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TRANSCRIPT
Debt Be Not Proud
Robert Arnott
Hedging With Inverse ETFs
Joanne Hill and Solomon Teller
Gold As An Asset Class
Juan Carlos Artigas
Commodities Indexing Roundtable
Chatting with Rouwenhorst, Rogers, Prestbo and others
Plus Blitzer on commodities investing, Haslem on
fund advertising, an excerpt from Swedroe and more!
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www.journalo�ndexes.com
Debt Be Not Proudby Robert Arnott . . . . . . . . . . . . . . . . . . . . . . . . 10 An alternative method of weighting bond indexes.
Hedging With Inverse ETFsby Joanne Hill and Solomon Teller . . . . . . . . . 18 Suggested methods for managing portfolio risk.
Rediscovering Gold As An Asset Classby Juan Carlos Artigas . . . . . . . . . . . . . . . . . . . . 26 Understanding gold’s diversification benefits.
Commodities Indexing Roundtableby Journal of Indexes staff . . . . . . . . . . . . . . . . . . 34How to properly construct a commodities index.
The Image Of The Investmentby David Blitzer . . . . . . . . . . . . . . . . . . . . . . . . . 40Has investment affected the commodities market?
Mutual Funds And Investor Choiceby John A. Haslem . . . . . . . . . . . . . . . . . . . . . . . 42Fund advertising can lead investors astray.
Wise Investing Made Simplerby Larry Swedroe . . . . . . . . . . . . . . . . . . . . . . . . 46An excerpt from Swedroe’s latest book for investors.
Post-Apocalyptic Investing: The Index Approachby Lara Crigger. . . . . . . . . . . . . . . . . . . . . . . . . . 64Surviving an apocalypse with your portfolio intact.
f e a t u r e s
PowerShares Revamps Junk Bond ETF . . . . . . . . 52
FTSE Acquires FXI . . . . . . . . . . . . . . . . . . . . . . . . 52
Vanguard In Massive ETF Rollout . . . . . . . . . . . . 52
Select Sector Sues PowerShares Over Tickers . . 52
Indexing Developments . . . . . . . . . . . . . . . . . . . . 53
Around The World Of ETFs . . . . . . . . . . . . . . . . . 55
Back To The Futures . . . . . . . . . . . . . . . . . . . . . . 57
Know Your Options . . . . . . . . . . . . . . . . . . . . . . . 57
From The Exchanges . . . . . . . . . . . . . . . . . . . . . . 57
On The Move . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Selected Major Indexes . . . . . . . . . . . . . . . . . . . 59
Returns Of Largest U.S. Index Mutual Funds . . . 60
U.S. Market Overview In Style . . . . . . . . . . . . . 61
U.S. Industry Review . . . . . . . . . . . . . . . . . . . . . 62
Exchange-Traded Funds Corner . . . . . . . . . . . . 63
d a t a
n e w s
1November/December 2010www.journalofindexes.com
Contributors
November/December 20102
Larr
y Sw
ed
roe
Joan
ne H
ill
Joh
n H
asl
em
Davi
d B
litz
er
Juan
Carl
os
Art
igas
Ro
bert
Arn
ott
Robert Arnott is chairman and founder of asset management firm Research Affiliates LLC. He is also the former chairman of First Quadrant LP and has served as a global equity strategist at Salomon Brothers (now part of Citigroup) and as the president of TSA Capital Management (now part of Analytic). Arnott was editor-in-chief at the Financial Analysts Journal from 2002 through 2006. He graduated summa cum laude from the University of California, Santa Barbara.
Juan Carlos Artigas is a manager in investment research for the World Gold Council in New York, where he is in charge of writing strategic and research notes putting gold in the context of global financial markets. He was previ-ously employed by JPMorgan Securities as a U.S. and emerging markets strate-gist. He holds a B.S. in actuarial sciences from ITAM (Mexico), and an MBA and M.S. in statistics from the University of Chicago.
David Blitzer is managing director and chairman of the Standard & Poor’s Index Committee. He has overall responsibility for security selection for S&P’s indices and index analysis and management. Blitzer previously served as chief economist for S&P and corporate economist at The McGraw-Hill Companies, S&P’s parent corporation. He received his M.A. in economics from Georgetown University and his Ph.D. in economics from Columbia University.
John A. Haslem is professor emeritus of finance in the Robert H. Smith School of Business at the University of Maryland, and the author of six banking and mutual funds books. He served as the Smith School’s first academic dean and its first chair of finance. Haslem is most recently the author of “Mutual Funds: Risk and Performance Analysis for Decision Making” and editor of “Mutual Funds: Portfolio Structures, Analysis, Management, and Stewardship.”
Joanne Hill, Ph.D., is head of investment strategy for ProShare and ProFund Advisors LLC. Prior to joining ProFunds, she was employed by Goldman Sachs for 17 years, where she was a managing director, leading a team focused on global equity index and derivatives strategy. She has published extensively on quantitative investment topics and derivatives, with recent articles in the Journal
of Portfolio Management, Financial Analysts Journal and Journal of Trading.
Larry Swedroe is a principal and the director of research for the Buckingham Family of Financial Services. He holds an MBA in finance from New York University. Swedroe is the author of several books, of which “Wise Investing Made Simpler” is the most recent. He is also the co-author of “The Only Guide to Alternative Investments You’ll Ever Need” and “The Only Guide to a Winning Bond Strategy You’ll Ever Need.”
Solomon Teller is the head of investment analytics at ProShare and ProFund Advisors LLC. He is responsible for product research and strategies, and new product analysis. Prior to joining ProShares, Teller was a senior portfolio manager at Trumbower Financial Advisors. He holds the Chartered Financial Analyst designation and has a B.A. in economics and philosophy from the University of Maryland.
So
lom
on
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November/December 2010
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David Blitzer: Standard & Poor’s
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November/ December 2010
Editor’s Note
Jim Wiandt
Editor
Thoughts FromThe Edges
Jim Wiandt
Editor
8
This issue we take a look at the edges of index investing. Indeed, in some of the
cases outlined here, you might even say “index” investing.
Commodities exchange-traded products in particular have exploded onto
the scene in recent years, with GLD now coming in as the second-largest ETP in the
world and many other commodities-focused funds joining the party as well. And this
issue, even where we’re looking at as old-school an asset class as possible—bonds—
we’ve got a new twist on the formula for you.
So who’s making all the noise this issue? Leading off we definitely have one
of the usual suspects in that area—Rob Arnott—weighing in (so to speak) with
some insightful thoughts on weighting bond indexes. Rob is always compelling and
thought-provoking enough that there is no resisting publishing his insights. Next up
is another longtime friend of the publication, Joanne Hill, along with Solomon Teller,
asserting that inverse ETFs are not just about trading.
On the gold front is Juan Carlos Artigas making the case for gold as a portfolio
diversifier. Obviously with more than $50 billion invested in GLD alone, that message
must have already sunk in with some investors.
Following the gold piece, we’ve got a high-profile commodities-focused round-
table with some very provocative commentary by the likes of Geert Rouwenhorst, Jim
Rogers, Mike McGlone, John Prestbo, Martin Kremenstein and Ed Carroll.
If that was not enough to wet your whistle regarding commodities indexing, try David
Blitzer’s piece on whether commodities investing is moving the commodities markets.
Finally, we have a piece from Professor Haslem on bogus fund advertising, some
pearls of wisdom from Larry Swedroe and a hilarious send-off to gold nuts with bun-
kers in South Dakota from Lara Crigger to close out the issue.
Happy investing. Try to keep your eggs on the shelf.
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Project1 9/28/10 4:42 PM Page 1
November/December 201010
By Robert Arnott
Getting down to the fundamentals of sovereign debt
Debt Be Not Proud*
* With apologies to John Donne: “Debt be not proud, though some have called thee / Mighty and dreadful, for
thou art not so / For those, whom thou think’st, thou dost overthrow / Die not, poor debt, nor yet canst thou kill me.”
November/December 2010www.journalofindexes.com 11
We live in a world profoundly addicted to debt-
financed consumption.
For most of us, our first car and our first home
were financed with debt. We borrowed with intent to repay,
and most of us did just that. We were, of course, no richer
because we’d just borrowed to buy a house or a car: We had
a new asset, exactly offset by a new liability. Our expected
future consumption was reduced, not advanced, by this bor-
rowing. While we were realigning our lifestyle to improve
the subjective mix (with a nice house and a car), our lifestyle
was improved in some ways and reduced in others (fewer
restaurants and holidays), with no objective net difference.
Today, many people, companies and countries borrow to
fund current consumption, with no evident intent to repay. As
it comes due, our debt is something we intend to replace with
new (and often larger) debt. We’re not just borrowing from
Peter to pay Paul; we’re borrowing a bit more from Peter, to
pay Paul … and to finance additional consumption with the
difference. How naive of us, as young adults, to have once
thought we might never have to pay back the principal!
Greece recently hit a wall, and had to break a lot of
promises to its citizens, notably the retirees and prospec-
tive retirees from government employment. Iceland’s banks
hit their wall a couple of years ago. Many people who
were late buyers during the U.S. housing bubble hit a wall
and are in default. Italy, Spain, Portugal, Ireland, Illinois,
California and New Jersey are all fast-careening toward their
respective walls.
The nature of that wall is generally the same: We cannot
find a lender willing to lend us more, to pay off our old
debts, and so those debts truly come due. Our choice, in
each case, is either to reduce our consumption, in order to
pay down that debt, or default.
Of course, with each default, the failed borrower suffers
damage, not least being a string of broken promises to trusting
stakeholders. But the lenders suffer reciprocal damage. While
debt is extinguished for some, so too are assets for others. It is
in this fashion that wealth is destroyed in a financial crisis.
Is the U.S. the lead junkie in a world addicted to debt-financed
consumption? Are we careening toward perhaps the biggest
array of sovereign defaults in world history? Time will tell, but the
sheer magnitude of global sovereign debt is not reassuring.
Why Are Bond Indexes Capitalization Weighted?Bond investors are lenders. As creditors, why should we
deliberately choose to lend more to those who are most
deeply in debt?
Bond indexes are mostly capitalization weighted. Consider
Table 1a. Greek debt is nearly three times the debt of
Australia, meaning cap-weighted sovereign bond investors
have loaned three times as much money to Greece as to
Australia. If Greece has three times the debt service capac-
ity of Australia, this should be fine, because Greece is just
as able to service its debt—ceteris paribus—as Australia. But
Australia has three times the GDP of Greece. Therefore, on
this simple measure, Greece has about nine times as much
debt, per dollar of GDP, as Australia. If the yields are similar,
as they were a year ago, one might reasonably prefer to own
more Australian debt than Greek debt.
Consider an efficient markets perspective. In an efficient
market, what does it matter if Greece owes more and is less
able to service its debt? If Greek debt is more risky than
Australian debt, we should garner exactly the right amount
of incremental yield to provide the same risk-adjusted
expected return for both countries’ debt. However, we see
little evidence of this sort of market efficiency.
If we do not believe that prices are always and every-
where correct, then we should be curious about debt levels,
as measured against a borrower’s ability to meet their debt
obligations. In other work, we’ve examined this very ques-
tion. We find that a Fundamental Index methodology applied
to bonds—weighting companies according to the size of
their business, or weighting countries according to the size
of their economy—adds considerable value, relative to cap
weighting.1 It is not our intent in this paper to explore the
correct weighting scheme for bonds in any detail. Rather, we
want to examine the debt loads themselves.
Measuring Sovereign Capacity To Service DebtHow might we estimate a country’s ability to produce
goods and services—and eventually wealth—that might
be accessed for debt service? There is no direct measure.
However, we can consider the factors of production in a capi-
talist economy. Economics literature typically identifies two
or three factors of production: capital, labor and sometimes
resources (a subsector of capital). We take it one step further
by breaking out energy (normally a subsector of resources,
but we think it’s large enough to merit its own category).
We have identified four factors that crudely proxy for
these factors of production; hence, for a country’s ability to
service its debt.
VËËCapital: GDP is imperfect, equally crediting the creation
of consumables (e.g., auto production and car wash
services), alongside destruction of wealth (e.g., litiga-
tion expenses and wars) and expenditures that do not
enhance wealth (e.g., regulatory compliance). Still, it’s
the most widely used gauge of the size of an economy.
VËLabor: A nation’s population is the simplest gauge.2
VËËResources: A nation’s landmass is a very rough gauge of
access to resources.3
VË Energy: The aggregate energy consumption of a nation
is a measure of the energy that goes into production
of goods and services. One caveat is that this may be
sourced externally, through petroleum imports.
In Figure 1a, the “Bond Cap Weight” column measures the
capitalization-weighted exposure of a country’s bond market
debt, as a percentage of global sovereign bond issuance,
spanning the developed economies of the world.4 These data
include local-currency bonds, as well as debt denominated
in dollars, euros or other benchmark currencies. As a quality
control check, we also include a column labeled “Public Net
Debt,” which measures the aggregate 2009 public debt—less
gold and foreign currency reserves—as a percentage of the
world total, as drawn from the 2010 CIA Fact Book.
The next four columns compare the fundamental scale of
these economies, using the above four metrics as proxies
November/December 201012
for the four factors of production for goods and services.
On the far right is the equal-weighted average of the four
fundamental weights (or three measures, for those instances
where there is no energy consumption data).
We should reflect on what’s missing from this table. We
exclude countries with no tradable bond debt. This includes
solvent countries like Kuwait, Lichtenstein, Monaco and
Saudi Arabia, as well as insolvent countries like Zimbabwe.
Based on the CIA World Fact Book data, these countries col-
lectively owe barely 5 percent of net world sovereign debt
and 3.6 percent of sovereign bond debt. Wouldn’t it be nice
if we could choose to own the debt of Monaco or Saudi
Arabia, rather than Greece or Belgium!
We also exclude any debt that is not in the form of publicly
traded bond debt. There are several categories, some of which
can dwarf the sovereign bond debt.
VËËUnfunded entitlement programs: The unfunded por-
tions of Social Security and Medicare are vivid examples,
as are the unfunded pay-as-we-go pension obligations
of Western Europe. These shortfalls are huge in the U.S.
and most of Europe. But in Japan, Australia, Sweden,
the Netherlands and New Zealand, such programs are
largely prefunded.
VËËOff-balance-sheet debt: A domestic example would
be the modest prefunded portion of Social Security
and Medicare, in the form of “trust funds,” which own
nonmarketable U.S. Treasury Bonds.5 While several
countries have replaced these entitlement programs
Developed Markets, Share Of Global Sovereign Debt
Figure 1a
Developed CountryBond
Cap Weight
Public
Net DebtGDP Weight
Population
WeightArea Weight
Energy
Weight
RAFI
Weight
Source: Research Affiliates, on data drawn from the CIA World Fact Book and IMF databases
Australia 0.5% 0.4% 1.5% 0.4% 5.2% 1.2% 2.1%
Austria 1.0% 0.8% 0.6% 0.2% 0.5% 0.3% 0.4%
Belgium 1.5% 1.3% 0.7% 0.2% 0.3% 0.7% 0.5%
Canada 1.6% 2.9% 2.2% 0.6% 6.0% 3.3% 3.0%
Denmark 0.5% 0.0% 0.4% 0.1% 0.4% 0.2% 0.3%
Finland 0.3% 0.3% 0.3% 0.1% 1.1% 0.3% 0.5%
France 5.2% 6.0% 3.9% 1.2% 1.4% 2.7% 2.3%
Germany 5.3% 7.3% 5.0% 1.6% 1.1% 3.3% 2.7%
Greece 1.4% 1.1% 0.5% 0.2% 0.7% 0.4% 0.4%
Ireland 0.6% 0.4% 0.3% 0.1% 0.5% 0.2% 0.3%
Italy 6.2% 7.0% 3.2% 1.1% 1.0% 1.9% 1.8%
Japan 28.8% 26.3% 7.6% 2.5% 1.2% 5.4% 4.1%
Netherlands 1.4% 1.4% 1.2% 0.3% 0.4% 1.0% 0.7%
New Zealand 0.1% 0.1% 0.2% 0.1% 1.0% 0.2% 0.4%
Norway 0.1% 0.5% 0.5% 0.1% 1.1% 0.4% 0.5%
Poland 0.5% 0.5% 0.9% 0.8% 1.1% 1.0% 0.9%
Portugal 0.6% 0.5% 0.4% 0.2% 0.6% 0.2% 0.3%
South Korea 1.6% 0.7% 1.8% 0.8% 0.5% 2.4% 1.4%
Slovakia 0.1% 0.1% 0.2% 0.1% 0.4% 0.2% 0.2%
Slovenia 0.1% 0.0% 0.1% 0.0% 0.3% N/A 0.1%
Spain 2.4% 2.6% 2.3% 0.8% 1.3% 1.5% 1.5%
Sweden 0.3% 0.4% 0.6% 0.2% 1.3% 0.5% 0.6%
Switzerland 0.4% 0.0% 0.7% 0.1% 0.4% 0.3% 0.4%
United Kingdom 5.8% 4.4% 3.5% 1.2% 0.9% 2.3% 2.0%
United States 23.2% 26.5% 23.6% 5.9% 5.9% 24.1% 14.7%
Prudent Nine 3.6% 5.2% 6.4% 2.4% 17.7% 7.2% 8.3%
“PIIGS” 11.2% 11.7% 6.7% 2.5% 4.1% 4.2% 4.3%
G-5 68.3% 70.5% 43.7% 12.5% 10.6% 37.8% 25.8%
ALL DEVELOPED 89.5% 91.7% 62.4% 19.1% 35.3% 54.1% 42.2%
November/December 2010www.journalofindexes.com 13
Emerging Markets, Share Of Global Sovereign Debt
Figure 1b
Emerging CountryBond
Cap Weight
Public
Net DebtGDP Weight
Population
WeightArea Weight
Energy
Weight
RAFI
Weight
Source: Research Affiliates, on data drawn from the CIA World Fact Book and IMF databases
Argentina 0.2% 0.4% 0.7% 0.8% 3.1% 0.7% 1.3%
Brazil 0.8% 2.3% 3.0% 3.7% 5.5% 2.2% 3.5%
Bulgaria 0.0% 0.0% 0.1% 0.2% 0.6% 0.2% 0.3%
Chile 0.0% 0.0% 0.3% 0.3% 1.6% 0.3% 0.6%
China 2.3% -1.4% 11.9% 25.9% 5.9% 17.8% 15.1%
Colombia 0.2% 0.3% 0.5% 0.9% 2.0% 0.3% 0.9%
Croatia 0.0% 0.1% 0.1% 0.1% 0.4% N/A 0.2%
Czech Republic 0.2% 0.1% 0.4% 0.2% 0.5% 0.5% 0.4%
Dominican Republic 0.0% 0.1% 0.1% 0.2% 0.4% N/A 0.2%
Ecuador 0.0% 0.1% 0.1% 0.3% 1.0% 0.1% 0.4%
Egypt 0.1% 0.4% 0.6% 1.4% 1.9% 0.6% 1.1%
El Salvador 0.0% 0.0% 0.1% 0.1% 0.3% N/A 0.2%
Gabon 0.0% 0.0% 0.0% 0.0% 1.0% N/A 0.3%
Ghana 0.0% 0.0% 0.0% 0.4% 0.9% N/A 0.5%
Hong Kong 0.0% -0.3% 0.4% 0.1% 0.0% 0.3% 0.2%
Hungary 0.2% 0.2% 0.3% 0.2% 0.6% 0.3% 0.3%
India 1.8% 2.1% 4.2% 21.6% 3.4% 4.0% 8.2%
Indonesia 0.3% 0.4% 1.3% 4.4% 2.6% 1.2% 2.3%
Israel 0.2% 0.3% 0.3% 0.1% 0.3% N/A 0.2%
Lebanon 0.0% 0.1% 0.1% 0.1% 0.2% N/A 0.1%
Lithuania 0.0% 0.0% 0.1% 0.1% 0.5% 0.1% 0.2%
Malaysia 0.4% 0.2% 0.5% 0.5% 1.1% 0.6% 0.7%
Mexico 0.6% 1.0% 2.0% 2.1% 2.7% 1.6% 2.0%
Morocco 0.0% 0.1% 0.2% 0.6% 1.3% N/A 0.7%
Pakistan 0.0% 0.2% 0.5% 3.0% 1.7% 0.6% 1.4%
Panama 0.0% 0.0% 0.1% 0.1% 0.5% N/A 0.2%
Peru 0.1% 0.1% 0.3% 0.5% 2.1% 0.1% 0.8%
Philippines 0.2% 0.2% 0.4% 1.7% 1.0% 0.3% 0.8%
Romania 0.0% 0.0% 0.3% 0.4% 0.9% 0.4% 0.5%
Russia 0.4% -0.5% 2.8% 2.8% 7.8% 7.0% 5.0%
South Africa 0.4% 0.2% 0.7% 0.9% 2.1% 1.3% 1.2%
Serbia 0.0% 0.0% 0.1% 0.1% 0.6% N/A 0.3%
Singapore 0.2% 0.2% 0.4% 0.1% 0.0% 0.5% 0.3%
Sri Lanka 0.0% 0.1% 0.1% 0.4% 0.5% N/A 0.3%
Taiwan 0.6% -0.3% 1.3% 0.5% 0.2% 1.2% 0.8%
Thailand 0.3% 0.2% 0.7% 1.3% 1.4% 0.9% 1.0%
Tunisia 0.0% 0.0% 0.1% 0.2% 0.8% N/A 0.4%
Turkey 0.5% 0.8% 1.3% 1.4% 1.7% 1.0% 1.3%
Ukraine 0.0% 0.1% 0.3% 0.9% 1.5% 1.4% 1.0%
Uruguay 0.0% 0.0% 0.1% 0.1% 0.8% N/A 0.3%
Venezuela 0.1% 0.1% 0.5% 0.5% 1.8% 0.7% 0.9%
Vietnam 0.0% 0.1% 0.3% 1.6% 1.1% N/A 1.0%
BRICs 5.2% 2.5% 21.9% 54.0% 22.6% 30.9% 31.9%
ALL EMERGING 10.5% 8.3% 37.6% 80.9% 64.7% 45.9% 57.8%
TOTAL (Emrg + Dev) 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
November/December 201014
with national defined contribution pension funds, with
individuals owning their share of these funds, others
pursue a pay-as-you-go approach. Outside the U.S.,
trust funds for prefunding national entitlement pro-
grams are not the norm. In any event, our own “trust
funds” don’t come close to fully prefunding for the
projected entitlements.
VËËGovernment-sponsored entities: GSEs—such as Fannie
Mae, Freddie Mac and others of their ilk—are backed
by the full faith and credit of the government; hence,
by future tax receipts. In the U.S., they’re bigger than
our external national debt. Japan has much smaller
GSEs; most other developed economies have none of
any consequence.
VËËState and local debt and unfunded pension obliga-
tions: These are excluded for the U.S.; they’re roughly
half as large as the direct public debt. No other country
in the world has as much state and local debt—a con-
sequence of U.S. tax policy that allows local and state
debt to remain exempt from federal tax and also allows
prospective obligations to remain unfunded.
VË Bank-owned sovereign debt: Sovereign debt, owed to
banks, is not uncommon in the emerging markets. In
the column labeled “Public Net Debt,” which measures
the aggregate 2009 public debt—including bank debt,
while subtracting gold and foreign currency reserves—
we can see that this is not a major “missing link.”
In the U.S., the combination of GSE debt, state and local
debt, unfunded pensions and entitlements all add up to just
under $60 trillion, roughly 10 times the official U.S. public
debt. By contrast, none of these hidden forms of debt, apart
from bank debt, is consequential in the emerging markets.
We’ll come back to this topic shortly.
Figure 1a color-codes the debt burden for the developed
economies, with purple indicating better debt coverage
ratios and red indicating possible debt service trouble spots.
If any Fundamental weight for a country, as a share of the
world economy, exceeds the cap weight by more than 100
percent, it’s flagged in dark purple with white text. If it
exceeds the cap weight by at least 25 percent, it’s flagged in
pale purple with dark purple text. Reciprocally, if a country’s
cap-weighted bond market debt exceeds any Fundamental
weight for that country, as a share of the world economy, by
more than 100 percent, it’s flagged in dark red with white
text. If it exceeds the fundamental weight by at least 25 per-
cent, it’s flagged in pale red with dark red text.
In the developed economies of the world, there’s a lot of
red ink in Figure 1a. Many countries carry debt—not even
counting often-vast off-balance-sheet debt—which is out of
proportion with their scale in the world economy.
Still, there are pockets of discipline. Australia, Poland and
Slovakia show no “red” at all, meaning that the sovereign
bond debt isn’t 25 percent greater than their economic
factors of production, on any of the four metrics. Canada,
Finland, New Zealand, Norway, Slovenia and Sweden are
each “out of bounds” on only one of the four measures.6
Collectively these “Prudent Nine” comprise less than 4
percent of world sovereign bond debt, and over 6 percent
of world GDP, 17 percent of world landmass, 7 percent of
world energy use and 8 percent of world capacity for sov-
ereign debt, as approximated by the RAFI weighting meth-
odology, which combines the four previously mentioned
factors of production.7 Furthermore, several of the “Prudent
Nine” have less hidden debt. For instance, Australia, New
Zealand, Norway and Sweden largely prefund their future
pension obligations. A cynic might suspect that those who
have too much explicit debt will begin to pursue hidden
debt, either off-balance-sheet or unfunded entitlements, as
was revealed in the case of Greece.
Greece is looking to Germany to save it from the gaping
maw of debt. So, let’s consider Germany. Germany has a
reputation for prudence and probity in the eurozone. But
that’s only true by comparison with the Mediterranean rim;
Germany is strained on all four of our measures. Germany
has 5 percent of the world GDP (proxying for available
capital), 1.6 percent of the population (available labor), 1.1
percent of the land area (available resources) and 3.3 per-
cent of the world’s energy consumption (the energy factor
of economic production). Its share of world sovereign bond
debt exceeds all four of these measures.
Germany’s capacity for carrying debt—approximated by
averaging these four fundamental measures of the scale of
Germany’s economy—is 2.7 percent of the world total. This
is barely half as large as Germany’s share of world sovereign
bond debt. Greece has a RAFI weight of 0.4 percent, about
one-third of its sovereign bond cap weight. These German
and Greek debt coverage ratios are not dissimilar. The
perceptions of German prudence, contrary to this objective
evidence, illustrate why we think the debt addiction of the
developed markets is so very dangerous.
Worse, Germany has:
VËË?�Ë?~��~ˬ�¬Ö�?Í���Ë?�aË?Ëw���aË�wˬÁ�ĬjWÍ�ÜjËÁjÍ�ÁjjÄË
in the coming 20 years
VËËa?Ö�Í��~Ë �ww�M?�?�Wj�Ä�jjÍË �M��~?Í���Ä^Ë ��ÄÍ�ßË ��Ë Í�jË
form of pay-as-you-go pensions
VËË?Ë ß�Ö�~Ë ?�aË ~Á�Ý��~Ë ����~Á?�ÍË ¬�¬Ö�?Í���Ë Í�?ÍË Ý?ÄË
not consulted in creating these long-horizon entitle-
ment programs, and that will not benefit proportionally
from these programs
If one were to mark these obligations to market, as if
they were prefunded with debt, and compare the total with
Germany’s ability to service the debt and future entitlements,
it’s possible that Germany’s total debt burden exceeds any
credible exit strategy. This would mean that Germany—a
bulwark of fiscal prudence in Europe—is probably near-
bankrupt, on a mark-to-market basis.
In effect, one might argue that Portugal, Ireland, Italy,
Greece and Spain (derisively—and unfairly—characterized
as “PIIGS”), are bankrupt states seeking shelter from larger
near-bankrupt states. The collective bond debt of PIIGS is
2.6 times its collective RAFI weight in the world economy,
which relates to its ability to service debt. That’s an
acknowledged problem. Belgium serves as the governance
center for the EU, yet its debt burden is near-identical to
this figure … as is the ratio for the G-5 in aggregate! Isn’t it
a sad irony that the G-5 economies have a debt burden—
November/December 2010www.journalofindexes.com 15
relative to the scale of these economies based on the four
factors of production—as the so-called PIIGS. And yet we
have the temerity to label the Mediterranean rim coun-
tries the “PIIGS”?!
Finally, it bears mention that the cap-weighted sover-
eign debt indexes are happy to include nations that carry
hefty debt burdens, until the ratings agencies catch up
with reality and downgrade their debt. Then, the index
providers apparently don’t know what to do with these
newly fallen angels. After being downgraded to BB in
June, and after the bond prices had cratered, Greece was
removed from the developed world sovereign indexes and
not added to the emerging markets indexes. As far as we
can tell, Greek bonds no longer have a home in the major
international fixed-income indexes.
The Emerging Markets Debt ConundrumEmerging markets debt commands a premium yield. And
yet, by objective measures, their debt coverage ratios are far
better than the developed markets.
On June 30, the Merrill Lynch Global Emerging Markets
Sovereign Plus Index, which spans the dollar-denominated
debt of the emerging markets, was priced to yield 6.0 per-
cent. This was 3 percent higher than U.S. 10-year Treasurys.
This 3 percent “risk premium” rewards us for bearing the
incremental default risk and political risk associated with
serving as a lender to the emerging markets. In 2008,
President Rafael Correa of Ecuador repudiated his nation’s
debt, despite ample financial resources to pay the debt.
This kind of disrespect for international law—and for the
integrity of a nation’s agreements—prompts investors to
fear investing in emerging markets, perhaps particularly
emerging markets debt.
