رحیم بیات

Unlike conventional banking, whose the main goal is to maximize profits based on the loans granted, Islamic banks operate based on the laws and rules of Islamic jurisprudence, in which the use of interest is prohibited. It was precisely due to this characteristic of Islamic banking that many experts were hesitant about its continued existence when the first Islamic bank was established in 1963 and predicted that interest-free banking would not last long. Despite these doubts, Islamic banks are now the fastest growing and developing financial industry in the world.
The basic question here is whether Islamic banking can meet the monetary and banking needs of the economy despite conventional banking? Do Islamic banks have the necessary stability and efficiency? In general, factors such as the ceiling of granted loans, rate of return on assets, rate of return on shares, value of bank assets,diferent types of bank expenses and various other factors can cause differences in the efficiency and performance of banks. Answering to these questions requires investigating the performance of Islamic banking compared to conventional banking.
In order to investigate the performance of Islamic banking compared to conventional banking, we used four different approaches: Data Envelopment Analysis (DEA), Stochastic Frontier Analysis (SFA), Markowitz Model, and Capital Asset Pricing Model (CAPM). DEA approach is one of the non-parametric methods based on mathematical methods and linear programming to measure the efficiency of decision-making units. In contrast, SFA is a parametric method based on econometrics and production function estimation to evaulate the efficiency of decision making units. Then we used the Markowitz model to obtain the optimal rate of return of banking stocks and unsystematic risk, as well as CAPM to find the optimal rate of return of banking stocks and systematic risk.

In order to evaluate the technical efficiency of banks based on DEA and SFA methods, the input and output variables of each model have been introduced, which include three input variables and three output variables in DEA method and three input variables with an output variable in SFA method. In the next stage, the following steps were taken to perform the Markowitz model optimization process:Some classes of the assets that are considered to be invested must be selected and according to the limitations of the Markowitz model and considerations related to the selection of variables, and collection of the research statistical sample, during developing the appropriate mathematical and statistical model, the optimization process is performed.
Expected returns are estimated for each class of assets.
The risk of each class of assets is estimated.
The process of optimization and graphical structure are performed to find the efficient frontier, based on which, the set of efficient and profitable assets is determined.
By implementing the mentioned steps and using the time series data related to the rate of return on shares of each of the banks from 2012 to 2020, the average and standard deviation related to the rate of return on the assets of each of the banks in both Islamic and conventional banking systems was measured and then the efficient frontier curve was extracted for Islamic and conventional banks. Finally, to apply the CAPM and calculate the systematic risk of banks, the value of bank shares was used for this purpose. Due to the statistical limitations in this field where the necessary data was not available for all 40 investigated banks, and on the other hand it was necessary to use the monthly return rates of these data, therefore subject to the DEA and SFA results, 5 banks from the Islamic banking system and 5 banks from the conventional banking system were selected. Then, according to the data of the monthly stock returns of each of these 10 banks since the second month of 2012 through the end of 2020 with 107 observations, the performance of both banking systems based on the CAPM and estimating the systematic risk rate for each bank was compared. For this purpose, we considered the regression model as Where  is the rate of return for asset i during period t,  is the rate of return for the market portfolio M during t,  (also called excess return) is the difference between estimated return and expected return,  is the beta coefficient and 𝜀 is the error term. To estimate above regression for the selected banks, we used the monthly return rates of the MSCI global index as , since the beginning of 2012 through the end of 2020  and the monthly stock return rates as   for each of the banks. First, this regression was performed for the portfolio of Islamic banks and conventional banks, and then we repeated it for each selected bank, and accordingly, the beta coefficients related to the stock portfolio of conventional and Islamic banks, as well as each of the selected banks, were calculated.

The average efficiency of Islamic banks in data DEA and SFA models was evaluated as 74% and 56.15% respectively, and for conventional banks as 69.5% and 48.13% respectively. By implementing the Markowitz model during the period 2012-2020, the average and standard deviation of Islamic banks' stock returns were calculated as 0.76% and 4.09% respectively, and for conventional banks as 0.68% and 6.35% respectively. Next, by developing ten optimization problems, ten pairs of optimal stock returns and their corresponding risk rates were calculated for each of the two Islamic and conventional banking systems, and accordingly, the efficient frontier curve for each of these two systems was extracted. The average correlation coefficients of stock return rates for two Islamic and conventional banking systems were calculated as 40.4% and 44.1%, respectively, which indicates the  lower amount for the Islamic banking system compared to conventional one. Finally, so as to implement the CAPM for measuring the beta coefficient for Islamic and conventional portfolios, firstly this regression was estimated for the portfolios of Islamic banks and conventional banks. The results are compiled in Table No. (1).

The main goal of this research is to investigate the performance of the Islamic banking system compared to the conventional banking system in terms of efficiency criteria by two parametric and non-parametric approaches. For this purpose, two approaches of data envelopment analysis and stochastic frontier analysis were used. The results of the investigation, in terms of both approaches, indicate that Islamic banks are more efficient than banks operating in the conventional system according to the studied input and output variables. The average efficiency of Islamic banks in DEA and SFA models evaluated as 74 and 56.15 percent, respectively, and for conventional banks as 69.5 and 43.18 percent, respectively. The average efficiency of all banks was 71.8% in DEA and 37.8% in SFA. The results also indicated that Iranian banks performed weaker than other Islamic banks in terms of granting the loans, but better than conventional banks. Also, public banks in Iran perform better than private banks in terms of granting loans. Also, the findings of this research showed that the risk of stock returns related to selected Islamic banks is lower compared to conventional banks, both in terms of systematic risk and unsystematic risk.

In order to evaluate the effect of different levels of sowing dates and plant density on grain yield and its components in chickpea var. Arman, a field study was conducted at Ekbatan Research Field Station of Agricultural and Natural Resources Research Center of Hamedan province under rainfed condition during 2009-2010 cropping season. Treatments were arranged as split plot on the basis of randomized complete block design with four replications. The main plots were date of sowing in three levels including Entezari (16 December), spring planting (7 March) and late spring planting (4 April) and the sub-plots were plant densities (20, 30 and 40 plants/m2). Results showed that pod number per plant, 100-seed weight and grain yield were affected by sowing dates, significantly. The results also showed significant differences for pod number per plant, grain yield and harvest index in different plant densities. The interaction of sowing date×plant density was significant (p< 0.01) only for seed yield. Finally, the highest and the lowest seed yield was achieved in Entezari planting with 30 plants/m2 and late spring planting with 40 plants/m2.