Exchange Rate Prediction Using a Multi Layer Feed-Forward Artificial Neural Networks
| dc.contributor.author | Adu, Sakyi | |
| dc.date.accessioned | 2013-12-10T09:01:22Z | |
| dc.date.accessioned | 2023-04-19T21:28:56Z | |
| dc.date.available | 2013-12-10T09:01:22Z | |
| dc.date.available | 2023-04-19T21:28:56Z | |
| dc.date.issued | 2013 | |
| dc.description | A Thesis Submitted to the Department of Mathematics, Kwame Nkrumah University of Science and Technology in partial fulfillment of the requirement for the degree of Master of Philosophy Applied Mathematics | en_US |
| dc.description.abstract | This research aimed at forecasting the Ghanaian Cedi – US Dolar rate with Treasury bill rates, money supply, consumer price index and inflation With the aim of exploring the efficiency of Artificial Neural Network (ANN) which is an imitation of the Human Brain, a two (2) layer feedforward network using Levenberg – Marquardt Backpropagation Algorithm was used for the forecast and the results were measured by the Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and the Weighted Absolute Percentage Error (WAPE). After careful and extensive training, validation and testing, the ANN produced MSE, RMSE, WAPE and an R-value of 0.0010, 0.0324, 2.30%, and 0.99634 respectively. An Artificial Neural Network model was obtained which was compared with the traditional multiple regression model with the ANN model producing a prediction accuracy of 97.70% as compared to 76.79% of the Regression model. | en_US |
| dc.description.sponsorship | KNUST | en_US |
| dc.identifier.uri | https://ir.knust.edu.gh/handle/123456789/5377 | |
| dc.language.iso | en | en_US |
| dc.title | Exchange Rate Prediction Using a Multi Layer Feed-Forward Artificial Neural Networks | en_US |
| dc.type | Thesis | en_US |
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