Robotrading 101
Sophisticated computer programs take the human element out of picking winners on Wall Street
Neural networks function more like the human brain. They can compare existing stock-trading patterns with previous situations and eventually "learn" what works and what doesn't as the program digests more data. Unlike traditional financial models, neural nets capture interconnections among financial variables. At Case's AIT, neural nets search out linkages between stock performance and variables such as price momentum, free cash flow, and the state of the overall economy.
AIT's neural nets have discovered, for instance, that with some stocks, the price-earnings ratio is a key indicator of its future return during good economic times. But when the economy is slowing, the stock's price momentum becomes more critical. Gaming company Aztar is one of AIT's largest positions. With low inflation and a steepening yield curve (a widening gap between short- and long-term interest rates), AIT's models show valuation and price patterns for the stock similar to those that have been bullish in the past. AIT's AllCap large-stock portfolio has beaten the overall market by an average of 3 percentage points a year since 1999. Those strong, though not otherworldly, results back up Case's cautionary contention that while AI is a formidable investing tool, "it's not some holy grail."
Still, the results can sometimes be astounding. Standard & Poor's uses a neural net to compile its Neural Fair Value 20 portfolio--available in its Outlook newsletter for $19.50 a month--which gained 29 percent last year, compared with a 13 percent loss for the S&P 500. The network constantly looks back six months to find the factors that seem to affect stock prices to predict the best performers over the next six months. Among the stocks in the portfolio are Computer Associates, PacifiCare Health Systems, and Tommy Hilfiger.
The VirtualHamilton presents a more tantalizing use of the technology. Why not also a VirtualBuffett or VirtualLynch? These digital doppelgangers might beat the originals by quantifying the unconscious intuition of these fabled investors. Just as an Ichiro Suzuki doesn't run trajectory and velocity calculations before catching a fly ball, many managers probably don't fully understand how they analyze stocks. Digitize a superstar manager's moves, and you might be able to hack his financial mind. "That's called reverse engineering," says Yale finance professor William Goetzmann. "And I suspect it is scaring some managers away from using a single broker who can view all of their trades." Using available information, Goetzmann himself has been attempting to reverse engineer the decisions made by managers of some unnamed mutual funds. "The idea being to see what makes managers trade, what signals they use, and if there is a magic formula," he says.
Math whiz. If there's a wild card in this investing arms race, it may be FatKat. The company may sound like a villain in a James Bond flick, but it's really a fledgling investment firm in Wellesley, Mass., founded by inventor and AI evangelist Ray Kurzweil. Although he's currently mum on FatKat, Kurzweil has written about the potential of mathematical formulas known as genetic algorithms to beat the market. The Darwinist process would begin with software randomly generating a million sets of rules for buying and selling stocks. Each set is a financial organism with the rules constituting its DNA. The ones that can't beat the market are killed, while the stock-savvy survivors mutate and breed until the population is back to a million. Rinse and repeat 100,000 times. "The surviving software creatures should be darn smart investors," he writes in The Age of Spiritual Machines.
How smart might AI programs get? By the year 2050, perhaps, investment software programs may be able to "come up with their own investment hypotheses, test them out, and implement them," says Andrew Lo, director of MIT's Laboratory for Financial Engineering. For now, though, humans still have a big role to play in the AI investment process. While the numbers are being crunched, the world keeps spinning and you need humans to keep track of it. At AIT, it takes all weekend to download data and update investment models. You also need humans to monitor the world for events that aren't reflected immediately in the data, such as terrorist attacks. And what happens if supersmart computers eventually get so good at the prediction game that all investors are made of silicon rather than carbon? Then the computers, as Kurzweil puts it, "will be trying to outpredict each other."
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