We study the out-of-sample and post-publication return-predictability of 97 variables that academic studies show 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...

Using a sample of 97 stock return anomalies, we find that anomaly returns are 50% higher on corporate news days and are 6 times higher on earnings announcement days. These results could be explained by dynamic risk, mispricing via biased expectations, and data mining. We develop and conduct...

We study how 9 different market participants trade with respect to 130 different stock return anomalies and how each participant's trades predict returns. Retail investors trade against anomalies, while firms' and short sellers' trades agree with anomalies. Institutional portfolios are weighted...