IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v167y2023ics0960077922012231.html
   My bibliography  Save this article

Stable versus fragile community structures in the correlation dynamics of Chinese industry indices

Author

Listed:
  • Nie, Chun-Xiao
  • Song, Fu-Tie

Abstract

Correlation dynamics in the stock market is recently gained wide attention, especially its relationship to financial crises. This study examines the correlation dynamics of the Chinese market based on time series data of industry indices. We examine the dynamics of linear and nonlinear dependencies using PCC (Pearson correlation coefficient) and MIC (Maximal information coefficient), respectively. The influence-strength (IS) analysis showed that the former was more closely associated with major events. We analyse the structure of the correlation networks from both the short- and long-term perspectives. For the short-term perspective, we employ IS analysis to extract the time stamps of structural breaks in the correlation structure. The timestamps of critical events in the correlation dynamics usually correspond to major events in the real market, including the 2015 stock market crash in China. We construct a network for each correlation matrix to extract the main correlation structure and detect the community structure. We create the long-term community-based network based on community structure. The long-term community structure of the correlation dynamics has clear economic implications and is thus stable, whereas the short-term community structure is fragile. This study analyses the correlation dynamics in the Chinese market, which is valuable for asset allocation, such as identifying market states and investing in index funds.

Suggested Citation

  • Nie, Chun-Xiao & Song, Fu-Tie, 2023. "Stable versus fragile community structures in the correlation dynamics of Chinese industry indices," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    • Handle: RePEc:eee:chsofr:v:167:y:2023:i:c:s0960077922012231
    DOI: 10.1016/j.chaos.2022.113044
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922012231
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2022.113044?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. Chun-Xiao Nie, 2021. "Studying the correlation structure based on market geometry," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(2), pages 411-441, April.
    2. Stefano Giglio & Bryan Kelly & Dacheng Xiu, 2022. "Factor Models, Machine Learning, and Asset Pricing," Annual Review of Financial Economics, Annual Reviews, vol. 14(1), pages 337-368, November.
    3. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    4. M.C. M�nnix & R. Sch�fer & O. Grothe, 2014. "Estimating correlation and covariance matrices by weighting of market similarity," Quantitative Finance, Taylor & Francis Journals, vol. 14(5), pages 931-939, May.
    5. Xianbo Wu & Xiaofeng Hui & Lijun Pei, 2021. "The Impact of COVID-19 on the Dependence of Chinese Stock Market," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-11, May.
    6. Wang, Gang-Jin & Xie, Chi, 2015. "Correlation structure and dynamics of international real estate securities markets: A network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 176-193.
    7. Yangguang Zhu & Feng Yang & Wuyi Ye, 2018. "Financial contagion behavior analysis based on complex network approach," Annals of Operations Research, Springer, vol. 268(1), pages 93-111, September.
    8. Michael C. Munnix & Takashi Shimada & Rudi Schafer & Francois Leyvraz Thomas H. Seligman & Thomas Guhr & H. E. Stanley, 2012. "Identifying States of a Financial Market," Papers 1202.1623, arXiv.org.
    9. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    10. Civitarese, Jamil, 2016. "Volatility and correlation-based systemic risk measures in the US market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 55-67.
    11. Nie, Chun-Xiao, 2020. "Correlation dynamics in the cryptocurrency market based on dimensionality reduction analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    12. Dror Kenett & Shlomo Havlin, 2015. "Network science: a useful tool in economics and finance," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 14(2), pages 155-167, November.
    13. Nie, Chun-Xiao & Song, Fu-Tie, 2018. "Analyzing the stock market based on the structure of kNN network," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 148-159.
    14. Guo, Hongfeng & Zhao, Xinyao & Yu, Hang & Zhang, Xin, 2021. "Analysis of global stock markets’ connections with emphasis on the impact of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    15. Jung, Woo-Sung & Chae, Seungbyung & Yang, Jae-Suk & Moon, Hie-Tae, 2006. "Characteristics of the Korean stock market correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 361(1), pages 263-271.
    16. Haiming Long & Ji Zhang & Nengyu Tang, 2017. "Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
    17. Nie, Chun-Xiao, 2022. "Analysis of critical events in the correlation dynamics of cryptocurrency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    18. Weibo Li & Wei Liu & Lei Wu & Xue Guo, 2021. "Risk spillover networks in financial system based on information theory," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    19. Brito, M. R. & Chávez, E. L. & Quiroz, A. J. & Yukich, J. E., 1997. "Connectivity of the mutual k-nearest-neighbor graph in clustering and outlier detection," Statistics & Probability Letters, Elsevier, vol. 35(1), pages 33-42, August.
    20. Teh, Boon Kin & Goo, Yik Wen & Lian, Tong Wei & Ong, Wei Guang & Choi, Wen Ting & Damodaran, Mridula & Cheong, Siew Ann, 2015. "The Chinese Correction of February 2007: How financial hierarchies change in a market crash," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 225-241.
    21. Gang-Jin Wang & Chi Xie & H. Eugene Stanley, 2018. "Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 607-635, March.
    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. Nie, Chun-Xiao, 2023. "Time-varying characteristics of information flow networks in the Chinese market: An analysis based on sector indices," Finance Research Letters, Elsevier, vol. 54(C).

