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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Transcript
Debt Be Not Proud
Robert Arnott
Hedging With Inverse ETFs
Joanne Hill and Solomon Teller
Gold As An Asset Class
Juan Carlos Artigas
Commodities Indexing Roundtable
Chatting with Rouwenhorst, Rogers, Prestbo and others
Plus Blitzer on commodities investing, Haslem on
fund advertising, an excerpt from Swedroe and more!
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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.
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.
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November/December 2010
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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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dividend yields) similar to the developed economies, we
might wonder why the stocks get a “free pass” on the
feared political risk of these markets, while the sovereign
debt does not. Similarly, when we saw a “flight to quality”
in the fall of 2008 and spring and summer of 2010, why did
this imply a shift in investment preferences away from the
emerging markets, toward the U.S., Germany and Japan,
and not the opposite?
One might reasonably argue that—absent political risk—
emerging markets are collectively more creditworthy than U.S.
Treasurys. Which invites a provocative question: When will
U.S. Treasurys be priced to offer a “risk premium”—a high-
er yield—than the most stable and solvent of the so-called
emerging markets?
November/December 201016
Appendix: Debt Burden And GDP GrowthIt is beyond the scope of this short paper to explore the
wisdom of our surging public debt, though our views on
the topic are self-evident. Still, we might pose the question:
Which countries have skated through the “global financial
crisis” largely unscathed? Again, we might turn to the CIA
World Fact Book for some simple evidence.
If we regress 2009 GDP growth against debt burden—defined
as the size of a country’s debt relative to the fundamental RAFI
scale of its economy—and against the 2008-09 average deficit,
we find the results on Figures 2 and 3. The bivariate regression
results, across the 75 countries, are as follows:
2009 Growth = 3.33% [t-Stat is 10.2]
-0.005% x ln (Debt / RAFI Weight) [t-Stat 5.3]
- 0.18% x (2009 Fiscal Deficit / GDP) [t-Stat 3.7]
R2 = 0.453
Every 1 percent increase in the ratio of a country’s debt,
relative to its RAFI-weighted share of the world economy
(proxying for the country’s ability to service its debt),
reduced GDP growth in 2009 by 5 basis points (7 basis points
in a univariate regression). If the real cost of sovereign debt
is 2 percent (i.e., if the yield that the country must pay the
bondholders is 2 percent above inflation), then the damage
that debt inflicts on GDP growth would appear to be roughly
three times as large as this direct cost. The univariate cor-
relation is -49 percent; this result is significant at the 0.1
percent level.
Figure 3 shows that every 1 percent of deficit spending, as
a percentage of GDP, reduced a country’s 2009 GDP growth
by 18 basis points (22 basis points on a univariate basis).
The univariate correlation is -59 percent; this result is also
significant at the 0.1 percent level.
Neo-Keynesians will argue that our causality is confused:
They would argue that it’s the plunging GDP that triggers
additional debt and deficit spending, not the other way
around. Causality is difficult to prove in either direction.
But, it merits mention that Keynes himself never argued
for structural deficits. That seems to be the war cry of the
neo-Keynesians. Keynes argued for budget surpluses in most
years, affording a nation an opportunity for deficit spending
to soften the impact of economic downturns.
While the sample period is only one year and one financial
crisis, and therefore must be taken with a grain (or even a
shaker-full) of salt, both results are highly statistically sig-
nificant. However, since we do not have access to data from
multiple “global financial crises,” we should perhaps take
heed of the implications of this admittedly limited result.
While Figures 2 and 3 examine the economies of the
world for one year (2009), Figure 4 examines one economy
(the U.S.) for over 50 years. Milton Friedman observed that
the true tax rate is the rate of spending: Spending must be
covered by current or future taxes, so deficits merely repre-
sent deferred taxation. So, how does growth in the private
sector economy respond to growth in spending? Badly.
There is a 73 percent correlation between increases in
federal spending and decreases in private sector GDP (the
gross GDP, less public sector spending). This evidence would
suggest that every 1 percent increase in federal outlays—as
a percentage of GDP—reduces the private sector GDP by
1.85 percent. Again, the neo-Keynesians will argue that the
causality is backward: Plunging private-sector GDP requires
soaring expenditures to arrest the damage. Again, causality
is difficult to prove, either way. However, the relationship is
overwhelming, with a t-Statistic of 3.1.
Figure 5 updates the graph from our 2009 white paper,
“The 3-D Hurricane: Deficit, Debt and Demographics.”10 As yet,
there has been no material deleveraging in the U.S. economy.
We’ve taken a breather on accumulating net new debt, and
we’ve transferred some private-sector debt to the govern-
ment. However, deleveraging has yet to begin in earnest.
Most of us know someone who has taken on debt amount-
ing to several years of income. If it’s for a first home, and our
friend’s income is rising quickly, we would not think them
foolish to take on that first mortgage. But, if it’s a middle-
aged friend with stable income, especially one fast approach-
ing retirement, we would likely think it very unwise for them
2009 GDP Growth Vs. Debt Burden, All Debtor Nations
10%
8%
6%
4%
2%
0%
-2%0.01 0.10 1.00 10.00
Debt Relative to RAFI Weight in World Economy
China
India
BrazilRussia
US
Germany
France UK
Japan
20
09
GD
P G
row
th, p
er
CIA
Wo
rld
Fa
ct B
oo
k
L Developed Markets L�Emerging Markets
Source: Research A�liates, on data drawn from CIA World Fact Book database
Figure 2
2009 GDP Growth Vs. De�cit, All Debtor Nations
10%
8%
6%
4%
2%
0%
-2%-15% -10% -5% 0% 5% 10% 15% 20%
2008-09 De�cit as % of GDP
China
India
BrazilRussia
US
Germany
FranceJapan
UK
20
09
GD
P G
row
th, p
er
CIA
Wo
rld
Fa
ct B
oo
k
L Developed Markets L�Emerging Markets
Source: Research A�liates, on data drawn from CIA World Fact Book database
Figure 3
November/December 2010www.journalofindexes.com 17
to take on massive debt. Most of us are unsurprised when
these friends encounter serious difficulties: They’ve boosted
their consumption lifestyle on borrowed funds. The creditors
eventually want to get paid.
Many observers fret that, if we deleverage (indeed, even if
we stop running up additional debt), we face a serious reces-
sion. They confuse credit-funded consumption with prosper-
ity. Is the entry-level clerk who borrows to buy a Mercedes
and a condo, and then finds that he cannot afford the pay-
ments, prosperous? Does he have a natural, inalienable right
to continue consuming beyond his means?
As a nation, regardless of our decisions to borrow more
or to reduce our borrowings, we’ll still be producing as much
in goods and services as in the past. We’ll just no longer be
consuming goods and services beyond what we produce as a
nation. If our lifestyle has been funded in part on debt, then
deleveraging will mean a reduced lifestyle for all, but only to
the extent that we’ve been consuming more than we were
able to produce. That consumption is unsustainable, regardless
of our fiscal and monetary policies and regardless of our
intentions with regard to future debt.
If we would counsel our overleveraged friends to cut their
spending and start whittling down their debt, why should our
counsel to nations be any different? Should we be surprised that
the economies for creditor nations are soaring, while the debtor
nations find their growth crippled by every economic shock?
Endnotes1 See Arnott, Hsu, Li and Shepherd, “Valuation-Indifferent Indexing for Bonds,” Journal of Portfolio Management, Spring 2010. Just as we damage our returns when we weight
stocks according to their popularity—i.e., cap weighting—we also hurt our bond results, if we weight bonds according to the magnitude of a borrower’s debt load.
2 The working age population might be a better gauge. We chose total population because it’s universally available for all countries.
3 We chose to use the square root of landmass, in order to avoid grossly rewarding big, sparsely populated countries like Russia, Australia and Canada, or penalizing small, crowded
countries like Luxembourg, Hong Kong and Singapore. For midsize countries like Argentina or Germany, this adjustment makes little difference.
4 Based on the UN definition of developed and emerging economies.
5 One interesting “factoid” is that the 2010 CIA Fact Book shows the U.S. as having far less debt in 2009 than it did in 2007. How’s that? In 2007, the unmarketable debt held in
the Social Security, Medicare and other national trust funds were correctly counted as U.S. public debt. In 2009, this $5 trillion debt was excluded. Was there political pressure
to make this change? Is there a growing intent to spend the trust funds, rather than to continue even partially prefunding these obligations? We may never know! Either way,
for our analyses in this paper, we added the unmarketable Treasury bonds back into the U.S. Bond and Public Debt columns.
6 Interestingly, in each case, the population is the sole outlier; it would appear that its debt is well within bounds on three factors of production: capital, resources and energy.
7 It’s interesting to note that most of these countries also breezed through the “global financial crisis” better than the countries with more debt. They enjoyed average GDP growth
in 2009 of 1.7 percent, double that of the G-5 and of the eurozone.
8 We’ve long found this label puzzling: four countries with almost nothing in common but a shared acronym! Even though China shares borders with Russia and India, the three
countries have less in common—culturally, economically or legally—than essentially any countries on the developed economies list. Consider it a labeling-cum-marketing coup
by Goldman Sachs!
9 Note also that Singapore has a sovereign wealth fund that is larger than its aggregate debt. So, as with Chile, China, Hong Kong, Russia and Taiwan, their net debt is nonexistent.
10 See our Fundamentals white paper, “The 3-D Hurricane: Deficit, Debt and Demographics,” Research Affiliates, November 2009.
US Federal Outlays And Private Sector Growth, 1953-2009
9
6
3
0
(3)
(6)
(9)(3) 0 3 6 9
Growth in Outlays, % of GDP
Growth =
-2.4*Outlays + 2.6%
Correl. = 0.69, to 2008
Growth =
-1.85*Outlays + 2.7%
Correl. = 0.73, to 2009
Gro
wth
in P
riv
ate
Se
cto
r G
DP,
%
Source: Research A�liates, on data drawn from OMB Budget of the U.S. Government 2010, Historical Tables
N Growth in Outlays
Linear (Growth in Outlays)
Figure 4
US Aggregate Debt, By Source, Through Q1 2010
900%
800%
700%
600%
500%
400%
300%
200%
100%
0%
March1950
March1960
March1970
March1980
March1990
March2000
March2010
Source: Research A�liates, on data drawn from Federal Reserve Flow of Funds database
N Entitlement Programs N Households and Nonpro�ts
N Business, Excluding OBS N Total Government + GSEs
Figure 5
November/December 201018
Hedging With Inverse ETFs
By Joanne Hill and Solomon Teller
A primer
November/December 2010www.journalofindexes.com 19
In designing hedging strategies, investors can choose from
a variety of tools and approaches. In recent years, inverse
exchange-traded funds (ETFs)1 have joined the list of avail-
able hedging tools used by institutional and other investors.
In this article, we first discuss the factors investors should
consider when constructing any hedging strategy. We then
explore the critical aspects of hedging with single inverse
(e.g., -1x) ETFs. We show that while these tools can be effec-
tive hedging vehicles, they require careful monitoring and
rebalancing to maintain the hedge. We finish by comparing
hedging with single inverse ETFs to hedging with leveraged
inverse ETFs (e.g., -2x), the latter requiring less upfront capi-
tal but more frequent rebalancing.
Key Hedging Strategy ConsiderationsA hedging strategy involves adding positions to a portfo-
lio with the objective of reducing volatility of returns. Many
investors choose to hedge risk rather than sell positions in
their portfolios because of liquidity, tax, trading cost or other
portfolio management implications.2 To hedge a portfolio
investments that move in the opposite direction—to all or
a portion of the portfolio in an attempt to offset some or all
changes in value of the target position. In designing a hedging
strategy, investors should consider the following factors:
Choosing a Benchmark Index—Many investors use hedg-
ing instruments based on indexes to reduce risk associated
with broad market moves, referred to as benchmark risk.
Index-based hedges are often more liquid, accessible via
exchanges and may be less costly than customized portfolio
hedges using swaps or options in the OTC market. This can
make it easier to monitor, trade and adjust the size of hedges
over time, as well as to exit the hedging strategy. Selecting an
appropriate benchmark index typically involves comparing the
return and security characteristics of the target position with
those of various indexes and identifying the index, or set of
indexes, that have the highest correlation to the target posi-
tion. Hedging strategies can range from simple—hedging an
S&P 500 portfolio with an S&P 500 index product—to more
complex—hedging across multiple asset classes that may
require blending a group of index products and that would
need to be regularly rebalanced to maintain consistency with
the target position. This article focuses on the former.
Determining How Much to Hedge—How much to hedge
depends on the amount of benchmark risk an investor is
seeking to reduce, with the maximum being a full hedge (100
percent of the long position) that would reduce the return
expectation of the hedged position to that of a cash equiva-
lent.3 Many investors attempt to hedge only a small portion
of a portfolio’s market exposure, such as 10 percent or 20
percent, to help reduce volatility of returns. In cases where
investors are interested in hedging a specific portfolio expo-
sure, such as a sector allocation, the amount of the hedge
will naturally be driven by the size of that exposure.
Selecting the Hedging Vehicle—When selecting a hedg-
ing vehicle, investors should consider various factors, such
as the return profile of the hedging vehicle, effectiveness,
expected duration of the hedge, liquidity, cost, financing and
ease. Investors looking to hedge equity risk, for instance, can
short stocks or ETFs or choose from a variety of derivative
strategies, such as selling futures or swap contracts, buying
put options or selling call options. More recently, the choice
of buying inverse ETFs has been added to the hedging menu.
