Document Type : Original Article
Authors
1 Ph.D. Candidate in Finance, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
2 Associate Prof. in Economics, Department of Public Administration, Faculty of Management, University of Tehran, Tehran, Iran.
3 Assistant Professor in Economics, Department of Humanities, Abhar Branch, Islamic Azad University, Zanjan, Iran.
Abstract
Keywords
The risk is a mental and partly unacceptable issue, so it is possible to exert personal opinion. Hence, one of the important options in relation to risk, especially in countries with a lack of investor protection law, will be corporate governance mechanisms. Accordingly, this study intends to investigate the effect of one corporate governance mechanisms (management structure) on credit risk in listed commercial banks in the Tehran Stock Exchange. The present study will answer this question: Do management structure have a significant effect on credit risk in Iranian commercial banks?
In many aspects, this subject is important; first, credit risk in commercial banks is an emerging issue in the world that is considered by institutions, legislators and researchers. However, in Iran, the effect of corporate governance mechanism (ownership structure and management structure) on credit risk in commercial banks has received relatively less attention. Second, evidence concerning other countries (mainly developed countries) is not exactly generalizable to Iran due to the difference in the legal environment, requirements, and governance structures. Third, corporate governance mechanisms and their functions are a topic in the current situation in Iran that needs further investigation in the field of credit risk in commercial banks. Therefore, this study helps to expand the risk literature in this field.
Finally, the answer of the research’s question is important for researchers, compilers of reporting requirements, investors and analysts, since no study has examined the effect of corporate governance mechanisms (management structure) on credit risk in listed commercial banks in the Tehran Stock Exchange so far and their relationship with corporate governance is unclear. Indeed, the issue of asymmetry and incomplete information is always a fundamental problem for the effective function of the market.
Economic and financial studies have shown that the political connections of the board of directors can have economic consequences for firms. Based on the theoretical and experimental literature of the subject, we expect government-controlled banks tend to accept more risk than the other banks mentioned. Facilitation of the entry of the private sector into the bank sector will lead to an increase in the share of private banks in the banking market and improve competitiveness and the performance of banks in the banking network.
As regard 60% of the banking network' assets belong to public banks in Iran, which have a lower degree of competitiveness than private banks, decrease in the size of public banks and transfer of the part of public banks to the private sector can reduce the concentration on the banking market. It causes an increase in competitiveness and efficiency in the banking market. This study attempts to investigate the effects of interactions between politically connected CEO (PCCEO), independent directors, and credit risk of banks in an emerging country context where corporate governance systems appear weak.
Political connections
For being of international evidence about the existence of political connections between government and companies, a large number of studies have investigated the phenomenon of political connections in companies. Political connections are a common phenomenon throughout the world which companies associated with various government interventions benefit from it (Hung et al., 2017).
The index of political connections has three dimensions: economic, social and individual. The economic dimension of political connections includes the percentage of direct ownership of government from company stocks, so that more than fifty per cent of company stock belongs to the government (Dinc, 2005; Faccio et al., 2006). The social dimension of political connections includes the investment of government-affiliated institutions in the ownership structure of the company, which represents the government institutional support. In other words, the social dimension includes the percentage of stocks held by state-owned and public companies from the total capital stock. These companies include insurance companies, financial institutions, banks, state-owned companies and other components of government. The individual dimension of political connections also includes companies that one of the major shareholders of the company (a shareholder who owns more than 84% of the company's shares) is a former or current character in the government.
In recent research (Boateng, Liu and Brahma, 2019), another criterion has been used to assess political connections. In this paper, the long-term debt is used as a criterion for political connections. Many studies have indicated that further political connections lead to access opportunity of companies to long-term and less expensive resources thereby increasing the ratio of long-term debt in companies with higher political connections (Rezaei F., Afrouzi L., 2015).
Politically Powerful companies are able to take advantage of rents in public banks by exerting threats to bank executives, such as fear of relocation and loss of job position, or the promise of new job position and promotions. As result, the companies with political connections will benefit from loans resulting from such rent-seeking, against bank losses. In addition, this matter persuades the government to prioritize creditors over bank shareholders to ensure the security of the bank in this way. Recent studies such as Nys et al. (2015) and Hung et al. (2017) point out that companies with political connections are more capable of accessing short-term and long-term debts. In fact, such companies use political connections as a competitive advantage and they are in a better position to finance through bank loans than other companies. Therefore, companies with political connections have more debts in their capital structure. Faccio et al. (2006) believe that lenders are more willing to lend to companies with political connections that are abler than their non-political counterparts to provide a guarantee in the face of financial and economic crises.
