Bank failure prediction: corporate governance and financial indicators

Both statistical and non-statistical models have been used to predict firms’ failures. Among statistical methodologies, the most common is the DA, which was initially used by Altman (1968) and then developed and adopted by Boyacioglu et al. (2009), Canbas et al. (2005), Cielen et al. (2004), Cox and Wang (2014), du Jardin (2017), du Jardin (2016), Haslem et al. (1992), Kao and Liu (2004), Karels and Prakash (1987), Ohlson (1980) and Serrano-Cinca and Gutiérrez-Nieto (2013). Other methodologies include LR used by Boyacioglu et al. (2009), Brédart (2014a), Canbas et al. (2005), Daily and Dalton (1994), du Jardin (2017), du Jardin (2016), Kao and Liu (2004), Lee and Yeh (2004), Ohlson (1980), Serrano-Cinca and Gutiérrez-Nieto (2013), Wang et al. (2014), West (1985) and Wu (2016), Principal Component Analysis (PCA), used by Boyacioglu et al. (2009), Canbas et al. (2005) and Kao and Liu (2004), and PLS-DA, used by Serrano-Cinca and Gutiérrez-Nieto (2013).

Among non-statistical models, artificial intelligence tools are widely used for failure prediction. In most studies, they have proven to be highly accurate. However, Boyacioglu et al. (2009) used both statistical and artificial intelligence techniques to predict the failure of Turkish banks during the crisis. Their findings show that, while artificial intelligence tools are superior prediction techniques, the other statistical techniques also provide satisfying results in prediction. Similarly, Jones et al. (2017) show that simple classifiers such as LR and DA perform reasonably well in bankruptcy prediction. In addition, Alaka et al. (2018) use several prediction tools including two statistical tools (DA and LR) and six artificial intelligence tools. They found that no single tool is predominantly better than other tools.

Other studies compare several statistical and intelligence methodologies. Boyacioglu et al. (2009) find that DA and LR analysis are better failure predicting models among other models including neural network, support vector machine, and cluster analysis. In assessing bank crisis, Davis and Karim (2008a) compare LR with signal extraction in early warning systems, and in another study, Davis and Karim (2008b) compared LR with binomial tree-based early warning systems. The results of both studies suggest that LR performs better than the rest of the techniques.

Pioneers in failure prediction have utilised financial ratios for the prediction of firm failure using statistical models (Beaver 1966; Altman 1968; Ohlson 1980). Subsequently, studies have mainly incorporated the traditionally used financial ratios but with different feature selection techniques, including Boyacioglu et al. (2009), Chauhan et al. (2009), Cox and Wang (2014), du Jardin (2010, 2016, 2017), Feki et al. (2012), Hosaka (2019), Lin et al. (2011), López Iturriaga and Sanz (2015), Serrano-Cinca and Gutiérrez-Nieto (2013) and Wang et al. (2014).

The selection of the financial ratios is an important process in failure prediction (Wang et al. 2014). du Jardin (2017) was able to have up to three years’ horizon prediction using variables selected based on prior literature. Because the financial structure and characteristics of banks differ from other sectors (Cielen et al. 2004; Wu 2016), common financial ratios used in non-financial sectors might not apply to banks. As a result, researchers have tried to adopt ratios in the CAMELS rating system as predictors.

CAMELS is a six-part rating system to evaluate banks’ overall condition based on their Capital adequacy, Asset quality, Management expertise, Earning strength, Liquidity, and Sensitivity to market risk. This rating system was developed by the Uniform Financial Institutions Rating System (UFIRS) in 1979 and is mandated by the Federal Deposit Insurance Corporation Improvement Act (FDICIA) of 1991 (Federal Deposit Insurance Corporation 1997).

Initially, the rating system consisted of only five groups which are Capital, Assets, Management, Earnings and Liquidity. In 1995, an additional group was added which is the Sensitivity to market which formed the currently used CAMELS rating system. 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. For this reason, this study incorporates the initial CAMEL rating system. Incorporating financial ratios that will test these five aspects of the rating system will enable us to have an overall coverage of the banks’ financial conditions.

