Shahid Beheshti UniversityFinancial Management Perspective2645-4637103220201221Multi-Factor asset pricing model in Iranian Capital MarketMulti-Factor asset pricing model in Iranian Capital Market93210072810.52547/JFMP.10.32.9FARezaEyvazloAssistant Prof., Department of Financial Management and Insurance, University of Tehran, Tehran, Iran.YasamanHashemiMaster's student in Financial Management, University of Tehran, Tehran, IranAmiraliQorbaniMaster's student in Financial Management, University of Tehran, Tehran, Iran.0000-0002-9336-078XJournal Article20200911Eight-factor pricing model of Skocˇir and Loncˇarski (2018) is a development of Fama and French (2016). They attempt to improve the explanatory power of the model by adding three factors; default risk, liquidity and momentum, in response to some anomalies of the model. Findings show that explanatory power of the model depends highly on variable selection. Efficiency of the model has not been approved in the mentioned confidence interval. But, evidence of the new factors` power and their impact on improvement of the model shows that in combination with 5- factor model, momentum has better performance and default has the worst performance. In addition, through combination of 2 factors, intercept analysis implies that combination of liquidity and momentum leads to a better performance.Eight-factor pricing model of Skocˇir and Loncˇarski (2018) is a development of Fama and French (2016). They attempt to improve the explanatory power of the model by adding three factors; default risk, liquidity and momentum, in response to some anomalies of the model. Findings show that explanatory power of the model depends highly on variable selection. Efficiency of the model has not been approved in the mentioned confidence interval. But, evidence of the new factors` power and their impact on improvement of the model shows that in combination with 5- factor model, momentum has better performance and default has the worst performance. In addition, through combination of 2 factors, intercept analysis implies that combination of liquidity and momentum leads to a better performance.https://jfmp.sbu.ac.ir/article_100728_01c262a2f7d8fa498b3ca5dd8837353e.pdfShahid Beheshti UniversityFinancial Management Perspective2645-4637103220201221Optimization Portfolio Selection in Risk Situations with Combined Meta-Heuristic Algorithm of Genetic Algorithm (GA) and Lion Optimization Algorithm (LOA)Optimization Portfolio Selection in Risk Situations with Combined Meta-Heuristic Algorithm of Genetic Algorithm (GA) and Lion Optimization Algorithm (LOA)335610072910.52547/JFMP.10.32.33FAMohammadMirabiAssistant professor, Department of Industrial Engineering, Meybod University, Meybod, Iran0000-0003-0077-9889MohammadZarei MahmoudabadiAssistant Professor, Department of Industrial Management, Meybod University, Meybod, Iran0000-0001-9544-2490Journal Article20200917Portfolio selection is one of the most concerns of any investor and the goal is to distribute the capital in different assets in such a way that it has the highest rate of return with considering the minimal risk from the investor's point of view. Saving in financial institute or buying bonds and investment in housing market, stock market, foreign currency market or precious metals such as gold and silver are one of the most important choices for any investor with different degrees of risk. Decision situations can be completely certainly, risky and completely uncertainly and solving techniques can be optimization or heuristics. So far during the past decade, different methods are presented depending on the conditions of the capital portfolio selection issue. In this research, a meta-heuristic algorithm based on genetic algorithm and based on the group life of lions is introduced to find a suitable capital portfolio for the investor in risky conditions. Using optimistic, most likely and pessimistic estimates is a strategy used in risky situations. The results of the research confirmed the efficiency of the proposed algorithm in distribution of capital in different sectors with the criterion of maximum return on capital. Also, the proposed algorithm performed better than the whale optimization algorithm in optimizing the portfolio of the top 50 listed companies in terms of stock portfolio return and risk criteria and the time to reach the answer.Portfolio selection is one of the most concerns of any investor and the goal is to distribute the capital in different assets in such a way that it has the highest rate of return with considering the minimal risk from the investor's point of view. Saving in financial institute or buying bonds and investment in housing market, stock market, foreign currency market or precious metals such as gold and silver are one of the most important choices for any investor with different degrees of risk. Decision situations can be completely certainly, risky and completely uncertainly and solving techniques can be optimization or heuristics. So far during the past decade, different methods are presented depending on the conditions of the capital portfolio selection issue. In this research, a meta-heuristic algorithm based on genetic algorithm and based on the group