The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index; 4 of the 49 sources its claims cite were checked for support.
Bank failure prediction: corporate governance and financial indicators
Open accessAbstract
This paper reiterates the importance of corporate governance in banks. Failure prediction studies have mainly relied on using financial ratios as predictors. The most suitable financial predictors for banks are financial ratios following the CAMEL rating system. Also, corporate governance has been proven to be an important aspect of banks, especially after the financial crisis. Given its importance, the novelty of this paper is to test the ability of corporate governance to increase the accuracy and extend the time-horizon of bank failure prediction in the US market. Using discriminant analysis, we predict the failure of banks insured by the Federal Deposit Insurance Corporation from 2010 to 2018. Using financial and non-financial predictors, we find that combining CAMEL ratios with corporate governance variables not only increases the accuracy of prediction but also extends the time horizon to three years before failure. We also show that bank earnings is a more significant predictor than capital structure and asset quality. The results further reveal that CEO compensation, voting rights and institutional ownership are significant predictors. These results are robust when using logit regression and out-of-sample examination. This study shows that corporate governance plays a key role in the success or failure of banks. The regulatory implication of this paper is that more attention needs to be directed to corporate governance and earnings aspects of banks rather than focusing on capital structure.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This paper tests whether adding corporate governance variables to CAMEL financial ratios improves US bank failure prediction. It finds that combining CAMEL ratios with governance variables increases accuracy and extends the prediction horizon to three years before failure.
Key findings
- Combining CAMEL ratios with corporate governance variables raised three-year-lagged prediction accuracy to 71.4%, compared with 60.3% using CAMEL ratios alone and 64.1% using CG variables alone.
- In out-of-sample tests, the CAMEL + CG model achieved 61.3% accuracy one year before failure and 72.4% three years before failure, while the CAMEL-only model fell from 60.20% to 55.17% over the same horizons.
- Among CAMEL categories, earnings and liquidity were more significant predictors than capital structure and asset quality; among governance variables, CEO pay slice, unequal voting rights, and institutional ownership were significant predictors.
- Replacing Bloomberg CG variables with ISS governance scores produced relatively similar results for 2013–2018, again supporting the early predictive power of CG.
Methods
- The study uses discriminant analysis as the main model, with logit regression and out-of-sample examination as robustness checks.
- Data cover FDIC-insured failed and non-failed US banks from 2010 to 2018; financial data come from FDIC and CG data from Bloomberg, with failed banks limited to delisted or merged banks according to Bloomberg.
- Five datasets are used—CAMEL, CG, ISS, CAMEL + CG, and CAMEL + ISS—with variables lagged one, two, and three years before failure; samples are matched by stratified random sampling based on bank size.
Limitations
- The paper states that data availability limited variable selection: the sensitivity-to-market CAMEL category was excluded because its only significant variable, stock return volatility, had no available data, and many CG variables were dropped for low data availability.
- The paper states that ISS variables in dataset 4 were not significant and had much lower accuracy, so those results were excluded from the paper.
- The text makes evident that findings are limited to FDIC-insured US banks during 2010–2018, with failure defined as delisting or merger per Bloomberg, which may restrict generalizability; the paper does not include a dedicated limitations section.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
4 of 49 cited sources checked · 1 supported · 0 flagged · accuracy not yet measured
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We also show that bank earnings is a more significant predictor than capital structure and asset quality.”
§ Bank failure prediction: corporate governance and financial indicators > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results further reveal that CEO compensation, voting rights and institutional ownership are significant predictors.”
- Also stated in: § 1 Introduction · plain
“On the other hand, amongst the CG variables, CEO pay slice, unequal voting rights and institutional shareholding are the most significant predictors.”
§ Bank failure prediction: corporate governance and financial indicators > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § 1 Introduction · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The findings also show that, amongst the CAMEL ratios, earnings and liquidity are the more significant predictors.”
§ Bank failure prediction: corporate governance and financial indicators > 1 Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the other hand, the other variables that represent the capital structure, asset quality, and management of banks are not significant across all models.”
