This paper presents an augmented deep factor model that generates latent factors for cross-sectional asset pricing. The conventional security sorting on firm characteristics for constructing long-short factor portfolio weights is nonlinear modeling, while factors are treated as inputs in linear models. We provide a structural deep learning framework to generalize the complete mechanism for fitting cross-sectional returns by firm characteristics through generating risk factors -- hidden layers. Our model has an economic-guided objective function that minimizes aggregated realized pricing errors. Empirical results on high-dimensional characteristics demonstrate robust asset pricing performance and strong investment improvements by identifying important raw characteristic sources.

翻译：本文提出了一种增强型深度因子模型，用于生成横截面资产定价的潜在因子。传统的基于公司特征进行证券排序以构建多空因子组合权重的过程属于非线性建模，而因子在传统方法中被视为线性模型的输入。我们构建了一个结构化深度学习框架，通过生成风险因子（隐藏层）来泛化拟合公司特征与横截面收益关系的完整机制。该模型采用经济导向的目标函数，以最小化聚合实现的定价误差。基于高维特征的实证结果表明，通过识别重要的原始特征来源，该模型展现了稳健的资产定价性能与显著的投资优化效果。