mehrzad minouei

The profitability of banks, as one of the key performance indicators, depends on their ability to generate income and manage costs. In recent decades, the emergence of modern banking technologies has led to significant transformations in the banking industry, which have had a direct impact on the profitability of banks. The purpose of this study is to apply the meta-synthesis technique to the bank profitability model, considering the role of modern banking technologies.The researcher employed a systematic review and meta-synthesis approach to analyze the results and findings of previous researchers. By following the seven steps of the Sandelowski and Barroso method, the researcher identified the influential factors. Out of 277 articles,25 articles were selected based on the CASP method, and the validity of the analysis was confirmed with a Kappa coefficient of 0.760. In this context, to assess reliability and quality control, thetranscription method was used, and the level of agreementfor the identified indicators was found to be excellent. The analysis of the collected data using ATLAS.ti software led to the identification of 52 initial codes in 12 categories.Based on the meta-synthesis technique, the 12 categories were classified according to these concepts. The 12 categories are: system sustainability, cost-effectiveness, system performance, data security, accessibility, regulations, system intelligence, customer satisfaction, system reliability, system documentation, data management, and quality.System sustainability in the bank profitability model, considering the role of modern banking technologies, refers to the bank's ability to maintain and enhance its financial performance in the long term. Modern technologies such as artificial intelligence, blockchain, and advanced security systems help banks effectively manage risks and prevent cyber threats and financial fraud. These technologies increase customer confidence and trust in the banking system and ensure sustainable profitability for banks by reducing costs associated with risks and potential losses.

A model for determining the oil market transferability turmoil on the financial markets of the Iranian economy using the dynamic systems approach. At first, data related to oil, gold, stock exchange and foreign exchange were extracted from statistics related to the World Bank, Central Bank and Statistics Center of Iran and were analyzed with statistical analysis and simulation software. Then the research model was constructed using simulation methods and system analysis and the results were analyzed. The oil market in supply and demand for price determination is based on global systemic behavior. this simulation has used the factors affecting oil supply, oil demand, the expectations that shape this supply and demand, as well as macro factors such as macroeconomic indicators of the US economy, sanctions on the oil sector in Iran, the rate of world industry development and the available knowledge on oil substitution. Hidden mechanisms are the main reason for some oil price behaviors. The results of the research have led to the forecast of oil prices in the baseline scenario until 2025. The presence of political problems due to the interconnectedness of parallel markets in Iran causes widespread fluctuations in the currency and gold sectors in the Iranian economy

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 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.

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

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.

This study aims at optimizing the portfolio of financial assets and in particular focuses on the stock market with conditional value at risk (CVaR) as the portfolio risk measure. This study uses generalized conditional heterogeneity variance methods, the dependency structure, the extreme value theory, and with the GARCH-EVT-Vine-Copula approach to optimize the portfolio and minimize the CVaR of a stock portfolio during a certain period by the re-weighting method. Modeling is based on the performance data of 7 companies among the top 50 listed companies during the period 2015 to 2021. The results show that considering the extreme values and structural dependence between the examined time series improves the risk identification between these markets. In addition, among the studied models, the out-of-sample results for the accumulated wealth function of different models show that when considering the dependence structure, the EGARCH-EVT model based on the Coppola Vine function results outperforms other models.

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.

One of the most important favorite topics for economists and financial analysts is to explain the reason and trend of price fluctuations because a large number of factors affecting profitability in this market are associated with risk.Due to the changes in the industry index in recent years and the complexity of the economic environment of construction in Iran, one of the most important issues for capital market participants and shareholders of the mentioned group is the possibility of forecasting stock prices. Therefore, the purpose of this study is to design and explain the stock price forecasting model in real estate mass construction companies on Tehran Stock Exchange using the data panel regression model. The research is quantitative in terms of the data type and practical in terms of the result and descriptive and exploratory in terms of the purpose.The statistical population of the study consists of all companies listed in the group "Mass Construction and Real Estate" on Tehran Stock Exchange. The results showed that for the overall stock index, both moving average and autoregressive factors have a positive and significant effect with more than 99% certainty and the retrospective trend of the stock index is predictable.

