How to Kill a Black Swan Remy Briand and David Owyong ...

How to Kill a Black Swan
Remy Briand and David Owyong
Benchmarking Asset Allocation
David Krein
Minimizing ETF Trading Costs
Gary Gastineau
How to Save America’s Financial System
John Bogle
Plus Callin and Jones on portable alpha, Mangiero interview and Blitzer on psychological risk

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Vol. 12 No. 4
features
How To Kill A Black Swan
By Remy Briand and David Owyong ..........10
A road map for managing risk in times of crisis.
Benchmarking Policy Portfolios
By David Krein ...........................18
A look at the importance of a well-fitted benchmark.
An Interview With Susan Mangiero
By Journal of Indexes Editors ..................22
A pension risk management expert considers 2008.
How To Minimize Your Cost Of Trading ETFs
By Gary Gastineau . ........................24
Trading ETFs is different from trading stocks.
The Future Of Portable Alpha
By Sabrina Callin and Steve Jones. ............32
Portable alpha must adapt to the new era of risk.
The Fiduciary Principle
By John Bogle. ............................38
A clarion call for a return to fiduciary responsibility.
Managing The Risks In Ourselves
By David Blitzer ...........................42
2008 was part of the normal boom and bust cycle.
Fly On The Wall
By Dave Nadig . ...........................56
How Dow components are really chosen.
news
CVC To Buy iShares … Or Maybe Not ..........44
Dow Jones Rebrands Two Index Families .......44
Grim Results For Active Managers In 2008 . .....44
MacroMarkets Closes Oil Fund, Cancels Housing IPO 44
Indexing Developments ....................45
Around The World Of ETFs . .................47
Back To The Futures .......................49
Know Your Options . .......................49
From The Exchanges .......................49
On The Move . ............................49
data
Selected Major Indexes ................... 51
Returns Of Largest U.S. Index Mutual Funds .. 52
U.S. Market Overview In Style ............... 53
U.S. Economic Sector Review .............. 54
Exchange-Traded Funds Corner ............ 55
10
18
32
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www.journalofindexes.com July/August 2009
1

Contributors
John Bogle
John Bogle is the founder and former CEO of The Vanguard Group, Inc., and
president of the Bogle Financial Markets Research Center. He created Vanguard
in 1974 and had been associated with a predecessor company since 1951, following
his graduation from Princeton University magna cum laude in economics.
Bogle founded the Vanguard 500 Index Fund, the first index mutual fund, in 1975.
Vanguard now holds some $1.3 trillion in assets. Bogle’s seventh book, “Enough.
True Measures of Money, Business, and Life,” was published in November 2008.
Remy Briand
Remy Briand is managing director and global head of Index Research at MSCI
Barra. He is responsible for the continual expansion, refinement and improvement
of the MSCI indices, as well as overseeing their day-to-day management.
Briand also leads MSCI Barra’s applied research team, which identifies earlystage
investment trends. He joined MSCI Barra in 2001 from Crédit Lyonnais
Asset Management, where he was head of Research.
Sabrina Callin
Sabrina Callin is a managing director and member of Pimco’s alternative
investment solutions team, and a product manager for global portable alphabased
equity and unconstrained bond strategies. She is the author of the
book “Portable Alpha Theory and Practice” (2008). Prior to joining Pimco in
1998, Callin was a manager in the assurance and business advisory services
group at KPMG Peat Marwick. She holds an M.B.A. from Stanford University
Graduate School of Business and undergraduate degrees from Texas Christian
University. Callin is also a certified public accountant.
Gary Gastineau
Gary Gastineau is a managing member of Managed ETFs LLC, a firm interested
in launching actively managed ETFs; and a partner in Skyhawk Management,
LLC, the adviser to a market-neutral hedge fund that buys ETFs and sells them
short. He is the author of “The Exchange-Traded Funds Manual,” as well as
“Someone Will Make Money on Your Funds—Why Not You? (A Better Way to
Select Mutual and Exchange-Traded Funds).” Gastineau is a frequent contributor
to the Journal of Indexes.
Steve Jones
Steve Jones is a vice president and product manager for Pimco’s portable
alpha-based equity strategies in the Newport Beach, Calif., office. He was previously
a member of the short-term portfolio management team and also held
positions in account management. Jones is a contributing author of “Portable
Alpha Theory and Practice,” published in 2008. Prior to joining Pimco in 2000,
he was an analyst at Collins Associates, a fund-of-funds investment management
firm. Jones holds an M.B.A. from the Paul Merage School of Business at
the University of California, Irvine.
David Krein
David Krein is senior director of institutional markets for Dow Jones Indexes,
where he serves as a liaison to the institutional investment community globally,
and guides the research and development of new and enhanced index benchmarks.
Prior to joining DJI, Krein was president of DTB Capital, a firm he founded
in 2006 to develop indexes and structured investment products for derivatives
exchanges and the over-the-counter marketplace. Krein holds an M.B.A. with
honors from the University of Chicago Graduate School of Business.
David Owyong
David Owyong, an MSCI vice president, leads the applied research team for
MSCI Barra in the Asia-Pacific region. Prior to joining the firm in 2006, he was the
director of Quantitative Research and Risk Management at Ajia, a Hong Kongbased
hedge fund. Owyong holds an M.S. in statistics and a Ph.D. in economics
from Stanford University, and has also obtained the Investment Management
Certificate (IMC Level 3) from the U.K. Society of Investment Professionals.
2
July/August 2009

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Editorial Board
David Blitzer: Standard & Poor’s
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Commodities’
future.
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As of May 7, the Dow Jones-AIG Commodity Indexes SM
have been rebranded as the Dow Jones-UBS Commodity
Indexes SM . The name has changed—but the indexes have not.
Backed by industry leaders, the family is maintained according
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the indexes still steadfastly measure an asset class that has
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Learn more about the future of commodities as measured
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Visit www.djindexes.com today.
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All information as of May 2009.
“Dow Jones-AIG Commodity Indexes” and “Dow Jones-UBS Commodity Indexes” are
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Presented by:

