发表机构
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出将残差学习应用于经验资产定价，通过加深神经网络模型提升经济价值，实证表明深度残差模型样本外夏普比率显著优于浅层和前馈模型。
AI 中文摘要
浅层模型是深层模型的特殊情况，理论上深层模型有潜力超越浅层模型。然而，现有的经验资产定价文献为浅层模型提供了强有力的基准。残差学习通过保留并精炼浅层模型，使资产定价中的神经网络模型能够向更深层次发展。基于价值加权的多空组合，深度残差模型的样本外夏普比率为2.07，高于对应浅层模型的1.92，并且是深度前馈模型0.89的两倍以上。我们表明，模型深度是资产定价中额外经济价值的来源。如果其他基于神经网络的资产定价模型包含中间层，残差学习可用于加深这些模型。我们的设计还提供了一种扩展资产定价模型的方法，使原生“大型资产定价模型”更加可行。
英文摘要
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 show that model depth is a source of additional economic value in asset pricing. Residual learning can be used to deepen other neural-network-based asset pricing models if they contain intermediate layers. Our design also provides one way to scale asset pricing models, making native "large asset pricing models" more feasible.
Comments58 pages, 8 figures
