مهدی معدن چی زاج

The design of automated trading systems has increased in various countries, including Iran, recently. The advantage of such systems is the increase in speed and accuracy of trading decisions without emotional interference. Having a model that can simultaneously estimate opening, maximum, minimum, and closing prices is a significant advantage for investors. In this study, using the LSTM neural network, simultaneous estimation of these four prices is performed, and various classification algorithms are used for buying, selling, and holding points to design an optimized algorithmic trading system using a genetic algorithm. To evaluate the proposed model, data from Shapna, Khodro, and Fajr stock markets in Iran from 2012 to 2020 and MATLAB software are used. The results of this research show that the proposed method has a very desirable performance in both simultaneous multi-price estimation and classification of buy, sell, and hold classes. Therefore, it can be considered a very suitable method for automated trading, and a robot based on this method can be designed and implemented in the Iranian stock market for this purpose.

The purpose of this research is to provide a decision support system to assess the credit risk of real bank customers. For this purpose, firstly, using the library method, the effective indicators on the credit risk assessment of real customers were identified and then, using the fuzzy Delphi method, the indicators were screened. The community of this section was formed by banking experts (Bank Mellat) who were selected by the snowball method. In the following, The decision support system was designed and implemented to evaluate the credit risk of real bank customers in SPSS Modeler software. The final data related to the indicators included the files of 7318 real customers of Bank Mellat during the years 2013-2016.The results of the research showed that the decision support system consisting of support vector machine and random forest correctly predicted the status of customers in the four categories of on-time delivery, past due, overdue and questionable delivery by 67.09% and 65.10%, respectively. Meanwhile, the combined support vector machine-random forest model has provided a better prediction with a rate of 77.17%.

This study examines the prediction of volatility through jump risk in Exchange Traded Funds (ETFs) on the Tehran Stock Exchange, aiming to play a crucial role in the pricing options of financial assets through volatility forecasting mechanisms.

The study employs three main families of heterogeneous autoregressive (HAR) models for volatility forecasting, incorporating jumps within an econometric framework by estimating heterogeneous autoregressive models. By comparing various models, the study utilizes high-frequency data-based realized volatility to provide precise measurements. The data set includes daily and 15-minute interval data collected from six equity and fixed-income funds over the financial period from 2020 to 2022.

The results indicate that among the funds, the predictive power of volatility is higher in equity funds. Additionally, the findings reveal that the second-order heterogeneous autoregressive model with jumps (HAR-J) is the most effective heterogeneous autoregressive model for modeling and forecasting realized volatility, as determined by the mean squared error and quasi-likelihood criteria. Furthermore, strong evidence supports that second-power variation-based models outperform their counterparts in predicting realized volatility, offering more accurate forecasts and better volatility estimations than second-order heterogeneous autoregressive models.

This study provides insights into the differences among heterogeneous autoregressive models, specifically within the heterogeneous autoregressive model group, so that its results can be used by analysts and fund managers for improved financial decision-making.

This study examines the accuracy of Heterogeneous Autoregressive (HAR) models in forecasting the Conditional Value-at-Risk (CVaR) of Exchange-Traded Funds (ETFs) on the Tehran Stock Exchange. The significance of this study stems from the need for better risk management in financial markets, where volatility and jumps significantly affect investment decisions.

Data from nine equity, index, and fixed-income funds were analyzed intraday with high frequency (daily and fifteen-minute intervals) from 2019 to 2022. Three main families of HAR models were evaluated by considering the relevant variables.

The results revealed that models based on second-order variations outperformed others in forecasting Realized Volatility (RV). Additionally, CVaR prediction was more accurate for index funds than for equity and fixed-income funds, with the HARQ model demonstrating superior performance.

Originality/Value: 

This study investigates the application of HAR models in predicting ETF risks and provides a novel framework for risk management and investment decision making, particularly in the Iranian financial market.

