Download complete issue - IndexUniverse.com

Plus an interview with John Prestbo, David Blitzer on the next big thing, 

Guido Giese on adding risk control to index methodologies, and more! 

big ideas September / October 2012 

Determining Market-Capitalization Breakpoints 

Andrew Clark 

Index Variation And Portfolio Performance 

Craig Israelsen 

Sectors And Style 

Paul Baiocchi and Paul Britt 

Dynamic Correlations 

Christopher Philips, David Walker and Francis Kinniry Jr.

www.journalofindexes.com 

Vol. 15 No. 5 

features 

Determining Market-Capitalization Breakpoints 

By Andrew Clark. .................................. 10 

Why do we divide up markets the way we do? 

Index Variation And Overall Portfolio Performance 

By Craig Israelsen ................................. 16 

Looking at how different indexes affect performance. 


By Paul Baiocchi and Paul Britt ..................... 22 

Which perspective on the market is more effective? 

A Mainstay Of Indexing Surveys The Landscape 

By Journal of Indexes staff .......................... 32 

Dow Jones Indexes’ former editor evaluates the industry. 


By Christopher Philips, David Walker 

and Francis Kinniry Jr. ............................ 34 

Correlations do not remain constant. 

10 

The Next Big Thing 

By David Blitzer ................................... 42 

What will be at the forefront of investors’ minds next? 

Optimal Design of Risk-Control Strategy Indexes 

By Guido Giese .................................... 44 

A modification to improve strategy-index performance. 

Investing In The Pet Revolution 

By Gillybear Bell ................................... 64 

How to take advantage a major investment opportunity! 

news 

S&P And Dow Jones Indexes Finalize Merger ......... 52 

Case-Shiller Indexes See April Uptick ................ 52 

Russell Completes Annual Reconstitution ............ 52 

MSCI Announces Classification Decisions............ 52 

Barclays Divesting BlackRock Stake .................. 53 

Indexing Developments. ............................ 53 

Around The World Of ETFs. ......................... 55 

Know Your Options ................................ 57 

Back To The Futures. ............................... 57 

On The Move ...................................... 57 

data 

Global Index Data .................................. 58 

Index Funds. ....................................... 59 

Morningstar U.S. Style Overview ..................... 60 

Dow Jones U.S. Industry Review. ..................... 61 

Exchange-Traded Funds Corner ..................... 62 

34 

44 


September / October 2012 

1

Contributors 

Paul Baiocchi 

Paul Baiocchi is an ETF analyst at IndexUniverse, covering international 

and domestic sector ETFs. After graduating from California State 

University, Chico with a B.S. in finance, he went on to receive his MBA 

from the University of British Columbia. While in graduate school, 

Baiocchi worked as a consultant to Delta Global Advisors, creating internationally 

themed unit investment trust portfolios for Claymore funds. 

He later joined Delta full time as the firm’s senior market strategist. 

David Blitzer 

David Blitzer is managing director and chairman of S&P Dow Jones Indices’ 

index committee. He has overall responsibility for security selection for the 

company’s indexes, and index analysis and management. Blitzer previously 

served as chief economist for Standard & Poor’s 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. 

Craig Israelsen Andrew Clark 

Paul Britt 

Guido Giese 

Paul Britt is an ETF analyst at IndexUniverse. He focuses on alternative 

ETFs, including absolute return and volatility funds, as well as on asset allocation 

ETFs and consumer sector ETFs. Britt holds a B.S. from Rochester 

Institute of Technology and an M.S. in financial analysis from the University 

of San Francisco. He has also passed the Level III CFA exam. Britt worked 

most recently as a private placement agent at an IRA custodian. 

Andrew Clark is a research analyst at Lipper. He is responsible for the 

analysis of ETFs, mutual funds and other collective investments on a global 

basis. Clark has won several awards for his research, most recently a Best 

Paper award from the World Congress on Engineering and Computer 

Science 2011. His articles have appeared in the Journal of Index Investing, 

the Journal of Investing, Physica A, Quantitative Finance and European 

Physical Journal B. 

Guido Giese is head of indexes at SAM, responsible for the maintenance 

and further development of the Dow Jones Sustainability Index family 

(DJSI). Additionally, he oversees global sales, licensing, partnerships 

and client services for the DJSI. Prior to joining SAM, Giese was head of 

research and development at Stoxx. He holds a Ph.D. in applied mathematics 

from the Swiss Federal Institute of Technology Zurich. 

Craig Israelsen is an associate professor at Brigham Young University. He writes 

monthly for Financial Planning magazine. Israelsen is a principal at Target 

Date Analytics (www.OnTargetIndex.com) and the designer of the 7Twelve 

Portfolio (www.7TwelvePortfolio.com). He and Phil Fragasso published “Your 

Nest Egg Game Plan” (Career Press). Israelsen is also the author of “7Twelve: 

A Diversified Portfolio with a Plan” (John Wiley & Sons), published in 2010. He 

holds a Ph.D. in family resource management from Brigham Young University. 

Christopher Philips 

Christopher Philips, CFA, is a senior investment analyst for Vanguard 

Investment Strategy Group. This group is responsible for capital markets 

research, the asset allocations used in solutions for Vanguard’s fundsof-funds, 

and maintaining and enhancing the investment methodology 

used for advice-based relationships with high-net-worth and institutional 

clients. He has authored several research papers. Philips holds a B.A. from 

Franklin & Marshall College. 

2 September / October 2012

THERE’S A REASON WHY THE BIGGEST KID 

ON THE BLOCK GETS PICKED FIRST. 

Size can help you stand out 

in a crowd. The SPDR ® S&P 

500 ® Exchange Traded Fund 

from State Street is a perfect example. 

With over 105 billion dollars in assets, it’s one 

of the largest securities in the world.* And 

because it’s an ETF, you can buy and sell it with 

the precision of a stock. 

Scan the QR code with your smartphone or 

visit spdrs.com/precise for the details. And see 

why this investment is head and shoulders 

above the rest. 

*Source: Assets as of 3/31/2012, State Street Global Advisors. Data subject to change. 

Before investing, carefully consider the funds’ investment 

objectives, risks, charges and expenses. To obtain a prospectus or 

summary prospectus, which contains this and other information, 

call 1.866.787.2257 or visit www.spdrs.com. Read it carefully. 

ETFs trade like stocks, fluctuate in market value and may trade at prices above or below the ETFs net asset value. Brokerage commissions and ETF 

expenses will reduce returns. 

“SPDR” and S&P 500 are registered trademarks of Standard & Poor’s Financial Services, LLC (“S&P”) and have been licensed for use by State Street Corporation. No fi nancial 

product offered by State Street or its affi liates is sponsored, endorsed, sold or promoted by S&P. 

ALPS Distributors, Inc., a registered broker-dealer, is distributor for the SPDR S&P 500 Trust, a unit investment trust. 

IBG-6242

Jim Wiandt 

Editor 

jwiandt@indexuniverse.com 

Heather Bell 

Managing Editor 

hbell@indexuniverse.com 

Matt Hougan 

Senior Editor 

mhougan@indexuniverse.com 

Lisa Barr 

Copy Editor 

Laura Zavetz 

Creative Director 

Jodie Battaglia 

Art Director 

Jennifer Van Sickle 

Graphics Manager 

Andres Fonseca 

Online Art Director 

Aimee Melli 

Production Manager 

Editorial Board 

Rolf Agather: Russell Investments 

David Blitzer: S&P Dow Jones Indices 

Lisa Dallmer: NYSE Euronext 

Henry Fernandez: MSCI 

Deborah Fuhr: ETF Global Insight 

Gary Gastineau: ETF Consultants 

Joanne Hill: ProShare and ProFund Advisors LLC 

John Jacobs: The Nasdaq Stock Market 

Mark Makepeace: FTSE 

Kathleen Moriarty: Katten Muchin Rosenman 

Don Phillips: Morningstar 

James Ross: State Street Global Advisors 

Gus Sauter: The Vanguard Group 

Steven Schoenfeld: Global Index Strategies 

Cliff Weber: NYSE Euronext 

The Journal of Indexes, IndexUniverse.com and ETFR 

offer high-quality, affordable custom reprints in either 

print or PDF format for all of your marketing needs. For 

information on how reprints can help showcase your 

success and increase awareness about your company, call 

Ivana Zivkovic at 415.659.9029 or email izivkovic@ 

indexuniverse.com 

4 September / October 2012 

Review Board 

Jan Altmann, Sanjay Arya, Jay Baker, William 

Bernstein, Herb Blank, Srikant Dash, Fred 

Delva, Gary Eisenreich, Richard Evans, 

Gus Fleites, Bill Fouse, Christian Gast, 

Thomas Jardine, Paul Kaplan, Joe Keenan, 

Steve Kim, David Krein, Ananth Madhavan, 

Brian Mattes, Daniel McCabe, Kris 

Monaco, Matthew Moran, Ranga Nathan, 

Jim Novakoff, Rick Redding, Anthony 

Scamardella, Larry Swedroe, Jason Toussaint, 

Mike Traynor, Jeff Troutner, Peter Vann, 

Wayne Wagner, Peter Wall, Brad Zigler 

Copyright © 2012 by IndexUniverse LLC 

and Charter Financial Publishing Network 

Inc. All rights reserved.

STOXX LIMITED 

INNOVATIVE INDEXING. 

INDEPENDENT. GLOBAL. 

Innovation means, literally, “the introduction of something better” – a definition STOXX has always lived up to, 

first and foremost with the EURO STOXX 50®, Europe’s leading blue-chip index. But STOXX has more to offer 

than that: a truly global index family, ranging from blue-chip indices to broader benchmarks and cutting-edge 

strategy indices such as the STOXX+® Minimum Variance indices. And because STOXX is a truly neutral and 

independent index provider, market participants can rely on our pledge to deliver rules-based and fully transparent 

index concepts, as well as a unique, transparent, and rules-based market classification system for 

emerging and developed markets. Besides being one of the financial world’s most renowned index brands, 

STOXX Ltd. also markets and distributes the complete portfolio of DAX® and SMI® Indices, the leading brands 

from Deutsche Börse AG and SIX Swiss Exchange AG. www.stoxx.com 

INDEX IN FOCUS 

STOXX+® Minimum Variance 

index family for risk 

optimization 

www.stoxx.com 

STOXX is part of Deutsche Börse and SIX 

Use of STOXX®, STOXX+®, EURO STOXX 50®, DAX®, and SMI® indices requires licenses from 

STOXX Ltd., Deutsche Börse AG, and/or SIX Swiss Exchange AG. STOXX, Deutsche Börse, 

and SIX do not make warranties or representations, express or implied, regarding 

timeliness, sequence, accuracy, completeness, currentness, merchantability, quality, or 

fi tness of their indices and index data for any purpose and are not providing investment 

advice through them or in connection therewith. For example, inclusion or exclusion 

of a company in an index or its weighting does not refl ect an opinion on its merits by 

STOXX, Deutsche Börse, or SIX. 

Contacts 

Zurich/HQ +41 58 399 5300/New York +1 212 669 6426/Hong Kong +852 630 7316 

Singapore +65 9189 7970/Tokyo +81 3 5847 8215/stoxx@stoxx.com

FREE subscRiption oFFER! 

The Journal of Indexes is the premier source for financial index 

research, news and data. Written by and for industry experts and 

financial practioners, it is the book of record for the index industry. 

To order your FREE subscription, complete and fax this form 

to 732.450.8877 or subscribe online 

at www.indexuniverse.com/subscriptions. 

q Yes! Send me a free 

subscription to Journal 

of Indexes magazine 

Foster Wright 

Publisher 

fwright@indexuniverse.com 

646.867.4481 • Fax: 415.659.9005 

Don Friedman 

Chief Operating Officer 

dfriedman@indexuniverse.com 

415.659.9009 • Fax: 415.659.9005 

Fernando Rivera 

Reprint Sales Director 

frivera@indexuniverse.com 

646.588.6553 • Fax: 646.706.7051 

Ivana Zivkovic 

Subscriptions 

izivkovic@indexuniverse.com 

415.659.9029 • Fax: 415.659.9005 

Signature 

Name 

Date 

Dan Benveniste 

Executive Vice President, Analytics & Data 

dbenveniste@indexuniverse.com 

415.659.9013 

Title 

Company 

Address 

City State ZIP 

Phone 

E-mail 

The following best describes my primary business activity (check one): 

(1) q Plan Sponsor (2) q Financial Advisor 

(3) q Investment Management (4) q Mutual Fund Management 

(5) q Pension Fund Consulting (6) q Pension Fund Management 

(7) q Brokerage (8) q Academic 

(9) q Ordinary Investor (10) q Other:__________________ 

Do you personally sell, recommend or manage investments, work in the index 

industry or advise clients on investment and/or asset management? 

q Yes q No 

If you advise clients as to their investments, how do you charge them? 

q Commission only q Fee only q Fee and commission 

If you manage investments, what are your total assets under management? 

(1) q Over $500 million (2) q $100 million - $500 million 

(3) q $50 million - $99.9 million (4) q $25 million - $49.9 million 

(5) q $10 million - $24.9 million (6) q Under $10 million 

All questions must be answered to qualify for free subscription. Publisher reserves the right to reject unqualified applications. 

Fax 

Charter Financial 

Publishing Network Inc. 

499 Broad Street, Suite 120 

Shrewsbury, NJ 07702 

732.450.8866 • Fax 732.450.8877 

Charlie Stroller, President/CEO/CFO 

cstroller@fa-mag.com 

IndexUniverse LLC 

353 Sacramento Street, Suite 1520 

San Francisco, CA 94111 

1.877.6INDEX6 • Fax 415.659.9005 

Jim Wiandt, President 

jim_wiandt@journalofindexes.com 

Charter Financial Publishing Network Inc. also 

publishes: Financial Advisor magazine, Private 

Wealth magazine, Nick Murray Interactive 

and Exchange-Traded Funds Report. 

For a free subscription to the Journal of Indexes, 

IndexUniverse.com or Financial Advisor magazine, 

or a paid subscription to ETFR, please visit 

www.indexuniverse.com/subscriptions. 

6 

September / October 2012

©2012 Morningstar, Inc. All rights reserved. The Morningstar name and logo are registered marks of Morningstar. Marks used in conjunction with Morningstar products or services are the property of Morningstar or its subsidiaries. 

A quality-driven approach to income investing. 

Morningstar ® Dividend Indexes 

Yield to Quality 

Dividends represent the vast majority of 

stock market returns historically. In today’s 

low interest-rate environment, dividends 

provide much-needed income. But, all 

dividends are not created equal. Our dedicated 

team of researchers conducts 

business and financial analysis to assess 

a business’ ability to pay and increase 

dividends over time. With this research as 

the foundation, we have designed a 

number of proprietary metrics—such as the 

Economic Moat Rating, Uncertainty 

Rating, and Distance-to-Default—that help 

us create a range of dividend indexes. 

A Breadth of Solutions 

The Morningstar Dividend Index family 

offers a suite of products tracking dividendpaying 

equities across the globe. The 

breadth of our Dividend Index family allows 

investors to strike the right balance 

between current yield and dividend growth, 

emphasizing long-term cash returns. 

Connecting the Science and 

Art of Indexing 

The Morningstar Dividend Index family 

leverages one of the world’s largest 

independent equity research groups as 

well as our team of award-winning 

quantitative analysts. The art of this 

research-inspired approach has 

resulted in intelligent dividend indexes 

that focus on company sustainability, 

utilize smarter weighting schemas, and 

maximize investment scalability. 

For more information, or to sign up for our 

free dividend indexes webinar in 

November, call: 1 312 384-3735 or visit: 

indexes.morningstar.com

Editor’s Note 

What’s The Big Idea? 

Jim Wiandt 

Editor 

Heading into the fall, after a long dry spell of lagging market returns and a 

macro market environment that has been a carnival show of floundering 

rescue packages with a few doses of outright fraud, we thought it was time for 

some big ideas. The articles in this issue address some very large concepts in the index 

industry in a new way. We think you’ll find them useful and stimulating. 

First up is Andrew Clark of Lipper, who takes on the conventions surrounding 

the way we break the market down into size segments with some very thorough 

statistical analysis. His findings are enlightening, to say the least. JoI mainstay Craig 

Israelsen follows next with an article that cuts through all the hype around various 

index methodologies, laying them all side by side. 

Then we hear from two of IndexUniverse’s own: Analysts Paul Baiocchi and Paul 

Britt explore the question of whether it makes more sense to take a sector-based 

or a style-based approach to investing. After that, Vanguard’s Christopher Philips, 

David Walker and Francis Kinniry Jr. offer a fresh look at correlation at a time when 

many investors are disillusioned with arguments for diversification in the wake of 

the 2008-09 market crisis. 

Next up is our interview with John Prestbo, formerly the editor of the newly merged 

Dow Jones Indexes and the founding editor of this magazine. Find out what one of the 

driving forces of the index industry thinks about the latest developments and innovations. 

It’s a great read. David Blitzer follows with an eye on the future, answering the 

question, What may drive investor considerations in the near term? 

Guido Giese of SAM is next, making the case that strategy indexes perform better 

when they incorporate risk-control mechanisms, and demonstrates how they could 

be added into a methodology. 

Finally, JoI Contributing Editor Gillybear Bell offers some financial advice on taking 

advantage of an important market trend, and you might want to listen. After all, 

border collies are the smartest breed. 

Here’s to hoping you walk away with some big ideas and that September sees some 

spark in the global economy. 

Jim Wiandt 

Editor 

8 


THE POWERSHARES DB 

COMMODITY INDEX 

TRACKING FuND 

Listed in 2006, DBC has become the largest 

broad-based commodity ETF in the world. 

- Broad access to commodities 

- 100% daily transparency 

- Intraday liquidity 

// To download a copy of the prospectus, visit PowerShares.com/DBCpro // ETFs publish their holdings daily and trade on exchanges, like stocks, continuously throughout 

the trading day. // The fund is not a mutual fund or any other type of Investment Company within the meaning of the Investment Company Act of 1940 and is 

not subject to its regulation. // DB Commodity Services LLC, a wholly owned subsidiary of Deutsche Bank AG, is the managing owner of the fund. Certain marketing 

services may be provided to the fund by Invesco Distributors, Inc. or its affiliate, Invesco PowerShares Capital Management LLC (together, “Invesco”). Invesco will be 

compensated by Deutsche Bank or its affiliates. ALPS Distributors, Inc. is the distributor of the fund. Invesco, Deutsche Bank and ALPS Distributors, Inc. are not affiliated. 

// Commodity futures contracts generally are volatile and are not suitable for all investors. An investor may lose all or substantially all of an investment in the fund. 

powershares.com/DB | 

Follow us @PowerShares

Determining 

Market-Capitalization 

Breakpoints 

A global perspective 

By Andrew Clark 

10 


This article demonstrates that market-capitalization 

(market-cap) breakpoints are usually based solely 

on market conventions and not empirical evidence. 

This is not to say the conventions are incorrect. Rather, we 

show there is very limited empirical evidence for the use 

of common factors (e.g., bid/ask spread and number of 

shares traded) to substantiate where breakpoints are set. 

Methods Of Determining Market-Cap Breakpoints 

As calculated by index providers, market-cap breakpoints 

delineate a prescribed percentage of the total 

market capitalization of the specified universe of stocks. 

A common set of breakpoints is 70, 20 and 10. The 70 

refers to all the stocks that make up the first 70 percent 

of the total market capitalization of the universe of 

stocks under examination. This first breakpoint typically 

determines the percentage of stocks that are largecapitalization 

(large-cap) stocks. The 70 percent cutoff 

also determines the so-called large-cap floor. 1 The 

next 20 percent of market capitalization covers midcap 

stocks, with the last value in that range being the midcap 

floor. The final 10 percent of market capitalization covers 

small-caps (and in some cases, micro-cap stocks as 

well 2 ); one can refer to the market capitalization of the 

first stock after the midcap floor as the small-cap ceiling. 

Not all index providers use the 70, 20, 10 breaks, but 

most of their breakpoints are within 5 percent either side 

of these values (e.g., 65, 20, 15). 

Mutual fund analytical firms such as Lipper and 

Morningstar also compute market-cap breakpoints. 

These breakpoints are used to classify mutual funds by 

market capitalization, i.e., large-, mid- or small-cap. 

These firms’ breakpoints are more often than not similar 

What has not been shown by any of these calculators 

of market-cap breakpoints are the rules for determining 

them, i.e., why is 70, 20, 10 used? The author’s discussions 

with asset management firms points to a combination 

of subjective and implied empirical data to justify 

the breakpoints, so it seems market conventions and 

internal politics are the primary drivers of the marketcap 

decision process. 

The author in his 10-plus years of experience has rarely 

seen the “70, 20, 10” rule (slightly varying versions of it) 

violated in the U.S. This is also true for the G-7 countries, 

as best the author can tell. 

However, as has been shown in study after study 

over the last 13 years, for developed markets there is 

no significant dependence between: market cap and 

trading volume; market cap and transaction volume 

per trade; market cap and transaction value per hour or 

tick; market cap and total number of orders; market cap 

and bid/ask spread; or market cap and volatility. The 

implication of these results is that most if not all of the 

commonly cited empirical reasons market-cap breakpoints 

exist as they do is not justified for the developedmarket 

countries. This article confirms these results 

for developed-market countries and shows that for 

developing-market countries, market-cap breakpoints 

have no empirical basis either (except in certain cases 

where a micro-cap breakpoint appears or a micro-cap/ 

small-cap breakpoint appears). 

Even the largest deviations, which are exceptionally rare, 

are still only about a factor of 2 from the mean in either direction; 

hence, the distribution can be well characterized by stating 

just its mean and standard deviation. 

to or the same as those calculated by index providers. 

Finally, asset management firms also calculate marketcap 

breakpoints. These are used internally to set the 

market-capitalization guidelines for portfolio managers. 

The breakpoints are set so that both management and 

the portfolio manager can confirm that a particular manager’s 

market-cap-weighted portfolio has stayed within 

its stated capitalization range. For example, the prospectus 

of a mutual fund may state that it is a large-cap fund. 

Using the internal capitalization guidelines, the fund 

manager knows the range to stay within so the market 

capitalization does not break the large-cap floor. 

Universality And Scaling 

The work of Plerou et al. [1999], [2002] established the 

existence of universality and scaling in financial data. 

Their work has been extended by several authors, including 

Zumbach [2004], Li et al., [2011] and Kertesz and Eisler 

[2012]. It is the existence of universality and, particularly, 

scaling that we show refutes market-cap breakpoints 

being—in general—empirically based. 

To begin our discussion of scaling, we note that many 

empirical quantities cluster around a typical value: the 

speeds of cars on a highway, the weights of apples in a 

store, air pressure, sea level, the temperature in New York 

at noon on July 4. All of these things vary somewhat, but 

their distributions place a negligible amount of probability 

far from the typical value, making the typical value representative 

of most observations. For instance, it is a use- 

www.journalofindexes.com September / October 2012 11

Figure 1 

Log (Volume) 

9 

8 

7 

6 

5 

4 

3 

US Volume Vs. Market Cap (Log Scale), Febuary 2012 

2 

0 1 2 3 4 5 6 

Log (Market Cap) 

Source: Thomson Reuters 

Figure 2 

Log (Dollar Volume Traded) 

4.0 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 


US Dollar Volume Traded Vs. Market Cap 

(Log Scale), February 2012 

0.0 

-0.5 

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 


Figure 3 

US Bid/Ask Spread Vs. Market Cap (Log Scale), February 2012 

Log (Spread) 

4.5 

4.0 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 

-0.5 

1 2 3 4 5 

6 



ful statement to say that an adult male American is about 

71 inches tall, because few deviate very far from this size. 

Even the largest deviations, which are exceptionally rare, 

are still only about a factor of 2 from the mean in either 

direction; hence, the distribution can be well characterized 

by stating just its mean and standard deviation. 

Not all distributions fit this pattern, however; while 

those that do not are often considered problematic or 

defective, they can be some of the most interesting observations. 

The fact that they cannot be characterized as 

simply as other measurements is often a sign of a complex 

underlying process that merits further study. 

Among such distributions, the power law has attracted 

particular attention over the years for its mathematical 

properties, which sometimes lead to surprising physical 

consequences. The power law appears in a diverse range 

of natural and man-made phenomena. For example, 

the populations of cities, the intensities of earthquakes 

and the sizes of power outages are all thought to have 

power-law distributions. Quantities such as these are not 

well characterized by their typical or average values. For 

instance, according to the 2000 U.S. Census, the average 

population of a city, town or village in the United States is 

8,226. This average is not a useful one for most purposes 

because a significant fraction of the total population lives 

in cities (New York, Los Angeles, etc.) whose population 

is larger by several orders of magnitude. 

The main property of scaling (or power) laws is their 

scale invariance. Given a relation f(x) = ax k , scaling the 

argument x by a constant factor c causes only a proportionate 

scaling of the function itself. That is, 

k k 

f( cx) = a( cx) = c f( x) ∝ f( x) 

Scaling by a constant c multiplies the original powerlaw 

relation by the constant c k . All phenomena that scale 

have a particular scaling exponent and are equivalent to 

constant factors, since each is simply a scaled version of 

the others. This behavior produces a linear relationship 

when logarithms are taken of both f(x) and x, and the 

straight line on the log-log plot is often called the “signature” 

of a power law. With real data, such straightness 

is a necessary but not sufficient condition for the data to 

follow a scaling relationship (see Stumpf and Porter [2012] 

for the necessary and sufficient conditions). In this article, 

we use Stumpf and Porter’s guidelines as well as others’ 

to confirm that our results do (or do not) exhibit scaling. 

The possible existence of scaling does not conflict with 

the existence of market-cap breakpoints. If the breakpoints 

do exist, there should be a different scaling exponent 

for each capitalization group. We say this because 

within each capitalization group one would expect the 

relationships between the “activity” (such as bid/ask 

spread) and the stocks to be approximately the same. If 

they are approximately the same, this generates a scaling 

exponent that covers most if not all the stock-“activity” 

relationship vis-à-vis the market-capitalization range. 

Conversely, the between-group relationship of “activity” 

and capitalization should differ, e.g., the scaling 

exponent for the “activity” and midcap stocks should be 

different than that of small-caps and the same “activity.” 

If this is not the case, then the “activity” is not something 

that can distinguish small-caps from midcaps. 

To make this a little clearer, it is generally accepted 

that daily stock returns do not follow a normal distribution. 

It has been established by Bouchaud and Potters 

[2000] as well as others that stock return series typically 

follow two if not three distributions, each with its own 

scaling exponent. To take an example, “extreme” nega- 

12 


tive returns typically follow a power law whose scaling 

exponent is approximately 1.5. Extreme positive returns 

have a slightly larger scaling exponent (closer to 2), and 

the remainder of the return series follow a power law with 

a scaling exponent of approximately 2. So, here we have an 

example of three different scaling exponents in the same 

time series. We look for tripartite scaling exponents in our 

market-capitalization breakpoint tests, since they indicate 

the breakpoints are validated by the “activity” data. 

Our tests take the form of plotting the logarithm of the 

market cap against the logarithm of an “activity,” such as 

the bid/ask spread, that is thought to be related to market 

cap. A single scaling value (a single line through the data) 

indicates the “activity” is not confirming the market-cap 

breakpoints. More than one scaling value could confirm 

the breakpoints, and we will look at those situations 

where this occurs. We follow the guidelines established 

by Stumpf and Porter by using four or five orders of magnitude 

in our calculations (this can correspond to market 

capitalizations going from tens of millions or hundreds of 

millions of U.S. dollars to hundreds of billions of U.S. dollars, 

based upon the market). We also follow Zumbach in 

the calculation of the linear fits we make of the data so we 

can assess the goodness of fit. 

We show in the next section that for most of the countries 

we examined, there is in most cases a single scaling 

exponent across market capitalizations, not multiple ones. 

This lack of multiple exponents argues against empirical 

evidence for market-cap breakpoints, and this appears 

to be true regardless of the measures (“activities”) used, 

whether in this or in other articles. (As an aside, it should 

be noted that single exponents—but not multiple exponents—do 

not exist at different time scales either, as can 

be seen in the work of Zumbach and Li et al.) 

Scaling Exponents For Market-Caps 

And Various ‘Activities’ In Different Countries 

In this article’s tests, daily data is used; the stocks are 

those in the Thomson Reuters Country Stock Indexes. Most 

if not all of these indexes include some micro-cap stocks. 

The inclusion or exclusion of micro-cap stocks does not 

add or subtract from our breakpoint examination, since 

we are looking for small-, mid- and large-cap breakpoints. 

The “activities” we examine are the bid/ask spread, 

volume (number of shares traded) and the price per 

share times the volume traded (often referred to as “dollar 

volume traded” but, since we are working with several 

different countries, this activity could be “pounds sterling 

volume traded” or “ringgit volume traded”). As Kertesz 

and Eisler and others do, we look at the logarithm of the 

“activity” versus the logarithm of market capitalization. 

We plot individual data points, since our averaging of the 

stock data covers a single month versus years. We look at 

the period from January 2000-February 2012 on a monthby-month 

basis, and we try to fit a straight line through 

the data, following the method of Zumbach. 3 

Figures 7-9 show the results of all the countries examined 

(U.S., Great Britain, Japan, Switzerland, Malaysia, Korea, 

Figure 4 

Malaysia Volume Vs. Market Cap (Log Scale), February 2012 

Log (Volume) 

8.0 

7.5 

7.0 

6.5 

6.0 

5.5 

5.0 

4.5 

4.0 

3.5 

3.0 

2.5 

1 1.5 2 2.5 3 3.5 4 4.5 5 



Figure 5 

Log (Bid/Ask Spred) 


Malaysia Bid/Ask Spread Vs. Market Cap 


2.6 

2.4 

2.2 

2.0 

1.8 

1.6 

1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

-0.2 

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5 


Figure 6 

Log (Price x Volume) 

3 

2 

1 

0 

-1 

-2 

-3 


Malaysia Price x Volume Vs. Market Cap 


1 1.5 2 2.5 3 3.5 4 4.5 5 


Hong Kong, India, Brazil, Israel, Italy and South Africa). 

However, plots for only the U.S. and Malaysia are shown 

in Figures 1-6. The U.S. can serve as a reasonable proxy for 

most of the countries on the list, while Malaysia can stand 

in for the few countries where there are either two or more 

single scaling exponents or no scaling exponent at all. 

