Comparison of the accuracy of forecasts based on neural networks before and after the outbreak of the COVID-19 pandemic on the example of selected exchange rates

Jakub Morkowski

doi:10.59139/ps.2022.04.4

Jakub Morkowski Poznań University of Economics and Business, Applied Mathematics Department, al. Niepodległości 10, 61-875, Poznań ORCID:https://orcid.org/0000-0001-5727-5089 Przegląd Statystyczny. Statistical Review, vol. 69, 2022, 4, pages: 41-60 Published online: 31 May 2023 DOI 10.59139/ps.2022.04.4 Citation: Morkowski, J. (2022). Comparison of the accuracy of forecasts based on neural networks before and after the outbreak of the COVID-19 pandemic on the example of selected exchange rates. Przegląd Statystyczny. Statistical Review, 69(4), 41–60. https://doi.org/10.59139/ps.2022.04.4.

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ARTICLE

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ABSTRACT

This article examines the impact of the COVID-19 pandemic on the accuracy of forecasts for three currency pairs before and after its outbreak based on neural networks (ELM, MLP and LSTM) in terms of three factors: the forecast horizon, hyper parameterisation and network type.

KEYWORDS

neural network, currency market, forecasts, COVID-19 pandemic

JEL

C45, C53, E44

REFERENCES

Abedin, M. Z., Moon, M. H., Hassan, M. K., & Hajek, P. (2021). Deep learning-based exchange rate prediction during the COVID-19 pandemic. Annals of Operations Research. Advance online publication. https://doi.org/10.1007/s10479-021-04420-6.

Abirami, S., & Chitra, P. (2020). Energy-efficient edge based real-time healthcare support system. Advances in Computers, 117(1), 339–368. https://doi.org/10.1016/bs.adcom.2019.09.007.

Akser, M. (2020). Cinema, Life and Other Viruses: The Future of Filmmaking, Film Education and Film Studies in the Age of Covid-19 Pandemic. CINEJ Cinema Journal, 8(2), 1–13. https://doi.org/10.5195/cinej.2020.351.

Ashraf, B. N. (2020). Stock markets’ reaction to COVID-19: Cases or fatalities?. Research in International Business and Finance, 54, 1–7. https://doi.org/10.1016/j.ribaf.2020.101249.

Balbo, N., Kashnitsky, I., Melegaro, A., Meslé, F., Mills, M. C., de Valk, H., & Vono de Vilhena, D. (2020). Demography and the Coronavirus Pandemic (Population and Policy Brief no. 25). https://www.population-europe.eu/research/policy-briefs/demography-and-coronavirus-pandemic.

Boserup, B., McKenney, M., & Elkbuli, A. (2020). Alarming trends in US domestic violence during the COVID-19 pandemic. The American Journal of Emergency Medicine, 38(12), 2753–2755. https://doi.org/10.1016/j.ajem.2020.04.077.

Cullen, W., Gulati, G., & Kelly, B. D. (2020). Mental health in the COVID-19 pandemic. QJM: An International Journal of Medicine, 113(5), 311–312. https://doi.org/10.1093/qjmed/hcaa110.

Daniel, S. J. (2020). Education and the COVID-19 pandemic. Prospects, 49(1–2), 91–96. https://doi.org/10.1007/s11125-020-09464-3.

Das, A. K., Mishra, D., & Das, K. (2021). Currency Exchange Prediction for Financial Stock Market: An Extensive Survey. In P. K. Mallick, A. K. Bhoi, G. Marques & V. H. C. de Albuquerque (Eds.), Cognitive Informatics and Soft Computing (pp. 697–709). Springer. https://doi.org/10.1007/978-981-16-1056-1_54.

Gunay, S. (2021). Comparing COVID-19 with the GFC: A shockwave analysis of currency markets. Research in International Business and Finance, 56, 1–13. https://doi.org/10.1016/j.ribaf.2020.101377.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.

Van Houdt, G., Mosquera, C., & Nápoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review, 53(8), 5929–5955. https://doi.org/10.1007/s10462-020-09838-1.

Jastrzębski, M., Kabziński, J., Wasiak, G., & Zawiślak, R. (2015). Ocena efektywności techniki Extreme Learning Machine (ELM) do modelowania dwuwymiarowych nieliniowości w układach napędowych. Prace Naukowe Instytutu Maszyn, Napędów i Pomiarów Elektrycznych Politechniki Wrocławskiej. Studia i Materiały, 71(35), 83–94.

Kartono, A., Febriyanti, M., Tri Wahyudi, S., & Irmansyah. (2020). Predicting foreign currency exchange rates using the numerical solution of the incompressible Navier-Stokes equations. Physica A: Statistical Mechanics and its Applications, 560. https://doi.org/10.1016/j.physa.2020.125191.

Li, C., Su, Z.-W., Yaqoob, T., & Sajid, Y. (2022). COVID-19 and currency market: a comparative analysis of exchange rate movement in China and USA during pandemic. Economic Research. Ekonomska Istraživanja, 35(1), 2477–2492. https://doi.org/10.1080/1331677X.2021.1959368.

Markova, M. (2019). Foreign exchange rate forecasting by artificial neural networks. Application of Mathematics in Technical and Natural Sciences: 11th International Conference for Promoting the Application of Mathematics in Technical and Natural Sciences, Albena.

Pfefferbaum, B., & North, C. S. (2020). Mental Health and the Covid-19 Pandemic. The New England Journal of Medicine, 383(6), 510–512. https://doi.org/10.1056/NEJMp2008017.

Raifu, I. A., Kumeka, T. T., & Aminu, A. (2021). Reaction of stock market returns to COVID-19 pandemic and lockdown policy: evidence from Nigerian firms stock returns. Future Business Journal, 7(1), 1–16. https://doi.org/10.1186/s43093-021-00080-x.

Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408. https://doi.org/10.1037/H0042519.

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by backpropagating errors. nature, 323(6088), 533–536. https://doi.org/10.1038/323533a0.

Smagulova, K., & James, A. P. (2019). A survey on LSTM memristive neural network architectures and applications. The European Physical Journal Special Topics, 228(10), 2313–2324. https://doi.org/10.1140/epjst/e2019-900046-x.

Sun, S., Wei, Y., & Wang, S. (2018). AdaBoost-LSTM ensemble learning for financial time series forecasting. International Conference on Computational Science, Wuxi.

Usher, K., Durkin, J., & Bhullar, N. (2020). The COVID-19 pandemic and mental health impacts. International journal of mental health nursing, 29(3), 315–318. https://doi.org/10.1111/inm.12726.

Wu, Y., & Gao, J. (2018). AdaBoost-based long short-term memory ensemble learning approach for financial time series forecasting. Current Science, 115(1), 159–165. https://doi.org/10.18520/cs/v115/i1/159-165.

Zeren, F., & Hizarci, A. E. (2020). The impact of COVID-19 coronavirus on stock markets: evidence from selected countries. Muhasebe ve Finans Incelemeleri Dergisi, 3(1), 78–84. https://doi.org/10.32951/mufider.706159.

Zhang, W., & Hamori, S. (2021). Crude oil market and stock markets during the COVID-19 pandemic: Evidence from the US, Japan, and Germany. International Review of Financial Analysis, 74, 1–13. https://doi.org/10.1016/j.irfa.2021.101702.