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Deep Learning in Asset Pricing

Author

Listed:
  • Luyang Chen

    (Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305)

  • Markus Pelger

    (Department of Management Science & Engineering, Stanford University, Stanford, California 94305)

  • Jason Zhu

    (Department of Management Science & Engineering, Stanford University, Stanford, California 94305)

Abstract

We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, keeps a fully flexible form, and accounts for time variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation, and pricing errors and identifies the key factors that drive asset prices.

Suggested Citation

  • Luyang Chen & Markus Pelger & Jason Zhu, 2024. "Deep Learning in Asset Pricing," Management Science, INFORMS, vol. 70(2), pages 714-750, February.
    • Handle: RePEc:inm:ormnsc:v:70:y:2024:i:2:p:714-750
    DOI: 10.1287/mnsc.2023.4695
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