Studies in companies with political connections (Johnson and Mitton, 2003; Aggarwal et al., 2012; and Hung et al., 2017) show that political connections can affect the company in both positive and negative direction. On the one hand, some studies have pointed to the many benefits of political connections, including 1- Easier access to financial resources, such as bank loans and budgets with appropriate conditions. 2- Increase confidence in the legal system 3- Performance improvement 4 - High probability of release from seizing 5 - Increase the value of the company for example by increasing the value of stocks, lower capital costs 6 - Transaction points such as priority in government contracts. On the other hand, some studies have shown that political connections can have a negative impact on the company, including 1- accounting information with lower quality 2- Selection of managers and board members with low qualifications, reduced long-term performance of the company arising from reduction of managerial motivation or inefficiency 3- Higher debt cost (Salehinia, M., Tamoradi, A., 2019).
In addition to the matter of lending, banks with political connections possess easier access to financial resources and timely liquidity support from the central bank, opposite groups or other affiliated banks. They also take advantage of benefits such as extending loan repayment periods or easy passage through legal inspections (Mazandarani, S., Saeedi, P., 2018). Moreover, by balancing the proposed demands of the government, banks must act in a way that they do not sacrifice their economic motivation for some non-economic motivation. Compared to banks without political connections, banks with political connections possess a high power in recognizing and interpreting political signs, correctly use of diplomatic language, implementation prudent actions and thus their performance improvement.
Companies with political connections benefit from profits of soft budget. A soft budget refers to the attraction of political support and takes advantage of this support. It seems that companies which benefit from political support hold less cash. Because the managers of such companies can provide their needs with a higher level of credit that does not require to fast payment of cash, or if they need financial resources, they will have access to these resources in a shorter time. Hence, the attraction of political support prevents the company from keeping a lot of cash.
In a study, Hong et al. (2017) examined the relationship between political connections and financial performance and risk in Chinese banks. For this purpose, 90 banks were analyzed between 2009 and 2014. The results of this study showed that there is a positive and significant relationship between political connections and financial performance and the risk of Chinese banks. In another study, Georgantopoulos and Philos (2017) investigate the board structure and performance of the bank and evidence from the Greek banking industry during the financial crisis. For this purpose, 13 banks were analyzed in the financial crisis period between 2008 and 2014. The results of this study show that there is a positive and significant relationship between independence of board members and financial performance indicators (including return on assets, return on equity, net profit margin and pre-tax operating income).
Credit risk
Risk is a situation where the actual return on investment is different from the expected one. Risk means the possibility of losing the main investment and the amount of interest earned on it (Schmitz, A., 2012). Credit risk is the risk-based on which the customer either pays late or not pay the loan and this causes problems in the cash flow of banks and harms the liquidity and return on investment of banks. To control and reduce credit risk, the bank needs to identify properly its credit facilities applicants and be able to distinguish between applicants who can repay their loans on time, in fact, have low risk, and high-risk applicants. This matter is possible through efficient and effective credit risk management. For this purpose, the credit rating and rating system of the bank's customers are required as the main tool in credit risk management. By using the credit rating system, it is possible to categorize credit facility applicants and divide them into two categories: group 1; who pays his/her debts on time and group 2; who do not pays his/her debts on time.
In the research literature, so far, less attention has been paid to the effect of political connections on credit rating. Hence, for investigating this matter, past research sources can be expanded. Politically supported companies with political connections are more able to receive government grants in adverse situations and are less likely to have trouble with repaying debts.
In a study, Chen (2015) found that, in the period of the financial crisis, companies with political connections possess less limitation of financial than other companies and they were more able to repay their obligations, Due to government support. Houston et al. (2014) believe that companies with political connections benefit from subsidies and financial aid in critical conditions and by political relationships, the risk of non-payment of debts to companies with political connections is lower and their credit rating is higher. On the other hand, the results of some studies show that companies with political connections have easier access to financing resources due to their special conditions (political relations with the government); therefore, there are more debts in the capital structure of such companies. Existence of large debts in the capital structure limits the ability to repay their debts, so it is to be expected that companies with political connections possess lower credit ratings than other companies. Debt expense is the interest paid to creditors that the company pays to creditors in exchange for the risk compensation of receiving the facility.
