For this project, you are going to evaluate the systematic risk of an investment portfolio. It can be an imaginary…

For this project, you are going to evaluate the systematic risk of an investment portfolio. It can be an imaginary portfolio that you would like to put together to park your retirement savings, or your real 401(k) portfolio. To simplify the project, we are not going to include bonds, ETFs, mutual funds, etc. Or in other words, we are going to investigate a portfolio composed of only stocks. For example, your ideal portfolio can comprise the following stocks: IBM (5%), APPL (5%), GOOG (10%), DIS (10%), C (5%), WMT (20%), ALM (5%), GM (10%), JNJ (10%), GE (5%), KFT (5%), MCD (10%). Note the weights in the parenthesis (percentage of total funding) have to add up to 100%. Your portfolio may include more stocks than this example because it normally takes more than 30 stocks (Statman, 1987) for a portfolio to be qualified as a well diversified portfolio. Beta measures the systematic risk (non-diversifiable risk) of a stock. Mysterious as it may sound, it can be easily found out by regressing the stock’s monthly excess return (return in excess of risk free rate) on market monthly excess return according to the following model: R_it-R_ft=α_i+β_i (R_mt-R_ft )+e_it Equivalently Equivalently the dependent variable Y the independently variable X where, R_it is the return of stock i on month t; R_ft is the risk free return on month t, normally a 3-month treasury bill serves a good proxy; R_mt is the market return on month t, normally S&P 500 return serves a good proxy; β_i is the beta for stock i; α_i is the intercept for stock i; e_it is the error term. To find the beta for stock i, you need to obtain the data of R_it-R_ft as dependent variable Y and R_mt-R_ft as the independent variable X. beta is simply the regression coefficient. After you find beta for each individual stock i, the portfolio beta is simply β_p=∑_(i=1)^n▒w_i β_i where w_i is the weight of stock i in the portfolio. Note you need to run regression analysis for each stock. If your portfolio contains 30 stocks, you will need to run 30 regressions and find 30 beta’s respectively. Comment on how you find beta is related to the stock’s industry (Finance? Manufacturing? Retail? Utility?) or relate to the company’s size (market capitalization). For example, it is generally accepted that utility stocks have low beta’s while technology stocks have high beta’s. In your report, I am looking for: 1. What stocks are in your portfolio? What is the weight (percentage of total investment) for each stock? 2. What is the historical period you choose to access the data? (Example: Jan 1981- Dec 2000) 3. Report beta for each stock. 4. Report beta for the portfolio. 5. Any observations on beta’s are encouraged