IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v63y2024i3d10.1007_s10614-023-10418-y.html
   My bibliography  Save this article

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

Author

Listed:
  • Kaihao Liang

    (Zhongkai University of Agriculture and Engineering
    Zhongkai University of Agriculture and Engineering)

  • Shuliang Li

    (Zhongkai University of Agriculture and Engineering)

  • Wenfeng Zhang

    (Zhongkai University of Agriculture and Engineering)

  • Zhuokui Wu

    (Zhongkai University of Agriculture and Engineering)

  • Jiaying He

    (Zhongkai University of Agriculture and Engineering)

  • Mengmeng Li

    (Zhongkai University of Agriculture and Engineering)

  • Yuling Wang

    (Zhongkai University of Agriculture and Engineering)

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.

Suggested Citation

  • Kaihao Liang & Shuliang Li & Wenfeng Zhang & Zhuokui Wu & Jiaying He & Mengmeng Li & Yuling Wang, 2024. "Evolution of Complex Network Topology for Chinese Listed Companies Under the COVID-19 Pandemic," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1121-1136, March.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:3:d:10.1007_s10614-023-10418-y
    DOI: 10.1007/s10614-023-10418-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-023-10418-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-023-10418-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    2. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    3. Réka Albert & Hawoong Jeong & Albert-László Barabási, 1999. "Diameter of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 130-131, September.
    4. Brida, Juan Gabriel & Matesanz, David & Seijas, Maria Nela, 2016. "Network analysis of returns and volume trading in stock markets: The Euro Stoxx case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 751-764.
    5. Chen, Shiu-Sheng, 2012. "Revisiting the empirical linkages between stock returns and trading volume," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1781-1788.
    6. Behrendt, Simon & Schmidt, Alexander, 2021. "Nonlinearity matters: The stock price – trading volume relation revisited," Economic Modelling, Elsevier, vol. 98(C), pages 371-385.
    7. Aman Gupta & Cyril Shaju & Pratibha & Kamal, 2023. "A Study of the International Stock Market Behavior During COVID-19 Pandemic Using a Driven Iterated Function System," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 57-68, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Changtai Li & Weihong Huang & Wei-Siang Wang & Wai-Mun Chia, 2023. "Price Change and Trading Volume: Behavioral Heterogeneity in Stock Market," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 677-713, February.
    2. Xue Guo & Hu Zhang & Tianhai Tian, 2019. "Multi-Likelihood Methods for Developing Stock Relationship Networks Using Financial Big Data," Papers 1906.08088, arXiv.org.
    3. Jae Woo Lee & Ashadun Nobi, 2018. "State and Network Structures of Stock Markets around the Global Financial Crisis," Papers 1806.04363, arXiv.org.
    4. Fatih Cavdur & Soundar Kumara, 2014. "Network mining: Applications to business data," Information Systems Frontiers, Springer, vol. 16(3), pages 473-490, July.
    5. Wang, Junjie & Zhou, Shuigeng & Guan, Jihong, 2011. "Characteristics of real futures trading networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(2), pages 398-409.
    6. Kanjamapornkul, K. & Pinčák, Richard & Bartoš, Erik, 2016. "The study of Thai stock market across the 2008 financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 117-133.
    7. Jae Woo Lee & Ashadun Nobi, 2018. "State and Network Structures of Stock Markets Around the Global Financial Crisis," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 195-210, February.
    8. Guo, Xue & Li, Weibo & Zhang, Hu & Tian, Tianhai, 2022. "Multi-likelihood methods for developing relationship networks using stock market data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    9. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    10. khoojine, Arash Sioofy & Han, Dong, 2019. "Network analysis of the Chinese stock market during the turbulence of 2015–2016 using log-returns, volumes and mutual information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1091-1109.
    11. Polovnikov, Kirill & Kazakov, Vlad & Syntulsky, Sergey, 2020. "Core–periphery organization of the cryptocurrency market inferred by the modularity operator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    12. Coletti, Paolo, 2016. "Comparing minimum spanning trees of the Italian stock market using returns and volumes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 246-261.
    13. Xie Chi & Zhou Yingying & Wang Gangjin & Yan Xinguo, 2017. "Investigating the Disparities of China’s Insurance Market Based on Minimum Spanning Tree from the Viewpoint of Geography and Enterprise," Journal of Systems Science and Information, De Gruyter, vol. 5(3), pages 216-228, June.
    14. Kazemilari, Mansooreh & Mardani, Abbas & Streimikiene, Dalia & Zavadskas, Edmundas Kazimieras, 2017. "An overview of renewable energy companies in stock exchange: Evidence from minimal spanning tree approach," Renewable Energy, Elsevier, vol. 102(PA), pages 107-117.
    15. Fatih Cavdur & Soundar Kumara, 2014. "A network view of business systems," Information Systems Frontiers, Springer, vol. 16(1), pages 153-162, March.
    16. Yao, Hongxing & Memon, Bilal Ahmed, 2019. "Network topology of FTSE 100 Index companies: From the perspective of Brexit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1248-1262.
    17. Brida, Juan Gabriel & Gómez, David Matesanz & Seijas, Maria Nela, 2017. "Debt and growth: A non-parametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 883-894.
    18. Akgüller, Ömer & Balcı, Mehmet Ali, 2018. "Geodetic convex boundary curvatures of the communities in stock market networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 569-581.
    19. Mohd-Zaid, Fairul & Kabban, Christine M. Schubert & Deckro, Richard F. & White, Edward D., 2017. "Parameter specification for the degree distribution of simulated Barabási–Albert graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 141-152.
    20. Trindade, Graça & Dias, José G. & Ambrósio, Jorge, 2017. "Extracting clusters from aggregate panel data: A market segmentation study," Applied Mathematics and Computation, Elsevier, vol. 296(C), pages 277-288.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:63:y:2024:i:3:d:10.1007_s10614-023-10418-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.