Testing Currency Predictability Using An Evolutionary Neural Network Model

A. Andreou, E. Georgakopoulos, S. Likothanassis and George Zombanakis

MPRA Paper from University Library of Munich, Germany

Abstract: Two alternative learning approaches of a MLP Neural Network architecture are employed to forecast foreign currencies against the Greek Drachma, a Back-Propagation with a hyperbolic tangent activation scheme and an evolutionary trained model. Four major currency data series, namely the U. S. Dollar, the British Pound, the French Franc and the Deutsche Mark, are used in this forecasting experiment. Extended simulations have shown a high predictive ability, which is significantly better when using the actual rates compared to using the logarithmic returns of each series. The genetic algorithm performs best on FF and DM, while the back-propagation on USD and BP.

Keywords: Currency; Forecasting; Artificial; Neural; Networks (search for similar items in EconPapers)
JEL-codes: F31 F47 (search for similar items in EconPapers)
Date: 1998-05-12, Revised 1998-03-24
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Citations: View citations in EconPapers (1)

Published in Proceedings of the International Conference on Forecasting Financial Markets, BNP/Imperial College 1.1(1998): pp. 1-23

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