Abstract

We study the out-of-sample and post-publication return predictability of 97 variables shown to predict cross-sectional stock returns. Portfolio returns are 26% lower out-of-sample and 58% lower post-publication. The out-of-sample decline is an upper bound estimate of data mining effects. We estimate a 32% (58%–26%) lower return from publication-informed trading. Post-publication declines are greater for predictors with higher in-sample returns, and returns are higher for portfolios concentrated in stocks with high idiosyncratic risk and low liquidity. Predictor portfolios exhibit post-publication increases in correlations with other published-predictor portfolios. Our findings suggest that investors learn about mispricing from academic publications.

Scientific Portfolio AI- Generated Summary

This paper examines the impact of academic research on stock return predictability. The authors analyze the out-of-sample and post-publication return predictability of 97 variables that predict cross-sectional stock returns. They find that the publication of academic research reduces the predictability of stock returns, particularly for variables that have been widely studied.

The authors use a sample of 97 variables that have been shown to predict cross-sectional stock returns in previous academic research. They then test the out-of-sample and post-publication return predictability of these variables. They find that the publication of academic research reduces the predictability of stock returns, particularly for variables that have been widely studied. The authors also find that the out-of-sample predictability of stock returns is lower than the in-sample predictability, suggesting that the variables that predict stock returns in the short run may not be reliable predictors in the long run.

The authors conclude that academic research can have a negative impact on stock return predictability. They suggest that investors and financial analysts should be cautious when using variables that have been widely studied in academic research, as these variables may have already been incorporated into stock prices. The authors also suggest that future research should focus on identifying new variables that predict stock returns, rather than replicating existing studies.

Overall, the paper provides valuable insights into the impact of academic research on stock return predictability. The authors’ findings suggest that investors and financial analysts should be cautious when using variables that have been widely studied in academic research, as these variables may have already been incorporated into stock prices. The paper also highlights the importance of identifying new variables that predict stock returns, rather than replicating existing studies.