Long-Term Asset Class Forecasting at State Street Global Advisors

State Street Global Advisors’ strategic asset allocation recommendations for our clients depend on the long-term assumptions we make about the future risks and returns of portfolio components. These assumptions inform our portfolio decisions and, because of that, are crucial to the success of the investment process. This paper is an updated,high-level summary of State Street’s long-term asset-class forecasting approach, recent innovations, and advantages.

Senior Research Analyst
Global Head of Research

Asset class returns can vary substantially through a business cycle. A given asset class may have negative short-term return expectations (because of short-term obstacles) while still having overall positive return expectations over the medium or long term. As a result, State Street Global Advisors’ long-term asset class forecasts (LTACF) must explicitly incorporate both short- and long-term investment horizons with potentially diverging outcomes.

A key component of our strategic asset allocation process is the need to be forward-looking with respect to the inputs used. While this brings a level of uncertainty into the process, as with any forecast, we believe this practice gives a better perspective on how markets will tend to behave. This improved perspective, in our view, supports the ability of investors to meet their goals. And while we use historical price patterns as a guide for the future “equilibrium value” of asset prices, in most cases we also use some forward-looking indicators. Such indicators may be based on the views of our economists, aggregated views of the Street’s analysts, or our Investment Solutions Group’s own informed, collective predictions about future asset price behavior.

Finally, our forecasts highlight the relative attractiveness of asset classes. For example, when we see 10-year bond return forecasts projected to be low or negative, we know that investors will need to consider assets further along the risk spectrum to achieve desired returns. Risk-return analysis per asset class also guides us in portfolio construction, highlighting which asset classes are eligible for inclusion given a client’s desired risk and return objectives.

“Building Blocks” Approach to Return Forecasting

We use a “building blocks” approach to return forecasting, focusing on the drivers having an inner frequency commensurate with the time horizon of the forecast. For example, when formulating forecasts for the next five or 10 years, we use building blocks that include 10-year growth and inflation views, as well as long-term historical averages for term premia and price multiples.


Future equity returns are calculated as a sum of our expectations for earnings growth, inflation, buybacks/share issuance yield, and income (equity dividends). The final forecast also includes a correction, reflecting the propensity of earnings multiples to mean-revert over the long term (see Figure 1). Dividends and growth prospects are the foundation of this analysis, as their combination is a known starting point for expected returns. A blend of current and forward dividend yields can be used to estimate the income return that is likely to be realized by equity holders. Real earnings growth, adjusted for new share issuance, drives market valuations. The inflation forecast helps to create a view on future nominal equity prices. Finally, the experience of equity investors is highly influenced by the level of valuation at the time of purchase, especially when price multiples stray far away from the historical levels. We make adjustments to return expectations that reflect our view that over time, extreme deviations from the norm tend to dissipate.

Figure 1
Equity Return Forecasting Building Blocks

Source: State Street Global Advisors, Investment Solutions Group.

This methodology is applied to 21 equity markets individually, then rolled up to generate more than a dozen regional and global index forecasts, based on the market capitalization of constituents.

Sovereign Bonds

Our return forecasts for sovereign bond indices are derived from a comparison of current yield conditions with expectations for how the nominal yield curve will evolve going forward. For each bond index, the return forecast is calculated as a combination of price return and income return (see Figure 2).

When deriving the expected shape of the sovereign yield curve, we implicitly link that shape to our expectations of future growth. This is done by starting with the cash rate forecast (which is conditioned on growth expectations) and then adding a term premia component conditioned on the cash forecast.

Figure 2
Sovereign Bond Return Forecasting Building Blocks

Source: State Street Global Advisors, Investment Solutions Group.


