Speaker: Agostino Capponi
Title: The Nonstationarity-Complexity Tradeoff in Return Prediction
Abstract: We study machine learning models for stock return prediction in non-stationary environments and identify a fundamental nonstationarity–complexity tradeoff: more complex models reduce misspecification error but require longer training windows that exacerbate non-stationarity. We address this tension with a novel tournament-based model selection procedure that jointly optimizes the model class and training window that adaptively evaluates candidates on non-stationary validation data. Our theory shows that the method balances misspecification, estimation variance, and non-stationarity, performing close to the best model in hindsight. Applied to 17 industry portfolios, the approach improves out-of-sample R2 by 14–23% relative to rolling benchmarks, delivers markedly stronger performance during recessions, and yields trading strategies with 31% higher cumulative returns (joint work with Chengpiao Huang, Antonio Sidaoui, Kaizheng Wang, and Jiacheng Zou).