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Portfolio perspectivesUS dollar | April 2019

BLACKROCK INVESTMENT

INSTITUTE

We update our capital market assumptions (CMAs) and apply our portfolio construction framework to our strategicasset allocation views. In our latest Portfolio Perspectives, we explain how we incorporate uncertainty in our returnestimates. We also introduce strategic asset views for euro- and sterling-based investors. This is a rst step towardslaunching new strategic asset allocation reference portfolios for specic investor types in coming months.

Our strategic views convey our asset class preferences for unconstrained, long-only investors over a 10-year horizonrelative to our long-term equilibrium portfolio. The views are based on our risk and return expectations and,importantly, uncertainty aversion. We favour a barbell strategy comprising equities and government bonds. Withinequities, we generally prefer emerging market (EM) and developed market (DM) ex�US equities to the US. Ourbiggest tilt is towards global government bonds. High valuations and late-cycle concerns tilt us away from globalcredit. We focus on downside protection as we account for uncertainty. This drives a more negative tilt to privatemarket income assets than we would have if the economic expansion was not in a late stage. Yet our long-termallocation to private markets remains sizeable. We have similar strategic views in our newly introduced euro- andsterling-based unconstrained portfolios.

Our returns expectations, updated with data to end�February, have broadly fallen compared to our previous updatefour months earlier, largely due to the hefty rebound in risk assets since then. Equities – particularly EM equities –have jumped higher just as earnings growth estimates have eroded, making starting valuations richer. Bond yieldshave also fallen across most major markets, resulting in lower xed income return expectations. These returnexpectations feed into our strategic views.

The incorporation of uncertainty is an important part of our revamped approach to portfolio construction. Byincorporating uncertainty we recognise that mean expected returns for assets are estimated with error rather thanassuming they are known, as is the case with mean variance techniques. We consider the distribution around themean, effectively reducing the weight placed on our mean (central) estimate. How much uncertainty should we takeinto account? We highlight some criteria used to identify a suitable amount of uncertainty in our process. Theyinclude the back-tested predictive power of our asset class return models, the historic volatility of assets and thedesire for diverse portfolios when optimising. Uncertainty in mean returns feeds in to our stochastic simulations, thatgive a range of potential return pathways from ve years out to the long term. When constructing portfolios, thesesimulated pathways and our mean return uncertainty enable us to use robust optimisation techniques that generallylead to less concentrated portfolios compared with those portfolios resulting from mean variance optimisation. Italso gives exibility to focus on certain upside or downside scenarios when constructing portfolios to t client needs.

AuthorsPhilipp HildebrandBlackRockVice Chairman

Anthony ChanPortfolio Research

Paul HendersonPortfolio Research

Jean BoivinGlobal Head of ResearchBlackRock InvestmentInstitute

Vivek PaulPortfolio Research

Misha van BeekFinancial Modeling Group

ContentsStrategic views 2

Capital market assumptions 3

Uncertainty 4�5

From theory to practice 6

Assumptions at a glance 7

ContributorsNatalie GillMichael Palframan

Christian OlingerLaszlo Antal

Strategic views

Our strategic views lay out our asset class preferences based on our assessment of the market environment over the comingyears. We express these views as tilts relative to a long-term equilibrium, or “neutral”, portfolio constructed using cycle-agnosticreturn expectations. Our strategic tilts are derived from our latest capital market assumptions and our recently modernisedportfolio construction framework incorporating multiple return pathways and our views on the uncertainty embedded in thereturn estimates. The strategic views do not account for implementation considerations or practical limitations some investorsmay face. In coming quarters, we will publish representative strategic asset allocation portfolios for different client types thatwill account for typical client objectives and constraints.

Interpreting the strategic views

We apply our portfolio construction framework to generate our strategic asset allocation tilts on a 10-year horizon relative tothe long-term equilibrium portfolio. The chart summarises our current tilts. This is from the perspective of a local currencyinvestor targeting around 7% volatility on an asset-only basis with few constraints.

