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

Doron Avramov (), Si Cheng () and Lior Metzker ()
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Doron Avramov: Arison School of Business, Reichman University (IDC Herzliya), Herzliya 4610101, Israel
Si Cheng: Whitman School of Management, Syracuse University, Syracuse, New York 13244
Lior Metzker: School of Business Administration, The Hebrew University of Jerusalem, Jerusalem 9190501, Israel

Management Science, 2023, vol. 69, issue 5, 2587-2619

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.

Keywords: machine learning; return prediction; neural networks; financial distress; Fintech (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:69:y:2023:i:5:p:2587-2619

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