The purpose of this study is to identify and then manage the most important environmental risks affecting financial performance of Ports and Maritime Organization (PMO) in Iran. In this research, risks management of port equipment and facilities, operational, human resource, pollution, maritime transport, natural, security and rules has been considered as environmental risks management affecting financial performance of PMO. This research is a mixed data in terms of type, applied and developmental purpose, exploratory and descriptive research in terms of nature and a survey in terms of data collection method. The statistical population of the research is certified and experienced specialists in the field of financial management and health, safety and environment (HSE) of PMO in Iran, which the sample size of 70 was selected by purposeful sampling method. Structural equation modeling and Smart PLS software were used to analyze the research data. The results showed that to improve the financial performance of PMO from environmental risks management, human resource risk management, pollution risk management and operational risk management with path coefficients based on the research structural model of 0.948, 0.914 and 0.905 has been the highest, and rules risk management with path coefficient 0.629 has been the lowest effects. As a result, special attention should be paid to environmental risks management in order to improve the financial performance of PMO in Iran. Therefore, PMO can increase the level of their professional knowledge and skills by considering training programs for employees, and thus reduce human error.

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 study is to design a warning system for price bubble and financial crisis in the Iranian stock market. In this study, the statistical information of the seasonal data of the period 1990-2019 and the MGARHC have been used to extract and identify the variable of financial crisis and price bubble in the capital market. The results of this study showed that variables such as production, inflation rate, exchange rate, total stock market index, etc. have a significant impact on the occurrence of financial crisis and the possibility of price bubbles in financial markets. Based on the estimation, it can be stated that the effect of the shocks of the research variables on the occurrence of the price bubble in the stock market leads to an increase in the instability of the variables. This can be due to the dependence of the economic structure on its markets, and this can have heavy consequences due to a shock on the country's structure.

The purpose of this article is to provide a model for calculating the economic capital of a bank loan portfolio and compare the obtained results with regulatory capital based on Basel II Models. The widely used asset value approach is used to model the default correlation. The method of estimating the parameters is based on the Method of moments and the method of maximum likelihood. Finally, the economic capital is calculated based on the distribution of losses obtained from the Monte Carlo simulation method. According to research hypothesis, calculated economic capital is compared with calculated regulatory capital based on Basel II Models. Regarding obtained results regulatory capital is more than economic capital. Considering the difference between economic capital and regulatory capital in the selected portfolio, it is not sufficient to rely on regulatory capital to assess banks’ risk. In order to have an accurate assessment of banks’ risk, economic capital must be calculated with proper modeling.

After-sales service can play an important role in customer satisfaction and maintaining it customers are loyal to a brand after sell to a brand make a constructive connection to it.Thus improving the quality and enhancing the performance of a business. Successful organizations are those that have the ability to consciously use customer information and provide better after-sales service. Therefore, in this research, the credit risk management model in Saipa Yadak dealer network has been designed using financial and non-financial components of after-sales serviceThe results showed that using financial and non-financial components including, service cost, performance of after-sales service agencies, good accounting of agents, the amount of collateral of agents, the quality of services of the network of agencies, the speed of services of the network of agencies Agencies, network age (or age) of agencies, and regional location of the network of after-sales service providers affect 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.

Access control in the blockchain network is one of the challenges we face with the growth of the blockchain network. In the blockchain network, the set of financial activities of users that require a digital signature is performed, this information is stored in the blockchain server. Manually signing digitally and verifying the authenticity of transactions is a time consuming and user-friendly process and is one of the reasons why blockchain technology is not fully accepted. In this paper, a new method is proposed based on a combination of clustering and classification methods. First, the data is labeled using the clustering method and then the labeled data is used to teach the SVM algorithm to determine healthy transactions. The proposed method is a machine learning method for access control that automatically blocks blockchain transactions and detects abnormal transactions. In order to evaluate the proposed method, atrium data have been tested and analyzed. And with the help of KMEANS clustering algorithm and machine vector support method, healthy transactions are detected from suspects, which shows the ability to identify with 89% accuracy

The title of the present study is designing and explaining the decision-making model of shareholders with a comparative approach to classical finance and behavioral finance in the capital market. The traditional finance viewpoint assumes that people make rational decisions to maximize wealth at a certain level of risk or minimize risk at a certain level of wealth. Such an approach, which states "how people should behave," is called norm. In this study, in the first stage, based on the literature review and using the previous related studies, a complete list of fifty-seven factors affecting the decision-making model of shareholders was provided to the members of the experts’ panel in the form of a questionnaire for the sake of weighting. The DANP technique was then used. The results show that political factors, economic factors, market psychological factors, cognitive factors, emotional factors, and finally financial factors have the highest effect at the company level.

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.