Studies that used CAMELS include Boyacioglu et al. (2009), Feki et al. (2012) and Kristóf and Virág (2022)to predict failure in Turkish, Tunisian, and European banks respectively. Similarly, López Iturriaga and Sanz (2015) declare that their variables selection approach is close to the CAMEL rating system in studying the failure prediction in banks. Their model shows that the three financial ratios that have the most predictive power are the provision ratio, the risk concentration in the construction industry, and the equity support to loans. Also, the Canbas et al. (2005) study aims to construct an early warning system as a decision-support tool in banks. In studying Turkish banks, they find that PCA can be used as an alternative or supportive tool to the CAMELS rating system (Gasbarro et al. 2002).

Existing studies that have examined the failure prediction of banks in the US include Serrano-Cinca and Gutiérrez-Nieto (2013) who use financial ratios to compare Partial Least Square Discriminant Analysis (PLS-DA) with eight other techniques. They assert that the US banking crisis is not over and that The FDIC recognizes that there are many banks at risk of failure. Also, López Iturriaga and Sanz (2015) predict the failure of US banks using a variables selection approach that is close to the CAMEL rating system. Other studies that have utilized financial ratios to study the failure prediction of US banks include Chauhan et al. (2009) and Cox and Wang (2014). However, none of these variables incorporates non-financial variables to predict bank failure.

In studying corporate bankruptcies, Jones (2017) finds that bankruptcy is better explained and predicted in a multi-dimensional setting. The author uses multiple non-financial and financial variables to predict bankruptcy and finds that non-traditional variables, such as ownership structure/concentration and CEO compensation, are among the strongest predictors. Also, Ioannidis et al. (2010) use several financial and non-financial variables to assess banks’ soundness; they find that the accuracy of classification of the models that include only financial variables is poor. This gives enough reason to believe that adding non-financial variables, such as CG, to the CAMEL ratios will enhance the accuracy of predicting bank failure.

Some studies have incorporated non-financial variables such as market-driven variables. Studies that use non-financial predictors include Cheng et al. (2018), Beaver et al. (2005), Jones (2017), Shumway (2001) and Charalambakis and Garrett (2016). CG is among the non-financial variables used in prediction (Daily and Dalton 1994; Lee and Yeh 2004; Brédart 2014a, b; Liang et al. 2016; Wu 2016; Jones 2017). However, 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. They also declare that previously conducted studies have only used some selected features of CG, which suggests the need for a thorough examination of various CG indicators.

Similarly, Jones (2017) asserts that, despite having good theoretical reasons that relate CG indicators to failure, few studies examine them as alternative failure predictors. 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). Lee and Yeh (2004) and Wu (2016) state that CG leads to corporate value reduction, but the question remains as to whether it also leads to financial distress. Also, Al-Faryan and Dockery (2021) find that the period following the CG change of firms listed in the Saudi Stock Markets shows sub-period improvement in market efficiency, and Enache and Hussainey (2020) find that CG has a positive effect on current and future firm performance up to two years ahead. While Zhai et al. (2022) find that CG drives the negative effect of bank risk-taking incentives on lending decisions. These arguments and findings give us reasons to believe that CG plays an important role in the success of firms.

To study financial distress in listed firms, Lee and Yeh (2004) use both financial ratios and CG indicators including board and ownership. They assert that weak CG leads to economic downturns and increases the probability of falling into financial distress. Likewise, Wu (2016) studies the relationship between CG variables and the risk of bankruptcy in firms. The author finds that board size and board independence are most significantly related to bankruptcy risk. The results show that CG variables are strong predictors of failure, but their prediction accuracy increases only nearer the time of bankruptcy. On the other hand, 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. Brédart (2014b, 2014a) finds that board size, CEO ownership, and CEO duality are significantly related to the financial distress of a firm.

One of the few studies that use CG as an alternative failure predictor is a study by Liang et al. (2016), who combine financial ratios with CG variables to predict failure. They conduct their study on non-financial firms in Taiwan by using statistical and artificial intelligence techniques. Their results suggest that CG enhances the accuracy of prediction and improves the performance of all models utilised in their study. They assert that their results may not apply to other markets due to the differences in the definition of distressed companies and CG indicators. They find that the most important CG indicators to predict failure are the ones related to the board and ownership structure. Jones (2017) uses 91 different predictor variables, including financial and non-financial predictors. He finds that the most significant predictors are ownership structure and CEO compensation, then market and accounting variables, and finally macro-economic variables. 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. 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.

