فصلنامه دانش سرمایه گذاری
پیاپی 50 (تابستان 1403)

The purpose of this study was to explain the role of investors' emotional tendencies in pricing the capital assets of companies listed on the Tehran Stock Exchange. For this purpose, information of 150 companies from the financial reports of companies listed on the Tehran Stock Exchange have been collected and tested. Findings show that in all three capital asset pricing models (Fama and French three-factor model, (2001) Kahrat four-factor model and Fama and French five-factor model, (2014) investors' emotional tendencies in price The investment of capital assets has an effect and increases the sheer risk of the portfolio.The results also show that in all three pricing models, presenting a capital asset pricing model based on investors' emotional tendencies increases the predictive power of common capital asset pricing models.

The present study was conducted with the aim of combining multi-criteria decision making approach and mathematical optimization in evaluating financial efficiency and refining the affecting factors. In this research, based on a quantitative analysis, a distinction is made between capital decision supporting and decision-making, and data envelopment analysis has been used in selecting the investment option. The type of research has been done in terms of theoretical-applied purpose and with survey research design. Using the fuzzy Dematel method, the most effective factors of financial efficiency was: 1) Current ratio, 2) Liquidity of assets, 3) Ownership ratio, 4) Operating cost rate, 5) Firm size, 6) Operating return on assets, 7) Net return Assets, 8) return on equity, 9) current asset turnover, 10) average monthly return, 11) daily price to earnings per share, 12) earnings growth. The statistical population of the research in the refinement of inputs and outputs includes professors and experts and, 183 selected companies of Tehran Stock Exchange in the selection of the optimal capital combination. The evaluation of financial efficiency led to the identification of 42 companies as justified investment options.

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

The importance of risk management in the pharmaceutical industry of each country reflects its ability to provide health in that community. The purpose of this article is to present a comprehensive risk management model in the pharmaceutical industry. In this study, it was attempted to collect data by means of a survey method after receiving the opinions of senior industry experts and experts familiar with the industry through a researcher-made questionnaire, and after confirming its validity and reliability. After that, risk types including financial and non-financial risks were identified and by using PLS software proposed a comprehensive risk management model. To evaluate the confirmatory factor analysis model, it is used the second most important index of estimation of variance error (SRMR). The value of the index is 0.079 for this model, which can be said that the model having had an appropriate fitting. This pattern shows that, non-financial risks are more effective than financial risks. Among non-financial risk sub indicators, operational risk with path coefficient of 0.970 has the highest effect, followed by management risk with path coefficient of 0.896 and then country risk with path coefficient of 0.818 and the legal risk with path coefficient of 0.765 have been determined as the latest influential indicator. And among the financial risk sub indicators, market risk with path coefficient of 0.969 has the most effect, followed by liquidity risk with path coefficient of 0.855 and the credit risk have been determined as the last effective indicators with path coefficient of 0.769.

Enterprise risk management can identify, measure, control, and monitor risk levels. The issue of enterprise risk management has been of interest to companies in recent years. The purpose of this study is to determine the implementation of enterprise risk management programs and then to examine the factors affecting the management of organizational risk 118 companies during the years 1388 to 1398. Factors such as firm size, growth opportunity, return on assets, financial leverage and dividends were also used as factors influencing the implementation of enterprise risk management. The test results of the hypotheses showed that the size of the company with 0.2853 and growth opportunity with 0.2349 multiplaiers is meaningful and positive. Also, financial leverage with -0.03935 and dividend with -0.01095 don’t have significance. In other words, at the 95% confidence level, firm size, growth opportunity, return on assets have an impact on enterprise risk management and financial leverage, dividend variables have no effect on enterprise risk management in companies listed on the Tehran Stock Exchange. In other words, enterprise risk management can improve the operational performance and increase the value of the company, and companies that are exposed to high risk due to environmental uncertainty can prevent and control the adverse effects of risk

Behavioral financial theory shows that changes in securities prices have no fundamental reason and the behavioral tendencies of investors play an important role in determining prices.In recent studies, the rationality of investors has been seriously challenged, and the results of studies suggest that prices are determined more by psychological attitudes and factors than fundamental variables, so the study of stock market psychology has become more important. Therefore, the purpose of this study is to Investigating the effect of investors' behavioral tendencies on stock Market liquidity in the capital markets of selected Islamic countries. The statistical sample consists of the capital markets of 14 selected Islamic countries during the period 2008 to 2020.Quantile regression method was used to estimate the research model using Eviews 9.0 software. The results show that the behavioral tendencies of investors have a positive and significant effect on stock market liquidity. Also, among the macroeconomic variables, the growth rate of money supply and the growth rate of industrial production have a positive and significant effect on stock market liquidity and the inflation rate has a negative effect on stock market liquidity. Based on the results, investors' behavioral tendencies increase the volume of transactions and stock market liquidity. In fact, the results of the research reinforce the argument that the behavioral tendencies of investors can be the source of changes in liquidity.