§ Bank failure prediction: corporate governance and financial indicators > 4.1 Dataset 1: CAMEL ratios
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Accuracy % | 61.1% | 61.2% | 60.3%”
§ Bank failure prediction: corporate governance and financial indicators > No Variable Definition Database
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Accuracy % | 63.1% | 62.4% | 64.1%”
§ Bank failure prediction: corporate governance and financial indicators > No Variable Definition Database
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results of the CG for both the DA and the LR in are relatively similar to the analysis using different sizes of paired samples.”
§ Bank failure prediction: corporate governance and financial indicators > 5 Robustness test
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“% Accuracy-Non-failed | 50.00 | 51.20 | 37.50”
§ Bank failure prediction: corporate governance and financial indicators > No Variable Definition Database
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“InstitutO | 0.552*** | 0.588*** | 0.618**”
§ Bank failure prediction: corporate governance and financial indicators > No Variable Definition Database
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results show that IBEItoA and REtoTA are significant at 1% in all models”
§ Bank failure prediction: corporate governance and financial indicators > 4.1 Dataset 1: CAMEL ratios
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 52
- The quote only partly states this claim. 2
- Probably not a substantial claim. 24
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Brédart (2014bBrédart ( , 2014a) ) finds that board size, CEO ownership, and CEO duality are significantly related to the financial distress of a firm.”
§ Bank failure prediction: corporate governance and financial indicators > 2.3 Non-financial ratios
What the sources say
- Partly supportedcites Brédart 2014b sourceExperimental — accuracy not yet measured
Section 4.2 and Table 3 report significant associations for CEO ownership only after 15 September 2008 and CEO duality only before that date. Board size is described as only partially significant in both periods (p < .10), so the unqualified claim that all three are significantly related overstates the results. (LLM, conf=0.88)
- Partly supportedcites Brédart 2014a sourceExperimental — accuracy not yet measured
Section 4.2, “Logit Regressions” (page unavailable), finds board size significantly negatively related to financial distress (b = −0.15, p < .01), but explicitly says CEO/chairman duality is not significantly related. The “Independent Variables” section lists the measures studied and does not include CEO ownership; our copy does not support that part of the claim. (LLM, conf=0.92)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The propensity score approach helps in alleviating the omitted variable concern, allows for a more accurate analysis.(Rosenbaum and Rubin 1983;Heckman et al 1997; Houston et al. 2014).”
§ Bank failure prediction: corporate governance and financial indicators > 5 Robustness test
What the sources say
- Partly supportedcites Rosenbaum and Rubin 1983 sourceExperimental — accuracy not yet measured
The Abstract says propensity-score adjustment removes bias due to observed covariates, and the Theory section says it can yield unbiased treatment-effect estimates when treatment assignment is strongly ignorable. These passages support more accurate analysis under that condition, but not the unqualified implication that the approach resolves concerns about omitted, unobserved variables. (LLM, conf=0.86)
- Not checked — no full text of the sourcecites Heckman et al 1997 source
- Supportedcites Houston et al. 2014 source
Section 5.2 says propensity score matching helps alleviate concern about nonlinear control-variable effects and allows for a more accurate analysis. Section 5.3 cautions that it may not help when unmeasured covariates remain, but the citing sentence says only that it helps alleviate the concern. (LLM, conf=0.92)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using financial and non-financial predictors, we find that combining CAMEL ratios with corporate governance variables not only increases the accuracy of prediction but also extends the time horizon to three years before failure.”
- Also stated in: § 1 Introduction · plain
“The results show that adding CG variables to the traditionally used financial ratios enhances the accuracy rate and extends the time horizons.”
§ Bank failure prediction: corporate governance and financial indicators > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § 1 Introduction · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, the robustness test using the LR confirms the findings of the DA, where adding CG as a non-financial predictor to the financial ratios enhances the accuracy of failure prediction and extends the time horizon.”
