Forecasting the Fluctuation of Currency Exchange Rate Using Random Forest
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32797
Forecasting the Fluctuation of Currency Exchange Rate Using Random Forest

Authors: L. Basha, E. Gjika

Abstract:

The exchange rate is one of the most important economic variables, especially for a small, open economy such as Albania. Its effect is noticeable on one country's competitiveness, trade and current account, inflation, wages, domestic economic activity and bank stability. This study investigates the fluctuation of Albania’s exchange rates using monthly average foreign currency, Euro (Eur) to Albanian Lek (ALL) exchange rate with a time span from January 2008 to June 2021 and the macroeconomic factors that have a significant effect on the exchange rate. Initially, the Random Forest Regression algorithm is constructed to understand the impact of economic variables in the behavior of monthly average foreign currencies exchange rates. Then the forecast of macro-economic indicators for 12 months was performed using time series models. The predicted values received are placed in the random forest model in order to obtain the average monthly forecast of Euro to Albanian Lek (ALL) exchange rate for the period July 2021 to June 2022.

Keywords: Exchange rate, Random Forest, time series, Machine Learning, forecasting.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 552

References:


[1] Ö. Karahan, “Influence of Exchange Rate on the Economic Growth in the Turkish Economy”, Financial Assets and Investing, vol. 11, no. 1, 2020, https://doi.org/10.5817/FAI2020-1-2
[2] M. M. Habib, E. Mileva, and L. Stracca, “The Real Exchange Rate and Economic Growth: Revisiting the case using External Instruments”, Journal of International Money and Finance, 73, pp. 386–398, 2017, http://dx.doi.org/10.1016/j.jimonfin.2017.02.014.
[3] R. S. M. Ribeiro, J. S. L. McCombie, and G. T. Lima, “Does Real Exchange Rate Undervaluation Really Promote Economic Growth?” Structural Change and Economic Dynamics, 50, pp. 408-417, 2019.
[4] G. Zhang, Q. Zhang, and M. T. Majeed, "Exchange Rate Determination and Forecasting: Can the Microstructure Approach Rescue Us from the Exchange Rate Disparity?", International Scholarly Research Notices, vol. 2013, Article ID 724259, 12 pages, 2013. https://doi.org/10.1155/2013/724259.
[5] S. A. Gyamerah, and E. Moyo, "Long-Term Exchange Rate Probability Density Forecasting Using Gaussian Kernel and Quantile Random Forest", Complexity, vol. 2020, Article ID 1972962, 11 pages, 2020. https://doi.org/10.1155/2020/1972962.
[6] M. B. Oskooee, A. M. Kutan. and D. Xi, (2015) “Does exchange rate volatility hurt domestic consumption? Evidence from emerging economics.”, Science Direct 144, pp. 53-65, 2015, https://doi.org/10.1016/j.inteco.2015.05.002.
[7] T. Ito, S. Koibuchi, K. Sato, and J. Shimizu, “Exchange rate exposure and risk management: the case of Japanese exporting firms,” Journal of the Japanese and International Economies, vol. 41, pp. 17–29, 2016, https://doi.org/10.1016/j.jjie.2016.05.001.
[8] C. Kwofie, I. Akoto, and K. Opoku-Ameyaw, “Modelling the Dependency between Inflation and Exchange Rate Using Copula”, Journal of Probability and Statistics, vol. 2020, Article ID 2345746, 7 pages, 2020. https://doi.org/10.1155/2020/2345746.
[9] J. Chen, C. Zhao, K. Liu, J. Liang, H. Wu, and S. Xu, “Exchange Rate Forecasting Based on Deep Learning and NSGA-II Models”, Computational Intelligence and Neuroscience, vol. 2021, Article ID 2993870, 13 pages, 2021. https://doi.org/10.1155/2021/2993870.
[10] M. Khashei, and B. Mahdavi Sharif, “A Kalman filter-based hybridization model of statistical and intelligent approaches for exchange rate forecasting”. Journal of Modelling in Management, https://doi.org/10.1108/JM2-12-2019-0277
[11] C. Amat, M. Tomasz, and S. Gilles, “Fundamentals and exchange rate forecast ability with machine learning methods.”, Journal of International Money Finance 88, pp. 1–24, 2018, https://doi.org/10.1016/j.jimonfin.2018.06.003
[12] Y. Zhang, and S. Hamori, “The Predictability of the Exchange Rate When Combining Machine Learning and Fundamental Models.” Journal Risk Financial Manag, vol.13, no.48, 2020, https://doi.org/10.3390/jrfm13030048
[13] F. Morina, E. Hysa, U. Ergün, M. Panait, and M. C. Voica, “The Effect of Exchange Rate Volatility on Economic Growth: Case of the CEE Countries.” J. Risk Financial Manag 13, 177, 2020, https://doi.org/10.3390/jrfm13080177
[14] E. Gjika, Ll. Puka, and O. Zacaj, “Forecasting consumer price index (CPI) using time series models and multi regression models (Albania case study)”, 10th International Scientific Conference “Business and Management 2018”, May 3–4, 2018, Vilnius, Lithuania, ISBN 978-609-476-119-5, https://doi.org/10.3846/bm.2018.51
[15] E. Gjika, L. Basha, Ll. Puka, and I. Shahini, “ATMs and POS Diffusion: An Econometric Model, Albania case study”, 3rd European Conference on Design, Modelling and Optimization- ECDMO, Amsterdam, Netherland, February 16-18, 2019. Proceedings: ISBN:978-1-4503-6168-2 https://doi.org/10.1145/3328886.3328899
[16] E. Gjika, L. Basha, Xh. Allka, and A. Ferrja, “Predicting the Albanian Economic Development using Multivariate Markov Chain Model”, 11th International Scientific Conference “Business and Management 2020” May 7–8, 2020, Vilnius, Lithuania. Proceedings: ISSN 2029-4441. http://www.bm.vgtu.lt/index.php/verslas/2020
[17] C. E Holt, “Forecasting seasonals and trends by exponentially weighted averages”, O.N.R. Memorandum No. 52, Carnegie Institute of Technology, Pittsburgh USA, 1957.
[18] P. R. Winters, “Forecasting sales by exponentially weighted moving averages”, Management Science, vol. 6, no.3, pp. 324–342, 1960, https://doi.org/10.1287/mnsc.6.3.324
[19] R. G. Brown, Statistical forecasting for inventory control. New York: McGraw-Hill, 1959.
[20] P. J. Brockwell, and R. A. Davis, Introduction to time series and forecasting (3rd ed). New York, USA: Springer. ISBN978-3-319-29852-8, 2016, https://doi.org/10.1007/978-3-319-29854-2
[21] L. Breiman, “Random Forests”, Machine Learning, 45, pp. 5-32, 2001, doi: 10.1023/A:1010933404324
[22] Institute of Statistics INSTAT, Albania, http://www.instat.gov.al/
[23] Bank of Albania, https://www.bankofalbania.org/