(Mis)Understanding Benign Overfitting in Equity Return Prediction

2026-08-24

AI Summary

研究探讨了在股票回报预测中是否存在‘良性过拟合’现象，通过对比无岭回归模型和最优岭回归模型的表现，发现两者均未能超越简单的历史平均值。

Abstract

Highly overparameterized models often predict well despite interpolating training data in complex domains, challenging the classical bias--variance tradeoff. We investigate whether this ``benign overfitting'' phenomenon extends to equity return prediction. Consistent with recent statistical theory, we document two key phenomena: first, a double descent pattern in the ridgeless model's prediction risk; and second, that while the optimal ridge model consistently outperforms its ridgeless counterpart, this performance gap becomes negligible at large parameter-to-observation ratios. Ultimately, however, both models fail to outperform a simple historical average. This empirical evidence aligns with our asymptotic results under the null hypothesis of zero slope coefficients, suggesting that standard equity predictors lack true forecasting power---even within highly flexible, nonlinear machine learning architectures. These findings reconcile modern and classical machine learning in asset pricing: in the absence of a true signal, they asymptotically collapse to the historical average benchmark.

Problem

• benign overfitting
• equity return prediction
• overparameterized models

Innovation

• benign overfitting
• double descent
• ridge regression
• equity return prediction