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Plus an interview with Robert Maynard of PERSI, S&P DJI’s Blitzer on ETF closures,
Israelsen on bonds and diversification, Slivka et al. on covered-call ETFs, JOI’s Bell and more!
www.journalofindexes.com
SERIOUS IDEAS FOR SERIOUS INVESTORS
new perspectives March / April 2013
Global Value And 10-Year CAPE
Mebane Faber and Prabhat Dalmia
Investor Requirements For Indexes: A Survey
Felix Goltz, Véronique Le Sourd and Masayoshi Mukai
How Smart Is ‘Smart Beta’?
David Blitz
Are Active Mutual Funds Becoming Less Active?
David Blanchett

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www.journalofndexes.com
Vol. 16 No. 2
features
Global Value
By Mebane Faber and Prabhat Dalmia . . . . . . . . . . . . . . . 16
Looking at Shiller’s CAPE metric from a global perspective.
PERSI’s Maynard Favors ‘Traditional’ Approaches
By Journal of Indexes Staff ..........................22
How PERSI avoids typical pension-plan headaches.
Requirements For Standard
And New Forms Of Indexes
By Felix Goltz, Véronique Le Sourd and
Masayoshi Mukai ...................................26
What do investors really want from their benchmarks?
How Smart Is ‘Smart Beta’?
By David Blitz ......................................36
Pointing out the concerns around a popular trend.
Survival Of The Fittest
By David Blitzer ....................................40
Are strategy indexes driving the increase in ETF closures?
Are Active Mutual Funds Becoming Less Active?
By David Blanchett .................................42
Active funds are beginning to look more like index funds.
Taking A Long View Of Bond Performance
By Craig Israelsen ..................................46
Don’t let recent events disrupt your diversification.
Covered-Call ETFs For BRIC Countries
By Ronald Slivka, Sharad Bhat and
Sridhar Nonabur Srinivasamurthy . . . . . . . . . . . . . . . . . . . 52
A suggested blueprint for constructing a product.
How I Learned To Stop Worrying …
By Heather Bell.....................................64
Things are looking up. Now what?
news
SPY Turns 20....................................... 10
Housing Recovery Continues In November . . . . . . . . . . . 10
Nasdaq OMX Rolls Out Global Index Family . . . . . . . . . . 10
FINRA Targets ETFs In Letter. . . . . . . . . . . . . . . . . . . . . . . . 10
ISE To Acquire NYSE ............................... 11
IndexUniverse Debuts Currency ETFs. . . . . . . . . . . . . . . . 11
Indexing Developments............................. 12
Around The World Of ETFs.......................... 13
Know Your Options ................................ 15
Back To the Futures ................................ 15
On The Move ...................................... 15
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
42
46
52
www.journalofindexes.com
March / April 2013
1

Contributors
David Blitz
Craig Israelsen Mebane Faber
Ronald Slivka
Felix Goltz
David Blitzer
David Blanchett
David Blanchett, CFA, is head of retirement research for Morningstar
Investment Management, where he provides research support for the
group’s consulting and investment management activities. Blanchett holds
a bachelor’s degree in finance and economics from the University of
Kentucky, a master’s degree in financial services from the American College,
and an MBA from the University of Chicago Booth School of Business.
David Blitz is senior vice president and co-head of Quant Research at
Robeco, where he is responsible for coordinating all quantitative equity
research efforts. He joined Robeco in 1995 after graduating cum laude in
econometrics at Erasmus University in Rotterdam. In 2011, Blitz obtained a
Ph.D. in empirical finance from the same university. His research has been
published in multiple peer-reviewed academic journals.
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, as well as index analysis and management.
Blitzer previously served as chief economist for Standard & Poor’s and as
corporate economist at The McGraw-Hill Companies. 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.
Mebane Faber, CAIA, CMT, is co-founder and chief investment officer
of Cambria Investment Management. He is manager of Cambria’s Global
Tactical ETF (GTAA), separate accounts and private investment funds for
accredited investors. Faber is also author of the World Beta blog, and coauthor
of “The Ivy Portfolio: How to Invest Like the Top Endowments and
Avoid Bear Markets.” He graduated from the University of Virginia with a
double major in engineering science and biology.
Felix Goltz is head of applied research at EDHEC-Risk Institute. He does
research in empirical finance and asset allocation, with a focus on alternative
investments and indexing strategies. Goltz’s work has appeared in
various international academic and practitioner journals and handbooks.
He obtained his Ph.D. in finance from the University of Nice Sophia
Antipolis after studying economics and business administration at the
University of Bayreuth and EDHEC Business School.
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 and the designer of the 7Twelve Portfolio. He is also
the author of “7Twelve: A Diversified Investment Portfolio with a Plan”
(John Wiley & Sons), published in 2010. Israelsen holds a Ph.D. in family
resource management from Brigham Young University.
Ronald Slivka is an adjunct professor at the Polytechnic Institute of
New York University and a faculty member of the New York Institute of
Finance. During his more than 35 years of practical Wall Street experience,
Slivka held equity derivative sales and management positions at Salomon
Brothers, J.P. Morgan and ABN AMRO. He has written over 35 articles and
book chapters on a broad range of derivative topics and holds a Ph.D. in
physics from the University of Pennsylvania.
2 March / April 2013

McGRAW-HILL
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across sectors, strategies and themes, S&P Dow Jones Indices gives issuers the right fit for
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Copyright © 2012 by S&P Dow Jones Indices LLC, a subsidiary of The McGraw-Hill Companies, Inc., and/or its affiliates. All rights reserved.
S&P Dow Jones Indices LLC is a subsidiary of The McGraw-Hill Companies, Inc. Standard & Poor’s, S&P and S&P 500 are registered trademarks of Standard
& Poor’s Financial Services LLC, a subsidiary of The McGraw-Hill Companies, Inc. Dow Jones is a registered trademark and Dow Jones Industrial Average is a
trademark of Dow Jones Trademark Holdings LLC (“Dow Jones”). All Trademarks have been licensed to S&P Dow Jones Indices LLC. It is not possible to invest
directly in an index. S&P Dow Jones Indices LLC, Dow Jones, S&P and their respective affiliates (collectively “S&P Dow Jones Indices”) do not sponsor, endorse,
sell, or promote any investment fund or investment vehicle that seeks to provide an investment return based on the performance of an index. This document
does not constitute an offer of services in jurisdictions where S&P Dow Jones Indices does not have the necessary licenses. S&P Dow Jones Indices receives
compensation in connection with licensing its indices to third parties.

Jim Wiandt
Editor
jwiandt@indexuniverse.com
Heather Bell
Managing Editor
hbell@indexuniverse.com
Matt Hougan
Senior Editor
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Copy Editor
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Creative Director
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Art Director
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Editorial Board
Rolf Agather: Russell Investments
David Blitzer: S&P Dow Jones Indices
Lisa Dallmer: NYSE Euronext
Remy Briand: 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
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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
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Editor’s Note
Taking A Fresh Look
Jim Wiandt
Editor
Sometimes life is just blind luck. At no point in time did we solicit articles for an
issue titled “New Perspectives.” We had an entirely different topic planned for
March/April, but when we peeked in our hopper, we saw that we had a bevy of
very good but disparate independently submitted articles that offer new perspectives
on some established ideas. Not being ones to look a gift horse in the mouth, we took it
and ran with it. It has made for some great reading.
Mebane Faber and Prabhat Dalmia of Cambria Investment Management kick off
the issue with a discussion of the cyclically adjusted price-to-earnings, or CAPE, ratio
that was developed by Robert Shiller. They examine the metric’s applicability across a
range of foreign markets and how it can be used in building portfolios.
We then check in with Robert Maynard of the Public Employee Retirement System
of Idaho for our regular institutional investor feature to discuss how he manages one
of the country’s most successful public pension funds.
Felix Goltz, Véronique Le Sourd and Masayoshi Mukai of the EDHEC-Risk
Institute follow up with a discussion of a survey of American investment professionals
and their main concerns and requirements with regard to the benchmarks
they use, including their views on alternatively weighted indexes. On the same
general theme, David Blitz of Robeco then weighs in with a commentary on what
he sees as the problems with “smart beta” indexes.
Next up, S&P Dow Jones Indices’ David Blitzer offers a unique angle on the wave
of ETF closures that took place in 2012. David Blanchett of Morningstar steps in
after that to provide evidence that actively managed mutual funds have become
increasingly less active in recent years, raising the question of whether the trend is
because of the rise of index funds.
Brigham Young University Professor Craig Israelsen follows with a reality check
for investors who might be thinking about jettisoning (or significantly reducing)
their fixed-income allocation. And Ronald Slivka, Sharad Bhat and Sridhar Nonabur
Srinivasamurthy offer a blueprint for how one might go about constructing a coveredcall
ETF for an emerging market.
Finally, always-quick-on-the-uptake JOI Managing Editor Heather Bell puts the
issue to bed with a meditation on her shocking realization that the sky hasn’t fallen.
We hope you find the issue as useful as we have and that your 2013 is off to a good start.
Jim Wiandt
Editor
8
March / April 2013

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News
SPY Turns 20
“SPY,” the very first U.S.-listed
exchange-traded fund and the biggest
ETF in the world, turned 20 in
January—a $125 billion portfolio
that’s now in the company of some of
the biggest U.S. mutual funds, making
it the perfect symbol for an industry
that’s on a roll.
Officially known as the SPDR S&P
500 ETF (NYSE Arca: SPY), SPY was
seeded on Jan. 22, 1993, and began
trading on Jan. 29. It was dreamed
up by the late Nate Most as a vehicle
for traders that he hoped would help
pump up volume at the American
Stock Exchange. Most and his entourage
thought they’d be lucky if SPY
hauled in $1 billion.
SPY did indeed end up appealing
to traders, and a whole lot more. The
biggest ETF in the world now sits atop a
universe of more than 1,400 ETFs that’s
increasingly poaching market share
from actively managed mutual funds. It
represents about 9 percent of the record
$1.4 trillion now invested in ETFs.
Housing Recovery
Continues In November
U.S. home prices were again stronger,
year-on-year, in November, with
the recovery in housing moving forward,
even as the seasonal downdraft
in prices associated with the colder
months of the year was also in evidence.
Indeed, the latest S&P/Case-Shiller
Home Prices Indices report showed
that while home values around the
country were 5.5 percent higher in
November than they were in the same
prior-year period, on a month-tomonth
basis, several cities saw home
prices decline in November.
The 10-City and 20-City composites
dropped 0.2 percent and 0.1 percent,
respectively, in November from
October levels, with 10 out of the 20
cities surveyed seeing prices decline
month-on-month. That’s a departure
from the market’s performance
last summer when all cities surveyed
were posting higher and higher home
prices on a monthly basis for several
consecutive months.
But that slowing momentum isn’t
necessarily surprising given that, from
a seasonal perspective, the fall and
winter months tend to be the housing
market’s weakest periods.
Boston, Chicago and New York
have been some of the worst-faring
cities in recent months. Each has seen
more than six months of declining
prices in the past 12 months.
In November, Chicago was the
worst-performing city, with home
prices there declining 1.3 percent
from month-earlier levels. In
New York, home values remained
1.2 percent lower year-on-year in
November, while in Boston, homes
have appreciated 2.3 percent in the
12 months ended in November.
On the flip side, cities like
Phoenix, San Francisco, Detroit and
Las Vegas have all managed to tally
double-digit gains in home values in
the 12-month period.
All in all, an average home in the U.S.
in November cost roughly what it did in
the fall of 2003, and remains about 30
percent off its highest price level seen
when the market peaked in 2006.
Nasdaq OMX Rolls Out
Global Index Family
Nasdaq OMX continued its push
into the world of indexing in early
December with the rollout of the first
piece of a comprehensive global family
of equity indexes that will ultimately
canvass 98 percent of the investable
universe and cover 9,000 securities
with a combined float-adjusted market
capitalization of $32 trillion.
The initial rollout covered about
4,000 indexes calculated in dollars.
The remaining indexes were to be
organized around different currencies
and introduced in different phases at
unspecified future dates, Nasdaq said
in a press release.
The index family will ultimately
result in the development of 24,000
separate indexes, Nasdaq said. The
new index family covers 45 countries
classified as developed and emerging
markets across the following regions:
the Americas, Europe, Asia-Pacific
and Middle East-Africa.
Like all Nasdaq OMX indexes, the
Nasdaq Global Index Family is based
on a transparent, rules-based index
methodology.
More than 10 years of historical
backtested data are available for the
entire global family, and price, total
return and net total return versions
are calculated for each index, the
company said.
FINRA Targets ETFs In Letter
The Financial Industry Regulatory
Authority singled out exchange-traded
products as one area it will focus on
in 2013 as it seeks to manage risks
to investors, though sundry concerns
related to “significant downside risks”
in the bond market easily topped
FINRA’s list of 2013 regulatory worries.
Borrowing a page from an inquiry it
launched in 2009 concerning leveraged
funds, FINRA again singled out such
strategies in a letter dated Jan. 11 outlining
its priorities for the coming year.
Crucially, a class action suit charging
that ProShares didn’t properly warn
investors about the risks of its leveraged
and inverse funds was dismissed
by a federal court last summer.
More broadly, FINRA said in the letter
that it worries investors may not
grasp crucial differences among the
various ETP structures, and highlights
leveraged products and new investment
areas—such as volatility, emerging mar-
10
March / April 2013

kets and currencies—as particular areas
of concern. Such concerns from the
financial industry’s self-regulator are
shared in the industry, which is growing
rapidly. ETF and ETN inflows hit a
record $188 billion in 2012, according to
data compiled by IndexUniverse.
Concern about ETPs notwithstanding,
FINRA’s chief concerns center on
the bond market, which has been rallying
sharply on and off since the market
crashed in 2008. FINRA is hardly
alone in identifying the dangers of the
bond market unraveling, but is specifically
focused on limiting the possibility
of irresponsible marketing of
fixed-income products at a potentially
crucial time in financial markets.
ICE To Acquire NYSE
The New York Stock Exchange
entered into an agreement to be
acquired by the Atlanta-based
IntercontinentalExchange in a cash
and stock transaction that values the
NYSE at $8.2 billion, the companies
said in a Dec. 20 press release.
The transaction valued NYSE
Euronext at $33 a share—a 37.7 premium
over NYSE Euronext’s (NYSE:
NYX) closing share price on Dec.
19, 2012. The transaction, which is
expected to close in the second half of
2013, will leave NYSE Euronext shareholders
with an ownership stake in the
new company of about 36 percent.
The companies estimated about
$450 million in “run rate expense synergies,”
with the bulk of those coming
in the second full year following the
closing. They also estimated earnings
“accretion” of more than 15 percent in
the first year after closing.
It’s not the first time ICE has made
a bid for NYSE. It tried unsuccessfully
to acquire a piece of NYSE Euronext in
a joint bid with the Nasdaq exchange
in the spring of 2011. But the initiative
fell apart a bit more than a month later
after U.S. regulators, citing antitrust
concerns, signaled they would reject
the proposed transaction.
The companies noted that the
2013 second-half closing is subject to
approvals by regulators in Europe and
the United States and by shareholders.
IndexUniverse Debuts
Currency ETFs
IndexUniverse LLC and Structured
Solutions AG announced the full commercial
launch of a new family of currency
indexes in late January.
Jointly developed by IndexUniverse
and Structured Solutions, the indexes
are intended to fill a substantial
gap in the modern indexing space
by measuring the performance of the
U.S. dollar against broadly diversified
trade-weighted baskets of currencies,
according to IndexUniverse.
The benchmarks are available
through Bloomberg under the following
symbols and include “investable”
indexes:
• IndexUniverse–Solactive U.S.
Dollar TW Index (Long USD)
(IUSLATL Index)
• IndexUniverse−Solactive U.S.
Dollar TW Index (Short USD)
(IUSLATW Index)
• IndexUniverse–Solactive Developed
Markets Currencies TW Index
(IUSLADTW Index)
• IndexUniverse–Solactive Emerging
Markets Currencies TW Index
(IUSLAETW Index)
• IndexUniverse–Solactive Asia-
Pacific Currencies TW Index
(IUSLAATW Index)
• IndexUniverse−Solactive U.S. Dollar
TW Investable Index (Short USD)
(IUSLTWI Index)
IndexUniverse LLC and Structured Solutions
AG announced the launch of a new family of
currency indexes in late January.
www.journalofindexes.com March / April 2013 11

News
• IndexUniverse–Solactive Developed
Markets Currencies TW Investable
Index (IUSLDTWI Index)
• IndexUniverse–Solactive Emerging
Markets Currencies TW Investable
Index (IUSLETWI Index)
• IndexUniverse–Solactive Asia-
Pacific Currencies TW Investable
Index (IUSLATWI Index)
Index weights are set each year based
on Federal Reserve reported data on
the level of trade between the U.S. and
foreign countries. The index also takes
into account the overnight lending rate
of each currency, giving investors a full
picture of the exposure they get to the
currency markets. This process creates
a dynamic index series that reflects
the purchasing power of the U.S. dollar
measured against the currencies of
trading partners of the United States.
The flagship IU−Solactive U.S.
Dollar TW Index holds a trade-weighted
basket of 26 currencies, led by the
Chinese renminbi, which represents
20.37 percent of the index. The U.S.
Dollar Index, by comparison, holds six
currencies, led by the euro at a 57.60
percent weight. The renminbi is not
included in the U.S. Dollar Index.
INDEXING DEVELOPMENTS
MSCI Adds To Risk Premia Family
Global index provider MSCI expanded
its risk premia index family in
December with the launch of five new
quality indexes. The benchmarks look
to capture the performance of equities
with quality growth characteristics.
They include the MSCI ACWI
Quality Index, the MSCI World Quality
Index, the MSCI Emerging Markets
Quality Index, the MSCI Europe Quality
Index and the MSCI USA Quality Index.
The indexes capture two underlying
risk premia, growth and low leverage,
MSCI said in a press release.
The MSCI Quality Index methodology
selects stocks based on
high return on equity, stable yearover-year
earnings growth and low
financial leverage. The indexes are
designed to underlie investable products
and to be used as benchmarks,
the press release said.
Russell Adds IPOs To Global Index
Russell said in a December press
release that it was adding 48 initial
public offerings to the broad Russell
Global Index as of Dec. 21. Half of
The Chicago Board Options
Exchange launched the
CBOE Low Volatility Index
at the end of November.
those IPOs were added from the U.S.,
and half from outside the U.S.
The U.S. companies included three
large-cap stocks, 19 small-cap stocks
and two micro-cap stocks, the press
release said. Four of the added non-
U.S. companies are domiciled in the
U.K., with three from Singapore. In all,
10 of the non-U.S. stocks being added
non-U.S. to the global index are from
Asia, and 10 are from Europe.
CBOE Rolls Out
Low-Volatility Index
The Chicago Board Options Ex -
change launched the CBOE Low Volatility
Index (LOVOL) at the end of
November, according to a press release.
The new index basically combines
equity exposure to the S&P 500 Index
with an options overlay strategy that
sells calls on S&P 500 options and
buys one-month VIX 30-delta calls,
the press release said. The exchange
described the CBOE LOVOL as a combination
of the strategies represented
by the CBOE S&P BuyWrite Index and
the CBOE VIX Tail Hedge Index.
The index strategy’s objective is to
reduce the downside volatility of an S&P
500 portfolio without detracting significantly
from the upside performance of
the equities, the press release said.
Facebook Joins Nasdaq-100
Facebook joined the Nasdaq-100
Index in mid-December, meaning it
now will be included in the portfolio of
the $30 billion PowerShares QQQ Trust
(NasdaqGM: QQQ), as well as the portfolios
of other ETFs tied to the index.
Indeed, QQQ, which replicates the
Nasdaq benchmark, had already bought
more than 11.14 million shares of the
social media company, representing a
little over 1 percent of the fund’s portfolio,
by the time the stock was added
to the index, according to PowerShares.
Facebook, which went public in
May 2012, replaced Infosys, after the
India-based consulting and technology
firm transferred its listing to the
New York Stock Exchange.
Facebook also joined the
Nasdaq-100 Equal Weighted Index
12
March / April 2013

and the Nasdaq-100 Technology
Sector Index, Nasdaq said.
Many had hoped that after the
Nasdaq OMX Group changed its
“seasoning rules” in April, Facebook
would be entering Nasdaq’s flagship
index by September 2012. Instead, in
September, Facebook was first added
to the Nasdaq Q-50 Index—the feeder
index for the Nasdaq-100.
S&P DJI Teams With
Townsend On REIT Index
S&P Dow Jones Indices rolled out the
Dow Jones Townsend Core U.S. REIT
Index in December, according to a press
release. The index was developed with
input from The Townsend Group, a real
estate investment consulting firm serving
institutional investors.
The REIT portfolio of the index is
designed to mimic the return/risk
profile of a privately held investment
in real estate. The components must
be focused on longer-term leases and
are drawn from specific REIT categories.
REITs classified as factory outlets,
hotels, manufactured homes, mixed
industrial/office and suburban office
are excluded from the index, according
to the index’s fact sheet. Individual
constituent weights are capped in
order to improve diversification.
The new index is part of the Dow
Jones Real Estate index family, the
press release said.
FTSE Unveils
‘Super Liquid’ Family
In late November, FTSE Group said
in a press release that it had launched
a family of highly liquid, narrow-based
indexes that have similar risk/return
characteristics and sector breakdowns
to their broader parent indexes.
The FTSE Super Liquid Index
Series debuted with 11 initial indexes
covering different development levels,
regions and individual countries.
Components are selected from the
parent index primarily for liquidity
with the intention of creating a highly
tradable portfolio that can be easily
and cheaply replicated.
The FTSE Developed Large Cap
Super Liquid Index, for example,
exhibits a 12-month correlation of
0.995 with its parent index, the FTSE
Developed Large Cap Index, according
to a fact sheet. However, as of Dec.
31, 2012, it only had 235 constituents
versus 843 in the broader index.
Russell Launches
‘High Efficiency’ Indexes
Russell has rolled out a family of
indexes based on its existing “Defensive”
index methodology, the index provider
said in a late January press release.
The Russell High Efficiency Defensive
Indexes are designed to target highquality
stocks exhibiting low volatility.
They were developed jointly with
Westpeak Global Advisors, an investment
management and research firm.
The original Russell Defensive Index
methodology selects components
based on “stability” scores and weights
them by market capitalization; however,
the methodology for the “High
Efficiency” indexes also weights components
by their stability scores. Each
index targets a specific level of tracking
error relative to its parent Russell
index, the press release said.
When it debuted, the series included
22 indexes derived from broader
U.S. and global indexes.
Stoxx Rolls Out ‘EM
Exposure’ Benchmark
Stoxx Limited announced the
launch of the Stoxx Global 1800 EM
Exposed Index in a December press
release. The index targets the components
of the Stoxx Global 1800 Index
that derive a significant portion of their
revenues from emerging markets.
The index methodology relies on
regional revenue breakdowns from
the companies, but can also estimate
a company’s exposure to emerging
markets based on the company’s
home country’s GDP, imports and
exports. Companies achieving an
“exposure score” above 33 percent are
included in the index and are assigned
a weighting in the index based on their
market capitalization and exposure
level, the press release said.
‘Real Asset’ Index
Targets Inflation
Morningstar Inc. rolled out a multiasset
index in January that it says can
be used to help hedge against inflation.
The Morningstar US Real Asset
Index represents the performance of
liquid “real assets” such as inflationprotected
securities, REITs and commodities-based
investments.
According to Morningstar, investors
are concerned about inflation,
and a number of new funds launched
in the past few years hold portfolios of
real assets. The new index is designed
to serve as a benchmark for investors
in those types of products.
The index has a 40 percent allocation
toward TIPS and a 30 percent
weighting in commodities futures.
REITs and commodities-related equities
each receive weightings of 15 percent,
the press release said.
AROUND THE WORLD OF ETFs
ProShares Debuts Merger Arb ETF
ProShares rolled out a mergerarbitrage
ETF in mid-December that
tracks the S&P Merger Arbitrage Index;
the benchmark seeks to capture the
spread between the actual stock price
of a company targeted for acquisition
at the time of the deal’s announcement
and the price the acquiring company
has said it will pay.
The ProShares Merger ETF (BATS:
www.journalofindexes.com March / April 2013 13

News
At the start of 2013, Barclays Plc’s iPath division launched
its first ETN to cover master limited partnerships.
MRGR) will go head-to-head with
IndexIQ’s IQ ARB Merger Arbitrage ETF
(NYSE Arca: MNA), which employs a
similar strategy and launched in 2009.
However, after waivers and reimbursements
are taken into account, MRGR is
1 basis point cheaper than MNA, at a
net expense ratio of 0.75 percent.
iPath Joins MLP Bandwagon
At the start of 2013, Barclays Plc’s
iPath division launched its first ETN
to cover master limited partnerships.
While the ETN structure is favored
for MLP exchange-traded products,
and arguably the most successful MLP
products are ETNs, iPath had not yet
entered the space.
The iPath S&P MLP ETN (NYSE
Arca: IMLP) tracks an index that covers
both MLPs and publicly traded
limited liability companies, which
are similar in structure to MLPs.
The benchmark includes only U.S.-
listed components that fall under
the energy sector and gas utilities
industry designations in the GICS
classification system.
With the addition of IMLP, there
are now 12 long-exposure MLP ETFs
and ETNs listed in the U.S. IMLP
comes with an annual expense ratio
of 0.80 percent, undercutting many
of its competitors.
PowerShares Closing 13 ETFs
PowerShares announced in December
it would be closing 13 of its
ETFs in the early months of 2013.
The funds slated for liquidation
include a mix of specialty sector, strategy
and active products, with their
assets under management at the time
of the announcement mostly coming
in under $15 million. PowerShares said
the ETFs would stop trading Feb. 26
and would be liquidated on March 7.
The three largest funds in the group
include the PowerShares RiverFront
Tactical Growth & Income Portfolio
(NYSE Arca: PCA), with $15.8 million
in assets; the PowerShares RiverFront
Tactical Balanced Growth Portfolio
(NYSE Arca: PAO), with $15.5 million;
and the PowerShares Morningstar
StockInvestor Core Portfolio (NYSE
Arca: PYH), with $14 million. The smallest
fund on the list was the PowerShares
Global Steel Portfolio (NasdaqGM:
PSTL), with just $1.9 million in assets.
Van Eck’s KWT, MOO
Get New Indexes
In early January, Van Eck Global
announced that it was changing the
indexes on two of its sector-focused
equities ETFs to in-house benchmarks
in a move the company says will
improve liquidity and diversification.
The blockbuster Market Vectors
Agribusiness ETF (NYSE Arca: MOO)
now tracks the Market Vectors Global
Agribusiness Index, replacing its
previous benchmark, the DAXglobal
Agribusiness Index. Similarly, the
Market Vectors Solar Energy ETF
(NYSE Arca: KWT) has switched
to the Market Vectors Global Solar
Energy Index, which replaced the
Ardour Solar Energy Index. The new
indexes cap the weights of individual
constituents in the interests of diversification,
while KWT’s new index
methodology will also broaden its
selection universe.
Twenty-five of Van Eck’s 35 equities
ETFs now track in-house indexes. The
firm’s Market Vectors Index Solutions
is a wholly owned Germany-based
subsidiary that develops and publishes
all of Market Vectors’ indexes.
First Copper ETF To Launch Soon?
After more than two years in registration,
the JP Morgan XF Physical Copper
Trust was approved in December by the
Securities & Exchange Commission.
The approval had been delayed while
the SEC investigated the question of
whether the product would affect the
integrity of the copper market. There
were concerns from copper market
participants that the fund would arti-
14
March / April 2013

ficially drive up the price of copper. It
is unclear when the fund may launch.
However, in an interesting twist,
the SEC put off ruling on the approval
of the very similar iShares Copper
Trust, which has been in registration
for about as long as the JP Morgan
fund. The SEC was to have made a
decision on Dec. 24, but deferred the
decision until February for reasons
that weren’t immediately clear.
FlexShares Debuts 3 Dividend ETFs
Northern Trust’s FlexShares unit
launched three dividend-focused
ETFs in December that each track inhouse
indexes. The funds’ names and
expense ratios are as follows:
• FlexShares Quality Dividend Index
Fund (NYSE Arca: QDF), 0.37
percent after a 0.01 percent fee
reimbursement
• FlexShares Quality Dividend
Defensive Index Fund (NYSE Arca:
QDEF), 0.37 percent
• FlexShares Quality Dividend
Dynamic Index Fund (NYSE Arca:
QDYN), 0.37 percent
The portfolios each comprise highquality
U.S. securities. Companies
included in the various underlying
indexes are selected based on expected
dividend payments as well as fundamental
factors such as profitability,
solid management and reliable cash
flow, the company said in the prospectus.
Each fund targets a different
level of volatility, with QDF aiming for
volatility on par with the market, while
QDEF and QDYN target volatility levels
that are, respectively, lower and
higher than market levels.
ALPS Launches ETFs
Tracking GS Indexes
ALPS, mainly known as an ETF distributor,
rolled out four in-house ETFs
in late December that track indexes
provided by Goldman Sachs.
Three of the funds take momentumbased
approaches, although all four also
are designed to keep volatility in check.
The ETFs that target price momentum
are all “funds of funds” that invest
in other exchange-traded products,
including ETFs covering U.S. fixedincome
markets. They and their
expense ratios are as follows:
• ALPS/GS Momentum Builder
Growth Markets Equities and U.S.
Treasuries Index ETF (NYSE Arca:
GSGO), 1.29 percent
• ALPS/GS Momentum Builder
Multi-Asset Index ETF (NYSE Arca:
GSMA), 1.14 percent
• ALPS/GS Momentum Builder
Asia ex-Japan Equities and U.S.
Treasuries Index ETF (NYSE Arca:
GSAX), 1.22 percent
The fourth ETF, the ALPS/GS
Risk-Adjusted Return U.S. Large Cap
Index ETF (NYSE Arca: GSRA), uses a
Goldman-developed methodology to
target the U.S. large-cap stocks with the
highest risk-adjusted returns. It comes
with an expense ratio of 0.55 percent.
KNOW YOUR OPTIONS
CBOE Reports 2012 Volumes
The Chicago Board Options
Exchange reported a total annual
volume of 1.06 billion contracts
traded in 2012, for an average daily
volume of about 4.2 million contracts.
The ADV is down about 7
percent from the prior year.
Index options saw their ADV
decline about 5 percent to 1.2 million
contracts, while ETF options fell
a sharp 15 percent to an ADV of 1.1
million contracts.
In December 2012, the exchange’s
ADV was down 10 percent year-overyear
to 3.6 million contracts, with index
options registering a decline in ADV of
8 percent to 1.3 million contracts and
ETF options’ ADV down 15 percent.
BACK TO THE FUTURES
CME December Volume Up Y-O-Y
CME Group said in a press release
that its average daily volume for
December 2012 rose 1 percent yearover-year
to 9.6 million contracts.
However, its equity index contracts
saw a 5 percent ADV contraction to 2.7
million contracts per day, down from
2.8 million the prior December.
The exchange group’s most actively
traded index futures contracts included
the e-mini S&P 500 futures, which saw
their total volume for the month fall
18.4 percent from the prior year to 37
million contracts. However, the volume
of the e-mini Nasdaq-100 contracts was
up 14.1 percent to 5.5 million contracts,
while the mini $5 Dow futures contracts
saw their volume rise year-over-year by
6.9 percent to 2.6 million contracts.
MSCI Licenses More
Indexes To Eurex
In December, MSCI said it had
licensed a range of indexes to the
derivatives-focused Eurex Exchange.
The agreement authorizes Eurex
to launch derivatives based on a
range of MSCI indexes, including
the MSCI World, MSCI Europe,
MSCI Emerging Markets and MSCI
Frontier Markets indexes, according
to a press release. The exchange
already offered futures and options
on MSCI’s Russia index, as well as
futures on the MSCI Japan Index.
ON THE MOVE
Kranefuss Joins Warburg Pincus
Lee Kranefuss, the former chief
executive officer of iShares who
stepped down from the San Franciscobased
company in 2010, has joined
global private equity firm Warburg
Pincus to help the company’s expansion
into the ETF market.
Kranefuss, as an executive-in-residence,
will “work to help Warburg
Pincus identify and evaluate investment
opportunities in the areas of
ETFs, index investing and asset management,
particularly in Europe, Asia
and Latin America,” the firm said in a
December press release.
The New York-based private equity
firm, known for its focus on growth
investing, manages more than $30 billion
in assets, according to information
provided by the company.
Kranefuss oversaw the growth of
iShares from its dawn to $600 billion in
ETF assets by the time he left office in
2010 after having overseen BlackRock’s
acquisition of Barclays Global Investors,
iShares’ parent company.
continued on page 63
www.journalofindexes.com March / April 2013 15