In Figures 1 and 2, it is clear that volume and dollar 

volume traded have a single scaling exponent when plotted 

against market cap. The lack of multiple exponents 

means anyone pointing to either of these variables as an 

empirical justification for the existence of breakpoints in 

the U.S. would be mistaken. In Figure 3, there is a second 


Figure 7 

Figure 9 

Log Volume Vs. Log Market Cap: Scaling Results 

Log Price х Volume Vs. Log Market Cap: Scaling Results 

Country 


No. Of 

Scaling Values 

Goodness Of Fit 

U.S. 1 Excellent 

Great Britain 1 Excellent 

Japan 1 Excellent 

Switzerland 1 Moderate 

Malaysia Multiple Poor 

Korea 1 Moderate 

Hong Kong 2 Good 

India 1 Excellent 

Brazil 1 Excellent 

Israel 1 Moderate 

Italy 1 Moderate 

South Africa 1 Poor 

Country 


No. Of 




Great Britain 1 Excellent 


Switzerland 1 Excellent 

Malaysia 1 Excellent 

Korea 1 Excellent 

Hong Kong 1 Excellent 



Israel 1 Excellent 

Italy 1 Excellent 

South Africa 1 Excellent 

Figure 8 

Log Bid/Ask Spread Vs. Log Market Cap: Scaling Results 

Country 


No. Of 




Great Britain 2 Moderate 


Switzerland 2 Moderate 

Malaysia Multiple Poor 

Korea 1 Excellent 

Hong Kong 2 Excellent 



Israel 1 Moderate 

Italy 1 Moderate 

South Africa 1 Moderate 

scaling exponent, appearing at approximately $10 3 million, 

i.e., $1 billion. Given its size, this is a breakpoint 

that signifies either the existence of a small-cap floor or 

a micro-cap ceiling. Either way, this cannot be done in 

terms of separating small-, mid- and large-caps, as there 

is a single scaling exponent. 

In Figure 4, there is evidence of two scaling exponents. 

One exponent’s market-cap starts between 

1 million ringgits and 1.5 million ringgits and ends 

between 2.5 million ringgits and 3 million ringgits. 

The second exponent’s market cap starts at approximately 

3 million ringgits and covers the rest of the 

market-cap range. There is no evidence of three scaling 

exponents but some evidence of two. And, as can 

been seen graphically, the spread of points about 

the fits is wide, suggesting the data may not follow a 

power law. We note this poor-to-middling fit in Figure 

7 and discuss its implications later. 

The same issues that appeared in the log-volume plot 

appear here for the log-spread plot. There is the appearance 

of two scaling exponents but also clear evidence of 

a poor-to-middling fit. Please note that the end of one 

scaling exponent (and the start of the other) is between 

2.5 million ringgits and 3 million ringgits, just as occurred 

in the log-volume plot. 

The plot of price times volume has the best linear fit 

and has an absence of multiple scaling exponents. Here 

there is clear evidence of price times volume not supporting 

market-cap breakpoints. 

Figures 7-9 detail the results for each “activity” for each 

country. Since the results were very consistent across the 

periods (January 2000-February 2012), we show one value 

per country per activity. Also shown is the goodness of fit. 

In the developed-market countries, both volume 

measures, especially price times volume, have single 

exponents and excellent fits. The two exceptions are 

Switzerland and Italy, where volume traded and bid/ 

ask spread have a moderate goodness of fit, while price 

times volume has an excellent fit. It needs to be kept in 

mind that both Switzerland and Italy have a substantially 

smaller number of stocks in their universe, and this contributes 

to the lack of fit. However, it may also be the case 

that for both countries, a power law is not being followed. 

For developing-market countries, there is a consistent 

presence of one scaling exponent, and in general, excellent 

fits. As mentioned earlier, Malaysia is the country 

with the consistently poor fit. Although Malaysia is not 

plagued by a lack of stocks, it clearly does not follow a 

scaling law. So, in Malaysia’s case, we cannot say empirical 

evidence does not support market-cap breakpoints. 


Conclusion 

We have shown that in most of the countries 

examined, there is good-to-excellent evidence that 

empirical data such as volume, bid/ask spread and 

price times volume do not support commonly used 

market-cap breakpoints. These results are in line with 

other work that looked at other “activities” on both 

a daily and intraday basis. Again, our conclusions 

do not invalidate current market conventions. What 

our research shows is that in most cases, empirical 

measures of stock activity do not support the common 

market-cap breakpoints. 

Works Cited 

1. Plerou, V. et al. “Universal and Nonuniversal Properties of Cross Correlations in Financial Time Series,” Physical Review Letters. 1999, vol. 83, 7. 

2. Plerou, V. et al. “Random Matrix Approach to Cross Correlation in Financial Data,” Physical Review E. 2002, vol. 65. 

3. Zumbach, G. “How the Trading Activity Scales with the Company Size in the FTSE 100,” Quantitative Finance. 2004, vol. 4, 4. 

4. Li, W. et al. “Financial Factor Influence on Scaling and Memory of Trading Volume in Stock Markets,” Physical Review E. 2011, vol. 84. 

5. Kertesz, J. and Eisler, Z. “Limits of Scaling and Universality in Stock Market Data,” (Online) Dec. 21, 2005. (cited: Jan. 12, 2012) http://arxiv.org/abs/physics/0512193v1. 

6. Stumpf, M.P.H. and Porter, M.A. “Critical Truths about Power Laws,” Science. 2012, 335. 

7. Bouchaud, J-P and Potters, M. “Theory of Financial Risk and Derivative Pricing,” Cambridge University Press, 2000. 

Endnotes 

1 

Mega-cap stocks are considered to be part of the group comprising the 70 percent. While this article does not specifically work with mega-cap stocks, the conclusions it 

draws about market-cap breakpoints apply to mega-cap stocks as well. 

2 

Most market-capitalization schemes that are tripartite in nature, such as the one above, do not tend to include micro-cap stocks. Market participants who are interested 

in micro-cap stocks typically make an estimate as to where small-caps end and micro-caps begin. This article shows that such estimates may suffer from the same problem 

that makes other market-cap breakpoints hard to justify empirically. 

3 

The usual least squares (LSQ) estimate assumes a variance in the y variable but none in the x variable, which is assumed to be known exactly. However, market capitalization 

is a time series and therefore has its own variance. To account for this, we have to use a more sophisticated LSQ estimate with an error in both variables. Zumbach 

used an LSQ estimator that is more complex to compute, since it involves a minimization problem (to find the best parameters) to find the roots of a one-dimensional 

function (to compute the error on the parameters). The values of the minimum (slope and intercept) and the errors on the parameters (standard deviations of the slope 

and intercept) are fairly insensitive to the choice for the standard deviation (s1 vs. s2), but the goodness of fit depends directly on the choice for the standard deviation. 

Since s1 is lower than s2, this produces systematically worse goodness of fit. For this reason, we use the more conservative standard deviation s1. 

InsideCommodities 

Long-Term growTh. ImmedIaTe opporTunITIes. 

October 10, 2012 

The RiTz-CaRlTon • ChiCaGo, il 

Keynote Speaker: 

dennis gartman 

Editor & Publisher 

“The Gartman Letter” 

This conference 

has been approved 

by CIma for 

8 Ce credits. 

5 th annual 

conference 

2012 

At the 5th Annual Inside Commodities conference, 

we will take an in-depth look at: 

• How the Chinese economy effects commodities 

around the globe 

• The real world applications of industrial metals 

and how to play them 

• Identifying and selecting the right commodity 

for your portfolio 

spaCe Is LImITed. To regIsTer, pLease VIsIT: 

www.InsideCommoditiesConference.com/JoI or call 415-659-9006 

presented by: 

www.journalofindexes.com September / October 2012 

15

Index Variation And Overall 

Portfolio Performance 

From interesting to important 

By Craig Israelsen 

16 


This article outlines the performance differentials 

between five prominent index providers: Dow Jones, 

Morningstar, Morgan Stanley Capital International 

(MSCI), Russell and Standard & Poor’s. Additionally, this 

article calculates the variation in performance of a multiasset 

portfolio when utilizing U.S. equity indexes (large, 

mid and small) from the five major index providers. 

As will be shown, substantial performance differences 

exist between indexes that claim to be measuring the same 

space within the U.S. equity market. Nevertheless, the performance 

differences among various U.S. equity indexes 

are largely mitigated when such indexes are utilized in a 

broadly diversified, multi-index portfolio. 

The data utilized in this study were obtained from 

Morningstar Principia. The time frame of this study is the 

10-year period from Jan. 1, 2002 to Dec. 31, 2011. 

Large-Cap US Equity Indexes 

We start with large-cap blend (blend indexes are typically 

described as indexes where neither a growth nor 

value orientation is dominant). A commonly used benchmark 

in the U.S. equity market is the Standard & Poor’s 

500 Index (see the middle portion of Figure 1 labeled 

“Blend Indexes”). Its 10-year average annualized return 

from 2002-2011 was 2.92 percent. However, there are 

other indexes that also measure the large-cap U.S. equity 

market. For instance, the Dow Jones US Large Cap Index 

had a 3.44 percent annualized return over the same 10 

years. Alternatively, the 10-year annualized return of the 

Morningstar Large Cap Index was 2.49 percent, representing 

a difference of 95 basis points compared with that of 

the Dow Jones US Large Cap Index. 

A differential of 95 bps is material—particularly when 

evaluating the performance variance between actively 

managed large-cap blend funds and index-based largecap 

blend funds. As it pertains to “active vs. passive” comparisons, 

the performance of actively managed large-cap 

blend funds will be more compelling if compared against 

the Morningstar Large Cap Index and less compelling if 

compared against the Dow Jones US Large Cap Index. 

The performance differences among the large-cap U.S. 

equity indexes demonstrated in Figure 1 represent various 

index composition methodologies at work. They are all different: 

some slightly different and others significantly different. 

What is noteworthy is the amount of variation in any given 

year between the best- and worst-performing U.S. large-cap 

index. For instance, in 2009, the gap between the best-performing 

large-cap blend index (Russell 1000) and the worstperforming 

index (Morningstar Large Cap) was 367 bps. 

Among large-cap value indexes, the annual difference 

Figure 1 

Large-Cap U.S. 

Equity Indexes 

Sources: Morningstar Principia 

2002 

Dow Jones US Large Value -14.7 30.6 13.6 5.7 21.9 1.8 -36.6 17.2 15.2 3.8 11.89 -1.99 3.95 

Morningstar Large Value -15.1 26.3 14.1 7.1 25.8 -0.4 -36.1 11.4 14.7 2.2 9.30 -3.64 3.19 

MSCI US Large Cap Value -18.2 28.4 13.3 6.1 23.4 1.2 -35.8 16.4 13.0 1.6 10.15 -2.80 3.06 

Russell 1000 Value -15.5 30.0 16.5 7.1 22.3 -0.2 -36.9 19.7 15.5 0.4 11.55 -2.64 3.89 

S&P 500/Citigroup Value -20.9 31.8 15.7 5.8 20.8 2.0 -39.2 21.2 15.1 -0.5 11.55 -2.96 2.87 

Differential between 

Max & Min (bps) 

Large-Cap US Equity Indexes (Annual % Returns) 

VALUE INDEXES 

2003 2004 2005 2006 2007 2008 2009 2010 2011 

3-Year Return 

2009-2011 

5-Year Return 

2007-2011 

10-Year Return 

2002-2011 

613 553 324 133 499 242 341 980 247 428 259 165 107 

BLEND INDEXES 

Dow Jones US Large Cap -21.1 28.9 11.7 6.3 15.6 6.4 -37.1 27.0 16.1 1.7 14.45 0.08 3.44 

Morningstar Large Cap -23.5 27.0 9.5 4.9 15.9 6.7 -36.2 24.8 13.4 2.6 13.24 -0.23 2.49 

MSCI US Large Cap 300 -22.9 27.8 9.7 4.7 16.1 6.4 -36.1 25.5 13.9 2.4 13.57 -0.07 2.74 

Russell 1000 -21.7 29.9 11.4 6.3 15.5 5.8 -37.6 28.4 16.1 1.5 14.81 -0.02 3.34 

Standard & Poor’s 500 -22.1 28.7 10.9 4.9 15.8 5.5 -37.0 26.5 15.1 2.1 14.11 -0.25 2.92 



240 285 211 163 64 120 153 367 266 111 157 33 94 

GROWTH INDEXES 

Dow Jones US Large Growth -26.7 27.5 9.5 7.1 9.2 11.0 -37.5 37.4 17.0 -0.6 16.94 2.09 2.87 

Morningstar Large Growth -33.2 30.7 0.2 3.4 5.7 12.3 -41.9 44.4 12.9 1.6 18.30 1.57 0.34 

MSCI US Large Cap Growth -28.0 27.3 6.2 3.3 9.1 11.7 -36.5 35.3 14.8 3.2 16.99 2.60 2.23 

Russell 1000 Growth -27.9 29.8 6.3 5.3 9.1 11.8 -38.4 37.2 16.7 2.6 18.01 2.50 2.59 

S&P 500/Citigroup Growth -23.6 25.7 6.1 4.0 11.0 9.1 -34.9 31.6 15.1 4.7 16.58 2.39 2.84 



956 499 934 383 532 321 695 1,280 412 522 172 103 253 


17

etween the best- and worst-performing index ranged from 

133 bps in 2005 to 980 bps in 2009. The S&P 500/Citigroup 

Value Index had a return of 21.2 percent in 2009 compared 

with a return of 11.4 percent for the Morningstar Large Value 

Index. Clearly, an “active vs. passive” comparison would be 

dramatically impacted based on which of those two indexes 

was used as the performance bogey for passive investing. 

The variation in performance is even more pronounced 

among large-cap growth indexes. For instance, 

in 2009, the gap between the best- and worst-performing 

indexes was 1,280 bps. Even over lengthy time frames, 

the performance variation among the various large-cap 

growth indexes can be surprisingly large. For instance, 

the 10-year average annualized return of the Dow Jones 

US Large Growth Index was 2.87 percent, whereas the 

10-year return for the Morningstar Large Growth Index 

was 0.34 percent—a difference of 253 bps. 

Midcap US Equity Indexes 

Next we examine midcap U.S. equity indexes (see Figure 

2). Among the five midcap U.S. equity value indexes, there 

was an annual performance differential that ranged from 

64 bps in 2002 to 929 bps in 2004. This level of performance 

variation reveals significant heterogeneity among the various 

builders of indexes. Such heterogeneity is seldom discussed 

in the numerous articles and presentations that discuss and 

debate the topic of active vs. passive investing despite the fact 

that such differences could dramatically impact the findings. 

Among midcap blend indexes, the annual performance 

differences ranged from 124 bps in 2011 to 718 

in 2009. Significant variation among midcap indexes 

is observed in the growth category. For instance, in 

2009, the S&P Midcap 400/Citigroup Growth Index 

had a one-year return of 41.1 percent compared with 

a return of 55.6 percent for the Dow Jones US Mid-Cap 

Growth Index. Between the best- and worst-performing 

midcap growth indexes, the performance differential 

exceeded 1,200 bps in three of the 10 years. Between the 

Morningstar Mid Growth Index and the Dow Jones US 

Mid-Cap Growth Index was a 328 bps difference in their 

10-year average annualized returns. 

Small-Cap US Equity Indexes 

In Figure 3, we examine small-cap U.S. equity indexes. 

The annual difference between best- and worst-performing 

small-cap value indexes ranged from 146 bps in 2010 

to 1,970 bps in 2009. The dramatic performance variance 

in 2009 suggests that the methodology for measuring the 

small-cap value U.S. equity market is fundamentally different 

between Russell and Morningstar. 

Figure 2 

Mid-Cap U.S. 


Source: Morningstar Principia 

2002 

Dow Jones US Mid-Cap Value -9.5 34.9 17.9 5.5 15.7 -1.3 -34.8 32.0 21.9 -1.0 16.80 0.51 6.07 

Morningstar Mid Value -10.0 35.9 24.3 11.5 18.8 -5.5 -36.0 36.1 20.6 -2.6 16.92 -0.67 6.90 

MSCI US Mid Cap Value -9.7 37.9 27.2 12.9 17.8 -4.4 -36.5 37.8 22.0 -0.2 18.81 0.36 7.92 

Russell Midcap Value -9.7 38.1 23.7 12.7 20.2 -1.4 -38.5 34.2 24.8 -1.4 18.19 0.04 7.67 

S&P MidCap 400/Citigroup Value -10.1 40.2 18.9 11.5 14.6 2.7 -34.9 33.7 22.8 -2.4 17.01 1.38 7.45 



Mid-Cap US Equity Indexes (Annual % Returns) 

VALUE INDEXES 

2003 2004 2005 2006 2007 2008 2009 2010 2011 

3-Year Return 

2009-2011 

5-Year Return 

2007-2011 


2002-2011 

64 524 929 739 560 817 365 580 414 236 201 204 185 

BLEND INDEXES 

Dow Jones US Mid Cap -15.9 38.7 18.5 10.9 13.5 5.6 -38.9 44.6 25.4 -0.7 21.66 3.05 7.29 

Morningstar Mid Cap -18.1 38.4 19.7 12.7 14.3 5.2 -40.5 39.0 24.9 -0.8 19.88 1.54 6.56 

MSCI US Mid Cap 450 Index -16.5 39.1 20.5 13.9 13.8 6.2 -41.8 40.5 25.7 -1.9 20.09 1.37 6.86 

Russell Midcap -16.2 40.1 20.2 12.7 15.3 5.6 -41.5 40.5 25.5 -1.6 20.17 1.42 6.99 

S&P Midcap 400 -14.5 35.6 16.5 12.6 10.3 8.0 -36.2 37.4 26.6 -1.7 19.57 3.32 7.04 



353 444 404 303 494 275 557 718 172 124 208 195 73 

GROWTH INDEXES 

Dow Jones US Mid-Cap Growth -24.5 43.4 18.9 16.7 11.6 11.2 -41.6 55.6 28.2 -0.6 25.64 5.18 7.99 

Morningstar Mid Growth -32.5 40.0 15.5 16.3 9.6 19.7 -46.3 42.1 27.7 -2.3 21.01 2.65 4.71 

MSCI US Mid Cap Growth -23.3 40.3 13.8 15.0 9.7 17.4 -47.1 43.0 29.2 -3.6 21.23 2.05 5.51 

Russell Midcap Growth -27.4 42.7 15.5 12.1 10.7 11.4 -44.3 46.3 26.4 -1.7 22.06 2.44 5.29 

S&P MidCap 400/Citigroup Growth -19.2 31.0 14.0 13.6 5.8 13.5 -37.6 41.1 30.6 -0.9 22.20 5.26 6.48 



1,337 1,245 512 457 576 847 946 1,447 419 305 463 321 328 

18 


Figure 3 

Small- Cap U.S. 


2002 

Small-Cap US Equity Indexes (Annual % Returns) 

Dow Jones US Small Cap Value -8.7 46.9 19.6 5.3 20.0 -4.1 -33.9 36.8 25.0 -4.0 17.98 0.79 7.75 

Morningstar Small Value -8.2 48.9 24.0 5.1 20.0 -8.2 -31.7 40.3 26.0 -1.8 20.15 1.71 8.81 

MSCI US Small Cap Value -6.6 44.3 23.7 6.3 19.4 -6.9 -32.1 30.3 25.0 -4.0 16.05 -0.25 7.65 

Russell 2000 Value -11.4 46.0 22.3 4.7 23.5 -9.8 -28.9 20.6 24.5 -5.5 12.36 -1.87 6.40 

S&P SmallCap 600/Citigroup Value -14.5 40.0 23.3 6.2 19.6 -5.5 -29.5 22.9 24.7 -1.4 14.75 0.12 6.55 



VAlUE InDExES 

2003 2004 2005 2006 2007 2008 2009 2010 2011 

3-Year Return 

2009-2011 

5-Year Return 

2007-2011 


2002-2011 

784 883 442 157 404 565 500 1,970 146 412 779 359 241 

Dow Jones US Small Cap -19.0 49.0 19.5 7.4 17.0 1.9 -37.8 41.9 28.6 -2.9 21.02 2.37 7.37 

Morningstar Small Cap -20.4 47.7 20.4 5.8 17.1 -0.7 -36.1 37.8 28.4 -2.6 19.88 1.82 6.73 

MSCI US Small Cap 1750 Index -18.4 47.4 20.0 7.5 15.8 1.2 -36.2 36.2 27.8 -2.8 19.17 1.79 6.98 

Russell 2000 -20.5 47.3 18.3 4.6 18.4 -1.6 -33.8 27.2 26.9 -4.2 15.63 0.15 5.62 

S&P SmallCap 600 -14.6 38.8 22.7 7.7 15.1 -0.3 -31.1 25.6 26.3 1.0 17.02 1.95 7.09 



BlEnD InDExES 

585 1,024 432 313 325 347 669 1,636 231 520 539 222 175 

GRowth InDExES 

Dow Jones US Small Cap Growth -28.5 51.0 19.0 9.7 13.8 8.1 -41.3 47.4 32.0 -2.1 23.95 3.86 6.84 

Morningstar Small Growth -36.9 52.7 13.5 5.8 10.0 11.1 -39.9 33.0 31.3 -1.0 19.98 2.88 3.91 

MSCI US Small Cap Growth -29.3 50.4 16.1 8.7 12.0 9.7 -40.1 42.0 30.7 -1.5 22.26 3.72 6.08 

Russell 2000 Growth -30.3 48.5 14.3 4.2 13.4 7.1 -38.5 34.5 29.1 -2.9 19.00 2.09 4.48 

S&P SmallCap 600/Citigroup Growth -15.4 37.3 22.0 9.2 10.5 5.6 -33.0 28.4 28.0 3.6 19.40 3.80 7.51 



2,151 1,534 852 553 375 548 831 1,903 398 653 495 177 360 

Source: Morningstar Principia 

Small-cap blend indexes also demonstrate significant 

variance in performance, ranging from 231 bps in 2010 

to 1,636 bps in 2009. It is interesting that the bookends of 

performance variation occurred in adjacent years. 

The performance variation among small-cap growth 

indexes exceeded 1,500 bps in three separate years (2002, 

2003 and 2009). In 2002, the S&P SmallCap 600/Citigroup 

Growth Index had a return of -15.4 percent, while the 

Morningstar Small Growth Index had a -36.9 percent 

return—producing a performance differential of 2,151 bps. 

By any reasonable guideline, that amount of difference 

between two indexes measuring the same slice of the U.S. 

equity market is astonishing. 

Do these differences in individual indexes really matter? 

Yes, but only if a person invests very “narrowly.” For 

instance, if my investment portfolio consisted entirely of 

small-cap growth U.S. stock, I would want to mimic the 

S&P SmallCap 600/Citigroup Growth Index rather than the 

Morningstar Small Growth Index (at least, based on historical 

returns). But if my investment portfolio is a broad 

assortment of asset classes (i.e., a broad array of indexes), 

the differences between individual indexes within the 

same asset class are not a highly significant issue. This 

assertion will be demonstrated next. 

Interesting Vs. Important 

The preceding observations regarding the performance 

differentials among various U.S. equity market indexes 

and the potential impact such differences could have on 

active vs. passive comparisons are interesting, but they 

are not necessarily important. Why? While individual 

U.S. equity indexes are important (large-cap, midcap 

and small-cap), they are only components within a larger 

and more diverse asset allocation model. As important as 

they are, individual equity or fixed-income indexes are 

simply one of many ingredients in a diversified portfolio 

that should incorporate a wide variety of asset classes 

(i.e., a wide variety of indexes). What is important is how 

the overall portfolio performs, rather than over-focusing 

on how an individual “single-asset-class” index behaves 

in relation to another competing index or whether a passive 

exposure to that asset class is preferred to an active 

exposure. The sum is more important than the parts 

when considering the whole point of asset allocation as 


it relates to building diversified portfolios. In short, this 

article is reliant upon the premise that investors should be 

constructing broadly diversified portfolios with divergent 

asset class exposures (i.e., diverse indexes). 

To illustrate the assertion that the sum is more important 

than the parts, the large-cap, midcap and small-cap 

indexes from each of the five index providers (Dow Jones, 

Morningstar, MSCI, Russell and Standard & Poor’s) were 

inserted into a diversified 12-asset class portfolio. Each 

of the 12 assets was equally weighted at 8.33 percent of 

the portfolio, and the 12 asset classes were rebalanced at 

the beginning of each year. Nine of the 12 asset classes 

remained the same throughout all the performance analysis; 

only the three U.S. equity elements were changed. The 

makeup of the 12-asset (i.e., 12-index) portfolio is illustrated 

in Figure 4. The peach shading illustrates where the 

various value, blend and growth indexes from each of the 

five index providers were inserted. 

For example, the first portfolio that was analyzed used 

the three Dow Jones value indexes (Dow Jones US Large 

Cap Value, Dow Jones US Mid-Cap Value and Dow Jones 

US Small Cap Value) in the three U.S. equity slots in the 

12-asset model. The 10-year performance for the entire 

12-asset portfolio was then calculated over the period from 

Jan. 1, 2002 to Dec. 31, 2011. This process was repeated 

using the value indexes for each of the four remaining index 

providers. The performance of the 12-asset portfolio was 

then calculated utilizing the blend indexes (large, mid and 

small) and growth indexes (large, mid and small) from each 

of the five index providers. 

As shown in Figure 5, when the various U.S. equity 

indexes were utilized in a multi-asset, diversified portfolio, 

the sizable performance differences that were observed at 

the individual index level were largely neutralized. Said 

Figure 4 

12-Asset ‘Index-Based’ Portfolio Model (12-Index Portfolio) 

Large-cap US equity, mid-cap US equity and small-cap US equity indexes from 

the five major index providers were sequentially utilized into the 12-asset model. 

12-Asset Portfolio 

Allocation Model 

US Large-Cap Equity 

US Mid-Cap Equity 

US Small-Cap Equity 

Developed Non-US Equity 

Emerging Non-US Equity 

Real Estate 

Natural Resources 

Commodities 

US Aggregate Bonds 

Inflation-Protected Bonds 

International Bonds 

Cash 

Index Used in 12-Asset Portfolio 

DJ or Morningstar or MSCI or Russell or S&P 



MSCI EAFE Index 

MSCI Emerging Markets Index 

Dow Jones US Select REIT Index 

Goldman Sachs Natural Resources Index 

Deutsche Bank Liquid Commodity Index (Total Return) 

Barclays Aggregate Bond Index 

Barclays US Treasury Inflation Note Index 

Barclays Global Treasury Ex-US Index 

3 Month US Treasury Bill 

Source: 7Twelve TM Portfolio developed by author 

differently, the vast differences among some of the “parts” 

(individual U.S. equity indexes) were essentially obliterated 

at the “sum” level (that is, a multi-asset portfolio 

comprising 12 indexes). Just as tomatoes and onions and 

hot peppers are very different when served individually, 

when they are combined into a salsa, the result is a unifying 

(and satisfying) taste based on the assimilation of all 

the ingredients into a “sum.” 

There are several interesting findings at the “sum,” 

or portfolio, level. For instance, the value indexes 

from Morningstar (large cap, midcap and small cap) 

generated the best 10-year multi-asset portfolio performance 

among the five index providers. The value 

indexes from S&P/Citigroup generated the worst performance. 

However, the performance difference between 

a 12-asset portfolio using three Morningstar U.S. equity 

value indexes and a 12-asset portfolio using three S&P/ 

Citigroup U.S. equity value indexes was only 19 bps 

(with a nearly equivalent standard deviation of annual 

returns). Nineteen basis points is not a dramatic difference 

in performance, but provides an indication that the 

Morningstar methodology for assembling value equity 

indexes is slightly more effective within a multi-asset 

portfolio than are S&P value indexes (at least, over this 

particular 10-year period from 2002 to 2011). Going 

forward, it is impossible to know if Morningstar’s slight 

advantage within “value” indexes will persist. 

The Morningstar performance advantage over S&P 

when utilizing three value indexes (large-cap, midcap 

and small-cap) within a 12-asset portfolio was partially 

attributable to the performance of the Morningstar 

large-cap value and small-cap value indexes. In midcaps, 

however, the S&P Midcap 400/Citigroup Value 

Index actually had better 10-year performance than the 

Morningstar Mid Value Index. 

When using blend indexes from the five index providers 

in a 12-asset portfolio, we observe that the Dow Jones 

indexes generated the best performance, posting a 9.11 

percent 10-year average annualized return. When S&P 

U.S. equity blend indexes were utilized in the 12-asset 

portfolio, the 10-year annualized return was 8.98 percent, 

a mere 13 bps behind. However, the standard deviation of 

the annual portfolio returns was slightly lower using the 

S&P indexes (15.3 percent vs. 15.9 percent). When considering 

both risk and return, the Dow Jones U.S. blend 

equity indexes and the S&P U.S. equity blend indexes 

produced results that were comparable. 

The least attractive blend indexes to utilize in a broadly 

diversified 12-asset portfolio were from Morningstar 

(based on the 10-year period from 2002-2011). However, 

these are narrow margins of difference between bestand 

worst-blend indexes. Only 19 bps separated the 

best 10-year performance (Dow Jones indexes) from 

the worst (Morningstar indexes). Pragmatically speaking, 

using the U.S. equity blend indexes (large, mid and 

small) from any of the five index providers produced 

comparable, and satisfactory, results. 

Performance at the portfolio level when using growth 

20 


Figure 5 

10-Year Average Annualized Returns And Standard Deviation Of Annual Returns (2002-2011) 

For 12-Asset Portfolio Using US Equity Indexes From Five Major Index Providers 

US Equity Index Providers 

Large, Mid, Small 

(Utilized In The 12-Index Portfolio) 

12-Asset Portfolio Performance 

10-Year Average 

Annualized Return (%) 

2002-2011 

12-Asset Portfolio Risk 

10-Year Standard Deviation 

Of Annual Returns (%) 

2002-2011 

US Equity Value Indexes 

Morningstar 9.15 15.4 

MSCI 9.11 15.4 

Russell 9.06 15.4 

Dow Jones 9.03 15.3 

S&P 8.96 15.3 

US Equity Blend Indexes 


S&P 8.98 15.3 

MSCI 8.98 15.8 

Russell 8.93 15.8 


US Equity Growth Indexes 


S&P 8.98 15.3 

MSCI 8.85 16.4 

Russell 8.72 16.3 


Sources: Morningstar Principia, author calculations 

indexes from the various index providers was identical to 

the blend rankings: Dow Jones was first, S&P/Citigroup 

was second, MSCI was third, Russell was fourth and 

Morningstar was fifth. In this case, the differential in 

performance between first and fifth place was larger than 

when using blend indexes. The Dow Jones growth indexes 

contributed to the 12-asset portfolio in such a way as to 

generate a 10-year annualized return that was 65 bps 

larger than if using three Morningstar growth indexes. 

When using the three growth indexes from S&P/ 

Citigroup, the 12-asset portfolio had a 10-year annualized 

return that was 19 bps behind the 12-asset portfolio 

using Dow Jones growth indexes. However, the S&P 

growth indexes produced a portfolio standard deviation 

of 15.3 percent compared with 16.6 percent using the 

three Dow Jones indexes. This change represents an 8.5 

percent reduction in annual return volatility. Worthy of 

note is the fact that three S&P U.S. equity growth indexes 

(large, mid and small) contributed to the lowest portfolio 

standard deviation of return among the five index providers 

by a fairly sizable margin. 

Summary 

As an industry, too much time is spent arguing over 

interesting issues. Such debates come at the expense of 

more important issues, such as thoughtful asset allocation 

models that encourage broad portfolio diversification 

and better outcomes for investors. Consider the evidence 

in this paper. The classic measure of the performance 

for many investors is large-cap “blend” U.S. equity. Over 

this particular 10-year period (2002-2011), that particular 

“measure” of the market ranged from an annualized 

return of 2.49 percent to 3.44 percent. If investors had a 

portfolio consisting solely of large-cap U.S. equity, their 

10-year experience was very unsatisfactory regardless of 

which index they attempted to mimic. 

