**3.3 Literature review**

**3. Theoretical background**

**3.1 Financial distress concept**

oriented, and [5] Technical.

a different definition:

to meet its obligations.

without being insolvent.

negative.

**306**

There are several studies conducted in financial distress field, but there is no agreed of a formal definition of financial distress [3]. The absence of a formal definition of financial distress puts into questions on the validity of researches conducted within the domain. Different measures of standards would categorize non distressed firms as distressed and vice versa; thus, without a formal definition of financial distress, it would be very difficult to address this problem [3, 4]. Categorized financial distressed into three, namely: 1- event-oriented, 2- process-

*Linear and Non-Linear Financial Econometrics - Theory and Practice*

In the first category (event oriented), financial distress is mostly associated with

Failure, moreover, means that the realized rate of return on invested capital is significantly lower than prevailing rates on similar investments it should be noted that a company may have had an economic failure for many years, yet never failed

Insolvency, furthermore, is another term depicting negative firm performance, and is generally used in a more technical fashion; whereas technical insolvency may be a temporary condition although it is often the immediate cause of bankruptcy. [5] also defines that insolvency in bankruptcy sense is a condition where total liabilities exceed a fair value of total assets rendering the net worth of the firm

Default distress can be technical and/or legal and always involve the debtorcreditor relationship [5]. Technical default takes place when the debtor violates a condition of an agreement with a creditor, and can be grounds for legal action [5]. Bankruptcy may be understood as a formal process where a firm announces in court that it has gone bankrupt followed by the petition to liquidate its assets or to undergo a recovery program [6]. As for the second category, financial distress is defined as a process; this definition helps in understanding financial distress as a phenomenon in constructing a comprehensive theory of financial distress [3, 4] stated that financial distress is a process situated between solvent and insolvent, and considered as a condition where the company experiences low cash flow and losses

The third category defines financial distress through indicators used by various financial distress prediction models [3]. Though still criticized by many, the use of ratios in many financial distress prediction models is to produce results relating to the likelihood of financial distress and default within a company [3]. In general, ratios that measure profitability, liquidity and insolvency are commonly used in predicting financial distress, despite not knowing which one is the most significant [7]. Poor management has always been the core reason behind financial distress within companies [8]. Several non-internal factors, such as high interest rates, bad industrial performance, competition on the international level etc. may contribute to the occurrence of financial distress within a company [8]; conducted a research regarding the potential of financial distress within banks in UAE. In the research, [9] identifies several factors that are greatly relevant to financial distress, such as cost to income ratio as well as equity to asset ratio and non-performing loan ratio. Macroeconomic factors, on the other hand, do not play a significant role. [10] demonstrated that financial distress is considered as the financial problems faced by

terms such as default, failure and bankruptcy [4, 5]. Explained that Four terms mostly used interchangeably are default, failure, insolvency and bankruptcy; even though these terms are often used interchangeably, formally each of them presents

> Found that stock market information can be used to estimate leading indicators of bank financial distress [13]. Had selected 64 European banks [13] pacified a logit early warning model, designed for European banks, which tests if market based indicators add predictive value to models relying on accounting data [14] also study the robustness of the link between market information and financial downgrading in the light of the safety net and asymmetric information hypotheses Other of their results show that the accuracy of the predictive power depends on the extent to which bank liabilities are market traded [15]. Conducted a research to use the financial data to identify changes in bank conditions. They used the call-report data to predict deterioration in condition as measured by changes in two main factors. The call report data could be used to construct non statistical early-warning models that mimic the examination process. The two main factors are the CAMEL ratings, and the role of off-site monitoring in the banking examination process. Off-site monitoring is an alternative method for on-site monitoring system in a bank using the financial ratios. There are twenty two commonly used financial ratios selected [16]. Each ratio is included because it provides insight into a dimension of the financial condition of the sample banks that is reflected in the actual composite CAMEL rating. The ratios generally are similar to those used in previous earlywarning failure-prediction models. Fifty eight samples of banks in the US were chosen. They used logit regression and logit analysis ratio. They found five financial ratios that are significant as follows:


