جستجوی مقالات مرتبط با کلیدواژه « fama » در نشریات گروه « علوم انسانی »

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 mean and variance of stock returns alone are not sufficient to describe the distribution of returns. , 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 realm of time between 2014to 2021 ) as a statistical sample from the companies listed on the Tehran Stock Exchange based on the systematic removal method (CAPM & FF3). Also, by determining the superior regression model, the power of the superior regression model was compared with the neural network model. results showed that by increasing the unsystematic risk torques calculated with the capital asset pricing model and the Fama and French tree-factor model reduce future stock returns; These results can be justified in line with the concepts of capital market efficiency theory.Other results indicate that the difference in expected non-systematic risk calculated with CAPM and FF3 models is significant. Therefore, the strength of the expected non-systematic risk torques calculated by the capital asset pricing model is less than the Fama and French three-factor model. By comparing the neural network model coefficient and linear regression, it can be said that the neural network-based model has a better performance in predicting future stock returns based on non-systematic risk moments than linear regression.

Several studies have been conducted on the impact of unique fundamental factors of companies on their stock returns. The capital asset pricing model (CAPM) developed by Sharpe (1964) and Lintner (1965) and the three-factor model of Fama and French (1993) are the most famous models resulting from these studies. These two models have been widely accepted by academics and used in numerous types of research since its presentation. Significant studies such as those of Fama and French (2012 and 2017) and Hou et al. (2015) show the validity of some global factor models. Nevertheless, Findanza and Morsi (2015) believe that, despite numerous studies on the efficiency of factor models in non-financial companies, few pieces of research have been conducted on the explanatory capability of these models in financial companies. At the same time, a more recent stream of studies in the finance field, including the research of Baker and Wergler (2007) and Sime et al. (2013), show that the performance of traditional factor models may be improved by adding various behavioral factors such as investors' sentiment factor. The paradox of the efficiency of markets depends on the efforts of investors to find opportunities to earn abnormal returns by discovering anomalies. By trying to discover these opportunities, many researchers have focused on the investor sentiment factor and studied its effect on the stock price. The current research seeks to investigate the effectiveness of Fama and French's three-factor model and CAPM in the banking and credit institutions industry, as well as the role of investors' sentiment in this industry to increase the explanatory power of the aforementioned factor models.

The current research is applied in terms of purpose. In terms of method, the research uses regression analysis along with the generalized least squares (GLS) model. The statistical population of the research includes all the listed banks in Tehran Stock Exchange and IRAN FARA BOURSE (IFB). Also, the time domain of the research includes the monthly periods from April 2012 to November 2022. The sampling is based on the methodical elimination of the banks that make up the statistical population. After removing the banks with a shorter acceptance period than the period of the research, 10 banks make up the sample size of the research. The Excel software is used to classify the data and calculate the research variables. Also, the data are analyzed with the Eviews (13 edition) and Stata (17 edition) software programs.Due to cross-sectional dependence, which causes the efficiency of the ordinary least squares method to reduce, the model used the generalized least squares (GLS) method in the Eviews software. To eliminate the problem of heterogeneity of variance, White’s correction (Period Cluster) has been used.

According to the cross-sectional dependence identified in the model, the final estimation of the research model has been done using the generalized least squares (GLS) method and White's correction to solve the heterogeneity of variance. The probability of F statistic related to the significance of all the models is equal to 0.0000. As a result, all the research models are significant. The findings show that adding variables related to the investor sentiment to the asset pricing factor models increases the explanatory power of these models. The excess market return factor was found to be significant in all the models, and the value factor was found to have no significant effect on banks' excess return in any model. The factors related to the investor sentiment are significant in both combined models. At the same time, the addition of the investor's sentiment index to the CAPM and Fama and French three-factor model has increased the adjusted coefficient of determining these models.

Conclusions and policy implications: 

The purpose of this research is to investigate the effectiveness of the CAPM and Fama and French three-factor model in the banking industry and the role of investor sentiment in strengthening the explanatory power of these models. The findings of the research show the significant and positive effect of excess market returns on the risk-adjusted performance of the studied banks. The size factor also has a significant and positive effect on the excess return of the investigated banks, which shows that there is a direct relationship between the excess return of banks and the performance of the size portfolio or the performance superiority of large market companies. Market performance has had the strongest effect on the excess returns of banks. However, the positive effect of the value portfolio performance on the excess return of banks is not statistically significant. This indicates that, although the expectations of investors incited by future developments, regarding the growth of stock returns with a high ratio of book value to market value, increase the return of this type of stock in the medium term, the factors influencing the stock returns are not counted in the long term. Therefore, the factor of market value to book value does not have a significant relationship with the risk-adjusted performance of banks. The findings of the research also show the significant and positive impact of the investor's sentiment index on the excess returns of banks. The sentiment of investors, thus, has a direct relationship with the excess returns, and the increase of the sentiment of investors and the strengthening of their optimism towards the banking industry cause an increase in the excess returns.

