Shareholders as key players in a stock exchange make extensive reviews when buying or selling shares. .This study attempts to identify and rank the factors influencing shareholders' decision to invest in the Iranian stock market.To achieve this goal, the required information was collected by surveying and designing a questionnaire from investors, experts and experts in the Iranian Stock Exchange for the year 1398 and analyzed in the framework of fuzzy Delphi method.The results showed that exchange rate variables, parallel market returns and bank interest rates are the most important macroeconomic factors that influence stockholders' decision-making in the stock market. In addition, among the psychological and behavioral factors, three variables of mass behavior, formal and informal news from corporate assemblies, and the opinion of investment advisers and investment firms have had the greatest impact on shareholder decision making in the Iranian stock exchange. Finally, the results showed that earnings per share, share trading volume and liquidity of the three financial and accounting factors as well as the variables of internal political developments, technical analysis and political relations with other countries are the three technical and political factors that are most important in the decision making of the Iranian stock market shareholders..

This paper is to develop a pattern to forecast adjusted returns according to the downside risk for the Tehran Stock Exchange. For this pursues, first, it seeks to find the factors affecting the adjusted return of companies listed on the Tehran Stock Exchange, taking into account the role of downside risk in the context of information asymmetry. In the second stage, it provides a pattern of forecasting adjusted returns based on downside risk. In fact, the main problem is that several internal and external effective variables affect the adjusted return according to the downside risk, and each of these variables may have positive or negative (ambiguous) effects on the adjusted return according to the downside risk. This study is to investigate the ambiguity about the role of variables affecting adjusted returns. The results of the regression model test show that the variables of capital structure, net profit, return on assets, board of directors, and duality of duties of managing director and chairman of the board at 99% confidence level and variables of dividend ratio and operating cash flow at 95% confidence level have significant effects on the adjusted return.

The capital market is one of the financial markets that in a dynamic economy can pave the way for long-term economic growth.Futures contracts that derive their values from an underlying asset, are included these financial instruments.To enter the futures market, the investor needs to anticipate future trends to cover his risk. For this purpose, the appropriate random differential equation has been selected to model the prediction of future coin contracts in the present study. Thus, after providing the necessary explanations about the necessity of using random models and as a result of new principles called random accounts, to introduce the most important stochastic differential equation in financial sciences including geometric Brownian, geometric Brownian with jump term, Heston and the explained model are discussed. Then, the appropriate model is selected, with a practical approach and based on the ability of each model to predict the price of futures contracts by assembling the Monte Carlo. The results of the fitness criteria regarding the predictive power indicate the superiority of the model explained in these contracts.

One of the risks associated with a company's stock is liquidity. Stock with high liquidity are attractive to shareholders and investors and increase demand. Stock liquidity is one of the most effective measures of market efficiency, especially in terms of information and is widely used in examining the factors affecting the supply of useful information. in this study, we designed and explained the stock liquidity model with respect to the product competition component. To select the sample, all companies listed in Tehran Stock Exchange from 2007 to 2019 are surveyed. not eligible were removed. Ten main variables affecting liquidity based on previous research were entered into the research model and the hypotheses were tested using regression model. Then, stock liquidity model was introduced through teaching and learning in Fuzzy neural network, which is an analytical-mathematical research method. The prediction obtained from fuzzy neural network is very accurate due to the comprehensive search method and the final model is confirmed with adjusted coefficient of determination of 77%. According to the results, liquidity depends on five influential factors including stock return, firm size, investment opportunity, Herfindahl Hirschman index and Lerner index

