PT - JOURNAL ARTICLE AU - Marcos López de Prado TI - Building Diversified Portfolios that Outperform Out of Sample AID - 10.3905/jpm.2016.42.4.059 DP - 2016 May 31 TA - The Journal of Portfolio Management PG - 59--69 VI - 42 IP - 4 4099 - https://pm-research.com/content/42/4/59.short 4100 - https://pm-research.com/content/42/4/59.full AB - In this article, the author introduces the Hierarchical Risk Parity (HRP) approach to address three major concerns of quadratic optimizers, in general, and Markowitz’s critical line algorithm (CLA), in particular: instability, concentration, and underperformance. HRP applies modern mathematics (graph theory and machine-learning techniques) to build a diversified portfolio based on the information contained in the covariance matrix. However, unlike quadratic optimizers, HRP does not require the invertibility of the covariance matrix. In fact, HRP can compute a portfolio on an ill-degenerated or even a singular covariance matrix—an impossible feat for quadratic optimizers. Monte Carlo experiments show that HRP delivers lower out-ofsample variance than CLA, even though minimum variance is CLA’s optimization objective. HRP also produces less risky portfolios out of sample compared to traditional risk parity methods.TOPICS: Statistical methods, portfolio construction