فریدون اوحدی

In the past decades, analyzing the relationship between stock returns and bond issuance has been an inevitable topic, because the sign of this relationship plays a pivotal role for asset allocation, portfolio optimization, and risk management. Bond yields provide steady income, and stock yields reward investors for taking on risk. In a dynamic environment, investors can diversify their risk by choosing a portfolio that includes these two asset classes and adjusting their ratios. The studies conducted do not have a consensus opinion about the variable nature of stock and bond returns. The purpose of the article is to investigate the effect of stock market returns on the issuance of bonds of companies admitted to the Tehran Stock Exchange. The data needed to calculate the model for the companies accepted in the Tehran Stock Exchange and Bahadar have been collected using the information included in the audited financial statements of the companies from the software of Rehvard Navin and Codal. The variables used in the research were measured by Excel 2010 and Eviews 10 software. The statistical population of this research includes all the companies accepted in the Tehran Stock Exchange from 2017 to 2011 and the sample members were selected from among the statistical population by simple random method. According to the mentioned conditions, the number of 141 companies was determined as the statistical sample of the research. The results showed that the cumulative normal and abnormal returns of the stock market have a direct effect on bond issuance.

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.

The aim of this study is to accurately estimate the cost of capital and manage risk in energy projects based on public-private partnerships (PPP) in emerging markets.
 
To explore the mechanism of determining capital costs and evaluating capital budgeting, Dynamic Conditional Correlation (DCC) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were employed. Additionally, the pure-play method was used as a tool for measuring systematic risk in energy-related projects in emerging markets.
 
The results showed that the DCC-GARCH model is capable of more accurately predicting volatility and capital costs in emerging markets. Moreover, the use of the pure-play method for calculating systematic risk led to reduced volatility and improved capital cost estimation. The findings also indicated that market volatility and risks associated with economic and financial policies in emerging markets have a significant impact on capital costs.
 
This study demonstrates that the application of advanced financial models such as DCC-GARCH and the pure-play method can lead to more accurate capital cost estimations in PPP projects and provide a better understanding of risks in emerging markets. These models offer valuable tools for risk management in the dynamic economic conditions of emerging markets.

This research aims to investigate the mechanism of systemic liquidity risk transmission of corporate stocks in capital market of Iran. For this purpose, 486 listed companies in Tehran stock exchange and OTC from 2011 to 2022 were sampled and the companies were divided into ten deciles according to the Amihud illiquidity ratio, Then by using “Diebold & Yilmaz” approach which is based on forcast error variance decomposition in the framework of the generalized VAR model, spillover mechanism and systemic liquidity risk transmission among the formed decimals was investigatd.The preliminary results indicated the existence of spillover effect and systemic liquidity risk among all deciles, with difference intensity and weakness. In such a way that the fifth decile had the highest net transmission of liquidity risk spillover effects to other deciles, and the seventh & third deciles, respectively had the highest net effects of received spillover from other deciles. The general results of the total spillover index (TSI) indicated that risk transmission processes, change and increase after the occurrence of shocks and financial crises.

The goal is to select an optimal portfolio of stocks by allocating capital among various investment opportunities in the stock market to achieve maximum return at a specified level of risk. This constitutes an efficient portfolio.

Attaining an efficient portfolio involves solving an optimization problem. There are numerous techniques and tools available to solve this issue. In this study, 15 stocks from companies listed on the Tehran Stock Exchange, including symbols such as Khapars, Khazamiya, Vepasar, Foulad, Akhabar, Kegel, Femli, Tapiko, Sepaha, Fazer, Fakhas, Shohbaran, Shefan, Qamro and Qathabat, were selected using cluster sampling. First, the daily returns of these stocks were calculated over a 5-year period from 2015 to 2020 (1183 days). The risk of the optimal investment portfolio was then calculated using the Mean-Semi Variance and Conditional Value at Risk models. These two criteria were compared using a classic solution method. Subsequently, the output data obtained from these calculations were compared using MATLAB software, employing the Particle Swarm Optimization algorithm under the Mean-Semi Variance risk criterion and the Genetic Algorithm under the Conditional Value at Risk criterion.

The results of this study indicate that the meta-heuristic Particle Swarm Optimization method yields a higher portfolio return ratio compared to the Genetic Algorithm in the Mean-Semi Variance risk criterion.