We believe the US-led expansion is entering a late-cycle stage, prompting a more cautious stance due to elevated valuations inmany risk assets. We nd a barbell strategy tilting towards non�US equities and global government bonds more appealing thancredit. Public credit assets have higher mean expected returns than government bonds, yet we believe this marginal returncomes with greater vulnerability to late-cycle dynamics and provides lower portfolio diversication. The signicant interest ratedifferential between the US and other developed markets contributes to the appeal of holding non�US equities on an FX hedgedbasis. Emerging market (EM) equities appear attractive given their strong earnings growth potential and lower valuation dragthan some developed markets, such as the US.

Our overall allocation to private markets remains sizable and similar to our December update, with a tilt away from incomeassets. Private credit in particular faces similar late-cycle pitfalls to public credit, driving our relative underweight on privatemarket income assets on a 10-year horizon. The allocation would likely be higher if we were in an earlier stage of the economicexpansion. Higher aversion to uncertainty stemming from a lower conviction on our private credit expected returns than otherassets given the asset class' relatively short history also drives our underweight. It is important to note that our long-runallocation to private markets has actually risen from our last update. As discussed in our March 2019 paper — The core role ofprivate markets in modern portfolios– many investors have a greater capacity for holding private assets that they may think.Unlike public markets that can be accessed via index products, many private markets are only available through activemanagers. To account for this, we also assume a return from alpha generation when designing our strategic tilts. The size ofalpha potential depends on the investors’ ability to select good managers. The return potential – beta and alpha, is oneconsideration when sizing a private markets allocation in a portfolio.

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: The pie chart shows the breakdown of our 10-year hypothetical asset allocation for an unconstrained US dollar-based portfolio. The bar chart shows how this portfoliodeviates from our long-term allocation in percentage points. The strategic tilts are rounded to the nearest +/�2.5%, as a result they may not sum to zero. Income privatemarkets include infrastructure debt, direct lending, real estate mezzanine debt and US core real estate. Growth private markets include US private equity buyouts andinfrastructure equity. The hypothetical portfolio may differ from those in other jurisdictions, is intended for information purposes only and does not constitute investmentadvice.

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Page 2

10-year strategic asset allocationUS hypothetical unconstrained 10-year allocation

GlobalGlobalgovernmentsgovernmentsGlobalgovernments

Global IGGlobal IGcreditcreditGlobal IGcredit

DM high yieldDM high yieldand EM debtand EM debtDM high yieldand EM debt

DM equityDM equityDM equity

EM equityEM equityEM equity

IncomeIncomeprivateprivatemarketsmarkets

Incomeprivatemarkets

GrowthGrowthprivateprivatemarketsmarkets

Growthprivatemarkets

Bonds Equities Private markets

56%24%

20%

Strategic tilt10-year strategic allocation vs. our equilibriumview

10.0%10.0%10.0%

2.5%2.5%2.5%

2.5%2.5%2.5%

0.0%0.0%0.0%

�2.5%�2.5%�2.5%

�5.0%�5.0%�5.0%

�7.5%�7.5%�7.5%

Global governmentbonds

DM equity

EM equity

Growth privatemarkets

DM high yield andEM debt

Global IG credit

Income privatemarkets

Underweight           Overweight

Capital market assumptions

We estimate ve-year mean expected returns using asset class models. We have updated these models using data to endFebruary. Financial markets have had a bumpy run since our last CMA update at the end of October 2018. Risk assets tumbledin the fourth quarter of 2018 and then swiftly clawed back losses in the rst quarter of 2019. Equities have bounced sharplysince, particularly in EM, corporate credit spreads have shrunk and government bond yields have fallen signicantly on a softergrowth outlook despite the risk asset recovery. The market’s moves have resulted in several mean return expectations fallingfrom our last update.

Equities: Prices have risen alongside falling analyst earnings growth estimates, cutting the contributions from earnings andvaluations to our overall return expectations. This is particularly true in EM equities where our mean return expectationshave fallen most.

Government bonds: DM government bond yields have fallen across the board. We expect DM yields to modestly rise over ave-year horizon, yet to lower levels than observed historically. Recent market moves result in our expected returns fallingdue to higher starting valuations and lower expected income. Our return expectations for EM local and hard currencysovereign debt fell the most among government bonds.

Credit: Spreads on investment grade and high yield bonds were largely at since our last update. Lower government bondyields have dampened expected returns for credit assets.

