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. The stock market is a typical complex system with different types of interactions between individuals and listed companies. To understand how the returns of different companies are correlated with each other and identify their community structure is of crucial importance for the study of financial dynamics and portfolio risk estimation. For this purpose the study of stock correlations has attracted much interest [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. It is noted that there is a coexistence of random and collective interactions among stocks in financial markets. The majority of eigenvalues of the stock correlation matrix agree well with the predictions of random matrix theory (RMT) [1], while a few large eigenvalues contain information about the co-movements of particular stocks within specific industry sectors or communities [15,16]. Revealing the interactions between stocks and their variance over time has provided useful information for portfolio optimization and systemic risk estimation [3][4][5][17][18][19].Among the various methods used in detecting stock correlations, Planar Maximally Filtered Graph (PMFG), an extension of Minimal Spanning Tree (MST) is proposed as an efficient approach to file out the internal structure between complex data sets [20]. It has been used to study the collective behavior of stock prices in * Electronic address: spli@phys.sinica.edu.tw † Electronic address: fren@ecust.edu.cn the US equity market, and a cluster formation associated with economic sectors, is q...
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