Startup Valuation: From Strategic Business Planning to Digital Networking 3030716074, 9783030716073

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Roberto Moro-Visconti

Startup Valuation From Strategic Business Planning to Digital Networking

Startup Valuation

Roberto Moro-Visconti

Startup Valuation From Strategic Business Planning to Digital Networking

Roberto Moro-Visconti Catholic University of the Sacred Heart Milan, Italy

ISBN 978-3-030-71607-3 ISBN 978-3-030-71608-0 (eBook) https://doi.org/10.1007/978-3-030-71608-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1

Introduction

1

Part I Valuation 2

From 2.1 2.2 2.3 2.4

2.5 2.6 2.7 2.8 2.9

Business Models to Business Planning From Budgeting to Business Planning How to Write a Business Plan … Step After Step Upstarting and Forecasting a New Business The Accounting Picture: Interacting Balance Sheets with Income and Cash Flow Statements 2.4.1 Typologies of Cash Flow Statements 2.4.2 Sources of Funds and Uses of Capital 2.4.3 From the Balance Sheet and the Economic Flows to the Financial Flows 2.4.4 Cash Flow Statement Analytics Getting Information from Big Data and Networks Frame-Working the Strategic Environment with PESTLE and SWOT Analysis A Matrix for Risk Metrics Sensitivity and Scenario Analysis: Deterministic Versus Stochastic Planning Fixing the Sustainable Bottom Line: How to Avoid Cash or Equity Burn Outs

9 9 11 13 15 15 18 21 25 29 31 32 35 37

v

vi

CONTENTS

2.10

Periodically Monitoring and Upgrading the Model and Its Underlying Miscalibrated Expectations 2.11 A Corporate Governance Perspective 2.12 Augmented Business Planning 2.13 Business Incubators and Accelerators References

3

4

Profitability, Intangible Value Creation, and Scalability Patterns 3.1 Return on Equity, Return on Invested Capital, and Other Profitability Ratios 3.1.1 Return on Equity (ROE) 3.1.2 Return on Invested Capital (ROIC) and Return on Assets (ROA) 3.1.3 Ratio Tree and DuPont Formulation 3.2 Invested Capital 3.3 Relationships Between ROIC and ROE 3.4 From Economic Value Added (EVA) to Market Value Added (MVA) 3.5 Operating Leverage 3.6 Break-Even Analysis 3.7 Digital Scalability 3.8 The Impact of Intangible Investments on EBITDA-Driven Market Valuation 3.9 Valuation Drivers, Overcoming the Accounting Puzzle 3.10 From EBITDA to EBIT 3.11 The Scalable Impact of the Intangibles on Revenues and Monetary OPEX 3.12 The Impact of EBITDA on the Profitability Ratios 3.13 The Impact of the EBITDA on the Market Multipliers References Boosting Sustainable Growth with Innovative Intangibles 4.1 Intangible Assets 4.1.1 From the Accounting to the Book Value 4.2 (Digital) Trademarks

38 39 40 41 43 47 47 48 49 50 51 51 53 58 62 64 65 66 68 69 70 73 78 81 81 82 86

CONTENTS

Technological Intangibles: From Know-How to Patents 4.2.2 The Web Value Chain: Domain Names, M-Apps, and Internet Firms 4.2.3 Acquisition and Processing of Information: IoT, Big Data, Artificial Intelligence, and Blockchains 4.3 Residual Goodwill and the Intangible Portfolio 4.4 The Value of Growth: Multi-Stage Cash Flows and Dividends 4.4.1 Franchise Factor Model 4.5 Sustainable Growth, ESG Drivers, and Ethical Funding 4.6 Sustainable Patterns 4.7 Circular Economy 4.8 Resilient Supply and Value Chains 4.9 Digital Platforms and Networks 4.10 Sharing Economy and Collaborative Commons References

vii

4.2.1

5

Cherry-Picking Intermediaries: From Venture Capital to Private Equity Funds 5.1 Venture Capital, Private Equity, and Equity Crowdfunding 5.2 Risk Capital for Growth: The Role of Venture Capital, Private Equity and Business Angels 5.3 Types of Investments, Intermediaries, and Bankability 5.3.1 Startup Loans and Venture Capital Activities 5.3.2 Financing for Expansion and Development: The Role of Private Equity and Bridge Financing 5.3.3 Financing of Change and Modification of Ownership Structures: Replacement Capital, Buyout, Venture Purchase, and Turnaround Financing 5.4 The Investment Process 5.5 The Valuation Framework

89 91

93 97 99 100 101 104 105 107 108 108 109 113 113 114 118 120

122

123 124 130

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CONTENTS

5.6

The (Uneasy) Estimate of Cash Flows for Financial Companies 5.7 Applying DCF to Asset Management Firms 5.8 Multiples and Rules of Thumbs 5.9 The Dividend Discount Model 5.10 Pros and Cons of the Valuation Methods References 6

7

133 133 136 138 139 140

Early-Stage and Debt-Free Startups 6.1 Cash is King 6.2 The Integrated Economic, Financial, and Balance Sheet Accounting System 6.3 Cash Flow Metrics 6.4 From Contacts to Contracts: Budgeting, Sale Forecasting, and Market Traction 6.5 Scalability Drivers, Growth Opportunities, and Real Options 6.6 Sales-Driven Net Working Capital 6.7 OPEX and CAPEX 6.8 Monetary Equity 6.9 Runway Cash Planning 6.10 The Winter of Capital: Matching Cash Burnout with Monetary Equity Burnout, and Bridge Financing 6.11 Conclusion References

143 143

Leveraging Startup’s Development with Debt 7.1 Transition from a Debt-Free to a Levered Startup 7.2 Net Present Value, Internal Rate of Return, and Investment Payback 7.3 Modigliani & Miller Proposition II 7.4 Information Asymmetries and Leverage 7.5 The Theory of Capital Structure: A Startup’s Reassessment 7.6 A Practical Case of Corporate Profitability Analysis 7.7 Why Startups Fail? References

161 161

144 144 146 147 148 150 151 153

156 157 158

164 165 173 173 175 179 180

CONTENTS

ix

8

A Comprehensive Valuation Metrics 8.1 Purpose of the Startup Evaluation 8.2 The Balance Sheet-Based Approach 8.3 The Income Approach 8.3.1 Estimated Normalized Income 8.3.2 Choice of the Capitalization Rate 8.3.3 Choice of the Capitalization Formula 8.4 The Mixed Capital-Income Approach 8.5 The Financial Approach 8.6 Empirical Approaches 8.7 The Control Approach References

183 183 187 190 190 192 193 194 196 205 210 210

9

Startup Valuation 9.1 An Adaptation of the General Valuation Approaches 9.2 The IPEV Valuation Guidelines 9.3 The Fair Value of the Investments in the Target Firms 9.4 The Fair Value of the Investments in the Portfolio Companies 9.5 Startup Evaluation with Binomial Trees 9.6 The Venture Capital Method 9.7 The Break-up Value of Venture-Backed Companies 9.8 Stock Exchange Listing and Other Exit Procedures 9.9 Valuation of the Investment Portfolio with a Net Asset Value 9.10 Unicorns 9.11 Key Person Discounts, Founder Control, and Governance Implications 9.12 A Practical Valuation Case References

213

Part II 10

213 214 218 220 221 223 225 227 228 229 232 233 238

Industry Applications

FinTech Valuation 10.1 Introduction 10.2 The Ecosystem: Digital Platforms and Multilayer Networks

245 245 248

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CONTENTS

10.3

Financial Bottlenecks: Inefficiencies and Friction Points 10.4 The Accounting Background for Valuation 10.5 FinTech Business Models 10.6 Banks Versus FinTechs: Cross-Pollination and Scalability 10.7 Insights from Listed FinTechs 10.8 Valuation Methods 10.8.1 The Financial Approach 10.8.2 Empirical Approaches (Market Multipliers) 10.9 Market Stress Tests and Business Model Sensitivity 10.10 Competitive Advantage, Excess Returns, Economic Value Added, and Goodwill 10.11 Challenges and Failures: Why FinTechs Burn Out 10.12 Concluding Remarks References 11

12

From Informal Financial Intermediaries to MicroFinTech Valuation 11.1 Introduction 11.2 Sustainability Versus Outreach 11.3 Technological Innovation 11.4 FinTech-Driven Scalability and Economic Sustainability 11.5 A Pecking Order Reinterpretation of MFIs Funding 11.6 Expanding Outreach with Multilayer Digital Platforms References Digital Platforms and Network Catalyzers 12.1 Networked Digital Platforms 12.2 Network Theory 12.3 The Impact of Digital Platforms on Supply and Value Chains 12.4 Evolutionary Multilayer Startups References

250 252 252 257 258 261 264 268 271 272 275 277 277 281 281 285 287 289 290 291 292 297 297 300 301 303 306

CONTENTS

13

14

15

From Netflix to Youtube: Over-the-Top and Video-on-Demand Platform Valuation 13.1 Introduction 13.2 Digital Platforms and Scalability 13.3 Business Models 13.4 M-Apps 13.5 The Accounting Background for Valuation 13.6 Valuation Methods 13.6.1 The Customers’ Portfolio (e-Loyalty of Digital Clients) 13.6.2 The Financial Approach 13.6.3 Empirical Approaches (Market Multipliers) References E-Health and Telemedicine Startup Valuation 14.1 Introduction 14.2 The Healthcare Ecosystem 14.3 Business Models 14.4 Investors and Market Players 14.5 The Accounting Background for Valuation 14.6 Valuation Methods 14.6.1 The Financial Approach 14.6.2 The Financial Approach with Debt-Free Startups 14.6.3 Empirical Approaches (Market Multipliers) References FoodTech and AgriTech Startup Valuation 15.1 Introduction 15.2 The FoodTech Ecosystem (from the Farm to the Fork): Digital Platforms and the Circular Economy 15.3 Food Chains 15.4 Business Models 15.5 The Accounting Background for Valuation 15.6 Valuation Methods 15.6.1 The Financial Approach

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309 309 311 315 319 320 322 323 329 335 338 341 341 343 344 345 345 348 350 354 357 360 363 363

365 368 370 375 377 378

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CONTENTS

15.6.2 15.6.3 References Index

The Financial Approach with Debt-Free Startups Empirical Approaches (Market Multipliers)

383 384 389 391

List of Figures

Fig. Fig. Fig. Fig. Fig. Fig. Fig.

2.1 2.2 2.3 2.4 2.5 2.6 2.7

Fig. 2.8 Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16

Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 3.5

Subdivision of the cash flows Accounting derivation of the cash flow statement Typologies of cash flow statement Sources and uses of funds Monetary costs and revenues Subdivision of net working capital Variation of the balance sheet and identification of liquidity as the target variable Reclassification of the income statement propaedeutic to the determination of the cash flow statement Subdivision of the cash-generating EBITDA Upside and downside risk, due to revenues’ volatility Interactive risk matrix Example of binomial model Risk scoring matrix Equity- and cash-burnout Interaction of top-down and bottom-up strategies Startup Interactions with Incubators, Angels, and Accelerators Profitability ratio tree Assets and liability structure The link between invested and raised capital and the income statement From EVA to MVA Value creation: when ROIC exceeds the cost of capital

16 17 18 20 21 22 23 24 30 33 34 36 37 38 41 42 51 52 53 57 58

xiii

xiv

LIST OF FIGURES

Fig. 3.6 Fig. 3.7 Fig. 3.8 Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

4.1 4.2 4.3 4.4 4.5 5.1 5.2 5.3 5.4 5.5 5.6 6.1

Fig. 6.2 Fig. 6.3

Fig. 6.4 Fig. 7.1

Fig. 7.2 Fig. 7.3 Fig. 7.4

Fig. 7.5

Break-even analysis Break-even point The impact of the intangible investments on the EBITDA Multi-stage dividend growth ESG impact on cash flows, cost of capital, and DCF value Sustainability patterns Circular economy flowchart From digital platforms to Networks Startup investors The Gartner Hype-Cycle model Startup financing cycle The investment process Economic and financial performance of a venture capital Valuation methods of asset management firms Interactions of income statement and variations of the Balance Sheet to Produce the Cash Flow Statement in a Debt-free startup Book, monetary, tangible and intangible equity The financing and investing cycle 1 Funds acquisition of capital (equity), 2 Funds investment in net working capital and fixed assets (invested capital), 3 Generation of operating NOPAT (funds applications in net working capital and fixed assets →sales →operating NOPAT), 4 Operating NOPAT generates operating cash flows to payback investors (shareholders) Cash runway and equity refinancing The Financial-Economic Cycle ➀ Funds acquisition of capital and debt (raised capital), ➁ Funds investment in net working capital and fixed assets (invested capital), ➂ Generation of operating NOPAT (funds applications in net working capital and fixed assets  sales  operating NOPAT), ➃ Operating NOPAT generates operating cash flows for investors (debtholders and shareholders) Impact of a leverage increase on the cost of capital Evolution from a debt-free to a levered startup Modigliani & Miller (M&M)—Proposition II (where: K e = cost of equity; k o = WACC (weighted average cost of capital); K d = cost of debt) ROE and ROIC

64 65 69 99 103 105 107 108 116 118 120 125 131 132

145 152

153 155

162 163 164

167 172

LIST OF FIGURES

Fig. 7.6 Fig. 8.1 Fig. 8.2 Fig. 8.3 Fig. 9.1 Fig. 10.1 Fig. 10.2 Fig. 10.3 Fig. 10.4 Fig. 10.5 Fig. 10.6 Fig. 10.7 Fig. 11.1 Fig. 11.2 Fig. 11.3 Fig. 11.4 Fig. 12.1 Fig. Fig. Fig. Fig.

12.2 12.3 12.4 12.5

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

12.6 13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9 13.10

Impact of an increase in financial leverage (d) on the cost of debt (i) and ROE Functional analysis, business planning, and startup valuation Value of the startup and cash flows The integrated equity—economic—financial—empirical and market valuation Representation of the pay-off Main FinTech activities Interaction of FinTech with BigTechs and traditional banks Evaluation methodology Business model and value drivers FinTech versus technological and banking stock market index Business model and valuation approach of FinTechs Goodwill as a positive differential between the yield and the cost of invested capital From informal financial intermediaries to MicroFinTechs Operational functions in traditional and technological MFIs The financial ecosystems network Impact of microfinance evolution on the trade-off sustainability versus Outreach Interaction between the infrastructural network, the platform, and the startup Networked digital platforms Digital supply and value chains Multilayer networks Superimposed multilayer networks with a bridging digital platform Multilayer evolution of startup stakeholders The link between media, the internet, and the platforms The link between digital transformation and scalability The digital media ecosystem Interactions of intangibles Video on demand business models Evaluation methodology Business model and value drivers Business model and valuation approach From blitzscaling to client retention Internet traffic monetization process

xv

172 187 199 202 222 247 251 256 257 259 261 273 284 284 285 292 298 299 302 305 305 306 312 313 314 315 319 320 321 322 324 327

xvi

LIST OF FIGURES

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

14.1 14.2 14.3 14.4 14.5 14.6 14.7 14.8

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8

The link between e-Health, m-Health, and telemedicine The healthcare ecosystem Interactions of intangibles Healthcare supply chain Evaluation methodology Business model and value drivers Business model and valuation approach Interactions of income statement and variations of the balance sheet to produce the cash flow statement in a debt-free startup Food chain FoodTech and AgriTech value chains The Food Supply Chain Evaluation Methodology Business model and value drivers Business model and valuation approach of foodTechs Valuation framework—traditional firm Valuation framework—startup

342 343 344 347 347 348 349

356 368 369 370 376 376 377 385 386

List of Tables

Table 2.1 Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table

2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 3.1 3.2 3.3 3.4

Table 3.5 Table Table Table Table Table

3.6 3.7 3.8 4.1 5.1

From revenues to EBIT, differentiating between fixed and variable costs Composition of operating net working capital Cash flow statement Derivation of the EBITDA From the EBITDA to the operating cash flow Composition of the operating net working capital Composition of the fixed assets (CAPEX) From the operating to the net cash flow Net financial liabilities and equity Variation of reserves Cash flow statement reconciliation PESTE and SWOT definition Equity equivalent adjustments to EVA From revenues to operating profit Degree of operating leverage Degree of operating leverage with a revenue decrease (a) Hypothesis 1: revenues decrease to 50 Degree of operating leverage with a revenue increase (b) Hypothesis 2: revenues grow to 300 From sales to EBIT From EBITDA to EBIT Discounted operating cash flow The big data 10Vs and their impact on forecasting Operating and net cash flows in startups

14 22 24 25 26 26 27 27 28 29 29 31 55 59 61 62 62 67 69 75 96 130

xvii

xviii

LIST OF TABLES

Table 5.2 Table 5.3 Table Table Table Table Table

6.1 6.2 6.3 6.4 6.5

Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table

7.5 7.6 7.7 7.8 8.1 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 10.1 10.2

Table 10.3 Table 10.4 Table 11.1 Table 13.1 Table 13.2 Table 13.3

Value drivers for asset management firms Strengths and weakness of valuation methods of asset management firms From sales to EBIT Turnover ratios From EBITDA to operating cash flows Cash flow runway Impact of the monetary equity injections on the net cash flow Combination of profitability ratios Profitability ratios, leverage, and credit spread Impact of a leverage (d) increase on the cost of debt (i) and ROE Delta case. Balance sheet T3 and T4 (Asset and liabilities; income statement T4) Input data for the profitability equation Collected or invested capital Reclassified data for the profitability equation Subdivision of the profitability equation Cash flow statement and link with the cost of capital Pay-off calculation Mean forecast EBITDA From the net cash flow to the equity value Listed comparables Net financial position Adjusted multiple of the EBITDA Synthetic valuation Sensitivity analysis Cost of equity (ke) sensitivity FinTech typologies and business models Comparison of the main evaluation approaches of traditional firms, technological startups, and banks FinTech valuation approaches Cash flow statement of a FinTech and link with the cost of capital MFI Income Statement and Impact of Technology Business models of the AudioVisual Industry Comparison of the main evaluation approaches of traditional firms and technological startups Cash flow statement and link with the cost of capital of a VoD/OTT company

132 139 147 148 151 154 156 170 171 172 176 178 178 178 178 203 223 234 235 236 236 237 237 237 238 254 263 264 269 289 317 323 333

LIST OF TABLES

Table 14.1 Table 14.2 Table 14.3 Table 14.4 Table 15.1 Table 15.2 Table 15.3 Table 15.4

e-Health and telemedicine business models and value chain issues Comparison of the main evaluation approaches of traditional firms and technological startups Cash flow statement and link with the cost of capital of an E-health startup Working capital turnover FoodTech and agriTech business models Comparison of the main evaluation approaches of traditional firms and technological startups Cash flow statement and link with the cost of capital of a FoodTech startup Turnover Ratios

xix

346 350 355 357 371 378 383 386

CHAPTER 1

Introduction

A startup is a newly established business begun by an entrepreneur to seek, develop, and validate a scalable economic model, transforming a project into a hopefully viable commercial activity. Bringing ideas to fruition is the ultimate target of successful startuppers. Innovative startups are characterized by high growth potential, which usually absorbs a lot of liquidity in the early years of life, to finance development, against minimal collateralizable assets. This is unattractive for traditional banks, usually replaced by other specialized intermediaries as venture capital or private equity funds, which diversify their portfolio basing their strategies on a multi-year exit with substantial expected increases in value from investments that survive a Darwinian selection. Startups coexist in an evolving ecosystem with established firms, to which they transfer innovativeness, technology, flexibility, and time-to-market speed, contributing to reinvent the business models, and receiving from mature firms feedbacks on the current market features, the existing clients, and their unsatisfied needs. The valuation paradigms represent a central issue for any startupper seeking external finance, either from “family and friends” or through a wider and professional placement, from equity crowdfunding to venture capital or private equity underwriting. This book represents an updated guide to both practitioners, students, and academics about the trendy valuation patterns of the startups. The © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_1

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topic is very actual and shows the presence of a theoretical and practical gap in the literature. In particular, what is missing in the literature is the interaction between sound corporate finance theory and appraisal applications. Empirical cases, with industry applications, show how the theoretical background can be applied to real situations. The main audience may be tentatively represented by practitioners (investors, startuppers, venture capital /private equity managers, etc.), specializing students (attending MBA programs, etc.), and academics working in this wide and interdisciplinary field. This book innovatively combines classic aspects of valuation with personalized considerations for startups. Theoretical corporate finance aspects, ranging from discounted cash flows, capital budgeting, or capital structure issues, are combined with practical insights that describe and interpret the startup features. Startups increasingly incorporate in their business model ESG-compliant sustainability patterns and so represent a template for typically less reactive traditional businesses. Fifteen chapters describe these complementary topics. They are divided into two parts: the first (Chapters from 2 to 9), dedicated to valuation practices, and the second (Chapters from 10 to 15), devoted to some empirical cases and industry applications. Chapter 2 deals with an introductory framework, represented by the interaction between business models and business planning, describing the uneasy attempt to transform visionary ideas into feasible numbers. Chapter 3 is dedicated to value creation and scalability patterns that are intrinsic in any startup, and accompany it to the scaleup phase. Corporate profitability is a core issue of financial statement analysis and corporate finance that is consequentially examined. Economic profitability, deriving from positive marginality where revenues exceed costs, is considered in complementary ways. Chapter 4 follows the thread of Chapter 3 and analyzes how innovative intangibles may boost growth, and scalability, especially if they have digital features. Trendy intangibles that may well operate in coordination range from M-Apps, IoT, and big data to blockchains, artificial intelligence, and interoperable databases, showing potential for scale-up if properly combined within a firm. Chapter 5 describes “cherry-picking” intermediaries that select the best startups. They range from venture capitalists to private equity investors, depending on the life state of the target company. In the early stages of seed financing, professional intermediaries may be preceded by business angels or equity crowd-funders.

1

INTRODUCTION

3

The corporate finance structure of a typical startup is described in Chapters 6 and 7. Chapter 6 illustrates early-stage startups that are typically debt-free, being unable to collateralize their tiny assets or to produce enough liquidity to properly serve debt. Startups that survive the “Death Valley” and the equity- and cash- burnout may start raising debt, as shown in Chapter 7, leveraging their development with bank loans, and reaching scaleup status. After these framework chapters, a comprehensive valuation approach for standard firms is described in Chapter 8. Reference to traditional firms and their valuation standards is important because startups are like other companies, albeit showing some peculiarities. Specific reference to startup valuation issues is contained in Chapter 9, showing which are the approaches suggested by private equity associations or other practitioners. The second part of the book, as anticipated, is dedicated to some industry applications, concerning empirical examples of startup valuation issues. Six complimentary examples decline the appraisal issues in specific segments of activity. While the valuation approaches are consistent with the general principles illustrated in the first part, some fine-tuning is necessary to adapt the appraisal standards to specific cases. Chapter 10 is devoted to FinTechs, a paradigmatic example of fashionable startups that are reshaping the somewhat old-styled banking industry. Chapter 11 analyzes the complementary business of microfinance activities scaled up by technology, showing that innovation may also have a positive social impact. MicroFinTech businesses incorporate startup innovation in not-for-profit activities, representing a paradigm for similar ventures. Digital platforms, described in Chapter 12, are network catalyzers that represent more a product and device than a firm typology. They are consistent with the scalability features shown in Chapter 3 and easily interact with any kind of startup, boosting productivity. Entertainment startups, exemplified by Over-the-Top and video-ondemand platforms, are analyzed in Chapter 13. They are, for instance, represented by popular firms (Netflix, YouTube, Amazon Prime, etc.) that have evolved from the startup phase, or by newcomers that challenge incumbent competitors. E-health and telemedicine startups, illustrated in Chapter 14, refer to a trendy business where innovation tries to match growing quality expectations, driven by a patient-centric approach. FoodTech and complementary AgriTech startups, examined in Chapter 15, start from innovation in food chains and to the desire to

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discover new food products and tastes, in a vital sector where demand— like that of healthcare—is potentially unlimited. Whereas these six examples, illustrated in Part II, do not exhaust the full range of possible sectors—that is, of course, much wider—they offer, anyway, a template for the adaptation of the valuation patterns described in Part I to further business models that may be incorporated in a new entrepreneurial activity. Even startups can be innovatively interpreted with network theory, considering them as a node that is linked to other nodes (external stakeholders, etc.) through connecting edges. This structure also applies to this book, where each chapter is connected to the others, as graphically shown in the following representation.

Business Models (Chapter 2)

Early-Stage Startups (Chapter 6)

Venture Capital - Private Equity (Chapter 5)

Business Planning (Chapter 2)

Intangible Value CreaƟon (Chapter 3)

InnovaƟve Intangibles (Chapter 4)

Sustainable Growth - ESG (Chapter 4)

Maturing (levered) Startups (Chapter 7)

Startup ValuaƟon (Chapter 9)

(General) ValuaƟon Principles (Chapter 8) THEORY

PRACTICE

Digital Plaƞorms / Networks (Chapter 12)

FinTechs (Chapter 10)

Not-for-Profit Startups (MicroFinTech) (Chapter 11)

Media Startups (Chapter 13)

e-Health / Life Sciences Startups (Chapter 14)

FoodTech / AgriTech Startups (Chapter 15 )

1

INTRODUCTION

5

∗ ∗ ∗ Any useful comment may be sent to [email protected] or by visiting www.morovisconti.com. This book is dedicated to the loving memory of Alfredo Scotti (1948– 2020), a mentor and a friend. Milan, Italy, Catholic University of the Sacred Heart, March 2021.

PART I

Valuation

CHAPTER 2

From Business Models to Business Planning

2.1

From Budgeting to Business Planning

The successful commercialization of an innovation strongly depends on its business model (Ruseva, 2015; Gajewski & Rzemieniak, 2018). Management scholars and practitioners generally agree that the primary functions of a business model are value creation and value capture (Biloshapka & Osiyevskyy, 2018). Startups base their strategic ideas on a business model canvas that quantifies their managerial goals. Digital startups in the early stages of their development frequently undergo innovation to their value architecture and Business Model (Ghezzi & Cavallo, 2020). Lean Startup has been impacting how startups and incumbents innovate their business models (Bocken & Snihur, 2020). Startups struggle to align their business models coherently, particularly in the early phases. At the same time, their founders’ backgrounds and experiences have a critical influence on the design of the business model (Dopfer, 2018). Startup success is greatly attributed to pre-startup phase planning. Startups develop business plans to pitch their ideas to secure funding and/or partners (Khan, 2018). Investors need a practical, flexible, and comprehensive framework to model their management strategies, synthesizing their theoretical knowledge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_2

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In synthesis, two background skills are necessary for proper business planning: 1. basic accounting (to prepare the balance sheet, income statement, and cash flow account) 2. basic Excel knowledge (to transform ideas into IT numbers). Budgeting lies at the foundation of every perspective economic and financial plan, not only for startups. Budgeting considers the trendy outlook of sales and revenues, costs, production, marketing strategies, and their impact on cash flows. Budgeting and “power to predict” must be properly incorporated within a wider system of business planning (Teece, 2010). It should be made immediately clear that the accounting backbone of any business plan is first represented by the interactive matching of the three basic balance sheet documents: 1. the balance sheet, with a representation of assets, liabilities, and differential net equity; 2. the income statement (profit & loss account), with a coherent matching of revenues and costs; 3. the cash flow statement, showing the quantity (and quality) of liquidity created or absorbed by the ongoing business. The cash flow statement is automatically derived combining balance sheet variations (from the previous year) with the income statement of the year under investigation and so no new information is given, since they are already existing, just needing to be reclassified. Among the major mistakes in preparing a business plan, the mere forecast of expected revenues and costs over a conventional multi-year time horizon, accompanied by a rough and unsupported estimate of cash flows, stands out as a most likely banana skin. To those who look for shortcuts, avoiding deriving the cash flow statement from the necessary comparison of two consecutive balance sheets, matched with the income statement, it may suffice to mention that if they so behave (completely ignoring the necessity to prepare a balance sheet, which is the company’s cumulated “backbone”), they are likely to undergo unsolvable problems, such as:

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FROM BUSINESS MODELS TO BUSINESS PLANNING

11

• how to calculate the impact of a changing net working capital (due to modifications in accounts receivable and payable, in the inventory …); • how to determine any modification in the net financial position (i.e., financial debts net of financial credits). The balance sheet and the expected income statements along with the whole useful life of the investment horizon are the true backbones behind any business plan. Anything else (break-even analysis; financial ratios; market evaluation …), albeit useful or even necessary, is just complementary. Startuppers should try to transform their vision into feasible numbers, blending imagination with concreteness.

2.2

How to Write a Business Plan … Step After Step

Some basic questions for the preparation of the business plan (Osterwalder & Pigneur, 2010; Pinson, 2004; Rhonda & Kleiner, 2000; Shalman, 1997) concern the following issues: • • • •

How to conceive and prepare a business plan? Which are the consequential steps? How to be simultaneously effective and comprehensive? How to properly combine hard and soft skills?

These are just a few of the many issues that every practitioner must face. And even non-business plan experts that may never be asked to prepare a business plan (McKeever, 2011), are extremely likely to be in the position to evaluate, assess, or just read somebody else’s strategies. So, it is always useful to know how to read it and, to do so, any potential user should have at least a basic knowledge about how to prepare a business plan; not everybody is due to become an expert, but we are all likely to be somewhat involved in some business plan issues, so … it is better to start approaching them, at least broadly and intuitively. The steps may be the following (see http://www.wikihow.com/Writea-Business-Plan):

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1. Analyze the Potential Markets: Who will want your product or service? 2. Define the Company: What will you accomplish for others? What products and services will you produce or provide? 3. Choose a Winning Strategy: How will you distinguish the product or service from others? 4. Develop a Strong Marketing Campaign: How will you reach the customers and what will you say? 5. Build A Dynamic Sales Effort: How will you attract customers? 6. Design the Company: How will you hire and organize the workforce? 7. Identify the Company’s Initial Needs: What will you require to get started? Back and support the sources with appropriate and verifiable data; 8. Target the Funding Sources: Where will you find the financing? As the business concept begins to take shape, you can begin to home in on the most likely financing sources; 9. Explain the Financial Data: How will you convince others to invest in the endeavor? 10. Present the startup in the Best Light: What are the qualifications for bringing the plan to fruition? Other useful tips may include the following: • Many sources exist for finding information for the business plan. The local library and the Internet are always helpful sources for data. If you live near a university, you may be able to schedule an appointment with a mentor. • Make sure you cite the appropriate information. This way you will have support for any statistics you put into the business plan. • Soft skills are concerned with creativity, imagination, “immaterial” and pioneering entrepreneurship, stamina, flexibility, and resilience (…), willingness to create new unexploited value, beyond simple quantitative data entry or numbering. • Remember that business planning is always a work in progress: never stop refining, fine-tuning, upgrading, updating … • Never be satisfied: only enthusiasm can light up the fire of changing!

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13

• follow the Market traction: From Target Addressable Market (TAM) to Serviceable Addressable Market (SAM).

2.3

Upstarting and Forecasting a New Business

Starting up a new business, any entrepreneur is surrounded by initial excitement and following frustration, with ups and downs, and hope to take off, eventually. Soft skills must be carefully blended with an entrepreneurial spirit. And it should always be remembered that there are no self-igniting businesses. Preparing a business plan from scratch, after a tiring brainstorming, the entrepreneur needs to transform ideas and dreams into feasible numbers. A projection of the income statement across the useful life of the project or, at least, its starting phase, must consider a period of some 3–5 years: the longer the forecast, the lower its accuracy; but forecasts do not even have to be too short-termed: financial supporters are anxious to know about their payback. The extension of the business plan depends on the useful life of the project, often uneasy to exactly assess. Innovation exacerbates business discontinuity, increasing business planning volatility. The income statement forecast must be accompanied by a projection of pro forma balance sheet; the first balance sheet may be the photography of the company’s assets and liabilities at time zero, whereas successive balance sheets may be conceived mainly on an incremental/developmental basis, considering the impact of yearly income statement on the starting balance sheet: Balance sheet T0 → Income Statement T1 → →Balance sheet T1 →Income Statement T2 → →Balance sheet T2 ...

Some shortcuts may be useful: 1. First concentrate on the initial balance sheet, which is typically very simple, just containing the starting investments, covered by pocket money or some other equity/loan injections; for the moment, there are no stocks, no credits, no structured debt, no working capital … it is so not too difficult to make the initial photography; 2. Then concentrate on the income statement forecast for a sufficiently long period of some 3 years; this is the core part of your projections

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and assumptions. Again, to soften problems, concentrate on operating revenues and costs, trying to divide fixed from floating costs; an example may be the following (Table 2.1). It is so important to estimate initial revenues, their likely growth, and the portion of costs necessary to reach these revenues. Incremental balance sheet forecasts must consider some peculiarities and sensitive items, such as the net working capital, whose variation is represented by: Operating or commercial (not liquid, i.e., without liquidity that is added up to calculate the total net working capital, expressed by the difference between current assets and liabilities) net working capital, is mainly represented by the three key items: accounts receivable + stock—debt toward suppliers. Two fundamental aspects of Net Working Capital (NWC) should be remembered: 1. if economic margins grow—as they should—across time, then revenues grow more than operating costs, and also stock tends to increase; so Net Working Capital grows; 2. any growth in NWC, representing an Asset increase, must be properly backed by raised capital, in the form of Debt and/or Equity; NWC increases are so cash absorbing and have an implicit financial cost (often uneasy to sustain, especially for still fragile young companies). Table 2.1 From revenues to EBIT, differentiating between fixed and variable costs

Revenues Fixed costs Variable costs (20% of revenues) EBIT (Operating Profit)

T0

T1

Growth T0 − T1

T2

Growth T1 − T2

100 −50 −20

150 −50 −30

50%

200 −50 −40

33%

30

70

110

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15

In all this process, the entrepreneur should not particularly care about cash flow projections, not because they are not to be considered important—they are vital—but only since, from an accounting point of view, they automatically derive from a comparison of two consecutive balance sheets, matched with the income statement. This accounting matching will be dealt with in the next section.

2.4 The Accounting Picture: Interacting Balance Sheets with Income and Cash Flow Statements Balance sheet variations are combined with forecast income statements to get cash flow statement projections. The target of cash flow statement analysis is to have a comprehensive representation of the amount of liquidity that the startup generates or destroys (especially in the first years of its life). 2.4.1

Typologies of Cash Flow Statements

In financial accounting, a cash flow statement is a financial statement that shows how changes in balance sheet accounts and income affect cash and cash equivalents, and breaks the analysis down to operating, investing, and financing activities. Essentially, the cash flow statement is concerned with the flow of cash in and out of the business. The statement captures both the current operating results and the accompanying changes in the balance sheet. As an analytical tool, the statement of cash flows is useful in determining the short-term viability of a company, particularly its ability to pay bills. International Accounting Standard 7 (IAS 7) deals with cash flow statements. Monitoring the cash situation of any business is a priority. The income statement reflects the profits but does not give any indication of the cash components. The important information of what the business has been doing with its cash is provided by the cash flow statement. Like the other financial statements, the cash flow statement is also usually drawn up annually but can be prepared more often. The cash flow statement covers the flows of cash over some time (unlike the balance sheet that provides a snapshot of the business at a particular date). Also, it can be drawn up in a budget form and later compared to actual figures. The cash flow statement tells exactly where a firm got its money from and how it was spent. All cash received (inflows) and spent (outflows) by

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the company are shown. This statement is made up by listing the changes that have occurred in all the balance sheet items between any two balance sheet dates. The cash flow statement shows how changes in balance sheet accounts can affect the cash which is available to a business. The projections in the statement help businesses, especially when planning short-term goals or investments, to see the available amount of cash available for those actions. They use the cash flow statement to pick up healthy or unhealthy trends regarding a company’s trading activities. A Cash Flow Statement shows how much cash (liquidity) is generated and used during a given time. It is one of the main financial statements analysts use in building a three statement model (variations of the balance sheet matched to the income statement to sort out the cash flow statement). The main categories found in a cash flow statement are: 1. operating activities, 2. investing activities, 3. financing activities. The total cash provided from or used by each of the three activities is summed to arrive at the total change in cash for the period, which is then added to the opening cash balance to arrive at the cash flow statement’s bottom line, the closing cash balance (Fig. 2.1).

Fig. 2.1 Subdivision of the cash flows

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General accounting system

Balance sheet (variations)

Income statement

Cash flow statement

Fig. 2.2 Accounting derivation of the cash flow statement

The cash flow statement is routinely used in financial forecasts and for the calculation of the Discounted Cash Flows (DCF) that are used for startup valuation. The information contained in the cash flow statement is already present in the (variation of) the balance sheet combined with the income statement (profit & loss account). There is so no new data, but simply already existing information that is reclassified differently (Fig. 2.2). There are three different cash flow statements, considering their different temporal focus: (a) “Immediate” liquidity; (b) Short-term/current liquidity; (c) Medium-to-long-term liquidity. What mostly matters is “immediate” liquidity—cash already available or assets and debts that are going to be converted into (positive or negative) liquidity very soon (within a few weeks). Short-term liquidity is cash + assets and liabilities that will become liquid within one year (this being the conventional threshold between short versus long-term assets and liabilities). Long-term liquidity includes medium-term cash and they both include short-term liquidity (Fig. 2.3).

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Long-term Cash Flows Current Liquidity

Immediate Liquidity

Fig. 2.3 Typologies of cash flow statement

2.4.2

Sources of Funds and Uses of Capital

A “sources and uses of funds” statement is a summary of a firm’s changes in financial position from one period to another; it is also called a flow of funds statement or a statement of changes in financial position. It has been replaced by the cash flow statement but is still valid, especially whenever we need to distinguish between raised debt and invested capital. Generally, the statement consists of two sections: the source (where the money has come from) and the uses (where the money has gone). The sources of funds originate from: • an increase in liabilities or a decrease in assets • net income after tax • the disposal or revaluation of fixed assets

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proceeds of loans raised proceeds of issued shares repayments received on loans previously granted by the company any decrease in Net Working Capital (NWC).

The uses of funds originate from: • • • • • • •

losses to be met by the company the purchase of fixed assets/investments the full or partial payment of loans granting of loans liability for taxes dividends paid and proposed any increase in net working capital.

Sources express an increase in the availability of funds resulting from: (a) an increase in liabilities (b) an increase in equity (c) a decrease of assets (d) funds (=liquidity) generated by the income statement (= positive EBITDA). Conversely, sources are fully balanced (within a “balance” sheet) by corresponding uses: (a) asset increases (b) liabilities decreases (c) equity decreases (d) liquidity absorbed by the income statement (negative EBITDA) (Fig. 2.4). The sources generated from the income statement (=EBITDA) derive from the difference between monetary revenues and costs. This difference corresponds to the net profit + non-monetary costs—non-monetary revenues. Consider this example (in units of e):

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Increase in liabilities

Increase in equity

Decrease of assets

Sources created from income statement = EBITDA>0

Sources of funds (raised capital)

Uses of funds (invested capital)

Increase in assets

Decrease of liabilities

Decrease of equity

Sources destroyed by income statement = EBITDA< 0

Fig. 2.4 Sources and uses of funds

+ monetary revenues - monetary costs Sources generated from the income statement = EBITDA

855,500 - 845,500 ---------10,000

This corresponds from an accounting point of view to:

net profit + non-monetary costs - non-monetary revenues Sources generated from the income statement = EBITDA

35,940 16,660 - 42,600 ------------10,000

Considering a bi-sectional income statement, we have (Fig. 2.5). Sources (d) and then (b) and eventually (a) correspond to the Pecking Order Hypothesis (examined in Sect. 11.4). In corporate finance, the pecking order theory (or pecking order model) postulates that the cost of financing increases with asymmetric information. Financing comes from these three sources, internal funds (EBITDA), debt, and new equity. Companies prioritize their sources of financing,

2

Monetary costs

FROM BUSINESS MODELS TO BUSINESS PLANNING

845,500

Monetary revenues

855,500

Non-monetary costs 16,660

Net profit

35,940

21

10,000

Non-monetary revenues

42,600

Fig. 2.5 Monetary costs and revenues

first preferring internal financing, and then debt, lastly raising equity as a “last resort.” Hence: internal financing is used first; when that is depleted, then the debt is issued; and when it is no longer sensible to issue any more debt, equity is issued. This theory maintains that businesses adhere to a hierarchy of financing sources and prefer internal financing when available, and debt is preferred over equity if external financing is required (equity would mean issuing shares which meant “bringing external ownership” into the company). Thus, the form of debt a firm chooses can act as a signal of its need for external finance. The pecking order theory is popularized by Myers and Majluf (1984) where they argue that equity is a less preferred means to raise capital because when managers (who are assumed to know better about the true condition of the firm than investors) issue new equity, investors believe that managers think that the firm is overvalued, and managers are taking advantage of this over-valuation. As a result, investors will place a lower value on the new equity issuance. 2.4.3

From the Balance Sheet and the Economic Flows to the Financial Flows

We have anticipated that the cash flow statement has a simple accounting derivation, resulting from the combination of changes in the balance sheet (i.e., from a comparison between balance sheet 1 and balance sheet 0) and the income statement (of year 1). Some definition is preliminary to this comparison.

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The Net Working Capital (NWC) is expressed by the difference between the current (= short-term) assets and current liabilities. Any increase in the NWC is accounted for in the assets (credit) and it absorbs liquidity since it must be financed (covered) by sources: it is an asset increase and so uses of funds, as illustrated before. The total NWC can be divided into two basic components (Fig. 2.6). This subdivision is functional to our target—determining the cash flow statement—since the key parameter of this statement is represented by the change in net (immediate) liquidity. The operating NWC considers only non-financial items, like receivables, stock, and payables, as shown in Table 2.2. The balance sheet variation (from T 0 to T 1 ) to be considered in our case is the following (Fig. 2.7). The difference between Net Financial Debts (D) and liquidity (A) identifies the Net Financial Position that is used in firm valuation to assess the enterprise value (i.e., the company value that includes financial debt). Considering the income statement, we have the following representation (Fig. 2.8). We can now calculate the cash flow; this is the formal statement (Table 2.3). Operating Net Working Capital Net Working Capital (total) Net liquidity

Fig. 2.6 Subdivision of net working capital

Table 2.2 Composition of operating net working capital

       

of commercial credits (accounts receivable) of other credits of prepayments and accrued income stock of accruals and deferred income of other debts of amounts owed to trade creditors operating net working capital

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FROM BUSINESS MODELS TO BUSINESS PLANNING

23

Fig. 2.7 Variation of the balance sheet and identification of liquidity as the target variable

The Net Cash Flow for the shareholders (free cash flow to equity) corresponds to the residual area not considered above: the liquidity (A) that is the target parameter in the balance sheet variations illustrated above. The net result that represents the bottom line of the income statement is already considered in the equity and so it does not need to be represented again. Making a comparison with the income statement, the operating cash flow corresponds to the EBIT, as the net cash flow corresponds to the net result. The operating cash flow remunerates, from a financial point of view, the debtholders and—residually—the shareholders. The operating profit (= EBIT, in the income statement) remunerates, from an economic point

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Net Monetary Operating Revenues (F) - Net Monetary Operating Costs (G) [considering operating taxes but depreciation] Link with the cashflow statement

excluding

the

= EBITDA

- Depreciations / Amortizations (H) = EBIT - negative interests (I) +/- extraordinary profits & losses (L) - (non-operating) taxes (M) = Net Result

Fig. 2.8 Reclassification of the income statement propaedeutic to the determination of the cash flow statement Table 2.3 Cash flow statement

Net monetary operating revenues (F) − Net monetary operating costs (considering operating taxes but excluding the depreciation) (G) = EBITDA ± variation of operating NWC (B) ± variation of Capex/Fixed Assets (C) net of depreciation (H) = Operating Cash Flow (Unlevered o Debt-Free Cash Flow) − Negative interests (net of positive interests) (I) ± variation of financial debts (D) ± variation of equity (E) ± extraordinary revenues and costs (L) − Non-operating taxes (M) = Net Cash Flow (Levered Cash Flow) (A)

of view, the debtholders and—residually—the shareholders. Since shareholders underwrite risky capital, they follow the absolute priority rule and are paid back after the senior and junior debtholders. Since debtholders + shareholders = financial debt + equity = raised capital (sources of funds), we can compare raised capital to both the

2

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FROM BUSINESS MODELS TO BUSINESS PLANNING

operating cash flow (financially) and the EBIT (economically). This holds because the operating cash flow and the EBIT represent, respectively, the financial or economic marginality before debt service. Comparing the EBIT to the raised capital, we have EBIT/raised capital = Return On Invested Capital (ROIC), a profitability ratio examined in Chapter 3. 2.4.4

Cash Flow Statement Analytics

The calculation of the cash flow statement is sometimes tricky and may require some additional comment. In the cash flow statement, the depreciation (and other non-monetary operating costs, as provisions, and amortization of goodwill) concern: 1. the “backward” calculation of the EBITDA (starting from the EBIT and going “up”: EBIT + depreciation/amortization = EBITDA) 2. the variation of the CAPEX (CAPital EXpenditure, i.e., fixed assets). An example is the following (Table 2.4). The EBIT includes both monetary and non-monetary costs, but only monetary costs are considered for the cash flow estimate. The CAPEX variation matters, together with the variation in the operating NWC, for the calculation of the operating cash flow (Table 2.5). CAPEX investments are typically important in startups. The variation in the operating NWC is the following (Table 2.6). The variation of the Capex derives from the comparison between two consecutive years, net of the depreciations (Table 2.7). The algebraic sum starts from the EBITDA and arrives at the Free Cash Flow to the Firm (FCFF − = Operating Cash Flow). The first passage is: EBITDA ±  NWC ±  CAPEX = Operating (unlevered) cash flow. Table 2.4 Derivation of the EBITDA Cash flow statement (in euro)

T0

T1

T2

T3

EBIT (A − B) Depreciations and provisions EBITDA (A)

412,922 78,360 491,282

967,252 158,434 1,125,686

1,129,467 209,069 1,338,536

605,877 360,535 966,412

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Table 2.5 From the EBITDA to the operating cash flow Cash flow statement (in euro) EBITDA (A) Operating net working capital variation Fixed assets (CAPEX) variation Taxes Unlevered (operating/debt-free) cash flow (B)

T0

T1

T2

T3

491,282 1,443,983

1,125,686 −1,778,470

1,338,536 966,412 −5,016,976 −3,381,203

−229,170 −189,103 1,516,992

−608,130 −402,528 −1,663,442

−351,339 −792,628 −401,140 −215,992 −4,430,919 −3,423,411

Table 2.6 Composition of the operating net working capital Cash flow statement (in euro) EBITDA (A) Operating net working capital variation Fixed assets (CAPEX) variation Taxes Unlevered (operating/debt-free) cash flow (B) Variation of credits of the current assets Variation of prepayments and accrued income Variation of stocks Variation of accruals and deferred income Variation of debt owed to social security institutions Variation of fiscal debts Variation of other debts Variation of payments on account Variation of debts owed to trade creditors Variation of the operating net working capital

T0

T1

T2

T3

491,282 1,443,983

1,125,686 −1,778,470

1,338,536 966,412 −5,016,976 −3,381,203

−229,170 −189,103 1,516,992

−608,130 −402,528 −1,663,442

−351,339 −792,628 −401,140 −215,992 −4,430,919 −3,423,411

−81,199

−3,570,905

−4,320,556

2,837,678

−1955

2964

20,486

−219,376

−153,502 102,872

−165,327 61,941

3358

22,385

27,455

39,107

−75,208 3864 −352,693

332,668 320,117 −375,532

−298,028 346,220 −5,938

105,077 −545,882 −606,837

1,998,446

1,593,219

971,330 −3,632,941

1,443,983

−1,778,470

−5,016,976 −3,381,203

−1,801,652 −1,514,856 43,707 156,827

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FROM BUSINESS MODELS TO BUSINESS PLANNING

27

Table 2.7 Composition of the fixed assets (CAPEX) Cash flow statement (in euro) Variation of intangible fixed assets Variation of tangible fixed assets Variation of financial fixed assets Amortizations and depreciations Variation of fixed assets

T0

T1

T2

T3

−146,293 −4517 – −78,360 −229,170

−377,095 −72,601 – −158,434 −608,130

−102,060 −39,435 −775 −209,069 −351,339

−208,874 −223,219 – −360,535 −792,628

Table 2.8 From the operating to the net cash flow Cash flow statement (in euro) Unlevered cash flow (B) Financial incomes and charges Credits toward shareholders variation Funds for liabilities and charges variation Severance indemnity for employee’s variation Net financial liabilities variation Intercompany liabilities variation Value adjustments of financial assets Extraordinary incomes and charges Equity variation Levered = Net cash flow (C)

T0

T1

T2

T3

1,516,992 −162,452

−1,663,442 −183,199

−4,430,919 −427,917

−3,423,411 −809,392

–

–

–

–

−110,053

6112

−38,948

20,693

34,931

54,324

82,584

83,947

147,616

359,375

500,283

2,501,337

–

–

–

–

–

–

–

–

−17,887

−9183

−1

500,187

373 1,409,520

1 −1,436,012

2 −4,314,916

−2 −1,126,641

To get to the net cash flow (free cash flow to equity, belonging to the shareholders), we must consider (Table 2.8). The detail of net financial liabilities and equity is the following (Table 2.9). In the variation of equity, the net result is not considered, because it is already expressed in the cash flow statement, being the synthesis of the income statement. In other words, the analytical difference between all

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Table 2.9 Net financial liabilities and equity Cash flow statement (in euro) Unlevered cash flow (B) Net financial liabilities variation Equity variation Levered = Net cash flow (C) Final cash availability Initial cash availability Cash flow variation (D) = (C) Variation of bonds Variation of convertible bonds Variation of debts owed to shareholders for loans Variation of debts owed to other financiers Variation of long-term bank debts Variation of net financial liabilities Variation of subscribed capital Variation of reserves Result of the previous financial year Variation of equity

T0

T1

T2

T3

1,516,992 147,616

−1,663,442 359,375

−4,430,919 500,283

−3,423,411 2,501,337

373 1,409,520

1 −1,436,012

2 −4,314,916

−2 −1,126,641

−291,246 −1,700,766 1,409,520

−1,727,258 −291,246 −1,436,012

−6,042,174 −1,727,258 −4,314,916

−7,168,815 −6,042,174 −1,126,641

– –

– –

– –

– –

–

–

–

470,839

147,616

359,375

−19,717

589,979

–

–

520,000

1,440,519

147,616

359,375

500,283

2,501,337

–

–

–

–

148,485 −148,112

43,481 −43,480

372,344 −372,342

300,407 −300,409

373

1

2

−2

the revenues and all the costs of the year, synthetically represented in the income statement with the net result, is already reported in the cash flow statement in different parts: EBITDA, extraordinary revenues, etc. Another particularity is the netting of the result of the previous year, reflected in the reserves of the next year (unless the net profit is paid out as a dividend). For example, as shown above, the netting is the following (Table 2.10). The final step is to check the correspondence of the cash flow variation calculated analytically (through the cash flow statement) and synthetically, considering the variation of the balance sheet (A) (Table 2.11).

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29

Table 2.10 Variation of reserves Variation of reserves Result of the previous financial year

148,485 −148,112

43,481 −43,480

372,344 −372,342

300,407 −300,409

Table 2.11 Cash flow statement reconciliation Cash flow statement (in euro) Levered = Net cash flow = Free cash flow to equity (C) Final cash availability Initial cash availability Net cash flow variation (D) = (C) Check (C − D) must correspond to zero

T0

T1

T2

T3

1,409,520

−1,436,012

−4,314,916

−1,126,641

−291,246 −1,700,766 1,409,520

−1,727,258 −291,246 −1,436,012

−6,042,174 −1,727,258 −4,314,916

−7,168,815 −6,042,174 −1,126,641

–

–

–

–

Considering the EBITDA, we have the following “cake” whose slices must be divided (Fig. 2.9).

2.5 Getting Information from Big Data and Networks Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Optimal business planning and related corporate evaluations derive from an equilibrated mix of top-down and bottom-up approaches, as will be shown in Fig. 2.15. While the former follows a traditional methodology where companies set up their strategic goals, the latter are grass-rooted with big data-driven timely evidence. Real options can be embedded in big data-driven forecasting to make expected cash flows more flexible and resilient, improving the Value for Money of the investment and reducing its risk profile. More accurate and timely big data-driven predictions reduce uncertainties and information asymmetries, making risk management easier, and decreasing the cost of capital. Whereas stochastic modeling is traditionally used for budgeting and business planning, this probabilistic process is seldom nurtured by big data that can refresh forecasts in real-time, improving their predictive

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Net cash flow for the shareholders

Extraordinary items + non operating taxes

Negative interests + Debt payback

Increase Operating NWC

Capex Increase

Fig. 2.9 Subdivision of the cash-generating EBITDA

ability. The combination of big data and stochastic estimates for corporate appraisal and governance issues represents a methodological innovation that goes beyond the traditional literature and practice (Moro Visconti et al., 2018). Network theory rotates around nodes and their linking edges. Big data can be extracted from informative networks and the Internet of Things. The Internet of Things (IoT) is the network of physical objects or “things” embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data (https://en.wikipedia.org/wiki/Internet_of_Things). Network theory is the study of graphs as a representation of either symmetric relations or, more generally, of asymmetric relations between discrete objects. Network theory is a part of graph theory. It has applications in many disciplines including statistical physics, particle physics, computer science, electrical engineering, biology, economics, operations research, and sociology. Applications of network

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31

theory include logistical networks, the World Wide Web, Internet, gene regulatory networks, metabolic networks, social networks, epistemological networks, etc. (see https://en.wikipedia.org/wiki/Network_theory). Network theory is concerned with the study of graphs as a representation of (a)symmetric relations between discrete objects (nodes or vertices connected by links or edges), as synthesized in Moro Visconti (2019).

2.6 Frame-Working the Strategic Environment with PESTLE and SWOT Analysis PESTLE and SWOT analyses provide a systematic and comprehensive reflection of the external and internal operational environment. They can so be widely used in forecasting and business planning and may be combined with big data. PEST(LE) considerations, being essentially external, somewhat tend to precede SWOT analyses: while the former shape the analytical framework of the investment, the latter resume environmental analysis (scanning the business environment for Threats and Opportunities), with a subsequent internal focus on organizational issues (Strengths and Weaknesses) (Table 2.12). Some useful tips may include the following: • Never forget to strategize your vision, triggering events with entrepreneurship drive; • Big Data, the Internet of Things, and strategic processing with SWOT and PEST analysis may greatly help; Table 2.12 PESTE and SWOT definition Variable

Definition

PESTLE

Strategic methodology comprehensively using Political, Economic, Social, Technological, Legal, Environmental trendy analysis for reviewing the macro environment, with its external forces that impact the ability to plan Structured planning method used to evaluate the Strengths, Weaknesses, Opportunities, and Threats involved in a project, to consider if the objective (i.e., building and running a hospital) is attainable and, if so, how. Opportunities may be considered as real options

SWOT

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• Follow a discovery channel, with a “learning by doing” knowledge curve; • Avoid tunnel vision; be flexible and resilient; adapt Your strategies and revise continuously your budget; • Look at KPIs—key performance indicators; • Prepare a strategy against the syndrome of empty sheets (how can I prepare my business plan? Which data must be entered?).

2.7

A Matrix for Risk Metrics

Risk is a concept that identifies and—possibly—measures the expected probability of specific eventualities (possible states of the world). Technically, the notion of risk is independent of the notion of value and, as such, eventualities may have both beneficial (upside risk) and adverse (downside risk) consequences. Lenders intrinsically have downside sensitivity. However, in general, the convention is to focus only on the potential negative impact on some characteristic of value that may arise from a future event. Risk can conveniently be measured with the probability that the effective ex-post outcome is different (lower) than the envisaged ones. Pricing risk, cutting through complexity, is often more difficult than expected and unforeseen events are an additional and unpredictable source of risk (Fig. 2.10). The main risks can interact within the risk matrix, with many possible outcomes often difficult to model and forecast; in many cases, the interaction follows a sort of Shanghai model, according to which each stick can randomly hit the others, causing a chain effect with unforeseen results. Risk—due to uncertain events—is extremely hard to detect and measure. The most straightforward method is to estimate the statistical probability that a (negative) event occurs, associating to it a measurable cost (or cash outflow). But this is hardly possible in many cases, due to the difficulty to detect the risk source, to forecast the possible outcomes/states of the world, and to associate to each of them a weighted cost according to its probability of occurrence. Also, in a changing scenario, many risk factors simultaneously interact among them, within a wide and interlinked risk matrix. Risk assessment and scoring is a key step in a risk management process, consisting of the determination of the quantitative or qualitative value

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33

2

risk (σ revenues)

downside risk

upside risk

revenues

(safer) revenues

Fig. 2.10

(riskier) revenues

Upside and downside risk, due to revenues’ volatility

of risk related to a concrete situation and a recognized threat (also called hazard). Quantitative risk assessment requires calculations of two components of risk: • the magnitude of the potential loss; • the (statistical) probability that the loss will occur. An example of risk measurement can be given by the Probability of Default used in Basel II or III credit scoring systems. It is the likelihood that a loan will not be repaid and fall into default. This Probability of Default will be calculated for each startup that has a loan (see Chapter 7). The credit history of the counterparty and nature of the investment will all be considered to calculate the Probability of Default figures. The probability of default of a borrower does not, however, provide the complete picture of the potential credit loss. Banks also seek to measure how much they will lose should a borrower default on an obligation. This is contingent upon two elements: • First, the magnitude of likely loss on the exposure: this is termed the Loss Given Default (and is expressed as a percentage of the exposure);

34

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• Secondly, the loss is contingent upon the amount to which the bank was exposed to the borrower at the time of default, commonly expressed as Exposure at Default. Risk assessment consists of an objective evaluation of risk in which assumptions and uncertainties are considered and detected. Part of the difficulty of risk management is that measurement of both quantities in which risk assessment is concerned—potential loss and probability of occurrence—can be exceedingly difficult to identify or measure. Risk can also derive from corporate governance problems and conflicts of interest among stakeholders. The first step, hazard identification, aims to determine the qualitative nature of the potential adverse consequences of the risky situation. Quantitative risk assessments include a calculation of the single loss expectancy of an asset. Risk is a holistic system, like the human body. A risk matrix can be ideally represented by the following Fig. 2.11. The graph shows that risks, purposely unspecified in this example, are linked among them, often in an unpredictable—risky—way. The attempt to find out a synthetic measure of overall risk, albeit theoretically captivating and practically meaningful, is uneasy to carry forward, but still worthwhile. In the startup’s valuation, risk is incorporated in the discount factor (cost of capital) of expected cash flows.

risk*

risk*

risk*

Σ RISK

Fig. 2.11

risk*

risk*

risk*

risk*

risk*

risk*

Interactive risk matrix

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35

2.8 Sensitivity and Scenario Analysis: Deterministic Versus Stochastic Planning Sensitivity analysis is conducted changing a parameter at a time (e.g., interest rates; the period of the project …), seeing what happens to the rest, whereas with scenario analysis, two or more parameters change simultaneously, and this effect may be uneasy to model. Business plans need to be flexible and resilient to external shocks, changes, adaptation, etc. And so sensitivity/scenario analysis, albeit bringing a certain level of sophistication, may be welcome. A pragmatic and flexible approach is highly wanted in any business planning strategy. The usefulness of sensitivity/scenario analysis derives also from its expected impact on break-even analysis (which may be well combined with the iterative research of the Internal Rate of Return, i.e., the return that makes Net Present Value of projected cash flows equal to zero), asking “disaster case” questions such as which is the most pessimistic scenario (growing rates/costs; lowering returns …) which may still allow reaching break-even. Statistical binomial models (Arnold et al., 2004) may be applied to debt service patterns, to ascertain which is the break even (minimum) that may avoid cash burnout, keeping proper cover ratios (Fig. 2.12). Lenders may well be interested in having an idea about this issue and even more pessimistic disaster cases may be conveniently tested, wondering about the recoverable value of a business when going concern is no more a viable option. Break-Even Business Planning stands out as key information, trying to estimate and fine-tune the likelihood of events. A more sophisticated risk analysis can make use of Monte Carlo simulations that are useful for modeling phenomena with significant uncertainty in inputs, such as the calculation of risk in business. Monte Carlo methods in finance are often used to calculate the value of companies, to evaluate investments in projects at a corporate level, or to evaluate financial derivatives. The method is intended for financial analysts who want to construct stochastic or probabilistic financial models as opposed to the traditional static and deterministic models. In mathematical finance, Monte Carlo methods are used to value and analyze (complex) instruments, portfolios, and investments by simulating the various sources of uncertainty affecting their value, and then determining their average value over the range of resultant outcomes. The advantage of Monte Carlo methods over other

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Fig. 2.12 Example of binomial model

techniques increases as the dimensions (sources of uncertainty) of the problem increase (Du & Li, 2008). Risk Mitigation represents a benefit for all the parties and follows several complementary steps: 1. identification—a trivial but fundamental and uneasy task: one cannot avoid what he does not know—and a quick look to the risk matrix can give a rough idea of the problem; 2. selection—of the most suitable risk bearer and contractual insurance regulation; 3. measurement—with probability and severity quantitative estimates; 4. monitoring—during the tender and afterward in the building and management phase;

2

(Severity) Consequences

Probability of occurence (likelyhood) 1 Negligible 2 Minor 3 Moderate 4 Major 5 Catastrophic

FROM BUSINESS MODELS TO BUSINESS PLANNING

Rare - 1

Unlikely - 2

Possible - 3

Likely - 4

Almost Certain - 5

1 2 3 4 5

2 4 6 8 10

3 6 9 12 15

4 8 12 16 20

5 10 15 20 25

Legend: Blue - Low risk Grey - Moderate risk Yellow - Significant risk Red - High risk

Fig. 2.13

37

impact of risk mitigation

Risk scoring matrix

5. management—risk mitigation has a strong impact on corporate governance conflicts among different stakeholders: the lower the risk, the higher the harmony and convergence of interests. A traditional risk scoring matrix is the one described in Fig. 2.13.

2.9 Fixing the Sustainable Bottom Line: How to Avoid Cash or Equity Burn Outs Sustainability is a key concern for every stakeholder, for employees or external providers of finance, who did not underwrite any risky capital issue and may not benefit from potential upsides. Due to its intrinsic importance—a matter of life or death—sustainability needs to be constantly investigated: entrepreneurs and other stakeholders should periodically check and monitor ongoing results and continuously reengineer/fix the model, looking for strategic goals/milestones achievement. Continuous monitoring is so necessary, always looking for Business Plan release 2.0 or xx.0… The main threats to sustainability are represented by cash and/or equity burnouts; synthetically reconsidering the balance sheet projection, we may have (Fig. 2.14).

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BALANCE SHEET

Startup Phase Year 1 Year 2

Management / Consolidation Phase Year 3 Year 4 Year 5

Assets Total Current Assets Total Long-Term Assets Total Assets Equity & Liabilities Total Equity Current Liabilities Long-term Debt Total Liabilities Potential EQUITY Burn Out And for what concerns the cash flow: CASH FLOW STATEMENT

Management / Startup Phase Consolidation Phase Year Year Year Year 1 2 Year 3 4 5

Operating Cash Flow Net Cash Flow Cash Balance at the beginning of the year Cash Balance at the end of the year (S) Cash & Banks (= Liquidity) Variation

Cumulative Cash Flow Potential LIQUIDITY (CASH FLOW) Burn Out

Fig. 2.14

Equity- and cash-burnout

2.10 Periodically Monitoring and Upgrading the Model and Its Underlying Miscalibrated Expectations Dreams always differ from ex-post historic reality and miscalibrated expectations, which may prove over-optimistic and unrealistic, or difficult to meet. And dreams traditionally prove difficult to model and to put into numbers: hypotheses must be duly backed by realism, with transparent

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39

and straightforward strategies which enable to avoid arbitrary assumptions and to check if and to which extent expectations have been met, continuously fine-tuning the model. Inefficient implementation and improper monitoring (due also to late periodical checks which do not timely represent the ongoing situation and its likely trend) are two likely situations that represent an obstacle to problem-solving of dysfunctions, with a consequent risk of being out of touch with grass-root reality. Monitoring is mostly performed with an accounting comparison between forecast and historic balance sheets, income, and cash flow statements; and differences between reality and expectations must be duly analyzed, considering their impact and—mostly—their likely incidence on future occurrences. And early-stage projections, made before starting the game, need to be continuously updated and upgraded, facing reality, and incorporating its feedbacks in the numerical model. The purpose of the International Standard on Assurance Engagements (ISAE 3400) http://www.ifac.org/sites/default/files/downlo ads/b013-2010-iaasb-handbook-isae-3400.pdf is to establish standards and provide guidance on engagements to examine and report on prospective financial information including examination procedures for best-estimate and hypothetical assumptions.

2.11

A Corporate Governance Perspective

“Corporate governance deals with the ways in which suppliers of finance to corporations assure themselves of getting a return on their investment” (Shleifer & Vishny, 1997). In synthesis, it is essential to give investors legal protection from expropriation by managers, limiting self-dealing. In a broader sense, corporate governance sets the rules of cohabitation and the behavior of the different stakeholders that pivot around the startup (borrowers, lenders, shareholders, employees, suppliers, clients, supervisory authorities …). To the extent that stakeholders are properly involved and feel themselves as a part of the project, their efforts may be better aligned, minimizing conflicts of interest or information asymmetries, which are so damaging to the startup’s survival, increasing its cost of capital. Information asymmetries are mitigated by the presence of monitoring debtholders that, however, at not present in the seed stage (see Chapter 6). The

40

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presence of majority shareholders—the founding partners—brings to a concentration of information that is progressively mitigated when the capital is diluted, with new shareholders that join the startup. Crowdfunding also facilitates a shareholders’ dispersion, and risk diversification. Good governance may also strongly contribute to lowering the cost of collected capital, from both shareholders and debt underwriters that appear when the startup matures, as shown in Chapter 7. Governance concerns shape the relationships among the startup’ s stakeholders. Proper team building is the managerial glue behind any successful startup and greatly helps to achieve goals and develop fruitful synergies.

2.12

Augmented Business Planning

Traditional business planning follows a managerial top-down approach where forecasts are conceived within the firm and occasionally compared with market returns. The increasing availability of timely big data, sometimes fueled by the Internet of Things (IoT), allows receiving continuous feedbacks that can be conveniently used to refresh assumptions and forecasts, using a complementary bottom-up approach. Forecasting accuracy can be substantially improved by incorporating timely empirical evidence, with consequent mitigation of both information asymmetries and the risk of facing unexpected events. Bottom-up feedbacks fed by IoT and big data can also readdress realtime strategies, incorporating in the business model forecast value-adding real options that increase its resilience. Management-prepared forecasts and projections, collectively referred to as prospective financial information (PFI), serves as the critical foundation for discounted cash flow methods. Pro forma information, often used in (traditional) business planning, is not prospective or forwardlooking, but rather a restatement of historical information (Dufendach, 2020). Augmented business planning intends to go far beyond pro forma top-down strategies. Interaction of top-down and bottom-up strategies is examined in Daradkah et al. (2018). According to Hutchison-Krupat and Kavadias (2014), when senior managers make the critical decision of whether to assign resources to a strategic initiative, they have less precise initiative-specific information than project managers who execute such initiatives. Senior management chooses between a decision process that dictates the resource level (top-down) and one that delegates the resource

2

d e d u c t i v e

FROM BUSINESS MODELS TO BUSINESS PLANNING

Business Model

Physical/Digital Supply Chain Firm

Business Plan / AccounƟng Metrics

t o p

ValuaƟon Metrics

d o w n

Stock Market Prices

Intermedi aries

Client

Big Data / IoT / validaƟng Blockchains / ArƟficial Intelligence

Market Ecosystem

41

i n d u c t i v e b o t t o m u p

Fig. 2.15

Interaction of top-down and bottom-up strategies

decision and gives up control in favor of more precise information (bottom-up). Interaction of top-down and bottom-up strategies can be synthesized in Fig. 2.15.

2.13

Business Incubators and Accelerators

A business incubator is a firm that helps new and startup companies to develop by providing services such as management training or office space. Incubators are a catalyst tool for either regional or national economic development. NBIA categorizes members’ incubators by the following five incubator types: academic institutions; non-profit development corporations; for-profit property development ventures; venture capital firms, and a combination of the above (Rubin et al., 2015). The big businesses are comfortable making money off the shortcomings rather than solving them or changing their existing established business models. It is the startups that will come up with innovative disruptive business models to solve the challenges (Adhana, 2020). Cohen (2013) reports that accelerators help ventures define and build their initial products, identify promising customer segments, and secure resources, including capital and employees. They usually provide a small

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amount of seed capital, plus working space. They also offer a plethora of networking opportunities, with both peer ventures and mentors, who might be successful entrepreneurs, program graduates, venture capitalists, angel investors, or even corporate executives. Finally, most programs end with a grand event, a “demo day” where ventures pitch to a large audience of qualified investors. Seed accelerators (Cohen & Hochberg, 2014) coexist with incubators, early-stage business angels, often operating in co-working environments, as shown in Fig. 2.16. The synergistic interactions among these stakeholders can be interpreted with network theory and mastered by bridging digital platforms, as shown in Chapter 12. These adjuvating stakeholders help the startup to focus on the business idea and planning, improving their proof-ofconcept empirical evidence and readjusting their strategies as a response to external advisory and feedbacks.

Incubators

Angel Investors

Accelerators

Startup Networking Digital Plaƞorms

Fig. 2.16

Co-working Environments

Startup Interactions with Incubators, Angels, and Accelerators

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43

Both incubators and accelerators can use Artificial General Intelligence paradigms, cognitive blockchains (Williams & Moro Visconti, 2020), big data evidence stored in cloud databases, or other innovative tools to simulate potential outcomes, reshaping visionary strategies, and shortening their time-to-market implementation. According to Moschner et al. (2019), corporate accelerator programs—accelerators managed by or directly sponsored by one or multiple established firms—are becoming an integral part of startup ecosystems and an important startup engagement vehicle for established firms. Accelerators can be: • In-house, if they are created and operated internally. This mostly happens with startups that belong to listed groups; • Hybrid, if they combine inside functions with external expertise; • Independent (external), mostly used by companies that invest in startups; • Consortiated, if they offer their services to multiple clients. Pivoting, i.e., business model innovation in startups is a crucial topic for young firms, since the probability that startups create a first business model that immediately works without any errors in an environment of high uncertainty is often low. Although it is easier for startups to implement a new business model because of their agility; they often have just one shot to pivot due to limited financial resources (Comberg et al., 2014).