Investment decisions are examined based on financial concepts that are based on the efficient market hypothesis. Considering the confirmation of the complete inefficiency of Iran's capital market, there is an expectation of obtaining more returns. In this research, we seek to introduce investment portfolios based on the momentum investment strategy (which has been confirmed in various studies) and its combination with other factors affecting the company's value and profitability. Based on this, 119 companies were investigated in the period of 1388 to 1400 in three short-term periods of three and six months and medium-term of one year. According to the findings, the average returns obtained from the formation of different combined portfolios are positive in all periods and the returns of the combined portfolios are higher than the single-criteria portfolios. The investment strategy based on the asset growth rate and momentum in the short-term periods of three and six months and the medium-term of one year has the highest average return, then the strategy based on over-leverage and under-leverage and momentum and finally the strategy based on leverage

Understanding the potential of cryptocurrencies in the country's economy leads to targeted investment in them and stabilization of the investment portfolio value. Risk hedging of capital through cryptocurrencies can be a novel potential for preserving the value of the investment portfolio. In this study, the factors affecting the risk hedging of conventional investment portfolios using cryptocurrencies and the nature of the relationships between these factors have been identified.

In the present study, first, by examining domestic references, common investment methods forming the investment portfolio that are of interest to the general public in the country were identified. Then, by conducting an integrated review of existing references in the field of cryptocurrencies and other conventional investment methods, the factors influencing the hedging capacity of the investment portfolio using cryptocurrencies were identified. Subsequently, utilizing the opinions of experts including university professors and capital market managers, the relationships between variables were determined. Based on the interpretive structural modeling approach, a model of factors affecting the risk hedging of common investments in the country using cryptocurrencies has been presented.

The results of this study showed that the most influential factors in using cryptocurrencies for investment risk hedging in Iran are international political factors and global prices of gold and oil. On the other hand, the most dependent variables are related to the lack of user knowledge and regulations in the field of cryptocurrencies within the country.

Originality/Scientific Value-Added:

Based on the conducted research, a model is proposed to identify factors affecting capital risk hedging in the country with the help of cryptocurrencies. Considering the obtained relationships, it was determined that factors affecting the understanding and application of cryptocurrencies in Iran do not have an impact on the risk of conventional investment methods in Iran. Furthermore, in hedging investment risks in Iran using cryptocurrencies, international factors are considered the most influential factors.

Traditional models do not have sufficient ability to predict the return on investor portfolio due to changes in the external environment (systematic risk) and the internal environment (non-systematic) and this is mainly due to the identification of the explanatory variables and the experimental design of the model.Therefore, the present research, while explaining this issue and in order to adjust the uncertainty problem of the model, by averaging all the models (Bayesian averaging), has determined the effective risks on stock returns in Iran.The present study expresses this failure in identifying explanatory variables and empirical model design. The statistical sample of the research includes 138 listed companies in the period 1390 to 1399.In this study, 62 risks affecting stock returns in the form of 31 indicators in the field of systematic risk and 31 non-systematic indicators entered into nonlinear Bayesian models with time-varying parameters.The results show that among BMA, TVP-DMA, TVP-DMS, BVAR and OLS models, the TVP-DMA model is the most efficient model. According to the TVP-DMA model, 10 non-fragile risks include systematic risks (real GDP growth rate, unofficial market currency, inflation rate, interest rate) non-systematic risks (instantaneous ratio, liabilities, cash flows from operations, return on equity, debt ratio, and price-to-earnings ratio) as the most important risks affecting stock returns. All the mentioned risks, except interest rate and debt ratio, have a positive effect on stock returns.

