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

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  • Myoung Jong Kim
  • Ingoo Han
  • Kun Chang Lee

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.

Suggested Citation

  • Myoung Jong Kim & Ingoo Han & Kun Chang Lee, 2004. "Hybrid knowledge integration using the fuzzy genetic algorithm: prediction of the Korea stock price index," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(1), pages 43-60, January.
    • Handle: RePEc:wly:isacfm:v:12:y:2004:i:1:p:43-60
    DOI: 10.1002/isaf.240
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    Cited by:

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    2. Elfadil A. Mohamed & Ibrahim Elsiddig Ahmed & Riyadh Mehdi & Hanan Hussain, 2021. "Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 52-71, January.
    3. Akhter Mohiuddin Rather & V. N. Sastry & Arun Agarwal, 2017. "Stock market prediction and Portfolio selection models: a survey," OPSEARCH, Springer;Operational Research Society of India, vol. 54(3), pages 558-579, September.

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