As an emerging research field, the complex network theory is able to depict the most daily complex systems’ topologies, but in terms of financial market analysis, it still needs more attention. We can apply this theory to construct financial networks and detect them both from macro level and micro level to support a company in forecasting its revenue. This paper aims to explore the macro-characteristics of the UK stock market. We examine the properties of return ratio series of selected components in FTSE100 index, adopt the Kendall’s ( rank correlation coefficient between series to write adjacency matrices and transform these matrices into complex networks. Then we visualize the networks, analyze features of them at different thresholds and find evidence of WS small world property in the UK stock networks. All these work follow our research framework proposed at beginning of this paper. According to the framework, more future work needs to be done to achieve the goal and make decision support in a company.