جستجوی مقالات مرتبط با کلیدواژه « ریسک » در نشریات گروه « مالی »

In this research, asset and liability management, which is one of the most important things done by banks, is examined. The methods and complexity of the process used to manage assets and liabilities depend on the size and complexity of the portfolio. Bank Mellat, as one of the most central banks in the country, in line with its organizational goals and missions, needs accurate economic planning and high-yield investments in order to maintain the value of depositors' reserves and provide the necessary resources to fulfill its long-term and short-term obligations. According to the stated content, the main problem of this research consists in the fact that due to the importance of asset and liability management with the approach of liquidity risks, credit, balance sheet, capital structure as a comprehensive approach that includes different types of important risks and It includes the important, it should be noted. In this research, we consider asset and debt management with more important factors in this matter, so that the model is more efficient than the previous models presented, which has been taken into account with the approach of goal programming.

The purpose of this research is to choose the composition of the bank's foreign exchange portfolio to reduce the risk of managing net open positions (NOP). This realization aims to answer the question of whether it is possible to optimize the currency portfolio based on different value-at-risk models, including Var, Copula VaR, and Copula CVaR. In this research, on a quantitative scale and observations in the form of time series, the daily logarithmic return percentage of the two main and common currencies in the country's business, including the US Dollar and the EURO, in Sepah Bank from 2013 April 6 to 2021 September 22.The results of the selected portfolio based on these three methods show that, according to the obtained results, the Copula GARCH VaR model has a higher Sharpe value than the other two methods. On the other hand, the results of the average Sharpe obtained from the portfolios on the efficient border between the three methods are presented, according to the obtained values, we find that there is a significant difference at the 95% statistical confidence level between the average of the three methods presented, during which the Copula model GARCH VaR is ranked higher.

Investment, the choice of assets to maintain and earn more for future prosperity, is one of the most important issues in the economy of all countries, commanding attention from individuals and high-ranking officials alike. Enhancing stock returns through forecasting is a critical concern in capital markets, necessitating attention from both individual and institutional investors. Accordingly, the present study aims to optimize the forecast of risk-based stock returns in selected industries of the Tehran Stock Exchange.

Data analysis was performed in two phases. In the first phase, the data were estimated using a combined data method and AR (1) autoregressive process, from 2010 to 2019. This model forecasts stock returns for selected industries on the stock exchange. In the second stage, using Data Envelopment Analysis (DEA), stock return forecast optimization by the previous stage was optimized for selected industries of the Tehran Stock Exchange from 2010 to 2019.

The results of optimization of stock return forecast in three industries of oil and gas extraction except exploration, extraction of metal ores and petroleum products revealed that the oil and gas extraction industry, except exploration, exhibited a higher efficiency of 0.4214 compared to other industries. The metal ore mining industry with an efficiency of 0.3728 stood in second place and the petroleum products industry with an efficiency of 0.2516 ranked in third place in terms of efficiency.

Therefore, it can be said that optimizing stock return prediction in the oil and gas extraction industry, excluding exploration, is at a higher level compared to other industries examined in this study. Given the oil industry's higher efficiency and optimization within the country, it is feasible to integrate oil into the production cycle using effective methods and introduce technological advancements to enhance oil supply within the country. This is particularly viable as the production output of other industries is significantly lower compared to these three industries. basic measures should be taken to increase the production of industrial and non-oil goods to reduce imports and increase exports of final goods to other countries to increase economic growth. Hence, it is recommended to utilize the oil and gas extraction industry, excluding exploration, for the production of final goods, followed by leveraging other industries for further production of final goods. Also, since the oil products industry is influenced by more variables in predicting stock returns, it is recommended that investors in the oil products industry be aware of the variables studied in this research, especially the industrial production growth rate, and consider their stock returns and investment levels accordingly. Future research should explore and assess the optimization of stock return forecasting in other significant industries like pharmaceuticals, electricity, steam, hot water supply, etc. to compare and evaluate their outcomes alongside the findings of this study.

