Approaching Mean-Variance Efficiency for Large Portfolios

• 成果类型: Article
• 署名作者: Ao, Mengmeng; Li, Yingying; Zheng, Xinghua
• 署名单位: Xiamen University; Hong Kong University of Science & Technology
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhy105
• 发表日期: 2019
• 页码: 2890
• 关键词: NAIVE DIVERSIFICATION; selection; optimization; performance; COVARIANCES; parameter; MARKOWITZ; SPARSE; error

摘要

This paper introduces a newapproach 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.