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<nettime> The secret financial market only robots can see

The secret financial market only robots can see

By Tim Fernholz	@timfernholz	September 16, 2013

What if someone told you the stock market crashed and spiked 18,000
times since 2006, and you had no idea?	

That’s the contention of a group of scientists who study complex systems
after analyzing market data, collected by Nanex, since the advent of
high-speed trading. While the fallout of computerized algorithms has
been seen before, including the infamous 2010 “flash crash,” when
markets lost nearly 10% of value in just a few minutes, that same kind
of sudden volatility is going on all the time, unseen.	

In a new paper called “Abrupt rise of new machine ecology beyond human
response time,” researchers found a new trading ecosystem that humans
don’t even notice. [1]	

People can’t really respond to stimuli much faster than in one second.
The benchmark comes from cognitive scientists who find that it takes 650
milliseconds for a chess grandmaster to realize that a king has been put
in check after a move. Below that time period, you can find “ultrafast
extreme events,” or UEEs, in which trading algorithms cause prices to
change by 0.08% or more before returning to human-time market prices.
This appears to be the case when many simple algorithms, operating on
limited information, pile into a single trade.	

“Down in the sub-second regime, they are the only game in town,”
University of Miami Physics Professor Neil Johnson, who led the study,
says. “It’s almost like you’re seeing them in pure form.”	

If you’ve noticed that the number of extreme events spikes around the
time of the financial crisis, and the stocks most likely to experience
them are bank stocks, you’ll see why the researchers are so interested
in this hidden market: This pattern suggests the coupling between
extreme market behaviors and global instability—”how machine and human
worlds can become entwined across timescales from milliseconds to
months”—and is also are seen more often before and after the kinds of
“flash crashes” that people actually notice.	

Regulators, though, aren’t keeping track of these events. That’s a
problem, not just because of any potential forewarning, but also because
trading at that speed creates volatility that makes markets less efficient.	

“Are these 18,000 lucky breaks for one of the algorithms or 18,000
examples of a new form of inside trading?” Johnson says. “In terms of
the information availability, it’s really hard to tell. It’s sort of
strange to have that going on and have nobody know.”	

The researchers say there’s much more to learn, especially at the border
where human traders and robotic ones interact. One question is whether
moving at computer speeds is inefficient because there’s less
information available at that time scale—data just can’t move that fast,
even electronically. Laboratory experiments suggest computers are more
efficient on a human time-scale than a sub-second one. And if sub-second
trading does continue, do market participants need to come up with
sub-second hedges and derivatives to protect from this kind volatility?

Regardless, the complexity emerging naturally from high-frequency
trading tends to be hard to comprehend for market participants and
regulators alike.

“It’s sort of a collective, in some sense they all share responsibility
and yet nobody’s responsible,” Johnson says. “Am I responsible for the
traffic jam out on US 1? No, I’m just in it, but if no one was in it,
there wouldn’t be one.”


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