Universally-Useful Economic Indicators Can’t Last Forever ($$)
It opens with he following example:
The Economist has a bit of an obituary for “Dr. Copper,” the idea that copper prices were a strong leading indicator ($, Economist). Copper’s utility as an indicator makes sense: it’s an input into electrical equipment, housing, cars, and plenty of other durable goods. Since demand for durable goods fluctuates more than other kinds of demand, and since the companies that make these goods need to actually buy the physical copper before they can manufacture the products, it works, in theory, as a leading indicator.[1]
But to use copper this way, you need to imagine an economy where swings in demand for durable goods are a primary driver of the economic cycle. And you also need to assume away any countervailing force. One reason copper broke down as an economic indicator is that the biggest consumer, accounting for half of worldwide demand, is China. And, for a long period that probably ended in the last few years
In another post, Byrne highlights a similar sentiment showing how hard it is to compare data long-term:
Small Caps and Like-for-Like Comparisons
Verdad Research has yet another good piece on the gap between small-cap and large-cap valuations, where they note that the small-cap stock universe is less fundamentally impressive than it used to be. The relative comparison hurts in both directions: larger companies are better-run and faster-growing than they used to be, and investors in small-caps face an adverse selection problem courtesy of private equity firms: PEs will snap up bargains and lever them up enough to compensate for the M&A premium. It’s a good reminder that long-term comparisons between indices are not like-for-like comparisons; small caps got cheap in part because the best of them became large caps and the cheapest got acquired.
I always harp on how markets are biology not physics. On Wednesday I highlighted SIG’s Todd Simkin’s response when he was asked what aspect of trading students have the most difficulty with:
The most difficult aspect, not just for our students, but for our experienced traders as well, is handling the noisy outcome and the noise that comes after the fact. As I mentioned before, the types of people that we tend to hire are those with backgrounds in computer science, physics, finance. However, many of these individuals come from fields where if you can figure out a system, then you can move forward. Biologists are very much in this camp; if you can describe the way biological systems interact, no matter how complex they are, once you’ve described them, you can build on that. You’ve got a description of an underlying process. Germ theory, for example, once developed, everything that can bolt onto germ theory ends up being correct because germ theory itself is a good underlying description of the interaction of germs and health.
But in our world, once you’ve figured out how a system works, it changes the way you behave and once you behave differently, the system itself changes fundamentally. So, we are in this world of constant change and part of that change is our own impact on it. For an astrophysicist, the way a star behaves has nothing to do with whether or not we’re observing it. But for a trader, the way a stock moves has everything to do with our perception of how that stock should move. Once we have an opinion about it, we then go out and do something differently, and somebody else can see what we did and they’re building that into their system and their model of the way the world works. So, dealing with this constant change, I think, is the biggest surprise, especially since we’re bringing in really high-level smart people. We’re not bringing in people who are used to being wrong, and we’re putting them in a world where they’re going to be wrong a lot. Not necessarily in the direction of the trades they make, but certainly wrong in terms if they only evaluated the outcome. Even wrong in terms of having to change their mind frequently, and being open and willing to change your mind and having the right mindset to say this. “This, I think, is correct for now. But it might not be correct tomorrow.” It’s a new experience for a lot of these people who are accustomed to being A+ students, to getting things right. And we’re putting them in a world where they’re not getting a lot right all the time.
Almost every take you see that starts with some comparison of the past and what it should mean for us today materially underweights the biological nature of the system.
This reality is the subtext for the most popular finance post I ever published:
Why Investing Feels Like Astrology (19 min read)
The dynamic in the post is an example of trying to bridge the irreducible paradox of “no, this time is not different” with the plasticity required to incorporate financial actors’ adaptation into the most lindy aspects of your mental model [again, RIP Munger].
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