Date: prev next · Thread: first prev next last
2012 Archives by date, by thread · List index

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 

   "total model return" = "total market return" * beta + alpha
   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.

For unsubscribe instructions e-mail to:
Posting guidelines + more:
List archive:
All messages sent to this list will be publicly archived and cannot be deleted


Privacy Policy | Impressum (Legal Info) | Copyright information: Unless otherwise specified, all text and images on this website are licensed under the Creative Commons Attribution-Share Alike 3.0 License. This does not include the source code of LibreOffice, which is licensed under the Mozilla Public License (MPLv2). "LibreOffice" and "The Document Foundation" are registered trademarks of their corresponding registered owners or are in actual use as trademarks in one or more countries. Their respective logos and icons are also subject to international copyright laws. Use thereof is explained in our trademark policy.