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Structural Analysis of Projected Networks of Shareholders and Stocks Based on the Data of Large Shareholders’ Shareholding in China’s Stocks

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  • Ruijie Liu

    (College of Science, Beijing Forestry University, Beijing 100083, China)

  • Yajing Huang

    (College of Science, Beijing Forestry University, Beijing 100083, China)

Abstract

This paper establishes a shareholder-stock bipartite network based on the data of large shareholders’ shareholding in the Shanghai A-share market of China in 2021. Based on the shareholder-stock bipartite network, the statistically validated network model is applied to establish a shareholder projected network and a stock projected network, whose structural characteristics can intuitively reveal the overlapping portfolios among different shareholders, as well as shareholder allocation structures among different stocks. The degree of nodes in the shareholder projected network obeys the power law distribution, the network aggregation coefficient is large, while the degree of most nodes in the stock projected network is small and the network aggregation coefficient is low. Furthermore, the two projected networks’ community structures are analyzed, respectively. Most of the communities in the shareholder projected network and stock projected network are small-scaled, indicating that the majority of large shareholders hold different shares from each other, and the investment portfolios of large shareholders in different stocks are also significantly different. Finally, by comparing the stock projected sub-network obtained from the shareholder-stock bipartite sub-network in which the degree of shareholder nodes is 2 and the original stock projected network, the effectiveness of the statistically validated network model, and the community division method on the research of the shareholder-stock bipartite network are further verified. These results have important implications for understanding the investment behavior of large shareholders in the stock market and contribute to developing investment strategies and risk management practices.

Suggested Citation

  • Ruijie Liu & Yajing Huang, 2023. "Structural Analysis of Projected Networks of Shareholders and Stocks Based on the Data of Large Shareholders’ Shareholding in China’s Stocks," Mathematics, MDPI, vol. 11(6), pages 1-25, March.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1545-:d:1104179
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    References listed on IDEAS

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    1. Onnela, J.-P. & Chakraborti, A. & Kaski, K. & Kertész, J., 2003. "Dynamic asset trees and Black Monday," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 247-252.
    2. Glasserman, Paul & Young, H. Peyton, 2015. "How likely is contagion in financial networks?," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 383-399.
    3. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang, 2009. "A network analysis of the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2956-2964.
    4. Paul Glasserman & Peyton Young, 2015. "Contagion in Financial Networks," Economics Series Working Papers 764, University of Oxford, Department of Economics.
    5. Yajing Huang & Taoxiong Liu, 2023. "Diversification and Systemic Risk of Networks Holding Common Assets," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 341-388, January.
    6. Brida, Juan Gabriel & Risso, Wiston Adrián, 2008. "Multidimensional minimal spanning tree: The Dow Jones case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5205-5210.
    7. Huang, Yajing & Liu, Taoxiong & Lien, Donald, 2023. "Portfolio homogeneity and systemic risk of financial networks," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 248-275.
    8. G. Agarwal & D. Kempe, 2008. "Modularity-maximizing graph communities via mathematical programming," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 66(3), pages 409-418, December.
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    Cited by:

    1. Wenqiang Li & Juan He & Yangyan Shi, 2024. "Contracting Supply Chains Considering Retailers’ Marketing Efforts," Mathematics, MDPI, vol. 12(11), pages 1-24, May.

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