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

机译:金融市场结构与实体经济的关系:聚类方法的比较

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摘要

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. 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. 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.
机译:我们将股票结构之间的相关性与聚类结构与基础行业活动分类进行比较,从而量化通过不同层次聚类方法过滤的信息量。我们首次将新颖的层次聚类方法(Directed Bubble Hierarchical Tree)应用于财务数据,并将其与其他方法(包括Linkage和k-medoids)进行比较。通过将工业部门的股票分类作为基准分区,我们评估了不同方法如何检索此分类。结果表明,定向气泡层次树可以胜过其他方法,能够以更少的集群检索更多信息。此外,我们表明,根据聚类方法,经济信息隐藏在层次结构的不同层次上。滚动窗口上的动态分析还显示,不同的方法对影响金融市场(例如危机)的事件显示出不同程度的敏感性。这些结果对于聚类方法在投资组合优化和风险对冲中的所有应用都可能是有意义的。

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