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doi:10.1016/j.cor.2004.03.016 
Copyright © 2004 Elsevier Ltd. All rights reserved.
Forecasting stock market movement direction with support vector machine
Accepted 29 March 2004.
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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.
Author Keywords: Support vector machine; Forecasting; Multivariate classification