Originality / Value: 

This research utilizes multi-objective genetic algorithms and Particle Swarm Optimization, which are intelligent and novel algorithms, to minimize the objective function value using Conditional Value at Risk and Mean-Semi Variance criteria. These algorithms optimize the return and risk ratios of the stocks in the investment portfolio with the highest possible accuracy. Additionally, the efficiency comparison of these models using MATLAB software contributes an innovative aspect to this study.

Digital Currency is a virtual currency, which relies on the principles of encryption to authenticate transaction.In fact, the first electronic payment system is decentralized, which managed to solve the problem of spending twice the virtual currency. One of the most important goals of virtual money is to facilitate financial and monetary creation without the presence of intermediaries and banks, That the government cannot handle this money, and banks cannot be worth it either much or too much. Currently, the rules on the use of digital currencies have not been approved in Iranian Islamic Banking and the use of foreign currencies has created numerous risks for banks and the country . In the research done First, they evaluated the risk associated with a currency portfolio used by banks in Iran using the value-at-risk approach. Then add the digital currency to the portfolio It again evaluates the risk associated with the mixed portfolio At the end of the optimal portfolio model, a hybrid portfolio is presented. The results of the research indicate a reduction in the risk of a mixed portfolio.

Choosing the optimal stock portfolio is one of the main goals of capital management. Today, There are several tools and techniques for measuring portfolio risk and selecting the optimal stock portfolio. In this article, using data of 15 shares selected by purposeful sampling method from the top companies of Tehran Stock Exchange Organization including; PKOD, ZMYD, BPAS, FOLD, MKBT, GOLG, MSMI, PTAP, SSEP, AZAB, FKAS, NBEH, PFAN, GMRO and GSBE, the First return of these stocks are calculated daily in the period of 31/3/1394 -31/3/1399 for 5 years for 1183 days and then using MATLAB software models The Metaheuristic Optimization of the Genetic Algorithm under the MSV Risk Criterion and the Particle Swarm Algorithm under the CVaR risk Criterion are Compared. The results show that the genetic algorithm model under MSV risk criterion is more efficient and less risky, therefore the genetic algorithm model under MSV risk criterion is more efficient than the particle swarm algorithm model under CVaR risk criterion.

Non-financial data may refer to variables that represent the dimensions of the company's management. Company size and industry are other non-financial dimensions used when assessing business risk. In this regard, the purpose of this study is to explain the effect of micro and macro factors (specific characteristics of the company, industry characteristics, corporate governance, ownership structure and macroeconomic variables) on business risk in listed companies on the Tehran Stock Exchange. In order to achieve this purpose, 1752 firm-year (146 companies for 12 years) observations collected from the annual financial reports of listed companies on the Tehran Stock Exchange during the period 2007 to 2018 have been tested.Findings show that firm size, firm liquidity, firm profitability, firm growth opportunity, firm size, number of firms in industry, board independence, existence of audit committee and management ownership have a negative effect on business risk. while debt ratio and the exchange rate have a positive and significant effect on business risk. The results also show that statistically, firm life, industry decentralization, board size, duality of CEO duties, board financial expertise, audit committee size, audit committee independence, audit committee financial expertise, ownership type, Institutional shareholder ownership, major shareholder ownership, government ownership, interest rates and oil price growth have no effect on business risk.

Moral hazard is an opportunistic behavior of manager in order to increase self- interest in an unethical way. The moral hazard of managers has adverse consequences on the financial health of companies. Therefore, the purpose of this research is to investigate the relationship between moral hazard and the possibility of companies becoming zombies.               

The research method is applied in terms of purpose and correlational in nature. The statistical population is listed firms in the stock exchange and the sampling method is systematic exclusion. The research data was collected from the website of the Stock Exchange Organization. Also, SPSS software was used for data analysis and binary logistic multivariate regression.

The results show that moral hazard has a positive and significant relationship with the possibility of companies becoming zombies. The evidence of this research shows that with the increase of financial risk, the probability of companies becoming zombies increases significantly and with the increase of profitability, the probability of companies becoming zombies decreases significantly.

moral hazard as one of managers' behavioral biases; It can increase the risk of financial health of companies and as a result the possibility of them becoming zombies. Therefore, in evaluating policies to prevent the company from becoming a zombie, this issue should be considered by financial analysts and capital market policymakers.

