Quantitative Momentum E-Book

Wesley R. Gray

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Wiley Finance Editions
• Sprache: Englisch

Seitenzahl: 299

Veröffentlichungsjahr: 2016

Beschreibung

The individual investor's comprehensive guide to momentum investing

Quantitative Momentum brings momentum investing out of Wall Street and into the hands of individual investors. In his last book, Quantitative Value, author Wes Gray brought systematic value strategy from the hedge funds to the masses; in this book, he does the same for momentum investing, the system that has been shown to beat the market and regularly enriches the coffers of Wall Street's most sophisticated investors. First, you'll learn what momentum investing is not: it's not 'growth' investing, nor is it an esoteric academic concept. You may have seen it used for asset allocation, but this book details the ways in which momentum stands on its own as a stock selection strategy, and gives you the expert insight you need to make it work for you. You'll dig into its behavioral psychology roots, and discover the key tactics that are bringing both institutional and individual investors flocking into the momentum fold.

Systematic investment strategies always seem to look good on paper, but many fall down in practice. Momentum investing is one of the few systematic strategies with legs, withstanding the test of time and the rigor of academic investigation. This book provides invaluable guidance on constructing your own momentum strategy from the ground up.

• Learn what momentum is and is not
• Discover how momentum can beat the market
• Take momentum beyond asset allocation into stock selection
• Access the tools that ease DIY implementation

The large Wall Street hedge funds tend to portray themselves as the sophisticated elite, but momentum investing allows you to 'borrow' one of their top strategies to enrich your own portfolio. Quantitative Momentum is the individual investor's guide to boosting market success with a robust momentum strategy.

Leseprobe

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Table of Contents

Title Page

Copyright

Dedication

Preface

Acknowledgments

About the Authors

Part One: Understanding Momentum

Chapter 1: Less Religion; More Reason

Technical Analysis: The Market's Oldest Religion

A New Religion Emerges: Fundamental Analysis

The Age of Evidence-Based Investing

Don't Worry: This Book Is About Stock-Selection Momentum

Summary

Notes

Chapter 2: Why Can Active Investment Strategies Work?

Into the Lion's Den

Good Investing Is Like Good Poker: Pick the Right Table

Growth Investing Stinks, So Why Do It?

Summary

Notes

Chapter 3: Momentum Investing Is Not Growth Investing

The Efficient Market Mafia Kills Relative Strength

“Momentum” Rises from the Ashes

Behavioral Finance Theorists Explain Momentum

Wait a Minute: Momentum Investing Is Just Growth Investing, Which Doesn't Work!

Digging Deeper into Growth versus Momentum

But Why Does Momentum Work?

Summary

Notes

Chapter 4: Why All Value Investors Need Momentum

Momentum Is a Myth

Asness Separates Fact from Fiction

Expanding Your Horizons with Momentum

Marrying Value and Momentum

Summary

Notes

Part Two: Building a Momentum-Based Stock Selection Model

Chapter 5: The Basics of Building a Momentum Strategy

How to Calculate Generic Momentum

Three Types of Momentum

Why Momentum Portfolio Construction Matters

Summary

Notes

Chapter 6: Maximizing Momentum: The Path Matters

The Performance of Lottery Stocks

The Path to Momentum Profits

The Results

Summary

Notes

Chapter 7: Momentum Investors Need to Know Their Seasons

Window Dressing

Tax-Motivated Trading

Great Theories: But Why Do We Care?

Momentum Seasonality: The Results

Summary

Notes

Chapter 8: Quantitative Momentum Beats the Market

Transaction Costs

The Parameters of the Universe

Quantitative Momentum Analysis

A Peek Inside the Black Box

Beating the Market with Quantitative Momentum

Notes

Chapter 9: Making Momentum Work in Practice

A Two-Legged Stool: Value + Momentum

A Three-Legged Stool: Combo + Trend

Career Risk Considerations

What if I Can't Handle Poor Relative Performance?

