be that they ignore information that indicates this in the sense of selective perception, or they may underestimate the long-term higher interest rates in their compound interest effect. In the study, customers received an initial reminder two to three months before the interest rate was lowered. In addition, they were sent another reminder about the interest rate cut with the aim of finding out what effect further messages have on switching behavior depending on time. The result is that the reminders have a significant impact on customer behavior. The switching rate of customers who received a letter was 7.1 percent higher than that of customers without reminder. Here, the letter itself was more important than the actual content. A small proportion even switched to another provider, which may well be desirable from a competition policy perspective. So, in combination with the right timing, reminders are an effective instrument to influence people in their behavior. Individuals tend to take out short-term loans at high interest rates in order to fulfil their wishes in the present. In the long run, they may underestimate the cost of repayment and get into debt beyond their economic capacity. A hard paternalistic approach could make it more difficult for consumers to access such credit. Jonathan Zinman analyzed this in 2009 in the U.S. State of Oregon and found that the rate of borrowing declined. However, many people who previously took out overpriced loans switched to comparatively worse products, such as overdraft facilities and late bill payments with “penalty payments”. To this ex-

123

Further examples can be found in chapters 7 to 9 under the respective heuristics.

124 Adams/Hunt/Vale/Zaliauskas

(2015)

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tent, this policy approach has not achieved the socially desired goal (see Zinman, 2009, p. 554). At this point, the discussion could also be resumed as to whether such an intervention in consumer preferences is politically permissible at all. This would be justifiable if consumers were subject to systematic distortions, which in this case is probably not a matter of dispute. We tend to overestimate the value of today’s consumption in relation to that of tomorrow, and we often overestimate our ability to fulfil future financial obligations. In the same year Marianne Bertrand/Adair Morse125 chose a libertarian paternalistic approach for a study. They investigated whether borrowing can be reduced by providing information on the costs and risks of the product and possible alternatives. The study shows that, among other things, by graphically comparing alternative products and their lower costs, a reduction of 10 percent within 4 months was achieved. This approach seems to have the desired effect. Exorbitant sums of money are spent every year on emotionally charged advertising (e.g., for tobacco products or clothing). Can advertising, depending on how it is designed, have an impact on the demand for less than fancy financial products? A group of scientists conducted a study in South Africa in this context.126 The experiment was conducted with a local loan provider, which arranged the content of its advertising, the cost of the credit and the time periods of the offers according to the experimental design and sent them to former customers. The result suggests that the way the advertising is designed has a great influence on consumer demand. A smaller, easy to grasp number of loan examples, a picture of an attractive woman or the skilful presentation of possible uses have the same effect on demand as a reduction of the interest rate by a quarter. For Bertrand et al. it remains unclear at first why the advertising in the study influences people in such a way. Possible reasons could be that the advertising addresses the →Ambiguity Aversion of the customers. The complexity of the confusing decision-making situation is reduced by the advertising. This enables the customer to make a decision without getting caught up in cognitive dissonance. 12.2.3 Credit Cards

In many countries such as the U.S., UK, Australia or Sweden, the credit card127 is an intensively used payment instrument. However, the composition of the cost of a credit card is often not transparent and not easily understood by every consumer. For this reason, the UK government introduced annual credit card statements in 2011 to provide consumers with this information.

125

Bertrand/Morse, 2009

126

Bertrand/Karlan/Mullainathan/Shafir/Zinman, 2009

127 This

refers to a credit card for which not the entire accrued amount is payable at the end of the accounting period, but only a contractually agreed minimum amount. The remaining amount is granted as credit.

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The annual statements include information on usage, outstanding amounts, fee composition and alternative credit cards. They provide the customer with the most objective overview possible of credit card usage. In addition, consumers are always informed in detail before the interest rate is changed.128 Over the last two decades, the volume of credit card debt in Australia has increased significantly. Credit cards are the second most important credit product here after mortgages. The majority of users settle their debts within the contractual periods and without further consequences. However, a small proportion of credit card holders only repay a minimum amount and are responsible for the majority of the credit volume. By extending the loans, they are charged higher interest rates and are thus exposed to an increasing risk of payment default. This can have far-reaching social, educational and economic consequences, e.g., inadequate pension provision. From a Behavioral Finance perspective, such credit card users tend to display the →Optimism Bias and →Illusion of Control. They are of the overly optimistic opinion that they will be able to repay their debts in future. In addition, they often mistakenly believe that they can control the situation, but in fact they are often particularly susceptible to strokes of fate or other short-term developments. To support these people, Australia has introduced the National Consumer Credit Protection Amendment Act 2011, which consists of a mixture of libertarian paternalistic measures and hard paternalistic prohibitions. When a new credit agreement is signed, an information sheet on costs and repayment modalities must be submitted. In addition, a maximum credit limit must be set and the consumer must be informed if he or she reaches it or if only minimum repayments are made. A strict ban has been introduced on letters inviting consumers to increase their credit limit unless they have explicitly agreed to do so with appropriate justification. The breakdown of the various costs in the information sheet counteracts the optimism bias, since advertising is often sweetened with low fees in the short-term, but higher costs are incurred in the long-term. The consumer can now better judge whether he or she can afford the credit. Warnings from the credit card company when the limit is exceeded have an effect on the consumer’s behavior at the right time, as he or she has set the respective limit but may have lost control of his/her spending (see Ali/McRae/Ramsay, 2012, pp. 126.). A somewhat stronger nudging approach is an opt-out payback program. This would automatically demand the required repayment sum from a credit card holder when the defined limit is reached. If a consumer does not wish to do so, he or she can opt-out of the programme and agree to a different repayment period. However, this proposal also has possible disadvantages. Some consumers who follow the programme but would be able to repay their loans faster than the opt-out programme and wait for the payment signal would have to bear higher interest 128 See UK Department for Business, Innovation & Skills, Behavioral Insights Team and Cabinet Office (2011), pp. 21.

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costs than if they were to repay quickly. A paper by Michael S. Barr, Sendhil Mullainathan and Eldar Shafir includes proposals to take that into consideration. They were introduced in the U.S. in 2009 under the Credit Card Accountability Responsibility and Disclosure Act (CARD). The CARD Act aims to regulate various types of credit products (see Barr/Mullainathan/Shafir, 2008a, p. 12). Also, in the U.S. in connection with the CARD Act, a nudge is applied, which is based on the information received by credit card customers in a similar way as described above (see Agarwal/Chomsisengphet/Mahoney/Stroebel, 2013). Since it is quite possible that consumers only make minimum repayments, thus accumulating long-term debt and bearing higher interest costs, an information section has been added to each statement of account letter, which contains the following data: The full cost of repayment and the duration of the repayment in months, if only minimum repayments are made; in comparison, the full cost and the monthly repayment amount that would be needed to pay off the debt in 36 months. The cost of the loan if it is repaid within 36 months is lower compared to minimum repayments with a longer duration. However, some consumers only became aware of this when they were explicitly informed about it. This nudge has generated interest rate reductions of 0.5 percent per year for customers in the U.S. 12.2.4 Mortgages

One of the most important financial decisions in the life of the average consumer is the choice of property financing. The dream of owning your own home is firmly anchored in many people’s minds, also with regard to retirement provisions. Mortgages that are linked to financing usually involve combinations of long maturities and large amounts of money, where it is difficult even for experts to weigh up the alternatives sensibly. During the financial crisis as of 2008, the handling of mortgages in the U.S. has proven to be problematic and dangerous. Many mortgages have been underestimated by consumers not only in terms of expected costs but also in terms of the risks involved. Complex cost structures and information asymmetry between lender and borrower have contributed to this. For example, credit issuers took advantage of psychological characteristics of human beings when they emphasized a low initial interest rate and pushed long-term interest cost risks into the background. In addition, the majority of consumers are not financially educated enough to understand, let alone rationally compare product variations in the market (see Barr/Mullainathan/Shafir, 2008b). In order to improve the mortgage market from a consumer perspective, Barr/ Mullainathan/Shafir discuss a range of policy measures from weak to coercive. The range starts with the simple disclosure of relevant information to consumers and ends with the prohibition of certain products. With the sticky opt-out nudging approach the researchers propose a measure between the two extremes. It is based on a standard mortgage for consumers, which is bound to certain simple rules. However, the consumer can opt-out of the standard contract if he or she so wishes and choose a different mortgage. The contract would, for example, be tied to a fixed interest rate, a fixed term and clear risk assumption through standard rules set by an independent institution. A

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small range of standard contracts would also be conceivable. In addition to these, financial service providers could continue to offer other, individualized products. However, the consumer must be able to gain a quick overview of the risks and costs involved by means of clear information in the case of deviation from the standard contract. Moreover, the lender must demonstrably assume more risk in the case of such a non-standard contract. These additional requirements in the event of an opt-out would make the standard contract sticky129, as lenders would have an incentive to encourage consumers to stick with the standard version. Only in those cases where the individualized non-standard contract has clear advantages for both parties would the lender be expected to advise customers in the direction of the non-standard contract. Such a nudging approach is of course associated with possible disadvantages for both sides. In practice, for example, it could turn out that for too many people seeking credit, a mortgage other than the standard contract would be optimal due to an unforeseeable economic development. In this case, opting out of the standard mortgage would be nothing more than a bureaucratic burden and cost for both sides. On the other hand, minorities or people with low incomes could be disadvantaged, as it is precisely they who often benefit from more flexible and innovative products. If the standard contracts do not reflect this special feature, such population groups would be worse off. However, most of the unfinished ideas can be leveraged by taking appropriate measures as soon as the first experiences with the approach are made. 12.2.5 Pension provisions

In most people’s lives, a solid pension plan is of enormous importance in order to avoid getting into financial difficulties in old age. In many countries there is a trend towards shifting the responsibility for old age provision from the state to the individual. Government or employer programmes usually do not provide sufficient financial stability in the third phase of life, so that personal precautions and decisions have to be taken to be financially secure at retirement age. The timeline of old-age provision can be divided into the asset accumulation phase before retirement and the expenditure phase at retirement age. In order to gradually build a solid financial base, individuals often have to make decisions under uncertainty, because no one can correctly predict how interest rates, income or their own health will develop in the future. Under these circumstances, people are influenced by many factors such as emotions, the information base or the decision architecture. They understandably make use of rules of thumb or heuristics, which may be useful in some respects, but which can systematically lead to irrational decisions and thus to inadequate retirement provisions (see Benartzi/Thaler, 2007, p. 82). In addition to heuristics such as →Selective Perception, →Ambiguity Aversion, →Framing, →Loss Aversion, →Overconfidence or →Mental Ac129 A deviation from the standard contract would thus be made more difficult; it has a „strong attraction“, so to speak.

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counting, factors such as inertia (→Status-Quo-Bias), emotions and limited financial literacy also influence people’s behavior in the decision-making process when it comes to retirement provision. The following examples illustrate this in more detail (see Knoll, 2010, p. 2). Employers often offer programmes that subsidise employees’ voluntary savings by making further contributions. Such offers are thus potentially very lucrative for employees. However, by far not all employees take advantage of these offers, even if the full pension contribution is paid by the employer and only a registration of the employees is required. In the UK, only 51 percent of employees joined the scheme in such a situation, even though it did not involve any financial burden for the employee. This irrational behavior may be due, for example, to the inertia of individuals or the perceived complexity of the application process. One possible nudging approach is to automatically enrol employees in the program, but to allow them to leave without further complications if desired. This is called a change from opt-in to opt-out. This nudge is generally considered to be very effective and is used in many areas outside of retirement planning.130 In one case where an opt-in plan was offered, the membership rate was initially 20 percent and increased to 65 percent within 36 months after intensive conventional advertising. After the introduction of the automatic membership (opt-out plan), the rate rose directly to 90 percent and increased to 98 percent within a further 36 months. As a result of this nudge, employees are entering retirement programs not only earlier, but also in greater numbers, without any significant increase in the number of exits from the programmes in the examples known to date. A similar, but somewhat weaker, nudge is that employees are not automatically enrolled, but are asked directly the simple question of whether or not they want to take the program. Thus, individuals in a direct interaction situation have to indicate their preferences and deal with the issue. The New Zealand and UK governments are putting the above findings into practice. In New Zealand, the KiwiSaver program was introduced in 2007. A NZD $1,000 government start-up grant and other financial initiatives make the programme attractive. Everyone with a new employment contract automatically becomes a member and employees with existing contracts can join at any time. It is interesting to note that two thirds of the employees who actively joined the program also made an active investment decision regarding their pension strategy, while only 8 percent of the employees who were automatically integrated into the program became active. The rest were left with a standard investment product. This suggests, for example, that self-attribution/control illusion might be at work. In the United Kingdom, the National Pension Saving Scheme Act came into force in 2012, under which every employee is automatically integrated into a pension scheme under which he or she pays 4 percent of his or her salary while the employer adds 3 percent. The government was concerned that despite the financial attractiveness of the scheme, many employees would not take up the offer, as em130

See for example Reisch/Oehler (2009)

