Identifying Signals of the Cross Section of Stock Returns

Abstract

The proliferation of anomalies and the resulting “factor zoo” has challenged finance researchers to identify firm characteristics that are genuinely related to the cross-sectional variation in expected stock returns. We address this challenge using a Bayesian ensemble-of-trees approach which combines the advantages of machine learning with a Bayesian inference framework. Applying the methodology to U.S. stock returns we find that a firm’s market value is the sole consistently relevant
characteristic. We further confirm that the stochastic discount factor based on a sparse set of factors can successfully explain most of the variation in expected returns.

Keywords: Factor Zoo, Anomalies, Stochastic Discount Factor, Bayesian Inference, Machine Learning, Ensemble-of-Trees

JEL Classification: G11, G12, C11, C53, C58