Behavioral Machine Learning? Regularization and Forecast Bias

Murray Z. Frank, Jing Gao and Keer Yang

Papers from arXiv.org

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.

Date: 2023-03, Revised 2025-12
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
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