BlackRock Investment Institute | August 2023

Capital market assumptions

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Asset return expectations and uncertainty

Select return time period (years)
The chart below shows our annualised central return expectations (dots) across asset classes. There are two sets of bars. The darker bands show our estimates of uncertainty in our central return estimates. The lighter bands are based on the 25th and 75th percentile of simulation-generated potential return pathways – the interquartile range. Buttons at the top of the chart can be used to switch the horizon for the return expectations. For more details see the methodology tab above.

Latest update to our CMAs

In the latest update to our CMAs, we’ve integrated the insights on the transition to a low-carbon economy from our BlackRock Investment Institute Transition Scenario (BIITS) into our return estimates. The BIITS is a research-based, analytical forecast of how the low-carbon transition could unfold – and we’re leveraging these insights to estimate the impact of the transition at a more granular level, adjusting our estimates for returns across and within sectors. This builds on our previous work to make our CMAs and strategic allocations climate aware. We see the transition to a low-carbon economy among a handful of mega forces, or structural changes that are creating big shifts in profitability across economies and sectors. These mega forces – including digital disruption like artificial intelligence, geopolitical fragmentation, aging populations and the fast-evolving financial system – are not in the far future – but are playing out today.

BlackRock strategic views

We are in a new regime of greater macro and market volatility that provides different but abundant investment opportunities. Persistent supply constraints are compelling central banks to hold policy tight, stoking greater volatility. This is quite different to the stable inflation and economic growth seen in the four-decade period leading up to the pandemic. Those conditions supported a portfolio approach that relied on set-and-forget allocations to public equities and bonds. Today’s conditions of heightened volatility and tight monetary policy don’t bode well for that static approach. We think this new regime requires more dynamic and nimble strategic portfolios – staying static risks missing out on opportunities to take advantage of the market shocks that come with the volatility of the new regime. For example, we’re taking advantage of higher short-term bond yields by rotating away from investment grade (IG) credit and towards short-dated government bonds for income. The difference between IG credit and short-term bond yields has narrowed – and we prefer short-term bonds on a risk-adjusted basis. We still see more room for long-term yields to rise as we expect investors to demand more term premium, or the extra compensation for the risk of holding long-term government bonds, given stubborn inflation and growing debt burdens.

Show tilts relative to:

Expected returns by horizon

We provide a term structure of returns over different time horizons — from five years out to the long term. We incorporate uncertainty into our return projections. The range of uncertainty differs by asset class. See our paper Understanding uncertainty for more. Use the chart below to compare different assets.

The chart(s) below show our annualised mean return expectations (central line) across different time horizons. The darker areas show our estimates of uncertainty in our mean return estimates. The lighter areas are based on the 25th and 75th percentile of simulation-generated potential return pathways – the interquartile range. For more details see the methodology tab above.

Asset class return and volatility expectations

Return time period (years)

Assumptions at a glance

Select asset class
Asset Return expectations
(geometric, gross of fees)
Long-term
expected volatility
Long-term correlation
5-year 10-year 15-year 20-year Global equities Global government bonds

Our capital market assumptions are part of our wider portfolio construction toolkit. Using our capital market assumptions, that explicitly account for uncertainty and different pathways for asset class returns, we can employ robust optimisation techniques to design hypothetical downside-aware strategic portfolios. We blend portfolio return drivers alpha, factors and index – to help ensure the portfolio risk budget is used efficiently and cost effectively. To size allocations to private markets, we consider liquidity risk linked to the cashflow requirements of the investor. We show below how our toolkit can be deployed to design strategic asset allocations for specific client types, based on their individual needs, objectives and constraints.

