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Advice Providers Get a Grip on Stable Value

The debate over how to handle stable value funds in asset allocation models appears to be winding down now that Financial Engines, one of the advice industry’s biggest players, has modified its approach to simulating the risk and reward characteristics of the funds.

“I’m relatively pleased with how the (major) providers are handling stable value now,” says Wayne Gates, a general director with John Hancock Financial Services in Boston and chair of the SVIA’s Asset Allocation Task Force. “I’m not sure everyone on the committee would agree with that, but we certainly have made a lot of progress.”

Financial Engines and mPower, along with a handful of other firms, provide investment advice over the Internet to participants in defined-contribution retirement plans. They start by creating an asset allocation model for the investor based on his or her investment goals and tolerance for risk. They then recommend how the investor should divide their retirement money among the investment choices in their plan.

Stable value managers had long fretted that Financial Engines’ asset allocation model didn’t capture the true nature of stable value funds by failing to give them credit for the book-value guarantees that dampen their volatility. While the underlying assets in a stable value fund are comparable to the assets in an intermediate-term bond fund, the book-value guarantee results in decreased short-term volatility of the stable value fund’s returns to a level comparable to money market funds. Yet stable value investors still enjoy superior bond-like performance.

Chris Jones, executive vice president of financial research and strategy for Financial Engines, says his firm spent much of last year refining its stable value methodology and began rolling it out to clients in December. The undertaking, he says, “basically addressed appropriately modeling the serial correlation in a stable value fund’s crediting rate” and represented an “incremental enhancement” to the company’s asset allocation model.

A stable value fund’s crediting rate is the rate of return credited to its investors. The rate is periodically adjusted to reflect the actual performance of the fund’s underlying assets, plus the impact of contributions and withdrawals that take place when the market value of the underlying assets differs from their book value. In essence, any differences between the credited rate and the actual rate earned on the fund’s assets, as well as any gains or losses in the fund triggered by participant withdrawals, are amortized over ensuing months or years.

“If you want to show people a range of outcomes consistent with the crediting rate calculation, you need to be able to address the fact that when the value of the assets underlying the stable value fund changes, it is not fully reflected in the crediting rate for that period but is spread out over multiple periods in the future, maybe over a period of months or years,” Jones says. “One way to accurately capture the volatility characteristics of these funds is to model that smoothing effect. Our model didn’t do that before. It does now.”

For purposes of projecting the long-term returns of a stable value fund, Jones says, his firm models the expected returns of the fund’s underlying investment portfolio. Over longer time periods, he says, the returns of a stable value fund and a comparably configured bond fund, assuming they had the same expenses, would be identical.

For purposes of projecting risk characteristics, however, Financial Engines does recognize the lower volatility inherent in stable value funds over short time horizons. As a result, Financial Engines would almost never forecast a loss for a stable value fund over a one-year time horizon, although it could project a loss for longer time periods—albeit with a low probability. Unfortunately, these models do not offer a true forecast for stable value funds, which will never post negative returns.

In generating asset allocation advice for investors, Jones adds, Financial Engines’ does not consider the gap between the stable value fund’s crediting rate and the rates available on any competing money market funds. That way, it avoids giving advice that could trigger short-term trading in and out of the stable value fund. Such short-term arbitrage could have a detrimental effect on long-term investors in the stable value fund and on the insurers that provide the fund’s book-value guarantees, or “wrap” contracts.

Although it might seem that Financial Engine’s advice model wouldn’t favor stable value funds over intermediate-term bond funds in making investment recommendations—since their long-term performance characteristics are comparable—Jones says that in practice, it would. “Even if the two funds had the same underlying assets, other things that would influence our choice would be the expense ratio of the funds,” Jones says. “Generally speaking, the average stable value fund is less expensive than the average market-value fixed income fund. Because of that, we usually show a preference for the stable value fund.”

mPower doesn’t follow the exact same methodology used by Financial Engines to model stable value funds, but the outcomes it delivers are similar. And like Financial Engines, mPower tends to recommend stable value funds, when they are available, over money market funds and intermediate-term bond funds, according to Hal Ratner, mPower’s vice president of investments. He says the company’s advice service would have a “very low” probability of predicting a negative return for a stable value fund over any time period, and investors would have to “do a lot of work” on its Web site to even see that possibility.

For purposes of arriving at an asset allocation recommendation for a particular investor, Ratner says, mPower’s model tends to treat stable value as if it were a blend of cash and short-term bonds, with the proportions dependent upon the diversity of the fund’s portfolio and its other characteristics, such as the credit quality of its fixed-income assets.

Although stable value managers have found little to fault in the way mPower models their products, Ratner says the firm did make one minor change to its methodology recently. Where it used to make adjustments for changes in the composition of a fund’s underlying portfolio on a quarterly basis, he says, the company now does so on an annual basis, having concluded that stable value managers don’t typically change their portfolio strategy from quarter to quarter.

Morningstar Associates LLC, a subsidiary of Morningstar Inc., also markets an online investment advice service, which it calls ClearFuture. It, too, models stable value as a blend of cash and bonds, and like Financial Engines and mPower has a very low probability of forecasting a negative return for stable value funds. “One is more likely to be struck by a meteor,” says Paul Kaplan, vice president of research for Morningstar Associates and director of research for Morningstar Inc.

Kaplan says ClearFuture doesn’t factor the serial correlation of stable value crediting rates into its model because over the long-term time periods that are its focus, serial correlation has no material impact on performance. “Some would argue that you have to model every short-term feature, but we don’t believe that’s necessary,” he says.

Like its peers, ClearFuture tends to favor stable value over money market funds and intermediate bond funds when all three are represented among the investment options of a retirement savings plan, Kaplan says. In part, that’s because ClearFuture always assigns the highest quality score—a risk measure—to stable value funds.

As is the case at Financial Engines, both mPower and Morningstar say their advice models, by focusing on the long-term performance expectations for stable value in making investment recommendations, could not be used by investors to support a strategy of arbitraging differences between money market yields and stable value yields during periods of rising short-term interest rates.

“The wrap issuers don’t want to see any volatility caused by these advice models, and I think they (the advice providers) have gone out of their way to assist in that,” observes Gates.

While advice providers may not be able to provide a perfect model of stable value behavior, the consensus seems to be that they have come remarkably close.

“Hopefully, we’ve reached the conclusion of the debate,” says Jones. “While we will certainly continue to evaluate possible enhancements, we are, at the moment, very comfortable with our model.”

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