Enhanced Portfolio Optimization
49 Pages Posted: 2 Mar 2020 Last revised: 19 Nov 2020
Date Written: January 2, 2020
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.
Financial Analysts Journal, Forthcoming
Keywords: portfolio choice, optimization, robustness, Black-Litterman, machine learning
JEL Classification: C58, C61, G11, G14
Suggested Citation: Suggested Citation