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ployees may underestimate the long-term benefits and prefer consumption expenditures in the present. Therefore, the government automatically enrolled employees in the program. Reliable results on the opt-out rate are still pending. However, there are strong indications that over 90 percent of the employees remain in the program. For many people it is difficult to find out their optimal monthly savings amount, as factors such as future interest rates, income, preferences and health cannot be reliably predicted. In addition, many lack the self-discipline to save more or they keep putting it off. A popular aid in the U.S. are the so-called 401(k) programs. Under these programs, the employer retains a portion of the employee’s salary for retirement benefits and usually subsidizes the amount to a certain extent. A savings program based on findings of Behavioral Finance is called Save More Tomorrow (SMarT) and was developed by Shlomo Benartzi and Richard Thaler.131 It aims to help people who lack self-discipline to save. It has four characteristics. Firstly, employees are familiarized with SMarT as long as possible before a salary increase. Here one makes use of the tendency of people to underestimate future actions or events. The employee only has to agree to SMarT in the present without direct consequences and is therefore more willing to join. Secondly, SMarT does not come into effect until the next salary increase. This counteracts the loss aversion, because the individual does not feel a loss in income, but only a lost profit, which causes less suffering. Thirdly, the savings rate increases with every salary increase up to a fixed maximum limit. Since people tend to inertia and the status-quo-bias, they will usually remain enrolled in the plan and accept the increase. Finally, it is important that individuals always have the option to withdraw from the program. This lowers the hurdle for entering the programme, because the employee knows that he or she can be released from the obligation at any time (see Benartzi/Thaler, 2004, pp. 170. and Cussen, 2015). SMarT was presented and introduced in different ways in three companies in different years and locations. It turned out that it has a strong influence on the savings behavior of employees. In one company, the amount saved tripled within 28 months. Positive effects on the savings volume were also recorded in the other companies. Once employees have joined the programme, only few leave it again due to inertia. In addition, the savings rate automatically increases with every pay rise, which counteracts the human characteristic of postponing such actions. It is questionable, however, whether an automatic SMarT membership would not be too paternalistic, but in its described form the measure is well compatible with libertarian paternalism. Surprisingly many workers in the U.S. avoid traditional 401(k) programs, although the economic advantages are obvious. One reason given for this is the varying degrees of financial education and competence of the population. A large part of the population is not aware that a 401(k) program includes employer subsidies and 131

See also chapter 10.

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tax benefits. Simple information nudges at the level of information perception can increase employee participation in 401(k) programs. Explaining the benefits of the program to people can change their behavior. Various studies show that the amount of the employer’s contribution also has a positive influence on the behavior of employees. In this respect, the target group seems to be quite receptive to economic incentives. Another reason for holding back on retirement provisions may be that people temporarily pursue other financial priorities, such as paying off a property or providing financial support for children’s university education. It is also possible that people do not participate in 401(k) programmes because they are already sufficiently covered via other means, such as by their spouse’s pension entitlements or savings that have already been accumulated. A simple e-mail or flyer with information about the 401(k) program and examples of interest and tax benefits will have different effects on different groups of employees. For example, for young employees between 18-24 years of age the pure information nudge seems to work best. This result suggests that different target groups must be addressed in different ways by the above-mentioned information measures in order to have the full effect (see Clark/Maki/Sandler/Morrill, 2014, pp. 679). Conrad Ciccotello and Paul Yakoboski132 from the Teachers Insurance and Annuity Association ‒ College Retirement Equities Fund (TIAA-CREF) Institute, one of the largest American non-profit financial services providers, also argue for different nudging approaches for different age groups. They investigated this question with regard to two generations: Baby boomers133 and Generation-Y134 . Generation-Y seems to be more open to nudges that have standard specifications; for example, if they are automatically included in a program that has a predetermined savings rate and invests in a pre-selected fund structure. Generation-Y accepts a lower level of privacy and is more influenced by the behavior of people in the same situation. Although they do not proactively seek advice, training and workshops seem to have a pronounced effect. For them it seems to be important to attract their attention and to involve them. Optional things do not seem to be so important, so that automatic membership could be an effective nudge. Baby boomers, on the other hand, have already made their first retirement decisions and therefore face quite different problems. A standard nudge seems less effective for them. They value privacy more than Generation-Y and therefore need a guide, which they trust above all. Therefore, individual nudging approaches are more suitable for baby boomers. The nudging approach to retirement provision could even be pursued further. A study from the UK shows that people who live on welfare or have a low income

132

Ciccotello/Yakoboski (2014)

133 Years 134

of birth approx. mid 1950s to mid 1960s

Birth cohorts approx. 1980 to 1999

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save particularly little, even against the background of low income.135 Two thirds of this group have no significant savings, but three quarters felt they should save more. In this context, a nudging approach based on →Mental Accounting could be used. It is then to be organized in such a way that these people pay slightly more rent than necessary. The surplus amount could then be put into an account by the social welfare office or another institution. In this way, the old-age provision could be supported over time. The heuristic of mental accounting is used here to the extent that the rent automatically includes an amount saved. The social welfare recipients would not consider the amount as retirement provisions, but classify them under the “account” rent. 12.2.6 Shares and bonds

This chapter follows on directly from the previous reflections on pensions and chapter 10. It is about how people, having decided to save money, can invest it better136. Basically, money can be invested in many assets such as real estate, commodities, art, jewellery, shares or bonds. This chapter will concentrate on equity and bond products, as these are often intended for retirement programs. In addition, many people perceive the decisions in these asset classes as particularly complex. For investors, the first thing that matters is how much risk they want to take. Usually, riskier products such as shares offer higher returns than more secure investments such as government bonds. Depending on the investor’s risk preference, it is now possible to invest in a combination of different equities and bonds. This results in the so-called portfolio structuring. For many people it is a complex challenge to find the optimal portfolio composition. Even if the composition is known, the money still has to be invested as cost-effectively as possible. In order to facilitate this, investment funds are often offered for retirement provision. These can invest according to many aspects such as industry, risk or company. The fee structure also differs from fund to fund. Employers also offer their employees their own company shares. The investment possibilities are complex and do not always have obvious advantages and disadvantages at first glance. Thaler and Sunstein name two frequent sources of errors when investing in securities. First, investors tend towards unfavourable timing when buying and selling shares. Following the herd instinct, they buy securities when they are at a high and sell them at a low price level. Secondly, investors use rules of thumb like the “1/n” heuristic. If they have “n” options, they spread their investment evenly across them. This highly simplified approach to diversification can lead to suboptimal results when applied naively, such as simply dividing the portfolio into 50 percent stocks and 50 percent bonds without analysing the risk and return expectations more precisely. In addition, the approach is vulnerable to the →Framing 135 136

Lemos/Crane (2013)

By „better“, we mean that for an individual risk appetite, a maximum return can be achieved in relation to the entire investment portfolio.

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Bias. It is therefore conceivable that a larger proportion of assets could be invested in equities if a particularly large number of investment opportunities are offered and taken up. To this extent, a whole range of financial nudging approaches is conceivable in the process of selecting investment categories. Since the investment process for retirement provision often takes decades and the composition of the portfolio is rarely changed by investors after an initial decision, regular feedback alone is a possible nudging approach. In the context of the perception of information, this helps to sharpen understanding of the effects of investment decisions (see Thaler/ Sunstein, 2009, pp. 128). Another approach in this context, which can facilitate information processing, is to divide the funds available for selection into three risk categories: conservative, moderate, aggressive. In this way, investors can invest in a suitable fund according to his/her preference. Strategies could also be designed in such a way that they are based on the investors’ retirement date. The closer the retirement date is, the more people invest in less risky securities or funds (see Benartzi/Peleg/Thaler, 2007, p. 22). Kenneth French and James Poterba137 documented the so-called →Home Bias already back in 1991. This is the tendency of investors to invest only in domestic securities. Investors obviously overestimate the return expectation and ignore the “cluster risk” resulting from poor diversification. If they were to invest in international securities, they could generate a higher return for a given level of risk. As a nudging approach, the country distribution of the portfolio could be analyzed in the course of a feedback discussion and, thus, possible starting points for improvements could be identified. Employees are often also offered corporate shares. In the U.S., 11 million participants in a 401(k) program invest 20 percent or more of their savings in their employers’ shares; a subgroup of 5 million people invest 60 percent or more. This can be risky for several reasons. Firstly, large individual investments represent a “cluster risk” in terms of diversification. Along those lines, there is usually a positive correlation between the development of the company’s share price and one’s own job. In the worst case, employees not only lose their jobs, but also a considerable part of their assets. Employees can often realize tax advantages when investing in their employers’ corporate shares, which are not granted with other investments. However, many employees are not even aware of this advantage. In addition, the disadvantages of the lack of diversification usually outweigh these tax advantages, because the risk of larger individual positions of company shares in the portfolio of the employee is not compensated. To avoid this harmful distortion, one could try to automatically diversify investors’ portfolios more. If an individual investment exceeds a certain hurdle, e.g., 8 percent, further involvement in this investment would be

137

French/Poterba (1991)

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discontinued or a step-by-step reduction would be initiated (see Benartzi/Thaler/ Utkus/Sunstein, 2007, pp. 45). In 2000, a pension scheme was introduced in Sweden, the decision architecture of which was analyzed in more detail by Henrik Cronqvist/Richard Thaler.138 The programme is fundamentally based on a laissez-faire approach and encouraged participants to make their own decisions. They can either choose a standard fund or put together their own fund strategy (mixture of different funds). To this end, participants were provided with comprehensive information on all 456 funds that are eligible for inclusion in the programme and fund service providers were allowed to advertise their products. About two thirds of the participants chose the pre-selected fund, which was compiled by a panel of experts. The other third, mainly people with higher investment volumes, women and younger people, chose their own portfolio. Which group had made the better choice? After three years, the standard fund lost around 30 percent compared to an average loss of around 40 percent for the actively selected funds.139 The performance of the standard fund during this period was so good in comparison with the benchmark and other funds that it received a top award from rating service provider Morningstar. A comparison of the two investor groups yielded some other interesting results. The self-selected portfolios held a very high proportion in shares: 96 percent (standard funds: 82 percent). In addition, investors tended to use the above-mentioned home bias when actively selecting portfolios, as they invested around 48 percent in Swedish securities (standard funds: 17 percent). The fund fees were 77 basis points higher than the costs of the standard fund. This means that people who actively selected their funds invested more in national products and almost exclusively in shares. And they paid higher fees. The Swedish example shows that too many choices are not beneficial for the investor. For many investors it is advantageous in the context of their retirement planning if only a small number of funds are offered; for example, three funds that are structured according to their risk tolerance, as described above. Any deviation from such standard products could be linked to a deliberate opt-out decision, similar to what is described for mortgages. Financial nudging has its use where consumers make decisions about financial products that appear complex and opaque from a subjective perspective. So far, product simplifications with opt-out options and targeted information processing (information nudges) have proven to be particularly effective.

138 See 139

Cronqvist, Thaler (2004), pp. 424.

The losses were related to the international financial crisis starting in 2008.

Summary Chapter 12

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Summary Chapter 12 In this chapter, so-called financial nudges were presented, which are intended to help people make better decisions about financial products and services. In the context of libertarian paternalism, nudging is a policy approach that influences individuals in their decision-making behavior towards a certain desirable alternative, while at the same time preserving the individual’s freedom of choice. Choice architects determine, organize and form the context in which the decision situation of an individual is presented through the socalled decision architecture. In the sense of libertarian paternalism, a nudge should not only grant freedom of choice, but also be transparent and effective. A nudge can therefore be designed according to the "EAST" concept (Effective, Attractive, Social, Timely). Typical starting points for nudges are standard specifications (especially "opt-out"), warnings and graphics, communication of relevant information, reminders or the reduction of the complexity of the task. In addition, the social behavior of other people, e.g., work colleagues, friends or family, influences the respective decisions. Financial decisions can have far-reaching consequences for individuals. In a person’s life, a wide range of financial products and services are used, but they are often complex and exploit the weaknesses of consumers. Many people make irrational decisions and would have made a different choice according to their preferences if they had been given a little help in making the decision. Financial nudging starts at this point and tries to improve the decision-making behavior of individuals in this sense. Last but not least, we would like to point out an aspect that has received little attention in the discussion so far, but which is of central importance for the acceptance of financial nudging. In order for financial nudging to be politically and socially acceptable, it must always remain transparent whether a nudge should lead to better decisions according to individual preferences or whether a nudge is used to promote politically desired decisions by exploiting a distortion. To this end, the question of what is financial-economic rationality in the first place and how can risk be handled under bounded rationality must be addressed to a much greater extent in order to dispel any fears of manipulation through nudging.

13 Further development of Behavioral Finance ‒ a look into the future In the last chapter of this book an outlook on new research directions within the Behavioral Finance research will be given. New food for thought and correspondingly new research results have already shifted the existing boundaries of Behavioral Finance. In this sense, the chapter leads to the mentioned boundaries and then presents two new research directions in Behavioral Finance. The main focus is on Neurofinance, which aims to investigate the causes of limited rational behavior on the basis of brain research. In addition, Emotional Finance is presented as the latest research area in which unconscious mental processes are investigated.

