EXPERT: EXchange Rate Prediction Using Encoder Representation from Transformers

Efstratios Bilis, Theophilos Papadimitriou (), Konstantinos Diamantaras and Konstantinos Goulianas
Additional contact information
Efstratios Bilis: Department of Information and Electronic Engineering, International Hellenic University, 57400 Thessaloniki, Greece
Theophilos Papadimitriou: Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece
Konstantinos Diamantaras: Department of Information and Electronic Engineering, International Hellenic University, 57400 Thessaloniki, Greece
Konstantinos Goulianas: Department of Information and Electronic Engineering, International Hellenic University, 57400 Thessaloniki, Greece

Forecasting, 2025, vol. 7, issue 4, 1-26

Abstract: This study introduces a Transformer-based forecasting tool termed EXPERT (EXchange rate Prediction using Encoder Representation from Transformers) and applies it to exchange rate forecasting. We developed and trained a Transformer-based forecasting model, then evaluated its performance on nine currency pairs with various characteristics. Finally, we benchmarked its effectiveness against six established forecasting models: Linear Regression, Random Forest, Stochastic Gradient Descent, XGBoost, Bagging Regression, and Long Short-Term Memory. Our dataset covers the period from 1999 to 2022. The models were evaluated for their ability to predict the next day’s closing price using three performance metrics. In addition, the EXPERT system was evaluated on its ability to extend forecast horizons and as the core of a trading strategy. The model’s robustness was further evaluated using the Multiple Comparisons with the Best (MCB) metric on five dataset samples.

Keywords: exchange rates; time series forecasting; deep learning; Transformers (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-9394/7/4/65/pdf (application/pdf)
https://www.mdpi.com/2571-9394/7/4/65/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jforec:v:7:y:2025:i:4:p:65-:d:1782352

Access Statistics for this article

Forecasting is currently edited by Ms. Joss Chen

More articles in Forecasting from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().