Project financing has emerged as a critical mechanism for funding infrastructure and capital-intensive projects across various sectors, both in the public and private domains. Its significance has grown notably in recent decades within the financial market, reflecting its pivotal role in facilitating economic development. Notably, in developing countries, the establishment of robust economic infrastructure is a pressing priority, serving as a cornerstone for overall growth and advancement across multiple economic sectors. However, the insufficient attention given to meeting financing requirements in this domain poses substantial challenges to economic progress. Diving deeper into the realm of project financing, one finds a diverse array of financing mechanisms, each characterized by its unique set of risks and complex capital structures. These risks, if not effectively managed, can exert considerable influence on project outcomes, impacting crucial objectives such as project timelines, cost management, safety protocols, quality standards, and long-term sustainability goals. Understanding these risk factors and their interplay is paramount for stakeholders involved in project financing endeavors.
 
To explore these dynamics, this study employs an objective-oriented approach, leveraging a descriptive-correlational method for data collection. Central to this methodology is the utilization of Structural Equation Modeling (SEM), a powerful analytical tool that allows for the examination of complex relationships among variables. The study's statistical population comprises a diverse range of professionals, including experts, senior experts, supervisors, and managers from mining companies within the country under study. From this pool, a sample of 220 individuals was randomly selected to ensure robustness and representativeness in the analysis.
 
The study's findings shed light on the intricate relationships between various factors and project financing risk. Notably, the analysis unveils the significant impact of financing methods on political risk, with an observed effect intensity of 0.462 and a probability statistic of 0.686. This surpasses the critical t-value at the 5% error level, underscoring the statistically significant influence of financing methods on political risk. This underscores the importance of considering the choice of financing mechanisms in mitigating or exacerbating political risk factors, thereby enhancing the overall risk management strategy of project financing endeavors. Moreover, the study delves into the relationship between customer and management risks. While the overall effect of customer risk on management risk was determined to be 0.046, the calculated significance value of 0.851 fell below the critical t-value at the 5% error level. This indicates a lack of significant observed effect, suggesting that customer risk may not exert a substantial impact on management risk. Nevertheless, understanding these nuanced relationships contributes to a more comprehensive understanding of the multifaceted nature of project financing risk. Beyond these specific findings, the study highlights a myriad of factors that influence project financing risk, including social risk, contractor risk, construction risk, design risk, legal risk, financial risk, and subsidiary contracts, among others. Each of these factors contributes to the overall risk profile of project financing endeavors, necessitating a holistic approach to risk management and mitigation strategies.
 
In conclusion, the study underscores the critical importance of focusing on project organization and management capability, particularly in the context of developing countries. Inadequacies in these areas often result in suboptimal utilization of resources and capital, leading to delays in project commencement, implementation, and operation, ultimately yielding uneconomical outcomes. In light of the prevalent project-oriented approach adopted by organizations, there exists a heightened imperative to design and enhance project organizations to effectively navigate the complexities of project financing endeavors and achieve desired outcomes.

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.

The purpose of this research is to identify the effect of price acceleration in Tehran Stock Exchange. For this purpose, the data of 60 companies available in the stock exchange during a 10-year period (2012-2021) has been used. The regression model of the research has been tested using the time series data method. The hypotheses of this research have been examined in two boom and recession periods. In investigating the problem of risk, Fama and French's five-factor model was used to explain the effect of price acceleration, which shows that this model cannot explain the effect of price acceleration, and this result is still valid in the boom and recession periods, although it has a higher explanatory power in the boom period. Will have. In examining the second hypothesis, the five-factor model was expanded to measure the ability of the profit acceleration factor to explain the price acceleration effect by adding this factor to the model. The results of the hypothesis test show that the 6-factor model does not have sufficient ability to explain the effect of price acceleration, and this result has been confirmed in the investigation of an event, such as a downward trend of profit acceleration has been observed, in which the reaction is less than the limit to profit news. It is not visible. These results are still valid in two periods of boom and recession, although only in the j6k12 strategy in two periods of boom and recession, the second hypothesis was confirmed and it was observed that in this strategy, profit acceleration is a suitable behavioral factor to explain the effect of price acceleration, because by adding this factor According to the model, the model has an explanatory power of up to 59%.

