Carregant...
Tipus de document
Document de treballData de publicació
Llicència de publicació
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/182601
An application of deep learning for exchange rate forecasting
Títol de la revista
Director/Tutor
ISSN de la revista
Títol del volum
Recurs relacionat
Resum
This paper examines the performance of several state-of-the-art deep learning techniques for exchange rate forecasting (deep feedforward network, convolutional network and a long short-term memory). On the one hand, the configuration of the different architectures is clearly detailed, as well as the tuning of the parameters and the regularisation techniques used to avoid overfitting. On the other hand, we design an out-of-sample forecasting experiment and evaluate the accuracy of three different deep neural networks to predict the US/UK foreign exchange rate in the days after the Brexit took effect. Of the three configurations, we obtain the best results with the deep feedforward architecture. When comparing the deep learning networks to time-series models used as a benchmark, the obtained results are highly dependent on the specific topology used in each case. Thus, although the three architectures generate more accurate predictions than the time-series models, the results vary considerably depending on the specific topology. These results hint at the potential of deep learning techniques, but they also highlight the importance of properly configuring, implementing and selecting the different topologies.
Descripció
Matèries (anglès)
Citació
Citació
CLAVERÍA GONZÁLEZ, Óscar, MONTE MORENO, Enric, SORIĆ, Petar, TORRA PORRAS, Salvador. An application of deep learning for exchange rate forecasting. _IREA – Working Papers_. 2022. Vol. IR22/01. [consulta: 11 de desembre de 2025]. [Disponible a: https://hdl.handle.net/2445/182601]