مهرزاد مینویی

The aim of this study is to present an optimized model for bank profitability by considering the role of modern banking technologies. This research, being exploratory in nature due to its model development, is also considered applied because its results are utilized by beneficiaries. A quantitative method is used in this study to optimize bank profitability while considering the role of modern banking technologies. In the first step, influential components are identified through library techniques and document review, followed by mathematical programming for modeling. The proposed model is optimized using the epsilon-constraint algorithm in the MAPLE software and further optimized using MATLAB software. The research population, as a case study, includes documents and articles related to the banking industry. Initially, a single-component bank, considered as part of the system, was examined. Assume the bank operates in a seven-hour shift, six days a week. The maintenance activity time and fintech interventions were set to 7 time units, and the service delivery and corrective actions were set to 12 time units. With a 10% and 20% increase in the δ_E and δ_(M/C) data, significant changes in the objective function and decision variables were observed. However, changes in other data did not result in noticeable alterations in the model, indicating the importance of preventing the process from shifting to an out-of-control state and the significance of changes in the mean and standard deviation of the targeted quality characteristic. The proposed model determines the optimal value of each of the four decision variables: sample size (n), sampling interval (h), control limit coefficient (k), and intervals for preventive fintech actions (t_PM), which minimizes the total expected cost from integration per time unit. A numerical example demonstrated the economic impact of cost parameters on the integrated policy of profit control and preventive fintech actions. Additionally, in this study, the integrated model was compared with the independent model through a numerical example, and the significant difference in total cost highlighted the superiority of the integrated model over the independent model.

Protecting the privacy of internet customers is crucial in the field of health. In this area, there is sensitive and personal information, and privacy can increase customers’ trust in companies and create a stronger relationship between them.

The target sample was chosen using a criterion-oriented purposeful sampling method. The sampling procedure was continued until the theoretical saturation of data was reached. Accordingly, 12 professors and administrators participated in the study. The data collection tool was a semi-structured interview. Nvivo software was used for theme analysis.

Based on the theme analysis method, two constructive themes of level one and 14 themes of level two were identified. Constructive themes of level one were technological infrastructure and obligations of the seller to the consumer. The themes of technological infrastructure were personalization services, social interaction performance, access control, information technology security, security enforcement measures, safety algorithm and data-based planning, and decision making. The constructive themes of the seller’s obligations to the consumer were awareness, user and seller education, safety, maintenance and support of information, responsibility, framework and principles, and trust.

The proposed model showed that privacy protection was essential. Medical device businesses should implement a robust privacy policy, closely monitoring access, training employees on privacy protection, and upgrading security systems.

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.

The aim of this research is to analyze the Importance-Performance Matrix (IPMA) of FinTech indicators in the institutional banking environment. 

This research is applied in nature, with a descriptive-analytical method, and it was conducted as a cross-sectional survey. The nature of the research is exploratory mixed-method, combining qualitative (Delphi method) and quantitative (Importance-Performance analysis) approaches. The statistical population in the qualitative section includes banking experts with PhDs in financial management and information and communication technology, and in the quantitative section, it includes an unlimited number of banking customers. A purposive sampling method was used to select participants for the qualitative section, and for the quantitative section, 384 individuals were determined based on Cochran's formula. Semi-structured interviews were used to identify the desired components in the Delphi technique. In the quantitative section, to determine the relationships between variables and the related importance coefficients, the partial least squares method (PLS) was used, and the ranking of components was conducted using the Importance-Performance Matrix analysis. Based on the Delphi results, a researcher-developed questionnaire was used in the quantitative section. In the qualitative section, EXCEL software was used, and in the quantitative section, SMARTPLS software was employed. 

Based on the Delphi technique, 17 components and 5 dimensions were identified in the FinTech evaluation model within the institutional banking environment. The identified themes include performance management, FinTech efficiency in the banking industry, customer identification, FinTech implementation infrastructure, and bank objectives and planning based on FinTech. 

