Wall Street Journal
Wall Street is notorious for not learning from its mistakes. Maybe machines can do better.
That is the hope of an increasing number of investors who are turning to the science of artificial intelligence to make investment decisions.
With artificial intelligence, programmers don't just set up computers to make decisions in response to certain inputs. They attempt to enable the systems to learn from decisions, and adapt. Most investors trying the approach are using "machine learning," a branch of artificial intelligence in which a computer program analyzes huge chunks of data and makes predictions about the future. It is used by tech companies such as Google Inc. to match Web searches with results and NetFlix Inc. to predict which movies users are likely to rent.
One upstart in the AI race on Wall Street is Rebellion Research, a tiny New York hedge fund with about $7 million in capital that has been using a machine-learning program it developed to invest in stocks. Run by a small team of twentysomething math and computer whizzes, Rebellion has a solid track record, topping the Standard & Poor's 500-stock index by an average of 10% a year, after fees, since its 2007 launch through June, according to people familiar with the fund. Like many hedge funds, its goal is to beat the broader market year after year.
"It's pretty clear that human beings aren't improving," said Spencer Greenberg, 27 years old and the brains behind Rebellion's AI system. "But computers and algorithms are only getting faster and more robust."
Some sophisticated hedge funds such as Renaissance Technologies LLC, based in East Setauket, N.Y., are said to have deployed AI to invest. But for years, these firms were the exception. Some firms that have dabbled in AI are skeptical it is anywhere close to working.
Rebellion is part of a new wave of firms using machine learning to trade. Cerebellum Capital, a San Francisco hedge fund with $10 million in assets, started using machine learning to invest in 2009. A number of high-frequency trading firms, such as RGM Advisors LLC in Austin, Texas, and Getco LLC in Chicago, are using machine learning to help their computer systems trade in and out of stocks efficiently, according to people familiar with the firms.
The programs are effective, advocates say, because they can crunch huge amounts of data in short periods, "learn" what works, and adjust their strategies on the fly. In contrast, the typical quantitative approach may employ a single strategy or even a combination of strategies at once, but may not move between them or modify them based on what the program determines works best.
"No human could do this," said Michael Kearns, a computer-science professor at the University of Pennsylvania who has used AI to invest at firms such as Lehman Brothers Holdings Inc. "Your head would blow off." Link to complete article
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