§ Bank failure prediction: corporate governance and financial indicators > 5 Robustness test
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The three CG variables are the CPS, which represents the CEO's compensation in comparison to that of the other executives, UVR, which represents the voting rights of shareholders, and InstitutO, which represents institutional ownership.”
§ Bank failure prediction: corporate governance and financial indicators > 3.3 Discriminatory power test
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, the results show that unequal voting rights and institutional shareholding are the next significant non-financial predictors with positive effects.”
§ Bank failure prediction: corporate governance and financial indicators > 4.2 Dataset 2: CG variables
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Despite the decrease in the significance of the models, the eigenvalue, canonical correlation and chi-square show that the function has a better discriminant ability when combining the CAMEL ratios and the CG variables.”
§ Bank failure prediction: corporate governance and financial indicators > 4.3 Dataset 3: CAMEL and CG
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With regard to CAMEL, the REtoTA shows robust and significant findings across all models, which confirms that failed banks have fewer earnings relative to assets.”
§ Bank failure prediction: corporate governance and financial indicators > 4.3 Dataset 3: CAMEL and CG
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results based on propensity score matching reinforce the conclusion that the accuracy of failure prediction is enhanced when combining CG variables as non-financial predictor with financial predictors, which confirms the robustness of the results.”
§ Bank failure prediction: corporate governance and financial indicators > 5 Robustness test
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The propensity score matching adequately controls for differences between failed and non-failed banks.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“% Accuracy-failed | 73.7 | 75.0 | 72.2”
§ Bank failure prediction: corporate governance and financial indicators > No Variable Definition Database
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results are similar to the main analysis findings shown in Table 6 which shows that the combination of CG variables (non-financial variables) and CAMEL ratios (financial ratios) can predict failure up to three years before failure.”
§ Bank failure prediction: corporate governance and financial indicators > 6 Additional analysis
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The eigenvalue, canonical correlation, and chi-square show that all models have a good discriminant ability, and Wilk's Lambda p-value shows that the discriminant function is statistically significant.”
§ Bank failure prediction: corporate governance and financial indicators > 4.2 Dataset 2: CG variables
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The Wilks' Lambda P-value shows that the discriminant function is significant for one, two and three years' lagging, which shows that the categorising power of the function is high.”
§ Bank failure prediction: corporate governance and financial indicators > 4.1 Dataset 1: CAMEL ratios
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, the F-test is shown in Table 2. Reveal that small banks and large banks have unequal variances, with a higher mean value for large banks.”
§ Bank failure prediction: corporate governance and financial indicators > 3.2 Data sampling
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, the percentage for the three-year lagged model has increased to 71.4% (from 60.3% using CAMEL ratios, and 64.1% using CG variables).”
§ Bank failure prediction: corporate governance and financial indicators > 4.3 Dataset 3: CAMEL and CG
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, the models with added CG variables have better prediction accuracies that increase up to three years before failure.”
§ Bank failure prediction: corporate governance and financial indicators > 7 Conclusion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These results are robust to the out-of-sample examination which confirms the validity of the prediction model.”
§ Bank failure prediction: corporate governance and financial indicators > 7 Conclusion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one.
- Rests on an assumption: The sample of failed and non-failed banks is representative of the broader banking population, so the prediction models generalize.
- Rests on an assumption: The relationship between the predictors and bank failure is stable over time, so a model trained on 2010-2016 predicts 2017-2018.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“By combining financial and non-financial variables, we were able to not only prevent the accuracy rates from dropping dramatically but also in some cases to improve them.”
§ Bank failure prediction: corporate governance and financial indicators > 7 Conclusion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As for the CG variables, CPS shows robust significance and effects across all models, which confirms that in failed banks CEOs receive a higher percentage of remuneration.”
§ Bank failure prediction: corporate governance and financial indicators > 4.3 Dataset 3: CAMEL and CG
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the other hand, these models show that liquidity ratios are not significant predictors, in contrast to the results using the first dataset.”
§ Bank failure prediction: corporate governance and financial indicators > 4.3 Dataset 3: CAMEL and CG
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Replacing CG variables with ISS scores shows relatively the same results for the years 2013 to 2018 shown in Table 7, which again confirms the early predictive power of CG.”