The purpose of this study was to design a new model for predicting the Tehran Stock Exchange index using pattern recognition in a combination of hidden Markov model and artificial intelligence. The present study is an applied type and mathematical analytical method. Its location is the Tehran Stock Exchange and during the years 2010 to 2020. Findings showed that the prediction error rate with artificial neural network has a higher accuracy than Markov's hidden model. Also, the prediction error of the hybrid model is much lower than the other two models for predicting the total stock index of Tehran Stock Exchange, so it has higher accuracy for forecasting stocks. According to the MAPE index, the hybrid model method could improve the predictive power of the artificial neural network by 0.044% and also improve the predictive power of the hidden Markov model by 0.70%.

aim of this study was to evaluate the integrated risk of the banking system through the metaphysical algorithms of gray wolf, genetics and particle swarming. This research is applied research in terms of purpose and correlational in nature and method. Data collection has been done through library studies, articles and sites in deductive form and data collection to refute and confirm hypotheses inductively. The statistical population of this research is the banking system and the sample includes banks listed on the Tehran Stock Exchange during the fiscal years 1392 to 1397. In order to collect the required data, the financial database of the Ministry of Economic Affairs and Finance, codal site, etc. have been used. After extracting the information, and setting them in the form of an integrated risk model, the objective function and constraints are entered in MATLAB software and the variables of risk and return (profit and loss on assets and Debts) were obtained using particle swarm algorithms, genetics and gray wolves and we compared their results using SPSS 16 software. After that, first the descriptive statistics were analyzed and then inferential statistics were performed. after reviewing the results of comparing the evaluation indicators of algorithms, it was determined that the gray wolf algorithm is efficient. Provides better goal function optimization. Also, by examining the research hypotheses, it was found that particle swarm algorithms and genetics have the same efficiency for assessing the integrated risk of the banking system. Provides better problem solving.

In this study, the effect of government and stock market expenditures on income distribution has been investigated. The countries studied in this study are the selected countries of Mena (Iran, Algeria, Egypt, Tunisia and Jordan). 2018. The data panel method has been used to estimate the research model. The results of this study show that the logarithm of the ratio of the volume of transactions in the stock market to GDP with a coefficient of -0.02 has a negative and significant effect on the Gini coefficient. The volume of exchanges in the capital market increases the working capital of companies and ultimately their production. With the increase and prosperity of production, the total production increases and this issue increases the national income and as a result the government can Improve income distribution. Also, the logarithm of the ratio of consumption expenditures to GDP with a coefficient of 0.03 has a negative and significant effect on the Gini coefficient. Increasing government spending can have different effects on income distribution in two ways. In the direction of transfer payments to the people, so the distribution of income is improved No, they are not transferable, so the distribution of income does not improve.

The present study seeks to identify the factors affecting the strengthening of banks with the approach of antifragility and determine the priority of these factors and finally provide a solution to strengthen banks. The present study is mixed due to the use of qualitative and quantitative methods. The theoretical population of research is senior banking managers and experts in the field of resilience and economic resilience. The research sampling method was judgmental and non-probabilistic and samples were selected based on expertise in the field of research. Two tools of interview and questionnaire (expertise and SMART) were used to collect data. Binominal statistical test and SMART were used to analyze the research data. In the first stage, the effective factors on banking strengthening were extracted through literature systematic review and interviews with experts. Due to the large number of factors, low importance factors were eliminated by using Binominal test. The remaining 28 factors were classified into 6 main categories: risk acceptance, organizational structure, learning, high reliability, degree of system softness, and support mechanisms. Then, using the SMART, sub-factors were ranked in each category. According to the score of factors, the sub-criterion of the existence of stress analysis mechanisms in the risk acceptance factor has the highest priority. Sub-factors of system interchangeability, rational selection, creative destruction, holism, and the existence of support systems were given the highest priority in the remaining categories.