That is the focus of this article.
Monitoring and Rebalancing the Hedge over Time—
Effective hedging normally requires a dynamic process,
monitoring and rebalancing the hedge to maintain alignment
with the value of the position or portion of the portfolio
being hedged. Common sources of misalignment are active
(alpha) risk or benchmark (beta) differences between the
hedging vehicle and the index itself. A portfolio with active
risk may outperform or underperform its benchmark index
over a hedging period, calling for adjustment in the size of
the hedge. Consider, for example, an initial $100 investment in
an actively managed mutual fund that outperforms the index
by 5 percent. An investor who had hedged by being short the
benchmark index now has at least an additional $5 at risk and
should consider adding to the hedge to account for the alpha
achieved—a practice known as rebalancing the hedge.
Designing Rebalancing Strategies—The design of a rebal-
ancing strategy for a hedge should reflect the desired level of
monitoring and customization required to adjust for chang-
ing market and volatility conditions. Common rebalancing
approaches include calendar rebalancing, where adjustments
are made at regular time intervals, such as monthly or
quarterly, and fixed-percentage rebalancing, which triggers
rebalancing when the difference between the hedge and
the long position return reaches a certain percentage level,
such as 10 percent.4 A fixed-percentage trigger is more adap-
tive to market conditions than calendar-based rebalancing.
With a fixed-percentage trigger, more frequent rebalancing
typically occurs during high-volatility periods. The size of
the band or range should be based on the investor’s goals,
risk tolerance and expected transaction costs. Generally, the
tighter the band, the more frequent the rebalancing and the
smaller the deviation of net exposure. Rebalancing the hedge
also involves capital, transaction cost and tax considerations,
which largely depend on which of these rebalancing strate-
gies is utilized and on prevailing market conditions.
Hedging Using Inverse ETFs Now, let’s examine one particular hedging method in
greater detail—hedging using inverse ETFs. Inverse ETFs are
investments that seek to provide an inverse multiple (e.g.,
-1x or -2x or -3x) of the daily return of a benchmark before
fees and expenses. These ETFs debuted in 2006, although
similar inverse mutual funds have been in existence since
1994. Inverse ETFs have grown significantly. Today, more
than 100 ETFs cover a broad range of equity, fixed-income,
commodity and currency benchmarks.5 Many investors con-
sider inverse ETFs to be attractive hedging instruments for
the following reasons:
VËËInverse Correlation: An inverse ETF seeks to achieve the
inverse of the one-day performance (or a multiple there-
of) of the ETF’s stated benchmark index before fees and
November/December 201020
expenses.6 As such, buying an inverse ETF may provide
index returns with the negative correlation, on a daily
basis, necessary to implement an effective hedge, with-
out requiring investors to short securities.
VËËAccessibility: Inverse ETFs trade much like stocks on
security exchanges and are generally bought and sold
in the same way. Typically, no special accounts or other
special arrangements are needed.7
VËËIntraday Pricing and Liquidity: Since inverse ETFs trade
much like stocks, they are priced throughout the day to
reflect market fluctuations. For some investors, this can
facilitate better monitoring and rebalancing.
Rebalancing the hedge is a particularly important consid-
eration when hedging with inverse ETFs due to the single-day
objective of these ETFs. Figure 1 uses a simple two-day example
to illustrate the potential additional rebalancing requirements
when using single inverse ETFs. The table shows the impact of
both 5 percent up and 5 percent down daily moves on a fully
hedged $100 long position8 where the long position and the
single inverse ETF have the identical underlying benchmark.
In Scenario A, where there has been a rise of 5 percent,
we see that a purchase of an additional $10 of the single
inverse ETF is required to return net exposure back to 0
percent. In Scenario B, where there has been a decline of 5
percent, we see that a sale of $10 is required to return net
exposure to 0 percent.10
Case Studies: Hedging With Single Inverse ETFs In Different Market Conditions
We use case studies to further illustrate hedging with
single inverse ETFs, demonstrating the need to rebalance.
With case studies representing periods of rising and falling
benchmark returns and different volatility environments,
we can show how the frequency of rebalancing is linked
to market conditions and how the net exposure varies
between rebalancing points.
We present two different market scenarios using S&P 500
returns: 1) a period of declining returns (H2 2008); and 2) a
rising return period (H2 2009). To simulate the performance
objective of an inverse and leveraged ETF, we’ve taken each
of the S&P 500’s daily returns and multiplied them by -1 and
-2, thus ignoring fees, financing, interest and expenses.11 In
all of the case studies, we employ a fixed-percentage rebal-
ancing approach to keep the net exposure of the combined
long and hedge positions within a fixed-percentage band of
+/-10 percent. With a fixed-percentage approach, rebalanc-
ing occurs when this range is exceeded in either direction.
Case Study I: Single Inverse Hedge In A Declining Return Environment
Figure 2 shows the risk/return characteristics and net
exposure of fully (100 percent) and partially (50 percent)
hedged positions in the S&P 500 during the second half
of 2008. The table at the bottom of Figure 2 shows the
net exposure of the 100 percent hedged position12 and
the points where rebalances occurred, which are seen
where the black line pierces the +10 percent and -10
Figure 1
Hedge Rebalancing Example For A Single Inverse ETF Hedge With 5% Daily Index Moves
Position
Scenario A: Long Position Rises 5%
Day 1 Day 2Rebalance
Trade
Buy
additional
$10 of -1x
ETF position
Position
Scenario B: Long Position Falls 5%
Day 1 Day 2Rebalance
Trade
Sell $10 of
existing
-1x ETF
position
Long $100 $105
-1x ETF $100 $95
Net Exposure9 $0 $10
Long $100 $95
-1x ETF $100 $105
Net Exposure $0 -$10
Source: ProShares
Figure 2
Single Inverse ETF Hedging Strategy Reduces Volatility And
Mitigates Downside Losses In A Period Of Declining Returns
10%
0%
-10%
ReturnAnnualized
Volatility
Maximum
Drawdown
S&P 500 -28.48% 53.91% -40.63%
S&P 500 with 50% -10.08% 14.76% -14.01%
Hedge in -1x Strategy
S&P 500 with 100% -0.88% 1.23% -0.99%
Hedge in -1x Strategy
20%
10%
0%
-10%
-20%
-30%
-40%Jun08
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S&P 500 with 100% Hedge
S&P 500 with 50% Hedge
S&P 500
Buy To Rebalance The 100% Hedge
Sell To Rebalance The 100% Hedge
Aug08
Sep08
Oct08
Nov08
Dec08
Jun08
Jul08
Aug08
Sep08
Oct08
Nov08
Dec08
Note: Total returns of S&P 500 with 50% and 100% hedges in -1x strategy 6/2008–12/2008 with 10% rebalance trigger. Volatility is standard deviation of daily returns. Maximum drawdown is lowest cumulative return during period. Net exposure based on 100% hedging strategy.Sources: Bloomberg, ProShares
November/December 2010www.journalofindexes.com 21
percent rebalancing bands. Through early August 2008, net
exposure would have stayed relatively stable, only breaking
out of the band and requiring rebalancing twice between
June 30 and the end of August. At that point, the S&P 500
began to decline steeply, with higher volatility through year-
end. During this latter period, fluctuations in net exposure
increased as the gap between the return of the S&P 500 and
the inverse strategy increasingly diverged, prompting the
need for more frequent rebalancing. For the six months as a
whole, the 10 percent rebalancing band required the hedge
to be adjusted, on average, about every 10 days.
As summarized in the table at the bottom of Figure 2,
rebalancing helped maintain a consistent hedge during the
six-month horizon, and the hedge significantly reduced loss-
es and return volatility over the entire six-month period. A
50 percent hedged position declined by just over 10 percent
during this period when the index return was -28.5 percent,
and reduced volatility from 54 percent to less than 15 per-
cent.13 As hoped, the fully hedged position has close to zero
return and zero volatility.14
Case Study II: Single Inverse Hedge In A Rising Return Environment
In our next case study, we looked at the same hedging
strategies against S&P 500 exposure but in a period of rising
returns, specifically the second half of 2009 when the S&P
500 appreciated by 22.6 percent. Results for this market
scenario are shown in Figure 3.
Over this period, the volatility of the S&P 500 index was
17 percent, much less than that experienced during the
turbulent second half of 2008. Not surprisingly, the net
exposure of the hedging strategies was far less volatile as
well. A 10 percent band applied over this particular period
prompted rebalancing about every 31 days versus the
average of every 10 days in the second half of 2008. All of
these rebalances were additions to the size of hedge posi-
tion, as the inverse position declined relative to the index.
This would have required adding additional capital to the
hedging strategy over this period. The hedging strategies
succeeded in reducing the volatility of S&P 500 exposure
and maintaining the desired equity exposures near 0 per-
cent and 50 percent, but at the cost of lower returns.
In both market scenarios, we see that the -1x hedging
strategies, using a 10 percent rebalancing band for the
hedge, fulfilled the objective of reducing downside return
risk significantly, measured both by volatility and maxi-
mum drawdown. On balance, it is important to understand
that these hedging strategies may significantly reduce
upside returns as well.
Hedging With Leveraged Inverse ETFs Up to this point, our discussion has focused on single
(-1x) inverse ETFs. Investors could alternatively use lever-
aged inverse ETFs, which pursue returns equal to -2x or -3x
of a benchmark index’s one-day return. The primary benefit
of using a leveraged inverse ETF is that less up-front capital
may be needed to implement the hedging strategy. However,
maintaining a leveraged inverse hedging strategy over
time—keeping the net exposure close to zero—is likely to
require more frequent rebalancing than would a -1x inverse
ETF strategy. To illustrate how inverse exposure and upfront
capital requirements vary when using leveraged inverse ETFs,
Figure 4 compares inverse ETF hedging strategies with vary-
Figure 3
Single Inverse ETF Hedging Strategy Reduces Volatility
And Overall Return In Period Of Rising Index Returns
ReturnAnnualized
Volatility
Maximum
Drawdown
S&P 500 22.59% 17.00% -4.30%
S&P 500 with 50% 7.52% 6.13% -1.41%
Hedge in -1x Strategy
S&P 500 with 100% -0.01% 0.43% -0.13%
Hedge in -1x Strategy
30%
20%
10%
0%
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S&P 500 with 100% Hedge
S&P 500 with 50% Hedge
S&P 500
Buy To Rebalance The 100% Hedge
Sell To Rebalance The 100% Hedge
Aug09
Sep 09
Oct 09
Nov09
Dec09
Jun09
Jul09
Aug09
Sep 09
Oct 09
Nov09
Dec09
10%
0%
-10%
Note: Total returns of S&P 500 with 50% and 100% positions in -1x strategy 6/2009–12/2009 with 10% rebalance trigger. Volatility is standard deviation of daily returns. Maximum drawdown is lowest cumulative return during period. Net exposure based on 100% hedging strategy.Sources: Bloomberg, ProShares
Figure 4
Comparison Of Initial Investment And Exposure Sizes For -1x, -2x, And -3x Inverse ETFs As Full Hedges
-1x ETF
-2x ETF
-3x ETF
$100.00
$50.00
$33.33
$100.00
$100.00
$100.00
$50.00
$66.67
-$100 -$50 $0 $50 $100
Inverse ETF exposure assumed to equal fund assets multiplied by fund multiple. Source: ProShares
N Inverse Investment N Added Inverse Exposure from Leverage N Long Assets
November/December 201022
ing degrees of leverage: -1x, -2x and -3x.
Figure 4 presents a long position of $100 that is fully
hedged (100 percent) by -1x, -2x and -3x inverse ETFs.
Working from the midpoint of $0, we see the initial cost of
capital for each of the ETF hedges in the left-hand bars. The
bars show how the use of additional leverage (-2x and -3x)
can reduce the amount of upfront capital required for the
hedge ($50 and $33.33 vs. $100), while still maintaining the
desired net exposure (100 percent).