§ Bank failure prediction: corporate governance and financial indicators > 4.4 Datasets 4 and 5: CAMEL and ISS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear? Based: unclearIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“show that failed banks are less liquid and have fewer deposits in relation to loans and leases three years before failure, but are more liquid one year before failure.”
§ Bank failure prediction: corporate governance and financial indicators > 4.1 Dataset 1: CAMEL ratios
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It also confirms that adding CG variables to the model increases the prediction accuracy as the time horizon extends to three years (72.4% accuracy for three years before failure in comparison to 61.3% for one year before failure).”
§ Bank failure prediction: corporate governance and financial indicators > 6 Additional analysis
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The relationship between the predictors and bank failure is stable over time, so a model trained on 2010-2016 predicts 2017-2018.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The models also confirm that the capital structure, assets and management of banks are not significant predictors, except for PTItoE, which is only significant one year before failure.”
§ Bank failure prediction: corporate governance and financial indicators > 4.3 Dataset 3: CAMEL and CG
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Panel B in Table 9 shows that the accuracy rates of the model including the CAMEL ratios only range from 60.20% for one year before failure to 55.17% for three years before failure, while the model that includes the CG variables shown in Table 9 increases the accuracy rate to range from 61.3% for one year before failure to 72.4% for three years before failure.”
§ Bank failure prediction: corporate governance and financial indicators > 6 Additional analysis
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The relationship between the predictors and bank failure is stable over time, so a model trained on 2010-2016 predicts 2017-2018.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the fourth dataset, we used ISS scores as an alternative measurement of CG, but the variables were not significant and the accuracy was much lower.”
§ Bank failure prediction: corporate governance and financial indicators > 4.4 Datasets 4 and 5: CAMEL and ISS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All variables are significant, notably the CPS, which is significant at 1% in all models.”
§ Bank failure prediction: corporate governance and financial indicators > 4.2 Dataset 2: CG variables
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The robustness test also confirms that earnings and liquidity are the most significant aspects in CAMEL, while CPS and institutional shareholding are the most significant in CG.”
§ Bank failure prediction: corporate governance and financial indicators > 5 Robustness test
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results show that the earnings followed by the liquidity are the key determinants of bank failure, but capital, assets, and management are insignificant in failure prediction.”
§ Bank failure prediction: corporate governance and financial indicators > 7 Conclusion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Enache and Hussainey (2020) find that CG has a positive effect on current and future firm performance up to two years ahead.”
§ Bank failure prediction: corporate governance and financial indicators > 2.3 Non-financial ratios
What the sources say
- Not checked — no full text of the sourcecites Enache and Hussainey 2020 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For example, bank failure affects the whole economic stability (Boyacioglu et al. 2009), failure prediction enables banks to make appropriate lending decisions (Liang et al. 2016) and bank failure could have been prevented if appropriate failure prediction tools had been used (Kao and Liu 2004).”
§ Bank failure prediction: corporate governance and financial indicators > 2 Literature review
What the sources say
- Not checked — no full text of the sourcecites Boyacioglu et al. 2009 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“According to the review of prior literature on failure prediction, there is no variable in sensitivity to market that significantly contributes to failure prediction, except for one which has no available data, which is the volatility of stock return.”
§ Bank failure prediction: corporate governance and financial indicators > 2.2 Financial ratios
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Failure of banks not only affects the banks themselves but also reaches the global economy (Liang et al. 2016).”
§ Bank failure prediction: corporate governance and financial indicators > 1 Introduction
What the sources say
- Not checked — no full text of the sourcecites Liang et al. 2016 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For example, bank failure affects the whole economic stability (Boyacioglu et al. 2009), failure prediction enables banks to make appropriate lending decisions (Liang et al. 2016) and bank failure could have been prevented if appropriate failure prediction tools had been used (Kao and Liu 2004).”
