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: http://portal.psz.utm.my/psz/index.php?option=com_...
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.
|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 (Formerly known)|
|Deposited By:||Ms Zalinda Shuratman|
|Deposited On:||04 May 2009 03:33|
|Last Modified:||02 Jun 2010 01:55|
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