Choosing an optimal portfolio is one of the main goals of financial management. The initial strategy is to zero the risk of non-systematic risk by diversification in the optimal portfolio. In this research, WCVaR applied model and linear programming method for stock market risk measurement and selection of a weight optimized portfolio were used. WCVaR is one of the newest risk measurements and covers the defects of the VaR and CVaR models. This research 11-year period information (1387 -1397) was used from actual data of 10 different companies accepted in Tehran Stock Exchange. This research uses data from ten companies accepted in TESM during the period of 1397-1387. The results of model estimation using the linear programming method in Matlab software showed that changing the benchmark optimization would lead to a change in the weight of the basket stock and change the strategy of diversification in the optimal portfolio. The results of estimating the model using the linear programming method showed that changing the optimization criterion will lead to a change in the stock weight of the portfolio and a change in the diversification strategy in the optimal stock portfolio. Among the 44 baskets with different weights, the most optimal

Banks play an important role in the economic development of any country as the main part of the financial system. Calculating efficiency and finding the strengths and weaknesses of branches has a significant impact on increasing the efficiency of banks. Data envelopment analysis is one of the performance evaluation techniques that is able to introduce benchmarks for inefficient decision-making units in addition to estimating relative efficiency. In this paper, using a data envelopment analysis, a model for evaluating efficiency, studying sensitivity analysis of 18 branches of one of the commercial banks of Iran with financial ratios is presented. For this purpose, a model is proposed for output estimating with ratio data. By changing the input values the output values can be estimated. The amount of changes required in the outputs to maintain the efficiency as well as maintaining the rank is possible by using the proposed model with ratio data.

Although econometric models are appropriate for describing and evaluating the relationships between variables and statistical inference, but they have some limitations for financial analysis. Many efforts have been made to model nonlinear relationships in financial data using machine learning technologies. The purpose of this study is to apply machine learning models to form optimal stock portfolios and compare their performance. The statistical sample of the present study consists of 156 companies listed in Tehran Stock Exchange during the period 2009-2018. After data collection, the intended deep learning models in Anaconda software and Python programming language were tested, and then the ability of each model was determined by return evaluation, composite return, trenors and jensens criteria to form an optimal stock portfolio. According to the free-risk and market return rate, forming portfolio by investor to more profit than these two rates and portfolio valuation results of trenors and jensens indexes, it was concluded that the deep Convolutional Neural Network is able to for optimal portfolio. According to this reasoning, the long short-term memory model is not capable of optimal portfolio formation.

Today, the Forex market is the largest financial market in the world. Determining the right strategy for buying or selling in the Forex market is based on predicting the price trend. Therefore, to choose a suitable strategy in Forex, complex meta-heuristic models are used. In this research, by predicting the market trend and based on trading rules based on fuzzy technical indicators, a new method for investing in the Forex market is presented. For forecasting, a combination of hyper support vector machine (HSVM) algorithm is used and for market classification in three different classes (uptrend, downtrend, sideway) and a dynamic genetic algorithm is used to optimize trading rules. Five fuzzy technical indicators have been used to determine the trading rules. Euro-dollar pair data is used as daily training and test data for a daily period between 2010 and 2019. The results obtained compared to traditional methods have had promising results.

Due to the prevalence of granting interbank credit for collateral in order to start open market operations (OMO) in Iran and the need for more liquidity risk management in banks, in this study to manage liquidity risk in interbank payment systems, from the statistical community Daily Data of New Payment Systems in the Banking Industry and Statistical Sample of the Time Series The sum of the daily data balances of the payment systems of an Iranian bank from 01/01/94 to 05/31/1398 has been used. Then, according to the data structure and the fact that the time series were not the sums of the normal payment systems, the GlueVaR criterion was used, which was introduced to eliminate the shortcomings of the two CVaR and VaR criteria and is a linear combination of them. Accordingly, the Liquidity Risk appetite chart has been reported with six different scenarios so that banks can store the cash flow of liquid assets in proportion to their attitude. The results show using the GlueVaR criterion to manage liquidity risk, due to the use of two different levels of confidence and two metrics of risk and expected loss, has the necessary flexibility for different attitudes towards liquidity risk.

