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Smart Beta Strategies as Outcome-Oriented Solutions in the Equity SpaceURSULA MARCHIONI, SOFIA ANTROPOVA, AND CATHERINE MCNAUGHT

THE JOURNAL OF INDEX INVESTING SUMMER 2015

Smart Beta Strategies as Outcome-Oriented Solutions in the Equity SpaceURSULA MARCHIONI, SOFIA ANTROPOVA, AND CATHERINE MCNAUGHT

URSULA MARCHIONI

is a director and head of iShares EMEA Equity Strategy & ETP Research at BlackRock in London, [email protected]

SOFIA ANTROPOVA

is a director and invest-ment strategist at Black-Rock in London, [email protected]

CATHERINE MCNAUGHT

is vice president and investment strategist at BlackRock in London, [email protected]

The growth of index investing has been one of the key trends in f inancial markets over recent decades. Since the launch of the

first index fund in the early 1970s, indexing has expanded to account globally for over 5.3 trillion USD1 of assets under manage-ment (AUM) in retail funds and exchange-traded funds (ETFs) alone. In the U.S., index mutual funds and exchange-traded products (ETPs)—of which ETFs are the largest cate-gory—boast a total of almost 4 trillion USD in AUM, or 28% of the retail asset manage-ment industry. In Europe, this category of assets has almost tripled over the last 10 years, rising from 4.5% of total AUM in 2004 to 12.6% in 2014, or 952 billion USD.2

This stellar growth in index investing can be linked to several drivers. The f irst driver is the breadth of benefits delivered by these strategies to investors. Index investing may reduce portfolio risk through diversifi-cation, as indices are made up of a broad set of individual securities; turnover and trans-action costs can be controlled by embedding dedicated pre-defined rules within the index; index strategies can be efficiently included within portfolio risk-management frame-works, as their risk and return drivers are comparatively easy to model and monitor.

A second driver behind the growth in index investing is the breadth of appli-cations for indices. Indices are nowadays

broadly used as a gauge for market sentiment, allowing for a measure of a specific market or segment behavior; they can be used as a tool for performance measurement—helping investors to measure the “average” risk/return achieved by a “market,” and to assess the outperformance of an active strategy. Furthermore, by representing key drivers of investment returns—through style, size, sector, and country indices—indices can help investors achieve a more focused asset allocation strategy, as portfolio performance can be properly attributed to the different underlying return drivers.

A third driver—and the main focus of this article—lies in the ever-increasing range of available index strategies and in the capacity these solutions have to evolve and adapt to varying market conditions and investor needs. From the creation of the first index, a price-weighted instrument to mea-sure the performance of the U.S. railway sector introduced by Dow and Jones in 1884,3 we can now identify more than one million indices available today in the equity space.4 As this number indicates, the concept of index investing is continuously expanding, as the evolution of financial markets and that of providers’ research allows for new applica-tions for investors.

Traditional beta is often identified with a market capitalization-weighted approach to equity investing; which has its foundations

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SMART BETA STRATEGIES AS OUTCOME-ORIENTED SOLUTIONS IN THE EQUITY SPACE SUMMER 2015

in the capital asset pricing model (CAPM) and whereby index constituent weights are calculated as the product of freely traded shares times their current market price. While arguably the majority of the assets invested in index strategies remain in this type of beta portfolios, we are witnessing an ever-growing interest in new ways of indexing that go beyond market-capitalization-weighted solutions. These strategies are often collec-tively referred as smart beta. While differing greatly across many aspects, these strategies share the aim of identifying, describing, and providing exposure to dif-ferent sources of returns beyond those provided by tra-ditional market-capitalization-weighted indices.

One of the most fascinating aspects of this expan-sion of the definition of beta lies in the fact that smart beta strategies offer a passive access to risk and return drivers which have historically been associated with active investing alone. The notion of a continuum of beta, which ranges from traditional beta to pure alpha (with smart beta strategies falling in between) is increas-ingly replacing the idea of a barbell approach to alpha and beta.

In the following sections, we focus on traditional beta and on the middle ground of this beta continuum, by providing a sample review of market-capitalization-weighted and smart beta strategies in the equity space. We look at their respective growth, specifically in the ETP space, and at their risk/return characteristics, to understand if there is any merit in implying that certain solutions are more clever than others, and can therefore deliver consistent outperformance.

