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...

We identify the characteristics and specifications that drive the out-of-sample performance of machine-learning models across an international data sample of nearly 1.9 billion stock-month-anomaly observations from 1980 to 2019. We demonstrate significant monthly value-weighted (long-short)...