defining alternatives may / june 2012 - etf · pdf fileby kishore karunakaran ... laura zavetz...

Managed Futures Strategies

Jeremy Schwartz and Chris Jabara

Benchmarking Tail Risk Management

Vineer Bhansali

Indexed Approaches To Long/Short Investing

Peter Little and Greg King

Market-Neutral Factor Investing

Kishore Karunakaran

Plus an interview with Morgan Creek’s Yusko, thoughts on hedge fund indexes

from Bruno & Whitelaw and columns by Vogelzang, Blitzer and Krein

defining alternatives May / June 2012

Thu Apr 5 11:24:06 EDT 2012Trim Size = 8.125 X 10.875

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INDEX MAY-JUN INDEX_I.pdf

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www.journalofindexes.com


f e a t u r e s

V o l . 1 5 N o . 3

1May / June 2012

40

30

10

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Global Index Data 57

Index Funds 58

Morningstar U S Style Overview 59

Dow Jones U S Industry Review 60

Exchange-Traded Funds Corner 61

Index Providers Establish IIA 48

Case-Shiller Indexes Hit New Lows 48

BATS Calls Off IPO 48

Vanguard, SSgA In Price War? 49

iShares Launches Sector, Other Bond ETFs 49

Managed Futures Strategies By Jeremy Schwartz and Chris Jabara 10Capturing an active strategy in a benchmark

Benchmarking Tail Risk ManagementBy Vineer Bhansali 16Key considerations for a certain kind of index

A Chat With Mark YuskoBy Journal of Indexes Staff 18A hedge fund expert talks about alternatives

Investable Indexed Approaches To Long/Short InvestingBy Peter Little and Greg King 22Ways of measuring long/short strategies

Waiting For Their Big BreakBy Michael Vogelzang 28When will alternative ETFs truly take off?

Market-Neutral Thematic/Factor InvestingBy Kishore Karunakaran 30Adding a little something to your asset allocation

Trust Me By David Blitzer 38Absolute-return strategies don’t exist in a vacuum

Selecting A Hedge Fund Replication ApproachBy Salvatore Bruno and Robert Whitelaw 40A look at the index development process

‘Alternative’ Might Just Mean ‘Unfamiliar’By David Krein and John Prestbo 46Thoughts on the evolution of the alternatives space

The Ronko Alterna-MaticTM

By Heather Bell 64The solution to all your investing needs!


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INDEX MAY-JUN INDEX_1.pdf

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Contributors

2 May / June 2012

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Vineer Bhansali is a managing director and portfolio manager at Pimco, currently overseeing Pimco’s quantitative investment portfolios. Since 2000, he has also headed Pimco’s firmwide analytics department. Bhansali is the author of numerous scientific and financial papers and of three books. He holds a Ph.D. in theoretical particle physics from Harvard University. Bhansali has a master’s degree in physics and an undergraduate degree from the California Institute of Technology.

David Blitzer, is managing director and chairman of the Standard & Poor’s Index Committee. He has overall responsibility for security selection for S&P’s indexes, and index analysis and management. Blitzer previ-ously served as chief economist for S&P and corporate economist at The McGraw-Hill Companies, S&P’s parent corporation. A graduate of Cornell University, he received his M.A. in economics from George Washington University and his Ph.D. in economics from Columbia University.

Salvatore Bruno is chief investment officer at IndexIQ. Prior to joining IndexIQ, he was a portfolio manager at Deutsche Asset Management, where he was also the global head of quantitative research. Bruno earned a B.S. in applied economics and business management from Cornell University and an MBA in finance and economics from the Leonard N. Stern School of Business, New York University.

Kishore Karunakaran is president and co-founder of ETF issuer QuantShares. Prior to joining QuantShares, he was a director in the quanti-tative equities stock selection group at Platinum Grove Asset Management LP. Karunakaran previously was employed as a vice president at AQR Capital Management LLC and a senior portfolio manager at SSgA. He holds an MBA from the University of Chicago Booth School of Business and a master’s degree from the University of Sydney, Australia.

Peter Little, CFA, is a director at Credit Suisse and head of portfolio man-agement and implementation for alternative beta products within the liquid alternatives group. Prior to joining Credit Suisse in 2003, he worked for Barclays Capital as well as Royal Bank of Scotland, J.P. Morgan, Chase Manhattan Bank and Standard Chartered. Little earned a B. Comm in finance from the University of Port Elizabeth in South Africa in 1995.

Jeremy Schwartz is director of research at WisdomTree Investments Inc. Prior to joining WisdomTree, he was the head research assistant for Jeremy Siegel, WisdomTree’s senior investment strategy advisor and The Wharton School’s Russell E. Palmer Professor of Finance, and helped with the research and writing of Siegel’s books “Stocks for the Long Run” and “The Future for Investors.” Schwartz is a graduate of The Wharton School, University of Pennsylvania.

Michael Vogelzang, president and chief investment officer, has man-aged Boston Advisors since 1997 and led his firm’s management buyout in 2006. He also chairs the Boston Advisors’ independent board of direc-tors. Vogelzang serves on the investment committee of the $300 million Barnabas Foundation. He holds the Chartered Financial Analyst designa-tion and belongs to the Boston Security Analysts Society. Vogelzang earned a B.A. from Calvin College in Michigan.

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T H E P O W E R S H A R E S D B

COMMODITY INDEX TRACKING FUND

To download a copy of the prospectus, visit PowerShares.com/DBCpro

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8

Editor’s Note

Jim Wiandt

Editor

May / June 2012

Alternative investments aren’t typically what one thinks of when one thinks of index investing. For one thing, the space encompasses a wide range of asset classes and strategies, most of which are associated with active management.

For another, the very nature of the category defies quantification—indexes typically aim to capture beta and market performance, while the alternative investments space is all about alpha and uncorrelated returns. How do you index that?

As it turns out, our roster of authors for this issue of the Journal of Indexes has some suggestions.

We kick things off with an exploration of managed futures by WisdomTree’s Jeremy Schwartz and Chris Jabara. They first consider the development of managed futures strategies and then segue into a discussion of Alpha Financial Technologies’ Diversified Trends Indicator and how it captures the performance of a managed futures approach.

Next up, Pimco’s Vineer Bhansali offers a meditation on what is necessary to con-struct an index to hedge tail risk. He’s followed by a provocative discussion with Mark Yusko of Morgan Creek Capital, who’s not afraid to offer up opinions that might not resonate with die-hard passive investors.

Peter Little and Greg King of Credit Suisse discuss the difficulties of capturing long/short strategies in an index, and provide explanations of two successful methods: factor based and mechanical based. They are followed by Boston Advisors’ Michael Vogelzang, who offers a quick look at the existing pool of alternative ETFs and offers suggestions to issuers and end-users as to what is necessary for those products to thrive.

Kishore Karunakaran of QuantShares weighs in with an explanation of how factor-based or thematic indexes can round out an asset allocation, choosing to focus on a market-neutral strategy as an example. David Blitzer takes on absolute-return strate-gies, reminding readers that benchmarks are still necessary to evaluate performance.

IndexIQ’s Salvatore Bruno and Robert Whitelaw tackle hedge fund benchmarking and the choices and trade-offs that must be made to construct a representative and investable index. Next, David Krein and John Prestbo of Dow Jones Indexes bring a historical perspective to the alternatives space and note that it wasn’t that long ago that stocks were considered the real alternatives.

Finally, our own Heather Bell closes out the issue with a home investing solution you can’t afford to do without.

Happy (alternative) Investing,

Uncharted Territory

Jim Wiandt

Editor




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May / June 201210

Managed Futures Strategies

How to capture an ‘active’ strategy in a benchmark

By Jeremy Schwartz and Chris Jabara




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In the past, managed futures investing has been asso-ciated with complex trading strategies, high fees, high minimums for investment and lockup agree-

ments that resemble the unregulated hedge fund indus-try. Despite such obstacles, the managed futures indus-try has grown from virtually nothing in 1980 to more than $300 billion at the end of 2011.1 In recent years, the asset class and investment approach has started to become democratized in more retail-friendly vehicles. Today investors can access the benefits of managed futures by not only investing with the traditional com-modity trading advisors (CTAs),2 but also by investing in mutual funds3 and exchange-traded funds.4

In this paper, we review some history of managed futures strategies, discuss the reason why investors have included them in their portfolios, and discuss the challenges and various solutions for crafting passive, rules-based benchmarks for measuring the returns of this investment approach.

Origins Of Futures TradingPrior to the early 1970s, futures contracts exchanged

hands principally as a means for producers and consum-ers of agricultural commodities to protect and lock in prices for their production or their supply. The history of futures contracts for hedging purposes is believed to date back thousands of years to the ancient city of Babylon where people exchanged contracts on livestock—goats, pigs, sheep and other items—to trade one good for anoth-er and lock in a set of prices for these goods.

The growth of futures trading expanded with the introduction of interest rate and currency trading that typified “financial futures” in the early 1980s. Now the markets are accessed by speculators, hedgers and investors alike in over 100 liquid markets ranging from equity futures, financial futures and commodity futures 24 hours per day. This rapid increase in trading instru-ments also gave birth to the CTA, or a third-party decision-maker who is charged with making buy or sell decisions on an investor’s behalf.

The term “CTA” tends to be vague, and the term “commodity” may not always accurately reflect the nature of the underlying securities in many of these strategies. Over time, the composition of CTAs has shifted; in the 1980s, agricultural futures represented about 64 percent of market activity, metals futures accounted for 16 percent, and currency and interest rate futures totaled approximately 20 percent.5 Today financial futures such as currencies, interest rates and stock indexes dominate trading in the global futures markets. CTA composition has also reflected this evo-lution, as many CTA portfolios are heavily invested in noncommodity-related futures contracts.

Benefits Of Incorporating Managed Futures In A Portfolio

As managed futures have grown in popularity, it is important to understand why many seek to diversify

www.journalofindexes.com May / June 2012 11

their traditional stock and bond portfolios to include this alternative asset class, including:1. Potential for returns in up and down markets: The

flexibility and ease in taking long and short positions allows profit both from rising as well as falling markets.

2. Noncorrelation to traditional investments: Returns of managed futures strategies have historically been noncorrelated to traditional stock and bond market returns over long-term periods.

3. Enhanced diversification: The noncorrelation of man-aged futures, combined with their potential ability to provide returns during up and down markets, help provide enhanced overall portfolio diversification. During the volatility experienced in the markets during

the financial crisis of 2008, there were few asset classes that provided adequate desired diversification and nega-tive correlation. Managed futures were one of the select areas that did provide that diversification potential. A growing number of retail-friendly vehicles like mutual funds and ETFs are making access to this once-institu-tional-only product set more easily attainable.

An Institutional Product? Active Managed Futures Strategies

In recent years, the vast majority of current assets under management in the managed futures space are with active CTA managers. Active managers in the managed futures space have traditionally charged hedge-fundlike fees (base investment management fees plus performance fees) for the prospects of gener-ating exposure to a managed futures program.

CTAs tend to be perceived as a complex and an almost scientific sector of the investment management indus-try, but the general objective of many of these strategies is to simply follow trends. Surveys show that upward of 70 percent of CTAs report that they make trend-follow-ing or momentum-based trading decisions.6

While managers employ a variety of proprietary processes and techniques to identify and capture price trends, the general objective does not change. Active managers justify their high fees by claiming they gener-ate meaningful alpha,7 but academic research suggests that CTA returns comprise a significant amount of sys-tematic exposure to trend-following strategies.

The academic paper by Professors Gorton, Rouwenhorst and Bhardwaj had a provocative title: “Fooling Some of the People All of the Time: The Inefficient Performance and Persistence of Commodity Trading Advisors.” They write:

We estimate that CTAs on average … captured most

of their performance through charging fees. Yet, even

before fees we find that CTAs display no alpha relative to

simple futures strategies that are in the public domain.

We argue that CTAs appear to persist as an asset class

despite their poor performance, because they face no

market discipline based on credible information. Our

evidence suggests that investors’ experience of poor per-

formance is not common knowledge.




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This research suggests that exposure to simple trend-following strategies can explain most of the aver-age outperformance of CTAs before fees.

In Search Of A Managed Futures Benchmark

If the majority of CTA performance can be attributed to systematic exposure to the market, then how does one identify that exposure?

Investors have grown accustomed to comparing investments within asset classes to a representation of the asset class, but does such a representation of CTAs exist? While there are certainly indexes that claim to represent returns of the asset class going back to 1980, such as the Barclays CTA Index8 or the CASAM/CISDM CTA indexes (equal weighted and asset weighted),9 these indexes have two main drawbacks in estimating returns from managed futures strategies: 1. Returns may be biased upward: The returns for

indexes of CTA managers tend to be biased upward as a result of the voluntary nature of self-reporting performance. A CTA with poor performance for a period of time is less likely to report unfavorable returns to these types of databases, resulting in an index that includes mostly favorable performance.

2. Lack of a natural measuring stick: Other asset classes like equities or bonds have a natural benchmark for performance reporting. Market-capitalization-weighted benchmarks for equities or bonds math-ematically represent the average return in aggregate to investors in the equity or bond markets. These are meaningful performance measures. How would one apply that to the managed futures space? CTAs are providing exposure to various asset class-

es—from equities, bonds, currencies and commodi-ties. In the end, a benchmark for managed futures must systematize the type of exposure these CTAs provide.

There is much debate about the best way to measure the performance of managed futures strategies. Given the academic research that suggests CTA performance can be explained by exposure to systematic trend-following rules, we propose that a passive, rules-based selection and weighting approach based on a meaningful measure of size of the underlying constituents provides an effec-tive representation of managed futures strategies.

The Rise Of Systematic Trading Approaches In CTAs

While discretionary CTA managers still exist, today approximately 80 percent of the universe of managed futures trading advisors comprises strategies that rely on systematic, computerized approaches to generate market-trading decisions.5 In theory, systematic trading strategies strive to eliminate any element of luck in generating alpha. As with most investment decisions, there are strengths and weaknesses associated with systematic strategies:Strengths

• Decisions are determined by computer models, which

help maintain a consistent and disciplined invest-

ment approach by removing emotion and reliance on manager discretion

• Allows for the historical study of price data to research,

develop and test strategies that results in a repeat-able process that can be quantified and studied to improve consistency

• Portfolio construction using various markets and sec-tors to increase diversification

• Investing in a passive manner diminishes the impact

of some of the traditional obstacles to investing in CTAs and also lessens the burden of how to find and monitor the best CTA managers

Weaknesses

• Systematic trading systems cannot adapt to news or

environments that are different from past environ-ments from which the models were initially derived

Constructing A Systematic Approach To Managed Futures

In constructing a passive approach, two index design decisions must be made:1. What constituents to include2. How to weight them

While other index-based approaches exist in the market, we will focus on the Diversified Trends Indicator (DTI) in this piece for illustration. Developed by Alpha Financial Technologies LLC10 the DTI com-prises 24 liquid commodity and financial futures contracts that are grouped into 17 sectors with 50 per-cent exposure to commodity futures and 50 percent exposure to financial futures (defined as currency and interest-rate futures only).

While equity futures certainly are some of the most actively traded futures contracts, there is a reasonable debate about whether they add value to a managed futures program if the goal is creating a noncorrelated vehicle to the traditional equities futures and bond portfolios. In our judgment, leaving equities out of the constituent list lowered correlation of a managed futures strategy to traditional equity allocations.

Figure 1 illustrates the futures composition of the DTI; note this is a long/short index based on trends. In the DTI, energy can only be long or flat, never short. When energy is flat, the allocations are spread pro rata to the remaining sectors.

The weights for the commodities subsectors are designed to reflect and approximate the relative pro-duction value and liquidity of the various commodi-ties, one of the most natural ways of measuring their performance. Meanwhile, for currencies and interest rates, the weights reflect and approximate the various country exposures to global GDP.

DTI Employs A Rules-Based MethodologyTo determine whether a sector should be long or

short, the DTI compares a sector’s close at month-end to a seven-month weighted moving average for the sector. If the sector closes above its average at month-

May / June 201212




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end, it will be held long for the forthcoming month. If the sector should close at month-end below its seven-month weighted moving average, it will be short in the upcoming month (except for energy, which would be flat). Sectors are rebalanced back to their base weights (as highlighted above) on a monthly basis. The exam-ple above assumes the energy sector is long; if the energy sector is flat, the weightings would be different than those shown in Figure 1. The underlying compo-nents of multicomponent sectors are only rebalanced annually. Using a weighted moving average method places a greater weight and importance on prices that have occurred more recently.

One of the big dilemmas that creators of trend-following strategies face is determining the appropriate length of time to establish a trend. Short-term trend-following strategies aim to generate favorable returns at the risk of eroding profits by excessively trading and repeatedly investing in false trends or losing strategies. Longer-term trend-following strategies seek favorable

returns by indentifying and correctly trading long-term trends in the futures markets at the risk of overlooking profits from short-term volatility.

In short, the determination of the length of the moving average line involved trade-offs, but historical simulations of the strategy’s success were not depen-dent on just this one specific seven-month average. Other moving-average periods could also be used to form the basis of the buy/sell decision without com-promising the nature of the strategy.

Characteristics Of DTIThe DTI has been calculated live in real time since

2004. Over that period, it has been negatively corre-lated to both U.S. equities and U.S. bonds: -0.21 and -0.17, respectively (through Dec. 31, 2011).11 The nega-tive correlation is a key element in the diversification that managed futures strategies can bring.

There are few asset classes that can provide mean-ingful negative correlation. Commodities was an asset


DTI Component & Sector Weights

The Diversified Trends Indicator is a long/short rules-based index constructed of 24 liquid commodity and financial futures contracts comprising 17 sectors. These are the approximate market sectors and sector weightings included in the DTI Index as of the beginning of each year (assuming the energy sector is long). Each month, the DTI Index sector exposure is rebalanced back to the fixed weights: 50 percent physical commodities and 50 percent financials when energy is long; and approximately 38.5 percent commodities and 61.5 percent financials when energy is flat. The sectors are positioned either long or short depending on the current market environment. The DTI Index individual market components, sectors and related weightings, as well as other aspects of the calculation of the DTI Index are subject to change at any time.Source: Alpha Financial Technologies

*(RBOB) Reformulated Blendstock for Oxygenate Blending

50% FINANCIAL FUTURES

CANADIAN DOLLAR 1.00%

AUSTRALIAN DOLLAR 2.00%

SWISS FRANC 2.00%

BRITISH POUND 5.00%

US TREASURY

NOTE 7.50%

US TREASURY

BONDS 7.50%

JAPANESE YEN 12.00%

EURO 13.00%

50% COMMODITY FUTURES

ENERGY 18.75%

LIGHT CRUDE 8.50%

NATURAL GAS 4.25%

RBOB* 3.00%

HEATING OIL 3.00%

GRAINS 11.50%SOYBEANS 5.00%

CORN 4.00%

WHEAT 2.50%

PRECIOUS

METALS 5.25%GOLD 3.50%

SILVER 1.75%

INDUSTRIAL

METALS 5.00%COPPER 5.00%

LIVESTOCK 5.00%LIVE CATTLE 3.00%

LEAN HOGS 2.00%

SOFTS 4.50%COFFEE 1.50%

COCOA 1.00%

COTTON 1.00%

SUGAR 1.00%

Figure 1




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class that many assumed would provide noncorrela-tion, and for a large amount of time, that was true. During the financial crisis, however, one can see that the correlation of a commodity index such as the S&P GSCI Index spiked higher and started to approach 0.8 from a negative correlation in 2005.11

The DTI rolling three-year correlation also shows mostly negative correlations on a three-year basis. However, in the middle of 2011, the rolling three-year correlation was impacted by the spike in overall com-modity correlations, as commodities do represent 50 percent of the DTI.11 (See Figure 2.)

Risk/Return Statistics That Complement Traditional Portfolios

Since the index went live in 2004, the DTI has pro-vided superior returns to the S&P 500 Index,12 with 679

basis points less annualized volatility and lower down-side risk. Relative to commodity strategies, such as the S&P GSCI Index and the Dow Jones UBS Commodity Index, the DTI has performed admirably, with greater risk-adjusted returns and lower downside risk. Figure 3 also shows that the DTI’s returns since 2004 have been in line with the Newedge CTA Trend Sub-Index and the Barclay Systematic Traders Index.

A key point: The DTI was able to achieve returns within 1 percentage point per year of the Newedge CTA Trend Sub-Index with less volatility, even taking into account the fact that the Newedge CTA Trend Sub-Index involves reporting biases inherent in CTA indexes.

Maximum Drawdown13

The diversification benefits and the flexibility of a long/short strategy can benefit investors in rising as well as falling markets. Investors point to low correla-tion and sometimes negative correlations to tradition-al investments, as well as favorable CTA performance in crisis events.

In recent memory, managed futures attracted atten-tion for positive performance in the face of the finan-cial crisis of 2008, when the S&P 500 Index was down approximately 37 percent. The ability to diversify and go both long and short has proven advantageous for the DTI, as the index has shown lower volatility than other major asset classes, save U.S. bonds.

In addition to the favorable volatility relative to the other asset classes shown, the DTI has had a maxi-mum drawdown of 15.65 percent since 2004. This maximum drawdown was one-third of that of U.S. stocks. To put it into perspective, commodity strate-gies experienced a maximum drawdown of between 54 and 67 percent.

May / June 201214

Figure 3

Source: Zephyr StyleADVISORNote: DTI returns are total returns. Past performance does not guarantee future results.

Index

Diversified Trends Indicator Index -7.39% 7.80% 5.80% -4.01% 8.21% 5.85% 1.17% 9.59% 6.51% 4.04% 8.72% 6.05%

Newedge CTA Trend Sub-Index -7.04% 11.61% 7.39% 0.04% 11.47% 7.66% 5.62% 12.80% 8.87% 4.95% 12.24% 8.62%

Barclay Systematic Traders Index -3.77% 7.34% 5.02% 0.08% 6.57% 4.46% 5.19% 7.34% 4.98% 3.67% 7.21% 4.92%

S&P GSCI Index -1.18% 20.79% 15.35% 6.93% 22.32% 15.71% -2.79% 27.51% 21.17% 1.03% 25.84% 19.69%

DJ-UBS Commodity Index -13.32% 19.45% 15.13% 6.39% 18.37% 13.36% -2.07% 21.62% 16.55% 2.49% 19.22% 14.74%

Barclays Capital U.S. Aggregate Index 7.84% 2.35% 1.55% 6.77% 2.82% 2.15% 6.50% 3.60% 2.51% 5.44% 3.48% 2.52%

S&P 500 Index 2.11% 15.94% 9.85% 14.11% 18.97% 13.83% -0.25% 18.88% 14.12% 3.63% 15.51% 11.85%

Annualized Risk And Return Characteristics: 1/1/2004-12/31/2011

1-YeAR 3-YeARS 5-YeARS 1/24/2004-12/31/0211

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Rolling 3-Year Correlation Of Various IndexesVs. S&P 500 Index: 1/31/2003-12/31/2011

Source: Zephyr StyleADVISORNote: Past performance does not guarantee future results.

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■ Newedge CTA Trend Sub-Index ■ Barclay Systematic Traders Index

■ S&P GSCI Index ■ Diversified Trends Indicator Index

Figure 2




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15www.journalofindexes.com May / June 2012

Calendar-Year ReturnsManaged futures strategy funds certainly raised their

profile among investors during the 2008 financial crisis.

While global markets were falling off the cliff in 2008, there

was a clear and discernible pattern that allowed trend fol-

lowers to go short in many of the declining markets and

go long those futures that reflect a flight-to-safety quality.

The DTI showed returns of 8.29 percent, and the Newedge

CTA Trend Sub-Index had returns of 20.88 percent. The

higher returns might be explained by funds employing

more leverage on their positions, while the DTI provides

only one-to-one exposure to the market.

Trend-following strategies suffered in 2009 and 2011

when the equity markets were positive. A long/short man-

aged futures strategy like the DTI can be described as one

gauge of volatility in its components or strong trends in

those underlying markets. When there is a lack of strong

trends in the DTI’s components, the DTI’s performance

is apt to suffer. This environment showed that one of

the limitations of a trend-following approach could be

a challenge identifying profitable trends during volatile

markets. These specific volatile markets were influenced

by the zero-interest-rate policy established by the Federal

Reserve, as well as ongoing interventions in the cur-

rency and interest rate market by global central banks that

caused wide deviations and fluctuations of price trends in

commodity, currency and interest-rate markets.

ConclusionManaged futures strategies represent exposure that

was once only available to the institutional investor

community and characterized by high fees, lockups,

leverage and opaque strategies. Only recently have

managed futures strategies started to become available

in liquid, transparent vehicles. Although the DTI rep-

resents exposure to one specific managed futures sys-

tematic trading strategy, there is research indicating the

vast majority of CTA returns can be explained by such

exposure to systematic trading approaches. The DTI

serves to crystallize the exposure in a simple rules-based

algorithm providing exposure to effective and represen-

tative managed futures strategies.

As the number of asset classes that provides low cor-

relation to traditional equity and bond portfolios becomes

increasingly sparse, we believe investors and advisors

would benefit from understanding the pros and cons of

the various managed futures offerings coming to market.

Figure 4


Standard

DeviationIndex

# of Down

Months

Average Down

Return

Maximum

Drawdown

Maximum Drawdown

Months

Diversified Trends Indicator Index 8.72% 41 -1.81% -15.65% 20

Newedge CTA Trend Sub-Index 12.24% 44 -2.72% -17.53% 6

Barclay Systematic Traders Index 7.21% 46 -1.40% -10.13% 6

S&P GSCI Index 25.84% 39 -6.68% -67.64% 8

DJ-UBS Commodity Index 19.22% 37 -5.08% -54.26% 8

Barclays Capital U.S. Aggregate Index 3.48% 29 -0.70% -3.83% 7

S&P 500 Index 15.51% 35 -4.03% -50.95% 16

Downside Table: 1/1/2004-12/31/2011

Figure 5


2011Index 2010 2009 20062008 20052007 2004

Diversified Trends Indicator Index -7.39% 1.62% -6.02% 8.29% 10.66% 5.74% 7.55% 13.92%

Newedge CTA Trend Sub-Index -7.04% 13.13% -4.80% 20.88% 8.58% 8.24% 0.75% 2.68%

Barclay Systematic Traders Index -3.77% 7.82% -3.38% 18.16% 8.72% 2.10% 0.95% 0.54%

S&P GSCI Index -1.18% 9.03% 13.48% -46.49% 32.67% -15.09% 25.55% 17.28%

DJ-UBS Commodity Index -13.32% 16.83% 18.91% -35.65% 16.23% 2.07% 21.36% 9.15%

Barclays Capital U.S. Aggregate Index 7.84% 6.54% 5.93% 5.24% 6.97% 4.33% 2.43% 4.34%

S&P 500 Index 2.11% 15.06% 26.46% -37.00% 5.49% 15.79% 4.91% 10.88%

Calendar-Year Return

continued on page 63




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Problem Solving

May / June 2012

By Vineer Bhansali

16

Addressing the problem of hedging tail risks

Benchmarking Tail

Risk Management

No topic has gathered more interest since the finan-cial crisis of 2008 than the topic broadly called “tail risk management.” The term and its practice

have been open to much interpretation; this phenomenon of initial confusion is not particularly different from the growing pains experienced by many other market sectors. Mutual funds, hedge funds, even ETFs at the very begin-ning of their life cycle operated without much uniformity or proper reference indexes. As the market for tail-hedging solutions evolves, it will become critical that the end-user at least have a framework within which to evaluate the potential and realized costs and benefits of particular practices. We believe that to add value over time, tail risk management has to be active rather than purely passive; thus, a proper benchmarking framework is not simply a luxury but a necessity. The purpose of this article is to start to lay out exactly such a framework, which we have evolved over almost a decade of implementation.

Defining A Hedge MandateAs discussed in much detail elsewhere,1 a small set of

inputs or guidelines is the natural starting point for defining a tail-hedge mandate. In our view, the minimal set consists of the following:

1. Exposures2. Attachment3. Cost4. Basis riskThe first step is quantifying exposures. Our analysis of the

long-term history of many different types of assets shows that a small set of risk factors drives the returns of these assets. The two major secular exposures are the equity beta and the interest-rate or duration exposure. In addition, over cyclical

periods, factors like liquidity, currency exposure, momentum and monetary policy also play important and significant roles. In our practice, we first try to quantify the exposures of each underlying portfolio to these key factors, both for normal and stressed periods. Interestingly, both our research and the work of others show that even very diversified portfolios exhibit similar exposures to the key risk factors, with equity beta as the dominant risk exposure.

