Abstract

Two easily measured variables, size and book-to-market equity, combine to capture the cross-sectional variation in average stock returns associated with market β, size, leverage, book-to-market equity, and earnings-price ratios. Moreover, when the tests allow for variation in β that is unrelated to size, the relation between market β and average return is flat, even when β is the only explanatory variable.

Scientific Portfolio AI- Generated Summary

“The Cross-Section of Expected Stock Returns” explores the relationship between stock returns and easily measured variables such as size and book-to-market equity. The authors use data from the Center for Research in Security Prices (CRSP) to examine the cross-sectional variation in average stock returns and test the asset-pricing model’s predictions.

The authors find that size and book-to-market equity are the two easily measured variables that capture the cross-sectional variation in average stock returns. They also find that the asset-pricing model predicts a positive relationship between expected returns on securities and market betas. However, the empirical evidence contradicts the Sharpe-Lintner-Black model, which assumes that all investors have the same expectations and that the market is efficient.

The authors also discuss the limitations of the asset-pricing model and suggest that other factors, such as liquidity and momentum, may also play a role in explaining the cross-sectional variation in average stock returns. They conclude that the asset-pricing model is a useful tool for understanding the relationship between expected returns and risk in the market, but it should be used in conjunction with other models and factors to make informed investment decisions.

Overall, this paper provides valuable insights into the factors that drive average stock returns and the limitations of the asset-pricing model. It is a must-read for investors and researchers interested in understanding the dynamics of the stock market and making informed investment decisions.