For Dhar, the markets are the ultimate AI lab. “Reality is the acid test,” says Dhar, a 1978 graduate of the Indian Institute of Technology, whose campuses are India’s best schools for engineering and computer science. He collected his doctorate in artificial intelligence from the University of Pittsburgh. A professor of information systems at Stern, Dhar left the school to become a principal at Morgan Stanley from 1994 to 1997, where he founded the data-mining group and focused on automated trading and the profiling of asset management clients. He still builds computer models to help Wall Street firms predict markets and figure out clients’ needs. Since 2002, his models have correctly predicted the stock prices from month to month 61% of the time, he says.
Next frontier
Dhar says AI programs typically start with a human hunch about the markets. Let’s say you think that rising volatility in stock prices may signal a coming “breakout,” Wall Street-speak for an abrupt rise or fall in prices. Dhar says he would select market indicators for volatility and stock prices, feed them into his AI algorithms and let them check whether that intuition is right. If it is, the program would look for market patterns that hold up over time and base trades on them.
Dhar says many AI scientists are questing after NLP programs that can understand human language. “That’s the next frontier,” he says, adding that computer scientists eventually will stitch together advances in machine learning and NLP and set the combined programs loose on the markets.
A crucial step will be figuring out the types of data AI programs should employ. The old programmer principle of GIGO—garbage in, garbage out—still applies. If you tell a computer to look for relationships between, say, solar flares and the Dow industrials and base trades on the patterns, the computer will do it. You might not make much money, however. “If I give an NLP algorithm ore, it might give me gold,” Dhar says. “If I give it garbage, it’ll give me back garbage.”
Collective Intellect, financed by Denver-based venture capital firm Appian Ventures Inc., is trying to sell hedge funds and investment banks on NLP technology. Wolters says traders and money managers simply can’t stay on top of all the information flooding the markets these days. Collective Intellect seeds its NLP programs with the names of authors, websites and blogs that its programmers think might yield moneymaking information. Then, the company lets the programs search the Web, make connections and come up with lists of sources they can monitor and update. Collective Intellect is pitching the idea to hedge funds, Wolters says.
Michael Thiemann, CEO of San Diego-based hedge fund firm Investment Science Corp., calls his program Deep Green. The name recalls IBM’s Deep Blue—and money. Deep Green evaluates market data, learns from it and scores trading strategies for stocks, options and other investments, he says.
Thiemann declines to discuss his computerized hedge fund, beyond saying that he’s currently investing money for friends and family and that he plans to seek other investors this year. “This is hard, like a moon launch is hard,” Thiemann says of the task ahead of him. Dhar says he doubts thinking computers will displace human traders anytime soon. Instead, the machines and their creators will learn to work together. “This doesn’t get rid of the rule of human creativity; it actually makes it more important,” he says. “You have to be in tune with the market and be able to say, ‘I’m smelling something here that’s worth learning about”.