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Forecasting stock market movement direction with support vector machine
Huang, W; Nakamori, Y; Wang, SY
2005-10-01
发表期刊COMPUTERS & OPERATIONS RESEARCH
ISSN0305-0548
卷号32期号:10页码:2513-2522
摘要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. (c) 2004 Elsevier Ltd. All rights reserved.
关键词support vector machine forecasting multivariate classification
DOI10.1016/j.cor.2004.03.016
语种英语
WOS研究方向Computer Science ; Engineering ; Operations Research & Management Science
WOS类目Computer Science, Interdisciplinary Applications ; Engineering, Industrial ; Operations Research & Management Science
WOS记录号WOS:000228207700003
出版者PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型期刊论文
条目标识符http://ir.amss.ac.cn/handle/2S8OKBNM/2301
专题中国科学院数学与系统科学研究院
通讯作者Wang, SY
作者单位1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Beijing 100080, Peoples R China
2.Japan Adv Inst Sci & Technol, Sch Knowledge Sci, Tatsunokuchi, Ishikawa 9231292, Japan
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Huang, W,Nakamori, Y,Wang, SY. Forecasting stock market movement direction with support vector machine[J]. COMPUTERS & OPERATIONS RESEARCH,2005,32(10):2513-2522.
APA Huang, W,Nakamori, Y,&Wang, SY.(2005).Forecasting stock market movement direction with support vector machine.COMPUTERS & OPERATIONS RESEARCH,32(10),2513-2522.
MLA Huang, W,et al."Forecasting stock market movement direction with support vector machine".COMPUTERS & OPERATIONS RESEARCH 32.10(2005):2513-2522.
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