Skip to main content
SpringerLink
Log in

Undirected and Directed Network Analysis of the Chinese Stock Market

  • Published:
Computational Economics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. Shandong Nanshan Aluminium

References

  • Albert, R., Jeong, H., & Barabási, A. (2000). Error and attack tolerance of complex networks. Nature, 406(6794), 378–382.

    Article  Google Scholar 

  • Bandyopadhyay, A., & Kar, S. (2018). Coevolution of cooperation and network structure in social dilemmas in evolutionary dynamic complex network. Applied Mathematics and Computation, 320, 710–730.

    Article  Google Scholar 

  • Bandyopadhyay, A., & Kar, S. (2018). Impact of network structure on synchronization of Hindmarsh–Rose neurons coupled in structured network. Applied Mathematics and Computation, 333, 194–212.

    Article  Google Scholar 

  • Bank of China Association. (2019). 2019 China Banking Top 100 List. http://www.chinabankingnews.com/2019/02/27/china-banking-association-releases-list-of-top-100-lenders/. Accessed 23 August (2020).

  • Baumöhl, E., Kočenda, E., Lyócsa, Š, & Vỳrost, T. (2018). Networks of volatility spillovers among stock markets. Physica A: Statistical Mechanics and its Applications, 490, 1555–1574.

    Article  Google Scholar 

  • Cohen, R., Erez, K., Ben-Avraham, D., & Havlin, S. (2000). Resilience of the internet to random breakdowns. Physical Review Letters, 85(21), 4626.

    Article  Google Scholar 

  • Danon, L., Diaz-Guilera, A., Duch, J., & Arenas, A. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(9), 09008.

    Article  Google Scholar 

  • Fan, Q., & Wang, F. (2020). Detrending-moving-average-based bivariate regression estimator. Physical Review E, 102(1), 012218.

    Article  Google Scholar 

  • Fortunato, S. (2009). Community detection in graphs. Physics Reports, 486(3), 75–174.

    Google Scholar 

  • Gao, C., Ma, Z., Zhang, A. Y., Zhou, H. H., et al. (2018). Community detection in degree-corrected block models. Annals of Statistics, 46(5), 2153–2185.

    Article  Google Scholar 

  • Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 7821–7826.

    Article  Google Scholar 

  • Han, D., et al. (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, 523, 1091–1109.

    Article  Google Scholar 

  • Huang, W., Zhuang, X., & Yao, S. (2009). A network analysis of the Chinese stock market. Physica A: Statistical Mechanics and its Applications, 388(14), 2956–2964.

    Article  Google Scholar 

  • Ji, P., & Jin, J. (2016). Coauthorship and citation networks for statisticians. Annals of Applied Statistics, 10(4), 1779–1812.

    Google Scholar 

  • Jin, J. (2015). Fast community detection by score. Annals of Statistics, 43(1), 57–89.

    Article  Google Scholar 

  • Kang, S. H., & Lee, J. W. (2019). The network connectedness of volatility spillovers across global futures markets. Physica A: Statistical Mechanics and its Applications, 526, 120756.

    Article  Google Scholar 

  • Kullmann, L., Kertész, J., & Kaski, K. (2002). Time-dependent cross-correlations between different stock returns: A directed network of influence. Physical Review E, 66(2), 026125.

    Article  Google Scholar 

  • Kumar, S., & Deo, N. (2012). Correlation and network analysis of global financial indices. Physical Review E, 86(2), 026101.

    Article  Google Scholar 

  • Liu, X., Pan, L., Stanley, H. E., & Gao, J. (2019). Multiple phase transitions in networks of directed networks. Physical Review E, 99(1), 012312.

    Article  Google Scholar 

  • Ma, J., Yang, J., Zhang, X., & Huang, Y. (2015). Analysis of Chinese stock market from a complex network perspective: Better to invest in the central. In 2015 34th Chinese control conference (CCC), IEEE, pp. 8606–8611.

  • Majapa, M., & Gossel, S. J. (2016). Topology of the South African stock market network across the 2008 financial crisis. Physica A: Statistical Mechanics and its Applications, 445, 35–47.

    Article  Google Scholar 

  • Newman, M. E. (2001). Scientific collaboration networks, I: Network construction and fundamental results. Physical Review E, 64(1), 016131.

    Article  Google Scholar 

  • Qin, S., Chen, X., & Sun, W. (2011). Optimal investment portfolio model with degree risk in complex stock network. In 2011 Fourth international workshop on chaos-fractals theories and applications, IEEE, pp. 161–165.

  • Radicchi, F. (2015). Predicting percolation thresholds in networks. Physical Review E, 91(1), 010801.

    Article  Google Scholar 

  • Tabak, B. M., Serra, T. R., & Cajueiro, D. O. (2010). Topological properties of stock market networks: The case of Brazil. Physica A: Statistical Mechanics and its Applications, 389(16), 3240–3249.

    Article  Google Scholar 

  • Volpp, L. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4), 175–308.

    Google Scholar 

  • Xixi, L., Qiang, W., & Suling, J. (2017). Analysis of topological properties of complex network of Chinese stock based on copula tail correlation. In 2017 International conference on service systems and service management, IEEE, pp. 1–6.

  • Yang, T., Chi, Y., Zhu, S., Gong, Y., & Jin, R. (2010). Directed network community detection: A popularity and productivity link model. In Proceedings of the 2010 SIAM international conference on data mining, pp. 742–753.

  • Yao, H., & Memon, B. A. (2019). Network topology of FTSE 100 index companies: From the perspective of BREXIT. Physica A: Statistical Mechanics and its Applications, 523, 1248–1262.

    Article  Google Scholar 

  • You, T., Fiedor, P., & Hołda, A. (2015). Network analysis of the shanghai stock exchange based on partial mutual information. Journal of Risk and Financial Management, 8(2), 266–284.

    Article  Google Scholar 

  • Zhang, J., & Chen, Y. (2017). A hypothesis testing framework for modularity based network community detection. Statistica Sinica, 27(1), 437–456.

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 12001557); the Youth Talent Development Support Program (QYP202104), the Emerging Interdisciplinary Project, the Disciplinary Funding, and the School of Statistics and Mathematics in Central University of Finance and Economics.

Author information

Authors and Affiliations

  1. School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China

    Binghui Li & Yuehan Yang

Authors
  1. Binghui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuehan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization and methodology: YY; formal analysis and investigation: BL and YY; writing—original draft preparation: BL and YY; funding acquisition: YY; resources: YY; supervision: YY.

Corresponding author

Correspondence to Yuehan Yang.

Ethics declarations

Conflict of interests

The authors declare no potential conflict of interests.

Human and Animal Rights

The authors declare no research involving human participants and/or animals.

Informed Consent

The authors declare informed consent.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

1.1 Additional Results

Tables 6 and 7 show the top 20 stocks of the betweenness centrality and eigenvector centrality respectively. Tables 8, 9, 10 and 11 show all the stocks in each community.

Table 6 Top 20 stocks of the betweenness centrality
Full size table
Table 7 Top 20 stocks of the eigenvector centrality
Full size table
Table 8 The first community and the related stocks
Full size table
Table 9 The second community and the related stocks
Full size table
Table 10 The third community and the related stocks
Full size table
Table 11 The fourth community and the related stocks
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, B., Yang, Y. Undirected and Directed Network Analysis of the Chinese Stock Market. Comput Econ 60, 1155–1173 (2022). https://doi.org/10.1007/s10614-021-10183-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10614-021-10183-w

Keywords

Search

Navigation