The second step is to define what we have called the “attachment” level (taking a term from the reinsurance indus-try), which is not very different than the deductible one would have in a policy for automobile or earthquake insurance. The closer the attachment level is to the current value of the portfolio, the higher one should expect the cost of the tail risk protection. Generally, we believe that broadly diversified portfolios should have an attachment level anywhere from 10 to 15 percent below the current portfolio value.

This brings us to the important question of cost. We gen-erally do not believe that tail hedging can be done efficiently in a perfectly costless manner over short-term horizons. Yes, there are structures (especially exotics) that purport to reduce the cost, or in many cases even eliminate the cost, but usually they consist of embedded sales of options that one would frequently rather not sell. Instead of this hidden dis-count, we believe that an explicit cost target is essential both to thinking of tail risk management as an asset allocation decision and as a commitment that one can continue to sup-port in periods where fat-tail events do not occur. Because of the natural difficulty in forecasting the time and form of the next tail event, we believe that tail hedging is an “always on” part of any risky investment portfolio. Our empirical and the-oretical research validates the belief that over longer periods (three to five years), tail hedging is generally self-financing




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when one accounts for both the ability to tilt portfolios more aggressively and following a systematic approach to rebal-ancing in the presence of such hedges.

Finally, one has some freedom to replace what might be expensive direct hedges with relatively cheaper indirect hedges, taking advantage of the tendency for correlations to increase, especially when extreme events happen. This cheapening comes with a trade-off, that the indirect hedges will not perform as well as the direct hedges conditional on the extreme event happening. To quantify this basis risk, we specify a level of confidence within which the likely outcomes of the actual portfolio are likely to fall relative to the direct hedge through simulations. The performance of a particular hedge program should be quantified in terms of the trade-off between basis risk and cost savings relative to a low- or no-basis risk benchmark.

Creating A Proper IndexOnce the framework for proper tail-hedge construction is

defined, the task of creating a proper index becomes relatively straightforward. If the benchmark is equity beta, we can use the most liquid traded market sectors that carry the key risk-factor exposures to start with a shortlist of potential bench-mark constituents. For instance, it would make sense to use S&P 500 Index options close to the maturity of the hedge mandate as a reference instrument, since by definition this index has an equity beta of 1 to itself (one can choose another equity index for this reference, e.g., the MSCI World, if that is the index of reference for the underlying portfolio). If the ref-erence portfolio is a blend of equity beta and fixed income—for instance, something like the MSCI World Index combined with the Barclays Aggregate Bond Index—then the tail hedge will be a blend of the best equity beta and duration hedges for this combination. The best reference market instruments will therefore be options on the equity and bond indexes. But since options on bond indexes are not very liquid, it makes sense to select options on tradable markets such as Treasury futures for index construction. Also, note that tail options on a portfolio are not the same as the sum of options on the indi-vidual constituents, so adjustments for the correlations of the underlying constituents need to be made.

Once the proper sectors are identified, the next step is to set a “strike” for the portfolio of reference market options. As an example, if the attachment level for an overall 60 percent equity, 40 percent bond portfolio is set at 85 percent (i.e., 15 percent out of the money for the whole portfolio), then assuming that the bond part remains static, the reference equity option strike is 15 percent/0.60 = 25 percent. So the natural strike of the reference equity option is 25 percent out of the money. One can proceed in a similar manner for the other underlying risks as a crude starting point.

The advantage of constructing the basket of reference securities in such a way is that they can be monitored in real time. Options-based tail hedges have various “Greeks,” such as time-decay, gamma, vega, theta, etc., which are very dynamic and have to be actively monitored and traded. The value added by an investment manager is proportional to how the actual portfolio of hedges behaves over time relative

to the theoretical benchmark. It also solves the problem of behavioral aversion to cost. Once the actual hedge cost and time decay is put relative to the cost of a theoretical hedge, it is much easier to commit to the cost as a long-term asset alloca-tion decision and to compare this cost versus the implied cost of de-risking or buying government bonds. The important point is that all types of tail hedging cost something, and this includes de-risking and moving to cash. The process of going through the relative value comparison of different types of hedges allows the investor to anchor the tail-hedging analysis to something realistic.

We should emphasize that the use of market-traded options is a simplification that works only if the underlying hedge objective is rather plain vanilla. If the objective is more complex, e.g., “hedge so that at no point in time the portfo-lio suffers a loss more than x percent,” the reference index security would have to be more of an exotic option such as a knock-in option. While these options are traded heavily in the over-the-counter markets, their prices are not as easily available as vanilla index options. More complex replicating option portfolios can be constructed to index these payoffs. Complexity vs. transparency is an important trade-off when it comes to tail hedging. We generally err toward simple portfo-lios and hence simple benchmarks to measure them against.

Measuring PerformanceFor traditional indexes, the task of performance measure-

ment is relatively straightforward. One can look at the returns of the actual portfolio versus the index and discern whether the decisions of the manager are adding or subtracting value. For tail risk hedging, the problem is only simple if all the hedges are relatively plain vanilla and the underlying instru-ments are liquid and replicate the portfolio without any basis risk. The moment the hedges become complicated, perfor-mance measurement takes a new twist. The reason simply is that the current price of the hedge does not reflect the poten-tial it has for a large tail payoff, and since tail events are rare events, observation of a few nontail periods is not sufficient to identify the prospects of the tail hedge. Naively, a tail hedge could look like it is performing better than a reference index of securities by losing time value slower than the reference hedges, but this is most likely to offer less potential of payoff if there is a jump event in the market (if the option hedges have less time decay, they probably, though not necessarily, have less gamma as well). To compensate for this shortcoming of real-time performance measurement, we believe that tail hedges need to be evaluated on the basis of scenario analysis. By identifying scenarios of concern and shocking the under-lying market factors at different horizons, one can evaluate the potential of these hedges to pay off in the situations that matter. Robust technology and sensible stress testing systems are thus of paramount importance for this exercise.

ConclusionsWhile tail risk hedging is a new and critically impor-

tant area of modern portfolio management practice, the relative newness of the area means standard frameworks





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Alternative Investing

May / June 201218

Morgan Creek’s chief investment officer

offers his thoughts

A Chat With Mark Yusko

Mark Yusko is the founder and head of Morgan Creek

Capital Management LLC. From 1998 to 2004, he led the

endowment office at the University of North Carolina at

Chapel Hill. Journal of Indexes caught up with him recently

to discuss the nature of alternative investments.

JOI: How would you define the alternative investment

concept? What do you see as its boundaries?

Yusko: There are only four primary investments: stocks, bonds, currencies and commodities. What people think of as alternative investments includes hedge funds, private investments, real estate, managed futures—things that are not traditional investments like stocks, bonds or cash.

A hedge fund is literally just a legal structure, and the term “hedge fund” is about as meaningless as the word “mutual fund.” You have mutual funds that invest in stocks. You have mutual funds that invest in bonds. There are mutual funds that invest in commodities and curren-cies. But if I own shares of IBM in a mutual fund, a hedge fund, a private partnership or a separate account, it doesn’t change the fact that I own shares of stock.

Another way that people think about defining alterna-tives is by the “structure” of the investment. For example, private equity is considered an alternative investment. But in reality, it’s just a partnership structure instead of a fund structure. In the end, you own equity or debt in a business. I just don’t see the difference.

We tend to think about alternatives such as hedge funds, private equity, real estate, commodities and struc-tured products as vehicles that allow people to get access to strategies that are different from traditional long-only exposure to stocks, bonds and cash and provide diversifi-cation benefits to your portfolio.

JOI: Alternatives are seen more as investments for

endowments or institutional investors. Has this

always been the case?

Yusko: Alternatives were definitely utilized originally by high-net-worth individuals and then much later by institutions like endowments and foundations. It took years for the average institution, like pension funds and insurance companies, to embrace these strategies—and there are still a lot of institutions that don’t have any exposure at all. The investor base in alternatives was a very small group back in the ’60s and ’70s, and it really wasn’t until maybe the last couple of decades that there was any really big institutional acceptance or adoption. Now, there’s fairly wide acceptance of most strategies, but the endowments and foundations have significantly higher exposures than the average pension fund. In fact, there is still a large percentage of pension funds that have hardly any exposure to alternatives.

JOI: What is the role of alternatives in the endowment

model, in your opinion?

Yusko: It depends on which part of the alternative market you’re talking about. The endowment model is about tak-ing a diversified global portfolio and building an exposure that is heavily overweight to equity risk in order to achieve a real return above your spending rate (usually around 5 percent). That doesn’t mean stocks per se, but different forms of equity—public equities, private equities, real estate equity, commodity equity, etc.

Then you want to take advantage of the illiquidity pre-mium, which you have with private equity, private real estate, private energy and private debt. If you think about that model, what exposures are you going to reduce to get




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exposure to alternatives? Well, you’ll have less exposure to traditional fixed income and traditional equities.

A normal endowment is going to have significantly less exposure to traditional equity in particular. If you look at Yale, for example, I think they’ve got 6 or 7 percent domes-tic equity, maybe double that amount international equity. That doesn’t mean that’s their only exposure to equity. They have lots of exposure to private equity in both domes-tic and international markets. They have a lot of exposure to real estate equity, commodity equity, hedge fund equity. It’s just that, in an endowment portfolio, or a sophisticated pension portfolio, or a sovereign wealth fund portfolio, you’re going to get exposure to those four risks that I talked about—equity risk, fixed-income risk, commodity risk and currency risk—in different structures.

If you think about an average large endowment, they’ve probably got 10 percent in fixed income. They’ve got 10 to 15 percent in traditional equity. Then they’ve got 40 to 50 percent in private investments. And that’s where they get the most alpha. They get their biggest excess returns from all the things that they do on the private side of the portfolio.

The largest pools of capital in the world are all managed in the same way, roughly, and they are all heavily weighted toward these things that are called alternatives. I think “alternatives” is a little bit of a misnomer, because ultimately you can own stocks, bonds, currencies and commodities, and how you own them, meaning what structure—mutual fund, hedge fund, private partnerships, separate account—doesn’t really change the risk you’re exposed to. But the pro-cess for managing the risk does change. There is a difference between managing a long/short portfolio and managing a long-only portfolio. There is a difference between managing a private portfolio of oil and gas assets versus an MLP.

JOI: Why can they do this?

Yusko: What the big endowments figured out is, “I know I’m not going to need my money immediately. I’m only going to spend a certain amount each year, so I can take some portion of my assets and bear illiquidity risk.” Because ultimately as an investor, you have to choose one of four risks that you’re willing to take. You can either take credit risk, and you buy a bond, and the risk is that you don’t get paid if the bond defaults. You can take equity risk, the risk that there is no money left for the equity owners after the bond holders are paid. You can take on structuring risk, which would include using leverage, or you could buy some sort of option or future or some other structure that has an embedded differential exposure like a derivative. Or you can take on illiquidity risk, which is the risk that you have to lock your money up for some period of time—and you should be compensated something for taking any of those risks.

Illiquidity risk is a very interesting risk, because what we have found is that private investments tend to outperform public investments over the long term because of that illi-quidity premium. As a result, the big endowments engaged in a much larger allocation to private investments. Private investments are considered alternatives, but the risk is to equities and illiquidity, which doesn’t seem alternative to me.

Traditional portfolios of stocks and bonds over the last decade earned around 2 or 3 percent. The bigger schools made 8 to 10 percent. Where did that big giant difference come from? About half of it came from the illiquidity pre-mium—exposure to private equity, private real estate, private energy, private debt. The other half of it came from just hav-ing better managers in the public markets, hedge funds and other trading strategies that were able to extract return even in the bad times, like 2000-2002, 2005 or 2008. That doesn’t mean that they necessarily made money, but they lost sig-nificantly less and kept the power of compounding working in their favor.

JOI: Should the average investor care about alternatives?

Yusko: They should first consider the question, Why do you invest? You invest to take advantage of risk factors that compensate you for putting your capital to work, and therefore, you get a return on your capital. If you leave your money in risk-free assets, you get the risk-free rate, which today is zero. And you can’t live on zero. So you’ve got to choose what risk you’re going to take. You can take credit risk and buy bonds, and then you don’t need alternative exposure. But the problem is, bonds today don’t yield enough to cover your lifestyle costs.

So then, you’re going to have to take equity risk. Well, if you take equity risk, and there’s lots of volatility, you may be forced to sell at the wrong time. You may not realize the same level of return as you think you’re going to over the long term. People get shaken out of the markets at pre-cisely the wrong time due to volatility.

If you have hedged strategies, you’re not only going to get lower volatility and a more stable rate of return, but you’re going to get access to the best talent. Because in every busi-ness we know, the most talented players go to the place where they can make the most money. Since the fee structure in alternatives is higher than traditional, you tend to find that the best, most talented managers go into that side of the business. The best doctor charges the most. The best lawyer charges the most. The best basketball coach gets paid the most.

I always joke: If you’re on a gurney, about to go into surgery, do you raise your hand and ask, “Can I have the cheapest surgeon?” No, you want the best surgeon. So I think the average investor has to think about having expo-sure to alternatives, because all that means is you have exposure to skill-based managers. We know that in a zero-interest-rate world, it’s tough to get any return from market beta. Bonds are yielding 2 percent. Equities haven’t made money for a decade. So if you have a balanced portfolio, just a passive portfolio of stocks and bonds, over a decade you made 2 percent. That’s not enough.

Yale’s portfolio in the last decade made 10 percent. UNC’s, where I was, made 8 percent. How did they do that? They had exposure to these alternative structures.

JOI: Are these investment opportunities out of reach for

the average individual investor?

Yusko: They shouldn’t be, but there are currently some mean-ingful restrictions imposed by the SEC on access to alternative




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20 May / June 2012

strategies for what we call “the average retail investor.” It doesn’t mean that they’re 100 percent restricted, but there are a very small number of products and services out there for the true retail investor. For the investor who is what we call the “mass afflu-ent”—an accredited investor who has $1 million of net worth or makes a certain amount of income over a couple of years— there are many more options, but there are still restrictions.

I don’t believe that there should be restrictions, because if you look at the risk of these strategies, it’s much, much lower, on every measure of risk compared to traditional investments. People think fixed-income investments are safe. Well, as long as interest rates are falling, fixed-income investments are safe. As long as companies aren’t default-ing, fixed-income investments are safe. But if you have a really bad market and companies default, like they did in ’90-’91, like they did in ’98, like they did in 2002, then fixed-income investments aren’t so safe at all; you can lose all your money. If you have rising interest rates, you can have very large capital losses in fixed income.

Take a strategy like equity market neutral, which is a form of hedge fund strategy. Its volatility or risk is about 25 percent of the risk of fixed income over the long term, with similar returns. Although the risk is significantly lower, it’s perceived to be higher.

If you read the Wall Street Journal any day of the month, you’ll find some story about how risky hedge funds are. But the data doesn’t bear that out. The data doesn’t bear out that private real estate and private equity and private energy are more risky than equities and fixed income, although that’s the perception. I think alternatives are entirely appropriate for retail investors, and I think investors should have exposure to alternative strategies to lower the risk of their portfolios. But it is more difficult in the current environment for them to gain access to the most talented alternative managers.

JOI: But what about the new ways to provide exposure to

alternatives, like hedge fund replication ETFs?

Yusko: There are now liquid alternative mutual funds, although I think that’s a little bit of an oxymoron. What makes an alternative investment “alternative” is the illiquid-ity premium. The reason a hedge fund can, in many cases, outperform a mutual fund or a more liquid strategy is that the hedge fund has a differential ability to hold assets over a longer period of time. Hedge funds don’t have the same liquidity needs. When you are able to have a differentiated time horizon, you can take advantage of people who have shorter time horizons. It’s just the nature of any business.

Think about selling a house: If you had to sell your house by tomorrow, you’d get a significantly lower price than if you had a year to sell it. When your time horizon shrinks, your ability to hold out for the best price falls. There is an arbitrage in many of these strategies, which is just because they have a differential time horizon.

JOI: Do you think that these kinds of strategies can be

captured by indexes?

Yusko: I think these strategies are actually the anti-index. Traditional indexing is what I’ll call “dumb strategies”—and

that doesn’t mean unintelligent. It just means they are rule-based strategies. Indexes aren’t allowed to think. If you have your 401(k) set to buy an S&P 500 index, and a company has gone up a lot, so its valuation is at a silly level, that index fund can’t make a decision not to buy that stock. It must buy what’s on the list. Regardless of price, it’s going to buy that security. That’s why, in an ebullient period, like ’95 to ’99, or ’09 to ’11, index funds look really good, because they’re capitalization weighted, meaning more money goes into the biggest companies because that is what the rules say.

What happens in ETFs and index funds (and more pas-sive mutual funds to a lesser extent) is money flows in, and because of this capitalization-weighting phenomenon, it flows to a smaller and smaller number of names. For exam-ple, today Apple is close to 20 percent of Nasdaq. It’s ridicu-lous—one company being 20 percent of an index. If you go back to ’95 to ’99, it looked like indexing was the best strat-egy, and everybody poured money into indexing. Then, from 2000 to 2002, it was a terrible strategy. Eventually you get to the breaking point, where you just can’t buy any more, and the valuation just gets so silly that people say, “I’m not going to buy it.” Once that happens, and the flows go the other way, now you’ve turned a virtuous cycle into a vicious cycle. And they have to unwind. What you’ll see in indexes is these peri-ods of hyper-performance and then under-performance.

Alternative strategies can actually take advantage of that cyclicality, and they try to provide liquidity when there is no liquidity in the market, and pick up payment for that service. As Warren Buffett says, they try to be greedy when others are fearful and fearful when others are greedy. They tend to be a little more hedged and a little more reserved in a time like today, when people are trying to jump on the latest move in Apple. And they tend to be much more interested in buying things, like back in 2002, when nobody wanted to own stocks because Enron and Adelphi and all these bad companies had done all these bad things. There is no chance that you could have an index of alternatives, in my mind, just because the strategies aren’t designed to be capitalization weighted.

JOI: Are alternatives managers actually providing alpha?

Yusko: I teach a class at UNC on hedge funds. A recent ses-sion focused on alpha. For the last 20 years, the return to the average hedge fund has been around 11 percent versus about 7.5 percent for a balanced 60/40 portfolio. There has been a significant increased return to hedge funds relative to traditional stocks and bonds.

And they have lower volatility, about 6.3 percent instead of 7.4 percent—much lower volatility, better returns. The alpha is actually very, very large, because your net exposure to market risk is very low— around 50 percent. When you’re getting all your excess returns, it’s from alpha, from the indi-vidual stock-picking skill of those managers. Now, alpha is a zero-sum game and because of that every manager doesn’t have alpha. What it means is there are a number that have very significant alpha and some that have negative alpha.

Overall, there has been significant alpha available in these markets over the last 20 years. The data is a little sketchier when you go back 30 or 40 years. However, over the last 20




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years, there has been on the order of 600-700 basis points of alpha over traditional strategies. Some will point to the last three years and say, “Well, all that alpha has disappeared over the last three years.” But it always does in periods of ebullient markets and recoveries. When we see the other side of the full market cycle, we are likely to see a different outcome between alternatives and traditional investments.

JOI: Does beta exist in the alternative space?

Yusko: Beta is a measure of your exposure to something else—you have to have beta relative to something. If I have a real estate fund, I have to have beta relative either to a real estate index or an equity index or a fixed-income index or the price of butter in Botswana. Beta is a statistical term that is the relative movement. Basically people think of it as market movement. If I have a portfolio structure, I have my beta exposure, which is my percentage that can be explained by the movement of the market. Then there’s the alpha, the per-centage that can be explained by my skill. And then there’s idiosyncratic, which is the part that can’t be explained.

Beta is just a statistical term, as is alpha. The difference is that, if we look at any stream of returns, we have to decide what we want to judge that beta relative to. So, if I look at hedge funds relative to equity, there clearly is beta to the equity markets, and it ebbs and flows. In some periods it’s quite low, and in some periods it’s quite high. Usually it’s quite high in periods like we’ve experienced the last few years, when everything is going up together. Correlations are very high, with stimulus caus-ing the bad and the good to go up. There’s no differentiation between good companies and bad companies—in that period, correlations tend to rise and beta tends to rise.

In periods where the market works normally, which is the bulk of the time, there is less stimulus. You end up with very significant alpha from the managers, and you end up with very low beta relative to whatever index you want to track.

So is there a beta in alternative sites? Of course there is. Remember, you can only be exposed to equity, debt, currencies and commodities. Say I’m a hedge fund that trades currencies, and if I run my track record relative to equities, you’ll see very little beta. You might even see negative beta. But you will either see a lot of alpha or not a lot of alpha, depending on my skill.

If I run a portfolio long and short stocks, and I’m always 60 percent net long, you’re going to see a very high beta to equi-ties. But if you ran it relative to currencies, you’d see low, or maybe even negative, beta. Beta only describes the comove-ment relative to something else, and we get to choose what that something else is when we make the comparison.

If I look at equity market neutral, it has had very nice stable returns, roughly equivalent to bonds over the past 20 years, but the volatility has only been 25 percent of that of bonds. If I run that portfolio relative to bonds, I actually get a pretty low beta. Because the markets that they’re playing in are equities, not fixed income, they are generating the returns using totally different systematic risks.

Therefore, if I run the track record around fixed income, it doesn’t show much beta at all. All of it’s coming from alpha.

If I run it based on the equity markets, I still get very low beta, because there is no net exposure. I’m a dollar

long and a dollar short—my money is sitting in cash. Where you get your big beta is if you ran it relative to cash; then you get almost perfect correlation. But then you’ve got to decide how much alpha you have above cash. Beta all depends on which benchmark you pick.

JOI: Are the higher fees that are generally paid for alterna-

tive strategies—like two and 20—justified in your view?

Yusko: I don’t know of any business where the best per-son doesn’t charge the most money. You could hire a law-yer for $100 an hour, or you could hire a lawyer for $5,000 an hour. It just depends what you need the lawyer to do. If you need him to look at your will, you can probably pay $100 an hour. If you need him to negotiate a multibillion-dollar settlement, you probably want to pay $5,000 an hour. It all depends on the scale of the problem and on what your tolerance for underperformance is.

What really matters in this world is not the fee you pay, but the net result. What I think the problem is, particularly for retail investors, is they tend to think that investing is about cost minimization. Nothing could be further from the truth. You actually want to maximize your return, not minimize your cost. In many cases, the evidence shows that there is a high correlation between fees and performance. The best venture funds, like Sequoia, have the highest fees. The best buyout funds, like KKR, have the highest fees. The best hedge funds, like Renaissance, have the highest fees.

Over the last decade, I could have bought an index fund for 10 basis points in equities, and I would have made 1 percent. Or I could have paid two and 20 to a fantastic hedge fund, and I could have netted almost 10 percent. Now 10 is far better than 1, and I don’t care how much I paid to get that.

People talk about fees in a vacuum, and you really can’t. You can only talk about fees relative to what they produce. Steve Cohen, a famous hedge fund manager, keeps 50 percent of profits. People say, “Wow, I would never pay that. It’s just ridic-ulous.” Well, even with that fee structure, he’s compounded far above equities for 20 years and had you decided not to invest based on fees, you would have missed the opportunity to make spectacular returns. Other large hedge funds charge something like five and 35, or four and 24. In fact, there are a lot of people who have very high fees, but as long as their net returns (the return I get to keep) justify those fees, it doesn’t bother me at all.

I really want to be around the best of the best, which could mean I have to pay them a little more to get a little more, because what matters is the returns I earn and, more importantly, the quality of those returns in terms of being lower volatility than traditional investments.

JOI: Do you think that hedge fund strategies can be suc-

cessfully replicated outside of the hedge fund structure? Yusko: I think that the idea of a hedge fund replication strategy is another oxymoron. If you look at the big bro-kerage firms that try to do this, they basically say, “Well, we have insight into what all the managers are doing. So let’s group all their positions together and give you the amalgam of those positions.” The problem with that is

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May / June 201222

Investable Indexed Approaches

To Long/Short Investing

Factor- and mechanical-based replication methods

By Peter Little and Greg King




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The term “long/short” (LS) has wide and varied applications in the world of investments. In its broadest definition, an LS strategy signifies an

investment process not constrained to holding only long positions. In perhaps its most common usage, an LS fund tends to refer to an equity hedge fund that takes both long and short positions. Whatever the application of the term, LS investing usually implies a search for absolute returns and an attempt to decrease or neutralize market beta. This combination of absolute returns with low market beta can also be described as “alpha” and has histori-cally been considered an “active” investment approach. Here we examine the LS investing landscape from the perspective of an indexer. We make the case that there are sensible and investable ways to index these strategies, thereby creating a “passive” approach to various sectors of LS investing.

Certain questions arise in such an undertaking: Which types of strategies are suitable for indexing and which are not? What are the pitfalls of index construction when con-sidering long/short strategies? When looking at LS fund results, how can we separate those attributed specifically to the superior skill of some managers from those manag-ers who are able to deliver purely because of the uncon-strained nature of the LS investing techniques at their disposal? To address these and other questions, we first provide an overview of the four main types of LS models. Next, we examine the case for passive approaches to LS investing. Finally, we present case studies of the Credit Suisse long/short index as an example of factor-based replication and the Credit Suisse merger arbitrage index as an example of mechanical replication.

The Long/Short LandscapeWhenever a manager decides to pursue a long/short

strategy, her investment approach can be distilled into one of four models by answering certain questions about the approach: Are positions taken based on company- or issue-specific information? Are decisions primarily based on the outputs of a quantitative model or do they come from funda-mental research? Is there a high or low degree of turnover?

There are four basic models used for selecting long/short positions in these strategies:

• Valuation• Trend-following• Macroeconomic• ArbitrageValuation-based approaches are typically buy-and-

hold strategies. The manager selects undervalued secu-rities for the long side of the portfolio, and overvalued securities for the short side. Generally this approach leads to more stable positions with lower turnover than other strategies. Alpha in this space is derived from fundamen-tal analysis of securities. Managers have a high amount of discretion in the selection process and focus on com-pany fundamentals more than on the macro environment. Typically, these managers are long biased and, in addition to potential alpha from security selection, add value by


varying their market exposure to deliver beta in a generally more efficient or better risk-adjusted format.

Trend-following models use technical indicators to identify trades. Typically, managers in this space use quan-titative or systematic processes to generate buy or sell signals and have somewhat limited discretion in these decisions. This is a market-timing approach and is suitable across asset classes so long as the instruments traded are liquid. In addition, managers prefer instruments that are not company specific so as to remove idiosyncratic risk from the model. Returns from trend-following strategies are typically relatively uncorrelated to traditional beta, provid-ing investors with diversification as well as the potential for alpha from the superior market timing of some managers’ models. A “managed futures” strategy would be an example of this approach.

Macroeconomic-based approaches are cross-asset-class in nature and are driven by global macroeconomic trends. Similar to trend-following approaches, they use liquid instruments that are not company specific and have a fairly high degree of turnover. Alpha in this space is derived from market timing. As with trend following, these manag-ers invest across a variety of asset classes and can have net long or short exposures, so in addition to potential for alpha from superior market timing, these managers offer inves-tors diversification to their traditional exposures. Managers take into consideration a variety of information that could affect the economy, either on a relative basis (e.g., long/short country pairs) or on an absolute basis (e.g., expect a global downturn and go short risky assets). Data that man-agers factor into their decision-making process are likely to include anything that could impact the local or global economy, including economic releases, political environ-ment, supply/demand and a variety of other information. Managers in this space also use relative value techniques to extract diversified risk premia like currency carry. A “global macro” manager would be an example of this approach.

Finally, arbitrage strategies seek to exploit relative pric-ing inefficiencies in different markets or securities. They focus on issuer-specific securities, and generate alpha from security selection. The strategies are risk-managed in a quantitative approach, and tend to be market neutral. As such, they provide good diversification to traditional expo-sures based on the managers’ ability to extract uncorrelated risk premia. Managers will look to identify different securi-ties that should have a strong relationship (based on some shared characteristics) but that are temporarily valued dif-ferently (e.g., convertible bonds that should theoretically have a similar value to a combination of the company’s debt and options on its stock). Managers then take a relative value position in those securities in order to profit as their values converge.

