Deep learning based residuals in non-linear factor models: Precision matrix estimation of returns with low signal-to-noise ratio
非线性因子模型中的深度学习残差：低信噪比收益精度矩阵估计
This paper introduces a consistent estimator and rate of convergence for the precision matrix of asset returns in large portfolios using a non-linear factor model within the deep learning framework. Our estimator remains valid even in low signal-to-noise ratio environments typical for financial markets and is compatible with the weak factor framework. Our theoretical analysis establishes uniform bounds on expected estimation risk based on deep neural networks for an…