An association mining method for time series and its application in the stock prices of TFT-LCD industry

Abstract

TFT-LCD is one of industries currently promoted by the "Two Trillion and Twin Star Industries Development Plan" in Taiwan. This study endeavors to find out the stock price associations between the suppliers and manufacturers in the value chain of the TFT-LCD industry by means of data mining techniques, and meanwhile, to improve the Apriori algorithm so that it can facilitate association mining of discrete data points in a time series. An efficient data mining method which consists of two phases is proposed. In the first phase, data are classified and preprocessed using the algorithm proposed by R. Agrawal et al. (1996), then Apriori algorithm is applied to extract the strong association rules. The second phase further improves the Apriori algorithm by breaking down the traditional limitation of relying on pattern matching of continuous data for disclosing stock market behavior. By mining the association rules from the discrete data points in a time series and testing the corresponding hypotheses, statistically significant outcomes can be obtained. The proposed data mining method was applied to some real time-series of the stock prices of companies in the supply chain of TFT-LCD industry in Taiwan. It is suggested that a positive correlation does not necessarily exist between the companies' stock prices in the supply chain of TFT-LCD industry. For instance the result shows that, if the stock price of Sintek Phonrotic Corp., a company in the up stream of the value chain, soars for more than 5% in a day, the stock price of Tatung, a company in the down stream of the same value chain, may not respond positively accordingly. If an investor can short the stock of Tatung on the 7th day and long it back on the 10th day after Sintek's stock price soaring for more than 5%, the annual return of investment is 199.88% with 95% confidence interval. In conclusion, the results may reveal helpful information for the investors to make leveraged arbitrage profit investing decisions, and it might be interesting to apply this proposed data mining method to the time series in other industries or problems and investigate the results further.