Indexing Long/Short Investment StrategiesTo answer why indexing is worthwhile for LS investments,

we need to look at what it accomplishes. First, the invest-able indexing process removes manager-specific alpha. This idiosyncratic risk is nearly impossible to index and tends to




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May / June 201224

Figure 1

be diversified away through most index processes. What then remains are the alternative betas. These risk premia—such as relative value arbitrage, long positive trends versus short negative trends and the ability to introduce leverage to cre-ate convex return profiles—are available only in the universe of LS investing. Removing the long-only constraint of the traditional indexing approach brings this access to new risk premia. In trying to index the strategies of a certain style of long/short investing, the indexer is essentially trying to sys-tematically capture the main benefits of the strategy beyond manager alpha and deliver a new “passive” source of returns to investor portfolios.

Long/short strategies are typically indexed either by observing actual hedge fund returns in the sector or via various replication strategies. Although aggregation of hedge fund returns data is an approach that has been taken by a handful of firms and is useful for many purposes, including research, the approach does not generally lend itself to invest-ability. Various constraints may stand in the way of broad investability here, such as a lack of daily liquidity, limited access to certain funds, capacity constraints of certain funds, and investor class constraints, to name a few. Conversely, rep-lication strategies—of which there are two main types—tend to start with investability in mind.

Factor-Based Replication

The first replication style, “factor-based replication,” uses quantitative measures to attempt to replicate hedge fund index returns by defining models that bear a high correlation to the hedge fund index return stream. Factor-based replication tries to identify both the market seg-ments (“factors”) and the level of current risk that manag-ers currently favor. Such models only “invest” in liquid market betas (and alternative betas) rather than the hedge funds themselves. This replication method is typically used in fundamentally based strategies where exposures are expected to be more stable.

Factors are typically proxied using a basket of securities that represent a certain risk premium, e.g., U.S. large-cap

equities (e.g., S&P 500), value companies (e.g., Russell 2000 Value Index), high-yield bonds (e.g., iBoxx Liquid High Yield Bond Index), etc. Factors can also represent certain investing behaviors, such as price momentum, to which managers can be exposed when they become susceptible to performance chasing. These can be proxied by creating a factor that buys securities that have been going up and shorts securities that have been going down. Finally, factors can also represent a type of risk that man-agers can become exposed to by nature of the instruments they invest in; for example, illiquidity risk, which manag-ers take on when they invest in securities that are difficult to sell. This risk can be interpreted as short exposure to volatility, since managers are likely to be paid a premium for holding illiquid securities while being susceptible to sharp losses in volatile environments when illiquid secu-rities are likely to trade at steep discounts. This factor can be proxied by selling options that give a similar profile of being short volatility (and risking sharp losses if volatility spikes) while earning a premium over time.

To empirically determine the market factors that drive LS equity hedge fund returns, two key assump-tions are made. The first, “view commonality,” suggests that managers tend to have clustered “views” (defined as exposures to various beta and alternative beta mar-ket factors). Analysis of actual hedge fund holdings shows this type of clustering exists. (See Figure 1.) The second and corollary assumption, “exposure inertia,” holds that, on average, managers will adjust these exposures to market factors fairly slowly—a few man-agers tend to adjust their exposures first, then, if their views turn out to be correct, others tend to follow. This exposure inertia allows multifactor-based replication models to identify and track the changes in the core factors over time. However, as factor-based replica-tion relies on these assumptions about the behavior of managers when they encounter certain environments, these types of models can be slow to adapt to systemic changes in the way managers, in general, react to new environments in the future.

Manager A Manager B Manager C Manager D (etc.)

CommonViews

Individual ManagerPositions

Beta Factor Clustering

Source: Credit Suisse Asset Management, LLC

Long

Neutral

Short

Traditional-Beta Factors Alternative-Beta Factors

Small Cap Large Cap US Equities Non-US Equities Value Growth Momentum




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Case Study – Factor Based Replication (Credit Suisse Long/Short Liquid Index)

The Credit Suisse Long/Short Liquid Index uses factor-based replication to achieve a high correlation to the equity long/short segment of the hedge fund universe (as represented by the Dow Jones Credit Suisse Long/Short Equity Hedge Fund Index, which we refer to as the ”target index”) in an investable way.

In this case, Credit Suisse designed a stepwise multiple regression process to determine the markets in which long/short equity managers are investing, identify the amount of risk long/short equity managers are taking and capture these exposures, as reflected in the performance of the target index, using the liquid instruments that cor-relate to the target index.

The process begins with an analysis of historical per-formance data to identify core, broad-market exposures in which managers in the aggregate appear to have taken positions during the prior 12 months. As described previ-ously, common manager views are the main drivers of hedge fund index performance, and these return drivers can be captured using a combination of factors. These factors are not hedge funds but are actual market indexes that are investable with reasonable liquidity. For example, U.S. large-cap exposure can be proxied by the S&P 500 Index, while equity momentum can be proxied by an index representing long positions in companies that are performing well and short positions in poorly perform-

ing companies. Once identified, core factors are selected from specified liquid tradable indexes. Exposures to these indexes can be long or short, with net exposures changing monthly depending on the market environment.

An overview of the factors used by the long/short model is shown in Figure 2.

The model estimates factor weightings by performing a stepwise regression of index returns on factor returns. A stepwise regression is similar to an ordinary least-squares


Figure 3



Long/Short Liquid Index Model

• Historical index and factor data

• Fit returns of the target index using five

equity index factors

• Only include significant factors

• Use residuals from the base stage

• Fit using style factors (value/growth and

momentum

• Only include significant factors

• Use residuals from the style stage

• Fit using nine S&P 500 sector index factors

• Include at most one factor with ≤25%

allocation

Fa

ctor W

eig

hts

Rotation Stage

Base Stage

Input

Style Stage

Market Factor/Proxy Index Market Represented

Credit Suisse Long/Short Liquid Index Model Factors

Figure 2

S&P 500 Index U.S. Broad Large Cap

Russell 2000 Index U.S. Small Cap

MSCI EAFE Daily Total Return Index (Net)1 International Large Cap

NASDAQ 100 Index U.S. Concentrated Large Cap

MSCI Emerging Markets Daily Total Return Index (Net)2 Emerging Markets

Russell 2000 Value Index Value

Russell 2000 Growth Index Growth

Credit Suisse High Price Momentum Index High Price Momentum

Credit Suisse Low Price Momentum Index Low Price Momentum

Consumer Discretionary Select Sector Index Consumer Discretionary

Technology Select Sector Index Technology

Consumer Staples Select Sector Index Consumer Staples

Energy Select Sector Index Energy

Financial Select Sector Index Financials

Health Care Select Sector Index Health Care

Industrial Select Sector Index Industrial

Materials Select Sector Index Materials

Utilities Select Sector Index Utilities




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(OLS) regression regression, but with one main advan-tage—it identifies and retains only those factors deemed to be statistically significant return drivers. This procedure is important because it avoids allocating to less relevant fac-tors, while trying to mitigate the effects of factor co-linear-ity resulting from the statistical similarity of many of these equity factors.

Next, as illustrated in Figure 3, the model analyzes the extent to which the returns of the hedge fund index that were not explained by stage one are attributable to dynamic market-neutral-style trades. The model examines the differ-ence between the performance of the hedge fund index and the performance of the core exposures and fits these residual returns using a stepwise regression.

Finally, the model seeks to identify which short-term, industry-specific trades are driving the hedge fund index returns in excess of the core exposures and the dynamic style themes. Once again, in employing a stepwise regres-sion, this time over a short time horizon, the model identi-fies which industry sector best explains the previously unex-plained returns. In this stage, the regression is constrained to select only one factor in order to identify the single most important exposure and to avoid over-fitting the model.

This process is performed every month as the most recent hedge fund index returns are available. These three stages identify the factors that should be included in the replication model in any given month, as well as how much weight the factor should be assigned. The LS equity replication index is then calculated based on the performance of the factors determined by this process. Overall, the results demonstrate the potential available in this type of an approach: Since inception, the Credit Suisse Long/Short Liquid Index has had an annualized return of 5.5 percent versus a return of 3.7 percent for the target hedge fund index (the Dow Jones Credit Suisse Long/Short Equity Hedge Fund Index) and has had a 92.8 percent correlation to the target index.3

Mechanical Replication

The second replication method, “mechanical replica-tion,” skips using hedge fund returns completely. Rather than seeking to correlate to a target index or segment, this method’s purpose is to fully replicate, if possible, the particular LS strategy in question by systematically mimicking the trades managers in that strategy make. Mechanical replication is used where the investing tech-nique is well defined and the universe of securities is identifiable, investable and not excessively large. This approach seeks to define the strategy and implement rules around how it should be carried out in a defined universe of investments.

For example, the “carry” strategy in currency invest-ing can be mechanically replicated. The carry tech-nique involves going long high-yielding currencies while simultaneously shorting low-yielding currencies, implic-itly wagering that forward rates will not converge to their then-current levels. To replicate this mechanically, a set of rules regarding when to buy/sell a particular cur-rency could be defined, and a universe of tradable cur-

rency pairs could be identified upon which to execute the indexed strategy. Another strategy that is conducive to mechanical replication is managed futures, where the universe of securities that managers trade is identifiable (typically liquid listed futures contracts) and the tech-nique is well defined (i.e., investing in pricing trends, typically momentum-based trends over a variety of dif-ferent trend horizons). This strategy can be systematically mimicked with a generic trend-following model that uses a variety of liquid futures contracts and trend horizons to determine the trading strategy.

While mechanical replication is appealing because of its straightforward nature and lack of correlation assump-tions, there are unfortunately relatively few sectors of LS investing where it is reasonably possible.

Case Study: Mechanical-Based Replication (Credit Suisse Merger Arbitrage Liquid Index)

The Credit Suisse Merger Arbitrage Liquid Index (the “merger index”) serves as an example of mechanical rep-lication. Merger arbitrage involves capturing the spread between the price at which the stock of a company trades after the announcement of a proposed acquisition of that stock and the price that the acquiring company has proposed to pay for it. The spread typically exists due to the uncertainty that the announced merger or acquisition will close, and, if it closes, that such merger or acquisition will take place at the initially proposed economic terms. For successful transactions, the spread is expected to approach zero by the closing date of the transaction. The size of the spread itself depends on the perceived risk of the deal closing, as well as the length of time expected until the deal is completed.

All of these factors lend themselves to mechanical rep-

May / June 201226

Figure 4

Credit Suisse Merger Arbitrage Liquid Index Methodology

• Narrow deal universe to include only

North American and European merger

deals with the following liquidity features:

–Target has market cap of >$500 M

–Target has sufficient trading volume

–Acquirer for stock deals is easy to borrow

to enable shorting

• Apply systematic contraints to ensure deals

have arbitrage potential:

– Positive acquisition premium

– Offer is for substantially all shares

outstanding of target

– Acquirer does not already own

substantially all of the target’s shares

• Incorporate rebalancing procedure and

risk constraints:

– Deals are modified cap weighted and

weight caps apply

– Leverage and short exposure are constrained

– Turnover is controlled through rebalancing

procedures


Liquidity

Constraints

Arbitrage

Constraints

Daily

Rebalancing




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lication. The merger index uses a quantitative methodol-ogy (see Figure 4) to select a diversified sample of liquid merger arbitrage deals in order to represent the returns of the merger arbitrage space.

The selection process begins once a new deal is announced. The model determines each deal’s eligibility for inclusion in the index; eligible deals can involve bids offering cash, stock or some combination of both in exchange for the target’s stock. In cash deals, the target’s stock typically trades slightly below its bid price immediately following the deal announce-ment and moves toward bid price as the probability of deal closing increases. In an eligible cash deal, the merger index will “buy” the target. In stock deals, the price of the target and acquirer typically trade at a spread post-announcement and converge as the probability of deal closing increases. In an eligible stock deal, the merger index will “buy” the target and “short” the acquirer.

To ensure the investability of the merger index, the process focuses on the deals that are likely to be liquid. Specifically, this means the deal universe is limited to deals in Western Europe and North America, where deal activity is highly regulated, and as such typically allows for more orderly trading. In addition, these companies are more likely to have larger market capitalizations and meaningful trading volumes, and therefore their stock should be easier to borrow (in the case of acquiring com-panies that need to be shorted in the index).

Once the eligible universe is defined, constraints are applied to ensure there is sufficient arbitrage potential, i.e., to ensure that the offer is for most or all shares out-standing and that the acquirer does not already own the vast majority of the target company stock.

Once selected, deals are generally held in the index until they are completed or officially terminated (occasionally deals will be removed if they breach liquidity constraints, such as if the acquirer’s stock is no longer easy to borrow).

Endnotes

1 In the case of a negative weight for this market factor, the gross index version of this market factor will be used.

2 Ibid.

3 Data as of 2/29/12. Represents performance of the Credit Suisse Long/Short Liquid Index from 10/16/09, the inception date of the index, to 2/29/12.

Past performance is not necessarily indicative of future performance.

4 Data as of 2/29/12. Represents performance of the merger index from 12/31/09, the inception date of the merger index, to 2/29/12. Past performance

is not necessarily indicative of future performance.

Disclosures

For more information on these indices, including information about their historical performance, as well as information about certain securities being offered by Credit

Suisse that link to these indices, see www.credit-suisse.com/etn and you may access the pricing supplements relating to some of these securities on the SEC website at:

Credit Suisse Long/Short Equity Index Exchange Traded Notes: http://www.sec.gov/Archives/edgar/data/1053092/000110465912020682/a12-7748_1424b2.htm

Credit Suisse Merger Arbitrage Index Exchange Traded Notes: http://www.sec.gov/Archives/edgar/data/1053092/000095010312001483/dp29447_424b2-etn3a.htm

Credit Suisse Merger Arbitrage Index Leveraged Exchange Traded Notes: http://www.sec.gov/Archives/edgar/data/1053092/000095010312001484/dp29449_424b2-etn4a.htm

Before investing in any these securities, you should, in particular, review the “Risk Factors” section in the applicable pricing supplement, which sets forth risks related to

an investment in the securities.

The indices have limited history and may perform in unexpected ways. The performance of the Credit Suisse Long/Short Index and the Credit Suisse Merger Arbitrage

Index may not be entirely representative of the performance of their respective strategies, and there is no assurance that the strategy on which these indices are based will

be successful. Additionally, because Credit Suisse is affiliated with the sponsor of the indices, and certain of its employees are members of the index committees for the

indices, conflicts of interest may exist between Credit Suisse and investors in securities whose return is based on the indices.

The merger index takes the approach of holding deals as long as possible because when a deal is removed based on stop-losses, maximum deal spreads or time constraints, such removal often simply crystalizes any negative perfor-mance while removing any possibility of recovery, rather than tracking the potential future success of the deal.

The index is rebalanced daily and risk constraints are applied. As for weighting, the algorithm weights deals based on the cube root of their market cap relative to the cube root of the market caps of the eligible universe. This is done to reflect the fact that, inevitably, larger amounts of merger arbitrage capital are able to be deployed in the larger deals while recognizing that occasionally, there can be a 100-to-1 ratio in deal sizes and therefore some scaling is desirable. All deals are capped at a maximum weight of 7.5 percent within the merger index to avoid over-concentration in large deals.

The merger index uses this passive approach to extract the merger arbitrage risk premium and diversification benefits of merger arbitrage investing from a large and defined universe of potential deals. It has generated posi-tive returns in both years since its inception in 2009, with an annualized return of 4.9 percent.4

Conclusion

Despite the perception of long/short investment strat-egies as “active,” it is possible to capture the perfor-mance characteristics of certain strategies in this catego-ry through a passive approach. By providing examples of Credit Suisse indexes that rely on factor-based replica-tion and mechanical replication, respectively, we have demonstrated how these very different methods reflect the underlying market trends that drive the performance of the targeted strategies. The resulting indexes, which reflect investable executions of these strategies, provide examples of how indexing can provide a passive alterna-tive to these traditionally “active” investment strategies.





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An Alternative View

May / June 2012

By Michael Vogelzang

28

Alternative ETFs have a long way to go

Waiting For

Their Big Break

The road to long-term viability for alternative ETF (Alt-E) sponsors and for the successful imple-mentation of Alt-Es in portfolios will require

many adaptations for our industry. Interesting alter-native strategies are being created, many with solid theoretical bases, strong long-term value potential and an ease of use that only a few years ago would have seemed impossible. Unfortunately, the short track records of the existing pool of Alt-Es, the poor returns many had in 2011 and the lack of assets to create mar-keting scale make for a long road ahead. For investors with the sophistication, patience and ability to find creative uses for Alt-Es, many existing funds offer an interesting array of options. Undoubtedly, more will come to market in the near future. However, to date—and it’s early in the development cycle of Alt-Es—these funds have seen weak asset flows amid limited uptake in both the retail and institutional investment commu-nities. I believe the lack of commercial success stems from the complexity of Alt-Es.

Alt-Es have a radically different foundation than tra-ditional ETFs. Rather than mirroring the returns of a traditional index, alternative ETF prices, returns and vola-tilities are derived from an algorithm or a specific rules-based decision process. Specifically, the ETF price moves based on how the rules (or model) interact with the self- defined investment universe. By definition, an Alt-E will not perform as a vanilla index-based strategy, where the underlying index can be easily measured, understood and monitored. Rather, the decision engine provides the critical inputs for returns and so requires the practitioner to have a deeper understanding of how positions are selected.

These nonindex, rules-based decision processes of

Alt-Es have clear and important implications. Generally, we can expect the understanding and sophistication of the buyer to be significantly greater than that of some-one buying a simple index ETF. Alt-Es are not made for grandma, at least not unless she has a master’s degree from MIT. These are professional-grade tools and will mostly be used by professional investors.

Additionally, creative and thoughtful portfolio construc-tion using Alt-Es will be critical to their successful growth. Potentially complex interactions with other portfolio hold-ings, model unpredictability and shifting correlations with other portfolio assets heighten the risks of using Alt-Es. Finally, costs, given the added complexity, lack of scale (currently) and additional required education, will be higher than those of simple index-based ETFs.

The first challenge for the Alt-E marketplace is identi-fying what is and what is not “alternative” and building a categorization system to better compare differing types of Alt-Es. IndexUniverse has broken out a still-small group of 27 ETFs as “alternative.” One can go a step further and break down those Alt-Es into four broad categories: • Hedge Fund Replication Strategies: These strategies

use advanced statistical techniques to attempt to mimic the returns (not the holdings) of a cross section of the hedge fund universe. Much academic ink has been spilled in an effort to gauge the efficacy of statistically replicating hedge fund returns. Generally, these funds work effectively during times of low stress and normalcy in the market. However, they can vary widely from their target (replicating hedge funds) during periods of abnormal market stress and returns. A few HF rep-lication ETFs include the ProShares Hedge Replication (NYSE Arca: HDG), the IndexIQ Hedge Multi-Strategy




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Tracker (NYSE Arca: QAI) and the iShares Diversified Alternatives Trust (NYSE Arca: ALT). Each fund relies on its own set of algorithms to target the returns of a specific slice of the hedge fund universe.

• Long/Short Funds: Using predetermined rules, these funds harvest what they believe to be long-term ineffi-ciencies (generally using fundamental factors) by having offsetting long and short positions with differing char-acteristics. For example, QuantShares has rolled out a number of factor-based ETFs that attempt to profit from tactical as well as strategic factor tilts. Its U.S. Market Neutral Value Fund (NYSE Arca: CHEP) owns shares of the cheapest companies while shorting those companies the model determines are the most expensive.

• Allocators: This smaller group of Alt-Es includes those funds that use a wide array of asset classes (equity, fixed, commodities, real estate, currencies) and a predetermined set of rules for tactically allocating a fund’s assets. An example is the AdvisorShares Meidell Tactical Advantage ETF (NYSE Arca: MATH), the pro-spectus for which includes a description of its decision engine with this language: “MATH’s quantitative pro-

cess measures price velocity to determine which assets to invest in.” Forgiving the editorial sin of ending the sentence in a preposition, the process of “price veloc-ity” is open to discovery for potential investors.

• Managed Futures Funds: Another small category of Alt-Es, these funds invest in any asset that trades in the financial futures markets and are generally managed using various forms of price momentum and trend- following techniques. The WisdomTree Managed Futures Strategy Fund (NYSE Arca: WDTI), for example, uses a broad array of commodities, currencies and financial futures. These funds typically have reasonably high levels of volatility and equitylike long-term returns, but with a very low correlation to equity markets.

Even with better classification, the hurdle remains high for alternative ETFs due to the lack of clarity in the security selection process. State Street Bank has no input into the decisions of Standard and Poor’s as to which stocks go into the S&P 500—SSgA merely builds the portfolio from the publicly available index informa-tion. BlackRock doesn’t time the purchase of gold based on a price-timing model. Rather, the iShares Gold Trust (NYSE Arca: IAU) simply tracks the price with its huge holdings. The lack of this simplicity—exemplified in the use of statistical models to generate return—makes an alternative ETF a very different investment.

To further growth in the Alt-E category, ETF sponsors must look deeply into the following issues, to overcome the natural disadvantage of the complexity of an alternative ETF:

• Continue to innovate with new offerings.

• Find creative portfolio uses for their new and existing

strategies. As discussed, the buyers of Alt-Es will be by definition more sophisticated than the buyers of simpler ETFs; therefore, opportunities will exist to find ways to increase diversification, lower volatility and/or enhance returns with Alt-Es.

• Educate, educate, educate. The higher level of com-plexity demands a higher level of buyer education before tickets will be written.

• Keep implementation costs low so the higher fees can be

used to pay for the additional education. Costs will only come down with much greater scale than exists today.

Buyers of alternative ETFs—portfolio managers, research analysts and consultants—must take a differ-ent approach to using Alt-Es than with traditional, well-known ETFs. Specifically:• The decision engine that underlies the basic risk/

return characteristics of the ETF must be fully under-stood. Akin to buying an actively managed mutual fund, buying an Alt-E requires a level of diligence generally not conducted in ETF management. Ask the

questions: “What is the theoretical basis for the deci-sion algorithm to perform as presented? What are the practical implementation hurdles the fund must clear to perform as expected?”

• Think creatively and openly about how a particular

Alt-E might fit a specific portfolio application. For example, using an “anti-momentum” long/short ETF to hedge momentum factor exposure in an equity portfolio might make sense after a long period of price momentum outperformance.

• Go slowly. Learn as you go. Regardless of the level of

research and diligence before the initial purchase, noth-ing forces an investor to understand an investment like owning it for the first time. Some funds will not deliver on their promises, either through implementation slip-page or poor model construction. In these cases, being on the bleeding edge is no fun.

Total combined assets in Alt-Es (using the IndexUniverse list of 27 funds) remain mired at a mod-est $1.4 billion. “Traditional” ETFs have seen runaway asset flows and accompanying massive commercial suc-cess, with just the top five largest funds combining for over $300 billion in AUM. To break this ceiling and to drive significant asset growth, Alt-E sponsors must focus on education and user development. Practitioners need to think critically about how to use Alt-Es in portfolios, either as complements to other portfolio pieces (most likely) or as stand-alone holdings. The future may be bright, but it’s likely a slow, long road to arrive there.

Even with better classification, the hurdle remains high for alternative ETFs due to the lack of clarity in the security selection process.




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May / June 201230

Market-Neutral

Thematic/Factor Investing

The future of asset allocation?

By Kishore Karunakaran




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A series of global financial crises over the past 15 years have made clear that traditional methods of portfolio risk management are no longer up

to the task. Investors employing traditional asset alloca-tion, such as the mechanical apportioning of portfolios between stocks and bonds, and the geographic distribu-tion of funds between multiple markets, have found that when markets are hit by extreme volatility, the diversi-fication enjoyed in risk-engaging markets disappears. Massive covariance ensues as investors flee to quality.

Investors digging deeper to uncover the true drivers of contemporary bond and equity returns have found that strategies focusing on themes and factors such as size, momentum and particularly value deliver reduced correlation. Investors using thematic/factor strategies can derive additional risk protection through the use of market- and sector-neutral investing techniques because valuable information can be extracted from rising as well as falling markets.

Diversification and CrisisIn this paper, we set up the motivation for structuring

an investment program in terms of some common well-documented investment themes. Consideration of stock market investment arises because of the equity risk pre-mium (which consists of the excess return that stock market investment provides over the risk-free rate). We outline themes that challenge some standard workhorse models of asset pricing such as the capital asset pricing model (CAPM).

Additionally, we focus on the value investment theme. Specifically, we show that a simplistic investment program that, rather than investing in the traditional 60/40 percent split between stocks and bonds, allocates 10 percent from each to the value theme, is a superior alternative on a num-ber of grounds. The purpose of this paper is to scratch the surface of a new approach to asset allocation and to suggest some complementary methods that can be used when allo-cating assets.1 Finally, we show that adding market- and sector-neutral conditions to a value investment theme are critical components to building uncorrelated returns that can complement a core investment portfolio.

Thematic Investing, Horse Racing And FinanceGiven the changing nature of contemporary financial

markets—and the extraordinary speed with which efficient market structures transmit negative investor sentiment and contagion—it is clear that new investment themes have emerged. The global financial crisis has made clear that equity and bond markets are evolving, and to succeed, investors need to adapt to this changing environment. Because of this, it is critical that investors set aside tradi-tional diversification and risk management tools and dig deeper to discern more precisely the true drivers of equity and fixed-income market returns.

Thematic investing seeks to explore a new means of understanding markets by giving investors insight into investing as a result of studying the properties of securities with similar characteristics. To illustrate this new approach


to understanding securities characteristics, we might con-sider two analogies: one from horse racing and another from the world of stocks.

Horse breeding has become a large commercial enter-prise predicated on the belief that the speed of a horse is an inherited characteristic. Investors paying top dollar for the foals of champions obviously believe that the racing suc-cess of a given horse is in large measure a function of genes inherited from their winning parents. But racing success is not determined solely by birthright. In addition to the genetic heritage of a horse, there are several external fac-tors, such as diet, training and even which jockey is riding the horse, that all conspire to define a horse’s speed.

The same holds true in the financial world. Positive returns are a desirable trait for investments, just as speed is a desirable trait for horses. But positive returns, like rac-ing speed, are subject to several common influences that define the expected returns of various investments. For example, in the 1970s, several academics came to recog-nize that assets with similar characteristics tend to behave in a uniform manner—a notion that was first captured by the arbitrage pricing theory (APT) developed by econo-mist Stephen Ross in 1976.2 The APT holds that security and portfolio expected returns are linearly related to the expected returns of an unknown number of underlying systematic common influences/themes. At the core of the APT is the notion that the price of a security is driven by a number of common influences/themes, and these can be divided into two distinct groups: macro themes; and com-pany specific themes.

Arbitrage Pricing Theory (APT)r = r

f + β

1f

1 + β

2f

2 + β

3f

3 + ... + β

Nf

N

Where r is the expected return on the security, rf is the

risk-free rate, each f1 f

2 and f

3 … f

N are separate factors

and each β is a measure of the relationship between the security price and that factor.

The feature that distinguishes the APT over the CAPM is that the APT separates out noncompany themes/factors into as many as prove necessary, while the CAPM has a single noncompany theme/factor and a single beta. The single-factor approach of the CAPM has been drawn into question since the 1980s by empirical analysis that has tended to sup-port asset characteristics associated with risk premia.

Each of these themes/factors requires a separate beta, and the beta of each factor is the price sensitivity of the security to that factor. Under the APT approach, the poten-tially large number of factors means several more betas must be calculated. In so calculating, there is no guarantee that investors will successfully identify all relevant factors. Taking this one step further, there is the successful three-factor model developed by Eugene Fama and Ken French3 in 1992, and later the four-factor model, with the Carhart momentum factor4 building on some of the anomalies first documented by Banz [1981] on the size theme/effect, and Jegadeesh and Titman on the momentum/effect theme.

Moving on from this theoretical construct, we might con-




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sider two tangible investing examples that articulate com-mon influences on returns. Take, for instance, two stocks: Applied Materials (AMAT) and Intel (INTC). In consider-ing these securities, we might intuit that their investment returns would behave in a similar manner given that they belong to the same sector (technology) and that both are in the semiconductor business line.

Now let’s consider a third stock, Starbucks Corp (SBUX), which is more involved in soy lattes than semiconductors. Because all three stocks are traded on major U.S. exchang-es, we can safely assume that all three securities will be impacted by events that affect the stock market as a whole (beta events). But the more critical question is the degree to which each security will be impacted by whole-market events. While all three are impacted by market events, each has a unique sensitivity to these events.

Similarly, AMAT and INTC would both likely react to events affecting the semiconductor industry as a whole, but INTC would be impacted by the destruction of one of its fabrication facilities in an earthquake. And if both semi-conductor firms endured catastrophic flooding, the market for coffee beans would likely be unaffected. The industry groups and sectors that pertain to individual securities therefore are another way of thinking about firms and their business lines and what is driving returns.