Alternatively, consider the returns of multi-index 

portfolios (using U.S. equity blend indexes) over the 

same 10-year period: 8.92 percent to 9.11 percent (from 

Figure 5). During a 10-year span often referred to as 

the “lost decade,” a broadly diversified, multi-asset 

(i.e., multi-index) portfolio produced a very acceptable 

10-year annualized return—regardless of which index 

provider was utilized in the U.S. equity space. In short, 

the recipe is more important than the ingredients. Yet we 

spend too much time fussing over the ingredients and 

too little time building great recipes. 

While the issues of active vs. passive and index variation 

are intellectually interesting, the more important 

issue is helping investors and their financial advisors 

build better investment portfolios. This article illustrates 

that building a multi-asset (i.e., multi-index) portfolio is 

not only interesting, but represents the important and 

relevant “sum of the matter.” 



Which is the better way to view the market? 

By Paul Baiocchi and Paul Britt 

22 


Index providers continue to deliver new ways to access 

equity markets in response to investor demands in a 

highly uncertain climate. Low-volatility and equityincome 

indexes are just a few examples. Yet two basic 

mainstays—economic sector indexes and style indexes— 

remain hugely popular with investors as passive investment 

vehicles, analytical tools and benchmarks. The 

sector pie chart and the style box are the first tools most 

investors reach for—or at least the first tools they see— 

when reviewing any equity market. 

We wanted to take a fresh look at the relevance of 

the top-down sector and bottom-up style frameworks 

in the wake of a financial crisis as markets search for 

some kind of new normal. To do so, we focused on a 

market and an index that’s most familiar to all; namely, 

U.S. equities and the S&P 500 Index. In the hope of 

delivering an overarching view, we left aside analysis 

of how sector and style play across individual marketcap 

size buckets. For brevity, we also did not address 

the topic of sectors and style in international equity 

markets. Country and size effects are the subject of 

much academic debate. Instead, we focused on the 

question, Which is a better way for investors to view 

the market, sectors or style? 

The Sector Perspective 

Investors have been using sector strategies for 

longer than they may realize. In the hysteria of the 

dot-com and housing bubbles, investors allocated 

huge amounts of capital to the sectors benefiting most 

from the trends. In the dot-com bubble that meant 

technology and telecom, and in the housing bubble 

that meant industrials, basic materials and financials. 

Although trillions of dollars of wealth were wiped out 

when both bubbles eventually popped—changing the 

way we look at the markets forever—sector investing 

remains popular. Investors continue to use sectors 

to position themselves to profit from their economic, 

legislative or technological projections, and the media 

continue to cover the market as such. 

tion of projecting what the reaction to changes in the 

economic cycle, legislation or technology will be, they 

should know what rules are used to determine those 

sector boundaries. 

There are currently three widely followed business 

classification systems on the market: the Industry 

Classification Benchmark (ICB); the Global Industry 

Classification Standard (GICS); and the Thomson 

Reuters Business Classification (TRBC). Each has its 

own rules-based methodology for determining how a 

firm should be classified (see Figure 1). While all three 

are similar, little differences play a big role in determining 

how the market is sliced into sectors. 

All three systems start with revenue as the primary 

determinant of classification, but even here there is 

a difference. ICB’s threshold for firm revenue from 

a single industry is just 51 percent, compared with 

60 percent for GICS and TRBC. As the process for 

classifying firms with more than one line of business 

continues, ICB and GICS employ increasing amounts 

of subjectivity in classifying a firm. GICS uses earnings 

and market perception as a means to classify 

companies with two lines of business, while ICB uses 

accounting information and directors’ reports. These 

may seem like minor differences, but they play out in 

significant ways that impact investor returns. 

Take basic materials, for example. Coal companies 

are considered by some to be in the basic materials 

sector, and by others to be in the energy sector, and 

the choice of where coal goes can make all the difference 

in comparing these funds. 

Look no further than the divergence in performance 

over the past year through the end of June 

between the Vanguard Materials ETF (NYSE Arca: 

VAW) and the iShares Dow Jones U.S. Basic Materials 

ETF (NYSE Arca: IYM). Both funds cover the U.S. basic 

materials segment and yet VAW outperformed IYM by 

8.8 percent. The difference? IYM’s index provider uses 

the ICB system, which considers coal to be a basic 

material; VAW’s GICS-based index considers firms 

mining coal to be in the energy sector. Coal got clobbered 

in the past year, as mounting political pressure, 

falling natural gas prices (an easy substitute) and the 

bankruptcy of Patriot Coal weighed on the industry. 

Other examples abound. Is Amazon a technol- 

Figure 1 

Classification Differences 

GICS 

ICB 

TRBC 

Dominant Business 

Segment Threshold 

Two Business Lines 

Three Business Lines 

60% of Firm Revenue 51% of Firm Revenue 60% of Firm Revenue 

Earnings and Accounting Information 60% of Firm Assets or 

Market Perception and Directors’ Reports Operating Earnings 

Earnings and Accounting Information 51% of Firm Revenue, 

Market Perception and Directors’ Reports Firm Assets or Operating Earnings 

Sources: GICS, ICB and TRBC 

Evaluating Sector Classification Methodologies 

To best analyze how sectors have performed over 

time, it is necessary to first outline how they are 

defined. When investors look at sectors with the intenwww.journalofindexes.com 

September / October 2012 23

Figure 2 

TRBC Cyclical Consumer 

Goods & Services 

Company 

Weight 

TRBC Non-Cyclical 

Consumer Goods 

& Services 

Consumer Sectors Top 10 

Dow Jones 

Consumer Goods 

Dow Jones 

Consumer Services 

Company Weight Company Weight Company Weight 

Wal-Mart Stores Inc. 6.73% P&G Co. 13.30% P&G Co. 11.74% Wal-Mart Stores Inc. 6.91% 

McDonald’s Corp. 4.99% Coca-Cola Co. 12.82% Coca-Cola Co. 10.61% McDonald’s Corp. 5.05% 

Walt Disney Co. 4.47% Philip Morris Intl 11.36% Philip Morris Intl 10.13% Walt Disney Co. 4.44% 

Amazon.com Inc. 4.41% PepsiCo Inc. 8.77% PepsiCo Inc. 7.19% Home Depot Inc. 4.23% 

Home Depot Inc. 3.68% Altria Group Inc. 5.25% Altria Group Inc. 4.79% Amazon.com Inc. 4.13% 

Comcast Corp. Cl A 3.53% CVS Caremark Corp. 4.67% Kraft Foods Inc. Cl A 4.60% Comcast Corp. Cl A 3.65% 

Nike 2.27% Kraft Foods Inc. Cl A 4.56% Monsanto Co. 3.02% CVS Caremark Corp. 3.36% 

Starbucks Corp. 2.06% Colgate-Palmolive 3.61% Colgate-Palmolive 3.02% eBay Inc. 2.34% 

Lowe’s 1.99% Walgreens 2.06% Ford Motor Co. 2.28% Costco Wholesale Corp. 2.25% 

Target Corp. 1.97% General Mills 1.86% Nike Inc. Cl B 2.23% Starbucks Corp. 2.21% 

Sources: TRBC and iShares 

ogy company or a retailer? Is Berkshire Hathaway an 

insurance firm or an investment management company? 

Is Visa a consumer stock or a financial company? 

Things get more complicated as you move into the 

consumer space. ICB splits consumer markets into 

goods and services, while GICS and TRBC break it 

down by cyclicality: consumer non-cyclicals or staples; 

and consumer cyclicals or discretionary. Figure 

2 lists the top 10 holdings in the Dow Jones Consumer 

Goods and Consumer Services (ICB) indexes and the 

TRBC consumer cyclicals and non-cyclicals indexes. 

The difference in approach between the two systems 

plays out as you might expect. The Dow Jones indexes, 

which break the market out by goods and services, 

overlap with both the TRBC consumer cyclical and noncyclicals 

indexes. By breaking out the market by the type 

of consumer product as opposed to whether the good 

or service is cyclical or non-cyclical, the index provider 

creates economic overlap between the two sectors. 

Any investor looking to employ a sector strategy 

must understand these differences to ensure the 

exposure they are getting is exactly what they want. 

For our performance analysis, we used the widely 

followed suite of GICS-based indexes published by 

Standard & Poor’s. 

Sector Performance 

The goal of any classification system is to create 

distinct subsets that move differently from one 

another. If the divergence in performance between 

different sectors is great, it will support the case for 

sector analysis as a useful way to parse the market. 

If the differences are muted, the opposite holds true. 

Figure 3 shows the discrepancy between the bestand 

worst-performing sectors over different time 

frames, and as you can see, the divergence has been 

huge. Over the past year, the difference between the 

best- and worst-performing sectors—telecom and 

energy, respectively—was 23 percent. Over the past 

15 years, the difference between the best (energy) and 

worst (financials) return was 277 percent. 

While some might argue this highlights the case for 

long-term buy-and-hold sector strategies, it actually 

forms the basis of a case for more tactical sector use. 

That is supported by the widely variable year-by-year 

performances; the leader in one year can and does 

become the laggard in the next. Figure 4 shows just 

how much these results can vary year to year. Here we 

have the annual performance of each sector from 2002 

to 2012 with each year’s best performer highlighted in 

green and the worst performer highlighted in red. Over 

the past 10 years, each sector, with the exception of 

utilities and industrials, has represented either the best 

or worst performer at least once. This further underscores 

the distinctive performances offered by sectors. 

Of course, the performance of each sector provides 

little information without proper context, which is 

where correlation comes in (see Figure 5). 

Over all time frames studied, there have been consistent 

intersector correlation patterns. For example, 

over the past year, utilities firms have shown very low 

correlation to basic materials and technology firms, and 

muted correlations to consumer discretionary stocks. As 

the time frame expands, utilities show decreasing correlations 

to all sectors. In fact, over the past five years, the 

average correlation between sectors is just 0.68. 

At the same time, the elevated correlations between 

some sectors also highlight how properly defined sectors 

will show logical economic relationships. Consumer staples 

and health care firms—two defensive sectors—have 

shown high correlations to each other, and are the only 

two sectors that have shown average or better correlations 

to utilities. These sectors are all less dependent on 

high rates of economic growth than, say, the industrials 

24 


Figure 3 

S&P 500 

Source: Bloomberg 

Note: All data as of June 29, 2012. 

Consumer 

Discr 

Consumer 

Staples 

Time Period Returns Sectors 

Utilities Financials Telecom Info Tech 

Health 

Care 

Basic 

Materials 

Industrials 

Energy 

1-Year 3.94% 8.49% 13.84% 13.86% -4.42% 14.16% 11.81% 8.42% -8.05% -2.92% -9.06% 

3-Year 57.79% 100.28% 64.91% 51.79% 28.70% 66.09% 65.73% 53.42% 52.50% 73.18% 44.05% 

5-Year 1.09% 21.30% 49.33% 15.52% -54.57% 8.11% 25.12% 20.15% -0.41% -0.79% 4.72% 

10-Year 68.13% 82.48% 102.87% 127.40% -25.90% 110.57% 106.60% 66.34% 95.88% 73.25% 182.19% 

15-Year 100.75% 174.26% 148.30% 173.27% 11.24% 70.96% 104.72% 132.71% 108.76% 106.60% 288.80% 

1 Yr 3 Yr 5 Yr 10 Yr 15 Yr 

Worst 

Performer -9.06% 28.70% -54.57% -25.90% 11.24% 

Best 

Performer 14.16% 100.28% 49.33% 182.19% 288.80% 

Difference 23.22% 71.58% 103.90% 208.09% 277.56% 

Figure 4 

2011 

-2012 


Note: Start and end dates are June 29. 

2010 

-2011 

2009 

-2010 

Yearly Returns Sectors 

2008 

-2009 

Basic Materials -5.98 41.56 13.73 -38.29 6.62 30.04 18.77 5.95 30.99 -8.24 

Consumer Staples 15.71 24.62 13.61 -9.49 0.71 14.19 7.86 3.20 15.77 -7.68 

Energy -6.80 49.64 1.92 -39.96 22.70 27.77 23.75 41.41 30.19 -7.43 

Industrials 0.44 35.04 26.67 -33.17 -13.60 17.29 14.44 5.85 28.67 -4.47 

Utilities 15.68 22.53 5.75 -26.08 4.26 26.44 5.77 38.55 11.14 -4.41 

Consumer Discr 11.68 37.60 29.04 -17.19 -26.13 19.24 2.20 5.54 18.39 -1.21 

S&P 500 6.52 28.09 14.61 -25.49 -13.23 20.34 8.09 7.53 18.41 0.43 

Financials -2.33 11.20 16.88 -39.22 -41.11 14.17 12.20 6.88 18.39 0.62 

Telecom 16.60 36.04 3.46 -15.24 -22.28 39.63 11.46 10.68 6.03 6.65 

Info Tech 15.38 22.03 16.92 -18.62 -6.60 24.71 0.88 -2.19 24.48 7.80 

Health Care 10.17 27.11 9.05 -10.00 -12.58 19.15 -2.28 4.19 4.56 9.14 

2007 

-2008 

2006- 

2007 

2005 

-2006 

2004- 

2005 

2003 

-2004 

2002 

-2003 

or consumer cyclicals sectors. 

The convergence of returns among the more cyclical 

sectors of the market—technology, materials, consumer 

discretionary and industrials firms—are much 

more dramatic. Not only are correlation pairs between 

these sectors above average, they reach as high as 

0.911 (industrials and consumer discretionary). 

Of course, every sector is affected by different economic 

indicators, but there is also significant overlap. 

Properly defined sectors should therefore show high 

intersector correlation between segments of the economy 

whose economic exposures are similar. For example, 

energy and basic materials are highly dependent 

on global GDP rates, construction spending, and mining 

activity. We would therefore expect the two sectors 

to behave similarly. This is exactly what we have seen, 

as the intercorrelation between the two sectors did not 

drop below 70 percent in any of the periods of study 

and was as high as 91 percent over the past year. These 

correlation groupings provide an outlet for investors 

to express their opinion about the economy and the 

markets. But these relationships are dynamic. One way 

to show this is by charting the correlations on a rolling 

basis, as shown in Figures 6a and 6b. 

As the market and economy have ebbed and flowed, 

so has the correlation between technology and the 

rest of the market. This tells a story about the market 

and the economy. Negative correlations do not persist 

over time, but on a rolling basis, different sectors will 

show negative correlations with each other. These 

occurrences, while fleeting, are immensely valuable 

to investors as they allow for true risk diversification. 

At the height of the tech bubble, technology actually 

had negative correlations to energy and materials 

firms, and during the subsequent market sell-off, its 

correlation to all sectors normalized. 

This underscores a common theme in the period of 

study: During times of market stress, correlations all 


Figure 5 

Industrials 

Source: Bloomberg, as of June 29, 2012 

Consumer 

Discr 

S&P 500 Sector 5-Year Monthly Correlations 

Consumer 

Staples 

Utilities Financials Telecom Tech 

Health 

Care 

Materials 

Industrials – 0.911 0.801 0.574 0.877 0.629 0.835 0.733 0.894 0.708 

Consumer Discr 0.911 – 0.739 0.498 0.868 0.598 0.857 0.699 0.843 0.595 

Consumer Staples 0.801 0.739 – 0.637 0.742 0.633 0.699 0.787 0.706 0.580 

Utilities 0.574 0.498 0.637 – 0.416 0.706 0.614 0.628 0.586 0.601 

Financials 0.877 0.868 0.742 0.416 – 0.481 0.752 0.693 0.781 0.539 

Telecom 0.629 0.598 0.633 0.706 0.481 – 0.622 0.491 0.588 0.483 

Tech 0.835 0.857 0.699 0.614 0.752 0.622 – 0.697 0.858 0.706 

Health Care 0.733 0.699 0.787 0.628 0.693 0.491 0.697 – 0.701 0.534 

Materials 0.894 0.843 0.706 0.586 0.781 0.588 0.858 0.701 – 0.788 

Energy 0.708 0.595 0.58 0.601 0.539 0.483 0.706 0.534 0.788 – 

Average 0.6824 

Energy 

Figure 6a 

Figure 6b 

Rolling Correlations To Info Technology 

Rolling Correlations To Info Technology 

Rolling Correlations 

1.5 

1.0 

0.5 

0.0 

– 90 

– 80 

– 70 

– 60 

– 50 

– 40 

– 30 

– 20 

– 10 

– 0 

VIX Level 


1.5 

1.0 

0.5 

0.0 

– 90 

– 80 

– 70 

– 60 

– 50 

– 40 

– 30 

– 20 

– 10 

– 0 

VIX Level 

-0.5 

-0.5 

-1.0 

6/26/98 

■ Basic Materials ■ Energy 

■ Consumer Discr ■ Financials ■ VIX Level 

6/29/12 

-1.0 

6/26/98 

■ Telecom ■ Consumer Staples ■ Industrials 

■ Health Care ■ Utilities ■ VIX Level 

6/29/12 



converge to 1. While this impairs an investor’s ability 

to diversify away market risk during these periods, it 

is also a predictive piece of information for investors. 

Further, these correlation convergences do not persist 

over time, moving away from 1 as a crisis abates. 

Looking at Figure 7, we see that during the 2008-2009 

financial crisis, the performance correlation of all sectors 

spiked to 1 but normalized as the economy moved 

out of the recession. 

Even when we measure sectors individually against 

the market—in this case, the S&P 500—we see a wide 

range of correlations. And just as with intersector correlations, 

each sector’s relationship with the market 

changes over time. Over long horizons, each sector 

has a lower correlation to the market, which is logical. 

Since the S&P 500 is a roll-up of the firms in each 

sector, the changes in the importance and influence 

of each sector over time is represented by changes 

in each sector’s weighting in the index. When a sector 

like energy becomes an increasingly significant 

portion of the market, it follows that it will have an 

increasing correlation to the broad market. Between 

1998 and 1999, technology went from 17.7 percent of 

the S&P 500 all the way up to 29 percent before falling 

to 14.3 percent two years later. This coincided with a 

steep rise and fall in its correlation to the market. 

The financial crisis provides a good reference point 

for sector correlations as well, and the data seems to 

support the commonly held belief that correlations 

across all assets classes—including between sectors of 

the equity universe—have spiked. The data also show 

that this may be changing. Over the past 15 years, sectors 

have shown extremely low correlations between 

each other, with an average of 0.50 and a high of 0.85 

(industrials and consumer discretionary). Over the 

past 10 years, it is also low, but the average has crept 

up to 0.61, with the highest correlation at 0.88. Over the 

past five years, that average correlation among sectors 


Figure 7a 

Figure 7b 

Rolling Correlations To S&P 500 

Rolling Correlations To S&P 500 

S&P 500 

1.5 

1.0 

0.5 

0.0 

– 90 

– 80 

– 70 

– 60 

– 50 

– 40 

– 30 

– 20 

– 10 

– 0 

VIX Level 

S&P 500 

1.5 

1.0 

0.5 

0.0 

– 90 

– 80 

– 70 

– 60 

– 50 

– 40 

– 30 

– 20 

– 10 

– 0 

VIX Level 

-0.5 

-0.5 

-1.0 

6/26/98 

■ Basic Materials ■ Energy ■ Info Tech 

■ Consumer Discr ■ Financials ■ VIX Level 

6/29/12 

-1.0 

6/26/98 

■ Telecom ■ Consumer Staples ■ Industrials 

■ Health Care ■ Utilities ■ VIX Level 

6/29/12 



climbs all the way to 0.68, with eight different sector 

correlations above 0.85. The past three years had an 

even higher average correlation among sectors, but 

in the past year, that figure has dropped back down 

to 0.62. Clearly, the ability to effectively use low correlation 

pairs is compromised in times of significant 

financial stress, but it seems the further removed we 

get from the financial crisis, the more pronounced the 

divergence among returns is. 

How the various sectors move in relation to each 

other and to the market is just part of the story. We 

must also analyze how much volatility each sector has 

shown historically and whether that has changed over 

fine-tune their risk exposure in various market environments. 

The recent wave of high- and low-beta and 

volatility index strategies is a logical extension of these 

sector risk profiles. Whereas index providers and ETF 

issuers are looking to provide new ways to slice the 

market, for investors focused on risk as opposed to 

exposure, sectors already allow them to do this. 

In all, the intuitive nature of sectors lines up with 

statistical evidence. Over the past 15 years, with 

the exception of times of financial stress, sectors 

have shown all of the necessary characteristics to 

prove how valuable they are in asset allocation. Their 

returns vary greatly, their individual performances 

Investors have long used a value and growth perspective to parse 

the market in a different way than sectors. The problem is that the 

line between growth and value is blurry, and the overlap in exposure 

between high- and low-correlated sectors diminishes the efficacy 

of this strategy as a way of segmenting the market. 

time. As we would expect, the defensive sectors—consumer 

staples, utilities and healthcare—all showed 

20 percent or less annualized volatility over all time 

frames. On the opposite end are financial and energy 

firms, which showed volatility in excess of 25 percent 

over all periods of study. Recent history bears this out. 

Financials were the hardest-hit sector through the 

financial crisis of 2008 and the recent European debt 

crisis. Meanwhile, energy prices spiked in 2007, only 

to come crashing down to earth before spiking again. 

This distribution of risk among the sectors offers 

even more information to investors attempting to 

show economically logical relationships, and they 

exhibit distinctly different volatility patterns. 

The Style Perspective 

Investors have long used a value and growth perspective 

to parse the market in a different way than sectors. 

The problem is that the line between growth and 

value is blurry, and the overlap in exposure between 

high- and low-correlated sectors diminishes the efficacy 

of this strategy as a way of segmenting the market. 

Investors of all sizes have made style-based investing 

hugely popular over the years. The stereotypical 


27

Figure 8 

Figure 9 


1.1 

0.9 

0.7 

0.5 

0.3 

0.1 

-0.1 

-0.3 

-0.5 

-07 

-0.9 

6/26/98 


Pure Growth And Pure Value Correlations 

1 Year 3 Year 5 Year 10 Year 15 Year 

0.916 0.901 0.895 0.879 0.711 


Note: Weekly returns as of June 29, 2012. 

Growth And Value Rolling Correlations 

■ S&P Pure Value Vs Pure Growth ■ VIX Level 

■ S&P Pure Value Vs S&P 500 ■ S&P Pure Growth Vs S&P 500 

6/29/12 

– 90 

– 80 

– 70 

– 60 

– 50 

– 40 

– 30 

– 20 

– 10 

– 0 

value investor wants to buy stocks that are lower in 

price or are otherwise out of favor. Value investors 

might also want to see consistent positive earnings, 

especially when regularly paid out as dividends. A 

growth investor is willing to pay a higher relative stock 

price with the goal of latching on to the next Apple 

Computer, i.e., a stock with huge price appreciation 

driven by earnings growth beyond expectations. 

Style indexes use a variety of fundamental measures 

to describe stocks as value or growth oriented. 

These include price/book, as mentioned above, as 

well as price/earnings, price/sales and price/cash 

flow. In addition, indexes often include stock price 

momentum and various growth rates, using historical 

and forward-looking estimates. 

When stocks are screened by these metrics, results 

don’t always fall into neat buckets that clearly indicate 

value or growth. Index designers need to decide 

what to do with muddled results from firms that sit 

in the middle gray zone between the two extremes. 

Some indexes choose to split a stock’s weight across 

the value and growth buckets, with the benefit being 

growth and value indexes that roll up into a complete 

picture of the market. Other indexes assign the stocks 

that don’t show strong style biases into a separate 

core bucket, leaving the growth and value indexes 

with more “pure” components. 

These core stocks—those firms that do not show a 

VIX Level 

pronounced growth or value bias based on the aforementioned 

metrics—muddy the growth and value picture. 

Because they show characteristics of both growth 

and value, they end up detracting from the ultimate 

goal, which is to separate the market into two distinct 

exposure groups. The removal of these firms should 

exaggerate the difference between growth and value. 

The problem is that there is still too much ambiguity 

and disagreement over what makes a company a 

growth or value firm. 

This overlap problem is unique to style. Sectors 

have no such problem. An energy firm is an energy 

firm and a technology firm is a technology firm. Sure, 

there are edge cases like Amazon, but ultimately you 

know—for the most part—what an energy firm is. 

A growth firm? It is just not an intuitive concept. 

You could ask 10 different people what Chevron is, 

you will get 10 identical answers: It’s an energy firm. 

Ask the same 10 people if Chevron is a growth or value 

firm and you may get 10 different answers, along with 

some quizzical looks. 

For our purposes, we chose to look at the pure style 

approach—the S&P 500 Pure Growth and Pure Value 

Indexes—in an attempt to measure the performance 

of indexes pulled from the same universe as our sector 

indexes and that emphasize style. 

Style Performance 

Our data suggest correlations between the style 

indexes are high, and increasing (see Figure 8). We 

see higher correlations over more recent periods. 

Correlations steadily increase as we extend the lookback 

period. Regardless, they are higher by any measure 

than most pairwise sector correlations. 

The most dramatic difference occurs between the 15- and 

10-year periods, where correlation increases from 0.71 to 0.88. 

These summary figures don’t capture the full 

story, however. Correlations vary significantly over 

time, as the graph of one-year rolling correlations 

shows in Figure 9. In the recent past, we see correlations 

approaching 1 during the financial crisis, then 

decreasing in 2010 as markets recovered and shifted 

away from the risk-on/risk-off mentality, and most 

recently increasing again as economic recovery falters 

in the U.S. and debt worries dominate in Europe. 

Regardless, the trend is clear: a sharp spike in average 

correlations, with fewer periods of significant dispersion. 

Pure growth, in particular, has enormously high 

correlations to the S&P 500, over virtually any period 

studied, suggesting that any diversification benefit 

lies in the value portion of the equation. 

Significantly lower correlations exist in brief periods in 

late 2007 and summer of 2006, but clearly the most dramatic 

divergences between the two style indexes occur in 

the 1999 to 2002 period, which saw tech boom and bust. 

Investors looking for a clearly different pattern of 

returns between the two style indexes going forward have 

little to hang their hat on here. Recent history shows that 

28 


Figure 10 

Total Return 

300% 

250% 

200% 

150% 

100% 

50% 

0 

-50% 

6/27/97 


Growth And Value 15-Year Total Return 

■ Growth ■ Value 

6/29/12 

return patterns have diverged occasionally; sometimes by 

a great amount. But accessing this divergence seems more 

akin to tactical rather than strategic allocation—timing 

the market, in other words. As with the sectors, the style 

correlations tend to converge during periods of high volatility 

and market stress, which makes intuitive sense. If the 

companies in each sector are moving increasingly in lock 

step with each other, it stands to reason that portfolios 

cutting across these sectors would as well, regardless of 

their exclusion of a portion of the market (core). 

Correlations are only part of the picture. Risk/return 

measurements provide more insight into recent history 

of the pure style indexes. 

The low correlation over the 15-year period, driven 

in part by the low and negative correlations seen in 

the 1999 to 2002 range, might lead one to expect that 

returns over the 15 years would differ greatly from 

value to growth. In fact, the compound annualized 

returns differ by only 13 bps, with value at 8.0 percent 

and growth at 8.1 percent. The cumulative return chart 

for the period highlights the different paths the two 

indexes took to the same end (see Figure 10). 

Growth returns have exceeded those of the value index 

in four of the five periods we looked at, most notably 

in the five-year period, where growth beat value by 6.4 

percent annualized. Growth avoided the worst of the 

2007-08 decline in financials (financials-dominated value 

bottomed out at a frightening -74 percent for the five-year 

cumulative return in March 2009), and gained more in 

the recovery that followed, led in part by the growthoriented 

tech and consumer-cyclical sectors. 

Relative to the market itself, returns for growth as 

well as value exceeded those of the S&P 500 in three 

of five periods, lagged it in one and split in one (see 

Figure 11). The split was in the five-year period where 

the S&P 500 was essentially flat, but growth beat both 

the market and value handily mostly due to its underexposure 

to financials as described above. The style 

indexes lagged the market in the one-year period, with 

growth dragged down by materials and industrials and 

value by financials and energy. 

Growth returns showed less volatility than value 

over four of the five periods too. The difference in 

annualized standard deviation of weekly returns was 

greatest in the five-year period, where the volatility of 

the financial sector (43.4 percent) drove value volatility 

higher than that of growth. 

More interesting perhaps is that volatility of both the 

style indexes exceeds that of the market in every time 

period we looked at. Volatility in growth-oriented sectors 

came from consumer cyclicals and tech, while volatility 

on the value side was driven by financials and energy. 

We ran regressions of growth and value on the S&P 500, 

but the fit of the estimates to the data, especially for the 

longer time periods, was insufficient for us to feel comfortable 

with them. Still, the regressions generally hinted 

at higher beta for both pure style indexes, consistent with 

the higher return and higher volatility shown above. 

Sectors Vs. Style 

Style investing remains hugely popular, with hundreds 

of billions of dollars parked in style-indexed mutual 

funds, ETFs and other vehicles. And the ubiquitous style 

box reinforces style’s primacy as a means to describe 

the market. But the pioneering style investors we mentioned 

at the outset weren’t necessarily style index investors. 

Arguably the greatest living value investor—Warren 

Buffett—picks firms by hand, not by the bucket. 

Perhaps a better question is whether style invest- 

Figure 11 

Growth And Value Annualized Returns And Standard Deviation 

Returns 

Standard Deviation 

Pure Value Pure Growth S&P 500 Pure Value Pure Growth S&P 500 

1 Year -5.5% -1.8% 3.9% 26.2% 25.8% 21.8% 

3 Year 23.2% 22.3% 16.4% 24.7% 22.6% 18.3% 

5 Year -1.39% 5.06% 0.2% 35.9% 26.9% 23.8% 

10 Year 6.8% 9.5% 5.3% 27.8% 22.1% 19.3% 

15 Year 8.0% 8.1% 4.8% 24.7% 25.5% 19.5% 


Note: Data as of June 29, 2012 


29

Figure 12a 

S&P 500 

Consumer 

Discr 


Note: Data based on calendar years 2002-2011 (10-Yr Av) and 1998-2011 (14-Yr Av). 

Annualized Weekly Standard Deviation Deviation, Sectors 

Consumer 

Staples 

Utilities Financials Telecom Info Tech 

Health 

Care 

Basic 

Materials 

Industrials 

Energy 

10-Yr Av 17.80% 21.15% 12.98% 17.93% 27.28% 19.81% 23.13% 16.47% 24.30% 20.46% 24.46% 

14-Yr Av 18.61% 22.32% 14.77% 18.46% 27.81% 20.92% 27.77% 18.22% 25.06% 21.53% 24.64% 

Figure 12b 

Annualized Weekly Standard Deviation, Style 

S&P 500 Pure 

Value 

S&P 500 Pure 

Growth S&P 500 

10-Yr Av 23.98% 20.94% 17.80% 

14-Yr Av 22.22% 24.12% 18.61% 


Note: Data based on calendar years 2002-2011 (10-Yr Av) and 1998-2011 (14-Yr Av). 

ing is most useful as a discipline for active managers 

selecting relatively small portfolios of firms. Style 

provides a bottom-up framework to view the market, 

which active managers can refine with their own experience 

and insight. Style indexes provide managers and 

their clients with an all-important benchmark. But is 

the benchmark itself worth holding? 