Global Value
Building trading models with the 10-year CAPE
By Mebane Faber and Prabhat Dalmia
16
March / April 2013

Over 70 years ago, Benjamin Graham and David
Dodd proposed valuing securities with earnings
smoothed across multiple years. Robert Shiller
popularized this method with his version of the cyclically
adjusted price-to-earnings ratio (CAPE) in the late 1990s,
and issued a timely warning of poor stock returns to follow
in the coming years. We apply this valuation metric across
approximately 40 foreign markets and find it both practical
and useful. Indeed, we witness even more examples of
bubbles and busts abroad than in the United States. We
then create a trading system to build global stock portfolios
based on valuation, and find significant outperformance by
selecting markets based on relative and absolute valuation.
The Futility Of Forecasting
Investors spend an inordinate amount of time and effort
forecasting stock market direction, often with very little success.
The conventional efficient market theory is that markets
are not predictable and cannot be forecasted. Value has
no place in the efficient market ivory tower, but does it seem
reasonable for an investor, or perhaps a retiree, to have allocated
the same amount of a portfolio to stocks in December
1999 that they did in 1982? Of course not.
However, valuation is best used as a strategic guide rather
than as a short-term timing tool. It is most useful on a time scale
of years and decades rather than weeks and months (or even
days). While we can formulate a hypothesis for where the S&P
500 “should” be trading, the animal spirits contained in the marketplace
invariably cause prices to deviate quite substantially
from “reasonable” levels, often for years and even decades.
There are numerous models to consider when valuing stock
markets, and a great summary can be found in a publication
by The Leuthold Group titled “Stock Market Valuation: What
Works and What Doesn’t?” The paper covers a number of
models, including price-to-earnings (P/E) on trailing 12-month
Figure 1
50
45
40
35
30
25
20
15
10
5
US 10-Year CAPE
1881 – 2011
-
1881 1893 1905 1917 1929 1941 1953 1965 1977 1989 2001
Source: Robert Shiller, http://www.econ.yale.edu/~shiller/data.htm
earnings per share (EPS), P/E on five-year normalized EPS,
return on equity (ROE)-based normalized EPS, dividend yield,
price-to-book, price-to-cash flow and price-to-sales. In general,
they find that many of these metrics are decent at forecasting
stock returns. Other models include the Q-ratio, and market
capitalization to GNP/GDP (Buffett’s favorite). Another great
summary is set forth in the paper “Estimating Future Stock
Market Returns” by Adam Butler and Mike Philbrick.
However, we are not going to summarize all of the stock
valuation models in existence; rather, we will focus on just one.
A Simple Model: 10-Year Normalized Earnings
Benjamin Graham and David Dodd are universally seen
as the fathers of valuation and security analysis. In their 1934
book “Security Analysis,” they were early pioneers in comparing
stock prices with earnings smoothed across multiple years,
Figure 2
US Stock Real Returns Vs. 10-Year CAPE
1881 – 2011
Top
Bottom
#
Date
10-Year
Real Return
CAPE
Date
10-Year
Real Return
CAPE
1 12/31/1948 18.1% 9.88 12/31/1910 -4.7% 12.99
2 12/31/1918 17.7% 5.93 12/31/1964 -4.0% 22.87
3 12/31/1949 17.0% 10.49 12/31/1998 -3.9% 39.87
4 12/31/1988 16.1% 14.68 12/31/1968 -3.6% 21.60
5 12/31/1920 15.8% 4.72 12/31/1999 -3.5% 45.08
6 12/31/1919 15.7% 6.11 12/31/1909 -3.0% 15.16
7 12/31/1946 15.7% 11.43 12/31/1967 -2.7% 21.93
8 12/31/1989 15.3% 17.82 12/31/1965 -2.5% 23.80
9 12/31/1951 15.1% 12.29 12/31/1911 -2.4% 12.82
10 12/31/1990 14.8% 15.86 12/31/1971 -2.3% 17.02
Avg 16.1% 10.92 -3.3% 23.31
Source: Robert Shiller, http://www.econ.yale.edu/~shiller/data.htm
www.journalofindexes.com March / April 2013
17

Figure 3
US Stock Average Real Compound Returns Vs. 10-Year CAPE
1881-2011
CAPE
%
Occurrence
1-Year Fwd
Real CAGR
3-Year Fwd
Real CAGR
5-Year Fwd
Real CAGR
7-Year Fwd
Real CAGR
10-Year Fwd
Real CAGR

are high as they do when valuations are low. But does that
seem even remotely reasonable looking at Figure 1?
The 10 Best, And Worst, Times In History To Invest
To illustrate this point, we examined all year-end periods
with a holding period for the next 10 years. What
have been the 10 best, and worst, years to invest since
1871? Figure 2 details these years and their corresponding
10-year compounded real returns.
Many of the best starting points seem obvious in retrospect.
1948 and 1949 were great entries, preceding the Nifty Fifty
mania, and of course 1918-1920—right before the Roaring
Twenties—are on the list. 1988 and 1989 certainly would not
be left out, with the Internet bull market ahead, as well.
The same hindsight applies for the bad years, as they
often fell at the end of these massive bull runs. Bear markets
set the stage for future bull markets and vice versa.
One simple takeaway from Figure 2 is the valuations at
Figure 7
Global Countries Included In Study And 10-Year CAPE
As Of December 2012
Country
Start Date Latest Min Max
Median
Australia 12/31/1969 14.06 7.65 31.60 16.93
Austria 10/31/1981 8.43 6.04 59.16 26.53
Belgium 12/31/1969 10.27 4.88 29.48 14.84
Brazil 1/31/1988 12.07 11.04 29.73 17.20
Canada 12/31/1969 18.00 5.83 63.34 19.75
Chile 1/31/1988 21.09 9.71 32.95 21.29
China 1/31/1995 15.56 13.52 63.85 23.95
Colombia 12/31/1992 33.81 9.25 48.66 35.00
Egypt 1/31/1996 13.57 10.03 56.43 17.38
France 9/30/1971 11.95 6.20 57.17 19.68
Germany 12/31/1969 14.01 7.83 56.87 17.85
Greece 12/31/1987 2.57 1.95 39.82 15.20
Hong Kong 12/31/1972 16.84 8.55 34.55 18.03
India 12/31/1992 19.50 12.69 47.80 23.86
Indonesia 1/31/1990 24.92 5.05 34.96 16.90
Ireland 5/31/1990 5.00 3.08 23.28 12.21
Israel 6/30/1999 10.55 10.55 21.64 17.29
Italy 4/30/1984 7.41 5.92 52.92 21.61
Japan 12/31/1969 15.41 13.27 94.26 43.79
Malaysia 12/31/1987 20.19 7.77 26.49 18.77
Mexico 12/31/1987 21.31 11.69 35.34 19.68
Netherlands 12/31/1969 11.20 4.62 38.51 11.89
New Zealand 1/31/1988 13.28 9.50 20.34 13.79
Norway 12/31/1969 12.39 6.76 30.55 14.17
Peru 1/31/1993 33.57 16.00 61.17 31.36
Poland 12/31/1992 13.90 7.55 27.47 15.23
Portugal 1/31/1988 8.79 7.02 39.36 15.97
Russia 1/31/1996 7.22 5.13 22.87 8.96
Singapore 12/31/1972 12.48 9.40 37.81 21.74
South Africa 12/31/1992 18.45 10.25 24.30 16.41
South Korea 12/31/1987 15.72 4.74 27.65 17.73
Spain 12/31/1979 8.50 6.50 40.05 17.27
Sweden 12/31/1969 15.04 4.82 74.18 19.42
Switzerland 12/31/1969 16.22 7.12 57.95 18.07
Taiwan 1/31/1988 14.29 9.18 42.28 19.36
Thailand 12/31/1987 16.35 3.00 17.97 12.10
Turkey 12/31/1987 15.72 8.24 42.95 16.77
UK 12/31/1927 12.56 4.43 28.69 11.84
USA 12/31/1881 20.70 4.72 45.08 15.87
Sources: Global Financial Data, MSCI
www.journalofindexes.com March / April 2013 19

Figure 8
10-Year CAPE Levels And Future Average Real Compound Returns For 39 Countries
1980 – 2012
Avg CAPE
By Bucket
%
Occurence
1-Year
Real CAGR
3-Year
Real CAGR
5-Year
Real CAGR
7-Year
Real CAGR
10-Year
Real CAGR
< 10 10.8% 23.3% 15.1% 17.6% 15.8% 12.3%
10 to 15 21.2% 24.8% 16.7% 14.0% 12.1% 9.1%
15 to 20 25.5% 13.1% 11.7% 10.8% 9.3% 8.8%
20 to 25 18.0% 8.6% 7.8% 6.5% 7.5% 6.1%
25 to 30 10.5% 1.0% 2.6% 5.6% 5.4% 4.4%
30 to 40 7.4% 7.1% 1.0% 0.7% 2.6% 2.9%
40 to 50 4.6% -6.0% 0.8% 2.7% 1.5% 1.0%
>50 1.9% -10.7% -11.7% -4.0% -1.5% -2.9%
Sources: Global Financial Data, MSCI
Figure 9
Portfolios Sorted On CAPE Levels With No Filters, Real Returns
1980-2011
Yearly
Top 33%
Yearly
Top 25%
Yearly
Top 10%
Strategy
Sources: Global Financial Data, MSCI
Eq Wt
Cheapest
Eq Wt
Expensive
Eq Wt
Spread
Eq Wt
All
CAGR 12.0% 8.2% 2.5% 9.8%
Stdev 27% 26% 18% 25%
Maxdd 54% 47% 59% 50%
CAGR 12.3% 6.3% 4.5% 9.8%
Stdev 27% 26% 20% 25%
Maxdd 55% 48% 62% 50%
CAGR 16.6% 6.0% 7.0% 9.8%
Stdev 35% 29% 34% 25%
Maxdd 59% 67% 81% 50%
Figure 10
Portfolios Sorted On CAPE Levels With Filters, Real Returns
(Max CAPE Of 15 For Long; Min CAPE Of 30 For Short), 1980-2011
Yearly
Top 33%
Yearly
Top 25%
Yearly
Top 10%
Strategy
Sources: Global Financial Data, MSCI
Eq Wt
Cheapest
Eq Wt
Expensive
Eq Wt
Spread
Eq Wt
All
CAGR 12.6% 0.4% 11.7% 9.8%
Stdev 23% 12% 21% 25%
Maxdd 20% 43% 27% 50%
CAGR 13.3% 0.7% 12.0% 9.8%
Stdev 23% 13% 21% 25%
Maxdd 20% 45% 30% 50%
CAGR 17.5% 1.5% 11.1% 9.8%
Stdev 32% 27% 36% 25%
Maxdd 32% 73% 81% 50%
There is very little in the literature regarding global CAPEs
for international equity markets.
the start of these 10-year periods. The average valuation for
the 10 best years was 10.92. The average valuation for the 10
worst years was 23.31, double that of the best starting points.
Buy Low, Sell High
Figure 3 is a table of all of CAPE year-end readings from
1881-2011. We list how often they occur, as well as the real
forward returns. The red bar in Figure 4 is where we find
ourselves as of the summer of 2012.
What we find is no surprise: It very much matters what
price one pays for an investment! Indeed, it is an almost perfect
stair step: Future returns are lower when valuations are
high, and future returns are higher when valuations are low.
While more sophisticated models can be built, Figure
5 simply shows the shockingly similar trend lines of an
inverse CAPE and future 10-year real stock returns. 3
Valuation And Inflation
Besides general sentiment, what might cause this large
variation in what multiples investors are willing to pay for
stocks? After all, at a current value of around 1374, this
means the S&P 500 could trade at either 315 or 2800 based
on historical low and high multiples of 5 and 45, respectively.
It is difficult for most investors to comprehend the possibility
of stocks declining 80 percent or increasing over 100 percent,
but both of these multiples have occurred in the past.
One of the determinants of the valuation multiple investors
are willing to pay is the inflation rate as seen in Figure
6. The red bar is where we find ourselves as of the summer
of 2012. When inflation is in the 1-4 percent “comfort zone,”
investors are willing to pay a valuation premium compared
with when there is either high inflation or outright deflation. 4
Global CAPE
There is very little in the literature regarding global CAPEs
for international equity markets. 5 We examined 39 countries
with data from MSCI and Global Financial Data, including as
much data as we could find, although there is some bias in
the study. All the returns are real dollar returns.
Two countries had a century’s worth of data (U.S. and
20 March / April 2013

the U.K.), but most of the other countries go back to the
1970s and 1980s (see Figure 7).
We examined all the countries on a yearly basis since
1980, CAPE levels and future returns. The sample includes
approximately 10 countries in 1980, 20 in 1990 and 30 by
2000. The results are in Figure 8 and largely confirm the
U.S. data: Buy low, sell high.
We found most CAPEs averaged around 15-20, bottomed
out around 7, and maxed out around 45 (a few made
the U.S. bubble in the late 1990s look pathetic in comparison,
as when Japan reached a value of nearly 100 in 1989).
A Global Stock Trading System
But can we turn this into a trading system? There is evidence
that sorting countries on other measures of value
works well. A good summary of the dividend literature can
be found in the Tweedy, Browne paper titled “The High
Dividend Return Advantage.” 6 In the paper, they summarize
a 1991 study by Michael Keppler titled “The Importance of
Dividend Yields in Country Selection“ 7 that found that ranking
the universe of countries by dividend yield also resulted
in outperformance. He found that the highest-yielding countries
outperformed the lowest-yielding from 1969-1989 by
more than 12 percentage points per year.
Running a similar study using a different database
(Global Financial Data), 8 we sorted countries by quartiles
from 1920-2011, beginning with nine countries and
expanding to 18 by study end. We found that countries in
the highest-dividend-paying quartile outperformed the
countries in the lowest-paying quartile by 11 percentage
points per year. (Also see the Appendix for tests on book
value, dividends, cash flow and earnings.)
We then set out to test CAPE in a similar manner.
Starting in 1980, we sort all countries by CAPE, and invest
in the most undervalued x percent, rebalanced yearly. We
also show the effects of investing in the most overvalued x
percent, as well as a long/short portfolio. These returns are
real returns net of inflation, and with yearly data (which
will naturally understate drawdown figures). The sample
includes approximately 10 countries in 1980, 20 in 1990 and
30 by 2000. Investing in the cheapest countries produces 2
to 7 percentage points of outperformance, while the overvalued
countries underperform (see Figure 9). The spread
is approximately similar to those appearing in the previously
mentioned dividend studies, albeit slightly lower.
However, investing in the cheapest countries on a relative
basis does not protect the investor when all countries are
expensive in a global equity bubble like 1999. We repeated
the study, but only invested long if the country was below a
CAPE of 15, and only short above a CAPE of 30. If the country
does not qualify for the valuation filter, then that part of
the portfolio sits in cash (although we do not receive any
interest income in this test) (see Figure 10).
For the most part, adding the absolute CAPE-level filter
results in better performance with lower drawdowns. This
is to be expected, as the portfolio could be sitting in 20, 50
or even 100 percent cash (as with 1999 or 2007). In this case,
the returns are higher as well. As many investors look at
Figure 11
25,600
12,800
6,400
3,200
1,600
800
400
200
100
Portfolios Sorted On CAPE Levels, Real Returns
1980 – 2011
50
1979 1983 1987 1991 1995 1999 2003 2007
■ Cheapest 33% ■ Expensive 33% ■ Eq Wt ■ Cheapest 33% with flter
Sources: Global Financial Data, MSCI
Figure 12
Appendix Data: Cheapest X Percent Of Countries, 1975-2011
33%
CAGR
STD
MaxDD
25%
CAGR
STD
MaxDD
10%
CAGR
STD
MaxDD
Buy
& Hold
12.7% 13.5% 15.6% 13.2% 14.3%
22% 24% 24% 25% 24%
47% 46% 50% 46% 49%
Buy
& Hold
12.7% 15.4% 14.3% 14.0% 14.9%
22% 25% 26% 25% 24%
47% 43% 61% 46% 46%
Buy
& Hold
Book
Yield
Book
Yield
Book
Yield
Earnings
Yield
Earnings
Yield
Earnings
Yield
12.7% 18.1% 16.4% 13.5% 12.9%
22% 29% 28% 34% 26%
47% 38% 54% 55% 48%
Source: Global Financial Data, MSCI, Fama & French
Cash Flow
Yield
Cash Flow
Yield
Cash Flow
Yield
Dividend
Yield
Dividend
Yield
Dividend
Yield
this table and salivate over the prospect of 15 percent real
returns, recall Figure 7 and note that most of the cheapest
countries fall in the troubled eurozone. How many investors
have the stomach to invest in these countries with potential
for the markets to get even cheaper? How many professional
investors would be willing to bear the career risk associated
with being potentially wrong in buying these markets?
Figure 11 depicts the equity curves from taking the cheapest
33 percent of countries (also with filter), the most expensive
33 percent of countries and the equal-weight benchmark.
Summary
Warren Buffett famously said, “Price is what you pay. Value
is what you get.” Over periods of years and decades, it is evident
that an investor’s real return is heavily dependent on the price
paid for the asset. Investors can use CAPE valuation as a guidecontinued
on page 41
www.journalofindexes.com March / April 2013
21

Profiles In Pensions
PERSI’s Maynard Favors
‘Traditional’ Approaches
Advises picking a strategy
and sticking with it
The $12.6 billion Public Employee Retirement System of
Idaho has been around since 1963, and is one of the most successful
public pension funds in operation, with a funding percentage
of more than 87 percent. Robert Maynard has been
the pension fund’s CIO since 1992. The Journal of Indexes
caught up with him recently for a chat about sound pension
fund management policies and the role of passive investment.
Public Employee Retirement System of Idaho
Assets (as of 12/20/2012): $12.6 billion
Funding: 87.3%
Passive/Active Mix: 35%/65%
JOI: Tell us a little bit about PERSI.
Maynard: It’s a multiple-employer trust. We have over
100,000 members. About 65 percent of them are active, and
about 35 percent are retired. We have teachers and public
employees. The mandated ones are the state employees
and the teacher systems. Basically, we include all statewide
employees and teachers. The discretionary participants,
who can choose to go in or out, are cities, hospital districts,
water districts, those types of things.
JOI: How much does the fund have in assets?
Maynard: As of Dec. 20, the fund has $12.585 billion. As of
this morning, we are 87.3 percent funded.
JOI: That’s an amazing number. To what do you attribute
the fact that PERSI is so well funded?
Maynard: Our political system. People will always complain
about legislatures and whatnot, but our political
system has been a hero on this over the years. It has basically
kept the liabilities under control and has always
fully funded everything.
Even if you look at the people that are underfunded,
it’s been a great 20 or 30 years. All of us have made 9
percent per year, for example. Very few of us—and I
can’t think of any, except those who had to be in bonds
for a lot of that time—haven’t made well above their
hurdle rates. The difference in funding levels is due to
either over-promising benefits or under-funding on
contributions, or both. For example, New Jersey didn’t
pay contributions for years, and that’s the problem. Our
legislature has always paid for the benefits, hasn’t overpromised
benefits, and has always kept up with what the
law requires. They always have to put in the minimum
above that 15 percent level, basically what’s called “normal
cost.” As a result, we’re in fine shape.
If we were a corporation, we would be probably 110
percent funded. But one thing the public pension funds
do is that we assume everybody right now is making
their salary at the time of retirement. That basically
doubles that liability for active members. If we were corporate,
we would be in even better shape than that.
If you look at the returns of the New Jersey Division of
Investment, for example, the returns have been fine over
the decades. I’ve known that system for 30 years. The
problem is, for years nobody put in the contributions.
JOI: Would you describe PERSI’s overall investment
approach as “conservative,” even in the context of other
pension funds?
Maynard: I would call it “traditional.” We’re a traditional
investment fund. We’re 70 percent at risk. Most people,
when they say they are conservative, look more towards
bonds and things of that nature.
22
March / April 2013

JOI: But PERSI’s strategy does seem conservative in the
sense that it seems to be strongly opposed to making
spur-of-the-moment decisions or chasing returns.
Maynard: That is correct. The traditional investment approaches
and modern portfolio theory were built and designed to
work over five- to 15-year periods. That means: Don’t make
quick decisions. Stick to the plan. Expect interim volatility.
If all of a sudden you find yourself in a situation where
you can’t lose a fair amount on a year-to-year basis, you
can’t take the volatility, or you find that your return needs
are more than the market can bear—like endowments,
which have real return needs oftentimes of 7 or 8 or 9 percent
above inflation—then modern portfolio theory gives
you issues. But we don’t have those problems.
JOI: What are the pitfalls for pension fund investing?
What trips up pension fund managers?
Maynard: Switching approaches too often, or chasing
the most recent good idea. Generally, if you look at the
most successful funds over the years, you’ll find that the
You use indexes for a number of different reasons.
First of all, it’s an easy way to see what the market return
would be if you have active management.
ones that have been the most successful stick to the same
investment approach over decades.
And sometimes they’ll do completely different things.
If you look at the Washington State Investment Board,
it’s a stunningly good fund. They do huge amounts of
private equity and always have, much more than we do—
something like 25 percent. Over time, we’ll all get about
the same return. The South Dakota Retirement System
has one of the best investment records in the world over
the last 35 years because they stuck to a certain type of
investment. Whatever investment approach you have,
you need to stick with it in good times and bad—and particularly
the bad times. The market always takes longer to
go through a cycle than you think it will—oftentimes just
beyond that three years that’s the average tenure of the
CIO, or average of five years for a board member. When
you have turnover of staff, they get rid of things that were
put in place by a previous group. Then they leave and
someone else comes in. Those are the systems—the ones
without those traditions—that can get in trouble.
In large measure, if you look at a period of time over years
and years, we all kind of end up around the same place, just
at different times. There are different routes to investing, and
you’ve got to pick one and stick with it. For us, because our
liabilities are under control, we stick to basic, market-oriented
investments—we don’t have to try for anything bigger.
JOI: How do you use index-based investment strategies
in the fund?
Maynard: In all of our areas, we have basic, cap-weighted
indices, and that tends to be about a third of our fund.
JOI: Why is cap-weighted the way to go?
Maynard: You use indexes for a number of different reasons.
First of all, it’s an easy way to see what the market
return would be if you have active management. It is an easy
alternative to the active managers or any other approach. It
definitely represents the money-weighted opinion of value.
It gives you the fewest transaction costs: Cap-weighted
automatically rebalances. We’re like big tuna being carved
up like sashimi out there when we go out there and try and
trade, given the high-frequency traders and everything. It
includes pretty much all stocks. You are not leaving some
stocks out like you would with a fundamentally weighted
index. It includes all stocks and all styles. And it may or may
not be the best way to get return for the risk involved, however
you measure risk.
When you hear people talking about different sorts of
indices, like fundamentally weighted indices, their argument
is it makes you more money with less risk. That may or may
not be true—I’m agnostic on that. But clearly other forms of
indices beyond cap weighting don’t address all the other reasons
you use an index in a broad portfolio—easy transaction,
get money in and out, use it to rebalance for other things,
good risk control, easy alternative, all of that stuff.
JOI: How do you evaluate an active manager?
Maynard: We evaluate whether or not they are being
true to their style and with the same sort of resources. If
they are, our basic policy is that we don’t fire for nearterm
performance—and by “near term,” I mean three to
five years. Poor near-term performance can be an indicator
of something going wrong, either that something is
going wrong within the firm or that it is a different firm
than you thought you had. But if you look at them and
they are the same firm, following the same style, picking
Figure 1
PERSI Asset Allocation
December 2012
Source: Public Employee Retirement System of Idaho
www.journalofindexes.com March / April 2013 23

the same types of stocks, we keep those managers—we
don’t fire for performance alone.
As a result, we tend to have managers who have very
clear styles, so it is easy to tell if they stray from the mandate.
We have deep-value managers, for example, so if
they pick Pets.com, you know something is wrong. Or
our managers have concentrated portfolios. We tend to
prefer those sorts of managers.
JOI: Are there areas where index-based investing is not
necessarily the way to go or isn’t appropriate?
Maynard: It used to be that I had doubts about emerging
markets and REITs because of the newness of the areas.
Now, I think the markets are sufficiently mature, and
we are starting to do index investing. But, clearly, bank
loans, you can’t index. There are some niche markets
pick the top quartile or top decile, it is worth it. I have no
problem with people who go into that area if they are one
of those types of funds who can’t handle market returns for
one of two reasons—either because market returns are too
volatile, or they need much-better-than-market returns.
This traditional approach we use is fine as long as you
can meet your liability with around 3 to 5 percent real
returns above inflation. If your needs are significantly
above that—if you are an endowment that has to make 8
percent real returns or you are so deep in the hole you’ve
got to make 8 or 9 percent real returns—then picking the
top-quartile active public equity manager doesn’t get you
what you need, but picking the top-quartile or -decile
hedge fund manager might.
If you have to go into that market, you’d better be
aware that the odds are 3-to-1 or 4-to-1 against you. We
Hedge funds promise you equitylike returns with bondlike volatility.
What they have given you is below-bond returns with equitylike volatility.
I think everybody recognizes that the average institutional
hedge fund or fund of funds isn’t worth it.
where indexing is less appropriate, and I have concerns
about index investing in some areas where you may be
putting in a bias you don’t want if you index.
For example, high-yield debt, you index according
to market weightings. Or in emerging market debt, your
assets will tend to go to those countries that are trying to
borrow too much money. But other than that, for the big,
major markets, U.S. large, mid and small; developed-market
international; TIPS; REITs; emerging markets; general
bond; the credit bond market; the government bond market—for
all of those, I think index investing is fine.
JOI: What sort of role do you see alternative investments
playing in the PERSI portfolio?
Maynard: We don’t see any role for those—I’m leaving out
private real estate, private equity and the Idaho commercial
mortgage program from that group. Those, oftentimes, are
thrown into the alternative category. But we’re fine with some
private equity. We’re fine with some private real estate. We’re
fine with our local private commercial mortgage program.
But beyond that, what is called the “hedge fund movement,”
the letting 100 flowers bloom and diversifying into
all these little areas like Sri Lankan distressed debt or
whatever you like—that sort of stuff we don’t see a role for.
A hedge fund from our standpoint is levered active management
and very expensive. It’s doubling down on active
management—and it hasn’t done its job.
Hedge funds promise you equitylike returns with bondlike
volatility. What they have given you is below-bond
returns with equitylike volatility. I think everybody recognizes
that the average institutional hedge fund or fund of
funds isn’t worth it. What they say, though, is that if you
don’t have to go there. We’re perfectly fine even with
subpar market returns for a decade or two. But if we
had to go there, if we had to make 8 percent real returns,
then you’re in a different world. Then you’ve got to go
into hedge funds and heavily into alternatives or do
leveraged strategies, like risk parity.
JOI: Do you use ETFs at all?
Maynard: We have our passive index fund that’s cheaper
than a Russell 3000 ETF because of our size, so there is no
reason to use ETFs. Within our passive index funds, sometimes
as money goes in or out, we may want an Indian
exposure or a U.K. exposure. An ETF may be the cheapest
way to do it temporarily, while we rebalance monies in and
out, and our passive managers can use them.
We just did a transition where we allowed the transition
manager, in order to keep our exposure in the market, to
buy ETFs. As a marginal, transactional tool, sure. But as a
base exposure, we can get that exposure cheaper directly.
JOI: How much of a concern is the fact that the markets
are more volatile?
Maynard: Recently, for the last year, markets have been
extremely well-behaved; in fact, much less volatile than
normal. What we saw in the last decade or two is actually
what was predicted for annual volatility. Even 2008-2009,
that was maybe a two-standard deviation when looked at
on a monthly or a yearly basis.
People were fooled by how calm the ’90s were, leading
them to say that’s normal volatility. What we’ve seen
in the last 20 years is actually fairly well within the range
of what was expected. Remember, our numbers that we
24
March / April 2013