The Value Style/ThemeFour main challenges have been posed to the standard

CAPM as it relates to equity markets: the equity premium theme; the size premium theme; the momentum pre-mium theme; and the value theme. Note that all these themes rely on the notion of a self-financing long/short

portfolio. The equity risk premium is investing in stocks and shorting the risk-free asset (T-bills); the size premium is buying small-cap stocks and shorting large-cap stocks; and finally the value premium is buying long cheap stocks and selling short expensive stocks.

The equity risk premium is based on the premise that over the 90-year period from 1889-1978, the real annual return on the S&P 500 Index was 7 percent, while the return on T-bills was less than 1 percent. The next chal-lenge to the CAPM was the size premium. In an early aca-demic study that highlights some of the shortcomings5 of the CAPM, Banz [1981]6 looked at returns on stocks from 1936-1977 and concluded that investing in the smallest companies (the bottom 20 percent of NYSE firms in terms of capitalization) would have generated returns of about 6 percent greater than larger-cap companies. Fama and French [1992] also document this risk premium, and we calculate it to be an average of about 2.29 percent annually over the risk-free rate between July 1926 and January 2012.

In the same study, Fama and French [1992] also high-lighted the “value premium,” noting that stocks with low price-to-book ratios outperformed stocks with high price-to-book ratios. In the U.S., this premium averaged 3.82 percent per year over the risk-free rate for the period of July 1927 to January 2012. In another study, Jegadeesh and Titman [1993] documented the “momentum premium.”7 By ranking stocks based on their prior six-month return, they found that the highest decile (prior winners) outperformed the lowest decile (prior losers) by an average of 10 percent per annum. The momentum premium measures the return of stocks with relatively high recent returns, minus the return of stocks with relatively low recent returns. In the

May / June 201232

The Dow Jones Thematic Market Neutral Index comprises all stocks in the Dow Jones U.S. Index, which aims to provide 95 percent market-capitaliza-tion coverage of U.S.-traded stocks. Stock selection is achieved through the following process: • Stocks that have a six-month average daily trading

volume of less than $10 million will be excluded.• The largest 1,000 stocks with the highest total mar-

ket capitalization in the Dow Jones U.S. Index con-stitute the universe of eligible companies.

• The stocks in the eligible universe are separated

into 10 industries as defined by Dow Jones Indexes’ proprietary classification system.

• Sector neutrality is introduced and maintained

within each index by allowing a proportionate number of stocks based on the composition of the top 1,000 stocks. The total number of companies within each industry is divided by 1,000 to achieve a target percentage of components for inclusion in long and short indexes for each industry. The tar-get percentage is multiplied by 200 to identify the

sector-neutral component count for each industry.• The value factor uses a multifactor ranking process.

Each company in the top 1,000 is ranked by book value to price ratio, projected earnings per share to price ratio and trailing 12-month operating cash flow to price ratio. If any of the three ratios is not available for a security, then the security is consid-ered ineligible.

• The 200 stocks with the highest ranking according

to the value factor from each industry are selected until sector neutrality is reached for the Dow Jones U.S. Thematic Long Value Index.

• The 200 stocks with the lowest ranking according

to the value factor from each industry are selected until sector neutrality is reached for the Dow Jones U.S. Thematic Short Value Index.

• The Dow Jones U.S. Thematic Market Neutral Value

Index measures a long/short strategy with a 100 per-cent long position in the Dow Jones U.S. Thematic Long Value Index and a 100 percent short position in the Dow Jones U.S. Thematic Short Value Index.

The Dow Jones U.S. Thematic Market Neutral Indexes8




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U.S., this premium averaged 8.25 percent per year above the risk-free rate for the period January 1927 to January 2012.

For purposes of this paper, we will concentrate mainly on the value theme.

Investing in value stocks is one of the oldest and most successful strategies, dating back to Benjamin Graham and David Dodd’s teachings in the early part of the 20th cen-tury. In their 1934 book, “Security Analysis,” they outlined how value investing works: buying stocks whose shares are underpriced as defined by fundamental or comparable analysis. Armed with this knowledge, two of Graham’s students from Columbia University—Warren Buffett and Charlie Munger—launched two of the most successful investment careers in the history of financial markets.

The conventional definition of a value investor is one who invests in low price/book value or low price/earn-ings ratios stocks. Value investors come in many differ-ent stripes. One type of value investor uses simple price to book value, price earnings and price sales ratios, and other relative value measures to generate persistent excess returns. “Contrarian” value investors take positions in companies that have done badly in terms of a battered stock price or have a temporary setback. Finally, there are activist investors who take positions in undervalued or poorly managed companies and—through their strong voting bloc—are able to force actions such as removing management or disposing of unusable assets. Using key valuation metrics for a firm, one can try to establish a notion of cheap vs. expensive stocks.

So how do investors invest in a “pure” value theme? As mentioned earlier, the themes or premia that are well documented rely on the notion of a self-financing long/short portfolio as the vehicle to unlock the theme. We will discuss market-neutral investing in more detail in the next section after we have developed the value theme.

We are only aware of one index in the U.S., the Dow Jones U.S. Thematic Market Neutral Value Index, that delivers a “pure value” long/short market-neutral return. The stated objective of the index, which is designed to be market neutral, is to reflect the performance of a strategy utilizing a long position in value companies and a short position in growth companies, thus isolating the “pure value” theme. We have developed a longer-term time series by mimicking the rules and methodology of this index over a longer time series starting in February 1980.

Over the 30-year period, the value strategy seems to deliver a similar performance to the Fama-French value theme/factor (which goes back to 1927). There are some notable differences between the proxy for the Dow Jones Market Neutral Value Index and the Fama-French value factor. First, the universe that the proxy index uses includes the largest 1,000 companies in the U.S. that trade on an exchange (NYSE and Nasdaq stocks), and most importantly, the index is sector neutral, whereas the Fama-French Value theme/factor (HML—high minus low book-to-price stocks) is constructed using the entire CRSP9 universe and is reconstituted annually and is not sector neutral. Also, the Dow Jones U.S. Thematic Market Neutral Value Index is constructed by using the three valuation metrics outlined above.

Figure 1 is a summary of the performance of the value premium over a 30-year period, using monthly data since 1980 to arrive at the average annualized return to the market-neutral sector neutral index.

Let us suppose that an investor wants to adjust his 60/40 stock/bond mix to instead allocate 10 percent of his stock allocation to a market-neutral value index and also 10 percent from his bond allocation into the same value index. The net result is a considerable improvement in performance and lower volatility. The final portfolio, i.e., 50/30/20 percent in stocks, bonds and value has a


VAL-Rf

Annualized Returns And Volatility For Value

R1000-Rf

Figure 1

Sources: QuantShares and FactSet

10th Percentile -4.59 -2.75

90th Percentile 5.86 4.00

Average Annual Return 6.81 5.94

Annualized Volatility 14.87 11.46

Sharpe Ratio 0.46 0.52

Annualized Returns And Volatility For Value

VAL-Rf

STKBND-Rf

STKBNDVAL-Rf

Figure 2


10th Percentile -2.75 -2.79 -2.27

90th Percentile 4.00 3.63 3.20

Average Annual Return 5.94 5.44 5.61

Annualized Volatility 11.46 9.31 8.14

Sharpe Ratio 0.52 0.58 0.69

Adjusted 60-40 Stock Bond Mix To Include Value


■ STKBND ■ STKBNDVAL

1000

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0

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Figure 3




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Sharpe ratio that is higher than value by itself or even a 60/40 percent stock/bond portfolio. It has slightly higher annualized returns, with 1.17 percent less volatility on an annualized basis. (See Figures 3 and 4.)

Correlation Grid For Stock And Bond Market Indexes With Themes/Factors

An extremely large percentage of world investments are allocated to mutual funds, ETFs and other investments that are either passively or actively seeking to match or beat a given benchmark. In practice, this means the betas to such portfolios are in the 70-95 percent range depending on the degree of stock-specific concentration. If we examine the proxy for the Dow Jones Thematic Market Neutral Index and look for the correlations with broad market indexes, one can see they are extremely low. The index is picking stocks from the top 1,000 names by market capitalization from the Dow Jones US index (a large-cap universe) and then generating a correlation of 0.04 to the R1000 or any other large-cap universe after imposing market neutral-ity and sector neutrality. It has a high correlation with the Fama-French HML theme/factor (high minus low book to price stocks) but it would be higher if not for the construc-tion methodology differences outlined earlier.

Figure 4, which covers the period 1988 to July 2011 and includes the highly correlative impacts of the past 15 years

of financial crises, illustrates that themes/factors such as value exhibit extraordinarily low correlation to markets. In fact, equity market-neutral value themes even rival the low-correlation properties of the Barclays Capital Aggregate Bond Index, which represents investment-grade bonds being traded in the U.S. The proxy for the Dow Jones Value index has a high correlation to the Fama-French Value theme (HML)—60 percent of which is designed to be so.

In Figure 4, the correlations in the Proxy DJ Value column (in green) highlight the benefits of a marriage of value in a market-neutral setting. This is the theme that the index/fund seeks to capture. This table contains a representative sampling of typical investments that investors view as components to achieving diversification benefits. For example, the EAFE and World (in red) expe-rience a correlation of a whopping 95 percent. The MSCI World and Russell 1000 is 88 percent and the R1000 cor-relation with R2000 is a surprising 82 percent. For the next tier of correlations (in blue), the MSCI World and R2000 have a correlation of 73 percent and the MSCI World and MSCI Emerging Markets are 72 percent. The last tier of correlations (in orange) is the least correlated, with EAFE and R2000 exhibiting a 62 percent correlation and the R1000 and EM, 67 percent.

Looking at these correlation coefficients (which include the period of the global financial crisis), one can

May / June 201234

Figure 4

Correlations Of Value Index With Fama-French Value And Select Equity And Bond Indexes

Proxy DJ

ValueHML EAFE WORLD USAGG R1000 R2000 EM

Proxy DJ Value 1.00

HML 0.60 1.00

EAFE 0.05 -0.11 1.00

WORLD 0.05 -0.16 0.95 1.00

USAGG 0.03 0.04 0.11 0.13 1.00

R1000 0.04 -0.21 0.71 0.88 0.17 1.00

R2000 0.01 -0.23 0.62 0.73 0.05 0.82 1.00

EM 0.06 -0.19 0.68 0.72 0.01 0.67 0.67 1.00

Source: QuantShares, FactSet and Kenneth French Data Library

Nuts And Bolts Of Market-Neutral Investing

Source: QuantShares

SpreadReturn

$9MM Longs

Buy attractive stocks

Investment gains value

when stock prices rise and

loses value when stocks fall

$9MM Shorts

Sell short

unattractive stocks

Investment gains value

when stock prices fall and

loses value when stocks gain

$1MM Cash

Short proceeds are

invested in T-Bills

Figure 5




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www.journalofindexes.com 35

appreciate the benefits that accrue to including long/short self-financing portfolios of factors such as value (Fama-French HML), as well as other factors such as size (Fama-French SMB) and momentum (Fama-French UMD) that are not included in the table.

Market-Neutral InvestingInvestors using thematic/factor-based strategies can

derive additional risk protection through the use of mar-ket-neutral investing techniques. Investment managers invest substantial resources, scanning the globe for stocks to buy. Yet they spend only a fraction of this amount in pursuit of overvalued company stocks that they can prof-itably sell short. Critical investment information can be gleaned from both ends of the spectrum. In some instanc-es, investors may derive more benefit from identifying companies with poor fundamentals or negative growth prospects than by seeking out the winners. A symmetrical approach to investing—dedicating equal energy to pursu-ing successful as well as flawed securities—is at the heart of market-neutral investments. The downside of market-neutral structures is that in exchange for greater stability of returns over time, investors will miss out on the thrill of achieving outsized returns when the overall equity asset class is screaming along. Market-neutral strategies date from 1949 when Alfred Winslow Jones operated the first long/short hedge fund holding long as well as short secu-rities. Today it is estimated that investors have committed some $40 billion to market-neutral strategies.10 Market-neutral equity strategies of this type seek to deliver returns in excess of Treasury bills; on average, the strategy has yielded between 5 and 6 percent returns with less volatility

than the broad market net of fees.11

There are three sources of returns for a long/short mar-ket-neutral portfolio: the long portfolio, which buys attrac-tive stocks; the short portfolio, which sells short unattract-ive stocks; and a cash portfolio, which invests in T-bills. In a long portfolio, investment managers buy stocks long and profit when these securities experience price gains. Conversely, this manager will lose money if the selected stocks decline in value. In the short portfolio, where the manager borrows unfavorable stocks from a prime broker with the intention of selling them short, the manager will profit from declines in the value of these securities. It goes without saying that short portfolio strategies will be unsuc-cessful if the unfavorable stocks rise in value.

They key to the success of this market-neutral strategy is that the longs outperform the shorts, even if the investor

May / June 2012

Market-Neutral Value Summary Statistics For All The Months The Russell 1000 Is Up

Value

Figure 7


Average Annual Return 8.00

Annualized Volatility 10.49

Median 0.58

10th Percentile (2.25)

90th Percentile 3.60

Information Ratio 0.76

Hit Rate 0.60

Equity Market-Neutral Value In US Stock Market Positive Months (January 1927 – July 2011)

■ Mkt ■ HML

Sources: QuantShares, FactSet and Kenneth French Data Library

50%

40%

30%

20%

10%

0%

-10%

-20%

Figure 6




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36 May / June 2012

loses value in the short portfolio due to unwanted gains in stock prices. For example, if the market is rising and the longs are rising slightly more than the shorts, the market-neutral strategy will be profitable. On the other hand, when the market is falling, the market-neutral strategy will be successful if the shorts are falling more than the longs. In this way, market-neutral investment strategies seek to gen-erate profit in any type of environment.

To envision the mechanics of a market-neutral strategy, suppose an investor wants to invest $10 million. She uses $9 million to buy favorable stocks with positive characteristics and at the same time shorts $9 million worth of unfavor-able stocks, receiving cash proceeds for that amount. The remaining $1 million is deployed as cash for use in meeting margin requirements. Our investor will also receive divi-dend income on the long portfolio, while generally paying

an offsetting amount to the owner of the borrowed stocks. In addition, the investor will also receive interest income (from T-bills) on the short-sale proceeds. (See Figure 5.)

Benefits Of Sector-Neutral InvestingA long/short fund that is market neutral can gain addi-

tional risk management advantages by also being sector neutral. This is because sector-neutral portfolios are insu-lated from shocks that are generated by any one sector or industry group. For instance, a market-neutral fund that is highly concentrated in a few sectors such as oil and gas will unintentionally (or intentionally) take on additional risk by being directionally long or short that sector.

Sector neutrality refers to investing an equal dollar amount long and short within the same sector (across the entire 10 sectors of the Global Industry Classification Standard (GICS) or Industry Classification Benchmark (ICB) classifications), thereby insulating the fund from an adverse shock in any one sector. For example, sup-pose there are 10 stocks in the utilities sector; the sec-tor-neutral strategy will ensure that the weights/dollars allocated to the long utilities is the same for the utility stocks the strategy seeks to sell short.

Market-Neutral Value InvestingEquity market-neutral strategies are designed to have a

very low beta with markets as a whole. This is why market-neutral investing is so valuable in an era of unprecedented and unwanted correlation, in which all manner of securi-ties plunge in lock step to increasingly frequent bad news. But in exchange for this downside protection, market-

Equity Market-Neutral Value In U.S. Stock Market's Negative Months (Jan. 1927 – July 2011)

■ Rm

■ HML

Sources: QuantShares, FactSet and Kenneth French Data Library

20%

15%

10%

5%

0%

-5%

-10%

-25%

-20%

-15%

-30%

-35%

Figure 8

Summary Statistics For All Months The Russell 1000 Is Down

Value

Figure 9


Average Annual Return 14.39

Annualized Volatility 11.09

Median 0.92

10th Percentile (2.42)

90th Percentile 5.09

Information Ratio 1.30

Hit Rate 0.69





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Talking Indexes

May / June 2012

By David Blitzer

38

Absolute-return investing can’t be measured in a vacuum

Trust Me

“Absolute return strategies, by definition, pursue returns independent of a traditional benchmark index like the S&P 500 Index or the Barclays Capital Aggregate Bond Index. Absolute return is unconstrained and can ‘go anywhere’ as well as use modern tools, such as hedging strategies, in seek-ing to reduce risk for investors.”

—Investment management marketing brochure

Absolute return versus traditional investing trans-lates into the flexibility to use a wide range of tactics across multiple asset classes versus limits on asset

classes and performance measured against benchmarks. Proponents of absolute-return investing argue that with this flexibility they almost always achieve positive returns, usually with less volatility, than traditional investment managers. Such flexibility offers more opportunity than what traditional investment managers enjoy. Traditional investments usually come with benchmarks. The majority of ETFs track published indexes. While a substantial major-ity of mutual funds are not index trackers, over 90 percent have benchmarks and are restricted to a few asset classes. Among institutionally managed funds the pattern is simi-lar—investment managers are restricted to a few asset classes and may be compensated by their performance against a benchmark. The best practice is for the plan sponsor to determine the benchmark and the asset class.

In the last few decades, absolute-return investing emerged to challenge traditional investment management. The issues posed by absolute-return investing are which asset classes are reasonable and whether going short is dif-ferent from going long and if derivatives are foolish or evil (they’re neither). However, it is the agency relation between the investor/asset owner and the investment manager that

really matters. An investor hires a manager and gives her discretion over how the funds are invested. This means that there is potential conflict in the agency relationship: Will the funds be lost? Will the risks be exorbitant? Will the invest-ments be so timid that gains are missed? No matter what the investment, results won’t match expectations.

Agency relationships are everywhere. Early work goes back to the 1930s, with Berle and Means1 discussing man-agers in large corporations. As business moved from part-nerships and proprietorships to stockholder-owned cor-porations where managers had little ownership and little of their own wealth in the business, were the stockholders sure that the managers wouldn’t embellish themselves at the true owners’ expense? Who wouldn’t like a palatial office or the use of a private jet? Anyone who thinks this problem has been solved hasn’t read a newspaper or a blog recently. Though less dramatic and maybe with less wealth at issue, the same relationship—and question—exists inside any organization: When a manager delegates a task to a subordinate, what assurances does the manager have that it will be done as well as possible?

Investors try to monitor the efforts and performance of their investment managers. Benchmarks, investment policy statements and limits on asset classes are crucial for monitoring investor/manager relations. Yet these tools for monitoring and understanding actual returns are missing in absolute-return investing.

The Prudent Man

In investment practice, there are generally three moni-toring approaches: 1) the “prudent man rule;” 2) bench-marking; and 3) transparency. The prudent man rule goes back to a court case (Harvard College v. Armory) in 1830 in




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Massachusetts where a trustee was sued for mismanage-ment of funds. The court noted that the trust agreement instructed the trustee to invest “in safe and productive stock, either in the public funds, bank shares, or other stock, according to their best judgment and discretion” and then found that while the investments had not been completely successful, that trustees should “observe how men of pru-dence, discretion and intelligence manage their own affairs, not in regard to speculation, but in regard to the permanent disposition of their funds, considering the probable income

as well as the probable safety of the capital to be invested… Do what you will, the capital is at hazard.”2

In an agency/investment relation, behavior and prudence mattered more than results since the results were subject to the vagaries of the markets; the capital is at risk. The court decision is often portrayed as protecting the investment manager. While it does limit the investor’s ability to recover investment losses, it also sets the terms for monitoring. The results are with us today: Stock picking should be based on financial analysis, not rolling dice or turning tarot cards. The rule, renamed the “prudent person rule,” is with us today in the Labor Department’s ERISA regulations. The question for absolute-return managers and their clients is, When do or whether short positions, option writing or leverage reflect the intelligence of people of prudence?

Monitoring investments with the prudent person rule requires extensive investigation and analysis to deter-mine if the investment manager met the standard. Benchmarking is much simpler: Instead of analysis, agree in advance on asset classes and compare the results to the benchmark. If large-cap U.S. equities are the asset class and the S&P 500 is the benchmark, then success is mea-sured by comparing the investment returns to the S&P 500. If several asset classes such as government and cor-porate bonds and stocks in the U.S. or Canada are used, a benchmark can be constructed that reflects the overall opportunities across those asset classes.

But there can be perverse results when a benchmark, such as the S&P 500, suffers negative returns. A “success-ful” investment manager could lose money while outper-forming the benchmark. To absolute-return investment managers, this demonstrates that benchmarks don’t work. Further, some absolute-return investors claim that by giving them flexibility, they have a much better chance of avoiding losses than the benchmark-bound tradition-alists. The absolute-return alternative is to agree on a goal of doing better than cash or T-bills by some number of percentage points of return.

Transparency is the third approach to monitoring; it is also one of the more-sought-after attributes of any invest-

ment strategy, second only to achieving large returns. The argument against failed instruments of the financial cri-sis—e.g., credit default swaps, collateral debt obligations, securitizations and others—is that they were opaque and no one understood them. Accolades for ETFs and objec-tions to hedge funds cite transparency. But transparency only solves the monitoring problem if the asset owner can evaluate investments. Would an investor whose bro-ker placed three-quarters of his funds in Enron in 2001 have realized what might happen if he knew what was in

his account? Maybe not. But if he knew his portfolio was skewed compared to the S&P 500 benchmark, he might have asked for an explanation.

Transparency in absolute-return investing would be welcome, but it cannot replace the missing benchmarks. Monitoring performance over time still requires a measure of how well the asset classes would perform without ben-efit of investment management and insight. Substituting a promise to earn a positive return for a benchmark leaves only the promise. It doesn’t offer the investor any way to monitor the manager.

A Question Of Fees

But there may be another way: fee arrangements. When monitoring in an agency relation is difficult, incentivizing the agent to perform well may work. There are many pos-sible incentives. If one takes pride in doing a job well, he will do it well without extra incentives. If doing the job well is a professional responsibility or duty, the agent will strive to fulfill his responsibilities. Of course, money is a commonly used incentive. Performance fees, fees based on returns or returns above a benchmark, are common in some investment arrangements but prohibited in others. Some fear that performance fees can encourage excessive risk-taking, leading to more agency problems.

Absolute-return investing arrangements often include performance fees. A common arrangement is “2 and 20,” meaning 2 percent of the assets under management plus 20 percent of the returns. Fees for mutual funds, ETFs and most traditional institutional funds do not include a performance fee and are usually less than 2 percent of the assets. The arguments for 2 and 20 cite the needed skills and experience of the manager and promise that the 20 percent will align the manager’s goals with those of the client to solve the agency problem.

Whether 2 and 20 is a reasonable price for providing investment management is best judged in the market-place for investment management services, not by debat-ing costs or skills or experience or business overhead. So

Monitoring investments with the prudent person rule requires extensive investigation and analysis to determine if the

investment manager met the standard.





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May / June 201240

Selecting A Hedge Fund

Replication Approach

Some factors to consider

By Salvatore Bruno and Robert Whitelaw




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Hedge funds have historically been important asset allocation components of well-diversified portfo-lios for sophisticated investors. The endowment

model pioneered by David Swensen1 to manage the Yale Endowment Fund argues for alternative investments in general and hedge funds in particular to play more signif-icant roles in portfolios. The limited-constraint nature of hedge funds is intuitively appealing to investors, as stud-ies have shown that constraints limit the alpha potential of portfolios. Perhaps the most widespread example of the limitation on regulated investment products is the limita-tion on short positions. Traditional long-only managers can only invest in long positions up to 100 percent of the value of their portfolios, by definition. By relaxing the long-only constraint, researchers have shown an increase in the potential to add alpha and minimize risks.2 This research has led to the widespread adoption of long/short portfolios (also called active-extension strategies or 130/30 portfolios representing long weights of 130 percent and short weights of -30 percent).

Despite the benefits of hedge funds, a number of characteristics have limited their desirability and acces-sibility from the perspective of the average investor. As virtually all hedge funds are organized as limited partner-ships (LPs), there are limits on the number and types of investors a fund can have. These limits relate to minimum investor asset levels and minimum income requirements, among other things. These restrictions effectively elimi-nate access to hedge funds for most individual investors. For those investors that do meet the accredited investor minimums, obstacles may still remain. Among these are:

• Manager selection − Performing the necessary due diligence to find a good hedge fund can be very time-con-suming and requires a certain level of investment acumen. Further, once an investor identifies a good fund, it is pos-sible that the manager may not be accepting new clients or that the hedge fund selected may not perform as desired.

• Liquidity − Hedge fund industry standards are to provide liquidity on a monthly or quarterly basis with advance notice of up to 45 days or more. Some investors want additional liquidity.

• Transparency − Reporting requirements in the hedge fund industry are far more relaxed than for registered investment products. In an attempt to protect their intel-lectual capital, many hedge funds do not provide regular updates on positions held in their portfolios.

• Fees − Hedge funds typically charge management fees of 1-2 percent of assets plus performance fees of 10-20 per-cent of the profits of the portfolio. To avoid some of the man-ager selection issues noted above, some investors choose to invest in hedge funds via a fund-of-funds structure. The fund-of-funds will add a fee for their service that may be an additional 1 percent of assets plus a percentage of the profits.

Academic researchers began to study the positive performance characteristics of hedge funds in the 1990s and 2000s. Several influential papers established that up to 77 percent of hedge fund returns can be attributed to beta, i.e., exposure to broad asset classes or factors, with


the remaining 23 percent being alpha, i.e., performance specific to the strategies of the fund. Further, studies have shown that by using variants of Sharpe’s returns-based style analysis, it is possible to estimate the beta expo-sures of hedge funds. Subsequently, researchers showed that by using liquid, exchange-traded instruments, it is possible to create a return series that approximates the beta returns of hedge funds. Investment professionals began to use the building academic body of research to develop indexes designed to mimic the performance of hedge fund beta. Starting in the mid-2000s, Merrill Lynch introduced the Merrill Lynch Factor Index, followed by Goldman Sachs, who developed the Goldman Sachs Absolute Return Tracker (GSART) Index. Both of these indexes are designed to track broad hedge fund indexes. IndexIQ followed these launches with the first suite of hedge fund replication indexes designed to replicate indi-vidual hedge fund strategies rather than broad indexes. More recently, Credit Suisse has also introduced indexes tracking individual hedge fund strategies.

All of the hedge fund replication strategies referenced above use a factor-based approach; however, there are significant differences in the development and imple-mentation of the factor models that create meaningful variations in the final product. The next section of this article discusses the academic research in more depth. While not meant to be an exhaustive review of academic work in the area, we introduce what may be some of the most influential published articles. Next, this article identifies some of the most important decisions that need to be made when developing a factor-based hedge fund replication model, using the IndexIQ models and the aca-demic research that underlies them as examples. Finally, the article provides a summary of the factor-based invest-ment products currently available in the United States.

It is important to note that hedge fund replication also exists in a very meaningful way in Europe, and there are substantial assets invested in both listed and OTC prod-ucts in Europe. A review of these products is beyond the scope of this article, but it is important to recognize their existence and the contributions to the body of research from academics and investors in Europe.

Hedge Fund Returns Attract Academic InterestFrom 1995 through 2007, hedge funds in aggregate had

13 consecutive years of positive returns, as reported by Dow Jones Credit Suisse (formerly Credit Suisse/Tremont),3 Notably, this period covers the rise and fall of the equity markets coinciding with the creation (1995-1999) and sub-sequent bursting of the technology bubble (2000-2003). During the five-year period from 1995-1999, hedge funds returned, on average, 18.16 percent, while the U.S. equity market, as measured by the S&P 500 Index, gained 28.56 percent. Hedge funds captured, on average, over 63 percent of the upside returns of the market. During the period when the technology bubble deflated from 2000-2003, hedge funds returned 4.81 percent per year, while the equity market lost 14.55 percent per year. Clearly, this type of upside partici-




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May / June 201242

pation with downside protection can be very beneficial to a portfolio, and investors took note. As assets began to flow into hedge funds in large sums, the number of hedge funds grew in response. It is estimated by Hedge Fund Research4 that the number of hedge funds grew from just over 600 in 1990 to almost 4,000 by the year 2000. By 2007, the estimated number of hedge funds exceeded 10,000.

Not surprisingly, the compelling investment results and explosive growth in hedge funds began to attract academic interest. Initially, academics were not concerned with repli-cating hedge fund results. Rather, their interest centered on analyzing and understanding the key drivers of hedge fund returns. Two of the early researchers to look into the sources of hedge fund returns were William Fung and David Hsieh.5 They published articles in 1997 and in 2001 that used William Sharpe’s return-based style analysis (originally published in 1992)6 to analyze hedge fund returns, stating “the article finds five dominant investment styles in hedge funds, which when added to Sharpe’s [1992] asset class factor model can provide an integrated framework for style analysis of buy-and-hold and dynamic trading strategies.” This research established time series analysis of hedge fund returns as a viable method for estimating hedge fund exposures.

