mohammad ebrahim mohammadpour zarandi

Liquidity management is one of the biggest challenges of the banking system. One of the problematic factors in liquidity management is that some loans, which are defined as non-performing loans, do not return to the liquidity cycle. The prediction of non-performing loans helps in the optimal management of banks' cash. Forecasting is possible by extracting the determining factors and using a suitable forecasting model. Therefore, this study was conducted with the aim of predicting the management of cash flow in non-performing loans of banks in three stages. In the first stage, all related factors were extracted and categorized using the meta-synthesis method and extensive background review (review of 194 articles). The categories included 10 classes of determining factors (95 factors), 11 mitigation strategies and 12 consequences. Due to the multiplicity of factors, it was not possible to include all of them in the prediction model. Therefore, in the second stage, the most important determining factors were selected by conducting three Delphi rounds and using experts' opinions. The output of this stage was the selection of 4 macroeconomic variables and 5 bank-specific variables. In the third step, the data of model variables for the years 1389 to 1400 were extracted and three machine learning algorithms, random forest, K nearest neighbor and Tobit logistic regression were used for prediction. The results showed that the prediction accuracy of random forest algorithm is higher than other used models.

Taxation is one of the main incomes of the government, and the government uses tax incentives as a key tool to improve tax compliance among taxpayers. This study aimed to understand the effects of tax incentives on tax compliance and business outcomes of taxpayers. Considering its purpose, this study is an applied research, and regarding its data collecting method, both field and secondary research methods have been used. In secondary research, we used books, national journals, and relevant articles form national and international databases, while in field research we used direct observations and interviews. Using various methods and tools for collecting the data, we investigated the agents, their interactions with each other and with the environment, and at the end, we analyzed the collected data using AnyLogic software, and the three hypotheses of the article were investigated. The results indicate that the tax incentives for any taxpayer can decrease the corresponding tax perception, which leads to tax compliance and change in the profitability and costs.

Finding the relationship between the components of the financing chain and improving the performance of the production cycle requires identifying the gaps and gaps between these two important categories. In the current research, it was tried to first take the basic steps to identify these gaps, then formulate them to optimize the financing chain to improve production. After building and solving the model, it was implemented through mathematical algorithms and relevant software, and then a multi-objective problem was designed to analyze the results and compare each of the components. Using them, the output of the algorithm was analyzed as a result. Finally, to check the validity of the findings, an interview was conducted with the managers and experts of the production units and the results were applied in making decisions to provide mathematical and computational planning. The qualitative part of the research was also conducted using the opinions of managers of manufacturing companies and experts and professors in production and financial management. The statistical population in the quantitative part of the research is all the machinery and equipment industry companies accepted in the Tehran Stock Exchange, out of 20 companies,17 active companies were considered. The results showed that the development of supply chain financial resources brings a new incentive for companies and society. In general, this research studies a model of the relationship between the financing chain and the production cycle and finally shows what factors can improve and promote the production cycle and can affect the performance of companies.

This research is a sequential mixed study in the sense that qualitative data from the literature review is first collected through in-depth interviews and then analyzed. After collecting the data and identifying them, the coding stage was done using the Swara method using the opinion of experts. The data was collected according to the reliable information and data of large companies accepted in the stock exchange and the Kodal system, and the reports and financial statements of the aforementioned machinery and equipment industry companies were used. The examined experts are all managers and experts of machinery and equipment industry companies and university professors, who are somehow considered experts in the concepts of the current research.In general, the main goal of this research is provide a model of the financing supply chain that can identify the optimized indicators influencing the financial supply chain in order to improve its performance and introduce the degree of influence of each one.This research creates a model of the financing chain that studies the management and control of risk and obstacles and bottlenecks, as well as the effects of the financing decision, and the relationship between the management of the financing chain and finally shows what Factors can improve and upgrade the production cycle and affect the performance of the company.

