BLACKROCK INVESTMENT INSTITUTE | February 2020

Capital market assumptions

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Asset return expectations and uncertainty

Select return time period (years)
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The chart below shows our annualised mean return expectations (dots) across asset classes. There are two sets of bars. The darker bands show our estimates of uncertainty in our mean 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.

BlackRock strategic views

Our strategic asset class views show our asset class preferences over a 10-year horizon relative to a long-term, cycle-agnostic equilibrium asset allocation. Based on our cyclical views and current market valuations, asset classes may be more or less favorable compared with a long-run, steady-state environment. The charts below summarize our current views. We favor a barbell approach comprising equities and government bonds – and prefer both to credit. Our conviction in the role of global government bonds in providing portfolio resilience, with a preference for U.S. Treasuries over lower-yielding peers. We are now more overweight government bonds compared with our prior update as the rise in yields means we see more carry potential and lower duration risk. Within equities, we are neutral on developed market equities and prefer emerging markets due to our views on Chinese equities. We add to our underweight on income private market assets slightly as we see them as particularly vulnerable to late-cycle dynamics, yet our overall allocation to private markets is still relatively higher. The drag on expected returns for public assets underscores the need for investors to include private markets and Chinese assets as diversified sources of relatively higher returns. Our views broadly hold across our investor-specific strategic asset allocations.

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.

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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

The dramatic drop in government bond yields we saw in 2019, taking nearly a third of the global bonds into negative-yielding territory at one point, has important implications for designing strategic asset allocations. Importantly, aside from the low return prospects, a potential breakdown of the negative equity-bond correlation due to a supply-shock or a shift in policy frameworks toward fiscal stimulus would further undermine the role of government bonds as portfolio ballast. We see a growing role for Chinese assets in global, long-term portfolios. We believe the size, importance and gradual opening of China’s onshore markets to foreign investors mean it should no longer be seen just as part of a broader emerging markets bloc. We show below how different investors can deploy our toolkit and incorporate our explicit China views in their SAAs based on individual needs, objectives and constraints. Read more in our paper on strategic asset allocation in an era of ultra-low interest rates.

 

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, December 2019. 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. 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.

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, 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, December 2019. Notes: The tables show hypothetical SAA and certain performance metrics for the peer groups used in our analysis. Net asset return expectations are as of 31 December, 2019. 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. 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.
 
 
Fixed income assumptions
Our five-year local-currency return assumptions for fixed income assets have five components that are shown in the chart below:
Equities assumptions
Our five-year return assumptions for equities have three components that are shown in the chart below:

Five-year macro assumptions

 

USEurozoneUKJapan
Yield in five years


3-month nominal government bond 2.1% 0.3% 1.2% 0.3%
10-year nominal government bond 3.2% 1.0% 2.1% 0.8%
30-year nominal government bond 3.5% 1.4% 2.7% 1.4%
Values in five years

CPI inflation 1.8% 1.6% 1.9% 1.0%
GDP growth 2.0% 1.1% 1.2% 0.6%

Source: BlackRock Investment Institute, February 2020. Data as of 31 December, 2019.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.

  • Uncertainty and optimisation

    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.

  • Stochastic engine

    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.

  • Equity

    We put estimates of the equity risk premium (ERP) at the heart of our approach to setting return expectations. 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.

  • Interest rates

    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.

  • Credit

    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.

  • Private markets

    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 returns (risk-free rates, corporate credit spreads) and private-market specific return drivers such as the public-private spread, losses due to default and downgrades, leverage and borrowing costs. Unlike most public debt markets, infrastructure debt and direct lending are modelled as ‘buy and hold’ investments, in line with how investors access these asset classes. Accounting for fees in private equity is challenging due to limited data, a wide variety of clauses that allow funds to adjust fees over time and the variety of fees involved (management, carried interest, fund expenses, transaction costs). We take a conservative approach that incorporates slightly higher fees than some industry surveys suggest. We use Preqin data to look at feet net of fee free cash flows for funds, add back estimated fees, aggregate cash flows and net asset values to create our gross returns –our goal to make private equity returns more comparable to public equity beta-plus-alpha returns. We then net out fees when creating and optimising portfolios. These steps are necessary to account for carried interest, which changes over time and depends on fund performance.

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

Adrian, T., Crump, R.K. and Moench, E. (2013). Pricing the Term Structure with Linear Regressions. Federal Reserve Board of New York Staff Report No. 340.

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.

Black, F. and Litterman, R. B. (1991). Asset allocation: combining investor views with market equilibrium. The Journal of Fixed Income, 1(2):7–18

Ceria, S., and R.A. Stubbs. “Incorporating Estimation Errors into Portfolio Selection: Robust Portfolio Construction.” Journal of Asset Management, Vol. 7, No. 2 (July 2006), pp. 109-127.

Doeskeland, Trond and Stromberg, Per. 2018. "Evaluating investments in unlisted equity for the Norwegian Government Pension Fund Global (GPFG)." Norwegian Ministry of Finance.

Garlappi, Lorenzo, Wang, Tan and Uppal, Raman, 2004. “Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach”. EFA 2005 Moscow Meetings Paper; Sauder School of Business Working Paper

Grinold, Richard C., and Ronald N. Kahn, 2000. Active portfolio management Second Edition, McGraw Hill Kalman, Rudolph Emil. “A new approach to linear filtering and prediction problems.” Journal of basic Engineering 82, no. 1 (1960): 35-45.

Kalman, R.E. 1960. "A new approach to linear filtering and prediction problems." Journal of Basic Engineering 82, no. 1, pp. 35-45.

Li, Y., Ng, D.T. and Swaminathan, B., 2013. Predicting market returns using aggregate implied cost of capital. Journal of Financial Economics, 110(2), pp.419-436.

Piazzesi, M. (2010). Affine term structure models. Handbook of financial econometrics, 1, pp. 691-766.

Ross, Stephen A., 1976, “The arbitrage theory of capital asset pricing,” Journal of Economic Theory 13: pp. 341-60.

Sharpe, William F., 1964. “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk.” The Journal of Finance, 19.3, pp. 425-442

Tütüncü, R.H., and M. König“Robust Asset Allocation.” Annals of Operations Research, Vol. 132, No. 1-4 (2004), pp. 157-187.

Scherer, B. “Can robust portfolio optimization help to build better portfolios?” Journal of Asset Management, Vol. 7, No. 6 (2006), pp. 374-387.