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Evolving Least Squares Support Vector Machines for Stock Market Trend Mining

文献类型：期刊论文

作者Yu, Lean1,2; Chen, Huanhuan3; Wang, Shouyang1; Lai, Kin Keung2
刊名IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
出版日期2009-02-01
卷号13期号:1页码:87-102
关键词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
ISSN号1089-778X
DOI10.1109/TEVC.2008.928176
英文摘要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.
资助项目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研究方向Computer Science
语种英语
WOS记录号WOS:000263161700007
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/9028]  
专题系统科学研究所
通讯作者Yu, Lean
作者单位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
推荐引用方式
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.

入库方式： OAI收割

来源：数学与系统科学研究院

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