Clustering heterogeneous financial networks
Clustering heterogeneous financial networks
复制标题
集群异构金融网络
DOI：
10.1111/mafi.12407
复制
发表时间：
2023
影响因子：
1.6
通讯作者：
Qian, Xin
中科院分区：
文献类型：
--
作者：
Amini, Hamed;Chen, Yudong;Minca, Andreea;Qian, Xin
我们开发了一种用于异构金融网络的凸优化聚类算法，在存在任意甚至对抗性离群值的情况下。在具有异质性参数的随机块模型中，我们惩罚那些度表现出超出内点异质性的异常行为的节点。我们证明，在温和的条件下，这种方法实现了准确的恢复的基础集群。在没有任何假设的离群值，他们不妨碍聚类的内点。我们测试了该算法在半合成异构网络上的性能，该网络被重建以匹配韩国金融部门的聚合数据。与现有算法相比，我们的方法允许以显着更低的错误率恢复子部门。对于重叠的投资组合网络，我们发现了一个聚类结构，支持投资管理中的多元化效应。
We develop a convex‐optimization clustering algorithm for heterogeneous financial networks, in the presence of arbitrary or even adversarial outliers. In the stochastic block model with heterogeneity parameters, we penalize nodes whose degree exhibit unusual behavior beyond inlier heterogeneity. We prove that under mild conditions, this method achieves exact recovery of the underlying clusters. In absence of any assumption on outliers, they are shown not to hinder the clustering of the inliers. We test the performance of the algorithm on semi‐synthetic heterogenous networks reconstructed to match aggregate data on the Korean financial sector. Our method allows for recovery of sub‐sectors with significantly lower error rates compared to existing algorithms. For overlapping portfolio networks, we uncover a clustering structure supporting diversification effects in investment management.