نشریه مطالعاتی در مدیریت بانکی و بانکداری اسلامی
پیاپی 23 (تابستان 1402)

Capital adequacy, as a necessary indicator for protecting the debt payment and profitability of banks, is regarded as one of the most important and fundamental indices in a financial institution. Economic efficiency is achieved when banks use the best combination of inputs and reach an optimal level of output and service delivery by reducing operating costs. In order to promote a healthy financial system, the legislator requires banks to have sufficient capital to deal with losses and limit credit risks. This study is based on the Fama hypothesis, which states that the regulation of banking improves the economic efficiency of banks, as well as seeks to answer the question of whether the improvement in the capital regulations of banks has an effect on their economic efficiency. To answer this question, the data of seven state banks and the panel data econometric method were used for the period of 2016 to 2021. The results of the model estimation show that the coefficients of bank size variables, profitability, NPL, and the ratio of total facilities to total deposits are negative and statistically significant at the 1% level, while the variable coefficient of capital adequacy is positive and significant at 10% level. This result indicates that the increase in regulatory rules regarding banks' capital affects the change of banks' strategy regarding their internal operations, which will ultimately lead to better bank performance or the improvement of the bank's economic efficiency index.

In this study, the importance of crisis test in bank risk management is discussed. In order to conduct this research project, information on the financial statements of 20 banks from the stock exchange was collected in 58 quarterly periods from the beginning of 2007 to the middle of 2021. Next, the research variables were calculated for these banks. The collected data was processed through Excel software. Then, the research hypotheses were analyzed and tested according to the type of data and panel data method, using R 4.1.1 open-source software. The results show that among the major variables that have been shocked, the exchange rate variables and the stock market price index have had an important effect on increasing the liquidity risk, which indicates that owners of funds are paying attention to alternative markets such as the foreign exchange market and the capital market.

According to the role that banks play in monetary policy, payment system, lessening the transaction costs, and controlling risks, it is important to study their performance and efficiency. Ownership is one of the factors that affects bank performance and efficiency. In addition to ownership, banks’ decisions on diversification affect their performance. In order to investigate the effects of bank ownership and scope economies of diversification, this study aims to estimate the linear and non-linear cost functions for selected banks of Iran during 2005 and 2021 and extract quasi scope economies of diversification by introducing a new method. Subsequently, using censored regression pattern (Tobit method), the effects of government ownership and quasi economies of diversification on banks’ risk return are investigated. The results indicate that there is always a positive and significant relationship between quasi economies of diversification and bank return. However, the effects of quasi economies of diversification on bank risk depends on the estimated cost function. In addition, government ownership always has a positive relationship with bank return but a negative relationship with bank risk.

The purpose of this study is to investigate the effects of real interest rate on inequality in different income deciles in Iran. For this purpose, the current model was estimated with the quantile approach and based on the data of 1990-2020. Based on the estimation results, there is a positive and significant relationship between the real interest rate and income inequality in the study period, and as the share of income deciles increases, income distribution becomes more unequal. As such, the bottom 60 percent of the decile lose 0.05 percent of their income share in total, while the top 10 percent gain 0.16 percent, which indicates the transfer of income from the poor and middle-income groups to the rich ones. It should be noted that the top 10 percent receive income transfers from the bottom 60 percent as a result of the increase in interest payments. The effect of such income transfer can also be seen in the increase of the Gini index, because the Gini index increases by 0.10% as a result of the increase in interest payments. Therefore, this transfer of income increases income inequality at the expense of low- and middle-income groups, thereby reducing social welfare. According to the above-mentioned findings, it can be claimed that if the interest rate is increased by the monetary authorities, households with more savings will benefit more, while households with more debts will suffer a lot. This reveals that the result of such a decision will eventually cause income inequality. Thus, it is better to focus on stabilization and reduction of inflation, as well as inflation targeting policy, instead of the policy of excessive profit increase to prevent the transfer of the real interest rate from the poor to the rich.

This study seeks to investigate the effect of variables and data related to people's social network on their credit score. In this study, two main goals are pursued: reducing information asymmetry and increasing financial inclusion. Achieving the above goals is done by finding meaningful information about people's social data to measure how such data affects their credit score. The basic hypothesis of this study is that people with a high credit score have social relationships with people who are similar and of the same age. A data set of more than 300,000 loans that have been paid by an Iranian bank to real people has been used to confirm and explain the effect of social network variables on credit score. In order to determine the variables of the study, an in-depth interview was conducted with a number of banking experts and people actively involved in the field of credit scoring, and at the end, the variables were determined and classified into three categories: financial, behavioral, and social. The study continued with the logistic regression method and finally with various regression models based on deep machine learning, including gradient. The results of the analyses conducted using the logistic regression method show that, statistically, people's social variables can predict the probability of their loan default. The results of machine learning algorithms also indicate that social network information can significantly improve the performance of loan default prediction.

Considering the importance of risk management in the banking industry, several studies can be found on the relationship between the effects of intellectual capital and banking risk. In some studies, it has been observed that intellectual capital affects the credit risk and insolvency risk of banks. Investigating the existence of a stable relationship between intellectual capital and banks' risk-taking is still one of the important research topics. In addition, the fact that banks are more ambiguous compared to some other industries, which can result in more risk-taking behaviors, leads to the interest of researchers to investigate the issue and the necessity of its importance. The present study examines the value-added of intellectual coefficient (VAIC) and its components on the risk-taking of banks in Iranian banking industry. Quantile regression method was used to investigate the issue and the sample was the stock market banks in Iran in the period of 2012 to 2021. The results of the study show that the VAIC in the first to fourth quantiles has a significant relationship with credit risk. Examining the components of the VAIC shows that capital employed efficiency (CEE) has an effect on insolvency risk in the second to ninth quantiles. This factor has no effect on credit risk in any of the quantiles. The human capi‏tal efficiency (HCE) has a significant relationship with credit risk in all quantiles, but it has a significant relationship with liquidity risk in the second to fifth, eighth and ninth quantiles. Regarding structural capital, the structural capital efficiency (SCE) has no significant effect on Insolvency risk, but a significant relationship has been observed between this variable and credit risk in the first to fifth quantiles.