---
title: Interpretable Machine Learning Recovery Rates
url: https://www.ml-quant.com/papers/repec/eee-jbfina-v-164-y-2024-i-c-s0378426624001043/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:eee:jbfina:v:164:y:2024:i:c:s0378426624001043
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0378426624001043%3Bh%3Drepec%3Aeee%3Ajbfina%3Av%3A164%3Ay%3A2024%3Ai%3Ac%3As0378426624001043
featured: 2024-06-12
citations: unknown
topic: ML & AI Methods
---


# Interpretable Machine Learning Recovery Rates

Machine learning methods offer better performance and insights in modeling corporate bond recovery rates than traditional methods.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0378426624001043%3Bh%3Drepec%3Aeee%3Ajbfina%3Av%3A164%3Ay%3A2024%3Ai%3Ac%3As0378426624001043
- Identifier: RePEc:eee:jbfina:v:164:y:2024:i:c:s0378426624001043
- Released: 2024-06-12
- First featured: Quant Letter No. 53 (2024-06-12): https://www.ml-quant.com/issues/2024-06-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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