Optimization of banks' foreign exchange portfolios aiming to determine an optimized combination of foreign exchange assets is in such a manner that it leads to a minimum risk and maximum yield. The approach used in this article, which is actually a priority over other methods is to use a combination of inexact mathematical models (fuzzy) and optimization. Thus, in this research, linear programming with fuzzy target coefficients has been used. The target coefficients are the same as the exchange rate. In other words, the daily rates of all currencies due to fluctuations are considered as fuzzy numbers. Therefore, in this investigation, by developing a multi-purpose mathematical model and by applying the fuzzy data relating to transaction rate of six foreign exchanges in year ended on March 20, 2020, including US dollar, Emirates Dirham, Japanese Yen, Turkish Lira, Korean Won and Euro, case by case risks related to the fluctuations of noted foreign exchanges and also their yield in the foreign exchange portfolio of Bank Mellat were measured and estimated for the future time horizon. All banks may benefit from the developed model. By determination of the optimal weight of each foreign exchange, upon describing and analyzing the existing status it specifies the optimal status, enabling banks to fulfill their foreign exchange obligations on due dates by properly and optimally making investment in foreign exchange assets meanwhile gaining a competitive advantage. "Gams Software" was used for solving the model.

Explaining the competitive strategies of supply chain management is vital. Therefore, examining supply chain risks to choose the best criteria is a new strategy to manage and control risks and reduce the vulnerability of the supply chain process in order to improve the process and productivity. The purpose of the current research is to identify and rank the risks of the  three-level supply chain (supplier, producer and distributor) with a focus on level (2) of production and operations in order to explain the new strategy in the operating company of oil and gas production. This article has been done by reviewing the studies conducted in the field of identifying and ranking the risks of lean, agile, resilient and green supply chain management, by presenting a hybrid analytical model based on fuzzy Delphi and the network analysis process in the Super-Decision software environment. The risks identified using the literature review, 32 effective components with the contexts and the real space of the supply chain and with a survey of the expert team of 17 people, have been carried out in three stages according to the fuzzy Delphi method. Finally, after refining and evaluating the extracted factors, it led to the identification of 6 criteria (environment, supply, information system, process, control and demand) consisting of 26 risks, categories and ratings. According to the findings of the research, the risks of the process criterion with the highest weight were chosen as the superior criteria and the criteria of supply, control, environment, demand and information system were placed in the next priorities. Explaining the new strategy, agile, lean, resilient and green competitive strategies were prioritized in order.

The bank’s real profitability by adsorbing the deposits, payment loans, and making investments may create cash flows different from what has been anticipated for the apportioned budgets. In line with this, the banks must predict cash flows and factors influencing their profitability. The present study tries to investigate and elucidating the effects of loan repayment losses and deposit costs on the growth in the banks’ profitability.Since there is no model for measuring the loan payment losses and deposit costs, the present study engages in designing and pretesting of models for measuring the loan payment losses and deposit costs based on Iran’s banking; then, the designed models will be used for measuring the loan payment losses and deposit costs. GMM-based multiple-regression is the method used herein along with the financial statements of Sarmayeh Bank as well as central bank’ information for a time span from 2011 to 2019.Corresponding to this research’s results, the loan payment losses and deposit costs are factors influencing the profitability of Sarmayeh Bank. In other words, the increase in the loan payment losses and the deposit costs brings about reductions in the profitability rate of Sarmayeh Bank.

Data envelopment analysis is a non-parametric method for measuring the performance score of a set of units under evaluation. Recently, the application of data envelopment analysis models in networked or multi-stage structures has been considered by researchers. This paper seeks to reinforce the first steps taken to develop DEA network models based on asynchronous technology. To this end, it provides a way to consider undesirable outputs in an asynchronous technology. The models presented in this paper, while calculating the overall performance score in a network system, are able to calculate the performance of each step separately in the presence of undesirable factors without any additional calculation and provide it to the system administrators. Also, to show the accuracy of the proposed model compared to the basic CCR model, we conclude the computational accuracy of the model due to the identification of a number of less efficient units than the CCR model. According to the results, despite the fact that some units are considered as efficient, but due to inefficiency in some stages are considered inefficient and only the National Industrial Holding is considered as the only efficient unit due to the fact that it is efficient in both stages.