[18] Stated that the CAMEL ratings generally assess overall soundness of the banks, and identify and/or predict different risk factors that may contribute to turn the banks into a problem or failed banks. These banks tend to perform the FFS. Bangladesh Banks have included an additional key point of "Sensitivity to market risk" to be the CAMELS. However, [18)] has recommended the CAMELS Rating Framework to be the CAMELSS in order to comprehend the Islamic Banking that is "Shari'ah Rating". In line with [18].

to risk as compared to their traditional counterparts [23]. Aimed to analyze the financial performance of three selected Islamic Banks (Islami Bank Bangladesh Limited, Export Import Bank of Bangladesh Limited, Shahjalal Islami Bank Limited) over a period of eight years (2007–2014) in Bangladeshi banking sectors. For this reason, CAMEL Rating Analysis approach has been conducted and it is found that all the selected Islamic Banks are in strong position on their composite rating system. [1] aimed to analyze the performance of Islamic banks and conventional banks during the crisis and after the crisis, by comparing the performance of Islamic and conventional banks based in the Gulf Cooperation Council (GCC) during the period of 2008–2011 by deploying the CAMEL testing factors, his results showed that Islamic banks possessed adequate capital structure but have recorded lower ROAE and poor management efficiency. Asset quality and liquidity for both the modes of banking system have not recorded any significant difference. [2], Directed a study on the GCC for a period of 2002–2009, to assess the factors that affect the Islamic bank and conventional banks. The study included a sample of 38 conventional banks, and 13 Islamic banks. The factors that were studied were foreign ownership, bank specific variable and macroeconomic variables. Some interesting results were found. The cost-income was found to have a negative and significant impact on banks performance for Islamic and conventional banks. Equity was found out to be important factor in maximizing the profitability of Islamic banks. The size of the banks supported the economies of scale utilizing the ROE for Islamic banks. GDP was found to be positively related, while inflation negatively related to the banks performance [25]. Aimed to evaluate the soundness of Islamic banks in the GCC for the period 2008 to 2014. Methodology- The study involves 11 listed Islamic banks based in the GCC countries of Saudi Arabia, United Arab Emirates, Qatar, Bahrain, and Kuwait [25]. Applied the CAMEL parameters, which include Capital Adequacy, Asset quality, Management, Earning and liquidity.

*Determinants of Islamic Banks Distress in Gulf Council Countries (GCC)*

*DOI: http://dx.doi.org/10.5772/intechopen.95028*

Multivariate Z- score model is also used to ensure robustness of the results. Findings-The findings suggest that although the Islamic banks in the GCC have adequate capital, their asset quality and earning ability have deteriorated over the

The applications of Altman Z score on banks have previously been researched by several researches like [26, 27], for banks in India and [28] suggested that Altman Z score is an analytical tool that may be applied in the banking industry. Additionally, [29] stated that Altman Z score has better predicting capabilities than CAEL model

However, several studies indicated the inappropriateness of Altman Z score in predicting financial distress within banks. A study conducted [25] applied Altman Z score model, CAEL model and bankometer model altogether within the Bank of Papua in Indonesia. His results showed that the results of Altman Z score model in many occasions were contradicted with the results of CAEL model. Altman Z score model was initially formed from an empirical study of manufacturing companies

Z ¼ 6*:*56 X1 þ 3*:*26 X2 þ 6*:*72 X3 þ 1*:*05 X4 (1)

which is very much different from banking institutions [30].

Z = a proxy variable of insolvency risk. X1 = working capital to Total Assets [28]. X2 = retained earnings to Total Assets [28]. X3 = earnings before interest &tax to Total Assets. X4 = Total book equity to Total liabilities [28].

Z score indicator as follow: Altman, Edward (May, 2002) [31].

period of study.

Whereas

**309**

when predicting bankruptcy.

[18] Stated that Recommendation on the "Shari'ah Rating" is the Ethical Identity Index (EII) [18]. Said that EII is the Shari'ah compliance determination identified by the existence of discrepancy between the communicated (based on information disclosed in the annual reports) and ideal (disclosure of information deemed vital based on the Islamic ethical business framework), which was termed by [19] as Ethical Identities Index (EII) [19]. Examined seven Islamic Banks over a three-year period of longitudinal survey in the Arabian Gulf region [19]. Found that six out of seven Islamic Banks suffered from disparity between the "communicated" and "ideal" ethical identities. They demonstrated that: From both functions of the CAMELSS and EII, they could ensure the Shari'ah compliance in the IBs. They have Recommend that False Financial Statements (FFS) detection model in CBs could be applied similarly by adding this Shari'ah compliance control variable.