A BRIEF HISTORY OF THE EXPANSION OF THE BETA CONTINUUM IN EQUITY SPACE

The idea of combining stocks to accurately rep-resent the risk and return drivers of a given market or exposure based on weights other than the market capi-talization of the companies themselves is very old; the first index ever created was not actually a market-cap-italization-weighted one. Nevertheless, today market-capitalization indices still represent the lion’s share of index investing: the near totality of indices used as a market gauge is represented by market-capitalization indices5 and the majority of the assets invested in index strategies is linked to market-capitalization-weighted benchmarks.

This status quo was initially driven by the benefits of market-capitalization-weighted strategies.6 In addi-tion, as market capitalization strategies became more popular, more products appeared in this space, more investors bought into them, and more portfolios were built with a risk framework tailored to this specific type of beta. This ultimately led several investors to identify beta with market-capitalization-weighted indices. For the broad investing public, many alternative beta strat-egies were generally neglected, despite being heavily used by a more restricted group of highly sophisticated investors, or indeed by active managers.

During the last 10 years, however, this situation has evolved as we have witnessed a significant interest by the press and by the broader investment community in non-cap-weighted forms of index investing—also known as smart beta strategies. A good proxy to measure the growth in the category can be found in the history and evolution of the ETP industry—which is relatively easy to measure and report on.

The f irst two so-called smart beta ETFs were launched in the U.S. in 2005;7 since then, we have witnessed a strong wave of issuance of U.S. domiciled smart beta ETFs, which now dominate the offering within the global smart beta ETF universe. The first three European-domiciled smart beta ETFs were also launched in 2005, offering exposure to dividend focused local-exposure benchmarks; these ETFs have been very successful, having now reached a total of over 1.7 bil-lion USD in AUM8 and several solutions are available in Undertakings for Collective Investments in Trans-ferable Securities (UCITS) format across fundamen-tally weighted, single-factor, multi-factor, and equally weighted strategies. Globally, AUM allocated to smart beta ETPs have steadily increased over the last five years, from accounting for 6% of the total equity ETP AUM in 2009 to accounting for 11% now.9 As measured by the cumulative annualized growth rate (CAGR),10 tra-ditional beta ETP AUM have grown at a rate of 14%, while smart beta ETPs have had a substantially stronger growth rate of 37%.

Looking more closely at the popularity of different strategies, dividend-weighted ETFs11 currently represent the largest portion of the equity smart ETP space in terms of AUM, both globally and in Europe: Four out of f ive of the largest smart beta global ETFs provide exposure to high dividend stocks, accounting for over



60 billion USD in AUM;12 in Europe, we observe an identical picture, with ETFs tracking dividend strategies and accounting to over 5.5 billion USD13 in AUM.

Minimum-volatility, equally weighted, and multi-factor exposures are the next most popular smart beta solutions delivered in ETP format. Specif ically, after the launch of the first minimum-volatility ETF in 2011, these trackers now account for over 12% of the smart beta equity ETP AUM, globally.14

Historical ETP f lows illustrate the continued growth over the last five years. The trend continues in 2015, with YTD ETP f lows into global smart beta ETPs contributing to 28% of the total equity ETP f lows so far—while only representing 11% of the total assets.15

Based on this growth, a series of questions is cur-rently under discussion among investors, academics, and market practitioners. Are smart beta solutions superior to traditional beta, as the adjective “smart” implies? Should investors consider substituting a signif icant portion

E X H I B I T 1Global Equity ETPs AUM: Traditional and Smart Beta

Source: BlackRock ETP Landscape. As of the end of Mar-2015. AUM for the two categories are measured in USD. Figures in brackets represent the compounded annual growth rate (CAGR) of the assets since the end of 2009.

E X H I B I T 2Global Equity ETP Flows: Traditional and Smart Beta

ETPs have seen continued growth over the last 5 years, with global smart beta f lows growing faster in 2015 YTD.

Source: BlackRock ETP Landscape. As of the end of Mar-2015. Flows for the two categories are measured in USD.



of their allocation to traditional beta with smart beta solutions?

While there is no doubt that smart beta solutions will continue to grow, we believe that the key to their success within portfolios lies in an outcome-oriented approach, which we outline in the next two sections.

TRADITIONAL AND SMART BETA IN THE EQUITY SPACE: BENEFITS AND CONSTRAINTS

Non-cap-weighted index strategies deviate from traditional beta by definition. Regardless of the meth-odology, any alternatively weighted index will include explicit or implicit biases towards specific risk factors or themes. These exposures—intended or otherwise—will drive any outperformance or underperformance relative to a standard index. Understanding the nature and per-sistence of these biases is therefore a critical component of smart beta investing. In this section, we will review a sample of equity beta strategies—highlighting their fun-damental characteristics (benefits and limitations) and examining different scenarios that may lead to superior or inferior performance.

