NDLI: Intelligent stock trading system based on SVM algorithm and oscillation box prediction

Content Provider	IEEE Xplore Digital Library
Author	Qinghua Wen Zehong Yang Yixu Song Peifa Jia
Copyright Year	2009
Description	Author affiliation: Tsinghua University, Beijing, 100084 China (Qinghua Wen)
Abstract	The stock market is considered as a high complex and dynamic system. Many machine learning and data mining technologies are used for stock analysis, but it still leaves an open question about how to integrate these methods with the plentiful knowledge and techniques accumulated in stock investment which are critical to the successful stock analysis. In this paper, we propose an intelligent stock trading system by combining support vector machine (SVM) algorithm and box theory of stock. The box theory believes a successful stock buying/selling generally occurs when the price effectivley breaks out the original oscillation box into another new box. In the system, support vector machine algorithm is utilized to make forecasts of the top and bottom of the oscillation box. Then a trading strategy based on the box theory is constructed to make trading decisions. The different stock movement patterns, i.e. bull, bear and fluctuant market, are used to test the feasibility of the system. The experiments on S&P500 components show a promising performance is achieved.
Starting Page	3341
Ending Page	3347
File Size	4272873
Page Count	7
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178843
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intelligent systems Support vector machines Learning systems Machine learning algorithms Stock markets Machine learning Data mining Investments Economic forecasting System testing
Content Type	Text
Resource Type	Article

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