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Analysis of network clustering behavior of the Chinese stock market

Author

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  • Chen, Huan
  • Mai, Yong
  • Li, Sai-Ping

Abstract

Random Matrix Theory (RMT) and the decomposition of correlation matrix method are employed to analyze spatial structure of stocks interactions and collective behavior in the Shanghai and Shenzhen stock markets in China. The result shows that there exists prominent sector structures, with subsectors including the Real Estate (RE), Commercial Banks (CB), Pharmaceuticals (PH), Distillers&Vintners (DV) and Steel (ST) industries. Furthermore, the RE and CB subsectors are mostly anti-correlated. We further study the temporal behavior of the dataset and find that while the sector structures are relatively stable from 2007 through 2013, the correlation between the real estate and commercial bank stocks shows large variations. By employing the ensemble empirical mode decomposition (EEMD) method, we show that this anti-correlation behavior is closely related to the monetary and austerity policies of the Chinese government during the period of study.

Suggested Citation

  • Chen, Huan & Mai, Yong & Li, Sai-Ping, 2014. "Analysis of network clustering behavior of the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 360-367.
    • Handle: RePEc:eee:phsmap:v:414:y:2014:i:c:p:360-367
    DOI: 10.1016/j.physa.2014.07.039
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Charu Sharma & Amber Habib, 2019. "Uncovering networks amongst stocks returns by studying nonlinear interactions in high frequency data of the Indian Stock Market using mutual information," Papers 1903.03407, arXiv.org.
    2. 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.
    3. 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).
    4. Xue Guo & Hu Zhang & Tianhai Tian, 2018. "Development of stock correlation networks using mutual information and financial big data," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-16, April.
    5. Jiao, Yang & Wu, Jianshe & Jiao, Licheng, 2018. "An image segmentation method based on network clustering model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1532-1542.
    6. Seyed Soheil Hosseini & Nick Wormald & Tianhai Tian, 2019. "A Weight-based Information Filtration Algorithm for Stock-Correlation Networks," Papers 1904.06007, arXiv.org.

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