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Forecasting Foreign Exchange Rates Using Recurrent Neural Networks

This article proposes the use of recurrent neural networks in order to forecast foreign exchange rates. Artificial neural networks have proven to be efficient and profitable in fore casting financial time series. In particular, recurrent networks in which activity patterns pass through the network more than once before they generate an output pattern can learn ex tremely complex temporal sequences. Three recurrent architectures are compared in terms of prediction accuracy of futures forecast for Deutsche mark currency. A trading strategy is then devised and optimized. The profitability of the trading strategy taking into account trans action costs is shown for the different architectures. The methods described here which have obtained promising results in real time trading are applicable to other markets.