Portfolio allocation is an important tool for portfolio managers and investors interested in diversification as well as improvements in out-of-sample portfolio performance. Recently, new portfolio allocation strategies based on unsupervised machine learning have been proposed in the literature, with hierarchical risk parity being one of the most popular. This article uses assets from the Brazilian financial market to perform an extensive out-of-sample comparison of hierarchical risk parity against widely-known, traditional portfolio allocation techniques. The results suggest that, in general, hierarchical risk parity does not report the best performance but, in some performance measures, performs equally well to other approaches. Overall, hierarchical risk parity outperforms the market index.