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The Virtue of Complexity in Return Prediction

Author

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  • Malamud, Semyon
  • Kelly, Bryan
  • Zhou, Kangying

Abstract

We theoretically characterize the behavior of return prediction models in the high complexity regime, i.e. when the number of parameters exceeds the number of observations. Contrary to conventional wisdom in finance, return prediction R2 and optimal portfolio Sharpe ratio generally increase with model parameterization, even when minimal regularization is used. Empirically, we document this "virtue of complexity" in US equity market prediction. High complexity models deliver economically large and statistically significant out-of-sample portfolio gains relative to simpler models, due in large part to their remarkable ability to predict recessions.

Suggested Citation

  • Malamud, Semyon & Kelly, Bryan & Zhou, Kangying, 2022. "The Virtue of Complexity in Return Prediction," CEPR Discussion Papers 17194, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:17194
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    More about this item

    Keywords

    Portfolio choice; Machine learning; Random matrix theory; Benign overfit; Overparameterization;
    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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