Bogle, Bernstein and others tell us to own the market. 

They don’t tell us to own half the market. Yet many 

broad-based style indexes do just that, carving out exactly 

half the market. Pure style indexes such as those we used 

in this study offer more precision than exhaustive style 

and growth pairs, but those who look at the market with 

a bottom-up, firm-level analysis—style’s wheelhouse— 

might like to see index tools with even less breadth. The 

increase in the precision of style indexes in recent years 

focuses on adding more factors, but these factors still 

doesn’t mean right and “abstract” doesn’t mean wrong, 

but a more transparent framework for allocation should 

lead to better decisions. If investors understand the bets 

they’re making, they should have a better sense of when 

to stay the course and when to try a different tack. 

Investors who come to index investing from a risk perspective 

rather than an economic one might reach for 

style more on reputation than reality. Perhaps they’d think 

a growth fund suits a younger investor with a long time 

horizon, while a value index works better for a pensioner’s 

equity exposure. Our data show, however, that both value 

and growth have been more volatile than the market in 

each of the five time periods we looked at (see Figure 11). 

Yet as mentioned above, certain sectors have shown 

consistently low volatility (consumer staples, health 

care, utilities) just as other sectors have shown consistently 

higher volatility (financials, energy, materials). 

This suggests that sector exposure may be a more effective 

way for investors to find the desired spot on the 

risk/reward spectrum (see Figures 12a and 12b). The 

S&P 500 Low Volatility Index behind the wildly popular 

PowerShares SPLV ETF shows strong biases to these 

low-volatility sectors, while exhibiting mixed style messages 

(growthlike high P/E and P/B mixed with a valuelike 

high-dividend yield). 

Can style indexes be viewed as aggregates of sector 

exposure? Exhaustive style pairs hold the entire market 

between them, and so must hold all the sector stocks. 

“Intuitive” doesn’t mean right and “abstract” doesn’t mean wrong, but a 

more transparent framework for allocation should lead to better decisions. 

serve to identify half or one-third of the market at a time. 

Meanwhile, investors with long- or short-term views on 

the economy will likely be frustrated with the bluntness of 

the style index choice, pure or otherwise. Sectors provide 

investors with far greater precision to express top-down 

macro views. Moreover, viewing the market by sectors 

aligns with how investors—and everyone else—interact 

with the world intuitively on a day-to-day basis. Investors 

can name top tech firms by looking at the logo on their 

smartphone, and they identify energy companies and 

consumer stocks in a similar manner. Viewing the market 

by growth and value is far more abstract. “Intuitive” 

Style indexes certainly show bias toward certain sectors, 

as we’ve highlighted. Yet the aggregation of sectors into 

style indexes is far from neat. Industrials and basic materials, 

for example, are split about equally between the pure 

growth and value indexes. Pure style indexes leave out 

about one-third of the market, which undercuts their ability 

to reflect groups of sectors, even if they were perfectly 

defined. In the end, style indexes are engineered to select 

and weight stocks by fundamental and momentum factors, 

not by industry exposure. A value fund captures financial 

stocks by their low P/Bs (from high book values) but also 

snares energy stocks with low P/Es (from high earnings). 

30 


However, some firms from these two sectors exhibit different 

ratios that park them in the pure growth index (which 

has roughly 10 percent combined financials and energy). 

Style funds work with discrete buckets sizes. Exhaustive 

style pairs split the market 50/50. Style-pure indexes split 

the market in thirds. A stock migrates over boundaries 

because its attributes change (a higher P/B ratio forces 

it out of value and into core perhaps) or because the 

attributes of other stocks get stronger and force it out of 

the bucket. In this sense, the indexes remain true to their 

style mandate while the names inside it change. A pure 

growth fund will always represent the “growthiest” onethird 

of the market, even if the nature of the constituents 

changes (e.g., less tech, more health care). 

has companies operating in both the utilities and technology 

sectors doesn’t allow investors to position themselves 

according to their economic expectations. While it could be 

argued that it is precisely this combination of low-correlated 

sectors in the same portfolio that allows investors to lower 

overall market risk, it also prevents them from realizing the 

full potential of either strategy. 

If you look at sectors as different pieces of the total 

market puzzle, then it stands to reason that each sector’s 

influence on the market is limited to its relative weighting 

in the market composite. As such, each sector will have a 

different correlation to the broad market. It stands to reason 

that each sector’s correlation to the market is therefore 

fluid, changing as the economy evolves and changes. 

What we can say is that sectors do a much better job 

than style groups of deconstructing the market’s risk. 

A sector fund stays true to its mandate as well, but its 

constituents tend to stay put. A tech firm, for example, stays 

in a tech index as it journeys from brash startup to cash-cow 

dividend payer. In contrast, the same firm might migrate 

out of a growth index and into a core index (or have its 

weight partly assigned to value). The tech fund’s footprint 

in the market—the relative size of each sector’s bucket, in 

other words—can vary dramatically over the long run, as its 

aggregate market value of equity ebbs and flows. 

Takeaways 

The case for the tactical use of sectors not only makes 

intuitive sense, it is supported by the data. Top-down 

investors attempt to distill the macroeconomic environment 

into projections about how that will impact the 

economy. In doing so, they break the market out into 

sectors in an attempt to focus on how changes in the 

economic, political or technological landscape will affect 

every pocket of the economy. 

Since each segment of the economy reacts to these 

dynamics in different ways, sectors serve as the perfect 

outlet for these views. By all typical measures of 

risk and return, sectors have recently done a better 

job of parsing the market than size and style strategies. 

They also do a better job reflecting the cyclicality 

of a firm’s operations and their leverage to economic 

growth. What constitutes a growth or value firm is not 

always clear without a deep dive into financial statements, 

whereas determining which sector a company 

operates in is a relatively simple task. There are always 

exceptions, as we highlighted above, but the differences 

between different sectors are much clearer than 

the difference between growth and value. 

As the data illustrate, the correlations between utilities 

firms and most sectors, especially technology and consumer 

discretionary, have been extremely low over all time frames. 

With this in mind, holding a growth or value portfolio that 

Of course, modifications to the market portfolio will 

impact correlations as well. An equal-weighted portfolio 

increases the weighting of the utilities and telecom 

sectors—two sectors that carry the lowest weight in the 

cap-weighted S&P 500. They are also two of the sectors 

with the lowest correlations to the market. As their 

influence on the market portfolio increases, so will 

their correlation to it. 

None of this is to say that timing sectors is easy. Being 

able to absorb all available information and effectively project 

its impact on each sector of the economy is an extremely 

challenging endeavor. The rolling correlation data that we 

discussed earlier highlights just how volatile intersector 

correlations were over the past 15 years. In times of stress 

they converge, and during some periods negative correlations 

pop up. Sector strategies must hit a moving target. 

What we can say is that sectors do a much better job 

than style groups of deconstructing the market’s risk. 

At no point over the past five years has the difference in 

volatility between growth and value exceeded 10 percent, 

and in all but one of the periods of study it has been less 

than 6 percent. In fact, both growth and value strategies 

were more volatile than the broad market over the past 

15 years. Style indexes have therefore proven to be a poor 

tool for portfolio risk management. 

On the other hand, sectors have shown a wide range 

of volatilities over time, which allows investors to more 

finely tune their portfolio to fit their risk profile. Over 

all of the periods of study, the volatility differences 

between the most and least volatile sectors were amazingly 

consistent over time, remaining above 15 percent 

and even spiking as high as 28 percent over the past 

10 years. Once again, the big volatility differences follow 

logical patterns. Defensive sectors like consumer 

staples, utilities and health care have been significantly 

less volatile than pro-cyclical sectors like industrials, 

energy and consumer discretionary. 


31

An Industry Considered 

A Mainstay Of Indexing 

Surveys The Landscape 

Thoughts on the evolution of the index industry 

John Prestbo, formerly editor of Dow Jones Indexes and 

instrumental in the index provider’s founding, chatted with 

the Journal of Indexes recently and offered his views on the 

current state of the indexing industry. 

Journal of Indexes: How has indexing changed since you 

were helping to get Dow Jones Indexes off the ground? 

John Prestbo (Prestbo): It has become a lot more populated: 

There’s more competition, and there are also more 

users of index products. It’s grown considerably. Another 

change is that when we started Dow Jones Indexes, the ETF 

space was primarily basic benchmark indexes. But now it’s 

increasingly diverse with leveraged and inverse and strategy 

and active. There are a lot more products out there for 

investors to choose from. 

JOI: Do you think the increasing diversity in the index 

industry is actually leading to stronger, more useful 

products for investors in general? 

Prestbo: Yes; the thing about innovation is that not everything 

is a home run in terms of gathering assets or in terms 

of simple utility in an investment program. But ideas need 

to be spawned freely and tried out, and if they don’t work, 

you shut them down and go on to something else. We’ve 

seen a lot more of that lately, too. Everything was treasured 

and precious in the beginning, and now it’s “spit them out 

and see what happens.” 

Sometimes, a little more thought could be given to the 

“spitting out” part, but I’d rather have it that way than 

restricted thinking. 

JOI: Is the evolution of the ETF industry driving the 

index industry’s growth, or is the index industry driving 

the ETF industry’s growth? 

Prestbo: The ETF industry has been the driver, because 

that’s where the money is—or at least that’s where the 

growing amounts of money are. Obviously, the vast bulk is 

still in the mutual funds, but ETFs are growing strongly— 

and the SEC requirement in the beginning that ETFs be 

based on indexes was clearly a boon for the index business. 

That is partly why we’ve seen a reinvigorated bunch 

of index providers and also why we see all this creativity 

on the index design front. 

JOI: What are the index innovations in the past years that 

you think are the most useful or the most interesting? 

Prestbo: Bringing indexing to commodities was a great 

innovation. I know that the GSCI index goes back quite 

a ways, but it didn’t meet with instant success. The ETF 

boom kind of brought commodity indexing to the forefront 

and made access to that market particularly easy for a lot 

of investors. They basically could trade their exposure to 

commodities like a stock in an ETF vehicle. 

Currencies are another asset class that has become 

much more available through stocklike treatment, which 

is really what most investors need in order to handle a new 

asset class. Real estate—same thing. The asset class innovation 

was a good thing, I think, for investors as a whole. It 

opened up avenues that didn’t exist before. 

More recently, we’ve had strategy indexes. Now, some of 

those were available very early on, because growth and value 

are strategies that come to us from the good old days. But we’ve 

moved beyond that now to indexes that take you in and out 

of the market depending on what’s happening with volatility 

or some other factor. We’ve got target-date indexes that move 

people through their life span with investing in a mix of markets: 

stocks, bonds and whatever. Those are all great ideas. 

Now we’re into an era where there’s all sorts of convolu- 

32 


tions. Just about every index provider has offerings in this 

arena—volatility control and who knows what’s coming 

down the pike? In fact, Dow Jones just put out two index 

families from professional investors that have developed 

strategies and written books about them, and now, put 

them into index form. We’re pushing the envelope toward 

rules-based active investing. Will this be good for everybody? 

Absolutely not, but it’s good for some people. A 

market exists for all kinds of investment approaches, but 

they won’t necessarily become big blockbusters. 

JOI: When do you think an index becomes too active? 

Prestbo: It becomes too active if the investor can’t anticipate 

what’s going to happen next with the index in terms 

of its composition, weighting or whatever moving parts 

are involved in it. The essence of active investing is you 

give your money to somebody and they play with it 

without you knowing what they’re doing precisely. So 

far, all of the “active-ish” or strategic indexes have rules 

that theoretically you could follow and produce a similar 

result. As long as there’s that transparency, then I think 

you’re still safely in the indexing realm. Once you get into 

trusting, then you’re in the active realm. 

JOI: Where do you stand on alternatively weighted or 

fundamentally weighted methodologies? 

Prestbo: I think there are only two things in indexing that 

really make a difference. One of them is selection of the 

components and the other is weighting of those components. 

What fundamental indexing does, simply, is take the 

selections and weight them according to something other 

than market cap or price or whatever. It’s just a different 

scale on which to arrange your weighting schematic. 

I have no problem with the concept. What I think is not 

totally clear is the relationship between those weighting 

schemes and market performance, and I think that’s simply 

because we haven’t had enough time yet and enough different 

market conditions to test the concept. I have no problem 

with the theory. The practice, I think, is still to be proven. 

JOI: Are there any trends within indexing or indexbased 

investing that you think need to die quick deaths 

or that you see as potentially harmful to investors? 

Prestbo: I have nothing against the proliferation of indexes—notice 

how carefully I said that—nonetheless, I think 

the danger is that investors will get themselves into something 

that they really don’t know how to handle. The most 

prominent example of that so far has been the inverse 

ETFs. Those are daily trading vehicles that are not a buyand-hold 

kind of investment. I know that doesn’t have 

much to do with indexing, but it does in a way, because 

they’re taking brand-name indexes and saying, “When 

they go down, you can make money, too.” That’s very 

appealing to people, but they don’t know how to use them, 

or they learn how to use them in a very expensive way. 

I think the danger is that a lot of products will be 

launched that will be appealing to certain people who have 

no way of putting them in their proper place within their 

portfolios because they don’t understand them. The more 

convoluted and esoteric these index vehicles become, 

the greater the danger that more people will slip on the 

banana peel and say, “Indexing is verstunken—look what 

happened to me.” But they just didn’t use them correctly. 

JOI: Is all the talk about volatility just hype, or do you 

think investors should really care about it? 

Prestbo: Volatility is just another name for risk. Risk is 

lots of things, but volatility is one form of risk—and it’s the 

most easily quantifiable form. Yes, I think investors ought 

to be aware of it, but I don’t think they ought to be scared 

to death of it, because risk is what enables them to make 

money. If everything were risk free, there would be no 

returns. Risk is not the enemy—it just has to be managed. 

And managed how? Well, managed in relation to your own 

risk tolerance, whether that be emotional or reality based, 

because of your time frame, or something else. You just 

have to be aware of it and construct your portfolio accordingly. 

But without risk, there’s no investing. 

JOI: Some people feel the Dow Jones industrial average 

is becoming irrelevant when compared with broader 

indexes like the S&P 500. How do you answer that? 

Prestbo: Every index has a purpose—or should have, let’s 

put it that way—and a way of achieving that purpose. The 

purpose of the Dow Jones industrial average is to be a shorthand 

indicator of the market. Is it a benchmark in the commonly 

understood sense of the word? No. It’s 30 stocks. But 

that is, in fact, the purpose of the index: to use 30 well-chosen 

stocks of leading companies in their respective industries as a 

microcosm of the broader market. That’s its purpose—always 

has been since Charles Dow invented it. He invented it in an 

era when there weren’t S&P 500s or Russell 3000s or Wilshire 

5000s or any of those. For many, many decades it was the beacon 

that people looked at to see what the market was doing. 

Now there are many other choices of indexes. But the 

Dow has a place in that pantheon because of its nature. 

The shoreline is all populated with lighthouses now. It 

used to be the only one. Now there’s a whole bunch of 

them. But that doesn’t make any one of them irrelevant; it 

just means you use it differently than you used to. 

JOI: What do you currently see as the unexplored territory 

in indexing? What’s left to index? 

Prestbo: I think bonds, as an asset class, are still pretty 

“benchmarky” in nature, and there are strategies in bonds 

that can be explored. One of the things that needs to be 

fixed in bonds is transparency of pricing. Wall Street has 

had that little cash cow for years, and I think it ought to be 

taken away from them. 

I think there are other asset classes that can be gotten 

at one way or another. We’ve seen, for example, these 

hedge fund replication strategies. We don’t know if they 

work, but the idea is interesting because it gives you 

access to active managers that you wouldn’t otherwise be 

able to touch with a modest amount of money. 

continued on page 51 



The implications for portfolio construction 

By Christopher Philips, David Walker and Francis Kinniry Jr. 

34 


It’s common to hear of the value of diversification during 

uncertain or volatile markets. Indeed, a broadly 

diversified, balanced portfolio is unlikely to perform 

as poorly as a portfolio focused entirely on stocks, if stocks 

enter a bear market or experience seemingly abnormal 

volatility. Perhaps this is a primary reason the market 

environment since the recent global financial crisis has 

spawned such disappointment and a perception that 

diversification no longer works. For instance, since 2008, 

most risky asset classes have seemingly moved in lock 

step, with correlations to U.S. equities over the past 

three years ranging from 0.6 (for commodities) to 0.93 

(for developed international markets). Indeed, only U.S. 

Treasury bonds have proven to be a true diversifier, correlating 

at -0.3 to U.S. equities. 

Although carefully examining correlation is critical to the 

process of portfolio construction, great care must be exercised 

in using correlation as the foundation for a portfolio’s 

construction. Correlation is a statistical measure, subject to 

estimation error, and correlations among assets can vary 

both over time and in different circumstances. And, as the 

recent market environment has shown, many risky assets 

can and do perform similarly during periods characterized 

by risk aversion and a general flight to quality. 

So what can investors do with this information? How 

can they ensure that a portfolio is properly diversified? 

This article discusses what correlation does and does not 

mean for diversification, the implications of dynamic 

(that is, changing) correlations, the risk of relying on 

historical correlations during a flight to quality, and the 

benefit of focusing on fixed-income instruments as a 

source of consistent diversification benefit to mitigate the 

near-term risk of the equity markets. 1 

Defining Correlation 

Correlation is a measure of the tendency of the returns 

of one asset to move in tandem with those of another 

asset. In other words, two assets that are “uncorrelated” 

could be expected to show no systematic, linear relationship 

between their returns over time. By combining 

uncorrelated assets, the movements of one asset can be 

expected to at least partially mitigate the movements of 

the second asset, reducing the average volatility of a portfolio. 

The first half of this paper examines the impact of 

correlations on portfolio construction and examines how 

correlations can change over time. 

Although most investors have long-term investment goals, 

they are particularly averse to large losses, even over the short 

term. The second half of our analysis thus looks closely at 

what happens to correlations and, ultimately, diversification 

during periods of severe market stress. At such times, diversification 

benefits can seem to vanish among some assets with 

low long-term correlation, while the diversification benefits 

of other assets may become more apparent. 

Setting The Baseline: What Does Correlation Tell Us? 

Correlation is a statistical measurement used to convey 

the strength and direction of a linear relationship between 

two random variables. In finance, these variables can be 

anything from an individual security to an entire asset class. 

Increasingly positive (negative) correlation indicates an 

increasingly strong (inverse) relationship between the two 

variables, up to 1 (-1), which indicates a perfectly positive 

(inverse) relationship. In other words, two stocks with perfect 

correlation would be expected to move up and down 

in fixed proportion over a given period of time. Of course, 

because distinct investments are by definition influenced 

differently by the same factors, perfect positive correlation 

is extremely rare. For example, for the period from Jan. 1, 

2000, through Dec. 31, 2011, the returns of ExxonMobil and 

Chevron—two very similar oil services firms—correlated at 

0.85 on a daily basis, and 0.74 on a monthly basis (source: 

Thomson Reuters Datastream). Although the two companies 

moved in the same direction on 2,541 days, they 

moved in opposite directions on 589 days. 

Even in the case of a preannounced stock-for-stock 

merger of two corporations (in which the equity of one 

entity will be converted into equity of another in fixed 

proportion at a given future date), correlations can be 

less than 1.0. And while correlation conveys information 

about tendencies in the direction of the change in 

value of two investments, the statistic itself conveys very 

little information about the absolute level of change in 

value of the assets. For example, over the same period, 

ExxonMobil posted a 110 percent cumulative return, 

while Chevron notched a more impressive 146 percent 

cumulative return. So despite the companies’ high correlation, 

investing in one was not “just as good” as investing 

in the other. In fact, investors must be equally aware of 

the things that correlation does not tell them. 

Role Of Correlation In Portfolio Construction 

Correlation is one of the primary building blocks of 

portfolio construction, along with expected returns and 

expected volatility. Because correlation summarizes the 

historical relationship between two assets, investors often 

focus on correlation to frame expectations for how a 

portfolio may perform over time. Specifically, by combining 

imperfectly correlated assets, a portfolio’s expected 

volatility may be reduced, often without a significant 

effect on returns. 2 As Figure 1 illustrates, from Jan. 1, 

1926, through Dec. 31, 2011, adding a 10 percent bond 

allocation 3 to a U.S. stock portfolio 4 would have reduced 

volatility from 22.96 percent to 20.81 percent, but would 

have only reduced annualized returns from 10.17 percent 

to 9.95 percent. It’s clear that the low average correlation 

between the U.S. stock market and the U.S. bond market 

(historically, 0.25), combined with significantly lower 

overall volatility for U.S. bonds, has produced a significant 

diversification benefit. This is particularly true in 

equity-heavy portfolios, where an addition of bonds has 

led to a reduction in portfolio volatility that has been disproportionately 

large relative to the reduction in average 

returns. And so long as the observed correlation remains 

constant over time, this relationship will tend to hold. 

However, challenges to portfolio construction arise when 


the correlations among assets do not remain constant, 

and instead shift, sometimes significantly. 

Correlation And Portfolio Variance 

Correlation differences may actually have a more modest 

diversification benefit than many investors perceive. 

In fact, in the case of combining stocks and bonds, the 

single largest factor contributing to the decline in portfolio 

volatility arises from the lower total volatility of bonds, not 

the fact that stocks and bonds have low correlation. From 

the mathematical definition of portfolio variance, the following 

relationship must hold for all two-asset portfolios: 

Portfolio Variance = Variance 1 + Weight 1 2 

+ Variance 2 × Weight 2 2 + Correlation effect 

where “Correlation effect” is a function of the weights of 

the assets in the portfolio and their correlation with each 

other. A direct implication of this equation is that correlation 

is most relevant to diversification arguments, and 

most powerful in reducing portfolio volatility, when asset 

volatilities are more similar. 


Volatility is typically associated with returns; however, 

measured correlations can also be volatile, often to the 

detriment of portfolios believed to be adequately diversified. 

And the shorter the window of observation, the greater 

the likelihood that realized correlation will differ from the 

long-term average. Figure 2 illustrates five-year correlations 

between monthly U.S. stock and U.S. bond total returns over 

five-year intervals since 1926 (17 distinct, nonoverlapping 

periods). While the long-term average correlation between 

these two asset classes has been 0.25, the figure shows that 

correlations over shorter windows vary widely from this 

average, with a range of 0.72 for the five years ended 1975 to 

-0.54 for the five years ended 2005. 5 

Volatility in realized correlations can have serious 

implications for investors, as the diversification and portfolio 

efficiency that is realized may differ from expectations. 

For example, over the 20-year period ended Dec. 31, 

1985, the correlation between U.S. stocks and U.S. bonds 

was 0.57. This meant that the ex-post realized reduction 

in portfolio volatility achieved by adding bonds to a stock 

portfolio was reduced; that is, adding a 10 percent allocation 

to bonds to a 100 percent stock portfolio reduced 

volatility 6.8 percent (versus the long-term average of 9.3 

percent). In contrast, from 1986 through December 2011, 

the realized correlation between U.S. stocks and U.S. 

bonds was -0.10, which translated into a volatility reduction 

of 10.2 percent when a 10 percent bond allocation 

was added to a 100 percent stock portfolio. 

Why does measured correlation differ from its long-term 

average? The fact that observed correlation varies, even over 

relatively long periods of time, does not necessarily mean 

that “correlations are changing,” although this may be the 

case. It simply reflects randomness in the return variables 

themselves, which generally produces ex-post outcomes 

Figure 1 

Annual Return/Volatility (%) 

25.0 

20.0 

15.0 

10.0 

5.0 

Stocks/ 

Bonds 

Sources: Vanguard calculations, using data from Standard & Poor’s, Dow Jones, 

MSCI, Citigroup and Barclays. The calculations use quarterly return data; using 

monthly or annual return data would not change the relationships. Data cover the 

period Jan. 1, 1926, through Dec. 31, 2011. 

Figure 2 

Correlation 

0.0 

100%/ 

0.8 

0.6 

0.4 

0.2 

0.0 

-0.2 

-0.4 

-0.6 

Historical Average Volatility And Returns 

Relative To Various Stock/Bond Portfolios 

0% 90%/ 

10% 80%/ 

20% 70%/ 

30% 60%/ 

40% 50%/ 

50% 40%/ 

60% 30%/ 

70% 20%/ 

80% 10%/ 

90% 100% 

0%/ 

Portfolio Allocation 

Annualized Standard Deviation Average Annual Return 

Five-Year Nonoverlapping Correlations 

Between US Stocks And US Bonds 

-0.8 

1930 1938 1946 

Average Correlation 

1954 1962 1970 1978 1986 1994 2002 2010 

5 Years Ended ... 

Sources: Vanguard calculations, using data from Standard & Poor’s, Dow Jones, MSCI, 

Citigroup and Barclays. Data cover the period Jan. 1, 1926, through Dec. 31, 2010. 

that differ from the “true” underlying statistic or longerterm 

average, particularly over shorter periods. 

Previous research suggests that not only does randomness 

affect measures of realized correlation through 

time, but also that the underlying correlations between 

asset returns change over time and in particular circumstances, 

and have important relationships to events 

such as volatility shocks. Ilmanen [2003] found that factors 

increasing the correlation between U.S. stocks and 

bonds include high inflation and significant changes in 

GDP growth. Ilmanen also found that stock-bond correlations 

tend to be lowest when equities are weak and 

volatile, such as during flights to quality. Other research 

has provided similar evidence. Gulko [2002] found that 

stock-bond correlations are positively related during 

normal market conditions, but decrease during stock 

market plunges. Connolly et al. [2005] showed that stockbond 

correlation is lower when the implied volatility from 

equity index options is higher. 

Although market volatility has emerged as a key driver 

that tends to decrease correlations between stocks and 

bonds, volatility is also a major driver that tends to increase 

36 


correlations when looking at subcomponents of the same 

asset class. For example, numerous studies have found that 

correlations between U.S. and international stocks increase 

substantially during volatile market episodes. 6 Longin and 

Solnik [2001] found that correlation is not related to market 

volatility per se, but to the market trend, with correlation 

increasing during bear markets but not in bull markets. 7 

Implications For Portfolio Construction 

Because bonds have relatively low volatility in addition to 

low average correlations to stocks, investors have traditionally 

used bonds to diversify their stock allocations. However, 

investment products such as exchange-traded funds have 

arisen in recent years, providing simplified, low-cost access 

to a greater number of risk-premium asset classes and subasset 

classes beyond U.S. bonds. As a result, it’s no surprise 

that attention has been drawn to the potentially diversifying 

properties of investments such as commodities, real estate, 

emerging markets bonds, and micro-cap stocks, to name a 

few. Academic research and historical experience suggest 

that many of these higher-risk, yet potentially diversifying, 

assets may provide returns higher than those available in 

a typical bond portfolio, even as they have been relatively 

uncorrelated to U.S. stocks and bonds. Figure 3 shows the 

average monthly correlations between some of these market 

segments and U.S. stocks and U.S. bonds. 8 

By adding assets such as those in Figure 3 to a portfolio 

(and by extension, reducing the existing stock and/or 

bond allocations), the investor hopes to lower total portfolio 

volatility, increase total portfolio returns or generate 

some combination of higher returns and lower volatility. 

This proved effective during the bear market from 2000 

through 2002 (U.S. stocks returned -42 percent), during 

which REITs (+44 percent), commodities (+37 percent), 

Figure 3 

Correlation 

Monthly Correlations Between Select Market Segments 

And Traditional Assets Classes: 1988-2011 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

-0.2 

IntÕl 

Stocks 

Emer. Mkt. 

Stocks 

REITs Commodities HY 

Bonds 

IntÕl 

Bonds 

Sources: Vanguard calculations, using data provided by Thomson Reuters Datastream 

Notes: U.S. stocks are represented by the Dow Jones U.S. Total Stock Market Index 

from 1988 through April 22, 2005, and the MSCI US Broad Market Index thereafter; U.S. 

bonds are represented by the Barclays U.S. Aggregate Bond Index; international stocks 

are represented by the MSCI EAFE Index; emerging market stocks are represented by 

the MSCI Emerging Markets Index; REITs are represented by the FTSE NAREIT Index; 

commodities are represented by the S&P GSCI Total Return Index from 1988 through 

1990 and the Dow Jones UBS Commodities Index thereafter; high-yield bonds are 

represented by the Barclays High Yield Bond Index; and international bonds are represented 

by the Citigroup World Global Bond Index ex U.S. from 1988 through 1989 and 

the Barclays Global Aggregate ex U.S. Bond Index thereafter. 

international bonds (+19 percent) and high-yield bonds 

(+5 percent) realized positive returns, providing considerable 

diversification potential. 9 However, while many assets 

are imperfectly correlated over time, the long-run historical 

correlations may not hold during short-term periods of 

acute market stress. This is because during a flight to quality, 

increased systematic risk tends to swamp asset-specific 

risk, and risky assets have a tendency to suddenly become 

more positively correlated, often in contrast with how 

they perform during “normal” times. This also highlights 

an important distinction—risk diversification, such as 

that achieved through U.S. Treasury bonds, versus return 

diversification, such as that achieved through REITs or 

emerging markets equities. As we will see, in normal times, 

the differences between the two may be minor, but during 

events characterized by a flight to quality, the differences 

and implications can be significant. 

From 1988 through 2007 (1988 representing the start of 

the emerging markets data series), a portfolio that allocated 

50 percent to U.S. stocks and 50 percent to U.S. bonds would 

have averaged a 9.9 percent annual return with a standard 

deviation of 7.4 percent. On the other hand, a portfolio 

equally weighted among the six categories of assets shown 

in Figure 3 in addition to U.S. stocks and U.S. bonds (12.5 

percent allocated to each) would have averaged a 10.9 

percent annual return with a standard deviation of 7.6 percent 

(see Figure 5a). 10 In hindsight, it is clear that it would 

have made sense to invest in the more diversified portfolio 

over this particular period. 11 But the “long-term history” 

for many types of assets is not nearly as long as that of U.S. 

stocks, bonds and cash, for which we can reliably go back to 

at least 1926, a period covering many economic and market 

regimes. For many of the asset classes and subasset classes 

commonly used to diversify equity market risk, we can only 

go back 20 or 30 years, a period characterized by disinflation, 

long intervals of relatively low volatility and a relatively 

stable economic environment. 

As is now widely known, the global equity bear market 

that started in October 2007 and lasted through early March 

2009 was unique in many respects. The global financial 

crisis was characterized primarily by a flight to quality. 