Traditional, or Market-Capitalization-Weighted, Beta Strategies

Traditional market-cap-weighted index strategies are based on a set of rules whereby security weights are calculated on the basis of a number of its freely traded shares and the current market price. A cap-weighted index is weighted by company size; larger stocks have a higher weight than do smaller stocks.

There are multiple benef its offered by cap-weighted index strategies. These indices are essentially self-rebalancing: when the price of a stock moves, so does its market capitalization, resulting in low turnover and transaction costs. Furthermore, because larger com-panies are more heavily weighted by definition, these strategies tend to be highly liquid and generally easily replicable in real-world portfolios. Finally, by defini-tion, these strategies are a very effective means for inves-tors to participate in rallying markets. This last benefit nevertheless also coincides with the main limitation of market-capitalization-weighted strategies as they can be more exposed to price bubbles.

In Exhibits 3 and 4, we look at a well-known market-capitalization-weighted index: the S&P 500 total return gross index (SP500)—during the dot-com “bubble” of the early 2000s. We compare this index with perhaps the most straightforward smart beta strategy, the S&P 500 equally weighted total return gross index (S&P 500 EW).16

Technology companies rallied significantly from late 1996 to Dec-1999, many of which were notoriously overvalued by the end of the 1990s. During this period, the S&P 500 outperformed the S&P 500 EW index by over 10% p.a.—hence representing a “smart” choice for investors during the period. The same choice over the period from Mar-2000 to Sep-2002 nevertheless led to a very different result: when the dot-com bubble burst, its heavy overweight in highly priced technology stocks led to a significant underperformance versus the more diversified S&P 500 EW. This example illustrates the very different performance experience that may result from simple index construction choices. We fur-ther touch upon the benefits and limitations of equal-weighted strategies below.

A similar situation was observed during the finan-cial crisis of 2007–2008, when investors with large allo-cations to market-capitalization-weighted indices were hit the hardest, due in large part to the indices’ high concentration in financial names. The massive draw-down in equities experienced during the financial crisis was arguably one of the key drivers in the development and adoption of smart beta strategies, as it led investors to seek alternative ways to build index strategies—for example minimum-volatility strategies—to seek better diversification, the potential for incremental returns and reduced risk.

Smart Beta: Minimum-Volatility Strategies

In minimum-volatility strategies,17 the weights of the index constituents are linked to the stocks’ realized historical volatility levels—with the least volatile names being overweighted with respect to the more volatile ones through different mechanisms.18 As a result, the least volatile stocks are overweighted compared with their weight in the equivalent market-capitalization-weighted indices.19

Minimum-volatility strategies seek to provide equity market participation with less volatility com-



pared to the standard (market-capitalization-weighted) index. This feature has generated investor interest in a variety of minimum-volatility strategies, ranging from U.S., developed, and emerging markets equities. Over the long run, minimum-volatility strategies have lower losses during market downturns while providing a disproportional amount of upside participation. The marked asymmetry of minimum-volatility portfolios, as illustrated in Exhibit 6, may be explained by a combina-tion of behavioral and structural anomalies that result in the mispricing of low-risk securities relative to their higher risk peers.20

Minimum-volatility strategies are also useful whenever investors have a desire to maintain exposure to a given market but with softening conviction sur-

rounding expected performance. Minimum volatility strategies can be a very effective tool to capture part of the upside of a market while limiting losses in downturn scenarios (Exhibit 6). Despite their strong long-term performance, low-volatility strategies will tend to lag market-capitalization-weighted indices in rallying mar-kets, as shown in Exhibit 7.

Smart Beta: Fundamental and Equally Weighted Strategies

Many of the first forms of smart beta equity strate-gies were based on simple, heuristic rules for portfolio construction. Two of the most widely adopted forms of equity smart beta are fundamentally weighted and

E X H I B I T S 3 A N D 4Comparative Behavior of the S&P 500 and the S&P 500 Equally Weighted Gross Total Return Indices, Before and After the Dot-Com Bubble

Notes: As implied by its name, this latter index invests in the same 500 companies included in the traditional index, however each of them has the same weight in the benchmark index.

