Markets have turned ugly lately, but return expectations are improving. So it goes with the inverse relationship between prices changes and implied ex ante performance. Suffer today, benefit tomorrow.
The waiting is the hardest part, of course. And even then there’s never a guarantee that you’ll earn what you expect — or anything at all, for that matter. But leaving the possibility of a financial apocalypse aside, the outlook continues to brighten — in theory — for return expectations as markets sink deeper into the hole.
For a rough estimate, let’s turn to the our periodic update of long-run returns for the major asset classes as of April 2022. For details on the model design, see summary at the end of this post.
The main takeaway: forward estimates are looking relatively attractive, at least in some cases. Notably, there are a growing number of long-term estimates (based on the average forecast) that compare favorably with the trailing 10-year realized return. These instances are highlighted by the green cells in the far-right column.
Stocks in emerging markets, for example, are expected to earn a 7.3% annualized total return for the long run, based on the average of three models (details below). That compares with a 3.0% annualized trailing return for the past 10 years, which translates to a positive 4.3 percentage-point spread in favor of future performance.
Forecasting specific slices of global markets is a risky affair, of course. Some of the risk fades as you aggregate forecasts into a portfolio. Our Global Market Index (GMI), for instance, holds all the asset classes listed above in market-based weights. But on this front the future suggests we continue to manage expectations down. That is, GMI’s ex ante return is 4.9%, or 2.1 percentage points below its trailing 10-year 7.0% performance.
Overweighting assets indicated by the green cells in the right-hand column can, in theory, boost expected return. That opens the possibility of customizing a portfolio relative to the passive GMI benchmark. Should you go down this rabbit hole? Much depends on how much confidence you have in the forecasts.
With that in mind, here’s how the forecasts are generated:
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: Trailing 10-year return less average-model forecast. ■