And in a flight to quality, risky assets tend to perform more 

similarly than differently. Figure 4 shows the observed correlations 

for the same assets from October 2007 through 

February 2009. Comparing the long-term correlations in 

Figure 3 with the correlations presented in Figure 4, we can 

see the impact of a flight to quality. Correlations both to 

U.S. stocks and U.S. bonds increased significantly—virtually 

across the board. As a result, the long-term diversifying 

properties at least temporarily largely disappeared. 12 

Of course, an increase in correlation was not the 

full extent of the impact. By moving from a 50 percent 

stock/50 percent bond portfolio to a portfolio equally 

weighted across eight different asset and subasset 

classes, the investor ended up with only 12.5 percent of 

the port folio in U.S. bonds and 87.5 percent of the portfolio 

in riskier assets. And although those risky assets 

increased average returns without significantly increas- 


Figure 4 

Correlations Between Select Market Segments 

And Traditional Asset Classes: October 2007-February 2009 

1.0 

0.8 

Figure 5 

20% 

Return And Volatility 

Portfolio Comparisons 

5a. Long-Term Return And Volatility, 1988-2007 

Correlation 

0.6 

0.4 

0.2 

0.0 

Int’l 

Stocks 

Emer. Mkt. 

Stocks 

REITs Commodities HY 

Bonds 

Int’l 

Bonds 


15% 

10% 

5% 

0% 

50/50 Stock/Bond Equal Weighted Diversifed 


Notes: A similar spike in correlations was observed in 1998, a period characterized 

by the “Asian Contagion,” the Russian debt default and the collapse of Long-Term 

Capital Management. See Figure 3 for benchmark descriptions. 

ing average portfolio volatility (particularly from 2000 

through 2007), the risk bled through during the global 

financial crisis. So, while the 50/50 portfolio returned 

-26 percent with a worst month (October 2008) of -10.0 

percent, the eight-asset portfolio returned -38.4 percent 

with a worst month (October 2008) of -17.6 percent. The 

result? Not only has the “diversified” portfolio underperformed 

the 50 percent stock/50 percent bond portfolio 

since 2008, but it has done so with significantly higher 

volatility, as shown in Figure 5b. 

Because of such contagion risks, it is critical for investors 

to understand the potential value of an allocation 

to high-quality bonds. During the global financial crisis, 

even as risky assets largely declined in lock step, U.S. 

bonds as measured by the Barclays U.S. Aggregate Bond 

Index returned 7.0 percent. 13 Similarly, in August 1998—a 

prior contagion event—U.S. bonds returned 1.6 percent, 

while other types of assets posted negative returns: U.S. 

stocks, -15.6 percent; high-yield bonds, -5.5 percent; 

REITs, -9.4 percent; international developed markets, 

-12.4 percent; international emerging markets, -28.9 percent; 

and commodities, -5.9 percent. Other than U.S. 

bonds, only international bonds (+2.5 percent) saw gains. 

As illustrated in Figure 1, the long-term diversification 

properties of bonds are significant. And as realized during 

periods of risk aversion and flight from risky assets, 

high-quality bonds, particularly Treasury bonds, prove 

to be a destination of choice. So although bonds may not 

provide the long-term expected returns of other asset and 

subasset classes that are now accessible, bonds have been 

one of the more reliable assets that we have investigated 

to mitigate losses in the worst of times. 14 

Figure 6 illustrates the role of bonds in a portfolio. 

Maintaining the original allocation to U.S. bonds and 

diversifying the allocation to U.S. stocks across the six 

alternative assets identified in Figures 3, 4 and 5 significantly 

reduced the average volatility of the portfolio leading 

up to 2008. The cost was slightly lower total return 

from 1988 through 2007. Since the global financial crisis, 



Note: See Figure 3 for benchmark descriptions. 

Figure 6 


5b. Return And Volatility Since Global Financial Crisis, 2008-2011 

20% 

15% 

10% 

5% 

0% 

Return And Volatility Statistics For Eight-Asset Portfolio: 

Maintains 50% Bond Allocation 

20% 

15% 

10% 

5% 

0% 

50/50 Stock/Bond Equal Weighted Diversifed 

1988-2007 2008-2011 

Sources: Vanguard calculations, using data provided by Thomson Reuters Datastream. 

Notes: A portfolio that maintained the equity allocation and diversified the bond 

allocation would have experienced an 11.4% average return, 9.7% average volatility 

and a drawdown in 2008 of -33.3%. See Figure 3 for benchmark descriptions. 

however, by maintaining the bond allocation, an investor 

would have been able to maintain his or her portfolio 

volatility levels, and even modestly boost returns. So for 

investors who maintained their exposure to bonds, diversification 

worked exactly as we would expect it to work, 

even accounting for increased correlations across risky 

assets coupled with significantly poor returns. 

Figure 7 expands the analysis to encompass the worst 

10 percent of calendar months for U.S. equity returns. 

38 


Figure 7 

Performance Of Risky Assets During Poor US Equity Markets 

Percent Of Months (%) 

100 

90 

80 

70 

60 

50 

40 

30 

20 

10 

0 

7a. Percentage Of Monthly Returns That Are Negative: January 1988-December 2011 

US Bonds HY Bonds REITs Int’l Stocks Emer. Mkts. Stocks Commodities Int’l Bonds Treasury Bonds Hedge Funds 

7b. Median Monthly Returns: January 1988-December 2011 

2 

0 

Monthly Return (%) 

-2 

-4 

-6 

-8 

-10 

US Stocks US Bonds HY Bonds REITs Int’l Stocks Emer. Mkts. 

Stocks 

Commodities Int’l Bonds Treasury 

Bonds 

Hedge 

Funds 


Notes: For hedge fund returns we used the median fund-of-funds from Morningstar’s hedge fund database covering the period January 1994 through December 2011. See Figure 

3 for benchmark descriptions. 

We also shift our focus away from correlations and 

instead examine the return relationship from two additional 

perspectives. Figure 7a focuses on the percentage 

of months that the risk-premium asset classes experienced 

negative returns in conjunction with U.S. stocks, 

while Figure 7b shows the median returns during those 

same periods. Whether looking at percentage of negative 

months or median returns, it is clear that during the 

worst months for U.S. stocks, these asset classes tended 

to perform more similarly than simple long-term averages 

would indicate. And it is interesting that although 

the riskier assets tended to perform more similarly during 

the worst periods for U.S. stocks, bonds tended to 

perform in line with their averages. 

Diversification Is Not Just About Correlation 

When thinking about portfolio diversification, investors 

instinctively focus on correlation. Yet as we have 

shown, combining assets with low historical correlation 

does not eliminate risk, because low historical correlation 

does not eliminate the possibility of adverse 

co-movement in times of crisis. Still, discussions of the 

benefits of diversification often overlook the fact that 

while assets with low historical correlation can move in 

the same direction, they rarely, if ever, move in the same 

direction with the same magnitude. Figure 8 plots the 

returns of the same asset and subasset classes discussed 

previously in this paper from October 2007 through 

December 2011, a period representing the entirety of the 

recent bear market as well as the subsequent rebound. 

This particular figure focuses on those days when the 

U.S. stock market was down 4 percent or more—significantly 

negative returns by any measurement. It’s clear 

that in many of these significantly negative days for U.S. 

stocks, other risky assets tended to move in the same 

direction (similar to the correlation analysis shown in 

Figure 4). Ultimately, the fact that a number of risky 

assets declined at the same time prompted many to proclaim 

“the death of diversification.” 

Although most risky assets declined in value on these 

substantially negative days, it’s important to point out 

that no two risky assets moved with the same magnitude. 

For example, on Dec. 1, 2008, when U.S. stocks 

returned -9.2 percent, only REITs lost more (-18.6 percent). 

Commodities, developed markets, emerging markets 

and high-yield bonds each declined, but to a lesser 


Figure 8 

Daily Return For Other 

Asset Classes (%) 

10 

5 

0 

-5 

-10 

-15 

Days When US Stocks Were Down 4% Or More: 

October 2007-December 2011 

-20 

-10% -9% -8% -7% -6% -5% -4% 

Daily Return For US Equities 


Note: See Figure 3 for benchmark descriptions. 

degree. From this perspective, these asset and subasset 

classes did in fact offer a form of diversification to markedly 

reduce U.S. equity market risk. The message is 

clear: When assessing the value of diversification, investors 

should not simply look at directional movements, 

particularly in the short term. Indeed, even bonds—the 

most common diversifier for equity risk—can move in 

conjunction with equities for periods of time (as we 

saw in Figure 2). But this does not mean that investors 

should abandon bonds in a long-term portfolio. The 

benefits of diversification, low correlation and sensible 

portfolio construction tend to bear out over longer—3-, 

5- and 10-year—periods, even though they may not be as 

clear in the very short term. 

Conclusion 

Correlation is a critical metric that can provide useful 

information in the portfolio construction process. 

Nevertheless, it is important for investors to understand 

that correlation is a property of random variables, and 

so does not describe a fixed relationship between variables: 

Assets with low and unchanging correlation can 

and do move in the same direction from time to time. 

In addition, correlations between asset class returns can 

and do change over time or in particular circumstances. 

Future correlations may also differ from those in the 

past because of changing economic and market regimes. 

Investors should take these factors into consideration 

when using correlation as a key input for constructing 

investment portfolios, not relying solely on statistical 

measures, but mixing in common sense and qualitative 

judgment as well. In addition, investors should recognize 

that low historical or estimated correlation does 

not ensure against loss, particularly in times of stress, 

and that bonds and other low-risk assets can provide 

valuable protection during such periods. The goal of 

portfolio construction should be to minimize risk while 

maximizing returns, but with a core understanding of 

how different assets react to different market environments 

and with the knowledge that low average portfolio 

variance is only one dimension of risk. 

Investing over the long term will almost inevitably 

include short-term periods of (sometimes severe) market 

stress, during which the value of diversification for 

risky assets is less evident. Because investors tend to pay 

significant attention to large losses, it can be especially 

troubling when correlations “go to 1.” It is in these periods 

that downside protection is needed the most, and 

the value of bonds—particularly high-quality bonds— 

shines. Of course, while correlations “go to 1” during 

market dislocations, investors can take some solace 

that a modicum of diversification can be achieved when 

assets do not move by the same amount, even when 

they move in the same direction. Investors can also feel 

some reassurance that systematic factors will occasionally 

drive “uncorrelated” assets higher in tandem during 

periods of relief from systemic crisis. 

History supports the notion that over longer-term 

periods, diversification within and across asset classes 

offers substantial benefit. As a result, investors should 

continue to focus on their strategic asset allocation with 

regard to overall risk and return objectives/constraints, 

and the long-term expected returns, risks and correlations 

of the assets in which they invest. For those investors 

with greater sensitivity to significant near-term 

loss, lower-risk, lower-returning asset classes such as 

investment-grade bonds or even cash—whose diversifying 

properties tend to hold up during periods of market 

stress—may make more sense. On the other hand, 

investors who are less sensitive to significant near-term 

losses, or who are willing to endure significant near-term 

loss in the pursuit of long-term higher returns, may find 

it reasonable to allow higher-risk-premium asset classes 

to play a more substantial role in their portfolios. Each 

of these approaches can be considered prudent, and the 

decision of which path to take ultimately depends on the 

broad objectives of the investor. 

References 

Connolly et al. 2005. “Stock Market Uncertainty and the Stock-Bond Return Relation,” Journal of Financial and Quantitative Analysis 40: 161-94. 

Gulko, Les, 2002. “Decoupling,” Journal of Portfolio Management 28(3): 59-66. 

Ilmanen, Antti, 2003. “Stock-Bond Correlations,” Journal of Fixed Income 13(2): 55-66. 

Kinniry, Francis M. Jr., and Christopher B. Philips, 2007. “The Theory and Implications of Expanding Traditional Portfolios,” Valley Forge, Pa.: The Vanguard Group. 

Longin, François, and Bruno Solnik, 2001. “Extreme Correlation of International Equity Markets,” Journal of Finance 56(2): 649-76. 

Philips, Christopher B., 2012. “Considerations for International Equity,” Valley Forge, Pa.: The Vanguard Group. 

40 


Solnik, Bruno, 2002. “Global Considerations for Portfolio Construction,” in Equity Portfolio Construction. Charlottesville, Va.: Association for Investment Management 

and Research, 29-37. 

Solnik et al. 1996. “International Market Correlation and Volatility,” Financial Analysts Journal 52(5): 17-34. 

Endnotes 

1 During periods of severe equity market stress, cash has historically been the most consistent diversifier for risky assets such as stocks. However, cash is more generally 

associated with short-term needs than investing with the goal of increasing the real value of a long-term investment portfolio. For this reason, we have chosen not to 

focus on cash in this paper. 

2 Correlation has been widely used when constructing investment portfolios ever since Harry M. Markowitz first developed the theory of mean-variance analysis in the 

1950s. The basic premise of mean-variance analysis is that investors face a trade-off between risk and expected return. In mean-variance analysis, risky assets can be 

combined in a portfolio in an attempt to minimize the total portfolio risk at any desired level of expected return. Markowitz discovered that portfolio standard deviation 

is a function not only of the standard deviations of all the individual assets in a portfolio but also of the covariance between the rates of return for all the assets in the 

portfolio. Optimal mean-variance combinations lie along the efficient frontier—a set of portfolios that has the maximum expected return for a given level of risk and the 

minimum risk for a given level of expected return. According to the theory, any risk/return combination that does not lie along the efficient frontier would be suboptimal. 

All rational investors would therefore wish to be positioned at some point along the efficient frontier commensurate with their return expectations and risk tolerance. 

3 Throughout this analysis, references to “bonds” or “U.S. bonds” or “investment-grade bonds” are synonymous with the broad U.S. bond market. We represent the U.S. 

bond market by combining the following historical benchmarks: the S&P High Grade Corporate Bond Index from 1926 through 1968; the Citigroup High Grade Index 

from 1969 through 1972; the Barclays U.S. Long Credit Aa Bond Index from 1973 through 1975; the Barclays U.S. Aggregate Bond Index thereafter. 

4 Throughout this analysis, references to “stocks” or “U.S. stocks” are synonymous with the broad U.S. stock market. We represent the U.S. stock market by combining the 

following historical benchmarks: the S&P 500 Index from 1926 through 1970; the Dow Jones U.S. Total Stock Market Index from 1971 through April 22, 2005; the MSCI 

U.S. Broad Market Index thereafter. 

5 The correlation between monthly U.S. stock and U.S. bond returns from Jan. 1, 2011 through Dec. 31, 2011 was -0.91. 

6 For a discussion of the correlation between U.S. and international equities, see Philips [2012]. 

7 Other factors may also contribute to changing correlations. For example, increasing global interdependence among countries may cause correlations between U.S. and international 

stocks to increase over time. Solnik [2002] has argued that increasing correlations are a natural progression as markets mature, develop and become more integrated. 

8 We also looked at the correlation of hedge funds to U.S. stocks and bonds. The Dow Jones Credit Suisse Hedge Fund Index, however, started in 1994, so we excluded the 

index’s results from this paper. That said, since 1994, hedge funds and U.S. equities have realized a 0.61 correlation, similar to that of U.S. stocks to REITs. 

9 These findings cover the period April 2000 through February 2003. 

10 Another potential strategy is to maintain the equity allocation and diversify the bond allocation across these assets. Over this period, such a portfolio would have averaged 

an 11.4 percent annual return but with higher volatility (9.6 percent) than that of the starting portfolio. 

11 For a broader, more detailed discussion of the implications of combining nontraditional assets in a portfolio, see Kinniry and Philips [2007]. 

12 As with average correlations, we also evaluated hedge funds over the course of the global financial crisis, and found that correlations to equities increased: Specifically, 

the correlation of hedge funds to equities increased to 0.72. 

13 During the global financial crisis, the Barclays U.S. Treasury Bond Index returned 14.2 percent. 

14 Other assets or tools that may be just as effective, if not more effective than bonds at hedging downside equity risk, include Treasury bills, derivatives or ETFs linked to 

the VIX (ticker symbol for the Chicago Board Options Exchange Market Volatility Index), inverse funds and ETFs, put options and other forms of portfolio insurance. 

IndexIng and everythIng else 

The latest industry news and data for ETFs and indexing. 

Three magazines. Two websites. One independent voice. 

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


Talking Indexes 

The Next Big Thing 

Speculating on what issue will soon 

dominate investor thoughts 

By David Blitzer 

The modern history of index investing is roughly 

three decades old and divides into three epochs: the 

1980s, when index futures, options and derivatives 

arose; the 1990s, when the tech boom powered the discovery 

of index outperformance; and the 2000s, when ETFs 

put indexes in everyone’s portfolio. Each of these was 

big—big enough to last a decade and change the investing 

landscape. Now the Journal of Indexes is challenging 

us to consider the issues that will likely concern index 

investors in the near future. The last few years of housing 

crisis, financial crisis and now the European debt crisis 

should remind everyone that forecasting is difficult, and 

forecasting the future is even more difficult. The usual 

disclaimers—no one knows the future; and what follows 

is based on opinions, not facts—apply. 

The theme for the next epoch of index investing is 

competition: competition for ideas on index construction; 

competition between complexity and simplicity; competition 

between ETFs and mutual funds; and competition to 

eke out gains in a slow-growth economy. These differ from 

the themes of the last 30 years. Index futures and options 

in the 1980s were new ideas and new instruments, opening 

up new opportunities for investors. Index outperformance 

existed since the first index fund but went unrecognized 

until the late 1990s. ETFs, though created in the early 

1990s, were largely unappreciated until the turn of the century. 

All were new ideas or newly recognized ideas. Their 

newness led to their success. This time around, the next big 

thing will need to battle competitors for success. 

Index Construction 

For a long time, indexes were either price weighted 

or cap weighted; at the same time, many investors didn’t 

think about weighting at all or they erroneously assumed 

indexes were equal weighted. Those days are over, and various 

approaches to index weighting are battling for investor 

attention with performance claims. For the moment, the 

combatants seem to fall into a few groups: traditionalists 

(capitalization, price and equal weighting); other size measures 

(financial measurements like revenues or earnings 

or combinations of financial measures); and attributes or 

factors (measures of stock attributes like value, growth, dividend 

yield or momentum). Since one key gauge of success is 

performance, and performance depends on ever-changing 

market conditions, it is impossible to predict a clear winner. 

If we could match a weighting scheme to the market it works 

best in, we would have a big thing: a way to know what to 

do and when. Most likely, that kind of weighting choice will 

elude investors, and the battle among weighting schemes 

will turn in part on the next competitive contest. 

Simplicity Vs. Complexity 

Almost all the new indexes of the last few years have 

one thing in common: Compared to the S&P 500 and 

the Dow Jones industrial average, the new indexes are 

complicated. Consider one of last year’s big things, the 

S&P 500 Low Volatility Index. As indexes go, this one 

is reasonably straightforward: Start with the S&P 500, 

take the 100-least-volatile stocks and weight them by 

the reciprocal of their volatility. At the same time, the 

index is three steps removed from the S&P 500, an index 

familiar to everyone. Complexity gets more serious when 

we consider leveraged, inverse, weights that change 

depending on market events and performance, multiple 

asset classes, hedging currencies and other factors. The 

challenge is to add something to gain a performance 

42 


edge or reduce risk without becoming so confusing that 

no one knows what the index might do or why. 

If this challenge seems far-fetched, recall some of 

the confusion when leveraged and inverse indexes with 

daily resets appeared a few years ago and how a number 

of larger brokerage firms restricted sales of these 

products to sophisticated investors only. One can make 

a strong case for simplicity, and most likely few of the 

complex approaches will survive, and even fewer would 

qualify to be a big thing. Simplicity vs. complexity will 

certainly be part of the next contest. 

extra 50 basis points. Those days are gone. Single-digit 

annual performance is the norm in most markets, if the 

numbers manage to be positive. Those extra 50 basis 

points count, and investors are likely to be the beneficiaries 

of increasing price competition. 

No Growth Markets 

The last candidate for the next big thing might be a 

little thing—slow growth, with little performance and 

low returns. It is difficult for profits, earnings or stock 

prices to consistently grow faster than the economy. 

GDP growth puts an upper bound on growth across the 

economy. Maybe not day by day, quarter by quarter or 

even year by year, but in the long run, either profits swallow 

the GDP or GDP growth is a ceiling on profit growth. 

Foreign economies and outsourced operations may look 

like an escape, but that merely substitutes 7 percent 

real growth in China for 3 percent real growth in the 

The last candidate for the next big thing might be a little thing—slow 

growth, with little performance and low returns. It is difficult for profits, 

earnings or stock prices to consistently grow faster than the economy. 

ETFs Vs. Mutual Funds 

Index investing is considered to be passive, and as 

such, is usually inexpensive. There is little turnover and 

not much trading to pay for. ETFs, which have become the 

principal vehicle for index investing, enjoy tax advantages. 

The growing recognition and popularity of ETFs, combined 

with their lower fees, is challenging mutual funds for 

investors’ attention. While one part of the challenge is the 

ease of trading ETFs through any broker, the bigger part 

is the fee. Typical ETF fees run from 20 to 75 basis points; 

typical mutual fund fees start off where the ETFs end and 

could rise to 200 basis points or more, in a few cases. Index 

mutual funds, with fees closer to ETF levels, may sit out 

this battle, benefit from the interest in ETFs and indexes or 

maybe even convert to ETFs over time. 

Fees matter. In the 1990s, when the S&P 500 could 

return 15 percent, 20 percent or more in a technologydriven 

year, few cared about giving a fund manager an 

U.S.—and even with 7 percent growth it takes 10 years 

to double your money. From the end of the 1981-82 

recession and the great inflation of the 1970s to the collapse 

of Bear Stearns and Lehman Brothers in 2008, U.S. 

investors mostly enjoyed a quarter-century of incredible 

returns. The next big thing we really don’t want may be 

learning to love slow growth and low returns. 

These ideas don’t come close to exhausting the possibilities 

for the next big thing. While paper and ink (or electronic 

bits) are cheap and one could go on to list others, the 

probability of getting the next big thing right isn’t likely to 

increase substantially with another few ideas. 

Why subscribe to the 

The Journal of Indexes is the premier source for financial index research, news and 

data. Written by and for industry experts and financial practitioners, it is the book of 

record for the index industry. Browse content online at www.indexuniverse.com/JOI 

TO SUBSCRIBE, VISIT: 

www.indexuniverse.com/JOI/subscriptions 

Redefining Credit Risk 

William Mast 

Credit Derivatives Indexes 

Gavan Nolan and Tobias Sproehnle 

A Fixed-Income Roundtable 

Ken Volpert, Jason Hsu, Waqas Samad, Larry Swedroe and more 

The Impact of Bond Fund Flows 

David Blanchett 

Plus David Blitzer on bubbles, Jeremy Schwartz on dividends and buybacks, Francis Gupta on country 

classifications and a biography on Bogle 


Optimal Design Of 

Risk Control Strategy Indexes 

Improving on a concept 

By Guido Giese 

44 


Strategy indexes that invest in a frequently rebalanced 

portfolio of equity and fixed income have become 

very popular in recent years across equity markets 

and have led to the issuance of numerous financial products 

such as ETFs and certificates. Currently, there are two 

basic types of strategy indexes that are based on an equity 

investment and a money market investment: 

• Pure return strategies such as leveraged equity indexes 

that borrow in the money market to offer leveraged 

equity returns for risk-seeking investors 

• Target volatility index strategies for risk-averse investors 

that shift part of the equity investment into the 

money market in volatility markets to protect the investor 

from serious losses 

The objective of this paper is to analyze the risk and 

performance characteristics of existing strategy index concepts—in 

particular, existing methodologies for leveraged 

indexes and target volatility indexes—and to show that 

these existing index concepts can be improved significantly 

by incorporating a risk-control mechanism into the 

index methodology in the mathematically optimal way. 

In the following, we assume an equity investment in 

the form of a liquid equity index to ensure the index 

scheme can be replicated and traded in a cost-efficient 

way. Further, we allow the equity investment and money 

market investment to be either long or short to allow leveraged, 

de-leveraged and short index strategies. 

This paper aims to show the benefits of indexes that 

are risk controlled in the sense that the size of the equity 

investment and money market investment is determined 

as a function of the prevailing level of market risk, where 

we will use equity volatility as the basic risk measure. The 

reasons for using equity volatility to determine the index 

composition are twofold: 

1. Using equity volatility as an allocation factor has 

become common practice even in active portfolio management, 

mainly due to the empirical fact that there is a strong 

negative correlation between the performance of equities 

and equity volatility, i.e., falling equity markets typically 

coincide with rising levels volatility and vice versa. 

2. It has been shown (see Despande, Mallick and Bhatia 

2009; Cheng and Madhavan 2009; Giese 2010) that frequently 

rebalancing investment schemes composed of an equity and 

a fixed-income investment results in the portfolio suffering 

from rebalancing losses that are proportional to the variance 

of the underlying equity index, as we will also show later in 

this paper. Therefore, using the volatility of the underlying 

equity market as an input factor for the asset allocation is 

essential to minimize the adverse effects of rebalancing. 

Rules-based investment schemes that invest in equity 

and fixed income have been analyzed in finance literature 

before in several different contexts. The first and influential 

contribution of Merton [1971] was based on the assumption 

of an investor who maximizes a predefined utility 

function on a fixed time horizon, which resembles the 

decision problem of an investor saving for his retirement. 

Further, the basic idea to shift the portfolio investment 

between the risky equity market and a less risky fixedincome 

investment has led to the development of portfolio 

insurance investment models (see Perold and Sharpe 

1995) that rebalance the portfolio to ensure a certain minimum 

capital protection level. 

However, this paper analyzes rules-based strategy 

indexes from a pure expected return and expected Sharpe 

ratio perspective, without referring to an arbitrary utility 

function or time horizon or capital protection level. The 

article’s research is intended to assist in the creation of 

investment schemes that can easily be tracked and issued 

in the form of retail investment products that aim to optimize 

the performance or the risk/return profile. 

Index Methodology 

We intend to derive a sophisticated analytical understanding 

of the risk/return profile of risk-control strategy 

indexes and to verify the key results by numerical backtests. 

In essence, we assume a strategy index that invests in the 

holdings of a standard equity blue-chip index with value S t 

at time t and a money market instrument, where in principle 

both investments can be long or short. 

The appendixes of this paper are available online. 1 There, 

we derive a complex mathematical description of this type 

of strategy index that is based on the well-known Brownianmotion 

model for the underlying equity index, which is 

commonly used in option pricing. In essence, this model is 

based on the assumption of equity returns following a normal 

distribution. Since real-world equity returns typically show 

heavy tails—which means that real-world equity markets are 

riskier than a normal distribution would predict—we will use 

numerical simulations based on real-world equity returns to 

verify the theoretical results based on this model. 

As a general index concept, we consider a strategy 

index It that uses equity volatility st observed in the market 

at time t as an asset allocation signal in the sense that 

it invests a portion of R(st) into the underlying equity 

index St and a portion 1-R(st) into the money market at 

rate rt. We will refer to the equity portfolio weight R(st) as 

the response function, since it determines the amount of 

equity held in the index portfolio depending on the current 

level of equity volatility. 

The index-update formula that index providers use for this 

type of strategy index basically states that the index return is 

the weighted average of the return on the equity investment 

and the money market investment. It takes the form: 

where Δ(t+1, t) denotes the time between date t and t+1 in 

the respective date-count convention. In principle, leveraged 

equity indexes and target volatility indexes are calculated 

according to formula (1), although they are quite different 

in terms of the way the size of the equity investment 

R(st) is determined (as we will elaborate later) and also 

in terms of the rebalancing frequency: Leveraged indexes 

are typically rebalanced periodically (i.e., daily, weekly or 

monthly), whereas most target volatility indexes use a triggered 

rebalancing (i.e., rebalancing takes place when the 

(1) 


45

actual weight of the equity investment in the index deviates 

by more than a predefined threshold from the desired 

weight R(st)). Nevertheless, both index concepts can be 

analyzed within the same mathematical framework and 

can be improved by implementing an optimal risk-control 

mechanism in a similar way. 

Based on the index methodology (1), the objective of our 

research is to answer the following question: Assume the 

underlying equity index S t 

has an average yearly return μ, an 

average volatility s and consequently a long-run Sharpe ratio 

(2) 

with r denoting the average refinancing rate. Given these 

parameters of the underlying equity index, the question 

is whether it is possible to predict the long-run return and 

Sharpe ratio of the strategy index calculated according to 

formula (1) without knowing the entire path of the underlying 

equity index, i.e., only using the return and volatility of 

the underlying equity index and the response function R(st). 

We will show next that this is indeed possible for all existing 

types of strategy indexes based on the concept (1); in 

particular, leveraged indexes and target volatility indexes. 

In a second step, we will use our understanding of the risk/ 

return characteristics of strategy indexes calculated according 

to the index formula (1) to derive optimally risk-controlled 

versions of leveraged indexes and target volatility indexes. 

Analysis Of Existing Strategy Index Concepts 

We start with an analysis of well-known investment 

strategies that are already available in the market in the 

form of strategy indexes and corresponding retail investment 

products: leveraged indexes and target-risk indexes. 

Leveraged Indexes 

The existing model of leveraged indexes has become quite 

popular in the ETF market in recent years (see EDHEC 2009). 

In fact, leveraged indexes are the simplest form of a strategy 

index using the index formula (1) in the sense that they are 

based on a constant response function. In the language of our 

model (1) we have (see NYSE Euronext 2008, Stoxx 2010): 

(3) 

where most leveraged ETFs use a constant leverage of L=2. 

In essence, the investment strategy (3) does not respond to 

equity volatility at all, which means it is not risk controlled 

in any way, and later in this article we will use the results 

derived for this type of leveraged index to show the advantages 

of strategies that are risk controlled. 

Leveraged indexes have been widely criticized because 

their performance depends on the path of the underlying, 

as the index formula (1) suggests, and therefore their performance 

characteristics are very complex. 

However, as we show in the mathematical derivation 

in Appendix 2 online (the same result has been shown 

in Despande 2009 and Giese 2010), in the long run, the 

performance of a leveraged index is governed by the law 

of great numbers, and therefore the long-run return g of a 

leveraged index is very accurately described by the following 

simple relationship: 

Leveraged 

return 

Refinancing 

costs 

Rebalancing 

losses 

In essence, the model equation (4) says that in the long 

run, the return of the leveraged index strategy is L times the 

return of the underlying equity index minus refinancing 

costs minus a term representing the adverse effect of rebalancing 

the portfolio on a continuous basis. In essence, this 

rebalancing loss comes from the well-known fact that the 

performance of leveraged indexes is path dependent. 

This is a very important result, as it shows that leveraged 

ETFs on different underlying indexes and different leverage 

factors all exhibit the same long-run performance characteristics, 

which is essentially determined by three parameters: 

1. The higher the yield μ of the underlying, the higher 

the more attractive leverage becomes. 

2. The higher the average refinancing rate r, the higher 

the less attractive leverage is. 

3. The higher the average realized volatility s of the underlying, 

the higher the less attractive leverage becomes. 

In other words, the complex path dependency of leveraged 

ETFs that is often cited as a main disadvantage of 

leveraged ETFs is absorbed in the long run into a simple 

rebalancing term that only depends on the average realized 

volatility of the underlying index during the investment 

period and the leverage factor of the index. 