References Adhana, D. (2020, September 20). Start-up ecosystem in India: A study with focus on entrepreneurship and university business incubators. SSRN. Available at https://ssrn.com/abstract=3702510. Arnold, T. M., Crack, T. F., & Schwartz, A. (2004, May 14). Implied binomial trees in excel without VBA. SSRN. Available at http://ssrn.com/abs tract=541744. Biloshapka, V., & Osiyevskyy, O. (2018, June). Value creation mechanisms of business models: Proposition, targeting, appropriation, and delivery. The International Journal of Entrepreneurship and Innovation. Bocken, N., & Snihur, Y. (2020). Lean startup and the business model: Experimenting for novelty and impact. Long Range Planning, 53(4).

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Cohen, S. (2013). Innovations: Technology, governance, globalization. MIT Press. Available at https://www.mitpressjournals.org/doi/pdf/10.1162/INOV_a_ 00184. Cohen, S., & Hochberg, Y. V. (2014, March 30). Accelerating startups: The seed accelerator phenomenon. SSRN. Available at https://ssrn.com/abstract=241 8000. Comberg, C., Seith, F., German, A., & Velamuri, V. K. (2014, June). Pivots in startups: Factors influencing business model innovation in startups. In The XXV ISPIM conference—Innovation for sustainable economy and society, Dublin, Ireland. Daradkah, M. M., Al Jounidy, E., & Qusef, A. (2018). Top-down vs. bottomup in project management: A practical model. ResearchGate. Available at https://www.researchgate.net/publication/326918337_Top-Down_vs_B ottom-Up_in_Project_Management_A_Practical_Model. Dopfer, M. (2018). Why business model innovation matters to startups. In N. Richter, P. Jackson, & T. Schildhauer (Eds.), Entrepreneurial innovation and leadership: Preparing for a digital future. Cham: Palgrave Macmillan. Du, X., & Li, A. N. (2008). Monte Carlo simulation and a value-at-risk of concessionary project: The case study of the Guangshen Freeway in China. Management Research News, 31(12), 912–921. Dufendach, D. C. (2020). The use of management’s prospective financial information: a focus on fair value measurement using discounted cash flow techniques. Business Valuation OIV Journal, Spring. Gajewski, P., & Rzemieniak, M. (2018, May 16–18). Startups—factors determining the achievement of a scalable business model. In Conference paper, integrated economy and society: Diversity, creativity, and technology, Naples, Italy. Ghezzi, A., & Cavallo, A. (2020). Agile business model innovation in digital entrepreneurship: Lean startup approaches. Journal of Business Research, 110, 519–537. Hutchison-Krupat, J., & Kavadias, S. (2014). Strategic resource allocation: Topdown, bottom-up, and the value of strategic buckets. Management Science, 61(2). Khan M. R. (2018). Constructing a startup strategy frame. In CBU international conference on innovations in science and education, March Prague. McKeever, M. P. (2011). How to write a business plan. Delta Printing Solutions Inc. Moro Visconti, R. (2019). Combining network theory with corporate governance: Converging models for connected stakeholders. Corporate Ownership and Control, 17 (1), 125–139.

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Moro Visconti, R., Montesi, G., & Papiro, G. (2018). Big data-driven stochastic business planning and corporate valuation. Corporate Ownership and Control, 15(3–1). Moschner, S., Fink, A. A., Kurpjuweit, S., Wagner, S. M., & Herstatt, C. (2019). Toward a better understanding of corporate accelerator models. Business Horizons, 62(5), 637–647. Myers, S. C., & Majluf, N. S. (1984). Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics, 13(2), 187–221. Osterwalder, A., & Pigneur, Y. (2010). Business model generation. Wiley. Pinson, L. (2004). Anatomy of a business plan: A step-by-step guide to building a business and securing your company’s future (6th ed.). Chicago, USA: Dearborn Trade. Rhonda, A., & Kleiner, E. (2000). The successful business plan: Secrets and strategies. Palo Alto: Running ‘R’ Media. Rubin, H. T., Aas, T. H., & Stead, A. (2015). Knowledge flow in Technological Business Incubators: Evidence from Australia and Israel. Technovation, 41–42, 11–24. Ruseva, R. (2015, July). Patterns for startup business models. In EuroPLoP ‘15: Proceedings of the 20th European conference on pattern languages of programs. Shalman, W. A. (1997). How to write a great business plan. Harvard Business Review. Available at http://serempreendedor.files.wordpress.com/2008/09/ how-to-write-a-great-business-plan.pdf. Shleifer, A., & Vishny, R. W. (1997, June). A survey of corporate governance. Journal of Finance. Teece, D. (2010). Business model, business strategy and innovation. Long Range Planning, 43, 172–194. Williams, A. E., & Moro Visconti, R. (2020, December). The application of artificial general intelligence to the cognitive blockchain and the Internet of value. ResearchGate. Available at https://www.researchgate.net/publication/ 346715411_The_Application_of_Artificial_General_Intelligence_to_the_Cog nitive_Blockchain_and_the_Internet_of_Value.

CHAPTER 3

Profitability, Intangible Value Creation, and Scalability Patterns

3.1 Return on Equity, Return on Invested Capital, and Other Profitability Ratios The profitability ratios indicate the economic returns of the firm, comparing key economic margins to their corresponding balance sheet variables. According to Ye (2018), results suggest that financial resources have positive impacts on startup firms’ profitability; whereas the impacts of the initial firm size on profitability are negative. Startups are more likely to be profitable when the firm size is small at the newborn stage. The positive impact of financial resources on profitability is greater when entrepreneurial teams have strong industry experience; whereas entrepreneurial teams’ industry experience and intangible resources have a negative interaction effect on profitability. The entrepreneurial team’s startup experience has the most negative interaction effects on newborn startup firms’ profitability. This finding indicates that the entrepreneurial team’s startup experience plays a stronger role in venturing into profitable startups when the number of financial resources and initial firm size are small; however, the team’s startup experience and intangible resources have positive interaction effects on the profitability of newborn startups.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_3

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3.1.1

Return on Equity (ROE)

In corporate finance, the return on equity (ROE) is a measure of the profitability of a business concerning the book value of shareholder equity, derived from the difference between assets and liabilities. ROE is a measure of how well a company uses investments to generate earnings growth, fostering value co-creation patterns (Beirão et al., 2017; Galvagno & Dalli, 2014). ROE is equal to net income (Rn), divided by mean total equity [E = (E 0 + E 1 )/2], expressed as a percentage: RO E =

Rn E0+E1 2

(3.1)

ROE is always positive unless the net return is negative or corresponds to zero. ROE is especially used for comparing the performance of companies in the same industry. As with return on capital, ROE is a measure of management’s ability to generate income from the equity available to it. ROE is also a factor in stock valuation, in association with other financial ratios. In general, stock prices are influenced by earnings per share (EPS), so that the stock of a company with a 20% ROE will generally cost twice as much as one with a 10% ROE. The benefit of low ROEs comes from reinvesting earnings to aid company growth. It can also come as a dividend on common shares or as a combination of dividends and company reinvestment. ROE is less relevant if earnings are not reinvested. The DuPont formula, also known as the strategic profit model, is a common way to break down ROE into three important components. Essentially, ROE will equal the net profit margin multiplied by asset turnover multiplied by financial leverage. Splitting return on equity into three parts makes it easier to understand changes in ROE over time. For example, if the net margin increases, every sale brings in more money, resulting in a higher overall ROE. Similarly, if the asset turnover increases, the firm generates more sales for every unit of assets owned, again resulting in a higher overall ROE. Finally, increasing financial leverage means that the firm uses more debt financing relative to equity financing. Interest payments to creditors are tax-deductible, whereas dividend payments to shareholders are not. Thus,

3

PROFITABILITY, INTANGIBLE VALUE CREATION …

49

a higher proportion of debt in the firm’s capital structure leads to higher ROE. Since: net result = R n = retained earning + dividends, ROE as represented by formula (3.1) can be split: retained earnings Dividends Rn = + E E E

(3.2)

ROE can also be linked to stock market ratios, multiplying the Price/book value for the earnings yield: Rn P Rn = ∗ E BV P

(3.3)

And then: E = BV (equity = book value of equity). Rn/P represents the earnings yield or, reciprocally, the P /E ratio: P /E = P /Rn (where Rn = E = net profit). The price earnings ratio (P /E ratio) is the ratio for valuing a listed company that measures its current share price relative to its per share earnings. The price earnings ratio indicates the e amount an investor can expect to invest in a company to receive one e of that company’s earnings. Therefore, the P/E is sometimes referred to as the price multiple because it shows how much investors are willing to pay per e of earnings. If a company was currently trading at a multiple (P /E) of 20, the interpretation is that an investor is willing to pay e20 for e1 of current earnings. Financial leverage benefits diminish as the risk of defaulting on interest payments increases. So if the firm takes on too much debt, the cost of debt rises as creditors demand a higher risk premium, and ROE decreases. Increased debt will make a positive contribution to a firm’s ROE only if the matching return on assets (ROA) of that debt exceeds the interest rate on the debt. 3.1.2

Return on Invested Capital (ROIC) and Return on Assets (ROA)

The Return on invested Capital (ROIC) is the ratio between the operating profit (EBIT) and raising (investing) capital, defined in Sect. 3.2. A high ROIC means the investment’s gains compare favorably to its cost.

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As a performance measure, ROIC is used to evaluate the efficiency of an investment or to compare the efficiencies of several different investments. In purely economic terms, it is one way of relating profits to capital invested. In business, the purpose of the return on invested Capital (ROIC) metric is to measure, per period, the rates of return on money invested in a firm to decide whether to undertake an investment. It is also used as an indicator to compare different investments within a portfolio. The investment with the largest ROIC is usually prioritized, even though the spread of ROIC over the time-period of an investment should also be considered. Return on invested Capital (ROIC) is a ratio that represents the profitability of the invested capital, i.e., the ratio between the operating profit (EBIT), and the average invested capital of the period. The formula is the following: RO I =

EBIT Invested Capital

(3.4)

Return on assets (ROA) is an indicator of how profitable a company is relative to its total assets. ROA gives a manager, investor, or analyst an idea as to how efficient a company’s management is at using its assets to generate earnings. Return on assets is displayed as a percentage and it is calculated as: R O A = N et I ncome / T otal Assets

(3.5)

Sometimes, the ROA is referred to as “net return on investment” if total assets = invested capital = raised capital. 3.1.3

Ratio Tree and DuPont Formulation

ROE, ROI, and other profitability ratios can be expressed together within the so-called “ratio tree,” depicted in Fig. 3.1. The DuPont formula that inspires this partitioning is the following: (3.6)

3

PROFITABILITY, INTANGIBLE VALUE CREATION …

51

ROS ROIC rotaƟon of invested capital

ROE

debt raƟo raƟo of extraordinary revenues and costs Fig. 3.1 Profitability ratio tree

3.2

Invested Capital

The invested capital corresponds to the raised capital (uses = sources). Raised capital is given by net equity + financial debts (short-term + longterm). Financial debts correspond to Net Financial Position if liquidity and financial credits are equal to zero. Net Financial Position is financial liabilities minus cash and cash equivalents. Net financial position may be negative, in which case it is referred to as net debt. In early-stage startups, financial debt is typically non-existent, and so the Net Financial Position is never negative. Invested capital also corresponds to Net fixed assets + net working capital—leaving indemnity and other provisions + net financial assets, as shown in Fig. 3.2.

3.3

Relationships Between ROIC and ROE

Return on sales (ROS) is a ratio used to evaluate a company’s operational efficiency; ROS is also known as a firm’s operating profit margin. This measure provides insight into how much profit is being produced per e of sales. An increasing ROS indicates that a company is growing more efficiently, while a decreasing ROS could signal looming financial troubles. Digital scalability positively affects ROS.

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Assets

LiabiliƟes Accounts Payable

Cash

OperaƟng Net Working Capital Accounts Receivable Inventory

Short Term Financial Debt Financial debt Long Term Financial debt

Fixed Assets (CAPEX) Intangibles and Goodwill Tangible Assets Financial Assets

Capital Reserves Net Earnings

Total Assets= Invested Capital

Total LiabiliƟes = Raised Capital

Financial debt / Equity = Leverage Equity

Fig. 3.2 Assets and liability structure

ROS measures the performance of a company by analyzing the percentage of total revenue that is converted into operating profits. ROS expresses the profitability of sales: ROS =

EBIT sales

(3.7)

The relationship between ROS, ROE, and ROIC is the following: RO E =

net profit EBIT invested capital net profit = Equi t y invested capital Equi t y EBIT

(3.8)

And so: EBIT EBIT sales = invested capital sales invested capital

(3.9)

sales E B I T invested capital net profit invested capital sales Equi t y EBIT

(3.10)

RO I = We also have: RO E =

ROE can be represented as a sub-sample of ROIC, considering the following (simplified) balance sheet (Fig. 3.3): We know that: net result + taxes + negative interests = EBIT. And so: RO I =

net result + taxes + negative interests EBIT = (3.11) invested capital Equi t y + financial debts and taxes

The bold part corresponds to ROE that is so a sub-sample of ROIC. The other part expresses the cost of debt and taxes.

3

Operating Net Working Capital Net Fixed Assets

Invested capital

53

PROFITABILITY, INTANGIBLE VALUE CREATION …

Financial Debts (and taxes)

Operating revenues - operating costs = EBITDA - depreciation =EBIT 100 - negative interests - taxes

equity

= net income

(25) (5) 70

Raised capital

Fig. 3.3 The link between invested and raised capital and the income statement

It should be mentioned that in a debt-free context, EBIT ≈ Net Result, and Invested (Raised) Capital ≈ Equity. So: RO IC =

Net Result EBIT ≈ ≈ RO E Raised Capital Equit y

(3.12)

3.4 From Economic Value Added (EVA) to Market Value Added (MVA) The Economic value added ® (EVA) expresses the difference between the return and the cost of the invested capital (expressed in market terms). The Market Value Added (MVA) represents the present value of a stream of (cumulated) future EVA. EVA is a performance measure devised by Stewart (1991), based on the difference between the return and the cost of capital. It is obtained by subtracting the cost of capital employed from the operating result (= EBIT) normalized and after taxes (NOPAT): EVA = NOPAT − WACC ∗ Ic = (r − WACC) ∗ Ic where: • NOPAT = normalized operating income after taxes;

(3.13)

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R. MORO-VISCONTI

• Ic = [adjusted] invested capital (shareholders’ equity + financial debts + equity equivalents); • r = NOPAT/Ic = ROIC = return on invested capital; • WACC = weighted average cost of capital. Being EVA (Stern et al., 1995) expressed in terms of WACC, it is independent of the financial structure (unless the latter has an impact on the WACC) and therefore does not discriminate between levered and unlevered companies (Moro Visconti, 2020, Chapter 17). This is never the case in an “ideal” Modigliani and Miller world, where the financial leverage does not impact the firm’s value that only depends on its DCF. In any case, if a company is not indebted (D = 0), the invested capital corresponds to the net assets and the NOPAT ≈ net profit; so, NOPAT/Ci = Return on Equity (ROE) and WACC = k e (cost of equity). Since both the NOPAT and the invested capital are expressed at market value (thanks to the adjustments made with the Equity Equivalents), then NOPAT/Ic ≈ WACC = k e and consequently EVA ≈ 0. Based on EVA, a company: • Creates wealth (EVA > 0) when the return on capital (r = ROIC) is higher than the weighted average cost of capital (WACC); • Destroys wealth in the opposite case (r = ROIC < WACC). Earlystage startups typically destroy value. NOPAT is the profit derived from a company’s operations (a sort of EBIT) after cash taxes but before financing costs and non-monetary revenues and costs. It represents the total pool of profits available to provide a cash return to those who provide capital to the firm. Invested capital is the amount of cash invested in the business, net of depreciation. It can be calculated as the sum of interest-bearing debt and equity or as the sum of net assets less non-interest-bearing current liabilities. The capital charge is the cash flow required to compensate investors for the riskiness of the business given the amount of economic capital invested. The cost of capital is the (minimum) rate of return on capital required to compensate investors (debt and equity) for bearing risk. Here we consider the Weighted Average Cost of Capital (WACC). Being EVA expressed in terms of WACC, it is independent of the financial structure

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(unless the latter does not have an impact on the WACC, as anticipated) and it does not discriminate between levered and unlevered firms. EVA improvements are consistent with the target of maximizing the market value of a company. According to EVA: • a company generates added value (EVA > 0) generates added value (EVA > 0) if the capital return (r) is higher than the weighted average cost of capital (WACC); • burns added value in the opposite case (r < WACC). The original EVA method considers some adjustments to the NOPAT and invested capital values (exemplified in Table 3.1), to express a fair measure of the capital invested by the debtholders and of the available monetary profit. Table 3.1 Equity equivalent adjustments to EVA

Invested capital integrations Accounting invested capital + Reserves for deferred taxes + LIFO reserve + Goodwill amortizations + net Intangibles costs + Reserves for future expenses + Net extraordinary incomes and expenses + Generic risk founds = Adjusted invested capital Operating profit adjustments Net operating profit + Provisions for deferred taxes + Variation of LIFO reserve + Goodwill amortization + Cost to be capitalized + Provision for future expenses − Taxes saved on financial charges + Generic risk provisions = NOPAT

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In the NOPAT calculation, accounting adjustments are carried out considering the accrual basis accounting instead of a cash basis, according to the financial method of firm evaluation. For example: • the taxes attributable to the operating profit are those effectively due; • losses on receivables are attributable to the financial year in which they are definitively ascertained and not to those in which the revenue is achieved; • intangible costs are amortizable in 5 years; The Market Value Added (MVA) consists in the difference between the market value and the accounting invested capital: MVA = market value − invested capital = present value of the expected EVA   = EVA / WACC − g (3.14)

where g is a (sustainable) growth rate. MVA could also represent a measure of the extra-value (goodwill) generated by a company compared to the bound resources. Considering that EVA is positive when r > WACC, a company has a MVA > 0 when is expected that r/WACC > 1 (Fig. 3.4). If markets are efficient, companies with EVA and MVA > 0 should have growing stock prices and improving credit rating, reducing the market risk premium and consequently the cost of capital and the WACC, increasing the difference r-WACC (growing EVA) and decreasing WACC-g (improving MVA). The original EVA calculation method prescribes some adjustments to the “raw” NOPAT and invested capital book values. These adjustments to the accounting parameters, to make them compliant with market values (equity equivalents) are necessary to express a correct measure of both the capital invested by the corporate lenders and the income available for them. Market Value Added (MVA) is the difference between the market value and the invested capital, equivalent to the sum of the discounted future EVA: MVA = market value − invested capital = present value of all future EVA   = ( EVA1 )/ WACC − g

3

PROFITABILITY, INTANGIBLE VALUE CREATION …

Value Burning

Value CreaƟon

EVA1/(Wacc-g)+ EVA2/(Wacc-g)2+...

EVA1/(Wacc-g)+ EVA2/(Wacc-g)2+...

MVA Created value

Burned value

MV

57

MV Invested Capital

Market Value

Invested Capital

Market Value

Fig. 3.4 From EVA to MVA   = economic profit of existing assets and growth opportunities / WACC (3.17)

The MVA is the measure of the value that a company has created in excess (goodwill) compared to the resources already bound to the company. This measures the excess market value (referring to the value of the current and fixed assets, including intangible assets) of the book value of the capital raised (or invested). The book value is an expression of the accounting liabilities (shareholders‘ equity + financial debts = current assets + fixed assets + equity equivalents). The MVA estimate can be broken down using a mixed capital-income valuation approach. Since EVA is positive when r > WACC, a company has an MVA > 0 when it is expected that in the future r/WACC > 1. Whenever there is value creation, the Return on Invested Capital (≈ Return on Equity in a debt-free firm) exceeds the cost of capital (i.e., the cost of equity, in a debt-free context), as shown in Fig. 3.6. The meaning of this relationship is intuitive: in a positive outlook, the startup raises capital from the shareholders at an opportunity cost known as the cost of equity. If the startup creates value, then the return on the capital is higher

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(Implicit) Goodwill

R O I C

ROIC > Cost of Equity

Fixed Assets - Tangible Assets / CAPEX - Intangibles - (Financial Assets)

OperaƟng Net Working Capital

Equity - UnderwriƩen Capital - Reserves

cost of equity

Quasi equity - shareholders loans

Liquidity

Fig. 3.5 Value creation: when ROIC exceeds the cost of capital

than its cost, and this produces implicit goodwill that cannot be recorded in the accounts but is considered for the valuation. If ROIC > cost of equity, it becomes easier (and cheaper) for the startup to raise additional capital, and to have access to debt (Fig. 3.5).

3.5

Operating Leverage

Operating leverage is a measure of how revenue growth translates into operating income increases. It is a measure of how risky, or volatile, a company’s operating income is. Operating leverage is the degree to which a firm or project can increase operating income by increasing its revenues. A startup that generates sales with a high gross margin and low variable costs has high operating leverage. The higher the degree of operating leverage, the higher the potential danger from forecasting risk, where a relatively small error in forecasting sales can be magnified into significant errors in cash flow projections. When a company reaches its break-even point (where operating revenues equal costs), then it can translate most of its incremental revenues on the EBIT if variable costs are negligible and fixed costs relevant. The opposite, however, occurs when revenues shrink: in this case, the presence of fixed costs is a burden that increases the operating losses.

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There is so a boomerang effect and startups with higher fixed costs are more volatile and riskier. The formula is the following: operating leverage =

E B I T /E B I T sales/sales

(3.18)

Operating leverage so expresses the ratio between the percentage variation in the operating income (Earnings Before Interests and Taxes, EBIT) and the percentage variation of sales. The elements that influence the operating leverage are: • • • •

Sale prices Volumes of sale Variable costs Fixed costs.

We can consider an income statement where fixed and variable costs are represented separately (Table 3.2). The contribution margin is the selling price per unit minus the variable cost per unit. It represents the portion of sales revenue that is not consumed by variable costs and so contributes to the coverage of fixed costs. This concept is one of the critical building blocks of break-even analysis. The contribution margin analysis is a measure of operating leverage; it expresses how growth in sales translates to an increase in operating profits (EBIT). The contribution margin is computed by using a management accounting version of the income statement that has been reformatted to group the fixed and variable costs. The overall contribution margin is given by the product of the unitary contribution margin and the sold quantities (or the services provided). Table 3.2 From revenues to operating profit

1. 2. 3. 4. 5.

Revenues (sales) (variable costs) = Contribution margin (1–2) (fixed costs) = Operating profit = EBIT (3–4)

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The unitary contribution margin is mainly determined by the relationship between “prices and revenues” of the sold products and the “prices and costs” of the variable input factors of production. Companies with a higher structure of fixed costs (that do not follow the variation of sold quantities, remaining unchanged—fixed) show a higher operating leverage. If a startup has only variable costs, then the operating leverage has a unitary level, and its contribution margin will coincide with the EBIT; to double the EBIT, sales will have to double, as they grow at the same pace as the variable costs and the contribution margin. A classic dilemma is represented by the difference between a startup with only fixed costs and another one with just variable costs: which one is better? Both corner solutions have pros and cons: the former companies find it more difficult to reach a break-even point, but when it does, the marginal growth of revenues is fully translated into higher EBIT, with a scalable impact on marginality. Companies with higher variable costs are on the contrary safer but less profitable when the outlook is positive, compensating lower risk with smaller returns. The contribution margin can be increased: (a) With a higher profit margin, expressed by the difference “price/revenue” vs. “price/cost”; (b) Improving the efficiency of variable factors of production; (c) Increasing the volumes of sales. Fixed costs are the second determinant of EBIT. Costs are “fixed” if they do not vary when production changes. The cost structure and the mix of fixed vs. variable costs is a strategic option of any startup but it also depends on the industry. For example, retail companies can choose from shops that are fully owned or in franchising; staff can be represented by employees or freelance workers, etc. Some sectors, however, have strategic constraints that limit the possibility of the startup to select its cost structure. For instance, in the automotive sector, fixed costs and investments are typically high, and they are difficult to reduce below certain thresholds. Fixed costs are typically significant in the banking sector, where staff costs and IT investments matter.

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Labor cost is just partially fixed and it has extraordinary components that are linked to performance (stock options, etc.). The degree of operating leverage (DOL) is a synthetic indicator of the operating risk, estimated comparing the contribution margin (total revenues—total variable costs) to the EBIT (EBIT = total revenues—total variable costs—fixed costs): DOL = (TR − VC) / (TR − VC − FC) = CM / EBIT

(3.19)

where: DOL = degree of operating leverage TR = total revenues VC = variable costs FC = fixed costs CM = contribution margin. The higher the weight of fixed cost over total costs, the higher the degree of operating leverage (Table 3.3). When the incidence of fixed costs grows, the economic and structural capacities of the startup worsen, as it needs to sell more to reach the break-even point, since higher operating leverage increases the contribution margin. Let us consider two alternative scenarios, with the same starting figures (Tables 3.4 and 3.5): The cost structure has a remarkable impact on the (operating) net working capital, which is given by the difference between accounts receivable, stock, and accounts payable. Fixed costs impact the working capital, producing accounts receivable that are independent of the trend of sales. Variable costs, on the contrary, follow the dynamics of sales, with a double impact on the working capital: Table 3.3 Degree of operating leverage Total revenues Total variable costs Contribution margin Total fixed costs EBIT Degree of operating leverage (DOL)

Startup A

Startup B

150 (120) 30 (10) 20

150 (60) 90 (70) 20

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Table 3.4 Degree of operating leverage with a revenue decrease (a) Hypothesis 1: revenues decrease to 50

Table 3.5 Degree of operating leverage with a revenue increase (b) Hypothesis 2: revenues grow to 300

Total revenues Total variable costs Contribution margin Total fixed costs EBIT Degree of operating leverage (DOL)

Total revenues Total variable costs Contribution margin Total fixed costs EBIT Degree of operating leverage (DOL)

Startup A

Startup B

50 (40) 10 (10) 0

50 (20) 30 (70) (40) (0.75)

Startup A

Startup B

300 (240) 60 (10) 50 1.2

300 (120) 180 (70) 110 1.64

sales produce receivables whereas variable costs generate payables. The working capital balance depends on the rotation of the credits and debts (average days of collection and payment).

3.6

Break-Even Analysis

The break-even point (BEP) in economics, business—and specifically cost accounting—is the point at which total cost and total revenue are equal, i.e. “even.” There is no net loss or gain, and one has “broken even,” though opportunity costs have been paid and capital has received the riskadjusted, expected return. In short, all costs that must be paid are paid, and there is neither profit nor loss. The break-even point (BEP) or break-even level represents the sales amount—in either unit (quantity) or revenue (sales) terms—that is required to cover total costs, consisting of both fixed and variable costs to the startup. Total profit at the break-even point is zero. It is only possible for a firm to pass the break-even point if the value of sales is higher than

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the variable cost per unit. This means that the selling price of the good must be higher than what the startup paid for the good or its components for them to cover the initial price they paid (variable costs). Once the break-even price is surpassed, the startup can start making a profit. The break-even point is one of the most used concepts of financial analysis and is not only limited to economic use but can also be used by entrepreneurs, accountants, financial planners, managers, and even marketers. Break-even points can be useful to all avenues of a business, as it allows employees to identify required outputs and work toward their meeting. The main purpose of break-even analysis is to determine the minimum output that must be exceeded for a business to profit. It also is a rough indicator of the earnings impact of marketing activity. A firm can analyze ideal output levels to be knowledgeable on the number of sales and revenue that would meet and surpass the break-even point. If a business does not meet this level, it often becomes difficult to continue operation. The break-even point is one of the simplest, yet least-used analytical tools. Identifying a break-even point helps provide a dynamic view of the relationships between sales, costs, and profits. For example, expressing break-even sales as a percentage of actual sales can help managers understand when to expect to break even (by linking the percent to when in the week or month this percent of sales might occur). The break-even point is a special case of Target Income Sales, where Target Income is zero (breaking even). Any sales made past the breakeven point can be considered profit (after all initial costs have been paid). Break-even analysis can also help businesses see where they could restructure or cut costs for optimum results. This may help the business become more effective and achieve higher returns. In many cases, if an entrepreneurial venture is seeking to get off the ground and enter a market it is advised that they formulate a break-even analysis to suggest to potential financial backers that the business has the potential to be viable and at what points (Fig. 3.6). A startup’s scalability implies that the underlying business model offers the potential for economic growth within the startup. In broader terms, scalability is the capability of a system, network, or process to handle a growing amount of work, or its potential to be enlarged to accommodate that growth (Fig. 3.7).

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profit Revenues and costs

Break-Even Point

Total costs Total revenues

loss

Fixed costs

Variable costs

Produced and sold quantities

Fig. 3.6 Break-even analysis

3.7

Digital Scalability

Scalability (Hoffman & Yeh, 2018) represents an essential feature of any business. It indicates the ability of a process, network, or system to handle a growing amount of work, or its potential to be enlarged to accommodate growth. Scalability (Smith & Rawnet, 2015) can be intended as the ability of a device to adapt to the changes in the environment and meet the changing needs of customers. So, in broader terms, scalability means flexibility, which allows to better address and achieve the specific needs of customers, which are never static. People’s interests and tastes, as well as environmental conditions, change continuously over time. Scalability is therefore vital as it contributes to competitiveness, efficiency, and quality. Scalability helps in the system to work gracefully without any undue delay and unproductive resource consumption while making good use of the available resources (Gupta et al., 2017). Digital businesses—examined in Chapter 12—are those which carry out transactions that are digitally mediated or involve products or services

3

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65

Revenues and costs Total revenues Break-Even Point Total costs

Fixed costs

Variable costs

Produced and sold quantities

Fig. 3.7 Break-even point

that are experienced digitally (Weill & Woerner, 2013). It is the digitalized, non-material nature of such goods and services that gives them the potential for scalability. So, the term “Digital Scalability” basically refers to the application of the scalability concept by digital companies and devices, to optimize as much as possible digital circuits and operations.

3.8 The Impact of Intangible Investments on EBITDA-Driven Market Valuation What “happens” in the upper part of the income statement—between the sales and the operating profit (EBIT)—has key implications in terms of value generation. Managers are normally remunerated according to their ability to improve operating marginality. And economic margins as the EBITDA—just one step before the EBIT—are the drivers of the liquidity creation (whenever EBITDA > 0) within the income statement. EBITDA is given by the difference between sales (operating monetary revenues) and monetary operating expenses (OPEX). The subdivision of

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OPEX into its main constituents—fixed and variable costs—unveils the role of operating leverage and scalability on value creation patterns. The link between scalability and liquidity is a fundamental concept to understand the cash flows that may be generated by an incremental economic margin. The key margin is represented by EBITDA that is the only parameter that simultaneously expresses both an economic and a financial marginality. EBITDA is close to EBIT—the target parameter of operating leverage—since it can be calculated from EBIT just summing up non-monetary operating costs like depreciation and amortization. Scalability indicates the ability of a process, network, or system to handle a growing amount of work. Scalability fosters economic marginality, especially in intangible-driven businesses where variable costs are typically negligible. Massive volumes may offset low margins, producing economic gains. Intangibles have a scalable impact on the EBITD(A) if they contribute to boost monetary revenues and/or to decrease monetary OPEX. The impact may concern both fixed and variable costs. The operating leverage defined in Sect. 3.5 can be reformulated in terms of EBITDA: operating leverage =

E B I T EBIT

=

E B I T D A−depr eciation E B I T D A−depr eciation

sales/sales

(3.20)

3.9 Valuation Drivers, Overcoming the Accounting Puzzle Capitalized intangibles are part of the Capital Expenditure (CAPEX), whereas intangible costs recorded in the income statement are part of the monetary OPEX. Amortization is a non-monetary operating cost that reduces the balance value of the intangible CAPEX, an important component of the assets of a typical startup. The two most common valuation approaches for the estimate of the enterprise value (i.e., the firm’s value comprehensive of financial debt) are based on the operating cash flows (before debt service) discounted at the WACC or on the EBITDA times a multiple of comparable firms, as shown in Chapter 8.

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The following formulation recalls the two methodologies (for simplicity, DCF does not consider any terminal value): Enterprise Value =

n  sales − monetary OPEX ± Net Working Capital ± C A P E X (1 + W ACC)n i=1

=

Operating Cash Flow (1 + W ACC)n

∼ = (basic E B I T D A + intangible − driven E B I T D A) ∗ market multiplier (3.21)

Intangibles impact on sales (due to their scalability), on monetary OPEX (cost of not-capitalized intangibles, net of the savings from synergies), and the CAPEX (capitalized intangibles less the yearly amortization). For a better understanding of these complex issues that are still partially unsolved, we might consider an “ideal” world without capitalizations, where all the expenses concerning the intangibles are reflected as monetary OPEX in the income statement. The absence of any capitalization also implies that there is no amortization of the goodwill or other intangibles, and that yearly intangible-CAPEX (i.e., investments in intangibles that occur each year) is also reflected by comprehensive monetary OPEX. The accounting puzzle, according to which investments can be either expensed as OPEX within the income statement, or capitalized as CAPEX in the balance sheet (assets), is irrelevant in monetary terms. This is so because investments produce a monetary outflow when they are undertaken (paid for), and their depreciation that follows capitalization does not modify the liquidity. The accounting breakdown of the income statement may be summarized as follows (Table 3.6): Table 3.6 From sales to EBIT

Sales − Intangible Monetary OPEX = EBITDA +  Intangible CAPEX (net of amortization) = normalized EBITD − depreciation = EBIT

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As non-monetary costs, both depreciation and amortization have not any impact on the cash flows (including the EBITDA), which consider only monetary items. However, in absence of depreciation and amortization, the EBITDA corresponds to the EBIT. As it will be illustrated in the following paragraphs, EBITD and EBITDA have a different range of multipliers. An increase of the monetary revenues can be obtained, for example, with growing sales (thanks to a new patent …) or/and with a rise in prices (branded products …). The contribution margin is the selling price per unit minus the variable cost per unit. It represents the portion of sales revenue that is not consumed by variable costs and so contributes to the coverage of fixed costs. Intangibles have a scalable impact on the EBITD(A) if they contribute to boost monetary revenues and/or to decrease monetary OPEX. A patented production process could reduce the monetary costs, with an impact on the economic margins.

3.10

From EBITDA to EBIT

As Nissim (2019) points out “The primary argument for excluding amortization (but not depreciation) from measures of operating profitability is related to the differential treatment of acquired versus internallydeveloped intangibles. Acquired intangibles are recognized on the balance sheet and subsequently amortized, but costs incurred to internally develop intangibles are generally expensed as incurred. This differential treatment can lead to distortions and a lack of comparability across companies and over time. For example, with no growth, reported earnings may be unbiased even for companies with substantial organic investments in intangibles. This follows because the expensing of current organic investments in intangibles is offset by the omission of amortization of past investments that contribute to current revenue (those investments were expensed in the past and so there is no book value to amortize). However, if such a startup is acquired, the increase in EBIT of the acquiring company will be smaller than the pre-acquisition EBIT of the acquired startup because the intangible assets of the acquired startup will be recognized by the acquiring company and subsequently amortized. Thus, by excluding amortization

3

Table 3.7 From EBITDA to EBIT

Operating Monetary Revenues -

variable monetary OPEX

-

fixed monetary OPEX

= EBITDA / EBITD* -

fixed non-monetary costs / depreciation

PROFITABILITY, INTANGIBLE VALUE CREATION …

69

EBITDA − amortization = EBITD − depreciation = EBIT

1

2 Risk reduction / Resilience 3

= EBIT * if it incorporates amortization, not considering intangible CAPEX

Fig. 3.8 The impact of the intangible investments on the EBITDA

expense, EBITA may give an unbiased estimate of the profitability of the combined company.” The classification can be summarized as follows (Table 3.7): In an “ideal” world without intangible capitalizations, amortization is absent and so EBITDA = EBITD. Startups frequently capitalize initial costs to reduce their economic losses, and delay equity burnouts.

3.11

The Scalable Impact of the Intangibles on Revenues and Monetary OPEX

EBITDA represents the real engine behind value creation and economicfinancial growth. Based on these premises, further consideration concerns the impact of the intangible investments on the EBITDA’s components, represented by the difference between the operating (monetary) revenues and the (monetary) OPEX. The representation may be synthesized in Fig. 3.8. 1. intangible-driven growth in monetary revenues may be given by:

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• their contribution in the approach of new markets; • the sales-driving digital platforms (Asadullah et al., 2018; Baldwin & Woodard, 2009; Basole & Karla, 2011; de Reuven et al., 2018; Gander, 2015; Gawer & Cusumano, 2014; Kenney & Zysman, 2016; Parker et al., 2017); • the incremental /differential role of brands, patents, and other intangibles; • revenue and market share protection, with entry barriers; • digital scalability, driven by Metcalfe (Odlyzko & Tilly, 2005) or Moore law externalities; • real options (to expand, contract out …). 2. intangible-driven savings in monetary OPEX may be given by: • productivity and efficiency gains; • (digital) supply chain savings. 3. Risk reduction: • Affects the denominator of DCF (discount factor of projected cash flows, incorporating the cost of capital); • Reduces the difference between expected and real outcomes, even thanks to timely re-engineering of the business planning (Moro Visconti, 2019) incorporating big data (Moro Visconti et al., 2018); • Improves the resilience and flexibility of the supply and value chain.

3.12 The Impact of EBITDA on the Profitability Ratios EBITDA has an impact on the most used profitability ratios, which are the following: (a) ROE The return on equity (ROE), as shown in § 3.1.1, is a measure of the profitability of a business concerning the book value of shareholder equity, also known as net assets or assets minus liabilities. ROE is a

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71

measure of how well a startup uses investments to generate earnings growth. ROE is equal to net income (Rn), divided by mean total equity [E = (E0 + E1)/2], expressed as a percentage: RO E =

Net Result E B I T D A − depr eciation − negative interests − taxes . . . = Equit y Equit y (3.22)

ROE is always positive unless the net return is negative or corresponds to zero. ROE is especially used for comparing the performance of companies in the same industry. As with return on capital, ROE is a measure of management’s ability to generate income from the equity available to it. EBITDA impacts both the net result and equity (E). (b) ROIC The return on invested capital (ROIC), introduced in Sect. 3.1.2, is the ratio between the operating profit (EBIT) and the resources that back it (raised = invested capital). A high ROIC means the investment’s gains compare favorably to its cost. As a performance measure, ROIC is used to evaluate the efficiency of an investment or to compare the efficiencies of several different investments. In purely economic terms, it is one way of relating profits to invested capital. The formula is the following: RO IC =

E B I T D A − depr eciation EBIT = I nvested = Raised Capital Invested Capital (3.23)

A high ROI (ROIC) means the investment’s gains compare favorably to its cost. As a performance measure, ROI is used to evaluate the efficiency of an investment or to compare the efficiencies of several different investments. In purely economic terms, it is one way of relating profits to capital invested. ROIC is often benchmarked to the WACC. If ROIC>WACC, then the return on invested capital exceeds the cost of invested capital, generating a positive goodwill that can even be expressed in EVA terms. Investments in intangibles may affect both the numerator (EBIT) and the denominator (Invested Capital) of this ratio.

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(c) ROA Return on assets (ROA), also illustrated in Sect. 3.1.2, is an indicator of how profitable a startup is relative to its total assets. ROA gives a manager, investor, or analyst an idea as to how efficient a company’s management is at using its assets to generate earnings. Return on assets is displayed as a percentage and it is calculated as: RO A =

Net Result E B I T D A − depr eciation − negative interests − taxes . . . = Total Assets Total Assets (3.24)

Sometimes, the ROA is referred to as ROI if total assets = invested capital = raised capital. ROA tells us how efficiently a business uses its existing assets to generate profits. As for ROI, investments in intangibles may affect both the numerator (Net Income) and the denominator (Total Assets) of this ratio. (d) ROS Return on sales (ROS) is a ratio used to evaluate a company’s operational efficiency; ROS is also known as a firm’s operating profit margin. This measure provides insight into how much profit is being produced per e of sales. An increasing ROS indicates that a startup is growing more efficient, while a decreasing ROS could signal looming financial troubles. ROS, introduced in Sect. 3.3, measures the performance of a startup by analyzing the percentage of total revenue that is converted into operating profits. ROS expresses the profitability of sales: ROS =

E B I T D A − depr eciation EBIT = sales sales

(3.25)

Intangibles may impact the ROS if they contribute to increasing sales and if this increase raises EBIT. (e) Economic profit Economic profit is the difference between the total revenue received by a business and the total implicit and explicit costs of a firm. It is often the

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73

extra profit left over after considering the next best alternative investment and can be either positive or negative in value. Intangibles may have an impact on the economic profit (net result) in terms of increased revenues and/or of decreased costs, as illustrated before. However, from an economic point of view, investing in intangible assets leads to an increase in depreciation, if the correlated costs are capitalized. Moreover, if the investment is supported by debt, the correlated financial charges harm economic profit. (f) EVA Intangibles may also affect EVA and MVA (described in Sect. 3.4), considering their impact on EBITDA and invested capital: EVA = NOPAT − WACC ∗ Ci

  = EBITDA− depreciation − operating taxes − WACC ∗ Ic (3.26)

where: EVA = Economic Value Added NOPAT = Net Operating Profit After Taxes WACC = Weighted Average Cost of Capital Ic = Invested Capital (Equity + financial debts + provisions) r = NOPAT/Ci = capital return (adjusted ROI).

3.13 The Impact of the EBITDA on the Market Multipliers EBITDA also has an impact on the most popular market multipliers, like the following: (a) EV/EBITDA Enterprise value (EV) is the sum of a company’s equity value or market capitalization plus its debt less cash. EV is typically used when evaluating

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a company for a potential buyout or takeover. The EV/EBITDA ratio is calculated by dividing EV by EBITDA to achieve earnings multiple. Improved intangible-driven scalability reflects on the Enterprise Value/EBITDA (EV/EBITDA) multiplier that compares a firm’s market value (inclusive of debt), to its overall economic-financial profitability. Financial analysts use the EV/EBITDA ratio to measure a company’s value over its earnings. The metric is better than the P/E ratio because it considers the enterprise value irrespectively of the company’s capital structure (Modigliani & Miller, 1958; Miller, 1988; Bradley et al., 1984; Ross, 1988). For instance, if a startup raises additional capital through equity financing, the company’s P/E ratio will be higher because the price will rise. Besides, the P/E ratio is used only for listed firms, and startups aren’t … EBITDA may impact either the market capitalization or/and the net financial position. An intangible-driven improved EBITDA creates additional liquidity, so increasing the net financial position, and may be reflected in higher market prices. The impact of the higher denominator EBITDA on the numerator EV may, however, change. The multiplier is: Market Value of Equity + Market Value of Debt EV = EBIT DA EBIT DA

(3.27)

(b) EV/FCFF Enterprise Value (EV) to Free Cash Flow to Firm (FCFF) compares the total valuation of the company with its ability to generate operating cash flows. FCFF correspond to the operating cash flow, before debt service. Investments in intangibles may have an impact on the FCFF, both in terms of economic margin (EBITDA) and CAPEX. EV = FC F F

Mar ket V alue o f Equit y + Mar ket V alue o f Debt O perating (debt − f r ee)Cash Flow = E B I T D A ±O perating N et W orking Capital ± C A P E X

(3.28)

FCFF corresponds to Operating (unlevered or debt-free) Cash Flows that are used for the calculation of Discounted Cash Flows. Unlevered cash flows are determined by using operating income before taxes and financial charges: Net operating income

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− taxes on operating income + amortization/depreciation and provisions (non-monetary operating costs) + technical divestments (-investments) + divestments (-investments) in other assets + decrease (-increase) in operating net working capital = Cash flow available to shareholders and lenders (operating cash flow). The link with EBITDA can be determined with a “bottom-up” approach (starting from the net income) or with a “top-down” reclassification (Table 3.8): Once the present value of the cash flows has been determined, the calculation of the market value W of the startup may correspond to: (a) the unlevered cash flow approach (consistent with an indebted startup): W =

 C F0 E B I T D A ± N W C ± C A P E X = D E W ACC ki (1 − t) D+E + ke D+E

(3.29)

where:  C F0 /W ACC = present value of operating cash flows NWC = (operating) net working capital D = market value of financial debt E = market value of equity t = corporate tax rate. The parameters of formula (3.29) are sensitive to the impact of intangible investments: Table 3.8 Discounted operating cash flow EBIT + Depreciation and amortization = EBITDA (A) ± Operating Net Working Capital ± fixed assets (CAPEX) = Operating cash flow (unlevered cash flow to the firm) (B)

To be discounted at the Weighted average cost of capital (WACC)

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• EBITDA depends on scalability, intangible-driven growth, etc. • The NWC (management of stock; optimization of payments to suppliers and collection from customers) and its forecasting may sometimes be optimized with the intangibles • The CAPEX may be minimized if tangible investments are complemented by intangible digitalization, etc. • Risk in the denominator (i.e., the WACC) may be decreased with digitalization of the managerial processes and real-time data acquisition. • P/FCFE. This multiplier Price to free cash flow is an equity valuation metric used to compare a company’s per share market price (P) to its per share amount of free Cash Flow to Equity (FCFE). This metric is very similar to the valuation metric of price to cash flow but is considered a more exact measure, since it uses free cash flow, which subtracts capital expenditures (CAPEX) from a company’s total operating cash flow, thereby reflecting the actual cash flow available to fund non-asset-related growth. Being a value metric, lower numbers generally indicate that a company is undervalued, and its stock is relatively cheap concerning its free cash flow. Again, as it refers to listed stocks, it represents a mighty parameter for promising but still privately held startups. Also, in this case, investments in intangibles may have an impact on the FCFE, in terms of economic margin (EBITDA), CAPEX, and (eventually) financial charges. P Mar ket Price = FC F E E B I T D A ± O perating N et W orking Capital ± C A P E X debt ± equit y = FC F F − negative inter ests ±  f inancial (3.30)

(d) Price to earnings (P/E) The price-earnings, also known as P/E ratio (P/E—already illustrated in Sect. 3.1), is the ratio of a company’s share (stock) price to the company’s earnings per share. The ratio is used for valuing companies and to find out whether they are overvalued or undervalued. The price/earnings ratio is the most widely used method for determining whether listed shares are “correctly” valued concerning one

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another. But the PER does not in itself indicate whether the share is a bargain. The P/E depends on the market’s perception of the risk and future growth in earnings. Intangibles may impact earnings, contributing to the growth of the net profit. Price Market Price = Ear nings Net Result = E B I T D A − depr eciation − negative interests − taxes . . . (3.31)

(e) Price to operating profit (P/OP) The price (P) to operating profit (OP) is the ratio of a company’s share (stock) price to the company’s operating profit per share. Operating profit is strictly correlated to the EBITDA and may be affected by intangibles. Market Price Price = Operating Profit E B I T D A − depr eciation = E B I T

(3.32)

(f) Price to sales (P/S) The price to sales (P/S) is a valuation ratio that compares a company’s stock price to its revenues. It is an indicator of the value placed on each currency unit of a company’s sales or revenues. The P/S ratio can be calculated either by dividing the company’s market capitalization by its total sales over a designated period – usually twelve months, or on a per share basis by dividing the stock price by sales per share. Market Price Price = Sales E B I T D A + monetary OPEX

(3.33)

The P/S ratio is also known as “sales multiple” or “revenue multiple”; as stated before, intangibles may impact sales. (g) Price to book value (P/BV)

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Analysts use the price to book (P/BV) ratio to compare a firm’s market capitalization to its book value. It is calculated by dividing the company’s stock price per share by its book value per share. An asset’s book value is equal to its carrying value on the balance sheet, and companies calculate it netting the asset against its accumulated depreciation. Intangibles assets impact the company’s book value, contributing to the growth of the net profit that is then stored in the book value of equity (unless paid out as a dividend). Price Market Price   = Book Value Book Value incor porating E B I T D A − driven retained earnings (3.34)

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Hoffman, R., & Yeh, C. (2018). Blitzscaling: The lightning-fast path to building massively valuable companies. Amazon’s top 20 Business and Leadership book. Kenney, M., & Zysman, J. (2016). The rise of the platform economy. Issues in Science and Technology, 32(3), 61. Miller, M. H. (1988). The Modigliani-Miller propositions after thirty years. Journal of Economic perspectives, 2(4, Fall), 99–120. Modigliani, F., & Miller, M. H. (1958, June). The cost of capital, corporation finance and the theory of investment. American Economic Review, 48(3), 261– 297. Moro Visconti, R. (2019). How to prepare a business plan with excel. ResearchGate. Available at https://www.researchgate.net/publication/255728204_ How_to_Prepare_a_Business_Plan_with_Excel. Moro Visconti, R. (2020). The valuation of digital intangibles. Cham: Palgrave Macmillan. Moro Visconti, R., Montesi, G., & Papiro, G. (2018). Big data-driven stochastic business planning and corporate valuation. Corporate Ownership & Control, 15(3–1), 189–204. Nissim, D. (2019, October 1). EBITDA, EBITA, or EBIT? Columbia Business School Research Paper No. 17–71. Available at SSRN: https://ssrn.com/abs tract=2999675. Odlyzko, A., & Tilly, B. (2005). A refutation of Metcalfe’s law and a better estimate for the value of networks and network interconnections. Minneapolis: University of Minnesota. Parker, G., Van Alstyne, M., & Jiang, X. (2017). Platform ecosystems: How developers invert the firm. MIS Quarterly, 41(1), 255–266. Ross, S. A. (1988). Comment on the Modigliani-Miller propositions. Journal of Economic perspectives, 2(4, Fall), 127–133. Smith, A., & Rawnet, M. D. (2015). How can businesses achieve digital scalability? Available at Freshbusinessthinking.com. Stern, J. M., Stewart, G. B., & Chew, D. H. (1995). The EVA® financial management system. Journal of Applied Corporate Finance, (Summer), 32–46. Stewart III, B. G. (1991). The quest for value. New York: Harper Business. Weill, P., & Woerner, S. L. (2013, March). Optimizing your digital business model. MIT Sloan Management Review, 53(3), 28–36. Ye, Q. (2018, February). New-born startups performance: Influences of resources and entrepreneurial team experiences. International Business Research, 11(2), 1–15.

CHAPTER 4

Boosting Sustainable Growth with Innovative Intangibles

4.1

Intangible Assets

Startups are characterized by their growth potential that is mostly due to the presence of intangibles. According to IVS 210 (https://www.ivsc.org/files/file/view/ id/647): 20.1. An intangible asset is a non-monetary asset that manifests itself by its economic properties. It does not have physical substance but grants rights and economic benefits to its owner. 20.2. Specific intangible assets are defined and described by characteristics such as their ownership, function, market position, and image. These characteristics differentiate intangible assets from one another. 20.3. There are many intangible assets, but they are often considered to fall into one of the following five categories (or goodwill): a. marketing-related: marketing-related intangible assets are used primarily in the marketing or promotion of products or services. Examples include trademarks, trade names, unique trade design, and Internet domain names, b. customer-related: customer-related intangible assets include customer lists, backlog, customer contracts, and contractual and non-contractual customer relationships,

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c. artistic-related: artistic-related intangible assets arise from the right to benefits such as royalties from artistic works such as plays, books, films, and music, and from non-contractual copyright protection, d. contract-related: contract-related intangible assets represent the value of rights that arise from contractual agreements. Examples include licensing and royalty agreements, service or supply contracts, lease agreements, permits, broadcast rights, servicing contracts, non-competition agreements, and natural resource rights, and e. technology-based: technology-related intangible assets arise from contractual or non-contractual rights to use patented technology, unpatented technology, databases, formulae, designs, software, processes, or recipes. 4.1.1

From the Accounting to the Book Value

Intangibles constitute an ongoing challenge for accountants (Giuliani & Marasca, 2011; Roslender & Fincham, 2001) and their recording is a constant dispute, with problematic consequences even on market and performance valuation, exemplified by the increasing gap—softened during recessions—between market and book values, mostly attributable to relevant but not (adequately) accounted intangibles. International homogeneous accounting treatment for intangibles is still a daunting target (Còrcoles, 2010). Intangible value (Clausen & Hirth, 2016; Demmou et al., 2019; Eisfeldt & Papanikolaou, 2014; Ewens et al. 2020; Falato et al., 2013; Giuliani & Marasca, 2011; Glova & Mrázková, 2018; Hasprová et al., 2019; Lev, 2001; Lev & Gu, 2016) is hidden in the balance sheet by inadequate accounting, but not in the income or the cash flow statement, where the intangible contribution to profit is detectable. Issues relating to the valuation of intangibles are surfacing with unprecedented regularity and posit an intriguing challenge for the accounting fraternity that is entrenched in the traditional ascendancy of “reliability” over “relevance” (Singh, 2013). As the intangibles are non-monetary assets with no physical form, it is difficult to find evidence for their existence. Intangible assets may be recorded as an asset in the balance sheet if future economic benefits can be expected. An intangible asset is identifiable when it is separable (capable of being separated and sold, transferred, licensed, rented, or exchanged, either

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individually or together with a related contract) or arises from contractual or other legal rights, regardless of whether those rights are transferable or separable from the entity or from other rights and obligations (IAS 38.12). The requirement for an intangible asset to be “identifiable” is included to distinguish the asset from the (internally generated) goodwill that cannot be recorded. Many intangibles will not be recognized in the financial statements as they fail to meet the definition of an asset or the recognition criteria. Examples include staff training, brand-building through advertising, and the development of new business processes. As no asset is recognized because of expenditure on such activities, it will be reported as an expense, even though it is undertaken to enhance the financial returns in subsequent accounting periods (Lennard, 2018). Financial statements can only deal with those intangibles that meet the definition of assets and satisfy the recognition criteria, as set out in the IASB’s Conceptual Framework. Intangibles can be acquired by: a. Separate purchase; b. Being part of a business combination; c. Government grant; d. Exchange of assets, and e. Self-creation (internal generation). IAS 38 permits intangible assets to be recognized at fair value, measured by reference to an active market. While acknowledging that such markets may exist for assets such as “freely transferable taxi licenses, fishing licenses or production quotas” it states that “it is uncommon for an active market to exist for an intangible asset.” The lack of an active market makes it difficult to estimate the fair market value of an intangible. According to Lev (2018): • Most of the strategic, value-creating resources of business firms, such as patents, IT, or brands, are currently expensed, and, therefore, not recognized as assets in financial reports, thereby understating the

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earnings and assets of intangibles-growing firms, and overstating the earnings and assets of intangibles-declining firms; • The fundamental inconsistency between the accounting treatment of internally generated intangibles (expensed) and that of the functionally similar acquired intangibles (capitalized) precludes a meaningful performance comparison of peer companies with different innovation strategies (internal generation vs. acquisition); • The disclosure of intangible expenditures in financial reports is seriously deficient. Except for R&D, all other intangible expenditures are generally aggregated within large expense items, mainly the cost of sales and Selling General &Administrative expenses. These inconsistences severely impair the capacity to rely on the accounting data to infer the market value of the intangibles, even for listed companies (Kai & Seiwai, 2020; That et al., 2018; Peters & Taylor, 2017; Park, 2019; Chan et al., 2001). The accounting treatment is nevertheless a prerequisite for valuation. The issue is overly complex, given that intangible assets are often not directly accounted for in the balance sheet or, in some cases, only appear in the income statement, within the operating expenses (OPEX) . In the attribution of value to intangible assets, it is necessary to consider the income capacity they generate, without which it is difficult to assign a specific value to the “intangible.” The accounting treatment and the consequent under-representation in the balance sheet of the real value of the intangibles often implies the necessity to appraise the growth opportunities that are naturally embedded in the intangibles. Another accounting issue concerns the net present value of growth opportunities (NPVGO) (Makrominas, 2016). that calculates the net present value of all future cash flows involved with the growth opportunities of the firm. The NPVGO is not recorded in the balance sheet and is used to estimate the intrinsic value of these opportunities to determine how much of the firm’s current per share value is determined by them. The estimation of NPVGO is consistent with the appraisal of the intrinsic value of the real options linked to the intangible assets. According to Damodaran (2018), firms with intangible assets have the following characteristics:

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a. Inconsistent accounting rules that prudentially prevent capitalization of most operating expenses (OPEX); b. Conservative financing since intangibles lack any physical collateral; c. Extensive use of stock options to remunerate the management; d. The compressed life cycle of tech firms that grow faster and stay mature for shorter periods (Damodaran, 2018). Accounting practice tends to divide intangible assets into two categories: a. Intangible assets in the strict sense; b. Intangible assets not represented by assets. The first category includes patents, intellectual property rights (IPR), concession or rights, licenses, and trademarks; the second category includes capitalized costs, such as startup and expansion costs, bond issue discounts, study and research costs, design costs, advertising and propaganda costs and representation costs (…). Capitalized costs (intangible assets not represented by assets, like all elements not identifiable with certainty and not separable from the company) are not independently transferable and, therefore, do not represent straightforward intangible assets. The valuation of intangible assets must also consider the subdivision into specific and generic (not represented by assets) intangibles: the former usually are subject to a separate estimate, which mainly uses the criterion of the cost of reproduction or the incremental income that the intangible asset guarantees. Intangible assets are characterized by a lack of tangibility. They are made up of costs that do not exhaust their usefulness in a single period but show the economic benefits for several years. Intangible (fixed) assets include: • Deferred charges (startup and expansion costs; development costs); • Intangible assets (industrial patents and intellectual property rights; concessions, licenses, trademarks, and similar rights); • Goodwill; • Intangible assets in progress; • Advances.

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Future economic benefits arising from an intangible asset (Haskel & Westlake, 2018) include revenues from the sale of products or services, cost savings, or other benefits arising from the use of the intangible asset by the company.

4.2

(Digital) Trademarks

Trademarks (brands) are typically registered intangibles that represent distinctive characters (with originality, truthfulness, novelty, and lawfulness as requirements) that identify a good of which they represent quality, provenience, and distinctive capacity. The surplus value that the trademark confers a product (compared to an unmarked equivalent) is an expression of the value of this classic intangible asset, which can be exploited internally or licensed. The international standard ISO 10668 (2010) (https://www.iso.org/ standard/46032.html) defines and identifies a methodology for assessing the economic value of brands, outlining the objectives, approaches, valuation methods, and the modes of selection and identification of the baseline data, to be used during the valuation process with the scope of guiding the evaluator, reducing the discretional margins, and suggesting a sort of evaluation “protocol.” Digital brands represent an informatic extension of the trademarks operating on Internet platforms and connected to other intangibles as the domain names. The trademark, in law terms, indicates any sign susceptible to be graphically represented, specifically words (including the name of a person), drawings, letters, numbers, sounds, the shape of a product or its packaging, combinations or color tones, if it is suitable to distinguish the goods or the services of a company from those of others. According to OECD (2017): • “a trademark is a unique name, symbol, logo or picture that the owner may use to distinguish its products and services from those of other entities. Proprietary rights in trademarks are confirmed through a registration system. The registered owner of a trademark may exclude others from using the trademark in a manner that would create confusion in the marketplace. A trademark registration may continue indefinitely if the trademark is continuously used and the registration appropriately renewed. Trademarks may be established

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for goods or services and may apply to a single product or service, or a line of products or services. Trademarks are perhaps most familiar at the consumer market level, but they are likely to be encountered at all market levels” (par. 6.21); • “A trade name (often but not always the name of an enterprise) may have the same force of market penetration as a trademark and may indeed be registered in some specific form as a trademark” (par. 6.22); • “The term “brand” is sometimes used interchangeably with the terms “trademark” and “trade name.” In other contexts, a brand is thought of as a trademark or trade name imbued with social and commercial significance. A brand may, in fact, represent a combination of intangibles and/or other items, including among others, trademarks, trade names, customer relationships, reputational characteristics, and goodwill. It may sometimes be difficult or impossible to segregate or separately transfer the various items contributing to brand value. A brand may consist of a single intangible, or a collection of intangibles” (par. 6.23). Trademarks are the core component of marketing intangibles. According to OECD (Glossary), a marketing intangible “relates to marketing activities, aids in the commercial exploitation of a product or service and/or has an important promotional value for the product concerned. Depending on the context, marketing intangibles may include, for example, trademarks, trade names, customer lists, customer relationships, and proprietary market and customer data that is used or aids in marketing and selling goods or services to customers.” Most countries offer some form of trademark protection whose registration is stored in the national or regional Trademark Register. The World Intellectual Property administers two treaties that comply with the System of International Registration of Marks: the Madrid Agreement concerning the International Registration of Marks and the Madrid Protocol. Citizens who live in a country that adhered to either or both the agreements, belong to the Madrid Union and are therefore allowed to register with the trademark office of a single country and simultaneously receive international protection in as many other Madrid Union countries the applicant prefers (as of April 2014, 91 countries were members of the Union).

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There are different types of trademarks. Concerning the breadth of the product portfolio to which they refer, trademarks are: • Mono-brand: adopted for one or a few products, and therefore evoking specific functional characteristics of the product to which it relates; • Family-brand: referring to many products, they recall non-specific features (given that they differ for every product of the “family”), as emotional situations or abstract values. Then there are the umbrella brands, in which the leading brand is associated with the specific product (e.g., Alfa Romeo—Giulietta). According to the distance from the corporate identity, we can identify a: • Corporate brand: adopted both for the products and for recalling the image of the company and its distinctive competencies (usually the company brand itself); • Furtive brand: distant from the corporate identity, traceable only to specific products. There are hybrid forms: • Brand endorsed: incorporates two brands that belong to two different typologies among those mentioned above. • Individual brand: different brands for each product. The “de facto” trademark (brand) differs from the registered trademark: • the latter enjoys reinforced protection for its certain date due to the registration process at the Patents and Trademarks Office; • the former must prove both its reputation and its extensive pre-use. The registration lasts ten years starting from the date of filing of the application, except in the case of renunciation of the holder, and at the expiry date, it can be renewed each time for a further ten years. In practice, in the valuation, it is assumed that trademarks never expire. The trademark is closely related to the Web-related domain names.

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Technological Intangibles: From Know-How to Patents

The know-how (to do it) and trade (industrial) secrets are proprietary information or knowledge that assist or improve a commercial activity, but that is not registered for protection in the manner of a patent or trademark. According to OECD (2017) “Know-how and trade secrets are proprietary information or knowledge that assist or improve a commercial activity, but that are not registered for protection in the manner of a patent or trademark. Know-how and trade secrets generally consist of undisclosed information of an industrial, commercial, or scientific nature arising from previous experience, which has practical application in the operation of an enterprise. Know-how and trade secrets may relate to manufacturing, marketing, research and development, or any other commercial activity. The value of know-how and trade secrets is often dependent on the ability of the enterprise to preserve the confidentiality of the know-how or trade secret. In certain industries, the disclosure of information necessary to obtain patent protection could assist competitors in developing alternative solutions. Accordingly, an enterprise may, for sound business reasons, choose not to register patentable know-how, which may nonetheless contribute substantially to the success of the enterprise. The confidential nature of know-how and trade secrets may be protected to some degree by (i) unfair competition or similar laws, (ii) employment contracts, and (iii) economic and technological barriers to competition” (par. 6.20). “There are also intangibles that are not protectable under specific intellectual property registration systems, but that are protected against unauthorized appropriation or imitation under unfair competition legislation or other enforceable laws, or by contract. Trade dress, trade secrets, and know-how may fall under this category of intangibles ” (par. 6.38). A trade secret is any information about a business that could give a competitive advantage to another person or business. A trade secret can include any of the following: • • • • • •

Formulas, practices, processes designs; Instruments, patterns. algorithms; commercial methods, such as distribution or sales methods; advertising strategies; lists of suppliers or clients, or consumer profiles;

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• Physical devices, ideas, compilations of information. Patents are the result of risky and costly R&D, and the developer will try to recover its costs (and earn a return) through the sale of products covered by the patent, licensing others to use the invention (often a product or process), or through its outright sale. Patents are registered for protection and typically valued for litigation or licensing purposes. A patent is a limited monopoly that is granted for 20 years in return for the disclosure of technical information (Benty & Sherman, 2014, p. 375). A patent is a set of exclusive rights granted by a sovereign state or intergovernmental organization to an inventor or assignee for a limited period in exchange for detailed public disclosure of an invention. An invention is a solution to a specific technological problem and is a product or a process (WIPO, 2008). The word patent originates from the Latin patere, which means “to lay open” (i.e., to make available for public inspection). According to OECD (2017) “A patent is a legal instrument that grants an exclusive right to its owner to use a given invention for a limited period within a specific geography. A patent may relate to a physical object or a process. Patentable inventions are often developed through risky and costly research and development activities. In some circumstances, however, small research and development expenditures can lead to highly valuable patentable inventions. The developer of a patent may try to recover its development costs (and earn a return) through the sale of products covered by the patent, by licensing others to use the patented invention, or by an outright sale of the patent. The exclusivity granted by a patent may, under some circumstances, allow the patent owner to earn premium returns from the use of its invention. In other cases, a patented invention may provide cost advantages to the owner that are not available to competitors. In still other situations, patents may not provide a significant commercial advantage” (6.19). The value of the intangibles is linked to their continuous upgrade through R&D: «In some industries, products protected by intangibles can become obsolete or uncompetitive in a relatively short period of time in the absence of continuing development and enhancement of the intangibles. As a result, having access to updates and enhancements can be the difference between deriving a short-term advantage from the intangibles and deriving a longer-term advantage» (OECD, 2017, par. 6.125).

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Patents are usually the result of risky and costly research and development and the developer will try to recover its costs (and earn a return) through the sale of products covered by the patent, licensing others to use the invention (often a product or process), or through the outright sale of the patent. The very fact that costs are incurred mainly before patentability for inventions may have important transactional implications: patents are ripe for sale or licensing even immediately after registration, considering their finite useful life, with typically soon peaking and then declining values. The terminal value of an expiring patent is not necessarily zero if it can still be used as a distinctive, albeit no more protected, invention, during and after its phaseout. The brand associated with the expired patent (e.g., Aspirine) may still be worth it. The protection provided by a patent is limited to 20 years, and so is shorter than the protection of copyright law or (potentially unlimited) trademark registration, but the rights are more extensive and cover most commercial uses. Patents are granted only after a long and expensive registration process. Patent rights help firms keep unique competitiveness in the market, under the protection of the law, avoiding the copying and plagiarism of other competitors (Danchev, 2006). Justifications and economic rationale for patents derive from: • The natural right of inventors to the proceeds of their mental labor; • The grant of a reward for the inventive activity that otherwise would lack proper incentives. 4.2.2

The Web Value Chain: Domain Names, M-Apps, and Internet Firms

Web domain names represent the gateway to Internet connections and access to specific websites. Their value depends on several parameters, as Web traffic or search engines, and is typically calculated with “quick and dirty” algorithms freely available on the Web. The value of a web domain depends on its capacity to attract traffic, i.e., visitors, and to transform them into cash-generating customers. A domain name is a group of alphanumeric symbols that compose a name, followed by an extension defined by the Registration Authority

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of a specific country or an organization. The domain name is directly associated with a DNS, which is a system that allows converting a domain name (easier to remember) to an IP address. Domain names are the gateway to websites. A website is a collection of related web pages typically identified with a common domain name and published on at least one web server. All publicly available websites collectively form the world wide web. The valuation of domain names can take place autonomously or jointly. Websites cannot exist without access domains, whereas domain names can be an empty shell, with little if any contents. M-Apps (a shortening of the term “Mobile Application Software”), represent a computer program (software) designed to run on mobile devices such as smartphones, tablet computers, phablets, smartwatches, or other mobiles, such as notebooks (with specific extensions). Each app is associated with a logo that represents the touchscreen gateway to the app. A logo is a graphical label even more difficult to conceive than a domain name, due to its stricter constraints (no different extensions, predefined measure). The relationship between M-Apps logos and domain names is still under-investigated: they both convey Internet traffic but in a different (complementary) way. M-Apps are increasingly popular and by now represent the trendiest software device. Investigations about their valuation paradigms are so increasingly common. Even if M-Apps belong to the broad category of Intellectual Property (IP) assets, their underlying business model is so innovative and different from traditional intangibles (such as patents, brands, etc.) that standard appraisal patterns, normally used for IP, may only be used as a starting point for the valuation. Internet companies represent a composite group of companies, including Internet Service Providers (ISP), which provide users with access to the web and e-mail. Information technology platforms are becoming more and more popular, with functionalities such as ecommerce or M-Apps. The ISP, as far as web access is concerned, are the nodes of the Internet’s computer network. The Internet is one of the most significant examples of the network. ISP perform various types of services in the information society, such as:

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• Editorial services (content /information providers); • Activities of content providers and competitive intelligence systems which interact with B2B2C transactions; • Storage and domiciliation services (hosting); • Connection and transmission services (conduit or connectivity providers); • Search and indexing services (search engine). At the level of the value chain, various segments can be distinguished, for example, at the systemic and infrastructural level, by backbone service providers or access service providers. The various business models have an impact on revenue models and consequently on valuation methodologies, to be considered (for each ISP) or at a systemic level, in which the overall value created is distributed among the stakeholders involved in the virtual value chain. The valuation of Internet companies, to be adapted to the specificities deriving from the ISP business models, can follow traditional methods such as those based on the Enterprise Value /EBITDA multipliers (Francis, 2018) of comparable transactions, considering business parameters such as: • The contractual terms that bind the customers, with the remaining duration of the contracts and the renewal percentages, through the churn analysis (abandonment rate); • The resulting rate of retention/loyalty of customers; • Average Revenue per User (ARPU), based on total revenue about the number of active subscribers; • Monthly Recurring Revenue, which is the sum of the value generated by customers (ARPU * number of customers). 4.2.3

Acquisition and Processing of Information: IoT, Big Data, Artificial Intelligence, and Blockchains

The Internet of Things (IoT) is based on a family of innovative technologies (chips, wired and wireless sensors, tags, QR codes and barcodes, radio frequency Rfid identifications, GPS, etc.), which connect objects (gadgets…)—in and of itself inanimate—in smart devices always connected to the web (such as mobile phones), to collect, exchange and

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process data in real-time. The IoT is the extension of the Internet to the world of physical objects and places, which through the web are delocalized and made potentially usable anywhere, acquiring an electronic identity and an active role linking to the network sensors that interface with the physical world. Protocols, interchangeable computing platforms, and enabling technologies to rotate around the IoT, and allow to combining functions of hardware, software, data, and services to obtain new products in which the physical component is intimately connected with the intangible. Intangibles connected to objects through the Internet acquire a potentially high added value, depending on the new economic exploitation prospects deriving from the network. The connectivity between objects, the network-web (as a virtual exchange platform) and the intangible, represents a lever of value creation especially if the intangible resources interact with each other within a synergistic portfolio of Intellectual Property (IP). The term big data is used to describe a data collection so extensive in volume, speed, and variety as to require specific technologies and analytical methods for the extraction of value. Big data represents the interrelation of data potentially coming from heterogeneous sources, thus including not only structured data, such as databases but also unstructured data, such as images, emails, GPS data, information taken from social networks (Snijders et al., 2012). Big data are increasingly becoming a strategic factor in production, market competition, and growth, considering the continuous evolution of business models and markets in the modern era (Zillner et al., 2014). The progressive increase in the size of the datasets is linked to the need for analysis on a single set of data to extract additional information compared to that which could be obtained by analyzing small series, with the same total amount of data. Data mining is the set of techniques and methodologies having as their object the knowledge, coming from large amounts of data (through automatic or semi-automatic methods), and the scientific, industrial, or operational use of this knowledge (Xintong et al., 2014). Data analysis technologies are being integrated into many aspects of everyday life (sensors, biometrics, home automation, communications, and healthcare, etc.).

