Financial time series forecasting using support vector machines

Support vector machines (SVMs) are promising methods for the prediction of financial time-series because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies SVM to predicting the stock...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2003-09, Vol.55 (1), p.307-319
Main Author: Kim, Kyoung-jae
Format: Article
Language:English
Subjects:
Back-propagation neural networks
Case-based reasoning
Financial time series
Support vector machines
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Summary:Support vector machines (SVMs) are promising methods for the prediction of financial time-series because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies SVM to predicting the stock price index. In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.
ISSN:0925-2312
1872-8286
DOI:10.1016/S0925-2312(03)00372-2