جستجوی مقالات مرتبط با کلیدواژه « d » در نشریات گروه « مالی »

Financing has always been one of the main challenges of SMEs in Iran. Therefore, developing financial instruments for these companies and reducing their dependence on bank financing is always one of their essential needs. Despite the development of the Sukuk market in recent years, the capacity of this market has not been provided for SMEs. This article seeks to provide an optimal model for financing SMEs. The present research is considered to be mixed (qualitative-quantitative) from the point of view of the applied goal and qualitative-quantitative from the point of view of the nature of the data and descriptive-survey from the point of view of the data collection method. In order to design a suitable model, first, in interviews with experts in the field of financing SMEs, as well as available resources, the financing challenges of these companies were analyzed. In the next step, based on two assumptions, 7 models were designed for issuing Sukuk. Then, based on the TOPSIS method, these models were prioritized and Wakalah model is choosed.

Regarding the problems and challenges facing Islamic banking, of course, after nearly forty years since the adoption and implementation of the law on interest-free banking in Iran, many writings, researches and materials have been formed. During this period, in order to solve them, solutions have been presented and implemented. Therefore, acknowledging that some of the problems and challenges have been resolved, but the Islamic banking system is still facing challenges, some of which have a relatively long history. Therefore, in order to find the roots of the challenges, it seems more important than recognizing the challenges themselves, to understand correctly the aspects and dimensions of the problems and challenges against the institutionalization of the Islamic banking system, especially in Iran and in the world in general. Accordingly, in this article, an attempt has been made to examine the dimensions of these challenges in order to avoid repetition, almost in a comprehensive manner. With regard to the above, the present study has examined the dimensions of challenges in the two sections of Islamic banking challenges in Iran and the world separately. In any case, the main challenge that unfortunately still exists in Iran almost fourteen years after the enactment of the Law on Interest-Free Banking Operations, and unfortunately has received less attention, is the problem or challenge It is "Qarz al-Hasna", which can be claimed to be the basis of Islamic thought and thought in the context of the simplest and at the same time the most complex concept of economics, namely "money" in the space of "Qarz al-Hasna" finds meaning and manifestation. It is hoped that this study can be used as an example of the second step of the Islamic Revolution in the direction of institutionalizing the Islamic economic system.

The purpose of this research is evaluation of Sharia-based sustainability reporting framework influenced by the emergence of metaverse axes in accounting. From the point of view of methodology, this study is considered as a combined research that seeks to achieve the research goals based on a set of qualitative and quantitative analysis methods. Therefore, in the qualitative part, through ground theory, the dimensions of Sharia-based sustainability reporting should be identified. Then, based on the systematic content screening process, the axes of the emergence of metaverse in accounting were identified so that in the last part of the qualitative analysis through fuzzy Delphi, the reliability of the identified dimensions of both bases of this study could be confirmed. Then, in the quantitative part, through the Gray VIKOR Matrix, an attempt was made to identify the most effective axis of the emergence of metaverse in accounting to increase the effectiveness of Sharia-based sustainability reporting. The findings of the study in the qualitative part regarding the dimensions of Sharia-based sustainability reporting, indicate the identification of 3 categories; It has 6 components and 41 propositional themes in the form of a multidimensional theoretical framework. Also, in this section, 5 axes of the emergence of metaverse in accounting were identified through systematic content screening, and in the form of fuzzy Delphi analysis, all dimensions were confirmed. In the quantitative part, the result indicates the determination of the development of the data mining process in accounting analysis as the most central influence on the dimension of adherence to Sharia ethics in sustainability reporting.

