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Title: A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression
Authors: Hsu, SH
Hsieh, JJPA
Chih, TC
Hsu, KC
Keywords: Stock price prediction
Support vector machine
Self-organizing map
Issue Date: May-2009
Publisher: Pergamon Press
Source: Expert systems with applications, May 2009, v. 36, no. 4, p. 7947-7951 How to cite?
Journal: Expert systems with applications 
Abstract: Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series. One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction. Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model.
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2008.10.065
Rights: © 2008 Elsevier Ltd. All rights reserved.
NOTICE: this is the author’s version of a work that was accepted for publication in Expert Systems With Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems With Applications, vol. 36, no. 4 (May 2009), DOI: 10.1016/j.eswa.2008.10.065
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