Abstract
In recent years, there has been an increasing number of papers in the literature that use neural networks for financial time-series prediction. This paper focuses on the Greek Foreign Exchange Rate Currency Market. Using four major currencies, namely the U.S. Dollar (USD), the Deutsche Mark (DM), the French Franc (FF) and the British Pound (BP), against the Greek Drachma, on a daily basis for a period of 11 years, we try to forecast the above time-series, using two different approaches. The first one involves RBF training using a Kalman filter, while the second one uses genetic algorithms in order to optimize the RBF network parameters. The goal of this effort, is, first, to predict, as accurately as possible, currencies future behaviour, in a daily predicting horizon and second, to have a comparison between the two methods.
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Adamopoulos, A., Andreou, A., Georgopoulos, E., Ioannou, N., Likothanassis, S. (1998). Currency Forecasting Using Recurrent RBF Networks Optimized by Genetic Algorithms. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_38
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DOI: https://doi.org/10.1007/978-1-4615-5625-1_38
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