基于深度学习的非线性因子模型残差：低信噪比下收益的精度矩阵估计
Deep Learning Based Residuals in Non-linear Factor Models: Precision Matrix Estimation of Returns with Low Signal-to-Noise Ratio
AI总结：
本文提出一种基于深度学习非线性因子模型的资产收益精度矩阵一致估计方法，在低信噪比和弱因子环境下依然有效，并通过理论界限与模拟实证验证其优越准确性。
摘要：
本文在深度学习框架内，利用非线性因子模型，为大投资组合中的资产收益精度矩阵引入了一个一致估计量及其收敛速率。我们的估计量即使在金融市场典型的低信噪比环境中仍然有效，并且与弱因子兼容。我们的理论分析基于深度神经网络，为不断增加的资产数量建立了期望估计风险的统一界限。此外，我们提供了一种新的、数据依赖的深度神经网络误差协方差一致估计量。我们的模型在广泛的模拟和实证中展现了卓越的准确性。
英文摘要：
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 weak factors. Our theoretical analysis establishes uniform bounds on expected estimation risk based on deep neural networks for an expanding number of assets. Additionally, we provide a new consistent data-dependent estimator of error covariance in deep neural networks. Our models demonstrate superior accuracy in extensive simulations and the empirics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