The higher the risk of getting credit, the higher the cost of debt. There are two viewpoints on the effect of political connections and credit risk. According to the first viewpoint, political connections is associated with increasing credit risk. According to this view, political connections leads to an increase in opportunistic behaviors of managers and a decrease in the performance of the company, and this will increase the risk of creditors to the company; As a result, creditors demand higher interest rate for granting of facilities to the company; hence the cost of debt increases.
On the other hand, according to the second viewpoints, that is named the dominant viewpoints, political connections are associated with reducing credit risk. According to this view, managers use political connections efficiently and the company's performance is improved, and this will reduce the risk of creditors to the company; As a result, the cost of debt will be reduced.
In different studies, various indicators have been used to evaluation of the company’s credit risk borrowing from the bank; in short, we can refer to the research of Shi et al. (2018):
1-Financial indicators include liquidity ratios, leverage ratios, profitability ratios
2- Market indicators include company sensitivity to macroeconomics, company sensitivity to the legal and political environment, market share, market orientation, demand diversity, technology and innovation, production flexibility, mixed products, price level, placement, brand value and dependence on customers and suppliers.
3- Management indicators including planning and forecasting data, timely and reliable reporting, certified accounting, behavior towards bank loans, behavior towards tax obligations, behavior towards suppliers, behavior toward customers, collateral, manpower motivation and productivity, management and business coherence, manager's past experience and performance, management team commitment and skill, management performance, ethical management and orientation towards the value of the environment and the customer.
Effective credit risk management is inseparable from the development of banking technology, which causes an increase in the speed of decision-making and a decrease in the costs of credit risk control simultaneously. This requires a complete base of partners and contractors. Credit risk is one of the significant risks of banks due to the nature of their activities. Through effective credit risk management, banks not only support the survival and profitability of their jobs but also contribute to systemic stability and efficient capital allocation in the economy.
Dong et al. (2014) in a study, entitled " Ownership structure and risk-taking: comparative evidence from private and state-controlled banks in China", by considering the number of bank branches and the number of staff as input and investment and volume of deposits as output, got the efficiency of banks through influencing the variables of credit risk and total risk using the DEA model. According to their research findings, they have ranked the banks and obtained a significant relationship between risk and assets.
Political connections of the board of directors
What is the impact of politicians on the board of directors? According to Resource Dependence Theory, the need for environmental connection is a function of the levels and types of dependencies ahead of the company. In a theoretical framework, resource dependence theory, the government is an important source of external dependence for regulated firms such as banks. As a result, various studies have focused on the impact of politics on the banking industry. In principle, existence politicians in the board of directors improve coordination between banks and the government; political relations can be expected to have positive effects.
In contrast to positive effects, political presence can have potentially negative consequences for banks' performance, loan quality, efficiency, and overall risk. One way to reduce uncertainty is to appoint politicians to the board of directors. In fact, these political connections can affect the performance of a company or organization: (1) Stock value (2) legislators' actions in approval the relevant regulations (3) the possibility of financial debt, (4) access to financial resources under more favorable conditions, and (5) market power[1].
Signs of company political management include the existence of Board of Directors affiliated with the government, parliament, and other political institutions, or the existence of a major shareholder (with at least 10%) of governmental and quasi-governmental owners.
To ensure banking security, Banks tend to prioritize creditors over bank shareholders, so the Board of Directors of the bank affiliated with politics may act in favor of the government instead of trying to maximize stock value, due to pressure from the officials. Such agency problems can also arise in the form of a political loan or inclination towards political goals, which can damage the bank's performance.
Ling et al. (2016), claim that politically connected banks are inefficient because they are under influence of politicians who are only interested in pursuing their own personal goals. Finally, researches show that the appointment of Manager Directors related to politics cannot increase the company's efficiency but it meets the political goals of politicians. On the other hand, Charumilind et al. (2006) show that access to banking finance is an important channel through which political connections act.
Habib et al. (2018) investigate the effect of Board of Director's connections on the relationship between audit quality and financial reporting quality in a listed company in the Tehran Stock Exchange. By citing the results of this study, they aim to determine whether the Board of Directors’ political connections have a significant effect on the relationship between the auditing quality and the financial reporting quality or not. Finally, according to the research results, it is possible that useful suggestions are offered to stock market investors as well as company managers.
