论文标题
非线性因子模型中的深度学习残差：信噪比低的回报的精确矩阵估计
Deep Learning Based Residuals in Non-linear Factor Models: Precision Matrix Estimation of Returns with Low Signal-to-Noise Ratio
论文作者
论文摘要
本文使用深度学习框架中的非线性因素模型引入了大型投资组合中资产回报精确矩阵的一致估计量和收敛速率。即使在金融市场典型的信噪比环境中，我们的估计器仍然有效，并且与弱因素兼容。我们的理论分析基于深度神经网络的预期估计风险建立了统一的界限，以扩大资产数量。此外，我们还提供了深层神经网络中误差协方差的新一致的数据依赖性估计值。我们的模型在广泛的模拟和经验中表现出卓越的准确性。
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.
