Approaching Mean-Variance Efficiency for Large Portfolios
提出一种基于无约束回归表示和高维稀疏回归的新方法，在资产和观测数量增长时构建最优均值-方差投资组合，实证表明加入个股能显著提升表现。
This paper introduces a new approach to constructing optimal mean-variance portfolios. The approach relies on a novel unconstrained regression representation of the mean-variance optimization problem combined with high-dimensional sparse-regression methods. Our estimated portfolio, under a mild sparsity assumption, controls for risk and attains the maximum expected return as both the numbers of assets and observations grow. The superior properties of our approach are demonstrated through comprehensive simulation and empirical analysis. Notably, using our strategy, we find that investing in individual stocks, in addition to the Fama-French three-factor portfolios, leads to substantially improved performance.