§ Bank failure prediction: corporate governance and financial indicators > 2 Literature review
What the sources say
- Not checked — no full text of the sourcecites Liang et al. 2016 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In studying corporate bankruptcies, Jones (2017) finds that bankruptcy is better explained and predicted in a multi-dimensional setting.”
§ Bank failure prediction: corporate governance and financial indicators > 2.3 Non-financial ratios
What the sources say
- Not checked — no full text of the sourcecites Jones 2017 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These arguments and findings give us reasons to believe that CG plays an important role in the success of firms.”
§ Bank failure prediction: corporate governance and financial indicators > 2.3 Non-financial ratios
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Their review reveals that most studies were conducted on firms and not banks, and mainly focused on the period from 1980 to 2003.”
§ Bank failure prediction: corporate governance and financial indicators > 2 Literature review
What the sources say
- Not checked — no full text of the sourcecites Ravi Kumar and Ravi 2007 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“Also, Sinnadurai et al. (2022) find that distressed companies are more likely to recover if their distress is diagnosed at early stages.”
§ Bank failure prediction: corporate governance and financial indicators > 2 Literature review
What the sources say
- Not checked — no full text of the sourcecites Sinnadurai et al. 2022 source
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Their results suggest that CG enhances the accuracy of prediction and improves the performance of all models utilised in their study.”
§ Bank failure prediction: corporate governance and financial indicators > 2.3 Non-financial ratios
What the sources say
- Not checked — no full text of the sourcecites Liang et al. 2016 source
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The Basel Committee on Banking Supervision states that the effectiveness of CG is critical to ensure the proper functioning of the banking sector and the whole economy (Basel Committee on Banking Supervision 2015).”
§ Bank failure prediction: corporate governance and financial indicators > 2.3 Non-financial ratios
What the sources say
- Not matchedcites Basel Committee on Banking Supervision 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“The review also shows that the optimal prediction accuracy is one year before failure, from that point the accuracy rates decrease, where the average rate for a one-year horizon is 85% and decreases to 69.5% for five years horizons.”
§ Bank failure prediction: corporate governance and financial indicators > 1 Introduction
What the sources say
- Not checked — no full text of the sourcecites du Jardin 2017 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“For example, bank failure affects the whole economic stability (Boyacioglu et al. 2009), failure prediction enables banks to make appropriate lending decisions (Liang et al. 2016) and bank failure could have been prevented if appropriate failure prediction tools had been used (Kao and Liu 2004).”
§ Bank failure prediction: corporate governance and financial indicators > 2 Literature review
What the sources say
- Not checked — no full text of the sourcecites Kao and Liu 2004 source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“Similarly, López Iturriaga and Sanz (2015) state that the reliability of failure prediction is a concern when the time horizon exceeds the short term.”
§ Bank failure prediction: corporate governance and financial indicators > 1 Introduction
What the sources say
- Not checked — no full text of the sourcecites López Iturriaga and Sanz 2015 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The selection of the financial ratios is an important process in failure prediction (Wang et al. 2014).”
§ Bank failure prediction: corporate governance and financial indicators > 1 Introduction
What the sources say
- Not checked — no full text of the sourcecites Wang et al. 2014 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Daily and Dalton (1994) study the characteristics of failed banks and find that less board independence and more CEO duality show significant association with failure at three years before the bankruptcy event.”
§ Bank failure prediction: corporate governance and financial indicators > 2.3 Non-financial ratios
What the sources say
- Not checked — no full text of the sourcecites Daily and Dalton 1994 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The review shows that most studies provide predictions up to three years horizons, while fewer extend it to four-or five-year horizons.”
§ Bank failure prediction: corporate governance and financial indicators > 1 Introduction
What the sources say
- Not checked — no full text of the sourcecites du Jardin 2017 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“They assert that weak CG leads to economic downturns and increases the probability of falling into financial distress.”
§ Bank failure prediction: corporate governance and financial indicators > 2.3 Non-financial ratios
What the sources say
- Not checked — no full text of the sourcecites Lee and Yeh 2004 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The results show that CG variables are strong predictors of failure, but their prediction accuracy increases only nearer the time of bankruptcy.”
