Capital market assumptions
We introduce a modernised framework for the use of capital market assumptions in strategic asset allocation. This framework delivers an enhanced toolkit that allows investors to build resilient portfolios by bringing together the research and expertise from teams across BlackRock. We have revamped our capital market assumptions by designing a suite of models that explicitly reflect cross-asset and macroeconomic linkages. These models are the key input to inform our views on returns across asset classes, including new assumptions for private markets and equity style factors.
To build resilient portfolios, we have developed a few necessary building blocks. Central return assumptions are not enough on their own. We need to assess and account for the uncertainty around these return assumptions. To do so, we need to see how financial markets may evolve in different scenarios by looking at multiple potential return pathways – especially adverse ones. These pathways – from five years onwards – then allows us to tailor portfolios to specific points in time. With these elements in hand, we can fortify portfolios against potential losses through robust optimisation. Incorporating views on private markets and equity style factors add another layer to asset allocation, incorporating returns that go beyond traditional asset classes.
Our 10-year strategic asset allocation preferences are derived from bringing these building blocks together. Our strategic preferences include a specific desire to minimise potential losses from risks such as a recession through more diverse portfolios. We currently favour emerging market (EM) equities and government bonds at the expense of credit. This reflects favourable valuations in EM equities and our outlook for structurally low interest rates. Yet this also due to credit’s lack of return compensation relative to the diversification benefits of government bonds. Our strategic preferences are also shaped by our view that we are late in the US-led economic cycle. That is one reason why we have negative allocation tilts on US equities and credit assets. Yet our revamped approach to private markets leads to a higher overall allocation compared with our previous framework.
Asset return expectations and uncertainty
The chart below shows our annualised median return expectations (dots) across asset classes. The bars show uncertainty bands around those expectations. The bands are based on the 25th to 75th percentile of expected outcomes. 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
On equities, we currently prefer EM over 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 in that negative correlation (equities up and bonds down), we expect the correlation 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. We believe private markets can offer greater potential alpha reward available if investors can select good managers. We use the principles underpinning our alpha, factors and indexing framework to help identify allocations to private equity where we see the most evidence of systematically higher alpha potential relative to public market equivalents if good managers are chosen. The strategic tilt to private market growth assets is broadly neutral.
Expected returns by horizon
Our prior CMAs expressed return expectations as two point estimates –a five-year and a long-term view. We introduce two important changes. First, for any given time horizon, we generate a range of pathways around a central return expectation. Second, we map out asset class returns along a full spectrum from five years onwards, allowing investors to build time horizon-specific portfolios. Use the chart below to compare different assets.
The chart below shows our annualised median return expectations (central line) across different time horizons. The shaded areas show uncertainty bands through time around those expectations. The bands are based on the 25th to 75th percentile of expected outcomes. For more details see the methodology tab above.
Asset class return and volatility expectations
Assumptions at a glance
(geometric, gross of fees)
|5-year||10-year||15-year||20-year||Global equities||Global government bonds|
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.
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.
Fixed income assumptions:
Our five-year return assumptions for fixed income assets have five components that are shown in the chart below:
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.
Our five-year return assumptions for equities have five components that are shown in the chart below:
Variability in equity returns can be explained by cash flow news, and discount rate news (Campbell, J. Y., 1990). We have used this insight to develop a discounted cash flow (DCF) model, with a couple of key innovative features. Most academic research is focused on the question of whether stock returns are predictable at all. We are concerned with making the best estimates that we can. We make two additional contributions. First, the baseline DCFmodel estimates earnings by leveraging analyst earnings estimates in the near term in a similar way to that used by academic researchers in the implied cost of capital (Li, Y., Ng, D. T. and Swaminathan, B. 2013). The common assumption in implied cost of capital (ICC) studies is that earnings growth implied by analyst earnings estimates in the near term should trend towards GDP growth in the long-term. This can introduce an unintended assumption of continued margin expansion. We have introduced a modification to account for late economic cycle dynamics, and especially to allow for margin reversion near peaks of the margin cycle. ICC literature typically tests for equity returns using linear regression tests. We instead arrive at equity return expectations over specific horizons by solving the DCFmodel explicitly over the desired horizon, and estimating equity prices at the end of the horizon by modelling time variation in the discount rate (estimated from the aggregate implied cost of capital). This way, we account for both key sources of variability in equity returns,. namely changes in cash flows and changes in the discount rate.
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.
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.
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