Despite extensive research on the effects of political connections on firm value, recent studies such as Nys et al. (2015); Hong et al. (2017) point out that the influence of politically connected banks has received less attention. An exception to the previous literature is the study by Hong et al. (2017) that examines the effects of a politically connected CEO on bank performance and risk-taking. There are few studies examining the effects of politically connected boards and the distinct ownership structure on the risk-taking of banks in the developing country context.
This research in terms of purpose is applied type. Also, research in terms of the method is a descriptive research of regression and correlation type. This research uses historical data to test hypotheses and it is post-event type in terms of time. The data of this research based on real statistics and information of the market, and financial statements of banks.
In this study, to collect the required data, we use the information database of Codal publishers for the listed banks in the Tehran Stock Exchange and the information existing in the performance report of the public banks that collected by the Iran Banking Institute. For the investigation of this issue, the following regression model is used. This model has taken from the studies of Boateng, Liu and Brahma (2019). In order to estimate this model, we employ the SGMM method (System Generalized Method of Moments) or in other words, dynamic GMM approach which is a valid method in econometrics. Prior studies on internal governance indicate that research in this area should consider that governance variables are endogenous (Boone et al., 2007). The regression of board characteristics on credit risk is likely to suffer from three endogeneity problems such as omitted variables, reverse causality and measurement error. Wintoki et al. (2012) provide strong evidence that the instruments associated with a dynamic GMM approach are valid and more appropriate for corporate governance research. To address the problem of endogeneity, we employ the two-step Arellano and Bover (1995)/Blundell and Bond (1998) dynamic panel-data system estimator with Windmeijer (2005) bias-corrected robust standard errors in all models. We test for second-order serial correlations AR(2) and Sargan test of overidentifying restrictions for the validity of our model and the GMM instruments. Our basic econometric model is specified as follows:
CRiskit =α0 + β1 PCCEOit + β2 B_INDit + (1)
+ γi + εit
The variables according to the measurement of variables in this model are as follows:
1) Dependent variables
CRisk represents the credit risk of banks, which is measured by loan loss provision ratio (LLPR).
2- Explanatory and independent variables:
PCCE and B_IND are variables that represents politically connected CEO and independent members in the board of directors, respectively.
Politically Connected CEO: In the research of Nikomram et al. (2013), the symptoms of political management of the company include the presence of board members affiliated with the government, parliament and similar political institutions, or the presence of a major shareholder (with at least 10% of governmental and quasi-governmental owners). According to the research of Neli Sari and Anugerah (2011), the percentage of direct or institutional ownership of the government is considered as a variable representative of PCCEO (Standard No. 20; "significant influence").
Therefore, the operational definition of the variable of PCCEO is as follows:
If the percentage of people with a background in government positions on the board to be more than 20% in the bank, the value of this variable will be equal to one and otherwise, be zero.
3- Control variables:
GDPR: GDP growth rate
(UNEMP): unemployment rate
(INFR): Inflation rate
(SIZE): Bank size
(LEVR): bank leverage ratio
(CEO_TC): CEO total compensation
To study the general characteristics of variables and their detailed analysis, it is necessary to know descriptive statistics of variables. The following table presents the descriptive statistics for all variables used in this study. For avoiding the influence of outlier data on the results of the research, all variables can be adjusted or deleted using the capabilities of Eviews software at a 95% confidence level(Winsorized the variables at level 95 %)[2].