International Petroleum Contracts (IPCs) has been made between Host Countries (HCs) and International Oil Companies (IOCs). By this kind of contracts, IOCs provide financial resources, technology and management necessary for oil and gas fields’ development and benefit from the produced oil and gas.
Needlaess to say, due to various and broad operations, unpredictable increase and decrease in market prices, unforeseeable rservoire behavior, and in one word, Because of major technical and financial risks, there is a great uncertainty in both sides of the contract, the amount and conditions of investment and outcomes. From the Islamic jurisprudence viewpoint, those risks would cause “Qarar” and affect the validity of contract.
For reviewing abovementioned hypothesis, first, the precedent studies has been examined and the consequent is only those uncertainties that may lead to dispute from commom sense view would enter “Qarar” into the contract. After that, this measuremen is applied to the IPC’s contents.
At the end, it is concluded that it is not necessary for parties to know every aspect of the contract and its outcome in full and they could put the contract out of the “Qarar” area by implementing risk management tools such as price formulas, Adjustment terms and industrial standards. In other word, according to the oil and gas professionals’ course of conduct such risks could be waived and consequently, the common risks of these contracts can not affct their validity totally.

The present study analyzes the content of financial risk management studies in Iranian banks. The main purpose of the research is to provide a conceptual framework, create a database and provide a comprehensive picture of the current state of the research. The present study is applied, review and retrospective and the data were collected by document analysis. The statistical population of the research includes 509 articles that have been published in scientific research and extension scientific journals approved by the Ministry of Science, Research and Technology between 2005 and 2019. This research presents a conceptual model for banking risk studies with a descriptive approach and content analysis. The content of the research has been reviewed and analyzed based on the analysis of journals, time course, keywords, thematic literature, objectives, data collection method, analysis tools, evaluation models, research variables, statistical tests and thematic trends in previous researches. Credit risk, practical purpose, correlation analysis, archival method, ivory software, statistical method, regression test, dependent variable of default probability and independent variable of interest rate had the highest frequency in articles. Sanctions, individual and social norms, cultural values and inclinations, climatic and geographical conditions, ethics, religiosity and family values that may contribute to risk were not found in the content of the articles.

This study examines the risk of the Tehran Stock Exchange and the S&P 500 index, as well as gold and silver. Each investigation was performed separately before the outbreak of Covid-19 and during it. To do so, we use the fractional cumulative residual entropy (FCRE) for risk analysis. Also, we investigated the effect of Covid-19 on the Hurst exponent of the aforementioned markets and discussed the dynamic of each time series as well. The results showed that the Hurst exponent increases to one after the Covid-19 pandemic for all investigated markets. Therefore, the pandemic led to higher persistence trends in all markets that were studied. The results of FCRE showed that for all markets, randomness and irregularity have increased since the outbreak, which indicates that the risk and uncertainty have generally increased after the outbreak. Gold, however, showed the lowest entropy level since the pandemic, indicating that information perceived by gold investors did not significantly affect their fears during the Covid-19 pandemic. The results provide insights into the response of financial markets and precious metals to investor expectations after the Covid-19 pandemic.

During project management, certain risks need to be effectively mitigated. While risks are an inevitable part of every project, their perception, analysis, and management help organizations achieve goals. Risk management is now an important part of project management and has a specific function for improving the efficiency of implementing the project, saving prices, and improving the profitability of projects. Theories have demonstrated risk management as an influential and scientific management instrument to decrease risk levels. Accordingly, investigating the new methods of eliminating financial risk and financial risk management of projects is necessary to develop and improve risk management.
 
In this study, the related models and variables were introduced to identify the factors influencing project financing risk. Subsequently, general criteria were derived from the examined models and their associated variables. The study samples were drawn from experts and professionals within Iranian mining companies who had direct involvement in the subject matter. Variables were identified using the content analysis method, which involved utilizing MAXQDA software for theoretical and qualitative data analysis.
 
Financing methods, social risk, contractor risk, construction risk, political risk, design risk, legal risk, financial risk, management risk, customer risk, and sub-contracts are the categories governing the current research. Each item is composed of one or several concepts that represent “features,” “conditions,” and the current or desirable “situation” of the item in the country. Another important point is the synchronicity of “description” and “prescription” in secondary or pivotal items of the research. It means that the constituted items not only represent the descriptive meaning that reflects the description of the current or the desirable situation but also represent the prescriptive meaning. The highest-ranking item is "financing methods," with 99 allocated codes. Following that is "financial risk" in the second rank, with 86 codes, and "management risk" secures the third rank with 26 codes. Ultimately, the categorized items can be approached through a model. Therefore, one of the most important results of the country's economic enterprise challenges is the lack of proper modeling for the financing method. A specific model should be created for financing, based on the risk and return of financing. In addition, it should be noted that the structure used to finance the project in one country may not be effective due to the differences in its laws and conditions, while it is used effectively in another country. according to the conditions of each project and the host country of the project.
 