§ Bank failure prediction: corporate governance and financial indicators > 2.3 Non-financial ratios
What the sources say
- Not matchedcites Wu 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“Liang et al. (2016) declare that, even though the importance of CG is well recognised in the literature, little effort has been made to conduct empirical studies that test the contribution of CG indicators in failure prediction along with the financial ratios.”
§ Bank failure prediction: corporate governance and financial indicators > 1 Introduction
What the sources say
- Not checked — no full text of the sourcecites Liang et al. 2016 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Also, Cheng et al. (2018) results show that specific types of institutional investors can determine which firms will file for bankruptcy among a set of equally distressed firms.”
§ Bank failure prediction: corporate governance and financial indicators > 2.3 Non-financial ratios
What the sources say
- Not checked — no full text of the sourcecites Cheng et al. 2018 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“They find that the most important CG indicators to predict failure are the ones related to the board and ownership structure.”
§ Bank failure prediction: corporate governance and financial indicators > 2.3 Non-financial ratios
What the sources say
- Not checked — no full text of the sourcecites Liang et al. 2016 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These findings imply that incorporating different aspects will give a better view of the bank's condition and hence improve the prediction accuracy.”
§ Bank failure prediction: corporate governance and financial indicators > 7 Conclusion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The sample of failed and non-failed banks is representative of the broader banking population, so the prediction models generalize.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Existing studies that examine bank failure prediction have restricted their prediction models to financial ratios only.”
§ Bank failure prediction: corporate governance and financial indicators > 7 Conclusion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
In the paper
“This is in line with the proposition that the potential costs of a dual-class structure increase with time, while the potential benefits decrease, which indicates the importance of sunset provisioning (Bebchuk and Kastiel 2017).”
§ Bank failure prediction: corporate governance and financial indicators > 4.2 Dataset 2: CG variables
What the sources say
- Not checked — no full text of the sourcecites Bebchuk and Kastiel 2017 source
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results show that CG variables not only enhance the accuracy but also extend the time horizon of prediction.”
§ Bank failure prediction: corporate governance and financial indicators > 4.3 Dataset 3: CAMEL and CG
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The increase can also be due to the bailouts provided by the government for failing banks.”
§ Bank failure prediction: corporate governance and financial indicators > 4.1 Dataset 1: CAMEL ratios
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Above that, the accuracy increases as the lagging increases, which shows that CG is better than CAMEL for long-term prediction.”
§ Bank failure prediction: corporate governance and financial indicators > 4.2 Dataset 2: CG variables
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, this paper shows that adding CG variables (as nonfinancial predictors) to the traditional financial ratios not only enhances the accuracy of bank failure but also extends the time horizon of bank failure prediction.”
§ Bank failure prediction: corporate governance and financial indicators > 7 Conclusion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The sample of failed and non-failed banks is representative of the broader banking population, so the prediction models generalize.
In the paper
“Also, these results imply that the deterioration of earnings in failed banks starts early, which might be due to the provisioning for loan losses that have a direct impact on a bank's earnings (Gopalan 2010).”
§ Bank failure prediction: corporate governance and financial indicators > 4.1 Dataset 1: CAMEL ratios
What the sources say
- Not matchedcites Gopalan 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Also, the CPS represents CEO power, which has been found to have a negative effect on the monitoring power of boards (Pathan 2009), accounting profitability and stock returns (Bebchuk et al. 2011).”
§ Bank failure prediction: corporate governance and financial indicators > 4.2 Dataset 2: CG variables
What the sources say
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These studies of failure prediction include few aspects of CG and do not include important characteristics such as CEO duality, board meetings and gender diversity.”
§ Bank failure prediction: corporate governance and financial indicators > 2.3 Non-financial ratios
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results confirm that that adding CG variables not only enhance the accuracy but also extend the time horizon of prediction to three years before failure.”
§ Bank failure prediction: corporate governance and financial indicators > 6 Additional analysis
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The relationship between the predictors and bank failure is stable over time, so a model trained on 2010-2016 predicts 2017-2018.