The health, efficiency and liquidity of the capital market and especially the stock exchanges are of particular importance. Common stock transactions account for a significant amount of Tehran Stock Exchange transactions, and this issue reveals the importance of careful attention and monitoring of these securities transactions. In addition to protecting the interests of legal investors, the establishment of appropriate regulations and serious monitoring of these transactions will attract new investors. Manipulation of common stock transactions in stock exchanges is one of the common phenomena of these transactions, which brings destructive effects, and supervisory institutions have been and are trying to reduce all types of manipulation. Also, the religion of Islam is full of valuable teachings in the field of transactions. One of these teachings is the prohibition of impurity in transactions. In this article, we are looking for an answer to this question, can some types of common stock transactions be considered as examples of fraud? We have used descriptive-analytical method to answer this question. In the descriptive part of this article, we defined common stock, explained the process of stock trading in Tehran Stock Exchange, the phenomenon of manipulation in common stock trading and Najesh. In the analytical section, we also examined the flow of prohibition of najesh in stock transactions. The result is that according to famous jurists, impurity is forbidden, therefore laws, trainings, mechanisms and crimes should be such that impurity does not occur in the stock trading market, but if impurity occurs and ordinary shares are sold, the sale is valid. But Mukhtar's opinion is that the option to cancel the deal by cucumber is for losers.

The present study analyzes the prediction of the total index of the Tehran Stock Exchange with the singular spectral analysis method based on the genetic algorithm and overlapping singular spectral analysis. It is practical in terms of purpose and descriptive-analytical in terms of method. Its statistical population is the daily price of the total index of the Tehran Stock Exchange in the ten-year return (2009 to 2018) and the research sample is 2411 data from the logarithmic return of the target index.First, the genetic algorithm was implemented in the SSA method on the index. Then, using the Ov-SSA method in order to improve the reconstruction and resolution of the components, the initial and large time series were divided into small and common consecutive parts and the standard SSA analysis method was used for each part.The results of the research showed that the Ov-SSA analysis method has a higher performance than the GA-SSA analysis method with a lower mean absolute value error.

The goal of the asset allocation strategy is to select a portfolio from the set of available assets that, in addition to minimizing the portfolio risk, also meets the minimum level of portfolio return for investment.Dynamic allocation of assets and identification of regime change as soon as possible can continue the return of the investment portfolio with acceptable credit and taking advantage of the new trend. In this research, the performance of equal weight strategy, equal risk share strategy, and minimum variance strategy was evaluated in recession and boom periods according to Sharpe and Trainor criteria. Finally, with the help of ELECTRE multi-criteria decision-making method, asset allocation strategies were ranked during recession and prosperity. To do this important, weekly data of 25 indexes of different industries of Tehran Stock Exchange was collected from the website of Tehran Stock Exchange Technology Management Company and the website of Stock Exchange. The results showed that the equal weight asset allocation strategy has a better performance and a higher rank according to the Sharpe and Trainor criteria during recession and prosperity.

Funding in the healthcare supply chain has always faced challenges, and the challenges of this industry have increased due to inflation and the government's financial problems. One of the most important methods of financing in the healthcare supply chain is a public-private partnership, therefore it is important to identify the drivers of this method of financing. According to the research gap in the field of identification of drivers in this supply chain, modeling, and analysis of their scenario, this study has been carried out to cover this research gap. This research has been done in two qualitative and quantitative stages. In the qualitative stage, with semi-structured interviews and thematic analysis, drives were identified, and in the quantitative stage, using a researcher-made questionnaire and fuzzy cognitive map, modeling, and scenario analysis of drives were done. The results of the qualitative stage show 23 drives that were grouped into 8 categories. The results of the fuzzy cognitive map indicate the independence of the ‘risk transfer’ drive, the dependence on the ‘profit’ drive, and the highest degree of centrality of the ‘hardware upgrade’ drive. The analysis of forward and backward scenarios shows the high importance of ‘asset management and financing’ and ‘infrastructure improvement’ drivers. According to the findings of the research, it is suggested to formulate a road map for the development of infrastructures, pay according to the quality, and determine the correction factor for the improvement of important drivers.

