Predicting credit rating changes conditional on economic strength

Chanaka Edirisinghe, Julia Sawicki, Yonggan Zhao and Jun Zhou

Finance Research Letters, 2022, vol. 47, issue PB

Abstract: This paper develops a new structural model for predicting credit rating changes using firms’ accounting data in a regime-switching multinomial logistic regression analysis. The empirical analysis indicates that the probabilities of upgrade, downgrade, or no-change, are asymmetric across economic regimes. The asymmetry of credit rating changes between the high and low credit-rated firms appears to be significantly different. While high credit-rated firms’ upgrade probabilities do not differ in expansions and contractions, low credit-rated firms’ upgrade probabilities are significantly asymmetric in expansions and contractions. Furthermore, the probabilities of downgrade appear to be asymmetric in expansions and contractions for most of the credit rating levels.

Keywords: Corporate bond rating; Credit risk; Macroeconomic indicators; Accounting variables; Markov regime-switching (search for similar items in EconPapers)
JEL-codes: G11 G21 G28 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612322000794
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:finlet:v:47:y:2022:i:pb:s1544612322000794

DOI: 10.1016/j.frl.2022.102770

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().