Investing in the capital market requires deciding on issues such as selection, timing, price and share buybacks with market research. One of the ways to do this is to use econometric modelers. In the studies performed to compare methods or to present hybrid models, most econometric models have been studied without comparing and predicting the error of prediction error of other algorithms. In this research, the most efficient algorithm for solving this defect is implemented and compared with the proposed methods on selected shares and based on the proposed parameters.On the other hand, often the order of the regression and the mean of the moving average sentence are considered for the finite number of studies, which is based on Bayesian criteria for determining the p and q degrees to obtain the optimal response. This paper compares the methods of self-regressive moving average, cumulative self-regressive moving average, self-regulated seasonal moving average, self-regressive moving average with explanatory variable, cumulative mean self-regression with explanatory variable, self-regression model with variance. Generalized conditional, exponential self-regression model with generalized conditional heterogeneity variance and regression model with moving average self-regression errors for selected symbols of Tehran Stock Exchange.

Unlike strategy formulation and monitoring models, which are often general applicable to different industries, strategy implementation models are always a function of the specific characteristics of each firm, so in this study, the researcher seeks to design a native model for one of the bank's functional strategies in the area of financing technological venture capital strategy. The present study is a descriptive study using qualitative-quantitative mixed methods. Using exploratory factor analysis(EFA), the identified factors were prioritized in previous research and then the final relationships were identified using structural equation modeling(SEM) and confirmatory factor analysis(CFA). In order to investigate the changes of factors and their impact on the effectiveness of the research model, a system dynamics approach was used. Then, by setting up an interview protocol with 12 experts, the effectiveness of the strategy's implementation as a dependent variable was evaluated and analyzed in the synergy sessions by the effect of changes in factors and sub-components in different scenarios. Factors Evaluating venture capital Plans, Business and Operational Characteristics of Investment Plans, Entrepreneurial Factors, and venture capital Subcomponents are important factors and sub-components of the model.

The outbreak of the corona virus has led to strong negative reactions from stock markets in various countries, and the side effects of the virus have caused the fall in prices of many macroeconomic variables worldwide.These have drawn the attention of analysts and investors to the negative effects of the spread of the virus on stock markets.The goal of the study is to create a complex network of corona viruses on the stock market of 75 countries with oil, gold, silver and copper.The results show that the interconnectedness of the modern economy of stock markets and economic variables has made the health crisis to a global economic crisis.Corona virus has a direct negative impact on 35% of stock market markets, this virus has had the greatest impact on stock market markets in European and Asian countries,It has also had the least impact on the stock markets of the Arab and African countries.The coronavirus has indirectly affected stock market markets by affecting economic variables.The unprecedented drop in oil prices has caused the stock markets to fall 56 percent,and gold price fluctuations have affected 29 percent of these markets.Silver and copper prices have fallen between 25 and 32 percent in stock markets.

The Social Security Organization is the main focus of insurance in the private sector and is the largest insurer and is one of the major economic institutions responsible for providing support to other economic, social and household institutions. Therefore, in order to provide optimal service in the social security organization, it is necessary to design and implement a system of performance evaluation of the appropriate supply chain. For this purpose, in this study, NDEA was used to evaluate the efficiency and performance of the service supply chain in the social security organization. Because conventional DEA models operate on historical information, their greatest weakness is to determine the efficiency of decision-making units in the past and do not provide a framework for the future, so to solve this problem in this study and to predict future inputs and outputs from the method Simulation of system dynamics is used. In order to show the theoretical findings of the new model presented in a practical way, the performance prediction and performance evaluation of 15 Hormozgan provincial social supply chain branches in three years from 95 to 97 have been discussed.Finally, the efficient and inefficient branches were identified.

