An Enhanced Factor Model for Portfolio Selection in High Dimensions
在Fama-French因子模型基础上引入潜在因子，并采用对角占优结构估计残差协方差，提出一种增强因子模型，在高维投资组合选择中实现了更低的组合方差和更高的净夏普比率。
Abstract This article extends Fama and French (FF) models of observed factors by introducing latent factors (LFs) to further extract information from FF residual returns. A diagonally dominant (DD) rather than a diagonal or sparse matrix structure is adopted in this study to estimate remaining covariance between disturbance terms. Such an enhanced factor (EF) model provides a more comprehensive analysis for portfolio selection in high dimensions and also has certain advantages of estimation stability and computational efficiency. It is shown that the proposed EF–DD approach achieves overall better performance than competing models in terms of portfolio variance and the net Sharpe ratio.