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U.S. News & World Report

Artificially intelligent investing

Cynics might wonder whether there's intelligent life to be found on Wall Street, but within the financial community there's plenty of life in artificial intelligence. AI, as it's commonly known, is the programming of computers to approximate the workings of the human brain. Far more than mere number crunching, the focus is on sophisticated tools like neural networks that simulate thought processes. Neural networks allow computers to discern subtle patterns as they winnow mounds of data and to actually learn as they see new patterns emerge.

In today's volatile markets, the capacity to learn makes AI especially intriguing to portfolio managers. It would be naive to expect a black box that could automatically pinpoint money-making opportunities, says Andrew Lo, a professor at MIT's Sloan School of Management. But AI has "proven potential to identify temporary inefficiencies, which can then be used to beat the market." Lo is heading a recently launched Research Project in Computational Finance to look into the ways computer intelligence can be applied to investing.

Most financial organizations are experimenting with AI, but only a few have actually put it to use. Citibank's currency traders, for example, are working with a neural network. Merrill Lynch uses an AI program to help price 1 million corporate bonds each day.

Risk assessment. A couple of money managers are producing impressive results. Since its founding in 1986, LBS Capital Management has been using AI to "decide how much risk there is in the market and to create portfolios that are either aggressive or defensive," states LBS President Walter Loick. With nearly $300 million in individual and institutional money under management, LBS has outpaced the Standard & Poor's 500-stock index by 24 percent over the past six years; its average annual gain has been 16.3 percent against the S&P's 14.0 percent. The LBS portfolio of midsize company shares has done even better. Over the past five years, the S&P Midcap index has gained an average of 21.6 percent annually, while the comparable LBS investment fund has compounded by 35.8 percent.

The Safety Harbor, Fla., firm, one of several companies helping to fund Lo's research, uses a computer model that decides when to be in stocks and when to be out. A "buy" went into effect at the end of February. The LBS computers have recently focused on interest-sensitive shares such as insurers Chubb and General Re, Pittsburgh's Mellon Bank and bond trader Salomon Brothers. Battered medical stocks like Mylan Laboratories, U.S. HealthCare and United Healthcare have, meanwhile, been added to the LBS midcap portfolio.

The previous buy signal, which came near the end of last October, correctly anticipated the stock market's strong ascent during the final quarter of 1992. At year's end, however, perception of rising risk produced a "sell." And, in fact, the market did take a sharp, though brief, tumble in mid-February just before President Clinton unveiled his economic program.

Brad Lewis, who runs the Fidelity family's Disciplined Equity fund, also has done well with AI stock-picking techniques. Each day, his neural web checks some 2,000 stocks for 11 variables, such as projected earnings and debt levels. The object is to find out "inefficiencies in stock prices, a process the human brain can't perform very well with 2,000 companies."

Disciplined Equity gets a top five-star ranking from Morningstar, the mutual fund research firm. The fund averaged a 15.2 percent annual return in the past three years, compared with 8.8 percent for the S&P 500. And in 1992, Disciplined Equity registered a 13.3 percent gain, compared with 7.6 percent for the S&P. "Even more remarkable, it didn't have a losing quarter in 1992's rotational market," notes Morningstar analyst Tom Desmond. "Investors who have not yet discovered this fund should take a closer look, because it's a gem."

Lewis, who also runs the Fidelity Stock Selector, a two-year-old sibling of Disciplined Equities, has become more defensive recently, loading up on the shares of utilities like Long Island Lighting and oil stocks like Mobil and Chevron. "This looks like a time when capital preservation is more important than capital gains," he says.

That AI is hardly a precision instrument is apparent from the mixed signals coming from the systems used by LBS and Fidelity. Human judgment is still a constant--until someone comes up with a neural network to reconcile the disparities.

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