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I use the past 12 months of sales and eps data to estimate what the future performance might be. My analysis accounts for seasonal behavior. AmSurg just had their "slowest" quarter of the year, which ahs historically produced about 24% of their revenue. The spring quarter produces ~26%. In this case, it's not such a big deal but the model works pretty well on toy companies and other's with big swings so I trust it for many cases.

Once I seasonally adjust the past data, I use linear regression to forecast what the next few quarters sales will be. I use the eps data to measure the net profit margins to get net income and then earnings per share.

I use a formula developed by Ben Graham to estimate a fair PE for the stock and that's where I get the fair price. The formula is

PE = 4.4*(8.5+2*G)/Yaaa
G is the growth, 20% growth is entered as 20.0
Yaaa is the yield on AAA rated bonds, 8.125% is entered as 8.125. This 4.4/Yaaa adjusts for the changes in interest rates. The yield on AAA bonds was 4.4% when Graham built the model.

Before I buy, I check to see how well the model fits. This is measured using a statistical function called the correlation coefficient. It can be interpreted as the % of the variation that is accounted for by the model. In the AmSurg case, I get R^2 of .99 meaning that 99% of the variation in the past sales is being treated by the model. In laymen's terms, the model fits like a glove for this stock.

I have been using this model for several years and it has a good track record of finding undervalued stocks. Sometimes it finds them way too soon but so far, it has rewarded me very well. Right now, it is highlighting a number of automotive supplier firms as undervalued. They may stay that way for awhile but if they continue to grow at 20+% per year, they won't continue to trade at PE's of 6. Look at TWR and ARM.

Questions, comments and derogatory remarks are always invited. Thanks.

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