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Firms have seen the emergence of the professional figure of the data scientist, the person who analyzes the data to provide useful information to management, to make decisions and strategies to be undertaken. The analysis of big data requires, first, the management, acquisition, organization, storage, and processing of data; second, it is necessary to identify the data to be extracted and the methods of this operation. Finally, the process requires the ability to communicate (storytelling), with different forms of representation, what the extracted data suggest. A privileged channel for the collection of big data is represented by the information released, voluntarily or not, by users who surf the Internet. The information requested with personal data, often related to the possibility of using free apps (e.g., antivirus, and online reservations, etc.), are collected and transmitted on databases for commercial purposes, and then sold to third parties. This usually happens without the user’s knowledge and not always in compliance with the protection of his data, taking advantage of the extraterritoriality of many servers, which hampers the imposition and enforcement of rules to protect privacy. The spread of portable devices (smartphones, tablets, and i-watch…) is continuously increasing, and most transactions are online. The actions, activities, and behaviors of individuals are now measurable by data that can be combined with other data and analyzed by special tools. Thanks to technological sensors and biometric identification, it is possible to arrive at a collection of increasingly representative quantitative data on the inhabitants of the world. The development of data and technology has brought out the sector known as the Internet of Things, which indicates a family of innovative technologies, whose purpose is to make any type of object, even without a digital vocation, a device connected to the Internet, which can enjoy all the features that have objects born to use the web. The characteristics of big data are expressed in Table 4.1, with the so-called 10 v, which refers to volume, velocity, variety, veracity, validity, variability, virality, visualization, viscosity, and value.

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Table 4.1 The big data 10Vs and their impact on forecasting Big data characteristics

Impact on the forecast

Volume

Large amounts of data significantly increase the depth, accuracy, and quality of available information. Using big data for budgeting and reporting purposes may allow for more accurate estimates. Differences between forecasts and actual data are reduced, as is the risk (inversely proportional to the Value for Money ratio), strengthening the supply chain. Relationships between different stakeholders are likely to improve Data are accumulated in real-time and quickly. The speed of the data increases when the system (represented, for example, by the databases) improves, thanks to artificial intelligence and the learning of the machine. Speed, like volume, can reduce the gap between forecast data and actual data Empirical samples, together with big data, analyze a variety of structured, semi-structured, and unstructured data to match forecasts with actual results, predict risk models, and provide more in-depth and effective analysis. Variety increases the understanding of stakeholders‘ needs One of the key parameters in investments is the reliability of the data. Increased variety and speed can hamper the ability to filter data before analyzing it and making decisions, amplifying the problem of data truthfulness, which can help reduce opportunistic behavior and conflicts of interest Integrity, associated with truthfulness, can be defined as the validity, accuracy, reliability, timeliness, and consistency of data The variability of the data increases their informative value and should be correlated with other parameters, such as speed and variety. When variability is considered and readily reflected in updated business models, the risk is reduced Measures the rate of data dissemination (speed of sharing) across the network. Increases the involvement of different stakeholders

Velocity

Variety

Veracity

Validity

Variability

Virality

(continued)

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Table 4.1 (continued) Big data characteristics

Impact on the forecast

Visualization

The connection between the display of information and visual analysis through an IT system and technological representations could help users to better understand the data. The synthesis reproduced by data visualization tools is a key element in transforming the information revealed by big data processing, including only by specialists, into user-accessible knowledge It characterizes the resistance to navigating in the data set or the complexity of data processing. It is a common feature of complex data in many industries The monetizable value is the synthesis of the characteristics of big data, considering data as a resource to be exploited to produce innovation and new information-sensitive products and services

Viscosity

Value

4.3 Residual Goodwill and the Intangible Portfolio According to IVS 210, § 20.6. “Goodwill is any future economic benefit arising from a business, an interest in a business, or from the use of a group of assets which has not been separately recognized in another asset. In general terms, the value of goodwill is the residual amount remaining after the values of all identifiable tangible, intangible, and monetary assets, adjusted for actual or potential liabilities, have been deducted from the value of a business. It is typically represented as the excess of the price paid in a real or hypothetical acquisition of a company over the value of the company’s other identified assets and liabilities.” Artificial intelligence is a broad and varied concept, based on processes and reasoning of thought and behavioral dynamics, faithful to human performances and tending toward rationality. Russell and Norvig (2016) recall complementary definitions, according to which artificial intelligence consists in designing computers and machines that think and act as human beings (even though limited to specific predefined applications, such as decision-making, problem-solving, and learning). Artificial intelligence consists, therefore in the study of mental faculties through computational models making possible actions such as learning, reasoning, and action, to design intelligent agents. These agents must be able to act humanely

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and to this end must be able to communicate with human beings through processes defined by the Turing test: a. Natural language processing (NLP), for language communication; b. Representation of knowledge to store it; c. Automatic reasoning to use stored knowledge to answer questions and draw new conclusions; d. Dynamic learning (machine learning) to adapt to new circumstances or to extrapolate new paths; e. Computer vision to perceive objects; f. Robotics to manipulate objects and move around. The pervasiveness of artificial intelligence and its applicability to wider solutions and business models represents a strong element of innovation, which can operate within a portfolio of intangible resources, enhancing their characteristics and potential. Blockchain is a consequential list (chain) of blocks (records) that are linked using cryptography. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data (generally represented as a Merkle tree root hash). Blockchain could be regarded as a public ledger technology in which all committed transactions are stored in a chain of blocks. This chain continuously grows when new blocks are added to it. Blockchain technology has characteristics such as decentralization, persistence, anonymity, verifiability, and auditability. It can be then used to ensure authenticity, reliability, and integrity of data and business activities. Blockchain can work in a decentralized environment thanks to the integration of technologies such as cryptographic hash, digital signature (based on asymmetric cryptography), and distributed consensus mechanisms. With blockchain technology, a transaction can take place in a decentralized manner.

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4.4 The Value of Growth: Multi-Stage Cash Flows and Dividends Intangibles foster growth, eventually bringing to the possibility for maturing startups to payout dividends. The multi-stage dividend discount model is a technique used to calculate the intrinsic value of a stock by identifying different growth phases of a stock; projecting dividends per share for each period in the high growth phase and discounting them to the valuation date, finding terminal value at the start of the stable growth phase using the Gordon growth model, discounting it back to the valuation date and adding it to the present value of the high-growth phase dividends. The basic concept behind the multi-stage dividend discount model is the same as the constant growth model, i.e., it bases intrinsic value on the present value of expected future cash flows of a stock. The difference is that instead of assuming a constant dividend growth rate for all periods in the future, the present value calculation is broken down into different phases. Figure 4.1. shows the growth rates of dividends in a three-stage timesheet. The cost of equity (a part of the denominator of the DCF formula, as shown in Sect. 8.5) should ideally be decreasing across time,

Growth rate of earnings

No dividends in the startup phase

High growth rate

Decreasing growth

Stable infinite growth

Cost of equity

Growing Payout Low Payout

Fig. 4.1 Multi-stage dividend growth

Payout rate

High Payout

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as initial high growth rates coincide with the startup phase where equity risk is higher (also due to the lower level of leverage that normally characterizes this phase). The dividend discount model (DDM) (Damodaran, 1996) is a method of valuing a company’s stock price based on the theory that its stock is worth the sum of all its future dividend payments, discounted back to their present value. In other words, it is used to value stocks based on the net present value of future dividends. The equation most widely used is called the Gordon growth model. The formula is:

(4.1) Where: – P0 is the current stock price – g is the constant growth rate in perpetuity expected for the dividends. – r is the constant cost of equity capital for that company. – D1 is the value of the next year’s dividends. This model embeds constant growth that is a theoretical oversimplification of the real world. It is also used to estimate the market value of intermediaries, as shown in Sect. 5.9. 4.4.1

Franchise Factor Model

Many alternatives to DDM have been formulated. One of them is a Franchise Factor Model, focused on wealth creation. This model defines franchise value as the value additive component of growth.

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This interpretation is fully consistent with the circumstance that the startup’s growth is likely to change across time. Growth affects the numerator of the DCF formulation—the forecast cash flows—but also the denominator of the DDM. The franchise factor may so affect even the cost of capital used in the DCF formulation. According to Petkov and Patev (2018) “Models based on economic profit divide the value of the company to “base value” and “added value.” The idea behind base value is that at the current moment of valuation the company has a value that does not depend on the company’s performance or growth projections. On the other hand, the added value represents the premium/discount when a company’s performance is compared with market expectations. If the company were able to beat market expectations then there would be value added, oppositely if the company’s rate of return is lower than the market’s then this business destroys shareholder value. Best-known economic profit models are EVA (illustrated in Sect. 3.4; see Stewart, 2019) and Residual income. The franchise value approach considers not the balance sheet, but rather the earning power of the company. The tangible value of the company is introduced and is equal to the present value of current EPS repeated in the future, for estimation is used basic Gordon Growth Model. The second major innovation is the separation of the growth model from the performance evaluation. While in most valuation models for growth estimation is used Gordon Growth Model that is implemented in the terminal value, here growth separated in “Growth factor.”

4.5 Sustainable Growth, ESG Drivers, and Ethical Funding Ignoring environmental, social, and corporate governance (ESG) aspects expose firms to risks that diminish value, shrink returns, and even lead to failure. Firms considering ESG aspects are perceived as less risky by capital providers. Such capital suppliers accept lower returns and lending rates when providing capital to firms with superior ESG practices and disclosure (Johnson, 2020). Both the EBITDA (Bouwens et al., 2019; Nissim, 2019; Verriest et al., 2018) and the cost of capital are sensitive to ESG - Environmental, Social, and Governance—parameters. The growing relevance of sustainability suggests that managerial decisions that improve corporate environmental

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footprint, and risks might be priced by investors, thus reducing the cost of capital (Gianfrate et al., 2019; Ng & Rezaee, 2015) for global companies. According to Wikipedia, Environmental, Social, and Corporate Governance (ESG) refer to the three central factors in measuring the sustainability and societal impact of an investment in a company or business. These criteria help to better determine the future financial performance of companies (return and risk). From a consumer perspective, firms that behave responsibly provide goods and services that protect the environment, satisfy needs, protect the consumer, and do so at a reasonable price; from an investor perspective, the socially responsible firms create value while minimizing risk. However, the identification of socially responsible firms is difficult ex-ante due to information asymmetries (Minutolo et al., 2019). Some studies analyze the relationship between corporate social performance and market returns. Preliminary results found no extra returns in Italy (Landi & Sciarelli, 2019). The impact of ESG compliance on cash flows and the cost of capital is still controversial; whether there is a hope of a positive impact on (higher) cash flows and (lower) cost of capital, the evidence is still mixed. The ESG scores developed by Bloomberg are one mechanism that signals to the market the level of transparency and disclosure by the firm and an indicator of overall social responsibility. Scores such as Bloomberg’s ESG have become an important measure for many investors because it conveys a level of risk (Huber & Comstock, 2017). ESG parameters may impact both the numerator and the denominator of DCF metrics, so affecting corporate valuation. The impact of ESG parameters on the estimated cash flows reported in the numerator, the discounting cost of capital in the denominator, and the overall market estimate represented by the sum of the DCF is asymmetric if E1 = E2 = E3, S1 = S2 = S3, and finally, G1 = G2 = G3. This means that the same parameters have a different impact on the cash flows, the cost of capital, and their discounted sum (DCF). This may well be the case because cash flows are an internal parameter (calculated within the firm), whereas the cost of capital reflects the discount risk of the cash flows but also incorporates external factors (the risk-free market interest rates, the market equity premium, etc.). The impact of ESG factors must be interpreted dynamically, as it changes across time.

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Figure 4.2 shows the impact on DCF according to this asymmetric interpretation. Ethical funding may concern corporate bond issues that are the main component of the cost of debt. According to Koelbel and Busch (2013), there is a significant effect of stakeholder pressure regarding ESG issues on corporate bond spreads.

Environmental(1)

ΣCash Flows

Governance (1)

Social (1)

Environmental(3) DCF (market value)

= Social (3)

Governance (3)

Environmental(2)

Cost of Capital

Social (2)

Governance (2)

Fig. 4.2 ESG impact on cash flows, cost of capital, and DCF value

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4.6

Sustainable Patterns

The forecast of future cash flows is possibly the main criticality of DCF metrics, and the risk that effective cash flows may (greatly) differ from expected ones needs to be incorporated in the cost of capital. This wellknown consideration is difficult to put into practice, especially when projections are long-termed or when they concern volatile businesses, such as startups or technological industries. Sustainability concerns so affect estimates, and the cost of capital discount factor may conveniently incorporate heterogeneous functions that refer to: a. Circular economy patterns; b. Sharing economy c. The resilience of the supply and value chains; d. Digital platforms and networks; e. Intangible-driven scalability potential and real options. Sustainability factors are expected to lower the cost of capital, improving the occurrence and stability of expected cash flows. The cost of debt and the cost of equity are lower for firms that disclose sustainability performance information when compared to firms that do not disclosure similar information (Ng & Rezaee, 2012). Sustainability so impacts the firm’s value, expected cash flows, and systematic risk of an overall market portfolio. Figure 4.3 recalls the main sustainability patterns. Sustainability patterns may affect both the systematic and the specific cost of capital components of a firm. The systematic component relates to the market (risk-free interest rates, then summed up with the firm-specific spread to determine the cost of debt; firm’s beta, to express its sensitivity toward the stock market premium, as a proxy of the cost of equity). Sustainability strategies impact both the ecosystem and the individual firm. The ecosystem, eventually related to the stock markets, benefits from the impact of sustainability factors, especially if they are coordinated and synergistic, and is sensitive to ESG achievements that improve the overall wealth. At the firm level, a better ecosystem may lower the overall cost of capital, making the capital markets more efficient and resilient.

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Circular Economy

[Resilient] Supply & Value Chains

Sharing economy

Digital plaƞorms and Networks

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Scalability and Real OpƟons

Fig. 4.3 Sustainability patterns

Digital platforms improve the architectural frame-working of the ecosystems and catalyze its functioning, operating as an orchestra director that coordinates and fine-tunes the market players. Scalability is both market- and firm-driven, as it benefits from an overall ecosystem functioning that creates the market conditions for individual firm achievements. Jiménez and Grima (2020) point out that the link between the cost of equity and sustainability is extremely timely as it can have great potential in reinforcing good practices regarding sustainable engagement among listed companies, which can also be regarded as trendsetters by other types of companies and institutions.

4.7

Circular Economy

A circular economy is an economic system aimed at eliminating waste and the continual use of resources. The impact of the circular economy model on the cost of capital is still largely undetected. Initial green investments may have long-term payback but should eventually become sustainable. The cost of capital of these investments may so increase in the first years but then gradually decrease,

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especially if there are incentives to carry on green investments (and restrictions for polluting ones). The overall collective cost (of capital) borne by a comprehensive ecosystem matters more than the individual cost of capital (within each firm). Circular systems employ reuse, sharing, repair, refurbishment, remanufacturing, and recycling to create a closed-loop system, minimizing the use of resource inputs and the creation of waste, pollution, and carbon emissions. Combining sustainable consumption with the circular economy concept could help tackle challenges, such as resource scarcity and climate change by reducing resource throughput and increasing the cycling of products and materials within the economic system, thereby reducing emissions and virgin material use (Tunn et al., 2019). According to the Center for Economic Development & Social Change,1 global economic growth is facing increasing challenges in terms of sustainability. Under this assumption, the new model of Circular Economy takes place: it promises economic growth with low or zero costs in terms of materials, energy, and environmental impact. As the Industrial Revolution promised benefits from an excess availability of resources, the Circular Economy takes advantage of resource constraints. Increasing efficiency means to improve the ratio between input (environmental impact) and output (return) through behavior, technology, and planning. The reasons to achieve better efficiency are many: the scarcity of resources, the increasing environmental impact, and the promised economic return. The challenges are also many. First, incentives are low. That is because of the low cost of some resources, too low to encourage recycling and efficiency. Moreover, investments in efficiency require payback periods longer than the industrial standard, beyond a large financial capital. Figure 4.4 shows an example of a circular economy flowchart.

1 Center for Economic Development & Social Change, http://www.ced-center.it/en/ 2016/09/26/economia-circolare-verso-un-nuovo-modello-di-sviluppo-economico-sosten ibile/.

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Fig. 4.4 Circular economy flowchart

4.8

Resilient Supply and Value Chains

Supply and value chains are becoming more resilient, thanks to digitalization and networking. This impacts the cost of capital, softening overall risk. Resilience is also embedded in real options and may be estimated with a differential approach, comparing a standard (and somewhat rigid) supply chain with a resilient one (which is the added value?). Resilient networks (e.g., digital supply chains) are elastic to external shocks, for instance, given by a node deletion (what happens if a physical or digital bridging node is deleted? Railway systems are more vulnerable than aviation systems since it is easier to replace a missing airport than a central station. Node deletion is, however, useful during pandemics). Upson and Wei (2019) examine the impact of supply chain concentration on a firm’s financing costs, showing that purchasing firms engaging in multiple supplier relationships are subject to higher firm risk and cost of equity.

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(Scalable) Digital Plaƞorms

•plaƞorms as a bridging (networking) virtual stakeholder •eCommerce links (B2B/B2C) •Plaƞorms as a Service

Networks

•interacƟng networks •mulƟplex supply and value chains leveraging value cocreaƟon paƩerns

Fig. 4.5 From digital platforms to Networks

Digitalization, examined in Chapter 12, is likely to improve the resilience of the supply and value chain.

4.9

Digital Platforms and Networks

Networks connect discrete objects or intangible assets with relations that establish an edging link among otherwise dispersed nodes. Physical networks may be used to explain traditional supply and value chains that can become “smart” following innovation patterns and other industry 4.0 applications, as exemplified in Fig. 4.5. Chapter 12 is dedicated to digital platforms and network catalyzers.

4.10 Sharing Economy and Collaborative Commons The sharing economy (Mallinson 2020) is an economic model defined as a peer-to-peer (P2P) based activity of acquiring, providing, or sharing access to goods and services that is often facilitated by a community-based online platform. The capitalist sharing economy is a socio-economic system built around the sharing of resources. It often involves a way of purchasing goods and services that differs from the traditional business model of companies hiring employees to produce products to sell to consumers. It includes the shared creation, production, distribution, trade, and consumption of goods and services by different people and organizations (https://en.wik ipedia.org/wiki/Sharing_economy).

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The key assumptions of the sharing economy are consistent with the sustainability of the supply chain (Banaszyk & Łupicka, 2020). In The Zero Marginal Cost Society, Rifkin (2014) describes how the emerging Internet of Things is speeding us to an era of nearly free goods and services, precipitating the meteoric rise of a global Collaborative Commons and the eclipse of capitalism. These visionary theories are consistent with sharing economy patterns. The impact on the cost of capital and valuation is still debated.

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Tahat, Y. A., Ahmed, A. H., & Alhadab, M. M. (2018). The impact of intangibles on firms’ financial and market performance: UK evidence. Review of Quantitative Finance and Accounting, 50, 1147–1168. Tunn, V. S. C., Bocken, N. M. P., van den Hende, E. A., & Schoormans, J. P. L. (2019). Business models for sustainable consumption in the circular economy: An expert study. Journal of Cleaner Production, 212, 324–333. Upson, J., & Wei, C. (2019, December 1). Supply chain concentration and cost of capital. SSRN. Available at https://ssrn.com/abstract=3532089. Verriest, A., Bouwens, J., & de Kok, T. (2018, June 11). The prevalence and validity of EBITDA as a performance measure. SSRN. Available at https:// ssrn.com/abstract=3171131. Wipo Intellectual Property Handbook: policy, law and use. (2008) Fields of Intellectual property protection WIPO, chap 2. Xintong, G., Hongzhi, W., Song, Y., & Hong, G. (2014, December). Brief survey of crowdsourcing for data mining. Expert Systems with Applications, 41(17), 7987–7994. Zillner, S., Rusitschka, S., & Skubacz, M. (2014). Big data story: Demystifying big data with special focus on and examples from industrial sectors. Whitepaper, Siemens AG. https://www.bibsonomy.org/bibtex/2ec1ca5231e88bd230216 bfe5c4cb6a7f/bigfp7.

CHAPTER 5

Cherry-Picking Intermediaries: From Venture Capital to Private Equity Funds

5.1 Venture Capital, Private Equity, and Equity Crowdfunding According to Wallmeroth et al. (2018), entrepreneurial finance is a vague term and can refer to numerous, interconnected elements of alternative investment finance. Entrepreneurial finance covers several sources of capital, such as angel investors, venture capital, private equity, hedge funds, microfinance, project finance, and more. The terms “venture capital” and private equity” are generally used to describe the provision of equity capital flowing from specialized intermediaries to unlisted companies with high growth and development potential. The basic assumption of this activity is the acquisition of shareholdings in startups from a long-term perspective, to obtain a capital gain when the exit strategy is executed. In general, private equity (Harris et al., 2014) refers to all operations carried out during the companies’ life cycle stages after the initial one, while venture capital (R‚ohm & Kuckertz, 2018) refers to those investments carried out in companies’ early stages of life. In this sense, a typical example is represented by technological startups. Equity crowdfunding is the online offering of private company securities to a group of people for investment. Startups may recur to crowdfunding in different stages of their life, either at the very beginning or for second round refinancing. Corporate governance implications are many. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_5

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According to Walthoff-Borm et al. (2018), there are crucial adverse selection issues on equity crowdfunding platforms, although these platforms also catalyze innovative activities. There is a more complex relationship between dispersed versus concentrated crowd shareholders and firm performance than currently assumed in the literature. The trade-off between concentrated and dispersed ownership shows that conflicts of interest may arise when management is separated from control, this being the case with a crowdfunding platform of shareholders. Any governance criticality might increase the cost of capital, even if more shareholders bring to larger visibility of the firm, so incorporating social networking options. Critical success factors change over time (Santisteban & Mauricio, 2017): experience about the previous startup of the founding team and government support factors affect the seed stage; the venture capital factor affects the early stage; the clustering, technological/business capabilities of the founding team and venture capital factors affect the growth stage; and the clustering factor affects the expansion stage.

5.2 Risk Capital for Growth: The Role of Venture Capital, Private Equity and Business Angels Innovative startups are newly formed companies with high growth potential, which usually absorb a lot of liquidity in the early years of life, to finance development, against minimal collateralizable assets. This is unattractive for traditional banking intermediaries, usually replaced by other specialized intermediaries as venture capital or private equity funds, which diversify their portfolio basing their strategies on a multi-year exit with substantial expected increases in value from investments that survive a Darwinian selection. Despite the rich and abundant research on the relationship between Venture Capital investment and startup performance, there is no clear evidence about the contribution of Venture Capital investment to the performance and market value of invested firms (Jeong et al., 2020). The evaluation of the target companies follows traditional methodologies, accompanied by specific features deriving from varied probabilistic scenarios and multiple exit methods. The technological footprint implies

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evaluation analogies with patents, know-how, and intangibles linked to specific sectors (biomedical, Internet, etc.). The invested capital can be allocated to various projects, as the development of new products, the expansion of working capital, the strengthening of the financial structure of a startup. Private equity can be used to solve problems related to the ownership structure of a startup (diluting the founding shareholders) or its strengthening or restructuring. It is the most appropriate tool for management buyouts (MBOs) and management buy-ins. Venture capital investment offers several advantages for target companies. First, specialized intermediaries offer companies the opportunity to exploit their expertise in the field of financial support to companies aimed at creating value over time. This means that the startup will be able to use the capital made available for a relatively long period, enough to carry out its projects (strategies, company acquisitions, new product development, company reorganizations, etc.). The support of the institutional investor is not limited to the mere provision of risk capital but often provides the startup with its managerial knowledge to run the business. The institutional investor can take advantage of a vast experience based on diversified entrepreneurial realities, on sectoral knowledge matured for similar investments, and usually has specific expertise to which the startup can have recourse. As far as intermediaries are concerned, there are now two major types of risk capital investors: 1. “business angels” (Avdeitchikova et al., 2008), often being part of a club deal of “family and friends”; 2. Venture capital (Cavallo et al., 2019; Davila et al., 2003) and private equity funds. The timesheet can be synthesized in Fig. 5.1 (consistent with Figs. 5.3 and 5.4). The investment timing is of crucial importance for the overall expost profitability. Correct—and lucky—entry timing is perhaps even more important than wise exit. Potential incoming investors often wonder when and how they can enter the startup. It is well known that early-stage equity underwriting is riskier and so cheaper. Default possibilities rank highest at the beginning and during the

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Venture Capital Private Equity

Founders - Family & Friends

Business Angels Crowdfunding

Fig. 5.1 Startup investors

“Death Valley” season when cash- and equity- burnouts are accompanied by disillusion. But this may be the optimal timing of a “buy-low and resell-high” strategy. Exit strategies are somewhat easier to plan, since they may be linked to the fund’s expiring date, or the possibility to cash in a sufficient capital gain, or to another milestone (equity increase; cash- or equity- burnout, etc.) where a clear-cut choice is unavoidable. Within this context, early-stage business angels are informal investors with significant personal assets, who acquire shares of small-medium firms with high growth potential. Business angels favor a personal relationship with the entrepreneur they finance and deal mainly with startups or early development. Angel finance forms an integral part of the larger venture capital industry as it often serves as the primary source of external equity for high-risk, early-stage startups. The term business angel characterizes a high net-worth individual, often with an entrepreneurial background, who invests in small, private firms on his account. Angel finance plays a major role in the high-technology industries. It is hard to single out large high-tech corporations that, at the crucial growth stage as a startup company, did not receive funding from such business angels. Apple, Amazon, Facebook, and Google are only a few examples of the large spectrum of today’s technology giants that—without the contribution of business angels in their early growth stage—ultimately may have never emerged (Werth, 2017). The latter are generally borne by banks, insurance companies, pension funds, or large companies (corporate venture capital) and are specialized by the investment sector.

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Private equity is a financing instrument through which an investor provides new capital to a target startup, generally not listed on the stock exchange, which has promising growth potential, thanks to the support to management with the sharing of strategies and the contribution of additional financial resources. The investor intends to disinvest in the medium term, realizing a capital gain from the sale of the shareholding. Private equity funds are investment vehicles that operate as venture capital (high-risk innovative startups) or through leveraged buyouts (LBOs), mainly with debt acquisitions. The growing attention of private equity derives from a combination of factors, including the returns of funds, often higher than those of the stock market (public equities), and the weak correlation with the market, with a significant diversification of risk if the investment in private equity funds is included in an equity and bond portfolio represented by listed securities. In countries where private equity has had less of an impact so far, like continental Europe and Japan, investors access private equity even through funds of funds, with an intermediate investment that diversifies the funds but leads to an increase in fees. Funds that operate through direct investments are, however, increasingly popular. Governance issues have a cascade effect on the fund’s portfolio companies, which are an investment objective compliant with the features suitable for these types of investors. These concerns include the dilution of the shareholding structure (reference shareholders who enjoy private benefits of control), the presence of independent directors, the voluntary production of quality information, and standards for the protection of corporate democracy and minorities. The members of the fund (limited partners) allocate capital to the fund, and the resources are called up by the fund managers (the general partners of the management company) and invested when equity is called. When an investment is liquidated, the general partners share the proceeds among the members. Institutional investors (pension funds, investment funds, hedge funds, funds of funds …) prefer private equity funds with advanced corporate governance, capable of mitigating conflicts of interest between stakeholders, protecting minority shareholders, and reducing agency costs, which are traditionally high in private equity funds even because they incorporate information asymmetries.

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Visibility

Peak of expectaƟons Plateau of producƟvity

Slope of Enlightment

Deep-Down Disillusionment Technological trigger

Time

Fig. 5.2 The Gartner Hype-Cycle model

Within this institutional framework, some market considerations emerge about the investment prospects. The Gartner Hype Cycle (Diller et al., 2009; Fenn, 2007) model represents the five phases of the life cycle of a technological investment: 1. The trigger for innovative activity; 2. Peak of inflationary expectations; 3. Disillusionment; 4. (Ascending) curve of the Enlightenment; 5. Plateau of productivity (Fig. 5.2).

5.3 Types of Investments, Intermediaries, and Bankability Financial intermediaries and institutional investors involved in startups are often different from those who traditionally assist conventional firms. This is the result of a series of factors including, first, the young age of these

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companies, which have no history and therefore no previous performance score, which is an important element of creditworthiness. Another distinctive feature of startups is the nature and composition of their assets, often represented by high-tech investments that take years to produce revenues and are characterized by a high level of risk, with modest collateral value. It is not surprising that traditional banking intermediaries are far from a world they are not familiar with, and that is not fully compliant with their characteristics. The startups’ ability to repay debts emerges only in the mid-term, once the initial phase is over, and the startup passes unscathed the so-called “death valley” (a phase in which the startup runs out of its initial capital, entering into a context of cash and equity burnout, without having yet activated a sustainable revenue model). Many young companies do not survive the test of commercial viability. Further considerations are made in Sects. 6.10 and 7.5. Investments in startups, which are intrinsically risky, do not allow the lender who underwrites standard debt to take up an upside profitsharing option that is typical of either shareholder (direct underwriters of risk capital) or quasi-equity underwriters (as convertible bonds or cum warrants). The impact on bankability is typically significant, to the extent that only specialized intermediaries as venture capital or private equity funds tend to assist startups that only after consolidation can access the traditional credit. There are different types of investments that institutional investors can make, depending on the different phases of the startup’s life cycle. Each stage of a startup’s life corresponds to different needs, which must be considered by the institutional investor. Potential outside investors generally do not want to be the first person to write a check to the startup. They look for previous financial investment from the founders, friends, and family, and outside members of the board of directors as evidence that the entrepreneurs are serious and legitimate (Everett & Casparie, 2018). The interventions of specialized intermediaries can be grouped and classified into three main categories: startup financing, financing for expansion and development, and financing for change. Venture capital deals with the first category, while private equity deals with the following two. The types of investment depend on the performance of the target startup, which often records negative results in the

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Fig. 5.3 Startup financing cycle

early years, and then recovers and grows in the medium term (if it successfully survives Darwinian selection). Investment record is reflected in the performance of venture capital or private equity funds, which may follow a yield curve (in terms of Internal Rate of Return that start negative and may reach break-even after some years) known as the J curve (Fig. 5.3). 5.3.1

Startup Loans and Venture Capital Activities

Within this category of interventions, it is possible to distinguish different types of actions that a venture capitalist may decide to take. The request for intervention is generally made through the presentation of a business plan to several institutional investors, by an entrepreneur intending to start a new business, to develop a new product, a new service, or a new technology. What the entrepreneur needs most is not only the amount of capital made available by the institutional investor but, above all, the contribution he can make in terms of entrepreneurial ability and competence in defining a successful strategy.

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It is possible to analyze the various types of intervention that the venture capitalist can implement, depending on the development of the new business reality: 1. Seed financing or financing the idea of business if the investor intervenes in the early stages of conception and testing of new products, which exist only at the level of the concept. The skills that the investor must have are not only managerial but also technical and scientific, i.e., aimed at the practical transformation of the idea into a working business. The risk associated with this type of investment is high. However, with a high risk of failure, the expected return is a multiple of the initial investment. Startups are sustained and perform better as they receive their Venture Capital investment at the initial stage (Jeong et al., 2020). 2. Research & development financing: funds are granted to finance the development of a new product both in a new startup and in a firm that is already established. 3. Startup financing is granted to complete the development of the product and the initial marketing, to pass market testing (albeit at the level of the prototype). The companies financed are in the organizational phase, since the conditions for the development of a startup already exist, even though they have not yet put their products on the market. The problems are still technical, and the characteristics of the intermediaries are essentially the same as in the previous cases. 4. First-stage financing is finally granted to companies that have used their initial capital to test the prototype on the market and need funds to start large-scale production and then sale. Venture Capital (VC) investors can be coordinated in syndicates, and their geographic concentration affects firm performance and ex-ante contractual terms. Firms with geographically concentrated VC investors are more likely to exit successfully than other firms. Geographically proximate VC investors are also more likely to form syndicates in follow-up rounds, use less intensive staged financing and fewer convertible securities, and are less likely to send their representatives to firm boards (Jun-Koo et al., 2020).

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5.3.2

Financing for Expansion and Development: The Role of Private Equity and Bridge Financing

Expansion financing or development capital interventions historically represent the most important activity of private equity. These are all the interventions that the institutional investor makes when the target startup is faced with problems concerning its development. The tools that a startup can use for this purpose are mainly: • Increasing or diversifying production capacity; • The acquisition of other companies or business units; • The integration with other business realities. It is possible to identify a series of different interventions, related to the stage of development of the startup: 1. Second-stage financing is represented by the working capital for the initial expansion of a startup that produces and sells its products, which has increasing receivables and goods ready in stock; 2. Third-stage financing occurs when the private equity grants funds for the expansion of startups that have already reached the breakeven point and whose turnover is growing. These funds are used to finance the purchase of additional machinery and plant, market research, or the development of an existing product to improve it; 3. Bridge financing, or transition financing, is granted to a startup that intends to be listed on the stock exchange. This investment can be defined as the intervention in risk capital representing a bridge financing between the startup with closed capital and the future listed startup. Bridge financing is often structured to be repaid by the proceeds of the IPO but it can be a restructuring of the positions of the major shareholders through secondary transactions if there are investors who want to reduce or liquidate their shareholdings. In all the cases analyzed above, the intervention of the intermediary is more complex than that of the startup phase. The institutional investor will have to negotiate with a higher number of shareholders, who may have divergent interests. Furthermore, the startup to be financed already

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has its track-record, which leads the investor to a detailed preliminary analysis. When investors commit capital to a private equity fund, the money is not immediately invested but is called by the fund manager throughout an investment period of up to five years. This business model allows private equity fund managers to invest the committed capital at their discretion, which gives them the flexibility to time the markets (Jenkinson et al., 2018). 5.3.3

Financing of Change and Modification of Ownership Structures: Replacement Capital, Buyout, Venture Purchase, and Turnaround Financing

Change processes may be financed with replacement capital or other forms of intervention. The reasons are different, even if they generally lead to a substantial change in the ownership structure. Companies that undergo this type of financing often find themselves in a stalemate, with a consequent need for rethinking their structure. A first cause is related to the situation where there is a change in the equity composition of the target startup, and in which, one or more shareholders want to leave the business. If only minority shareholders want to exit, there is a replacement capital situation, normally with no problems related to the change in the strategy of the startup. The situation is different if most shareholders want to leave the startup. There can be different situations leading to this solution, even if the intervention of the institutional investor is always aimed at financially supporting the new entrepreneurial group in the purchase of the target startup, thus favoring the change of the ownership structure. The transaction is part of the more general category of buyouts. There is a management buyout if inside management takes control of the startup or management buy-in if it is an external group to take over. If the institutional investor favors the involvement of employees of the startup itself, this is referred to as workers buy out. There may be a venture purchase of quoted shares up to the possibility of delisting. In this situation, the investor buys, through a takeover bid, directly on the market the securities available to allow delisting and uses her managerial knowledge to restructure the startup.

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Finally, there may be a need for restructuring in the event of a corporate crisis, which often requires a change in the composition of the corporate structure. The operation that the institutional investor operates in these cases is called turnaround financing and is often the only way to save loss-making companies in need of a relaunch.

5.4

The Investment Process

After analyzing the general characteristics of private equity and venture capital activities and the different methods of intervention by institutional investors, it is worth focusing on the phases through which the investment process is structured. The first phase of the investment process consists of identifying the target startup. At the end of this activity, when the most interesting investment opportunities have been identified, the intermediaries will have to evaluate in-depth the profile of the target startup. At this point, the most delicate stages of the entire process consist of a comprehensive evaluation of the startup and the structure of the operation. In addition to the general characteristics of the entrepreneur, other factors are being considered, including the market position of the startup and its potential location, the potential value growth, its technological capabilities, and the possibilities of divestment of the shareholding. If this analysis leads the investor to make a favorable decision, the investor will be concerned with structuring the operation, in terms of time and method of execution. This will be followed by a phase of negotiation aimed at defining the price, in which the decisions regarding the timing of disbursement of the loan and the methods of payment will be crucial, ranging from a capital increase to the purchase of shares from the old partners. Once the participation has been acquired, the investor will have to monitor the operation, constantly following the trend of the investee startup, to be able to detect and resolve any problems in time. In this phase the investor can directly contribute to the management of the target startup, appointing her managers. The last stage is the most critical and consists of the divestment, whose outcome determines the profit or loss for the institutional investor (Fig. 5.4).

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Fig. 5.4 The investment process

Investing funds typically have predetermined exit schedules, following the fund’s contractual agreements (Benftsson & Sensoy, 2011). The divestment operation is generally planned, in its manner and timing, at the time of the initial investment, although often the initial project undergoes inevitable changes, always concerning the objective of the investor who is to maximize their ROIC compared to the WACC (creating value if ROIC > WACC). a. The value chain in risk capital intermediation in the presence of information asymmetries and debt constraints. Technological startups or those with a core business in other innovative sectors and with interesting growth prospects are unlikely to obtain the financial resources necessary for the implementation of the business plan, up to the achievement of the financial breakeven. This happens because they can offer limited guarantees, with no significant assets collateralized and being the payback of loans distant and uncertain. Hence the need to look for alternative financial resources, which share the business risks in a noncontingent time perspective and aligned with the development cycle of the business model. The value chain in risk capital intermediation is based on the tendency to overcome traditional information asymmetries between historical and potential shareholders (venture capital or private equity funds …).

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Conflicts of interest between existing and future shareholders following a capital increase have been addressed in the Myers and Majluf model (1984), according to which: 1. The managers of each startup have a more in-depth knowledge of current earnings and investment opportunities than external investors; 2. Managers act in the interest of existing shareholders. The existence of information asymmetries and the tendency to favor existing shareholders means that managers who develop profitable investment initiatives are unable to channel good news to new shareholders, who are suspicious and fear to underwrite new capital at a high price, with a consequent unfair transfer of wealth from new to old shareholders. Managers, on the other hand, have an incentive to communicate the good news, otherwise, it would be difficult to raise stock prices (which are linked to stock options and other incentives). Only time could tell if the news communicated by the management is true or false. If in doubt, the potential new shareholders will accept to underwrite the new shares only at a discounted price compared to a hypothetical equilibrium value in the absence of informative asymmetries, unless full disclosure is given by managers. Managers understand these problems and, in some cases, prefer not to undertake new investments—even if they are considered profitable—if such investments can only be financed through the issuance of additional equity. If the capital increase takes place at an excessive discount, there is an unfair transfer of wealth from the old to the new shareholders. The paradoxical consequence of the Myers and Majluf (1984) model is that if investment projects cannot be financed through self-financing and/or debt issuance, then there is a disincentive for the management and historical shareholders to undertake profitable new investment projects. There is consequent underinvestment that does not allow to create value. Venture capitalists are intermediaries who perform this function, especially in capital rationing contexts where bank credit or alternative means of financing is limited or excessively expensive. Capital rationing phenomena occur with greater frequency and intensity in a negative market situation, in which interest rates are low (to encourage economic recovery, taking advantage of the low bargaining

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power of workers, as not to feed inflationary spirals with the price–wage race). In the presence of credit crunch phenomena, the low-interest rate differentials between lending and borrowing, associated with an increase in the riskiness of loans (induced by the low growth economic cycle), represent a disincentive for banks to grant loans. Loans become unprofitable and characterized by potential non-performing loans that may affect their capital adequacy. b. Prospective evaluation of the target startup (venture-backed) and peculiarities of the cash flows of the startups. According to ISAE 3400, prospective financial information is based on assumptions about events that may occur in the future and possible actions by an entity. Following this reasoning, the prospective evaluation of the target startup turns out to be a fundamental step in the screening of the venture capitalist’s investment possibilities, aimed at a strong increase in the expected value of the investee startup. Expected capital gains must be: • Discounted at a rate that considers an adequate risk premium and incorporates the lack of marketability discount, typical of unlisted companies; • Suitable to include a possible terminal value of the investment, to be discounted to re-express it in current currency. The longer the initiative in an early-stage phase, the longer the cycle of the business model is extended. The expected remuneration of the target startup must be sufficiently high to ensure an adequate return for the venture capitalist, considering the possibility of failure of the investment, which in many cases involves a full write-off of the holding. Most companies in the first years of life generate negative financial flows, due to significant startup costs and consequent cash disbursement that anticipates revenues. There are some characteristics of intangible-intensive startups:

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• Monetary operating revenues tend to be modest or even nonexistent for an extended period (especially if investments are high and reach a threshold of profitability only in the medium or long term); • Monetary operating costs tend to be high, despite the use of outsourcing (which is limited to making them more flexible), and because of leasing fees; • The low level of indebtedness means that EBITDA is substantially in line with the operating loss, typical of the startup phases and destined to last for several years; • The greater propensity of stakeholders (directors, managers, and employees, etc.) to be paid through stock options decreases monetary operating costs and increases—with a time dilution—risk capital; • The presence of high fixed costs (which tend to increase as entry barriers go up) is accompanied by variable costs that are usually limited (once the breakeven is reached); • Taxes (operating and non-operating) tend to be low or zero, in the presence of negative taxable income (which generates a possibility of carrying forward losses to subsequent years); • The cash flow is negative, even significantly; • Trade receivables follow the trend of operating revenues and are modest or non-existent; • The warehouse is virtual and therefore does not absorb financial resources; • Suppliers follow the trend of operating costs; • Operating Net Working Capital tends to be negative and represents a source of funding; • The change in fixed assets (CAPEX) tends to be positive and significant for new investments; • The operating cash flow is usually negative and is financed by working capital and risky capital; • Financial debts are negligible since the risk profile is high; their size is associated with (limited) cash outflows for financial charges; • The residual cash flows attributable to the shareholders are normally negative, and shareholders often need to recapitalize the startup until the financial break-even is reached.

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Performance in private equity investing is traditionally measured via (i) the internal rate of return (IRR) which captures a fund’s time-adjusted return, and (ii) multiple of invested capital which captures return on invested capital (ROIC). IRR reflects the performance of a private equity fund by considering the size and timing of its cash flows (capital calls and distributions) and its net asset value at the time of the calculation. Internal Rate of Return for the shareholders (IRRequity ) expresses the rate that makes the Net Present Value equal to zero: I R Requit y = N P Vequit y =



n t=1

NC F − C0 = 0 (1 + r )n

(5.1)

where: NCF = Net cash inflow during the period t; C0 = initial investment costs; r = discount rate; t = number of periods. The Multiple on Invested Capital (MOIC) allows measuring the value generated by an investment. MOIC is a gross return traditionally calculated before fees and carry (share of any profits that the general partners of private equity receive as compensation): MOIC =

Reali zed V alue + U nr eali zed V alue T otal Amount I nvested

(5.2)

MOIC expresses a multiple of the initial investment; a ratio of 1.8 means that an initial investment of 100 has generated a final payoff of 180. Table 5.1 shows the cash flow generated by startups, with the most significant items shown in bold. The topic will be further analyzed in Sect. 8.5.

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Table 5.1 Operating and net cash flows in startups

Net monetary operating revenues net monetary operating costs (excluding depreciation and amortization) operating taxes = Cash flow of the operating area +/- Δ trade receivables +/- Δ stock +/- Δ suppliers and other current payables = change in operating net working capital +/- Δ assets net of amortization and depreciation EBITDA ±ȴNet Working Capital ±ȴCAPEX

= Operating cash flow (unlevered or debt-free cash flow) - financial charges net of financial income +/- Δ net financial liabilities +/- Δ shareholders' equity Net Cash flow (levered cash flow- Free cash flow to Equity)

5.5

The Valuation Framework

Asset Management companies can be independent investment companies or a part of a bank; they usually invest on behalf of their clients. This is one distinguished difference between asset management companies with other financial institutions such as commercial banks, investment banks, or insurers (Elliott, 2014).

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MorningStar defines investment management firms as “firms offering diversified services such as asset administration, investment advice, portfolio or mutual fund management, money management, venture capital, and investment research.” The three methods commonly used for the valuation of asset management firms are: 1. the discounted cash flows—DCF (Damodaran, 2013); 2. the multiples / rule of thumb (Bigelli & Manuzzi, 2019); 3. the dividend discount model—DDM (Damodaran, 2018; Joenväärä & Scherer, 2017), as illustrated in the following Fig. 5.5. DCF and multiples are the most used appraisal methods even for traditional firms, as shown in Chapter 8 (Fig. 5.6). These approaches are analogically referrable to the standard valuation approaches (Moro Visconti, 2020, chapter 2), with some specific adaptions (Borroni & Rossi, 2019; Huberman, 2006; Malkiel, 2013). Table 5.2 shows the principal factors that directly impact the valuation of those firms, including company-specific risk as well as relative valuation multiples (Elliott, 2014; Iannotta, 2010): Once the most suitable evaluation approach has been defined, it might be appropriate to use another evaluation approach, to double-check the evaluation carried out with the main approach (Fazzini, 2018). The use of a control approach is applied in all cases where it is possible to estimate the market value of the company from complementary angles

Fig. 5.5 Economic and financial performance of a venture capital

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Asset Management Firms ValuaƟon Methods

Dividend Discount Model (DDM) Discounted Cash Flows (DCF)

Market MulƟples Rules of Thumb

Fig. 5.6 Valuation methods of asset management firms

Table 5.2 Value drivers for asset management firms Driver

Description

Size Revenue Growth Revenue Source Client Demographics

Reaching scale is important Organic growth or market growth Commission-based or fee-based Client concentration, client tenure, new client ratio, client age Relationship of revenue to the owner of the asset management firm Compensation and expense management Number of employees, tenure, relationships with clients

Relationships Management Employee Demographics

to arrive at a range of values within which the market value must be positioned (Koller & Goedhart, 2015).

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5.6 The (Uneasy) Estimate of Cash Flows for Financial Companies The financial approach is based on the principle that the market value of the company is equal to the discounted value of the cash flows that the company can generate (“cash is king”). The determination of the cash flows is of primary importance, as is the consistency of the discount rates adopted (Singh, 2013). Damodaran (2013) highlights that it is not easy to estimate cash flows made by financial firms (not only asset management firms but also banks, insurance firms, etc.). Even if financial firms work in a regulated framework, their cash flows cannot be easily estimated, since items like capital expenditures, working capital, and debt are not clearly defined. Financial service firms are so best valued using equity valuation models, rather than enterprise valuation models. When we evaluate an asset management firm, it is also reasonable to assume that Free Cash Flows to Equityholders (FCFE) are proxied by net earnings because of a negligible level of investments, depreciation, and net working capital. Asset management firms are characterized by having quite high margins, although working in a highly competitive industry (Berk & Green, 2004). Joenväärä and Scherer (2017), and Bigelli and Manuzzi (2019), state that the net earnings by an asset management firm can be determined as the product of three components—the assets under management (AUM ), the ratio of fees on AUM (f ), and the earnings margin, given by the ratio of the net earnings: N et ear nings = AU M ∗ f ees/AU M ∗ N et ear nings/ f ees = AU M ∗ f ∗ q (5.3)

5.7

Applying DCF to Asset Management Firms

Huberman (2006) adapts the Discounted Cash Flow model (DCF) to asset management firms and concludes that the 2–4% P/AUM ratio (Price relative to Assets Under Management) at which asset management firms are usually traded is relatively low, as their value on AUM should be closer to the earnings margin (q), which is usually around a 20% value. His model is based on the following hypothesis:

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• net earnings equal the Free Cash Flow to Equity-holders; • assets under management have already reached a steady state. No money flows into or out from managed assets, except for the management fees that are debited yearly. All dividends and capital gains are reinvested in the managed mutual funds or the managed clients’ portfolios; • the level of fees on AUM is equal to “f ” as in formula [5.4]; • the assets under management, net of transaction costs, but gross of (management) fees, yield a yearly return equal to “r”; the discount rate of the cash flows, “R,” is equal to the return on the assets under management, “r.” Given the above hypothesis, using the more intuitive notation from Joenväärä and Scherer (2017), the Huberman model can be explained in the following way. Net earnings at the end of the first year can be defined as: N et ear nings1 = AU M ∗ (1 + r ) ∗ f ∗ q

(5.4)

While earnings at the end of a generic year “i” can be defined as: N et ear ningsi = AU M ∗ (1 + r )i ∗ (1 − f )i−1 ∗ f ∗ q

(5.5)

If the returns on the assets under management (r) are equal to the equity cost of capital of the firm (R): P = P V0 = AU M ∗ q ∗ [1 − (1 − f )n−1 ]

(5.6)

Besides, by considering an infinite valuation horizon (n = ∞), and rearranging the equation to have the P/AUM ratio on the left-hand side, the model can finally be reduced to the following result: P/AU M = q

(5.7)

Since the earning margin (q) is, on average, around 20%, while asset management firms are usually priced about 2–4% of AUM, Huberman (2006) concludes that they somehow quote at a discount. According to this model, the value seems to be insensitive to the level of fees because an increase in fees will increase the earnings in the short run at the expense of earnings in the long run. The two effects offset each other when the asset growth rate (gross of fees), “r,” is equal to the discount rate “R” (Bigelli & Manuzzi, 2019).

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However, the average return of assets under management should be lower than the equity cost of capital because assets under management are also composed of bonds and money market portfolios. Besides, the risk of equity of an asset management firm is anyway amplified by operational and regulatory risk. The equivalence between the perpetual return of the assets under management and the equity cost of capital of the firm is also identified as the main limitation of Huberman’s model by Joenväärä and Scherer (2017). They also argue that it cannot be assumed that the level of assets under management increases ad infinitum, as the investment industry suffers from a diseconomy of scale, as shown by Berk and Green (2004). Latzko (1999) also empirically reports that economies of scale in mutual fund administration vanish when about $3.5 billion in fund assets is reached. Joenväärä and Scherer (2017) therefore amend Huberman’s model. By assuming that the asset management firm has already reached its optimal size, they introduce the hypothesis that assets under management are constant over time, eliminating the previous assumption of a yearly gross revaluation at a yearly rate equal to “r.” In this way, by assuming that the level of fees and the net margin is also constant over time, the net earnings also become constant over time. The net earnings made in year i can, therefore, be expressed in the following way: N et ear ningsi = AU M ∗ f ∗ q

(5.8)

Assuming net earnings equal to the FCFE again, the present value of the discounted stream of future perpetual cash flows becomes as follows: P = AU M ∗ f ∗ q ∗ 1/R

(5.9)

If we express the final equation indicating the P/AUM on the lefthand side, the results become more easily comparable with those of the previous model, as in the following equation: P/AU M = f ∗ q ∗ 1/R

(5.10)

The Price/AUM ratio is, therefore, simply given by the present value of perpetuity whose perpetual cash flow is the product of the level of fees on AUM (f ) and the net margin (q). The model developed by Joenväärä and Scherer (2017) results in the valuations of asset management firms in line with the empirical ones

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observed on the market. If we take some hypothetical values not far from real ones and we set a level of fees equal to 1% of assets under management (f ), a net margin equal to 20% (q), and a discount rate equal to 6% (R), the resulting multiple P/AUM would be equal to 3%. This value is remarkably like the average one observed on the market and in the acquisitions of asset management firms (Zask, 2000; Constant, 2004).

5.8

Multiples and Rules of Thumbs

As Bigelli and Manuzzi (2019) point out, “academic literature has addressed how multiples can be used in firm valuation (Lie & Lie, 2002; Liu et al., 2002), how the selection of comparable firms can be relevant (Alford, 1992; Bhojraj & Lee, 2002), how the comparable company method should be adjusted for the value of corporate control (Finnerty & Emery, 2004), how emerging markets may have specifics factors affecting multiples (Farah Freihat, 2019), how some multiples can be combined to obtain a better estimate (Yoo, 2006), how firm’s value in different industries is better proxied by different multiples (Fidanza, 2010), and which multiples work better for banks’ valuation (Forte et al., 2018).” In the case of comparable companies, the approach estimates multiples by observing similar companies (Alford, 1992; Fidanza, 2010). The problem is to determine what is meant by similar companies. In theory, the analyst should check all the variables that influence the multiple. In practice, companies should estimate the most likely price for a nonlisted company, taking as a benchmark some listed companies, operating in the same sector, and considered homogeneous. Two companies can be defined as homogeneous when they present, for the same risk, similar characteristics, and expectations (Lie & Lie, 2002). According to widespread estimates (Fernandez, 2001; Yoo, 2006), the main factors in establishing whether a company is comparable are: • • • •

Size; Belonging to the same sector; Financial risks (leverage); Historical trends and prospects for the development of results and markets; • Geographical diversification; • Degree of reputation and credibility;

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• Management skills; • Ability to pay dividends. The multiple can be reduced by a proper percentage to consider the non-perfect comparability of the sample in terms of activity, location, and turnover (size discount) and the non-listing of a company (illiquidity discount) (Damodaran, 2005). Empirical approaches will be more comprehensively analyzed in Sect. 8.6. The calculation is: • A company whose price is known (P 1 ); • A variable closely related to its value (X 1 ). The ratio (P 1 )/(X 1 ) is assumed to apply to the company to be valued, for which the size of the reference variable (X 2 ) is known. Therefore: (P1 )/(X 1 ) = (P2 )/(X 2 )

(5.11)

so that the desired value P2 will be: P2 = X 2 [(P1 )/(X 1 )

(5.12)

For the valuation of the Asset Management firms, the multiples commonly used are the ratios EV/AuM and EV/Revenues, considering that the most diffuse ratio EV/EBITDA (examined in Sect. 3.13) would be supposedly dependent on the different accounting policies used. Besides those multiples, there is another empirical method, the rules of thumb, which are a short-cut way to arrive at a value, i.e., the “average” firm in the industry is valued at two times revenues or 5 times cash flow. Rules of Thumb usually fail to consider (among other items): 1. Differences in effective management fees; 2. Profitability; 3. Differences in growth rates; 4. Quality of AUM, clients. As a result, firms of above-average quality can be undervalued, while firms of below-average quality can be overvalued.

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5.9

The Dividend Discount Model

The dividend discount model (DDM), examined in Sect. 4.8, is a method of valuing a company’s stock price based on the theory that its stock is worth the sum of all its future dividend payments, discounted back to their present value (Farrell, 1985). In other words, it is used to value stocks based on the net present value of future dividends. The model simply discounts cash flows at a given rate, just like any other DCF model. The difference lies in the fact that dividend discount models consider only “dividends” as being legitimate cash flows. Therefore, if a firm pays no dividends at all, this model cannot be applied to the firm regardless of how profitable or cash flow efficient its operations are. According to Damodaran (2013), many analysts accept the reality that estimating cash flows for financial service firms is not feasible and fall back on the only observable cash flow—dividends. Analysts are implicitly assuming that the dividends that are paid out are sustainable and reasonable. The focus on current dividends can also create problems when valuing financial service firms that have growth potential. If we start with the assumption that equity in a publicly-traded firm has an infinite life, we arrive at the most general version of the dividend discount model: V alue per Shar e o f Equit y =



∞ t=1

D P St (1 + ke )t

(5.13)

where DPSt = Expected dividend per share in period t k e = Cost of equity. In the case where the expected growth rate in dividends is constant forever, this model collapses into the Gordon Growth model. V alue per Shar e o f Equit y in Stable Gr owth =



∞ t=1

D P S1 (5.14) (ke − g)

In this equation, g is the expected growth rate in perpetuity, and DPS1 is the expected dividends per share next year. In the more general case, where dividends are growing at a rate that is not expected to be sustainable or constant forever during a period (called the extraordinary growth period), we can still assume that the growth rate will be constant forever.

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The cost of equity must reflect the portion of the risk in the equity that cannot be diversified away by the marginal investor in the stock. There is an inherent trade-off between dividends and growth. When a company pays a larger part of its earnings as dividends, it is reinvesting less and should thus grow more slowly. With financial service firms, this link is reinforced by the fact that the activities of these firms are subject to regulatory capital constraints (Damodaran, 2013).

5.10

Pros and Cons of the Valuation Methods

The three valuation methods previously illustrated (DCF, multiples, and DDM) need proper adaptions for their applications to the Asset Management firms, and each of them has its strengths as well its weakness. Table 5.3 synthetically shows the principal pros and cons of the aboveindicated valuation methods of Asset Management firms: Table 5.3 Strengths and weakness of valuation methods of asset management firms Method

Strengths

Weaknesses

DCF

The value of a firm ultimately derives from the inherent value of its future cash flows (“Cash is king”) Not influenced by depreciations / capitalisations policies Easy to use (“quick and dirt”)

Reliability of the future cash flows estimation

Multiples

Few parameters DDM

DCF strengths Dividends appear as the more objective cash flows for Asset Management firms

Subjectivity of the discount rates determinations Fairness of the selected multiples / comparables Accuracy of the results (rule of thumbs) Current dividends ignore the growth potential Implicitly assumes that the dividends paid out are sustainable and reasonable

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References Alford, A. W. (1992). The effect of the set of comparable firms on the accuracy of the price-earnings valuation method. Journal of Accounting Research, 30(1), 94–108. Avdeitchikova, S., Landström, H., & Månsson, N. (2008). What do we mean when we talk about business angels? Some Reflections on Definitions and Sampling, Venture Capital, 10(4), 371–394. Benftsson, O., & Sensoy, B. A. (2011). Investor abilities and financial contracting: Evidence from venture capital. Journal of Financial Intermediation, 20(4), 477–502. Berk, J. B., & Green, R. C. (2004). Mutual fund flows and performance in rational markets. Journal of Political Economy, 112(6), 1269–1295. Bhojraj, A., & Lee, C. M. C. (2002). Who is my peer? A valuation-based approach to the selection of comparable firms. Journal of Accounting Research, 40(2), 407–439. Bigelli, M., & Manuzzi, F. (2019). The valuation of asset management firms. Corporate Ownership & Control, 16(4), 103–110. Borroni, M., & Rossi, S. (2019). Bank profitability: Measures and determinants. In Banking in Europe. Palgrave Macmillan Studies in Banking and Financial Institutions. Cham: Palgrave Pivot. Cavallo, A., Ghezzi, A., Dell’Era, C., & Pellizzoni, E. (2019). Fostering digital entrepreneurship from startup to scaleup: The role of venture capital funds and angel groups. Technological Forecasting and Social Change, 145, 24–35. Constant, M. I. (2004). Brokers and asset managers. September Quarter Broker/Investment Bank Earnings Preview. Lehman Brothers Report. Damodaran, A. (2005). Marketability and value: Measuring the illiquidity discount. Available at http://people.stern.nyu.edu/adamodar/pdfiles/pap ers/liquidity.pdf. Damodaran, A. (2013). Valuing financial service firms. Journal of Financial Perspectives, 1, 59–74. Damodaran, A. (2018). The dark side of valuation. Pearson FT Press PTG. Davila, A., Foster, G., & Gupta, M. (2003). Venture capital financing and the growth of startup firms. Journal of Business Venture, 18(6), 689–708. Diller, C., Herger, I., & Wulff, M. (2009). The private equity J-Curve: Cash flow considerations from primary and secondary points of view. Investing in private equity. Available at https://www.capdyn.com/Customer-Content/ www/news/PDFs/the-private-equity-j-curve_private-equity-mathematics_ apr-09__2_.pdf. Elliott, D. (2014). Systemic risk and the asset management industry. Available at http://www.brookings.edu/~/media/research/files/papers/2014/05/ systemic%20risk%20asset%20management%20elliott/systemic_risk_asset_mana gement_elliott.pdf.

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Everett, C. R., & Casparie, J. (2018). Equity investment by startup board members can attract new capital. Graziadio Business Review, 21(1). Farah Freihat, A. R. (2019). Factors affecting price to earnings ratio (P/E): Evidence from the emerging market. Risk Governance and Control: Financial Markets & Institutions, 9(2), 47–56. Farrell, J. L. (1985). The dividend discount model: A primer. Financial Analyst’s Journal, 41(6), 16–25. Fazzini, M. (2018). Business valuation: Theory and practice. Cham: Palgrave MacMillan. Fenn, J. (2007). Understanding Gartner’s Hype cycles. Available at https://www. gartner.com/en/documents/509085. Fernandez, P. (2001). Valuation using multiples: How do analysts reach their conclusions? Madrid: IESE Business School. Fidanza, B. (2010). The valuation by multiples of Italian firms. Corporate Ownership & Control, 7 (3–1), 228–241. Finnerty, J. D., & Emery, D. R. (2004). The value of corporate control and the comparable company method of valuation. Financial Management, 33, 91–99. Forte, G., Gianfrate, G., & Rossi, E. (2018). Does relative valuation work for banks? Global Finance Journal, 1–25. Harris, R. S., Jenkinson, T., & Kaplan, R. (2014). Private equity performance: What do we know? Journal of Finance, 69(5), 1851–1882. Huberman, G. (2006). Is the price of money managers too low? (Working Paper). Columbia Business School. Iannotta, G. (2010). Investment Banking. Cham: Springer. Jenkinson, T., Morkoetter, S., & Wetzer, T. (2018). Buy low, sell high? Do private equity fund managers have market abilities? (Working Papers on Finance 1813). University of St. Gallen, School of Finance. Jeong, J., Kim, J., Son, H., & Nam, D. I. (2020). The role of venture capital investment in startups’ sustainable growth and performance: Focusing on absorptive capacity and venture capitalists’ reputation. Sustainability, 12, 3447. Joenväärä, J., & Scherer, R. (2017). A note on the valuation of asset management firms. Journal of Financial Markets and Portfolio Management, 31(2), 181– 199. Jun-Koo, K., Yingxiang, L., & Oh Seungjoon, O. (2020, September 21). Venture capital coordination in syndicates, corporate monitoring, and firm performance. https://ssrn.com/abstract=3216018. Koller, T., & Goedhart, M. (2015). Valuation: Measuring and managing in the value of companies. McKinsey & Company. Latzko, D. A. (1999). Economies of scale in mutual fund administration. Journal of Financial Research, 22(3), 331–339.

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CHAPTER 6

Early-Stage and Debt-Free Startups

6.1

Cash is King

Cash represents for companies what blood means for the human body. Whenever its circulation or amount dries up, the firm rapidly decays, eventually facing death. “Monetary transfusions” can keep it alive in the meantime, if it is possible to find available “blood” from equity-holders. Consistently with this statement, liquidity can be measured (in accounting, economic, and financial terms), and then estimated in a debt-free environment, typical of early-stage startups (Laitinen, 2017). Traditional banks are typically reluctant to fund vulnerable startups with what they perceive as unproven ideas until they have passed through market testing. Moreover, though there are numerous nontraditional startup financing options, not all are suitable in all situations. Scalable volumes, albeit appealing, are insufficient to guarantee survival, unless backed by appropriate economic marginality and consequent liquidity generation. To the extent that startup valuation is often based on Discounted Cash Flows, the forecast of its liquidity is a prerequisite for appraisal. The risk that the real liquidity may be different from the expected one needs to be fairly incorporated in the cost of capital used to discount the risky cash flows. Forward-looking valuations should never underestimate the importance of liquidity, remembering that “cash is king” and that … all roads bring to cash. Wise valuations cool down irrational expectations, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_6

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avoiding to back exuberance, and may deflate prices after a pumped listing of promising startups. Within this framework, this chapter considers some basic accounting and corporate finance issues, reinterpreting them consistently with the research target, based on the liquidity estimate of equity-backed startups.

6.2 The Integrated Economic, Financial, and Balance Sheet Accounting System Startups are normally debt-free since they have little if any collateral value of their assets and they produce negative cash flows, especially in the first years of their existence. Consequently: • in the balance sheet raised capital (funds) tend to coincide with equity; • in the income statement, EBIT is similar to the net result (considering that interest rates are non-existent, and taxes also, due to a negative tax base); • in the cash flow statement, the operating cash flow tends to coincide with the net cash flow; • In the absence of the cost of debt, the cost of capital (WACC) coincides with the cost of equity. The difference between a traditional firm and a startup economic, financial, and balance sheet system can be represented in Fig. 6.1.

6.3

Cash Flow Metrics

In financial accounting, a cash flow statement (already examined in Sect. 2.4), is a financial statement that shows how changes in balance sheet accounts and income affect liquidity and break the analysis down to operating, investing, and financing activities. Essentially, the cash flow statement is concerned with the flow of cash in and out of business. The statement captures both the current operating results and the accompanying changes in the balance sheet. As an analytical tool, the statement of cash flows is useful in determining the short-term viability of a startup, particularly its ability to pay bills. The International Accounting Standard 7 (IAS 7) deals with cash flow statements.

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ȴ Fixed Assets (CAPEX) ȴ Equity and Quasi-Equity

Cost of Equity = WACC (being debt = 0)

ȴ OperaƟng Net Working Capital ȴ Liquidity Invested Capital = Raised Capital = Equity Value = Enterprise Value

Income statement Operating monetary revenues - operating monetary costs (monetary OPEX) = EBITDA - amortization, depreciation, provisions and writedowns = EBIT +/- balance of extraordinary operations = Pre-Tax Result - (taxes, if any) = Net result (similar to EBIT and Pre-Tax result)

Cash flow statement EBIT + amortization, depreciation = EBITDA +/- Δ operating net working capital +/- Δ fixed assets = Operating cash flow (unlevered) +/- extraordinary income/expense - (taxes, if any) +/- Δ shareholders contributions in kind +/- Δ shareholders’ equity = Net Cash Flow

Fig. 6.1 Interactions of income statement and variations of the Balance Sheet to Produce the Cash Flow Statement in a Debt-free startup

Monitoring the cash situation of any business is the key. The income statement would reflect the profits but does not give any indication of the cash components. Like the other financial statements, the cash flow statement is also usually drawn up annually but can be drawn up more often. It is noteworthy that the cash flow statement covers the flows of cash over a period (unlike the balance sheet that provides a snapshot of the business at a particular date). Also, the cash flow statement can be drawn up in a budget form and later compared to actual figures. Liquidity derives from: • EBITDA (initially negative and so cash-absorbing but with high increase potential);

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• Change in operating net working capital (sales growth is fueled by the cash-absorbing expansion of receivables and stock [wherever present, e.g., not in FinTechs], partially counterbalanced by the cash-generating increase in payables); • Change in the CAPEX (net of cashless depreciation /amortization) • Equity and quasi-equity injections (considering only liquidity cashed in, and not a cashless contribution in kind).

6.4

From Contacts to Contracts: Budgeting, Sale Forecasting, and Market Traction

Sales forecasting is the primary and first value driver to consider, as it generates the revenues (Bednar et al., 2018) that, net of monetary operating expenditure (OPEX), form the EBITDA. Business planning activities pivot around the income statement forecasts. What mostly matters is the forecast of the “upper part” of the income statement (from the sales to the EBIT), since debt service costs do not exist, and taxation is limited or completely offset by carrying of losses. Revenue forecasting impacts on: • Economic and financial marginality (monetary EBITDA, etc.); • Net working capital; • CAPEX necessity. Revenues are output factors made possible by input parameters like: 1. The raised capital (equity); 2. The invested capital (fixed assets /CAPEX and Operating Net Working Capital); 3. Operating costs (e.g., purchases and salaries to produce sales). Business traction refers to the progress of a startup and the momentum it gains as the business grows. When traction is lacking, sales dry up, the churn rate grows, and the customer base dwindles, regardless of the effort put into the enterprise. Traction is the rate at which a business model captures monetizable value from its users.

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Questions may be formulated as follows: Is the startup targeting—and solving—a real market need? Which is its competitive advantage (what is new in its business model?). The passage from sales volumes to economic and financial margins is delicate but crucial, as it is cash generating (absorbing) element within the startup.

6.5 Scalability Drivers, Growth Opportunities, and Real Options Scalability is a crucial growth driver for any business, and intangibledriven startups. The income statement of the startup must consider the accounting parameters that influence the operating leverage: • Volumes and margins of the revenues, identifying if they are recurrent, and subject to potential blitzscaling; • The mix between fixed and variable operating expenditure (OPEX), with a further distinction between monetary and non-monetary OPEX. The classification may be the following (Table 6.1). According to Moro Visconti (2020, Chapter 3), scalability indicates the ability of a process, network, or system to handle a growing amount of work. Scalability fosters economic marginality, especially in intangibledriven businesses where variable costs are typically negligible. Massive volumes may offset low margins, producing economic gains. Digitalization is defined as the concept of “going paperless”—the technical process of transforming analog information or physical products into digital form. Table 6.1 From sales to EBIT

sales • Variable (monetary) OPEX = contribution margin • Fixed (monetary) OPEX = EBITDA → monetary income • Depreciation and amortization = EBIT

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Digital scalability operates in a web context, where networked agents interact to generate co-created value. Economic and financial margins that represent a primary parameter for valuation are boosted by cost savings and scalable increases in expected revenues. Digitalized intangibles synergistically interact through networked platforms that reshape traditional supply chains.” Growth opportunities incorporate real options (to expand, defer, abandon the business …) that make the overall business plan more flexible, improving the resilience of the supply and value chain. Digitalization (examined in Chapter 12) is a further component, intrinsically embedded in the business model of most digitally born startups.