Editor’s Note
Got Risk?
Jim Wiandt
Editor
The basic theme of this issue is one we’ve all become acutely aware of over the last
year: risk. Risk of default, risk of a depression, risk of standing on the sidewalk
in front of your (former) front yard, shirtless. Over the past year, things we never
dreamed could be real risks suddenly became frighteningly real.
The index business has gotten in on the act, reflecting as always, the soaring hopes and
then the terrifying fears of investors—all in brutally clear numbers, available for easy reference.
When the pendulum shifts toward fear, products that track and help manage risk
become popular with investors, and the index industry has been working hard to deliver.
One question that’s been on everyone’s minds recently is, “When it feels like the ‘six
sigma’ or ‘black swan’ event has become a near-daily occurrence, what can we do about it?”
An outstanding submission from Remy Briand and David Owyong of MSCI tells you.
The article “How To Kill A Black Swan” not only explains how to systematically manage
tail risk, but proposes an entirely new methodology for thinking about asset allocation
in a risk-managed context.
David Krein of Dow Jones takes a different tack. Krein wonders why, if asset allocation
explains 90–100 percent of portfolio risk and return, people spend so little time creating good
asset allocation benchmarks. He proposes a set of first principles for building better ones.
Next up is an interview with Susan Mangiero, president of Pension Governance, Inc.,
and one of the leading pension plan risk management experts in the world, examining the
latest thinking on risk mitigation at the institutional level. Gary Gastineau joins in with
a practical examination on how investors can minimize the risk of getting scalped when
trading ETFs, while Sabrina Callin and Steve Jones of Pimco examine how portable alpha
strategies must adapt to the new era of risk.
Rounding out the issue is John Bogle, who issues a broadside against the American financial
system and examines the urgent need to embrace fiduciary principles, and David Blitzer,
who argues that many of the biggest risks in the market lie with investors themselves.
And don’t forget Dave Nadig, who takes a humorous back page look at how that
iconic barometer of market sentiment, the Dow Jones Industrial Average, really picks
its constituents.
Risk may not be something we want to focus on during these halcyon days of summer,
when we’d rather be thinking about BBQ, apple pie and the Fourth of July. But if last
year taught us anything, it’s that risk—and how to manage it—should always be kicking
around somewhere in the back of our minds.
Enjoy the summer,
Jim Wiandt
Editor
8
July/August 2009

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How To Kill A Black Swan
Risk and asset allocation in crises
By Remy Briand and David Owyong
10
July/August 2009

To most of us in the financial sector, the events of
2008 were shocking. We have all struggled with the
aftershocks and asked ourselves what happened and
what led us there. Some have argued that this is an unprecedented
situation, which may be true in terms of the speed
and magnitude of the shock, and possibly the long-term
impact on the real economy. But others see echoes of the
past in terms of the elements that triggered the crisis and
the way it has been unfolding.
In fact, history points to many precedents. Rogoff and
Reinhart [2008] identified 18 major banking crises since
World War II, and a casual count of recent crises shows a pattern
of a major market event at least every 10 years. Most of
the time, these crises have been linked to excess liquidity in
the economy, combined with a general misjudgment on the
benefits of a certain type of innovation. This time is no different.
Excess savings from emerging markets were reinvested
in developed financial and housing markets, while financial
innovation in the form of sophisticated securitized instruments
contributed to a false sense of security surrounding
systemic risk reduction.
The experience of this last market shock highlights several
shortcomings in the risk management and asset allocation
practices that have been commonly adopted by industry
practitioners. It is now clear that the underestimation of
the frequency and magnitude of extreme events left many
unprepared. In particular, the consequences of contagion
on portfolios have been overlooked. By increasing correlation
across asset classes, contagion significantly reduces
the benefits of diversification in otherwise-well-diversified
portfolios. In addition, liquidity dried up in many parts of the
capital markets, leading to cash-flow issues for most investors,
including many long-term investors such as endowments
and pension funds. Finally, the intensity of the crisis
exacerbated the negative consequences of nonmarket risks,
such as counterparty risk, operational risk and concentration
risk. This is vividly captured by Warren Buffett’s now-famous
quote: “Only when the tide goes out do you discover who’s
been swimming naked.”
In our view, the events of last year will force a number of
changes in investment practices in the following areas:
• Management of Extreme Events: Business continuity
planning (BCP) is a standard operational risk control
practice for organizations, enabling critical processes
to function in all conditions. Asset managers and plan
sponsors need BCP plans for their portfolios. Achieving
this will require methods to measure and model tail risk,
rules to trigger BCP plans on predefined market conditions,
and a consistent framework for stress-testing
portfolios and planning for extreme event scenarios.
• Strategic Asset Allocation: Investors are rethinking
asset allocation practices with a view to better managing
downside risk and avoiding investment horizon mismatches,
in particular for individual retirement products.
They may more formally take into consideration
cash-flow requirements and eventually move toward
more risk-based allocations.
• Integrated Risk Management: There is wider recognition
now that investors need an all-encompassing view
of portfolio risk. They need tools that help them understand
the sources of risk, not just the absolute levels.
Breaking asset class silos will allow investors to view
the sources of risk across all assets in the portfolio. New
tools will be needed to look beyond market risk into
other types of portfolio risks, such as counterparty risk,
concentration and liquidity risk.
The rest of this paper addresses these three areas in more
detail by looking at the main issues of last year’s crisis and
possible solutions to mitigate their impact.
I. Managing Extreme Events
Extreme events can be characterized by volatility jumps,
increased risk aversion, negative returns for risky assets,
and increased correlation across asset classes. Such events
actually happen more often than is commonly perceived. In
just the last 21 years, we have experienced 10 major market
events: Black Monday (1987), Gulf War (1990), European
ERM Crisis (1992), Mexican Crisis (1994), Asian Crisis (1997),
Long-Term Capital Management (LTCM) (1998), Tech Bubble
Crisis (2000), September 11 (2001), Quant Crisis (2007) and
Credit Crisis (2008).
Aside from the frequency of occurrence, it is also interesting
to note the volatility spikes and clusters through time.
Figure 1 shows the one-day losses in the MSCI World Index
over a period of 20-plus years and reveals how market volatility
can change drastically and unexpectedly in extreme events.
Understanding Risk Regime Shifts And Flight To Quality
A high-volatility regime usually develops with a shock in
one asset class, extends to other asset classes as investors
worry about the consequences of the shock and, in a herding
movement, snowballs into panic. At that point, returns from
assets are inversely proportional to their riskiness, which is
the opposite of expected behavior in normal times. This is
illustrated in Figure 2, which compares the returns of various
asset groups in the worst month of five major crises.
While equities were the worst performers in all these
crises, bonds were also affected by the widening of credit
spreads. In addition, assets that were previously negatively
correlated may no longer remain so in a crisis, which significantly
alters the diversification picture of a portfolio.
Figure 1
One-Day Losses In MSCI World Index
(January 1987–April 2009)
10%
8
6
4
2
0
–2
–4
–6
–8
–10
1/87 1/89 1/91 1/93 1/95 1/97 1/99 1/01 1/03 1/05 1/07 1/09
Source: MSCI Barra
Note: This graph plots losses, and hence negative values denote gains.
www.journalofindexes.com July/August 2009
11