life of lions is introduced to find a suitable capital portfolio for the investor in risky conditions. Using optimistic, most likely and pessimistic estimates is a strategy used in risky situations. The results of the research confirmed the efficiency of the proposed algorithm in distribution of capital in different sectors with the criterion of maximum return on capital. Also, the proposed algorithm performed better than the whale optimization algorithm in optimizing the portfolio of the top 50 listed companies in terms of stock portfolio return and risk criteria and the time to reach the answer.https://jfmp.sbu.ac.ir/article_100729_4c64598fcde9e4990e4c9848089d5401.pdfShahid Beheshti UniversityFinancial Management Perspective2645-4637103220201221Assessment of the Effect of CAMELS Indicators on Risk-Adjusted Return on Capital (RAROC) in the Banks listed in Iran’s Stock MarketAssessment of the Effect of CAMELS Indicators on Risk-Adjusted Return on Capital (RAROC) in the Banks listed in Iran’s Stock Market578010102010.52547/JFMP.10.32.57FAMohammad SadeghAbdollahi PoorMA in Financial Management, Allameh Tabataba’i University, Tehran, Iran0000-0002-6084-7532Mohammad HashemBotshekanAssociate Prof, Department of Finance and Banking, Allameh Tabataba’i University, Tehran, Iran.0000-0002-1685-8808MostafaSargolzaeiAssistant Prof, Department of Finance and Banking, Allameh Tabataba’i University, Tehran, Iran.0000-0002-8245-9258Journal Article20201003In this Research, The Risk-Adjusted Return on Capital (RAROC), as economic performance measurement and risk-adjusted index, was introduced and has been calculated for all the registered banks in the Tehran Stock Exchange and Over-the-Counter Market of Iran, based on contemporary methods which were extracted from earlier researches. The way in which this variable was calculated is one of the distinctions of this research. The period of this research is 8 years, from 2012 to 2019. At the next stage, the CAMELS indicators were introduced and their importance were declared. Then, the impact of these indicators on RAROC were assessed by a multiple linear regression model and Panel Data approach. The results illustrated this fact that there are numerous banks which even disclose net income in their financial statements, while based on RAROC index are not financially as safe as they seem. Also, it has been concluded that Capital Adequacy ratio, Management Quality, Earnings Quality, and Liquidity Quality affect the RAROC. Meaning that, by improving those indicators, RAROC index will be enhanced. On the other hand, Asset Quality and Sensitivity to Market Risk have no significant effect on the RAROC.In this Research, The Risk-Adjusted Return on Capital (RAROC), as economic performance measurement and risk-adjusted index, was introduced and has been calculated for all the registered banks in the Tehran Stock Exchange and Over-the-Counter Market of Iran, based on contemporary methods which were extracted from earlier researches. The way in which this variable was calculated is one of the distinctions of this research. The period of this research is 8 years, from 2012 to 2019. At the next stage, the CAMELS indicators were introduced and their importance were declared. Then, the impact of these indicators on RAROC were assessed by a multiple linear regression model and Panel Data approach. The results illustrated this fact that there are numerous banks which even disclose net income in their financial statements, while based on RAROC index are not financially as safe as they seem. Also, it has been concluded that Capital Adequacy ratio, Management Quality, Earnings Quality, and Liquidity Quality affect the RAROC. Meaning that, by improving those indicators, RAROC index will be enhanced. On the other hand, Asset Quality and Sensitivity to Market Risk have no significant effect on the RAROC.https://jfmp.sbu.ac.ir/article_101020_c98ef9c7736abd148cbdbd858f62b151.pdfShahid Beheshti UniversityFinancial Management Perspective2645-4637103220201221The Effect of Monetary and Financial Instability on Energy-Intensive Industries StocksThe Effect of Monetary and Financial Instability on Energy-Intensive Industries Stocks8110710105310.52547/JFMP.10.32.81FASaraBeiranvandMaster of Energy Economics, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran.MohammadRezaeiAssistant Prof, Department of Economics, Faculty of Business and Economics, Persian Gulf University, Bushehr, IranHadiKeshavarzAssistant Prof, Department of Economics, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran.Journal Article20201127Capital markets are created with the goal of allocating and equipping resources, and one of the most important tasks of these markets is to provide liquidity. A minimum of liquidity is essential for the survival of the capital market. Given that liquidity is considered a type of risk for financial assets and in recent decades has been considered by many economists, so the study of monetary and fiscal policy shocks and their effect on liquidity is important. It is a significant step in orienting the capital market. In this study, using the Structural vector autoregressive (SVAR) model, the impact of monetary and financial instabilities on the