Extended Modeling of Banks’ Credit Ratings

Karminsky, Alexander; Khromova, Ella

doi:10.1016/j.procs.2016.07.058

Cited by 21 publications

(13 citation statements)

References 8 publications

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“…In many cases, it turned out that the qualitative methodology (CAMEL) with only specific indicators explained the majority of bank's rating (Chodnicka-Jaworska, 2019;Shen et al, 2012;Pagratis, Stringa, 2007;Karminsky, Khromova, 2016;Cole, White, 2012). On the other side, banks located in the sovereign regions are strongly influenced by support factors and sovereign variability (Drago, Gallo, 2017;Chen et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…According to many literacies, risk appetite, economic and operational conditions, and financial ratios explain a large percentage (between 62-95%) of risk model change (Chodnicka-Jaworska, 2019). This argument is supported by states that the qualitative measures are most imported in credit risk assessment (Karminsky, Khromova, 2016;Cole, White, 2012).…”

Section: A Methodological Review Of Rating Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

Assessing methodology of banks' ratings and their competitive positions: overview of main rating agencies

Grigorieva

Михайловна²,

Vuković

et al. 2020

Vestn. Ross. univ. družby nar., Ser. Èkon.

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This paper analyzes methodologies of credit rating assessment of major rating agencies, with the focus on Fitch Ratings (Fitch). The methodology data were collected from Fitch Ratings. Also, the article review international literacy for factors and indicators adequacy in rating assessments. The purpose of this research is to overview literacy for the primary methodologies of banks' credit rating assessment. It was explained that ratios from financial indicators are most important for credit rating assessment. In some cases, in sovereign countries, support and macroeconomic factors are significant. We concluded that this area is still not enough researched and that in the future, the number of studies will appear with different methodological proposals and improvements.

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Section: Resultsmentioning

confidence: 99%

Section: A Methodological Review Of Rating Assessmentmentioning

confidence: 99%

Assessing methodology of banks' ratings and their competitive positions: overview of main rating agencies

Grigorieva

Михайловна²,

Vuković

et al. 2020

Vestn. Ross. univ. družby nar., Ser. Èkon.

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“…Gangolf et al (2016) provide a complete comparison of quantitative methods in credit rating forecasting with different statistical and AI techniques. Karminsky and Khromova (2016) approach the forecasting of bank ratings using ordered probit models, with a sample from Bankscope database. Khemakhem and Boujelbène (2015) forecast credit rating in Tunisia.…”

Section: Introduction and Statistical Methods In Rating Forecastingmentioning

confidence: 99%

Corporate rating forecasting using Artificial Intelligence statistical techniques

Caridad¹,

Hančlová

Bousselmi

et al. 2019

Investment Management and Financial Innovations

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Forecasting companies long-term financial health is provided by Credit Rating Agencies (CRA) such as S&amp;P, Moody’s, Fitch and others. Estimates of rates are based on publicly available data, and on the so-called ‘qualitative information’. Nowadays, it is possible to produce quite precise forecasts for these ratings using economic and financial information that is available in financial databases, utilizing statistical models or, alternatively, Artificial Intelligence techniques. Several approaches, both cross section and dynamic are proposed, using different methods. Artificial Neural Networks (ANN) provide better results than multivariate statistical methods and are used to estimate ratings within all the range provided by the CRAs, obtaining more desegregated results than several proposed models available for intervals of ratings. Two large samples of companies ‘public data’ obtained from Bloomberg are used to obtain forecasts of S&amp;P and Moody’s ratings directly from these data with high level of accuracy. This also permits to check the published rating’s reliability provided by different CRAs.

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“…При этом следует обратить внимание на узкую квалификацию специалистов в соответствии с идентифицированной категорией рисков инновационного развития. В-третьих, учет при построении модели управления рисками инновационного развития международного опыта, а также требования регулятора (ЦБ) к обеспеченности обязательств и иных показателей финансовой устойчивости [7]. В-четвертых, преобладание результатов потенциальных рисков над ожидаемыми выгодами с учетом нового выбранного направления деятельности.…”

Section: рисунок 1 -отзывы и аннулирование лицензий банков за 2012-20unclassified

Preventive risk management of innovative development in commercial banks

Валерьевна¹

2018

ОПЭП

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Сереброва Мария Валерьевна аспирант Департамента финансовых рынков и банков Финансового университета при Правительстве Российской Федерации ПРЕВЕНТИВНОЕ УПРАВЛЕНИЕ РИСКАМИ ИННОВАЦИОННОГО РАЗВИТИЯ В КОММЕРЧЕСКИХ БАНКАХ Аннотация: Темпы экономического развития любого государства всегда зависели от уровня разработки и освоения новых технологий, а в условиях современной технологической революции инновационные процессы превращаются в один из основных и постоянно действующих факторов развития. Сегодня все больше внимания уделяется процессам, связанным с управлением инновационной деятельностью. Инновационная деятельность, в свою очередь, непрерывно связана с риском. Банковская сфера имеет важные отличия от сферы материального производства при устроении инновационного процесса. К ним относятся большое количество клиентов и высокая взаимозависимость банков и клиентов, сильная межбанковская конкуренция, которая делает необходимым ведение инновационной деятельности. В статье проведен анализ актуального вопроса превентивного управления рисками инновационного развития коммерческих банков.

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Extended Modeling of Banks’ Credit Ratings

Cited by 21 publications

References 8 publications

Assessing methodology of banks' ratings and their competitive positions: overview of main rating agencies

Assessing methodology of banks' ratings and their competitive positions: overview of main rating agencies

Corporate rating forecasting using Artificial Intelligence statistical techniques

Preventive risk management of innovative development in commercial banks

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