Excessive co-movement effect and evolution network analysis of Chinese stock market

Abstract

In order to better understand mutual influences among stock price fluctuations, we treat stocks on the market as nodes, collecting closing price data of The Shanghai and Shenzhen 300 Index from 2014 to 2016. And we use the partial correlation coefficient to measure the linkage effect between stocks. Specifically, through the selection of a certain threshold, we get a financial complex network with stock linkage effect. Furthermore, we use Newman Fast analysis algorithm to divide Shanghai and Shenzhen 300 Index network into 15 communities. It turns out that the correlation within the community is significantly closer than the correlation between the community and the outside community, which is called close price-fluctuating plate. Moreover, through the time division that is used to analyze the structure of stock network community, we may conclude that banks, securities and real estate stocks' prices are basically a synchronous change during this period. And there are also some stocks which have excessive co-movement effect changes asynchronously. The results of our work can not only provide inner information and linkage on the stock market capital flows, but also can explore the evolution of the stock market rules.