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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

Portuguese

Modelos Shallow são casos especiais de modelos profundos, e modelos profundos teoricamente têm o potencial de ultrapassar os modelos baixos. No entanto, a literatura de preços de ativos empíricos existentes fornece fortes referências para modelos baixos. Aprendizagem residual permite que modelos de rede neural em preços de ativos para ir mais fundo, preservando e refinando suas contrapartes baixos. A proporção de Sharpe fora de amostra para portfólios longos e curtos ponderados por valor de modelos profundos residuais (2.07) é maior do que para os correspondentes modelos baixos (1.92) e mais do que o dobro do que para os modelos profundos feed forward (0.89).

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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