6.6

Sales-Driven Net Working Capital

Sales are also related to the operating net working capital components, as they influence stock and receivables. Sales are also output factors depending on input purchases that influence payables. It may so be argued that the Operating Net Working Capital is a function of the sales. The following ratios can be used to interpret and forecast the expected outcome of the working capital (Table 6.2). A further input factor of sales is represented by CAPEX. Liquidity forecasts and occurrences are typically negative in the startup phase, with the absorption of cash that is mainly due to a negative EBITDA, and a CAPEX increase (due to the investments necessary to Table 6.2 Turnover ratios

Inventory turnover Inventory Turn-Days Accounts Receivable Turnover Average Collection Period Accounts Payable Turnover Average Payment Period

Cost of goods sold/Inventory 360/Inventory Turnover Sales/Accounts Receivable 360/Accounts Receivable Turnover Cost of Goods Sold/Account Payable 360/Accounts Payable Turnover

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startup the firm). The operating net working capital may be less significant, but it usually grows (so absorbing cash) when sales boost. The intervention of the shareholders is so periodically necessary to keep a cash equilibrium, avoiding both cash and equity burnout. Debt capacity indicators like the debt service cover ratio are obviously meaningless in debt-free startups. Startups often produce negative operating cash flows, at least in the first years, due to a combination of their composing factors (EBITDA, cash-absorbing increase in operating Net Working Capital, and CAPEX). Whenever this happens, they sooner or later face a cash burnout, under which all the liquidity dries up. Waiting for cash-generating income (that needs to be financed by investments and payments that keep a going concern), startups need a second round of equity injections, to the extent that residual equity is still there. Otherwise, a cash burnout is accompanied (or sometimes even preceded) by an equity burnout. Whenever the cash burnout is accompanied by an equity burnout, refinancing—issuing new equity, sometimes allowing new shareholders to join the firm—is the only manageable option. Its timing is crucial for success, also considering the prevailing market conditions and the willingness of the shareholders to fund the firm again. If the “winter of capital” prevails and the startup is unable to trespass its “Death Valley,” liquidation is the only option left. The pecking order theory (examined in Sect. 11.4) states that firms first rely on self-financing (i.e., EBITDA), and then issue debt and eventually raise equity. The second component of this hierarchical chain is, however, missing. Startups so rely on self-financing and equity. Agency costs and corporate governance concerns follow this pattern, being simplified to the primary relationship between managers and shareholders that often tend to coincide, at least partially. Equity crowdfunding makes this occurrence less likely, to the extent that crowdfunders are many, and managers just represent a subset of the shareholders.

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6.7

OPEX and CAPEX

A reported in Moro Visconti (2020, Chapter 3) “Capital expenditures (CAPEX) and operating expenses (OPEX) represent two complementary categories of business expenses, deriving from the capital and operating budgets which are created by companies to support growth and adjust resources. Capital budgets cover capital expenses, which are capitalized and appear as long-term assets on the balance sheet. CAPEX corresponds to the amounts that companies use to purchase long-term assets as primary physical goods or services that will be used for more than one year. These assets may be physical (plant, equipment, property, and vehicles, etc.) or represented by intangibles that are not directly expensed in the income statement. As these long-term assets depreciate over their useful lives, the depreciation for a given year shows up on the income statement as a non-monetary expense in that year. Therefore, CAPEX is subject to depreciation (with a linear methodology in regular installments over the useful life or with an impairment test, whenever applicable). OPEX refers instead to the ordinary costs for a startup to run its daily business operations (purchases; salaries; rents; sales, general, & administrative expenses; property taxes, etc.). OPEX can be divided into monetary OPEX + depreciation/amortization.” The accounting treatment of intangibles—which represents a core component of the startup investments—is often controversial, with a dilemma between expensed or capitalized costs. This uneasy choice is, however, irrelevant for the assessment of the startup liquidity since these operating costs affect the operating cash flow anyway. If they are recorded in the income statement, they are represented by: • Monetary OPEX (e.g., purchases of goods and services, payroll, etc.) that absorbs liquidity; • Non-monetary OPEX (depreciation and amortization) that is subtracted from CAPEX (capital expenditure variations, net of depreciation/amortization). If they are capitalized and so recorded within the assets in the balance sheet, their “liquid” cost (that occurs when the asset is purchased) is partitioned (and then depreciated) within the useful life of the asset, along several years.

6

Table 6.3 From EBITDA to operating cash flows

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Monetary Revenues (sales) • Monetary OPEX = EBITDA ±  CAPEX (net of depreciation) ±  Operating Net Working Capital = Operating Cash Flow (≈ Net Cash Flow if the debt is irrelevant)

It might be remembered that the operating (debt-free or unlevered) cash flow can be calculated as follows (Table 6.3). Whenever costs are capitalized, they do not affect the EBITDA since they are not recorded within the monetary OPEX, and they increase the CAPEX (in the year of occurrence). Any CAPEX increase absorbs liquidity and so affects the Operating Cash Flow exactly like a recording of the cost in the income statement. In the following years, the CAPEX variation is expressed net of the depreciation/amortization that, however, represent a non-monetary item. The variation of the Capex derives from the comparison between two consecutive years, net of the depreciation, as exemplified in Table 2.7.

6.8

Monetary Equity

Net equity, as anticipated, represents the total raised capital in any debtfree startup. The balance sheet representation is in Fig. 6.2. Monetary equity has a temporal dimension, and it may be subdivided in: • “immediate” monetary equity, considering in the assets only the liquidity; • Short-term (“net working”) monetary equity, also including the liquidity that is going to be generated (and absorbed) by the evolution of the operating net working capital (cashed in receivables, paid out payables, etc.). The concept of monetary equity allows bypassing the controversial accounting treatment of intangibles that include capitalized costs. Work-for-equity is common in startups that wish to mix retention policies with monetary savings. The promised working effort from workers

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liquidity Monetary Equity OperaƟng Net Working Capital (receivables + stock - payables) (Tangible) fixed assets

Tangible Equity

equity

Financial Assets

Intangibles

Intangible Equity

Fig. 6.2 Book, monetary, tangible and intangible equity

(especially for skilled co-operators) may be paid in kind with a capital increase. Apart from the legal issues and the necessity to estimate this contribution to avoid any unjustified equity dilution, what matters here is the financial issue. Since work-for-equity is a cashless contribution, it does not have any immediate impact on monetary equity. Nevertheless, it can bring to future monetary OPEX savings, if (monetary) staff cost is replaced by dedicated equity increase. The input/output cycle that starts from equity and returns to its remuneration can be synthesized in Fig. 6.3. This representation is similar to the “levered” Fig. 7.1. Monetary equity can be underwritten by many complimentary shareholders, ranging from founding partners to business angels, up to venture capital (Davila et al., 2003) or private equity funds (as shown in Chapter 5; see also Braun, 2009). Belz (2020) shows that debt and a related instrument, the Simple Agreement for Future Equity (SAFE), are used successfully in both channels as early-stage vehicles, but the selection and funding outcomes differ. Equity crowdfunding investors strongly prefer the SAFE to a traditional debt instrument.

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Fig. 6.3 The financing and investing cycle 1 Funds acquisition of capital (equity), 2 Funds investment in net working capital and fixed assets (invested capital), 3 Generation of operating NOPAT (funds applications in net working capital and fixed assets →sales →operating NOPAT), 4 Operating NOPAT generates operating cash flows to payback investors (shareholders)

Oranburg (2015) points out that startups should obtain financing from professional investors first, and then turn to crowdfunding. Bridgefunding takes crowdfunding to its logical conclusion, allowing crowdfunding to act as a means by which to bridge the funding gap, and thus occupy a valuable niche in the startup investment market.

6.9

Runway Cash Planning

“Cash runway“ refers to the length of time in which a startup will remain solvent, assuming that they are unable to raise more money. In short, a cash runway is the amount of time the startup can operate at a loss before running out of money. For example, a startup reveals that they have a “cash runway“ until the middle of 2020. This means that they expect to have enough money to fund operations until the middle of 2020. At this time, they will likely need to raise capital to keep the business running. Cash runway is particularly essential for startups who have received funding to monitor closely. In most cases, startups are not immediately profitable and expect to burn through their funding over a specified period when they will either expect to start turning a profit (Havard, 2018) or seek another round of funding.

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The formula is: Cash RunWay=

Total Cash Burn Rate

(6.1)

So, if a startup has $10 million in cash and is burning through $2 million per month, they will have a cash runway of five months before they would have to raise more money. An example of runway cash flow is the following (Table 6.4; Fig. 6.4). Forecast of runway cash planning can usefully consider sensitivity and scenario analysis, where various possible states of the world are considered and weighed, in probability terms. Expectations can be incorporated in either deterministic or stochastic models (Moro Visconti et al. 2018). Table 6.4 Cash flow runway

Survival day Weeks from today End of month balance Cash Flow Runway 31-03-20 30-04-20 31-05-20 30-06-20 31-07-20 31-08-20 30-09-20 31-10-20

30-09-2020 26

1,92,500 e 1,65,100 e 1,07,800 e 95,600 e 35,600 e 25,600 e −34,400 e 1,65,600 e

30-11-20 31-12-20 31-01-21 28-02-21 31-03-21 30-04-21 31-05-21

1,31,200 e 96,800 e 62,400 e 28,000 e −6,400 e −40,800 e 24,800 e

30-06-21

24,800 e

31-07-21

34,800

Milestones

progressive losses monthly loss +200,000 equity injection net of Monthly loss

progressive loss e 34,400 per month +100,000 equity injection net of Monthly loss monthly breakeven monthly profit e 10,000

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Fig. 6.4 Cash runway and equity refinancing

Liquidity projections also need to incorporate seasonality factors that in many industries are relevant. This consideration must be consistent with the business model and the underlying market where the startup operates. Some months are more expensive than others. Runway length and cash out date should conveniently be updated in real-time, interpreting timely big data that represent the basic informative input factor, to be stored in the cloud, possibly validated with blockchains, and processed with artificial intelligence patterns. Continuous update and consequent reformulation of the business and cash plan reduces the volatility of the startup returns, with marking-tomarket adaptation. To the extent that the difference between expectations and reality reduces, risk also softens, minimizing the cost of (equity) capital.

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6.10 The Winter of Capital: Matching Cash Burnout with Monetary Equity Burnout, and Bridge Financing Whenever a cash burnout occurs, and the managers realize that this is not a temporary seasonality and it is not going to be recovered soon by incoming liquidity, they must use the monetary equity (the shareholders’ cash available within the startup) to refinance the firm. Equity refinancing (kicker) provides survival liquidity, either using the monetary equity available or with new monetary equity injections. This process involves the cash flow statement. As an example, the following case should be considered (Table 6.5). Any capital increase might either involve only the historical (existing) shareholders or open the capital to newcomers, even with crowdfunding options. In the latter case, a share premium account may be considered if the startup, despite its cash- and equity- burnout, incorporates implicit goodwill (in the form of future growth opportunities). This should be consistent with an updated business plan that the new shareholders accept as a starting point to fix their entry price. In some cases, negotiations may become flexible, foreseeing earn-out or real options. Table 6.5 Impact of the monetary equity injections on the net cash flow Before equity intervention Monetary Revenues (sales) • Monetary OPEX = EBITDA ±  CAPEX (net of depreciation) ±  Operating Net Working Capital = Operating Cash Flow (≈ Net Cash Flow if debt is irrelevant) + (existing) monetary equity + new monetary equity injection = Net Cash Flow

After equity intervention

+100 −120 −20 −10 −5 = +35 +15 +50 = −20

= +30

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Should the startup be unable to collect additional funding to overcome its Death Valley phase, it might be forced to transform runway cash burnout to … a “run-away” option. In such a case, the firm might face liquidation with a fire sale of the assets. The absence of debt should shelter the startup from bankruptcy if it is still able to pay its current operating costs (payroll, etc.). Whenever shareholders are willing to fund again the startup but want to avoid a time-consuming capital increase, they may underwrite bridge financing to extend the cash runway. Loans from shareholders formally represent a financial debt that increases leverage. In practice, any payback is conditional to the availability of enough free cash flow. For this reason, shareholder loans belong to the “quasi-equity” section of the balance sheet. When the startup has positive liquidity, it may start paying periodical interests on the loan. Interests may be considered a sort of shadow dividend.

6.11

Conclusion

Liquidity needs to be a significant survival concern for any firm. This elementary concept also applies to startups, even if they are debt-free. In this case, they need to be backed by monetary equity that provides the necessary liquidity until the startup reaches a cash flow breakeven. If it does not, and the shareholders are unwilling to sponsor it again, the firm passes from a going concern to a breakup scenario, needing to be wound up. The absence of financial debts may soften the liquidation criticalities. Liquidity forecasts and consequent runway cash flow estimates are crucial not only for the short-term survival of the startup but also for its not ephemeral development. Cash flows are intrinsically challenging to estimate, representing a conundrum for evaluators. These criticalities may be softened with a continuous reformulation of forecasting, based on timely evidence of the outstanding cash flows. A model that incorporates big data in the cash projections is so essential, and its IT characteristics are consistent with the digital features that most startups incorporate in their business models. Startup valuation (Achleitner, 2005; Aggarwal et al., 2009; Batista de Oliveira & Perez Zotes, 2018; Burger & Kohn, 2017; Charsios et al., 2016; Damodaran, 2018; Festel et al., 2013; Halt et al., 2017; Hering et al., 2006; Kohn, 2017; Koller & Goedhart, 2015; Miloud et al., 2012; Nasser, 2016; Polimenis, 2018; Sassi, 2016; Sokol, 2018;

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Venture Valuation, 2019; Trichkova & Kanaryan, 2015) is often linked to the uneasy estimate of its discounted cash flows (Moro Visconti, 2020, Chapter 6). Liquidity forecasts are so essential even in this crucial aspect that backs the shareholders’ expectations and motivates their willingness to underwrite monetary equity.

References Achleitner, A. K. (2005). First Chicago method: Alternative approach to valuing innovative startups in the context of venture capital financing rounds. Betriebswirtschaftliche Forschung Und Praxis, 57 (4), 333–347. Aggarwal, R., Bhagat, S., & Rangan, S. (2009). The impact of fundamentals on IPO valuation. Financial Management, 38(2), 253–284. Batista de Oliveira, F., & Perez Zotes, L. (2018). Valuation methodologies for business startups: A bibliographical study and survey. Brazilian Journal of Operations & Production Management, 15(1), 96–111. Bednar, R., Tariskova, N., & Zagorsek, B. (2018). Startup revenue model failures. Montenegrin Journal of Economics, 14(4), 141–157. Belz, A. (2020, April 20). Terms of endearment: Financing terms for deep technology startups on a crowdfunding platform. https://ssrn.com/abstract=363 1931. Braun, R. (2009). Risk of private equity fund-of fund investments—a detailed cash flow-based approach. SSRN Electronic Journal. Burger, E. S. C., & Kohn, A. (2017). Exploring differences in early-stage startup valuation across countries. Academy of management proceedings, 1. Charsios, G., Moutafidis, K., & Foroglou, G. (2016). Valuation model for internet-of-things (IoT) startups. Conference Paper. Damodaran, A. (2018). The dark side of valuation. Pearson FT Press PTG. Davila, A., Foster, G., & Gupta, M. (2003). Venture capital financing and the growth of startup firms. Journal of Business Venture, 18(6), 689–708. Festel, G., Wuermseher, M., & Cattaneo, G. (2013). Valuation of early-stage high-tech startup companies. International Journal of Business, 18(3). Halt, G. B., Donch, J. C., Stiles, A. R., & Fesnak, R. (2017). Valuing startup companies. In: Intellectual property and financing strategies for technology startups. Cham: Springer. Havard, E. (2018). Internet startups’ profit dilemma: A theoretical paper on using two-sided markets theory as a framework in a valuation setting. The Arctic University of Norway. Hering, T., Olbrich, M., & Steinrucke, M. (2006). Valuation of startup internet companies. International Journal of Technology Management, 33(44), 406– 419.

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Kohn, A. (2017). The determinants of startup valuation in the venture capital context: A systematic review and avenues for future research. Springer Link, 68, 3–36. Koller, T., & Goedhart, M. (2015). Valuation: Measuring and managing the value of companies. McKinsey & Company. Laitinen, E. K. (2017). Profitability ratios in the early stages of a startup. The Journal of Entrepreneurial Finance, 19(2), 1–28. Miloud, T., Cabrol, M., & Aspelund, A. (2012). Startup valuation by venture capitalists: An empirical study. Venture Capital, Taylor and Francis. Moro Visconti, R. (2020). The valuation of digital intangibles: Technology, marketing and internet. Cham: Palgrave Macmillan. Moro Visconti, R., Montesi, G., & Papiro, G. (2018). Big data-driven stochastic business planning and corporate valuation. Corporate Ownership & Control, 15(3–1), 189–204. Nasser, S. (2016). Valuation for startups—9 methods explained. ICT Strategic Consulting. Oranburg, S. C. (2015). Bridgefunding is crowdfunding for startups across the private equity gap. SSRN Electronic Journal. Polimenis, V. (2018). Valuation issues with early equity finance. Hephaestus Research Repository, NUP Academic Publications, School of Economic Sciences and Business. Sassi, R. (2016). An improved valuation method for startups in the social-media industry. RUN Nova School of Business and Economics (NSBE). Sokol, M. (2018). What drives the magic of startups? People & Strategy, 4. Trichkova, R., & Kanaryan, N. (2015). Startups valuation: Approaches and methods. Paper presented at 1st Balkan Valuation conference “Best valuation practices”, 19–21, Sofia, Bulgaria. Venture Valuation. (2019). Valuation methods. Available at https://www.ventur evaluation.com/en/methodology/valuation-methods.

CHAPTER 7

Leveraging Startup’s Development with Debt

7.1

Transition from a Debt-Free to a Levered Startup

The dynamics of capital structure refer to ways in which a business finances its overall operations and growth over time, as needs for different sources of funds—such as debt, equity, or a combination of the two— differ in progressive stages of its development. In their attempts to raise debt and equity, new and small firms face different challenges than publicly held firms do. Startup capital, as well as future capital injections, are crucial to firm survival (Cotei & Farhat, 2017). Hanssens et al. (2016) find out that the debt policy of entrepreneurial firms is remarkably stable over time. The debt policy in the initial year of operation is a very important determinant of future debt policies, even after controlling for traditional contemporaneous determinants. The founder-CEO has an important impact on the stability of debt policies: the influence of initial debt policies on future debt policies is significantly reduced when the founder-CEO is replaced or when (s)he dies. When the startup solves (or at least, softens) the initial criticalities that obstacle debt issuing, it can start being financed (Huynh et al., 2012; Tech, 2018) by the banks or other external financial intermediaries. Introducing financial debts worsens the financial leverage (debt/ equity) and other ratios of the startup and, ceteris paribus decreases the net result due to the financial charges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_7

161

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The borrowers will be expected to make monthly payments against both the interest and principal amounts. In many cases, the lender will demand collateral guarantees on sizeable assets that the startup progressively stores in its CAPEX. The presence of active shareholders, like venture capital or private equity funds, might help, both for their relational ties and their belonging, in some cases, to financial institutions that may switch from the equity to the debt side. Figure 7.1 represents a variety of Fig. 6.3, introducing debt. The cycle of funds can be summarized in the following figure. Startup raises funds (capital and debt) from investors (debtholders and shareholders); funds raised are invested in fixed assets and net working capital, which generates an operating result and then the cash flows for investors. Whenever the startup becomes levered, conflicts of interest between shareholders and managers are extended to debtholders. And with increasing leverage, the cost of equity reduces with a compensative increase in the cost of (riskier) debt. The cost of debt also incorporates the tax shield (fiscal deductibility) of the negative interests. The carry-over of the tax losses influences the tax base of the startup that starts raising debt and begins to produce taxable The cycle: Sources

operating NOPAT

Funds

operaƟng NOPAT

financial NOPAT

Investors OPERATING NET WORKING CAPITAL

FINANCIAL DEBTS

FIXED ASSETS

EQUITY

financial NOPAT

Fig. 7.1 The Financial-Economic Cycle ➀ Funds acquisition of capital and debt (raised capital), ➁ Funds investment in net working capital and fixed assets (invested capital), ➂ Generation of operating NOPAT (funds applications in net working capital and fixed assets  sales  operating NOPAT), ➃ Operating NOPAT generates operating cash flows for investors (debtholders and shareholders)

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profits. In some cases, the startup may benefit from tax vacancies or other incentives, linked for instance to a pay-check protection program. One of the most contentious issues in the theory of finance during the past quarter-century has been the theory of capital structure. The geneses of this controversy were the seminal contributions by Modigliani & Miller (Mikkelson, 1984; Miller, 1988; Chen, 2017; Brusov et al., 2018). In a Modigliani & Miller world where the capital structure is irrelevant and leverage does not affect the market value of the firm, the WACC is invariant to any change in the leverage, as shown in Fig. 7.2. Elia and Quarta (2020) show that it becomes crucial to provide clear and valuable support to each phase of startup creation (e.g., ideation, validation, build, launch, growth, and maturity), with a specific focus on the financial sources that can be used to implement each phase. The transition from a debt-free to a levered startup typically occurs beyond the early-stage period and rotates around the composition of the

Fig. 7.2 Impact of a leverage increase on the cost of capital

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Assets

LiabiliƟes

CAPEX Net Working Capital (Liquidity)

Equity

CAPEX

Equity

* Passage from NegaƟve to PosiƟve and Stabler Cash-Flows

Net Working Capital * Growing presence of collateral CAPEX

Liquidity Financial Debt

* ReducƟon of InformaƟon Asymmetries * AggregaƟng role of Professional Investors (VC, PE ...)

Equity CAPEX Net Working Capital Liquidity

Financial Debt

Fig. 7.3 Evolution from a debt-free to a levered startup

raised capital. What mostly matters for such a composition is the debt-toequity ratio (financial leverage), as a function of the asset subdivision, as shown in Fig. 7.3.

7.2 Net Present Value, Internal Rate of Return, and Investment Payback Capital budgeting formulations are mainly based on Net Present Value (NPV) and Internal Rate of Return (IRR) metrics.

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NPV and its specular IRR are based on Discounted Cash Flows. It so incorporates liquidity projections and must include in its denominator the risk of cash—and monetary equity—burnout. The firm is unlevered, and so there is no difference between operating and net cash flows. So NPV equity = NPV project , and IRR equity = IRR project . In formulae: N P Vequit y =

n  N et Cash Flows − I nitial (cash) I nvestment (1 + i)n

(7.1)

i=1

and: I R Requit y = N P Vequit y = 0 =

n  N et Cash Flows  n 1 + I R Requit y i=1

− I nitial (cash) I nvestment

(7.2)

A complementary indicator is represented by the (discounted) investment payback. The payback period is the time required to earn back the amount invested in an asset from its net cash flows. It is a simple way to evaluate the risk associated with a proposed project. An investment with a shorter payback period is better since the investor’s initial outlay is at risk for a shorter period. The payback period measures the length of time an investment reaches a financial breakeven point, matching cash outflows with subsequent cash returns. From these broad definitions, it seems intuitive that also the payback is influenced by runway forecasts. The shortcomings of the Payback method are well known. For instance, it ignores the cash flows beyond the liquidity break even (payback period). Most major capital expenditures, including the profitable startup businesses, however, have a long lifespan and continue to provide positive cash flows even after the payback threshold.

7.3

Modigliani & Miller Proposition II

The Modigliani–Miller theorem (1958) considers the (optimal) capital structure, forming the basis for modern thinking on corporate finance. The basic theorem states that in the absence of taxes, bankruptcy costs, agency costs, and asymmetric information, and in an efficient market, the value of a firm is unaffected by how it is financed. Since the value of the

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firm depends neither on its dividend policy nor its decision to raise capital by issuing stock or selling debt, the Modigliani–Miller theorem is often called the capital structure irrelevance model. The key Modigliani–Miller theorem was developed in a world without taxes. However, if we move to a world where there are taxes when the interest on the debt is tax-deductible, and ignoring other frictions, the value of the company increases in proportion to the amount of debt used. And the source of additional value is due to the amount of taxes saved by issuing debt instead of equity. Consider two firms that are identical except for their financial structure. The first (Firm U) is unlevered: that is, it is financed by equity only (this being the case considered in Chapter 6). The other (Firm L) is levered: it is financed partly by equity, and partly by debt. The Modigliani–Miller theorem (including taxation) states that the value of the two firms is the same. VL = VU + Tc D

(7.3)

where: • V U is the value of an unlevered firm = price of buying a firm composed only of equity; • V L is the value of a levered firm = price of buying a firm that is composed of some mix of debt and equity; • Tc D = tax rate * (market) value of debt. To see why this should be true, suppose an investor is considering buying one of the two firms, U or L. Instead of purchasing the shares of the levered firm L, he could purchase the shares of firm U and borrow the same amount of money B that firm L does. The eventual returns to either of these investments would be the same. Therefore, the price of L must be the same as the price of U minus the money borrowed B, which is the value of L’s debt. This discussion also clarifies the role of some of the theorem’s assumptions. We have implicitly assumed that the investor’s cost of borrowing money is the same as that of the firm, which need not be true in the presence of asymmetric information, in the absence of efficient markets, or if the investor has a different risk profile than the firm (Fig. 7.4).

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Fig. 7.4 Modigliani & Miller (M&M)—Proposition II (where: K e = cost of equity; k o = WACC (weighted average cost of capital); K d = cost of debt)

M&M Proposition II with risky debt states that as leverage (D/E) increases, the WACC (k0 ) remains constant. The formula (considering taxation) is the following:    EBIT EBIT N egative inter ests f inancial debt + − invested capital invested capital f inancial debt equit y net pr o f it (7.4) ad justed pr etax pr o f it 

RO E =

where: • • • •

EBIT/ invested capital = ROIC Negative interests/ financial debt = i = ki = cost of debt Financial debt/ equity = (financial) leverage = d Adjusted pretax profit = net profit, before taxes, extraordinary items, and positive interests = Rn/Rn*.

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And so: R O E = [R O I + (R O I − i)d]

net pr o f it ad justed pr etax pr o f it

(7.5)

M&M II deals with the WACC. It says that as the proportion of debt in the company’s capital structure increases, its return on equity to shareholders increases in a linear fashion. A higher debt-to-equity ratio leads to a higher required return on equity, because of the higher risk involved for equity-holders in a company with debt. The formula is derived from the theory of weighted average cost of capital (WACC). These propositions are true under the following assumptions: • no transaction costs exist and • individuals and corporations borrow at the same rates. These results might seem irrelevant (after all, none of the conditions are met in the real world), but the theorem still tells something particularly important. That is, capital structure matters precisely because one or more of these assumptions is violated. It tells where to look for determinants of optimal capital structure and how those factors might affect optimal capital structure. The difference (ROIC − i) expresses the gap between the return on invested capital and the cost of debt: ROIC (compared to WACC, in market terms) shows the return of the investment whereas—i-corresponds to the cost of debt. The difference (ROIC − i) is complementary to the difference (ROIC—cost of equity) illustrated in Fig. 3.6. To understand the convenience of the investment, we must consider the following cases: 1) ROIC > i so (ROIC − i) > 0 2) ROIC = i so (ROIC − i) = 0 3) ROIC < i so (ROIC − i) < 0 The debt ratio—d—operates as a “leverage” on the differential (ROIC-i). Even for d there are three possible cases:

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(1) d = 0 when financial debts correspond to zero in an unlevered firm (with no debt). If so, equity corresponds to the raised capital (= invested capital), and profitability of equity = profitability of raised/invested capital. So, ROE = ROIC. (2) d > 1 when financial debts > equity. This hypothesis is very frequent, especially for under-capitalized companies. In this case, d acts as a multiplier of the difference (ROIC − i). (3) d < 1 when financial debts < equity, and leverage (d) acts as a demultiplier. When the difference (ROIC − i) is positive, there may be a temptation to increase, even substantially, the leverage (raising debt and keeping the equity unchanged or decreasing the equity paying dividends …). When leverage (d) is increased, the denominator of the cost of debt (i = negative interests/financial debt) grows but if debt trespasses manageable levels then the cost of debt grows, incorporating higher bankruptcy costs and a worse rating that require a higher risk premium (see also the adverse selection issue in Stiglitz and Weiss [1981]). Any increase in the leverage has an impact on ROIC: the denominator of ROIC (EBIT/invested = raised capital) grows since debt is a component of raised capital. Any leverage increase normally bears an increase of raised capital (if equity is unchanged). The company has more sources of funds that are translated into more uses in the invested capital (CAPEX, stock, credits …). What happens if any increase of invested = raised capital (that is part of the ROIC denominator) does not bring to a proportional increase of EBIT (the numerator of ROIC)? If it is so, ROIC decreases and so does the differential (ROIC − i) that can even become negative. And if (ROIC − i) becomes negative, the situation can be complicated since leverage has substantially grown. There is so a dangerous boomerang effect. Considering an increase in financial debts and invariant equity, we have: 

negative inter ests net pr o f it + negative inter ests = E B I T − invested capital = f inancial debts ↑ +equit y ∼ f inancial debts ↑ = f inancial debts ↑ equit y ∼ =



(7.6)

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But any increase of financial debts bears an increase (at least proportional) of negative interests. If equity is unchanged, then higher debt brings to higher invested=raised capital and this should increase both the revenues and the operating/net economic marginality, with a positive impact on EBIT and net profit. If this virtuous process is blocked, then wealth is diluted, since we need more capital to get the same economic results. And so, considering an uncertain change in net profit (net profit?), we have: 

net pr o f it? + negative inter ests ↑= E B I T negative inter ests ↑ − invest capital = f inancial debts ↑ +equit y ∼ f inancial debts ↑ = f inancial debts ↑ equit y ∼ =



(7.7)

Beyond a confidence threshold, negative interests increase more than proportionally, and the (ROIC − i) differential shrinks: 

net pr o f it? + negative inter ests ↑↑= E B I T negative inter ests ↑↑ − invest capital = f inancial debts ↑ +equit y ∼ f inancial debts ↑ = f inancial debts ↑ equit y ∼ =



(7.8)

The example in Table 7.1 shows the possible combinations of ROIC, i (cost of debt) and d (financial leverage), remembering that if ROIC = i and/or d = 0, then (ROIC − i) * d = 0. As it is shown, when the difference (ROIC − i) is positive, high leverage is convenient, to increase the gearing. Considering case a), increasing the value of d, there is a consequent (exponential) growth in the value of (ROIC − i) * d. In economic terms, when investments have profitability that exceeds the cost of their financing, it is convenient to get into debt. Table 7.1 Combination of profitability ratios

(a) (b) (c) (d)

ROIC (%)

i(%)

(ROIC − i)(%)

d

(ROIC − i)*d(%)

15 15 15 15

12 12 20 20

3 3 −5 −5

1.5 0.5 1.5 0.5

4.5 1.5 −7.5 −2.5

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This reasoning is however simplistic and does not consider the collateral effects of excessive debt. Banks and other financial lenders typically apply a variable spread to Euribor (or other Interbank rates). An example is given in Table 7.2 (where interest rates are expressed in basis points; 1 basis point = 0.01%). • The debt/equity ratio corresponds to the financial leverage; • The ratio EBITDA/net negative interests links EBITDA (i.e., the economic or financial margins deriving from the current business activity) to net negative interests that are consequent to a negative Net Financial Position. Considering ROE as a function of d (financial leverage), we can have the following representation (Fig. 7.5). ROE is a function of ROIC and leverage: the profitability grows and reaches a peak when ROE is still positive but no greater than the cost of debt (ROE ≤ i). Beyond the break even point (ROE = 0), the equity decreases. According to the formulation of Modigliani & Miller (proposition II), ROE increases linearly as a function of leverage, provided that the cost of debt (i) remains constant. When i > ROIC, ROE collapses and may soon become negative. Consider the following example in Table 7.3 and its graphical representation in Fig. 7.6.

Table 7.2 Profitability ratios, leverage, and credit spread Parameter

Debt/equity (D/E) [financial leverage] EBITDA/ net negative interests (E/OFN) Spread

Financially sound company

Company in financial equilibrium

Company close to financial disequilibrium

Company close to insolvency or bankruptcy

D/E < 2

2 < D/E < 2.5

2.5 < D/E < 3.25

> 3.25

E/OF > 5

4 < E/OF < 5

3 < E/OF < 4

+175

+200

+225

i

ROE ROI i (%)

ROI = i

d (>1) ROI < i

Fig. 7.5 ROE and ROIC Table 7.3 Impact of a leverage (d) increase on the cost of debt (i) and ROE

ROIC(%)

d

i (%)

ROE(%)

15 15 15 15 15 15 15 15 15

0.5 1 1.5 2 2.5 2.8 3 3.5 4

12 12 12 12 13 14 15 17 20

16.5 18.0 19.5 21 20 17.8 15 8 −5

19

14

9

4

-1 ROI

ROE

i

d

-6

Fig. 7.6 Impact of an increase in financial leverage (d) on the cost of debt (i) and ROE

7

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Information Asymmetries and Leverage

Financing investments in a knowledge-intensive sector may be more difficult as there is a greater degree of uncertainty and asymmetries of information (Nigam et al., 2020). Information asymmetries represent a major concern for potential debtholders, and this is a further reason behind the difficulty of startups to raise debt. Little if any history (track record), equity concentration (especially in the seed phase), absence of valuable collateral adds additional concern. As shown in Moro Visconti (2015), intangibles intrinsically incorporate information asymmetries and may so discourage debt but are also a vital component of cash-generating value, so representing a key factor for debt servicing, with paradoxical effects (more guarantees with less collateral?). Debt service is guaranteed by the capacity of the firm to generate adequate future cash flows. The primary component of generated liquidity is represented by revenue growth, and its marginal by-products (EBITDA, operating and net cash flow, etc.). If companies can hardly survive without increasingly sophisticated intangibles, even their sponsoring banks are more and more challenged by path-breaking changes in the strategies of their clients. Therefore, intangible valuation is so significant (also) for lending institutions. Lack of proper intangible “soft” lending may also cause credit misallocation and consequent market failures. Startups are intrinsically intangible-intensive, and this represents a double-edged sword for their creditworthiness. Managers may, however, soften some criticalities, and have an incentive to disclose information to make credit rationing less binding. Shimizu (2017) analyses how startups can utilize intellectual properties for their financing.

7.5 The Theory of Capital Structure: A Startup’s Reassessment The founding model of Modigliani and Miller (1958) brought to many scientific and practical applications in the following decades. The seminal study of Harris and Raviv (1991), represents a still valid survey of the topic, consistent with Sects. 2.11, 7.3, and 7.4. Agency costs, asymmetric information, product/input market interactions, and corporate

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control considerations influence the capital structure, even considering the peculiar startup case. The agency approach aims to ameliorate the conflicts of interest among the main stakeholders that rotate around the startup. The two main conflicts arise between shareholders and managers or debtholders versus equity-holders. In early-stage startups, these conflicts may be minimal because shareholders and managers tend to coincide, being represented by the founding investors, and the startup collects little if any debt. But the situation changes across time, when the startup develops, and gets more articulated, differentiating ownership from control, and introducing debt and its unavoidable conflicts. New conflicts arise, while others soften. Debt reduces free cash flow, almost nonexistent in newborn startups but potentially dangerous when they grow, and accumulate liquidity, so increasing managerial discretion. Monitoring is another positive by-product of debt, made necessary by the articulation of the stakeholders that increasingly rely on concentrated equity-holders and large creditors to reduce managerial free-riding temptations. Asset substitution occurs when the raised capital and its composition (financial debt + equity), even in relative terms (financial debt/equity = leverage) are confronted with the invested capital (asset) composition. Shareholders may for instance be tempted to payout generous dividends, so reducing asset liquidity. This would increase the riskiness of assets (which become more concentrated in their CAPEX, largely represented by risky intangibles), to the detriment of debt service capacity. Dividends are unlikely in young startups, but other asset-substitution strategies are frequent, especially if represented by larger investments in risky intangibles. Asymmetric information is intrinsically incorporated in the startup model, for many complimentary reasons, already mentioned, which may be worth recalling. The asset composition reflects the startup’s business model that fosters investments in risky intangibles (with little if any collateral, and a volatile capacity to produce positive liquidity in the short run), often predominant in an asset composition where liquidity is initially negative and net working capital is limited (as it depends on the revenue model and invoicing capacity that takes time to takeoff). Product/input market interactions have deep strategic implications, even for startups that compete with other firms in an evolving ecosystem. Product prices and payoffs, their features, the selected business models,

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and implementing strategies, all concur to shape the startup’s operations. Innovative startups get closer to oligopolistic or monopolistic models that impact their goodwill, reshaping the competitive forces within the market and threatening the existence of the incumbents. The market for corporate control also affects the startups, as it happens whenever the equity composition is modified by new shareholders (diluting the founding partners, accommodating for Venture Capital or Private Equity intermediaries that sooner or later exit, etc.). Takeovers and Leveraged-Buy-Outs are unlikely in young startups, less so when they become desirable firms. All these considerations, apparently theoretical and well-investigated by refined models, have practical implications that affect—for the better or the worse—the startup valuation issues that represent the core research question of this book.

7.6

A Practical Case of Corporate Profitability Analysis

A practical case of corporate profitability analysis starts from a balance sheet and income statement and then calculates the profitability equation, step by step. The example is represented in Table 7.4. Considering the profitability Eq. (7.4):    EBIT EBIT N egative inter ests f inancial debt + − invested capital invested capital f inancial debt equit y net pr o f it ad justed pr etax pr o f it 

RO E =

Considering the T0 -T1 balance sheet, the data for the equation are reported in Table 7.5. The invested capital for T0 and T1 is reported in Table 7.6. The total collected (invested) capital is represented by the sum of equity and total financial debts. The reclassified data for the profitability equation are reported in Table 7.7. Once we have determined ROE, ROIC, I, D/E, and Rn/Rn*, the profitability equation can be calculated as shown in the following Table 7.8.

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Table 7.4 Delta case. Balance sheet T3 and T4 (Asset and liabilities; income statement T4) Intangible fixed assets Tangible fixed assets Shares Financial long-term credits Commercial long-term receivables Intercompany long-term credits Total long-term assets Current assets (within 12 months) Financial current credits Credits towards shareholders for subscribed capital unpaid Stocks Commercial current receivables Intercompany current credits Financial assets not constituting fixed assets Cash availability Prepayments and accrued income Total current assets Total assets Check Equity Subscribed capital Reserves Shareholders payments Result of the financial year Total Long-term liabilities (over 12 months) Severance indemnity for employees Long-term financial debts Long term commercial debts Long-term intercompany debts Total long-term liabilities Current liabilities (within 12 months) Funds for liabilities and charges Current financial debts Current commercial debts Current intercompany debts Accruals and deferred income

702,930 234,528 775 0 0 0 938,233

911,804 457,747 775 0 16,880 0 1,387,206

0 0

0 0

3,264,083 13,575,477 0 0 54,665 56,015 16,950,240 17,888,473 –

4,778,939 10,720,919 0 0 802,785 275,391 16,578,034 17,965,240 –

103,480 721,798 – 300,409 1,125,687

103,480 1,022,206 – 80,680 1,206,365

234,990 520,000 1,500 – 756,490

318,937 1,968,961 1,500 – 2,289,398

26,742 6,628,894 9,142,140 – 208,520

47,435 9,556,031 4,500,664 – 365,347

(continued)

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Table 7.4 (continued) Total current liabilities Total liabilities check

16,006,296 17,888,473 –

Income Statement reclassified by value added Net revenues Other revenues Stocks variations of finished goods Capitalized costs (A) Production of the financial year Purchases Costs for services Costs for utilisation of assets belonging to third parties Sundry operating charges Stocks variations of raw materials (B) Costs of production VALUE ADDED (A − B) Wages and salaries Severance indemnity for employees Other staff costs (C) Staff costs GROSS OPERATING MARGIN (EBITDA) (A − B − C) Amortisation of intangible fixed assets Depreciation of tangible fixed assets Devaluations Other provisions (D) Depreciations and provisions OPERATING NET RESULT (EBIT) (A − B − C − D) Financial charges Financial incomes Profits (losses) on currency exchanges (E) Financial incomes and charges CURRENT INCOME Extraordinary incomes Extraordinary charges (F) Extraordinary incomes and charges PRE-TAX RESULT Taxes NET RESULT

14,469,477 17,965,240 – T4 22,822,493 122,456 0 0 22,944,949 14,194,538 5,417,179 901,789 458,818 −1,514,856 19,457,468 3,487,481 1,805,356 141,112 574,601 2,521,069 966,412 222,402 87,357 50,776 0 360,535 605,877 −518,277 4,878 −295,993 −809,392 −203,515 500,188 1 500,187 296,672 215,992 80,680

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Table 7.5 Input data for the profitability equation

Net Profit Average equity (E) Ebit Average invested capital Negative interests Financial debt (D) Adjusted pretax profit

T3

T4

Average

1,125,687

80,680 1,206,365

1,166,026

7,742,526

605,877 11,138,484

9,440,505

479,547 8,274,479 126,330

Table 7.6 Collected or invested capital Collected (invested) capital

T3

T4

Average

Equity Current financial debts Long-term financial debts Total financial debts Total collected (invested) capital

1,125,687 520,000 6,096,839 6,616,839 7,742,526

1,206,365 1,960,519 7,971,600 9,932,119 11,138,484

1,166,026 1,240,260 7,034,220 8,274,479 9,440,505

Table 7.7 Reclassified data for the profitability equation

Net Profit/Equity EBIT/Invested capital Negative interests/financial debt Financial debt/Equity Net Profit/adjusted pretax profit ROI − i (ROI − i)*D/E

6.92% 6.42% 5.80%

ROE ROI i

709.63% 0.64

D/E (leverage) Rn/Rn*

0.62% 4.42%

Table 7.8 Subdivision of the profitability equation Net Profit/Equity

ROI

(ROI − i)

(ROI − i) * D/E

Rn/Rn*

6.92%

6.42%

0.62%

0.0442

0.64

ROE =

6.92%

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7.7

179

Why Startups Fail?

Startups are capable of achieving great growth with exclusive cash flow but the bitter truth is that 90% of startups get failed (Chitkara & Jamal Mahmood, 2019). While the overall contribution of startups is crucial, the high-risk and high-reward strategy followed by these startups lead to significant failure rates and a low ratio of successful startups. The lack of a structured Business Development strategy emerges as a key determinant of startup failure in most cases (Cantamessa et al., 2018). The reasons for the failure of a startup (Davila et al., 2015; Bednar et al., 2018) are many, and not too different from those of other established firms (https://www.cbinsights.com/research/startup-failurereasons-top/). One main concern, consistently with the above sections, is lack of liquidity—cash burnout (Laitinen, 2012). The top 10 causes of small business failure (https://smallbiztrends. com/2019/03/startup-statistics-small-business.html) are the following: 1. No market need: 42%; 2. Ran out of cash: 29%; 3. Not the right team: 23%; 4. Got outcompeted: 19%; 5. Pricing/Cost issues: 18%; 6. User un-friendly product: 17%; 7. Product without a business model: 17%; 8. Poor marketing: 14%; 9. Ignore customers: 14%; and 10. Product mistimed: 13%. The mortality rate of newborn European firms after 3 or 5 years is reported in Eurostat (2016), and in further updates.1 According to Feinleib (2011), startup failures are due to poor productmarket fit, bad products, a missing entrepreneur, too early investment in sales and marketing, losing money on sales. Entrepreneurial firms are important sources of patented inventions. Whenever startups fail, their patents might be re-deployable. At odds with

1 http://appsso.eurostat.ec.europa.eu/.

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the view that the resale market for patented inventions is illiquid, most patents from these startups are sold, are sold quickly, and remain “alive” through renewal fee payment long after the startups are shuttered. The patents tend to be purchased by other operating companies in the same sector and retain value beyond the original venture and team (Serrano & Ziedonis, 2019). This consideration could be generalized to accommodate residual value concern, typical of any firm that passes from a going concern to a breakup scenario. Unpatented inventions (know-how), and other internally generated intangibles with little if any market value (internal goodwill, capitalized expenses, etc.) normally suffer this situation and find it difficult to retain even a small residual value. Financial ratios, starting from Altman’s zeta score, can be useful in predicting the bankruptcy of companies, but in the case of new companies their usefulness is questionable. Many of the firms that are successful today made few profits when they were first created. On the other hand, structural inertia from the theory of organizational ecology and the “survival of the fitter” principle advocate that companies that are healthy in their early years will go ahead in greater proportion than those that start with many difficulties. Fuertes-Callén et al. (2020), found healthier financial indicators in the first years of startup companies that survived eight years than in those that failed, supporting the organizational ecology theory. The authors found statistically significant differences in profitability, productivity, liquidity, leverage, and size. Outcome predicting remains a difficult but crucial task (Krihna et al., 2016).

References Bednar, R., Tariskova, N., & Zagorsek, B. (2018). Startup revenue model failures. Montenegrin Journal of Economics, 14(4), 141–157. Brusov, P., Filatova, T., Orekhova, N., & Eskindarov, M. (2018). Capital structure: Modigliani–Miller theory. In Modern corporate finance, investments, taxation and ratings. Cham: Springer. Cantamessa, M., Gatteschi, V., Perboli, G., & Rosano, M. (2018). Startups’ roads to failure. Sustainability, 10, 2346.

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Chen, J. (2017, January 31). Modigliani and Miller propositions: The scope of their applicability. Available at https://ssrn.com/abstract=2909306. Chitkara, B., & Jamal Mahmood, S. M. (2019, November–December). An overview of the literature on startups failure: Trends and contributions. Test Engineering and Management, 81. Cotei, C., & Farhat, J. (2017). The evolution of financing structure in U.S. startups. The Journal of Entrepreneurial Finance, 19(1), 1–32. Davila, A., Foster, G., He, X., & Shimizu, C. (2015). The rise and fall of startups: Creation and destruction of revenue and jobs by young companies. Australian Journal of Management, 40(1), 6–35. Elia, G., & Quarta, F. (2020). Financing the development of technology startups. In G. Passiante (Ed.), Innovative entrepreneurship in action. International Studies in Entrepreneurship, 45. Cham: Springer. Feinleib, D. (2011). Why startups fail and how yours can succeed. Cham: Springer. Fuertes-Callén, Y., Cuellar-Fernández, B., & Serrano-Cinca, C. (2020, August). Predicting startup survival using first years financial statements. Journal of Small Business Management. https://www.tandfonline.com/action/showCi tFormats?doi=10.1080%2F00472778.2020.1750302&area=000000000000 0001. Hanssens, J., Deloof, M., & Vanacker, T. (2016). The evolution of debt policies: New evidence from business startups. Journal of Banking & Finance, 65(C), 120–133. Harris, M., & Raviv, A. (1991). The theory of capital structure. The Journal of Finance, 46(1), 297–355. Huynh, K. P., Petrunia, R. J., & Voia, M. (2012). Duration of new firms: The role of startup financial conditions, industry and aggregate factors. Structural Change and Economic Dynamics, 23, 354–362. Krihna, A., Agrawal, A., & Choudhary, A. (2016). Predicting the outcome of startups: less failure, more success. Paper presented at IEEE 16th International Conference on Data Mining workshops (ICDMW). Laitinen, E. K. (2012). Profitability, growth, and different flow ratio concepts: Implications for failing firms. Review of Economics & Finance, 2(4), 112–130. Mikkelson, W. H. (1984). On the existence of an optimal capital structure: Theory and evidence: discussion. The Journal of Finance, 39(3), 878–880. Miller, M. H. (1988). The Modigliani-Miller propositions after thirty years. Journal of Economic Perspectives, 2(4, Fall), 99–120. Modigliani, F., & Miller, M. H. (1958, June). The cost of capital, corporation finance and the theory of investment. The American Economic Review, 48(3), 261–297. Moro Visconti, R. (2015). Leveraging value with intangibles: More guarantees with less collateral? Corporate Ownership & Control, 13(1–2), 241–252.

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Nigam, N., Mbarek, S., & Boughanmi, A. (2020). Impact of intellectual capital on the financing of startups with new business models. Journal of Knowledge Management, ahead-of-print. https://www.researchgate.net/publication/ 344477138_Impact_of_intellectual_capital_on_the_financing_of_startups_ with_new_business_models. Serrano, C. J., & Ziedonis, R. (2019, August). How Redeployable are patent assets? Evidence from failed startups. Academy of Management, 19(1). Shimizu, T. (2017). Intellectual properties and debt finance for startups. In T. Kono (Ed.), Perspectives in law, business and innovation. Security interests in intellectual property. Singapore: Springer. Stiglitz, J. E., & Weiss, A., (1981, June). Credit rationing in markets with imperfect information. The American Economic Review, 71(3), 393–410. Tech, R. (2018). Financing high-tech startups: Using productive signaling to efficiently overcome the liability of complexity. Cham: Springer.

CHAPTER 8

A Comprehensive Valuation Metrics

8.1

Purpose of the Startup Evaluation

Startups are young firms that need to be appraised adapting the standard valuation approaches to their peculiar nature and stage of development. The value of a startup is primarily the result of a series of factors, including: • Net assets, i.e., all the funds contributed by the partners to finance the business activity; • Ability to generate income, i.e., the ability to produce positive income flows; • Finanial capacity. The attitude of the net assets to produce income depends on the quality of the means of production and the entrepreneurial capacity. This last circumstance allows understanding the presence of profoundly different profit margins between companies operating in the same sector. Under ideal conditions, the subjective “value” must tend to coincide with an objective “price” at the negotiation stage. Value is estimated from the application of one or more valuation criteria, chosen concerning the type of corporate transaction, the identity

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_8

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of the parties involved, and the activity of the startup. It is ideally independent of the contractual strength of the parties and other subjective factors. The price is the meeting point of expectations and benefits formulated by the supply and demand involved in the negotiation of the startup. A startup (or, more generally, a firm) can be evaluated, among other things: 1. With a view to trade (transaction purposes); • Purchases/sales of shareholdings (the underlying startup is valued), companies or business units; • Extraordinary financial transactions (relating to the startup/branch of business), M&A, demergers, contributions, disposals, transformations, securitization …; 2. For litigation (e.g., compute damage awards in an infringement lawsuit); 3. For arbitration or similar proceedings; 4. For bankruptcy (valuation is required by the Court to dispose of the assets properly, and payback creditors); 5. Because of changes in the equity: • Issue of shares (excluding pre-emptive rights; with share premium …); • Issue of convertible bonds; • Issue of warrants; • Linked to extraordinary operations (transfers, transformations, mergers, contributions, demergers, etc.); 3. With a view to the purchase of assets by the founding partners; 4. To provide guarantees; 6. For listing on the Stock Exchange (IPO); 7. For “internal” cognitive purposes (financial reporting, etc.); 8. For the evaluation of the withdrawal of the shareholder. Kumar (2016) claims that: • Closely held companies (like startups, at least in their early stages) can be valued basically by three methods—discounted income approach, comparative analysis, and capitalized earnings.

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• In the case of a high probability of bankruptcy, the estimation of liquidation value is the best estimate of the valuation of distressed firms. • The life cycle of a firm is also a determinant of negative earnings for firms. Cyclical firms are subject to significant swings in profitability. Cyclical companies can be valued using a modified discounted cash flow approach involving scenario analysis. The earnings of the cyclical firms must be normalized for the economic cycle covering 5 or 10 years. • In the life cycle stage of firms, startup firms highlight the initial stage of the life cycle of firms. The value of a startup depends on its future growth potential. Venture capital firms estimate the exit or terminal value of startup firms at the time of the initial public offerings. The discount rate for estimation of discounted cash flow valuation for emerging market firms must factor in additional risk factors like high levels of inflation and macroeconomic volatility. • To consider the inflation impact on cash flows, the estimation of future cash flows in discounted cash flow valuation must be done in both nominal and real terms. • Cash flow valuation, relative valuation, and real option valuation methods can be used to value high growth firms. High growth companies offer a higher rate of return to shareholders. The main approaches for estimating the market value of companies (Damodaran, 2018; Fazzini, 2018; Koller & Goedhart, 2015) are different and can be divided into empirical and analytical approaches. Empirical approaches are based on the practical observation of market prices of assets that are sufficiently similar and, as such, comparable. Analytical approaches, on the other hand, have a more solid scientific basis and a more significant tradition in the professional sphere and are based on a revenue-financial approach, to estimate what an asset is worth today based on expected future returns or an estimate of the costs incurred for its reproduction/replacement. The main approaches to evaluating companies commonly used in practice are: 1. The balance sheet-based approach, simple, and complex; 2. The income approach;

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3. The mixed capital-income approach; 4. The financial approach, using DCF; 5. Market approaches and valuation through multiples. Balance sheet-based approaches are seldom used for some established firms (holdings, real estate companies, etc.) but they are hardly applicable to startups. The central element in determining the value of a startup is the estimate of its future ability to generate an income or financial flow capable of adequately rewarding its shareholders after debt service. Among the approaches to identify the market value of the startup, the financial and income approaches are the most appropriate to represent the expected fair remuneration of shareholders. While the balance sheet-based approach values tangible and intangible resources summing up the values of individual assets, the income, and financial approaches consider them as comprehensive elements able to participate in the context of the entire set of factors for the creation of value. The startup’s market value is the result of the interaction of internal variables relating to its tangible and intangible assets and external variables relating to the market. The combined consideration of both makes it possible to estimate the startup’s future results and to assess its risk. Recent valuation trends have led to the use of two approaches: the financial approach based on the estimate of discounted operating cash flows at the weighted average cost of capital (WACC) and the market approach based on the EBITDA multipliers of comparable companies. In both cases, the enterprise value (value of the startup, including debt) is estimated, which is then added algebraically to the net financial position to arrive at the residual equity value. In the evaluation of the intangibles, “distinctions are sometimes made between trade intangibles and marketing intangibles, between ‘soft’ intangibles and ‘hard’ intangibles, between routine and non-routine intangibles, and between other classes and categories of intangibles” (OECD, 2017). The choice of the correct approach and parameters depends on a bottom-up analysis of the business plan of the target startup (Moro Visconti, 2019; see also Chapter 2). This helps in the estimate of trendy parameters (operating and net cash flows; economic margins, etc.) and in the functional analysis that eases the selection of comparable startups.

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187

FuncƟonal analysis

Comparables; industry/ market survey

Top-down approach Business planning

BoƩom-up feedback

Economic/financial/ strategic drivers

Startup valuaƟon

Fig. 8.1 Functional analysis, business planning, and startup valuation

The functional analysis is traditionally used for transfer pricing purposes (OECD, 2017). It analyzes the functions performed (considering assets used and risks assumed) by associated startups in a transaction, providing an overview of value creation within the supply chain (Fig. 8.1).

8.2

The Balance Sheet-Based Approach

The valuation of the market value according to the balance sheet approach (Fernandez, 2001) is based on the current value of the equity contained in the last available balance sheet. There are three approaches: • Simple balance sheet-based approach; • Complex balance sheet-based approach grade I; • Complex balance sheet-based approach grade II; This approach has been traditionally used in continental Europe and less in Anglo-Saxon countries. As anticipated, this methodology is almost inapplicable to startups. The starting point for the use of the balance sheet-based approach, both simple and complex, is represented by the shareholders’ equity of the

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financial statements including the profit for the year net of the amounts approved for distribution. Based on the values shown in the financial statements, an analysis of assets and liabilities must be carried out, representing non-monetary assets (technical fixed assets, inventories of goods, securities, and, depending on the approach used, intangible fixed assets) in terms of current values, to highlight implicit capital gains or losses compared to the accounting data (Lev & Gu, 2016). For assets with a significant exchange market (e.g., real estate or traded securities), the calculation of present values is generally based on the prices recorded during the most recent negotiations. When there is no reference market, estimates based on reconstruction or training costs may be alternatively used. The simple balance sheet-based is significant in the case of companies with high equity content (real estate companies, holding companies, etc.). In such companies, the overall profitability/risk profile may represent the synthesis of the patterns implicitly or explicitly considered in the valuation of the individual assets. This methodology makes the value of the capital coincided with the difference between the current value of the assets and the value of the liabilities that contribute to determining the startup’s assets. The asset value corresponds with the net investment that would be necessary to start a new company with the same asset structure as the one being valued. The simple asset value is, therefore, not the liquidation value of the assets, but the value of their reconstruction from a business operating perspective. Accounting of liabilities should never be underestimated and so their value should be consistent with their bookkeeping or lower. The formula is: Enterprise value = book equity + asset adjustments−−liability adjustments = adjusted equity = K1 = W1

(8.1)

where assets and liability adjustments are defined as capital gains and losses net of the tax impact. The simple valuation considers, to estimate equity stocks, only tangible assets in addition to loans and liquidity. The valuation provides for a detailed estimate of the assets at current replacement values, in particular:

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– Assets at current repurchase value; – Assets and liabilities based on settlement values. The “first-grade complex balance sheet-based approach,” diffused in Continental Europe, also considers intangible assets that are not accounted for but have a market value. In formulae: K 1 + intangible Intangibles assets not accounted for but with market value = K 2 = W2 (8.2)

(e.g., bank deposits, insurance premium portfolio, shop licenses, and large-scale distribution) where K 1 is the value of assets determined according to the principles of the simple balance sheet-based approach. Finally, the complex Tier II balance sheet-based approach also refers to intangible assets that are not accounted for and do not have a specific market value, bringing to the “second-grade complex balance sheet-based approach.” K 2 + unrecognized intangible Intangibles assets without market value = K 3 = W3 (8.3)

(e.g., product portfolio, patents and industrial concessions, know-how, market shares and corporate image sales network, management, the value of human capital). where K 2 is the value of assets determined according to the complexgrade I balance sheet-based approach. Intangible assets that are not accounted for and do not have a market value are: • Strategy, concerning products and life cycle, customers, markets, market positioning, and market share achieved, orientation toward growth and partnership policies; • Customers and market; • Processes and innovation; • The organization, which includes all the elements related to corporate governance; • Human resources.

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8.3

The Income Approach

Profitability valuation, consistent with the income forecasting, can be appropriate when the startup has a sufficiently defined profitability trend, or the approach is deemed reliable for startup projections. Or even if there is a significant intangible component that influences the income (as shown in Chapter 4). The income approach makes it possible to estimate the market value based on profits, which the startup is deemed to be able to produce in future years. This methodology is suitable for the evaluation of cyclical companies, which have very volatile incomes, but with a tendency to compensate for overtime. In the presence of cyclical companies, normalization is a process that can identify a stable trend line, underlying the volatile trend of income flows that occur in the various periods of management. The fundamental elements in an evaluation of an income approach are: – The estimate of normalized income, – The choice of the capitalization rate, – The choice of the capitalization formula, based on the adopted valuation time horizon. 8.3.1

Estimated Normalized Income

As regards the determination of the income to be used as a basis for the valuation, reference is made to the average normalized value of income that the startup is expected to produce permanently in future years. Therefore, it is not considered as a series of future incomes, but rather as the expected average normalized value able to reflect the startup’s average long-term income capacity, in a time horizon consistent with the business model. Normalized income can be derived from: • Study of the income statement (historical and perspective); • Analysis of the financial structure (leverage), investigated in Chapter 7; • Consistency between the normalized operating result and the equity evaluation process; • Normalized income, i.e., average perspective income;

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Alternatively, the evaluator may consider operating result/EBIT, pretax result, net income, operating or net cash flow (if referring to a complementary financial approach). It is essential to transform the net profit (income) into a “normalized and integrated value” capable of expressing the startup’s ability to generate income, through three corrective processes: 1. Normalization: this is an articulated process aimed primarily at: – Redistribute “extraordinary” income and expenses over time; – Eliminate “non-operating” income and expenses; – Neutralization of the effects caused by budgetary policies; 2. The integration of changes in the stock of intangible assets; 3. Neutralization of the distorting impacts of inflation, to avoid fictitious losses or profits that could affect the valuation process. The longer the extension of the evaluation scenario, the likelier the distortions. The normalization process aims to subtract a series of income components from randomness, to bring them back to a relationship of adequate competence (accrual) with the reference period. Extraordinary income and expenses are significant and sometimes nonrecurring, components of operating income. Extraordinary income may, for example, include the realization of substantial assets on the assets side, such as real estate (hardly the case for seed startups). Costs include the economic consequences of exceptional events, such as restructuring costs, costs arising from the effects of natural disasters, and plant removals. These elements must be redistributed over time to express a measure of normalized income, not burdened by components that do not present the usual manifestation. The objective of the redistribution is to replace a random size with an average value to avoid that some businesses are particularly underweighted, and others are overestimated. Normalization is more difficult to assess when historical patterns are scarce or inexistent, as it happens in most startups. The elimination of income and costs unrelated to ordinary operations must be carried out by bringing the values in the income statement to size in line with the market or practice.

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As regards the neutralization of budgetary policies, reference is made to the fundamental estimates (amortization and depreciation, inventories, provisions for risks in industrial and commercial startups, fiscal policies). The integration process is based on the observation that the dynamics of some values regarding intangible assets that are or not adequately recorded in the accounts. The neutralization of the distortive effects of inflation makes it possible to separate real outcomes from apparent and illusory results since they derive from the sum of values that are not uniform in monetary terms. The most used corrections are as follows: • The adjustment of the depreciation rates of fixed technical assets at reconstruction costs, i.e., to the updated values of recent estimates; • Adoption of the LIFO procedure in the valuation of inventories of products, semi-finished products, and raw materials; • Determination of economic results. 8.3.2

Choice of the Capitalization Rate

The capitalization rate of normalized income represents the opportunity cost of capital employed. This rate depends on the expected return on the risk-free securities and the risk premium (Fernandez et al., 2020) that the market is expected to require for the type of investment being valued. The expected return on risk-free securities is generally identified with that on government bonds. The market return refers to all risky investments available on the market. This is consistent with the Capital Asset Pricing Model, used to assess an appropriate expected rate of return of a listed security, in proportion to its risk, to make decisions about adding assets to a well-diversified portfolio. An alternative criterion for determining the capitalization rate may be to base it on the cost of invested capital from the perspective of the purchaser. In this case, the value of the startup is understood as a series of future incomes that must be discounted based on the average cost of money for the purchaser. Its value, therefore, no longer depends on the degree of risk of the startup.

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The first approach of determining the rate of capitalization presents a theoretical-practical structure of greater importance but presupposes efficient financial markets since the entire evaluation is based on indicators that can be traced back to them. 8.3.3

Choice of the Capitalization Formula

The determination of the market value, through the discounting of income flows, occurs in many cases using the perpetual annuity formula since the startup is an institution destined to last over time. The attribution, instead, of limited duration to the production of income (from 3–5 to 8–10 years) is an assumption not verified in the business reality and tends to be arbitrary, considering the determination of the time boundary. It is, therefore, possible to proceed with the calculation of the value of the startup, based on the average normalized value of the income flows, estimated synthetically, generated in protracted-time horizons. Based on the chosen capitalization period, one of the two alternative formulas can be used: – The limited capitalization:

W2 = R an−i

(8.4)

– The unlimited capitalization:

W1 = R/i where: • • • •

W is the market value of the startup; R is the integrated normalized income; i is the income capitalization rate; n is the period (years) of limited capitalization.

(8.5)

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Whenever the capitalization period is illimited, the Terminal Value tends to zero. Terminal value is the value of a business or project beyond the forecast period when future cash flows can be estimated. Terminal value assumes a business will grow at a set growth rate forever after the forecast period. In some cases (and frequently in promising but still young startups), the Terminal Value represents a substantial component of the overall estimated value.

8.4

The Mixed Capital-Income Approach

The mixed approach (Fernandez, 2019) is based on the belief that in the long term the company’s asset value is reflected in its earnings and is, therefore, based on the assumption that the use of assets generates an average normalized return. For example, the mixed approach is suitable in the case of companies with significant equity holdings, which temporarily do not have a regular income capacity. In these cases, the mixed criterion can capture the value linked to the temporary ability for differential income, concerning the norm, under the hypothesis that the remuneration of the assets then returns to normal. The market value is estimated by referring to the adjusted equity, calculated based on the simple or complex balance sheet-based approach, and the value of the excess-revenue (goodwill) that the startup can produce compared to the average of the companies in the sector to which it belongs. It has already been shown that the balance sheet-based method is mostly unsuitable for startups. So even its derived mixed approach is unlikely to be used. The mixed approach “may incorporate different analytical values, including net book value, liabilities, goodwill, and even some specific intangibles (e.g., brands, technologies, customer lists, etc.).” Goodwill is any future economic benefit arising from a business, an interest in a business, or from the use of a group of assets that have not been separately recognized in another asset. In general terms, the value of goodwill is the residual amount remaining after the benefits of all identifiable tangible, intangible, and monetary assets, adjusted for actual or potential liabilities, have been deducted from the value of a business. It is typically represented as the excess of the price paid in a real or hypothetical acquisition of a startup over the value of the startup’s other identified assets and liabilities (IVS 210, https://www.ivsc.org/files/file/view/id/647).

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This methodology allows combining the requirements of objectivity and verifiability, typical of the equity component, with those of rationality expressed by the estimate of expectations regarding the future income capacity of the startup. The integration of the equity estimate with the value of the goodwill (positive/goodwill or negative/badwill) can be particularly convenient when the profitability of the startup shows deviations (positive or negative) concerning the level considered normal by the investors, expressed by the rate of remuneration. The market value is, therefore, composed of both an equity component and an income component. In this way, the value of the startup is always included in an interval that has as its lower limit the net assets at liquidation value and as its upper limit the value of the startup that can be determined by the income approach. The mixed-income approach has two different formulations: (a) Average value; (b) Independent (autonomous) goodwill estimate. (a) The average value The market value is determined as the average of the adjusted assets and the value obtained for the capitalization of income, using the perpetual capitalization formula. W = 1/2(K + R/i) = K + 1/2(R/i − K )

(8.6)

where: – K is the equity expressed at replacement cost according to the balance sheet-based approach. It is an adjusted capital measure, including intangible assets and capital gains, and considering any higher market values compared to the accounting data. – R is the normalized income expected for the future. – i is the normalized rate of return for equity, concerning both the level of operational risk borne by the startup and the level of risk deriving from the financial structure chosen.

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(b) Autonomous goodwill estimate The mixed balance sheet-based approach with an independent estimate of goodwill provides various alternatives, formulated concerning the different assumptions made for the projection and discounting of the over-returns to estimate the goodwill. b.1. Limited capitalization of average profit This approach considers the market value of the startup as the adjusted equity plus the limited capitalization of the average profit (the difference between the expected income and the return on equity = goodwill), based on the following formula: W = K + a n − i ∗ (R − i K )

(8.7)

where: – i = normalized rate concerning the type of investment. It expresses the measure of the return considered normal, considering the levels of risk incurred by the startup. – i* = discount rate of the over-income. – n = number of years, defined and limited. b.2. Unlimited capitalization of average profit The market value is the sum of the adjusted net asset value plus goodwill calculated as the perpetual annuity of the surplus profits. It assumes that the startup can generate extra profits for an indefinite period, to be taken with caution considering the intrinsically ephemeral nature of goodwill, which over time inevitably tends to erode. The formulation is as follows: W = K + [(R − i K )/i∗]

(8.8)

and provides for the replacement of a n−i* with 1/i*.

8.5

The Financial Approach

The financial approach is based on the principle that the market value of the startup is equal to the discounted value of the cash flows that the startup can generate (“cash is king”). The determination of the cash flows is of primary importance in the application of the approach, as is the

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consistency of the discount rates adopted. This methodology is frequently used for startups. The doctrine (especially the Anglo-Saxon one) believes that the financial approach is the “ideal” solution for estimating the market value for limited periods. It is not possible to make reliable estimates of cash flows for longer periods. “The conceptually correct methods are those based on cash flow discounting. I briefly comment on other methods since— even though they are conceptually incorrect—they continue to be used frequently” (Fernandez, 2019). This approach is of practical importance if the individual investor or startup with high cash flows (leasing companies, retail trade, public and motorway services, financial trading, project financing SPVs, etc.) are valued. Financial evaluation can be particularly appropriate when the startup’s ability to generate cash flow for investors is significantly different from its ability to generate income and forecasts can be formulated with a sufficient degree of credibility and are demonstrable. There are two criteria for determining cash flows: I. The cash flow available to shareholders The first configuration considers the only flow available for members’ remuneration. It is a measure of cash flow that considers the financial structure of the startup (levered cash flow). It is the cash flow that remains after the payment of interest and the repayment of equity shares and after the coverage of equity expenditures necessary to maintain existing assets and to create the conditions for business growth. In M&A operations, the Free Cash Flow to the Firm (operating cash flow) is normally calculated, to estimate the Enterprise Value (comprehensive of debt). The residual Equity Value is then derived by subtracting the Net Financial Position. The cash flow for the shareholders is determined, starting from the net profit: Net profit (loss) + amortization/depreciation and provisions + divestments (− investments) in technical equipment + divestments (− investments) in other assets + decrease (− increase) in net operating working capital

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+ increases (− decreases) in loans + equity increases (− decreases) = Cash flows available to shareholders (Free cash flow to equity). The discounting of the free cash flow for the shareholders takes place at a rate equal to the cost of the shareholders’ equity. This flow identifies the theoretical measure of the startup’s ability to distribute dividends, even if it does not coincide with the dividend paid. II. The cash flow available to the startup (Free cash flow to the firm) The second configuration of flows is the one most used in the practice of startup valuations, given its greater simplicity of application compared to the methodology based on residual flows to partners. It should be remembered that the two variants tend to coincide when the startup is debt-free, as shown in Chapter 6. It is a measure of cash flows independent of the financial structure of the startup (unlevered cash flows) that is particularly suitable to evaluate companies with high levels of indebtedness, or that do not have a debt plan. In these cases, the calculation of the cash flow available to shareholders is more difficult because of the volatility resulting from the forecast of how to repay debts. This methodology is based on the operating flows generated by the typical management of the startup, based on the operating income available for the remuneration of own and third-party means net of the relative tax effect. Unlevered cash flows are determined by using operating income before taxes and financial charges. Net operating income − taxes on operating income + amortization/depreciation and provisions (non-monetary operating costs) + technical divestments (−investments) + divestments (−investments) in other assets + decrease (−increase) in operating net working capital = Cash flow available to shareholders and lenders (operating cash flow).

