The Art of Streetplay

Tuesday, August 23, 2005

Useful Applications for Quantitative Ability with Fundamental Analysis

I just finished successfully coding up a data miner on another database and am starting to reach a few tentative conclusions on how one can go about building a more structured, more efficient, overall better value framework using a quantitative skillset in addition to a qualitative one.

Benefits of Quantification:
When you think about it, what exactly is it which quantitative operations have which their more qualitative counterparts have less of? I would sum it up with two things:
  1. Quantitative programs can be more precise (many times perhaps overly so!) than the human brain on its own.
  2. Quantitative programs can be far more systematic than the human brain on its own.
Case in point regarding (1): when I proposed a correlation-based trading strategy to one of the smartest quants I know, one of his first reactions was to replace correlation with the correlations of the wavelet spectra and eliminate the leptokurtosis which may muddy results with a shrinker of some sort. By understanding the underlying properties driving our process (or set of processes), one can leverage a technical background to be more precise.

Case in point regarding (2): see some of the below posts for some of the more boring applications of data miners. Programs allow me scale analysis up and across the entire stock market. By slicing the market in intuitively reasonable ways, one would hope to find stocks or situations which are deviant enough from what one would expect to merit diving in with fundamental analysis. As an individual investor though, I couldn't possibly with my one brain look through the whole stock market in multiple ways. Quantitative programs are what allow me to be systematic.

Using Quant in a Value Framework
I guess for right now, the conclusion I've come to is that for a deep value investor, quant can be helpful when providing econometric analytics because of its ability to be more systematic than humans can. So when I pull up a stock that I want to research, with a few clicks I will know where my stock fits in the entire universe of stocks in intuitive and useful ways on many levels (ie. how is the industry doing, where is the P/E of my stock relative to overall market and industry and how has this evolved over time, how does the size of my company stack up with others in the industry, what are similar stocks so that I can scrutinize them, how is insider buying in my industry and in my company relative to other companies in the industry, etc etc). Those are all things I can get immediately with a fine tuned program, and I can delve as deep or shallow as I want because I created the programs and am familiar with manipulating the databases. A human couldn't do that except with great effort or at the least a lot more time expended.

Pros and Cons of the Brain and of Machines; Creating Complementarity
The question then becomes how I can combine these two distinct skillsets in useful ways. I think it boils down to identifying where each can add value relative to the other.

The human brain is much more capable of identifying idiosyncrasy. One of the common problems with relying entirely on a quantitative methodology is its inability to pick up on all the idiosyncrasies which the human brain can see. And yet at the same time quant can be far more systematic than the human brain ever could. Therefore I think it makes sense to tune my brain with quantitative analytics, and tune the analytics with my brain so that I can leverage idiosyncrasy while at the same time leveraging a systematic approach. I'm not sure how much quant's precision can help in a value framework, but I think its ability to be systematic can be of great value.

Thoughts are welcome.


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