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Development of an Intelligent Trading System Using Support Vector Machines and Genetic Algorithms  

Kim, Sun-Woong (The Graduate School of Business IT, Kookmin University)
Ahn, Hyun-Chul (School of Management Information Systems, Kookmin University)
Publication Information
Journal of Intelligence and Information Systems / v.16, no.1, 2010 , pp. 71-92 More about this Journal
Abstract
As the use of trading systems increases recently, many researchers are interested in developing intelligent trading systems using artificial intelligence techniques. However, most prior studies on trading systems have common limitations. First, they just adopted several technical indicators based on stock indices as independent variables although there are a variety of variables that can be used as independent variables for predicting the market. In addition, most of them focus on developing a model that predicts the direction of the stock market indices rather than one that can generate trading signals for maximizing returns. Thus, in this study, we propose a novel intelligent trading system that mitigates these limitations. It is designed to use both the technical indicators and the other non-price variables on the market. Also, it adopts 'two-threshold mechanism' so that it can transform the outcome of the stock market prediction model based on support vector machines to the trading decision signals like buy, sell or hold. To validate the usefulness of the proposed system, we applied it to the real world data-the KOSPI200 index from May 2004 to December 2009. As a result, we found that the proposed system outperformed other comparative models from the perspective of 'rate of return'.
Keywords
Intelligent Trading Systems; Support Vector Machines; Genetic Algorithms; Two-threshold Mechanism; KOSPI200;
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Times Cited By KSCI : 4  (Citation Analysis)
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