Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods

TL;DR

This paper compares hierarchical clustering methods on stock return correlations to assess how well they reveal underlying industrial structures and market dynamics, introducing a novel method that outperforms existing techniques.

Contribution

It introduces the Directed Bubble Hierarchical Tree clustering method and demonstrates its superior ability to recover industrial classifications from financial data.

Findings

Directed Bubble Hierarchical Tree outperforms other methods in information retrieval.

Economic information is captured at different hierarchical levels depending on the method.

Clustering methods show varying sensitivity to market crises.

Abstract

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.