Many view deep learning as a "black box" used only for forecasting. However, this paper provides an alternative application by constructing a structural deep neural network to generate latent factors in asset pricing. The conventional approach of sorting firm characteristics to generate long-short factor portfolio weights is underappreciated nonlinear modeling. First, we describe the complete mechanism for fitting crosssectional returns by firm characteristics through risk factors. Second, unlike statistical models, our model has an economic-guided objective function that minimizes pricing errors. Empirically, we find asset pricing and investment improvements using individual stocks and test portfolios for in-sample and out-of-sample analysis.

翻译：许多人认为深层次的学习只是用于预测的“黑盒”,然而,本文却提供了一种替代应用,即建立一个结构深厚的神经网络,以产生资产定价的潜在因素。传统的分类公司特征以产生长期短因子组合权重的方法被低估了非线性模型。首先,我们描述了通过风险因素通过公司特征使跨部门回报相匹配的完整机制。第二,与统计模型不同,我们的模型有一个经济导向的目标功能,可以最大限度地减少定价错误。很典型的是,我们发现资产定价和投资改进利用单个股票和测试组合进行内模和外样分析。