الفهرس 
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Abstract
In finance, investment is one of the most researched fields. For decades, many researchers tried to develop methods to understand and measure the way investors value their investments and assess the risk of their cash flows. Researchers tried over the years to estimate the expected investments’ return and cost of Equity using different economic and financial factors. Investors want to correctly assess the expected returns of an investment to be able to improve the future performance of an investment. Moreover, many researchers tried to explain the way investors assess the investment’s risk premium. It is known that the investors are risk averse, it is assumed that they wanted to maximize an asset’s expected return compared to its variance.
The Capital Asset Pricing Model (CAPM) that was developed by Sharpe- Lintner- Black is the most used model for assessing the risk of the cash flows of an investment. The CAPM is the simplest asset pricing model. However, it faces a lot of criticism. Since then, many improvements to the model have been proposed. The focus of the CAPM is to measure the excess return of an investment relying on the financial indicators of the company. However, in the knowledge era this is not sufficient to provide the investors with a satisfactory representative of the investment’s true excess return. The intellectual capital plays an important role in measuring the company’s performance in the current knowledge era. According to (Laing, et al., 2010) the VAIC (Value Added Intellectual Coefficient) model, which was developed by Pulic (2000), is a robust tool that can be used to measure of the efficient use of intellectual capital.
The aim of this research was to propose an improvement to the CAPM model by adding the intellectual capital components to the CAPM model to help in improving the calculation of the excess return. To assess the relationship, the quarterly panel data of the 75 firms listed in the EGX 100 (after removing the Financial Companies), for the period from December 2013 till September 2018, were used to conduct the test. The researcher assessed whether the intellectual capital components, as measured by Pulic (VAIC), can be used as a factor in measuring the excess return of a stock or not. This is done to evaluate the reliability of the VAIC components in predicting the Excess Return of the EGX100 non-financial companies and compare its results to the CAPM and the FF models. The research also identified the direction of the relationship between them. Moreover, the researcher created three regression equations for the VAICCAPM, CAPM, and the FF model to evaluate for the robustness of the results when using the EGX100.
To do so the researcher first performed some diagnostic test to assess the reliability of the Regression models. After conducting the linearity RESET test, it was found out that the VAICCAPM and the CAPM models were linear. However, the FF model was found to be non-linear, which was then treated to linearity by raising the entire model to the power of 3. Moreover, after conducting the Hausman test to find out whether the fixed or random effects model should be used for each of the three models. It was found out that the VAICAPM, and the CAPM should use the Random effects model and the FF model should use the Fixed effects model. Furthermore, the researcher conducted a Breusch-Pegan test to see which model is heteroscedastic and which one is Homoscedastic. It was found out that the VAICCAPM and the FF model are Homoscedastic and the CAPM is heteroscedastic, which means that the errors of the CAPM are bias, this means that the OLS regression cannot be used for it. Moreover, the results show that all the variables are stationary, and that the dependent variable are co-integrated with the independent variables, which means that the ER is walking randomly together with its independent variables.
The GLS regression analysis shows that the most reliable forecasting equation among the three regression models is, according to the information criterion, is the CAPM model. Moreover, the MRP in the VAICCAPM and the CAPM is the only independent variable that has a significant relationship with the ER. All the other independent variables have a positive insignificant relationship with the ER, except for the HML, which has a negative insignificant relationship with the ER.
Finally, the researcher conducted the Fama-MacBeth regression analysis to see which model has a better explanatory power for the Excess Return in the EGX100. To do so the researcher first conducted a time series analysis for each company to estimate the time series beta for each company. After that, the researcher used these betas to perform a cross-sectional analysis for each quarter to be able to compare the estimated risk premiums derived from the cross-sectional regression analysis with the risk premiums of the data set. The results of the cross-sectional analysis of the VAICCAPM shows that the model has an insignificant explanatory power on the individual Stock returns of the EXG100 companies. As for the results of the cross-sectional regression analysis done on the CAPM, it shows that the MRP has a positive statistically significant explanatory power on the ER. Finally, the analysis of the FF model shows that it cannot be used to price the individual stocks in the EGX100, as its components have an insignificant explanatory power on the ER. This result complies with the result of the Information Criterion, that CAPM is the best model to be used to estimate the Stock Return of the companies listed in the EGX100. Finally, both the VAICCAPM and the FF cannot be used to estimate the Stock returns of the Companies listed in the EGX100 during this period.