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

Hazar Altınbaş; Gülay Selvi Hanişoğlu

Makale detayları

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

Yazarlar:

Hazar Altınbaş | Gülay Selvi Hanişoğlu

Yayıncı:

Istanbul University

Ana Başlıklar:

Business Systems in Context (Other)

Dergi:

Istanbul Business Research Vol.52 Issue 2, pp.381-406

Belge Tipi:

Makale

Yayın Tarihi:

11/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001304312

pISSN:

2630-5488

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Hazar Altınbaş | Gülay Selvi Hanişoğlu

Yayıncı:

Istanbul University

Ana Başlıklar:

Business Systems in Context (Other)

Dergi:

Istanbul Business Research Vol.52 Issue 2, pp.381-406

Belge Tipi:

Makale

Yayın Tarihi:

11/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001304312

pISSN:

2630-5488

Banking Regulations

Financial System

Time-Series Forecasting

Machine Learning

Sayı

Makale detayları

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

Yazarlar: Hazar Altınbaş | Gülay Selvi Hanişoğlu

Yayıncı: Istanbul University

Ana Başlıklar: Business Systems in Context (Other)

Dergi: Istanbul Business Research Vol.52 Issue 2, pp.381-406

Belge Tipi: Makale

Yayın Tarihi: 11/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001304312

pISSN: 2630-5488

Banking Regulations

Financial System

Time-Series Forecasting

Machine Learning

Sayı

Yazarlar:

Hazar Altınbaş | Gülay Selvi Hanişoğlu

Yayıncı:

Istanbul University

Ana Başlıklar:

Business Systems in Context (Other)

Dergi:

Istanbul Business Research Vol.52 Issue 2, pp.381-406

Belge Tipi:

Makale

Yayın Tarihi:

11/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001304312

pISSN:

2630-5488