According to the Importance-Performance Matrix, performance management ranks at the highest level, followed by FinTech efficiency in the banking industry and bank objectives and planning based on FinTech

The effort of this article is to solve one of the main problems in the field of banking, which is closely related to the field of information technology. The combination of the management discussion of this topic with the field of information technology will be one of the important topics in the field of information technology management. The main goal of this article is the clustering of bank customers.At first, all customer characteristics were extracted from the bank's database, which was randomly extracted for 900,000 customers, which will be provided as input to the proposed method of this article. All the characteristics of these customers were extracted and 10 characteristics (except four characteristics of the LRFM method) were listed using the opinions of experts. The proposed method should be able to choose among these 10 features for clustering customers, which results in more resolution in clustering. Due to the high number of cases of this problem, it is not possible to do it manually, and the proposed method tries to provide a separate model for clustering for the customers of each bank by examining different cases. Also, the problem of choosing the right value for the number of clusters in the K-means method is solved by the method proposed in this article. The results show that it is better than the basic RFM and LRFM methods.Keywords: relationship management with bank customers, clustering, RFM model, LRFM model, particle swarm algorithm, K-means method.

This research sought to find the determinants and consequences of non-performing loans. For this purpose, meta-synthesis method was used and 7 steps of meta-synthesis proposed by Sandelowski and Barroso (2007) were implemented. The intended sample for meta-synthesis consists of selected studies based on their relationship with the research question. In the present study, the initial sample included 194 sources related to the research topic, which were extracted from databases without time limits. After screening the sources in terms of various items such as title, abstract, content and quality, 151 sources remained, whose results were examined more precisely and combined with each other. The result of this process was the extraction of 118 codes, which were categorized into 10 categories of determinants, 11 strategies to reduce and 12 consequences of non-performing loans. Determinants include international factors, macroeconomic factors, bank-specific factors, industry-related, market, borrower, and loan, political, macro-financial and institutional factors. 36% of determinants were classified in the category of macroeconomics determinants, 31% in the category of bank-specific factors, and 33% in the other 9 categories. Another result of this study is the circular causality observed among the consequences and determinants.

The purpose of this research is to combine the genetic algorithm in the optimization of the stock portfolio in the financial decision making of investors; in a simulation project, the final use of the input data is to build the simulation model. This process includes collecting input data, analyzing the input data, and using these analyzed input data in the simulation model. The statistical population of the research includes 20 symbols (companies) from among the industries (Vabsadar, Vetjarat, Akhaber, Fakhuz, Fars, Balbar, Tapampi, Khasapa, Khodro, Sasharq, Sosofi, Shobhorn, Shapna, Ghopino, Fould, Ghasabat, Kesra, Vanbank, Vanneft, Veniki) and the information related to the daily stock price and the daily index value from Decembre 22, 2008 to January 16, 2020 was considered as a sample. The tool for collecting information and data is using the Phipiran site, and the amount of beta (risk) of stocks is calculated monthly using Excel software, and the frequency of return and beta (risk) calculated using Spss software, and distribution functions were discussed using Easy fit software; the results showed that if the agents are beginners to earn more profit than normal behavior and accept 40% risk, the amount of profit obtained after optimizing the model with genetic algorithm is more than the initial model. If the agents are professionals to earn more profit than risk-averse behavior and accept 80% risk, the amount of profit obtained after optimizing the model with genetic algorithm is more than the initial model.

Considering the current economic conditions of the country, the number of helpless companies and the importance of financial helplessness are increasing day by day. The increase in economic factors affecting financial helplessness has also increased the complexity of investment decisions for these companies. For this purpose, the approach presented in this research, taking into account various financial criteria, provides the possibility of dynamic forecasting of Financial Distress for these decision makers. makes The approach introduced in this research is first by clustering the companies in the proportional cluster of financially helpless and non-helpless with the help of artificial neural network method, self-organizing mapping (SOM) and then by using the data envelopment analysis method based on the worst performance (WPF-DEA). A dynamic forecast of the financial helplessness of the companies admitted to the Tehran Bahadur Stock Exchange was carried out. Using the mentioned method, 105 companies were evaluated and the result of the inefficiency of these companies was predicted during 5 time periods from 2015 to 2019. The dynamic data coverage analysis model based on the worst performance has the ability to evaluate the inefficiency of the examined units, including companies that are members of the Stock Exchange and Securities Organization. Data envelopment analysis has been able to successfully identify the financial helplessness of companies as inefficient decision units.

