Application of a New Quantitative Approach to Stock Markets: Minimum Spanning Tree

The systems involving interacting agents such as food networks, scientific citations, social networks, communication networks, the Internet, and the companies interacting in stock portfolios have long been studied by many researchers under the concept of complex systems. Such systems are expressed in terms of weighted networks. The dense connections and entwined relations amongst the agents play important roles for forecasting or risk analysis. In this study we present a novel approach to determine hierarchical structure of Industrial sector in the globally operating stock market network. By using the subdominant ultra-metric topology emerge from the minimum spanning tree of the stock market network; it becomes possible to extract the important properties of this complex system. Moreover, we use the dynamic time warping distance to determine the taxonomy and to measure similarity between time series of the operating Industrial sectors. It is found that United States, United Kingdom, Netherlands and Denmark are the most dominant stock exchanges in Industrials sector. We also find three hierarchical clusters and then topologically analyze the structure of considered clusters.