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Community detection and clustering characteristics analysis of the stock market

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Listed:
  • Jia Xing
  • Binghui Li
  • Yuehan Yang

Abstract

In this paper, we study and analyze the community detection algorithms and the related evaluation metrics to explore the clustering characteristics of China's stock market. We build the stock market network by the constituent companies of the CSI 300 index and study the tightness between nodes. We first choose an appropriate adjacency matrix, which is obtained by the relation coefficient. Then, based on the singular value decomposition (SVD) of the adjacency matrix, we analyze the optimal network based on the directed spectral clustering on the ratio of eigenvectors and the spectral clustering followed by local refinement. The optimal network is chosen based on the best clustering effect according to the clustering effectiveness indexes Calinski–Harabasz and Davies–Bouldin. We explore the community structure in the stock market and study the influence of each community. Through the index out‐degree and in‐degree, we also obtain the most important companies in the market. Different from the traditional strategies that are always based on qualitative methods and undirected networks, this paper explores the characteristics of how to detect China's stock market. Specifically, we offer suitable strategies and measurements to evaluate the clustering results objectively and fairly. The qualities of community detection are measured too. Based on the above analysis, we further provide valuable insights by exploring the characteristics of the stock market.

Suggested Citation

  • Jia Xing & Binghui Li & Yuehan Yang, 2023. "Community detection and clustering characteristics analysis of the stock market," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(7), pages 3893-3906, October.
    • Handle: RePEc:wly:mgtdec:v:44:y:2023:i:7:p:3893-3906
    DOI: 10.1002/mde.3929
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    1. D. S. Choi & P. J. Wolfe & E. M. Airoldi, 2012. "Stochastic blockmodels with a growing number of classes," Biometrika, Biometrika Trust, vol. 99(2), pages 273-284.
    2. Francis Bloch & Gabrielle Demange & Rachel Kranton, 2018. "Rumors And Social Networks," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(2), pages 421-448, May.
    3. Binghui Li & Yuehan Yang, 2022. "Undirected and Directed Network Analysis of the Chinese Stock Market," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 1155-1173, October.
    4. Huang, Chuangxia & Wen, Shigang & Li, Mengge & Wen, Fenghua & Yang, Xin, 2021. "An empirical evaluation of the influential nodes for stock market network: Chinese A-shares case," Finance Research Letters, Elsevier, vol. 38(C).
    5. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    6. Bayer, Péter & Herings, P. Jean-Jacques & Peeters, Ronald, 2021. "Farsighted manipulation and exploitation in networks," Journal of Economic Theory, Elsevier, vol. 196(C).
    7. Vasiliki Plerou & Parameswaran Gopikrishnan & Bernd Rosenow & Luis A. Nunes Amaral & H. Eugene Stanley, 1999. "Universal and non-universal properties of cross-correlations in financial time series," Papers cond-mat/9902283, arXiv.org.
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