Prediction of Bank Failures Based on Zmisky and Toffler Models in the Banking Industry of Iran

Bani, Mahmoud; Zadehbagher, Mostafa; Jalili, Jalal; Saberimanesh, Mohammad Ali

doi:10.12816/0018813

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…In this case, an extreme value theory-based model has a higher predictive value than classic logistic regression. Bani, Zadehbagher, and Saberimanesh (2014) have conducted research on bank failure in Iran. Data used was 17 commercial and private banks in Iran from 2008 to 2012.…”

Section: Literature Reviewmentioning

confidence: 99%

Bank Failure Prediction Model Based on Governance: A Case of Rural Banks in Indonesia

Suwandi¹,

Achsani²,

Hakim³

et al. 2019

ASS

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Since it was first operating in 2005 until 2017, Indonesia Deposit Insurance Corporation (IDIC) has liquidated 91 rural banks which were determined as failed banks by supervision authority. The cause of the failing of the bank is mainly due to the incapability of the bank to meet the minimum Capital Adequacy Ratio (CAR). Bank’s capital was shrunk by the loss caused by fraud. The fraud is mostly induced by the lack of good corporate governance implementation. By using the logistic regression, it can be concluded that (1) the incomplete of responsibility letter which will be used in the event of bank failure, submitted by the bank commissioners; (2) the incomplete of responsibility letter which will be used in the event of bank failure, submitted by the bank directors; (3) role duplication between shareholders and board of directors; and (4) bank had classified as special supervision, have impact on the increase of rural banks failure. At the same time, the compliance level of rural banks to a correct premium payment has impacted to decrease of rural banks failure possibilities.

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Section: Literature Reviewmentioning

confidence: 99%

Bank Failure Prediction Model Based on Governance: A Case of Rural Banks in Indonesia

Suwandi¹,

Achsani²,

Hakim³

et al. 2019

ASS

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Evaluating Financial Performance of Saudi Listed Firms: Using Statistical Failure Prediction Models

Radhi

Sarea

2019

IJBEG

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The study aims to compare the classification power of three statistical failure prediction models for evaluating financial performance of Saudi Listed Firms. The study sample consisted of 122 listed industrial companies in the Saudi Stock Exchange for the period from 2014 to 2016. Altman model 1968, Kida model and Zmijewski are used as examples of statistical failure prediction models to evaluate the classification power of the given models to assess the financial performance of firms listed on Saudi Stock Exchange. The results showed that Zmijewski model was more powerful in predicting the financial performance of Saudi listed firms than Altman model (1986) and Kida model. The results showed that there are a statistical relationships between some ratios included in the three models and the financal performance of industrial companies, which was measured by EPS. The study recommended users of financial statements of Saudi listed companies to use Zmijewski ?model, which performs well in evaluating their finacial position to be used when making the ?financial decisions.

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Prediction of Bank Failures Based on Zmisky and Toffler Models in the Banking Industry of Iran

Cited by 2 publications

References 6 publications

Bank Failure Prediction Model Based on Governance: A Case of Rural Banks in Indonesia

Bank Failure Prediction Model Based on Governance: A Case of Rural Banks in Indonesia

Evaluating Financial Performance of Saudi Listed Firms: Using Statistical Failure Prediction Models

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