www.journalofindexes.com
have been basing modern portfolio theory on go back
now to the Civil War. That includes all the stuff that happened
in the late 1800s and the Great Depression.
There’s this idea that modern portfolio theory and that the
expectations of people when they put these numbers together
were wildly off—but no, they were pretty much right on. In
1996—about the time Greenspan spoke about irrational exuberance—our
estimated volatility of the funds back then was
12 percent annual standard deviation. The standard deviation
of our funds, since 1996—through the supposedly most
volatile time since the Great Depression—has been 11.
If you could just not react on a day-to-day basis or a
month-to-month basis, the annual volatility has been well
within that predicted range, I think for pretty much everybody.
But what happened is that people didn’t recognize
what those numbers actually meant when they experienced
them on a day-to-day basis or a month-to-month basis.
Remember: Modern portfolio theory doesn’t work if
you wake up in under five years. If you wake up once
every five years, it works perfectly.
JOI: What would induce you to make significant changes
in the portfolio or your overall approach?
Maynard: I’d have to be able to see someone implement
an approach that is describable and repeatable. Most of my
time is spent looking at new stuff coming in: We would make
a change if there was an opening of a new, basic capital market—like
when they opened up REITs and TIPS in the late
’90s—with a clear portfolio benefit.
When TIPS were launched, they went from 3 up to 4.2
percent real yield. None of my bond managers would buy
it because they were getting crushed by it, but in a portfolio
like ours, it’s a beautiful instrument. And so we had to go
in and put 10 percent of our portfolio into TIPS because
our active bond managers weren’t doing it. It’s that sort
of opening of a new capital market that has clear benefits
and can be easily accessed that would cause us to change.
We’re a fund that does basic equity or fixed-income
investing with five or six special things that we think have
clear, describable portfolio benefits. If they opened up a
new capital market, that’s the type of thing we do—but
we haven’t seen that this decade.
The other part of this, too, is if you aren’t willing to put
5 to 10 percent of your total fund into it, it doesn’t make a
difference on the risk level. It just doesn’t move the needle.
Adding a lot of little things isn’t going to do it.
JOI: Are there any particular asset classes that you see
as driving returns or dragging them down in the future?
Maynard: I’m not going to put my fund at risk because I
have particular ideas. The way I manage is if I get a bright
idea, I go to a dark room, lay down and wait for the feeling
to pass. But for cocktail party conversation, I like the
equity markets. People assume that this “new normal”
means that equities won’t return what they have returned
in the past in terms of real returns. I think they do.
I think the equity markets are nicely set up to do so—
I’m not talking “spectacular.” But the idea that equities
can get you 5 to 7 percent real returns, like they have
over the last 200 years, I think is fine. Bonds are likely to
be dead money for a while, but you never rely on bonds
to get you the returns anyway. They’re the Armageddon
asset class. You’ve got to have something to rebalance
from if everything else goes down, like in 2008-2009.
They did their job just fine there.
I think Europe probably is going to be fine as an equity
investment, because we invest in companies; we don’t invest
in countries. I think leveraged strategies could run into a
period of difficulty, but I thought that last year, and last year
they turned out to be the best strategies in the world.
Why advertise in the Journal of Indexes?
SERIOUS IDEAS FOR SERIOUS INVESTORS
the bogle issue March / April 2012
defining alternatives May / June 2012
commodities beta? July / August 2012
big ideas September / October 2012
emerging emerging January / February 2013
The Economic Role Of The Investment Company
John Bogle
Managed Futures Strategies
Jeremy Schwartz and Chris Jabara
Commodities Sectors And The Business Cycle
Geetesh Bhardwaj and Adam Dunsby
Determining Market-Capitalization Breakpoints
Andrew Clark
The Next Emerging Markets
Amy Schioldager and Heather Apperson
The Bogle Impact: A Roundtable
Featuring Gus Sauter, William Bernstein, Burton Malkiel, Don Phillips, Ted Aronson and more!
Benchmarking Tail Risk Management
Vineer Bhansali
Commodities In A Portfolio
Sal Gilbertie
Index Variation And Portfolio Performance
Craig Israelsen
The Man Who Invented ‘BRIC’ Weighs In
An Interview with Jim O’Neill
Lessons From SPIVA
Srikant Dash
Indexed Approaches To Long/Short Investing
Peter Little and Greg King
Keeping Current With Commodities
Featuring Jim Rogers, Victor Sperandeo, Shonda Warner, Jodie Gunzberg and more
Sectors And Style
Paul Baiocchi and Paul Britt
Better Beta
Robert Holderith
The Case For Indexing
Christopher Philips
Market-Neutral Factor Investing
Kishore Karunakaran
Better Beta In Commodities Indexing
Jonathan Guyer
Dynamic Correlations
Christopher Philips, David Walker and Francis Kinniry Jr.
Exploring Emerging Market Debt
Rick Harper and Bradley Krom
Plus an excerpt from Bogle’s forthcoming book and an interview with the man himself,
as well as thoughts on indexes and investing from Agather and Blitzer
Plus an interview with Morgan Creek’s Yusko, thoughts on hedge fund indexes
from Bruno & Whitelaw and columns by Vogelzang, Blitzer and Krein
Plus Mulvey on managed commodities futures, Kaplan on capturing long/short strategies,
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Plus an interview with John Prestbo, David Blitzer on the next big thing,
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www.journalofindexes.com March / April 2013 25

Requirements For Standard
And New Forms Of Indexes
Insights from a survey of North American investors
By Felix Goltz, Véronique Le Sourd and Masayoshi Mukai
26
March / April 2013

Figure 1
Main Index Providers And Recent Index Activity
As Of July 1, 2012 2
Source: Index providers
No. Of New Indexes
Launched
01/01/09–01/07/12
No. Of Index Series
Launched
In Partnership
With 3rd Party
Dow Jones 25 9
FTSE 20 19
MCSI 11 1
Russell 22 10
S&P 3 45 27
Stoxx 16 2
Sum across providers 139 68
Standard cap-weighted market indexes have traditionally
been used as a reference for determining
how much additional risk one is willing to tolerate, in
terms of deviating from peer group behavior. This presupposes
a basis to consider any index or method as a universally
applicable and neutral starting point, but there is
ample empirical evidence and theoretical support that this
attribution of neutrality is unwarranted and that standard
indexes suffer from poor risk/return properties (see e.g.,
Amenc, Goltz, Martellini and Retkowsky [2011] for a review
of the various drawbacks of cap-weighted indexes). 1
More recently, index providers have been expanding
their offerings to an array of different types of indexes,
while the proliferation of ETFs has given a broader range
of investors’ direct access to indexing. A clear trend
has been emerging in recent years and reflects a movement
away from standard indexes towards constructing
indexes with the purpose of fulfilling a specific set of
investment objectives, such as obtaining low risk, high
risk-adjusted returns, or calibrated exposure to a given
risk factor. While most traditional indexes can be clearly
seen as “market indexes” that provide a representation of
the average performance of investors in a given market
segment, many recent indexes can be seen as “strategy
indexes” that aim at achieving a given risk/return objective
through a set of systematic rules.
As illustrated in Figure 1, there have been numerous
index launches in the past few years, many of which
involved collaborations of index providers with asset management
firms or other third parties.
Due to such heavy index launch activity over recent
years, investors are now faced with an increasing variety of
index offerings with different construction methods, often
from the same provider. In particular, rather than sticking
to the default index construction scheme where stocks
within a geographic or industry segment are selected by
their market cap and then weighted in proportion to their
market cap, new index launches often draw on alternative
constituent selection schemes and/or alternative weighting
schemes. Such innovation naturally raises the question
of what desirable properties an index should have in
the first place. Also, in view of this increasing variety of
index supply, another relevant question is whether investors
who make up the potential demand side accept such
new index construction schemes and how they integrate
the corresponding products into their overall investment
process. In fact, while recent innovation and the continuous
extension of strategies that are offered from index providers
have led to informal debate on where the limits are
of what one could reasonably refer to as “an index,” little
evidence is available on where index investors actually
draw this line. This article focuses on results of the aforementioned
survey that provide insights into this question.
To better gauge the attitudes of investment professionals
with respect to such innovation and to help
anticipate the direction indexing is likely to go in the
future, EDHEC-Risk Institute has conducted a survey of
investment management professionals in North America.
A broad and comprehensive set of questions was asked
to allow us to present a clear picture of which attributes
of indexes users value. The EDHEC survey consisted of
a questionnaire given in Q1 and Q2 2011 and answered
by 139 North American investment professionals, 4 most
of whom are institutional investors or asset managers;
further, the overwhelming majority of all respondents
have used indexes as investments. This survey was done
in parallel with similar surveys conducted in Asia and
Europe, to help provide a comprehensive global picture
of the indexing industry and note any regional disparities
(see Amenc, Goltz, Mukai, Narasimhan and Tang [2012]
and Amenc, Goltz and Tang [2011]. This article provides
a summary of key results of the American survey concerning
investors’ quality requirements for indexes in general
and their use of alternative index strategies. We discuss
results for equity indexes as well as fixed-income indexes.
An analysis of the complete results of the survey is presented
in Amenc, Goltz, Tang and Vaidyanathan [2012].
We first discuss survey results on the current use and
satisfaction with indexes in equity and fixed-income
investing. We then focus on investors’ views on what the
fundamental quality requirements for indexes are before
providing an overview of respondents’ views on alternative
weighting schemes.
Current Use Of Indexes And Satisfaction Rates
Before discussing the results obtained for each class of
indexes, we first present the general adoption rate and satisfaction
rate of indexes for each asset class. Index usage among
respondents to our survey is high and broad, as depicted in
Figure 2, especially when it comes to equity investments, with
about 89 percent of respondents using indexes.
However, as shown in Figure 3, results indicate satisfaction
with these indexes was surprisingly low, further
emphasizing the importance of investigating the sources
of dissatisfaction and issues that index providers need to
account for when constructing indexes that would truly
address investors’ needs. For example, only about 69 percent
of equity index users were satisfied with equity indexes,
March / April 2013 27

which means that although the vast majority of equity
investors use equity indexes, equity indexes do not appear
good enough for quite a lot of them. The rate of satisfaction
is even lower with corporate bond indexes, with only
53 percent of respondents answering they were satisfied.
Figure 2
Equity
Indexes
Gov’t
Bond
Indexes
Corp.
Bond
Indexes
Source: EDHEC
Figure 3
Equity
Indexes
Gov’t
Bond
Indexes
Corp.
Bond
Indexes
Source: EDHEC
Usage Of Indexes Across Asset Classes
Have you used the following indexes in your investments,
in the respective asset classses?
73.0%
70.5%
88.9%
0% 20% 40% 60% 80% 100%
Satisfaction Of Those Who Use Indexes
In Diferent Asset Classes
Are you satisfed with the products you have used?
53%
68.8%
68.5%
0% 20% 40% 60% 80% 100%
As the academic literature in recent years has produced
several formal criticisms 5 of indexes, we examine in Figure
4 the extent to which these criticisms are shared by practitioners.
We first asked respondents whether they thought
that significant problems are associated with standard
cap-weighted equity indexes. The results in Figure 4 show
that about 53 percent of respondents answered that they
did, while only about 35 percent answered that they did
not see any problems, with the remainder of respondents
having indicated that they did not know. For fixed-income
indexes, the percentage of respondents who did not see
any issues is even lower than for equity indexes, at 33 and
29 percent for government bond indexes and corporate
bond indexes, respectively. However, there is a notable
gap in familiarity with issues across index types, with
corporate and government bond indexes receiving “I
don’t know” responses of 27 and 29 percent, respectively,
compared with only 12 percent for equity indexes. This
suggests that the issues surrounding bond indexes are
obscured, in contrast with equity indexes.
Equity Indexes
To elaborate with more precision on the specific concerns
held by respondents regarding the indexes in different
asset classes, we asked respondents to express their
agreement with common concerns about equity indexes
and bond indexes. For equity indexes, we examined five
prominent issues—including style biases, overinvestment
in overpriced stocks, sector biases, poor diversification
and lack of representativity of the economy—seeking
confirmation of their perceived importance (see
Figure 5). In particular, we assessed agreement with the
criticisms formulated in the existing literature. Several
research papers (see Strongin et al. [2000]; Bernstein
[2003]; Tabner [2007] among others) have found that capweighting
leads to concentration in the stocks with the
largest market caps (concentration effect, or “size bias”).
Figure 4
Satisfaction Levels Across Asset Classes
Do you think there are signifcant problems with cap-weighted equity indexes, government bond Indexes and corporate bond Indexes?
Equity Indexes Gov’t Bond Indexes Corp Bond Indexes
I don’t
know
12%
No
35%
Yes
53%
No
33%
I don’t
know
29%
Yes
38%
Yes
44%
No
29%
I don’t
know
27%
Source: EDHEC
Note: Expanding on the information presented in Figure 3, which displays levels of satisfaction, the above charts depict the percentage of respondents who recognize significant
problems with indexes across asset classes. Note the significant disparity between the percentage of respondents who stated “I don’t know” for equity indexes and for both
types of bond indexes.
28
March / April 2013

Figure 5
Five Key Issues With Equity Indexes And Their Importance To Investors
What do you think are important issues?
Size biases
37.3% 55.2%
Overinvestment in
overpriced stocks
Sector biases
31.3% 58.2%
40.3% 32.8%
Poor diversifcation
38.8% 29.9%
Lacking of representativity
of the economy
31.3% 25.4%
0% 20%
40% 60% 80% 100%
Important Very Important
Source: EDHEC
Note: The above chart illustrates the percentages of respondents who think five issues that have been discussed in academic literature in relation to equity indexes are important
or very important. The figures are the result of questions that offered the following possible responses: not important; I don’t know; slightly important; important; and very
important. The percentages exclude nonresponses.
Moreover, Hsu [2006] and Arnott and Hsu [2008] have
shown theoretically that cap-weighting automatically
leads to an overinvestment in overpriced stocks; however,
that theory has been shown to be based on flawed
assumptions (see Perold [2007]; and Graham [2012]).
Literature has also evidenced the poor diversification
of cap-weighted indexes, which are often concentrated in
a few large firms (Malevergne et al. [2009]). For example,
Strongin et al. [2000] conclude that the S&P 500 Index
mainly reflects the performance of 86 stocks and the
Russell 1000 of 118. Meanwhile, Bernstein [2003] finds
that the 10 largest companies accounted for 25 percent
of the S&P 500 market value, and the top 25 companies
accounted for 40 percent. In addition, Tabner [2007]
finds a dramatic increase in the concentration of the top
10 firm/sector holdings between 1984 and 2005 for the
FTSE 100 Index. Due to the heavy weightings of the biggest
companies in the index, cap-weighted indexes are
not necessarily providing the diversification-related risk
reduction most investors expect from a benchmark.
The results of the survey (Figure 5) show that size biases
and overinvestment in overpriced stocks were perceived by
the respondents as being the biggest problems with indexes,
with 92.5 and 89.5 percent of respondents, respectively,
finding those issues to be very important or important. In
addition to these concerns that standard cap-weighted
indexes are being exposed to a performance drag, about
73 percent of survey respondents think that biased exposures
to sector factors may be problematic with standard
equity indexes. This concern corroborates findings that the
commonly used equity indexes are exposed to significant
shifts in their style or sector exposures (e.g., Amenc et al.
[2006]). About two-thirds of respondents find poor diversification
an important to very important concern. It is
worth noting that respondents do not see the potential lack
of representativity of cap-weighted indexes as the major
issue, as only about 57 percent of respondents mentioned
it as important or very important. In conclusion, the main
issues that respondents see with cap-weighted indexes are
issues related to the risk and return properties of indexes.
Bond Indexes
We conducted a similar analysis with common criticisms
of bond indexes. In fact, fixed-income benchmarks
are of particular concern because there are several prominent
problems with bond indexes long noted in academic
literature, including the so-called bums problem—the
overweighting of heavily indebted issuers—as well as the
problem of frequently changing characteristics, which
result in fluctuating risk-factor exposures (see e.g., Siegel
[2003]). A more recent study documents unstable duration
and credit ratings over time in corporate bond indexes
(Goltz and Campani [2011]).
Respondents were queried for their opinions on specific
issues with government bond indexes, helping illustrate
that a variety of concerns were held by investors
ranging from practical implementation matters to riskfactor
exposure and risk-factor stability. The results show
that difficulty replicating the index is the main issue for
respondents, as 81.6 percent of them consider it important
or very important. Overinvestment in more indebted
countries—an issue due to the nature of debt-weighting
indexes—is also a major concern for 65.8 percent of
respondents. This finding parallels those obtained for
equity indexes, where a majority of respondents considered
overinvestment in overpriced stocks to be a critical
issue. Lack of liquidity and proprietary pricing models
are also each revealed as important issues for about 63
percent of respondents. Thus, respondents recognize the
issues involved in creating an investable product based
on government bond indexes (difficulties in tracking
and replicability or illiquidity). Other practical concerns
March / April 2013
29

Figure 6
Issues With Government Bond Indexes That Are Important To Investors
What do you think are important issues?
Difculties to replicate the index
31.6% 50.0%
Overinvestment in more
indebted countries
Lack of liquidity
23.7% 42.1%
26.3% 36.8%
Lack of stable duration in the index
39.5% 23.7%
Proprietary models for pricing
36.8% 23.7%
Lack of stable sovereign credit
risk exposure
39.5% 18.4%
Inconsistent security selection rules
42.1% 15.8%
Exposed to security risk
31.6% 7.9%
0% 20%
40% 60% 80% 100%
Important
Very Important
Source: EDHEC
Note: The above chart depicts the percentages of respondents who think issues related to government bond indexes are important or very important. Similar to the chart in
Figure 5, the percentages are the results of questions that offered the following possible responses: not important; I don’t know; slightly important; important; and very important.
The percentages exclude nonresponses.
Figure 7
Issues With Corporate Bond Indexes That Are Important To Investors
What do you think are important issues?
Overinvestment in more
risky companies
42.9% 35.7%
Lack of stable credit risk exposure
50.0% 21.4%
Proprietary models for pricing
42.9% 28.6%
Lack of liquidity
26.2% 38.1%
Inconsistent security selection rules
Difculties in replication due to the
changing index characteristics
Lack of stable duration in the index
42.9% 21.4%
38.1% 23.8%
42.9% 19.0%
Exposed to currency risk
26.2% 7.1%
0% 20%
40% 60% 80% 100%
Important
Very Important
Source: EDHEC
Note: The above chart depicts the percentages of respondents who think issues related to corporate bond indexes are important or very important. Similar to the charts in
Figures 5 and 6, the percentages are the result of questions that offered the following possible responses: not important; I don’t know; slightly important; important; and very
important. The percentages exclude nonresponses.
30 March / April 2013

Figure 8
Important Issues When Selecting/Assessing An Index
Do you consider the following characteristics important for selecting and assessing an index?
Full information on the construction
methodology is publicly available
Using objective guidelines
to select constituents
15.1%
82.0%
30.2% 66.2%
Full information on historical
index weights is available
27.3% 62.2%
The index has high liquidity
34.5%
54.7%
Backed by economic and/or
theoretical concepts
The index has a reasonably
low turnover
The index represents
a buy-and-hold strategy
38.1% 44.6%
43.9% 37.4%
35.3% 34.5%
Avoiding any discretionary choices
through, e.g. committee decisions
35.3% 34.5%
0% 20%
40% 60% 80% 100%
Important
Very Important
Source: EDHEC
Note: The above chart depicts the percentages of respondents who think certain construction and management issues are important or very important when selecting and
assessing an index. The questions apply to indexes generally, and thus reflect the basic qualities and characteristics of an index’s construction and management rules deemed
important by respondents.
regarding liquidity and pricing sources similarly received
strong recognition as important issues.
Another implication of the results is that government
bond indexes, generally, may lack sufficient coordination
with investors’ needs. A substantial percentage of respondents
recognized risk-factor instability, with stable duration
exposure appearing to be more important to respondents
than stable credit risk: 63.2 percent of respondents
found duration instability important or very important,
while 57.9 percent found the same for credit risk exposure
instability. Thus, a substantial percentage of investors recognize
the incongruity between investors’ needs and the
unstable risk factor exposures present in government bond
indexes—and the accompanying difficulty in integrating
unpredictable risk into a portfolio.
Inconsistent security selection rules across index
providers received fair attention from investors, with
about 58 percent of respondents considering it important
or very important. Finally, exposition to currency risk
appears to be a major issue for only about 40 percent of
respondents. These findings also suggest that the issues
associated with government bond indexes are very different
from those with equity indexes (Figure 5).
A similar assessment was conducted with corporate
bond indexes, with liquidity and the instability of risk-factor
exposure emerging as important issues. As Figure 7 illustrates,
overinvestment in risky bonds is evidently a crucial
concern held by investors, and although the distinction
may be less profound in light of the current sovereign credit
crisis, corporate bond indexes were perceived as more susceptible
to the “bums problem” than their sovereign counterparts.
Similar views on the importance of credit risk with
corporate bond indexes are reflected in the large percentage
of respondents (71.4 percent) who view the instability of
credit risk exposure as important or very important.
It is evident that there are problems that are recognized
by investors, and there has been a very recent, rapid
trend in indexing development; further changes will likely
come as indexing innovation matures. Thus, it is also
important to analyze perceptions on index properties and
construction methodologies, which will both guide and
constrain index development in the future.
Basic Quality Requirements:
What Indexes Need To Deliver
As a result of the sheer variety of new index offerings
and the lack of clarity of market participants on the meaning
of key terms and concepts such as “active” vs. “passive”
management and “beta” vs. “alpha,” the current
framework for analyzing indexes is convoluted, leaving
some investors clinging to familiar standard indexes as a
response. One of the key revelations of our survey was the
elucidation of the qualities of indexes deemed most important
by users. Fifteen questions related to various characteristics,
rules, and general construction and rebalancing
methods were asked. Most notable were the responses to
March / April 2013
31

the questions seeking assessment of the perceived level of
importance of key index attributes, including the use of
objective guidelines for constituent selection, backing by
economic concepts and access to full information on the
index construction methodology.
In accordance with the related academic literature, 6
transparency and liquidity are recognized as the most
important qualities of an index. Concerning index transparency,
97.1 percent of respondents stated that public
disclosure of index construction methodology was important
or very important, while 89.9 percent of respondents
said that the availability of constituent weights was important
or very important (Figure 8). Objectivity also emerged
as one of the most important characteristics, with over 96.4
percent of respondents finding the objectivity of rules governing
constituent selection important or very important. 7
The perceived prominence of transparency and objectivity
are notable in light of the increasingly innovative
indexing strategies being provided. Indeed, in light of our
results, it is clear that investors will have very little tolerance
for obscurity or discretionary decisions with new
index offerings. It should also be noted that some existing
standard market indexes may not completely fulfill these
basic quality requirements of investors, as the literature on
indexes has highlighted issues with discretion (see Arnott,
Hsu and West [2008], p. 64) and lack of transparency (see
Kamp [2008]) with standard indexes.
Another attribute of indexes cited as prominent in academic
literature is the buy-and-hold characteristic. Malkiel
[1995] and Bogle [2002] view this as a defining attribute of
an index, and one serving a key purpose for investors.
The responses we received from index users, however,
illustrate an interesting contrast between a strong concern
about low turnover (81.3 percent viewing it as important or
very important), and a relatively less profound desire for a
pure buy-and-hold strategy (only 69.8 percent viewing it as
important or very important), indicating that indexes that
use some form of dynamic weighting of stocks—and thus
deviate from a pure buy-and-hold strategy—are likely to be
acceptable, subject to constraints related to transparency
and subject to maintaining low levels of turnover.
In addition to identifying the basic qualities valued, an
attempt was made to establish which index construction
methods are seen as favorable and which are outside of
investors’ comfort zone. As depicted in Figure 9, respondents
were asked which approaches they found acceptable,
and were presented with several possible qualities
that an index construction methodology could exhibit.
The results indicate substantial acceptance of characteristics-based
weighting. Characteristics-based weighting
refers to index methodologies that attribute weights to
stocks in proportion to an attribute such as an accounting
variable like earnings or book value. As the traditional
indexing method—weighting by market capitalization—
also simply allocates based on a single characteristic,
the use of characteristics may not be seen as much of a
deviation from precedent methods, and coupled with its
lack of complexity, may prove to be a method that finds
Figure 9
Acceptability Of Various Index Approaches
Do you accept that indexes can adopt the following
approaches in their construction process?
New Forms Of Indexes
% Of Yes
Simple Characteristics Weighted 71.3%
Systematic Quantitative Weighted 50.0%
Qualitative Choices 35.7%
Could Deviate From Reflecting Passive Strategies 49.6%
Can Be Based On Alpha 25.2%
Source: EDHEC
Note: This chart expands on the information presented in Figure 8 and depicts the
percentages of respondents who identified various index construction approaches
as acceptable. The percentages shown have been normalized by excluding nonresponses
and those who answered “I don’t know.” The response rates are above
90 percent for all questions.
widespread popularity with investors.
An interesting contrast also emerged from respondents’
views on the acceptability of qualitative versus quantitative
rules. In accordance with the strong affirmation of the importance
of objectivity, only a little over one-third of respondents
found qualitative choices acceptable, while one-half
of respondents stated that systematic quantitative weighting
methods that depart from standard weighting schemes would
be acceptable. Thus, after characteristics-based weighting,
respondents find systematic quantitative methods as the
most acceptable, while a turning point emerges with qualitative
methods. This reluctance to accept qualitative assessments
as part of an indexing method is consistent with the
strong affirmation of the importance of objectivity described
previously (96.4 percent of respondents stated that objective
guidelines were important or very important), as it is clear
that qualitative assessments necessarily introduce a degree of
subjectivity into index construction.
Central to the discourse surrounding indexing innovation
is whether indexes must be “passive” in nature. Nearly
half the respondents in our survey stated that it would
be acceptable for indexes to deviate from purely passive
strategies. However, only 25 percent of respondents
stated it would be acceptable for an index to be based on
“alpha.” This contrast between relatively wide acceptance
of indexes that are not purely “passive” and relatively wide
rejection of indexes that try to create “alpha” implies that
for most respondents, an “index” is not required to be
“passive” in the sense of a buy-and-hold portfolio without
any trading—but most importantly, an “index” is required
to be “passive” in a broader sense of refraining from trying
to generate alpha. Therefore, our results allow us to
conclude that the main objective of an index portfolio
should be to reflect the risk premia or normal returns available
from investing within an asset class as opposed to
abnormal returns through the pursuit of alpha strategies.
However, the way of implementing this access to risk premia
within an asset class does not necessarily constitute a
“buy and hold” strategy. Our respondents’ acceptance of
32
March / April 2013

indexes that do not rely on a buy-and-hold strategy probably
reflects an acceptance of new and enhanced index
offerings that maintain systematic rules, and thus allow
investors to reconcile their concern over reliability and
transparency with improvement of risk/reward properties.
After examining the basic quality requirements for
indexes and acceptable construction methods, we now
turn to an assessment of investors’ views on alternative
methods that have been proposed as improvements
over traditional indexes.
Figure 10
Source: EDHEC
Note: The above chart depicts the percentage of respondents who think each respective
use is the appropriate approach of using alternative-weighted indexes. The chart,
thus, shows how respondents see the relationship between cap-weighted indexes
and alternative weighting schemes.
Figure 11
How To Use Alternatively Weighted Indexes
What is the most appropriate approach to
use alternative-weighted indexes in practice?
To complement the
cap-weighted indexes
To replace active
managers
To replace the
cap-weighted indexes
58.6%
23.0%
Views On Current And Future Use
Of Alternative Weighted Equity Indexes
Do you use alternative weighting schemes of equity indexes?
Yes, we have
No, but we are going to
No, we are still
considering
No, and we are
not going to
We are not familiar with
these approaches
42%
6%
24%
27.6%
23%
Source: EDHEC
Note: The above chart depicts respondents’ views on current and future use of
alternative weighted equity indexes. The full range of possible responses (excluding
nonresponses) are given in the chart.
Views On Alternative Weighting Schemes
Although there has been rapid development of indexing
methods in recent years, cap-weighted indexes have
for decades been the standard, and enjoy a place as an
established method. This can be explained by the fact that
cap-weighting indexes were made popular by the capital
asset pricing model (CAPM), formulated by Sharpe [1964],
and are thus assumed to represent the average decisions of
investors. In addition, extensive track records are available
for those indexes. Thus, the place for alternative indexing
schemes is not entirely clear, particularly with regard to
whether they should be used as substitutes for cap-weighted
indexes or as complements. When asked about that, the
majority of respondents (58.6 percent) stated that the most
appropriate approach to employing alternative methods
would be as a complement to cap-weighted indexes. Only
23 percent viewed alternatives as a replacement to capweighted
indexes. However, 27.6 percent of respondents to
the survey viewed indexes following alternative weighting
schemes as a potential replacement for active managers.
This highlights the potential for alternative indexes as possible
methods for exercising an investor’s tracking error
budget—a task traditionally awarded to active management.
These results are displayed in Figure 10.
It is likely inevitable, however, that alternative weighting
schemes will always be evaluated relative to their capweighted
counterparts. The findings displayed in Figure
10 are consistent with the notion that any alternatives to
standard cap-weighted indexes are perceived as creating
a relative risk with respect to the investor’s peer group,
leading investors to become reluctant to completely
move away from cap-weighted indexes, which are riskfree
relative to the peer group.
In regard to general usage of alternative weighting methods
for equity indexes, the responses indicate that further
development of alternative methods can be expected, as
about 30 percent of respondents stated that they have not
yet implemented alternative schemes, but are going to, or
are still considering doing so (Figure 11).
To provide further context around the likely areas for
development of alternative weighting schemes, respondents
were asked which types of data or information were
integrated into their portfolio construction process. It is
clear that one would expect that, if investors accept the use
of indexes that deviate from cap weighting, the data used
in the construction of those indexes would need to correspond
to information that investors consider to be relevant
for constructing their own equity portfolios. Figure
12 depicts the percentage of respondents who use each
respective information type “frequently.”
Figure 12 indicates that most of the respondents are
more concerned with country or regional exposure than
style and sector exposures, though academic literature
did not find clear evidence of dominance of any single
type of exposure over the others (Hamelink et al. [2001]
Ferreira [2006]). The results demonstrate that risk properties—such
as volatility and stock correlation—are viewed
as essential, but also that expected returns are perceived
to be a very important ingredient in constructing equity
portfolios, a result in accordance with concepts of modern
portfolio theory (Markowitz [1959]). Given that traditional
cap-weighted indexes and even some alternative indexing
methods ignore such parameters (notably correlation
5%
March / April 2013
33

