# Credit spread approximation and improvement using random forest regression

arXiv: https://arxiv.org/abs/2106.07358 · HTML: https://mlsanity.com/paper/2106.07358/

## 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.

## Builds on (most cited first, 6 of 6)

- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Opening the Black Box of Deep Neural Networks via Information](https://mlsanity.com/paper/1703.00810/) (2017)
- [Self Paced Deep Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/1605.07651/) (2016)
- [Predicting the direction of stock market prices using random forest](https://mlsanity.com/paper/1605.00003/) (2016)
- [Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations](https://mlsanity.com/paper/1712.05043/) (2017)
- [CDS Rate Construction Methods by Machine Learning Techniques](https://mlsanity.com/paper/1705.06899/) (2017)

## 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.