One of the main concerns of financial institutions for investing in companies is to evaluate financial performance and, most importantly, the financial distress of organizations applying for investment. Therefore, various approaches and tech-niques are used in this evaluation. Financial decision-making has always been associated with the risk of uncertainty. One way to help investors is to provide forecasting models for the overall corporate prospect. It is noteworthy that in all these approaches, various criteria are used to identify corporate financial distress. In this study, a dynamic worst-practice-frontier DEA model was used to identify financially distressed decision-making units over several time-periods. Another feature of the model presented in this study was to provide some improvement solutions for financially distressed decision-making units. Finally, a new ranking approach was introduced to evaluate companies based on the inefficiency trend over several time-periods. The study's approach provides decision-makers with the ability to evaluate the inefficient DMUs during each time-period according to the relationships between these time-periods. The efficiency slope can also be evaluated over time-periods, and companies can be ranked based on this slope. Finally, it is suggested to use this model to dynamically predict financial distress in various industries, including metals, rubber, automobiles, etc., so that compa-nies are informed of their financial distress promptly and take appropriate measures to prevent bankruptcy.

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.

One of the most important problems of the proposed methods in measuring the portfolio Efficiency frontier patterns is not considering the uncertainty in financial variables such as risk and return. In this regard, the development of various optimization theories and Extract optimal efficiency frontier of a portfolio in financial sciences to understand and identify aspects of uncertainty in the decision environment and possible events in space of uncertainty and ambiguity of capital markets have been invented and developed. Among the theories proposed in terms of uncertainties, the theory of possibility can be considered the most appropriate and accurate theory in interpreting and proving uncertainties in stock portfolio optimization. For this reason in the forthcoming research to further adapt portfolio optimization models And recognizing the efficient space congruous to reality to extract returns and portfolio risk by mathematical inference in an uncertainty space with emphasis on possible theory. In this study of the concept of fuzzy random variable and fuzzy theory and Fuzzy obligation, In order to cover the uncertainty existing in Cognition and determine the efficiency frontier Mean - portfolio variance Used under the theory of possibility.

The present research examines the Financial Contagion or Volatility Spillover by financial assets such as exchange rates, gold and global variables on the stock market index. The correlation and Contagion between variables of global prices of gold, oil, and the dollar exchange rate on the index of 8 selected Tehran stock exchange industries over a period of 10 years (2008-2018) was examined. Method of the research is applied in terms of purpose and analytical-descriptive in terms of the nature. To test the research hypotheses using econometric approach based on Copula models, programming was performed in MATLAB software. The results of the show that the effects of volatility spillover of these variables on the index of selected industries are significant but different. The different models of the Copula method show that the Clayton and Gumbel models are most suitable for transmitting spillover effects in the upper and lower distribution of the range. The t-student model is in the next rank. In other words, the overflow effects of macro variables mostly affect one of the high (positive return) and low (negative return) domains, which indicates the existence of asymmetric effects on the return behavior of the selected industries of the stock exchange.

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.

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.

Blockchain can reduce bureaucracy and increase the efficiency and performance of administrative processes through a platform possessing features and attributes such as storing and exchanging electronic messages in a decentralized environment and executing high level of security transactions and transparency, if used in government public service delivery. Many scholars believe that this distributed technology can bring new utilizations to a variety of industries and fields, including finance and banking, economics, supply chain, and authentication and increase economic productivity and efficiency dramatically by transforming many industries in the context of today's economy. The present study, presents the characteristics of the localized blockchain and e-currency conceptual model for the evolution of e-government services. It also examines the impact of the blockchain and e-currency model on the economy and electronic financial transactions as a viable, practical and constructive solution (rather than blocking and filtering of e-currency and blockchain). Ultimately designing a localized block chain and e-currency model, has played an effective role in exploit its high potential to speed up the administrative processes and reduce costs related to electronic transactions and payments in e-government and increase e-government revenues and ultimately it can speed up the customer service delivery and increase their satisfaction with the government.

Venture capital companies play an important role in the economy of countries and greatly influences economic and employment growth. VC is the provision of capital for companies and entrepreneurs that is prone to leaping and growing value and, of course, a lot of risk. However, the volume of venture capital in our country is far less than the economic capacity. Many of analysts consider having no model for venture capital in our country as the main reason for this. Therefore, the present study by the qualitative method aims to design decision-making native model for selecting venture capital investment in emerging companies. To achieve this goal, by collecting qualitative data through literature reviews and having deep interview with experts and venture capital firms, a native decision-making model for selecting venture capital in emerging companies is presented. The methodology of this research based on purpose, is fundamental and through the qualitative methods, thematic analysis method is used. Purposeful sampling method is used and interviewing experts continued to theoretical saturation level that means the number of selected samples includes 16 elites. The native decision-making model for selecting venture capital in emerging companies presented in this research has 16 main themes and 86 sub-themes.