Approaching Mean-Variance Efficiency for Large Portfolios
69 Pages Posted: 6 Dec 2015 Last revised: 19 Jul 2018
Date Written: July 7, 2018
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 the 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, we find that investing in individual stocks in addition to the Fama-French three factor portfolios using our strategy leads to substantially improved performance.
Keywords: Large portfolio selection; Mean-variance portfolio; Sharpe ratio; Unconstrained regression; LASSO
JEL Classification: G11; C10
Suggested Citation: Suggested Citation