Stock market predicton using support vector machines
The stock market is a complex, nonstationaty, chaotic and non-linear dynamical system. Therefore, predicting stock price movements is quite difficult. A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original applic...
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| Format: | Article |
| Language: | en |
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Penerbit UTM Press
2005
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| Online Access: | http://eprints.utm.my/8515/1/MohdNoorMdSap2005_StockMarketPredictionUsingSupportVectorMachines.PDF http://eprints.utm.my/8515/ https://core.ac.uk/display/11784350 |
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| author | Md. Sap, Mohd. Noor Awan, A. Majid |
| author_facet | Md. Sap, Mohd. Noor Awan, A. Majid |
| author_sort | Md. Sap, Mohd. Noor |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The stock market is a complex, nonstationaty, chaotic and non-linear dynamical system. Therefore, predicting stock price movements is quite difficult. A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. Some results for stock price prediction are also presented. Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast financial time series. |
| format | Article |
| id | my.utm.eprints-8515 |
| institution | Universiti Teknologi Malaysia |
| language | en |
| publishDate | 2005 |
| publisher | Penerbit UTM Press |
| record_format | eprints |
| spelling | my.utm.eprints-85152017-11-01T04:17:32Z http://eprints.utm.my/8515/ Stock market predicton using support vector machines Md. Sap, Mohd. Noor Awan, A. Majid HF Commerce QA75 Electronic computers. Computer science The stock market is a complex, nonstationaty, chaotic and non-linear dynamical system. Therefore, predicting stock price movements is quite difficult. A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. Some results for stock price prediction are also presented. Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast financial time series. Penerbit UTM Press 2005-12 Article PeerReviewed application/pdf en http://eprints.utm.my/8515/1/MohdNoorMdSap2005_StockMarketPredictionUsingSupportVectorMachines.PDF Md. Sap, Mohd. Noor and Awan, A. Majid (2005) Stock market predicton using support vector machines. Jurnal Teknologi Maklumat, 17 (2). pp. 27-35. ISSN 0128-3790 https://core.ac.uk/display/11784350 |
| spellingShingle | HF Commerce QA75 Electronic computers. Computer science Md. Sap, Mohd. Noor Awan, A. Majid Stock market predicton using support vector machines |
| title | Stock market predicton using support vector machines |
| title_full | Stock market predicton using support vector machines |
| title_fullStr | Stock market predicton using support vector machines |
| title_full_unstemmed | Stock market predicton using support vector machines |
| title_short | Stock market predicton using support vector machines |
| title_sort | stock market predicton using support vector machines |
| topic | HF Commerce QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/8515/1/MohdNoorMdSap2005_StockMarketPredictionUsingSupportVectorMachines.PDF http://eprints.utm.my/8515/ https://core.ac.uk/display/11784350 |
| url_provider | http://eprints.utm.my/ |