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Stock market predicton using support vector machines

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


Official URL: https://core.ac.uk/display/11784350


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.

Item Type:Article
Uncontrolled Keywords:financial time series forecasting, nonstationarity, support vector machines (SVM)
Subjects:H Social Sciences > HF Commerce
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System
ID Code:8515
Deposited By: Zalinda Shuratman
Deposited On:04 May 2009 03:33
Last Modified:01 Nov 2017 04:17

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