Private markets: We estimate major private markets returns using data and models that capture dynamics in each specicmarket (further detail on our return models can be found in our private markets paper). In US core real estate, updated datapointed to more expensive starting valuations, cutting our expected returns.

Return time period (years)  

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: Return assumptions are total nominal returns. US dollar return expectations for all asset classes are shown in unhedged terms, with the exception of global ex�USTreasuries, hedge funds, and global ex�US large cap equities. Our CMAs generate market, or beta, geometric return expectations. Asset return expectations are gross of fees.For a list of indices used, visit our Capital Market Assumptions website at blackrock.com/institutions/en-us/insights/portfolio-design/capital-market-assumptions and clickon the information icon in the Asset class return and volatility expectations table. We use BlackRock proxies for selected private markets because of lack of sufcient data.These proxies represent the mix of risk factor exposures that we believe represents the economic sensitivity of the given asset class. There are two sets of bands around ourmean return expectation. The darker bands show our estimates of uncertainty in our mean return estimates. The lighter bands are based on the 25th and 75th percentile ofexpected return outcomes – the interquartile range - of potential return pathways (Garlappi, Wang and Uppal, 2006 ). Indices are unmanaged and used for illustrativepurposes only. They are not intended to be indicative of any fund or strategy’s performance. It is not possible to invest directly in an index.

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Understanding uncertainty

Most people would be suspicious of any meteorologist claiming precision about the weather next week or even just tomorrow.Meteorology has advanced over the years, yet any realistic weather forecast acknowledges an element of uncertainty, andprobability, to avoid giving a false sense of security.

We nd a parallel to the process of estimating asset returns and building portfolios. We can analyse historical data, studycorrelations and build models to create forward-looking views. Yet claiming the long-run mean is known with certainty would befoolish. In this Portfolio perspectives, we take a deeper look into how we incorporate uncertainty in our portfolio constructionprocess by introducing mean return uncertainty — a distribution of values for the mean expected return for each asset class.

Distinguishing between uncertainty and risk is important. We dene uncertainty as the range of outcomes for the mean andrisk as the range of outcomes around the mean. For example, instead of saying an asset has a mean return of 6%, we say amean return in range of 5�7% even if the risk, or volatility, of the asset stays the same. We believe overlooking uncertainty,combined with common mean variance optimisation (MVO) techniques, can lead to undesirable results such as unstable oroverly concentrated asset allocations without the use of ad-hoc constraints.

Our approach does two important things: First, it acknowledges that we should not place full conviction on a specic value forthe mean expected return. Instead, we allow for other potential return pathways where the mean expected return is different.Observing market data over the last 20 years only gives us information on one state of the world, or one regime. We cannot baseour expected returns only on historic observation as future regimes can differ from the past, resulting in structural changes tothe mean return. Second, our uncertainty varies by asset class. Why is this important? A lower ability to estimate returns for oneasset class (for example when an asset’s returns are poorly explained by well-known public market factors) should be reectedwith a wider uncertainty range, all else equal. For two assets of the same mean risk and return, we would hold less of the assetwhere we have greater uncertainty in the mean return assuming investors are averse to uncertainty.

Our expected returns with uncertainty are illustrated in the Banding together chart below. The central path of mean returns isinformed by our long-run expectations of macro factors and the ve-year return estimates derived from our asset class models.Using a Monte Carlo simulation we generate thousands of potential return pathways centered around a distribution of meanpathways. The lighter shaded areas show an interquartile range between the 25th and 75th percentile of these return pathways.

The difference in the size of mean return uncertainty between government bonds and EM debt in the chart below comes downto facets of each asset class. Well-understood factors can explain much of the returns from US government bonds but less sofor EM debt. EM debt also has higher volatility than US government bonds. Together, these dampen our conviction in the meanpath of returns for EM debt relative to US government bonds.

