Performance of Hierarchical Equal Risk Contribution Algorithm in China Market

37 Pages Posted: 9 Nov 2020 Last revised: 6 Jun 2022

See all articles by Weige Huang

Weige Huang

Zhongnan University of Economics and Law; Temple University

Date Written: September 19, 2020

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

Suggested Citation

Huang, Weige, Performance of Hierarchical Equal Risk Contribution Algorithm in China Market (September 19, 2020). Available at SSRN: https://ssrn.com/abstract=3695598 or http://dx.doi.org/10.2139/ssrn.3695598

Weige Huang (Contact Author)

Zhongnan University of Economics and Law ( email )

182# Nanhu Avenue
East Lake High-tech Development Zone
Wuhan, Hubei 430073
China

Temple University ( email )

Philadelphia, PA 19122
United States

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