Common risk factors in stock returns in the MENA Region

• Autor(a) principal: Abadi, Rasha Tawfiq Yousif
• Data de Publicação: 2019
• Outros Autores: Silva, Florinda
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/1822/69298
• topic: CAPM; Factor Models; Illiquidity Factor; MENA Market; Social Sciences

Research aims: This paper examines risk factors comprising size, value, profitability, investment, momentum and illiquidity to see if they are relevant for the stock markets in the Middle East and North Africa (MENA) region. Design/Methodology/Approach: Stock market data, from January 2007 to December 2015, are used to construct the risk factors for the stock market in the MENA region. The single factor models and the multifactor models are used to explain the constructed portfolios’ excess returns. Research findings: Findings show that the risk factors of size, value and profitability are the most important to be applied in asset pricing models within the MENA region. In addition, most of the models analysed in this study are unable to perfectly capture the average excess returns of the datasets, with the seven-factor model performing better than the other competing models. Theoretical contribution/Originality: This paper is possibly one of the first to construct and apply the above-mentioned risk factors in the MENA markets. It further proposes using two additional risk factors, such as momentum and illiquidity, within the Fama and French’s three-factor and five-factor models so as to examine the stock markets of the MENA region. Other researchers before have not made this proposition. Practitioner/Policy implication: The findings raise the awareness that additional and important factors must be considered by investors in the emerging financial markets when they want to diversify the risks so as to achieve higher excess returns. Research limitation/Implications: This study also has some drawbacks in that the dataset period is short while the analysis comprises different markets with different levels of development, thereby affecting some degree of generalisability.