The Alpha Formula: High Powered Strategies to Beat The Market With Less Risk 9780578530840, 9781792313394

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Copyright © 2019, Chris Cain, Larry Connors, and Connors Research, LLC. Published by The Connors Group, Inc., 185 Hudson St., Suite 2500, Jersey City, NJ 07311 ALL RIGHTS RESERVED. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher and the author. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that the author and the publisher are not engaged in rendering legal, accounting, or other professional service. Authorization to photocopy items for internal or personal use, or in the internal or personal use of specific clients, is granted by The Connors Group, Inc., provided that the U.S. $7.00 per page fee is paid directly to The Connors Group, Inc., 1-973-494-7311. Publisher’s Notice The publisher has provided this eBook to you without Digital Rights Management (DRM) software applied so that you can enjoy reading it on your personal devices. This eBook is for your personal use only. You may not print or post this eBook, or make this eBook publicly available in any way. You may not copy, reproduce, or upload this eBook except to read it on your personal devices. Copyright infringement is against the law. If you believe the copy of this eBook you are reading infringes on the author’s copyright, please notify the publisher at [email protected]. ISBN 978-0-578-53084-0 EBook ISBN 978-1-7923-1339-4 Printed in the United States of America.

Disclaimer The Connors Group, Inc., Connors Research, LLC, Chris Cain, and Laurence A. Connors (collectively referred to as “Company”) are not investment advisory services, nor registered investment advisors or broker-dealers and do not purport to tell or suggest which securities or currencies customers should buy or sell for themselves. The analysts and employees or affiliates of Company may hold positions in the stocks, currencies or industries discussed here. You understand and acknowledge that there is a very high degree of risk involved in trading securities and/or currencies. The Company, the authors, the publisher, and all affiliates of Company assume no responsibility or liability for your trading and investment results. Factual statements on the Company’s website, or in its publications, are made as of the date stated and are subject to change without notice. It should not be assumed that the methods, techniques, or indicators presented in these products will be profitable or that they will not result in losses. Past results of any individual trader or trading system published by Company are not indicative of future returns by that trader or system, and are not indicative of future returns which be realized by you. In addition, the indicators, strategies, columns, articles and all other features of Company’s products (collectively, the “Information”) are provided for informational and educational purposes only and should not be construed as investment advice. Examples presented on Company’s website are for educational purposes only. Such set-ups are not solicitations of any order to buy or sell. Accordingly, you should not rely solely on the Information in making any investment. Rather, you should use the Information only as a starting point for doing additional independent research in order to allow you to form your own opinion regarding investments. You should always check with your licensed financial advisor and tax advisor to determine the suitability of any investment. HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN INHERENT LIMITATIONS. UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING AND MAY NOT BE IMPACTED BY BROKERAGE AND OTHER SLIPPAGE FEES. ALSO, SINCE THE TRADES HAVE NOT ACTUALLY BEEN EXECUTED, THE RESULTS MAY HAVE UNDER- OR OVERCOMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN. The Connors Group, Inc. 185 Hudson St. Suite 2500 Jersey City, NJ 07311 Connors Research, LLC 185 Hudson St. Suite 2500 Jersey City, NJ 07311

CONTENTS Acknowledgments Dedication Chapter 1 If I Lose This Account Chris, I’m Going to F***ing Kill You!!! Chapter 2 Welcome to The Alpha Formula Section 1 – Trend Following, Mean Reversion, and Why Prices Behave the Way They Do Chapter 3 Why Trend Following Works Chapter 4 Why Mean Reversion Works Section 2 – The Strategies Chapter 5 Rising Assets Strategy™ Chapter 6 Connors Research Weekly Mean Reversion Strategy Chapter 7 Connors Research Dynamic Treasuries™ Chapter 8 ETF Avalanches Section 3 – The Alpha Formula Portfolio Chapter 9 Introducing The Alpha Formula Portfolio Chapter 10 The Alpha Formula - A Better Solution to Money Management Section 4 – Appendix A1 Trend Following Reduces Risk A2 RSI - Definition, Calculation and Historical Edges A3 Additional Notes A4 More Books from Larry Connors A5 Learn to Program in Python Directly From Chris Cain In Only 10 Hours A6 Credits and Additional Acknowledgements About the Authors

ACKNOWLEDGMENTS As with all of the books we have published over the years, this one required a team of dedicated individuals to get it from a raw manuscript to the finished product you now hold in your hands. We’d like to send a special thank you to the following people who assisted in the creation of this book. Special thanks to Drew Markson, Brett Nelson, Tim Kiggins, Ken Levey, Marcus Dunne, Ernie Chan, Mike Hanyok, Dion Chu, Glenn Williams Jr. and Vishal Mehta. On the production side, we’d also like to acknowledge Danilo Torres, Kyle Bowes, Studio02, Jose Pepito Jr., and Susan Tyler. I would like to thank the best parents in the world, Gary and Antonette Cain, for their continued love and support. Thank you to my Aunt, Lisa DeSimone, for the constant career guidance and for being a great role model. Thank you to my beautiful wife, Lindsay Cain, for being my loving partner, best friend and for always pushing me to be better. Finally thank you to all my ex co-workers, clients and friends at both GX Clarke and Co. and INTL FC Stone.

DEDICATION In memory of Mike Duke, a great trader and an even better friend. -Chris Cain In memory of Joseph Simonetty, a great husband, father, father-in-law, grandfather, great grandfather, and my friend. -Larry Connors

CHAPTER 1

If I Lose This Account Chris, I’m Going to F***ing Kill You!!! 2008 was a year when the world turned upside down. It was a year that showed everyone that markets are not only driven by supply and demand; they’re driven by human emotion. Human emotion, and the subsequent actions (buys and sells) driven by emotion, helps create identifiable market behavior. We can then use this market behavior to profit and capture Alpha. Throughout this book, we’re going to show you how to do just that. What you’ll see is that investors’ behavioral biases lead to predictable market behavior which can then be used to produce strong risk-adjusted performance and Alpha.

Think Markets Are Rational? Think Again! On October 15, 2008, at the ripe old age of 22 years old, Chris was in the epicenter of one of the largest one day moves in US Treasury bond history. Markets around the world were collapsing and the world was fleeing into the safety of US Treasuries. On the one side of the trade was one of the largest and most sophisticated asset management firms in the world. On the other side was Chris, with a total of four months of institutional trading experience. If this was a bet, Vegas wouldn’t even post the odds. But it wasn’t a gambling event. It was an event with $200 million at stake! Here’s what happened...

“They’re Paying Interest to Park Their Money???” It was October 2008 and the global financial crisis was kicking into high gear. It was only a few short weeks earlier that Lehman Brothers stunned Wall Street and the world by declaring bankruptcy.

Fannie Mae and Freddie Mac, Congress-created, government-sponsored enterprises, were essentially taken under government control. Congress had to pass a huge bailout bill for the collapsing financial systems, known as TARP (Troubled Asset Relief Program), to stabilize the entire financial system.

The Trade Chris will now take you inside this once in a generation event... Here I was, nervously sitting in front of four computer monitors, watching the destruction of global markets firsthand. Five months earlier I was crashing frat parties for entertainment. Today I was responsible for $500 million of trading capital. As luck would have it, I was filling in for the senior treasury bond trader who was out that day. Around midday, with the stock market in a freefall, an enormous client came in and asked us to offer $200 million worth of one-month treasury bills. They wanted to buy the bills and inquired at what price (yield) I was willing to sell it to them. For me and our firm, $200 million was a huge order. In fact, it would be a large order for even the largest firms on Wall Street. Being the four-month junior trainee that I was, I unsuccessfully attempted to remain calm. I nervously went through the thought process I was taught by my bosses. Priority number one was protecting the firm - especially as panic had ensued throughout Wall Street. A recent email, sent to the trading desk by the head of trading earlier that day, reiterated this point - namely that the financial system seemed to be melting down and protecting our neck is priority number one. That being said, this wasn’t just any client that came in looking for an offer. This was the number one client of the firm and one of the largest money managers in the world! They were obviously seeking the safety of short-term US Treasuries in an attempt to protect themselves from the relentless sell-off. The panic in the market over the last two months was painfully clear. Risk assets continued to sell-off at an alarming pace, with the US dollar and US treasury bonds catching a huge safe haven bid, sending the prices of the bonds higher (yields lower). When I looked at the screen to offer the one-month treasury bill, it was trading at an unbelievable 5 basis points (0.05%). This meant that US Treasuries were yielding only 0.05% - a level never seen before in modern times.

I checked my position. I was currently long only about $2 million. If I was to sell $200 million to this customer, I would have to short the Bills, at least for a while, until I could find the Bills and cover my short position. Due to the excessive volatility, if prices rose while I was short, I’d cause serious damage to my three-decade old firm. I checked to see what rate we would have to pay for the right to borrow the bonds to sell short. The result was not what I was expecting. The cost to borrow was so high that we would be bleeding money the longer we held the short position, thereby increasing the risk of the trade even more. To make matters worse, the other large investment banks, the ones still standing at least, had been backing away from bids and offers all day as they were worried about their own future survival. Liquidity in the market was basically non-existent. I didn’t know if I would be able to even find the bonds to cover the short position in a timely manner. Taking all of this information into consideration, I took a deep breath and told the salesperson in charge of the account (a senior salesperson to boot) what our offer was: -10bps (negative .10%). That’s right, I just offered to sell one-month treasury bills to one of the largest and most sophisticated money managers in the world, and our best client, at a NEGATIVE YIELD! Think about that for a second. The buyer of the bonds would be PAYING for the privilege of parking their money in a safe place for a month. The salesperson looked at me like I was out of my mind. “Look kid”, he said, “I know you are new here and the world is coming to an end, but we can’t offer bonds with negative yields, especially to this client. Do you know how much money this client made the firm last year? We run the risk of looking like total a**holes if you give this offer!” My heart was racing. I summoned the courage to explain myself, telling the salesperson how much this will cost us to borrow and that I was not sure if we would be able to cover the short, as the other Wall Street dealers have not been providing liquidity. He would hear none of it. “We just can’t do that!” he screamed. “You’re about to lose me my best f***ing client!” This issue was rapidly escalated, at the salesperson’s request, to heads of trading and sales. We all got in a room and I, profusely sweating at this point, walked everybody through the math.

I went over where the bonds were trading in repo (the cost to borrow) and how I wasn’t confident we would be able to cover the short in a timely fashion. I was basically telling them we don’t want to do this trade. If it took a couple of days to cover the roughly $200 million short position this transaction would cause us, we would stand to lose a lot of money from the financing costs alone. After a heated discussion, it was determined that I made an appropriate offer. That -10bps (-0.10%) was a “fair” offer given the extreme circumstances. The general belief in the room was that the client was going to look for and get a better price away from us. We even went through what the salesperson was going to say to the client, explaining in great detail why our offer is -10bps, and letting them know that they will most likely see a better offer away from us. The plan was to give the offer and save face by explaining the situation. At that point the client would do the trade with somebody else and hopefully not hold it against us for too long. To put it mildly, the salesperson in charge of the account was not happy.

“If I Lose this Account Chris, I’m Going to F***ing Kill You!!!” We walked out of the meeting room and I went back to my seat, sick to my stomach from nerves. The salesperson was clearly upset with me. He raged at me that if he lost the client as a customer, he was “going to f***ing kill me!” Being that this person was 15 years older than me and one of the top producers in the firm, he was not a guy you want to have on your bad side. I heard him call the client and meekly mumbled my offer, -10bps. I then heard him begin to explain himself and was quickly cut short... “Excuse me,” I heard him say. Oh no, my heart sunk. This client definitely just told us to go f*ck ourselves. My mind quickly went to the worst-case scenario - we lose the client forever and I get fired - my tenure as a Wall Street trader lasting all of four months, another victim of the great financial crisis. The salesperson stood up and put his phone on his shoulder. The whole trading floor stopped at this point as word got around as to what was happening. Here we go, I thought. I was about to be reamed out in front of everybody. Instead the salesperson looked at me, and to all of our shock, said, “Chris, you’re done!” The order was filled.

Heightened Emotions Create Market Inefficiencies

If markets were truly efficient, that trade, and likely hundreds of thousands of trades during that period, shouldn’t have happened. That day, in the height of the financial crisis, we sold $200 million worth of one-month Treasury bills at a negative yield. Not only that, one of the largest, most sophisticated money management firms in the world accepted a negative interest rate on a debt instrument as they looked to avoid further losses and were clearly herding into safe assets. I never looked at markets the same way again. The panic and herding instinct of investors was so strong that this firm (and in my opinion, one of the greatest, most brilliant money management firms ever built) was willing to PAY for the right to park their money in a safe place. I learned a valuable lesson that day - most notably that herding and other behavioral biases can cause even the most sophisticated investors in the world to act irrationally at times. This realization guided my thought process and approach to the market for years to come. Little did I realize at the time, but that day would be the seed for The Alpha Formula.

CHAPTER 2

Welcome to The Alpha Formula The Alpha Formula is a combination of: • Quantitative Investment Strategies • Behavioral Finance, and • Applying First Principles™ to portfolio construction In this book we will touch on a handful of the most pervasive behavioral biases that befall investors and how they can lead to certain, predictable, repeatable market behavior. We will then use this market behavior to construct four minimally correlated trading strategies, complete with rules and historical test results. Each strategy attacks a First Principle, or self-evident truth, about the market. This results in strategies that are inherently different and uncorrelated. Finally, combining our four strategies leads us to The Alpha Formula portfolio.

What are First Principles™? First Principles are basic, self-evident truths. They are the truths that are obvious, will not invoke controversy, and that everybody can agree on. First Principles thinking is a way of attacking a problem by addressing these basic, selfevident truths. This type of thinking has been used by everyone from the philosopher Aristotle to Thomas Edison to Nikola Tesla. It is behind some of the greatest technological breakthroughs in history. First Principle thinking often unleashes creativity and leads to novel solutions to difficult or complex problems. It is a way of reverse engineering complex issues, often breaking them down into simple axioms and “truths” which can then be tackled individually.

First Principles Applied to the Markets When we think about the complex global financial markets, there isn’t a shortage of

beliefs, schools of thought and opinions about where the market is going or the best way to go about investing your money. Many investors naturally “think by analogy,” or take widely held beliefs, assumptions or best practices, when we look to create a framework to solve complex problems. In this book, we look to avoid such thinking and, instead, break down the complex problem of portfolio management into simple, self-evident truths, or First Principles. Our three First Principles for the markets are simple and are as follows: 1. Markets Go Up 2. Markets Go Down 3. Markets Go Through Times of Stress These rather obvious statements are self-evidently true and are very difficult to argue against. For decades, portfolios have basically been built the same way, applying things like subjective fundamental analysis, compelling stories, static asset allocation and dozens of other techniques that are not First Principles. None of these techniques are truths! Markets going up, down and through times of stress are self-evident truths and can be used to build a suite of robust trading strategies. Starting with these truths is the first step in building portfolios that have the potential to perform in many different market environments for a sustained period of time. Let’s now expand on each First Principle and comment on the tendencies within each principle.

First Principle #1 - Markets Go Up We know that markets around the world, especially equity markets throughout history, have tended to trend higher for extended periods of time. Most bull market moves last several years and are characterized by relatively low volatility. We want to have strategies in place to take advantage of this rising tide.

First Principle #2 - Markets Go Down Not only do prices of assets rise, they also decline. This bearish phase is typically shorter in duration compared to bull moves and often more violent. This can empirically be seen by measuring volatility during bear phases, which we will document later in the book. We want to have strategies in place to take advantage of times when markets go down.

First Principle #3 - Markets Go Through Times of Stress The final First Principle is that market participants tend to panic at times, and display severe bouts of risk aversion, causing the markets to go through periods of stress. These times of stress lead to an increase in demand for assets that are perceived as “safe havens.” One of the most consistent safe haven assets throughout history has been US Treasury bonds. Ideally, we want part of our portfolio to always be allocated to these “safe haven” assets, allowing us to benefit from times of market stress.

Building a Portfolio with First Principles in Mind Our goal is to design a portfolio of strategies where at least one strategy in our portfolio will do well during each of the First Principles above. This includes strategies that perform in prolonged bull markets, strategies that participate on the short side when markets decline, and strategies that benefit from safe haven flows when markets go through times of stress. By designing a portfolio in this manner, we also take advantage of the holy grail in investing - diversification. A lot of lip service is given in the investment industry regarding the importance of diversification. Unfortunately, many “diversified” portfolios fail to provide the protection they claim during extreme market events such as 2008. By designing strategies with First Principles in mind, our strategies will be inherently different from one another, allowing us to take full advantage of diversification’s benefits. This leads to world class performance of the portfolio as a whole, which we will demonstrate in this book.

Designing Strategies Consistent with Historical Market Tendencies Not only do we want to have strategies that apply First Principles, or self-evident market truths, but we also want our strategies to take advantage of what we believe to be the nature of markets. Specifically, we want the time frames of our strategies to be congruent with historical market behavior. Based on decades of research, by us and many others, we view market behavior to be mean reverting over the short-term (days to weeks) and trend following over the longer term (months to years). We expand on this in great detail in the coming chapters, complete with statistical evidence and behavioral explanations.

Understanding Your Source of Alpha is Important Another theme of this book is that it is not enough for a strategy to “work” in a backtest, we need to know why it works. We aim to understand where our Alpha is coming from and, more importantly, if the Alpha that provided strong historical results is persistent and likely to continue going forward. This guards against results being data mined. For the explanation about why these strategies have worked in the past and why they should continue to work going forward, we lean heavily on behavioral finance. Of the two market tendencies outlined above (short-term mean reversion and long-term trend following), we view human behavior as a primary driver of both. We will expand on the behavior that leads to these market tendencies in the coming chapters but let’s look at a brief description. 1. We view short-term mean reversion to be driven by the tendency for markets to move “too far too fast” in the short run, typically over a couple of days to a couple of weeks. These moves have a tendency to then mean revert, especially in equity markets. This tendency for markets to move “too far too fast” is at least partially driven by short-term fear / greed, including loss aversion on the downside, and fear of missing out (FOMO) on the upside. This can often cause herding behavior among market participants. 2. We view the tendency for markets to have longer-term, trend following characteristics to also be rooted in human behavior. This behavior can best be summarized by initial underreaction to a news item or catalyst, followed by a positive or negative feedback loop as markets begin to trend higher or lower, followed by a longer-term overreaction as recency bias, greed and herding behavior sets in. This long-term overreaction tends to take trends much further than what is “rationally” warranted. We explain this behavioral cycle in detail in Chapters 3 and 4, because once you understand the behavioral aspects, you’ll better understand how to construct strategies and portfolios to take advantage of the built-in edges and Alpha they offer.

The Holy Grail Diversification - the only free lunch in investing. Most traders and investors have heard this before. Almost everyone qualitatively understands that you should diversify your investments. This makes sense, after all. “Don’t put all your eggs in one basket,” is an intuitive and simple idea to wrap our minds around.

What is often underappreciated, however, is the true quantitative effect of having a basket of truly diversified markets or strategies. Simply put, if you have a portfolio of diversified strategies, you will realize the average weighted returns of each strategy individually, but the risk of your portfolio as a whole will go down markedly. The amount of risk reduction will be a function of how uncorrelated each strategy is to one another. The more uncorrelated the strategies are, the larger the risk-reducing power of diversification will be.

