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Exchange Rate Forecasting with Advanced Machine Learning Methods

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

Author - Jonathan Felix Pfahler
Journal - Journal of Risk and Financial Management

Source - https://www.mdpi.com/1911-8074/15/1/2