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

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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)

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.

Suggested Citation

  • Abolfazl Saghafi & Maryam Bagherian & Farhad Shokoohi, 2025. "Forecasting Forex EUR/USD Closing Prices Using a Dual-Input Deep Learning Model with Technical and Fundamental Indicators," Mathematics, MDPI, vol. 13(9), pages 1-17, April.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:9:p:1472-:d:1646213
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    References listed on IDEAS

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    3. Gunho Jung & Sun-Yong Choi & Benjamin Miranda Tabak, 2021. "Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques," Complexity, Hindawi, vol. 2021, pages 1-16, March.
    4. Manav Kaushik & A K Giri, 2020. "Forecasting Foreign Exchange Rate: A Multivariate Comparative Analysis between Traditional Econometric, Contemporary Machine Learning & Deep Learning Techniques," Papers 2002.10247, arXiv.org.
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