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

Joseph Acquah; Alex Emmanuel Nti; Isaac Ampofi; David Akorli

doi:doi:10.11648/j.ajmcm.20220701.11

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

Joseph Acquah, Alex Emmanuel Nti, Isaac Ampofi, David Akorli

Published in American Journal of Mathematical and Computer Modelling (Volume 7, Issue 1)

Received: 9 October 2021 Accepted: 15 February 2022 Published: 9 April 2022

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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.

Published in	American Journal of Mathematical and Computer Modelling (Volume 7, Issue 1)
DOI	10.11648/j.ajmcm.20220701.11
Page(s)	1-11
Creative Commons	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.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Artificial Neural Network, Training, Validation, Performance Index, Mean Absolute Error, Generalized Regression Neural Network

References

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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

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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

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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

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@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}
}

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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  -

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Author Information

Joseph Acquah

Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana
Alex Emmanuel Nti

Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana
Isaac Ampofi

Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana
David Akorli

Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana

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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

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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

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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

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@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}
}

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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  -

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