Performance of Hierarchical Equal Risk Contribution Algorithm in China Market

Abstract

This paper studies the performance of the portfolios based on the Hierarchical Equal Risk Contribution algorithm in China stock market. Specifically, we consider a variety of risk measures for calculating weight allocations which include equal weighting, variance, standard deviation, expected shortfall and conditional draw-down risk and four types of linkage criteria used for agglomerative clustering, namely, single, complete, average, and Ward linkages. We compare the performance of the portfolios based on the HERC algorithm to the equal-weighted and inverse-variance portfolios. We find that most HERC portfolios are not able to beat the equal-weighted and inverse-variance portfolios in terms of several comparison measures and HERC with Ward-linkage seems to dominate the ones with other linkages. However, the results do not show that any risk measures can beat other measures consistently.

Note: The paper has been renamed as "Evaluating Hierarchical Equal Risk Contribution Portfolios in the Chinese Stock Market" which was published at Journal of Mathematical Finance.

Keywords: Hierarchical Equal Risk Contribution, Asset Allocation, Machine Learning, Portfolio Optimization, Inverse-Variance Portfolio, Hierarchical Risk Parity

JEL Classification: G00, G10, G11