Support Vector Machine and Least Square Support Vector Machine Stock Forecasting Models

Abstract

This paper explores the Support Vector Machine and Least Square Support Vector Machine models in stock forecasting. Three prevailing forecasting techniques - General Autoregressive Conditional Heteroskedasticity (GARCH), Support Vector Regression (SVR) and Least Square Support Vector Machine (LSSVM) are combined with the wavelet kernel to form three novel algorithms Wavelet-based GARCH (WL_GARCH), Wavelet-based SVR (WL_SVR) and Wavelet-based Least Square Support Vector Machine (WL_LSSVM) to solve the non-linear and non-parametric financial time series problem. This paper presents a platform for comparison of the wavelet-based algorithm using Hang Sang Index, Dow Jones and Shanghai Composite Index which has significant influence to each other. It has been discovered that wavelet-based model is not as good as the LS-SVM model. The best result is from LS-SVM without wavelet-based kernel.