Banding togetherMean return uncertainty and return pathways on a 5� to 25-year horizon

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: Return assumptions are total nominal returns. US dollar return expectations for all asset classes are shown in unhedged terms, with the exception of global ex�USTreasuries, hedge funds, and global ex�US large cap equities. Our CMAs generate market, or beta, geometric return expectations. Asset return expectations are gross of fees.For a list of indices used, visit our Capital Market Assumptions website at blackrock.com/institutions/en-us/insights/portfolio-design/capital-market-assumptions and clickon the information icon in the Asset class return and volatility expectations table. We use BlackRock proxies for selected private markets because of lack of sufcient data.These proxies represent the mix of risk factor exposures that we believe represents the economic sensitivity of the given asset class. There are two sets of bands around ourmean return expectation. The darker bands show our estimates of uncertainty in our mean return estimates. The lighter bands are based on the 25th and 75th percentile ofexpected return outcomes – the interquartile range - of potential return pathways (Garlappi, Wang and Uppal, 2006 ). Indices are unmanaged and used for illustrativepurposes only. They are not intended to be indicative of any fund or strategy’s performance. It is not possible to invest directly in an index.

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Years

Exp

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US government bonds (all maturities)US government bonds (all maturities)

US government bonds (all maturities)Mean return uncertaintyInterquartile range

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USD EM debtUSD EM debt

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7.5

10%

Should uncertainty converge over time?

An assumption still commonly held in traditional portfolio construction techniques is that simulated asset returns (on anannualised cumulative basis) converge to a single point — the mean return — in the long-run. Our belief that there is meanreturn uncertainty leads to a different conclusion. The chart Questioning convergence compares two return simulations for USequities — one where there is no mean return uncertainty (the left chart) and one where we assume mean return uncertainty(the right chart).

Simulated returns with no uncertaintyWith no uncertainty in the mean return, we see only a smaller, light yellow range. The range reects the inherent risk of assetreturns, or the standard deviation. It falls through time before eventually converging to the mean return estimate, or 7.5%, inthis example. This will be a familiar picture to many. Simulated returns may have a forward-looking estimate of mean returnsbut assume no uncertainty in that mean. Importantly, the range of outcomes (the light yellow) is often based only on historicvolatility.

Simulated returns with uncertaintyWith uncertainty added, we see both a dark and light yellow region. The range of outcomes does not and should never vanish tozero in theory and we see that the dark yellow region is persistent. Why? Collapsing to zero would require that the long-termexpected return is known exactly (7.5% in our example). Instead of converging to a single point, the range of outcomesconverges to the dark yellow band of uncertainty around our mean estimate at a horizon beyond 50 years. Another importantconsequence of adding uncertainty? The range of potential return pathways is wider than the range excluding uncertainty. Thisis because we now allow for more than just a single possible path of mean expected returns.

Questioning convergenceMedium to long-term return pathways and mean return estimates for US large-cap equities

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: The line shows our mean (central) return estimate for large-cap US equities (MSCIUSA) on a ve-year to long-term horizon. Assumptions are total return and in US dollars. The interquartile range in light yellow shows the potential return pathways betweenthe 25th and 75th percentiles generated by our stochastic simulation. The chart on the left shows this range based on the historical volatility of the asset class alone. Thechart on the right includes the mean return uncertainty in orange. We discuss the factors determining the size of the mean return uncertainty above. Indices are notintended to be indicative of any fund or strategy’s performance. It is not possible to invest directly in an index.

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Years

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From theory to practice

The right amount of uncertainty

Incorporating uncertainty brings the portfolio construction process closer to real-world investing experience: reality can strayconsiderably from any central expectation and the central expectation can be wrong, so we do not want to place all our bets ongetting this right. How much uncertainty should we consider in return estimates? There is no single answer but several criteriaguided our decision. We highlight the following:

1. Uncertainty and return simulations The range of potential return pathways widens when including mean return uncertaintyas shown in the Questioning convergence chart. But the bands representing the mean return uncertainty should be narrowerthan this full range of pathways including the historical volatility of an asset. This is intuitive: We would expect the range of dailystock prices to be larger than the range of the rolling average of those stock prices. This determines the relative scale of theshaded areas on the chart – the dark area should be narrower than the light.

2. Uncertainty and our cyclical views We use asset class models, that capture the cyclical economic and market dynamics, toestimate returns at the ve-year horizon. These views inform the ve-year mean expected return in our simulations. At thishorizon, the uncertainty band should be in line with the predictive power of our asset class models. In other words, we cannothave greater conviction in our ve-year expected returns than is warranted by the back-testing of our asset class models.