An important consideration when hedging with lever-
aged ETFs is that variations in net exposure are magni-
fied in response to index moves. This means that hedges
with leveraged inverse ETF exposure will most certainly
require more frequent rebalancing. Figure 5 illustrates
this point by showing the impact of a 5 percent market
move on a -1x, -2x and -3x inverse ETF hedge. When the
market rises 5 percent, the $5 gain in the long portfolio
triggers exposure gaps across all three ETFs, but in vary-
ing degrees. The use of higher multiple inverse ETFs leads
to larger net exposure gaps over the course of the hedg-
ing period.15 For instance, the use of a -1x ETF results in a
10 percent performance gap and a $10 net exposure gap
($105 vs. $95), but the same position hedged with a -3x
ETF results in a 20 percent gap with a $20 net exposure
gap ($105 vs. $85). This potential for larger net exposure
variances demonstrates the need to increase the fre-
quency of rebalancing when hedging with leveraged ETFs
rather than single inverse ETFs.16
Figure 6
Declining Index Return Scenario: Relative Performance Of
Single And Leveraged Inverse ETF Hedges
ReturnAnnualized
Volatility
Maximum
Drawdown
Average # of Days Between
Rebalances
S&P 500 -28.48% 53.91% -40.63% —
-1x Hedging -10.08% 14.76% -14.01% 10.2
Strategy
-2x Hedging -11.31% 18.18% -16.17% 5.1
Strategy
20%
10%
0%
-10%
-20%
-30%
-40%
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S&P 500 with 50% -2x HedgeS&P 500 with 50% -1x Hedge
S&P 500
-2x Hedging Strategy-1x Hedging Strategy
40
30
20
10
0
Note: Total returns of S&P 500 with 50% positions in -1x and -2x strategy 6/2008–12/2008 with 10% rebalance trigger. Volatility is standard deviation of daily returns. Maximum drawdown is lowest cumulative return during period.Sources: Bloomberg, ProShares
Jun08
Jul08
Aug08
Sep08
Oct08
Nov08
Dec08
Jun08
Jul08
Aug08
Sep08
Oct08
Nov08
Dec08
Figure 7
Rising Index Return Scenario: Relative Performance Of
Single And Leveraged Inverse ETF Hedges
ReturnAnnualized
Volatility
Maximum
Drawdown
Average # of Days Between
Rebalances
S&P 500 22.59% 17.00% -4.30% —
-1x Hedging 7.52% 6.13% -1.41% 30.7
Strategy
-2x Hedging 9.04% 7.41% -1.69% 20.4
Strategy
30%
20%
10%
0%
-10%
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S&P 500S&P 500 with 50% -2x HedgeS&P 500 with 50% -1x Hedge
-2x Hedging Strategy-1x Hedging Strategy
10
5
0
Note: Total returns of S&P 500 with 50% positions in -1x and -2x strategy 6/2009–12/2009 with 10% rebalance trigger. Volatility is standard deviation of daily returns. Maximum drawdown is lowest cumulative return during period.Sources: Bloomberg, ProShares
Jun09
Jul09
Aug09
Sep 09
Oct 09
Nov09
Dec09
Jun09
Jul09
Aug09
Sep 09
Oct 09
Nov09
Dec09
Figure 5
Comparison Of Initial Investment And Exposure Sizes For -1x, -2x, And -3x Inverse ETFs As Full Hedges
$105
Long Assets -1x Exposure -2x Exposure -3x Exposure
$50 $105 $95 $90 $85
+$5-$5
-$10-$15
$10Gap
$15Gap $20
Gap
$0
$100Initial Value
Chart is not drawn to scale.
Assumes inverse ETFs achieve exact multiple of long position’s total returns over period in question. Inverse ETF exposure assumed to equal fund assets multiplied by fund multiple.Source: ProShares
November/December 2010www.journalofindexes.com 23
Case Studies: Hedging With Leveraged Inverse ETFs In Different Market Conditions
To examine the effects of leverage across market con-
ditions, we compare single- and leveraged-ETF hedging
strategies across the declining and rising market-return
scenarios presented earlier in the article, as well as across
a third, choppy index-return scenario (H1 2009), where the
index experiences high volatility but has flat return over
the entire six months. Case Studies III, IV and V show the
performance of the S&P 500 when hedging with a leveraged
ETF, which for illustration purposes is represented by a -2x
strategy. As a point of comparison, we include the single
inverse ETF hedge (-1x) in the exhibits.
Case Study III: Leveraged Inverse Hedge In A Declining Market
Overall, the -2x strategy, with the lower initial investment,
showed slightly higher volatility of hedged positions but a
very similar pattern of returns compared with the -1x inverse
hedging strategy. In Figure 6, we see that in the second half
of 2008, returns were slightly lower and somewhat more
volatile with the -2x strategy given the index volatility and
corresponding size of daily moves. Rebalancing frequency
doubled, moving from a -1x strategy to a -2x strategy.
Case Study IV: Leveraged Inverse Hedge In A Rising MarketFigure 7 shows that during the second half of 2009 when
the index was rising in value, the -2x hedging strategy had
slightly higher returns than the comparable -1x example but
also slightly higher risk.
Case Study V: Leveraged Inverse Hedge In A Choppy MarketIn Figure 8, we compare the inverse ETF hedging strat-
egies in a choppy index return period where the index
was volatile but ended the period with only a 3.2 percent
return. Rebalancing frequencies were much greater, mov-
ing from the -1x to the -2x hedging strategies. The -2x
strategy was rebalanced on average every eight days ver-
sus every 23 days for the -1x strategy. Performance was
very similar among both hedged strategies during these
choppy market conditions, indicating that rebalancing the
size of the hedge was effective in mitigating the impact of
the volatile market conditions on the effectiveness of the
leveraged hedging tools.
Another way of thinking about how a hedging strategy
with a -2x inverse ETF would compare with one using a
-1x ETF is that for a given trigger, say 10 percent, more
frequent rebalancing would be required since the ETF
returns are a multiple of the inverse index moves. In the
tables under the previous three charts, you can see that the
frequency of rebalancing was greater with the addition of
leverage.17 This illustrates that the leveraged inverse ETF is
more likely to appeal to investors who are looking to lower
the upfront investment associated with the hedge and
who are comfortable with rebalancing on a more frequent
basis. An alternative to reduce the frequency of rebalanc-
ing is to have a wider trigger (e.g., 15 percent instead of
10 percent) when using leveraged inverse ETFs, with the
trade-off being that the investor assumes greater variation
in net exposure between rebalances.
ConclusionHedging is a risk management practice that requires
investment discipline and agility. Whether managing the risk
of a specific sector allocation or an entire portfolio, inves-
tors are best served by having a process addressing a range
of hedging considerations including benchmark selection,
how much to hedge, the hedging vehicle and an approach to
monitoring and rebalancing.
Investors are increasingly considering single and lever-
aged inverse ETFs as potential hedging instruments. With
proper monitoring and rebalancing, a single inverse ETF may
provide the inverse correlation on a daily basis necessary
for an effective hedge and can offer the benefits of acces-
sibility and intraday pricing/liquidity. Additionally, leveraged
inverse ETFs require less capital to initiate the hedge than
single inverse strategies. On balance, these vehicles, like any
other hedging instrument, must be carefully monitored and
managed. Leveraged inverse ETFs, in particular, may magnify
benchmark exposure with less capital but require more fre-
quent rebalancing to maintain the hedge.
In terms of measuring the effectiveness of an inverse ETF
Figure 8
Choppy Index Return Scenario: Relative Performance
Of Single And Leveraged Inverse ETF Hedges
ReturnAnnualized
Volatility
Maximum
Drawdown
Average # of Days Between
Rebalances
S&P 500 3.16% 34.78% -24.63% —
-1x Hedging 0.35% 10.99% -8.55% 22.6
Strategy
-2x Hedging 0.97% 13.04% -9.67% 7.9
Strategy
10%
5%
0%
-5%
-10%
-15%
-20%
-25%
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S&P 500 with 50% -2x HedgeS&P 500 with 50% -1x Hedge
-2x Hedging Strategy-1x Hedging Strategy
25
20
15
10
5
0
Note: Total returns of S&P 500 with 50% positions in -1x and -2x strategy 12/2008–6/2009 with 10% rebalance trigger. Volatility is standard deviation of daily returns. Maximum drawdown is lowest cumulative return during period. Sources: Bloomberg, ProShares
S&P 500
Dec08
Jan09
Feb09
Mar 09
Apr 09
May09
Jun09
Dec08
Jan09
Feb09
Mar 09
Apr 09
May09
Jun09
November/December 201024
hedge, we evaluated relative return, volatility and maximum
drawdown results of the hedged portfolio, as well as the
pattern and frequency of rebalancing. As we saw across
very different index return scenarios, inverse ETF hedges,
with and without leverage, potentially reduced volatility and
magnitude of returns. It’s important to note that while we
presented illustrations for different market scenarios, the
examples are still theoretical, and other hedging vehicles
could be more effective than inverse ETFs. Market conditions
can vary considerably, and transaction costs, cost of capital,
and tax consequences will all affect the final outcome of a
hedging strategy. Regardless of your hedging method, it is
important to carefully customize and closely monitor and
calibrate your hedging strategies to achieve and maintain
your desired risk targets.
This article is not intended as a recommendation for
any specific investment program. It is not intended to be an
investment strategy and does not infer or guarantee a profit
by using the strategy.
References
Joanne Hill and Solomon Teller, “Rebalancing Leveraged and Inverse Funds,” Eighth Annual Guide to Exchange Traded Funds & Indexing Innovations, Institutional Investor Journals (Fall
2009): 67-76
Nassim Taleb, “Dynamic Hedging,” John Wiley & Sons, Inc. 1997
John Hussman, “How Hedging Works,” HussmanFunds.com, April 18, 2005
Matt Hougan, “How Long Can You Hold Leveraged ETFs?” Journal of Indexes, March/April 2009
Mark Miller, “Hedging Strategies for Protecting Appreciation in Securities and Portfolios,” FPA Journal, August 2002
Joanne Hill and George Foster, “Understanding Returns of Leveraged and Inverse Funds,” Journal of Indexes, September/October 2009
Werner Keller, “Dynamic Risk Control for Equity Portfolios,” Keller Partners, LLC, April 2008
Ira Kawaller, “Tailing Futures Hedges/Tailing Spreads,” The Journal of Derivatives, Winter 1997
Tom Konrad, “Five Hedging Strategies,” Seeking Alpha, Sept. 8, 2009
Investopedia Staff, “A Beginner’s Guide to Hedging,” Investopedia, August 2003
Endnotes1Inverse exchange-traded funds are designed to provide an inverse multiple (e.g., -1x or -2x) of the daily return of a benchmark (before fees and expenses).
2Hedging also differs from diversification in that hedging’s sole purpose is to mitigate the risk of return volatility rather than to serve as a potential new source of returns.
3 In a situation where the beta sensitivity of the hedging tool to portfolio risk is less than 1.0, a fully hedged position may require a notional hedge amount of more than 100%
of the portfolio value. For example, if the portfolio has a beta of 1.2 to the hedging vehicle, a full hedge could entail the dollar value of the hedge position being 120% of the
portfolio value.
4 Another more dynamic rebalancing approach uses percentage triggers that are larger in volatile market conditions and smaller in lower-volatility markets, such as Bollinger
bands.
5 Total inverse ETP assets were $21.6 billion, with average daily volume of $5.8 billion for the first six months of 2010. Source: Bloomberg. Inverse exchange-traded product data
as of June 30, 2010.
6 Some exchange-traded products have monthly objectives or even multiyear holding periods with knockout features. ETPs with nondaily objectives are beyond the scope of this
article.
7With all investments, users should take care to read the prospectus and fully understand how inverse ETFs work and what risks are involved.
8 The long position and single inverse returns are chosen to provide an illustration of the direction and size of the rebalancing trades. Returns are not intended to predict fund
performance levels in particular market conditions. Inverse ETF returns over periods other than one day will likely differ in amount and possibly direction from the target return
for the same period.
9 Net long exposure is equal to the value of the long assets multiplied by any explicit leverage minus the short assets multiplied by any explicit leverage. Note, this assumes the long
position’s beta equals that of the inverse fund’s underlying index. Investors hedging based on beta comparisons can adjust the inverse fund weightings accordingly.
10 Proceeds from selling this position could be invested elsewhere or held for future funding needs for the rebalance process. In practice, investors not facing any constraints on
the long position may consider rebalancing both the long and the inverse positions simultaneously, reducing long positions to augment inverse positions or vice versa, which is
conceptually similar to rebalancing between stocks and bonds.
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.
Note: Performance based on total return indexes except for gold in which spot price is used.
1) MSCI EM from Dec ‘87 and JPMorgan EM Sovereign Debt Index from Dec ‘90; 2) compounded annual growth rate; 3) projected returns used for simulation and optimization;
4) estimated using weekly returns; 5) ratio of nominal return and volatility, also known as avg. risk-adjusted return (a higher number indicates a better return per unit of risk); 6)
expected maximum loss during a week at a given confidence level (1— A) from a US$10 million investment; 7) EMBI prior to Jan ‘00 and EMBI Global after due to data availability.
Nominal Real Projected3
Annualised
Volatility4 (%)
Inf.
Ratio52.5% 1.0%
CAGR2 (%) Weekly VaR (US$ ‘000s)6
Performance Of Selected Assets In A Model Portfolio; Jan ’87 - Jul ’101
November/December 201030
Portfolio optimization produces a myriad of different
combinations that form the “efficient frontier.” While each
asset allocation that falls upon this frontier is considered
optimal, we perform 500 simulations to obtain an expected
efficient frontier curve. We find that adding gold to a portfolio
increased returns for a given level of volatility 68 percent of
the time. This means, on average, a 3.4 percent increase and
as much as 22 percent for some risk/return combinations.
