Exchange rate forecasting with advanced machine learning methods - Jonathan Felix Pfahler (Journal of Risk and Financial Management, 2022)
Historically, exchange rate forecasting models have exhibited poor out-of-sample performances and were inferior to the random walk model. Monthly panel data from 1973 to 2014 for ten currency pairs of OECD countries are used to make out-of sample forecasts with artificial neural networks and XGBoost models. Most approaches show significant and substantial predictive power in directional forecasts. Moreover, the evidence suggests that information regarding prediction timing is a key component in the forecasting performance.
Keywords: E00; E04; E05; E06; F00; F04; 330; machine learning; exchange rate forecasting; fundamentals