Title: Forecasting foreign exchange rates using artificial neural networks: a trader's approach
Authors: Adam Stokes; Ahmed S. Abou-Zaid
HAVI Global Solutions, 3075 Highland Parkway, Downers Groove, IL 60515, USA
Department of Economics, Eastern Illinois University, 600 Lincoln Avenue, Charleston, IL 61920, USA
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.
Keywords: foreign exchange rates; artificial neural networks; ANNs; exchange rate forecasting; US dollar; euro; pound sterling; yen; market timing ability; sign prediction; forecasting accuracy.
Int. J. of Monetary Economics and Finance, 2012 Vol.5, No.4, pp.370 - 394
Submission date: 18 Aug 2012
Date of acceptance: 12 Oct 2012
Available online: 06 Mar 2013