13.1 Limits of Behavioral Finance The findings of Behavioral Finance research led to considerable criticism of the →Neoclassical Capital Market Theory. The inadequate acknowledgement of the actual behavior of market participants was criticized. The correct incorporation of the information available on the market into the security prices was also strongly doubted, which led to ongoing criticism of Fama’s →Efficient Market Hypothesis. In view of the criticism voiced by Behavioral Finance, it is not surprising that this area of research is also subject to intense criticism. Two central points of criticism are discussed below:140  Lack of a consistent theoretical framework and  Doubts about the systematic existence of market anomalies The lack of a comprehensive and unambiguous explanatory model for the numerous behavioral anomalies is addressed with the criticism of the missing theoretical framework. The Prospect Theory is regarded as the decisive development for understanding the behavior of market participants in the investment process. However, it only covers a part of the investment and decisionmaking situations that are relevant in reality. In addition, numerous behavioral anomalies have been described, which provide a broad starting point for the development of behavioral explanatory approaches. However, the large number of psychological phenomena and their interdependence and contradictions present A further, more research-technical point of criticism is based on the fact that many distortions and heuristics were investigated in experiments with students. This experimental group is certainly not representative for all market participants. More recent research therefore also attempts to include other test persons in the analysis.

140

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Behavioral Finance scientists with the challenge of developing comprehensive, secure and stable structures and models. For example, the →Representativeness Bias shows that market participants either overreact or underreact to news, depending on the historical security returns. The same phenomenon is described with respect to overconfidence under the aspect of the market participant’s own assessment of the information gathered. The more private information the market participant has, the stronger is the overreaction to it with a corresponding effect on decision-making. The accusation that the existing explanatory approaches of Behavioral Finance are rather arbitrary is therefore quite understandable. Fama (1998), for example, criticises the fact that previous behavior-oriented concepts can only very selectively explain market anomalies (see Jaunich, 2008, pp. 64). In response to this criticism, it can be assumed that Behavioral Finance will have to produce further, more powerful explanatory approaches in the future, which will further deepen the understanding of the effects of limited rational behavior and help to better structure possible anomalies and distortions. Besides the lack of a consistent theoretical framework, the sheer existence of the market anomalies being researched is doubted. This point of criticism is even more serious than the first one, as they are one of the basic pillars of Behavioral Finance. In this sense, the following points of criticism are expressed specifically:  Allegation of targeted and biased data search MacKinlay (1995) and Black (1993) argue that research into market anomalies is plagued by pure ex post analysis of the data. Thus, market distortions are almost always found when a possibly biased selection of data is analyzed. In other words: Chances are that one will find “anomalies” in all probabilistic data sets.  Evidence of market anomalies depending on the methodology applied Fama confronts behavioral financial market research with the accusation that the detectability of market anomalies depends strongly on the methodology used and that long-term event studies in particular are prone to errors: “Reasonable changes in the approach used to measure abnormal returns typically suggest that apparent anomalies are methodological illusions.” (Fama, 1998) It also needs to be clarified whether the disappearance or diminishing of some market anomalies is an indication of more rational investor behavior and increasing market efficiency over time. Chapter 4.3.3 lists short-term market anomalies whose effects have weakened over time. For example, Hirshleifer (2001) documents the volatility of the value effect in the U.S. stock market at the end of the 1990s. Furthermore, Chordi and Shivakumar (2002) demonstrated that the momentum effect is only significantly detectable in cyclical upswings. According to Henker, Martens and Huynh (2006), this effect has no longer existed for the U.S. market since 2000 (see Jaunich, 2008, p. 68).

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Finally, Fama points out the lack of an alternative to the established neoclassical financial theory: “My view is that any new model should be judged [...] on how it explains the big picture. The question should be: Does the new model produce rejectable predictions that capture the menu of anomalies better than market efficiency? For existing behavioral models, my answer to this question [...] is an emphatic no.” (Fama, 1998) The continued recognizable importance of the models of neoclassical capital market theory is also evident from their broad application. Thus Goedhart, Koller and Wessels note: “It takes a better theory to kill an existing theory, and we have yet to see the better theory. Therefore, we continue to use the CAPM while keeping a watchful eye on new research in the area.” (Goedhart/Koller/Wessels, 2005) Nevertheless, some of the empirically proven anomalies turn out to be both methodically and temporally robust (see chapter 4.3.3). Investment strategies that exploit such systematic market distortions (as used by hedge funds, for example) therefore have the potential to generate sustainable excess returns (see Jaunich, 2008, p. 73). In this context, the empirical question can now be posed as to whether the corresponding market anomalies will continue to exist in the future and can provide for excess returns. Within the discussion about the limits of all economic theories, it must be noted that, according to the current state of the art, behavioral financial market research provides concrete lessons at least for the active investment management of private investors.

13.2 Emergence of Neurofinance/Neuroeconomics Over the last 40 years, researchers in the field of Behavioral Finance have used findings from psychology, sociology and game theory to analyze the decisionmaking of market participants. The findings have enabled the identification and description of market anomalies and limited rational behavior during the information and decision-making process. The exploration of the market participant and the formulation of recommendations to limit risk/return detrimental behavior is the first step in the development of a sound theoretical basis. However, there is no straightforward answer to the question of what causes the observable behavior. In the course of time, technical progress has enabled neuroscientists to record and analyze the chemical and electrical processes taking place in the brain during decision-making. The combination of Behavioral Finance and the research field of neurofinance now makes it possible to better understand the investment behavior of market participants. Neuroeconomics as an umbrella term for the research into the neuronal causes of economic action is capable of revealing the fundamental

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biological and psychological mechanisms that significantly cause the application of the heuristics presented and can also lead to herding, among other things (see Petterson, 2010, pp. 73). Thus, neuroeconomists are interested in the neuronal relationships that are responsible for the behaviors discussed in chapters 7 to 9 (see Walter/Abler, 2005, p. 368). In this respect, neuroeconomics is a subdiscipline of economics which, among other things, takes up the findings of Behavioral Finance and refines the knowledge gained from theoretical and practical studies based on the neural and biological processes of market participants. In this context, the application of neuroscientific research methods, such as magnetic resonance imaging (MRI), allows to investigate biological drivers for decision-making and thus to find the origin of the limited rational behavior. The aim of this field of research is therefore to decipher the neural basis for decisions and the observable behavior of market participants. The results obtained are particularly revealing, since the foundations of economic theories have so far been based on assumptions that could and did not incorporate neural research. The increasingly thorough investigation of the brain reveals new factors influencing the information and decision-making processes of market participants. As a result, the image of the Homo Economicus is being further pushed back and the Homo Economicus Humanus is coming to the fore as a kind of Homo Neurobiologicus based on the findings of neuroscience. This market participant is characterized by behavior, thought and decision patterns which can be explained mainly with the help of neurobiology. Both neuroeconomics and neurofinance are thus able to classify the results obtained from experimental games and explain the associated neuronal processes in the brain. This knowledge helps scientists to make assumptions about how the brain solves different tasks. In this way, the neurosciences improve the understanding of the reasons responsible for deviating from the assumptions of →Neoclassical Capital Market Theory during the decision-making process. As a result, new models can be created that are based on a realistic description of human behavior and take into account the driving forces in the background (see Kenning/Plassmann, 2005, p. 344). In the research of neuronal processes, neurofinance deals with the following three central questions:  How is certain information processed by market participants?  What effects does that have on mental processes of market participants?  What influence does personal risk perception have on the decision-making process? With regard to the research results, however, it should be noted that the empirical results may be limited in their validity by certain limitations. For example, research results cannot be repeated with a test subject with the same technical effort (see below), as the subject would already be biased in the follow-up test. It should also be noted that neural research can identify different areas of the brain that

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often have a simultaneous effect on decision-making. If a scientist decides on a particular area of the brain because of his research focus or because of technical obstacles, the reader of the scientific publication will only see a partial view of the processes that take place in the brain. For this reason, it is certainly advisable to view the research results with a healthy scepticism and to give this still young research direction time to develop further. 13.2.1 Research on the human brain

In order to answer the above questions, neuroeconomics makes use of the possibilities offered by neurology. A distinction is made between methods for measuring the electrical activity of the brain, methods for measuring neuronal metabolic processes and methods for measuring psychophysical processes in the body of the test person. The latter is limited to the measurement of body activities such as blood pressure and pupil dilation (see Schilke/Reimann, 2007, p. 250). The methods for measuring electrical activity, on the other hand, measure the electrical voltage fluctuations on the upper side of the brain. They are particularly suitable for answering the question of when a certain neuronal activity occurs. The oldest method in this group is the electroencephalography (EEG), which uses electrodes on the scalp to measure voltage fluctuations. Since this method only measures activities near the surface, no spatial representation of the brain areas involved is possible. However, the EEG is able to precisely determine the order in which the brain areas involved are used (see Ahlert/Kenning, 2006, p. 24). The procedures for measuring neuronal metabolic processes use PET or the fMRI procedure. The PET (positron emission tomography) method is used significantly less than the functional magnetic resonance imaging (fMRI) method (see Fig. 84). This is due to the fact that in the PET procedure, the examinee is administered a slightly radioactive agent, which subsequently measures the activated brain areas.

Abb. 84: Functional magnetic resonance tomograph (fMRI) for measuring brain activity

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Overall, the most frequently used form of examination is the fMRI. This examination method provides information about the areas where the oxygen concentration of the blood is higher due to neuronal activities. Active areas of the brain are detected by the fMRI which collects and evaluates the magnetic resonance signals emitted. The magnetic resonance signals have a different intensity depending on the oxygen content of the affected brain areas. The signals are finally picked up by receiving detectors and converted into images with the help of a statistical calculation method. The different images of the brain’s activity allow conclusions to be drawn about the brain regions involved in solving the problem. Since the mid-1990s, the investigation of brain activity has been carried out mainly by using fMRI. This allows an outpatient application and does not cause any radioactive radiation. Other examination methods include genetic tests, behavioral measurements, psychological tests and electrophysiology. As mentioned above, the latter is based on the measurement of heartbeat, blood pressure and skin changes (transpiration), which indicate activities in individual brain regions (see Petterson, 2010, pp. 78). For a better understanding of the triggers of certain behaviors and of the brain areas involved, the most important brain areas ‒ amygdala, prefrontal cortex and the nucleus accumbens as well as the two most important biogenic amines, also known as hormones ‒ dopamine and serotonin ‒ will be examined more closely below (see Fig. 85; see Pompian, 2006, pp. 296). The hormones mentioned have a significant influence on the perception processes of the market participant and lead to characteristic behavior. Structure of the human brain ‒ side view

Fig. 85: Structure of the human brain

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Prefrontal cortex The prefrontal cortex is a part of the frontal lobe in the cerebral cortex. It is involved in the planning of cognitive and social behavior and in the expression of one’s own personality. This area of the brain is considered to be the ultimate control center since it ultimately leads to the generation of decisions. Insofar as market participants commit cognitive errors in decision-making, this is often related to a lack of information that would be necessary for a correct decision. If a market participant experiences an injury to this area of the brain or if its functioning is impaired by the aging process, this often leads to short-sightedness in the conclusion/planning capacity. Amygdala The amygdala is an almond-sized, paired core area in the human brain. It plays a key role in the generation of primary emotions such as fear and joy. Diseases such as depression or schizophrenia are often due to the abnormal structure/function of the amygdala. The influence on the market participant can be observed especially in panic situations on the capital markets. Market participants act frantically and dump positions when prices suddenly fall sharply. The statement of the legendary investor Sir John Templeton: “Buy when pessimism is at its maximum, sell when optimism is at its maximum” fits in with this. The difficulty, however, lies in actually having the courage to invest in times of sharply falling prices, such as in autumn 2008 (U.S. mortgage crisis), June 2016 (Brexit vote) or March 2020 (during the Covid-19 pandemic). Nucleus Accumbens The nucleus accumbens is a key structure in the lower forebrain with a broad collection of neurons. In cooperation with the anterior cingulate, it helps to recognize patterns and decide between alternatives. For market participants, this means that they are constantly searching for certain patterns to speed up or facilitate their decision-making. Corresponding to the functionality of the nucleus accumbens, this may be used as a starting point for the explanation of the →Representativeness Bias. In recent years, the neurosciences have produced remarkable results on the functioning of the brain. For example, in 2005 Camelia Kuhnen and Brian Knutson identified areas in the brain that are activated when a market participant makes a risky investment (see left figure in Fig. 86) and brain areas that exhibit increased neuronal activity when a market participant tries to avoid losses (see right figure in Fig. 86). When expecting a gain, the nucleus accumbens, which is located deep behind the eyes at the back of the rear part of the frontal lobe in the brain, reacts. In the case of a loss, the two almond-shaped amygdalae on the left and right side of the brain react.

