نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 56 (پاییز 1402)

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.

Predicting time series of financial markets is a challenging issue in the field of specialized studies of time series and has attracted the attention of many researchers. Due to the presence of big data, this issue has led to the growth of developments in the field of machine learning models. Due to the importance of this issue, in this study, by using the comparison of different machine learning models such as random forest approaches, support vector machine, artificial neural network and deep learning-based recurrent neural networks to investigate the ability of different machine learning models in prediction. The total index of Tehran Stock Exchange during the period 2013 to 2020 has been discussed. The prediction results of 1, 3 and 6 day courses for the out-of-sample period show that the machine learning method based on the long short-term memory (LSTM) network, a recurrent neural networks, has a better result compared to other models.

In this study, a hybrid copula-stochastic volatility model based on Monte-Carlo Markov chain is developed to evaluate the latent volatilities of the TSE Index. The data used to estimate the models include the values of the total index of the TSE from the beginning of 2020 to the beginning of 2021 on a daily basis with a frequency of 30 minutes. Also, in order to determine the error, data from the date (03/27/2021) to (12/21/2021) has been used in 15-minute intervals. the square logarithm distribution of returns as a measure of realized volatilities is first simulated using a stochastic volatility model to obtain latent volatilities and then using a mixture of copula family distributions and the MCMC, modeling and estimation were performed in the training phase and finally in the test phase using out-of-sample data to estimate the stochastic volatility of the test phase was investigated. The results show that among the functions of Copula Gumble, Galambos, Joe, Clayton and Frank in the test phase, 3 Copula Gumble, Galambos, Joe have acceptable performance and among these functions, the Gumble-Stochastic Volatility based on MCMC with the lowest error rate among the out-sample data recorded better performance.

Blockchain technology is a meta-technology that integrates several technologies to form a secure platform for managing transactions . this study determine the effect of using blockchain technology on reducing costs in economic, in the period of 2019-2021. The hypothesis claims that blockchain enables us to reduce controlling, monitoring and enforcement costs. This research benefits from a pluralistic method to study the impact of applying blockchain technology in the field of economy. so, three different methods were used in order to investigate the research hypothesis including theoretical analysis, interview with experts and statistical inference. The findings confirmed the main claim of the hypothesis., the findings indicated that applying blockchain technology results in reduction of controlling, monitoring and enforcement costs. Among the well-surveyed features of the block chain technology, immutability and keeping record automatically by time- stamping reduce controlling, monitoring and enforcement costs more than other features, also confidentiality characteristics provided by encryption has the greatest impact on increasing transaction costs and in the other hand immutability of blockchain has the greatest impact on decreasing transaction costs. Moreover, blockchain can be aligned towards the goals of the resistance economy by bringing new and smart technology into the economy and reducing the costs.

The purpose of this research is to provide a model for evaluating the performance of bank branches using the network coverage analysis model using fuzzy data. In this research, an attempt was made to use a model based on the traditional model of data envelopment analysis, taking into account factors such as the network nature and internal relationships of each decision-making unit, undesirable output, non-optional input, and the variables having a fuzzy nature. which is more consistent with the actual conditions related to the decision-making units and calculates the efficiency in a more accurate way.After presenting the research model, first, based on the studies conducted in the past researches and field studies and obtaining opinions from experts in this industry, indicators were considered as input and output. After identifying the indicators, fuzzy Delphi method was used for initial screening of the indicators. In the following, confirmatory factor analysis technique was used to finalize the screened indicators. At the end of the research, the developed model was solved using the data collected from the studied branches, by Gams software and with the alpha cutting approach. The results indicate that among the 34 investigated branches, 8 are efficient and 26 are ineffective.

The aim of this research is to compare the performance of the value-at-risk model with the liquidity-t-copula approach with dynamic conditional correlation (t-copula-GARCH-LVaR) with the value-at-risk (VaR) model to optimize the portfolio in the Tehran Stock Exchange. In the current research, in order to test the desired hypotheses, the period is between 2001 and 2021. All the variables used in this research on a quantitative scale and observations in the form of time series are the daily logarithmic returns of 40 stock market indices, including 39 industry indices and one index of fixed-income bonds from the beginning of September 2011 to the end of July 2021. In this research, to perform the final analysis, all the calculations required for this research were done using the open-source software R 4.2.1. Our results showed that the t-copula-GARCH-LVaR optimization model performs better according to the Sharpe criterion based on Mann–Whitney U test at the 95% test level.

The purpose of this article is to investigate the impact of financial distress risk on the fall in the stock prices of companies listed on the Tehran Stock Exchange. In this research, statistical data of 195 companies admitted to the Tehran Stock Exchange during the years 2015-2019 have been used, and multiple regression using the panel data method has been used to analyze the data. To measure the variable of financial distress risk, Merton's distance to default index and to measure the fall of stock prices, four methods of risk period of stock price fall, negative skewness of stock returns, maximum sigma and low-to-high volatility have been used, as well as asset return ratio variables. leverage ratio, ratio of market value to book value and company size have been used as control variables. The results of the research show that there is a positive and significant relationship between financial distress risk and falling stock prices.

