sadegh bafandeh imandoust; Zahra Shaterian; Seyed Mohammad Fahimifard
Abstract
Credit is an important policy instrument that can facilitate the application of modern technologies and increase the production, especially in developing countries. Credit is also a key to poverty reduction, livelihood diversification, and increasing the business skills of small farmers. Keshavarzi banks, ...
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Credit is an important policy instrument that can facilitate the application of modern technologies and increase the production, especially in developing countries. Credit is also a key to poverty reduction, livelihood diversification, and increasing the business skills of small farmers. Keshavarzi banks, with more than seven decades of experience, are specialized institutions that provide the majority of credits for the development of the agricultural sector in Iran. These banks aim to enhance agricultural productions and fair distribution of income by granting loans to the farmers.
One of the important issues in bank lending is the probability of loan default. Many factors affects the default of bank loan default which their assessing will reduce the credit risk and improve the process of loan endowment. Agricultural lending involves giving out of credit (in cash and kind) to small- scale farmers for the purpose of farming. There is no doubt about the crucial roles of credit in economic development. Agricultural household models suggest that farm credit is not only necessitated by the limitations of self-finance, but also by uncertainty pertaining to the level of output and the time lag between input and output. Recent studies show that the growth rate of investment in agriculture is less than other economic sectors. So, financing agriculture is one of the most important factors to develop rural areas in developing countries. Banking system payment is a way of financing. Generally, credit accessibility is important for the improvement of quality and quantity of farm products, so that it can increase farmer’s’ income and reduce the rural migration. The present paper tries to find the effective factors on bank loan recovery rate of Keshavarzi bank of Khorasan Razavi province-Iran through Tobit econometrics model.
Methodology
The Tobit model is a statistical model proposed by James Tobin (1958) to describe the relationship between a non-negative dependent variable and an independent variable (or vector) . The term Tobit was derived from Tobin's name by truncating and adding -it by analogy with Probit model.
The model assumes that there is a latent (i.e. unobservable) variable *. This variable linearly depends on via a parameter (vector) \beta which determines the relationship between the independent variable (or vector) and the latent variable * (just as in a linear model). In addition, there is a normally distributed error term to capture random influences on this relationship. The observable variable is defined to be equal to the latent variable whenever the latent variable is above zero and zero otherwise.
Therefore, in this research, the effective factors on loan recovery rate with the case study of Keshavarzi bank of Khorasan Razavi province-Iran and through Tobit econometric model were studied.
For this purpose, using Cochran's random sampling, 215 customers which part of their loan from mentioned bank encountered with default were selected and the required data was gathered.
Results and Discussion
Results of the estimated Tobit model using STATA software showed that loan size has the weak direct, rate of loan payment has the not significant effect, length of loan payment has the strong direct, guarantee has the positive, assurance has the negative, natural accident has the inverse and loan payment deadline coincident with the sale season has the strong direct effect on loan recovery rate.
Conclusion
Empirical results of this study highlight the importance of taking into account factors including interest rate, loan size, type of guarantor, extension of the loan and loan payment deadline coincident with the sale season. Therefore, it is recommended to the policy makers and agricultural banks to consider a comprehensive credit risk management process to monitor and control credit risks for reducing the risk of delinquencies and defaults. And also try to allocate the lending to the farmers which have off-farm incomes and activities. In addition, it seems necessary to ask the loan applicants to provide appropriate collateral and to enforce loan repayment obligations for effective credit delivery to the agricultural sector.
Therefore, most of the factors are easily achievable and should be utilized by policy makers in Khorasan Razavi province.
Taghi Ebrahimi Salary; Seyed Mohammad Fahimifard; Hanif Kheirkhah
Abstract
Abstract
In this research the comparative prediction of Iran's banking system (included 14 banks) was carried out by using econometric and artificial neural network models. Accordingly, at first, by using the Kohonen neural network model, the considered banks were divided into two categories of high ...
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Abstract
In this research the comparative prediction of Iran's banking system (included 14 banks) was carried out by using econometric and artificial neural network models. Accordingly, at first, by using the Kohonen neural network model, the considered banks were divided into two categories of high performance and low performance groups and then using the output of Kohonen neural network model, financial proportions and Panel Data econometric model, the performance of Iran's banking system was estimated for the period 2004-2010 and finally by using models evaluation criteria, the performance of Panel Data and ANN models was compared.
The results of Kohonen neural network model indicated that from 14 considered bank, 4 banks belong to high performance group and 10 banks are belong to low performance group. Also the results of Panal Data estimations showed that “capital income/total income "portion has the lowest and “cash/total deposits", has the haighes effect on the Iran's banking system. Finally the results of models comparison stated that the ANN model outperforms the Panel Data model to predict the performance of Iran's banking system.
Sadegh Bafandeh Imandoust; Seyed Mohammad Fahimifard; Somayeh Shirzady
Abstract
سیاستگذاران پولی به منظور جلوگیری از زیانهای ناشی از تغییرات از همگسیخته نرخ ارز، همواره درصدد یافتن روشی مناسب برای پیشبینی نرخ ارز بودهاند. لیکن ویژگیهای ...
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سیاستگذاران پولی به منظور جلوگیری از زیانهای ناشی از تغییرات از همگسیخته نرخ ارز، همواره درصدد یافتن روشی مناسب برای پیشبینی نرخ ارز بودهاند. لیکن ویژگیهای چند بعدی نرخ ارز باعث رفتار پیچیده و غیرخطی آن شده و مدلهای غیرخطی از عملکرد بهتری در پیشبینی آن برخوردار میباشند. لذا، در این مطالعه کارایی مدلهای غیرخطی ANFIS و NNARX و مدل خطی ARIMA در پیشبینی برابری نرخ ارز ریال/دلار آمریکا و ریال/یورو برای 2، 4 و 8 روز آتی با استفاده از مهمترین معیارهای ارزیابی کارایی مدلها و دادههای روزانه مربوط به دوره 1/9/1387-1/1/1381 مقایسه شد. نتایج نشان داد که مدلهای ANFIS و NNARX در مقایسه با مدل ARIMA و مدل ANFIS در مقایسه با مدل NNARX در همه افقهای مورد نظر، از کارایی بیشتری در پیشبینی نرخ ارز ریال/دلار آمریکا و ریال/یورو برخوردار میباشد.