In this study, quarterly data of 16 banks (including Eghtesad Novin, Ansar, Iran Zamin, Parsian, Pasargad, Day, Saman, Sarmayeh, Shahr, Tejarat, Mellat, KarAfarin, Gardeshgari, Khavarmianeh, Hekmat Iranian and Ghavamin bank) for the period 2011 - 2017 have been used. The results of descriptive statistics are presented in table (1):
Table 1. Descriptive statistics of variables
|
|
PCCEO |
B_IND |
GDPR |
INFR |
LEVR |
SIZE |
UNEMP |
LLPR |
|
Mean |
0.483606 |
0.600440 |
0.017795 |
19.06510 |
0.070942 |
13.05490 |
11.53154 |
0.019475 |
|
Median |
0.400000 |
0.600000 |
0.019777 |
15.90000 |
0.055706 |
13.08971 |
11.50000 |
0.017965 |
|
Maximum |
1.000000 |
0.857143 |
0.144061 |
39.90000 |
0.377497 |
15.07543 |
14.10000 |
0.097335 |
|
Minimum |
0.000000 |
0.200000 |
-0.094578 |
8.700000 |
-0.190977 |
9.673707 |
9.500000 |
0.000000 |
|
Std. Dev. |
0.379904 |
0.119798 |
0.057841 |
9.624516 |
0.059454 |
0.990576 |
0.991016 |
0.011939 |
|
Skewness |
0.168234 |
-0.057847 |
0.045072 |
0.844074 |
1.331534 |
-0.531628 |
0.226718 |
1.541613 |
|
Kurtosis |
1.522607 |
3.955767 |
2.863458 |
2.548755 |
8.728679 |
3.315118 |
2.813372 |
10.18372 |
|
|
|
|
|
|
|
|
|
|
|
Jarque-Bera |
37.30836 |
15.06173 |
0.435006 |
49.61886 |
648.5324 |
19.98445 |
3.907069 |
993.0718 |
|
Probability |
0.000000 |
0.000536 |
0.804525 |
0.000000 |
0.000000 |
0.000046 |
0.141772 |
0.000000 |
|
|
|
|
|
|
|
|
|
|
|
Sum |
188.6063 |
234.1714 |
6.940010 |
7435.390 |
27.66725 |
5091.409 |
4497.300 |
7.595175 |
|
Sum Sq. Dev. |
56.14311 |
5.582782 |
1.301450 |
36033.58 |
1.375008 |
381.7023 |
382.0421 |
0.055445 |
|
Observations |
390 |
390 |
390 |
390 |
390 |
390 |
390 |
390 |
Source: Research Findings
One of the suitable econometric methods to solve or reduce the problem of endogenous variables such as corporate governance variables and the correlation between corporate governance variables and other explanatory variables is Generalized Method of Moments (GMM) Dynamic Panel Data. Using this method has advantages such as taking into account more information and eliminating the existing biases in cross-sectional regressions, resulting in more accurate and efficient estimation and less collinearity in GMM. The GMM Dynamic Panel Data method is used when the number of cross-sectional variables (N) is more than the number of times and years (T) (N> T).
In general, the GMM dynamic panel data approach has the following advantages over other methods (Green, 2008):
1) To solve the problem of endogenous variables, 2- Reducing or eliminating collinearity in the model, 3- Omitting fixed variables over time, 4- Increasing the time dimension of variables.
There are two methods for model estimation in the GMM dynamic panel data method. The first basic of dynamic GMM models was proposed by Arellano and Bond (1991), that it is called the first-order differential GMM method. Arellano and Bover (1995) and Blundell and Bond (1998) introduced the orthogonal GMM method through modifications of the first-order differential GMM method (hereinafter the differential GMM method is referred to as DGMM and GMG orthogonal method is referred to as OGMM). The difference between the two methods is based on how individual influences are included in the model. The Arellano and Bover method use all available lag as an instrumental variable, but the OGMM method uses lag levels as an instrumental variable. Although the Arellano and Bover method is more popular than the OGMM method, the OGMM method has advantages over the DGMM method that researchers prefer to use it. One of the notable advantages is that the OGMM method provides more efficient and accurate estimates than the DGMM method by improving the accuracy and reducing the bias of the sample volume restriction (Baltaji, 2008).
According to Covariance Analysis, the variables B-IND and PCCEO * B_IND are strongly correlated. According to the above description, SGMM solves the collinearity problem to some extent. Also in order to the regression results not to be false, the stationarity of the variables must be verified before estimating the medals. If all variables data are stationary, the model can be easily estimated, and if even one variable is non-stationary, it must be stationarity first and then be used in the model unless they have long-term co-integrated. According to research findings, some data are non-stationary, and the relationship is also co-integrated. Furthermore, assumptions for estimating research models in this study are:
- irst-order differential GMM
- orthogonal GMM
According to the estimated results, it is recommended to use the orthogonal deviation method because the results are more reasonable and also, as mentioned in the description, have fewer limitations than the First Differences method. When Orthogonal Deviation Method is used, Arellano-Bond statistic is no longer applicable and only Sargan statistics are used for the validity of our model and the GMM instruments through Orthogonal Deviation Method. Tables (2) presents the results of the SGMM estimation based on the dependent variable, namely, LLPR.