Notes

Appendix A. Investigating Alternative Momentum Concepts

How is Momentum Related to Fundamentals?

Is the 52-Week High a Better Momentum Signal?

Can Absolute Strength Improve Relative Strength Momentum?

Can the Volatility of Momentum be Constrained?

Notes

Appendix B: Performance Statistics Definitions

About the Companion Website

Index

End User License Agreement

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Guide

Table of Contents

Begin Reading

List of Illustrations

Chapter 2: Why Can Active Investment Strategies Work?

Figure 2.1 The Two Pillars of Behavioral Finance

Figure 2.2 Identifying Opportunity in the Market

Figure 2.3 CGM Focus Fund from 1999 to 2009

Figure 2.4 The Long-Term Performance Equation

Figure 2.5 Investors Extrapolate Past Growth Rates into the Future

Figure 2.6 Realized Growth Rates Systematically Mean-Revert

Figure 2.7 Value Investing Can Underperform

Figure 2.8 Value and Growth Chart

Figure 2.9 New Style Box Paradigm

Chapter 3: Momentum Investing Is Not Growth Investing

Figure 3.1 CAGR: Growth Monkeys versus Momentum Monkeys

Figure 3.2 Volatility: Growth Monkeys versus Momentum Monkeys

Figure 3.3 Drawdown: Growth Monkeys versus Momentum Monkeys

Chapter 4: Why All Value Investors Need Momentum

Figure 4.1 Modern Portfolio Theory Chart (1927-2014)

Figure 4.2 Modern Portfolio Theory with Momentum

Figure 4.3 US Rolling Five-Year Spreads

Figure 4.4 UK Rolling Five-Year Spreads

Figure 4.5 Europe Rolling Five-Year Spreads

Figure 4.6 Japan Rolling Five-Year Spreads

Figure 4.7 Global Rolling Five-Year Spreads

Chapter 5: The Basics of Building a Momentum Strategy

Figure 5.1 Short-Term Momentum Portfolio Returns

Figure 5.2 Long-Term Momentum Portfolio Returns

Figure 5.3 Intermediate-Term Momentum Portfolio Returns

Chapter 6: Maximizing Momentum: The Path Matters

Figure 6.1 Alliance and International Rectifier Past Performance

Figure 6.2 Alliance and International Rectifier Future Performance

Figure 6.3 Frog-in-the-Pan Portfolio Alphas

Figure 6.4 Quality of Momentum Portfolio Returns

Chapter 7: Momentum Investors Need to Know Their Seasons

Figure 7.1 Momentum Seasonality from 1984 to 2004

Figure 7.2 Momentum Spread from 1974 to 2014

Chapter 8: Quantitative Momentum Beats the Market

Figure 8.1 Quantitative Momentum Process

Figure 8.2 Cumulative Value for Quantitative Momentum (1927–2014)

Figure 8.3a Five-Year Rolling CAGR for Quantitative Momentum

Figure 8.3b Ten-Year Rolling CAGR for Quantitative Momentum

Figure 8.4 Summary Drawdown Analysis

Figure 8.5a Five-Year Rolling Max Drawdown for Quantitative Momentum

Figure 8.5b Ten-Year Rolling Max Drawdown for Quantitative Momentum

Figure 8.6 Market Cycle Performance for Quantitative Momentum

Figure 8.7 Short-Term Stress Event Tests for Quantitative Momentum

Figure 8.8a Five-Year Rolling Alpha for Quantitative Momentum

Figure 8.8b Ten-Year Rolling Alpha for Quantitative Momentum

Chapter 9: Making Momentum Work in Practice

Figure 9.1 Rolling Five-Year Spreads

Figure 9.2 Histogram of Five-Year Spreads

Figure 9.3 Histogram of 5-Year Spreads

Figure 9.4 Histogram of Five-Year Spreads

Appendix A. Investigating Alternative Momentum Concepts

Figure A1.1 Fundamental Momentum Returns

Figure A1.2 Decile Returns to 52-Week High Screen

Figure A1.3 Absolute Momentum Breakpoints

Figure A1.4 Absolute Momentum Number of Firms

List of Tables

Chapter 2: Why Can Active Investment Strategies Work?