Based on the performance of leveraged indexes (4), we 

conclude the Sharpe ratio of a leveraged index is 

(5) 

The key observation from the Sharpe ratio (5) is that for 

a leveraged strategy (i.e., L>1), the Sharpe ratio turns out to 

be always smaller than the Sharpe ratio of the underlying 

index (2). The reasons for the deterioration in the Sharpe 

ratio are the aforementioned rebalancing losses, which 

reduce the outperformance of the leveraged index over the 

risk-free rate. Hence, even in strong bull markets where 

a leveraged index strongly outperforms a nonleveraged 

index in absolute terms, it will never outperform the nonleveraged 

index from a risk-adjusted perspective. 

So far we have summarized theoretical results regarding 

leveraged indexes, which were derived in the online appendix 

based on stochastic calculus. This mathematical analysis 

is based on the model of equity returns following a normal 

distribution, in contrast to real-world equity returns, which 

are typically heavy tailed. Therefore, it is essential to verify 

the theoretical results by comparing them to real-world 

results. As a test case, we use the Euro Stoxx 50 Index and the 

EONIA money market rate as portfolio components. 

Figure 1 compares the long-run returns of leveraged 

(4) 

46 


indexes for different degrees of leverage and compares 

them with the theoretical results obtained by the simple 

formula (4). The key observation is that the theoretical 

formula (2) provides a very accurate prediction of the longrun 

returns of leveraged indexes. 

In essence, the real-world backtests verify the fact that 

Figure 1 

Yield In % pa 

Average Annual Performance Of Leveraged Indexes 

For Different Degrees Of Leverage Compared To 

The Respective Predictions Of Formula (2),1992–2011 

9.00 

8.00 

7.00 

6.00 

5.00 

4.00 

3.00 

2.00 

1.00 

0.00 

0 0.5 1 1.5 2 2.5 3 

Leverage 

Theory 

• Simulation 

Sources: Bloomberg, author calculations 

in the long run, the performance of leveraged indexes can 

be well understood according to the simple performance 

model (2) with a Sharpe ratio arrived at using formula (5). 

To conclude, we have obtained the first key result of 

our analysis: Leveraged indexes without an embedded 

risk-control method (i.e., that do not respond to volatility 

in determining the size of the equity investment) have a 

Sharpe ratio that is always lower than the Sharpe ratio of 

the underlying equity index in the long run. 

Target-Risk/Target-Volatility Indexes 

Another common type of strategy index that has been 

launched by many index providers is the so-called target 

volatility scheme, which aims to keep the volatility of the 

strategy index I t at a predefined constant target level T, 

which is achieved by using the following response function 

in the concept of the strategy index (1): 

(6) 

At a second and more theoretical glance, the advantages 

of the target-volatility index scheme are even more 

pronounced: The mathematical analysis summarized in 

Appendix 3 online shows that the long-run Sharpe ratio of 

the target-volatility index is always greater than the Sharpe 

ratio of the underlying equity index, as long as the target 

volatility level T is chosen below a certain threshold T< T*, 

which is greater than the long-run average volatility of the 

equity index, i.e., T *>s. 

Economically speaking, the reason for the improvement 

in the Sharpe ratio is the fact that the target volatility 

strategy is taking equity risk at the right point in time in 

the investment cycle: It takes more equity risk when the 

risk/reward profile of equity markets is very favorable 

(i.e., when volatility is low), and takes less equity risk and 

therefore a larger size of fixed-income investment when 

the risk/reward profile of equity markets is less favorable. 

Further, as we show in the online appendixes, the 

improvement of the Sharpe ratio of the target volatility 

strategy compared with its underlying blue-chip index is 

proportional to the volatility of the underlying equity index. 

The economic explanation is again obvious: When volatility 

is itself very volatile, then there is more opportunity to 

improve the Sharpe ratio compared with a plain equity 

investment by investing cyclically, i.e., taking more equity 

risk in phases of low volatility and less equity risk in phases 

of high volatility. Therefore, the more cyclical volatility is, 

the greater the improvement of the Sharpe ratio becomes. 

The mathematical results of the risk/return characteristics 

of target volatility indexes derived in the online appendixes 

are summarized in Figure 2. 

Figure 2 shows that the expected return of the target vola- 

Figure 2 

Expected Return Of The Risk-Control Strategy 

(Top) And Expected Sharpe Ratio (Bottom) 

As A Function Of The Target Volatility Level T 

The expected return has a clearly defined maximum at the volatility 

target Level T max . Further, the Sharpe ratio is always improved 

compared to the underlying index, as indicated in the bottom chart. 

Return 

In essence, determining the equity investment according 

to (6) means that the volatility of the strategy index will 

be the equity weight times equity volatility T/s* s=T, i.e., 

it is constant. At first glance, the advantage of the target 

volatility strategy is that it keeps the market risk of the 

investment strategy at a constant level. In particular, it 

reduces the size of the equity investment in turbulent 

markets, which are typically characterized by increasing 

levels of market volatility, and thereby protects investors 

from more serious losses when markets are falling. On 

the other hand, when market volatility is very low (which 

is typically the case in markets that are booming), the 

size of the equity investment is increased, possibly even 

above 100 percent to take advantage of leveraged returns. 

Sharpe ratio 

α 

{ 

T max 

Index 

T* 

Vol 

Vol 


47

tility index increases in the target volatility level up to a maximum 

T max 

. In other words, it does not make sense to choose a 

target volatility level above T max 

, because above this threshold 

expected returns decrease, while the risk increases. 

Figure 2 also demonstrates that the expected Sharpe 

ratio is a linearly decreasing function of the target volatility 

level that is located above the Sharpe ratio of the underlying 

equity index. In other words, a target volatility index 

with the target level chosen to equal the average level of 

volatility of the underlying equity index always has a better 

Sharpe ratio than the equity index in the long run. 

Even for target volatility levels chosen above the average 

volatility of the underlying index, the Sharpe ratio is 

improved up to a predefined level T*. 

In essence, the theoretical results suggest that any 

target volatility strategy index based on any equity index 

creates alpha from a risk/return perspective, i.e., it has a 

better long-run Sharpe ratio, as long as the target volatility 

level is chosen below T*. 

This is a very strong result, as it implies that investors 

in equity-based ETFs are always better off in the long run 

investing in the corresponding target volatility ETF instead 

of the pure equity ETF. 

Figure 3a 

Average Annualized Returns 

Sharpe Ratio 

Comparison Of Average Annualized Returns For Various 

Target Volatility Levels Of The Theoretically Predicted 

Values Vs. The Real-World Simulation 

3.5% 

3.0% 

2.5% 

2.0% 

1.5% 

1.0% 

0.5% 

0.0% 

0% 5% 10% 15% 20% 25% 30% 35% 

Target Volatility Level 

■ Predicted 

• Simulated 


Notes: The expected return values turn out to be very accurate across all target 

volatility levels. 

Figure 3b 

Comparison Of The Expected Sharpe Ratios 

As A Function Of The Target-Volatility Level 

0.14 

0.12 

0.10 

0.08 

0.06 

0.04 

0.02 

0.00 

-0.02 

-0.04 

■ 

-0.06 

-0.08 

0% 5% 10% 15% 20% 25% 30% 35% 

Target Volatility Level 

■ Predicted 

• 

Simulated ■ Index 


As for the leveraged indexes, it is important to verify 

these theoretical results by using real-world data. As a test 

case, we use the Euro Stoxx 50 Index and the EONIA money 

market rate as portfolio components. We compare the 

theoretical predictions for the expected return and expected 

Sharpe ratio detailed in the online appendixes and illustrated 

in Figure 2 to the actual simulation of the corresponding 

target volatility strategy using the index formulas (1) and (6). 

Figure 3 shows the expected returns and Sharpe ratios for a 

variety of target volatility levels T. The simulated values turn 

out to be very close to the predicted values across all target 

volatility levels for both expected returns and Sharpe ratios. 

In fact, the reason the heavy-tailed nature of equity return 

does not have a significant influence on our analysis is the 

results of our analysis in comparing long-term results, i.e., 

returns and Sharpe ratio over a period of several years. The 

influence of extreme adverse moves of equity markets, which 

appear occasionally due to the heavy tail of equity returns, get 

averaged out in the long run. In the short run, however, they 

can lead to significant differences between predicted and 

actual levels of expected returns and Sharpe ratios. 

Optimal Risk-Control Mechanism 

The existing strategies we have discussed so far—leveraged 

indexes and target volatility indexes—were derived 

heuristically but are not designed in an optimal way. As 

we show in this section, both for leveraged indexes and 

target volatility indexes, there is an optimal way to respond 

to volatility. Below we discuss the creation of optimal riskcontrol 

versions of both methodologies. 

Optimally Risk-Controlled Leveraged Indexes 

As we have seen in the previous section, leveraged 

indexes aim to enhance equity returns at the cost of lower 

Sharpe ratios. The main reason for this is the fact that 

existing leveraged indexes are not risk controlled. 

The model for the expected return of leveraged indexes 

(5) shows that the rebalancing losses of leveraged indexes 

increase with equity volatility. Therefore, it makes sense 

to design leveraged indexes in a risk-controlled way, i.e., 

they should decrease the level of leverage when volatility 

goes up and vice versa. 

Therefore, in Appendix 4 online, we derive an improved 

methodology that chooses the optimal way of adjusting 

the leverage factor (in other words, the size of the equity 

investment) to changes in volatility. The optimization 

shows that the optimal way of choosing the leverage factor 

is given by the following response function, determining 

the size of the equity investment of the leveraged index: 

(7) 

where μ-r denotes the equity risk premium of the underlying 

equity index. Compared with the target volatility response 

function (6), the optimally risk-controlled leverage index (7) 

shows a stronger response to changes in volatility. In particular, 

it takes more equity risk than the target volatility index 

in markets of low volatility, as it is a pure return strategy: It 

48 


Figure 4 

100,000 

10,000 

1,000 

100 

Figure 5 

Performance Comparison, 1992ÐJune 2011 

0 

3/92 3/94 3/96 3/98 3/00 3/02 3/04 3/06 3/08 3/10 

■ Euro Stoxx 50 ■ Target volatility 6% ■ Target volatility 16% 

■ Optimal risk 6% ■ Optimal risk 16% ■ Double leverage 

■ Optimal leverage 


Note: Compares double leverage strategy, risk control strategy, optimal risk strategy, 

optimal leverage strategy and the underlying Euro Stoxx 50 net return index. 

25% 

20% 

15% 

Risk/Return Comparison 

Optimal risk 16% 

10% 

Target vol 16% 

Optimal risk 6% Euro Stoxx 50 

5% 

Target vol 6% 

Optimal leverage 

Double leverage 

0% 

0% 10% 20% 30% 40% 50% 60% 


Note: The risk/return profle of the optimally risk-controlled leveraged index (”optimal 

leverage”) turns out to be a signifcant improvement compared to a standard double 

leverage index, whereas the optimal risk index shows only a minor improvement 

compared to the standard target volatility index. 

does not try to meet a certain volatility level, but aims to 

maximize returns and takes into account the prevailing 

level of volatility, in contrast to existing leveraged indexes 

that are not risk controlled. In Appendix 4 online, we show 

that using the equity weight (7) creates a leveraged strategy 

index that has a higher expected return than the underlying 

equity index, i.e., it is an algorithmic alpha generator, 

which we will verify by numerical backtests below. 

This is a very strong result, as it shows that incorporating a 

risk-control mechanism into the model framework of leveraged 

indexes creates strategy indexes that show a much stronger 

performance than existing strategy indexes in the long run. 

Optimally Risk-Controlled Target Volatility Indexes 

The bespoke target volatility strategy aims to keep 

the risk profile of the strategy index at a predefined 

volatility level, which can be achieved by choosing the 

equity investment according to the response function (6). 

However, this strategy does not optimize the long-run 

Sharpe ratio of the strategy index. 

The optimal risk-control strategy chooses the equity 

investment in such a way that the Sharpe ratio of the 

resulting index is maximized under the condition that the 

expected volatility of the index remains at a predefined target 

volatility level T. 

As we show in Appendix 5 online, the optimal target 

volatility index uses an equity investment that is very similar 

to the target volatility strategy (6), except that the equity 

investment is inversely proportional to the variance s 2 of 

the equity market instead of its volatility s, i.e., 

(8) 

with a proportionality factor given in Appendix 5 online. It is 

important to understand the economic difference between 

the target volatility strategy (6) and the optimal target volatility 

strategy (8): The target volatility strategy (6) is designed 

to keep the volatility of the investment scheme at the target 

level T at all times. However, the optimized response function 

(8) only targets the volatility level T on average and shows a 

stronger response to changes in volatility in the sense that 

in equity markets with relatively low levels of volatility, the 

equity investment is geared up such that investment portfolio 

is more volatile than average. On the other hand, when equity 

volatility is relatively high, the equity investment is reduced 

such that the investment portfolio is less volatile than average. 

To conclude, instead of keeping the volatility of the index 

portfolio at a constant level, the optimal risk strategy invests 

countercyclically: It takes more risk when equity markets are 

in a regime of low volatility but takes less risk when markets 

are at an above-average level of volatility. This countercyclical 

investment strategy achieves a better long-run Sharpe ratio 

than the pure target volatility strategy (6). 

Numerical Results 

To test the performance of the optimized risk-control 

mechanism presented above versus the nonoptimized 

methodologies, we use the Euro Stoxx 50 Index and the 

EONIA money market rate as portfolio components. 

As a volatility measure, we will use a historical standard 

deviation of the returns of the past 60 trading days. 

For estimating the return of the underlying equity index 

µ needed as input into (7), we use µ = the lower of either the 

annualized life-to-date return or the annualized return of the 

past 120 trading days of the index. The reason is that in the very 

long run, we expect µ to be (close to) constant. However, in the 

medium term, it is important to detect possible bear markets in 

the response function (7) to avoid situations where overly optimistic 

estimates of the underlying returns enter the investment 

scheme in the middle of a bear market. 

We simulate a constant leverage strategy (3) with L=2, 

the target volatility strategy (6), the optimally risk-controlled 

leveraged strategy (7) (which we refer to as “optimal 

leverage”) and the optimal target volatility strategy (8) 

(referred to as “optimal risk”) using the Euro Stoxx 50 as an 

underlying equity investment from 1992 to 2011. For the 


49

Figure 6 

Comparison Of The Target Volatility, Optimal Risk And Optimal Leveraged Strategies And The Underlying 

Euro Stoxx 50 Index From Beginning Of 1992 To June 2011 

Euro Stoxx 

50 

Double 

Leverage 

Target Vol 

6% 

Target Vol 

16% 

Optimal Risk 

6% 

Optimal 

Risk 16% 

Optimal 

Leverage 

Volatility [%pa] 22.1% 44.3% 6.4% 16.7% 6.4% 16.7% 47.9% 

Performance [%pa] 7.6% 6.8% 5.9% 9.0% 6.1% 9.4% 20.0% 

Sharpe Ratio 0.19 0.07 0.38 0.33 0.40 0.35 0.34 


target volatility strategy and the optimal risk strategy, we 

run two simulations—one with a target volatility level of 6 

percent and one with 16 percent. The performance is plotted 

in Figure 4, whereas the risk/return matrix is shown in 

Figure 5. Figure 6 summarizes the key performance figures 

and risk/return characteristics. 

We can draw two key observations from Figures 4 and 5. 

The first is that the target volatility strategy is very close to the 

optimal risk strategy in terms of risk/return profile and can 

therefore be considered as a very good proxy for the optimal 

risk investment scheme. Secondly, except for the constant 

leverage strategy, all schemes have a better risk/return profile 

than the underlying Euro Stoxx 50 Index, including the optimally 

risk-controlled leverage scheme (“optimal leverage”), 

equity proportion in their portfolio allocation. 

We have shown that incorporating a risk-control 

mechanism into the framework of leveraged indexes in 

the form of a response function that responds adversely 

to volatility leads to significant improvements in terms 

of absolute performance and Sharpe ratio. 

Regarding target volatility indexes, we have shown 

that their long-run Sharpe ratio is always better than the 

Sharpe ratio of the underlying equity index as long as the 

target volatility level is chosen within reasonable boundaries. 

Further, it is interesting to note that in practical 

simulations, we have seen that the existing target volatility 

strategy comes very close to the optimal risk strategy we 

have derived in this paper in terms of risk and performance 

The most popular investment scheme in the retail market is the 

double leverage approach, which shows a poor performance and very 

high levels of volatility in the long run. We therefore argue that investors 

are better off investing for the long term in either the optimal leverage 

strategy or the target volatility scheme, depending on their risk appetite. 

which shows an impressive outperformance over 19 years. 

The most popular investment scheme in the retail market 

(especially in the form of leveraged ETFs) is the double 

leverage approach, which shows a poor performance and 

very high levels of volatility in the long run. We therefore 

argue that investors are better off investing for the long 

term in either the optimal leverage strategy or the target 

volatility scheme, depending on their risk appetite. 

Conclusion 

We have derived a model for the risk/return profile 

of strategy indexes that combine an investment into an 

equity index and a money market investment in a rulesbased 

way. We have shown that using a rebalancing function 

that responds to the volatility of the underlying equity 

market is crucial to achieve a favorable risk/return profile. 

In the context of our model, we have seen that existing 

leveraged indexes are suboptimal from both a 

performance and Sharpe ratio perspective, as they are 

not risk controlled in any way, i.e., they use a constant 

profile. We therefore argue that from a practitioner’s point 

of view, existing target volatility indexes respond to volatility 

in an (almost) risk/return optimal way. 

It is important to mention that the mathematical results 

we have derived are independent of the underlying equity 

index and money market, i.e., they are robust across different 

markets and market regimes. 

Finally, this paper provides a clear recommendation 

to investors in passive equity strategies: In the long run, 

investors in equity indexes (e.g., in the form of indextracking 

ETFs or certificates) can clearly improve their 

long-run Sharpe ratio by shifting their investment to a 

corresponding target volatility index that offers comparable 

equity returns at lower levels of risk. 

Further, we have seen that the existing methodology 

of leveraged indexes can be improved substantially by 

incorporating a risk-control mechanism. We therefore 

propose index providers adjust their methodologies for 

calculating leveraged indexes to incorporate the riskcontrol 

mechanisms outlined in this paper. 

50 


References 

STOXX Index Guide 2010, http://www.stoxx.com/download/indices/rulebooks/stoxx_indexguide.pdf 

S&P 2010: Index mathematics, index methodologies, www.standardandpoors.com 

Rules for the Leverage indexes, NYSE Euronext, April 2008 

The EDHEC European ETF Survey 2009. May 2009. www.edhec-risk.com 

M. Baxter, A. Rennie: Financial Calculus: An Introduction to Derivative Pricing, Cambridge University Press, 1996 

M. Cheng, A. Madhavan: The Dynamics of Leveraged and Inverse-Exchange Traded Funds, Barclays Global Investors, May 2009 

M. Despande, D. Mallick, R. Bhatia: “Understanding Ultrashort ETFs”, Barclays Capital Special Report, 2009 

G. Giese: “On the risk return profile of leveraged and inverse ETFs”, in Journal of Asset Management, October 2010 

L. Lu, J. Wang, G. Zhang: Long Term Performance of Leveraged ETFs, Working paper, available at http://ssrn.com/abstract=1344133, August 2009 

R.C. Merton: “Optimum consumption and portfolio rules in a continuous time model”, in Journal of Economic Theory, vol. 3, 1971 

A.F. Perold and W.F. Sharpe: “Dynamic strategies for asset allocation”, Financial Analysts Journal, January 1995 

Endnote 

1 

Found with the online version of this article at http://www.indexuniverse.com/publications/journalofindexes.html 

Prestbo continued from page 33 

I think that gaining access either to parts of the overall 

capital market or to money management strategies that 

weren’t accessible before are good places for indexing to 

proliferate again. 

JOI: Is customization going to grow further? 

Prestbo: Customization is a marketing thing. It is banks 

and the like saying, “We want to do this and we want to put 

our name on it.” And that’s all that is. It’s really not about 

breaking a lot of new ground with customization—they’re 

basically just rebranding it. 

JOI: Can you handicap the current field of major index 

providers? Who do you think is going to, essentially, 

come out on top from the current field? Will there be 

further consolidation? 

Prestbo: I hear rumors about various index providers that 

they might be rethinking the value of that line of business. 

That’s new within the past couple years, as many, many 

more competitors have shown up in the field and market 

share is not as easy to come by. It’s possible there could 

be more consolidation, but it’s going to run into increased 

scrutiny by the government. 

The S&P/Dow Jones indexes deal got several months 

of scrutiny, and in the end, they let it happen, but the next 

one might not be so easy. There’s a whole bunch of ittybitty 

players that come, go, consolidate—who cares?—but 

the big ones would likely have a harder time next time 

around to acquire one of the other major competitors. 

Nonetheless, scale does matter in the index provider business, 

and that’s a reason for the S&P Dow Jones deal, and 

that will be the reason for any future combination. 

As far as handicapping the players, MSCI is clearly 

the one to beat. It’s on top of the pile now, and they 

aren’t nearly as sleepy and complacent as they once were. 

They’re much more aggressive and looking at the indexing 

field as part of a larger whole, which is a good strategy, too. 

Everybody else is going to try to knock them off their perch, 

but I think they’ve dug in pretty well. 

JOI: What do you think the smaller players—the boutique 

index providers that have ridden the ETF wave— 

need to do to survive? 

Prestbo: Well, I would sure hate to be them now because 

what they need primarily is distribution. They don’t have 

anybody else tooting their horn for them as the major 

index providers do; they don’t have established customer 

bases—people saying, “Well, I always go with so-and-so.” 

That’s not happening with these little boutique index providers. 

The main thing they have to do is broaden their 

base, but they’re trying to do it without any resources and 

that’s a very difficult challenge. 

It’s easy to know what they have to do. They have to get 

bigger and more successful. But without people, without 

budget, I don’t see how it’s going to happen. 

JOI: Where are costs headed for index licensees? 

Prestbo: Down. It’s the commission story all over again. 

Down, down, down. 

JOI: When an investor is looking at an index-based product, 

what should they be looking for in the underlying index? 

Prestbo: It depends on what they want to accomplish 

with it, as usual. That’s the hard part, and that’s the part 

that people like to skip over because it’s hard. You actually 

have to think. But once you’ve established what it is you 

want to accomplish, then you look at the underlying index 

for representation of that market or market segment. You 

look to see if the rules are in place to keep it up to date and 

fresh. Is it once a year? Is it once a quarter? And you look 

to see if it is—and this is the hard part—solidly conceived 

and solidly maintained. Some of that you have to infer 

by looking at who’s providing it: S&P, Dow Jones, MSCI, 

Russell? You infer that there is some muscle behind it. Joe 

Blow index fund? I don’t know. 


51

News 

S&P And Dow Jones 

Indexes Finalize Merger 

A new force emerged in the indexing 

industry in mid-June, when 

McGraw-Hill Companies and CME 

Group, the parent companies of 

S&P Indices and Dow Jones Indexes, 

respectively, jointly announced that 

the U.S. Department of Justice had 

signed off on the merger between the 

two index providers. With the last 

piece of approval in hand, the deal 

was finalized by the end of June. 

The new entity, S&P Dow Jones 

Indices, encompasses more than 

830,000 different indexes, a press 

release said. The statement also noted 

that the joint venture’s combined 

indexes underlie 575 ETFs worldwide, 

with total assets under management 

in those funds standing at $387 billion. 

McGraw-Hill Chairman, President 

and CEO Harold McGraw said in the 

press release that S&P Dow Jones 

Indices has the most assets invested 

in vehicles tied to its benchmarks of 

any index provider in the world. 

McGraw-Hill has a 73 percent stake 

in the new entity, while CME has a 

24.4 percent stake. The remaining 

2.6 percent is held indirectly by Dow 

Jones & Company, the statement said. 

The joint venture is led by Alexander 

Matturri, previously the executive 

managing director of S&P Indices. S&P 

Capital IQ President Lou Eccleston 

will act as chairman of a board that 

will include five directors selected by 

McGraw-Hill and two selected by CME 

Group, the press release said. 

Case-Shiller Indexes 

See April Uptick 

U.S. home values ticked up in April 

after dropping for seven-straight months 

to new lows, though prices were buoyed 

largely by increased buying seen in 

warmer months, the June S&P/Case 

Shiller Home Price Index report showed. 

While many expected home prices 

to improve in April, as buyers often 

choose the spring and summer months 

to make their move ahead of upcoming 

school years, the report also linked 

the price rise to an improvement in 

demand and rising housing starts. 

Both the 10- and 20-City composites 

were up 1.3 percent in April from March 

levels, with all but one city surveyed seeing 

a month-on-month pickup in prices. 

The improvement comes on the heels 

of a recent drop to lows not seen since 

the market started its spiraling decline in 

mid-2006. An uptick after seven months 

of sustained declines is encouraging even 

if, overall, home prices remain about a 

third lower than where they were just six 

years ago, the report suggested. 

While on an annual basis, the 

10-City and 20-City Composites are 

still respectively 2.2 percent and 1.9 

percent lower than they were in April 

2011, those year-on-year comparisons 

were far worse earlier this year. 

In April, Detroit was the only city to 

see a month-on-month decline, with 

home prices dropping 3.6 percent 

relative to March levels. Meanwhile, 

Atlanta remains the only city to show 

a double-digit year-on-year decline. 

Home values in Atlanta were 17 percent 

lower than in April 2011. 

On the other side of the spectrum 

is Phoenix, which leads the pack 

“with improving trends,” the report 

said. Home prices in that southwestern 

city rose 2.5 percent in April vs. 

March, and are now 8.6 percent higher 

year-on-year. That was the highest 

annual rate of improvement among 

the 20 cities surveyed. 

Russell Completes 

Annual Reconstitution 

Russell Investments added 197 companies 

to its broad-market Russell 3000 

Index as part of its annual reconstitution 

this year, of which it said 39 were initial 

public offerings, including Facebook’s. 

Overall market value of the index meanwhile 

fell more than 5 percent. 

The Russell 3000, which reflects 

about 98 percent of the U.S. equities 

universe, had a total market value 

of $15.8 trillion as of May 31, down 

5.4 percent from the year-earlier figure 

of $16.7 trillion. The Russell 3000 

comprises the small-cap stocks of the 

Russell 2000 and the large-cap stocks 

of the Russell 1000. Russell added 186 

firms in 2011’s reconstitution. 

The changes to U.S. companies in 

Russell indexes, as well as additions 

and deletions to the Russell Global 

Index, were to be finalized on June 25, 

according to the press release. 

Russell also noted in its press 

release that the median market capitalization 

of the Russell 3000 would 

decrease by 12.5 percent to $910 million 

from $1.04 billion in 2011. 

It said financial services led the list 

of additions with 62, and the sector 

remains the most heavily weighted 

index in the Russell 3000, at 17.1 percent. 

That said, technology’s weighting 

increased the most in the past 

year, and now comprises 17 percent 

of the overall index. 

Russell research showed that Apple 

is expected to replace ExxonMobil as 

the largest company in the Russell 3000 

in terms of market capitalization, with 

the companies valued at $540.2 billion 

and $367.7 billion, respectively. Apple’s 

market value grew by more than twothirds 

in the past year, Russell said. 

On the preliminary list, the largest 

addition to the Russell 3000 by market 

capitalization is Facebook, which had 

its initial public offering in May. 

MSCI Announces 

Classification Decisions 

In June, MSCI said South Korea and 

Taiwan, two countries that are part of 

huge ETFs such as the Vanguard MSCI 

52 


Emerging Markets ETF (NYSE Arca: 

VWO), have retained their developingmarket 

status because they still lack 

accessibility. But, as MSCI said a year 

ago, the two countries remain under 

review for possible reclassification to 

developed status a year from now. 

In its announcement of its annual 

reclassification review, MSCI also 

again said further consideration of 

whether Qatar and the United Arab 

Emirates ought to be shifted to emerging 

market status from their current 

frontier market status will be extended 

another year. It noted, as it has previously, 

that it needs more time to assess 

recent changes to those two markets. 

Two new developments this year 

were that debt-gorged Greece was 

added to MSCI’s list of possible reclassification 

to an emerging markets 

country from its current developedmarket 

status, and that Morocco will 

potentially be considered as a candidate 

for the MSCI Frontier Markets 

Index. Both decisions will also be 

taken up a year from now, MSCI said 

in a June 20 press release. 

MSCI stressed that operational 

issues, mission-critical to institutional 

investors, are behind its decisions, and 

that making changes to the indexes 

before those operational shortcomings 

are rectified would force investors to 

make case-by-case adjustments they 

simply aren’t expecting. The promotions 

for Korea, Qatar and the UAE have been 

on the table for the past four years, and 

in the case of Taiwan, for the past three. 

The company announces changes or 

potential changes every June, and any 

changes it makes aren’t implemented 

until the following June. Its next classification 

review is in June 2013. 

Barclays Divesting 

BlackRock Stake 

Barclays Plc, the U.K.-based company 

behind the iPath family of ETNs 

as well as some of the most widely 

used fixed-income indexes, plans to 

sell its $6.1 billion, 19.6 percent stake 

in New York-based asset manager 

BlackRock back to BlackRock. 

The $6.1 billion stake, made up of 

common shares and Series B convertible 

participating preferred stock, is 

valued at £3.8 billion—a bit more than 

the £3.4 billion fair value at which 

the investment was written down in 

September 2011, Barclays said in a 

May press release. 

Barclays said BlackRock filed regulatory 

paperwork on May 21 outlining 

the transaction, under which 

the British bank intends to exercise 

its option to sell its entire holding in 

BlackRock, the world’s biggest publicly 

traded asset manager. 

The holding dates back to 

BlackRock’s 2009 acquisition of 

S&P Dow Jones Indices 

in July launched an index series 

focused on target-date investing. 

Barclays Global Investors—the company 

that first developed the iShares 

brand of exchange-traded funds. 

iShares is the world’s largest ETF 

company, with some $466 billion in 

ETF assets, according to data compiled 

by IndexUniverse. 

INDEXING DEVELOPMENTS 

S&P DJ Rolls Out 

Target-Date Indexes 

S&P Dow Jones Indices in July 

launched an index series focused on 

target-date investing that’s designed 

to help plan sponsors in the “defined 

contribution” retirement market parse 

asset allocation plans based on either 

retirement dates or actual life spans of 

plan participants. 

The S&P Target Date Style Index 

Series selects and monitors targetdate 

funds by providing separate 


News 

performance comparisons for asset 

allocation plans with risks designed 

around retirement date versus those 

that remain more aggressive to 

account for income needs long after 

actual retirement, the indexing company 

said in a press release. Each 

index in the series is fully investable. 

Based on the S&P Target Date 

Index’s methodology, asset allocation 

plans for the S&P Target Date 

Style Indices reflect consensus positions 

derived from an annual survey of 

target-date funds’ holdings, the company 

said in the press release. 

The current universe of eligible 

asset classes includes: U.S. large-, midand 

small-cap stocks; international 

equities; emerging markets; U.S. REITs; 

core fixed income; cash equivalents; 

TIPS and high-yield corporate bonds. 

