Using The O-Metrix Score To Find Cheap Stocks With High Potential

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 |  Includes: CLF, CTB, INT, JBL, MNK, STX, TRTN
by: Jae Jun

I get a good stream of comments and requests by current users of the OSV stock analysis spreadsheet to add certain valuation tools and metrics, but rather than just adding user requests, I like to make sure that it will really benefit and adds to fundamental analysis instead of just trying to pack as much features as possible.

One interesting valuation method that came up was designed by Jeremy Siegel from his book, "Stocks the for Long Run."

As far as I know, there is no official name for the valuation method, and there is only one website that uses this method and has called it the O-Metrix score.

I’ll stick with the name. So let’s go through what it is, how it is used and my verdict on how useful I find it.

What is the O-Metrix Score?

You know about the Piostroki F Score, Altman Z Score and the Beneish M Score. All created by Professors based upon their intense research.

The O-Metrix score too was developed by a professor. Jeremy Siegel is a professor of Wharton School of Business, and describes the O-Metrix method as well as other in his book, "Stocks for the Long Run."

The O-Metrix is based upon a combination of three metrics.

A rule of thumb for stock valuation that is found on Wall Street is to calculate the sum of the growth rate of a stock’s earnings plus its dividend yield and divide by its P-E ratio. The higher the ratio the better, and the famed money manager Peter Lynch recommends investors go for stocks with a ratio of two or higher, avoiding stocks with a ratio of one or less.

We all want to buy stocks with high growth, a dividend and low price and the following equation is supposed to be able to identify such stocks.

O-Metrix = (Dividend Yield + Earnings Growth) / PE Ratio

and if you are a dividend income investor, more emphasis is required for dividends, so the dividend variation is referred to as the Double Dividend O-Metrix Score.

DD O-Metrix = (2 x Dividend Yield + Earnings Growth) / PE Ratio

Going back to that quote a little higher up; “Peter Lynch recommends investors go for stocks with a ratio of two or higher, avoiding stocks with a ratio of one or less,” the above two equations can be multiplied by 5 to make the scale range from 0 to 10 instead of between 0-1, which makes it easier to identify and classify stocks.

The equations now become:

O-Metrix = 5 x [(Dividend Yield + Earnings Growth) / PE Ratio]

DD O-Metrix = 5 x [(2 x Dividend Yield + Earnings Growth) / PE Ratio]

EFSInvestment has provided his grading system, which I’ll go by.

  • 10+ : A+ Grade Stock
  • 8 to 10: A Grade Stock
  • 6 to 8: B Grade Stock
  • 4 to 6: C Grade Stock
  • 2 to 4: D Grade Stock
  • 0 to 2: F Grade Stock
  • <0 : Sub-F Grade Stock

The objective of the O-Metrix score is to find stocks that score above 10.

Hunting for a 10+ O-Metrix Stock

This took me a while to compile but based on a random search through various tickers, here is a compilation of 13 stocks that are rated in the A+ to B grade using the O-Metrix score.

Rather than just using the current PE, I took the average of the current and Forward PE.

Growth rates were taken from the Yahoo Finance analyst prediction section.

Click to enlarge

Topping the list is STX, which perfectly represents what the O-Metrix score tries to find. Stocks trading at a low PE, high-dividend yield and good growth potential.

Potential Adjustments to the O-Metrix Formula

More testing and work will have to be done, but here are a couple of changes that could be made.

  • Instead of PE, EV/EBITDA could be used.
  • Could make it FCF based, but using FCF growth instead of EPS growth and P/FCF or EV/FCF instead of PE.

Closing Thoughts on the O-Metrix Score (For Now)

I was hesitant at first but I can see how it could be useful with some adjustments.

In the current format, I believe a lot of companies could come up incorrectly, based on how EPS has been calculated, and what the expected growth rate is, but I do see potential.

I'm not ready to employ it full time, but it is interesting enough to merit some further research and testing.

Any thoughts?