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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

Japanese

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. 深い残留モデルの価値重視の長期ショートポートフォリオ(2.07)のサンプル外のシャープ比(Sharp ratio)は、相応の浅いモデル(1.92)と深いフィードフォロワーモデルの2倍(0.89)よりも高い。

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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