Table 2.1 Value versus Growth (1927 to 2014)

Table 2.2 Value Investing Can Underperform (1994–1999)

Table 2.3 Annual Returns

Table 2.4 Summary Statistics (2000–2014)

Table 2.5 Summary Statistics (1994–2014)

Table 2.6 Combining Value and Growth Lowers Volatility (1994–1999)

Table 2.7 Annual Returns for Combo Portfolio

Table 2.8 Combining Value and Growth Lowers Volatility (1994–2014)

Chapter 3: Momentum Investing Is Not Growth Investing

Table 3.1 Momentum Performance (1927–2014)

Table 3.2 Momentum Investing Can Underperform (2008–2009)

Table 3.3 Momentum Investing Can Underperform (2008–2014)

Chapter 4: Why All Value Investors Need Momentum

Table 4.1 Japanese Equity Market Performance (1982–2014)

Table 4.2 Asset Class Historical Results (1927-2014)

Table 4.3 Momentum Performance (1982–2014)

Table 4.4 Value Performance (1982–2014)

Table 4.5 Correlation of Value and Momentum

Table 4.6 Value and Momentum Combination Portfolios

Chapter 5: The Basics of Building a Momentum Strategy

Table 5.1 Simple 12-Month Momentum Example for Apple

Table 5.2 Short-Term Momentum Portfolio Returns (1927–2014)

Table 5.3 Long-Term Momentum Portfolio Returns (1931–2014)

Table 5.4 Intermediate-Term Momentum Portfolio Returns (1927–2014)

Table 5.5 Momentum Portfolio Returns: Varying Holding Period and Number of Firms in the Portfolio (1927–2014)

Chapter 6: Maximizing Momentum: The Path Matters

Table 6.1 Lottery Stock Results

Table 6.2 Average Monthly Returns Sorting Stocks on Beta and the “Lottery” Ranking

Table 6.3 Frog-in-the-pan Results to Long/Short Momentum Portfolios

Table 6.4 Quality of Momentum Portfolio Annual Results

Chapter 7: Momentum Investors Need to Know Their Seasons

Table 7.1 Average Returns by Month

Table 7.2 Seasonality of Momentum Portfolio Annual Results

Chapter 8: Quantitative Momentum Beats the Market

Table 8.1 Universe Selection Parameters

Table 8.2 VW Quantitative Momentum Performance (1927–2014)

Table 8.3 CAGR Across Different Decades

Table 8.4 Top 10 Drawdown Analysis

Table 8.5 Market Cycle Definitions

Table 8.6 Asset Pricing Coefficient Estimates for Quantitative Momentum

Table 8.7 December 31, 2014, Quantitative Momentum Portfolio Holdings

Chapter 9: Making Momentum Work in Practice

Table 9.1 Combining Quantitative Value and Quantitative Momentum

Table 9.2 Combining Quantitative Value and Quantitative Momentum

Table 9.3 Core-Satellite Returns

Appendix A. Investigating Alternative Momentum Concepts

Table A1.1 Top Decile Portfolio Summary Statistics

Table A1.2 Bottom Decile Portfolio Summary Statistics

Table A1.3 Long/Short Momentum Portfolio Annual Returns

Table A1.4 Long/Short Momentum Portfolio Factor Loadings

Table A1.5 Value and Momentum Portfolio Annual Returns

Table A1.6 Absolute Momentum Long/Short Returns

Table A1.7 Absolute Momentum Long-Only Portfolio Returns

Table A1.8 Equal-Weighted Stop-Loss Momentum Monthly Returns

Table A1.9 Momentum Stop-Loss Performance

Table A1.10 Time-Series Momentum Performance

Appendix B: Performance Statistics Definitions

Table A2.1 Performance Statistics Definitions

Quantitative Momentum

A Practitioner’s Guide to Building a Momentum-Based Stock Selection System

WESLEY R. GRAYJACK R. VOGEL

Copyright © 2016 by Wesley R. Gray and Jack R. Vogel. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Names: Gray, Wesley R., author. | Vogel, Jack R., 1983- author.