Be Like Ray! You only have to look at Ray Dalio, the billionaire founder of the largest hedge fund in the world - Bridgewater Associates. In his recent book “Principles,” Mr. Dalio speaks of the power of diversification, which he calls the “holy grail” of investing. Mr. Dalio asked one of his junior associates to put together a chart showing how the volatility of a portfolio would decline, and its return/risk metrics would increase, by incrementally adding investments with different correlations. Below is an excerpt from “Principles,” outlining Mr. Dalio’s epiphany regarding the power of diversification: “That simple chart struck me with the same force I imagine Einstein must have felt when he discovered E=MC2: I saw that with fifteen to twenty good, uncorrelated return streams, I could dramatically reduce my risks without reducing my expected returns.” Mr. Dalio went on to call this insight the “Holy Grail of investing.” Mr. Dalio went to say, “On paper, this new approach improved our returns by a factor of three to five times per unit of risk, and we could calibrate the amount of return we wanted based on the amount of risk we could tolerate. In other words, we could make a ton more money than the other guys, with a lower risk of being knocked out of the game…” “The success of this approach taught me a principle that I apply to all parts of my life: Making a handful of good uncorrelated bets that are balanced and leveraged well is the surest way of having a lot of upside without being exposed to unacceptable downside.” Here is the chart Mr. Dalio referred to in the quote. We believe this is one of the most powerful charts in all of finance. 2.1. Uncorrelated Return Streams Decrease Risk

Stare at this chart for a while, it is very powerful. The Y (vertical) axis shows the standard deviation of a hypothetical portfolio, a proxy for risk. The X axis (horizontal) shows the number of different return streams in the portfolio. “Return streams” here could be different asset classes, different trading strategies, etc. The declining lines on the chart show what adding return streams with different correlations together does to the overall risk of the portfolio. A couple of key takeaways from the chart: 1. The more uncorrelated the return streams are to each other, the more dramatic the risk reducing nature of diversification is. Notice how the line representing 0% (no correlation) reduces risk much faster than the line representing 60% correlation does. 2. The first couple of return streams added has a much larger risk reducing effect compared to return streams added later. A diminishing effect is observed as more return streams are added. Notice how the lines decline markedly over the first four assets but then flatten out after about five or six.

The takeaway? True, elite trading performance, as measured by risk-adjusted returns, can be achieved by having only a few truly diversified assets or trading strategies! This is a very powerful concept - one that we put into practice in the pages ahead.

Looking Ahead In Chapters 3 & 4, we will go over both the tendency for markets to trend over the longterm and mean revert over the short-term. We will show quantitative evidence and comment on the behavioral biases that lead to these market tendencies. We will look to profit from these tendencies later in the book. You will learn, from decades of research, that one of the great frontiers yet to be conquered is the use of behavioral finance supported by quantitative analysis. Unfortunately, some of behavioral finance has been influenced by social scientists who didn’t rely upon large sample size research to support their claims. For decades, too many of these people relied upon non-quantified nonsense such as taxi driver stories of how much money they’re making in the market, or how many times an investment theme appeared in a newspaper or on a magazine cover. Those days have fortunately been replaced by researchers applying hard science and statistics to support their theories. What you will see throughout this book is, by taking a quantified approach and marrying it with the best of behavioral finance, Alpha can be realized. Chapters 5 & 6 apply the First Principle that markets go up. You will learn our Rising Assets Strategy™ which is designed to be long global assets that are rising. You will also learn the Connors Research Weekly Mean Reversion strategy which buys stocks that have pulled back in a bull market, taking advantage of the tendency for such stocks to revert to their mean. Combining these two strategies provides you with multiple ways to profit from times when markets go up. Chapter 7 focuses on the First Principle that markets go through periods of stress and introduces our Connors Research Dynamic Treasuries™ strategy. This strategy trades US Treasury bonds, a consistent safe haven asset. Our Connors Research Dynamic Treasuries strategy, as the name suggests, dynamically adjusts the duration of our portfolio based on market conditions and provides constant exposure to US Treasuries. This ensures that we benefit from safe haven flows when markets go through times of stress. Chapter 8 is predicated on the First Principle that markets fall. Through the use of a global universe of ETFs, our ETF Avalanches strategy triggers in bear markets, climbing

aboard ETFs that are in longer-term declines. Chapter 9 is “The Alpha Formula.” We combine the four strategies above and place them into a systematic multi-strategy portfolio. With First Principles in mind, capital is allocated to the times when markets go up, down and through times of stress. The end result is a non-optimized portfolio that looks like this: 2.2. Alpha Formula Portfolio Results - No Leverage

Data and analysis tools provided by Quantopian.com.

Applying 50% leverage to The Alpha Formula portfolio looks like this: 2.3 Alpha Formula Portfolio Results - Leverage

Data and analysis tools provided by Quantopian.com.

A Quick Peak at the Key Performance Results 1. Higher Returns -The annual returns for the Alpha Formula portfolio, both nonlevered and levered versions, are higher compared to both SPY and 60/40. 2. Lower DrawDowns - The maximum drawdown for the non-leveraged version was only -7.1%. This compares to -55.2% for SPY and -34.7% for 60/40. This preservation of capital changes lives! 3. Less Risk - These higher returns are accomplished with significantly less risk, both in the form of standard deviation and maximum drawdowns. 4. Higher Sharpe Ratio - With higher returns and lower risk, naturally the Sharpe Ratio is significantly higher. Our Alpha Formula portfolio shows Sharpe Ratios of 1.29 for the non-levered version and 1.24 for the levered version compared to 0.35 for SPY and 0.53 for 60/40. 5. Significant Alpha - The annualized Alpha versus SPY, the core concept of this book, shows 6.5% for the non-leveraged version and 9.5% for the leveraged version. This is impressive in a world where many believe Alpha doesn’t exist! 6. Low Beta – The Beta for our Alpha Formula portfolio is close to 0 (coming in at 0.07 to be exact). This shows these returns are not dependent on, nor correlated to, the US stock market.

Summary To summarize, The Alpha Formula is about: • Attacking portfolio management with First Principles. • Developing diversified strategies that profit from market tendencies in the compatible time frames - short-term mean reversion and long-term trend following. • Having a logical explanation as to why these market tendencies exist - rooted in inherent human behavior. • Putting uncorrelated strategies together into a portfolio to achieve consistent, market-beating returns, low volatility, and significant Alpha. This is The Alpha Formula. Enjoy!

SECTION 1 Trend Following, Mean Reversion, and Why Prices Behave the Way They Do

CHAPTER 3

Why Trend Following Works Yes, Markets Do Trend Identifying

and following trends in the marketplace is perhaps the oldest trading strategy. Practitioners have been measuring and following trends profitably for at least the last 100 years, and likely long before that. The classical British economist David Ricardo is credited with saying, “Cut short your losses, let your profits run on” over 200 years ago, which is the basic tenant underlying many trend following strategies today. Ricardo used this philosophy to accumulate great wealth in his life. Trend following philosophy dominated the classic trading book “Reminiscences of a Stock Operator” by Edwin Lefèvre, which was the biography of trader Jesse Livermore. Livermore is quoted as saying, “the big money is not in the individual fluctuations but in the main movements — that is, not in reading the tape but in sizing up the entire market and its trend.”

Real World Results There are many decades of examples of practitioners identifying and riding price trends to great profits throughout history. One of the earlier practical users of trend following ideas was Richard Donchian. Donchian was a trader, trend follower and the founder of the first publicly managed futures fund, Futures, Inc., established in 1949. He is credited as the creator of the managed futures industry, an industry that now has over $300 billion in assets under management. Donchian also began writing a weekly commodity “Trend Timing” letter which became widely followed. He can be credited with making popular a number of trend following ideas, such as the use of two moving averages, as well as breakout type trading strategies (buying new highs and selling new lows). Many others have used trend following and longer-term momentum principles to accumulate great wealth for themselves and their investors. From John Henry, billionaire owner of the Boston Red Sox and the Liverpool Football Club, to David Hardings, billionaire founder of Winton Group, trend following has produced great

performance and handsome profits for managers of the strategy as well as their investors for numerous decades.

Academic Results One of the first academic efforts to empirically investigate if past performance could in fact be predictive of future results was performed by Alfred Cowles and Herbert Jones back in 1937. Using stock market data from 1920 to 1935, Cowles and Jones concluded that the strong stocks of the previous year tended to outperform going forward, and the weakest stocks of the preceding year tended to underperform on a go-forward basis. Academic interest in studying the tendency of markets to trend waned in the decades that followed, as the efficient market hypothesis became the dominant model in academic finance. Using past performance of an investment to forecast future performance cuts right to the heart of the efficient market hypothesis, and as such was generally ignored by academia up until the early 1990s. Academic treatment of price momentum took a significant step forward with the publication of the seminal academic study “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency” by Jegadeesh and Titman in 1993. In this paper, the professors showed that buying stocks that have performed the best over the last 12 months tended to continue to outperform in the subsequent months ahead. Conversely, stocks that have performed the worst over the preceding 12 months tended to continue to underperform. These findings were consistent with the results that Alfred Cowles and Herbert Jones found some 60 years earlier! The rigor and statistical significance of the findings, helped by computer technology, finally caused academics to stand up and take notice. Around this same time, Clifford Asness, who would go on to found and run Goldman Sachs Global Alpha Fund followed by establishing his own hedge fund - AQR, used similar price momentum in his PhD thesis. His advisor, Eugene Fama, no less than the father of the efficient market hypothesis, had to take notice of this observation as the data was and continues to be overwhelming. Since these early academic research efforts in the early / mid 1990s, momentum research has been given the full academic treatment, with countless studies and papers confirming what was observed by Jagadeesh and Titman in 1993, namely that past outperformance tends to lead to future outperformance. Price momentum has since been shown to be consistent and robust across different asset classes - including equities, fixed income, commodities and currencies. Momentum has been effective when applied both to individual securities as well as broad indexes.

Some standout academic work on longer-term momentum investing includes: • “Time Series Momentum” - Moskowitz, Ooi, Pedersen • “A Century of Evidence in Trend Following Investing” - Hurst, Ooi, Pedersen • “Harvest Risk Premia Through Dual Momentum” - Antonacci • “A Quantitative Approach to Tactical Asset Allocation” - Faber • “Value and Momentum Everywhere” - Asness, Moskowitz, Pedersen • “Two Centuries of Multi-Asset Momentum” - Geczy, Samonov

Research Highlight: A Century of Evidence on Trend Following Investing Researchers from AQR published a 2014 paper titled “A Century of Evidence on TrendFollowing Investing.” In the paper, the researchers constructed a simple, equalweighted trend signal comprised of one-month, three-month and twelve-month time series momentum measures and went long or short each market based on past performance. The researchers tested this simple strategy on 67 markets across four major asset classes (29 commodities, 11 equity indices, 15 bond markets and 12 currency pairs) from 1903 to 2013. The conclusion from this study was that trends have been a consistent feature of the markets for over 100 years. Specifically, annualized returns of the strategy in the paper were 20% by combining momentum and implementing volatility targeting over the entire period tested (over 100 years). More impressively, this return was realized with roughly half the volatility of equities alone, with an annual standard deviation of 9.9%. Performance was consistent throughout this 100-year-plus backtest, including being positive every decade. Here is the performance summary from the AQR study: 3.1. Trend Following Performance by Decade, 1903-2012

A Century of Evidence on Trend-Following Investing, Brian Hurst, Yao Hua Ooi, and Lasse Heje Pedersen (2017), The Journal of Portfolio Management, 44 (1), 15-29. Thanks to AQR Capital Management for facilitating permission to feature this information.

The above backtest includes many monetary policy regimes, as well as times of high and low inflation (and stagflation). It also includes dramatic historical events such as the Great Depression, multiple wars, the inflationary 1970s, the tech bubble of the late 1990s, the Global Financial Crisis of 2008, etc. This paper, and many others like it, provides a strong argument that trends have been inherent in the financial markets since the beginning.

Above the 200-Day Moving Average - Higher Returns and Lower Volatility Let’s further investigate the characteristics of markets that have been trending up versus down in the recent past. This will advance our understanding of certain market environments, and ensure we are designing strategies consistent with historical market behavior. A ubiquitous long-term trend following signal, that is well known and widely used, is the 200-day simple moving average. We will use this unoptimized trend following signal to separate markets into generic “bull” or “bear” phases and look at some characteristics of each market environment. Later in the book we’ll introduce an equally

powerful trend following filter we developed in order to further identify market regimes. Below are summary statistics for days when the Wilshire 5000, a representation of the entire US stock market, was above and below its 200-day moving average. This data spans from 1980 to December 2018 (9,761 trading days) using data from the Federal Reserve Banks of St. Louis (FRED). 3.2. Wilshire 5000 Performance Above and Below the 200-day Moving Average, 19802018

Wilshire Associates, Wilshire 5000 Price Index [WILL5000PR], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/WILL5000PR, June 17, 2019.

A few things especially stand out here. Notice that the average daily returns when the market has been trending higher (Days > 200-day SMA) is significantly higher than when the market was trending lower (Days < 200-day SMA). Specifically, the average daily percent change for the 7478 days when the closing price was above the 200-day moving average was 9bps (0.09%) while the average percent change for the 2283 days when the market was below its 200-day moving average was -10bps (-0.10%). The fact that the average daily return for the market has actually been negative when the market has been trending lower in the past is extraordinary. Remember this is on an index that has grown over 6,450% during this time frame! Another, perhaps more important, observation is the large difference in volatility. We consistently find that uptrending markets display significantly lower volatility and downtrending markets consistently display higher volatility. In this sample, the Wilshire 5000 had an average daily volatility (standard deviation of returns) of 0.80% during uptrending markets and 1.65% during downtrending markets. What we consistently see is a more volatile market environment when markets are declining. We believe investor behavior has a large role to play in this observation. This reduction in volatility and increase in returns when markets are trending higher

and increases in volatility and decreases in returns when markets have been trending lower is consistent when we look across other markets. 3.3. Wilshire US REIT Performance Above and Below the 200-day Moving Average, 1996-2018

Wilshire Associates, Wilshire US Real Estate Investment Trust Total Market Index (Wilshire US REIT) [WILLREITIND], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/WILLREITIND, June 17, 2019.

3.4. Wilshire US Small Cap Performance Above and Below the 200-day Moving Average, 1999-2018

Wilshire Associates, Wilshire US Small-Cap Total Market Index [WILLSMLCAP], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/WILLSMLCAP, June 17, 2019.

We observe largely the same phenomenon when inspecting US Real Estate (US equity REITs) and US Small Cap stocks, namely markets that have been trending higher in the past have significantly higher returns and lower volatility, with the opposite being observed when markets have been trending lower (lower returns and higher volatility). Going further, let’s take a look at this study applied to fixed income investments. Below is a look at this study applied to Long Duration (15 years+) US Corporate Bonds. We will use the Bank of America Merrill Lynch US 15+ year Total Return Index for this analysis.

3.5. ICE BofAML US Corporate Bonds 15+ year Performance Above and Below the 200-day Moving Average, 1999-2018

ICE Benchmark Administration Limited (IBA), ICE BofAML US Corp 15+yr Total Return Index Value [BAMLCC8A015PYTRIV], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/BAMLCC8A015PYTRIV, June 17, 2019.

Results here are constant when applied to another asset class. Notice the higher return and lower volatility of uptrending markets and lower return and higher volatility of downtrending markets. Quantifying market behavior, such as these studies, is an important step in trading system development, ensuring your rules are consistent with market behavior. We keep macro studies such as this in mind when designing trading strategies, ensuring that any rules we add are backed up by quantitative research.

Why Do Securities Trend? By this point, we should be satisfied with the plethora of historical quantitative evidence that trends are an inherent feature of markets and have been for hundreds of years. An obvious question that then emerges from the research is why is this the case? Why do security prices tend to exhibit persistence in performance? According to the efficient market hypothesis, past performance should tell us nothing about future performance, but research and real-world results strongly argue otherwise. Many professionals are now turning to behavioral finance for the explanation. The following is a quote directly from the AQR paper mentioned earlier regarding the role behavior plays in the tendency of the markets to trend: “A large body of research has shown that price trends exist in part due to long-standing behavioral bias exhibited by investors, such as anchoring and herding, as well as the trading activity of non-profit seeking participants, such as central banks and corporate hedging programs.”

Summary of Behavioral Biases Before we explain the behavior behind the tendency for markets to trend, we need to quickly define some of the most common behavioral biases that befall traders and investors. We will define them below. 1. Anchoring - The tendency for people to allow experiences or observations from the past to serve as anchors going forward. This anchor then affects how we view an item or situation and serves as a reference point for future value judgements and the subsequent actions taken thereafter. Example - Veteran traders, who traded bonds in the 80’s when interest rates were in the double digits, think rates are absurdly low today. In contrast, traders who began trading bonds in 2009, with rates near zero, view today’s 3% rates as absurdly high. This is an example of traders from different generations being influenced by past experiences, which creates anchors going forward. 2. Herding - The tendency for people do things because others are doing it, and not based on their own independent thoughts or information. Herding results in individuals mimicking the actions of the larger group, no matter how logical or rational such actions are. Example - Investors piling into a hot investment because others are doing it and not based on their own research or judgement. 3. Confirmation Bias - The tendency for people to give more credence to evidence that supports their already formed point of view and give less weight to evidence that rejects it. Example - Liberals watch CNN and MSNBC to get confirmation of their liberal views. Conservatives watch Fox News to hear their conservative perspective.

Related: Cognitive Dissonance Cognitive Dissonance refers to the mental discomfort felt when people hold contradictory beliefs. People naturally want to avoid this cognitive dissonance, and the pain it can cause, so they seek out evidence that agrees with their already formed opinion (confirmation bias). 4. Loss Aversion - The tendency for people to prefer avoiding losses to acquiring equivalent gains. Example - An investor who holds on to their losing trades for too long in an effort to avoid realizing the loss. Related: The Disposition Effect The disposition effect is the tendency for investors to hold on to losing trades too long and sell winning positions too soon. Loss aversion plays a central role in the disposition effect. Studies, such as Daniel Kahneman and Amos Tversky’s seminal 1979 paper “Prospect Theory: An Analysis of Decision under Risk,” shows that people prefer to gamble for the chance to avoid a sure loss and tend to take a sure gain if it is available. In the real world, this results in investors selling winners too early (realizing sure gains) and holding on to losers for too long (gambling to avoid a sure loss). 5. Relativity - The tendency for people to make constant comparisons and relative judgments, rarely viewing things in absolute terms. Example - Traders constantly comparing their results to others. Even though the trader is doing very well in the market, they are jealous of others doing substantially better. 6. Recency Bias - The tendency for people to assume that the near future will look like the recent past. Example - Observing past performance of a mutual fund or stock and assuming those results will continue into the future.

Summary of Investor Behavior Underlying Price Trends The Story of “Bob”

The behavioral explanations as to why financial securities tend to trend can best be summarized as an initial underreaction to a catalyst, which often is contrary to the established market narrative, caused in large part by anchoring and confirmation bias. This is followed by a positive feedback loop as the trend gets going, as investor’s anchors / biases begin to change and higher (lower) prices lead to better (worse) fundamentals as well as better(worse) perceptions by market participants. This eventually leads to delayed overreaction, as investors herd into the security or market following the observed trend. This last phase can vary greatly in size and scope - from mild trends to extended and at times extreme market bubbles, for example the internet bubble in the late 1990s. We will explain this in a simplified “story” about a fictional investor, who we will call “Bob.” Bob goes through a somewhat typical cycle of initial underreaction and delayed overreaction as the market and the economy, in our story, transitions from “bad” to “good.” While, obviously, all markets and situations are different, and thus prompts somewhat different reactions from investors, the following simplified framework is still constructive to understand.

