Prediction of default probability in banking industry using CAMELS index: A case study of Iranian banks

Valahzaghard, Mohammad Khodaei; Bahrami, Maryam

doi:10.5267/j.msl.2013.03.016

Cited by 6 publications

(8 citation statements)

References 7 publications

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“…ratio (Loans to Total Assets), and third ratio (Loans to Deposits), which are considered the best predictors of bank distress. Valahzaghard and Bahrami (2013) found a meaningful relationship between default probability and management quality, earning quality and liquidity quality. Samad (2011) found significant differences in capital adequacy (capital to average total assets, capital to risk weighted assets, equity capital to assets, and Tier 1 capital to risk-weighted assets) between unhealthy and healthy banks.…”

Section: Related Literaturementioning

confidence: 86%

“…Since then, logistic regression (Al-Saleh & Al-Kandari, 2012;Valahzaghard & Bahrami, 2013;Zaghdoudi, 2013), genetic algorithms (Martin, Gayathri, Saranya, Gayathri, & Venkatesan, 2011), multivariate discriminant analysis (Canbas, Cabuk, & Kilic, 2005;Demyanyk & Hasan, 2009), multivariate regression analysis (Meyer & Pifer, 1970), artificial neural networks (Ravi & Pramodh, 2008), and fuzzy models (Tung, Quek, & Cheng, 2004;Yildiz & Akkoc, 2010) have served as general models for predicting bankruptcies. ratio (Loans to Total Assets), and third ratio (Loans to Deposits), which are considered the best predictors of bank distress.…”

Section: Related Literaturementioning

confidence: 99%

“…It is widely believed that the majority of existing studies on bank failure has relied too heavily on bank-level accounting data (Al-Saleh & Al-Kandari, 2012;Boyacioglu, Kara, & Baykan, 2009;Männasoo & Mayes, 2009;Ravisankar & Rav, 2010;Serrano-Cinca & Gutiérrez-Nieto, 2013;Valahzaghard & Bahrami, 2013;Yildiz & Akkoc, 2010;Zaghdoudi, 2013). These solutions appear to have shown that financial ratios are more capable of detecting bank distress than other factors.…”

Section: Introductionmentioning

confidence: 99%

“…These solutions appear to have shown that financial ratios are more capable of detecting bank distress than other factors. However, most of these studies have considered a single country for analysis (Al-Saleh & Al-Kandari, 2012;Boyacioglu et al, 2009;Erdogan, 2008;Lanine & Vennet, 2006;Serrano-Cinca & Gutiérrez-Nieto, 2013;Sinha, Taneja, & Gothi, 2009;Valahzaghard & Bahrami, 2013;Zaki, Bah, & Rao, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Cross-Country Study on the Determinants of Bank Financial Distress

Zhen-Jia-Liu

2015

Rev. adm. empres.

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Bank failures affect owners, employees, and customers, possibly causing large-scale economic distress. Thus, banks must evaluate operational risks and develop early warning systems. This study investigates bank failures in the Organization for Economic Co-operation and Development, the North America Free Trade Area (NAFTA), the Association of Southeast Asian Nations, the European Union, newly industrialized countries, the G20, and the G8. We use financial ratios to analyze and explore the appropriateness of prediction models. Results show that capital ratios, interest income compared to interest expenses, non-interest income compared to non-interest expenses, return on equity, and provisions for loan losses have significantly negative correlations with bank failure. However, loan ratios, non-performing loans, and fixed assets all have significantly positive correlations with bank failure. In addition, the accuracy of the logistic model for banks from NAFTA countries provides the best prediction accuracy regarding bank failure.KEYWORDS | Bank failure prediction, bank, distress, global, cross-country. RESUMOFalências bancárias afetam proprietários, funcionários e clientes, e têm potencial para causar crises econômicas de grande escala. Portanto, os bancos devem avaliar os riscos operacionais e desenvolver sistemas de alerta preventivo. O presente estudo investiga falências bancárias na Organização para a Cooperação e Desenvolvimento Econômico, nos países integrantes do Tratado de Livre Comér-cio da América do Norte (NAFTA, na sigla em inglês), na Associação de Nações do Sudeste Asiático, na União Europeia, em países recentemente industrializados, no G20 e no G8. Utilizamos indicadores financeiros para analisar e explorar a adequação de modelos preditivos. Os resultados demonstram que índices de capital, juros ativos comparados a despesas com juros, receitas excluindo juros comparadas a despesas excluindo juros, rentabilidade sobre o patrimônio e provisões para prejuí-zos sobre empréstimos têm correlações significantemente negativas com falências bancárias. No entanto, índices de empréstimos, créditos de liquidação duvidosa e ativos fixos têm correlações significantemente positivas com falências bancárias. Além disso, a exatidão do modelo logístico para os bancos dos países que fazem parte do NAFTA fornece melhor precisão em termos de previsão no que diz respeito à falência bancária. PALAVRAS-CHAVE | Previsão de falência bancária, bancos, crise, global, cross-country. RESUMEN Las quiebras bancarias afectan a los propietarios, empleados y clientes, y pueden causar dificultades económicas a gran escala. Por lo tanto, los bancos deben evaluar los riesgos operativos y desarrollar sistemas de alerta temprana. Este estudio investiga las quiebras bancarias en la Organización para la RAE-Revista de Administração de Empresas | FGV-EAESP