Figure 2
Comparison Of Asset Returns During Major Crises
Global Equities
Global Bonds
Month
MSCI
ACWI
MSCI
World (DM)
MSCI
EM
U.S.
T-Bills
Sovereign
High-Yield
Premium
Corporate
Premium
MSCI EM
Currency
Index
LTCM Aug-1998 -14.2% -13.5% -29.3% 0.9% 2.5% -2.2% -1.5% -2.0%
Tech Bubble Nov-2000 -6.2% -6.1% -8.8% 0.7% 2.0% -1.3% -0.7% -0.2%
Sept 11 Sep-2001 -9.1% -8.8% -15.5% 1.0% 0.8% -1.4% -0.7% -2.0%
Quant Crisis Aug-2007 -0.2% 0.0% -2.1% 0.7% 1.6% -0.8% -0.9% -0.8%
Lehman Oct-2008 -19.8% -18.9% -27.4% 0.7% -1.9% -7.4% -6.3% -6.3%
Note: The bond indices are sourced from Merrill Lynch, the T-bill data from the Federal Reserve and the rest are from MSCI Barra. All returns are based in U.S. dollars. The MSCI
EM Currency Index measures the strength of emerging market currencies relative to the U.S. dollar. The high-yield and corporate bond premia are based on the differential in
returns between the high-yield or corporate bond index and the sovereign bond index.
Figure 3 illustrates this effect by displaying the correlations
between equities and bond premia in the last 10 years. In
general, correlations are unstable and tend to change over
time. However, during the periods of crisis as highlighted
in orange, correlations generally rose substantially, which
caused equities to become more correlated with bonds and
hence reduced diversification benefits.
Since correlations can change quickly over time, particularly
in crises, a well-diversified portfolio in normal times may still
be subject to unexpected volatility in extreme circumstances.
It is therefore also important for us to understand tail risk—
particularly, how to estimate and manage it appropriately.
Modeling Extreme Events
A common statistic currently used to measure downside
risk is value at risk (VaR). This statistic is defined as the maximum
loss a portfolio is expected to incur over a specified
time period, with a specified probability or confidence level.
For example, if the one-week 99 percent VaR of a portfolio
is 20 percent, then there is a 99 percent chance that the loss
of this portfolio over a week is not more than 20 percent. In
other words, there is only a 1 percent chance that the weekly
loss would exceed 20 percent.
Traditional VaR methods, however, tend to underestimate
the likelihood of extreme events because they usually assume
Figure 3
that returns are normally or lognormally distributed. In reality,
the empirical distribution of asset returns shows that
extreme events, referred to as “fat tails,” are more likely to
occur than normal distribution patterns imply. Assuming a
normal distribution may underestimate the likelihood and
magnitude of extreme losses. Risk estimates acceptable during
normal periods are prone to fail during severe downturns.
This problem of fat tails is not unique to financial markets
and has received much attention in other disciplines, such as
hydrology and structural engineering. Researchers in these
disciplines approach this problem using extreme value theory,
which focuses on the distribution of returns at the tails. This
is precisely what is required for analyzing market crises, since
these are extreme or tail events. The framework of normal
distribution is only adequate as a reflection of average returns,
not extreme ones. In extreme value theory, the distribution of
the tail values instead follows the generalized extreme value
(GEV) distribution. Once the tail distribution is determined,
the VaR can then be computed as in the normal distribution.
Figure 4 provides an example, extended from Goldberg
et al. [2008], of how extreme value theory can provide a better
reflection of the downside risk. We compare the relative
robustness of the traditional VaR and the extreme-value VaR
for a variety of portfolios composed of U.S. equities. Daily
returns are taken from December 1996 to October 2007, a
Tech Bubble
Correlation Between Equities And Bond Spreads In Last Ten Years
Sept. 11
WorldCom
Bull Market: Correlations fell
into negative territory
12/98 6/99 12/99 6/00 12/00 6/01 12/01 6/02 12/02 6/03 12/03 6/04 12/04 6/05 12/05 6/06 12/06 6/07 12/07 6/08 12/08
Quant Crisis
Lehman
0.8
0.6
0.4
0.2
0.0
–0.2
–0.4
–0.6
■ MSCI ACWI vs. High Yield Bond Premium
■ Emerging Market Equity Premium vs. High Yield Bond Premium
■ MSCI ACWI vs. Corporate Bond Premium
■ Emerging Market Equity Premium vs. Corporate Bond Premium
Sources: MSCI Barra, Merrill Lynch
Note: Correlations are monthly, computed using all daily returns within each month, and smoothed by using a six-month moving average.
12
July/August 2009

