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The purpose of this study is to investigate the ability of mutual fund investors to predict the performance of mutual funds and select a fund with higher returns. To predict whether investors and the cash they enter into investment funds will be able to select a more efficient fund, first, the information of 34 funds in the period 2012 to 2020 was Collected to examine the intelligence of cash. The economic prosperity periods of the market were then examined separately to determine whether investors had acted differently in different market periods. Also, real and legal investors are separated from each other to examine the performance of each separately. Using Carhart's four-factor model, the adjusted return on risk of the funds was calculated to assess the intelligence of investors, which showed that group of real and legal investors, at the time of entry during market blooming and at the time of exit money during market stagnation act wisely in mutual funds.

The purpose of this research is to design a financial stress index to predict the occurrence of a financial crisis. In this research, a composite index has been designed to measure the financial system of Iran and the effects of financial turbulence in the conditions of uncertainty in the financial markets and the Tehran Stock Exchange between 2008 and 2020. Since shock variables were used in previous studies, in this study, the factors of currency volatility, stock market index volatility, banking industry volatility, coin price volatility, energy carrier volatility, and insurance industry volatility were used as variables in the final model. It was used, and three factors of currency volatility, stock market volatility, and banking industry volatility were used to design and construct the financial stress index. This research was conducted in five steps based on the DCC-GHARCH approach. Finally, based on the variables of financial institutions and the stock market index, a prediction model based on neural networks for the financial stress index was presented. From the results, we find that all the independent variables of the research have a positive and significant effect on the financial stress index, except for the coin price volatility index, which has a negative and significant impact.

Objective:

 The pricing and valuation of initial public offerings (IPOs) are the subject of extensive literature in finance, primarily focusing on well-documented pricing anomalies such as IPO underpricing and significantly abnormal post-issuance returns. The use of peer company information to value IPOs has received attention due to their signaling role. Among the things that can be compared in the field of peer companies is the quality of their accounting information and financial reporting. Due to the lack of accounting information related to IPOs, practitioners rely significantly on comparable Peer Accounting Information when attempting IPO Valuation. Firms desiring to obtain capital through an IPO depend on more accounting information than their own when transitioning from a relatively opaque to a relatively transparent information environment, such as the accounting information of their priced peers. According to these cases, this research aims to present the model of the effect of the degree of group similarity, the quality of accounting information and the quality of financial reporting on the valuation of the initial public offering of shares using the algorithm of artificial neural networks.

This research is practical in terms of purpose and data collection method; it is ex-post facto research in accounting proof research. The statistical population of this research includes 92 companies listed on the Tehran Stock Exchange between 2012 and 2020, which uses the multiple regression method to test the hypotheses. The degree of similarity of the quality of accounting information of the supplier company with the peer group has been calculated and tested separately using three indicators of abnormal accruals, profit predictability and profit stability. Also, considering that artificial network patterns can be used to predict the valuation of an initial public offering of stocks and probably have different powers, this research compares the power of three algorithms (firefly algorithm, machine regression algorithm, decision, and tree algorithm).

The comparable companies' approach to pricing IPOs depends largely on the availability of accounting information from peer companies already priced in the market. However, to be most effective, peer accounting information should be useful in making decisions about how to use the accounting information of peer companies. The results showed that the valuation of shares in an initial public offering is possible based on the degree of similarity of the companies in the group and the quality of the accounting information. Stock valuation in initial public offerings based on profit and sales approaches differs in terms of the similarity of group companies and the quality of the company's accounting information. Group companies and the quality of accounting information aren’t associated with incorrect pricing (overvaluation and undervaluation) in initial public offerings. The results of neural networks also indicated that the firefly algorithm has a higher power to predict the initial public offering valuation than the machine regression algorithm; the firefly algorithm had a higher power to predict the initial public offering valuation than the tree algorithm. The decision tree algorithm is more likely to predict the valuation of the initial public offering of stocks than the support vector machine regression algorithm.

in the comparison between valuation theory and practice (use of information of peer companies), the second view is more effective, and the accuracy of information of peer companies is a key component of pre-publication estimates of the accuracy of information of IPO companies, and the information set of an IPO, it is not limited to its own accounting information, but also includes the information of its peers. Given that companies often decide to offer their initial shares when they think they can maximize their equity earnings (such as when they feel their stock is overvalued or during periods of booming stock markets and heightened investor sentiment), the results of this research can be used by potential investors to reduce the effect of underwriters' incorrect valuation and to monitor them, especially in the emotional stock market; Therefore, it is suggested that buyers of shares of companies that are listed on the stock market for the first time should consider the accounting information and quality of financial reporting of similar and peer companies, especially the quality of accruals and the predictability and stability of their profits; because it contains information about the future price estimation of initial supply companies. Underwriters and initial price estimators are also suggested to consider the quality of accounting and financial reporting of their peers in estimating the initial price of initial public offering companies. In the end, due to the higher power of the Firefly algorithm to predict the valuation of the initial public offering of stocks compared to the vector machine regression algorithm and the decision tree, it is suggested to use these algorithms for valuation due to the lower error.