Phase 1 - Initial Underreaction Behavior Biases - Anchoring, Confirmation Bias Our fictional story starts in the midst of a bear market, with stocks declining over the previous two years. News about the economy is consistently negative. The president just lost his reelection bid, with the lousy economy and horrible stock market performance largely to blame. Stocks are currently ~50% off their highs of a few short years ago. The job market is terrible, with double digit unemployment and seemingly little hope at a turnaround. Our fictional character, Bob, personally knows many talented people that have been out of work for months on end and are barely scraping by. Bob is feeling lucky. About a year ago, he inherited a large amount of money that he is now looking to deploy in the market. A stock market observer his whole life and given the constant negativity regarding the stock market and the economy as a whole, Bob decided to wait to invest the funds. This proved to be a prudent decision, as stocks continued to sell-off since he obtained his inheritance. Feeling smart, Bob subconsciously seeks out news that agrees with his decision, as this causes emotional pleasure and reaffirms to himself that it was the right decision not to invest the funds as soon as he inherited the money. Reading about a potential for a coming great depression on a message board, Bob begins to think that maybe the stock

market isn’t the right place to invest his newfound wealth no matter how long the market declines, or how cheap it gets. The narrative of a bad economy, accompanied by a stock market in a freefall, dominates the public mood. Slowly but surely, over the next few months, some marginally better news regarding the economy starts to be reported. While still terrible numbers compared to historical averages, the economic numbers are relatively improving, with unemployment beating the consensus and companies narrowly beating Wall Street’s pessimistic earnings forecasts. This is hardly noticed, however, as investors view most news through a lens of “this economy is bad, the stock market is for suckers.” The market starts to go up, which everybody assumes is just a bear market bounce before further pain is administered. Bob doesn’t buy into this “fake, central bank manipulated” rally. “This is the calm before the next storm,” he thinks, as a double-dip recession seems imminent. Many of us lived through this in 2009. The S&P 500 experienced a 55% drawdown from its high in 2007 to its low in the first quarter of 2009. In spite of the fact that there were signs things were beginning to stabilize, the overwhelming majority of the news was painted as negative and the rumors of pending doom were even greater. In early March 2009, I (Larry) got a call after the close from a well-established hedge fund manager I had a business relationship with. He told me he was hearing that a meltdown was going to occur overnight and that the financial markets in the US may not be able to open the next day! The futures were also reflecting this rumor. On my drive up the West Side Highway, I remember telling my driver to look at the Empire State building on our right because tonight may be the last time we ever see it lit up. The takeaway is that at market bottoms, everything looks terrible because everyone is anchoring to the recent past.

Behavioral Analysis We start our story with an objectively bad economy and worse stock market. As such, the financial media is constantly negative regarding the future prospects for stocks. Bob, who fortunately hesitated in investing his newfound funds, is happy with himself. He pats himself on the back for avoiding further wealth destruction that would have resulted from dipping his toes into the water. Bob, like many others in this scenario, is displaying anchoring bias as he anchors to the prevailing narrative, specifically that the economy is bad, and the stock market is likely

to continue to decline. Bob is guilty of confirmation bias as well, as he seeks out stories and opinions about the continuation of the stock market declines and ignores or completely dismisses any evidence of the economy getting relatively better. This is an easy, and at times subconscious, thing to do as this feels good emotionally for Bob. Many other investors are like Bob, as the prevailing wisdom of the bad market and economy are hard to ignore.

Implications for Market Behavior This anchoring to the prevailing narrative and filtering all new information through the lens of the anchor, causes prices to initially underreact to news that the economy is getting relatively better. Many market participants, like Bob, seek out evidence that supports the view that the economy is “bad”, and the stock market is not a good place to invest money (confirmation bias). This serves to further entrench his viewpoint. As such, prices only slowly begin to trend higher after the better than expected news, and don’t immediately jump to “fair value.” This initial underreaction begins the trend to higher prices, though at this point most market participants, Bob included, remain skeptical.

Phase 2 - Slow Grind Higher Behavior Biases - Anchoring, Confirmation Bias, Loss Aversion, the Disposition Effect A few more months go by and news continues to be positive, again in a relative sense, for both the stock market and the economy. The market has slowly grinded higher over this time. While Bob still believes that another, more violent decline is coming, he begins to entertain the possibility that the market could continue to go up, at least in the short-term. Still a skeptic, however, Bob views every small rally as suspect and prone to a sharp reversal. Bob dips his toes in the water and buys a few of his favorite companies with a very small percentage of his capital. Once in the trade, loss aversion dominates his behavior. The bear market and 50% stock decline are firmly in his mind as he gets into positions. If any of his positions show a profit, Bob is quick to take the profit for fear that the bear market will resume, and his profit will quickly disappear. If a position shows a loss, Bob thinks it prudent to give it some more time in an effort to avoid the painful loss-taking experience. Besides, it should come back to even, at which point he tells himself he will get out.

Behavioral Analysis

At this stage, anchoring and confirmation bias are still influencing Bob’s behavior, though he begins to entertain the idea that the market can continue to trend higher like it has over the past couple of months. He begins to buy some stocks but takes much less risk than he would in another market environment. Loss aversion dominates Bob’s psychology as he begins to invest again. Deep down, he is still very skeptical of the market and thinks a larger crash is likely coming in the near future. This desire to avoid the pain of a loss causes Bob to sell his winners early and hold onto his losers for a longer time period; this is the well-known disposition effect.

Implications for Market Behavior This disposition effect, caused by loss aversion, can result in securities slowly “grinding” higher. There is a natural ceiling on price moves in the short-term as skeptical and loss averse investors like Bob are biased to sell positions that show a profit for fear that this profit will soon disappear. As such, these trends typically unfold over a longer time period than people assume, slowly grinding higher in a stair step fashion. Most mechanical systems designed to follow price trends will be entering the trend around this time. In spite of the fact that the market rose 26.4% in 2009, I (Larry) attended an investment conference in early 2010 where the participants were wearing buttons with the words “Buy and Hold” crossed out with a red line. They were still displaying loss aversion and continued to anchor to 2008 because they refused to believe the rally was real.

Phase 3 - The Trend Accelerates Behavior Biases - Relativity, Recency Effect, Positive Feedback Loops Fast forward another couple of months. The trend is now squarely higher for the stock market. Economic numbers and company earnings estimates have routinely beaten consensus. People begin piling back into the market as their previous anchors begin to change. The prevailing feeling now is that “the coast is clear” and that the likelihood of a continuation of the uptrend is likely. Market “experts” are routinely going on TV, predicting further price appreciation. The rising stock market begins a positive feedback loop, as investors begin feeling better, or at least wealthier on paper. The wealth effect spurs an increase in consumer spending. The increase in consumer spending then shows up in corporate profits, causing more stock market buying. The improving economy and general “risk on”

mood also causes corporate bond spreads to tighten to US treasuries, making it cheaper for companies to raise money via debt financing (issuing bonds), which further accelerates earnings growth. Over this time frame, Bob misses most of the rally as he continues to anchor to his bad stock market narrative, one which is reinforced by “confirming” evidence Bob seeks out. The few times he did buy some stocks, those positions that showed a profit were quickly sold as the fear of the profit being taken away is overwhelming. Seeing the error in his ways, Bob’s opinion begins to change, and his previously held anchors start to fade into the distance. He can see now that the economy and the stock market are getting better. He projects the recent past into the future and assumes the uptrend will continue. Bob goes on Morningstar.com and begins to see the strong performance of many mutual funds and assumes this should continue as well, buying some of the best performing funds over the last couple months. He finally now deploys most of his money in a diversified, but conservative, mix of investments.

Behavioral Analysis As the market rally gets going, opinions and previously held anchors begin to change. Those previously anchored to the economy being “bad” most likely missed out on the majority of the rally of the last year, this includes Bob. Driven by fear of missing out on a continuation of the rally, most of these investors begin to invest again. The recency effect causes investors to extrapolate recent results (market rallying) into the future. Investors routinely invest in mutual funds which have done the best job of capturing the turnaround over the last year, chasing recent performance. The positive feedback loop of rising asset prices begins, both in consumer spending (consumers feel wealthier and thus spend more) as well as cheaper corporate financing (tighter corporate bond spreads). Finally, relativity effects market participants as investors begin to compare their investment results to the broad indexes as well as to their neighbors and peers. This encourages more risk taking, especially in investors that have missed out on the rally thus far. This behavior was especially seen in the first half of 2011, after the S&P had risen over 85% from its lows two years earlier. Slowly but surely investors returned to the market as their biases had clearly changed. They began taking more risk, as the 2008 anchor faded and was replaced with the new anchor - an 85% rise in market prices.

3.6. SPY Performance, March 2009 - March 2011

Implications for Market Behavior As more and more people’s opinions and previous anchors begin to change, more buyers come into the market lifting prices higher. The recency effect causes more buying, as investors project the future from the recent past. The rising stock market feedback loop begins to show up more in corporate profits as the public feels better about their economic future, both through the improved economy and job prospects as well as higher net worth as their holdings increase in price. This causes an increase in spending from the public, which results in larger corporate profits and higher share prices.

Phase 4 - All In! Behavior Biases - Herding, Relativity, Recency Effect, Fear of Missing Out, Greed The rally continues for several more months. New investment fads are taking the country by storm. There seems to be a story every week of a new millionaire crowned in this new super stock market. While Bob missed the first part of the rally, he has been invested in a diversified and relatively conservative portfolio over the last couple of months, enjoying some of the market gains. However, Bob can’t help but be disappointed. While he is showing some decent performance, it is nothing compared to the people he sees in the news who have

become newly wealthy following the latest investment fad and hot company / sector. Bob takes a look at the parabolic price chart of the new hot sector and does some quick mental math. If he would have put most of his money into that sector months ago, a decision that looks obvious in hindsight, he would have been on easy street by now! His brother-in-law, somebody he considers significantly dumber than himself, actually caught some of this rally. He can’t stop talking about it over Thanksgiving dinner. “This is really easy, like taking candy from a baby,” he tells Bob over turkey, “this technology will revolutionize how we do everything from communicate to make payments in the future!” Bob can’t take lagging behind his dim-witted brother-in-law anymore. The trend will obviously continue, he thinks, besides this time is different, this technology really will change our way of life. While Bob has enjoyed a decent rate of return since correcting his overly bearish stance months ago, there is much more money to be made by taking a concentrated position in the new sector. As such, Bob sells most of his conservative, diversified portfolio and invests heavily in the leading companies whose business is centered around the new technology. For both of our careers this has been the hardest phase because the human urge to move money from poorer performing assets into the rapidly rising assets is great. Why tie up money in cash or conservative assets when you’re only weeks away from making millions!

Behavioral Analysis Bob is displaying multiple behavioral biases in this phase, including relativity, recency bias, loss aversion (in the form of “fear of missing out”) and just plain old greed. Bob’s mind is hardwired to make consistent comparisons, and as such he compares the performance of his conservative diversified portfolio to the performance of the hot new sector. This causes Bob emotional pain as he has missed out on great riches. Relativity also comes into play as he compares himself to others. Bob can’t believe people with much lower IQs, like his brother in law, are doing so much better than he is. Recency bias causes Bob to assume the explosive performance of the hot new sector will likely continue into the future, further prompting him to jump in. Bob thinks one of the most dangerous thoughts in investing, “this time is different,” to justify chasing the hot new sector / investment.

These biases cause Bob, along with many others, to abandon their investing plan and pile into the best performing companies and sectors, convinced that there is easy money to be made.

Implications for Market Behavior In this stage of the cycle, relativity, loss aversion (fear of “losing” perceived easy future gains), recency bias and greed cause investors to herd into certain investment vehicles. This causes a continuation and acceleration of the price trend, and often takes the instrument well beyond “fair value.” This phase can have a high variance in length and severity, factors that are very hard to predict. Sometimes this exuberance is relatively mild and fails to cause extreme moves. Other times, however, these behavioral biases can lead to extreme parabolic moves and price bubbles, followed inevitably by spectacular crashes. 3.7. Bitcoin Performance, 2017-2018

Coinbase, Coinbase Bitcoin [CBBTCUSD], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CBBTCUSD.

Adding It Up As you can see from this example, our established behavioral biases can lead to sustained price trends like the ones that have been empirically seen in financial market research as well as practitioner results for decades. This cycle can best be summarized as initial underreaction, as investors are slow to abandon currently held narratives and anchors, followed by a self-sustaining feedback loop of higher (lower) prices resulting in better (worse) economic numbers / results, followed by the last stage of the cycle where greed (fear) and recency bias cause investors to herd into certain market positions. Loss aversion and the disposition effect

cause the prices to slowly trend throughout the process, as investors sell winners too early and hold onto losers too long. The later herding phase can come to fruition in many ways - from a mild trend and shallow correction to a full-blown mania or bubble situation, followed by a crash. Instead of forecasting how long the trend will go or hypothesizing about investor behavior, we prefer logical, quantified, systematic trading rules that allow one to objectively participate while the market is trending in one direction and to get out when that trend begins to reverse.

Systematic and Quantitative Investing Provides a Dual Benefit Having a systematic investing process provides the further dual benefit of helping investors avoid some of the behavioral mistakes above while systematically taking advantage of the behavioral mistakes of others. Knowing this, we will present a handful of trading strategies to take advantage of this behavior in an unemotional, systematic way in the coming chapters. First though, let’s explore the quantitative evidence and behavioral explanations for the other price tendency we will look to profit from - short-term overreactions and subsequent mean reversion.

CHAPTER 4

Why Mean Reversion Works Have you ever checked a stock you are following to see a large move in a relatively short time, say over a day or a week? We are talking about huge percentage gains or declines for large companies with thousands of employees and diversified lines of businesses. Have you ever thought how can the future prospects of such a large and established company change so quickly? Aren’t stock prices supposed to reflect a company’s future prospects? Is the prospect of said company really 30% different compared to a couple of days ago? Does that negative or positive news item or catalyst really affect the future prospects of that company to such a dramatic degree? Such large and dramatic moves, as most market watchers can attest to, are typically short-term overreactions that tend to mean revert. In this chapter we are now going to switch our focus to shorter term time frames and study the tendency for securities to overreact, moving “too far too fast,” and subsequently mean revert.

In Short Time Frames, Markets Tend to Mean Revert To generalize, we can label market movement over the shorter term (days to a few weeks) as mean reverting in nature, especially after a large move has occurred. This is opposed to the trending nature of markets in the longer term, which we demonstrated in the last chapter. It is clear that these extreme moves tend to correct (mean revert) after the hysteria, especially to the downside, subsides. Decades of research bear out this market behavior. By systematically identifying large short-term price moves and taking mean reversion trades, we look to take advantage of this market behavior. These short-term overreactions are mainly caused by the behavioral biases of investors / traders, as investors emotionally herd into or out of securities often causing them to move beyond what is reasonable. We outline some of the behavioral biases that contribute to this phenomenon and how they play out in this time frame - specifically loss aversion, herding and recency bias.

We then discuss the emotional difficulty of “rejecting the herd” and fading these price moves, pointing out the fact that this goes against our nature as humans. This emotional difficulty especially contributes to the sustainability of this edge.

Equity Markets Tend to Mean Revert over the Short-Term Period! In the previous chapter we showed multiple studies, spanning several asset classes and market conditions, clearly showing that in the long-term, markets trend. Outperformance over the last 2-12 months tends to persist going forward, and underperformance over this time frame tends to persist as well. This is one of the most studied phenomena in modern finance with years of practitioner results to boot. Larry is considered to be one of the world’s foremost authorities on quantified shortterm mean reversion trading strategies. His published research spans over 25 years and repeatedly shows that there are statistical edges in place in systematically fading extreme price moves over a short-term time period, usually a few days to a few weeks. As an illustration, let’s examine a study he published in 2009 in his book Short Term Trading Strategies That Work. This study trades the ETF SPY. The simple, unoptimized rules are: 1. SPY is above its 200-day moving average 2. SPY has had a short-term pullback to the downside. We quantify this by buying the ETF on the close when the 4-period RSI (see appendix for the calculation of RSI) is below 25. 3. If the 4-period RSI closes below 20 anytime we are in the trade, we add another unit to the trade. This effectively doubles our position if SPY continues to decline. 4. Sell SPY on the close after it has reverted to the mean. In this case we will measure that by selling when the same 4-period RSI indicator is above 55. Notice that these rules are very straightforward applications of mean reversion trading and meant to highlight general market tendencies. In this specific study, we are looking at the tendency for short-term extreme moves to the downside for SPY to mean revert higher provided the overall market trend is higher (measured by the 200-day MA). Here are the test results from 1993-2008, first published in Short Term Trading Strategies That Work: 4.1. SPY Mean Reversion, 1993-2008

Source: The Connors Research Traders Journal

Tens of thousands of copies of “Short Term Trading Strategies That Work” have been purchased. As such, this information has been disseminated globally and has been in the public domain for over 10 years. Did the behavior of the market change as this research was read by traders around the globe? Did the publication of these findings diminish the effectiveness of the short-term mean reversion signals? Or is there something else going on? Is this behavior a function of human nature, which is unlikely to change in a meaningful way? Nearly a decade later, Larry updated the results in his book Buy the Fear, Sell the Greed. Below are the updated test results. In spite of the fact that many thousands of traders know these rules, mean reversion is so strong and inherent in stock prices that not only did the test results continue to hold up, they actually IMPROVED over the next decade! 4.2. SPY Mean Reversion, 2009-2017

Source: The Connors Research Traders Journal

Let’s now examine the full history, which covers over a quarter century. It is worth noting that this time period covered great changes in the financial markets, not least of which was a move to electronic trading. This has not changed the market behavior, however, as human emotion remains a part of the market and human behavior doesn’t change. 4.3. SPY Mean Reversion, 1993-2017

Source: The Connors Research Traders Journal

To even the most adamant skeptics of short-term mean reversion trading, these kinds of studies challenge those assumptions. These results, and the consistency of the findings now spanning over 25 years, are very strong evidence that short-term mean reversion trading offers significant edges in the marketplace.

Academic Researchers Are Well Aware of This Short-Term Mean Reverting Behavior The observation that markets tend to mean revert in the short-term is not lost on the academic researchers we spoke about in the previous chapter. For instance, in momentum research regarding individual stocks such as the seminal research conducted by Jegadeesh and Titman in the early 1990’s, it is customary to EXCLUDE the most recent month when forming momentum portfolios. The most widely used academic measure of price momentum, especially when applied to individual securities, is what is known as “12M-1M.” “12M-1M” means that the researchers look at the trailing 12-month momentum of a security and EXCLUDE the most recent month. Why is excluding the most recent month the standard in academic research regarding the momentum factor? Simply because academics realize what we are highlighting in

this chapter, that over a short-term time horizon, stocks and other securities tend to be MEAN REVERTING in nature.

Overall Market Conditions Matter An assessment of the overall market conditions is important when attempting to take advantage of the tendency for securities to overreact then mean revert. One observation, again informed by years of research, is that downside fear is much quicker to subside in longer-term, uptrending markets. This is a big reason why we often add long-term trend filters to short-term mean reversion strategies, such as the 200-day moving average rule applied earlier in this chapter. We also saw this dynamic play out in studies in the last chapter, which showed that volatility in downtrending markets in consistently higher than volatility in uptrending markets. As a reminder, let’s take a look at the volatility numbers for markets when they were above or below their 200-day MA. Notice the volatility is consistently and meaningfully higher (in some cases over double) when the market has been in a downtrend. 4.4. Comparison of Market Volatility Above and Below the 200-day Moving Average

Data derived from sources cited previously. See citations in Figures 3.2, 3.3, 3.4, 3.5.

