of the book.
This book is based on my 35 years in teaching, research, and actual portfolio management. I hope you find what I present useful in building a successful portfolio. And, finally, I leave you with the investor’s blessing: may you always buy low and sell high (or at least 60% of the time)!
C. Thomas Howard, Denver, Colorado, 2014
Acknowledgements
In building the case for BPM, I frequently reference three books: Daniel Kahneman’s Thinking, Fast and Slow, Nassim Taleb’s Fooled by Randomness, and Hersh Shefrin’s Behavioralizing Finance.
Kahneman, winner of the 2002 Nobel Memorial Prize in Economic Sciences, does a masterful job of presenting the major conclusions of Behavioral Science, providing numerous insights into how individuals actually make decisions using shortcuts and heuristics. The rational model it is not. Behavioral science came of age during the same time period over which MPT became the de facto standard in the investment management industry. I came to wish my own education and early experience would have included much more behavioral science and much less MPT, but it is better to be late than never as they say.
Taleb provides an eminently readable exposition on the challenge facing individuals in mastering one of the most difficult realties of the world: the random nature of events and markets. He contends that the complex and random nature of the investing world we face has outrun our brains’ evolutionary hard wiring. To be successful, an investor must think of the world in terms of probabilities and previously unobserved dramatic events (black swans in his parlance). This requires a dose of heavy-duty analytic thinking – what Kahneman refers to as System 2 thinking. The BDI spends most of the time engaging the cerebral cortex portion of the brain and little time engaging the intuitive limbic portion of the brain.
Shefrin, in Behavioralizing Finance, provides a systematic analysis of how behavioral assumptions impact various aspects of modern finance theory. He posits a world in which investors begin with unequal wealth endowments, have different probability assessments of future events (some are pessimistic, some are optimistic, some are overconfident, and some are rational, with probability assessments changing over time), and have different risk preferences.
The result is a composite probability distribution of future events which differs in important respects from the true probability distribution. The differences between composite and true probabilities leads to pricing distortions. Individual securities, as well as market-wide indices, can go from being undervalued to overvalued and back again. Shefrin believes that “the future of finance will combine realistic assumptions from behavioral finance and rigorous analysis from neoclassical finance.” I hope to take a step in this direction by showing the implications of such a dynamic pricing model for how investing decisions should be made in light of emotional investors.
I must mention Robert Haugen and his book The Inefficient Stock Market. In the mid-90s as I was actively questioning the validity of MPT, I came across this excellent book which confirmed many of the things I was thinking at the time. It provided arguments and empirical results challenging the pillars of MPT. He essentially posited a behavioral market in which pricing distortions were common. He captured these distortions by means of a multi-factor proxy model based on PE ratios, growth rates, debt ratios, and other company characteristics. I used his book in my securities classes at Daniels for nearly 20 years. Haugen provided an important stepping stone on my journey to BPM, and for that I am thankful.
I would like to thank Craig Callahan, friend and colleague, for posing the initial questions and providing support for the research project which has produced one surprising result after another and is the basis for this book. Gary Black of Janus Capital saw early potential in this project and provided initial research funding. For many years I have enjoyed and benefited from my conversations with my friend and industry veteran Andrew Cox. Academic colleagues Russ Wermers, Levon Goukasian, Hersh Shefrin, Oliver Boguth, Russ Goyenko, Randy Cohen, Malcolm Baker and Gene Fama have provided useful insights over the years. Jeff Wurgler, Ken French and Jay Ritter generously provided data for testing purposes. This book would not have been possible without the support and infrastructure development provided by my colleagues at AthenaInvest: Andy Howard, Joel Coppin and Lambert Bunker.
And most importantly, the unwavering love and support of my wife Mitch has been indispensable. She has stood by me for 40 years, through the trying years of the PhD program and now through the trying years of launching a new business. To her I dedicate this book with love.
Executive Summary
In this executive summary I present the most important features and conclusions of my research, broken down by chapter so that readers can navigate to topics of interest. This Executive Summary thus acts as a more detailed contents page – helping you find your way to specific areas – and also provides a guide to the most significant conclusions that I present.
Behavioral Portfolio Management (BPM) is presented as a superior way to make investment decisions. BPM’s first basic principle is that Emotional Crowds dominate the determination of both prices and volatility, with fundamentals playing a small role. The second basic principle is that Behavioral Data Investors earn superior returns. The third basic principle is that investment risk is the chance of underperformance. (See Chapter 1.)
Emotions and heuristics dominate our decision process – they act as brakes on our investing, preventing us from making good decisions. How do we avoid falling prey to emotionally-driven and ineffective heuristics? This is a challenge since money and market volatility stir up strong emotions and so we have to constantly work to not let them control our thinking. (See Chapter 2.)
We do not have the ability to intuitively identify trends in financial time series. Thus a successful market timing approach must be based on rigorous analysis, revealing both statistical and economic significance. The identification of such a process is complicated by investor myopic loss aversion, driving us to seek relief from short-term losses, while making us susceptible in any number of poorly tested timing techniques. Achieving a DNA level understanding of randomness is a critical step to becoming a successful Behavioral Data Investor. (See Chapter 3.)
The investment industry is dominated by a Cult of Emotions in which investors make emotional decisions and the industry does little to discourage them to do otherwise. Particularly pernicious are the Cult Enforcers who use the emotional tools of MPT to keep investors from building superior portfolios. (See Chapter 4.)
The typical emotional, anecdotal investor makes poor decisions. Myopic loss aversion interacts with social validation which interacts with availability bias which interacts with mental accounting and so on, leading to the careful creation of inferior portfolios. (See Chapter 5.)
Like any good scientific, rational, truth-seeking organization, the finance profession has chosen to reject the world rather than reject the empirically discredited MPT. Now that we have spent the last 40 years, essentially my entire professional career, waiting for evidence supportive of MPT to appear and seeing none, it is time to move on. Luckily an alternative is emerging: behavioral finance. (See Chapter 6.)
Diversification, remembering the name of the stock in which you invested, and the price you paid for it, are a few of the things that lead to poor investment decisions and should be avoided. (See Chapter 7.)
The traditional mountain chart for representing investment performance is an emotionally-driven, random representation of portfolio returns. Instead, return histograms and matched returns are a more meaningful way to present what an investor can expect when making an investment. Equity
