Abstract Risk-based portfolio allocation strategies using the so-called hierarchical risk clustering have recently attracted attention from both academics and practitioners, mainly because of their ability to construct well-diversified portfolios through machine learning algorithms without the need to invert the covariance matrix. However, despite this innovative approach, the existing literature remains inconclusive regarding the outperformance of this methodology compared to traditional risk-b