Perhaps the most groundbreaking research came in 2006, when Jasmina Hasanhodzic and Andrew Lo published a paper titled “Can Hedge Fund Returns Be Replicated?: The Linear Case.”7 Hasanhodzic and Lo analyzed the returns of over 1,600 individual hedge funds from the TASS database and wrote, “For certain hedge-fund style categories, we find that a significant fraction of both [expected returns and volatility] can be captured by common factors corresponding to liquid exchange-based instruments.” This paper clearly moved the discussion forward, as it shifted the emphasis from analyzing hedge fund returns as part of performance analysis into the realm of actively trying to create synthetic returns using liquid, exchange-traded instruments that shared simi-lar risk and return attributes to actual hedge funds.

In 2007, Thierry Roncalli and Guillaume Weisang8 present-ed a framework for hedge fund replication using Bayesian filters. An important outgrowth of their research is that by creating a reliable model, it became possible to estimate the proportion of returns due to alpha and to beta. When analyz-ing the returns of the Hedge Fund Research Index (HFRI), they wrote “… a large part of the HF [hedge fund] returns are not explained by the traditional alpha but by the alternative beta. For the entire period [1994-2008], the alternative alpha explains about 23% of the HF returns whereas the alternative beta explains about 77%.” This result supported the notion of a core/satellite approach, using hedge fund replication as a core component that could “… still be supplemented by other illiquid instruments to capture and reproduce more efficiently the risk profile of the hedge fund industry.”

In 2009, Noel Amenc, Lionel Martellini and others at EDHEC9 published a paper titled “Passive Hedge Fund Replication—Beyond the Linear Case.” The paper made several important contributions to the growing field of hedge fund replication by extending the paper of Hasanhodzic and Lo. Amenc et al. examined different approaches to hedge

fund replication. They wrote, “We find that going beyond the linear case does not necessarily enhance the replication power. On the other hand, we find that selecting the factors on the basis of an economic analysis allows for a substan-tial improvement in the out-of-sample replication quality, whatever the underlying form of the factor model.” This was an important piece of research because it documented the importance of factor selection in the investment pro-cess. Amenc et al. also wrote, “[W]e confirm the findings in Hasanhodzic and Lo that the performance of the replicating strategies is systematically inferior to that of actual hedge funds.” In other words, hedge funds returns still offer alpha even after identifying and capturing the beta. This conclu-sion confirmed the research of Roncalli and Weisang.

One of the most recent papers to be published added an interesting wrinkle to the analysis of hedge fund returns. All of the previous papers looked at performance using reported hedge fund returns. Adam Aiken, Christopher Clifford and Jesse Ellis10 sought to determine if hedge fund alpha truly existed after controlling for biases introduced by the self-selective nature of hedge fund reporting to commercial databases. They found “evidence that most of the average fund’s alpha can be explained by its decision to voluntarily report its performance to a database; 95 percent of a typical fund manager’s measured skill can be explained by whether they report to a database.” This is an important contribution to the field because it calls into question whether investors are actually benefiting from the returns purported to be achieved by hedge funds. This bias in reported returns effec-tively raises the bar for hedge fund replication strategies, as they are being compared to an artificially high benchmark. To the extent that a hedge fund replication product can produce returns that are very close to reported hedge fund returns at a lower cost and without the negative character-istics of limited transparency and liquidity, the benefit of the replication strategy becomes more apparent.

Not All Hedge Fund Replication Strategies Are The Same

Despite the fact that many of the current hedge fund repli-cation strategies are based on the solid principles established by academic researchers, significant differences can be seen in the products based upon the investment process. In this section, we discuss some of the key decisions that need to be

December 31, 2001 - December 31, 2011

HFRI- FWI

DJCSHFI

S&P500

BarCapAgg

Bond

Annualized Return 5.92% 6.44% 2.92% 5.78%

Annualized Std. Dev. 6.52% 5.84% 15.93% 3.70%

Return/Risk 0.91 1.10 0.18 1.56

Correlation to S&P 500 80.11% 68.42% 100.00% -5.55%

Correlation to BarCap Agg Bond -3.04% 3.30% -5.55% 100.00%

Sources: Dow Jones Credit Suisse, Hedge Fund Research, Bloomberg, IndexIQ research

Figure 1

Index Performance Comparison




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made and use the IndexIQ methodology when necessary as an illustrative example.

Hedge Fund Return Providers

The two major providers of hedge fund returns are Dow Jones Credit Suisse (DJCS) and Hedge Fund Research (HFR). While there are other providers such as Barclay Hedge and MSCI Barra, it is generally acknowledged in the industry that DJCS and HFR are the dominant provid-ers. Both provide returns for individual strategies as well as for broad-based composites. Additionally, both provide returns for investable (open for new investors) and non-investable (closed to new investors) hedge funds. DJCS and HFR both include funds that have at least $50 million in assets. While HFR requires assets greater than $50 mil-lion or 12 months of trading history, DJCS requires assets greater than $50 million and 12 months of trading.

The most important distinction, however, is that DJCS is asset weighted whereas HFR is equal weighted. Asset weighting, we believe, provides a more accurate picture of the asset class, as it represents the total performance of the actual assets invested. This distinction can have an effect on the risk and return profile. As detailed in Figure 1, for the 10 years ending Dec. 31, 2011, DJCS returned 6.44 percent per year with a standard deviation of 5.84 percent for a return/risk ratio of 1.10. Over the same time period, the HFRI Fund Weighted Composite Index (HFRIFWI) had a 5.92 percent annualized return with a standard deviation of 6.52 percent for a return/risk ratio of 0.91. DJCS produced higher returns with a lower standard deviation. Perhaps more importantly, the correlation of DJCS returns to the S&P 500 was 68 percent vs. 80 percent for HFRIFWI. Both had virtually zero correlation to bonds as represented by the Barclays Capital Aggregate Bond Index (BarCap Agg). As many investors use hedge funds to diversify away some of the equity risk in their portfolios, a lower correlation to equities is a very desirable trait. With a better representation of the investments in hedge funds supported by higher returns, lower standard deviation and lower correlation to the S&P 500, IndexIQ selected the DJCS as the basis for its hedge fund replication strategy.

Return Level

There are three levels of returns available when design-ing a hedge fund replication strategy. First, one can choose to replicate the returns of individual hedge funds. However, any individual hedge fund can completely turn its portfolio over in a short period of time. Thus, using any time series analysis method on individual hedge funds leaves one open to the possibility that prior information becomes irrelevant very quickly.

Alternatively, one can opt to replicate the returns of a broad hedge fund index that aggregates across mul-tiple hedge fund strategies. With the inherent diversifica-tion across multiple managers and strategies, one would expect the exposures to be more stable over time as port-folio changes by any individual manager are far less likely to impact the aggregate exposures. However, there is a risk

that with so many return series aggregated together, the signal-to-noise ratio falls and it becomes difficult to iden-tify the meaningful exposures. Also, to the extent that the broad indexes have a bias toward a certain strategy based solely on either the assets invested or the number of funds classified in that strategy, the resulting replication product will also have a similar bias. This can be suboptimal, as the allocation to that particular strategy may not be efficient given the expected returns and volatilities of strategies at that point in time. For example, at the start of 2008, the DJCS index had over 35 percent allocated to the equity long/short strategy. The equity long/short strategy went on to lose -19 percent for the year.

The third option is to replicate the returns of individual hedge fund strategies. By using the aggregate returns of a homogenous group of managers, one can potentially avoid the risk of any individual manager changing exposures frequently. Much as a portfolio of securities, such as an industry portfolio, exhibits a much more stable exposure-and-return pattern, a group of hedge funds will also have more stable and consistent exposures and returns due to the natural diversification that occurs. For example, as one fund is increasing its exposure to a certain factor, another fund may be reducing its exposure to the same factor. In such an instance, the net change in the factor-loading at the strategy level will be much smaller than at the fund level. Strategy-level returns also allow for the selection of factors based on an economic analysis as recommended by Amenc et al. [2009]. Finally, having individual hedge fund strategies as building blocks allows for greater flex-ibility in creating better allocations across strategies.

Statistical Properties

Hedge fund indexes generally comprise hedge funds that report their returns on a voluntary basis. As such, these indexes are susceptible to biases that can arise from managers deciding not to report their returns once the returns are no longer attractive or from manag-ers choosing to begin reporting returns only when they have a successful track record that they can add to the database. Numerous articles have been written about survivorship and back-fill biases in hedge fund returns. Fung and Hsieh [2009]11 found that “[i]n general, return measurement biases can be traced to two key events: when a hedge fund elects to enter one or more databases and when a hedge fund exits a database.” Estimates of the effect of these biases on reported returns range from 4-6 percent per annum.12 Surprisingly, this bias is often used as a counterargument against hedge fund replication. The bias is actually supportive of replication. Replication is often attacked for targeting the “average” manager. Most investors would prefer to have the returns of an “above average” manager. If the performance as reported by the hedge fund indexes is overstated by at least 4 per-cent per year, then a replication product that can deliver these returns must be “above average.”

When working with data sets that contain performance information, it is important to review the data with an eye





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toward quality control. Extreme data points can cause a process to produce undesirable outcomes if the data quality is not verified. An example of this issue occurred in November 2008 in the Dow Jones Credit Suisse Equity Market Neutral Hedge Fund Index. Typically, a mar-ket-neutral hedge fund will have very low volatility. Indeed, this index had an annualized standard deviation of 2.92 percent from its inception in January 1994 through October 2008. However, in November 2008, a single manager comprising over 40 percent of the index had a return of -100 percent, as its assets were written down to zero due to its exposure to a Madoff Investments fund. As a consequence, the index was down 40.45 percent in November 2008. This return is an almost 14-standard-deviation event. Clearly, using this return in a replication process would cause an undesirable result.

One solution is to identify returns that are extreme rela-tive to the environment and the strategy being evaluated. If an extreme value is detected, an algorithm can be used to estimate a more suitable value to be used in the replica-tion process. This ensures that extreme data values do not corrupt the replication process.

An oft-cited characteristic of hedge funds is the some-times-illiquid nature of their underlying holdings. This illi-quidity can perhaps lead to superior returns over the longer run but can cause difficulties in valuing an asset in the short-er run. If assets are not marked-to-market accurately at the end of each reporting period, the volatility of the reported returns can be muted and the fund returns can appear to be less correlated to exchange-listed assets than they actually are. Low volatility and low correlation to exchange-listed assets are obviously desirable attributes of an investment vehicle. However, misestimation of the true volatility and correlation can introduce errors in the replication process. IndexIQ chooses to employ a process that measures the degree of misestimation of the volatility and correlation, and applies a correction factor designed to yield a better estimate of the true returns as opposed to the reported returns.

Estimation Methods

One of the key decisions one needs to make when designing a replication product is which estimation method to employ. The choices can range from a simple ordinary least squares (OLS) regression method to a more complicated Kalman filter. OLS is best suited for estimating stable, linear relationships, such as factor exposures. Given that many hedge fund returns exhibit

time-varying and nonlinear properties, more sophis-ticated methodologies may be better suited. Amenc et al. researched the impact of using conditional and nonlinear models to create hedge fund clones. They wrote, “… it appears that conditional and non-linear models, which are less parsimonious than their linear counterparts, do not necessarily lead to improved out-of-sample replication.” Thus it appears that despite the intuitive appeal of more complex models, the out-of-sample efficacy of OLS is supported by the evidence.

Asset Universe

As Hasanhodzic and Lo showed, exchange-traded assets can serve as viable assets in hedge fund replica-tion. Within the broad universe of assets encompassed by this definition, there are obvious distinctions. One could partition the universe by asset class and choose, for example, to use assets that represent equities only. One could also segment the universe by underlying asset type. Such delineation might group assets into exchange-trad-ed funds versus exchange-traded derivative products. Each group has certain advantages and disadvantages. For example, ETFs may provide potential exposure to a greater list of factors, especially on the corporate fixed-income side where exchange-listed derivatives do not exist. ETFs also provide greater transparency as they are typically index based and are required to disclose posi-tions daily. On the other hand, ETFs may have a higher cost of ownership as there will be expense ratios in addi-tion to transaction costs. Exchange-listed derivatives will have lower fees but may have fewer potential exposures and can also be less transparent and more difficult for end-users to understand.

Strategy Allocation

Given the decision made earlier to replicate hedge fund returns at the strategy level, one must then select a method for allocating across the strategies. There are at least four possible methods:

1. Equal weight − This is the simplest method to under-stand and implement. One would simply average the exposures across all of the underlying strategies to arrive at the final portfolio.

2. Asset weight − This slightly more complex method assigns a weight to each strategy based upon the distribu-tion of assets across strategies in the hedge fund universe. A key limitation of this approach is that one must have

May / June 201244

Figure 2

IQ Hedge

Long/Short

Beta Index

Dow Jones

Credit Suisse

Long/Short Equity

Hedge Fund Index

Dow Jones Credit

Suisse Blue Chip

Long/Short Equity

Hedge Fund Index

HFRI Equity

Hedge LS

HFRX Equity

Hedge LS

2008 -27.56 -19.74 -29.39 -26.64 -25.45

Sources: Dow Jones Credit Suisse, Hedge Fund Research, Bloomberg, IndexIQ research

Hedge Fund Index Performance In 2008 (%)




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www.journalofindexes.com 45May / June 2012

access to the strategy asset weights. Even if one were to have access to this data, it is not clear that this approach is optimal. Asset weighting is very much like market-cap-italization weighting in equity indexes. An ongoing debate exists in the industry as to whether asset (market-capital-ization) weighting is superior to other methods (such as weighting based on fundamental factors).

3. Subjective weight − This method relies on the forward-looking forecasts of strategists to accurately determine the proper allocation. This requires a person or team to possess a level of skill that must be repeatable over time.

4. Optimized weight − This method uses a rules-based model to determine the allocation based on measurable metrics. For example, one could run a mean-variance (or perhaps some other type of nonlinear) optimization using inputs on estimated return, volatility or correlation.

The IndexIQ methodology employs the last method, providing allocations to each strategy that reflect the best allocation given the available data. Specifically, the combi-nation of strategies is selected to have the highest expected return and correlation to a broad hedge fund index with the lowest expected standard deviation. One interesting feature of this type of process is the ability to allow for short exposures to a particular strategy.

To create a short exposure to a strategy, one simply reverses the signs on the underlying factor exposures. This will create a return series that is designed to track the inverse of that particular hedge fund strategy, which

can be advantageous during periods when a strategy has negative returns with high volatility. For example, in 2008, the equity long/short strategy was in the midst of a sharp drawdown. Figure 2 shows that Equity Hedge Long/Short index returns ranged from -19.74 percent (DJCS Long/Short Equity Hedge Fund Index) to -29.39 percent (DJCS Blue Chip Long/Short Equity Hedge Fund Index). By comparison, the IQ Hedge Long/Short Beta Index (which is the IndexIQ index designed to deliver the beta component of returns for the equity long/short strategy) had a return of -27.56 percent. Clearly, the IQ Hedge Long/Short Beta Index had returns similar to the average long/short fund in 2008. However, the allocation to the IQ Hedge Long/Short Beta Index in the composite strategy was on average -33.33 percent in 2008. Thus, the IQ Alpha Hedge Index benefited from having a negative exposure to an underperforming strategy.

Registered Hedge Fund Replication ProductsHedge fund replication products come in a wide range

of investment vehicles. Initially, they were only available as indexes. Mutual funds were the first listed products to appear. They were quickly followed by ETFs. Of course, there are also structured products and separately managed accounts available. Figure 3 contains key characteristics of listed hedge fund replication investment products.

While these products clearly differ on a number of dimen-

Figure 3

TickerAsset

Manager

Incep.

Date3-Mo (%)

3-Yrb

(%)

1-Yra

(%)

3-Yr Corr

To S&P

Net Exp

Ratiod

Max

Sales

Charge

Structure

3-Yr

Sharpe

Ratio

3-Yrc

Std

Dev

Name

IQHIX IQ Alpha Hedge Mutual Strategy, Inst Cl IndexIQ 6/30/08 2.72 -1.91 5.53 8.54 0.66 0.69 1.30 0 Fund

QAI IQ Hedge Multi-Strategy Tracker IndexIQ 3/25/09 1.98 0.26 — — — — 0.75 0 ETF

MCRO IQ Hedge Macro Tracker IndexIQ 6/9/09 2.23 -3.42 — — — — 0.75 0 ETF

GARTX Goldman Sachs Absolute Mutual Return, Cl A Goldman Sachs 5/30/08 2.08 -3.77 1.49 5.80 0.26 0.86 1.66 5.50 Fund

GAFAX ASG Global Alternatives, Mutual Cl A Alpha Simplex 9/30/08 0.98 -3.29 4.06 7.91 0.53 0.72 1.61 5.75 Fund

HDG ProShares Hedge Replication ProShares 7/12/11 3.36 — — — — — 0.95 0 ETF

CSLS Credit Suisse Long/ Short Liquid Credit Suisse 2/9/10 3.64 -0.37 — — — — 0.95 0 ETN

CSMN Credit Suisse Market Neutral Equity Credit Suisse 9/20/11 1.52 — — — — — 1.05 0 ETN

HFRIFoF HFRI Fund of Funds Hedge Fund Index Research — -0.39 -5.64 3.59 4.69 0.75 -0.70 — — Index

HFRIFWI HFRI Fund Weighted Hedge Fund Index Research — 1.26 -4.83 7.97 6.78 1.15 0.84 — — Index

BarCap BarCap Aggregate Agg Bond Index Barclays Capital — 1.12 7.84 6.77 2.82 2.30 0.00 — — Index

S&P 500 S&P 500 Standard & Poor’s — 11.82 2.11 14.11 18.97 0.79 1.00 — — Index

Hedge Fund Replication Investment Products & Indexes

Sources: Bloomberg, IndexIQ researcha As of 12/31/2011. bPeriods greater than one year are annualized. cAnnualized standard deviation of monthly returns. dMay include a contractual fee waiver





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In Perspective

May / June 201246

‘Alternative’ Might Just

Mean ‘Unfamiliar’

Once upon a time, the stock market was considered an “alternative” market.

In the late 19th and early 20th centuries, the stock market was a Wild West free-for-all where prudent investors feared to tread. Protective rules and regula-tions were virtually nonexistent, and what few there were went largely unenforced. Stock certificates could be stolen or forged. Pricing was usually shallow, opaque and thus vulnerable to extreme manipulation, especially by insiders who didn’t care about wiping out hundreds or thousands of investors. Realizing favorable returns by trading stocks demanded nerves of steel, and perhaps some aggressive arm-bending of one’s own.

Instead, “real” investors were almost exclusively focused on physical assets—buying and selling them, or lending against them in the form of bonds. Real property such as land, goods and equipment represented true wealth, and dealing in them one way or another was subject to fewer of the challenges that plagued the stock market. It would, for example, be impossible to forge a shipment of oil, and land was about as permanent as things could be. Although such assets were far from perfect, they enjoyed reasonable liquidity and could be readily resold to other investors.

Today, of course, the situation has entirely reversed. Most in-vestors assemble stock-centric portfolios, and everything other than stocks (and bonds) is classified as “alternatives.” This includes the same land, goods and equipment markets that had dominated the investment landscape a century or so ago.

The Rise Of SecuritiesHow did this come to pass? Over time, it became clear

that the efficiencies of scale accrued to the largest players,

which were corporations. They could produce profits rel-atively quickly, making shares of ownership much livelier investment vehicles than were loans paid off at a plod-ding, fixed rate. Corporations used both stocks and bonds to raise capital, of course, but stocks were in the enviable position of being able to capture economic growth.

Naturally, the investors attracted to these wondrous instruments demanded legitimate market improve-ments and efficiencies: price transparency, trading tech-nologies, securities clearing, regulatory oversight and so on. Ultimately, the stock market sufficiently addressed virtually all of the earlier challenges. It became the most liquid and transparent of investment markets.

The equities market’s very long, continuous and public track record allows it to become a readily accessible invest-ment measure that permits investors everywhere to refer-ence and analyze its data. Stocks have been transformed from investment outcasts to being the “devil you know.” Almost everything else—the catchall definition of “alterna-tives” —has become the devil that most investors don’t know.

And this devil’s name is “risk.” What are our chances of los-ing money? Do we know how we could be rewarded? Do we understand the economics of the investment marketplace?

The devil-you-know moniker for stocks is relative, of course, since we cannot have perfect knowledge and each new cycle seems to bring an event that breaks old expectations: the 1987 crash, the Internet bubble, the 2008 financial crisis and the “lost decade.”

Prospect TheoryAn interesting model for something as basic as defining

alternatives as “alternatives” might be “prospect theory,” which was developed and published by Daniel Kahneman

David Krein John Prestbo

Unknown risks can tempt investors—

or give them pause

By David Krein and John Prestbo




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and Amos Tversky in 1979. In their paper, they demon-strated that individuals (and investors) are risk-averse and therefore do not always make rational decisions in which they opt for the highest expected value outcome.

In today’s investment environment, in which stocks are widely and easily available, the default decision is to opt for stocks, whose risks are largely known. An alternative-investment option—whether commodi-ties, real estate, managed futures or anything else—may be a desirable addition to a portfolio from an economic perspective, but the risks are less known and therefore more threatening.

Stated differently, the relative predictability of stock risk is favored over the uncertainty surrounding the risk in alternatives, even if the portfolio outcome is suboptimal. Of course, this is not to imply that stocks are more legitimate than “alternatives” and other investment options. Rather, the default preference for stocks can be partly explained by the time and expense required, whether spent directly or through advisors, for coming to understand alternatives and their associated risks. It is not a trivial task.

For example, contrast stocks with commodities. There are large and small stocks, growth and value stocks and lots of sectors—but they are all stocks, and a major market move can sweep most of them higher or lower regardless of whether they individually deserve to be so reposi-tioned. They are a true asset class, a cohesive grouping of securities that usually react similarly to economic cycles

and that have many other common characteristics.But beware of transposing this asset-class attitude to

commodities. There is no singular commodities market. There are, instead, commodity markets. Each one is driv-en by its own supply and demand factors, which can be radically different from one to another—even when the commodities themselves are related, such as crude oil, gasoline and heating oil. Stock-oriented investors almost always got burned when venturing into commodity mar-kets because of this diversity.

Until, that is, the advent of diversified commodity indexes led to investment vehicles that bundled these individual, disparate markets into one price. Then the floodgates opened, and some $200 billion to $300 billion has flowed into commodity funds, notes and other invest-ment vehicles over the past 20 years because the compli-cated was magically rendered simple. Or so it seemed.

Underneath, though, commodities are still the churlish individualists they always have been. Just ask the immi-grated investors who have reluctantly expanded their “price-earnings” vocabularies to include “Brent” and “WTI,” not to mention “contango” and “backwardation.”

Perhaps the time will come when the now-murky risks of commodities, commercial real estate, tim-berlands and even vintage wines also will be “known devils.” Until then, the alternatives bucket will con-tinue to seduce some investors and repel others with mysterious booms and busts.

sions, their overall performance, albeit over a relatively short time period, shows the potential of hedge fund replication products to play a significant role in investors’ portfolios. With returns, volatilities and correlations that are approach-ing those of actual hedge fund indexes, the synthetic products are establishing themselves as viable investment solutions. Moreover, the level of assets under management indicates that investors are beginning to recognize this potential.

ConclusionThe hedge fund replication industry has grown dra-

matically over the last five years. Thanks to contribu-

tions from both the academic community as well as to asset managers, the body of research has expanded to cover the topic from a number of different perspec-tives. Asset managers have also introduced numerous products to provide synthetic hedge fund exposure. However, despite the fact that most products are based on identifying the common factors that drive hedge fund returns, there are important distinctions in the methodologies employed and the vehicles offered. The key distinctions are discussed in this article to enable an investor to better understand the different replication products being offered and to intelligently select the one that best fits their investment needs.

Endnotes1 Swensen, D., 2000, “Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment.”2 Armfelt, C. and Somos, D., 2008, “Performance, Benefits and Risk of Active-extension Strategies.” 3 Sources: Dow Jones Credit Suisse, Bloomberg, IndexIQ research4 HFR 2008 Industry Report, Hedge Fund Research Inc.5 Fung, W. and Hsieh, D., 1997, “Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds.” 6 Sharpe, W., 1992, “Asset Allocation: Management Style and Performance Measurement,” Journal of Portfolio Management.7 Hasanhodzic, J. and Lo, A., 2006, “Can Hedge-Fund Returns Be Replicated?: The Linear Case.”8 Roncalli, T. and Weisang, G., 2008, “Tracking Problems, Hedge Fund Replication and Alternative Beta.”9 Amenc, N. et al. 2009, “Passive Hedge Fund Replication—Beyond the Linear Case.” 10 Aiken, A., Clifford, C. and Ellis, J., 2011, “Out of the dark: Hedge fund reporting biases and commercial databases.”11 Fung, W. and Hsieh, D., 2009, “Measuring Biases in Hedge Fund Performance Data: An Update,” Financial Analyst Journal, vol. 65, No. 3.12 Ennis, R. and Sebastian, M., 2003, “A Critical Look at the Case for Hedge Funds,” Journal of Portfolio Management.

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News

May / June 201248

Index Providers Establish IIAMSCI, S&P Indices and FTSE are

to launch a new trade body to repre-sent index providers, called the Index Industry Association (IIA), the three firms said in a mid-March press release.

The IIA will have a remit that includes investor education and the promotion of the interests of index users and product providers, say MSCI, S&P Indices and FTSE. The not-for-profit trade body is to be based in New York and will have a board of directors that initially com-prises representatives from the three founding companies. The IIA will be open to membership from index businesses worldwide. A search is currently under way for an executive director to lead the body.

The IIA says its aims also include working with regulatory bodies and the support of index providers’ intel-lectual property rights.

Regulators, particularly in Europe, are taking a closer look at how index-es are used in funds approved for retail distribution, and some fund managers have justified recent moves to “self-index,” or to run their own benchmarks, as being motivated part-ly by competitive pressures and the costs of paying licensing fees to third-party index firms.

Case-Shiller Indexes Hit New Lows

The U.S. housing market did in December what many feared, with home prices sliding to their lowest readings since the housing crisis began in mid-2006, the February S&P/Case-Shiller report showed.

As all major index composites that track the performance of the U.S. hous-ing market forged new lows, hopes that the market was stabilizing after having bounced off a recent bottom for the past two years all but evaporated.

Both the 10-City and the 20-City composites, as well as the national index—a measure of all nine census divisions—ended the year roughly 4 percent lower than where they were a year earlier. Overall, both the 10-City and 20-City composites are nearly 34 percent off their mid-2006 peaks.

On a broader level, the national composite, which fell by 3.8 percent in the fourth quarter of 2011 alone from last year’s third quarter, ended the year nearly 34 percent off of its mid-2006 peak—a new record low.

All in all, home prices in the U.S. are now back to where they were at the end of 2002.

On a monthly level, 18 of the 20 cit-ies surveyed saw home prices decline in December vs. November, with 17 of them posting month-on-month declines for at least three straight months, the report showed.

Atlanta led the losses, with home prices there nearly 13 percent below where they were a year ago, leaving them at a new low in December since prices started moving downward in 2006. Las Vegas, Seattle and Tampa also set new record lows at the end of 2011.

Detroit was the only market where prices ended the year on a posi-tive note, gaining 0.5 percent from December 2010 levels.

BATS Calls Off IPOBATS Global Markets, the third-

biggest U.S. stock exchange, on March 23 called off its plans for an initial public offering, apparently in the wake of a slew of aberrant trades on the day of the IPO that involved both its own stock and that of Apple Inc.

BATS, which first announced plans to go public in May 2011, actually had begun the IPO on March 22, with more than 6 million class A common shares pricing at $16 per share, the company said then in a press release.

The IPO was scheduled to be wrapped up by Wednesday, March 28.

However, Joe Ratterman, chair-man, president and chief executive officer of BATS, said in a statement issued by the company that call-ing off the IPO was the “appropriate action” to take. Another company official later confirmed that the IPO was being called off rather than post-poned, but declined to discuss the exact reasons behind the decision or whether BATS might have another go at an IPO at some time in the future.

