Forecasting Forex EUR/USD Closing Prices Using a Dual-Input Deep Learning Model with Technical and Fundamental Indicators

Abolfazl Saghafi, Maryam Bagherian and Farhad Shokoohi ()
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Abolfazl Saghafi: Department of Mathematics, Saint Joseph’s University, Philadelphia, PA 19131, USA
Maryam Bagherian: Department of Mathematics & Statistics, Idaho State University, Pocatello, ID 83209, USA
Farhad Shokoohi: Department of Mathematical Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA

Mathematics, 2025, vol. 13, issue 9, 1-17

Abstract: Predicting foreign exchange prices is a challenging yet important task due to the complex, volatile, and fluctuating nature of the data. Although deep learning models are efficient, accurate predictions of closing prices and future price directions remain difficult. This study proposes a dual-input deep-learning long short-term memory (LSTM) model for forecasting the EUR/USD closing price and predicting price direction using both fundamental and technical indicators. The model outperforms the second-best model, achieving a 29% reduction in mean absolute error (MAE) and root mean squared error (RMSE) in the training set and reductions of 24% and 23% in MAE and RMSE, respectively, in the test set. These results are confirmed through forecasting simulations, where performance metrics are consistent with those from the training phase. Finally, the model generates reliable three-day price forecasts, providing valuable insights into price direction.

Keywords: multivariate time-series; forex; forecasting; technical indicators; fundamental indicators; dual-input network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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