Figure 12
Types Of Information Used When Constructing Equity Portfolio
When you construct your equity portfolio, how often do you integrate information on the following perspectives?
Country/regional exposure
63.5%
Expected returns of stocks
or categories of stocks
Correlation between stocks
or categories of stocks
52.4%
54.8%
Volatility of stocks or
categories of stocks
50.8%
Sector exposure
50.8%
Style exposure
49.2%
Economic risk-factor exposure
42.1%
Accounting measures
(e.g., earnings, cash fow, etc.)
38.1%
0% 20%
40% 60% 80%
Source: EDHEC
Note: The above chart depicts the percentages of respondents who integrate each respective information type often or very often when constructing equity portfolios. The possible
responses were: never; rarely; sometimes; often; and very often.
between stocks, volatility and expected return), it will be
interesting to see whether index providers will account
for these considerations in their future index innovations.
Another notable result is that accounting measures
are seen as the least relevant of the various information
types that are available to build an equity portfolio, while
simple characteristics-based indexes that draw precisely
on such accounting information are the most widely
accepted among practitioners with regard to alternative
index strategies methodologies (see Figure 9). All these
results should be taken into consideration by index providers
for future developments of indexes.
Conclusion
Some prominent conclusions can be drawn from the
responses of a broad set of investment professionals.
Although index usage is high, there is a lack of satisfaction
across asset classes, and the widespread receptiveness to
new methods that are apparent from our survey will likely
prompt providers to continue to develop innovative indexing
methods; however, respondents make it very clear that
they think all indexes should be subject to quality constraints,
including the transparency and objectivity of methodology
and construction rules as well as high liquidity and low
turnover levels. Two general characteristics that consistently
govern investors’ requirements are an index’s objectivity and
systematic nature. Thus, further development of any kind
in the index space will likely be very systematic, with assurances
to investors that index characteristics will be evident
and predictable (precluding indexes that pursue “alpha” and
eschew buy-and-hold methods). Our survey results clearly
show that investors draw a dividing line around transparency
and objectivity of rules to differentiate between what they
deem acceptable for an index and what they believe brings an
approach outside the realm of indexing.
Although alternative methods are widely used and will
likely continue to grow in popularity, most investors do
not currently see them as replacements to cap-weighted
indexes, but rather as serving a supplementary purpose.
Standard cap-weighted indexes have remarkably low satisfaction
rates; however, they remain a de facto reference
point, while alternative methods are largely seen as
tools for spending relative risk budgets. While alternative
index strategies in that sense are seen as substitutes of
active management—rather than as substitutes of market
indexes—a key difference between active managers and
advanced beta indexes is that the decision processes of
index strategies are more systematic and rules-based, and
hence it is easier in principle to document risks. Ultimately,
alternative index strategies hold the promise of allowing
investors to obtain more precise information on the risk
choices that are made within their portfolios and to make
more explicit decisions about their desired risk exposures.
Alternative weighting methodologies may serve investors
in two different ways. They could serve as a substitute of
cap-weighting indexes, in order to obtain long-term higher
performance. Those alternative weighing indexes will have
a potentially high tracking error with regard to a cap-weighting
index, and possibly a rather high drawdown (Figure 13),
as reported in Amenc, Goltz, Lodh and Martellini [2012].
34
March / April 2013

Figure 13
Relative Risk of Alternative Weighting Schemes
Risk Measures
S&P 500
Equal
Weight
Index
MSCI USA
Minimum
Volatility
Index
FTSE
EDHEC-
Risk
Efficient
US Index
FTSE RAFI
US 1000
Index
Max Relative Drawdown 13.60% 12.23% 8.43% 12.67%
Start Date 2/23/07 11/21/08 4/21/06 6/29/07
End Date 11/21/08 4/23/10 11/21/08 3/6/09
Annualized Excess Return
Over Cap-Weighted index
3.24% 1.21% 3.75% 2.18%
Tracking Error (TE) 4.64% 5.69% 3.82% 4.78%
Extreme Tracking Error
(95th Percentile Of 9.57% 7.42% 6.72 % 11.92%
Rolling One Year TE)
Source: EDHEC
Note: The above table shows historical extremes of underperformance, annualized
excess return over the cap-weighted index (the S&P 500 Index), annualized tracking
error and extreme tracking error of the S&P 500 Equal Weight Index, the MSCI USA
Minimum Volatility Index, the FTSE EDHEC-Risk Efficient US Index and the FTSE RAFI
US 1000 Index with respect to the S&P 500 Index. Maximum relative drawdown for a
strategy is the maximum drawdown of the long/short index whose return is given by
the fractional change in the ratio of strategy index to the benchmark index. Extreme
tracking error corresponds to the 95th percentile of rolling one-year tracking error,
i.e., the annualized standard deviation of a portfolio long in the alternatively weighted
index and short in the S&P 500 Index, over the entire horizon. All statistics are
annualized and are based on weekly data from Jan. 3, 2003 to Dec. 30, 2011. Source:
Amenc, Goltz, Lodh and Martellini [2012].
On the other hand, alternative weighting schemes
could also provide overperformance relative to capweighted
indexes while not deviating too far from such
standard indexes. In that case, the alternative weighting
indexes are used as a replacement of benchmarked
active management. In fact, generating smooth outperformance,
while keeping relative risk in check, is a
common objective of traditional active managers. When
an alternative weighted index is used as a substitute for
traditional active management, the cap-weighted index
is still considered the reference index. In this context,
the alternative weighted index—rather than replacing
the cap-weighted index—serves as a complement to the
cap-weighted index. An important question that investors
need to address in this context is how to manage the
relative risk behind the alternative weighted strategy.
The fact that alternative weighted indexes may be used as
a substitute for traditional active managers, however, poses
an important question. In fact, it is clear that compared to the
widespread procedures on manager selection, the industry
has dedicated relatively little attention historically to benchmark
selection. Thus, an important issue for the adoption and
future development of alternative index strategies will be the
establishment of a framework for analyzing such strategies,
and for making selection and investment decisions across the
growing range of alternative index strategies.
References
Amenc, N., F. Goltz and V. Le Sourd. 2006. Assessing the quality of stock market indices: Requirements for asset allocation and performance measurement. EDHEC-Risk Institute.
Amenc, N., F. Goltz, A. Lodh and L. Martellini. 2012. Diversifying the diversifiers and tracking the tracking error: Outperforming cap-weighted indices with limited risk of
underperformance. The Journal of Portfolio Management 38(3): 72-88.
Amenc, N., F. Goltz, L. Martellini and P. Retkowsky. 2011. Efficient indexation: An alternative to cap-weighted indices. Journal of Investment Management 9(4): 1-23.
Amenc, N., F. Goltz, M. Mukai, P. Narasimhan and L. Tang. 2012. EDHEC-Risk Asian index survey 2011. EDHEC-Risk Institute. (see: http://docs.edhec-risk.com/ERI-Days
Asia-2012/documents/EDHEC-Risk_Asian_Index_Survey_2011.pdf).
Amenc, N., F. Goltz, L. Tang and V. Vaidyanathan. 2012. EDHEC-Risk North American index survey 2011. EDHEC-Risk Institute (see: http://docs.edhec-risk.com/
mrk/000000/Press/EDHEC-Risk_North_American_Index_Survey.pdf).
Amenc, N., F. Goltz and L. Tang. 2011. EDHEC-Risk European index survey 2011. EDHEC-Risk Institute (see: http://docs.edhec-risk.com/mrk/000000/Press/EDHEC
Risk_European_Index_Survey.pdf).
Arnott, R.D. and J.C. Hsu. 2008. Noise, CAPM and the size and value effects. Journal of Investment Management 6(1): 1-11.
Arnott, R., J. Hsu and J. West. 2008. The fundamental index: A better way to invest. New Jersey: John Wiley & Sons, Inc.
Bernstein, P. 2003. Points of inflection: Investment management tomorrow. Financial Analysts Journal 59(4): 18-23.
Bogle, J. 2002. An index fund fundamentalist. Journal of Portfolio Management 28(3): 31-38.
Ferreira, A. 2006. The importance of industry and country effects in the EMU equity markets. European Financial Management 12(3): 341-373.
Fuller, R.J., B. Han and Y. Tung. 2010. Thinking about indices and “passive” versus active management. Journal of Portfolio Management 36(4): 35-47.
Goltz, F. and C.H. Campani. 2011. Review of corporate bond indices: Construction principles, return heterogeneity, and fluctuations in risk exposures.
EDHEC-Risk Institute publication, June.
Goltz, F. and V. Le Sourd. 2010. Does finance theory make the case for capitalization-weighted indexing? EDHEC-Risk Institute.
Graham, J. 2012. Comment on the theoretical and empirical evidence of fundamental indexing. Journal of Investment Management, First Quarter.
Grinold, R. 1992. Are benchmark portfolios efficient? Journal of Portfolio Management 19(1): 34-40.
Hamelink, F., H. Harasty and P. Hillion. 2001. Country, sector or style: What matters most when constructing global equity portfolios? Working paper. FAME.
Haugen, R. and N. Baker. 1991. The efficient market inefficiency of capitalization-weighted stock portfolios. Journal of Portfolio Management 17(3): 35-40.
Hsu, J. 2006. Cap-weighted portfolios are sub-optimal portfolios. Journal of Investment Management 4(3): 1-10.
Kamp, R. 2008. Debunking 130/30 benchmarks. Invesco.
Malevergne, Y., P. Santa-Clara and D. Sornette. 2009. Professor ZIPF goes to wall street. Working paper. National Bureau of Economic Research.
continued on page 63
March / April 2013
35

Evaluating Alternatives
How Smart Is
‘Smart Beta’?
The efficiency of
alternative index approaches
By David Blitz
Worldwide, investors are increasingly keen on
“smart beta” investing. By this we mean passively
following an index in which stock weights
are not proportional to their market capitalizations, but
based on some alternative weighting scheme. Well-known
examples of smart beta include fundamentally weighted
and minimum-volatility indexes.
In this article, we first take a critical look at the pros and
cons of smart-beta investing in general. After this, we discuss
in turn the most popular types of smart indexes that have
been introduced in recent years. The added value of smartbeta
indexes has been shown to come from systematic tilts
toward classic factor premiums that are induced by their
weighting schemes. We will argue that investors should be
aware of the potential pitfalls of smart-beta indexes, which
arise because they are not specifically designed for harvesting
factor premiums in the most efficient manner, but primarily
for simplicity and appeal. And although passive management
can be used to replicate smart indexes, we believe it is important
for investors to realize that, without exception, smart
indexes themselves represent active strategies.
Smart-Beta Investing In General
The argument that is typically used to motivate smartbeta
investing is that the capitalization-weighted index
is inefficient, and that a more efficient portfolio can be
constructed by applying some alternative stock-weighting
scheme. We agree with this view, but think it is important
to understand where the added value of such weighting
schemes really comes from. Research has shown that the
weighting schemes used by alternative indexes tend to result
in structural tilts toward stocks that score high (or low) on
certain factors, and that the premiums that are known to
be associated with these factors are driving performance. 1
For example, when compared with capitalization-weighted
indexes, fundamentally weighted indexes have a systematic
tilt toward value stocks. These exposures enable the strategy
to benefit from the well-known value premium, which, in
fact, turns out to fully explain its performance. Similarly, a
minimum-volatility index captures the low-volatility premium
by tilting the portfolio toward low-volatility stocks.
Although this may seem obvious to some, many smart-beta
index providers are still reluctant to acknowledge that their
performance is driven by factor exposures, and that their
weighting schemes are merely a novel way of establishing
exposures toward classic factor premiums.
We are often asked whether smart-beta investing is a
form of passive investing. It is important to realize that it is
not. Although passive management can be used to replicate
smart indexes, smart indexes themselves are essentially
active strategies. The only truly passive investment strategy
is the capitalization-weighted broad market portfolio,
which represents the only buy-and-hold portfolio that
could, in principle, be held in equilibrium by every investor.
Smart-beta indexes are fundamentally different, because
they require various subjective assumptions and choices.
Their active nature is also illustrated by the fact that they
require periodic rebalancing to maintain their profile. It is
true that smart-beta indexes may bear some resemblance
to true passive investing (for example, by investing in a
large number of stocks with relatively low turnover), but it
is important to realize that their deviations from the capitalization-weighted
index, which are the key to their added
value, represent active investment decisions.
In sum, so far, smart-beta investing is a way to tilt a
portfolio actively toward certain factor premiums. As we
are proponents of factor investing, this makes smart-beta
investing a potentially promising investment approach.
36
March / April 2013

For example, in a recent paper, we argued that equity
investors should strategically allocate a sizable part of their
portfolio to the value, momentum and low-volatility factor
premiums. 2 Smart-beta investing represents one way in
which this could be implemented in practice.
Our view on smart-beta investing can be summarized as
follows: Although smart-beta investing may be a good start,
we believe investors can do better. The reason is that the
main appeal of smart-beta indexes—namely their simplicity—is
at the same time their biggest weakness. Specifically,
we find that the simple tilts toward factor premiums provided
by smart-beta indexes often involve significant risks
that are undesirable. In addition, smart-beta strategies
can be inefficient from a turnover perspective, or can have
unattractive exposures to factor premiums other than the
one that is primarily targeted.
Another concern with smart-beta indexes is that they
are often based on backtests that only go back 10 or 15
years in time. Investors should therefore be careful to avoid
chasing recent performance. To properly understand the
behavior of a smart index in different environments, we
recommend analyzing its performance over long historical
periods, covering multiple economic cycles. Investors
should also carefully think about whether the factor premiums
that are driving historical smart-beta index returns are
likely to persist in the future.
In the following sections, we will elaborate on these points
by discussing the pros and cons of the most popular types of
smart-beta indexes that have been introduced in recent years.
Fundamentally Weighted Indexes
In a fundamentally weighted index, stocks are weighted
in proportion to their fundamentals, such as book value or
earnings. In other words, instead of letting the market decide
on the appropriate weight of a stock, one might say that
fundamentally weighted index investors prefer to rely on
the assessment of accountants. The differences in weights
between a traditional, capitalization-weighted index and a
fundamentally weighted index are, by definition, entirely
due to differences in valuation ratios of individual stocks,
such as differences in book-to-price or earnings-to-price
ratios. Compared with the capitalization-weighted index, a
fundamentally weighted index is tilted toward stocks that are
cheap on such ratios, i.e., value stocks. Studies have shown
that the added value of fundamentally weighted indexes
is, in fact, entirely attributable to this tilt toward the value
premium. 3 For a long time, Research Affiliates, the inventors
of fundamentally weighted indexation, denied that the
success of fundamentally weighted indexation is critically
dependent on the existence of a value premium. Instead,
they argued that, even in the absence of a value premium,
random mispricing causes capitalization-weighted indexes
to be biased toward overvalued stocks, resulting in a structural
drag on performance. 4 Nowadays, however, Research
Affiliates acknowledges that the value premium does indeed
explain most or all of their indexes’ performance. 5
Our main concern with straightforward value strategies
such as fundamentally weighted indexation is that they
tilt toward financially distressed firms. To understand this,
consider a firm that actually gets into financial distress. As a
result, its share price drops, and its weight in the cap-weighted
index drops correspondingly. Initially, the same happens
in a fundamentally weighted index. At a certain point, however,
a fundamentally weighted index rebalances back to the
weight based on past and current fundamentals, which have
typically not (or only partly) adapted to the new situation.
This exposure to distressed firms might not be a problem
if, as some have conjectured, distress risk is the source of
the value premium. Studies have shown, however, that the
stocks of financially distressed firms tend to underperform,
and that the tilt to distressed firms of naive value strategies
increases risk and is harmful to returns. 6 This implies that
the value premium can be captured more efficiently by
avoiding cheap stocks of financially distressed firms.
A related concern is that, since rebalancing involves
buying stocks that have recently experienced a large price
drop, fundamentally weighted indexes tend to go against the
momentum premium. As the momentum premium appears
to be at least as strong as the value premium, this suggests that
the return of a value strategy may be enhanced by avoiding its
natural tendency of going against the momentum premium.
Another concern with fundamentally weighted indexes
is their sensitivity to settings choices. For example, it has
been shown that, in certain calendar years, the arbitrary
choice of the annual rebalancing moment of the fundamentally
weighted FTSE RAFI indexes can make the difference
between an outperformance of 10 percent or a small
underperformance. 7 The more recently launched fundamentally
weighted indexes of MSCI, called MSCI Value
Weighted indexes, address this concern by rebalancing
every six months, while those of Russell rebalance a quarter
of the portfolio every quarter. In light of these developments,
FTSE recently announced that it will also provide
such a staggered quarterly rebalanced variant of the FTSE
RAFI indexes this year, although these will not replace their
current indexes but will coexist with them.
Finally, we note that fundamentally weighted indexes
represent a low-conviction approach to capturing the value
premium. To understand this, note that a fundamentally
weighted index is not exclusively concentrated in stocks with
the most attractive valuation characteristics. For example,
the FTSE RAFI US and Developed ex-U.S. indexes each
invest in 1,000 stocks, and the MSCI Value Weighted indexes
invest in all the stocks that are in the regular MSCI indexes.
In other words, stocks with the least attractive valuations are
still included in these indexes, only with smaller weights.
Low-Volatility Indexes
Low-volatility indexes are designed to benefit from the
low-volatility premium: the empirical finding that low-risk
stocks have similar or better returns than the market average,
with substantially lower risk. Minimum-volatility indexes use
optimization techniques to create a portfolio with the lowest
expected future volatility. The resulting portfolio tends to
consist mainly of stocks with low past volatility, although it
may also include some higher-volatility stocks if these help
www.journalofindexes.com March / April 2013 37

more than 5 percent from their weight in the regular, capitalization-weighted
index. In our view, both approaches
are too extreme. The MSCI Minimum Volatility index is
overly constrained, while the S&P 500 Low Volatility Index
is overly concentrated. Our assessment is that the optimum
lies somewhere between these two approaches.
Russell recently launched its so-called defensive equity
indexes, which can be regarded as a “low-volatility light”
alternative. This is because the weight of low-volatility factors
in these indexes amounts to only 50 percent. The other
50 percent is based on “quality” factors, such as earnings
stability, profitability and leverage. The reason for blending
in these other factors is not entirely clear. The backtested
index returns indicate that these factors tend to increase,
rather than further reduce, volatility. So if volatility does not
improve, the benefit should probably come from improved
returns. Thus, investors should be convinced that the incremental
return from tilting toward quality more than offsets
the higher volatility induced by these factors.
Maximum Sharpe Ratio Indexes
We next discuss two closely related smart-beta indexes;
namely, the FTSE/TOBAM Maximum Diversification
Index and the FTSE/EDHEC Risk Efficient indexes. Both
approaches essentially try to maximize the expected
Sharpe ratio, i.e., the ratio of expected return to expected
risk. Although the way in which expected risk and return
are defined is not identical, the differences are relatively
small. For example, the Maximum Diversification index
In our view, the main challenge involved with harvesting the momentum
premium is how to control the high risk involved with the strategy.
to reduce volatility through low correlations. A drawback of
optimized low-volatility indexes is their lack of transparency.
For example, the most popular minimum-volatility index, the
one provided by MSCI, uses the proprietary Barra risk model
and optimization algorithm, as a result of which, many investors
regard the index to be a “black box.” Another concern is
that the raw turnover of minimum-volatility strategies tends
to be very high. MSCI addresses this concern by imposing
turnover constraints, 8 but this causes a new drawback;
namely, path-dependency. This means that today’s composition
of the MSCI Minimum Volatility index depends on its
past composition—a feature that is undesirable for investors
interested in a fresh minimum-volatility portfolio because
they wish to invest in the strategy from scratch.
A more transparent alternative is provided by the S&P
500 Low Volatility Index, which simply invests in the 100
stocks in the S&P 500 index with the lowest volatility over
the preceding 12 months. 9 Empirical studies have shown
that this simple ranking approach results in a very similar
risk/return profile to more sophisticated optimization
approaches. 10 The added value of both approaches comes
from their tilt toward low-volatility stocks, which enables
them to capture the low-volatility premium. 11 We believe,
however, that both represent a suboptimal way of benefiting
from the low-volatility premium.
Our first concern with low-volatility indexes is their onedimensional
view of risk, focusing mainly on past volatility
and correlations. We believe that risk cannot be captured by
a single number, and our research confirms that a multidimensional
approach, which also includes forward-looking
risk measures, is able to reduce risk—in particular, tail risk—
further. 12 A second concern with low-volatility indexes is that
they completely ignore expected return considerations. We
find that there is, in fact, a large dispersion in the expected
returns of stocks with similar volatility characteristics. For
example, stocks that are attractive from a volatility perspective,
but that go against other factor premiums—for example,
by having unattractive valuation or momentum characteristics—tend
to have below-average expected returns; however,
low-volatility stocks that are supported by other factor premiums
tend to have above-average expected returns. These
insights are entirely ignored by generic low-volatility indexes.
We also observe some significant differences in the
composition of different low-volatility index portfolios.
The S&P 500 Low Volatility Index does not constrain sector
weights, resulting in a huge sector concentration. For
example, at the time of writing, around 60 percent of this
index was invested in only two sectors (utilities and consumer
staples). The MSCI Minimum Volatility index, on
the other hand, does not allow sector weights to deviate
assumes that expected returns are proportional to volatility,
while the Risk Efficient index assumes that expected
returns are proportional to downside volatility.
The Maximum Diversification and Risk Efficient indexes
are often regarded to be alternative low-volatility approaches.
To understand this, note that lowering portfolio volatility
helps to maximize the Sharpe ratio, which has volatility in
the denominator. However, the indexes actually go against
the low-volatility premium by assuming that expected
returns are proportional to (downside) volatility, which
makes high-risk stocks more attractive in the numerator of
the Sharpe ratio. These two opposing forces (i.e., a preference
for low-volatility stocks from a risk perspective versus
a preference for high-volatility stocks from a return perspective)
can cause the indexes to have either a low-volatility or
a high-volatility profile. In the long term, the high-volatility
profile actually appears to dominate. 13 Compared with the
capitalization-weighted index, the indexes also appear to
load on the small-cap and value-factor premiums. 14
In sum, it seems that, similar to other smart-beta
indexes, classic factor premiums fully explain the added
38
March / April 2013

value of the Maximum Diversification and Risk Efficient
indexes. Unlike fundamentally weighted and minimumvolatility
indexes, however, the tilt toward factor premiums
is less direct and more dynamic in nature.
Momentum Indexes
Historically, the momentum premium has been at least
as large and consistent as the value and low-volatility
factor premiums. Momentum indexes are much scarcer
though, probably due to the fact that momentum struggled
during the most recent decade (while value and lowvolatility
strategies showed very strong performance over
this period) and because the relatively high turnover of
momentum strategies fits less well with the idea of a “passive”
index strategy. We believe, however, that momentum
deserves more attention, if only because it tends to do well
when value and low-volatility struggle simultaneously,
such as during the tech bubble of the late 1990s.
Although momentum strategies have shown impressive
long-term average returns, they can show a large
underperformance over shorter periods of time. For
example, the generic long/short momentum strategy
that is typically considered in the academic literature
shows a return of -83 percent over the year 2009. 14 In
our view, the main challenge involved with harvesting
the momentum premium is how to control the high
risk involved with the strategy. AQR, which recently
introduced the first serious momentum indexes, seems
to do so by limiting the tilt toward momentum stocks.
Specifically, they invest in a relatively broad set of stocks
(the top 33 percent, based on a ranking on return over
the past 12 months, excluding the most recent month)
and they weight these stocks in proportion to their market
capitalization. Although these choices are indeed
effective for controlling the risk of a momentum strategy,
they also prevent investors from benefiting from the
full potential magnitude of the momentum premium.
Our research shows that in order to earn the momentum
premium, it is not necessary to be exposed to the large risks
involved with naive momentum strategies. Specifically,
we find that a more sophisticated momentum strategy is
highly effective at eliminating precisely those risks that are
not properly rewarded, thereby resulting in significantly
better risk-adjusted returns. 15 The essence of our approach
is to adjust the momentum of each stock for the part that
is driven by its systematic risk characteristics (for example,
high-beta stocks are expected to outperform the market
in proportion to their beta). By ranking stocks according
to their remaining, idiosyncratic momentum, we obtain
a more sophisticated momentum strategy, which is much
less sensitive to systematic risk, such as a broad market
reversal. This enables us to create a portfolio that is tilted
more aggressively toward the momentum premium, while
staying within the same risk budget.
Turnover is also a major concern with momentum
strategies, which have relatively high turnover by definition.
From this perspective, the AQR momentum indexes
are clearly not entirely optimal, because they may involve
buying a stock ranked just above the selection threshold
and selling it at the next rebalancing, three months later, if
its rank has dropped to just below the selection threshold.
More sophisticated buy-sell rules may be able to avoid
such unnecessary turnover. 16
Equally Weighted Indexes
Several index providers, including MSCI and S&P, have
introduced equally weighted indexes. These are typically
regarded as a means to harvest the small-cap premium,
which is another example of a premium that has been
extensively documented in the literature. However, we
believe that a word of caution is appropriate here. The
evidence for a small-cap premium in the literature mainly
concerns the smallest, least liquid stocks in the market.
Equally weighted indexes do not actually invest in these
stocks, but continue to invest in large- and medium-sized
firms. For example, the S&P 500 Equal Weight Index still
invests in 500 of the largest U.S. stocks, while the total
number of U.S. stocks is well over 5,000. Thus, equally
weighted indexes are better described as strategies that
try to exploit a possible difference in return between large
stocks and even larger stocks. Equally weighted indexes
are thus able to profit only partly, at best, from the smallcap
effect considered in the literature.
Another concern with equal weighting is that portfolio
weights tend to move continuously away from their target
levels, so frequent rebalancing is required to maintain
equal weights. As this rebalancing involves selling recent
winners and buying recent losers, this tends to go against
the momentum effect (e.g., in case of annual or semiannual
rebalancing). A nice anecdote in this regard is that back in
the early 1970s, when the concept of passive investing was
conceived, some of the early adopters in fact chose equally
weighted portfolios, but soon abandoned this approach
because of these practical issues. 17 In our view, therefore, a
traditional capitalization-weighted (buy-and-hold) index
of true small stocks is a more appropriate and also a more
efficient way to capture the small-cap premium.
Summary
In smart-beta indexes—such as fundamentally weighted
and minimum-volatility indexes—stock weights are based
not on their market capitalizations, but on some alternative
formula. We have argued that the added value of smart-beta
indexes comes from systematic tilts toward classic factor
premiums that are induced by these alternative weighting
schemes. We also showed that smart-beta indexes are
not specifically designed for harvesting factor premiums in
the most efficient manner, but primarily for simplicity and
appeal. For a number of popular smart-beta indexes, we
have discussed the main pitfalls, and how investors may capture
factor premiums more efficiently by addressing these
concerns. Finally, it is important to remember that although
passive management can be used to replicate smart indexes,
investors should realize that, without exception, smart
indexes themselves always represent active strategies.
continued on page 50
March / April 2013 39

