Author(s)

Weige Huang¹, Xiang Gao^2*

¹Wenlan School of Business, Zhongnan University of Economics and Law, Wuhan, China.
²Research Center of Finance, Shanghai Business School, Shanghai, China.

This paper investigates the usefulness of the Hierarchical Equal Risk Contribution algorithm to exploit correlation structure in China’s equity market over 2001-2020. By running a horse race of different combinations of metrics and linkages, we demonstrate that the winner strategy always beats traditional portfolio construction techniques. Better-performing risk-based hierarchy strategies vary with stock-sorting methods by size, mean return, volatility, and Sharpe ratio. However, our treatment results in extremely imbalanced asset allocation, implying that we capture information other than the standard Chinese industrial classification.

Hierarchical Equal Risk Contribution, Machine Learning, Hierarchical Risk Parity, Asset Allocation, Critical Line Algorithm, Inverse-Variance Portfolio

Huang, W. and Gao, X. (2022) Evaluating Hierarchical Equal Risk Contribution Portfolios in the Chinese Stock Market. Journal of Mathematical Finance, 12, 179-195. doi: 10.4236/jmf.2022.121011.