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Regulators use Silicon Valley's AI to catch rogue traders

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In Robert Harris's 2011 novel The Fear Index a secretive hedge fund builds a computer capable of making its own trading decisions. Gobbling up information, the machine starts to confuse its human creators by building huge stakes and making a handsome profit from a market panic. As they assess the outcome, one of the protagonists notes: "The beauty of it is that it was but 0.4 per cent of total market volatility. No one will ever notice, except us." As markets increasingly rely on computer algorithms, reality is imitating fiction: artificial intelligence is becoming a bigger part of investing and it is also helping regulators ensure that traders do not get away with bad behaviour.


Meet Wall Street's New AI Sheriffs

#artificialintelligence

Inc.'s 11th annual 30 Under 30 list features the young founders taking on some of the world's biggest challenges. In 2013, a high-frequency trader named Michael Coscia was arrested in New Jersey for an activity called "spoofing"--essentially manipulating the market by flooding trading systems with future orders he had no intention of completing. He was fined 6 million--with the possibility of jail time. It was the first such prosecution under a new set of financial regulations from the 2010 banking reform law called the Dodd-Frank Act. That was an aha! moment for David Widerhorn, 28, and it became his reason for founding Neurensic.


Meet Wall Street's New A.I. Sheriffs

#artificialintelligence

Inc.'s 11th annual 30 Under 30 list features the young founders taking on some of the world's biggest challenges. In 2013, a high-frequency trader named Michael Coscia was arrested in New Jersey for an activity called "spoofing"--essentially manipulating the market by flooding trading systems with future orders he had no intention of completing. He was fined 6 million--with the possibility of jail time. It was the first such prosecution under a new set of financial regulations from the 2010 banking reform law called the Dodd-Frank Act. That was an aha! moment for David Widerhorn, 28, and it became his reason for founding Neurensic.


Navinder Sarao: Who is the so-called 'flash crash' trader?

BBC News

Navinder Sarao, who traded from his parents' home in Hounslow, west London, has been accused of market manipulation that caused a 1,000-point fall on the US Dow Jones index in 2010. He faces 22 charges in the US, including fraud charges, all of which he denies - which include "spoofing" - the practice of buying or selling with the intent to cancel the transaction before execution. What he did was astonishing. From a computer in the bedroom he'd grown up in, at his parents' semi-detached house under the Heathrow flight path, this casually dressed 37-year old traded remotely on an exchange in Chicago he had never visited. He was operating on the Chicago Mercantile Exchange, a highly computerised market where normal human beings stand little chance.


Trader pleads guilty to sparking stock market 'Flash Crash'

Engadget

If you were trading on an American stock market on May 6th, 2010, you probably had a minor heart attack: the "Flash Crash" that day sent the Dow Jones Industrial Average down 1,000 points (600 in the first 5 minutes) and recovered virtually all its value in the space of just 15 minutes. However, investigators eventually discovered that the crash was the result of intentional manipulation... and now, investors are getting some justice for that manufactured crisis. Navinder Sarao (above), a British trader extradited to the US, has pleaded guilty to charges of both wire fraud and spoofing that came from using automated trading software to make "at least" $12.8 million in illegal profit from the crash and beyond. Sarao says he used the program to make thousands of bogus orders for E-mini S&P 500 futures contracts (including 85 sell orders on the day of the crash), creating an illusion of supply that fooled others into trading when they otherwise wouldn't. When that happened, he'd make real trades to capitalize on the artificial values.