Talking Indexes
Survival Of The Fittest
Why some ETFs fail
By David Blitzer
In the indexing and ETF world, 2012 may be remembered
as the year ETF closings reached sufficient numbers
to dominate industry gossip and news about ETFs.
IndexUniverse’s ETF Watch lists about 100 ETF closures in
2012, almost twice the annual pace seen in 2008-2010 during
the financial crisis and recession. There is little chance
we will run out of ETFs or stop launching new ones any
time soon: The total number of ETFs continues to expand,
with nearly 180 added in 2012, and money continues to
flow into ETFs, both new and old. By now, some 20 years
after the launch of the SPDR S&P 500 ETF (NYSE Arca: SPY)
kicked off the rise of the ETF industry, most new ETFs—
and recently closed ones—are based on strategy indexes
rather than broad-based market indexes. Strategy dominates
the new issues because there are few, if any, markets
left that aren’t already covered by ETFs. Further, strategy
indexes—and hence the ETFs linked to those indexes—
focus on various investor interests such as dividends or low
volatility. Digging into the nature of strategy may explain
the 2012 rise in ETF terminations.
While we shouldn’t ignore some of the financial factors
cited for ETF closures—rising operating expenses; increases
in the minimum size needed to break even; or competition
among ETF issuers—understanding the nature of strategies
and the indexes that track them is important for understanding
why some ETFs survive and others fade away.
Financial and economic research going back several
decades focuses on why some stocks tend to outperform
the market. The results of this research are the raw material
of strategy indexes. Many investors are familiar with
ideas that small-cap or value stocks tend to outperform
large-cap or growth stocks. The more formal statement of
these arguments is the three-factor model of Gene Fama
and Ken French, 1 who identified size measured by market
capitalization and a value bias measured by the ratio of
book value to market value and then added market performance
as the third factor driving stock performance.
Later work by Mark Carhart 2 introduced momentum
measured by the difference between short-term and
intermediate-term performance as a fourth factor. Recent
research has expanded some of these ideas with different
measures of value or momentum and with new factors
such as volatility or liquidity. The three- or four-factor
models underlie the first generation of strategy indexes
focusing on combinations of growth or value and large-,
mid- or small-cap stocks and momentum.
These efforts were only the beginning of strategies.
Other factors soon joined, including dividends, specific
sectors or industries, mergers, acquisitions, spinoffs and
other corporate actions or such company characteristics
as family ownership or social policies. Strategy indexes
are attempts to exploit times when the market deviates
from the theory that all stocks offer the same returns after
adjustment for risk and correlation. Some strategies—for
example, buying stocks in only one sector—have limited
lifetimes, since the market is constantly evolving. Other
strategies seek longer lifetimes and more staying power;
some claim to do well in various markets.
All strategies face three challenges that could limit
their performance. An ETF based on a strategy that
underperforms is living on borrowed time. Consider an
ETF that holds stocks in only one sector: Markets shift
over time, and what works one day may fail miserably the
next day. Financial stocks were shunned in 2007-2009 but
gained twice as much as the S&P 500 in 2012.
The second challenge is data mining: Given a big database,
a fast computer and enough time, an analyst can “discover”
some rule guaranteed to pick yesterday’s winning stocks.
40
March / April 2013

Despite high t-statistics, statistical and economic significance,
and an R 2 of 99.99 percent, the discovery most often doesn’t
work going forward. The cause is simple: Try enough models,
equations and ideas, and a few are certain to look good. Even
when data mining creates fool’s gold instead of the real thing,
some of these do become filings for new ETFs.
The last challenge is “success.” Once word gets around
that some new strategy works, everyone rushes in.
Suppose you designed an index of technology stocks that
pay dividends to combine the stability of dividend payers
with growth and it beats the market quite handsomely.
Other tech-dividend indexes appear, hedge funds buy
up high-dividend tech stocks and CNBC runs a hot idea
story, while IndexUniverse lists all the newly registered
ETFs targeting the area. Prices of dividend-paying technology
stocks would be bid up, and performance going
forward would collapse amid falling dividend yields for
the group. There was no market rotation and the idea
wasn’t data-mined, yet success breeds its own failure.
A recent research paper by McLean and Pontiff 3 examines
the loss in stock predictability and strategy return caused by
research publication. Their study explores 82 investment
ideas going back over 20 years, testing losses that might be
blamed on data mining as well as crowds. The impact of data
mining and statistical analysis is mixed and not statistically
significant. The average effect of popularity through publication
reduces the expected returns after publication by 35
percent of the returns before publication.
Investment strategies, like many other investment
ideas, are often ephemeral. Moreover, the attractiveness
of strategy ETFs differs from the attractions of investing
in an ETF that tracks a broad-based market index like
the S&P 500 or a total market index. An investor owning
a strategy ETF hopes it is a good idea that will last long
enough; the investors who choose an ETF tracking the
S&P 500 or a total market index believe in low costs and
participating in the stock market.
Endnotes
1
Fama, Eugene F. and French, Kenneth R. (1993). “Common Risk Factors in the Returns on Stocks and Bonds,” Journal of Financial Economics 33 (1): 3–56
2
Carhart, Mark M. (1997). “On Persistence in Mutual Fund Performance,” Journal of Finance 52 (1): 57–82
3
McLean, R. David and Pontiff, Jeffrey E. “Does Academic Research Destroy Stock Return Predictability?” (Oct. 3, 2012). AFFI/EUROFIDAI, Paris, December 2012 Finance
Meetings Paper. Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2156623
Faber continued from page 21
post for both opportunities arising from negative geopolitical
events as well as a sanity check against bubbling stock markets.
Comparing global equity markets on a relative basis allows the
portfolio manager to create portfolios of cheap stocks markets,
while avoiding or even shorting expensive markets.
Appendix: Other Valuation Models
Samuel Lee has a great article titled “The Hedgehog’s
Error” 9 on Morningstar’s website that sorts global
countries based on value (price/book) using the
French/Fama database. Not surprisingly, he finds that
sorting on value works well.
We utilize the database to sort the countries (12 in
1975 and rising to 20 by 1991) based on various measure
of value. In Figure 12, we demonstrate the results of
sorting the countries on a yearly basis and choosing the
cheapest x percent of the universe (from 10 to 33 percent).
Results are U.S. dollar based, nominal.
Endnotes
1 Shiller maintains a website with an Excel download that includes historical data with formulas illustrating how to construct his 10-year CAPE: http://www.econ.yale.
edu/~shiller/data.htm. For a step-by-step guide, Wes Gray at Turnkey Analyst has a good post that walks through the steps necessary to construct the metric: http://turnkeyanalyst.com/2011/10/the-shiller-pe-ratio/
2 “Estimating Future Stock Market Returns” by Adam Butler and Mike Philbrick tackles the issue of different measurement periods from one to 30 years (as well as other
valuation models).
3 John Hussman has a few good articles on this topic: “Estimating the Long-Term Returns on Stocks” and “The Likely Range of Market Returns in the Coming Decade”;
Joachim Klement also recently published the paper “Does the Shiller-PE Work in Emerging Markets?” that performs a similar analysis.
4 Rob Arnott of Research Affiliates touches on this important topic in his white paper “King of the Mountain” (http://www.researchaffiliates.com/Our%20Ideas/Insights/
Fundamentals/Pages/F_2011_Sept_King_of_the_Mountain.aspx). Two other books speak of CAPEs and inflation/deflation levels. The first is “Unexpected Returns:
Understanding Secular Stock Market Cycles” by Ed Easterling, and John Mauldin’s “Bull’s Eye Investing: Targeting Real Returns in a Smoke and Mirrors Market.”
5 One such resource is Russell Napier, who authored Anatomy of the Bear: Lessons From Wall Street’s Four Great Bottoms, and who discusses global CAPEs in a video here:
http://video.ft.com/v/946244201001/Long-View-Historian-sees-S-P-fall-to-400 . We also found two great recently published papers: “Does the Shiller-PE Work in Emerging
Markets?” by Joachim Klement (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2088140), and “Value Matters: Predictability of Stock Index Returns” by Angelini,
Bormetti, Marmi and Nardini (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2031406).
6 http://www.tweedy.com/research/papers_speeches.php
7 http://www.iijournals.com/doi/abs/10.3905/jpm.1991.409327
8 http://www.mebanefaber.com/2011/11/17/sorting-countries-by-dividend-yield-2/
9 http://etf.morningstar.com/BlogArticle.aspx?postid=3281399
www.journalofindexes.com March / April 2013 41

Are Active Mutual Funds
Becoming Less Active?
(Hint: The answer is ‘yes’)
By David Blanchett
42
March / April 2013

There is a growing body of research noting that return
correlations for individual securities have been
increasing. A recent paper by Sullivan and Xiong [2012]
titled “How Index Trading Increases Market Vulnerability”
not only documents this occurrence, but also cites a potential
culprit: the increasing popularity of index funds. Since index
funds tend to be value-weighted—and therefore trade the
same securities in the same relative portion—as index funds
gain more assets, more and more securities are being traded
in the same way at the same time, regardless of the underlying
attributes of the stocks themselves.
The increasing “commonality” across individual securities
doesn’t appear to bode well for active managers, who
by definition seek to add value through individual security
selection. This paper will provide insight as to how the level
of “active” management has been changing in actively managed
mutual funds over the last 21 years by reviewing the
historical relationship between gross returns and a benchmark
based on each fund’s respective Morningstar category.
As one might expect, given the increase in individual security
correlations, the average mutual fund correlation to its
benchmark has increased over the test period, suggesting
that active managers are in fact becoming less active.
Here Come The Index Funds
Index investing has exploded over the last two decades,
growing at roughly twice the rate of active investments. Of
households that owned mutual funds, 31 percent owned
at least one index mutual fund in 2010. The Investment
Company Institute estimates that 37 percent of all index
Just as indexing has changed the nature of stock ownership,
so too has the rise of institutional investors. The
average fraction of a firm’s equity shares held by institutions
has grown from 24 percent in 1980 to 44 percent in
2000, and reached 70 percent in 2010. 2 In a world where
all institutional investors are trading according to their
own respective beliefs, this may not be a problem; however,
with the rise in indexing, more stocks are being held
by institutions that seek to replicate the return of a given
index and minimize tracking error.
Since the vast majority of indexes are value-weighted,
they tend to hold the same stocks in the same relative portions.
Therefore, when an investor buys (or sells) an index
that holds one of these securities, the security is bought
(sold) in conjunction with the other securities that make
up the index, regardless of the relative attractiveness of
the stock itself. This creates an increase in “commonality”
among stocks, especially those in the more popular “baskets,”
such as those in the S&P 500.
Just as indexing has changed the nature of stock ownership,
so too has the rise of institutional investors. The average fraction of
a firm’s equity shares held by institutions has grown from 24 percent
in 1980 to 44 percent in 2000, and reached 70 percent in 2010.
fund assets were invested in S&P 500 index funds, while 32
percent were tracking some other domestic equity index.
About 40 percent of the new money that flowed into index
funds was invested in funds indexed to bond indexes, while
one-third was directed toward funds indexed to global and
international stock indexes, and one-quarter went to funds
indexed to domestic stock indexes.
While equity index assets are only 14.5 percent of mutual
fund assets, Sullivan and Xiong estimate indexes represent
roughly one-third of total fund assets today when factoring in
ETF assets. The first ETF, the SPDR S&P 500 ETF (NYSE Arca:
SPY), was introduced in January 1993. Significant growth in ETF
assets really didn’t start until 2000, though, when there were
roughly $66 billion in assets and 100 options; those numbers
have grown to more than $1 trillion in assets with more than
1,400 ETFs available. ETF trading has grown from virtually nil
in 2000 to now accounting for roughly 30 percent of total dollar
trade volume and about 20 percent of total share volume. 1
Impact On Active Managers
The most obvious impact on actively managed portfolios
from increasing individual security correlations
would be higher levels of market correlations (i.e.,
a decrease in the “active” portion of the portfolio).
Although this might seem intuitive, it may not necessarily
be the case, if (for example) the portfolio manager
was trying to maintain some level of tracking error
against his or her respective size and style benchmark.
If the portfolio manager were to recognize that correlations
among individual securities were increasing, he or
she could decide to hold fewer stocks or tilt the portfolio
more toward certain sectors.
If a portfolio does not maintain a constant level of
tracking error (on average), the portfolio effectively
becomes either more active or passive through time. If
the portfolio is becoming less “active” and charging the
same fee, it becomes increasingly unlikely that the portfolio
manager will outperform his or her benchmark. This
is because, in the absence of any active management skill
(which should cancel out in the aggregate, regardless),
the active manager should be expected to underperform
the appropriately selected benchmark by the total fees
of the portfolio. 3 Therefore, in order to outperform the
benchmark, the portfolio manager will need to take on
active risk, thereby deviating from the benchmark.
If individual securities are, in the aggregate, exhibiting
less idiosyncratic risk and the portfolio manager
www.journalofindexes.com March / April 2013
43

does not change the risk profile through some other
means, the probability of having a return more similar
to the benchmark increases. This in turn increases the
probability the portfolio manager will underperform the
benchmark by the total amount of fees.
Analysis
In order to determine whether or not active managers
are becoming more or less “active,” an analysis was
performed. For the analysis, all data were obtained from
Morningstar Direct. The mutual funds included in the
analysis are those categorized by Morningstar as domestic
equity mutual funds from January 1991 to December
2011. Funds are included regardless of when they exit or
leave the test set, and since the available test population
is updated monthly, survivorship bias is not a concern.
In order to be included, the mutual fund must be in
one of the nine domestic equity style boxes. Also, to
ensure fund “purity,” only those funds with the same
Morningstar Category and Style Index are included for
a given test period. The oldest share class for each fund
Figure 2
Correlation Standard Deviation
Average Rolling Category Correlation Standard Deviation
14%
12%
10%
8%
6%
4%
2%
0%
Dec-91 May-97 Nov-02 May-08
Source: Morningstar
One-Year Period Ending
y = 6E-0.6x + 0.2932
R 2 = 0.2517
uses a 12-month rolling historical period for all calculations.
All returns are gross returns; i.e., they do not include
the impact of investment management fees. The purpose
of the analysis is not to opine on the great active versus
passive debate, but rather to determine how the nature of
Past research by Sharpe [1992] noted that style and size
explain approximately 80 to 90 percent of mutual fund
returns, while stock selection explains only 10 to 20 percent.
The results of this analysis confirm these general findings.
is used, and any fund classified as an enhanced index,
index fund, or fund of funds is excluded from the analysis.
These screens limited the number of funds to 2,059
over the entire test period.
Each fund is compared with its respective Russell Index,
across value, blend and growth, using the Russell 1000,
Russell Mid Cap and Russell 2000 for large cap, midcap and
small cap, respectively. The analysis is conducted on a rolling
monthly basis and the available test set is determined
for that respective historical rolling period. The analysis
Figure 1
Average Correlation
1.00
0.95
0.90
0.85
0.80
Average Rolling Annual Correlation
To Respective Category Index
0.75
Dec-91 May-97 Nov-02 May-08
Source: Morningstar
One-Year Period Ending
y = 1E-0.5x + 0.4614
R 2 = 0.3697
active management has changed over time. Note, however,
that since expense ratios are relatively constant, they would
have little effect on the statistics calculated for this paper
(primarily correlation and standard deviation).
While the average correlations are determined at the
individual Morningstar category level, the category results
are aggregated into a single value for each rolling test period
based on the weighted average number of funds available
for the given test period by category. This approach
overweights styles that typically receive more assets (e.g.,
large cap versus small cap). However, although only the
aggregate results are presented, the overall results are virtually
identical across the individual categories.
Results
Past research by Sharpe [1992] noted that style and
size explain approximately 80 to 90 percent of mutual
fund returns, while stock selection explains only 10 to 20
percent. The results of this analysis confirm these general
findings, with the average correlation of the respective
fund to its Russell index based on its category being
0.93 (and the average correlation 0.94). That translates
into a coefficient of determination (R²) of 86 percent,
which is right in the middle of Sharpe’s original estimate.
However, the correlation has not been constant through
time, as exhibited in Figure 1, which includes the average
44
March / April 2013

olling annual correlation to the respective category index.
Since 1991, the average correlation for actively managed
mutual funds has been increasing. As of December
2011, it was at its approximate highest level in history,
with the average fund having a correlation of 0.982 to its
respective category index. Viewed differently, a correlation
of 0.982 means that 98.2 percent of the return of a
given active manager can be described entirely by the
underlying benchmark index. This suggests the active
manager is only adding roughly one-thirtieth of the total
deviation in returns, but in many cases charging 10 times
or more than what a comparable passive strategy costs.
Note that the t-statistic associated with slope is 11.73, suggesting
an incredibly high level of statistical significance.
Another way to view the changing “active” exposure
of mutual funds through time is the standard deviation
of the correlations. This metric captures the dispersion
of all active managers through time. It is possible that
while the average is increasing, there could also be a
greater level of dispersion among portfolio managers.
Unfortunately, as demonstrated in Figure 2, the average
rolling category correlation standard deviations
have also been decreasing. This suggests that, on average,
an increasing number of actively managed mutual
funds are clustering more and more tightly around their
respective category benchmarks. The t-statistic associated
with slope is -8.84, suggesting an incredibly high
level of statistical significance.
The final test to determine the direction of the “active”
portion of actively managed mutual funds is based on the
average tracking error of the fund versus its respective category
index (Figure 3). For this test, other than two noticeable
spikes, the clear trend has been a decreasing level
of tracking error through time. Again, this suggests active
managers are in fact less “active” than they used to be. The
t-statistic associated with slope is -3.13, suggesting a relatively
high level of statistical significance.
Conclusion
It is impossible to pinpoint an exact reason why actively
managed mutual funds are becoming less “active,” but
the evidence would certainly suggest this is the case. One
theory this author believes could be a driving force behind
Figure 3
Average Tracking Error
4.5%
4.0%
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
Average Rolling Average Category Tracking Error
Dec-91 May-97 Nov-02 May-08
Source: Morningstar
One Year Period Ending
y = 6E-0.7x + 0.0394
R 2 = 0.0409
this change is increased movement to index investing. As
more investors “index” their portfolios, more and more
trading volume is based not on some technical analysis
about the future earnings of a given company (for example),
but instead whether or not a given security is included
in a particular index (the S&P 500 in particular) and to
what extent. Alternatively, it could be that “style purity” is
becoming increasingly important for benchmarking purposes.
Regardless of the reason, active funds have clearly
become less active through time.
These findings present an interesting environment
for active management. First, as more money flows to
index funds, less money must be flowing to active strategies.
Although active as well as passive investments can
be “winners” from a positive flows perspective, the relative
gain of either category definitely comes at the cost
of the other. Second, if higher levels of flows into passive
funds do create an environment that makes it more difficult
for active management to outperform, and if an
increasing amount of flows go to passive/index strategies,
the ability of an active manager to outperform is
likely to become increasingly hampered. Finally, for an
efficient market to exist, there must be some active management.
It is beyond the scope of this article to venture
a guess as to where this point is, but we don’t appear to
be there yet. It will certainly be interesting to see what
happens if we do ever get there.
References
Investment Company Fact Book. 2011. http://www.ici.org/pdf/2011_factbook.pdf
Sharpe, William. 1992. “Asset Allocation: Management Style and Performance Measurement.” Journal of Portfolio Management, vol. 18 No. 2: 7-19
Sias, R.W., L. Starks and S. Titman. 2006. “Changes in Institutional Ownership and Stock Returns: Assessment and Methodology.” Journal of Business, vol. 79 No. 6:2869-2910.
doi:10.1086/508002
Sullivan, R. and Xiong, J. X. 2012. “How Index Trading Increases Market Vulnerability.” Financial Analysts Journal. Forthcoming, available at SSRN: http://ssrn.
com/abstract=1908227
Endnotes
1
Sullivan and Xiong [2012]
2
Sias, Starks and Titman [2006]; Sullivan and Xiong [2012]
3
Both explicit management fees and implicit fees that result from trading, such as the bid/ask spread and commissions
www.journalofindexes.com March / April 2013 45

Taking A Long View
Of Bond Performance
Don’t be distracted by the short term
By Craig Israelsen
46
March / April 2013

Interest rates go up. And down. And up.
Over the past 64 years (1948-2011), that is exactly
what has happened. During the 34-year period from
1948-1981, the Federal discount rate increased—not every
year, but as a general trend, as shown in Figure 1. In 1948,
the Federal discount rate was 1.34 percent, and by 1981, it
was 13.42 percent. During this time frame of rising interest
rates, the 34-year average annualized return for U.S. bonds
was 3.83 percent. The year-to-year performance of U.S.
bonds is represented in the graph by the vertical bars.
Starting in 1982, the Federal discount rate began its
downward trend. At the end of 2011, the rate was 0.75
percent. During the last 30 years (1982-2011), the average
annualized return of U.S. intermediate bonds has
been 8.98 percent (see Figure 1).
Clearly, the last 30 years have provided a wonderful
With this review of history now in mind, the question
of the day is, If I expect interest rates to rise, should I avoid
bonds going forward?
First, let’s clarify something. Are we talking about avoiding
bonds as our only investment asset, or, are we talking about
avoiding bonds as one of the asset classes in our overall asset
allocation models? I will assume we are talking about the latter
question. To those who invest all their money in one asset
class—such as a 100 percent stock portfolio or a 100 percent
bond portfolio—this article is not for you.
Let me demonstrate. A one-asset portfolio that held only
U.S. bonds (U.S. intermediate government bonds from 1948-
1975 and the Barclays Capital Aggregate Bond Index from
1976-2011) was clearly impacted by the period of time.
During the 34-year period of rising interest rates, a nondiversified
all-bond portfolio averaged 3.83 percent per year,
The last 30 years has been a wonderful environment for bonds
to perform well as the Federal Discount rate steadily descended.
environment for bonds to perform well as the Federal
discount rate steadily descended. Interestingly, U.S. stocks
(represented by the S&P 500 Index) performed essentially
the same during both periods. From 1948 to 1981, when
interest rates were rising, the S&P 500 Index had an annualized
return of 11.00 percent. During the recent 30-year
period of declining interest rates, the S&P 500 Index
generated a 10.98 percent annualized return. Whereas
bond returns are markedly impacted by interest rate
movement, stocks are largely immune—they march to a
variety of drummers. Furthermore, cash (as represented
by the three-month T-bill) averaged 4.49 percent during
the 34-year period of rising interest rates, and 4.88 percent
during the 30-year period of declining interest rates.
whereas during the last 34 years, it would have produced
an average annualized return of 8.98 percent (see Figure 2).
Realistically, a one-asset portfolio is not a prudent design.
How about a two-asset portfolio? Let’s assume the
classic “balanced” design with a 60 percent allocation
to stocks (S&P 500) and a 40 percent allocation to bonds
(rebalanced annually). As shown in Figure 2, the differential
in performance between the two time periods
(1948-1981 and 1982-2011) is much less dramatic, but
it clearly favors the more recent 30-year time period,
which was more favorable to bond performance—which
affected 40 percent of the two-asset portfolio.
A four-asset portfolio that allocated 40% to large U.S.
stocks, 20 percent to small U.S. stocks, 30 percent to
Figure 1
The Rise And Fall Of Interest Rates
35
30
25
20
15
10
5
0
Rising Interest Rates: 1948-1981
34-Year Period
Average Annual Bond Return = 3.83%
Average Annual S&P 500 Return = 11.00%
Average Annual T-Bill Return = 4.49%
Declining Interest Rates: 1982-2011
30-Year Period
Average Annual Bond Return = 8.98%
Average Annual S&P 500 Return = 10.98%
Average Annual T-Bill Return = 4.88%
(5)
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Annual Return of US Bonds (%) Federal Discount Rate (%)
Source: Raw data from Lipper for Investment Management
Note: Intermediate term U.S. government bond returns from 1948-1975 and the Barclays Capital Aggregate Bond Index returns from 1976-2011
www.journalofindexes.com March / April 2013
47

Figure 2
Asset Allocation Across Time
Portfolio
Period of Rising Interest Rates
34-Year Period from 1948-1981
Period of Declining Interest Rates
30-Year Period from 1982-2011
1-Asset Portfolio
100% US Bonds
2-Asset Portfolio
60% Large US Stock
40% Bonds
4-Asset Portfolio
40% Large US stock
20% Small US Stock
30% Bonds
10% Cash
3.83% Annualized Return
4.32% Standard Deviation
8.52% Annualized Return
10.49% Standard Deviation
9.52% Annualized Return
11.80% Standard Deviation
8.98% Annualized Return
7.05% Standard Deviation
10.54% Annualized Return
11.52% Standard Deviation
9.96% Annualized Return
11.17% Standard Deviation
Source: Raw data from Lipper for Investment Management
As a portfolio is more diversified, the impact of the performance
of one asset class on the overall portfolio is dramatically reduced.
bonds and 10 percent to cash (with annual rebalancing)
generated an annualized return of 9.52 percent during
the 34-year period when interest rates were rising, and a
9.96 percent annualized return during the last 30 years
in which rates were falling. There was a modest difference
of 44 basis points between the two time frames.
Clearly, as a portfolio is more diversified, the impact of
the performance of one asset class on the overall portfolio
(assuming the allocations are not heavily skewed toward
only one asset) is dramatically reduced. This is precisely
why portfolios should be diversified—by doing so, we
lower the risk of allowing the bad performance of one particular
asset class to sink the portfolio’s overall returns.
Let’s now examine how the performance of bonds
(actual, worst-case, and best-case) impacted a broadly
diversified 12-asset portfolio. I will utilize a portfolio known
as the 7Twelve Portfolio.
As shown in Figure 3, the 7Twelve Portfolio includes 12
asset classes that are equally weighted at 8.33 percent of the
portfolio. Each asset class is rebalanced annually. During
the 10-year period from Jan. 1, 2002 to Dec. 31, 2011, the performance
of the 7Twelve Portfolio (using the performance of
12 raw indexes) was 8.93 percent, with a standard deviation
of annual returns of 15.30 percent. The actual performance
of U.S. bonds during this 10-year period (using the Barclays
Capital U.S. Aggregate Bond Index) was 5.78 percent.
Figure 3
Performance Of A Broadly Diversified Portfolio (2002-2011)
7Twelve Portfolio Asset Category
(Using Raw Index Performance)
10-Year Annualized % Return
1/1/2002-12/31/2011
10-Year Standard Deviation
of Annual Returns
US Large Cap Equity 2.92 20.50
US Mid Cap Equity 7.04 22.63
US Small Cap Value Equity 6.40 22.52
Developed Non-US Equity 4.67 25.05
Emerging Non-US Equity 13.86 38.00
Real Estate 10.12 25.16
Natural Resources 10.99 26.87
Commodities 14.97 20.34
US Aggregate Bonds 5.78 2.23
Inflation-Protected Bonds 7.57 5.99
International Bonds 8.38 8.34
Cash 1.91 1.86
7Twelve Portfolio Return 8.93 15.30
Source: Raw data from Lipper for Investment Management
48
March / April 2013

Figure 4
Impact Of Worst-Case Bond Performance In A Broadly Diversified Portfolio (2002-2011)
7Twelve Portfolio Asset Category
(Using Raw Index Performance)
10-Year Annualized % Return
1/1/2002-12/31/2011
(US Govt Bonds 1950-1959)
10-Year Standard Deviation
Of Annual Returns
US Large Cap Equity 2.92 20.50
US Mid Cap Equity 7.04 22.63
US Small Cap Value Equity 6.40 22.52
Developed Non-US Equity 4.67 25.05
Emerging Non-US Equity 13.86 38.00
Real Estate 10.12 25.16
Natural Resources 10.99 26.87
Commodities 14.97 20.34
US Bonds (1950-1959) * 1.34 2.71
Inflation-Protected Bonds 7.57 5.99
International Bonds 8.38 8.34
Cash 1.91 1.86
7Twelve Portfolio Return 8.54 15.47
Source: Raw data from Lipper for Investment Management
Note: Worst-performing 10-year return for U.S. bonds during 1948-2011 period
Now, let’s insert the worst 10-year performance for
U.S. bonds since 1948, and measure the impact on a
broadly diversified 12-asset portfolio. As shown in Figure
4, the worst 10-year period for U.S. bonds between 1948
and 2011 was from 1950-1959. During that 10-year span,
U.S. bonds produced an average annualized return of
1.34 percent. The overall return of the 12-asset portfolio
dropped from 8.93 to 8.54 percent—a decline of 39 basis
points. The standard deviation of the 12-asset portfolio
was essentially unchanged.
Next, as shown in Figure 5, I inserted the returns of
the best 10-year period for U.S. bonds, which happened
to be the period from 1982-1991. During this 10-year
period, U.S. bonds generated a 10-year annualized
return of 14.09 percent. The impact of superior bond
returns on the portfolio was beneficial, of course. The
10-year return of the 12-asset portfolio was 9.65 percent,
with a standard deviation of 15.32 percent.
A summary of the scenarios (based on actual bond
performance, worst-case bond performance and best-case
bond performance) is provided in Figure 6.
For an investor placing all her investments in one
asset, such as bonds or stocks or real estate, timing is
everything. As it pertains to bond performance, the difference
between the worst-case 10-year time period and
best-case 10-year time period for a 100 percent U.S. bond
portfolio was nearly 1,300 basis points—resulting in a
performance differential of nearly $26,000.
Figure 5
Impact Of Best-Case Bond Performance In A Broadly Diversified Portfolio (2002-2011)
7Twelve Portfolio Asset Category
(Using Raw Index Performance)
Source: Raw data from Lipper for Investment Management
Note: Best-performing 10-year return for U.S. bonds during 1948-2011 period
10-Year Annualized % Return
1/1/2002-12/31/2011
(US Agg Bonds 1982-1991)
10-Year Standard Deviation
Of Annual Returns
US Large Cap Equity 2.92 20.50
US Mid Cap Equity 7.04 22.63
US Small Cap Value Equity 6.40 22.52
Developed Non-US Equity 4.67 25.05
Emerging Non-US Equity 13.86 38.00
Real Estate 10.12 25.16
Natural Resources 10.99 26.87
Commodities 14.97 20.34
US Bonds (1982-1991) * 14.09 8.43
Inflation-Protected Bonds 7.57 5.99
International Bonds 8.38 8.34
Cash 1.91 1.86
7Twelve Portfolio Return 9.65 15.32
www.journalofindexes.com March / April 2013 49

