NDLI: Stock Trend Prediction by Sequential Chart Pattern via K-Means and AprioriAll Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Yung-Piao Wu Kuo-Ping Wu Hahn-Ming Lee
Copyright Year	2012
Abstract	In this paper we present a model to predict the stock trend based on a combination of sequential chart pattern, K-Means and AprioriAll algorithm. The stock price sequence is truncated to charts by sliding window. Then the charts are clustered by K-Means algorithm to form chart patterns. Therefore, the charts form chart pattern sequences, and frequent patterns in the sequences can be extracted by AprioriAll algorithm. The existence of frequent patterns implies that some specific market behaviors often show accompanied, thus the corresponding trend can be predicted. Experiment results show that the proposed system can produce better index return with fewer trade. Its annualized return is also better than award winning mutual funds. Therefore, the proposed method makes profits on the real market, even in a long-term usage.
Starting Page	176
Ending Page	181
File Size	477419
Page Count	6
File Format	PDF
ISBN	9781467349765
DOI	10.1109/TAAI.2012.42
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-16
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation K-Means Clustering algorithms Haar wavelet Market research AprioriAll Indexes Data mining Stock markets Forecasting Stock Trend Prediction Sequential Chart Pattern
Content Type	Text
Resource Type	Article

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