فصلنامه دانش مالی تحلیل اوراق بهادار
سال چهاردهم شماره 51 (پاییز 1400)

As financial markets become more complex in today's world, terms such as financial capability and financial knowledge (literacy) have recently become widely used in the literature and everyday conversation. However, there are still challenges in explaining the meanings of each word, its dimensions and elements, and how to measure it, and many consider the two words to be the same. In addition, most of the research conducted so far has focused on the term financial literacy and identifying the factors affecting it, and the concept of financial capability (ability) has not been specifically addressed. Therefore, the purpose of this study, while explaining the conceptual differences between the two words, is to provide a structural model regarding the factors affecting the financial ability of individuals from the perspective of experts through a phenomenological approach. This research is based on a library and interpretive method of qualitative research that has examined the views of 12 Iranian financial experts. The results showed that the proposed model of factors affecting the financial ability of individuals including 3 main themes of ability, mental framework and communication, 4 categories of skills, attitudes and general and financial motivations as well as 39 factors are influential.

This study examine the Merton structural model based on market data and the discriminant analysis model based on accounting data in banks during 1386 to 1398. Due to the different structure of banks' balance sheets, for the first time, using the transformed data maximum likelihood estimation method and other liablilty with an adjustment and calculate the market value of assets and their volatility Using the stock price, we calculated the distance to default and the probability of default with the modified Merton model. Then, with the discriminant analysis model and Wilkes lambda index, we introduced a model based on accounting data to measure credit risk in banks. Among variables, inactive credits to total credits, total credits to main deposits and reserves to inactive credits have the most impact on determining the credit risk of banks, respectively, which is determined by z coefficients. The lower  z score, the greater credit risk and vice versa. Finally, using the cumulative accuracy profile and accuracy ratio, which is a new method in determining an efficient model for credit risk, Merton's structural model is compared with z-score model, and finally Merton's structural model with an accuracy ratio of 70.97 as an efficient model for measuring credit risk in banks

In this study based on the historical statistics of deposits of a private bank in four different categories , the purpose is to estimate the optimal share of the bank's quadrilateral deposits with the aim of minimizing the interest paid on these deposits and observing the limit of the bank's deposit matrix within the framework of the bank's upstream documents. To solve this optimization, optimization algorithms in Matlab Package have been used due to the nonlinear objective function. Initially, the results and data prediction for each of the bank deposits are based on previous data and using machine learning and regression methods in the relevant section. Then, by constructing the objective function and constraints and inserting the bank deposit and deposit data, the optimal deposit share is extracted using the internal point method. The results show that long-term stable sensitive deposit with expensive and then unstable current deposit sensitive to cheap business have the largest share in total bank deposits, which should be considered in the planning of bank deposits.

In the capital market, due to the separation of ownership from management, the values and benefits of interest between shareholders and companies are different, and the existence of these differences has led to the development of theoretical approaches in corporate decision-making to balance equity. Congestion decision making as a concept with pluralism of values at the level of the capital market has been considered as a philosophical theoretical approach in recent years. The existence of such values in decision-making, while it can lead to greater interaction between the company and stakeholders, can help increase sustainability in various areas such as environment, taxation, economics, etc. Evaluating the Isomorphism of Green Tax in the Presence of the Element of Thick Decision by Interpretive Ranking Process (IRP). In this research, in order to identify the components (consequences of glass ceiling) and research propositions (hegemonic of power acceptance), a combined analysis was performed with the participation of 15 accounting experts at the university level and In the quantitative part, the components and propositions identified in the form of matrix questionnaires were evaluated by 22 CEO in Capital Market. The results showed that the statement of social responsibility in decision making (B2); Reducing conflict of interest in decision-making (B5) and legitimacy in decision-making (B6) are the most influential philosophical themes in crowdfunding decision-making at the capital market level, which reinforces the consequence of reducing environmental pollution as a component of green tax sustainability. This result suggests that crowdfunding decision-making, which describes the pursuit of a significant level of pluralistic social values, reinforces the implications of green taxes for the future, as companies increase their social responsibilities by expanding their social responsibilities to strengthen productive infrastructure. Reduce themselves and, through the disclosure of voluntary information in the form of financial statements, enrich social confidence and trust through insight into congestion decisions.

The most effective asset for survival and efficiency in any organization is manpower. But in the process, there are various dangers that threaten manpower at any moment. These threats upset the psychological balance of individuals and are known as psychosocial risks and dangers. Therefore, reducing the effects and managing these risks is important and necessary for organizations. The purpose of this study is to provide a model for managing psychological risk in the organization using the structural model of ISM interpreter. The research method is descriptive. The statistical population includes employees and experts of tax departments in the east of  Mazandaran province (Neka, Behshahr, Galugah) and the sample is estimated by a simple random method of 100 people. The data collection tool is a questionnaire and the data analysis is performed by the structural method of ISM interpreter. Key indicators of psychological risk in the organization (such as: job content, job design, volume and method of work, work schedule, etc.) were reviewed with the help of experts and at the end, a model for managing psychological risk in the organization was presented by ISM method.