period that covers major crises that include the Asian crisis,
LTCM, Tech Bubble, Sept. 11 and the Quant Meltdown in
August 2007. VaR figures are generated using two methods:
the traditional way in which returns are assumed to be normally
distributed and exponentially weighted across time, and
through using extreme value theory. We choose a confidence
level of 99 percent and a time horizon of one day, so that the
resultant VaR figures should represent the maximum daily loss
that would be incurred with 99 percent probability.
To compare the two measures over a variety of different
portfolios, we evaluated 74 factor-tilted portfolios. It is
important to note that the VaR numbers generated here are
forecast values. In Figure 4, the horizontal axis is divided into
interval ranges that denote the percentage of days in which
actual losses are greater than the VaR, while the vertical axis
displays the number of portfolios (out of the possible 74 in
our sample) within each interval. Ideally, all portfolios should
be in the expected range, left of the broken line. This is true
for the majority of the portfolios when using the extremevalue
VaR, but not in the case of the traditional VaR. While
about 80 percent of the portfolios meet this criterion under
the extreme-value measure, only 3 percent do so in the case
of the traditional measure. Clearly, the estimates of risk generated
using VaR are starkly different depending on whether
the distribution is normal or not.
In addition to better modeling of fat tails, extreme value
theory also introduces a tail-risk measure that provides a
more complete reflection of the expected loss in a worstcase
scenario. While VaR tells an investor his worst expected
loss in 99 percent of the trading days, it does not indicate
how severe the loss would be in the remaining 1 percent.
Expected shortfall measures the expected loss within that
worst 1 percent. Goldberg et al. [2009] have demonstrated
how this new concept can be integrated in the standard tool
kit used to measure portfolio risk, and show that different
portfolios have distinct downside characteristics.
Options To Manage Tail Risk
BCP is a standard risk control practice for organizations,
enabling critical processes to stay in operation even if a disaster
strikes. This practice is well-established in many industries,
including finance, as well as in nonbusiness organizations such
as the military. We suggest that an analogous concept, which
will be referred to as Portfolio BCP, would be relevant for
portfolio management.
Generally, Portfolio BCP would require the following steps
to be implemented. Firstly, the definition of an extreme
event has to be determined. This could be based on returns,
volatility, tracking error, VaR, drawdown or a combination
of these measures as captured in a scenario. The probability
and severity of the extreme events can then be quantified, as
was carried out in Bhansali [2008].
Secondly, thresholds have to be decided upon, so that the
conditions that trigger BCP are clear to everyone involved.
Thirdly, scenarios should be elaborated to cover the most
likely current threats. In order to rehearse these potential
extreme events, stress tests should be performed to simulate
the performance of portfolios under such situations. Finally,
portfolio trades reflecting the mitigating decisions should
be prepared and preapproved by the various investment
committees for fast and consistent execution, should those
extreme events happen.
In this context, the importance of stress-testing under
extreme conditions should be emphasized, since this would
help determine how much tail risk should be hedged away.
In recent years, stress-testing has attracted the attention
both of regulators and practitioners as an important
measure that complements traditional risk measures such
as volatility, tracking error and VaR. Increasingly, regulators
such as the Basel Committee and the EU Commission
(UCITS III Directive) require that practitioners incorporate
stress-testing into their regular risk management practice.
Stress-testing is particularly important because it helps to
mitigate the overdependence on recent historical data.
Multi-asset class risk systems often include dozens of predefined
stress-testing scenarios.
II. Rethinking Strategic Asset Allocation
The issues of 2008 also highlighted the mismatch between
the investment horizon, the level of risk investors can bear
and the characteristics of their portfolio. The most problematic
cases were seen in the defined contribution space, but
this issue also affects mature pension plans.
A retiring worker who expects to exit the workforce in
one year should take on much less risk in his investments
than a young worker in his 20s who is looking at another 40
years of employment. Sounds obvious? Yet individuals retiring
in 2009 who put their savings in a 2010 target-date fund
would typically have seen the value of their savings shrink by
20–30 percent. For someone purchasing an annuity at retirement
under these circumstances, it means a permanent loss
of revenues in the same proportion.
Understanding Investment Horizon
And Downside Risk
Asset allocation decisions have to be made in the context
of the risk-return characteristics of various asset classes and
the tolerable downside risk. The latter is linked to the length
of the investment horizon. The key investment problem is
Figure 4
Expected Range
Extreme Value Theory Yields Better
Reflection Of Downside Risk
Risk Underestimated
2
–3
<
0.5 0.5-
0.6 0.6-
0.7 0.7-
0.8 0.8-
0.9 0.9-
1.0 1.0-
1.1 1.1-
1.2 1.2-
1.3 1.3-
1.4 1.4-
1.5 1.5-
1.6 1.6-
1.7 1.7-
1.8 1.8-
1.9 1.9-
2.0 > 2.0
Percentage of Times Value at Risk Was Exceeded
■ Traditional Value at Risk ■ Value at Risk Using Value Theory
Source: Barra Extreme Risk (BXR) model
22
17
12
7
Number of Portfolios (total =74)
www.journalofindexes.com July/August 2009
13

Figure 5
Historical Real Returns For U.S. Stocks And Bonds (1926-2008)
1-Year 5-Year 10-Year 20-Year 30-Year
Holding Period
■ Mean stocks ■ Mean bonds ■ Stocks, 5% Tail ■ Bonds, 5% Tail
10%
5
0
–5
–10
–15
–20
–25
–30
Sources: MSCI Barra, U.S. Census Bureau, Federal Reserve and Global Financial Data
determining the acceptable level of downside risk while at
the same time maximizing long-term real returns, subject to
protection against inflation or deflation.
The investment horizon is an important element of this
problem because it is related to the appetite for downside risk.
While the returns of an asset may be positive in the long run, in
the short run the possibility of losses cannot be ruled out. Longhorizon
investors have greater capacity to withstand short-term
losses because they see beyond these short-run fluctuations
to the long-term trend that will in time reassert itself. Figure 5
illustrates the various dimensions of the problem.
This chart compares the historical returns of U.S. stocks
and bonds, net of inflation (real returns), over different
holding periods or investment horizons from one year to
30 years. The dotted lines represent the average returns
for stock and bonds for all periods and the solid ones show
the bad outcomes (divider between the best 95 percent and
the worst 5 percent). When you look at the simple average,
stocks offered systematically higher real returns, even after
the two major down markets of 2002 and 2008. These results
for the U.S. markets are largely consistent with findings in
other markets around the world over similarly long periods,
even if the U.S. equity market has one of the highest returns,
as highlighted by Dimson, Marsh and Staunton [2009].
However, when looking at downside risk, as measured by
the worst 5 percent of returns, the picture is completely different.
Equities were very volatile over a one-year horizon, while
bonds were less so. Only cash provided more protection in
Return
extreme events. More generally, the downside risk was higher
in the case of stocks for relatively short holding periods of up
to five years. For longer investment horizons, the drawdown
risk was not as significant, and in fact for horizons of more
than 10 years, stocks even enjoyed a slight advantage.
Why is this particularly important for individual investors?
Most retirement solutions offered to individuals today are
trying to find optimal solutions for average investors, which
is different than solving for the constraint of an individual
retiring at a unique point in time. This is not surprising given
that most research has been conducted in the context of
vehicles such as defined benefit plans that pool assets and
liabilities. Unfortunately, an individual in a defined contribution
world does not benefit from pooling liabilities with
others. An individual planning for retirement needs to have
sufficient funds for his or her maximum life expectancy, not
the average one. On the contrary, an insurance company
providing an annuity service would be pooling the liabilities,
allowing it to focus on the average life expectancy, since
individual differences are canceled out at the aggregate
level. Similarly, while the average investor will not retire in a
catastrophic year since such years are not regular events, the
prudent individual investor cannot rule out the possibility
that he or she will retire in a year like 2008.
Segregation of assets and liabilities forces individual
investors to put more emphasis on tail risk than pension
funds with very long horizons. The optimal solution for an
individual is therefore found in the context of the worst case
scenarios, not the average ones. The logical consequence
of this statement should lead to a dramatic change in the
asset allocation of individual retirement products, such as
target-date funds, toward less risky assets as retirement
dates approach.
Toward Risk-Based Asset Allocations
Another reason why diversified portfolios did not offer as
much downside protection as anticipated during the events
of 2008 is that diversification strategies were often misapplied.
In particular, it has become clear that the categorization
of asset classes in a portfolio influences the approaches
chosen for diversification. Many pension plans are still using
a categorization of asset classes that groups assets into
three buckets: equities, fixed income and alternatives. The
category of alternatives includes private equity, private real
Figure 6
Risk-Based Asset Allocation
Category
Role
Equities
To provide the highest
long-term real returns
Real Assets Liability Hedging Absolute Returns Strategies
To provide protection
against inflation
To match duration of assets
with liabilities
To enhance returns with
uncorrelated risk premia
Includes
Developed markets
Emerging markets
Small-cap equities
Private equity
Equity hedge funds
Real estate
Timberland
Farmland
Low-risk sovereign bonds
Inflation-protected bonds
Value/growth
Momentum
Credit spread
High-yield spread
Merger arbitrage
Convertible arbitrage
Currency strategies
14
July/August 2009

