Forecasting stock market movement direction with support vector machine

  • Authors:

  • Wei Huang;Yoshiteru Nakamori;Shou-Yang Wang

  • Affiliations:

  • Sch. of Knowl. Sci., Japan Adv. Inst. of Sci. and Tech., Tatsunokuchi, Ishikawa 923-1292, Japan and Inst. of Sys. Sci., Acad. of Math. and Sys. Sci., Chinese Academy of Sciences, Beijing 100080, C ...;School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Tatsunokuchi, Ishikawa 923-1292, Japan;Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100080, China and University of Tsukuba in Japan and Hunan University in China

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2005

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Abstract

Support vector machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare its performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms the other classification methods. Further, we propose a combining model by integrating SVM with the other classification methods. The combining model performs best among all the forecasting methods.