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

Abstract : 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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Preprints, Working Papers, ...
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https://halshs.archives-ouvertes.fr/halshs-01003914
Contributor : Gilles Stoltz <>
Submitted on : Saturday, July 12, 2014 - 2:22:21 AM
Last modification on : Friday, November 6, 2020 - 3:10:07 PM
Long-term archiving on: : Sunday, October 12, 2014 - 10:50:12 AM

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ExchangeRates--HAL-July-11.pdf
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  • HAL Id : halshs-01003914, version 2

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Christophe Amat, Tomasz Michalski, Gilles Stoltz. Forecasting exchange rates better than the random walk thanks to machine learning techniques. 2014. ⟨halshs-01003914v2⟩

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