Abstract
The foreign exchange market is of the utmost importance for many sectors of the economy, therefore attempts to forecast changes in currency price levels are the research area of many practitioners and theorists. The article aims at examining the impact of settings of various neural network parameters on the results of currency forecasts. The three currency pairs the US dollar, British pound, and Swiss franc to EUR were selected for the analysis. The forecast results for different network settings are examined with three different indicators: forecast error, the ratio of correctly forecasted changes in the course direction and the potential profit generated. The neural network used for the study is Extreme Learning Machine and the forecast horizons taken into account are in the range of one to ten days. The better-quality forecasts based on price levels than on rates of return was shown and good quality forecasts for two out of three currency pairs was obtained in the study. The article also presents the relationship between the results generated by the neural network and the settings of these networks - in particular, the impact of the number of delays on forecast errors and the number of hidden nodes on all three assessment parameters.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdelsalam, M., Hatem, A., Abdulwahed, W.F.: Evaluation of differential evolution and particle swarm optimization algorithms at training of neural network for prediction. IJCI Int. J. Comput. Inf. 3(1), 2–14 (2014)
Abdual-Salam, M.E., Abdul-Kader, H.M., Abdel-Wahed, W.F.: Comparative study between differential evolution and particle swarm optimization algorithms in training of feed-forward neural network for stock price prediction. In: 2010 The 7th International Conference on Informatics and Systems (INFOS), pp. 1–8. IEEE (2010)
Agrawal, M., Khan, A.U., Shukla, P.K.: Stock price prediction using technical indicators: a predictive model using optimal deep learning. Learning 6(2), 7 (2019)
Anastasakis, L., Mort, N.: Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach. Expert Syst. Appl. 36(10), 12001–12011 (2009)
Anjum, H., Malik, F.: Forecasting risk in the US Dollar exchange rate under volatility shifts. North Am. J. Econ. Financ. 54, 101257 (2020)
Das, S.R., Mishra, D., Rout, M.: A hybridized ELM using self-adaptive multi-population-based Jaya algorithm for currency exchange prediction: an empirical assessment. Neural Comput. Appl. 31(11), 7071–7094 (2018). https://doi.org/10.1007/s00521-018-3552-8
Dash, R., Dash, P.K., Bisoi, R.: A self adaptive differential harmony search based optimized extreme learning machine for financial time series prediction. Swarm Evol. Comput. 19, 25–42 (2014)
De Myttenaere, A., Golden, B., Le Grand, B., Rossi, F.: Using the mean absolute percentage error for regression models. In: Proceedings, p. 113. Presses universitaires de Louvain (2015)
Dritsaki, C.: Modeling the volatility of exchange rate currency using GARCH model. Economia Internazionale/Int. Econ. 72(2), 209–230 (2019)
Epaphra, M.: Modeling exchange rate volatility: application of the GARCH and EGARCH models. J. Math. Financ. 7(1), 121–143 (2016)
Ferreira, T.A., Vasconcelos, G.C., Adeodato, P.J.: A new intelligent system methodology for time series forecasting with artificial neural networks. Neural Process. Lett. 28(2), 113–129 (2008)
Kartono, A., Febriyanti, M., Wahyudi, S.T.: Predicting foreign currency exchange rates using the numerical solution of the incompressible Navier-Stokes equations. Physica A Stat. Mech. Appl. 560, 125191 (2020)
Markova, M.: Foreign exchange rate forecasting by artificial neural networks. In: AIP Conference Proceedings, vol. 2164, no. 1, p. 060010. AIP Publishing LLC (2019)
Rout, A.K., Dash, P.K., Dash, R., Bisoi, R.: Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach. J. King Saud Univ.-Comput. Inf. Sci. 29(4), 536–552 (2017)
Rout, M., Majhi, B., Majhi, R., Panda, G.: Forecasting of currency exchange rates using an adaptive ARMA model with differential evolution based training. J. King Saud Univ.-Comput. Inf. Sci. 26(1), 7–18 (2014)
Sekmen, F., Ravanoğlu, G.A.: The modelling of exchange rate volatility using Arch-Garch models: the case of Turkey. MANAS Sosyal Araştırmalar Dergisi 9(2), 834–843 (2020)
Shittu, O.I., Yaya, O.S.: Measuring forecast performance of ARMA and ARFIMA models: an application to US Dollar/UK pound foreign exchange rate. Eur. J. Sci. Res. 32(2), 167–176 (2009)
Tahersima, H., Tahersima, M., Fesharaki, M., Hamedi, N.: Forecasting stock exchange movements using neural networks: a case study. In: 2011 International Conference on Future Computer Sciences and Application, pp. 123–126. IEEE (2011)
Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15(1), 55–66 (2011)
Willmott, C.J., Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 30(1), 79–82 (2005)
Yang, H.L., Lin, H.C.: Applying the hybrid model of EMD, PSR, and ELM to exchange rates forecasting. Comput. Econ. 49(1), 99–116 (2017)
Yildirim, H., Özkale, M.R.: The performance of ELM based ridge regression via the regularization parameters. Expert Syst. Appl. 134, 225–233 (2019)
Yu, L.Q., Rong, F.S.: Stock market forecasting research based on neural network and pattern matching. In: 2010 International Conference on E-Business and E-Government, pp. 1940–1943. IEEE (2010)
Zong, W., Huang, G.B., Chen, Y.: Weighted extreme learning machine for imbalance learning. Neurocomputing 101, 229–242 (2013)
Kourentzes, N.: nnfor: Time Series Forecasting with Neural Networks. R package version 0.9.6 (2019). https://CRAN.R-project.org/package=nnfor
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Morkowski, J. (2021). Impact of ELM Parameters and Investment Horizon for Currency Exchange Prediction. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12854. Springer, Cham. https://doi.org/10.1007/978-3-030-87986-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-87986-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87985-3
Online ISBN: 978-3-030-87986-0
eBook Packages: Computer ScienceComputer Science (R0)