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But these, said Leinweber, are like applying the rules of the road to aircraft. Slowing, rather than suddenly halting, markets is less traumatic.
In early 2011, using the lab’s Cray XE6 “Hopper” supercomputer, Leinweber’s team found that a supercomputer could use a recently identified measure to warn of a looming flash crash.
Called Volume-synchronized Probability of INformed trading, or VPIN, it detects an imbalance between buy and sell orders, and growing volatility, about 45 minutes before a crash. It reveals “flow toxicity,” that is, when high-tech traders’ computers generate so many buy or sell orders that it becomes all but impossible to match orders. Amid this volatility, one side will stop trading to stem its losses, causing a sudden drop of prices that triggers an avalanche of similar withdrawals called a “flash crash.”
A second measure of market instability, the Herfindahl-Hirschman Index, or HHI, also rose sharply for some stocks, although not for others.
These two signals of instability — and that uneasy feeling that someone else has more information than you — do not give a clear direction of specific trades, said the lab’s John Wu. So they’re not likely to be exploited by someone hoping to jump in and profit.
But they might be useful “for regulators to impose some rules that might slow down the market so we don’t get into a sort of feeding frenzy,” he wrote in an e-mail.
Market experts said they saw promise in the idea. Leinweber “is well situated to speak to the role that machine learning and artificial intelligence can play for detecting patterns in markets,” said David Andre of San Francisco’s Cerebellum Capital.