One of the most important challenges for Islamic financial engineers is to pay attention to the differences between religions in Islamic countries, including Iran, and plan to discover the differences, resolve legal ambiguities, and finally lay the groundwork for increasing public participation in various financing projects. The importance of the present research is to resolve the legal ambiguities and doubts in order to increase participation in the financing of Mudarabah bonds. The lack of financial research in Iranian Sunni jurisprudence in connection with the issue of Mudarabah bonds, the population and the high economic and development potentials of Iranian Sunnis, as well as the potential ability to participate in various financing projects by this special segment of the Iranian society, from It is one of the necessities of conducting the present research. . Therefore, in this research, with the aim of reducing the jurisprudential doubts of the Iranian Sunnis, from the two perspectives of Shafi'i and Imami jurisprudence, the Mudarabah papers were examined and analyzed jurisprudently, in order to clarify and resolve its jurisprudential ambiguities, in fact, the present study seeks to answer this question. What are the similarities, differences and the percentage of jurisprudence and difference in Mudarabah papers based on the standards of Imamiyyah and Shafiiyyah jurisprudence. The main approach in this research is a comparative approach. In order to analyze and issue jurisprudence in Imami jurisprudence, the document analysis method (documents and approvals of the Jurisprudence Committee of the Stock Exchange Organization) has been used, and in connection with Sunni jurisprudence (Shafi'i jurisprudence), in-depth interview and focus group methods have been used, a statistical sample It includes 9 experts of Shafi'i jurisprudence. The results of the research show that the similarities and common points of jurisprudence are very high and the percentage of jurisprudence and jurisprudence in the two jurisprudence systems of Imamiyyah and Shafiiyya in relation to Mudarabah papers is at a very high level (100%), which is important. It can be the basis for increasing the participation of Iranian Sunnis in projects related to Mudarabah bonds and other Islamic securities issued in Iran.

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

The importance of predicting and knowing the future in order to plan and formulate economic strategies is not hidden from anyone. The accuracy of forecasts is one of the most important factors in choosing the type of forecasting method. The stock price index is one of the effective variables in economic systems that these very complex time series are usually assumed to be random and as a result their changes are assumed to be unpredictable. Such time series variables have the property that the shock to the variable takes a long time to disappear due to the possibility of long-term memory. The aim of the present study was to test the fractal market hypothesis with the Markov regime change model with a possible combination and convergence in the Tehran Stock Exchange. In this paper, the amount of long-term memory and stability of financial time series resulting from the total stock market index for the period 1388-1386 were examined. For this purpose, first the existence of long-term memory was examined, then the fractal nature of the market was examined using the Harst view index. The results indicate the existence of long-term memory in this variable. In this case, with one differentiation, it becomes more differentiated, so the stock price index series in Iran has long-term memory and the effects of each shock on this variable due to its long-term memory remain for long periods. It stays. The results also showed that the overall stock market index is fractal.

The purpose of this paper is to analyze and measure the systemic risk between the cryptocurrency and real currencies using the conditional risk exposure value approach and the expected marginal loss. In this study, statistical data of real and virtual currencies during the years 2015-2021 have been used. For this purpose, systemic risk indices have been calculated using CoVaR and MES indices and then the correlation between systemic risk of currencies has been evaluated. In this study, the statistical data of the currencies of the exchange rate of the pound to the dollar, the exchange rate of the yuan to the dollar, the exchange rate of the lira to the dollar, the exchange rate of the euro to the dollar, bitcoin, atrium, ripple, litcoin and atrium based on daily price returns Currencies and real currencies were used. The results showed that there was a correlation between systemic risk indicators for the studied currencies and virtual currencies had a lower systemic risk index than real currencies. The purpose of this paper is to analyze and measure the systemic risk between the cryptocurrency and real currencies using the conditional risk exposure value approach and the expected marginal loss. In this study, statistical data of real and virtual currencies during the years 2015-2021 have been used. For this purpose, systemic risk indices have been calculated using CoVaR and MES indices and then the correlation between systemic risk of currencies has been evaluated.

In this study, we provide a model for evaluating the performance of capital market professionals (Case Study of Mutual Fund Managers) on how to apply market timing strategy in predicting future market movements, using a combination of multi-indicator decision making and We have analyzed the scenario of "expert system proposed by Hosseini Nasab" using the network analysis process (ANP) [3]. In the mentioned method for modeling and analyzing data, the matrix of internal and external dependencies of strategies and criteria was calculated and using the designed network model, strategies for determining weight and the most important strategies were evaluated. This method is used to implement the model of capital allocation to corporate stocks, with the three objectives of return, risk and liquidity.