liquidity of energy industry stocks has been investigated. The data used are 43 companies active in the energy industry on the stock exchange for 2008 to 2018. The results indicate that monetary and financial instability have a negative impact on the liquidity of energy-intensive industries.Capital markets are created with the goal of allocating and equipping resources, and one of the most important tasks of these markets is to provide liquidity. A minimum of liquidity is essential for the survival of the capital market. Given that liquidity is considered a type of risk for financial assets and in recent decades has been considered by many economists, so the study of monetary and fiscal policy shocks and their effect on liquidity is important. It is a significant step in orienting the capital market. In this study, using the Structural vector autoregressive (SVAR) model, the impact of monetary and financial instabilities on the liquidity of energy industry stocks has been investigated. The data used are 43 companies active in the energy industry on the stock exchange for 2008 to 2018. The results indicate that monetary and financial instability have a negative impact on the liquidity of energy-intensive industries.https://jfmp.sbu.ac.ir/article_101053_6912d2cf8d6f0319d2bed09e2d2e6b37.pdfShahid Beheshti UniversityFinancial Management Perspective2645-4637103220201221The impact of Tedan system analytical reports on the informational efficiency of Tehran Stock ExchangeThe impact of Tedan system analytical reports on the informational efficiency of Tehran Stock Exchange10913010108810.52547/JFMP.10.32.109FAMortezaRafieeMA in Accounting, Department of Accounting, Ferdowsi University of Mashhad, Razavi Khorasan, Iran.RezaHesarzadehAssociate Prof, Department of Accounting, Ferdowsi University of Mashhad, Razavi Khorasan, Iran.FarzanehNasirzadehAssociate Prof, Department of Accounting, Ferdowsi University of Mashhad, Razavi Khorasan, Iran.Journal Article20210313Informational efficiency is an influential variable in financial markets that affects the flow of capital and the optimal allocation of resources. There is extensive literature on the factors affecting information efficiency and our aim in this study is to examine the reports of analysts as one of the factors affecting this variable. For this purpose, the effect of analysts' reports in the Tedan system in general and also in a classified way on the information efficiency of 291 companies listed on the Tehran Stock Exchange during the years 2015 to 2020 was studied using multivariate regression. The information efficiency in this study has been calculated by two methods of variance ratio and Fama and French three-factor model and the results indicate that the Tedan system reports do not affect the information efficiency of Tehran Stock Exchange companies.Informational efficiency is an influential variable in financial markets that affects the flow of capital and the optimal allocation of resources. There is extensive literature on the factors affecting information efficiency and our aim in this study is to examine the reports of analysts as one of the factors affecting this variable. For this purpose, the effect of analysts' reports in the Tedan system in general and also in a classified way on the information efficiency of 291 companies listed on the Tehran Stock Exchange during the years 2015 to 2020 was studied using multivariate regression. The information efficiency in this study has been calculated by two methods of variance ratio and Fama and French three-factor model and the results indicate that the Tedan system reports do not affect the information efficiency of Tehran Stock Exchange companies.https://jfmp.sbu.ac.ir/article_101088_d019eb089e65903455cc52308f00b997.pdfShahid Beheshti UniversityFinancial Management Perspective2645-4637103220201221Compare Canonical stochastic volatility model of focal MSGJR-GARCH to measure the volatility of stock returns and calculating VaRCompare Canonical stochastic volatility model of focal MSGJR-GARCH to measure the volatility of stock returns and calculating VaR13115810110410.52547/JFMP.10.32.131FAAliFarhadianAssistant Prof., Dep of Management and entrepreneur, Faculty of Human Science, University of Kashan, Isfahan, IranMojtabaRostamiPostdoctoral researcher in Economics, Tehran, Iran.MoslemNilchiPh.D. Candidate in Financial Engineering, Yazd Universtity, Yazd, Iran0000-0001-8946-4693Journal Article20210228One of the most important challenges in examining the behavior of investors in financial markets is measuring the volatility of financial assets. This is because stock price volatility is a latent variable. There are two basic approaches to modeling volatility in financial economics that differ in their probabilistic structure. In the first approach, volatility is modeled using shocks to stock returns, and in the second approach, volatility is transformed based on a stochastic process that can be independent of stock return dynamics over time. The models presented in the first approach of the GARCH class and in the second approach of the class constitute random volatility and Markov regime change. Despite the superiority of the probabilistic structure of these