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Exchange-Rates Forecasting: A Hybrid Algorithm Based on Genetically Optimized Adaptive Neural Networks

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  • Andreou, Andreas S
  • Georgopoulos, Efstratios F
  • Likothanassis, Spiridon D

Abstract

The use of neural networks trained by a new hybrid algorithm is employed on forecasting the Greek Foreign Exchange-Rate Market. Four major currencies, namely the U.S. Dollar (USD), the Deutsche Mark (DEM), the French Franc (FF) and the British Pound (GBP), versus the Greek Drachma, were used as experimental data. The proposed algorithm combines genetic algorithms and a training method based on the localized Extended Kalman Filter (EKF), in order to evolve the structure and train Multi-Layered Perceptron (MLP) neural networks. The goal of this effort is to predict, as accurately as possible, exchange-rates future behavior. Simulation results show that the method gives highly successful results, while the diversification of the structure between the four currencies has no effect on the performance. Copyright 2002 by Kluwer Academic Publishers

Suggested Citation

  • Andreou, Andreas S & Georgopoulos, Efstratios F & Likothanassis, Spiridon D, 2002. "Exchange-Rates Forecasting: A Hybrid Algorithm Based on Genetically Optimized Adaptive Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 20(3), pages 191-210, December.
    • Handle: RePEc:kap:compec:v:20:y:2002:i:3:p:191-210
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    Cited by:

    1. Medel, Carlos & Camilleri, Gilmour & Hsu, Hsiang-Ling & Kania, Stefan & Touloumtzoglou, Miltiadis, 2015. "Robustness in Foreign Exchange Rate Forecasting Models: Economics-based Modelling After the Financial Crisis," MPRA Paper 65290, University Library of Munich, Germany.
    2. Fat Codruta Maria & Dezsi Eva, 2011. "Exchange-Rates Forecasting: Exponential Smoothing Techniques And Arima Models," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 499-508, July.
    3. Nguyen, Hang T. & Nabney, Ian T., 2010. "Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models," Energy, Elsevier, vol. 35(9), pages 3674-3685.

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