In this article, the goal is to design and present a model to determine the effect of macroeconomic and banking variables on the occurrence of asset freezing in the country's banking system using meta-heuristic models. The current research is applied in terms of purpose, in terms of research method, correlation analysis type and in terms of overall research design, post-event and retrospective. In order to answer the research questions, the annual data of macroeconomic and banking variables, during the period of 1399-1390, were collected and using the test of regression models in EViews, Smart PLS software and also the neural network model. It was estimated in SPSS Modeler software. The estimation results of the regression model of the first hypothesis in EViews software showed that the economic variables of GDP, unemployment rate and interest rate, consumer price index, currency strength at the error level of one percent and the economic growth rate variable at the error level of ten percent have a significant relationship. They have a dependent variable (asset freezing). Also, the estimation results of the structural model of the first hypothesis in the PLS software are significantly aligned with the output of the Eviuse software. So; The first research hypothesis is confirmed. Also, the results of the regression model estimation of the second hypothesis in EViews software showed that the intra-bank variable of the bank size ratio, return on equity, and the amount of liquidity at the error level of ten percent, and the variables of capital adequacy, return on assets, bank capital, at the error level of one percent. The percentage has a significant relationship with the dependent variable (asset freezing). Also, the estimation results of the structural model of the second hypothesis in the PLS software are significantly aligned with the output of the Eviuse software. So; The second research hypothesis is also confirmed.

The purpose of this research is to provide a model of recognition and evaluation of factors affecting the acceptance of fintechs. The statistical population included experts and experts in the banking industry, who were selected as participants in the research using a targeted sampling approach. The data was collected by interview method with general guidance method and in a semi-structured way. In order to obtain the reliability and validity of the data, two methods of reviewing the participants and also the review of non-participating experts were used in the research. The reliability of the model was evaluated using the Kappa index. The value of Kappa index equal to 0.748 was calculated and it was placed at the level of valid agreement. Interviews and coding were analyzed using Maxqda software. Finally, based on the identified final criteria, the model derived from the foundation''s data analysis method is presented. Based on the results of open coding of qualitative data collected using the interview tool, it can be seen that 60 open codes have been identified among 434 parts of the interviews. All the created codes are reviewed again and compared with the texts so that nothing is missed. In the results of core coding, it can be seen that 60 primary codes are in the form of 15 categories of evaluation and monitoring, public trust and security, interaction and communication, human and social development, satisfaction, infrastructure, administrative and organizational health, transparency and accountability, and core justice. , financial factors, information technology, efficiency and effectiveness, quality of laws and regulations, quality of electronic services and participation are categorized.

One of the most vital research programs of financial knowledge today, which is at the forefront of rejecting the theory of efficient markets, is financial behavior theory, which is the cause of joint efforts between financial sciences and social sciences and has deepened our knowledge of financial markets. Today's financial topics, which we are going to explain, describe and compare, are called financial behavior, which in simple terms is a pattern of thinking in which markets use patterns consisting of social sciences, psychology, finance and several other disciplines. They are studied, and in other words, economic agents are not rational in behavioral patterns contrary to neoclassical theories, but are normal either because of their preferences or because of cognitive biases. The purpose of this study is to design and explain a model for identifying behavioral factors affecting stock price fluctuations in the Tehran Stock Exchange. This research is applied in terms of purpose and in terms of working method is a type of survey research. The data of this research were analyzed using SmartPLS software. The results of the study showed that the emotional bias of short-sightedness, the emotional bias of optimism and the emotional bias of adaptation affect the fluctuations of stock prices of companies.

