Deep Learning in Asset Pricing

Luyang Chen (), Markus Pelger and Jason Zhu ()
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Luyang Chen: Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305
Jason Zhu: Department of Management Science & Engineering, Stanford University, Stanford, California 94305

Management Science, 2024, vol. 70, issue 2, 714-750

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.

Keywords: conditional asset pricing model; no arbitrage; stock returns; nonlinear factor model; cross-section of expected returns; machine learning; deep learning; big data; hidden states; GMM (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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http://dx.doi.org/10.1287/mnsc.2023.4695 (application/pdf)

Related works:
Working Paper: Deep Learning in Asset Pricing (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:70:y:2024:i:2:p:714-750

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