[20] used a simple stress test method, including three stress test areas: profitability stress test, capital stress test and liquidity stress test. His results showed that in term of profitability, Islamic banks in Indonesia are immune from losses if the default rate (Non-Performing Loan) is less than 8.5%. If the industry can improve the profit margin, the resistance will be higher. In term of capital position, by assuming loss given default (LGD) is constant at 40%, the industry will not go bankrupt if probability of default (PD) is less than 9%. If the PD is more than 9%, total expected loss is more than available capital.

[21] focused on cutting-edge FDP models and applied them to Islamic. They had employed three models: Altman Z-Score and Altman Z-Score for service firms, and Standardized Profits method, their results indicated that there is a need for a specific financial distress mechanism for Islamic banks, as variables that are indicative of a bank's status differ between the old Altman [7] standard and novel approaches. "Working Capital/Total Assets" was the most predictive variable for forecasting financial distress in Islamic banks. As for the Standardized Profits method, "Return On Revenue" was the most influential variable banks, they employed three models [22]. Examined, evaluated and compared the financial activities of selected Islamic and conventional banks of Pakistan for period (2003– 2012.). Various parameters of CAMEL model were tested by employing simple ttest. His result showed that: there are significant differences between Islamic and conventional banks in risk-weighted credit exposures, regulatory capital, advances in proportion to asset portfolios, long-term debt paying abilities, management's control over expenses in proportion to income, return on assets, and liquidity.

[23], analyzed the financial performance of three selected Islamic Banks in Bangladesh over 8 years (2007–2014), he was using Camel Rating model to evaluate banks' performance, he demonstrated that all the selected Islamic Banks are in strong position on their composite rating system (CAMEL).

[24] Their study has conducted with the objective of comparing shariah compliance and traditional banks of Pakistan from performance perspective. The relative investigations were conducted by means of t.test, for the period 2010–2017. Ratios based on CAMELS approach are applied to identify the managerial and monetary performance of shariah compliance and traditional banks of Pakistan. They demonstrated that Shariah compliance banks are significantly better in managing capital adequacy, management adequacy/quality, earning ability, liquidity and sensitivity

*Determinants of Islamic Banks Distress in Gulf Council Countries (GCC) DOI: http://dx.doi.org/10.5772/intechopen.95028*

to risk as compared to their traditional counterparts [23]. Aimed to analyze the financial performance of three selected Islamic Banks (Islami Bank Bangladesh Limited, Export Import Bank of Bangladesh Limited, Shahjalal Islami Bank Limited) over a period of eight years (2007–2014) in Bangladeshi banking sectors. For this reason, CAMEL Rating Analysis approach has been conducted and it is found that all the selected Islamic Banks are in strong position on their composite rating system. [1] aimed to analyze the performance of Islamic banks and conventional banks during the crisis and after the crisis, by comparing the performance of Islamic and conventional banks based in the Gulf Cooperation Council (GCC) during the period of 2008–2011 by deploying the CAMEL testing factors, his results showed that Islamic banks possessed adequate capital structure but have recorded lower ROAE and poor management efficiency. Asset quality and liquidity for both the modes of banking system have not recorded any significant difference. [2], Directed a study on the GCC for a period of 2002–2009, to assess the factors that affect the Islamic bank and conventional banks. The study included a sample of 38 conventional banks, and 13 Islamic banks. The factors that were studied were foreign ownership, bank specific variable and macroeconomic variables. Some interesting results were found. The cost-income was found to have a negative and significant impact on banks performance for Islamic and conventional banks. Equity was found out to be important factor in maximizing the profitability of Islamic banks. The size of the banks supported the economies of scale utilizing the ROE for Islamic banks. GDP was found to be positively related, while inflation negatively related to the banks performance [25]. Aimed to evaluate the soundness of Islamic banks in the GCC for the period 2008 to 2014. Methodology- The study involves 11 listed Islamic banks based in the GCC countries of Saudi Arabia, United Arab Emirates, Qatar, Bahrain, and Kuwait [25]. Applied the CAMEL parameters, which include Capital Adequacy, Asset quality, Management, Earning and liquidity. Multivariate Z- score model is also used to ensure robustness of the results. Findings-The findings suggest that although the Islamic banks in the GCC have adequate capital, their asset quality and earning ability have deteriorated over the period of study.