For corporate bonds, we analyze credit spreads and their term structures, with separate assessments of investment grade and high yield bonds.
• For investment grade bonds, we consider long-term credit spreads along with our forecast for the sovereign yield curve to generate the investment-grade yield curve forecast. We then follow the same approach to calculating the expected returns of corporate bond indices as we did for sovereign ones, but with one important caveat: Some investment grade corporate bonds will be downgraded over their lifetime, and when they are, they will be removed from the index. Losses experienced by the holders of those bonds because of the downgrades would not be reflected in the index option-adjusted spread (OAS), which would lead to the OAS over-estimating the actual extra spread-related income that bondholders receive. To account for this phenomenon, in our methodology, only a portion of the option-adjusted spread gets converted to income return.
• For the high yield universe, we consider the long-term high yield spread along with the forecasted sovereign yield curve to generate the high-yield curve forecast. One key difference from investment grade bonds is that, instead of bond downgrades, we incorporate a market-implied level of defaults that reduces total return expectations. The default impact is itself a function of the current high yield spread and bond maturity.

In both cases, we analyze bond indexes by country and type, then produce forecasts for the regional and broad credit indexes by calculating market capitalization-based averages.

Alternatives and Smart Beta

We provide forecasts for a range of alternative asset classes, including private equity, hedge funds, commodities, and real estate. We also provide a number of smart beta forecasts. Generally, our approach to forecasting alternatives aims to link alternatives to global market factors for which we have forecasts – including equity markets, levels of interest rates and inflation, and credit spreads. Linkage is not always trivial and each forecast incorporates asset class-specific parameters. For a detailed description, please contact your State Street Global Advisors relationship manager.

Risk Forecasting

Our focus so far has been on the expected return component of our forecasts. Most investors are also interested in risk and correlation expectations for a more complete picture of the relative attractiveness of various asset classes. For that reason, we provide both traditional risk estimates, based on monthly data, and long-horizon risk estimates and correlation figures alongside expected returns.

Our risk estimates rely heavily on analyzing long windows of historical data, spanning numerous market environments. Our long-term correlation forecasts are derived from analyzing long-term, equal-weighted correlation from data going back to 1990, with negative long-term correlations systematically reduced in magnitude to moderate possibly over-optimistic, in-sample bias.

Recent Long-Term Asset Class Forecasting Innovations

We incorporated several innovations into the LTACF over the course of 2020-2021, including long horizon risk estimation, effects of buybacks, and ESG scoring.

Long Horizon Risk Estimation

Most long-term allocators use monthly or quarterly performance data to assess the riskiness of financial assets. A typical process – including that employed by State Street Global Advisors until recently – involves taking monthly performance figures over a sufficiently long period of time (e.g., 10 or 20 years), calculating monthly asset volatilities and correlations over these periods, annualizing volatilities by multiplying monthly figures by the square root of 12 (while leaving correlation estimates untouched), and then deploying results as “long-term risk estimates.” This approach is quite sensible if we need to estimate asset riskiness over a short investment horizon, for example a month or a quarter. But what if our investment horizon were much longer? Wouldn’t it make more sense to lengthen the time horizon of our risk estimation as well?

One way to go about this would be to decompose historical asset-price patterns into a sum of persistent components and short-term, transient “noise” and then focus on the former. Figure 3 brings this discussion to life using the example of the S&P 500.

Figure 3
Decomposition of Price Series into Trend and Cycle Components

Source: State Street Global Advisors, Investment Solutions Group, as of August 31, 2021.

The two components of the S&P 500 differ not only in the speed of change, but also in their long-term dynamic. The slow, “persistent” component grows over time, reflecting growth in the real economy and corporate earnings. The fast, “transient” component is directionless and strongly mean-reverting.

Ever since Robert Shiller published his seminal work, it has been well-accepted that, over the long term, public equity returns are anchored to economic fundamentals, while in the short term they are also subject to so-called “excess” volatility. The smooth, “persistent” component of equities represents economic fundamentals and business impact, while the mean-reverting component in Figure 3 is the manifestation of Shiller’s excess volatility.

Augmenting traditional volatility and correlation estimates with their long-horizon versions may seem like common sense, but we believe it could be transformational. For publicly traded equities, credit, and real estate instruments such an approach would largely expunge excess volatility, reducing public-asset volatility figures to levels close to those of their private counterparts (after leverage adjustments). The change would also clarify the long-term relationships between public and private assets, which are obscured in short-term correlation analysis.

