Analytics 1.0 = Find underpriced assets
Analytics 1.1->1.9 = Keep frantically searching diminishing pool of underpriced assets
Analytics 2.0 = Improve the quality of your own assets
Most investors have no idea what they are good and bad at. They are stuck at Analytics 1.5. They need to evolve to Analytics 2.0.
The Current “Leading Edge”
Today, “quantitative” investing is the rage. Investment firms design computer programs that pick stocks based on algorithms that can overcome human’s neurological biases and generate better performance.
However, I am here to talk about the future, not the present. In the (likely near) future, the rage in investing will be analytics that can teach investors rather than teach computers. Black boxes may do well, but they have flaws. They can stop working (and inevitably will) and can lead to systemic contagion.
Rather than just trust the black box, investors will demand their portfolio managers be more hands on. This will lead to the development of training investors through quantitative assessment.
All investors have what I’ll call “leans”. Others might call them biases. Some are better at growth stocks, others at value. Some are more contrarian while others like to ride momentum. Some care only about the direction of fundamentals, while others think the macro environment is more important to getting individual stocks right.
The thing is, nobody knows if their individual leans create value. This is the great mystery of investing. It is very hard to attribute what styles actually make a portfolio manager money. It is it’s own black box.
Learning From Baseball
To use a baseball analogy, it is like if a pitcher leans on his fastball because he thinks it his best pitch, but the data says his curveball is actually more effective. It used to be most pitchers didn’t know which pitch was better for them. They just went on their intuition. Now, they have tools that show them what works and what doesn’t.
While “Moneyball” has become eponymous with analytics in sports due to the book and movie, the concepts identified by the Oakland A’s were really Sports Analytics 1.0: the focus was finding mispriced assets, such as players who walked a lot or were good defenders. This is analogous to what quant investors began as.
This process evolved into more and more complex ways of identifying mispriced players, whether it be WAR (wins above replacement) or FIP (field independent pitching) or BABIP (batting average on balls in play). Again, this resembles the evolution of quant into more and more complex algorithms to find better trades. Call this Moneyball 1.5 (M1.5).
Moneyball 2.0: Changing the Focus
The problem with a strategy focused on finding underpriced assets is eventually others catch up to you and you lose your edge. You then end up at M1.5 where you have to spend more and more money and intellectual resources to find the next edge first and monetize it before it too disappears. There is no sustainable advantage.
Smart franchises (particularly the Houston Astros) realized there was a better approach: stop looking for undervalued assets and try to improve the assets they already had. This took a number of forms. Some teams have focused on improving a pitcher’s biomechanics to improve the “spin rate”. Others have, as mentioned above, worked to help a pitcher identify which of his pitches produce the best results and emphasize those more. The lesson is it’s more of a sustainable advantage to buy a fairly priced asset and increase it’s efficiency than to keep playing the game of hunting for underpriced assets with diminishing returns.
A new book covers these ideas for those who want to learn more. It’s called “The MVP Machine: How Baseball’s New Nonconformists Are Using Data to Build Better Players”. You can find an excerpt here or the whole thing on Amazon (no, I’m not getting a cut if you click, I haven’t even bought the book myself yet, but the excerpt is recommended).
I thought this was a great quote (from one of the Astros’ executives) describing the problem with the classic approach.
“When I came there and saw our player-development goals…It was stuff like, ‘Improve your command.’ How’s a pitcher supposed to go into the off-season and improve his command? He needs a drill. He needs to know how to measure if he’s getting better.”Mike Fast, Houston Astros
“Improve your command” is the kind of thing a portfolio manager hears from a CIO when they’re struggling. “Stop trading so much” (or even “trade more”) or “make bigger bets” or “be more aggressive” or “don’t follow the crowd“. They’re all general statements without any suggestions on how to actually do them.
What portfolio managers need is analytics that show them where they make money and where they lose money. This exists a little bit with factor investing (“you own too much momentum”) but that’s just scratching the surface.
Really useful measurements would explain if you make or lose money when you put on certain types of trades. For example, do you do better calling sector moves or ranking stocks within your sector? Are you good at trading events? Do you use technicals well? Do you put too much or too little emphasis on valuation? Do you tend to catch falling knives too early? Do you get spooked out of good ideas because of a bad data point? There is so much to look at!
This may sound hard and, to some extent, it is. When I’ve suggested it in the past, that was always the answer I got. Too hard. Too expensive. But if it even added 5bp of performance, it would easily pay for itself. Baseball teams have much smaller budgets than hedge funds, yet they have found the money.
The issue isn’t cost. It’s resistance to change. But if you want to build an investment organization with a sustainable advantage, you need to invest in tools like this. Sustained outperformance doesn’t come from doing what everyone else does. It comes from being early to identify change.