Professors successfully apply statistical technique to picking mutual funds.

Five for five. That's the heady batting average of a new, if not experimental, mutual fund selection technique that began predicting outperformers last year. Its recommended portfolios have beaten the Wilshire 5000 by an average of about 300 basis points, risk adjusted, in each of the last five quarters (ignoring load fees).

What does the model use to forecast fund performance? "Just historical returns, nothing else," says Matthew Spiegel, a professor of finance at the Yale School of Management who developed the approach with Harry Mamaysky, a former Yale assistant professor who is now a vice president at Morgan Stanley, and Hong Zhang, an assistant finance professor at INSEAD in Singapore. "How can we do what we're doing? We use a different model than others have in the past," Spiegel says.

In Search Of Alpha

To understand the new model, a brief review of investment basics is necessary. You probably know that the 90-day Treasury bill yield is considered investing's risk-free rate. Stocks, of course, since they carry risk, are expected to return more-i.e., a market premium, or excess return. A statistic called alpha indicates a stock or fund's excess return on a risk-adjusted basis.

The adjustment for risk is made with the beta statistic. It is constructed so that a value of 1.0 denotes average market risk, while a beta above or below that figure indicates, respectively, above- or below-average risk relative to the overall stock market. Take a fund with a beta of 1.25. If equities are expected to return 4% above T-bills, then this fund would be expected to generate a 5% excess return (4% expected market premium times 1.25 fund beta). After all, if the fund is 25% riskier than the norm, its rewards should be that much greater, too.

Suppose the fund actually earns 6% above the risk-free rate. Then its alpha is 1% (6% actual excess return minus 5% expected risk-adjusted return). The goal of the mutual fund manager is to buy stocks with high current alphas-that is, ones expected to outperform the market. Likewise, investors seek to buy funds that own high-alpha stocks and therefore have high alphas themselves.

Against that background, the new model advanced by Spiegel and his colleagues makes some important assumptions that depart from previous work in the field. The first is intuitive: Fund managers trade their portfolios based on a signal received from the investment environment. The signal could be macroeconomic data, company earnings projections, actual or forecasted changes in interest rates-who knows what the information is? And that's the point. Investors can't see the signal, yet presumably a fund manager follows some beacon to his investment decisions. Isn't that why managers who sit on conference panels are routinely asked about their buy and sell disciplines?

Paradigm Shift

The other assumptions are more novel. The researchers contend that the signal from the investment environment varies in strength, so that a manager may possess valuable information at some points in time but not others. "Above-market returns are achieved only when a manager has valuable information at the moment," Spiegel asserts. In other words, not only does a fund's expected alpha depend on a signal that cannot be seen by investors, the signal may or may not have value at any given time. Alpha thus changes over time, Spiegel says. "Previous empirical studies of mutual fund performance assume that the ability to beat the market sticks with the manager, that if he can beat the market by 40 basis points in one year, he can do it every year"-a static alpha. "Our view was that that didn't make sense," Spiegel says.

He also observes that a fund's beta changes, even though theories heretofore have considered it to be constant. Clearly, though, as a manager trades the portfolio's risk profile changes, morphing beta. Properly gauging beta is important because an inaccurate estimate of it leads to misestimating alpha. By assuming that a fund's alpha and beta parameters are both in motion, "our model has a lot of flexibility," Spiegel says.

The model is described in Estimating the Dynamics of Mutual Fund Alphas and Betas, a 2003 Yale International Center for Finance working paper now making the rounds in academic circles. The model was developed in hopes that it could identify mutual funds that are poised to outperform-that currently have high expected alphas. But how do you calculate alpha if it is a function of something unseen?

With the Kalman filter, an obscure statistical technique used to solve signal extraction problems, where "you have data that's noisy about some signal and you're trying to figure out what the signal is," says Spiegel. The technique was originally used by the Navy to track submarines-you know they're moving, but you don't know where to, or why. Or consider an engineer reading Geiger counter data: He's getting a noisy signal about what he would like to get.

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