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Investment decisions lead to activity in different brain regions

Fig. 86: Neuronal activities of a market participant when making a risky investment (left) and when avoiding a loss (right)

Dopamine Dopamine is a neurotransmitter whose release leads to feelings of happiness. In expectation of a corresponding positive stimulation, dopamine causes an increasing focus on the expected event (e.g., rising security prices). The concentration of dopamine increases if the positive event occurs unexpectedly. However, the release of dopamine is immediately stopped as soon as the expected event has not occurred. In this case a negative change of mood occurs. Dopamine is particularly important for the behavior of market participants in the financial markets, as it influences their approach to risk. Dopamine causes market participants to invest in growth stocks or to hold on to under-diversified portfolios ‒ always in expectation of rising prices. The release of dopamine thus appears to be responsible for the emergence of certain heuristics ‒ such as →Overconfidence, which plays a major role in causing greed. Serotonin Serotonin is a neurotransmitter that is produced in the central nervous system. The reduction of the serotonin level leads to a decrease in well-being, which leads to depression, impulsiveness and anxiety. If the market participant experiences an unexpected negative turn in the course of an investment, the serotonin level in the brain drops. This leads to the market participant behaving passively and not being able to correctly assess the risks taken. It can also happen that the market participant is tempted to trade frequently in order to compensate for losses suffered and to reduce the depressive feeling caused by losses through possible gains from a risky strategy. A low serotonin level may thus be associated with the causes of loss aversion among market participants.

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With the help of neurological research methods, a number of factors were identified as the cause of the observable behavior of market participants. Thus, the basic genetic disposition of the individual also plays a decisive role in attitudes toward risk in financial matters. In addition, the molecular composition of the brain undergoes a change when taking chemical substances such as drugs or medications. As a result, changes in the behavior of market participants are also becoming apparent. Finally, behavioral changes can be explained from the point of view of anatomical changes made transparent using the fMRI. These occur as soon as information is presented in a certain way (framing) or decisions are made based on certain reference points. 13.2.2 Decision processes from the perspective of Neurofinance

Although neurosciences are a relatively young field of research, they can already explain certain behaviors of market participants on the basis of neural processes, as mentioned above. In the following, findings are presented that explain certain decision-making processes from the perspective of neurofinance and that can shed light on the cause of the behaviors described in chapters 7 to 9. Research in behavioral science has already shown that market participants make their decisions not only on the basis of cognitive processes but also on the basis of emotional processes. Colin Camerer, George Loewenstein and Drazen Prelec (2005) have constructed a four-field system to differentiate human reactions. In this matrix they have extended the dimension of cognitive/emotional processes by another dimension, the controlled/automatic processes. Together, the two dimensions define the four quadrants in Figure 87.

controlled

Run parallel effortless Evoked reflexively Run unconsciously

I

II

automatic

Run sequentially Complex Deliberately induced Run conciously

Type of Processes – Dimension 1

Neuronal processes based on two dimensions ‒ overview

III

IV

cognitive

affectiv/emotional

Type of Processes – Dimension 2 Fig. 87: Dimensions of neuronal processes according to Camerer et al (2005)

The individual dimensions and their respective quadrants are executed in different thought processes. Quadrant I is used when the market participant decides on the financing of an acquisition object. Quadrant II is rather untypical for human be-

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ings, since emotions do not usually run in a controlled manner. If anything, actors can be given as an example who, if necessary, show a certain emotion by putting themselves in the respective situation. The processes from Quadrant III play an important role in sports activities, while the processes from Quadrant IV are activated when an escape reaction is triggered in humans, e.g., when they are frightened. In theory, the behavior patterns caused can be assigned to the individual quadrants, but in reality, many processes are based on the interaction of all four quadrants (see Camerer/Loewenstein/Prelec, 2005, p. 29). Controlled and automatic processes Controlled processes are characterized by the fact that they run sequentially, i.e., one after the other, and are deliberately induced by the decision-maker. This type of process is often perceived as “elaborate”. Humans can usually reflect on these controlled processes very well themselves, since they are consciously experienced. The reason for a decision can be explained. Automatic processes, in contrast, run parallel and are evoked reflexively. They run unconsciously and are therefore perceived by people as effortless. Since automatic processes run unconsciously, it is difficult for people or market participants to justify their behavior (see Camerer/Loewenstein/Prelec, 2005, p. 29). Herding or the regret aversion are therefore more damaging to return/risk, since the market participant uses them unconsciously. Cognitive and affective/emotional processes Within the second dimension, decision-making processes are distinguished according to whether they have a cognitive or affective/emotional origin. Affective processes lead to motivating or avoiding behavior. Thus, in affective processes, the decision is made according to whether the decision is executed or not (yes/no). Cognitive processes, on the other hand, focus on the question of “right” or “wrong” (see Camerer, Loewenstein, Prelec, 2005, p. 18). The processing of information based on the four quadrants can therefore lead to different types of reactions in humans. If the brain is provided with two different pieces of information, only one of the two pieces of information can be used during the evaluation of the information, whereby the other may be suppressed (see LeDoux, 1996, p. 19). This finding can be an explanation for the observable behavior in the context of →Cognitive Dissonance (selective perception from chapter 7.1.2 and selective decision from chapter 9.1.1). According to this, the competition between cognitive and affective/emotional processes plays a role in limited rational behavior. Rational decision-making behavior is only possible through balanced cooperation and activity in all four quadrants. Scientists therefore suspect that many distortions in the decision-making process are due to the “wrong distribution of work” between the individual quadrants (see Camerer/Loewenstein/Prelec, 2005, pp. 28). The neuronal processes from the four quadrants can also explain the decisionmaking behavior of market participants in situations of risk and uncertainty. The

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four-quadrant matrix of Camerer, Loewenstein and Prelec can provide an explanation for the subjective misinterpretation of objective probabilities by market participants. In this sense, the competition between the individual quadrants becomes visible as the market participant moves between the individual dimensions (automatic/controlled vs. cognitive/emotional) during the decision-making process. Thus, on the one hand, market participants attempt to evaluate risk according to objective criteria, as suggested by neoclassical capital market theory (quadrant 1). On the other hand, they also react on an emotional level (quadrant 4). These affective reactions have a major influence on the cognitive system and thus on the behavior of a market participant (see Camerer/Loewenstein/Prelec, 2005, p. 43). Applied to the decision-making process, this means that the market participant can be disturbed by the affective system despite rational considerations of the cognitive system. These neuroscientific findings could explain, for example, why market participants misinterpret objective probability distributions in the decisionmaking process and, according to the →Probability Weighting, unjustifiably take more risk or reduce risk more than necessary (see Sunstein, 2003, pp. 122). Neuroscientific findings suggest that a purely rational decision according to the →Expected Utility Theory by Morgenstern and von Neumann (see chapter 1.2.3) does not exist. Rather, the decision-making process is based on rational or cognitive and limited rational or affective processes in the human brain. These are both controlled and automatic. The decision-making process can be complicated in particular by affective processes that occur unconsciously. According to Antonio Damasio’s141 findings, avoiding emotions to improve decision-making is not a solution. Accordingly, the interaction of all quadrants is necessary in order to be able to make a decision under risk and uncertainty. Research by Damasio, for example, has shown that brain-damaged patients who no longer felt emotions were not able to make the most trivial decisions (see Damasio, 1997, pp. 262). The results suggest that the neuronal systems for reason and emotions do not function independently of each other and therefore the decision-making process is closely linked to emotions. This insight makes it clear that the Homo Economicus Humanus is not able to make purely rational decisions, since emotions are indispensable for decision-making. The impact of emotions on decision-making receives special attention later in the context of Emotional Finance (see chapter 13.3). Human brain systems In addition to the research of behavior-influencing processes, neurofinance also involves research into the brain systems that are of fundamental importance for behavior and decision-making in financial matters. There are four systems ‒ reward system, loss prevention system, memory system and decision system ‒ which are described in more detail below (see Elger/Schwarz, 2009, pp. 157).

141

Antonio Damasio | Portuguese-American neuroscientist | born 1944

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Basically, it should be noted that although the human brain accounts for only two percent of body mass, it consumes up to 20 percent of the energy used for human activities. Most of the energy is employed in conscious thought processes, but a part is used for unconscious activities. This explains why market participants make certain decisions with the help of heuristics. Although these may be energyefficient, they lead to systematic distortions in the information and decision-making process in financial markets, as was illustrated in chapters 7 to 9. The reward system The reward system reinforces, modulates, modifies or inhibits unconsciously thought processes. It is activated as soon as the person perceives a potential reward. This system leads from the midbrain through the limbic system to the cortex and coordinates the evaluation of and alignment with potential rewards. The release of dopamine makes the person want to reach the potential reward, allowing them to rejoice when the reward is secured. Neurological studies (Knutson et. al., 2001) demonstrated neuronal activity in three areas of the brain, indicating activity when individuals developed an expectation of winning. These regions included the thalamus, the nucleus caudate and the nucleus accumbens. The nucleus accumbens (NAcc) showed activity exclusively in phases of increasing gains, but thalamus and nucleus caudates were also active in anticipation of a loss (see Knutson et al., 2001, pp. 3). In addition to the three brain areas mentioned above, areas of the prefrontal cerebrum (PFC) were also identified which show increased activity after the realisation of a profit. These areas therefore assess whether the expectation of a profit was fulfilled (see Knutson/Fong et al., 2001, p. 3685). The reward system also provides an explanation of why market participants think in relative rather than absolute terms when making financial decisions. This mechanism is observed, for example, in the pricing of consumer goods. It is therefore not surprising that prices very often end with an unrounded number. These are perceived as lower than the next higher round number. In this way, the reward system takes a difference of only one cent (in the case of EUR 9.99 and EUR 10.00) as equivalent to a difference of EUR 1, whereas in reality the difference is only EUR 0.01. The rewards system has another characteristic that can be interpreted as a source of error in monetary decisions. It succumbs to the money illusion. It calculates in nominal values of an amount of money and not in real ones. The loss avoidance system The loss avoidance system is based on four elementary reaction patterns: expectation, anger, fear and panic. The brain associates expectation with the tendency to make predictions; this is, for example, the driving force of anticipation. Anger as a second reaction pattern is activated by frustration, i.e., by the impossibility of being able to carry out targeted behavior.

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The best researched reaction patterns include fear and anxiety. Their basic function is to improve the speed of reaction and to increase attention to physical reactions as well. Fear has the property of bundling the capacity of the brain, since in this situation the market participant only concentrates on processing the fear-induced situation and puts everything else on hold. Consequently, fear leads to limited rational actions in financial markets and increases the risk of panic reactions, which can then seize the entire market. In other areas of life, however, fear can be vital for survival. The main component of the loss prevention system are the two amygdalae. Serotonin serves as an activator of the system. The loss aversion of the market participant is caused in this system by the activation of the amygdala. In addition to the amygdala, neuroscientific studies also suggest that the increased activity in the area of the insula, a slightly dented part of the cerebral cortex, may provide indications of loss aversion. Paulus et al. show in a study a correlation between insula activity and the degree of risk preparedness shown by a market participant. If a risky alternative was chosen, the activity was significantly higher compared to a safe alternative. The activity in the insula also depends on how strongly the respondent wanted to avoid the potential negative effects (see Paulus/Rogalsky et al., 2003, pp. 1444). The memory system A person’s memory system develops during childhood and has a decisive impact on the future lifestyle of the market participant. The attitude towards money is thus shaped in the parental home, with the consequence that the market participant will tend to act according to the maxim observed in the parental home. Thus, a market participant who was brought up to save often later behaves as a defensive investor who is keen to invest his savings in low-risk investments. The decision-making system The decision-making system takes up almost half of the human brain. All the information needed to make a decision is gathered in this area. The decision-making system basically takes over the final control of what the market participant’s intentions are and how he or she behaves. The interaction of all four brain systems is of eminent importance for making a decision. It becomes apparent that the market participant tends to make limited rational decisions, since the decisions are not only based on pure rational calculation, but also on emotions and experiences. In summary, the findings of brain research, namely the use of different areas of the brain and different messenger substances, could possibly offer a neurological explanation for the different behaviors of market participants, which became visible in the context of Behavioral Finance. The findings, especially on the reward and loss avoidance system, show that people use different approaches as well as different neural mechanisms when they expect gains or losses. Also, according to the →Prospect Theory, market participants show different behaviors depending on whether they are confronted with a potential gain or loss (see Kahneman/Tversky, 1979, p. 278).

