@article{article_1680301, title={Döviz Kurunu Belirleyen Değişkenlerin Tahmini: Doğrusal Zaman Serisi Ve Doğrusal Olmayan Yapay Sinir Ağı (YSA) İle Bir Model Önerisi}, journal={Bankacılık ve Finansal Araştırmalar Dergisi}, volume={12}, pages={192–209}, year={2025}, url={https://izlik.org/JA32YS96SL}, author={Sünbül, Ersin and Keskin Benli, Yasemin}, keywords={Theory of Exchange Rate, Toda-Yamamoto Causality Analysis, Artificial Neural Network}, abstract={The aim of this study is to identify the most significant variables influencing the exchange rate and to propose a high-performing predictive model using both linear and nonlinear time series methods. A dataset comprising 16 variables obtained from the Central Bank of the Republic of Turkey was analyzed. Three different models were applied: a traditional Artificial Neural Network (ANN) model (97.2% accuracy, RMSE 2.7960), a combined VAR-ANN model (99.6% accuracy, RMSE 0.3184), and a Multi-Stage Data Cleaning–Causality–ANN model (99.7% accuracy, RMSE 0.3062). The third model demonstrated the highest predictive accuracy. The key determinants of the exchange rate were identified as the consumer price index, real interest rate, unemployment rate, current account balance, gross domestic product, and exports. The findings offer strategic insights for policymakers and researchers, contributing meaningfully to the existing literature.}, number={2}