On 06/14/2012 08:47 PM, Robert Peirce wrote:
If I use a small amount (3 pairs) of dummy information, the intercept,
slope and R^2 are exactly what I expect them to be. If I use something
on the order of 400 pairs it makes no sense at all. When I use Linest,
the results are reasonable.
I am evaluating stock trading systems against holding the market. The
data pairs are the monthly percentage change in the model vs. the
market. A regression produces the y-intercept (called alpha), the slope
(called beta) and R^2.
Generally, if I know the total return for the model and the market the
calculation
"total model return" = "total market return" * beta + alpha
or
y = mx + b
is fairly close if I get the results from linest. They are way off from
intercept and slope.
If the dependent variable is in A and the independent in B and there are
400 pairs, I can use INTERCEPT|SLOPE(A1:A400; B1:B400). The results
aren't even close and I don't know why. Plugging the average returns
for the market into the equation to calculate the expected return for
the model produces nothing reasonable.
Any ideas? I am probably missing something really obvious.
What are your R^2 values? Near 1 indicates a high correlation between x
and y.
--
Jay Lozier
jslozier@gmail.com
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