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Section: Related Literaturementioning

confidence: 86%

Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cross-Country Study on the Determinants of Bank Financial Distress

Zhen-Jia-Liu

2015

Rev. adm. empres.

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“…For this last pillar, nancial institutions should maintain e ective processes for disclosing information and displaying transparency to the market. e studies found in the literature on predicting financial distress are based on samples of financial institutions from the European Union (Betz, Oprica, Peltonen, & Sarlin, 2014), Russia (Peresetsky, Karminsky, & Golovan, 2011), North America (Cleary & Hebb, 2016;Lane, Looney, & Wansley, 1986), Iran (Valahzaghard & Bahrami, 2013), and Malaysia (Wanke, Azad, & Barros, 2016), as well as cross-country samples (Liu, 2015).…”

Section: Introductionmentioning

confidence: 99%

Financial distress in Brazilian banks: an early warning model,

Rosa

Gartner

2017

Rev. contab. finanç.

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ABSTRACTis study aims to propose an early warning model for predicting nancial distress events in Brazilian banking institutions. Initially, a set of economic-nancial indicators is evaluated, suggested by the risk management literature for identifying situations of bank insolvency and exclusively taking public information into account. For this, multivariate logistic regressions are performed, using as independent variables nancial indicators involving capital adequacy, asset quality, management quality, earnings, and liquidity. e empirical analysis was based on a sample of 142 nancial institutions, including privately and publicly held and state-owned companies, using monthly data from 2006 to 2014, which resulted in panel data with 12,136 observations. In the sample window there were nine cases of Brazilian Central Bank intervention or mergers and acquisitions motivated by nancial distress. e results were evaluated based on the estimation of the in-sample parameters, out-of-sample tests, and the early warning model signs for a 12-month forecast horizon. ese obtained true positive rates of 81%, 94%, and 89%, respectively. We conclude that typical balance-sheet indicators are relevant for the early warning signs of nancial distress in Brazilian banks, which contributes to the literature on nancial intermediary credit risk, especially from the perspective of bank supervisory agencies acting towards nancial stability.

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Bank Failure Prediction Models for the Developing and Developed Countries: Identifying the Economic Value Added for Predicting Failure

Wang

Jiang

Liu

2016

Asian Economic and Financial Review

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This study used data from 2003-2013, and used a logistic model to analyze the factors that influence financial early warning systems in developing and developed countries. We employed a bank capital adequacy ratio less than 8%, Tier I capital ratio less than 4%, and nonperforming loan ratio more than one third to measure bank failure and identify the financial ratio that most accurately predicts bank failure. The results indicate that the economic value added index is more effective than other indexes in predicting bank failure in NAFTA, ASEAN, EU, NIC, and G20 nations.

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Prediction of default probability in banking industry using CAMELS index: A case study of Iranian banks

Cited by 6 publications

References 7 publications

Cross-Country Study on the Determinants of Bank Financial Distress

Cross-Country Study on the Determinants of Bank Financial Distress

Financial distress in Brazilian banks: an early warning model,

Bank Failure Prediction Models for the Developing and Developed Countries: Identifying the Economic Value Added for Predicting Failure

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