فهرست مطالب mohammadreza pourfakharan

The mean and variance of stock returns alone are not sufficient to describe the distribution of returns. According to Markowitz, paying attention to higher torques such as skewness and kurtosisas a risk index instead of variance leads to more accurate results. Therefore, due to study contradictions, the study of stock returns reaction to models for measuring non-systematic risk moments was significant. For this purpose, 152 companies were selected( In the time domain of the years between 2013to 2020 ) as a statistical sample from the companies listed on the Tehran Stock Exchange based on the systematic removal method. results showed that by increasing the unsystematic risk torques calculated with the capital asset pricing model and the Fama and French five-factor model reduce future stock returns; These results can be justified in line with the concepts of capital market efficiency theory; Other results showed that the effect of unsystematic risk moments on stock futures is intensified by the simultaneous interaction between limited shareholder attention and arbitrage constraints. These results are based on the concepts of information asymmetry theory and the concepts of attentional limitations theory; Investors are taking risks due to irrational preferences for positively skewed stocks. This puts companies at greater risk for future stock price declines. This effect is exacerbated when institutional investors' disregard for managers coincides with increasing arbitrage restrictions. And managers will hide bad news through earnings management to affect stock returns.The obtained results are consistent with the documents mentioned in the theoretical framework of research and financial literature.

The main purpose of this study is to investigate the tail risk measures (VaR and ES) in Tehran Stock Exchange using the dynamic multi-scale generalized autoregressive ranking approach (DMS- GAS-1F). In this regard, using the daily data of the total index of Tehran Stock Exchange in the period 2011/03/26 - 2022/03/19 and the maximal overlap discrete wavelet transform (MODWT)algorithm, the short-run, medium-run and Long-run components of time series returns are extracted. Then, using the approach of generalized autoregressive ranking models (GAS), the tail risk measures at different time horizons are dynamically estimated and finally using the inverted wavelet transform, the final results of estimating the risk criteria based on the proposed model (DMS- GAS-1F) is provided. The results of backtests show that the proposed model has performed better in out-of-sample forecasting of tail risk measures than competing and traditional models in this field, including GARCH models and rolling window models. In addition, the results show that the use of the Maximum Overlap Discrete Wavelet Transform (MODWT) algorithm to extract information components at different time horizons has increased the predictive efficiency of the model.

Market disorder and irrational behavior of investors escalate stock market volatility. This research has been conducted to identify drivers and consequences of behavioral bias and strategies for reducing the effects of bias behavioral in institutional investors by implementing the correlation research method. The evidence collected from a possible statistical sample of 607 legal investors has been analyzed using a questionnaire, confirmatory factor analysis, and structural equation modeling. The results have shown that increasing the causes and bases of bias (social and psychological factors, knowledge and experience, and market) leads to an increase in behavioral biases and the use of behavioral biases strategy in investors' decision-making. Also, the strategy to reduce behavioral biases is influenced by behavioral biases, background factors, and intervening factors. In the end, the success of investors is significantly influenced by behavioral bias reduction strategy, the causes of the behavior biases, and the behavioral biases. Evidence has shown that the investigated model has predictive validity for investment performance, and institutional investors can benefit from this study by understanding the influence of behavioral factors on investors' decisions.

The development of transportation plays an important role in the development of the country's oil industry. Sustainable financing of transportation in this industry will help the sustainable development of the oil industry by considering all dimensions, including economic and environmental. The aim of the current research is to identify and analyze the drivers affecting the future of sustainable financing in the transportation of the oil industry.The present study is post-positivist in terms of philosophical foundations and applied in terms of orientation. To analyze the research data, two quantitative methods of fuzzy Delphi and modified fuzzy Vikor were used. The theoretical population of the research was the managers and senior experts of the oil industry in the field of transportation. Sampling was done judgementally based on the expertise of experts and the sample size was 10 people. Research tools for data collection were interview and questionnaire.After reviewing the literature and interviewing the experts, 29 drivers affecting the future of sustainable financing in the transportation of the oil industry were extracted. Research drivers were screened using fuzzy Delphi. After the fuzzy screening, nine drivers had a defuzzy coefficient higher than 0.7 and were selected for final prioritization with modified fuzzy vikor. Finally, after the final prioritization with the fuzzy Vikor technique, the development drivers of the banking industry had the highest priority. The driver of bunkering industry development had the highest priority. Applied research proposals were developed based on priority drivers. The most important practical suggestions of the research were: The government's investment in providing infrastructure for the bunkering industry, improving the business environment and removing annoying laws to facilitate investment in the country and using the capacity of universities and science and technology parks to strengthen startups and knowledge-based companies.

