کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
474339 | 698866 | 2005 | 10 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Forecasting stock market movement direction with support vector machine Forecasting stock market movement direction with support vector machine](/preview/png/474339.png)
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.
Journal: Computers & Operations Research - Volume 32, Issue 10, October 2005, Pages 2513–2522