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            <front>

                <journal-meta>
                                    <journal-id></journal-id>
            <journal-title-group>
                                                                                    <journal-title>Bankacılık ve Finansal Araştırmalar Dergisi</journal-title>
            </journal-title-group>
                            <issn pub-type="ppub">2148-4090</issn>
                                        <issn pub-type="epub">2148-4090</issn>
                                                                                            <publisher>
                    <publisher-name>Ankara Hacı Bayram Veli University</publisher-name>
                </publisher>
                    </journal-meta>
                <article-meta>
                                        <article-id/>
                                                                <article-categories>
                                            <subj-group  xml:lang="en">
                                                            <subject>International Banking</subject>
                                                    </subj-group>
                                            <subj-group  xml:lang="tr">
                                                            <subject>Uluslararası Bankacılık</subject>
                                                    </subj-group>
                                    </article-categories>
                                                                                                                                                        <title-group>
                                                                                                                        <article-title>Döviz Kurunu Belirleyen Değişkenlerin Tahmini: Doğrusal Zaman Serisi Ve Doğrusal Olmayan Yapay Sinir Ağı (YSA) İle Bir Model Önerisi</article-title>
                                                                                                                                                                                                <trans-title-group xml:lang="en">
                                    <trans-title>Forecastıng The Determınants Of Exchange Rate: A Model Proposal Usıng Lınear Tıme Serıes And Nonlınear Artıfıcıal Neural Network (Ann) Approaches</trans-title>
                                </trans-title-group>
                                                                                                    </title-group>
            
                                                    <contrib-group content-type="authors">
                                                                        <contrib contrib-type="author">
                                                                <name>
                                    <surname>Sünbül</surname>
                                    <given-names>Ersin</given-names>
                                </name>
                                                                    <aff>UFUK ÜNİVERSİTESİ</aff>
                                                            </contrib>
                                                    <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0002-8386-2620</contrib-id>
                                                                <name>
                                    <surname>Keskin Benli</surname>
                                    <given-names>Yasemin</given-names>
                                </name>
                                                                    <aff>ANKARA HACI BAYRAM VELI UNIVERSITY</aff>
                                                            </contrib>
                                                                                </contrib-group>
                        
                                        <pub-date pub-type="pub" iso-8601-date="20250716">
                    <day>07</day>
                    <month>16</month>
                    <year>2025</year>
                </pub-date>
                                        <volume>12</volume>
                                        <issue>2</issue>
                                        <fpage>192</fpage>
                                        <lpage>209</lpage>
                        
                        <history>
                                    <date date-type="received" iso-8601-date="20250420">
                        <day>04</day>
                        <month>20</month>
                        <year>2025</year>
                    </date>
                                                    <date date-type="accepted" iso-8601-date="20250716">
                        <day>07</day>
                        <month>16</month>
                        <year>2025</year>
                    </date>
                            </history>
                                        <permissions>
                    <copyright-statement>Copyright © 2014, Journal of Banking and Financial Research</copyright-statement>
                    <copyright-year>2014</copyright-year>
                    <copyright-holder>Journal of Banking and Financial Research</copyright-holder>
                </permissions>
            
                                                                                                <abstract><p>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.</p></abstract>
                                                                                                                                    <trans-abstract xml:lang="en">
                            <p>Çalışmanın amacı, döviz kurunu etkileyen en önemli değişkenleri belirlemek ve doğrusal ile doğrusal olmayan zaman serisi modelleri kullanarak tahmin gücü yüksek bir model önermektir. Türkiye Cumhuriyet Merkez Bankası&#039;ndan alınan verilerle 16 değişken analiz edilmiştir. Üç farklı model uygulanmıştır: Geleneksel Yapay Sinir Ağı (YSA) modeli (%97,2 doğruluk, RMSE 2.7960), VAR-YSA birleşik modeli (%99,6 doğruluk, RMSE 0.3184) ve Çok Aşamalı Veri Temizleme-Nedensellik-YSA modeli (%99,7 doğruluk, RMSE 0.3062). Üçüncü model en yüksek tahmin başarısını göstermiştir. Döviz kurunu etkileyen başlıca değişkenler tüketici fiyat endeksi, reel faiz oranı, işsizlik oranı, cari açık, gayri safi yurt içi hasıla ve ihracattır. Sonuçlar, ekonomik karar vericiler ve araştırmacılar için stratejik öngörüler sunmakta ve literatüre önemli katkılar sağlamaktadır.</p></trans-abstract>
                                                            
            
                                                            <kwd-group>
                                                    <kwd>Döviz Kuru Teorileri</kwd>
                                                    <kwd>  Toda-Yamamoto Nedensellik Analizi</kwd>
                                                    <kwd>  Yapay Sinir Ağı.</kwd>
                                            </kwd-group>
                                                        
                                                                            <kwd-group xml:lang="en">
                                                    <kwd>Theory of Exchange Rate</kwd>
                                                    <kwd>  Toda-Yamamoto Causality Analysis</kwd>
                                                    <kwd>  Artificial Neural Network</kwd>
                                            </kwd-group>
                                                                                                            </article-meta>
    </front>
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