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

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 due to 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.

Keywords: Machine Learning, Return Prediction, Neural Networks, Financial Distress, Fintech

JEL Classification: G10, G11, G12, G14

Suggested Citation: Suggested Citation

Avramov, Doron and Cheng, Si and Metzker, Lior, Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability (September 30, 2021). Management Science, Vol. 69, No. 5, 2023, Available at SSRN: https://ssrn.com/abstract=3450322 or http://dx.doi.org/10.2139/ssrn.3450322