How precarious are the debt burdens in the emerging
economies? Surprisingly benign! Consider the so-called
BRICs.8 As we can see on Figure 1b, they collectively com-
prise 22 percent of world GDP, and yet have only 5 percent
of world bond debt (and, according to the 2010 CIA World
Fact Book, net of gold and foreign currency reserves, just
2.5 percent of the world’s total public debt). India and
China have issued only local currency debt, which is dif-
ficult or impossible for foreign investors to access. India’s
debt is held in part by the IMF and/or World Bank and
otherwise not traded or investable. Most cap-weighted
indexes exclude these two countries, because their debt
is not investable.
Even this overstates the debt picture, from a global
investor’s perspective: The second column of Figure 1b
shows that Chile, China, Hong Kong, Russia and Taiwan
have gold reserves, foreign currency reserves and/or
investments in the developed economies’ stocks and
bonds, amply exceeding their total debt. No wonder, then,
that Greater China is on a roll: They’re the bankers; we’re
the debt-addled consumers, who can’t stop consuming on
borrowed funds!
Similarly, Saudi Arabia, Kuwait, Qatar, the Emirates, as
well as tax havens like the Cayman Islands, Monaco and
Liechtenstein, all have no net debt. Most such countries, as
with China and India, have no bond debt that any foreign
investor would be permitted to buy. These “net creditors”
would have a significant collective “fundamental weight,” if
only there were bonds to buy!
If the BRICs—especially Greater China—are carrying less
debt than they can comfortably support (based on their GDP,
their population, their resources or their energy consump-
tion), then surely there must be trouble spots in the emerg-
ing markets. Otherwise, why should investors demand a
substantial risk premium for emerging markets debt?
Indeed, there are some pockets of “red” on Figure 1b:
Across all four factors of production, Singapore and Taiwan
each have a share of world bond markets rivaling their
fundamental economic footprint in the world economy.9 Of
course, many investment professionals would consider these
to be part of the developed world—belonging in Figure 1a,
not Figure 1b. For example, FTSE includes Singapore in
the Developed World indexes. But we’re using the United
Nations definition of emerging markets; according to the
UN, Taiwan and Singapore are emerging markets.
Let’s consider the rest of the emerging markets list. Not
one of the other 40 emerging markets in this list—which
spans all countries that are included in any of the major EM
debt indexes—has as much debt as any of the G-5 countries,
whether measured relative to GDP or relative to the RAFI
fundamental economic footprint of these countries. The
emerging markets are bathed in purple ink in Figure 1b,
because in almost all cases, their debt is modest relative to
their evident ability to carry debt, based on the four factors
of economic production.
The developed markets comprise 62 percent of the
world’s GDP and owe 89 percent of the world’s sovereign
bond debt (and 92 percent of total world public debt). The
emerging markets collectively produce 38 percent of the
world’s GDP and owe just over 10 percent of world sover-
eign bond debt. Do hidden debt and off-balance-sheet debt
change this picture? Yes. As with the role of gold and cur-
rency reserves, these factors skew the picture in the “wrong”
direction.10 In many instances, the developed economies have
vast off-balance-sheet debt, while most of the emerging
markets have little off-balance-sheet debt, and often have
substantial gold or foreign currency reserves.
Given that emerging market stocks are now priced at
valuation ratios (price-earnings ratios, price-book ratios,
dividend yields) similar to the developed economies, we
might wonder why the stocks get a “free pass” on the
feared political risk of these markets, while the sovereign
debt does not. Similarly, when we saw a “flight to quality”
in the fall of 2008 and spring and summer of 2010, why did
this imply a shift in investment preferences away from the
emerging markets, toward the U.S., Germany and Japan,
and not the opposite?
One might reasonably argue that—absent political risk—
emerging markets are collectively more creditworthy than U.S.
Treasurys. Which invites a provocative question: When will
U.S. Treasurys be priced to offer a “risk premium”—a high-
er yield—than the most stable and solvent of the so-called
emerging markets?
November/December 201016
Appendix: Debt Burden And GDP GrowthIt is beyond the scope of this short paper to explore the
wisdom of our surging public debt, though our views on
the topic are self-evident. Still, we might pose the question:
Which countries have skated through the “global financial
crisis” largely unscathed? Again, we might turn to the CIA
World Fact Book for some simple evidence.
If we regress 2009 GDP growth against debt burden—defined
as the size of a country’s debt relative to the fundamental RAFI
scale of its economy—and against the 2008-09 average deficit,
we find the results on Figures 2 and 3. The bivariate regression
results, across the 75 countries, are as follows:
2009 Growth = 3.33% [t-Stat is 10.2]
-0.005% x ln (Debt / RAFI Weight) [t-Stat 5.3]
- 0.18% x (2009 Fiscal Deficit / GDP) [t-Stat 3.7]
R2 = 0.453
Every 1 percent increase in the ratio of a country’s debt,
relative to its RAFI-weighted share of the world economy
(proxying for the country’s ability to service its debt),
reduced GDP growth in 2009 by 5 basis points (7 basis points
in a univariate regression). If the real cost of sovereign debt
is 2 percent (i.e., if the yield that the country must pay the
bondholders is 2 percent above inflation), then the damage
that debt inflicts on GDP growth would appear to be roughly
three times as large as this direct cost. The univariate cor-
relation is -49 percent; this result is significant at the 0.1
percent level.
Figure 3 shows that every 1 percent of deficit spending, as
a percentage of GDP, reduced a country’s 2009 GDP growth
by 18 basis points (22 basis points on a univariate basis).
The univariate correlation is -59 percent; this result is also
significant at the 0.1 percent level.
Neo-Keynesians will argue that our causality is confused:
They would argue that it’s the plunging GDP that triggers
additional debt and deficit spending, not the other way
around. Causality is difficult to prove in either direction.
But, it merits mention that Keynes himself never argued
for structural deficits. That seems to be the war cry of the
neo-Keynesians. Keynes argued for budget surpluses in most
years, affording a nation an opportunity for deficit spending
to soften the impact of economic downturns.
While the sample period is only one year and one financial
crisis, and therefore must be taken with a grain (or even a
shaker-full) of salt, both results are highly statistically sig-
nificant. However, since we do not have access to data from
multiple “global financial crises,” we should perhaps take
heed of the implications of this admittedly limited result.
While Figures 2 and 3 examine the economies of the
world for one year (2009), Figure 4 examines one economy
(the U.S.) for over 50 years. Milton Friedman observed that
the true tax rate is the rate of spending: Spending must be
covered by current or future taxes, so deficits merely repre-
sent deferred taxation. So, how does growth in the private
sector economy respond to growth in spending? Badly.
There is a 73 percent correlation between increases in
federal spending and decreases in private sector GDP (the
gross GDP, less public sector spending). This evidence would
suggest that every 1 percent increase in federal outlays—as
a percentage of GDP—reduces the private sector GDP by
1.85 percent. Again, the neo-Keynesians will argue that the
causality is backward: Plunging private-sector GDP requires
soaring expenditures to arrest the damage. Again, causality
is difficult to prove, either way. However, the relationship is
overwhelming, with a t-Statistic of 3.1.
Figure 5 updates the graph from our 2009 white paper,
“The 3-D Hurricane: Deficit, Debt and Demographics.”10 As yet,
there has been no material deleveraging in the U.S. economy.
We’ve taken a breather on accumulating net new debt, and
we’ve transferred some private-sector debt to the govern-
ment. However, deleveraging has yet to begin in earnest.
Most of us know someone who has taken on debt amount-
ing to several years of income. If it’s for a first home, and our
friend’s income is rising quickly, we would not think them
foolish to take on that first mortgage. But, if it’s a middle-
aged friend with stable income, especially one fast approach-
ing retirement, we would likely think it very unwise for them
2009 GDP Growth Vs. Debt Burden, All Debtor Nations
10%
8%
6%
4%
2%
0%
-2%0.01 0.10 1.00 10.00
Debt Relative to RAFI Weight in World Economy
China
India
BrazilRussia
US
Germany
France UK
Japan
20
09
GD
P G
row
th, p
er
CIA
Wo
rld
Fa
ct B
oo
k
L Developed Markets L�Emerging Markets
Source: Research A�liates, on data drawn from CIA World Fact Book database
Figure 2
2009 GDP Growth Vs. De�cit, All Debtor Nations
10%
8%
6%
4%
2%
0%
-2%-15% -10% -5% 0% 5% 10% 15% 20%
2008-09 De�cit as % of GDP
China
India
BrazilRussia
US
Germany
FranceJapan
UK
20
09
GD
P G
row
th, p
er
CIA
Wo
rld
Fa
ct B
oo
k
L Developed Markets L�Emerging Markets
Source: Research A�liates, on data drawn from CIA World Fact Book database
Figure 3
November/December 2010www.journalofindexes.com 17
to take on massive debt. Most of us are unsurprised when
these friends encounter serious difficulties: They’ve boosted
their consumption lifestyle on borrowed funds. The creditors
eventually want to get paid.
Many observers fret that, if we deleverage (indeed, even if
we stop running up additional debt), we face a serious reces-
sion. They confuse credit-funded consumption with prosper-
ity. Is the entry-level clerk who borrows to buy a Mercedes
and a condo, and then finds that he cannot afford the pay-
ments, prosperous? Does he have a natural, inalienable right
to continue consuming beyond his means?
As a nation, regardless of our decisions to borrow more
or to reduce our borrowings, we’ll still be producing as much
in goods and services as in the past. We’ll just no longer be
consuming goods and services beyond what we produce as a
nation. If our lifestyle has been funded in part on debt, then
deleveraging will mean a reduced lifestyle for all, but only to
the extent that we’ve been consuming more than we were
able to produce. That consumption is unsustainable, regardless
of our fiscal and monetary policies and regardless of our
intentions with regard to future debt.
If we would counsel our overleveraged friends to cut their
spending and start whittling down their debt, why should our
counsel to nations be any different? Should we be surprised that
the economies for creditor nations are soaring, while the debtor
nations find their growth crippled by every economic shock?
Endnotes1 See Arnott, Hsu, Li and Shepherd, “Valuation-Indifferent Indexing for Bonds,” Journal of Portfolio Management, Spring 2010. Just as we damage our returns when we weight
stocks according to their popularity—i.e., cap weighting—we also hurt our bond results, if we weight bonds according to the magnitude of a borrower’s debt load.
2 The working age population might be a better gauge. We chose total population because it’s universally available for all countries.
3 We chose to use the square root of landmass, in order to avoid grossly rewarding big, sparsely populated countries like Russia, Australia and Canada, or penalizing small, crowded
countries like Luxembourg, Hong Kong and Singapore. For midsize countries like Argentina or Germany, this adjustment makes little difference.
4 Based on the UN definition of developed and emerging economies.
5 One interesting “factoid” is that the 2010 CIA Fact Book shows the U.S. as having far less debt in 2009 than it did in 2007. How’s that? In 2007, the unmarketable debt held in
the Social Security, Medicare and other national trust funds were correctly counted as U.S. public debt. In 2009, this $5 trillion debt was excluded. Was there political pressure
to make this change? Is there a growing intent to spend the trust funds, rather than to continue even partially prefunding these obligations? We may never know! Either way,
for our analyses in this paper, we added the unmarketable Treasury bonds back into the U.S. Bond and Public Debt columns.
6 Interestingly, in each case, the population is the sole outlier; it would appear that its debt is well within bounds on three factors of production: capital, resources and energy.
7 It’s interesting to note that most of these countries also breezed through the “global financial crisis” better than the countries with more debt. They enjoyed average GDP growth
in 2009 of 1.7 percent, double that of the G-5 and of the eurozone.
8 We’ve long found this label puzzling: four countries with almost nothing in common but a shared acronym! Even though China shares borders with Russia and India, the three
countries have less in common—culturally, economically or legally—than essentially any countries on the developed economies list. Consider it a labeling-cum-marketing coup
by Goldman Sachs!
9 Note also that Singapore has a sovereign wealth fund that is larger than its aggregate debt. So, as with Chile, China, Hong Kong, Russia and Taiwan, their net debt is nonexistent.
10 See our Fundamentals white paper, “The 3-D Hurricane: Deficit, Debt and Demographics,” Research Affiliates, November 2009.
US Federal Outlays And Private Sector Growth, 1953-2009
9
6
3
0
(3)
(6)
(9)(3) 0 3 6 9
Growth in Outlays, % of GDP
Growth =
-2.4*Outlays + 2.6%
Correl. = 0.69, to 2008
Growth =
-1.85*Outlays + 2.7%
Correl. = 0.73, to 2009
Gro
wth
in P
riv
ate
Se
cto
r G
DP,
%
Source: Research A�liates, on data drawn from OMB Budget of the U.S. Government 2010, Historical Tables
N Growth in Outlays
Linear (Growth in Outlays)
Figure 4
US Aggregate Debt, By Source, Through Q1 2010
900%
800%
700%
600%
500%
400%
300%
200%
100%
0%
March1950
March1960
March1970
March1980
March1990
March2000
March2010
Source: Research A�liates, on data drawn from Federal Reserve Flow of Funds database
N Entitlement Programs N Households and Nonpro�ts
N Business, Excluding OBS N Total Government + GSEs
Figure 5
November/December 201018
Hedging With Inverse ETFs
By Joanne Hill and Solomon Teller
A primer
November/December 2010www.journalofindexes.com 19
In designing hedging strategies, investors can choose from
a variety of tools and approaches. In recent years, inverse
exchange-traded funds (ETFs)1 have joined the list of avail-
able hedging tools used by institutional and other investors.
In this article, we first discuss the factors investors should
consider when constructing any hedging strategy. We then
explore the critical aspects of hedging with single inverse
(e.g., -1x) ETFs. We show that while these tools can be effec-
tive hedging vehicles, they require careful monitoring and
rebalancing to maintain the hedge. We finish by comparing
hedging with single inverse ETFs to hedging with leveraged
inverse ETFs (e.g., -2x), the latter requiring less upfront capi-
tal but more frequent rebalancing.
Key Hedging Strategy ConsiderationsA hedging strategy involves adding positions to a portfo-
lio with the objective of reducing volatility of returns. Many
investors choose to hedge risk rather than sell positions in
their portfolios because of liquidity, tax, trading cost or other
portfolio management implications.2 To hedge a portfolio
position, investors add negatively correlated investments—
investments that move in the opposite direction—to all or
a portion of the portfolio in an attempt to offset some or all
changes in value of the target position. In designing a hedging
strategy, investors should consider the following factors:
Choosing a Benchmark Index—Many investors use hedg-
ing instruments based on indexes to reduce risk associated
with broad market moves, referred to as benchmark risk.
Index-based hedges are often more liquid, accessible via
exchanges and may be less costly than customized portfolio
hedges using swaps or options in the OTC market. This can
make it easier to monitor, trade and adjust the size of hedges
over time, as well as to exit the hedging strategy. Selecting an
appropriate benchmark index typically involves comparing the
return and security characteristics of the target position with
those of various indexes and identifying the index, or set of
indexes, that have the highest correlation to the target posi-
tion. Hedging strategies can range from simple—hedging an
S&P 500 portfolio with an S&P 500 index product—to more
complex—hedging across multiple asset classes that may
require blending a group of index products and that would
need to be regularly rebalanced to maintain consistency with
the target position. This article focuses on the former.
Determining How Much to Hedge—How much to hedge
depends on the amount of benchmark risk an investor is
seeking to reduce, with the maximum being a full hedge (100
percent of the long position) that would reduce the return
expectation of the hedged position to that of a cash equiva-
lent.3 Many investors attempt to hedge only a small portion
of a portfolio’s market exposure, such as 10 percent or 20
percent, to help reduce volatility of returns. In cases where
investors are interested in hedging a specific portfolio expo-
sure, such as a sector allocation, the amount of the hedge
will naturally be driven by the size of that exposure.
Selecting the Hedging Vehicle—When selecting a hedg-
ing vehicle, investors should consider various factors, such
as the return profile of the hedging vehicle, effectiveness,
expected duration of the hedge, liquidity, cost, financing and
ease. Investors looking to hedge equity risk, for instance, can
short stocks or ETFs or choose from a variety of derivative
strategies, such as selling futures or swap contracts, buying
put options or selling call options. More recently, the choice
of buying inverse ETFs has been added to the hedging menu.
That is the focus of this article.
Monitoring and Rebalancing the Hedge over Time—
Effective hedging normally requires a dynamic process,
monitoring and rebalancing the hedge to maintain alignment
with the value of the position or portion of the portfolio
being hedged. Common sources of misalignment are active
(alpha) risk or benchmark (beta) differences between the
hedging vehicle and the index itself. A portfolio with active
risk may outperform or underperform its benchmark index
over a hedging period, calling for adjustment in the size of
the hedge. Consider, for example, an initial $100 investment in
an actively managed mutual fund that outperforms the index
by 5 percent. An investor who had hedged by being short the
benchmark index now has at least an additional $5 at risk and
should consider adding to the hedge to account for the alpha
achieved—a practice known as rebalancing the hedge.
Designing Rebalancing Strategies—The design of a rebal-
ancing strategy for a hedge should reflect the desired level of
monitoring and customization required to adjust for chang-
ing market and volatility conditions. Common rebalancing
approaches include calendar rebalancing, where adjustments
are made at regular time intervals, such as monthly or
quarterly, and fixed-percentage rebalancing, which triggers
rebalancing when the difference between the hedge and
the long position return reaches a certain percentage level,
such as 10 percent.4 A fixed-percentage trigger is more adap-
tive to market conditions than calendar-based rebalancing.
With a fixed-percentage trigger, more frequent rebalancing
typically occurs during high-volatility periods. The size of
the band or range should be based on the investor’s goals,
risk tolerance and expected transaction costs. Generally, the
tighter the band, the more frequent the rebalancing and the
smaller the deviation of net exposure. Rebalancing the hedge
also involves capital, transaction cost and tax considerations,
which largely depend on which of these rebalancing strate-
gies is utilized and on prevailing market conditions.
Hedging Using Inverse ETFs Now, let’s examine one particular hedging method in
greater detail—hedging using inverse ETFs. Inverse ETFs are
investments that seek to provide an inverse multiple (e.g.,
-1x or -2x or -3x) of the daily return of a benchmark before
fees and expenses. These ETFs debuted in 2006, although
similar inverse mutual funds have been in existence since
1994. Inverse ETFs have grown significantly. Today, more
than 100 ETFs cover a broad range of equity, fixed-income,
commodity and currency benchmarks.5 Many investors con-
sider inverse ETFs to be attractive hedging instruments for
the following reasons:
VËËInverse Correlation: An inverse ETF seeks to achieve the
inverse of the one-day performance (or a multiple there-
of) of the ETF’s stated benchmark index before fees and
November/December 201020
expenses.6 As such, buying an inverse ETF may provide
index returns with the negative correlation, on a daily
basis, necessary to implement an effective hedge, with-
out requiring investors to short securities.
VËËAccessibility: Inverse ETFs trade much like stocks on
security exchanges and are generally bought and sold
in the same way. Typically, no special accounts or other
special arrangements are needed.7
VËËIntraday Pricing and Liquidity: Since inverse ETFs trade
much like stocks, they are priced throughout the day to
reflect market fluctuations. For some investors, this can
facilitate better monitoring and rebalancing.
Rebalancing the hedge is a particularly important consid-
eration when hedging with inverse ETFs due to the single-day
objective of these ETFs. Figure 1 uses a simple two-day example
to illustrate the potential additional rebalancing requirements
when using single inverse ETFs. The table shows the impact of
both 5 percent up and 5 percent down daily moves on a fully
hedged $100 long position8 where the long position and the
single inverse ETF have the identical underlying benchmark.
In Scenario A, where there has been a rise of 5 percent,
we see that a purchase of an additional $10 of the single
inverse ETF is required to return net exposure back to 0
percent. In Scenario B, where there has been a decline of 5
percent, we see that a sale of $10 is required to return net
exposure to 0 percent.10
Case Studies: Hedging With Single Inverse ETFs In Different Market Conditions
We use case studies to further illustrate hedging with
single inverse ETFs, demonstrating the need to rebalance.
With case studies representing periods of rising and falling
benchmark returns and different volatility environments,
we can show how the frequency of rebalancing is linked
to market conditions and how the net exposure varies
between rebalancing points.
We present two different market scenarios using S&P 500
returns: 1) a period of declining returns (H2 2008); and 2) a
rising return period (H2 2009). To simulate the performance
objective of an inverse and leveraged ETF, we’ve taken each
of the S&P 500’s daily returns and multiplied them by -1 and
-2, thus ignoring fees, financing, interest and expenses.11 In
all of the case studies, we employ a fixed-percentage rebal-
ancing approach to keep the net exposure of the combined
long and hedge positions within a fixed-percentage band of
+/-10 percent. With a fixed-percentage approach, rebalanc-
ing occurs when this range is exceeded in either direction.
Case Study I: Single Inverse Hedge In A Declining Return Environment
Figure 2 shows the risk/return characteristics and net
exposure of fully (100 percent) and partially (50 percent)
hedged positions in the S&P 500 during the second half
of 2008. The table at the bottom of Figure 2 shows the
net exposure of the 100 percent hedged position12 and
the points where rebalances occurred, which are seen
where the black line pierces the +10 percent and -10
Figure 1
Hedge Rebalancing Example For A Single Inverse ETF Hedge With 5% Daily Index Moves
Position
Scenario A: Long Position Rises 5%
Day 1 Day 2Rebalance
Trade
Buy
additional
$10 of -1x
ETF position
Position
Scenario B: Long Position Falls 5%
Day 1 Day 2Rebalance
Trade
Sell $10 of
existing
-1x ETF
position
Long $100 $105
-1x ETF $100 $95
Net Exposure9 $0 $10
Long $100 $95
-1x ETF $100 $105
Net Exposure $0 -$10
Source: ProShares
Figure 2
Single Inverse ETF Hedging Strategy Reduces Volatility And
Mitigates Downside Losses In A Period Of Declining Returns
10%
0%
-10%
ReturnAnnualized
Volatility
Maximum
Drawdown
S&P 500 -28.48% 53.91% -40.63%
S&P 500 with 50% -10.08% 14.76% -14.01%
Hedge in -1x Strategy
S&P 500 with 100% -0.88% 1.23% -0.99%
Hedge in -1x Strategy
20%
10%
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S&P 500 with 100% Hedge
S&P 500 with 50% Hedge
S&P 500
Buy To Rebalance The 100% Hedge
Sell To Rebalance The 100% Hedge
Aug08
Sep08
Oct08
Nov08
Dec08
Jun08
Jul08
Aug08
Sep08
Oct08
Nov08
Dec08
Note: Total returns of S&P 500 with 50% and 100% hedges in -1x strategy 6/2008–12/2008 with 10% rebalance trigger. Volatility is standard deviation of daily returns. Maximum drawdown is lowest cumulative return during period. Net exposure based on 100% hedging strategy.Sources: Bloomberg, ProShares
November/December 2010www.journalofindexes.com 21
percent rebalancing bands. Through early August 2008, net
exposure would have stayed relatively stable, only breaking
out of the band and requiring rebalancing twice between
June 30 and the end of August. At that point, the S&P 500
began to decline steeply, with higher volatility through year-
end. During this latter period, fluctuations in net exposure
increased as the gap between the return of the S&P 500 and
the inverse strategy increasingly diverged, prompting the
need for more frequent rebalancing. For the six months as a
whole, the 10 percent rebalancing band required the hedge
to be adjusted, on average, about every 10 days.
As summarized in the table at the bottom of Figure 2,
rebalancing helped maintain a consistent hedge during the
six-month horizon, and the hedge significantly reduced loss-
es and return volatility over the entire six-month period. A
50 percent hedged position declined by just over 10 percent
during this period when the index return was -28.5 percent,
and reduced volatility from 54 percent to less than 15 per-
cent.13 As hoped, the fully hedged position has close to zero
return and zero volatility.14
Case Study II: Single Inverse Hedge In A Rising Return Environment
In our next case study, we looked at the same hedging
strategies against S&P 500 exposure but in a period of rising
returns, specifically the second half of 2009 when the S&P
500 appreciated by 22.6 percent. Results for this market
scenario are shown in Figure 3.
Over this period, the volatility of the S&P 500 index was
17 percent, much less than that experienced during the
turbulent second half of 2008. Not surprisingly, the net
exposure of the hedging strategies was far less volatile as
well. A 10 percent band applied over this particular period
prompted rebalancing about every 31 days versus the
average of every 10 days in the second half of 2008. All of
these rebalances were additions to the size of hedge posi-
tion, as the inverse position declined relative to the index.
This would have required adding additional capital to the
hedging strategy over this period. The hedging strategies
succeeded in reducing the volatility of S&P 500 exposure
and maintaining the desired equity exposures near 0 per-
cent and 50 percent, but at the cost of lower returns.
In both market scenarios, we see that the -1x hedging
strategies, using a 10 percent rebalancing band for the
hedge, fulfilled the objective of reducing downside return
risk significantly, measured both by volatility and maxi-
mum drawdown. On balance, it is important to understand
that these hedging strategies may significantly reduce
upside returns as well.
Hedging With Leveraged Inverse ETFs Up to this point, our discussion has focused on single
(-1x) inverse ETFs. Investors could alternatively use lever-
aged inverse ETFs, which pursue returns equal to -2x or -3x
of a benchmark index’s one-day return. The primary benefit
of using a leveraged inverse ETF is that less up-front capital
may be needed to implement the hedging strategy. However,
maintaining a leveraged inverse hedging strategy over
time—keeping the net exposure close to zero—is likely to
require more frequent rebalancing than would a -1x inverse
ETF strategy. To illustrate how inverse exposure and upfront
capital requirements vary when using leveraged inverse ETFs,
Figure 4 compares inverse ETF hedging strategies with vary-
Figure 3
Single Inverse ETF Hedging Strategy Reduces Volatility
And Overall Return In Period Of Rising Index Returns
ReturnAnnualized
Volatility
Maximum
Drawdown
S&P 500 22.59% 17.00% -4.30%
S&P 500 with 50% 7.52% 6.13% -1.41%
Hedge in -1x Strategy
S&P 500 with 100% -0.01% 0.43% -0.13%
Hedge in -1x Strategy
30%
20%
10%
0%
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S&P 500 with 100% Hedge
S&P 500 with 50% Hedge
S&P 500
Buy To Rebalance The 100% Hedge
Sell To Rebalance The 100% Hedge
Aug09
Sep 09
Oct 09
Nov09
Dec09
Jun09
Jul09
Aug09
Sep 09
Oct 09
Nov09
Dec09
10%
0%
-10%
Note: Total returns of S&P 500 with 50% and 100% positions in -1x strategy 6/2009–12/2009 with 10% rebalance trigger. Volatility is standard deviation of daily returns. Maximum drawdown is lowest cumulative return during period. Net exposure based on 100% hedging strategy.Sources: Bloomberg, ProShares
Figure 4
Comparison Of Initial Investment And Exposure Sizes For -1x, -2x, And -3x Inverse ETFs As Full Hedges
-1x ETF
-2x ETF
-3x ETF
$100.00
$50.00
$33.33
$100.00
$100.00
$100.00
$50.00
$66.67
-$100 -$50 $0 $50 $100
Inverse ETF exposure assumed to equal fund assets multiplied by fund multiple. Source: ProShares
N Inverse Investment N Added Inverse Exposure from Leverage N Long Assets
November/December 201022
ing degrees of leverage: -1x, -2x and -3x.
Figure 4 presents a long position of $100 that is fully
hedged (100 percent) by -1x, -2x and -3x inverse ETFs.
Working from the midpoint of $0, we see the initial cost of
capital for each of the ETF hedges in the left-hand bars. The
bars show how the use of additional leverage (-2x and -3x)
can reduce the amount of upfront capital required for the
hedge ($50 and $33.33 vs. $100), while still maintaining the
desired net exposure (100 percent).
