Session 7: The Beta Regression
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Episode description
This class covered the conventional approach to estimating betas, which is to run a regression of returns on a stock against returns on the market index. We first covered the estimation choices: how far back in time to go (depends on how much your company has changed), what return interval to use (weekly or monthly are better than daily), what to include in returns (dividends and price appreciation) and the market index to use (broader and wider is better). We also looked at the three key pieces of output from the regression: 1. The intercept: This is a measure of how good or bad an investment your stock was during the period of your regression. To compute the measure correctly, you net out Rf(1-Beta) from the Intercept: Jensen's alpha = Intercept - Riskfree rate (1- Beta) If this number is a positive (negative) number, your stock did better (worse) than expected, after adjusting for risk and market performance. 2. The slope: is the beta, albeit with standard error 3. The R squared: measures the proportion of the risk in your stock that is market risk, with the balance being firm specific/diversifiable risk. Finally, we used the beta to come up with an expected return for stock investors/cost of equity for the company. Slides: http://www.stern.nyu.edu/~adamodar/podcasts/cfspr17/session7.pdf Post class test: http://www.stern.nyu.edu/~adamodar/pdfiles/cfovhds/postclass/session7test.pdf Post class test solution: http://www.stern.nyu.edu/~adamodar/pdfiles/cfovhds/postclass/session7soln.pdf
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