A Reign of Robots Is Closer Than You Think: Mark BuchananMark Buchanan
March 7 (Bloomberg) -- The futurist Ray Kurzweil has famously predicted that humanity is approaching a “singularity,” a fateful moment when our technology becomes smarter than us and able to learn faster than we can, when it becomes the principal creator of new technologies and machines race far ahead of us. Humans may effectively fall out of the loop -- a species demoted, if not eliminated.
For now, this world remains science fiction, at least at the level of humanity. But finance is flirting with a similar transition, as ever-faster computing and communications technology takes high-frequency trading into a regime of speed where human beings can no longer keep up. In fact, we may have already arrived.
The Flash Crash of May 6, 2010, was a landmark event hinting that something may be amiss in the high frequency markets. Now it is clear that odd market behavior at high frequencies is systematic.
In a recent study, physicist Neil Johnson and colleagues found more than 18,000 instances over the past five years where markets, in about a second and a half or less, either ticked up or down at least 10 times in a row, making prices rise or fall in that span by more than 0.8 percent. Many of these mini-crashes and mini-booms took place in well under a tenth of a second, effectively instantaneous from a human perspective. And they have been happening roughly 10 times per day. It’s as if the markets are throwing off sparks reflecting mysterious frictions or stresses.
These sparks show up in market statistics, too. The same study looked at the incidents on different timescales, both above one second and below, and found a striking difference. Over periods of one second or longer, the distribution of events by size has the familiar “fat tailed” distribution -- the norm for markets, broadly speaking, which reflects their pronounced susceptibility to large price changes.
In contrast, the distribution for events that last less than one second looks very different. Here, the distribution is “fatter than fat” and shows an even greater than normal tendency for Black Swan-type upheavals.
What’s so special about one second? Why is this sharp and distinctive boundary located at that period of time, rather than at, say, one minute or a 10th of a second? Well, it is more than a little suspicious, the researchers point out, that one second happens to be right around the speed limit for fast human decision making. Experiments with chess grandmasters, for example, show they can assess a complex chess situation and identify a threat of checkmate in about two thirds of a second. Other people operate at comparable speeds in their own areas of expertise.
When it comes to making conscious decisions, one second is about the limit. Coincidence? Or are the markets at this timescale showing the signs of an emerging all-machine phase of trading over which human decisions have little influence or control?
Further evidence for the latter interpretation comes from simple models of markets as ecologies of interacting strategies, models of the kind I wrote about in my last column. These models reproduce many of the realistic qualities -- or “stylized facts” -- of real markets, and can help us anticipate how markets might do surprising things. In particular, they can give hints about how seemingly innocuous, gradual changes might push markets across a threshold and into a regime of dramatically different behavior.
Mathematical studies of these models show that one of the most fundamental factors influencing their basic dynamics is how “crowded” the market is -- crowded in an intellectual and strategic, rather than physical sense. If the participants in a market use a wide and diverse range of trading strategies, then the market is uncrowded. In this case, the typical behavior is akin to that in a world with few predators and relatively plentiful prey. A healthy diversity of participants earns profits in different ways -- thinking and acting on different timescales, taking different views on the future and so on.
Real markets, the models suggest, look a lot like this uncrowded phase, with highly irregular market fluctuations and fat tails.
In contrast, if a market becomes overcrowded -- that is, if many traders chase few opportunities and use very similar strategies to do so -- then the continuity of the market tends to break down. In this regime, the market becomes prone to what might be called “glitches” or “fractures,” sudden moves up or down much like those now observed in the sub-one-second trading regime.
There are good reasons, Johnson and colleagues argue, to think that high frequency markets have indeed entered such a crowded phase. After all, high frequency algorithms by their nature compete on speed and have to act extremely quickly. As a consequence, they must be relatively simple, and can’t waste time analyzing too much information about the past. Given these constraints on the range of possible strategies, and given the number of traders operating within them, overcrowding is quite likely -- as are the fractured, troubled market dynamics arising from it.
This is an instructive example of how a simple adaptive model of markets can give rise to important and non-obvious insights. As trading moves to inhumanely short timescales, we shouldn’t be surprised, but should actually expect to see increasingly frequent Black Swan events in microscopic timescales. They may well be the natural consequence of machine trading that is becoming uncoupled from the strong influence of conscious human decision making.
We’re moving, as Johnson and colleagues put it, “from a mixed phase of humans and machines, in which humans have time to assess information and act, to an ultrafast all-machine phase in which machines dictate price changes.” We’re crossing a boundary into a trading twilight zone, and doing so without much thought or awareness of the potential dangers.
(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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