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A big data set can often be illustrated by the nodes and edges of a big network. A large volume of data is generally produced by the stock market, and complex networks can be used to reflect the stock market behavior. The correlation of stock prices can be examined by analyzing the stock market based on complex networks. This paper uses the stock data of Tehran Stock Exchange from March 21, 2014, to March 21, 2017, to construct its stock correlation network using the threshold method. With an emphasis on centrality in complex networks, this article addresses key economic and financial implications that can be derived from stock market centrality. Central industries and stocks are thus identified. The results of the analysis of stock centrality suggest that stocks with a higher market capitalization, a greater risk, a higher volume of transactions and a lower debt ratio (i.e. greater liquidity) are more central. These stocks attract more customers due to their attractive investment features and thus have a greater market influence. The review of the relationship between centrality and the growth of industries shows that an industry or a sector with greater economic growth has a higher centrality value and is positioned more centrally in the stock market network. |
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