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Dynamic structure of stock communities: A comparative study between stock returns and turnover rates


  • Li-Ling Su
  • Xiong-Fei Jiang
  • Sai-Ping Li
  • Li-Xin Zhong
  • Fei Ren


The detection of community structure in stock market is of theoretical and practical significance for the study of financial dynamics and portfolio risk estimation. We here study the community structures in Chinese stock markets from the aspects of both price returns and turnover rates, by using a combination of the PMFG and infomap methods based on a distance matrix. We find that a few of the largest communities are composed of certain specific industry or conceptional sectors and the correlation inside a sector is generally larger than the correlation between different sectors. In comparison with returns, the community structure for turnover rates is more complex and the sector effect is relatively weaker. The financial dynamics is further studied by analyzing the community structures over five sub-periods. Sectors like banks, real estate, health care and New Shanghai take turns to compose a few of the largest communities for both returns and turnover rates in different sub-periods. Several specific sectors appear in the communities with different rank orders for the two time series even in the same sub-period. A comparison between the evolution of prices and turnover rates of stocks from these sectors is conducted to better understand their differences. We find that stock prices only had large changes around some important events while turnover rates surged after each of these events relevant to specific sectors, which may offer a possible explanation for the complexity of stock communities for turnover rates.

Suggested Citation

  • Li-Ling Su & Xiong-Fei Jiang & Sai-Ping Li & Li-Xin Zhong & Fei Ren, 2016. "Dynamic structure of stock communities: A comparative study between stock returns and turnover rates," Papers 1608.03053,
  • Handle: RePEc:arx:papers:1608.03053

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    Cited by:

    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,, revised Nov 2020.

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