The Impact of Herding on the Risk Pricing in the Egyptian Stock Exchange

We test the empirical validity of the three-factor model of Fama and French in the Egyptian Stock Exchange (EGX) using monthly excess stock returns of 50 stocks listed on the EGX from January 2014 to December 2018. Our findings do not support Fama and French three-factor model, where the coefficient of the beta was insignificant. The “SBM” coefficient and the “HML” coefficient were equal to zero and insignificant, which confirms the absence of the small firm effect and book-to-market ratio effect in the market. We conclude that there is no relation between expected return and Fama-French risk factors.

This paper examines the impact of financial leverage on the risk pricing in the Egyptian Stock Exchange (EGX) by adding an additional risk factor reflecting financial leverage to the three factor model of Fama and French. We used monthly excess stock returns of 50 stocks listed on the (EGX) from January 2014 to December 2018 to test Fama and French model before and after adding the financial leverage factor. Total debt to equity ratio was used to calculate the financial leverage. The results do not support Fama and French model before and after adding the financial leverage factor, therefore there is no effect of financial leverage on the risk pricing.

This paper examines the impact of herding behaviour on the expected return in the Egyptian Stock Exchange by adding an additional risk factor reflecting herding behaviour to the capital asset pricing model. The study used monthly excess stock returns of 50 stocks listed on the Egyptian Stock Exchange from January 2014 to December 2018. The results do not support the capital asset pricing model before and after adding the herding behaviour factor, therefore there is no effect of herding behaviour on the expected return.

Abstract: 
 Objective: In this paper, we test and compare the explanatory power of the two asset pricing models: the conventional CAPM and the empirical Fama and French three-factor model (1993) in the Moroccan stock market.
 Method: According to the Fama and French (1993) methodology, we analyze monthly data covering the sample period from July 2012 to June 2020.
 Results: The main findings support the superiority of the Fama and French three-factor model. The mimic risk factors pertained to the size and the book-to-market ratio have a significant role in explaining the Moroccan returns. Moreover, results show the existence of weak value effect but significant size effect. Despite the preeminence of the Fama and French model in describing the time-series of sock returns, the model leaves an unexplained fraction of the variation of Moroccan stock returns.
 Originality/Relevance: Our study is the first to compare the two popular models in the financial literature in the Moroccan stock which is an emerging market. Thus, our study corroborates others studies conducted in emerging markets and confirms the hypothesis that the characteristics of those markets impact the explanatory power of asset pricing models.

By conducting ordinary least square estimations using the Fama and French Three-Factor and Five-Factor models on thirty U.S. based industry portfolios, the significant rate of all the variables is compared. Using the comparison, the impacts of the COVID-19 Pandemic on the markets and the Fama and French models are significant. As a result, the significance level of all the independent variables has increased during the COVID-19 Pandemic. The Five-Factor model fares a more substantial increase in efficiency during the Pandemic, and some variables, such as HML and CMA, see tremendous changes. The market becomes less sophisticated during the Pandemic, and the Fama and French Five-Factor model may be more suitable for estimation under certain market environments, contrary to many previous studies.

Purpose This study aims to investigate the market timing strategy in different market conditions (i.e. up, down, normal and in-financial-crisis situation) in the emerging market of Pakistan over the period 1995 to 2015. Furthermore, this study tests the validity of the capital asset pricing model (CAPM) and Fama and French model. Design/methodology/approach This study considers monthly stock returns of 167 firms and constructs six different portfolios on the basis of different size and book to market ratio. The Treynor and Mazuy model is used to capture the market timing strategy. Findings The results indicate evidence of the market timing in normal market conditions. However, there is less supportive evidence of market timing in up-market, down-market and in-financial-crisis situations. This study also confirms the validity of the capital asset pricing model and Fama and French three-factor model with strong support of value premium and size premium in the stock market. Practical implications The findings of this study are helpful to companies in estimating the cost of issuing equity more accurately. The investors can use market timing to make their investment in a more better and profitable manner. Originality/value Unlike other previous studies, this study considers an extended period to test the validity of the capital asset pricing model and Fama and French model. In addition, this study is novel in testing the marketing timing of the firms in the context of emerging economy of Pakistan.

Comparative Study of CAPM, Fama and French Model and Reward Beta Approach in the Brazilian Market

This is the first paper to test the ability of conventional asset pricing models to explain the excess returns of European infrastructure stocks. Specifically, we firstly run the well-known Fama and French three-factor model, including three common stock market factors (market risk, size risk and value risk), and subsequently augment the model with two common bond risk factors (term and default risk), as infrastructure firms should be closely related to bond markets. The times-series regressions span the period from July 1992 to June 2014 and are conducted using an individually created infrastructure equity data-set. With the help of an intensive screening process, we only include those infrastructure stocks that in fact own and/or operate physical infrastructure. The results reveal that the three-factor model is unable to capture most of the variation in infrastructure returns. Therefore, bond risk factors should be included in asset pricing models in order increase the goodness of fit, as infrastructure stocks prove to be sensitive to interest rate changes. Nevertheless, even the augmented asset pricing model leaves a substantial part of the variance unexplained, thus indicating that infrastructure firms exhibit a high level of idiosyncratic risk. In addition, the results suggest that there may be further risk factors which should be investigated in future studies.