Source: Bloomberg, BlackRock. Based on USD denominated gross total return indices. Index values in the bottom charts have been re-based to 100 as of 31-Dec-1999 and 31-Mar-2000, respectively, in order to measure and compare index performances. Past performance is not an indicator of future results.



equally weighted strategies. The weights of index con-stituents within fundamental strategies are based on fun-damental indicators such as a company’s sales, cash f low, or book value. Security weights in equally weighted strategies, as the name implies, is determined by 1/n, with each security receiving the same weight. Both methodologies result in a bias towards smaller, more value-oriented securities. These factor biases help explain the majority of the strategies’ relative per-formance, as both value and size have been demonstrated to earn a return premium over the long term.21 Exhibit 8 compares the performance of the MSCI World Value Weighted Index (a fundamentally weighted strategy) compared to the standard MSCI World Index, illustrating the impact of these implicit style biases on index performance.

While the biases towards value- oriented and smaller companies may result in strong long-term performance, they also increase the complexity of implementation. Smaller names are generally less liquid, with higher potential transactions costs.

Turnover can also be significantly higher than that of the respective traditional index, further increasing the importance of thoughtful implementation that mind-fully trades return, risk, and cost.

E X H I B I T 6Asymmetrical Performance of Minimum Volatility Strategies

Source: Morningstar. Based on monthly index returns from 1-Dec-2009–1-Dec-2014. Past per-formance is not an indicator of future results. MSCI USA MV Index was launched on 30-May-2008, all other MSCI MV Indices above incepted on 30-Nov-2009.

E X H I B I T 5Comparative Behavior of the S&P 500 Total Return Net Index and the S&P 500 Minimum Volatility Total Return Net Index During the Financial Crisis

While allocation to the S&P 500 Minimum Volatility Strategy would not have fully removed the underperformance, it would have helped to absorb some of the impact.

Source: Bloomberg, BlackRock. Based on USD denominated net total return indices. Index values have been re-based to 100 as of 31-Oct-2007. Past performance is not an indicator of future results.



E X H I B I T 7Rallying Markets Present Challenges for Minimum Volatility Strategies

Source: Bloomberg,BlackRock. For the period 9-Mar-2009–15-Apr-2010. Based on USD denominated net total return indices. Index values have been re-based to 100 as of 9-Mar-2009. Past performance is not an indicator of future results.

E X H I B I T 8Comparative Behavior of the MSCI World and the MSCI World Enhanced Value Indices

Source: MSCI, Bloomberg, BlackRock. Based on USD denominated Net total return indices as of the end of Mar-2015. Index values have been re-based to 100 as of 1-Jan-2011. Past performance is not an indicator of future results.



FACTOR STRATEGIES: A POWERFUL SET OF SMART BETA SOLUTIONS

As we have noted, any non-cap-weighted strategy will include implicit style biases that result from portfolio construction rules. The recognition of these implicit biases has given rise to a new category of smart beta strategies that are explicitly constructed to isolate and capture one or more style factors. Factor-based strategies are now one of the largest and fastest growing categories of smart beta ETPs: Of the total 248 billion USD in smart beta ETPs (from Exhibit 1) “Factor” categories like “Reduced Volatility,”22 Single-Factor Exposures,” and “Multi-Factor Exposures” contribute c. 88 billion USD (Exhibit 9).

Factor strategies seek to identify common charac-teristics driving risk and returns in equity markets. Cer-tain factors have historically delivered a positive return over the long term, compensating investors for bearing risk or capturing long-standing market anomalies. These “factors” are nothing more than the intuitive invest-ment ideas that have driven stock selection for decades: Value ref lects the notion of buying low and selling high; Quality ref lects the idea of overweighting strong com-panies and underweighting weak companies. Exhibit 10 illustrates the long-term incremental return delivered by two well-understand risk factors: volatility and quality. Specifically, over the longer term, low-volatility and high-quality stocks have outperformed high-volatility and low-quality stocks respectively, as shown in Exhibit 10.

The innovation of smart beta strat-egies—and factor strategies in particular is not the discovery of these factors, but rather the ability capture them in a low-cost and transparent form. In essence, smart beta strategies aim to rewrite investment rules to create deliberate, diversif ied outcomes based upon this factor-based view of the world.

One potential challenge for inves-tors, though, is linked to the selection of the most appropriate index to match an investor’s portfolio needs and goals. Many index strategies may sound alike, but subtle differences in portfolio con-struction choices may lead to large

differences in performance. For example, traditional (cap-weighted) value indices, fundamentally weighted indices and value-factor indices all provide investors with a tilt towards value-oriented securities—but with a very different total performance experience.

