NDLI: Forecasting stock price using Nonlinear independent component analysis and support vector regression

Content Provider	IEEE Xplore Digital Library
Author	Chi-Jie Lu Jui-Yu Wu Cheng-Ruei Fan Chih-Chou Chiu
Copyright Year	2009
Description	Author affiliation: Department of Industrial Engineering and Management, Ching Yun University, Jung-Li, Taiwan (Chi-Jie Lu) \|\| Department of Business Administration, Lunghwa University of Science and Technology, Taipei, Taiwan (Jui-Yu Wu) \|\| Institute of Commerce Automatic and Management, National Taipei University of Technology, Taiwan (Cheng-Ruei Fan; Chih-Chou Chiu)
Abstract	In developing a stock price forecasting model, the first step is usually feature extraction. Nonlinear independent component analysis (NLICA) is a novel feature extraction technique to find independent sources given only observed data that are mixtures of the unknown sources, without prior knowledge of the mixing mechanisms. It assumes that the observed mixtures are the nonlinear combination of latent source signals. This study propose a stock price forecasting model which first uses NLICA as preprocessing to extract features from forecasting variables. The features, called independent components (ICs), are served as the inputs of support vector regression (SVR) to build the prediction model. Experimental results on Nikkei 225 closing cash index show that the proposed method can produce the best prediction performance compared to the SVR models that use linear ICA, principal component analysis (PCA) and kernel PCA as feature extraction, and the single SVR model without feature extraction.
Starting Page	2370
Ending Page	2374
File Size	689758
Page Count	5
File Format	PDF
ISBN	9781424448692
DOI	10.1109/IEEM.2009.5372995
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	stock price prediction Independent component analysis Predictive models Nonlinear independent component analysis Vectors support vector regression Technology management Engineering management feature extraction Feature extraction Economic forecasting Technology forecasting Kernel Principal component analysis
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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