Dynamic analysis and community recognition of stock price based on a complex network perspective

In the context of the 2008 financial crisis, this paper focuses on the Chinese A-share stock prices in ten years (from January 1, 2003 to December 31, 2012) to explore some possible cautionary phenomena based on a complex network perspective. Firstly, with the help of the threshold method and taking monthly and fixed time (20 days, 40 days and 60 days) as time windows, this paper establishes highly correlated networks of daily opening price and backward answer authority closing price respectively. Notice that the correlation between stock price and its category in Citic One-level industry index is misty. And then, it is interesting to discover some special behaviors of stock prices by the evolution of different network features and community identification, so as to provide guidance for crisis identification and control. The results show that the size of time windows has a certain influence on the evolution, and network parameters appear obvious particularity near the 2008 crisis for the networks with fixed times of monthly and 20 days. For example, the average path length reaches a maximum around May 2009; the average degree is low and stable around May 2005 to December 2006 but fluctuates greatly around May 2008 to September 2008; the clustering coefficient fluctuates greatly and reaches the maximum value 0.78 during May 2008 to August 2008; the assortativity coefficient reaches its maximum value at a certain time and fluctuates relatively greatly. In addition, the community identification results indicate that near the crisis, cluster communities with high network density, which contain a relatively large number of nodes, appear.