Asset Pricing and Machine Learning: A Critical Review of Empirical Findings

Abstract

The latest development in Empirical Asset Pricing is the employment 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. I review and critically assess the most recent and relevant contributions in the literature with special attention to the interpretation of non-standard statistical tools applied to this field and I summarize the empirical findings in detail into hints for further developments.