Combining Wavelet Decomposition with Machine Learning to Forecast Gold Returns

34 Pages Posted: 25 Oct 2017 Last revised: 21 Dec 2017

See all articles by Marian Risse

Marian Risse

University of the German Federal Armed Forces - Helmut Schmidt Universität

Date Written: December 18, 2017

Abstract

I combine the discrete wavelet transform with support vector regression to forecast gold-price dynamics. I investigate the advantages of this approach using a relatively small set of economic and financial predictors. In order to measure model performance, I differentiate between statistical and economic forecast evaluation, where the economic valued-added of forecasts is simulated using a trading strategy. I show that disentangling the predictors with respect to their time and frequency domain leads to improved forecast performance. Results are robust to alternative forecasting approaches. Findings on the relative importances of such wavelet decompositions suggest that the influence of short-term and long-term trends is not stable over the full evaluation period.

Keywords: Forecasting, Discrete Wavelet Transform, Support Vector Regression, Trading Rule

JEL Classification: C22, C53, C55, C58

Suggested Citation

Risse, Marian, Combining Wavelet Decomposition with Machine Learning to Forecast Gold Returns (December 18, 2017). Available at SSRN: https://ssrn.com/abstract=3059293 or http://dx.doi.org/10.2139/ssrn.3059293

Marian Risse (Contact Author)

University of the German Federal Armed Forces - Helmut Schmidt Universität ( email )

Holstenhofweg 85
Hamburg, 22008
Germany

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