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

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.