Proceedings of the Korea Inteligent Information System Society Conference (한국지능정보시스템학회:학술대회논문집)
- 2003.05a
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- Pages.329-337
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- 2003
Application of Support Vector Machines to the Prediction of KOSPI
- Kim, Kyoung-jae (Department of Information Systems, College of Business Adminstration, Dongguk University)
- Published : 2003.05.01
Abstract
Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using artificial neural networks in this area. Recently, support vector machines (SVMs) are regarded as promising methods for the prediction of financial time-series because they me a risk function consisting the empirical ewer and a regularized term which is derived from the structural risk minimization principle. In this study, I apply SVM to predicting the Korea Composite Stock Price Index (KOSPI). 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.
Keywords
- Support vector machines;
- Back-propagation neural networks;
- Case-based reasoning;
- Stock market prediction