<p>Hierarchical Risk Parity methods address instability, concentration, and underperformance in asset allocation by taking advantage of machine learning techniques to build a diversified portfolio. HRP methods produce a hierarchical structure to the correlation between assets by means of tree clustering that results in a reorganization of the covariance matrix of returns. However, HRP admits multiple variations in terms of clustering algorithms and distance metrics. In this paper, we evaluate the out-of-sample performance of alternative hierarchical distance metrics for clustering purposes using real stock markets in three different market scenarios: bull market, sideways trend, and bear market. We pay special attention to the mean-variance performance of the output portfolios as an estimation of the ability of alternative methods to estimate future return and risk. Our results show that correlation-based metrics provide better performance than non-correlation metrics. In addition, HRP methods outperform quadratic optimizers in two of the three stock market scenarios.</p>