The accuracy of forecasting neural networks and the impact of using fuzzy sets for the currency market


currency exchange rate
neural networks
fuzzy sets


How to Cite

Morkowski, J. (2024) “The accuracy of forecasting neural networks and the impact of using fuzzy sets for the currency market”, Scientific Journal of Bielsko-Biala School of Finance and Law. Bielsko-Biała, PL, 28(1), pp. 24–31. doi: 10.19192/wsfip.sj1.2024.3.


The aim of the article is to check the accuracy of forecasts of neural networks on the currency market and the impact of fuzzy sets on their accuracy. The study presented in this article uses an original approach that considers the use of neural networks and fuzzy sets in the mechanism of investment decision making. The empirical study is based on projections of the three currency pairs of the Swiss franc, British pound, and the dollar against the euro. These currencies are forecasted using three different neural networks - ELM, MLP and LSTM, for ten different forecast horizons (from 1 to 10 days). In forecasting, neural networks use historical data, both for price levels and rates of return. The research carried out confirmed that the presented method is in many cases more accurate than the methods compared to it in this study


Alakhras, M. N. (2005). Neural network-based fuzzy inference system for exchange rate prediction. Journal of Computer Science (Special Issu

Babu, A. S., & Reddy, S. K. (2015). Exchange rate forecasting using ARIMA. Neural Network and Fuzzy Neuron, Journal of Stock & Forex Trading, 4(3), 01-05.

De Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. (2015). Using the mean absolute percentage error for regression models. In Proceedings (p. 113). Presses universitaires de Louvain.

Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190.

Hao, Y., & Gao, Q. (2020). Predicting the trend of stock market index using the hybrid neural network based on multiple time scale feature learning. Applied Sciences, 10(11), 3961

Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226-251.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.

Kodogiannis, V., & Lolis, A. (2002). Forecasting financial time series using neural network and fuzzy system-based techniques. Neural computing & applications, 11(2), 90-102.

Lazzerini, B., Jain, L. C., & Dumitrescu, D. (2000). Fuzzy Sets & their Application to Clustering & Training. CRC Press.

Markova, M. (2019, October). Foreign exchange rate forecasting by artificial neural networks. In AIP Conference Proceedings (Vol. 2164, No. 1, p. 060010). AIP Publishing LLC.

Moghar, A., & Hamiche, M. (2020). Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, 1168-1173.

Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 53(4), 3007- 3057.

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by backpropagating errors. nature, 323(6088), 533-536.

Schrimpf, A., & Sushko, V. (2019). Sizing up global foreign exchange markets. BIS Quarterly Review, December.

Strader, T. J., Rozycki, J. J., Root, T. H., & Huang, Y. H. J. (2020). Machine learning stock market prediction studies: Review and research directions. Journal of International Technology and Information Management, 28(4), 63-83.

Zadeh, L. A. (1965). Information and control. Fuzzy sets, 8(3), 338-353.

Zhang, Y. Q., & Wan, X. (2007). Statistical fuzzy interval neural networks for currency exchange rate time series prediction. Applied Soft Computing, 7(4), 1149-1156.

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Copyright (c) 2024 Jakub Morkowski


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