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- W213896379 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 the perfor-mance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms other classification methods. Furthermore, we propose a combining model by integrating SVM with other classification methods. The combining model performs the best among the forecasting methods." @default.
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- W213896379 date "2003-01-01" @default.
- W213896379 modified "2023-09-24" @default.
- W213896379 title "FORECASTING NIKKEI 225 INDEX WITH SUPPORT VECTOR MACHINE" @default.
- W213896379 hasPublicationYear "2003" @default.
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