[zerosum]

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The selection process is an expected return factor model, where the expected return is relative to the universe of stocks from which the factor measures and sensitivities are derived. Multivariate regression is used to disentangle the interrelationships among factors, allowing for consideration of the *pure* expected return to each particular factor. Each stock's sensitivity to each factor is estimated, normalized and expressed as *B* or Beta. A particular stock's expected return is the sum of its betas multiplied by their respective expected return factor values.

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<font color="blue"> You would have been better served by simply saying you weren't willing to answer the question!!</font>

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I don't understand why. I can only guess that you may feel that the answer is not informative, or that the answer is too informative.

If you believe the latter, I can assure you that the answer does not provide sufficient information to allow someone to reverse-engineer the process.

I have had business relationships with a number of quant shops that conduct stock selection in a similar manner (i.e., multivariate regression analysis using similar factors), yet produce quite different selection results. On the surface, their processes look extremely similar, beneath the surface, they're decidedly dissimilar. So, again, I'm not worried that someone will be able to take my general description and reverse-engineer my selection process. Its simplicity is truly deceptive.

By the way, I saw your post that incorporated Louis Navellier's recent speculation about the future of index investing. Navellier's qauant shop is one of the shops that I include in the above reference.

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<font color="blue"> FWIW, how big of a secret could it possibly be to mention some of the factors you use for your analysis? </font>

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Each category contains multiple factors. To mention some, but exclude others, could be misleading. You can identify any number of factors that would reasonably fit into the broad categories of growth, valuation and momentum.