I love Value investing, but it’s not always so easy to execute. The ratios are all well known and easy to calculate, so the low hanging fruit tends to get quickly arbitraged out of existence as everybody and anybody pounces, often leaving behind stocks that have modest ratios because the companies are dogs whose stocks deserve to be cheap. So if you really want to play Value, you have to think outside the box. Here’s an idea for doing just that.

A Non-Judgmental Perspective

In school and in dummies-level books, we’re allowed to come away believing that stocks are supposed to trade for what they are worth and that if they don’t, somebody (or many somebodies) are being dumb and/or evil. The quest for Value, according to such thinking, is tantamount to a crusade against ignorance.

Whatever. I have no problem with fuzzy thinking, emotion, hype, etc. That’s what creates opportunities. And unlike the Value Police, I’m not prone to outrage or disappointment because I know these irrational characteristics will never go away. They are an inherent part of the market’s fabric; so much so that we cannot ever suggest that the normal, or even ideal condition for stocks is one in which P (Price) is equal to V (Value). The correct framework is P equals V plus N (Price equals Value plus Noise).

The key here is not to see N as something horrid you wish would go away. Instead, let’s try to see N as something we can quantify and deal with through disciplined screen- and rank-based selection models. Specifically, what I’m going to do now is define and measure N, and then build a portfolio of stocks whose prices (1) are less influenced than most by the presence of N, and (2) for which there appears to be a catalyst that might presage increased N in the near future. The goal is to wind up with a realistic, investableSmart Alpha model that holds 20 stocks and is refreshed once every three months.

This is not the only thing I can do. Stocks high in Noise can and do win as well, if properly understood and assessed. That’s the innovation in this framework. We want to respect understand and possibly use N, not condemn it. But high-noise stocks are a topic for another time. Today is Value day.

(And speaking of innovation, I wish I could claim full credit for this approach. But I can’t. What I’ll be doing here is builds on ideas introduced by Robert Schiller with an assist from Fischer Black and amplified by Stanford’s Dr. Charles M.C. Lee.)

This is actually a straightforward task. If we quantify Value, then we can assume that the difference between the stock price and the stock’s value is attributable to Noise. In other words, N = P-V.

The key is in how we quantify value. It’s not something we’re used to doing, except those who compute those monstrosities that pass for Discounted Cash Flow spreadsheet models that are so plagued by so many assumptions that range from tenuous to ridiculous as to make them not worth the cost of the cloud-drive memory used to store them. I’m going to define Value much more simply. I’ll use NOPAT (net operating profit after tax – the emphasis being “operating,” as in free from crazy whacky non-recurring things companies do form time to time and from the impact of financing choices) divided by the cost of capital.