Today, in the vast majority of leading and successful countries, one of the main pillars of maintaining material and intangible resources of governments, organizations, institutions, etc. is the insurance industry.Insurance companies try to provide people with the coverage they need, by insuring the risks that threaten their health and even their lives, to provide peace of mind to people.Among the various disciplines of insurance, life insurance is of particular importance due to its significant benefits. Life insurance plays an important role in the financial lives of individuals and families. Because this type of insurance is insurance against loss of income due to the death of the head of the familyThe present study aimed to identify the factors affecting the concept Brand Value Creation in Insurance Industry with Focus on Investment Life Insurance. This was a fundamental research trying to work out a conceptual model for the explanation of Brand Value Creation in Insurance Industry with Focus on Life Insurance. To this end, the author employed a mixed methodology. In the qualitative section, the Grounded Theory including open, axial, and selecting codings was used in order to develop the research model. In the quantitative section, a descriptive survey came to use and the model was tested via Amos software. Afterwards, the results were confirmed in a goodness-of-fit model, and presented in the form of 6 categories and 28 minor subcategories.

In the world of investment, decision making is the most important part of the investment process, in which investors need to make the most optimal decisions in order to achieve their maximum benefits and wealth. In this regard, the most important factor in the decision-making process is information. Information can have a significant impact on the decision-making process. Because it makes different decisions in different people. In the stock market, investment decisions are also affected by information. Therefore, this study seeks to provide and explain a model to create the value of the company according to the role of management of accounting standards, financial reporting quality and audit quality using meta-innovative models. To achieve this goal, the data of 101 companies listed on the Tehran Stock Exchange during the period 1392 to 1397 were collected, and the optimized algorithm method was used to analyze the data. The research findings indicate that all three meta-functional methods have the power to estimate economic value added and market value added. However, the estimated value of economic value added and market value added in the night cream algorithm is higher than the two decision tree algorithms and the regression machine-supporting algorithm algorithm. Is higher.

Investment is one of basic necessity of economical growth for each country. Non-economicalfactors such as , the sort of government and its quality and competency of human resource areeffective in economic growth and direct foreign investment. Foreign direct investment is one ofthe most important factors in economic growth and development in most countries in the world.The main aim of this study is the investigating role of good government and human capital inattracting the direct foreign investment and economic growth in a group of selected developingcountry with panel data and extracted data from world Bank during interval of 1990-2018,byusing the systemic generalized method of moments(GMM).The result of models estimation of the study showed that the direct foreign investment and human capital and the factor of government have positive and significant effect on economic growth in developing country. Therefore it can be said the human capital and factor of good government in attracting direct foreign investment and improving economic growth have effective roles.

The purpose of this study is to present a model of marketing capacity in Khuzestan steel industry and its impact on financial performance in order to develop investment. The method of the present study is mix method with an exploratory design that was performed through two qualitative and quantitative stages using the data theory of the foundation. In Qualitative stage data were collected through interviews with 15 experts and marketing experts who were selected through purposive sampling method and analyzed with Maxqda software. The results of the analysis of theoretical foundations and research interviews led to the final model of marketing capacity, which has 7 main categories of product, distribution, market, customer, competitor analysis, advertising and brand. Competitors analysis and product in the role of "underlying factors" of the market in the role of "intervening conditions", advertising, brand and distribution network in the role of "strategies and strategies" and the role of marketing capacity in the role of "outcome" have been identified. In order to fit the mentioned model, 200 employees of Khuzestan Steel Industry who were selected by random sampling method, answered the researcher-made marketing capacity questionnaire that resulted from the results of the quality section and also Financial dimension of Kaplan and Norton (2001) organizational performance questionnaire. Data were analyzed using LISREL software. Findings indicated a good fit of the proposed model

The impact of information on prices is the core of the market. This means that as soon as new information is released, an immediate and immediate response occurs, and prices change. Research on how stock prices react to market-level information and the company's specific information in the financial literature has received special attention. The main purpose of the present experimental study is to determine the effect of quality of management performance on information confidence indicators and market confidence indicators. To measure the quality of management performance from the management ability index of Demerjian et al (2012), to measure information reliability from the interaction between information quality assurance and cash flow reliability and to measure market confidence from three indicators of arbitrage risk, stock volatility and value risk. The stock market has been used. The statistical population of this study includes companies listed on the Tehran Stock Exchange and the data studied in this study include 150 Firm from 2008 to 2018. The research method is correlational and postoperative causality and the hypothesis testing method is correlation and regression test. The results of this study showed that the quality of management performance has a positive and significant effect on market confidence and no evidence was obtained on the effect of quality of management performance on information confidence.

