Bringing Data to Life

• Posted by Derek
• on October 20th, 2012

I’ve been keeping stats since I was a kid. I vividly recall lying on the floor with an NFL game on, and a legal pad creating my own ranking system for the teams. San Diego and Philadelphia had my attention as top teams, probably based on point differential or some composite rank based on yards for and against. Looking back, this must have been 1979-80, so I was around 8 years old at the time. This progressed to trying to figure out the QB rating system(harmless), and eventually thinking I could predict game outcomes(oops!).

The seed was probably planted even earlier with baseball cards, where the front was a picture and the back was all numbers…guess which side I liked? The numbers had meaning to me, they told me a story more so than the guy in a uniform on the front. Has this guy gotten better from year to year? Is he a known name but with his big numbers in the past? Why do I have 12 Eduardo Rodriguez cards but can’t find a Reggie Jackson?

To answer those questions, I was learning a) accumulation and distribution, b) sentiment, and c) supply and demand. 30 years later, I spend my days dissecting a) accumulation and distribution, b) sentiment, and c) supply and demand. Nobody put that together for me, so the path was a little curvier than it had to be; I write this stuff partly so my kids can have some idea of what I actually do. If I can teach it right, they’ll go into some other field but use my lessons to make smart investment choices.

So my love for numbers was stamped early, with evolving applications but always as my yardstick. Fast forward to today, where instant access to infinite data can be a statistician’s greatest ally or worst enemy. Nate Silver says the government produces 45,000 different series of economic data; it’s comical to think of the extrapolations people make from some of these.

What we need to do with numbers is to rip them out of the spreadsheet and give them meaning. This is not a spectator sport, for me it means a) physically entering each trusted piece of evidence into my analysis sheet, b) printing that sheet, and c) using a pen or highlighter to draw attention to changes. Technology plays a huge role, calculating what is actually 6000 inputs for me a few times/day and copying the raw files into a more readable form. But as much as I enjoy becoming more efficient in my data gathering, I never want to automate the pre-judgment step of placing those outputs in the right cells.

Shortly after I met Brian Shannon, he pulled out a folded piece of paper with his watchlist for the week. As he does every weekend, he had gone through hundreds of charts and noted by hand the ones he wanted to consider further. I chuckled at its archaic nature and said I thought Art Cashin and I were the only ones that scribbled notes by hand. Then we did Art proud with a few drinks and some chart scribbling on cocktail napkins.

As I read about other great market operators, I continue to see that many do the same.  A blog post on Ralph Acampora. An old video with Linda Raschke. A podcast with Ralph Vince. Everywhere I turn, I’m reminded that the optimal analysis of data is a physical act. By keeping records this way, we are engaging in deliberate practice and activating our intuition to help us make decisions.

Those ugly spreadsheets I’ve been sharing? Those aren’t numbers. They are the composite actions of my thousands of competitors. When I mark the number of upthrusting stocks at 41% vs. 71% the day before, to me that represents a broad urgency of selling and more importantly, the injection of emotion where complacency existed the day before. When I see fast/moderate/slow represented as 41%/52%/57%, I know that those emotions are still spreading, not receding. And we all know that it often takes time for people to convert new feelings into action, so when numbers look like that there’s more conversion of feelings into action ahead.

Dr. Alexander Elder refers to technical analysis as applied social psychology. I can live with that; fundamentals may dictate the destination but market mood dictates the path to get there. I’m not confident I have the skills to forecast the destination of $SPX, but I’m comfortable that my methods will always tell me when I need to switch lanes, or take the exit ramp, or make a u-turn. And even when they lead me astray, they are responsive enough to get me moving while the investment committees hold meetings. Unlike predicting sports outcomes, a disciplined investor can take back 90-99% of our investment after we know we’re wrong; my calling indeed.

The information in this blog post represents my own opinions and does not contain a recommendation for any particular security or investment. I or my affiliates may hold positions or other interests in securities mentioned in the Blog, please see my Disclaimer page for my full disclaimer.

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