Figure 6
Summary Of Three Bond Scenarios
Time Period
Description Of US
Bond Performance
10-Year Annualized
Return Of US Bonds
Growth Of $10,000
In US Bonds
10-Year Annualized
Return Of
12-Asset Portfolio *
Growth Of
$10,000 In A
Diversified Portfolio
2002-2011 Actual Performance 5.78% 17,540 8.93% 23,522
2002-2011
(US bond returns
from 1950-1959)
2002-2011
(US bond returns
from 1982-1991)
Worst-case
Performance
Best-case
Performance
1.34% 11,423 8.54% 22,693
14.09% 37,365 9.65% 25,123
Difference between
Worst-case and Best-case Bond Performance
1,275 bps 25,942 111 bps 2,430
Source: Raw data from Lipper for Investment Management
Note: U.S. bonds have an 8.33% allocation (or 1/12th) in the 7Twelve portfolio. All 12 assets were rebalanced annually over the 10-year period.
For an investor who used a diversified approach (in this
analysis, a 12-asset portfolio), the performance differential
between the worst-case bond period and the best-case bond
period was 111 basis points, or $2,430 in ending account value.
Completely avoiding any asset class in a diversified
portfolio amounts to a guess that it will underperform
and that another asset class will outperform. Building
prudent portfolios is not about guessing and timing;
it’s about broad diversification. A broadly diversified
portfolio is naturally insulated—not completely, but
largely—from the normal swings in performance among
its various components. The “underperformance” of one
or several of its ingredients will not sink the performance
of the overall portfolio.
Blitz continued from page 39
Endnotes
1 See, for example, Chow, Hsu, Kalesnik & Little (2011), “A Survey of Alternative Equity Index Strategies,” Financial Analysts Journal, vol. 67, No. 5, pp. 37-57.
2 Blitz (2012), “Strategic Allocation to Premiums in the Equity Market,” Journal of Index Investing, vol. 2, No. 4, pp. 42-49.
3 See Asness (2006), “The Value of Fundamental Indexation,” Institutional Investor, (October), pp. 94-99; Blitz & Swinkels (2008), “Fundamental Indexation: an Active Value
Strategy in Disguise,” Journal of Asset Management, vol. 9, No. 4, pp. 264-269.
4 See Arnott, Hsu & Moore (2005), “Fundamental Indexation,” Financial Analysts Journal, vol. 61, No. 2, pp. 83-99.
5 See Chow, Hsu, Kalesnik & Little (2011), “A Survey of Alternative Equity Index Strategies,” Financial Analysts Journal, vol. 67, No. 5, pp. 37-57.
6 See de Groot & Huij (2011), “Is the Value Premium Really a Compensation for Distress Risk?” SSRN working paper no. 1840551.
7 See Blitz, van der Grient & van Vliet (2010), “Fundamental Indexation: Rebalancing Assumptions and Performance,” Journal of Index Investing, vol. 1, No. 2, pp. 82-88.
8 We note that although MSCI aims for a one-way turnover of no more than 20 percent per annum, on several occasions they have relaxed this constraint. For example, a
methodology change implemented at the end of 2009 caused a turnover of 45 percent at that moment.
9 Stock weights in this index are set inversely proportional to their volatility, so the lowest-volatility stocks get the highest weights.
10 See, for example, Soe (2012), “Low-Volatility Portfolio Construction: Ranking versus Optimization,” Journal of Index Investing, vol. 3, No. 3, pp. 63-73.
11 For a discussion of the low-volatility premium, we refer to Blitz & van Vliet (2007), “The Volatility Effect: Lower Risk Without Lower Return,” Journal of Portfolio
Management, vol. 34, No. 1, pp. 102-113.
12 See Huij, van Vliet, Zhou & de Groot (2012), “How Distress Improves Low-Volatility Strategies: Lessons Learned Since 2006,” Robeco research note.
13 See Clarke, de Silva & Thorley (2011), “Minimum Variance, Maximum Diversification, and Risk Parity: An Analytic Perspective,” SSRN working paper no. 1977577. In their
Table 2, they report a volatility of 19.0 percent for a maximum diversification strategy applied to U.S. equities over the 1968-2010 period, which compares with a volatility
of only 15.6 percent for the cap-weighted index over the same period.
14 Returns for this strategy are publicly available on the website of Prof. Kenneth French: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
15 See Blitz, Huij & Martens (2011), “Residual Momentum,” Journal of Empirical Finance, vol. 18, No. 3, pp. 506-521.
16 In all fairness, AQR also acknowledges that mechanically following their momentum indexes would be a suboptimal approach, and recognizes the need for a more efficient
implementation strategy.
17 Quoting Eric Falkenstein: “[…] It should be noted that there were several missteps among the index founding fathers. John McQuown and David Booth at Wells Fargo, and
Rex Sinquefield at American National Bank in Chicago, both established the first passive Index Funds in 1973. These were portfolios targeted at institutions. The Wells
Fargo fund was initially an equal-weighted fund on all the stocks on the NYSE, which, given the large number of small stocks, and the fact that a price decline meant you
should buy more, and at a price increase sell more, proved to be an implementation nightmare. It was replaced with a value-weighted index fund of the S&P500 in 1976,
which eliminates this problem. […]” See http://falkenblog.blogspot.nl/2011_09_01_archive.html.
50
March / April 2013

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Covered-Call ETFs
For BRIC Countries
Can covered-call ETFs benefit global investors in BRIC stock markets?
By Ronald Slivka, Sharad Bhat and Sridhar Nonabur Srinivasamurthy
52
March / April 2013

While the global financial crisis that began in
2007 left emerging markets less affected than
G-10 countries, the subsequent price behavior
of BRIC stock markets highlighted once again the
need for global investors to control risks through portfolio
diversification. Typically within a single BRIC country,
however, the means to diversify are less available
or less well developed than found elsewhere. Within
the ETF investment universe, the means for portfolio
diversification are even further restricted. For ETFs to
continue their growth among emerging markets, then,
will require wider coverage of assets and employment of
strategies beyond traditional passive indexation.
ETF choices among nontraditional, or so-called second-generation
funds, increased notably in 2012. Active
management strategies for stocks and bonds now form
the basis for rapid growth in developed-market ETFs. This
development, which currently lags in BRIC countries, could
offer the prospect of increasing the choices for domestic
as well as global investors to further diversify their local
BRIC holdings and so to increase risk control. BRIC equity
markets remain volatile and only mildly trending or even
trendless, raising the possibility that covered-call ETFs on
recognized indexes could find a foothold in that impending
wave of second-generation ETFs. Historical evidence
strongly supports the claim that in such an environment,
a covered-call strategy delivers superior returns with less
than index volatility, thereby making itself an attractive
candidate for use in risk control.
Moreover, the continuing strong demand for emerging
markets ETFs and investors’ need for enhanced returns in
our current low-yield environment suggest that coveredcall
strategies targeting emerging markets could find a
warm welcome in the ETF arena.
In this article, we examine the feasibility of structuring a
specialty covered-call ETF to satisfy the rising demand for
emerging market ETFs.
Equity Covered-Call ETFs
Covered-call ETFs presently can be found linked to
stock markets in the U.S., Canada, Europe and even Korea,
a borderline “emerging” market (Figure 1). However, no
covered-call ETFs yet exist linked to indexes in the most
prominent of the emerging markets: the BRIC countries.
Figure 2 shows which BRIC markets have elements from
which to construct such a covered-call ETF denominated
in local currency. The requirements are simple: There
must be both a convenient ability to acquire equity index
exposure and an ability to sell index options. The presence
of stocks or ETFs that replicate the index can satisfy the
exposure requirement. It may also be possible to obtain
synthetic equity exposure using index futures and cash
[Slivka & Li, 2010] but this method is sometimes inconvenient,
unnecessarily complicated and likely to provide
more variable returns. The presence of exchange-traded
index calls can satisfy the second requirement.
Any instruments used to construct BRIC covered-call
ETFs should have sufficient liquidity to support continuous
call writing over a multiyear period. Two countries
(Brazil and Russia) have options on index futures, but not
options on the index itself, making covered-call construction
impractical. China presently has no options at all.
India, on the other hand, appears to have the necessary
requirements for covered-call construction. Index exposure
can be acquired by direct purchase of stocks replicating
the index, while a liquid index options market allows
call writing for maturities up to one month and sometimes
longer. This makes it possible to construct an ETF using a
semi-passive strategy in which one-month covered calls
replace written calls as they expire, a topic we next explore.
Covered-Call Returns
One generally recognized benefit of selling calls against
long positions in stocks and indexes is that receipt of the
time-premium component of the call premium can raise
the return on the underlying asset above the return from
holding the asset alone. Since time premium for a call is
greatest near-the-money, covered-call writers seeking
return enhancements often choose strike prices close
to the current asset price. A second benefit is that the
premium received creates a partial hedge against asset
price decline. If the call is written out-of-the-money,
the amount of this partial hedge is limited to the premium
received. If the call is written in-the-money, the
Figure 1
Equity Covered-Call ETFs/ETNs
Equity Covered-Call
ETFs/ ETNs
Bloomberg
Ticker
Country/
Region
BMO Covered-Call Canadian Banks ZWB:CN Canada
Horizons Enh Inc Equity HEX:CN Canada
Horizons Enh Inc Financials HEF:CN Canada
Can-60 Covered Call LXF:CN Canada
Can-Energy Covered Call OXF:CN Canada
Can-Financials Covered Call FXF:CN Canada
Can-Materials Covered Call MXF:CN Canada
Lyxor ETF EURO STOXX 50 BuyWrite BWE:BQ Europe
MIDAS KOSPI200 Covered Call 137930:KS Korea
PowerShrs S&P 500 BuyWrite PBP:US US
iPath S&P 500 BuyWrite ETN BWV:US US
Source: Bloomberg
Figure 2
BRIC
Country
Availability Of Exchange-Traded Instruments
In BRIC Countries For Covered-Call Construction
Stock
Index
Source: Local exchanges
Stock
Index
Futures
Options
on Stock
Index
Options
on Index
Futures
ETF on
Index
Brazil Ibovespa Yes No Yes Yes
Russia RTS Yes No Yes Yes
India NIFTY Yes Yes No Yes
China CSI 300 Yes No No Yes
March / April 2013 53

full amount of premium offers protection against loss.
Investors using covered calls to protect against meaningful
losses often choose options deeper in-the-money.
In our study, we selected pairs of NIFTY index call options
listed on the National Stock Exchange of India (NSE) having
a one-month time-to-expiration (maturity) and strike prices
just above and just below the index price at the time the calls
were written. A one-month time-to-option maturity offered
excellent call liquidity, while the choice of near-the-money
strike prices allowed for high time-premium income.
Two standard returns are customarily calculated to evaluate
the attractiveness of covered-call candidates prior to
trade execution. They are the stand-still return (RSS) and
the return-if-called (RIC). The RSS is the percentage returnto-option
expiry, assuming the underlying index remains
unchanged. This return recognizes costs and any dividends
payable during the period ending at option expiry. The RIC
is the return if the option is in-the-money at maturity and is
exercised. The RIC calculation also includes costs and any
dividends received by the asset holder between trade date
and expiry. A third return, the realized return (RR), is the
actual return realized at call expiry, and its calculation must
account for any capital gains or losses arising from changes
in the underlying index.
Formulas used to calculate these three returns at expiry
are as follows:
RSS = [P - MAX[ 0, (I-K) ] + D - G] x t / [ I + G - D - P ] (1)
RIC = [P - MAX[ 0, I-K ] + MAX[ 0, K-I ]
+ D - G] x t / [ I + G - D - P ] (2)
RR = [ P - MAX[0, Im-K ] + D - G + Im - I ]
x t / [I + G - D - P] (3)
Where:
I = Index level in points on trade date
Im = Index level in points at call expiry
K = Call strike price
P = Call premium in index points
D = Dividends payable to call expiry in index points
G = Costs in index points = Index times a = a I
a = 0.65 percent
t = 365 / n
n = number of days to call expiration
For the RIC calculation, the quantity K-I in the numerator
of RIC has a convenient interpretation, as it represents the
capital appreciation of the asset if the call is initially written
out-of-the-money. Otherwise, this quantity will cancel the
intrinsic value of the premium if the call is in-the-money
when written. Any such cancellation will correctly leave only
the time premium to contribute to the return. When an inthe-money
call is written, the RIC and RSS are the same. The
RIC equation can also be written simply to reflect these facts
and isolate the capital appreciation component as follows:
RIC = RSS + MAX[0, K-I] x t / (I + G - D - P ] (4)
Prices for NIFTY calls and projected dividends payable
each month were available on Bloomberg and
quoted in index points. Typical costs to an institutional
covered-call writer appear in Figure 3. For a hypothetical
institutional investor, total round-turn covered-call
costs amount to an estimated 0.65 percent of traded
index value for transactions in which securities are purchased
and held for one or more days (delivery transactions).
For retail clients transacting covered calls in
smaller size, such costs are easily double this amount.
Covered-Call Performance
Call-writing strategies employed among the covered-call
ETFs in Figure 1 range from active to semi-passive. The
PowerShares S&P 500 BuyWrite Portfolio ETF, which follows
the strategy used to create the CBOE S&P 500 BuyWrite
Index (BXM), provides an example of a semi-passively managed
strategy [CBOE, 2010]. The BXM Index is constructed
from the results of systematically writing one-month slightly
out-of-the-money calls on the S&P 500 Index where the
underlying portfolio is an S&P 500 Index fund. At each call
expiration, a replacement one-month call is written.
The performance of this covered-call index has been
carefully studied by five authors over the period from
1986 through 2012 (“key studies”). Ibbotson Associates
studied the BXM behavior over the period 1988 to 2004
[Ibbotson, 2004], while Callan Associates studied the
same index from 1988 to 2006 (Callan, 2006), as did
Hewitt EnnisKnupp from 1986 to 2012 [EnnisKnupp, 2012]
and Asset Consulting Group from 1986 to 2011 [Asset
Consulting, 2012]. Separately, Kapadia and Szado studied
covered-call writing on another index, the Russell 2000
Figure 3
Common Institutional Costs For Covered-Call Writing
Charges On Each Leg Of Delivery Transactions
As A % Of Traded Value
Stock Charges
Brokerage on Turnover (Traded Value) 0.10%
Service Tax on Brokerage 10.30%
Securities Transaction Tax (STT) For Delivery Trades 0.100%
Exch Transaction Charges for NSE and BSE Trades 0.0035%
Stamp Duty 0.010%
SEBI Charges 0.0001%
Subtotal for Stock 0.2239%
Option Charges
Brokerage on Turnover (Traded Value) 0.035%
Service Tax on Brokerage 10.30%
Securities Transaction Tax (STT) 0.0085%
Exch Transaction Charges for NSE and BSE Trades 0.050%
Stamp Duty 0.002%
SEBI Charges 0.0020%
Subtotal for Options 0.1011%
Round Turn for Stock + Options 0.6500%
Source: Interactive Brokers at interactivebrokers.com
54
March / April 2013

[Kapadia & Szado, 2007] and reached similar conclusions.
All five key studies agreed that:
1. In falling markets, covered-call writing returns outperform
pure index portfolios.
2. In markets trading in a range, covered-call writing
also outperforms index portfolios.
3. In rising markets, covered-call-writing returns underperform
index portfolios.
These general findings could have been anticipated by
comparing a typical index covered-call gain or loss (P/L) at
call expiry, with the gain or loss on the underlying index. A
simplified hypothetical P/L profile for this purpose appears
in Figure 4, where two breakeven points are easily identified
for an at-the-money covered call. The lower breakeven at 95
occurs at a value equal to the strike price of 100 less the initial
call premium of 5, while an upper breakeven point at 105
occurs at the value equal to the strike price of 100 plus the
initial call premium of 5. These two breakeven points divide
the covered-call P/L outcomes into three regions of return:
Region 1: When the index level at call expiry is below the
lower breakeven, the investor experiences a realized loss on
the covered call but outperforms the index at all outcomes
in this region. This result corresponds to the first of the three
covered-call findings from the key studies cited above.
Region 2: When the index level at call expiry is
between the two breakeven points, the covered call also
outperforms the index at all levels, corresponding to the
second finding of the key studies.
Region 3: When the index level at call expiry is above the
upper breakeven, there is a realized gain on the covered
call but this gain is less than that on the index, corresponding
to the third finding of the key studies.
While each of these findings could have been anticipated
from inspection of Figure 4, many useful numerical results
from the key studies required careful detailed analysis.
Important among the additional findings was that coveredcall
returns were above that of the index for the period
1986 to 2011 and were achieved with a volatility about onethird
lower than that of the index alone. This same period
included rising, falling and range-bound markets. In rising
markets, covered-call writing underperformed the market.
Because returns are capped when calls become in-themoney,
correlations between index and strategy returns
drop. This latter finding suggests that covered-call writing
occupies a position on the capital market line that offers
returns in rising markets that are generally below that of
the index but above that of cash, much as graphically represented
in Figure 5. This position of covered-call writing on
the capital market line conveniently suggests that there is
also portfolio diversification potential in this strategy.
Covered-Call ETF Benefits And Concerns
Financial journalists often make three negative warning
observations about covered-call ETFs. The first warning is
that covered-call writing is a strategy that underperforms
the market, implying that the strategy should be avoided.
The warning is based on the mistaken assumption that
this strategy is designed to outperform the market at all
Figure 4
Maturity P/L
10
8
6
4
2
0
-2
-4
-6
-8
-10
Comparison Of Index And Covered-Call
Outcomes At Call Expiry
Maturity P/L For At The Money Covered Call Vs. Index
Strike Price At 100; Call Premium 5; No Costs Or Dividends
Realized
Loss
Lower Breakeven
Upper Breakeven
90 92 94 96 98 100 102 104 106 108 110
Index At Cal Maturity
Source: Author’s calculations
Figure 5
Return %
20
15
10
5
0
■ Cover-Call P/L
Index P/L
Representation Of Covered-Call Performance
In Rising Markets
Cash
0 2 4 6 8 10 12 14 16 18 20
Source: Author’s calculations
Covered-Call Writing
Volatility (%)
Index
times. As the key studies clearly demonstrate, covered-call
returns should be expected to underperform in rapidly
rising markets because capital gains are limited above the
call strike price. Conveniently overlooked by journalists is
that outperformance of covered-call ETFs can be expected
both in range-bound and declining markets.
A second mistake made by journalists is to compare
price-only returns from covered-call funds against total
market returns. Proper return comparisons between strategies
can be made only by including costs and dividends
received. Such inclusions sometimes reveal a favorable
comparison, as was the case from 1986 to 2011, when covered-call
writing in the U.S. market outperformed indexes.
A third erroneous claim found in the financial press is that
covered calls fail to provide a hedge against declining markets.
Professional covered-call writers know that sold options provide
two sources of partial protection against price declines.
For out-of-the-money calls, the premium received provides
a partial hedge by lowering the cost basis of the underlying
asset. For in-the-money calls, the option premium also
contains intrinsic value that provides an additional powerful
1-for-1 offset against asset price declines. Neither in- nor
out-of-the-money written calls can provide a complete hedge
against a declining market, nor are they designed to do so, as
March / April 2013
55

any knowledgeable options investor can confirm.
Benefits and concerns that are appropriate to coveredcall
ETFs include the following:
Figure 6
Volume And Open Interest
NIFTY January 2012 Expiry Call; Strike Price 4900
Benefits
• Lower fees than actively managed ETFs: The costs of
acquiring and managing an index fund are meaningfully
lower than for actively managed strategies. Thus, while
covered-call ETFs on index funds can be expected to have
fees slightly higher than for purely passive index ETFs, the
fees will be less than those for actively managed ETFs.
• Diversification: The lower risk of covered-call writing on
the capital market line (Figure 5) suggests this strategy has
potential diversification benefits for investment portfolios.
• Yield: The systematic capture of call premiums and
dividends from covered-call writing both raises the yield
and lowers the cost basis of the underlying equity. In
Figure 4, it can be seen that returns from covered-call
writing dominate those from an index investment right
up until the “upper breakeven” point.
• Tax treatment: In countries where dividends receive
more favorable treatment than capital gains, the aftertax
total return on a covered-call ETF will be further
enhanced by receipt of dividends.
• Intraday trading: Pricing and trading are available intraday.
Concerns
• Execution costs: Important to understand is that
in India, the costs for delivery transactions (Figure
3) are significantly higher than for intraday trades.
Covered-call writing does not lend itself to intraday
high-frequency trading, so execution costs can create a
significant drag on returns. The monthly rolling over of
Volume / Open Interest
600,000
500,000
400,000
300,000
200,000
100,000
0
0 10
20 30 40 50 60
Days To Expiration
■ Open Interest ■ Volume
Source: National Stock Exchange of India (exchange holiday at 20 days to expiration)
Figure 7
Number Of Intraday Covered Calls
With RSS Exceeding Mibor*
Trade Date ITM Strike OTM Strike
December 29, 2012 2,981 10,948
January 25, 2012 3,129 6,933
February 23, 2012 9,953 12,891
April 26, 2012 3,299 8,493
Source: Author’s calculations
Note: Using prices for the NIFTY index and NIFTY calls matched to within one second
Covered-Call ETF Analysis
While the period of our analysis was brief (December
2011 through March 2012), it was sufficient to arrive at
useful conclusions regarding the feasibility of ETF creation,
especially knowing that the outcome of any lengthier
study of risks and returns would be virtually certain to
fit consistently with the findings of the key studies.
The following steps were taken in this covered-call
ETF analysis:
In India, the costs for delivery transactions are significantly
higher than for intraday trades. Covered-call writing
does not lend itself to intraday high-frequency trading,
so excecution costs can create a significant drag on returns.
options as they expire also adds to the total costs of this
semi-passive strategy.
• Management fees: The analysis of which options to
write and when to write them requires extra care by the
ETF manager. The attending costs for this effort must be
passed along to the investor.
• Underperformance risk: As has been explained,
covered-call ETFs can be expected to underperform the
index in rapidly rising markets.
Step 1: To identify the best options maturity for covered-call
writing, trading volume and open interest
were captured for near-the-money NIFTY options from
which profiles were constructed. A representative profile
appears in Figure 6. Similar open-interest and volume
statistics for four consecutive expiry dates suggested that
the optimal period for covered-call writing was one to
four weeks prior to contract maturity. Writing calls with
greater than four weeks to maturity was not sensible due
to lower liquidity. Writing calls with less than one week to
maturity afforded too little absolute return.
Step 2: In 2011, NIFTY index options were the secondmost-actively
traded equity options contract in the
world [Ackworth, 2012]. Despite this comforting statis-
56
March / April 2013

tic, we found it prudent to test the regularity with which
profitable covered calls on the NIFTY could be written
on any trading day. For this purpose, high-frequency
intraday time and options price data was captured and
matched to the index price and times within one second.
The number of covered calls having an RSS exceed -
ing Mibor was recorded on successive expiration trade
dates and appears in Figure 7 for contracts both in-themoney
(ITM) and out-of-the-money (OTM).
Step 3: Following a methodology consistent with BXM
construction for January through April 2012 expiry contracts,
nearest in-the-money and the nearest out-of-themoney
one-month covered calls were written on each of
four consecutive option expiry dates and held to the next
expiry date. For example, at December expiry (Dec. 29,
2011), two covered calls were separately written for comparison
using January 2012 expiry calls, one being slightly
in-the-money and one being slightly out-of-the-money,
and these positions were held to their Jan. 26 expiry.
Step 4: In addition to expiry dates on which steps 1-3
were taken, intermediate sample dates were chosen on
which to conduct the same analysis. Results confirm the
intraday availability of covered-call writing opportunities
was substantial on a regular basis using NIFTY options.
As expected, in all three critical regions of the covered-call
return profile (Figure 1), observed behavior
was consistent with the findings of key studies. In-themoney
calls provided greater protection against market
declines, while out-of-the-money calls provided greater
returns in a rising market. Realized returns were also
achieved with less volatility than the index, confirming
the diversification benefits of covered-call ETFs.
Conclusions
With more investors turning their attention to emerging
markets, demand is growing for more ways to access
them. Pairing covered-call strategies with emerging
markets exposure makes sense due to the fact that such
strategies tend to dampen volatility and offer some
protection from downside risk. However, a developing
market may not possess all the features necessary to
execute such a strategy. Our findings suggest that currently
among BRIC nations, India alone has stock and
options markets that make an index covered-call ETF
practically achievable. Such an ETF on India’s NIFTY
index could provide the valuable benefits of yield
enhancement over cash with volatility below that of the
index, diversification benefits due to a lower correlation
with the index, and a degree of protection against falling
markets. Using spot exchange rates, this ETF could
also be listed and quoted on local exchanges in Europe,
the U.S. or other major financial centers.
References
Ackworth, W. (March 1, 2012), Annual Volume Survey. Futures Industry, pp. 24-33.
Asset Consulting, G. (2012), “An Analysis of Index Option Writing for Liquid Enhanced Risk-Adjusted Returns,” Saint Louis: Asset Consulting Group.
CBOE. (2010), BXMDescription-Methodology.pdf, Chicago: Chicago Board Options Exchange.
Callan, A. (2006), “An Historical Evaluation of the CBOE S&P 500 BuyWrite Index Strategy,” San Francisco: Callan Associates Inc.
EnnisKnupp, H. (2012), “The CBOE S&P 500 BuyWrite Index (BXM),” Lincolnshire: Hewitt EnnisKnupp.
Ibbotson, A. (2004). “Highlights from Case Study on BXM Buy-Write Options Strategy,” Chicago: Ibbotson Associates.
Kapadia, N. & Szado, E. (2007), “Risk and Return Characteristics of the Buy-Write Strategy on the Russell 2000 Index,” Chicago: Options Industry Council.
Slivka, R.T. & Li, X. (September/October 2010). “Hedging and Synthetic Funds Creation in the China Market,” Journal of Indexes, pp. 50-55.
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March / April 2013
57