Conversely, for the other 32 percent of portfolios where gold
does not increase returns, the average was -0.4 percent and
the maximum differential -0.8 percent. In particular, including
gold in the asset mix increases the value of the portfolio with
Summary Statistics And Asset Weight Allocation For Each Of The Selected Portfolios
BarCap Global ex US Treasury Aggregate 7% 6% 7% 7%
BarCap US Credit Index 3% 2% 2% 2%
BarCap US High Yield Index 11% 11% 5% 7%
JP Morgan EM Sovereign Debt 3% 3% 10% 8%
MSCI US Equity Index 4% 4% 19% 17%
MSCI EAFE Equity Index 2% 2% 15% 14%
MSCI EM Equity Index 3% 3% 25% 26%
S&P Goldman Sachs Commodity Index 2% 1% 8% 7%
Expected annual return (%) 3.2 3.1 6.9 6.9
Annualized volatility (%) 2.4 2.3 11.9 11.7
Information ratio 1.301 1.342 0.583 0.586
Portfolio weights
Gold (US$/oz) - 4% - 9%
JP Morgan 3-month T-Bill Index 30% 34% 0% 0%
BarCap US Treasury Aggregate 37% 33% 15% 14%
BarCap Global ex US Treasury Aggregate 9% 7% 10% 9%
BarCap US Credit Index 0% 0% 1% 1%
BarCap US High Yield Index 17% 18% 7% 8%
JP Morgan EM Sovereign Debt 4% 3% 6% 5%
MSCI US Equity Index 0% 0% 21% 19%
MSCI EAFE Equity Index 0% 0% 9% 9%
MSCI EM Equity Index 2% 1% 25% 24%
S&P Goldman Sachs Commodity Index 0% 0% 5% 3%
1) Correlation estimation using all weekly returns from Jan ‘87 to Jul ‘10; 2) expected return divided by volatility, also known as avg. risk-adjusted return (a higher number indi-
cates a better return per unit of risk); 3) correlation estimation using only weekly returns in which the MSCI equity index fell by more than 2 std. deviations over the same period.
* Portfolio selection based on allocations that achieved the maximum information ratio available. † Portfolio selection based on allocations that resembled benchmark portfolio
of 55% equities, 40% fixed income, and 5% alternative assets, with similar expected returns.
November/December 2010www.journalofindexes.com 31
the maximum information ratio (expected return divided by
volatility). In other words, an investor choosing to include gold
in their portfolio allocation is likely to obtain similar returns at
a lower level of risk than an investor who does not include it.
For simplicity, to compare the effect on VaR, we select
a finite number of portfolios. For each scenario (allocation
based on long-term correlation and high-risk correlation)
we find optimal asset allocations with and without gold. We
then choose: 1) the portfolio with the maximum risk-adjust-
ed return; and 2) a portfolio with a similar composition to a
typical benchmark allocation (50 to 60 percent equities, 30
to 40 percent fixed income and 5 to 10 percent alternative
assets), such that the portfolios with and without gold during
the optimization have similar expected returns. Therefore,
we compare a total of eight portfolios.
Figure 5 shows the expected return, volatility and
information ratio for each portfolio, as well as the weight
assigned to each asset. On one hand, the selected portfo-
lios with maximum information ratios produce more “con-
servative” asset allocations, with heavy weights in cash
and fixed income. On the other hand, “optimal” bench-
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.
Scenario 2: “High Risk” Correlation2Scenario 1: Average Correlation1
November/December 201032
Conclusion
Gold is first and foremost a consistent portfolio diversifi-
er. Moreover, we find that gold effectively helps manage risk
in a portfolio, not only by means of increasing risk-adjusted
returns, but also by reducing expected losses incurred in
extreme circumstances. Such tail-risk events, while unlikely,
can be seen to have a damaging effect on an investor’s capital.
On one hand, short- and medium-term holders—individual
and institutional alike—can take advantage of gold’s unique
correlation to other assets to achieve better returns during
times of turmoil. This is especially true given that gold’s
correlation tends to change in a way that benefits investors
who hold it within their portfolios. On the other hand, by
including gold in their portfolios, long-term holders—such
as retirement savings accounts, pension plans, endowments
and other institutional investors—can manage risk without
necessarily sacrificing much sought-after returns.
Our analysis suggests that even relatively small allocations
to gold, ranging from 2 to 9 percent, can have a positive impact
on the structure of a portfolio. We find that, on average, such
allocations can reduce the VaR of a portfolio, while maintain-
ing a similar return profile to equivalent portfolios that do not
include gold. For the eight portfolios analyzed using data from
January 1987 to July 2010, adding gold reduced the 1 and 2.5
percent VaR by between 0.1 and 18.5 percent.
We also note that investors who hold gold only in the form
of a commodity index are likely to be under-allocated.5 There is a
strong case for gold to be allocated as an asset class on its own
merits. It is part commodity, part luxury consumption good and
part financial asset, and as such, its price does not always behave
like other asset classes and especially not other commodities.
Finally, while most of this analysis concentrates on risk in the
form of tail-risk and volatility, gold has other unique risk-related
attributes that make it very useful in periods of financial distress.
For example, the gold market is highly liquid and many gold bul-
lion investments have neither credit nor counterparty risk.
Endnotes
1 For a more in-depth analysis on negative economic news and gold, see Roach S.K. and M. Rossi (2009), “The Effects of Economic News on Commodity Prices: Is Gold Just Another
Commodity?” IMF Working Paper.
2 In statistical terms, the VaR of a portfolio, at a given confidence level α between zero and one, is the minimum loss, such that the probability that any other loss exceeds that
value, is not greater than (1 − α) during a period of time.
3 Michaud, R. and R. Michaud (2008), “Efficient Asset Management: A Practical Guide to Stock and Portfolio Optimization and Asset Allocation,” 2nd edition, Oxford Press, New York.
4 A more detailed listing of historical examples can be found in the World Gold Council’s “Gold: hedging against inflation,” October 2010.
5 Gold’s weight in typical benchmark commodity indexes, such as the S&P GSCI or the Dow-Jones UBS Commodity Index, tends to be small, usually between 2 and 6 percent. Even
if an investor holds a 10 percent allocation in one of these indexes, their effective gold exposure is between 0.2 percent and 0.6 percent.
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2 “Commodity Index Investing: Speculation or Diversification?” Vanderbilt University, July 2010, available at http://ssrn.com/abstract=1633908, later version Journal of Applied
Corporate Finance 1-40 (2010)
Carroll: I wouldn’t say traders are gaming commodity indices.
That implies that what traders are trying to do is cannibal-
ize the returns of commodity indices to their own benefit to
the detriment of their investors, which I don’t think is what
they’re trying to do.
Over recent years, we’ve seen traders and structurers
starting to recognize what’s popularly referred to as the
contango effect, or negative roll yield, and they have taken
steps to alleviate it. … It’s a new market, and with any new
market, not everything is known on day one. As time’s gone
by, banks have improved the products we’re offering to try
and improve the returns to our investors.
The CMCI’s daily rolling mechanism means that even as
you add a large amount to the commodity index in terms of
investment, it’s still not subject to price action around certain
roll times. There’s little danger of a sudden event in wheat,
for example—a drought—exposing the index to unfavorable
conditions around the roll because it is continuously rolling.
That means any conditions that it’s exposed to are fair and
representative of the commodity space over time.
JOI: Does index-based investing distort the commodities market?
Carroll: I don’t think we are distorting the market. It’s
certainly true that any large inflow of money into a market
that previously didn’t have that capital in there is going to
impact the market in some way. But it doesn’t necessarily
mean it’s a negative impact. The CFTC at the start of this
year, and the OECD, said that index investment hadn’t actu-
ally distorted commodity markets, and in fact, if anything,
possibly had dampened volatility.
I think that’s true in the commodities market where
you’ve had a lot more index money flowing in. What’s actu-
ally happened is we’ve improved liquidity and improved
price discovery. And that’s certainly true further down the
commodities futures curve where now there’s liquidity
in two- and three-year natural gas, where previously you
would have struggled. If anything, there have been massive
improvements in the state of the commodity markets and the
price discovery of those commodity markets that are being
overlooked. There have been impacts, but everyone always
focuses on the negative impacts. I think people very much
miss the positive impacts. I don’t think it distorts the com-
modity market. Has it affected it? Of course it has, but I think
it’s positively affected it more than anything.
Roundtable continued from page 39
November/December 201042
By John A. Haslem
Paths to the ‘Wizards of Advertising and Overconfidence’
Mutual Funds
And Investor Choice
November/December 2010www.journalofindexes.com 43
This article discusses mutual fund advertising and
investor skill in making fund choices. The research
Euronext and its affi liates do not recommend or make any representation as to possible benefi ts from any securities or investments, or third-party products or services. Investors should undertake their own due
diligence regarding securities and investment practices. This material may contain forward-looking statements regarding NYSE Euronext and its affi liates that are based on the current beliefs and expectations
of management, are subject to signifi cant risks and uncertainties, and which may differ from actual results. *Data as of May 2010. “SPDR®” is a registered trademark of Standard & Poor’s Financial Services, LLC
(“S&P”) and has been licensed for use by State Street Corporation. No fi nancial product offered by State Street Corporation or its affi liates is sponsored, endorsed, sold or promoted by S&P. iShares® is a
registered trademark of BlackRock Institutional Trust Company, N.A.
Project1 7/29/10 10:55 AM Page 1
46
By Larry Swedroe
An excerpt
Wise Investing Made Simpler
November/December 2010
November/December 2010www.journalofindexes.com 47
Larry Swedroe is well-known to many investors and financial
professionals as a source of common-sense investment wisdom.
Wise Investing Made Simpler (CFPN, 2010) is the follow-up
to Swedroe’s Wise Investing Made Simple (CFPN, 2007), and
continues the author’s efforts to expose the misconceptions many
investors have about financial markets through anecdotes and
empirical data. Below is an excerpt containing Chapters 10-12.
Wise Investing Made Simpler hit shelves in June 2010.
CHAPTER 10The Fed Model And The Money Illusion
Magic, or conjuring, is the art of entertaining an audi-
ence by performing illusions that baffle and amaze, often
by giving the impression that something impossible has
been achieved, as if the performer had supernatural pow-
ers. Practitioners of this art are called magicians, conjurors
or illusionists. Specifically, optical illusions are tricks that
fool your eyes. Most magic tricks that fall into the category
of optical illusions work by fooling both the brain and the
eyes together at the same time.
Fortunately, most optical illusions don’t cost the par-
ticipants anything, except perhaps some embarrassment at
being fooled. However, basing investment strategies on illu-
sions can lead investors to make all kinds of mistakes.
There are many illusions in the world of investing. The
process known as data mining—torturing the data until
it confesses—creates many of them. Unfortunately, iden-
tifying patterns that worked in the past doesn’t necessar-
ily provide you with any useful information about stock
price movements in the future. As Andrew Lo, a finance
professor at MIT, points out: “Given enough time, enough
attempts, and enough imagination, almost any pattern
can be teased out of any data set.”1
The stock and bond markets are filled with wrongheaded
data mining. David Leinweber, of First Quadrant Corp.,
illustrates this point with what he calls “stupid data miner
tricks.” Leinweber sifted through a United Nations CD-ROM
and discovered the single best predictor of the S&P 500 Index
had been butter production in Bangladesh.2 His example is a
perfect illustration that the mere existence of a correlation
doesn’t necessarily give it predictive value. Some logical rea-
son for the correlation to exist is required for it to have cred-
ibility. For example, there is a strong and logical correlation
between the level of economic activity and the level of interest
rates. As economic activity increases, the demand for money,
and, therefore, its price (interest rates), also increases.
An illusion with great potential for creating investment mis-
takes is known as the “money illusion.” The reason it has such
potential for creating mistakes is it relates to one of the most
popular indicators used by investors to determine if the market
is under- or overvalued, what is known as The Fed Model.
The Fed Model
In 1997, in his monetary policy report to Congress,
Federal Reserve Chairman Alan Greenspan indicated that
changes in the ratio of prices in the S&P 500 to consensus
estimates of earnings over the coming 12 months have often
been inversely related to changes in long-term Treasury
yields.3 Following this report, Edward Yardeni, at the time
a market strategist for Morgan Grenfell, speculated that
the Federal Reserve was using a model to determine if the
market was fairly valued—how attractive stocks were priced
relative to bonds. The model, despite no acknowledgment of
its use by the Fed, became known as the Fed Model.
Using the “logic” that bonds and stocks are competing
instruments, the model uses the yield on the 10-year Treasury
bond to calculate “fair value,” comparing that rate to the
E/P ratio (the inverse of the popular price-to-earnings, or
P/E, ratio). For example, if the yield on the 10-year Treasury
were 4 percent, fair value would be an E/P of 4 percent, or a
P/E of 25. If the P/E is greater (lower) than 25, the market is
considered overvalued (undervalued). If the same bond were
yielding 5 percent, fair value would be a P/E of 20. The logic is
that higher interest rates create more competition for stocks,
and this should be reflected in valuations. Thus, lower interest
rates justify higher valuations, and vice versa.
Since Yardeni coined the phrase, it seems almost impos-
sible to watch CNBC for even a day without hearing about
the market relative to “fair value.” The Fed Model as a valu-
ation tool has become “conventional wisdom.” However,
conventional wisdom is often wrong. There are two major
problems with the Fed Model. The first relates to how the
model is used by many investors. Yardeni speculated that
the Federal Reserve used the model to compare the valua-
tion of stocks relative to bonds as competing instruments.
The model says nothing about absolute expected returns.
Thus, stocks, using the Fed Model, might be priced under
fair value relative to bonds, and they can have either high
or low expected returns. The expected return of stocks is
not determined by their relative value to bonds. Instead, the
expected real return is determined by the current dividend
yield plus the expected real growth in dividends. To get the
estimated nominal return, we would add estimated inflation.
This is a critical point that seems to be lost on many inves-
tors. The result is that investors who believe low interest
rates justify a high valuation for stocks without the high
valuation impacting expected returns are likely to be disap-
pointed (and perhaps not have enough funds with which to
live comfortably in retirement). The reality is when P/Es are
high, expected returns are low and vice versa, regardless of
the level of interest rates.
