The total return forecast for the Global Market Index (GMI) continued to tick higher in August for the long-run outlook. Despite the increase in the GMI forecast in recent months, today’s estimate remains well below the index’s trailing 10‑year return.
GMI is The Capital Spectator’s proxy for a market‑value‑weighted blend of the major asset classes (excluding cash) using ETF proxies. The expected return is calculated as the average of three models, defined below.
The latest GMI return forecast — an annualized 8.1% performance — is modestly above last month’s estimate but still far short of the benchmark’s trailing return for the past ten years, although the gap has been narrowing lately.
Echoing recent history, several of GMI’s underlying components continue to post expected returns well below posted results for the past decade, highlighted by the red boxes in the far‑right column in the table below — a gap that suggests adopting a relatively cautious stance in these markets relative to recent history’s implied outlook. The widest divergence is still found in U.S. equities, although commodities are a close second. The caveat also applies to GMI, which is projected to earn an 8.1% return, moderately below its trailing 9.9% performance for the past decade.
GMI represents a theoretical benchmark for the “optimal” portfolio that’s suited for the average investor with an infinite time horizon. Given those practical limitations, GMI is useful as a starting point for customizing asset allocation and portfolio design to match a particular investor’s expectations, objectives, risk tolerance, etc. GMI’s history suggests that this passive benchmark’s performance will be competitive with most active asset-allocation strategies, especially after adjusting for risk, trading costs and taxes.
It’s reasonable to assume that some, most, or possibly all of the forecasts above will be wide of the mark to some degree. GMI’s projections, however, are expected to be somewhat more reliable vs. the estimates for its components. Predictions for the specific markets (US stocks, commodities, etc.) are subject to greater variability compared with aggregating the forecasts into the GMI estimate, a process that may reduce some of the errors through time.
Another way to view the projections above is to use the estimates as a baseline for refining expectations. For instance, the point forecasts above can be adjusted with additional modeling that accounts for other factors and assumptions not used here, such as current valuation and the implied return forecasts. Customizing portfolios for a specific investor, to reflect risk tolerance, time horizon, and so on, is also recommended.
For perspective on how GMI’s realized total return has evolved through time, consider the benchmark’s track record on a rolling 10-year annualized basis. The chart below compares GMI’s performance vs. ETFs tracking US stocks and US bonds through last month. GMI’s current return for the past ten years is a robust annualized 9.9%, modestly above July’s performance.
Here’s a brief summary of how the forecasts are generated and definitions of the other metrics in the table above:
BB: The Building Block model uses historical returns as a proxy for estimating the future. The sample period used starts in January 1998 (the earliest available date for all the asset classes listed above). The procedure is to calculate the risk premium for each asset class, compute the annualized return and then add an expected risk-free rate to generate a total return forecast. For the expected risk-free rate, we’re using the latest yield on the 10-year Treasury Inflation Protected Security (TIPS). This yield is considered a market estimate of a risk-free, real (inflation-adjusted) return for a “safe” asset — this “risk-free” rate is also used for all the models outlined below. Note that the BB model used here is (loosely) based on a methodology originally outlined by Ibbotson Associates (a division of Morningstar).
EQ: The Equilibrium model reverse engineers expected return by way of risk. Rather than trying to predict return directly, this model relies on the somewhat more reliable framework of using risk metrics to estimate future performance. The process is relatively robust in the sense that forecasting risk is slightly easier than projecting return. The three inputs:
* An estimate of the overall portfolio’s expected market price of risk, defined as the Sharpe ratio, which is the ratio of risk premia to volatility (standard deviation). Note: the “portfolio” here and throughout is defined as GMI
* The expected volatility (standard deviation) of each asset (GMI’s market components)
* The expected correlation for each asset relative to the portfolio (GMI)
This model for estimating equilibrium returns was initially outlined in a 1974 paper by Professor Bill Sharpe. For a summary, see Gary Brinson’s explanation in Chapter 3 of The Portable MBA in Investment. I also review the model in my book Dynamic Asset Allocation. Note that this methodology initially estimates a risk premium and then adds an expected risk-free rate to arrive at total return forecasts. The expected risk-free rate is outlined in BB above.
ADJ: This methodology is identical to the Equilibrium model (EQ) outlined above with one exception: the forecasts are adjusted based on short-term momentum and longer-term mean reversion factors. Momentum is defined as the current price relative to the trailing 12-month moving average. The mean reversion factor is estimated as the current price relative to the trailing 60-month (5-year) moving average. The equilibrium forecasts are adjusted based on current prices relative to the 12-month and 60-month moving averages. If current prices are above (below) the moving averages, the unadjusted risk premia estimates are decreased (increased). The formula for adjustment is simply taking the inverse of the average of the current price to the two moving averages. For example: if an asset class’s current price is 10% above its 12-month moving average and 20% over its 60-month moving average, the unadjusted forecast is reduced by 15% (the average of 10% and 20%). The logic here is that when prices are relatively high vs. recent history, the equilibrium forecasts are reduced. On the flip side, when prices are relatively low vs. recent history, the equilibrium forecasts are increased.
Avg: This column is a simple average of the three forecasts for each row (asset class)
10yr Ret: For perspective on actual returns, this column shows the trailing 10-year annualized total return for the asset classes through the current target month.
Spread: Average-model forecast less trailing 10-year return.