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Credit Spread Approximation and Improvement using Random Forest Regression

Author

Listed:
  • Mathieu Mercadier

    (LAPE - Laboratoire d'Analyse et de Prospective Economique - GIO - Gouvernance des Institutions et des Organisations - UNILIM - Université de Limoges)

  • Jean-Pierre Lardy

    (JPLC SASU)

Abstract

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.

Suggested Citation

  • Mathieu Mercadier & Jean-Pierre Lardy, 2019. "Credit Spread Approximation and Improvement using Random Forest Regression," Post-Print hal-02057019, HAL.
    • Handle: RePEc:hal:journl:hal-02057019
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    Cited by:

    1. Nielson, Jordan & Bhaganagar, Kiran & Meka, Rajitha & Alaeddini, Adel, 2020. "Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction," Energy, Elsevier, vol. 190(C).
    2. Mercadier, Mathieu & Strobel, Frank, 2021. "A one-sided Vysochanskii-Petunin inequality with financial applications," European Journal of Operational Research, Elsevier, vol. 295(1), pages 374-377.
    3. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023. "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print hal-04223161, HAL.
    4. Santiago Carbo-Valverde & Pedro Cuadros-Solas & Francisco Rodríguez-Fernández, 2020. "A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-39, October.
    5. Efstathios Polyzos & Aristeidis Samitas & Ghulame Rubbaniy, 2024. "The perfect bail‐in: Financing without banks using peer‐to‐peer lending," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3393-3412, July.
    6. Solomon Y. Deku & Alper Kara & Artur Semeyutin, 2021. "The predictive strength of MBS yield spreads during asset bubbles," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 111-142, January.
    7. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2025. "Bankruptcy prediction using machine learning and Shapley additive explanations," Review of Quantitative Finance and Accounting, Springer, vol. 65(1), pages 107-148, July.
    8. Sami Ben Jabeur & Salma Mefteh-Wali & Jean-Laurent Viviani, 2024. "Forecasting gold price with the XGBoost algorithm and SHAP interaction values," Annals of Operations Research, Springer, vol. 334(1), pages 679-699, March.
    9. Nami, Nazanin & Pishchulov, Grigory & Quariguasi Frota Neto, Joao, 2025. "Circular economy application in pharmaceutical supply chains in the UK: a holistic evolutionary game approach," European Journal of Operational Research, Elsevier, vol. 326(3), pages 451-466.
    10. Tolga Yalçin & Pol Paradell Solà & Paschalia Stefanidou-Voziki & Jose Luis Domínguez-García & Tugce Demirdelen, 2023. "Exploiting Digitalization of Solar PV Plants Using Machine Learning: Digital Twin Concept for Operation," Energies, MDPI, vol. 16(13), pages 1-17, June.
    11. Mohammad S. Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib, 2022. "Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3713-3729, July.
    12. Yang, Cai & Zhang, Hongwei & Weng, Futian, 2024. "Effects of COVID-19 vaccination programs on EU carbon price forecasts: Evidence from explainable machine learning," International Review of Financial Analysis, Elsevier, vol. 91(C).
    13. Mercadier, Mathieu & Tarazi, Amine & Armand, Paul & Lardy, Jean-Pierre, 2025. "Monitoring bank risk around the world using unsupervised learning," European Journal of Operational Research, Elsevier, vol. 324(2), pages 590-615.
    14. Yılmaz, Ömer Faruk & Guan, Yongpei & Yılmaz, Beren Gürsoy & Yeni, Fatma Betül & Özçelik, Gökhan, 2025. "A comprehensive methodology combining machine learning and unified robust stochastic programming for medical supply chain viability," Omega, Elsevier, vol. 133(C).
    15. Chengyuan Li & Haoran Zhu & Hanjun Luo & Suyang Zhou & Jieping Kong & Lei Qi & Congjun Rao, 2023. "Spread Prediction and Classification of Asian Giant Hornets Based on GM-Logistic and CSRF Models," Mathematics, MDPI, vol. 11(6), pages 1-26, March.

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