 

Criteria Description
Base currency
Investment objective
Risk target
Investment opportunity set
Investment horizon
Parameter Value
Expected SAA return range excluding alpha, net of fees
Contribution from net alpha
Expected SAA return range including alpha, net of fees
Return estimate assuming bottom half of outcomes
Past performance is not a reliable indicator of current or future results. This information is not intended as a recommendation to invest in any particular asset class or strategy or as a promise - or even estimate - of future performance.
Sources: BlackRock Investment Institute, with data from Refinitiv Datastream and Bloomberg, . Notes: The chart shows a hypothetical SAA, based on the metrics provided in the table above. Index proxies can be found on the Assumptions tab under the info icons in the Assumptions at a glance table. Fee assumptions are listed on the methodology tab. The expected returns range is based on the 25th and 75th percentile of our simulated return pathways. The return estimate assuming bottom half of outcomes shows how the asset allocation may perform in adverse economic outcomes. It is derived by considering only the bottom-half of our simulated return pathways and applying the weighted average of expected returns from this set to the asset allocation mix. For assets without indices (private markets), we have assumed top-quartile performance. ‘Contribution from net alpha’ in the table relates to the alpha opportunity in public market assets only, according to the definitions and methodology detailed in our paper on blending returns. The allocation shown above does not represent any existing portfolio, and as such, is not an investible product. The construction of the hypothetical asset allocation is based on criteria applied with the benefit of hindsight and knowledge of factors that may have positively affected it's performance, and cannot account for risk factors that may affect the actual portfolio's performance. The actual performance may vary significantly from our modelled CMAs due to transaction costs, liquidity or other market factors. Indexes are unmanaged, do not account for management fees and one cannot invest directly in an index. We use BlackRock proxies for selected private markets because of lack of sufficient data. These proxies represent the mix of risk factor exposures that we believe represents the economic sensitivity of the given asset class.

Our view: 

 

 

 


Peer group for:

We derive the peer groups for our SAAs from a variety of sources listed below. These peer groups are purely illustrative, broadly share similar objectives and constraints as our hypothetical SAAs and are intended to be an approximate guide of average industry practice. They do not represent any actual portfolio. We apply our CMAs and robust optimization techniques to these allocations using the same assumptions as our SAAs. We do not assume any alpha in the expected returns for peer groups as we have no visibility into what blend of returns (index, factors and alpha-seeking) various investors in our data sets use, their ability to pick top-quartile managers or their fees and governance costs.

Peer group derived from:

Assumed asset class breakdown:

Parameter Value
Expected return range excluding alpha, net of fees
Risk target
Return estimate assuming bottom half of outcomes
Past performance is not a reliable indicator of current or future results. This information is not intended as a recommendation to invest in any particular asset class or strategy or as a promise - or even estimate - of future performance.
Sources: BlackRock Investment Institute, with data from Refinitiv Datastream and Bloomberg, . Notes: The tables show hypothetical SAA and certain performance metrics for the peer groups used in our analysis. Index proxies can be found on the Assumptions tab under the info icons in the Assumptions at a glance table. Fee assumptions are listed on the methodology tab. The expected returns range is based on the 25th and 75th percentile of expected return outcomes as detailed here. The return estimate assuming bottom half of outcomes shows how the asset allocation may perform in adverse economic outcomes. It is derived by considering only the bottom-half of our simulated return pathways and applying the weighted average of expected returns from this set to the asset allocation mix. Peer groups return ranges do not include alpha potential. Hedge fund allocations are included in private markets for peer groups. For assets without indices (private markets), we have assumed top-quartile performance. The allocation shown above does not represent any existing portfolio, and as such, is not an investible product. The construction of the hypothetical asset allocation is based on criteria applied with the benefit of hindsight and knowledge of factors that may have positively affected it's performance, and cannot account for risk factors that may affect the actual portfolio's performance. The actual performance may vary significantly from our modelled CMAs due to transaction costs, liquidity or other market factors. Indexes are unmanaged, do not account for management fees and one cannot invest directly in an index. We use BlackRock proxies for selected private markets because of lack of sufficient data. These proxies represent the mix of risk factor exposures that we believe represents the economic sensitivity of the given asset class.
 
