Regression Analysis: Optimal Portfolio

The “excess rate of return for the firm” data is the Input Y Range (dependent variable) and the “excess rate of return for the market” is the Input X Range (In Excel, the Data Analysis menu is under Tools (older version of Excel) or Data (newer version)). If you include the row with the variable name in your Input Y Range and your Input X Range, check the box LABELS, and Excel will automatically name your variables in the Excel output. Hint: the alpha coefficient estimate is the estimated intercept coefficient. The beta coefficient is the estimated coefficient for the independent or X variable, the excess rate of return for the market. (10 points)

However by looking at the results in Table 4 it may be seen that not all of the variables which are included in the model may be significantly contributing to the model. As the variable X5, which is the visibility of the store, has a p-value of more than 0.05 this suggests that the variable is not contributing significantly to the model. This would suggest that removing this variable may further improve the model. In addition to this it would be necessary to remove any variables which were collinear as this could interfere with the results of the regression. After using the program PHStat to analyse the variable inflation factors (VIFs) of the variables these are all below 5, which shows that there is no collinearity between variables. Therefore the improved model would be one which included all variables except X5.

Fama and French’s three factor model attempts to explain the variation of stock prices through a multifactor model that includes a size factor and BE/ME factor in addition to the beta risk factor. Fama-French model essentially extended the CAPM (which breaks up cause of variation of stock price into systematic risk which is non-diversifiable and idiosyncratic risk which is diversifiable) by introducing these two additional factors. Fama and French find that stocks with high beta didn’t have consistently higher returns than stocks with low beta and this indicates that beta was not a useful measure under their model. Their model is based on research findings that sensitivity of movements of the size and BE/ME factor constituted risk, and

CAPM is a model that describes the relationship between risk and expected return, and the formula itself measures the expected return of the portfolio. Mathematically, when beta is higher, meaning the portfolio has more systematic risk (in comparison to the market portfolio), the formula yields a higher expected return for the portfolio (since it is multiplied by the risk premium and is added to the risk free interest rate). This makes sense because the portfolio needs to

I collaborated with Ryan and Michael to work on this white paper portfolio. It is not easy to complete the white papers even though we have been working together for several times. Initially, we conducted research on 3 different products- self-driving cars, Google glass and Alpha Go. We put our comments on each items on Google doc so it can be shared and revised in real time. It was a really good start. It did not take a long time for us to reach an agreement on writing the self-driving cars. The most effective part of the work is building the content for the first white papers as Ryan is an expert in automobile and he did most of the writing. Michael also contributed a lot to the second white papers. I am responsible for the visual design and writing the second half of the white paper on white paper. We set up a meeting after spring break, which helps us get most of the work done (80%). Meeting face-to-face is an effective way of

The R-squared of this new model is around 60%, even a little lower than the original model. The statistical significance of other independent variables keeps almost unaffected. The analysis of coefficients in this model with logarithms is not intuitive. So we still stick to the reduced linear model estimated in (a). We analyzed the OLS residuals to check for possible heteroskedasticity for a cross-sectional data set. We computed the fitted values of Price and OLS residuals in our model. Then residuals are sorted by fitted values of Price, and a scatter diagram of residuals is plotted against the fitted values in Figure 2. It is observed that all points are fairly scattered and there is no obvious pattern for the dependence of the residuals on the fitted values. We conclude that a linear model with homoskedastic errors is satisfactory for this data set. We evaluated the validity of this linear regression model by checking the accuracy of its prediction.

The variables with insignificant regression coefficients can be removed from the regression model. Thus the modified regression model is

Imagine you are applying to become a trainee in a management consulting company, Solutions Inc., which claims to deliver innovative solutions. They are looking for innovative employees who engage with their work. The selection process will be rigorous. You know you will be asked to submit reports based on questions regarding your knowledge of management accounting practice and strategic management accounting. To provide a context for the reports, you have been provided with a scenario in the form a case study on which the questions are based. To answer the questions you are going to have to do some research in the library. Giving you the questions is a method to test your information

This paper aims to analyze the validity of the CAPM model of predicting returns for stocks by empirically testing the model with past financial data. The CAPM model is defined as R_i=r_f+ β_i (R_m-r_f). R_i represents the return on stock i, and is what the CAPM model attempts to define or predict. r_f represents the risk free rate available at the time the model is being analyzed, a figure that’s important for understanding both minimum return figures and the return premium offered by the market. β_i represents the Beta of stock i and is a measure of a given company’s volatility relative to the market they are in. If β_i is one, then the company is at market risk, if it is lower than one then it is below market risk, and if it is higher than one then it is above market risk. The only stock that would have a Beta of 0 would be a risk free stock, or whatever security you are using for your risk free rate. β_i is calculated as (COVARIANCE(r_i-r_f,〖 r〗_m-r_f))/(VARIANCE(〖 r〗_m-r_f)).(R_m-r_f) represents the Market Risk Premium, or the level of return an average stock in the market would return in excess of the risk free rate.

This research study investigates the firm-specific return prediction through CAPM, Fama and French three factor model and Augmented FF three factor models. The sample consists of financial and non-financial sectors listed on KSE. The

Foreign Direct Investment (FDI) is the dependent variable measure in terms of RM Million. Gross Domestic Product (GDP), Debt (DT) and Exchange Rate (EX) are the independent variables or explanatory variables in this multiple regression model .Both GDP and DT measures in terms of RM Million whereas for the Exchange Rate, the unit of measurement is official exchange

CAPM is a highly acclaimed theory of risk and return for securities in a competitive capital market. The path breaking theory won Sharpe, Markowitz, and Miller the Nobel Memorial Prize in Economics in 1990. CAPM establishes the Beta coefficient as a measure of the systematic risk of an asset. The systematic risk is also known as market risk. This risk cannot be eliminated. This systematic risk is uncontrollable. The unsystematic risks include the risk that influences a single company or a small group of company and the same is controllable and can be mitigated through diversification.

CAPM is a highly acclaimed theory of risk and return for securities in a competitive capital market. The path breaking theory won Sharpe and Markowitz the Nobel Memorial Prize in Economics in 1990. CAPM establishes the beta coefficient as a measure of the systematic risk of an asset. Systematic risk is also known as market risk. This risk cannot be eliminated nor is it controllable. Unsystematic risks include the risk that influences a single company or a small group of companies, and it is controllable and can be mitigated through

During the interviews, it was understood that Portfolio Theory is difficult to apply to loaning administration. A bank is required to have a flexible credit evaluating process to capture the individuality of each loan. These differences depend on the size of the loan and the different clients’ risks. This makes it difficult to apply loan portfolio diversification compared to a portfolio of bonds or stocks. Nevertheless, more than the fixed principles banks seem to follow the intuition behind it not to expose themselves more than a limit.

While the CAPM uses the β largely as an adjusting factor, the FFM, mainly uses the two introductory factors for adjustments as their models reflect little to no adjustments to β causing a disconnection between the two models. If sensitivity to market risk as captured by β in CAPM does not motivate investors, it is, on the face of it, difficult to envisage how the book-to-market equity and firm size variables in the FFM can be expected to motivate them (Dempsey,