Applying Dynamic Fuzzy Model in Combination with Support Vector Machine to Explore Stock Market Dynamism

  • Authors:

  • Deng-Yiv Chiu;Ping-Jie Chen

  • Affiliations:

  • Department of Information Management, ChungHua University, Hsin-chu City, Taiwan 300, R.O.C.;Department of Information Management, ChungHua University, Hsin-chu City, Taiwan 300, R.O.C.

  • Venue:
  • ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
  • Year:
  • 2007

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Abstract

In the study, a new dynamic fuzzy model is proposed in combination with support vector machine (SVM) to explore stock market dynamism. The fuzzy model integrates various factors with influential degree as the input variables, and the genetic algorithm (GA) adjusts the influential degree of each input variable dynamically. SVM then serves to predict stock market dynamism in the next phase. In the meanwhile, the multiperiod experiment method is designed to simulate the volatility of stock market. Then, we compare it with other methods. The model from the study does generate better results than others.