February 8, 2012

Moneyball in Your Shares

We missed part of the Super Bowl tromping snowshod through a chamber-of-commerce snapshot 3,000 feet above Denver. We’ve tallied 20 inches of grade-A, premium Rockies powder the past five days.

Speaking of grade A, have you seen the movie Moneyball? If not, rent it. For story, cast, script, direction and acting, we don’t think better has been done in years. We were riveted.

“Moneyball” is a term writer Michael Lewis (The Big Short, Liar’s Poker, Blindside) coined to describe the application of statistics to baseball-team construction. Pressed to deliver returns economically, Billy Beane, manager of the Oakland Athletics, turned to proprietary data analytics, aided by a young gun from Harvard with a degree in economics. It changed baseball.

Before Beane, who’d heard of OBP or OBA? – on-base percentage or on-base average. Baseball teams bought players on how they looked, how they swung, their runs batted in, their average, even if they had a good-looking girlfriend (sign of confidence). Classic old-school fundamentals.

Of course, everybody looked for fundamentals. Players with top fundamentals were expensive. Teams with deep pockets bought them, and won. In 2002, the Oakland A’s changed all that with proprietary data analytics. Beane turned a hundred-plus years of baseball theory and practice upside down.

The same realities exist in the market where your shares trade. All the desired behaviors, the valued attributes, are known. There’s a limited and shrinking pool of publicly traded companies (in the US at least). A lot of money chases them.

So money turns to proprietary data analytics, just like baseball, to find a bargain somebody else has missed. It may lie in plain sight, overlooked. Like how do you win baseball games? Score more runs than the other team. But how? You get people on base. Period.

Same with stocks. You need stocks that get on base. Clifford Asness of AQR Capital has turned this notion into a hedge fund managing $42 billion. He refined his approach, applied quantitative research, running Goldman Sachs’s Global Alpha Fund in the 1990s. It’s data analytics. Now, this idea of cost-efficient momentum investing abounds. We know. We track it.

And we track it because we apply proprietary data analytics to investor relations. In IR as in baseball, what matters are good statistics and how you measure them. Sure, focus on fundamentals. It’s a must. But if you can’t identify statistical differentiation, you’re playing a game that has passed you by. The world is in motion.

The next big opportunity for distinguishing IR is where baseball found it.

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