The purpose of this paper was to assess the overall risk in various financial sectors, including banking, insurance and investment companies. Pervasive risk in general indicates the possibility of collapse of the entire financial system in a crisis. While in most cases, investors in different markets worry about losing the value of a stock or commodity and measure the risks involved, the risk is pervasive, focused on the market as a whole and likely to fall. In this study, the method of measuring changes in value at risk based on the returns of financial institutions has been used. In this study, statistical information of banks, investment companies and commercial insurances during the years 1380-1399 has been used. The results show that all three sectors during this time period are significantly involved in hedging risk in Iran and investment companies have the largest share in hedging risk, followed by segments respectively. Banking and insurance are included.

Credit risk is the probability of default of the borrower or the counterparty of the bank in fulfilling its obligations, according to the agreed terms. In other words, uncertainty about receiving future investment income is called risk, which is of great importance in banks. The purpose of this article is to estimate the credit risk of individual and corporate customers. In this study, the statistical information of 400 individual customers and7500 corporate customers was used. In this regard, the results of neural network model and support vector machine model have been compared. The obtained results have shown that the components considered in this study based on their personal, financial and economic characteristics had significant effects on the probability of customer default and credit risk calculation. Also, the results of this study showed that the application of control policies at the beginning of the repayment period suggests facilities that have the highest probability of default with long life and high repayment. The comparison of the results of the prediction accuracy shows the higher explanatory power of the support vector machine model and the use of the survival probability function than the simple neural network model for both groups of customers.

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

AbstractThe purpose of this study is to design a financial stress index to predict the occurrence of a financial crisis. In this study, a composite index has been designed to measure the Iranian financial system and the effects of financial turmoil in conditions of uncertainty in the financial markets and the Tehran Stock Exchange between 2009 and 2021. Since the shocks of variables were used in previous studies, in this study, three factors of currency turbulence, stock market index turmoil, and banking industry turbulence have been used to design and construct the financial stress index. This research is conducted in five steps based on the DCC-GHARCH approach and finally, based on the variables of financial institutions and the stock index, a predictive model for the financial stress index is presented. From the results, we find that all independent variables of the research have a positive and significant effect on the financial stress index, except for the coin price volatility index, which has a negative and significant effect. The value of the model determination coefficient is 0.8736, which indicates that the quality of the fitted model is desirable.

Brand value is an important concept in doing business as well as scientific research; Because companies gain competitive advantage with the power of their brand. The purpose of this study is to investigate the effect of financial performance criteria on brand value in the automotive industry of Tehran Stock Exchange. For this purpose, the variables of stock return, sales return, asset return, equity return and operating cash ratio were used as performance criteria and Kyotubin criterion was used as a variable expressing the brand value of the company. The results of this study, which was obtained from the financial data of 32 companies in the automotive industry during the years 1391 to 1398 and the multivariate regression method showed that the factors affecting brand value in the short term (this year) and next year are different and only stock return as a performance measure can Affect the brand value of this year and next year. These results indicate that in order to explain the brand value in the short and long term, not all company performance metrics have information content and stock returns have shown their role in explaining the company's brand value.Keywords: Brand Value, Performance Criteria, Automotive Industry

The use of cryptocurrencies is a creative and technology-based way to circumvent unilateral sanctions against Iran. Due to the importance of this issue, this article was compiled with the aim of designing and validating the pattern of using blockchain technology and cryptocurrencies in the conditions of sanctions. The present study is a cross-sectional survey in terms of applied purpose and in terms of method and time frame of data collection. The statistical population includes managers and senior experts of the Central Bank, the Ministry of Industry, Mines and Trade and the Ministry of Economic Affairs and Finance. The sample size was estimated to be 384 using Cochran's formula and sampling was done by simple random method. The data collection tool was a questionnaire that was validated by structural validity, convergent validity and divergent validity. Using Cronbach's alpha and combined reliability, the reliability of the questionnaire was assessed as desirable. The partial least squares method and Smart PLS software were used to validate the pattern of application of blockchain technology and cryptocurrencies. Finally, the indicators of using blockchain technology and cryptocurrencies were prioritized by Savara method. According to the results, the financial sanctions of the Islamic Republic and the threats of virtual currency are the underlying elements of the model that have a direct impact on risk management. Risk management also affects the technological infrastructure and management of virtual currency information, and thus strategic strategies can be developed.