An important consideration when hedging with lever-
aged ETFs is that variations in net exposure are magni-
fied in response to index moves. This means that hedges
with leveraged inverse ETF exposure will most certainly
require more frequent rebalancing. Figure 5 illustrates
this point by showing the impact of a 5 percent market
move on a -1x, -2x and -3x inverse ETF hedge. When the
market rises 5 percent, the $5 gain in the long portfolio
triggers exposure gaps across all three ETFs, but in vary-
ing degrees. The use of higher multiple inverse ETFs leads
to larger net exposure gaps over the course of the hedg-
ing period.15 For instance, the use of a -1x ETF results in a
10 percent performance gap and a $10 net exposure gap
($105 vs. $95), but the same position hedged with a -3x
ETF results in a 20 percent gap with a $20 net exposure
gap ($105 vs. $85). This potential for larger net exposure
variances demonstrates the need to increase the fre-
quency of rebalancing when hedging with leveraged ETFs
rather than single inverse ETFs.16
Figure 6
Declining Index Return Scenario: Relative Performance Of
Single And Leveraged Inverse ETF Hedges
ReturnAnnualized
Volatility
Maximum
Drawdown
Average # of Days Between
Rebalances
S&P 500 -28.48% 53.91% -40.63% —
-1x Hedging -10.08% 14.76% -14.01% 10.2
Strategy
-2x Hedging -11.31% 18.18% -16.17% 5.1
Strategy
20%
10%
0%
-10%
-20%
-30%
-40%
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S&P 500 with 50% -2x HedgeS&P 500 with 50% -1x Hedge
S&P 500
-2x Hedging Strategy-1x Hedging Strategy
40
30
20
10
0
Note: Total returns of S&P 500 with 50% positions in -1x and -2x strategy 6/2008–12/2008 with 10% rebalance trigger. Volatility is standard deviation of daily returns. Maximum drawdown is lowest cumulative return during period.Sources: Bloomberg, ProShares
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Nov08
Dec08
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Aug08
Sep08
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Figure 7
Rising Index Return Scenario: Relative Performance Of
Single And Leveraged Inverse ETF Hedges
ReturnAnnualized
Volatility
Maximum
Drawdown
Average # of Days Between
Rebalances
S&P 500 22.59% 17.00% -4.30% —
-1x Hedging 7.52% 6.13% -1.41% 30.7
Strategy
-2x Hedging 9.04% 7.41% -1.69% 20.4
Strategy
30%
20%
10%
0%
-10%
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S&P 500S&P 500 with 50% -2x HedgeS&P 500 with 50% -1x Hedge
-2x Hedging Strategy-1x Hedging Strategy
10
5
0
Note: Total returns of S&P 500 with 50% positions in -1x and -2x strategy 6/2009–12/2009 with 10% rebalance trigger. Volatility is standard deviation of daily returns. Maximum drawdown is lowest cumulative return during period.Sources: Bloomberg, ProShares
Jun09
Jul09
Aug09
Sep 09
Oct 09
Nov09
Dec09
Jun09
Jul09
Aug09
Sep 09
Oct 09
Nov09
Dec09
Figure 5
Comparison Of Initial Investment And Exposure Sizes For -1x, -2x, And -3x Inverse ETFs As Full Hedges
$105
Long Assets -1x Exposure -2x Exposure -3x Exposure
$50 $105 $95 $90 $85
+$5-$5
-$10-$15
$10Gap
$15Gap $20
Gap
$0
$100Initial Value
Chart is not drawn to scale.
Assumes inverse ETFs achieve exact multiple of long position’s total returns over period in question. Inverse ETF exposure assumed to equal fund assets multiplied by fund multiple.Source: ProShares
November/December 2010www.journalofindexes.com 23
Case Studies: Hedging With Leveraged Inverse ETFs In Different Market Conditions
To examine the effects of leverage across market con-
ditions, we compare single- and leveraged-ETF hedging
strategies across the declining and rising market-return
scenarios presented earlier in the article, as well as across
a third, choppy index-return scenario (H1 2009), where the
index experiences high volatility but has flat return over
the entire six months. Case Studies III, IV and V show the
performance of the S&P 500 when hedging with a leveraged
ETF, which for illustration purposes is represented by a -2x
strategy. As a point of comparison, we include the single
inverse ETF hedge (-1x) in the exhibits.
Case Study III: Leveraged Inverse Hedge In A Declining Market
Overall, the -2x strategy, with the lower initial investment,
showed slightly higher volatility of hedged positions but a
very similar pattern of returns compared with the -1x inverse
hedging strategy. In Figure 6, we see that in the second half
of 2008, returns were slightly lower and somewhat more
volatile with the -2x strategy given the index volatility and
corresponding size of daily moves. Rebalancing frequency
doubled, moving from a -1x strategy to a -2x strategy.
Case Study IV: Leveraged Inverse Hedge In A Rising MarketFigure 7 shows that during the second half of 2009 when
the index was rising in value, the -2x hedging strategy had
slightly higher returns than the comparable -1x example but
also slightly higher risk.
Case Study V: Leveraged Inverse Hedge In A Choppy MarketIn Figure 8, we compare the inverse ETF hedging strat-
egies in a choppy index return period where the index
was volatile but ended the period with only a 3.2 percent
return. Rebalancing frequencies were much greater, mov-
ing from the -1x to the -2x hedging strategies. The -2x
strategy was rebalanced on average every eight days ver-
sus every 23 days for the -1x strategy. Performance was
very similar among both hedged strategies during these
choppy market conditions, indicating that rebalancing the
size of the hedge was effective in mitigating the impact of
the volatile market conditions on the effectiveness of the
leveraged hedging tools.
Another way of thinking about how a hedging strategy
with a -2x inverse ETF would compare with one using a
-1x ETF is that for a given trigger, say 10 percent, more
frequent rebalancing would be required since the ETF
returns are a multiple of the inverse index moves. In the
tables under the previous three charts, you can see that the
frequency of rebalancing was greater with the addition of
leverage.17 This illustrates that the leveraged inverse ETF is
more likely to appeal to investors who are looking to lower
the upfront investment associated with the hedge and
who are comfortable with rebalancing on a more frequent
basis. An alternative to reduce the frequency of rebalanc-
ing is to have a wider trigger (e.g., 15 percent instead of
10 percent) when using leveraged inverse ETFs, with the
trade-off being that the investor assumes greater variation
in net exposure between rebalances.
ConclusionHedging is a risk management practice that requires
investment discipline and agility. Whether managing the risk
of a specific sector allocation or an entire portfolio, inves-
tors are best served by having a process addressing a range
of hedging considerations including benchmark selection,
how much to hedge, the hedging vehicle and an approach to
monitoring and rebalancing.
Investors are increasingly considering single and lever-
aged inverse ETFs as potential hedging instruments. With
proper monitoring and rebalancing, a single inverse ETF may
provide the inverse correlation on a daily basis necessary
for an effective hedge and can offer the benefits of acces-
sibility and intraday pricing/liquidity. Additionally, leveraged
inverse ETFs require less capital to initiate the hedge than
single inverse strategies. On balance, these vehicles, like any
other hedging instrument, must be carefully monitored and
managed. Leveraged inverse ETFs, in particular, may magnify
benchmark exposure with less capital but require more fre-
quent rebalancing to maintain the hedge.
In terms of measuring the effectiveness of an inverse ETF
Figure 8
Choppy Index Return Scenario: Relative Performance
Of Single And Leveraged Inverse ETF Hedges
ReturnAnnualized
Volatility
Maximum
Drawdown
Average # of Days Between
Rebalances
S&P 500 3.16% 34.78% -24.63% —
-1x Hedging 0.35% 10.99% -8.55% 22.6
Strategy
-2x Hedging 0.97% 13.04% -9.67% 7.9
Strategy
10%
5%
0%
-5%
-10%
-15%
-20%
-25%
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S&P 500 with 50% -2x HedgeS&P 500 with 50% -1x Hedge
-2x Hedging Strategy-1x Hedging Strategy
25
20
15
10
5
0
Note: Total returns of S&P 500 with 50% positions in -1x and -2x strategy 12/2008–6/2009 with 10% rebalance trigger. Volatility is standard deviation of daily returns. Maximum drawdown is lowest cumulative return during period. Sources: Bloomberg, ProShares
S&P 500
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Jan09
Feb09
Mar 09
Apr 09
May09
Jun09
Dec08
Jan09
Feb09
Mar 09
Apr 09
May09
Jun09
November/December 201024
hedge, we evaluated relative return, volatility and maximum
drawdown results of the hedged portfolio, as well as the
pattern and frequency of rebalancing. As we saw across
very different index return scenarios, inverse ETF hedges,
with and without leverage, potentially reduced volatility and
magnitude of returns. It’s important to note that while we
presented illustrations for different market scenarios, the
examples are still theoretical, and other hedging vehicles
could be more effective than inverse ETFs. Market conditions
can vary considerably, and transaction costs, cost of capital,
and tax consequences will all affect the final outcome of a
hedging strategy. Regardless of your hedging method, it is
important to carefully customize and closely monitor and
calibrate your hedging strategies to achieve and maintain
your desired risk targets.
This article is not intended as a recommendation for
any specific investment program. It is not intended to be an
investment strategy and does not infer or guarantee a profit
by using the strategy.
References
Joanne Hill and Solomon Teller, “Rebalancing Leveraged and Inverse Funds,” Eighth Annual Guide to Exchange Traded Funds & Indexing Innovations, Institutional Investor Journals (Fall
2009): 67-76
Nassim Taleb, “Dynamic Hedging,” John Wiley & Sons, Inc. 1997
John Hussman, “How Hedging Works,” HussmanFunds.com, April 18, 2005
Matt Hougan, “How Long Can You Hold Leveraged ETFs?” Journal of Indexes, March/April 2009
Mark Miller, “Hedging Strategies for Protecting Appreciation in Securities and Portfolios,” FPA Journal, August 2002
Joanne Hill and George Foster, “Understanding Returns of Leveraged and Inverse Funds,” Journal of Indexes, September/October 2009
Werner Keller, “Dynamic Risk Control for Equity Portfolios,” Keller Partners, LLC, April 2008
Ira Kawaller, “Tailing Futures Hedges/Tailing Spreads,” The Journal of Derivatives, Winter 1997
Tom Konrad, “Five Hedging Strategies,” Seeking Alpha, Sept. 8, 2009
Investopedia Staff, “A Beginner’s Guide to Hedging,” Investopedia, August 2003
Endnotes1Inverse exchange-traded funds are designed to provide an inverse multiple (e.g., -1x or -2x) of the daily return of a benchmark (before fees and expenses).
2Hedging also differs from diversification in that hedging’s sole purpose is to mitigate the risk of return volatility rather than to serve as a potential new source of returns.
3 In a situation where the beta sensitivity of the hedging tool to portfolio risk is less than 1.0, a fully hedged position may require a notional hedge amount of more than 100%
of the portfolio value. For example, if the portfolio has a beta of 1.2 to the hedging vehicle, a full hedge could entail the dollar value of the hedge position being 120% of the
portfolio value.
4 Another more dynamic rebalancing approach uses percentage triggers that are larger in volatile market conditions and smaller in lower-volatility markets, such as Bollinger
bands.
5 Total inverse ETP assets were $21.6 billion, with average daily volume of $5.8 billion for the first six months of 2010. Source: Bloomberg. Inverse exchange-traded product data
as of June 30, 2010.
6 Some exchange-traded products have monthly objectives or even multiyear holding periods with knockout features. ETPs with nondaily objectives are beyond the scope of this
article.
7With all investments, users should take care to read the prospectus and fully understand how inverse ETFs work and what risks are involved.
8 The long position and single inverse returns are chosen to provide an illustration of the direction and size of the rebalancing trades. Returns are not intended to predict fund
performance levels in particular market conditions. Inverse ETF returns over periods other than one day will likely differ in amount and possibly direction from the target return
for the same period.
9 Net long exposure is equal to the value of the long assets multiplied by any explicit leverage minus the short assets multiplied by any explicit leverage. Note, this assumes the long
position’s beta equals that of the inverse fund’s underlying index. Investors hedging based on beta comparisons can adjust the inverse fund weightings accordingly.
10 Proceeds from selling this position could be invested elsewhere or held for future funding needs for the rebalance process. In practice, investors not facing any constraints on
the long position may consider rebalancing both the long and the inverse positions simultaneously, reducing long positions to augment inverse positions or vice versa, which is
conceptually similar to rebalancing between stocks and bonds.
11 Summary of Assumptions:
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12 To apply this methodology to partially hedged scenarios (e.g., 50%), the same band can be said to apply around the portion of the long position that is being hedged. For instance,
in the examples in Figure 1, a 50% hedge target would imply $50 of the long assets were hedged with $50 of inverse assets. A 10% increase in value of long assets could lead to
a $52.50 long position hedged with $47.50 of the inverse position. The net exposure would then be $5, which is also 10% of the initial $50 being hedged.
13Without any rebalancing of hedge, S&P 500 with 50% hedge return and risk was -12.06% and 8.57%; S&P 500 with 100% hedge return and risk was -3.85% and 14.21%.
14 The fully hedged portfolio began the period with zero net exposure and was only exposed to market movements to the extent net exposure did not exceed either + or -10% in
either direction. Without rebalancing, as the index position fell and the inverse position rose unchecked, net exposure would have peaked at negative 90% in this period.
15 Similarly, had the long positions declined in value, the ending net short portfolio exposures could be equivalently greater with increased leverage. The long position and -1x
inverse returns are chosen to provide an illustration of the direction and size of the rebalancing trades even if long positions were identical to the index. Returns are not intended
to predict fund performance levels in particular market conditions. Inverse ETF returns over periods other than one day will likely differ in amount and possibly direction from
the target return for the same period.
16 While trading frequency likely increases with more leverage, average trade size decreases, owing again to the use of less capital. Figure 5 shows that an investor would have to
purchase $15 of additional exposure when using a -2x fund and $10 when using a -1x fund. This equates to $7.50 of -2x fund units vs. $10 for the -1x fund.
17Despite a greater rebalancing frequency, total capital traded was still less for leveraged inverse ETFs, as many rebalance trades were also sells.
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Project1 5/26/10 4:10 PM Page 1
November/December 2010
By Juan Carlos Artigas
What it can do for your portfolio
Rediscovering Gold
As An Asset Class
26
November/December 2010www.journalofindexes.com 27
Traditionally, investors have looked at gold as an infla-
tion hedge and, sometimes, as an asset to protect
them only in times of financial distress. While gold
can serve in this role, its main value stems not only from
these traits. Gold provides a unique source of diversifica-
tion to an investor’s portfolio. It tends to have low cor-
relations to most assets usually held by institutional and
individual investors whether it is in good times or bad. It
preserves wealth: Besides providing inflation protection,
gold also acts as a currency hedge, in particular against the
dollar with which gold correlates negatively. Moreover, it
helps to manage risk more effectively by protecting against
infrequent or unlikely but consequential negative events,
often referred to as “tail risks.”
In recent years, investors have become more aware of
the value gold can add to their portfolio. However, many
do not realize that all the characteristics gold brings to an
investor’s portfolio (diversification, risk management, and
store of wealth) are underpinned by supply and demand
dynamics that have undergone important developments in
recent years. The global nature of the gold market and its
diverse uses make it a unique asset. Here, we discuss the
role that investment, jewelry, technology and official sector
purchases play in the gold market.
The 2007-2009 financial crisis has brought back into
perspective alternative strategies that place more emphasis
on risk management. By using lessons learned during these
tough times, investors may be better prepared when a new
unforeseen event occurs. We believe gold’s role extends
beyond affording protection in extreme circumstances.
There are cost-effective strategies that can provide such
protection without sacrificing return, and we show that gold
can be an integral part of these strategies both for short- and
long-term investors.
Using a portfolio optimizer, we find that including gold
in a portfolio can reduce the volatility of the portfolio
without necessarily sacrificing expected returns. Moreover,
we show that including gold in portfolios not only delivers
better risk-adjusted returns, but also can help to reduce
potential losses. Specifically, we show that gold can gener-
ally expand the efficient frontier and reduce the value at
risk (VaR) in a portfolio. We find that even relatively small
allocations to gold, ranging between 2.5 percent and 9.0
percent, can increase risk-adjusted returns and help reduce
the weekly 1 percent and 2.5 percent VaR of a portfolio by
between 0.1 percent and 18.5 percent based on data from
December 1987 to July 2010.
Developments In The Gold MarketLike any freely traded good or service, the price of gold
is determined by the confluence of demand and supply.
Demand for gold has traditionally come from three sec-
tors—jewelry, industry and investment—while supply has
come from newly mined gold, official sector sales and the
recycling of above-ground stocks.
However, the gold market has experienced important
developments over the past decade which, in turn, have
influenced the performance of gold’s price. On the sup-
ply side, mine production remains lower than 2001 levels,
despite a higher gold price. Producer de-hedging reduced
the supply of gold coming from the miners. At the same time,
rising mining costs put a higher floor underneath the gold
price. The period has also been marked by a fundamental
shift in the behavior of central banks, who were large suppli-
ers of gold to the market in 2001 but became net purchasers
starting in Q2 2009.
Meanwhile, on the demand side, strong GDP growth and
a growing middle class in key jewelry-buying markets like
India and China have contributed to higher price levels.
While new ways to access the gold market were releasing
pent-up investment demand, the advent of gold exchange-
traded funds have allowed investors to buy gold on stock
exchanges for the first time. However, the development of
gold-backed ETFs in 2003 was mirrored by growing general
interest in gold ownership, as evidenced by the concurrent
rise in coin and bars sales.
Over the past five years, on average, around 60 percent of
demand for gold came from jewelry, where growing econo-
mies such as India and China play a preeminent role. About
30 percent of demand came from investment and the remain-
ing 10 percent from technology. Clearly, despite the growing
importance of gold investment in general and ETFs in par-
ticular in the gold market in recent years, they are still part of
a larger picture. It is estimated that ETFs backed by physical
gold currently hold over 2,000 tonnes of gold, which, com-
pared with the total size of the above-ground stock of gold
(165,600 tonnes by year-end 2009, half of which is jewelry),
is a relatively small amount. Even when compared with the
amount of gold held by private investors and the official
sector—which accounts for about 56,000 tonnes—physical
gold ETFs equate to just 3.5 percent. While figures vary by
quarter, average ETF demand hovers around 10 to 15 percent
of the total demand for gold.
During this time, some investors have used gold to
express tactical views on the market, to hedge against
currency and monetary policy risk or as a store of wealth.
Other investors are increasingly recognizing gold’s diversifi-
cation benefits and as a vehicle for risk management. While
sometimes overlooked, we consider this a particularly
important role for gold.
The Strategic Case For Gold: Portfolio Diversification, Risk Management And Wealth Preservation
Asset allocation is a fundamental question any investor
or money manager faces: How best to distribute resources
across competing assets? It is not a simple problem, and
there are many possible solutions. One method widely
used in finance is based on the assumption that, with a
certain degree of uncertainty, assets tend to correlate to
one another in similar ways depending on macroeconomic
and financial conditions. The correlations among assets,
combined with the individual volatilities, allow an investor
to reduce the overall risk of a portfolio without necessarily
sacrificing expected returns.
Dynamics of supply and demand within the gold market
November/December 201028
make it an ideal tool for portfolio diversification and risk
management. Gold’s volatility characteristics are often
misunderstood. Many people tend to equate the behavior
of gold’s price to that of other commodities, which often
are very volatile. The volatility of gold, however, over the
past 20 years has been, on average, around 15 percent.
The general commodities complex, as measured by the
S&P GSCI, has been a third more volatile than gold, with
an average volatility of 21 percent over the same period.
Even U.S. equities, as measured by the MSCI US index, have
experienced higher volatility, at around 17 percent.
There are good reasons for gold’s relatively tame volatili-
ty. First, the gold market is deep and liquid, and is supported
by the availability of large above-ground stocks. The various
sources of supply allow the market to absorb unexpected
shocks. Unlike many other commodities, gold is extracted
from virtually every continent except Antarctica, making it
less susceptible to geopolitical risks.
Moreover, unlike other assets—equities, in particular—
gold tends to exhibit lower volatility on negative returns than it
does on positive returns (Figure 1). The economics behind this
phenomenon are, in part, due to what is commonly known as
flight-to-quality. As negative news hits the market (especially
the equity market) and risk aversion increases, investors usu-
ally retreat from equity and other risky assets into Treasurys,
gold and other assets that tend to protect wealth.1
In risk management and portfolio theory, it is not only
individual volatilities that matter; it is also how assets inter-
act with each other, i.e., their correlation structure. Gold
tends to have little correlation with many asset classes,
which makes it a strongly viable choice for portfolio diver-
sification. This lack of correlation with other assets is also
a function of its unique drivers of supply and demand that
are, in turn, affected by a wide range of factors. Some fac-
tors, like inflation and currency movements, are tied to
developments elsewhere in financial markets, but many
more are peculiar to the gold market, underpinning its lack
of correlation to other assets year after year. These include
fashion trends, marketing campaigns, the Indian wedding
season, religious festivals, gold mine exploration spending,
new discoveries of gold, the cost of finding and mining
gold, and central banks’ strategic reserve decisions.
More importantly, unlike other assets typically consid-
ered diversifiers, gold’s correlation to other assets tends to
change in a way that benefits portfolio returns. For example,
while gold correlation to U.S. equities is usually not statisti-
cally significant on average, historically it tends to decrease
as U.S. equities fall, and increase when they rise (Figure 2).
This behavior is more evident when one compares the
correlation of equities to gold and commodities in peri-
ods when equity returns fall by more than two standard
deviations from zero (Figure 3). Put simply, in economic
and financial downturns, most industrial-based commodi-
ties and equities tend to follow a similar pattern. On the
other hand, history shows that gold’s correlation to equi-
ties becomes more negative during these same periods. It is
by no means a strong negative correlation, but it serves to
exemplify the benefits that gold can offer when managing
the overall risk of a portfolio.
The combination of volatility and correlation, alongside
expected returns, helps investors allocate resources more
effectively. Portfolio optimization, in particular, is one
0%NegativeReturns
�Gold ($US/oz) �S&P 500
PositiveReturns
4%
8%
12%
16%
20%
Annualized Volatility Of Positive And Negative WeeklyReturns For Gold ($US/oz) And S&P 500 Jan ’87-Jul ’10
Sources: London Bullion Market Association, Bloomberg, WGC
Co
rre
lati
on
1.0 1,600
1,5001,400
1,300
1,200
1,100
1,000
900
800
700
600
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
Dec-00
Nov-01
Oct-0
2
Sep-03
Aug-04
Jul-0
5
Jun-0
6
May-0
7
Apr-08
Mar-0
9
Feb-10
Ind
ex Le
vel
1-Year Rolling Correlation Between Weekly Returns On Gold ($US/oz) And Equities Compared To The S&P 500 Index Level
Sources: London Bullion Market Association, Standard & Poor’s, WGC
�1Y rolling correlation b/w gold ($US/oz) and S&P 500 (LHS)�S&P 500 (RHS)
S&P 500 returnmore than +2s
S&P 500 returnless than -2s
-0.5 -0.25 0.25 0.50
�Correlation between S&P 500 and gold ($US/oz)�Correlation between S&P 500 and S&P GSCI
Weekly-Return Correlation Between Equities,Gold And Commodities When Equities Move By More
Than 2 Standard Deviations; Jan ’87-Jul ’10
Sources: London Bullion Market Association, Bloomberg, WGC
Figure 1
Figure 2
Figure 3
November/December 2010www.journalofindexes.com 29
method in which an investor can determine the appropriate
weight of a particular asset in order to improve the risk-
adjusted returns in a portfolio. Using historical data, we dem-
onstrate how gold allows investors in many cases to lower
the overall risk of a portfolio without sacrificing returns.
Furthermore, the characteristics that gold exhibits in terms
of its performance, volatility and correlation to other assets
should help reduce potential losses in a portfolio. We also
show how, using a common measure for “maximum expected
loss” in a given period of time, gold can be used to manage
risk more effectively and ultimately, protect an investor’s capi-
tal against potential losses in negative economic conditions.
Specifically, we use value at risk to achieve this observation.
Simply put, VaR is a way of measuring how much an investor
can expect to lose in a given portfolio, during a certain period
of time and with a specified confidence level.2
While the analysis is based on historical performance
and future uncertainty can affect the results, the data shows
gold’s usefulness in protecting against systemic risk in mul-
tiple scenarios.
Asset And Period Selection
We use a collection of assets representative of a typi-
cal investment portfolio, namely cash, U.S. Treasury and
corporate bonds, international debt from developed and
emerging markets, U.S. and international equities and a
commodity index, in addition to gold. Ideally, one would
use a series going back as far as 1972, the year by which
the gold window had been closed and the metal’s price was
allowed to float freely. However, a modern investor typically
holds many more assets in a portfolio than those available
in the ’70s and early ’80s, and for some—such as high-yield
bonds, or emerging markets sovereign debt and equities—
data is not available or is unreliable. Thus, the period under
consideration for this analysis spans from January 1987 to
July 2010, a period for which most data series are available.
Moreover, this period contains at least three business cycles
and includes multiple market crashes.
Figure 4 shows the assets selected to construct the model
portfolio, as well as their summary statistics over the period,
such as average return, volatility, information ratio (defined
as nominal return divided by volatility) and VaR. While gold
exhibits a lower information ratio than other assets listed
in Figure 4, its diversification properties make it a valuable
asset to hold in a portfolio. Furthermore, the maximum
expected loss experienced by gold is, in many cases, lower
than that of other assets with higher information ratios.
To find the optimal weights employed to construct
different sample portfolios, we use resampled efficiency
(RE) optimization developed by Michaud and Michaud.3
We apply “projected” long-term real returns to remove a
potential period bias. We then use the volatility and cor-
relation estimates based on weekly returns from January
1987 to July 2010. For the correlation structure estima-
tion, we use two scenarios. In the first scenario, we use
average correlations for the whole period as inputs for
the optimizer. This scenario produces portfolios designed
to maximize expected returns over the long run. For the
second scenario, we use the correlation structure observed
in periods of higher risk, or when U.S. equities fell by more
than two standard deviations, as explained above. This sce-
nario creates portfolios constructed to maximize expected
returns by taking advantage of asset interactions observed
during periods of higher risk.
Source: LBMA, JP Morgan, Barclays Capital, MSCI Barra, Standard & Poor’s, WGC.
Figure 4
Performance of selected assets in a model portfolio; Jan ‘87-Jul ‘101
Gold (US$/oz) 4.7 1.8 2.0 15.3 0.31 451 590
JP Morgan 3-month T-Bill Index 5.0 2.1 0.0 1.0 5.05 - -
BarCap US Treasury Aggregate 7.0 4.0 4.0 4.8 1.46 130 166
BarCap Global ex US Treasury Aggregate 7.5 4.5 4.0 8.9 0.85 223 252
BarCap US Credit Index 7.6 4.6 4.0 5.2 1.48 138 175
BarCap US High Yield Index 8.3 5.3 5.0 8.2 1.01 209 338
JP Morgan EM Sovereign Debt Index7 13.0 10.2 6.0 12.8 1.02 358 566
MSCI US Equity Index 8.6 5.5 8.0 17.3 0.50 466 708
MSCI EAFE Equity Index 5.7 2.7 8.0 18.1 0.31 490 736
MSCI EM Equity Index 10.7 7.6 10.0 22.2 0.48 686 946
S&P Goldman Sachs Commodity Index 6.8 3.7 2.0 21.1 0.32 636 896
Note: Performance based on total return indexes except for gold in which spot price is used.
1) MSCI EM from Dec ‘87 and JPMorgan EM Sovereign Debt Index from Dec ‘90; 2) compounded annual growth rate; 3) projected returns used for simulation and optimization;
4) estimated using weekly returns; 5) ratio of nominal return and volatility, also known as avg. risk-adjusted return (a higher number indicates a better return per unit of risk); 6)
expected maximum loss during a week at a given confidence level (1— A) from a US$10 million investment; 7) EMBI prior to Jan ‘00 and EMBI Global after due to data availability.
Nominal Real Projected3
Annualised
Volatility4 (%)
Inf.
Ratio52.5% 1.0%
CAGR2 (%) Weekly VaR (US$ ‘000s)6
Performance Of Selected Assets In A Model Portfolio; Jan ’87 - Jul ’101
November/December 201030
Portfolio optimization produces a myriad of different
combinations that form the “efficient frontier.” While each
asset allocation that falls upon this frontier is considered
optimal, we perform 500 simulations to obtain an expected
efficient frontier curve. We find that adding gold to a portfolio
increased returns for a given level of volatility 68 percent of
the time. This means, on average, a 3.4 percent increase and
as much as 22 percent for some risk/return combinations.
Conversely, for the other 32 percent of portfolios where gold
does not increase returns, the average was -0.4 percent and
the maximum differential -0.8 percent. In particular, including
gold in the asset mix increases the value of the portfolio with
Summary Statistics And Asset Weight Allocation For Each Of The Selected Portfolios
Sources: LBMA, JP Morgan, Barclays Capital, MSCI Barra, Standard & Poor’s, WGC
Figure 5
W/O
Gold
W/O
Gold
With
Gold
With
Gold
Max. Inf. Ratio* Benchmark †
Senario 1: Average Correlation1
W/O
Gold
W/O
Gold
With
Gold
With
Gold
Max. Inf. Ratio* Benchmark †
Scenario 2: “High Risk” Correlation3
Expected annual return (%) 3.4 3.3 7.0 7.0
Annualized volatility (%) 3.4 3.3 11.8 11.8
Information ratio2 1.002 1.002 0.589 0.591
Portfolio weights
Gold (US$/oz) - 3% - 6%
JP Morgan 3-month T-Bill Index 29% 30% 0% 0%
BarCap US Treasury Aggregate 36% 35% 8% 7%
BarCap Global ex US Treasury Aggregate 7% 6% 7% 7%
BarCap US Credit Index 3% 2% 2% 2%
BarCap US High Yield Index 11% 11% 5% 7%
JP Morgan EM Sovereign Debt 3% 3% 10% 8%
MSCI US Equity Index 4% 4% 19% 17%
MSCI EAFE Equity Index 2% 2% 15% 14%
MSCI EM Equity Index 3% 3% 25% 26%
S&P Goldman Sachs Commodity Index 2% 1% 8% 7%
Expected annual return (%) 3.2 3.1 6.9 6.9
Annualized volatility (%) 2.4 2.3 11.9 11.7
Information ratio 1.301 1.342 0.583 0.586
Portfolio weights
Gold (US$/oz) - 4% - 9%
JP Morgan 3-month T-Bill Index 30% 34% 0% 0%
BarCap US Treasury Aggregate 37% 33% 15% 14%
BarCap Global ex US Treasury Aggregate 9% 7% 10% 9%
BarCap US Credit Index 0% 0% 1% 1%
BarCap US High Yield Index 17% 18% 7% 8%
JP Morgan EM Sovereign Debt 4% 3% 6% 5%
MSCI US Equity Index 0% 0% 21% 19%
MSCI EAFE Equity Index 0% 0% 9% 9%
MSCI EM Equity Index 2% 1% 25% 24%
S&P Goldman Sachs Commodity Index 0% 0% 5% 3%
1) Correlation estimation using all weekly returns from Jan ‘87 to Jul ‘10; 2) expected return divided by volatility, also known as avg. risk-adjusted return (a higher number indi-
cates a better return per unit of risk); 3) correlation estimation using only weekly returns in which the MSCI equity index fell by more than 2 std. deviations over the same period.
