Asset Pricing and Machine Learning: A critical review

Abstract

The latest development in empirical Asset Pricing is the use of Machine Learning methods to address the problem of the factor zoo. These techniques offer great flexibility and prediction accuracy but require special care as they strongly depart from traditional Econometrics. We review and critically assess the most recent and relevant contributions in the literature grouping them into five categories defined by the Machine Learning (ML) approach they employ: regularization, dimension reduction, regression trees/random forest (RF), neural networks (NNs), and comparative analyses. We summarize the empirical findings with particular attention to their economic interpretation providing hints for future developments.