The list of relatively attractive markets on a total-return-forecast basis is growing. In today’s monthly update, there are seven components of the major asset classes with comparatively high ex ante performance estimates relative to the 10-year annualized return history. That compares with six markets in last month’s update and four in January.
Don’t confuse any of this with a guarantee of future returns, or even a high-confidence estimate. Nonetheless, it’s intriguing that there are more markets registering comparatively high return estimates.
For example, stocks in emerging markets are currently projected to earn a 7.3% annualized total return for the long run, based on the average for our ensemble model. That compares with a trailing 3.5% return over the past decade. The spread (forecast less historical performance) is 3.7 percentage points (that’s slightly off due to rounding), the highest for the major asset classes. For details on the model design, see summary at the end of this post.
For comparison, the Global Market Index, the benchmark, is projected to earn 4.7% for the long-term future.
At first glance, it’s tempting to overweight markets with relatively high expected return and underweight their counterparts on the downside. The markets with spreads highlighted in green represent one possibility for identifying relatively attractive overweights. Another possibility: favor average return forecasts that are well above GMI’s forecast.
The degree of tilting re: overweighting and underweighting depends on several factors, starting with your confidence in the model. Other factors include your tolerance for risk, time horizon and ability to bear tracking error (i.e., returns that differ from GMI, which offers a somewhat more reliable forward estimate due to aggregating the forecasts on a markets-weighted basis — a process that may minimize the forecast errors in the individual markets.
How much confidence should you ascribe to the model? Err on the side of caution, as with all things ex ante. That said, you could do a lot worse as a starting point for thinking about how or if to tilt a multi-asset-class portfolio.
With that in mind, here are brief definitions for each column of data 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: Trailing 10-year return less average-model forecast. ■