* Portfolio selection based on allocations that achieved the maximum information ratio available. † Portfolio selection based on allocations that resembled benchmark portfolio
of 55% equities, 40% fixed income, and 5% alternative assets, with similar expected returns.
November/December 2010www.journalofindexes.com 31
the maximum information ratio (expected return divided by
volatility). In other words, an investor choosing to include gold
in their portfolio allocation is likely to obtain similar returns at
a lower level of risk than an investor who does not include it.
For simplicity, to compare the effect on VaR, we select
a finite number of portfolios. For each scenario (allocation
based on long-term correlation and high-risk correlation)
we find optimal asset allocations with and without gold. We
then choose: 1) the portfolio with the maximum risk-adjust-
ed return; and 2) a portfolio with a similar composition to a
typical benchmark allocation (50 to 60 percent equities, 30
to 40 percent fixed income and 5 to 10 percent alternative
assets), such that the portfolios with and without gold during
the optimization have similar expected returns. Therefore,
we compare a total of eight portfolios.
Figure 5 shows the expected return, volatility and
information ratio for each portfolio, as well as the weight
assigned to each asset. On one hand, the selected portfo-
lios with maximum information ratios produce more “con-
servative” asset allocations, with heavy weights in cash
and fixed income. On the other hand, “optimal” bench-
marklike portfolios weighted fixed-income assets evenly
among various classes when average correlations were
used, while increasing exposure to cash and Treasurys
in the “high risk” scenario, as one would expect. Finally,
allocations to gold ranged from 3 to 9 percent, consistent
with findings in previous analysis performed by the World
Gold Council. Considering that gold’s correlations to other
assets generally dropped in the “high risk” correlation
scenario, it is not surprising that this scenario had the
largest weight for gold, at about 9 percent. More interest-
ingly, gold, unlike the commodity index, had positive (and
statistically significant) allocations not only in the selected
portfolios but throughout the whole efficient frontier.
Relatively small allocations to gold can be shown to
help investors reduce potential losses without substan-
tially sacrificing expected return. Using the empirical
distribution of all asset returns from January 1987 to
July 2010, we compute average returns, volatilities and
VaRs for each of the selected portfolios (Figure 6). We
consistently find that including gold in a portfolio deliv-
ers similar expected returns with lower volatilities, while
reducing weekly VaR by between 0.1 and 18.5 percent.
For example, using average correlation estimates, adding
gold to the portfolio mix reduces the weekly 2.5 percent
VaR by 6.9 percent for a maximum information ratio allo-
cation and by 18.5 percent when using a “high risk” port-
folio allocation. Similarly, using a benchmarklike portfo-
lio, including gold, reduces the weekly expected loss by
between 2.8 and 5.8 percent at a 97.5 percent confidence
level (2.5 percent VaR). Only in the benchmarklike port-
folio using average correlation estimates is the weekly 1
percent VaR similar in both cases.
We have established that, in general, there is a good
case to be made for adding gold to a portfolio. Indeed,
expected losses tend to diminish without necessarily sacri-
ficing return. To put this into perspective, we analyze the
performance of the selected portfolios during the period
between October 2007 and March 2009—in the midst of
the global recession. We find that for the benchmarklike
portfolios, by adding allocations to gold between 6 and
9 percent, investors would have reduced their losses by
$350,000 to $500,000 (3.5 to 5.0 percentage points) on a
$10 million investment during this period.4
Figure 6
Weekly Value At Risk (VaR) On A US$10 Million Investment For Selected Portfolios
With And Without Including Gold; Jan ’87-Jul ’10
Sources: LBMA, JP Morgan, Barclays Capital, MSCI Barra, Standard & Poor’s, WGC
Gold weight - 3% - 6% - 4% - 9%
Expected annual return (%) 6.6 6.5 8.1 8.0 6.6 6.5 7.9 7.7
Annualized volatility (%) 3.2 3. 12.1 11.7 2.9 2.6 11.0 10.4
Information ratio3 2.06 2.13 0.67 0.68 2.31 2.50 0.72 0.74
2.5% VaR (US$ ‘000) 76 71 348 338 69 58 318 301
Gain (loss) by including 4.9 9.6 10.7 17.5
gold in US$ ‘000 and % 6.9% 2.8% 18.5% 5.8%
1.0% VaR (US$ ‘000) 108 96 478 477 95 83 443 429
Gain (loss) by including 12.2 0.5 12.2 14.0
gold in US$ ‘000 and % 12.7% 0.1% 14.7% 3.3%
1) Correlation estimation using all weekly returns from Jan ‘87 to Jul ‘10; 2) correlation estimation using only weekly returns in which the MSCI equity index fell by more than 2 std.
deviations over the same period; 3) expected return divided by volatility, also known as avg. risk-adjusted return (a higher number indicates a better return per unit of risk).
* Portfolio selection based on allocations that achieved the maximum information ratio available. † Portfolio selection based on allocations that resembled benchmark portfolio
of 55% equities, 40% fixed income, and 5% alternative assets, with similar expected returns.
W/O
Gold
W/O
Gold
W/O
Gold
W/O
Gold
With
Gold
With
Gold
With
Gold
With
Gold
Max. Inf. Ratio* Max. Inf. Ratio*Benchmark† Benchmark†
Scenario 2: “High Risk” Correlation2Scenario 1: Average Correlation1
November/December 201032
Conclusion
Gold is first and foremost a consistent portfolio diversifi-
er. Moreover, we find that gold effectively helps manage risk
in a portfolio, not only by means of increasing risk-adjusted
returns, but also by reducing expected losses incurred in
extreme circumstances. Such tail-risk events, while unlikely,
can be seen to have a damaging effect on an investor’s capital.
On one hand, short- and medium-term holders—individual
and institutional alike—can take advantage of gold’s unique
correlation to other assets to achieve better returns during
times of turmoil. This is especially true given that gold’s
correlation tends to change in a way that benefits investors
who hold it within their portfolios. On the other hand, by
including gold in their portfolios, long-term holders—such
as retirement savings accounts, pension plans, endowments
and other institutional investors—can manage risk without
necessarily sacrificing much sought-after returns.
Our analysis suggests that even relatively small allocations
to gold, ranging from 2 to 9 percent, can have a positive impact
on the structure of a portfolio. We find that, on average, such
allocations can reduce the VaR of a portfolio, while maintain-
ing a similar return profile to equivalent portfolios that do not
include gold. For the eight portfolios analyzed using data from
January 1987 to July 2010, adding gold reduced the 1 and 2.5
percent VaR by between 0.1 and 18.5 percent.
We also note that investors who hold gold only in the form
of a commodity index are likely to be under-allocated.5 There is a
strong case for gold to be allocated as an asset class on its own
merits. It is part commodity, part luxury consumption good and
part financial asset, and as such, its price does not always behave
like other asset classes and especially not other commodities.
Finally, while most of this analysis concentrates on risk in the
form of tail-risk and volatility, gold has other unique risk-related
attributes that make it very useful in periods of financial distress.
For example, the gold market is highly liquid and many gold bul-
lion investments have neither credit nor counterparty risk.
Endnotes
1 For a more in-depth analysis on negative economic news and gold, see Roach S.K. and M. Rossi (2009), “The Effects of Economic News on Commodity Prices: Is Gold Just Another
Commodity?” IMF Working Paper.
2 In statistical terms, the VaR of a portfolio, at a given confidence level α between zero and one, is the minimum loss, such that the probability that any other loss exceeds that
value, is not greater than (1 − α) during a period of time.
3 Michaud, R. and R. Michaud (2008), “Efficient Asset Management: A Practical Guide to Stock and Portfolio Optimization and Asset Allocation,” 2nd edition, Oxford Press, New York.
4 A more detailed listing of historical examples can be found in the World Gold Council’s “Gold: hedging against inflation,” October 2010.
5 Gold’s weight in typical benchmark commodity indexes, such as the S&P GSCI or the Dow-Jones UBS Commodity Index, tends to be small, usually between 2 and 6 percent. Even
if an investor holds a 10 percent allocation in one of these indexes, their effective gold exposure is between 0.2 percent and 0.6 percent.
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34 November/December 2010
What’s the best way to represent a unique
and burgeoning asset class?
Commodities Indexing Roundtable
November/December 2010www.journalofindexes.com 35
The Journal of Indexes spoke with some of the best-known figures
in the field of commodities indexes and got their take on how best
to measure the asset class.
K. Geert Rouwenhorst
Co-founder of SummerHaven Investment
Management; professor of finance at the Yale
School of Management; deputy director of
the International Center for Finance at Yale
(SummerHaven Dynamic Commodity Index)
JOI: What is the best way to weight a commodities index, in
your view?
Rouwenhorst: It depends on whose perspective you are tak-
ing. Unlike stocks, there is really no natural way to choose
commodities in an index. For example, cap-weighting is a
common method for stocks, but for every long, there’s a
short in commodity futures, so cap-weighting is not really an
option. It all adds up to zero. That means that you have to
look in a different direction.
Investment banks are likely to prefer an index that has
the highest liquidity, and therefore the capacity to do client
business; however, investors probably have different objec-
tives than banks. And for an investor, the index ought to be
one that is the most beneficial in terms of contributing to the
overall risk and return of their portfolios. These are potentially
conflicting objectives. But whatever your perspective, I think
most people will agree on a middle ground: that any index
probably ought to have some representation of all sectors
in the commodity universe and that it should use weights to
guarantee that the index remains reasonably diversified.
JOI: How should the component commodities for an index
be selected?
Rouwenhorst: I think there are very few rules here. You
probably would like the index to be reasonably liquid so as
to provide a timely measure of overall price movements in
the market, and you would like the index to be investable
so as to be a useful benchmark for investors. You also want
the market for the components to be sufficiently deep in
terms of open interest. Finally, you probably would like the
component commodities to be investable. Some commodi-
ties don’t have a futures contract associated with them: You
might have spot prices of a commodity, but spot markets are
not easily investable for investors. That would be a reason to
exclude those component commodities from an index.
JOI: Can an index reliably avoid or alleviate contango?
Rouwenhorst: In our study, “Facts and Fantasies about
Commodity Futures,” Gary Gorton and I studied a large cross
section of commodities between 1959 and 2004. And over
this period, the average commodity was in contango, but an
equally weighted portfolio of commodities futures actually
had quite attractive returns. Contango in markets doesn’t
preclude investors earning positive investment returns.
However, there are reasons to prefer commodities that
are in backwardation. As we showed in our follow-up paper,
“The Fundamentals of Commodity Futures Returns,” [written
with Fumio Hayashi] when you select commodities that are
in backwardation, the compensation that you get per unit of
risk is a lot better than if you simply hold all commodities at
the same time, at least over the period of our study and the
commodities that we studied. But there are times when it
is very difficult to only invest in backwardated commodities
and maintain a diversified position at the same time. It may
well be that there are only four commodities in backwarda-
tion at a given time. Therefore, you couldn’t completely con-
struct an index around commodities that are backwardated
and still maintain a diversified position in all these markets.
JOI: Should the weighting of energy or any other commodity sec-
tor be capped within an index?
Rouwenhorst: Well, it certainly seems reasonable to think that
you would like an index to be diversified in some form. It seems
to me that if you chose an equity index, and 80 percent of that
index was Microsoft, it would not be a desirable benchmark for
investors. I’d think that a diversified index would be preferred
to an index that can be very slanted in its positions.
JOI: Are commodities indexes being gamed by traders? And is it
affecting the returns of products based on those indexes?
Rouwenhorst: The indexes are constructed based on rules
that are written down. And by construction, all commodity
indexes represent a trading strategy in the market, because
the underlying futures mature.
Because the trading strategy is completely predictable,
that invites timing on behalf of market makers who might
try to front-run the index investors. Having said that—and I
don’t have really hard facts to back this up—it’s my impres-
sion that many investors realize this danger of exactly holding
the index, and have, over time, decided to spread out the roll
dates away from those specified in the index handbooks. I
think a fair amount of the market now rolls at different times
than the indexes do—or they’re maybe in different contracts
than the index holds. I think that there has been a response by
investors to try to prevent that from happening.
JOI: Does index-based investing distort the commodities market?
Rouwenhorst: This is obviously a contentious debate, and
people come out very strongly on both sides. I like to collect
and study data and see if I can find evidence of whether this
type of investing influences prices.
For example, one market where this is hotly debated is crude
oil. If you look at the CFTC data on the number of oil contracts
held by index investors, it’s actually been quite stable over the
last four years. But this is also a period where crude oil went
from $80 to $145, then down to $40 and back up to $80.
I was not surprised when I saw this, because index inves-
tors tend to be long term. I expect their allocation to com-
modities to be a strategic one, and I don’t expect them to
November/December 201036
move in and out of the market based on short-term price
movements. I believe a CFTC report reached a very similar
conclusion. I am not saying investors do not influence prices,
but the evidence on index investors seems somewhat weak.
James Rogers
Commodities expert
(Rogers International Commodity Index)
JOI: What is the best way to weight a com-
modities index, in your view?
Rogers: Theoretically, the best weighting is based on what
people use and consume around the world. But that’s not
practical, since the listed markets are not adequate for that.
There is no listed market, for instance, in rare earth metals,
even though the world needs them desperately. You cannot
put uranium or water into a listed commodity index because
there’s no listed market. [On the other hand,] two-thirds of
the people in the world eat rice every day. Yet the liquid
traded market at the moment in rice is not conducive to
weighting rice in the index heavily enough to reflect its sig-
nificance in the world. You have to combine the fact of what
people use with the liquidity that exists.
JOI: How should the component commodities for an index
be selected?
Rogers: It’s got to be something that’s traded on a public,
visible market somewhere in the world. And it’s got to be
liquid enough so that it can be traded.
I wanted to start an index which would reflect the cost of
doing business around the world or the cost of staying alive
around the world, call it what you will. The more I got into it, the
more I realized the GSCI was very American-centric; Goldman
Sachs was arbitraging against the customers investing in their
index, and so they needed an index which was tradable during
hours when they could arbitrage. I didn’t have any customers—I
still don’t have any customers—to arbitrage against, so I wanted
something that was reflecting the whole world. I could see that
nobody had rice, even though two-thirds of the population eats
rice every day. They did not have rubber, even though rubber is
[unbelievably important] on the world market.
The other problem I had with the other indexes was that
they changed dramatically every year; you had no idea what
you were going to own in three years—and even today.
Because they had to follow formulas that locked them in,
Goldman Sachs’ index’s weighting in energy in the past couple
of decades has ranged from a 39 percent weighting to 76 per-
cent in the course of 22 years. Livestock in that same period
has ranged from 26 percent to 3 percent. I will not invest in
something like that, where it’s changing very dramatically over
the years, because I have no idea what I’m buying or selling,
because who knows how the changes are going to take place?
The GSCI and all the others are based on a principle: If
something goes up in price, they’re going to increase the
weighting. With my [index’s] weightings, I decided it had to
be stable, consistent and transparent. It had to try to balance
the consumption of the world with the liquidity. I set out to
have a stable, consistent, transparent index based on world-
wide consumption patterns.
JOI: Can an index reliably avoid or alleviate contango?
Rogers: My index cannot. If an index can, then it’s not an index—
it’s a managed account or [something similar]. Contango has
been coming and going for decades around these markets, and
I’m truly astonished with all the talk about contango and back-
wardation, as if they were all of a sudden new developments.
They come and go; they work themselves out—they always
have, in a way. If something gets out of whack, you can [avoid
contango], but I don’t worry about it too much.
I also have an enhanced index which does have a couple
simple modifications so that it buys other contracts, but
they’re very simple and straightforward modifications. If
you’re going to try to really move around contango, there
have been hundreds of thousands of people doing it for
decades. The commodities market has always known about
this, even though the press and Wall Street have just recently
learned about it. But thousands of people have been trying
to take advantage of the fluctuations in contango and back-
wardation for decades, and more power to them if they can
do it. But that’s a managed account, that’s not an index. An
index is something you can’t change.
All I’m trying to do is have a simple, straightforward
index. I’m not trying to outsmart anybody.
JOI: Should the weighting of energy or any other commodity sec-
tors be capped within an index?
Rogers: Not in my view. I know the Dow Jones AIG did that
as a “competitive” response, but it doesn’t make any sense
to me that you would say something can be capped because,
whether you like it or not, energy is extremely important to
the world. But I think the Goldman Sachs approach of hav-
ing up to 76 percent energy was [also] pointless. What’s the
sense in having an index that’s 76 percent energy?
JOI: Are commodities indexes being gamed by traders? And is it
affecting the returns of products based on those indexes?
Rogers: Well, they certainly were before, and they are now, to
some extent. But again, with some of the temporary fluctua-
tions in an index, most of the index guys have learned ways to
get around that. Unfortunately, in the financial markets, you’re
always going to have somebody trying to “game” anything. If
IBM announces they’re going to have a stock offering, there
are people running around taking advantage of that effect.
There always have been and always will be. A way to try to get
around it is to manage the account and try to outsmart those
guys, but then it’s not an index fund anymore; it’s a managed
account. Many studies have repeatedly demonstrated that
index or passive investing outperforms active investing 70 or
80 percent of the time year after year after year.
JOI: Does index-based investing distort the commodities market?
November/December 2010www.journalofindexes.com 37
Rogers: That is one of the most absurd things that [people
say]. It’s reactive. If their money’s invested in it, the investors
have an effect on the market. Money goes where the funda-
mentals are sound, and that’s always going to happen. If you
don’t want that to happen, you cannot have markets. But the
world needs markets, as we’ve learned over and over again.
If you’re going to talk about somebody who distorts mar-
kets, index trading of stocks is a huge distortion. If you look at
the S&P 500 index funds, they buy stocks and take them off the
market. That has a serious effect on the market. It reduces the
number of shares available of IBM [for example] and reduces
the liquidity and therefore has an ongoing effect, because those
shares are off the market and they don’t come back.
Commodity index investors don’t take delivery of any-
thing. Anything they buy, they sell in a few days, weeks, a
month, whatever. It’s money which comes back out of the
market. It’s not as though index investors are taking silver
off the market and putting it in a warehouse somewhere so
that there’s less available to the market and less liquidity [as
happens with physical ETFs].
Michael McGlone
Director of commodity indexing,
Standard & Poor’s
(S&P GSCI)
JOI: What is the best way to weight a com-
modities index, in your view?
McGlone: That’s very subjective. Our S&P GSCI is produc-
tion weighted and it’s generally considered the most widely
tracked commodity index. I suppose that has its advan-
tages. It’s subjective to whomever uses it, but the benefit
of that world production weighting is it gets you exposed
to the world’s economy.
In the case of the S&P GSCI, its highest exposure is to
energy and petroleum, partly because it’s the most signifi-
cant commodity in the world. If the price of food goes up
in a short period of time, that’s not going to impact global
economies as much as if the price of petroleum goes up a lot
in a short period of time.
JOI: How should the component commodities for an index
be selected?
McGlone: First and foremost, by significance in the world
economy. But our S&P GSCI tracks futures. One of the key
prerequisites is that they have to be as liquid, investable
and tradable as possible to be part of the index. If there’s
anything that’s not liquid, it can’t be included. For example,
iron ore and steel aren’t included in the S&P GSCI because
futures on steel are not liquid enough.
JOI: Can an index reliably avoid or alleviate contango?
McGlone: We have a number of indexes designed to do
exactly that. For instance, we have the S&P GSCI Enhanced
Index, which is specifically designed to reduce the potential
negative roll impacts of contango. And we have our forward
indices that are designed that do the same.
But one thing’s significant: Any index that’s designed to
alleviate contango or moves forward on the futures curve
generally has less liquidity, because it’s not using the most
liquid contract. The original GSCI is designed to be in the
most active liquid contracts all the time.
People also have to remember we’re in the aftermath of
one of the worst recessions post-World War II; and the futures
curve, contango and backwardation are directly correlated
with supply and demand—which is directly correlated with
economic activity. Of course, we’ve had a pretty substantial
falloff in economic activity and demand for commodities.
Contango’s been somewhat accentuated as a result. But these
things always work in cycles, and when demand picks up
globally, these things will generally reverse. Historically, in
the long term, contango and backwardation have not been big
factors. We’re in the midst of a unique historical aberration.
JOI: Are commodities indexes being gamed by traders? And is it
affecting the returns of products based on those indexes?
McGlone: I’m an ex-trader myself, and I remember one thing:
If it’s a free lunch, how long is it going to last? There’s always
going to be traders trying to predict the commodity index
roll. In the short term, they may make money, but often that
will invert completely the opposite way.
I think there were periods in the past in which traders
could predict the S&P GSCI roll, but generally, if it’s a free
lunch—as we know in trading—that doesn’t last very long.
JOI: Does index-based investing distort the commodities market?
McGlone: People have to remember that first, it’s basically
theoretically impossible. As far as our indices are concerned,
they’re all futures based. And when you use a future to make
or reflect a position in a commodity, you never make or take
deliveries. So that should never add or remove supplies from
the markets. On the big picture, Economics 101, there’s no
supply or demand impact.
On the short term, sure. If there’s a major reallocation to
a commodity index tracking futures, it is likely to boost the
futures market in the short term, but without actually tak-
ing physical supplies off the market, the longer-term impact
is minimal. In the long term, the fundamentals will always
prevail. Our indices are designed to always use only the most
liquid contracts, and they always sell the futures before they
expire, meaning before delivery. There’s an argument they
actually could put negative pressure on the cash commod-
ity.
John Prestbo
Editor and executive director,
Dow Jones Indexes
(Dow Jones-UBS Commodity Index)
JOI: What is the best way to weight a com-
modities index, in your view?
November/December 201038
Prestbo: It depends on the purpose of the index. The purpose of
the Dow Jones-UBS Commodity Index is to provide investors with
a diversified exposure to commodities. It’s not so much to track
the prices of commodities; there are lots of indexes that can do
that. And many [commodities are] unrelated markets, anyway.
Since the purpose is to provide this diversified exposure, the
weighting is done on the basis of production, to some extent, but
mainly on futures trading volume so that liquidity is involved in
the selection of components and in their weightings.
JOI: How should the component commodities for an index
be selected?
Prestbo: Again, it depends upon the purpose of the index. You
can have a narrow selection of commodities, or you can have a
wide selection. The Dow Jones-UBS Commodity Index is kind of
in the middle: On the one hand, you have diversification; on the
other hand, the components are restricted to liquid markets. We
had 20 commodities when we started out 10 years or so ago,
but we dropped cocoa because trading volume times production
was getting less significant, so we reduced the number to 19.
JOI: Can an index reliably avoid or alleviate contango?
Prestbo: You can’t avoid contango. You’re either in the mar-
ket or you’re not. If you’re in the market and that market
is in contango, you’re stuck. Assuming that you don’t want
to take delivery, you roll forward, and then you will pay a
higher price in the markets than what you are getting for
selling the expiring contracts.
There are ways to alleviate it in terms of reducing the
amount of roll loss by choosing, for example, a contract that
might have less of an incremental increase over the spot
amount. But you’re just reducing contango, not eliminating it.
What really has happened is that commodities prices
were going gangbusters and nobody noticed that contango
was robbing them to some extent on the roll. But when the
recession came and prices fell, all of a sudden they started
noticing and complaining about it.
JOI: Should the weighting of energy or any other commodity sec-
tor be capped within an index?
Prestbo: We do. No major sector can be more than 33 percent of
the Dow Jones-UBS Commodity Index. The reason for that goes
back to the purpose of the index, which is to assure diversified
exposure to commodities. If we weight strictly on the raw num-
bers of production times liquidity, we overweight some things
and underweight others. We mitigate that by imposing ceilings
to assure diversification. We place a higher value on diversifica-
tion than we do on representing the various commodity markets
according to production and futures trading volume.
JOI: Are commodities indexes being gamed by traders? And is it
affecting the returns of products based on those indexes?
Prestbo: I think we’ve all seen articles recently where traders
crow about gaming the indexes, so I don’t think there’s any
doubt about that. As to whether it affects the returns of prod-
ucts based on those indexes, I’m sure it does if it exacerbates
contango in a market or reduces the amount of backwardation.
But to what degree, I have no idea. And whether it is a mate-
rial problem, I have no idea either. I do know that index-based
commodity products give investors access to an asset class in a
way they never had before, and that adding commodities to your
portfolio probably has greater diversification benefits than what
might be lost to a magpie hopping around the roll month.
JOI: Does index-based investing distort the commodities market?
Prestbo: That’s one of those impossible questions. I don’t
think it does. I think the commodities markets are driven
by supply and demand—and that’s supply and demand for
the commodities themselves, not for the futures contracts,
which represent a subset of that and which represent a dif-
ferent aspect of commodities markets. Futures markets are
not where real commodities are bought and sold.
Does it distort the futures market? I don’t know what
distort means, but I’m sure it does have an influence. Then
you’d have to ask, “Does the futures market distort the spot
market?” I don’t think so. I think supply and demand for each
individual commodity is much larger than any set of futures
traders can influence, except maybe on a short-term basis.
Martin Kremenstein
Chief Investment Officer, DB Commodity
Services, Deutsche Bank
(Deutsche Bank Liquid Commodity Index)
JOI: What is the best way to weight a com-
modities index, in your view?
Kremenstein: There are several things to take into consider-
ation when looking at weighting commodity sector alloca-
tions: You’ve got to look at the size of the market and the
size of production of the commodities. If you create a pure
production-weighted index, you end up with something
that’s economically true to the importance of each commod-
ity in the world economy, but you also end up with a basket
that’s heavily skewed toward energy products.
To have an index that will actually respond to the other
subsectors, you need to trim down that energy weighting
and allocate to some of the others a bit more. That’s what
we did. We looked at the liquidity and the tradability of
the market when we allocated. What you end up with is
an index that takes into account both the economic signifi-
cance of a commodity and its tradability.
JOI: How should the component commodities for an index
be selected?
Kremenstein: Again, we look at their significance. You want
to look at the commodities that are significant in the econ-
omy, and also look at their tradability. With futures, there
are moving parts, so you need to make sure that you have
ongoing liquidity to roll those positions.
November/December 2010www.journalofindexes.com 39
JOI: Can an index reliably avoid or alleviate contango?
Kremenstein: I believe a well-constructed index can allevi-
ate it or mitigate it to a certain extent. You can’t avoid
it outright unless you disinvest completely. Commodity
futures are always subject to the curve. The “Optimum Yield”
methodology that Deutsche Bank uses helps to mitigate the
effects of contango on the portfolio.
In 2009, DBO [NYSE Arca: DBO] was still subject to the
contango in the oil futures markets, but using Optimum
Yield, it was able to mitigate that effect, and significantly
outperformed front-month rolling strategies.
JOI: Should the weighting of energy or any other commodity sec-
tor be capped within an index?
Kremenstein: If you want to get the full diversification benefit of
commodities and want the index to move with broad commodity
prices, you might want to cap a certain sector’s base weighting.
When people are looking to commodities for diversifica-
tion, they’re looking for broad inflation protection, and they’re
looking for that diversification benefit. If you have a sector
that overshadows the rest of the index, you end up with a
return profile and a correlation profile that isn’t as ideal.
JOI: Are commodities indexes being gamed by traders? And is it
affecting the returns of products based on those indexes?
Kremenstein: There’s been a lot of talk by people saying they
are, but it’s hard to actually find empirical evidence of that
fact. John Hyland [of United States Commodity Funds] pro-
duced research showing the spreads between oil contracts
before, during and after a USO roll, and showing that there’s
no reliable evidence of the futures being gamed. So if they’re
not [being gamed], then you must reach the conclusion that it
isn’t affecting returns of the products based on these futures-
based indices. I’ve yet to see any proof that they are.
JOI: Does index-based investing distort the commodities market?
Kremenstein: My answer is no. There’s been a lot of talk
about it, but nobody who’s made the accusation has actually
come up with any kind of proof to back it up. The fact is,
when the futures are going into delivery, there are no index
products in those contracts. None of the indices can take
delivery. If you thought they were distorting the market,
you’d expect to see prices collapse in that front month—as
the investor money rolls out—and it just doesn’t happen.
Ed Carroll
Head of Commodity Structured Products
Trading, UBS
(UBS Bloomberg Constant Maturity
Commodity Index)
JOI: What is the best way to weight a commodities index, in
your view?
Carroll: I think that the weighting of a commodity index
should be pretty even and broad. The objective of a com-
modity index is to be representative of the whole underly-
ing market, so it should be fairly equally weighted across
sectors. Of course, crude oil is currently more significant
globally than a rough rice or a soybean oil, and should have
a high weighting. But choosing a heavier weighting in one
component or another should be more the job for the asset
manager or trader. The job of the index is to provide a rep-
resentation of the market as a whole.
JOI: How should the component commodities for an index
be selected?
Carroll: I think considerations of global production and con-
sumption should be used when considering what commodi-
ties to include. Most people want their commodity index to
be representative of the things that are relevant to the global
community, so throwing in tiny commodities for the sake of
diversification doesn’t necessarily make sense. Furthermore,
I think you have to consider issues of liquidity and price
discovery to make sure that you’ve got a fair and balanced
representation of that commodity market.