According to the theory of behavioral psychology, the inner personality traits of a person can be examined through the external manifestations of his behavior. One of the most important personality traits of a manager that can affect different dimensions of the organization is narcissism. The purpose of this study is to investigate the effect of managers' narcissism on earnings quality in companies listed on the Tehran Stock Exchange. In order to select the research sample, systematic elimination method (screening) and non-random were used and thus 177 companies were selected over a period of 8 years, from 1390 to 1397. The research data were extracted annually from Rahavard Novin 3 software and after performing calculations in EXCEL software to enter the research relationships and hypotheses, they were entered into EVIEWS software. The results of examining the research hypotheses showed that managers' narcissism has a significant effect on various dimensions of profit quality such as stability, predictability, relevance and timeliness of company profits.

This research aims to model and present an appropriate pattern in order to forecast systemic liquidity risk of corporate stocks in capital market of Iran. For this purpose, 486 listed companies in Tehran stock exchange and OTC from 2011 to 2020 were sampled and then the companies were divided into four groups (portfolios) according to combination of indicators and types of activites of companies. Then by using types of multivariate GARCH models and comparing them, finanlly the VAR(1)-DBEKK(1,2) was selected as an optimum pattern . The results of research showed significant relationships among of liquidity shocks and volatilities with all of subsections, and consequently the main hypothesis based on “presence of systemic liquidity risk of corporate stocks in capital market of Iran” was accepted. In a way that the portfolios of company stocks with a “low level of liquidity- industry section” and “low level of liquidity- financial section” respectively had maximum and minimum liquidity shocks transmission of effects on future returns of the other portfolios, as well as the portfolio with a “high level of liquidity- financial section” had maximum volatility persistence and liquidity risk transmission to other portfolios.

Choosing the optimal stock portfolio is an important issue in the economy. There are several techniques and tools to solve problem the optimal portfolio. In this research, using data of 15 stocks which randomly selected from the Tehran Stock Exchange including; PKOD, ZMYD, BPAS, FOLD, MKBT, GOLG, MSMI, PTAP, SSEP, AZAB, FKAS, NBEH, PFAN, GMRO and GSBE, the first return of these stocks are calculated daily in the period of 31/3/1394 -31/3/1399 for 5 years for 1183 days. Then and their portfolio risk is calculated using the models of mean–semi variance risk and conditional value at risk, and these two criteria are compared by the classical solution method. The portfolio optimization output with each of these risks represents a different weight per share. In the following, the mean–semi variance risk model and conditional value at risk model of metaheuristic method using MATLAB (R2019) software are compared. The results showed that the metaheuristic method of genetic algorithm compared to the classical method of mean–semi variance and conditional risk value of deviation in solving the problem of portfolio optimization had higher returns and lower risk under the risk criterion of mean–semi variance and therefore a better method for solve optimization problems.

In general, economic development requires financing. However, exchange rate volatility hinders securing the capital needed for economic projects resulting from economic risks, transaction and money exchange. Taking into amount the importance of the role of exchange rate volatility, the main purpose of this study is to evaluate the effect of exchange rate volatility along with other macroeconomic variables on the inflow of foreign direct investment in various economic sectors of Iran (industry and oil, services, agriculture) by using the Approach of Autoregressive Distributed Lag (ARDL) during the period 1994 -2018. By using the theoretical literature presented in this study, FDI in Iran is considered a function of exchange rate volatility, capital productivity index, index of economic freedom, inflation rate, and economic growth rate, which according to the results, other than the variable the economic growth rate, the coefficients of all the variables are significant and the sign of their coefficients agrees with the theoretical expectations. Furthermore, the effect of capital productivity index and index of economic freedom variables on FDI was positive and the effect of exchange rate volatility and inflation rate on FDI is negative and increasing exchange rate volatility leads to a decrease in foreign direct investment in the Iranian economy.

The first purpose of this study is to investigate the professional socialization of accounting graduate students of Islamic Azad University. Also, its next goal is to study the effect of motivational strategies on the professional sociability of these students. The statistical sample of the study includes 505 graduate students (M.Sc. - PhD) in Accounting, Islamic Azad University throughout the country, which was done using the non-probability sampling method. To test the hypotheses, multivariate regression model was used using SPSS software number 22. Findings show that intrinsic motivation has a positive and significant effect on professional socialization. But external motivation does not have a significant effect on professional socialization. Also, gender and educational level have a significant effect on  professional socialization. But the age of the studied students does not have a significant effect on their professional socialization.

