Credit spread approximation and improvement using random forest regression
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Abstract (arXiv)
Credit Default Swap (CDS) levels provide a market appreciation of companies' default risk. These derivatives are not always available, creating a need for CDS approximations. This paper offers a simple, global and transparent CDS structural approximation, which contrasts with more complex and proprietary approximations currently in use. This Equity-to-Credit formula (E2C), inspired by CreditGrades, obtains better CDS approximations, according to empirical analyses based on a large sample spanning 2016-2018. A random forest regression run with this E2C formula and selected additional financial data results in an 87.3% out-of-sample accuracy in CDS approximations. The transparency property of this algorithm confirms the predominance of the E2C estimate, and the impact of companies' debt rating and size, in predicting their CDS.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 59 references that are in the map. First posted on arXiv 4 Jun 2021.
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References · 6
- 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,487
- 2017Opening the Black Box of Deep Neural Networks via Informationcited by 503
- 2016Self Paced Deep Learning for Weakly Supervised Object Detectioncited by 25
- 2016Predicting the direction of stock market prices using random forestcited by 15
- 2017Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representationscited by 4
- 2017CDS Rate Construction Methods by Machine Learning Techniquescited by 1
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