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Journals / Çağ Üniversitesi Sosyal Bilimler Dergisi / 2026 / Cilt: 23 Sayı: 1

A Study on Prediction and Determinants of CDS Premiums Using Different Machine Learning Algorithms

CDS Primlerinin Farklı Makine Öğrenme Algoritmalarıyla Kestirimi ve Belirleyenleri Üzerine Bir Çalışma

Serkan Nas(Tarsus Üniversitesi)0000-0001-6551-8933
Ayşe Ergin Ünal(Tarsus Üniversitesi)0009-0004-5084-6431
Pages
86–99
Publication Date
2026-06-29
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

While the analysis findings indicate the effects of USD/TRY (61%), DTA (14%) and foreign exchange reserves (10%) on CDS predicting, the best results according to the OMYH criterion were realized with Gradient Boost algorithm (GB) by (74.38%.).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The proportional increase in Credit Default Swaps (CDS), which are widely used as part of the proper management of credit risks, reduces the country's creditworthiness and increases the cost of borrowing.
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Key Findings & Data:Another purpose of the study is to determine the most accurate machine learning algorithm that can predict the CDS risk premium in the period 2001:01-2023:12 in Turkey.
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Conclusion & Impact:While the analysis findings indicate the effects of USD/TRY (61%), DTA (14%) and foreign exchange reserves (10%) on CDS predicting, the best results according to the OMYH criterion were realized with Gradient Boost algorithm (GB) by (74.38%.).
🔬Design:Modeling & Simulation
📍Scope:2001 Verileri
⚙️Variables:CDS · Makine Öğrenme Algoritmaları · Denetimli Öğrenme

Abstract

The proportional increase in Credit Default Swaps (CDS), which are widely used as part of the proper management of credit risks, reduces the country's creditworthiness and increases the cost of borrowing. In this context, the first purpose of the study is to draw a picture of the market financial situation and to reveal the determinants of CDS that indicate the reliability of investments to be made in the country. Another purpose of the study is to determine the most accurate machine learning algorithm that can predict the CDS risk premium in the period 2001:01-2023:12 in Turkey. In this context, the effects of the selected attributes on CDS prediction were tested using supervised learning and ensembled learning algorithms. While the analysis findings indicate the effects of USD/TRY (61%), DTA (14%) and foreign exchange reserves (10%) on CDS predicting, the best results according to the OMYH criterion were realized with Gradient Boost algorithm (GB) by (74.38%.).

Özet

Kredi risklerinin doğru yönetilmesi kapsamında yaygın olarak kullanılan Kredi Temerrüt Swaplarında (CDS) görülen oransal artış, ülkenin kredibilitesini azaltmakta ve borçlanma maliyetini yükseltmektedir. Bu bağlamda çalışmanın ilk amacı piyasa finansal durumunun resmini çizmesi ve ülkeye yapılacak yatırımların güvenilirliğini gösteren CDS’in belirleyenlerini ortaya koymaktır. Çalışmanın diğer bir amacı ise, Türkiye’de 2001:01-2023:12 dönemlerinde CDS risk primini öngörebilen en isabetli makine öğrenme algoritmasının belirlenmesidir. Bu bağlamda seçilen özniteliklerin CDS kestirimindeki etkileri denetimli öğrenme ve toplulaştırılmış öğrenme algoritmaları kullanılarak sınanmıştır. Analiz bulguları; USD/TRY (%61), DTA (%14) ve döviz rezervlerinin (%10) CDS kestirimi üzerindeki etkilerine işaret ederken, OMYH kriterine göre en iyi sonuçlar Gradient Boost algoritması (GB) (%74,38) ile gerçekleşmiştir.

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Cite This Article

Nas, S., Ünal, A. E. (2026). A Study on Prediction and Determinants of CDS Premiums Using Different Machine Learning Algorithms. Çağ Üniversitesi Sosyal Bilimler Dergisi, Cilt: 23 Sayı: 1, 86–99. https://ajindex.com/articles/1116713

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