Management is effective in all areas of an organization. An organization achieves the expected results when the management of the organization uses jihadi components in its approaches. An organization can achieve its planned financial and non-financial and transcendent goals, if jihadi management approaches are implemented in that organization. Jihadi management as the most logical and efficient management model for achieving the goals of organizations in a revolutionary system has been considered and emphasized in recent years. This type of executive agent management is an "Iranian-Islamic model of progress" and is based on management knowledge derived from management science and schools as well as the Islamic value system. The present study aimed to identify the dimensions and components of Jihadi management with the aim of optimal management of credit risk in the network of car manufacturers’ dealerships. Since this after-sales service network is primarily responsible for serving customers of Saipa Company, it requires having a professional and jihadist view with the aim of promoting the level of servicing.

The type of customer service during the warranty is crucial for each complex. The purpose of customer service will be to meet the satisfaction of customers. Many components can contribute to accomplish this goal. One of the most important components is financial components. Today's world is a world of wide developments in all dimensions. The majority of companies are, more than ever, aware that the delivery of after-sales service is very effective in the loyalty and repetition of customer purchases. The intense focus on the quality of service causes the product to be valuable in terms of customers and their loyalty. Therefore, in this study, designing a credit risk management model for the for the Saipa Yadak Company and its Representatives Network using financial components of after-sales service and meta-heuristic algorithms was discussed. The sample studied in this research is the representatives of Saipa Company.The results showed that using financial components including, service cost, performance, good accounting, the amount of collateral and the amount of after-sales service agents have an impact on optimal credit risk management. Also, firefly algorithm and bee colony algorithm have the ability to predict the optimal management of credit risk using financial components.

This analytical-descriptive article seeks to explain the impact of overall and partial of efficiency governance and residents on selected financing methods in the Javanmard Qassab neighborhood. The statistical population was all residential properties located in this neighborhood, of which 50 homogeneous license plates were selected in terms of land area by non-random sampling method. Using fuzzy data envelopment analysis model, Network Fuzzy Range Adjusted Measure Data Envelopment Analysis, overall and partial efficiency was calculated and classified into three groups by using the K-means Clustring technique. Findings from Pearson correlation coefficient and F-test & test of significance (Two-tailed) of variance in SPSS software environment show that there is a direct and positive relationship between overall efficiency and part of residents in all financing methods with an average correlation coefficient of 0.834 with a significant level of 99%. It is more prominent in the process of improvement and modernization. The findings show a direct relationship between overall efficiency and governance with a mean correlation coefficient of 0.570. The results suggest that there is no significant relationship between residents' performance efficiency and governance, which indicates a lack of correlation in financing between government and people's interests.

One of the financial institutions of the countries in the world is the Stock Exchange, which is used as indicators of economic health. As for a large volume of capital is managed through the stock market and is placed in the production and industry, the recession in this market can have important effects. In this paper, we seek to extract the cycles of prosperity and recession in the Tehran Stock Exchange using the TEPIX and the Pagan and Sossounov method. Then, using the PSO algorithm, the most important predictor variables are determined and in the next step We predict market financial cycles using ANFIS, MLP, RBF and PNN networks. The results show that according to the mean square error, the root mean square error, the accuracy of the model and the Kappa coefficient, the MLP model provides better results in predicting future market conditions.

Banks as fund intermediaries in providing and allocating resources to the community, encounter market risk, liquidity risk and etc. In this study, the market risk, is taken into consideration in order to determine the optimal currency basket, one of the fundamental aspects of Foreign Currency Reserve Management in banks, which itself is also affected by fluctuating interest rates, exchange rates, stock prices and etc. The approach used in this paper is the value-at-risk criterion (VaR) the variance-covariance method, along with the exponentially weighted moving average (EWMA) technic. Value at risk actually summarizes the types of risks in a single digit, and it releases the senior management from bunches of risk calculations. The purpose is to design a model which provide an optimal combination for holding 6 currency reserves such as U.S. dollar, Dirham, Yen, Lira, Won, and Euro in Bank Mellat using the reference rates data of the aforementioned currencies in 2018. At the end, the model was solved using LINGO and Excel software. The results show that the maximum share of the US dollar and the dirhams in the currency basket of Bank Mellat are 33% and 67%, respectively. Accordingly, if the share of that currencies mentioned above exceed the obtained digits in the currency basket, then the maximum expected losses on the currency portfolio increase over the time and at the level of desired level of confidence. Also, other currencies are so risky, therefore Mellat Bank, to hold these currencies must plan more based on its trading needs.