In particular, security selection (screening) and security weighting decisions have the largest impact on portfolio characteristics and performance. Some smart beta indices re-weight all securities in a particular uni-verse; others include only a subset of securities that are ranked highest along particular factor dimensions. Unlike traditional style indices, which cap-weighted a subset of value-oriented securities for example, smart beta indices are generally not cap-weighted.

While these differences might appear subtle, the index rules governing stock selection and weighting criteria have a profound impact both on the perfor-mance of these benchmarks and on their exposure to the underlying market’s factors. Exhibit 12 shows, as an example, a comparison in the historical performances of the MSCI World, MSCI World Value (a style index), MSCI World Value-Weighted (a fundamental index), and MSCI World Enhanced Value (a factor index) net total return indices.23

It should be noted that the majority of index-based factor strategies available are long-only strategies.

E X H I B I T 9Global Equity Smart Beta ETFs Assets—Factor ETPs is the Second Largest Growing Category

Source: BlackRock ETP Landscape. As of the end of Mar-2015.



A large proportion of portfolio risk is therefore related to the underlying market exposure and its inherent macroeconomic risk. While long-only factor strate-gies can provide a “tilt” towards the desired factor, a

long/short implementation would be required to pro-vide “pure” exposure to a desired factor. The shorting required makes such strategies more challenging in an ETF format, but still worthwhile investment proposi-tions for those investors seeking to isolate factor expo-sures from market exposures.

PUTTING SMART BETA TO WORK

The proliferation of smart beta strategies can make it challenging to understand the potential role smart beta can play in investor portfolios. Some strategies are focused on delivering exposure to a single smart beta factor, and others provide exposure to several factors (intended or otherwise!). Some forms of smart beta are not explicitly anchored to factor investing, instead focusing on using the smart beta toolkit to seek specific outcomes or more efficient versions of exposure to a particular asset class.

At the risk of oversimplifying, we divide the world of smart beta into two types of strategies: precision tools and solutions. Some investors are more tactical by nature and prefer to build custom portfolios for themselves. Single-factor funds, for example, provide a toolkit to

E X H I B I T 1 1Comparing Portfolio Construction Rules

An investor’s key challenge is the selection of the most appropriate index to match their portfolio needs and goals.

Source: BlackRock, for illustrative purposes only.

E X H I B I T 1 0Cumulative Active Returns for Minimum Volatility and Quality Indices

Source: Bloomberg, BlackRock. As of the end of March 2015. Based on USD denominated net total return indices. Risk premiums measured as the rela-tive performance of the World Minimum Volatility and the World Quality indices with respect to the parent MSCI World Index. Minimum Volatility: MSCI World Minimum Volatility Index (M00IWO$O Index), Quality: MSCI World Sector Neutral Quality Index (M1WONQ Index). Past per-formance is not an indicator of future results.



express an investment view. Other investors are more interested in well-diversified strategies that have been devised by an asset manager or index provider that seeks to perform well in a variety of market environments.

Multi-factor strategies, for example, are designed to be a part of investors’ long-term core holdings.

For more tactically minded investors, the distinct cyclicality of style factors, along with their historically

E X H I B I T 1 3Factor Investing Across Business Cycles

Source: BlackRock. The chart above is an indicative illustration by the authors of various phases of macro-economic/business cycles and the factors that could potentially produce better returns in each stage.

E X H I B I T 1 2Historical Performance of MSCI World Value (Style), MSCI World Value-Weighted (Fundamental), and MSCI World Enhanced Value (Factor) Indices Compared to MSCI World

Source: MSCI, Bloomberg, BlackRock. As of the end of Mar-2015. Based on USD denominated net total return indices.

Index values have been re-based to 100 as at 1-Jan-2001. Past performance is not an indicator of future results.



low correlations with each other, can make single factor strategies a useful tool to implement investment views or hedge risks along factor dimensions. Certain factors tend to be naturally pro-cyclical (such as Value, Size and Momentum) and others counter-cyclical (such as Quality and Minimum Volatility. Indeed, factors have been highly cyclical historically, with sensitivity to var-ious stages of the economic cycle. Just as many investors make tactical adjustments to country or sector alloca-tions, factor strategies provide another tool to express a market view.

