RELIABILITY AND ACCURACY OF NEURAL NETWORKS FOR EXCHANGE RATE

Abstract

Most exchange rates are volatile and mainly rely on the principle of supply and demand. Millions of people around the world are influenced, one way or another, by the variation in exchange rates. In this research we demonstrate that the Artificial Intelligence, specifically Artificial Neural Networks (ANN), can improve the accuracy of forecasting exchange rates compared to statistical techniques such as regression. When we compared the results from regression and artificial neural network, it was clear that the ANN outperformed regression in forecasting exchange rates. Moreover, it became clear that using ANNs instead of regression for forecasting exchange rates is rewarding and necessary because the average error given by an ANN is smaller than the average error given by regression. Accuracy in forecasting became a major issue and not a minor detail. It was the combination between Artificial Intelligence and Macro Economics that made these two models come into reality, making it possible to use computer sciences and engineering fields in the service of an economical problem. The results of our research showed that using ANN with the right parameters and variables rather than using a regression model will yield a result with a lower error margin.