Predictive Abilities of Machine Learning and Deep Learning Approaches for Exchange Rate Prediction

Furkan Turkoglu, Eda Gocecek and Yavuz Yumrukuz

Journal of BRSA Banking and Financial Markets, 2024, vol. 18, issue 2, 186-210

Abstract: This study evaluates the efficacy of forecasting models in predicting USD/TRY exchange rate fluctuations. We assess Support Vector Machine (SVM), XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models with 96 and 21 feature sets. Data from 01.01.2010 to 30.04.2024 were sourced from Bloomberg, CBRT, and BDDK. Findings indicate that LSTM and GRU models outperform traditional models, with GRU showing the highest predictive accuracy. SVM performs poorly with highdimensional data, while XGBoost offers moderate predictive power but lacks in capturing intricate patterns. This study highlights the importance of model and feature selection in financial time series forecasting and underscores the advantages of advanced neural networks. The results provide valuable insights for analysts and policymakers in developing robust economic forecasting models.

Keywords: Exchange Rate; Machine Learning; Deep Learning; Time Series Forecasting; Nelson Siegel Model; Yield Curve. (search for similar items in EconPapers)
JEL-codes: C45 C53 F31 G17 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:bdd:journl:v:18:y:2024:i:2:p:186-210

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