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Journal of Information Science and Engineering, Vol. 30 No. 3, pp. 571-585 (May 2014)


A Novel Episode Mining Methodology for Stock Investment*


YU-FENG LIN1, CHIEN-FENG HUANG2 AND VINCENT S. TSENG1
1Department of Computer Science and Information Engineering
National Cheng Kung University
Tainan, 701 Taiwan
2Department of Computer Science and Information Engineering
National University of Kaohsiung
Kaohsiung, 811 Taiwan
E-mail: tsengsm@mail.ncku.edu.tw

In this paper, we present a novel methodology for stock investment using episode mining and technical indicators. The time-series data of stock price and the derived moving average, a class of well-known technical indicators, are used for the construction of complex episode events and rules. Our objective is to devise a profitable episodebased investment model to mine associated events in the stock market. Using Taiwan Capitalization Weighted Stock Index (TAIEX), the empirical results show that our proposed model significantly outperforms the benchmark in terms of cumulative total returns. We also show that the level of the precision by our model is close to 60%, which is better than random guessing. Based upon the results obtained, we expect this novel episodebased methodology will advance the research in data mining for computational finance and provide an alternative to stock investment in practice.

Keywords: episode mining, technical indicators, stock investing strategy, complex event sequence, cross validation

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Received February 28, 2013; accepted June 15, 2013.
Communicated by Hung-Yu Kao, Tzung-Pei Hong, Takahira Yamaguchi, Yau-Hwang Kuo, and Vincent Shin-Mu Tseng.
* This research was supported by National Science Council, Taiwan, under Grant No. NSC101-2221-E-006-255- MY3.