An Improved Support Vector Regression Modeling for Taiwan Stock Exchange Market Weighted Index Forecasting

Abstract

This study applies a novel neural network technique, support vector regression (SVR), to Taiwan stock exchange market weighted index (TAIEX) forecasting. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVR s optimal parameters using real value genetic algorithms. The experimental results demonstrate that SVR outperforms the ANN and RW models based on the normalized mean square error (NMSE), mean square error (MSE) and mean absolute percentage error (MAPE). Moreover, in order to test the importance and understand the features of SVR model, this study examines the effects of the number of input node