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The cash flow available to the startup is, therefore, determined as the cash flow available to shareholders, plus financial charges after tax, plus loan repayments and equity repayments, minus new borrowings and flows arising from equity increases. An example is given in Fig. 8.2. The difference between the two approaches is, therefore, given by the different meanings of cash flows associated with debt and equity repayments. Cash flows from operating activities are discounted to present value at the weighted average cost of capital. This configuration of flows offers an evaluation of the whole startup, independently from its financial structure. The value of the debt must be subtracted from the value of the startup to rejoin the value of the market value, obtained through the cash flows for the shareholders.

Value of the firm and cash flows 100

Operating free Cash flow

35 Value of financial debt

Cash flow

Value of equity

To shareholders

to creditors

10

55

15

35

Operating assets

Cash flow

25 t0

Fig. 8.2 Value of the startup and cash flows

40 t1

65

t2

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The relationship between the two concepts of cash flow is as follows: cash flow available to the startup = cash flow available to shareholders + financial charges (net of taxes) + loan repayments − new loans (8.9) Cash flow estimates can be applied to any type of asset. The differential element is represented by their duration. Many assets have a defined time horizon, while others assume a perpetual time horizon such as shares. Cash flows (CF) can, therefore, be estimated using a normalized projection of cash flows that it uses, alternatively: • unlimited capitalization: W1 = C F / i

(8.10)

• limited capitalization: W2 = C F a n − i

(8.11)

where W 1 and W 2 represent the present value of future cash flows. The discount rate to be applied to expected cash flows is determined as the sum of the cost of equity and the cost of debt, appropriately weighted according to the leverage of the startup (the ratio between financial debt and equity). This produces the Weighted Average Cost of Capital (WACC): W ACC = ki (1 − t)

E D + ke D+E D+E

(8.12)

where: k i = cost of debt; t = corporate tax rate; D = market value of debt; E = market value of equity; D + E = raised capital; k e = cost of equity (estimated with the CAPM or the Dividend Discount Model). The cost of debt capital is easy to determine, as it can be inferred from the financial statements of the startup. The cost of equity or share capital, which represents the minimum rate of return required by investors for equity investments, is instead more complex and may use the Capital Asset Pricing Model or the Dividend Discount Model (see Sect. 5.9)—a

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method of valuing a startup’s stock price considering the sum of all its future dividend payments, discounted back to their present value. It is used to value stocks based on the net present value of future dividends. Once the present value of the cash flows has been determined, the calculation of the market value W of the startup may correspond to: (a) the unlevered cash flow approach:

W =

 C F0 +VR−D W ACC

(8.13)

(b) the levered cash flow approach:

W =

 C Fn Ke

+VR

(8.14)

where:   C F0 /W ACC = present value of operating cash flows C Fn /K e = present value of net cash flows VR = terminal (residual) value D = initial net financial position (financial debt—liquidity). The residual value is the result of discounting the value at the time n (before which the cash flows are estimated analytically). As anticipated, it is often the greatest component of the global value (above all in intangible-intensive companies) and tends to zero if the time horizon of the capitalization is infinite (VR/∞ = 0). The two variants (levered versus unlevered) give the same result if the value of the startup, determined through the cash flows available to the lenders, is deducted from the value of the net financial debts. Operating cash flows (unlevered) and net cash flows for shareholders (levered) are determined by comparing the last two balance sheets (to dispose of changes in operating Net Working Capital, fixed assets, financial liabilities, and shareholders’ equity) with the income statement of the last year, as can be seen in Fig. 8.3, that shows the accounting scheme of the cash flow statement (Table 8.1).

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Δ Tangible and intangible fixed assets

Δ Net operating working capital

Δ equity

Market value of equity

Balance sheet Δ Implicit goodwill Δ Equity gain

Δ Net financial position

Intangible gain

Basic capital method

Balance sheetbased

Mixed equity/income method

Invested Capital = raised capital= Enterprise Value Income statement Operating monetary revenues - operating monetary costs (monetary OPEX) = EBITDA - amortization and depreciation = EBIT (A – B) +/- balance of financial management +/- balance of extraordinary operations = Pre-Tax Profit - taxes = Net result

Income method

Cash flow statement EBIT + amortization, depreciation = EBITDA +/- Δ operating net working capital +/- Δ fixed assets = Operating cash flow (unlevered) +/- extraordinary income/expense +/- financial income/expense +/- Δ other activities - taxes +/- Δ financial debts +/- Δ shareholders’ equity = Net Cash Flow

Financial method

Market Prices

Market mulƟplescomparable companies

Market interest rates- other macroeconomic variables

Fig. 8.3 The integrated equity—economic—financial—empirical and market valuation

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Table 8.1 Cash flow statement and link with the cost of capital Cash flow statement EBIT + Depreciation and amortization = EBITDA (A) ± ±

Operating Net Working Capital fixed assets (CAPEX)

= Operating cash flow (unlevered cash flow to the firm) – Free Cash Flow to the Firm (B)

To be discounted at the Weighted average cost of capital (WACC)

– Financial charges ± net financial liabilities ± Extraordinary income and charges – Taxes ± Equity = Net (free) cash flow to the shareholders (levered cash flow – Free Cash Flow to Equity) (C) Reconciliation statement:

To be discounted at the cost of equity (Ke)

Closing cash and cash equivalents – Opening cash and cash equivalents = Change in net cash flow = liquidity (D) = (C)

The net cash flow for the shareholders coincides with the free cash flow to equity and, therefore, with the dividends that can be paid out, once it has been verified that enough internal liquidity resources remain in the startup. This feature, associated with the ability to raise equity from third parties and shareholders, allows the startup to find adequate financial coverage for the investments deemed necessary to maintain the startup’s continuity and remain on the market in economic conditions (minimum objectives). They should allow for the creation of incremental value in favor of shareholders, who are the residual claimants (being, as subscribers of risky capital, the only beneficiaries of the variable net returns, which, as such, are residual and subordinate to the fixed remuneration of the other stakeholders). The estimate of cash flows can be applied to any activity.

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The differential element is service life. Many activities have a defined time horizon, while others assume a perpetual time horizon such as startup shares. The discounted cash flow (DCF) approach can be complemented with real options that incorporate intangible-driven flexibility in the forecasts. DCF is ubiquitous in financial valuation and constitutes the cornerstone of contemporary valuation theory (Singh, 2013). The robustness of the model, as well as its compatibility with the conventional twodimensional risk-return structure of investment appraisal, makes it suited to a multitude of valuations. Accounting standards across the globe recognize the efficacy of this model and advocate its use, wherever practicable. FAS 141 and 142 of the United States and IAS 39 that relate to the accounting of intangible assets, recommend the use of DCF methodology for attributing a value to such assets. Some caveats should be considered. According to OECD (2017): • “Valuation techniques that estimate the discounted value of projected future cash flows derived from the exploitation of the transferred intangible or intangibles can be particularly useful when properly applied. There are many variations of these valuation techniques. In general terms, such techniques measure the value of an intangible by the estimated value of future cash flows it may generate over its expected remaining lifetime. The value can be calculated by discounting the expected future cash flows to present value. Under this approach valuation requires, among other things, defining realistic and reliable financial projections, growth rates, discount rates, the useful life of intangibles, and the tax effects of the transaction. Moreover, it entails consideration of terminal values when appropriate” (par. 6.157). • “When applying valuation techniques, including valuation techniques based on projected cash flows, it is important to recognize that the estimates of value based on such techniques can be volatile. Small changes in one or another of the assumptions underlying the valuation approach or in one or more of the valuation parameters can lead to large differences in the intangible value the approach produces. A small percentage change in the discount rate, a small percentage change in the growth rates assumed in producing financial projections, or a small change in the assumptions regarding the useful life of the intangible can each have a profound effect on the ultimate valuation. Moreover, this

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volatility is often compounded when changes are made simultaneously to two or more valuation assumptions or parameters ” (par. 6.158). • “The reliability of a valuation of a transferred intangible using discounted cash flow valuation techniques is dependent on the accuracy of the projections of future cash flows or income on which the valuation is based” (par. 6.163). • “The discount rate or rates used in converting a stream of projected cash flows into a present value is a critical element of a valuation approach. The discount rate considers the time value of money and the risk or uncertainty of the anticipated cash flows. As small variations in selected discount rates can generate large variations in the calculated value of intangibles using these techniques ” (par. 6.170). • “It should be recognized in determining and evaluating discount rates that in some instances, particularly those associated with the valuation of intangibles still in development, intangibles may be among the riskiest components ” (par. 6.172).

8.6

Empirical Approaches

Comparative approaches are often used for startups that can be appropriately benchmarked, even to (target) seasoned firms. The market value identifies: (a) The value attributable to a share of the equity, expressed at stock exchange prices; (b) The price of the controlling interest or the entire share equity; (c) The traded value for the controlling equity of comparable undertakings; (d) The value derived from the stock exchange quotations of comparable undertakings. Sometimes comparable trades of companies belonging to the same product sector with similar characteristics (in terms of cash flows, sales, costs, etc.) are used. In practice, an examination of the prices used in negotiations with companies in the same sector leads to quantifying average parameters (already examined in Sect. 3.13):

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• • • • •

R. MORO-VISCONTI

Price/EBIT Price/cash-flow Price/book-value Price/earnings Price/dividend

These ratios seek to estimate the average rate to be applied to the startup. However, there may be distorting effects of prices based on special interest rates, on a historical context, on difficulties of comparison, etc. In financial market practice, the multiples methodology is frequently applied. Based on multiples, the startup’s value is derived from the market price profit referring to comparable listed companies, such as net profit, before tax or operating profit, cash flow, equity, or turnover. The attractiveness of the multiples approach stems from its ease of use: multiples can be used to obtain quick but dirty estimates of the startup’s value and are useful when there are many comparable companies listed on the financial markets and the market sets correct prices for them on average. Because of the simplicity of the calculation, these indicators are easily manipulated and susceptible to misuse, especially if they refer to companies that are not entirely similar. Since there are no identical companies in terms of entrepreneurial risk and growth rate, the assumption of multiples for the processing of the valuation can be misleading, bringing to “fake multipliers.” The use of multiples can be implemented through: A. Use of fundamentals; B. Use of comparable data: B.1. Comparable companies; B.2. Comparable transactions. The first approach links multiples to the fundamentals of the startup being assessed: profit growth and cash flow, dividend distribution ratio, and risk. It is equivalent to the use of cash flow discounting approaches. Discount factors incorporate risk. According to OECD (2017): • “When identifying risks in relation to an investment with specificity, it is important to distinguish between the financial risks that are linked

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to the funding provided for the investments and the operational risks that are linked to the operational activities for which the funding is used, such as for example the development risk when the funding is used for developing a new intangible” (par. 6.61). • “Particular types of risk that may have importance in a functional analysis relating to transactions involving intangibles include: (i) risks related to development of intangibles, including the risk that costly research and development or marketing activities will prove to be unsuccessful, and considering the timing of the investment (for example, whether the investment is made at an early stage, mid-way through the development process, or at a late stage will impact the level of the underlying investment risk); (ii) the risk of product obsolescence, including the possibility that technological advances of competitors will adversely affect the value of the intangibles; (iii) infringement risk, including the risk that defense of intangible rights or defense against other persons’ claims of infringement may prove to be time-consuming, costly and/or unavailing; (iv) product liability and similar risks related to products and services based on the intangibles; (v) exploitation risks, uncertainties in relation to the returns to be generated by the intangible” (par. 6.65). • In some industries, products protected by intangibles can become obsolete or uncompetitive in a relatively short period in the absence of continuing development and enhancement of the intangibles. As a result, having access to updates and enhancements can be the difference between deriving a short-term advantage from the intangibles and deriving a longer-term advantage. • The following types of risks, among others, should be considered: – Risks related to the future development of the intangibles. This includes an evaluation of whether the intangibles relate to commercially viable products, whether the intangibles may support commercially viable products in the future, the expected cost of required future development and testing, the likelihood that such development and testing will prove successful and similar considerations.

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– Risks related to product obsolescence and depreciation in the value of the intangibles. This includes an evaluation of the likelihood that competitors will introduce products or services in the future that would materially erode the market for products dependent on the intangibles being analyzed. – Risks related to the infringement of intangible rights. – Product liability and similar risks related to the future use of the intangibles (par. 6.128). For the second approach, it is necessary to distinguish whether it is a valuation of comparable companies or comparable transactions. The comparability concerns different firms but is also related to their contents. Intangible assets are however often hard to compare. According to OECD (2017): • “‘Unique and valuable’ intangibles are those intangibles (i) that are not comparable to intangibles used by or available to parties to potentially comparable transactions, and (ii) whose use in business operations (e.g., manufacturing, provision of services, marketing, sales or administration) is expected to yield greater future economic benefits than would be expected in the absence of the intangible” (par. 6.17) “intangibles often have unique characteristics, and as a result have the potential for generating returns and creating future benefits that could differ widely. In conducting a comparability analysis with regard to a transfer of intangibles, it is, therefore, essential to consider the unique features of the intangibles ” (par. 6.116). • “In conducting a comparability analysis, it may be important to consider the stage of development of particular intangibles ” (par. 6.123). In the case of comparable companies, the approach estimates multiples by observing similar companies. The problem is to determine what is meant by similar companies. In theory, the analyst should check all the variables that influence the multiple. In practice, companies should estimate the most likely price for a nonlisted startup, considering homogeneous listed companies operating in the same sector. Two companies can be defined as homogeneous when they present, for the same risk, similar characteristics, and expectations.

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The calculation is: – A startup whose price is known (P 1 ), – A variable closely related to its value (X 1 ). The ratio (P 1 )/(X 1 ) is assumed to apply to the startup to be valued, for which the size of the reference variable (X 2 ) is known. Therefore: (P1 )/(X 1 ) = (P2 )/(X 2 )

(8.15)

so that the desired value P 2 will be: P2 = X 2 [ (P1 )/(X 1 )]

(8.16)

According to widespread estimates, the main factors to establish whether a startup is comparable are: – Size; – Belonging to the same sector (see for instance the Statistical Classification of Economic Activities in the European Community, commonly referred to as NACE.Rev.2); – Financial risks (leverage); see Huffman (1983); – Historical trends and prospects for the development of results and markets; – Geographical diversification; – Degree of reputation and credibility; – Management skills; – Ability to pay dividends. Founded on comparable transactions, the basis of valuation is information about actual negotiations (or mergers) of similar—i.e., comparable— companies. The use of profitability parameters is usually considered to be the most representative of startup dynamics. Among the empirical criteria, the approach of the multiplier of the EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) is widely diffused, to which the net financial position must be added algebraically, to pass from the estimate of the enterprise value (total value

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of the startup) to that of the equity value (value of the net assets). The formulation is as follows: W = average perspective EBITDA ∗ Enterprise Value / sector EBITDA = Enterprise Value of the startup (8.17) And then: Equity Value = Enterprise Value ± Net Financial Position

8.7

(8.18)

The Control Approach

Once the most suitable evaluation approach has been defined, it might be appropriate to use another evaluation approach, to double-check the evaluation carried out with the main approach. The use of a control approach is applied in all cases where it is possible to estimate the market value of the startup from complementary angles to arrive at a range of values, within which the market value must be positioned. The comparison between the “main approach” and the “control” approach can lead to significant differences in absolute terms, especially if the reference values are high. It is, however, to be considered appropriate if, from a relative point of view, the deviations between the two approaches do not exceed an indicative percentage in the order of 20–25%. Figure 8.3 synthetically represents an integrated valuation, showing how the main approaches may conveniently interact.

References Damodaran, A. (2018). The dark side of valuation. Pearson FT Press PTG. Fazzini, M. (2018). Business valuation: Theory and practice. Cham: Palgrave Macmillan. Fernandez, P. (2001). Valuation using multiples: How do analysts reach their conclusions? Madrid: IESE Business School. Fernandez, P. (2019). Valuation and common sense (7th ed.). Available at SSRN: https://ssrn.com/abstract=2209089.

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Fernandez, P., de Apellániz, E., & Acín, F. J. (2020). Survey: Market risk premium and risk-free rate used for 81 countries in 2020 (March 25). IESE Business School Working Paper No. WP-1244-E. Available at SSRN: https:// ssrn.com/abstract=3560869. Huffman, L. (1983). Operating leverage, financial leverage, and equity risk. Journal of Banking & Finance, 7 (2), 197–212. Kumar, R. (2016). Valuation: Theories and concepts. Amsterdam: Academic Press. Koller, T., & Goedhart, M. (2015). Valuation: Measuring and managing in the value of companies. McKinsey & Company. Lev, B., & Gu, F. (2016). The end of accounting and the path forward for investors and managers. Hoboken, NJ: Wiley. Moro Visconti, R. (2019). How to prepare a business plan with excel (June 1). Available at https://ssrn.com/abstract=2039748. OECD. (2017). Transfer pricing guidelines for multinational enterprises and tax administrations. Available at https://www.oecd.org/tax/oecd-transferpricing-guidelines-for-multinational-enterprises-and-tax-administrations-207 69717.htm. Singh, J. P. (2013). On the intricacies of cash flow corporate valuation. Advances in Management, 6(3), 15–22.

CHAPTER 9

Startup Valuation

9.1

An Adaptation of the General Valuation Approaches

The valuation of a startup (Achleitner, 2005; Batista de Oliveira & Perez Zotes, 2018; Berger & Köhn, 2017; Braun, 2009; Festela et al., 2013; Hering et al., 2006; Jogekar, 2009; Koller & Goedhart, 2015; IPEV, 2018; Nasser, 2016; Polimenis, 2018; Sassi, 2016; Sokol, 2018; Venture valuation, 2019; Trichkova & Kanaryan, 2015; Wasserman, 2017; Havard, 2018) follows the general appraisal rules illustrated in Chapter 8 with some important adaptations suggested by the peculiar nature of these innovative and still immature firms. Because of the peculiar features of early-stage companies, it is not easy to find an adequate method to assess their value. The valuation of a startup is complicated because of the newness of the business and industry, and because of the nature of the business structure (Ali & Khalidi, 2020). Traditional valuation methods are often unsuitable for startups. Therefore, over time, academic literature and experienced investors created alternative and innovative valuation models (Montani et al., 2020). The various appraisal methods (Sivicka, 2018; Akkaya, 2020) include: 1. Discounted Cash Flow (DCF) Method (examined in Sect. 8.5).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_9

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2. Berkus Approach, considering five key success factors: (1) Basic value, (2) Technology, (3) Execution, (4) Strategic relationships in its core market, and (5) Production, and consequent sales. 3. Risk Factor Summation Method, an evolved version of the Berkus Method. It includes Management risk, Stage of the business risk, Legislation/Political risk, Manufacturing risk, Sales and marketing risk, Funding/capital raising risk, Competition risk, Technology risk, Litigation risk, International risk, Reputation risk, Potential lucrative exit risk. 4. Reproduction Cost Approach. The cost to duplicate from scratch is a variant of the cost methodologies illustrated by the International Valuation Standard 210 (§ 130.1): the value [of an intangible asset] is determined based on the replacement cost of a similar asset or an asset providing similar service potential or utility. 5. Future Valuation Method, estimating the return on investment that the investors can expect shortly. 6. Market Multiple Approach (considering comparables, as illustrated in Sect. 8.6). 7. Real options, to assess the flexibility embedded in the startup’s business model (Milanesi et al., 2013), as shown in Sect. 6.5. 8. Fair value—defined in IFRS 13 as “the price that would be received to sell an asset or paid to transfer a liability in an orderly transaction between market participants at the measurement date (an exit price).”

9.2

The IPEV Valuation Guidelines

Assigning a valuation to a startup in the Venture Capital context is remarkably challenging because startup investments are characterized by high risk, high cash burn rates, and asymmetric information (Sahlman, 1990; Sievers et al., 2013). It is so even more important to understand the different determinants that impact startup valuations. The International Private Equity and Venture Capital Valuation (IPEV) Guidelines1 set out recommendations, intended to represent current best practice, on the valuation of Private Capital Investments. 1 IPEV Guidelines, http://www.privateequityvaluation.com/Portals/0/Documents/ Guidelines/IPEV%20Valuation%20Guidelines%20-%20December%202018.pdf?ver=201812-21-085233-863.

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The Valuation Guidelines are applicable across the whole range of Alternative Funds (seed and startup venture capital, buyouts, growth/development capital, infrastructure, credit, etc., collectively referred to as Private Capital Funds) and financial instruments commonly held by such Funds. They provide a basis for valuing Investments by other entities, including Fund-of-Funds, in these Private Capital Funds. Furthermore, the Valuation Guidelines have been prepared with the goal that Fair Value measurements derived when using these guidelines are compliant with both International Financial Reporting Standards (IFRS) and the United States Generally Accepted Accounting Principles (US GAAP). According to the EVCA Guidelines,2 Fair Value is the price that would be received to sell an asset in an Orderly Transaction between Market Participants at the Measurement Date. It is the amount for which an asset could be exchanged between knowledgeable, willing parties in an arm’s length transaction. A Fair Value measurement assumes that a hypothetical transaction to sell an asset takes place in Principal Market or in its absence, the Most Advantageous Market for the asset. For actively traded (quoted) Investments, available market prices will be the exclusive basis for the measurement of Fair Value for identical instruments. For Unquoted Investments, the measurement of Fair Value requires the Valuer to assume the Investment is realized or sold at the Measurement Date whether the instrument or the Investee Startup is prepared for sale or whether its shareholders intend to sell soon. Some Funds invest in multiple securities or tranches of the same Investee Startup. If a Market Participant would be expected to transact all positions in the same underlying Investee Startup simultaneously, for example, separate Investments made in series A, series B, and series C, then Fair Value would be estimated for the aggregate Investment in the Investee Startup. If a Market Participant would be expected to transact separately, for example, purchasing series A independent from series B and series C, or if Debt Investments are purchased independent of equity, then Fair Value would be more appropriately determined for each financial instrument. Fair Value must be estimated using consistent Valuation Techniques from Measurement Date to Measurement Date unless there is a change in market conditions or Investment-specific factors, which would modify 2 https://www.investeurope.eu/uploadedFiles/Home/Toolbox/Industry_Standards/ evca_international_valuation_guidelines_2009.pdf.

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how a Market Participant would determine value. The use of consistent Valuation Techniques for Investments with similar characteristics, industries, and/or geographies would be expected. The Price of a Recent Investment, if resulting from an orderly transaction, generally represents the Fair Value as of the transaction date. At subsequent Measurement Dates, the Price of a Recent Investment may be an appropriate starting point for estimating the Fair Value. However, adequate consideration must be given to the current facts and circumstances, including, but not limited to, changes in the market or changes in the performance of the Investee Startup. Inputs to Valuation Techniques should be calibrated to the Price of a Recent Investment, to the extent appropriate. The valuation of a startup is consistent with its ability to produce expected cash flows and so with the outline of this study, alternatively considering a debt-free (see Chapter 6) or levered context (see Chapter 7). The International Private Equity and Venture Capital Valuation (IPEV) Guidelines (cit.) set out recommendations, intended to represent current best practice, on the valuation of Private Capital Investments. In selecting the appropriate Valuation Technique, the Valuer should use one or more of the following Valuation Techniques as of each Measurement Date, considering Market Participant assumptions as to how Value would be determined: 1. Market Approach • Multiples • Industry Valuation Benchmarks • Available Market Prices 2. Income Approach • Discounted Cash Flows • Replacement Cost Approach • Net Assets

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Discounted Cash Flows (DCF) are traditionally used even for startups, even if their liquidity forecasting is difficult. The general principles shown in Sect. 8.5 apply even for startups. According to Laitinen (2019), in general, DCF is found the most popular method in startup valuation followed by the internal rate of return (IRR) and the payback period methods. However, the consequences of using this method in startup valuation are rarely analyzed in financial research. The use of DCF favors startups that grow slowly and have a short payback period but that also exhibit a high IRR. The longer the time series of the startup used in the analysis, the more significant role IRR tends to play in DCF. To identify the current value of a startup, before an investment is made (pre-money valuation), the Valuation Capital Method can be applied. This valuation approach was first described by Bill Sahlman in the late ‘80s. The basic keywords used in this valuation approach are: • Harvest year: the time (year) that the investor plans to exit the startup; • Pre-money Valuation: the value of the startup before any investment has been made; • Post-money Valuation: the value of the startup after the investment has been made; The formula for post-money valuation is: Post − Money Valuation = Pre − Money Valuation + Investment Amount (9.1)

The Venture Capital Method is organized into a 2-step process: • The terminal value of the business in the harvest year is derived. • The (desired) ROIC (examined in Sect. 3.1.2) and the investment amount are used to derive the pre-money valuation. The return on investment can be estimated by determining what return an investor could expect from that investment with the specific level of risk attached. In calculating the terminal value, the following inputs are required:

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• Projected revenue in the harvest year; • Projected (or industry average) profit margin in the harvest year; • Industry P/E ratio. The formula is: Terminal Value = projected revenue ∗ projected margin ∗ P/E = earnings ∗ P/E (9.2) In the same way, when calculating the pre-money valuation, the inputs needed are: • Required Return on Investment Capital (ROIC) • Investment amount. The formula is: Pre − Money Valuation = Terminal value / ROIC − Investment amount (9.3)

The advantages of the Venture Capital Valuation Method are linked to its simplicity in understanding and implementation.

9.3 The Fair Value of the Investments in the Target Firms The valuation of target companies presupposes a startup valuation. The evaluation methodology should be referred to as the type of startup being evaluated. In most cases in which the appraisal refers to industrial, commercial, or service companies, the evaluation methodologies—even if different, depending on the type of startup—will be those typically adopted for such companies. The general considerations made in Chapter 8 on balance sheet-based, income, mixed (capital-income), market (empirical) approaches may apply to startups, with adaptations that presuppose a preliminary analysis of: • The different business model applicable from time to time, with repercussions on value drivers, the value chain, strategic and market aspects (…);

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• The financial statements (balance sheet, income statement, and cash flow statement); • The presence of accounting parameters relevant to the valuation, as EBIT, EBITDA, the Net Financial Position, etc. In the evaluation of the target startup, especially when estimating the enterprise value, it is necessary to adequately consider the changes in the financial structure induced by the entry of private equity funds. Leveraged buyouts are frequent, through which the fund finances part of its investment making the target startup indebted. Debt sustainability, subject to the startup’s ability to generate adequate cash flows and the level of market interest rates, has a decisive impact in determining the overall risk level of the startup, which in turn influences its value. In private equity buyouts, debt is typically reshaped by type and maturity to make it compatible with the new business plans (Moro Visconti, 2019; see also Chapter 2) formulated with the contribution of the fund and usually characterized by a strategic orientation more oriented toward the creation of value assisted by an extension of debt maturities. Among the various approaches to estimating the fair value, the following matter: • Price of recent investments, including those made by others (a reasonable estimate of fair value, the validity of which erodes rapidly over time); • Income and market multiples (appropriate, sustainable and equitable, usually applicable to consolidated businesses): P/E; EV/EBIT; EV/EBITDA (analyzed in Sect. 3.13) …; • Discounted cash flows, including terminal value; • Sector benchmarks, whenever applicable (e.g., rate of occupancy of hotel rooms; the price per hospital bed; the price per subscriber to cable TV…); • Market prices if the subsidiary is eventually listed. If the fair value is challenging to estimate, the best estimate is often represented by the fair value referred to the previous report, adjusted, if necessary, by applying the impairment test.

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9.4

The Fair Value of the Investments in the Portfolio Companies

The valuation of the investment portfolio is the most critical aspect and the fundamental prerequisite for a valuation of the target startup. This valuation must refer first to the existing holdings in the portfolio and must not prudently consider possible future investments and the ability to expand the portfolio, which relates to purely potential goodwill. In the estimate, the entire investee startup and its underlying business must first be evaluated, and then the fund’s investment in that startup must be evaluated accordingly. The estimate of the periodic performance of private equity funds is essential to establish portfolio benchmarks, the fair remuneration of asset managers, and to analyze the degree of efficiency and sophistication of investors concerning unlisted companies. Once the individual investee companies have been evaluated, the investments in these companies are estimated using a procedure that can be summarized as follows: a. The starting point is the gross equity value of the investee startup, adjusted to consider the surplus assets and estimated by applying the approaches described above; b. The pro-rata share of this Equity Value, based on the fund’s percentage shareholding in the startup; the percentage is adjusted to consider the minority discount or majority premium; the net Equity Value is estimated. If the target is to value a loan (bridge financing or mezzanine loan) instead of a shareholding, the market value of this financial loan must be estimated. According to the valuation guidelines, in private equity, the value is usually crystallized by the sale or listing of the entire investment portfolio, rather than by the sale of individual holdings. The market value of the fund is determined by estimating the adjusted Enterprise Value of each startup, using the most appropriate valuation approaches, and then arriving at an estimate of the fair value of all the investments.

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Startup Evaluation with Binomial Trees

The valuation of startup portfolios can be carried out with binomial trees (grids), frequently used in decision-making processes under uncertainty. Uncertain outcomes can be interpreted even with Monte Carlo methods, traditionally used to solve any problem having a probabilistic interpretation using randomness. Suppose, for example, that a venture capital decides to invest in three startups at the same time, underwriting capital for 100 in each startup and with an exit after 2 years. The three startups have a different risk profile: • The S1 startup has an expected variance in value, on an annual basis, of 20% (defensive investment); • The S2 startup has an expected variance in value, on an annual basis, of 40% (risky investment); • The S3 startup has an expected variance in value, on an annual basis, of 60% (highly speculative investment). The pay-off (expected results) is as follows (Fig. 9.1): The total pay-off is the following (Table 9.1): The total Net Present Value of the three projects, which are considered not mutually exclusive (since venture capital can take over all of them), nor with synergistic effects between them (which is possible, especially if the sectors and business models intersect), is positive if the rate at which they are discounted is less than 13.93%. This rate represents the watershed, which expresses the breakeven point at which the Net Present Value of the project portfolio is zero, calculated through the iterative search for the Internal Rate of Return (which is the rate that makes the NPV = 0). Venture capital must make a comparison between its cost of raising financial resources (weighted average cost of capital, which coincides with the cost of equity capital if the venture capital has not resorted to debt) and the return that the investments offer. In all cases where the expected return is lower than the cost of capital (IRR < WACC), it will not be convenient to undertake the investment. Binomial networks (traditionally used as decision trees or to determine the pricing of options) are flexible: by adapting the parameters of the expected variance of the value and the probability that the value increases (upside potential) or decreases (downside risk), it is possible to estimate, with basically unlimited ramifications, a wide range of scenarios.

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Fig. 9.1 Representation of the pay-off

The possibility of correcting the estimates along the way, refining them based on what happened in the portion of time passed (which declines a chronological process of time decay), represents a further element to improve the forecasts. Timely big data may conveniently be introduced in the model.

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Table 9.1 Pay-off calculation

9.6

The Venture Capital Method

The valuation of a new venture is often considered to be a combative point of negotiation between venture capitalists and entrepreneurs (Dhochak & Doliya, 2020). How to value a new venture is critical in entrepreneurial financing. The attractiveness of the industry, the quality of the founder and top management team, as well as external relationships of a new venture significantly and positively affect its valuation by venture capitalists when it seeks venture capital financing in its early stages of development (Miloud et al., 2012). Startup valuation in the venture capital (VC) context is often said to be more art than science (Köhn, 2018). To identify the current value of a startup, before an investment is made (pre-money valuation), the Valuation Capital Method can be applied. This valuation approach was first described by Sahlman and Scherlis (1987). The basic keywords used in this valuation approach are: • Harvest year: the time (year) that the investor plans to exit the startup;

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• Pre-money Valuation: the value of the startup before any investment has been made; • Post-money Valuation: the value of the startup after the investment has been made; The formula for post-money valuation is3 : Post − Money Valuation = Pre − Money Valuation + Investment Amount (9.4)

The Venture Capital Method is organized into a 2-step process: 1. The terminal value of the business in the harvest year is derived. 2. The (desired) ROIC and the investment amount are used to derive the pre-money valuation. The return on investment can be estimated by determining what return an investor could expect from that investment with the specific level of risk attached. In calculating the terminal value, the following inputs are required: • Projected revenue in the harvest year; • Projected (or industry average) profit margin in the harvest year; • Industry P/E ratio. The formula is as follows: Terminal Value = projected revenue ∗ projected margin ∗ P/E Terminal Value = earnings ∗ P/E (9.5) In the same way, when calculating the pre-money valuation, the inputs needed are: • Required Return on Investment Capital (ROIC) • Investment amount. 3 An Excel application can be found in, https://docs.google.com/spreadsheets/d/1BivgVc5VtXjlbrmA_Fn3r4a8JLidtS6CKCnwQGUnAs/edit#gid=35.

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• The formula is as follows:

Pre − Money Valuation = Terminal value / ROIC−Investment amount (9.6) Besides the industry average P/E ratio, many of the required inputs are based on not so reliable assumptions. It follows that, if one feeds the model with wrong assumptions, a wrong value will be derived.

9.7 The Break-up Value of Venture-Backed Companies Most venture-backed investments are directed toward companies that are unable to takeoff and that will not only never reach the stock exchange listing, which is the primary exit approach for the intermediary but are typically tricky to demobilize. When they are, the disposal of the investment typically takes place at a significant discount compared to the investments made by the venture capitalist. This may be the case for startups in which a private equity fund has an interest, although in this case the target startup is typically at a more advanced stage of its life cycle and is generally more likely to limit losses in value, thanks to a more stable and well-established business model. If going concern is irreparably compromised and no more shareholders are willing to recapitalize the startup, which is typically affected by cash and equity burnouts, different prospects open up. Scenarios range from a liquidation, with disposal of attractive business units or sale of individual assets, to insolvency, if the market value of the assets realized is not sufficient to meet the liabilities (typical event of equity burnout, in which the shareholders’ equity has zeroed or even become negative). The breakup value of the assets represents the lower limit in the valuations and is related to the possibility of selling them to third parties. Venture-backed companies are typically represented by a composition of assets in which intangibles (that fuel the present value of growth opportunities) have a predominant weight. The existence of intangible assets, especially if they are not included in the balance sheet, limits the startup’s capacity for indebtedness (as shown

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in Chapters 6 and 7) because of the difficulty of establishing a guaranteed title on them and their uncertain or sometimes nonexistent market value. The idea that the capacity for indebtedness increases in the presence of tangible assets with collateral value on which a guarantee can be provided is confirmed by numerous studies. The fact that venture-backed startups have a portfolio of assets with a book value and—a fortiori—a market value represented essentially by intangibles sharply limits their ability to borrow but at the same time minimizes conflicts of interest between shareholders and third-party creditors, which are typical of situations of insolvency or prodromal to a state of crisis (Moro Visconti, 2015). The transition from a going concern scenario to a break-up context implies the disappearance of the startup’s income expectations. It only considers the market value of the individual assets (including the intangibles). The market value is usually well below the operating value and sometimes even below the book value (as is the case for intangibles without market value): using a concept introduced by Adam Smith, the exchange value is prevalent over the value in use of the individual asset. The presence in the startup of highly specialized activities (firm-specific) increases the difference between the value in use and exchange and, while it makes these activities more challenging to sell, it reduces agency costs between shareholders and creditors, since the former will have greater difficulties in making substitutions with other activities, making it more challenging—but less necessary—to collateralize them (Smith & Warner, 1979). The market value of an intangible (as well as a tangible asset) depends, to a large extent, on the existence of a large and well-established secondary market for the goods being traded. The secondary market is reduced by the more specific the intangible is, even though it is usually more value added (Titman & Wessels, 1988). Internally generated goodwill (not accounted for, as the startup has not paid a sum in this regard) of the venture capitalist is incorporated in the value of the investment in the venture-backed startup. In the case of bad will related to a startup in crisis, it emerges the need to write-off the investment, with a significant impact on the shareholders’ equity of the intermediary. Equity can be eroded substantially, especially if the write-off is material compared to a portfolio of investments in other assets unable to offset the loss of some initiatives with the gains of others.

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9.8 Stock Exchange Listing and Other Exit Procedures The listing of the venture-backed startup on the stock exchange is the traditional way for the venture capitalist to exit the investment. The fact that this intermediary typically represents a minority facilitates the sale of the share package, on the occasion of the Initial Public Offering and/or subsequently and does not help to signal to the market any feeling of distrust since the market knows in advance the nature and mission of the intermediary. If the startup to be listed has a value that is strongly influenced by the permanence of some key managers (typically historical shareholders of the startup), it may require them to have a lock-up period. This constraint is not imposed on the intermediary, for the reasons mentioned above and for the disappearance of its strategic role (the financial resources are guaranteed by the stock market and the advisory service of the intermediary may continue, if necessary, through external consultancy). The stock market, when the time windows that allow divestments—usually fractioned—are opened to key managers bound by lock-up clauses, questions their behavior (often reacting negatively to their exit, knowing that they have privileged information). The exit of some shareholders and, more generally, the purchases and sales, are influenced by the degree of liquidity of the stock, which is expressed in the ability to allow significant purchases or sales without having a significant impact on the price. In an illiquid market, which characterizes thin stocks and is typical of technological companies, strongly growing and with recent history, the price typically moves against the shareholder who carries out the transaction. The intermediary plays an active role in assisting the startup in the listing process, not only because it is involved in the success of the operation but because of its privileged relations with the financial community and with the intermediary that takes care of the IPO (Aggarwal et al., 2009). With the quotation on the stock exchange (going public), the structure of the stakeholders is divided, and the process of fragmentation involves the passage from concentrated shareholders to a more intense fragmentation. This transition can preserve some reference shareholders, expression of a majority (individually or—more frequently, especially as the size increases—through coalitions of shareholders through syndicate

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agreements). A public company model may apply, in which many small shareholders live together, none of whom can exert a significant influence on the former startup (in this context, the power of management is growing, and this can lead to a loss of value, counterbalanced by the value of the contestability of control). The majority premium inherent in the controlling shareholding is gradually eroded with the splitting up of the equity until it tends to zero in a public company. Stock exchange listing creates intrinsic value not only by allowing the intermediary shareholder (or other shareholders, subject to any lock-up constraints) to sell all or part of their holding but—above all—by facilitating the search for a counterparty, to the point of making it anonymous and, in a liquid market, free of implicit costs. With the listing, the so-called lack of marketability discount, which consists of the depreciation usually applied to unlisted companies, is eliminated. As a result, unlisted companies have traditionally been granted a discount resulting from the lack of marketability discount. Empirical evidence allows identifying a range of variation of the discount for lack of marketability (Novak, 2016).

9.9 Valuation of the Investment Portfolio with a Net Asset Value The valuation of venture capital or private equity fund must be based on the book value of equity, to which the market value of the investments must be added, and the book value of the investments subtracted. The market value of investments is based on their Net Asset Value and any capital gain over their book value must be expressed, whenever appropriate (where the participation exemption is not applicable …), net of potential tax charges. In formulae: Net Asset Value = NAV = Market Value of the Fund = Book Value of the Fund + (market value - book value of the investments) (1− tax rate) (9.7) The valuation of equity investments at fair value, applying international accounting standards (IAS/IFRS), should decrease the difference between the market and the book value of equity investments and the book value of shareholders’ equity will already reflect the market value of the venture capital or private equity fund.

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In the case of the valuation of venture capital or private equity funds, the following aspects should be considered: • The value of each subsidiary must be estimated by discounting it at the cost of capital that incorporates the systematic risk of the most similar stock index (Nasdaq, Numtel …), increased by a firm-specific risk premium, as to include the lack of marketability, the volatility of economic and financial flows (higher in startups …); • The valuation of the shareholding must sometimes consider the size of the package, which may be subject to a minority discount (i.e., less frequently—a specular majority premium) in the absence of co-sale options, non-participation in syndicate agreements, etc.; • The financial flexibility of the intermediary, which may or may not intervene in the event of cash or equity burnout of the investee startup, with the possibility of retaining at least part of its value (post-equity recapitalization burnout involves a reshuffling of the shareholding structure, in the event of failure by all shareholders to exercise the option right); • There may be a synergistic value of the investment portfolio (if it relates to vertically integrated companies operating in contiguous segments of the value chain …), such that the market value of that portfolio is higher than the sum of the NAVs per share of each holding. This synergistic value expresses goodwill not accounted for but considered in the estimate of the market value; • The holding of a stake can generate management consulting services, placement fees, intermediation, which must be independently evaluated; • The intermediary has its intrinsic value linked to its portfolio of holdings but dependent on its reputation (which is an asset of primary importance for any financial intermediary).

9.10

Unicorns

In the venture capital industry, a unicorn refers to any tech startup founded after 2003 and reaching 1 billion-dollar market value, as determined by private or public investment. Since the term was coined in 2013 by the founder of Cowboy Ventures, Aileen Lee, the number of unicorns has increased manifold.

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According to the Economist (2019), 156 unicorns exist worldwide, and the top five are: • • • • •

Uber, with a valuation of $68 billion; Didi Chuxing, with a valuation of $56 billion; Xiomi, with a valuation of $46 billion; Meituan Dianping, with a valuation of $30 billion; and Airbnb, with a valuation of $29.3 billion.

What most characterizes these innovative businesses has to do with their being data providers and innovation disruptors, given their Internetfocused business models. Digital data are to this century what oil was to the last one: a driver of growth and change. Digital information is unlike any previous resource; it is extracted, refined, valued, bought, and sold in different ways. The potential for digital scalability in the unicorn businesses is confirmed by their ability to provide customers with the same products and services as traditional companies while cutting on physical plant, staff, and other expenses thanks to the online digital platforms put in place. For example, Uber came into the market as a ride-sharing app and it became the best known and most valuable startup because it owns the biggest pool of data about supply (drivers) and demand (passengers) for personal transportation. Uber’s business model is based on a digital platform which makes it possible for people to simply tap their smartphone and have a cab arrive at their location in the minimum possible time. According to this innovative B2C mechanism, Uber customers would eventually be both the passenger booking a cab, as well as the driver (not official taxi driver) offering the lift. Today there is a “new regime of company formation” (Kenney & Zysman, 2019). The design and manufacture of unicorns have become gradually industrialized, and many of the ingredients needed are available on tap as online services. Smartphones let companies distribute what they offer at home and abroad, social media allow them to market it, and cloud computing lets them ramp as demand grows. If from one side unicorns are a paradigm representing the dream for any startup, on the other side there is still some concern about their reliability.

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While the production of unicorns gathered pace and slickness, their disposal did not keep up. The rate at which venture-backed companies make public offerings has slowed. New forms of regulation came out after the dot-com bubble burst, which gave protection to investors and increased the number of shareholders beyond which startups must disclose financial information, thus making going public much riskier.4 And there was no significant shortage of private capital willing, indeed eager, to help with that. As Komisar, a venture capitalist at Kleiner Perkins said: “Silicon Valley’s lust for scaling … is more a result of the desires of capital than the needs of innovation”.5 Several factors have come together to bring this period of reticence to an end. For example, a lot of venture-capital funds were started around 2010, and they mostly have a ten-year term; investors now want to cash out. Several public listings in 2018 showed that markets have a bigger appetite for tech shares. And the window of opportunity may soon close, meaning that a global downturn would both limit investors’ appetite and severely test some of the unicorns’ business models. Much the same might happen if several IPOs failed to live up to their hype. So again, the incentives are to go big and go quick. To get a sense of the going, The Economist (2019) has examined a panel of a dozen former and current Internet-focused unicorns in Silicon Valley and elsewhere. This one includes most of the larger prospects and covers a range of industries. Uber and Lyft are in transport, Spotify in music-streaming, WeWork in real estate, Meituan, and Pinduoduo in Chinese e-commerce. Given they offer the same opportunity as their precursors but through more innovative and efficient means, these businesses, which are now only a subset of their market, may hope to dominate soon. Apart from the issues described above, what they lack, are profits. Today, according to Gao et al. (2013) 84% of companies pursuing IPOs have no profits, while ten years ago, this proportion was just 33%. If all this dearly bought growth has not supplied profits, what will happen? The answers for the

4 Sarbanes-Oxley Act and JOBS Act of 2012. 5 https://pdfs.semanticscholar.org/3087/addf40b7ed3215423ca6286f0c4c0a7cad23.

pdf.

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unicorns could be more growth, more spending by existing customers, and higher margins. However, the first is not necessarily that plausible. Among the companies that disclosed the number of customers they have in America, growth slowed to 9% in 2018. Moreover, few of the firms sit behind barriers to entry as strong as those that protected Alibaba, Facebook, and Google. They can lose customers as well as gain them. Lots of property companies can rent out office space, as WeWork does. Spotify customers can get music from Apple, too. Drivers often toggle between Lyft and Uber apps; so, do passengers. There are already several big Chinese e-commerce firms to choose from. None of these considerations necessarily mean unicorn startups are bad businesses. But they do make them look like pricey ones. Another growing concern is that innovation produced by some unicorns does not leave society better off than it is intended to. There are real benefits, but critics point to real downsides, as increased congestion and other environmental costs, a weakening of public transport systems, and the precarious lives of the workers who make these platforms function.

9.11 Key Person Discounts, Founder Control, and Governance Implications A further characteristic of young firms is that they are typically highly dependent on the founder/owner and a few other key people until the firm gets sufficiently stable and large. The impact of key person losses on the value can be significant, especially if the replacement is challenging. According to Damodaran (2018), the key person discount can be estimated as follows:  key person discount =

Value of Firmsstatus quo − Value of firmkey Value of Firmstatus quo

 person lost

(9.8)

A fair estimation of the discount is in practice extremely difficult, and often the loss of key people is a major threat to business continuity, with a strong impact on value. Does the degree to which founders keep control of their startups affects company value? Wasserman (2017) argues that founders face a

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“control dilemma” in which a startup’s resource dependence drives a wedge between the startup’s value and the founder’s ability to retain control of decision-making. The composition of the equity-holders, the diluting role of founders, and their relations with new shareholders (family & friends, business angels, crowdfunding underwriters, venture capital, private equity, up to the stock market if the firm is eventually listed) has a deep impact on the governance equilibria, and on the degree of disclosure of information asymmetries. Managerial control and monitoring is also affected by the changing ruling guidelines. Factors such as bargaining power, monitoring costs, private benefits, and risk aversion impact the allocation of control rights (Wang et al., 2017).

9.12

A Practical Valuation Case

This valuation example is taken from a real case, duly anonymized. The startup is represented by a food processing firm that operates in a mature market with an innovative business model. Its scalability is somewhat limited but the same applies to the overall market risk. This template can be easily extended to other startups, even operating in different businesses. The Excel files are here simply “copied and pasted” from a repository of the author. The two methodologies used are the DCFequity and the market comparables (to assess the Enterprise Value and then the Equity Value). The target of this estimate is to assess the fair (equity) market value for potential new investors. Startup Epsilon is evaluated at the end of year 0 and has a business plan forecast (consistent with the insights illustrated in Chapter 2) for the following five years. The two methodologies are described below. To Estimate the DCFequity, we first need to estimate the forecast EBITDA, considering both the (short) history of the startup and mainly its market perspectives. The EBITDA is the key variable that brings to the operating and then net cash flow that must be discounted using the cost of equity, to estimate the DCFequity (Table 9.2). The EBITDA brings to the net cash flow following the passages described in Sect. 2.4. The net cash flow is then discounted year after

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Table 9.2 Mean forecast EBITDA EBITDA mean year 1 to year 5

€ €

year 1 1,287,000 1,875,600

€

year 2 1,644,000

€

year 3 2,008,000

€

year 4 2,145,000

€

year 5 2,294,000

year with a (constant) cost of equity (Ke)6 that is calculated with the Capital Asset Pricing Model formula: E(Ri ) = R f + βi [E(Rm ) − R f ]

(9.9)

where: E(Ri ) = expected return of investment Rf = risk-free rate βi = beta of the investment (sensitivity to the market index) E(Rm ) − Rf = market risk premium. It is normal to assess a terminal value since the business model goes beyond the proposed five-year “gosplan” (already difficult to estimate). As shown below, the Terminal Value is calculated with the “exit multiple” methodology (Ouidad, 2010). Exit multiple7 is one of the methods used to calculate the terminal value of the free cash flows of a startup. Terminal value refers to the value at a future point in time of all future cash flows of a company when the growth rate is expected to be consistent and stable. However, terminal value limits the cash flow projections to a several-year period. Usually, predicting the value of a business (Krihna et al., 2016) beyond such a period is often impossible, and it exposes such a process to a variety of risks that question the validity of the forecast. Terminal value 6

Sources: Beta risk free MRP I

http://pages.stern.nyu.edu/~adamodar/New_Home_Page/da[Total beta by industry sector, Europe, Jan 2020, Food Processing, Average Levered Beta] http://www.dt.mef.gov.it/export/sites/sitodt/modules/documenti_it/debito_pubblico/risultati_aste/risultati_aste_btp_5_anni/BTP_5_Anni_Risultati_Asta_del_27-28.02.2020.pdf http://pages.stern.nyu.edu/~adamodar/New_Home_Page/da[Risk premium for other markets, Jan 2020, ERP + CRP, Italy]

7 See https://corporatefinanceinstitute.com/resources/knowledge/finance/exit-mul tiple/#:~:text=Exit%20multiple%20is%20one%20of,the%20existing%20public%20market% 20valuations.

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addresses such limitations by allowing the inclusion of future cash flow values beyond the projection period while mitigating any issues that may arise from using the values of such cash flows. The very fact that the Terminal Value represents most of the total value, as shown in this case, is frequent in startup valuation, but should anyway be considered with caution, and periodically re-checked updating the estimates. Overwhelming Terminal Value is a typical matter of discussion between existing shareholders and potential newcomers. While the former stick on it to back what they consider a minimum value, incoming shareholders tend to be skeptical, and unwilling to pay for mighty future value. A possible compromise solution may consist of an earn-out clause (linked to real options), according to which the Terminal Value is paid to historic shareholders only when and if it concretely materializes. Business planned milestones represent the expected hard evidence that backs earn-out triggering. The discounting passages of the Net Cash Flow are the following (Table 9.3): The comparables are found using the Nace Rev.2 industry code and then selecting them from a databank after careful scrutiny (Table 9.4): The Net Financial Position (NFP) at the end of year 0 (reference date of the estimate) is considered to pass from the Enterprise Value (estimated through DCF of operating flows—not reported here for simplicity—or through market multipliers of the EBITDA) to the Equity Value that corresponds to the final target of the appraisal. It should be noted that NFP is the only “punctual” parameter with a “photo” at the reference date of the estimate. All the other sensitive parameters are perspective. Table 9.3 From the net cash flow to the equity value year 1 1

year 2 2

year 3 3

year 4 4

year 5 5

Terminal Value 6

Net Cash Flow (NCF) € 6,000 € 277,000 € 609,000 € 714,000 € 994,000 € 4,326,359 exit multiple Discount factor 0.933415723 0.871264912 0.813252367 0.759102546 0.708558252 0.661379413 Discounted NCF € 5,600 € 241,340 € 495,271 € 541,999 € 704,307 € 2,861,365 Equity value €

Ke Beta Risk free rate Market risk premium Ke

4,849,883

0.71 Damodaran [ 2020] 0.36% Long-Term Treasury Bonds 9.54% Damodaran [2020] 7.13%

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Table 9.4 Listed comparables Comparables

Listed Competitor Alpha Listed Competitor Beta Listed Competitor Gamma

Country

US IT JP

Nace Rev. 2

4632 4729 4617

Average

year 0

EV/EBITDA year - 1

year - 2

5.28 13.49 7.14

5.56 n.a. 8.40

5.82 9.00 6.91

5.55 11.25 7.48

Average

8.09

Table 9.5 Net financial position Net Financial Position

End of year 0 Value

Item Debts owed to quotaholders Short-term Debts owed to banks Long-Term Debts owed to banks Liquidity Total NFP (end of year 0)

-€ -€ -€ -€

212,000 1,007,000 527,000 0 1,746,000

It may also be possible to estimate a perspective NFP, but this assessment is arbitrary for a startup with a little history and a volatile outlook (Table 9.5). The adjusted multiple (taken from the Orbis database) starts from the average of the selected sample and then incorporates a dimensional and illiquidity discount. This is often necessary since it may be inappropriate to compare established listed firms with a promising but still unlisted and small startup (Table 9.6). The valuation synthesis with the two described methods is reported in Table 9.7. It is also advisable to make a sensitivity analysis of the main input parameters of the two methods. Startup estimates are, as known, difficult and volatile, and they need constant refreshing, possibly exploiting realtime big data sources and augmented business planning (see Sect. 2.12). The sensitivity starts from the “base case” estimating EBITDA variations from −20% to +20% and cost of equity (ke) variations from −2% to +2%, as shown in Table 9.8. Cost of equity (ke) variations are calculated as follows (Table 9.9): Wise evaluators are aware of the difficulties to make a fair market value estimate. The caveats may be softened with periodical updating

9

STARTUP VALUATION

Table 9.6 Adjusted multiple of the EBITDA EBITDA EBITDA [year 1 to year 5] * adj. multiple [EV/EBITDA] = Enterprise value +/- Net Financial Position = Equity value

€ € -€ €

1,875,600 3.24 6,072,359 1,746,000 end of year 1 4,326,359

Orbis MULTIPLE (EV/EBITDA) years from -2 to 0 Listed Competitor Alpha 5.55 Listed Competitor Beta 11.25 Listed Competitor Gamma 7.48 Average 8.09 Dimensional discount 25% Illiquidity discount 35% Total discount 60% Adjusted multiple 3.24

EBITDA mean year 1 to year 5

€ €

year 1 1,287,000 1,875,600

Table 9.7 Synthetic valuation

Table 9.8 Sensitivity analysis

€

year 2 1,644,000

€

year 3 2,008,000

€

year 4 2,145,000

€

year 5 2,294,000

237

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Table 9.9 Cost of equity (ke) sensitivity Ke year

5.13% year 1 1

year 2 2

year 3 3

year 4 4

year 5 5

Terminal Value 6

Levered Cash Flow (LCF) € 6,000 € 277,000 € 609,000 € 714,000 € 994,000 € 4,326,359 Discount factor 0.95117251 0.904729144 0.860553492 0.818534825 0.778567824 0.740552312 Discounted LCF € 5,707 € 250,610 € 524,077 € 584,434 € 773,896 € 3,203,895 Equity value € 5,342,620 Ke year

6.13% year 1 1

year 2 2

year 3 3

year 4 4

year 5 5

Terminal Value 6

Levered Cash Flow (LCF) € 6,000 € 277,000 € 609,000 € 714,000 € 994,000 € 4,326,359 Discount factor 0.942210463 0.887760557 0.836457286 0.788118807 0.742573787 0.699660792 Discounted LCF € 5,653 € 245,910 € 509,402 € 562,717 € 738,118 € 3,026,984 Equity value € 5,088,784 Ke year

8.13% year 1 1

year 2 2

year 3 3

year 4 4

year 5 5

Terminal Value 6

Levered Cash Flow (LCF) € 6,000 € 277,000 € 609,000 € 714,000 € 994,000 € 4,326,359 Discount factor 0.924783647 0.855224794 0.790897903 0.731409447 0.676395496 0.625519494 Discounted LCF € 5,549 € 236,897 € 481,657 € 522,226 € 672,337 € 2,706,222 Equity value € 4,624,888 Ke year

9.13% year 1 1

year 2 2

year 3 3

year 4 4

year 5 5

Terminal Value 6

Levered Cash Flow (LCF) € 6,000 € 277,000 € 609,000 € 714,000 € 994,000 € 4,326,359 Discount factor 0.916309764 0.839623584 0.769355288 0.704967762 0.645968844 0.591907559 Discounted LCF € 5,498 € 232,576 € 468,537 € 503,347 € 642,093 € 2,560,805 Equity value € 4,412,856

(refreshing) of the appraisal, incorporating in the model the timely market evidence with bottom-up feedbacks (illustrated in Fig. 2.15). This sample starts from the EBITDA but what mostly matters is represented but its constituting elements, expressed by the difference between expected revenues and monetary OPEX. The assessment of the revenue model is, probably, the most difficult but also fascinating task.

References Achleitner, A. K. (2005). First Chicago method: Alternative approach to valuing innovative startups in the context of Venture capital financing rounds. Betriebswirtschaftliche Forschung und Praxis, 57 (4), 333–347. Aggarwal, R., Bhagat, S., & Rangan, S. (2009). The impact of fundamentals on IPO valuation. Financial Management, 38(2), 253–284.

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Akkaya, M. (2020). Startup valuation: Theories, models, and future, valuation challenges and solutions in contemporary businesses. ICI Global Publisher. Ali, S. B., & Khalidi, M. A. (2020). Valuation of equity securities, private firms, and startups. IBT Journal of Business Studies, 16, 125–140. Batista de Oliveira, F., & Perez Zotes, L. (2018). Valuation methodologies for business startups: A bibliographical study and survey. Brazilian Journal of Operations & Production Management, 15(1), 96–111. Berger, E. S. C., & Köhn, A. (2017, November). Exploring differences in earlystage startup valuation across countries. Academy of Management. Braun, R. (2009). Risk of private equity fund-of fund investments—A detailed cash flow-based approach. SSRN Electronic Journal. https://ssrn.com/abs tract=1368277. Damodaran, A. (2018). The dark side of valuation. Pearson FT Press PTG. Dhochak, M., & Doliya, P. (2020). Valuation of a startup: Moving towards strategic approaches. Journal of Multi-Criteria Decision Analysis, 27 (1–2), 39–49. Economist. (2019). Herd instincts—The wave of unicorn IPOs reveals Silicon Valley’s groupthink. Available at https://www.economist.com/briefing/ 2019/04/17/the-wave-of-unicorn-ipos-reveals-silicon-valleys-groupthink. Festela, G., Wuermseherb, M., & Cattaneo, G. (2013). Valuation of early-stage high-tech startup companies. International Journal of Business, 18. Gao, X., Ritter, J. R., & Zhu, Z. (2013). Where have all the IPOs gone? Journal of Financial and Quantitative Analysis, 48(6), 1663–1692. Havard, E. (2018). Internet startups’ profit dilemma: A theoretical paper on using two-sided markets theory as a framework in a valuation setting. The Arctic University of Norway. Hering, T., Olbrich, M., & Steinrucke, M. (2006). Valuation of startup internet companies. International Journal of Technology Management, 33(44), 406– 419. IPEV. (2018). IPEV guidelines. Available at http://www.privateequityvaluation. com/Portals/0/Documents/Guidelines/IPEV%20Valuation%20Guidelines% 20-%20December%202018.pdf?ver=2018-12-21-085233-863. Jogekar, N. (2009). Marketing R&D, and startup valuation. IEE Transaction on Engineering Management. Kenney, M., & Zysman, J. (2019). Unicorns, Cheshire cats, and the new dilemmas of entrepreneurial finance. Venture Capital, 21. Köhn, A. (2018). The determinants of startup valuation in the venture capital context: A systematic review and avenues for future research. Management Review Quarterly, 68, 3–36. Koller, T., & Goedhart, M. (2015). Valuation: Measuring and managing the value of companies. McKinsey & Company.

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Krihna, A., Agrawal, A., & Choudhary, A. (2016). Predicting the outcome of startups: Less failure, more success. Paper presented at IEEE 16th International Conference on Data Mining Workshops (ICDMW). Laitinen, E. K. (2019). Discounted Cash Flow (DCF) as a measure of startup financial success. Theoretical Economics Letters, 9(8). Milanesi, G., Pesce, G., & Alabi, E. E. (2013). Technology-based startup valuation using real options with Edgeworth expansion. Journal of Finance and Accounting, 1(2), 54–61. Miloud, T., Cabrol, M., & Aspelund, A. (2012). Startup valuation by venture capitalists: An empirical study. Venture Capital, an International Journal of Entrepreneurial Finance, 14(2–3). Montani, D., Gervasio, D., & Pulcini, A. (2020). Startup company valuation: The state of art and future trends. International Business Research, 13(9), 31–45. Moro Visconti, R. (2015). Leveraging value with intangibles: More guarantees with less collateral? Corporate Ownership & Control, 241–252. Moro Visconti, R. (2019, June 1). How to prepare a business plan with excel. Available at https://ssrn.com/abstract=2039748. Nasser, S. (2016). Valuation for startups—9 methods explained. ICT Strategic Consulting. Novak, N. P. (2016). Measuring the discounts for lack of marketability for noncontrolling nonmarketable ownership interests. Insights. Ouidad, Y. (2010, March 20). Exit routes in LBO projects. SSRN: https://ssrn. com/abstract=1552316. Polimenis, V. (2018). Valuation issues with early equity finance. Hephaestus Research Repository, NUP Academic Publications, School of Economic Sciences and Business. Sahlman, W. A. (1990). The structure and governance of venture-capital organizations. Journal of Financial Economics, 27 (2), 473–521. Sahlman, W. A., & Scherlis, D. R. (1987, July). A method for valuing highrisk, long-term investments: The “venture capital method”. Harvard Business School Background Note, 288–006 (Revised October 2009). Sassi, R. (2016). An improved valuation method for startups in the social-media industry. RUN Nova School of Business and Economics (NSBE). Sievers, S., Mokwa, C. F., & Keienburg, G. (2013). The relevance of financial versus non-financial information for the valuation of venture capital-backed firms. European Accounting Review, 22(3), 467–511. Sivicka, J. O. (2018). Features of valuation of startup companies. Economic Scope, 132. Smith, C. W., & Warner, J. (1979). On financial contracting: An analysis of bond covenants. Journal of Financial Economics, 7 (2), 117–161. Sokol, M. (2018). What drives the magic of startups? People & Strategy, 4.

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PART II

Industry Applications

CHAPTER 10

FinTech Valuation

10.1

Introduction

Financial technology companies (FinTechs) are gaining momentum, fueled by drivers such as the sharing economy, and include peer-to-peer lending platforms that have opened marketplaces for multiple economic actors and enabled the co-creation of value as Uber has for cars (Hommel & Bican, 2020). Technological startups include companies operating in the FinTech segment, providing services and financial products with ICT technologies. FinTechs reformulate business models (Schallmo & Williams, 2018; Gomber et al., 2018), making use of innovative software and algorithms, value chains based on interactive computer platforms, artificial intelligence, and big data. Financial services, which focus on the transmission of information on digital platforms, rely on innovative activities (Sironi, 2016) concerning the processing of data and their interpretation in real-time with automated descriptive, prescriptive, and predictive technologies. FinTech (Fatás, 2019) has become a hot term due to many driven forces, which include technical development, business innovation expectations (market), cost-saving requirements, and customer demands (Gai et al., 2018). Other factors concern the regulatory framework and the macroeconomic scenario characterized by low-interest rates, leading to

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_10

245

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a reduction of the institutions’ profitability, and promoting investments aimed to increase the organizations’ efficiency (Piobbici et al., 2019). FinTech refers to a vast and diverse industry that disrupts the industry (Vives, 2019), solving friction points for consumers and businesses. The banking industry is facing radical transformation and restructuring, as well as a move toward a customer-centric platform-based model. The competition will increase as new players enter the industry, but the long-term impact is more open. The regulation will decisively influence to what extent BigTech will enter the industry and who the dominant players will be. The challenge for regulators will be to keep a level playing field that strikes the right balance between fostering innovation (Chen et al., 2019) and preserving financial stability. Consumer protection concerns rise to the forefront (Vives, 2019). The main areas of activity are (Haddad & Hornuf, 2019; Gai et al., 2018; Sarhan, 2020): • Financial technologies applied to blockchains (Skinner, 2016) and distributed ledger technology based on data archives, whose records are public on a computer network and without the need for a central register; • Crypto and digital money; • Peer-to-peer loans (P2P); • Smart contracts (using the blockchain) that automatically execute contracts between buyers and sellers; • Open banking supported by the blockchain applications that create a service through a connected network of financial institutions and third-party providers. • IT security, through or decentralized storage of data, and anti-fraud systems; • Applications in the insurance field (InsurTech) or regulation (RegTech); • Asset management (robo-advice, social trading, wealth management, personal financial management apps, or software). Figure 10.1 contains a complementary taxonomy of the main FinTech areas (Eickhoff et al., 2017; Gimpel et al., 2018; Lee & Shin, 2018).

10

Payments

Investments

FINTECH VALUATION

MicroFinTech

BlockChains

(Cyber) Security

Data AnalyƟcs & Planning

Asset Management

FinTechs

Credit / Debit Cards

Banking as a Service

Crowd-

PropTech

Funding

InsurTech

Fig. 10.1

247

RegTech / SupTech

(Crowd) Lending

Main FinTech activities

While cryptocurrencies raise several ethical concerns, including the lack of market transparency, controls, and money laundering, other blockchain applications are based on more solid perspectives. The valuation issues of FinTech companies must be adapted to often young companies, given the novelty of the sector, which have all the prerogatives of startups (in terms of expected growth, survival rate, volatility, etc. …). The valuation methodologies must consider first the underlying business model. According to Accenture (2016), there are two types of FinTech companies: competitive and collaborative. Competitive companies are mature firms, not necessarily specializing in FinTech, looking to squeeze out

248

R. MORO-VISCONTI

new competitors applying lower prices. In this case, it would be any of the previously mentioned larger companies, as they make up the bulk of investments in FinTech. Collaborative companies are those who offer services to enhance the position of competitors.

10.2 The Ecosystem: Digital Platforms and Multilayer Networks The digital ecosystem (Drummer et al., 2016; see also Chapter 12) is a prerequisite for the evaluation of any FinTech. PoliticalEconomic-Sociocultural-Technological-Legal-Environmental (PESTLE) analysis may help in this preliminary activity. In particular: • Political factors concern governmental policies to control the banking industry; • Economic factors are influenced by expected savings and competitivity gains; • Sociocultural influences concern the changing attitudes and necessities of consumers that look for a seamless banking experience; • Technological factors are the engine behind FinTech; • Legal issues are concerned with the regulation of the industry and the consistency of FinTech products and services with banking rules; • Environmental concerns may be softened with paperless digital choices. Platforms are digital enablers and facilitators of exchange (of goods, services, and information) between different types of stakeholders that could not otherwise interact with each other. Transactions are mediated through complementary players that share a network ecosystem (Rochet & Tirole, 2003; Armstrong, 2006). Due to their digital characteristics, they have a global outreach that gives them the potential to scale. FinTechs find their rationale and natural habitat in a digital ecosystem where they act as an intermediating platform among networked stakeholders. Incumbents in the financial industry (e.g., established banks, traditional financial intermediaries, etc.) are threatened by iconic Big Techs and startups that innovate the business models and may erode market shares.

10

FINTECH VALUATION

249

Digital platforms are at the basis of technology-enabled business models that facilitate exchanges between multiple groups—such as endusers and producers—who do not necessarily know each other. The continuous upgrade of the technological environment creates new possibilities and reshapes the value and supply chain of financial intermediation, disrupting the existing business models. Whereas traditional firms create value within the boundaries of a company or a supply chain, digital platforms utilize an ecosystem of autonomous agents to co-create value (Hein et al., 2019). Digital platforms can be represented by FinTechs, and they act as a bridging node that connects digital clients to traditional or innovative financial intermediaries. Whenever platforms connect different layers (each representing a network sub-system), they can increase the overall systemic value. Digital platforms are multisided digital frameworks that shape the terms on which participants interact. Digitalization is defined as the concept of “going paperless,” namely, as the technical process of transforming analog information or physical products into digital form. The term “digital transformation” refers, therefore, to the application of digital technology as an alternative to solve traditional problems. As a result of digital solutions, new forms of innovation and creativity are conceived, while conventional methods are revised and enhanced. Digitally born startups or similar tech-businesses are not the only ones interested in adopting digital processes. Traditional businesses may be digitalized as well (e.g., a simple farmer willing to increase exponentially his/her production of tomatoes may digitalize the production activities through new systems or machines). In practice, with digitalization, traditional firms improve their key economic and financial parameters, as the EBITDA, which increases, while the WACC reduces, so improving the DCF and the overall enterprise value (EV): DCF(unlevered) = 

OCF ↑ ∼ = Enterprise Value ↑↑ WACC ↓

(10.1)

In synthesis, digitalization brings speed and quality at a low cost, thus representing a key driver for scalability itself. Digitalization enables a business process reengineering of traditional firms, which may presuppose an incremental production growth.

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Network theory (see Barabási, 2016), is the study of graphs as a representation of either symmetric or asymmetric relations between discrete objects. In computer science and network science, network theory is a part of graph theory: a network can be defined as a graph in which nodes and/or edges have attributes (e.g., names). Digital platforms are intrinsically networked, and within networks, they represent a bridging node that connects users (stakeholders). The properties of networked platforms are intrinsically consistent with the FinTech ecosystem. Digital platform analysis can give an interpretation of FinTechs that considers from an unconventional perspective their properties and potential.

10.3 Financial Bottlenecks: Inefficiencies and Friction Points An analysis of the main bottlenecks of the supply and value chain of the financial industry goes beyond the narrow scope of this chapter. It might, however, be mentioned that frictions increase the costs charged to the consumers, burdening the intermediation process with undue inefficiencies and longer passages that fuel rigidity. Challenges and opportunities facing the financial services industry (Burlakov, 2019) concern: • • • • •

Cybercrime threats; Regulatory compliance; Customer and employee retention; Blockchain integration; Artificial intelligence and big data applications.

Two main value drivers are represented by: a. Savings due to disintermediation and efficiency gains; b. Improved availability and fungibility of access to the services. Cheaper and always available financial services substantially increase the perceived Value for Money for the consumers and the other stakeholders that form the financial ecosystem, fostering its long-term sustainability. The joint impact of savings and improved fungibility is likely to have a

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scalable impact in terms of client outreach. Higher volumes (due to more frequent negotiations and a wider set of products) may partially offset lower margins for traditional banking intermediaries (Fig. 10.2). Bottlenecks determine the throughput of a supply chain. Recognizing this fact and making improvements will increase cash flow. A bottleneck (or constraint) in a supply chain means the resource that requires the longest time in operations of the supply chain for certain demands. Financial bottlenecks are intrinsic in the supply chain design, where intermediation is a long labor-intensive process. Each additional chain increases the marginal costs eventually charged to the final user and makes the whole supply chain more rigid. Digital applications contribute to shortening the supply chain that also becomes more resilient. Positive economic marginality derives from this reengineering process and should be shared among the supply chain stakeholders that include consumers.

