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Complexity in Factor Pricing Models

Author

Listed:
  • Didisheim, Antoine
  • Ke, Barry
  • Kelly, Bryan
  • Malamud, Semyon

Abstract

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance---in terms of SDF Sharpe ratio and test asset pricing errors---is improving in model parameterization (or "complexity''). Our empirical findings verify the theoretically predicted "virtue of complexity'' in the cross-section of stock returns. Models with an extremely large number of factors (more than the number of training observations or base assets) outperform simpler alternatives by a large margin

Suggested Citation

  • Didisheim, Antoine & Ke, Barry & Kelly, Bryan & Malamud, Semyon, 2024. "Complexity in Factor Pricing Models," CEPR Discussion Papers 18812, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:18812
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    More about this item

    Keywords

    Stochastic discount factor; Portfolio choice; Alpha; Pricing errors;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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