平衡数据长度与模型复杂度，提升股市收益预测能力
The Nonstationarity-Complexity Tradeoff in Return Prediction
- 动态调整训练窗口与模型复杂度，应对市场非平稳性
- 美国股市30年数据验证，预测效果提升14%（相对基准）
- 尤其在经济衰退期表现优异，适合高频金融预测场景
金融市场具有非平稳性，更长的训练窗口虽能提升复杂模型的预测能力，但会引入过时的经济周期信息；而简单模型需数据少，对经济变化不敏感。本文正式刻画了非平稳性与模型复杂度之间的权衡关系，提出一种具有理论性能保证的自适应选择方法。在长达三十余年的美国股市数据中，该方法在行业组合的样本外预测中，$R^2$ 相比固定窗口和制度切换基准平均提升14%，并在经济衰退期取得显著增益。
原文摘要 · Abstract (English)
Does more data improve return prediction? In non-stationary financial markets, longer training windows improve prediction of complex models but incorporate outdated economic regimes, whereas simpler models require less data and are less vulnerable to changes in economic conditions. We formally characterize this nonstationarity-complexity tradeoff, showing that model complexity and training window length must be jointly optimized. We propose an adaptive selection procedure with formal performance guarantees. Over three decades of U.S. equity markets, our method improves out-of-sample $R^2$ on industry portfolios by 14% relative to fixed-window and regime-switching benchmarks, with large gains during recessions.
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