Market Commentary for the week of January 11, 2010

All tech, all the time.

Speed kills.

No, I’m not talking about drugs or automobiles or other metaphors. I’m talking about the advent of technology and its impact upon today’s qualitative decision-making.

In our world of internet and ubiquitous connectivity I hear more about day-trading, weekly evaluations, and 24 hour market cycles than ever before. As if speed has replaced accuracy, some investors measure their portfolio in staccato beats rather than long term sine waves.

In just the first four trading days of this calendar year I have heard investors ask about “annualized portfolio projections” from trades executed on Monday, management teams projecting budgets based upon first week revenues, and companies issuing year-end prognostications deriving from sales figures in the first week of 2010.

Please, get real.

If decisions about 2010 emanate from first week statistics something is more wrong with the mindset than the numbers themselves.

Turn that off!!

Quantitative statistics that existed on December 31st, 2009, still exist in nearly identical proportions today as on that date. If you were optimistic and aggressively allocated in December, you should be so today. If you were cautious and conservatively positioned last month, you must be so now. Quantitative, and fundamental, data evolve over time, they do not reposition or reaccelerate week-by-week, minute-by-minute.

But more importantly, to think that they do, or that this Monday is statistically any different from last Monday, is an assumption that might lead to more portfolio ruin than simply being a bad stock-picker, or having poor information to start with.

Stubbornly, many investors believe that because instant information exists they must use it.

At the risk of sounding old-fashioned (and I am frequently accused of being just that) let me posit that any portfolio methodology which derives from technology, alone, is often a black-box surrogate for common sense and subjective evaluation. Even more interesting is that my own discipline, Arlington Econometrics, a quantitative evaluation methodology, is also mostly tech-oriented. The difference, I would argue, is in how those data are executed, and how they are evaluated.

There is no substitute for experience, common sense, or long-term perspective.

The best requirement for being a turn-key scientist is an unwavering belief in one’s methodology, a few “grey hairs” worth of experience, and a healthy dose of skepticism. There is no excuse for not believing in “manual override” regarding any data output.

These characteristics I have, even before I turn on my computer.