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Abstract:
Managers and corporate directors need to recognize two key behavioral impediments that obstruct the process of value maximization, one internal to the firm and the other external. I call the first obstruction behavioral costs. Behavioral costs, like agency costs, tend to prevent value creation. Behavioral costs are the costs associated with errors that people make because of cognitive imperfections and emotional influences. The second obstruction stems from behavioral errors on the part of analysts and investors. These errors can create gaps between fundamental values and market prices. When they do, managers may find themselves conflicted, unsure of how to factor the errors of analysts and investors into their own decisions.

Proponents of value based management emphasize that with properly designed incentives, managers will maximize the value of the firms for which they work. As such, either they treat behavioral costs as simply another form of agency costs, or they deny the relevance of cognitive errors. In contrast, proponents of behavioral finance argue that behavioral costs are typically large, and cannot be addressed though incentives alone. This is not to say that incentives are immaterial. On the contrary, incentives are of critical importance. The point, however, is that there are limits to incentives. If employees have a distorted view of what is in their own self-interest, or if they have a mistaken view of what actions they need to take in order to maximize their self-interest, then incentive compatibility, although necessary for value maximization, will not be sufficient.

Abstract:
The relationship between risk and return lies at the heart of modern finance. This relationship is embodied within such core concepts as the capital market line and the security market line. Both of these graphs feature a positive slope, meaning that the higher the risk the higher the expected return. I propose that even though investors may state that in principle, risk and expected return are positively related, in practice they form judgments in which the two are negatively related.

I have organized my remarks around the following six questions: (1) What evidence is there that investors judge risk and return to be negatively related? (2) What psychological forces would lead investors to form such judgments about risk and return? (3) What implications do such judgments hold for the broad debate between proponents of market efficiency and proponents of behavioral finance? (4) How robust are judgments about risk and return to judgments about the expected equity premium? (5) To what extent are investors consciously aware of the manner in which they form judgments about risk and return? (6) How reliable is the evidence on risk and return presented here?

Abstract:
The relationship between risk and return lies at the heart of modern finance. This relationship is embodied within such core concepts as the capital market line and the security market line. Both of these graphs feature a positive slope, meaning that the higher the risk the higher the expected return. I propose that even though investors may state that in principle, risk and expected return are positively related, in practice they form judgments in which the two are negatively related.

I have organized my remarks around the following six questions: (1) What evidence is there that investors judge risk and return to be negatively related? (2) What psychological forces would lead investors to form such judgments about risk and return? (3) What implications do such judgments hold for the broad debate between proponents of market efficiency and proponents of behavioral finance? (4) How robust are judgments about risk and return to judgments about the expected equity premium? (5) To what extent are investors consciously aware of the manner in which they form judgments about risk and return? (6) How reliable is the evidence on risk and return presented here?

Abstract:
The paper analyzes the manner in which sentiment affects the pricing kernel. Sentiment is another term for traders' errors. There are two main questions addressed in the paper. The central question is: How can the concept of sentiment be formally defined so as to identify the manner in which traders' errors are manifest in the pricing kernel? The subsidiary question is: Because the pricing kernel underlies the pricing of all assets, how do traders' errors impact the pricing of major asset classes such as fixed income securities, options, mean-variance portfolios, and the market portfolio?

The central result in the paper is that the log-pricing kernel can be decomposed into two stochastic processes, one pertaining to fundamentals and the other to sentiment. Hence, prices are efficient if and only if sentiment is uniformly zero. In the model, investors differ from each other in respect to beliefs, risk tolerance, and time preference. This heterogeneity has far reaching implications for asset pricing theory. When sentiment is nonzero, heterogeneity can lead to "smile" effects both in the graph of the kernel and in option prices, and "frown" effects in mean-variance returns. In this respect, heterogeneity can prevent the conditions necessary for equilibrium option prices to be given by Black-Scholes. As a result, the smile effect for call options can differ from the smile effect for put options. Nonzero sentiment distorts the mean-variance frontier from its "efficient" position, thereby giving rise to behavioral betas. In addition, nonzero sentiment interferes with the expectations hypothesis of the term structure, and can affect the volatility of the return to the market portfolio, depending on traders' risk tolerance spectrum.