    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. Nie, Chun-Xiao, 2022. "Analysis of critical events in the correlation dynamics of cryptocurrency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    2. Nie, Chun-Xiao, 2023. "Time-varying characteristics of information flow networks in the Chinese market: An analysis based on sector indices," Finance Research Letters, Elsevier, vol. 54(C).
    3. 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.
    4. Chun-Xiao Nie, 2021. "Studying the correlation structure based on market geometry," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(2), pages 411-441, April.
    5. Nie, Chun-Xiao, 2020. "Correlation dynamics in the cryptocurrency market based on dimensionality reduction analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    6. Haiming Long & Ji Zhang & Nengyu Tang, 2017. "Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
    7. 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.
    8. Peng Yue & Qing Cai & Wanfeng Yan & Wei-Xing Zhou, 2020. "Information flow networks of Chinese stock market sectors," Papers 2004.08759, arXiv.org.
    9. Djauhari, Maman Abdurachman & Gan, Siew Lee, 2015. "Optimality problem of network topology in stocks market analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 108-114.
    10. Peralta, Gustavo & Zareei, Abalfazl, 2016. "A network approach to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 157-180.
    11. Erick Treviño Aguilar, 2020. "The interdependency structure in the Mexican stock exchange: A network approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-31, October.
    12. Sindhuja Ranganathan & Mikko Kivelä & Juho Kanniainen, 2018. "Dynamics of investor spanning trees around dot-com bubble," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-14, June.
    13. Seyed Soheil Hosseini & Nick Wormald & Tianhai Tian, 2019. "A Weight-based Information Filtration Algorithm for Stock-Correlation Networks," Papers 1904.06007, arXiv.org.
    14. Bentian Li & Dechang Pi, 2018. "Analysis of global stock index data during crisis period via complex network approach," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-16, July.
    15. Rehman, Mobeen Ur & Shahzad, Syed Jawad Hussain & Ahmad, Nasir & Vo, Xuan Vinh, 2022. "Dependence dynamics of US REITs," International Review of Financial Analysis, Elsevier, vol. 81(C).
    16. Chen, Yanhua & Li, Youwei & Pantelous, Athanasios A. & Stanley, H. Eugene, 2022. "Short-run disequilibrium adjustment and long-run equilibrium in the international stock markets: A network-based approach," International Review of Financial Analysis, Elsevier, vol. 79(C).
    17. Chun-Xiao Nie & Fu-Tie Song, 2021. "Entropy of Graphs in Financial Markets," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1149-1166, April.
    18. Wen, Danyan & Ma, Chaoqun & Wang, Gang-Jin & Wang, Senzhang, 2018. "Investigating the features of pairs trading strategy: A network perspective on the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 903-918.
    19. Papana, Angeliki & Kyrtsou, Catherine & Kugiumtzis, Dimitris & Diks, Cees, 2017. "Financial networks based on Granger causality: A case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 65-73.
    20. Chuangxia Huang & Xian Zhao & Renli Su & Xiaoguang Yang & Xin Yang, 2022. "Dynamic network topology and market performance: A case of the Chinese stock market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1962-1978, April.

    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:chsofr:v:167:y:2023:i:c:s0960077922012231. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.