A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction

• Published in: Risks (Basel) Vol. 12; no. 9; p. 139
• Main Authors: Iqbal, Farhat, Koutmos, Dimitrios, Ahmed, Eman A., Al-Essa, Lulwah M.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2024
• Subjects: Accuracy; Algorithms; Analysis; BiGRU; Currency; data augmentation; Deep learning; Econometrics; Forecasting; Foreign exchange; foreign exchange (FX) forecasting; Foreign exchange market; Foreign exchange markets; Foreign exchange rates; hybrid deep learning; hyperparameter optimization; International finance; Machine learning; Methods; Neural networks; Prevention; Time series; United Kingdom
• ISSN: 2227-9091; 2227-9091
• DOI: 10.3390/risks12090139

The global foreign exchange (FX) market represents a critical and sizeable component of our financial system. It is a market where firms and investors engage in both speculative trading and hedging. Over the years, there has been a growing interest in FX modeling and prediction. Recently, machine learning (ML) and deep learning (DL) techniques have shown promising results in enhancing predictive accuracy. Motivated by the growing size of the FX market, as well as advancements in ML, we propose a novel forecasting framework, the MVO-BiGRU model, which integrates variational mode decomposition (VMD), data augmentation, Optuna-optimized hyperparameters, and bidirectional GRU algorithms for monthly FX rate forecasting. The data augmentation in the Prevention module significantly increases the variety of data combinations, effectively reducing overfitting issues, while the Optuna optimization ensures optimal model configuration for enhanced performance. Our study’s contributions include the development of the MVO-BiGRU model, as well as the insights gained from its application in FX markets. Our findings demonstrate that the MVO-BiGRU model can successfully avoid overfitting and achieve the highest accuracy in out-of-sample forecasting, while outperforming benchmark models across multiple assessment criteria. These findings offer valuable insights for implementing ML and DL models on low-frequency time series data, where artificial data augmentation can be challenging.