The second problem with the Fed Model, leading to a
false conclusion, is it fails to consider that inflation impacts
corporate earnings differently than it does the return on
fixed-income instruments. Over the long term, the nominal
growth rate of corporate earnings has been in line with
the nominal growth rate of the economy. Similarly, the real
growth rate of corporate earnings has been in line with the
real growth of the economy.4 Thus, in the long term the real
growth rate of earnings is not impacted by inflation. On the
other hand, the yield to maturity on a 10-year bond is a nomi-
nal return—to get the real return you must subtract infla-
tion. The error of comparing a number that isn’t impacted
by inflation to one that is leads to what is called the “money
illusion.” Let’s see why it’s an illusion.
We begin by assuming the real yield on a 10-year TIPS
(treasury inflation-protected security) is 2 percent. If the
expected long-term rate of inflation were 3 percent, a
10-year Treasury bond would be expected to yield 5 percent
(the 2 percent real yield on TIPS plus the 3 percent expected
rate of inflation). According to the Fed Model, that would
mean a fair value for stocks at a P/E of 20 (E/P of 5 percent).
Let’s now change our assumption to a long-term expected
rate of inflation of 2 percent. This would cause the yield on
the 10-year bond to fall from 5 to 4 percent, causing the
fair value P/E to rise to 25. However, this makes no sense.
Inflation doesn’t impact the real rate of return demanded by
equity investors. Therefore, it shouldn’t impact valuations. In
addition, as stated above, over the long term, there is a very
strong relationship between nominal earnings growth and
inflation. In this case, a long-term expected inflation rate of
2 percent, instead of 3 percent, would be expected to lower
the growth of nominal earnings by 1 percent, but have no
impact on real earnings growth (the only kind that matter).
Because the real return on bonds is impacted by inflation,
while real earnings growth is not, the Fed Model compares a
number that is impacted by inflation with a number that isn’t
(resulting in the money illusion).
Let’s also consider what would happen if the real interest
rate component of bond prices fell. The real rate is reflective
of the economic demand for funds. Thus, it’s reflective of the
rate of growth of the real economy. If the real rate falls due
to a slower rate of economic growth, interest rates would
fall, reflecting the reduced demand for funds. Using the same
example from above, if the real rate on TIPS fell from 2 per-
cent to 1 percent, that would have the same impact on nomi-
nal rates as a 1 percent fall in expected inflation, and, thus, the
same impact on the fair value P/E ratio—causing fair value to
rise. However, this too does not make sense. A slower rate of
real economic growth means a slower rate of real growth in
corporate earnings. Thus, while the competition from lower
interest rates is reduced, so will be future earnings.
Since corporate earnings have grown in line with nominal
GNP growth over the past 70 years, a 1 percent lower long-
term rate of growth in GNP would lead to a 1 percent lower
expected growth in corporate earnings. The “benefit” of
falling interest rates would be offset by the equivalent fall
in future expected earnings. The reverse would be true if a
stronger economy caused a rise in real interest rates. The
negative effect of a higher rate of interest would be offset
by a faster expected growth in earnings. The bottom line is
there is no reason to believe stock valuations should change
if the real return demanded by investors has not changed.
Clifford S. Asness studied the period 1881–2001. He
concluded the Fed Model had no predictive power in terms
of absolute stock returns—the conventional wisdom is
wrong. (As we discussed, however, this is not the purpose
for which Yardeni thought the Fed Model was used. Given
the purpose for which the model was designed, it would
have been more appropriate for Asness to study the rela-
tive performance of stocks vs. bonds given the “signal”—
under/overvalued—the model was giving.) Asness also
concluded that over 10-year horizons, the E/P ratio does
have strong forecasting powers. Thus, the lower the P/E
ratio, the higher the expected returns to stocks, regardless
of the level of interest rates, and vice versa.5
There is one other point to consider. A stronger econ-
omy, leading to higher real interest rates, should also be
expected to lead to a rise in corporate earnings. The stron-
ger economy reduces the risks of equity investing. In turn,
that could lead investors to accept a lower risk premium.
Thus, it is possible that higher interest rates, if caused by a
stronger economy and not higher inflation, could actually
justify higher valuations for stocks. The Fed Model, how-
ever, would suggest that higher interest rates mean stocks
are less attractive. The reverse would be true if a weaker
economy led to lower real interest rates.
The Moral Of The Tale
While gaining knowledge of how a magical illusion works
has the negative effect of ruining the illusion, understand-
ing the “magic” of financial illusions is beneficial because it
should help you avoid mistakes. In the case of the money
illusion, understanding how the illusion is created will
prevent you from believing an environment of low (high)
interest rates allows for either high (low) valuations or for
high (low) future stock returns. Instead, if the current level
of prices is high (a high P/E ratio), that should lead you to
conclude that future returns to equities are likely to be lower
than has historically been the case, and vice versa. Note that
this doesn’t mean investors should either avoid equities
because they are “highly valued” or increase their allocations
because they have low valuations.
Hopefully, you are now convinced that the Fed Model should
not be used to determine if the market is at fair value and that a
much better predictor of future real returns is the E/P ratio.
The next tale explains why it’s important to keep control
over your emotions.
CHAPTER 11Don’t Let Emotions Take Control
A friend who is also a financial adviser, Sherman Doll,
related the following story. He has been a two-line sport
kite flier for many years. While not a pro, he has learned a
few tricks by observing the flying behavior of these kites.
He told me one of the most difficult skills for beginners
to master is what to do when their kite starts to plunge
earthward. The natural, panicky impulse is to yank back-
ward on the lines. However, this action only accelerates the
kite’s death spiral. The simple kite-saving technique is to
calmly step forward and thrust your arms out. This causes
the kite’s downward acceleration to stop, allowing you to
regain control of the kite and end its plunge. What does
this have to do with investing?
On January 21, 2008, equity markets around the globe all
collapsed. In just that one day, stock markets fell from about
5 percent to as much as 10 percent. For some markets it was
the worst day since the Great Depression. The Australian
market had its worst day ever. The U.S. market, which was
closed for Martin Luther King Day, saw the futures market
trading down more than 500 points ahead of the opening
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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.
The Moral Of The Tale
Just as when a kite starts to plunge earthward, the
natural, panicky reaction is to yank backward on the lines,
the natural, panicky reaction to a dive in your portfolio’s
value is to pull back (sell). In both cases, pulling back is the
wrong strategy. The right strategy is the less intuitive one of
remaining calm and stepping forward (actually buying stocks
to rebalance your portfolio to the desired asset allocation).
Warren Buffett is probably the most highly regarded
investor of our era. Listen carefully to his statements regard-
ing efforts to time the market.
“Inactivity strikes us as intelligent behavior.”9
“The only value of stock forecasters is to make fortune
tellers look good.”10
“We continue to make more money when snoring than
when active.”11
“Our stay-put behavior reflects our view that the stock
market serves as a relocation center at which money is
moved from the active to the patient.”12
Buffett also observed: “Long ago, Sir Isaac Newton gave
us three laws of motion, which were the work of genius. But
Sir Isaac’s talents didn’t extend to investing: He lost a bundle
in the South Sea Bubble, explaining later, ‘I can calculate the
movement of the stars, but not the madness of men.’ If he had
not been traumatized by this loss, Sir Isaac might well have
gone on to discover the Fourth Law of Motion: For investors as
a whole, returns decrease as motion increases.”13
Perhaps Buffett’s views on market-timing efforts are
best summed up by the following from his 2004 Annual
Shareholder Letter of Berkshire Hathaway:
“Over the 35 years, American business has delivered
terrific results. It should therefore have been easy for inves-
tors to earn juicy returns: All they had to do was piggyback
Corporate America in a diversified, low-expense way. An
index fund that they never touched would have done the job.
Instead many investors have had experiences ranging from
mediocre to disastrous.
There have been three primary causes: first, high costs,
usually because investors traded excessively or spent far
too much on investment management; second, portfolio
decisions based on tips and fads rather than on thoughtful,
quantified evaluation of businesses; and third, a start-and-
stop approach to the market marked by untimely entries
(after an advance has been long underway) and exits (after
periods of stagnation or decline). Investors should remem-
ber that excitement and expenses are their enemies. And if
they insist on trying to time their participation in equities,
they should try to be fearful when others are greedy and
greedy only when others are fearful.”
The above observation is perhaps why Buffett has stated
that investing is simple, but not easy.14 The simple part is
that the winning strategy is to act like the lowly postage
stamp that adheres to its letter until it reaches its destination.
Investors should stick to their asset allocation until they reach
their financial goals. The reason it is hard is that it is difficult
for most individuals to control their emotions—emotions of
greed and envy in bull markets and fear and panic in bear
markets. In fact, bear markets are the mechanism that serves
to transfer assets from those with weak stomachs and without
investment plans to those with well-developed plans—with
the anticipation of bear markets built right into the plans—
and the discipline to adhere to those plans.
The bottom line: If you don’t have a plan, develop one. If
you do have one, stick to it.
The next tale is about finding the magic formula to be
able to successfully time the market.
CHAPTER 12Using Market Valuations To Time The Market
According to Christian mythology, the Holy Grail was
the dish, plate or cup with miraculous powers that was
used by Jesus at the Last Supper. Legend has it that the
Grail was sent to Great Britain where a line of guardians
keeps it safe. The search for the Holy Grail is an important
part of the legends of King Arthur and his court.
50 November/December 2010
For many investors, the equivalent of the Holy Grail
is finding the formula allowing them to successfully time
the market. Trying to time the market is certainly tempt-
ing, as the rewards for success can be great. The idea is
made even more tempting when one looks at data such
as the following from a study reported in the August 16,
1999 issue of Fortune. The average historical P/E ratio for
the market had been around 15. For the period 1926
through the second quarter of 1999, an investor buying
stocks when the market traded at P/E ratios of between
14 and 16 earned a median return of 11.8 percent over
the next 10 years. However, investors purchasing stocks
when the P/E ratio was greater than 22 earned a median
return of just 5 percent per year over the next 10 years.
On the other hand, investors who purchased stocks when
P/E ratios were below 10 earned a median return of 16.9
percent per year over the next 10 years. Sounds simple,
right? Buy stocks when the P/E of the market is below the
historical average and sell them when the P/E is above
average. Tempting, isn’t it?
Authors Ben Stein and Phil DeMuth presented similar
evidence in their 2003 book Yes, You Can Time the Market!
They advocated buying stocks when the real price was
below the 15-year moving average of real stock prices and
abstaining otherwise.15 The problem with this type of analy-
sis is it fails to consider that when an investor is out of the
market, they must invest in an alternative. In other words,
the winning strategy isn’t dependent on whether you buy
stocks when they are “cheap” and avoid them when they
are “expensive.” Instead, the winning strategy depends on
whether the alternative investments purchased with the
proceeds of the stock sales outperform the stocks you sold
because the stocks were “expensive,” and “doomed” to
produce lousy returns.
The study “Very Long Term Equity Investment
Strategies: Real Stock Prices and Mean Reversion,” exam-
ined the returns from mean reversion strategies using
various valuation metrics (i.e., real stock prices, P/E ratios
and dividend yields, and combinations of these metrics)
in both the U.S. and the U.K. The U.S. data covered the
period 1871–2004, and the U.K. data covered the period
1899–2004. They used the risk-free asset as the alterna-
tive investment when the strategy called for being out
of the market because prices were expensive (above
average). Not surprisingly, they found buying cheap does
outperform buying expensive—by from 3 percent to
5.4 percent per year, depending on the holding period.
However, they also found that mean reversion strategies
don’t work. The reason is during periods when stocks are
expensive relative to historic averages (and, thus, pro-
duce below-average returns), there is still an equity risk
they concluded: “a simple buy-and-hold strategy is far
superior.”16 And for taxable accounts it is certainly more
tax efficient.
Before you are tempted by seemingly surefire ways to
beat the market, consider the following from John Bogle,
founder and former CEO of the Vanguard Group:
“ The idea that a bell rings to signal when investors should get
into or out of the stock market is simply not credible. After
nearly fifty years in this business, I do not know of anybody
who has done it [market timing] successfully and consistently.
I don’t even know anybody who knows anybody who has done
it successfully and consistently.”17
The Moral Of The Tale
While it certainly seems tempting to try to time the
market, the evidence suggests it is a mug’s game. What
is perhaps most surprising is the following. Given most
investors acknowledge Warren Buffett as one of the
greatest investors of all time, you would think they would
listen to his advice. As you have seen, Buffett is vociferous
about his belief that investors should avoid trying to time
the market; yet his advice is ignored.
Endnotes
1 Kiplinger’s Personal Finance, February 1997.
2 Wall Street Journal, April 5, 1996.
3 Humphrey-Hawkins Report, Section 2: Economic and Financial Developments in 1997, Alan Greenspan, July 22, 1997.
4 William Bernstein, “The Efficient Frontier,” (Summer 2002).
5 Clifford S. Asness, “Fight the Fed Model: The Relationship Between Stock Market Yields, Bond Market Yields, and Future Returns,” (December 2002).
6 Charles Ellis, Investment Policy (Irwin Professional Pub 2nd edition 1992).
7 David Dreman, Contrarian Investment Strategies (Simon & Schuster 1998), p. 57.
8 Morningstar FundInvestor (July 2005).
9 1996 Annual Report of Berkshire Hathaway.
10 1992 Annual Report of Berkshire Hathaway.
11 1996 Annual Report of Berkshire Hathaway.
12 1991 Annual Report of Berkshire Hathaway.
13 2005 Annual Report of Berkshire Hathaway.
