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 forecasting 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 extremely 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 transaction 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. For years, opposing views have existed between the trading and academic communities about the statistical properties of foreign exchange rates. Traders considered exchange rates to have persistent trends that permitted mechanical trading systems (systematic methods for repeatedly buying and selling on the basis of past prices and technical indicators) to consistently generate profits with relatively low risk. Researchers, on the other hand, presented evidence supporting the random