BlackRock Capital market assumptions

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Asset return expectations and uncertainty

Latest update to our CMAs

Our latest capital market assumptions (CMAs) update reflects the impact of the waves of transformation rippling through the global economy and market moves to the end of the second quarter of 2024. Buzz around artificial intelligence (AI) powered U.S. stocks to record highs in Q2, while 10-year Treasury yields hit a 16-year peak before retreating sharply. Japanese stocks also rallied, buoyed by mild inflation and corporate reforms. Credit spreads slightly widened after over a year of contraction. Some of these moves partially reversed – and snapped back – after our quarterly update cutoff. We are focused on the big picture and lean into the opportunities we see from the unfolding transformation. We anticipate greater sectoral dispersion with strong corporate earnings growth in sectors poised to benefit from structural shifts like the surge in AI investment, the low-carbon transition, and the rewiring of global supply chains. We expect tech sector profit margins to hold up or even expand, potentially justifying valuations that appear historically high. The highly uncertain impact of the transformation underway leads to a wide range of possible outcomes. That calls for a nimbler approach to long-term asset allocation, in our view.

BlackRock strategic views

We're leaning into opportunities from the surge in real economy investments, rather than simply reducing risk in an uncertain outlook. An investment boom in AI data centers, semiconductor facilities, and manufacturing plants favors infrastructure equity within private markets, in our view. We like income-oriented private markets as the rapidly evolving U.S. financial landscape supports private credit. Private markets may not suit all investors. In public markets, we upgrade investment-grade (IG) credit to neutral, and favor short- and medium-term, high-quality corporate bonds where we think investors are better compensated for credit risk and face less interest rate risk than in longer-dated bonds. We expect sticky inflation, coupled with high public debt, keeping long-term yields elevated as investors seek greater compensation for holding long-dated bonds. We prefer short-dated over long-term government bonds and favor non-U.S. over U.S. within developed market government bonds overall. Sticky inflation also means inflation-linked bonds remain a core, strategic portfolio holding yet we cut it to a neutral from overweight as we see more compelling risk-reward elsewhere in fixed income. Our strategic preference for Japanese equities remains unchanged – we still see a brightening macro environment and specific investment opportunities driven by structural forces, justifying an above-benchmark allocation to Japan's stocks.

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. Source: BlackRock Investment Institute, August 2024. Data as of 28 June 2024. Notes: The equilibrium view tab shows our asset views on a 10-year view from an unconstrained U.S. dollar perspective against a long-term equilibrium allocation. The representative view tab shows the tilts of representative multi-asset portfolios in the U.S., UK and euro area with a similar risk target of 8%. The representative allocations are averages of client surveys in the U.S. conducted by the BlackRock U.S. Wealth Advisory group. The survey comprises 3319 advisor models in the U.S. Global government bonds and EM equity allocations include respective China assets. Income private markets comprise infrastructure debt, direct lending and real estate mezzanine debt. Growth private markets comprise global private equity buyouts, infrastructure equity and Global core real estate. The hypothetical portfolio may differ from those in other jurisdictions, is intended for information purposes only and does not constitute investment advice. We use BlackRock proxies for growth and income private market assets due to 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.

Notes: U.S. dollar return expectations for all asset classes are shown in unhedged terms, with the exception of global ex-US Treasuries and hedge funds. Euro return expectations for all asset classes are shown in hedged terms, with the exception of regional equity markets, Chinese government bonds, local-currency EM debt and private markets other than hedge funds. Sterling return expectations for all asset classes are shown in hedged terms, with the exception of regional equity markets, Chinese government bonds, local-currency EM debt and private markets other than hedge funds. Japanese yen return expectations for all asset classes are shown in hedged terms, with the exception of regional equity markets, Chinese government bonds, local-currency EM debt and private markets other than hedge funds. Swiss franc return expectations for all asset classes are shown in hedged terms, with the exception of EM equity, US large cap, European large cap, Chinese equities, China A-share equities, Chinese government bonds, local-currency EM debt and private markets other than hedge funds. Canadian dollar return expectations for all asset classes are shown in unhedged terms, with the exception of global corporate bonds, hedge funds and global government bonds. Australian dollar return expectations for all asset classes are shown in unhedged terms, with the exception of global corporate bonds, hedge funds, global aggregate bonds and global government bonds. New Zealand dollar return expectations for all asset classes are shown in unhedged terms, with the exception of hedge funds, global corporate bonds, global aggregate bonds and global government bonds. Chinese yuan return expectations for all asset classes are shown in hedged terms, with the exception of regional equity markets, local-currency EM debt and private markets other than hedge funds. South African rand return expectations for all asset classes are shown in unhedged terms, with the exception of hedge funds. Mexican peso return expectations for all asset classes are shown in unhedged terms, with the exception of hedge funds, global government bonds. Singapore Dollar return expectations for all asset classes are shown in hedged terms, with the exception of regional equity markets, local-currency EM debt and private markets other than hedge funds. Hong Kong Dollar return expectations for all asset classes are shown in hedged terms, with the exception of regional equity markets, local-currency EM debt and private markets other than hedge funds.

