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Artificial Intelligence Asset Pricing Models

Author

Listed:
  • Bryan T. Kelly

    (Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER))

  • Boris Kuznetsov

    (Swiss Finance Institute)

  • Semyon Malamud

    (Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute)

  • Teng Andrea Xu

    (École Polytechnique Fédérale de Lausanne (EPFL))

Abstract

The core statistical technology in artificial intelligence is the large-scale transformer network. We propose a new asset pricing model that implants a transformer in the stochastic discount factor. This structure leverages conditional pricing information via cross-asset information sharing and nonlinearity. We also develop a linear transformer that serves as a simplified surrogate from which we derive an intuitive decomposition of the transformer's asset pricing mechanisms. We find large reductions in pricing errors from our artificial intelligence pricing model (AIPM) relative to previous machine learning models and dissect the sources of these gains.

Suggested Citation

  • Bryan T. Kelly & Boris Kuznetsov & Semyon Malamud & Teng Andrea Xu, 2025. "Artificial Intelligence Asset Pricing Models," Swiss Finance Institute Research Paper Series 25-08, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2508
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