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The Virtue of Complexity in Machine Learning Portfolios

Author

Listed:
  • Bryan T. Kelly

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

  • Semyon Malamud

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

  • Kangying Zhou

    (Yale School of Management)

Abstract

We theoretically characterize the behavior of machine learning portfolios in the high complexity regime, i.e. when the number of parameters exceeds the number of observations. We demonstrate a surprising \virtue of complexity:" Sharpe ratios of machine learning portfolios generally increase with model parameterization, even with minimal regularization. Empirically, we document the virtue of complexity in US equity market timing strategies. 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

  • Bryan T. Kelly & Semyon Malamud & Kangying Zhou, 2021. "The Virtue of Complexity in Machine Learning Portfolios," Swiss Finance Institute Research Paper Series 21-90, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2190
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    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3984925
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    Cited by:

    1. Lassance, Nathan, 2021. "Maximizing the Out-of-Sample Sharpe Ratio," LIDAM Discussion Papers LFIN 2021013, Université catholique de Louvain, Louvain Finance (LFIN).

    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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