We follow a two-step variable selection approach for the financial ratios. First, we use prior literature to select the financial ratios which have been used to predict bankruptcy or failure in studiesshown in Appendix 1, which resulted in 176 ratios. Next, we selected ratios that were found to be significant, which resulted in 43 ratios.¹ Then, 23 ratios were chosen out of the 43 based on the data availability. The second step is using the CAMEL rating system as a criterion for categorising the ratios into five groups, namely Capital, Assets, Management, Earnings, and Liquidity. It is worth mentioning that our review showed that the only significant variable in the sensitivity to market category which contributes to failure prediction is the volatility of stock return. This variable had no data availability; hence, we incorporate the 1991 CAMEL rating system in our study and exclude the Sensitivity to market category. The 23 CAMEL ratios are detailed in Panel B in Table 1.

As for CG variables, we have chosen all variables related to CG available on the Bloomberg database. We started with 72 variables, then eliminated variables with low data availability, and ended up with 23 variables that represent board characteristics, compensation structure, voting rights and ownership structure. The CG variables are detailed in Panel C in Table 1.

To confirm the results of CG in predicting bank failure, we replace the CG variables obtained from Bloomberg with another set of CG variables, which are the governance scores developed by the Institutional Shareholder Services (ISS). The ISS scores are detailed in Panel D in Table 1.

This study includes samples of failed and non-failed banks that are insured by FDIC from 2010 to 2018. The financial data were obtained from the FDIC website and the CG data from the Bloomberg database. Failed banks in the FDIC database include institutions entering receivership, had their deposits assumed by others, and merged into others under federal assistance plans (Bell 1997). However, in this study, failed banks are limited to either delisted or merged banks according to the Bloomberg database. The models are performed with five different datasets, as detailed in Table 1, namely CAMEL, CG, ISS, CAMEL with CG, and CAMEL with ISS.

The analysis includes matched samples that were constructed following Altman (1968) and Beaver (1966) in pairing the datasets based on a stratified random sampling, in which a non-failed bank of similar size is matched for every failed bank for the corresponding year. 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. Therefore, the effect of the bank size is controlled for in constructing the sample.

The stratified random sampling technique has been recently used by Hartnett and Shamsuddin (2020), Islam et al. (2019) and Sarhan et al. (2018). This technique avoids a biased sample by ensuring that the samples for both the failed and non-failed banks include the best match. Beaver (1966) declares that this sampling technique controls for factors that might affect the relationship between ratios and failure prediction. In addition, this sampling technique accounts for the class imbalance problem caused by the difference between the number of failed and non-failed cases, which could lead to a degradation in the performance of the prediction (Liang et al. 2016).

We use a Mann–Whitney test to assess the discriminatory power of each variable and ratio by testing the discrepancies between failed and non-failed banks for one year before failure. Eight CAMEL ratios and three CG variables showed significant discrimination between failed and non-failed firms, as shown in Table 3.

The eight CAMEL ratios are PTItoE, ECofNCO and AperE, which are under the Capital, Assets and Management categories respectively, NIEtoTI, IBEItoA and IBEItoA under the Earnings category, and, finally, NLLtoD and GLtoTD under the Liquidity category.

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. These results show that none of the variables that represent board characteristics has discriminatory power.

We investigate the prediction accuracy using only CAMEL ratios; this will enable us to compare the results with the other datasets when CG variables are added. The Mann–Whitney test resulted in eight significant ratios; the discriminant function for the CAMEL ratios is as follows:where \(D_{1}\) is a discriminant score, \(B_{0}\) is the constant, \(B_{1}\) to \(B_{8}\) are the coefficients. \(PTItoE\) is the Pre-Tax Income to Equity ratio, \(ECofNCO\) is Earnings Coverage of Net Charge Offs, \(AperE\) is the Assets per Employee, \(NIEtoTI\) is the Non-Interest Expenses to Total Income ratio, \(IBEItoA\) is the Income Before Extraordinary Items to Assets ratio, \(REtoTA\) is the Retained Earnings to Total Assets ratio, \(NLLtoD\) is the Net Loans and Leases to Deposits ratio, and \(GLtoTD\) is the Gross Loans to Total Deposits ratio.

The result of measuring the accuracy of predicting failure by using the CAMEL ratios are reported in Table 4. The overall accuracy ranges from 60.3% for three years before failure to 61.1% for one year before failure. 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. Also, the canonical correlation and the Chi-square show that the models have acceptable discriminant ability.