This sudy aims to compare the pricing error of Barles-Soner and Bakstein-Howison equation, in the S&P500 index option market. Option pricing equations are solved using Lie algebra. Using the historical data of the S&P500 index from August 18, 2022, to August 18, 2023, the price of this asset has been calculated with each model considered. In the sequel, the obtained data are classified using multilayer Perceptron and Probabilistic neural networks. The networks show which model is closest to the real market. In addition, the prices obtained from Lie algebra have been compared with the actual values of the options in the market. PNN and MLP have been tested with statistical data after August 18, 2023. Two assumed models were priced with the same data and then compared with the real market. In testing the networks, MLP put 60% of the test data and PNN put all the data into the Barels-Soner category. By calculating the difference between the results of the Lie groups and the real data, 80% of the data were less different from the Barrels-Soner model. With the results obtained in S&P option pricing, the Barels-Soner model has less error than other models.

The present study deals with redesigning the design of the financial stability model in order to respond to the return of Islamic banking to the shock of the value of the national currency for the years 1400-1373. For this purpose, using the structural vector autoregression model (SVAR), which are known as impulse models; The response of Islamic banking returns to the shocks of national currency value, financial stability, inflation and inflation were investigated. According to the results, the response coefficient of the yield impulses of cooperative banking contracts to oil revenues, weakening of the value of the national currency and inflation is negative and equal to 0.01, 0.25 and 0.06. Also, the response of financial development momentum coefficient to the yield of cooperative banking contracts is positive and equal to 0.32. The increase in oil and foreign exchange revenues of the central bank causes an increase in the monetary base of the country, and the volume of liquidity also increases, and therefore inflation will also increase. The expectation of inflation in the future period and uncertainty about the inflation rate are also effective in fueling the intensity of inflation, and as uncertainty increases, the amount of investment in the production sector decreases and the production situation and the yield of contracts also worsen. On the other hand, the artificial pricing of the exchange rate in the years before thein order to reduce the pressure of the foreign exchange market, it is suggested that the annual off

Financing ultimately is aimed at increasing the value of the business through planning, executing, as well as applying financial resources besides balancing profitability and risk. This is realized when financial managers make proper decision about which financing methods to adopt. This study is aimed at providing a hybrid approach based on fuzzy ISM technique and MICMAC analysis in order to design a model to help choose financing sources for Iranian industrial enterprises through overcoming the restrictions of decision-making process. To this end, the fuzzy ISM technique has been first employed to specify the interrelationships between the indicators as well as modeling their hierarchical structure; subsequently, by means of MICMAC analysis, the dominant indicators involved in the financing process have been classified in one of the four clusters based on their Driving power and Dependence. The results indicate that the index of “government fiscal policies” has the highest effect on other indicators of the system and it is a basic criterion for making fundamental decisions to choose the financing sources of Iranian industrial enterprises. Hence, in their management decisions, decision makers should pay attention to the perspective of government fiscal policies before considering any other indicators.

Investment, the choice of assets to maintain and earn more for future prosperity, is one of the most important issues in the economy of all countries, commanding attention from individuals and high-ranking officials alike. Enhancing stock returns through forecasting is a critical concern in capital markets, necessitating attention from both individual and institutional investors. Accordingly, the present study aims to optimize the forecast of risk-based stock returns in selected industries of the Tehran Stock Exchange.

Data analysis was performed in two phases. In the first phase, the data were estimated using a combined data method and AR (1) autoregressive process, from 2010 to 2019. This model forecasts stock returns for selected industries on the stock exchange. In the second stage, using Data Envelopment Analysis (DEA), stock return forecast optimization by the previous stage was optimized for selected industries of the Tehran Stock Exchange from 2010 to 2019.

The results of optimization of stock return forecast in three industries of oil and gas extraction except exploration, extraction of metal ores and petroleum products revealed that the oil and gas extraction industry, except exploration, exhibited a higher efficiency of 0.4214 compared to other industries. The metal ore mining industry with an efficiency of 0.3728 stood in second place and the petroleum products industry with an efficiency of 0.2516 ranked in third place in terms of efficiency.