After the 2007/9 financial crisis, liquidity risk became the most dreaded financial risk of all times. However, our modern finance theories are modeled on the premises that markets are frictionless and hence liquidity plays no role, yet there is a plethora of literature that attest to the fact that liquidity is a cost that needs to be priced. This confirms the fact that markets are indeed full of friction and therefore, liquidity needs to be modeled accordingly. In this paper, a time-series regression approach is adopted to investigate the relationship between stock illiquidity and stock excess return on the JSE. The basis of the methodology followed in this study is that of Fama and MacBeth's (1973) cross-sectional multiple regression analysis. Specifically, Fama and French three-factor model plus liquidity is employed to test empirically the relationship between stock excess returns and liquidity together with other known, important time-series determinants of stock returns, such as beta, size, and book-to-market ratio. Liquidity is then added to the three factor model as the fourth factor thus, in addition to the excess return on the market. The results from this study show that liquidity is an important factor in pricing returns on the JSE as the stock excess returns are positively related to illiquidity and the relationship is significant. More specifically, the study revealed that the expected excess returns are positively and significantly related to stock systematic risk as measured by beta. This was the case across all the portfolios that were investigated. The three factor model shows a negative relationship though not significant. The there-factor model plus liquidity showed a positive relationship but not significant. Given the exhibited importance of liquidity in determining stock return, stock liquidity should be incorporated in dynamic stochastic general equilibrium models since markets in reality are not frictionless. Given that highly illiquid stocks are associated with higher expected returns as compared to liquid stocks, the conversional finance theories should be revised to reflect this new insight as this is a confirmation that markets have friction, thus liquidity plays an important role in the modeling of required return.

This study comparatively evaluated the Capital Asset Pricing Model (CAPM), the Fama and French three-factor model (FF3), and the Fama and French five-factor model (FF5) in key US market sectors (finance, energy, and utilities). The goals were to optimize financial decisions and reduce valuation errors. The historical daily returns of ten-stock portfolios, selected from sectors with the highest trading volume in the S&P 500 Index between 2020 and 2024, were analyzed. Companies with the lowest beta were prioritized. Models were compared based on the metrics of the root mean square error (RMSE) and mean absolute error (MAE). The results demonstrate the superiority of the multifactor models (FF3 and FF5) over the CAPM in explaining returns in the analyzed sectors. Specifically, the FF3 model was the most accurate in the financial sector; the FF5 model was the most accurate in the energy and utilities sectors; and the FF4 model, with the SMB factor eliminated in the adjustment of the FF5 model, was the least error-prone. The CAPM’s consistent inferiority highlights the need to consider factors beyond market risk. In conclusion, selecting the most appropriate asset valuation model for the US market depends on each sector’s inherent characteristics, favoring multifactor models.

Emerging stock market returns have been extensively studied by the academic community over the past two decades. However, there is still no consensus among the researchers and practitioners as to which asset pricing models should be used to explain returns in these markets. The fundamental objective of the study is to evaluate the power and performance of multifactor asset pricing models (three-and four-factor models) over the traditional one-factor capital asset pricing model (CAPM), using the data from one of the fastest growing emerging markets: India. The study, using a large sample data of 470 listed stocks over a period of 16 years stretching from January 1997 to March 2013, evaluates the relevance of Fama and French three-factor model as well as liquidity augmented four-factor model in explaining the stock return variations in the Indian stock market. The study employs time series regression approach to examine the impact of market risk, size risk, value risk and liquidity risk on stock returns. The overall results of the study provide support to the multidimensional nature of risk and suggest the use of multifactor asset pricing models for consideration in investment decisions. Both Fama and French three-factor model and liquidity augmented four-factor model were found to be superior to traditional one-factor CAPM, though liquidity increased four-factor model was found to be slightly better in explaining Indian stock returns as compared with Fama and French three-factor model.

The authors study the Fama and French three-factor (FF-3F) model in relation to a developing market. To this end, they consider Chinese stock markets over the period 1995–2008, which is to say, over a period when these markets are recognized as “developing” markets influenced by speculative activity. The authors find that the model appears to be working as a form of “principal component analysis for the determinants of stock price formation with book-to-market (B/M) as the “variable of choice” on account of that it captures the earnings-to-price (E/P), cash-flow-to-price (C/P) and sales-to-price (S/P) variables while remaining largely uncorrelated with firm size (whereas E/P, C/P and S/P are themselves positively correlated with firm size). The variables, however, are unrelated to risk as represented by market exposure, volatility, or leverage.

This study is focussed on estimating the real interest and inflation sensitivity in Spanish market, proposing an extension of the Stone (1974) two-factor model and controlling for size and growth of the companies [Fama and French (1993) three-factor model], because of its importance in the stock sensitivity shown by previous literature. I also study the classical explanatory factors of the stock sensitivity: leverage and liquidity level of the firms. The Spanish stock response is similar to the response in other markets, and the ‘size’ is higher than ‘growth’ effect.

Estimation of expected return: CAPM vs. Fama and French

This article addresses the topic of nonlinear dependencies in the Fama and French three-factor model. Five time-series models, including nonlinear terms, are assessed using US and European data and compared with a benchmark linear model. The analysis found that nonlinear dependencies in the modified Fama and French three-factor model were statistically significant and provided additional explanatory power for the underlying return-generating process. However, these nonlinear dependencies are of secondary importance in the economic sense.