Title: Quantitative momentum : a practitioner’s guide to building a momentum-based stock selection system / Wesley R. Gray, Jack R. Vogel.

Description: Hoboken, New Jersey : John Wiley & Sons, Inc., [2016] | Series: Wiley finance series | Includes index.

Identifiers: LCCN 2016023789 (print) | LCCN 2016035370 (ebook) | ISBN 9781119237198 (cloth) | ISBN 9781119237266 (pdf) | ISBN 9781119237259 (epub)

Subjects: LCSH: Stocks. | Investments. | Technical analysis (Investment analysis)

Classification: LCC HG4661 .G676 2016 (print) | LCC HG4661 (ebook) | DDC 332.63/2042—dc23

LC record available at https://lccn.loc.gov/2016023789

Cover image: Wiley

Cover design: © Frank Rohde/Shutterstock

Buy cheap; buy strong; hold 'em long.

—Wes and Jack

Preface

The efficient market hypothesis suggests that past prices cannot predict future success. But there is a problem: past prices do predict future expected performance and this problem is generically labeled “momentum.” Momentum is the epitome of a simple strategy even your grandmother would understand—buy winners. And momentum is an open secret. The track record associated with buying past winners now extends over 200 years and has become the ultimate black eye for the efficient market hypothesis (EMH). So why isn't everyone a momentum investor? We believe there are two reasons: hard-wired behavioral biases cause many investors to be anti-momentum traders, and for the professional, who wants to exploit momentum, marketplace constraints make this a challenging enterprise.

As long as human beings suffer from systematic expectation errors, prices have the potential to deviate from fundamentals. In the context of value investing, this expectation error seems to be an overreaction to negative news, on average; for momentum, the expectation error is surprisingly tied to an underreaction to positive news (some argue it is an overreaction, which cannot be ruled out, but the collective evidence is more supportive of the undereaction hypothesis). So investors that believe that behavioral bias drives the long-term excess returns associated with value investing already believe in the key mechanism that drives the long-term sustainability of momentum. In short, value and momentum represent the two sides of the same behavioral bias coin.

But why aren't momentum strategies exploited by more investors and arbitraged away? As we will discuss, the speed at which mispricing opportunities are eliminated depends on the cost of exploitation. Putting aside an array of transaction and information acquisition costs, which are nonzero, the biggest cost to exploiting long-lasting mispricing opportunities are career risk concerns on behalf of delegated asset managers. The career risk aspect develops because investors often delegate to a professional to manage their capital on their behalf. Unfortunately, the investors that delegate their capital to the professional fund managers often assess the performance of their hired manager based on their short-term relative performance to a benchmark. But this creates a warped incentive for the professional fund manager. On the one hand, fund managers want to exploit mispricing opportunities because of the high expected long-term performance, but on the other hand, they can do so only to the extent to which exploiting the mispricing opportunities doesn't cause their expected performance to deviate too far—and/or for too long—from a standard benchmark. In summary, strategies like momentum presumably work because they sometimes fail spectacularly relative to passive benchmarks, creating a “career risk” premium. And if we follow this line of reasoning, we only need to assume the following to believe that a momentum strategy, or really any anomaly strategy, can be sustainable in the future:

Investors will continue to suffer behavioral bias.

Investors who delegate will be short-sighted performance chasers.

We think we can rely on these two assumptions for the foreseeable future. And because of our faith in these assumptions, we believe there will always be opportunities for process-driven, long-term focused, disciplined investors.

