Impact of the subprime crisis on bank ratings: The effect of the hardening of rating policies and worsening of solvency

Salvador, Carlos; Pastor, José Manuel; Guevara, Juan Fernández de

doi:10.1016/j.jfs.2013.10.005

Cited by 31 publications

(29 citation statements)

References 35 publications

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“…The reduced number of research on modeling and forecasting the BCRR (Salvador et al, 2014) concerned mainly the "stand-alone" ratings: «Bank Financial Strength Ratings "BFSR"» of Moody"s (Poon, Firth, & Fung, 1999;Laruccia & Revoltella, 2000;Peresetsky & Karminsky, 2011;Ö güt, Doğanay, Ceylan, &Aktaş, 2012 andSalvador et al, 2014) et «Bank Viability Rating "BVR"» (previously called "Individual Rating "IR"") of FitchRatings (Hammer, Kogan, &Lejeune, 2012 andSalvador et al, 2014). Since 2011, researchers have started to be interested in the "all-in" ratings (Van Laere & Baesens, 2011;Bissoondoyal-Bheenick & Treepongkaruna, 2011;Peresetsky & Karminsky, 2011;Chen, 2012;Van Laere, Vantieghem, & Baesens, 2012;Shen, Huang, & Hasan, 2012;Orsenigo & Vercellis, 2013;Salvador et al, 2014 andGogas, Papadimitriou, &Agrapetidou, 2014). The issuing ratings used by Pagratis and Stringa (2009)…”

Section: The Literature Reviewmentioning

confidence: 99%

“…The second way is to go with a relatively smaller set of variables over factors predefined based on previous studies and use the methods of analysis directly on these variables (Pagratis & Stringa, 2009;Van Laere & Baesens, 2011;Bissoondoyal-Bheenick & Treepongkaruna, 2011;Peresetsky & Karminsky, 2011;Chen, 2012;Van Laere et al, 2012;Shen et al, 2012;Salvador et al, 2014;Gogas et al, 2014). Some studies have used the stepwise procedure to select the best model (Laruccia & Revoltella, 2000;Godlewski, 2004;Van Laere & Baesens, 2011;Van Laere et al, 2012;Distinguin, Hasan, & Tarazi, 2013;and Gogas et al, 2014).…”

Section: The Literature Reviewmentioning

confidence: 99%

“…Estrella, Park and Peristiani (2002) showed that the information provided by the bank credit ratings is correlated to those provided by the capital ratios. Several authors (Shen et al, 2012;Salvador et al, 2014; for example) have found that well-capitalized banks have a better probability of getting a high rating.…”

Section: Capital (C)mentioning

confidence: 99%

“…The empirical works, to our knowledge, that have distinguished the second component of the first (the intrinsic credit quality) explicitly in modeling the "all-in" rating are those of Peresetsky and Karminsky (2011) and Salvador et al (2014) but without classification of the variables in the different support sub-factors. We will follow these authors and present them independently of the environment support sub-factors defined by Moody"s because of the affiliation measurement variables to more than one factor.…”

Section: Analytical Quantifiable Factors Of the Environment Support (mentioning

confidence: 99%

“…Salvador et al (2014) showed that CRAs punish not diversified banks that concentrate their balance sheet on the credit activity. They state that with the onset of the sub-prime crisis, the small and medium-sized Spanish saving banks were penalized more intensely by the CRAs because of the business model followed by a number of saving banks, based on the credit traditional activity and focused on real estate-related activities.…”

Section: Activity Of the Bankmentioning

confidence: 99%

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CAMELS Model With a Proposed ‘S’ for the Bank Credit Risk Rating

Damak¹

2018

IJEF

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The purpose of this article is to adopt the CAMELS model to the bank credit risk rating by using simple indicators from publicly available quantifiable information retrieval from their financial statements. Then, it is to test its empirical validation after completion of its revised methodology in 2012 as response to the sub-prime crisis using the rating "all-in" of 128 banks rated by Moody"s of 29 EMENA countries. We use "ordered logit" regression for the variable to explain the rating classes and the bootstrap resampling techniques to assess the stability degree of the best model selected with the information criteria"s AIC. Under this scheme, the explanatory powers measured by Pseudo R2 of the best model is 56.47%. The results show that the two components: intrinsic credit quality and the support of the environment measured respectively by CAMEL factors and the proposed "S" factor determine well the "all-in" ratings. The sovereign rating of the bank establishment country, the size and the "stand-alone" rating of the bank are the most relevant variables.

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Section: The Literature Reviewmentioning

confidence: 99%

Section: The Literature Reviewmentioning

confidence: 99%

Section: Capital (C)mentioning

confidence: 99%

Section: Analytical Quantifiable Factors Of the Environment Support (mentioning

confidence: 99%

Section: Activity Of the Bankmentioning

confidence: 99%

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CAMELS Model With a Proposed ‘S’ for the Bank Credit Risk Rating

Damak¹

2018

IJEF

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Are bank risk disclosures informative? Evidence from debt markets

et al. 2020

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This study examines whether financial reporting with a specific focus on risk disclosures have a predictive (informative) effect on banks' credit ratings (BCRs) and, consequently, ascertains whether governance structures can moderate such an association. Using one of the largest bank‐level datasets collected from 12 Middle East and North African (MENA) countries over the 2006–2013 period to‐date, our findings are as follows. First, we find that risk disclosures have a predictive effect on BCRs. Second, we find that the relationship between risk disclosures and BCRs is contingent on the quality of governance structures. Specifically, we find that the informativeness of risk disclosures on BCRs is higher in banks with larger board size, greater independence, higher government ownership, and better Shariah supervisory board, but lower in banks with greater block ownership, higher foreign ownership and the presence of CEO duality. The central tenor of our findings remains unchanged after controlling for a number of firm‐ and country‐level factors, alternative risk disclosure measures, firm‐ and national‐level governance proxies, different types of banks, and potential endogeneities. The findings have important implications for investors, especially bondholders, standard‐setters, regulators, and central governments.

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Stock Returns and Bank Ratings in the PIIGS

Muñoz¹

2016

Liquidity Risk, Efficiency and New Bank Business Models

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Impact of the subprime crisis on bank ratings: The effect of the hardening of rating policies and worsening of solvency

Cited by 31 publications

References 35 publications

CAMELS Model With a Proposed ‘S’ for the Bank Credit Risk Rating

CAMELS Model With a Proposed ‘S’ for the Bank Credit Risk Rating

Are bank risk disclosures informative? Evidence from debt markets

Stock Returns and Bank Ratings in the PIIGS

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