Abstract: The complex network theory is a powerful tool to study the internal structure and function of the stock market. The research of the relevance of stock indicators has become a new perspective in the research of stock market. Traditional financial research using equity-linked network to dig intrinsic clustering structure, lack of formulation of the relationship between the complex structures. This paper based on complex network theory to build the network topology on CSI 300 Index (July 28 2011-February 28 2012), the relationship between the complex structures is analyzed, the existing empirical analysis methods of complex network are improved. The data is tiltered by DFA, and the absolute correlation coefficient of the stock index is calculated. According to the absolute correlation coefficients of stock index probability distribution, the threshold is determined and build the network topology is build. Network statistics characteristic index of the stock is calculated, and the network structure of the rate of return, trading volume and price-earnings ratio are analyzed. The results show that the stock returns and trading volume indicators have strong correlation with the small-world character,while price-earnings ratio index has a weak correlation, and does not have the small-world character, and is a random network that subject to outside influence with low efficiency and high-risk. Such new tools and empirical results are useful to study the relevance and empirical findings of stock indicators in China's financial market.

Key words: complex network, the DFA, stock indicators, topology