Total Return Forecast: Major Asset Classes – August 2, 2022
The long-term expected return of the Global Market Index (GMI) remains slightly below the realized 10-year performance of the index through July, based on the average of three forecasting models (defined below). below). The key to remember: it is always prudent to manage expectations downwards for multi-asset class strategies, compared to the last decade.
The table below offers a first approximation for developing long-term return estimates for major asset classes and GMI, an unmanaged market-weighted portfolio that holds all major asset classes (excluding cash) and represents a theoretical benchmark for optimal return. portfolio for the average investor with an infinite time horizon. Based on this, GMI is useful as a starting point for asset allocation research and portfolio design. GMI’s track record suggests that the performance of this passive benchmark is competitive with most active asset allocation strategies overall, especially after adjusting for risk, trading costs and taxes .
Returns highlighted in green represent relatively attractive return forecasts. These are asset classes whose average return expectations are higher than the relevant 10-year performance.
Keep in mind that all forecasts are likely to be incorrect to some degree, although GMI’s projections should be more reliable compared to the estimates for individual asset classes shown in the table above. Forecasts for specific market components (US stocks, commodities, etc.) are subject to greater volatility and tracking error than forecast aggregation into the GMI estimate, a process that can reduce some of the errors over time.
For a historical perspective on how GMI’s realized total return has evolved over time, consider the history of the benchmark on a 10-year rolling annualized basis. The chart below compares GMI’s performance against the equivalent for US stocks (Russell 3000) and US bonds (Bloomberg Aggregate Bond) up to the last month. GMI’s current 10-year yield (green line) is a solid 7.0%. Although this has fallen from recent levels, it remains well above the current long-term projection. This is an indication to remain relatively cautious in projecting returns for multi-asset class portfolios relative to the all-time high of the past decade.
Here is a brief summary of how forecasts are generated:
BB: The Building Block model uses historical returns as a proxy to estimate the future. The sample period used begins in January 1998 (the earliest date available for all asset classes listed above). The procedure involves calculating the risk premium for each asset class, calculating the annualized return, and then adding an expected risk-free rate to generate a total return forecast. For the expected risk-free rate, we use the latest 10-year TIPS (Treasury Inflation Protected Security) yield. This return is considered a market estimate of a real risk-free (inflation-adjusted) return for a “safe” asset – this “risk-free” rate is also used for all the models described below. Note that the BB model used here is (loosely) based on a methodology originally described by Ibbotson Associates (a division of Morningstar).
Equalizer: The Equilibrium model reverses the engineering of expected return through risk. Rather than trying to predict return directly, this model relies on the somewhat more reliable framework of using risk measures to estimate future performance. The process is relatively robust in that it is slightly easier to predict risk than to project return. The three entries:
* An estimate of the market price of expected risk for the entire portfolio, defined as the Sharpe ratio, which is the ratio of risk premia to volatility (standard deviation). Note: “portfolio” here and throughout is defined as GMI
* The expected volatility (standard deviation) of each asset (market components of GMI)
* The expected correlation for each asset relative to the portfolio (GMI)
This model for estimating equilibrium returns was originally described 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, then adds an expected risk-free rate to arrive at the total return forecast. The expected risk-free rate is described in BB above.
AD: This methodology is identical to the equilibrium model (EQ) described above, with one exception: forecasts are adjusted for short-term momentum and longer-term mean reversion factors. Momentum is defined as the current price relative to the moving average of the last 12 months. The average reversion factor is estimated as the current price relative to the moving average of the last 60 months (5 years). Equilibrium forecasts are adjusted for current prices relative to 12-month and 60-month moving averages. If current prices are above (below) moving averages, estimates of unadjusted risk premia are decreased (increased). The adjustment formula simply takes the inverse of the average of the current price to the two moving averages. For example: if the current price of an asset class is 10% above its 12-month moving average and 20% above 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 relative to recent history, equilibrium forecasts are reduced. On the other hand, when prices are relatively low compared to recent history, the equilibrium forecast is raised.
Avg: This column is a simple average of the three forecasts for each line (asset class)
10 years of retirement: For a perspective on real returns, this column shows the 10-year annualized total return for the asset classes up to the current target month.
Spread: Average forecast of the model minus yield over 10 years.
Editor’s note: The summary bullet points for this article were chosen by the Seeking Alpha editors.