Table 2. Model Estimation Results
|
Dependent Variable: LLPR |
|
|
|||
|
Method: Panel Generalized Method of Moments |
|||||
|
Transformation: Orthogonal Deviations |
|
|
|||
|
Periods included: 26 |
|
||||
|
Cross-sections included: 16 |
|||||
|
The white diagonal instrument weighting matrix |
|
|
|
||
|
Instrument specification: @DYN (LLPR, -2 @LEV(PCCEO)@LEV(B_IND_PCCCEO) Constantly added to the instrument list |
|||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
|
LLPR(-1) |
0.848270 |
0.005254 |
161.4606 |
0.0000 |
|
|
PCCEO |
0.020368 |
0.002008 |
10.14155 |
0.0000 |
|
|
B_IND |
0.018160 |
0.000915 |
19.85490 |
0.0000 |
|
|
GDPR |
-0.001224 |
0.000922 |
-1.328075 |
0.1850 |
|
|
INFR |
-7.00E-05 |
5.66E-06 |
-12.37066 |
0.0000 |
|
|
LEVR |
-0.012489 |
0.002256 |
-5.534959 |
0.0000 |
|
|
SIZE |
-0.001318 |
5.65E-05 |
-23.33548 |
0.0000 |
|
|
UNEMP |
1.79E-05 |
7.54E-05 |
0.237321 |
0.8125 |
|
|
Effects Specification |
|
|
|
|
|
|
Cross-section fixed (orthogonal) )deviations) |
|
|
|
||
|
Mean dependent var |
-0.002671 |
S.D. dependent var |
0.006470 |
||
|
S.E. of regression |
0.004295 |
Sum squared resid |
0.006345 |
||
|
J-statistic( Sargan Test) |
278.8518 |
Instrument rank |
287 |
||
|
Prob(J-statistic) |
0.474343 |
|
|
||
Source: Research Findings
In this estimation, considering the nature of GMM approach in using intermittent instrumental variables, the Sargan test has been used to make the instruments suitable. The Sargan test is a predefined constraint that tests the validity of tools and is used to determine any correlation between tools and errors. For tools to be valid, there must be no correlation between tools and error statements. The null hypothesis for this test is that the instruments are valid insofar as they are not correlated with the errors in the first-order differential equation. Failure to reject the null hypothesis can provide evidence that the tools are appropriate. The significance of the statistics of this test shows that the tools used in the model under study are appropriate.
In this study, the T- Student test was also used to validate the estimated coefficients in the research model. For our model, the P-value should be less than the significance level (0.05), which indicates that our results are statistically significant. Similarly, our 95% confidence interval does not include the null hypothesis and we draw the same conclusion. As can be seen, the results of this test also indicate the validity and significance of all estimation coefficients except GDPR and UNEMP. Therefore, all diagnostic tests performed show that the GMM estimator results are reliable and valid. The results show that PCCEO has a positive and significant effect on LLPR. This means that the presence of board members with political connections increases the credit risk in the studied banks in Iran. But INFR, LEVR and SIZE have a negative and significant effect on LLPR.
Based on what has been done in previous studies, it is easy to conclude that political connections and special support from the government are like a double-edged sword; That is, such relationships, on the one hand, can be beneficial to the companies or banks and on the other hand, can harm it. Studies show that state-owned companies and banks have better conditions than their counterparts, especially in times of economic turmoil and crisis, these relationships will be very life-saving for them. Despite all these advantages, as mentioned, government-affiliated banks will also have problems that can lead to their destruction. Regarding the credit risk of banks and the political connections of board members, it can be said that banks are no exception to this rule and political connections can have advantages and disadvantages for them. However, it seems that political connections have more advantages for banks and its negative effects are less visible (especially in economic conditions similar to the current situation in Iran, these connections are very important for banks). According to the results of this study, the political connections of the board members have a positive and significant effect on the credit risk of banks. Based on this finding and the special conditions of the Iranian economy, while many bank embezzlements have taken place, it is suggested that more attention be paid to this point in the appointment of board members. Also, it is worth to say that the political connections of board members should be considered as a negative point in evaluating banks' performance in Iranian bank ranking.
Funding: This research received no external funding
)
