Man vs. machine

Dec 3, 2007 @ 12:01 am

By Jeff Benjamin

When computers manage the portfolio, the first challenge is often preventing the human brain from getting in the way.

This has become the mantra of Shashi Mehrotra, chief investment officer at Legend Advisory Corp., an asset-management firm based in Palm Beach Gardens, Fla., which hands over responsibility for more than $2 billion in investor assets to an artificial-intelligence computer program.

The current message coming out of the computer is that the Standard & Poor's 500 stock index will climb as much as 18% by the middle of next year.

Beyond that, things don't look so good, but the computer continues its four-and-a-half-year love affair with international investing, according to Mr. Mehrotra, who dutifully follows the lead of the Asset Allocation Neural Network, AANN.

"I used to second-guess her, but I was wrong nine out of 10 times," he said.

The use of artificial intelligence, also known as neural networks or genetic algorithms, has been described by some as the second generation of quantitative investing because it has the flexibility to get smarter through the expansion of input data.

The technology, which has been in place for more than a decade at Legend Advisory and is quietly spreading throughout the money management industry, also has wide applications for use in science and medical technology.

It was an artificial-intelligence program that enabled IBM's Deep Blue computer to defeat chess champion Gary Kasparov in 1997.

In the financial services industry, the biggest obstacle is often the old-school mentality that pits man against the machine.

"It's almost like an intramural college rivalry," said Caleb Wong, senior portfolio manager at OppenheimerFunds Inc. in New York, which has $260 billion under management and 60 mutual funds.

Mr. Wong oversees the use of artificial-intelligence programs in the management of four of the company's mutual funds.

Aside from a general reluctance to let a computer manage money, Mr. Wong said resistance to the technology can also be traced to a lack of understanding.

"People don't always know how to use it right," he said. "We're mostly a fundamental shop here, but people are recognizing that there is room for algorithms."

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