The results show that IBEItoA and REtoTA are significant at 1% in all models, while NIEtoTI decreases from very significant in the one-year lagged model to not significant in the three-year lagged model. These ratios represent the earnings of a bank and their coefficients illustrate that banks with lower earnings relative to assets are more likely to fail. These results are in line with Kristóf and Virág (2022) who find that earneds is one of the strongest predictors of bank failure. Also, NLLtoD and GLtoTD, which represent liquidity, are significant at 5% in all models and 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. On the other hand, the other variables that represent the capital structure, asset quality, and management of banks are not significant across all models. These results are interesting and unexpected since they show that the earnings and liquidity of a bank are more significant than its capital structure and asset quality to predict failure. 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. 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). In addition, the increase of liquidity in failed banks implies that failed banks liquidate their assets nearer to their failure. The increase can also be due to the bailouts provided by the government for failing banks. On the other hand, the results related to the ratios of the capital structure and asset quality show that they are insignificant in comparison to the other aspects. This insignificance might be due to the banks’ capability to increase their capital ratios through reducing lending or selling assets (Gopalan 2010), which will result in concealing the capital’s deterioration in failed banks.

In this model, we investigate the prediction accuracy using CG variables. The discriminant function is as follows:where \(D_{2}\) is a discriminant score, \(B_{0}\) is the constant, \(B_{1}\) to \(B_{3}\) are the coefficients. \(CPS\) is the CEO Pay Slice, \(UVR\) is the Unequal Voting Rights, and \(InstitutO\) is the Institutional Ownership.

The results of the DA for the second dataset, which represents the CG variables, are reported in Table 5. 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. 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, which shows that CG is better than CAMEL for long-term prediction. All variables are significant, notably the CPS, which is significant at 1% in all models. 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. These results are in line with the findings of Jones (2017): that CEO compensation and ownership structure are among the strongest non-traditional predictors. 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).

In addition, the results show that unequal voting rights and institutional shareholding are the next significant non-financial predictors with positive effects. 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). Also, institutional shareholders pressurise management to deliver short-run performance because they do not internalise the social costs and institutional arrangements of financial institutions’ failures (Erkens et al. 2012; Andreou et al. 2016).

In this model, we investigate the prediction accuracy using CAMEL ratios with CG variables together. The discriminant function is as follows:where \(D_{4}\) is a discriminant score, \(B_{0}\) is the constant, \(B_{1}\) to \(B_{11}\) are the coefficients. \(PTItoE,\) \(ECofNCO,\) \(AperE,\) \(NIEtoTI,\) \(IBEItoA,\) \(REtoTA,\) \(NLLtoD,\) and \(GLtoTD\) are the CAMEL ratios. \(CPS,\) \(UVR,\) and \(InstitutO\) are the CG variables.

The results of the DA for both the CAMEL ratios with the CG variables are reported in Table 6. 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. Also, the accuracy percentages have increased significantly in comparison to using them individually. 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). Another notable finding is that the accuracy of prediction is increasing as the time horizon increases, comparing one and three years before failure. These results show that CG variables not only enhance the accuracy but also extend the time horizon of prediction. This finding confirms the crucial role of CG in assuring the proper functioning of banks, as suggested by the Basel Committee on Banking Supervision (Basel Committee on Banking Supervision 2015). 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. This confirms that having a multi-dimensional setting by including different aspects of the bank provides a better prediction of failure (Jones 2017).

Also, the coefficients and their significance confirm and complement the previous findings using the first and the second datasets. 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. 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. On the other hand, these models show that liquidity ratios are not significant predictors, in contrast to the results using the first dataset. 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. In addition, the unequal voting rights and institutional ownership are less significant and have fewer impacts in comparison to the models using the CG variables only.

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. We think that this is due to combining many variables in four indices that are not suitable for the prediction of failure. We excluded the results from the paper.

Using the fifth dataset, we investigate the prediction accuracy using CAMEL ratios with the four ISS scores. The discriminant function is as follows:where \(D_{5}\) is a discriminant score, \(B_{0}\) is an estimated constant, \(B_{1}\) to \(B_{12}\) are the estimated coefficients. \(PTItoE,\) \(ECofNCO,\) \(AperE,\) \(NIEtoTI,\) \(IBEItoA,\) \(REtoTA,\) \(NLLtoD,\) and \(GLtoTD\) are the CAMEL ratios. \(ISSB,\) \(ISSS,\) \(ISSC,\) and \(ISSA\) are the ISS scores that represent CG.

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.