Abstract
Understanding stock return variations and accounting for their drivers help academics and practitioners estimate expected returns and gauge risk exposures, thereby optimizing investment strategies. This paper seeks to study the effect of systemic risk, size and valuation on stock return, in the Lebanese stock market. The research design and methodology are the Fama French Factor Model (FFFM) as developed by Fama and French in their seminal work of 1993. The research demonstrates validity of the three variables in question, and that is consistent with results obtained for global equity markets. However, the results exhibit a negative market risk premium with respect to US T-bills, and a high level of factor inter-correlation for the period in question.
Highlights
The Fama French Factor Model (FFFM) and other Arbitrage Pricing Theory (APT) derivative models are utilized by academics and finance practitioners to estimate expected returns of securities and portfolios, and to gauge risk exposures to markets, size, and valuation
Given the significance of systemic risk, size and valuation as explanatory variables of cross-sectional average stock return variations, in American stock markets, obtained in FFFM, it was interesting to see if these same factors are significant in the context of a small and emergent economy stock market, such as the Beirut Stock Exchange
The FFFM is represented by the following equation: Where: E(ri,t) = E(rf,t) + β1i {E(rm,t) − E(rf,t)} + β2i SMBt + β3iHMLt ri,t is the total return of a stock or portfolio, i at time t; rf,t is the risk free rate of return at time t; rm,t is the total market portfolio return at time t; SMBt is the size premium; HMLtt is the value premium; β1,2,3 refer to the factor coefficients
Summary
The Fama French Factor Model (FFFM) and other Arbitrage Pricing Theory (APT) derivative models are utilized by academics and finance practitioners to estimate expected returns of securities and portfolios, and to gauge risk exposures to markets, size, and valuation. An analyst can use stock market data to identify over/under exposure to risk factors and construct an optimally designed portfolio that meets investment objectives. Given the significance of systemic risk, size and valuation as explanatory variables of cross-sectional average stock return variations, in American stock markets, obtained in FFFM, it was interesting to see if these same factors are significant in the context of a small and emergent economy stock market, such as the Beirut Stock Exchange. Investors in the Lebanese stock market can always use the Fama French Factors (FFF) as derived from global emerging equity markets. That will depend on the level of exposure to global markets, at the expense of country specific conditions, which may taint performance
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