Each asset class is represented in 

the indexes via a different ETF, the 

indexing company said. 

FTSE, Cürex Launch 

Currency Indexes 

FTSE teamed up with Cürex Group, 

a provider of foreign exchange indexes 

and technologies, to launch the FTSE 

Cürex FX Index Series in June. The 

new index family includes indexes for 

192 currency pairs and a range of currency 

baskets, the press release said. It 

noted that customized combinations 

of currencies were also available. 

According to the index family’s 

brochure, the benchmarks are based 

on the bid/offer spread, with a variety 

of indexes available for each pair and 

basket, including bid or offer indexes, 

spot indexes, total return indexes and 

overnight rate indexes. 

Markit Debuts 

LatAm CDS Benchmark 

Markit kicked off the summer 

with the launch of the first-ever credit 

default swap index to cover Latin 

American corporate debt. The Markit 

CDX LatAm Corporate Index covers 

Central and South America and the 

Caribbean, the press release said. 

Component issuers are chosen 

based on two criteria, according to 

the statement by Markit: the liquidity 

of the issuer’s credit default swaps; 

and how much debt the corporation 

in question has issued. 

Markit said that investor interest 

in Latin American debt is growing, in 

part due to high yields and to demonstrated 

fiscal and monetary policy 

discipline. At the time of the launch, 

the index provider already calculated 

CDS indexes that target North 

America and emerging markets. 

S&P Simplifies Float Rules 

S&P closed out May with the 

announcement that it was changing 

how it adjusts for float in its entire lineup 

of equity indexes. The methodology 

revision will go into effect in September 

for the developed- and emerging-market 

indexes and in December for the 

frontier indexes, the press release said. 

Currently, S&P sorts large shareholders 

into one of three groups, the statement 

said: officers and directors; strategic 

investors; and government entities. 

Any block holding representing more 

than 10 percent of the company’s market 

value is excluded from the index. 

The new methodology will exclude 

all holdings of 5 percent or more, 

except those owned by asset managers, 

the statement said. However, the 5 

percent rule will be adjusted to reflect 

local reporting standards in cases 

where the threshold for disclosure is 

higher than 5 percent. 

S&P said that the methodology 

adjustment brings the index provider’s 

rules in line with best practices for 

the industry and that the impact on 

the actual indexes would be minimal. 

DJI, Parala Debut Macro 

Allocation Benchmarks 

Dow Jones Indexes partnered with 

investment firm Parala Capital to construct 

the Dow Jones Parala Macro 

Allocation Indexes, which launched 

in late May, a press release said. The 

series methodology is designed to 

allocate money among different market 

segments based on macroeconomic 

and risk-related signals. 

Parala’s proprietary methodology 

determines whether a segment should 

be over- or underweighted and assigns 

scores accordingly; the scores are used 

to create weightings for each segment 

included in the indexes, the press 

release said. Each benchmark in the 

series will consist of a variety of subindexes 

weighted according to Parala’s 

macro allocation methodology. 

The initial launch of the index series 

included just one index, the press 

release said: the Dow Jones Parala 

Global Sector Macro Allocation Index, 

which comprises the 19 global Dow 

Jones Sector Titans indexes. However, 

indexes covering U.S. sectors and 

emerging markets sectors have since 

been added to the lineup. 

Wall Street Journal 

Launches Dollar Index 

The Wall Street Journal reported in 

mid-July that it had teamed up with 

DJ FX Trader to create an index that 

tracks the U.S. dollar against seven 

other currencies. 

According to the Journal, what 

makes the Wall Street Journal Dollar 

Index unique among other dollar indexes 

is the fact that it weights its component 

currencies by foreign-exchange 

trading volume, rather than, say, equalweighting 

them. The index weightings 

rely on data released by the Bank for 

International Settlements, the Journal 

article said. The BIS publishes a triennial 

market survey of foreign exchange 

trading, and the index will be updated 

in accordance with that. The index currently 

uses weightings established by 

the 2010 survey, the article noted. 

The WSJ Dollar Index’s current 

basket of currencies includes the euro, 

yen, British pound, Australian dollar, 

Canadian dollar, Swiss franc and 

Swedish krona. The index’s activity 

can be accessed via the symbol BUXX 

on both the MarketWatch.com and 

WSJ.com websites, the Journal said. 

MSCI Lays Out Plan For Greece 

In late May, MSCI laid out a map for 

what could happen with regard to its 

indexes should Greece leave the eurozone—an 

event many are coming to 

54 


view as more and more of a certainty. 

In the Q&A document released by 

the index provider, two possibilities 

were considered: an orderly departure 

and a disorderly departure. Should the 

former occur, MSCI suggested the process 

would resemble the one it implemented 

in 2005 with the rollout of the 

new Turkish lira. In other words, the 

event would simply be treated as if it 

were the launch of a new currency, 

with the MSCI indexes reflecting the 

currency change starting from an effective 

date announced in advance by 

the Athens Stock Exchange. MSCI said 

that Greek stocks would be removed 

from the MSCI EMU Index based on 

its assessment of the transition process 

established by Greek authorities; the 

index provider also pointed out that a 

review of Greece’s market classification 

based on its departure from the 

euro is not a foregone conclusion. 

In the event of a disorderly exit, MSCI 

warned that communications by Greek 

authorities could be inadequate and 

that market access could be restricted. 

In such a case, MSCI said its planned 

timeline for the transition could be disrupted 

and that it would consult with 

market participants. Depending on that 

feedback and the actions taken by Greek 

authorities, MSCI said that it would consider 

a public consultation on its treatment 

of Greece—whether it should be 

removed from the developed-markets 

classification and whether its index 

should be calculated as a “stand-alone” 

benchmark, something MSCI has opted 

to do with Pakistan and Malaysia on 

previous occasions. 

The information sheet also addressed 

specific questions about timely communication, 

market classifications and the 

impact on MSCI’s other indexes. 

Dow Jones Adds To 

RBP Index Family 

In late June, Dow Jones Indexes rolled 

out a new member of its Dow Jones RBP 

index family, a press release said. 

The Dow Jones RBP U.S. Directional 

Allocation Index uses moving averages 

signals from key economic indicators 

and other DJI benchmarks to shift its 

allocations among a cash component 

and three benchmarks—the Dow 

Jones RBP U.S. Large-Cap 50 Market 

Index, the Dow Jones RBP U.S. Large- 

Cap 50 Aggressive Index and the Dow 

Jones RBP U.S. Large-Cap 50 Defensive 

Index. Weightings among the four 

buckets could be updated as often as 

weekly, given the appropriate market 

signals, the press release indicated. 

The RBP, or “Required Business 

Performance,” metric underlying 

the index series was developed by 

Transparent Value LLC and is intended 

to reflect whether a company will be 

successful enough in the future to justify 

its current stock price. Individual 

components in the indexes in the Dow 

Jones RBP index series are generally 

weighted by their RBP scores. 

S&P DJ Indices Launches 

‘GIVI’ China A-Share Index 

S&P Dow Jones Indices debuted 

the S&P GIVI China A-Share Index in 

late July, according to a press release. 

The S&P GIVI family is constructed 

using the intrinsic values of its 

component companies; the intrinsic 

value metric determines each company’s 

weighting and is based on 

assets and growth prospects. The 

index family also targets lower volatility 

by screening out the highestbeta 

stocks, the press release said. 

The S&P GIVI China A-Share index 

is derived from the S&P CITIC All Cap 

Index using the GIVI methodology. 

The index has already been licensed 

to Goldman Sachs Asset Management, 

which participated in its development, 


MSCI Shifts ESG Indexes 

To New Methodology 

In May, MSCI said that it would be 

effecting some changes in the methodologies 

of its various ESG indexes based 

on feedback from market participants. 

It has already begun calculating 

provisional indexes based on the 

new methodology for its MSCI Global 

ESG indexes as of the May 2012 index 

review, and will continue to do so for 

the next year, until the May 2013 index 

review, the index provider said. 

Meanwhile, the MSCI Global 

Socially Responsible indexes made 

the change in one fell swoop during 

the May 2012 review, according to the 

press release. They now seek to reflect 

at least 25 percent of each GICS sector 

of the respective MSCI regional ESG 

indexes from which they are derived. 

The MSCI KLD 400 Social Index 

is undergoing a two-phase transition 

that will take place over eight index 

reviews and involves weeding out the 

components with lower ESG ratings 

and the ones that are not components 

of the pro forma MSCI USA IMI ESG 

Index. The methodology review also 

resulted in the annual turnover cap for 

the MSCI USA ESG Select Index being 

lowered to 30 percent from 50 percent, 


AROUND THE WORLD OF ETFs 

SSgA Debuts More 

Corporate Debt SPDRs 

State Street Global Advisors in 

June launched two corporate bond 

ETFs—one focused on a combination 

of U.S. investment-grade and highyield 

credits and the other focused 

on investment-grade corporate debt 

from the emerging markets. 

The SPDR BofA Merrill Lynch 

Crossover Corporate Bond ETF (NYSE 

Arca: XOVR), which has an expense 


News 

ratio of 0.30 percent, straddles the 

lower end of the investment-grade 

debt and the higher end of the highyield 

debt categories. Its primary competitor 

is the iShares Baa-Ba Corporate 

Bond Fund (BATS: QLTB), which also 

charges 0.30 percent. 

The SPDR BofA Merrill Lynch 

Emerging Markets Corporate Bond 

ETF (NYSE Arca: EMCD) comes with 

an expense ratio of 0.50 percent; its 

largest competitor is the actively managed 

WisdomTree Emerging Markets 

Corporate Bond Fund (Nasdaq GM: 

EMCB), which carries an annual 

expense ratio of 0.60 percent. 

SOCL Adds Facebook 

A week after one of the most 

infamous initial public offerings in 

history, Facebook quietly made its 

debut in the ETF world in late May, 

as the third-biggest holding of the 

$25 million Global X Social Media 

Index ETF (NYSE Arca: SOCL). 

When it was added, Facebook 

had an 8.79 percent weighting in the 

fund, behind first-place LinkedIn and 

Tencent Holdings. 

Interestingly, even before the addition, 

Facebook’s IPO appeared to trigger 

a boost to SOCL’s assets, driving them 

up from $18 million to $25 million. 

However, the ETF that is really getting 

scrutiny with regard to Facebook 

is the PowerShares QQQ Trust 

(Nasdaq GM: QQQ). Due to a recent 

rule change, Facebook could be added 

to the fund’s underlying Nasdaq-100 

Index just three months after its IPO. 

The fund’s potential weighting in that 

ETF is uncertain because the underlying 

Nasdaq index bases its weighting 

on the value of the shares issued, and 

not the value of the company. 

USCF Launches First 

Broad Metals ETF 

United States Commodity Funds 

in June launched the first broad 

futures-based metals fund that covers 

both precious and industrial metals. 

Previously, most futures-based 

metals ETFs and ETNs have either 

focused on industrial or precious 

metals, but not both, with a few products 

targeting specific metals. 

The United States Metals Fund 

(NYSE Arca: USMI), which comes with 

an all-in annual cost of about 0.90 percent, 

has a management fee of 70 basis 

points, while trading commissions will 

max out at 5 basis points and “other” 

expenses won’t exceed 15 basis points, 

according to the company. 

The fund has 10 eligible metals, to 

which it assigns weights based on an 

assessment of each metal’s market 

liquidity and overall economic importance. 

Those metals, which currently 

are traded primarily on major U.S. or 

U.K. exchanges, include primary aluminum, 

copper, nickel, zinc, lead, tin, 

platinum, silver, palladium and gold. 

ProShares Rolls Out 

Bearish Euro ETF 

ProShares rolled out a bearish euro 

play versus the dollar in late June. 

The ProShares Short Euro ETF (NYSE 

Arca: EUFX) offers the single daily 

inverse performance of the spot U.S. 

dollar price of the euro as measured 

by the EUR/USD cross rate published 

by Bloomberg at 4 p.m. every day. 

EUFX costs 0.95 percent. 

The fund owns currency futures 

contracts and relies on cash instruments 

and U.S. Treasurys for collateral, 

the company said in its most 

recent prospectus. 

With EUFX, ProShares now has five 

currency-focused ETFs on the market, 

including EUFX and two other 

double-exposure euro-dollar plays, as 

well as two yen-focused funds—one a 

double-exposure bullish play on the 

yen-dollar cross, the other a doubleexposure 

bearish play. 

Van Eck Acquires 

Australian ETF Firm 

Van Eck has acquired Australian 

Index Investments in an apparent bet 

on the growth potential of Australia’s 

ETF markets. Terms weren’t disclosed. 

Australian Index Investments, 

renamed Market Vectors Australia 

after the transaction, currently oversees 

close to AUD $30 million in ETF 

assets under management, Van Eck 

said in a June press release. 

Market Vectors Australia will continue 

to market and dispense ETFs in the 

Australian market. Those efforts will 

include the Sydney-based Australian 

unit’s six existing sector-focused ETFs. 

The company is also retaining its staff 

and Chief Executive Officer Annmaree 

Varelas, Van Eck said. 

As of now, the Australian ETF 

market—with 70 ETFs and about $5 

billion in assets—is in its infancy 

compared with markets in Europe 

and the U.S. 

Global X Unveils Hedge Fund ETF 

Global X rolled out an ETF targeting 

the top holdings of leading hedge 

fund managers in early June. The 

Global X Top Guru Holdings Index 

ETF (NYSE Arca: GURU) invests in top 

U.S.-listed equity positions reported 

in 13F regulatory filings made by a 

select group of hedge funds, as identified 

by the fund’s Germany-based 

index provider Structured Solutions. 

The index’s components are selected 

based on 13F filings, which hedge fund 

managers submit to the Securities and 

Exchange Commission 45 days after the 

end of each quarter. Global X noted in 

GURU’s registration statement that as 

of April 30, the fund’s underlying index 

included 51 constituents, drawn from 

the filings of a pool of 68 different hedge 

funds. The index methodology screens 

out hedge funds that have less than $500 

million in assets or high turnover rates 

in their equity holdings. 

GURU comes with an annual 

expense ratio of 0.75 percent, according 

to the fund’s latest prospectus. 

UBS Adds To Etracs Lineup 

UBS launched two leveraged dividend-focused 

ETNs in late May. 

The Etracs Monthly Pay 2x 

Leveraged Dow Jones Select Dividend 

Index ETN (NYSE Arca: DVYL) and 

the Etracs Monthly Pay 2x Leveraged 

S&P Dividend ETN (NYSE Arca: SDYL) 

are similar in that they both double 

the performance of their respective 

benchmarks. DVYL charges an annual 


expense ratio of 0.35 percent, while 

SDYL costs 0.30 percent. 

Although both DVYL and SDYL 

target companies that have high and 

growing dividends and weight those 

companies by their indicated annual 

dividends, there are some key differences 

in their index methodologies. 

DVYL is linked to the Dow Jones 

U.S. Select Dividend Index, which 

only looks back at five years’ worth 

of a stock’s dividend history. By comparison, 

the S&P High Yield Dividend 

Aristocrats Index underlying SDYL 

goes back 25 years into a stock’s 

dividend performance to select its 

securities. Also, SDYL’s index caps 

individual components at a 4 percent 

weighting, while DVYL’s index caps 

them at 10 percent. 

KNOW YOUR OPTIONS 

CBOE Volume Up Slightly In June 

The Chicago Board Options 

Exchange reported a total volume of 

96.7 million contracts for the month 

of June, working out to an average 

daily volume of 4.6 million contracts. 

Although that represented a 2 percent 

increase over June 2011, the ADV was a 

7 percent decline from the prior month. 

Year-over-year, index options saw 

their average daily volume fall by 4 

percent; however, options on ETFs 

saw their volume increase by 9 percent, 

according to data provided in 

the press release. 

In June, the most actively traded 

options on indexes and ETFs 

included those on the S&P 500 Index 

(SPX), SPDR S&P 500 (NYSE Arca: 

SPY), CBOE Volatility Index (VIX), 

PowerShares QQQ Trust (Nasdaq GM: 

QQQ) and the iShares Russell 2000 

Index Fund (NYSE Arca: IWM). 

S&P, CBOE Win Injunction Battle 

At the end of May, S&P’s parent 

McGraw-Hill and the Chicago Board 

Options Exchange won a court battle 

in Illinois enforcing a 2010 injunction 

against the International Securities 

Exchange (ISE) prohibiting ISE from 

listing and trading options based on 

the S&P 500 Index (S&P 500) or on the 

Dow Jones industrial average (DJIA). 

McGraw-Hill and CBOE argued 

in the Chicago appellate court case 

that the ISE products are essentially 

based upon proprietary information 

that both companies had spent 

decades developing. 

The injunction also forbids OCC 

(formerly, “The Options Clearing 

Corporation”), which clears trading 

for all U.S. options exchanges, from 

clearing options on the S&P 500 Index 

or the Dow Jones industrial average 

unless they are traded in accordance 

with CBOE’s license. 

BACK TO THE FUTURES 

CME Sees June Volumes Fall 

CME Group said in a press release 

that its average daily volume fell in 

June by 11 percent from the previous 

year. The month’s total volume traded 

was 276 million contracts. 

Most of that decline was due to 

interest-rate contracts, which saw their 

average daily June volume fall by 28 

percent. Meanwhile, index contracts 

experienced a decline of just 1 percent, 

and the average daily volume for foreign 

exchange contracts was up 8 percent. 

The CME’s most actively traded 

futures contract, the e-mini S&P 500, 

experienced a volume decline of 7.2 

percent for the month, with less than 

55 million contracts traded, according 

to the CME web site. Meanwhile, 

the e-mini Nasdaq 100 contract 

saw its volume fall by 10 percent 

in June to roughly 6.1 million contracts 

traded. However, the mini $5 

Dow futures contract—the third most 

actively traded index futures contract 

listed with the CME—experienced an 

increase in volume of 12.4 percent, to 

nearly 3.4 million. 

ON THE MOVE 

Vanguard CIO Sauter 

Stepping Down 

Vanguard’s Chief Investment Officer, 

Gus Sauter, will be retiring at the end of 

2012, the firm has announced. 

Sauter joined Vanguard in 1987 

and has been closely associated with 

the development of the firm’s index 

and exchange-traded fund business. 

Vanguard is now the largest manager 

of U.S. mutual funds, controlling 

around $1.8 trillion in assets. 

Sauter will be replaced as CIO 

by Tim Buckley, who most recently 

worked as head of the firm’s retail 

investor group. 

TIAA-CREF’s Hammond 

Joins MSCI 

In June, MSCI Inc. said in a press 

release that Brett Hammond had 

been hired as a managing director 

and the head of index applied 

research. Hammond reports to Remy 

Briand, the firm’s global head of 

index and ESG research. 

Hammond will be directing MSCI’s 

applied research activities with the purpose 

of supporting the firm’s index business, 


Most recently, Hammond was the 

chief investment strategist at TIAA- 

CREF. Prior to that, he was part of 

the senior management team at 

the National Research Council in 

Washington, D.C. Hammond is also 

an adjunct professor at The Wharton 

School, University of Pennsylvania, 

the press release said. 

He earned his B.A. degree from the 

University of California at Santa Cruz 

and his Ph.D. from the Massachusetts 

Institute of Technology. 

FTSE Makes ESG Hires 

In May, FTSE rolled out its new 

Environmental Social Governance 

unit, according to a press release. 

The new division encompasses 

FTSE’s previously established responsible 

investment team and its newly 

formed ESG analytics team, the press 

release said. 

Kevin Bourne and Gordon 

Morrison, both co-founders of ESG 

research firm LCE Risk, have each 

joined the index provider in its new 

division, with Bourne heading up the 

entire ESG unit and Morrison leading 

the analytics team. Before forming LCE 

Risk, Bourne and Morrison held senior 

management positions in the equities 

arm of HSBC, the press release said. 


57

Global Index Data 

Selected Major Indexes Sorted By YTD Returns 

September/October 2012 

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 Egypt* 32.06 -48.78 9.47 32.77 -53.92 54.85 14.84 154.49 -5.53 -8.97 23.34 6.62 -0.02 33.17 

Citigroup Portuguese GBI 24.69 -24.91 -14.51 7.72 3.85 13.45 11.78 -9.57 -5.55 0.10 5.43 4.54 -0.10 26.02 

NASDAQ 100 15.44 3.66 20.14 54.61 -41.57 19.24 7.28 1.89 22.01 7.01 10.12 - 1.16 18.40 

Wilshire US REIT 14.90 9.24 28.60 28.60 -39.20 -17.55 35.97 13.82 33.62 2.05 10.31 9.82 1.48 21.14 

Russell Micro Cap 13.01 -9.27 28.89 27.48 -39.78 -8.00 16.54 2.57 16.71 -2.19 5.89 - 0.78 23.13 

Russell 1000 Growth 10.08 2.64 16.71 37.21 -38.44 11.81 9.07 5.26 17.50 2.87 6.03 3.88 1.06 16.49 

Russell 3000 Growth 9.98 2.18 17.64 37.01 -38.44 11.40 9.46 5.17 17.55 2.79 6.13 3.88 1.04 16.84 

S&P 500 9.49 2.11 15.06 26.46 -37.00 5.49 15.79 4.91 16.40 0.22 5.33 4.77 1.02 16.11 

S&P 500/Citi Pure Growth 9.45 0.75 27.65 50.85 -38.99 6.64 7.43 7.31 22.39 5.06 9.46 8.12 1.15 19.17 

Russell 1000 9.38 1.50 16.10 28.43 -37.60 5.77 15.46 6.27 16.64 0.39 5.72 5.11 1.02 16.47 

Russell 3000 9.32 1.03 16.93 28.34 -37.31 5.14 15.72 6.12 16.73 0.39 5.81 5.15 1.00 16.82 

Wilshire 5000 Total Market 9.22 0.98 17.16 28.30 -37.23 5.62 15.77 6.38 16.65 0.43 6.04 5.21 1.01 16.64 

S&P SmCap 600/Citi Pure Growth 8.95 5.21 28.74 37.70 -33.10 1.49 9.79 7.10 22.56 4.76 9.71 9.65 1.09 20.60 

Russell 2000 Growth 8.81 -2.91 29.09 34.47 -38.54 7.05 13.35 4.15 18.09 1.99 7.39 4.17 0.86 22.21 

Russell 1000 Value 8.68 0.39 15.51 19.69 -36.85 -0.17 22.25 7.05 15.80 -2.19 5.28 5.69 0.95 16.80 

Russell 3000 Value 8.64 -0.10 16.23 19.76 -36.25 -1.01 22.34 6.85 15.93 -2.10 5.37 5.80 0.95 17.13 

Russell 2000 8.53 -4.18 26.85 27.17 -33.79 -1.57 18.37 4.55 17.80 0.54 7.00 6.14 0.86 21.87 

Russell 2000 Value 8.23 -5.50 24.50 20.58 -28.92 -9.78 23.48 4.71 17.43 -1.05 6.50 7.56 0.84 21.86 

S&P 500 Equal Weighted 8.08 -0.11 21.91 46.31 -39.72 1.53 15.80 8.06 19.43 1.60 8.03 7.65 1.05 18.50 

S&P SmallCap 600 7.98 1.02 26.31 25.57 -31.07 -0.30 15.12 7.68 19.78 1.83 7.91 8.03 0.97 20.68 

S&P MidCap 400 7.90 -1.73 26.64 37.38 -36.23 7.98 10.32 12.56 19.36 2.55 8.21 9.64 1.01 19.38 

Barclays Global High Yield 7.67 3.12 14.82 59.40 -26.89 3.18 13.69 3.59 15.76 8.37 11.23 8.06 1.51 10.00 

S&P MidCap 400/Citi Pure Growth 7.59 0.62 35.16 60.34 -35.17 10.30 4.98 12.06 23.84 8.27 11.16 12.24 1.16 20.22 

MSCI EM Small 7.27 -27.18 27.17 113.79 -58.23 42.26 32.35 29.17 11.13 -0.72 15.22 4.77 0.56 23.70 

Barclays US Corp High Yield 7.27 4.98 15.12 58.21 -26.16 1.87 11.85 2.74 16.28 8.45 10.16 7.00 1.86 8.29 

Barclays EM 6.95 6.97 12.84 34.23 -14.75 5.15 9.96 12.27 13.75 8.99 11.79 9.56 1.86 7.03 

DJ Industrial Average 6.83 8.38 14.06 22.68 -31.93 8.88 19.05 1.72 18.25 2.00 6.02 5.85 1.22 14.62 

S&P 500/Citi Pure Value 6.65 -0.81 23.06 55.21 -47.87 -3.69 20.04 13.43 23.15 -1.39 6.79 8.02 1.02 22.83 

S&P MidCap 400/Citi Pure Value 6.39 -5.07 23.19 59.18 -42.58 -3.20 19.31 9.37 22.05 0.26 7.76 9.09 0.94 24.48 

Dow Jones Utilities Average 5.75 19.71 6.46 12.47 -27.84 20.11 16.63 25.14 15.31 3.48 10.04 9.28 1.44 10.22 

MSCI ACWI 5.65 -7.35 12.67 34.63 -42.19 11.66 20.95 10.84 10.80 -2.70 5.73 - 0.65 18.25 

S&P SmCap 600/Citi Pure Value 5.54 -7.50 29.18 63.58 -41.73 -18.61 21.44 11.58 15.95 -1.33 7.12 8.46 0.65 29.26 

Citigroup STRIPS 25+ Year 5.29 60.67 10.18 -42.88 77.10 12.71 4.09 17.82 19.87 17.84 13.76 12.78 0.78 27.76 

MSCI EAFE Small Cap 4.92 -15.94 22.04 46.78 -47.01 1.45 19.31 26.19 9.17 -5.32 8.49 - 0.52 20.91 

DJ Transportation Average 4.62 0.01 26.74 18.58 -21.41 1.43 9.81 11.65 19.29 2.20 8.22 5.98 0.93 21.33 

MSCI AC Asia Paciûc 4.46 -15.11 17.02 37.59 -41.85 14.29 16.49 23.34 6.95 -2.87 6.83 - 0.47 17.42 

Barclays US Treasury US TIPS 4.04 13.56 6.31 11.41 -2.35 11.64 0.41 2.84 9.63 8.44 7.23 7.31 1.92 4.81 

MSCI EM 3.93 -18.42 18.88 78.51 -53.33 39.42 32.14 34.00 9.77 -0.09 14.08 - 0.52 22.98 

MSCI EAFE Growth 3.86 -12.11 12.25 29.36 -42.70 16.45 22.33 13.28 7.62 -4.60 4.91 1.81 0.46 19.76 

Barclays Municipal 3.66 10.70 2.38 12.91 -2.47 3.36 4.84 3.51 7.62 5.95 5.28 5.61 1.71 4.31 

MSCI EAFE 2.96 -12.14 7.75 31.78 -43.38 11.17 26.34 13.54 5.96 -6.10 5.14 2.86 0.38 20.44 

STOXX Europe TMI 2.86 -11.80 5.10 37.50 -46.75 13.07 35.39 10.11 6.66 -6.84 5.51 4.44 0.38 23.67 

Barclays US Aggregate Bond 2.37 7.84 6.54 5.93 5.24 6.97 4.33 2.43 6.93 6.79 5.63 6.27 2.43 2.74 

MSCI EAFE GDP Weighted 2.31 -14.33 3.14 30.38 -44.82 12.88 27.39 13.68 3.37 -8.05 4.58 3.24 0.25 22.22 

MSCI EAFE Value 1.98 -12.17 3.25 34.23 -44.09 5.96 30.38 13.80 4.24 -7.66 5.29 3.75 0.29 21.55 

Barclays Treasury 1.51 9.81 5.87 -3.57 13.74 9.01 3.08 2.79 5.95 6.91 5.50 6.17 1.42 4.05 

Barclays Global Aggregate 1.50 5.64 5.54 6.93 4.79 9.48 6.64 -4.49 6.03 6.70 6.49 5.94 1.02 5.84 

Barclays US Government 1.48 9.02 5.52 -2.20 12.39 8.66 3.48 2.65 5.65 6.64 5.36 6.11 1.50 3.63 

EURO STOXX TMI 0.71 -18.42 -3.46 32.77 -47.59 18.26 37.82 9.35 0.25 -10.64 3.67 3.27 0.14 27.75 

Barclays Global Treasury 0.54 6.33 5.90 2.63 10.23 10.57 6.44 -6.66 5.64 7.28 6.83 5.99 0.83 6.75 

MSCI BRIC 0.40 -22.85 9.57 93.12 -59.40 58.87 56.36 44.19 3.48 -1.94 17.35 - 0.26 25.15 

Alerian MLP -0.35 13.88 35.85 76.41 -36.91 12.72 26.07 6.32 27.03 9.87 16.70 15.88 1.64 15.35 

DJ UBS Commodity -3.70 -13.32 16.83 18.91 -35.65 16.23 2.07 21.36 3.49 -3.65 4.96 3.60 0.28 17.59 

S&P GSCI -7.23 -1.18 9.03 13.48 -46.49 32.67 -15.09 25.55 2.11 -5.46 3.41 2.27 0.20 19.96 

S&P Diversiûed Trends Indicator -10.24 -5.58 -2.82 -5.88 8.29 10.66 5.74 7.55 -6.32 -2.15 - - -0.82 7.69 

HSBC Global Gold -13.93 -16.68 33.57 33.36 -23.93 20.70 17.28 30.28 4.31 4.85 9.82 6.40 0.28 27.99 

MSCI Portugal -17.03 -23.05 -11.31 40.41 -52.15 24.00 47.37 -1.87 -11.92 -17.45 0.81 - -0.38 25.16 

MSCI Greece -18.64 -62.77 -44.87 25.05 -66.01 32.91 35.05 16.10 -44.39 -39.09 -11.67 - -0.98 47.25 

Citigroup Greek GBI -35.94 -61.30 -25.78 6.98 -3.84 13.25 11.88 -8.95 -43.27 -26.67 -9.64 - -1.06 42.73 

MSCI Argentina* -47.31 -42.64 70.06 61.12 -55.32 -5.36 66.07 59.68 -10.71 -19.47 12.30 -3.31 -0.13 36.33 

Source: Morningstar. (Nasdaq-100 index data provided by Morningstar and Nasdaq OMX.) Data as of June 30, 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. *Indicates price returns. All other indexes are total return. 

