Forecasting Exchange Rates Using Artificial Neural Networks

Emmanuel Erem^{1, 2,}

¹Department of Economics and Finance, Maynooth University, Ireland

²Faculty of Business and Management Sciences, Mountains of the Moon University, Uganda

Cite this paper:
Emmanuel Erem. Forecasting Exchange Rates Using Artificial Neural Networks. Journal of Finance and Economics. 2023; 11(2):68-77. doi: 10.12691/jfe-11-2-1

Abstract

This article uses the single hidden layer perceptron neural network structure to forecast daily, weekly and monthly exchange rate data on the Swiss Franc (CHF), British Pound Sterling (GBP), and United States Dollar (USD), all per Euro (EUR). The results show good accuracy of the model as evidenced by the low mean absolute error and root mean square error, especially for the daily frequency data. Furthermore, the neural network performs best in out-of-sample predictions for the CHF/EUR currency pair for daily and weekly predictions, and best for the GBP/EUR pair when it comes to monthly frequency data. The USD/EUR pair proves more difficult to model, performing worst, especially in the validation period. The non-linear nature of the neural network goes a long way in learning and capturing complex movements in the exchange rates as shown in the in-sample and out-of-sample graphs; a clear advantage when compared to the traditional linear prediction models. The contribution of this research is that it demonstrates the applicability of machine learning techniques to financial and economic data, clearly demonstrating the data frequencies that perform best when subjected to these algorithms. These findings are very relevant to forex traders, including commercial banks, central banks, and other monetary policy authorities. It can be argued that when it comes to risk mitigation, especially with the complexity and patterns in exchange rate movements, the neural network-based models may do a much better job compared to the traditional linear models.

Keywords:
predicting/forecasting exchange rates artificial neural networks training period validation period in-sample out-of-sample

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