It is helpful in trading system development to account for the overall market trend, and thus the overall volatility. This makes taking advantage of short-term mean reversion much more powerful and predictable. We incorporate such overall market regime filters when constructing short-term mean reversion trading strategies, which we cover later in this book.

Behavior Behind Extreme Short-Term Moves Why Does Mean Reversion Exist? It’s Human Nature! We have seen via quantified results that markets tend to mean revert in the short-term after large price moves. Now the question becomes, why is this the case? Why has this tendency been so strong over such a long period of time? As we did in explaining the tendency of markets to trend over the long-term, we rely on behavioral explanations as to why markets overreact in the short-term then mean

revert. In short, it is in our nature to display loss aversion, herd along with the crowd, and assume the recent past will look like the immediate future. These behavioral biases cause investors to push prices beyond what is reasonable in the short-term, creating potential trading edges. Many of the same behavioral biases that contribute to long-term trends also contribute to short-term mean reversion, they just play out in a shorter-term time frame.

Panic and FOMO! The Role of Loss Aversion in Short-Term Trading Loss aversion is a fundamental human quirk. This desire to avoid losses stems from our evolution, where avoiding the “loss” and surviving another day was the logical choice. This loss aversion behavior was shown very convincingly by Daniel Kahneman and many others after him. In short - losses hurt more than gains feel good and thus we are designed to avoid losses, whether real or imaginary. In the short-term, this desire to avoid losses quickly turns into fear. Fear is hardwired in humans. The science community has proven this over and over again. There are many scientific studies using MRIs showing that the brain changes when fear is present. This is similar to what Daniel Kahneman refers to as “system 1 thinking,” which is reflexive, emotional and automatic. The Flash Crash of 2010 is a great example. I (Larry) was briefly out of the office when the intra-day sell-off started and when I returned, it was already a bloodbath. Traders panicked, and the sell-off was vicious. As you can see from that day, the ensuing intraday reversion to the mean was just as sharp giving time to buy to only the nimblest of traders. Events like this occur all the time to varying degrees and is why mean reversion in equities has continuously shown edges. 4.5. SPY Performance during The Flash Crash, May 5-10, 2010

Panic, Fear and Loss Aversion is a Hardwired Human Instinct To speak to the sustainability of the panic / fear / FOMO / loss aversion behavior of market participants, which we think greatly contributes to short-term overreactions and subsequent mean reversion, consider the fact that most scientists believe that loss aversion has been ingrained in us through hundreds of thousands of years of evolution! This loss aversion served us well during our past, as our natural instinct is to protect ourselves (avoid the “loss”) in order to pass our genes along to the next generation. As such, this instinct is very unlikely to go away anytime soon.

Loss Aversion - It Works Both Ways It is rather obvious how fear and loss aversion can cause traders to abandon an investment when observing a large short-term move against their position. Our instincts take over, we look to avoid the loss, we feel the pull of the crowd and, more often than not, get out of the trade at the wrong time. What is less obvious is that this fear and loss aversion can play itself out on the upside as well. This comes in the form of FOMO, or fear of missing out. We become greedy when the perception of “easy money” is seemingly on the table. This is a powerful human urge. Imagine not being in a hot stock or sector that is experiencing a large move to the

upside. This will likely cause emotional pain, as you are missing out on seemingly easy profits. We are all greedy, and it’s hard not to imagine the money we could be making if we only made the trade a few days ago. This causes many investors to throw caution to the wind and jump in the appreciating stock, further exacerbating the move higher. It drives people crazy when others are making money in the market and they are not. Our inherent relativity comes into play here, as we compare our performance to that of our peers and to the broad market.

The Role of Herding in Short-Term Trading Humans are herd animals. We can all appreciate the inherent need to be accepted into the group and to not be an outcast. This, again, was an effective tactic during our evolution, as following the crowd increased our chances of survival, even if the crowd was often times incorrect. We have seen how this herding instinct can sustain longer-term price trends in the last chapter, usually towards the later parts of the trend as investors jump on the outperforming security or sector / market. Similar herding instincts affect shorter-term traders as well. This instinct causes market participants to herd into or out of securities that have had large short-term price moves, going with the crowd (which, as we have seen, is emotionally comforting). If you, say, own a stock that has reported some negative news, you will likely feel the pull of the crowd to get out of the investment. “Experts” come on TV and explain that this stock’s prospects are now badly damaged and that the “smart money” is selling the stock. Feeling the need to go with the crowd, you (and undoubtedly many others like you) take action to sell the stock, which exacerbates the downward price move. Similarly, if a stock that you don’t own is having a large one- or two-day move higher, it will likely be in the financial media. Other “experts” will be discussing the great prospects of the company and how they expect the current outperformance to continue. This can trigger powerful emotions within us such as our desire to go with the herd, our fear of missing out, and just plain old greed. These emotions often lead investors to jump in the hot stock, sector or investment, exacerbating the short-term price overreaction. Fear and Herding During the 2016 US Presidential Election On the night of November 8th 2016, Donald Trump’s victory led to an overnight panic in equity futures. As his surprise victory became more and more likely, many traders and asset

managers panicked as fear of the unknown took over. These fearful traders aggressively sold stock futures, taking Dow futures down 870 points at the height of the election night panic. These traders were clearly herding together. After the hysterical overnight reaction, futures rapidly reversed and actually closed significantly higher the following business day. Stocks went on to rally for the rest of the week. Savvy traders and investors who bought into the overreaction were handsomely rewarded.

The Role of Recency Bias in Short-Term Trading Recency bias, or the tendency to assume that the observed recent past will look like the immediate future, is another contributor to the tendency for markets to move beyond what is reasonable over short time frames. If you are an active short-term trader, you likely have fallen victim to recency bias similar to what “Bob” fell into in our longer-term trend behavior story. As your time frame is shorter, your recency bias plays out on a short time frame as well. If a security is moving up strongly over the past couple of days, it’s easy to observe that price action and assume it will continue in the near future, causing more buying pressure and an even larger short-term price move higher. Similarly, if a security has been getting killed over the last couple of days, it’s easy to assume that the sell-off will continue over the short-term, prompting further selling and further price overreaction. This recency bias again leads to more extreme short-term movements than what is logically expected.

Mean Reversion Trading is Emotionally Difficult! Think, for a moment, about the simple test performed earlier in this chapter. In this test, we are identifying times when there has been intense selling of the ETF “SPY.” Our strategy then steps in and buys into the panic. This panic is, more often than not, accompanied by bad news and worse predictions from so called experts in the financial media. Stepping up and buying a security that is experiencing an extreme short-term move to the downside is very difficult emotionally. At the time, it certainly feels like the wrong thing to be doing. Why are you stepping in to buy a security currently in freefall? This action would be a firm rejection of short-term herding behavior, which we are all programmed to do. This causes emotional discomfort. It is emotionally uncomfortable to reject the group, which is exactly what you would be doing if you step in to buy a security in free fall. A

systematic rules-based process for identifying and fading short-term extreme price moves can help us overcome the emotional pain. It is often said colloquially in trading / investing circles that the trades that feel the worst at the time usually end up being the most profitable. Fading extreme price moves is a good example of that. William Eckhart, in his interview in the classic book “New Market Wizards” said: “If you’re playing for the emotional satisfaction, you’re bound to lose, because what feels good is often the wrong thing to do.” This difficulty is exacerbated by the constant news feed that modern life provides, as the news and sentiment will likely be strongly negative after large short-term, downward price movement. This difficulty contributes to the edges quantitatively displayed earlier in this chapter. It also speaks to the lasting sustainability of the edge, as human behavior is unlikely to change.

Alpha Can Be Realized by Taking Advantage of Short-Term Overreactions In this chapter, we have seen that time frame is the key differentiator between what is seemingly contradictory market observations - specifically the tendency for markets to mean revert over the shorter term and trend over the longer term. The short-term mean reverting nature of markets is quantitatively shown by the simple trade setup shown earlier in this chapter, which has correctly predicted the next move for SPY over 90% of the time for the last quarter century. This is not lost on academic researchers, who routinely exclude the most recent month when forming momentum portfolios (such as the seminal Jegadeesh and Titman study in the early 1990s). Why do they do this? Simply because markets tend to mean revert in the shorter time frame! Similar behavioral biases are behind each phenomenon (short-term mean reversion and long-term trend), they just play out in different time frames. This has the effect of moving prices beyond what is reasonable over the short-term which we can take advantage of with mean reversion trades. Finally, we spoke about the emotional difficulty in bucking the short-term trend and entering mean reversion trades, especially after large moves in which the media is predictably either overly positive or overly negative. This speaks to the sustainability of the edge, and we believe is a large reason this market behavior has existed for more than a quarter of a century.

SECTION 2 The Strategies

CHAPTER 5

Rising Assets Strategy™

Strategy: Rising Assets First Principle: Markets Go Up Style: Trend Following Strategy Objective: Long Risk Assets When They’re Rising. Long Bonds When They’re Not

The first strategy we will present is our long only, dynamic trend following strategy titled “Rising Assets.” Our Rising Assets strategy aims to go long risk assets when they’re rising and move into treasuries and other fixed income instruments when they’re not. Consistent with the academic literature we presented in Chapter 3, we expect this past outperformance to continue moving forward. In other words, we expect persistence in performance of these assets, positioning ourselves for a continuation of market trends. This strategy is trend following in nature. Trading it on a longer-term time frame is consistent with the evidence that markets trend over this longer time frame. Our holding period for this strategy is typically a couple of months but can extend to over a year if the trend continues. This is a long only, “go anywhere” strategy. The design of the strategy is such that the model is not contained to any one asset class or region of the world. We aim to profit from global, cross asset trends no matter where they come from in the future. With this goal in mind, it is important that our choice of assets represents a broad cross section of available global assets across and within different asset classes. While the strategy is technically long only as no short positions are initiated, we add several “safe haven” type assets such as US Treasury bonds and gold to the mix. These asset choices serve to protect us during prolonged equity (or general “risk”) bear markets, as our model will invest in these safer, lower volatility instruments during

such times. For further risk management, this strategy employs inverse volatility weightings for our asset choices. This means, assets with lower historical volatility receive more of an allocation and assets with higher volatility receive less of an allocation. This serves to manage risk by increasing the diversification of the strategy and ensuring that no one volatile asset dominates the risk of the strategy.

First Principle - Markets Go Up This strategy is primarily designed to perform well when risk assets, such as global equities, and real estate, are in bull markets. As such, the first principle this strategy is rooted in is the truth that markets go up. These bull phases are typically characterized by lower overall volatility and at times have lasted for years on end.

What Market Tendencies Are We Looking to Take Advantage Of? We are looking to take advantage of the trending nature of markets over the longer term, which as we showed in Chapter 3, has a long history of both real-world profits and academic support. Stated another way, we are looking to take advantage of markets showing persistence in performance.

What Investor Behavior Contributes to this Market Tendency? As we outlined in Chapter 3, some of the behavioral biases that contribute to long-term trends in the financial markets include anchoring, confirmation bias, positive feedback loops, herding, recency bias, loss aversion and relativity. The behavior behind long-term trends can best be summarized as initial underreaction, followed by slow adoption to the trend and positive feedback loops, finally followed by herding behavior toward the later part of the trend. This most typically plays itself out over several months to, at times, over a year. For a more detailed look at how we view these behavioral biases contributing to longerterm trends, refer to Chapter 3.

Strategy Asset Choices An important step in a strategy like this is to carefully select asset choices. We resist the urge to simply choose assets that have performed strongly over this time frame, as that would introduce selection bias to our trading strategy. Instead of choosing assets that have performed the strongest over the last 10-15 years,

we instead choose to select a global, cross asset representation of markets. We aim to cover most equity markets around the world, via regional index funds, in our asset choices. We also aim to have multiple asset class choices, including equities, fixed income, real estate (equity REITs) and gold. Finally, we have found it important to include safer / lower volatility assets in our universe. This allows our strategy to dynamically allocate capital to such assets should the trends in risk assets, such as equities and real estate, turn negative. The universe for our Rising Assets strategy is as follows. Notice that we separated the assets into “risk” and “risk off” categories: 5.1. Rising Assets Investment Universe

Rising Assets Strategy Rules 1. On the last business day of every month, invest in the top 5 assets with the highest momentum score. 2. Sell any asset that was previously in the top 5 but has since dropped out. 3. Weight each asset by the inverse of its 63-day historical volatility.

Momentum Score Our momentum score is the average of the last 1mo, 3mo, 6mo and 12mo trailing total returns. We opt for an unoptimized average of lookbacks. This is consistent with the method AQR employed in their paper “A Century of Evidence in Trend-Following Investing” mentioned in Chapter 3. Momentum Score Example:

SPY 1 Month Return = +2.0% SPY 3 Month Return = -4.0% SPY 6 Month Return = +11.0% SPY 12 Month Return = +8.0% Average Return = (2% - 4% + 11% + 8%) / 4 = 4.25% Result is a Momentum Score of 4.25%

Inverse Volatility Asset Weights Asset weights are equivalent to the inverse of each asset’s trailing 3-month (63 trading days) historical volatility. Volatility here is measured by the standard deviation of each ETF’s daily returns. This step serves to increase diversification in the portfolio as inverse weighting every asset avoids one volatile asset from dominating the portfolio. The typical effect of this step, compared to equal weighting the assets, is a slight decrease in returns but a larger decrease in risk. This increases the return / risk metrics such as the Sharpe Ratio. Inverse Volatility Example: SPY Std Dev = 0.10 EEM Std Dev = 0.20 Take 1 divided by each Std Dev observations and sum the results: SPY 1 / 0.10 = 10 EEM 1 / 0.20 = 5 10 + 5 = 15 Divide each by the sum to get percent capital allocation: SPY 10 / 15 = 0.666 EEM 5 / 15 = 0.333 Result is a 66.6% allocation to SPY and a 33.3% allocation to EEM 5.2. Rising Assets Performance Summary, November 2004-December 2018 Historical Test Results for the Rising Assets Strategy

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5.3. Rising Assets Monthly Returns, November 2004-December 2018

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5.4. Cumulative Returns - Rising Assets vs. SPY and EFA, November 2004-December 2018

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5.5. Drawdowns - Rising Assets vs. SPY and EFA, November 2004-December 2018

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Highlights of the Results Notice our Rising Assets strategy did a great job of capturing the upside while, more importantly, prudently managing downside risk. The strategy had a higher return than buying and holding SPY, with much less volatility (11.6% vs 18.6%) and a far smaller maximum drawdown (-14.8% vs -55.2%). In many ways, SPY is a bit of a tough benchmark for a strategy like this because US stocks have outperformed most of our available universe during this time period. Despite that, our strategy still handily outperformed, especially on a risk-adjusted return basis. Our Rising Assets strategy also dramatically outperformed EFA (developed markets ex US), which represents most of the investable equities outside the US. Our strategy has about triple the returns, half the risk (as measured by annualized volatility) and much

less of a maximum drawdown (-14.8% vs -61%).

Final Thoughts Our Rising Assets strategy is designed with the first principle that markets go up. The markets tendency we look to profit from is long-term trend following. This strategy serves as an anchor of sorts for our overall portfolio, ensuring that we capture sustained global trends regardless of which asset class or region of the world they come from. Rising Assets has shown a historically strong track record of higher returns and much less risk compared to equity markets globally.

CHAPTER 6

Connors Research Weekly Mean Reversion Strategy

Strategy: Connors Research Weekly Mean Reversion First Principle: Markets Go Up Style: Mean Reversion Strategy Objective: Buy Pullbacks in Liquid US Equities in Rising Markets

In

the previous chapter, we introduced our Rising Assets strategy, a longer-term momentum approach. Continuing with the First Principle that markets rise, let’s now add the next element - a mean reversion strategy. When the overall market trend is higher, stocks which have sold off over a short-term time frame tend to consistently revert to the mean. Stated simply, short-term oversold conditions in bull markets tend to reverse higher. Connors Research, and companies affiliated with Connors Research, have been publishing quantified studies about this behavior as far back as 1995. Deep analysis of short-term mean reverting behavior was originally published in the 1995 book Street Smarts, co-authored by Larry Connors and Linda Raschke. Street Smarts was selected by Stocks & Commodities Magazine as one of the top ten trading books published in the 20th century. Short-term mean reverting research was further quantified in subsequent books, including Short-Term Trading Strategies That Work (2009), How Markets Really Work (2012) and Buy the Fear, Sell the Greed (2018). Each of these books showed statistically, over multiple decades, that US stocks and world-wide equity ETFs have an inherent nature of reversing higher on a short-term basis after pulling back, especially when the longerterm trend is higher. Connors Research has been able to create dozens of trading strategies over the years

based on this quantified behavior. In this chapter, we’re going to introduce our Connors Research Weekly Mean Reversion strategy, which has performed consistently well for over 15 years.

What the Strategy Is Designed to Do The Connors Research Weekly Mean Reversion strategy is designed to trade liquid US stocks from the long side. At its heart, this strategy systematically identifies and invests in oversold stocks, benefiting from the tendency of such oversold stocks to mean revert. Our CR Weekly Mean Reversion strategy also employs a trend following overlay, only taking new long positions when the trend of the overall market is higher. Furthermore, all else being equal, the strategy favors stocks with lower historical volatility. This strategy, like all quality strategies, is based on a simple premise. We first aim to identify stocks that have overreacted to the downside, ones that we identify as having gone down “too far too fast.” We then step in and buy these stocks, but only if the overall S&P 500 index is in an uptrend. These simple ideas are consistent with the research we have already touched upon in this book, as well as evidence presented by the academic finance community. Furthermore, there are good behavioral explanations as to why these advantages are likely to persist going forward.

What Market Tendencies Are We Looking to Take Advantage Of? The main market tendency we are looking to profit from here is short-term mean reversion. We look to buy highly liquid US stocks that have sold off sharply over the last couple of weeks. This shorter time frame is consistent with mean reversion behavior. By identifying times when individual stocks have had strong moves downward, we position ourselves for attractive entry points and look to profit from the subsequent mean reversion. We also employ a longer-term trend following filter with a strategy like this. We have found this filter greatly increases performance. For our results in this chapter, we employ a simple absolute momentum filter, looking at trailing total return numbers for our index to determine the overall trend of the market. With this trend following filter in place, our CR Weekly Mean Reversion strategy effectively shuts itself off by not taking new trades when the general stock market enters bear market mode. This trend following regime filter serves to allow us to

participate if stocks trend higher and manage our risk if the overall indexes begin to decline. Finally, we also look to take advantage of the differing volatility profiles between uptrending and downtrending markets. Recall the volatility studies we presented in Chapter 3, which clearly showed that markets tend to be less volatile when they are trending higher and more volatile when trending lower. For a strategy like CR Weekly Mean Reversion, which looks to step in and buy stocks that have gone down significantly in a short time frame, we prefer a less volatile market. Our trend following filter provides this for us.

What Behavior Contributes to the Market Tendencies? The main objective of this strategy is to successfully identify times when liquid individual stocks have overreacted to the downside, declining “too far too fast.” Many behavioral biases play into the observation that markets tend to overreact in the short-term. Some of these biases include loss aversion, panic / fear, recency bias and herding. For a more granular discussion of how these hardwired behavioral biases contribute to short-term overreactions, refer to Chapter 4.

