Dynamic structure of stock communities: a comparative study between stock returns and turnover rates
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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. An empirical study using the overall data set shows that for both returns and turnover rates the largest communities are composed of specific industrial or conceptional sectors and the correlation inside a sector is generally larger than the correlation between different sectors. However, the community structure for turnover rates is more complex than that for returns, which indicates that the interactions between stocks revealed by turnover rates may contain more information. This conclusion is further confirmed by the analysis of the changes in the dynamics of community structures over five sub-periods. Sectors like banks, real estate, health care and New Shanghai take turns to comprise a few of the largest communities in different sub-periods, and more interestingly several specific sectors appear in the communities with different rank orders for returns and turnover rates even in the same sub-period. To better understand their differences, a comparison between the evolution of the returns and turnover rates of the stocks from these sectors is conducted. We find that stock prices only had large changes around important events while turnover rates surged after each of these events relevant to specific sectors, which shows strong evidence that the turnover rates are more susceptible to exogenous shocks than returns and its measurement for community detection may contain more useful information about market structure.
KeywordsStatistical and Nonlinear Physics
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