Forecasting stock market movement direction with support vector machine

Support vector machine (SVM) is a very speci1c 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 1nancial 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 classi1cation methods. Further, we propose a combining model by integrating SVM with the other classi1cation methods. The combining model performs best among all the forecasting methods. ? 2004 Elsevier Ltd. All rights reserved.