3. Uncertainty and diversication A portfolio construction framework incorporating uncertainty should deliver a solution thatis between the MVO solution, that places full conviction on mean returns, and the minimum-variance solution, that places noconviction in mean returns. Too little uncertainty and an optimisation will retrieve something close to mean-variance portfolio —typically very concentrated and requiring constraints. Too much uncertainty and an optimisation will retrieve something closeto a minimum-variance solution that prioritises diversication only. We are looking for a sensible middle ground.

Portfolio implications

A key benet, in our view, of incorporating uncertainty is that we can allow for different conviction levels in return expectations.Suppose an investor is choosing between two assets with the same expected return, and similar risk, the condence inestimating the return is likely the deciding factor in making a choice between the two. We believe that the preferred assetshould be the one where the investor has the higher conviction – and this preference should intensify as an investor’s aversionto uncertainty grows.

A large part of the uncertainty around our mean return estimates comes from the quality and availability of data that feeds intoour models for various asset classes. Our conviction levels in our estimates for public assets – especially xed income andequities – with a long history of granular, observable data are higher than those for certain private market assets where theabsence of indices and benchmarks requires the use of proxies.

To design portfolios or to derive our strategic views, we use our simulated return expectations in a robust optimisation. Wefollow a methodology that identies the portfolio that performs ‘least badly’ over a range of scenarios, it is thefore robust toerrors where we have over-estimated returns. Robust optimisation generally requires fewer constraints to retrieve a diverseportfolio than MVO. This approach also brings greater exibility; we can specify a level of uncertainty aversion that ts aparticular investor. For example, when designing our strategic views (page 2) we specify a level of uncertainty aversion thatfocuses on downside scenarios and is betting our current views on the cycle.

Our bottom line: Uncertainty in expected returns is well-known but often overlooked. Tackling uncertainty is hard. We believe wehave developed a systematic way of explicitly allowing for uncertainty in our CMAs that ultimately leads to more resilient andinvestor-specic portfolios.

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Assumptions at a glance

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: Return assumptions are total nominal returns. US dollar return expectations for all asset classes are shown in unhedged terms, with the exception of global ex�USTreasuries, hedge funds, and global ex�US large cap equities. Our CMAs generate market, or beta, geometric return expectations. Asset return expectations are gross of fees.Forecasted future performance is not a reliable indicator of future results. We use long-term volatility and correlation expectations. We break down each asset class intofactor exposures and analyse those factors' historical volatilities and correlations over the past 18 years. Correlations with global equities and bonds are based on globalmeasures excluding domestic equities and bonds. We combine the historical volatilities with the current factor makeup of each asset class to arrive at our assumptions. Thisapproach takes into account how asset classes evolve over time. Example: Some xed income indices are of shorter or longer duration than they were in the past. Ourexpectations reect these changes, whereas a volatility calculation based only on historical monthly index returns would fail to capture the shifts. Indices are unmanagedand used for illustrative purposes only. They are not intended to be indicative of any fund or strategy’s performance. It is not possible to invest directly in an index. For a list ofindices used, visit our Capital Market Assumptions website at blackrock.com/institutions/en-ch/insights/portfolio-design/ capital-market-assumptions and click on theinformation icon in the Assumptions at a glance table.

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Asset

Return expectations (geometric, gross of fees) Long-term

expectedvolatility

Long-term correlation

5�year

10�year

15�year

20�year

Globalequities

Global governmentbonds

US government (10+ years) 0.9% 1.3% 1.7% 2.0% 14.0% �30% 76%

Global ex�US government bonds 2.1% 2.3% 2.6% 2.7% 3.3% �21% 100%

US ination-linked governmentbonds

2.2% 2.4% 2.6% 2.7% 5.6% 1% 49%

US government bonds (allmaturities)

