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Almost Everything You Wanted to Know about Recoveries on Defaulted Bonds

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  • Edward I. Altman
  • Vellore M. Kishore

Abstract

This study documents, for the first time, the severity of bond defaults stratified by Standard Industrial Classification sector and by debt seniority. The highest average recoveries came from public utilities (70 percent) and chemical, petroleum, and related products (63 percent). The differences between those sectors and all the rest are statistically significant, even when adjusted for seniority. The original rating of a bond issue as investment grade or below investment grade has virtually no effect on recoveries once seniority is accounted for. In addition, neither the size of the issue nor the time to default from its original date of issuance has any association with the recovery rate. These results should provide important information for investors as well as analysts.

Suggested Citation

  • Edward I. Altman & Vellore M. Kishore, 1996. "Almost Everything You Wanted to Know about Recoveries on Defaulted Bonds," Financial Analysts Journal, Taylor & Francis Journals, vol. 52(6), pages 57-64, November.
    • Handle: RePEc:taf:ufajxx:v:52:y:1996:i:6:p:57-64
    DOI: 10.2469/faj.v52.n6.2040
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    Cited by:

    1. Anand, Kartik & Gai, Prasanna & Kapadia, Sujit & Brennan, Simon & Willison, Matthew, 2013. "A network model of financial system resilience," Journal of Economic Behavior & Organization, Elsevier, vol. 85(C), pages 219-235.
    2. Frame, W. Scott & Lazaryan, Nika & McLemore, Ping & Mihov, Atanas, 2024. "Operational loss recoveries and the macroeconomic environment: Evidence from the U.S. banking sector," Journal of Banking & Finance, Elsevier, vol. 165(C).
    3. Theodore M. Barnhill & Panagiotis Papapanagiotou & Liliana Schumacher, 2002. "Measuring Integrated Market and Credit Risk in Bank Portfolios: An Application to a Set of Hypothetical Banks Operating in South Africa," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 11(5), pages 401-443, December.
    4. Nazemi, Abdolreza & Fabozzi, Frank J., 2024. "Interpretable machine learning for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 164(C).

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