The purpose of this study is ‎to establish and introduce a new financial network and to examine centrality ‎measures for optimizing the portfolio of investors as well as identifying ‎stock market leaders. In this study, 100 top stock companies with largest ‎average market capitalization were selected from January 2009 to January ‎‎2020. The financial network was converted to logarithmic returns using ‎adjusted closing price. The concepts of graph theory and prim algorithm ‎were used to explore the relationships and distances between stocks to ‎construct a minimum spanning tree. The results showed that based on the ‎degree centrality measure, Iranian telecommunication stocks and Ayandeh ‎Bank, based on closeness centrality measure, Bahman investment stocks, ‎Omid capital financing and tourism bank, based on Betweenness centrality ‎measure, Omid capital financing stocks, Bahman investment and Asia ‎insurance, based on the bottleneck centrality measure, Asian Insurance ‎stocks, Tourism Bank and Omid Capital has the most impact on the financial ‎network and stock market. Finally, the financial network was divided into 9 ‎clusters, each cluster showing the stronger relationship of its components ‎with each other. ‎

During the last decade, studies on the factors affecting stock market returns have reached a peak with the advances of financial economics in the field of statistics and mathematics, and modeling is of great importance in this regard. Accordingly, this study seeks to present a new approach to modeling the nonlinear relationship between financial variables and stock returns. This paper employs Bayesian Markov chain Monte Carlo (MCMC) sampling methods for updating the estimates and quantile threshold regression with heteroscedasticity. To study and model this approach, we used returns of the Tehran Stock Exchange, Coin Price, Oil, and Gold Price from 2011 to 2019. The results show that these variables have different effects under low and upper quantile levels. Also, the impact of the financial variables on the stock returns is different under higher and lower threshold amount for each quantile levels. Based on the result, we can say that stock returns have a nonlinear relationship with other markets in the bullish and bearish market.

The volatility of financial returns plays an important role in many empirical applications, such as portfolio allocation, risk management and derivative pricing. The purpose of this research is to explain the relationship between undesirable risk and desirable risk in predicting market return volatility. The research is descriptive in nature and applied in purpose. The statistical population of the study is the companies listed in Tehran Stock Exchange and the target sample of the companies listed in the cement industry from which the required research data can be extracted. The research period is from 1392 to 1397. This research has a theoretical model and the self-regression model was used to test the hypotheses. In the cement industry, according to the t-statistic and its coefficient of determination, it is clear that the predictor of market yield fluctuations correlates with undesirable and desirable risk. Also, the adjusted coefficient of determination is 51%, which indicates this effect.

Today's the world is more competitive than ever therefore in order to survive in such an environment for organizations having with a supply management network has become an essential issue a recyclable and environmentally friendly product. The purpose of this article is to design Green Closed Loop Network (GCLN) to integrate financial- physical flows. The innovation of this article designing a four objective model to minimize deviations in financial indicators from desired limits; Costs; The rate of emission of pollutant gases throughout the environmental network and maximizing profits of shareholders using mathematical modeling of Fuzzy Goal Programming (FGP) with considering financial flows and its integration with physical flow. Modeling problem of FGP in a multi level multi period and multi product supply network with aim of maximizing shareholder wealth. To deal with multi-objective nature of FGP has been used. The model is designed and implemented in the dairy industry. The proposed model has been designed with GAMS24 software CPLEX solver tested using epsilon method of constraint. The results of modeling and solving numerical model show importance of the role of financial ratios and at the same time consider operational financial dimensions in mathematical model to gain a stable competitive advantage.

The choice of stock portfolio is a special issue in the field of investment. Given the wide range of options in the stock market, one of the major concerns of investment groups is the optimal allocation of assets. Therefore, most of these collections use portfolio selection models. The conditional value at Risk, which is one of the models of portfolio selection, follows the Quadratic Programming. Given that Quadratic Programming requires extensive computations, the use of metaheuristic algorithms in solving these problems increases the speed and accuracy of computations. The aim of this study is to minimize the Conditional Value at Risk by using two algorithms of Imperialist Competitive Algorithm and Particle Swarm Optimization. Therefore, using 800 days of data from 12 companies listed on the Tehran Stock Exchange in the period of 2/5/92 to 1/28/98, portfolio has been formed, and the weight of each stock in the optimal portfolio and the risk and return of the portfolio has been calculated using MATLAB2018 software. Then, using SPSS software, the average difference between risk and return of the two algorithms was tested. The results showed that the risk and return of the two algorithms were not statistically significant,.