Therefore, it can be said that optimizing stock return prediction in the oil and gas extraction industry, excluding exploration, is at a higher level compared to other industries examined in this study. Given the oil industry's higher efficiency and optimization within the country, it is feasible to integrate oil into the production cycle using effective methods and introduce technological advancements to enhance oil supply within the country. This is particularly viable as the production output of other industries is significantly lower compared to these three industries. basic measures should be taken to increase the production of industrial and non-oil goods to reduce imports and increase exports of final goods to other countries to increase economic growth. Hence, it is recommended to utilize the oil and gas extraction industry, excluding exploration, for the production of final goods, followed by leveraging other industries for further production of final goods. Also, since the oil products industry is influenced by more variables in predicting stock returns, it is recommended that investors in the oil products industry be aware of the variables studied in this research, especially the industrial production growth rate, and consider their stock returns and investment levels accordingly. Future research should explore and assess the optimization of stock return forecasting in other significant industries like pharmaceuticals, electricity, steam, hot water supply, etc. to compare and evaluate their outcomes alongside the findings of this study.

Blockchain finds application in the public sector, notably in the tax system. The extant literature points to the benefits of employing blockchain in the tax system. A review of studies provides a general view of why and how to implement blockchain technology as well as the identification of existing defects to solve it. In this paper, we investigate how to implement blockchain technology in the value-added tax (VAT) system and answer three questions through a systematic literature review: (1) what are the characteristics of blockchain that can help in the VAT system; (2) what is the appropriate blockchain framework for the VAT system; and (3) what are the appropriate solutions to protect privacy in a blockchain-based VAT system.

We conducted a systematic literature review following the guidelines proposed by Okoli and Prisma flow diagrams. The screening process was carried out in three stages to ensure that each article fits the research questions. The first article in this field was published in 2017, and the number of articles has risen significantly since then.

The increasing number of papers reflects the growing interest in implementing blockchain technology. Statistical charts indicate that approximately 60% of the documents are conference papers. Our systematic literature review revealed that blockchain technology can play a crucial role in enhancing the VAT system, particularly in the electronic invoicing process. By identifying existing challenges in electronic invoices, such as transaction forgery, the long process of issuing electronic invoices, and hacking of the central database, blockchain technology, with its features like decentralization, tamper-proofness, transparency, and automation, emerges as a suitable solution. We propose a blockchain-based architecture and a five-step process that includes: authentication and authorization, sending electronic invoice(s), storing the e-invoice(s) in the blockchain, reviewing and verifying the e-invoice(s), and last but not least, payment of VAT. For the security and privacy of taxpayers, we propose the use of a private blockchain, opting for a permissioned blockchain to ensure that only trusted users are part of the network. Network users can include companies, banks, tax authorities, and auditors. In the third and fifth steps, the utilization of smart contracts is recommended. Smart contracts play a role in documenting taxes in the third step. Furthermore, in this stage, leveraging off-chain storage is possible, where the data hash is stored in the blockchain, and the original data is kept separately. In the fifth step, a smart contract can periodically calculate VAT to be deducted from the payers' accounts by banks. Encryption alone cannot guarantee the privacy of electronic invoices, and while zero-knowledge proofs seem to be a suitable solution, they come with considerable complexities.

According to the inherent characteristics of the blockchain, it can be concluded that a blockchain is a suitable option for the VAT system and the right architecture is a permissioned private blockchain. However, further study is required to address privacy concerns.

In recent years, the focus on earnings characteristics has intensified, spurred by the financial disclosures of some major corporations. Financial analysts and investors not only consider the accounting profit figure when assessing future cash flows but also prioritize the sustainability and repeatability of reported earnings. More than the final earnings figure, they refer to the items that make up real earnings, usefulness in predicting future earnings, as well as the stability, sustainability, and immutability of reported earnings. Earnings are one of the most important factors affecting economic decisions. Users' awareness of the reliability of earnings can help analysts make better decisions about profitability and financial statement analysis. This study examines the relationship between financial reporting models and earnings characteristics of companies listed on the Tehran Stock Exchange.