Determining the appropriate strategy for buying or selling in the foreign exchange market is very important for companies to cover exchange rate fluctuations against the national currency. This study proposes a new approach based on genetic algorithms and support vector machines for trading in the foreign exchange market.In this research, a new algorithm with the ability to generate technical rules for investment based on forecast certainty is presented. For prediction, a combination of the Combined Support Vector Machine (HSVM) algorithm for classifying the market into three different classes (uptrend, downtrend, sideway) and a dynamic genetic algorithm for optimizing trading rules based on several technical indicators Different has been used. Rials-dollar pair data is used as training and test data for the period between 1392 and 1398. The proposed architecture for machine learning, as well as the implementation and study of the proposed trading system are fully described. The research shows promising results during the test period in which the return on investment was 129%.

AbstractThe purpose of this study is to study the market timing and provide a model for investors to make the right and responsible decisions against their capital and tries to examine the capabilities of man-angers of mutual funds in the Iranian capital market, whether they are time skills Have market assessments in choosing their investment portfolio or not. Therefore, the variables of the present study, using the implementation of a combined method of multi-attribute decision making and scenario analysis of "expert system proposed by Hosseini Nasab" [2], we used the A N P method. The results showed that it is possible to provide a comprehensive "timing" model for the capital market and In-prove the performance of professionals, and the future movements of the market can be predicted using the market timing strategy; Also, comparing the models of market timing strategy, the results showed that the timing strategy of seasons and dates, in comparison with the strategies of using moving average and seasonal timing of Sai Harding, have an effect on managers' performance and predicting future market movements; But compared to Mark Walker's seasonal timing strategies and Hirsch's best six-month timing strategies, it is less effective.

Due to the importance of predicting economic variables, different models have been created to predict the future values of variables. In fact, economic models can be tested by checking the level of forecasting accuracy. The main purpose of this study is prediction of Iran interbank offered rate and Iran treasury exchange rate as interest rates indicators for facilitating interest rate risk management. Two econometric models including ARFIMA and ARIMA have been used for forecasting. Thus, the ARFIMA model considering long-term memory and the ARIMA model without considering long-term memory have been considered. The evaluation of the prediction accuracy of the two models using the monthly Iran interbank offered rates data and also the monthly Iran treasury exchange rates data shows that both the interbank offered rates data and the Islamic treasury bond rates data, ARIMA model has a better performance compared to ARFIMA model in predicting data.

Examining the results in the case where there is a liquidity cost and a diversification index in the model, shows that the industries that have more stability in their stock prices over time have more weight in the optimal portfolio. In addition, performing statistical analysis with total index return data in this case does not show the existence of a significant relationship between the average portfolio return data and the average return of the total index .By removing the cost of liquidity from the model, the examination of the output data shows that the average weight share of the petroleum products industry and the metal ore industry increases compared to the previous state, which means that these two industries are less liquid; Meanwhile, the average return and value at risk of the portfolio increases in this case. Performing statistical analysis with total index return data in this case shows a significant relationship between the average return of the portfolio and the average return of the total index. In the case of removing the diversification limit from the model, the results of the research in this case show that the average weight of the selected industries in the optimal portfolio changes, but this change is not very noticeable, and the result is that this limitation can be ignored in the model; In addition, performing statistical analysis with total index return data shows a significant relationship between the average portfolio data in this case and the average index return.

Application of technical analysis and Markov filterIn future optimal portfolio managementEmphasizing the amount of investor riskMetaphysical artificial intelligence approachAbstractIn decision making for investment, two factors are very important and are the basis of investment. These two factors are risk and return, and in this regard, the study and study of investors to select the best investment portfolio is done according to the amount of risk and its return. A portfolio is a combination of assets formed by an investor to invest. The process of selecting a stock portfolio is one of the issues that has been the focus of many researchers. The aim of this study is to create an optimal stock portfolio using the predicted data. The statistical sample of the research includes the financial data of Iranian stock exchange companies during the years 1390 to 1397. In this study, using stock regression algorithm to predict stock returns, and finally using Markov method and spectral clustering algorithm, the necessary filter to select the appropriate initial data was performed and cultural meta-processing method with prediction data, It provided the optimal portfolio of stocks for the investor group with risk-taking as well as risk-averse. The research results show that the cultural transcendental algorithm, according to Sharp's method, has the ability to create an optimal stock portfolio using predicted data using the Marquis method for venture capitalists and risk averse investors.Keywords: Optimal stock portfolio, cultural meta-innovation algorithm, spectral clustering algorithm, Markov method, technical analysis

 The purpose of this research is to create a model of ethical decision-making that applies to accountants and the accounting profession. In this research, an integrated pattern of five factors affecting ethical decision-making by accountants was identified, which include professional codes of conduct, philosophical orientation, religious orientation, values derived from culture, and moral maturity.