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The fact that the human brain uses two different areas of the brain to process profits and losses may explain the differentiated risk behavior that is being worked out in Behavioral Finance. Neuroeconomics shows that the decision-making process is significantly influenced by whether the decision-maker is motivated by the “wanting to achieve” a reward or the “wanting to avoid” a loss. It can be assumed that the increased activity of the nucleus accumbens in the course of profits and the increased activity of the insula anterior in the course of losses can explain the shift in the risk preference of market participants. According to the findings of prospect theory, market participants behave risk-averse in the profit range of the value function and risk-loving in the loss range of the value function. The phenomenon of loss aversion, which in prospect theory is represented by the steep curve in the loss range of the value function, may therefore also be due to increased activity of the insula anterior. In Behavioral Finance, it also became clear that market participants changed their risk behavior towards an investment product as a result of the →Framing Bias. Although there are no clear neuroscientific results on the presentation effect, it is possible to deduce assumptions about the neurological causes from the findings so far. Studies by Kuhnen and Knutson (2011) have shown that certain areas of the brain change as soon as the test persons are shown images that have a strong influence on their emotional state. Through their experiments they were able to establish a direct link between certain feelings and the resulting risk behavior. The emotional state of the test persons was influenced directly from the outside by showing pictures. The pictures, which were either highly stimulating and positive (e.g., erotic scenes), highly stimulating and negative (e.g., spoiled food) or neutral (e.g., a book), were each shown shortly before the financial decision was made. The decision-making process was therefore preceded by stimuli that had no connection to the actual financial decision. From a rational point of view, the decision as to whether a market participant opts for a high-risk investment (securities) or for a low-risk investment (government bonds) should be independent of information or stimuli that do not relate to the investment. However, experiments have shown that objects associated with positive and stimulating emotions such as “excitement” lead to a more risk-taking decision. On the other hand, the willingness to take risks can be reduced by negative and stimulating emotions such as “discomfort” (see Kuhnen/Knutson, B., 2011, p. 623). The change in risk attitude was also supported by the activity of certain brain regions. If the test persons were shown stimulating images in a positive sense, the nucleus accumbens reacted, with the result that the test persons became willing to take risks in the run-up to the investment. If instead images were shown in a negative sense, the insula anterior reacted, and the test persons did not want to make the investment. The reaction of the two brain areas to external influences thus provides a neurological basis for observed behavioral patterns from Behavioral Finance. In principle, it can be shown that certain brain structures have an effect on a decision-maker’s risk preferences. A decision-making process at the neural level in-

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volves two reciprocal processes that evaluate information at both the emotional and the cognitive level. This exchange mechanism offers a possible explanation for the course of the neural decision process and also a possible approach to explain the use of simplifying heuristics. Concrete examples of explanatory approaches based on the findings of neural processes have been published in 2016 by Arman Eshragi and Adam Moore from the University of Edinburgh. Thus, the focus on individual securities, as opposed to a portfolio of a large number of securities created with a view to diversification, appears to lead to higher returns for investors sampled in that study. The analysis of a large number of securities can quickly overwhelm the human brain and lead to highly distorted decisions due to the increasing selection possibilities. In addition, research in the field of neuromarketing shows that there can be an emotional influence on our decision-making by well-known brands (such as Apple or Tesla), as these brands address different areas of the brain compared with “noname” products. The scientists argue that influencing emotions and unconscious tendencies can be reduced by changing the name of the securities to A, B or C. In practice, however, changing the names will prove difficult, as otherwise market data systems will not be able to recognize the securities. Rather, the use and weighting of different selection criteria can reduce the influence of emotions in the selection of individual securities in a comparable way. Focusing on index tracker funds could be an approach to simplify the selection process. fMRI scans have also clearly shown that potential losses activate far more brain regions than potential gains. In order to avoid the damaging effects of loss aversion (see chapter 9.2.1 in the context of the disposition effect), some investment managers (including AthenaInvest’s CIO Dr. Tom Howard) advocate “forgetting” the purchase prices of securities (see →Podcast of May 2014: Behavioral finance wins playing blind). In this way, investors avoid overweighting short-term volatility and the trading activities following from it, which are detrimental to returns. 13.3 Origin of Emotional Finance The final subchapter of this fourth section aims to explain the behavior of market participants from a new perspective related to Behavioral Finance. As was shown in the first section (chapters 1 and 2), neoclassical capital market theory has considerable weaknesses in capturing the actual behavior of market participants. The development of Behavioral Finance, which attempts to compensate for precisely these weaknesses by interpreting the observable behavior of market participants, has made significant progress in the interpretation of limited rational behavior. Decision-making under uncertainty plays a central role here and is analyzed by investigating mostly cognitive limitations. In a next step, the causes of limited rational behavior were investigated in the context of neuroeconomics. The neuronal processes as well as the brain areas in-

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volved offer additional starting points for explaining the sometimes risk/returndamaging behavior of market participants. In researching and explaining the limited rational behavior, the effects of unconscious processes such as emotions have already been analyzed in the context of heuristics research. However, this attention has so far been denied to the effects of such general emotions as fantasies and fears. The first signs of recognizing the importance of emotionally driven decisions became apparent through the work of Keynes (1936) in the description of the →Animal Spirits. Recently, demands for a stronger inclusion of psychology and social sciences were made by Robert Shiller and George Akerlof (2009). In their research on financial crises, the two scientists identified key attributes (faith, confidence and trust) that decisively influence the decision-making of market participants and revive animal spirits in the capital markets (see Tuckett, 2011, pp. 14). Special attention to the interpretation of emotions is being paid by the new research area, Emotional Finance. This field of research, which is influenced by the findings of the scientists Richard Taffler and David Tuckett, is based on the mental processes originally described by Sigmund Freud. The central starting point is the effect of unconscious processes on the decision-making of market participants. Emotional Finance describes the consequences of unconscious and highly complex processes that lead market participants to emotionally driven behavior. It thus attempts to raise the awareness of such unconscious processes (see Taffler/Tuckett, 2010, pp. 95). The importance of Emotional Finance can be seen in particular in the interaction of market participants. This interaction results in certain emotional reactions such as uncertainty, concern and stress. As has already been shown, the valuation of securities is based, among other things, on the formation of expectations as a mass phenomenon. Uncertainty about what all the other investors do leads to emotional reactions such as anxiety, which ultimately results in stress. Emotional reactions are not only visible in the context of →Herding, but also during the information intake and the subsequent evaluation of the information. Due to the high degree of complexity associated with the valuation of securities, Taffler and Tucket believe that market participants are urged to rely on their intuition. Due to the perceived uncertainty, market participants feel more →Stress with the consequence that limited rational behavior becomes visible (see Fig. 88). Investment

Uncertainty

Concern

Stress

Fig. 88: Overview of emotional reactions in the investment process

In Tuckett’s eyes, the uncertainty associated with an investment decision illustrates the central importance of Emotional Finance: “The central implication of Emotional Finance is that, if the future is taken as inherently uncertain, conventional equilibrium modelling isn’t a useful way to

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start and, especially as far as understanding instability is concerned, isn’t helpful” (Tuckett, 2011, p. 184). This makes it clear that research into human emotions and the insights gained from it should not be seen as a paradigm shift again, but rather as a supplement to the existing basic concepts of the →Neoclassical Capital Market Theory. The concern of market participants about the future development of their investments can also be seen as the epitome of emotional reactions (see Taffler/Tuckett, 2010, pp. 100). 13.3.1 Emotions as a basis for investment decisions

The interaction of emotions in the human psyche has been systematically researched by Sigmund Freud, among others. In his opinion, opposing thoughts temporarily remain in the subconsciousness until they reach the surface of the human psyche again through emotions. A core aspect of his research work is the interaction between thoughts and feelings: Thought leads to feelings and these feelings lead to thoughts. The feelings caused by thoughts embody pleasant (exciting) and painful (worrying) feelings. According to Freud (1908) the perception of pleasant or worrying feelings is a constant development process of the human mind, in which the tendency to suppress worrying feelings is permanent: “But whosoever understands the human mind knows that hardly anything is harder for a man than to give up a pleasure which he has once experienced. Actually we can never give anything up; we only exchange one thing for another.” (Freud, 1908, quoted after Taffler/Tuckett, 2010, p. 96) Decision-driving factors from the perspective of Emotional Finance  Information processing based on depressive (D) or paranoid-schizophrenic (PS) state of mind: − D: Attention pos./neg. information − PS: Separation of pos./neg. feelings and projection onto outsiders  Conception from childhood expectation formation  Desire of the market participant to achieve unlimited return with a given investment  Loss of objective view about current situation; activation of paranoid-schizophrenic state of mind

 Overweighting of information that is considered correct within the group  Driver for herding  Unconsciously perceived sense of security against worries and fears  Perception: “This time is different”

Fig. 89: Overview of decision driving factors from the perspective of Emotional Finance

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From the perspective of Emotional Finance, these ambivalent feelings play an important role in the investment behavior of market participants. Investors are aware of the danger of considering a share as their favorite and consequently keeping it for too long, but they suppress the pain of having to sell the share quickly under certain circumstances ‒ and may not sell it or sell it too late due to the loss aversion. Painful or unpleasant thoughts are suppressed into the subconscious and are therefore particularly dangerous because the market participant does not actively pay attention to these thoughts, which have a considerable influence on investment decisions. In psychoanalysis, however, ignored emotions are considered a fundamental component of unconscious mental processes and are therefore responsible for people’s decision-making. In the course of Emotional Finance research, three factors have been identified which play a decisive role in the subconscious decision-making behavior of market participants (see Taffler/Tuckett, 2010, pp. 99). The mental state of the market participants, group thinking as a preliminary stage of group behavior and the search for fantastic objects are at the forefront of the analysis (see Fig. 89). State of Mind Decisions, and thus also investment decisions, are caused by the activity of states of mind. According to Melanie Klein (1935) two states of mind can be distinguished ‒ the depressive and the paranoid-schizophrenic state of mind. In a depressive state of mind people see themselves and their surroundings more or less as they actually are. In the paranoid-schizophrenic state, positive and negative feelings are considered separately. Here, the schizophrenic way of thinking consists in the strict separation of positive or negative experiences with the simultaneous projection of these experiences onto persons who are either idealized or feared/hated. The paranoid way of thinking results from the feeling of being persecuted by the persons who are now feared or hated. Tuckett and Taffler describe the interaction of these ambivalent thoughts as follows: “... a depressive state involves giving up the feeling that one is all-powerful and all-knowing, ... feeling a certain amount of regret about the consequences of past actions, and a potential anticipatory feeling of depressive anxiety or guilt when contemplating potentially repeating past actions which led to failure or suffering. In a paranoid-schizoid state all such feelings are evaded by evacuating them from awareness...” (Tuckett/Taffler, 2010, p. 98) Applying the research findings of Emotional Finance to the investment behavior of market participants, two strategies for dealing with uncertainty and stress emerge. Basically, with every investment the market participant enters into an emotional bond with an ambivalent character ‒ consciously or unconsciously.  If the market participant tends to process information in a depressive state of mind, he will recognize both the opportunities and the risks and will accordingly be aware of the uncertainty regarding the profitability of the investment.  However, it is also possible that the market participant is in a paranoid schizophrenic state during information processing and therefore negative infor-

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mation is considered separately from the positive information. In this case, the market participant unconsciously idolizes the investment on the basis of exclusively positive information. If the investment develops contrary to expectations, the suppressed negative associations are evoked and the market participant reacts with stress or even panic. “Actually the pressure can be horrendous, a trade goes badly wrong, you are staring into black hole, frozen, knowing you should get out but just hoping the market will turn. I rushed off the desk and threw up in the toilet ‒ I was terrified.” (an anonymous securities trader, quoted by Emotional Finance Blog, 2015) Mark Fenton-O’Creevy, Professor of Organisational Behavior at the Open University Business School, has conducted numerous studies and interviews to research the behavior of traders. In one interview, a young trader, as in the quote above, spoke about the emotions he felt when a trade went against his expectations. In view of the reactions described above, consciously promoting knowledge about the emotional reactions during an investment could limit the risk/return-damaging consequences. Market participants who know the cause of the emotional processes could better deal with the uncertainty and the resulting stress. If this really would be possible it would contradict the “Adapt” approach from chapter 10. Group Thinking Psychoanalytical research analyzed not only ambivalent ways of thinking of the market participant, but also the way in which the market participants interact. Wilfried Bion (1952) distinguishes between two types of groups ‒ work groups and groups based on elementary assumptions (basic assumptions groups). While the members of a working group make an individual contribution to the achievement of objectives and actively cooperate with each other, the members of the basic assumption groups do not perform individual thinking but limit themselves to collective group thinking (Janis, 1982). Here, group thinking promotes the unconscious feeling of security which is radiated by the group through the processing of information to the members of the basic assumption groups. The processing of information is different in both groups. In the working group, the members are concerned to analyze positive and negative information in order to obtain an objective assessment of the respective situation. In the group based on elementary assumptions, however, the information collected is not used by the members for an active way of thinking, but rather to suppress information which the members sometimes do not want to know.142 In the course of this selective information processing, the mental state of the members of the basic assumptions groups changes to the paranoid-schizophrenic state. Information is evaluated to see if it can be used for the development of positive, exciting feelings. All information that does not serve this purpose is separated 142

