Hybrid knowledge integration using the fuzzy genetic algorithm: prediction of the Korea stock price index: Research Articles

Authors:
Myoung Jong Kim;Ingoo Han;Kun Chang Lee
Affiliations:
Graduate School of Management, Korea Advanced Institute of Science & Technology, Korea;Graduate School of Management, Korea Advanced Institute of Science & Technology, Korea;School of Business Administration, Sungkyunkwan University, Korea
Venue:
International Journal of Intelligent Systems in Accounting and Finance Management
Year:
2004

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Abstract

This paper proposes the hybrid knowledge integration mechanism using the fuzzy genetic algorithm for the optimized integration of knowledge from several sources such as machine knowledge, expert knowledge and user knowledge. This mechanism is applied to the prediction of the Korea stock price index. Machine knowledge is generated by applying neural networks to technical indicators, while expert knowledge and user knowledge are generated from the evaluations of external factors that affect the stock market. Cooperative knowledge is generated from the weighted sum of these sources using a genetic algorithm. Experimental results show that the hybrid mechanism can provide more accurate and less ambiguous results. It means that this mechanism is useful in integrating knowledge from multiple sources for an unstructured environment such as the stock market. Copyright © 2004 John Wiley & Sons, Ltd.