(Corrects VIX index peak in first paragraph. For more Bloomberg View, click on VIEW <GO>.)
Feb. 7 (Bloomberg) -- The aftermath of the Lehman Brothers Holdings Inc. bankruptcy in 2008 was a scary time: One measure of stock-market volatility, known as the VIX or the “fear index,” reached a peak daily closing price of more than 80, compared with about 18 today. Scarier is the knowledge that we’ll be there again sometime. That’s how markets go: Unexpected chaos is the rule.
Economists have wondered for decades why markets have “excess volatility” -- that is, why prices move up and down more than they should by any sensible reckoning of assets’ fundamental value. We routinely have things like the crash of 1987, or the Flash Crash of May 6, 2010, shocking events that seem to strike out of nowhere.
Yet the nature of this chaos might not be as mysterious as it seems. From the right perspective -- not that of mainstream economics -- it looks like a natural consequence of a market game that often deludes investors into thinking they know how it works.
The idea comes by way of a silly thought experiment invented in 1994 by Brian Arthur, an economist then at Stanford University. Imagine a college bar with music and cheap drinks every Thursday night. Naturally, lots of students want to go. Trouble is, it’s a tiny place, and they will enjoy it only if 60 percent or fewer of them go. Otherwise, they will suffer miserably in the cramped heat. Hence, each week, every student faces a tricky decision: How to do what most other people will not do. (No cheating -- everyone has to decide at the same time.)
By tradition, economists analyze situations like this using game theory, which attempts to explain strategic interactions. It assumes that every student will think hard about the best strategy, and remain aware that others will do the same, everyone taking into account everyone else’s likely actions. By this method, the bar problem is devilish. Deductive thinking can’t give a good solution because if everyone chose one “best” solution, all students would go to the bar or stay at home, and be miserable either way. Strict reasoning falls down.
Drawing on psychology, Arthur argued that people might make decisions in more practical terms using simple theories or hypotheses. For example, a person might think, “crowded last week, should be less crowded this week,” and choose to go. Another might think differently, “crowded two weeks in a row, likely to be crowded again,” and stay at home. Psychologists have shown that people often make decisions by holding a handful of such theories in their minds, using whichever one seems recently to be working best.
Looking at the bar puzzle this way, Arthur found, you can quickly see how what happens at popular nightspots might fluctuate quite randomly from night to night. He used a computer to simulate a group of people using various theories about whether to go to the bar, and learning by trial and error. Quite quickly, the weekly attendance settled at an average of about 60 percent. But -- and this is the significant point -- the number didn’t settle down to 60. Rather, it kept fluctuating above and below in a random way, as people changed their tactics from week to week, responding to others who were also changing theirs.
Notice that there’s no “equilibrium” here of the kind that economists often like and expect. There’s no state of unchanging balance into which things settle. Lots of surprises emerge from a completely static situation: people just trying to solve the same problem week after week.
OK, cute puzzle. So what?
With a few small adjustments, this game can be used to describe the stock market. Replace “go” with “buy” and “stay at home” with “sell,” and suppose the difference in the number buying and selling drives a price change up or down. The bar game then takes a step toward the kind of thing that John Maynard Keynes had in mind when he suggested that markets resemble a beauty contest in which people guess whom they think most others will choose as the most beautiful. In the markets, Keynes suggested, we must “devote our intelligences to anticipating what average opinion expects the average opinion to be.”
Two decades later, the bar game now looks more profound than anyone would have imagined it could. Step by step, a handful of physicists, computer scientists and economists have transformed this curiosity into what are now arguably the most realistic models of markets. Such models treat markets as ever-evolving ecologies of investors who aim to profit on the basis of strategies they keep updating as they learn, or at least try to learn, from the past. Among other things, these ecological models reproduce the same kind of “excess” price fluctuations and sporadic upheavals we see in real markets.
In reality, of course, markets are often whacked by external events such as bank failures and policy announcements. But in these models, tumultuous market plunges can also emerge from nothing more than a chance momentary concentration of investors’ strategies -- a crowd-like similarity in thinking and behavior. There are periods when many market participants see patterns they interpret similarly. As a result, they react similarly and in concert, yet in a way that no one sees coming in advance.
This kind of natural herding leads to the so-called fat-tailed statistics of markets, reflecting their susceptibility to large crashes and rallies over minutes, hours and months. It also leads to many other more subtle, but realistic, features of real markets, such as their “long memory” -- an episode of high market volatility now has measurable effects lasting as long as 10 years into the future. None of these things emerges in any natural way from traditional equilibrium models.
Of course, no one working on these models would claim that they offer anything more than a crude picture. But the picture gets a few things right, and takes a first step into seeing the consequences of real human behavior in all its adaptive, changeable and less-than-rational glory. (I’ve given some further detail on market models of this kind on my blog.)
No theory of markets will ever come to a final set of elegant equations, like one often finds in physics. Understanding markets, like understanding evolution or the weather, means dealing in approximations, in decent but flawed models, and exploring any scheme that helps make sense of a very chaotic and changeable reality. Even if it does start off as a silly game about a bar.
(Mark Buchanan, a theoretical physicist and the author of “The Social Atom: Why the Rich Get Richer, Cheaters Get Caught and Your Neighbor Usually Looks Like You,” is a Bloomberg View columnist. The opinions expressed are his own.)
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