Predicting credit rating changes conditional on economic strength

Chanaka Edirisinghe, Julia Sawicki, Yonggan Zhao, Jun Zhou

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Article number102770
JournalFinance Research Letters
DOIs
Publication statusAccepted/In press - 2022

Bibliographical note

Funding Information:
This research was partially supported by theRowe Research Foundation at the Faculty of Management, Dalhousie University. Financial support from the Social Sciences and Humanities Research Council of Canada is acknowledged.

Funding Information:
This research was partially supported by the Rowe Research Foundation at the Faculty of Management, Dalhousie University . Financial support from the Social Sciences and Humanities Research Council of Canada is acknowledged.

Publisher Copyright:
© 2022 Elsevier Inc.

ASJC Scopus Subject Areas

  • Finance

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Edirisinghe, C., Sawicki, J., Zhao, Y., & Zhou, J. (Accepted/In press). Predicting credit rating changes conditional on economic strength. Finance Research Letters, Article 102770. https://doi.org/10.1016/j.frl.2022.102770