The technical analysis followed in this paper has yielded results which are to a considerable extent anticipated and lead to plausible conclusions as regards the predictability of the behaviour of the drachma fluctuations versus the four currencies involved, namely the US dollar, the British Pound, the Deutsche Mark and the French Franc. Both algorithms perform successfully as regards the prediction of the exchange rate levels of all currencies, which is not the case for the logarithmic version of the data despite its satisfactory MREs, given that the correlation coefficients obtained are very low. At the risk of being diverted to technical discussions we can not help recalling that traditional econometric techniques often show preference for logarithmic functions when it comes to equation specifications, frequently without any theoretical justification. This is a complication which the use of the neural networks helps to avoid since it seems that it performs much better in the case of the levels of the exchange rates.
The prediction performance in the case of the MLP with Back-Propagation is very successful for all four drachma rates. The same applies for the case of the MLP using a Genetic Algorithm, with the exception of the Drachma / US Dollar rate.. This seems reasonable to support because the US Dollar exhibits more pronounced fluctuations in the international markets since it is subjected to exchange-rate discipline to a much lesser extent, unlike the rest of the currencies, which are ECU participants, two of them being ERM members. On the other hand, the drachma exchange rate policy is to a large extent and has, for a considerably long time period, been determined on the basis of the
drachma fluctuations versus all ECU participant currencies, which include all currencies involved in the analysis except the US dollar. This leads to expecting the predictability of the rate of the drachma versus the US dollar to be reduced compared to that of the drachma rates vis-à-vis the rest three currencies due to the absence of any discipline framework in this case. Thus, the results obtained seem very reasonable, particularly in the case of the Genetic Algorithm: Very good prediction results for the three ECU participants, and even better ones for the DM and the FF which are, in addition, ERM members, a fact which introduces additional discipline in the relevant exchange rates, the fluctuations of which are bound by the ERM band. The Pound, on the contrary has participated in the ERM only for a short time period until its membership suspension on the 16th of September 1992, a period too short to provide for disciplined behaviour in the sample period chosen.
The poor performance of the drachma / dollar rates, however, was not the case with the Back-Propagation algorithm, which, has provided for a remarkably low MRE on just one occasion. This exceptional case may be the result of a wide variety of factors most important of which is the varying number of training patterns in this case as opposed to the constant number of training patterns in the case of the Genetic Algorithm, something which is a decisive determinant of the prediction performance of the network.
6. Conclusions
This paper has employed two advanced artificial learning approaches to an MLP Neural Network architecture, a Back-Propagation and a Genetic Algorithm, based on a hyperbolic tangent activation scheme, in order to forecast the drachma exchange rates versus four major currencies, namely the US Dollar, the Deutsche Mark, the French Frank and the British Pound.
In general, despite the difficulties encountered due to the nature of the Greek exchange rate policy and the sample period in use which includes major noise elements, like a drachma devaluation, the results have been proved very satisfactory, since they have provided for successful exchange rate predictions in all four cases with both algorithms.
Specifically, two pre-processed data sets have been used: The first includes the raw data, that is, the fixing rates of each currency rescaled, and the second involves with the logarithmic returns. Both the methods have exhibited very high quantitative and qualitatively accuracy of forecasting as regards the former, where results have provided for very small MRE's on one hand (accurate forecasts) and high Correlation Coefficients on the other (very close follow-up of the actual series). The latter case resulted to poor performance for both methods, regarding mostly the CC, where while accuracy was good in some cases, the predicted series were either shifted upwardly or downwardly compared to the actual ones, or failed to follow-up for the entire forecasting period.
The conclusion, therefore, is that the promising results obtained for the raw data have been as expected and the explanation lies with the degree of discipline of behaviour that
characterises the fluctuations of each of the currencies involved with respect to the drachma. In three out of four cases, with the exception of the dollar case, the drachma factor introduces a considerable degree of consistency in its behaviour thanks to its exchange rate versus the ECU which is a policy guideline. The increased predictability in the case of the DM and the FF comes as a result of their ERM membership, which, unlike the case of the Pound, provides for fluctuation bands and consequently for increased predictability.
The pending issue of the variation in the number of the training patterns indicates the need for further research on the topic, concentrating on modifying the algorithms used so as to indicate the number of training and testing patterns that yield the best prediction results. Our efforts will concentrate on developing a genetic algorithm that will supervise the selection of the most appropriate length for both the training and the testing sets, so as to improve forecasting regarding this highly sensitive performance factor.
7. Acknowledgement
The authors would like to thank John Gourdoulis, currently a student at the Department of Computer Engineering & Informatics, University of Patras, for his help in performing the simulations.
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