The present study is applied in terms of purpose. In the present study, the required data have been extracted from Rahavard Novin software, financial statements of companies and text mining as well as the Codal website. The statistical population of the present study includes companies listed on the Tehran Stock Exchange from 2012 to 2022. Stata 14 was the software utilized for data preparation and model estimation in this study. The panel data model was used to test the research hypotheses.

The findings of this study indicate that the net assets approach negatively impacts the quality of accruals, positively influences income smoothing, negatively affects earnings predictability, and does not impact earnings sustainability. Additionally, the results obtained indicate that the transactional approach positively affects the quality of accruals, has no effect on income smoothing or earnings predictability, and negatively impacts earnings sustainability.

This study has examined earnings characteristics from four perspectives: accruals quality, income smoothing, earnings predictability, and earnings sustainability. Accruals quality associates accruals with past, present, and future cash flows, and then tests them. This criterion (called accruals quality) is related to debt cost and equity cost. Earnings sustainability indicates the stability and persistence of earnings. Because management cannot address all the requirements of financial statement users, reliance on financial information as companies' output is heightened. Thus, the significance of earnings quality is paramount prior to decision-making, as higher earnings quality correlates with better company performance. For reasons such as retention in the company, bonus receipt, and other factors, the manager consciously or unconsciously presents the status of the company as favorable; therefore, the characteristics of corporate earnings are influenced by the foundations of reporting and the discretion of their managers. Less reliable earnings equate to less useful information they offer.

Predicting the valuation of blockTrade transaction allows the market to evaluate control over companies in an efficient manner.In this research, by measuring the indicators affecting block transactions in three active industries in Tehran Stock Exchange During the period of 1390 to the end of 1400, on a daily basis with utilization a deep learning neural network, specifically the GRU model. The study focused on industries with a significant number of market participants, namely basic metals (steel), automotive and parts manufacturing (Khodro), and pharmaceuticals (Darou). The results of the hypothesis testing indicate that, at three industry level, Nine variables significantly affect blockTrade transaction valuation: stock returns, block size, trading volume, company size, price fluctuations, industry returns, market returns, institutional ownership, and market-to-book ratio It affects the valuation of block transactions. At the separate level of industries, the results of the effect of financial indicators on the valuation of blockTrade transaction in each industry are different from other industries, which indicates the independence of industries from each other. The findings of this research will help the managers of industries in the stock market and the users of the valuation indices of blockTrade transactions in better evaluation of pricing.

The present study is to develop a model based on Barberis investor's sentiment model and in the framework of DGPS dynamic psychological games. This model is related to herding behaviors biases and influenced by general and macroeconomic factors that emerge in the form of stock price return shocks and in the stock market index. These shocks, in the framework of explanatory model, evaluate changes in stock trading volume in weekly intervals and monetary inflation simultaneously in two unexpected and expected inflations in Tehran Stock Exchange from the beginning of 2015 to the beginning of 2020. The findings based on this rotational and dynamic model showed that in the periods when the stock market exclusively passes the period of optimism and pessimism, a significant relationship between the sales volume of ordinary investors and the total volume of transactions has a unilateral convergence. However, in the periods when the stock market and the foreign exchange market will be aligned with each other, the period of optimism and pessimism will be unique, this relationship has a two-way convergence. In addition, the results of this study showed that psychological games of investors with expected inflation variable can provide a suitable fit for the ability distribution of asymmetric strategies model of Banerjee's asymmetric behaviors.

