Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability

Abstract

This paper shows that investments based on deep learning signals extract profitability from difficult-to-arbitrage stocks and during high limits-to-arbitrage market states. In particular, excluding microcaps, distressed stocks, or episodes of high market volatility considerably attenuates profitability. Machine learning-based performance further deteriorates in the presence of reasonable trading costs because of high turnover and extreme positions in the tangency portfolio implied by the pricing kernel. Despite their opaque nature, machine learning methods successfully identify mispriced stocks consistent with most anomalies. Beyond economic restrictions, deep learning signals are profitable in long positions and recent years and command low downside risk. This paper was accepted by Kay Giesecke, finance. Funding: D. Avramov acknowledges the Israel Science Foundation (Grant 288/18) for financial support. S. Cheng acknowledges the General Research Fund of the Research Grants Council of Hong Kong [Project 14502318] for financial support. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4449 .