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.
Published in | American Journal of Mathematical and Computer Modelling (Volume 7, Issue 1) |
DOI | 10.11648/j.ajmcm.20220701.11 |
Page(s) | 1-11 |
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
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Copyright © The Author(s), 2022. Published by Science Publishing Group |
Artificial Neural Network, Training, Validation, Performance Index, Mean Absolute Error, Generalized Regression Neural Network
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APA Style
Joseph Acquah, Alex Emmanuel Nti, Isaac Ampofi, David Akorli. (2022). Artificial Neural Network Model for Predicting Exchange Rate in Ghana: A Case of GHS/USD. American Journal of Mathematical and Computer Modelling, 7(1), 1-11. https://doi.org/10.11648/j.ajmcm.20220701.11
ACS Style
Joseph Acquah; Alex Emmanuel Nti; Isaac Ampofi; David Akorli. Artificial Neural Network Model for Predicting Exchange Rate in Ghana: A Case of GHS/USD. Am. J. Math. Comput. Model. 2022, 7(1), 1-11. doi: 10.11648/j.ajmcm.20220701.11
AMA Style
Joseph Acquah, Alex Emmanuel Nti, Isaac Ampofi, David Akorli. Artificial Neural Network Model for Predicting Exchange Rate in Ghana: A Case of GHS/USD. Am J Math Comput Model. 2022;7(1):1-11. doi: 10.11648/j.ajmcm.20220701.11
@article{10.11648/j.ajmcm.20220701.11, author = {Joseph Acquah and Alex Emmanuel Nti and Isaac Ampofi and David Akorli}, title = {Artificial Neural Network Model for Predicting Exchange Rate in Ghana: A Case of GHS/USD}, journal = {American Journal of Mathematical and Computer Modelling}, volume = {7}, number = {1}, pages = {1-11}, doi = {10.11648/j.ajmcm.20220701.11}, url = {https://doi.org/10.11648/j.ajmcm.20220701.11}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20220701.11}, 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.}, year = {2022} }
TY - JOUR T1 - Artificial Neural Network Model for Predicting Exchange Rate in Ghana: A Case of GHS/USD AU - Joseph Acquah AU - Alex Emmanuel Nti AU - Isaac Ampofi AU - David Akorli Y1 - 2022/04/09 PY - 2022 N1 - https://doi.org/10.11648/j.ajmcm.20220701.11 DO - 10.11648/j.ajmcm.20220701.11 T2 - American Journal of Mathematical and Computer Modelling JF - American Journal of Mathematical and Computer Modelling JO - American Journal of Mathematical and Computer Modelling SP - 1 EP - 11 PB - Science Publishing Group SN - 2578-8280 UR - https://doi.org/10.11648/j.ajmcm.20220701.11 AB - 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. VL - 7 IS - 1 ER -