Topological data analysis of Chinese stocks’ dynamic correlations under major public events

Frontiers in Physics

Physics

Bing Xing¹  Ziwei Ming¹  Hongfeng Guo² 

[1] School of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan, China;null;

关键词: topological data analysis;    persistence landscape;    L-norm;    complex network;    major public event;    systemic financial risk;   

DOI  :  10.3389/fphy.2023.1253953

 received in 2023-07-06, accepted in 2023-08-07,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Guo, Ming and Xing.