“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 barrier
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.
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