FinTechs

BigTechs

Digital pla orms

TradiƟonal Banks Fig. 10.2

Interaction of FinTech with BigTechs and traditional banks

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10.4

The Accounting Background for Valuation

The evaluation is sensitive to forward-looking data that can be used to build up a sound business plan with a time horizon coherent with the average life cycle of the products and services of the FinTech. As shown in Chapter 2, a business plan is a formal accounting statement that numerically describes a set of business goals, the reasons why they are believed attainable, and the strategic plan and managerial steps for reaching those goals. Hypotheses and visionary ideas of gamechangers must be transformed into numbers and need to be backed by reasonable and verifiable assumptions about future events and milestones (Moro Visconti, 2019b). The accounting background is composed of pro forma balance sheets (of some 3–5 years) and perspective income statements. The matching of these two documents produces expected cash flow statements. Economic and financial margins are the key accounting parameters for valuation that are represented by the EBITDA, the EBIT, the operating and Net Cash Flows, and the Net Financial Position, as it will be shown in the formulation of the appraisal approaches.

10.5

FinTech Business Models

FinTech is an elastic business that can concentrate on market niches and specific customer segments, leveraging an innovative use of (big) data, and proposing new disruptive products and services. Osterwalder et al. (2005, p. 12) identify nine common business model elements: value proposition, target customer, distribution channel, relationship, value configuration, core competency, partner network, cost structure, and revenue model. FinTechs can complementarily be a: (a) A catalyzer/upgrader (digital enabler) of traditional business models, bringing to efficiency gains and pollinating the activity of ordinary banks or other financial intermediaries; FinTech providers use technology to disrupt these services by offering consumers a more compelling offering such as enhanced capabilities, convenience, or lower prices and fees (EY, 2019).

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(b) A pioneer of innovative products and services, normally through a B2B channel. An invented service is one that did not exist before but is now possible by technology and alternative business models, such as peer-to-peer lending and mobile-phone payments. Some invented services fill niches in the market, and others have the potential to redefine and transform entire financial subsectors (EY, 2019). Innovation may for instance concern: • Digital platform economy: handling of third parties: improving existing processes—coopetition as a new business model; • Open architectures & cloud: open vision—biometric & geolocalization to improve security standards; • Change management—new legacies; • Frictionless processes for client onboarding. Table 10.1 synthesizes the FinTechs main typologies and business models (see also Tanda & Schena, 2019; Das, 2019). The appraisal methodology may conveniently start from a strategic interpretation of the business model (that derives from accounting data) to extract the key evaluation parameters to insert in the model, as shown in Fig. 10.3. An analysis of the business model may conveniently consider: 1. The 2. The 3. The 4. The 5. The

revenue model; strategic goals; growth drivers; expected investments; market trends (Fig. 10.4).

FinTechs cooperate with banks (Dorfleitner & Hornuf, 2018). Cooperation is primarily geared to the integration or use of a FinTech application (product-related cooperation). An interpretation of the business model of each FinTech can be given using the SWOT analysis.

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Table 10.1 FinTech typologies and business models Typology

Business model

Financing solutions

Pure equity crowdfunding (retail); club deals; funding from institutional investors The blockchain is a decentralized and distributed digital ledger that corresponds to an open database with a pattern of sharable and unmodifiable data that are sequenced in chronological order. The main applications are cryptocurrencies; banking and payments; cyber-security; supply chain management; forecasting; networking & IoT; insurance; private transport & ride-sharing; cloud storage; charity; voting; healthcare; crowdfunding Credit cards; mobile payments through apps; virtual POS; online wallet; money transfers. Payment innovations throughout the year have been largely all about mobile e-wallets and contactless payments. PayTech firms also focused on ensuring the security of transactions leveraging artificial intelligence and machine learning technologies Global consumers have grown less reliant on cash, enhancing the growth profile of mobile payments firms Peer-to-peer (P2P) lending is the practice of lending money to individuals or businesses through online services that match lenders with borrowers. Peer-to-peer lending companies often offer their services online and attempt to operate with lower overhead and provide their services more cheaply than traditional financial institutions In October 2015, the European Parliament adopted a revised Payment Services Directive, known as PSD2. The new rules included aims to promote the development of neo-banks or challenger banks’ use of innovative online and mobile payments through open banking

Blockchain

Payment systems and processing (PayTech)

P2P loans

Open banking

(continued)

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Table 10.1 (continued) Typology

Business model

Big data and analytics

Big data analytics is the often-complex process of examining large and varied data sets, or big data, to uncover information—such as hidden patterns, unknown correlations, market trends, and customer preferences—that can help organizations make informed business decisions. Big data based on payment transaction data provide insight into customer retention, identification of criminal activities, or future customer behavior InsurTech refers to the use of technology innovations designed to squeeze out savings and efficiency from the current insurance industry model Regulatory technology, in short, RegTech, is a new technology that uses information technology to enhance regulatory processes. With its main application in the Financial sector, it is expanding into any regulated business with an appeal for the Consumer Goods Industry. RegTech, post-financial crisis—with MiFiD II, Basel III, and GDPR—may have been the initial external driver to ensure full compliance, and this has ensured a dramatic rise in technological solutions, and crucial in increasing efficiency, for example, by reducing gap-analysis time Use of innovative technology (big data, artificial intelligence, blockchains, etc.) by supervisory agencies to support supervision. SupTech will help authorities to become more data-driven (Di Castri et al., 2019) FinTech applications to microfinance activities (microcredit; microdeposits; microinsurance; micro-consulting). M-banking boosts volumes and fosters marginality gains (Moro Visconti, 2019a). See Chapter 11

InsurTech

RegTech

SupTech

Micro FinTech

(continued)

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Table 10.1 (continued) Typology

Business model

Banking-as a service

End-to-end process ensuring the overall execution of a financial service provided over the web AI will transform nearly every aspect of the financial service industry. Automated wealth management, customer verification, and open banking all provide opportunities for AI solution providers Property technology, in short called PropTech, sometimes also called Real estate technology, is a term that encompasses the application of information technology and platform economics to real estate markets

Artificial intelligence

PropTech

(Perspec ve) Accoun ng data

•Balance sheet •Income statement •Cash Flow statement

Business Plan

•Ɵme horizon •strategic assumpƟons •sensiƟvity/scen ario analysis

Evalua on parameters

•economic/finan - cial data •book versus market values

An analysis of the business model may conveniently consider: 1. The revenue model; 2. The strategic goals; 3. The growth drivers; 4. The expected investments; 5. The market trends.

Fig. 10.3

Evaluation methodology

A further issue to be considered in the strategic analysis of the business model is the patentability of the algorithm that is behind the FinTech’s formulation. Software applications may be protected by patent law (in the US) or copyright law (in the EU). The potential of FinTechs (in terms of products and services offered, strategic goals, etc.) concerns:

10

Fig. 10.4

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257

Business model and value drivers

1. Problem-solving capacity (disruptive solutions to existing problems); 2. Total Addressable/Available Market; 3. New applications/Products/Services enabled by technology; 4. Lower Distribution/Intermediation and Operational costs (efficiency gains); 5. Revenue Model (market traction); 6. Cross-selling opportunities.

10.6 Banks Versus FinTechs: Cross-Pollination and Scalability The business model of a bank is vastly different from that of a typical FinTech and this difference reflects in the balance sheet and the income statement.

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The balance sheet of a bank is characterized by a binding structure, due to the presence of the supervisory capital and bank deposits (in the liabilities) and loans to customers (within the assets). The assets and liabilities structure of a typical FinTech is much “lighter,” being represented by net working capital and some capitalized assets (tangible and intangible), against equity and financial debt in the liabilities. The income statement reflects these differences: • the bank has economic margins represented by the interest rate differential and the net contribution of commissions; • the FinTech has a more standard EBITDA and EBIT, sourced by the difference between operating revenues (from services) and monetary OPEX (to get to the EBITDA) or comprehensive OPEX, including depreciation and amortization, to determine the EBIT. The different income statements, driven by the respective business model of either the bank or the FinTech, reflect a completely different attitude toward (digital) scalability. FinTechs have a revenue model that is much more scalable than that of a typical bank. Whereas a bank is limited in its growth potential by constraints such as the supervisory capital (a percentage of its loans, weighted for risk), huge fixed costs for personnel, and difficult upside in a mature market, FinTechs incorporate a digital potential in an intrinsically scalable business model. Even if FinTechs have a higher marginality potential, they still need the volumes (client base, etc.) and the market caption bound to traditional banks.

10.7

Insights from Listed FinTechs

FinTechs has a hybrid business model, as they operate in the financial (banking) sector deploying their technological attitudes. Evaluators may so wonder if FinTechs follow the typical evaluation patterns of bank/financial intermediaries or those of technological firms. Preliminary empirical evidence—reported below—shows that the latter interpretation is the one consistent with the stock-market mood. This indication is important for the assessment of the best evaluation criteria.

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Stock Market Prices - July 31, 2015 to June, 30, 2020 300 250

FinTechs

200 150

IT

100

Banks

50 0 31/07/2015

31/07/2016 IFINXNT Index

Fig. 10.5

31/07/2017 MXWD0BK Index

31/07/2018

31/07/2019

MXWO0IT Index

date

FinTech versus technological and banking stock market index

The following graph (with data sourced from Bloomberg) contains the comparative stock market price (from August 1, 2015 to June 30, 2020) of: (a) IFINXNT—Indxx Global FinTech Thematic Index (the statistics about the index are reported in the Appendix) (b) MXW00BK—MSCI World Banks Weighted Equity Index1 (c) MXW00IT—MSCI World (ex-Australia) Information Technology Index (Fig. 10.5). Despite the young age of FinTechs, many of these firms are experiencing significantly faster growth than their traditional financial services peers. This reflects in the performance of FinTech companies tracked by the

1 The MSCI ACWI Banks Index is composed of large and mid-cap stocks across 23 Developed Markets (DM) countries and 26 Emerging Markets (EM) countries*. All securities in the index are classified in the Banks industry group (within the Financials sector) according to the Global Industry Classification Standard (GICS® ). The top 5 constituents are: JPMORGAN CHASE & CO US; BANK OF AMERICA CORP US; WELLS FARGO & CO US; HSBC HOLDINGS (GB) GB, and CITIGROUP US. See https://www.msci. com/documents/10199/1b714b5e-5e20-405d-acfa-cb18ae63f669.

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Indxx Global FinTech Thematic Index,2 the underlying index for the Global X FinTech ETF (FINX), relative to the Financial Select Sector Index. The differences in the stock prices reflect not only a different market mood but also a cost of capital (cost of equity) that is not the same and influences the valuation of each firm. FinTechs seem far from the banks even because they have a different model, as they do not collect deposits and lend money, intermediating financial resources; FinTechs are not hyper-regulated deposit-taking institutions, and they just provide financial service and do not intermediate “money” as a product, and they do not need a supervisory capital like banks. The preliminary conclusion that FinTechs follow the evaluation parameters of technological firms has, however, some caveats that may tentatively be summarized as follows: (a) If FinTech firms are the purchase target of (much bigger and consolidated) ordinary banks/financial intermediaries, then the valuation criteria of the latter predominate, at least after the acquisition (and especially if FinTechs are merged into traditional banks); (b) The underlying market and business model of maturing FinTechs may become less technological and more “client-based”; (c) Some established criteria used in the evaluation of traditional banks are, however, hardly applicable even in perspective (e.g., consideration of “physical” banking branches as a positive element).

2 The Indxx Global Fintech Thematic Index is designed to track the performance of companies listed in developed markets that are offering technology-driven financial services which are disrupting existing business models in the financial services and banking sectors. The index has been backtested to June 30, 2015 and has a live calculation date of August 29, 2016. https://www.indxx.com/indxx-global-fintech-thematic-index-tr.

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10.8

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Valuation Methods

Most of the concepts recalled in these paragraphs are similar to those already illustrated in other chapters, and restated here with some personalization, considering the peculiar startup of this chapter that is so intended to be self -containing. In particular, the main concepts that are here directly or indirectly restated concern: a. The preliminary phase, from business modeling to business planning (Chapter 2); b. The main valuation approaches (Discounted Cash Flows —DCF, and market multipliers), described in Chapter 8; c. The valuation of specific startups (see Part II —Industry Applications).

The evaluation criteria typically follow the (actual and prospective) business model of the target company. The technological value driver seems, at least in this historical phase, prevalent over the banking/financial activity, as shown in Fig. 10.6. A

Business model

Technological Firms (Startups)

FinTechs

Banks / Financial intermediaries

Valua on approach

Fig. 10.6

Business model and valuation approach of FinTechs

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preliminary consideration may, however, indicate that the business model is slightly more “bank-centric” than the evaluation criteria. The reasons for this divergence are manifold: banks are capital- and labor-intensive institutions and are strictly supervised (not only since they are financial institutions but also because they collect deposits and are so regulated by Central Bank authorities). FinTechs are quite different, although they share with banks a common underlying framework. Banking and financial activities (Damodaran, 2009) follow peculiar valuation patterns that often concentrate on parameters like adjusted equity or dividends. These parameters are, however, not particularly meaningful with FinTechs since they are not capital-intensive firms, and their capacity to payout dividends is absent in the startup phase. If the FinTech activity is developed within a banking group by a captive company, its strategic meaning may be that of a catalyzer of (traditional) banking activity. In this case, what mostly matters is not the value of FinTech (Yao, 2018) as a stand-alone reality, but rather its contribution to the incremental marginality of the (traditional) banking group to which it belongs. FinTechs naturally tend to cooperate with banks, as in most cases they represent their customers. (Product-related) cooperation is primarily geared to the integration or use of a FinTech application cooperation (Brandl & Hornuf, 2017). In this case, the value may be inferred even with differential income methodologies, traditionally used in the evaluation of intangible assets (within the income approaches). According to the International Valuation Standard IVS 210, § 80: 80. Premium Profit Method or With-and-Without Method 80.1 The premium profit method, sometimes referred to as the withand-without method, indicates the value of an intangible asset by comparing two scenarios: one in which the business uses the subject intangible asset and one in which the business does not use the subject intangible asset (but all other factors are kept constant). (…) 80.2 The comparison of the two scenarios can be done in two ways: (a) calculating the value of the business under each scenario with the difference in the business values being the value of the subject intangible asset, and

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(b) calculating for each future period the difference between the profits in the two scenarios. The present value of those amounts is then used to reach the value of the subject intangible asset. In this case, what matters for the evaluation is the with-an-without availability of the FinTech business that can be considered as the “intangible” asset indicated in IVS 210. Demyanova (2018) considers several methodologies that, in most cases, are hardly applicable to FinTechs. For example, the liquidation value or book value method are not consistent with the innovative nature of startups that become valueless if wound up and derive most of their potential value from intangible assets. The Berkus method appears too undetermined, and real options may be embedded in the estimate of future cash flows with multiple scenarios. A synthesis is reported in Table 10.3. According to Moro Visconti et al. (2020), in an equity valuation theory and practice, there are generally two valuation approaches— discounted cash flows (DCF) and comparables. A comparison of the primary evaluation criteria in traditional (nonfinancial) firms, high-tech firms (startups), and banks/financial intermediaries is reported in Table 10.2. Table 10.2 is complementary to Table 10.3. Table 10.2 Comparison of the main evaluation approaches of traditional firms, technological startups, and banks Traditional Firm

Technological startup (IPEV, 2018; other methods)

Bank (Financial intermediary)

Balance-sheet based (Fernandez, 2001)

Venture capital method

Income

Binomial trees

Expected dividends per share/dividend discount models Adjusted book value of equity (to proxy market value) Excess return models

Mixed capital-income Net asset value Financial (DCF) Market multiples (comparable firms) (IPEV, 2018)

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Table 10.3 FinTech valuation approaches Method

Description

Liquidation value Book value Discounted cash flows

Break-up value of tangible assets Accounting value of tangible assets Discount of Operating Cash Flows to get Enterprise Value or Net Cash Flows to get Equity Value Situation-specific business valuation approach used by venture capital and private equity investors for early-stage companies. This model combines elements of market-oriented and fundamental analytical methods Weighted average value compared to similar firms Considers five key success factors: (1) Basic value, (2) Technology, (3) Execution, (4) Strategic relationships in its core market, and (5) Production, and consequent sales An economically valuable right to make or else abandon some choice that is available to the managers of a company, often concerning business projects or investment opportunities

First Chicago

Payne scoring Berkus

Real options

10.8.1

The Financial Approach

The financial approach is based on the principle that the market value of the company is equal to the discounted value of the cash flows that the company can generate (“cash is king”). The determination of the cash flows is of primary importance in the application of the approach, as is the consistency of the discount rates adopted. The doctrine (especially the Anglo-Saxon one) believes that the financial approach is the “ideal” solution for estimating the market value for limited periods. It is not possible to make reliable estimates of cash flows for longer periods. “The conceptually correct methods are those based on cash flow discounting. I briefly comment on other methods since—even though they are conceptually incorrect —they continue to be used frequently” (Fernandez, 2001). This approach is of practical importance if the individual investor or company with high cash flows (leasing companies, retail trade, public and motorway services, financial trading, project financing SPVs, etc.) are valued. Financial evaluation can be particularly appropriate when the company’s ability to generate cash flow for investors is significantly different from its ability to generate income, and forecasts can be formulated with a sufficient degree of credibility and are demonstrable.

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There are two complementary criteria for determining the cash flows: 10.8.1.1

The Cash Flow Available to the Company (Free Cash Flow to the Firm) This configuration of expected flows is the one most used in the practice of company valuations, given its greater simplicity of application compared to the methodology based on flows to partners. It is a measure of cash flows independent of the financial structure of the company (unlevered cash flows) that is particularly suitable to evaluate companies with high levels of indebtedness, or that do not have a debt plan. In these cases, the calculation of the cash flow available to shareholders is more difficult because of the volatility resulting from the forecast of how to repay debts. This methodology is based on the operating flows generated by the typical management of the company, based on the operating income available for the remuneration of own and third-party means net of the relative tax effect. Unlevered cash flows are determined by using operating income before taxes and financial charges. The cash flow available to the company is, therefore, determined as the cash flow available to shareholders, plus financial charges after tax, plus loan repayments and equity repayments, minus new borrowings and flows arising from equity increases. The difference between the two approaches is, therefore, given by the different meanings of cash flows associated with debt and equity repayments. Cash flows from operating activities are discounted to present value at the weighted average cost of capital. This configuration of flows offers an evaluation of the whole company, independently from its financial structure. The value of the debt must be subtracted from the value of the company to rejoin the value of the market value, obtained through the cash flows for the shareholders. The relationship between the two concepts of cash flow is as follows: cash flow available to the company = cash flow available to shareholders + financial charges (net of taxes) + loan repayments − new loans (10.2)

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10.8.1.2 The (Residual) Cash Flow Available to Shareholders This configuration considers the only expected flow available for members’ remuneration. It is a measure of cash flow that considers the financial structure of the company (levered cash flow). It is the cash flow that remains after the payment of interest and the repayment of equity shares and after the coverage of equity expenditures necessary to maintain existing assets and to create the conditions for business growth. In M&A operations, the Free Cash Flow to the Firm (operating cash flow) is normally calculated to estimate the Enterprise Value (comprehensive of debt). The residual Equity Value is then derived by subtracting the Net Financial Position. The discounting of the free cash flow for the shareholders takes place at a rate equal to the cost of the shareholders‘ equity. This flow identifies the theoretical measure of the company’s ability to distribute dividends, even if it does not coincide with the dividend paid. Cash flow estimates can be applied to any type of asset. The differential element is represented by their duration. Many assets have a defined time horizon, while others assume a perpetual time horizon, such as shares. Cash flows (CF) can, therefore, be estimated using a normalized projection of cash flows that it uses, alternatively: • unlimited capitalization: W 1 = C F/i

(10.3)

W2 = CF a n − i

(10.4)

• limited capitalization:

where W 1 and W 2 represent the present value of future cash flows. The discount rate to be applied to expected cash flows is determined as the sum of the cost of equity and the cost of debt, appropriately weighted according to the leverage of the company (the ratio between financial debt and equity). This produces the Weighted Average Cost of Capital (WACC): WACC = ki (1 − t) where:

E D + ke D+E D+E

(10.5)

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k i = cost of debt; t = corporate tax rate; D = market value of debt; E = market value of equity; D + E = raised capital; k e = cost of equity (to be estimated with the Capital Asset Pricing Model − CAPM or the Dividend Discount Model). The cost of debt capital is easy to determine, as it can be inferred from the financial statements of the company. The cost of equity or share capital, which represents the minimum rate of return required by investors for equity investments, is instead more complex and may use the CAPM or the Dividend Discount Model (a method of valuing a company’s stock price considering the sum of all its future dividend payments, discounted back to their present value. It is used to value stocks based on the net present value of future dividends). The formula of the CAPM is the following:  (10.6) E(r )FinTech = rfree + βFinTech [(E(r )market − rfree where: E(r )FinTech = expected return of the FinTech listed stock rfree = risk − free rate of return (e.g., of a long-term Government bond) βFinTech = sensitivity of the FinTech’s stock to the market price (E(r )market = expected return of the (benchmark) Stock market. A central element is represented by the beta (β) of the FinTech to be evaluated that consists of the ratio between the covariance of the FinTech security with its stock market, divided by the variance of the market. Market betas, subdivided by industry, may be detected from the dataset of A. Damodaran (see for instance http://pages.stern.nyu.edu/ ~adamodar/New_Home_Page/datafile/Betas.html). Once the present value of the cash flows has been determined, the calculation of the market value W of the company may correspond to: (a) the unlevered cash flow approach: W =

 C F0 +VR−D WACC

(10.7)

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(b) the levered cash flow approach: W =

 C Fn Ke

+VR

(10.8)

where:   C F0 /WACC = present value of operating cash flows C Fn /K e = present value of net cash flows VR = terminal (residual) value D = initial net financial position (financial debt − liquidity). The residual value is the result of discounting the value at the time n (before which the cash flows are estimated analytically). It is often the greatest component of the global value W (above all in intangible-intensive companies) and tends to zero if the time horizon of the capitalization is infinite (VR/∞ = 0). The two variants (levered versus unlevered) give the same result if the value of the firm, determined through the cash flows available to the lenders, is deducted from the value of the net financial debts. Operating cash flows (unlevered) and net cash flows for shareholders (levered) are determined by comparing the last two balance sheets (to dispose of changes in operating Net Working Capital, fixed assets, financial liabilities, and shareholders‘ equity) with the income statement of the last year. The accounting derivation of the cash flow and its link to the cost of capital (to get DCF—Discounted Cash Flows) is illustrated in Table 10.4. The net cash flow for the shareholders coincides with the free cash flow to equity and, therefore, with the dividends that can be paid out, once it has been verified that enough internal liquidity resources remain in the company. 10.8.2

Empirical Approaches (Market Multipliers)

The market value identifies: a. The value attributable to a share of the equity expressed at stock exchange prices; b. The price of the controlling interest or the entire share equity; c. The traded value for the controlling equity of comparable undertakings;

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Table 10.4 Cash flow statement of a FinTech and link with the cost of capital

d. The value derived from the stock exchange quotations of comparable undertakings. Sometimes comparable trades of companies belonging to the same product sector with similar characteristics (in terms of cash flows, sales, costs, etc.) are used. In practice, an examination of the prices used in negotiations with companies in the same sector leads to quantifying average parameters: • • • • •

Price/EBIT Price/cash-flow Price/book-value Price/earnings Price/dividend

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These ratios seek to estimate the average rate to be applied to the company being assessed. However, there may be distorting effects of prices based on special interest rates, in a historical context, on difficulties of comparison, etc. In financial market practice, the multiples methodology is frequently applied. Based on multiples, the company’s value is derived from the market price profit referring to comparable listed companies, such as net profit, before tax or operating profit, cash flow, equity, or turnover (see Damodaran, 2018). The attractiveness of the multiples approach stems from its ease of use: multiples can be used to obtain quick but dirty estimates of the company’s value and are useful when there are many comparable companies listed on the financial markets and the market sets correct prices for them on average. Because of the simplicity of the calculation, these indicators are easily manipulated and susceptible to misuse, especially if they refer to companies that are not entirely similar. Since there are no identical companies in terms of entrepreneurial risk and growth rate, the assumption of multiples for the processing of the valuation can be misleading, bringing to “fake multipliers.” The use of multiples can be implemented through: A. Use of fundamentals; B. Use of comparable data: B.1. Comparable companies; B.2. Comparable transactions. Comparables may be looked for consulting databases like Orbis (https:// www.bvdinfo.com/en-gb/our-products/data/international/orbis). Among the empirical criteria, the approach of the multiplier of the EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) is widely diffused. The net financial position must be added algebraically to the EBITDA, to pass from the estimate of the enterprise value (total value of the company) to that of the equity value (value of the net assets). The formulation is as follows: W = average perspective EBITDA ∗ Enterprise Value/sector EBITDA = Enterprise Value of the company (10.10)

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And then: Equity Value = Enterprise Value ± Net Financial Position

(10.11)

The DCF approach can be linked to the market approach since they both share as a starting parameter the EBITDA.

10.9 Market Stress Tests and Business Model Sensitivity Market stress tests are now routinely for banks, especially after the big crisis of 2008. The impact of market crises on FinTechs is mixed and may be quite different from that concerns other industries, as shown for example in Fig. 10.5. The relationship between the business model and its surrounding environment is evident, and it is normally the latter that influences the former. The sensitivity of a (listed) FinTech over its stock market can be measured by the beta (β) coefficient that is given by the covariance between the listed FinTech and its market of listing, divided by the variance of the market: βFinTech =

Cov(FinTech, Market) Variance Market

(10.12)

FinTech’s sensitivity to crises is uneasy to assess since its historical track record is limited. They were nonexistent during the big Internet/NASDAQ bubble of March 2000 and hardly present during the double-dip recession of 2008–2011. Early indications from the Covid-19 pandemic crisis, started in January 2020, show a sharp decline followed by a recovery, with a pattern again similar to that of the technological firms, and much more volatile than that of the bank index. In broader terms, the business model sensitivity impacts the valuation since this appraisal process should carefully consider different scenarios. Whereas sensitivity analysis analyzes the impact of one change at a time of a valuation parameter, more comprehensive scenario analysis incorporates several parameters that are simultaneously changing. The total Net Present Value of the three projects, which are considered not mutually exclusive (since venture capital can take over all of them),

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nor with synergistic effects between them (which is possible, especially if the sectors and business models intersect), is positive if the rate at which they are discounted is less than 13.93%. This rate represents the watershed, which expresses the breakeven point at which the Net Present Value of the project portfolio is zero, calculated through the iterative search for the Internal Rate of Return (which is the rate that makes the NPV = 0). Venture capital (Cumming & Schwienbacher, 2018) must make a comparison between its cost of raising financial resources (weighted average cost of capital, which coincides with the cost of equity capital if the venture capital has not resorted to debt) and the return that the investments offer. In all cases where the expected return is lower than the cost of capital (IRR < WACC), it will not be convenient to undertake the investment. Binomial networks (traditionally used as decision trees or to determine the pricing of options) are flexible: by adapting the parameters of the expected variance of the value and the probability that the value increases (upside potential) or decreases (downside risk), it is possible to estimate, with basically unlimited ramifications, a wide range of scenarios. The possibility of correcting the estimates along the way, refining them based on what happened in the portion of time passed (which declines a chronological process of time decay), represents a further element to improve the forecasts. Timely big data may conveniently be introduced in the model.

10.10 Competitive Advantage, Excess Returns, Economic Value Added, and Goodwill FinTechs that survive Darwinian selection typically incorporate a competitive advantage. This reflects in goodwill that is not accounted for (unless purchased from third parties). The concepts of this section are excerpted and adapted from Moro Visconti (2020, Chapter 17). Goodwill is a residual intangible since it incorporates all the added value that cannot be directly allocated to any other specific immaterial asset. Goodwill indicates the ability of a company or one of its branches to generate an extra-profit (new incremental wealth), that is the concrete attitude to produce profits higher than the average of the reference sector; this is represented by a typically indistinct set of intangible conditions (the image and the prestige of the company, the clientele, the organization, the management, the

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quality of the products, the commercial network, etc.) that express the competitive capacity of the company on the market. The Competitive Advantage Period (CAP) considers the time frame during which the company is expected to be able to achieve returns on invested capital higher than the weighted average cost of capital (ROIC > WACC) and so represents positive goodwill. The implicit surplus value in the CAP is conveyed into the strategic components of the company (competitive advantages, linked to product differentiation or cost advantages; technological, marketing, and organizational resources and skills; industry attractiveness, etc.) and in the economic and financial aspects (first, the incremental EBITDA margin). The intensity and duration of the CAP are at the base of the valuation models of the surplus value (implicit goodwill), driven by the intangible sources of the expected competitive advantages, which allow reinvestments at a rate of return on invested capital higher than the weighted average cost of capital (ROIC > WACC). Figure 10.7 (similar to Fig. 3.5) shows the formation of goodwill. The sustainable enterprise value corresponds to the valorisation of the existing assets, added to the (typically intangible) value of growth opportunities. The CAP is consistent with the notion of goodwill, in its meaning of excess return concerning the industry average. The concept is connected to the Economic Value Added and, in a multi-year cumulated perspective, to the Market Value Added (examined in Sect. 3.4).

Implicit goodwill / intangible-driven goodwill

R O I C

Fixed assets, tangible and intangible Current assets

ROIC > WACC

Equity

Net Financial PosiƟon

WACC

Fig. 10.7 Goodwill as a positive differential between the yield and the cost of invested capital

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The acquisition of resources (funding sources or collected capital) is preparatory to their use (invested capital), even in intangibles that generate a positive economic and financial margin/flow (and a NOPAT) at the operational level. This positive economic margin assumes a financial connotation (through the EBITDA incorporated in the EBIT and then in the NOPAT), creating cash flows primarily allocated to debt service (operating cash flows) and, residually, to the remuneration of the shareholders (free cash flow to equity). The competitive advantage of a FinTech depends on several factors that are reflected in its business model. Concepts like the CAP, EVA, MVA, are fully consistent with the Franchise Factor Model of Leibowitz (2004). The accounting background of this value creation is represented not only by the difference between the return and the cost of capital (ROIC—WACC), expressed in book value and then market terms but also by what happens in the upper part of the income statement, considering sales and other revenues, operating fixed and variable costs, in monetary and non-monetary terms. Economic margins like the Added Value, the EBITDA (the difference between revenues and monetary OPEX), the EBIT are a cornerstone of any value appraisal. The EBITDA is the starting point for the evaluation approaches based on DCF or market multipliers. The EBIT is consistent with the NOPAT (used the estimate of EVA), after adjusting for operating taxes. The application of these standard concepts to FinTechs needs some fine-tuning. The FinTech industry is relatively recent, and it so discounts a novelty factor, with an expected growth above the commodity level. This incorporates a promise of earnings exceeding the cost of capital so that ROIC > WACC. Above-average growth reflects in the digital scalability features of promising FinTechs. The rate of above-average earnings growth is the main component of goodwill (whose cumulation brings to a positive MVA), and this process is typically marginally decreasing across time. In their first years, startups often experience superheated growth that is, however, limited in scope and duration. Longer-lasting growth stabilizes on sustainable patterns. When the market matures, entry barriers become porous, cheaper imitations are developed, innovation leads to improved or even radically different product models, distribution channels are penetrated, cost advantages are homogenized, pricing power erodes, and the market becomes commoditized (Leibowitz, 2004, p. 23). What happened to mature banks is likely to occur even to FinTechs.

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In equilibrium, there is no extra-growth, and goodwill tends to zero. If this is the (general) case, successful FinTechs may just be a temporary exception.

10.11 Challenges and Failures: Why FinTechs Burn Out Startup failures are so common that they cannot refrain from influencing valuation, for instance, increasing the risk embedded in the discount rate of expected cash flows. Failures have common features among the different startups but are industry-specific. And the financial sector has its own rules. Among the reasons that may cause the default of FinTech startups, the following are worth mentioning3 : • Underfunding. • Choosing an inexperienced Venture Capital. • Overlooking compliance. Regulatory complexity is often underestimated. • Thinking a FinTech startup is the same as any other tech startup. psychological behaviors around money, credit, savings, and payments are different from those concerning IT, biotechnologies, etc. • Competing solely on cost. banks have massive (traditional) scale advantages. Going digital, FinTechs may reengineer traditional business models but the task is uneasy and risky. • Overconfidence. creating a new market is no easy task. Many FinTechs think that their business model is so innovative that they have no competitors. Whenever there is competition, geographical segmentation may represent a weak barrier, due to increasing financial globalization. Innovation may become increasingly challenging in a crowded and over-competitive market. • Underestimation of the length of the sales cycle. financial institutions are notoriously slow purchasers of anything new. • Missing sales strategy. FinTech startups are often the brainchild of software experts that have limited sales and marketing skills.

3 See https://www.forbes.com/sites/ronshevlin/2019/07/29/why-fintech-startupsfail/#30c33e6a6440.

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• Lack of understanding of the financial market. FinTech startups pursuing a B2C business model often overestimate the extent to which consumers will: (1) change their behavior and (2) pay for a new product or service in addition to all the things they already pay for. While a B2B model may be a better path for some FinTech startups, some fail by not understanding that they are a vendor—not a partner—which may require a completely different set of skills and capabilities from those they already have. According to a survey (Endeavor Insight, Mapping Milan FinTech, 2019): • Decision-makers in the private sector and the public sector alike should focus on helping companies reaching scale; • FinTech entrepreneurs identified access to capital, access to talent, and compliance with the regulation as their top challenges in scaling their companies today; • Network Analysis points to challenges and opportunities: – There is an absence of productive mentorship and angel investment connections between FinTech entrepreneurs in Milan; – The FinTech community has strong ties with the banking sector because of former employment; – Entrepreneurial networks are the most influential actors in the entrepreneurship community and they can become a vehicle to transmit resources; • Decision-makers in the public sector and the private sector who wish to support FinTech entrepreneurship in Milan need to focus on: – Channel resources to companies with a potential to scale; – Foster relationships with international investors. – Address the shortage of tech talent and non-financial managerial talent in the sector. – Leverage entrepreneurship networks to foster angel investment and mentorship among entrepreneurs in the sector.

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Concluding Remarks

FinTechs are reshaping the banking industry, proposing innovative technological solutions that foster customer-centricity. The main thesis of this study is that the evaluation of FinTechs follows appraisal approaches that are (unsurprisingly) like those of technological startups. Even if the underlying industry is represented by bank activities, FinTechs are innovators/facilitators of financial activities and are not personally involved in the borrowing/lending intermediation business. Due to their nature as technological providers of financial services, FinTechs can so be assimilated to innovative startups (or more mature companies). Evaluation methodologies are important to assess and refine not only to ease the M&A activity but also to foster value recognition for all the stakeholders that are involved in the value co-creation paradigm. The customer’s experience (and the big data continuously fuelled by feedbacks) is a central factor in the digital economy as it adds value to the whole process. Fair remuneration of the clients remains, however, a hot issue.

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Demyanova, E. A. (2018). The topical issues of valuation of companies under the conditions of fintech. Strategic Decisions and Risk Management, 1, 88–103. Di Castri, S., Hohl, S., Kulenkampff, A., & Prenio, J. (2019). The SupTech generations. FSI Insights on policy implementation, 19. Dorfleitner, G., & Hornuf, L. (2019). FinTech business models. In FinTech and data privacy in Germany. Springer Nature. Drummer, D., Jerenz, A., Siebelt, P., & Thaten, M. (2016). FinTech—Challenges and opportunities: How digitization is transforming the financial sector. McKinsey & Co. Eickhoff, M., Muntermann, J., & Weinrich, T. (2017). What do FinTechs actually do? A taxonomy of FinTech business models. ICIS Proceedings (p. 22). Endeavor Insight. (2019). Mapping Milan FinTech. Available at https:// endeavor.org/blog/events/40-entrepreneurs-15-countries-selected-isp-80milan/. EY. (2019). Global FinTech adoption index 2019. Available at https://www.ey. com/en_gl/ey-global-fintech-adoption-index. Fatás, A. (Ed.). (2019). The economics of FinTech and digital currencies. Available at https://voxeu.org/content/economics-fintech-and-digital-currencies. Fernandez, P. (2001). Valuation using multiples: How do analysts reach their conclusions ? IESE Business School, Madrid. Gai, K., Qiu, M., & Sun, X. (2018). A survey on FinTech. Journal of Network and Computer Applications, 103, 262–273. Gimpel, H., Rau, D., & Röglinger, M. (2018). Understanding FinTech startups—A taxonomy of consumer-oriented service offerings. Electronic Markets, 28, 245–264. Gomber, P., Kauffman, C., & Weber, B. W. (2018). On the Fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of Management Information Systems, 35(1), 220–265. Haddad, C., & Hornuf, L. (2019). The emergence of the global FinTech market: Economic and technological determinants. Small Business Economics, 53, 81– 105. Hein, A., Schreieck, M., Riasanow, T., Setzke, M., Wiesche, M., Bohm, M., & Krcmar, H. (2019, November). Digital platform ecosystems. Electronic Markets. Hommel, K., & Bican, P. M. (2020). Digital entrepreneurship in finance: FinTechs and funding decision criteria. Sustainability, 12, 8035. IPEV. (2018). Valuation guidelines. Available at http://www.privateequityvalua tion.com/Valuation-Guidelines. Lee, I., & Shin, Y. J. (2018). Fintech: Ecosystem, business models, investment decisions, and challenges. Business Horizons, 61(1), 35–46.

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Leibowitz, M. (2004). Franchise value. New York: Wiley. Moro Visconti, R. (2019a). Microfintech: Outreaching financial inclusion with cost-cutting innovation. Available at https://www.researchgate.net/public ation/332818363_microfintech_outreaching_financial_inclusion_with_costcutting_innovation. Moro Visconti, R. (2019b). How to prepare a business plan with excel. Available at https://www.researchgate.net/publication/255728204_How_to_Pre pare_a_Business_Plan_with_Excel. Moro Visconti, R. (2020). The valuation of digital intangibles: Technology, marketing and internet. Cham: Palgrave Macmillan. Moro-Visconti, R., Cruz Rambaud, S., & López Pascual, J. (2020). Sustainability in FinTechs: An explanation through business model scalability and market valuation. Sustainability, 12, 10316. Osterwalder, A., Pigneur, Y., & Tucci, C. L. (2005). Clarifying business models: Origins, present, and future of the concept. Communications of the Association for Information Systems, 16(1). Piobbici, F., Rajola, F., & Frigerio, C. (2019). Open innovation effectiveness in the financial services sector. In N. Mehandjiev & B. Saadouni (Eds.), Enterprise applications, markets and services in the finance industry. FinanceCom 2018. Lecture Notes in Business Information Processing (vol. 345). Cham: Springer. Rochet, J. C., & Tirole, J. (2003). Platform competition in two-sided markets. Journal of the European Economic Association, 1(4), 990–1029. Sarhan, H. (2020, July). Fintech: An overview. Available at https://www.resear chgate.net/publication/342832269. Schallmo, D. R. A., & Williams, C. A. (2018). Digital transformation of business models, digital transformation now! SpringerBriefs in Business. Cham: Springer. Skinner, C. (2016). ValueWeb: How FinTech firms are using mobile and blockchain technologies to create the Internet of Value. Marshall Cavendish. Sironi, P. (2016). FinTech innovation. Chichester: Wiley. Tanda, A., & Schena, C. (2019). FinTech, BigTech and banks: Digitalisation and its impact on banking business models. Cham: Palgrave Macmillan. Vives, X. (2019). Digital disruption in banking. Annual Review of Financial Economics, 11(1), 243–272. Yao, J. (2018). Valuation of a FinTech company. Available at https://repositorio. iscte-iul.pt/bitstream/10071/18806/1/Master_Jiayu_Yao.pdf.

CHAPTER 11

From Informal Financial Intermediaries to MicroFinTech Valuation

11.1

Introduction

This chapter goes beyond the traditional model of for-profit startups, showing how microfinance Institutions—a good template for NGOs—can follow the typical startup patterns, with some adaptations. Trespassing traditional banking, microfinance is by now a consolidated and successful mean to provide credit to the neediest, helping the poor to sort out bank exclusion, which is one of the main misery traps (Collier, 2007; Moro Visconti, 2014a; Armendariz De Aghion & Morduch, 2010) that prevents billions of underserved, especially women, from escaping atavistic poverty. While the success of microfinance (Fanconi & Scheurle, 2017), since the pioneering intuition of Yunus, has gone beyond any expectation, its implementation is still typically subsidized and raises growing concerns. Self-sufficiency and economic sustainability (de Oliveira Leite et al., 2019) represent in most cases a mighty goal, whose attainment would allow MFI to broaden their clientele (potentially unlimited, being represented by billions of unbanked poor). Consistently with this view, Ghosh (2013), claims that microfinance cannot be a silver bullet for development, and profit-oriented MFIs are problematic (“better unbanked that unable to repay loans”). The business industry remains opaque, and mission drift is a constant temptation (Engels, 2010), especially in India (Saxena & Deb, 2014). Microfinance © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_11

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must be regulated and subsidized, and other strategies for viable financial inclusion of the poor and small producers must be more actively pursued. Financial inclusion is generally considered as a pro-growth strategy and improved access to (micro)finance reduces income inequality and poverty (Agyemang-Badu, 2018). Mader (2017) however claims that high expectations of financial inclusion serving as a core pro-poor, private sector-led development lack justification. Bateman and Chang (2012) are even more skeptical, arguing that microfinance constitutes a powerful institutional and political barrier to sustainable economic and social development, and so also to poverty reduction. Mission drift often prevents MFIs from achieving their outreach potential (Shu & Oney, 2012). Technology, starting from electronic payments, may foster financial inclusion and availability/affordability of financial services in developing economies, softening the perverse effects of microcredit (Dos Santos & Kvangraven, 2017). Digital finance companies work a mile further, in the name of digital financial inclusion, by serving excluded, marginalized, neglected individuals and Small and Medium Enterprises through their innovative, affordable, quality, and speedy digital financial services and products (Ravikumar, 2019). While there is an impressive literature on microfinance (for a comprehensive introduction, Armendariz De Aghion & Morduch, 2010; for recent surveys, García-Pérez et al., 2017; Moro Visconti, 2016), and m-banking (Shaikh & Karjaluoto, 2015), little attention has been dedicated to other more innovative strands, as FinTech (Gai et al., 2018) or social networking applied to microfinance group lending (Ali et al., 2016; Sharma et al., 2017; Altinok, 2018) and peer-to-peer (P2P) lending (Bruton et al., 2015). Financial technology (FinTech), examined in Chapter 10, is a new technology that uses software and digital platforms (AFI, 2018) to deliver financial services to consumers (Schüffel, 2017). These digital tools often disrupt well-established business models by creating new and efficient means of providing services. The use of smartphones for mobile banking (Tomic & Stojanovic, 2018), investing services, and cryptocurrency are examples of technologies aiming to make financial services more accessible. FinTech is related to complementary businesses as InsurTech or RegTech that may both interact with microfinance, due to its contiguity with microinsurance or with regulatory issues (especially for deposit-taking MFIs, supervised by Central Bank authorities).

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There are two main reasons for the emergence of FinTech companies (Saksonova & Kuzmina-Merlino, 2017). First, the global financial crisis of 2008, has vividly demonstrated to consumers the shortcomings of the traditional banking system that led to the crisis. Second, the emergence of new technologies that helped provide mobility, ease of use (visualization of information), speed, and lower cost of financial services (Anikina et al., 2016). Whereas some studies examine the impact of technology on microfinance (Ashta, 2011; Moro Visconti & Quirici, 2014; Moro Visconti, 2015), there are no papers dedicated to “MicroFinTech,” a neologism that combines financial technology (FinTech) with microfinance, reshaping the delivery of financial services to make them more accessible and affordable. In emerging markets where financial inclusion is a challenge, FinTechs are helping bridge the exclusion gap and may be financed for instance by social impact funds (described in Chiappini, 2017). Rapid urbanization, mobile and internet penetration, and ease of use are driving individual demand for FinTech services. Leapfrog innovation can provide cutting-edge solutions for the unbanked (Ernst & Young, 2019). Ashta (2018) illustrates some best practices in the use of digital technologies by highly innovative fintech firms in areas that could be of use to MFIs in diverse sectors such as mobile payments, credit scoring, card readers, ATMs, and management information systems (Budampati, 2018). According to Liu et al. (2020), the latest fintech business model research hotspots are mobile payment, microfinance, P2P lending, and crowdfunding. Mobile money facilitated by mobile technology stands out as the most successful innovation in extending financial inclusion in Africa. The second most promising innovation that has the potential to alleviate SME funding constraints is crowdfunding (Makina, 2019). Informal financial intermediaries can grow and transform into supervised intermediaries, following a pattern that resembles that of evolving startups, as shown in Fig. 11.1. Traditional and technological MFIs share a common organizational playground that presides over their main functions. An increasing level of personalization is, however, possible for technology-driven MFIs, as exemplified in Fig. 11.2. Figure 11.3. shows that the financial ecosystem is sufficiently elastic to accommodate for new intermediaries, from FinTechs to MicroFinTechs. The networked systems, mainly mastered by digital interactions, follow

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Self-driven

moneylender

ASCA

ROSCA

village bank / SHG

financial cooperative / credit union

Market-driven

MF bank

private commercial bank

Microfinance Startup State (or postal) bank

MF (deposit taker) NGO

MF NGO

MicroFinTech sponsor

informal (unchecked) institution

unranked

driven

(supervised) formal institutions Tier 4

Tier 3

Tier 2

Fig. 11.1

From informal financial intermediaries to MicroFinTechs

Fig. 11.2

Operational functions in traditional and technological MFIs

Tier 1

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Fig. 11.3 The financial ecosystems network

the network theory principles and the interactions among the complementary nodes may be mathematically measured, easing their economic and financial valuation. Blockchain validation (Davradakis & Santos, 2019; Moro Visconti, 2019) increases the value of data, and is useful for fighting poverty (Kshetri, 2017).

11.2

Sustainability Versus Outreach

Startups may soften the sustainability versus outreach trade-off, focusing on resilient business plans that incorporate the main microfinance features. The success of microfinance does not imply that it can solve all the existing socio-economic problems which affect the poor. Such a false and simplified conviction is both dangerous and deceiving, as it generates exaggerated expectations that are going to remain mostly unsatisfied. MFIs, according to their current tide, are limited in their ability to serve the poorest (this being a practical but also theoretical obstacle to optimal outreach), for many complementary reasons such as the poorest natural unwillingness to borrow—life is already risky enough without taking on debt—or exclusion (often self-exclusion) from group

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lending membership. The poorest also desperately need primary goods and services such as food, grants, or guaranteed employment before they can make good use of financial products. Highly subsidized safety net programs are what the destitute at the bottom of the economic ladder primarily need. Microfinance business is often unprofitable or—in the luckiest cases— it offers only decent returns and consequently it does not readily attract ambitious and profit-maximizing managers unless they have a charitable background and are looking for “values” beyond money and success. MFIs have a high-interest rate burden due to the small monetary amount and high operating cost per transaction. To ensure financial viability and to expand the depth and breadth of their operations, MFIs must adopt cost recovery interest rates. Hence MFIs must charge interest rates high enough, substantially higher than the bank loan risk-free interest rate. The main factors in determining the interest rate on microcredit are the cost of funds, operating costs, loan loss cost, and capital for business expansion (Song et al., 2014). Trendy strategies suggest privileging technological investments instead of opening new physical branches. The key for a feasible and progressive solution of the main microfinance target—maximizing outreach and impact while preserving long term, possibly unsubsidized, sustainability—is to insist on the search for financial innovation, to find smart and unconventional solutions to unorthodox problems. Among the interchanging examples of financial flexibility and innovation, there are changing sizes in target groups, different loan maturities, individual rather than group lending, feasible ad hoc forms of guarantee (forcing deposits from retained earnings; pledging notional assets psychologically worthy for the borrower …). Other characteristics are represented by the frequency of repayment instalments (Santandreu et al., 2020), synergies between financial products (e.g., loans linked with deposits and insurances), specific methods of monitoring (from primary rural supervision to technology-driven devices). Outreach and sustainability are much concerned with the risk that may affect already tiny margins, especially for MFIs who are also enabled to collect deposits that can conveniently reduce their risk profile. This may happen both on an aggregate basis, matching assets (credits toward borrowers) with liabilities toward depositors, and on a single base, since many depositors are also borrowers, partially counterbalancing their overall exposure toward the MFI.

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Cultural changes (Moro Visconti, 2014b: Ch. 9) and improvements are by far the most difficult and longest to look for since they entail a mentality shift that needs plenty of time—often measured by generations—to develop solid roots. The frantic and increasingly interlinked world we live in might speed up the process, but velocity tends to go along with superficiality whereas long-lasting deepness requires its due time. Accounting and financial indicators such as the “financial selfsufficiency ratio,” which calculates the ability to generate enough revenues to cover running and fixed costs, can measure the threshold to profitability. Institutions serving poor customers charge higher interest rates and have fewer default rates than those addressing better-off clients. The classical trade-off between outreach and sustainability stands as a real key point in microfinance issues. Maximum outreach and the potential involvement of as many as possible between the poorest is a primary goal, and sustainability is a crucial element for its persistence over time.

11.3

Technological Innovation

Technical or social innovation (Reinhardt et al., 2019), also concerning the creation and commercialization of new products, strategies, and management, has a deep impact on MFIs, contributing to reshaping their business model, with an impact on their overall risk profile. Innovation ignites a Schumpeterian “creative destruction” that reengineers the business model, making it sounder and more resilient to external shocks, albeit requiring initial investments on both sides, concerning not only MFIs but also increasingly sophisticated clients. Innovation is accelerated by globalization and the deregulation of banking systems, and it promotes economic growth through improved allocation, efficiency, and a reduction of financial service costs. Technology stands out as a big disrupting factor, which segments haves from haves-not, so creating a market barrier among different MFI, where only the strongest are fit for upgrading. Technology is reshaping the banking industry mainly since the advent of IT applications as home banking. Spill-over effects on microfinance are reengineering old-fashioned business models and, in some cases, MFIs are pioneering change, as it happens for M-banking. Whereas technology typically originates in Western countries and then trickles down in poorer areas, with microfinance, emerging markets represent a pioneering lab for financial innovation (Sharma & Al-Muharrami, 2018).

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Even if technology has many different applications, some strands are predominating the actual landscape. IT applications through the digital web are the bridging platform where technologies converge. This is the case for M-banking, social networks, FinTech applications, etc. The impact on the different stakeholders, starting from the microborrowers, is meaningful, mainly because they face a transition from an oral to digital culture. In many backward environments, the oral tradition is seldom complemented by a written culture that is nowadays incorporated in a digital environment where data are created and stored. This cultural leap forward has profound, albeit under-investigated, socioeconomic implications. And the very fact that technology has nonrival characteristics eases its spread and simultaneous use, boosting scalability and economies of experience. Technology is possibly the most potent transmittable tool within a globalized world, subject to unprecedented movements of capitals, goods, people and their know-how, a common denominator which represents the “software” behind any “hardware” transfer, with a demiurgic impact that makes it a cornerstone of internationalized economic value. Technology is also introducing new stakeholders as TLC operators or social networks, who respectively carry and intermediate data. Digital information is exchanged through web platforms and data carriers are becoming the dominant player, with possible abuses (threatening privacy, overcharging their services with the extraction of monopolistic rents, etc.). In the globalization trend, technology is easier to spread than other factors, as it represents a cultural bridge among different experiences. An example is given by the penetration rate of smartphones that are readily accepted everywhere, much more than cultural differences in food, dressing, religion, etc. The most common microfinance processing tasks, such as credit analysis, recording disbursements, payments, and monitoring, can be positively affected and reengineered by ad hoc technology. Innovation can concern either back-office or front-office activities. While the former involves the inside organization of the MFI, the latter interact with the end-users, typically with a mobilephone and connected digital platforms. Technology can be easily customer-tailored, and its client-centricity attitudes are crucial in microfinance, driving value co-creation that levers both sustainability and outreach.

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11.4 FinTech-Driven Scalability and Economic Sustainability Economic sustainability can be detected considering the income statement of a typical MFI and the impact of technology that can disrupt and reengineer existing business models, as shown in Table 11.1. According to Adeyeye and Oyetayo (2016), sustainability can be measured with financial and operational self-sufficiency, capital adequacy, and subsidy dependence ratio. The key parameter to assess business sustainability is represented by the Earning Before Interests and Taxes (EBITDA). The dynamic interpretation of Table 11.1 represents the canvas for the answer to the research question. MFIs traditionally face high staff costs (6.a) and related operating expenses (6.c.) for their core credit scoring and lending activities. Delinquency from untrustworthy borrowers represents another significant cost that contributes to the economic and financial absorption of resources. Table 11.1 MFI Income Statement and Impact of Technology

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To the extent that technology contributes to decreasing costs, economic marginality automatically improves. This surplus can be allocated, at least partially, to decreasing unitary interest rate margins, converging toward fair loan rates (Jarrow & Protter, 2019). MFIs may be tempted to cash in these extra margins, with a consequent mission drift from their original vocation; competition and the will of philanthropic shareholders may, however, minimize this risk, pushing toward a decrease in the level of interest rates. This reduction improves outreach, and so higher volumes of loans may partially compensate for lower marginality, preventing sustainability concerns. Technology can improve the supply and value chain on different layers, reducing the costs but also improving the revenues, not only with outreach-driven higher volumes but even with extra gains from innovative business models. For instance, the digitalization of information from profiling customers produces big data that represent a worthy asset, whose revenues can be shared with the clients, following a value co-creation pattern. Business model extensions can also derive from the interaction with complementary activities and stakeholders. For instance, digital group lending through social networks eases the convergence with peer-to-peer lending, as shown later. A core component of sustainability is represented by the business scalability that expresses the capability to handle growing revenues, dramatically improving economic marginality, so contributing to making the business profitable. MFIs can incorporate in their business models many FinTech features. This may lead to cost savings and revenue increases. Even if what matters for sustainability is positive economic and financial marginality, there is a trade-off that derives from the intrinsic riskiness of lending (banking) activity.

11.5 A Pecking Order Reinterpretation of MFIs Funding Economic and financial sustainability, emblematically represented by (positive) EBITDA, impacts the MFI’s capacity to raise capital from financial lenders and equity-holders. This capacity can be explained with the Pecking Order Theory which postulates that the cost of financing increases with asymmetric information.

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Financing comes from three sources, internal funds (i.e., self-financing, mainly represented by the EBITDA), debt, and new equity. Companies prioritize their sources of financing, first preferring internal financing, and then debt, lastly raising equity as a “last resort.” Hence: internal financing is used first; when that is depleted, then the debt is issued; and when it is no longer sensible to issue any more debt, equity is issued. This theory maintains that businesses adhere to a hierarchy of financing sources and prefer internal financing when available, and debt is preferred over equity if external financing is required (equity would mean issuing shares which meant “bringing external ownership” into the company). Thus, the form of debt a firm chooses can act as a signal of its need for external finance. The pecking order theory is popularized by Myers and Majluf (1984) where they argue that equity is a less preferred means to raise capital because when managers (who are assumed to know better about the true condition of the firm than investors) issue new equity, investors believe that managers think that the firm is overvalued, and managers are taking advantage of this over-valuation. As a result, investors will place a lower value on the new equity issuance. Most MFIs are initially financed with equity provided by sponsoring shareholders, often with a philanthropic attitude. Self-financing is a difficult target, being the EBITDA typically negative. And leverage through bank debts is difficult to get unless it is guaranteed by the same shareholders. The pecking order is so often reversed, and equity injections may represent the first source of funding. One of the theses of this study is that technology can improve self-financing, up to the ideal point of making it positive, so leaving room for debt-taking that may replace equity injections to cover economic losses and financial imbalances.

11.6 Expanding Outreach with Multilayer Digital Platforms Outreach can be further expanded with multilayer digital networks. Multilayer networks are networks with multiple kinds of relations. Digital platforms operate as bridging nodes, as shown in Fig. 11.3. The multilayer dimension is dynamic and changes across time, reshaping the relationships among the different stakeholders, with an impact on value creation and appraisal. Further analysis is contained in Sect. 12.4.

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moneylender

ASCA - ROSCA - SHG - village bank

financial cooperaƟve - credit union - deposit taking NGO

outreach

microfinance bank - private commercial bank - FinTech - NeoBank

Fig. 11.4 Impact of microfinance evolution on the trade-off sustainability versus Outreach

The evolution of MFIs—from rural moneylenders to licensed banks— eases the social lift of development and goes along with the increase of their outreach potential. Each network layer can be represented by a financial intermediary, linked among them through a Darwinian chain by bridging nodes. Catalyst nodes may be represented by digital platforms or other technological devices that upgrade the outreach potential. Multilayer networks are so connected and represent a segmented ecosystem where different actors coexist, as exemplified in Fig. 11.4.

References Adeyeye, P., & Oyetayo, O. (2016). Balance sheet management and outreach success for microfinance banks in Nigeria. International Journal of Economic Development Research and Investment, 7 (1). AFI. (2018). Digital transformation of microfinance and digitization of microfinance services to deepen financial inclusion in Africa. Available at https:// www.afi-global.org/publications/2830/Digital-Transformation-of-Microfina nce-Digitization-of-Microfinance-Services-to-Deepen-Financial-Inclusion-inAfrica.

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Agyemang-Badu, A. (2018). Financial inclusion, poverty and income inequality: Evidence from Africa. Spiritan International Journal of Poverty Studies, 2(2), 1–38. Ali, A., Jamaludin, N., & Othman, Z. H. (2016). Modeling microfinance acceptance among social network women entrepreneurs. International Journal of Economics and Financial Issues, 6(S4), 72–77. Altinok, A. (2018). Group lending, sorting, and risk sharing. Available at https://www.researchgate.net/publication/324953074_Group_Len ding_Sorting_and_Risk_Sharing. Anikina, I. D., Gukova, V. A., Golodova, A. A., & Chekalkina, A. A. (2016). Methodological FinTech as financial innovation—The possibilities and problems of implementation 972 aspects of prioritization of financial tools for stimulation of innovative activities. European Research Studies Journal, 19(2), 100–112. Armendariz De Aghion, B., & Morduch, J. (2010). The economics of microfinance. Cambridge: MIT Press. Ashta, A. (Ed.). (2011). Advanced technologies for microfinance, 206–224. IGI Global: Hershey. Ashta, A. (2018, October). News and trends in FinTech and digital microfinance: why are European MFIs invisible? FIIB Business Review. Bateman, M., & Chang, H. (2012). Microfinance and the illusion of development: From hubris to nemesis in thirty years. World Economic Review, 1. Bruton, G., Siegel, D., & Wright, M. (2015). New financial alternatives in seeding entrepreneurship: Microfinance, crowdfunding, and peer-to-peer innovations. Entrepreneurship Theory and Practice, 39(1), 9–26. Budampati, S. (2018). Impact of information technology on microfinance industry. Advanced Science and Technology Letters, 150, 116–121. Chiappini, H. (2017). Social impact funds: Definition, assessment and performance. Palgrave Macmillan. Collier, P. (2007). The bottom billion. Oxford: Oxford University Press. Davradakis, E., & Santos, R. (2019). Blockchain, FinTechs and their relevance for international financial institutions (EIB Working Papers No. 2019/01). Luxembourg, European Investment Bank. de Oliveira Leite, R., dos Santos Mendes, L., & Sacramento, L.C. (2019, February). To profit or not to profit? Assessing financial sustainability outcomes of microfinance institutions. International Journal of Finance & Economics, 24(3), 1287–1299. Dos Santos, P. L., & Kvangraven, I. H. (2017). Better than cash, but beware the costs: Electronic payments systems and financial inclusion. Developing Economies, 48(2), 205–227.

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Ernst & Young. (2019). FinTech ecosystem playbook. Available at https://www. ey.com/Publication/vwLUAssets/EY-fintech-ecosystem-playbook/$FILE/ EY-fintech-ecosystem-playbook.pdf. Fanconi, P., & Scheurle, P. (2017). Small money—Big impact: Fighting poverty with microfinance. New Jersey: Wiley & Sons. Engels, P. (2010). Mission Drift in microfinance. Stuttgart: Ibidem-Verlag. Gai, K., Qiu, M., & Sun, X. (2018). A survey on FinTech. Journal of Network and Computer Applications, 103, 262–273. García-Pérez, I., Muñoz-Torres, M., & Fernández-Izquierdo, M. (2017). Microfinance literature: A sustainability level perspective survey. Journal of Cleaner Production, 4(20), 3382–3395. Ghosh, J. (2013). Microfinance and the challenge of financial inclusion for development. Cambridge Journal of Economics, 37 (6), 1203–1219. Jarrow, R., & Protter, P. (2019). Fair microfinance loan rates. International Review of Finance, 19(4), 909–918. Kshetri, N. (2017). Potential roles of blockchain in fighting poverty and reducing financial exclusion in the global South. Journal of Global Information Technology Management, 20(4), 201–204. Liu, J., Li, X., & Wang, S. (2020). What have we learnt from 10 years of FinTech research? A scientometric analysis. Technological Forecasting and Social Change, 155,. Mader, P. (2017). Contesting financial inclusion. Development and Change, 49(2), 461–483. Makina, D. (2019). The potential of FinTech in enabling financial inclusion. In Extending financial inclusion in Africa (Chapter 14, pp. 299–318). Academic Press. Moro Visconti, R. (2014a). From microfinance to business planning: Escaping poverty traps. Stuttgart: Ibidem. Moro Visconti, R. (2014b). Clan governance and landless social capital: An anthropological stakeholdership model. Corporate Ownership & Control, 11(1), 477–484. Moro Visconti, R. (2015). Leveraging development with technology and microfinance. ACRN Journal of Finance and Risk Perspectives, 4(3), 19–33. Moro Visconti, R. (2016). Microfinance vs. traditional banking in developing countries. International Journal of Financial Innovation in Banking, 1, 43– 61. Moro Visconti, R. (2019). Blockchain valuation: Internet of value, digital networks and smart transactions. Available at https://www.researchgate.net/ publication/329916782_Blockchain_Valuation_Internet_of_Value_digital_n etworks_and_smart_transactions.

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Moro Visconti, R., & Quirici, M. C. (2014). The impact of innovation and technology on microfinance sustainable governance. Corporate Ownership & Control, 11(2), 420–428. Myers, S. C., & Majluf, N. S. (1984). Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics, 13(2), 187–221. Ravikumar, T. (2019). Digital financial inclusion: A payoff of financial technology and digital finance uprising in India. International Journal of Scientific & Technology Research, 8(11), 3434–3438. Reinhardt, R., Hietschold, N., & Gurtner, S. (2019). Overcoming consumer resistance to innovations—An analysis of adoption triggers. R&D Management, 49(2), 139–154. Saksonova, S., & Kuzmina-Merlino, I. (2017). FinTech as Financial innovation— The possibilities and problems of implementation. European Research Studies Journal, XX (3A), 961–973. Santandreu, E. M., López Pascual, J., & Cruz Rambaud, S., (2020). Determinants of repayment among male and female microcredit clients in the USA: An approach based on managers perceptions. Sustainability, 12, 1701. Saxena, A., & Deb, A. T. (2014). Paradigm paranoia or mission drift? Lessons from microfinance crisis in India. Journal of Business Thought, 4, 38–49. Schüffel, P. (2017). Taming the beast: A scientific definition of fintech. Journal of Innovation Management, 4(4), 32–54. Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129–142. Sharma, S. K., & Al-Muharrami, S. (2018). Mobile banking adoption: Key challenges and opportunities and implications for a developing country. In Y. Dwivedi, et al. (Eds.), Emerging markets from a multidisciplinary perspective: Advances in theory and practice of emerging markets. Cham: Springer. Sharma, S., Singh, P., Singh, K., & Chauhan, B. (2017). Group lending model—A panacea to reduce transaction cost? Zagreb International Review of Economics and Business, 20(2), 46–63. Shu, C. A., & Oney, B. (2012). Outreach and performance analysis of microfinance institutions in Cameroon. Economic Research, 27 (1), 107–119. Song, I., Lui, C., & Vong, J. (2014). Lowering the interest burden for microfinance. International Journal of Process Management and Benchmarking, 4(2), 213–229. Tomic, V., & Stojanovic, D. (2018). Trends and innovations in mobile banking. Available at https://www.researchgate.net/publication/330468035.

CHAPTER 12

Digital Platforms and Network Catalyzers

12.1

Networked Digital Platforms

Digital entrepreneurship highly relies on external sources of financing to foster growth (Cavallo et al., 2019). Networked platforms act as an accelerator (see Sect. 2.13) of startups, strengthening their strategic positioning within the ecosystem, and favoring their growth opportunities. Digital platforms broadly represent the environment in which a piece of software is executed, typically online, through a browser. Platforms can also be interpreted as bridging nodes that connect other virtual or physical nodes (e.g., an e-Commerce platform intermediating between a seller and a buyer in a B2C transaction). “Platforms” are “frameworks that permit collaborators—users, peers, providers—to undertake a range of activities, often creating de facto standards, forming entire ecosystems for value creation and capture” (Mattila & Seppala, 2015). Networks are a powerful catalyzer of interactive activities (exchanges of information; transactions, etc.) that can be boosted and scaled up when they are digitized. The interaction between the infrastructural network, the platform, and the startup is exemplified in Fig. 12.1. Platforms do not represent a specific industry or business segment, as the other realities illustrated in Chapters 10, 11, 13, 14, or 15. They rather refer to a cross-sectional business process that encompasses different industries and products. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_12

297

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Startups

Digital Plaƞorms

Networks

Fig. 12.1 Interaction between the infrastructural network, the platform, and the startup

Digital technologies imply homogenization of data, editability, reprogrammability, distributedness, and self-referentiality (Yoo et al., 2010; Kallinikos et al., 2013). Such features can lead to multiple inheritances in distributed settings, meaning there is no single owner that owns the platform core and dictates its design hierarchy (Henfridsson et al., 2014). Digital scalability (Moro Visconti, 2020, chapter 3) is increasingly dependent on IT platforms. Spagnoletti et al. (2015, p. 364) define a digital platform as “a building block that provides an essential function to a technological system and serves as a foundation upon which complementary products, technologies, or services can be developed.” Digital platforms and supply chains are naturally linked with the networked firm and enhance a scalability multiplier, benefitting also from the Metcalfe’s effect. An example is represented in Fig. 12.2. Platforms are facilitators of exchange (of goods, services, and information) between different types of stakeholders that could not otherwise interact with each other. Transactions are mediated through complementary players that share a network ecosystem (Rochet & Tirole, 2003).

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Clients

Digital Plaƞorm

digital supply chain

Networked Startup

suppliers clustering triangular networks of sub-suppliers

banks / financial intermediaries

Fig. 12.2

other firms

Networked digital platforms

Digital platforms are multisided digital frameworks that shape the terms on which participants interact with one another. Digital platforms are also complicated mixtures of software, hardware, operations, and networks (de Reuven et al., 2018; Gawer, 2014). They provide a set of shared techniques, technologies, and interfaces to a broad set of users; social and economic interactions are mediated online, often by apps (Kenney & Zysman, 2016). Digital platforms are complementarily defined as “software-based external platforms consisting of the extensible codebase of a softwarebased system that provides core functionality shared by the modules that interoperate with it and the interfaces through which they interoperate” (Tiwana et al., 2010). Software platforms represent a technological meeting ground where application developers and end-users converge (Evans et al., 2006). Multisided platforms continue to disrupt long-established industries and have governance structures ranged from a very centralistic and autocratic organization to a more split approach with an empowerment on the

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user side. Also, the accessibility varies from a high degree of openness to detailed background checks users need to pass to participate in the platform (Schreieck et al., 2018). These characteristics may strongly impact healthcare digital platforms. Digital platforms have become a major mode for organizing a wide range of human activities, including economic, social, and political interactions (e.g., Tan et al., 2015; Kane et al., 2014). Platforms leverage networked technologies to facilitate economic exchange, transfer information and connect people (Fenwick et al., 2019). Studies adopting this view focus on the technical developments and functions that form the foundation upon which complementary products and services can be developed i.e., building on the top of the technical core that a platform owner offers and facilitates (Tiwana et al., 2010; Ghazawneh & Henfridsson, 2015; Ceccagnoli et al., 2012). Due to their plasticity, platforms represent an ideal bridging node between complementary intangibles (e.g., big data sourced by IoT, vehiculated through M-Apps, stored in the cloud, shared through interoperable databases, validated with blockchains, and interpreted with artificial intelligence patterns), enhancing value co-creating patterns.

12.2

Network Theory

Network theory (Bapat, 2011; Barabási, 2016, Caldarelli & Catanzaro, 2011; Estrada & Knight, 2015; Jackson, 2008; Van Steen, 2010), is the study of graphs as a representation of either symmetric relations or asymmetric relations between discrete objects. In computer science and network science, network theory is a part of graph theory: a network can be defined as a graph in which nodes and/or edges have attributes. As shown in Fig. 12.1, networks interact with digital platforms, shaping a virtual ecosystem where startups can evolve. Networks are a fundamental feature of complex systems whose connected structure may give an innovative interpretation of the interactions among (linked) stakeholders. Network theory has applications in many disciplines, including statistical physics, particle physics, computer science, electrical engineering, biology, economics, finance, operations research, climatology, ecology, and sociology. Applications of network theory include logistical networks, the www, Internet, gene regulatory networks, epidemiology (even concerning pandemic patterns, like

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those of Covid-19 coronavirus), metabolic networks, social networks, epistemological networks, etc. Stakeholding nodes, as those depicted in Fig. 12.2 are typically symmetric (i.e., bidirectional, or undirected), and this increases the informative and decisional value of the network, particularly when digital platforms are introduced, as they directly mediate the relationships among other stakeholders. A key property of each node is its degree: its number of links to other nodes. The degree is an important parameter even in corporate governance, as it identifies the connections among stakeholders and their intensity. Network theory may greatly contribute to the interpretation of how startups work and interact, showing which are the links with external realities, within their innovative ecosystem.

