Predicting credit rating changes conditional on economic strength

Chanaka Edirisinghe; Julia Sawicki; Yonggan Zhao; Jun Zhou

doi:10.1016/j.frl.2022.102770

Predicting credit rating changes conditional on economic strength

Chanaka Edirisinghe, Julia Sawicki, Yonggan Zhao, Jun Zhou

Rowe School of Business

Research output: Contribution to journal › Article › peer-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 language	English
Article number	102770
Journal	Finance Research Letters
DOIs	https://doi.org/10.1016/j.frl.2022.102770
Publication status	Accepted/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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10.1016/j.frl.2022.102770

Cite this

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title = "Predicting credit rating changes conditional on economic strength",

abstract = "This paper develops a new structural model for predicting credit rating changes using firms{\textquoteright} 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{\textquoteright} upgrade probabilities do not differ in expansions and contractions, low credit-rated firms{\textquoteright} 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.",

author = "Chanaka Edirisinghe and Julia Sawicki and Yonggan Zhao and Jun Zhou",

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: {\textcopyright} 2022 Elsevier Inc.",

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AU - Edirisinghe, Chanaka

AU - Sawicki, Julia

AU - Zhao, Yonggan

AU - Zhou, Jun

N1 - 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.

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Y1 - 2022

N2 - 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.

AB - 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.

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Predicting credit rating changes conditional on economic strength

Abstract

Bibliographical note

ASJC Scopus Subject Areas

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