BLACKROCK INVESTMENT INSTITUTE | APRIL 2019

Capital market assumptions

Free Form Html-1
Free Form Html-2,Paragraph-1,Accordion-1,Paragraph-2
 

Asset return expectations and uncertainty

Select return time period (years)
×
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 stimulation-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

We apply our framework to generate our strategic asset allocation tilts on a 10-year horizon relative to the long-term equilibrium portfolio. The charts below summarise our current tilts and an update to the long-term portfolio. On equities, we currently prefer emerging market over developed market (DM) given stronger expected earnings growth and better starting valuations. We also prefer DM equities outside the US relative to the US – also a valuations story. Within fixed income, we prefer DM government bonds over credit, reflecting the view that the diversification benefits of government debt are preferable – in an overall portfolio context – to the marginally higher returns from the credit assets. Even with the occasional, short-term breakdown, we expect that negative correlation (equity returns up and bonds down) to hold over a strategic time horizon. On private markets, we are modestly underweight private market income assets. Yet our overall strategic allocation to private markets is higher than in our previous allocations, reflecting the better risk/reward potential relative to some public market assets, especially credit.

Expected returns by horizon

Financial markets have had a bumpy run since our last CMA update in early December. Risk assets fell sharply in the fourth quarter of 2018 and then swiftly clawed back losses in the first quarter of 2019. Equities have bounced sharply, particularly in EM; corporate credit spreads have shrunk sharply; and yet government bond yields have plumbed new lows on a softer growth outlook despite the risk asset recovery. The market’s moves have resulted in several central return expectations falling from our last update. 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 stimulation-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
 
Fixed income assumptions
Our five-year 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 2.6% 0.6% 1.8% 0.4%
10-year nominal government bond 3.6% 1.4% 2.6% 1.2%
30-year nominal government bond 3.9% 2.0% 3.2% 2.0%
Five-year average

CPI inflation 1.9% 1.6% 2.4% 1.1%
GDP growth 2.0% 1.2% 1.2% 0.5%

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, April 2019. Data as of 28 February, 2019.
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

    Expected returns and asset price volatility are difficult to predict. We believe any technique that builds portfolios should incorporate this inherent uncertainty. We consider both long-and short-term drivers of return. In the long run, we expect a relatively small number of macroeconomic drivers —economic growth, rates, inflation, credit and currencies —to determine an asset’s returns. In the short-run, other factors can overpower the structural drivers causing wider fluctuations from an asset’s fair value. Valuations can be helpful in estimating short-term returns. We combine contributions from the long-and short-term return drivers to produce a final set of return expectations with a range of uncertainty around each.

    The next step is to use this set of return expectations in an optimisation engine that offers the best trade-off between risk and return. Mean-variance optimisation would produce a portfolio that maximises expected return under one base scenario with a given level of risk. In contrast, we look to build a “least-worst” portfolio –one that maximises returns for each risk level across the worst outcomes, say for the bottom 50% of the distribution, from a set of stochastically generated scenarios (see below). This helps ensure the portfolio is not overly reliant on just the median return. This process seeks to produce a portfolio that is robust to small changes in the central return estimates.

  • 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.

  • Interest rates

    The CMA 2.0 Rates model provides a way to map 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 macro 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 spread at a given horizon and 2) our estimated 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 transition matrix which captures 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 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 Preqindata to look at feet 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.

References

Bernanke, S.Ben, Boivin, Jean, EliaszPiotr, 2005, “Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach.”

Black, F. and Litterman, R., 1992. Global portfolio optimization. Financial Analysts Journal, pages 28–43.

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.

Fischer Black and Robert Litterman. Asset allocation: Combining investor views with market equilibrium. Fixed income research, Goldman Sachs, September 1990.

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

Garlappi, Lorenzo, Raman Uppal, and Tan Wang. “Portfolio selection with parameter and model uncertainty: A multi-prior approach.” The Review of Financial Studies 20, no. 1 (2006): 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.

Michaud, R., 2004.

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.