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

Salim Lahmiri
Additional contact information
Salim Lahmiri: Department of Computer Science, University of Quebec at Montreal, Montreal, QC, Canada, & ESCA School of Management, Casablanca, Morocco

International Journal of Strategic Decision Sciences (IJSDS), 2013, vol. 4, issue 1, 79-89

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.

Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 4018/jsds.2013010105 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jsds00:v:4:y:2013:i:1:p:79-89

Access Statistics for this article

International Journal of Strategic Decision Sciences (IJSDS) is currently edited by Saeed Tabar

More articles in International Journal of Strategic Decision Sciences (IJSDS) from IGI Global
Bibliographic data for series maintained by Journal Editor ().