The Connors Research Weekly Mean Reversion Strategy Rules Here are the rules for our Connors Research Weekly Mean Reversion strategy. All rules are checked at the end of the business week, with the exception of our percentage stop rule which is checked at the end of every business day. 1. Long-term Trend Following Regime Filter. SPY’s total return over the last six months (126 trading days) is positive. 2. Liquidity Filter. The stock must be one of the 500 most liquid US stocks. Our 500 most liquid US stock universe is determined every month by taking the 500 US stocks with the highest 200-day average dollar volume. 3. The Weekly 2-period RSI of the stock is below 20. This confirms that the stock has overreacted to the downside. 4. All stocks that meet these criteria are then ranked by their trailing 100-day historical volatility. We then BUY on the close, in equal weight, the 10 stocks with the LOWEST historical volatility. 5. SELL the stock on the close if its weekly 2-period RSI is above 80. This is checked at the end of every business Week. 6. SELL the stock on the close if the current price is more than 10% below the entry

price. This is checked at the end of every business Day. 7. Any capital that is not allocated is put into SHY (1-3-year US Treasuries ETF). Please note, we have run test results entering and exiting on the next day’s open, and also on the average price of the next day (open + high + low + close divided by 4). There was minimal difference in the test results. The message here is you do not need to be in front of the screen at the close because the test results are robust. There you have it. Remember our goal here is straight forward logic that looks to take advantage of times when individual stocks have overreacted to the downside in an otherwise uptrending overall market. These simple rules provide this for us, allowing us to systematically take advantage of this repeatable market pattern. How to Get To 10 Positions The CR Weekly Mean Reversion strategy only takes trades if we have open spots available in the portfolio. As currently constructed, our strategy can invest in 10 stocks. As such, at the end of every business week we only buy the amount of stocks in which we have open slots for. Example: Say we come into the last trading day of the week with 5 positions. At the end of the day one position is exited. This leaves you with four positions and six open slots. Therefore, you can buy up to six new positions. If more than six stocks meet our buy criteria, we buy the six with the lowest historical 100-day trailing volatility. 6.1. Connors Research Weekly Mean Reversion Performance Summary, 2003-2018 Historical Test Results for the Connors Research Weekly Mean Reversion Strategy

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6.2. Connors Research Weekly Mean Reversion Monthly Returns, 2003-2018

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6.3. Cumulative Returns - Connors Research Weekly Mean Reversion vs. SPY, 20032018

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6.4. Drawdowns - Connors Research Weekly Mean Reversion vs. SPY, 2003-2018

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Highlights of the Results The results for this straightforward strategy are quite impressive. The Connors Research Weekly Mean Reversion performed well in both bull and bear markets. Performance highlights are as follows: 1. Overall Performance - Our strategy beat buying and holding SPY during this time by 3.6% per year with significantly less volatility (10.1% vs 18.2%) and a much lower max drawdown (-12.8% vs -55.2%). 2. Bull Market Performance - The strategy did quite well in the bull market years. In spite of the fact that the strategy often holds some short duration Treasury bonds, CR Weekly Mean Reversion strategy saw returns of over 15% in seven of the bull market years. For example, CR Weekly Mean Reversion returned 25.13% in 2014

while SPY returned 13.5%. 2016 was another year of significant outperformance, with our strategy returning 38.43% while SPY returned 11.8% 3. Bear Market Performance - On a relative basis, our performance showed particularly strong results in the bear year of 2008. Whereas SPY lost over -37% in 2008, the CR Weekly Mean Reversion strategy lost only -2.18%, preserving investors capital. This is the trend following regime filter in action, as our strategy automatically turns itself off if stocks as a whole begin to show weakness. We view this filter as an additional way to protect portfolios from the devastation that occurs in a long-term bear market, with 2000-2002, and 2008 being recent examples.

Some Notes Regarding the Strategy Logic and Parameters We presented an unoptimized version of this strategy. There are a number of variations that performed better. Our philosophy is applying straightforward rules for systematic trading strategies intended to capture repeatable market patterns. Our goal is NOT to curve fit this strategy to the past data, as that would give us unrealistic expectations for the strategy going forward. As such, we did not publish the optimized results in this chapter. For those interested, we tested this strategy with a number of different combinations of the strategy parameters. From our testing, a broad set of differing parameters provides attractive results, with all runs handily beating the S&P 500 over this time and most showing Sharpe Ratio’s above 1.0. We found that: • Stop losses ranging from 5% to 15% work well • RSI lookbacks from 2-4 weeks work well • RSI entry thresholds from 10-30 work well • RSI exits (take profit) thresholds from 60-90 work well • Total return momentum lookbacks (for the longer-term trend following regime filter) ranging from three months (63 days) to one year (252 days) work well

Robustness: The bottom line is that our CR Weekly Mean Reversion strategy has shown robust results over the past 16 years. The strategy performs well under a variety of parameters which is what you want to see in a systematic strategy. We believe the robustness of this strategy comes from the inherent market edges in place when you combine systematic pullback buying on highly liquid US stocks with proper risk management.

Final Thoughts Our CR Weekly Mean Reversion strategy is designed to buy stocks which have overreacted to the downside over a short-term time frame. Our strategy then steps in to buy these oversold stocks but only in an overall rising stock market. We use straightforward and robust measures to achieve this goal. Our strategy results are impressive, handily beating the S&P 500 during this time with less risk and less drawdown.

The Alpha Formula So Far So far, we have presented our Rising Assets strategy which is designed to participate in bull markets no matter where they occur - both around the globe and across asset classes. We then presented our CR Weekly Mean Reversion strategy, designed to participate in a rising US stock market by identifying stocks that have overreacted to the downside on a short-term basis. We will now continue building the Alpha Formula portfolio by introducing a fixed income component to further broaden and protect the portfolio.

CHAPTER 7

Connors Research Dynamic Treasuries™

Strategy: Connors Research Dynamic Treasuries First Principle: Markets Go Through Times of Stress Style: Trend Following Strategy Objective: Provide constant exposure to US Treasuries while dynamically adjusting interest rate sensitivity.

So far, we have presented our Rising Assets strategy as well as our CR Weekly Mean Reversion strategy. As a quick recap: • Rising Assets is designed to participate in upward trending markets from around the world and across asset classes. This “go anywhere” strategy looks to identify the strongest markets and ride the trend. • CR Weekly Mean Reversion is designed to buy high quality US stocks that have pulled back dramatically over recent weeks, but only when the overall market is trending higher. While these strategies have risk management features built in, in the form of “risk off” asset choices in the Rising Assets strategy and the ability to turn itself off for the CR Weekly Mean Reversion strategy, they both are designed to benefit the most when “risk assets” are performing well. To complement our existing strategies, we are now going to present a strategy specifically designed to prosper when there is panic in the marketplace. This panic typically results in increased demand for safe haven assets. This is exactly what our Connors Research Dynamic Treasuries strategy looks to profit from.

The Flight to Safety It would be nice if “risk assets” always performed well. In reality, we know this is far from the truth. Markets go through both short-term periods of panic (for example

August 2011 and February 2018) and also long-term bear markets (2008). If you look at where money flows to during times of distress, the main asset it flows to is US Treasuries.

US Treasuries, the Ultimate Flight to Safety Asset US Treasury bonds have historically been one of, if not the strongest, asset class when there is fear in the marketplace. When market participants become risk averse, they have historically “herded” into the perceived safety of US Treasuries. US Treasuries are looked at as basically “risk free” assets, at least from a credit quality standpoint, as the US government remains one of the most credit worthy government bond issuers in the world. Spending over a decade as a market maker in US Treasury bonds, Chris can personally attest to the consistent bid that US Treasuries garner when investors are fearful. After almost every news item or catalyst that is perceived as bearish, investors tend to herd into the safety of US Treasuries. This is a consistent behavior, and something that seems unlikely to change in the coming decades. For example, recall the story about the 2008 credit crisis that opened this book. One of the largest and most sophisticated money managers in the world herded into the safety of short-term US government debt (US Treasury Bills). The demand was so strong that they agreed to pay for the right to own the Treasury Bills, through negative interest. We have witnessed this behavior from investors consistently. To explore this further, let’s look at historical correlations for US Treasury bonds versus S&P 500, as well as stocks from the rest of the world. We will then look at how US Treasuries have performed during times when stocks (and thus risk assets) performed poorly. For our initial analysis, we will use the ETF “IEF,” which is included in our Connors Research Dynamic Treasuries strategy. This ETF tracks the 7-10-year part of the US Treasury yield curve and has an effective duration of 7.5 years.

Correlation Analysis - SPY versus IEF The goal of this strategy is to be uncorrelated to equity markets. For our correlation analysis, let’s look at the rolling 1-year correlation coefficient between IEF and SPY as well as the rolling correlation between IEF and EFA. SPY tracks the S&P 500, aka US stocks, and EFA tracks developed market equities ex US. Between SPY and EFA, the majority of the investable worldwide equities are covered. This provides us with a broad look at how 7-10-year Treasury bonds have been

correlated to equities around the world. It is important to observe the rolling correlation through time, as correlations tend to change. This will give us a good idea of what to expect going forward. The following two charts present the rolling correlation, over the last 252 trading days (1 year), for IEF versus SPY and IEF versus EFA spanning 2004-2018: 7.1. Rolling 252 Day Correlation - SPY and IEF, 2004-2018

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7.2. Rolling 252 Day Correlation - EFA and IEF, 2004-2018

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First notice that the correlation is far from static. The rolling 1-year correlation for SPY versus IEF and EFA versus IEF here has consistently been below 0, which is good to see,

ranging from slightly positive to firmly negative. We can conclude that US treasuries are at times not correlated at all with US and foreign developed stocks (correlation around 0), and at times decidedly negatively correlated (correlation well below 0), over the last 15 years. There is a clear lack of positive correlation, making IEF a good candidate to provide diversification to our portfolio. It should be noted that there have been times in history, much of the 1990s for example, where stocks and bonds exhibited mildly positive correlation as both markets largely went higher during this time. The correlation over the last 15+ years, however, has been zero to negative as shown above.

Crisis Alpha Using rolling historical correlations, however, is incomplete. What we really want is a strategy that becomes negatively correlated to stocks during market panics. We will explore this important characteristic now. US Treasury bonds have the benefit of historically acting as a safe haven asset. Given their safe haven status, it is typical to see US Treasury bonds rally when there is a prolonged bout of “risk off” sentiment. Simply stated, when US or world markets panic, money flows into US Treasuries. We have already seen how fear and loss aversion can play out in the marketplace, and how people are programmed to react in such a way. This behavior is not going to change, and US Treasuries should continue to act as a safe haven during times of stress.

The Proof Is in the Numbers To further examine this tendency, let’s take a look at the worst percentage moves for SPY by quarter from 2003-2018. Let’s look at the following table, sorted by worst quarterly SPY performance, and the subsequent quarterly performance of IEF: 7.3. IEF Returns Sorted by Worst Quarterly Performance for SPY, 2003-2018

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It is clear to see that IEF has provided value at most of the worst times for US equities over the last 15+ years. Taking a look at IEF performance, notice that during each of the worst quarters for SPY, IEF was positive 9 out of 10 times. The one-time performance wasn’t positive was Q1 2009, where IEF was down a modest -1.16% while SPY was down -11.27%! Notice the average percent change for these 10 quarters for the two asset classes. The average percent change during the 10 worst quarters for SPY came out to -10.22%. This is compared with the +5.06% average performance from IEF during such times. Let’s now repeat the same analysis, this time comparing the worst quarters for EFA and the subsequent quarterly performance of IEF: 7.4. IEF Returns Sorted by Worst Quarterly Performance for EFA, 2003-2018

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A similar pattern emerges. Notice the average -13.31% decline for EFA compared to an average gain of +4.77% for IEF. The bottom line is that IEF has historically tended to rally when risk assets such as US and foreign stocks are weak, benefiting from safe haven flows. This highlights a very attractive feature of US Treasuries, providing investors with crisis alpha and valuable diversification when they need it most.

Introducing the Connors Research Dynamic Treasuries™ Strategy Now that we know about the strong tendency of US Treasuries to rally during the worst periods for stocks, let’s put that knowledge into practice. Our four goals for the Connors Research Dynamic Treasuries strategy are as follows: • Profit from periods of risk aversion in the marketplace and the subsequent rush for

safe haven assets • Develop a strategy fundamentally different than “Rising Assets” and “CR Weekly Mean Reversion,” one that just invests in high quality fixed income. • Be non-correlated with risk assets, such as US and World stocks. Given that our Rising Assets and CR Weekly Mean Reversion strategies benefit from outperformance in risk assets, having a strategy uncorrelated with such assets ensures we are adding a complementary strategy. • Preserve capital in a rising interest rate environment by shortening duration.

Dynamic Duration Duration is the price sensitivity of a bond to changes in interest rates. The longer the duration, the more the price of the bond will move given a change in rates. As such, if you are bullish on the bond market (expect interest rates to decrease and prices to increase), all else being equal, you would want to extend the duration of your portfolio. This way, if the expected move to lower interest rates happens, your bonds will benefit (rally) more. If you have a bearish outlook on bonds, subsequently, you would want to shorten the duration of your portfolio. With our Connors Research Dynamic Treasuries strategy, we look to do just that. We use momentum signals to inform our strategy as to whether extending or shortening duration is the appropriate course of action. The goal here is to jump on board, by extending duration, should US treasuries show strength over the recent past. On the other hand, the strategy dynamically shortens duration should US Treasuries show weakness. While our strategy dynamically adjusts the duration based on market conditions, it will always be long US Treasuries thus benefiting from a flight to quality bid no matter when it comes.

Strategy Design Our strategy trades four US Treasury ETFs with different durations: IEI: 3-7yr Treasuries | Duration = 4.5 years IEF: 7-10yr Treasuries | Duration = 7.5 years TLH: 10-20yr Treasuries | Duration = 11.5 years TLT: 20yr+ Treasuries | Duration = 17.4 years The amount allocated to each of the three longer duration ETFs (IEF, TLH and TLT) can range from 0-25%. The allocation is based on trailing momentum scores for each ETF.

We employ an “ensemble” method of trend following here, meaning we use a variety of different lookback periods to dictate positioning. We use five different momentum lookbacks for our signals, with each lookback either increasing or decreasing the allocation to each ETF. IEI, the shortest duration instrument, serves as an anchor of sorts for our strategy. Any amount not allocated to the other three longer duration ETFs (IEF, TLH and TLT) is then allocated to IEI.

Connors Research Dynamic Treasuries Rules: 1. Allocate anywhere from 0-25% of the portfolio to each of our longer duration ETFs (IEF, TLH and TLT). This allocation is based on trailing 1,2,3,4,5-month total returns (21, 42, 63, 84 and 105 trading days), with each positive lookback contributing 5% to the total allocation for that ETF. 2. Any part of the portfolio not allocated to our duration ETFs is allocated to IEI. As such our allocation to IEI can range from 25% (if all longer duration ETFs have positive momentum in all lookbacks) to 100% (if none of the longer duration ETFs have positive momentum in any lookback). 3. We rebalance this strategy once a week, at the end of the business week. Connors Research Dynamic Treasuries Logic Example: IEF Momentum Scores: 1mo = +4%, 2mo = +3%, 3mo = +1%, 4mo = -3%, 5mo = +3.5% Result = 4 / 5 lookbacks are positive | Allocate 20% of capital to IEF TLH Momentum Scores: 1mo = +1.5%, 2mo = -2%, 3mo = -1%, 4mo = +4%, 5mo = +8% Result = 3 / 5 lookbacks are positive | Allocate 15% of capital to TLH TLT Momentum Scores: 1mo = -3%, 2mo = +1%, 3mo = +3%, 4mo = +4%, 5mo = +1% Result = 4 / 5 lookbacks are positive | Allocate 20% of capital to TLT IEI Allocation: 20% + 15% + 20% = 55% allocation to our longer duration ETFs We allocate the remaining capital to IEI (45%) | Allocate 45% to IEI The following table summarizes the possible allocations for this strategy: 7.5. Possible Allocations for the Connors Research Dynamic Treasuries Strategy

Note that if all total return lookback signals are above 0 for each longer duration ETF (IEF, TLH and TLT), then our portfolio would be 25% IEI, 25% IEF, 25% TLH and 25% TLT. If none of the momentum lookbacks are positive, we would be 100% in IEI. The duration of our portfolio can dynamically change from 4.5 years (if we are 100% allocated to IEI) to approximately 10 years if we have 25% of the portfolio in each ETF. This is the design of the strategy, a fixed allocation to US Treasuries and dynamically changing duration based on market conditions. 7.6. Connors Research Dynamic Treasuries Performance Summary, 2007-2018 Historical Test Results for the Connors Research Dynamic Treasuries Strategy

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7.7. Connors Research Dynamic Treasuries Monthly Returns, 2007-2018

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7.8. Cumulative Returns - Connors Research Dynamic Treasuries, 2007-2018

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7.9. Drawdowns - Connors Research Dynamic Treasuries, 2007-2018

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Comparing Connors Research Dynamic Treasuries to IEF When comparing our Connors Research Dynamic Treasuries strategy to a static allocation to US Treasuries (IEF), the results are fairly similar. So why go through all this work? Is it worth it? We believe it is. Here’s why - having the ability to dynamically adjust the duration based on market conditions better protects you should a prolonged rise in interest rates occur. The strategy will move into short maturities during such times, better protecting your capital. When rates are rising you want to shorten your duration. The Connors Research Dynamic Treasuries strategy does this. Yes, there’s been a 30+ year bull market in bonds but it’s not going to last forever. By dynamically adjusting your duration you’re more intelligently protecting your portfolio.

The Connors Research Dynamic Treasuries Strategy in Action When analyzing the results, it is important to keep our goals in mind. Our main goal is to have a strategy that benefits from safe haven flows, providing a hedge for our Rising Assets and CR Weekly Mean Reversion strategies.

Crisis Analysis - 2008 and 2011 To examine our performance, let’s take a look at two tough years for risk assets - 2008 and 2011. We compare the return of our CR Dynamic Treasuries strategy to SPY. 7.10. Crisis Analysis - CR Dynamic Treasuries vs. SPY, 2008 and 2011

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2008 was the global financial crisis and needs little introduction. This period saw the S&P 500 get cut roughly in half from peak to trough. For the year, SPY was down 36.8%. It is no coincidence that 2008 was the best year for our CR Dynamic Treasuries strategy. The strategy benefited from the strong demand for safe haven assets and extended duration of the portfolio throughout the year, allowing our strategy to rally significantly. The CR Dynamic Treasuries strategy ended 2008 up 14.03% Let’s take a further look at the performance of SPY versus CR Dynamic Treasuries during the worst of the crisis, the fall of 2008. Notice the strong negative correlation here, with the worst months for SPY being the best months for our CR Dynamic Treasuries strategy. 7.11. Crisis Analysis - CR Dynamic Treasuries vs. SPY, August-December 2008

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Another year of global financial market turbulence was 2011. The problems in 2011 began in Europe, with contagion fears of the sovereign debt crisis causing market participants to become risk averse. The problems came to a head in August 2011, when Standard and Poor’s, for the first time in history, downgraded the US’s sovereign debt rating from AAA (risk free) to AA. This caused an acute bout of risk averse behavior in the marketplace. Here is a look at monthly results for this tumultuous time for CR Dynamic Treasuries versus SPY. 7.12. Crisis Analysis - CR Dynamic Treasuries vs. SPY, May-September 2011

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Performance During the Worst Months Over the Last 15 Years The following is the performance of our CR Dynamic Treasuries strategy for some of the toughest months for risk assets over the past 15 years. Specifically, November 2008 (the Global Financial Crisis), May 2010 (the Flash Crash and aftermath), August 2011 (US Sovereign Debt Downgrade), January 2016 (China bear market) and December 2018 (fear of excessive Fed tightening). 7.13. Crisis Analysis - CR Dynamic Treasuries vs. SPY and EFA, Selected Periods

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It is clear to see that our Connors Research Dynamic Treasuries strategy provided consistent value during some of the worst times of stocks and other risk assets. This is the main reason for its inclusion in the Alpha Formula portfolio.