Improvement and redevelopment measures in Worn-Out texture, Urban Recreation Targets, and Neighborhoods,are a multifaceted and progressive issue, and the extent to which these reductions have been beneficial has increased in both dimensions and dimensions. This study examines the idea of whether data envelopment analysis models (Network Fuzzy Range Adjusted Measure Data Envelopment Analysis (NFRAM DEA)) can be used to make the right decisions to finance these areas. This study, through reviewing the literature and examining the theoretical foundations, identifies the factors that influence the selection of financing methods for 50 Javanmard Qasab neighborhood district 20 of Tehran municipality plates (homogeneous plaque in terms of land area). Determines the government and residents and then proposes financing options for each license plate. The results of this study indicate the practical application of data envelopment analysis method (Network Fuzzy Range Adjusted Measure Data Envelopment Analysis (NFRAM DEA)) to calculate the efficiency of financing methods.

The effective role of capital in every country flows through giving guidelines for capital and resources, generalizing companies and sharing development projects with public, and also adding accredited companies stock market requires appropriate decision making for shareholders and investors who are willing to buy shares based on price mechanism. Forecasting stock price has always been a challenging task, since it is affected by many economic and non-economic factors and variables; therefore, selecting the best and the most efficient forecasting model is tough and essential. Up to now applying weighted mean called weighted mean price has been used to forecast industry average price for companies in the stock market and investors were forecasting based on this method. First we have identified 10 accredited banks in TSE and 10 banks in Iran Fara Bourse. In this article, by applying one of the mathematical optimizing techniques, industry means got calculated based on optimized parameters and compared with the industry average; in this statement we strived to find another variable that could forecast with less deviation.  In the following study, by calculating frequency level of deviations, average for price forecasting in banking industry during five years is examined. Finally, the research suggests that, instead of using mean of industry average, it is better to use mean average of golden number, which will lead us to more accurate results.

The stock exchange is considered to be an important establishment to finance long term projects, on one hand, and to collect savings and finance of private section. The stock exchange can be a safe and secure place to invest surplus funds to purchase corporate stocks. As recession and prosperity in this market can have a great role in stockholders` decision-making, it becomes vital to predict these cycles. In this paper, using model MSMH(4)AR(2), we extract the financial cycles of the market. Then, using the ant colony algorithm, we determine the most significant predictors and predict the market financial cycles using neural networks. The results show that the PNN model performs better in predicting the future market with respect to the criteria of mean squared error, the root mean squared error, the model accuracy and kappa coefficient.

The stock exchange is one of the financial instruments of countries around the world. The recession in this market can have important effects, for example reducing liquidity, reducing the profitability of companies admitted to the stock exchange, and reducing economic growth. In this paper, we are looking for extraction and prediction of time cycles in the stock market. Initially, using the total stock index and the MSI (3) AR (2) model, three cycles of recession, medium prosperity and high prosperity are extracted in the stock market. Then the most important predictor variables are determined by using the integration of the NSGA (II) algorithm and the three types of neural network models and predicted the market situation for the next three months. Finally, the performance of three types of multilayer perceptron neural network, radial basis and probable network were compared in terms of feature selection and prediction of future market situation. The results indicate that all three models have acceptable error rates, accuracy, and Kappa coefficients, and the probable network model has lower error rate, more accuracy and kappa coefficient than other models.