The purpose of this research is to study the explanation of the credit rating pattern in companies accepted in the investment industry of Tehran Stock Exchange. For this purpose, relevant information was extracted from the financial statements of the companies listed on the stock exchange. The research period was considered from 1390 to 1400. For statistical analysis, regression estimation with panel data was used. The results of the first hypothesis test of the research show that the variables of presence in foreign exchange industries, ratio of operating cash flow to debt, ratio of operating profit to interest expense, company size, financial leverage, current ratio, cash ratio have a significant effect on credit rating. have.

This study was aimed to predict financial distress using financial, economic and stock market variables in the form of binary logistic regression models, Merton and hybrid models. For this purpose, the information of 168 distressed companies selected based on specific criteria of distress and 168 healthy companies listed on the Tehran Stock Exchange between2006-2019 and two years ago, one year ago and distress year has been used. In this study, from 17 financial ratios, 4 economic variables and 4 stock market variables have been used. The innovation of the present study is the development of a hybrid financial distress prediction model that for the first time combines the financial, economic and stock market variables of the accounting model with the default variable of the structural model.The results showed that the hybrid model has better explanatory power than Merton and binary logistic regression model and although the existence of the variable probability of Merton model improves the explanatory power of the hybrid model, but the explanatory power of binary logistic regression model is better than the Merton model

This study is aimed to investigate the effect of the 5 personality phases of managers of mutual funds in dealing with smart money by using the structural equation model. Meanwhile, managers' financial intelligence has also been examined as a moderating variable. This survey is carried out by collecting statistics from all mutual funds. This analysis was carried out in 1399 and all data have been collected from mutual funds during that time. Five studied phases of personality are: Hypomania, Psychopathic Deviate, Psychasthenia, Paranoia, Depression.The results show that out of the five personality phases studied, four of them have a significant relationship with smart money and there is no significant relationship between only one personality phase and smart money. Also, the moderating role of financial intelligence was accepted only concerning one type of personality phase( Hypomania,) and concerning the other four personality phases(Psychopathic Deviate, Psychasthenia , Paranoia, Depression), the effect of the moderating variable was not confirmed.

The capital structure influences various management decisions such as cash maintenance. The capital structure of companies does not necessarily correspond to the structure of the goal. Companies adjust the capital structure by considering adjustment and deviation costs. The role of liquidity in the dynamic nature of capital structure, especially the speed with which companies adjust their capital structure to the goal, is examined. It seems that a company with higher liquidity will face lower costs associated with issuing new securities and is therefore more inclined to quickly correct any deviations from its optimal leverage ratio from its target ratio. To verify this claim, In this study, 106 companies listed on the Tehran Stock Exchange between 1384-1398 were examined. Through rolling regression, the effect of each heading of cash flow statement on the adjustment speed in upper and lower lever companies was tested. The results show that the speed of reaching the optimal leverage in companies with low optimal leverage is higher than companies with high optimal leverage. This indicates a lack of attention to future debt capacity in companies listed on the Tehran Stock Exchange. The results also show that the only cash flow from operations is a moderating factor in the speed of adjustment of the optimal leverage for companies above the optimal leverage.

Given the increasing changes and transformations in various fields, including public policy-making, critics of public administration believe that hierarchical models are not efficient enough to address current challenges. For this reason, in pursuit of new solutions, the concept of networking has been entered the field of public administration issues and public policies. Networks make it possible to identify all stakeholders and involve them in the policy-making process. Regarding to the important fact that the digitalization of the economy and following that, the banking industry is one of the phenomena of the present age, the digital banking requires networking public policy-making with keeping actors, stakeholders and influencers in mind.The aim of this study has been the identification and validation of the dimensions and components of the digital banking network policy-making model in the Iran banking system. The research method is using qualitative (focusing on Thematic Analysis) and quantitative analysis techniques (based on the Confirmatory Factor Analysis - CFA).

The current research studies and examines the resilience of financial technology businesses. Resilience is determining strategies that can prevent inefficiency and collapse of businesses, or in case of system inefficiency, return to the pre-failure state or better than that in the shortest possible time. In addition, in this research, a comprehensive model of resilience of financial technological businesses and its introduction to the studied society is presented. All the failure modes that cause the inefficiency of financial systems for technological businesses include 12 failure modes that have been identified through research literature and experts' opinions. The statistical population of this research are managers and experts in the field of financial technology business and related groups in Tehran. In the analysis, the FMEA technique is used to determine resilience strategies in financial technology businesses before failure and the new FAAO technique is used to determine resilience strategies of financial technology businesses after failure. Through the FAAO technique, 6 strategies were determined to make risk management more resilient to failure.

