IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v60y2022i3d10.1007_s10614-021-10183-w.html
   My bibliography  Save this article

Undirected and Directed Network Analysis of the Chinese Stock Market

Author

Listed:
  • Binghui Li

    (Central University of Finance and Economics)

  • Yuehan Yang

    (Central University of Finance and Economics)

Abstract

To study the characteristics of Chinese stock market, this paper analyses the undirected and directed stock market networks of the constituent stocks of CSI 300. We first apply the spectral clustering on the ratio of eigenvectors (SCORE) (Jin in Ann Stat 43(1):57–89, 2015) to detect the community structure of the undirected market network. Four communities are found and analysed in detail: “Financial industry (Securities category) community”, “Real estate industry community”, “Financial industry (Bank category) community” and “Heavy industry and Manufacturing industry community”. We then test the stability of the undirected stock network by analysing its topological stability, showing that the network is stable to random attacks but vulnerable to the particular type of deliberate attacks. We establish a directed market network to further analyse the characteristics of the Chinese stock market, exploring the characteristics of stocks with high in-degrees and out-degrees. During the network analysis, we describe the characteristics of the Chinese stock market from the perspective of network analysis, analysing the key companies and providing suggestions for the researchers and investors. Stock market network analysis also provides an effective practice and expansion of the statistical clustering algorithm. Our findings shed light on trends and topological patterns of stock market networks.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:compec:v:60:y:2022:i:3:d:10.1007_s10614-021-10183-w
    DOI: 10.1007/s10614-021-10183-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-021-10183-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-021-10183-w?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. 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.
    2. Bandyopadhyay, Abhirup & Kar, Samarjit, 2018. "Impact of network structure on synchronization of Hindmarsh–Rose neurons coupled in structured network," Applied Mathematics and Computation, Elsevier, vol. 333(C), pages 194-212.
    3. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    4. Majapa, Mohamed & Gossel, Sean Joss, 2016. "Topology of the South African stock market network across the 2008 financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 35-47.
    5. Kang, Sang Hoon & Lee, Jang Woo, 2019. "The network connectedness of volatility spillovers across global futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    6. Tao You & Paweł Fiedor & Artur Hołda, 2015. "Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information," JRFM, MDPI, vol. 8(2), pages 1-19, June.
    7. Sunil Kumar & Nivedita Deo, 2012. "Correlation, Network and Multifractal Analysis of Global Financial Indices," Papers 1202.0409, arXiv.org.
    8. khoojine, Arash Sioofy & Han, Dong, 2019. "Network analysis of the Chinese stock market during the turbulence of 2015–2016 using log-returns, volumes and mutual information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1091-1109.
    9. Bandyopadhyay, Abhirup & Kar, Samarjit, 2018. "Coevolution of cooperation and network structure in social dilemmas in evolutionary dynamic complex network," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 710-730.
    10. Yao, Hongxing & Memon, Bilal Ahmed, 2019. "Network topology of FTSE 100 Index companies: From the perspective of Brexit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1248-1262.
    11. Tabak, Benjamin M. & Serra, Thiago R. & Cajueiro, Daniel O., 2010. "Topological properties of stock market networks: The case of Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3240-3249.
    12. L. Kullmann & J. Kertesz & K. Kaski, 2002. "Time dependent cross correlations between different stock returns: A directed network of influence," Papers cond-mat/0203256, arXiv.org, revised May 2002.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Mbatha, Vusisizwe Moses & Alovokpinhou, Sedjro Aaron, 2022. "The structure of the South African stock market network during COVID-19 hard lockdown," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    3. khoojine, Arash Sioofy & Han, Dong, 2019. "Network analysis of the Chinese stock market during the turbulence of 2015–2016 using log-returns, volumes and mutual information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1091-1109.
    4. Muzi Chen & Nan Li & Lifen Zheng & Difang Huang & Boyao Wu, 2024. "Dynamic Correlation of Market Connectivity, Risk Spillover and Abnormal Volatility in Stock Price," Papers 2403.19363, arXiv.org.
    5. 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.
    6. Bilal Ahmed Memon & Rabia Tahir, 2021. "Examining Network Structures and Dynamics of World Energy Companies in Stock Markets: A Complex Network Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 329-344.
    7. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2019. "Return spillovers around the globe: A network approach," Economic Modelling, Elsevier, vol. 77(C), pages 133-146.
    8. Hongxing Yao & Yanyu Lu & Bilal Ahmed Memon, 2019. "Impact of US-China Trade War on the Network Topology Structure of Chinese Stock Market," Journal of Asian Business Strategy, Asian Economic and Social Society, vol. 9(2), pages 235-250, December.
    9. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    10. Dariusz Siudak, 2021. "Sectoral Analysis of the US Stock Market through Complex Networks," European Research Studies Journal, European Research Studies Journal, vol. 0(3B), pages 951-966.
    11. Yong Tang & Jason Jie Xiong & Zi-Yang Jia & Yi-Cheng Zhang, 2018. "Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets," Complexity, Hindawi, vol. 2018, pages 1-31, November.
    12. Coletti, Paolo, 2016. "Comparing minimum spanning trees of the Italian stock market using returns and volumes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 246-261.
    13. Bilal Ahmed Memon & Hongxing Yao & Rabia Tahir, 2020. "General election effect on the network topology of Pakistan’s stock market: network-based study of a political event," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-14, December.
    14. Bhattacharjee, Biplab & Kumar, Rajiv & Senthilkumar, Arunachalam, 2022. "Unidirectional and bidirectional LSTM models for edge weight predictions in dynamic cross-market equity networks," International Review of Financial Analysis, Elsevier, vol. 84(C).
    15. Hosseini, Seyed Soheil & Wormald, Nick & Tian, Tianhai, 2021. "A Weight-based Information Filtration Algorithm for Stock-correlation Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    16. Chen, Muzi & Li, Nan & Zheng, Lifen & Huang, Difang & Wu, Boyao, 2022. "Dynamic correlation of market connectivity, risk spillover and abnormal volatility in stock price," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    17. Chu, J. & Nadarajah, S., 2017. "A statistical analysis of UK financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 445-459.
    18. Esmalifalak, Hamidreza, 2022. "Euclidean (dis)similarity in financial network analysis," Global Finance Journal, Elsevier, vol. 53(C).
    19. Nobi, Ashadun & Maeng, Seong Eun & Ha, Gyeong Gyun & Lee, Jae Woo, 2014. "Effects of global financial crisis on network structure in a local stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 135-143.
    20. Jae Woo Lee & Ashadun Nobi, 2018. "State and Network Structures of Stock Markets around the Global Financial Crisis," Papers 1806.04363, arXiv.org.

    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:kap:compec:v:60:y:2022:i:3:d:10.1007_s10614-021-10183-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.