14 Financial Analysts Journal (November/December 2005), p. 51.
15 Ben Stein and Phil DeMuth, Yes, You Can Time the Market! (Wiley 2003).
16 Owain Ap Gwilym, James Seaton, and Stephen Thomas, “Very Long Term Equity Investment Strategies: Real Stock Prices and Mean Reversion,” Journal of Investing (Summer 2008).
17 John Bogle, Commonsense on Mutual Funds, Wiley (March 1999).
November/December 2010www.journalofindexes.com 51
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Invesco PowerShares kicked off
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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��ˤåååË 0�Ë
(NasdaqGM: VONE), 0.12 percent
VËË7?�~Ö?ÁaË-ÖÄÄj��ˤåååË7?�ÖjË 0�Ë
(NasdaqGM: VONV), 0.15 percent
VËË7?�~Ö?ÁaË-ÖÄÄj��ˤåååË�Á�ÝÍË 0�Ë
(NasdaqGM: VONG), 0.15 percent
VËË7?�~Ö?ÁaË-ÖÄÄj��ËÔåååË��ajÞË�Ö�aË
(NasdaqGM: VTWO), 0.15 percent
VËË7?�~Ö?ÁaË-ÖÄÄj��ËÔåååË7?�ÖjË��ajÞË
Fund (NasdaqGM: VTWV), 0.20 percent
VËË7?�~Ö?ÁaË-ÖÄÄj��ËÔåååË�Á�ÝÍË��ajÞË
Fund (NasdaqGM: VTWG), 0.20 percent
VËË7?�~Ö?ÁaË-ÖÄÄj��ËÏåååË��ajÞË�Ö�aË
(NasdaqGM: VTHR), 0.15 percent
Vanguard still has a real estate ETF
and three municipal bond ETFs in reg-
istration that were part of the same
group of filings.
Select Sector Sues PowerShares Over Tickers
In late July, the Select Sector SPDR
Trust, the entity that holds the trade-
mark on the Select Sector SPDRs, filed
suit against Invesco PowerShares over
the trading symbols used by the new
PowerShares ETFs tracking domestic
small-cap sectors.
State Street Global Advisors has
marketed Select Sector SPDRs since
1998; the funds divide the S&P 500
Index into nine individual sectors. The
PowerShares offering, launched in April,
divides the S&P SmallCap 600 Index
into the same nine sectors. The tickers
on the two sets of ETFs are identical,
save for an “S” PowerShares added
onto the end of each of its funds. For
example, the Select Sector Financials
SPDR’s ticker is XLF, while the tick-
November/December 201052
er for the PowerShares S&P SmallCap
Financials Portfolio is “XLFS.”
A Select Sector SPDRs representa-
tive called PowerShares’ ticker choice
“a deliberate and unconscionable act
on the part of PowerShares to confuse
both institutional and retail investors.”
The suit seeks to block PowerShares
from using the nine “XL Family of
Marks” members or anything simi-
lar in creating tickers for ETFs and
requests that PowerShares pay the
SPDR Trust’s legal costs.
The suit was filed in the U.S. District
Court in Houston against PowerShares
Exchange-Traded Fund Trust II, Invesco
PowerShares Capital Management, LLC,
and Invesco Distributors, Inc., accord-
ing to a press release from Select
Sector SPDRs.
A representative for PowerShares
said the firm doesn’t comment on ongo-
ing litigation.
Select Sector SPDRs is a trademark
of the McGraw-Hill Companies, Inc.,
and has been licensed for use.
INDEXING DEVELOPMENTSS&P Launches Equal- Weighted GSCI Index
In September, Standard & Poor’s
launched an equal-weighted version of
its S&P GSCI index. The index was cre-
ated in response to investor demand for
more equal distribution of commodities
in investment vehicles, S&P said in a
press release. The original S&P GSCI is
weighted by world production levels.
Compared with the S&P GSCI, the S&P
GSCI Equal Weight Select Index will tend
to have higher exposure to commodi-
ties with lower production weights as a
result of the equal weighting. In 2010,
the new index includes 14 commodities
selected from the 28 covered by the S&P
GSCI, with weightings reset on a quar-
terly basis. One end result of including
fewer commodities is that products and
investors tracking the index will have
fewer monthly rolls to contend with.
The equal-weighted index selects
only the largest and most liquid com-
modities from each of six commodi-
ties groups: Agriculture – Grains and
Oilseeds; Agriculture – Softs; Energy;
Industrial Metals; Livestock; and
Precious Metals.
SummerHaven Adds More Commodities Indexes
SummerHaven Index Management,
the index provider behind the U.S.
Commodity Funds’ broad commodi-
ties ETF (NYSE Arca: USCI), is expand-
ing its footprint with the launch of
two active commodities indexes that
aim to boost returns.
The methodology behind the
SummerHaven Dynamic Metals Index
(“SDMI”) and the SummerHaven Dynamic
Agriculture Index (“SDAI”) is simple: The
bigger the physical inventory of a com-
modity, the smaller the weight that com-
modity will carry in the mix. The indexes
are rebalanced monthly. SummerHaven
says that research has shown that com-
modities with low inventories tend to
outperform commodities with high
inventories over time.
The indexes track commodity futures
contracts. The SDMI provides exposure
to 10 industrial and precious metals
ranging from gold and palladium to
nickel and tin, while the SDAI covers 14
agricultural commodities such as soy-
beans, sugar, wheat and lean hogs.
Nasdaq Debuts ‘Green’ Index Family
In late September, Nasdaq announ-
ced the launch of a family of indexes
tracking companies with products
and services focused on the environ-
November/December 2010www.journalofindexes.com 53
Nasdaq announced the launch of a family of indexes tracking companies with products and services focused on the environment and sustainability.
News
ment and sustainability.
The composite index, the Nasdaq
OMX Green Economy Index, covers 350
stocks winnowed down from a universe
of 460. It covers 13 sectors, includ-
ing advanced materials; biofuels and
clean fuels; energy efficiency; financial;
green building; healthy living; lighting;
natural resources; pollution mitigation;
recycling; renewable energy genera-
tion; transportation; and water.
Nasdaq has said it will be rolling out
subindexes for each of the sectors as
well as regional indexes covering the
U.S., Europe, Asia and the world ex-U.S.
The index family was developed
through a partnership with consultancy
firm SustainableBusiness.com LLC; a rep-
resentative of SustainableBusiness.com
selects the components of the indexes.
Nasdaq Partners With DWS On Volatility Target Index
In August, Nasdaq and DWS
Investments launched the DWS
Nasdaq-100 Volatility Target Index. The
new index is designed as a risk man-
agement tool for investors, enabling
them to control their exposure to the
popular Nasdaq-100 Index by shifting
their allocation between exposure to
the Nasdaq-100 and cash in response to
changes in volatility.
When the Nasdaq-100’s volatility
increases, the volatility target index
shifts more weight into its cash alloca-
tion. When the Nasdaq-100’s volatility
decreases, the volatility target index
increases its exposure to the other
index. Although it is a popular bench-
mark, the Nasdaq-100 is also known for
its volatility, so the new index poten-
tially allows investors to access the
Nasdaq-100’s growth-oriented stocks
without taking on too much risk.
DWS Investments is a subsidiary of
Deutsche Bank.
Barclays Capital Unveils Astro Index
Barclays Capital debuted a new index
series in mid-September. The Barclays
Capital Astro indexes track mean rever-
sion in the equity markets of Europe
and the U.S.
The index series is meant to be
a hedging tool for equity investors
who can use it to gain tail-risk protec-
tion and potentially mitigate any hits
to their equities portfolio. The Astro
indexes are designed with the inten-
tion that they outperform in highly
volatile markets when mean reversion
typically spikes, a Barclays represen-
tative noted. The index typically will
underperform slightly during long-
term bull markets, he said.
According to Barclays, backtesting
indicates that the index has not been
plagued by a negative cost of carry,
which is often the case with volatility
investments; the firm says this could
make the index appealing to long-
term investors.
Barclays calculates excess and total
return versions of the Barclays Capital
Astro US Index and the Barclays Capital
Astro Europe Index.
SAM Expands Relationship With DJI
Zurich-based sustainability invest-
ment firm SAM said in August it has
widened its relationship with Dow Jones
Indexes. The move follows the dissolu-
tion of DJI’s involvement in European
index provider Stoxx Ltd.; until recently,
Stoxx was partially owned by DJI.
SAM previously collaborated with
both DJI and Stoxx on the manage-
ment, marketing and dissemination of
sustainability-based indexes, with Stoxx
responsible for the European bench-
marks. SAM has since terminated its
relationship with Stoxx, and under a
new agreement, DJI will collaborate with
SAM on a set of European sustainability
indexes, similar to the ones that had
been calculated by Stoxx.
The new lineup includes the broad
Dow Jones Sustainability Europe Index
and Dow Jones Sustainability Eurozone
Index, and the narrow-based Dow Jones
Sustainability Europe 40 and Dow Jones
Sustainability Eurozone 40 indexes.
Their construction and methodology
align with those of the other Dow Jones
Sustainability Indexes. SAM will continue
to be responsible for the evaluation and
selection of the indexes’ components.
S&P Debuts Factor Index SeriesStandard & Poor’s rolled out the
S&P Factor Indexes in August; the new
benchmarks each consist of two equal-
weighted subindexes representing dif-
ferent asset classes or market seg-
ments. The point is to capture the risk
premium between the two subindexes.
The main index for each pairing
tracks a long position and a short
position in two front-month futures
indexes, seeking to measure the price
difference between the positions in the
two component subindexes.
Currently there are four indexes in the
series. The Equity Risk Premium Index
tracks the spread between the return
of U.S. stocks (represented in the long
subindex) and the return of 30-year U.S.
Treasury bonds (represented in the short
index). The other indexes include the
Non-US Dollar Equity Index (U.S. stocks
vs. the U.S. dollar); the Crude Oil – Equity
Spread Index (crude oil vs. U.S. stocks);
and the Gold – Equity Spread Index
(gold vs. U.S. stocks).
S&P Rolls Out International Preferred Stock Index
In late August, S&P said it had
launched the S&P International Preferred
Stock Index tracking preferred stocks in
developed markets other than the U.S.
The index currently has holdings from
49 companies, with Canada, Germany
and the U.K. showing the most repre-
sentation in the index. The index itself
is weighted by modified market capital-
ization, with individual issuer weights
capped at 4 percent of the index.
Constituents eligible for addition are
required to have market capitalizations
greater than $100 million, and must be
at least 12 months from any mandatory
conversion or scheduled maturity.
Preferred stocks resemble a hybrid
of stocks and bonds, and are valued by
investors for the high yields and diversi-
fication benefits that they offer.
AFT Launches Long-Short Currency Futures Index
Alpha Financial Technologies, LLC
unveiled the FX Trends Index (FXTI) in
August; the index is designed to take
November/December 201054
advantage of both rising and declining
price trends in individual currencies in
order to boost returns.
The FXTI does this by taking long
and short positions in 11 different cur-
rencies based on their individual price
trends. It uses GDP, liquidity and credit
stability to determine the weighting of
each currency.
The component currencies in the
index include the euro, Japanese yen,
Swiss franc, Brazilian real, British pound,
Canadian dollar, Mexican peso, Australian
dollar, New Zealand dollar, Norwegian
krone and South African rand.
The index is rebalanced monthly.
AROUND THE WORLD OF ETFsVan Eck, WisdomTree Launch Emerging Market Debt ETFs
Van Eck and WisdomTree both
launched emerging market debt ETFs
recently; importantly, both funds hold
only bonds denominated in local cur-
rencies. Previously, the only emerging
market debt ETFs available held dollar-
denominated debt.
The Market Vectors Emerging
Markets Local Currency Bond ETF (NYSE
Arca: EMLC) tracks the J.P. Morgan
Government Bond Index-Emerging
Markets Global Core Index. As of July
1, the benchmark had 171 constituents
with maturities ranging from one to 30
years. At the fund’s launch, the index
covered 13 countries, each capped at
a 10 percent weight. EMLC charges an
expense ratio of 0.49 percent.
WisdomTree followed up in August
with the WisdomTree Emerging Markets
Local Debt Fund (NYSE Arca: ELD). Unlike
EMLC, though, ELD is actively managed.
Its allocation model divides 13 emerging
markets into three tiers based on size
and risk parameters. ELD charges an
expense ratio of 0.55 percent.
Global X Debuts First Lithium ETF
Global X recently rolled out the first
ETF to tap into the renewable energy
theme through lithium companies.
Launched in July, the Global X Lithium
ETF (NYSE Arca: LIT) invests both in
lithium miners and lithium battery mak-
ers. By investing in battery manufactur-
ers, the fund captures the “high-tech
component” of the lithium story. The
metal, which is widely used in batteries
for cell phones and laptop computers,
is also key for the electric car industry,
which uses lithium-ion batteries in its
vehicles. And because lithium is not
traded on any commodities exchanges,
investors previously have had no way to
gain targeted exposure to the metal.
At launch, LIT’s basket was split
nearly 50-50 between miners and pro-
ducers in seven different countries. The
fund, which tracks the Solactive Global
Lithium Index, comes with an annual
expense ratio of 0.75 percent.
iShares Unveils Nine Ex-US Sectors
Mid-July saw the launch of nine new
iShares ETFs targeting sector subin-
dexes of the MSCI All Country World
ex USA Index.