12.3 The Impact of Digital Platforms on Supply and Value Chains Suppliers, partners, companies, and dealers in supply chains generate, use, and share information with others. These associations lead to a multitude of challenges and opportunities within the supply chains. A Digital Supply Chain is a smart, value-driven, efficient process to generate new forms of revenue and business value for organizations and to leverage new approaches with novel technological and analytical methods (Büyüközkan & Göçer, 2018). Stakeholders interact and co-create value around the chain. The flowchart is exemplified in Fig. 12.3. The interactions among the networked firm, the digital platform, and the other external stakeholders can be examined with a value chain analysis that outlines its networked and digital features. The platform economy is concerned with economic and social activity facilitated by platforms. Such platforms are typically online matchmakers or technology frameworks. By far the most common types are “transaction platforms,” also known as “digital matchmakers.” Examples of transaction platforms include Amazon, Airbnb, Uber, and Baidu. A second type is the “innovation platform,” which provides a common technology framework upon which others can build, such as the many independent developers who work on Microsoft’s platform (Moazed, 2016).

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1

Supplier

2

Manufacturer

Digital Supply Chain

Digital Value Chain

Stakeholder A

Stakeholder B

Stakeholder C

Fig. 12.3

Digital supply and value chains

3

Consumer

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The interactions ignited by digital platforms make the whole network more densely connected and increase its overall value (even in terms of Metcalfe’s formulation). Metcalfe’s law concerns one of the scalability factors and states that the effect of a telecommunications network is proportional to the square of the number of connected users of the system (n 2 ). First formulated in this form by George Gilder in 1993 and attributed to Robert Metcalfe (the inventor of Ethernet), Metcalfe’s law was originally presented, c. 1980, not in terms of users, but rather of “compatible communicating devices” (for example, fax machines, telephones, etc.). Only later, with the globalization of the Internet, did this law carryover to users and networks as its original intent was to describe Ethernet purchases and connections. The law is related to economics and business management, especially with competitive companies looking to merge. Metcalfe’s law characterizes many of the network effects of communication technologies and networks such as the Internet, social networking, and the World Wide Web. Metcalfe’s law is related to the fact that the number of unique possible connections in a network of nodes can be expressed mathematically. If a network is composed of n people and each of them assigns to the network a value that is proportional to the number of other participants, then the value that all the n people assign to the network is the following: n ∗ (n − 1) = n 2 − n

(12.1)

The law has often been illustrated using the example of now oldfashioned fax machines: single fax is useless, but the value of every fax machine increases with the total number of fax machines in the network because of the total number of people with whom each user may send and receive documents increases. Likewise, in social networks, the greater the number of users with the service, the more valuable the service becomes to the community. Digital marketplaces and platforms operate as scalability drivers, as shown in Sect. 3.7.

12.4

Evolutionary Multilayer Startups

Traditional studies of networks generally assume that nodes are connected by a single type of static edge that encapsulates all connections between

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them. This assumption is almost always an oversimplification, and it can lead to misleading results and even the sheer inability to address certain problems. For example, ignoring time-dependence throws away the ordering of pairwise human contacts in the transmission of diseases, and ignoring the presence of multiple types of edges (which is known as “multiplexity”) makes it hard to consider the simultaneous presence and relevance of multiple modes of transportation or communication (De Domenico et al., 2013). Multilayer networks are networks with multiple kinds of relations with multiplex or multidimensional configurations (Bianconi, 2018; Lee et al., 2015; Tomasini, 2015). In a multiplex network, the same set of nodes are connected via more than one type of link, so enhancing scalability. In most real-world systems an individual network is one component within a much larger complex multi-level network (is part of a network of networks). Most real-world network systems continuously interact with other networks (Kennet et al., 2015). There is a wide range of systems in the real world where components cannot function independently so that these components interact with others through different channels of connectivity and dependencies. Complex Networks theory is, in fact, the formal tool for describing and analyzing fields as disparate as sociology (social networks, acquaintances or collaborations between individuals), biology (metabolic and protein networks, neural networks) or technology (phone call networks, computers in telecommunication networks) (Boccaletti et al., 2015). Many real-world networks interact and depend on other networks via dependency connectivity, forming “networks of networks.” The interdependence between networks has been found to largely increase the vulnerability of interacting systems, when a node in one network fails, it usually causes dependent nodes in other networks to fail, which, in turn, may cause further damage on the first network and result in a cascade of failures with sometimes catastrophic consequences (Liu et al., 2015). An example of multilayer networks is reported in Fig. 12.4. Digital platforms represent a virtual stakeholder (Moro Visconti, 2019) that can link previously unrelated layers (each representing a network). The platform is a bridging node that is virtually present in each layer, with edges that connect inter-layer nodes, i.e., nodes placed in different layers.

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Product 1 Country A

Product 2

Country B

Fig. 12.4

Multilayer networks

paƟents

Product 1

Country A

Bridging Digital Plaƞorm

Country B

Fig. 12.5

Product 2

Superimposed multilayer networks with a bridging digital platform

If networks (Barabási, 2016) get closer and are superimposed, the representation of Fig. 12.4. becomes slightly different, as shown in Fig. 12.5. Here the bridging digital platform acts as a super-network. Multilayer networks are also consistent with platforms that operate with other platforms (e.g., Internet platforms serving social media apps). Networks are weighted if the intensity (traffic of data; transactions, etc.) of each interaction among nodes is measured. Bridging digital platforms are typically weight-intensive, improving the overall value and functionality of the network’s ecosystem (consistently with Metcalfe’s law). Multilayer networks may also contribute to explaining evolutionary dynamic processes that may illustrate how startups evolve with their

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crowdfunders

founders business angels

banks venture capital

T

Fig. 12.6

i

m

private equity

e

Multilayer evolution of startup stakeholders

complex web of interdependencies. Temporal networks—i.e., networks that change over time—follow multilayer patterns where the interacting stakeholders adapt and change. For instance, business angels, a family and friends club deal, and crowdfunding equity-holders, are present in the first phase, and may eventually be diluted in second-round financing, when venture capital and private equity investors intervene. When the startup matures and starts collecting debt, also banks get involved. An example of this chronological evolution is represented in Fig. 12.6. Figure 12.6. is complementary to Fig. 2.16, as it shows how the “adjuvating stakeholders” that rotate around the startup can catalyze its growth and impact the dynamic value creation.

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and angel groups, Technological Forecasting and Social Change, 145(C), 24– 35. Ceccagnoli, M., Forman, C., Huang, P., & Wu, D. J. (2012). Co-creation of value in a platform ecosystem: The case of enterprise software. MIS Quarterly, 36(1), 263–290. De Domenico, M., Solé-Ribalta, A., Cozzo, E., & Kivelä, M., (2013). Mathematical formulation of multilayer networks. Physical Review, X (3), 041022. de Reuven, M., Sørensen, C., & Basole, R. C. (2018). The digital platform: A research agenda. Journal of Information Technology, 33, 124–135. Estrada, E., & Knight, P. A. (2015). A first course in Network theory. Oxford: Oxford University Press. Evans, D. S., Hagiu, A., & Schmalensee, R. (2006). Invisible engines: How software platforms drive innovation and transform industries. Cambridge: MIT University Press. Fenwick, M., McCahery, J. A., & Vermeulen, E. P. M. (2019). the end of ‘corporate’ governance: Hello ‘platform’ governance. European Business Organization Law Review, 20(1), 171–199. Gawer, A. (2014). Bridging differing perspectives on technological platforms: Toward an integrative framework. Research Policy, 43(7), 1239–1249. Ghazawneh, A., & Henfridsson, O. (2015). A paradigmatic analysis of digital application marketplaces. Journal of Information Technology, 30(3), 198–208. Henfridsson, O., Mathiassen, L., & Svahn, F. (2014). Managing technological change in the digital age: The role of architectural frames. Journal of Information Technology, 29, 27–43. Jackson, M. O. (2008). Social and economic networks. Princeton University Press: Princeton. Kallinikos, J., Aaltonen, A., & Marton, A. (2013). The ambivalent ontology of digital artifacts. MIS Quarterly, 37 (2), 357–370. Kane, G. C., Alavi, M., Labianca, G., & Borgatti, S. P. (2014). What’s different about social media networks? A framework and research agenda. MIS Quarterly, 38(1), 275–304. Kennet, D. Y., Perc, M., & Boccaletti, S. (2015). Networks of networks—An introduction. Chaos, Solitons & Fractals, 80, 1–6. Kenney, M., & Zysman, J. (2016). The rise of the platform economy. Issues in Science and Technology, 32(3). Lee, K. M., Min, B., & Goh, K. I. (2015). Towards real-world complexity: an introduction to multiplex networks. European Physical Journal B, 88(2). Liu, X., Peng, H., & Gao, J. (2015). Vulnerability and controllability of networks of networks. Chaos, Solitons & Fractals, 80, 125–138. Mattila, J., & Seppala, T. (2015). Machines in a Cloud—Or a Cloud in Machines? Emerging New Trends of the Digital Platforms in Industry and Society, June.

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Moazed, A. (2016). Modern monopolies. Cham: Palgrave Macmillan. Moro Visconti, R. (2019). Combining Network theory with corporate governance: Converging models for connected stakeholders. Corporate Ownership & Control, 17 (1). Moro Visconti, R. (2020). The valuation of digital intangibles. PalgraveMacmillan. Rochet, J. C., & Tirole, J. (2003). Platform competition in two-sided markets. Journal of the European Economic Association, 1(4), 990–1029. Schreieck, M., Hein, A., Wiesche, M., & Krcmar, H. (2018). The challenge of governing digital platform ecosystems. In C. Linnhoff-Popien, R. Schneider, & M. Zaddach (Eds.), Digital marketplaces unleashed. Berlin, Heidelberg: Springer. Spagnoletti, P., Resca, A., & Lee, G. (2015). A design theory for digital platforms supporting online communities: A multiple case study. Journal of Information Technology, 30(4), 364–380. Tan B., Pan S. L., Lu X., & Huang L. (2015). The role of IS capabilities in the development of multi-sided platforms: The digital ecosystem strategy of Alibaba.com, Journal of the Association for Information Systems, 16(4), 248– 280. Tiwana, A., Konsynsky, B., & Bush, A. A. (2010). Platform evolution: Coevolution of platform architecture, governance, and environmental dynamics. Information Systems Research, 21(4), 675–687. Tomasini, M. (2015). An introduction to Multilayer Networks BioComplex Laboratory. Florida Institute of Technology. Available at https://www.resear chgate.net/profile/Marcello_Tomasini/publication/321546271_An_Introd uction_to_Multilayer_Networks/links/5a26fe48aca2727dd8839dee/An-Int roduction-to-Multilayer-Networks.pdf. Van Steen, M. (2010). Graph theory and complex networks. Maarten Van Steen: An introduction. Yoo, Y., Henfridsson, O., & Lyytinen, K. (2010). The new organizing logic of digital innovation: An agenda for information systems research. Information Systems Research, 21(4), 724–735.

CHAPTER 13

From Netflix to Youtube: Over-the-Top and Video-on-Demand Platform Valuation

13.1

Introduction

The broadband video revolution has brought on the content industry an exponential growth of demand for online video services, driving the bandwidth demand and hence as a possible ground for the evolution of ultra-broadband networks. The growing variety and availability of devices brought to a radical change in consumers’ habits, amplifying the options and modes of fruition not bound to the single and specific television device. This process is driven by consumer demand as well as by the industry’s relentless tendency toward innovation. (IT Media Consulting—LUISS Dream, 2016)

An over-the-top (OTT) media service is a streaming function offered directly to viewers via the Internet. OTT bypasses cable, broadcast, and satellite television platforms, the companies that traditionally act as a controller or distributor of such content. It has also been used to describe no-carrier cellphones, where all communications are charged as data, avoiding monopolistic competition, or apps for phones that transmit data in this manner, including both those that replace other call methods and those that update software. The term is most synonymous with subscription-based video-ondemand (SVoD) services that offer access to film and television content

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_13

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(including existing series acquired from other producers, as well as original content produced specifically for the service). OTT also encompasses a wave of “skinny” television services that offer access to live streams of linear specialty channels, similar to a traditional satellite or cable TV provider, but streamed over the public Internet, rather than a closed, private network with proprietary equipment such as set-top boxes. Over-the-top services are typically accessed via websites on personal computers, as well as via apps on mobile devices (such as smartphones and tablets), digital media players (including video game consoles), or televisions with integrated Smart TV platforms (Wikipedia, 2020a). Video on demand (VOD) is a video media distribution system (Baladron & Rivero, 2019) that allows users to access video entertainment without a traditional video entertainment device and the constraints of a typical static broadcasting schedule. Television VOD systems can stream content, either through a traditional set-top box or through remote devices such as computers, tablets, and smartphones. VOD users can permanently download content to a device such as a computer, digital video recorder, or a portable media player for continued viewing. The majority of cable and telephone company–based television providers offer VOD streaming, whereby a user selects a video program that begins to play immediately or download to a digital video recorder (DVR) rented or purchased from the provider, or to a PC or a portable device for delayed viewing. Internet television has emerged as an increasingly popular medium of VOD provision. Desktop client applications such as the Apple iTunes online content store and Smart TV apps such as Amazon Prime Video allow temporary rentals and purchases of video entertainment content. Other internet-based VOD systems provide users with access to bundles of video entertainment content rather than individual movies and shows. The most common of these systems, Netflix, Hulu, and Disney+, use a subscription model that requires users to pay a monthly fee for access to a selection of movies, television shows, and original series. In contrast, YouTube, another internet-based VOD system, uses an advertising-funded model in which users can access most of YouTube’s video content free of charge but must pay a subscription fee for premium content. Some airlines offer VOD services like in-flight

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entertainment to passengers through video screens embedded in seats or externally provided portable media players (Wikipedia, 2020b). Over-the-top is any service that previously used satellite or cable to transmit but now uses the internet. OTT can describe many services used daily, like: • • • •

WhatsApp; Skype; Livestreaming; Netflix (Wayne, 2018).

All these services once depended on satellite or cable to exist (texts, video calls, and television) but now use the internet to perform the function instead (Fig. 13.1). Some examples of video-on-demand are: • • • • •

YouTube; BBC iPlayer; In-flight entertainment systems; OnDemand on your television (cable set-top box); Even videos posted on your Facebook feed.

The main megatrends are represented by: • Increasing penetration of smartphones and tablets; • Personalized media entertainment.

13.2

Digital Platforms and Scalability

Digital platforms (examined in Sects. 3.7, 12.1, and 12.3) are at the basis of technology-enabled business models that facilitate exchanges between multiple groups—such as end-users and producers—who do not necessarily know each other. The continuous upgrade of the technological environment creates new possibilities and reshapes the value and supply chain of financial intermediation, disrupting the existing business models.

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streaming media

Internet

digital plaƞorms

Fig. 13.1 The link between media, the internet, and the platforms

Whereas traditional firms create value within the boundaries of a company or a supply chain, digital platforms utilize an ecosystem of autonomous agents to co-create value (Hein et al., 2019). Digital platforms act as a bridging node that connects digital clients to traditional or innovative producers. Whenever platforms connect different layers (each representing a network sub-system), they can increase the overall systemic value. Digital platforms are multisided digital frameworks that shape the terms on which participants interact. Digitalization is defined as the concept of “going paperless”, namely as the technical process of transforming analog information or physical products into digital form. The term ‘digital transformation’ refers, therefore, to the application of digital technology as an alternative to solve traditional problems. As a result of digital solutions, new forms of innovation

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and creativity are conceived, while conventional methods are revised and enhanced. Digitally born startups or similar tech-businesses are not the only ones interested in adopting digital processes. Traditional businesses may be digitalized as well (e.g., a simple farmer willing to increase exponentially his/her production of tomatoes may digitalize the production activities through new systems or machines). In practice, with digitalization, traditional firms improve their crucial economic and financial parameters, as the EBITDA, which increases, while the WACC reduces, so improving the DCF and the overall enterprise value (EV): DC F(unlever ed) =

 OC F ↑ ∼ = Enter prise V alue ↑↑ W ACC ↓

(13.1)

In synthesis, digitalization brings speed and quality at a low cost, thus representing a crucial driver for scalability itself. Digitalization enables a business process reengineering of traditional firms, which may presuppose an incremental production growth. Figure 13.2 shows the link between digital transformation and scalability.

Fig. 13.2

The link between digital transformation and scalability

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As shown in Sect. 12.2, digital platforms can be interpreted in terms of network theory (see Barabási, 2016), the study of graphs as a representation of either symmetric or asymmetric relations between discrete objects. In computer science and network science, network theory is a part of graph theory: a network can be defined as a graph in which nodes and/or edges have attributes (e.g., names). Digital platforms are intrinsically networked, and within networks, they represent a bridging node that connects users (stakeholders). The properties of networked platforms are intrinsically consistent with the digital media ecosystem, depicted in Fig. 13.3.

smartphones and other devices

M-Apps

Internet

Streaming Media / Entertainment Ecosystem (VerƟcal)

Technology providers

AdverƟsing

Big Data

Fig. 13.3

The digital media ecosystem

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Trademarks

ArƟsƟc Plays, Movies, Songs, Gaming ...

Patents / Know-How

SoŌware

Copyright

Intangible Assets

Domain Names / Websites

M-Apps

ArƟficial Intelligence

Blockchains Big Data / IoT

Fig. 13.4

315

Internet Companies / Digital Plaƞorms

Goodwill

Interactions of intangibles

A portfolio of intangibles embeds synergistic interactions, as illustrated in Fig. 13.4, and triggers levered scalability upside.

13.3

Business Models

According to ITMedia Consulting LUISS DREAM, 2016, the growth of internet based video is mainly driven by: • the broadband deployment that allows video to be smoothly transmitted and widely distributed; • the increase in demand for quality services that provides a strong incentive in producing HD and Ultra HD (4K) contents, which are bandwidth-hungry.

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Technologies have an impact on business models, affecting cost structure, availability of new products, market size, as well as the possibility to provide news services, information available to consumers, payment mechanisms. Digitization has aligned the audio, video, and data transmission systems, enabling convergent networks to convey a growing number and variety of services. Over-the-Top business models are characterized by remarkable scalability properties that make them geographically adaptable to various countries. The business model is a pre-requisite for the value proposition (social and economic value, to be appraised with a cost-benefit analysis). The most challenging estimate is probably represented, as it happens in most industries, by the revenue streams. Mighty contacts need to be monetized and transformed into … contracts. Table 13.1 summarizes the main business model propositions, linked to value chain patterns. The business model may include: • Music in streaming Spotify (Apple Music; iTunes; Amazon prime Music; Amazon Prime HD—Tidal, etc.); • TV: Apple/Disney/NowTV; • Captive firms within a larger media/digital group: e.g., Apple, Amazon through Google Chromecast (a device that makes a traditional television “smart” through a Wi-Fi connection through HDMI); • Smart TV connected to Internet; • Synergies with satellite TVs/cross-selling, platform convergence; • Acquisition and protection of a competitive advantage; • M-App: User Interface (functionality/usability …); • A Library/catalogue to represent the portfolio of products, and the quality of audio-video, to segment the clients according to their degree of sophistication and spending capacity/willingness; • sharing economy patterns, according to which users and service providers interact on a peer-to-peer basis: through online platforms, it is possible to share contents, resources, time, and skills. The value proposition is represented in Fig. 13.5.

• Subscription VoD: subscription-based broadcasting with periodic fees, offering a large library of contents. SVOD is like traditional TV packages, allowing users to consume as much content as they desire at a flat rate per month. Retention of customers and constant search for new ones with aggressive marketing represent a common strategy. With SVOD, there is far greater freedom to opt-out, as consumers are not tied into a long-term contract. This offers greater flexibility to users, and providers of SVOD are continually challenged with retaining consumers, by providing exclusive new content, aggressive pricing schemes—and probably both. Examples include Netflix, Amazon Prime Video, Infinity, Hulu, HBO, Disney, Now TV, etc. • Transactional VoD: pay-per-view broadcasting with rent (purchase) of contents (a movie, etc.) through the Web. TVOD is the opposite of subscription video, where consumers purchase content on a pay-per-view basis. There are two sub-categories, known as electronic sell-through (EST), where you pay once to gain permanent access to a piece of content; and download to rent (DTR), where customers access a piece of content for a limited time for a smaller fee. TVOD services tend to offer more recent releases, providing rights holders with higher revenues and giving consumers timely access to new content. TVOD services typically retain customers by offering attractive price incentives, so they continue to return in the future. Examples are given by iTunes, Sky Box Office, Chili, Google Play, etc. • Advertising VoD: free-of-charge broadcasting contents, supported by segmented (vertical) advertising. Content may be generic, and the library limited. Examples are represented by Youtube, Raiplay, Mediaset Play, etc. Premium content owners rarely use AVOD as it generates lower amounts of revenue than SVOD and TVOD • Freemium VOD model, which allows all users to have a limited free tier and pay service offerings on higher tiers (Hulu Plus) • Multiple business models. Some services operate with multiple business models. Take Amazon Video and Sky for example audiences pay a fixed subscription per month for access to a library of content, but brand-new movie releases and specific sporting events command an additional fee.

Value proposition/technology

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(continued)

Description

Issue

Table 13.1 Business models of the AudioVisual Industry

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317

Internet (broadband) access typically through M-Apps; viral social networking Unique expertise—Management team—Innovation—Patented Inventions—Sales team—Platform scalability—Platform competition—Economies of Scale Trusted partnerships—Investment team—Advisory team—stakeholders Business purpose—see vertical applications, market outlook, and customer segmentation Customer relationships—B2C solutions—Revenues from licensing and service fees—sales of M-apps—in-app purchases—Big Data collection and (anonymous) resale Purchase of contents—platform development—R&D—Marketing and Advertising Inventor—Family and friends—Crowdfunding—Venture Capital—Private Equity—Bridge financing

Delivery mode Key resources

Cost structure Startup funding

Key partners Key activities Revenue streams

Description

Issue

Table 13.1 (continued)

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TransacƟonal Video on Demand

AdverƟsing Video on Demand

SubscripƟon Video on Demand

Fig. 13.5 Video on demand business models

13.4

M-Apps

M-Apps, (a shortening of the term “Mobile Application Software”) represent a computer program (software) designed to run on mobile devices such as smart-phones, tablet computers, phablets, smartwatches, or other mobiles, such as notebooks (with specific extensions). Each app is associated with a logo that represents the touchscreen gateway to the app. A logo is a graphical label even more difficult to conceive than a domain name, due to its stricter constraints (no different extensions, predefined measure). The relationship between M-Apps logos and domain names is still under-investigated: they both convey internet traffic but in a different (complementary) way. M-Apps are increasingly popular and now represent the trendiest software device. M-Apps can be sold for free (freemium = free + premium) or paid. Some 90% of the MApps downloaded tend to be freemium. Revenue streams for freemium app providers follow different patterns and are mainly represented by

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following premium services (e.g., a free app that introduces to paying services) (Moro Visconti, 2020, Chapter 12). M-Apps are associated with iconic digital logos and they represent the bridging shortcut to the web. They are most popular with touchscreen devices like smartphones or tablets.

13.5

The Accounting Background for Valuation

The evaluation is sensitive to forward-looking data that can be used to build up a sound business plan with a time horizon coherent with the average life cycle of the products and services. As shown in Chapter 2, a business plan is a formal accounting statement that numerically describes a set of business goals, the reasons why they are believed attainable, and the strategic plan and managerial steps for reaching those goals. Hypotheses and visionary ideas of gamechangers must be transformed into numbers and need to be backed by reasonable and verifiable assumptions about future events and milestones (Moro Visconti, 2019). The accounting background is composed of pro forma balance sheets (of some 3–5 years) and perspective income statements. The matching of these two documents produces expected cash flow statements. Economic and financial margins are the crucial accounting parameters for valuation that are represented by the EBITDA, the EBIT, the operating and Net Cash Flows, and the Net Financial Position, as it will be shown in the formulation of the appraisal approaches. The appraisal methodology may conveniently start from a strategic interpretation of the business model (that derives from accounting data) to extract the key evaluation parameters to insert in the model, as shown in Fig. 13.6.

(Perspec ve) Accoun ng data

Fig. 13.6

•Balance sheet •Income statement •Cash Flow statement

Business Plan

•Ɵme horizon •strategic assumpƟons •sensiƟvity/scenario analysis of OTT / VoD

Evaluation methodology

Evalua on parameters

•economic/ financial data •book versus market values

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321

An analysis of the business model may conveniently consider: 1. The revenue model; 2. The strategic goals; 3. The growth drivers; 4. The expected investments; 5. Market trends. The interaction between the business model and the strategic value drivers is illustrated in Fig. 13.7.

OTT / VoD Revenue Model

Business Model & Strategic Goals

Market trends

Expected Investments

Fig. 13.7

Business model and value drivers

Growth Drivers

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13.6

Valuation Methods

Most of the concepts recalled in these paragraphs are similar to those already illustrated in other chapters, and restated here with some personalization, considering the peculiar startup of this chapter that is so intended to be self -containing. In particular, the main concepts that are here directly or indirectly restated concern: (a) The preliminary phase, from business modeling to business planning (Chapter 2); (b) The main valuation approaches (Discounted Cash Flows—DCF, and market multipliers), described in Chapter 8; c) The valuation of specific startups (see Part II —Industry Applications).

The evaluation criteria typically follow the (actual and prospective) business model of the target company, as illustrated in Fig. 13.8. A comparison of the primary evaluation criteria in traditional firms versus high-tech firms (startups) is reported in Table 13.2.

Business model

Technological Firms (Startups)

VoD firms Valua on Approach

Fig. 13.8

Business model and valuation approach

TradiƟonal Broadcasters

13

Table 13.2 Comparison of the main evaluation approaches of traditional firms and technological startups

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Traditional firm (see Chapter 8)

323

Technological startup (see Chapter 9 and IPEV, 2018)

Balance-sheet based Venture Capital method (Fernandez, 2001) Income Binomial trees Mixed capital-income Financial approach (Discounted Cash Flows) Market multiples (comparable firms)

In this case, the value may be inferred even with differential income methodologies, traditionally used in the evaluation of intangible assets (within the income approaches). Among the main evaluation methodologies, the following are the most relevant: 1. the customers’ portfolio and the Internet traffic generated by the platform and the related web analytics; 2. Financial approach (Discounted Cash Flows—DCF); 3. Market comparables. 13.6.1

The Customers’ Portfolio (e-Loyalty of Digital Clients)

The customers (client base) unsurprisingly represent even for VoD firms a primary strategic target and the basis for any long-lasting revenue model. Consistently with this basic insight, any valuation approach must first consider the revenue basis and then its impact on other parameters (EBITDA, operating cash flows, other economic/financial margins, etc.). Even if the VoD firms may have a different business model (transactional, advertising, or subscription-based), they all converge towards a client base. The plasticity of the business models and the digital competition among different firms make the clients’ portfolios intrinsically unstable. It is so immediate for any customer to dismission a temporary subscription, not to surf anymore on a free domain, or stop any purchase from a pay-for-view provider that the churn rate (the annual percentage rate at which customers stop subscribing to a service) represents a major concern for incumbents—and an element of hope for newcomers.

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Two complementary strategic targets are represented by the acquisition of new clients and their retention, possibly increasing their spending willingness. The acquisition of new clients follows digital paradigms that are based on a combination of contents (services/products offered), price, advertising/marketing, competitors, technology (digital platforms, broadband Internet, smart devices, etc.). Clients are often easy to attract but difficult to keep. They continuously compare similar offers and easily swap to more convenient ones. Some client retention program ideas may be found in: • https://blog.hubspot.com/service/customer-retention-strategies • https://www.barilliance.com/what-is-retention-marketing/ Figure 13.9 shows how sales can boost with blitzscaling (a specific set of practices for igniting and managing dizzying growth) but then need retention strategies. Retention strategies may be based, whenever possible, on loyalty programs that primarily attract existing customers, and improve network building

s a l e s

g r o w t h

Fig. 13.9

retenƟon markeƟng

blitzscaling

From blitzscaling to client retention

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The contents of this sub-paragraph are excerpted and readapted from Moro Visconti (2020, Chapter 17). Whereas the customer list may be considered an identifiable asset (according to the accounting standard IAS 38), customer loyalty is typically difficult to assess and identify and cannot be negotiated autonomously. The customers’ portfolio is a strategic asset for management, to monitor the company’s performance, favouring personalized marketing. It can accordingly be segmented and structured by: • Sales channel (consumer, industry, other); • Type of customer (B2B or, most likely in the VoD business, B2C); • Supply methods (platform technology); This may allow us to carry out strategic analyses in terms of: • Sales trend (which is relevant for estimating lost profits in the event of disputes); • Collection days; • Service level; • Comparison with the budget; • Comparison with the previous year. The customers’ portfolio is included in the broader concept of goodwill and can have, in a synergistic portfolio, correlations with other intangibles, first of all, the trademarks and, sometimes, the patents and know-how, if the sales (and the corresponding customers, more or less loyal) are linked to a marketing and technological surplus value. The customers’ equity (client assets) represents the economic value of customer relationships, in an overall stratification of the value of the customers’ portfolio that is discounted over time. The strategic drivers of the customers’ equity are represented by: • Equity value (value attributed by the customer to the goods or services produced/supplied by the company); • Brand equity (value of the brand/trademark); • Retention equity (brand loyalty even when it involves an incremental price compared to other comparable products or services).

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More sophisticated models (Estrella-Ramon et al., 2013) for estimating the customers’ value (Customer Lifetime Value—CLV) consider future flows generated by each customer and the retention rate (loyalty). The CLV can be defined as the current value of all the cash flows generated by each user, dividing all the present and future consumers into different groups. The calculation of the Customer Lifetime Value (CLV) is complex, but it can be approximated considering the current value of the constant customer in the future: C LV = A × S × G

(13.2)

where: A = duration, in years, of the relationship between customer and company; S = average cost of a customer per year; G = percentage gain. The main drivers of the CLV are: • • • • •

The abandonment (churn) rate; The average number of purchases during the year; The frequency of purchases during the year; The costs of acquiring a new customer; Marketing costs for development and retention activities.

Starting from the assumption that a buyer cannot be appreciated according to what he/she has just bought, but for all the potential purchases that he/she will be able to make in the future, the CLV allows to classify customers into different segments and to implement more targeted marketing actions. Alongside the traditional methodologies for evaluating companies (mixed capital-income approach with an independent assessment of goodwill), for the valuation of the customers’ portfolio, there are specific approaches proposed by appraisal practitioners (Customer Lifetime Value—CLV). Concerning the customers’ portfolio, the valuation metrics should take into consideration the following specific aspects:

13

• • • •

The The The The

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cost of reconstruction/replacement; aging (schedule); incremental EBITDA; additional cash flows.

These considerations need to be adapted to a digital world where scalability is enhanced by the market base, mainly represented by an increasing number of loyal clients. Ephemeral retention rates need however to be carefully considered, in a volatile scenario where higher opportunities are mitigated by a correspondingly higher risk profile. Scalability-driven higher expected cash flows are discounted at higher rates. DCF metrics incorporate both in its formulation. A further aspect is represented by the Internet traffic. Web-based VoD firms rotate around Internet platforms, and traffic is a vital measure of the value of each platform. This concept can be interpreted even considering the platform as a bridging node within a network. The traffic (of data and transactions) around this node contributes to determining its intrinsic value and strategic importance. As shown in Moro Visconti (2020, Chapter 11), Internet traffic is the flow of data across the Web. To the extent that traffic turns out into contacts that can be monetized (through the sale of e-commerce goods or the supply of services or web advertising), it becomes a key parameter for the evaluation of domain names, websites, and other web intangibles. Internet traffic represents the flow of data across the Web. The monetization process is the following (Fig. 13.10). Internet traffic can be evaluated with sophisticated algorithms and can be divided into (Kemmis, 2018):

Occasional visitor

• churn rate of uninterested surfers

interested contact (lead)

•synergies with web brand, MApps ...

Fig. 13.10 Internet traffic monetization process

(paying) client

•moneƟzaƟon •verƟcal adverƟsing

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(a) Direct Traffic Direct access (direct traffic) to a website occurs when a visitor arrives directly on a website, without having clicked on a link on another site. Direct traffic can come from different sources: • If a visitor knows the URL and enters it directly into his/her browser’s address bar; • If a visitor has bookmarked the site or saved it as a favorite in his/her browser; • If a visitor clicks on a link contained in an email (the URL has been shared by a third person). VoD firms struggle for the fidelization of their clients, and direct traffic may represent a primary source, especially for first-time contacts. (b) Organic Traffic Organic traffic is defined as visitors coming from a search engine, such as Google or Bing. Paid search ads are not counted in this category. In HubSpot and Google Analytics, paid search traffic or PPC is marked in a separate category. Organic traffic deals directly with SEO. The better the ranking for competitive keywords, the more organic traffic will result. Websites that blog consistently will see a steady increase in organic search traffic and improved positioning in the search results. As a marketer, it is important to look at keywords and identify new ranking opportunities each month. These should guide the blogging efforts. Direct traffic is so different from referred traffic, with implications on the appraisal of domain names versus internet search engines, etc. Web traffic information may include visitors, visitor base, subscribers, subscriber base, and/or web traffic. Web traffic information may include the number, type, demographics, language, income, attributes of the visitors and/or subscribers; most requested entry and exit pages; top path (way visitors navigate the site); type, number, quality, attributes of the referrers and backlinks; search engine listings; reach, rank, page views, ranking on a search engine; and/or web traffic logs. All these parameters matter in web analytics.

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329

The Financial Approach

The financial approach is based on the principle that the market value of the company is equal to the discounted value of the cash flows that the company can generate (“cash is king”). The determination of the cash flows is of primary importance in the application of the approach, as is the consistency of the discount rates adopted. The doctrine (especially the Anglo-Saxon one) believes that the financial approach is the “ideal” solution for estimating the market value for limited periods. It is not possible to make reliable estimates of cash flows for longer periods. “The conceptually correct methods are those based on cash flow discounting. I briefly comment on other methods since - even though they are conceptually incorrect - they continue to be used frequently” (Fernandez, 2001). This approach is of practical importance if the individual investor or company with high cash flows (leasing companies, retail trade, public and motorway services, financial trading, project financing SPVs, etc.) are valued. Financial evaluation can be particularly appropriate when the company’s ability to generate cash flow for investors is significantly different from its ability to generate income, and forecasts can be formulated with a sufficient degree of credibility and are demonstrable. There are two complementary criteria for determining the cash flows: a.1. The cash flow available to the company (Free cash flow to the firm) This configuration of expected flows is the one most used in the practice of company valuations, given its greater simplicity of application compared to the methodology based on flows to partners. It is a measure of cash flows independent of the financial structure of the company (unlevered cash flows) that is particularly suitable to evaluate companies with high levels of indebtedness, or that do not have a debt plan. In these cases, the calculation of the cash flow available to shareholders is more difficult because of the volatility resulting from the forecast of how to repay debts. This methodology is based on the operating flows generated by the typical management of the company, based on the operating income available for the remuneration of own and third-party means net of the relative tax effect. Unlevered cash flows are determined by using operating income before taxes and financial charges.

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The cash flow available to the company is, therefore, determined as the cash flow available to shareholders, plus financial charges after tax, plus loan repayments and equity repayments, minus new borrowings and flows arising from equity increases. The relationship between the two concepts of cash flow is as follows: cash flow available to the company = cash flow available to shareholders + financial charges (net of taxes) + loan repayments − new loans

(13.3)

a.2. The (residual) cash flow available to shareholders This configuration considers the only expected flow available for members’ remuneration. It is a measure of cash flow that considers the financial structure of the company (levered cash flow). It is the cash flow that remains after the payment of interest and the repayment of equity shares and after the coverage of equity expenditures necessary to maintain existing assets and to create the conditions for business growth. In M&A operations, the Free Cash Flow to the Firm (operating cash flow) is normally calculated to estimate the Enterprise Value (comprehensive of debt). The residual Equity Value is then derived by subtracting the Net Financial Position. The discounting of the free cash flow for the shareholders takes place at a rate equal to the cost of the shareholders’ equity. This flow identifies the theoretical measure of the company’s ability to distribute dividends, even if it does not coincide with the dividend paid. Cash flow estimates can be applied to any type of asset. The differential element is represented by their duration. Many assets have a defined time horizon, while others assume a perpetual time horizon, such as shares. Cash flows (CF) can, therefore, be estimated using a normalized projection of cash flows that it uses, alternatively: • unlimited capitalization:

 W1 = C F i

(13.4)

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• limited capitalization:

W2 = C F a n¬i

(13.5)

where W 1 and W 2 represent the present value of future cash flows. The discount rate to be applied to expected cash flows is determined as the sum of the cost of equity and the cost of debt, appropriately weighted according to the leverage of the company (the ratio between financial debt and equity). This produces the Weighted Average Cost of Capital (WACC): W ACC = K i (1 − t)

E D ke D+E E+D

(13.6)

where: k i = cost of debt; t = corporate tax rate; D = market value of debt; E = market value of equity; D + E = raised capital; k e = cost of equity (to be estimated with the Capital Asset Pricing Model − CAPM or the Dividend Discount Model). The cost of debt capital is easy to determine, as it can be inferred from the financial statements of the company. The cost of equity or share capital, which represents the minimum rate of return required by investors for equity investments, is instead more complex and may use the CAPM or the Dividend Discount Model (a method of valuing a company’s stock price considering the sum of all its future dividend payments, discounted back to their present value. It is used to value stocks based on the net present value of future dividends). The formula of the CAPM is the following: E(r )V oD = r f r ee + βV oD [13.(E(r )mar ket − r f r ee ]

(13.7)

where: E(r)VoD = expected return of the VoD listed stock r free = risk − free rate of return (e.g.,of a long term Government bond)

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R. MORO-VISCONTI

β VoD = sensitivity of the VoD’s stock to the market price (E(r)market = expected return of the (benchmark) stock market. A central element is represented by the beta (β) of the startup to be evaluated that consists of the ratio between the covariance of the security with its stock market, divided by the variance of the market. Market betas, subdivided by industry, may be detected from the dataset of A. Damodaran (see, for instance, http://pages.stern.nyu.edu/~adamodar/ New_Home_Page/datafile/Betas.html). Once the present value of the cash flows has been determined, the calculation of the market value W of the company may correspond to: (a) the unlevered cash flow approach:

W =

 C F0 +VR−D W ACC

(13.8)

(b) the levered cash flow approach:

W =

 C Fn Ke

+VR

(13.9)

where:   C F0 /W ACC = present value of operating cash flows C Fn /K e = present value of net cash flows VR = terminal (residual) value D = initial net financial position (financial debt − liquidity). The residual value is the result of discounting the value at the time n (before which the cash flows are estimated analytically). It is often the greatest component of the global value W (above all in intangible-intensive companies) and tends to zero if the time horizon of the capitalization is infinite (V R/∞ = 0). The two variants (levered versus unlevered) give the same result if the value of the firm, determined through the cash flows available to the lenders, is deducted from the value of the net financial debts. Operating cash flows (unlevered) and net cash flows for shareholders (levered) are determined by comparing the last two balance sheets (to

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dispose of changes in operating Net Working Capital, fixed assets, financial liabilities, and shareholders’ equity) with the income statement of the last year. The accounting derivation of the cash flow and its link to the cost of capital (to get DCF—Discounted Cash Flows) is illustrated in Table 13.3. The net cash flow for the shareholders coincides with the free cash flow to equity and, therefore, with the dividends that can be paid out, once it has been verified that enough internal liquidity resources remain in the company. This feature, associated with the ability to raise equity from third parties and shareholders, allows the company to find adequate financial coverage for the investments deemed necessary to maintain the company’s continuity and remain on the market in economic conditions Table 13.3 Cash flow statement and link with the cost of capital of a VoD/OTT company Cash flow statement EBIT + Depreciation and amortization = EBITDA (A) ± Δ Operating Net Working Capital ± Δ fixed assets (CAPEX) = Operating cash flow (unlevered cash flow to the firm) (B) - Financial charges ± Δ net financial liabilities ± Extraordinary income and charges - Taxes ± Δ Equity = Net (free) cash flow to the shareholders (levered cash flow) (C) Reconciliation statement: Closing cash and cash equivalents - Opening cash and cash equivalents = Change in net cash flow = liquidity (D) = (C)

To be discounted at the Weighted average cost of capital (WACC)

To be discounted at the cost of equity (Ke)

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R. MORO-VISCONTI

(minimum objectives). They should allow for the creation of incremental value in favor of shareholders, who are the residual claimants (being, as subscribers of risky capital, the only beneficiaries of the variable net returns, which, as such, are residual and subordinate to the fixed remuneration of the other stakeholders). When the DCF metrics have a limited time horizon, a terminal value is typically estimated. the terminal value (TV, also known as “continuing value” or “horizon value”) is the present value at a future point in time of all future cash flows when we expect a stable growth rate forever. In growing and innovative businesses, the TV may represent a consistent proportion of the overall value. In some exceptional cases the TV can even offset an initial negative value. Let us consider, for instance, the (typically negative) DCF over some 3 years of a VoD startup, counterbalanced by a mighty TV: (Overall) Enterprise Value = DCF + TV = − 20 + 120 = 100. This context should be considered with care since it embeds optimistic expectations in the long run. The appraised firm needs to survive over the negative years (bypassing the “Death Valley” phase if it is a startup) and then keep/develop a winning business model that lasts over time in a volatile market where technological shifts are frequent and may jeopardize current strategies. It so seems wiser to limit the extent of the TV and to discount it using rates that are consistent with private equity/venture capital IRR benchmarking (≥ 20–25%). Part of the TV may be included in an earnout package to be paid out to the selling entity subject to the verification of agreed milestones. The perpetuity growth method is an estimate of the TV that assumes that the firm will continue its historic business and generate FCFs at a steady-state forever. TV can be calculated as follows: TV =

OC Fn (1 + g) W ACC − g

(13.10)

where: OCFn = Operating Cash Flow for the last 12 months of the projection period g = Perpetuity growth rate (at which FCFs are expected to grow forever) WACC = Weighted-average cost of capital.

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Empirical Approaches (Market Multipliers)

The market value identifies: (a) The value attributable to a share of the equity expressed at stock exchange prices; (b) The price of the controlling interest or the entire share equity; (c) The traded value for the controlling equity of comparable undertakings; (d) The value derived from the stock exchange quotations of comparable undertakings. Sometimes comparable trades of companies belonging to the same product sector with similar characteristics (in terms of cash flows, sales, costs, etc.) are used. In practice, an examination of the prices used in negotiations with companies in the same sector leads to quantifying average parameters: • • • • •

Price/EBIT Price/cash-flow Price/book-value Price/earnings Price/dividend

These ratios seek to estimate the average rate to be applied to the company being assessed. However, there may be distorting effects of prices based on special interest rates, in a historical context, on difficulties of comparison, etc. In financial market practice, the multiples methodology is frequently applied. Based on multiples, the company’s value is derived from the market price profit referring to comparable listed companies, such as net profit, before tax or operating profit, cash flow, equity, or turnover. The attractiveness of the multiples approach stems from its ease of use: multiples can be used to obtain quick but dirty estimates of the company’s value and are useful when there are many comparable companies listed on the financial markets and the market sets correct prices for them on average.

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Because of the simplicity of the calculation, these indicators are easily manipulated and susceptible to misuse, especially if they refer to companies that are not entirely similar. Since there are no identical companies in terms of entrepreneurial risk and growth rate, the assumption of multiples for the processing of the valuation can be misleading, bringing to “fake multipliers”. The use of multiples can be implemented through: A. Use of fundamentals; B. Use of comparable data: B.1. Comparable companies; B.2. Comparable transactions. The first approach links multiples to the fundamentals of the company being assessed: profit growth and cash flow, dividend distribution ratio, and risk. It is equivalent to the use of cash flow discounting approaches. For the second approach, it is necessary to distinguish whether it is a valuation of comparable companies or comparable transactions. The comparability concerns different firms but is also related to their contents. In the case of comparable companies, the approach estimates multiples by observing similar companies. The problem is to determine what is meant by similar companies. In theory, the analyst should check all the variables that influence the multiple. In practice, companies should estimate the most likely price for a nonlisted company, taking as a reference some listed companies, operating in the same sector, and considered homogeneous. Two companies can be defined as homogeneous when they present, for the same risk, similar characteristics, and expectations. The calculation is: • A company whose price is known (P 1 ), • A variable closely related to its value (X 1 ). The ratio (P 1 )/(X 1 ) is assumed to apply to the company to be valued, for which the size of the reference variable (X 2 ) is known.

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Therefore: (P1 )/(X 1 ) = (P2 )/(X 2 )

(13.11)

so that the desired value P 2 will be: P2 = X 2 [13. (P1 )/(X 1 )]

(13.12)

According to widespread estimates, the main factors in establishing whether a company is comparable are: • Size; • Belonging to the same sector (see for instance the Statistical Classification of Economic Activities in the European Community, commonly referred to as NACE); • Financial risks (leverage); • Historical trends and prospects for the development of results and markets; • Geographical diversification; • Degree of reputation and credibility; • Management skills; • Ability to pay dividends. Founded on comparable transactions, the basis of valuation is information about actual negotiations (or mergers) of similar—i.e., comparable— companies. The use of profitability parameters is usually considered to be the most representative of company dynamics. Comparables may be looked for consulting databases like Orbis (https://www.bvdinfo.com/en-gb/our-products/data/international/ orbis). Among the empirical criteria, the approach of the multiplier of the EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) is widely diffused. The net financial position must be added algebraically to the EBITDA, to pass from the estimate of the enterprise value (total value of the company) to that of the equity value (value of the net assets). The formulation is as follows: W = average perspective EBITDA ∗ Enterprise Value/sector EBITDA

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= Enterprise Value of the company

(13.13)

And then: Equity Value = Enterprise Value ± Net Financial Position

(13.14)

The DCF approach can be linked to the market approach since they both share as a starting parameter the EBITDA.

References Baladron, M., & Rivero, E. (2019). Video-on-demand services in Latin America: Trends and challenges towards access, concentration and regulation. Journal of Digital Media & Policy, 10(1), 109–126. Barabási, A. (2016). Network science. Cambridge: Cambridge University Press. Estrella-Ramon, A. M., Sánchez-Pérez, M., Swinnen, G., & VanHoof, K. (2013). A marketing view of the customer value: Customer lifetime value and customer equity. South African Journal of Business Management, 44(4), 47–64. Fernandez, P. (2001). Valuation using multiples: How do analysts reach their conclusions ? IESE Business School, Madrid. Hein, A., Schreieck, M., Riasanow, T., Setzke M., Wiesche M., Bohm M., & Krcmar H. (2019, November). Digital platform ecosystems. Electronic Markets. Kemmis, A. (2018). The difference between direct and organic search traffic sources. SmartBug. Available at https://www.smartbugmedia.com/blog/ what-is-the-difference-between-direct-and-organic-search-traffic-sources?utm_ medium=social&utm_source=email. IPEV. (2018). Valuation guidelines. Available at http://www.privateequityvalua tion.com/Valuation-Guidelines. IT Media Consulting—LUISS Dream. (2016). The rise of video and the third internet revolution market trends and policy perspectives. Available at www.itm edia-consulting.com/DOCUMENTI/rise_of_video.pdf. Moro Visconti, R. (2019). How to prepare a business plan with excel. Available at https://www.researchgate.net/publication/255728204_How_to_Pre pare_a_Business_Plan_with_Excel. Moro Visconti, R. (2020). The valuation of digital intangibles: Technology, marketing and internet. Cham: Palgrave Macmillan. Wayne, M. L. (2018). Netflix, Amazon, and branded television content in subscription video on-demand portals. Media, Culture and Society, 40(5), 725–741.

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Wikipedia. (2020a). Available at https://en.wikipedia.org/wiki/Over-the-top_ media_service. Wikipedia. (2020b). Available at https://en.wikipedia.org/wiki/Video_on_ demand.

CHAPTER 14

E-Health and Telemedicine Startup Valuation

14.1

Introduction

E-Health is a healthcare practice supported by electronic processes and communication that covers everything related to medicine and computers. This industry is relatively young and rapidly evolving. It is so unsurprising that many innovative firms are still in their infancy, belonging to a startup phase. Telemedicine has become an increasingly popular option for long-distance/virtual medical care and education, but many telemedicine ventures fail to grow beyond the initial pilot stage (Chen et al., 2013). E-Health is often described as telemedicine or m-Health. These concepts, although overlapping, remain distinct. Telehealth is the distribution of health-related services and information via electronic information and telecommunication technologies. It allows long-distance patient and clinician contact, care, advice, reminders, education, intervention, monitoring, and remote admissions. Telemedicine (Elliott & Yopes, 2019) is sometimes used as a synonym or is used in a more limited sense to describe remote clinical services, such as diagnosis and monitoring (Fig. 14.1). According to Prescient & Strategic Intelligence (2020), m-Health is the practice of delivering healthcare services with the help of mobile devices, such as cell phones, laptops, tablets, and personal digital assistants © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_14

341

342

R. MORO-VISCONTI

m-Health

Telemedicine

e-Health

Fig. 14.1 The link between e-Health, m-Health, and telemedicine

(PDAs), through wireless networks. The factors fueling the popularity of m-Health technologies include the increasing prevalence of chronic diseases, rising geriatric population, growing popularity of at-home services, rising healthcare costs, and supportive healthcare regulatory norms. Moreover, several m-Health applications are available for disease and wellness management, which has increased their adoption rate. The main megatrends are represented by: • • • • •

Increasing penetration of smartphones and tablets; The growing need for remote patient monitoring services; Innovative and advanced applications of m-Health technologies; Government support for digital health solutions; Increasing demand for advanced healthcare information systems.

14

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The Healthcare Ecosystem

Digital health has become a real buzz word in recent discussions about transforming the healthcare system. One driver for the digitization of healthcare is represented by startups. Startups are newly emerging companies with a new business model that identifies a certain problem and tries to fix it (Rinsche, 2017). The properties of networked platforms are intrinsically consistent with the healthcare ecosystem, depicted in Fig. 14.2.

Public/Private Hospitals / Labs Insurance companies

Technology providers

Pharma Companies

Healthcare Ecosystem

e-Health / Telemedicine / m-Health

Investors / ChariƟes / UniversiƟes Government / regulators / policy makers

Fig. 14.2

PaƟents

The healthcare ecosystem

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R. MORO-VISCONTI

Patents

Trademarks

Know-How

Copyright

SoŌware

Intangible Assets

Domain Names / Websites

M-Apps

Big Data / IoT

Fig. 14.3

ArƟficial Intelligence

Blockchains

Internet Companies / Digital Plaƞorms

Goodwill

Interactions of intangibles

A portfolio of intangibles embeds synergistic interactions, as illustrated in Fig. 14.3, and triggers levered scalability upside.

14.3

Business Models

E-Health business models are characterized by remarkable scalability properties that make them geographically adaptable to various countries. There are, however, differences between developed and developing nations that are still important, albeit decreasing thanks to globalization. Healthcare ecosystems are increasingly interconnected, as the Covid-19 pandemics show, and they need shared platforms and responses to joint challenges. The business model is a prerequisite for the value proposition (social and economic value, to be appraised with a cost–benefit analysis).

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The most challenging estimate is probably represented, as it happens in most industries, by the revenue streams. Mighty contacts need to be monetized and transformed into … contracts. The Digital Healthcare Market may be segmented by Technology, Application, Delivery Mode, Components, and End User. Chen et al. (2013), introduce an interesting grid to describe the telemedicine business models. Table 14.1 summarizes the main business model propositions, linked to value chain patterns. Table 14.1 can be used as a starting point to describe the supply chain that is represented in Fig. 14.4.

14.4

Investors and Market Players

Investors in e-Health startups are represented by classic shareholders, like founders (that are also … funders), family & friends, crowdfunded equityholders, etc. There are, however, some peculiar shareholders and strategic partners like insurance companies, (big) pharma, etc. The revenue model is often peculiar, and some key customers like National Health Services may guarantee long-term payments, linked to proof-of-concept or other milestones. This may severely impair the survival capability of cash-absorbing startups that are constantly in need of bridge financing or other financial facilities to overcome the Death Valley period.

14.5

The Accounting Background for Valuation

The evaluation is sensitive to forward-looking data that can be used to build up a sound business plan with a time horizon coherent with the average life cycle of the products and services of the e-Health industry. A business plan is a formal accounting statement that numerically describes a set of business goals, the reasons why they are believed attainable, and the strategic plan and managerial steps for reaching those goals. Hypotheses and visionary ideas of game-changers must be transformed into numbers and need to be backed by reasonable and verifiable assumptions about future events and milestones (Moro Visconti, 2019). The accounting background is composed of pro forma balance sheets (of some 3–5 years) and perspective income statements. The matching of these two documents produces expected cash flow statements. Economic and financial margins are the crucial accounting parameters for valuation

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R. MORO-VISCONTI

Table 14.1 e-Health and telemedicine business models and value chain issues Issue

Description

Value proposition

Business Target—Market opportunities—Support from Government, NGOs, Health Insurance companies, Patients, TLC operators, Big Pharma, and other Stakeholders Healthcare providers—Hospitals (public and/or private)—Outpatient Clinics—Home Care Settings—healthcare payers—(segmented) patients—big Pharma—NGOs—charities—healthcare technology incubators/accelerators Streamline access to healthcare services—Wireless health—Mobile health—EHR—Telehealth—Global Healthcare Information Software Market by geography (Asia-Pacific, Europe, Middle East—Africa, North America, and South America), deployment (on-premises and cloud-based), and application (HIS and PIS) Cardiology, Diabetes, Neurology, Dermatology, Sleep Apnea, Oncology, Orphan pathologies, and others. Non-communicable diseases that do not require hospitalization (unless for acute treatment) are particularly fit for remote telemedicine applications Digital Health Systems (Electronic Health/Medical Records), Tele-healthcare (Activity Monitoring; Remote Medication Management; LTC Monitoring; Video Consultation), mHealth (wearables: Glucose Meters; Neurological Monitors; Sleep Apnea Monitors; Pulse Oximeters; BP Monitors, etc.; m-health apps: fitness/medical apps), Healthcare Analytics—ePrescribing System On-Premise—Cloud-Based Software; services; hardware Unique expertise—Management team—Innovation—Patented Inventions—Sales team Trusted partnerships—Investment team—Advisory team—stakeholders Business purpose—see vertical medical applications, market outlook, and customer segmentation

Customer segments—end users

Market outlook

Medical application

Technology

Delivery mode Component Key resources

Key partners Key activities

(continued)

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Table 14.1 (continued) Issue

Description

Revenue streams

Customer relationships—B2B/B2C solutions—Revenues from licensing and service fees—sales of m-apps—in-app purchases—Big Data collection and (anonymous) resale R&D—Marketing and Advertising Inventor—Family and friends—Crowdfunding—Venture Capital—Private Equity—Bridge financing Goals—Metrics—Sustainability (economic, social, and environmental)

Cost structure Startup funding

Social impact

Manufacturer

Fig. 14.4

(Perspec ve) Accoun ng data

Fig. 14.5

Warehouse

Hospital

PaƟent

Healthcare supply chain

•Balance sheet •Income statement •Cash Flow statement

Business Plan

•Ɵme horizon •strategic assumpƟons •sensiƟvity/scena rio analysis

Evalua on parameters

•economic/ financial data •book versus market values

Evaluation methodology

that are represented by the EBITDA, the EBIT, the operating and Net Cash Flows, and the Net Financial Position, as it will be shown in the formulation of the appraisal approaches. The appraisal methodology may conveniently start from a strategic interpretation of the business model (that derives from accounting data) to extract the key evaluation parameters to insert in the model, as shown in Fig. 14.5.

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R. MORO-VISCONTI

Revenue Model

Strategic Goals

Market trends

Growth Drivers

Expected Investments

Fig. 14.6

Business model and value drivers

An analysis of the business model may conveniently consider: 1. The revenue model; 2. The strategic goals; 3. The growth drivers; 4. The expected investments; 5. Market trends. The interaction between the business model and the strategic value drivers is illustrated in Fig. 14.6.

14.6

Valuation Methods

Most of the concepts recalled in these paragraphs are similar to those already illustrated in other chapters, and restated here with some personalization, considering the peculiar startup of this chapter that is so intended to be self-containing. In particular, the main concepts that are here directly or indirectly restated concern: (a) The preliminary phase, from business modeling to business planning (Chapter 2);

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(b) The main valuation approaches (Discounted Cash Flows—DCF, and market multipliers), described in Chapter 8; (c) The valuation of specific startups (see Part II—Industry Applications).

The evaluation criteria typically follow the (actual and prospective) business model of the target company, as illustrated in Fig. 14.7. A comparison of the primary evaluation criteria in traditional firms versus high-tech firms (startups) is reported in Table 14.2. In this case, the value may be inferred even with differential income methodologies, traditionally used in the evaluation of intangible assets (within the income approaches). Among the main evaluation methodologies, the following are the most relevant: 1. Financial approach (Discounted Cash Flows—DCF); 2. Market comparables.

Business model

Technological Firms (Startups)

e-Health Startups Valua on Approach

Fig. 14.7

Business model and valuation approach

TradiƟonal Healthcare Firms

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R. MORO-VISCONTI

Table 14.2 Comparison of the main evaluation approaches of traditional firms and technological startups

14.6.1

Traditional firm (see Chapter 8)

Technological startup (see Chapter 9 and IPEV, 2018)

Balance-sheet based Venture capital method (Fernandez, 2001) Income Binomial trees Mixed capital-income Financial approach (Discounted Cash Flows) Market multiples (comparable firms)

The Financial Approach

The financial approach is based on the principle that the market value of the company is equal to the discounted value of the cash flows that the company can generate (“cash is king”). The determination of the cash flows is of primary importance in the application of the approach, as is the consistency of the discount rates adopted. The doctrine (especially the Anglo-Saxon one) believes that the financial approach is the “ideal” solution for estimating the market value for limited periods. It is not possible to make reliable estimates of cash flows for longer periods. “The conceptually correct methods are those based on cash flow discounting. I briefly comment on other methods since - even though they are conceptually incorrect - they continue to be used frequently” (Fernandez, 2001). This approach is of practical importance if the individual investor or company with high cash flows (leasing companies, retail trade, public and motorway services, financial trading, project financing SPVs, etc.) are valued. Financial evaluation can be particularly appropriate when the company’s ability to generate cash flow for investors is significantly different from its ability to generate income, and forecasts can be formulated with a sufficient degree of credibility and are demonstrable. There are two complementary criteria for determining the cash flows: a.1. The cash flow available to the company (Free cash flow to the firm) This configuration of expected flows is the one most used in the practice of company valuations, given its greater simplicity of application compared to the methodology based on flows to partners. It is a measure

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of cash flows independent of the financial structure of the company (unlevered cash flows) that is particularly suitable to evaluate companies with high levels of indebtedness, or that do not have a debt plan. In these cases, the calculation of the cash flow available to shareholders is more difficult because of the volatility resulting from the forecast of how to repay debts. This methodology is based on the operating flows generated by the typical management of the company, based on the operating income available for the remuneration of own and third-party means net of the relative tax effect. Unlevered cash flows are determined by using operating income before taxes and financial charges. The cash flow available to the company is, therefore, determined as the cash flow available to shareholders, plus financial charges after tax, plus loan repayments and equity repayments, minus new borrowings and flows arising from equity increases. The relationship between the two concepts of cash flow is as follows: cash flow available to the company = cash flow available to shareholders + financial charges (net of taxes) + loan repayments − new loans (14.1) a.2. The (residual) cash flow available to shareholders This configuration considers the only expected flow available for members’ remuneration. It is a measure of cash flow that considers the financial structure of the company (levered cash flow). It is the cash flow that remains after the payment of interest and the repayment of equity shares and after the coverage of equity expenditures necessary to maintain existing assets and to create the conditions for business growth. In M&A operations, the Free Cash Flow to the Firm (operating cash flow) is normally calculated to estimate the Enterprise Value (comprehensive of debt). The residual Equity Value is then derived by subtracting the Net Financial Position. The discounting of the free cash flow for the shareholders takes place at a rate equal to the cost of the shareholders‘ equity. This flow identifies the theoretical measure of the company’s ability to distribute dividends, even if it does not coincide with the dividend paid.

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Cash flow estimates can be applied to any type of asset. The differential element is represented by their duration. Many assets have a defined time horizon, while others assume a perpetual time horizon, such as shares. Cash flows (CF ) can, therefore, be estimated using a normalized projection of cash flows that it uses, alternatively: • unlimited capitalization:

W1 =

CF i

(14.2)

• limited capitalization:

W2 = C F a n¬i

(14.3)

where W 1 and W 2 represent the present value of future cash flows. The discount rate to be applied to expected cash flows is determined as the sum of the cost of equity and the cost of debt, appropriately weighted according to the leverage of the company (the ratio between financial debt and equity). This produces the Weighted Average Cost of Capital (WACC): W ACC = ki (1 − t)

E D + ke D+E D+E

(14.4)

where: k i = cost of debt; t = corporate tax rate; D = market value of debt; E = market value of equity; D + E = raised capital; k e = cost of equity (to be estimated with the Capital Asset Pricing Model—CAPM or the Dividend Discount Model). The cost of debt capital is easy to determine, as it can be inferred from the financial statements of the company. The cost of equity or share capital, which represents the minimum rate of return required by investors for

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equity investments, is instead more complex and may use the CAPM or the Dividend Discount Model (a method of valuing a company’s stock price considering the sum of all its future dividend payments, discounted back to their present value. It is used to value stocks based on the net present value of future dividends). The formula of the CAPM is the following:   (14.5) E(r ) FoodT ech = r f r ee + β FoodT ech [14. E(r )mar ket − r f r ee where: E(r ) FoodT ech = expected return of the FoodTech listed stock r f r ee = risk-free rate of return (e.g., of a long term Government bond) β FoodT ech = sensitivity of the FinTech’s stock to the market price E(r )mar ket = expected return of the (benchmark) Stock market. A central element is represented by the beta (b) of the startup to be evaluated that consists of the ratio between the covariance of the e-Health security with its stock market, divided by the variance of the market. Market betas, subdivided by industry, may be detected from the dataset of A. Damodaran (see, for instance, http://pages.stern.nyu.edu/~ada modar/New_Home_Page/datafile/Betas.html). Once the present value of the cash flows has been determined, the calculation of the market value W of the company may correspond to: (a) the unlevered cash flow approach:

W =

 C F0 +VR−D W ACC

(14.6)

(b) the levered cash flow approach:

W =

 C Fn Ke

+VR

where:   C F0 /W ACC = present value of operating cash flows C Fn /K e = present value of net cash flows

(14.7)

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R. MORO-VISCONTI

VR = terminal (residual) value D = initial net financial position (financial debt—liquidity). The residual value is the result of discounting the value at the time n (before which the cash flows are estimated analytically). It is often the greatest component of the global value W (above all in intangible-intensive companies) and tends to zero if the time horizon of the capitalization is infinite (VR/∞ = 0). The two variants (levered versus unlevered) give the same result if the value of the firm, determined through the cash flows available to the lenders, is deducted from the value of the net financial debts. Operating cash flows (unlevered) and net cash flows for shareholders (levered) are determined by comparing the last two balance sheets (to dispose of changes in operating Net Working Capital, fixed assets, financial liabilities, and shareholders’ equity) with the income statement of the last year. The accounting derivation of the cash flow and its link to the cost of capital (to get DCF—Discounted Cash Flows) is illustrated in Table 14.3. The net cash flow for the shareholders coincides with the free cash flow to equity and, therefore, with the dividends that can be paid out, once it has been verified that enough internal liquidity resources remain in the company. This feature, associated with the ability to raise equity from third parties and shareholders, is such as to allow the company to find adequate financial coverage for the investments deemed necessary to maintain the company’s continuity and remain on the market in economic conditions (minimum objectives). They should allow for the creation of incremental value in favor of shareholders, who are the residual claimants (being, as subscribers of risky capital, the only beneficiaries of the variable net returns, which, as such, are residual and subordinate to the fixed remuneration of the other stakeholders). 14.6.2

The Financial Approach with Debt-Free Startups

Even e-health startups are normally debt-free (as shown in Chapter 6) since they have little if any collateral value of their assets and they produce negative cash flows, especially in the first years of their existence. Consequently: • in the balance sheet raised capital (funds) tend to coincide with equity;

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Table 14.3 Cash flow statement and link with the cost of capital of an E-health startup Cash flow statement EBIT + Depreciation and amortization = EBITDA (A)

= Operating cash flow (unlevered cash flow to the firm) (B) - Financial charges ± net financial liabilities ± Extraordinary income and charges - Taxes

To be discounted at the Weighted average cost of capital (WACC)

= Net (free) cash flow to the shareholders (levered cash flow) (C) Reconciliation statement:

To be discounted at the cost of equity (Ke)

Closing cash and cash equivalents - Opening cash and cash equivalents = Change in net cash flow = liquidity (D) = (C)

• in the income statement, EBIT is similar to the net result (considering that interest rates are nonexistent, and taxes also); • in the cash flow statement, the operating cash flow tends to coincide with the net cash flow; • in the absence of cost of debt, the cost of capital (WACC) coincides with the cost of equity. The interactions of the three main accounting statements in a debtfree startup (consistent with the findings illustrated in Chapter 6) are illustrated in Fig. 14.8. Liquidity so derives from: • EBITDA (initially negative and so cash-absorbing but with high increase potential);

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ȴ Fixed Assets (CAPEX) ȴ Equity and Quasi-Equity

Cost of Equity = WACC (being debt = 0)

ȴ OperaƟng Net Working Capital ȴ Liquidity Invested Capital = Raised Capital = Equity Value = Enterprise Value

Income statement Operating monetary revenues - operating monetary costs (monetary OPEX) = EBITDA - amortization, depreciation, provisions and writedowns = EBIT +/- balance of extraordinary operations = Pre-Tax Result - (taxes, if any) = Net result (similar to EBIT and Pre-Tax result)

Cash flow statement EBIT + amortization, depreciation = EBITDA +/- Δ operating net working capital +/- Δ fixed assets = Operating cash flow (unlevered) +/- extraordinary income/expense - (taxes, if any) +/- Δ shareholders contributions in kind +/- Δ shareholders’ equity = Net Cash Flow

Fig. 14.8 Interactions of income statement and variations of the balance sheet to produce the cash flow statement in a debt-free startup

• Change in operating net working capital (sales growth is fueled by the cash-absorbing expansion of receivables and stock [wherever present], partially counterbalanced by a cash-generating increase in payables); • Change in the CAPEX (net of cashless depreciation/amortization) • Equity and quasi-equity injections (considering only liquidity cashed in, and not a cashless contribution in-kind). Sales forecasting is the main and first value driver to consider, as it generates the revenues that, net of monetary operating expenditure (OPEX), form the EBITDA. Sales are also related to the operating net working capital components, as they influence stock and receivables. Sales are also output factors

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Table 14.4 Working capital turnover

Inventory turnover Inventory turn-days Accounts receivable turnover Average collection period Average payable turnover Average payment period

357

Cost of goods sold/inventory 360/inventory turnover Sales/accounts receivable 360/accounts receivable turnover Cost of goods sold/account t payable 360/accounts payable turnover

depending on input purchases that influence payables. It may so be argued that the Operating Net Working Capital is a function of the sales. The following ratios can be used to interpret and forecast the expected outcome of the working capital as shown in Table 14.4. A further input factor of sales is represented by CAPEX that fosters the income statement’s economic marginality. Liquidity forecasts and occurrences are typically negative in the startup phase, with the absorption of cash that is mainly due to a negative EBITDA, and a CAPEX increase (due to the investments necessary to startup the firm). The operating net working capital may be less significant, but it normally grows (so absorbing cash) when sales boost. The intervention of the shareholders is so periodically necessary to keep a cash equilibrium, avoiding both cash burnout and equity burnout. Debt capacity indicators like the debt service cover ratio are meaningless in debt-free startups. 14.6.3

Empirical Approaches (Market Multipliers)

The market value identifies: (a) The value attributable to a share of the equity expressed at stock exchange prices; (b) The price of the controlling interest or the entire share equity; (c) The traded value for the controlling equity of comparable undertakings;

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(d) The value derived from the stock exchange quotations of comparable undertakings. Sometimes comparable trades of companies belonging to the same product sector with similar characteristics (in terms of cash flows, sales, costs, etc.) are used. In practice, an examination of the prices used in negotiations with companies in the same sector leads to quantifying average parameters: • • • • •

Price/EBIT Price/cash flow Price/book value Price/earnings Price/dividend.

