Stock market predicton using support vector machines

Abstract

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.