TY  - JOUR
T1  - Predictive Abilities of Machine Learning and Deep Learning Approaches for Exchange Rate Prediction
TT  - Makine Öğrenmesi ve Derin Öğrenme Yaklaşımlarının Döviz Kuru  Tahmini Konusundaki Tahmin Yeteneği
AU  - Türkoğlu, Furkan
AU  - Göçecek, Eda
AU  - Yumrukuz, Yavuz
PY  - 2024
DA  - December
Y2  - 2024
DO  - 10.46520/bddkdergisi.1600294
JF  - BDDK Bankacılık ve Finansal Piyasalar Dergisi
PB  - Bankacılık Düzenleme ve Denetleme Kurumu
WT  - DergiPark
SN  - 1307-5705
SP  - 186
EP  - 210
VL  - 18
IS  - 2
LA  - en
AB  - This study evaluates the efficacy of forecasting models in predicting USD/TRY exchange ratefluctuations. We assess Support Vector Machine (SVM), XGBoost, Long Short-Term Memory (LSTM), andGated Recurrent Unit (GRU) models with 96 and 21 feature sets. Data from 01.01.2010 to 30.04.2024 weresourced from Bloomberg, CBRT, and BDDK. Findings indicate that LSTM and GRU models outperformtraditional models, with GRU showing the highest predictive accuracy. SVM performs poorly with highdimensional data, while XGBoost offers moderate predictive power but lacks in capturing intricatepatterns. This study highlights the importance of model and feature selection in financial time seriesforecasting and underscores the advantages of advanced neural networks. The results provide valuableinsights for analysts and policymakers in developing robust economic forecasting models.
KW  - Exchange rate
KW  - Machine learning
KW  - Deep learning
KW  - Time series forecasting
KW  - Nelson Siegel model
KW  - Yield curve
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UR  - https://doi.org/10.46520/bddkdergisi.1600294
L1  - https://dergipark.org.tr/tr/download/article-file/4435130
ER  -