Firm-level variables that predict cross-sectional stock returns, such as price-to-earnings and short interest, are often averaged and used to predict the time series of market returns. We extend this literature and limit the data-snooping bias by using a large population of the literature's...

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...

Analysts' price targets and recommendations contradict stock return anomaly variables. Using an index based on 125 anomalies, we find that analysts' annual stock return forecasts are 11% higher for anomaly-shorts than for anomaly-longs. Anomaly-shorts' return forecasts are excessively...