Creating a value creation chain is essential for those bank customers who have a significant share of financial resources, opportunities and potentials in the financial markets to attract resources, allocate and invest and perform financial and economic activities. This segment of customers includes legal customers, or in other words, companies, public, private and semi-private companies. The purpose of this study is to design an appropriate tool to determine value creation factors and evaluate its validity and reliability. First, using the snowball method, experts, key specialists and customers of the company were identified in the Welfare Bank of Tehran and Isfahan provinces, which according to the theoretical saturation of the researchers, 18 people were interviewed. Then, using their opinions, 61 items of the questionnaire were extracted and by calculating the content validity ratio and content validity index, the number of acceptable items was reduced to 51 items and the reliability of the questionnaire was confirmed using Cronbach's alpha and ICC.

In the past years, financial markets have faced uncertainties such as financial crises, oil shocks, changes in currency policies and similar cases. The statistical population in the current research are financial intermediaries such as insurance, banks, active investment companies (PGPIC) in the stock market on a daily basis in 1990-98. For the insurance market, by increasing one unit of its efficiency, the value at risk of the whole system decreases by 0.016. With the increase of one unit of its yield, the value at risk decreases by 0.051. With an increase of a unit or one percent of profit in the banking sector, the efficiency of the whole system increases by 0.0014. The results of estimating the value exposed to conditional differential risk indicate that the share of value exposed to conditional risk of capital is equal to 0.045, bank 0.026 and insurance 0.037, and the value exposed to conditional risk of capital and insurance sector is more and bank is less, and the share of value exposed to conditional risk of capital equals 0.045, bank 0.026 and insurance 0.037, which means the value exposed to conditional risk of capital and insurance sector is more and bank is less.

The possibility to renewal of loan contracts in Iran may lead to the identification of fictitious profits by banks and ultimately lead to a banking crisis and disruption of the country's monetary system, so to prevent banks from reaching the stage of this, Measuring customers' credit risk is essential. The aim of this study is to increase the accuracy of customer accreditation using the structure of hybrid models and has been done as a case study on Mellat Bank. In this regard, 14 learning models were compared with each other and their ability to validate customers was determined. Learning models show that based on both accuracy criteria (success rate) and measurement F (harmonic mean between accuracy and recall), the combined learning model (KNN-NN-SVMPSO) - (DL) - (DBSCAN) with an accuracy rate of 99.90 is the highest It has more performance than other basic and hybrid models. Also, using the principal component analysis (PCA), Gini index, interest rate ratio (IGR) and interest rate (IG) methods to calculate the weight of features and their average rank, it was shown that features such as collateral, The type of collateral and the amount of facilities have been the most important features in distinguishing good from bad customers, respectively.

Internal Control has long been a concern with the directors, shareholders and other beneficiaries in trading units, because they well know that how such a control is vital for achieving the objectives set by a given trading unit. The present study is aiming at identifying and ranking the components of Coso internal control in preventing financial corruption among the companies listed on Tehran stock exchange using a hierarchal cluster analysis. To this end, the components of Coso internal control in preventing corruption were identified through literature review and interviews with the informants. These components include control environment, risk assessment, control activities, information & communication and monitoring activities. Then, using a pairwise comparison matrix, a questionnaire was designed. The questionnaires were distributed among 13 active shareholders with an experience of more than years in stock exchange through snowball sampling. Analysis of the responses confirmed the results obtained and the ranking of the components. The results showed the priority of the control environment with a weight of 0.43, risk assessment and monitoring with the same weight of 0.16, communication information of 0.17 and control activities of 0.12.

Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. Deep Learning (DL) is a type of Artificial Neural Network (ANN) that consists of multiple processing layers and enables high-level abstraction to model data. The key advantage of DL models is extracting the good features of input data automatically using a general-purpose learning procedure which is suitable for dynamic time series such as stock price.In this research the ability of Long Short-Term Memory (LSTM) to predict the stock price is studied; moreover, the factors that have significant effects on the stock price is classified and legal and natural person trading is introduced as an important factor which has influence on the stock price. Price data, technical indexes and legal and natural person trading is used as an input data for running the model. The results obtained from LSTM with Dropout layer are better and more stable than simple form of LSTM and RNN models.

