KMS Of Academy of mathematics and systems sciences, CAS
| Evolving Least Squares Support Vector Machines for Stock Market Trend Mining | |
Yu, Lean1,2; Chen, Huanhuan3; Wang, Shouyang1 ; Lai, Kin Keung2
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| 2009-02-01 | |
| Source Publication | IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
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| ISSN | 1089-778X |
| Volume | 13Issue:1Pages:87-102 |
| Abstract | In this paper, an evolving least squares support vector machine (LSSVM) learning paradigm with a mixed kernel is proposed to explore stock market trends. In the proposed learning paradigm, a genetic algorithm (GA), one of the most popular evolutionary algorithms (EAs), is first used to select input features for LSSVM learning, i.e., evolution of input features. Then, another GA is used for parameters optimization of LSSVM, i.e., evolution of algorithmic parameters. Finally, the evolving LSSVM learning paradigm with best feature subset, optimal parameters, and a mixed kernel is used to predict stock market movement direction in terms of historical data series. For illustration and evaluation purposes, three important stock indices, S&P 500 Index, Dow Jones Industrial Average (DJIA) Index, and New York Stock Exchange (NYSE) Index, are used as testing targets. Experimental results obtained reveal that file proposed evolving LSSVM can produce some forecasting models that tire easier to be interpreted by using a small number of predictive features and are more efficient than other parameter optimization methods. Furthermore, the produced forecasting model can significantly outperform other forecasting models listed in this paper in terms of the hit ratio. These findings imply that the proposed evolving LSSVM learning paradigm can be used as a promising approach to stock market tendency exploration. |
| Keyword | Artificial neural networks (ANNs) evolutionary algorithms (EAs) feature selection genetic algorithm (GA) least squares support vector machine (LSSVM) mixed kernel parameter optimization statistical models stock market trend mining |
| DOI | 10.1109/TEVC.2008.928176 |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China (NSFC)[70601029] ; National Natural Science Foundation of China (NSFC)[70221001] ; Knowledge Innovation Program of the Chinese Academy of Sciences ; NSFC/RGC Joint Research Scheme[N_CityU110/07] |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods |
| WOS ID | WOS:000263161700007 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/9029 |
| Collection | 系统科学研究所 |
| Corresponding Author | Yu, Lean |
| Affiliation | 1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Beijing 100190, Peoples R China 2.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China 3.Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England |
| Recommended Citation GB/T 7714 | Yu, Lean,Chen, Huanhuan,Wang, Shouyang,et al. Evolving Least Squares Support Vector Machines for Stock Market Trend Mining[J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,2009,13(1):87-102. |
| APA | Yu, Lean,Chen, Huanhuan,Wang, Shouyang,&Lai, Kin Keung.(2009).Evolving Least Squares Support Vector Machines for Stock Market Trend Mining.IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,13(1),87-102. |
| MLA | Yu, Lean,et al."Evolving Least Squares Support Vector Machines for Stock Market Trend Mining".IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 13.1(2009):87-102. |
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