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Forecasting foreign exchange rates using artificial neural networks: a trader's approach

Author

Listed:
  • Adam Stokes
  • Ahmed S. Abou-Zaid

Abstract

This study investigates the use of two different types of the Artificial Neural Networks (ANNs), Feed-Forward (FF) Neural Network and Nonlinear Autoregressive with Exogenous Input (NARX) neural network, in forecasting the exchange rate of the US dollar against the three major currencies: the Euro, the Pound and the Yen. Although the ANNs technique is not very common in economic discipline, the results are expected to be more accurate in terms of market timing ability and sign prediction than those of the standard econometric techniques such as ARMA. ANNs are, in fact, capable of dealing with high-frequency data as well as the nonlinearities in exchange rate movements. Our results support the notion that ANNs is an effective method in forecasting the exchange rates. The NARX networks output shows a significant market timing ability. Both FF and NARX proved to forecast at a higher accuracy (sign prediction) than random walk and ARMA models.

Suggested Citation

  • Adam Stokes & Ahmed S. Abou-Zaid, 2012. "Forecasting foreign exchange rates using artificial neural networks: a trader's approach," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 5(4), pages 370-394.
    • Handle: RePEc:ids:ijmefi:v:5:y:2012:i:4:p:370-394
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    Citations

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    Cited by:

    1. Catalina Lucia COCIANU & Mihai-Serban AVRAMESCU, 2018. "New Approaches of NARX-Based Forecasting Model. A Case Study on CHF-RON Exchange Rate," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 22(2), pages 5-13.
    2. Paulo Canas Rodrigues & Olushina Olawale Awe & Jonatha Sousa Pimentel & Rahim Mahmoudvand, 2020. "Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks," Stats, MDPI, vol. 3(2), pages 1-21, June.

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