Abstract In order to analyze the evolution of financial markets, the problem of dynamic network model construction and topology analysis s proposed. We use the daily closing prices of portfolio comprising of 884 A share stocks traded in the Shanghai Stock Exchange in the period 2000~2011 and estimate financial networks where nodes correspond to stocks and edges correspond to none vanishing correlation coefficients. Then we use the weighted edge filter technology to build a dynamic network model of financial markets, and analyze the network’s basic characteristics and community structures to explore the evolution of financial markets. The degree distribution of the dynamic financial network has a power law form, and keeps stable except the connectivity shows a trend of stronger. We use the weighted CNM algorithm to detect community structure of the dynamic network, it can be found that the modularity Q become larger over the time, which showed a strong community structure development. e further analyze these communities by their industry classification, and it can be revealed that manufacturing is the core of the network industry, followed by wholesale and retail trade of the five industries. The financial dynamic network modeling and analysis of evolution can be extended to the general theory of complex networks.
2013, Vol. 10 