 The present study is a review study in which the articles indexed in scientific databases such as Academic Jihad Scientific Information Center, Marja Danesh, Noor specialized magazines database and financial analysis publication were made. Research texts from 1992 to the end of 2023 have been examined. In this study, the researchers searched and extracted the valid English articles from the valid electronic sources by using the determined keywords, and by examining the full texts of these articles, they described the obtained data in a classified manner.

 According to the findings of the research, most of the financial crises are caused by the non-observance of ethics in decision-making processes, and following ethical principles in decisions and professional behavior can prevent society from moving towards financial crises in the long term.

Originality / Value:

 Based on the findings of the research, it was finally determined that the ethical requirements in investment is one of the categories of the model of ethics in investing in the financial crisis. The organization and development of the capital market depends on the regulation of the structure of the financial market in accordance with the macroeconomic characteristics and the coordinates of the companies active in the country's economy.

In recent years, with the developments in Iranian Capital Markets and also the influence of Social Networks, new trading strategies like as social trading emerged which makes the need to a tailored social platform for Iranian capital market. The purpose of the study is to design and explain the trading social platform in iran’s capital market. Using the qualitative research based on theme and content analysis and through deep interviews with fintech and investment experts, the research was performed. The participants were selected using targeted sampling and the interviews were continued until Theoretical saturation and 12 interview were performed. In order to analyze the interviews, open and selective encoding, continuous comparative analysis, and creating contents and categories were used. Social trading platforms, considering extensive interactions between individual participants with social media, are inevitable developments and according to encoding of interview, the experts pointed to 11 dimensions and 60 categories essential for development of a Trading Social Platforms in order to improve the trading and information in capital market.The resulting dimensions are as follows: information and trading transparency, the characteristics of social network, stock analysis and returns, financial developments indices and the interaction of money and securities markets, financial and market ratios, owners equity and financial statements, risk management, technologic characteristics, competitiveness ability, training and investor education and reducing herd behavior, and inventory management systems.

The main purpose of this study is to identify and refine the factors affecting the efficiency of investment in insurance companies based on the fuzzy Delphi approach. In order to identify the factors affecting the financial stability of companies, while reviewing the studies, semi-structured interviews were conducted using a qualitative method of theme analysis. The experts interviewed are 20 university professors, CEOs of insurance companies and institutions, as well as senior auditors with accounting and management degrees. A review of the literature, research, and interview results reveals two main themes, including inputs and outputs, that categorize the factors affecting the efficiency of investment in companies and institutions. In this study, key factors have been identified and then the fuzzy Delphi technique has been used to rank and find the degree of importance of the factors. The results showed that return on assets, return on investment, value of investment, return on investment and net return on capital from the category of outputs and market share variables, ability to fulfill obligations, business diversity, technical reserves and risk transfer from the category of inputs, respectively. Have the greatest impact on the investment efficiency of insurance companies.

Structural diversity in Islamic financial instruments is growing rapidly. One of the newest Islamic financial instruments is the issuance of Perpetual Sukuk. These bonds structure are similar to debt and equity forms and have regular returns with no maturity date. These bonds are issued by Islamic banks, the government and the private sector in the form of Al-Musharaka, Mudaraba and Wakalat contracts. Such tools face serious criticism in terms of Shari'ah nd operational principles. In this study, a hierarchical model has been performed to improve the financing method using the issuance of Perpetual Sukuk. The methodological approach of this research is qualitative-quantitative, in the qualitative part 13 indicators are extracted and in the quantitative part with the help of self-interaction matrix, the research model is designed by interpretive structural modeling. The result of this research is a 9-level model, the most influential index of which is the verses of the Holy Quran and the most influential index of secondary transactions.