Global Index Data
Selected Major Indexes Sorted By YTD Returns
March/April 2013
Total Return % Annualized Return %
Index Name 2012 2011 2010 2009 2008 2007 2006 2005 3-Yr 5-Yr 10-Yr 15-Yr Sharpe Std Dev
Citigroup Greek GBI 89.73 -61.30 -25.78 6.98 -3.84 13.25 11.88 -8.95 -18.32 -10.93 -0.62 - -0.08 55.10
MSCI Turkey* 60.53 -36.78 18.36 92.00 -63.38 70.04 -9.22 51.60 6.30 -3.32 19.79 5.42 0.34 32.47
Citigroup Portuguese GBI 58.27 -24.91 -14.51 7.72 3.85 13.45 11.78 -9.57 0.53 2.59 6.54 6.18 0.15 27.15
S&P 500/Citi Pure Value 25.59 -0.81 23.06 55.21 -47.87 -3.69 20.04 13.43 15.30 4.40 11.26 8.14 0.82 19.78
S&P SmallCap 600/Citi Pure Value 21.64 -7.50 29.18 63.58 -41.73 -18.61 21.44 11.58 13.28 6.74 10.71 8.40 0.62 24.88
MSCI EMU 21.17 -17.64 -4.25 31.41 -47.57 19.55 36.29 8.80 -1.51 -8.02 7.33 4.19 0.07 26.76
STOXX Europe TMI 20.06 -11.80 5.10 37.50 -46.75 13.07 35.39 10.11 3.63 -4.01 8.96 5.01 0.26 22.72
MSCI EAFE Small Cap 20.00 -15.94 22.04 46.78 -47.01 1.45 19.31 26.19 7.17 -0.86 11.93 - 0.44 20.12
Russell Micro Cap 19.75 -9.27 28.89 27.48 -39.78 -8.00 16.54 2.57 11.87 1.46 8.42 - 0.62 21.50
S&P MidCap 400/Citi Pure Value 19.62 -5.07 23.19 59.18 -42.58 -3.20 19.31 9.37 11.84 5.04 10.84 8.79 0.65 20.32
Barclays Global High Yield 19.60 3.12 14.82 59.40 -26.89 3.18 13.69 3.59 12.30 10.54 11.63 8.50 1.32 9.10
JPM EMBI Global 18.54 8.46 12.04 28.18 -10.91 6.28 9.88 10.73 12.94 10.47 11.56 10.22 1.89 6.53
NASDAQ 100 18.35 3.66 20.14 54.61 -41.57 19.24 7.28 1.89 13.80 5.89 11.12 - 0.81 17.92
MSCI EM 18.22 -18.42 18.88 78.51 -53.33 39.42 32.14 34.00 4.66 -0.92 16.52 - 0.31 21.80
Russell 2000 Value 18.05 -5.50 24.50 20.58 -28.92 -9.78 23.48 4.71 11.57 3.55 9.50 7.19 0.64 20.17
Wilshire 4500 Completion 17.99 -4.10 28.43 36.99 -39.03 5.39 15.28 10.03 13.27 3.95 10.67 6.43 0.75 18.81
Barclays EM 17.95 6.97 12.84 34.23 -14.75 5.15 9.96 12.27 12.50 10.25 11.63 10.09 1.80 6.66
S&P MidCap 400 17.88 -1.73 26.64 37.38 -36.23 7.98 10.32 12.56 13.62 5.15 10.53 9.14 0.79 18.16
MSCI EAFE Value 17.69 -12.17 3.25 34.23 -44.09 5.96 30.38 13.80 2.19 -4.34 8.57 5.41 0.20 20.56
Wilshire US REIT 17.59 9.24 28.60 28.60 -39.20 -17.55 35.97 13.82 18.21 5.25 11.57 9.08 0.99 18.54
Russell 3000 Value 17.55 -0.10 16.23 19.76 -36.25 -1.01 22.34 6.85 10.92 0.83 7.54 5.38 0.72 16.03
MSCI EAFE 17.32 -12.14 7.75 31.78 -43.38 11.17 26.34 13.54 3.56 -3.69 8.21 4.38 0.27 19.65
S&P MidCap 400/Citi Pure Growth 17.29 0.62 35.16 60.34 -35.17 10.30 4.98 12.06 16.84 10.64 14.01 12.41 0.90 19.18
MSCI EAFE Growth 16.86 -12.11 12.25 29.36 -42.70 16.45 22.33 13.28 4.85 -3.09 7.77 3.18 0.34 19.12
MSCI ACWI Ex USA 16.83 -13.71 11.15 41.45 -45.53 16.65 26.65 16.62 3.87 -2.89 9.74 - 0.29 19.53
MSCI AC Asia Paciûc 16.78 -15.11 17.02 37.59 -41.85 14.29 16.49 23.34 5.07 -1.48 9.73 - 0.37 16.88
Wilshire 5000 Growth 16.78 -0.75 16.57 37.93 -37.91 10.65 9.73 7.43 10.55 2.96 8.04 3.95 0.66 17.26
Russell 3000 16.42 1.03 16.93 28.34 -37.31 5.14 15.72 6.12 11.20 2.04 7.68 4.81 0.74 15.95
Russell 2000 16.35 -4.18 26.85 27.17 -33.79 -1.57 18.37 4.55 12.25 3.56 9.72 5.89 0.66 20.48
S&P SmallCap 600 16.33 1.02 26.31 25.57 -31.07 -0.30 15.12 7.68 14.07 5.14 10.45 7.71 0.78 19.22
MSCI ACWI 16.13 -7.35 12.67 34.63 -42.19 11.66 20.95 10.84 6.63 -1.16 8.11 - 0.45 17.37
Wilshire 5000 Total Market 16.06 0.98 17.16 28.30 -37.23 5.62 15.77 6.38 11.15 2.03 7.85 4.86 0.74 15.80
S&P 500 16.00 2.11 15.06 26.46 -37.00 5.49 15.79 4.91 10.87 1.66 7.10 4.47 0.74 15.30
Russell Top 50 Mega Cap 15.66 4.35 9.47 20.46 -33.85 4.92 18.21 0.82 9.73 1.03 5.55 - 0.70 14.58
S&P 500/Citi Pure Growth 15.43 0.75 27.65 50.85 -38.99 6.64 7.43 7.31 14.07 6.44 10.76 7.97 0.80 18.41
Wilshire 5000 Value 15.24 2.76 17.53 18.77 -36.31 1.11 21.63 5.71 11.65 1.03 7.54 5.48 0.82 14.77
Russell 3000 Growth 15.21 2.18 17.64 37.01 -38.44 11.40 9.46 5.17 11.46 3.15 7.69 3.60 0.74 16.21
Russell 2000 Growth 14.59 -2.91 29.09 34.47 -38.54 7.05 13.35 4.15 12.82 3.49 9.80 4.04 0.67 21.01
MSCI BRIC 14.54 -22.85 9.57 93.12 -59.40 58.87 56.36 44.19 -1.07 -5.36 19.78 - 0.07 24.01
S&P Global 100 13.43 -3.18 5.79 26.71 -36.44 11.38 20.42 5.47 5.13 -1.32 6.68 4.08 0.36 17.62
S&P SmallCap 600/Citi Pure Growth 13.03 5.21 28.74 37.70 -33.10 1.49 9.79 7.10 15.25 7.12 11.73 9.51 0.85 18.65
DJ Industrial Average 10.24 8.38 14.06 22.68 -31.93 8.88 19.05 1.72 10.87 2.62 7.32 5.80 0.82 13.62
Barclays US Credit 9.37 8.35 8.47 16.04 -3.08 5.11 4.26 1.96 8.73 7.65 6.23 6.58 2.27 3.69
Dow Jones Transportation Average 7.55 0.01 26.74 18.58 -21.41 1.43 9.81 11.65 10.88 4.90 10.28 4.72 0.61 19.99
Barclays US Treasury US TIPS 6.98 13.56 6.31 11.41 -2.35 11.64 0.41 2.84 8.90 7.04 6.65 7.32 1.94 4.40
Barclays Municipal 6.78 10.70 2.38 12.91 -2.47 3.36 4.84 3.51 6.57 5.91 5.10 5.42 1.69 3.76
Alerian MLP 4.80 13.88 35.85 76.41 -36.91 12.72 26.07 6.32 17.48 12.53 16.48 15.17 1.26 13.56
Barclays Global Aggregate 4.32 5.64 5.54 6.93 4.79 9.48 6.64 -4.49 5.17 5.44 5.98 5.87 1.02 4.97
Barclays US Aggregate Bond 4.21 7.84 6.54 5.93 5.24 6.97 4.33 2.43 6.19 5.95 5.18 5.96 2.46 2.42
Barclays US Government 2.02 9.02 5.52 -2.20 12.39 8.66 3.48 2.65 5.48 5.23 4.66 5.69 1.60 3.32
Barclays Treasury 1.99 9.81 5.87 -3.57 13.74 9.01 3.08 2.79 5.84 5.40 4.75 5.74 1.54 3.68
Barclays Global Treasury 1.83 6.33 5.90 2.63 10.23 10.57 6.44 -6.66 4.67 5.34 6.08 5.89 0.82 5.65
Citigroup WGBI 1.65 6.35 5.17 2.55 10.89 10.95 6.12 -6.88 4.37 5.27 6.04 5.97 0.78 5.59
Dow Jones Utilities Average 1.64 19.71 6.46 12.47 -27.84 20.11 16.63 25.14 9.01 1.01 12.00 7.49 0.89 10.18
S&P GSCI 0.08 -1.18 9.03 13.48 -46.49 32.67 -15.09 25.55 2.54 -8.12 2.75 3.18 0.22 20.45
DJ UBS Commodity -1.06 -13.32 16.83 18.91 -35.65 16.23 2.07 21.36 0.07 -5.17 4.09 4.04 0.09 17.91
Citigroup Japanese GBI -9.43 7.74 17.54 -1.76 27.82 9.47 -0.65 -12.56 4.67 7.57 4.78 4.74 0.50 9.82
S&P Diversiûed Trends Indicator -11.21 -5.58 -2.82 -5.88 8.29 10.66 5.74 7.55 -6.60 -3.65 - - -0.86 7.70
HSBC Global Gold -11.85 -16.68 33.57 33.36 -23.93 20.70 17.28 30.28 -0.64 -0.09 9.32 8.64 0.10 26.64
MSCI Argentina* -38.86 -42.64 70.06 61.12 -55.32 -5.36 66.07 59.68 -15.82 -15.56 10.30 -2.25 -0.28 36.74
Source: Morningstar. (Nasdaq-100 index data provided by Morningstar and Nasdaq OMX.) Data as of December 31, 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
March / April 2013

Morningstar Index Funds U.S. Style Overview XXXX –XXXX, 2011
Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions
March/April 2013
Total Return % Annualized Return %
Fund Name Ticker Assets Exp Ratio 3-Mo 2012 2011 2010 3-Yr 5-Yr 10-Yr 15-Yr P/E Std Dev Yield
Vanguard Total Stock Mkt, Inv Shrs VTSMX 78,935.9 0.18 0.15 16.25 0.96 17.09 11.18 2.18 7.83 4.87 15.5 15.98 2.02
Vanguard Institutional, Instl Shrs VINIX 68,055.1 0.04 -0.39 15.98 2.09 15.05 10.85 1.69 7.11 4.51 15.3 15.30 2.17
Vanguard Total Stock Market, Adm Shrs VTSAX 59,771.5 0.06 0.18 16.38 1.08 17.26 11.32 2.29 7.93 4.95 15.5 16.00 2.13
Vanguard 500, Adm Shrs VFIAX 59,749.3 0.05 -0.39 15.96 2.08 15.05 10.85 1.68 7.09 4.47 15.3 15.31 2.17
Vanguard Institutional, Instl+ Shrs VIIIX 49,286.1 0.02 -0.37 16.00 2.12 15.07 10.88 1.72 7.14 4.54 15.3 15.30 2.19
Vanguard Total Bond Mkt II, Inv Shrs VTBIX 45,757.9 0.12 0.00 3.91 7.59 6.41 5.96 - - - - 2.51 2.30
Vanguard Total Stock Mkt, Instl Shrs VITSX 39,366.6 0.05 0.21 16.42 1.09 17.23 11.32 2.31 7.96 4.99 15.5 15.99 2.14
Vanguard Total Intl Stock, Inv Shrs VGTSX 37,659.4 0.22 6.66 18.14 -14.56 11.12 3.90 -3.03 9.41 5.11 11.8 20.17 2.92
Vanguard Total Bond Mkt, Adm Shrs VBTLX 35,532.6 0.10 0.13 4.15 7.69 6.54 6.11 5.91 5.17 5.81 - 2.54 2.65
Vanguard 500, Sig Shrs VIFSX 27,304.4 0.05 -0.38 15.97 2.08 15.05 10.85 1.68 7.05 4.44 15.3 15.30 2.17
Vanguard 500, Inv Shrs VFINX 24,821.4 0.17 -0.42 15.82 1.97 14.91 10.72 1.57 6.99 4.39 15.3 15.30 2.05
Vanguard Total Bond Mkt, Instl Shrs VBTIX 22,493.5 0.07 0.13 4.18 7.72 6.58 6.15 5.95 5.21 5.87 - 2.54 2.68
Vanguard Instl Total Stock Mkt, Instl+ Shrs VITPX 20,731.2 0.03 0.24 16.53 1.11 17.25 11.37 2.37 8.04 - 15.5 15.99 2.17
Fidelity Spartan 500, Adv Cl FUSVX 20,409.3 0.05 -0.38 15.97 2.06 15.01 10.83 1.65 7.05 4.39 15.1 15.30 2.14
Vanguard Total Bond Mkt II, Instl Shrs VTBNX 18,704.2 0.05 0.02 3.99 7.67 6.47 6.03 - - - - 2.51 2.37
Fidelity Spartan 500, Instl Cl FXSIX 16,513.0 0.04 -0.39 15.96 2.09 14.98 10.82 1.63 7.03 4.38 15.1 15.30 2.16
Vanguard Total Intl Stock, Adm Shrs VTIAX 16,418.7 0.18 6.69 18.21 -14.52 11.04 3.91 -3.03 9.41 5.11 11.8 20.12 2.98
Vanguard Total Bond Mkt, Instl+ Shrs VBMPX 16,294.1 0.05 0.14 4.20 7.74 6.57 6.16 5.89 5.12 5.77 - 2.54 2.70
T. Rowe Price Equity 500 PREIX 15,443.4 0.30 -0.43 15.68 1.87 14.71 10.57 1.45 6.83 4.21 15.3 15.30 2.01
Vanguard Total Intl Stock, Instl+ Shrs VTPSX 13,840.4 0.10 6.69 18.30 -14.49 11.09 3.97 -3.00 9.43 5.12 11.8 20.15 3.03
Schwab S&P 500 SWPPX 12,758.7 0.09 -0.38 15.91 2.07 14.97 10.80 1.67 7.05 4.38 14.7 15.25 2.21
Vanguard Total Bond Mkt, Sig Shrs VBTSX 12,585.5 0.10 0.13 4.15 7.69 6.54 6.11 5.91 5.14 5.79 - 2.54 2.65
Vanguard Total Bond Mkt, Inv Shrs VBMFX 11,794.4 0.22 0.10 4.05 7.56 6.42 6.00 5.80 5.07 5.74 - 2.54 2.55
Fidelity Spartan 500, Inv Cl FUSEX 10,370.3 0.10 -0.39 15.93 2.03 14.98 10.79 1.61 7.03 4.37 15.1 15.30 2.11
Vanguard Total Stock Mkt, Sig Shrs VTSSX 8,025.3 0.06 0.19 16.39 1.09 17.23 11.31 2.29 7.90 4.92 15.5 15.99 2.14
Fidelity Series 100 FOHIX 8,015.0 0.20 -1.92 15.77 2.98 12.39 10.24 1.11 - - 14.7 14.96 8.42
Vanguard Total Intl Stock, Instl Shrs VTSNX 7,882.1 0.13 6.69 18.28 -14.51 11.09 3.95 -3.00 9.43 5.12 11.8 20.15 3.01
Vanguard Balanced, Adm Shrs VBIAX 7,406.9 0.10 0.19 11.49 4.29 13.29 9.62 4.26 7.18 5.73 15.5 9.09 2.15
Fidelity Spartan Total Mkt, Adv Cl FSTVX 7,325.5 0.06 0.19 16.35 1.01 17.44 11.34 2.18 7.87 4.89 15.3 15.91 1.89
Vanguard Emerging Mkts Stock, Adm Shrs VEMAX 7,300.5 0.20 6.84 18.86 -18.67 18.99 4.78 -0.87 16.30 9.50 9.3 22.25 2.20
Vanguard Mid-Cap, Instl Shrs VMCIX 7,057.1 0.08 2.84 16.01 -1.96 25.67 12.64 3.18 10.07 - 16.8 17.71 1.43
Vanguard REIT, Adm Shrs VGSLX 6,916.8 0.10 2.49 17.69 8.62 28.49 17.99 6.07 11.68 8.86 43.3 18.29 3.56
Vanguard Mid-Cap, Adm Shrs VIMAX 6,895.1 0.10 2.83 15.99 -1.97 25.59 12.61 3.15 10.02 - 16.8 17.71 1.41
Vanguard Small-Cap, Adm Shrs VSMAX 6,541.2 0.16 2.79 18.24 -2.69 27.89 13.74 5.12 10.96 6.92 16.9 20.05 1.86
Vanguard Intermed-Tm Bond, Adm Shrs VBILX 6,251.7 0.11 0.46 7.02 10.73 9.49 9.07 7.81 6.34 6.84 - 4.03 3.13
Spartan U.S. Bond, Inv Shrs FBIDX 6,227.7 0.22 0.06 4.06 7.68 6.29 6.00 5.64 4.94 5.76 - 2.48 2.35
Vanguard Growth, Instl Shrs VIGIX 6,188.5 0.08 -1.08 17.04 1.89 17.17 11.80 3.35 7.53 4.61 17.9 16.49 1.53
PIMCO EM Fundamental IndexPLUS, Instl Cl PEFIX 6,097.4 1.25 6.20 28.19 -16.81 25.86 10.31 - - - - 23.38 7.43
Vanguard Extended Mkt, Adm Shrs VEXAX 5,969.6 0.14 3.17 18.48 -3.59 27.57 13.37 4.24 10.73 6.51 16.8 19.27 1.64
Vanguard Small-Cap, Instl Shrs VSCIX 5,954.7 0.14 2.81 18.26 -2.65 27.95 13.78 5.17 11.01 6.99 16.9 20.06 1.87
Vanguard Growth, Adm Shrs VIGAX 5,774.2 0.10 -1.11 17.01 1.87 17.12 11.77 3.31 7.49 4.56 17.9 16.49 1.51
Vanguard Short-Term Bond, Sig Shrs VBSSX 5,570.3 0.11 0.15 2.05 3.08 4.03 3.05 3.80 3.67 4.66 - 1.38 1.54
Vanguard Balanced, Instl Shrs VBAIX 5,554.3 0.08 0.19 11.51 4.31 13.34 9.65 4.30 7.22 5.76 15.5 9.10 2.16
Vanguard Developed Mkts, Instl Shrs VIDMX 5,520.9 0.08 7.51 19.00 -12.44 8.76 4.26 -3.24 8.42 - 11.8 20.06 3.61
Vanguard Extended Mkt, Instl Shrs VIEIX 5,495.8 0.12 3.16 18.50 -3.57 27.59 13.39 4.28 10.78 6.58 16.8 19.28 1.65
Vanguard Extended Mkt, Instl+ Shrs VEMPX 5,476.7 0.10 3.17 18.52 -3.57 27.37 13.33 4.16 10.62 6.43 16.8 19.27 1.68
Vanguard Mid-Cap, Instl+ Shrs VMCPX 5,428.4 0.06 2.83 16.03 -1.91 25.67 12.67 3.20 10.07 - 16.8 17.70 1.45
Fidelity Spartan Extended Mkt, Adv Cl FSEVX 5,240.8 0.07 2.82 18.05 -3.79 28.62 13.46 4.22 10.72 6.39 16.5 19.02 1.74
TIAA-CREF Equity, Instl Cl TIEIX 4,892.5 0.07 0.19 16.33 0.99 16.88 11.15 2.04 7.63 - 15.3 15.93 1.74
Schwab 1000 SNXFX 4,851.3 0.29 0.00 15.77 1.27 15.96 10.78 1.71 7.21 4.59 14.8 15.54 2.02
Vanguard Mid-Cap, Sig Shrs VMISX 4,834.2 0.10 2.84 16.02 -1.99 25.62 12.62 3.15 10.04 - 16.8 17.72 1.42
Fidelity Spartan US Bond, Adv Cl FSITX 4,402.6 0.10 0.07 4.17 7.71 6.29 6.05 5.66 4.95 5.77 - 2.49 2.45
Vanguard Short-Term Bond, Adm Shrs VBIRX 4,401.2 0.11 0.15 2.05 3.08 4.03 3.05 3.80 3.70 4.68 - 1.38 1.54
Vanguard Value, Instl Shrs VIVIX 4,368.8 0.08 0.88 15.20 1.17 14.49 10.09 0.49 7.49 4.64 13.4 15.03 2.75
Fidelity Series Inüation-Protected Bond FSIPX 4,347.3 0.20 0.28 4.77 8.63 5.06 6.14 - - - - 2.85 0.07
Vanguard Small Cap, Sig Shrs VSISX 4,328.6 0.16 2.78 18.25 -2.68 27.85 13.74 5.12 10.92 6.88 16.9 20.07 1.86
Northern Stock NOSIX 4,216.2 0.11 -0.40 15.86 1.89 14.82 10.67 1.46 6.79 4.09 15.3 15.31 2.05
ING US Stock, Cl I INGIX 4,129.6 0.26 -0.38 15.79 1.81 14.74 10.59 1.43 - - 15.2 15.32 1.83
Vanguard Total Intl Stock, Sig Shrs VTSGX 4,009.5 0.18 6.69 18.21 -14.52 11.06 3.92 -3.02 9.42 5.11 11.8 20.15 2.99
TIAA-CREF Bond, Instl Cl TBIIX 3,983.9 0.13 0.18 4.10 7.65 6.32 6.01 - - - - 2.42 1.98
Source: Morningstar. Data as of December 31, 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.
www.journalofindexes.com March / April 2013 59

Morningstar U.S. Style Overview Jan. 1-Dec. 31, 2012
Trailing Returns %
3-Month YTD 1-Yr 3-Yr 5-Yr 10-Yr
Morningstar Indexes
US Market 2.61 16.17 16.17 10.91 2.07 7.91
Large Cap 1.69 15.93 15.93 10.15 1.30 6.84
Mid Cap 5.33 16.98 16.98 12.75 3.65 10.46
Small Cap 4.92 16.39 16.39 12.85 4.97 10.92
Morningstar Market Barometer YTD Return %
US Market
16.17
Value
14.11
Core
17.53
Growth
17.17
US Value 4.19 14.11 14.11 9.98 0.26 7.32
US Core 3.62 17.53 17.53 11.68 3.35 8.67
US Growth 0.25 17.17 17.17 11.06 2.40 7.47
Large Cap
15.93
12.81 17.48 17.87
Large Value 3.04 12.81 12.81 9.45 –1.31 6.15
Large Core 2.94 17.48 17.48 10.72 2.54 7.81
Large Growth –0.68 17.87 17.87 10.23 2.40 6.18
Mid Cap
16.98
17.47 17.86 15.72
Mid Value 7.51 17.47 17.47 10.89 3.75 9.83
Mid Core 5.61 17.86 17.86 14.60 5.27 10.74
Mid Growth 3.04 15.72 15.72 12.68 1.85 10.54
Small Cap
16.39
18.19 16.60 14.41
Small Value 6.95 18.19 18.19 12.97 6.92 11.68
Small Core 5.27 16.60 16.60 11.88 4.73 10.57
Small Growth 2.58 14.41 14.41 13.69 3.30 10.32
–8.00 –4.00 0.00 +4.00 +8.00
Sector Index YTD Return %
Communication 32.39
Financial Services 29.05
Industry Leaders & Laggards YTD Return %
Residential Construction 85.84
Oil & Gas Refining & 80.48
Biggest Influence on Style Index Performance
Best Performing Index
YTD
Return %
Small Value 18.19
Constituent
Weight %
Consumer Cyclical 24.56
Healthcare 19.32
Real Estate 18.61
Basic Materials 16.46
Industrials 15.28
Technology 13.30
Consumer 10.08
Energy 4.32
Utilities 2.19
1-Year
Consumer Electronics 77.16
Building Materials 58.57
Real Estate - General 51.75
Broadcasting - Radio 51.68
–9.02 Business Equipment
–14.12 Gold
–17.53 Electronic Gaming & Multimedia
–21.54 Industrial Metals & Minerals
–26.67 Coal
–38.23 Education & Training Services
3-Year
Terex Corp 108.07 0.49
US Airways Group 166.27 0.27
Tenet Healthcare 58.24 0.73
First American Financial Corp 93.66 0.44
Community Health Systems 77.65 0.52
Worst Performing Index
Large Value 12.81
Bank of America Corp 109.83 1.70
JP Morgan Chase & Co 36.18 3.82
General Electric Co 21.25 5.72
Citigroup Inc 50.55 2.31
Pfizer Inc 20.41 5.03
5-Year
Value
Core
Growth
Value
Core
Growth
Value
Core
Growth
Large Cap
12.81
17.48
17.87
Large Cap
9.45
10.72
10.23
Large Cap
–1.31
2.54
2.40
Mid Cap
17.47
17.86 15.72
Mid Cap
10.89
14.60 12.68
Mid Cap
3.75
5.27 1.85
Small Cap
18.19
16.60 14.41
Small Cap
12.97
11.88 13.69
Small Cap
6.92
4.73 3.30
–20 –10 0 +10 +20
–20 –10 0 +10 +20
–20 –10 0 +10 +20
Source: Morningstar. Data as of Dec. 31, 2012
Source: Morningstar. Data as of Feb. 29, 2012.
Notes and Disclaimer: ©2013 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.
60
March / April 2013

Exchange-Traded Dow Jones U.S. Industry Funds Review Corner
1-Year Performance
Cumulative Performance
Comparison Period Ending December 31, 2012 Comparison Period: December 31, 1991 to December 31, 2012
130
1600
125
1400
120
1200
115
110
105
100
95
90
12/11 1/12 2/12 3/12 4/12 5/12 6/12 7/12 8/12 9/12 10/12 11/12 12/12
Basic Materials Consumer Goods Consumer Services Financials
Health Care Industrials Oil & Gas Technology
Telecommunications Utilities Dow Jones U.S. Index
1000
800
600
400
200
0
12/91
7/92
2/93
9/93
4/94
11/94
6/95
1/96
8/96
3/97
10/97
5/98
12/98
7/99
2/00
9/00
4/01
11/01
6/02
1/03
8/03
3/04
10/04
5/05
12/05
7/06
2/07
9/07
4/08
11/08
6/09
1/10
8/10
3/11
10/11
5/12
12/12
Basic Materials Consumer Goods Consumer Services Financials
Health Care Industrials Oil & Gas Technology
Telecommunications Utilities Dow Jones U.S. Index
Short-Term Performance Statistics
Long-Term Performance Statistics
Comparison Period Ending December 31, 2012 Comparison Period Ending December 31, 2012
Avail. Hist.
1-Month 3-Month 6-Month 1-Year YTD 3-Year 5-Year 7-Year 10-Year (12/31/91)
Dow Jones U.S. Basic Materials Index 4.03% 3.14% 8.61% 10.49% 10.49% Dow Jones U.S. Basic Materials Index
7.47% 0.21% 6.74% 9.78% 7.46%
Dow Jones U.S. Consumer Goods Index -0.98% 1.63% 6.14% 12.80% 12.80% Dow Jones U.S. Consumer Goods Index 13.62% 6.18% 7.89% 9.33% 8.90%
Dow Jones U.S. Consumer Services Index 0.16% 1.18% 8.15% 24.17% 24.17% Dow Jones U.S. Consumer Services Index 18.07% 8.76% 7.09% 8.77% 8.51%
Dow Jones U.S. Financials Index 3.95% 5.10% 11.45% 26.85% 26.85% Dow Jones U.S. Financials Index
7.61% -6.26% -4.74% 1.29% 8.03%
Dow Jones U.S. Health Care Index -0.27% -0.67% 6.27% 19.26% 19.26% Dow Jones U.S. Health Care Index
11.68% 5.53% 6.12% 7.44% 8.85%
Dow Jones U.S. Industrials Index 2.64% 4.58% 9.12% 17.87% 17.87% Dow Jones U.S. Industrials Index
13.80% 2.35% 5.48% 9.05% 8.09%
Dow Jones U.S. Oil & Gas Index 0.91% -2.67% 7.39% 4.71% 4.71% Dow Jones U.S. Oil & Gas Index
9.27% -0.35% 7.20% 13.77% 11.52%
Dow Jones U.S. Technology Index 0.16% -6.41% -0.25% 12.08% 12.08% Dow Jones U.S. Technology Index
8.12% 3.50% 6.09% 9.16% 10.61%
Dow Jones U.S. Telecommunications Index -0.48% -5.53% 2.22% 18.79% 18.79% Dow Jones U.S. Telecommunications Index 13.29% 1.39% 7.07% 7.03% 5.49%
Dow Jones U.S. Utilities Index 0.05% -2.56% -2.27% 1.76% 1.76% Dow Jones U.S. Utilities Index
9.33% 0.52% 5.61% 10.11% 7.26%
Dow Jones U.S. Index 1.12% 0.14% 6.37% 16.32% 16.32% Dow Jones U.S. Index
11.20% 2.16% 4.53% 7.82% 8.29%
Annualized Risk
Risk / Return
Comparison Period Ending December 31, 2012 Comparison Period: December 31, 1991 to December 31, 2012
Avail. Hist.
3-Year 5-Year 7-Year 10-Year (12/31/91)
25.53% 31.29% 27.48% 24.63% 22.22%
Dow Jones U.S. Consumer Goods Index 11.49% 15.45% 13.49% 12.58% 12.98%
Dow Jones U.S. Consumer Services Index 15.59% 19.65% 17.37% 16.04% 17.12%
Dow Jones U.S. Financials Index 19.75% 29.41% 25.57% 22.33% 20.33%
Dow Jones U.S. Health Care Index 12.05% 15.80% 14.13% 12.81% 14.75%
Dow Jones U.S. Industrials Index 19.77% 24.81% 21.50% 19.11% 17.84%
Dow Jones U.S. Oil & Gas Index 22.39% 24.56% 23.00% 21.76% 19.64%
Dow Jones U.S. Technology Index 19.47% 23.10% 20.80% 19.63% 27.62%
Dow Jones U.S. Telecommunications Index 14.15% 18.36% 17.10% 16.30% 19.89%
Dow Jones U.S. Utilities Index 9.54% 14.54% 13.59% 13.15% 14.62%
Dow Jones U.S. Index 15.79% 19.57% 17.06% 15.21% 15.18%
Annualized Return
14.0%
12.0%
10.0%
8.0%
6.0%
4.0%
2.0%
0.0%
10.0% 15.0% 20.0% 25.0% 30.0%
Annualized Risk
Basic Materials Consumer Goods Consumer Services Financials
Health Care Industrials Oil & Gas Technology
Telecommunications Utilities Dow Jones U.S. Index
Copyright © 2013 by S&P Dow Jones Indices LLC, a subsidiary of The McGraw-Hill Companies. All rights reserved. “Dow Jones” is a registered trademark of Dow Jones Trademark Holdings LLC (“Dow Jones”). STANDARD & POOR’S and S&P
are registered trademarks of Standard & Poor’s Financial Services LLC.
The Dow Jones U.S. Index and the Dow Jones U.S. Industry Indices were first calculated in February 2000. All information presented prior to this date is back-tested. Back-tested performance is not actual performance, but is hypothetical. The backtest
calculations are based on the same methodology that was in effect when the index was officially launched. Complete index methodology details are available at www.spindices.com. Past performance is not an indication of future results.
Prospective application of the methodology used to construct the Dow Jones U.S. Index may not result in performance commensurate with the back-test returns shown. The back-test period does not necessarily correspond to the entire available
history of the index. Please refer to the methodology paper for the index, available at www.spdji.com or www.spindices.com for more details about the index, including the manner in which it is rebalanced, the timing of such rebalancing, criteria for
additions and deletions, as well as all index calculations. It is not possible to invest directly in an Index.
Another limitation of back-tested hypothetical information is that generally the back-tested calculation is prepared with the benefit of hindsight. Back-tested data reflect the application of the index methodology and selection of index constituents in
hindsight. No hypothetical record can completely account for the impact of financial risk in actual trading. For example, there are numerous factors related to the equities (or fixed income, or commodities) markets in general which cannot be, and
have not been accounted for in the preparation of the index information set forth, all of which can affect actual performance.
The index returns shown do not represent the results of actual trading of investor assets. S&P/Dow Jones Indices LLC maintains the indices and calculates the index levels and performance shown or discussed, but does not manage actual assets.
Index returns do not reflect payment of any sales charges or fees an investor would pay to purchase the securities they represent. The imposition of these fees and charges would cause actual and back-tested performance to be lower than the
performance shown. In a simple example, if an index returned 10% on a US $100,000 investment for a 12-month period (or US$ 10,000) and an actual asset-based fee of 1.5% were imposed at the end of the period on the investment plus accrued
interest (or US$ 1,650), the net return would be 8.35% (or US$ 8,350) for the year. Over 3 years, an annual 1.5% fee taken at year end with an assumed 10% return per year would result in a cumulative gross return of 33.10%, a total fee of US$
5,375, and a cumulative net return of 27.2% (or US$ 27,200).
Source: S&P Dow Jones Indices; data as of December 31, 2012.
For more information, please visit the S&P Dow Jones Indices web site at www.spdji.com.
www.journalofindexes.com March / April 2013 61

