Enhanced Portfolio Optimization

Abstract

Portfolio optimization should provide large benefits to investors, but standard mean-variance optimization (MVO) works so poorly in practice that optimization is often abandoned. The approaches developed to address this issue are often surrounded by mystique regarding how, why, and whether they really work, so we seek to simplify, unify, and demystify optimization. We identify the portfolios that cause problems in standard MVO and present a simple enhanced portfolio optimization (EPO) method. Applying EPO to industry momentum and time series momentum across equities and global asset classes, we find significant alpha beyond the market, the 1/N portfolio, and standard asset pricing factors.

Citation: Pedersen, Lasse Heje, Abhilash Babu, and Ari Levine, Enhanced Portfolio Optimization,
Financial Analysts Journal, 2021, 77(2): 124-151.
Available at SSRN: https://ssrn.com/abstract=3530390
Published version (open access) at https://doi.org/10.1080/0015198X.2020.1854543

Keywords: portfolio choice, optimization, robustness, Black-Litterman, machine learning

JEL Classification: C58, C61, G11, G14