Contrary to the popular belief in modern financial theory that states decision makers behave completely rationally to maximize their profits, studies conducted in the field of behavioral finance show that the human decision-making process is not completely rational and is not based on it. Most of the time, the financial-behavioral factors of the decision-making process examine the perceptions of investors and their reactions to different conditions of the financial market, and emphasize the influence of the personality, culture and judgment of investors based on investment decisions. Recognizing behavioral biases makes investors more aware of their decision-making process, and if faced with biases, they can react well and avoid deviations in decision-making. Therefore, the purpose of this research is to design a model of stock return fluctuations and investors affected by cognitive biases. The statistical population of this research is experts, managers, consultants and experts in financial affairs and the statistical sample includes 160 people.This research is practical in terms of purpose, and in terms of working methods, it is a type of survey research. The study period is in 2022 AD. For sampling, purposeful and snowball sampling methods were used, and with 30 semi-structured interviews with experts, the research reached theoretical saturation. The results obtained from the research showed that cognitive bias, ambiguity avoidance, familiarity, self-documentation, and tardiness have an effect on the volatility of companies' stock returns. The path coefficients (beta) for each bias have been calculated separately, which determines its effect on stock return fluctuations.

The main goal of this research is to present a model for the feasibility of deploying blockchain technology for the Tehran Stock Exchange (in 1400). This research is a survey in terms of the practical purpose of the data collection method. The tool used in this research includes a 14-question questionnaire made by the researcher. Its content validity has been confirmed by experts and its reliability was calculated through Cronbach's alpha of 0.915. The research community was Tehran Stock Exchange industry experts, 293 people were selected based on the available sample. The results obtained from the implementation of the confirmatory structural model show that in order to establish blockchain technology in the Tehran Stock Exchange, the highest path coefficient related to the organizational component (with a coefficient of 0.81) is in the second place of the technology component (with a path coefficient of 0.77) and in the third place is the environmental component (with a coefficient of path 0.61) is located. In addition, the highest path coefficient of the indicators is related to the knowledge of human resources of the organization with a path coefficient of 0.89

A major factor in the Iranian stock market is the risk-taking of the economy. The stock index falls when the economy is at higher risk, either by protests or the possibility of war. The present study was conducted to investigate the existence of fluctuations in the currency, stock, gold, and oil markets during the period 2017-2021 using the common t-test and the DCC-GARCH approach. The statistical population includes all companies listed in the Tehran Stock Exchange during the period 2017 to the end of 2021. The period was chosen due to significant fluctuations in exchange rates, coins, oil, and stocks. To achieve the purpose of the study, first, a descriptive study of coin price trends, exchange rates, and stock and oil indices in the Iranian economy was presented and then the conditional correlation between the returns of these assets was estimated using the dynamic DCC-GARCH conditional correlation method. Conditional correlation between currency and stock markets as well as coins and stock exchanges has increased significantly from a period of calm to a period of turmoil. These results are consistent with the results of the transfer test using t-test statistics. The results showed the existence of transitions between the markets and transitions between the foreign exchange and gold markets. The results also showed that the oil market, because it is the most important source of Iran's income, has a major impact on the gold, currency, and stock markets.

Blockchain technology is a relatively new technology, and due to its decentralization, consensus and encryption, it is one of the safest technologies in the world. Considering the importance of the security and accuracy of taxpayer information for the tax administration on the one hand and the capabilities of blockchain technology on the other hand, the present study examined the possibility of using this technology in the country's Tax Affairs Organization. In this research, since the possibility of using blockchain technology in the design of the value added tax information exchange system in the country's tax affairs organization should be investigated first, a model was presented by examining the existing theoretical and empirical foundations based on the technology acceptance model in the organization (TOE). Opinions on the factors affecting the adoption of technology in the organization were investigated. Then, using the qualitative method (content analysis), a semi-structured interview was conducted with 27 participants and the factors affecting the adoption of blockchain in the organization were identified. Lastly, the final model was presented by analyzing 15 questionnaires using the fuzzy Delphi method. The results of the present research showed that the perceptions of the employees of the Tax Affairs Organization regarding the three components of technology, organization and environment confirm the possibility of using this technology which was considered acceptable by them.