The erroneous trades on the BATS BZX Exchange affected trades in secu-rities in the symbol range A through BF, BATS said in a statement issued in the wake of the problems, but before the company made the deci-sion to abandon the IPO. The glitch included three erroneous trades of Apple stock, one of which triggered a “volatility halt” in that stock.

The problem was rectified in just a few hours, with the erroneous trades broken under BATS’ “clearly errone-ous” trade policy, the exchange said. Normal trading in the affected securities resumed by 12:50 p.m. ET on March 23.

Vanguard, SSgA In Price War?State Street Global Advisors cut

expense ratios on its nine “Select Sector SPDR” sector ETFs, leaving their pric-es 1 basis point lower than competing funds from Vanguard.

The Select Sector SPDRs are the larg-est U.S.-focused sector ETFs. In a regu-latory filing effective Jan. 31, Boston-based SSgA said that expense ratios on all nine of its Select Sector ETFs would be lowered by 10 percent, or 2 basis points, to 0.18 percent.

On the surface, the move appears to be a response to price-cutting made by Valley Forge, Pa.-based Vanguard in late December. At that time, Vanguard said it was able to cut expense ratios




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on its family of sector funds by some 20 percent—to 0.19 percent each—thanks to gains in economies of scale and growth in assets.

A representative for the Select Sector SPDR Trust said the board of direc-tors had already decided to lower fees on the suite of SPDR sector ETFs at its last meeting in November, but an announcement could only be made once a prospectus was filed with the Securities and Exchange Commission.

In late February, Vanguard again slashed the fees on six of its ETFs, includ-ing the popular Vanguard Emerging Markets ETF (NYSE Arca: VWO), which now costs 0.20 percent, a 10 percent reduction; it was already the cheapest in its class. The other funds and their lowered prices are:

• Vanguard Total World Stock ETF (NYSE Arca: VT), 0.22 percent from 0.25 percent

• Vanguard FTSE All-World ex-US ETF (NYSE Arca: VEU), 0.18 per-cent from 0.22 percent

• Vanguard FTSE All-World ex-US Small Cap ETF (NYSE Arca: VSS), 0.28 percent from 0.33 percent

• Vanguard Total International Stock ETF (NYSE Arca: VXUS), 0.18 per-cent from 0.20 percent

• Vanguard High Dividend Yield ETF (NYSE Arca: VYM), 0.13 percent from 0.18 percent.

iShares Launches Sector, Other Bond ETFs

iShares charted new territory in the bond space in mid-February with the launch of seven fixed-income ETFs, including multiple “first-to-market” funds.

The iShares Financial Sector Bond Fund (NYSE Arca: MONY), the iShares Industrials Sector Bond Fund (NYSE Arca: ENGN) and the iShares Utilities Sector Bond Fund (NYSE Arca: AMPS) each cost 0.30 percent a year in fees,

and are the market’s first funds to parse the corporate debt sectors. Invesco PowerShares and State Street Global Advisors both have similar sector funds in registration that have yet to launch.

The rollout also included the iShares Aaa-A Rated Corporate Bond Fund (NYSE Arca: QLTA), the first-ever ETF to tease out the most highly rated corporate bonds. QLTA includes only bonds with at least one year to maturity and at least $500 million of outstanding face value, with as much as 15 percent of the portfolio being tied to non-U.S. issues. It has an annu-al expense ratio of 0.15 percent.

The iShares Barclays CMBS Bond Fund (NYSE Arca: CMBS), anoth-er first-to-market ETF, taps into investment-grade commercial mort-gage-backed securities, which are in essence securities that represent interests in “pools” of commercial mortgages. It costs 0.25 percent.

At the same time, iShares debuted the also-unique iShares Barclays

GNMA Bond Fund (Nasdaq GM: GNMA). GNMA’s underlying index measures the performance of mort-gage-backed pass-through securities issued by the Government National Mortgage Association, also known as Ginnie Mae. It comes with an annual expense ratio of 0.32 percent.

Less exotic is the iShares Barclays U.S. Treasury Bond Fund (NYSE Arca: GOVT), which invests in Treasurys with at least one year to maturity, and that have a minimum of $250 million of out-standing face value. The Treasurys also need to be fixed rate and nonconvert-ible. GOVT costs 0.15 percent.

INDEXING DEVELOPMENTSDow Jones Indexes Launches Position Sizing Index

Working with LSP Partners LLC, Dow Jones Indexes debuted the Dow Jones LSP Position Sizing Equal Sector U.S. Large-Cap 50 Index in late January, a press release said.

The index’s methodology applies

State Street Global Advisors cut expense ratios on its nine “Select Sector SPDR” sector ETFs




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News

LSP Partners’ “Leveraged Space Portfolio” strategy to the largest stocks in the Dow Jones U.S. Total Stock Market Index, selecting the five largest stocks from each of the 10 sectors in DJI’s sector classification system, the index’s meth-odology said. Based on an algorithm, the index adjusts its weight between that equity component and Treasury bills, with the goal of “maximiz(ing) the probability of positive performance,” according to the press release.

The index is the first in a series of planned indexes, the press release said.

FTSE Debuts WPU FTSE rolled out the FTSE Wealth

Preservation Unit in February. The index provider said in a press release that the WPU is a “currency unit” that was created as a hedging tool to “mini-mize risk for investors and exhibit great-er stability than any single currency.”

The WPU is calculated based on a range of currencies from emerging and developed markets and commodities. According to a fact sheet, developed market currencies represent about 82 percent of the WPU, while emerging market currencies represent 14 percent, and commodities 4 percent.

The unit was developed with Mountain Pacific Group, a U.S. invest-ment management firm that specializes in commodity, currency and emerging

market strategies. The firm’s chairman asserted in the original press release that currency risk was one of the great-est risks faced by institutional investors.

Markit Launches Int’l High-Yield Bond Indexes

Markit said in early February it had launched two high-yield corporate bond indexes. The Markit iBoxx Global Developed Markets High Yield Index and the Markit iBoxx Global Developed Markets ex-US High Yield Index each cover bonds from more than 30 coun-tries denominated in Canadian dol-lars, euros and pounds sterling, the press release said. The former index also includes USD-denominated debt, while the latter does not.

The indexes are weighted by market value, according to the press release.

S&P Rolls Out ‘High Quality’ Index

In mid-February, S&P rolled out the S&P International Developed High Quality Rankings Index, a press release said.

The index includes stocks from developed markets, excluding the U.S., that are highly rated in the S&P Quality Rankings. The ranking system uses an algorithm that relies primarily on earnings and dividends to evaluate companies, the press release said.

According to a fact sheet from S&P, the new index includes all stocks in the selection universe that are rated A- or higher, and weightings in the index are determined by a stock’s score in the rankings system. As of the end of January, the S&P International Developed High Quality Rankings Index had 294 components, with Japan carrying the heaviest county weight, at 20.2 percent of the index, followed by the U.K. at 15.4 percent and Canada at 11.5 percent, the fact sheet said.

S&P Debuts New Version Of S&P GSCI

In late January, S&P rolled out yet another version of the well-known S&P GSCI, the index provider said in a press release. The S&P GSCI Multiple Contract Index basically combines three versions of the S&P GSCI, hold-ing front-month, one-month forward and two-month forward contracts.

According to an S&P fact sheet, the contracts of the original S&P GSCI rep-resent 55 percent of the new index, with the contracts of the 1-Month S&P GSCI Forward Index receiving a 30 per-cent weighting and the 2-Month S&P GSCI Forward Index weighted at 15 percent. The purpose of this, the press release said, is to mitigate the effects of contango and lower volatility.

New Russell Series Focuses On Customized Volatility

In early March, Russell joined an index industry trend with the launch of the Russell Volatility Control Index Series. Many of the major index pro-viders—most notably S&P—have been launching indexes targeting volatility levels as investors become more aware of the risks associated with higher market volatility.

According to the press release, the new Russell indexes shift their allo-cation between equities and cash in order to maintain the desired level of volatility, pulling out of the stock market when volatility is high and increasing exposure to equities when volatility is low. The methodology can be used with any index in Russell’s

May / June 201250

The WPU is a “currency unit” created as a hedging tool to “minimize risk for investors and exhibit greater stability than any single currency.”




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U.S. and global benchmark families, and can target any volatility level, the press release said.

The Russell website further noted that the indexes can also be custom-ized according to rebalancing fre-quency, levels of leverage and volatil-ity calculation methodology.

DB Rolls Out Inflation Expectation Index

In mid-January, Deutsche Bank launched an index tracking the spread between the yields on U.S. Treasury inflation-protected securities and the yields on U.S. nominal Treasurys, a press release said. The purpose of the DB Market-Implied US Inflation Rate Index is to provide investors with a tool for gauging inflation expectations.

The index is calculated based on the breakeven rates for five-year, 10-year and 30-year TIPS, paired with the Treasurys that most closely match their respective maturity dates, the press release said. According to a Deutsche Bank fact sheet, the index allocates a 25 percent weight to the five-year and 30-year breakeven rates, and a 50 percent weight to the 10-year breakeven rate.

Nasdaq Teams With Axioma On Commodities Indexes

Nasdaq recently teamed with port-folio tools provider Axioma to develop a family of equities-based commodity indexes, a press release said. On March 5, the exchange rolled out the Nasdaq Axioma Equity-Commodity Oil Index, the Nasdaq Axioma Equity-Commodity Gold Index and the Nasdaq Axioma Equity-Commodity Agriculture Index.

While commodities equities indexes abound, the Nasdaq Axioma Equity-Commodity Index series offers a dif-ferent twist—rather than simply trying to capture the performance of com-modies equities, it tracks commodi-ties equities that correlate closely with commodities prices in an effort to mimic spot commodities price perfor-mance, the press release said. A fact sheet noted that the indexes aim for a beta of 1 with respect to the spot prices of their targeted commodities.

Russell Launches European ‘SMID’ Index

In March, Russell Investments unveiled a new index designed to give investors in ETFs and other structured products access to the most liquid small- and midcap European stocks.

According to the index provider, the Russell Europe SMID 300 Index was cre-ated after lengthy consultation with large financial institutions such as Deutsche Bank, Goldman Sachs and UBS.

Russell said its consultation with market participants revealed that many thought existing indexes cover-ing small- and midcap stocks lacked the liquidity needed to underlie prod-ucts such as ETFs, where traders need to be able to buy and sell the index constituents quickly. The index meth-odology has therefore been designed with tradability in mind.

To qualify for inclusion, stocks must be listed on a European exchange and fall between the 75th and 95th per-centile of the Russell Global Index in terms of global market capitalization. Companies are then ranked in order of their “speed to trade,” which is the length of time it takes to complete a trade in its stock.

FTSE Focuses On Bribery For ESG Indexes

FTSE Group is to expand the scope of its bribery criteria to cover an addi-tional 1,200 companies included in the group’s indexes.

The move follows the strengthening of bribery regulations in various juris-dictions, including the introduction of the 2011 UK Bribery Act. FTSE called bribery a “risk to long-term investors” in a press release about the policy change.

In the past, the index provider applied bribery criteria only to firms deemed to be at the greatest potential risk of bribery, but it will now roll out its assessment to cover companies that are defined as “medium risk” based on their industrial sector, countries of operation and public sector contracts.

Of the additional 1,200 firms to be assessed, about 550 are in the FTSE4Good index series, and 130 of

these will need to make improve-ments in order to stay in the index.

The changes were announced with the March FTSE4Good and ESG Ratings semiannual review.

FTSE Adds To Carbon Index Lineup

Index provider FTSE Group expanded its FTSE CDP Carbon Strategy index series in February; the index family was originally launched in December 2011. The new index-es include the FTSE CDP Carbon Strategy Australia 200 Index, the FTSE CDP Carbon Strategy Australia 300 Index, the FTSE CDP Carbon Strategy Europe Index and the FTSE CDP Carbon Strategy Japan Index.

The indexes, developed in partner-ship with CDP and ENDS Carbon, are aimed at helping investors factor the costs associated with climate change reg-ulation into their investment strategies.

The index provider said that pen-sion funds and asset managers in particular had expressed a desire to incorporate carbon risk into their investment strategies.

The index series uses established FTSE indexes and then reweights com-panies within each index according to their future carbon risks. Regulations related to climate change are expected to have an increasing impact on the profit-ability of companies in the near future. This is particularly true in Australia, where a controversial carbon tax is set to come into force in July of this year.

DJI Launches Volatility-Adjusted Titans Index

Dow Jones Indexes expanded its family of Volatility Risk Control indexes in mid-February, according to a press release.

The Dow Jones Global Titans 50 Volatility Risk Control indexes cur-rently come in two flavors, targeting market volatility levels of 10 percent and 15 percent. The indexes shift their weightings between the Dow Jones Global Titans 50 Index and cash to achieve their designated volatility lev-els, the press release said.





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According to the index methodology, volatility is calculated daily for 20-day and 40-day periods, with changes to the index’s allocation made only when the indicated day-to-day allocation shift is greater than 10 percent; otherwise, the allocations remain the same.

Dow Jones Indexes first launched its Volatility Risk Control index family in September 2011; it has licensed the newest additions to the series to J.P. Morgan and Barclays Capital for use as the bases for tradable products, the press release said.

AROUND THE WORLD OF ETFssPY Crosses $100 Billion Mark

In early February, “SPY,” the very first U.S.-listed exchange-traded fund, became the first ETF to gather more than $100 billion, almost 19 years after its launch, putting it in the company of some of the more legendary U.S. mutual funds and making it the perfect meta-phor for an ETF industry that’s on a roll.

The fund, officially known as the SPDR S&P 500 ETF (NYSE Arca: SPY), gathered $1.31 billion in fresh assets as of Jan. 20, ending the day’s trad-ing session with $101.03 billion in assets, according to data compiled by IndexUniverse. It’s a milestone that’s almost unbelievable to those who cre-ated SPY. The ETF was dreamed up by the American Stock Exchange as a

vehicle for traders its creators hoped would help pump up volume.

When it crossed the threshold, SPY was just about as big as the $101.8 billion Vanguard 500 Index Fund (VFINX), the world’s first index mutu-al fund, launched in 1975.

Van Eck Debuts Indonesia small-Cap ETF

Van Eck Global on March 21 rolled out the market’s first equities ETF to invest solely in Indonesia’s smallest companies, providing a new way to access Southeast Asia’s biggest economy.

The Market Vectors Indonesia Small Cap ETF (NYSE Arca: IDXJ) is essential-ly the small-cap version of the Market Vectors Indonesia ETF (NYSE Arca: IDX), which is a large-cap portfolio that has gathered $527.4 million since it came to market in January 2009. The new fund, IDXJ, has a net expense ratio of 0.61 percent, which is capped until May 1, 2013. It’s just 1 basis point above IDX’s 0.60 percent fee.

IDXJ tracks a rules-based, modi-fied market-capitalization-weighted, float-adjusted Market Vectors index that focuses on small-cap companies either headquartered in or that gener-ate most of their revenues in Indonesia. The index includes 27 securities.

ishares Rolls Out Commodity-stock ETFs

In early February, iShares launched five separate equities focused on commodities.

The five funds and their expense ratios are:

• iShares MSCI Global Gold Miners

Fund (NYSE Arca: RING), 0.39 percent

• iShares MSCI Global Select

Metals & Mining Producers Fund (NYSE Arca: PICK), 0.39 percent, including fee waiver of 0.02 per-cent through Dec. 31, 2014

• iShares MSCI Global Energy

Producers Fund (NYSE Arca: FILL), 0.39 percent

• iShares MSCI Global Agricultural

Producers Fund (NYSE Arca:

VEGI), 0.39 percent, including 0.01 percent fee waiver through Dec. 31, 2014

• iShares MSCI Global Silver

Miners Fund (NYSE Arca: SLVP), 0.39 percent

All five new iShares funds use representative sampling strategies, meaning they don’t seek to own all the companies in the MSCI indexes they track. They also are broad-based funds that provide diversification by including stocks in a wide variety of regions and countries, both devel-oped and developing.

WisdomTree First To Market With EM Corporates Fund

WisdomTree debuted the market’s first broad-based emerging market corporate bond ETF in early March, beating iShares and State Street Global Advisors to the punch.

The WisdomTree Emerging Markets Corporate Bond Fund (Nasdaq GM: EMCB) is an actively managed port-folio consisting of dollar-denominated investment-grade corporate bonds from issuers in Asia, Latin America, Eastern Europe, Africa and the Middle East. The fund, which is listed on the Nasdaq exchange, has an annual expense ratio of 0.60 percent.

EMCB’s portfolio consists of bonds between two to 10 years, according to the company. The fund’s holdings currently have nearly eight years’ average maturity. Legg Mason’s sub-sidiary Western Asset Management is EMCB’s manager.

EMCB will eventually go head-to-head with similar strategies from iShares and SSgA, but those funds, which are passively managed, are still sitting in the Securities and Exchange Commission’s pipeline.

The ETF was seeded with almost $45 million, according to information posted on WisdomTree’s website.

Guggenheim Rebrands Rydex ETFs

Guggenheim Investments finally put its name on 27 Rydex funds as of March 1, in the latest chapter of an acquisition




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that began in 2010. The rebranding left all of the fund tickers unchanged.

However, Rydex’s 10 CurrencyShares ETFs are not part of the latest name change, and for now continue to oper-ate with the CurrencyShares name. Guggenheim officials have said the CurrencyShares ETFs will one day carry the Guggenheim name, but they didn’t specify when.

The rebranding just about com-pletes money manager Guggenheim’s arrival in the world of ETFs, which began with its acquisition of Claymore Investments in 2009. Guggenheim is the No. 8 U.S. ETF company, with roughly $12 billion in assets—a figure that includes the legacy funds from Rydex and Claymore, as well as the CurrencyShares ETFs.

PowerShares Adds More ‘Low-Vol’ ETFs

In mid-January, Invesco PowerShares brought two low-volatility ETFs to mar-ket. The launches follow on the suc-cess of the PowerShares S&P 500 Low Volatility Portfolio ETF (NYSE Arca: SPLV), which has gathered more than $1 billion since its launch in May 2011.

The PowerShares S&P Emerging Markets Low Volatility Portfolio (NYSE Arca: EELV) and the PowerShares S&P International Developed Low Volatility Portfolio (NYSE Arca: IDLV), like SPLV, each track indexes derived from broader benchmarks that have been screened to exclude higher-vol-atility stocks.

EELV and IDLV currently come with net annual expense ratios of 0.29 percent and 0.25 percent, respectively. The prices reflect fee waivers effective through April 20, 2013 of 16 basis points on EELV and 10 basis points on IDLV.

Global X Debuts ‘Permanent’ ETFIn February, Global X Funds

launched a multi-asset-class index fund designed to profit in different macroeconomic scenarios. It also happens to be the company’s first ETF to include bonds.

The Global X Permanent ETF (NYSE Arca: PERM) uses a low-volatil-

ity investment strategy called “risk par-ity,” an investment approach that was pioneered in 1996 by the Connecticut-based money manager Bridgewater Associates, with its All Weather hedge fund. Risk parity is intended to damp-en volatility through a diverse portfolio of various noncorrelated assets.

PERM is based on the Solactive Permanent Index and comes with an annual expense ratio of 0.49 percent.

The fund’s asset allocation strategy is as follows:

• U.S. large-cap stocks: 9 percent• U.S. small-cap stocks: 3 percent• International stocks: 3 percent• U.S. real estate stocks: 5 percent• U.S. and foreign natural resourc-

es stocks: 5 percent• U.S. long-term Treasury bonds,

with remaining maturity greater than 20 years: 25 percent

• Short-term U.S. Treasury bills and notes, remaining maturity less than three years: 25 percent

• Gold ETFs and exchange-traded commodities: 20 percent

• Silver ETFs and exchange-traded commodities: 5 percent

Guggenheim Shutters 8 Funds

Guggenheim Funds closed eight of its ETFs on March 23 due to their fail-ure to generate significant demand.

The closings of the ETFs are part of an effort “to focus resources on prod-ucts that have demonstrated the most marketplace demand,” Guggenheim said in a press release. The com-pany, which acquired Claymore in 2009 and Rydex in 2010, is also about to rebrand its Rydex funds with the Guggenheim name. It did the same with Claymore in 2010.

The eight closed funds together had gathered $105 million at the time of the announcement, or less than 1 per-cent of Guggenheim’s total ETF assets.

The liquidated funds include:• CurrencyShares Russian Ruble

Trust (NYSE Arca: FXRU)• CurrencyShares Mexican Peso

Trust (NYSE Arca: FXM)• Guggenheim EW Euro-Pacific

LDRs ETF (NYSE Arca: EEN)

• Guggenheim International Small Cap LDRs ETF (NYSE Arca: XGC)

• Guggenheim Ocean Tomo Growth Index ETF (NYSE Arca: OTR)

• Guggenheim Ocean Tomo Patent ETF (NYSE Arca: OTP)

• Guggenheim Sector Rotation ETF (NYSE Arca: XRO)

• Rydex MSCI All Country World Equal Weight ETF (NYSE Arca: EWAC)

FROM THE EXCHANGESEC Prohibits Deutsche Boerse-NYSE Euronext Merger

European competition authorities decided in early 2012 to prohibit the proposed merger between Deutsche Boerse and NYSE Euronext, bring-ing an end to a $10.2 billion takeover process that would have created the world’s largest stock exchange.

Joaquín Almunia, the European competition commissioner, said the tie-up would have hindered competi-tion in the European exchange-traded derivatives market.

Via their futures exchanges, Eurex and Liffe, the two exchange groups control more than 95 percent of trad-ing in several short-term interest rate and German government bond futures contracts.

A merger of the exchanges would also have concentrated European trading in exchange-traded funds. According to a BlackRock report from June 2011, Deutsche Boerse and NYSE Euronext were the most active European exchanges by aver-age ETF trading volumes.

Regulators had been pushing for the sale of one of the derivatives busi-nesses owned by the groups, but although the bourses offered some concessions, they considered a sell-off unacceptable. Almunia commented that the merger would have resulted in a “near-monopoly in European financial derivatives” and that none of the offered solutions was viable.

Following the announcement, NYSE Euronext said it would resume a $550 million share repurchase program.




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The London Metal Exchange is one of the last member-owned exchanges—

and home to Europe’s last trading pit.

CBOE Offers Futures, Options On EWZ Volatility Index

CBOE Holdings added to its roster of products with the launch of futures and options on its volatility index tied to the iShares MSCI Brazil Index Fund (NYSE Arca: EWZ).

The CBOE Brazil ETF Volatility Index applies the VIX methodology to CBOE-listed options contracts on EWZ. Straight options on EWZ were one of the most traded options con-tracts on the CBOE last year, the exchange said in a press release.

According to the press release, CBOE Brazil ETF Volatility Index Security Futures (VXEW) went live on Feb. 21, while CBOE Brazil ETF Volatility Index Options (VXEWZ) began trading on March 6.

Exchanges Compete On LME BidsNYSE Euronext, CME Group and

IntercontinentalExchange all sub-mitted bids for the London Metal Exchange, according to a February Financial Times report.

The offers, said to value the exchange in the region of £1 billion, came after the exchange said it had received pre-liminary expressions of interest from 10 potential buyers in September, and set a deadline of Feb. 15 for takeover bids.

Other parties speculated to have expressed an interest in the 135-year-old exchange include Deutsche Boerse, the London Stock Exchange and Hong Kong Exchanges and Clearing, though the first two are not thought to have put in bids in February.

The world’s largest metals futures market is something of an anomaly in modern times, being one of the last member-owned exchanges—it is owned by the banks and brokers that use it—and home to Europe’s last trading pit.

Shareholders were to meet on Feb. 23 to discuss the offers. They were said to be divided on the prospect of a sale, and a minimum of 75 percent shareholder support is needed for any takeover to occur.

BACK TO THE FUTURESCME Group, BM&FBOVESPA And S&P In Futures Agreement

In early March, the CME Group, Latin American exchange BM&FBOVESPA (BVMF) and S&P said in a press release that they would be entering into a cross-listing and -licensing futures-related alliance.

Under the terms of the agreement, futures tied to the Bovespa Index, which tracks the performance of stocks listed on the Sao Paulo Stock

Exchange, will list on the Chicago Mercantile Exchange, while futures on the S&P 500 will list on the BVMF, the press release said. The BVMF will also cross-list two commodities futures contracts offered by the CME Group—the Mini-Sized Soybean futures listed on the CBOT and the Light Sweet Crude Oil (WTI) futures listed on the NYMEX. The agreement leaves the door open for other similar cross-listing efforts in the future, the statement added.

According to the press release, the soybean futures are expected to roll out in the second quarter, followed by the oil futures in the third quarter, and the Bovespa and S&P 500 futures within the last six months of the year.

CME Contracts Down Year-Over-Year In February

The CME Group saw its average daily volume fall 13 percent year-over-year to 12.8 million contracts for the month of February. In particular, the average daily volume of the equity index contracts fell 14 percent during the month from the prior year.

The e-mini S&P 500 futures con-tract, the most actively traded CME index futures contract, saw its total volume fall 10.5 percent. The e-mini Nasdaq-100 futures contract, another high-volume contract, was down 10.3 percent, and the Mini $5 Dow futures contract’s volume was up by 1.8 per-cent. Meanwhile, the e-mini S&P 500 options contract volume was up 8.7 percent year-over-year in February.

KNOW YOUR OPTIONSCBOE Volume Up Slightly From Feb. 2011

According to a press release, CBOE Holdings recorded an average daily volume in options of 5.1 million con-tracts in February, a 1 percent increase from the prior year.

While the average daily volume for equity options was down 8 percent, index options ADV was up 12 percent and ETF options ADV up 5 percent. The most actively traded of those con-tracts were the options on the S&P




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500 Index, the SPDR S&P 500 Index Fund (NYSE Arca: SPY), the CBOE Volatility Index (VIX), the PowerShares QQQ Trust (Nasdaq GM: QQQ) and the iShares Russell 2000 Index Fund (NYSE Arca: IWM), the press release said.

S&P, CBOE Sue ISE Over ‘SPY’ Options

Standard & Poor’s parent McGraw Hill and the CBOE asked a court in Chicago to enforce a pre-existing injunction against the International Securities Exchange to prohibit the ISE from listing and trading options on the ISE Max SPY Index, arguing the ISE products are essentially S&P index options.

The two companies said they obtained an injunction against the ISE in July 2010 that prohibited ISE’s listing or providing an exchange market for the trading of S&P 500 index options.

CBOE Chairman and Chief Executive Officer William Brodsky said in the press release that the two companies had filed for the injunc-tion to protect “CBOE’s license rights in S&P 500 index options and S&P’s proprietary rights in the S&P 500.”

He further noted that there was a legal precedent set in 2010 with an injunction that specifically blocked the ISE from offering options on the S&P 500.

A VIX On The VIX?In mid-March, the CBOE rolled out

its “VIX of VIX” index, or VVIX, a press release said. The index essentially measures the volatility of the CBOE Volatility Index, the statement noted.

The VVIX uses the same basic methodology as the VIX to arrive at the expected volatility of a theoretical VIX futures contract expiring in 30 days. It bases its calculations on the prices of at- and out-of-the-money VIX options, the CBOE website noted, similar to how the VIX itself relies on the prices of S&P 500 options.

The CBOE suggested in the press release that investors would be able to develop investment strategies using the VVIX and VIX in conjunc-tion with each other.

ON THE MOVESEC Hires Pershkow

Reuters reported in late January that the Securities and Exchange Commission had appointed Barry Pershkow as senior special counsel in its investment management division.

Pershkow has extensive experi-ence in the area of exchange-traded funds, and the Reuters article says he was likely hired to help the regulatory agency refine its policies with regard to ETFs. The SEC is currently investigat-ing whether ETFs contribute to mar-ket volatility and how extensively they should be allowed to invest in deriva-tives, among other topics, the Reuters article noted.

Pershkow joined the SEC from law firm Morgan, Lewis and Bockius; prior to that, he was employed as an attorney by ProFunds Advisors.

NYSE Liffe Hires Flax For Repo Futures

The NYSE Liffe U.S. exchange said in a February press release that it had hired Harvey Flax as the busi-ness manager responsible for its GCF Repo Index futures. In his new role, Flax will oversee the development and rollout of the contracts, the press release said. He reports to NYSE Liffe U.S. COO Lynn Martin.

Flax was previously employed by Morgan Stanley for 25 years, and was the executive director of the equity and fixed-income repo financing division when he left the company, the press release said.

Repurchase agreements, or “repos,” involve a securities dealer selling a security and repurchasing it within a brief, agreed-upon time period, a sort of short-term loan. NYSE Liffe announced plans in January to launch futures tied to the Depositary Trust & Clearing Corp.’s GCF Repo Index, which tracks the repo interest rate on Treasury securities.

