Procedure for the forecast of exchange rates. Applying artificial neural networks: EUR/USD
DOI:
https://doi.org/10.33936/eca_sinergia.v13i2.4149Keywords:
inteligencia artificial, pronósticos, redes neuronales recurrentes, series de tiempo.Abstract
The forecast of the exchange rate is an essential part of foreign exchange risk management strategy, which drives the search for new techniques that are more effective. The procedure presented in this work was created in order to facilitate the use of artificial intelligence algorithms for exchange rate forecasting. The procedure is based on the use of quantitative methods to obtain the forecast, the most important being the use of artificial neural networks, specifically those called long short-term memory. The construction of different models is carried out, from which the optimal one is chosen using the square root of the mean square error as the selection criterion. The efficacy of the procedure was demonstrated after obtaining models that perform with an effectiveness comprised in the range of 97.28% - 99.55%. Keywords: artificial intelligence, prognostic, recurrent neural networks, time series.Downloads
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Trapletti, T., & Hornik,K. (2019). tseries: Time Series Analysis and Computational Finance. R package version 0.10-47. Recuperado de https://CRAN.R-project.org/package=tseries
Wickham, H., & Seidel,D. (2020). scales: Scale Functions for Visualization. R package version 1.1.1. Recuperado de https://CRAN.R-project.org/package=scales
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Published
2022-05-20
How to Cite
Oliva de Con, F. de la, Molina Fernández, R., & Díaz Rodríguez, D. (2022). Procedure for the forecast of exchange rates. Applying artificial neural networks: EUR/USD. ECA Sinergia, 13(2), 107–117. https://doi.org/10.33936/eca_sinergia.v13i2.4149
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Copyright (c) 2022 Fidel Oliva
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
