Behavioral Machine Learning? Regularization and Forecast Bias
This challenges the interpretation of forecast violations as cognitive failure, suggesting they may instead reflect statistical sophistication, which is significant for researchers and practitioners in economics and finance.
The paper tackles the problem of distinguishing behavioral bias from statistical sophistication in forecast efficiency tests, showing that rational forecasters using optimal regularization systematically violate these tests, with machine learning forecasts exhibiting near zero bias at one year but strong overreaction at two years.
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.