An Empirical Evaluation of Hierarchical Clustering-Based Portfolio Selection Models

This paper investigates the empirical performance of hierarchical clustering-based as-set allocation models. These models use clustering techniques to inform portfolioconstruction without relying on traditional optimization.We evaluate several variants of these methods and compare their out-of-sample perfor-mance with standard portfolio selection strategies. Our results suggest that hierarchicalclustering models offer competitive and often superior risk-adjusted returns, demon-strating robustness across different market conditions and asset universes.