We present a set of analyses aiming at quantifying the amount of information filtered by different hierarchical clustering methods on correlations between stock returns . In particular we apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree (DBHT), and we compare it with other methods including the Linkage and k-medoids. In particular  by taking the industrial sector classification of stocks as a benchmark partition  we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree outperforms the other methods, being able to retrieve more information with fewer clusters. Moreover, we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis  also reveals that the different methods show different degrees of sensitivity to financial events , like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging.
We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing it with the underlying industrial activity structure. Specifically, we apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. In particular , by taking the industrial sector classification of stocks as a benchmark partition , we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover, we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets , like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging.
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