estate, hedge funds, commodities and other real assets.
This segmentation reflects more the structure of asset
management practices than the role that the assets are
supposed to play in the portfolio, and that has led to some
undesirable effects. Firstly, the fixed-income category has
evolved to include a mix of low-risk government bonds with
higher-yielding assets like corporate high-yield bonds and
emerging markets bonds. The rationale for including these
higher-risk fixed-income instruments in the fixed-income
segment was that these assets were providing higher returns
and diversification to the rest of the bond portfolio. That is
true, but only if the bond portfolio is viewed in isolation.
However, as we have seen in the first section, high-risk assets
tend to be highly correlated in times of crisis. At the portfolio
level, those risky fixed-income assets are in fact reducing the
downside diversification that you expect from your allocation
to bonds. Peters [2008] offers a particularly clear and
elegant explanation of this phenomenon.
Secondly, the alternative asset class has often been viewed as
in a world of its own, where its risk-return profile has no relation
with the two other segments. Obviously, that is not correct. One
of the characteristics of these alternative asset classes is illiquidity.
Illiquidity can create the illusion that assets are uncorrelated
if naively compared with publicly listed equivalents. In reality,
there are many fundamental reasons to link alternatives to
equities and bonds. Many studies have shown that hedge funds’
strategies have a high component of traditional beta. Private
equity returns should closely follow public equity returns.
Privately held and managed real estate assets are subject to the
same real estate cycles as public real estate assets.
This general misperception of asset characteristics and
correlation demonstrates how investors, in general, have lost
sight of the fundamental aim of portfolio construction. In
response, several leading asset owners are moving away from
the traditional asset class categorization to a system that more
explicitly accounts for the role of each asset in the portfolio.
We will refer to this approach as risk-based asset allocation.
Under this approach, as illustrated in Figure 6, a riskbased
asset allocation could be structured along four broad
segments: equities, real assets, liability hedging bonds and
absolute returns strategies. For investors adopting this
approach, the equity segment opportunity set could be
represented by global public equities covering the broadest
investable universe, such as the one captured by the
MSCI All Country World Investable Market Index (ACWI IMI).
Allocations to private equity and long/short equity hedge
funds could complement this core equity allocation, providing
a more diversified return stream along with a strong
alpha component. The primary purpose of the equity allocation
is to provide the highest long-term real returns possible,
matching long-term economic growth.
This core equity allocation could be complemented by
real assets. The real assets category could cover real estate,
timber and farmland, as well as commodities. Infrastructure
investments could also be put in this category, although
some may argue it belongs to the equity segment. Real assets
would principally be included to provide additional and more
effective protection against inflation risk. The third component
of this asset allocation framework is liability-hedging.
Given the high level of downside risk of risky assets, the closer
a retirement plan is to the payout phase, the better the
liabilities need to be matched with assets of similar nature
and duration. This constraint calls for a mix of low-risk government
bonds, including some allocation to TIPS.
A framework that included only these assets would still
leave a high number of sources of risky returns unexploited.
These sources could be found in the various risk premia associated
with the fundamental factors driving traditional asset
classes, such as the small-cap or value premium in equities
or the credit premium for corporate bonds. For example,
an investor holding small-caps receives a market return for
investing in equities, plus a risk premium to compensate for
the risks of small-cap securities.
Similarly, high-yield bonds yield a return or beta that equals
the corresponding yield on a government bond of a comparable
maturity, plus a risk premium or spread for assuming the
higher risk of holding such a bond. Strategies that aim to capture
a specific risk premium through the execution of systematic
trading rules also qualify for the risk premium approach.
For example, arbitrage strategies such as merger arbitrage or
convertible arbitrage qualify under that scheme.
These additional sources of return could be captured in
the absolute returns strategies segment of this strategic
allocation framework. Briand, Nielsen and Stefek [2009]
analyze the risk return characteristics of these risk premia
and show that it is possible to create portfolios of risk
premia that offer similar returns to a traditional portfolio
composed of 60 percent equities and 40 percent bonds
with significantly lower volatility. The returns of an equalweighted
one are displayed in Figure 7.
III. Developing An Integrated View Of Risk
Finally, the recent crisis in financial markets has raised
awareness of the importance of some types of risk that
often receive little attention under normal market conditions.
These risks include counterparty risk, operational risk,
liquidity risk and concentration risk.
Counterparty risk captures the potential loss from derivative
contracts (including swaps, CDS, etc.) should a counterparty
default. The bankruptcy of Lehman Brothers left many
Figure 7
Performance Of Equal-Weighted Risk
Premia Portfolio Vs. 60/40 Portfolio
5/95 5/97 5/99 5/01 5/03 5/05 5/07
■ 60/40 Index ■ Risk Premia Index
Sources: MSCI Barra, Merrill Lynch
280
260
240
220
200
180
160
140
120
100
www.journalofindexes.com July/August 2009
15