The purpose of this study was to investigate the role of financial uncertainty, economic uncertainty, economic policy uncertainty as a systematic risk and the fama french five factor model along with Momentum as a unsystematic risk in the US capital market and short-term and long-term effects on stock returns (S&P New York Stock Exchange Index) Using a structural vector autoregressive model Quarterly data. The results show that in the short run, the greatest impact of negative shocks, respectively, due to variables such as stock returns, profitability factor, small minus big (SMB) and momentum respectively. Economic policy uncertainty has also had a positive short-term shock to stock returns. Therefore, the variables of economic uncertainty and financial uncertainty, like other Unsystematic risk factors, have almost equally and negatively affected stock returns. In the long run, the greatest impact of negative shocks was due to variables such as economic policy uncertainty, stock returns, economic uncertainty and investment factor, respectively. In the long run, the greatest impact of positive shocks is due to variables such as Small Minus Big (SMB), High Minus Low (HML), profitability factor, momentum, Market portfolio return ratio and risk free rate of return and finally financial uncertainty.

The purpose of this study was to explain the role of investors' emotional tendencies in pricing the capital assets of companies listed on the Tehran Stock Exchange. For this purpose, information of 150 companies from the financial reports of companies listed on the Tehran Stock Exchange have been collected and tested. Findings show that in all three capital asset pricing models (Fama and French three-factor model, (2001) Kahrat four-factor model and Fama and French five-factor model, (2014) investors' emotional tendencies in price The investment of capital assets has an effect and increases the sheer risk of the portfolio.The results also show that in all three pricing models, presenting a capital asset pricing model based on investors' emotional tendencies increases the predictive power of common capital asset pricing models.

evaluating the return on investment is one of the main concerns of investors, which is conducted using different models including the single-factor model CAPM, three and five factors Fama and French, and six factors Roy and Shijin, etc. known as multifactorial models. In spite of the widespread use of the models, their main disadvantages include sensitivity to unexpected changes, sudden shocks, severe turbulence of price bubble, and so on. To solve the disadvantages, a multi-factor model is estimated based on the use of the penalty function method, instead of using the average method, which would act based on optimization and avoiding the impact of unusual changes and other factors affecting the capital market. It is possible to select effective factors and model to evaluate stock returns and present a model appropriate to the conditions prevailing in the Iranian capital market. In this article, the classification and estimation of the integrated model of penalty and multi-factor (P & PCA) was performed by forming investment portfolios and identifying the effective factors and refining it based on performance data over a period of time. The study results showed that the use of an extensive simulation algorithm for penalty function by estimation method (P & PCA) improves the efficiency of multifactorial methods in evaluating stock returns. The use of the finite and multi-factor combination algorithm has higher accuracy and explanatory power during the review period in estimating stock returns than the 5-factor Fama and French model.

Understanding why and how asset prices rise is a major concern for industry, policymakers and investors. This has attracted the attention of researchers to the issue of asset pricing, especially high-risk assets such as stocks. The single-factor capital asset pricing model (CAPM) and the Fama and French (1993) three-factor model are some of the well-known models that seek to explain investors' expectations for high-risk asset returns, and various multifactorial extensions of these models have been proposed. One of the latest versions of these models is the seven-factor model, which is expected to have a higher explanatory power than the CAPM model and the Fama and French three-factor model. The purpose of this study is to evaluate the seven-factor model in Tehran Stock Exchange and compare it with CAPM model and Fama and French model in order to predict the expected returns of companies operating in the financial sector of Tehran Stock Exchange during the period 2016: 1 to 2019: 12. In this study, the estimation method is the Auto Regressive Distributed Lag (ARDL). The results of the study show that the seven-factor model gives a better estimate of the expected return than the CAPM and Fama and French models. Also in the seven-factor model, the variables of book value to market value ratio, momentum, liquidity turnover ratio and commodity index have a negative and significant effect on expected returns. But market factor, government bond index and company size factors do not have a significant effect on expected returns.