The purpose of this article was to investigate the relationship between the diversification of banking resources and expenses with systemic risk in the country's banking system. In this regard, the accepted banks in the Tehran Stock Exchange were evaluated. In order to estimate the systemic risk, the criterion of conditional value at risk was used. Also, in order to estimate the impact of diversification of banking resources and expenses on systemic risk, panel data and quantile regression were used for a selection of banks in the country in the period of 2010-2022. The results obtained from this study indicated that the diversification of banking resources and expenses, because it leads to more dependence between financial institutions, as well as a different combination of financial assets, leads to an increase in systemic risk and contagion. It goes to other financial institutions. In the estimated model, the coefficient of influence of bank diversification index on systemic risk in panel data regression model is equal to 1.45 and in quantile regression it is equal to 1.33. In addition, according to the results of the quantile regression, it can be stated that the effect of diversification index of sources and expenses in different banking groups has been different according to the level of systemic risk. The high level of systemic risk in Iran shows that despite the existence of supervision of some financial institutions, the systemic risk of financial activities is excessive, and this perhaps suggests the need for an effective supervisory authority.

Investor sentiment plays an important role in financial markets and it is important to study the factors affecting it. Therefore, this paper, using the TVP-SVAR-SV method, has studied the time-varying effect of macroeconomic factors on investor sentiment in TSE during 2004-2 to 2022-4 seasonally. The findings showed that the impact of macroeconomic factor shocks on investors' sentiment is negative in the short term. After 2014-2, the exchange rate has had a positive effect on investor sentiment in the medium term. GDP and inflation also had a positive effect in the medium term. The fluctuation of investor reaction to the oil price shock in the short term is more than other macroeconomic factors and the biggest reaction was related to the drop in oil price in 2008. Compared to other macroeconomic factors, liquidity has had the strongest impact on investor sentiment in the short, medium and long term. The study of investor sentiment reaction to the shocks of macroeconomic factors in the three events of sanctions in 2012, the JCPOA and the coexistence of Covid-19 and the withdrawal of the United States from the JCPOA shows that the sentiment response to the shock of each of the factors in all three events was similar and no factor has had a lasting positive or negative effect on investor sentiment. In the first (third) event, compared to the other two events, the sentiment reaction to the shock of macro variables (except inflation) was stronger (weaker). The findings showed that the use of static approaches may lead to misleading results regarding the effect of macroeconomic factors on investor sentiment. In addition, the strong effectiveness of the investor's sentiment of self compared to the macroeconomic variables, explains the investors' behavior better.

In banking, customer management and validation are crucial to maintain financial security and organizational stability. A fundamental challenge in this area is the identification of appropriate indicators for validating and ranking customers. Because these customers generally have limited access to financial and credit information and cannot provide guarantors or good credit records, it is very challenging to determine correct and reliable indicators. Also, to score customers, there is a need to determine an appropriate and fair scoring system. This system should be able to place customers in different categories by considering credit and behavioral criteria and assigning them appropriate points based on their performance. In addition, there is a need to develop methods for evaluating and monitoring customers over time. This research aims to identify the indicators of validation and ranking of customers in micro-lending in Iran’s Middle East Bank.

This research is applied-contextual in terms of purpose and exploratory in terms of method. The statistical population of this research includes all retail banking clients of Digital Middle East Bank who seek low-interest loans at an annual rate of 2%, with a penalty rate of 6%. Statistical methods in this study were carried out in two phases: descriptive and inferential statistics. In the descriptive statistics section, various personality factors including age, gender, education, occupation, current debt status within the banking system, bounced checks history, money laundering records, bank account balance, transaction history, geographic location (residence and workplace), mobile phone model and operating system, as well as credit rating obtained from Iran's credit rating consulting company, were analyzed and presented using tables and graphs. Naive Bayes, Meta, Attribute Selected Classifier, and j48 algorithms were implemented and WEKA software was used to classify criteria and create patterns. Also, to evaluate the validation model and ranking of customers of unsupported microlending, the T-test was used at the significance level of 0.25.