The applications of Altman Z score on banks have previously been researched by several researches like [26, 27], for banks in India and [28] suggested that Altman Z score is an analytical tool that may be applied in the banking industry. Additionally, [29] stated that Altman Z score has better predicting capabilities than CAEL model when predicting bankruptcy.

However, several studies indicated the inappropriateness of Altman Z score in predicting financial distress within banks. A study conducted [25] applied Altman Z score model, CAEL model and bankometer model altogether within the Bank of Papua in Indonesia. His results showed that the results of Altman Z score model in many occasions were contradicted with the results of CAEL model. Altman Z score model was initially formed from an empirical study of manufacturing companies which is very much different from banking institutions [30].

Z score indicator as follow: Altman, Edward (May, 2002) [31].

$$Z = 6.56 \,\text{X1} + 3.26 \,\text{X2} + 6.72 \,\text{X3} + 1.05 \,\text{X4} \tag{1}$$

Whereas

Z = a proxy variable of insolvency risk.

X1 = working capital to Total Assets [28].

X2 = retained earnings to Total Assets [28].

X3 = earnings before interest &tax to Total Assets.

X4 = Total book equity to Total liabilities [28].

[18] Stated that the CAMEL ratings generally assess overall soundness of the banks, and identify and/or predict different risk factors that may contribute to turn the banks into a problem or failed banks. These banks tend to perform the FFS. Bangladesh Banks have included an additional key point of "Sensitivity to market risk" to be the CAMELS. However, [18)] has recommended the CAMELS Rating Framework to be the CAMELSS in order to comprehend the Islamic Banking that is

*Linear and Non-Linear Financial Econometrics - Theory and Practice*

[18] Stated that Recommendation on the "Shari'ah Rating" is the Ethical Identity Index (EII) [18]. Said that EII is the Shari'ah compliance determination identified by the existence of discrepancy between the communicated (based on information disclosed in the annual reports) and ideal (disclosure of information deemed vital based on the Islamic ethical business framework), which was termed by [19] as Ethical Identities Index (EII) [19]. Examined seven Islamic Banks over a three-year period of longitudinal survey in the Arabian Gulf region [19]. Found that six out of seven Islamic Banks suffered from disparity between the "communicated" and "ideal" ethical identities. They demonstrated that: From both functions of the CAMELSS and EII, they could ensure the Shari'ah compliance in the IBs. They have Recommend that False Financial Statements (FFS) detection model in CBs could be

applied similarly by adding this Shari'ah compliance control variable.

total expected loss is more than available capital.

strong position on their composite rating system (CAMEL).

**308**

[20] used a simple stress test method, including three stress test areas: profitability stress test, capital stress test and liquidity stress test. His results showed that in term of profitability, Islamic banks in Indonesia are immune from losses if the default rate (Non-Performing Loan) is less than 8.5%. If the industry can improve the profit margin, the resistance will be higher. In term of capital position, by assuming loss given default (LGD) is constant at 40%, the industry will not go bankrupt if probability of default (PD) is less than 9%. If the PD is more than 9%,

[21] focused on cutting-edge FDP models and applied them to Islamic. They had employed three models: Altman Z-Score and Altman Z-Score for service firms, and Standardized Profits method, their results indicated that there is a need for a specific financial distress mechanism for Islamic banks, as variables that are indicative of a bank's status differ between the old Altman [7] standard and novel approaches. "Working Capital/Total Assets" was the most predictive variable for forecasting financial distress in Islamic banks. As for the Standardized Profits method, "Return On Revenue" was the most influential variable banks, they employed three models [22]. Examined, evaluated and compared the financial activities of selected Islamic and conventional banks of Pakistan for period (2003– 2012.). Various parameters of CAMEL model were tested by employing simple ttest. His result showed that: there are significant differences between Islamic and conventional banks in risk-weighted credit exposures, regulatory capital, advances in proportion to asset portfolios, long-term debt paying abilities, management's control over expenses in proportion to income, return on assets, and liquidity. [23], analyzed the financial performance of three selected Islamic Banks in Bangladesh over 8 years (2007–2014), he was using Camel Rating model to evaluate banks' performance, he demonstrated that all the selected Islamic Banks are in