In this study, the influential items or factors affecting project financing risk are investigated. The results of the study showed that financing methods, social risks, contractor risks, construction risks, political risks, designing risks, legal risks, financial risks, management risks, customer risks, and secondary contracts have influential impacts on project financing risk. After project formation and establishment, project financing emerges as a key determinant. Effectively managing a project to successfully achieve its goals within defined time, cost, and quality parameters demands a holistic understanding of the project's stakeholders and its environmental context. Contract frameworks dictate how foreign companies can leverage their financial capabilities to execute operations within different countries.

Investors, in their pursuit to maximize expected returns, minimize risks in their stock portfolios, and achieve the desired benefits, require suitable methods and criteria to select stocks for their portfolios and allocate capital. One of the most important things in stock portfolio optimization is the use of a suitable optimization algorithm. The function of the multi-objective portfolio optimization model is quadratic. Quadratic functions are a special class of nonlinear programming problems in which the objective function is quadratic and the constraints are linear. Common algorithms for quadratic programming require certain parameters with fixed values. Such algorithms are extensively employed for solving real-world problems, particularly in financial contexts. The major objective of this research is to apply the inner point mathematical algorithm to optimize the stock portfolio and to use this algorithm to address the multi-objective portfolio optimization problem. With the GlueVaR risk measurement criterion, the problem of portfolio optimization takes into account the two objectives of maximizing returns during the research period and reducing investment risk, reassuring investors to make better and more accurate decisions about the final object of this research.

The necessary information for this study was provided by 50 active companies listed on the Tehran Stock Exchange. However, due to the availability of their daily prices during the study period, the final number of companies considered was reduced to 33. The inner point mathematical approach was utilized to optimize the model with the dual objectives of increasing efficiency and reducing risk. To demonstrate the effectiveness and capability of the algorithm in solving the problem of two-objective optimization, its output was compared with other risk measurement criteria such as variance, and value at risk (VaR). The investment risk in the stock portfolio was also calculated using the GlueVaR criterion. Comparing conditional (CVaR) was also done. The GlueVaR criterion has the advantage over the other criteria since it takes the investor's attitude toward risk into account. This advantage formed the basis of the calculation method in this research according to the mentioned algorithm.

According to the research, value at risk (VaR) and conditional value at risk (CVaR) perform better than other variance risk measures in the portfolio optimization model with the GlueVaR risk measure and the internal point optimization method for determining the most effective border. Additionally, when applied to optimization problems, the internal point method discovers the optimal point with fewer iterations, providing strong evidence of the algorithm's effectiveness.

Based on the current findings, it is evident that the internal point algorithm is effective in resolving stock portfolio optimization issues. Additionally, the GlueVaR risk measurement criterion outperforms VaR, variance, and CVaR for most investors with diverse risk and return preferences

aim of this study was to evaluate the integrated risk of the banking system through the metaphysical algorithms of gray wolf, genetics and particle swarming. This research is applied research in terms of purpose and correlational in nature and method. Data collection has been done through library studies, articles and sites in deductive form and data collection to refute and confirm hypotheses inductively. The statistical population of this research is the banking system and the sample includes banks listed on the Tehran Stock Exchange during the fiscal years 1392 to 1397. In order to collect the required data, the financial database of the Ministry of Economic Affairs and Finance, codal site, etc. have been used. After extracting the information, and setting them in the form of an integrated risk model, the objective function and constraints are entered in MATLAB software and the variables of risk and return (profit and loss on assets and Debts) were obtained using particle swarm algorithms, genetics and gray wolves and we compared their results using SPSS 16 software. After that, first the descriptive statistics were analyzed and then inferential statistics were performed. after reviewing the results of comparing the evaluation indicators of algorithms, it was determined that the gray wolf algorithm is efficient. Provides better goal function optimization. Also, by examining the research hypotheses, it was found that particle swarm algorithms and genetics have the same efficiency for assessing the integrated risk of the banking system. Provides better problem solving.