The findings indicate that when assessing a loan application, the following indicators should be considered to determine the applicant's ability to repay the loan or provide suitable collateral. Applicants demonstrating most or all of the following indicators are more likely to meet loan repayment requirements or offer adequate collateral: First, the person's previous loans have been settled 30 days and 60 days after the loan maturity date in the granting bank. Second, the person's previous loans have been settled in other banks. Third, the higher the requested loan amount, the better. Fourth, the person's age is above middle age. Fifth, the person's degree is not a bachelor's degree, diploma, or sub-diploma. Sixth, the person's score is above 40. Seventh, the operating system of the person's phone is not Android. Eighth, the person's phone model is not SAMSUNG or XIAOMI. Ninth, if the investigations related to the person's money laundering status are negative, then preferably, and if necessary, the loan of that person can be approved.

In this approach (unsupported micro-lending), banks should extend loans to individuals only after acquiring comprehensive knowledge about them before initiating any new credit arrangement. It is vital to gather the required data and ensure their creditworthiness and positive reputation. Banks need to obtain reliable and detailed information about borrowers, as extending facilities for maximum profitability also exposes the bank to associated risks.

The banking industry is one of the most important and influencing industries in today’s world and highly relies on identifying customer needs besides planning and providing solutions for proper response to their needs. Emphasis on exclusivity and strong competitive power within the banking network can significantly impact the performance and portfolios of banks. To prevent crises and future risks in banking networks, more attention should be paid to managing banks' exclusive power. Foreign currency services form a major part of banks' services and generate substantial income in both rials and foreign currencies. Therefore, the main purpose of this research is to provide a pattern for promoting the capability of competitiveness of customer foreign currency services in the banking industry.

This study is applied research in which a mixed research method has been used. Resources and articles were studied using documentary analysis. The Delphi test was applied to determine dimensions, elements, and indices. Structural equations analysis test, exploratory factor analysis, and Lisrel software were used to determine the current status and influencing elements. The statistical population in the quantitative phase comprised managers and employees engaged in banking marketing and foreign currency operations. The participants in the qualitative section consisted of experts specializing in the same field. The sample size consisted of 166 individuals, including experts and knowledgeable individuals, selected using a random sampling method.

Using the Delphi method, a pattern for promoting the capability of competitiveness of customer foreign currency services in the banking industry was extracted. The method was applied three times to reach an agreement between the experts. Moreover, the level and extreme effectiveness were confirmed using structural equations tests and exploratory factor analysis. Based on the obtained results, it can be concluded that the coefficient of all factors had a significant effect on the final model, confirming the effectiveness of each factor in promoting the competitiveness of customer foreign currency services within the banking industry.

Foreign currency services constitute one the most important parts of banking services. They result in high incomes for banks, therefore, banks need to undertake requisite measures to establish a competitive edge in attracting, retaining, and servicing customers, while also evaluating the prevailing conditions in the banking sector. For each foreign currency service, banks adopt various procedures simultaneously when handling requests for foreign currency services, including submission, documentation completion, and customer file creation. Banks can possess a greater share of the foreign currency market by adopting proper strategies and determining elements for the capability of competitiveness. This study shows that major elements such as “banking activity”, “credit portfolio”, “monetary and financial”, “market share” and “extra-organizational” shape patterns for promoting the capability of competitiveness of customers’ foreign currency services in the banking industry.

  In general, financial literacy is the ability to effectively understand and apply various financial skills, including budgeting for money management and investment, which leads to self-sufficiency in financial principles and ultimately leads to improved financial life. Accordingly, the main purpose of this study can be considered the effect of financial literacy on the intention to invest in the stock market by considering the tendencies and behavioral controls of investors and emphasizing ethical and subjective norms in the Tehran Stock Exchange.The statistical method used for the data analysis is the Structural Equation Model. According to the research questionnaire, the sample size of the research is 125 to 375, which 310 questionnaires were collected and analyzed. The results show that the financial literacy of the investors are meaningfully effective on the tendency of the investor in the stock market. Furthermore, the moral norms of the person also affect the intention of investment.: On the other hand, financial institutions and stock exchanges, considering the issues raised in this study, can consider the rational and irrational reaction of investors, based on financial literacy and emotions along with the source of risk, so that they can make recommendations and instructions