The expected risk premium for the Global Market Index (GMI) continued to trend lower through December. GMI — an unmanaged, market-value weighted mix of the major asset classes — is now projected to earn an annualized 3.6% over the “risk-free” rate for the long term. (For details on the equilibrium-based methodology that's used to generate the forecasts, see the summary below). Today's updated estimate, which is based on data through the close of last month, fell 30 basis points from November's 3.9% projection. But if yesterday's sharp drop in financial and commodity markets persists in the days ahead, GMI's expected risk premium will probably rebound in the near-term future. Meanwhile, using data through the end of 2014 suggests that GMI's anticipated return over the risk-free rate suffered another decline last month.
Adjusting for short-term momentum and longer-term mean-reversion factors (defined below) squeezed GMI's current ex ante risk premium a bit more: 3.4%. The current adjusted estimate is below last month's adjusted 3.7% projection.
The current risk premia forecasts remain substantially below the realized return in recent history. GMI's risk premium is an annualized 9.7% for the trailing 3-year period through December 2014. But as yesterday's market tumble suggests, the wide gap between recent history and the long-run outlook is narrowing. Over a relatively long stretches, realized results and expectations will generally fall in line with one another. In the short run, however, the dynamic aspect of markets insures that wide deviations will prevail at times. These short-term deviations are the raw material that provide opportunity, as well as risk, in the art/science of second-guessing Mr. Market's passive asset allocation.
Here's a summary of the current risk premia projections for GMI and the major asset classes that comprise the benchamrk:
For additional perspective on recent history, here's a recap of rolling three-year annualized risk premia for GMI, US stocks (Russell 3000) and US Bonds (Barclays Aggregate Bond Index) through last month.
Finally, here's a brief summary of the methodology and rationale for the estimates above. The basic idea here is to reverse engineer expected return based on assumptions about risk. Rather than trying to predict returns directly, this approach relies on the somewhat more reliable model of using risk metrics to estimate performance of asset classes. The process is relatively robust in the sense that forecasting risk is slightly easier than projecting return. With the necessary data in hand, we can estimate theimplied risk premia using the following inputs:
