The expected risk premium continued to decline in April for the Global Markets Index (GMI). Tuesday’s revision reflects a decline to a long-term 5.4% annualized increase — a relatively large reduction of 40 basis points from last month’s estimate. The new estimate reinforces the outlook, long presented in these updates, for managing downside expectations for globally diversified multi-asset class portfolios from returns achieved in recent years.

The forecast reflects the long-term projected return relative to the “risk-free” rate, according to a risk-based model (detailed below). GMI is an unmanaged market-weighted portfolio that holds all major asset classes (except cash) and represents a theoretical benchmark of the optimal portfolio for the average investor with an infinite time horizon.

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.

Using short-term momentum and medium-term mean reversion market factors (defined below) to adjust the forecast raises GMI’s ex ante risk premium (slightly) to 5.5% in annualized rate.

All forecasts are generally off to some degree, although GMI’s projections are believed to be relatively reliable (i.e. less off) compared to the estimates for individual asset classes shown in the table below. above. Forecasts for specific market components (US stocks, commodities, etc.) are subject to greater uncertainty than the aggregate forecasts in the GMI outlook, a process that can reduce some of the errors over time.

For a historical perspective on how GMI’s realized risk premium has changed over time, consider the history on a 10-year annualized basis. The chart below compares GMI’s risk premia against the equivalent for US stocks (Russell 3000) and US bonds (Bloomberg Aggregate Bond) up to last month.

GMI’s current 10-year performance (red line) is 6.4%. Although this has fallen from recent levels, it remains well above the current long-term projection mentioned above, suggesting that the performance of multi-asset class portfolios will generally be lower going forward compared to to the historic record of the last decade.

Let’s review the methodology and rationale for the above estimates. The basic idea is to reverse engineer the expected return, based on risk assumptions. Rather than trying to predict return directly, this approach relies on the moderately more reliable model of using risk measures to estimate asset class performance. The process is relatively robust in that it is slightly easier to predict risk than to project return. With the necessary data in hand, we can calculate the implied risk premia with the following inputs:

- an estimate of the expected market price of GMI risk, defined as the Sharpe ratio, which is the ratio of risk premia to volatility (standard deviation).
- the expected volatility (standard deviation) of each asset
- the expected correlation for each asset with the overall portfolio (GMI)

The estimates are taken from historical records since the end of 1997 and are presented as a first approximation for modeling the future. The projected premium for each asset class is calculated as the product of the three inputs above. GMI’s ex ante risk premia are calculated as the market value weighted sum of individual projections for asset classes.

The framework for estimating equilibrium returns was originally described in a 1974 paper by Professor Bill Sharpe. For a more practical summary, see Gary Brinson’s explanation of the process in Chap. 3 of the Portable MBA in Investment. I also review the model in my book Dynamic Asset Allocation. Here is how Robert Litterman explains the concept of estimating the equilibrium risk premium in Modern Investment Management: An Equilibrium Approach:

We don’t need to assume that the markets are always in equilibrium to find a useful equilibrium approach. Rather, we view the world as a complex and highly random system in which there is a constant barrage of new data and shocks to existing valuations that, more often than not, push the system away from equilibrium. However, while we expect these shocks to constantly create deviations from the financial market equilibrium, and recognize that frictions prevent these deviations from immediately disappearing, we also assume that these deviations represent opportunities. Wise investors who attempt to take advantage of these opportunities take actions that create the forces that continually push the system back towards equilibrium. Thus, we consider financial markets as having a center of gravity defined by the balance between supply and demand. Understanding the nature of this equilibrium helps us to understand financial markets as they are constantly jolted and pushed back towards this equilibrium.

The adjusted risk premia estimates in the table above reflect changes based on two factors: short-term momentum and long-term mean reversion. Momentum is defined here as the current price relative to the moving average of the last 10 months.

The average reversion factor is estimated as the current price relative to the moving average of the last 36 months. Estimates of gross risk premiums are adjusted for current prices relative to 10-month and 36-month moving averages.

If current prices are above (below) moving averages, estimates of unadjusted risk premia are decreased (increased). The adjustment formula is simply to take the inverse of the average of the current price at the two moving averages as a signal to change the projections.

For example: If the current price of an asset class is 10% above its 10-month moving average and 20% above its 36-month moving average, the risk premium estimate will not adjusted is reduced by 15% (the average of 10% and 20%) .

What can you do with the predictions in the table above? You might start by asking yourself whether the expected risk premia are satisfactory… or not. If the estimates are lower than your required return, you may consider designing a higher return rate by customizing the asset allocation and rebalancing rules.

Keep in mind that GMI’s gross implied risk premia are based on an unmanaged market value-weighted combination of major asset classes. In theory, this is the optimal asset allocation for the average investor with an infinite time horizon. Unless you are a foundation or pension fund, this time horizon assumption is impractical and so there is a reasonable case for a) changing Mr. Market’s asset allocation based on your particular needs and risk budget, and b) add a rebalancing component to your investment. strategy.

You can also estimate risk premia with alternative methodologies to gain additional insight into the short-term future (a great resource on this topic: Antti Ilmanen’s Expected Returns).

For example, suppose you are confident in the Dividend Discount Model (DDM) to predict stock market performance over the next 3-5 years. After analyzing the numbers, you find that DDM tells you that the expected performance of the stock market will differ significantly from the estimate based on the long-term equilibrium. In this case, you have some tactical information to consider.

Also keep in mind that combining forecasts through multiple models can provide a more reliable set of forecasts than estimates from a given model. Indeed, a number of studies published over the years show that combined forecasts tend to be more robust than projections from a single model.

What you can’t do is remove blood from a stone. Nobody really knows what the risk premia will be in the months and years to come, so relying solely on forecasts (especially for the near-term future) is frustrating. In other words, you should deviate from Mr. Market’s asset allocation carefully, thoughtfully, and for reasons other than assuming you’re smarter than everyone else (i.e. the market).

*Original post*

**Editor’s note:** The summary bullet points for this article were chosen by the Seeking Alpha editors.