The Nonstationarity-Complexity Tradeoff in Return Prediction

• URL: http://arxiv.org/abs/2512.23596v1
• Date: Mon, 29 Dec 2025 16:49:19 GMT
• Title: The Nonstationarity-Complexity Tradeoff in Return Prediction
• Authors: Agostino Capponi, Chengpiao Huang, J. Antonio Sidaoui, Kaizheng Wang, Jiacheng Zou,
• Abstract summary: We investigate machine learning models for stock return prediction in non-stationary environments.<n>We show that a novel model selection method balances misspecification error, estimation variance, and non-stationarity, performing close to the best model in hindsight.
• Score: 5.8720142291102135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate machine learning models for stock return prediction in non-stationary environments, revealing a fundamental nonstationarity-complexity tradeoff: complex models reduce misspecification error but require longer training windows that introduce stronger non- stationarity. We resolve this tension with a novel model selection method that jointly optimizes model class and training window size using a tournament procedure that adaptively evaluates candidates on non-stationary validation data. Our theoretical analysis demonstrates that this approach balances misspecification error, estimation variance, and non-stationarity, performing close to the best model in hindsight. Applying our method to 17 industry portfolio returns, we consistently outperform standard rolling-window benchmarks, improving out-of-sample $R^2$ by 14-23% on average. During NBER- designated recessions, improvements are substantial: our method achieves positive $R^2$ during the Gulf War recession while benchmarks are negative, and improves $R^2$ in absolute terms by at least 80bps during the 2001 recession as well as superior performance during the 2008 Financial Crisis. Economically, a trading strategy based on our selected model generates 31% higher cumulative returns averaged across the industries.

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