Predicting Bank CAMELS and S&amp;P Ratings: The Case of the Czech Republic

Derviz, Alexis; Podpiera, Jiří

doi:10.2753/ree1540-496x440107

Cited by 40 publications

(26 citation statements)

References 18 publications

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“…In recent years CAMELS model was one of the most used models for the estimation of a bank performances and soundness (Baral, 2005). This model is used as a bank supervision instrument by the regulatory authorities (Gilbert et al, 2000;Hays et al, 2009) and also as a main model for the evaluation of the banks performances (Evans et al, 2000;Derviz and Podpiera, 2008;Atikogullari, 2009;Mishra et al, 2012). CAMELS model was developed in 1979 in the U.S. as a supervisory rating system which would help analyzing the overall condition of banks.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2019

Financial Risk and Management Reviews

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Evaluating the financial performance of banks has always been in the center of attention among both academics and practitioners. In that matter, CAMELS framework has always been one of the most widely used model in evaluating the financial soundness of banks and exploring the weakness areas a bank has. The model is used to distinguish good banks from bad ones. This study aims to evaluate the financial soundness of Kuwaiti banks that are listed at Kuwait stock exchange over the period 2011-2016 using CAMELS framework. Results obtained from this study showed that Ahli united bank was the top performing bank in Kuwait during the study period despite showing weakness in terms of capital adequacy and liquidity while the worst performing bank was Kuwait finance house. Kuwait finance house showed very poor performance in capital adequacy and management efficiency. The results from this research would be useful for the Kuwaiti banks to address their weak areas and try to improve on them. Contribution/Originality: This study is one of very few studies which have investigated the financial soundness of Kuwaiti banks using CAMELS framework. The results highlighted the weak areas in every bank listed at Kuwait stock exchange which must be addressed by the bank to improve its performance. from the five main segments of a bank operations: Capital adequacy, Asset quality, Management quality, Earnings ability and Liquidity. Since 1996, out of the desire to stronger focus on risk, to the five components was added the

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Section: Introductionmentioning

confidence: 99%

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2019

Financial Risk and Management Reviews

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“…(1) Studies on credit rating indicator system First category is credit rating indicator system of international authority. There are mainly credit evaluation indicator system of enterprises constructed by authorities Moody [1], Standard & Poor [2] and Fitch Ratings [3] and rating indicator system based on principle "5C" [4] and "5P" [5] recognized by international conventions.…”

Section: Introductionmentioning

confidence: 99%

Model of credit rating of micro enterprise based on fuzzy integration

Li¹,

Hou²,

Su³

et al. 2018

Filomat

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This paper selects credit data of 1688 micro enterprises in a certain commercial bank in China from 2013 to 2016 as empirical research objects. We use the combination method of t test and correlation analysis to construct credit rating indicator system, then use entropy weight method and fuzzy integration to construct credit evaluation model, and lastly divide credit rating of micro enterprises. The empirical results indicate three main points. Firstly, final constructed indicator system can significantly discriminate credit state and avoid information overlapping, and curve ROC proves credit rating indicator system is effective. Secondly, we use entropy weight method to weight indicators objectively, and evaluate credit state comprehensively based on fuzzy integration model. At last, credit rating of micro enterprises proves that upper rating has minor default enterprises, and overall micro enterprises' credit state is basically on average credit level.

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“…Derviz and Podpiera (2008) see that the CAMELS model, under its composite and component form, is generally accepted as a monitoring instrument and important research subject for those who are interested in the behavior of the banks for academic or applied purposes. A lot of research, such as Derviz and Podpiera (2008) and Yüksel et al (2015), illustrate the relationship between several financial ratios (classified into CAMEL(S) model factors or not) and bank credit risk rating but this famous model is not fully adapted to the "all-in" rating. Therefore, since the BCRR is a relative measure of the credit risk only (without taking into account other banking risk in a separate way), we will ignore this added factor and take the first five factors as the first component of our "all-in" rating model (Note 3).…”

Section: Analytical Quantifiable Factors Of the Bank Intrinsic Creditmentioning

confidence: 99%

“…In 1996, the "Sensitivity to Market Risk" is added as a sixth factor to this model (Yüksel et al, 2015). Derviz and Podpiera (2008) see that the CAMELS model, under its composite and component form, is generally accepted as a monitoring instrument and important research subject for those who are interested in the behavior of the banks for academic or applied purposes. A lot of research, such as Derviz and Podpiera (2008) and Yüksel et al (2015), illustrate the relationship between several financial ratios (classified into CAMEL(S) model factors or not) and bank credit risk rating but this famous model is not fully adapted to the "all-in" rating.…”

Section: Analytical Quantifiable Factors Of the Bank Intrinsic Creditmentioning

confidence: 99%

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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Predicting Bank CAMELS and S&P Ratings: The Case of the Czech Republic

Cited by 40 publications

References 18 publications

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Model of credit rating of micro enterprise based on fuzzy integration

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

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