The new funds are the first family
of sector ETFs to cover developed and
emerging markets, but exclude the United
States. They include the following:
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë��ÄÖ jÁË
Discretionary Sector Index Fund
(NYSE Arca: AXDI)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë��ÄÖ jÁË
Staples Sector Index Fund (NYSE
Arca: AXSL)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë �jÁ~ßË
Sector Index Fund (NYSE Arca: AXEN)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë�j?�ÍË
Care Sector Index Fund (NYSE Arca:
�9� ¯
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë��aÖÄÍÁ�?�ÄË
Sector Index Fund (NYSE Arca: AXID)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë
Information Technology Sector Index
Fund (NYSE Arca: AXIT)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë ?ÍjÁ�?�ÄË
Sector Index Fund (NYSE Arca: AXMT)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë
Telecommunication Services Sector
Index Fund (NYSE Arca: AXTE)
VËË�.?ÁjÄË .�Ë�8�ËjÞË2.Ë2Í���Í�jÄË
Sector Index Fund (NYSE Arca: AXUT)
The iShares MSCI ACWI ex US
Financials Sector Index Fund (NYSE
Arca: AXFN) launched separately back in
January. Each fund charges an expense
Á?Í��Ë �wË å±|oË ¬jÁWj�Í±Ë ���a��~ÄË Á?�~jË
from roughly 60 for the health care ETF,
�9� ^ËÍ�ËÝj��Ë�ÜjÁËÔÉåËw�ÁË�9�!±
Schwab Enters Fixed-Income ETFsCharles Schwab, which just entered
ÍjË 0�Ë ?Á�jÍË ��Ë !�Üj MjÁË Ôåå�^Ë
made its first foray into fixed income
with the launch of three U.S. Treasury
ETFs in early August.
The new funds include the Schwab
2±.±Ë 0�+.Ë 0�Ë ®!:. Ë �ÁW?]Ë .�+¯^Ë ÍjË
Schwab Short-Term U.S. Treasury ETF
®!:. Ë �ÁW?]Ë .�#¯Ë ?�aË ÍjË .WÝ?MË
Intermediate-Term U.S. Treasury ETF
®!:. Ë �ÁW?]Ë .�-¯±Ë 0jË 0�+.Ë wÖ�aË ?ÄË
an annual expense ratio of 0.14 per-
cent, while the other two funds both
?ÜjË jÞ¬j�ÄjË Á?Í��ÄË �wË å±¤ÔË ¬jÁWj�Í^Ë
according to the company’s Web site.
As with other Schwab funds, Schwab cli-
ents aren’t charged trading commissions
when they buy and sell the funds.
iPath Adds Eight Treasury ETNsIn August, the iPath ETN family
added eight exchange-traded notes to
its lineup. Each is linked to a U.S.
Treasury futures index. The products
are Barclays’ first foray into fixed-
income-based ETNs.
iPath’s new crop of ETNs includes
November/December 2010www.journalofindexes.com 55
News
three bull-and-bear pairs:
• iPath US Treasury 10-year Bull ETN
(NYSE Arca: DTYL)
• iPath US Treasury 10-year Bear ETN
(NYSE Arca: DTYL)
• iPath US Treasury 2-year Bull ETN
(NYSE Arca: DTUL)
• iPath US Treasury 2-year Bear ETN
(NYSE Arca: DTUS)
• iPath US Treasury Long Bond Bull
ETN (NYSE Arca: DLBL)
• iPath US Treasury Long Bond Bear
ETN (NYSE Arca: DLBS)
In addition, iPath launched a pair
of ETNs designed to give investors the
ability to take a view on whether the
yield curve will steepen or flatten:
• iPath US Treasury Steepener (NYSE
Arca: STPP)
• iPath US Treasury Flattener (NYSE
Arca: FLAT)
Each ETN comes with an expense
ratio of 0.75 percent.
Barclays Launches New Volatility-Linked ETN
Barclays Capital launched a new ETN
based on the S&P 500 Dynamic Veqtor
Index, the fourth volatility-linked
exchange-traded product for the global
banking giant.
The Barclays ETN+ S&P Veqtor
Exchange Traded Note (NYSE Arca:
VQT) began trading Sept. 1. It carries an
annual expense ratio of 0.95 percent.
VQT tracks the S&P 500 Veqtor
Index, which combines broad equity
market exposure with a built-in volatil-
ity hedge by allocating assets among
the S&P 500 Index, the S&P 500 Short-
Term VIX Futures Index and cash. VIX,
a product of the Chicago Board Options
Exchange, reflects the prices of S&P 500
options and is a benchmark for measur-
ing near-term volatility.
Claymore Closes Four FundsClaymore Securities, which was
acquired by Guggenheim Partners in
October, closed four of its ETFs on
Sept. 10. The company said in a state-
ment issued in August that the funds
had been lightly traded, and were being
closed so it can turn its attention to
“areas of greater investor interest.”
The list of funds included the fol-
lowing: the Claymore/Zacks Dividend
Rotation ETF (NYSE Arca: IRO), which
had $12.5 million in assets at the time of
the announcement; the Claymore/Zacks
Country Rotation ETF (NYSE Arca: CRO),
with $3 million in assets; the Claymore/
Beacon Global Exchanges, Brokers & Asset
Managers Index ETF (NYSE Arca: EXB),
with $2.8 million; and the Claymore/Robb
Report Global Luxury Index ETF (NYSE
Arca: ROB), with $16.2 million.
All shareholders remaining on Sept.
17 received a cash distribution into their
brokerage account representing the value
of their shares as of that date, including
any capital gains and dividends.
Vanguard Trumps iSharesIn Adviser Loyalty
Vanguard is increasingly popu-
lar among investment advisers, out-
ranking iShares for the first time to
become the most popular ETF provider
in terms of adviser loyalty, a study
from Cambridge, Mass.-based Cogent
Research showed. The firm surveyed
1,560 investment advisers.
According to the 2010 Advisor
Brandscape report compiled by the
market research firm, advisers who use
Vanguard ETFs are more committed
to the brand than those using iShares
products. John Meunier, a Cogent prin-
cipal, noted that Vanguard is the only
top-five ETF provider to grow its mar-
ket share over the past year.
iShares still outperforms Vanguard in
the range of products it offers, Meunier
said, but Vanguard outperformed its com-
petitor in just about every other category
Cogent measures, especially in “aspects
of service and client experience.”
State Street and Pimco ranked
third and fourth place among advisers,
respectively.
Alerian Debuts First-Ever MLP ETFIn late August, MLP research firm
Alerian launched the first ETF to tap
into the MLP space. Previously, investors
seeking access to the asset class could
only do so through various ETNs offered
by JP Morgan, UBS and Credit Suisse.
The Alerian MLP ETF (NYSE
Arca: AMLP) tracks the Alerian MLP
Infrastructure Index, and charges an
annual expense ratio of 0.85 percent.
Alerian also says that the ETF will retain
the tax benefits of MLP distributions. MLPs
are typically a nightmare to hold in a fund
setting since funds are typically taxed as
November/December 201056
Claymore Securities, which was acquired by Guggenheim Partners in October, closed four of its ETFs on Sept. 10.
registered investment companies, which
may only invest 25 percent of their assets
in MLPs before becoming subject to vari-
ous tax penalties. AMLP has elected to be
taxed as a corporation, which helps it get
around this restriction.
ALPS Advisors is AMLP’s distributor,
with Arrow Investment Advisors serving
as its subadviser.
BACK TO THE FUTURESCME Group Volume Up In August
Volumes at the CME Group stood
at an average of 11.7 million contracts
traded per day in August 2010, a 15 per-
cent increase from the prior year, and
an 8 percent increase from July 2010.
A total of 258 million contracts were
traded on the exchange in August 2010.
However, index-based contracts were
up only 5 percent from August 2009 to a
daily average of 2.6 million contracts. Of
those, the most actively traded contract,
the e-mini S&P 500 futures, was up 4.8
percent to an average daily volume of
1.9 million contracts; that same fig-
ure for the second-most actively traded
index futures, the e-mini Nasdaq-100
contracts, was up 6 percent for an ADV
of 6 percent. The mini $5 Dow futures,
on the other hand, saw their average
daily volume for August fall nearly 6 per-
cent to 125,383 contracts.
US Investors Can AccessTurkish Futures
In August, Reuters reported that the
Commodity Futures Trading Commission
had given the OK via a “no-action letter”
for U.S. investors to access an index-
based futures contract listed on the
Turkish Derivatives Exchange.
The contract is tied to the Istanbul
Stock Exchange 30 Stock Index, or
ISE-30, which consists of 30 of the larg-
est and most liquid stocks listed on the
Turkish stock exchange. According to
the CFTC letter, the index represents
70 percent of the total market capital-
ization of the Turkish stock market.
KNOW YOUR OPTIONSCFE Lists VIX Contracts
In early September, the CBOE Futures
Exchange (CFE) unveiled plans to begin
trading weekly options on VIX futures.
The contracts would be the first options
to be listed on the CFE.
Regular cash-settled options and
futures on the VIX, as well as options on
VIX-linked ETNs, are already available on
the CBOE’s trading platform.
With the weekly options at the CBOE,
four different contracts are generally
available, expiring in one week, two
weeks, three weeks and four weeks.
The options on the VIX futures will
be settled American style. They were
scheduled to launch Sept. 28.
CBOE Sees Volumes FallThe Chicago Board Options Exchange
saw its average daily volume for August
fall 21 percent from the prior year to
3.5 million contracts. The ADV was also
down from July 2010 by 9 percent.
However, index options saw their
ADV rise by 8 percent from the prior
year. ETF options saw their ADV fall, but
still outperformed, with a decline of just
13 percent. It was really equity options
that dragged down the exchange’s over-
all volume—they saw their ADV fall by a
whopping 33 percent.
Options on the S&P 500 index,
the SPDRS S&P 500 ETF, the VIX,
the PowerShares QQQ Trust and the
iShares Russell 2000 Index Fund remain
the most actively traded index and ETF
options listed on the CBOE.
FROM THE EXCHANGESCBOE Rolls Out Indexes For CME
In September, the CBOE publicly
debuted the first two indexes it has
developed through a partnership with
the CME Group.
The indexes are constructed using
the same methodology used to cre-
ate the CBOE’s VIX (which is based
on options contracts on the S&P 500),
except options on futures on gold and
crude oil that are traded on the CME are
substituted for the S&P 500 contracts.
The CBOE/NYMEX WTI Volatility Index
and the CBOE/COMEX Gold Volatility
Index and their underlying methodolo-
gies are owned by the CBOE. However,
the agreement gives CME the right to
create products based on the indexes,
including futures and options on futures.
Nasdaq To Launch Price-Size Exchange
On Oct. 8, the Nasdaq OMX Group,
Inc. was to launch the first U.S. equity
trading platform with a price-size prior-
ity model to encourage greater trans-
parency in public securities markets.
The platform, called the Nasdaq OMX
PSX, or PSX, will encourage participants
to display more shares at a price level,
making it easier to trade large blocks of
stock and increasing market efficiency.
The allocation of shares is prorated
based on a participant’s size relative to
the total size at that price level.
The platform, which will be oper-
ated as a facility of the Nasdaq OMX
PHLX exchange, formerly known as the
Philadelphia Stock Exchange, has been
approved by the SEC.
ON THE MOVERussell Hires Zyla
Russell Investments said in Sep-
tember it had hired Kurt Zyla, pre-
viously head of investment strategy
for indexes and ETFs at BNY Mellon,
Mellon Capital Management.
Zyla’s new title is regional director
for listed derivatives, and he is respon-
sible for the licensing of the Russell
indexes for use as the basis of futures
and options. The scope of his activi-
ties will encompass the development
of new products and the support of
existing products.
Zyla has an MBA from New York
University.
ETFS Adds To US Sales TeamIn early September, ETF Securities
announced it had expanded its U.S. sales
team with the hiring of Patrick Carter.
Carter’s responsibilities will be
focused on the California and West
Coast markets, particularly institutional
clients and national accounts.
Carter has been working in finan-
cial services for more than 20 years
and joins ETFS from Dimensional Fund
Advisors. Prior to moving to DFA, he
worked in a sales capacity for Merrill
Lynch for 13 years.
November/December 2010www.journalofindexes.com 57
November/December 201058
Summary And ConclusionsSome key takeaways can be drawn from our analysis of
the four factors affecting the relationship between mutual
fund advertising and investors’ fund choices, and whether
they lead investors down the path of the “wizards of over-
confidence” or the path of the “wizards of advertising.”
One key point is that (self-identified) financial literacy
doesn’t seem to improve investors’ mutual fund choices. The
“smart money” effect states sophisticated investors invest in
actively managed mutual funds due to superior investment
skills that enable them to outperform index funds. However,
this effect is apparently false, as sophisticated investors
with higher financial literacy scores and above-average self-
assessed financial skills are not “smart investors.”
Financial literacy scores are strongly associated with inves-
tor awareness of index funds. However, investors with lower
financial literacy scores invest in actively managed funds based
on fund advertising and brokers, while investors with higher
financial literacy scores and self-assessed above-average invest-
ment skills still invest in actively managed funds. The latter
group of investors believe they make superior fund choices,
but they are overconfident investors, not “smart investors.”
Most fund advertisements display just-prior fund perfor-
mance, but they do not signal superior future performance. It
works—increasing assets and profits—because many inves-
tors focus on past performance.
