(Mis)Understanding Benign Overfitting in Equity Return Prediction
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...
| Authors | Hui Guo, Jiawei Huang, Runze Li et al. |
|---|---|
| Primary category | stat.ML |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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