A Comparative Study of the Equal-Weight Method and Hierarchical Risk Parity in Portfolio Construction

Abstract

Purpose: Portfolio optimization is a process in which the capital is allocated among the portfolio assets such that the return is maximized while the risk is minimized. Portfolio construction and optimization has long been an active research area in finance. For the portfolios with highly correlated assets, the performance of traditional risk-based asset allocation methods such as, the mean-variance (MV) method is limited because quadratic optimizers require an inversion of the covariance matrix of the portfolio to distribute weight among the portfolio assets.

Methods: A possible solution to the limitations of traditional risk-based asset allocation methods can be provided by a hierarchical clustering-based Machine Learning method because it uses hierarchical relationships between the covariance of assets in the portfolio to distribute the weight, and inversion of the covariance matrix is not required. A comparison of the performance of a simple non-optimization technique called the Equal-weight (EW) method to the two optimization methods, the Mean-variance method and the HRP method, which is a machine learning method, was conducted in this research.

Results: It was found that in terms of cumulative returns, the equal-weight method has outperformed several more sophisticated optimization techniques, the mean-variance method, and the HRP method. For most of the period, the Sharpe ratio of the HRP method was observed to be similar to the mean-variance method and equal-weight method.

Implications: This research supports the idea that HRP is a feasible method to construct portfolios with correlated assets because the performance of HRP is comparable to the performances of the traditional optimization method and the non-optimization method.