Assuming we are prepared to be a momentum investor and we've internalized the reality that the journey has to be painful in order to be sustainable, we need to address a simple question: How do we build an effective momentum strategy? In this book we outline the multiyear research journey we undertook to build our stock selection momentum strategy. The conclusion of our adventure is the quantitative momentum strategy, which can be summarized as a strategy that seeks to buy stocks with the highest quality momentum. And to be clear up front, we do not claim to have the “best” momentum strategy, or a momentum strategy that is “guaranteed” to work, but we do think our process is reasonable, evidence-based, and ties back to behavioral finance in a coherent and logical way. We also provide radical transparency into how and why we've developed the process. We want readers to question our assumptions, reverse engineer the results, and tell us if they think our process can be improved. You can always reach us at AlphaArchitect.com and we'll be happy to address your questions.

We hope you enjoy the story of quantitative momentum.

Acknowledgments

We have had enormous support from many colleagues, friends, and family in making this book a reality. We thank our wives, Katie Gray and Meg Vogel, for their continual support and for managing our chaotic kids so we could write our manuscript. We'd also like to thank the entire team at Alpha Architect, for dealing with the two of us while we drafted the initial manuscript. David Foulke provided invaluable comments and read the manuscript so many times his head is still spinning. Walter Haynes also played a pivotal role in making the manuscript a lot better. Yang Xu was immensely helpful on the research front, grinding numbers into the late hours of the night. Finally, to the rest of the Alpha Architect team—Tian Yao, Yang Xu, Tao Wang, Pat Cleary, Carl Kanner, and Xin Song—we are forever indebted! We'd also like to thank outside readers for their early comments and incredible insights. Andrew Miller, Larry Dunn, Matt Martelli, Pat O'Shaughnessy, Gary Antonacci, and a handful of anonymous readers made the book so much better than it would have been had we been working alone. Finally, we think our editor Julie Kerr for her invaluable feedback.

About the Authors

Wesley R. Gray, PhD

After serving as a Captain in the United States Marine Corps, Dr. Gray received a PhD, and was a finance professor at Drexel University. Dr. Gray's interest in entrepreneurship and behavioral finance led him to found Alpha Architect, an asset management firm that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and multiple academic articles. Wes is a regular contributor to the Wall Street Journal, Forbes, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.

John (Jack) R. Vogel, PhD

Dr. Vogel conducts research in empirical asset pricing and behavioral finance, and has published two books and multiple academic articles. His academic background includes experience as an instructor and research assistant at Drexel University in both the Finance and Mathematics departments, as well as a Finance instructor at Villanova University. Dr. Vogel is currently a Managing Member of Alpha Architect, an SEC-Registered Investment Advisor, where he serves as the Chief Financial Officer and Co-Chief Investment Officer. He has a PhD in Finance and a MS in Mathematics from Drexel University, and graduated summa cum laude with a BS in Mathematics and Education from the University of Scranton.

Part One

Understanding Momentum

This book is organized into two parts. Part One sets out the rationale for using momentum as a systematic stock selection tool. In Chapter 1, “Less Religion; More Reason,” we provide a discussion of the two dominant investment religions: fundamental and technical. We propose that evidence-based investors consider both approaches. Next, in Chapter 2, “Why Can Active Investment Strategies Work?” we outline our sustainable active investing framework, which helps us identify why a strategy will work over the long haul (i.e., the “edge”). In Chapter 3, “Momentum Investing is Not Growth Investing,” we propose that momentum investing, like value investing, is arguably a sustainable anomaly. Finally, we end Part One with Chapter 4, “Why All Value Investors Need Momentum,” a discussion of the evidence related to momentum investing, which suggests that most investors should at least consider momentum investing when constructing their diversified investment portfolio.

CHAPTER 1Less Religion; More Reason

Child: “Dad, are you sure Santa brought the presents?”

Father: “Yes, Santa carried them on his sleigh.”

Child: “I guess that makes sense. He did eat the cookies and milk we left by the fireplace.”

