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Dergiler / Istanbul Business Research / 2023 / Cilt: 52 Sayı: 2

Forecasting and Evaluation of Non-Performing Loans in the Turkish Banking Sector

Hazar ALTINBAŞ
Gülay Selvi HANİŞOĞLU
Sayfa
381–406
Yayın Tarihi
2023-11-28
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Our results indicate partial dependencies and positive relationship between non-performing loans and inflation, interest rate and capital adequacy ratios, and negative relationship with credit to gross domestic product ratio.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In recent years, there is an increasing trend in non-performing loan levels in Turkey which causes stress both on the real and financial sectors.
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Temel Bulgular & Veri:For this purpose, non-performing loans to credit ratio in Turkey for the dates between the first quarter of 2015 and fourth quarter of 2019 were forecasted with two machine learning methods, namely random forests and boosted trees, by using data starting from the first quarter of 2003.
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Sonuç & Katkı:Our results indicate partial dependencies and positive relationship between non-performing loans and inflation, interest rate and capital adequacy ratios, and negative relationship with credit to gross domestic product ratio.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2015 Verileri
⚙️Değişkenler:Banking Regulations · Financial System · Time-Series Forecasting

Abstract

In recent years, there is an increasing trend in non-performing loan levels in Turkey which causes stress both on the real and financial sectors. Increasing non-performing loan volumes are an indication of problems in sectors or the general economy. It is also closely related with the stability of the banking system. It is therefore important for regulatory/ supervisory institutions and banks to be able to predict problematic loan levels successfully, for better policy making and management. For this purpose, non-performing loans to credit ratio in Turkey for the dates between the first quarter of 2015 and fourth quarter of 2019 were forecasted with two machine learning methods, namely random forests and boosted trees, by using data starting from the first quarter of 2003. Lagged values of several macroeconomic, bankspecific and uncertainty factors are included as determinant variables in the analyses. Methods provide insight about the relationship of included variables with non-performing loans. Our results indicate partial dependencies and positive relationship between non-performing loans and inflation, interest rate and capital adequacy ratios, and negative relationship with credit to gross domestic product ratio.

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Bu Makaleye Atıf Yap

ALTINBAŞ, H., HANİŞOĞLU, G. S. (2023). Forecasting and Evaluation of Non-Performing Loans in the Turkish Banking Sector. Istanbul Business Research, Cilt: 52 Sayı: 2, 381–406. https://ajindex.com/articles/1020318

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  1. Aiyar, M. S., Bergthaler, M. W., Garrido, J. M., Ilyina, M. A., Jobst, A., Kang, M. K.,. Moretti, M. M. (2015). A strategy for resolving Europe’s problem loans. International Monetary Fund. google scholar Altınbaş, H. (2020). Modern kredi sınıflandırma çalışmaları ve metasezgisel algoritma uygulamaları: Sistematik bir derleme (Metaheuristic algorithms and modern credit classification methods: A systematic review). İstanbul Business Research, 49(1), 146-175. https://doi.org/10.26650/ibr.2020.49.0033 google scholar Arrawatia, R., Dawar, V., Maitra, D., & Dash, S. R. (2019). Asset quality determinants of Indian banks: Em-pirical evidence and policy issues. Journal of Public Affairs, 19(4), e1937. google scholar Balgova, M., Nies, M., & Plekhanov, A. (2016). The economic impact of reducing non-performing loans. google scholar Banking Regulation and Supervision Agency of Turkey. (2019). Turkish Banking Sector Main Indicators. google scholar Bellotti, A., Brigo, D., Gambetti, P., & Vrins, F. (2021). Forecasting recovery rates on non-performing loans with machine learning. International Journal of Forecasting, 37(1), 428-444. google scholar Berger, A. N., & DeYoung, R. (1997). Problem loans and cost efficiency in commercial banks. Journal of Banking & Finance, 21(6), 849-870. google scholar Bergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics and Data Analysis, 120, 70-83. https://doi. org/10.1016/j.csda.2017.11.003 google scholar Boudriga, A., Taktak, N. B., & Jellouli, S. (2009). Banking supervision and nonperforming loans: a cross-country analysis. Journal of Financial Economic Policy. google scholar Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. google scholar Cerqueira, V., Torgo, L., & Mozetic, I. (2020). Evaluating time series forecasting models: an empirical study on performance estimation methods. Machine Learning, 109(11), 1997-2028. https://doi.org/10.1007/ s10994-020-05910-7 google scholar Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? -Ar-guments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014 google scholar Changqing Cheng, Akkarapol Sa-Ngasoongsong, Omer Beyca, Trung Le, Hui Yang, Zhenyu (James) Kong & Satish T.S. Bukkapatnam (2015) Time series forecasting for nonlinear and non-stat