In the paper
“Also, the increase in prediction accuracy when combining CG variables with CAMEL rations are in line with Brogi and Lagasio (2022) who find that CG is not important by itself.”
§ Bank failure prediction: corporate governance and financial indicators > 4.3 Dataset 3: CAMEL and CG
What the sources say
- Not checked — no full text of the sourcecites Brogi and Lagasio (2022) source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CPS is associated with agency problems and banks are more likely to fail if their CEOs receive high compensation in comparison to their executive directors.”
§ Bank failure prediction: corporate governance and financial indicators > 4.2 Dataset 2: CG variables
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“He finds that the most significant predictors are ownership structure and CEO compensation, then market and accounting variables, and finally macro-economic variables.”
§ Bank failure prediction: corporate governance and financial indicators > 2.3 Non-financial ratios
What the sources say
- Not checked — no full text of the sourcecites Jones 2017 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The prediction power of earnings that extends up to three years before failure indicates that the decisions related to earnings have a long-term effect.”
§ Bank failure prediction: corporate governance and financial indicators > 4.1 Dataset 1: CAMEL ratios
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The author uses LR alongside DA because it has two advantages over the latter: does not require optimality of explanatory variables and allows the use of qualitative variables.”
§ Bank failure prediction: corporate governance and financial indicators > 5 Robustness test
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
The paper relies on this without support.
The sample of failed and non-failed banks is representative of the broader banking population, so the prediction models generalize.
“The out-of-sample results shown in panel A in Table 9 indicate that the combination of CAMEL ratios and CG variables identifies a high number of failures.Therefore, the outof-sample analysis confirms that the prediction model has a good predictive ability.”
The paper relies on this without support.
The relationship between the predictors and bank failure is stable over time, so a model trained on 2010-2016 predicts 2017-2018.
“The sample of the earlier subperiod (2010-2016) is used to create the in-sample dataset and develop the prediction model.The second subperiod (2017-2018) is used to create the out-of-sample dataset and examine the prediction accuracy by employing the developed prediction model based on the in-sample dataset.”
The paper relies on this without support.
The propensity score matching adequately controls for differences between failed and non-failed banks.
“We match failed banks with non-failed banks using the propensity score and then re-estimate the discriminant analysis using CAMEL ratios ad CG variables.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Daily and Dalton study is described as both non-financial and about banks
MinorThe introduction includes Daily and Dalton (1994) among studies it says were conducted on non-financial firms, but the literature review describes the same study as examining failed banks.
“However, these studies were conducted on non-financial firms”
“On the other hand, Daily and Dalton (1994) study the characteristics of failed banks”
CG accuracy does not increase at every longer lag
MinorTable 5 assigns 62.4% to the two-year model, not the one-year model. Accuracy also falls from 63.1% at one year to 62.4% at two years, contrary to the stated increasing trend.
“The accuracy of predicting bank failure using CG variables is higher in comparison to the CAMEL ratios, where the percentage ranges from 62.4% one year before failure to 64.1% three years before failure.Above that, the accuracy increases as the lagging increases”
“| Accuracy % | 63.1% | 62.4% | 64.1% |”
List of eight significant CAMEL ratios repeats one ratio and omits another
MinorThe list names IBEItoA twice among the earnings ratios, whereas Table 3 reports significant results for both IBEItoA and REtoTA.
“NIEtoTI, IBEItoA and IBEI-toA under the Earnings category”
“IGR 0.284 IBEItoA 0.000 IEtoTE 0.708 REtoTA 0.000”
CAMEL accuracy exceeds the stated upper end of its range
MinorThe text gives 61.1% as the upper end of the accuracy range, but Table 4 reports 61.2% for the two-year model.
“The overall accuracy ranges from 60.3% for three years before failure to 61.1% for one year before failure.”
