Behavioral Finance Meeting.

The NBER Group on Behavioral Finance held its fall meeting in Cambridge on December 9. Robert J. Shiller, NBER and Yale University, and Richard H. Thaler, NBER and University of Chicago, organized this program:

Joseph Chen and Harrison Hong, Stanford University, and Jeremy C. Stein, NBER and MIT, "Forecasting Crashes: Trading Volume, Past Returns, and Conditional Skewness in Stock Prices"

Discussant: Kent Daniel, NBER and Northwestern University

Narasimhan Jegadeesh, University of Illinois, and Sheridan Titman, NBER and University of Texas, "Profitability of Momentum Strategies: An Evaluation of Alternative Explanations" (NBER Working Paper No. 7159)

Discussant: Werner Debondt, University of Wisconsin

Malcolm Baker, Harvard University, and Jeffrey Wurgler, Yale University, "The Equity Share in New Issues and Aggregate Stock Returns"

Discussant: Robert J. Shiller

Shlomo Benartzi, University of California; Los Angeles, "Why Do Employees Invest Their Retirement Savings in Company Stock?"

Discussant: Andrei Shleifer, NBER and Harvard University

Brad M. Barber and Terrance Odean, University of California, Davis, "Online Investors: Do the Slow Die First?"

Discussant: Kenneth R. French, NBER and MIT

Randall Morck, Harvard University; Bernard Yeung, University of Michigan; and Wayne Yu, Queens University, "The Information Content of Stock Markets: Why Do Emerging Markets Have Synchronous Stock Price Movements?"

Discussant: Kenneth A Froot, NBER and Harvard University

Chen, Hong, and Stein investigate the determinants of asymmetries in stock returns. With a series of cross-sectional regression specifications, they attempt to forecast skewness in the daily returns of individual stocks. Negative skewness is most pronounced in stocks that have experienced an increase in trading volume relative to trend over the prior six months and positive returns over the prior 36 months. The first finding is consistent with a model predicting that negative asymmetries are more likely to occur when there are large differences of opinion among investors. The latter finding fits with a number of theories. Analogous results also obtain when the authors attempt to forecast the skewness of the aggregate stock market, although here their statistical power is limited.

Jegadeesh and Titman evaluate various explanations for the profitability of momentum strategies. Evidence indicates that momentum profits have continued in the 1990s, which suggests that the original results were not a product of data snooping bias. The authors also examine the predictions of recent behavioral models that propose that momentum profits are attributable to delayed overreactions that are eventually reversed. This supports the behavioral models, but this support should be taken with caution, Although the authors find no evidence of significant return reversals in the two to three years after the following formation date, there are significant return reversals four to five years after the formation date. The authors' analysis of post-holding period returns sharply rejects a claim in the literature that the observed momentum profits can be explained completely by the cross-sectional dispersion in expected returns.

The share of equity issues in total new equity and debt issues is a strong predictor of U.S. stock market returns between 1928 and 1997. In particular, firms issue relatively more equity than debt just before periods of low market returns. The equity share in new issues has stable predictive power in both halves of the sample period, and after controlling for other known predictors. Baker and Wurgler do not find support for efficient market explanations of the results. Instead, the fact that the equity share sometimes predicts significantly negative market returns suggests inefficiency and that firms time the market component of their returns when issuing securities.

About one-third of the assets in large retirement savings plans are invested in company stock. From a diversification perspective, this is a dubious strategy. Benartzi offers a behavioral explanation: individuals excessively extrapolate past returns into the future. Consistent with the excessive extrapolation hypothesis, employees of firms that experienced the worst stock performance over the past 10 years have 10.37 percent allocated to company stock, while employees whose firms experienced the best stock performance have 39.70 percent allocated to company stock. The author also examines more traditional explanations of company stock holdings and finds that they can only explain part of the story. Results show that employees invest 20-30 percent of their discretionary funds in company stock (as opposed to being required to own company stock), and on average, employees are unable to predict the future performance of company stock. Thus, employees invest a nontrivial fraction of their discretionary funds in s tocks that provide superior past performance but no superior future performance.

Barber and Odean examine changes in the stock trading behavior and investment performance of 1,607 investors who switched from phone-based to online trading between 1992 and 1995. They document that young men who are active traders with high incomes and a preference for investing in small growth stocks with high market risk are more likely to switch to online trading. They also find that those who switch to online trading experience unusually strong performance prior to going online, beating the market by more than 2 percent annually. After going online, they trade more actively, more speculatively, and less profitably than before -- lagging the market by more than 3 percent annually. A rational response to reductions in market frictions (lower trading costs, improved execution speed, and greater ease of access) does not explain these findings. The increase in trading and reduction in performance of online investors can be explained by overconfidence augmented by self-attribution bias, the illusion of knowle dge, and the illusion of control.

Stock prices move together more in low-income economies than in high-income economies. This finding is clearly not attributable to market size differences and is only partially explained by slightly higher fundamentals correlation in low-income economies. However, measures of a country's institutionalized respect for property rights do appear to explain these differences. Morck, Yeung, and Yu propose that weak private property rights impede informed trading and increase systematic noise trader risk. They also conjecture that, in countries that protect public investors poorly from corporate insiders, intercorporate income shifting may make firm-specific information less useful to risk arbitrageurs and therefore impede its capitalization into stock prices.