2.4% 2.5% 2.6% 2.6% 4.8% �38% 77%

US credit (10+ years) 2.6% 3.4% 4.3% 4.9% 12.1% 28% 53%

US aggregate bonds 2.7% 2.9% 3.0% 3.1% 4.3% �15% 75%

US agency MBS 2.8% 2.9% 2.9% 3.0% 3.5% �25% 69%

US credit (all maturities) 2.9% 3.3% 3.7% 3.9% 5.8% 21% 58%

Local currency EM debt 3.4% 3.4% 3.5% 3.5% 12.4% 54% 8%

USD EM debt 3.8% 4.2% 4.6% 4.8% 9.4% 40% 33%

US high yield 5.2% 5.3% 5.3% 5.4% 8.2% 63% �6%

US large cap equities 5.4% 5.9% 6.5% 6.9% 16.1% 88% �19%

US small cap equities 5.9% 6.4% 6.9% 7.2% 18.5% 87% �19%

Emerging large cap equities 6.7% 6.7% 6.6% 6.6% 22.7% 81% �13%

Europe large cap equities 7.8% 7.9% 8.1% 8.1% 18.9% 89% �14%

Developed infrastructure debt 3.0% 3.6% 4.1% 4.4% 8.6% 28% 45%

US core real estate 4.9% 5.2% 5.4% 5.6% 14.7% 43% 6%

Hedge funds (global) 6.4% 6.6% 6.7% 6.8% 7.6% 82% �26%

Global direct lending 8.3% 8.6% 8.8% 9.0% 13.8% 78% �20%

US private equity (buyout) 13.0% 13.3% 13.5% 13.7% 30.1% 80% �24%

BlackRock Investment Institute

The BlackRock Investment Institute (BII) provides connectivity between BlackRock’s portfolio managers, originates economic,markets and portfolio construction research, and publishes investment insights. Our goals are to help our portfolio managersbecome even better investors and to produce thought-provoking investment content for clients and policymakers

BlackRock’s Long�Term Capital Market Assumption Disclosures: This information is not intended as a recommendation to invest in any particular asset class or strategy or product or asa promise of future performance. Note that these asset class assumptions are passive, and do not consider the impact of active management. All estimates in this document are in USdollar terms unless noted otherwise. Given the complex risk-reward trade-offs involved, we advise clients to rely on their own judgment as well as quantitative optimisation approaches insetting strategic allocations to all the asset classes and strategies. References to future returns are not promises or even estimates of actual returns a client portfolio may achieve.Assumptions, opinions and estimates are provided for illustrative purposes only. They should not be relied upon as recommendations to buy or sell securities. Forecasts of nancialmarket trends that are based on current market conditions constitute our judgment and are subject to change without notice. We believe the information provided here is reliable, but donot warrant its accuracy or completeness. If the reader chooses to rely on the information, it is at its own risk. This material has been prepared for information purposes only and is notintended to provide, and should not be relied on for, accounting, legal, or tax advice. The outputs of the assumptions are provided for illustration purposes only and are subject tosignicant limitations. “Expected” return estimates are subject to uncertainty and error. Expected returns for each asset class can be conditional on economic scenarios; in the event aparticular scenario comes to pass, actual returns could be signicantly higher or lower than forecasted. Because of the inherent limitations of all models, potential investors should notrely exclusively on the model when making an investment decision. The model cannot account for the impact that economic, market, and other factors may have on the implementationand ongoing management of an actual investment portfolio. Unlike actual portfolio outcomes, the model outcomes do not reect actual trading, liquidity constraints, fees, expenses,taxes and other factors that could impact future returns.

Index Disclosures: Index returns are for illustrative purposes only and do not represent any actual fund performance. Index performance returns do not reect any management fees,transaction costs or expenses. Indices are unmanaged and one cannot invest directly in an index.

General Disclosure: This material is prepared by BlackRock and is not intended to be relied upon as a forecast, research or investment advice, and is not a recommendation, offer orsolicitation to buy or sell any securities or to adopt any investment strategy. The opinions expressed are as of April 2019 and may change as subsequent conditions vary. The informationand opinions contained in this material are derived from proprietary and nonproprietary sources deemed by BlackRock to be reliable, are not necessarily all-inclusive and are notguaranteed as to accuracy. As such, no warranty of accuracy or reliability is given and no responsibility arising in any other way for errors and omissions (including responsibility to anyperson by reason of negligence) is accepted by BlackRock, its ofcers, employees or agents. This material may contain ’forward looking’ information that is not purely historical in nature.Such information may include, among other things, projections and forecasts. 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