12 public and private banks are currently present in the stock exchange market in the “bank and credit institution” group. This study examines the behavioral bias of legal clients of stock exchange investors according to the literature on the behavioral finance. The grounded theory approach has been used to examine the patterns of behavioral bias of legal clients. The research data are the narratives of the participants (legal clients of stock market investors) for 2021. Nineteen people were selected using the purpose-based sampling method, and their narratives were collected through semi-structured interviews and coded by MaxQda software and analyzed in three stages. The findings of the study have shown that institutional investor behavioral bias is formed on social, emotional and psychological bias platforms, whose drivers are social factors, market, knowledge and experience, and psychological factors. To reduce the impact of behavioral bias, strategies for portfolio diversification strategies, investment strategies, investment analysis, and the use of information, are used which in turn result in improved capital market efficiency and investor success. Furthermore, the strategies are influenced, on the one hand, by cultural, political and economic factors, and on the other hand by the financial characteristics of the company, the policy of the company, and the institutional characteristics.

The oil industry is one of the key drivers in the economy of Iran and the world. Every year, many projects are defined and implemented in this industry. Without proper financing and the use of efficient financing methods, the success of this industry for the exploration, production, profitability and development of the value chain is impossible. The future of financing and its methods are completely changing under the influence of financial technology and new trends. Therefore, the present study seeks to identify the drivers that affect the future of financing methods in the oil industry.

The present study is applied in terms of orientation and a quantitative multi method from a methodological point of view. In this study, two quantitative methods of Best-worst and Copras were used to analyze the data. The theoretical community consisted of managers and senior experts in the field of financing oil projects. The sample size in this study was 10 people and sampling was done by judgment. To collect research data, three questionnaires of expertise, pairwise comparison and prioritization were used.

At first, research drivers were extracted using analytical literature review. Twenty-five drivers were screened using Binominal test and eight drivers were selected for final prioritization due to a significance coefficient of less than 5%. The remaining drivers were prioritized using the Copras technique, taking into account the three indicators of severity, degree of certainty, and expertise. The best-worst method was used to weight the indices and the degree of certainty index (0.542) had the highest weight. Finally, Copras analysis showed that the drivers of investment risks in the country, the situation of the capital market in Iran and legal and sharia restrictions in the field of financing and investment, respectively, had the highest priority in terms of influencing the future of financing methods of the oil industry.

Research proposals were presented with respect to important drives. The most important suggestions of this study were: considering the nature of project risks to choose the appropriate financing method, improving the capital market for foreign investors, removing legal barriers and adjusting legal restrictions to encourage investment and project financing, supporting fintechs by traditional financial institutions and finally, the efforts of diplomacy stakeholders to resolve foreign sanctions and restrictions.

Universities and higher education institutions have moved towards diversifying their sources of income due to declining government budgets, economic pressures, intense competition, declining demand for graduate education, and international financial crises. Therefore, the present study seeks to identify the most important drivers of the future of universities' revenue sources.

The present study is applied in terms of orientation and its methodology is quantitative multiple. The theoretical population of the research is the planning and programming managers of the major universities in the country and experts in the field of education financing. The sampling method was judgmental according to the skill of experts in the field of higher education financing. The sample size in this study was 10 people. Research drivers were obtained through a literature review and interviews with experts. Then, two questionnaires of expert evaluation and effectiveness evaluation were used to screen and analyze the effectiveness of the drivers. These questionnaires were analyzed by two techniques of fuzzy Delphi and fuzzy Dematel.

25 drivers were obtained through a literature review and interviews with experts. Among the factors of the study, 10 drivers had a defuzzy value greater than 0.6 and were selected for final analysis. The effectiveness of the remaining drivers was evaluated with fuzzy Dematel. According to the net effect index, the drivers of sanctions and external restrictions, the economic conditions of the society, the nature of the relationship between universities and industries and businesses in the society, and the policies of the Ministry of Science regarding student admission had the most impact.

The practical suggestions of the research were presented according to the most effective drivers. In this regard, suggestions such as the resilience of universities, attention of universities to the production of wealth in society, directing graduate theses and research towards problem-oriented and attracting students based on the needs of industry and market, land use planning, and local requirements were presented.

The main purpose of the present study is to investigate the tail risk in the Tehran Stock Exchange using dynamic methods and time series spectral analysis in the form of the dynamic multi-scale Expected Shortfall (DMS-ES).

