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

Christophe Amat (1) , Tomasz Michalski (1) , Gilles Stoltz (1)

1 GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC

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.

Mots clés: exchange rates; forecasting; machine learning; purchasing power parity; uncovered interest rate parity; monetary exchange rate models

Domaines: Economies et finances

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