Many investors seek bespoke combinations of fac-tors to ref lect their market views and goals for risk and

return. For example, Exhibit 14 compares the historical performance of two bespoke factor portfolios, compared to the standard MSCI World Index. The “defensive” factor portfolio includes quality and minimum volatility, while the “aggressive” factor portfolio includes expo-sures to momentum, size, and value.24 Not surprisingly, we observe that the defensive portfolio performed well in f light-to-quality environments when market vola-tility was rising. The aggressive portfolio performed best in risk-seeking environments when market volatility was low or falling. While factor timing is potentially chal-lenging, the notion of factor rotation is attractive to many investors and many practitioners are investigating

E X H I B I T 1 4Historical Evolution of the Excess Returns of the Aggressive and Defensive Portfolios vs. the MSCI World’s Returns (lhs axis). On the rhs axis, we Show the Behavior of Markets’ Volatility, as Proxied by the Realized Volatility of the MSCI World Index

Notes: The above data refers to simulated past performance, which is not a reliable indicator of future performance.

Source: BlackRock, Bloomberg. As of the end of Jan-2015. Based on USD denominated net total return indices.

Our Defensive factor portfolio, denominated in USD, rebalances annually, provides exposure to 50% MSCI World Sector Neutral Quality Index NR USD and 50% MSCI World Minimum Volatility Index NR USD. Our Aggressive factor portfolio, denominated in USD and rebalances annually, provides exposure to 33.3% MSCI World Momentum Index NR USD, 33.3% MSCI World Mid-Cap Equal Weighted Index NR USD and 33.4% MSCI World Enhanced Value Index NR USD. For a full assessment of the investment strategy, the different levels of risk implied by each solutions should also always be considered. Past performance is not an indicator of future results. Index and portfolio returns do not ref lect any management fees, transaction costs or expenses. Indexes are unmanaged and one cannot invest directly in an index. Data for the time periods prior to the index inception date (MSCI World Minimum Volatility Index NR USD was launched on 14-Apr-2008; MSCI World Momentum Index NR USD was launched on 11-Dec-2013; MSCI World Mid Cap Equal Weighted Index NR USD was launched on 25-Jul-2014; MSCI World Enhanced Value Index NR USD and MSCI World Sector Neutral Quality Index NR USD were both launched on 11-Aug-2014) is hypothetical back-tested data. The MSCI World Index was launched on 31-Mar-1986.



both subjective and rules-based methods of dynamic factor allocations.

Bull market periods are identif ied as follows: Period 1: Mar-2003 to Oct-2007. Period 2: Apr-2009 onwards to Jan-2015. The financial crisis in the U.S. and the accompanying crash in global equity markets between Nov-2007 and Mar-2009 are identified as a bear markets.

In contrast to the case examined above, many smart beta investors seek well-diversif ied smart beta strategies that can offer the potential for incremental returns, perform well in a variety of market condi-tions and logically complement traditional index or active equity strategies. For investors seeking broadly diversified smart beta equity strategies, allocating across many style factors can ameliorate the need for factor

timing as one factor may perform well just as another is struggling.

A simple base case for the construction of a multi-factor portfolio is equal weighting across factors. In Exhibit 15, we examine an equally weighted combi-nation of four MSCI World Factor indices to repre-sent exposures to value, quality, momentum, and size, compared to the MSCI World Index. Based on simu-lated past performance, in each of the 1-year, 3-year and 5-year periods examined ending 31-Mar-2015, the equally weighted combination of factors provides positive incremental returns. Exhibit 15 also includes the historical returns for the MSCI World Diversif ied Multi-Factor Index, which is deliberately constructed to maximize exposure to securities with strong rank-ings across an equal-weighted combination of value,

quality, momentum, and size criteria. The historical outperformance of the MSCI World Diversif ied Multi-Factor Index compared to the combination of single-factor indices ref lects the efficiency gain inherent in its portfolio construc-tion. A unif ied portfolio construction process allows the index to take advantage of netting opportunities across securities: the long-only constraint is applied only once, taking into consideration all avail-able information about potential risk and return. In contrast, single-factor indices are by def inition constructed without reference to each other. A momentum index may overweight the same stock that is underweighted in a quality index. For multi-factor investors, simultaneously trading off style metrics means a more efficient spend of risk on those securities that exhibit the highest combined ranking across the desired style dimensions.