The purpose of this research is to evaluate the investment methods and compare between the behavioral model and conventional rational models in choosing the optimal portfolio using cumulative perspective theory because the behavior of the stock market does not follow a linear pattern, for this reason, the methods A common line cannot be used to describe this behavior and be useful. Considering that choosing a portfolio is a difficult task according to different criteria and goals, in this research, an attempt was made to provide a solution for this problem by using cumulative perspective theory and the usefulness of the mentioned technique in choosing the best portfolio according to several Different criteria should be tested. The analyzed statistical sample is for 31 active companies in the period from the beginning of April 2015 to March 2014. The results of using cumulative perspective theory based on elliptic two-point distribution show that using this method over time and using more training data leads to the creation of a portfolio with better performance and a high standard. Also, the total portfolio, which is obtained by using the monthly average of the total stocks, has a favorable trainer criterion, which indicates better portfolio performance and creation of an optimal portfolio.

Non-performing loans are a critical factor in assessing the profitability of banks and their influence on the financial system. This research tries to identify the influential macro factors in forming non-performing loans of Bank Mehr Eghtesad branches in the country. This research is qualitative and exploratory based on the type and nature of the data. Therefore, it benefits from the theme analysis method. An interview protocol is used to collect data. Also, in terms of the purpose, it is considered among applied and developmental research. The research population includes 20 persons who are the managers and deputy managers of Bank Mehr Eghtesad branches in the provinces and experts of accounting, economics, financial management, and banking. The participants' method in the interview is an exploratory focus group; the purposeful snowball sampling method is used to define the target groups of informants according to the research topic, problem, and situation. They participated in this study From May to August 2022. The data from the interviews has been analyzed with the help of MAXQDA software to analyze the theme during the three main stages of open, central, and selective coding. The results indicate that in cretating non-performing loans the three significant macro factors of the lack of transparency of administrative laws; inflation rate and profit; the pressures of banking interest groups have had a significant impact compared to other factors.

Considering the extreme fluctuations of the cryptocurrency market and also the importance of predicting the value at risk in such conditions, the purpose of the present study is to predict the value at risk in the cryptocurrency market and also to compare different models for predicting the value at risk. In addition, the impact of different distributions of model innovation terms has been investigated. In this research, we use different models to predict the value at risk of return of four well-known cryptocurrencies. The data used in the research covers the period from 1/1/2018 to 16/3/2022. This research uses CAViaR and DQR models that directly predict the return distribution quantiles as value at risk. In addition to the mentioned models, several types of common models have been used to predict value at risk. In order to check the performance of the used models, we have used the back-test method, which is one of the common methods for testing the performance of the models. The results show that the models that directly use the quantiles of the return distribution to predict value at risk (specifically CAViaR and DQR models) have a much better performance than other common models for predicting value at risk.

Issuance of asset sales declaration is one of the strategies of voluntary disclosure of information that can be used to measure and evaluate the correct market reactions and analyze the type of decisions taken in order to evaluate companies. Accordingly, the purpose of this study is to investigate the effect of voluntary disclosure of asset sales on the financial performance of companies using the data contained in the financial statements of 108 companies listed on the Tehran Stock Exchange in the period 2013 to 2019. The method of research is applied in terms of purpose, applied in terms of approach, rationalistic and in terms of nature because it causes the effect of several variables on a variable to be examined. It is also quantitative in nature and quantitative in duration. To test the hypotheses, the generalized least squares linear multiple regression model was used using the combined data. Findings from the study show that voluntary disclosure of asset sales has a positive and significant effect on companies' financial performance.

Financial fragility is one of the terms that has recently been widely used in financial and accounting issues and its improvement has become one of the important issues and challenges in various organizations in the public and private sectors. This mixed (qualitative-quantitative) research has been conducted with the aim of presenting a model for promoting financial fragility with a screening approach in companies listed on the Iranian Stock Exchange. After interviewing 19 experts, 28 components of the model were identified in the qualitative section, but fuzzy Delphi method was used to rely on very important components and screen the identified components. Then, to achieve the model in the quantitative part, based on interpretive structural modeling (ISM) and self-interaction matrix was used to collect the required data. Findings The research presents a four-level model consisting of 9 components, of which the most effective component and the only independent component of the financial fragility improvement model of this research is the amount of economic stability.