Data mining of Iranian stock market by modeling complex network filtering based on MST

One of the most important problems in modern finance is finding efficient ways to summarize and visualize stock market data. Modeling the filtering of complex networks in the stock market allows this to be achieved by reducing the market size, obtaining reliable information with less disturbance. Since stock price changes are not independent of each other, the study of the correlation between stock price changes provides a better understanding of market performance for investors. Stock market analysis based on complex networks allows studying the correlation of stock prices. In this paper, using the stock market data in the Tehran Stock Exchange, the Iranian stock market network is created by the threshold method, and then the network filtering is based on MST. The results show that the filtration modeling of Iran's stock market network based on the MST can form a subset of the stock market that follows the performance of the entire market with a significant reduction in size and has a similar degree of diversification with the entire market. These analyzes provide a more in-depth insight into the structure of the stock market while reducing the size.