New York: Way up in a New York skyscraper, inside the headquarters of Lehman Brothers Holdings Inc., Michael Kearns is trying to teach a computer to do something other machines can’t: think like a Wall Street trader.
In his cubicle overlooking the trading floor, Kearns, 44, consults with Lehman Brothers traders as Ph.D.s tap away at secret software. The programs they’re writing are designed to sift through billions of trades and spot subtle patterns in world markets.
Kearns, a computer scientist who has a doctorate from Harvard University, says the code is part of a dream he’s been chasing for more than two decades: to imbue computers with artificial intelligence, or AI.
His vision of Wall Street conjures up science fiction fantasies of HAL 9000, the sentient computer in 2001: A Space Odyssey. Instead of mindlessly crunching numbers, AI-powered circuitry one day will mimic our brains and understand our emotions—and outsmart human stock pickers, he says.
“This is going to change the world, and it’s going to change Wall Street,” says Kearns, who spent the 1990s researching AI at Murray Hill, New Jersey-based Bell Laboratories, birthplace of the laser and the transistor.
As finance Ph.D.s, mathematicians and other computer-loving disciples of quantitative analysis challenge traditional traders and money managers, Kearns and a small band of AI scientists have set out to build the ultimate money machine.
Human-intelligent: Vasant Dhar, a former Morgan Stanley quant, at New York University's Stern School of Business in New York, where he now teaches
For decades, investment banks and hedge-fund firms have employed quants and their computers to uncover relationships in the markets and exploit them with rapid-fire trades.
Quants seek to strip human emotions such as fear and greed out of investing. Today, their brand of computer-guided trading has reached levels undreamed of a decade ago. A third of all US stock trades in 2006 were driven by automatic programs, or algorithms, according to Boston-based consulting firm Aite Group LLC. By 2010, that figure will reach 50%, according to Aite.
AI proponents say their time is at hand. Vasant Dhar, a former Morgan Stanley quant who teaches at New York University’s Stern School of Business in Manhattan’s Greenwich Village, is trying to program a computer to predict the ways in which unexpected events, such as the sudden death of an executive, might affect a company’s stock price.
To believers such as Dhar and Kearns, all of this is only the beginning. One day, a subfield of AI known as machine learning, Kearns’s speciality, may give computers the ability to develop their own smarts and extract rules from massive data sets. Another branch, called natural language processing, or NLP, holds out the prospect of software that can understand human language, read up on companies, listen to executives and distil what it learns into trading programs.
Collective Intellect Inc., a Boulder, Colorado-based startup, already employs basic NLP programs to comb through 55 million Web logs and turn up information that might make money for hedge funds. “There’s some nuggets of wisdom in the sea,” says Collective Intellect chief technology officer Tim Wolters.
The hope is that these systems will ape living neurons, think like people and, like traders, understand that some things are neither black nor white but rather in varying shades of gray. The computers have done well. A November 2005 study by Darien, Connecticut-based Casey, Quirk & Associates, an investment management consulting firm, says that from 2001 to 2005, big-cap US stock funds run by quants beat those run by nonquants.
The quants posted a median annualized return of 5.6%, while nonquants returned an annualized 4.5%. Both groups beat the Standard & Poor’s 500 Index, which returned an annualized negative 0.5% during that period.
Rex Macey, director of equity management at Wilmington Trust Corp. in Atlanta, says computers can mine data and see relationships that humans can’t. Quantitative investing is on the rise, and that’s bound to spur interest in AI, says Macey, who previously developed computer models at Marietta, Georgia-based American Financial Advisors LLC, to weigh investment risk and project clients’ wealth. “It’s all over the place and, greed being what it will, people will try anything to get an edge,” Macey, 46, says. “Quant is everywhere, and it’s seeping into everything.”
AI proponents are positioning themselves to become Wall Street’s hyperquants. Kearns, who previously ran the quant team within the equity strategies group at Lehman Brothers, splits his time between the University of Pennsylvania in Philadelphia, where he teaches computer science, and the New York investment bank, where he tries to put theory into practice.
Neither he nor Lehman executives would discuss how the firm uses computers to trade, saying the programs are proprietary and that divulging information about them would cost the firm its edge in the markets. At Lehman, Kearns is the big thinker on AI. He leaves most of the actual programming to a handful of Ph.D.s, most of whom he’s recruited at universities or computer conferences.
Kearns himself was plucked from Penn. Ian Lowitt, who studied with Kearns at the University of Oxford and is now co-chief administrative officer of Lehman Brothers, persuaded him to come to the firm as a consultant in 2002.
Kearns hardly looks the part of a professor. He has closely cropped black hair and sports a charcoal gray suit and a crisp blue shirt and tie. At Penn, his students compete to design trading strategies for the Penn-Lehman Automated Trading Project, which uses a computerized trading simulator.
Kearns says AI’s failure to live up to its sci-fi hype has created many doubters on Wall Street. He says people should be sceptical: Trading requires institutional knowledge that is difficult, if not impossible, to program into a computer.
AI holds perils as well as promise for Wall Street, Kearns says. Right now, even sophisticated AI programs lack common sense, he says.
Chasing dreams: Lehman Brothers’ Michael Kearns, a computer scientist, poses at the University of Pennsylvania
“When something is going awry in the markets, people can quickly sense it and stop trading,” he says. “If you have completely automated something, it might not be able to do that, and that makes you subject to catastrophic risk.”
During the 1960s and 1970s, AI research yielded few commercial applications. As Wall Street firms deployed computer-driven program trading in the 1980s to automatically execute orders and allow arbitrage between stocks, options and futures, the AI world began to splinter. Researchers broke away into an array of camps, each focusing on specific applications rather than on building HAL-like machines.
Financial service companies have already begun to deploy basic machine-learning programs, Kearns says. Such programs typically work in reverse to solve problems and learn from mistakes.
Like every move a player makes in a game of chess, every trade changes the potential outcome, Kearns says. Machine-learning algorithms are designed to examine possible scenarios at every point along the way, from beginning to middle to end, and figure out the best choice at each moment.
Kearns likens the process to learning to play chess. “You would never think about teaching a kid to play chess by playing in total silence and then saying at the end, ‘You won’ or ‘You lost’,” he says.
As an exercise, Kearns and his colleagues at Lehman Brothers used such programs to examine orders and improve how the firm executes trades, he says. The programs scanned bids, offers, specific prices and buy and sell orders to find patterns in volatility and prices, he says. Using this information, they taught a computer how to determine the most cost-effective trades.
The program worked backward, assessing possible trades and enabling trader-programmers to evaluate the impact of their actions. By working this way, the computer learns how to execute trades going forward.
Language represents one of the biggest gulfs between human and computer intelligence, Dhar says. Closing that divide would mean big money for Wall Street, he says.
Unlike computers, human traders and money managers can glimpse a CEO on television or glance at news reports and sense whether the news is good or bad for a stock. In conversation, a person’s vocal tone or inflection can alter—or even reverse—the meaning of words. Let’s say you ask a trader if he thinks US stocks are cheap and he responds, “Yeah, right.” Does he mean stocks are inexpensive or, sarcastically, just the opposite? What matters is not just what people say, but how they say it. Traders also have a feel for what other investors are thinking, so they can make educated guesses about how people will react.
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.
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”.