Figure 8
Subset Of A Multi-Asset Class Risk Correlation Matrix
Equity
Fixed Income Currencies Commodities Hedge Funds
World
Momentum
Size
Value
Volatility
JP Shift
US Shift
EU Shift
AUD
JPY
CHF
GBP
EUR
Agriculture
Energy
Industrial Metals
Livestock
Precious Metals
Nonlinearity
Global Macro
Implied Volatility
Managed Futures
Equity
Fixed Income
Currencies
Commodities
Hedge Funds
World
Momentum
Size
Value
Volatility
JP Shift
US Shift
EU Shift
AUD
JPY
CHF
GBP
EUR
Agriculture
Energy
Industrial Metals
Livestock
Precious Metals
Nonlinearity
Global Macro
Implied Volatility
Managed Futures
Source: Barra Integrated Model
pension plans and asset managers scrambling to measure
their counterparty exposure to Lehman, which is not a good
sign of preparedness.
Operational risk refers to the risks attached to people,
processes, technology or external events such as fraud.
Brown, Goetzmann, Liang and Schwarz [2009] provide a
detailed description of how to build a scoring model to
detect operational risk in hedge funds.
Concentration risk is the risk arising from all investments
in securities of a particular issuer, sector, country or
other common factor. Concentration risk emerged in this
recent crisis when securitized subprime assets were found
to be present in many parts of investors’ portfolios without
proper management of the aggregate exposure. Many cash
management products, money market funds and other commonly
used investment vehicles were found to be exposed
to subprime assets.
The recent credit crisis has also reminded us of the importance
of liquidity risk. Market liquidity may fall very quickly
as risk aversion suddenly rises in a crisis situation, effectively
shutting down certain segments of the market and increasing
the cost of selling in most others. For investors, this raises
the danger of being unable to meet cash-flow obligations. In
2008, the endowments that were heavily invested in illiquid
asset classes, such as private equity and real estate, found
themselves with a sharp liquidity mismatch and had to liquidate
part of their holdings at distressed values.
Given the variety of problems, the dynamic nature of
portfolios and the links between assets and asset classes, it
is hard to imagine efficiently managing risk at the portfolio
or enterprise level without a proper framework to measure,
monitor and aggregate risk. Multi-asset class risk systems
can help address this problem.
Fundamental Factors Driving Portfolio Risk And Return
While VaR and volatility act as barometers to provide an
advance warning signal against heightened volatility, these
measures are not useful in understanding the sources of risk.
Looking at the fundamental factors driving each asset reveals
the sources of rising risk. The individual impact of such factors
can be derived using multiple-factor models, which not
only measure and forecast risk for a portfolio, but also break
down that risk according to the contribution from the various
factors used in the model.
In the Barra risk models, for example, each stock receives
an exposure to various factors such as country, sector or
style (value, growth, leverage, liquidity, size, nonlinear size
and momentum) that drive risk. These country and industry
factors help to determine how much of a portfolio’s risk is
specific to a particular country or industry after netting out
other effects. For instance, a portfolio of Korean stocks is
clearly subject to the country risk of Korea, but also reflects
16
July/August 2009

the risk of the technology sector that has a disproportionately
high weight in that market. Using country and industry
factors helps to disentangle these effects and identify
“pure” exposures of a portfolio to country and industry risk.
Similarly, a portfolio of bonds has different exposures to different
changes in the term structure, whether it is a shift of
the whole curve, a steepening or flattening of the curve or a
change in its convexity.
At the portfolio level, these fundamental factors will
be linked across different asset classes. For example, the
value and size factors in equities will be linked to yield
curve factors in fixed income, as well as other factors from
commodities and other asset groups. Figure 8 illustrates
how factors can be integrated into a multi-asset class correlation
matrix.
With an integrated factor model, factors from the country
level are in fact combined together to arrive at the global factors
that apply to all markets. This greater granularity allows
for different factors in different markets, thereby facilitating
a finer analysis of exposures of various assets worldwide. By
combining all factors from different asset groups, a portfolio
manager can more effectively analyze portfolio exposure to
multiple levels across asset classes.
In the recent crisis, many investors did not have sufficiently
strong modeling capabilities to comprehensively
cover the many derivatives in their portfolio, thus causing
them to miscalculate the true risk of these instruments.
It is now clear that there was an overreliance on credit
ratings, which in some cases were based upon wrong or
flawed models. Given the importance of models in risk
measurement, modeling risk cannot be overlooked. The
integrity of the models—and that of the modelers—should
be assessed. Complex and untested instruments should be
subject to additional prudent constraints.
Conclusion
This review has revealed the complexity of dealing with
the many facets of portfolio risk. Hopefully, it has also introduced
a high-level road map that may help investors find
better ways to handle extreme market events.
The 2008 crisis has been an acid test for risk management.
It has shone a bright light on institutions that put risk management
front and center, and cast in bold relief those that
may have neglected the tools and key concepts. While risk
management did not fully prevent downside in portfolios, it
is clear now that the organizations that invested in the intelligent
dissection of risks and acted on their findings fared
significantly better than those that did not.
At the end of the day, risk management is a state of mind,
not a technique. To be successful at managing risk, one
needs a desire to be prudent, the means to understand risk
and the discipline to make difficult decisions.
References
Bhansali, Vineer [2008]. “Tail Risk Management,” Journal of Portfolio Management (Summer 2008), pp 68–75.
Briand, Remy, Frank Nielsen and Dan Stefek [2009]. “Portfolio of Risk Premia: A New Approach to Diversification,” MSCI Barra Research Insights (January).
Brown, Stephen, William Goetzmann, Bing Liang and Christopher Schwarz [2009]. “Estimating Operational Risk for Hedge Funds,” Financial Analysts Journal (January/February),
Vol. 65, No. 1, pp 43–53.
Dimson, Elroy, Paul Marsh and Mike Staunton [2009]. “Looking to the Long Term,” Credit Suisse Investment Returns Yearbook 2009, pp 11–18.
Goldberg, Lisa, Michael Hayes, Jose Menchero and Indrajit Mitra [2009]. “Extreme Risk Management,” MSCI Barra Research Insights (February).
Goldberg, Lisa, Guy Miller and Jared Weinstein [2008]. “Beyond Value at Risk: Forecasting Portfolio Loss at Multiple Horizons,” Journal of Investment Management, Vol. 6, No. 2, pp 73–98.
Peters, Ed [2008]. “Does Your Portfolio Have Bad Breadth?” First Quadrant Perspective, Vol. 5, No. 4.
Rogoff, Kenneth and Carmen Reinhart [2008]. “Is the 2007 U.S. Sub-Prime Financial Crisis So Different? An International Historical Comparison,” American Economic Review,
Vol. 98, pp 339–344.
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www.journalofindexes.com July/August 2009
17

Benchmarking Policy Portfolios
How best to benchmark asset allocation decisions
By David Krein
18
July/August 2009