This is closely related to “information bubbles” in social media

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from conscious perception. The individual behavior leads to group behavior with the corresponding macro-phenomena as shown in chapter 5. This kind of separated state of mind is not only recognizable in the context of herding, but also when evaluating the return chances of new investment opportunities. Market participants subconsciously begin to develop wishful thinking, whereby the risks assessments are separated from the whole amount of information and often ignored. This way of thinking leads to the creation of phantastic objects. Phantastic Objects Investments in securities are uncertain and have great potential to cause excitement for market participants. Emotional Finance picks up the excitement of market participants and uses it to explain limited rational behavior. The concept of “Phantastic Objects” (Tuckett/Taffler, 2008) is introduced. The concept stands for the unconscious desire of market participants to find an undervalued security or a new type of investment (e.g., New Economy) with which they hope to achieve undreamt-of returns. The concept of Phantastic Objects is based on two ideas from psychoanalytical research. The term Objects stands for the mental representation of a symbol in our imagination. The object can take on any shape, it is not precisely defined. The term Phantastic broadly stands for unconscious wishes and ideas that have been formed in early childhood development. Consequently, a Phantastic Object is the mental representation of something (or someone or an idea) that fulfils the innermost wishes of a market participant to have a certain thing at a certain time. An example of this is the dotcom bubble. In the unconscious imagination of market participants, technology stocks around the globe were transformed from ordinary securities into highly exciting ones. Old valuation approaches were pushed aside as insufficient and no longer relevant. Market participants let their subjective perception be shaped by the new valuation approaches and thus slipped from reality into fantasy. The new world of thought of the market participants also had a lasting effect on the portfolio management of many asset management firms. Investors withdrew their liquidity when the target funds had not invested in sectors considered promising. Portfolio managers suddenly changed their investment approach in order not to jeopardise their employment. Some analysts’ coverage of the securities under review gave the impression that the analyst was praising one of his favourite stocks and was completely convinced of its positive prospects (Fogarty/Rogers, 2005). Analysts such as Abby Cohen or Henry Blodget became internet stars at the time of the dotcom bubble and their assessments had a major impact on the equity markets. In retrospect, from the perspective of Emotional Finance, all speculative bubbles, from the Tulip Mania and the South Sea Bubble to the Subprime Bubble, can be described as phantastic objects. In this way, the Collateralized Debt Obligations (CDO) and Credit Default Swaps (CDS) were also elevated to phantastic objects. Consequently, all investment products or investment strategies (including bio-

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technology, renewable energies, etc.) can appear as Phantastic Objects if, in the course of media coverage, market participants get the feeling that they can participate in an extraordinary investment. If the expectations of an investment are not fulfilled or if the speculative bubble bursts, the views of market participants will change as soon as security prices fall significantly in value. Securities that were once considered Phantastic Objects are now despised. In the course of their paranoid-schizophrenic mindset, market participants blame others for the bitter losses. 13.3.2 Interpretation of market movements from an Emotional Finance perspective

Only in the recent past, since 2009 or so, Emotional Finance has led to remarkable research results, which have been given more attention due to the subprime crisis. For this reason, it is still too early to hope for far-reaching practical applications. However, it is already able to explain certain market movements and thus sensitise market participants to the unconsciously occurring phenomena. Risk assessment from the perspective of Emotional Finance The interpretation of risk from the perspective of Emotional Finance and traditional economics could not be more different. In neoclassical capital market theory, a variety of models can be applied to measure the risk of a security investment. For example, →CAPM is one of the best known and most frequently used calculation methods to assess the risk of an investment. Further possibilities (in total up to 63 different approaches according to Ricciardi (2008)) result from the consideration of the standard deviation of returns, the →Beta Factors or the concept of the →Value at Risk (VaR). All methods used have the common goal of weighing up risks and returns and thus being able to decide for or against an investment. The risk is quantified according to the methods used. However, the numerous models and formulas cannot capture the uncertainty about the actual risks and expected returns. This uncertainty, which often can neither be evaluated nor correctly identified, is responsible for the sometimes-burdensome concerns of market participants and leads to panic-like changes in behavior in specific situations. In this sense, Emotional Finance is in a position to make the real scope of risk clear to market participants. From Emotional Finance’s point of view, the quantification of risk is the unconscious protection against uncertainty. Instead of consciously understanding the unpredictability of return and risk development of securities, market participants try to control the subconsciously anchored panic about the unpredictability of the future by measuring risk (see Taffler/Tuckett, 2010, p. 101). Negative news from the perspective of Emotional Finance The →Post-Earnings-Announcement Drift (see chapter 4.3.3), researched by Victor L. Bernard (1993), already showed that market participants only incorporate information into the evaluation after a delay. Negative information seems to

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make market participants take even longer to incorporate into the securities’ pricing than positive information does. Research has shown that securities with a negative analyst forecast continue to lose value for up to a year after publication of the forecast, whereas securities only experience a short period of price increases due to positive news (Womack 1996, Mokaleli-Mokoteli/Taffler/Agarwal 2009). This phenomenon was also observed in the course of the downgrading of credit ratings by the rating agencies during the financial and debt crisis from 2010 onwards. The downgrading of individual countries was reflected in significantly falling bond prices of the affected bonds in the following months. In contrast, only brief attention was paid to the upgrading of the country’s government bonds following Greece’s debt cut in March 2012, for example. On the one hand, this subsequent valuation adjustment may be related to the limits of arbitrage (Lesmond/Schill/Zhou; 2004). Accordingly, →Arbitrage can be limited by the fact that the arbitrageurs simply have no interest in reducing a possible mispricing. Rather, they have the incentive to maintain or extend the misjudgment through their actions instead of limiting it through their actions. From the perspective of Emotional Finance, there is another explanation for the delayed valuation adjustment. According to this, the market participant processes the information in a depressive or paranoid-schizophrenic state of mind. In the above-mentioned case of the credit rating adjustment of Greece, information processing takes place in a paranoid-schizophrenic state of mind, whereby negative information is separated from positive information. The separation of the information has the aim of protecting oneself from the admission of having made a wrong decision. Bad news is emotionally seen as stress and anxiety, which is why market participants want to avoid it and it may take longer for the news to be accepted and incorporated into the valuation. Good news, on the other hand, is valued with joy. This may be one reason why markets tend to incorporate positive news into the valuation of securities more quickly than negative news. Although Prospect Theory has already shown that losses are valued twice as strongly as gains, this emotional phenomenon is likely to be so deeply rooted in the psyche that it will continue to lead to the delayed pricing of negative information, even if it is recognized and understood by investors (see Taffler/Tuckett, 2010, pp. 102). Speculative bubbles from the perspective of Emotional Finance Chapter 5 presented not only significant speculative bubbles but also the five phases of a bubble. It became apparent that at the peak of speculative investment behavior, even the most cautious market participants were drawn under the spell of rising prices as a result of boom thinking in the context of social contagion. To better illustrate the following remarks, the dot-com speculative bubble at the beginning of 2000 is used as an example. The Dow Jones Internet Index rose by 500 percent within 18 months until its peak in March 2000. The market capitalization of the listed companies, which were

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mainly based on retrospectively unrealistic business models and were heavily in debt, reached almost USD 1,000 billion. Six weeks after its peak, the Dow Jones Internet Index lost 50 percent of the price level reached up to that point. By the end of 2002, the loss was a staggering 92 percent. Over the course of almost two years, market participants have accepted the apparently unconsciously suppressed negative information and have aligned the prices of the securities with their fundamental valuation. This emotional turnaround of market participants can be seen in the development of a speculative bubble. Just as the bubble according to Kindleberger/Minsky comprises five phases, the emotional tightrope walk between excitement and desperation of the market participants also takes place in five phases: Excitement ‒ Mania ‒ Clouded Mania ‒ Panic ‒ Despair. The gradual turning away from the objective view of reality begins when market participants are fascinated by the possibility of being able to invest in a phantastic object due to media coverage. In this phase of excitement, the state of mind begins to shift from the depressive to the paranoid-schizophrenic state. Suddenly, the managers of the companies concerned are adored as true superstars. Market participants firmly believe that by investing in the relevant securities, their deepseated subconscious desires can be fulfilled. Market participants were certain that the companies of the new economy would fundamentally change all previous views on the valuation of companies. Old valuation approaches had become obsolete. This is how Mary Meeker, “star analyst” of the investment bank Morgan Stanley in 1997, euphorically commented on the new valuation approaches: “...we believe that we have entered a new valuation zone, the internet has introduced a brave new world for valuation methodologies.” (Meeker, 1997 quoted after Taffler/Tuckett, 2010, p. 104) It is not surprising that in an environment of new beginnings and euphoria, market participants’ sense of reality was significantly clouded by the phantastic object that is now becoming real. Reality-oriented thoughts, the ability to deal with the possibility of losses and real risks were completely suppressed. Market participants gave up thinking for themselves and questioning events ‒ they acted on the basis of basic assumptions groups, with the consequence of extreme herd formation. The second phase ended in the mania of the market participants, in which any negative information was unconsciously suppressed and ignored. The dotcom bubble also became a symbol of a generation struggle in which young, technology-oriented market participants openly demonstrated their perceived superiority over older market participants with a penchant for the old economy (industrial production). This attitude is exemplified by the declared aim of Josh Harris, founder of the Web TV site Pseudo.com, in an interview with CBS in 1997: “...to take you guys out of business. I’m in a race to take CBS out of business.” (Harris, 1997, quoted after Taffler/Tuckett, 2010, p. 104) The third phase of the clouded mania manifests itself in the increasing pain re-

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sulting from the need to abandon unrealistic expectations regarding the phantastic object. Concern about losses, especially for investors who were the last to invest in technology stocks, increases as soon as unconsciously occurring mechanisms of repression of the paranoid-schizophrenic state of mind are no longer able to suppress objective reality. As soon as the repressed fears and worries are transported from the subconscious by incoming and conspicuous information, panic sets in and the stock prices begin to collapse abruptly. The dot-com bubble burst overnight and the relationship to the phantastic object suddenly tilted into the negative. Technology stocks were now despised, market participants were traumatized by their helplessness. Analysts and former dazzling corporate leaders felt persecuted and were blamed for the considerable impairments. In this schizophrenic state of mind, the worries that emerged from the subconscious were projected onto others. Even in this moment, market participants did not see the blame on themselves. In a series of articles in the New York Times, for example, analysts were accused of having further strengthened the speculative bubble with their newly developed valuation methods. In the course of criminal investigations, ten Wall Street investment banks were sentenced to a total fine of USD 1.4 billion. Using the above example, Emotional Finance impressively demonstrates how market participants remain in a paranoid schizophrenic state of mind both during the creation of a speculative bubble and when it bursts, projecting negative feelings or responsibility for the misconduct onto others. The return to an integrated state of mind is only possible when market participants take responsibility for their actions. This happens, but often quite late, as could be seen from the decline in value of technology stocks up to the end of 2002 (see Taffler/Tuckett, 2010, pp. 104). Emotional Finance describes the consequences of unconscious and highly complex processes that lead market participants to emotionally driven behavior. It thus attempts to make investors aware of unconscious processes. The central finding of the research results to date is the conscious perception of mental processes in order to be able to limit risk/return-damaging behavior more quickly.

Biographies of Taffler and Tuckett Richard Taffler, together with David Tuckett, is considered the founder of Emotional Finance. After studying at the LSE, he completed his doctorate at the City University Business School. He continued his academic career with teaching positions at the University of Edinburgh Business School, Manchester Business School and since January 2011 at Warwick Business School.

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Taffler is a recognized expert in the field of Behavioral Finance. He has written over 100 academic papers and books. He is currently working with David Tuckett to develop Emotional Finance as a new paradigm to complement the traditional and the behavioral perspective on decision-making. His focus is on the role of emotions and unconscious decision-making processes. After studying economics, medical sociology and psychoanalysis at Cambridge University and Bedford College, David Tuckett has held numerous positions in the field of psychoanalysis since 1977. Since 2014 he has continued his academic career at the Center for the Study of Decision-Making and Uncertainty at UCLA as Director. In 2006 Tuckett won the Leverhulme Research Fellowship Award for his essay “Psychoanalytic study of investment markets”. In 2007 he received the Sigourney Award for his contribution to psychoanalysis. Together with numerous scientists, especially with Richard Taffler, he is researching psychoanalytic processes in the decision-making process and is considered a pioneer of Emotional Finance.

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Summary Chapter 13

The last chapter was dedicated to the discussion of possible limits of Behavioral Finance on the one hand, and to the development of Neurofinance and Emotional Finance as next steps in the exploration of the Homo Economicus Humanus on the other hand. Within the boundaries of Behavioral Finance, the lack of a consistent theoretical framework is noted. It can be assumed that in the future, Behavioral Finance will have to produce further, more powerful explanatory approaches that will further deepen the understanding of the effects of limited rational behavior and at the same time promote more rational action by market participants. There are also doubts about the systematic existence of market anomalies. It should be noted that investment strategies that exploit such systematic market distortions still seem to have the potential to generate sustainable excess returns. In the ongoing exploration of the Homo Economicus Humanus, the results of neurofinance make it possible to apply the knowledge gained from theoretical and practical studies based on neural and biological processes among market participants. This opens up the possibility to explain the Behavioral Finance findings by neuronal processes in the brain. In addition, research into the functioning of certain hormones makes it possible to determine the neuronal/biological causes for the observable behavior of market participants. The special importance of the interpretation of emotions is emphasized by the new research direction of emotional finance. This research direction, which is influenced by the research results of the scientists Richard Taffler and David Tuckett, is based on the mental processes originally described by Sigmund Freud. The central point of research is the effect of unconscious processes on the decision-making of market participants. Emotional Finance describes the consequences of unconscious and highly complex processes that lead the market participant to emotionally driven behavior and thus tries to bring unconscious processes into consciousness. In the course of the research results, three factors were highlighted which play a decisive role in the subconscious decision-making behavior of market participants. Here, the mental state of the market participants, group thinking as a preliminary stage of group behavior and the search for phantastic objects are in the foreground of the analysis. Emotional Finance can support the market participant in the objective assessment of the situation or help in the identification of phantastic objects. The central finding of the research results to date is the conscious perception of mental processes in order to be able to limit risk/return-damaging behavior faster.