The success of any asset pricing model should depend on the proportionality of the real options in the value of the companies whose returns are tested in the model test. The purpose of this study is to investigate the Investment Option Model Indicators and its impact on stock returns. The statistical population of this study consisted of 146 companies in the period 1390-1397. This research is applied in terms of and in terms of nature and content is correlational and the research hypotheses are estimated using multivariate regression approach and panel data method. The results of the hypotheses test showed that investment options based on Grullon's four-factor model affect stock In addition, the results showed that there is a significant difference between the explanatory power of Fama and French's five-factor asset pricing model and the investment options model based on Grullon's four-factor model in explaining stock returns. And the explanatory power of the Grullon's four-factor model in explaining stock returns is more than the Fama and French five-factor model.

This research is based on an eight-factor model consisting of the Fama and French five-factor model plus the variables of market stress, market fragility, and market liquidity risk in order to investigate the explanatory power of this model in Tehran securities market during the years 2009 to 2018 for 117 companies on a monthly basis. The results of the research indicated that the explanatory capability of the eight factor model is better than the Fama and Farnach five-factor model in the Iranian capital market.The results show that fragility has a significant negative relationship with stock returns. The results also show that stress has a significant negative relationship with stock returns and but liquidity risk has a significant positive relationship with stock returns. This result can be of interest to policymakers in the field of finance and investment and other stakeholders.Keywords: Liquidity Risk, Fragility, Market Stress, Fama and French Five-Factor Model, Stock Return

The purpose of this study is to investigate the possibility of combining the ranking period and the maintenance period of classical momentum and contrarian strategies and evaluate the performance of portfolios resulting from hybrid strategies in comparison with classical strategies. For this purpose, using the information of 120 companies in the period 2011 to 2020, the amount of excess return, the explanatory power of excess return by the risk factors of the seven-factor model Fama and French and the explanatory power of future return by excess return of hybid strategy and classical strategy are evaluated and compared.The results show that contratum (contractionary hybrid) strategy, which rank like contrarian strategies in the long run but are maintained in the medium term like momentum strategies, perform better than momentum and contrarin strategies. In addition, even after controlling risk factors (Fama and French seven-factor model), contratum, contrarian and momentum investment strategies in the Iranian capital market are profitable. Finally, the results of the present study support the explanatory power and more predictive power of the excess return of the contratum strategy than the classical strategies for future returns. Therefore, the contribution of the present study is to provide evidence of excess return and performance of the new hybrid strategy is much higher than the classic investment strategies proven in the Iran capital market.

Knowing effective factors on optimum portfolio assignment is one of the main issues facing finance market investors. Therefore, the present empirical study aims to assign optimum portfolio pricing pattern in Tehran Stock Exchange. Hence, R- CAPM, Fama and French and Carhat pricing pattern were studied. A combination of two methods (panel data and apparent portfolio) were used for 2012-2020. Data sample consisted of 176 active companies in Tehran Stock Exchange. Initially, data were divided into two groups: the first group used for portfolio making and model estimation and the second group used for optimum portfolio assignment. For optimum assignment MAD, MSE, RMSE, MAPE index were used. Results indicated that portfolio comprising big companies had a negative effect on investment return, while portfolio comprising small companies had positive return. Momentum factor of portfolio comprising winning companies was positive while in portfolio comprising losing companies was negative. Finally, it can be concluded that in Tehran Stock Exchange Carhat pricing pattern has better performance compered to RCAPM or Fama and French pricing pattern during the mentioned period.

Identifying the factors affecting stock returns has become very important in recent years. Therefore, the purpose of this study is to investigate the effect of two behavioral factors of peak and end based on prospect theory on the explanatory power of Fama and French three-factor and five-factor models. The statistical population of the present study is the companies listed on the Tehran Stock Exchange during the years 2006 to 2020. In this study, multivariate regression method has been used to analyze the data. The results show that the five-factor model of Fama and French has more explanatory power than the three-factor model, in other words, adding two factors of profitability and investment to the three-factor model increases its explanatory power. Findings also indicate that peak and end behavioral factors have very little effect on increasing the three-factor and five-factor models of Fama and French. In other words, the addition of peak and end factors to the two three-factor models and the five-factor Fama and French models does not increase the explanatory power of these models. Although the results show that the peak power is slightly higher than the end, they have little effect on the models as a whole.