Abstract:
This paper examines the empirical relation among trading volume, informational variables (i.e., precision and differential beliefs), the bid-ask spread components, and price volatility using a structural model that treats the spread components, trading volume, and price volatility as endogenous. Specifically, we investigate the direct and indirect effects of informational precision and differential beliefs on trading volume via a structural model representation in general and examine the theoretical ambiguity issue of George, Kaul, and Nimalendran (1994) in particular. Our empirical results indicate that although liquidity trading dampens the effect of informational precision on trading volume, the net effect of informational precision is positive. Also, while higher differential beliefs lead to greater trading by informed traders, it exerts a commensurate negative impact on liquidity trading. As a result, the net effect of differential beliefs on trading volume is insignificant. This new finding refutes the generally accepted belief in the volume literature that greater differences of opinion induce more trading.

Trading volume, Information precision, Differential belief, Bid-ask spreads, Structural model

Abstract:
Although many economists, most notably Strotz, have discussed dynamic inconsistency and precommitment, none have dealt directly with the essence of the problem: self-control. This paper attempts to fill that gap by modeling man as an organization. The Strotz model is recast to include the control features missing in his formulation. The organizational analogy permits us to draw on the theory of agency. We thus relate the individual's control problems with those that exist in agency relationships.

Abstract:
Behavioral finance endeavors to bridge the gap between neoclassical finance and cognitive psychology. Now an established field, behavioral finance looks at the investors' decision making formula as well as at their behavior, which in turn sheds light on the observed departures from the traditional finance theory. The paper provides an overarching view of the behavioral finance area. It begins by reviewing a few fundamental questions and lead slowly to the future of behavioral finance. The study unveils the move from the traditional approach that decision making is based on rational individuals using all available information, to including heuristics and biases as the background for framing choices under uncertainty. A new class of asset pricing models is put forward as a solution to overcome the obstacles of the neoclassical finance paradigm. The behavioral element is central to the new proposition without disputing the value of the traditional approach. It is suggested that with all its strengths and weaknesses the new paradigm will combine the best of neoclassical and behavioral elements.

Behavioral finance, Neoclassical finance, Asset valuation, Behavioral SDF-based pricing

Abstract:
More than twenty years ago, Meir Statman and I coined the term disposition effect to describe the predisposition of investors to sell their winners too early and to ride their losers too long. We identified a series of psychological phenomena that we believed explained the disposition effect, presented data consistent with the effect, and proposed some testable hypotheses. Since that time, a literature on the disposition effect has developed to test those hypotheses and extend the focus of discussion from investor behavior to pricing.

In this article, I survey highlights of the disposition effect literature that are of special interest to investment professionals. Recent research concludes that the disposition effect impacts investment professionals, both directly and indirectly. The direct effect involves investment professionals tending to sell their winners too quickly and/or riding their losers too long. The indirect effect involves momentum in pricing that in part stems from some investors behaving in accordance with the disposition effect. Notably, the disposition effect and momentum are key determinants in the separation of outperforming investors from underperforming investors.

Disposition Effect, Behavioral Finance, Momentum, Investment Consultants, Investment Professionals

Abstract:
6/22/00: web retrieval--abstract only--Kathy
University of Washington
School of Business Administration
Journal of Financial and Quantitative Analysis
http://depts.washington.edu/jfqa/
Vol. 35, No. 2, June 2000

Behavioral Portfolio Theory

Hersh Shefrin and Meir Statman

Abstract:
We develop a positive behavioral portfolio theory (BPT) and explore its implications for portfolio construction and security design. The optimal portfolios of BPT investors resemble combinations of bonds and lottery tickets, consistent with Friedman and Savage's (1948) observation. We compare the BPT efficient frontier with the mean-variance efficient frontier and show that, in general, the two frontiers do not coincide. Optimal BPT portfolios are also different from optimal CAPM portfolios. In particular, the CAPM two-fund separation does not hold in BPT. We present BPT in a single mental account version (BPT-SA) and a multiple mental account version (BPT-MA). BPT-SA investors
integrate their portfolios into a single mental account, while BPT-MA investors segregate their portfolios into several mental accounts. BPT-MA portfolios resemble layered pyramids, where layers are associated with aspirations. We explore a two-layer portfolio where the low aspiration layer is designed to avoid poverty while the high aspiration layer is designed for a shot at
riches.

Abstract:
We know from empirical studies that stocks of small companies with high book-to-market ratios have provided higher returns than stocks of large companies with low book-to-market ratios. But do senior executives, outside directors and financial analysts believe that? We show that senior executives, outside directors and financial analysts surveyed annually by Fortune magazine rank companies as if they believe that good companies are large companies with low book-to-market ratios. They rank stocks as if they believe the opposite of what empirical research has demonstrated; they rank stocks as if they believe that good stocks are stocks of good companies. We argue that a misperception of the relationship between the quality of a company and the expected rate of return of its stock underlies the superior performance of stocks of small, high book-to-market companies and the weak relationship betweenrealized returns and beta.