{"title":"重大公共事件下中国股票动态相关性的拓扑数据分析","authors":"Hongfeng Guo, Ziwei Ming, Bing Xing","doi":"10.3389/fphy.2023.1253953","DOIUrl":null,"url":null,"abstract":"Topological data analysis has been acknowledged as one of the most successful mathematical data analytic methodologies in many fields. Additionally, it has also been gradually applied in financial time series analysis and proved effective in exploring the topological features of such data. We select 100 stocks from China’s markets and construct point cloud data for topological data analysis. We detect critical dates from the Lp-norms of the persistence landscapes. Our results reveal the dates are highly consistent with the transition time of some major events in the sample period. We compare the correlations and statistical properties of stocks before and during the events via complex networks to describe the markets’ situation. The strength and variation of links among stocks are clearly different during the major events. We also investigate the neighborhood features of stocks from topological perspectives. This helps identify the important stocks and explore their situations under each event. Finally, we cluster the stocks based on the neighborhood features, which exhibit the heterogeneity impact on stocks of the different events. Our work demonstrates that topological data analysis has strong applicability in the dynamic correlations of stocks.","PeriodicalId":12507,"journal":{"name":"Frontiers in Physics","volume":"28 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Topological data analysis of Chinese stocks’ dynamic correlations under major public events\",\"authors\":\"Hongfeng Guo, Ziwei Ming, Bing Xing\",\"doi\":\"10.3389/fphy.2023.1253953\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Topological data analysis has been acknowledged as one of the most successful mathematical data analytic methodologies in many fields. Additionally, it has also been gradually applied in financial time series analysis and proved effective in exploring the topological features of such data. We select 100 stocks from China’s markets and construct point cloud data for topological data analysis. We detect critical dates from the Lp-norms of the persistence landscapes. Our results reveal the dates are highly consistent with the transition time of some major events in the sample period. We compare the correlations and statistical properties of stocks before and during the events via complex networks to describe the markets’ situation. The strength and variation of links among stocks are clearly different during the major events. We also investigate the neighborhood features of stocks from topological perspectives. This helps identify the important stocks and explore their situations under each event. Finally, we cluster the stocks based on the neighborhood features, which exhibit the heterogeneity impact on stocks of the different events. Our work demonstrates that topological data analysis has strong applicability in the dynamic correlations of stocks.\",\"PeriodicalId\":12507,\"journal\":{\"name\":\"Frontiers in Physics\",\"volume\":\"28 1\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2023-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.3389/fphy.2023.1253953\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.3389/fphy.2023.1253953","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
Topological data analysis of Chinese stocks’ dynamic correlations under major public events
Topological data analysis has been acknowledged as one of the most successful mathematical data analytic methodologies in many fields. Additionally, it has also been gradually applied in financial time series analysis and proved effective in exploring the topological features of such data. We select 100 stocks from China’s markets and construct point cloud data for topological data analysis. We detect critical dates from the Lp-norms of the persistence landscapes. Our results reveal the dates are highly consistent with the transition time of some major events in the sample period. We compare the correlations and statistical properties of stocks before and during the events via complex networks to describe the markets’ situation. The strength and variation of links among stocks are clearly different during the major events. We also investigate the neighborhood features of stocks from topological perspectives. This helps identify the important stocks and explore their situations under each event. Finally, we cluster the stocks based on the neighborhood features, which exhibit the heterogeneity impact on stocks of the different events. Our work demonstrates that topological data analysis has strong applicability in the dynamic correlations of stocks.
期刊介绍:
Frontiers in Physics publishes rigorously peer-reviewed research across the entire field, from experimental, to computational and theoretical physics. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, engineers and the public worldwide.