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arXiv Machine Learning By Dexin Peng, Xiaoyu Wang

Residual Learning in Empirical Asset Pricing

Read the original on arXiv Machine Learning →

The paper demonstrates that residual learning enables neural network models in asset pricing to become deeper while preserving and refining shallow counterparts. Deep residual models achieve a higher out‑of‑sample Sharpe ratio (2.07) compared to shallow models (1.92) and significantly outperform deep feedforward models (0.89). This suggests that model depth can add economic value and offers a scalable approach for large asset pricing models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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