CDS Approximation Accuracy Improvement with Cart and Random Forest Algorithms Based on a Time Span Including the COVID-19 Pandemic Period

Mathieu Mercadier

Chapter 3 in Recent Trends in Financial Engineering:Towards More Sustainable Social Impact, 2022, pp 39-63 from World Scientific Publishing Co. Pte. Ltd.

Abstract: This study uses decision tree and random forest regressions to improve the accuracy of an approximation of credit default swap (CDS) spreads called the Equity-to-Credit (E2C) formula based on a time span including the COVID-19 pandemic period. Certain sections are dedicated to explaining deeper important concepts in machine learning. Random forest regressions run with the E2C and selected additional financial data results in an accuracy in CDS approximations of 82% out-of-sample. The transparency property of these algorithms confirms that, for CDS spreads’ forecasting, the most used feature is the E2C formula and to a lower extent companies’ debt rating and size.

Keywords: Innovation; Equity-Crowdfunding; Capital Structure; Credit Default Swap; Machine Learning: Green Bonds; Impact Bonds; Shareholder Engagement; ESG; Systemic Risk; Sharing Economy; Impact Accounts (search for similar items in EconPapers)
JEL-codes: C02 F3 G3 G32 (search for similar items in EconPapers)
Date: 2022
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