Optimization of banks' foreign exchange portfolios aiming to determine an optimized combination of foreign exchange assets is in such a manner that it leads to a minimum risk and maximum yield. The approach used in this article, which is actually a priority over other methods is to use a combination of inexact mathematical models (fuzzy) and optimization. Thus, in this research, linear programming with fuzzy target coefficients has been used. The target coefficients are the same as the exchange rate. In other words, the daily rates of all currencies due to fluctuations are considered as fuzzy numbers. Therefore, in this investigation, by developing a multi-purpose mathematical model and by applying the fuzzy data relating to transaction rate of six foreign exchanges in year ended on March 20, 2020, including US dollar, Emirates Dirham, Japanese Yen, Turkish Lira, Korean Won and Euro, case by case risks related to the fluctuations of noted foreign exchanges and also their yield in the foreign exchange portfolio of Bank Mellat were measured and estimated for the future time horizon. All banks may benefit from the developed model. By determination of the optimal weight of each foreign exchange, upon describing and analyzing the existing status it specifies the optimal status, enabling banks to fulfill their foreign exchange obligations on due dates by properly and optimally making investment in foreign exchange assets meanwhile gaining a competitive advantage. "Gams Software" was used for solving the model.

The daily observations of the total index of the Tehran Stock Exchange show that in the last few years, stock prices have been very volatile. This volatility can harm the economic environment of Iran. Modeling and predicting price volatility in this market can provide important information about uncertainty and risk to actors and thus help with managing possible unwanted changes in the field of financial investments. This, along with the rise in the value of the stock market in recent years, has caused the investigation of the issue of volatility to become increasingly popular among academicians and financial policymakers. Volatility and risk measurements are essential parameters in risk management programs and can affect a country’s economic activity and public confidence. These are also key parameters in studies that examine the relationship between the stock market, economic growth, and other financial variables. Tehran Stock Exchange markets have been volatile in recent years. Controlling the negative effects caused by stock price volatility has made it necessary to predict and model price dynamics for participants in this market. In this paper, the class of Parameter-Driven volatility models (stochastic volatility models) is used to predict the price volatility and calculate the risk of the price index in the Tehran stock market. Therefore, four stochastic volatility models were used. To make a comprehensive review, the asymmetry in the volatility (leverage effect) and the heavy tail of the stock price return distribution (with t- student distribution and Skew normal) have been included in the models. To estimate the models, the Gibbs sampling method was used, and to accurately compare the models, the test based on the posterior distribution of the models and the Bayesian factor was used. The results indicated that the canonical stochastic volatility model with Skew normal distribution (SNSV) is more effective than other stochastic volatility models in predicting the price of stock market volatility based on the Bayesian factor. Therefore, to analyze stock market risks using stochastic volatility models, there is no need to include the leverage effect in the state space of the Volatility equation. The SNSV model makes it possible to observe volatility and make predictions related to it, thereby improving market transparency and ultimately making diversification and risk management easier to implement. Also, the backtests of VaR and CVaR market risk assessment using Kupiec and DQ tests do not show evidence that the estimation is over or under the risk limit. As a result, the calculation of volatility and pricing with this model will lead to more precision risk management for professionals, especially fund managers who intend to include Tehran Stock Exchange stocks for asset allocation.

Due to the dynamics of the business world and due to the uncertainty in the current economic environment of the country, there are various risks in this environment that can affect the performance of companies.In this survey, the statistical population is accountants in companies admitted to the Tehran Stock Exchange and includes information from the year1400. For risk graphs two methods fuzzy ANP and network analysis have been used. Also a questionnaire related to pairwise comparisons was used and completed by10 experts in the field of stock exchange with more than10 years of work experience.The selection of people was carried out using the snowball method until theoretical saturation was reached.The obtained results were analyzed, ranked and clustered by the fuzzy ANP method. Also by using the topology method based on network theory, the shape related to the most important risks with their interactions has been drawn and by using the opinion of 10 experts of the pairwise comparisons department, the probabilities, the degree of influence of the risks have been determined and then the intensity of the effect has been determined and ranked.MATLAB 2022 and Excel 2016 software were used for this research. Also,the effectiveness of the most important risks has been determined. Inflation risks(179), liquidity(119), liquidity(80) as critical risks and political risks(72),human resources(52),credit(40) as key risks and operational risks(39) industry(32) interest rate(28), stock price(24) were classified as important risks.Therefore by identifying important risks with a high center of gravity, risks can be managed, and therefore suggestions have been made.

