Beta assignment

Prof. M. Muhtaseb Beta Estimation Assignment

For the following THREE exercises A, B & C 1. Calculate daily or weekly or monthly returns for each one, or a complete year of daily

returns. You must use at least 100 observations.

2. Estimate a simple linear regression model.

3. Indicate the source of your data.

4. You may use Excel or any software to estimate the regression model.

5. Interpret/explain the output: parameter estimates (alpha and beta), R2, F statistic,

T-Statistics, Systematic and unsystematic risks etc.

Include scatter plot of the returns.

6. Work with your partner. One report per team is due on ----

7. Feel free to use your Book for TOM 302 or FRL 363.

8. Place the output i.e. regression models on One page and explain the output in NO more than

two pages.

A. Select a STOCK and the appropriate STOCK MARKET index.

The dependent variable is the rate of return on the stock, and the independent variable is the

stock market index.

B. For the stock Selected in A use the stock market index as well as a sector/industry index.

That is use TWO independent variables.

Estimate a multiple regression model.

The dependent variable is the rate of return on the stock, and the independent variables are the

rates of return on the stock market index and the industry index.

An industry ETF can substitute for the industry index.

C. Select a STOCK MUTUAL FUND and the appropriate market index.

The dependent variable is the rate of return on the fund. The independent variable is the RoR on

the appropriate market index.

Compare the beta and R2 of stock vs. fund.

Helpful links: http://courses.statistics.com/software/Excel/XL_Ch02.htm

http://www.cba.nau.edu/allen-

d/excel%20regression%20tutorial/excel_regression_tutorial.htm

http://www.jeremymiles.co.uk/regressionbook/extras/appendix2/exce

l/

Beta Estimation Example

REGRESSION SUMMARY OUTPUT: Chevron vs S&P 500

Multiple R 0.8022

R Square 0.6435

Adjusted R Square 0.6407

Standard Error 0.0101

Observations 128

ANOVA

df SS MS F Significance F

Regression 1 2.32E-02 2.32E-02 2.27E+02 5.31E-30

Residual 126 1.28E-02 1.02E-04

Total 127 3.60E-02

Coefficients STD Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 0.000592 0.000895 0.661549 0.509468 -0.001179 0.002363 -0.001179 0.002363

S&P Daily Return 1.058407 0.070183 15.080681 0.000000 0.919517 1.197297 0.919517 1.197297

Simple Linear Regression Equation

RoR = 0.000592 + 1.0584(S&P 500)