Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi
Abstract
:1. Introduction
- (i)
- Enhancement can be achieved in exchange rate predictability using information fusion from historical microeconomic data and public search queries (Google Trends).
- (ii)
- The long short-term memory offers a better prediction accuracy than the traditional statistical models.
2. Related Works
Ref. | Algorithm | Data Source | Evaluation Metric | Study Origin |
---|---|---|---|---|
[22] | PPP, Random walk, Monetary model, interest parity | GT | MSPE | NS |
[5] | PLSSEM | MV | R2, f2, Q2 and SGT | Ghana |
[28] | Multivariate GARCH DCC and BEKK models using | MV | Correlation | Ghana |
[1] | ARIMA | MV | RMSE, MAE, MPE, MAPE | Nigeria |
[12] | Neural Network | MV | RMSE | NS |
[10] | Morlet wavelet transform | MV | Mean, variance, skewness, kurtosis | Ghana |
[30] | ARIMA | MV | MAPE, RMSE | Ghana |
[13] | SVAR | MV | UK | |
[11] | Taylor rules fundamentals, yield curve factors | MV | MSE | NS |
[4] | FFVAR, Bayesian vector auto-regression | MV | MSE | US |
[31] | ARIMA and Random walk | MV | Correlation | Ghana |
[38] | Neural network | MV | MSE, MAPE | Indian |
[9] | MV | Skewness, p-value, Kurtosis | Ghana | |
[39] | RNN and CNN | MV | RMSE | China |
[40] | SVM, ANN and LSTM | MV | Accuracy | India |
[37] | LSTM | MV | RMSE, accuracy | NS |
[41] | Public sentiments | RMSE, MAE | Korea | |
[24] | Extreme Learning Machines (ELMs) and the Jaya optimisation technique | MV | MAPE, Theil’s U, ARV, and MAE | India |
Proposed model | LSTM | GT + MV | RMSE, MAE, Accuracy | Ghana |
3. Materials and Methods
3.1. Study Framework
3.1.1. Data Download and Integration
3.1.2. Data Pre-Processing and Partitioning
3.1.3. Machine-Learning Model
3.2. Theoretical Background of Benchmark Models
3.2.1. Support Vector Regressor
3.2.2. Back-Propagation Neural Network (BPNN)
3.2.3. Evaluation Metrics
4. Results and Discussions
4.1. Dataset Visualisation
4.2. Model Performance Measure
4.2.1. Automatic Feature Selection
4.2.2. Prediction with Macroeconomic Variables
4.2.3. Prediction with Macroeconomic Variables and Google Trend
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
S/N | MACROECONOMIC VARIABLES | Abbreviation |
---|---|---|
MONTHLY MONETARY SURVEY | ||
1. | Net Foreign Assets | NFA |
2. | BOG | BOG |
3. | DMBs | DMB |
4. | Net Domestic Assets | NDA |
5. | Claims on Gov’t | CoG |
6. | Govt. Deposits | GD |
7. | Claims on private sector | CPS |
8. | Other Items (net) | OI |
9. | Total Assets | TA |
10. | Currency outside banks | COB |
11. | Demand deposits | DD |
12. | Savings & Time deposits | STD |
13. | Foreign currency deposits | FCD |
14. | Total Liabilities | TL |
15. | Reserve Money (RM) | RM |
16. | Narrow Money (M1) | M1 |
17. | Broad Money (M2) | M2+ |
18. | Total Liquidity (M2+) | TLM2+ |
MONTHLY INTEREST RATES | ||
19. | Monetary Policy Rate | MPR |
20. | 91-Day Treasury Bill Interest Rate Equivalent | 91-Tbill |
21. | Inter-Bank Weighted Average | IBWA |
22. | Average Commercial Banks Lending Rate | ACBLR |
23. | Average Savings Deposits Rate | ASDR |
24. | Average Time Deposits Rate (3-Month) | ATD |
COMMODITY PRICES MONTHLY | ||
25. | International Cocoa Price (US$/Tonne) | CP |
26. | International Gold (US$/fine ounce) | GP |
27. | International Brent Crude Oil (US$/Barrel) | BCOP |
INFLATION | ||
28. | Headline Inflation | HF |
29. | Food Inflation | FI |
31. | Non-Food Inflation | NFI |
32. | Bank of Ghana Composite Index of Economic Activity (Nominal Growth) | BGCIEA_NG |
33. | Bank of Ghana Composite Index of Economic Activity (Real Growth) | BGCIEA_RG |
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Hyperparameter | Value |
---|---|
Input time steps | 20 |
Input feature dimension | 10 |
Learning rate | 0.002 |
Adam optimiser | |
Batch size | 128 |
# Epochs | 100 |
# nodes in LSTM input layer | 34 |
# nodes in LSTM output layer | 1 |
Output layer | single value prediction |
USD–GH₵ | EUR–GH₵ | GBP–GH₵ | |
---|---|---|---|
Mean | 2.321627451 | 2.901645 | 3.434115 |
Standard Error | 0.02308252 | 0.023675 | 0.02839 |
Median | 1.5859 | 2.1533 | 2.4558 |
Mode | 0.9122 | 1.9176 | 1.7076 |
Standard Deviation | 1.446484115 | 1.483602 | 1.779061 |
Sample Variance | 2.092316295 | 2.201076 | 3.165059 |
Kurtosis | −1.09615245 | −1.05057 | −1.37613 |
Skewness | 0.671644346 | 0.661981 | 0.541371 |
Range | 4.5275 | 5.0625 | 5.4178 |
Minimum | 0.8124 | 0.922 | 1.5343 |
Maximum | 5.3399 | 5.9845 | 6.9521 |
Sum | 9117.031 | 11,394.76 | 13,485.77 |
Confidence Level (95.0%) | 0.045254859 | 0.046416 | 0.05566 |
Currency | Models | MAE | MSE | RMSE | R2 | RMSLE | MAPE |
---|---|---|---|---|---|---|---|
EUR | LSTM | 0.0327 | 0.0035 | 0.0551 | 0.9983 | 0.0129 | 0.0121 |
BPNN | 0.0433 | 0.0056 | 0.0688 | 0.9974 | 0.0172 | 0.0168 | |
SVR | 0.0508 | 0.0051 | 0.0683 | 0.9973 | 0.0191 | 0.0241 | |
USD | LSTM | 0.0805 | 0.0172 | 0.1208 | 0.9939 | 0.0218 | 0.0218 |
BPNN | 0.0973 | 0.0217 | 0.1406 | 0.9916 | 0.0274 | 0.0287 | |
SVR | 0.095 | 0.0238 | 0.146 | 0.9911 | 0.0275 | 0.0269 | |
GBP | LSTM | 0.0634 | 0.0096 | 0.0923 | 0.9953 | 0.0268 | 0.0257 |
BPNN | 0.0794 | 0.0139 | 0.1135 | 0.993 | 0.0329 | 0.0328 | |
SVR | 0.0928 | 0.0172 | 0.1255 | 0.9914 | 0.0333 | 0.0362 |
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Adekoya, A.F.; Nti, I.K.; Weyori, B.A. Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi. FinTech 2022, 1, 25-43. https://doi.org/10.3390/fintech1010002
Adekoya AF, Nti IK, Weyori BA. Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi. FinTech. 2022; 1(1):25-43. https://doi.org/10.3390/fintech1010002
Chicago/Turabian StyleAdekoya, Adebayo Felix, Isaac Kofi Nti, and Benjamin Asubam Weyori. 2022. "Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi" FinTech 1, no. 1: 25-43. https://doi.org/10.3390/fintech1010002