Can Machine Learning Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification

The Hierarchical risk parity (HRP) approach of portfolio allocation, introduced by [Lopez de Prado, 2016], applies graph theory and machine learning to build a diversified portfolio. Like the traditional risk based allocation methods, HRP is also a function of the estimate of the covariance matrix, however, it doesn't require its invertibility. In this paper we first study the impact of covariance misspecification on the performance of the different allocation methods. Next we study under appropriate covariance forecast model whether the machine learning based HRP out-performs the traditional risk based portfolios. For our analysis we use the test for superior predictive ability, on out-of-sample portfolio performance, to determine whether the observed excess performance is significant or occurred by chance. We find that when the covariance estimates are crude, inverse volatility weighted portfolios are more robust, followed by the machine learning based portfolios. Minimum variance and maximum diversification are most sensitive to covariance misspecification. HRP follows the middle ground, it is less sensitive to covariance misspecification when compared with minimum variance or maximum diversification portfolio, while it is not as robust as the inverse volatility weighed portfolio. We also study the impact of different rebalancing horizon and how the portfolios compare against a market-capitalization weighted portfolio