CONCLUSION

Smart beta strategies deviate from traditional indices by definition, and result in implicit or explicit biases towards cer-tain factors. Smart beta strategies therefore represent a powerful set of tools to seek incremental performance, reduce risk, or enhance diversif ication along factor

E X H I B I T 1 5Comparing Multi-Factor Portfolio Approaches

Source: BlackRock, Bloomberg. As of the end of Mar-2015. The MSCI World Factor Equal Weight Portfolio is a simulated portfolio, denominated in USD and rebalances annually, pro-vides exposure to 25% MSCI World Sector Neutral Quality Index NR USD, 25% MSCI World Momentum Index NR USD, 25% MSCI World Enhanced Value Index NR USD and 25% MSCI World Mid-Cap Equal Weighted Index NR USD. Past performance is not an indicator of future results. Index and portfolio returns do not ref lect any management fees, transaction costs or expenses. Indexes are unmanaged and one cannot invest directly in an index. Data for the time periods prior to the index inception date (MSCI World Momentum Index NR USD was launched on 11-Dec-2013; MSCI World Mid Cap Equal Weighted Index NR USD was launched on 25-Jul-2014; MSCI World Enhanced Value Index NR USD and MSCI World Sector Neutral Quality Index NR USD were both launched on 11-Aug-2014; MSCI World Diversified Multi-Factor Index was launched on 19-Mar-2015.) is hypothetical back-tested data. The MSCI World Index was launched on 31-Mar-1986.


The Journal of Index InvesTIng summer 2015

dimensions. We encourage smart beta investors to con-sider their own goals and unique needs when evaluating potential strategies.

• What is the objective of the potential smart beta strategy: return enhancement or risk mitigation?

• What is the investors risk tolerance and time horizon? How much cyclicality can the investor endure” Understanding what the “bad times” may look like can help investors withstand those uncomfortable times.

• What is the investor’s expectation for the future, including market returns and the persistence of style factors? How prominent a role should these views take in the formulation of the investment strategy?

As we have shown, the portfolio construction decisions surrounding index methodology can have a significant impact on a strategy’s risk and return results. As with any investment strategy, its underlying charac-teristics will drive its behavior along with the ultimate success and durability of the investment strategy. We firmly believe that there is a valuable role for smart beta alongside traditional index and active investing.

ENDNOTES

BlackRock Advisors (U.K.) Limited, which is autho-rized and regulated by the Financial Conduct Authority (“FCA”), having its registered off ice at 12 Throgmorton Avenue, London, EC2N 2DL, England, Tel +44 (0)20 7743 3000, has issued this document for access by Professional Cli-ents only and no other person should rely upon the informa-tion contained within it. For your protection, calls are usually recorded. Investment in the products mentioned in this docu-ment may not be suitable for all investors. Past performance is not a guide to future performance and should not be the sole factor of consideration when selecting a product. The price of the investments may go up or down and the investor may not get back the amount invested. Hypothetical data results are based on criteria applied retroactively with the benefit of hindsight and knowledge of factors that may have positively affected its performance, and cannot account for risk factors that could affect any actual fund performance. Any actual fund performance could vary significantly from the hypo-thetical index performance due to transaction costs, liquidity or other market factors. BLACKROCK, BLACKROCK SOLUTIONS, iSHARES, BUILD ON BLACKROCK,

SO WHAT DO I DO WITH MY MONEY and the styl-ized i logo are registered and unregistered trademarks of BlackRock, Inc. or its subsidiaries in the United States and elsewhere. All other trademarks are those of their respective owners.

1Source: BlackRock Investment Institute, BlackRock ETP Landscape, Simfund. Data excludes assets in Cayman Islands, British Virgin Islands, and Bermuda-domiciled funds. As of the end of December 2014. Throughout this docu-ment, ETF data is referenced from a number of sources by BlackRock including provider websites, fund prospectuses, provider press releases, provider surveys, Bloomberg, the National Stock Exchange, Strategic Insight Simfund, Wind, and the Bank of Israel. Mutual fund data is sourced from EPFR.

2As at the end of December 2014. By retail asset man-agement industry we define the aggregation of ETP with index and active mutual funds offered to retail clients.

3In 1884, Charles Dow and Edward Davis Jones began to list the Dow Jones Average index: a price-weighted bench-mark of 11 U.S. railways companies.

4BlackRock estimates, based on number of indices maintained by a sample of index providers as of March 2015.Providers included: MSCI, FTSE, Stoxx, and S&P.

5With the exception of the Nikkei 225 Stock Average index, which is used as the default choice to represent the Japanese equity market and which uses a price weighting scheme.

6Which we review in the following section. 7Both investment vehicles were tracking indices with

underlying securities selected on a basis of a range of fun-damental values and combined using a modif ied equally weighted methodology. This meant that out of a total of 50 stocks, the top 15 constituents were given 50% of the weights (equally distributed) and the remaining 35 constituents were contributing for 50% weight (again, equally distributed).