In order to survive in the modern world, organizations must be equipped with the mechanisms that not only maintain their competitive advantage, but also result in their progress and improvement. Prediction of banks’ performances is an important issue, and a poor performance in banks may primarily lead to their bankruptcy, thereby affecting national economics. The bank performance prediction model uses scientific and systematic approaches to diagnose the financial operations of institutes. According to a precise and strict evaluation, the model can detect the weakness of institutions in advance and provide early warning signals to related financial governments. In the present study, we have used three data mining models to predict the future performance of the banks accepted in Tehran Stock Exchange (TSE) and Iran Fara Bourse. Initially, 53 financial ratios were selected and, consequently, reduced to 28 using the fuzzy Delphi technique. The statistical population included 18 banks listed on TSE and Iran Fara Bourse, which   provided their financial statements during the period of 2011 to 2017. Data were collected from the Codal site based on 28 financial ratios using C4.5 decision tree, AdaBoost, and Naïve Bayes algorithm. According to the findings, the Naïve Bayes algorithm was the optimal predictive model with the accuracy of 88.89%.

The recent financial crisis has induced many discussions about stability of financial system, which is naturally dependent primarily on banks. Bank stability issue became crucial for both depositors and governments of different countries. This paper focuses on the corporate governance of banks. In this paper we discuss the state of corporate governance in Iranian commercial banks and try to find a link between corporate governance mechanisms and financial stability indexes. We use Structured Estimation Modeling (SEM) and find that there are some relationships between auditing quality measures, characteristics of CEO and board of directors as measures of corporate governance mechanisms and asset quality measures, efficiency measures, profitability measures, liquidity measures and capital sufficiency as measures of financial stability in Iranian commercial banks. We report that the finding can improve the governance roles and performance evaluation of Iranian commercial Banks. This findings provide useful contributions for managers in making decisions in banking industry

One of the main goals of governments in privatization implementation, along with goals such as increasing revenues and improving economic efficiency, is the development of stock markets. In this study, using the Bay and Peron technique, structural break is observed in the liquidity variable as one of the signs of the stock market depth. The model implies the existence of at least two effective and valid structural breaks and shows that in many related events, the implementation of privatization led to increased market liquidity as one of the principles of market development and led to a stock market has more depth. Also, using the MRS-GARCH method, it was determined that privatization on the emergence of regime change in the yield variables of stock market index has affected the duration of the third, fourth and fifth development plans in the form of dynamic models.

One of the primary goals in an accounting system is to provide relevant, in-time, and reliable information for the users. The accounting structure of historical costs considers past events of companies, on the other hand investors, mostly, need to have a clear image of companies’ future. Therefore, this idea comes to existence that the accountants should provide such information that makes it possible for the investors to forecast the future of the companies better
In this paper, we study the relationship between accounting information (earning per share and book value per share), adjusted based on inflation, and stock market prices
To this end, the sample includes 169 companies in a six-year period and we analyzed the data by using Eviews software. The statistical method in this research is based on Ohlson assessments model.
After adjusting the financial historical information based on the Consumer Price Index, we tested the relationship between market value of stocks and adjusted financial data.
The results of the current study show that both the historical financial statements and inflation-adjusted financial statements indicate significant relationship with the stock market, so it is more practical to present both type of statements together as a complementary and not as a substitute of one another.

The banking system, as an intermediate in the role of society’s financial resources allocator, faces different risks relating its activities, such as credit risk, market risk, liquidity risk, operational risk, etc. In this paper, our focus is on market risk, which is influenced by interest rate, exchange rate and other fluctuations, especially on exchange rate risk. By studying the different methods of evaluating this risk, studies and data on this matter, the risk of foreign exchange portfolio is estimated using the VaR method. In this study, theoretical basis of this issue are discussed and the concept was implemented on historical data. The software designed for this approach helps us to evaluate the optimum portfolio of exchange basket according to banks’ obligations and capital and exchange rates fluctuations.

Lending is one of the main services of the banks. It is notable that the process of funding money and lending it to the customers has a cost of capital, so, the lending condition should be economical for both bank and customers. Hence, in this article we’re going to provide a model of banking loans loan portfolio which is designed as a soft-ware application that supported by Data Mining Technology and mathematical models. This model could provide optimum combination of bank’s loan portfolio and contribute as a decision support system for bank credit management.