Because of the numerous environmental, industrial, micro, and macro elements that influence the index and stock price trend, it is undeniably difficult to predict changes in the index and stock price trend. Although the aforementioned factors are difficult or impossible to measure in some cases, micro factors such as price history and trade volume are simply measurable and available. The goal of this research is to use feature selection and classification algorithms to find the most effective features and indicators for predicting the total index and total weighted index. Then we will examine the proposed model in the daily and weekly time period from January 2020 to May 2022. The results show that it is possible to predict the trend of changes with high accuracy using a limited number of indicators, and that the prediction accuracy is much higher in the weekly time interval than in the daily time interval.

The purpose of the research is to examine special financial institutions; In the financing of tourism industry projects in Iran, it is based on the capital market. Based on the philosophical premise, this research is interpretive. Also, its paradigm is pragmatism and its approach in the field of mixed research. Here, there is a tendency towards the applicability of "what works" and solutions to problems. Therefore, the important dimensions of the research and its questions are emphasized, so no hypotheses are examined and its purpose is to answer the research questions. In this method, the researcher has compared and summarized the qualitative and quantitative results with the triangulation approach by reviewing the documents and asking the experts' opinions through preliminary semi-structured interviews and questionnaires using the Delphi method. The results have shown that real estate investment funds, project joint stock company, risk investment fund, tourism infrastructure investment fund, bond guarantee syndicate by the tourism development fund with banks, playing the role of reinsurer by the fund The development of tourism, the formation of a joint insurance association for the compensation of tourism projects has been agreed upon.

Financial risk evaluation of companies is crucial to identify potential financial risks, provide a decision basis for senior chief financial officer, and prevent and reduce risk losses. In evaluating the financial risk of companies, the basic issue is related to uncertainty and inaccuracy. In this research, in order to evaluate the financial risks of companies in the Tehran Stock Exchange, we use group decision-making methods. The Pythagorean fuzzy set (PFS), which is depicted by degrees of membership and non-membership, is a new and more effective tool for dealing with ambiguity. In this paper, a new Pythagorean fuzzy score function is presented to solve the comparison problem. In addition, we present composite weights that reflect both subjective and objective preferences. Then, the weight of the criteria and the correlation between the pair of criteria by D-CRITIC, and the prioritization of financial risk of companies by the EDAS method (Evaluation Based on Distance from Average Solution) based on the new score function are calculated. This method is very useful when we have conflicting criteria. Finally, we express the feasibility of the algorithm to evaluate the financial risk of four investment group with six criteria with the corresponding sensitivity analysis.

Today, one of the most important challenges in the capital market is stock price prediction. Stock price data represents a financial time series whose trend is very difficult to predict due to its characteristics and dynamic nature. One of the most recent methods used in predicting financial time series is ANN with back propagation of error. In this article, artificial neural networks based on three different Levenberg-Marquardt learning algorithms, scaled conjugate gradient and Bayesian regularization were used to predict the stock market based on the data of the lowest price range as well as the 30-minute data of the stock market index and compared their results together. We compare. All three algorithms provide a 99.9% estimate using the lowest price range data. But when using 30-minute data, the estimation accuracy decreases to 96.2%, 97.0%, and 98.9% for Levenberg-Marquarat algorithm, scaled conjugate gradient, and Bayesian regularization, respectively, which compares with the results Obtained by using the data of the lowest price range, the accuracy of the prediction is significantly reduced. Finally, the optimal neural network is compared with the regression method to determine that the results of the ANN in complex nonlinear time series are more efficient than linear methods.

Sentiment risk factors at the market level include a small portion of possible factors that include factors related to competition, suppliers, employees and customers, financing, foreign operations, regulations, lawsuits, governance, the environment. The severity of this disclosure gap is only addressed by regulatory efforts that require companies to discuss their quantitative and qualitative assessments of perceived risks and uncertainties. One of dimensions of behavioral finance is behavioral risk, which is also known mental risk or perceived risk; which actually the investors' subjective judgment of the risk, which may be estimated more or less than the real risk. The main purpose of the present research is investigate the relationship between the risk Sentiment annual reports and the investor's risk Sentiment, which was conducted based on data of companies admitted the Tehran Stock Exchange in 13-year period between 2007 and 2019. To test the hypotheses of the research, the system of simultaneous equations and the analysis of two-stage regression coefficients have been used. The estimation results indicate that there significant relationship between the change in the standard deviation of monthly returns, the change in beta risk, and the change the volume of stock transactions with changes in risk Sentiment of annual reports.

Algorithmic trading has a significant share in global markets. These types of transactions are also emerging in the domestic financial and capital markets. In this regard, in this study, to design and present several algorithms and trading strategies and implement and output five of the most important of these strategies using the Python programming language and compare their profitability. Has been. The study's statistical population includes all companies listed in the Iranian capital market. The sampling method is a stock selection from 30 large companies on the Tehran Stock Exchange index. The present research is applied in terms of purpose and terms of data collection, survey, and cross-sectional, in terms of subject, field, and time in terms of retrospective research. The results show that he used a different trading strategy in algorithmic trading to optimally use and compare and earn dividends of stocks traded in the future of the Iranian capital market. In the present study, five types of studies are implemented on one of the symbols of the Tehran Stock Exchange (Fars). One of the most practical strategies is to follow the trend that is welcomed by traders.