In this study, the daily data of Tehran Stock Exchange total index in the period 2011/03/26 - 2022/03/19 were used then extracting the short-term, medium-term and long-term information components of the index return time series, four predictive models of expected fallout (ES) were estimated using Taylor (2017) approach at different time horizons.

The results of estimating the models and back tests show that the expected shortfall (ES) is short-term in nature and the dynamic model using short-term components in the one-day time horizon has the highest efficiency among the introduced models. In addition, the results of comparing the models based on the average loss test show that the use of spectral analysis components increases the efficiency of dynamic predictions of the expected shortfall (ES) at different time horizons.

Originality / Value:

 Finally, according to the results of this study, it is suggested to use dynamic models and signal decomposition analysis algorithms to increase the efficiency of risk criteria predictions.

LARG supply chain is a combination of four supply chains: lean, agile, resilient and green. Each type of supply chain has its own requirements that may conflict with other supply chain requirements. The study seeks to identify the types of risks facing the LARG supply chain.

The present study has a positive nature in terms of philosophical foundations due to the use of quantitative methods. From the orientation point of view, the research has an applied approach and its methodology is multiple. The statistical population of the study is the experts of the supply chain of the automotive industry who are selected using a judgmental method for sampling. In this study, two methods of fuzzy Delphi and best-worst were used to analyze the data.

Initially, 41 risks were identified through literature and interviews with experts. In the following, these 41 risks are presented in the form of 13 main risks. Each of these risks is related to a specific supply chain. These risks were then screened by fuzzy Delphi and 25 risks were excluded. The remaining risks were prioritized using the BWM. The results showed that the risks of financing problems, production planning, changing customer demands and weakness of processes and recycling technology have the highest priority in each component of the LARG supply chain.

Diversifying financing tools, using new marketing methods and having a team approach in product development, paying attention to trends affecting future consumer behavior and examining changes in customers' buying behavior are the most important suggestions.

The financial helplessness of companies is one of the factors that, when faced with it, the management of the company takes certain actions and accordingly manipulates the accounting profit as one of the most obvious performance evaluation items when faced with financial helplessness. do The companies that are facing financial helplessness to get rid of the bankruptcy costs start profit management and use more accrual items than other companies. In this research, the relationship between financial helplessness and profit management was investigated according to the role of the auditor during the period of 2015-2019. In terms of classification according to the purpose, this research is an applied research, and in terms of classification based on the method, it is considered a descriptive research, and among the descriptive researches, it is of the correlational type. The statistical sample of the research was selected by the systematic elimination method of 120 companies. In this research, three indicators of negative net profit, negative working capital, and simultaneous negativity of profit and working capital were used to investigate financial helplessness. The results of the hypothesis test at the five percent error level showed that there is a significant relationship between financial helplessness and profit management. Also, the size of the auditor has a significant effect on the relationship between the company's financial helplessness and profit management.

The purpose of this study was to investigate the response of stock returns to non-systematic risk torque measurement models. The spatial scope of this research was the companies listed on the Tehran Stock Exchange and the time domain was between 2013and 2020. The present research is in the category of applied research. If the classification of types of research is considered based on the nature and method, the method of the present research is descriptive in terms of nature and is considered as a correlational research in terms of method. Based on the systematic elimination method, 152 companies were selected as a statistical sample. Descriptive and inferential statistics have been used to describe and summarize the collected data. In order to analyze the data, first the variance heterogeneity pre-tests, F-Limer test and Jark-Bra test were used, and then multivariate regression were used to confirm and reject the research hypotheses (EVIEWS software). The results showed the effect of unsystematic risk torques on future stock returns with increasing limited shareholder attention; Arbitrage restrictions as well as the simultaneous interaction between limited shareholder attention and arbitrage restrictions are intensifying; The obtained results eliminate the contradiction of studies and are consistent with the documents mentioned in the theoretical framework of research and financial literature.

In the present study, the role of auditing financial statements in improving the company's business credit in the framework of contract theory has been discussed. The research question of the present research is that audit of financial statements (auditor's type ˛audit quality ˛ auditor's opinion) ˛ affects the firm's firm rank or not . The statistical population in this research is all companies accepted in Tehran Stock Exchange. According to the criteria and systematic deletion, 172 companies have been selected as the sample in this research. In order to test the hypotheses of the research, multiple linear regression model based on generalized least squares and panel template has been used. The result of testing the research hypotheses shows that the quality of the independent auditor and the quality of the audit of the financial statements improve the company's Commercial Credit, Further findings indicate that the audited opinion is not affected by the company's corporate credibility. Further findings show that the controlling variables include the firm size ˛Leverage ˛ operating cash flow ˛ return on asset are effective on company's Commercial Credit.