models, the calculation of model parameters and volatility prediction is very complex, which makes it necessary to use Bayesian methods and MCMC simulations. The results of this study indicate that in the period of this study, the existence of a leverage effect in the Tehran stock market is not confirmed and the MSGJR-GARCH method is more efficient in predicting fifty more active companies of Stock Exchange return volatility based on Bayesian information deviation criteria. Finally, based on the more efficient model, the out-of-sample VaR was calculated for the first seven days.One of the most important challenges in examining the behavior of investors in financial markets is measuring the volatility of financial assets. This is because stock price volatility is a latent variable. There are two basic approaches to modeling volatility in financial economics that differ in their probabilistic structure. In the first approach, volatility is modeled using shocks to stock returns, and in the second approach, volatility is transformed based on a stochastic process that can be independent of stock return dynamics over time. The models presented in the first approach of the GARCH class and in the second approach of the class constitute random volatility and Markov regime change. Despite the superiority of the probabilistic structure of these models, the calculation of model parameters and volatility prediction is very complex, which makes it necessary to use Bayesian methods and MCMC simulations. The results of this study indicate that in the period of this study, the existence of a leverage effect in the Tehran stock market is not confirmed and the MSGJR-GARCH method is more efficient in predicting fifty more active companies of Stock Exchange return volatility based on Bayesian information deviation criteria. Finally, based on the more efficient model, the out-of-sample VaR was calculated for the first seven days.https://jfmp.sbu.ac.ir/article_101104_db758a12fe660765e6c66b502c52369b.pdfShahid Beheshti UniversityFinancial Management Perspective2645-4637103220201221The Ability of Firm Life Cycle Patterns in Explaining Financial Flexibility (Based on the Adjusted Financial Flexibility Index)The Ability of Firm Life Cycle Patterns in Explaining Financial Flexibility (Based on the Adjusted Financial Flexibility Index)15918810110310.52547/JFMP.10.32.159FAVahidTaghavi FardoudPh.D. Candidate in Accounting, Tabriz branch, Islamic Azad University, Tabriz, Iran.0000-0002-1283-1931RasoulBaradaran HasanzadehAssociate Prof, Department of Accounting, Tabriz branch, Islamic Azad University, Tabriz, Iran0000-0002-2161-0321AhmadMohammadiAssistant Prof, Department of Accounting, Tabriz branch, Islamic Azad University, Tabriz, IranJournal Article20210125The purpose of the present study is to investigate the ability of different models of firm life cycle in explaining financial flexibility in Iran Stock Exchange. Therefore, to achieve the research objective, an adjusted and multidimensional financial flexibility index to reflect the current characteristics of the Iranian Stock Exchange in the form of composite index based on the experts' views using the hierarchical analysis method and the coefficient of variation was extracted, and then using this adjusted criterion of financial flexibility, the ability of different patterns of firm life cycle to explain financial flexibility was tested. The research hypotheses were tested on a sample of 180 companies listed on the Tehran Stock Exchange during the years 2009 to 2019 using panel data regression. The results showed that generally, among the three life cycle models, the Dickinson model combines the net cash flow from operating, investing and financing activities of a company and provides a more complete and comprehensive life cycle map of the company at any date of financial statements, and this reason has increased the ability of Dickinson’s model in explaining financial flexibility.The purpose of the present study is to investigate the ability of different models of firm life cycle in explaining financial flexibility in Iran Stock Exchange. Therefore, to achieve the research objective, an adjusted and multidimensional financial flexibility index to reflect the current characteristics of the Iranian Stock Exchange in the form of composite index based on the experts' views using the hierarchical analysis method and the coefficient of variation was extracted, and then using this adjusted criterion of financial flexibility, the ability of different patterns of firm life cycle to explain financial flexibility was tested. The research hypotheses were tested on a sample of 180 companies listed on the Tehran Stock Exchange during the years 2009 to 2019 using panel data regression. The results showed that generally, among the three life cycle models, the Dickinson model combines the net cash flow from operating, investing and financing activities of a company and provides a more complete and comprehensive life cycle map of the company at any date of financial statements, and this reason has increased the ability of Dickinson’s model in explaining financial flexibility.https://jfmp.sbu.ac.ir/article_101103_59a5a519d252f9f89a099fe4d680be4a.pdf