نشریه مطالعاتی در مدیریت بانکی و بانکداری اسلامی
پیاپی 4 (پاییز و زمستان 1395)

The purpose of this study is to identify the criteria of venture capital (VC) financing and to determine the importance of each criterion in VC financing and finally to use TOPSIS method to rank Islamic contracts in VC financial systems. To rank the contracts, 8 criteria were identified through literature review and research works in the related fields. Then, for calculating the ranking of each criterion and performing sensitivity analysis toward making proper decisions, both quantitative and qualitative techniques is used. Shannon Entropy, as a quantitative technique, was used to calculate the importance of criteria using a questionnaire completed by bank experts in Islamic contracts of VC. However, using a qualitative method, 3 different states were considered for the importance level of criteria. Finally, the ranking of Islamic contracts in the VC financial system was calculated using TOPSIS method.

Since the banks are important influential sections in the development of every country, it is important to evaluate their performance. Data Envelopment Analysis (DEA) is one of the common tools used for evaluating banks performance. The focus of this paper is mainly to evaluate the efficiency of decision making units over a specific time period. To analyze the changes in efficiency, Window DEA based on the input-oriented model is used under the assumption of non-decreasing return to scale (NDRS). This research identified the most efficient and inefficient 3rd degree branches of Bank Sepah in Mashhad during 2008 to 2013. Input included personnel costs and the total deposits attracted, and output included granted loans and the amount of gross profit of branches. Based on the results of window analysis, 6 out of 39 3rd degree branches of Bank Sepah in Mashhad had an average efficiency of 1. In addition, 70% of 3rd degree branches of Bank Sepah had efficiencies of over 0.72 and also their average efficiency was 0.78 during 6 years. It is notable to mention that 70% of these branches enjoy the efficiency of over 0.80 with an average efficiency of 0.87 in four windows.

The aim of this research is to focus on systemic risk by assessing the extent to which the distress caused by the main different financial sectors including banks, insurance and investment companies can be spread over the risk of financial system. For this purpose, a method of measuring changes in Conditional Value at Risk (CoVaR) based on the financial sectors return is used and its value is estimated using quantile regression. Also, two-sample Kolmogorov-Smirnov test was used to determine the impact of risk imposed by financial institutions to financial system and to achieve a ranking of financial sectors contributing to systemic risk. In this paper, 24 financial institutions listed in Tehran Stock Exchange during the time period of 2011-15 was selected. The results show that all 3 sectors contribute significantly to systemic risk in Iran during this time period and the investment companies have the highest share in creating systemic risk and then the banking and insurance sectors, respectively.

Loan loss provisions (LLP) and its related costs are factors having reducing effect on the profit and assets of banks. Iranian banks estimate their LLP each year according to the instructions provided by Central Bank of Islamic Republic of Iran. In this study, the main questions are: 1-whether LLP estimation is acceptable or not considering the financial and risk situation of banks, 2- if the estimated LLP is significantly different from the optimal value, how much it should be, and 3- which factors are affecting on this difference. In this regard, by adopting stochastic frontier model and using 19-bank financial data from 2007 to 2013 time period we examine the efficiency of Iranian banks’ LLP estimates. The results show that banks’ inefficiency scores in estimating LLP are different in various years. Iranzamin Bank with inefficiency score of 0.00054 was the most efficient bank and Bank Mellat with inefficiency score of 0.042 has been considered as the least efficient bank in estimating LLP in 2011.

Opening a proper bank account is one of the ways by which parents ensure a promising financial future for their family particularly for their children. Since Agriculture Bank is deeply involved in the bank acceptance of children and teenagers, present study was conducted in Agriculture Bank branches to identify and analyze the factors influencing bank acceptance of children and teenagers. The present study is an applied research in which two types of questionnaires were used to collect experts as well as customers’ opinions. To examine the validity of questionnaires, content validity was used. Statistical sample includes Agricultural Bank experts and customers who have already opened accounts for their children. The sample consists of 36 experts and 526 customers. The results indicate that environmental factors have the greatest influence on the other variables. Individual factors lie in the middle level and organizational factors have the lowest influence. Also, the results indicate that social factors have the most significant impact on the children and teenagers’ bank acceptance, bank credibility and families’ welfare level ranked as second and third, respectively.

In this study, the relationship between weighted average cost of capital (as one of the economic approach to assess the performance) with intellectual capital was studied. In order to compute intellectual capital Value-Added Intellectual Coefficient (VAIC) model and to compute weighted average cost of capital (WACC) Gordon model were used. Statistical sample consists of 80 companies and 380 company-year observations for period of 2010 till 2014. To examine the relationship between independent and dependent variables linear regression model and to perform calculations and data analysis Excel, SPSS  and Eviews  have been used. The results showed that in the case of banks the relationship between human capital efficiency (HCE), structural capital efficiency (SCE), and intellectual capital efficiency (ICE) with weighted average cost of capital (WACC) is positive and significant. So, intellectual capital caused an increase in company wealth and a decrease in the cost of capital. While relationship among capital employed efficiency (CEE) and weighted average cost of capital (WACC) regardless of positive or negative relationship coefficient is not significant. Also, in the case of both banks and companies, the relationship between human capital efficiency (HCE), intellectual capital efficiency (ICE) and capital employed efficiency (CEE) with weighted average cost of capital (WACC) regardless of positive or negative relationship coefficient is not significant. In other words, intellectual capital did not lead to an increase in company’s wealth. While relationship between structural capital efficiency (SCE) and weighted average cost of capital (WACC) is positive and significant. Also, the results showed that the relationship between value-added intellectual coefficient (VAIC) with intellectual capital efficiency (ICE) is positive and significant. The correlation coefficient in banks was 0.99 and correlation coefficient for both banks and companies calculated to be 0.94. Therefore, intellectual capital causes an increase in value-added intellectual coefficient