After graduation, you begin a job with an investment firm. Your firm uses a commercial data vendor for information about its holdings. The vendor will not share how it calculates its numbers. Your bosses are uncomfortable with not knowing how these numbers are calculated and believe it could be less expensive to calculate the necessary statistics in-house. They have asked you to estimate a few commonly used statistics.

The first statistic requested is beta. Beta is often estimated by linear regression. A model commonly used is called the market model, which is:

R_t R_ft = a_i +b_i[R_Mt R_ft] + e_t

In this regression, R_t is the return on the stock in period t, R_ft is the risk-free rate in period t, R_Mt is the return on a stock market index such as the S&P 500 in period t, and b_i is the slope (and the stocks estimated beta).

To start, you will estimate the beta of Campbell Soup Company (ticker: CPB). You have already compiled the returns data necessary for your analysis. Click here to download the data.

Part 1: Use the market model to estimate the beta of Campbell Soup using the most recent 60 months of returns. What is Campbell Soup's beta? What does your estimate tell you about the level of systematic risk in Campbell Soups stock? Here is a short review of how to estimate a market model in Excel:

Part 2: When the beta is calculated using monthly returns, there is a debate over the number of months that should be used in the calculation. Rework the previous questions using the last 36 months of returns to estimate beta. How does this answer compare to what you calculated previously? Does Campbell Soup appear riskier or less risky using the shorter estimation window? What could account for the increase or decrease in risk during the more recent period?

Part 3: Compare your beta for Campbell Soup to the beta you find on finance.yahoo.com. How similar are they?