Background and Theoretical Foundations: 

Ports are the main routes for transporting products around the world and play an important role being the vital links of land and sea. Also, they act as gateways and linking transport corridors, and enhance trade and communication. Marine and coastal environments face countless threats, including pollution sources, oil and gas extraction, tourism, overfishing, invasive species, destruction and loss of habitats, and climate change. Each of these factors imposes the highest cost on Ports and Maritime Organization, which reduces the financial performance of organization and its profitability. Since financial performance is a central issue in all organizational analyses, risk management is necessary to reduce failures caused by various risks that lead to large financial losses. Therefore, identifying the environmental risks of ports and managing them increases safety and, as a result, effective financial performance and increases their profitability. Therefore, The purpose of this research is weighting and prioritizing the environmental risks management of port equipment and facilities, operational, human resources, pollution, maritime transport, natural, security, and rules affecting on financial performance of Ports and Maritime Organization.

This research is a mixed data in terms of type, an applied and developmental in terms of purpose, exploratory research in terms of nature and a survey in terms of data collection method. The time period of this study is cross-sectional and was conducted in the years 2021 and 2022. The statistical population of the research is certified and experienced specialists in the field of financial management and health, safety and environment of Ports and Maritime Organization. In order to determine the weight and prioritization of environmental risks management, the fuzzy analytic hierarchy process technique and with the purposeful sampling method, the opinions of 12 people were used. Excel 2013 software was used to analyze the research data.

The demographic characteristics of experts showed that most experts have a master level of education, work experience between 21 and 30 years and the position of head of maritime safety and protection department. According to the presented results of weighting and prioritization of the main research criteria with fuzzy analytic hierarchy process technique, human resources risk management with a weight of 0.313 got the first rank and it was recognized as the most important criterion. After that, operational environmental risks management with a weight of 0.219, pollution with a weight of 0.138, natural with a weight of 0.118, maritime transport with a weight of 0.090, security with a weight of 0.064, port equipment and facilities with a weight of 0.030 and rules with a weight of 0.028 were in the next priorities, respectively.

As a result, in order to improve the financial performance of Ports and Maritime Organization, special attention should be paid to environmental risks management. When the Ports and Maritime Organization identifies and fully implements the national and international requirements in the field of environment, it can integrate environmentally friendly processes and reduce their pollution by reducing environmental risks and in this way, to improve its environmental performance and financial performance at the same time. Also, by considering the training programs for organization's employees, increase the level of their skills and professional knowledge and thus reduce human error.

The present paper deals with the way of designing a model describing the effect of corporate and macroeconomic factors on the profit forecast of companies listed on the Tehran Stock Exchange. To this end, the capability of profit forecast in stock exchange companies was investigated using four machine learning models including: support vector machine learning model of regression (SVR), simple and deep neural network (ANN & DNN) and linear regression (LM). This research is of applied research type and it is of descriptive type (correlation) in terms of data collection method, with relative data measurement scale. For testing the research questions, accounting data between 2010-2019 were prepared and input variables for the model were calculated accordingly. In order to check the results of data analysis using regression support vector models, simple neural network, deep neural network, linear regression, first the data set is divided into two training and testing parts so that 90% of the data is used for training and 10% is reserved for testing. The results of data analysis show the deep neural network model for the experimental data set has lower RMSE and MAE values than the support vector regression model. Therefore, it can be concluded that using macro variables and intra-company variables, the model of artificial neural networks can be a good approach to forecast profit of listed companies.

