This book is an ambitious effort by three well-known and well-respected scholars to fill an acknowledged void in the literature-a text covering the burgeoning field of empirical finance. As the authors note in the preface, there are several excellent books covering financial theory at a level suitable for a Ph.D. class or as a reference for academics and practitioners, but there is little or nothing similar that covers econometric methods and applications. Perhaps the closest existing text is the recent addition to the Wiley Series in Financial and Quantitative Analysis written by Cuthbertson (1996). The major difference between the books is that Cuthbertson focuses exclusively on asset pricing in the stock, bond, and foreign exchange markets, whereas Campbell, Lo, and MacKinlay (henceforth CLM) consider empirical applications throughout the field of finance, including corporate finance, derivatives markets, and market microstructure. The level of anticipation preceding publication can be partly measured by the fact that at least three reviews (including this one) have appeared since the book arrived. Moreover, in their reviews, both Harvey (1998) and Tiso (1998) comment on the need for such a text, a sentiment that has been echoed by numerous finance academics.So, does the book live up to its advance billing? For the most part, the answer is yes. The book is comprehensive and up to date, yet also relatively accessible and self-contained. As such, it no doubt will serve as the basic text for Ph.D. courses in empirical finance throughout the country. It also will find a role as an invaluable reference for academics and practitioners in the field. Finally, those interested in learning more about the study of financial markets will find it an excellent introduction to the relevant issues, applications, and methodologies.

Motivated by existing evidence of a preference among investors for assets with lottery-like payoffs and that many investors are poorly diversified, we investigate the significance of extreme positive returns in the cross-sectional pricing of stocks. Portfolio-level analyses and firm-level cross-sectional regressions indicate a negative and significant relation between the maximum daily return over the past one month (MAX) and expected stock returns. Average raw and risk-adjusted return differences between stocks in the lowest and highest MAX deciles exceed 1% per month. These results are robust to controls for size, book-to-market, momentum, short-term reversals, liquidity, and skewness. Of particular interest, including MAX generally subsumes or reverses the puzzling negative relation between returns and idiosyncratic volatility recently documented in

for helpful suggestions and David Hait (Option Metrics) for providing the options data. We are especially grateful to comments from Owen Lamont, Jeff Wurgler, the anonymous referee and seminar participants at UBC, NYU, USC and the NBER. The views expressed herein are those of the authors and not necessarily those of the National Bureau of Economic Research.

This article investigates empirically the comovements of the conditional mean and volatility of stock returns. It extends the results in the literature by demonstrating the role of the commercial paper-Treasury yield spread in predicting time variation in volatility. The conditional mean and volatility exhibit an asymmetric relation, which contrasts with the contemporaneous relation that has been tested previously. The volatility leads the expected return, and this time series relation is documented using offset correlations, short-horizon contemporaneous correlations, and a vector autoregression. These results bring into question the value of modeling expected returns as a constant function of conditional volatility.THE TIME SERIES PROPERTIES of the expectation and volatility of stock returns have recently attracted much attention in the financial economics literature. Empirical evidence suggests that variables such as yields and yield spreads in the corporate and Treasury bond markets, and dividend yields have predictive power for returns (Breen, Glosten, and Jagannathan (1989), Fama and French (1989), Kandel andStambaugh (1989, 1990), and Keim and Stambaugh (1986)). This explanatory power over different time periods and return horizons leads some researchers to conclude that there is significant time variation in expected returns over the business cycle. See, for example, Fama and French (1989, p. 23). In addition, significant time variation in the volatility of returns has been documented using these and other economic variables (Schwert (1989) and Kandel andStambaugh (1989, 1990)). A natural extension of this research is to examine the covariation between the mean and volatility of returns. On a market-wide level, strong intuition suggests that risk and return should be positively related.1 Consequently, researchers have searched for both a positive relation between expected returns and the conditional volatility of returns and a negative relation* Stern School of Business, New York University. I would like to thank Kobi Boudoukh, Allan Kleidon, Ken Singleton, the editor, Rene Stulz, an anonymous referee, and seminar participants at for helpful comments. 'For example, Campbell (1987) develops a specialization of the intertemporal CAPM in which the expected excess real return is approximately proportional to the variance of the return. Of course, if international capital markets are integrated, the appropriate market index is the world market. 515 516 The Journal of Finance between unanticipated volatility and realized returns. This latter effect arises if unanticipated increases in volatility increase required returns and cause a corresponding decline in price. Yet, prior empirical investigations into the contemporaneous correlation between the first two moments of stock market returns yield decidedly mixed results (Campbell (1987), French, Schwert, and Stambaugh (1987), Glosten, Jagannathan, and Runkle (1993), and Harvey (1991)). For example, French, Schwert, and Stambaugh (1987) find a statistically signif...