{"title":"The topology of overlapping portfolio networks","authors":"Weilong Guo, Andreea Minca, Li Wang","doi":"10.1515/strm-2015-0020","DOIUrl":null,"url":null,"abstract":"Abstract This paper analyzes the topology of the network of common asset holdings, where nodes represent managed portfolios and edge weights capture the impact of liquidations. Asset holdings data is extracted from the 13F filings. We consider the degree centrality as the degree in the subnetwork of weak links, where weak links are those that lead to significant liquidations. We explore the applications of this network representation to clustering and forecasting. To validate the weight attribution and the threshold used to define the weak links, we show that the degree centrality is correlated with excess returns, and is significant after we control for the Carhart four factors. The network of weak links has a scale free structure, similar to financial networks of balance sheet exposures. Moreover, a small number of clusters, densely linked, concentrate a significant proportion of the portfolios.","PeriodicalId":44159,"journal":{"name":"Statistics & Risk Modeling","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2015-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/strm-2015-0020","citationCount":"43","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics & Risk Modeling","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/strm-2015-0020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 43
Abstract
Abstract This paper analyzes the topology of the network of common asset holdings, where nodes represent managed portfolios and edge weights capture the impact of liquidations. Asset holdings data is extracted from the 13F filings. We consider the degree centrality as the degree in the subnetwork of weak links, where weak links are those that lead to significant liquidations. We explore the applications of this network representation to clustering and forecasting. To validate the weight attribution and the threshold used to define the weak links, we show that the degree centrality is correlated with excess returns, and is significant after we control for the Carhart four factors. The network of weak links has a scale free structure, similar to financial networks of balance sheet exposures. Moreover, a small number of clusters, densely linked, concentrate a significant proportion of the portfolios.
期刊介绍:
Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.