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Über dieses Buch

This book explains how investor behavior, from mental accounting to the combustible interplay of hope and fear, affects financial economics. The transformation of portfolio theory begins with the identification of anomalies. Gaps in perception and behavioral departures from rationality spur momentum, irrational exuberance, and speculative bubbles. Behavioral accounting undermines the rational premises of mathematical finance. Assets and portfolios are imbued with “affect.” Positive and negative emotions warp investment decisions. Whether hedging against intertemporal changes in their ability to bear risk or climbing a psychological hierarchy of needs, investors arrange their portfolios and financial affairs according to emotions and perceptions. Risk aversion and life-cycle theories of consumption provide possible solutions to the equity premium puzzle, an iconic financial mystery. Prospect theory has questioned the cogency of the efficient capital markets hypothesis. Behavioral portfolio theory arises from a psychological account of security, potential, and aspiration.



Chapter 1. The Structure of a Behavioral Revolution

This book represents one of the first two volumes in the series, “Quantitative Perspectives on Behavioral Economics and Finance.” Its companion volume, Postmodern Portfolio Theory: Navigating Abnormal Markets and Investor Behavior, addresses leading departures from the putative efficiency of financial markets.1 Intense pressure on the conventional capital asset pricing model gave rise to theoretical innovations such as Eugene Fama and Kenneth French’s three-factor model. Postmodern Portfolio Theory traces this story through the four statistical moments of the distribution of financial returns: mean, variance, skewness, and kurtosis.
James Ming Chen

Chapter 2. Mental Accounting, Emotional Hierarchies, and Behavioral Heuristics

If there is one fixed star in the firmament of economic science, it is the principle that sunk costs are just that, sunk.1 Vorbei ist vorbei; reden wir nicht mehr davon. 2 Or in the words of plain English taught to generations of American college students: “One of the most important lessons of economics is that you should look at the marginal costs and marginal benefits of decisions and ignore past or sunk costs.”3
James Ming Chen

Chapter 3. Higher-Moment Capital Asset Pricing and Its Behavioral Implications

Mental accounting, as depicted in the preceding chapter, organizes potentially bewildering financial information in ways that address humans’ physiological, social, and emotional needs. These processes operate at the level of the self and at the level of civilized society addressing risk management and resource allocation questions of global proportions. Having introduced these simplifying frames of thought in purely qualitative terms, this book will now present some of the quantitative tools that undergird not only mathematical finance in the traditional sense, but also behavioral finance as an extension beyond strictly rational considerations of risk and return. After describing the capital asset pricing model (CAPM) in its conventional form, this chapter will present a four-moment CAPM as a Taylor series expansion of mean returns. Treating returns as the sum of their mean, variance, skewness, and kurtosis enables us to ascribe behavioral significance to the odd and even moments of the distribution of returns.1
James Ming Chen

Chapter 4. Tracking the Low-Volatility Anomaly Across Behavioral Space

In his popular guide to asset allocation, neurologist-turned-financial-analyst William J. Bernstein offers a bit of jarring advice to investors: “Good companies are usually bad stocks; bad companies are usually good stocks.1 Bernstein’s practical prescription stems from an academic insight: “Growth opportunities are usually the source of high betas.”2 In principle, these high betas should impart higher risk and higher returns to growth stocks: “[B]ecause growth options tend to be most valuable in good times and have implicit leverage, which tends to increase beta, they contain a great deal of systematic risk.”3 Nevertheless, even though “growth options hinge upon future economic conditions and must be riskier than assets in place,” the historical pattern cuts in the opposite direction: “[G]rowth stocks earn lower average returns than value stocks.”4 From these observations flows Bernstein’s advice to the individual investor: “Favor a value approach in your stock and mutual fund choices.”5
James Ming Chen

Chapter 5. The Intertemporal Capital Asset Pricing Model: Hedging Investment Risk Across Time

The previous \ter traced the nuances of the low-volatility anomaly across behavioral space. Specifically, it explored whether examining beta on either side of mean returns or separately evaluating its relative volatility and correlation components might offer insight into why low-volatility stocks offer higher returns. An even fuller explanation of the mechanics of the low-volatility anomaly lies in the work of John Campbell. That explanation, in turn, traces its origins to Robert Merton’s intertemporal CAPM.1
James Ming Chen

Chapter 6. Risk Aversion

If only because protective instincts are pervasive among humans and prized in many social settings,1 behavioral finance demands a credible account of risk aversion.2 That account begins with the decline of expected utility theory.3 Behavioral economics arose as a response to the limitations of conventional game theory and expected utility theory.4 Behavioral economics adds a host of considerations that elude these conventional models of utility and risk.5 Because conventional definitions of risk aversion hold the key to solving behavioral challenges such as the equity risk premium and the equity premium puzzle,6 I will now propound some of the foundations of expected utility theory. I start by presenting the absolute and relative versions of the Arrow–Pratt measures of risk aversion, named for Kenneth Arrow7 and John Pratt.8 These measures are also known as the coefficients of absolute and relative risk aversion.9
James Ming Chen

