The Usefulness of Artificial Neural Networks in Forecasting Exchange Rates

This article contributes to the neural network literature by demonstrating how potent and useful they can be as a tool in the process of economic and financial decision makings. We probe into the usefulness of Nonlinear Autoregressive Networks (NAR) in comparison to the ARIMA models that are commonly used as a benchmark for forecasting exchange rates. To demonstrate it we chose the USD/EUR exchange rate, as a considerably volatile and a highly transacted asset in the international financial market, yet very disputed in academic works due to its often large divergences from the fundamental levels suggested by economic theories. Although through a modest application, our findings show that neural network models can add value and possibly outperform traditional models used to forecast exchange rates. The results were affirmative that the nonlinear autoregressive net consistently beat the ARIMA (and the random walk) static forecasts of the USD/EUR exchange rate.