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The shareholding similarity of the shareholders of the worldwide listed energy companies based on a two-mode primitive network and a one-mode derivative holding-based network

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  • Li, Huajiao
  • Fang, Wei
  • An, Haizhong
  • Yan, LiLi

Abstract

Two-mode and multi-mode networks represent new directions of simulating a complex network that can simulate the relationships among the entities more precisely. In this paper, we constructed two different levels of networks: one is the two-mode primitive networks of the energy listed companies and their shareholders on the basis of the two-mode method of complex theory, and the other is the derivative one-mode holding-based network based on the equivalence network theory. We calculated two different topological characteristics of the two networks, that is, the out-degree of the actor nodes of the two-mode network (9003 nodes) and the weights of the edges of the one-mode network (619,766 edges), and we analyzed the distribution features of both of the two topological characteristics. In this paper, we define both the weighted and un-weighted Shareholding Similarity Coefficient, and using the data of the worldwide listed energy companies and their shareholders as empirical study subjects, we calculated and compared both the weighted and un-weighted shareholding similarity coefficient of the worldwide listed energy companies. The result of the analysis indicates that (1) both the out-degree of the actor nodes of the two-mode network and the weights of the edges of the one-mode network follow a power-law distribution; (2) there are significant differences between the weighted and un-weighted shareholding similarity coefficient of the worldwide listed energy companies, and the weighted shareholding similarity coefficient is of greater regularity than the un-weighted one; (3) there are a vast majority of shareholders who hold stock in only one or a few of the listed energy companies; and (4) the shareholders hold stock in the same listed energy companies when the value of the un-weighted shareholding similarity coefficient is between 0.4 and 0.8. The study will be a helpful tool to analyze the relationships of the nodes of the one-mode network, which is constructed based on the two-mode network, and it provides a means to discover the similarity of the shareholding behavior among the shareholders; in addition, this study will be useful for further research studies regarding the stability of the structure of the energy institutes and the level of risk in the energy stock market.

Suggested Citation

  • Li, Huajiao & Fang, Wei & An, Haizhong & Yan, LiLi, 2014. "The shareholding similarity of the shareholders of the worldwide listed energy companies based on a two-mode primitive network and a one-mode derivative holding-based network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 525-532.
    • Handle: RePEc:eee:phsmap:v:415:y:2014:i:c:p:525-532
    DOI: 10.1016/j.physa.2014.08.035
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    References listed on IDEAS

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    Cited by:

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    3. Xi, Xian & An, Haizhong, 2018. "Research on energy stock market associated network structure based on financial indicators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1309-1323.
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    5. Hossein Dastkhan & Naser Shams Gharneh, 2019. "Simulation of Contagion in the Stock Markets Using Cross-Shareholding Networks: A Case from an Emerging Market," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1071-1101, March.
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    9. Sun, Qingru & Gao, Xiangyun & Zhong, Weiqiong & Liu, Nairong, 2017. "The stability of the international oil trade network from short-term and long-term perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 345-356.
    10. Qing Yao & Tim S Evans & Kim Christensen, 2019. "How the network properties of shareholders vary with investor type and country," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-19, August.
    11. Guan, Qing & An, Haizhong & Li, Huajiao & Hao, Xiaoqing, 2017. "The rapid bi-level exploration on the evolution of regional solar energy development," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 49-61.
    12. Li, Huajiao & An, Haizhong & Wang, Yue & Huang, Jiachen & Gao, Xiangyun, 2016. "Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 657-669.
    13. Qing Guan & Haizhong An & Xiaoqing Hao & Xiaoliang Jia, 2016. "The Impact of Countries’ Roles on the International Photovoltaic Trade Pattern: The Complex Networks Analysis," Sustainability, MDPI, vol. 8(4), pages 1-16, March.
    14. Li, Huajiao & An, Haizhong & Huang, Jiachen & Huang, Xuan & Mou, Songtao & Shi, Yanli, 2016. "The evolutionary stability of shareholders’ co-holding behavior for China’s listed energy companies based on associated maximal connected sub-graphs of derivative holding-based networks," Applied Energy, Elsevier, vol. 162(C), pages 1601-1607.
    15. Qing Yao & Tim Evans & Kim Christensen, 2018. "How the network properties of shareholders vary with investor type and country," Papers 1812.06694, arXiv.org, revised Sep 2019.
    16. Hossein Dastkhan & Naser Shams Gharneh, 2016. "Determination of Systemically Important Companies with Cross-Shareholding Network Analysis: A Case Study from an Emerging Market," IJFS, MDPI, vol. 4(3), pages 1-17, June.
    17. Li, Huajiao & Fang, Wei & An, Haizhong & Gao, Xiangyun & Yan, Lili, 2016. "Holding-based network of nations based on listed energy companies: An empirical study on two-mode affiliation network of two sets of actors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 224-232.
    18. Li, Huajiao & An, Haizhong & Fang, Wei & Wang, Yue & Zhong, Weiqiong & Yan, Lili, 2017. "Global energy investment structure from the energy stock market perspective based on a Heterogeneous Complex Network Model," Applied Energy, Elsevier, vol. 194(C), pages 648-657.
    19. An, Pengli & Zhou, Jinsheng & Li, Huajiao & Sun, Bowen & Shi, Yanli, 2018. "The evolutionary similarity of the co-shareholder relationship network from institutional and non-institutional shareholder perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 439-450.
    20. Li, Jie & Zhang, Yongjie & Wang, Lidan, 2021. "Information transmission between large shareholders and stock volatility," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    21. An, Qier & An, Haizhong & Wang, Lang & Gao, Xiangyun & Lv, Na, 2015. "Analysis of embodied exergy flow between Chinese industries based on network theory," Ecological Modelling, Elsevier, vol. 318(C), pages 26-35.

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