On the Nature of Outliers: Quant Vs. Fundie Analysis

I've been thinking a lot about the implications of one of my prior posts, "Useful Applications for Quantitative Ability with Fundamental Analysis." I think there are a few things worth mentioning about the nature of residuals versus outliers, and how that plays into this whole schema I've written about god knows how many times.

The bottom line:

• Quants may toss outliers to denoise their data so that they can properly estimate the "true" relationship between two variables. Once they have established the "truth," they can simply trade the noise. That is the game of tons of prop trading shops, and it does make some sense. I have done it myself while at a big bank. And we traded a ton of bonds.
• Fundamental analysts ('FA's') actively seek those same outliers which were thrown out. Rather than trade continuously, they sit on their hands in waiting most of the time. And when those outliers surface themselves, the FA's put on their positions in size. This also makes sense.

Those simple facts have huge implications on the applicability of quantitative methods in a qualitative setting!

Sure, I could de-noise my time series prior to calculating rolling correlations of every stock on every other stock, and sure I could calculate the correlations of the wavelet spectra, but while that may be more technically precise, it first of all dramatically increases the computational time. But even assuming computational time wasn't an issue, it's not really hitting at the point.

Traditional correlation and the correlation of wavelet spectra are not orthogonal concepts. They are generally jabbing in the same direction. If that is true, then turn to what the goal of analytics are in a deep value setting, and what deep value investors are attempting to do. They are attempting to find situations which are completely out of the ordinary, and are content on sitting on their hands until they are able to find such a situation.

If I am looking for a situation that is truly out of the ordinary, then statistics and hardcore mathematics will not help me 99% of the time, because we aren't trading noise, we are trading outliers. Whatever intuitive concept I am trying to pick up with statistics would have to be so extraordinary that at that point, any statistic generally pointing in a similar direction should be flashing red lights!

I know that a lot of times, a good investment comes as a result of many small oddities lumped on top of eachother. In this sort of situation it does help to have the additional precision. But the driving notion is to keep in mind the nature of the diminishing returns due to precision in a value framework.