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 main purpose of this study is to level the indicators of extreme events affecting the behavior of investors and capital market risk using a structural-interpretive modeling approach.This research is based on purpose, is practical and descriptive in terms of data collection. The statistical population of this study is 15 professors and PhD students in accounting, financial management, economics and business management, who were selected as experts through purposive sampling, and these, and these experts, through an interpretive structural questionnaire, identified 15 indicators of extreme events that had previously been identified in another study by the authors of this article, through 3 stages of fuzzy Delphi and among 72 indicators,in 5 categories, they were graded and prioritized. The validity of the questionnaire was confirmed by respected professors, advisors and financial experts. Also, Cronbach's alpha coefficient was used for the reliability of the questionnaire, which was more than 0.7 for all variables,and MIC MAC software was also used to analyze the data.Findings from the structural-interpretive method in this study show that the extreme events indices are ranked at five levels. So that, inflation and sanctions indices in the fifth level, have the greatest impact and air pollution index in the first level, have the least impact on investor behavior and capital market risk.

The existence of bubbles in the market,especially the capital market,can be a factor in preventing the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country.On the other hand,due to the goal of investors in achieving a high return portfolio with the least amount of risk,it is necessary to pay more attention to these markets In this study,in order to maximize returns and minimize investment risk,an attempt was made to create an optimal portfolio in conditions where the capital market has a price bubble.According to the purpose,the research is of applied type,and in terms of data,quantitative and post-event,and in terms of analysis,is descriptive-correlation.In order to identify bubble months in the period from2015to2019in Tehran Stock Exchange,sequence tests and skewness and kurtosis tests were used and after identifying bubble periods,artificial bee colony algorithm was used to optimize the portfolio.The results indicate the identification of 10 periods with a price bubble in the study period.Also,in portfolio optimization, selected stock portfolios are formed with maximum returns and minimum risk.This research will be a guide for investors in identifying bubble courses and how to form an optimal portfolio in these conditions.

Applying tax minimization policies can increase uncertainty about future tax payments due to uncertainty in the tax situation and thus increase the company's risk. However, if a company maintains a consistently low effective tax rate, it is unlikely that this corporate tax policy will increase the company's risk; But if tax minimization leads to instability in the tax situation, it will increase the company's risk. Low tax rates can also increase uncertainty about future cash flows. In this study, the relationship between tax avoidance and corporate risk (future fluctuations in tax rates and future fluctuations in stock returns) is investigated. The research is applied in terms of purpose and using a post-event approach and is correlational based on the method. Two hypotheses have been developed based on theoretical foundations in the direction of research objectives and by selecting 148 companies in the years 2014 to 2020 (1036 firm-year) these hypotheses have been tested. The results of testing the hypotheses show that there is a direct relationship between tax avoidance and tax rate fluctuations and the relationship between tax avoidance and future fluctuations in stock returns is not significant. Based on these results, it can be argued that tax avoidance carries tax risk, but due to the lack of specialization of the Iranian capital market, it cannot be considered effective in stock returns fluctuations.

The main purpose of this study was to evaluate the tolerance of returns to the variations of stocks idiosynchratic volatilities, in order to present an explicit response to previous empirical evidences that documents existence of positive or negative relationship between idiosynchratic volatilities and stock returns. The statistical population of study is all active listed firms in Tehran stock exchange during years 2013 to 2018, which among them, 155 firms were active in market in whole period of research and were studied. The data of research were analyzed through regression models with approach of panel data. The results of regression models in testing hypotheses of study showed that the general relationship between stock idiosynchratic volatilities and future stock returns is in a negative direction. But, the more accurate evaluations under second model showed that this relationship is affected by risk tolerance of return and there is a U-shape relationship between idiosynchratic volatilities and future stock returns.