Always Be Long Dynamic Duration Treasuries! Our Connors Research Dynamic Treasuries strategy is designed around the First Principle that markets panic. The strategy was built to give us exposure to US Treasury bonds, which, as we showed in this chapter, have both historically benefited from times of market panic (crisis alpha) and have been consistently negatively correlated to stocks. Our strategy dynamically adjusts the duration of the portfolio based on market conditions using momentum signals. We use an “ensemble” method of trend following here, using several different total return lookbacks to inform the duration of our strategy. Our Connors Research Dynamic Treasuries strategy achieves its main goal, which is to

perform well during market panics, providing us crisis alpha when we need it most.

CHAPTER 8

ETF Avalanches

Strategy: ETF Avalanches First Principle: Markets Go Down Style: Mean Reversion Strategy Objective: Profit from global bear markets

So far, we have included strategies that generally do well when equity and other “risk” markets rise, specifically our Rising Assets strategy and Connors Research Weekly Mean Reversion strategy. Note that while these strategies both prosper historically when risk assets rise, they go about it in fundamentally different ways. Rising Assets looks to climb aboard long-term trends no matter where they emerge throughout the world. The Connors Research Weekly Mean Reversion strategy looks to buy stocks that have gone down “too far too fast,” thus providing attractive entry points. We also added a diversifying strategy in the form of our Connors Research Dynamic Treasuries strategy, designed to do well when there is a bout of risk aversion in the market and a subsequent increase in demand for safe haven assets. This strategy dynamically adjusts duration based on market conditions. To add further diversification to round out our portfolio, we will now add a true unicorn in the investing arena, a profitable short only strategy.

Goals for Our Short Only Strategy The goals for our short only strategy are as follows. If we can satisfy these objectives, our short only strategy will provide significant value on a portfolio level: 1. Our short strategy should prosper during prolonged bear markets. This is the main goal of the strategy. Accomplishing this will make the strategy as good as gold from a portfolio context, providing profit opportunities when markets are imploding. 2. Our short strategy, ideally, will not consistently lose money during bull markets,

such as the one that occurred from 2009-2017. In summary, our short strategy aims to provide value during bear markets, allowing us to prosper when markets go down, and not incur much of a drag when markets are in bull mode.

Keeping Expectations in Check First lets manage expectations. From 2009 to 2018, many major indexes experienced bull markets led by the S&P 500, which rose over 200%. We are not aware of any equity short only investment vehicle that profited during this period of time (most got crushed). As such, it would be unrealistic to expect a strategy with stand-alone performance numbers to be anywhere near the long only strategies we have presented so far in this book. With the goal of keeping expectations in check for a strategy like this, let’s keep a couple of things in mind: 1. Being long risk assets, such as stocks and real estate, has provided positive returns for decades. While there are no guarantees, there is a well-known risk premium in such assets, which is logical considering the fact that investors are exposing themselves to uncertainty and risk (not to mention the >50% drawdowns that have occurred throughout history). They should be rewarded for taking on such risk with positive returns over time. The same cannot be said for short positions. 2. Our backtest period covers 2007-2018. The markets from 2009 onward have largely been in bull mode. Going back to First Principles, we know that markets go down. As such, having a short only strategy to profit from such times, as well as provide valuable diversification, is a significant value add. It is practical to keep expectations in check when observing the results of a short only strategy. Remember our main goal is to further diversify our portfolio and have a strategy that can prosper when bear markets occur.

The Struggle of Short Only and Long / Short Hedge Funds Given these facts, it’s little wonder why short only hedge funds (and many long / short hedge funds) have had a particularly rough go of it over the last decade. Some of the failed ways managers have looked to trade on the short side include: • Subjective analysis of a business or an industry, making bold predictions about future prosperity and sustainability.

• Traditional fundamental analysis, looking to find overvalued stocks that (by the portfolio managers estimation) are unlikely to meet elevated earning expectations. • Technical identification of companies that are overbought and “due for a sell-off,” even though the overall trend of the market is clearly higher. • Always being short the weakest performing stocks. This has worked well in many bear markets but gets crushed when markets rally. As we know, based on First Principles, one can’t go about attacking a problem by using the same approaches used for decades that have not worked!

Observations from Years of Research Over the years, Connors Research has created a number of systematic short only strategies, some on a short-term basis, and others on a longer-term basis. A few of the research conclusions for short only strategies are as follows: • As we have seen, markets tend to trend higher for years at a time. As such, a longerterm trend filter, such as price being below the 200-day moving average or showing negative total returns over the past year, is necessary to identify regimes. • Ideally, we want our short strategy to “turn on” during longer-term bear markets and “turn off” during longer-term bull markets, because constantly fighting a bull trend is a losing proposition. • While in a longer-term bear market, the stock or ETF will often times go through short bursts of optimism. These bear market rallies (exacerbated by traders quickly covering shorts) are often violent, as volatility tends to be higher during downtrending markets. This results in the security being overbought in the shortterm within a longer-term bear trend. We have found these to be attractive short entry points, as these rallies typically fade quickly and the existing downtrend resumes.

We Favor Dynamic Short Strategies As mentioned previously, we generally don’t agree with holding static short positions or having an always “on” short only strategy. Static short only strategies, or positions, introduce too much of a drag on performance when equity and other risk markets are trending higher, which is the majority of the time. Instead, we prefer strategies that use longer-term trend following indicators to essentially turn the strategy on and off based on market conditions. Think of this as a regime filter, identifying times when we should more aggressively take short positions

and times when we should be passive on the short side. Remember, our goal for this strategy is to do well during bear markets, providing valuable diversification during such times, and not be too much of a drag during bull markets. Our longer-term trend following regime filter helps us achieve this goal.

Strategy Rules Our short strategy aims to take advantage of times when there is a short-term overbought situation in an otherwise downtrending market. As we mentioned, bear market rallies are often violent. We look to use such rallies as attractive entry points to set up our short positions. Unlike the other strategies presented so far in this book, we use limit orders for entries in our short only strategy. Again, we aim to sell into short-term euphoria (and FOMO), anticipating that the euphoria dissipates and the existing downward trend resumes. The rules for the strategy are as follows. Note this logic is run on every security in our ETF universe (identified after the rules).

ETF Avalanches Rules Entry Rules: 1. Long-Term Trend Following Regime Filter. At the beginning of the day, the security is showing a negative total return over the past year (252 trading days). 2. Intermediate-Term Trend Following Confirmation. At the beginning of the day, the security is showing a negative total return over the past month (21 trading days). This confirms that not only is the longer-term trend down, the intermediateterm trend is down as well. 3. Check for Short-Term Strength. At the beginning of the day, the 2-period RSI is above 70. This ensures that our security is moderately overbought when we put our limit order in. We’ve found this rule to be universally true when building short strategies. We want the security to be mildly to extremely overbought before shorting it. 4. Limit Order to Enter Short. At the beginning of the day, if all of the above are true, put a sell limit order 3.0% above the current price. If the order gets filled during that day, then we are short the ETF. If the order doesn’t get filled by the end of the day, it is canceled. Note: This 3.0% limit order is not a magic number, you will find positive test results using other limit order entry levels ranging from 1.5% - 3.5%. Exit Rules:

There are two separate ways to exit a short position. 5. RSI Exit. Exit if the 2-period RSI is below 15. This is checked every business day at the close. 6. Intermediate-Term Momentum Exit. Exit if the trailing one-month (21 trading days) total return turns positive. This is checked every business day at the close. Portfolio Allocation Rules: 7. Maximum Positions. Our strategy holds a maximum of 5 short positions. 8. Signal Ranking. Positions are ranked by their 100-day historical volatility (standard deviation), from high volatility to low volatility. We have found that shorter term trades benefit from higher volatility. 9. How to Allocate Cash. The percentage of capital that is not allocated to the short positions is placed into SHY (iShares 1-3-year Treasury Bond ETF) Note that our strategy holds a maximum of 5 short positions. Most times it will hold less than that, effectively shutting itself off if no markets in our universe are in bear mode.

What If Many Securities Are Meeting Our Short Criteria Above? In this case, the potential securities are ranked by their 100-day historical volatility (standard deviation), from high volatility to low volatility. The algorithm then looks to see how many open slots are available and puts in limit orders for that number of open slots, ranked by highest volatility. For example, let’s say we currently have three short positions, we then have two open slots. If there are five ETFs that meet our criteria, you’ll place orders for the two ETFs with the highest historical volatility since we only have two slots open.

ETF Universe The guidelines we followed to create our universe for this strategy is as follows: 1. The ETF is either a country equity ETF, a US sector equity ETF, or a broad regional equity ETF. 2. The inception date was 2004 or earlier, which captures equity ETFs with long trading histories. Following those two simple guidelines, our ETF universe for this strategy is as follows: 8.1. ETF Avalanches Investment Universe

Historical Test Results for the ETF Avalanches Strategy Below are the results of our short only strategy. We compare our strategy with an alternative some investors use to achieve what this strategy aims to achieve, being statically short the S&P 500. We will use the ETF “SH,” the short S&P 500 ETF, for this purpose. 8.2. ETF Avalanches Performance Summary, 2007-2018

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8.3. ETF Avalanches Monthly Returns, 2007-2018

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8.4. Cumulative Returns - ETF Avalanches vs. SH, 2007-2018

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8.5. Drawdowns - ETF Avalanches vs. SH, 2007-2018

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First of all, it is clear that our strategy is a better alternative than establishing a static short position in the S&P 500 (SH), which has eroded over 80% over the last decade. This strategy had a specific purpose - to add value to our portfolio as a whole. We aimed to achieve this purpose by focusing on the two goals for the strategy: • Make money during prolonged bear markets, providing valuable diversification at a time when some of our other strategies are potentially struggling. • Not continually lose money during extended bull markets, like we have experienced over much of the last decade. Did our strategy achieve its stated goals? Taking a look at the monthly / yearly performance, we would expect large outperformance in the 2008 global financial crisis. We would also expect some positive performance during other trying times over the last decade, such as late 2015 / early 2016 and Q4 2018. This is, in fact, what occurred. Notice the 19.49% return in 2008 when the rest of the world was falling apart. This is the value our ETF Avalanches strategy was designed to deliver. Also, unlike nearly all other short funds and long volatility strategies, this strategy didn’t continually erode during one of the longest bull markets in history. In fact, it can be viewed as a “positive carry put” for the portfolio.

A Closer Look at the Results During 2008 With our first goal in mind, prosper during prolonged bear markets, let’s take a look at the last major bear – 2008 / 2009. The following is the cumulative return of our strategy versus SPY from January 2008 to March 2009. Notice the 29.2% return for our short strategy during this time compared to the

approximately 47.8% decline for US stocks. In fact, a theoretical $1,000,000 turned into $1,292,000 in ETF Avalanches from January 2008 - February 2009 while $1,000,000 in SPY turned into $522,000. 8.6. Global Financial Crisis, ETF Avalanches vs. SPY, January 2008-February 2009

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The bottom line is our short strategy achieved its goals: performing well during bear markets and not losing too much during bull markets. As such, our ETF Avalanches strategy adds value to our overall portfolio by being short in times of stress in many regions of the world, while mostly being in cash during global bull markets.

You Can’t Predict Bear Markets Around the Globe but You Can Participate in Them Finding a good short strategy is a difficult endeavor and it is why so many short hedge funds have shuttered over the decades. Being constantly short the S&P 500, shorting “overvalued” companies, or using long only volatility strategies have been losing strategies for decades. There’s no reason to believe they will work in the future! Risk assets such as stocks generally go up over time, as investors are compensated for the significant risk they are undertaking by holding such assets. It’s further confirmation that short only funds and other “black swan” market insurance type products have failed since 2009. With these realities in mind, our short strategy does what few other short strategies do. It identifies an established bearish trend on multiple time frames and only sells short, via a limit order, during times when there are violent short-term moves to the upside. From there, if the market continues higher, our one-month momentum > 0 rule will get us out. If the market resumes its downtrend, our 2-period RSI < 15 rule usually locks in

gains. Everything is supported by 12 years of test results, which includes major bull and bear market conditions, on both a domestic and global basis. One final note - there’s a generation of naive investors who believe that markets only rise. Unfortunately, they’re very wrong. As we learned when looking at markets using First Principles, markets rise, markets fall and markets go through periods of times of stress. This truism is best amplified by having strategies for all three market conditions something you now have.

Moving On to Building the Alpha Formula Portfolio We have now presented four systematic trading strategies, along with well-defined rules and historical performance. These four strategies each attack a specific First Principle and are consistent with observed market tendencies - short-term mean reversion and long-term trend following. In the next chapter, we will put these strategies together into a portfolio - The Alpha Formula portfolio. We will discuss important considerations when putting together a portfolio of systematic strategies. We will then examine the in-depth results of the portfolio with you.

SECTION 3 The Alpha Formula Portfolio

CHAPTER 9

Introducing The Alpha Formula Portfolio We

have now presented four robust, and very different, trading strategies, all with First Principles in mind. Going back to our First Principles, we know that: • Markets go up. • Markets go down. • Markets go through times of stress. Based on years of research, we also know that markets tend to behave in certain ways based on time frames: • In the short-term, a few days to a few weeks, markets tend to mean revert. This is especially true after large moves in either direction. This is primarily driven by the behavior of market participants over this short-term time frame, specifically loss aversion and herding. • In the longer term, usually over several months, markets tend to trend. Past performance, in this sense, is in fact predictive of future performance. This is one of the most empirically studied phenomena in modern finance. We view the trending nature of markets over the longer term to also be rooted in investor behavior - with anchoring, confirmation bias, loss aversion, recency bias, relativity and herding conspiring together to create lasting price trends. With these First Principles in mind, as well as our observations of how markets work over multiple time frames, we created our four trading strategies. We designed these strategies that when they are combined together we have a portfolio that delivers significant Alpha and prospers during most market conditions. We will now put data behind these facts in this chapter. First, let’s quickly review the strategies and their characteristics. We then combine these strategies together in a portfolio with different weights, showing the historical test results. We will go on to examine how our portfolio performed under different market

environments, from financial crashes to runaway bull markets. Finally, we will investigate the historical correlation of our strategies, both to each other and to the US stock market.

A Qualitative Summary of Our Four Trading Strategies Based on First Principles Before we look at the portfolio results, let’s review one more time the reasoning for each strategy. This is important because it allows you to see how the strategies integrate when they’re combined.

First Principle #1 - Markets Go Up Rising Assets - Our Rising Assets strategy uses momentum signals to rotate into the strongest asset classes and regions in the world on an ongoing tactical basis. This strategy looks to take advantage of momentum, one of if not the most researched phenomena in academic finance. This strategy’s objective is to be in risk assets, such as equities and real estate, when they are rising and to be in bonds and other safe assets when they are not. Rising Assets has the flexibility to dynamically change risk exposure, moving from relatively risky equities and real estate to safer fixed income based on market conditions. It also has the ability to “go anywhere,” taking advantage of long-term trends no matter where they come from around the world and across asset classes. Connors Research Weekly Mean Reversion - Our Connors Research Weekly Mean Reversion strategy primarily aims to invest in individual, high quality / liquid US stocks after they have pulled back over a short period of time. Connors Research Weekly Mean Reversion then buys these stocks looking to take advantage of the subsequent mean reversion. For risk management purposes, a trend following overlay / regime filter is employed. This effectively turns the strategy off should US equities enter a prolonged bear market. Percentage stops for individual positions, checked every day, are also employed to further manage risk and keep overall volatility in check.

First Principle #2 - Markets Go Down ETF Avalanches - Our ETF Avalanches strategy is our short only strategy. ETF Avalanches is primarily designed to profit in bear markets around the globe. One of the First Principles is that markets go down, and we want to have a strategy in place that can benefit from prolonged weakness in global markets. Our ETF Avalanches strategy looks to short ETFs that have had a large spike higher in

an otherwise downtrending market. We have found this to be an advantageous setup for setting short positions as these bear market, short covering rallies often dissipate and the longer-term downtrend resumes. Designing a short only strategy has historically been difficult. Many times, such as 19821987, 1995-2000, 2003-2007, and 2009-2017, markets have tended to rise for years on end. As such, we prefer short only strategies that shut themselves off during such time, as to not be a drag on the rest of the portfolio. Our longer-term trend following regime filter provides this for us in our ETF Avalanches strategy.

First Principle #3 - Markets Go Through Times of Stress Connors Research Dynamic Treasuries - Our Connors Research Dynamic Treasuries strategy is primarily designed to provide diversification to our Rising Assets and Connors Research Weekly Mean Reversion strategies, which benefit from uptrending risk markets. This strategy is based on the premise that there are times of risk aversion in the market, and such times result in an increase in demand for safe haven assets. The most consistent of these safe haven assets over time has been US Treasury bonds. Our Connors Research Dynamic Treasuries strategy, as the name suggests, dynamically changes the duration of our strategy based on market trends. This has the effect of making the strategy more or less sensitive to changes in interest rates. This strategy performs well when risk assets panic or go through periods of acute risk aversion, which we demonstrated earlier in this book. This is the main goal of the strategy.

Introducing The Alpha Formula Portfolio We now present The Alpha Formula portfolio, a combination of the four strategies we outlined so far in this book. We present different weightings for these strategies within the portfolio in order for you to see the flexibility of the formula, including running the portfolio with leverage. To begin, let’s take a quick look again at the performance of each strategy individually. Our time period for these tests are January 2007 to the end of 2018. Though we showed longer backtests for some of the individual strategies in the previous chapters, 20072018 is the time period where we have data for all of the strategies combined which we need to construct our portfolio and analyze the results.

Individual Strategy Performance Please note the results for Rising Assets and CR Weekly Mean Reversion are slightly different

than the results in their dedicated chapters because of the different start dates. 9.1. Individual Strategy Performance Summary, 2007-2018

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Combined Performance - The Alpha Formula Portfolio The following tables display the Alpha Formula portfolio results, a combination of our four strategies. We show the results using no leverage and using 1.5 to 1 leverage. The percent of capital allocated to each strategy is shown in the table. We also added two benchmark comparisons, specifically SPY and the ubiquitous 60/40 portfolio, represented by 60% SPY and 40% AGG. 9.2. Alpha Formula Portfolio Results - No Leverage, 2007-2018

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9.3. Alpha Formula Portfolio Results - Leverage, 2007-2018

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Performance Analysis We combined the strategies for our non-leveraged portfolio in weights of: • 30% - Rising Assets • 30% - CR Weekly Mean Reversion • 20% - CR Dynamic Treasuries • 20% - ETF Avalanches For our leveraged portfolio, we increased the weights to: • 45% - Rising Assets • 45% - CR Weekly Mean Reversion • 30% - CR Dynamic Treasuries • 30% - ETF Avalanches Resulting in total leverage coming in at 1.5:1. Please note that our backtest checks the worst drawdown numbers on a daily frequency, as opposed to the standard in the Hedge Fund industry, which checks at a monthly frequency. This makes the results even more impressive.