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 December 31, 2012.
Fund Name Ticker ER 1-Mo 3-Mo Mkt Cap P/E Inception Assets
PIMCO Total Return BOND 0.55 -0.02 1.60 - - 2/29/2012 3,870.5
SPDR Barclays Short-Term HiYld Bond SJNK 0.40 1.32 2.56 - - 3/14/2012 621.6
iShares Aaa-A Rated Corporate Bond QLTA 0.15 -0.04 0.48 - - 2/14/2012 316.5
iShares Core MSCI EAFE IEFA 0.14 4.19 - 22,472 12.4 10/18/2012 279.4
iShares Core MSCI Emerging Markets IEMG 0.18 6.78 - 13,804 9.8 10/18/2012 261.0
iShares MSCI Glb Metals/Mining Prod PICK 0.39 9.15 10.96 23,810 11.5 1/31/2012 253.6
Market Vectors Intl HiYld Bond IHY 0.40 2.80 5.80 - - 4/2/2012 209.9
iShares Emerging Markets HiYld Bond EMHY 0.65 2.09 5.65 - - 4/3/2012 198.3
iShares Barclays US Treasury Bond GOVT 0.15 -0.56 -0.24 - - 2/14/2012 146.2
UBS FI Enh Big-Cap Growth ETN FBG 1.20 -0.57 -3.07 - - 6/8/2012 140.2
First Trust North Amer Energy Infrastr EMLP 0.95 0.23 -1.65 8,303 20.1 6/20/2012 123.7
Market Vectors Mstar Wide Moat MOAT 0.49 2.08 3.55 13,221 16.3 4/24/2012 115.4
PIMCO Glb Adv Inü-Linked Bond Strat ILB 0.60 2.19 2.71 - - 4/30/2012 110.4
Vanguard Short-Tm Inüation-Prot Sec VTIP 0.10 0.06 - - - 10/12/2012 108.3
Market Vectors Pref Sec ex Financials PFXF 0.40 1.52 2.00 4,397 7.9 7/16/2012 100.1
Yorkville High Income MLP YMLP 0.82 -2.47 -6.32 1,683 12.0 3/13/2012 98.1
WisdomTree Emrg Mkts Corp Bond EMCB 0.60 1.19 3.56 - - 3/8/2012 96.4
PowerShares S&P Emrg Mkts Low Vol EELV 0.29 5.54 6.47 5,478 11.0 1/13/2012 88.3
iShares Morningstar Multi-Asset Inc IYLD 0.60 -0.08 0.37 10,295 11.1 4/3/2012 86.3
ALPS Sector Dividend Dogs SDOG 0.40 0.25 0.25 15,430 16.1 6/29/2012 70.1
Source: Morningstar. Data as of December 31, 2012. ER is expense ratio. 1-Mo is 1-month. 3-Mo is 3-month.
Mkt Cap is market cap. P/E is price-to- earnings ratio.
Selected ETFs In Registration
Direxion Daily European Equity Bull 3X
EGShares Beyond BRICs Emrg Asia Infrastr
ETFS Physical Zinc
First Trust Mstar Diversiûed Futures
Forensic Accounting ETF
Global X Risk Parity
Guggenheim Intl High Dividend
IQ Bear Industry Leaders US Equity
iShares MSCI USA Risk Weighted
LocalShares Nashville
Market Vectors Non-Agency RMBS
Pimco EM Agg US$-Denominated Bond
PowerShares Fundamental EM Local Debt
ProShares Global Direct Infrastructure
SPDR Russell 1000 Low Volatility
United States Nat Gas Double Inverse
Vanguard Total International Bond
VelocityShares Russia Select DR
Yorkville High Inc Infrastructure MLP
Zacks MLP
Source: IndexUniverse.com’s ETF Watch
Largest U.S.-listed ETFs Sorted By Total Net Assets In $US Millions
Total Return % Annualized Return %
Fund Name Ticker Exp Ratio Assets 3-Mo 2012 2011 2010 3-Yr 5-Yr Mkt Cap P/E Std Dev Yield
SPDR S&P 500 SPY 0.09 123,001.0 -0.38 15.99 1.89 15.06 10.79 1.66 54,844 15.0 15.28 2.18
SPDR Gold GLD 0.40 72,239.3 -5.74 6.60 9.57 29.27 14.72 14.46 - - 18.68 -
Vanguard MSCI Emerging Markets VWO 0.20 59,875.2 7.83 19.20 -18.75 19.46 4.98 -0.63 19,047 9.3 23.69 2.20
iShares MSCI Emerging Markets EEM 0.69 48,189.6 8.00 19.10 -18.82 16.51 4.05 -0.55 20,339 9.5 24.08 1.71
iShares MSCI EAFE EFA 0.34 38,814.7 8.44 18.82 -12.25 8.15 4.08 -3.33 32,256 12.4 20.18 3.14
iShares Core S&P 500 IVV 0.07 34,911.5 -0.23 16.06 1.86 15.09 10.81 1.64 54,822 15.0 15.25 2.10
PowerShares QQQ QQQ 0.20 30,416.9 -4.48 18.11 3.38 19.91 13.55 5.71 77,323 16.4 17.96 1.26
iShares iBoxx $ Inv Gr Corp Bond LQD 0.15 25,350.4 0.59 10.58 9.73 9.33 9.88 8.06 - - 5.34 3.83
Vanguard Total Stock Market VTI 0.06 24,270.5 0.24 16.46 0.97 17.42 11.35 2.32 32,918 15.5 15.98 2.14
iShares Barclays TIPS Bond TIP 0.20 22,284.7 0.35 6.39 13.28 6.14 8.55 6.75 - - 4.42 2.21
Vanguard Total Bond Market BND 0.10 17,968.3 -0.01 3.89 7.92 6.20 5.99 5.78 - - 2.48 2.72
iShares Russell 1000 Growth IWF 0.20 16,907.0 -1.20 15.22 2.33 16.52 11.17 3.02 45,094 18.0 15.88 1.66
iShares Russell 2000 IWM 0.23 15,997.1 1.92 16.69 -4.44 26.93 12.28 3.68 1,044 16.4 20.53 2.00
iShares iBoxx $ HiYld Corp Bond HYG 0.50 15,972.3 3.33 11.66 6.77 11.89 10.08 7.20 - - 9.37 6.63
Vanguard REIT VNQ 0.10 15,406.7 2.51 17.63 8.62 28.37 17.93 6.11 8,100 43.3 18.28 3.56
iShares Core Total US Bond Market AGG 0.08 15,335.8 -0.02 3.76 7.69 6.37 5.93 5.72 - - 2.58 2.54
iShares Russell 1000 Value IWD 0.20 14,536.2 1.60 17.46 0.12 15.49 10.74 0.56 37,841 13.5 15.74 2.30
iShares Core S&P Mid-Cap IJH 0.15 13,558.4 3.65 17.79 -2.18 26.72 13.45 5.13 3,636 18.0 18.09 1.43
SPDR Barclays High Yield Bond JNK 0.40 12,502.3 3.48 13.46 5.12 14.20 10.85 7.11 - - 9.62 6.78
Vanguard Dividend Appreciation VIG 0.13 12,042.9 0.64 11.65 6.16 14.74 10.79 3.59 42,697 15.5 12.84 2.37
iShares Gold Trust IAU 0.25 11,645.3 -5.74 6.89 9.57 29.46 14.88 14.57 - - 18.72 -
Vanguard MSCI EAFE VEA 0.12 10,981.8 8.21 18.57 -12.30 8.35 4.05 -3.14 29,401 11.8 20.46 3.00
SPDR DJ Industrial Average Trust DIA 0.17 10,923.4 -1.89 9.94 8.06 14.01 10.64 2.44 114,113 13.6 13.65 2.53
iShares S&P US Preferred Stock PFF 0.48 10,747.3 1.59 18.20 -2.00 13.81 9.65 6.90 - - 8.75 6.02
SPDR S&P MidCap 400 MDY 0.25 10,616.7 3.67 17.82 -2.13 26.28 13.35 4.96 3,585 17.8 18.06 1.14
Source: Morningstar. Data as of December 31, 2012. Exp Ratio is expense ratio. 3-Mo is 3-month. Mkt Cap is market cap. 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.
62
March / April 2013

News continued from page 15
Malkiel Named CIO
Of Wealthfront
Burton Malkiel, the investmentworld
legend and author of the seminal
work on indexing “A Random
Walk Down Wall Street,” was named
chief investment officer of investment
advisor firm Wealthfront.
Wealthfront has a minimum account
size of $5,000 and manages clients’ first
$25,000 free. The firm provides its services
online using the tenets of modern
portfolio theory as the backbone of its
asset allocation plans.
As CIO, Malkiel will help Wealthfront
improve its services, including the
choice of asset classes, the way it allocates
among different classes, the
choice of securities and the methods
by which it evaluates risk and applies
those evaluations to client portfolios,
the company said in a blog it published
on its website in November 2012.
Wealthfront’s advisory fee, which
is separate from fund expense ratios,
is considerably lower than what many
advisors charge. The firm puts together
its low-cost portfolios using only ETFs.
Malkiel, who is also a Princeton
University professor emeritus of economics,
will additionally meet with
select groups of Wealthfront clients,
and offer investing insights to clients
and the public, the registered investment
advisor said.
Schapiro Leaves SEC,
Succeeded By Walter
The Securities and Exchange
Commission is under new leadership
after the year-end departure of
Chairwoman Mary Schapiro, one of the
longest-serving heads of the agency. Her
successor for a year is Elisse Walter, who
has been an SEC commissioner since
2008 and whose term as a commissioner
comes to a close at the end of 2013.
Walter previously served as an executive
charged with regulatory policy
and programs at the Financial Industry
Regulatory Authority, and also led an
in-depth review of the municipal securities
markets at the SEC, according to
her bio on Wikipedia.
Appointed by President Barack
Obama and unanimously confirmed by
the U.S. Senate, Schapiro first took the
helm of the SEC in January 2009 in the
wake of the credit crisis that sent the
U.S. economy into its worst downturn
since the Great Depression. As head
of the SEC, Schapiro has also served
on the Financial Stability Oversight
Council, the FHFA Oversight Board, the
Financial Stability Oversight Board and
the IFRS Foundation Monitoring Board.
The White House statement didn’t
mention a successor for Walter in the
late November statement it issued
on the transition.
Changes To Morningstar’s
Passive Fund Research Team
Morningstar said in early December
that it had appointed Ben
Johnson global director of passive
funds research. Johnson was already
overseeing the firm’s passive fund
research teams for Europe and Asia,
and the new role means he now also
oversees the North American team.
Johnson earned a bachelor’s
degree in economics at the University
of Wisconsin. He was hired
by Morningstar in 2006.
Paul Justice previously had responsibility
for Morningstar’s passive fund
research team in North America;
currently, he is focused on research
aimed at institutional investors.
Goltz continued from page 35
Malkiel, B.G. 1995. Returns from investing in equity mutual funds 1971-1991. Journal of Finance 50(2): 547-572.
Markowitz, H. 1959. Portfolio selection: efficient diversification of investments. John Wiley & Sons, Inc., New York and Chapman & Hall, Limited, London.
Perold, A.F. 2007. Fundamentally flawed indexing. Financial Analysts Journal 63(6): 31-37.
Ranaldo, A. and R. Häberle. 2007. Wolf in sheep’s clothing: the active investment strategies behind index performance. European Financial Management 14(1): 55-81.
Sharpe, W.F. 1964. Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance. 19(3): 425-441.
Siegel, L.B. 2003. Benchmarks and investment management. The Research Foundation of the Association for Investment Management and Research, Charlottesville, Virginia.
Strongin, S., M. Petsch and G. Sharenow. 2000. Beating benchmarks. Journal of Portfolio Management 26(4): 11-27.
Tabner, I. 2007. Benchmark concentration: Capitalization weights versus equal weights in the FTSE 100 index. Working paper. University of Stirling.
Endnotes
1 Further, investors have different profiles and investment contexts (liability constraints, income risk, etc.), which makes a unique reference somewhat questionable.
2 Data taken from press releases on each provider’s website. Note that, more often than not, a press release announcing the launch of an index actually refers to a new index
series being launched, which may contain several indexes such as different indexes for geographic segments. For the purpose of our analysis, we count the number of
announcements by the index provider and hence tend to capture the launch of new index series rather than of individual indexes.
3 Information on index launches was only available on the S&P website going back to Jan. 1, 2012.
4 The largest category was institutional investment management professionals, including asset owners and third-party asset managers with a focus on institutional clients,
making up 80 percent of respondents. Eighty-one percent of respondents were from the United States and the remainder from Canada.
5 For equity indexes, see Haugen and Baker [1991]; Grinold [1992]; Amenc, Goltz and Le Sourd [2006]; Hsu [2006]; Ranaldo and Häberle [2007]; Tabner [2007]; Malevergne et
al. [2009]; Fuller et al. [2010]; Goltz and Le Sourd [2010] among others.
6 See, e.g., Kamp (2008).
7 While they are related concepts, objectivity and transparency do not necessarily go together. For example, an index can be completely transparent about data, history and
the fact that it has delegated all decision-making responsibilities, not to an objective set of rules, but to a committee; similarly, an index can have very objective, systematic
rules, but impose barriers to the access of key attributes such as data and application of methodology (e.g., an index with the goal of minimizing risk does not provide precise
descriptions of statistical methods employed).
March / April 2013
63

ME Musings
HUMOR
How I Learned
To Stop Worrying . . .
By Heather Bell
. . . and love gardening.
SHHHH! Don’t tell anyone, but we put
the March/April issue of Journal of
Indexes together in January. It’s just
how the publication cycle works. And putting
the publication together at the start of
2013 really brings home the topic of “New
Perspectives.” I mean, what is the start of
a brand-new year if not a chance to take
stock of what has come before and to tweak
your approach for what lies ahead?
I don’t know about you, but since late
2008—what with the financial collapse and
the growing political polarization in this
country—I’ve had it in the back of my mind
that I should invest in a bunker instead
of my 401(k). I mostly gravitate that way
anyway—when there’s a snowstorm coming,
I’m right in there with the rest of the
nutsos, desperately buying up bread and
milk, even though I don’t consume much
of either item. But that 24 hours when
I might be snowbound COULD be the
24-hour period in which I am struck by an
unconquerable desire for a double-decker
sandwich and a big frosty glass of milk.
(Note: Head out to the grocery store
early if you don’t want to be stuck with
the raisin bread—it does NOT go well with
peanut butter and jelly.)
This kind of neurotic response to
unpleasant global events runs in my family.
Not long after the market tanked,
I had to talk my 60-something mother
out of putting her entire retirement savings
into physical silver—and explain
that her jewelry collection didn’t qualify.
However, when things took a downward
turn, I elected not to take up political
extremism, load up on ammo or move
to Canada. Instead, I bought a Costco
membership and started a garden.
The former has led to an impressive
stockpile of toilet paper, paper towels, plastic
wrap and frozen broccoli florets (if you
have a good recipe involving them, please
email me as soon as possible). My friends
started talking about a Hoarders-style intervention
this past autumn when they realized
I had enough rolls of toilet tissue to
last me to the next Olympics. They don’t
hesitate to stop by for a few rolls when they
run out, however, so who’s the crazy one?
The garden I view as an investment.
OK, right now I probably spend about $20
for every luscious heirloom tomato I harvest,
but one day—hopefully before I’m
ready for retirement—I will have a costeffective
supply of organic vegetables for
my post-crisis Cobb salad.
So after all that warehouse shopping and
a ton of potting, I awoke on this past New
Year’s Day to the realization that, contrary
to my radical fears and the vehement beliefs
of market pundits, society might just be
safe and sound for a while. And here I sit
with enough Saran Wrap for the world’s
leftovers. There’s a lesson here somewhere.
No, I’m not giving up my Costco membership,
even if I’ll probably never have to
buy TP again. Nor am I abandoning my
gardening efforts—it appeals to my crunchy
granola side. But I’m not going to let irrational
fears of the U.S. turning into Greece—or
Somalia, God forbid—affect how I manage
my port folio. I’m not going to allocate an outsized
portion of my investments to gold. I’m
not going to put all my money into emerging
markets because the U.S. and its fellow
developed markets have a bit too much debt.
And I’m certainly not burying an Airstream
trailer in the backyard as an ad hoc bomb
shelter, even if Costco has them on sale.
I am, however, going to continue to
invest responsibly and not spend my ducats
on frivolous things. Repeat after me:
The odds are good that it’s gonna be OK—
maybe not great or even pretty cool, but
definitely, at a minimum, “OK.”
64
March / April 2013

ETF Analytics
There are over 1,000 eTFs on The markeT. own The righT ones.
Equity: U.S. Energy
Equity Segment Report
OVERVIEW
IndexUniverse Insight
Eight ETFs offer very different approaches to the energy space, providing
investors with access to everything from the broad market—dominated
by names like Exxon Mobil or
ConocoPhillips—to quant-based
strategies that attempt to pick
winners from the many companies
and sub industries in U.S. energy.
Eight ETFs offer
very different
approaches to the
energy space.
Four funds—VDE, IYE, XLE and
FEG—deliver broad, market-like
sector exposure that ranges from very good to great, though they diverge
sharply on costs and risks. IYE and XLE offer the most representative
portfolio of stocks, but IYE's 0.47% expense ratio is far and away the
highest of these four “plain vanilla” funds. In contrast, XLE delivers the
best combination of broad exposure at the segment-lowest fee: 0.18%.
The fund brings something else to the party--massive liquidity. As one of
the most liquid ETFs in the world, XLE trades about $1 billion a day,
15 June 2012
making its all-in costs from trading and fees tough to beat. VDE also
delivers a market-like basket at a low fee (0.19%). Then there’s FEG, the
polar opposite from XLE on trading volume. Despite an excellent
portfolio offered at the second lowest price in the segment (tied with VDE
at 0.19%), FEG suffers from poor on-screen liquidity and carries high
fund-closure risk from its low asset base.
Four other funds offer clear alternatives to traditional sector exposure
due to their strategy or selection universe. PSCE differs the most from the
sector by focusing exclusively on small-cap energy companies. FXN and
PXI use quant strategies to pick winners from the sector instead of merely
owning the market like the vanilla funds. RYE offers an equal-weighted
version of XLE. As they stray from vanilla exposure, all four funds
come—in varying degrees—with greater risks, higher price tags and
less-than-perfect tracking, but contribute to a well-rounded segment:
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Snapshot
Overall
Ticker Fund Name
Rating Efficiency Tradability Fit Notes
IYE iShares Dow Jones U.S. Energy A 97 88 98 97
VDE Vanguard Energy A 93 91 98 93
XLE Energy Select SPDR A 93 94 99 93 TRADABILITY
FEG Focus Morningstar Energy B 94 85 78 94
IndexUniverse Tradability Insight
PXI PowerShares Dynamic Energy Portfolio B 65 81 86 65
U.S. energy funds vary dramatically in Tradability, with XLE reigning over tier, averaging around 0.14% ($0.06). Still, like the top tier, PXI scores
RYE Guggenheim S&P Equal Weight Energy B 52 82 79 52 all. For on-screen liquidity, XLE is not only the most liquid ETF in the high with regard to block liquidity—indicating it’s still easy to trade in
FXN First Trust Energy AlphaDEX B 41 80 83 41
segment, it’s one of the most liquid size.
PSCE PowerShares S&P SmallCap Energy B 26 89 74 26
U.S. energy funds ETFs in the world, trading an
average of about $1 billion daily. Funds that trade around and below $1 million a day in volume land at
Segment Average | Ranked by Overall Score
vary dramatically in
1-Year Total Return
Tradability.
VDE and IYE are distant seconds by the bottom tier of Tradability—PSCE, RYE, FXN and FEG. Of these, FEG
volume, with ADV averaging $13 may be the least lucky, considering its portfolio of securities is among the
million and $9 million,
most liquid. FEG was victim to a pricing error on its first day of trading
respectively. Still, VDE and IYE deliver excellent liquidity for most
that led to wacky trades and bad press—likely scaring away investors at a
1-Year
investors. All of the top three funds trade multiples of their creation unit critical early stage for the new fund. While all four funds experience
PXI -7.97%
sizes (50,000 shares), with average spreads between 0.01% and 0.04%. wider spreads—with averages as high as 0.24% in the case of PSCE—their
10%
Bench -8.44%
holdings are incredibly liquid, as indicated by their high block-liquidity
PXI occupies IYE a -9.08% second tier of Tradability within the U.S. Energy segment. scores. The irony of this is that large investors will find these funds easy
0%
With
FEG
its ADV of
-9.41%
$2 million, it trades well above our minimum threshold to trade in size (with the help of a liquidity provider), but small investors
of $1 million. Spreads range a good deal wider for PXI than for the top only have limit orders to use as an aid when placing trades.
XLE -10.11%
FIT
-10%
VDE -10.54%
Median
IndexUniverse Fit Insight
RYE -16.60%
Average Daily
Average
Premium/
Maximum
Maximum
Creation
Segment funds deliver clear choices regarding their portfolios, roughly exposure: It overweights service and equipment firms and completely
Ticker Tradability Rating
Volume ($)
Spread
Discount
Premium
Discount
Basket Size
-20%
PSCE -19.54%
split along the lines of those that try to match the sector and those that ignores the integrated oil & gas industry. (PSCE passes on names like
XLE 99 1.07 B 0.02% 0.00% 0.38% -0.17% 50,000
don’t. VDE, IYE, XLE and FEG all Chevron and Exxon Mobil in favor of SEACOR Holdings and Lufkin
FXN -24.28%
IYE 98 9.55 M 0.04% 0.00% 0.16% -0.20% Segment 50,000funds
land in the first camp. They aim to Industries.) Though PSCE has outperformed its competitors over the
Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
2012
VDE 98 13.08 M 0.04% 0.00% 0.17% -0.15% 100,000
deliver the broad market and past year, its beta of 1.42 indicates that it’s risky relative to the broad
deliver clear
generally do a great job at it, with market, though this shouldn’t surprise (or necessarily deter) investors
PXI 86 2.32 M 0.14% -0.03% 0.60% -1.03% 50,000
As of 06/14/12
FXN 83 963.16 K 0.13% -0.04% 0.38% -0.50% choices 50,000 regarding
RYE 79 309.18 K 0.12% -0.02% 1.97% -1.80% their portfolios.
IYE and XLE edging out the rest. looking for small-cap energy companies.
These four funds all follow a
50,000
market-cap weighting scheme that Investors looking for an equal-weight strategy will want to examine RYE.
gives them top-heavy
The fund strays from the broad market—instead it overweights midcap
FEG 78 309.04 K 0.14% 0.00% 1.93% -1.35% 50,000
concentration of major players such as Exxon Mobil and Chevron, and companies with zero exposure to small-caps. As a result, RYE loads up on
PSCE 74 621.02 K 0.24% -0.05% 0.68% -0.52% generally heavy 50,000 exposure to the integrated oil & gas industry. However, the exploration & production industry—a space that tends to be
Segment Average | Ranked by Tradability Score
there are differences to note. For one, XLE selects from a mid-to-large dominated by midcap firms. RYE’s beta of 1.18 indicates the fund is
universe within the S&P 500, but due to the capping restrictions of the relatively risky compared to the broad market.
Spread Dispersion
Premium/Discount Dispersion
S&P indexes, the fund ends up with a portfolio slightly skewed toward
Premium
midcaps. VDE, the second-broadest fund in the segment, slightly
PXI and FXN follow similar strategies: They use quant strategies to select
| Equity: U.S. Energy | 15 June 2012 | Source data provided by
Page 1 of 6
overweights exploration & production firms, making it a tad riskier than winners within the energy space. While their goals are similar, their
the market with a beta of 1.03. That said, it’s FEG that tilts the most selection screens differ. As a result, PXI heavily overweights mid- and
0.8%
1%
toward smaller companies (among the four vanilla funds), with a
small-cap stocks. FXN also dips into the small-cap space, but loads up
PSCE
PSCE
weighted average market cap of $130 billion vs. the sector’s $150 billion. massively on midcaps. By industry, both funds skimp on the integrated
0.6%
FXN
VDE
oil & gas space, while loading up on exploration & production firms. In
PXI
The other 4 funds FXN diverge from the market, and from each other, by the case of PXI, its current portfolio is particularly exposed to refining
0%
FEG
nature of their XLE unique strategies. PSCE scores lowest in Fit, but with good and marketing firms. Both funds are relatively risky in comparison to the
0.4%
RYE
reason—the fund IYE doesn’t attempt to represent the broad market—it broad market. FXN and PXI have yet to show any statistically significant
VDE
focuses solely
-1%
FEG
on small-caps. PSCE’s firm size-universe affects industry alpha relative to the market.
0.2%
IYE
PXI
XLE Discount
RYE
Index Methodology
Average # of Goodness
JunJul Aug Sep Oct Nov Dec Jan Feb Mar Apr May
JunJul Aug Sep Oct Nov Dec Jan Feb Mar Apr TickerMay
Fit Rating Weighting Selection P/E P/B
Market Cap Holdings of Fit (R) Note
2012
2012
IYE 97 Market Cap Market Cap 9.7 1.6 $144.96 B 91 99.94% Broad exposure, high
cost
IndexUniverse / Knight Block Liquidity
FEG 94 Market Cap Market Cap 10.0 1.6 $130.36 B 100 99.86% Youngest fund
IYE VDE XLE FEG PXI RYE FXN PSCE This measure shows how easy it is to trade
VDE 93 Market Cap Market Cap 9.8 1.6 $131.08 B 172 99.90% Broad exposure,
25,000 shares of any given ETF. It reflects
5
5
5
5
5
5
5
5
second for lowest cost
the liquidity and hedgeability of a fund’s
4
4
4
4
4
4
4
4 underlying securities. A score of 5 means XLE a
93 Market Cap Proprietary 9.8 1.6 $126.55 B 44 99.80% AUM giant, cheapest
3
3
3
3
3
3
3
3 fund is extremely liquid.
fund
2
2
2
2
2
2
2
2
PXI 65 Tiered Multi-Factor 9.9 1.5 $26.45 B 60 95.75% Quant-based strategy
|
1
1
1
1
1
1
1
1
RYE 52 Equal Proprietary 10.7 1.3 $31.77 B 44 96.91% Equal-weighted
exposure
FXN 41 Tiered Multi-Factor 9.2 1.2 $27.27 B 54 95.42% Quant-based strategy
PSCE 26 Tiered Proprietary 15.0 1.1 $1.02 B 24 90.04% Small-cap play
Segment Average | Ranked by Fit Score
Geographic Exposure
United States Switzerland Bermuda Netherlands Brazil Canada
Benchmark 96.84% 2.00% 0.65% 0.32% 0.10% 0.09%
IYE 97.15% 2.19% 0.22% 0.44% 0.00% 0.00%
| Equity: U.S. Energy | 15 June 2012 | Source data provided by
VDE
Page 3 of 6
97.74% 1.26% 0.43% 0.42% 0.00% 0.15%
XLE 98.87% 0.60% 0.52% 0.00% 0.00% 0.00%
PXI 96.15% 0.00% 1.30% 1.24% 0.00% 1.30%
RYE 96.04% 2.20% 1.76% 0.00% 0.00% 0.00%
FXN 96.51% 0.00% 2.16% 1.34% 0.00% 0.00%
PSCE 100.00% 0.00% 0.00% 0.00% 0.00% 0.00%
FEG 97.81% 1.88% 0.32% 0.00% 0.00% 0.00%
Balanced Underweight 5%+ 3% to 5% 1% to 3% Overweight 1% to 3% 3% to 5% 5%+
} Complete eTF due
diligence process
} robust scoring system:
efficiency, Tradability, Fit
} Unbiased institutional analysis
} Plain-english explanations
} Best data in the eTF industry
| Equity: U.S. Energy | 15 June 2012 | Source data provided by
Page 4 of 6
Learn more at analytics.indexuniverse.com/joi or by contacting our
ETF Analyst team at 415-501-0939 or analytics@indexuniverse.com
©2012 IndexUniverse LLC, IndexUniverse ETF Analytics

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