Forecasting Direction of the S&P500 Movement Using Wavelet Transform and Support Vector Machines

Abstract

Using the wavelet analysis for low-frequency time series extraction, we conduct out-of-sample predictions of the S&P500 price index future trend (up and down). The support vector machines (SVMs) with different kernels and parameters are used as the baseline forecasting model. The simulation results reveal that the SVMs with wavelet analysis approach outperform the SVMs with macroeconomic variables or technical indicators as predictive variables. As a result, we conclude that the wavelet transform is appropriate to capture the S&P500 trend dynamics.