We propose a latent multi-factor asset pricing model that estimates risk exposure based on firm characteristics and connectivity between assets. To handle connected high-dimensional characteristics, we adopted a graph convolutional network while estimating the connectivity between assets from the correlation of asset returns. Unlike recent literature involving the deep-learning-based latent factor model, we propose a forward stagewise additive factor modeling architecture that constructs latent factors sequentially to maintain the previous stage’s factors. Our empirical results on individual U.S. equities show that the proposed graph factor model outperforms other benchmark models in terms of explanatory power and the Sharpe ratio of the factor tangency portfolio.

我们提出了一个潜在的多因素资产定价模型，该模型根据公司特征和资产之间的连通性来估计风险敞口。为了处理连接的高维特征，我们采用了图卷积网络，同时从资产回报的相关性估计资产之间的连通性。与最近涉及基于深度学习的潜在因子模型的文献不同，我们提出了一种前向阶段加性因子建模架构，该架构按顺序构建潜在因子以保持前一阶段的因子。我们对单个美国股票的实证结果表明，所提出的图因子模型在解释力和因子切线组合的夏普比率方面优于其他基准模型。