A multivariate analysis of the determinants of Moody’s bank financial strength ratings

Poon, Winnie P. H.; Firth, Michael; Fung, Hung‐Gay

doi:10.1016/s1042-4431(99)00011-6

Cited by 86 publications

(66 citation statements)

References 19 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).…”

Section: The Literature Reviewmentioning

confidence: 99%

“…The first way is to go from a major set of variables without ranking prior in categories or factors predefined by theoretical and empirical studies to use linear dimensionality reduction techniques (Poon et al, 1999;Laruccia & Revoltella, 2000;Ö güt et al, 2012;Orsenigo & Vercellis, 2013) and/or non-linear (Orsenigo & Vercellis, 2013) and then proceed with the application of different methods of analysis (linear regression, discriminant analysis,...) of the coefficients of the main factors or variables that represent the better.…”

Section: The Literature Reviewmentioning

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%

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

Damak¹

2018

IJEF

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“…These studies are mainly conducted within two broad research strands focusing on statistical and machine learning techniques, and may address both feature selection and classification. Poon et al (1999) developed logistic regression models for predicting financial strength ratings assigned by Moody's, using bank-specific accounting variables and financial data. Factor analysis was applied to reduce the number of independent variables and retain the most relevant explanatory factors.…”

Section: An Analysis Of Banks' Credit Ratingmentioning

confidence: 99%

Estimating Dual Deposit Insurance Premium Rates and Forecasting Non-Performing Loans: Two New Models

2015

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Asian Development Bank InstituteThe Working Paper series is a continuation of the formerly named Discussion Paper series; the numbering of the papers continued without interruption or change. ADBI's working papers reflect initial ideas on a topic and are posted online for discussion. ADBI encourages readers to post their comments on the main page for each working paper (given in the citation below). Some working papers may develop into other forms of publication. The views expressed in this paper are the views of the authors and do not necessarily reflect the views or policies of ADBI, ADB, its Board of Directors, or the governments they represent. ADBI does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequences of their use. Terminology used may not necessarily be consistent with ADB official terms.Working papers are subject to formal revision and correction before they are finalized and considered published. ADBI Working Paper 510Yoshino, Taghizadeh-Hesary, and Nili AbstractRisky banks that endanger the stability of the financial system should pay higher deposit insurance premiums than healthy banks and other financial institutions that have shown good financial performance. It is necessary, therefore, to have at least a dual fair premium rate system. In this paper, we develop a model for calculating dual fair premium rates. Our definition of a fair premium rate in this paper is a rate that could cover the operational expenditures of the deposit insuring organization, provides it with sufficient funds to enable it to pay a certain percentage share of deposit amounts to depositors in case of bank default, and provides it with sufficient funds as precautionary reserves. To identify and classify healthier and more stable banks, we use credit rating methods that employ two major dimensional reduction techniques. For forecasting non-performing loans (NPLs), we develop a model that can capture both macro shocks and idiosyncratic shocks to financial institutions in a vector error correction setting. The response of NPLs/loans to macro shocks and idiosyncratic innovations shows that using a model with macro variables only is insufficient, as it is possible that under favorable economic conditions some banks show negative performance or vice versa. Our final results show that stable banks should pay lower deposit insurance premium rates. JEL Classification: G28, G21, E44Asian D...

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“…Statistical and machine learning techniques have been applied for the examination of bank credit ratings that are assigned by CRAs. Statistical techniques include linear regression (Poon et al 1999) and ordered probit (Bissoondoyal-Bheenick and Treepongkaruna (2011); Pagratis and Stinga (2007) Pasiouras et al (2006Pasiouras et al ( , 2007. In the first paper they used UTADIS for the Agency.…”

Section: B) Bank Credit Ratingsmentioning

confidence: 99%

Forecasting Bank Credit Ratings

2014

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Purpose -This study presents an empirical model designed to forecast bank credit ratings using only quantitative and publicly available information from their financial Then these sets of variables are used in an ordered probit regression setting to forecast the long term credit ratings. Findings -Under this scheme, the forecasting accuracy of our best model reaches 83.70% when 9 explanatory variables are used. Originality/value -The results indicate that bank credit ratings largely rely on historical data making them respond sluggishly and after any financial problems are already known to the public.

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A multivariate analysis of the determinants of Moody’s bank financial strength ratings

Cited by 86 publications

References 19 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

Estimating Dual Deposit Insurance Premium Rates and Forecasting Non-Performing Loans: Two New Models

Forecasting Bank Credit Ratings

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