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The Virtue of Complexity in Return Prediction

Author

Listed:
  • Bryan T. Kelly
  • Semyon Malamud
  • Kangying Zhou

Abstract

Much of the extant literature predicts market returns with “simple” models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in US equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning.

Suggested Citation

  • Bryan T. Kelly & Semyon Malamud & Kangying Zhou, 2022. "The Virtue of Complexity in Return Prediction," NBER Working Papers 30217, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:30217
    Note: AP
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Complexity in Prediction
      by Francis Diebold in No Hesitations on 2022-08-19 13:54:00

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    Cited by:

    1. Victor Olkhov, 2023. "Market-Based Probability of Stock Returns," Papers 2302.07935, arXiv.org, revised Feb 2024.
    2. Bagnara, Matteo, 2024. "The economic value of cross-predictability: A performance-based measure," SAFE Working Paper Series 424, Leibniz Institute for Financial Research SAFE.
    3. Antonio Marsi, 2023. "Predicting European stock returns using machine learning," SN Business & Economics, Springer, vol. 3(7), pages 1-25, July.
    4. 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
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G1 - Financial Economics - - General Financial Markets

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