{"title":"Dynamics of NYSE correlation structure during global crisis in 2008: Evidence from complex network analysis","authors":"M. A. Djauhari, G. Lee","doi":"10.1109/ICACSIS.2015.7415196","DOIUrl":null,"url":null,"abstract":"Stocks market is a complex system. To understand its behavior, random matrix theory and/or graph theory are/is usually used. In this paper, the latter is used to analyze the dynamics of correlations network at New York Stock Exchange (NYSE) during global crisis in 2008. For that purpose, first, correlations network stability is tested. Second, complex network representation is provided to study the correlations network dynamics and their minimal spanning tree (MST) is constructed to study the evolution of network topological properties. Some changes of these properties in terms of stock's degree and graph diameter will be highlighted to demonstrate the advantages of complex network approach.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACSIS.2015.7415196","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Stocks market is a complex system. To understand its behavior, random matrix theory and/or graph theory are/is usually used. In this paper, the latter is used to analyze the dynamics of correlations network at New York Stock Exchange (NYSE) during global crisis in 2008. For that purpose, first, correlations network stability is tested. Second, complex network representation is provided to study the correlations network dynamics and their minimal spanning tree (MST) is constructed to study the evolution of network topological properties. Some changes of these properties in terms of stock's degree and graph diameter will be highlighted to demonstrate the advantages of complex network approach.
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