“| Accuracy % | 61.1% | 61.2% | 60.3% |”
Citations
Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY
Altman, E. I. (1968) · The Journal of Finance · doi:10.1111/j.1540-6261.1968.tb00843.x · Cited by 14,888
Financial Ratios and the Probabilistic Prediction of Bankruptcy
Ohlson, J. A. (1980) · Journal of Accounting Research · doi:10.2307/2490395 · Cited by 6,250
Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme
Heckman, J. J., Ichimura, H., & Todd, P. E. (1997) · The Review of Economic Studies · doi:10.2307/2971733 · Cited by 5,486
Financial Ratios As Predictors of Failure
Beaver, W. H. (1966) · Journal of Accounting Research · doi:10.2307/2490171 · Cited by 4,882
Forecasting Bankruptcy More Accurately: A Simple Hazard Model
Shumway, T. (2001) · The Journal of Business · doi:10.1086/209665 · Cited by 2,674
Erkens, D. H., Hung, M., & Matos, P. P. (2012) · Journal of Corporate Finance · doi:10.1016/j.jcorpfin.2012.01.005 · Cited by 1,202
Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review
Ravi Kumar, P., & Ravi, V. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.08.043 · Cited by 1,074
Strong boards, CEO power and bank risk-taking
Pathan, S. (2009) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2009.02.001 · Cited by 1,031
Bebchuk, L. A., Cremers, K. J. M., & Peyer, U. C. (2011) · Journal of Financial Economics · doi:10.1016/j.jfineco.2011.05.006 · Cited by 993
Flagged first, then by citations
Research works printed but not linked: 4
- Gopalan BY (2010) Earliest Indicator of Bank Failure Is Deterioration. Fed. Reserv. Bank, ST. Louis
- Haslem JA, Scheraga CA, Bedingfield JA (1992) An analysis of the foreign and domestic balance sheet strate. Manag Int Rev 32:55–76
- Islam SR, Eberle W, Bundy S, Ghafoor SK (2019) Infusing domain knowledge in AI-based “black box” models for better explainability with application in bankruptcy prediction. Preprint http://arxiv.org/abs/190511474
- Wu S (2016) Corporate governance and bankruptcy risk. J Acc Audit Financ 31:163–202
Cited by
Sorted by citations
Gholampoor, H., & Asadi, M. (2024) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer19020066 · Cited by 20
Serpa, L. J. V., Rodriguez, C. R., Pérez-Núñez, J.-R., & Navarro, C. (2025) · Journal of risk and financial management · doi:10.3390/jrfm18010026 · Cited by 8
Lee, S., Choi, K., & Yoo, D. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-02382-7 · Cited by 4
Rega, F. G., Russo, S., & Salerno, D. (2025) · Research in International Business and Finance · doi:10.1016/j.ribaf.2025.102977 · Cited by 4
Robust lessons learned from bank failures during the Great Financial Crisis
Goenner, C. F. (2023) · Review of Quantitative Finance and Accounting · doi:10.1007/s11156-023-01213-9 · Cited by 4
Metadata
- Authors
- Alzayed, N., Eskandari, R., & Yazdifar, H.
- Affiliations
- University of Bahrain (BH)
- University of Salford (GB)
- University of Derby (GB)
- Corresponding author
- Alzayed, Noora
- Venue
- Review of Quantitative Finance and Accounting
- Published
- 2023-05-22
- Volume
- 61
- Issue
- 2
- Pages
- 601-631
- ISSN
- 0924-865X (ISSN-L)
- 1573-7179
- OpenAlex
- W4377291306
- Semantic Scholar
- 2eee4dc444370d29d5f0269a5b51d0c75fc32494
- Cited by
- 23
- References
- 57
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 4 · 2023: 3
- FWCI
- 13.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Review of Quantitative Finance and Accounting (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Bournemouth University
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Social Sciences › Economics, Econometrics and Finance › Finance › Banking stability, regulation, efficiency
- Social Sciences › Economics, Econometrics and Finance › Finance › Corporate Finance and Governance
- Keywords
- bank failure prediction, corporate governance, financial ratios, Federal Deposit Insurance Corporation, bank earnings, CEO compensation, voting rights, institutional ownership, capital structure, asset quality, banking regulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-02, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-02 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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