Artificial Neural Network Model for Predicting Exchange Rate in Ghana: A Case of GHS/USD

Abstract

In today's global economy, accuracy in predicting the foreign exchange rate or at least predicting the trend correctly is of crucial importance for any future investment and this is mostly achieved by the use of computational intelligence-based techniques as explored in this paper. The aim of this study was to develop an Artificial Neural Network (ANN) Model for predicting the GHS/USD with inflation, nominal growth, monetary policy, interest rate, trade balance, gross international reserve, foreign currency deposit, broad money as the major indicators for Exchange rate. Three different ANN models which are Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN) and Generalized Regression Neural Network (GRNN) were developed and the results were measured by the Performance Index (PI), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). After extensive training, validation and testing of the data, the BPNN model was seen to be the adequate model for predicting the exchange rate with MAE of 0.28973, RMSE of 0.32274, PI of 0.10416 and MAPE of 7% and a prediction accuracy (R²) of 0.8460 as against the RBFNN which have MAE of 0.37265, RMSE of 0.48472, PI of 0.2349, MAPE of 8.52% and an R² of 0.3744, and the GRNN with MAE of 1.06482, RMSE of 1.15444, PI of 1.33274, MAPE of 24.07% and an R² of 0.2987.