The purpose of this study is to validate the real customers of Bank Mellat using fuzzy Delphi approach and multiple logistic regression. For this purpose, first, the effective indicators on assessing the credit risk of real customers were identified by library method and then, the fuzzy Delphi method was used and the indicators were screened. The community of this section was formed by banking experts (Bank Mellat) who were selected by snowball method. Then, the final data related to the indicators, including the files of 7318 real customers of Bank Mellat during the years 2014-2020, were collected and analyzed by multiple logistic regression method in four categories of timely receipt, past due date, delinquent and doubtful receipt. The results showed that the loan amount, loan repayment time, installment interval, installment amount, loan extension, previous loan, real estate collateral, inventory average, facility interest rate and education level have a significant effect on credit risk of real customers. Also, the significance of gender, age and occupation indices was not confirmed.

Liquidity of stocks is an important challenge in the capital market. Identifying the factors affecting liquidity helps to predict the stock liquidity situation and thus stock risk management. The purpose of this study is to find the factors affecting the liquidity of stocks. For this purpose, in the first stage, using the research literature and experts, the influencing factors are identified and using the methods of minimum redundancy and maximum correlation (MRMR) and genetic algorithm, the effective variables are selected. In this research, using Excel software and existing raw data, the required data was created and then using support software and neural network toolbox and support vector machine was created. . Finally, the extracted variables using MRMR include stock market value, intensity of product market competition, GDP growth, equity returns, stock returns, inflation rate and family ownership, and using the financial model of financial leverage, government ownership, Equity returns, GDP growth, share buoyancy percentage, market type and board (on the stock exchange and OTC), the intensity of competition in the product market were selected.

One of the most important prerequisites for public participation in investment and financing projects (in this research, endowment bonds), is to provide information needs and pay attention to different jurisprudential views in the country. Therefore, in this research, the mentioned papers are examined based on the standards of Shafi'i and Imami jurisprudence in order to clarify and resolve its legal ambiguities and doubts.

The main approach in this research is a comparative approach. In order to analyze and issue jurisprudence in Imami jurisprudence, the method of document analysis (documents and approvals of the Jurisprudence Committee of the Stock Exchange Organization) has been used, and in connection with Sunni jurisprudence (Shafi'i jurisprudence), in-depth interview and focus group methods have been used, a statistical sample The aforementioned include 8 experts of Shafi'i jurisprudence.

The findings of the research showed that the commonality of views in the two jurisprudence institutions is very high, and this can be a sign of increasing the participation of Sunnis in endowment bonds and other Islamic securities issued in Iran.

Originality / Value: 

Considering that the results of the research resolved the jurisprudential ambiguities related to the endowment documents, it is a very positive step for the development of this tool, and surely, with the wide dissemination of the above results, we will witness more and more participation of Sunnis in Iran in similar projects at the level We will be the country and eventually a very good synergy will happen in this area.

Analysts have found that securities prices do not change randomly. Rather, change is based on a reproducible and recognizable process. Investors are faced with a decision-making process when selecting and buying corporate stocks. In this process, they seek to select stocks that have the maximum return for them and are also liquid. Liquidity in the stock market is very important. Identifying the factors affecting liquidity helps to predict the stock liquidity situation and thus stock risk management. The purpose of this study is to find the factors affecting the liquidity of stocks. For this purpose, in the first stage, using the research literature and experts, the effective factors are identified and using the stepwise regression method, the effective variables are selected. Variable selection (feature selection) is an important step in model construction for prediction. A good feature selection algorithm should always provide benefits such as better data display, better classification model, increased generalization, and identification of irrelevant features. Also, reducing the number of variables to understand the relationship between features and target variables, reducing computational load and increasing accuracy in high-dimensional data sets where the number of observations is less than the number of features, improves predictor performance and increases efficiency in terms of cost and time. Finally, the extracted variables were selected using the Step Wise regression model, including firm size, risk-taking, return on equity, and free float ratio.