Fleites To Head Invest n RetireInvest n Retire, the Oregon-

based firm at the forefront of pro-viding software necessary to inte-grate index exchange-traded funds

into 401(k) retirement plans, said in March it had hired longtime invest-ment manager and ETF industry executive Agustin “Gus” Fleites as its chief executive officer to oversee the company’s push into the so-called defined contribution space.

Fleites most recently served as a managing director at Boston-based Beta Capital Advisors and helped establish the asset allocation group as State Street Global Advisors, where he worked closely with various retirement plans. He also worked at ProShares, the world’s largest purveyor of inverse and leveraged ETFs. Beta Capital helps its clients formulate passive invest-ment strategies.

Fleites is a graduate of the Wharton School of Business at the University of Pennsylvania, and he also holds a master’s degree in business adminis-tration from Wellesley, Mass.-based Babson College.

Fuhr Launches Own FirmETF veteran Deborah Fuhr

launched an independent research and consultancy firm, ETF Global Insight, in February.

The new company publishes both independent and commissioned research, and provides investors with assistance on product comparison and asset-allocation implementation.

Fuhr has been working in the ETF industry since 1997, and was most recently global head of ETF research and implementation strategy and a managing director at BlackRock.

Fuhr left the iShares parent com-pany in October last year and had been due to take up the post of head of global delta one strategy at Bank of America Merrill Lynch. However, the bank announced in January it would not be going ahead with the hiring.

Shane Kelly and Matthew Murray, former BlackRock colleagues of Fuhr’s, have joined her in the new venture. The trio was instrumental in devel-oping the first ETF global industry research reports and worldwide hand-books, and have won a number of awards for their research.




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ETFsPLUS A WHOLE LOT MORE

Subscriptions: www.indexuniverse.com/subscriptions. Advertising and Reprints Inquiries: 415.659.9029




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Global Index Data

May/June 2012Selected Major Indexes Sorted By YTD Returns

Total Return % Annualized Return %

Index Name YTD 2011 2010 2009 2008 2007 2006 2005 3-Yr 5-Yr 10-Yr 15-Yr Sharpe Std Dev

MSCI India 26.81 -37.17 20.95 102.81 -64.63 73.11 51.00 37.57 30.56 5.01 18.08 - 0.90 36.23

MSCI Russia 25.84 -19.55 19.07 104.22 -73.88 24.50 55.60 73.12 40.91 -2.97 16.39 - 1.10 37.85

MSCI EM Eastern Europe 24.43 -21.58 15.88 83.53 -69.10 25.66 46.31 49.18 37.85 -3.00 15.73 - 1.08 35.79

MSCI BRIC 21.46 -22.85 9.57 93.12 -59.40 58.87 56.36 44.19 29.00 6.17 18.57 - 1.02 28.83

MSCI EM 18.01 -18.42 18.88 78.51 -53.33 39.42 32.14 34.00 32.28 6.21 15.19 - 1.21 25.87

NASDAQ 100 15.40 3.66 20.14 54.61 -41.57 19.24 7.28 1.89 33.93 9.04 7.33 - 1.63 19.14

S&P 500/Citi Pure Value 15.28 -0.81 23.06 55.21 -47.87 -3.69 20.04 13.43 49.12 1.32 7.69 9.35 1.54 28.85

MSCI EAFE Small Cap 14.78 -15.94 22.04 46.78 -47.01 1.45 19.31 26.19 26.72 -2.19 10.61 - 1.17 22.37

EURO STOXX TMI 13.68 -18.42 -3.46 32.77 -47.59 18.26 37.82 9.35 16.07 -6.09 5.11 4.94 0.66 28.40

S&P MidCap 400/Citi Pure Growth 13.15 0.62 35.16 60.34 -35.17 10.30 4.98 12.06 41.66 11.28 10.55 13.31 1.66 22.71

S&P MidCap 400/Citi Pure Value 12.32 -5.07 23.19 59.18 -42.58 -3.20 19.31 9.37 44.64 2.06 8.58 10.20 1.38 30.00

STOXX Europe TMI 11.90 -11.80 5.10 37.50 -46.75 13.07 35.39 10.11 21.13 -3.07 6.56 5.84 0.91 24.30

MSCI EAFE GDP Weighted 11.88 -14.33 3.14 30.38 -44.82 12.88 27.39 13.68 17.43 -4.60 6.03 4.73 0.82 22.82

MSCI EAFE Growth 11.83 -12.11 12.25 29.36 -42.70 16.45 22.33 13.28 19.85 -1.28 5.89 3.21 1.01 19.85

Wilshire 5000 Growth 11.79 -0.75 16.57 37.93 -37.91 10.65 9.73 7.43 27.13 3.96 5.08 5.18 1.38 18.66

S&P SmallCap 600/Citi Pure Value 11.74 -7.50 29.18 63.58 -41.73 -18.61 21.44 11.58 50.81 0.19 9.25 9.75 1.17 42.06

MSCI Pacific 11.58 -13.74 15.92 24.18 -36.42 5.30 12.20 22.64 18.68 -2.32 7.00 2.07 1.10 16.85

Wilshire 4500 Completion 11.58 -4.10 28.43 36.99 -39.03 5.39 15.28 10.03 31.14 3.28 8.44 7.59 1.39 21.18

S&P MidCap 400 11.40 -1.73 26.64 37.38 -36.23 7.98 10.32 12.56 31.50 4.67 8.24 10.56 1.44 20.56

MSCI EAFE 11.38 -12.14 7.75 31.78 -43.38 11.17 26.34 13.54 19.74 -2.93 6.31 4.27 0.96 21.04

Russell 3000 Equal Weighted 11.34 -4.47 27.78 55.33 -36.49 -2.77 18.18 4.82 38.84 5.21 8.54 8.49 1.40 25.97

MSCI ACWI 11.14 -7.35 12.67 34.63 -42.19 11.66 20.95 10.84 23.71 0.07 5.72 - 1.21 19.17

Russell 3000 Growth 11.04 2.18 17.64 37.01 -38.44 11.40 9.46 5.17 27.78 4.48 4.48 4.74 1.51 17.34

Russell 1000 Growth 11.04 2.64 16.71 37.21 -38.44 11.81 9.07 5.26 27.51 4.54 4.30 4.76 1.52 16.97

Russell 2000 Growth 11.02 -2.91 29.09 34.47 -38.54 7.05 13.35 4.15 31.21 3.93 6.67 4.93 1.30 23.08

MSCI EAFE Value 10.92 -12.17 3.25 34.23 -44.09 5.96 30.38 13.80 19.62 -4.63 6.63 5.18 0.89 22.88

Russell Micro Cap 10.85 -9.27 28.89 27.48 -39.78 -8.00 16.54 2.57 28.57 -1.80 6.17 - 1.16 24.20

Russell Mid Cap 10.46 -1.55 25.48 40.48 -41.46 5.60 15.26 12.65 31.98 2.73 8.24 8.91 1.49 20.09

S&P 500/Citi Pure Growth 10.28 0.75 27.65 50.85 -38.99 6.64 7.43 7.31 34.99 6.52 7.68 9.09 1.59 20.33

MSCI World 10.15 -5.54 11.76 29.99 -40.71 9.04 20.07 9.49 22.66 -0.59 5.04 4.71 1.20 18.53

Russell 2000 9.63 -4.18 26.85 27.17 -33.79 -1.57 18.37 4.55 29.48 1.83 7.00 6.94 1.24 22.91

Russell 3000 9.49 1.03 16.93 28.34 -37.31 5.14 15.72 6.12 26.50 1.77 4.81 5.93 1.44 17.47

Russell 1000 9.48 1.50 16.10 28.43 -37.60 5.77 15.46 6.27 26.25 1.77 4.63 5.88 1.45 17.10

Wilshire 5000 Total Market 9.37 0.98 17.16 28.30 -37.23 5.62 15.77 6.38 26.36 1.88 5.08 6.00 1.44 17.29

S&P 500 9.00 2.11 15.06 26.46 -37.00 5.49 15.79 4.91 25.56 1.58 4.17 5.58 1.45 16.69

S&P SmallCap 600 8.83 1.02 26.31 25.57 -31.07 -0.30 15.12 7.68 31.41 3.37 8.09 8.91 1.35 22.18

S&P GSCI 8.43 -1.18 9.03 13.48 -46.49 32.67 -15.09 25.55 15.74 -1.58 6.38 3.28 0.79 21.33

Russell 2000 Value 8.23 -5.50 24.50 20.58 -28.92 -9.78 23.48 4.71 27.66 -0.36 7.04 8.37 1.17 23.10

Russell 3000 Value 7.95 -0.10 16.23 19.76 -36.25 -1.01 22.34 6.85 25.22 -1.03 4.93 6.48 1.35 17.93

Russell 1000 Value 7.92 0.39 15.51 19.69 -36.85 -0.17 22.25 7.05 25.01 -1.08 4.75 6.35 1.36 17.59

S&P SmallCap 600/Citi Pure Growth 7.70 5.21 28.74 37.70 -33.10 1.49 9.79 7.10 37.75 6.19 9.48 10.24 1.45 24.22

Barclays Global High Yield 7.24 3.12 14.82 59.40 -26.89 3.18 13.69 3.59 25.70 8.37 10.75 8.32 2.01 11.75

Wilshire 5000 Value 7.07 2.76 17.53 18.77 -36.31 1.11 21.63 5.71 25.48 -0.15 5.00 6.54 1.47 16.38

DJ Industrial Average 6.55 8.38 14.06 22.68 -31.93 8.88 19.05 1.72 25.84 3.92 5.11 6.65 1.63 14.84

Barclays US Corporate High Yield 5.48 4.98 15.12 58.21 -26.16 1.87 11.85 2.74 25.23 8.15 9.51 7.10 2.18 10.61

Wilshire US REIT 5.27 9.24 28.60 28.60 -39.20 -17.55 35.97 13.82 43.75 -2.19 10.53 9.57 1.47 27.27

DJ UBS Commodity 5.24 -13.32 16.83 18.91 -35.65 16.23 2.07 21.36 11.90 -1.76 6.97 4.11 0.72 17.79

Barclays EM 5.14 6.97 12.84 34.23 -14.75 5.15 9.96 12.27 19.20 8.55 11.01 9.80 2.39 7.47

JPM EMBI Global 4.76 8.46 12.04 28.18 -10.91 6.28 9.88 10.73 17.87 8.79 10.94 9.80 2.38 7.00

Barclays US Corporate Invest Grade 3.07 8.15 9.00 18.68 -4.94 4.56 4.30 1.68 13.56 7.03 6.52 6.80 2.56 4.99

Dow Jones Transportation Average 3.01 0.01 26.74 18.58 -21.41 1.43 9.81 11.65 29.67 2.92 7.78 7.00 1.24 23.13

Barclays US Credit 2.85 8.35 8.47 16.04 -3.08 5.11 4.26 1.96 12.59 6.98 6.47 6.80 2.66 4.48

Barclays Municipal 2.41 10.70 2.38 12.91 -2.47 3.36 4.84 3.51 7.94 5.50 5.32 5.67 1.74 4.41

Barclays US Treasury US TIPS 1.96 13.56 6.31 11.41 -2.35 11.64 0.41 2.84 11.22 7.89 7.56 7.12 1.89 5.67

Barclays Global Aggregate 1.60 5.64 5.54 6.93 4.79 9.48 6.64 -4.49 8.60 6.58 7.35 6.09 1.38 6.04

Barclays US Aggregate Bond 0.85 7.84 6.54 5.93 5.24 6.97 4.33 2.43 7.52 6.36 5.68 6.35 2.71 2.66

Citigroup WGBI 0.51 6.35 5.17 2.55 10.89 10.95 6.12 -6.88 7.49 7.04 7.97 6.15 1.07 6.91

Barclays US Government -0.21 9.02 5.52 -2.20 12.39 8.66 3.48 2.65 4.90 6.22 5.41 6.16 1.27 3.74

Dow Jones Utilities Average -2.05 19.71 6.46 12.47 -27.84 20.11 16.63 25.14 16.78 2.89 9.11 8.83 1.54 10.43

Citigroup STRIPS 25+ Year -5.24 60.67 10.18 -42.88 77.10 12.71 4.09 17.82 9.78 12.95 12.25 12.35 0.46 27.58

Source: Morningstar. (Nasdaq-100 index data provided by Morningstar and Nasdaq OMX.) Data as of February 29, 2012. All returns are in US dollars, unless noted.

3-, 5-, 10- and 15-year returns are annualized. Sharpe is 12-month Sharpe ratio. Std Dev is 3-year standard deviation.

May / June 2012www.journalofindexes.com 57




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Index Funds

58

May/June 2012Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions


Fund Name Ticker Assets Exp Ratio 3-Mo YTD 2011 2010 3-Yr 5-Yr 10-Yr 15-Yr P/E Std Dev Yield

Vanguard Total Stock Market, Inv Shrs VTSMX 68,078.1 0.18 10.43 9.56 0.96 17.09 26.60 2.01 5.05 5.99 13.9 17.49 1.64

Vanguard Institutional, Inst Shrs VINIX 65,810.6 0.04 10.10 9.00 2.09 15.05 25.58 1.61 4.19 5.62 13.7 16.70 1.88

Vanguard 500, Adm Shrs VFIAX 56,336.5 0.06 10.11 8.99 2.08 15.05 25.58 1.60 4.16 5.56 13.7 16.70 1.89

Vanguard Total Stock Market, Adm Shrs VTSAX 54,525.4 0.07 10.46 9.55 1.08 17.26 26.75 2.11 5.14 6.05 13.9 17.52 1.75

Vanguard Institutional, Inst+ Shrs VIIIX 38,580.4 0.02 10.12 9.00 2.12 15.07 25.61 1.64 4.21 5.65 13.7 16.70 1.90

Vanguard Total Bond Mkt II, Inv Shrs VTBIX 38,505.0 0.12 1.94 0.79 7.59 6.41 7.29 - - - - 2.74 2.81

Vanguard Total Stock Mkt, Inst Shrs VITSX 34,626.3 0.06 10.49 9.58 1.09 17.23 26.75 2.13 5.18 6.11 13.9 17.50 1.75

Vanguard Total Intl Stock, Inv Shrs VGTSX 32,880.6 0.22 9.81 12.79 -14.56 11.12 22.52 -1.32 7.55 4.77 10.8 22.49 2.72

Vanguard Total Bond Mkt, Adm Shrs VBTLX 32,207.5 0.11 1.95 0.85 7.69 6.54 7.45 6.36 5.51 6.20 - 2.80 3.11

Vanguard 500, Inv Shrs VFINX 27,531.3 0.17 10.07 8.96 1.97 14.91 25.43 1.50 4.06 5.49 13.7 16.70 1.78

Vanguard Total Bond Mkt, Inst Shrs VBTIX 24,105.9 0.07 1.95 0.85 7.72 6.58 7.49 6.40 5.55 6.26 - 2.80 3.14

Vanguard 500, Sig Shrs VIFSX 23,925.3 0.06 10.10 8.99 2.08 15.05 25.58 1.60 4.12 5.53 13.7 16.69 1.89

Vanguard Instl Total Stock Mkt, Inst+ Shrs VITPX 17,446.1 0.03 10.47 9.60 1.11 17.25 26.83 2.18 5.26 - 13.9 17.52 1.73

Fidelity Spartan 500, Adv Cl FUSVX 16,227.5 0.06 10.12 9.01 2.06 15.01 25.54 1.57 4.11 5.47 14.2 16.69 1.85

Vanguard Total Bond Mkt II, Inst Shrs VTBNX 16,200.9 0.07 1.96 0.80 7.67 6.47 7.35 - - - - 2.75 2.89

Fidelity Spartan 500, Inst Cl FXSIX 14,623.3 0.04 10.10 8.99 2.09 14.98 25.53 1.54 4.09 5.46 14.2 16.70 -

Vanguard Total Intl Stock, Adm Shrs VTIAX 14,280.5 0.18 9.86 12.82 -14.52 11.04 22.52 -1.32 7.55 4.77 10.8 22.46 2.74

T. Rowe Price Equity 500 PREIX 13,807.2 0.30 10.02 8.94 1.87 14.71 25.27 1.37 3.91 5.30 13.7 16.69 1.63

Fidelity Spartan 500, Inv Cl FUSEX 13,412.0 0.10 10.09 8.99 2.03 14.98 25.51 1.53 4.09 5.46 14.2 16.70 1.83

Vanguard Total Bond Mkt, Inv Shrs VBMFX 12,549.1 0.22 1.91 0.83 7.56 6.42 7.33 6.25 5.41 6.13 - 2.80 2.99

Vanguard Total Bond Mkt, Sig Shrs VBTSX 11,361.3 0.11 1.95 0.85 7.69 6.54 7.45 6.36 5.47 6.17 - 2.80 3.11

Schwab S&P 500 SWPPX 10,993.8 0.09 10.12 8.99 2.07 14.97 25.43 1.61 4.12 - 13.8 16.64 1.86

Vanguard Total Bond Mkt, Inst+ Shrs VBMPX 10,093.8 0.05 1.95 0.85 7.74 6.57 7.45 6.32 5.45 6.16 - 2.80 3.16

Vanguard Total Intl Stock, Inst+ Shrs VTPSX 9,974.5 0.10 9.87 12.86 -14.49 11.09 22.57 -1.29 7.56 4.78 10.8 22.48 2.82

Vanguard Emerging Mkts Stock, Adm Shrs VEMAX 7,202.9 0.20 13.45 17.34 -18.67 18.99 32.37 6.02 14.63 7.50 9.3 26.88 2.02

Fidelity Series 100 FOHIX 7,170.5 0.20 10.80 8.96 2.98 12.39 23.80 - - - 13.3 15.97 1.86

Vanguard Total Stock Mkt, Sig Shrs VTSSX 6,790.4 0.07 10.48 9.57 1.09 17.23 26.74 2.11 5.11 6.03 13.9 17.50 1.75

Vanguard Mid-Cap, Adm Shrs VIMAX 6,677.3 0.12 10.89 11.21 -1.97 25.59 31.76 2.77 7.89 - 15.5 20.08 1.22

Vanguard Mid-Cap, Inst Shrs VMCIX 6,554.8 0.08 10.94 11.22 -1.96 25.67 31.80 2.81 7.94 - 15.5 20.09 1.24

Fidelity Spartan Total Mkt, Adv Cl FSTVX 6,494.7 0.07 10.40 9.52 1.01 17.44 26.61 2.01 5.08 - 14.5 17.40 1.68

Vanguard Extended Mkt, Inst Shrs VIEIX 6,453.5 0.12 11.80 11.92 -3.57 27.59 32.07 3.37 8.50 7.75 15.2 21.87 1.04

Vanguard Short-Term Bond, Sig Shrs VBSSX 6,381.9 0.11 0.88 0.56 3.08 4.03 4.07 4.70 4.05 4.99 - 1.69 1.85

Vanguard Total Intl Stock, Inst Shrs VTSNX 6,122.6 0.13 9.87 12.85 -14.51 11.09 22.55 -1.30 7.56 4.78 10.8 22.48 2.81

Vanguard Balanced, Adm Shrs VBIAX 6,083.5 0.12 7.02 6.01 4.29 13.29 19.27 4.33 5.72 6.56 13.9 10.24 2.20

Vanguard Small-Cap, Adm Shrs VSMAX 6,082.9 0.17 10.66 10.42 -2.69 27.89 33.31 3.49 8.19 8.02 15.1 23.30 1.24

Spartan U.S. Bond, Inv Cl FBIDX 5,933.6 0.22 1.98 0.87 7.68 6.29 7.34 5.75 5.45 6.16 - 2.73 2.84

Vanguard Extended Mkt, Adm Shrs VEXAX 5,599.1 0.16 11.77 11.89 -3.59 27.57 32.01 3.33 8.45 7.66 15.2 21.86 1.02

Vanguard REIT, Adm Shrs VGSLX 5,592.4 0.12 10.19 5.30 8.62 28.49 42.79 -1.05 10.67 9.31 37.8 26.46 3.36

Vanguard Intermediate Bond, Adm Shrs VBILX 5,541.5 0.11 3.44 1.58 10.73 9.49 10.52 7.90 6.71 7.09 - 4.44 3.58

Vanguard Small-Cap, Inst Shrs VSCIX 5,514.8 0.13 10.68 10.42 -2.65 27.95 33.38 3.53 8.24 8.10 15.1 23.31 1.25

Vanguard Growth, Inst Shrs VIGIX 5,410.4 0.08 10.75 11.45 1.89 17.17 27.12 4.68 4.50 5.85 16.4 17.31 1.13

Vanguard Growth, Adm Shrs VIGAX 5,372.4 0.12 10.75 11.45 1.87 17.12 27.09 4.65 4.46 5.80 16.4 17.32 1.11

Vanguard Developed Mkts, Inst Shrs VIDMX 5,307.4 0.08 8.65 11.28 -12.44 8.73 20.27 -2.83 6.36 - 10.9 21.79 3.38

Vanguard Balanced, Inst Shrs VBAIX 4,701.8 0.08 7.03 6.01 4.31 13.34 19.32 4.37 5.76 6.59 13.9 10.25 2.22

Fidelity Series Infl-Protected Bond FSIPX 4,535.7 0.20 1.55 1.81 8.63 5.06 - - - - - - 0.26

Schwab 1000 SNXFX 4,491.2 0.29 10.27 9.36 1.27 15.96 25.75 1.68 4.42 5.71 14.0 16.94 1.79

PIMCO EM Fundmntl IndexPLUS, Instl Cl PEFIX 4,445.8 1.25 20.61 20.30 -16.81 25.86 43.33 - - - - 28.81 3.05

Vanguard Mid-Cap, Inv Shrs VIMSX 4,430.8 0.26 10.81 11.15 -2.11 25.46 31.55 2.64 7.77 - 15.5 20.07 1.06

Vanguard Small-Cap, Inv Shrs NAESX 4,241.5 0.31 10.61 10.37 -2.80 27.72 33.14 3.35 8.07 7.93 15.1 23.29 1.08

Vanguard Mid-Cap, Sig Shrs VMISX 4,215.0 0.12 10.91 11.23 -1.99 25.62 31.76 2.78 7.90 - 15.5 20.09 1.22

Vanguard Short-Term Bond, Adm Shrs VBIRX 4,166.4 0.11 0.88 0.56 3.08 4.03 4.07 4.71 4.08 5.02 - 1.69 1.85

ING U.S. Stock, Cl I INGIX 4,139.6 0.26 10.06 8.93 1.81 14.74 25.28 1.35 - - 13.7 16.74 1.79

Vanguard FTSE All-World ex-US, Inst Shrs VFWSX 4,073.3 0.13 9.84 12.81 -14.21 11.93 23.54 - - - 10.5 22.83 3.12

Fidelity Spartan Extended Mkt, Adv Cl FSEVX 3,894.3 0.07 11.79 11.84 -3.79 28.62 31.57 3.60 8.47 - 16.0 21.33 1.13

Vanguard Value, Inst Shrs VIVIX 3,821.3 0.08 10.04 7.47 1.17 14.49 24.79 -0.71 4.67 5.61 11.9 17.01 2.50

Vanguard Mid-Cap, Inst+ Shrs VMCPX 3,770.1 0.06 10.91 11.20 -1.91 25.67 31.81 2.82 7.95 - 15.5 20.07 1.26

Vanguard Short-Term Bond, Inv Shrs VBISX 3,719.2 0.22 0.86 0.54 2.96 3.92 3.95 4.60 4.00 4.96 - 1.69 1.74

Vanguard Balanced, Inv Shrs VBINX 3,699.2 0.26 6.94 5.97 4.14 13.13 19.12 4.20 5.62 6.48 13.9 10.27 2.07

ING U.S. Bond, Cl I ILBAX 3,616.1 0.43 2.00 0.81 7.20 6.14 7.12 - - - - 2.60 2.20

Vanguard Small Cap, Sig Shrs VSISX 3,562.4 0.17 10.69 10.44 -2.68 27.85 33.32 3.49 8.14 7.98 15.1 23.30 1.24

Source: Morningstar. Data as of February 29, 2012. Exp Ratio is expense ratio. 3-Mo is 3-month. YTD is year-to-date. 3-, 5-, 10- and 15-yr returns are annualized.

P/E is price-to-earnings ratio. Std Dev is 3-year standard deviation. Yield is 12-month dividend yield.




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Morningstar U.S. Style Overview Jan. 1 - Feb. 29, 2012

59

Source: Morningstar. Data as of Feb. 29, 2012.

Trailing Returns %

3-Month YTD 1-Yr 3-Yr 5-Yr 10-YrMorningstar Indexes

US Market 15.30 9.48 4.92 26.50 2.10 5.00

Large Cap 14.96 8.95 5.74 24.14 1.64 3.79

Mid Cap 16.02 11.28 3.29 32.43 2.98 8.00

Small Cap 16.81 10.03 0.83 33.81 3.51 8.22

US Value 14.41 6.61 0.72 24.26 –1.31 4.98

US Core 15.55 9.27 6.13 27.11 3.25 5.60

US Growth 16.21 12.89 7.77 28.06 4.21 3.89

Large Value 13.66 5.55 0.76 20.92 –2.32 3.80

Large Core 14.90 8.66 6.74 24.69 2.98 4.46

Large Growth 16.71 13.06 9.46 26.70 4.03 2.49

Mid Value 16.23 9.56 0.40 32.41 0.54 7.54

Mid Core 17.17 11.15 6.15 33.68 3.69 8.53

Mid Growth 14.72 13.29 3.23 31.27 4.43 7.37

Small Value 16.86 9.14 1.41 37.02 3.34 9.36

Small Core 18.41 10.94 –0.88 33.28 2.44 8.46

Small Growth 15.06 9.96 2.02 31.34 4.41 6.46

Morningstar Market Barometer YTD Return %

US Market9.48

6.61

Value

9.27

Core

12.89

Growth

8.95Larg

e C

ap

11.28Mid

Cap

10.03Sm

all C

ap

5.55 8.66 13.06

9.56 11.15 13.29

9.14 10.94 9.96

–8.00 –4.00 0.00 +4.00 +8.00

Sector Index YTD Return %

Technology 15.95

Financial Services 14.14

Consumer Cyclical 11.94

Basic Materials 11.42

Industrials 9.93

Energy 8.84

Communication 8.48

Real Estate 5.95

Healthcare 5.61

Consumer 2.71

–2.83 Utilities

Industry Leaders & Laggards YTD Return %

Data Storage 29.48

Broadcasting - Radio 24.68

Contract Manufacturers 23.50

Capital Markets 23.10

Oil & Gas Drilling 23.05

Oil & Gas Refining & 22.81

–3.82 Pollution & Treatment Controls

–6.91 Education & Training Services

–11.23 Electronic Gaming & Multimedia

–22.08 Diversified Industrials

–38.51 Apparel Stores

–49.59 Auto & Truck Dealerships

Biggest Influence on Style Index Performance

YTDReturn %

ConstituentWeight %

Best Performing Index

Mid Growth 13.29

Regeneron Pharmaceuticals Inc. 89.05 0.49

Teradata Corp. 37.19 1.00

Fastenal Co. 21.24 1.57

Illumina Inc. 68.14 0.46

Pioneer Natural Resources Co. 22.53 1.33

Worst Performing Index

Large Value 5.55

JPMorgan Chase & Co. 18.86 3.93

Citigroup Inc. 26.68 2.37

General Electric Co. 7.30 5.88

Goldman Sachs Group Inc. 27.71 1.20

Exxon Mobil Corp. 2.62 12.63

1-Year

0.76

Value

Larg

e C

ap

6.74

Core

9.46

Growth

0.40

Mid

Cap 6.15 3.23

1.41

Sm

all C

ap

–0.88 2.02

–20 –10 0 +10 +20

3-Year

20.92

Value

Larg

e C

ap

24.69

Core

26.70

Growth

32.41

Mid

Cap 33.68 31.27

37.02

Sm

all C

ap

33.28 31.34

–20 –10 0 +10 +20

5-Year

–2.32

Value

Larg

e C

ap

2.98

Core

4.03

Growth

0.54

Mid

Cap 3.69 4.43

3.34

Sm

all C

ap

2.44 4.41

–20 –10 0 +10 +20

Notes and Disclaimer: ©2012 Morningstar, Inc. All Rights Reserved. Unless otherwise noted, all data is as of most recent month end. Multi-year returns are annualized. NA: Not Available. Biggest Influence on Index Performance listsare calculated by multiplying stock returns for the period by their respective weights in the index as of the start of the period. Sector and Industry Indexes are based on Morningstar's proprietary sector classifications. The informationcontained herein is not warranted to be accurate, complete or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information.