Fixed income assumptions
Our five-year local-currency return assumptions for fixed income assets have five components that are shown in the chart below:
Source: BlackRock Investment Institute, Notes: All component numbers are geometric and are subject to rounding. Expected return estimates are subject to uncertainty and error. Expected returns for each asset class can be conditional on economic scenarios; in the event a particular scenario comes to pass, actual returns could be significantly higher or lower than forecasted.
Equities assumptions
Our five-year return assumptions for equities have three components that are shown in the chart below:
Source: BlackRock Investment Institute, Notes: All component numbers are geometric and are subject to rounding. Returns are in local currency except for emerging markets which are in US dollars. Expected return estimates are subject to uncertainty and error. Expected returns for each asset class can be conditional on economic scenarios; in the event a particular scenario comes to pass, actual returns could be significantly higher or lower than forecasted.

Five-year macro assumptions

 

      U.S. Euro area UK Japan  
Yield in five years


  10-year nominal government bond 3.9% 2.1% 3.8% 0.8%  
  30-year nominal government bond 3.9% 2.3% 3.9% 1.5%  
Values in five years

  CPI inflation 2.7% 2.2% 2.3% 1.3%  
  GDP growth 1.9% 1.7% 1.4% 1.0%  

Source: BlackRock Investment Institute, August 2023. Data as of 30 June 2023. This information is not intended as a recommendation to invest in any particular asset class or strategy or as a promise - or even estimate - of future performance.

Notes: All component numbers are geometric and are subject to rounding. Expected return estimates are subject to uncertainty and error. Expected returns for each asset class can be conditional on economic scenarios; in the event a particular scenario comes to pass, actual returns could be significantly higher or lower than forecasted.

  • Our macroeconomic and asset return forecasts account for the impact of the low carbon transition and climate change and our underpinned by the BII Transition Scenario. This is our research-based, analytical forecast of how the low-carbon transition could unfold. Its value is not in the forecast itself, but how it can be used to help investors navigate the transition’s risks and opportunities. It focuses on what is most likely to occur – rather than on what anybody thinks should happen or a specific outcome. The low-carbon transition’s speed and shape are highly uncertain and the BIITS cannot capture all the transition’s drivers and all the ways physical climate events affect the economy and markets. We approach this process with humility, knowing we may be wrong about some aspects.

    The BIITS is not designed to capture dynamics for companies or assets, nor does it assess whether markets have priced in opportunities and risks. It is not intended as a recommendation to invest in any particular asset class or strategy or as a prediction of future performance.

    Our methodology and assessment of its effects are inherently incomplete, especially further out in the future. We plan to adjust our views as we learn more.

    Macroeconomic impacts

    The BII Transition Scenario modelling allows us to account for the impact of physical climate damages, transition-related costs and capital investment – key channels for the transition’s impact on growth, in our view. We use an iterative framework to estimate the impact of the transition and physical climate change on economic growth, considering energy prices, energy production and consumption. across the global economy through 2100, and the expected economic damages that may result from worsening physical and transition risks.

    On inflation, we assess the likely impact of the transition across countries based on an estimated net increase in energy prices and capital investment to finance the transition.

    We arrive on a view on the impact on policy interest rates based on our assessment of the monetary policy response given the estimated inflation and GDP growth impact.

    Asset class return impacts

    We adapt the inputs to our asset return models to account for climate change, as described below. 

    Firstly, our estimated asset returns are underpinned by the macroeconomic impacts discussed above.

    Climate change and the low-carbon transition also impact expected returns via two further channels:

    Repricing – We think a consequence of shifting societal preferences for sustainability is that the average price investors are willing to pay for assets perceived to be sustainable is changing, meaning the discount rate we use to value these securities is also changing. For credit and equity markets we adjust our future cost of capital estimates, such that all else equal, more/less sustainable sectors have lower/higher future costs of capital.