Here Are the Key Performance Results

1. Higher Returns -The annual returns for the Alpha Formula portfolio, both unlevered and levered versions, are higher compared to both SPY and 60/40. 2. Lower DrawDowns - The maximum drawdown for the non-leveraged version was only -7.1%. This compares to -55.2% for SPY and -34.7% for 60/40. This preservation of capital changes lives! 3. Less Risk - These higher returns are accomplished with significantly less risk, both in the form of standard deviation and maximum drawdowns. 4. Higher Sharpe Ratio - With higher returns and lower risk, naturally the Sharpe Ratio is significantly higher. Our Alpha Formula portfolio shows Sharpe Ratios of 1.29 for the non-levered version and 1.24 for the levered version compared to 0.35 for SPY and 0.53 for 60/40. 5. Significant Alpha - The annualized Alpha versus SPY, the core concept of this book, shows 6.5% for the non-leveraged version and 9.5% for the leveraged version. This is impressive in a world where many believe Alpha doesn’t exist! 6. Low Beta – The Beta for our Alpha Formula portfolio is close to 0 (coming in at 0.07 to be exact). This shows these returns are not dependent on, nor correlated to, the US stock market. In this book, we calculated Alpha using the single factor capital asset pricing model (CAPM) using SPY as the market benchmark and assuming a risk free rate of 1.0%. We do not add in other risk factors, which are utilized in other factor models such as the Fama-French three-factor model and the Carhart four-factor model. These results were achieved during a time period from 2007 to 2018 that includes significant changes in the financial markets along with non-stop market moving news items and catalysts. Some of the many eventful happenings included in this time period include: • The global market crash of 2008, the worst financial crisis since the Great Depression. • The greater than 70% loss for MSCI BRIC (Brazil, Russia, India, China) in 2007-2008. • Greece imploding, beginning in late 2009. • The Flash Crash of 2010. • European sovereign debt crisis and the US debt credit rating downgrade in 2011.

• The China bear market of 2015-2016. • Many years of zero percent interest rates beginning in 2008 (including quantitative easing), followed by rising rates in 2017-2018. • Wide ranging presidential economic agendas from the policies of Presidents Bush, Obama, and Trump. The political and economic world continuously changed during this period of time, yet this portfolio continued to produce Alpha with solid, consistent returns no matter what happened in the world.

Additional Performance Statistics for the Alpha Formula Portfolio 9.4. Alpha Formula (No Leverage) Monthly Returns, 2007-2018

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9.5. Cumulative Returns - Alpha Formula (No Leverage) vs. SPY, 2007-2018

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9.6. Drawdowns - Alpha Formula (No Leverage) vs. SPY, 2007-2018

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9.7. Cumulative Returns - Alpha Formula (No Leverage) vs. 60/40, 2007-2018

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9.8. Drawdowns - Alpha Formula (No Leverage) vs. 60/40, 2007-2018

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9.9. Alpha Formula (With Leverage) Monthly Returns, 2007-2018

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9.10. Cumulative Returns - Alpha Formula (With Leverage) vs. SPY, 2007-2018

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9.11. Drawdowns - Alpha Formula (With Leverage) vs. SPY, 2007-2018

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9.12. Cumulative Returns - Alpha Formula (With Leverage) vs. 60/40, 2007-2018

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9.13. Drawdowns - Alpha Formula (With Leverage) vs. 60/40, 2007-2018

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Achieving Consistent, Uncorrelated Returns - The Holy Grail of Investing As can clearly be seen, our Alpha Formula portfolio provides us attractive risk-adjusted returns. Since we designed this portfolio and its strategies with First Principles in mind, the strategies are inherently different, taking advantage of different market truths and performing in most market environments. Having strategies that are inherently different results in such strategies having fundamentally low correlation to each other. Going back to Ray Dalio’s “holy grail” chart, this results in a portfolio with significantly less risk. This risk reduction, and subsequent increase in risk-adjusted returns, is evident in the results presented in this chapter.

Correlation Analysis - Individual Strategies The following is a correlation matrix containing our four strategies individually along with the US stock market, represented by SPY. 9.14. Correlation Matrix of The Alpha Formula Strategies and SPY, 2007-2018

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When analyzing this table, a few things stand out:

Rising Assets and CR Weekly Mean Reversion, our two strategies that were designed under the First Principle that markets rise, have a correlation coefficient of 0.57 to each other and 0.46 and 0.45 correlation respectively to SPY. This moderately positive correlation makes sense, as these strategies benefit from an overall rise in risk assets such as US stocks. Both CR Dynamic Treasuries and ETF Avalanches have zero to negative correlation Rising Assets and CR Weekly Mean Reversion. These two strategies also have significantly negative correlations to SPY, coming in at -0.48 and -0.41 respectively. This is the power of applying First Principles to design trading strategies. It results in strategies that are negatively correlated to each other, and negatively correlated to the overall stock market. The results from our correlation matrix is what we expected, with different strategies, designed using First Principles, resulting in low correlations to each other and to US Stocks. This leads to attractive portfolio level results when combined together which is displayed in this chapter.

The Alpha Formula Portfolio Also Displays Low Correlation versus SPY and 60/40 We have seen that each strategy having low correlation to each other results in a portfolio with attractive reward / risk characteristics. The Alpha Formula portfolio also displays low correlation to standard benchmarks, SPY and the 60/40 portfolio. Running the numbers, from 2007-2018, we get a correlation coefficient of 0.23 for The Alpha Formula portfolio versus SPY, and 0.27 for The Alpha Formula portfolio versus 60/40. The following table summarizes the results. 9.15. Correlation: Alpha Formula vs. SPY and 60/40, 2007-2018

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Low Correlation Leads to Lower Volatility - Putting Ray

Dalio’s Philosophy into Action As we saw in the Ray Dalio chart displayed in Chapter 2, combining uncorrelated strategies leads to large increases in risk-adjusted performance. Furthermore, as the chart showed, the first four or five strategies with low correlation leads to the most dramatic decrease in portfolio volatility. Interestingly, adding subsequent strategies has a diminishing risk reducing effect. 9.16. Uncorrelated Return Streams Decrease Risk

What’s eye-opening about this chart is that, according to Ray Dalio’s analysis, the expected volatility of four minimally correlated strategies, each with a 10% volatility, should be in the 6-7% range. What was the annual volatility for The Alpha Formula portfolio from 2007-2018? It was 6.2%, pretty much as Mr. Dalio stated it would be. The bottom line - it takes only a handful of uncorrelated strategies to give you the most “bang for your buck” from a risk reduction standpoint. Adding more is fine but you can go a long way just by applying First Principles and getting the first four right.

Broad Diversification: A Closer Look Within the Alpha Formula Portfolio Our Alpha Formula portfolio, by definition, is designed with different asset classes, markets, styles and time horizons in mind. Let’s go through some of the different characteristics within the strategies that make up the portfolio. These differences help lead to the attractive results we have witnessed. 1. Financial Instrument Diversification - Within our portfolio, we have diversification among a variety of different financial instruments. Our Rising Assets strategy, by design, is intended to participate in global financial market trends no matter where they come from around the world and across asset classes. We trade US stocks from the long side in our Connors Research Weekly Mean Reversion strategy. We trade high quality US Treasury bonds in our CR Dynamic Treasuries strategy. Finally, we trade country and sector ETFs on the short side in our ETF Avalanches strategy. 2. Geographical Diversification - Our portfolio also has geographical diversification. Stocks from around the globe are covered in our Rising Assets strategy. US stocks are covered with CR Weekly Mean Reversion. We participate on the short side through country ETFs from around the globe in our ETF Avalanches strategy. 3. Style Diversification - The strategies in our portfolio utilize multiple trading styles. We believe that the trading styles utilized have strong behavioral explanations as to why they exist and more importantly why they should continue to work. These styles include several forms of momentum and trend following trading as well as mean reversion trading. Rising Assets and CR Dynamic Treasuries are trend following in nature while CR Weekly Mean Reversion and ETF Avalanches incorporate mean reversion trading. 4. Time Horizon Diversification - The average time we hold positions within each strategy is also diversified. Time horizon diversification is one of the most underutilized concepts in portfolio construction and when it’s applied correctly it tends to improve returns while reducing volatility. a. Longer-Term: Our Rising Assets and CR Dynamic Treasuries strategies hold positions for the longest, with typical holds spanning several months to over a year. b. Intermediate Term: Our CR Weekly Mean Reversion strategy typically holds positions for an intermediate time period, averaging 40-60 trading days. c. Short-Term: Our ETF Avalanches short only strategy holds positions much shorter, with the majority of these trades lasting only a few days.

Looking Closer at the Alpha Formula Portfolio over Different Market Environments Now that we have the results of the Alpha Formula portfolio, let’s take a look at the results over several different market environments. No strategy, or combination of strategies, can outperform in all market environments. What we aim to do here is to further our understanding of our Alpha Formula portfolio. The more we know about the characteristics of our portfolio, the more likely we are to stay with the strategy and ultimately realize the superior risk-adjusted returns and performance. These charts show the performance of the non-leveraged Alpha Formula portfolio versus SPY (US Stocks) and EFA (Developed Market ex US Stocks). By comparing our strategy to these two ETFs, we are comparing against a vast majority of investable equities in the world. When observing the performance of our portfolio versus the global equity benchmarks, it is important to keep in mind that our unlevered portfolio displays markedly lower volatility than any equity index by itself, as displayed by the summary statistics earlier in this chapter.

Global Financial Crisis of 2008 Let’s start by taking a look at the growth of a theoretical $1,000,000 during the Global Financial Crisis of 2008, the worst financial crisis we have had in the US since the Great Depression. This chart spans January 2007 to March 2009. 9.17. Cumulative Returns - Global Financial Crisis, January 2007-March 2009

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9.18. Drawdowns - Global Financial Crisis, January 2007-March 2009

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During this time frame, our non-leveraged Alpha Formula portfolio grew $1,000,000 to $1,147,000. This is a dramatic outperformance compared to SPY and EFA during this time frame, which turned $1,000,000 into $589,000 and $542,000 respectively. Notice the large outperformance of the Alpha Formula portfolio here during the worst financial crisis since the Great Depression. Several factors contributed to the huge outperformance during this time period, most notably the risk management inherent in CR Weekly Mean Reversion and Rising Assets shifted these strategies into defensive bonds during this time. Also, our ETF Avalanches strategy was profitable on the short side and our CR Dynamic Treasuries strategy benefited from safe haven demand for US Treasuries. This resulted in an enormous outperformance for the Alpha Formula portfolio, when the rest of the world was blowing up!

Global Instability in 2011 How about a sideways market such as 2011? This was a very eventful year in finance. With the global financial crisis squarely in everybody’s mind, there were prevalent fears of a “double dip” recession. The downgrade of US sovereign debt (first time in history) and an ongoing crisis in Europe contributed to the anxiety of the market. 9.19. Cumulative Returns - Global Instability / Sideways Markets, January 2011December 2011

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9.20. Drawdowns - Global Instability / Sideways Markets, January 2011-December 2011

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Our unlevered Alpha Formula portfolio grew $1,000,000 to $1,075,000 during this time period. The global equity benchmarks turned $1,000,000 into $1,008,000 and $870,000 for SPY and EFA respectively. Again, the risk management aspects of our Alpha Formula portfolio are on display in the results from 2011.

Runaway Bull Markets - The Time the Alpha Formula Typically Underperforms No trading strategy outperforms all the time. There are market conditions where we shouldn’t expect a diversified strategy, especially one designed for a variety of market

conditions with risk management rules in place, to outperform. One of those conditions is in runaway bull market in equities, like we saw in 2013, where stocks made a large one-way upward move throughout the year. Thinking about our Alpha Formula portfolio, it makes sense that our strategy should lag during such times. After all, at least a portion of our portfolio is allocated to US Treasuries through our CR Dynamic Treasuries strategy. Furthermore, our ETF Avalanches strategy is likely “turned off” during such times, as there are few if any bear markets to profit from. With that in mind, let’s take a closer look at 2013, a year which saw a sustained upward move in US stocks. 9.21. Cumulative Returns - Sustained Upward Stock Trend, January 2013-December 2013

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9.22. Drawdowns - Sustained Upward Stock Trend, January 2013-December 2013

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As expected, our portfolio underperformed during this time on a total return basis. It should be noted, however, that this underperformance was accompanied by lower volatility for our strategy compared to the equity benchmarks. We feel it is important to analyze the times where a strategy underperforms, which increases our understanding of the strategy and increases the likelihood that we stick with the strategy through such underperformance. After all, sticking with a strategy over the long-term is the only way to realize the historical outperformance and Alpha.

China Crash, Brexit, and the Rise of Donald Trump 2015 into early 2016 was another eventful time for global finance. This time period saw turbulence in the global financial markets on three separate occasions: 1. Bear Market in China in 2015 into early 2016 2. Brexit chaos in mid-2016 3. The unexpected election of Donald Trump as President of the United States in late 2016 The steep and volatile bear market in the Chinese equities along with Britain’s vote to exit the European Union caused volatility throughout global markets. This lead to an increase in demand for safe haven assets such as US Treasuries. In fact, the increase in demand for Treasury bonds during this time took 10yr US Treasury yields to an all-time low of 1.38% in mid-2016. Here is the performance of The Alpha Formula portfolio from January 2015 to the end of 2016 versus SPY and EFA. 9.23. Cumulative Returns - China Crash, Brexit, and The Rise of Donald Trump,

January 2015-December 2016

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9.24. Drawdowns - China Crash, Brexit and The Rise of Donald Trump, January 2015December 2016

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$1,000,000 in our Alpha Formula portfolio grew to $1,112,000 during this time frame. US stocks, represented by SPY, outperformed our strategy on a total return basis, growing $1,000,000 into $1,134,000 but with much higher volatility. The Alpha Formula portfolio outperformed stocks throughout the rest of the world, with EFA growing $1,000,000 into $1,007,000. In spite of the upward move, both SPY and EFA saw double digit drawdowns, with much higher volatility whereas the Alpha Formula continued to perform with lower volatility.

The goal of our portfolio is to perform steadily in all market conditions and as you can see, this is exactly what it did for more than a decade. This includes the crash of 2008, the large sell-off in 2011, the outsized bull move in 2013, the events of 2015-2016 including the crash in China, Brexit and the emergence of Donald Trump.

The Alpha Formula - The Next Generation of Portfolio Management Construction In this chapter, we saw the benefit of designing strategies with First Principles in mind. Attacking portfolio management in this manner results in strategies that have low correlation to each other and, when combined, results in world class performance as we have shown.

CHAPTER 10

The Alpha Formula - A Better Solution to Money Management We’ve come

a long way in our journey together. We hope you found many of our insights, philosophies and strategies enlightening and can take some of our ideas and improve your own trading process. As you have seen, applying First Principle thinking to portfolio construction results in minimally correlated strategies. These strategies, when combined together, leads to superior performance (both on an absolute return basis and a risk-adjusted basis), versus buy and hold (indexing), 60/40, traditional long / short, and many popular hedge fund strategies. In our opinion, the Alpha Formula is a superior way to invest.

The Alpha Formula Recap We saw in the story to begin the book what happens in times of extreme stress in the markets. This stress caused normally rational (and highly paid) professionals to make, what in hindsight, turned out to be irrational decisions - buying bonds at negative interest rates. These times are not isolated! They occur over and over again, whether it was in 2008, the summer of 2011 or in any other time when the stress of the markets becomes overwhelming. In Chapter 2, we also introduced what we believe is at the heart of building high performing portfolios - First Principles. Through the introduction of First Principles as a way to build portfolios, we now are attacking an old problem in a new way. The application of “truths” to portfolio construction leads to having strategies in place for when market rise, markets fall, and for the times when markets go through periods of stress. For decades, investors have approached portfolio management using methods which are not self-evident truths and such are not First Principles! This has led to sustained underperformance from active managers and has led trillions of dollars to move into index investing. Few strategies have consistently performed in most markets. The tiny fraction of fund

managers that have been able to do so have accomplished the feat by attacking the problem differently than the majority of the asset management world. First Principles, in our opinion, are possibly the single best way to begin your portfolio construction because you are grounding your portfolio in objective truths. These truths will be there long after all of us are gone because this is the way that all markets, not only the equity and bond markets, have behaved for centuries. In Chapters 3 and 4, we looked at the evidence supporting the tendency for markets to mean revert in the short-term and trend in the long-term, as well as the underlying investor behavior behind these tendencies. These market tendencies can be used to capture Alpha in the marketplace. The behavioral underpinnings will not change because they’re based on inherent human conditions. These biases have been documented by many of the greatest social scientists of our time, including Nobel Prize winner Daniel Kahneman. Rooting an investment approach in inherent human behavior results in lasting edges! In Chapters 5 and 6, applying First Principles, we presented two strategies for the times when markets rise. In Chapter 5, our Rising Assets strategy was introduced. The strategy was inspired by the many academic studies which have shown that prices tend to trend over the longer term. Our objective with the strategy is to be in risk assets around the world, such as equities and real estate, while they’re rising, and in defensive assets, such as highquality bonds, while they’re not. The time frame tested included many large global economic and political events which crushed many other long only strategies during those volatile periods. In Chapter 6, our second long strategy was presented, the Connors Research Weekly Mean Reversion strategy. Decades of research, including some published by our research company, have shown that stocks tend to mean revert over shorter-time frames, especially after an extreme move has occurred. The Connors Research Weekly Mean Reversion strategy identifies stocks in bull markets which have pulled back too far, positioning ourselves to profit from the tendency of such stocks to mean revert. The strategy applies stops to contain the risk along with a non-optimized exit in order to take advantage of the times the prices move higher. The strategy takes advantage of a pattern that has been repeated over and over again in bull markets as money managers look to buy stocks inexpensively after they’ve dropped too far. Combining these two strategies allows the portfolio to take advantage of bull markets using both short-term mean reversion and long-term trend following, both of which are

inherent in the market over time. Each strategy also turns defensive by moving into fixed income during bear markets. In Chapter 7, we presented a strategy for the times when markets go through times of stress with our Connors Research Dynamic Treasuries strategy. One of the best assets to be in during times of stress is US Treasuries. Our CR Dynamic Treasuries strategy is always long US Treasuries and dynamically adjusts duration (sensitivity to interest rate changes) based on market conditions. No one can predict when the next 9/11 or 2008 will occur. We all hope such events never happen again in our lifetime. As realists, however, we need to be cognizant that calamitous political and economic events like this have occurred since the beginning of mankind. An allocation to safe haven assets will benefit us during such times. In Chapter 8, we looked to profit from the First Principle that markets go down with our ETF Avalanches strategy. ETF Avalanches trades ETFs from around the world, looking to sell into short-term rallies in otherwise bear markets. We are thus positioning ourselves for a continuation of the existing downtrend. Unlike so many other short strategies, which are always short no matter the market conditions, our ETF Avalanches strategy uses market trends to dictate how aggressive we will be on the short side. By approaching our short only strategy in this manner, we don’t allow static short positions to create a drag on returns when markets are rising. The ETF Avalanches strategy dynamically seeks out bear markets around the world. There is typically at least one country in the world that is in a downtrend, even in a global bull market such as what was experienced from 2009-2017. Examples include Greece in 2010, Russia and Brazil in 2011-2015, and China from 2015-early 2016. In Chapter 9, we put the strategies together in order to build a Long / Short multistrategy portfolio. The individual strategies range from moderately correlated to noncorrelated to each other. Combining the strategies together results in impressive reward / risk statistics, as the Ray Dalio “holy grail” chart showed in the second chapter. Our Alpha Formula portfolio shows a correlation to the S&P 500 of 0.23. Returns surpassed both those of SPY and 60/40, with a much higher Sharpe Ratio. Drawdowns dropped from over -55.2% for SPY to just -7.1% for the Alpha Formula, a drop of over 80%! In a world where some argue that there is no Alpha left in the marketplace, our portfolio displayed Alpha numbers of over 6% with a Beta of 0.07.

