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Fundamentals and exchange rate forecastability with machine learning methods

Listed author(s):
  • Christophe Amat

    ()

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

  • Tomasz Michalski

    ()

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

  • Gilles Stoltz

    ()

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

Using methods from machine learning we show that fundamentals from simple exchange rate models (PPP or UIRP) consistently allow to improve exchange rate forecasts for major currencies over the floating period era 1973--2014 at a 1 month forecast and allow to beat the no-change forecast. ``Classic'' fundamentals hence contain useful information and exchange rates are forecastable even for short forecasting horizons. Such conclusions cannot be obtained when using rolling or recursive OLS regressions as in the literature. The methods we use -- sequential ridge regression and the exponentially weighted average strategy both with discount factors -- do not estimate an underlying model but combine the fundamentals to directly output forecasts.

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

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Date of creation: 20 Dec 2016
Handle: RePEc:hal:wpaper:halshs-01003914
Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01003914v5
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