درخت حوزه‌های تخصصی

This paper examines the role of development banks in the fixed investment formation and economic growth of the country with emphasis on the facilities granted by development banks. To do this, quarterly data of the country's development banks in the period 2006-2020 and experimental tests related to the causal relationship of variables and estimating the long-run relationship between variables were used with the Granger causality test and vector autoregression method (var). The results show that the development sector facilities and total banking network facilities (except for development sector facilities) have had positive and significant effects on fixed investment formation and other banking network payment facilities except the development sector and liquidity volume have had no significant effcets. Regarding the relationship between the liquidity and total paid facilities of the banking system, with the variable of fixed investment formation, the results show that all paid facilities have had positive and significant effects and liquidity has insignificant effects on fixed investment formation. According to the results of the long-run relationship, the development sector facilities and the volume of liquidity, in the long run, have had a significant relationship with fixed investment formaton in the Iranian economy. According to the results of the short-run relationship, development sector facilities have had positive and significant impacts on fixed investment formation and investment in the economy. Therefore, it can be concluded that the development sector facilities in both the short-term and long-term have been able to have a positive and significant effect on fixed investment formation

Predicting bankruptcy risk is one of the most critical issues in corporate financial decision-making. Investors always try to predict the bankruptcy of a firm to reduce the risk of losing their assets, so they are looking for ways by which they can predict the risk of bankruptcy. We predict the position of companies active in the oil and gas industry based on their financial health in the 2020 ranking of S&P global up to three years before 2020. This study uses three data envelopment analysis models (CCR, BCC, and DDEA) and the traditional Altman model for forecasting. We have shown that dynamic data envelopment analysis is a powerful tool for predicting bankruptcy risk.

In the past, deciding over granting loans to bank customers in Iran would be made traditionally and based on personal judgments over the risk of repayment. However, increase in demands on banking facilities by economic enterprises and families on the one side, and increased as well as extended commercial competitions among banks and financial and credit institutions in the country for reduction of facility repayment risk on the other side, have caused application of novel methods such as some statistical ones in this context. Now to predict the risk of negligence in banking facility repayment and classification of the candidates, bankers use their customers’ credit ranking. Time efficiency, cost effectiveness, avoidance from personal judgments, and further accuracy in examining the candidates who apply for various funds are of its salient merits of this new combined method. Various statistical methods including biased analysis, logistic regression, non-parametric parallelism, and also some others such as neural networks have been employed for credit ranking. In this research, given the random forest metaheuristic algorithm-based smart pattern of real bank customers’ credit risk (case study: Bank Tejarat) was presented. According to the value of skewness, the data could be stated to have a normal distribution. Based on the observed results, the lowest mean was related to the variable of type of facility and its maximum value, to the amount of facility.

The purpose of this study is to make investment decisions with the approach of data envelopment analysis and making decision on selection of optimal stock portfolio employing meta heuristic algorithms for multi-objective functions subject to real-life constraints. The statistical population of this research in capital decision-making and selection of the optimal capital composition is 183 of the selected companies of Tehran Stock Exchange, which were finally 42 companies as justified investment options. After measuring the risk and return of efficient companies, the real limitations of the budget, requirements and expectations of the investor, determination and composition of the investment were formulated as a multi-objective model. For optimal decision, the modified genetic meta heuristic algorithm and MATLAB software with dual operators were used. Elimination of the risk minimization function in sensitivity analysis improved the level of decision return but also led to more risk. Eliminating the maximizing return function improved decision-making risk but also reduced investment return. Elimination of investment requirements and expectations improved returns and increased investment risk, but more companies became involved in optimal investment.