Trade and relations between countries are a necessary condition for the financial convergence. With this approach, the present study investigated the impact of the Iranian stock market on the stock market of Iran's trading partner in the MENA region. for this purpose, information about the total stock market index of selected countries was collected during September 2015 to June 2022 and index fluctuations calculated using wavelet analysis. Next, the VAR model was estimated and the Granger causality test was performed. Finally, the effect of the news on the country's stock market was investigated using GARCH models. The results of wavelet analysis showed that, the range of stock market fluctuations in MENA countries has increased. Based on the results of the VAR model and Granger causality test, Iran's stock market is unilaterally affected by fluctuations in the stock market of trading partner and OPEC member countries, including Kuwait, Oman, Qatar, Saudi Arabia, the United Arab Emirates, and Lebanon. However, there was no sign of Iran's stock market being affected by fluctuations in the stock market of Jordan, Bahrain, Egypt, Tunisia, and Morocco. Finally, the results of the conditional variance models indicate that the selected countries follow a similar asymmetric fluctuation pattern.

The financing of municipalities is of great importance in the current situation, and considering their structural assets, it is important to use these assets in financing with the support of assets. The purpose of this research is to provide a conceptual model for financing based on municipal assets. The current research is applied in terms of purpose, descriptive-survey in terms of inference method, and qualitative in terms of the nature of the data. In the qualitative part, in order to conduct semi-structured interviews of 18 managers and experts of the country's municipalities and rural districts, as well as financial experts of the country's metropolitan municipalities with at least 10 years of management experience in administrative, managerial and financial reporting fields, using the method Theoretical sampling, which is one of the sequential or consecutive purposeful sampling methods, was chosen. Then, using the data theory approach of the foundation, the interviews were analyzed and then in the quantitative part of the research, after obtaining the opinions of experts (30 people) through a questionnaire and summarizing their opinions, a quantitative model of municipal financing was developed. Based on the results of this research, the financing model can be classified based on four categories of income from public tolls, collection of tolls in a new way, income from funds and property, as well as the price of services and incomes of municipal for-profit institutions.

The trend of stock market movements and changes is a window for future economic behavior because it reflects investors' expectations of future economic conditions. The main purpose of this study was to identify and rank the factors affecting the forecast of stock returns in companies listed on the Tehran Stock Exchange. The present research raises two general questions. The first question is related to the factors affecting the forecast of companies' stock returns. The second question deals with the method of prioritizing effective factors and the importance assigned to each factor? Based on the analysis of the field of knowledge, microeconomic factors were identified and based on a survey of experts and Delphi-fuzzy technique, the importance of the identified factors was assessed and refined. According to the results, among the main factors affecting the forecast of stock returns of companies, the priority factors of dividend to price, price-to-profit ratio, price-to-profit adjustment ratio, profit growth ratio, risk-free return, return on distribution, fluctuation range yield, Pearson skewness coefficient, standard skewness coefficient, elongation coefficient, dividend ratio and average return deviation play an important role in the order mentioned.

This study aims to identify financial criteria to evaluate and analyze the financial distress of companies listed on the Tehran Stock Exchange and the dynamic forecast of corporate financial distress. Therefore, after a comprehensive review of the research literature and the main financial ratios used in previous studies, eight financial ratios widely used in previous research were selected. The research data was collected from the Exchange and Securities Organization data sources and existing data systems such as Tehran Stock Exchange, Codal.ir website, and Rahvard Novin software related to 106 companies. Then, the clustering process was performed for 105 companies using the SOM artificial neural network method. In this study, the number of existing clusters was considered equal to two clusters (financially distressed and non-distressed companies). After clustering the companies, the proposed DEA-DA model was implemented. Finally, the membership of the new company was predicted in the appropriate distressed or non-distressed cluster. The study's results indicated that the membership of the new company in the financial distress cluster was correctly predicted, and the proposed method made it possible to dynamize the financial distress forecast for decision-makers including corporate managers and investors by considering various economic and financial criteria.

