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

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  • Salim Lahmiri

    (Department of Computer Science, University of Quebec at Montreal, Montreal, QC, Canada, & ESCA School of Management, Casablanca, Morocco)

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.

Suggested Citation

  • Salim Lahmiri, 2013. "Forecasting Direction of the S&P500 Movement Using Wavelet Transform and Support Vector Machines," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 4(1), pages 79-89, January.
    • Handle: RePEc:igg:jsds00:v:4:y:2013:i:1:p:79-89
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    Cited by:

    1. Aggarwal, Divya & Chandrasekaran, Shabana & Annamalai, Balamurugan, 2020. "A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    2. Salim Lahmiri, 2016. "Features selection, data mining and finacial risk classification: a comparative study," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(4), pages 265-275, October.

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