Bismark may have been misquoted when he reportedly said “laws are like sausages, it is better not to see them being made.” Whoever is the source, the observation applies to estimating expected returns. The end result is forever in high demand, but the details for generating the numbers can get messy.
The subject of looking into the quantitative grinder that spits out return forecasts was topical on these pages after yesterday’s monthly update of risk premia projections for the major asset classes and the Global Market Index (GMI). Several readers inquired about the upward drift in the estimates, the US equity forecast in particular: the expected 8.5% annualized premium (for the unadjusted estimate) looked quite high. Could that be right?
Questioning the accuracy of forecasts is always and everywhere prudent, and so the latest risk premia estimates offer a teachable moment. Let’s go down this rabbit hole a bit, if only as an excuse to review best practices for using forecasts.
Let’s start with the big-picture observation that all forecasts deserve to be treated cautiously. The monthly estimates presented here are first and foremost offered as an academic exercise, which is to say that the obvious caveats apply. The equilibrium model that’s used to generate the data is quite useful as a starting framework, but the unavoidable task of making assumptions on the data inputs can lead to a variety of outcomes. The key point: any model you use needs to be tweaked to make it useful for you. Accordingly, there are no generic solutions that apply to everyone.
Inevitably, estimating performance of asset classes and portfolios is a mix of art and science, with a greater emphasis on the former than some folks realize.
More generally, a responsible way to use forecasts, in my view, is to combine predictions from several sources, each using a different methodology. Just as you shouldn’t rely on a single market or asset class for portfolio design, it’s unnecessarily risky to use one set of forecasts for anticipating risk premia.