The following article is by Tim Fernholz, and has been written on September 16-th, 2013 and is proposed here verbatim.
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.
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.”
This chart shows what an UEE crash looks like (box A), what a spike looks like (box B), and most interestingly, how the number of these events (in red and blue) has risen between 2006 and 2011 compared with the S&P index (in black). That list of stock symbols in green contains the equities that have the most extreme events, with the most likely at the bottom:
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.”
Allowing robots – programmed by people who often insist that the world is linear, the markets are efficient and in equilibrium, or that things are Gaussian – to happily move billions of dollars per second is playing with fire, to say the least. The Principle of Fragility, coined by Ontonix in 2005, states that:
Complexity X Uncertainty = Fragility
when it comes to business, or trading, we can say:
Complexity (of products) X Uncertainty (turbulence of markets) = Fragility
In other words, as the complexity of traded products increases, in a market with a given level of turbulence (volatility) exposure also increases. But the above equation can also be interpreted as follows:
Complexity (of products) X Ignorance (of traders) = Fragility
Placing complex and sophisticated products or systems into incompetent hands is like giving a sports car to a child, or a sophisticated airliner to a young untrained pilot. The higher the complexity of the system and the higher the ignorance of the operator the more fragility is being created. Now, trading robots are very specialized and are extremely efficient in what they do. However, they lack insight, intuition, wisdom. They don’t “feel”. For example, in April 2013 a false rumor of explosion at the White House has caused stocks to plunge. The Associated Press’ Twitter account has been hacked for a short time and has published the news. Trading robots have instantly reacted to the news causing billions of dollars of capitalization to evaporate. Had the attack been made simultaneously on the Twitter accounts of the major press agencies it could have collapsed the global financial system.
It is not a bad idea to trade stocks automatically and to do it based on millions of tweets or social networks. However, in order prevent computers from taking us 100 years back in time in just a few minutes, it is necessary to:
- Measure and monitor the complexity of financial products and derivatives in particular
- Measure and monitor the complexity of financial transactions
- Establish regulations and limits on the complexity of products that may be traded automatically and the of the associated transactions
Before things run (more) out of control we must limit just to what degree can we allow the global financial system to run on autopilot.