    Fundamentals – We see climate change and the low-carbon transition potentially impacting the profitability and growth prospects of companies. We estimate the impact of both physical and transition risks on corporate earnings at the sector level, taking into account macroeconomic shifts like changes in growth or demographic trends, changes in supply and demand for less carbon intensive  assets, and the ability of companies to adapt to such changes. We also account for the evolution in power and energy systems – as estimated by the BII Transition Scenario - from emissions-intensive companies to efficient and renewable business models.

  • By incorporating uncertainty we recognise that mean expected returns for assets are estimated with error rather than assuming they are known, as is the case with mean variance techniques. A key benefit is that we can allow for different conviction levels in return expectations. We consider the distribution around the mean, effectively reducing the weight placed on our mean (central) estimate. Distinguishing between uncertainty and risk is important. We define uncertainty as the range of outcomes for the mean and risk as the range of outcomes around the mean. The amount of uncertainty we take into account for each asset classes depends on a number of criteria. They include the back-tested predictive power of our asset class return models, the historic volatility of assets and the desire for diverse portfolios when optimising.

    Uncertainty in mean returns feeds in to our stochastic simulations, that give a range of potential return pathways from five years out to the long term. When constructing portfolios, these simulated pathways and our mean return uncertainty enable us to use robust optimisation techniques that generally lead to less concentrated portfolios compared with those portfolios resulting from mean variance optimisation. It also gives flexibility to focus on certain upside or downside scenarios when constructing portfolios to fit client needs. Read more.

  • We use Monte Carlo simulation to create random distributions informed by historical return distributions and centred on our expected returns. The engine simulates thousands of return pathways for each asset, representing the range of possible outcomes over a five-to 30-year time horizon. We leverage BlackRock’s risk models to ensure we respect co-dependencies between asset returns. The range of scenarios incorporate our work on incorporating uncertainty in return expectations. The Black-Littermanmodel (1990) –a well-known model for portfolio allocation -combines long-and medium-term views in a single-period setting. Our model uses a Kalman filter –an algorithm that extracts insights about potential future paths by bringing together a number of uncertain inputs -to extend this approach into a multi-period setting. This allows us to capture the variation of expected returns over time under various scenarios —from economy-related to market sentiment driven. A large part of these variations is not predictable. Constructing portfolios that are robust to, or can exploit, these variations is a major challenge for investors. The ability to calibrate the engine with asset class views with uncertainty at arbitrary time horizons, and to evolve this uncertainty stochastically, drives the dispersion of return outcomes. Highlighting the uncertainty that investors face when building portfolios helps ensure ostensibly precise return expectations do not lead investors to concentrated portfolios.

    Simulated return paths support a broader range of applications, such as asset-liability modelling. Stochastically generated return scenarios enable investors to move with ease beyond mean-variance and optimise portfolios against their individual needs. Investors can place more emphasis on the tails of the distribution or focus on the path of returns rather than just the total return. They can incorporate flows in or out of the portfolio over the course of the investor’s time horizon or place more emphasis on scenarios that are challenging for the investor’s business beyond their portfolio. Investors with complex asset-liability matching requirements, such as insurers, typically rely on stochastic simulations of returns to assess and construct portfolios.

  • We put estimates of the equity risk premium (ERP) at the heart of our approach to setting return expectations. We calculate the equity risk premium using an implied cost of capital approach (Li et al, 2013). We use a discounted cashflow model and take today’s market price and expectations of future dividends and growth and interest rates to arrive at an implied equity risk premium.  Changes in equity valuations are driven by both expected cash flows – earnings and dividends – and the ERP. Forming expected returns by looking solely at valuations – typically the price-to-earnings ratio – can miss the full picture, in our view. Our work finds that linking expectations for future interest rates and the ERP can be more telling for expected returns rather than attempting to find a fair value for the price-to-earnings ratio alone. This allows us to incorporate our views of the structural drivers of interest rates into expected equity returns –as well as other asset class returns. We also use bottom-up analyst earnings forecasts and the relationship between margins and the economic cycle to formulate our earnings expectations (using an augmented discounted cash flow model). We find corporate profit margins not only converge to long-term averages but do so at a faster pace when an economy reaches full capacity. We assume in future the ERP will mean-revert to levels observed in the post-1995 period. Our projections for risk-free rate, or “long-run short rate”, are described in the Interest rates methodology section.

