lecture10

# For a publicly traded company we observe past returns

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Unformatted text preview: ore, what are we going to do about this! But keep it in mind in case the CAPM doesn’t work as you expect it to work. • For a publicly traded company we observe past returns and we can compute covariances with the market return using recent history. Hopefully it will tell us something about the future covariance. • If it is a new project or ﬁrm, try to ﬁnd comparable publicly traded ﬁrms with returns you can observe. • Once you have the data (historical returns), how can we compute beta? • Formally, run a linear regression like this: e e rit = αi + β irmt + it e e • Where rit = rit − rF t and rmt = rmt − rF t • rit is the observed return on stock i and time t, and rmt is the observed return on the market for the same period. The "market" is usually approximated with a broad stock index like the S&P 500. • Take T periods: t = 1, ..., T. • In a linear regression: T X e (rit − ri )(rmt − rm) ¯e e ¯e β i = t=1 T X e (rmt − rm)2 ¯e t=1 • This is equivalent as estimating the covariance and then dividing that by the market variance. • Even easier: use the SLOPE function in Excel to compute β i. If you want to compute αi use the INTERCEPT...
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