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MITOCW | watch?v=JE80wLNIhjE The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation, or to view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ANDREW LO: What I want to do today is to continue where we left off last time in talking about the capital asset pricing model, and we're going to finish that off in the next 15 or 20 minutes. And then, I'm going to turn to applications of the capital asset pricing model. In particular, I want to focus on capital budgeting. That's going to be the last major topic we take on for this course. So let me finish the capital asset pricing model and then I'll talk a little bit about where we're going to go for the remainder of the lectures. You remember last time where we left off was, I decided to estimate the CAPM relationship for a couple of stocks, Biogen and Motorola. And we found that the estimated alphas, the deviations from the CAPM, were pretty sizable for both of these companies. And the interpretation is either, wow these companies are really exceptional values, they offer investors much, much larger expected return than justified by the appropriate market betas. That's one interpretation, or the other interpretation is there something missing with the CAPM. The CAPM doesn't quite capture all of the risks that are giving you these kinds of expected rates of return. So I'm going to come back to that debate in just a few minutes. But I thought, that to continue along these lines, let's explore a little bit about the goodness of fit of these measures. Now, I mentioned last time that the R-squareds, as a measure of goodness of fit, was 17.5 percent for Biogen and 33% for Motorola. Both of which are reasonably representative of the kind of goodness of fit measures that you're going to see with financial data, for the simple reason that financial data is very noisy. And so you're not going to get any single theory that can explain 99.9 percent of the fluctuations in any kind of financial series. So here's a plot of Biogen versus the market. Now the market, as I reminded you last time, is the valuated return, including dividends, of all stocks on the NYSE, Amex, and NASDAQ. So this market portfolio, you can think of as being a broader version than the S&P 500, but it's meant to capture the market. So this is a plot of Biogen versus the market. And you can see that there is some slope that fits this scatter of points, and of course the equation is 1.42 times the market, minus a particular value for the alpha, and then R-squared of about 33% over this particular sample period. So there is a line that goes through these points, but it's not a perfect straight line by any
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means. You can see there's a scatter. And so there's a tendency for Biogen to move together with the market, but it's not a perfect linear relationship by any means. That's why the R- squared is not 100%. It's because we're not able to explain all of the fluctuations with a simple
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  • Spring '17
  • Jim Angel

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