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

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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[3] Highfill J. and Wojcikewych R. (2011), “The US-China Exchange Rate Debate: Using Currency Offer Curves”, International Advances in Economic Research, Vol. 17, No. 4, pp. 386 – 396.
[4] Kamasa K. (2013), Do Financial Sector Reforms Promote Private Sector Investment? The Case of Ghana (Doctoral dissertation).
[5] Thorbecke W. (2008), “The effect of exchange rate volatility on fragmentation in East Asia: Evidence from the electronics industry”, Journal of the Japanese and International Economies, Vol. 22, No. 4, pp. 535 – 544.
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[7] Stavrakeva V. and Tang J. (2015), “Exchange rates and monetary policy”, Working Papers, No. 15 – 16, 48 pp.
[8] Amoako-Agyeman F. and Mintah E. (2014), “The Benefits and Challenges of Ghana's Redenomination Exercise to Market Women-A Case Study of Adum, Kejetia and Central Markets in Kumasi Metropolis”, Journal of Accounting, Vol. 2, No. 1, 27 pp.
[9] Menkhoff, L., 2013. Foreign exchange intervention in emerging markets: A survey of empirical studies. The World Economy, 36 (9), pp. 1187-1208.
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[12] Kwakye J. K. (2012), Determination of real exchange rate misalignment for Ghana, Institute of Economic Affairs, Vol. 10, No. 7, 41 pp.
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[14] Immurana M., Iddrisu A. A. and Kyei-Brobbey I. (2013), “The Determinants of the Real Exchange Rate in Ghana: A Focus on Inflation Using a Bound Test Approach”, Journal of African Development and Resources Research Institute, Vol. 3, pp. 20–37.
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[17] Mbaga, Y. V. and Olubusoye, O. E., 2014. Foreign exchange prediction: a comparative analysis of foreign exchange neural network (FOREXNN) and ARIMA models, 14 pp.
[18] Econ, I. J. and Hadrat, Y. M. (2015), “Inflation Forecasting in Ghana, Artificial Neural Network Model Approach”, International Journal of Economics, Vol. 4, No. 8, pp. 8-13.
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Cite This Article
  • 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
  • Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana

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

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

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

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