Fluctuations in commodity prices in global markets have always influenced the behavior and decisions of investors in financial markets. In this research, using the Copula family models, financial contagion or volatility spillover on global price of petrochemical products and base metals on the on the stock price index of eight selected industries of Tehran Stock Exchange listed companies during a period of 10 years (2008-2018) has been reviewed. The research method is descriptive-analytical in nature and applied in terms of purpose. The research hypotheses were tested using an econometric approach based on Copula models and programming in MATLAB software. The results show that the effects of overflow of these variables on the index of selected industries are significant but different.Examination of different models of Copula method showed that T-Student model is most suitable for transmitting spillover effects, which indicates the symmetrical effects of price variables in global markets of petrochemical products and base metals on the index performance of selected industries. And then Clayton and Gumble models are in the next rank.

Iran's stock market should be related to the stock market of other countries, especially the countries of the region; This connection and dependence accelerates the accumulation and formation of capital and provides many opportunities to investors. With this approach, the present study has investigated the dependence structure of the stock market of Iran and MENA countries. In order to achieve this goal, first, information about the total stock market index of MENA countries from September 2015 to June 2022 was collected and then the fluctuations of the total stock market index of the countriescalculated using wavelet analysis. In the continuation, the Vector Autoregresive (VAR) model estimation and Granger causality test regarding the relationship between the stock market fluctuations of Iran and the countries of the region was carried out. Finally, the quantile regression was estimated and the upper and lower limits of the correlation between Iran's stock market and MENA countries were determined. The results of the wavelet analysis showed that, the range of fluctuations of the total stock market index in MENA countries has increased, over time. Based on the results of the VAR model and the Granger causality test, Iran's stock market is unilaterally affected by the stock market fluctuations of Kuwait, Oman, Qatar, Saudi Arabia, the United Arab Emirates and Lebanony؛ if fluctuations occur in the stock market of these countries, This work will be transferred to the Iranian stock market immediately. In addition, there was no sign of the impact of the  stock market flactuations of Jordan and Bahrain as well as North African countries including Egypt, Tunisia and Morocco on Iranian stock market. The results of quantile regression also showed that the affectability of Iran's stock market from fluctuations is different for different countries and quantiles. In this regard, in the months when the volatility in the stock market of the mentioned countries was less, the effect of the fluctuations on the Iranian stock market was less, and on the other hand, in the months when significant fluctuations occurred in the stock market, the amount of volatility transferred to the Iranian stock market was also higher.

The purpose of this article is to predict probability of default of 60 corporations with structural Merton model. we used market data of 60 corporations from 2018/08/01 to 2019/09/01 witch are listed in Tehran Stock Exchange For predicting probability of default . to reach this goal we estimated assets value of corporations, volatility of assets and drift rate. We used Capital asset pricing method to estimate expected assets return. Then We used simple regression method and multivariate GARCH (MGARCH) to estimate Beta of corporations. in the end we compute d likelihood function of the average predicted default for each industry and compared the results with actual default rate of that industry in the next year after predicted date. Regarding obtained results of likelihood function probability of default prediction with multivariate GARCH (MGARCH) approach outperform the simple regression model, therefore we recommend using the MGARCH approach for its better prediction performance.

In modern monetary economies, the real and financial sectors are contiguous. In particular, the activities of the financial sector, which equip savings and direct them to investment schemes, link the real level of activity and development of the two real and financial sectors through two basic variables of the real sector of the economy, namely investment and capital formation. A healthy and active economic system must have a financial system to deliver the money that people have saved to those who have the opportunity to invest. Therefore, coordination and moving along with the financial sectors is a necessity to achieve economic growth and development. If banks and financial institutions manage liquidity and resource allocation properly with liquidity and cost management, the benefits and costs of lending will certainly be lower. The present study was formed with the aim of "Designing an optimal model for allocating bank resources and expenses with a portfolio distribution approach". In order to achieve this, first a two-objective mathematical model is designed based on the scenario that the objective functions of this model are risk minimization and wealth maximization. The SPP-CVAR approach was used to estimate the risk and the historical simulation method was used to estimate its values. Finally, the robust optimization model is designed and solved using two particle mass algorithms and genetics with improvement procedure based on variable neighborhood search (VNS) and the research results are obtained.