—Typical adult/child chat on Christmas Day

TECHNICAL ANALYSIS: THE MARKET'S OLDEST RELIGION

During the 1600s, the Dutch had a large merchant fleet and the port city of Amsterdam was a dominant commercial hub for trade from around the world. Based on the growing influence of the Dutch Republic, in 1602 the Dutch East India Company was founded, and its evolution into the first publicly traded global corporation drove a number of financial innovations to the Amsterdam Stock Exchange, including the subsequent listing of additional companies and even short selling.

In 1688, Joseph de la Vega, a successful Dutch merchant, wrote Confusion De Confusiones, one of the earliest known books to describe a stock exchange and stock trading. Some researchers today argue that he should be considered the father of behavioral finance. De la Vega vividly described excessive trading, overreaction, underreaction, and the disposition effect well before they were documented by modern finance journals.1

In his book, de la Vega describes the day-to-day business of the Exchange and alludes to how prices are set:

When a bull enters such a coffee-house during the Exchange hours, he is asked the price of the shares by the people present. He adds one to two per cent to the price of the day and he produces a notebook in which he pretends to put down orders. The desire to buy shares increases; and this enhances also the apprehension that there may be a further rise (for on this point we are all alike: when the prices rise, we think that they fly up high and, when they have risen high, that they will run away from us).2

De la Vega seems to be describing how rising prices themselves can beget continued price increases. Put another way, in the words of Wes's graduate school roommate who managed a market making desk at a large Wall Street bank, “High prices attract buyers, low prices attract sellers.”3

De la Vega continues:

The fall of prices need not have a limit, and there are also unlimited possibilities for the rise…Therefore the excessively high values need not alarm you…there will always be buyers who will free you from anxiety…the bulls are optimistic with joy over the state of business affairs, which is steadily favorable to them; and their attitude is so full of [unthinking] confidence that even less favorable news does not impress them and causes no anxiety…[It seems] incompatible with philosophy that bears should sell after the reason for their sales has ceased to exist, since the philosophers teach that when the cause ceases, the effect ceases also. But if the bears obstinately go on selling, there is an effect even after the cause had disappeared.4

Here de la Vega explicitly discusses how bulls can continue buying, and bears can continue selling, even when there is no direct reason or cause for them to do so, other than the price action itself. So here we see how, even in seventeenth-century Europe, price changes—independent of fundamentals—can affect future market prices.

While early technical analysis was evolving in stock trading in Europe, an even more fascinating financial experiment was taking place in Japan. During the 1600s, the peasant class, who made up the majority of the Japanese population, was forced into farming, thus supplying a tax base that could support the ruling military class, who, in turn, provided protection for agricultural land. Rice was the largest crop at that time, accounting for as much as 90 percent of government revenues, and became a staple of the Japanese economy.

The important role of rice in Japan led to the establishment of a formal exchange in 1697, and eventually to the emergence of what many believe to be the first futures market, the Dojima Rice Market. That market grew to include a network of warehouses, with established credit and clearing mechanisms.5

The rapidly evolving rice market in Japan was the fertile financial environment in which a young rice merchant, Munehisa Homma (1724–1803), found himself during the mid-1700s. Homma began trading rice futures and used a private communications network to trade advantageously. Homma also used the history of prices to make predictions about the direction of future prices. But his key insight involved the psychology of the markets.

In 1755, Homma wrote, The Fountain of Gold—The Three Monkey Record of Money, which described the role of emotions and how these could affect rice prices. Homma observed, “The psychological aspect of the market was critical to [one's] trading success,” and “studying the emotions of the market…could help in predicting prices.” Thus, Homma, like de la Vega, was perhaps one of the earliest documented practitioners of behavioral finance. His book was among the earliest writings covering markets and investor psychology.6

Homma invested on the long and the short side, and was thus an antecedent to today's hedge funds. He was so successful and became so wealthy that he inspired the adage: “I will never become a Homma, but I would settle to be a local lord.” He eventually became an adviser to the government, and to Japan's first sovereign wealth fund.7

On the other side of the globe, financial markets were also evolving. The late nineteenth and early twentieth centuries marked a time of increasing stock market participation in the United States. Among the most famous equity investors of that era was a man named Jesse Livermore. He began trading at the age of 14, and over his lifetime, he gained and lost several fortunes.

