Intertemporal defaulted bond recoveries prediction via machine learning

Abdolreza Nazemi, Friedrich Baumann and Frank J. Fabozzi

European Journal of Operational Research, 2022, vol. 297, issue 3, 1162-1177

Abstract: The recovery rate on defaulted corporate bonds has a time-varying distribution, a topic that has received limited attention in the literature. We apply machine learning approaches for intertemporal analysis of U.S. corporate bonds’ recovery rates. We show that machine learning techniques significantly outperform traditional approaches not only out-of-sample as documented in the literature but also in various out-of-time prediction setups. The newly applied sparse power expectation propagation approach provides the most compelling out-of-time prediction results. Motivated by the association of systematic factors with the time-varying characteristic of recovery rates, we study the effect of text-based news measures to account for bond investors’ expectations about the future which translate into market-based recovery rates. Especially during recessions, government-related news are associated with higher recovery rates. Although machine learning is a data-driven approach rather than considering economic intuition for ranking a group of predictors, the most informative groups of predictors for recovery rate prediction are nevertheless economically meaningful.

Keywords: Finance; Risk management; Recovery rates; Machine learning; News-based analysis; Power expectation propagation (search for similar items in EconPapers)
JEL-codes: C14 C52 G17 G33 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221721005634
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:297:y:2022:i:3:p:1162-1177

DOI: 10.1016/j.ejor.2021.06.047

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().