Investment performance cannot be measured without a
benchmark. A good benchmark should be unambiguous,
investable, measurable, appropriate, reflective of current
investment opinions and specified in advance. 1 Certain
indexes serve as good benchmarks because they are tools
designed to measure the performance of a particular market
over time without any unnecessary or unreasonable biases.
This article examines benchmarking issues in both single and
multi-asset class portfolios.
One Small Step …
Within a policy portfolio, single asset class benchmarking
is accomplished using widely accepted methods and procedures.
A benchmark, though, is not relevant on its own; it
must coexist with an investment in an asset class. In practice,
such an investment will typically be assigned to one or more
asset managers who will, in turn, each be given a mandatespecific
benchmark to gauge their performance. The mandates
themselves are often narrower than the asset class,
such as large-cap growth or small-cap value in the equity
space. Any mandate narrower than the asset class should
identify the appropriate subasset class benchmark along with
corresponding weight, specified in advance. 2 Otherwise, the
risk of benchmark misfit is significant.
Benchmark misfit is an investment decision that leads to
uncompensated risk. Prudent investors do not take uncompensated
risks because they do not receive additional return
for doing so. Therefore, benchmark misfit needs to be properly
measured and monitored.
Benchmark misfit is calculated as the difference between
the return of the asset class benchmark and the weighted average
return of all benchmark mandates assigned to individual
asset managers. In other words, misfit exists when the sum of
Figure 1
the assigned parts does not equal the desired whole.
Benchmark misfit can be decomposed into two categories:
(1) gaps and overlaps; and (2) allocation misfit.
Misfit #1: Gaps And Overlaps
Gaps and overlaps occur when the subasset class benchmarks
are mixed and matched among different index providers.
For example, many in the investment community use the
S&P 500 Index and the Russell 2000 Index as the standard
benchmarks for large-cap and small-cap domestic equities,
respectively. Figure 1 illustrates an example of the gaps in market
coverage when the large-cap and small-cap benchmarks
are set to the S&P 500 Index and Russell 2000 Index. The asset
class benchmark has been set to the Dow Jones U.S. Total
Stock Market Index, which covers the entire opportunity set of
all U.S. equity securities with readily available prices.
As of Jan. 1, 2009, the gap in market coverage of the S&P
500–Russell 2000 combination results in 2,165 missing constituents,
which is nearly half of all available constituents.
This equity gap leaves over $1 trillion, or 11 percent, of
the U.S. stock market unaccounted for. The median market
capitalization of the missing 2,165 stocks is $28 million, and
the median market capitalization of the top quartile of those
stocks is $1.51 billion, and includes such names as Genentech
($38.44 billion), Visa ($22.53 billion) and Accenture ($18.03
billion). No reasonable investor should use a benchmark that
excludes the top 11 percent of the U.S. equity market, the
bottom 11 percent or any other 11 percent.
Misfit #2: Allocation Misfit
Allocation misfit exists when asset allocations deviate from
the actual market coverage of the asset class benchmark. Figure
2 illustrates an example of allocation misfit. A hypothetical
Gaps In Market Coverage
Benchmark Constituents Market Cap ($ Billion) Market Coverage
Dow Jones U.S. Total Stock Market Index 4,599 9,662.04 100.00%
S&P 500 Index 500 7,851.81 81.26%
Russell 2000 Index 1,934 745.35 7.71%
S&P 500 + Russell 2000 Subtotal 2,434 8,597.16 88.98%
Gap in Market Coverage 2,165 1,064.88 11.02%
Source: Dow Jones Indexes, iShares fund fact sheets
Figure 2
Allocation Misfit
Benchmark
Subasset Class
Allocation
Actual Benchmark
Coverage
Quarter Ending
March 31, 2009
Dow Jones U.S. Large-Cap Total Stock Market Index 85.00% 88.25% -10.32%
Dow Jones U.S. Small-Cap Total Stock Market Index 15.00% 10.68% -12.42%
Dow Jones U.S. Micro-Cap Total Stock Market Index 0.00% 1.06% -10.06%
Weighted Average Benchmark Performance -10.56% -10.63%
Benchmark Misfit Impact -0.07%
Source: Dow Jones Indexes
www.journalofindexes.com July/August 2009
19

investor has decided to benchmark 85 percent of their portfolio
to the large-cap subasset class, 15 percent to small-caps and
no exposure to micro-caps. The actual market coverage of the
policy benchmark is listed as of Jan. 1, 2009. The result of these
decisions (i.e., underweighting large-cap, overweighting smallcap
and underweighting micro-caps) is approximately 9 basis
points in benchmark misfit. Those 9 basis points represent a
performance mismatch that can be directly attributed to the
allocation decisions exclusive of manager performance.
Compounding The Problem
Figure 3 illustrates an example of the compounding effect of
the allocation misfit along with gaps in market coverage. Using
the benchmarks from Figure 1 and the subasset allocation decisions
from Figure 2, the result of these decisions (i.e., ignoring
~500 mid-caps and 1,665 micro-caps) is, after one year, approximately
104 basis points in benchmark misfit. Stated differently,
the decision to deviate from the actual market coverage of the
asset class benchmark has, in this case, “cost” the investor 104
basis points. Note that the decision is on the part of the investor,
who has responsibility for assigning the individual benchmarks
and weights. The misfit excludes any “alpha” which, whether
positive or negative, is generated by the aggregate decisions
made by the managers. This distinction is critical.
Achieving A Zero Misfit Portfolio
Figure 4 illustrates a comprehensive asset class benchmark.
The subasset class benchmarks and weights are specified
in advance and properly aligned with it.
Under this scenario, investment performance can be
directly attributed to each mandate decision without any
Figure 3
Source: Dow Jones Indexes, iShares fund fact sheets
Figure 4
Source: Dow Jones Indexes
Benchmark
Benchmark
Compounded Misfit
Subasset Class
Allocation
Comprehensive Asset-Class Benchmark
Subasset Class
Allocation
benchmark misfit. Any mandate that deviates from this baseline
opportunity set will then not allow an investor to properly
measure the impact of their own decisions on their own
portfolio for a given asset class. The benchmark misfit analysis
must precede—and remain separate from—the active
versus passive decision as well as the decisions on the part of
their selected managers in attempting to generate alpha.
The Cost Of Inappropriate Benchmarking
Benchmark misfit is an investment decision that leads to
uncompensated risk. Prudent investors do not take uncompensated
risks because they do not receive additional return
for doing so. Therefore, benchmark misfit needs to be properly
measured and monitored.
Using an inappropriate benchmark can result in misleading
information being provided to all parties involved in
the investment process. If a manager is outperforming the
assigned benchmark, which is not an appropriate benchmark,
an investor may believe the manager is successful, when in
fact they may be under-performing by a material amount. In
such cases, an investor may be paying for less-than-expected
performance. The investor has spent more and received less
while being led to believe by the benchmark misfit that the
manager is doing well.
This can also occur in more-subjective benchmark assignments,
especially when managers are ranked relative to a
peer group. Unfortunately, peer group analysis falls short
of being a good benchmark because the average is neither
investable nor specified in advance. Further, the peers may
actually be a poor proxy for the asset class or subasset
class. Finally, the practical implementation of gathering
peer group data of sufficient
scale and relevance is quite
complicated and often incorporates
significant biases.
Quarter Ending
March 31, 2009
S&P 500 Index 85% -11.01%
Russell 2000 Index 15% -14.95%
Weighted Average Benchmark Performance -11.60%
Asset Class Benchmark Performance -10.56%
Benchmark Misfit Impact -1.04%
Year Ending
Dec. 31, 2008
Dow Jones U.S. Large-Cap Total Stock Market Index 88.25% -10.32%
Dow Jones U.S. Small-Cap Total Stock Market Index 10.68% -12.42%
Dow Jones U.S. Micro-Cap Total Stock Market Index 1.06% -10.06%
Weighted Average Benchmark Performance -10.56%
Asset Class Benchmark Performance -10.56%
Benchmark Misfit Impact 0.00%
From One To Many
In practice, investors hold
more than just equities or
any single asset class in their
portfolios. They more typically
assemble a portfolio with a
number of diversifying asset
classes (even if just two).
However, benchmarking such
multi-asset class portfolios is
not a straightforward exercise
since it is not simply an
extension of the single asset
class approach. Although
the decision to use a given
benchmark in both single
and multi-asset class portfolios
may be driven by similar
purposes and mechanics, the
process for establishing a
multi-asset class benchmark
is fundamentally different
20
July/August 2009

