Evolving Least Squares Support Vector Machines for Stock Market Trend Mining

doi:10.1109/TEVC.2008.928176

CSpace > 系统科学研究所

	Evolving Least Squares Support Vector Machines for Stock Market Trend Mining
	Yu, Lean 1,2; Chen, Huanhuan 3; Wang, Shouyang1 ; Lai, Kin Keung 2
	2009-02-01
发表期刊	IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
ISSN	1089-778X
卷号	13 期号:1 页码:87-102
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.
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
DOI	10.1109/TEVC.2008.928176
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:000263161700007
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/9029
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.