Enhanced Portfolio Optimization
50 Pages Posted: 2 Mar 2020 Last revised: 14 Aug 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. Several approaches have been developed to address this important issue, but they are often surrounded by mystique regarding how, why, and whether they really work. We seek to demystify, simplify, and enhance optimization: we identify the portfolios that cause problems in standard MVO and develop a simple enhanced portfolio optimization (EPO) method that addresses the problems. Applying EPO across equities and global asset classes, we find that EPO significantly enhances the performance of industry momentum and time series momentum factors, adding significant alpha beyond the market, the 1/N portfolio, risk parity, and standard asset pricing factors.
Keywords: portfolio choice, optimization, robustness, Black-Litterman, machine learning
JEL Classification: C58, C61, G11, G14
Suggested Citation: Suggested Citation