IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v415y2014icp525-532.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114007134
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2014.08.035?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. Zhong, Weiqiong & An, Haizhong & Gao, Xiangyun & Sun, Xiaoqi, 2014. "The evolution of communities in the international oil trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 42-52.
    2. An, Haizhong & Gao, Xiangyun & Fang, Wei & Huang, Xuan & Ding, Yinghui, 2014. "The role of fluctuating modes of autocorrelation in crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 382-390.
    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. Harrison Hong & Jeffrey D. Kubik & Jeremy C. Stein, 2005. "Thy Neighbor's Portfolio: Word‐of‐Mouth Effects in the Holdings and Trades of Money Managers," Journal of Finance, American Finance Association, vol. 60(6), pages 2801-2824, December.
    5. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
    6. Masini, Andrea & Menichetti, Emanuela, 2012. "The impact of behavioural factors in the renewable energy investment decision making process: Conceptual framework and empirical findings," Energy Policy, Elsevier, vol. 40(C), pages 28-38.
    7. Xiangyun Gao & Haizhong An & Weiqiong Zhong, 2013. "Features of the Correlation Structure of Price Indices," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-9, April.
    8. Andrea Masini & E. Menichetti, 2012. "The impact of behavioural factors in the renewable energy investment decision making process: Conceptual framework and empirical findings," Post-Print hal-00651706, HAL.
    9. Erik Dietzenbacher & Umed Temurshoev, 2008. "Ownership relations in the presence of cross-shareholding," Journal of Economics, Springer, vol. 95(3), pages 189-212, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. An, Pengli & Li, Huajiao & Zhou, Jinsheng & Chen, Fan, 2017. "The evolution analysis of listed companies co-holding non-listed financial companies based on two-mode heterogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 558-568.
    2. Arif, Asma & An, Pengli & Qi, Yajie & Li, Huajiao & An, Haizhong & Hussain, Mujahid & Wang, Yanli, 2021. "The influence factors of the national roles in the FDI network: A combined methods of complex networks and Panel Data Analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    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.
    4. Li, Huajiao & An, Haizhong & Liu, Xueyong & Gao, Xiangyun & Fang, Wei & An, Feng, 2016. "Price fluctuation in the energy stock market based on fluctuation and co-fluctuation matrix transmission networks," Energy, Elsevier, vol. 117(P1), pages 73-83.
    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.
    6. Zhu, Zhiyun & Dong, Zhiliang & Zhang, Yanxing & Suo, Guibin & Liu, Sen, 2020. "Strategic mineral resource competition: Strategies of the dominator and nondominator," Resources Policy, Elsevier, vol. 69(C).
    7. An, Feng & Gao, Xiangyun & Guan, Jianhe & Huang, Shupei & Liu, Qian, 2017. "Modeling the interdependent network based on two-mode networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 57-67.
    8. Sun, Bowen & Li, Huajiao & An, Pengli & Wang, Ze, 2020. "Dynamic energy stock selection based on shareholders’ coholding network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    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.

    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. 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.
    2. An, Pengli & Li, Huajiao & Zhou, Jinsheng & Chen, Fan, 2017. "The evolution analysis of listed companies co-holding non-listed financial companies based on two-mode heterogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 558-568.
    3. 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.
    4. 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.
    5. Bauwens, Thomas, 2019. "Analyzing the determinants of the size of investments by community renewable energy members: Findings and policy implications from Flanders," Energy Policy, Elsevier, vol. 129(C), pages 841-852.
    6. Arnold, Uwe & Yildiz, Özgür, 2015. "Economic risk analysis of decentralized renewable energy infrastructures – A Monte Carlo Simulation approach," Renewable Energy, Elsevier, vol. 77(C), pages 227-239.
    7. Hennessey, Ryan & Pittman, Jeremy & Morand, Annette & Douglas, Allan, 2017. "Co-benefits of integrating climate change adaptation and mitigation in the Canadian energy sector," Energy Policy, Elsevier, vol. 111(C), pages 214-221.
    8. Dirk Johan van Vuuren & Annlizé L. Marnewick & Jan Harm C. Pretorius, 2021. "A Financial Evaluation of a Multiple Inclination, Rooftop-Mounted, Photovoltaic System Where Structured Tariffs Apply: A Case Study of a South African Shopping Centre," Energies, MDPI, vol. 14(6), pages 1-26, March.
    9. Hao, Xiaoqing & An, Haizhong & Qi, Hai & Gao, Xiangyun, 2016. "Evolution of the exergy flow network embodied in the global fossil energy trade: Based on complex network," Applied Energy, Elsevier, vol. 162(C), pages 1515-1522.
    10. Jenner, Steffen & Groba, Felix & Indvik, Joe, 2013. "Assessing the strength and effectiveness of renewable electricity feed-in tariffs in European Union countries," Energy Policy, Elsevier, vol. 52(C), pages 385-401.
    11. Toka, Agorasti & Iakovou, Eleftherios & Vlachos, Dimitrios & Tsolakis, Naoum & Grigoriadou, Anastasia-Loukia, 2014. "Managing the diffusion of biomass in the residential energy sector: An illustrative real-world case study," Applied Energy, Elsevier, vol. 129(C), pages 56-69.
    12. Häckel, Björn & Pfosser, Stefan & Tränkler, Timm, 2017. "Explaining the energy efficiency gap - Expected Utility Theory versus Cumulative Prospect Theory," Energy Policy, Elsevier, vol. 111(C), pages 414-426.
    13. Zheng, Xiaotian & Zhou, Youcheng & Iqbal, Sajid, 2022. "Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 439-451.
    14. Shahriyar Nasirov & Carlos Silva & Claudio A. Agostini, 2015. "Investors’ Perspectives on Barriers to the Deployment of Renewable Energy Sources in Chile," Energies, MDPI, vol. 8(5), pages 1-21, April.
    15. Zhang, Xinhua & Yang, Hongming & Yu, Qian & Qiu, Jing & Zhang, Yongxi, 2018. "Analysis of carbon-abatement investment for thermal power market in carbon-dispatching mode and policy recommendations," Energy, Elsevier, vol. 149(C), pages 954-966.
    16. Huan Chen & Lixin Tian & Minggang Wang & Zaili Zhen, 2017. "Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks," Sustainability, MDPI, vol. 9(4), pages 1-29, April.
    17. Lone Werner & Bert Scholtens, 2017. "Firm Type, Feed-in Tariff, and Wind Energy Investment in Germany: An Investigation of Decision Making Factors of Energy Producers Regarding Investing in Wind Energy Capacity," Journal of Industrial Ecology, Yale University, vol. 21(2), pages 402-411, April.
    18. Shrimali, Gireesh & Nelson, David & Goel, Shobhit & Konda, Charith & Kumar, Raj, 2013. "Renewable deployment in India: Financing costs and implications for policy," Energy Policy, Elsevier, vol. 62(C), pages 28-43.
    19. Blondiau, Yuliya & Reuter, Emmanuelle, 2019. "Why is the grass greener on the other side? Decision modes and location choice by wind energy investors," Journal of Business Research, Elsevier, vol. 102(C), pages 44-55.
    20. Lim, Xin-Le & Lam, Wei-Haur, 2014. "Public Acceptance of Marine Renewable Energy in Malaysia," Energy Policy, Elsevier, vol. 65(C), pages 16-26.

    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:eee:phsmap:v:415:y:2014:i:c:p:525-532. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.