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Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi

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  • Adebayo Felix Adekoya

    (Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani BS0000, Ghana)

  • Isaac Kofi Nti

    (Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani BS0000, Ghana)

  • Benjamin Asubam Weyori

    (Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani BS0000, Ghana)

Abstract

An accurate prediction of the Exchange Rate (ER) serves as the basis for effective financial management, monetary policies, and long-term strategic decision making worldwide. A stable and competitive ER enables economic diversification. Economists, researchers, and investors have conducted several studies to predict trends and facts that influence the ER’s rise or fall. This paper used the Long Short-Term Memory Networks (LSTM) framework to predict the weekly exchange rate of one Ghanaian Cedis (GH₵) to three different currencies (United States Dollar, British Pound, and Euro), using Google Trends and historical macroeconomic data. We fused past exchange rates, fundamental macroeconomic variables, commodity prices (cocoa, gold, and crude oil) and public search queries (Google Trends) as input parameters. An empirical analysis using publicly available ER data from the Bank of Ghana (BoG) from January 2004 to October 2019 showed satisfactory results. We observed that the proposed LSTM model outperformed the Support Vector Regressor (SVR) and Back-propagation Neural Network (BPNN) models in accuracy and closeness metrics. That is, our LSTM model obtained (MAE = 0.033, MSE = 0.0035, RMSE = 0.0551, R2 = 0.9983, RMSLE = 0.0129 and MAPE = 0.0121) compared with SVR (MAE = 0.05, MAE = 0.005, RMSE = 0.0683, R2 = 0.9973, RMSLE = 0.0191 and MAPE = 0.0241) and BPNN (MAE = 0.04, MAE = 0.0056, RMSE = 0.0688, R2 = 0.9974, RMSLE = 0.0172 and MAPE = 0.0168). Moreover, we observed a strong positive correction (0.98–0.99) between Google Trends on the currency of focus and its exchange rate to the Ghanaian cedis. The study results show the importance of incorporating public search queries from search engines to predict the ER accurately.

Suggested Citation

  • Adebayo Felix Adekoya & Isaac Kofi Nti & Benjamin Asubam Weyori, 2021. "Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi," FinTech, MDPI, vol. 1(1), pages 1-19, December.
  • Handle: RePEc:gam:jfinte:v:1:y:2021:i:1:p:2-43:d:698263
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    References listed on IDEAS

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