Concluding remarks Section IV

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Concluding remarks Section IV Concluding the fourth section, the circle of observing the market participant closes. Contrary to neoclassical capital market theory, the market participant is by no means perfect and behaviors demonstrated do not correspond to theoretical expectations. Just as it is hardly possible to speak of a homo economicus in practice, it is also not correct to describe the market participant as an individual who makes his decisions solely on the basis of emotions and impulses. Rather, the market participant must be viewed and judged in all its facets. According to the latest research results, however, he/she is in no way in a position to oversee and evaluate all the information given. It can be assumed that in future Behavioral Finance will produce further, more powerful explanatory approaches that will further deepen the understanding of the effects of limited rational behavior and at the same time promote more “rational” action by market participants. In this context, financial market regulation is also required to provide guidelines for customer advisory services. The financial and economic crisis, triggered by two drivers of the homo economicus humanus ‒ namely greed and fear ‒ have made it clear that sensible regulation must keep a close eye on developments on the capital markets ‒ both in the development of new products and in the provisioning for unforeseeable developments. The Basel III capital adequacy rules adopted in the wake of the crisis, which are intended to regulate the backing of risk-weighted assets with increased equity capital, are a first, albeit very rough, step-in learning from the failures of earlier years, when the risk of apparently irrational behavior was underestimated. MiFID II (Markets in Financial Instruments Directive) goes a step further here. In response to the 2008 financial crisis, the EU Commission has initiated very ambitious reforms with MiFID II, which aim to limit risky financial transactions and increase transparency on the financial markets. The directive was introduced on a binding basis in 2018. However, no regulation will be able to keep within limits the behavior of market participants who make their investment decisions driven by greed and fear. Rather, an understanding of these behaviors is required, as well as the training of market participants in order to be able to limit risk/return-damaging behavior.

Glossary Term

Meaning

Page(s)

Ambiguity Aversion

This heuristic of cognitive origin represents the market participant’s aversion to unknown investments. He or She prefers the known to the unknown. This form of aversion occurs as soon as the market participant evaluates a piece of information as not known and cannot accurately assess the degree of ignorance. RRH-Index: 8

226, 254, 278, 321, 324

Anchoring & Adjustment

The anchoring & adjustment heuristic is used by market participants as a kind of benchmark with the help of which they try to assess the significance of a problem or issue. It shows its effect in the fact that the anchor set is not sufficiently adjusted as soon as new information is processed. This heuristic also reinforces the conservatism bias. RRH-Index: 5

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Animal Spirits

Irrational elements in economic activity, such as unreflective instincts, emotions and herd behavior. According to English economist John M. Keynes, these can lead to economic fluctuations and involuntary unemployment.

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Arbitrage

Arbitrage describes the exploitation of temporal price differences of securities. In doing so, the arbitrageur profits from different prices on two exchanges. Arbitrage profits can be achieved by exploiting differences between cash (spot) and futures or options prices. Bond markets offer arbitrage opportunities between securities with similar maturities but differences in liquidity. In practice, arbitrage is subject to increasing limitations due to risks and costs.

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Availability Bias

Heuristic of cognitive origin in the context of information perception. It describes the tendency of market participants to make the significance of information dependent on their imagination or estimated probability of occurrence. RRH-Index: 6

39, 122, 275, 287, 298

Bayes’ Theorem

Part of probability theory, named after the mathematician Thomas Bayes. Bayes’ theorem illustrates how a market participant’s probability assessment should change when new information is received. This in-

177

362

Glossary

volves the adjustment of a-priori probabilities to aposteriori probabilities. Behavioral Finance

Research field in financial market research that deals with the psychology of market participants. It examines cognitive and emotional rules of thumb that are used to facilitate decision-making. Behavioral Finance is based on the realization that market participants are only capable of rational behavior to a limited extent. A Homo Economicus Humanus emerges, who is often influenced by cognitive and emotional aspects.

28, 37, 180, 296

Behaviorism

A scientific-theoretical point of view based on the fact that the behavior of humans and animals can be studied with the methods of natural science.

28

Beta Factor

The beta factor expresses the systematic risk relative to the market and can be estimated using linear regression. It does not indicate how close the relationship is between security returns and market returns, but merely indicates the direction of dependence. Consequently, it is a sensitivity measure for the return of an investment with respect to changes in the market return.

119, 353

Bias

Result of the application of heuristics to reduce information. Biases are distortions in the information and decision-making process that can systematically result from the application of certain rules of thumb (heuristics).

39, 45, 181

CAPM

Capital Asset Pricing Model. Capital market equilibrium model that expands portfolio theory to include the question which part of the total risk of an investment object cannot be eliminated by spreading the risk and explains how risky investment opportunities are valued in the capital market.

62, 119, 353

Chart Analysis

Chart or Technical analysis consists of a variety of individual techniques that aim to predict future stock market prices on the basis of historical price developments. While fundamental analysis asks “if” an investment should be made, chart analysis focuses on the question of “when” the investment should be made. The central assumption of technical analysis is that securities’ prices move in trends. This assumption is based on the behavior of investors, which from the

31

Glossary

363

chartists’ point of view is often characterized by the herd instinct. Confirmation Bias

The confirmation bias describes how market participants try to confirm their assessments by consulting other market participants. This is the same heuristic as selective perception.

143, 204

Conservatism

Conservatism as a cognitive heuristic is the attitude of not adjusting existing views or expectations when new information arrives. New information tends to receive too little attention and is only priced into security prices with a delay. RRH-Index: 2

278

Debt Capital

Debt capital is the capital of a company financed by borrowing; it includes those parts of the liabilities side of a balance sheet that represent creditor claims. In contrast to equity capital, debt capital is external funds that are made available to the company from outside by creditors by way of loan financing or from within by way of provision financing in the short, medium and long term.

158

Discounted Cash Flow

The Discounted Cash Flow (DCF) model is based on the view that the present value of an investment is determined by how much money it will make in the future. The model typically applies the weighted average cost of capital (WACC) to discount projected future cash flows.

69

Disposition Effect

As a heuristic of emotional origin, the disposition effect unfolds its return-damaging effect in the context of loss aversion. The market participant holds on to loser shares, but sells the winner shares too early. The disposition effect increases with increasing self-commitment to the commitment made. It can be recognized by the slope of the value function in the loss area, which is significantly greater than in the profit area. RRH-Index: 5

175, 190, 226, 241, 254, 259, 281

Diversification Effect

The diversification effect is based on the dissimilarity of investments, which as a result lowers the arithmetic mean of the individual risks (variance) as soon as a portfolio consists of several securities. The diversification effect is described by the correlation coefficient of the securities under consideration.

53, 76, 226

Dividend Discount Model

The basic idea of the Dividend Discount Model (DDM) is based on the present value concept known from investment theory. Dividends are discounted at

99

364

Glossary

the cost of equity, which means that the shares of a company are the focus of consideration. The value of a share is composed of the sum of the present values of all dividends expected in the future. Efficient Market Hypothesis

The efficient market hypothesis describes a market as efficient if security prices completely reflect all available information. Three types of unusable information are defined, leading to the three known forms of the efficient market hypothesis. The weak form of market efficiency characterizes a market in which the price histories of the traded securities are included in the current prices. The medium-strict form of market efficiency is based on the idea that all other publicly available information is also priced into the prices of the securities. The strict form of market efficiency includes the correct processing of all conceivable information in the prices of securities (besides price histories and all publicly available information, also the non-public insider information).

66, 92, 119, 298, 329

Endowment Bias

The endowment bias describes the tendency of market participants to estimate the value of their investment higher when they have acquired it than when they have not yet acquired the investment. It leads to the market participant valuing his or her investment not only according to its actual value, but also according to his/her attachment or habituation to the investment. RRH-Index: 5

281

Equity Capital

Equity capital consists of financial resources that are made available to the company by its owners without any time limit. Equity capital includes paid-in capital in the case of a PLC (public limited company), share capital in the case of a LLC (limited liability company).

69, 359

Expected Utility Theory

Expected utility theory aims to analyze rational behavior while taking risks (uncertainty) into account. The central object of consideration is the making of decisions without knowing their results / consequences. Together with Bayes’ theorem of information processing, expected utility theory forms the basis for the efficient market hypothesis.

21, 81, 107, 343

Fat Tail Risk

Fat Tail Risk is a form of portfolio risk that arises when the possibility that an investment will move more than three standard deviations from the mean is greater than what is shown by a normal distribution.

75

Glossary

365

Framing Bias

The framing bias as a heuristic of cognitive origin describes the phenomenon that the presentation of one and the same fact in a different way leads to different decisions. RRH-Index: 5

193, 224, 276, 308, 324, 328, 346

Fundamental Analysis

In contrast to technical analysis, fundamental analysis assumes that the price of a security depends on current and future profits of the company. The central starting point of fundamental analysis is the determination of the “intrinsic value” of a security. Among other things, this can also be interpreted as the sum of the present values of all dividends expected in the future or determined within the framework of Discounted Cash Flow (DCF) analysis by discounting the entire company cash flows and subsequently deducting the cost of debt capital.

29, 31, 197, 220

Growth Shares

Opposite to value shares; are valued based on their potential to outperform the market. Growth stocks tend to have high P/E or high P/B ratios.

119

Herding

Behavior that can be observed in the majority of market participants and that drives speculative bubbles. Gustave Le Bon, founder of mass psychology, formulated the following statements on herd behavior:

71, 76, 143, 173, 259, 277, 301, 348

.

- Masses develop a collective soul - ways of acting are coordinated. There is a high degree of connectedness within the masses. - Overall interest infects individual interests - emotional contagion in the context of feedback theory. - Simple feelings prevail - individuals in the mass are characterized by impulsiveness and irritability. - Opinions and rumors build up and lead to opinion formation based on single rumors, assumptions. RRH-Index: 5

Heuristics

Rules of thumb with the help of which conclusions are drawn without having to use complicated and comparatively lengthy algorithms. The advantage of heuristics is that they lead to resource-saving conclusions that have sufficient quality in most life situations.

83, 210

Hindsight Bias

The hindsight bias leads market participants to believe in retrospect that they had a higher estimate of the probability of an event occurring. Therefore, they do not learn from their mistakes. RRH-Index: 3

281

366

Glossary

Home Bias

Heuristic of cognitive origin in which market participants focus on domestic capital markets. This bias suggests an increased degree of control to investors, making them unintentionally risk-seeking. Overweighting domestic securities lowers portfolio diversification with a view to regional dispersion of investments, which significantly increases portfolio risk.

247, 329

Homo Economicus Humanus

Concept of the market participant in behavioral financial market research; the market participant behaves rationally to a limited extent and is subject to cognitive and emotional heuristics in decision-making.

65, 81, 286

Information Efficiency

Measure of a market’s ability to process information and reflect it in the prices or quotes of goods or securities. Accordingly, a market is considered to be information efficient if all information relating to the value of an investment is immediately processed in its market price.

48

Illusion of Control Bias

The illusion of control bias gives market participants the feeling that they are in a better position to forecast the markets or that they have greater control than is actually the case. It is closely linked to overconfidence and additionally increases it. RRH-Index: 5

275, 322

LIBOR

Former reference interest rate for interbank trading known as the London Interbank Offered Rate. It used to be the reference interest rate set by the most important internationally active banks of the British Bankers’ Association in London. This rate represented the offered rate at which they borrow or are offered funds from other banks in the market. It has been replaced with fully transactions-based interest rates, such as the Secured Overnight Financing Rate (SOFR) in the U.S., the Sterling Overnight Index Average (SONIA) or the €STR (Euro Short-Term Rate), for example.

150

Limited Rationality

Characteristic of Homo Economicus Humanus that can lead to substantial and long-lasting deviations from the fundamental value of securities on the markets. In economics, this refers to behavior that is distinguished from unrestricted rationality and optimization under constraints on the one hand, but also from irrationality on the other.

232

Glossary

367

Loss Aversion

This behavioral pattern is considered to be the cause of the disposition effect. In a loss situation, the market participant tries to avoid losses by becoming more risk-averse on the one hand and holding on to losing positions on the other.

180, 248, 324

Market Portfolio

Total portfolio held by all market participants in equal proportions / weightings if lending and borrowing is possible.

62, 188

Mean Reversion Effect

Mean reversion effect stands for the tendency of securities’ prices to return to their “mean values” over the longer term. This mean value often corresponds to the fundamental value of a security. The deviation from fundamentally justified valuations can be explained by the heuristics applied by market participants within the decision-making process.