JOI: Can an index reliably avoid or alleviate contango?
Carroll: Basically, the goal of an index is and always has been
to represent a holding in underlying physical commodities,
which is why the early indices were front-month indices—it
was believed that the front-month price would most closely
resemble the price of the physical underlying. A commodity
index, however, isn’t a holding in physical commodities; it’s
a holding in financial futures based on underlying commodi-
ties. The two things are different, so they’re never going to
respond exactly the same to market events and moves.
That said, there are things that you can do to alleviate the
contango effect. UBS’ CMCI index holds positions all the way
down the curve …, which means you’re not exposed to the price
action and spreads over any specific period in time. We’ve tried
to closely emulate the holding in the physical by not exposing
the investor to any one particular part of the futures curve.
JOI: Should the weighting of energy or any other commodity
sector be capped within an index?
Carroll: No single component commodity or commodity sec-
tor should be allowed to dominate the weighting of a com-
modity index if that index is serving the goal of representing
the broader commodity market. Our benchmark indices are
designed to be representative of the whole commodity uni-
verse. They are not held hostage by news and market moves in
one specific underlying. Controlling the balance of the index is
important to remain representative and relevant.
JOI: Are commodities indexes being gamed by traders? And is it
affecting the returns of products based on those indexes?
continued on page 41
November/December 2010
Talking Indexes
By David Blitzer
40
Do index investors affect commodities markets?
The Image Of The Investment
While commodities as well as equities as invest-
ments date back to the 19th century, the modern
era of institutional investments in commodities is
only less than a decade old. The beginning is usually marked
by the publication of “Facts and Fantasies about Commodity
Futures” by Gorton and Rouwenhorst.1 For investors whose
traditional focus was stocks or bonds, the introduction of
commodities as a new asset class opened up opportunities
but also posed some questions. The opportunities most-
ly included improved diversification, while the questions
included factors such as backwardation, contango and rolling
one’s positions. Commodities investing also brought with it
new challenges in politics and image.
Politicians don’t often link buying stocks to the problems fac-
ing the “man on the street” or institutional investors indexing to
the S&P 500 as the cause of pushing gasoline prices over $4 per
gallon. However, indexed commodities investments were seen
by many, including some regulators, as the principal cause of
record-high oil and gasoline prices. Occasionally equity invest-
ments are mentioned or criticized in the media, but these are
usually narrowly defined questions about specific companies or
products, not entire markets. Moreover, for equities, the link
between the investment and the criticism is generally clear. In
the debate about commodities and oil prices, the link was often
buried in arguments about the economics and operations of dif-
ferent markets, especially commodities futures. The debate over
oil prices and commodities indexing is a reminder that while
equities and commodities may be complements in investment
strategies, they are certainly not the same in either their market
behaviors or how they are seen by politicians and the public.
The first thing people think of when considering markets
and prices is supply and demand—unfortunately it is maybe
the last thing that some people think about when trying
to understand markets: Increased demand raises prices;
increased supply lowers prices. The argument that commodi-
ties investing raised oil prices seemed simple: As more inves-
tors purchased commodities futures, this increased demand
and pushed prices up. The sharp rise in oil prices in 2008,
coming after a few years of growth in commodities investing
through indexes, was seen by many as evidence that index
investors were responsible for the higher gasoline prices.
The way some people think markets work, and the way they
actually work, is not always the same. The fact that prices—
especially as they change—affect supply and demand may not
be fully recognized: Increased demand may raise prices, but
rising prices can also increase demand. For example, consum-
ers tend to buy things such as brand-name goods or expen-
sive brands of liquor even though the prices are higher than
“The first thing people think of when considering markets and pricesis supply and demand—unfortunately it is maybe the last thing that
some people think about when trying to understand markets.”
November/December 2010www.journalofindexes.com 41
supermarket brands. This can also be seen with stocks, where
rising prices may attract buyers rather than encourage people
to look for alternatives. So, if one believes that there is a con-
nection between increased investment in commodities futures
and higher oil prices, it is not clear which one caused the other.
For commodities markets, the puzzle is a bit more complicated
because the supply or demand of futures contracts is not the
same as the supply or demand of the underlying commodity.
Most commodities investors use commodities futures con-
tracts rather than buying and holding the actual physical com-
modities. In the futures markets there is a short position for
every long, and a long position for every short. One cannot
buy oil in the commodities market unless someone is willing
to sell oil—that’s supply and demand, meaning the number
of outstanding positions in the market are always balanced.
Moreover, the number of long (or short) positions is not direct-
ly tied to the available physical supply. In fact, most contracts
are closed out without anyone ever taking delivery of the physi-
cal commodity. Of course, the futures markets are not wholly
independent of what is happening in the physical market.
The link between the physical and the futures contract
world depends on inventories in the physical world and
the relation between futures and spot prices in the con-
tract world. Inventories depend on supply, demand and the
costs of storage and tying up capital to hold inventories.
Futures prices can be more, or less, than the spot prices.
When futures prices are less than the spot, the market is
in backwardation, and buying futures is likely to be profit-
able as the futures price converges to the higher spot price.
The opposite—contango—occurs when the futures price
is higher than the spot and the market is likely to fall as
futures and spot prices converge. Whether the market is in
backwardation or contango depends on inventories and the
cost of carrying inventory. This represents the link between
the physical and the futures markets.
Index investors in commodities futures are only one
part of the market and only one of many factors affecting
demand, supply, inventories and prices. Supply and demand
theory alone cannot tell us if commodities indexers actu-
ally raised gasoline prices, as was discussed earlier. There is
research that addresses this issue empirically. Recent work
by Hans Stoll and Robert Whaley2 explore a wide range of
commodities included in the S&P GSCI index and finds that
neither investment flows related to rolling contracts nor
establishing new positions impacts prices. Therefore, this
research seems to demonstrate that when investing in either
commodities or equities, one should not jump to conclusions
about how either of the markets work.
Endnotes1 Financial Analysts Journal, vol. 62, No. 2 (2006)
2 “Commodity Index Investing: Speculation or Diversification?” Vanderbilt University, July 2010, available at http://ssrn.com/abstract=1633908, later version Journal of Applied
Corporate Finance 1-40 (2010)
Carroll: I wouldn’t say traders are gaming commodity indices.
That implies that what traders are trying to do is cannibal-
ize the returns of commodity indices to their own benefit to
the detriment of their investors, which I don’t think is what
they’re trying to do.
Over recent years, we’ve seen traders and structurers
starting to recognize what’s popularly referred to as the
contango effect, or negative roll yield, and they have taken
steps to alleviate it. … It’s a new market, and with any new
market, not everything is known on day one. As time’s gone
by, banks have improved the products we’re offering to try
and improve the returns to our investors.
The CMCI’s daily rolling mechanism means that even as
you add a large amount to the commodity index in terms of
investment, it’s still not subject to price action around certain
roll times. There’s little danger of a sudden event in wheat,
for example—a drought—exposing the index to unfavorable
conditions around the roll because it is continuously rolling.
That means any conditions that it’s exposed to are fair and
representative of the commodity space over time.
JOI: Does index-based investing distort the commodities market?
Carroll: I don’t think we are distorting the market. It’s
certainly true that any large inflow of money into a market
that previously didn’t have that capital in there is going to
impact the market in some way. But it doesn’t necessarily
mean it’s a negative impact. The CFTC at the start of this
year, and the OECD, said that index investment hadn’t actu-
ally distorted commodity markets, and in fact, if anything,
possibly had dampened volatility.
I think that’s true in the commodities market where
you’ve had a lot more index money flowing in. What’s actu-
ally happened is we’ve improved liquidity and improved
price discovery. And that’s certainly true further down the
commodities futures curve where now there’s liquidity
in two- and three-year natural gas, where previously you
would have struggled. If anything, there have been massive
improvements in the state of the commodity markets and the
price discovery of those commodity markets that are being
overlooked. There have been impacts, but everyone always
focuses on the negative impacts. I think people very much
miss the positive impacts. I don’t think it distorts the com-
modity market. Has it affected it? Of course it has, but I think
it’s positively affected it more than anything.
Roundtable continued from page 39
November/December 201042
By John A. Haslem
Paths to the ‘Wizards of Advertising and Overconfidence’
Mutual Funds
And Investor Choice
November/December 2010www.journalofindexes.com 43
This article discusses mutual fund advertising and
investor skill in making fund choices. The research
discussed below indicates unsophisticated investor
choices are dominated by fund advertising. Fund advertising
appeals to investor emotions by resonating with their cur-
rent beliefs, not by providing direct and objective informa-
tion that enables more informed fund choices.
Sophisticated investors with self-assessed above-average
investment skills view themselves as true “wizards of Oz.”
They believe their investment skills allow them to select
superior-performing actively managed funds. Why should they
purchase “boring” index funds that only provide “average
returns?” In reality, these actively managed funds do not out-
perform index funds, leaving these “sophisticated” investors
with the average returns they so desperately tried to avoid.
Four academic articles written in the last five years capture
some key truths about mutual fund advertising and investor
investment skills; each addresses a factor in the relationship
between mutual fund advertising and investors’ choices of
mutual funds: financial literacy, investor-revealed preferences,
advertising and choice, and advertising persuasion. The behav-
ioral model (vs. rational model) resonates most closely with
the prevailing beliefs of investors in making fund choices.
Investor Financial LiteracyMüller and Weber (2010)1 developed a financial literacy test
to analyze the relationship between investor financial literacy
and choice of mutual funds. It is agreed that individual inves-
tors should buy low-cost index funds. But why, then, do 85
percent of U.S. investors invest in actively managed funds?
Investors who choose actively managed mutual funds may
do so because of superior skills in selecting funds—the “smart
money” effect. If so, these investors rely on more than the com-
mon practice of “chasing past performance.” Unsophisticated
investors with lower financial literacy scores have limited
awareness of index mutual funds, and also appear inadequately
informed of the risks, returns and especially the costs of actively
managed funds. Rather, these investors depend on fund advertis-
ing and brokers to select actively managed funds.
The personal characteristics of the German mutual fund
investors in Müller and Weber’s study are primarily: (1) male,
(2) online investors, (3) employed in financial services, (4) more
highly educated, (5) wealthier, and (6) middle aged. However,
these variables are weak proxies for financial expertise.
Importantly, there is “. . . a discrepancy among highly lit-
erate subjects between knowing about passive mutual fund
alternatives and investing in them. This effect suggests that
a lack of financial literacy among mutual fund customers can
only partly explain their reliance on active management. Even
investors with a high level of financial literacy (who thus are
aware of passive funds) overwhelmingly select active funds.”
Further, sophisticated investors with self-assessed “better
than average” investment skills overwhelmingly select active-
ly managed funds. The continuing growth in actively man-
aged mutual funds is thus not explained solely by choices of
investors with lower financial literacy. The exception to this
is investors who purchase funds online (bypassing financial
advisers). These investors frequently purchase index funds.
The results indicate that “. . . financial literacy is not relat-
ed to mutual fund selection abilities. Investors with higher
financial literacy scores are not ‘smart investors.’” Further,
“. . . any fund selection skills among more sophisticated
respondents are minor and short-lived at best.”
The results also indicate that “. . . overconfidence is a pos-
sible explanation for why even highly sophisticated partici-
pants mostly select active funds.” While self-assessed above-
average investment skill may have been positively related to
prior performance of mutual funds held by the individual, it
is not related to future performance.
Moreover, any outperformance seen by investors in
their mutual fund holdings could possibly—though it is
unlikely—be due to “chasing past performance.” If so,
these investors may take too much personal credit for good
luck. To continue to invest in actively managed funds on
this basis reflects overconfidence.
“There is a strong positive relation between financial literacy
and better-than-average thinking [self-assessed investment skill].
Hence, investors with higher financial literacy scores believe
themselves to be better than average in their mutual fund choic-
es. Apparently they are not,” Müller and Weber assert.
To conclude, having higher financial literacy scores does
not necessarily mean those investors will buy low-cost index
mutual funds. Investors with higher literacy scores are over-
confident they have above-average investment skills. They
believe these skills enable them to choose actively managed
funds that will outperform “boring” index funds with “only”
average returns. Also, investors with lower financial literacy do
not invest in index funds, but depend on fund advertising to
choose actively managed funds.
Thus, there is ample evidence sophisticated mutual fund
investors are the “wizards of overconfidence” and choices of
unsophisticated investors are dependent on fund advertis-
ing, turning them into “wizards of advertising.”
Investor-Revealed PreferencesInvestors who exhibit non-normative “revealed preferenc-
es” in making investment decisions do not act in their own
“true interests,” according to Beshears, et al. (2008).2 The
characteristics of these investors include: (1) lower financial
literacy, (2) avoidance of complex investment decisions, or
(3) inability to engage actively in the investment process.
The major reasons behind flawed revealed preference
decisions, according to Beshears et al., include: (1) passivity
in investment choices, (2) avoidance of complex investment
decisions, (3) limited personal experience and feedback,
(4) dependence on mutual fund advertising, and (5) flawed
inter-temporal decisions based on improper methodology
and assumptions.
In general, advertising affects customer choice of products in
several ways. Advertising can provide direct information about
relevant product characteristics. Uninformative advertising may
also actually signal ex ante product quality. Repeated customer
exposure to product advertising may enhance customer attitudes
toward products, even if little or no information is provided.
Thus, there is evidence that mutual fund investors who
reveal non-normative preferences in fund choices are
November/December 201044
dependent on fund advertising, earning the designation
“the wizards of advertising.”
Advertising And Investor ChoiceCronqvist (2006)3 analyzed the role of mutual fund adver-
tising in investor fund choices. The data from Sweden’s
public pension system allows the identification of several
important findings. First, “. . . only a small proportion of fund
advertising can be construed as directly informative about
characteristics relevant for rational investors, such as funds’
expense ratios.” Advertisements that do not provide direct
information about fund fees are not fully informative.
Second, “. . . funds that advertise more do not produce higher
post-advertising excess returns, so advertising does not appear
to signal higher fund manager ability.” Nevertheless, 30 percent
of mutual fund advertising focuses on performance. There may
be a positive link between advertising and abnormal returns in
funds that consider quality signaling most important. However,
overall results do not find a link between fund advertising and
fund quality, specific fund managers or subsequent returns.
Third, “[f]und advertising affects investors’ portfolio choices,
even when advertising provided little information.” There is a
positive relation between mutual fund advertising and inves-
tor purchases. However, fund marginal returns from advertis-
ing do decline, perhaps due to investor overexposure.
Mutual funds that advertise the most may also differ on
other characteristics that investors care about. Investors
allocate more to funds with recent positive media coverage.
Also, local and larger funds with higher recent returns adver-
tise much more than other funds.
Fourth, “. . . fund advertising has significant economic
effects for investors; it steers people into portfolios with lower
expected returns [higher fees] and higher risk.” Portfolio-based
advertising almost exclusively focuses on equities, active man-
agement, “hot’” sectors and “home bias.” Advertisements that
include fund performance have a positive relation to investor
purchases, which suggests advertising is primarily designed to
sell what investors appear to care most about—past perfor-
mance. Advertising of fund performance is much more effective
than other types of advertising.
Mutual fund advertising has several economic and risk effects.
Advertising may: (1) not be related to higher post-advertising
returns, (2) attempt to avoid fee competition through fund
differentiation for which investors pay higher fees, (3) cause
investors to pay more for “meaningless differentiation” without
direct information, (4) motivate investors to contact fund web-
sites, (5) be used by funds with retail outlets that charge higher
fees, and (6) be based on risk characteristics of equity portfolios
funds tilted toward “hot” sectors and local funds.
Mutual fund advertising affects investor fund choices even
if it provides no direct or indirect information. These investor
fund choices are based on cognition and emotion. Advertising
can generate positive emotions that make investor attitudes
toward funds more favorable. Advertising can successfully dif-
ferentiate funds, which allows them to charge higher fees.
Thus, there is evidence that choices of mutual fund inves-
tors are greatly influenced by fund advertising—”the wizards
of advertising.”
Advertising As PersuasionAnalysis of traditional and behavioral models of persuasion—
as in Mullainathan and Shleifer (2006)4—provides very different
predictions of investor reactions to mutual fund advertising.
The traditional model is driven by the goal of updating ratio-
nal (or empirically useful) investor information about funds.
Investors do not take this information at face value, but rather,
assess it. These investors also take a negative view of funds that
lack rational information in their advertising.
The traditional model of mutual fund advertising assumes
rational investors. Advertising does not predict fund returns,
as they do not persist. If fund returns are provided, only rela-
tive measures should be used, which are better indicators
of fund manager quality. Without providing relative returns,
funds are considered poor performers. It is also important
for funds to provide risk measures.
In the behavioral model of persuasion, mutual fund advertis-
ing does not provide direct information per se, but the message
is designed to resonate with prevailing investor beliefs. That is,
investors get what they are prepared to accept. Investors take
such advertising at face value. Advertising that does not reso-
nate with prevailing investor beliefs is disregarded.
Mutual fund advertisers generally know the messages
that work, and they provide content directed to investors’
prevailing beliefs. Fund advertisements are designed to
maximize investor utility, which allows funds to charge the
maximum in fees.
The behavioral model best fits the emotional and cognitive
process of investor fund choices. For investors who view the
world as an interconnected system of associations, advertising
must connect with these associations to be effective.
For example, the “Marlboro Man” is the quintessential suc-
cessful advertisement because it so successfully tapped into male
psyches with images of masculinity, independence and freedom.
The advertisements were simple—just a handsome and mature
cowboy riding a horse, smoking of course.
Behaviorally oriented mutual fund investors hold two basic
beliefs concerning the concepts of risk/return: (1) growth
investing is for wealth generation, and (2) value investing
is for wealth conservation. Funds predominantly advertise
market returns when past market returns are high, and avoid
providing returns when they are low. Investor states of mind
are shaped by market returns, not by individual fund returns.
Investors do not necessarily assume past fund returns are
risk driven, but if they fear risk, they will assume as much.
Mutual fund advertisements do not report performance
after a market decline, even with superior past performance.
In this case, the number of advertisements approaches zero.
Advertisements focus on growth funds when the market is
rising, and focus on value funds when the market is declining.
Overall, mutual fund advertising fosters more investor specula-
tion than contrarian-style investing. The major focus of fund
advertising is to gain ever-more assets and profits, rather than
guiding investors to make appropriate fund choices.
Thus, mutual fund advertising is persuasive in inves-
tor fund choices when it resonates with their prevailing
beliefs—“the wizards of advertising” again!
continued on page 58
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Project1 7/29/10 10:55 AM Page 1
46
By Larry Swedroe
An excerpt
Wise Investing Made Simpler
November/December 2010
November/December 2010www.journalofindexes.com 47
Larry Swedroe is well-known to many investors and financial
professionals as a source of common-sense investment wisdom.
Wise Investing Made Simpler (CFPN, 2010) is the follow-up
to Swedroe’s Wise Investing Made Simple (CFPN, 2007), and
continues the author’s efforts to expose the misconceptions many
investors have about financial markets through anecdotes and
empirical data. Below is an excerpt containing Chapters 10-12.
Wise Investing Made Simpler hit shelves in June 2010.
CHAPTER 10The Fed Model And The Money Illusion
Magic, or conjuring, is the art of entertaining an audi-
ence by performing illusions that baffle and amaze, often
by giving the impression that something impossible has
been achieved, as if the performer had supernatural pow-
ers. Practitioners of this art are called magicians, conjurors
or illusionists. Specifically, optical illusions are tricks that
fool your eyes. Most magic tricks that fall into the category
of optical illusions work by fooling both the brain and the
eyes together at the same time.
Fortunately, most optical illusions don’t cost the par-
ticipants anything, except perhaps some embarrassment at
being fooled. However, basing investment strategies on illu-
sions can lead investors to make all kinds of mistakes.
There are many illusions in the world of investing. The
process known as data mining—torturing the data until
it confesses—creates many of them. Unfortunately, iden-
tifying patterns that worked in the past doesn’t necessar-
ily provide you with any useful information about stock
price movements in the future. As Andrew Lo, a finance
professor at MIT, points out: “Given enough time, enough
attempts, and enough imagination, almost any pattern
can be teased out of any data set.”1
The stock and bond markets are filled with wrongheaded
data mining. David Leinweber, of First Quadrant Corp.,
illustrates this point with what he calls “stupid data miner
tricks.” Leinweber sifted through a United Nations CD-ROM
and discovered the single best predictor of the S&P 500 Index
had been butter production in Bangladesh.2 His example is a
perfect illustration that the mere existence of a correlation
doesn’t necessarily give it predictive value. Some logical rea-
son for the correlation to exist is required for it to have cred-
ibility. For example, there is a strong and logical correlation
between the level of economic activity and the level of interest
rates. As economic activity increases, the demand for money,
and, therefore, its price (interest rates), also increases.
An illusion with great potential for creating investment mis-
takes is known as the “money illusion.” The reason it has such
potential for creating mistakes is it relates to one of the most
popular indicators used by investors to determine if the market
is under- or overvalued, what is known as The Fed Model.
The Fed Model
In 1997, in his monetary policy report to Congress,
Federal Reserve Chairman Alan Greenspan indicated that
changes in the ratio of prices in the S&P 500 to consensus
estimates of earnings over the coming 12 months have often
been inversely related to changes in long-term Treasury
yields.3 Following this report, Edward Yardeni, at the time
a market strategist for Morgan Grenfell, speculated that
the Federal Reserve was using a model to determine if the
market was fairly valued—how attractive stocks were priced
relative to bonds. The model, despite no acknowledgment of
its use by the Fed, became known as the Fed Model.
Using the “logic” that bonds and stocks are competing
instruments, the model uses the yield on the 10-year Treasury
bond to calculate “fair value,” comparing that rate to the
E/P ratio (the inverse of the popular price-to-earnings, or
P/E, ratio). For example, if the yield on the 10-year Treasury
were 4 percent, fair value would be an E/P of 4 percent, or a
P/E of 25. If the P/E is greater (lower) than 25, the market is
considered overvalued (undervalued). If the same bond were
yielding 5 percent, fair value would be a P/E of 20. The logic is
that higher interest rates create more competition for stocks,
and this should be reflected in valuations. Thus, lower interest
rates justify higher valuations, and vice versa.
Since Yardeni coined the phrase, it seems almost impos-
sible to watch CNBC for even a day without hearing about
the market relative to “fair value.” The Fed Model as a valu-
ation tool has become “conventional wisdom.” However,
conventional wisdom is often wrong. There are two major
problems with the Fed Model. The first relates to how the
model is used by many investors. Yardeni speculated that
the Federal Reserve used the model to compare the valua-
tion of stocks relative to bonds as competing instruments.
The model says nothing about absolute expected returns.
Thus, stocks, using the Fed Model, might be priced under
fair value relative to bonds, and they can have either high
or low expected returns. The expected return of stocks is
not determined by their relative value to bonds. Instead, the
expected real return is determined by the current dividend
yield plus the expected real growth in dividends. To get the
estimated nominal return, we would add estimated inflation.
This is a critical point that seems to be lost on many inves-
tors. The result is that investors who believe low interest
rates justify a high valuation for stocks without the high
valuation impacting expected returns are likely to be disap-
pointed (and perhaps not have enough funds with which to
live comfortably in retirement). The reality is when P/Es are
high, expected returns are low and vice versa, regardless of
the level of interest rates.
The second problem with the Fed Model, leading to a
false conclusion, is it fails to consider that inflation impacts
corporate earnings differently than it does the return on
fixed-income instruments. Over the long term, the nominal
growth rate of corporate earnings has been in line with
the nominal growth rate of the economy. Similarly, the real
growth rate of corporate earnings has been in line with the
real growth of the economy.4 Thus, in the long term the real
growth rate of earnings is not impacted by inflation. On the
other hand, the yield to maturity on a 10-year bond is a nomi-
nal return—to get the real return you must subtract infla-
tion. The error of comparing a number that isn’t impacted
by inflation to one that is leads to what is called the “money
illusion.” Let’s see why it’s an illusion.
We begin by assuming the real yield on a 10-year TIPS
(treasury inflation-protected security) is 2 percent. If the
expected long-term rate of inflation were 3 percent, a
10-year Treasury bond would be expected to yield 5 percent
(the 2 percent real yield on TIPS plus the 3 percent expected
rate of inflation). According to the Fed Model, that would
mean a fair value for stocks at a P/E of 20 (E/P of 5 percent).
Let’s now change our assumption to a long-term expected
rate of inflation of 2 percent. This would cause the yield on
the 10-year bond to fall from 5 to 4 percent, causing the
fair value P/E to rise to 25. However, this makes no sense.
Inflation doesn’t impact the real rate of return demanded by
equity investors. Therefore, it shouldn’t impact valuations. In
addition, as stated above, over the long term, there is a very
strong relationship between nominal earnings growth and
inflation. In this case, a long-term expected inflation rate of
2 percent, instead of 3 percent, would be expected to lower
the growth of nominal earnings by 1 percent, but have no
impact on real earnings growth (the only kind that matter).
Because the real return on bonds is impacted by inflation,
while real earnings growth is not, the Fed Model compares a
number that is impacted by inflation with a number that isn’t
(resulting in the money illusion).
Let’s also consider what would happen if the real interest
rate component of bond prices fell. The real rate is reflective
of the economic demand for funds. Thus, it’s reflective of the
rate of growth of the real economy. If the real rate falls due
to a slower rate of economic growth, interest rates would
fall, reflecting the reduced demand for funds. Using the same
example from above, if the real rate on TIPS fell from 2 per-
cent to 1 percent, that would have the same impact on nomi-
nal rates as a 1 percent fall in expected inflation, and, thus, the
same impact on the fair value P/E ratio—causing fair value to
rise. However, this too does not make sense. A slower rate of
real economic growth means a slower rate of real growth in
corporate earnings. Thus, while the competition from lower
interest rates is reduced, so will be future earnings.
Since corporate earnings have grown in line with nominal
GNP growth over the past 70 years, a 1 percent lower long-
term rate of growth in GNP would lead to a 1 percent lower
expected growth in corporate earnings. The “benefit” of
falling interest rates would be offset by the equivalent fall
in future expected earnings. The reverse would be true if a
stronger economy caused a rise in real interest rates. The
negative effect of a higher rate of interest would be offset
by a faster expected growth in earnings. The bottom line is
there is no reason to believe stock valuations should change
if the real return demanded by investors has not changed.
Clifford S. Asness studied the period 1881–2001. He
concluded the Fed Model had no predictive power in terms
of absolute stock returns—the conventional wisdom is
wrong. (As we discussed, however, this is not the purpose
for which Yardeni thought the Fed Model was used. Given
the purpose for which the model was designed, it would
have been more appropriate for Asness to study the rela-
tive performance of stocks vs. bonds given the “signal”—
under/overvalued—the model was giving.) Asness also
concluded that over 10-year horizons, the E/P ratio does
have strong forecasting powers. Thus, the lower the P/E
ratio, the higher the expected returns to stocks, regardless
of the level of interest rates, and vice versa.5
There is one other point to consider. A stronger econ-
omy, leading to higher real interest rates, should also be
expected to lead to a rise in corporate earnings. The stron-
ger economy reduces the risks of equity investing. In turn,
that could lead investors to accept a lower risk premium.
Thus, it is possible that higher interest rates, if caused by a
stronger economy and not higher inflation, could actually
justify higher valuations for stocks. The Fed Model, how-
ever, would suggest that higher interest rates mean stocks
are less attractive. The reverse would be true if a weaker
economy led to lower real interest rates.
The Moral Of The Tale
While gaining knowledge of how a magical illusion works
has the negative effect of ruining the illusion, understand-
ing the “magic” of financial illusions is beneficial because it
should help you avoid mistakes. In the case of the money
illusion, understanding how the illusion is created will
prevent you from believing an environment of low (high)
interest rates allows for either high (low) valuations or for
high (low) future stock returns. Instead, if the current level
of prices is high (a high P/E ratio), that should lead you to
conclude that future returns to equities are likely to be lower
than has historically been the case, and vice versa. Note that
this doesn’t mean investors should either avoid equities
because they are “highly valued” or increase their allocations
because they have low valuations.
Hopefully, you are now convinced that the Fed Model should
not be used to determine if the market is at fair value and that a
much better predictor of future real returns is the E/P ratio.
The next tale explains why it’s important to keep control
over your emotions.
CHAPTER 11Don’t Let Emotions Take Control
A friend who is also a financial adviser, Sherman Doll,
related the following story. He has been a two-line sport
kite flier for many years. While not a pro, he has learned a
few tricks by observing the flying behavior of these kites.
He told me one of the most difficult skills for beginners
to master is what to do when their kite starts to plunge
earthward. The natural, panicky impulse is to yank back-
ward on the lines. However, this action only accelerates the
kite’s death spiral. The simple kite-saving technique is to
calmly step forward and thrust your arms out. This causes
the kite’s downward acceleration to stop, allowing you to
regain control of the kite and end its plunge. What does
this have to do with investing?
On January 21, 2008, equity markets around the globe all
collapsed. In just that one day, stock markets fell from about
5 percent to as much as 10 percent. For some markets it was
the worst day since the Great Depression. The Australian
market had its worst day ever. The U.S. market, which was
closed for Martin Luther King Day, saw the futures market
trading down more than 500 points ahead of the opening
48 November/December 2010
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Project1 9/28/10 11:31 AM Page 1
on January 22. This type of market move generally leads to
panicked selling. And the media fuels the frenzy.
