(Mis)Understanding Benign Overfitting in Equity Return Prediction

• URL: http://arxiv.org/abs/2608.23761v1
• Date: Mon, 24 Aug 2026 18:54:34 GMT
• Title: (Mis)Understanding Benign Overfitting in Equity Return Prediction
• Abstract summary: We investigate whether this benign overfitting'' phenomenon extends to equity return prediction.<n>We find 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.
• Score: 14.203404489519242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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