Applying the CAPM and the Fama–French models to the BRVM stock market

Soumaré, Issouf; Amenounvé, Edoh; Diop, Ousmane M.; Méité, Dramane; N'sougan, Yao Djifa

doi:10.1080/09603107.2012.718062

Cited by 22 publications

(11 citation statements)

References 7 publications

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“…Bundoo (2008) provides evidence of the size and BM effects and argues that the Fama-French model captures these effects on the Mauritius market. Also, Soumaré et al (2013) found that the Fama-French model partly describes the returns of individual stocks on the Bourse Re=gionale desValeurs Mobilie'res. Liew and Vassalou (2000) explored the relationship between future economic growth and the Fama-French-Carhart factors.…”

Section: Introductionmentioning

confidence: 95%

“…This is attributable to the small size of most of these markets and the difficulty in assembling large enough stocks to construct the underlying portfolios of these models. For instance, prior African studies such as those like Bundoo (2008), Hearn and Piesse (2010) and Soumaré et al (2013) may suffer from RAF 14,4 small sample problems. Pastor and Stambaugh (2003) argue that the profits to the winner-loser portfolios are significantly driven by market liquidity.…”

Section: Introductionmentioning

confidence: 99%

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Robustness of the Carhart four-factor and the Fama-French three-factor models on the South African stock market

Boamah

2015

Review of Accounting and Finance

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Purpose – The purpose of this study is to explore the applicability of the Fama–French and Carhart models on the South African stock market (SASM). It examines the ability of the models to capture size, book-to-market (BM) and momentum effects on the SASM. The paper, additionally, explores the ability of the Fama–French–Carhart factors to predict the future growth of the South African economy. Design/methodology/approach – The paper relies on data of 848 firms from January 1996 to April 2012 to examine the size, BM and momentum effects on the SASM. The paper constructs the test assets from a 3 × 3 sort on size and BM and a 3 × 3 sort on size and momentum. The paper estimates momentum as the past six-months’ cumulative return. The momentum portfolios are monthly rebalanced. Additionally, the size and BM portfolios are formed annually at the end of each June. Findings – Evidence is provided that size, BM and momentum effects exist on the SASM; also, the small- and high-BM firm portfolios, respectively, appear riskier than the big- and low-BM firm portfolios. The paper provides evidence of past winners outperforming past losers aside from the small-firm group. Additionally, the models only partially capture the size and value effects on the SASM. The Carhart model partly captures the momentum effects, but the Fama–French model is unable to describe the returns to the momentum-sorted portfolios. The evidence shows that the models’ factors predict future gross domestic product growth. Originality/value – The models do not fully describe returns on the SASM; any application of the models on the SASM should be done with caution. The Carhart model better describes returns than the Fama–French model on the SASM. The Fama–French–Carhart factors may relate to the underlying economic risk of the South African economy.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Robustness of the Carhart four-factor and the Fama-French three-factor models on the South African stock market

Boamah

2015

Review of Accounting and Finance

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“…In order to confirm if the OLS hypotheses are fulfilled (normality, homoscedasticity, and independence), we performed several analyses similar to those in (Soumaré et al 2013). A Shapiro-Wilk test was run on each set of residuals, suggesting that, in general, the residuals of the TS regression are normally distributed, except for portfolios 1-1-1 (p-value = 0.007), 1-2-1 (p-value = 0.037), and 1-2-2 (p-value = 0.024).…”

Section: Model 1: Capmmentioning

confidence: 99%

Models for Expected Returns with Statistical Factors

Cueto

Grané

Cascos

2020

JRFM

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In this paper, we propose multifactor models for the pan-European Equity Market using a block-bootstrap method and compare the results with those of traditional inferential techniques. The new factors are built from statistical measurements on stock prices—in particular, coefficient of variation, skewness, and kurtosis. Data come from Reuters, correspond to nearly 2000 EU companies, and span from January 2008 to February 2018. Regarding methodology, we propose a non-parametric resampling procedure that accounts for time dependency in order to test the validity of the model and the significance of the parameters involved. We compare our bootstrap-based inferential results with classical proposals (based on F-statistics). Methods under assessment are time-series regression, cross-sectional regression, and the Fama–MacBeth procedure. The main findings indicate that the two factors that better improve the Capital Asset Pricing Model with regard to the adjusted R2 in the time-series regressions are the skewness and the coefficient of variation. For this reason, a model including those two factors together with the market is thoroughly studied. We also observe that our block-bootstrap methodology seems to be more conservative with the null of the GRS test than classical procedures.

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“…As reported by Kumar and Goyal (2015), the majority of studies concerning the behavioral biases among investors were conducted in the United States and other developed countries with a few studies conducted in the developing countries. Behavioral finance is important in emerging markets because the traditional asset pricing models are invalid in some of these markets including the capital asset pricing model (Alqisie & Alqurran, 2016;Chaudhary, 2017;Elshqirat & Sharifazdeh, 2018;Obrimah, Alabi & Ugo-Harry, 2015;Soumaré , Amé nounvé , Diop, Mé ité & N'sougan, 2013) and the arbitrage pricing theory (Elshqirat, 2019;Gul & Khan, 2013;Okoro, 2017). Concerning herding behavior, however, the same conclusion about the lack of studies in emerging markets can be noted (Kumar & Goyal, 2015).…”

Section: Herding Behaviormentioning

confidence: 99%

Testing Sectoral Herding in the Jordanian Stock Market

Elshqirat¹

2019

IBR

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The main purposes of this quantitative study were to examine the existence of herding behavior among investors in Amman stock exchange (ASE) at market and sector level in addition to testing the behavior during the market rising and falling and examining whether the behavior existence is different before and after the global financial crisis of 2008. The theoretical base of the study was the behavioral finance which assumes that investors are not completely rational and they may follow others when taking investment decisions. The main enquires of the study were about the existence of herding in the Jordanian market, whether it's affected by conditions of market rising and falling, and whether it's affected by the financial crisis. A quantitative design was employed to achieve the purposes of this study which covers the period 2000 - 2018. Data were obtained from ASE website and analyzed using ordinary least squares method. The results indicated that herding is absent in the Jordanian market if tested at market level while it exists in services and industrial sectors if tested at sectors level. The financial crisis did not affect the presence of herding at market level while it did affect the behavior in services and industrial sectors. Moreover, the results revealed that market condition of rising and falling affected herding at market level but not at sectors level. It is also concluded that the global financial crisis changed the presence of herding behavior during conditions of rising and falling in market and in each sector.

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Applying the CAPM and the Fama–French models to the BRVM stock market

Cited by 22 publications

References 7 publications

Robustness of the Carhart four-factor and the Fama-French three-factor models on the South African stock market

Robustness of the Carhart four-factor and the Fama-French three-factor models on the South African stock market

Models for Expected Returns with Statistical Factors

Testing Sectoral Herding in the Jordanian Stock Market

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