The second point discussed in this article is that investors
who exhibit non-normative “revealed preferences” in fund
choices do not act in their own “true interests.” They have
lower financial literacy scores, avoid complex decisions and
are psychologically unable to engage actively in investment
decisions. One type of revealed-preference decision is the
selection of funds based on advertising, which may affect
investor choices even if it provides little information.
The third part of this analysis discusses the disconnect
between fund advertising and its relevance to the product
it is promoting. Fund advertising affects investor choices
even if it provides little or no information. Only a small
proportion of fund advertising provides direct informa-
tion relevant for rational investors, such as expense ratios.
Moreover, funds that advertise the most may not provide
characteristics investors care about.
Funds that advertise more do not provide higher per-
formance or signal higher fund manager quality, and fund
advertising does not predict future performance. In fact, fund
advertising steers investors to funds with lower expected
returns (higher fees) and higher risk (equity exposure). It can
do this by successfully differentiating products—even when
the differentiation is meaningless—which can reduce fee
competition and allow the fund to charge higher fees.
Investors allocate the most to funds that receive the most
recent media attention. And investors may pay more for
funds that advertise heavily and provide no direct informa-
tion, but marginal returns to fund advertising decline over
time, perhaps due to investor overexposure.
Fund performance advertising is most effective in attract-
ing investors, and suggests that investors care most about
past performance. Funds that advertise portfolios are almost
always equity funds tilted toward “hot” sectors and local
funds. In general, fund advertising generates positive emo-
tions that make investor attitudes more favorable.
The final piece of the puzzle compares two advertising
persuasion models: The traditional model of advertising
assumes rational investors, while the behavioral model
best fits the emotional and cognitive process of investor
fund selection. In the behavioral model of persuasion, fund
advertising is designed to resonate with prevailing investor
beliefs that they accept at face value. Fund advertisers gener-
ally know which messages work, and content is directed to
changes in investor prevailing beliefs. For example, investors
believe growth investing is for wealth creation and that value
investing is for wealth conservation, so when the market is
rising, fund advertisements focus on growth funds, and when
it is declining, they focus on value funds.
Fund advertisements focus on market returns (because
investors do) when past market returns are high, and avoid
providing returns when they are not. They also do not
report performance following market declines even if per-
formance is superior—at such times, the number of fund
advertisements approaches zero.
Overall, fund advertising promotes speculative rather than
contrarian styles of investing. Ultimately, the purpose of mutu-
al fund advertising is to persuade investors to invest more and
to increase fund adviser profits. This is a far cry from advertis-
ing that provides investors with full and objective information
that enables them to become “smarter investors.”
This article explores the relationships of mutual fund advertis-
ing and investor skill in making fund choices. Advertising appeals
to investor emotions by resonating with current beliefs, not by
providing information that enables more informed fund choices.
Choices of unsophisticated investors are dominated by fund
advertising—“the wizards of advertising.”
On the other hand, sophisticated investors with self-
assessed above-average investment skills believe they make
superior choices of actively managed mutual funds that will
outperform index funds. However, sophisticated investors
are not superior investors, but overconfident investors—
“the wizards of overconfidence.”
Mutual funds will be forced to provide useful objective
information if investors “demand it,” but will this ever hap-
pen? The test of this change is when the traditional model of
persuasion replaces the behavioral model in best matching
investor perceived needs in making fund choices.
Endnotes1Müller, Sebastian and Martin Weber. Financial Literacy and Mutual Fund Investments: “Who Buys Actively Managed Funds?” Schmalenback Business Review, vol. 62 (April 2010), pp. 126-153.
2Beshears, John, James J. Choi, David Laibson, and Brigitte C. Madrian. “How are Preferences Revealed?” Working Paper Series, SSRN, April 25, 2008 (http://ssrn.com/abstract=1125043).
3Cronqvist, Henrik. “Advertising and Portfolio Choice.” Working Paper Series, SSRN, Sept. 11, 2008 (http://ssrn.com/abstract=920693).
4Mullainathan, Sendhil and Andrei Shleifer. “Persuasion in Finance.” Working Paper Series, SSRN, Jan. 11, 2006 (http://ssrn.com/abstract=864686).
Haslem continued from page 44
November/December 2010 59www.journalofindexes.com
Global Index DataNovember/December 2010Selected Major Indexes Sorted By YTD Returns
Total Return % Annualized Return %
Index Name YTD 2009 2008 2007 2006 2005 2004 2003 3-Yr 5-Yr 10-Yr 15-Yr Sharpe Std Dev
Source: Morningstar. Data as of August 31, 2010. All returns are in dollars, unless noted. YTD is year-to-date. 3-, 5-, 10- and 15-year returns are annualized. Sharpe is 12-month Sharpe ratio. Std Dev is 3-year standard deviation. *Indicates price returns. All other indexes are total return.
November/December 2010
Index Funds
60
November/December 2010Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions
Total Return % Annualized Return %
Fund Name Ticker Assets Exp Ratio 3-Mo YTD 2009 2008 3-Yr 5-Yr 10-Yr 15-Yr P/E Std Dev Yield
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.
Morningstar U.S. Style Overview Jan. 1 – Aug. 31, 2010
Source: Morningstar. Data as of August 31, 2010
November/December 2010
Dow Jones U.S. Industry Review
PerformanceIndex Name Weight 1-Month 3-Month YTD 1-Year 3-Year 5-Year 10-Year
Dow Jones U.S. Index 100.00% -4.58% -3.55% -4.29% 5.73% -8.04% -0.43% -1.33%
Dow Jones U.S. Basic Materials Index 3.43% -2.02% 1.29% -1.07% 18.29% -2.91% 6.60% 7.87%
Dow Jones U.S. Consumer Goods Index 10.76% -2.14% 2.11% 2.57% 13.51% -0.32% 3.54% 6.14%
Dow Jones U.S. Consumer Services Index 11.91% -4.04% -7.05% -0.16% 11.86% -5.18% -0.30% -0.24%
Dow Jones U.S. Financials Index 16.42% -7.32% -7.08% -4.66% -3.89% -21.05% -10.03% -2.72%
Dow Jones U.S. Health Care Index 11.42% -1.41% -2.11% -8.11% 1.36% -3.67% 0.18% 0.53%
Dow Jones U.S. Industrials Index 12.56% -7.02% -5.46% -0.78% 11.89% -8.77% 0.80% -0.80%
Dow Jones U.S. Oil & Gas Index 10.24% -4.23% -2.77% -9.88% -0.46% -8.53% 2.18% 7.22%
Dow Jones U.S. Technology Index 15.95% -7.13% -6.53% -10.05% 4.56% -4.36% 2.12% -8.50%
Dow Jones U.S. Telecommunications Index 3.16% 1.59% 10.18% 1.33% 12.00% -10.08% 2.17% -4.70%
Dow Jones U.S. Utilities Index 4.15% 1.10% 7.57% 1.93% 10.55% -4.01% 2.21% 3.05%
Risk-Return
Industry Weights Relative to Global ex-U.S. Asset Class Performance
Data as of August 31, 2010
Source: Dow Jones Indexes Analytics & Research
For more information, please visit the Dow Jones Indexes Web site at www.djindexes.com.
The Dow Jones U.S. Index, the Dow Jones Global ex-U.S. Index and the Dow Jones U.S. Industry Indexes were first published in February 2000. The Dow Jones Brookfield Infrastructure Index was first published in July 2008. To the extent this document includes information for the index for the period prior to its initial publication date,
such information is back-tested (i.e., calculations of how the index might have performed during that time period if the index had existed). Any comparisons, assertionsand conclusions regarding the performance of the Index during the time period prior to launch will be based on back-testing. Back-tested information is purely hypothetical
and is provided solely for informational purposes. Back-tested performance does not represent actual performance and should not be interpreted as an indication of actual performance. Past performance is also not indicative of future results.
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
Source: Morningstar. Data as of August 31, 2010. Exp Ratio is expense ratio. 3-Mo is 3-month. YTD is year-to-date. 3-Yr and 5-Yr are 3-year and 5-year annualized returns, respectively.Mkt Cap is geometric average market capitalization. P/E is price-to-earnings ratio. Std Dev is 3-year standard deviation. Yield is 12-month.
Claymore BulletShrs 2012 HiYld Corp Bond
Direxion Daily Euro Bull 3X
EGS INDXX Growing Asia Lrg Cap
Emerald Rock Dividend Growth
ETFS Leveraged Copper
First Trust Nasdaq CEA Smartphone
Global X Fishing
IQ International Indonesia Small Cap
L6KDUHV�*OREDO�,QüDWLRQ�/LQNHG�%RQG
Jefferies Natural Gas Equity
Market Vectors MLP
Pimco Govt Limited Maturity Strategy
PowerShares KBW High Div Yld Financial
ProShares Ultra Gold Miners
Riverfront Strategic Income
Russell Contrarian
Rydex Russell 3000 Equal Weight
SPDR Barclays Capital CMBS
Vanguard Long-Term Municipal Bond
Wilshire Mid-Cap Value
Source: IndexUniverse.com’s ETF WatchSource: Morningstar. Data as of August 31, 2010. ER is expense ratio. 1-Mo is 1-month. 3-Mo is 3-month. YTD is year-to-date.
Exchange-Traded Funds Corner
H U M O R
64
A tool for surviving
imminent Armageddon
November/December 2010
The End Is Nigh
By Lara Crigger
Post-Apocalyptic Investing: The Index Approach
With the price of gold crossing $1,300
an ounce, the Federal Reserve prepping
for more quantitative easing and Oprah’s
talk show finally ending, we here at Journal
of Indexes can read the writing on the wall:
The end of the world is nigh.
Food riots. Water riots. Beer riots. The
collapse of the world economy. Invasions
by the undead. You get the picture.
When the apocalypse comes, and all
your stocks and bonds won’t be worth the
paper you burn to fuel your cooking fires,
how can you be sure your portfolio will
remain protected?
To fill this critical investment need,
our crack team of analysts has developed
the very first post-apocalyptic investment
strategy, the Zero-Omega Markowitz/
Bernstein Index of Extinction. The ZOMBIE
benchmark will track a basket of stocks,
bonds and weapons designed to give
investors exposure to the most promising
post-Armageddon markets.
Fully 90 percent of the index will invest
in gold bullion, because, as everyone
knows, when the global economy ceases
to function and all previous conceptions
of money and fair value lose their mean-
ing in the wake of aching hunger, lumps
of inedible yellow rock will be the most
useful asset anyone can possess. Indeed,
gold has plentiful applications in the post-
apocalyptic economy:
VËË.¬��Ë �ÍË ��Í�Ë ÍÁ�¬Ý�Áj^Ë Í�Ë j�Ä�?ÁjË ÍÁjÄ-
passers who’d steal your canned food.
VËË.WÖ�¬ÍË�ÍË��Í�Ë?�Ë�~���ËÍ�ËÝ?ÁaË�wwËÍ�jËM�Í-
ter gales of an extended nuclear winter.
VËË ?�jË?Ë�jÝË~Á���Ëw�ÁËß�ÖÁËÍjjÍ�^ËÄ��Wj^Ë
as a zombie, yours will have long
since rotted away.
But gold isn’t the only asset worth own-
ing. A full 5 percent of the index will invest
in the luxury projectiles industry: gun
stores, hunting outlets, munitions depots,
antique musket manufacturers and cata-
pult-engineering firms. While the firearms
sector remains a niche market now, we pre-
dict it will experience extraordinary growth
potential once the food riots begin.
Another 2 percent of the index will
focus on big-box retailers and canning
operations. Why only 2 percent? While
durable foodstuffs are a crucial element of
any post-apocalyptic portfolio, we’ve kept
the overall percentage here small due to
its high exposure to loot risk and competi-
tion from cannibalization.
The final 3 percent will be spread out
among diversified decontamination opera-
tions, MRE manufacturers and defense con-
tractors. In a world of devastating plagues
and the restless undead, we foresee excit-
ing opportunities for companies in the con-
tainment and quarantine industries.
ZOMBIE’s equity exposure and custo-
dial relationships will focus on the U.K.,
Indonesia, Japan and Australia, since, as
islands, they have the best chance of survival
after a worldwide pandemic transforms the
continental populations into slavering, mind-
less brain-chewers. However, a substantial
portion of assets will also invest specifically
in Los Angeles, as the percentage of plastic
and silicon among the native population
should serve as a natural deterrent against
any impending zombie assault.
Future-minded investors holding
ZOMBIE-based products can relax, know-
ing their portfolios will be safe, even when
the dead roam the earth and feast on the
flesh of the living. They can turn to their
spouse or neighbor, huddling beside them
in abject fear and hunger, and say with con-
fidence, “We might not have food or water
or enough bullets to last until sunrise, but,
hey, at least we have some gold.”
Project1 9/28/10 11:38 AM Page 1
All investments are subject to risk. Vanguard funds are not insured or guaranteed.
Vanguard ETFs are not redeemable with an Applicant Fund other than in Creation Unit aggregations. Instead, investors must buy or sell Vanguard ETF Shares in the secondary market with the assistance of a stockbroker. In doing so, the investor will incur brokerage commissions and may pay more than net value when buying and receive less than net asset value when selling.
For more information about Vanguard ETF Shares, visit advisors.vanguard.com/equityetfs, call800-523-8845, or contact your broker to obtain a prospectus. Investment objectives, risks, charges, expenses, and other important information are contained in the prospectus; read and consider it carefully before investing.
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