Various theories have been proposed to explain momentum in stock returns. We test the gradual-information-diffusion model of Hong and Stein (1999) and establish three key results. First, once one moves past the very smallest stocks, the profitability of momentum strategies declines sharply with firm size. Second, holding size fixed, momentum strategies work better among stocks with low analyst coverage. Finally, the effect of analyst coverage is greater for stocks that are past losers than for past winners. These findings are consistent with the hypothesis that firm-specific information, especially negative information, diffuses only gradually across the investing public. SEVERAL RECENT PAPERS HAVE DOCUMENTED that, at medium-term horizons ranging from three to 12 months, stock returns exhibit momentum-that is, past winners continue to perform well, and past losers continue to perform poorly. For example, Jegadeesh and Titman (1993), using a U.S. sample of NYSE/ AMEX stocks over the period from 1965 to 1989, find that a strategy that buys past six-month winners (stocks in the top performance decile) and shorts past six-month losers (stocks in the bottom performance decile) earns approximately one percent per month over the subsequent six months. Not only is this an economically interesting magnitude, but the result also appears to be robust: Rouwenhorst (1998) obtains very similar numbers in a

We use Bayesian model averaging to analyze the sample evidence on return predictability in the presence of model uncertainty. The analysis reveals in-sample and out-of-sample predictability, and shows that the out-of-sample performance of the Bayesian approach is superior to that of model selection criteria. We find that term and market premia are robust predictors. Moreover, small-cap value stocks appear more predictable than large-cap growth stocks. We also investigate the implications of model uncertainty from investment management perspectives. We show that model uncertainty is more important than estimation risk, and investors who discard model uncertainty face large utility losses.

Using a comprehensive database of headlines about individual companies, I examine monthly returns following public news. I compare them to stocks with similar returns, but no identifiable public news. There is a di#erence between the two sets. I find strong drift after bad news. Investors seem to react slowly to this information. I also find reversal after extreme price movements unaccompanied by public news. The separate patterns appear even after adjustments for risk exposure and other e#ects. They are, however, mainly seen in smaller, more illiquid stocks. These findings support some integrated theories of investor over- and underreaction.

Recent literature in empirical finance is surveyed in its relation to underlying behavioral principles, principles which come primarily from psychology, sociology and anthropology. The behavioral principles discussed are: prospect theory, regret and cognitive dissonance, anchoring, mental compartments, overconfidence, over- and underreaction, representativeness heuristic, the disjunction effect, gambling behavior and speculation, perceived irrelevance of history, magical thinking, quasi-magical thinking, attention anomalies, the availability heuristic, culture and social contagion, and global culture. Theories of human behavior from psychology, sociology, and anthropology have helped motivate much recent empirical research on the behavior of financial markets. In this paper I will survey both some of the most significant theories (for empirical finance) in these other social sciences and the empirical finance literature itself. Particular attention will be paid to the implications of these theories for the efficient markets hypothesis in finance. This is the hypothesis that financial prices efficiently incorporate all public

We review extensive evidence about how psychological biases affect investor behavior and prices. Systematic mispricing probably causes substantial resource misallocation. We argue that limited attention and overconfidence cause investor credulity about the strategic incentives of informed market participants. However, individuals as political participants remain subject to the biases and self-interest they exhibit in private settings. Indeed, correcting contemporaneous market pricing errors is probably not government’s relative advantage. Government and private planners should establish rules ex ante to improve choices and efficiency, including disclosure, reporting, advertising, and default-option-setting regulations. Especially

This paper finds that trading volume is a significant determinant of the lead-lag patterns observed in stock returns. Daily and weekly returns on high volume portfolios lead returns on low volume portfolios, controlling for firm size. Nonsynchronous trading or low volume portfolio autocorrelations cannot explain these findings. These patterns arise because returns on low volume portfolios respond more slowly to information in market returns. The speed of adjustment of individual stocks confirms these findings. Overall, the results indicate that differential speed of adjustment to information is a significant source of the cross-autocorrelation patterns in short-horizon stock returns. BOTH ACADEMICS AND PRACTITIONERS HAVE LONG BEEN interested in the role played by trading volume in predicting future stock returns. 1 In this paper, we examine the interaction between trading volume and the predictability of short horizon stock returns, specifically that due to lead-lag cross-autocorrelations in stock returns. Our investigation indicates that trading volume is a significant determinant of the cross-autocorrelation patterns in stock returns. 2 We find that daily or weekly returns of stocks with high trading volume lead daily or weekly returns of stocks with low trading volume. Additional tests indicate that this effect is related to the tendency of high volume stocks to respond rapidly and low volume stocks to respond slowly to marketwide information.

A growing number of researchers argue that time-series patterns in returns are due to investor irrationality and thus can be translated into abnormal profits. Continuation of short-term returns or momentum is one such pattern that has defied any rational explanation and is at odds with market efficiency. This paper shows that profits to momentum strategies can be explained by a set of lagged macroeconomic variables and payoffs to momentum strategies disappear once stock returns are adjusted for their predictability based on these macroeconomic variables. Our results provide a possible role for time-varying expected returns as an explanation for momentum payoffs. THIS PAPER EXAMINES THE RELATIVE importance of common factors and firmspecific information in explaining the profitability of momentum-based trading strategies, first documented by Jegadeesh and Titman ~1993!. The profitability of momentum strategies has been particularly intriguing, as it remains the only CAPM-related anomaly unexplained by the Fama–French

We use a two-year panel of individual accounts in an S&P 500 index mutual fund to examine the trading and investment behavior of more than 91 thousand investors who have chosen a low-cost, passively managed vehicle for savings. This allows us to characterize investors ’ heterogeneity in terms of their investment patterns. In particular, we identify positive feedback traders as well as contrarians whose activities are conditional upon preceding day stock market moves. We test the consistency and profitability of these conditional strategies over time. We find that more frequent traders are typically contrarians, while infrequent traders are more typically momentum investors. The dynamics of these investor classes help us to partially examine the question of the marginal investor over the period of our study. We find that the behavior of momentum investors is typically more correlated to changes in the S&P 500 and we trace its dynamics over time. We build up “behavioral factors ” based on contrarian and momentum flows and show that they perform well against a benchmark of loadings on latent factors extracted from returns. We also use the behavior of momentum and contrarian investors to build a measure of “market polarization”. This captures the dispersion of beliefs among the investors and helps to account for asset pricing better than standard measures of dispersion of beliefs. Acknowledgments: We thank Fidelity for providing us with the data for this study. We thank the International Center for Finance at the

Lubos Pastor,

country. It was founded in 1969 through a grant from Oppenheimer & Company in honor of its late partner, Rodney L. White. The Center receives support from its endowment and from annual contributions from its Members. The Center sponsors a wide range of financial research. It publishes a working paper series and a reprint series. It holds an annual seminar, which for the last several years has focused on household financial decision making. The Members of the Center gain the opportunity to participate in innovative research to break new ground in the field of finance. Through their membership, they also gain access to the Wharton School’s faculty and enjoy other special benefits.