[24] Their study has conducted with the objective of comparing shariah compliance and traditional banks of Pakistan from performance perspective. The relative investigations were conducted by means of t.test, for the period 2010–2017. Ratios based on CAMELS approach are applied to identify the managerial and monetary performance of shariah compliance and traditional banks of Pakistan. They demonstrated that Shariah compliance banks are significantly better in managing capital adequacy, management adequacy/quality, earning ability, liquidity and sensitivity

"Shari'ah Rating". In line with [18].

A higher score indicates greater financial strength with a lower probability of default and vice versa.

Zones of discriminations: Z > 2.6 -"Safe" Zone

are<sup>1</sup> grouped<sup>2</sup> in **Table 4**.

**4. Data analysis**

**Model Variables Entered**

1 T.Liabilities/T. Assets

2 Equity/ total assets ratio

*Dependent Variable: Z Score*

*Variables entered/Removed.a,b*

*a*

*b*

*a*

*b*

*c*

**311**

**Table 6.** *Model summary.*

**Table 5.**

*Source researcher from data analysis.*

*Linear Regression through the Origin*

*Source researcher from data analysis.*

*Predictors: T.Liabilities/T.Assets, Equity/ total assets ratio*

<sup>1</sup> In Shariah compliance banks it is the profit paid to total assets. <sup>2</sup> 3 In Shariah compliance banks it is profit earned to total assets

*Predictors: T.Liabilities/T.Assets*

*which include an intercept.*

1.1 < Z < 2. 6 -"Grey" Zone Z < 1.1 -"Distress" Zone

*DOI: http://dx.doi.org/10.5772/intechopen.95028*

**4.1 Capital adequacy requirement (CAR) test**

*Determinants of Islamic Banks Distress in Gulf Council Countries (GCC)*

and capital adequacy ratios is developed as following:

than T .equities to T. assets. See the following **Table 8**.

**Variables Removed**

Capital Adequacy is debit to Equity ratio.

The concept of CAMELS'standard, It consists of six dimensions. Each dimension can be measured through different ratios. These ratios along with their measures

Two ratios (total Equities to total Assets ratio and Debt-to Equity ratio)are Examined using step wise method **Table 5** shows the result, **Table 6** shows model Summary between Z score and liabilities to Assets ratio, **Table 7** shows significance for each individual studied ratio As a result of **Table 7**, the model between Z score

Z score = 8.9 Total liabilities to Total assets ratio + 6.6 Equities to Assets ratio. Thus lesson to be learned that T. Liabilities/T. Assets has more effects on Z score

It shows exclude variables. **Table 8** indicates that the best ratio that can measure

. Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove > = .100).

. Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove > = .100).

**Method**

**Model R R Square<sup>b</sup> Adjusted R Square Std. Error of the Estimate**

*For regression through the origin (the no-intercept model), R Square measures the proportion of the variability in the dependent variable about the origin explained by regression. This CANNOT be compared to R Square for models*

1 .888<sup>a</sup> .789 .786 3.39385892 2 .896c .803 .797 3.30345566

The method examines liquidity, profitability, reinvested earnings and leverage which are integrated into a single composite score. It can be used with past, current or project data as it requires no external inputs such as GDP or market price.


*Source the researcher from literature review.*

*Determinants of Islamic Banks Distress in Gulf Council Countries (GCC) DOI: http://dx.doi.org/10.5772/intechopen.95028*

Zones of discriminations: Z > 2.6 -"Safe" Zone 1.1 < Z < 2. 6 -"Grey" Zone

Z < 1.1 -"Distress" Zone

The concept of CAMELS'standard, It consists of six dimensions. Each dimension can be measured through different ratios. These ratios along with their measures are<sup>1</sup> grouped<sup>2</sup> in **Table 4**.