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Performance

Index Name Weight 1-Month 3-Month YTD 1-Year 3-Year 5-Year 10-Year

Dow Jones U.S. Index 100.00% 4.35% 10.42% 9.51% 4.77% 26.50% 2.07% 4.93%

Dow Jones U.S. Basic Materials Index 3.45% -0.08% 7.06% 11.01% -7.74% 35.34% 4.72% 8.25%

Dow Jones U.S. Consumer Goods Index 10.63% 4.55% 7.23% 5.69% 13.08% 26.14% 6.65% 7.50%

Dow Jones U.S. Consumer Services Index 12.33% 4.10% 10.84% 9.25% 11.69% 31.19% 4.18% 4.37%

Dow Jones U.S. Financials Index 15.47% 4.95% 15.02% 12.39% -7.12% 26.37% -11.71% -1.03%

Dow Jones U.S. Health Care Index 10.64% 1.58% 8.53% 5.42% 13.23% 19.82% 4.44% 3.92%

Dow Jones U.S. Industrials Index 12.61% 3.02% 11.55% 10.90% 2.76% 32.62% 3.35% 5.67%

Dow Jones U.S. Oil & Gas Index 11.32% 6.29% 7.01% 8.34% -2.31% 22.52% 7.15% 12.46%

Dow Jones U.S. Technology Index 17.21% 7.27% 14.69% 16.22% 9.69% 32.10% 7.57% 5.83%

Dow Jones U.S. Telecommunications Index 2.66% 3.91% 6.06% 2.30% 6.53% 16.46% -0.38% 2.55%

Dow Jones U.S. Utilities Index 3.68% 0.71% 0.58% -2.58% 12.41% 17.73% 2.07% 7.56%

Risk-Return

Industry Weights Relative to Global ex-U.S. Asset Class Performance

Data as of February 29, 2012

Source: Dow Jones Indexes Analytics & Research

For more information, please visit the Dow Jones Indexes Web site at www.djindexes.com.

All information in these materials is provided “as is”. CME Indexes and its affiliates do not make any representation regarding the accuracy or completeness of these materials, the content of which may change without notice, and each of CME Indexes and its affiliates disclaim liability related to

these materials. All information provided by CME Indexes is impersonal and not tailored to the needs of any person, entity or group of persons. Dow Jones, its affiliates and CME Indexes do not sponsor, endorse, sell, promote or manage any investment fund or other vehicle that is offered by

third parties and that seeks to provide an investment return based on the returns of any index. CME Indexes is not an investment advisor, and CME Indexes makes no representation regarding the advisability of investing in any investment fund or other vehicle. Inclusion of a security or instrument

in an index is not a recommendation by Dow Jones, CME Indexes or their affiliates to buy, sell, or hold such security or instrument, nor is it considered to be investment advice. Exposure to an asset class is available through investable instruments based on an index. It is not possible to invest

directly in an index. There is no assurance that investment products based on the index will accurately track index performance or provide positive investment returns.

The Dow Jones U.S. Index and the Dow Jones U.S. Industry Indexes were first published in February 2000. To the extent this document includes information for the index for the period prior to its initial publication date, such information is back-tested (i.e., calculations of how the index might have

performed during that time period if the index had existed). Any comparisons, assertions and conclusions regarding the performance of the Index during the time period prior to launch will be based on back-testing. Back-tested information is purely hypothetical and is provided solely for

informational purposes. Back-tested performance does not represent actual performance and should not be interpreted as an indication of actual performance. Past performance is also not indicative of future results.

© CME Group Index Services LLC 2012. All rights reserved. The "Dow Jones U.S. Index" and "Dow Jones U.S. Industry Indexes" referenced in this piece are products of Dow Jones Indexes, the marketing name and a licensed trademark of CME Group Index Services LLC (“CME Indexes”).

“Dow Jones®”, “Dow Jones Indexes” and the names identifying the Dow Jones Indexes referenced herein are service marks of Dow Jones Trademark Holdings, LLC (“Dow Jones”), and have been licensed for use by CME Indexes. “CME” is a trademark of Chicago Mercantile Exchange Inc.

Chart compares industry weights within the Dow Jones U.S. Index to industry weights within the Dow

Jones Global ex-U.S. Index

U.S. = Dow Jones U.S. Index | Global ex-U.S. = Dow Jones Global ex-U.S. Index

Commodities = Dow Jones-UBS Commodity Index | REITs = Dow Jones U.S. Select REIT Index

Infrastructure = Dow Jones Brookfield Global Infrastructure Index

Composite

Basic Materials

Consumer Goods

Consumer Services

Financials

Health Care

Industrials

Oil & Gas

Technology

TelecommunicationsUtilities

15%

20%

25%

30%

35%

40%

5% 10% 15% 20% 25% 30%

3-Y

ear A

nn

uali

zed

Retu

rn

3-Year Annualized Risk

-0.19%

-2.19%

12.22%

0.33%

-0.65%

4.65%

-7.73%

4.85%

-2.85%

-8.43%

-15% -10% -5% 0% 5% 10% 15%

Utilities

Telecommunications

Technology

Oil & Gas

Industrials

Health Care

Financials

Consumer Services

Consumer Goods

Basic Materials

Underweight <= U.S. vs. Global ex-U.S. => Overweight

50

100

150

200

250

300

350

2/09 5/09 8/09 11/09 2/10 5/10 8/10 11/10 2/11 5/11 8/11 11/11 2/12

U.S. [202.43] Global ex-U.S. [186.86] Commodities [140.13]

REITs [295.80] Infrastructure [205.04]

Dow Jones U.S. Industry Review

May / June 201260




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Largest New ETFs Sorted By Total Net Assets In $US Millions Selected ETFs In Registration

Largest U.S.-listed ETFs Sorted By Total Net Assets In $US Millions

Covers ETFs and ETNs launched during the 12-month period ended February 29, 2012.


Fund Name Ticker ER 3-Mo YTD P/E Inception Assets

PowerShares S&P 500 Low Volatility SPLV 0.25 4.40 1.26 15.2 5/5/2011 1,319.6

iShares High Dividend Equity HDV 0.40 5.19 1.30 17.3 3/29/2011 1,250.3

Market Vectors Oil Services OIH 0.35 - 13.68 18.0 12/20/2011 1,091.2

WisdomTree Asia Local Debt ALD 0.55 3.94 4.41 - 3/17/2011 419.2

iShares MSCI China MCHI 0.58 12.31 16.13 10.5 3/29/2011 338.2

Market Vectors Semiconductor SMH 0.35 - 13.37 13.2 12/20/2011 309.5

Schwab US Dividend Equity SCHD 0.17 6.08 4.55 14.2 10/20/2011 301.0

Market Vectors Pharmaceutical PPH 0.35 - 2.71 16.8 12/20/2011 275.8

PowerShares Senior Loan BKLN 0.76 3.65 3.58 - 3/3/2011 260.5

FlexShares Glb Upstream Natrl Res GUNR 0.48 8.46 11.39 11.4 9/16/2011 247.9

Schwab U.S. Aggregate Bond SCHZ 0.10 2.04 0.75 - 7/14/2011 217.7

PIMCO 0-5 Year High Yld Corp Bond HYS 0.55 4.65 2.03 - 6/16/2011 204.4

Maxis Nikkei 225 NKY 0.50 7.73 9.56 - 7/13/2011 193.0

iPath S&P Dynamic VIX ETN XVZ 0.95 2.09 3.50 - 8/17/2011 186.9

FlexShares 5-Yr Target Dur TIPS TDTF 0.20 1.89 1.74 - 9/19/2011 185.0

FlexShares 3-Yr Target Dur TIPS TDTT 0.20 1.30 1.56 - 9/19/2011 182.9

Market Vectors Biotech BBH 0.35 - 15.78 22.9 12/20/2011 107.4

PIMCO Total Return TRXT 0.55 - - - 2/29/2012 103.0

iShares MSCI EM Min Volatility EEMV 0.25 10.14 12.93 14.1 10/18/2011 101.7

iShares S&P Intl Preferred Stock IPFF 0.55 6.18 5.14 - 11/15/2011 95.0

Fund Name Ticker Exp Ratio Assets 3-Mo YTD 2011 2010 3-Yr 5-Yr Mkt Cap P/E Std Dev Yield

SPDR S&P 500 SPY 0.09 99,595.1 10.27 9.15 1.80 15.04 25.34 1.55 53,325 15.0 16.66 1.88

SPDR Gold GLD 0.40 73,592.2 -3.44 8.09 9.57 29.27 21.05 19.85 - - 20.90 -

Vanguard MSCI Emerging Mkts VWO 0.20 55,083.4 11.96 16.80 -18.73 19.45 32.18 5.93 17,273 9.3 28.19 2.03

iShares MSCI Emerging Mkts EEM 0.67 40,936.7 11.82 16.84 -18.83 16.54 29.95 5.66 19,903 10.9 28.31 1.82

iShares MSCI EAFE EFA 0.34 39,415.9 7.92 10.36 -12.23 8.25 19.83 -3.16 30,577 11.9 21.91 3.11

PowerShares QQQ QQQ 0.20 33,268.3 14.55 15.37 3.36 19.89 33.67 8.84 65,824 16.2 19.23 0.72

iShares S&P 500 IVV 0.09 28,552.8 10.17 9.02 1.86 15.11 25.34 1.53 53,340 15.0 16.68 1.90

iShares Barclays TIPS Bond TIP 0.20 23,221.4 1.86 1.70 13.28 6.13 10.86 7.73 - - 5.68 3.89

Vanguard Total Stock Market VTI 0.07 20,766.9 10.57 9.50 0.93 17.45 26.66 2.09 27,488 13.9 17.47 1.75

iShares iBoxx $ Inv Gr Corp Bond LQD 0.15 19,563.4 7.43 3.82 9.75 9.33 13.25 7.07 - - 5.93 4.24

iShares Russell 2000 IWM 0.26 16,349.7 10.46 9.90 -4.44 26.90 29.22 1.86 1,056 17.2 22.82 1.27

iShares Russell 1000 Growth IWF 0.20 16,035.7 10.71 11.04 2.32 16.48 27.32 4.36 44,098 16.8 16.99 1.25

Vanguard Total Bond Market BND 0.11 15,026.5 1.86 0.68 7.93 6.20 7.10 - - - 2.65 3.06

iShares Barclays Aggregate Bond AGG 0.22 14,773.9 2.08 0.71 7.70 6.37 7.01 6.12 - - 2.86 2.81

iShares iBoxx $ HiYld Corp Bond HYG 0.50 14,249.6 8.31 3.60 6.81 11.96 21.29 - - - 12.48 7.26

iShares Russell 1000 Value IWD 0.20 11,937.7 9.83 7.61 0.10 15.44 24.53 -1.25 36,457 13.8 17.60 2.13

SPDR Barclays HiYld Bond JNK 0.40 11,930.8 8.46 4.84 5.16 14.20 25.13 - - - 13.10 7.28

SPDR DJ Industrial Avg DIA 0.17 11,826.7 8.02 6.24 8.04 13.96 25.41 3.71 115,941 14.4 14.84 2.34

iShares Silver SLV 0.50 11,654.1 4.84 24.54 -10.74 82.14 37.59 18.89 - - 43.78 -

iShares S&P 400 MidCap IJH 0.21 10,701.1 10.84 11.44 -2.18 26.73 31.09 4.51 3,484 18.4 20.38 1.14

Vanguard REIT VNQ 0.12 10,691.7 10.25 5.16 8.56 28.43 42.76 -1.08 6,587 37.8 26.28 3.36

iShares Barclays 1-3 Yr Treasury SHY 0.15 10,651.7 -0.01 -0.06 1.44 2.28 1.53 3.37 - - 1.04 0.77

iShares MSCI Brazil EWZ 0.59 10,568.9 18.08 20.54 -24.17 7.69 30.58 12.22 21,909 8.1 33.57 2.18

SPDR S&P MidCap 400 MDY 0.25 10,449.2 10.69 11.38 -2.16 26.26 30.76 4.27 3,312 17.6 20.27 0.97

iShares Gold IAU 0.25 10,329.0 -3.40 8.21 9.57 29.46 21.16 19.89 - - 20.93 -

Source: Morningstar. Data as of February 29, 2012. Exp Ratio is expense ratio. 3-Mo is 3-month. YTD is year-to-date. 3-Yr and 5-Yr are 3-year and 5-year annualized returns, respectively.

Mkt Cap is geometric average market capitalization. P/E is price-to-earnings ratio. Std Dev is 3-year standard deviation. Yield is 12-month.

AdvisorShares Glb Alpha & Beta

Direxion Primary Tactical Advantage

EGShares EM Domestic Demand

ETFS Physical Nickel

First Trust Glb Commodity AlphaDex

FlexShares Liquid Access

Global X SuperIncome REIT

IQ Physical Diamond

iShares Human Rights

Market Vectors Global Chemicals

Pimco Foreign Currency Strategy

PowerShares Commodity Rotation

ProShares UltraPro Oil/Gas

Russell Intl High Dividend Yld

Schwab US Small-Cap Growth

SPDR BofA ML Crossover Corp Bond

Sustainable North Amer Oil Sands

US Equity High Vol Put Write

WisdomTree China Div Ex-Financials

Zacks Sustainable Dividend

Source: IndexUniverse.com’s ETF WatchSource: Morningstar. Data as of February 29, 2012. ER is expense ratio. 3-Mo is 3-month. YTD is year-to-date. P/E is price-to- earnings ratio.

Exchange-Traded Funds Corner

May / June 2012www.journalofindexes.com 61




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62 May / June 2012

neutral investors will have to forgo a good measure of the “thrill” that accompanies a sharply rising market.

For example, when the Russell 1000 is rising, an equity market-neutral strategy will not experience the same bull market returns due to the fact that the strategy has been designed precisely to deliver a very low beta with the market.

When the market is on a tear, market-neutral investors will have to forgo the substantial returns synonymous with beta. (See Figures 6 and 7.)

The up beta is around 0.2 for the value market-neutral strategy. The median monthly returns are still reasonable for one of the market-neutral strategies with considerably less volatility than the R1000.

The hit rate is actually around 60 percent; in other words, every month the R1000 is up, the value strategy is also up 60 percent of the time but by a lower amount because of its beta.

The story is very different for the months in which the R1000 is down.12 (See Figures 8 and 9.) In a declining over-all market, the equity market-neutral value strategy will avoid much of the downturn, since the strategy’s down-side beta in this case is approximately 0.03.

The performance of the strategy is quite remarkable, as one can see from the red bars above the zero line in Figure 8. The median for all combinations of market-neutral factors is significantly better than the median

return for the R1000 over this period. The annualized expected return for the negative months for the R1000 is -42 percent, whereas it is above +8 percent for the market-neutral value index.

The value portfolio performs well with an annualized return of 14.39 percent, with an information ratio of around 1.3 with a hit rate of 69 percent. The value portfolio is up in 69 percent of months in which the R1000 is down. (See Figure 9.)

Conclusion

Several rounds of market dislocation over the past 15 years, culminating in the global financial crisis, have made clear the deficiencies of traditional methods of asset alloca-tion and risk mitigation. In times of market stress, assets that provide diversification and stability in more normal markets plunge in lock step as investments surrender to herding and unwanted correlation. To address this and build more efficient, robust portfolios, the world’s most innovative investors are moving beyond traditional asset allocation and geographic/industry/beta segmentation and drilling down to uncover themes/factors such as momen-tum, quality, size and particularly value that increasingly drive valuations. Additional risk mitigation can be achieved by adding market and sector neutrality to these strategies in the belief that valuable information can be extracted from both rising as well as falling markets.

Endnotes1 This approach to asset allocation, however, has been utilized by some of the world’s most sophisticated investors for decades. For instance, Norway’s $580 billion

Government Pension Fund Global (GPFG), by imposing low tracking error constraints and a rigorous asset allocation formula, relies largely on beta returns, as opposed

to alpha, in the belief that markets are generally efficient. Please also see Chambers, David, Dimson, Elroy and Ilmanen, Antti S., The Norway Model (October 10, 2011).

Available at SSRN: http://ssrn.com/abstract=1936806

2 Ross, Stephen (1976), “The arbitrage theory of capital asset pricing,” Journal of Economic Theory 13 (3): 341-360.

3 Fama E., French, K. (1992), “The Cross Section of Expected Stock Returns,” Journal of Finance 47: 427-465.

4 Carhart, Mark M. (1997), “On Persistence in Mutual Fund Performance,” Journal of Finance 52 (1): 57-82.

5 Beta alone is not the sole driver of expected returns.

6 Banz, Rolf W. (1981) “The Relationship Between Market Value and Return of Common Stocks,” Journal of Financial Economics.

7 Jegadeesh, N., Titman, S. (1993), “Returns to buying winners and selling losers.” Journal of Finance 48: 65-91.

8 See Dow Jones’ website for a description of the market-neutral indexes located at http://www.djindexes.com/thematicmarketneutral/.

9 http://www.crsp.com/documentation/product/ccm/ccm_data_guide.pdf.

10 HFRI database, though to this estimate, we might add nonoverlapping funds in other databases such as TASS and Dow Jones

11 Dow Jones Credit Suisse Equity Market Neutral Hedge Fund Index − January 1994 to September 2011.

12 Rm

-Rf, the excess return on the market, is the value-weight return on all NYSE, AMEX and Nasdaq stocks (from CRSP) minus the one-month Treasury bill rate (from

Ibbotson Associates).

Karunakaran continued from page 36

for benchmarking such portfolios have not developed. In this article, we sketched the rudiments of bench-mark construction that we have used. While much work remains to be done, we believe that standardization and benchmarking in this area will result in the same value

added to investors as it has done in the areas of tradi-tional equity and bond portfolio management. Most importantly, it will give end-users a means via which they can quantify the “distance” of a bespoke tail hedge portfolio versus an easily measurable index to evaluate the cost versus benefit trade-offs.

Endnote1 See, for example, V. Bhansali, “Tail Risk Management,” Journal of Portfolio Management, Winter 2008.

Bhansali continued from page 17




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how is the investor to know if the fees are reasonable? Merely saying that the investment will not lose money or outperform cash or Treasury bills says virtually nothing. Is 2 percent a good return? 10 percent? Without some gauge, it is impossible to tell. The benchmark is a measure of the opportunities for return if no investment skill is applied, if one just buys the market.

Recall alpha and beta: returns to investment skill and the market. The benchmark earns beta and there is no reason to pay for beta. If the promise is not to lose money, a money market fund will fulfill it—and pay the investor besides. Investors should only pay for alpha—the difference between the return earned and market beta. This is the problem of pretending that cash or T-bills or some fixed percentage

return are the benchmark for absolute-return investing. Suppose T-bills pay 1 percent, the market defined as stocks and bonds globally pays 5 percent and the absolute-return fund earns 7 percent. Should the performance fee be based on the 6 percentage point spread between T-bills and the absolute-return fund or on the 2 percentage point spread between the market and the fund? Since the manager wasn’t restricted to holding T-bills, the alpha earned is 2 percent-age points, not 5. The benchmark is essential: It determines what the investor surrenders—20 percent as part of the 2 and 20 fee. It shows the asset owner what the opportunity is and how much of it she gave up for her agent’s efforts. Without the benchmark, the investor sees little. Prudence can be hoped for, transparency is welcome but may be hard to find, and benchmarks are essential.

Endnotes1. Adolf Berle and Gardiner Means, “The Modern Corporation and Private Property,” 1932.

2. See http://en.wikipedia.org/wiki/Prudent_man_rule for “Prudent Man Rule” and Harvard College v. Armory 9 Pick. (26 Mass.) 446 (1830).

Blitzer continued from page 39

May / June 201263

then you’re canceling some of the best alpha-generative positions out in the process. There could be one guy who might be long health care, and another guy who might be short health care. That just doesn’t make any sense to me.

Ultimately, I believe it’s not the structure that makes it better, it’s the ability to attract the best talent, because in every business, the person executing the strategy is the key. The best pitcher

wins more baseball games. The best football player scores more touchdowns. The best doctor commits fewer errors in surgery. The best airline pilot crashes fewer plans. We have found that the best portfolio managers tend to find their way into the hedge fund business (that doesn’t mean they are all good, so you have to do your due diligence) and they tend to generate the highest-quality returns net of their fees and expenses. Replicating talent still seems like a very challenging proposition.

Yusko continued from page 21

Endnotes1 Barclay Hedge Alternative Investment Databases, 2011

2 Commodity trading advisors – investment managers charged with making buy and sell decisions usually in the commodity spot, physical and futures markets on behalf of inves-

tors. There is no guarantee that a CTA will meet his/her objective, and investors may lose money.

3 Mutual funds – an investment vehicle in which investors pool assets together and a professional money manager makes investment decisions on the shareholders’ behalf to try

to meet the fund’s objective. There is no guarantee that a mutual fund will achieve its stated objective, and investors may lose money.

4 Exchange-traded funds – an investment vehicle in which investors buy a share of a fund that attempts to provide returns, less fees and expenses, of a stated objective (e.g., U.S.

equity market, the price of gold). Investors may lose money in such investments.

5 L’habitant, F-S, “Handbook of Hedge Funds,” The Wiley Financial Series.

6 Bhardwaj, G., Gorton, G., Rouwenhorst, G. “Fooling Some of the People All of the Time: The Inefficient Performance and Persistence of Commodity Trading Advisors.” Yale IFC

Working Paper, October 2008.

7 Alpha is a risk-adjusted measure of the active return on an investment. It is the return in excess of the compensation for the risk borne, and thus commonly used to assess active

managers’ performance.

8 The Barclay CTA Index is an industry benchmark of representative performance of commodity trading advisors. There are currently 565 programs included in the calculation of

the Barclay CTA Index for the year 2011, which is unweighted and rebalanced at the beginning of each year.

9 The CASAM CISDM CTA/CPO indexes represent a series of both asset-weighted and equal-weighted performance indexes of commodity trading advisors and commodity pool

operators in the CASAM CISDM Hedge Fund/CTA Database. Currently there are 20 indexes.

10 Diversified Trends Indicator and DTI are registered marks of AFT and have been licensed by the fund. The fund is not sponsored, endorsed, sold or promoted by AFT.

11 Zephyr StyleADVISOR, WisdomTree

12 S&P 500 Index – capitalization-weighted index of 500 stocks selected by the Standard & Poor’s Index Committee designed to represent the performance of the leading industries

in the U.S. economy.

13 Maximum drawdown measures the peak-to-trough decline during a specific record period of an investment. A drawdown is usually quoted as the percentage between the peak

and trough.

Schwartz continued from page 15




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H U M O R

64

Don’t touch

that dial!

May / June 2012

From The Think Tank

By Heather Bell

The Ronko Alterna-Matic™

Tired of getting just average returns? Worried about what will happen to your portfolio the next time the market plung-es into a fiery chasm? Hoping to make a gazillion dollars without burning too many precious calories or brain cells?

Do we have a solution for you!The Ronko Alterna-matic™ adds

diversification and noncorrelated returns to your portfolio with just the push of a button. Utilizing the most sophisticated Magic-8 Ball randomiza-tion technology available, the Ronko Alterna-matic™ selects a customized portfolio of alternative investments just for you! It’s like having your own hedge fund manager in your own home!

Ed here is a professional foosball play-er looking to retire on his bar tourna-ment winnings. He has it wisely invested in a balanced portfolio of Google stock, Greek bonds and Japanese yen. But he needs something to ensure that it doesn’t tank should the no-longer-unthinkable happen. But PRESTO! With the push of a button, the Ronko Alterna-matic™ has the diversification solution for Ed!

It recommends an alternative invest-ment portfolio of 35 percent in baseball cards, 25 percent in Franklin Mint com-memorative coins, 20 percent in original paintings purchased at flea markets, 15 per-cent in vintage concert T-shirts and 5 per-cent in baked goods resembling celebrities.

What a combo! Pure genius! You’d better start shopping for your own tropical island now, Ed, because you will be retiring in style!

Our other guest, Thelma, is a golf ball diver who hopes to run her own

miniature golf course in her retire-ment. Thelma, let’s see what the Ronko Alterna-matic™ advises for you!

Ooooh! Look at that! Isn’t it amazing? This spectacular device recommends a portfolio that is 50 percent invested in ownership of a racehorse named Toxic Bunny, 30 percent in bouillon (yes, that’s chicken stock, not gold), 15 percent in the Mauritanian ouguiya, 3 percent in a time share in the Poconos and 2 percent in lottery tickets.

And that’s not all this genius inven-tion can do! The Ronko Alterna-matic™ is equipped with a nonstick surface and an inner heating coil. Just pick your favorite bread and your favorite sand-

wich fixings, and voila! Look at that panini, toasted to golden perfection!

How much would you expect to pay an actual qualified asset manager for an alternative investment (not to mention a tasty sandwich)? Most firms charge two and twenty, but have we got a deal for you! Not two and twenty! Not 1.99 and 19.99! And not one and 19 either! How about TWO-TWENTY? Yes, you heard correctly—that’s TWO DOLLARS and TWENTY CENTS! For a flat fee of $2.20, you too can purchase the Ronko Alterna-matic™ and all its financial acuity!

PLUS if you act now, we’ll throw in the Ronko Can Opener™, with extra-large handles that make it easier for elderly hands to open cat food cans at lunchtime. That’s right, you get the Ronko Alterna-matic™ AND the Ronko Can Opener™, all for TWO DOLLARS and TWENTY CENTS!

How much would you expect to pay an actual qualified asset manager for an alternative investment

(not to mention a tasty sandwich)?




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Is your focus in the right place?

+1 877 503 6437 www.russell.com/indexes

Risk or fortune? Whatever lies ahead,we’re here to help you see what matters most.

Russell Indexes. Opportunity Re-Defi ned.

Russell Investments is a Washington, USA Corporation which operates through subsidiaries worldwide and is a subsidiary of The Northwestern Mutual Life Insurance Company. Russell Investments is the owner of the trademarks, service marks and copyrights related to its respective indexes. Indexes are unmanaged and cannot be invested in directly. Copyright © Russell Investments 2011. All rights reserved.



INDEX MAY-JUN INDEX_II.pdf

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©2012 M

orningstar, Inc. All rights reserved. The M

orningstar name and logo are registered m

arks of Morningstar. M

arks used in conjunction with M

orningstar products or services are the property of Morningstar or its subsidiaries.

Changing the Game

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helped fill a critical void of data in the

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We are committed to using the best bond-

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INDEX MAY-JUN INDEX_III.pdf

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The best situation for your clients is the agility to avoid a bad one.

invescopowershares.com | Follow us @PowerShares

Taxes can bevenomous to

wealth creation.TAXES MAY BE ONE OF THE MOST CRITICAL AND YET OVERLOOKED FACTORS

IN WEALTH CREATION OVER TIME AS THEY CAN ERODE EVEN THE BEST FUND’S

RETURNS. DESIGNED WITH A UNIQUE STRUCTURE, POWERSHARES QQQ™ MAY

SERVE AS A TAX-EFFICIENT INVESTMENT TOOL FOR SHAREHOLDERS WHO

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// There are risks involved with investing in exchange-traded funds (ETFs) including possibleloss of money. The funds are not actively managed and are subject to risks similar to stocks, including those related to short selling and margin maintenance. Ordinary brokerage commissions apply. Shares are not FDIC insured, may lose value and have no bank guarantee. // Invesco PowerShares does not offer tax advice. Investors should consult their own tax advisors for information regarding their own tax situations. While it is not Invesco PowerShares’ intention,

there is no guarantee that the PowerShares ETFs will not distribute capital gains to their shareholders. // Shares are not individually redeemable and owners of the shares may acquire those shares from the Funds and tender those shares for redemption to the funds in Creation Unit aggregations only, typically consisting of 50,000 shares. // PowerShares® is a registered trademark of Invesco PowerShares Capital Management LLC. ALPS Distributors, Inc. is the distributor for QQQ. Invesco

PowerShares Capital Management LLC is not affi liated with ALPS Distributors, Inc. // An investor should consider the Fund’s investment objective, risks, charges and expenses carefully before investing. To obtain a prospectus, which contains this and other information about the QQQ, a unit investment trust, please contact your broker, call 800.983.0903 or visitwww.invescopowershares.com. Please read the prospectus carefully before investing.



INDEX MAY-JUN INDEX_IV.pdf

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defining alternatives may / june 2012 - etf · pdf fileby kishore karunakaran ... laura zavetz...

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