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.
Exchange rate Machine learning Deep learning Time series forecasting Nelson Siegel model Yield curve
Birincil Dil | İngilizce |
---|---|
Konular | Finansal Kurumlar, Bankacılık ve Sigortacılık (Diğer) |
Bölüm | Araştırma Makaleleri |
Yazarlar | |
Yayımlanma Tarihi | 12 Aralık 2024 |
Gönderilme Tarihi | 25 Temmuz 2024 |
Kabul Tarihi | 11 Eylül 2024 |
Yayımlandığı Sayı | Yıl 2024 Cilt: 18 Sayı: 2 |