The long-horizon volatility can be calculated as annualized standard deviation of monthly figures for the long-term trend component of Figure 3.2

Buyback Yield

Equity returns are driven by the earnings per share (EPS) growth as opposed to the earnings growth. One can either attempt to forecast EPS as a whole or split EPS into “E” and “S” components and build separate forecasts for them. For the earnings component, GDP can be used as a proxy. The share component reflects new share issuance by already listed firms. Notably, share issuance yields vary across countries, reflecting differences in how public companies return capital to shareholders. For example, an emerging market may have higher dividend yields while consistently issuing new shares, but a developed market may have a lower dividend yield while consistently issuing fewer new shares, or even buying shares back.

To capture this phenomenon, we built a bottom-up aggregate of buyback yields by country and included it in our return forecasts. Corporations in most countries are net equity issuers over a majority of history. However, in recent years, US companies have been doing more buybacks than issuance (see Figure 4).

Figure 4
Share Issuance or Buyback Yield

Source: State Street Global Advisors, Investment Solutions Group, as of August 31, 2021.

ESG Scoring

We believe that the use of Environmental, Social, and Governance (ESG) criteria by financial markets is part of a multi-year readjustment in the way that institutional investors approach price formation and risk estimation of financial assets. We therefore have a responsibility to systematically and explicitly include ESG metrics in our investment analysis and decision-making process.

In 2019, State Street launched its ESG scoring system, R-Factor™ (the “R” stands for responsible investing). R-Factor scores are based on SASB’s financial materiality framework and draw from raw data provided by several high-quality ESG data sources as well as governance insights from State Street Global Advisors’ asset stewardship team. The result is an ESG view by company that is based on multiple perspectives seen through the lens of financial materiality and therefore important to investors. Beginning in Q3 2020 we incorporated R-Factor into our long-term equity asset class forecasts.3

ESG score improvements may be rewarded by the marketplace in the form of (1) higher returns and (2) reduced risks. While ESG ratings may have a nuanced effect on returns, its impact on risks is perhaps most straightforward: Improved ESG R-Factor scores are likely to reduce tail risks associated with ESG issues, thereby delivering an overall lower level of risk (standard deviation). The inverse is true for those countries who have seen a deterioration in their ESG ratings. To account for these relationships, we built a framework that rewards higher-performing countries with lower risk expectations and vice versa.

Because some countries’ R-Factor scores are semi-permanently higher than others (due to structural differences in culture, law, or environment), we focus on how R-Factor scores rise or fall over time, rather than on absolute levels. In addition, to ensure that we do not change the risk expectation of an equity class as a whole, we subtract the weighted average of the normalized country scores (“global score”) (see Figure 5).

Figure 5
R-Factor Use in LTACF

Source: State Street Global Advisors, Investment Solutions Group.

The State Street Global Advisors Advantage

Reasonable inputs are a cornerstone to developing an asset allocation strategy aligned with investors’ goals and objectives. Many asset management organizations around the world publish long-term asset class forecasts, but we believe that many aspects of our approach are different.

Long-term asset class forecasting has been an area of focus for our Investment Solutions Group for over 15 years. Today, we provide long-term forecasts for over 150 asset classes on a quarterly basis, following a systematic process that also relies on fundamental inputs from a team of deeply experienced investment professionals across the firm. Over the years we have constantly enhanced our process - including the addition of new asset classes as markets have evolved, and refinements of the methodology used to capture key developments. These enhancements have allowed us to provide thorough, forward-looking asset class forecasts for our clients. Many of our clients have found our forecasts to be a valuable input in their long-term planning processes.

Each quarter, State Street Global Advisors publishes its Long-term Asset Class Forecast. This paper is best viewed in conjunction with that quarterly forecast. Please reach out to your representative from the Investment Solutions Group or from Investment Strategy and Research if you would like additional information.