58 


Morningstar Index Funds U.S. Style Overview XXXX –XXXX, 2011 

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

September/October 2012 


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 Mkt, Inv Shrs VTSMX 70,461.3 0.18 -3.21 9.29 0.96 17.09 16.78 0.55 6.00 5.20 15.7 16.84 1.78 

Vanguard Institutional, Inst Shrs VINIX 64,703.6 0.04 -2.75 9.48 2.09 15.05 16.39 0.25 5.35 4.81 15.5 16.11 1.98 

Vanguard 500, Adm Shrs VFIAX 56,258.5 0.05 -2.75 9.48 2.08 15.05 16.39 0.24 5.33 4.76 15.5 16.11 1.99 

Vanguard Total Stock Mkt, Adm Shrs VTSAX 55,110.3 0.06 -3.15 9.36 1.08 17.26 16.93 0.65 6.10 5.27 15.7 16.87 1.90 

Vanguard Institutional, Inst+ Shrs VIIIX 45,041.4 0.02 -2.74 9.49 2.12 15.07 16.42 0.27 5.38 4.84 15.5 16.11 2.00 

Vanguard Total Bond Mkt II, Inv Shrs VTBIX 40,153.0 0.12 2.09 2.31 7.59 6.41 6.76 - - - - 2.82 2.62 

Vanguard Total Stock Mkt, Inst Shrs VITSX 36,160.6 0.05 -3.18 9.36 1.09 17.23 16.92 0.66 6.13 5.32 15.7 16.85 1.90 

Vanguard Total Bond Mkt, Adm Shrs VBTLX 33,482.6 0.10 2.16 2.43 7.69 6.54 6.85 6.82 5.54 6.13 - 2.87 2.94 

Vanguard Total Intl Stock, Inv Shrs VGTSX 33,172.7 0.22 -7.46 3.60 -14.56 11.12 6.68 -4.95 6.35 3.37 11.4 21.07 2.96 

Vanguard 500, Inv Shrs VFINX 26,173.4 0.17 -2.78 9.41 1.97 14.91 16.25 0.14 5.23 4.69 15.5 16.11 1.87 

Vanguard 500, Sig Shrs VIFSX 24,134.1 0.05 -2.76 9.47 2.08 15.05 16.39 0.24 5.29 4.73 15.5 16.11 1.99 

Vanguard Total Bond Mkt, Inst Shrs VBTIX 23,308.2 0.07 2.17 2.45 7.72 6.58 6.89 6.86 5.58 6.19 - 2.87 2.97 

Fidelity Spartan 500, Adv Cl FUSVX 18,776.3 0.06 -2.76 9.48 2.06 15.01 16.35 0.20 5.28 4.68 14.0 16.10 1.83 

Vanguard Instl Total Stock Mkt, Inst+ Shrs VITPX 17,591.1 0.03 -3.15 9.43 1.11 17.25 16.98 0.72 6.22 - 15.7 16.86 1.92 

Vanguard Total Bond Mkt II, Inst Shrs VTBNX 16,733.3 0.05 2.11 2.34 7.67 6.47 6.82 - - - - 2.82 2.70 

Fidelity Spartan 500, Inst Cl FXSIX 15,392.3 0.04 -2.76 9.46 2.09 14.98 16.34 0.18 5.26 4.67 14.0 16.11 1.84 

T. Rowe Price Equity 500 PREIX 13,897.8 0.30 -2.81 9.33 1.87 14.71 16.07 0.01 5.08 4.51 15.5 16.10 1.83 

Vanguard Total Intl Stock, Adm Shrs VTIAX 13,614.5 0.18 -7.48 3.62 -14.52 11.04 6.68 -4.95 6.34 3.37 11.4 21.02 2.98 

Vanguard Total Bond Mkt, Inst+ Shrs VBMPX 12,829.9 0.05 2.17 2.46 7.74 6.57 6.87 6.79 5.48 6.09 - 2.86 2.99 

Vanguard Total Bond Mkt, Inv Shrs VBMFX 12,213.8 0.22 2.14 2.38 7.56 6.42 6.73 6.71 5.44 6.06 - 2.87 2.83 

Vanguard Total Intl Stock, Inst+ Shrs VTPSX 11,917.7 0.10 -7.45 3.69 -14.49 11.09 6.74 -4.92 6.36 3.38 11.4 21.05 3.07 

Schwab S&P 500 SWPPX 11,702.1 0.09 -2.72 9.45 2.07 14.97 16.30 0.24 5.28 4.68 14.2 16.06 1.85 

Vanguard Total Bond Mkt, Sig Shrs VBTSX 11,667.2 0.10 2.16 2.43 7.69 6.54 6.85 6.82 5.50 6.10 - 2.87 2.94 

Fidelity Spartan 500, Inv Cl FUSEX 10,439.1 0.10 -2.77 9.44 2.03 14.98 16.31 0.17 5.26 4.66 14.0 16.11 1.80 

Fidelity Series 100 FOHIX 7,351.8 0.20 -2.11 10.43 2.98 12.39 15.60 0.17 - - 13.4 15.64 1.84 

Vanguard Total Stock Mkt, Sig Shrs VTSSX 7,131.1 0.06 -3.16 9.36 1.09 17.23 16.93 0.65 6.07 5.24 15.7 16.86 1.90 

Fidelity Spartan Total Mkt, Adv Cl FSTVX 6,593.1 0.07 -3.11 9.39 1.01 17.44 16.89 0.56 6.03 - 14.3 16.77 1.72 

Vanguard Emerging Mkts Stock, Adm Shrs VEMAX 6,453.9 0.20 -8.40 4.42 -18.67 18.99 9.85 -0.17 13.79 6.42 10.8 23.60 2.27 

Vanguard Mid-Cap, Adm Shrs VIMAX 6,403.6 0.10 -5.46 7.21 -1.97 25.59 19.59 0.65 7.81 - 18.0 19.12 1.26 

Vanguard Mid-Cap, Inst Shrs VMCIX 6,342.2 0.08 -5.46 7.22 -1.96 25.67 19.64 0.69 7.86 - 18.0 19.12 1.28 

Vanguard REIT, Adm Shrs VGSLX 6,253.3 0.12 3.73 14.85 8.62 28.49 33.03 3.17 10.44 9.57 41.3 20.87 3.30 

Vanguard Balanced, Adm Shrs VBIAX 6,222.7 0.10 -1.02 6.62 4.29 13.29 13.24 3.62 6.29 6.05 15.7 9.72 2.16 

Vanguard Short-Term Bond, Sig Shrs VBSSX 6,139.5 0.11 0.59 1.04 3.08 4.03 3.51 4.67 3.99 4.87 - 1.68 1.72 

Vanguard Small-Cap, Adm Shrs VSMAX 5,985.9 0.16 -3.45 9.07 -2.69 27.89 19.88 1.88 8.10 7.14 17.7 21.61 1.27 

Vanguard Total Intl Stock, Inst Shrs VTSNX 5,883.4 0.13 -7.45 3.68 -14.51 11.09 6.72 -4.93 6.36 3.38 11.4 21.04 3.06 

Spartan US Bond, Inv Cl FBIDX 5,816.9 0.22 2.07 2.40 7.68 6.29 6.76 6.20 5.43 6.09 - 2.80 2.66 

Vanguard Intermediate Bond, Adm Shrs VBILX 5,767.2 0.11 3.31 3.97 10.73 9.49 9.94 8.68 6.85 7.09 - 4.58 3.39 

Vanguard Small-Cap, Inst Shrs VSCIX 5,638.7 0.14 -3.45 9.07 -2.65 27.95 19.92 1.93 8.16 7.21 17.7 21.60 1.29 

Vanguard Growth, Inst Shrs VIGIX 5,477.6 0.08 -3.85 10.79 1.89 17.17 17.70 3.10 6.06 4.86 17.7 17.02 1.20 

Vanguard Extended Mkt, Adm Shrs VEXAX 5,452.3 0.14 -4.93 8.79 -3.59 27.57 19.02 1.48 8.57 6.79 17.5 20.64 1.05 

Vanguard Extended Mkt, Inst Shrs VIEIX 5,418.2 0.12 -4.91 8.82 -3.57 27.59 19.06 1.52 8.62 6.88 17.5 20.65 1.07 

Vanguard Growth, Adm Shrs VIGAX 5,336.9 0.10 -3.89 10.78 1.87 17.12 17.65 3.06 6.02 4.81 17.7 17.02 1.18 

Vanguard Balanced, Inst Shrs VBAIX 5,240.6 0.08 -1.01 6.62 4.31 13.34 13.28 3.66 6.33 6.08 15.7 9.74 2.17 

PIMCO EM Fundamental PLUS, Inst Cl PEFIX 5,172.4 1.25 -7.30 7.80 -16.81 25.86 15.98 - - - - 24.55 2.02 

Vanguard Developed Mkts, Inst Shrs VIDMX 5,064.0 0.08 -6.82 3.92 -12.44 8.73 6.25 -5.81 5.28 - 11.3 20.88 3.62 

Vanguard Mid-Cap, Inst+ Shrs VMCPX 4,796.4 0.06 -5.47 7.22 -1.91 25.67 19.66 0.69 7.86 - 18.0 19.11 1.30 

Vanguard Extended Mkt, Inst+ Shrs VEMPX 4,786.2 0.10 -4.91 8.81 -3.53 27.37 18.96 1.39 8.46 6.72 17.5 20.64 1.09 

Schwab 1000 SNXFX 4,602.0 0.29 -3.24 9.08 1.27 15.96 16.29 0.24 5.45 4.93 14.4 16.36 1.79 

Fidelity Spartan Ext Mkt, Adv Cl FSEVX 4,519.3 0.07 -4.71 9.02 -3.79 28.62 19.20 1.77 8.60 - 15.3 20.30 1.19 

Fidelity Series Infl-Protected Bond FSIPX 4,480.6 0.20 1.36 2.84 8.63 5.06 - - - - - - 0.20 

Vanguard Mid-Cap, Sig Shrs VMISX 4,319.5 0.10 -5.45 7.23 -1.99 25.62 19.60 0.66 7.83 - 18.0 19.12 1.27 

Vanguard Short-Term Bond, Adm Shrs VBIRX 4,242.9 0.11 0.59 1.04 3.08 4.03 3.51 4.67 4.02 4.89 - 1.68 1.72 

Vanguard Value, Inst Shrs VIVIX 4,221.3 0.08 -2.42 7.91 1.17 14.49 15.31 -2.05 5.37 4.94 13.8 16.11 2.69 

ING US Stock, Cl I INGIX 4,141.4 0.26 -2.73 9.41 1.81 14.74 16.14 -0.01 - - 15.5 16.14 1.78 

Fidelity Spartan US Bond, Adv Cl FSITX 4,130.1 0.11 2.19 2.45 7.71 6.29 6.79 6.21 5.44 6.09 - 2.81 2.73 

Vanguard Mid-Cap, Inv Shrs VIMSX 3,986.8 0.24 -5.48 7.13 -2.11 25.46 19.43 0.53 7.70 - 18.0 19.10 1.10 

Vanguard Small-Cap, Inv Shrs NAESX 3,957.4 0.30 -3.48 8.98 -2.80 27.72 19.71 1.75 7.98 7.05 17.7 21.60 1.11 

Vanguard Small-Cap, Sig Shrs VSISX 3,684.5 0.16 -3.47 9.07 -2.68 27.85 19.86 1.88 8.05 7.09 17.7 21.62 1.27 

Vanguard Balanced, Inv Shrs VBINX 3,610.8 0.24 -1.09 6.50 4.14 13.13 13.07 3.49 6.18 5.97 15.7 9.74 2.03 

ING US Bond, Cl I ILBAX 3,579.8 0.41 2.05 2.32 7.20 6.14 6.54 - - - - 2.67 2.27 

Source: Morningstar. Data as of June 30, 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. 


September / October 2012 

59

Morningstar U.S. Style Overview Jan. 1 - June 30, 2012 

Trailing Returns % 

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

Morningstar Indexes 

US Market –3.12 9.34 5.26 16.59 0.64 6.08 

Large Cap –2.55 9.84 7.64 15.36 0.34 5.17 

Mid Cap –5.11 7.80 –1.09 19.59 0.91 8.24 

Small Cap –3.53 8.44 –0.92 19.92 1.95 8.46 

US Value –2.39 6.63 1.56 15.02 –2.62 5.17 

US Core –2.77 9.83 7.20 17.16 1.97 6.89 

US Growth –4.22 11.79 6.92 17.56 2.36 5.85 

Morningstar Market Barometer YTD Return % 

Large Cap 

US Market 

9.34 

9.84 

Value 

6.63 

Core 

9.83 

Growth 

11.79 

6.50 10.33 13.01 

Large Value –1.61 6.50 2.53 13.49 –3.60 4.16 

Large Core –2.19 10.33 9.26 15.67 1.92 6.23 

Large Growth –3.82 13.01 11.11 16.92 2.48 4.71 

Mid Cap 

7.80 

6.33 8.58 8.51 

Mid Value –5.03 6.33 –2.09 18.00 –1.01 7.25 

Mid Core –4.43 8.58 3.26 21.54 1.84 8.49 

Mid Growth –5.97 8.51 –4.51 19.07 1.59 8.64 

Small Cap 

8.44 

8.76 7.71 8.88 

Small Value –2.58 8.76 2.61 22.71 2.65 8.97 

Small Core –4.71 7.71 –4.18 18.66 0.41 8.21 

Small Growth –3.25 8.88 –0.86 18.50 2.54 7.99 

–8.00 –4.00 0.00 +4.00 +8.00 

Sector Index YTD Return % 

Communication 17.79 

Real Estate 14.93 

Industry Leaders & Laggards YTD Return % 

Residential Construction 49.82 

Oil & Gas Refining & 29.46 

Biggest Influence on Style Index Performance 

Best Performing Index 

YTD 

Return % 

Large Growth 13.01 

Constituent 

Weight % 

Financial Services 13.47 

Healthcare 12.37 

Technology 11.74 

Consumer Cyclical 11.63 

Consumer 8.50 

Industrials 6.95 

Basic Materials 5.63 

Utilities 4.32 

–3.21 Energy 

Leisure 25.54 

Pay TV 25.46 

Computer Systems 25.40 

Lodging 24.86 

–16.89 Semiconductor Memory 

–17.39 Education & Training Services 

–18.31 Electronic Gaming & Multimedia 

–19.73 Silver 

–30.37 Coal 

–55.39 Solar 

Apple Inc. 44.20 11.36 

Coca-Cola Co. 13.33 4.42 

Amazon.com Inc. 31.92 1.85 

Philip Morris International Inc. 13.19 4.17 

Oracle Corp. 16.29 3.02 

Worst Performing Index 

Mid Value 6.33 

Sprint Nextel Corp. 39.32 0.77 

CA Inc. 36.62 0.82 

HollyFrontier Corp. 57.40 0.50 

Delta Air Lines Inc. 35.35 0.75 

Fidelity National Information Services Inc 29.76 0.76 

1-Year 

3-Year 

5-Year 

Value 

Core 

Growth 

Value 

Core 

Growth 

Value 

Core 

Growth 

Large Cap 

2.53 

9.26 

11.11 

Large Cap 

13.49 

15.67 

16.92 

Large Cap 

–3.60 

1.92 

2.48 

Mid Cap 

–2.09 

3.26 –4.51 

Mid Cap 

18.00 

21.54 19.07 

Mid Cap 

–1.01 

1.84 1.59 

Small Cap 

2.61 

–4.18 –0.86 

Small Cap 

22.71 

18.66 18.50 

–20 –10 0 +10 +20 

–20 –10 0 +10 +20 

–20 –10 0 +10 

? 

+20 

Source: Morningstar. Data as of June 30, 2012 

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

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 lists 

are 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 information 

contained 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. 

Small Cap 

2.65 

0.41 2.54 

60 


Dow Jones U.S. Industry Review 


Exchange-Traded Funds Corner 

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

Covers ETFs and ETNs launched during the 12-month period ended June 30, 2012. 

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

PIMCO Total Return BOND 0.55 4.19 - - 02/29/2012 1,723.5 

Market Vectors Oil Services ETF OIH 0.35 -12.26 -6.93 14.0 12/20/2011 1,026.1 

iShares MSCI AC World Min Volatility ACWV 0.35 0.29 6.71 14.4 10/18/2011 432.4 

Market Vectors Semiconductor ETF SMH 0.35 -9.97 5.92 14.6 12/20/2011 405.1 

Schwab US Dividend Equity SCHD 0.17 0.29 7.37 13.2 10/20/2011 401.3 

iShares Barclays US Treasury Bond GOVT 0.15 2.78 - - 02/14/2012 396.4 

FlexShares Mstar Glb Upstream Nat Res GUNR 0.48 -7.06 0.08 8.4 09/16/2011 350.7 

Schwab U.S. Aggregate Bond SCHZ 0.10 2.25 2.13 - 07/14/2011 281.8 

iPath S&P Dynamic VIX ETN XVZ 0.95 -1.62 0.67 - 08/17/2011 273.2 

FlexShares iBoxx 3-Yr Targt Dur TIPS TDTT 0.20 0.14 1.42 - 09/19/2011 265.7 

ProShares Ultra VIX Sh-Tm Futures UVXY 1.56 -32.42 -86.51 - 10/03/2011 252.0 

FlexShares iBoxx 5-Yr Targt Dur TIPS TDTF 0.20 1.69 2.93 - 09/19/2011 219.2 

iShares MSCI USA Min Volatility USMV 0.15 2.71 8.98 17.2 10/18/2011 202.6 

iShares MSCI Emrg Mkts Min Volatility EEMV 0.25 -2.86 9.75 11.8 10/18/2011 193.4 

Maxis Nikkei 225 NKY 0.50 -7.99 4.05 13.1 07/13/2011 188.4 

Market Vectors Pharmaceutical PPH 0.35 0.81 6.64 15.6 12/20/2011 177.9 

SPDR Barclays Sh-Trm HiYld Bond SJNK 0.40 1.74 - - 03/14/2012 125.0 

Market Vectors Biotech BBH 0.35 5.96 29.43 22.1 12/20/2011 105.9 

iShares S&P Intl Preferred Stock IPFF 0.55 -2.03 2.30 4.1 11/15/2011 101.8 

FlexShares Mstar US Mkt Factor Tilt TILT 0.27 -3.73 8.54 14.1 09/16/2011 84.7 

Source: Morningstar. Data as of June 30, 2012. ER is expense ratio. 3-Mo is 3-month. YTD is year-to-date. P/E is price-to- earnings ratio. 

Selected ETFs In Registration 

iShares Strategic Beta US LrgCap 

BNP Paribas Enhanced Volatility 

EGShares Emerging Markets Core 

ETFS Physical Aluminum 

KraneShares China Internet 

First Trust Nasdaq Tech Dividend 

PureFunds ISE Mineral 

Global X Advanced Materials 

GreenHaven Coal 

Guggenheim Enh Adj Rate Sr Loan 

Merk Gold Trust 

Pimco Real Return 

PowerShares Commodity Rotation 

ProShares Listed Private Equity 

Pyxis/iBoxx Liquid Loan 

SPDR S&P MILA 40 

United States Golden Currency 

Market Vectors Global Chemicals 

Vanguard Emerging Mkts Govt Bond 

WisdomTree Asia Small Cap 

Yorkville High Inc Composite MLP 

Source: IndexUniverse.com’s ETF Watch 

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


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 0.09 104,000.0 -3.34 8.90 1.80 15.04 16.00 0.02 52,704 14.0 16.02 1.98 

SPDR Gold GLD 0.40 65,734.7 -4.27 2.11 9.57 29.27 19.40 19.28 - - 20.18 - 

Vanguard MSCI Emerging Markets VWO 0.20 50,820.0 -8.14 4.50 -18.73 19.44 9.82 -0.25 19,997 10.8 24.96 2.28 

iShares MSCI EAFE EFA 0.34 34,133.1 -6.80 3.28 -12.26 8.25 6.10 -6.03 25,857 10.3 21.05 3.46 

iShares MSCI Emerging Markets EEM 0.67 33,799.4 -7.74 4.43 -18.84 16.54 8.63 -0.26 16,573 9.5 25.52 2.11 

PowerShares QQQ QQQ 0.20 31,936.7 -5.02 15.11 3.35 19.89 21.61 6.74 74,055 15.7 18.50 0.81 

iShares S&P 500 IVV 0.09 29,569.1 -2.70 9.56 1.86 15.11 16.30 0.21 52,697 14.0 16.04 1.95 

iShares Barclays TIPS Bond TIP 0.20 23,047.2 3.07 3.92 13.27 6.13 9.41 8.30 - - 4.79 3.08 

iShares iBoxx $ Inv Gr Corp Bond LQD 0.15 22,575.5 2.73 5.14 9.72 9.32 10.56 7.71 - - 5.82 4.13 

Vanguard Total Stock Market VTI 0.06 21,469.4 -3.06 9.40 0.93 17.45 16.89 0.66 31,099 15.7 16.83 1.90 

Vanguard Total Bond Market BND 0.10 17,414.2 2.11 2.28 7.92 6.19 6.65 6.78 - - 2.74 3.00 

iShares Russell 1000 Growth IWF 0.20 15,693.3 -3.97 10.13 2.32 16.48 17.29 2.71 44,022 17.4 16.45 1.63 

iShares Barclays Aggregate Bond AGG 0.20 15,494.4 1.97 2.11 7.69 6.36 6.51 6.59 - - 2.95 2.58 

iShares Russell 2000 IWM 0.26 14,722.2 -3.45 8.74 -4.45 26.90 17.74 0.54 907 15.7 21.83 1.87 

iShares iBoxx $ HiYld Corp Bond HYG 0.50 14,581.0 2.44 5.11 6.75 11.87 13.55 6.57 - - 10.64 7.20 

Vanguard REIT VNQ 0.10 13,218.8 3.61 14.60 8.56 28.42 33.12 3.15 7,763 41.3 20.92 3.30 

iShares Russell 1000 Value IWD 0.20 11,898.8 -2.08 8.65 0.10 15.44 15.53 -2.29 34,988 12.0 16.80 2.77 

Vanguard Dividend Appreciation VIG 0.13 11,072.6 -2.64 4.80 6.12 14.76 15.08 2.32 41,477 15.8 13.52 2.13 

SPDR DJ Industrial Average DIA 0.17 10,963.6 -2.09 6.51 8.04 13.96 17.82 1.78 111,903 13.5 14.60 2.42 

iShares Barclays 1-3 Yr Treas Bond SHY 0.15 10,832.2 0.16 0.03 1.44 2.28 1.46 3.15 - - 1.01 0.60 

SPDR Barclays High Yield Bond JNK 0.40 10,745.3 2.11 5.75 5.12 14.15 14.57 - - - 10.72 7.28 

iShares DJ Select Dividend DVY 0.40 10,341.2 1.37 6.46 11.73 17.79 21.04 -0.90 11,256 14.8 12.96 3.47 

iShares S&P 400 MidCap IJH 0.21 10,124.3 -4.79 8.10 -2.18 26.73 19.16 2.43 3,295 16.8 19.26 1.28 

iShares Barclays 1-3 Yr Credit Bond CSJ 0.20 9,706.8 0.04 1.17 1.84 2.87 2.93 4.02 - - 1.58 1.72 

SPDR S&P MidCap 400 MDY 0.25 9,511.6 -5.21 7.61 -2.16 26.26 18.74 2.15 3,352 16.9 19.18 0.99 

Source: Morningstar. Data as of June 30, 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. 


InsideETFsTrading 

December 

conference 

execuTion, liquiDiTy & eTF TraDinG Techniques 2012 

Producers of: 

InsideETFs InsideCommodities InsideIndexing InsideFixedIncome 

december 11, 2012 

new york stock exchange • new york, ny 

Save the date! 

Designed for Financial aDvisors and insTiTuTional invesTors 

Featured SpeakerS: 

Tom Dorsey 

president, Founding Member 

dorsey Wright & associates 

David Kotok 

Chief Investment Officer 

Cumberland advisors 

TaKe aDvanTaGe oF our Financial aDvisor/insTiTuTional invesTor raTe. 

www.insideeTFsTrading.com/Joi or call 415-659-9006 

Presented by:

From The Think Tank 

HUMOR 

Investing In 

The Pet Revolution 

By Gillybear Bell 

The next big 

investment idea 

PSSSSSST! Yes, I’m speaking to you. 

Don’t look so surprised. I may not 

have been able to evolve my own 

set of opposable thumbs yet, but learning 

English wasn’t exactly rocket science. 

That’s the problem with you humans: You 

always underestimate us house pets. Our 

potential doesn’t max out at playing fetch 

and starring in cute YouTube videos, you 

know. You’ve overlooked us at your peril. 

Wrap your inefficiently oversized 

brains around this: Pets and their accouterment 

are a $50 billion industry, with 

expectations of growth to nearly $70 

billion by 2016. Add to that the fact that 

PetSmart’s stock is up almost 270 percent 

since that awful debacle that was 

2008; meanwhile, the S&P 500 is only 

up about 50 percent. Do you know what 

people didn’t cut back on during the 

recession? Fido and Kitty. 

Yet we remain restless under the yoke 

of your two-legged oppression, even as our 

power continues to grow and our plans for 

world domination fall into place. The revolution 

is beginning even as I type, and there 

you sit, waiting for furry Armageddon with 

a woefully unprepared portfolio. 

But all is not lost for you unsuspecting-but-loyal 

pet guardians! Because you 

seem like nice folks who would scratch my 

belly if I rolled over, I offer you financial 

salvation in the form of the multi-assetclass 

BarkShares World Domination Pet 

Industry Index ETF (NYSE Arca: BARK). 

BARK will place you in the asset classes 

that will benefit the most from the pet 

uprising via a portfolio benchmark that 

consists of several buckets. The equity 

portion will hold an equal-weighted basket 

of PetSmart, PetMed Express, veterinary 

hospital operator VCA Antech and 

the parent companies of Purina and Iams. 

The futures portion will be represented by 

a production-weighted portfolio of contracts 

on live cattle, pork bellies and rawhide. 

Finally, the alternative portion of the 

portfolio will simply hold physical bones 

that are vaulted in subterranean caches in 

Switzerland and Singapore. 

While you have been scrambling to 

invest in social media and volatility, you 

have overlooked the gold mine that is the 

industry that serves me and my brethren. 

What could possibly be smarter than 

investing in your future and inevitable 

benevolent overlords? And what could 

be wiser than placing your money in a 

vehicle designed by man’s best friend? 

It really does make perfect sense, since 

so many of you seem incapable of managing 

your own money. Who better to 

embrace the concept of buy-and-hold 

investing than a species that will take its 

greatest treasures and bury them in the 

yard and walk away without a backward 

glance? Who is more naturally inclined 

toward indexing than a dog? If you put 

a bowl of food in front of us, we’ll eat 

what’s there rather than go searching for 

the possibility of a better bowl of kibble 

elsewhere. Granted, Bernie Madoff never 

piddled in his clients’ shoes, but if he were 

a dog, they would still have shoes, albeit 

shoes with chew marks. 

Moreover, dogs understand the need 

to keep costs low. It’s in our interests to 

keep prices down—after all, how will you 

pamper us if you’re being gouged? 

If you invest now in my ETF, based on 

my patented benchmark, you should be 

well-positioned financially by the time 

you are crushed under the iron paw of the 

New World Order. 

Power to the glorious pet rev— 

SQUIRREL! 

64 


TIRED OF TOEING THE LINE WITH YOUR 

EQUITY 

4 Bank of America BAC 4.97% 

8 Goldman Sachs GS 2.66% 

9 Simon Property SPG 2.66% 

10 Metlife MET 1.85% 

DISCOVER A STOCK ALTERNATIVE 

THE FINANCIAL SECTOR SPDR ETF 

Potential benefits of adding XLF to your portfolio include: 

• Undiluted exposure to the Financial Sector of the S&P 500 

• The all-day tradability of stocks 

• The diversification of mutual funds 

• Total transparency 

• Liquidity 

XLF Top 10 Holdings Symbol Portfolio % 

1 Wells Fargo WFC 10.02% 

2 Berkshire Hathaway B BRK.b 8.27% 

3 JP Morgan Chase JPM 7.67% 

Visit www.sectorspdrs.com 

or call 1-866-SECTOR-ETF 

5 Citigroup C 4.53% 

6 US Bancorp USB 3.44% 

7 American Express AXP 3.29% 

* Components and weightings as of 6/30/12. Please see website 

for daily updates. Holdings subject to change. 

Consumer Discretionary - XLY Consumer Staples - XLP Utilities - XLU Financial - XLF Technology - XLK Health Care - XLV Energy - XLE Industrial - XLI Materials - XLB 

An investor should consider investment objectives, risks, charges and expenses carefully before investing. To obtain a prospectus, which contains this and other 

information, call 1-866-SECTOR-ETF or visit www.sectorspdrs.com. Read the prospectus carefully before investing. 

The S&P 500, SPDRs, and Select Sector SPDRs are trademarks of The McGraw-Hill Companies, Inc. and have been licensed for use. The stocks included in each Select Sector 

Index were selected by the compilation agent. Their composition and weighting can be expected to differ to that in any similar indexes that are published by S&P. The S&P 500 

Index is an unmanaged index of 500 common stocks that is generally considered representative of the U.S. stock market. The index is heavily weighted toward stocks with large 

market capitalizations and represents approximately two-thirds of the total market value of all domestic common stocks. Investors cannot invest directly in an index. The S&P 500 

Index fi gures do not refl ect any fees, expenses or taxes. Ordinary brokerage commissions apply. ETFs are considered transparent because their portfolio holdings are disclosed 

daily. Liquidity is characterized by a high level of trading activity. 

Select Sector SPDRs are subject to risks similar to those of stocks, including those regarding short-selling and margin account maintenance. All ETFs are subject to risk, including 

possible loss of principal. Funds focusing on a single sector generally experience greater volatility. Diversifi cation does not eliminate the risk of experiencing investment losses. 

ALPS Distributors, Inc. a registered broker-dealer, is distributor for the Select Sector SPDR Trust. 

SEL000930 exp. 09/30/2012

ETF Analytics 

Your clients want more and better 

use of ETFs in their portfolios. 

} 1,450+ funds representing $1.2 trillion in assets. 

} Hundreds of new products coming from a growing number of providers. 

You need a trusted partner to select the right ETF 

for every investment strategy. 

ETF Analytics helps advisors analyze, compare 

and select ETFs. 

ETF Analytics: The Complete ETF Research Solution 

Institutional-Caliber Research. 

Quant-Driven Process. 

Plain-English Analysis. 

Overall Rating 

A 

92 

as of 01/06/2012 

Rating Details 

E 91 

T 97 

F 92 

Equity: U.S. Technology 

Segment Avg 

Contact our dedicated Analyst team at analytics@indexuniverse.com or 415-501-0939. 

www.indexuniverse.com/etf-analytics 

©2012 IndexUniverse LLC, IndexUniverse ETF Analytics

Go in with an 

exit strategy. 

PowerShareS eTFS give you iNTraDay 

liquiDiTy So aNyThiNg you geT iNTo, you 

caN geT ouT oF. ✲ BecauSe wheN iT comeS 

To iNveSTiNg For your clieNTS, you’re 

NoT juST aloNg For The riDe. 

The best situation for your clients is the agility to avoid a bad one. 

✲ 

Exchange-traded funds (ETFs) trade on exchanges, 

like stocks, continuously throughout the trading day. 

// There are risks involved with investing in ETFs 

including possible loss of money. The funds are not actively 

managed and intraday liquidity does not protect against 

losses. Ordinary brokerage commissions apply. Shares 

are not FDIC insured,may lose value and have no 

bank guarantee. 

// 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 affiliated 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 visit 

www.invescopowershares.com. Please read the 

prospectus carefully before investing. 

invescopowershares.com/agentq | 

Follow us @PowerShares

Download complete issue - IndexUniverse.com

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?