As I had learned to expect, I received two phone calls
from the media to discuss what investors should be doing
in light of the bear market spreading around the globe.
What I find amusing is that I always give them the same
answer—investors should do nothing except adhere to their
well-developed investment plan, assuming they are knowl-
edgeable enough to have one.
While it is tempting to believe there are those who can pre-
dict bear markets and, therefore, sell before they arrive, there
is no evidence of the persistent ability to do so. This is why
I tell people there is only one person who knows where the
market is going and none of us gets to talk to that person.
There is a large body of evidence suggesting that trying
to time markets is highly likely to lead to poor results. For
example, one study on the performance of 100 pension plans
engaged in tactical asset allocation (TAA: a fancy term for
market timing, allowing the purveyors of such strategies to
charge high fees) found not one single plan benefited from
their efforts—an amazing result, as randomly we should
have expected at least some to benefit.6
Another study also found some amazing results. For
the 12 years ending in 1997, while the S&P 500 Index on
a total return basis rose 734 percent, the average equity
fund returned just 589 percent, but the average return
for 186 TAA funds was a mere 384 percent, about half the
return of the S&P 500 Index.7
A third example of the futility of trying to time the mar-
ket is the finding from a Morningstar study. They found that
investors in mutual funds, on average, significantly under-
perform the very funds in which they invest. The dollar-
weighted returns of investors are below the time-weighted
returns of the funds in which they invest.8 The reason for
this seemingly strange outcome is investors tend to buy after
periods of strong performance and sell after periods of weak
performance. Buying high when greed takes over and selling
low when panic sets in is not exactly a recipe for financial
success. Unfortunately, it is the way most investors act.
The Moral Of The Tale
Just as when a kite starts to plunge earthward, the
natural, panicky reaction is to yank backward on the lines,
the natural, panicky reaction to a dive in your portfolio’s
value is to pull back (sell). In both cases, pulling back is the
wrong strategy. The right strategy is the less intuitive one of
remaining calm and stepping forward (actually buying stocks
to rebalance your portfolio to the desired asset allocation).
Warren Buffett is probably the most highly regarded
investor of our era. Listen carefully to his statements regard-
ing efforts to time the market.
“Inactivity strikes us as intelligent behavior.”9
“The only value of stock forecasters is to make fortune
tellers look good.”10
“We continue to make more money when snoring than
when active.”11
“Our stay-put behavior reflects our view that the stock
market serves as a relocation center at which money is
moved from the active to the patient.”12
Buffett also observed: “Long ago, Sir Isaac Newton gave
us three laws of motion, which were the work of genius. But
Sir Isaac’s talents didn’t extend to investing: He lost a bundle
in the South Sea Bubble, explaining later, ‘I can calculate the
movement of the stars, but not the madness of men.’ If he had
not been traumatized by this loss, Sir Isaac might well have
gone on to discover the Fourth Law of Motion: For investors as
a whole, returns decrease as motion increases.”13
Perhaps Buffett’s views on market-timing efforts are
best summed up by the following from his 2004 Annual
Shareholder Letter of Berkshire Hathaway:
“Over the 35 years, American business has delivered
terrific results. It should therefore have been easy for inves-
tors to earn juicy returns: All they had to do was piggyback
Corporate America in a diversified, low-expense way. An
index fund that they never touched would have done the job.
Instead many investors have had experiences ranging from
mediocre to disastrous.
There have been three primary causes: first, high costs,
usually because investors traded excessively or spent far
too much on investment management; second, portfolio
decisions based on tips and fads rather than on thoughtful,
quantified evaluation of businesses; and third, a start-and-
stop approach to the market marked by untimely entries
(after an advance has been long underway) and exits (after
periods of stagnation or decline). Investors should remem-
ber that excitement and expenses are their enemies. And if
they insist on trying to time their participation in equities,
they should try to be fearful when others are greedy and
greedy only when others are fearful.”
The above observation is perhaps why Buffett has stated
that investing is simple, but not easy.14 The simple part is
that the winning strategy is to act like the lowly postage
stamp that adheres to its letter until it reaches its destination.
Investors should stick to their asset allocation until they reach
their financial goals. The reason it is hard is that it is difficult
for most individuals to control their emotions—emotions of
greed and envy in bull markets and fear and panic in bear
markets. In fact, bear markets are the mechanism that serves
to transfer assets from those with weak stomachs and without
investment plans to those with well-developed plans—with
the anticipation of bear markets built right into the plans—
and the discipline to adhere to those plans.
The bottom line: If you don’t have a plan, develop one. If
you do have one, stick to it.
The next tale is about finding the magic formula to be
able to successfully time the market.
CHAPTER 12Using Market Valuations To Time The Market
According to Christian mythology, the Holy Grail was
the dish, plate or cup with miraculous powers that was
used by Jesus at the Last Supper. Legend has it that the
Grail was sent to Great Britain where a line of guardians
keeps it safe. The search for the Holy Grail is an important
part of the legends of King Arthur and his court.
50 November/December 2010
For many investors, the equivalent of the Holy Grail
is finding the formula allowing them to successfully time
the market. Trying to time the market is certainly tempt-
ing, as the rewards for success can be great. The idea is
made even more tempting when one looks at data such
as the following from a study reported in the August 16,
1999 issue of Fortune. The average historical P/E ratio for
the market had been around 15. For the period 1926
through the second quarter of 1999, an investor buying
stocks when the market traded at P/E ratios of between
14 and 16 earned a median return of 11.8 percent over
the next 10 years. However, investors purchasing stocks
when the P/E ratio was greater than 22 earned a median
return of just 5 percent per year over the next 10 years.
On the other hand, investors who purchased stocks when
P/E ratios were below 10 earned a median return of 16.9
percent per year over the next 10 years. Sounds simple,
right? Buy stocks when the P/E of the market is below the
historical average and sell them when the P/E is above
average. Tempting, isn’t it?
Authors Ben Stein and Phil DeMuth presented similar
evidence in their 2003 book Yes, You Can Time the Market!
They advocated buying stocks when the real price was
below the 15-year moving average of real stock prices and
abstaining otherwise.15 The problem with this type of analy-
sis is it fails to consider that when an investor is out of the
market, they must invest in an alternative. In other words,
the winning strategy isn’t dependent on whether you buy
stocks when they are “cheap” and avoid them when they
are “expensive.” Instead, the winning strategy depends on
whether the alternative investments purchased with the
proceeds of the stock sales outperform the stocks you sold
because the stocks were “expensive,” and “doomed” to
produce lousy returns.
The study “Very Long Term Equity Investment
Strategies: Real Stock Prices and Mean Reversion,” exam-
ined the returns from mean reversion strategies using
various valuation metrics (i.e., real stock prices, P/E ratios
and dividend yields, and combinations of these metrics)
in both the U.S. and the U.K. The U.S. data covered the
period 1871–2004, and the U.K. data covered the period
1899–2004. They used the risk-free asset as the alterna-
tive investment when the strategy called for being out
of the market because prices were expensive (above
average). Not surprisingly, they found buying cheap does
outperform buying expensive—by from 3 percent to
5.4 percent per year, depending on the holding period.
However, they also found that mean reversion strategies
don’t work. The reason is during periods when stocks are
expensive relative to historic averages (and, thus, pro-
duce below-average returns), there is still an equity risk
premium (stocks outperform riskless instruments). Thus,
they concluded: “a simple buy-and-hold strategy is far
superior.”16 And for taxable accounts it is certainly more
tax efficient.
Before you are tempted by seemingly surefire ways to
beat the market, consider the following from John Bogle,
founder and former CEO of the Vanguard Group:
“ The idea that a bell rings to signal when investors should get
into or out of the stock market is simply not credible. After
nearly fifty years in this business, I do not know of anybody
who has done it [market timing] successfully and consistently.
I don’t even know anybody who knows anybody who has done
it successfully and consistently.”17
The Moral Of The Tale
While it certainly seems tempting to try to time the
market, the evidence suggests it is a mug’s game. What
is perhaps most surprising is the following. Given most
investors acknowledge Warren Buffett as one of the
greatest investors of all time, you would think they would
listen to his advice. As you have seen, Buffett is vociferous
about his belief that investors should avoid trying to time
the market; yet his advice is ignored.
Endnotes
1 Kiplinger’s Personal Finance, February 1997.
2 Wall Street Journal, April 5, 1996.
3 Humphrey-Hawkins Report, Section 2: Economic and Financial Developments in 1997, Alan Greenspan, July 22, 1997.
4 William Bernstein, “The Efficient Frontier,” (Summer 2002).
5 Clifford S. Asness, “Fight the Fed Model: The Relationship Between Stock Market Yields, Bond Market Yields, and Future Returns,” (December 2002).
6 Charles Ellis, Investment Policy (Irwin Professional Pub 2nd edition 1992).
7 David Dreman, Contrarian Investment Strategies (Simon & Schuster 1998), p. 57.
8 Morningstar FundInvestor (July 2005).
9 1996 Annual Report of Berkshire Hathaway.
10 1992 Annual Report of Berkshire Hathaway.
11 1996 Annual Report of Berkshire Hathaway.
12 1991 Annual Report of Berkshire Hathaway.
13 2005 Annual Report of Berkshire Hathaway.
14 Financial Analysts Journal (November/December 2005), p. 51.
15 Ben Stein and Phil DeMuth, Yes, You Can Time the Market! (Wiley 2003).
16 Owain Ap Gwilym, James Seaton, and Stephen Thomas, “Very Long Term Equity Investment Strategies: Real Stock Prices and Mean Reversion,” Journal of Investing (Summer 2008).
17 John Bogle, Commonsense on Mutual Funds, Wiley (March 1999).
November/December 2010www.journalofindexes.com 51
NewsPowerShares Revamps Junk Bond ETF
Invesco PowerShares kicked off
August with the relaunch of its junk
bond ETF with an index from Research
Affiliates, making it the first-ever fixed-
income ETF to use a fundamentally
weighted benchmark. The new index
is just one of a family of fundamentally
weighted bond indexes that Research
Affiliates developed with Ryan ALM, Inc.
While traditional bond indexes
weight the largest debtors most heav-
ily, potentially exposing investors to
greater risks of default, a fundamentally
weighted bond index uses financial
fundamentals, including sales, profits,
book value and dividends, to determine
holdings, leading funds to firms with
more manageable levels of debt.
The rechristened PowerShares
Fundamental High Yield Corporate Bond
Portfolio (NYSE Arca: PHB) now uses the
RAFI Corporate Bond Index; previously
it had tracked the Wells Fargo High
Yield Bond Index. The new underlying
index has fewer components than its
predecessor, as well as most traditional
bond indexes, enabling a replication
approach rather than sampling; it also
selects higher-quality debt than the
previous index.
PHB carries an expense ratio of 0.50
percent.
FTSE Acquires FXIFTSE announced in mid-September
that it had bought out its partner in
FTSE Xinhua Index Ltd. (FXI) to gain
complete control of the joint venture.
In 2001, FTSE teamed up with finan-
cial news and data provider Xinhua
Finance to create FXI, with the inten-
tion of offering indexes for both foreign
and domestic Chinese investors. FXI
currently offers a full suite of indexes
covering China’s complicated markets,
among them the blue-chip FTSE/Xinhua
China 25 Index. According to FTSE,
almost 60 percent of assets invested in
ETFs targeting China are benchmarked
to an FXI index.
The company is being renamed FTSE
China Index Ltd., or FCI, with the index-
es rebranded accordingly. The method-
ology, review schedules, management
of the indexes and free-float rules will
also be brought into alignment with
FTSE’s standards, and the FXI advisory
committee will be dissolved, with the
indexes now being overseen by FTSE’s
own committees.
Vanguard In Massive ETF RolloutOn Sept. 9, Vanguard Group real-
ized a long-standing objective with the
launch of an S&P 500 ETF amidst a mas-
sive expansion of its lineup.
The Vanguard S&P 500 ETF (NYSE
Arca: VOO) costs investors 0.06 percent
in annual fees, compared with 0.09 per-
cent for both the $68 billion State Street
Global Advisors’ SPDR S&P 500 (NYSE
Arca: SPY) and the $22 billion iShares S&P
500 Index Fund (NYSE Arca: IVV). Time
will tell if VOO is able to poach investors
from SPY, the world’s biggest ETF.
Vanguard also launched eight
other ETFs based on S&P indexes that
together amount to a full canvassing
of the U.S. equities investment land-
scape broken down by large-, mid-
and small-cap categories. The funds
have the cheapest expense ratios in
their categories.
The other S&P-based ETFs, their
tickers and prices are:
VËË7?�~Ö?ÁaË.F+ËyååË7?�ÖjË 0�Ë®!:. Ë
Arca: VOOV), 0.15 percent
VËË7?�~Ö?ÁaË.F+ËyååË�Á�ÝÍË 0�Ë
(NYSE Arca: VOOG), 0.15 percent
VËË7?�~Ö?ÁaË.F+Ë �a�?¬Ë|ååË 0�Ë
(NYSE Arca: IVOO), 0.20 percent
VËË7?�~Ö?ÁaË.F+Ë �a�?¬Ë|ååË7?�ÖjË
ETF (NYSE Arca: IVOV), 0.20 percent
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ETF (NYSE Arca: IVOG), 0.20 percent
VËË7?�~Ö?ÁaË.F+Ë.�?���?¬ËÉååË 0�Ë
(NYSE Arca: VIOO), 0.15 percent
VËË7?�~Ö?ÁaË.F+Ë.�?���?¬ËÉååË7?�ÖjË
ETF (NYSE Arca: VIOV), 0.20 percent
VËË7?�~Ö?ÁaË.F+Ë.�?���?¬Ë�Á�ÝÍË 0�Ë
(NYSE Arca: VIOG), 0.20 percent
Vanguard followed up the S&P
launch with another family of ETFs
based on Russell indexes a few weeks
later. They are:
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(NasdaqGM: VONE), 0.12 percent
VËË7?�~Ö?ÁaË-ÖÄÄj��ˤåååË7?�ÖjË 0�Ë
(NasdaqGM: VONV), 0.15 percent
VËË7?�~Ö?ÁaË-ÖÄÄj��ˤåååË�Á�ÝÍË 0�Ë
(NasdaqGM: VONG), 0.15 percent
VËË7?�~Ö?ÁaË-ÖÄÄj��ËÔåååË��ajÞË�Ö�aË
(NasdaqGM: VTWO), 0.15 percent
VËË7?�~Ö?ÁaË-ÖÄÄj��ËÔåååË7?�ÖjË��ajÞË
Fund (NasdaqGM: VTWV), 0.20 percent
VËË7?�~Ö?ÁaË-ÖÄÄj��ËÔåååË�Á�ÝÍË��ajÞË
Fund (NasdaqGM: VTWG), 0.20 percent
VËË7?�~Ö?ÁaË-ÖÄÄj��ËÏåååË��ajÞË�Ö�aË
(NasdaqGM: VTHR), 0.15 percent
Vanguard still has a real estate ETF
and three municipal bond ETFs in reg-
istration that were part of the same
group of filings.
Select Sector Sues PowerShares Over Tickers
In late July, the Select Sector SPDR
Trust, the entity that holds the trade-
mark on the Select Sector SPDRs, filed
suit against Invesco PowerShares over
the trading symbols used by the new
PowerShares ETFs tracking domestic
small-cap sectors.
State Street Global Advisors has
marketed Select Sector SPDRs since
1998; the funds divide the S&P 500
Index into nine individual sectors. The
PowerShares offering, launched in April,
divides the S&P SmallCap 600 Index
into the same nine sectors. The tickers
on the two sets of ETFs are identical,
save for an “S” PowerShares added
onto the end of each of its funds. For
example, the Select Sector Financials
SPDR’s ticker is XLF, while the tick-
November/December 201052
er for the PowerShares S&P SmallCap
Financials Portfolio is “XLFS.”
A Select Sector SPDRs representa-
tive called PowerShares’ ticker choice
“a deliberate and unconscionable act
on the part of PowerShares to confuse
both institutional and retail investors.”
The suit seeks to block PowerShares
from using the nine “XL Family of
Marks” members or anything simi-
lar in creating tickers for ETFs and
requests that PowerShares pay the
SPDR Trust’s legal costs.
The suit was filed in the U.S. District
Court in Houston against PowerShares
Exchange-Traded Fund Trust II, Invesco
PowerShares Capital Management, LLC,
and Invesco Distributors, Inc., accord-
ing to a press release from Select
Sector SPDRs.
A representative for PowerShares
said the firm doesn’t comment on ongo-
ing litigation.
Select Sector SPDRs is a trademark
of the McGraw-Hill Companies, Inc.,
and has been licensed for use.
INDEXING DEVELOPMENTSS&P Launches Equal- Weighted GSCI Index
In September, Standard & Poor’s
launched an equal-weighted version of
its S&P GSCI index. The index was cre-
ated in response to investor demand for
more equal distribution of commodities
in investment vehicles, S&P said in a
press release. The original S&P GSCI is
weighted by world production levels.
Compared with the S&P GSCI, the S&P
GSCI Equal Weight Select Index will tend
to have higher exposure to commodi-
ties with lower production weights as a
result of the equal weighting. In 2010,
the new index includes 14 commodities
selected from the 28 covered by the S&P
GSCI, with weightings reset on a quar-
terly basis. One end result of including
fewer commodities is that products and
investors tracking the index will have
fewer monthly rolls to contend with.
The equal-weighted index selects
only the largest and most liquid com-
modities from each of six commodi-
ties groups: Agriculture – Grains and
Oilseeds; Agriculture – Softs; Energy;
Industrial Metals; Livestock; and
Precious Metals.
SummerHaven Adds More Commodities Indexes
SummerHaven Index Management,
the index provider behind the U.S.
Commodity Funds’ broad commodi-
ties ETF (NYSE Arca: USCI), is expand-
ing its footprint with the launch of
two active commodities indexes that
aim to boost returns.
The methodology behind the
SummerHaven Dynamic Metals Index
(“SDMI”) and the SummerHaven Dynamic
Agriculture Index (“SDAI”) is simple: The
bigger the physical inventory of a com-
modity, the smaller the weight that com-
modity will carry in the mix. The indexes
are rebalanced monthly. SummerHaven
says that research has shown that com-
modities with low inventories tend to
outperform commodities with high
inventories over time.
The indexes track commodity futures
contracts. The SDMI provides exposure
to 10 industrial and precious metals
ranging from gold and palladium to
nickel and tin, while the SDAI covers 14
agricultural commodities such as soy-
beans, sugar, wheat and lean hogs.
Nasdaq Debuts ‘Green’ Index Family
In late September, Nasdaq announ-
ced the launch of a family of indexes
tracking companies with products
and services focused on the environ-
November/December 2010www.journalofindexes.com 53
Nasdaq announced the launch of a family of indexes tracking companies with products and services focused on the environment and sustainability.
News
ment and sustainability.
The composite index, the Nasdaq
OMX Green Economy Index, covers 350
stocks winnowed down from a universe
of 460. It covers 13 sectors, includ-
ing advanced materials; biofuels and
clean fuels; energy efficiency; financial;
green building; healthy living; lighting;
natural resources; pollution mitigation;
recycling; renewable energy genera-
tion; transportation; and water.
Nasdaq has said it will be rolling out
subindexes for each of the sectors as
well as regional indexes covering the
U.S., Europe, Asia and the world ex-U.S.
The index family was developed
through a partnership with consultancy
firm SustainableBusiness.com LLC; a rep-
resentative of SustainableBusiness.com
selects the components of the indexes.
Nasdaq Partners With DWS On Volatility Target Index
In August, Nasdaq and DWS
Investments launched the DWS
Nasdaq-100 Volatility Target Index. The
new index is designed as a risk man-
agement tool for investors, enabling
them to control their exposure to the
popular Nasdaq-100 Index by shifting
their allocation between exposure to
the Nasdaq-100 and cash in response to
changes in volatility.
When the Nasdaq-100’s volatility
increases, the volatility target index
shifts more weight into its cash alloca-
tion. When the Nasdaq-100’s volatility
decreases, the volatility target index
increases its exposure to the other
index. Although it is a popular bench-
mark, the Nasdaq-100 is also known for
its volatility, so the new index poten-
tially allows investors to access the
Nasdaq-100’s growth-oriented stocks
without taking on too much risk.
DWS Investments is a subsidiary of
Deutsche Bank.
Barclays Capital Unveils Astro Index
Barclays Capital debuted a new index
series in mid-September. The Barclays
Capital Astro indexes track mean rever-
sion in the equity markets of Europe
and the U.S.
The index series is meant to be
a hedging tool for equity investors
who can use it to gain tail-risk protec-
tion and potentially mitigate any hits
to their equities portfolio. The Astro
indexes are designed with the inten-
tion that they outperform in highly
volatile markets when mean reversion
typically spikes, a Barclays represen-
tative noted. The index typically will
underperform slightly during long-
term bull markets, he said.
According to Barclays, backtesting
indicates that the index has not been
plagued by a negative cost of carry,
which is often the case with volatility
investments; the firm says this could
make the index appealing to long-
term investors.
Barclays calculates excess and total
return versions of the Barclays Capital
Astro US Index and the Barclays Capital
Astro Europe Index.
SAM Expands Relationship With DJI
Zurich-based sustainability invest-
ment firm SAM said in August it has
widened its relationship with Dow Jones
Indexes. The move follows the dissolu-
tion of DJI’s involvement in European
index provider Stoxx Ltd.; until recently,
Stoxx was partially owned by DJI.
SAM previously collaborated with
both DJI and Stoxx on the manage-
ment, marketing and dissemination of
sustainability-based indexes, with Stoxx
responsible for the European bench-
marks. SAM has since terminated its
relationship with Stoxx, and under a
new agreement, DJI will collaborate with
SAM on a set of European sustainability
indexes, similar to the ones that had
been calculated by Stoxx.
The new lineup includes the broad
Dow Jones Sustainability Europe Index
and Dow Jones Sustainability Eurozone
Index, and the narrow-based Dow Jones
Sustainability Europe 40 and Dow Jones
Sustainability Eurozone 40 indexes.
Their construction and methodology
align with those of the other Dow Jones
Sustainability Indexes. SAM will continue
to be responsible for the evaluation and
selection of the indexes’ components.
S&P Debuts Factor Index SeriesStandard & Poor’s rolled out the
S&P Factor Indexes in August; the new
benchmarks each consist of two equal-
weighted subindexes representing dif-
ferent asset classes or market seg-
ments. The point is to capture the risk
premium between the two subindexes.
The main index for each pairing
tracks a long position and a short
position in two front-month futures
indexes, seeking to measure the price
difference between the positions in the
two component subindexes.
Currently there are four indexes in the
series. The Equity Risk Premium Index
tracks the spread between the return
of U.S. stocks (represented in the long
subindex) and the return of 30-year U.S.
Treasury bonds (represented in the short
index). The other indexes include the
Non-US Dollar Equity Index (U.S. stocks
vs. the U.S. dollar); the Crude Oil – Equity
Spread Index (crude oil vs. U.S. stocks);
and the Gold – Equity Spread Index
(gold vs. U.S. stocks).
S&P Rolls Out International Preferred Stock Index
In late August, S&P said it had
launched the S&P International Preferred
Stock Index tracking preferred stocks in
developed markets other than the U.S.
The index currently has holdings from
49 companies, with Canada, Germany
and the U.K. showing the most repre-
sentation in the index. The index itself
is weighted by modified market capital-
ization, with individual issuer weights
capped at 4 percent of the index.
Constituents eligible for addition are
required to have market capitalizations
greater than $100 million, and must be
at least 12 months from any mandatory
conversion or scheduled maturity.
Preferred stocks resemble a hybrid
of stocks and bonds, and are valued by
investors for the high yields and diversi-
fication benefits that they offer.
AFT Launches Long-Short Currency Futures Index
Alpha Financial Technologies, LLC
unveiled the FX Trends Index (FXTI) in
August; the index is designed to take
November/December 201054
advantage of both rising and declining
price trends in individual currencies in
order to boost returns.
The FXTI does this by taking long
and short positions in 11 different cur-
rencies based on their individual price
trends. It uses GDP, liquidity and credit
stability to determine the weighting of
each currency.
The component currencies in the
index include the euro, Japanese yen,
Swiss franc, Brazilian real, British pound,
Canadian dollar, Mexican peso, Australian
dollar, New Zealand dollar, Norwegian
krone and South African rand.
The index is rebalanced monthly.
AROUND THE WORLD OF ETFsVan Eck, WisdomTree Launch Emerging Market Debt ETFs
Van Eck and WisdomTree both
launched emerging market debt ETFs
recently; importantly, both funds hold
only bonds denominated in local cur-
rencies. Previously, the only emerging
market debt ETFs available held dollar-
denominated debt.
The Market Vectors Emerging
Markets Local Currency Bond ETF (NYSE
Arca: EMLC) tracks the J.P. Morgan
Government Bond Index-Emerging
Markets Global Core Index. As of July
1, the benchmark had 171 constituents
with maturities ranging from one to 30
years. At the fund’s launch, the index
covered 13 countries, each capped at
a 10 percent weight. EMLC charges an
expense ratio of 0.49 percent.
WisdomTree followed up in August
with the WisdomTree Emerging Markets
Local Debt Fund (NYSE Arca: ELD). Unlike
EMLC, though, ELD is actively managed.
Its allocation model divides 13 emerging
markets into three tiers based on size
and risk parameters. ELD charges an
expense ratio of 0.55 percent.
Global X Debuts First Lithium ETF
Global X recently rolled out the first
ETF to tap into the renewable energy
theme through lithium companies.
Launched in July, the Global X Lithium
ETF (NYSE Arca: LIT) invests both in
lithium miners and lithium battery mak-
ers. By investing in battery manufactur-
ers, the fund captures the “high-tech
component” of the lithium story. The
metal, which is widely used in batteries
for cell phones and laptop computers,
is also key for the electric car industry,
which uses lithium-ion batteries in its
vehicles. And because lithium is not
traded on any commodities exchanges,
investors previously have had no way to
gain targeted exposure to the metal.
At launch, LIT’s basket was split
nearly 50-50 between miners and pro-
ducers in seven different countries. The
fund, which tracks the Solactive Global
Lithium Index, comes with an annual
expense ratio of 0.75 percent.
iShares Unveils Nine Ex-US Sectors
Mid-July saw the launch of nine new
iShares ETFs targeting sector subin-
dexes of the MSCI All Country World
ex USA Index.
The new funds are the first family
of sector ETFs to cover developed and
emerging markets, but exclude the United
States. They include the following:
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë��ÄÖ jÁË
Discretionary Sector Index Fund
(NYSE Arca: AXDI)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë��ÄÖ jÁË
Staples Sector Index Fund (NYSE
Arca: AXSL)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë �jÁ~ßË
Sector Index Fund (NYSE Arca: AXEN)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë�j?�ÍË
Care Sector Index Fund (NYSE Arca:
�9� ¯
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë��aÖÄÍÁ�?�ÄË
Sector Index Fund (NYSE Arca: AXID)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë
Information Technology Sector Index
Fund (NYSE Arca: AXIT)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë ?ÍjÁ�?�ÄË
Sector Index Fund (NYSE Arca: AXMT)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë
Telecommunication Services Sector
Index Fund (NYSE Arca: AXTE)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë2Í���Í�jÄË
Sector Index Fund (NYSE Arca: AXUT)
The iShares MSCI ACWI ex US
Financials Sector Index Fund (NYSE
Arca: AXFN) launched separately back in
January. Each fund charges an expense
Á?Í��Ë �wË å±|oË ¬jÁWj�Í±Ë ���a��~ÄË Á?�~jË
from roughly 60 for the health care ETF,
�9� ^ËÍ�ËÝj��Ë�ÜjÁËÔÉåËw�ÁË�9�!±
Schwab Enters Fixed-Income ETFsCharles Schwab, which just entered
ÍjË 0�Ë ?Á�jÍË ��Ë !�Üj MjÁË Ôåå�^Ë
made its first foray into fixed income
with the launch of three U.S. Treasury
ETFs in early August.
The new funds include the Schwab
2±.±Ë 0�+.Ë 0�Ë ®!:. Ë �ÁW?]Ë .�+¯^Ë ÍjË
Schwab Short-Term U.S. Treasury ETF
®!:. Ë �ÁW?]Ë .�#¯Ë ?�aË ÍjË .WÝ?MË
Intermediate-Term U.S. Treasury ETF
®!:. Ë �ÁW?]Ë .�-¯±Ë 0jË 0�+.Ë wÖ�aË ?ÄË
an annual expense ratio of 0.14 per-
cent, while the other two funds both
?ÜjË jÞ¬j�ÄjË Á?Í��ÄË �wË å±¤ÔË ¬jÁWj�Í^Ë
according to the company’s Web site.
As with other Schwab funds, Schwab cli-
ents aren’t charged trading commissions
when they buy and sell the funds.
iPath Adds Eight Treasury ETNsIn August, the iPath ETN family
added eight exchange-traded notes to
its lineup. Each is linked to a U.S.
