Machine learning, anomalies, and the expected market return : evidence from China

Abstract

We investigate whether machine learning (ML) techniques that forecast overall U.S. market returns using cross-sectional stock return anomalies in Dong et al. (2022) are useful for the China equity market. We successfully forecast out-of-sample R² of the market return in China using a combined version of ordinary least squares and an elastic net model. However, the other four ML methods cannot forecast the market return. Overall, our exercise highlights the potential of ML techniques, but also calls for future research to rule out the possibility of model mining.