8Source: BlackRock, as of March 2015.9Source: BlackRock, ETP Landscape as of end of March

2015.10Calculated from January 2010 to December 2014.11By this we define ETFs that track an index whereby

the constituents are weighted based on the historical or pro-spective estimated dividend paid.

12Source: BlackRock, as of March 2015.13Source: BlackRock, as of March 2015.14Source: BlackRock, as of March 2015.15Source: BlackRock, as of March 2015.16As implied by its name, this latter index invests in

the same 500 companies included in the traditional index, however, each of them has the same weight in the bench-mark index.


17While we classify minimum-volatility strategies as part of factor strategies, we cover them in a dedicated section given their popularity among ETP investors.

18In low-volatility strategies, constituents’ weights are inversely correlated to the stock’s volatility. Minimum variance and minimum-volatility strategies are generally optimized to seek the risk minimizing portfolio subject to a number of constraints related to turnover, liquidity, posi-tion sizes, and country or sector mid-weights. Despite having similar names, these methodologies that result in very dif-ferent portfolios with very different behaviors.

19Among indexed minimum-volatility strategies avail-able, some of the most advanced ones employ optimization processes which are aimed at selecting a subset of stocks from the investible universe with minimum volatility, under cer-tain constraints. This optimization process typically considers the volatility of each stock and the correlation between stocks, and delivers strategies that are very close to the equivalent capitalization-weighted index (the “parent index”) in terms of sector, country, and factor exposures—except for the vola-tility factor. This aims to achieve an exposure with lower absolute risk, without deviating substantially from the char-acteristics of the parent index.

20With reference to Baker et al. [2010].21Style factors have been heavily researched in academic

literature. Rosenberg and Marathe were among the first to describe the importance of style risk factors in explaining stock returns. Fama and French put forward a model explaining U.S. equity market returns with three factors: the market (based on the traditional CAPM model), the size factor (large versus small capitalization stocks), and the value factor (low versus high book to market). The “Fama-French” model, which now includes Carhart’s momentum factor has become a trademark within factor investing.

22This category includes minimum-volatility, low vola-tility, and minimum-variance strategies.

23Source: www.msci.com. Global Investable Markets Value and Growth Index Methodology, December 2007. MSCI Value Weighted Indices Methodology. August 2012. MSCI Enhanced Value Indices Methodology. August 2014. Exhibit 5 containing cross-correlations of various style factor returns.

24As observed in Bender et al. [2013]. “Deploying Multi-Factor Index Allocations in Institutional Portfolios.” MSCI Research Insight.

REFERENCES

Antropova, S., and R. Tiwari. “Factoring in Stock DNA.” BlackRock, (2015).

Baker, M., B. Bradley, and J. Wurgler. “Benchmarks as Limits to Arbitrage: Understanding the Low Volatility Anomaly.” (March 2010). NYU Working Paper No. FIN-10-002. Avail-able at SSRN: http://ssrn.com/abstract-1585031.

Bender, J., R. Briand, D. Melas, and R.A. Subramanian. “Foundations of Factor Investing.” MSCI Research Insight, (2013).

Bender, J., R. Briand, D. Melas, R.A. Subramanian, and M. Subramanian. “Deploying Multi-Factor Index Allocations in Institutional Portfolios.” MSCI Research Insight, (2013).

Cohen, S., and U. Marchioni. “The Art of Indexing,” Black-Rock, (2014).

“ETP Landscape.” March 2015, BlackRock.

BIBLIOGRAPHY

Carhart, M. “On Persistence of Mutual Fund Performance.” Journal of Finance, 52, (1997) pp. 57-82.

Fama, E.F., and K.R. French. “The Cross-Section of Expected Stock Returns.” Journal of Finance, Vol. 47, No. 2 (1992).

——. “A Five-Factor Asset Pricing Model.” University of Chicago, Amos Tuck School of Business, Dartmouth Col-lege, (2014).

Kahn, R.N., and M. Lemmon. “Making Smart Decisions About Smart Beta.” (2014).

——. “Who Should Buy Smart Beta?” BlackRock, (2014).

Keynes, J.M. “General Theory of Employment, Interest, and Money.” Macmillan Cambridge University Press, for Royal Economic Society, (1936).

Rosenberg, B., and V. Marathe. “Common Factors in Secu-rity Returns: Macroeconomic Determinants and Macroeco-nomic Correlates.” University of California, (1976).

Schoenfeld, S.A. “Active Index Investing.” Wiley Finance, (2004).

To order reprints of this article, please contact Dewey Palmieri at [email protected] or 212-224-3675.


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