The main subject of this research is the experimental study of the use of currency shock on the stock market anomalies of Tehran Stock Exchange companies. For this purpose, a sample of 70 companies listed on the Tehran Stock Exchange during the period 2012 to 2019 was examined quarterly. For stock market anomalies, ten separate variables (momentum, abnormal stock returns, gross profitability, specific risk, liquidity, net operating assets, asset growth, return on assets, investment in assets and accruals) have been used as representatives. Multivariate regression was used to test the hypotheses and the relationship between the variables was addressed by forming an optimal model. The results show that currency shock has a positive and significant relationship with abnormal stock returns, special risk, gross profitability, stock liquidity, asset growth, accruals and return on assets of the company. The findings also did not show a relationship between currency shock and momentum, net operating assets and investment in assets.

The present study has endeavored to represent a more precise model to calculate the risk of banks in this study by ARIMA-GARCH-COPULA Model has been introduced.In obtaining the iid distributions and variance estimation the mean model and conditional variance have been determined and estimated simultaneously.In so doing, the ARIMA methodology has been employed to model the average return on assets of the study, and for modeling the research conditional variance of GARCH have been applied. Also mean error criterion has been used to compare the different models of VAR estimation, and for the purpose of testing statistical results backtesting methods have been employed. Based on mean error criterion, the proposed model of the study at hand has demonstrated the most accuracy The GEV model derived from the EVT has been ranked second The output of the Dow ranking method, however, has been very similar to one another According to Dow ranking method, the GEV model has had the lowest loss function at 5% level of significance, and at 1% level of significance, the HS model has demonstrated the least loss function. ES calculations have also been carried out for the four models with ARIMA-GARCH-COPULA model showing the least loss.

The purpose of this study is to investigate the factors affecting the stock price crash risk of listed companies between the years 2009 to 2016. Based on this, the researcher first calculated the stock price crash risk based on two different criteria and then tested the internal and external factors of the company on the crash risk. The statistical model used in this study is regression model and data type is panel data type. Dependent variables include calculating stock crash risk based on the first criterion, the Hutton model, which considers the monthly price fall in a fiscal year for a company when that company experiences a monthly return of 3.2 standard deviations below the company's average specific monthly return for the entire fiscal year and second criteria, Chen model, Based on the negative skewness criterion in the company's stock returns, Independent variables include in-company factors (financial ratios) and external factors. Based on the findings of the present study, there is a significant relationship between dependent variables and factors within the company and the organization external factors, can help in predicting stock crash risk.

Stock market as a part of the capital market plays a very important role in directing savings to the manufacturing sector in all countries. Today, in the economy of many developing countries, the situation of macroeconomic variables is not consistent with the ascension of stock indices, and in fact the relationship between the economy and the stock has been discontinued. Today, in the economy of many developing countries, the situation of macroeconomic variables is not consistent with the increase in stock indices, and in fact the relationship between the economy and the stock has been discontinued. In the present study, for the prediction of price bubbles, the daily data of 144 companies in the Tehran Stock Center during the period of 1389 (1396) has been analyzed by the generalized autoregressive conditional heteroscedasticity (GARCH). Based on the results of the data analysis, member firms in the stock center in the years under consideration have been priced bubbles that were higher in the first six months of the year. The factors that triggered price bubbles include political shocks, returns in parallel bubbles, such as oil, currency and gold.

Credit risk issues and methods for identifying and predicting it have been constantly evolving over the past few decades. When a company deals with a financial problem, it may not be able to fulfill its financial obligations, which can cause direct and indirect financial losses to shareholders, creditors, investors and other people in the community. Advanced credit risk models that are based on market value include improving credit quality as well as reducing or decreasing credit ratings. In the present study, we have Investigated two models of advanced credit risk models, so two samples were selected, namely companies with financial problems and companies with financial health, in each group probabilities of default are estimated by two models which are KMV and ZPP, and then we compared probabilities of default. We have concluded that the ZPP model has more predictive ability than the KMV model. This method is denoted the Zero-Price Probability or simply the ZPP model. The main focus is on the new simulation based approach rather than the older established models.