Despite the large number of mutual funds available to investors, paying attention to their ratings is important. Many ranking approaches use subjective weights to integrate fund performance across different time horizons, which can lead to completely different ranking. So far, the efficiency of the funds has been calculated in the valuation of the funds with the help of data envelopment analysis, and then the portfolio is selected from the efficient funds. In this paper, a new approach and application of the philosophy of data envelopment analysis (input reduction and output increase) is applied and generalized to the concept of risk and return. The proposed model is a new nonparametric technique that focuses on the simultaneous estimation of contractile (risk) and expansion (return) potentials. This approach eliminates any need for mental such as the importance, weighting, and meaningfulness of actions across different time horizons. This approach, based on the performance of the underlying fund relative to other funds, assigns a score to the underlying fund, on which it can rank the funds. This model has been applied to a sample community of 26 mutual funds. The clear results and unique ratings indicate the ability of this model to evaluate and ranking.

The purpose of this article was to designing four level supply network with the simultaneous consideration of operational and financial flow in LARG supply network framework in Saipa Yadak Company. The innovation of article was presenting an integrated financial- operational approach to LARG supply network and new aspect of research was considering four levels of suppliers factories distribution centers and customers by Fuzzy Multi-Objective (FMO) optimization at strategic and technical decision-making levels. One of the prominent features of proposed model was use of Goal Programing (GP) for modeling financial flow and achieving producer's financial goals. Since the proposed model was a two objective model SO and TH FMO interactive approaches which are able to adjust degree of satisfaction of the objective functions were used to solve model. Using these approaches in addition to GP the enables decision-maker to make final decision by choosing the right solution based on degree of satisfaction and priority of each objective function. The mathematical model presented in the GAMS optimization software were coded and solved with CPLEX solver. Finally to show the efficiency and fit of the model integrated financial approach of the present article was compared with a non-financial model.

Investors in stock market, always seeking for novel and efficient methods to predict stocks price and make the appropriate trading strategies. This paper propose a pattern for implementing appropriate strategies with a model composed of future studies, Fundamental analysis, expert trading rules and machine learning algorithms. First with use of feature study and expert’s opinion, stock market scenarios designed and a portfolio consist of 6 fundamental stocks is built. In next step for each selected stocks a model for stock price movement prediction is developed by means of related stocks data from 1393 to 1398 and 7 machine learning algorithms. Model inputs includes, technical indicators, technical trading rules, stock sign reading rules and stocks trading data. Results show that implementing proposed composed model for investment in stock market led to greater performance compared with Tehran stock market index and implementing short term trading strategies based on the model trained with light gradient boosting machine (LGBM) algorithm cause better performance in comparison with Buy& Hold and Technical trading Strategies.

Role of information in economy started from questioning one of the assumptions of perfect competition theory in market. In stock market, information plays an important role in determining the level of competitiveness. In this study tried to measure the competitiveness of firms in Tehran Stock Exchange by using income equations in the presence of asymmetric information. In this regard, first, using the income equations and a GLS- Panel has been paid the effect of various variables. The results indicate that equations without the effect of scale offer better specification of the firms’ conditions. Although asymmetric information is high in stock market and stock gains influenced by the degree of asymmetric information, but extent of this influence is limited. The “H competitiveness index” shows that monopolistic competition is best description of market conditions in equations of incomes from changes in stock prices, but in equations of incomes from stocks dividends and accumulated, the performance of studied firms be explained with monopolistic behavior.

Investor's emotional tendencies indicate the margin of shareholder's optimism and pessimism towards a stock. Investors' emotions, under the influence of psychological phenomena, direct people's behavior and, in many cases, make people to deviate from the rational behavior. The purpose of this study is to use meta-innovative methods to predict the emotional tendencies of investors. In this study, using 97 financial ratios related to 176 companies listed on the Tehran Stock Exchange during the period between 2006 and 2018, investors' emotional tendencies have been predicted with the help of support vector machine (SVM) and decision tree (DT) techniques.To measure the emotional tendencies of investors, four indicators of relative strength, psychological line, trading volume and stock turnover adjustment rate have been applied. Finally, we have combined these indicators with the help of PCA method. Mean absolute error (MAE) and root mean square error (RMSE) values were used to compare predicting methods. The results of data analysis indicate that the prediction error of the support vector machine method is less than the decision tree.

