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

Doron Avramov, Si Cheng, Lior Metzker

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2587-2619
Number of pages33
JournalManagement Science
Volume69
Issue number5
DOIs
StatePublished - May 2023

Keywords

  • Fintech
  • financial distress
  • machine learning
  • neural networks
  • return prediction

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research

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