Musmeci Nicoló, Aste Tomaso, Di Matteo T
Department of Mathematics, King's College London, The Strand, London, WC2R 2LS, UK.
Department of Computer Science, UCL, Gower Street, London, WC1E 6BT, UK; Systemic Risk Centre, London School of Economics and Political Sciences, London, WC2A2AE, UK.
PLoS One. 2015 Mar 18;10(3):e0116201. doi: 10.1371/journal.pone.0116201. eCollection 2015.
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 [corrected].
我们通过将聚类结构与基础产业活动分类进行比较，来量化不同层次聚类方法在股票回报相关性上过滤的信息量。我们首次将一种新颖的层次聚类方法——定向气泡层次树应用于金融数据，并将其与包括连锁法和k-中心点法在内的其他方法进行比较。通过将股票的行业分类作为基准划分，我们评估不同方法如何恢复这一分类。结果表明，定向气泡层次树能够优于其他方法，能够用更少的聚类恢复更多信息。此外，我们表明，根据聚类方法的不同，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表明，不同方法对影响金融市场的事件（如危机）表现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可能具有重要意义[已修正]。
