Revisiting the macroeconomic determinants of non-performing loans with a deep learning technique with causal inference: Evidence from Türkiye

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dc.authorid0000-0001-8261-5487
dc.authorid0000-0002-0442-8720
dc.contributor.authorRaşid Bakır, Muhammed
dc.contributor.authorAtalay Çetin, Mümin
dc.contributor.authorBakırtaş, İbrahim
dc.date.accessioned2025-07-10T11:34:25Z
dc.date.available2025-07-10T11:34:25Z
dc.date.issued2025
dc.departmentİktisadi ve İdari Bilimler Fakültesi
dc.description.abstractThis study revisits the macroeconomic determinants of non-performing loans using a deep neural network (DNN). We present the proposed DNN as a methodological framework that combines deep learning techniques and causal inference methods. We employ a rigorous triple-validation methodology that integrates deep learning architecture, random forest analysis, and the DoWhy causal inference framework. Furthermore, our optimized deep learning framework is validated and enhanced with causal inference capabilities, thus establishing a robust analytical framework for credit risk assessment in emerging markets. Although traditional analyses emphasize unemployment and debt stock as primary predictors, our causal inference methodology indicates that foreign direct investment exhibits the most substantial risk-mitigating effect. Real interest rates had substantial risk-mitigating effects compared with policy rates, suggesting the potential limitations of real interest rates in current monetary policy transmission mechanisms. The integration of deep learning and causal inference has significant implications for policy formulation, suggesting the efficacy of structural reforms over conventional monetary interventi.
dc.identifier.doi10.1016/j.bir.2025.02.006
dc.identifier.endpage551
dc.identifier.issn22148450
dc.identifier.issue3
dc.identifier.scopus105003213293
dc.identifier.startpage541
dc.identifier.urihttps://dx.doi.org/10.1016/j.bir.2025.02.006
dc.identifier.urihttps://hdl.handle.net/20.500.12451/13238
dc.identifier.volume25
dc.identifier.wosWOS:001479708900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorRaşid Bakır, Muhammed
dc.institutionauthorAtalay Çetin, Mümin
dc.institutionauthorBakırtaş, İbrahim
dc.institutionauthorid0000-0001-8261-5487
dc.institutionauthorid0000-0002-0442-8720
dc.language.isoen
dc.publisherBorsa Istanbul Anonim Sirketi
dc.relation.ispartofBorsa Istanbul Review
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCausal Inference
dc.subjectDeep Learning
dc.subjectNon-performing Loans
dc.titleRevisiting the macroeconomic determinants of non-performing loans with a deep learning technique with causal inference: Evidence from Türkiye
dc.typeArticle

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