Financial market participants are constantly exposed to uncertainty and investment risk. Predicting and calculating risk is one of the most important issues in the field of financial issues. Reviewing the financial crises of recent years, it can be inferred that one of the reasons for these crises is the excessive attention to the repetitive central data and the lack of attention to the extreme data. In other words, in the analysis of financial data, the end part of the distribution should also be considered. The purpose of this study is to provide a model for tail risk estimation using extreme value mixture models. Accordingly, four one-tailed models and one two-tailed model in two simple functions and GARCH have been used. Modeling is based on three categories of data. The studied data include total index, price index (homogeneous) and index of top 50 companies. According to the obtained results, simulation of models with GARCH significantly improves the performance of models and reduces the error rate of simulated data in GARCH-based models. The findings also indicate that two-tailed models are more accurate than one-tailed models.

Gold exchange traded fund is one of the new financial instrumentss that underlying asset is gold and traded in the capital markets. This article examines the pricing deviation of the four gold funds on the Iran Mercantile Exchange from their underlying asset index. The main purpose of this study is investigate the effect of underlying asset shocks on the Gold exchange traded funds’ pricing deviation. In order to achieve this purpose, employed daily data of Emami Coin, Gold ETFs in Iran (Tala, Ayar, Zar, Gohar), Vector Error Correction Model (VECM) and Impulse Response Function (IRF). Results The results shown that pricing deviation is stationarity and predictable. Therefore can be considered an implicit transaction cost an Gold ETF. The reason for the predictability of the pricing deviation stems from its stationarity and the specific price discovery processes for this asset class. Utilization of Impulse Response Function shown that the shock effect of the underlying asset was the same and do not persist for more than six trading days, Which indicates the relative pricing efficiency of these funds

Devaluation of assets in the future is one of the most important investment concerns that has led investors to choose the set of assets that have the lowest risk and highest return. The present study deals with the problem of stock portfolio optimization according to the Conditional Value at Risk based on the new and intelligent fireworks algorithm and compares it with genetic algorithm with the historical simulation method using MATLAB software. The parameters of meta-heuristic algorithms were adjusted by Taguchi method using MINITAB software. Not suspended, used. For reliability of the study, generalized Dickey-Fuller test and Phillips-Prone test were used. To evaluate the accuracy of the Conditional Value at Risk model, the kupiec proportion of failure test, Christoffersen independence test and Conditional coverage test are used.  A comparison was also made between the models by Lopez test. Findings showed that at %95 and %99  confidence levels, the conditional risk value model using the fireworks algorithm has a suitable and reliable validity for measuring market risk and optimizing the stock portfolio.

Due to the importance of stock selection and investment in recent times, this study was formed with the aim of evaluating the optimal portfolio portfolio using accounting criteria using multi-criteria decision criteria under conditions of uncertainty in the Iranian capital market. This study was an exploratory mixed study and its population in the qualitative section included the managers of Tehran Stock Exchange companies and university professors and in the quantitative statistical sample section, 30 managers and experts of listed companies that were purposefully selected. And participated in the research. Quantitative findings based on the identification of qualitative findings showed that the general criteria based on the AHP method in accounting criteria include non-systematic risk categories, respectively; 2) risk of financial liabilities; 3) operating profit risk; 4) risk of investment activities; 5) are the financial performance risk of the company. Then, to determine the coefficient of importance of each of the sub-criteria, the methods of AHP, Electro and TOPSIS were used and the results showed that the sub-criteria of reducing the rate of return on assets, corporate sales risk and net profit risk, respectively, have rankings. First, second and third are important in terms of accounting. Based on the results obtained, it can be used in the field of decision-making criteria based on accounting criteria in the uncertainty of Iran.

The political uncertainty caused by the instability of management and the political hypothesis leads to a market failure. In high-risk information environments where accounting information is less predictable, political uncertainty exacerbates market behavior. The purpose of this study is to investigate the political uncertainty caused by the volatility of management and size of the company under the influence of the capital market information environment against the rules of voluntary accruals and the capital cost of ordinary shares of companies. For this purpose, the data of 100 companies listed in Tehran Stock Exchange during the period 1387-1396 were examined and tested using combined data. The results show that the political uncertainty caused by management instability under the influence of the capital market information environment is negatively and significantly correlated with the cost of ordinary equity capital. Also, there is a positive and significant relationship between the political uncertainty associated with the size of the company affected by the capital market information environment and the cost of ordinary equity capital.

The main purpose of this study is to model and determine the ability of working capital management in predicting financial bankruptcy of companies using artificial intelligence algorithms. The statistical population of the study consists of 120 companies listed on the Tehran Stock Exchange during the years 2008-2019. In order to achieve the objectives of the research, first by studying previous research in the field of financial distress, 12 financial ratios affecting financial bankruptcy have been selected. After calculating the ratios, the mean comparison test was used to consider the ratios that have a significant difference between the two bankrupt and non-bankrupt financial groups for calculation in the forecasting models, which showed that all 12 variables are suitable for use in the models. Then, in order to evaluate the ability of working capital management in predicting companies' financial bankruptcy, to compare research models with and without working capital management variable based on five models of multilayer perceptron neural network, support vector machine, decision tree, logistic regression and multiple audit analysis is performed. The results of comparing bankruptcy prediction models showed that the multilayer perceptron neural network model has the highest power in predicting companies in terms of financial bankruptcy and soundness compared to other models. The results of comparing the models showed that with the development of the research model, by entering the working capital management variable, the training error of the multilayer perceptron neural network model is reduced to 0.036 and the accuracy of the model is increased to 75%.