Using Multiple Models
As an old saying goes, a man with a watch knows the time, but a man with two watches can never be sure. By having multiple models, have we simply muddied the waters?
If your goal in expectations modeling is to determine the exact return of an asset in the future, multiple models may, or may not be, useful. Trying to estimate an exact figure, however, is almost impossible to consistently achieve. None of the models discussed in this article would be a particularly effective approach for achieving this objective.
If your objective, as ours, is to use expectations as a mechanism for reducing the odds of a portfolio missing the level of return necessary to meet predefined spending needs, such as the 5% challenge in West and Masturzo (2016), multiple models can be extremely helpful. The use of multiple models allows investors to create portfolios based on different perspectives, similar to blending factor portfolios to gain particular exposures—the recent rage in equity investing.
Using each model, we can create a strategic asset allocation portfolio (called the “efficient portfolio” on the AAI platform) for varying levels of risk. Portfolios based on the yield-plus-growth model have an income focus, while those incorporating valuations adopt a mean-reversion perspective. Blending portfolio allocations based on different expectations models has the added benefit of reducing model risk inherent in any individual model, even these two models, which have some definitional overlap.
With those two extremes an investor is able to blend portfolios based on personal constraints (such as tracking error or investment beliefs about, for example, the richness or cheapness of assets) in order to construct a portfolio with the multiple perspectives that address their specific needs. This approach acknowledges that no single strategy is appropriate for all investors all the time by providing an extensible framework to meet the diverse needs of investors.
A Blend Is Better
With the newest release of our Asset Allocation Interactive platform, we introduce the functionality of viewing expected returns through a yield-plus-growth lens in addition to our traditional valuation-dependent model. Using each of the models, we show efficient allocations that maximize return for varying levels of risk. The two expected return models can be blended together, resulting in a blend of the portfolio allocations under each.
The performance of the one-factor CAPE model, which has underperformed other models over the past few decades, may or may not rebound in the future—we do not have a crystal ball. Nor do we know which of the other return expectations models will produce good results for investors. What we do know is that an assumption that any particular model is always the best one—and many investors take that approach with CAPE—will lead to model fatigue. We believe the most successful way for investors to meet future spending needs is to blend portfolios based on different models of return expectations.