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Evolution of Complex Network Topology for Chinese Listed Companies Under the COVID-19 Pandemic

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Abstract

The purpose of this study is to analyze the topological structure dynamics of the complex network of stocks before and after the outbreak of the COVID-19, so as to provide a basis for preventing financial risks. We calculate Pearson correlation coefficient between enterprises according to logarithmic rate of return and trading volume ratio of enterprises’ stocks, and then constructed a complex network of stock market price and volume before and after the outbreak of the COVID-19. First, through thresholding and heat map imaging of the correlation matrix, the change characteristics of the correlation between various industries in 2019 and 2020 are studied. Second, the node degree, average weighted degree, graph density, clustering coefficient, and average clustering coefficient are used to study the topological structure change of the complex network of stock correlation. Third, the principle of node betweenness centrality is used to analyze the characteristics of a complex network after removing the core nodes. The research shows that, first, under the influence of the COVID-19 pandemic, the correlation among industries has the characteristics of industrial clusters, that is, the correlation in a industry is strengthened. In addition to banking, the correlation between industries has weakened, and the correlation between the banking industry and other industries has strengthened. Second, the node difference in betweenness centrality of core nodes in 2020 is higher than that in 2019, indicating that the network stability in 2019 is higher than that in 2020. These two points indicate that under the influence of the COVID-19 epidemic, the complex network topology of China’s entire stock market has changed, and companies need to undertake countermeasures in the face of the crisis to effectively prevent and control systemic risks.

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Acknowledgements

We thank the reviewers for their comments, and we also thank Chengfeng Lin for his assistance in data collection and processing. This paper is one of the achievements of Agri-product Digital Logistics Research Center of Guangdong-Hong Kong-Macao Greater Bay Area. The corresponding authors of this paper are Kaihao Liang and Shuliang Li.

Funding

This research was partly supported by the NSF of China under grant nos. 11471012 and 11971491, the NSF of Guangdong under grant nos. 2018A0303130136 and 2017A030310650, the Science and Technology Planning Project of Guangdong under grant nos. 2015A070704059 and 2015A030402008, project of Education Department of Guangdong Province under grant no. 2020KZDZX1120, the college students’ innovation and entrepreneurship training program under grant no. 202211347036, the Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products under grant nos. 2021KJ145 and 2023KJ145, Guangzhou Science and Technology Project under grant no. 201704030131, the Characteristic Innovation Project of Universities in Guangdong (Natural Science) under grant no. 2018KTSCX094. The authors gratefully acknowledge all sponsors.

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Author notes

  1. Shuliang Li and Wenfeng Zhang have contributed equally to this work.

Authors and Affiliations

  1. College of Computational Science, Zhongkai University of Agriculture and Engineering, No. 501 Zhongkai Road, Guangzhou, 510225, People’s Republic of China

    Kaihao Liang

  2. College of Economic and Trade, Zhongkai University of Agriculture and Engineering, No. 501 Zhongkai Road, Guangzhou, 510225, People’s Republic of China

    Kaihao Liang, Shuliang Li, Wenfeng Zhang, Jiaying He, Mengmeng Li & Yuling Wang

  3. College of Automation, Zhongkai University of Agriculture and Engineering, No. 501 Zhongkai Road, Guangzhou, 510225, People’s Republic of China

    Zhuokui Wu

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  1. Kaihao Liang
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  2. Shuliang Li
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  3. Wenfeng Zhang
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  4. Zhuokui Wu
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  5. Jiaying He
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  7. Yuling Wang
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Correspondence to Kaihao Liang.

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Liang, K., Li, S., Zhang, W. et al. Evolution of Complex Network Topology for Chinese Listed Companies Under the COVID-19 Pandemic. Comput Econ 63, 1121–1136 (2024). https://doi.org/10.1007/s10614-023-10418-y

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