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Complexity in Factor Pricing Models

Author

Listed:
  • Antoine Didisheim
  • Shikun (Barry) Ke
  • Bryan T. Kelly
  • Semyon Malamud

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

  • Antoine Didisheim & Shikun (Barry) Ke & Bryan T. Kelly & Semyon Malamud, 2023. "Complexity in Factor Pricing Models," NBER Working Papers 31689, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:31689
    Note: AP
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    Cited by:

    1. Yuan Liao & Xinjie Ma & Andreas Neuhierl & Zhentao Shi, 2023. "Economic Forecasts Using Many Noises," Papers 2312.05593, arXiv.org, revised Dec 2023.

    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G1 - Financial Economics - - General Financial Markets
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • 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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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