The present study investigates the role and mechanism of affecting the emotional labor variable on employees' brand image of the organization through the intermediate variables of organizational commitment and service quality. The purpose of this study is providing a model of employees' brand image of the organization by considering the independent variable of emotional labor and the intermediate variables of organizational commitment and service quality. The study questionnaire was extracted from existing standard questionnaires, but in addition to expert opinion, confirmatory factor analysis was used to prove its validity. The study sample consisted of 367 personnel of Bank Melli Iran, all of whom completed the questionnaire. The results of the research indicate a positive and direct effect, as well as a positive and indirect effect of emotional labor on employees' brand image of the organization; The indirect effect of internal marketing on employees' brand image of the organization hrough intermediate variables of organizational commitment and service quality is far greater than its direct effect. In the indirect effect, the share of service quality on employees' brand image of the organization is greater than the share of organizational commitment

This main purpose of this study is to provide a generic process pattern for optimizing the investment management process in Iranian banks. To this end, the literature and related processes were examined, three types of legal, systemic, and goal constraints on investment were mentioned, and investment management constraints on banks, including bank liquidity management constraints, were emphasized. Then, the special distinction of banks in terms of criteria and indicators and two levels of investment performance evaluations were explained. Finally, the components and, accordingly, the generic process pattern for bank investment management were presented. In this pattern, the necessity of using a systemic approach and the necessity of coordinating its banking and non-banking activities in the form of a large set in modeling and optimization were emphasized. The proposed pattern through improving the coordination and a more logical sequence of the investment management process in banks will provide the basis for improving the efficiency of Iranian banks.

To optimize the investment portfolio, conditional value at risk is a new approach. To amend the non-normal distribution of return on assets, and the non-linear correlation between return, the modification of Copula-CVaR compound method has a better performance in measuring portfolio risk. In this study, it has been attempted to present a more efficient model for portfolio optimization that provides greater returns for investors, given the uncertainty investment conditions. The VaR model was compared variance-covariance approach and the Copula-CVaR model for their efficiency frontier. The research area entails of 2014 up 2018; The statistical population was the top 50 companies of TSE.The variance-covariance approach was used to estimate the VaR of the portfolio. Moreover to estimate the CopulaCVaR model we have used the ARIMA-GARCH time series disruption component of the asset return distribution; then the marginal distributions of the assets were estimated using the CAPA-Student function; finally through Monte Carlo simulation the return on assets and their CVaR for the 10-day period were calculated. The optimal portfolio composition was determined at 95% and 99% confidence levels for different levels of risk. The results of this study showed that the optimized portfolio formation using the compound model, the Copula-CVaR model, has performed better

Literature has been provided on the factors influencing the foreign Food products purchase intention , and each of the scholars in this field has referred to one or more of its dimensions, but so far a comprehensive model of the factors influencing the purchase intention and the relationship between are not provided. The purpose of this study is to identify and interpret the structural analysis of factors affecting customer foreign Food products purchase intention. First, through a systematic review of the literature, a comprehensive set of components related to customer purchase intention was extracted. Then, by analyzing the qualitative content of the components, 13 factors were identified and classified into five levels of the interpretive structural model. Level 5, "Corporate Social Responsibility", " customer’s personality characteristic", Level 4, "Product Features", Level 3, " retailer’s commercial image ", Level 2, " customer knowledge and awareness ", "Social Identity" , "Attitude", "country of origin", "subjective norm", first level, "perceived value", "loyalty", and "perceived behavioral control", "perceived risk", were identified. The contribution of research to producers and marketing managers is to become more familiar with the factors influencing purchase intention and to understand customer preferences in purchasing foreign Food products.

Risk management is part of investment decision making. Diversification as one of the risk management techniques can increase the utility by increasing return for every unit of risk. In this paper, we review the effect of gold on diversification of stock portfolios in Tehran Stock Exchange. For this purpose, we used the spot price of gold coin as a standard price for gold and we used different indexed portfolios such as Tehran Stock Exchange Price Index, Industry Index, index of 50 companies from 2008 to 2018.We used Stochastic Dominance to rank portfolios as one of the criteria for rating and evaluating performance is to select a portfolio that maximizes the expected utility. By implementing stochastic dominance at three levels we compared the portfolio’s performance. The portfolios were made of an indexed portfolios and a share of gold which could be from 0 to 50 percent of the value of the portfolio. The results showed that adding gold to the portfolios of almost all of the indexes can improve their performances at 95% confidence level. The diversification benefit of gold will be maximized when at least 20% of the portfolios consist of gold coin.

The analysis of the turbulence among markets has been widely applied and in details the emphasis on theorists and researchers in different fields. The reciprocal relationship between financial markets and the macroeconomic markets is based on the idea, which has negative shocks to the economy of the housing sector. The behavior of the housing market is also important because of the impact of housing prices on bank loans and other financial institutions. In the housing sector, a monetary and monetary policy is expected to increase demand for housing by increasing the volume of money in the asset portfolio. Of course, this will depend on a variety of issues. Therefore, the need for further reflection seems necessary in this part. In this study, using the time series analysis of vector regression (VAR), the relationship between financial and financial markets has been investigated. The results of the study show that financial markets, especially foreign exchange markets and gold markets have a significant relationship with housing.