These ratios seek to estimate the average rate to be applied to the company being assessed. However, there may be distorting effects of prices based on special interest rates, in a historical context, on difficulties of comparison, etc. In financial market practice, the multiples methodology is frequently applied. Based on multiples, the company’s value is derived from the market price profit referring to comparable listed companies, such as net profit, before tax or operating profit, cash flow, equity, or turnover. The attractiveness of the multiples approach stems from its ease of use: multiples can be used to obtain quick but dirty estimates of the company’s value and are useful when there are many comparable companies listed on the financial markets and the market sets correct prices for them on average. Because of the simplicity of the calculation, these indicators are easily manipulated and susceptible to misuse, especially if they refer to companies that are not entirely similar. Since there are no identical companies in terms of entrepreneurial risk and growth rate, the assumption of multiples for the processing of the valuation can be misleading, bringing to “fake multipliers.” The use of multiples can be implemented through: A. Use of fundamentals; B. Use of comparable data:

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B.1. Comparable companies; B.2. Comparable transactions. The first approach links multiples to the fundamentals of the company being assessed: profit growth and cash flow, dividend distribution ratio, and risk. It is equivalent to the use of cash flow discounting approaches. For the second approach, it is necessary to distinguish whether it is a valuation of comparable companies or comparable transactions. The comparability concerns different firms but is also related to their contents. In the case of comparable companies, the approach estimates multiples by observing similar companies. The problem is to determine what is meant by similar companies. In theory, the analyst should check all the variables that influence the multiple. In practice, companies should estimate the most likely price for a nonlisted company, taking as a reference some listed companies, operating in the same sector, and considered homogeneous. Two companies can be defined as homogeneous when they present, for the same risk, similar characteristics, and expectations. The calculation is: • A company whose price is known (P 1 ), • A variable closely related to its value (X 1 ). The ratio (P 1 )/(X 1 ) is assumed to apply to the company to be valued, for which the size of the reference variable (X 2 ) is known. Therefore: (P1 )/(X 1 ) = (P2 )/(X 2 )

(14.8)

so that the desired value P 2 will be: P2 = X 2 [14. (P1 )/(X 1 )]

(14.9)

According to widespread estimates, the main factors in establishing whether a company is comparable are: • Size;

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• Belonging to the same sector (see for instance the Statistical Classification of Economic Activities in the European Community, commonly referred to as NACE); • Financial risks (leverage); • Historical trends and prospects for the development of results and markets; • Geographical diversification; • Degree of reputation and credibility; • Management skills; • Ability to pay dividends. Founded on comparable transactions, the basis of valuation is information about actual negotiations (or mergers) of similar—i.e., comparable— companies. The use of profitability parameters is usually considered to be the most representative of company dynamics. Comparables may be looked for consulting databases like Orbis (https://www.bvdinfo.com/en-gb/our-products/data/international/ orbis). Among the empirical criteria, the approach of the multiplier of the EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) is widely diffused. The net financial position must be added algebraically to the EBITDA, to pass from the estimate of the enterprise value (total value of the company) to that of the equity value (value of the net assets). The formulation is as follows: W = average perspective EBITDA ∗ Enterprise Value/sector EBITDA = Enterprise Value of the company (14.10) And then: Equity Value = Enterprise Value ± Net Financial Position

(14.11)

The DCF approach can be linked to the market approach since they both share as a starting parameter the EBITDA.

References Chen, S., Cheng, A., & Khanjan Mehta, K. (2013, April). A review of telemedicine business models. Telemedicine and e-Health, 287–297.

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Elliott, T., & Yopes, M. C. (2019). Direct-to-consumer telemedicine. The Journal of Allergy and Clinical Immunology: In Practice, 7 (8), 2546–2552. Fernandez, P. (2001). Valuation using multiples: How do analysts reach their conclusions ? Madrid: IESE Business School. IPEV. (2018). Valuation guidelines. Available at http://www.privateequityvalua tion.com/Valuation-Guidelines. Moro Visconti, R. (2019). How to prepare a business plan with excel. Available at https://www.researchgate.net/publication/255728204_How_to_Pre pare_a_Business_Plan_with_Excel. Prescient & Strategic Intelligence. (2020). Digital health market research report. Available at https://www.psmarketresearch.com/market-analysis/digital-hea lth-market. Rinsche, F. (2017). The role of digital health care startups. In A. Schmid & S. Singh (Eds.), Crossing borders—Innovation in the U.S. health care system. Bayreuth: P.C.O. Verlag.

CHAPTER 15

FoodTech and AgriTech Startup Valuation

15.1

Introduction

There is no bigger industry on our planet than food and agriculture, with a consistent, loyal customer base of more than 7 billion. The World Bank estimates that food and agriculture comprise about 10% of the global GDP, meaning that food and agriculture would be valued at about $8 trillion globally based on the projected global GDP of $88 trillion for 2019. However, despite a stalwart customer base, the food industry is facing unprecedented challenges in production, demand, and regulations stemming from consumer trends. Consumer demands and focus have changed in recent years. An increasing focus by consumers on sustainability, health, and freshness has placed significant pressure on the food industry to innovate.1 The food ecosystem needs to be consistent with the sustainable development goals (Valentini et al., 2019). There is nothing more important than our food supply. But according to McKinsey & Company, about a third of food produced is lost or wasted every year. Globally, that is a $940 billion economic hit. Inefficiencies in planting, harvesting, water use, and trucking, as well as uncertainty about 1 https://techcrunch.com/2019/10/22/the-foodtech-investment-opportunity-presentand-future/?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8& guce_referrer_sig=AQAAAK3pfwLrLxQGzCasuAS1p5jnsnop2Pn-iY5X301B5AHY7CSSj8 dgNd9gT1oFgV1MOlZzlzHW8OB9_MieSTZpiXGBPqrg1IjHcgeOYN7MPiXwJv-aqK p4o4ir-Mn1aoEsO50E_LqtwQdlzj3qe-DfidBsL_vjh5Psg_Nz_eGEwGTG

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0_15

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the weather, pests, consumer demand, and other intangibles, contribute to the loss. On the consumer end, inadequate packaging and labeling can lead to waste and potentially life-threatening illness due to food-borne pathogens. These are problems desperately in need of solutions and many of those solutions can be found in emerging technologies. Big data is moving into agriculture in a big way. Several well-known investors recently dropped a combined $40 million into Farmers Business Network, a data analytics startup. Venture capital has flooded the ag tech space, with investment increasing 80% annually since 2012, as investors realize big data can revolutionize the food chain from farm to table. Sensors on fields and crops are starting to provide literally granular data points on soil conditions, as well as detailed info on wind, fertilizer requirements, water availability, and pest infestations. GPS units on tractors, combines, and trucks can help determine optimal usage of heavy equipment. Data analytics can help prevent spoilage by moving products faster and more efficiently. Unmanned aerial vehicles, or drones, can patrol fields and alert farmers to crop ripeness or potential problems. RFID-based traceability systems can provide a constant data stream on farm products as they move through the supply chain, from the farm to the compost or recycle bin. Individual plants can be monitored for nutrients and growth rates. Analytics looking forward and back assist in determining the best crops to plant, considering both sustainability and profitability. Agricultural technology can also help farmers hedge against losses and even out cash flow. The software market for these sorts of precision farming tools (such as yield monitoring, field mapping, crop scouting, and weather forecasting) is expected to grow 14% by 2022 in the United States alone. Researchers suggest the full-scale adoption of these technologies could mean an increase in farm productivity unseen since mechanization. For consumers, packaging sensors detect gases emitted as food starts to spoil and verify packaging integrity and freshness. Algorithms can even help create a recipe out of whatever you have in the pantry. Several startups (Meena, 2019) are building finger-sized scanners that tell the composition of food on your plate, from ingredients to nutrient content, by sending data to an app (Meenakshi & Sinha, 2019) on your smartphone. These applications help not only health-conscience consumers but also those with chemical sensitivities or food allergies. Some projections say it could help reduce overall health care costs, too,

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as consumers are increasingly empowered to customize their nutrition (Verma, 2018) and avoid potentially spoiled or contaminated foods.2 FoodTech is an ecosystem made of all the agri-food entrepreneurs and startups (from production to distribution) innovating on the products, distribution, marketing, or business model. FoodTech can be defined as “the intersection between food and technology; the application of technology to improve agriculture and food production, the supply chain and the distribution channel.” (http://dig ital-me-up.com/2016/11/27/foodtech/). Agritech is the use of technology in agriculture, horticulture, and aquaculture to improve yield, efficiency, and profitability. Agritech can be products, services, or applications derived from agriculture that improve various input/output processes (https://web.archive.org/web/201512 30102045/, http://www.sproutagritech.com/what-is-agritech). The Ag(ri)Tech industry (AgFunder, 2019) is concerned with startups that disrupt agriculture. They come up with solutions to improve farming output and quality using drones, sensors, and farm management software. AgTech is also about new farm products, next-generation farms, and urban farming.

15.2 The FoodTech Ecosystem (from the Farm to the Fork): Digital Platforms and the Circular Economy Technology is increasingly contributing to food’s journey from farm to fork. The food industry is a crucial link in that process. The competitiveness of food industry enterprises is intricately linked to their ability to implement new technologies (ING, 2019). The digital ecosystem is a prerequisite for the evaluation of any FoodTech startup. Within this ecosystem, platforms are digital enablers and facilitators of exchange (of goods, services, and information) between different types of stakeholders that could not otherwise interact with each other. Transactions are mediated through complementary players that share a network

2 https://www.forbes.com/sites/timsparapani/2017/03/23/how-big-data-and-techwill-improve-agriculture-from-farm-to-table/.

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ecosystem (Rochet & Tirole, 2003). Due to their digital characteristics, they have a global outreach that gives them the potential to scale. FoodTechs find their rationale and natural habitat in a digital ecosystem where they act as an intermediating platform among networked stakeholders. Digital platforms are at the basis of technology-enabled business models that facilitate exchanges between multiple groups—such as endusers and producers—who do not necessarily know each other. The continuous upgrade of the technological environment creates new possibilities and reshapes the value and supply chain of financial intermediation, disrupting the existing business models. Whereas traditional firms create value within the boundaries of a company or a supply chain, digital platforms (Schneider, 2018) utilize an ecosystem of autonomous agents to co-create value (Hein et al., 2019). Digital platforms can be represented by FoodTechs, and they act as a bridging node that connects digital clients to traditional or innovative food producers. Whenever platforms connect different layers (each representing a network sub-system), they can increase the overall systemic value. Digital platforms are multisided digital frameworks that shape the terms on which participants interact. Digitalization is defined as the concept of “going paperless,” namely as the technical process of transforming analog information or physical products into digital form. The term “digital transformation” refers, therefore, to the application of digital technology as an alternative to solve traditional problems. As a result of digital solutions, new forms of innovation and creativity are conceived, while conventional methods are revised and enhanced. Digitally born startups or similar tech-businesses are not the only ones interested in adopting digital processes. Traditional businesses may be digitalized as well (e.g., a simple farmer willing to increase exponentially his/her production of tomatoes may digitalize the production activities through new systems or machines). In practice, with digitalization, traditional firms improve their crucial economic and financial parameters, as the EBITDA, which increases, while the WACC reduces, so improving the DCF and the overall enterprise value: DCF (Unlevered) =



OCF ↑ ∼ = Enterprise Value ↑↑ WACC ↓

(15.1)

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In synthesis, digitalization brings speed and quality at a low cost, thus representing a crucial driver for scalability itself. Digitalization enables a business process reengineering of traditional firms, which may presuppose an incremental production growth. Digital platforms can be interpreted in terms of network theory (see Barabási, 2016), the study of graphs as a representation of either symmetric or asymmetric relations between discrete objects. In computer science and network science, network theory is a part of graph theory: a network can be defined as a graph in which nodes and/or edges have attributes (e.g., names). Digital platforms are intrinsically networked, and within networks, they represent a bridging node that connects users (stakeholders). The properties of networked platforms are intrinsically consistent with the FoodTech ecosystem. Digital platform analysis can give an interpretation of FoodTechs that considers from an unconventional perspective their properties and potential. FoodTech and AgriTech businesses are increasingly consistent with the circular economy patterns. A circular economy is an economic system aimed at eliminating waste and the continual use of resources. Circular systems employ reuse, sharing, repair, refurbishment, remanufacturing, and recycling to create a closed-loop system, minimizing the use of resource inputs and the creation of waste, pollution, and carbon emissions (Geissdoerfer et al., 2017). The circular economy aims to keep products, equipment, and infrastructure in use for longer, thus improving the productivity of these resources. All “waste” should become “food” for another process: either a by-product or recovered resource for another industrial process, or as regenerative resources for nature, e.g., compost. This regenerative approach is in contrast to the traditional linear economy, which has a “take, make, dispose of” model of production (https://web.archive.org/ web/20130110100128/, http://www.thecirculareconomy.org/).

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15.3

Food Chains

A food chain is a linear network of links in a food web starting from producer organisms and ending at apex predator species. Figure 15.1 illustrates an example of a food chain. If this is the original meaning of “food chain,” there is a complementary technological interpretation. The food chain provides a blockchain technology to trace and digitally authenticate food products, enabling a transparent, safe, and reliable supply chain ecosystem. In more general terms, a food chain is a linear network of links in a food web starting from producer organisms (such as grass or trees which use radiation from the Sun to make their food) and ending at apex predator species (like grizzly bears or killer whales), detritivores (like earthworms or woodlice), or decomposer species (such as fungi or bacteria).

grass (producer)

mushroom (decomposer)

grasshopper (primary consumer)

owl (apex predator) bird (secondary consumer))

snake (terƟary consumer)

Fig. 15.1

Food chain

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Visibility and traceability (of food provenance) are a crucial characteristic of food supply chains and may prevent frauds, favoring the immediate localization of intoxication threats. Data validation is a key characteristic of blockchains (Ge et al., 2017; Mao et al., 2018), and it may add great value to the food chain that ignites FoodTech or AgriTech applications, as shown in Fig. 15.2. The food supply chain (Renda, 2019) is exemplified in Fig. 15.3. Each link can be optimized, reducing the time to delivery, improving the resilience to external shocks. A short food supply chain, made possible by digitization and optimization of the process, reduces the intermediation chains, and so the costs for the end-consumer. For example, if a tomato can be sold by the producer directly to the final consumer, the product is cheaper, fresher, and more easily traceable.

FoodChain AgriTech FoodTech

Fig. 15.2

FoodTech and AgriTech value chains

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cow

Fig. 15.3

mill

transport

consumers

The Food Supply Chain

15.4

Business Models

B2C FoodTech is targeted toward consumers and may concern plantbased (meatless) meals, novel distribution systems, or nutrition-based tech. Food is a sensitive issue, with deep cultural implications. The issue of consumer acceptance of food technologies, and their applications, needs to be addressed early in technology development (Frewer et al., 2011). Industrial FoodTech is the sub-segment of FoodTech that focuses on addressing the fundamental business models and B2B pain points within the food industry. The companies include innovators in novel processing and packaging technology and new/functional ingredients that have improved nutritional, labeling, or formulation characteristics. A taxonomy of the main FoodTech & AgriTech business models (synthesized in Table 15.1, adapted from https://www.digitalfoodlab. com/) is propaedeutic to the evaluation assessment of the startup.

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Table 15.1 FoodTech and agriTech business models Typology

Features

Ag(ri)Tech startups are disrupting agriculture. They come up with solutions to improve farming output and quality using drones, sensors, and farm management software. Ag(ri)Tech is also about new farm products, next-generation farms, and urban farming

FARM MANAGEMENT SOFTWARE Startups are assisting farmers in managing, organizing, and optimizing all the tasks on their farm DRONES & ROBOTS Startups provide farmers with robots and drones. These tools are used to collect data or directly to replace human tasks URBAN AND NOVEL FARMS Startups developing urban farms to reduce the distance between production and consumption or developing new-generation farms to increase yields, quality, and sustainability AGRICULTURE MARKETPLACES Startups working on B2B e-commerce marketplaces for farmers (with products ranging from seeds to equipment) AG-BIOTECH Research and development-oriented startups with a focus on living systems and organisms for agriculture and food PRECISION AGRICULTURE/FARMING Precision agriculture, satellite farming, or site-specific crop management is a farming management concept based on observing, measuring, and responding to inter and intra-field variability in crops FUTURE FOODS Startups working on breakthrough food products, mostly to replace those currently in use with more sustainable and healthier alternatives

Food-science startups develop new food products answering the need for more transparency, health, and environmental concerns. Products range from market innovations to radical disruptions using revolutionary ingredients

(continued)

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Table 15.1 (continued) Typology

Foodservice: startups reinvent the restaurant industry. It means improving the management of restaurants and institutional catering, connecting customers and businesses directly to local chefs for catering and new experiences

Features MEAL SUBSTITUTES Startups reinvent the meal. Their bars, drinks, or powders replace the traditional meal with highly nutritious alternatives PACKAGING Startups develop smarter and more sustainable food (Schneider, 2018) and beverage packaging PRODUCT INNOVATION Startups work on already well-established ingredients or markets (such as chocolate or baby food). The innovation is either in the product itself, the transparency of its composition, the means of distribution, or greater customization of the products DRINKS Startups work on new forms of drinks, to promote new ingredients or a healthier lifestyle APPLIANCES AND COOKWARE Startups develop a new generation of appliances or cookware. They provide more technology, new distribution channels, or more personalization RESERVATION PLATFORMS Services to book a restaurant table, generally with a discount. These startups can specialize by focusing on unsold food, high-end restaurants, etc. FOODSERVICE MANAGEMENT Services to improve restaurant management. These startups help with an online presence, cash management, marketing, customer feedback, order taking, inventory management, traceability, recipes, etc.

(continued)

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Table 15.1 (continued) Typology

Coaching: startups answering the questions, “is my food good for me?” and “what should I eat?”. These services target the final customer and help him to have a better view of his food purchases and intakes to reach his personal goals

Features CATERING Startups enable anyone to hire the services of a local chef to organize a dinner or cocktail party based on their tastes and budget STAFFING SERVICES Startups help restaurants by hiring additional staff for rush hours. These “go-between” platforms enable restaurants to expand their workforce with a few clicks by managing administrative procedures COOKING ROBOTS Startups develop cooking robots to help or replace human tasks. This also includes 3D printers, automated kiosks, and bartending robots NUTRIGENOMICS Startups work on the genome or microbiota-based tests to establish the personalized nutritional needs of each customer RECOMMENDATION Startups answer the question “what should I eat (or drink)?” with recommendations of meals, recipes, shopping lists, or wines based on each customer’s expectations. These startups use manual recommendations from specialists or algorithms based on artificial intelligence (Liakos et al., 2018; World Economic Forum, 2018) RECIPES Startups reinvent the recipe as we know it with new formats such as interactive games or addictive videos broadcast on social networks

(continued)

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Table 15.1 (continued) Typology

Delivery: startups answering the delivery challenges in the food industry, with home delivery of groceries, restaurant meals, or meals prepared in their kitchens

Features TRANSPARENCY Startups enable consumers to access quality information on food products. They aim to create standardized content that is easily accessible by everyone and potentially exchangeable between different services FOOD EXPERIENCES Startups create tourist experiences around the food-related points of interest (brewery, vineyard, …) or reinventing access to cooking classes MEAL KITS Startups regularly deliver to their customers all the ingredients to make meals by adapting quantities to the home MARKETPLACES Startups develop food e-commerce platforms, including farm-to-home solutions and store delivery DISCOVERY BOX Delivery services to receive products selected by experts every month. Wine, tea, coffee, and exotic new products from around the world are among the most popular themes RESTAURANT DELIVERY Startups enable their customers to be delivered with prepared meals from nearby restaurants, mostly through independent drivers FULL STACK DELIVERY Startups deliver meals prepared in their kitchens. DELIVERY ROBOTS Startups develop food delivery drones or robots VENDING MACHINES A new generation of automated machines providing food groceries, meals, and snacks

(continued)

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Table 15.1 (continued) Typology

Features

Retail: startups developing solutions for the retail food industry, from the digitalization of the supply chain to a better in-store shopper experience

DATA FOR SUPPLY CHAIN Startups address the issues of the food supply chain with tools to improve data management LOYALTY Startups work to (re)build a bond between brands and their customers while providing food corporates with more in-store data on consumers’ behaviors OMNICHANNEL SERVICES Startup providing brands with solutions to digitalize, integrate, and manage all the channels to sell their products in-store and online

15.5

The Accounting Background for Valuation

The evaluation is sensitive to forward-looking data that can be used to build up a sound business plan with a time horizon coherent with the average life cycle of the products and services of the FoodTech. A business plan is a formal accounting statement that numerically describes a set of business goals, the reasons why they are believed attainable, and the strategic plan and managerial steps for reaching those goals. Hypotheses and visionary ideas of game-changers must be transformed into numbers and need to be backed by reasonable and verifiable assumptions about future events and milestones (Moro Visconti, 2019). The accounting background is composed of pro forma balance sheets (of some 3–5 years) and perspective income statements. The matching of these two documents produces expected cash flow statements. Economic and financial margins are the crucial accounting parameters for valuation that are represented by the EBITDA, the EBIT, the operating and Net Cash Flows, and the Net Financial Position, as it will be shown in the formulation of the appraisal approaches. The appraisal methodology may conveniently start from a strategic interpretation of the business model (that derives from accounting data) to extract the key evaluation parameters to insert in the model, as shown in Fig. 15.4. An analysis of the business model may conveniently consider:

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(Perspec ve) Accoun ng data

Fig. 15.4

•Balance sheet •Income statement •Cash Flow statement

FoodTrech's Business Plan

•Ɵme horizon •strategic assumpƟons •sensiƟvity/scenario analysis

Evalua on parameters

•economic/financial data •book versus market values

Evaluation Methodology

FoodTech's Revenue Model

Strategic Goals

Market trends

Expected Investments

Fig. 15.5

• • • • •

The The The The The

Business model and value drivers

revenue model; strategic goals; growth drivers; expected investments; market trends (Fig. 15.5).

Growth Drivers

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15.6

377

Valuation Methods

Most of the concepts recalled in these paragraphs are similar to those already illustrated in other chapters, and restated here with some personalization, considering the peculiar startup of this chapter that is so intended to be self-containing. In particular, the main concepts that are here directly or indirectly restated concern: a. The preliminary phase, from business modeling to business planning (Chapter 2); b. The main valuation approaches (Discounted Cash Flows – DCF, and market multipliers), described in Chapter 8; c. The valuation of specific startups (see Part II—Industry Applications).

The evaluation criteria typically follow the (actual and prospective) business model of the target company (Fig. 15.6). A comparison of the primary evaluation criteria in traditional firms versus high-tech firms (startups) is reported in Table 15.2.

Business model

Technological Firms (Startups)

FoodTechs

TradiƟonal Food Firms

Valua on approach

Fig. 15.6

Business model and valuation approach of foodTechs

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Table 15.2 Comparison of the main evaluation approaches of traditional firms and technological startups

Traditional firm (see Chapter 8)

Technological startup (see Chapter 9 and IPEV, 2018)

Balance-sheet based Venture Capital method (Fernandez, 2001) Income Binomial trees Mixed capital-income Financial approach (Discounted Cash Flows) Market multiples (comparable firms)

In this case, the value may be inferred even with differential income methodologies, traditionally used in the evaluation of intangible assets (within the income approaches). Among the main evaluation methodologies of FoodTech companies, the following are the most relevant: • Financial approach (Discounted Cash Flows—DCF); • Market comparables. 15.6.1

The Financial Approach

The financial approach is based on the principle that the market value of the company is equal to the discounted value of the cash flows that the company can generate (“cash is king”). The determination of the cash flows is of primary importance in the application of the approach, as is the consistency of the discount rates adopted. The doctrine (especially the Anglo-Saxon one) believes that the financial approach is the “ideal” solution for estimating the market value for limited periods. It is not possible to make reliable estimates of cash flows for longer periods. “The conceptually correct methods are those based on cash flow discounting. I briefly comment on other methods since - even though they are conceptually incorrect - they continue to be used frequently” (Fernandez, 2001). This approach is of practical importance if the individual investor or company with high cash flows (leasing companies, retail trade, public and motorway services, financial trading, project financing SPVs, etc.) are valued.

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Financial evaluation can be particularly appropriate when the company’s ability to generate cash flow for investors is significantly different from its ability to generate income, and forecasts can be formulated with a sufficient degree of credibility and are demonstrable. There are two complementary criteria for determining the cash flows: a.1. The cash flow available to the company (Free cash flow to the firm) This configuration of expected flows is the one most used in the practice of company valuations, given its greater simplicity of application compared to the methodology based on flows to partners. It is a measure of cash flows independent of the financial structure of the company (unlevered cash flows) that is particularly suitable to evaluate companies with high levels of indebtedness, or that do not have a debt plan. In these cases, the calculation of the cash flow available to shareholders is more difficult because of the volatility resulting from the forecast of how to repay debts. This methodology is based on the operating flows generated by the typical management of the company, based on the operating income available for the remuneration of own and third-party means net of the relative tax effect. Unlevered cash flows are determined by using operating income before taxes and financial charges. The cash flow available to the company is, therefore, determined as the cash flow available to shareholders, plus financial charges after tax, plus loan repayments and equity repayments, minus new borrowings and flows arising from equity increases. The relationship between the two concepts of cash flow is as follows: cash flow available to the company = cash flow available to shareholders + financial charges (net of taxes) + loan repayments − new loans (15.2) a.2. The (residual) cash flow available to shareholders This configuration considers the only expected flow available for members’ remuneration. It is a measure of cash flow that considers the financial structure of the company (levered cash flow). It is the cash flow that remains after the payment of interest and the repayment of equity shares and after the coverage of equity expenditures necessary to maintain existing assets and to create the conditions for business growth.

380

R. MORO-VISCONTI

In M&A operations, the Free Cash Flow to the Firm (operating cash flow) is normally calculated to estimate the Enterprise Value (comprehensive of debt). The residual Equity Value is then derived by subtracting the Net Financial Position. The discounting of the free cash flow for the shareholders takes place at a rate equal to the cost of the shareholders‘ equity. This flow identifies the theoretical measure of the company’s ability to distribute dividends, even if it does not coincide with the dividend paid. Cash flow estimates can be applied to any type of asset. The differential element is represented by their duration. Many assets have a defined time horizon, while others assume a perpetual time horizon, such as shares. Cash flows (CF) can, therefore, be estimated using a normalized projection of cash flows that it uses, alternatively: • unlimited capitalization: W1 = CF /i

(15.3)

W2 = CF a n − i

(15.4)

• limited capitalization:

where W 1 and W 2 represent the present value of future cash flows. The discount rate to be applied to expected cash flows is determined as the sum of the cost of equity and the cost of debt, appropriately weighted according to the leverage of the company (the ratio between financial debt and equity). This produces the Weighted Average Cost of Capital (WACC):

W ACC = ki (1 − t) where: k i = cost of debt; t = corporate tax rate; D = market value of debt; E = market value of equity; D + E = raised capital;

E D + D + E D + E

([15.5])

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381

k e = cost of equity (to be estimated with the Capital Asset Pricing Model − CAPM or the Dividend Discount Model). The cost of debt capital is easy to determine, as it can be inferred from the financial statements of the company. The cost of equity or share capital, which represents the minimum rate of return required by investors for equity investments, is instead more complex and may use the CAPM or the Dividend Discount Model (a method of valuing a company’s stock price considering the sum of all its future dividend payments, discounted back to their present value. It is used to value stocks based on the net present value of future dividends). The formula of the CAPM is the following: E (r )FoodTech = rfree + βFoodTech [(E(r )market − rfree ]

(15.6)

where: E (r)FoodT ech = expected return of the FoodT ech listed stock rfree = risk − f ree rate of return (e.g., of a long term Goverment band) βFoodT ech = sensi tivi ty of the Fintech’s stock to the market price (E (r)market = expected return of the (benchmark) Stock market. A central element is represented by the beta (b) of the FoodTech to be evaluated that consists of the ratio between the covariance of the FoodTech security with its stock market, divided by the variance of the market. Market betas, subdivided by industry, may be detected from the dataset of A. Damodaran (see, for instance, http://pages.stern.nyu.edu/ ~adamodar/New_Home_Page/datafile/Betas.html). Once the present value of the cash flows has been determined, the calculation of the market value W of the company may correspond to: (a) the unlevered cash flow approach:

W=

 C F0 +VR − D W ACC

(15.7)

(b) the levered cash flow approach:

W=

 C Fn Ke

+VR

(15.8)

382

R. MORO-VISCONTI

where: CF 0/ WACC = present value of operating cash flows CFn/Ke = present value of net cash flows VR = terminal (residual) value D = initial net financial position (financial debt − liquidity) The residual value is the result of discounting the value at the time n (before which the cash flows are estimated analytically). It is often the greatest component of the global value W (above all in intangible-intensive companies) and tends to zero if the time horizon of the capitalization is infinite (VR /∞ = 0). The two variants (levered versus unlevered) give the same result if the value of the firm, determined through the cash flows available to the lenders, is deducted from the value of the net financial debts. Operating cash flows (unlevered) and net cash flows for shareholders (levered) are determined by comparing the last two balance sheets (to dispose of changes in operating Net Working Capital, fixed assets, financial liabilities, and shareholders‘ equity) with the income statement of the last year. The accounting derivation of the cash flow and its link to the cost of capital (to get DCF – Cash Flows) is illustrated in Table 15.3. The net cash flow for the shareholders coincides with the free cash flow to equity and, therefore, with the dividends that can be paid out, once it has been verified that enough internal liquidity resources remain in the company. This feature, associated with the ability to raise equity from third parties and shareholders, allows the company to find adequate financial coverage for the investments deemed necessary to maintain the company’s continuity and remain on the market in economic conditions (minimum objectives). They should allow for the creation of incremental value in favor of shareholders, who are the residual claimants (being, as subscribers of risky capital, the only beneficiaries of the variable net returns, which, as such, are residual and subordinate to the fixed remuneration of the other stakeholders). The estimate of cash flows can be applied to any activity. The differential element is service life. Many activities have a defined time horizon, while others assume a perpetual time horizon, such as company shares. The discounted cash flow (DCF) approach can be complemented with real options that incorporate intangible-driven flexibility in the forecasts.

15

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383

Table 15.3 Cash flow statement and link with the cost of capital of a FoodTech startup Cash flow statement EBIT + Depreciation and amortization = EBITDA (A) ± Operating Net Working Capital ± fixed assets (CAPEX) = Operating cash flow (unlevered cash flow to the firm) (B) - Financial charges ± net financial liabilities ± Extraordinary income and charges - Taxes ± Equity

To be discounted at the Weighted average cost of capital (WACC)

= Net (free) cash flow to the shareholders (levered cash flow) (C) Reconciliation statement:

To be discounted at the cost of equity (Ke)

Closing cash and cash equivalents - Opening cash and cash equivalents = Change in net cash flow = liquidity (D) = (C)

15.6.2

The Financial Approach with Debt-Free Startups

Startups are normally debt-free (as shown in Chapter 6) since they have little if any collateral value of their assets and they produce negative cash flows, especially in the first years of their existence. Consequently: • in the balance sheet raised capital (funds) tend to coincide with equity; • in the income statement, EBIT is similar to the net result (considering that interest rates are nonexistent, and taxes also); • in the cash flow statement, the operating cash flow tends to coincide with the net cash flow; • in the absence of cost of debt, cost of capital (WACC) coincides with cost of equity.

384

R. MORO-VISCONTI

The difference between a traditional firm versus a startup economic, financial, and balance-sheet system can be represented as follows, respectively in Figs. 15.7 and 15.8. Liquidity so derives from: • EBITDA (initially negative and so cash-absorbing but with high increase potential); • Change in operating net working capital (sales growth is fueled by the cash-absorbing expansion of receivables and stock [wherever present, e.g., not in FinTechs], partially counterbalanced by cash-generating increase in payables); • Change in the CAPEX (net of cashless depreciation/amortization) • Equity and quasi-equity injections (considering only liquidity cashed in, and not a cashless contribution in-kind). Sales forecasting is the main and first value driver to consider, as it generates the revenues that, net of monetary operating expenditure (OPEX), form the EBITDA. Sales are also related to the operating net working capital components, as they influence stock and receivables. Sales are also output factors depending on input purchases that influence payables. It may so be argued that the Operating Net Working Capital is a function of the sales. The following ratios can be used to interpret and forecast the expected outcome of the working capital (Table 15.4): A further input factor of sales is represented by CAPEX. Liquidity forecasts and occurrences are typically negative in the startup phase, with the absorption of cash that is mainly due to a negative EBITDA, and a CAPEX increase (due to the investments necessary to startup the firm). The operating net working capital may be less significant, but it normally grows (so absorbing cash) when sales boost. The intervention of the shareholders is so periodically necessary to keep a cash equilibrium, avoiding both cash burnout and equity burnout. Debt capacity indicators like the debt service cover ratio are meaningless in debt-free startups. 15.6.3

Empirical Approaches (Market Multipliers)

The market value identifies:

15

FOODTECH AND AGRITECH STARTUP VALUATION

Balance sheet Δ Implicit goodwill Δ Equity gain Δ Tangible and intangible fixed assets

Δ Net operating working capital

Intangible gain

Δ equity

Basic capital method

Δ Net financial position

Balance sheet based approach

Mixed equity/income method

Invested Capital = raised capital= Enterprise Value

Income statement Operating monetary revenues - operating monetary costs (monetary OPEX) = EBITDA - amortization, depreciation, provisions and writedowns = EBIT (A – B) +/- balance of financial management +/- balance of extraordinary operations = Pre-Tax Profit - taxes = Net result Cash flow statement EBIT + amortization, depreciation = EBITDA +/- Δ operating net working capital +/- Δ fixed assets = Operating cash flow (unlevered) +/- extraordinary income/expense +/- financial income/expense +/- Δ other activities - taxes +/- Δ financial debts +/- Δ shareholders’ equity = Net Cash Flow

Fig. 15.7

Valuation framework—traditional firm

Income method

Market Prices

Market mulƟplescomparable Financial companies

method

Market interest rates- other macroeconomic variables

385

386

R. MORO-VISCONTI

Fig. 15.8 Valuation framework—startup

Table 15.4 Turnover Ratios

Inventory Turnover Inventory Turn-Days Accounts Receivable Turnover Average Collection Period Accounts Payable Turnover Average Payment Period

Cost of Goods Sold/Inventory 360/Inventory Turnover Sales/Accounts Receivable 360/Accounts Receivble Turnover Cost of Goods Sold/Account Payable 360/Accounts Payable Turnove

(a) The value attributable to a share of the equity expressed at stock exchange prices; (b) The price of the controlling interest or the entire share equity; (c) The traded value for the controlling equity of comparable undertakings; (d) The value derived from the stock exchange quotations of comparable undertakings. Sometimes comparable trades of companies belonging to the same product sector with similar characteristics (in terms of cash flows, sales, costs, etc.) are used.

15

FOODTECH AND AGRITECH STARTUP VALUATION

387

In practice, an examination of the prices used in negotiations with companies in the same sector leads to quantifying average parameters: • • • • •

Price/EBIT Price/cash flow Price/book value Price/earnings Price/dividend

These ratios seek to estimate the average rate to be applied to the company being assessed. However, there may be distorting effects of prices based on special interest rates, in a historical context, on difficulties of comparison, etc. In financial market practice, the multiples methodology is frequently applied. Based on multiples, the company’s value is derived from the market price profit referring to comparable listed companies, such as net profit, before tax or operating profit, cash flow, equity, or turnover. The attractiveness of the multiples approach stems from its ease of use: multiples can be used to obtain quick but dirty estimates of the company’s value and are useful when there are many comparable companies listed on the financial markets and the market sets correct prices for them on average. Because of the simplicity of the calculation, these indicators are easily manipulated and susceptible to misuse, especially if they refer to companies that are not entirely similar. Since there are no identical companies in terms of entrepreneurial risk and growth rate, the assumption of multiples for the processing of the valuation can be misleading, bringing to “fake multipliers.” The use of multiples can be implemented through: A. Use of fundamentals; B. Use of comparable data: B.1. Comparable companies; B.2. Comparable transactions. The first approach links multiples to the fundamentals of the company being assessed: profit growth and cash flow, dividend distribution ratio, and risk. It is equivalent to the use of cash flow discounting approaches. For the second approach, it is necessary to distinguish whether it is a valuation of comparable companies or comparable transactions.

388

R. MORO-VISCONTI

The comparability concerns different firms but is also related to their contents. In the case of comparable companies, the approach estimates multiples by observing similar companies. The problem is to determine what is meant by similar companies. In theory, the analyst should check all the variables that influence the multiple. In practice, companies should estimate the most likely price for a nonlisted company, taking as a reference some listed companies, operating in the same sector, and considered homogeneous. Two companies can be defined as homogeneous when they present, for the same risk, similar characteristics, and expectations. The calculation is: • A company whose price is known (P 1 ), • A variable closely related to its value (X 1 ). The ratio (P 1 )/(X 1 ) is assumed to apply to the company to be valued, for which the size of the reference variable (X 2 ) is known. Therefore: (P1 / (X 1 ) = (P2 )/ (X 2 )

(15.9)

so that the desired value P 2 will be: P2 = X 2 [(P1 ) / (X 1 ]

(15.10)

According to widespread estimates, the main factors in establishing whether a company is comparable are: • Size; • Belonging to the same sector (see for instance the Statistical Classification of Economic Activities in the European Community, commonly referred to as NACE); • Financial risks (leverage); • Historical trends and prospects for the development of results and markets; • Geographical diversification; • Degree of reputation and credibility; • Management skills; • Ability to pay dividends.

15

FOODTECH AND AGRITECH STARTUP VALUATION

389

Founded on comparable transactions, the basis of valuation is information about actual negotiations (or mergers) of similar—i.e., comparable— companies. The use of profitability parameters is usually considered to be the most representative of company dynamics. Comparables may be looked for consulting databases like Orbis (https://www.bvdinfo.com/en-gb/our-products/data/international/ orbis). Among the empirical criteria, the approach of the multiplier of the EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) is widely diffused. The net financial position must be added algebraically to the EBITDA, to pass from the estimate of the enterprise value (total value of the company) to that of the equity value (value of the net assets). The formulation is as follows: W = average perspective EBITDA * Enterprise Value /sector EBITDA = Enterprise Value of the company

(15.11)

Enterprise Value of the company And then: Equity Value = Enterprise Value ± Net Financial Position

(15.12)

The DCF approach can be linked to the market approach since they both share as a starting parameter the EBITDA.

References AgFunder. (2019). AfFunder AgriFoodTech investing report 2018. San Francisco, AgFunder. https://agfunder.com/research/agrifood-tech-investing-rep ort-2018/. Barabási, A. (2016). Network science. Cambridge: Cambridge University Press. Ge, L., Brewster, C., Spek, J., Smeenk, A., & Top, J. (2017). Blockchain for agriculture and food; findings from the pilot study. Wageningen Economic Research, Report, 2017-112. Fernandez, P. (2001). Valuation using multiples: How do analysts reach their conclusions ? IESE Business School, Madrid. Frewer, L. J., Bergmann, K., Brennan, M., Lion, R., Meertens, R., Rowe, G., et al. (2011). Consumer response to novel agri-food technologies: Implications for predicting consumer acceptance of emerging food technologies. Trends in Food Science & Technology, 22(8), 442–456.

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Geissdoerfer, M., Savaget, P., Bocken, N., & Hultink, E. (2017). The circular economy—A new sustainability paradigm? Journal of Cleaner Production, 143(1), 757–768. Hein, A., Schreieck, M., Riasanow, T., Setzke, M., Wiesche, M., Bohm, M., & Krcmar, H. (2019). Digital platform ecosystems. Electronic Markets, November. ING. (2019, April). Food tech: Technology in the food industry. Robot arm offers the food industry. A helping hand. ING Economics Department. Available at https://www.ingwb.com/media/2917160/food-tech-rep ort_april-2019.pdf. IPEV. (2018). Valuation guidelines. Available at http://www.privateequityvalua tion.com/Valuation-Guidelines. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18, 2674. Mao, D., Wang, F., Hao, Z., & Li H. (2018, August 1). Credit evaluation system based on blockchain for multiple stakeholders in the food supply chain. International Journal of Environmental Research and Public Health, 15(8), 1627. Available at https://doi.org/10.3390/ijerph15081627. Meena, G. P., Meena, R. L., & Kumar, D. (2019). Boosting of Indian agriculture through agritech startups—An overview. International Journal of Current Microbiology and Applied Sciences, 8(12), 396–405. Meenakshi, N., & Sinha, A. (2019). Food delivery apps in India: Wherein lies the success strategy? Strategic Direction, 35(7). Moro Visconti, R. (2019). How to prepare a business plan with excel. Available at https://www.researchgate.net/publication/255728204_How_to_Pre pare_a_Business_Plan_with_Excel. Renda, A. (2019). The age of foodtech: Optimizing the agri-food chain with digital technologies. In R. Valentini, J. Sievenpiper, M. Antonelli, & K. Dembska (Eds.), Achieving the sustainable development goals through sustainable food systems. Cham: Springer. Rochet, J. C., & Tirole, J. (2003). Platform competition in two-sided markets. Journal of the European Economic Association, 1(4), 990–1029. Schneider, T., Eli, K., Dolan, C., & Ulijaszek, S. (Eds.). (2018). Digital food activism. London: Routledge. Valentini, R., Sievenpiper, J. L., Antonelli, M., & Dembska, K. (2019). Achieving the sustainable development goals through sustainable food systems. Cham: Springer. Verma, M., Hontecillas, R., Tubau-Juni, N., Abedi, V., & Bassaganya-Riera, J. (2018). Challenges in personalized nutrition and health. Frontiers in Nutrition, 5, 117. World Economic Forum. (2018). Harnessing artificial intelligence for the Earth. Available at http://www3.weforum.org/docs/Harnessing_Artificial_Intellige nce_for_the_Earth_report_2018.pdf.

Index

A Advertising, 83, 85, 89, 310, 317, 318, 323, 324, 327, 347 Agency cost, 117, 149, 165, 173, 226 AgriTech, 3, 365, 367, 369–371 Algorithm, 89, 91, 245, 256, 327, 364, 373 Amortization, 25, 55, 66–69, 75, 146, 147, 150, 151, 192, 197, 198, 258, 356, 384 Artificial intelligence (AI), 2, 96–98, 155, 245, 250, 254, 255, 300, 373 Asset management, 130–137, 139, 246 Assets, 1, 3, 10, 13, 14, 17–19, 22, 25–27, 34, 48, 50–52, 57, 68, 70, 72, 73, 75, 78, 81–86, 92, 97, 108, 114, 116, 119, 125, 128, 131, 134, 135, 144, 146, 150, 151, 153, 157, 162, 164, 165, 174, 176, 184–189, 191, 192, 194, 195, 197, 198, 200, 201, 204, 208, 214,

215, 220, 225, 226, 229, 234, 258, 262–264, 266–268, 272, 273, 286, 290, 323, 325, 330, 331, 333, 349, 351, 352, 354, 378–383 Assets under management (AUM), 133–137 Audio Visual Industry, 317 Augmented business planning, 40, 236 Average profit, 196 B B2B, 253, 276, 325, 347, 370, 371 B2C, 230, 276, 297, 318, 325, 347, 370 Balance sheet, 10, 11, 13–17, 21–23, 28, 37, 39, 47, 52, 68, 78, 82, 84, 101, 144, 145, 150, 151, 157, 175, 176, 185–189, 194–196, 201, 218, 219, 225, 252, 257, 258, 268, 320, 332, 345, 354, 356, 375, 382–384 Banking as a Service (BaaS), 245

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Moro-Visconti, Startup Valuation, https://doi.org/10.1007/978-3-030-71608-0

391

392

INDEX

Bankruptcy, 157, 165, 169, 180, 184, 185 Berkus Approach, 214 Big data, 2, 29–31, 40, 43, 70, 94–97, 155, 157, 222, 236, 245, 250, 255, 272, 277, 290, 300, 318, 347, 364 Big Tech, 246, 248, 251 Binomial, 35, 36, 221, 263, 272, 323, 350, 378 BioTech, 371 Blitzscaling, 147, 324 Blockchains, 2, 43, 98, 155, 246, 247, 250, 254, 255, 285, 300, 368, 369 Bond, 28, 85, 103, 117, 135, 192, 267, 331, 375 Book value, 48, 49, 56, 57, 68, 70, 77, 78, 82, 226, 228, 263, 264, 274 Bottom-up, 29, 40, 41, 75, 186, 238 Brand, 70, 83, 86, 88, 91, 92, 317, 325, 375 Break-even analysis, 11, 35, 63, 64 Break-up, 180, 225, 226, 264 Bridge financing, 122, 157, 220, 318, 345, 347 Broadcasting, 310, 317 Budgeting, 2, 10, 29, 96, 164 Business angel, 2, 42, 115, 116, 152, 233, 306 Business model, 1, 2, 4, 9, 40, 41, 43, 63, 92–94, 96, 98, 108, 123, 125, 127, 146–148, 155, 157, 174, 179, 190, 214, 218, 221, 225, 230, 231, 233, 234, 245, 247–249, 252–254, 256–258, 260–262, 271, 272, 274–276, 283, 287, 289, 290, 311, 316, 317, 319–323, 334, 343–349, 365, 366, 370, 371, 375–377

Business planning, 2, 10, 12, 13, 29, 31, 35, 40, 70, 146, 187 Buyout, 74, 123, 215

C Capital expenditure (CAPEX), 25, 26, 66, 67, 74, 76, 128, 133, 146, 148–151, 156, 162, 165, 169, 174, 356, 357, 384 Capitalization, 67, 69, 73, 74, 77, 78, 85, 190, 192–196, 201, 229, 266, 268, 330–332, 352, 354, 382 Capital structure, 2, 49, 74, 161, 163, 165, 166, 168, 174 Cash, 10, 14–17, 28, 29, 32, 37, 51, 54, 56, 66, 73, 75, 76, 84, 91, 99, 102, 104, 116, 127–129, 133–135, 138, 143–145, 147– 149, 153–157, 162, 174, 185, 186, 196–199, 201, 204, 206, 214, 216, 225, 229, 231, 233, 234, 251, 252, 263–269, 275, 290, 326, 327, 329–332, 334– 336, 345, 350–354, 356–359, 364, 375, 378–380, 382, 384, 387 Cash burnout, 3, 35, 38, 116, 149, 156, 165 Cash flow statement, 10, 15–18, 21, 22, 24, 25, 27–29, 39, 82, 144, 145, 156, 201, 203, 219, 252, 269, 320, 333, 345, 355, 356, 375, 383 Churn rate, 146, 323 Circular economy, 104–107, 367 Comparable, 66, 93, 135, 136, 139, 185, 186, 205, 206, 208, 209, 214, 233, 235, 236, 263, 268–270, 323, 325, 335–337, 349, 357–360, 378, 386–389

INDEX

Competitive, 89, 93, 133, 147, 175, 247, 272–275, 303, 316, 328 Contribution margin (CM), 59–62, 68, 147 Convertible bond, 28, 119, 184 Cost approach, 214, 216 Cost of capital, 29, 39, 53, 54, 56–58, 70, 101–107, 109, 114, 134, 135, 143, 163, 192, 203, 229, 260, 268, 269, 274, 333, 354, 355, 382, 383 Cost of debt, 49, 52, 103, 104, 144, 162, 167–172, 200, 266, 267, 331, 352, 355, 380, 381, 383 Cost of equity, 54, 57, 58, 99, 100, 104, 105, 107, 139, 144, 162, 168, 200, 221, 233, 234, 236, 238, 260, 266, 267, 272, 331, 352, 355, 380, 381, 383 Cross-selling, 257, 316 Crowdfunding, 1, 40, 113, 114, 149, 152, 153, 156, 233, 254, 283, 306, 318, 347 Customer centricity, 277 Customer lifetime value (CLV), 326 Cybersecurity, 254

D Data analytics, 364 Database, 2, 43, 82, 94–96, 236, 254, 270, 300, 337, 360, 389 Death valley, 3, 116, 119, 149, 157, 334, 345 Debt-free, 3, 24, 26, 53, 57, 74, 143–145, 149, 151, 157, 163, 164, 198, 216, 354–357, 383, 384 Degree of operating leverage (DOL), 61, 62 Delivery, 283, 318, 345, 346, 369, 374

393

Depreciation, 24, 25, 27, 54, 66–69, 73, 75, 78, 133, 139, 146, 147, 150, 151, 156, 192, 197, 198, 208, 228, 258, 356, 384 Development, 1, 3, 9, 41, 83, 85, 90, 95, 113–116, 119, 121, 122, 125, 136, 157, 161, 179, 183, 207, 209, 215, 223, 245, 254, 281, 282, 292, 300, 318, 326, 337, 360, 370, 388 Digital enabler, 248, 252, 365 Digital experience, 65 Digitalization, 76, 107, 108, 147, 148, 249, 290, 312, 313, 366, 367, 375 Digital media, 310, 314 Digital money, 246 Digital platform, 3, 42, 70, 104, 105, 108, 230, 245, 249, 250, 253, 288, 291, 292, 297–301, 303–305, 311, 312, 314, 324, 366, 367 Digital scalability, 65, 70, 148, 230, 258, 274, 298 Discount, 70, 85, 101, 102, 104, 126, 127, 133, 134, 136–139, 143, 185, 196, 197, 200, 206, 220, 225, 228, 229, 232, 236, 264, 266, 274, 275, 329, 331, 334, 350, 352, 372, 378, 380 Discounted Cash Flows (DCF), 2, 17, 40, 54, 67, 70, 74, 99, 101–104, 131, 133, 138, 139, 143, 158, 165, 185, 186, 204, 213, 216, 217, 219, 235, 249, 263, 264, 268, 271, 274, 313, 323, 327, 333, 334, 338, 349, 354, 360, 366, 378, 382, 389 Dividend, 19, 28, 48, 49, 78, 99, 100, 134, 137–139, 157, 166, 169, 174, 198, 201, 203, 206, 209, 262, 263, 266–268, 330,

394

INDEX

331, 333, 336, 337, 351, 353, 354, 359, 360, 380–382, 387, 388 Dividend Discount Model (DDM), 99–101, 131, 138, 139, 200, 263, 267, 331, 352, 353, 381 Domain name, 81, 86, 88, 91, 92, 319, 327, 328 Drone, 364, 365, 371, 374 E Early-stage, 2, 3, 9, 39, 42, 113–116, 121, 127, 143, 152, 163, 174, 184, 213, 223, 264 Earnings before interest and taxes (EBIT), 14, 23, 25, 49, 50, 52–54, 59–62, 65–69, 71, 72, 144, 146, 147, 167, 169, 170, 191, 219, 252, 258, 274, 320, 347, 355, 375, 383 Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), 19, 25, 26, 28–30, 65, 66, 68–71, 73–77, 93, 101, 128, 137, 145, 146, 148, 149, 151, 156, 171, 173, 186, 209, 219, 233–238, 249, 252, 258, 270, 271, 273, 274, 289–291, 313, 320, 323, 327, 337, 338, 347, 355–357, 360, 366, 375, 384, 389 E-commerce, 92, 231, 232, 327, 371, 374 Economic forecasts, 357 Economic value added (EVA), 53–57, 73, 101, 273, 274 Ecosystem, 1, 43, 104–106, 174, 248–250, 283, 285, 292, 297, 298, 300, 301, 305, 312, 314, 343, 344, 363, 365–368 Edges, 4, 30, 31, 250, 283, 300, 303, 304, 314, 367

Efficiency, 50, 51, 60, 64, 70–72, 106, 220, 246, 250, 252, 255, 257, 287, 365 E-health, 3, 341, 342, 344–346, 353–355 Empirical approach, 137, 185 Enterprise value (EV), 66, 73, 74, 93, 186, 197, 209, 219, 220, 233, 235, 249, 264, 266, 270, 273, 313, 330, 334, 337, 351, 360, 366, 380, 389 Entertainment, 3, 310, 311 Equity, 1–3, 10, 13, 14, 19–21, 23, 24, 27, 28, 38, 48, 51, 53–57, 70, 71, 73–76, 78, 100, 102, 113–117, 123, 126, 133–135, 138, 139, 143, 144, 146, 149, 151–153, 155, 156, 161, 162, 164, 166–171, 173–175, 184, 186–188, 190, 194–203, 205, 206, 210, 215, 220, 225, 226, 228, 229, 233, 235, 254, 258, 259, 262–268, 270, 290, 291, 306, 325, 330, 331, 333, 335, 337, 345, 351–354, 356–358, 360, 379–384, 386, 387, 389 Equity burnout, 37, 116, 119, 149, 156, 225, 229, 357, 384 ESG drivers, 101 EVCA, 215 Excel, 10, 224, 233 Excess return, 263, 273 Exit, 1, 113–116, 121, 123, 125, 175, 185, 214, 217, 221, 223, 225, 227, 234, 328

F Failure, 101, 121, 127, 173, 179, 229, 275, 304 Fair value, 83, 214–216, 219, 220, 228

INDEX

Family & friends, 233, 306, 318, 345, 347 Financial debt, 11, 22, 24, 51, 54, 57, 73, 75, 128, 157, 161, 167, 169, 170, 174, 175, 200, 201, 258, 266, 268, 331, 332, 352, 354, 380, 382 Financial inclusion, 282, 283 Financial innovation, 286, 287 Financial intermediary, 118, 161, 229, 248, 249, 252, 258, 260, 263, 283, 284, 292 Financial technology (FinTech), 3, 146, 245–264, 267, 269, 271, 272, 274–277, 282, 283, 288, 290, 384 Firm evaluation, 56 Fixed costs (FC), 59–62, 68, 128, 258, 287 Food chain, 3, 364, 368, 369 Food technology (FoodTech), 3, 365–367, 369, 370, 375, 377, 378, 381, 383 Food waste reduction, 363, 367 Forecasting, 29, 31, 40, 76, 96, 146, 157, 190, 217, 254, 356, 364, 384 Franchise factor model, 100, 274 Free cash flow to equity (FCFE), 27, 76, 133–135, 203, 268, 274, 333, 354, 382 Free Cash Flow to Firm (FCFF), 25, 74 Functional analysis, 186, 187 Funding, 9, 12, 103, 116, 128, 152, 153, 157, 214, 254, 274, 283, 291, 318, 347 G Geo-localization, 253 Goodwill, 25, 55–58, 67, 81, 83, 85, 97, 156, 175, 180, 194–196,

395

220, 226, 229, 272–275, 325, 326 Governance, 30, 34, 37, 39, 40, 101, 113, 114, 117, 149, 189, 233, 299, 301 Growth, 1, 2, 14, 48, 56, 60, 63, 64, 69, 71, 76–78, 81, 94, 99–101, 106, 113, 114, 116, 117, 124, 125, 127, 132, 134, 137–139, 146, 147, 150, 161, 163, 170, 173, 179, 185, 189, 194, 197, 206, 215, 230–232, 234, 247, 249, 253, 254, 258, 259, 266, 270, 274, 275, 287, 297, 306, 309, 313, 315, 321, 324, 330, 334, 336, 348, 351, 356, 358, 359, 364, 367, 376, 379, 384, 387 Growth opportunities, 84, 148, 156, 225, 273, 297

I Income approach, 184–186, 190, 195, 216, 262, 323, 326, 349, 378 Income statement, 10, 11, 13, 15–17, 19–23, 27, 28, 53, 65–67, 84, 144–147, 150, 151, 175, 176, 190, 191, 201, 219, 252, 257, 258, 268, 274, 289, 320, 333, 345, 354–357, 375, 382, 383 Information asymmetry, 29, 39, 40, 102, 117, 125, 126, 173, 233 Information technology (IT), 10, 60, 83, 92, 97, 157, 246, 255, 256, 259, 275, 287, 288, 298, 309 Initial public offering (IPO), 122, 184, 185, 227, 231 Innovation, 3, 9, 13, 30, 43, 84, 97, 98, 101, 108, 189, 230–232, 245, 246, 249, 253–255, 274,

396

INDEX

275, 283, 286–288, 301, 309, 312, 318, 346, 366, 371, 372 InsurTech, 246, 255 Intangibles, 2, 27, 47, 55–57, 66–78, 81–87, 90, 92, 94, 97–99, 104, 108, 115, 127, 147, 148, 150–152, 173, 174, 180, 186, 188–192, 194, 195, 201, 204, 207, 208, 214, 225, 226, 258, 262, 263, 268, 272–274, 300, 315, 323, 325, 327, 332, 344, 349, 354, 364, 378, 382 Internal rate of return (IRR), 35, 120, 129, 164, 165, 217, 221, 272, 334 International Accounting Standards/International Financial Reporting Standards (IAS/IFRS), 15, 83, 144, 204, 214, 215, 228, 325 International Private Equity and Venture Capital Valuation (IPEV), 213, 214, 216, 263, 323, 350, 378 Internet broadband, 318, 324 Internet of Things (IoT), 2, 30, 31, 40, 93–95, 109, 254, 300 Invention, 90, 91, 179, 180, 318, 346 Invested capital, 18, 50, 51, 53–56, 71–73, 115, 129, 146, 153, 162, 167, 169, 174, 175, 178, 192, 273, 274 Investment, 1, 11, 13, 16, 19, 29, 31, 33, 35, 39, 48–50, 60, 67–69, 71–76, 96, 102, 105, 106, 113–131, 133, 135, 148–150, 153, 162, 165, 166, 168, 170, 173, 174, 179, 188, 192, 196–198, 200, 203, 204, 214–221, 223–229, 234, 246, 248, 253, 264, 267, 272, 276, 286, 287, 318, 321, 331, 333,

346, 348, 353, 354, 357, 364, 376, 381, 382, 384 Investor, 2, 21, 39, 42, 49, 50, 54, 72, 102, 113, 115–126, 139, 152, 153, 162, 165, 166, 174, 195, 197, 200, 213, 214, 217, 220, 223, 224, 231, 233, 254, 264, 267, 276, 291, 306, 329, 331, 345, 350, 352, 364, 378, 379, 381

K Key person, 232 Know-how, 11, 89, 115, 180, 189, 288, 325 Knowledge, 9–11, 32, 89, 94, 95, 97, 98, 115, 123, 126, 173

L Leverage, 48, 49, 54, 100, 136, 157, 161–164, 167–172, 174, 180, 190, 200, 209, 266, 276, 291, 300, 301, 331, 337, 352, 360, 380, 388 Leveraged buyout (LBO), 117, 219 Levered, 24, 27–29, 54, 55, 152, 162–164, 166, 197, 201, 216, 266, 268, 315, 330, 332, 344, 351, 353, 354, 379, 381, 382 Liabilities, 10, 13, 14, 17–19, 22, 27, 28, 48, 51, 52, 54, 57, 70, 97, 176, 188, 189, 194, 201, 208, 214, 225, 258, 268, 286, 333, 354, 382 Licensing, 82, 90, 91, 318, 347 Life cycle, 85, 113, 118, 119, 185, 189, 225, 252, 320, 345, 375 LIFO, 55, 192 Liquidation, 149, 157, 185, 188, 195, 225, 263, 264

INDEX

Liquidity, 1, 3, 10, 14–17, 19, 22, 23, 51, 65, 66, 74, 114, 137, 143–146, 148–151, 155–158, 165, 173, 174, 179, 180, 188, 201, 203, 217, 227, 268, 332, 333, 354–357, 382, 384 Listing, 16, 144, 184, 220, 225, 227, 228, 231, 271, 328 Loyalty, 93, 324–326, 375 M Management buyouts (MBO), 115, 123 M-apps, 2, 92, 316, 318–320, 347 Market approach, 186, 216, 271, 338, 360, 389 Market multiplier, 73, 235, 274 Marketplace, 245, 303, 371, 374 Market traction, 13, 257 Market value added (MVA), 53, 56, 57, 73, 273, 274 Matrix, 32, 34, 36, 37 Metcalfe, 70, 298, 303, 305 M-health, 341, 342, 346 Microfinance, 3, 113, 255, 281–283, 285–288, 292 MicroFinTech, 3, 283, 284 M-Apps, 92, 300, 318 Mobile banking, 282 Monetary equity, 151, 152, 156–158, 165 Multilayer network, 291, 292, 304, 305 Multiple, 43, 49, 66, 74, 77, 107, 114, 121, 129, 131, 136, 137, 139, 186, 206, 208, 214–216, 219, 234, 236, 237, 245, 249, 263, 270, 291, 298, 304, 311, 317, 335, 336, 358, 359, 366, 387, 388 Multiple on Invested Capital (MOIC), 129

397

N Nasdaq, 229, 271 Net asset, 54, 70, 183, 195, 210, 216, 270, 337, 360, 389 Net Asset Value (NAV), 129, 196, 228, 229, 263 Net earning, 133–135 Net financial position (NFP), 11, 22, 51, 74, 171, 186, 197, 201, 209, 219, 235, 236, 252, 266, 268, 270, 320, 330, 332, 337, 347, 351, 354, 360, 375, 380, 382, 389 Net Operating Profit After Taxes (NOPAT), 53–56, 73, 153, 162, 274 Net present value (NPV), 35, 84, 100, 129, 138, 164, 165, 201, 221, 267, 271, 272, 331, 353, 381 Net profit, 19, 28, 48, 54, 77, 78, 167, 170, 191, 197, 206, 270, 335, 358, 387 Network, 3, 4, 30, 31, 42, 63, 64, 66, 92, 94, 96, 104, 107, 108, 147, 189, 221, 246, 248–250, 252, 272, 273, 276, 285, 288, 290–292, 297–301, 303–306, 309, 310, 312, 314, 316, 324, 327, 342, 364–368, 373 Nodes, 4, 30, 31, 92, 107, 108, 249, 250, 285, 291, 292, 297, 300, 301, 303–305, 312, 314, 327, 366, 367 O OECD transfer pricing guidelines, 187 Operating cash flow (OCF), 23, 25, 26, 66, 74–76, 128, 144, 149–151, 153, 162, 186, 197, 201, 264, 266, 268, 274, 323, 330, 332, 334, 351, 353–355, 380, 382, 383

398

INDEX

Operating expense (OPEX), 65–70, 84, 85, 146, 147, 150–152, 156, 238, 258, 274, 289, 356, 384 Operating leverage, 60, 61, 66, 147 Operating net working capital, 22, 26, 75, 128, 146, 148, 149, 151, 156, 198, 201, 268, 333, 354, 356, 357, 382, 384 Outreach, 248, 251, 282, 285–288, 290–292, 366 Over-the-top (OTT), 3, 309–311, 316, 333 P P2P lending, 245, 253, 254, 282, 283, 290 Patents, 68, 70, 83, 85, 88–92, 115, 179, 180, 189, 256, 325 Patient, 3, 341, 342, 346 Payback, 13, 105, 106, 125, 153, 157, 165, 184, 217 Pay per view, 317 Paytech, 254 Pecking Order Hypothesis, 20 Peer-to-peer (P2P), 108, 246, 316 Political-Economic-SocioculturalTechnological-LegalEnvironmental (PESTLE), 31, 248 Pollination, 252, 257 Portfolio, 1, 35, 50, 88, 94, 98, 104, 114, 117, 131, 134, 135, 189, 192, 220, 221, 226, 229, 272, 315, 316, 323, 325, 326, 344 Precision farming, 364 Pre-money, 217, 218, 223, 224 Price earnings, 49, 76 Private equity, 1–3, 113–115, 117, 119, 120, 122–125, 129, 152, 162, 175, 219, 220, 225, 228, 229, 233, 264, 306, 318, 334, 347

Probability, 32–34, 36, 43, 154, 185, 221, 272 Productivity, 3, 70, 118, 180, 364, 367 Profitability, 2, 25, 47, 48, 50–52, 68–70, 72, 74, 115, 128, 137, 169–171, 175, 178, 180, 185, 188, 190, 195, 209, 246, 287, 337, 360, 364, 365, 389 PropTech, 256 Q Quasi-equity, 119, 146, 157, 356, 384 R Raised capital, 14, 24, 25, 50, 51, 53, 72, 144, 146, 151, 162, 164, 169, 170, 174, 200, 267, 331, 352, 354, 380, 383 Real options, 29, 31, 40, 70, 84, 104, 107, 148, 156, 185, 204, 214, 235, 263, 264, 382 RegTech, 246, 255 Replacement, 123, 185, 188, 195, 196, 214, 232, 327 Research and development (R&D), 84, 90, 91, 318, 347, 371 Retained earnings, 49, 286 Retention, 93, 151, 250, 255, 317, 324–327 Return on assets (ROA), 49, 50, 72 Return on equity (ROE), 48–50, 52, 54, 57, 70, 71, 168, 169, 171, 172, 175, 196 Return on invested capital (ROIC), 25, 49, 50, 52, 54, 57, 58, 71, 125, 129, 167–172, 175, 217, 218, 224, 273, 274 Return on investment (ROI), 50, 71–73, 214, 217, 224

INDEX

Return on sales (ROS), 51, 52, 72 Revenue, 2, 10, 14, 19, 21, 24, 28, 33, 52, 54, 56, 60–63, 65, 66, 68–70, 72, 73, 77, 86, 93, 119, 127, 128, 132, 137, 146–148, 151, 156, 170, 173, 174, 185, 194, 218, 224, 238, 252, 253, 257, 258, 274, 287, 290, 301, 316–319, 321, 323, 345, 347, 348, 356, 376, 384 Risk, 29, 32–37, 39, 40, 49, 54–56, 60–62, 70, 76, 77, 96, 100–102, 104, 107, 115–117, 119, 121, 122, 125, 127, 128, 131, 135, 136, 139, 143, 155, 165, 166, 168, 169, 179, 185–188, 192, 195, 196, 204, 206–209, 214, 217, 219, 221, 224, 229, 233, 234, 258, 267, 270, 272, 275, 286, 287, 290, 327, 336, 337, 358–360, 387, 388 Runway, 153–155, 157, 165

S Sales, 10, 12, 48, 51, 52, 59–63, 65, 67, 68, 70, 72, 77, 84, 86, 89–91, 117, 121, 146–151, 153, 156, 157, 162, 179, 184, 189, 205, 214, 215, 220, 225, 227, 264, 269, 274, 275, 318, 324, 325, 327, 335, 346, 347, 356–358, 384, 386 Scalability, 2, 3, 63–67, 74, 76, 104, 105, 147, 233, 249, 288, 290, 298, 303, 304, 313, 315, 316, 318, 327, 344, 367 Scale up, 2 Seed, 2, 39, 42, 114, 173, 191, 215, 371 Self-sufficiency, 281, 287, 289 sensitivity analysis, 35, 236, 237, 271

399

Shareholder, 23, 24, 27, 28, 39, 40, 48, 54, 57, 70, 75, 101, 114, 115, 117, 119, 122, 123, 125, 126, 128, 129, 149, 152, 153, 156–158, 162, 168, 174, 175, 184–187, 197–199, 201, 203, 215, 225–229, 231, 233, 235, 265, 266, 268, 274, 290, 291, 329, 330, 332–334, 345, 351, 354, 357, 379, 380, 382, 384 Sharing economy, 104, 108, 109, 245, 316 Smart contract, 246 Social networking, 114, 282, 303, 318 Software, 30, 82, 92, 94, 245, 246, 256, 275, 288, 297, 299, 309, 319, 346, 364, 365, 371 Spread, 50, 95, 103, 104, 171, 288 Stakeholder, 4, 34, 37, 39, 40, 42, 93, 96, 103, 117, 128, 174, 203, 227, 248, 250, 251, 277, 288, 290, 291, 298, 300, 301, 304, 306, 314, 318, 334, 346, 354, 365–367, 382 Stock, 14, 22, 26, 48, 49, 56, 61, 76–78, 85, 99, 100, 104, 117, 122, 126, 128, 138, 139, 146, 148, 166, 169, 184, 188, 191, 201, 205, 225, 227–229, 233, 258–260, 267–269, 271, 331, 332, 335, 353, 356–358, 381, 384, 386 Strategic goal, 29, 37, 253, 256, 321, 348, 376 Strategic planning, 35 Streaming, 231, 309, 310, 316 Strengths, Weaknesses, Opportunities, and Threats (SWOT), 31, 253 Subscription services, 309 Supervised, 262, 283

400

INDEX

Supply chain, 70, 96, 107, 109, 148, 187, 249, 251, 254, 298, 301, 311, 312, 345, 347, 364–366, 368–370, 375 SupTech, 255 Sustainability, 2, 37, 101, 102, 104–106, 109, 219, 250, 281, 285–290, 292, 347, 363, 364, 371 Sustainable development goals (SDG), 363

T Target, 1, 2, 12, 13, 15, 22, 23, 55, 63, 66, 82, 114, 115, 117, 119, 122–124, 127, 144, 186, 205, 218–220, 225, 233, 235, 252, 260, 261, 286, 291, 322–324, 346, 349, 373, 377 Technology, 1, 3, 82, 93–95, 98, 106, 116, 120, 214, 245, 246, 249, 252–257, 260, 264, 282, 283, 286–291, 298–301, 303, 304, 311, 312, 316, 317, 324, 325, 341, 342, 345, 346, 364–366, 368, 370, 372 Telemedicine, 3, 341, 342, 345, 346 Terminal value, 67, 91, 99, 101, 127, 185, 194, 217, 219, 224, 234, 235, 334 TLC, 288, 346 Top-down, 29, 40, 41, 75 Trademarks, 81, 85–89, 91, 325 Trade secret, 89 Traffic, 91, 92, 305, 319, 323, 327, 328 Turnaround, 124 Turnover, 48, 122, 137, 148, 206, 270, 335, 358, 386, 387

U Unicorns, 229–232 Unlevered, 24–28, 54, 55, 74, 75, 151, 165, 166, 169, 198, 201, 265, 267, 268, 329, 332, 351, 353, 354, 379, 381, 382 V Valuation framework, 385, 386 Value, 1, 2, 9, 12, 21, 27, 29, 32, 35, 48, 54–57, 62, 66, 69, 73–75, 77, 82–84, 86, 87, 90, 91, 94, 95, 97, 99–102, 104, 114, 119, 124, 126, 127, 131, 133–138, 144, 146, 148, 163, 165, 166, 170, 173, 180, 183, 185–196, 199–201, 203–206, 208–210, 214, 220, 221, 223, 225–229, 232, 233, 235, 249, 250, 261–265, 267, 268, 270, 274, 285, 288, 291, 297, 301, 303, 305, 306, 311, 316, 321, 323, 325, 327, 329, 331, 332, 334–337, 345, 348–350, 352–354, 357–359, 366, 369, 378, 380–384, 387–389 Value chain, 70, 93, 104, 107, 108, 125, 148, 218, 229, 245, 250, 290, 301, 302, 316, 345, 346, 369 Value co-creation, 48, 277, 288, 290 Variable costs (VC), 14, 59–63, 66, 68, 128, 147, 274 Venture capital (VC), 1, 2, 41, 113–117, 119–121, 124, 125, 131, 152, 162, 175, 185, 214, 215, 217, 218, 221, 223, 224, 228, 229, 233, 263, 264, 271, 272, 306, 318, 323, 334, 347, 350, 364, 378 Video-on-demand (VoD), 3, 309–311, 319

INDEX

Volatility, 13, 33, 155, 185, 198, 229, 247, 265, 329, 351, 379 Volume, 60, 66, 94–96, 143, 147, 251, 255, 258, 290 W Warrant, 119, 184

401

Website, 91, 92, 310, 327, 328 Weighted average cost of capital (WACC), 54–56, 66, 76, 125, 144, 163, 167, 168, 186, 199, 200, 221, 249, 265, 266, 272–274, 313, 331, 334, 352, 355, 366, 380, 383