Support Vector Machine for Regression and Applications to Financial Forecasting

The main purpose of this paper is to compare the support vector machine (SVM) developed by Vapnik with other techniques such as Backpropagation and Radial Basis Function (RBF) Networks for financial forecasting applications. The theory of the SVM algorithm is based on statistical learning theory. Training of SVMs leads to a quadratic programming (QP) problem. Preliminary computational results for stock price prediction are also presented.

• Type: Conference
• Year: 2000
• Where: IJCNN
• Authors: Theodore B. Trafalis, Huseyin Ince
• Keywords: IJCNN 2000, Neural Networks, Radial Basis Function, Support Vector Machine, SVM Algorithm