The purpose of this research is to investigate two controversial cases about the momentum in the Tehran Stock Exchange. These two cases include risk theory and under-reaction theory. This research will be followed in two parts. The first part tries to explain the momentum with respect to risk. The second part describes the excess return in momentum portfolios with the under-reaction hypothesis. The research hypotheses have been examined using the data of 58 non-financial companies from the Tehran Stock Exchange between 1389 and 1398. The results show that the risk-adjusted momentum profit is statistically significant. In addition, the results show that the five-factor risk model is not able to explain the momentum effect. However, the momentum effect can be explained by using the under-reaction hypothesis. The under-reaction is asymmetric for first six month after the earning announcement date. In general, the findings also rejects the efficient market hypothesis in favor of the under-reaction theory.

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.

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.

The necessity of research on predicting investment returns and presenting effective models of its explaining and compare the most efficient ones, affects both the expansion of investment in the financial market and the creation of more confident conditions for decision making and portfolio formation. Experimental testing of some models has shown that investment options are measurable as a way to create more options as one of the most common types of real options based on investment options and can be effective in explaining stock returns. The purpose of study is to explain the factors affecting equity returns using the investment options approach. The information from this study is taken from the financial statements of 146 companies during the period 1390-1397. The research is applied in terms of purpose; in terms of nature and content it is a correlation type. Multiple regression based on panel data analysis was used to test the relationship between variables and the significance of the model. The results of the hypotheses test showed that investment options based on Grullon's four-factor model affect stock returns and firms with more aggregated investment options based on Grullon's model have higher stock returns. In addition, the results showed that there is a significant difference between the explanatory power of Fama and French's five-factor asset pricing model and the investment options model based on Grullon's model in explaining stock returns. And the explanatory power of the Grullon's model in explaining stock returns is more than the Fama and French five-factor model.

 Since the 1990s, the determination of the fair value of financial assets and whether this value depends only on fundamental factors in pricing models or is influenced by behavioral and psychological factors has always been a debatable issue. The behavioral finance field focuses on issues and proposes asset pricing models that incorporate behavioral decision-making aspects. The purpose of this study is to investigate the simultaneous effect of the criteria of the sentiment and individual investors trading behavior on the pricing of capital assets in 77 listed firms in Tehran Stock Exchange during the period from 2009 to 2014, using combination data method.

 For this purpose, a revised Fama and French five-factor model is created by adding sentiment and individual investors trading behavior.

 The experimental results showed that these two factors have a significant effect on the return on the five-factor model. In a situation where all investors are not rational, decision-makers can use the investor's psychological aspects to understand how asset pricing works.

The purpose of this paper is to examine the explanatory power of Hou et al. four-factor model and Fama and French’s five-factor model to predict the expected stock return in listed companies in Tehran Stock Exchange. For this purpose, a sample consisting of 147 firms during the period of 2006 to 2015 has been selected. To analyze the data, descriptive and inferential statistics were incorporated and to test the research hypotheses, multiple regression approach with the pooled data approach through the software’s Eviews and Stata has been used. The findings indicate that the adjusted coefficient of determination, which shows the explanatory power of their two models together, is equal. The mean squared error and mean absolute error of both models also are not significantly different. Thus, the results demonstrated that in the course of the study, the explanatory power of the French’s five-factor model with Hou et al. four-factor model, in anticipation of expected returns is not significantly different from each other.  Therefore, investors can use both models to predict expected returns to form their investment portfolio.

The bubble in the stock market and in general in all markets is a real phenomenon that can cause losses to investors. The main problem facing each investor in the capital market is the decision to select the securities for investing and creating an optimal portfolio of stocks. Hence, stock valuation models have long been used by researchers  and investors. Solving  valuation puzzle reveals  the need to develop a comprehensive model that  describes the abnormal return . In this regards, although many efforts have been made and various models have been developed, none of these models has been able to fully explain this abnormal return. In this research, a pricing model in bubble conditions and an evaluation of the effective factors on stock returns are presented using the Fama-French model. For this purpose, the sample included 81 listed companies in the Tehran Stock Exchange( TSE) between 2009 and 2013, which have been selected . The results of this study reveal that among the five factors of the market, firm size, book value to price, momentum and bubble, only two factors of momentum and bubble affect the abnormal returns.

  This study examined the effects of investor sentiment index on excess return in the Fama-French five-factor model, in the companies listed in the Tehran Stock Exchange in the period 2009 to 2015. Data collection was based on document mining and referral to databases, and data analysis was performed using inference method and Stata and Eviews software. To test the research hypotheses a Panel data model was used. The results showed that the investor sentiment index plays an important role in capital assets pricing, and supports this role of the index in creating excess return and price formation. Attention to the factors such as investor sentiment, besides the fundamental factors, can have significant effect on the decisions of investors and other users, regarding challenges of the efficient markets hypothesis.