Chapter 7. The Equity Risk Premium and the Equity Premium Puzzle

All of finance rests on the proposition that investors dislike risk and demand higher returns as compensation for bearing risk. In behavioral terms, the equity risk premium may be regarded as the additional rate of return that risk-averse investors, as a class, demand in exchange for the burden of bearing volatility and the attendant risk of downside loss. Although one study has concluded that the replacement of standard deviation in the conventional CAPM by a downside risk measure would advise investors to lower the stock allocations within their portfolios,1 another study suggests that investors’ reliance on fixed-income positions vastly exceeds the allocation that any strictly rational, utilitarian evaluation of risk in equity investing would ever counsel.2 Given the presence of a “sizeable equity premium,” why indeed should “a substantial fraction of investable wealth [be] invested in fixed income instruments”?3
James Ming Chen

Chapter 8. Prospect Theory

This chapter explores prospect theory’s depiction of the impact of fear and greed on financial markets. Prospect theory posits that the human evaluation of uncertain gains and losses departs from a purely rational account of expected utility in three crucial ways. First, humans pay heed to a reference point, whether it is the price paid for a security or a target rate of return. Second, humans hate losing more than they like winning. Third, humans grow less sensitive to the magnitude of changes in welfare as gains or losses increase. Critically, diminished sensitivity applies to gains as well as losses. The resulting “fourfold pattern” of human responses to uncertainty provides a far more complete and persuasive account of risk-averse as well as risk-seeking behavior.
James Ming Chen

Chapter 9. Specific Applications of Prospect Theory to Behavioral Finance

Prospect theory illuminates multiple problems in behavioral finance. In economics generally and in finance in particular, the real challenge lies in “know[ing] exactly how to apply” prospect theory’s “many remarkable insights.”1 “Whatever its limitations, prospect theory represents a valuable refinement to the maximization assumption” of rational choice and expected utility theory, and accordingly “should inform … policymaking.”2
James Ming Chen

Chapter 10. Beyond Hope and Fear:Behavioral Portfolio Theory

This book and its companion volume, Postmodern Portfolio Theory,1 have devoted most of their attention to two models of finance. Each of these two models is sensitive to human behavior. Postmodern Portfolio Theory treated mathematical finance as a “pattern of timeless moments,” a deeply quantitative puzzle whose answer lies in statistical distributions and their properties. The presentation of a higher-moment CAPM in Chap. 3 of this book enables the overtly behavioral interpretation of moment-based theories of finance, which associate different statistical moments (mean, variance, skewness, and kurtosis) with different emotions.2 Other chapters in this book, so far, have presented financial models whose primary or even exclusive purpose is to describe economic behavior as undertaken by actual humans, as opposed to hypothetical economic reason dictated by quantitative logic. Prospect theory, in particular, reflects the “psychophysics of chances.”3
James Ming Chen

Chapter 11. Behavioral Gaps Between Hypothetical Investment Returns and Actual Investor Returns

Individual investors behave badly. “[I]nvestors with strong behavioral biases or lack of attention” to meaningful financial news are more likely to forgo equity ownership or to participate in capital markets “for the wrong reasons.”1 Individual investors, often the product of that lethal combination of ignorance and bias, “trade … frequently, tend to time their buys and sells badly, and prefer high expense [mutual] funds and active funds rather than index funds.”2 Behavioral biases cost investors. Narrow framing extracts a 2.16 % premium from the most heavily affected quintile of individual investors relative to the least affected quintile.3 The disposition effect costs the worst quintile 0.89 % in annual returns relative to best quintile.4
James Ming Chen

Chapter 12. Irrational Exuberance: Momentum Crashes and Speculative Bubbles

The ψ statistic described in Sect. 11.4 is specific to a particular investment vehicle, such as a mutual fund or a hedge fund, whose net inflows and outflows of cash enable us to quantify the gap between hypothetical investment returns and the actual returns that real-life investors realized in fact. Unlike other anomalous departures from hypothetically efficient markets and capital asset pricing, however, the effect that ψ measures is almost entirely an artifact of investor behavior. Indeed, among the many phenomena documented in this book, the performance gap may be the purest example of a behavioral departure from perfect efficiency, rational expectations, and platonically maximized utility.
James Ming Chen

Chapter 13. The Monster and the Sleeping Queen

Postmodern portfolio theory, as depicted in this book, provides an overarching view of abnormal markets and less than fully rational investor behavior. Whereas market abnormality could be described in terms of econophysics, complete understanding of investor irrationality demands knowledge of neuroscience, evolutionary biology, and epidemiology. A federal court has described agriculture as a field “so vast that fully to comprehend it would require an almost universal knowledge ranging from geology, biology, chemistry and medicine to the niceties of the legislative, judicial and administrative processes of government.”1 A comparable claim befits finance, particularly as it embraces the perspectives and methodologies of the behavioral sciences.
James Ming Chen


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