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Interpretable machine learning for creditor recovery rates · Study

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ArticleInterpretable machine learning for creditor recovery rates

Abdolreza Nazemi2024

OA

Articlegreen2024

Interpretable machine learning for creditor recovery rates

Abdolreza Nazemi · Frank J. Fabozzi

23 citations·0/0 files stored·Journal of Banking & Finance

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Financial Distress and Bankruptcy Prediction Credit Risk and Financial Regulations Explainable Artificial Intelligence (XAI)

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References

1984A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market 2020Empirical Asset Pricing via Machine Learning 2012A Simple Way to Estimate Bid‐Ask Spreads from Daily High and Low Prices 2019Characteristics are covariances: A unified model of risk and return 2005The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications*2007Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries 1996Almost Everything You Wanted to Know about Recoveries on Defaulted Bonds 2004What Do We Know About Loss Given Default?

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2021Intertemporal defaulted bond recoveries prediction via machine learningarticle · 19 shared references 2019Recovery Rates: Uncertainty Certainly Mattersarticle · 11 shared references 2021Predicting loss given default using post-default informationarticle · 10 shared references 2018Macroeconomic variable selection for creditor recovery ratesarticle · 9 shared references 2018Improving corporate bond recovery rate prediction using multi-factor support vector regressionsarticle · 9 shared references 2017Fuzzy decision fusion approach for loss-given-default modelingarticle · 9 shared references

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Year2024

Typearticle

Published2024-04-30

VenueJournal of Banking & Finance

ISSN-L0378-4266

Languageen

Citations23

Files0 public / 0

ShelfFinancial Distress and Bankruptcy Prediction

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doi10.1016/j.jbankfin.2024.107187

openalex_workW4396529039

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