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  Citation Number 1
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Ülke Kredi Notlarını Etkileyen Faktörlerin Çeşitli Sınıflandırma Analizleri ile İncelenmesi
2019
Journal:  
Ekoist: Journal of Econometrics and Statistics
Author:  
Abstract:

Kredi derecelendirmeleri, Standard and Poor’s Corporation, Moody’s Yatırımcı Servisi ve Fitch Ratings gibi uluslararası derecelendirme kuruluşları tarafından sağlanan kredi riskinin alfabetik göstergeleridir. Kredi notları hükümetlerin kamu borcunu zamanında geri ödeme kabiliyetinin ve istekliliğinin bir değerlendirmesi olduğundan, yatırımcılar, borç veren kuruluşlar ve ilgili piyasa katılımcıları, yayınlanan raporlar doğrultusunda yatırım kararları alabilmektedir. Bu nedenle verilen notlar oldukça önemlidir. Bu çalışmada, 85 ülkenin 2017 yılına ait verisi için lojistik regresyon analizi ve yapay sinir ağları tekniklerinden yararlanılarak Moody’s kredi derecelendirme kuruluşunun ülke kredi notlarını verirken baskın olarak hangi faktörleri ele aldığı belirlenmiş ve verilen kredi notlarına göre ülkeler yatırım yapılabilirlik durumuna göre sınıflara ayrılmıştır. Analiz sonucunda, kişi başına düşen gayrisafi yurtiçi hasıla (GSYİH), enflasyon, genel hükümet faiz dışı dengesi / GSYİH, devlet borcu, dış ödemeler ve resmi Forex rezervleri değişkenleri istatistiksel olarak anlamlı bulunmuş, lojistik regresyon modelinin doğru sınıflandırma oranının %90,6 ve yapay sinir ağları modelinin doğru sınıflandırma oranının %88 olduğu sonucuna varılmıştır. Türkiye zaman zaman yatırım “yapılabilir ülkeler” kategorisinde yer alsa da, kredi derecelendirme kuruluşu Moody’s, 2018 Ağustos ayında Türkiye’nin kredi notunu Ba2’den Ba3’e, 2019 Haziran ayında ise B1’e düşürerek not görünümünü durağandan negatife düşürmüştür. Analiz sonucunda da buna paralel olarak kredi notları açısından Türkiye’nin “yatırım yapılamaz” sınıfına dahil edildiği belirlenmiştir.

Keywords:

Review of factors affecting country credit ratings with various classification analyses
2019
Author:  
Abstract:

Credit ratings are alphabetical indicators of credit risk provided by international rating agencies such as Standard and Poor's, Moody's, and Fitch. Since credit ratings are an assessment of a government’s ability to repay the public debt on time, investors, lenders, and market participants can make investment decisions in line with published reports. Therefore, the scores given are very important. In this study, the factors handled by Moody's as sovereign ratings were determined using Logistic Regression Analysis and Artificial Neural Networks for the data of 85 countries for 2017 and the countries were divided into classes according to investment grade. As a result of the analysis, per capita GDP, inflation, general government primary balance / GDP, government debt, external payments, official forex reserves were found to be statistically significant. The correct classification rate of the logistic regression model was found to be 90%, while the correct classification rate of the artificial neural network model was found to be 88%. In August 2018, Moody's lowered Turkey's credit rating from 'Ba2' to 'Ba3' and in June 2019 to B1 so the rating outlook dropped from stable to negative. Similarly, the analysis concluded that Turkey had been placed onto the list of non-investable countries.

Keywords:

Investigation Of Factors Affecting Sovereign Ratings By Various Classification Analyses
2019
Author:  
Abstract:

Credit ratings are alphabetical indicators of credit risk provided by international rating agencies such as Standard and Poor’s, Moody’s, and Fitch. Since credit ratings are an assessment of a government’s ability to repay the public debt on time, investors, lenders, and market participants can make investment decisions in line with published reports. Therefore, the scores given are very important. In this study, the factors handled by Moody’s as sovereign ratings were determined using Logistic Regression Analysis and Artificial Neural Networks for the data of 85 countries for 2017 and the countries were divided into classes according to investment grade. As a result of the analysis, per capita GDP, inflation, general government primary balance / GDP, government debt, external payments, official forex reserves were found to be statistically significant. The correct classification rate of the logistic regression model was found to be 90%, whereas the correct classification rate of the artificial neural network model was found to be 88%. In August 2018, Moody’s downgraded Turkey’s credit rating from ‘’Ba2’’ to ‘’Ba3’’ and in June 2019 to B1 so the rating outlook dropped from stable to negative. Similarly, the analysis concluded that Turkey had been placed onto the list of non-investable countries.

Keywords:

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Ekoist: Journal of Econometrics and Statistics