Why Was So Much Alpha Achieved? There are a number of reasons for the outstanding performance and significant Alpha displayed in the results of the Alpha Formula portfolio:

1. To the best of our knowledge, we are not aware of any other published research which attacks portfolio development by applying First Principles. When you start with truths, major breakthroughs occur. This has been practiced for centuries by scientists and is behind some of the major technological breakthroughs throughout history. Markets go up, markets go down, and markets go through times of stress are truths. From these truths comes the framework to begin building high performing portfolios. 2. There are proven behavioral biases displayed consistently by investors that dictate how markets behave. These behaviors have been identified by giants in both the social sciences and finance academia. By applying their insights to the building of the strategies, we’re able to model inherent market behavior and form strategies to profit from it. 3. There is strong strategy diversification. Our individual strategies have low correlation to each other and thus produce attractive performance when combined together. This is a step in the direction that Ray Dalio has espoused for years - find low / non-correlated strategies and combine them into one portfolio. 4. The portfolio tends to recalibrate fairly quickly as you can see in the monthly results. Having personally traded the concepts in this book for years, we’re cognizant of just how quickly the strategies adjust themselves to market conditions. This creates a portfolio of dynamically adjusting strategies that looks to constantly be in sync with the market. 5. Risk management rules are built into the portfolio both on an individual strategy basis and on a portfolio level. We have strategies turning on and off, not based upon predicting the future, but based upon market prices and trends. We’ve stated this before: opinions are fine, quantified systematic rules are better. When you look at many of the best performing money managers in the world today, most have systematic strategies in place. The days of analysts looking into crystal balls has been replaced by the professionals that apply math and data science to their investment processes.

The Alpha Formula Checklist - Comparing Portfolios to the Alpha Formula Taking the five reasons why so much Alpha was generated, you now have the opportunity to compare your portfolio, or any money manager’s portfolio, to the Alpha Formula.

The following is a checklist for you to do so. 1. Do you know the correlation of your strategies both to each other and to the overall market? Do you have a portfolio of low-correlated strategies? 2. Can you systematically profit from bull markets anywhere around the world? 3. Are you able to profit in a bear market? 4. Do you have capital permanently allocated to safe haven investments for times of stress and especially extreme stress? 5. Do your strategies dynamically turn on and off based on ever changing market conditions? 6. Do you have high performing strategies that take advantage of both short-term mean reversion and long-term trend following? 7. Are there inherent behavioral reasons for why your strategies work and should continue to work going forward?

7 Best Practices Moving Forward If you decide to implement the concepts from the Alpha Formula, the following are a handful of best practices moving forward: 1. Start with First Principles. First Principles are objective truths in the marketplace. Ground your portfolio in these truths! Build your strategies from there. Long strategies, short strategies, and strategies for times of stress. 2. Trade minimally correlated strategies. Using either the strategies taught in this book, or your own, find a suite of strategies with low to non-correlation to each other. Ray Dalio is richer than only a few people in the world. As he wrote in his great book “Principles,” having a portfolio of uncorrelated strategies is the “holy grail” of investing. 3. Quantify everything! Either learn the skills to be able to quantify your strategies, or hire a team or an outside firm to do so for you. 4. Take advantage of repeatable human behavior. Ideally your strategies will have an inherent behavioral reason behind the market behavior you are looking to profit from. Hardwired human behavior is unlikely to change in any meaningful way and leads to edges and Alpha. It’s essentially like being in a poker game and knowing how your opponent is going to behave at certain times. If you know how he or she is going to behave, you can position yourself to put the odds of success in your favor.

5. See the trade, take the trade! Too many investors abandon their strategies because of emotional reasons. Whether it’s being caused by media hysteria or simply by the way they are wired, they walk away from long-term winning strategies at the wrong time - usually when the edges are most in their favor. Until proven otherwise, stay the course! 6. Risk management is essential. As you saw, we’ve applied risk management strategies, both on a portfolio and individual position basis. A few of these risk management rules include turning strategies off if market conditions are unfavorable, having a portion of your portfolio allocated to US treasuries and having stops in place for individual positions. No one can tell us where prices are heading or when they will move. No matter how certain any of us may be on the market direction, it’s vitally important to your long-term success to have proper risk controls in place! 7. Learn and grow from your experiences. Larry has traded through the Crash of 1987, the internet bubble that led to the bear market of 2000-2002, the Crash of 2008, and many other events that have occurred over the past 40 years. Chris traded Treasury bonds through the global financial crisis of 2008, including staring down a multi-trillion dollar fund management firm during the height of the crisis. The strategies in this book stem from our real life experiences. If you’re a veteran in the industry, you’ve lived through these types of events too. Use your experience to your advantage, including protecting yourself at all times. It’s the key to long-term success. In ending, building a portfolio with Alpha is a never ending pursuit and a worthwhile endeavor. Attack markets using First Principles, take advantage of inherent human biases, acknowledge that markets both trend and mean revert, apply risk management and embrace minimally correlated strategies. Finally, stay in touch with us! If you have any questions, email us at [email protected] or [email protected]. You can also stay in touch by subscribing to the Connors Research Traders Journal, a free Journal we publish, keeping traders and investors updated on new research findings. There’s no cost and you can find details in the appendix. We enjoy the interaction and feel free to comment or ask us anything related to the book. Thank you for reading and happy trading. Chris and Larry

APPENDIX

TREND FOLLOWING REDUCES RISK Trend following, one of the key themes in this book, is one of the oldest investment strategies. Sometimes called “time series” or “absolute” momentum, trend following uses a security’s recent performance in isolation to dictate positioning, looking to be long securities that have gone up in the recent past and be out of (or short) securities that have gone down in the recent past. A trend follower looks to be “in sync” with the market, and continually puts themselves on the right side of the trend. This type of momentum is slightly different than “cross sectional” momentum (sometimes called relative strength), which looks at a securities performance versus another security, or universe of securities, to inform trading decisions and positioning. In our strategies presented in this book we use both types of momentum. Rising Assets utilizes cross sectional momentum and CR Weekly Mean Reversion, ETF Avalanches and CR Dynamic Treasuries utilizing time series momentum. Trend following has decades of real world results behind it and is one of the most widely studied phenomena in modern academic finance, which we documented in Chapter 3. Academic results have clearly shown what practitioners have known for years; that markets tend to trend and that following such trends can be a profitable strategy.

A Closer Look at the Effect of Trend Following Rules What is a typical result of applying straightforward trend following rules to individual assets? Based on our research, we can conclude that: Simple trend following rules CONSISTENTLY decreases risk versus buy and hold. This risk reduction characteristic of trend following is very valuable, and in our opinion is the main benefit of adding trend following rules. Many want trend following to result in higher returns compared to buy and hold. While trend following at times results in higher returns, we don’t always find this to be the case. Trend following at times results in higher returns, but it always results in lower risk. We view trend following, or time series momentum, as a risk reducing strategy. This is an often overlooked characteristic of trend following. This risk reducing effect of simple trend following rules, in our opinion, is the most attractive characteristic of the strategy. You will see this play out in the numbers ahead.

Trend Following Test For our trend following test, we will use US sector ETFs. Our tests will run from January 2003 to the end of 2018. We will display buy and hold performance of each ETF, then apply a simple trend following rule to each, displaying the risk reducing nature of trend following. All data used at total returns, including dividends. Below are the ETFs we will use in this study. A1-1. ETF Universe for Trend Following Risk Reduction Study

The following table displays results for buying and holding each ETF from January 2003 to December 2018: A1-2. Buy and Hold Performance for US Sector ETFs, 2003-2018

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While the results per sector naturally vary, they all had steep drawdowns at one point or another. The max drawdown for each sector ranged from -32.8% for Consumer Staples (XLP) to a shocking -83.10% for Financials (XLF). Looking at averages across the sectors, we witness an average return of 8.62% per year with 20.54% volatility, a Sharpe Ratio of 0.46 and a max drawdown of -54.96%. Now let’s apply a simple trend following rule to each ETF individually and observe the results. For our unoptimized trend following measure, we will look at total returns over the past 6 months (126 trading days). Note: This is the same lookback we apply in our Connors Research Weekly Mean Reversion strategy. The rules are: 1. If the trailing 6-month total return is greater than 0, buy and hold that ETF. 2. If the trailing 6-month total return is less than 0, buy and hold SHY (1-3 year US Treasuries). 3. Rules are checked once a month, at the end of the month. The following table shows the results of this simple trend following strategy applied to each US sector individually. A1-3. Trend Following Performance for US Sector ETFs, 2003-2018

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Sometimes simple trend following rules increased returns and sometimes it didn’t. Averaging the returns of each together shows an annual return of 7.67%, slightly lower than the buy and hold returns. What is very clear, however, is that applying simple trend following rules consistently decreases risk. Significant risk reduction can be witnessed in every sector, with our simple trend following rule decreasing volatility and max drawdown

in every case. Again, taking a look at the averages, we saw a marked decrease in volatility, with average volatility for our trend following strategy decreasing to 13.82% versus 20.54% for buy and hold. This reduction in volatility results in increased Sharpe ratios (even though our return was slightly lower), taking the average Sharpe from 0.46 (buy and hold) to 0.56 (trend following). Furthermore, average max drawdown, another risk metric, decreased dramatically. The average max drawdown for buy and hold came in at -54.96% and decreased by over half to -25.97% for trend following! The real life experience of an approximately 25% drawdown versus over a 50% drawdown is not insignificant. This is the power of trend following. Let’s now take a closer look at the risk comparisons of buy and hold versus trend following. The ensuing table compared volatility and max drawdown of each sector versus trend following. Notice the consistent risk reduction here: A1-4. Volatility Comparison - Buy and Hold vs. Trend Following, 2003-2018

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A1-5. Max Drawdown Comparison - Buy and Hold vs. Trend Following, 2003-2018

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Notice that IN EVERY CASE, trend following decreases volatility and decreased max drawdown, often by a significant margin.

Conclusion: This simple study is not intended to be a stand-alone trading strategy, but rather an illustration of the consistent risk reducing effects of simple trend following rules. This risk reducing effect of trend following can improve strategy results, and is the main reason such rules are implemented, in one way or another, in every strategy in this book.

RSI - DEFINITION, CALCULATION AND HISTORICAL EDGES In two of the strategies presented in this book, Connors Research Weekly Mean Reversion and ETF Avalanches, we utilized the technical indicator RSI or Relative Strength Index. We will now define RSI and take a look at the historical edges (future returns) for this popular technical indicator.

Definition RSI was introduced by J. Welles Wilder, Jr. in his 1978 book “New Concepts in Technical Trading Systems.” The indicator was designed to measure the speed of the change in the price of a security over a given period. RSI is an oscillator, ranging in value from 0 to 100. There are many ways to use RSI, with the most popular being an indication of overbought / oversold conditions.

RSI Calculation and Intent RSI was designed to measure the speed and strength of recent prices changes. The heart of the calculation involves measuring the average up moves versus the average down moves over the given lookback period. Said another way, RSI looks to compare the strength / speed of the increases versus the strength / speed of the decreases. A2-1. RSI Calculation

RSI with Short Lookback Periods are Better for Mean Reversion Trading Connor’s Research was at the forefront of RSI research, specifically using RSI to build mean reversion trading strategies. For years now, CR has clearly shown that shorter period RSIs are a better way to identify mean reversion opportunities. RSI’s with short lookbacks are effective in signaling that a security has moved “too far too fast,” and is due for a move back to the mean.

There Has Consistently Been Clear Historical Edges at Low RSI Levels! To investigate this further, we are going to present research showing the historical edges for short period RSIs at different levels. In this simple study, we will explore the average future three-day percentage changes given different RSI levels. We will use the ETF SPY here to demonstrate. The time period covered in this study is January 2003 through December 2018. We will explore the average 3-day percent change for SPY for all days. We will then compare this to the average 3-day future percent changes given different RSI levels. The lookback we will use here is a 4-period RSI. This study was conducted on daily data, using daily closes. Prices are adjusted to splits and dividends.

A2-2. 3-day Future SPY Returns Based on 4-period RSI Values, 2003-2018

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A clear trend emerges when inspecting the results: • Notice the average 3-day returns of the whole sample was 0.11% covering 4022 trading days. • Notice that lower 4-period RSI values leading to higher 3-day future returns and higher RSI values leading to lower 3-day future returns. For example, the average 3-day future percent change for SPY on days when its 4-period RSI was below 10 (46 trading days) was 0.56%, significantly higher than the 0.11% observed for all days. Similarly, times when the RSI was between 10 and 20 (201 trading days) showed an average future 3-day gain of 0.38%. By contrast, notice the lower than average future returns given high RSI values. RSI values of 70-80, for example, occurred in 656 trading days and lead to a future percent return of -0.05%, well below the average for the whole sample. Below we conduct the same study, this time using a 2-period RSI instead of a 4-period RSI. A2-3. 3-day Future SPY Returns Based on 2-period RSI Values, 2003-2018

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A similar pattern emerges here. When the 2-period RSI is low, future 3-day returns are high and when 2-period RSI is high, future returns are low.

Conclusion In this note, we defined the popular RSI technical indicator and briefly touched on its intent and calculation. We then quantitatively displayed historical edges for this indicator after certain readings are observed. This results in a clear pattern - lower RSI readings lead to higher future returns and higher RSI readings lead to lower than average future returns. This clearly shows that RSI with short lookback periods are useful in identifying mean reversion trading opportunities and the main reason as to why we used this indicator for two of the strategies presented in this book.

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MORE BOOKS FROM LARRY CONNORS Street Smarts: High Probability Short-Term Trading Strategies by Laurence A. Connors and Linda Bradford Raschke (https://bit.ly/2xZeURy) Short Term Trading Strategies That Work by Larry Connors and Cesar Alvarez (https://bit.ly/2MkoIbB) How Markets Really Work, 2nd Ed. by Larry Connors, Cesar Alvarez, and Connors Research, LLC (https://amzn.to/2JIcE5z) Buy the Fear, Sell the Greed: 7 Behavioral Quant Strategies for Traders by Larry Connors and Connors Research, LLC (http://bit.ly/buythefear)

LEARN TO PROGRAM IN PYTHON DIRECTLY FROM CHRIS CAIN IN ONLY 10 HOURS Python has become the hottest programming language on Wall Street and is now being used by the biggest and best quantitative trading firms in the world. Chris will teach you the benefits of Python and how it can make you a more successful trader and allow you to build better trading strategies. All the strategies and research in The Alpha Formula were tested in Python. Chris teaches a highly successful 10-hour course that will make you proficient in programming trading and investing strategies using Python. For more information on Chris’s Programming in Python for Traders, please go to http://tradingmarkets.com/store/Programming-in-Python-ForTraders-p136510056

CREDITS AND ADDITIONAL ACKNOWLEDGEMENTS Special thanks to all involved with the development of the open-source software NumPy, Matplotlib, IPython, and Pandas. S. van der Walt, S. C. Colbert, and G. Varoquaux, The numpy array: A structure for efficient numerical computation, Computing in Science & Engineering, 13 (2011), pp. 22–30, https://doi.org/10.1109/MCSE.2011.37. J. D. Hunter, Matplotlib: A 2d graphics environment, Computing In Science & Engineering, 9 (2007), pp. 90–95, https://doi.org/10.5281/zenodo.44579. F. Perez and B. E. Granger ́, Ipython: A system for interactive scienti c computing, Computing in Science & Engineering, 9 (2007), pp. 21–29, https://doi.org/10.1109/MCSE.2007.53. Wes McKinney. Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science Conference, 51-56 (2010), http://conference.scipy.org/proceedings/scipy2010/mckinney.html

Sources and Citations for Figures in this Book Figure 3.1 - Hurst, Brian ; Ooi, Yao Hua ; Heje Pedersen, Lasse. / A Century of Evidence on Trend-Following Investing : Executive Summary. Greenwich, CT : AQR Capital Management, LLC, 2012. Figure 3.2 - Wilshire Associates, Wilshire 5000 Price Index [WILL5000PR], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/WILL5000PR, June 17, 2019. Figure 3.3 - Wilshire Associates, Wilshire US Real Estate Investment Trust Total Market Index (Wilshire US REIT) [WILLREITIND], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/WILLREITIND, June 17, 2019. Figure 3.4 - Wilshire Associates, Wilshire US Small-Cap Total Market Index [WILLSMLCAP], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/WILLSMLCAP, June 17, 2019. Figure 3.5 - ICE Benchmark Administration Limited (IBA), ICE BofAML US Corp 15+yr Total Return Index Value [BAMLCC8A015PYTRIV], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/BAMLCC8A015PYTRIV, June 17, 2019. Figure 3.7 - Coinbase, Coinbase Bitcoin [CBBTCUSD], retrieved from FRED, Federal

Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CBBTCUSD, June 16, 2019. Figures 2.2, 2.3, 5.2, 5.3, 5.4, 5.5, 6.1, 6.2, 6.3, 6.4, 7.1, 7.2, 7.3, 7.4, 7.6, 7.7, 7.8, 7.9, 7.10, 7.11, 7.12, 7.13, 8.2, 8.3, 8.4, 8.5, 8.6, 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9, 9.10, 9.11, 9.12, 9.13, 9.14, 9.15, 9.17, 9.18, 9.19, 9.20, 9.21, 9.22, 9.23, 9.24, A1-2, A1-3, A1-4, A1-5, A2-2, A2-3 – Charts generated with Matplotlib using data from Quantopian.com. Tables created using data and analysis tools from Quantopian.com.

ABOUT THE AUTHORS Christopher Cain, CMT, is a quantitative trader, researcher and trading system developer. He is currently a senior quantitative researcher at Connors Research, where he shares his insights weekly in the Connors Research Traders Journal. He is an expert on trading system design and development. Mr. Cain is also the creator and lead instructor for TradingMarket’s Programming in Python For Traders course. Mr. Cain has 10 years of institutional trading experience, serving as a market maker throughout the yield curve in various fixed income instruments. He is passionate about quantitative trading, investing, data science, coding, and behavioral finance. He lives in Hoboken, NJ with his wife, Lindsay. ---Larry Connors has over 35 years of experience in the financial markets industry and has authored top-selling books on market strategies and volatility trading. Mr. Connors started in the financial services industry in 1982 with Merrill Lynch. At the time, the Dow was at 800. In 1989, he was made a Vice President with Donaldson Lufkin and Jenrette (DLJ) until 1994 when he left to start his own firm and trade his own private investment partnership. Since then, Mr. Connors has built two multi-million dollar financial markets information companies. Two of his companies, The Connors Group and Connors Research, were listed in the Entrex Private Company Index as the fastest growing private companies in the United States. Mr. Connors has authored several well-regarded books on trading including Street Smarts, Short Term Trading Strategies That Work and most recently Buy the Fear, Sell the Greed. Street Smarts was selected by Technical Analysis of Stocks and Commodities magazine as one of “The Classics” for trading books written in the 20th century. He is widely regarded as one of the leading educators in the financial markets industry. Larry is also frequently quoted by major media sources including the Wall Street Journal, Bloomberg Magazine, and The New York Times, along with major financial television and radio networks. He lives in New York City with his wife, Karen.