A typical U.S. public pension plan invests globally on a long-only basis with an absolute return target.

Criteria	Description
Base currency	U.S. dollars
Investment objective	Target absolute return of at least 7%
Risk target	Risk budget based on volatility of a 70-30 equity-bond portfolio
Investment opportunity set	Long-only, global. Maximum allocation to private markets constrained by liquidity considerations to 35%
Investment horizon	20 years

A typical U.S. defined benefit plan is frozen/closed to new participants but aims for a liability-relative return target to fund accruing service costs and credit event headwinds.

Criteria	Description
Base currency	U.S. dollars
Investment objective	Maintain high current funding ratio (over 100%) and minimize surplus volatility
Return target	1.5% on surplus basis
Investment opportunity set	Long-only, global, with leverage permitted via government bonds. Maximum allocation to private markets constrained by liquidity considerations to 13%
Investment horizon	10 years

Family offices typically have long investment horizons, high risk tolerance and low liquidity needs, and tend to hold large private market and equity allocations.

Criteria	Description
Base currency	U.S. dollars
Investment objective	Maximise return for a given risk budget, while being downside aware
Risk target	10% volatility
Investment opportunity set	Long-only, global. Assets with holding periods of more than 10 years excluded
Investment horizon	20 years

This client type typically invests globally on a long-only basis, often with an absolute or 'cash plus' return objective.

Criteria	Description
Base currency	UK pounds
Investment objective	Seek to maximize excess return with a moderate risk target
Risk target	8% volatility
Investment opportunity set	Long-only investments across global public and private markets. Liquidity requirements limit the size of private market allocations
Investment horizon	10 years

Capital preservation, liquidity and return have historically been the main drivers of official reserve managers' investment.

Criteria	Description
Base currency	U.S. dollars
Investment objective	Maximise returns for given level of risk, subject to achieving material liquidity
Risk target	3.5% volatility
Investment opportunity set	Relatively restricted, yet less so than typical reserve managers
Investment horizon	10 years

UK charities typically invest globally on a long-only basis with an 'inflation-plus' return objective and with an income need to meet recurring cash outflows.

Criteria	Description
Base currency	UK pounds
Investment objective	Target absolute return of CPI + 4%
Risk target	Maintain high funding ratio (over 100%)
Investment opportunity set	Long-only investments across global public and private markets. Liquidity requirements limit the size of private market allocations.
Investment horizon	20 years

European high net worth investors focus on investing globally on a long-only basis. With a goal of achieving a desired level of return, they accept a relatively higher risk level as compared to the broader wealth segment.

Criteria	Description
Base currency	Euros
Investment objective	Maximise return for given level of risk
Risk target	9.2% volatility
Investment opportunity set	Long-only investments across global public and private markets.
Investment horizon	10 years

			U.S.	Euro area	UK	Japan
Yield in five years		10-year nominal government bond	4.9%	3.1%	4.0%	1.7%
		30-year nominal government bond	5.0%	3.2%	4.3%	2.5%
Values in five years		CPI inflation	2.7%	2.1%	2.3%	1.4%
	GDP growth	1.8%	1.2%	1.4%	0.9%

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 recognize that central 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 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 optimizing.

Uncertainty in central 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 central return uncertainty enable us to use robust optimization techniques that generally lead to less concentrated portfolios compared with those portfolios resulting from mean variance optimization. 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 beta	Alpha-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

Burke, M. et al. (2018). Large potential reduction in economic damages under UN mitigation targets (Nature, 557, 549–553). https://www.nature.com/articles/s41586-018-0071-9

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, L., Uppal, R. and Wang, T., 2007. Portfolio selection with parameter and model uncertainty: A multi-prior approach. The Review of Financial Studies, 20(1), pp.41-81

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.

We hold an above-benchmark allocation to Japanese equities in long-term portfolios as a benign macro backdrop brightens the outlook for corporate profits.

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