The Most Important Chart in Finance
What Crowding Does to Returns
For anyone who wants to have a career as an investor, or wants it to be an enjoyable hobby that's more profitable than not, there's a good case to be made that the following picture should be printed out, framed, and hung somewhere prominent:
The chart shows the sharpe ratio (i.e. the excess return over the risk-free rate divided by standard deviation of the returns) of a given trading strategy over time. There are other metrics that capture the same kind of risk/return tradeoff, and they'll all show similar results. The cycle goes like this:
A signal is discovered, whether it's some kind of systematic one ("if stocks rise in the last thirty minutes of trading, they tend to open higher the next morning!") or a more qualitative one ("companies that have recognizable brand names and make repeat sales, like consumer packaged goods, tend to be able to continuously raise prices").
Investors pile in to the trade, and the edge shrinks.1 Once the signal is well-known, traders respond to it, and the cheap stocks snap back from cheapness faster, while short-term momentum effects get smeared out a bit as traders get ahead of them earlier and earlier.
At some point, the main driver of the strategy's performance isn't the fact that it worked, but that a growing number of investors think it's working. The chart doesn't show this, but some strategies actually hit their peak returns when they're the most crowded they'll ever be.
Something breaks, somewhere. Often it's a matter of random bad luck, though occasionally what happens is that the returns on the strategy dip below the opportunity cost of alternatives, so people start to exit. But at that point, the natural buyers are people following the strategy, who are fully invested and levered. When the price moves in the wrong direction, they might want to add to the trade, but in practice they're more likely to be forced to exit, either by their risk manager or their prime broker.
The strategy reverses, and the smartest thing you can do in that market is the opposite of whatever a smart professional would have thought was the right move.
When the dust clears, track records have been dinged, perhaps some careers have been ruined—but often, the signal never fades, and the strategy continues to hum along at a positive but less impressive sharpe ratio.
It’s easy to see how systematic strategies are vulnerable to this cycle, but it’s worth emphasizing the fact that this happens to qualitative strategies as well—yes, there is such a thing as paying too much for what you perceive to be good management and high growth, as anyone who paid 50x earnings for GE or 200x for Cisco in the late 90s can attest.2
And that checks out, because this cycle needs to be true: every financial anomaly is an invitation for someone to exploit it. In the short term, asset prices are determined by flows and by the willingness of market participants to make a market, and leverage has the effect of enforcing shorter-term thinking by increasing the probability of forced sales or a total loss.
This process can happen very gradually, partly depending on market dynamics: if you discover some new quirk of market microstructure, it might be days or weeks before the edge is competed away. But the VCs who bet on technical founder-CEOs had a signal that kept providing upside for a long, long time before competition got too aggressive and founders learned that it was something investors liked.
Systematic investors are more aware of their reliance on signals, because that's explicitly how they model their process, and that's the level at which they track performance. For a fundamental investor, it can be hard to honestly admit that in their ten-item checklist, it turns out that item #8 has driven all of their excess returns and is also increasingly on everyone else's checklist, too.
This also gets noisy because sometimes the same thesis gets expressed in different ways. Yesterday's issue of The Diff talked about modeling loyal users as an abstract kind of real estate ($), with big platforms as the landlords. But plenty of real estate investors missed out on this because they took their real estate expertise too literally. Meanwhile, many value investors did very well by slowly adjusting their models to focus more on growth than on present earnings. And, as it turns out, buying a dollar bill for fifty cents still works even if calculating that dollar bill means paying more attention to growth rates and incremental margins than to the present level of cash burn.
But the real takeaway is that the thesis-generating process is really the output of a separate process for coming up with and tweaking investment approaches. It's not just the depth of expertise that makes a good investor, but the habit of regularly switching what, exactly, they intend to be experts on. And as the chart reminds us, doing that adjustment too slowly is a painful mistake.
Read More in The Diff
The Diff has written extensively about this kind of cycle, at multiple levels:
Value? Yes. "Systematic?" Not Necessarily ($): Systematic value strategies had a good run, but over time they’ve ended up selecting for optically cheap companies that stay cheap for a reason.
Why do "Value Traps" Persist ($)? Taking the same phenomenon in the other direction, we look at “value traps,” companies that are notoriously cheap and don’t improve.
Irish Banks: After the Hangover, a Party ($)? The Irish banking sector went through a massive cyclical upturn in the 90s and especially 2000s, and it’s taken about as long to recover.
The Economics of Alpha Capture ($): Hedge funds are well aware of these dynamics, and have actually built systems to identify and exploit the temporary systematic strategies that arise from discretionary trades.
1. Actually, for a while the realized returns go up! One thing that this risk-adjusted graph obscures is the actual shape of that change: often, what happens is that returns decline, but risk declines, too.
2. Many of the best-performing stocks of the 2001-3 period were pretty dowdy, low-growth business, often in industries that didn't have great long-term economics. But in the dot-com frenzy, they'd been left behind. It certainly helped that, after the collapse of that bubble, the big economic story was increasingly China's voracious demand for raw materials, which was good news for oil, mining, steel, and the like—all sectors investors were avoiding in the late 90s.
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