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Influence network in Chinese stock market

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  • Ya-Chun Gao
  • Yong Zeng
  • Shi-Min Cai

Abstract

In a stock market, the price fluctuations are interactive, that is, one listed company can influence others. In this paper, we seek to study the influence relationships among listed companies by constructing a directed network on the basis of Chinese stock market. This influence network shows distinct topological properties, particularly, a few large companies that can lead the tendency of stock market are recognized. Furthermore, by analyzing the subnetworks of listed companies distributed in several significant economic sectors, it is found that the influence relationships are totally different from one economic sector to another, of which three types of connectivity as well as hub-like listed companies are identified. In addition, the rankings of listed companies obtained from the centrality metrics of influence network are compared with that according to the assets, which gives inspiration to uncover and understand the importance of listed companies in the stock market. These empirical results are meaningful in providing these topological properties of Chinese stock market and economic sectors as well as revealing the interactively influence relationships among listed companies.

Suggested Citation

  • Ya-Chun Gao & Yong Zeng & Shi-Min Cai, 2015. "Influence network in Chinese stock market," Papers 1503.00823, arXiv.org.
    • Handle: RePEc:arx:papers:1503.00823
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    Cited by:

    1. Zhao, Yang & Noori, Mehdi & Tatari, Omer, 2016. "Vehicle to Grid regulation services of electric delivery trucks: Economic and environmental benefit analysis," Applied Energy, Elsevier, vol. 170(C), pages 161-175.
    2. Liu, Jian-Guo & Yang, Zhen-Hua & Li, Sheng-Nan & Yu, Chang-Rui, 2018. "A generative model for the collective attention of the Chinese stock market investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1175-1182.
    3. Gao, Yang & Wang, Yaojun & Wang, Chao & Liu, Chao, 2018. "Internet attention and information asymmetry: Evidence from Qihoo 360 search data on the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 802-811.
    4. Zhao, Longfeng & Wang, Gang-Jin & Wang, Mingang & Bao, Weiqi & Li, Wei & Stanley, H. Eugene, 2018. "Stock market as temporal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1104-1112.
    5. Yanhua Chen & Rosario N Mantegna & Athanasios A Pantelous & Konstantin M Zuev, 2018. "A dynamic analysis of S&P 500, FTSE 100 and EURO STOXX 50 indices under different exchange rates," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-40, March.
    6. 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.
    7. Qingru Sun & Xiangyun Gao & Shaobo Wen & Sida Feng & Ze Wang, 2019. "Modeling the impulse response complex network for studying the fluctuation transmission of price indices," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(4), pages 835-858, December.
    8. Longfeng Zhao & Chao Wang & Gang-Jin Wang & H. Eugene Stanley & Lin Chen, 2021. "Community detection and portfolio optimization," Papers 2112.13383, arXiv.org.
    9. Wen, Tiange & Wang, Gang-Jin, 2020. "Volatility connectedness in global foreign exchange markets," Journal of Multinational Financial Management, Elsevier, vol. 54(C).
    10. Wang, Shuliang & Lv, Wenzhuo & Zhao, Longfeng & Nie, Sen & Stanley, H. Eugene, 2019. "Structural and functional robustness of networked critical infrastructure systems under different failure scenarios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 476-487.

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