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Behavioral Machine Learning? Regularization and Forecast Bias

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  • Murray Z. Frank
  • Jing Gao
  • Keer Yang

Abstract

Standard forecast efficiency tests interpret violations as evidence of behavioral bias. We show theoretically and empirically that rational forecasters using optimal regularization systematically violate these tests. Machine learning forecasts show near zero bias at one year horizon, but strong overreaction at two years, consistent with predictions from a model of regularization and measurement noise. We provide three complementary tests: experimental variation in regularization parameters, cross-sectional heterogeneity in firm signal quality, and quasi-experimental evidence from ML adoption around 2013. Technically trained analysts shift sharply toward overreaction post-2013. Our findings suggest reported violations may reflect statistical sophistication rather than cognitive failure.

Suggested Citation

  • Murray Z. Frank & Jing Gao & Keer Yang, 2023. "Behavioral Machine Learning? Regularization and Forecast Bias," Papers 2303.16158, arXiv.org, revised Dec 2025.
    • Handle: RePEc:arx:papers:2303.16158
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    References listed on IDEAS

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    1. Sean Cao & Wei Jiang & Junbo L. Wang & Baozhong Yang, 2021. "From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses," NBER Working Papers 28800, National Bureau of Economic Research, Inc.
    2. Qi Chen & Itay Goldstein & Wei Jiang, 2007. "Price Informativeness and Investment Sensitivity to Stock Price," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 619-650.
    3. Begenau, Juliane & Farboodi, Maryam & Veldkamp, Laura, 2018. "Big data in finance and the growth of large firms," Journal of Monetary Economics, Elsevier, vol. 97(C), pages 71-87.
    4. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
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