Treasury futures index. The products
are Barclays’ first foray into fixed-
income-based ETNs.
iPath’s new crop of ETNs includes
November/December 2010www.journalofindexes.com 55
News
three bull-and-bear pairs:
• iPath US Treasury 10-year Bull ETN
(NYSE Arca: DTYL)
• iPath US Treasury 10-year Bear ETN
(NYSE Arca: DTYL)
• iPath US Treasury 2-year Bull ETN
(NYSE Arca: DTUL)
• iPath US Treasury 2-year Bear ETN
(NYSE Arca: DTUS)
• iPath US Treasury Long Bond Bull
ETN (NYSE Arca: DLBL)
• iPath US Treasury Long Bond Bear
ETN (NYSE Arca: DLBS)
In addition, iPath launched a pair
of ETNs designed to give investors the
ability to take a view on whether the
yield curve will steepen or flatten:
• iPath US Treasury Steepener (NYSE
Arca: STPP)
• iPath US Treasury Flattener (NYSE
Arca: FLAT)
Each ETN comes with an expense
ratio of 0.75 percent.
Barclays Launches New Volatility-Linked ETN
Barclays Capital launched a new ETN
based on the S&P 500 Dynamic Veqtor
Index, the fourth volatility-linked
exchange-traded product for the global
banking giant.
The Barclays ETN+ S&P Veqtor
Exchange Traded Note (NYSE Arca:
VQT) began trading Sept. 1. It carries an
annual expense ratio of 0.95 percent.
VQT tracks the S&P 500 Veqtor
Index, which combines broad equity
market exposure with a built-in volatil-
ity hedge by allocating assets among
the S&P 500 Index, the S&P 500 Short-
Term VIX Futures Index and cash. VIX,
a product of the Chicago Board Options
Exchange, reflects the prices of S&P 500
options and is a benchmark for measur-
ing near-term volatility.
Claymore Closes Four FundsClaymore Securities, which was
acquired by Guggenheim Partners in
October, closed four of its ETFs on
Sept. 10. The company said in a state-
ment issued in August that the funds
had been lightly traded, and were being
closed so it can turn its attention to
“areas of greater investor interest.”
The list of funds included the fol-
lowing: the Claymore/Zacks Dividend
Rotation ETF (NYSE Arca: IRO), which
had $12.5 million in assets at the time of
the announcement; the Claymore/Zacks
Country Rotation ETF (NYSE Arca: CRO),
with $3 million in assets; the Claymore/
Beacon Global Exchanges, Brokers & Asset
Managers Index ETF (NYSE Arca: EXB),
with $2.8 million; and the Claymore/Robb
Report Global Luxury Index ETF (NYSE
Arca: ROB), with $16.2 million.
All shareholders remaining on Sept.
17 received a cash distribution into their
brokerage account representing the value
of their shares as of that date, including
any capital gains and dividends.
Vanguard Trumps iSharesIn Adviser Loyalty
Vanguard is increasingly popu-
lar among investment advisers, out-
ranking iShares for the first time to
become the most popular ETF provider
in terms of adviser loyalty, a study
from Cambridge, Mass.-based Cogent
Research showed. The firm surveyed
1,560 investment advisers.
According to the 2010 Advisor
Brandscape report compiled by the
market research firm, advisers who use
Vanguard ETFs are more committed
to the brand than those using iShares
products. John Meunier, a Cogent prin-
cipal, noted that Vanguard is the only
top-five ETF provider to grow its mar-
ket share over the past year.
iShares still outperforms Vanguard in
the range of products it offers, Meunier
said, but Vanguard outperformed its com-
petitor in just about every other category
Cogent measures, especially in “aspects
of service and client experience.”
State Street and Pimco ranked
third and fourth place among advisers,
respectively.
Alerian Debuts First-Ever MLP ETFIn late August, MLP research firm
Alerian launched the first ETF to tap
into the MLP space. Previously, investors
seeking access to the asset class could
only do so through various ETNs offered
by JP Morgan, UBS and Credit Suisse.
The Alerian MLP ETF (NYSE
Arca: AMLP) tracks the Alerian MLP
Infrastructure Index, and charges an
annual expense ratio of 0.85 percent.
Alerian also says that the ETF will retain
the tax benefits of MLP distributions. MLPs
are typically a nightmare to hold in a fund
setting since funds are typically taxed as
November/December 201056
Claymore Securities, which was acquired by Guggenheim Partners in October, closed four of its ETFs on Sept. 10.
registered investment companies, which
may only invest 25 percent of their assets
in MLPs before becoming subject to vari-
ous tax penalties. AMLP has elected to be
taxed as a corporation, which helps it get
around this restriction.
ALPS Advisors is AMLP’s distributor,
with Arrow Investment Advisors serving
as its subadviser.
BACK TO THE FUTURESCME Group Volume Up In August
Volumes at the CME Group stood
at an average of 11.7 million contracts
traded per day in August 2010, a 15 per-
cent increase from the prior year, and
an 8 percent increase from July 2010.
A total of 258 million contracts were
traded on the exchange in August 2010.
However, index-based contracts were
up only 5 percent from August 2009 to a
daily average of 2.6 million contracts. Of
those, the most actively traded contract,
the e-mini S&P 500 futures, was up 4.8
percent to an average daily volume of
1.9 million contracts; that same fig-
ure for the second-most actively traded
index futures, the e-mini Nasdaq-100
contracts, was up 6 percent for an ADV
of 6 percent. The mini $5 Dow futures,
on the other hand, saw their average
daily volume for August fall nearly 6 per-
cent to 125,383 contracts.
US Investors Can AccessTurkish Futures
In August, Reuters reported that the
Commodity Futures Trading Commission
had given the OK via a “no-action letter”
for U.S. investors to access an index-
based futures contract listed on the
Turkish Derivatives Exchange.
The contract is tied to the Istanbul
Stock Exchange 30 Stock Index, or
ISE-30, which consists of 30 of the larg-
est and most liquid stocks listed on the
Turkish stock exchange. According to
the CFTC letter, the index represents
70 percent of the total market capital-
ization of the Turkish stock market.
KNOW YOUR OPTIONSCFE Lists VIX Contracts
In early September, the CBOE Futures
Exchange (CFE) unveiled plans to begin
trading weekly options on VIX futures.
The contracts would be the first options
to be listed on the CFE.
Regular cash-settled options and
futures on the VIX, as well as options on
VIX-linked ETNs, are already available on
the CBOE’s trading platform.
With the weekly options at the CBOE,
four different contracts are generally
available, expiring in one week, two
weeks, three weeks and four weeks.
The options on the VIX futures will
be settled American style. They were
scheduled to launch Sept. 28.
CBOE Sees Volumes FallThe Chicago Board Options Exchange
saw its average daily volume for August
fall 21 percent from the prior year to
3.5 million contracts. The ADV was also
down from July 2010 by 9 percent.
However, index options saw their
ADV rise by 8 percent from the prior
year. ETF options saw their ADV fall, but
still outperformed, with a decline of just
13 percent. It was really equity options
that dragged down the exchange’s over-
all volume—they saw their ADV fall by a
whopping 33 percent.
Options on the S&P 500 index,
the SPDRS S&P 500 ETF, the VIX,
the PowerShares QQQ Trust and the
iShares Russell 2000 Index Fund remain
the most actively traded index and ETF
options listed on the CBOE.
FROM THE EXCHANGESCBOE Rolls Out Indexes For CME
In September, the CBOE publicly
debuted the first two indexes it has
developed through a partnership with
the CME Group.
The indexes are constructed using
the same methodology used to cre-
ate the CBOE’s VIX (which is based
on options contracts on the S&P 500),
except options on futures on gold and
crude oil that are traded on the CME are
substituted for the S&P 500 contracts.
The CBOE/NYMEX WTI Volatility Index
and the CBOE/COMEX Gold Volatility
Index and their underlying methodolo-
gies are owned by the CBOE. However,
the agreement gives CME the right to
create products based on the indexes,
including futures and options on futures.
Nasdaq To Launch Price-Size Exchange
On Oct. 8, the Nasdaq OMX Group,
Inc. was to launch the first U.S. equity
trading platform with a price-size prior-
ity model to encourage greater trans-
parency in public securities markets.
The platform, called the Nasdaq OMX
PSX, or PSX, will encourage participants
to display more shares at a price level,
making it easier to trade large blocks of
stock and increasing market efficiency.
The allocation of shares is prorated
based on a participant’s size relative to
the total size at that price level.
The platform, which will be oper-
ated as a facility of the Nasdaq OMX
PHLX exchange, formerly known as the
Philadelphia Stock Exchange, has been
approved by the SEC.
ON THE MOVERussell Hires Zyla
Russell Investments said in Sep-
tember it had hired Kurt Zyla, pre-
viously head of investment strategy
for indexes and ETFs at BNY Mellon,
Mellon Capital Management.
Zyla’s new title is regional director
for listed derivatives, and he is respon-
sible for the licensing of the Russell
indexes for use as the basis of futures
and options. The scope of his activi-
ties will encompass the development
of new products and the support of
existing products.
Zyla has an MBA from New York
University.
ETFS Adds To US Sales TeamIn early September, ETF Securities
announced it had expanded its U.S. sales
team with the hiring of Patrick Carter.
Carter’s responsibilities will be
focused on the California and West
Coast markets, particularly institutional
clients and national accounts.
Carter has been working in finan-
cial services for more than 20 years
and joins ETFS from Dimensional Fund
Advisors. Prior to moving to DFA, he
worked in a sales capacity for Merrill
Lynch for 13 years.
November/December 2010www.journalofindexes.com 57
November/December 201058
Summary And ConclusionsSome key takeaways can be drawn from our analysis of
the four factors affecting the relationship between mutual
fund advertising and investors’ fund choices, and whether
they lead investors down the path of the “wizards of over-
confidence” or the path of the “wizards of advertising.”
One key point is that (self-identified) financial literacy
doesn’t seem to improve investors’ mutual fund choices. The
“smart money” effect states sophisticated investors invest in
actively managed mutual funds due to superior investment
skills that enable them to outperform index funds. However,
this effect is apparently false, as sophisticated investors
with higher financial literacy scores and above-average self-
assessed financial skills are not “smart investors.”
Financial literacy scores are strongly associated with inves-
tor awareness of index funds. However, investors with lower
financial literacy scores invest in actively managed funds based
on fund advertising and brokers, while investors with higher
financial literacy scores and self-assessed above-average invest-
ment skills still invest in actively managed funds. The latter
group of investors believe they make superior fund choices,
but they are overconfident investors, not “smart investors.”
Most fund advertisements display just-prior fund perfor-
mance, but they do not signal superior future performance. It
works—increasing assets and profits—because many inves-
tors focus on past performance.
The second point discussed in this article is that investors
who exhibit non-normative “revealed preferences” in fund
choices do not act in their own “true interests.” They have
lower financial literacy scores, avoid complex decisions and
are psychologically unable to engage actively in investment
decisions. One type of revealed-preference decision is the
selection of funds based on advertising, which may affect
investor choices even if it provides little information.
The third part of this analysis discusses the disconnect
between fund advertising and its relevance to the product
it is promoting. Fund advertising affects investor choices
even if it provides little or no information. Only a small
proportion of fund advertising provides direct informa-
tion relevant for rational investors, such as expense ratios.
Moreover, funds that advertise the most may not provide
characteristics investors care about.
Funds that advertise more do not provide higher per-
formance or signal higher fund manager quality, and fund
advertising does not predict future performance. In fact, fund
advertising steers investors to funds with lower expected
returns (higher fees) and higher risk (equity exposure). It can
do this by successfully differentiating products—even when
the differentiation is meaningless—which can reduce fee
competition and allow the fund to charge higher fees.
Investors allocate the most to funds that receive the most
recent media attention. And investors may pay more for
funds that advertise heavily and provide no direct informa-
tion, but marginal returns to fund advertising decline over
time, perhaps due to investor overexposure.
Fund performance advertising is most effective in attract-
ing investors, and suggests that investors care most about
past performance. Funds that advertise portfolios are almost
always equity funds tilted toward “hot” sectors and local
funds. In general, fund advertising generates positive emo-
tions that make investor attitudes more favorable.
The final piece of the puzzle compares two advertising
persuasion models: The traditional model of advertising
assumes rational investors, while the behavioral model
best fits the emotional and cognitive process of investor
fund selection. In the behavioral model of persuasion, fund
advertising is designed to resonate with prevailing investor
beliefs that they accept at face value. Fund advertisers gener-
ally know which messages work, and content is directed to
changes in investor prevailing beliefs. For example, investors
believe growth investing is for wealth creation and that value
investing is for wealth conservation, so when the market is
rising, fund advertisements focus on growth funds, and when
it is declining, they focus on value funds.
Fund advertisements focus on market returns (because
investors do) when past market returns are high, and avoid
providing returns when they are not. They also do not
report performance following market declines even if per-
formance is superior—at such times, the number of fund
advertisements approaches zero.
Overall, fund advertising promotes speculative rather than
contrarian styles of investing. Ultimately, the purpose of mutu-
al fund advertising is to persuade investors to invest more and
to increase fund adviser profits. This is a far cry from advertis-
ing that provides investors with full and objective information
that enables them to become “smarter investors.”
This article explores the relationships of mutual fund advertis-
ing and investor skill in making fund choices. Advertising appeals
to investor emotions by resonating with current beliefs, not by
providing information that enables more informed fund choices.
Choices of unsophisticated investors are dominated by fund
advertising—“the wizards of advertising.”
On the other hand, sophisticated investors with self-
assessed above-average investment skills believe they make
superior choices of actively managed mutual funds that will
outperform index funds. However, sophisticated investors
are not superior investors, but overconfident investors—
“the wizards of overconfidence.”
Mutual funds will be forced to provide useful objective
information if investors “demand it,” but will this ever hap-
pen? The test of this change is when the traditional model of
persuasion replaces the behavioral model in best matching
investor perceived needs in making fund choices.
Endnotes1Müller, Sebastian and Martin Weber. Financial Literacy and Mutual Fund Investments: “Who Buys Actively Managed Funds?” Schmalenback Business Review, vol. 62 (April 2010), pp. 126-153.
2Beshears, John, James J. Choi, David Laibson, and Brigitte C. Madrian. “How are Preferences Revealed?” Working Paper Series, SSRN, April 25, 2008 (http://ssrn.com/abstract=1125043).
3Cronqvist, Henrik. “Advertising and Portfolio Choice.” Working Paper Series, SSRN, Sept. 11, 2008 (http://ssrn.com/abstract=920693).
4Mullainathan, Sendhil and Andrei Shleifer. “Persuasion in Finance.” Working Paper Series, SSRN, Jan. 11, 2006 (http://ssrn.com/abstract=864686).
Haslem continued from page 44
November/December 2010 59www.journalofindexes.com
Global Index DataNovember/December 2010Selected Major Indexes Sorted By YTD Returns
Total Return % Annualized Return %
Index Name YTD 2009 2008 2007 2006 2005 2004 2003 3-Yr 5-Yr 10-Yr 15-Yr Sharpe Std Dev
MSCI Sri Lanka* 39.96 184.15 -62.09 -15.15 42.78 30.70 7.81 42.09 14.33 11.17 17.68 4.68 0.46 48.18MSCI Colombia* 39.57 76.50 -27.68 12.64 10.92 102.31 125.66 59.01 24.14 26.23 38.01 15.75 0.79 33.74Citigroup STRIPS 25+ Year USD 36.15 -42.88 77.10 12.71 4.09 17.82 16.33 -0.95 15.62 8.98 10.79 11.17 0.58 31.90Barclays US Treasury Long 21.00 -12.92 24.03 9.81 1.85 6.50 7.70 2.48 11.45 7.49 8.28 8.23 0.79 13.64Alerian MLP 17.14 76.41 -36.91 12.72 26.07 6.32 16.67 44.54 9.29 11.70 18.18 - 0.45 23.94AMEX Gold Miners* 16.05 37.30 -26.79 16.86 21.86 29.08 -9.56 47.07 13.18 17.27 - - 0.48 49.42FTSE NAREIT All REITs 13.44 27.45 -37.34 -17.83 34.35 8.29 30.41 38.47 -5.96 0.23 9.77 9.50 - 37.62JPM EMBI Global 12.26 28.18 -10.91 6.28 9.88 10.73 11.73 25.66 10.40 9.17 10.40 12.37 0.77 12.58Barclays EM 12.26 34.23 -14.75 5.15 9.96 12.27 11.89 26.93 10.27 9.25 10.47 11.97 0.66 15.16Barclays US Credit 9.82 16.04 -3.08 5.11 4.26 1.96 5.24 7.70 8.36 6.02 7.09 6.81 0.90 8.09Barclays Global High Yield 8.25 59.40 -26.89 3.18 13.69 3.59 13.17 32.42 9.05 8.28 8.95 9.14 0.52 17.89Credit Suisse HY USD 8.07 54.22 -26.17 2.65 11.92 2.26 11.95 27.94 7.57 7.08 7.83 7.43 0.48 15.85Barclays US Aggregate Bond 7.83 5.93 5.24 6.97 4.33 2.43 4.34 4.10 7.65 5.96 6.47 6.49 1.55 4.13Barclays Municipal 7.00 12.91 -2.47 3.36 4.84 3.51 4.48 5.31 6.62 5.02 5.69 5.81 0.92 6.03Barclays US Agency 5.22 1.53 9.26 7.90 4.37 2.33 3.33 2.59 6.71 5.55 6.14 6.23 1.57 3.57Barclays Global Aggregate 4.51 6.93 4.79 9.48 6.64 -4.49 9.27 12.51 7.33 5.85 7.06 6.11 0.83 7.71Dow Jones Transportation Avg 1.80 18.58 -21.41 1.43 9.81 11.65 27.73 31.84 -3.70 3.87 5.66 6.76 -0.04 27.58S&P MidCap 400/Citi Growth 1.42 41.08 -37.61 13.50 5.81 14.39 15.79 37.32 -2.87 2.57 3.21 11.67 -0.03 25.13Dow Jones Utilities Avg 0.92 12.47 -27.84 20.11 16.63 25.14 30.24 29.39 -3.16 2.87 4.56 8.74 -0.16 17.25S&P MidCap 400 0.24 37.38 -36.23 7.98 10.32 12.56 16.48 35.62 -4.29 1.73 4.20 9.96 -0.09 25.23Wilshire 4500 Completion 0.10 36.99 -39.03 5.39 15.28 10.03 18.10 43.84 -5.90 1.00 0.96 6.92 -0.15 25.36MSCI EM -0.33 78.51 -53.33 39.39 32.17 34.00 25.55 55.82 -1.50 12.38 - - 0.10 33.60Dow Jones Composite Average -0.45 19.35 -27.94 8.88 15.71 9.49 15.58 29.40 -4.89 2.71 3.71 7.92 -0.20 19.97S&P MidCap 400/Citi Value -0.88 33.73 -34.87 2.65 14.62 10.80 17.19 33.81 -5.73 0.80 5.12 8.30 -0.14 25.71S&P SmallCap 600/Citi Growth -1.73 28.35 -32.94 5.60 10.54 7.02 24.27 38.43 -6.49 -0.10 4.10 7.12 -0.16 26.11MSCI EAFE Small Cap -2.01 46.78 -47.01 1.45 19.31 26.19 30.78 61.35 -9.83 0.70 5.75 - -0.26 28.09DJ Industrial Average -2.11 22.68 -31.93 8.88 19.05 1.72 5.31 28.28 -6.47 1.77 1.23 7.62 -0.30 19.48S&P SmallCap 600 -2.46 25.57 -31.07 -0.30 15.12 7.68 22.65 38.79 -7.11 -0.38 4.75 7.94 -0.18 26.75Russell 2000 Value -2.54 20.58 -28.92 -9.78 23.48 4.71 22.25 46.03 -8.03 -1.33 6.56 8.33 -0.19 27.91Russell 3000 Value -2.96 19.76 -36.25 -1.01 22.34 6.85 16.94 31.14 -10.40 -1.66 2.25 7.13 -0.40 22.98Russell 2000 -2.97 27.17 -33.79 -1.57 18.37 4.55 18.33 47.25 -7.44 -0.69 2.48 6.01 -0.19 26.97Russell 1000 Value -3.03 19.69 -36.85 -0.17 22.25 7.05 16.49 30.03 -10.61 -1.69 1.92 7.08 -0.42 22.67S&P SmallCap 600/Citi Value -3.14 22.85 -29.51 -5.54 19.57 8.33 21.06 39.09 -7.84 -0.74 5.23 8.57 -0.19 27.72S&P 500/Citi Value -3.33 21.18 -39.22 1.99 20.80 8.71 15.03 30.36 -11.44 -1.78 0.76 5.99 -0.44 23.39Russell 2000 Growth -3.44 34.47 -38.54 7.05 13.35 4.15 14.31 48.54 -7.02 -0.17 -1.94 3.12 -0.17 26.76Russell Micro Cap -3.54 27.48 -39.78 -8.00 16.54 2.57 14.14 66.36 -11.34 -4.18 2.59 - -0.32 28.23S&P 1500 -4.16 27.25 -36.72 5.47 15.34 5.66 11.78 29.59 -8.26 -0.68 -1.15 6.42 -0.34 21.63Russell 3000 -4.26 28.34 -37.31 5.14 15.72 6.12 11.95 31.06 -8.27 -0.72 -1.26 6.21 -0.33 22.01Russell 1000 -4.37 28.43 -37.60 5.77 15.46 6.27 11.40 29.89 -8.34 -0.71 -1.55 6.30 -0.34 21.69NASDAQ 100 -4.51 54.61 -41.57 19.24 - - - - -3.25 - - - -0.04 25.50S&P 500 -4.62 26.46 -37.00 5.49 15.79 4.91 10.88 28.68 -8.66 -0.91 -1.81 6.14 -0.36 21.25MSCI AC World -5.42 34.63 -42.20 11.66 20.95 10.84 15.23 33.99 -8.68 1.14 0.14 - -0.30 24.09Russell 3000 Growth -5.52 37.01 -38.44 11.40 9.46 5.17 6.93 30.97 -6.32 0.08 -5.11 4.66 -0.24 21.73Russell 1000 Growth -5.68 37.21 -38.44 11.81 9.07 5.26 6.30 29.75 -6.26 0.10 -5.36 4.86 -0.24 21.42MSCI BRIC* -5.79 88.79 -60.27 56.12 52.87 39.81 13.63 84.18 -3.47 14.30 10.73 9.19 0.07 37.93S&P 500/Citi Growth -5.90 31.57 -34.92 9.13 11.01 1.14 6.97 27.08 -6.00 -0.17 -4.73 5.75 -0.26 20.14MSCI EAFE Growth -5.92 29.36 -42.70 16.45 22.33 13.28 16.12 31.99 -9.60 1.56 -0.57 2.55 -0.33 24.39DJ UBS Commodity -5.93 18.91 -35.65 16.23 2.07 21.36 9.15 23.93 -6.62 -2.83 4.39 5.40 -0.20 24.22S&P 100 -6.07 22.29 -35.31 6.12 18.47 1.17 6.43 26.25 -9.28 -1.02 -3.43 5.95 -0.43 20.25MSCI Kokusai -6.67 33.14 -41.96 10.66 21.95 7.67 14.62 32.83 -9.46 0.20 -0.39 6.05 -0.34 23.83MSCI EAFE -7.95 31.78 -43.38 11.17 26.34 13.54 20.25 38.59 -10.75 0.96 1.10 4.01 -0.35 25.62S&P Global 100 -9.07 26.71 -36.44 11.38 20.42 5.47 10.15 30.93 -8.69 0.35 -1.75 6.81 -0.34 22.53MSCI EAFE Value -10.00 34.23 -44.09 5.96 30.38 13.80 24.33 45.30 -11.96 0.28 2.64 5.36 -0.36 27.40STOXX Europe 600 -10.30 36.65 -46.54 13.49 35.04 9.93 20.95 40.58 -11.88 1.02 1.67 6.85 -0.52 26.67MSCI EAFE GDP Weighted -11.26 30.38 -44.82 12.88 27.39 13.68 22.57 42.95 -12.63 0.07 0.86 4.72 -0.39 27.27S&P GSCI -11.39 13.48 -46.49 32.67 -15.09 25.55 17.28 20.72 -12.84 -11.67 0.24 3.74 -0.29 32.30MSCI Spain -22.67 43.48 -40.60 23.95 49.36 4.41 28.93 58.46 -9.10 4.76 6.26 11.27 -0.13 35.02MSCI Ireland -27.88 12.28 -71.92 -20.09 46.81 -2.29 43.07 43.83 -41.86 -22.49 -8.07 -3.05 -1.37 34.75Citigroup Greek GBI USD -29.11 6.98 -3.84 13.25 11.88 -8.95 15.73 25.58 -7.48 -2.45 6.00 - -0.34 20.08MSCI Greece -38.82 25.05 -66.01 32.91 35.05 16.10 46.06 69.52 -33.09 -12.87 -3.66 - -0.60 48.05
Source: Morningstar. Data as of August 31, 2010. All returns are in dollars, unless noted. YTD is year-to-date. 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.
