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Forecasting exchange rates better than the random walk thanks to machine learning techniques

  • Christophe Amat

    ()

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959)

  • Tomasz Michalski

    ()

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959)

  • Gilles Stoltz

    ()

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959)

Using methods from machine learning - adaptive sequential ridge regression with discount factors - that prevent overfitting in-sample for better and more stable forecasting performance out-of-sample we show that fundamentals from the PPP, UIRP and monetary models consistently improve the accuracy of exchange rate forecasts for major currencies over the floating period era 1973-2013 and are able to beat the random walk prediction giving up to 5% improvements in terms of the RMSE at a 1 month forecast. "Classic" fundamentals hence contain useful information about exchange rates even for short forecasting horizons.

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Paper provided by HAL in its series Working Papers with number halshs-01003914.

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Date of creation: 10 Jun 2014
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Handle: RePEc:hal:wpaper:halshs-01003914
Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-01003914
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