An American author named Edwin Lefevre wrote the biography Reminiscences of a Stock Operator. The biography is an account of Livermore's life and experiences in the early years of 1900s. The book describes Livermore's success using technical trading rules. Lefevre also described Livermore's overarching philosophy on the market:

You watch the market…with one object: to determine the direction—that is the price tendency…Nobody should be puzzled as to whether a market is a bull or a bear market after it fairly starts. The trend is evident to a man who has an open mind and reasonably clear sight…8

We gain more insight into Livermore's investment philosophy when we examine comments regarding his buy and sell decisions. We would recognize these decisions today as modern “momentum” strategies: “It is surprising how many experienced traders there are who look incredulous when I tell them that when I buy stocks for a rise I like to pay top prices and when I sell I must sell low or not at all.”

Clearly, the ideas that investors are not completely rational, and prices are related to future prices are not new ideas. Collectively, the investors discussed above—Joseph de la Vega, Munehisa Homma, and Jesse Livermore—highlight how great investors across history have recognized the role of psychology in the markets, and that historical prices can help predict future prices—in other words, technical analysis works. But fast forward to the early twentieth century, when some investors began to question whether technical analysis represented a sensible approach to investing. Many thought analysis of a company's fundamentals might be a more reasonable technique. Investors began to investigate fundamental analysis, involving a careful review of a company's financial statements, in hopes that such analysis might provide a better rationale for making investment decisions. In particular, a new investing philosophy began to gain notoriety: value investing, which involves buying stocks trading at a low price versus various fundamentals, such as earnings or cash flow.

A NEW RELIGION EMERGES: FUNDAMENTAL ANALYSIS

Benjamin Graham is commonly known as the father of the value investing movement. Graham believed that if investors bought stocks at prices consistently below their intrinsic value, as determined by fundamental analysis, those investors could earn superior risk-adjusted returns. Graham outlined his value-investing framework in two of the most famous investing books of all time, Security Analysis and The Intelligent Investor.

Graham realized that there were many adherents to the technical analysis approach, but he was clear in expressing what he thought of the discipline: bogus witchcraft. A quote from The Intelligent Investor summarizes his views:

The one principle that applies to nearly all these so-called “technical approaches” is that one should buy because a stock or the market has gone up and one should sell because it has declined. This is the exact opposite of sound business sense everywhere else, and it is most unlikely that it can lead to lasting success on Wall Street.9

Graham's early criticism of technical analysis has been reinforced over time by other adamant adherents of the fundamental analysis religion. Graham's most famous protégé, Warren Buffett, took the boxing gloves from Graham and continued to beat on the technical analysis crowd. A statement attributed to him demonstrates his views: “I realized technical analysis didn't work when I turned the charts upside down and didn't get a different answer.” A more recent quote by Burt Malkiel, who penned the popular book A Random Walk Down Wall Street, brings the disdain for technical methods front and center: “The central proposition of charting is absolutely false…”10

One can almost hear the laughter from the fundamental analysts. They believe they are better informed and ultimately more rational than technical investors. Another statement attributed to Buffett is, “If past history was all there was to the game, the richest people would be librarians.” It's pretty obvious that, in Buffett's view, only obscure and harebrained librarians turning their charts around and around would ever consider technical analysis to be a legitimate discipline. And perhaps the religious adherents of the fundamental approach thought that the use of humor and ridicule would make their arguments more compelling.