Every investor is always looking for the investment portfolio that will bring him the most profit with the least risk. The standard deviation of an asset return is the amount of risk of that asset. In this study, the value-at-risk approach has been used as a measure of risk in the formation of the optimal stock portfolio. By selecting a statistical sample consisting of seven companies operating on the Tehran Stock Exchange, first the variance-covariance matrix is ​​extracted by the moving average weighted method (EWMA) and then the Markowitz model is calculated with the aim of reducing portfolio risk against an expected return level. Portfolio performance has been achieved. Then the value-at-risk limit is added to the efficient frontier chart, and then by analyzing the sensitivity of the value-at-risk value for different values ​​of the level of confidence and the maximum risk accepted by the investor, we show that with the risk-value approach in forming the optimal portfolio Stocks may not change the limit of the Markowitz model, or be limited, or become a point, or even disappear.

Systemic risk refers to the risk of failure of the financial system or failure of the entire market. This risk can arise from instability or crisis in financial institutions and can be transmitted to the entire financial system as a result of transmission. The purpose of this paper was to assess the pervasive risk of a financial crisis in the Iranian banking system. In this study, statistical information of banks during the years 1392-1397 has been used. In the first part, the comprehensive risk indicators of the financial crisis are calculated using the Delta CoVaR index, then the risk susceptibility is assessed using the ARFIMA-FIGARCH method. In the first step, the unit root test indicates the existence of a deficit root in the bank stock price index. Comprehensive risk indicators are then calculated and systemic risk transmission modeling is discussed. The results of the model indicated that the systemic risk situation in the country's banking system was abnormal, which was due to the leverage situation of the country's banks. Using the results of this study, it can also be stated that different financial sectors are required to consider sufficient capital for systemic

, is the existence of an efficient management system. To have an effective and efficient management system, organizations need to train qualified managers. Training managers needs to have a comprehensive and complete framework in order to achieve the goals and strategies of the organization with the correct selection and training of effective managers. The aim of this research is to develop a model for affecting factors in success of succession programs in Iranian banks. The present study is based on a mixed research method. The statistical population is academic experts specializing in human resource management and experts working in Iranian banks. The number of statistical samples in the qualitative phase is sufficient to conduct interviews with 12 people, and 161 employees of the banking sector are selected for quantitave phase. In the qualitative dimension, the phenomenological method is used and in the quantitative dimension, in order to validate the model, Delphi method, confirmatory factor analysis and multi-criteria decision fuzzy SWARA technique are used for screening and prioritization of effective factors. Finally, after determining the categories, the general classes of the model are identified in the form of three categories of succession requirements, contextual conditions and facilitating factors.

In this study, using dynamic averaging models and monthly data in the period 2001:4 until 2018:3, Tehran Stock Exchange returns be investigated. In this regard, macroeconomics variables and parallel markets indices have been used to forecast the stock returns. Initially, estimating various models such as Recursive models, time-varying parameter models (TVP), dynamic model selection (DMS) and dynamic model averaging (DMA) in Matlab software, It was observed that DMS model with α = β = 0.95 had higher forecast accuracy (based on MAFE, MSFE and Log (PL) metrics). Gold price (48-period), exchange rate (36-period) and inflation rate (30-period) had the highest effect on stock returns, respectively, and global oil prices and GDP had the lowest effect by 28 and 2, respectively. Finally, the results indicate that utilizing dynamic models by considering time variations in parameters and the variation of the model increases the efficiency of forecasting stock returns. Keywords: Forecasting, Stock Returns, time-varying Parameter (TVP), Dynamic Model Averaging (DMA).

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.