2011 : FOREIGN EXCHANGE FORECASTING USING A GENETIC RECURRENT NEURAL NETWORK
Prof. Ir. Arif Djunaidy M.Sc., Ph.D.
The use of recurrent neural networks (RNNs) for foreign exchange forecast gives proČmisČČČČČing results because of their excellent perforČmanČce in minimizing the error of the model, partiČcuČlarly for the data having chaotic behavior. HowČever, in practice, the use of a foreČcasting method that is merely capable of miniČmizing the modelĺs error but lacking of producing an optimal forecasting profit is impractical for most traders. To overcome such a drawback, in this study the modified RNN is integrated with a genetic algorithm (GA) in or-der to provide a forecasting model that is capable of both minimizing the error and maximizing the profit of the model. Modified RNN is placed on the performance function of neural network. Formerly, a default performance function of RNN is only error minimizing. In this research work, we build a new performance function, which is maximizing profit of Foreign ExchangeÂ …