Choosing the optimal stock portfolio is one of the main goals of capital management. There are several techniques and tools to solve problem the optimal portfolio. In this research, using data of 15 stocks which randomly selected from the Tehran Stock Exchange including; PKOD, ZMYD, BPAS, FOLD, MKBT, GOLG, MSMI, PTAP, SSEP, AZAB, FKAS, NBEH, PFAN, GMRO and GSBE, the First return of these stocks are calculated daily in the period of 31/3/1394 -31/3/1399 for 5 years for 1183 days. Then and their portfolio risk is calculated using the models of absolute deviation risk and conditional value at risk, and these two criteria are compared by the classical solution method. The portfolio optimization output with each of these risks represents a different weight per share. In the following, the deviation - absolute risk model and conditional value at risk model of metaheuristic method using MATLAB (R2019) software are compared. The results show that the PSO model of metaheuristic method compared to the classical method in solving portfolio optimization problem showed more return in PSO-MAD criteria and therefore it is a better method to solve such portfolio optimization problems.

Digital currency is a special form of digital money based on cryptography. The cost and time of transferring digital currency to different places is less than the traditional method. In banking, the method of transferring Fiat currencies has created many risks for banks due to different prices, long transfer time and high cost of Swift fees. The main purpose of this study was to provide a model for estimating the rate of return and risk of banks 'foreign exchange portfolio in Iran and to evaluate the effect of adding digital currencies to the banks' portfolio in terms of rate of return, risk and optimization using Value At Risk (VAR). To examine the extent of changes in banks' foreign exchange portfolio risk in combination with digital currencies, first the return and risk of a foreign exchange portfolio used in Iranian banks are calculated and optimized the using Value At Risk (VAR); then, by selecting a number of digital currencies and adding them to the foreign exchange portfolio of banks, the returns and risk related to the new portfolio are calculated and optimized using Lingo software. The results of the study show a reduction in the risk of the new portfolio.

In this study, the impacts of exchange rate volatility in terms of other economic variables on the financing cost of companies listed on the Tehran Stock Exchange have been evaluated using the panel data method during the period 2008-2018. Based on the theoretical literature presented in this study, the financing cost of the surveyed companies is a function of exchange rate volatility, interest rate, inflation rate, the growth rate of the total market price index, and economic growth rate. The results Show, except for the variable of the inflation rate, the coefficients of the variables are statistically significant, and their signs are consistent with the theoretical expectations. Furthermore, the coefficient of exchange rate volatility in the model of the financing cost investigated in the study is positive. That is to say, the variable has a direct effect on financing costs. An increase in exchange rate volatility results in a rise in the financing cost of the companies under review and decreases investment in the companies under study. Theoretically, there are two different approaches to the effect of inflation on the cost of financing. On the one hand, rising inflation due to creating uncertainty in the capital market will reduce capital attraction and increase the cost of financing. On the other hand, a steady rise in prices leads to an increase in accumulated profits. According to the theoretical literature and the results obtained in this study, the negative and positive effects of inflation on the financing of the companies under study neutralize and ineffective each other, because the coefficient of variable inflation in the estimated model, in terms of It is statistically meaningless.

One way to help you capitalize on investment opportunities and better allocate resources is to anticipate business risk. Predicting the probability of future events based on present and past information, In this way, first of all, by providing the necessary warnings, companies can be alerted to the occurrence of business failure so that they can take appropriate action accordingly. And second, investors and lenders distinguish favorable investment opportunities from unfavorable ones. And invest their resources in the right opportunities; Therefore, predicting the business risk of companies has always been one of the topics of concern for investors, creditors and the government. The purpose of this study is to design a business risk forecasting model using machine learning techniques.. The statistical population of the present study is the selected companies listed on the Tehran Stock Exchange during a period of nine years between 2007-2018. Hypothesis test results show that firm size, firm liquidity, firm profitability, firm growth opportunity, industry size, number of firms in industry have a negative effect on business risk. Meanwhile, the company's debt ratio has a positive and significant effect on business risk. The results also show that statistically, the life of the company, the decentralization of the industry has no effect on business risk. The results of model design and machine learning techniques show the efficiency of NB technique and then SVM technique compared to other machine learning techniques.

In this paper the effect of learning environment on the professional socialization of graduate students of accounting at the Islamic Azad University have been studied. This study is a descriptive correlational study whose data were collected through a questionnaire. The time of conducting the research is 2020. The statistical sample of the study includes 505 graduate students (Masters – PhD) in Accounting of Islamic Azad University across the country, which has been done using the probabilistic sampling method available. To test the hypotheses, the structural equation modeling technique has been used.  The results of hypothesis testing show that constructive learning environment has a positive effect on cognitive and motivational strategies .But this relationship between a constructive learning environment and the professional socialization of accounting students does not make sense. Also, the cognitive strategies of the professional sociability of accounting students have a significant and positive effect. While the motivational strategies of the professional sociability of accounting students do not have a significant effect. The results of this study can be useful and beneficial for policy makers and legislators of higher education in Islamic Azad University in order to develop educational programs that lead to greater awareness of university graduates about legal, professional, industry and ultimately meet their professional needs.