November/December 2010
Index Funds
60
November/December 2010Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions
Total Return % Annualized Return %
Fund Name Ticker Assets Exp Ratio 3-Mo YTD 2009 2008 3-Yr 5-Yr 10-Yr 15-Yr P/E Std Dev Yield
Vanguard Total Stock Market VTSMX 61,740.7 0.18 -3.86 -4.20 28.7 -37.04 -8.05 -0.51 -1.13 6.13 14.4 21.94 1.96Vanguard Institutional, Instl VINIX 45,058.3 0.05 -3.17 -4.63 26.63 -36.95 -8.60 -0.88 -1.79 6.19 14.3 21.24 2.28Vanguard 500, Inv VFINX 44,398.6 0.18 -3.20 -4.70 26.49 -37.02 -8.70 -0.99 -1.91 6.07 14.3 21.24 2.15Vanguard Total Stock Market, Adm VTSAX 28,315.1 0.07 -3.83 -4.12 28.83 -36.99 -7.96 -0.42 -1.05 6.19 14.4 21.96 2.07Vanguard 500 Admiral Class VFIAX 27,026.9 0.07 -3.18 -4.64 26.62 -36.97 -8.62 -0.90 -1.83 6.12 14.3 21.25 2.27Vanguard Total International Stock VGTSX 25,240.5 0.32 5.53 -6.04 36.73 -44.1 -8.96 2.79 2.46 - 12.1 27.78 2.54Vanguard Institutional Class Institution VIIIX 24,819.3 0.02 -3.18 -4.62 26.66 -36.94 -8.58 -0.86 -1.77 6.22 14.3 21.24 2.30Vanguard Total Bond Market II, Inv VTBIX 23,594.0 0.11 4.00 7.77 - - - - - - - - 3.37Fidelity Spartan 500, Inv FUSEX 22,398.1 0.10 -3.17 -4.65 26.51 -37.03 -8.68 -0.95 -1.90 6.01 15.2 21.26 1.93Vanguard Total Bond Market VBMFX 22,039.4 0.22 4.08 7.89 5.93 5.05 7.62 5.91 6.21 6.30 - 4.18 3.47Vanguard Total Bond Market, Adm VBTLX 21,913.8 0.12 4.11 7.97 6.04 5.15 7.73 6.01 6.29 6.35 - 4.18 3.58Vanguard Total Bond Market, Instl VBTIX 20,199.5 0.07 4.12 8.00 6.09 5.19 7.77 6.05 6.35 6.43 - 4.18 3.62Vanguard Total Stock Market, Instl VITSX 18,280.0 0.06 -3.83 -4.14 28.83 -36.94 -7.94 -0.40 -1.01 6.24 14.4 21.94 2.08Vanguard 500, Sig VIFSX 15,340.9 0.07 -3.19 -4.64 26.61 -36.97 -8.62 -0.92 -1.87 6.09 14.3 21.25 2.27T. Rowe Price Equity Index 500 PREIX 11,886.0 0.30 -3.24 -4.81 26.33 -37.06 -8.81 -1.13 -2.06 5.87 14.3 21.22 1.71Fidelity Spartan 500, Adv FUSVX 11,870.3 0.07 -3.18 -4.63 26.55 -37.01 -8.66 -0.92 -1.88 6.02 15.2 21.24 1.96Fidelity U.S. Bond Index FBIDX 11,313.0 0.32 4.07 7.73 6.45 3.76 7.02 5.44 6.23 6.30 - 3.82 3.14Vanguard Instl Total Stock Mkt, Instl+ VITPX 11,178.3 0.02 -3.82 -4.13 28.92 -36.89 -7.89 -0.35 - - 14.4 21.97 2.13Schwab S&P 500 SWPPX 8,950.3 0.09 -3.16 -4.61 26.25 -36.72 -8.60 -0.88 -1.86 - 17.3 21.16 1.48Vanguard Total Bond Market, Indx VBTSX 8,731.7 0.12 4.11 7.97 6.04 5.15 7.73 5.99 6.25 6.33 - 4.18 3.58Vanguard Total Bond Market II, Instl VTBNX 8,501.1 0.07 4.01 7.80 - - - - - - - - 3.39Vanguard Emerging Markets Stock VEIEX 8,170.1 0.40 6.76 -0.54 75.98 -52.81 -1.99 11.55 11.25 8.15 14.2 34.20 1.22Vanguard Mid Cap VIMSX 6,857.5 0.27 -4.22 -0.11 40.22 -41.82 -6.81 0.50 3.90 - 15.5 25.56 1.07Vanguard Short-Term Bond, Sig VBSSX 6,466.3 0.12 2.09 4.37 4.38 5.51 5.89 5.11 5.07 5.36 - 2.59 2.39Vanguard European Stock VEURX 6,440.4 0.27 6.83 -10.22 31.91 -44.73 -11.97 0.66 1.44 6.65 11.1 28.56 4.22Vanguard Mid Cap, Instl VMCIX 6,371.5 0.08 -4.15 0.02 40.51 -41.76 -6.64 0.65 4.07 - 15.5 25.58 1.21Vanguard Small Cap NAESX 6,073.0 0.28 -7.50 -1.30 36.12 -36.07 -5.90 0.47 3.46 7.04 15.4 27.89 1.01Vanguard Developed Markets, Instl VIDMX 6,009.1 0.08 5.17 -7.50 28.17 -41.62 -10.65 0.93 1.06 - 11.5 26.59 1.24Vanguard Short-Term Bond VBISX 5,927.1 0.22 2.06 4.29 4.28 5.43 5.79 5.05 5.04 5.33 - 2.59 2.28Vanguard Total Bond Market, Instl+ VBMPX 5,844.8 0.05 4.12 7.99 5.93 5.05 7.65 5.93 6.22 6.31 - 4.18 -Fidelity Spartan International, Inv FSIIX 5,690.7 0.10 5.10 -8.14 28.48 -41.43 -10.68 0.89 0.96 - 13.2 26.70 2.27Fidelity Series 100 FOHIX 5,246.6 0.20 -3.00 -6.18 22.14 -35.44 -9.42 - - - 14.8 20.26 2.42Vanguard Growth VIGRX 5,237.1 0.28 -3.47 -5.73 36.29 -38.32 -6.18 0.09 -3.68 6.13 16.6 21.43 1.08Fidelity Spartan Total Market, Inv FSTMX 5,141.7 0.10 -3.72 -3.88 28.39 -37.18 -8.07 -0.49 -1.11 - 15.3 21.88 1.87Vanguard Small Cap, Instl VSCIX 4,446.9 0.08 -7.46 -1.18 36.4 -35.98 -5.74 0.63 3.63 7.18 15.4 27.89 1.17Vanguard REIT VGSIX 4,444.6 0.26 2.69 14.44 29.58 -37.05 -5.36 1.50 10.03 - 36.3 40.38 3.04Vanguard Total Stock Market, Sig VTSSX 4,389.0 0.07 -3.83 -4.14 28.85 -36.99 -7.96 -0.44 -1.09 6.16 14.4 21.94 2.08Vanguard Extended Market, Instl VIEIX 4,281.5 0.08 -6.16 -0.74 37.69 -38.58 -6.04 0.80 1.00 7.06 15.8 26.35 1.18Vanguard Extended Market VEXMX 4,237.8 0.30 -6.20 -0.85 37.43 -38.73 -6.23 0.61 0.82 6.90 15.8 26.34 0.99Vanguard Intermediate-Term Bond VBIIX 4,224.7 0.22 6.73 12.06 6.79 4.93 9.38 6.77 7.40 7.00 - 6.93 3.93Schwab 1000 SNXFX 4,105.7 0.29 -3.34 -4.21 27.68 -37.28 -8.33 -0.76 -1.55 6.08 17.5 21.39 1.83)LGHOLW\�6HULHV�,QüDWLRQ�3URWHFWHG�%RQG FSIPX 3,623.6 0.20 2.03 3.92 - - - - - - - - -Fidelity Spartan Total Market, Adv FSTVX 3,583.4 0.07 -3.72 -3.87 28.43 -37.16 -8.05 -0.46 -1.10 - 15.3 21.87 1.90Vanguard FTSE All-World ex-US, Instl VFWSX 3,473.6 0.15 5.29 -5.53 39.01 -43.96 -8.02 - - - 12.5 28.24 2.179DQJXDUG�3DFLûF�6WRFN VPACX 3,465.2 0.27 2.28 -2.58 21.18 -34.36 -8.02 1.34 0.13 0.39 12.3 23.37 2.71Vanguard Balanced VBINX 3,461.1 0.25 -0.74 0.67 20.05 -22.21 -1.51 2.40 2.18 6.62 14.4 13.68 2.55Vanguard Small Cap Value VISVX 3,436.5 0.28 -7.83 -0.80 30.34 -32.05 -6.18 -0.38 6.25 - 13.4 28.68 1.88ING US Stock , Class I INGIX 3,396.3 0.26 -3.22 -4.82 26.22 -37.12 -8.85 -1.14 - - 14.3 21.30 0.73ING U.S. Bond, Class I ILBAX 3,281.1 0.46 3.91 7.46 5.88 - - - - - - - 2.45Vanguard Intermediate-Term Bond, Advr VBILX 3,226.6 0.12 6.76 12.14 6.89 5.01 9.48 6.86 7.47 7.05 - 6.94 4.05Vanguard Value VIVAX 3,168.4 0.26 -3.19 -3.55 19.58 -35.97 -10.53 -1.43 0.44 6.16 12.6 22.09 2.68Vanguard Growth, Instl VIGIX 3,098.8 0.08 -3.43 -5.61 36.5 -38.19 -6.01 0.26 -3.54 6.25 16.6 21.44 1.29Vanguard Emerging Markets Stock, Adm VEMAX 3,095.1 0.27 6.77 -0.44 76.18 -52.76 -1.86 11.67 11.31 8.19 14.2 34.22 1.31Vanguard Balanced, Instl VBAIX 3,006.8 0.08 -0.65 0.82 20.18 -22.1 -1.34 2.55 2.31 6.72 14.4 13.67 2.74Vanguard Small Cap Growth VISGX 2,949.7 0.28 -7.19 -1.78 41.85 -40 -5.86 1.11 3.25 - 18.3 27.92 0.28Vanguard Developed Markets, Inv VDMIX 2,935.5 0.22 5.01 -7.66 28.17 -41.62 -10.71 0.90 1.04 - 11.5 26.60 1.24VALIC Company I Stock VSTIX 2,886.8 0.39 -3.24 -4.80 26.16 -37.21 -8.93 -1.21 -2.14 5.83 14.3 21.38 2.25Vanguard Emerging Markets Stock, Instl VEMIX 2,855.6 0.23 6.79 -0.42 76.35 -52.74 -1.81 11.74 11.43 8.28 14.2 34.20 1.35Fidelity Spartan Extended Market, Inv FSEMX 2,835.7 0.10 -6.09 -0.13 36.65 -38.45 -5.75 1.11 1.04 - 15.6 25.40 1.15Vanguard Value, Instl VIVIX 2,820.6 0.08 -3.15 -3.41 19.79 -35.88 -10.37 -1.28 0.57 6.27 12.6 22.10 2.88
Source: Morningstar. Data as of August 31, 2010. Exp Ratio is expense ratio. 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.
November/December 2010
Source: Morningstar. Data as of 2/29/08
www.journalofindexes.com 61
Trailing Returns %
3-Month YTD 1-Yr 3-Yr 5-Yr 10-YrMorningstar Indexes
US Market –3.66 –4.24 5.72 –8.06 –0.43 –1.21
Large Cap –3.11 –5.82 3.39 –8.85 –0.92 –2.96
Mid Cap –4.25 0.18 12.55 –6.43 0.75 3.30
Small Cap –7.48 –1.22 9.85 –5.56 0.61 4.14
US Value –1.89 –1.59 4.97 –10.60 –1.49 3.18
US Core –4.23 –3.80 6.13 –6.45 0.74 1.22
US Growth –4.88 –7.46 6.07 –7.54 –0.94 –7.98
Large Value –0.35 –2.20 2.81 –12.49 –2.24 1.45
Large Core –3.89 –5.31 3.77 –6.75 0.60 –0.60
Large Growth –5.14 –10.08 3.58 –7.74 –1.71 –9.86
Mid Value –5.34 –0.05 10.22 –6.28 –0.02 7.37
Mid Core –3.64 0.51 12.93 –6.24 0.70 6.39
Mid Growth –3.86 0.01 14.59 –7.12 1.28 –3.24
Small Value –7.59 –0.13 12.11 –3.08 1.22 9.68
Small Core –9.34 –2.18 9.36 –6.83 0.43 6.63
Small Growth –5.22 –1.27 8.05 –6.98 –0.14 –3.45
Morningstar Market Barometer YTD Return %
US Market–4.24
–1.59
Value
–3.80
Core
–7.46
Growth
–5.82Larg
e C
ap
0.18Mid
Cap
–1.22Sm
all C
ap
–2.20 –5.31 –10.08
–0.05 0.51 0.01
–0.13 –2.18 –1.27
–8.00 –4.00 0.00 +4.00 +8.00
Sector Index YTD Return %
Consumer Goods 3.96
Media 2.49
Utilities 1.55
–0.97 Consumer Services
–1.03 Industrial Materials
–4.03 Financial Services
–6.08 Telecommunications
–6.12 Business Services
–7.55 Healthcare
–8.32 Hardware
–9.40 Software
–9.74 Energy
Industry Leaders & Laggards YTD Return %
Drug Related Products 38.47
Resorts & Casinos 36.28
Auto Parts Stores 29.90
Confectioners 28.19
Gold 27.60
REIT - Residential 25.79
–26.02 Rubber & Plastics
–30.02 Long-Term Care Facilities
–31.08 Printed Circuit Boards
–33.92 Education & Training Services
–35.37 Aluminum
–43.35 Dairy Products
Biggest Influence on Style Index Performance
YTDReturn %
ConstituentWeight %
Best Performing Index
Mid Core 0.51
Boston Properties Inc. 22.98 1.10
Dr Pepper Snapple Group Inc. 31.49 0.79
Avalonbay Communities Inc. 30.64 0.79
Cliffs Natural Resources Inc. 33.66 0.72
Family Dollar Stores Inc. 55.05 0.44
Worst Performing Index
Large Growth –10.08
Microsoft Corp. –21.90 9.25
Google Inc. Cl A –27.41 5.73
Cisco Systems Inc. –16.52 5.30
Monsanto Co. –34.78 1.70
Medtronic Inc. –27.40 1.87
1-Year
2.81
Value
Larg
e C
ap
3.77
Core
3.58
Growth
10.22
Mid
Cap 12.93 14.59
12.11
Sm
all C
ap
9.36 8.05
–20 –10 0 +10 +20
3-Year
–12.49
Value
Larg
e C
ap
–6.75
Core
–7.74
Growth
–6.28
Mid
Cap –6.24 –7.12
–3.08
Sm
all C
ap
–6.83 –6.98
–20 –10 0 +10 +20
5-Year
–2.24
Value
Larg
e C
ap
0.60
Core
–1.71
Growth
–0.02
Mid
Cap 0.70 1.28
1.22
Sm
all C
ap
0.43 –0.14
–20 –10 0 +10 +20
Notes and Disclaimer: ©2010 Morningstar, Inc. All Rights Reserved. Unless otherwise noted, all data is as of most recent month end. Multi-year returns are annualized. NA: Not Available. Biggest Influence on Index Performance listsare calculated by multiplying stock returns for the period by their respective weights in the index as of the start of the period. Sector and Industry Indexes are based on Morningstar's proprietary sector classifications. The informationcontained herein is not warranted to be accurate, complete or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information.
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Morningstar U.S. Style Overview Jan. 1 – Aug. 31, 2010
Source: Morningstar. Data as of August 31, 2010
November/December 2010
Dow Jones U.S. Industry Review
PerformanceIndex Name Weight 1-Month 3-Month YTD 1-Year 3-Year 5-Year 10-Year
Dow Jones U.S. Index 100.00% -4.58% -3.55% -4.29% 5.73% -8.04% -0.43% -1.33%
Dow Jones U.S. Basic Materials Index 3.43% -2.02% 1.29% -1.07% 18.29% -2.91% 6.60% 7.87%
Dow Jones U.S. Consumer Goods Index 10.76% -2.14% 2.11% 2.57% 13.51% -0.32% 3.54% 6.14%
Dow Jones U.S. Consumer Services Index 11.91% -4.04% -7.05% -0.16% 11.86% -5.18% -0.30% -0.24%
Dow Jones U.S. Financials Index 16.42% -7.32% -7.08% -4.66% -3.89% -21.05% -10.03% -2.72%
Dow Jones U.S. Health Care Index 11.42% -1.41% -2.11% -8.11% 1.36% -3.67% 0.18% 0.53%
Dow Jones U.S. Industrials Index 12.56% -7.02% -5.46% -0.78% 11.89% -8.77% 0.80% -0.80%
Dow Jones U.S. Oil & Gas Index 10.24% -4.23% -2.77% -9.88% -0.46% -8.53% 2.18% 7.22%
Dow Jones U.S. Technology Index 15.95% -7.13% -6.53% -10.05% 4.56% -4.36% 2.12% -8.50%
Dow Jones U.S. Telecommunications Index 3.16% 1.59% 10.18% 1.33% 12.00% -10.08% 2.17% -4.70%
Dow Jones U.S. Utilities Index 4.15% 1.10% 7.57% 1.93% 10.55% -4.01% 2.21% 3.05%
Risk-Return
Industry Weights Relative to Global ex-U.S. Asset Class Performance
Data as of August 31, 2010
Source: Dow Jones Indexes Analytics & Research
For more information, please visit the Dow Jones Indexes Web site at www.djindexes.com.
The Dow Jones U.S. Index, the Dow Jones Global ex-U.S. Index and the Dow Jones U.S. Industry Indexes were first published in February 2000. The Dow Jones Brookfield Infrastructure Index was first published in July 2008. To the extent this document includes information for the index for the period prior to its initial publication date,
such information is back-tested (i.e., calculations of how the index might have performed during that time period if the index had existed). Any comparisons, assertionsand conclusions regarding the performance of the Index during the time period prior to launch will be based on back-testing. Back-tested information is purely hypothetical
and is provided solely for informational purposes. Back-tested performance does not represent actual performance and should not be interpreted as an indication of actual performance. Past performance is also not indicative of future results.
© Dow Jones & Company, Inc. 2010. All rights reserved. "Dow Jones", "Dow Jones Indexes", "Dow Jones U.S. Index", "Dow Jones Global ex-U.S. Index" and "Dow Jones U.S. Industry Indexes" are service marks of Dow Jones & Company, Inc. "UBS" is a registered trademark of UBS AG. "Dow Jones-UBS Commodity Index" is a service
mark of Dow Jones & Company, Inc. and UBS. "Brookfield" is a service mark of Brookfield Asset Management Inc. or its affiliates. The "Dow Jones Brookfield Infrastructure Indexes" are published pursuant to an agreement between Dow Jones & Company, Inc. and Brookfield Asset Management. Investment products that may be based
on the indexes referencedare not sponsored,endorsed,sold or promoted by Dow Jones, and Dow Jones makes no representationregarding the advisability of investing in them. Inclusion of a company in these indexesdoes not in any way reflect an opinion of Dow Jones on the investment merits of such company. Index performance is for
illustrative purposes only and does not represent the performance of an investment product that may be based on the index. Index performance does not reflect management fees, transaction costs or expenses. Indexes are unmanaged and one cannot invest directly in an index.
Chart compares industry weights within the Dow Jones U.S. Index to industry weights within the Dow Jones
Global ex-U.S. Index
U.S. = Dow Jones U.S. Index | Global ex-U.S. = Dow Jones Global ex-U.S. Index
Commodities = Dow Jones-UBS Commodity Index | REITs = Dow Jones U.S. Select REIT Index
Infrastructure = Dow Jones Brookfield Global Infrastructure Index
Composite
Basic Materials
Consumer Goods
Consumer Services
Financials
Health Care
IndustrialsOil & Gas
Technology
Telecommunications
Utilities
-25%
-20%
-15%
-10%
-5%
0%
14% 16% 18% 20% 22% 24% 26% 28% 30% 32% 34% 36%
3-Year Annualized Risk
3-Y
ear
An
nu
alized
Retu
rn
-0.50%
-2.25%
11.14%
0.75%
-0.27%
5.55%
-8.52%
4.35%
-1.95%
-8.28%
-15% -10% -5% 0% 5% 10% 15%
Utilities
Telecommunications
Technology
Oil & Gas
Industrials
Health Care
Financials
Consumer Services
Consumer Goods
Basic Materials
Underweight <= U.S. vs. Global ex-U.S. => Overweight
20
40
60
80
100
120
140
160
8/07 11/07 2/08 5/08 8/08 11/08 2/09 5/09 8/09 11/09 2/10 5/10 8/10
U.S. [77.76] Global ex-U.S. [77.76] Commodities [81.42]
REITs [80.09] Infrastructure [94.14]
62
Dow Jones U.S. Industry Review
November/December 2010
Exchange-Traded Funds Corner
www.journalofindexes.com 63
Largest New ETFs Sorted By Total Net Assets In $US Millions Selected ETFs In Registration
Largest U.S.-listed ETFs Sorted By Total Net Assets In $US Millions
Covers ETFs and ETNs launched during the 12-month period ended August 31, 2010.
Total Return % Annualized Return %
Fund Name Ticker ER 1-Mo 3-Mo YTD Launch Date Assets
Market Vectors Junior Gold Miners GDXJ 0.59 13.76 11.75 17.87 11/10/2009 1,276.9
ETFS Physical Swiss Gold Shares SGOL 0.39 5.76 2.79 13.77 9/9/2009 782.0
Vanguard Short-Term Corp Bond VCSH 0.15 0.24 2.46 4.71 11/19/2009 706.9
PowerShares Build America Bond BAB 0.28 4.11 5.96 15.57 11/17/2009 542.3
PIMCO Enh Short Maturity Strategy MINT 0.35 0.19 0.73 1.02 11/16/2009 517.7
ETFS Physical Platinum Shares PPLT 0.60 -3.26 -1.72 - 1/8/2010 452.7
ETFS Physical Palladium Shares PALL 0.60 0.46 8.69 - 1/8/2010 371.5
Schwab International Equity SCHF 0.13 -3.92 4.34 -7.68 11/3/2009 285.9
Vanguard Intermediate Corp Bond VCIT 0.15 1.59 6.51 10.95 11/19/2009 274.3
Schwab U.S. Large-Cap SCHX 0.08 -4.51 -3.13 -4.30 11/3/2009 273.5
Schwab U.S. Broad Market SCHB 0.06 -4.68 -3.65 -4.07 11/3/2009 272.5
WisdomTree Emrg Mkts Local Debt ELD 0.55 - - - 8/9/2010 195.3
Schwab U.S. Small-Cap SCHA 0.13 -7.03 -8.13 -1.49 11/3/2009 193.3
SPDR Barclays Short-Term Corp Bond SCPB 0.12 0.52 1.95 2.29 12/16/2009 178.6
iShares Russell Top 200 Growth IWY 0.20 -5.00 -3.46 -8.23 9/22/2009 166.8
iShares Russell Top 200 Value IWX 0.20 -4.30 -3.54 -5.88 9/22/2009 164.7
Schwab Emerging Markets Equity SCHE 0.25 -2.79 7.56 - 1/14/2010 148.2
PowerShares CEF Income Composite PCEF 1.81 0.91 6.98 - 2/19/2010 131.2
Schwab U.S. Large-Cap Growth SCHG 0.13 -5.36 -4.56 -6.65 12/11/2009 108.9
UBS E-TRACS Alerian MLP Infrastr ETN MLPI 0.85 -1.35 11.31 - 4/1/2010 99.1
Fund Name Ticker Assets Exp Ratio 3-Mo YTD 2009 2008 3-Yr 5-Yr Mkt Cap P/E Std Dev Yield
SPDR S&P 500 SPY 62,241.0 0.09 -3.25 -4.66 26.31 -36.70 -8.64 -0.98 41,103 13.7 21.09 2.00
SPDR Gold Shares GLD 52,158.8 0.40 2.69 13.76 24.04 4.92 22.43 22.98 - - - -
iShares MSCI Emerging Markets EEM 39,767.1 0.72 5.88 -2.80 68.82 -48.87 -1.57 10.88 18,624 13.4 33.74 1.48
iShares MSCI EAFE EFA 32,097.3 0.35 5.25 -8.00 26.88 -41.00 -10.92 0.58 27,261 13.4 26.97 2.70
Vanguard Emerging Markets VWO 30,346.3 0.27 6.81 -0.49 76.29 -52.54 -2.17 11.44 15,706 14.2 33.52 1.34
iShares S&P 500 IVV 20,905.6 0.09 -3.11 -4.53 26.61 -37.00 -8.57 -0.94 40,524 14.3 21.20 2.00
iShares Barclays TIPS Bond TIP 20,622.3 0.20 3.00 6.00 8.95 -0.53 7.03 5.16 - - 8.98 3.32
PowerShares QQQ QQQQ 15,802.4 0.20 -4.51 -4.72 54.67 -41.72 -3.40 2.62 40,372 19.4 25.61 0.60
iShares iBoxx $ Inv Gr Corp Bond LQD 14,381.6 0.15 8.47 11.75 8.58 2.44 8.20 5.77 - - 12.26 4.90
Vanguard Total Stock VTI 13,628.5 0.07 -3.97 -4.07 28.89 -36.95 -7.90 -0.43 21,970 14.4 21.91 2.07
iShares Barclays Aggregate Bond AGG 12,493.0 0.24 3.96 7.81 3.01 7.90 7.45 5.74 - - 5.47 3.56
iShares Russell 2000 IWM 12,320.2 0.28 -8.87 -3.07 28.53 -34.15 -7.33 -0.76 747 14.4 26.43 1.28
iShares Russell 1000 Growth IWF 9,899.1 0.20 -3.45 -5.82 36.73 -38.21 -6.32 -0.07 31,236 16.8 21.45 1.53
iShares MSCI Brazil EWZ 9,240.5 0.65 7.13 -8.80 121.50 -54.37 7.04 22.78 25,682 15.1 42.45 3.79
Vanguard Total Bond Market BND 8,946.5 0.12 3.92 7.69 3.67 6.88 7.60 - - - 4.91 3.54
iShares Barclays 1-3 Yr Treasury Bond SHY 8,877.7 0.15 0.85 2.31 0.36 3.00 4.08 4.21 - - 1.96 1.18
SPDR S&P MidCap 400 MDY 8,567.5 0.25 -5.16 0.05 37.52 -36.40 -4.59 1.38 2,809 17.5 24.87 1.25
iShares Russell 1000 Value IWD 8,081.1 0.20 -3.61 -2.99 19.23 -36.45 -10.64 -1.78 28,787 12.6 22.54 2.17
iShares FTSE/Xinhua China 25 FXI 7,564.5 0.73 0.89 -5.99 47.28 -47.73 -6.00 15.84 71,483 15.0 39.04 1.73
SPDR DJIA DIA 7,557.7 0.17 -0.98 -2.59 22.72 -32.10 -6.66 1.53 91,197 13.3 19.33 2.23
iShares S&P 400 MidCap IJH 7,107.2 0.22 -5.15 0.23 37.81 -36.18 -4.37 1.54 2,626 16.9 24.80 1.29
iShares Barclays 1-3 Yr Credit Bond CSJ 7,050.2 0.20 2.27 2.82 7.17 3.84 5.41 - - - 4.28 2.92
Market Vectors Gold Miners GDX 6,935.3 0.53 7.52 16.01 36.72 -26.07 13.20 - 13,510 29.0 49.51 0.21
iShares iBoxx $ High Yield Corp Bond HYG 6,254.1 0.50 5.27 4.53 28.86 -17.40 4.38 - - - 19.31 8.97
Energy Select Sector SPDR XLE 5,778.8 0.21 -3.00 -9.42 21.81 -38.97 -8.38 1.67 44,678 12.7 27.55 1.96
Source: Morningstar. Data as of August 31, 2010. Exp Ratio is expense ratio. 3-Mo is 3-month. YTD is year-to-date. 3-Yr and 5-Yr are 3-year and 5-year annualized returns, respectively.Mkt Cap is geometric average market capitalization. P/E is price-to-earnings ratio. Std Dev is 3-year standard deviation. Yield is 12-month.
Claymore BulletShrs 2012 HiYld Corp Bond
Direxion Daily Euro Bull 3X
EGS INDXX Growing Asia Lrg Cap
Emerald Rock Dividend Growth
ETFS Leveraged Copper
First Trust Nasdaq CEA Smartphone
Global X Fishing
IQ International Indonesia Small Cap
L6KDUHV�*OREDO�,QüDWLRQ�/LQNHG�%RQG
Jefferies Natural Gas Equity
Market Vectors MLP
Pimco Govt Limited Maturity Strategy
PowerShares KBW High Div Yld Financial
ProShares Ultra Gold Miners
Riverfront Strategic Income
Russell Contrarian
Rydex Russell 3000 Equal Weight
SPDR Barclays Capital CMBS
Vanguard Long-Term Municipal Bond
Wilshire Mid-Cap Value
Source: IndexUniverse.com’s ETF WatchSource: Morningstar. Data as of August 31, 2010. ER is expense ratio. 1-Mo is 1-month. 3-Mo is 3-month. YTD is year-to-date.
Exchange-Traded Funds Corner
H U M O R
64
A tool for surviving
imminent Armageddon
November/December 2010
The End Is Nigh
By Lara Crigger
Post-Apocalyptic Investing: The Index Approach
With the price of gold crossing $1,300
an ounce, the Federal Reserve prepping
for more quantitative easing and Oprah’s
talk show finally ending, we here at Journal
of Indexes can read the writing on the wall:
The end of the world is nigh.
Food riots. Water riots. Beer riots. The
collapse of the world economy. Invasions
by the undead. You get the picture.
When the apocalypse comes, and all
your stocks and bonds won’t be worth the
paper you burn to fuel your cooking fires,
how can you be sure your portfolio will
remain protected?
To fill this critical investment need,
our crack team of analysts has developed
the very first post-apocalyptic investment
strategy, the Zero-Omega Markowitz/
Bernstein Index of Extinction. The ZOMBIE
benchmark will track a basket of stocks,
bonds and weapons designed to give
investors exposure to the most promising
post-Armageddon markets.
Fully 90 percent of the index will invest
in gold bullion, because, as everyone
knows, when the global economy ceases
to function and all previous conceptions
of money and fair value lose their mean-
ing in the wake of aching hunger, lumps
of inedible yellow rock will be the most
useful asset anyone can possess. Indeed,
gold has plentiful applications in the post-
apocalyptic economy:
VËË.¬��Ë �ÍË ��Í�Ë ÍÁ�¬Ý�Áj^Ë Í�Ë j�Ä�?ÁjË ÍÁjÄ-
passers who’d steal your canned food.
VËË.WÖ�¬ÍË�ÍË��Í�Ë?�Ë�~���ËÍ�ËÝ?ÁaË�wwËÍ�jËM�Í-
ter gales of an extended nuclear winter.
VËË ?�jË?Ë�jÝË~Á���Ëw�ÁËß�ÖÁËÍjjÍ�^ËÄ��Wj^Ë
as a zombie, yours will have long
since rotted away.
But gold isn’t the only asset worth own-
ing. A full 5 percent of the index will invest
in the luxury projectiles industry: gun
stores, hunting outlets, munitions depots,
antique musket manufacturers and cata-
pult-engineering firms. While the firearms
sector remains a niche market now, we pre-
dict it will experience extraordinary growth
potential once the food riots begin.
Another 2 percent of the index will
focus on big-box retailers and canning
operations. Why only 2 percent? While
durable foodstuffs are a crucial element of
any post-apocalyptic portfolio, we’ve kept
the overall percentage here small due to
its high exposure to loot risk and competi-
tion from cannibalization.
The final 3 percent will be spread out
among diversified decontamination opera-
tions, MRE manufacturers and defense con-
tractors. In a world of devastating plagues
and the restless undead, we foresee excit-
ing opportunities for companies in the con-
tainment and quarantine industries.
ZOMBIE’s equity exposure and custo-
dial relationships will focus on the U.K.,
Indonesia, Japan and Australia, since, as
islands, they have the best chance of survival
after a worldwide pandemic transforms the
continental populations into slavering, mind-
less brain-chewers. However, a substantial
portion of assets will also invest specifically
in Los Angeles, as the percentage of plastic
and silicon among the native population
should serve as a natural deterrent against
any impending zombie assault.
Future-minded investors holding
ZOMBIE-based products can relax, know-
ing their portfolios will be safe, even when
the dead roam the earth and feast on the
flesh of the living. They can turn to their
spouse or neighbor, huddling beside them
in abject fear and hunger, and say with con-
fidence, “We might not have food or water
or enough bullets to last until sunrise, but,
hey, at least we have some gold.”
Project1 9/28/10 11:38 AM Page 1
All investments are subject to risk. Vanguard funds are not insured or guaranteed.
Vanguard ETFs are not redeemable with an Applicant Fund other than in Creation Unit aggregations. Instead, investors must buy or sell Vanguard ETF Shares in the secondary market with the assistance of a stockbroker. In doing so, the investor will incur brokerage commissions and may pay more than net value when buying and receive less than net asset value when selling.
For more information about Vanguard ETF Shares, visit advisors.vanguard.com/equityetfs, call800-523-8845, or contact your broker to obtain a prospectus. Investment objectives, risks, charges, expenses, and other important information are contained in the prospectus; read and consider it carefully before investing.
*Source: Lipper Inc. as of 12/31/2009. Based on 2009 ETF industry average expense ratio of 0.52% and Vanguard ETF average expense ratio of 0.18%.© 2010 The Vanguard Group, Inc. All rights reserved. U.S. Pat. No. 6,879,964 B2; 7,337,138. Vanguard Marketing Corporation, Distributor.
With 7 new ETFs, you have more options than ever when it comes to Vanguarding™ your clients’ portfolios. And with expense ratios lower than the industry average,* your clients can keep more of their returns. Take a closer look at the new Vanguard Russell ETF lineup.Visit advisors.vanguard.com/equityetfs
All Russell ETFs were
created equal.
Until now.
7 new ETFs with expense ratios lower than the industry average.
Project1 9/29/10 9:28 AM Page 1
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