Enhancing Stock Market Anomalies with Machine Learning

Abstract

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 yield returns equal or larger than our linearly constructed baseline factor. The risk-adjusted returns are significant across alternative asset pricing models, considering transaction costs with round-trip costs of up to 2% and including only anomalies after publication. Our results indicate that non-linear models can reveal market inefficiencies (mispricing) that are hard to conciliate with risk-based explanations.

Keywords: Anomalies, machine learning models, efficient market hypothesis, asset pricing models

JEL Classification: G12, G29, M41