A digital currency is a complex form of electronic money.The process of transferring this system is quite direct, and compared to the traditional method, there is less cost and time for transactions to take place in different parts of the world. Digital currencies use a system called the Block chain,Anyone who understands the benefits of the Chinese block,It's empty in our country .Advantages such as the impossibility of manipulating information, intelligence and decentralizing processes, along with high transparency, are issues that optimistic about the future of this technology. In this research, we select a number of digital currencies with the highest transaction volume and liquidity to create portfolios. and calculated using the Value at Risk (VaR) approach and the return on the portfolio Finally, an optimal investment portfolio is offered.

Tax compliance is important for governments around the world to fund. Traditional ways to improve tax compliance, such as increasing auditing, are the cost to implement this goal. Meanwhile, the impact of social and psychological norms on various variables is an interesting topic recently investigated in the field of behavioral finance. The purpose of the research is to investigate the effect of social factors on the objectives of tax compliance of individuals. The statistical sample includes 550 accountants and financial managers of companies and auditors in the public sector and private sector in 1396. The research data were collected through a Delphi method and then a questionnaire was collected and analyzed using the structural equation model with the help of LISREL software. The findings suggest the indirect effect of social factors on tax compliance. Social norms have a significant impact on tax compliance indirectly through personal and psychological norms. Therefore, the greater the power of social norms in favor of tax compliance, the personal and psychological norms of tax compliance will also increase, which will lead to a further increase in the goals of subjection. The results of this study will provide useful information about the social factors affecting tax compliance with its users and can be used by the tax authorities in planning to develop more cost-effective and effective solutions to increase taxpayer's compliance.

Identifying the factors affecting company performance and its analysis in the past and its future prediction for decision making in the financial area is very important. Important factors that play a critical role in determining how firms and institutions work are corporate capital structure and firm strategies.The purpose of this study is to examine the impact of the business cycle on the capital structure and the impact of capital structure on their performance. Hence, in a descriptive-correlation study،a total of 71 companies fromTehran stock exchange from 2004to2015 were selected. For data analysis, panel regression model and Eviews8 software were used. The results indicate an insignificant effect of the business cycle on capital structure، also indicate the negative and significant effect of capital structure on the company's performance in different business cycles.

Tax is one of the important economic issues of any society. Wherever there is a tax, there will be an avoidance of it by itself. Tax compliance is important for all governments around the world to fund the budget. Exploring the effect of social factors on tax avoidance is the purpose of this study. The statistical sample includes 550 accountants and financial managers of companies and auditors in the public and private sector in 2018. The research data were collected through Delphi method and a questionnaire and analyzed using the structural equation model with the help of LISREL software. The findings of this study suggest that the indirect influence of social factors on tax avoidance is supported. The Political ideology through social trust has an indirectly significant negative effect on tax avoidance. The greater the power of social and economic conservatism, the greater the social confidence on the government, which leads to a reduction in tax avoidance. The results of this study will provide useful information about the social factors affecting tax avoidance for its users and can be used by the Taxation Organization to plan for the formulation of cheaper and more effective solutions to reduce taxpayer avoidance.

In a Competitive Market, Understanding Customer Demand and Effective Advertising is one of the most Important Factors in Survival. Extend the Internet and virtual networks have provided a great opportunity for companies to advertise, and thus studying electronic marketing methods and models is of great importance. One of the newest marketing methods is viral marketing that is based on mouth-to-mouth advertising and has a lot of power. Viral marketing relies on the principle that on any social network, a number of users have high power and influence on others, and by identifying them and creating good advertising messages, They can be used to effectively marketing. Therefore, The identification of important users is considered the most important activity in viral marketing. In this regard, various studies have been conducted to identify users using a variety of graph-based and publish-based methods. In this research, the capabilities of both methods have been used and by Using a semi-localized centrality criterion based on graph-based methods and Markov clustering model based on propagation methods, a new hybrid model for user clustering and identification of key users presented. The results show higher correlation between the proposed method and the SIR standard and, therefore, its higher efficiency than other methods used in the research.