Retno Aulia Vinarti, Arif Djunaidy : FOREIGN EXCHANGE FORECASTING USING A GENETIC RECURRENT NEURAL NETWORK
Retno Aulia Vinarti S.Kom., M.Kom.
Prof. Ir. Arif Djunaidy M.Sc., Ph.D.
forecasting of foreign currency exchange recurrent neural networks genetic algorithm minimum error maximum profit
The use of recurrent neural networks (RNNs) for foreign exchange forecast gives pro¬mis¬¬¬¬¬ing results because of their excellent perfor¬man¬ce in minimizing the error of the model, parti¬cu¬larly for the data having chaotic behavior. How¬ever, in practice, the use of a fore¬casting method that is merely capable of mini¬mizing the model’s 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 Exchange Trading. The re¬sult¬ing forecasting mo¬del was tested using a daily time series obtained from http://www.tradingblox.com for a period rang¬¬ing from January 4, 1999 until October 20, 2010. Experi¬men¬tal results—using three types of foreign exc¬ha¬nge that are fre¬qu¬ent¬ly used in practice i.e., USD/JPY, EUR/USD, and GBP/USD—show that the forecasting model that both minimizing the error and maxi¬mizing the profit of the fore¬casting model can be achieved using a modified RNN employing a pair of perfor¬mance functions that are optimized using GA.