  • We derive our expected returns for government bonds by mapping out the yield curve at multiple time horizons in the future. This is based on estimating (1) the short rate, and (2) model implied term premia. Estimates of short rates are based on market data in the near-term and on macroeconomic informed data in the long-term. More specifically, in the long-term, we assume investor views about long-run inflation and real growth, coupled with changing preferences as to savings and risk aversion, will determine expectations for short rates (the “long run short rate”). Model implied term premia are computed from a model based in the affine term structure class of models (Adrian, Crump and Moench, 2013) describing the yield curve using the first five principal components of yield. The model implied term premia from the affine term structure model are further calibrated to market implied term premia, with the relative weights dependent on the relevant time horizon.

  • Our model for credit asset (excess) returns is anchored on two key elements: 1) our estimate of credit spreads at a given horizon and 2) our estimates of loss due to defaults and downgrades over the horizon. The first component is projected in a consistent manner with our view of real GDP growth, as implied by BlackRock’s factor-augmented vector autoregressive macroeconomic model (Bernanke, Boivin and Eliasz, 2004) and the link between credit spreads and equity volatility. Our approach attempts to avoid overfitting, yet retains the ability to explain a high proportion of the variance in credit spreads and passing cross-validation tests against more complex approaches. The second component is estimated based on our outlook for spreads, the duration of the asset and an assumed credit rating transition matrix which captures rating migrations and defaults across multiple credit cycles. We currently base our transition matrix on Moody’s long-run transition data. We aim to further develop our model by directly modelling transitions based on macroeconomic conditions in order to better capture cycle dynamics and the respective variation in losses due to credit events. In addition to making our estimates of credit spreads consistent with our macroeconomic views, the our new credit (excess) return model allows the flexibility of calibrating our expected returns to various credit rating compositions which may prevail over the entire time horizon.

  • The private market return models can be grouped into two categories – equity and debt. The equity models – relevant for core real estate and private equity buyouts – are based on an accounting statement framework. We estimate earnings growth and future valuations, which are used in conjunction with observable private and public market data (current valuations, financing cost, leverage, etc.) to model the evolution of the capital structure over time and infer equity returns. Estimated earnings growth and future valuations are linked to components of our public market return expectations for equity, rates, and credit spreads. Crucially, they also consider the unique dynamics of each asset class, such as the changing occupancy rates for real estate. Returns for private market debt – infrastructure debt and direct lending – are estimated using a ‘build up’ approach. The total return is a build-up of underlying public market factors (interest rates) and private-market specific return drivers such as credit spreads, losses due to default and downgrades, leverage and borrowing costs. Unlike most public debt markets, direct lending is modelled as a ‘buy and hold’ investment, in line with how investors access the asset classes. The published returns are gross of fees and we net out representative client fees, accounting for management fees, carried interest, and hurdle rates, when creating and optimizing portfolios.

Fee assumptions

Index or betaAlpha-seeking
Equities 0.15%-0.5% 0.4%-0.8%
Government bonds 0.15%-0.3% 0.2%-0.25%
Investment grade credit 0.1%-0.3% 0.2%-0.25%
Sub-investment grade credit 0.4%-0.5% 0.4%-0.5%
Private markets N/A 0.5%-5.0%

Sources: Mercer Global Asset Manager Fee Survey 2017, Morningstar, BlackRock estimates. Note: Fee assumptions are given as ranges given the wide range of asset classes, currencies and datasets we consider in our calculations.

References

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Bernanke, B.S., Boivin, J. and Eliasz. P. (2005). Measuring The Effects Of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach, Quarterly Journal of Economics, 2005, v120: 387-422.

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