More recently, Seth Klarman, the billionaire founder of the Baupost Group hedge fund, has also denigrated technical analysis. In his cult-classic value investing book Margin of Safety: Risk-Averse Value Investing Strategies for the Thoughtful Investor, Klarman is clear about his views:11

Speculators…buy and sell securities based on the whether they believe those securities will next rise or fall in price. Their judgment regarding future price movements is based, not on fundamentals, but on a prediction of the behavior of others…They buy securities because they “act” well and sell when they don't…Many speculators attempt to predict the market direction by using technical analysis—past stock price fluctuations—as a guide. Technical analysis is based on the presumption that past share prices meanderings, rather than underlying business value, hold the key to future stock prices. In reality, no one knows what the market will do; trying to predict it is a waste of time, and investing based on that prediction is a speculative undertaking…speculators…are likely to lose money over time.

It is illuminating that Klarman views underlying fundamentals as the only justifiable signal for insight into future stock prices. Price action is “meandering” and meaningless, and efforts to predict the behavior of others are in vain. But Klarman doesn't stop here. He goes on to reject any systematic means of predicting future stock prices:

Some investment formulas involve technical analysis, in which past stock-price movements are considered predictive of future prices. Other formulas incorporate investment fundamentals such as price-to-earnings (P/E) ratios, price-to-book-value ratios, sales or profits growth rates, dividend yields, and the prevailing level of interest rates. Despite the enormous effort that has been put into devising such formulas, none has been proven to work.

It is perhaps surprising that Graham, Malkiel, Buffett, and Klarman would be so dismissive of technical analysis, given what seems to be a rich vein of successful historical practitioners and a stack of academic research that is arguably higher than the research that supports the merits of a fundamental, or value investing, approach. Nevertheless, these fundamental investors' views are reflective of those of many in the value investing community and of fundamental practitioners in general. The value investing religion is alive and well.

THE AGE OF EVIDENCE-BASED INVESTING

“Avoid extremely intense ideology because it ruins your mind.”

—Charlie Munger, Vice Chairman, Berkshire Hathaway12

Why did Ben Graham, a data-driven financial economist at heart, have a knee-jerk distrust for technical methods? Perhaps some of this doubt relates to how technical analysis differs from fundamental analysis. For value investors, fundamentals lead, and prices follow, albeit noisily. However, for technical investors, prices lead, and perhaps even drive fundamentals, but fundamentals are not the core driver of stock movements. Moreover, the technician label captures a larger group of the investing public, with a much larger distribution of skills, ranging from the peon to the preeminent. This wider distribution means the average technician tends to be more subjective, less professional, and generally less sophisticated than the average fundamental investor. Thus, one criticism of technical analysis might be that investors are seeking out patterns where no patterns really exist—a reasonable concern, given what we know about human behavior.

Contrast the technical analyst with the fundamental analyst. The fundamental analyst is looking at concrete data—financial statements—that are based on established conventions. For example, positive net income ratios, ample free cash flow, and low levels of debt can be considered fairly objective measures of good financial health. Additionally, the fundamental analyst must do a lot of hard work to conduct her security analysis: after all, she is trying to identify the present value of all future cash flows from a business and discount them to the present time.

The fundamental analyst is thus arguably engaged in a more thoughtful and intellectually rigorous pursuit. In this sense, she is perhaps more credible. Buying based on fundamentals seems more reasonable than examining recent price charts with a Ouija board. The technical analyst is assumed to have a simpler job because one can reasonably argue that a history of prices is a limited and simplistic signal, whereas for the fundamental analyst, there is a much wider and deeper array of financial information to digest and consider.

But in the end, does effort and sophistication really matter? Taking a step back, the mission for long-term active investors is to beat the market. Active investors should focus on the scientific method to address a basic question: What works? Warren Buffett obviously showed that value investing, irrespective of technical considerations, can work. But Stanley Druckenmiller, George Soros, and Paul Tudor Jones also showed that technical analysis can work just as well. An ever-growing body of academic research formalizes the evidence that fundamental strategies (e.g., value and quality) and technical strategies (e.g., momentum and trend-following) both seem to work.13