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

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  • 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.

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  • 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
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    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).

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