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Residual Learning in Empirical Asset Pricing

Dexin Peng, Xiaoyu Wang · 2026 · SSRN Electronic Journal

DOI: 10.2139/ssrn.7528558 · cited by 0 works (OpenAlex) · open-access PDF

Korean

Shallow models are special cases of deep models, and deep models theoretically have the potential to outperform the shallow ones. However, the existing empirical asset pricing literature provides strong benchmarks for shallow models. Residual learning allows neural network models in asset pricing to go deeper by preserving and refining their shallow counterparts. Deep residual models (2.07)의 가치가 무게 된 길고 짧은 포트폴리오에 대한 샘플 밖의 Sharpe ratio는 해당 shallow models (1.92)에 대한 것보다 높고, deep feed forward models (0.89)에 대한 것보다 두 배 이상 높습니다.

Machine translation (Meta M2M100); the English original follows.

Abstract

Shallow models are special cases of deep models, and deep models theoretically have the potential to outperform the shallow ones. However, the existing empirical asset pricing literature provides strong benchmarks for shallow models. Residual learning allows neural network models in asset pricing to go deeper by preserving and refining their shallow counterparts. The out-of-sample Sharpe ratio for value-weighted long-short portfolios of deep residual models (2.07) is higher than that for the corresponding shallow ones (1.92) and more than twice that of the deep feedforward models (0.89). We sh

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Topics: Capital asset pricing model, Residual, Consumption-based capital asset pricing model, Asset (computer security), Arbitrage pricing theory, Econometrics

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