Interpretable machine learning for creditor recovery rates

Abdolreza Nazemi and Frank J. Fabozzi

Journal of Banking & Finance, 2024, vol. 164, issue C

Abstract: Machine learning methods have achieved great success in modeling complex patterns in finance such as asset pricing and credit risk that enable them to outperform statistical models. In addition to the predictive accuracy of machine learning methods, the ability to interpret what a model has learned is crucial in the finance industry. We address this challenge by adapting interpretable machine learning to the context of corporate bond recovery rate modeling. In addition to the best performance, we show the value of interpretable machine learning by finding drivers of recovery rates and their relationship that cannot be discovered by the use of traditional machine learning methods. Our findings are financially meaningful and consistent with the findings in the existing credit risk literature.

Keywords: Interpretable machine learning; Risk management; Recovery rate; Corporate bonds (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:164:y:2024:i:c:s0378426624001043

DOI: 10.1016/j.jbankfin.2024.107187

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