This reminds of the old adage that it's time to sell when your shoeshine boy starts giving you stock tips. When running money starts to suck in a sizeable fraction of all brainpower (as internet startups seemed ready to, in the last bubble), it probably means we've reached a peak. Either that, or it's a secular revolution with the financial machine taking over the whole world :-) In that case, it will only be a matter of time before predicting short term market movements has surpassed the sexiness of any of the Clay Prize problems in mathematics, or quantizing gravity in physics...
Via DealBook: this publishing event is another sure sign of a market top. Tell your shoeshine boy!
NYTimes: Ray Kurzweil, an inventor and new hedge fund manager, is describing the future of stock-picking, and it isn’t human.
“Artificial intelligence is becoming so deeply integrated into our economic ecostructure that some day computers will exceed human intelligence,” Mr. Kurzweil tells a room of investors who oversee enormous pools of capital. “Machines can observe billions of market transactions to see patterns we could never see.”
The listeners, attendees of a conference sponsored earlier this month by the Capital Group Companies, are slightly skeptical. Some have heard that Mr. Kurzweil, 58, who takes more than 150 vitamins and supplements a day, believes people will eventually live forever. Others know he has said that in 2045, man and machine will achieve “singularity,” and humans will hold their breath for hours thanks to nanomachines in our bloodstreams.
But some are aware that a former Microsoft executive and chairman of the Nasdaq stock market, Michael W. Brown, is an investor in Mr. Kurzweil’s new hedge fund, FatKat, and that Bill Gates once described him as “the best person I know at predicting the future of artificial intelligence.”
More important, many of them have seen Mr. Kurzweil’s ideas used by stock speculators. So, they want to learn more about his brave, new world.
“These ideas are the future,” said David Atkinson, a private investor who attended another lecture later that day by Mr. Kurzweil. “I’m not really sure I understand them, but they’re making some folks rich.”
Complicated stock picking methods are nothing new. For decades, Wall Street firms and hedge funds like D. E. Shaw have snapped up math and engineering Ph.D.s and assigned them to find hidden market patterns. When these analysts discover subtle relationships, like similarities in the price movements of Microsoft and I.B.M., investors seek profits by buying one stock and selling the other when their prices diverge, betting historical patterns will eventually push them back into synchronicity.
Today, such methods have achieved a widespread use unimaginable just five years ago. The Internet has put almost every data source within easy reach. New software programs, like the Apama Algorithmic Trading Platform, have made it possible for day traders to build complicated trading algorithms almost as easily as they drag an icon across a digital desktop.
“Five years ago it would have taken $500,000 and 12 people to do what today takes a few computers and co-workers,” said Louis Morgan, managing director of HG Trading, a three-person hedge fund in Wisconsin. “I’m executing 1,500 to 2,000 trades a day and monitoring 1,500 pairs of stocks. My software can automatically execute a trade within 20 milliseconds — five times faster than it would take for my finger to hit the buy button.”
Studies estimate that a third of all stock trades in the United States were driven by automatic algorithms last year, contributing to an explosion in stock market activity. Between 1995 and 2005, the average daily volume of shares traded on the New York Stock Exchange increased to 1.6 billion from 346 million.
But in recent years, as algorithms and traditional quantitative techniques have multiplied, their successes have slowed.
“Now it’s an arms race,” said Andrew Lo, director of the Massachusetts Institute of Technology’s Laboratory for Financial Engineering. “Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits.”