Minimum Spanning Tree

Nowadays, financial markets in Iran have attracted the attention of many managers, investors and financial policymakers. Therefore, in order to make the optimal decision and reduce the risks in such a market, it is important to identify and analyze the network behavior of the financial markets at different times to obtain the optimal decision. The current study aims to answer the following research question; how is it possible to use the minimum spanning tree and hierarchical clustering in the network analysis of the Tehran Stock Exchange? The period examined was 2013 to 2018. The population consisted of all the companies accepted in Tehran Stock Exchange. The sampling was selected purposefully and contained the companies which had at least one trading day in the time span from the beginning of 2013 to the end of 2018. The stock of the investigated companies was considered as the vertexes of one graph and the coherent information criterion was considered as the weight of the edge. First, the minimum spanning tree of the graph was calculated. The results revealed that the stocks of DarooAbuReihan, DarooPakhsh and Alborzdaroo had a high influence on directing the prices of the other stocks. Furthermore, the results of hierarchical clustering classified the stocks of the companies into 8 clusters. This study presents a viewpoint about the modern method designed for the analysis of complex financial networks. Moreover, the study offers an analysis of Iran's stock market structure which can be the center of finance researchers and analysts' attention.

Examining the importance and influence of financial market companies is one of the main issues in the field of financial management because sometimes the collapse of a stock exchange company can affect an entire financial market. One systematic way to analyze the significance and impacts of companies is to use complex networks based on Interaction Graphs (IGs). There are different methods for quantifying the edge weight in an IG. In this method, the graph vertices represent the stock exchange companies that are connected by weighted edges (corresponding to the extent to which they relate to each other). In this paper, using the GARCH model (1,1) and the Clayton copula, we obtained the lower tail dependence interaction network of the first 52 companies of the Tehran Stock Exchange in terms of average market value, between June 2017 and October 2020. Then, based on the minimum spanning tree of the interaction network, we divided the companies into different communities. Using this classification, it was observed that the companies of the first group (Food Industry) and the second group (Oil Refinery) have the greatest impact on other companies. We also calculated the central indexes of the minimum spanning tree for each company. According to the results, the companies of the third group (Steel) have the highest average in the central indicators.