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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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V o l . 1 3 N o . 6

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

Tell

er

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David Blitzer: Standard & Poor’s

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6

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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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Beyond Borders



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


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%


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%


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?~��~Ë¬�¬Ö�?Í��Ë?�aË?Ëw��aË�wË¬Á�Ä¬jWÍ�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—


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?


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


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


Hedging With Inverse ETFs

By Joanne Hill and Solomon Teller

A primer


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


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%


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S&P 500 with 100% 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


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%


S&P 500 with 100% -0.01% 0.43% -0.13%


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S&P 500

Buy To Rebalance The 100% Hedge

Sell To Rebalance The 100% Hedge

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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


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

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0%

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-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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Aug08

Sep08

Oct08

Nov08

Dec08

Jun08

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Aug08

Sep08

Oct08

Nov08

Dec08

Figure 7

Rising Index Return Scenario: Relative Performance Of

Single And Leveraged Inverse ETF Hedges

ReturnAnnualized

Volatility

Maximum

Drawdown


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

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S&P 500S&P 500 with 50% -2x HedgeS&P 500 with 50% -1x Hedge


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


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


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

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S&P 500 with 50% -2x HedgeS&P 500 with 50% -1x Hedge


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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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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.

Taxes on capital gains take away money you could be investing. We strive

to manage our exchange-traded fund (ETF) holdings in a way that minimizes

capital gains incurred by the fund. Since our inception in 2003, this

process has resulted in zero capital gains distributions on 99% of our ETFs.

Visit InvescoPowerShares.com today to learn about our tax-efficient ETFs,

including PowerShares QQQ, a large-cap equity ETF.

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By Juan Carlos Artigas

What it can do for your portfolio

Rediscovering Gold

As An Asset Class

26


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


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


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


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.


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


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


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


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.


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.


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.


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?



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.



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)



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.


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.


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.



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.


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)




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.


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.


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.



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.



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.


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)



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.


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.



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.



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.



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.


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.


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.


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.



continued on page 41


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.”


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.


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


By John A. Haslem

Paths to the ‘Wizards of Advertising and Overconfidence’

Mutual Funds

And Investor Choice


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


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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46

By Larry Swedroe

An excerpt

Wise Investing Made Simpler



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


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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.


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.


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


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.





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).


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

VËË7?�~Ö?ÁaË.F+Ë �a�?¬Ë|ååË�Á�ÝÍË

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:

VËË7?�~Ö?ÁaË-ÖÄÄj��Ë¤åååË 0�Ë

(NasdaqGM: VONE), 0.12 percent

VËË7?�~Ö?ÁaË-ÖÄÄj��Ë¤åååË7?�ÖjË 0�Ë

(NasdaqGM: VONV), 0.15 percent

VËË7?�~Ö?ÁaË-ÖÄÄj��Ë¤åååË�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-


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-


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


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


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


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


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


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.



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.


Index Funds

60

November/December 2010Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions


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.


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.

?

Morningstar U.S. Style Overview Jan. 1 – Aug. 31, 2010

Source: Morningstar. Data as of August 31, 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


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.


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


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.”

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.


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