We examine the predictability of 299 capital market anomalies enhanced by 30 machine learning approaches and over 250 models in a dataset with more than 500 million firm-month-anomaly observations. We find significant monthly (out-of-sample) returns of around 1.8-2.0%, and over 80% of the models...

I estimate a theory-based behavioral momentum using analysts' predictable underreaction (APU) as a proxy for newswatchers underreaction. The results show that APU strongly predicts analysts' errors and, more importantly, stock returns. A long-short strategy based on APU generates a...

We examine the out-of-sample performance of 240 stock market anomalies enhanced by 49 machine learning algorithms and over 260 individually trained models across an international data sample of nearly 1.9 billion stock-month-anomaly observations from 1980 to 2019. We demonstrate significant...

We examine the value of analyst recommendations across 45 countries and 3.8 million firm-month observations from 1994 to 2019. Recommendation-based portfolio strategies lead to highly significant (insignificant) abnormal returns in international markets (in the U.S.). In line with...

We propose a proxy for global equity mispricing (mispricing $R^2$) based on an instrumented principal component analysis of the return variation of 198 mispricing anomalies. We find that mispricing $R^2$ is higher for countries with lower market development, lower accounting quality, and higher...