268, 298

Mental Accounting

Mental accounting stands for the limited rational tendency of market participants to book their assets in mental accounts depending on certain categories. The booked investment sums are independently valued according to the prospect theory. RRH-Index: 7

241, 250, 255, 268, 278, 295, 304, 324, 328

Myopic Loss Aversion

Myopic loss aversion is the idea that the more market participants evaluate portfolios, the higher the chance of seeing a loss and, thus, they are more susceptible to loss aversion.

304

Neoclassical Capital Market Theory

Research field from the 20th century assuming a perfect and complete capital market, based on the concept of a rational homo economicus. The postulated assumptions represent the maximization of the market value of the payment stream flowing from an investment as an approach through which the objectives of all capital providers are pursued.

107, 329, 334, 349

Neoclassical Economics

Neoclassical economics is a broad theory that focuses on supply and demand as the driving forces behind the production, pricing, and consumption of goods and services. It emerged in around 1900 to compete with the earlier theories of classical economics.

22, 329

Optimism Bias

The optimism bias characterizes the behavior of market participants to assess positive market developments as more likely than negative ones. This heuristic also evokes overestimation of oneself as well as investments in geographically or otherwise known areas. RRH-Index: 8

162, 281, 322

368

Glossary

Overconfidence Bias

The overconfidence bias manifests itself as an unjustified belief in one’s own cognitive abilities. Market participants overestimate their level of knowledge, underestimate risks and tend to exaggerate their belief that they can control market movements. RRHIndex: 4

143, 162, 166, 243, 279, 285, 299, 324, 340

Portfolio Selection Theory

Portfolio Selection Theory is a financial theory that represents a relationship between the risk and return of multiple securities held within a portfolio. It goes back to the U.S. economist Harry M. Markowitz. The portfolio theory is based on the two-parameter approach, which is able to assess the future return of investments through the expected value and the standard deviation.

115, 271

Post-EarningsAnnouncement Drift

Delayed price adjustment after announcement of price-sensitive information. Cautious attitude towards company earnings announcements leads to further gains on positive announcements and further losses on negative announcements.

216, 353

Probability Weighting

Part of Prospect Theory. In this process, objective probabilities are transformed via own assessments. Low probabilities are overvalued, while high probabilities are undervalued. Disregarding objective probabilities changes the risk attitude of market participants.

201, 218, 227, 301, 343

Prospect Theory

Prospect theory is the foundation of Behavioral Finance. It was developed in 1978 by Daniel Kahneman and Amos Tversky as a descriptive decision theory. The theory was conceived as an alternative and generalization of the expected utility theory. It allows the description of decision-making in situations of uncertainty. This includes decisions where imponderable risks or the probabilities of occurrence of future environmental states are unknown.

27, 91, 220, 224, 241, 268, 299, 303, 345

Random Walk Theory

It is a direct consequence of the market efficiency theory. The random walk theory describes the time course of market prices mathematically. It states that price trends of financial instruments are subject to a non-quantifiable and non-calculable random principle. The proponents of this theory thus oppose the view that financial markets exhibit trend developments and recurring patterns that can be used to achieve above-average results when investing in equities.

44, 76, 92, 120, 129

Glossary

369

Recency Bias

The recency bias describes the tendency of market participants to remember recently experienced events better and to give them a higher weighting than events that occurred further in the past. This behavior distorts the objective reality and the market participant makes his decision based on recently published data. RRH-Index: 5

143, 279

Reflection Effect

According to this effect, which is also referred to as the reversal of risk appetite, the market participant’s attitude to risk changes when confronted with losses. This effect results from the decreasing sensitivity in the loss area. This leads to the market participant preferring risky alternatives in the loss range and thus becoming more risk-averse. RRH-Index: 3

191, 249, 279

Regret Aversion

According to this heuristic of emotional origin, the market participant regrets the investment in an asset if it is subsequently sold at a loss and a mistake has to be admitted. Thus, regret aversion stands for the market participant’s effort to avoid making wrong decisions that could be regretted in retrospect. This heuristic also evokes conservatism and herd behavior. RRH-Index: 8

250, 283

Representativeness Bias

Heuristic of cognitive origin during information perception. The market participant integrates an observation into a certain schema through personal experience and thus arrives at a biased probability assessment. It is an estimation about the final result of a full analysis of all information before all information has actually been processed. RRH-Index: 3

278, 334, 339

Risk Perception

Risk perception is a central aspect for the decision for or against an investment. It can be observed that risk perception changes temporarily. This phenomenon is based on the fact that market participants change their risk perception and risk appetite depending on a loss or gain that has occurred. This change is based on non-objective probability assessments. RRH-Index: 2

279

Selective Perception

Selective perception is used by market participants in the context of cognitive dissonance. Consciously or unconsciously, information is neglected with the objective of obtaining confirmation for a decision to be made or a decision that has already been made. RRHIndex: 4

40, 162, 243, 276, 324

370

Glossary

Selective Decision-Making Bias

The selective decision-making bias has the objective to lead a previous decision, which was made under high self-commitment, to the desired success in any case. This heuristic amplifies the disposition effect and the sunk cost effect. RRH-Index: 5

40, 280, 299

Self-Attribution

Self-attribution leads market participants to attribute success to their own ability, but to blame other, external, circumstances for failure. RRH-Index: 7

280, 286, 290, 299

Self-Commitment

Self-commitment is considered to be a measure of the degree to which the market participant is affected in the context of selective perception. If the level of commitment is high, market participants perceive only the information that supports the decision they have made.

179

Self-Control Bias

The self-control bias represents the market participant’s shortcoming in not always consistently and without interruption pursuing an investment objective such as retirement provision. It leads to unbalanced portfolio composition and disregard for fundamental investment principles, such as the compound interest effect. RRH-Index: 4

250, 282, 295

Standard Deviation

Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance.

31

Status-Quo Bias

Status-quo bias leads market participants to leave the composition of their portfolios unchanged, although an adjustment of individual weights would be necessary in the course of market changes. It reinforces loss aversion, the ambiguity heuristic and also the tenure effect. RRH-Index: 7

246, 258, 282, 325

Stress

The term stress was introduced into psychology by the Austrian-Canadian researcher Hans Selye to describe the response of biological systems - i.e., animals and humans - to stress. Stress has become a symbol for strain in general. Originally, the term was meant only to describe what happens in the body when it is stressed. “Stress” is thus initially a neutral term. Selye had originally called the negative component distress, while he called positive stress eustress.

348

Sunk Cost Effect

The sunk cost effect is the tendency to continue investing in something that has moved deep into the loss territory. Because it is human nature to want to avoid failure, people will often continue spending

240

Glossary

371

time, effort or money to try and fix what isn’t working instead of cutting their losses and moving on. This tendency, which is known as the sunk cost effect, can be illustrated by the adage “throwing good money after bad.” Theory of Cognitive Dissonance

Cognitive dissonance describes the imbalance between individual psychological cognitions, such as attitude, emotions or belief in a decision made. In the context of cognitive dissonance, the market participant attempts to correct these imbalances by suppressing negative information about an investment decision and emphasizing positive information. The attempt to avoid cognitive dissonance influences investment behavior in two ways: - First, information selection impairs the ability to monitor and review one’s own investment decisions. - Second, the active investment action is overshadowed by the discomfort of the situation.

40

Value at Risk

Specific risk measure with applications in the area of financial risks (risk). Based on a fixed time interval and a specified probability of default (confidence level), the Value at Risk (VaR) of a financial position is the level of loss that will not be exceeded with the specified probability (probable maximum loss).

80, 353

Value Shares

Opposite to growth shares; are valued according to their current value. In contrast to growth stocks, value stocks are based on their current value substance. Characterized by low P/E ratios.

295

Volatility

Volatility is a statistical measure of the dispersion of returns for a given security or market index. In most cases, the higher the volatility, the riskier the security. Volatility is often measured from either the standard deviation or variance between returns from that same security or market index.

31

Winner-Loser Effect

Market anomaly in which formerly losing stocks outperform and formerly winning stocks underperform over several years. Correction of an initial overreaction to both positive and negative news. As a consequence, the market prices of securities approach their fundamental values again.

45

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Index Allais, Maurice 25

Ellsberg, Daniel 25

Ambiguity Aversion 254

Endowment Bias 245, 281

Ambiguity Aversion Bias 278

Expected Utility Theory 21, 29

Anchoring and Adjustment Bias 278

Experiments 95

Animal Spirits 25

Fat Tail Risk 76

Arbitrage 62, 108

Festinger, Leon 40

Arbitrage Pricing Theory 26

Fama, Eugene F. 25, 42, 47

Framing Bias 276

Availability Bias 39, 275

Fundamental Analysis 29, 31

Bachelier, Louis 24, 31, 34

Gauss, Johann Carl Friedrich 31

Bayes’ Theorem 29

Gigerenzer, Gerd 87

Behavioral Finance, Starting Point and Objective 81

Graham, Benjamin 119

Behavioral Life-Cycle Theory 255 Bernard, Victor L. 41 Black, Fischer S. 26, 57 Bounded Rationality 88 Capital Asset Pricing Model 26 Capital Market Anomalies 119, 121 Chart Analysis 31 Cognitive Dissonance 76 Conservatism Bias 278 Cowles, Alfred 24 Darwin, Charles 23 Discounted Cash Flow Analysis 66 Disposition Effect 248, 254, 281 Diversification Effect 53 Dividend Discount Model 66, 99 Ebbinghaus, Hermann 23 Efficient Market Hypothesis 25, 333

Greater Fool Theory 116 Greenspan, Alan 101 Herding 71 Hindsight Bias 243, 281 Home Bias 247 Illusion of Control Bias 279 Information Cascades 105 January effect 120 Kahneman, Daniel 27 Keynes, John M. 24 Kindleberger, Charles P. 107, 111 Le Bon, Charles-Marie Gustave 104 Life-cycle Hypothesis 255 Lintner, John V. 26, 58 Litterman, Robert B. 57 Loss Aversion 248 Luhmann, Niklas 87 Market Portfolio 62

402

Index

Markowitz, Harry M. 25, 51

Schelling, Thomas C. 96

Mean Reversion Effect 121, 268

Scholes, Myron S. 26

Mental Accounting 250

Selective Decision-Making 40, 240, 280

Mental Accounting Bias 278 Merton, Robert C. 26, 99 Miller, Merton H. 26, 99 Minsky, Hyman 111 Modigliani, Franco 26 Momentum Effect 121 Morgenstern, Oskar 25, 37 Mossin, Jan 58 Muth, John F. 25 Neoclassical Capital Market Theory 333

Selective Perception 40, 276 Self-Attribution Bias 242, 280 Self-Control Bias 254, 282 Sharpe, William F. 26, 58, 60 Shefrin, Hersh 99 Simon, Herbert A. 27, 82 Smith, Adam 23, 29 Smith, Vernon L. 27 Standard Deviation 31 Statman, Meir 99

Neoclassical Economics 22

Status Quo Bias 253, 282

Neuberger, Oswald 85

Subjective Expected Utility Theory 37

Noise Trader 108 Objective Expected Utility Theory 37

Taffler, Richard 28 Technical Analysis 44

Optimism Bias 247, 281, 314, 322

Thaler, Richard H. 21, 27

Overconfidence Bias 279

Theil, Henri 56

Portfolio Selection Theory 25, 52

Theory of Bounded Rationality 27, 82

Post-Earnings-Announcement Drift 42, 120, 216

Theory of Cognitive Dissonance 40

Probability Weighting 343

Thomas, Jacob K. 41

Prospect Theory 27

Thorndike, Edward L. 28

Random Walk Theory 24, 35, 44

Tuckett, David 28

Recency Bias 279

Tulip Mania 131

Reflection Effect 279

Tversky, Amos N. 27

Regret Aversion Bias 257, 283

Value at Risk 80

Representativeness Bias 278

Volatility 31

Risk Perception 275

von Neumann, John 25

Ross, Stephen A. 26

Winner-Loser Effect 122

Save More Tomorrow Program 255

Working, Holbrook 24

Dr. Rolf J. Daxhammer is professor for Financial Markets at ESB Business School, Reutlingen University. Máté Facsar is Vice President Sales, Global Account Manager at FactSet, a global provider of integrated financial information and analytical applications. Zsolt Papp, Managing Director, is a senior investment specialist in the Global Fixed Income, Currency and Commodities group of J.P.Morgan Asset Management, a global leader in asset management service.

ISBN 978-3-7398-3119-0

ut

Layo

ut

Layo

www.uvk.de

3rd ed.

Behavioral Finance

For students and practitioners alike, our book aims at opening the door to another perspective on financial markets: a behavioral perspective based on a Homo Oeconomicus Humanus. This agent acts with limited rationality when making decisions. He/she uses heuristics and shortcuts and is prone to the influence of emotions. This sounds familiar in real life and can be transferred to what happens in financial markets, too.

Daxhammer / Facsar / Papp

Over the last 50 years, neoclassical financial theory has been dominating our perception of what is happening in financial markets. It has spurred numerous valuable theories and concepts all based on the concept of Homo Economicus, the strictly rational economic man. However, humans do not always act in a strictly rational manner.

Rolf J. Daxhammer / Máté Facsar / Zsolt Papp

Behavioral Finance Limited Rationality in Financial Markets 3 rd edition