A novel discovery model for revealing substitution relationships from international stock markets: With association rule analysis

Abstract

At present, the trading volume of the stock market is huge, and the traditional method can not effectively find the relationship between the rise and fall of the stock market, but the machine learning method can find their interrelated data from a large number of data. This research aims to determine the effectiveness of association mining technology in analyzing the relationship between the ups and downs of stock markets in various countries, and it found the highest level of association between stock market items as investor references. The research data takes Taiwan's stock market as the target market and the international mainstream stock index as the related stock market. Through the analysis, it is found that association mining can accurately find the associated stock market according to the relevant parameters. The Taiwan stock market is more closely related to the top ten economies such as the Mainland, the United States, the United Kingdom and France, which shows that the rise of the international or mainland stock market will drive foreign capital to actively buy the Taiwan stock market, and vice versa. At last, the study sorted out three groups of stocks with the highest correlation degree according to the results of association mining, Namely Foxconn Stock (2354) and TSMC (2330), which are most closely related to the rise and fall of the international stock market. Therefore, the results of this study can also be used as a reference for investors to choose the stock price of Taiwan stock market.