than that for a single asset class benchmark.
Many single asset classes have a known universe of securities
or investment options that constitute “the market”
and can be used to establish a nearly definitive single asset
class benchmark. Other single asset classes, such as commodities,
do not have such a defined market, so reasonable
proxies are established and marketed with one or two
emerging as the standard.
In either case, any single asset class benchmark is designed
to identify the aggregate performance of the class and allow
for standardized comparisons between and among market
participants and managers within that class. Advisers can
then measure the implementation skill, or “alpha,” behind a
specific investment assignment.
However, a multi-asset class benchmark is generally
designed to measure something else—the total alpha of all
such investment assignments plus the alpha, if any, of a policy
portfolio itself.
What is a policy portfolio in a multi-asset class context?
It is the mix of asset classes and weights derived from the
set of short- and long-term investment objectives and capital
market assumptions for an investor or investment plan. This
mix may change over time in certain ways to reflect changing
objectives (e.g., liabilities) or assumptions (an attempt
to capture excess returns from short-term asset class price
fluctuations in (market timing). 2
Establishing a policy portfolio is not an easy task. Numerous
challenges and complexities arise almost immediately.
• How are these decisions made in practice?
• How are specific asset classes included?
• How are the weights set?
• Where do the capital market assumptions come from?
• How do these decisions evolve over time?
For example, multi-asset class efficient frontiers are
based on, and highly sensitive to, their inputs and may
contain volatile or inaccurate data (private equity and
venture capital performance data come to mind), making
it difficult to gain agreement on what that efficient frontier
looks like. This is further complicated by the fact that
there is no standing consensus on the asset-class palette;
after all, the simple decision to invest strictly in domestic
equities (or any other set of asset classes) creates the
opportunity cost of not investing in some other market,
such as international equities, commodities, real estate or
bonds, to name just a few.
Establishing a policy portfolio is not a trivial exercise either.
Analysis shows that the asset allocation decision explains
about 90 percent of the variability of a fund’s returns over
time, and on average across funds, explains approximately 100
percent of the level of returns. 3 The constraints and decisions
that drive the policy portfolio, whatever they may be, clearly
have a big impact and must be evaluated appropriately.
Of course, an investor (and their adviser or consultant)
must construct a policy portfolio even in the absence of
specific guidance on these matters. After all, establishing
an investment policy, delineating responsibility and measuring
the performance contribution of those activities
form the core of the investment management process. 2
Yet benchmark identification is routinely dismissed as a
lesser element of the process; since there is no single definitive
approach, the ultimate decision is viewed as relatively
inconsequential.
This perception is far from accurate. The need for benchmarking
is heightened, not diminished, in a multi-asset class
environment given the much wider spectrum of required
decision making and investment opportunities.
The Case Of Target Date
In recent years, the individual retirement market has
witnessed an explosion of “target date” products. These are
multi-asset class products that dynamically and automatically
evolve an investor’s “policy portfolio”—their asset class mix
and weights—at each stage of an investor’s working life.
The benchmark process in this realm is very challenging.
How should such evolving policy portfolios be measured
against the finite horizons of individual investors
who have real-world and often conflicting needs that may
vary with age, existing wealth, living expenses and personal
circumstances?
Given existing tools and capabilities, there may be a reasonable
approach to benchmarking. In general, the proper
design of a portfolio involves at least four decisions 2 : (1) the
asset-class palette; (2) strategic asset allocations; (3) tactical
asset allocations (market timing); and (4) security selection.
The first two decisions form the core of the policy portfolio,
and should be the focus of benchmarking in a multi-asset
class environment.
Ultimately, though, there will be ongoing debate about
the objective nature and best practices of benchmarking
such multi-asset class portfolios. The different approaches
will each bring their own strengths and weakness, but the
growing competition can only help benefit the market drive
toward the optimal approach.
Jeffrey Fernandez, project manager, Analytics & Research,
Dow Jones Indexes, contributed to this article.
References
1 Bailey, Jeffery V., “Are Manager Universes Acceptable Performance Benchmarks?” Journal of Performance Management, Spring 1992.
2 Brinson, Gary P., L. Randolph Hood, and Gilbert L. Beebower, “Determinants of Portfolio Performance,” Financial Analysts Journal, July/August 1986.
http://www.cfapubs.org/doi/pdfplus/10.2469/faj.v51.n1.1869
3 Ibbotson, Roger G. and Paul D. Kaplan, “Does Asset Allocation Policy Explain 40, 90, or 100 Percent of Performance?” Financial Analysts Journal, January/February 2000.
http://corporate.morningstar.com/ib/documents/MethodologyDocuments/IBBAssociates/AssetAllocationExplain.pdf
www.journalofindexes.com July/August 2009
21

An Interview
With Susan Mangiero
By Journal of Indexes Editors
The challenges of late-2008
and early-2009 have many
institutional investors looking
to get a better handle on
risk management. Journal of
Indexes contributing editor
Heather Bell caught up with
Susan Mangiero, president of
Pension Governance, Inc., a
research and consulting firm
focused on risk and other
fiduciary concerns related to
benefit plans, to discuss the latest in the field. Dr. Mangiero is
the author of “Risk Mangement for Pensions, Endowments, and
Foundations” (John Wiley & Sons, 2005) and also writes a blog,
Pension Risk Matters (www.pensionriskmatters.com).
Journal of Indexes (JoI): What has the last year done to change
the way pension plans manage risk, and what lessons were
learned in the last year with regard to that?
Dr. Susan Mangiero (Mangiero): First of all, I hope things
have changed. I think it is fair to say that people are adopting
more of a risk focus because of what’s happened. But
whether that’s occurring across all plans and across all plan
sponsors, I’m not convinced.
What are some of the lessons learned? [To take one
example], people are looking at some of the problems
with alternative funds, where people couldn’t redeem out
even if they were contractually allowed to do so. That surprised
quite a few investors. A lot of fund managers—particularly
on the hedge fund side—said, “Well, even though
contractually you’re allowed to redeem, if we allow you
to redeem, it could cause a run on the funds, which could
exacerbate problems; then everybody loses. And so, we’re
just going to forbid you to take your money.”
We know there are some lawsuits, now, against some of the
asset managers that have done that. I don’t know how they’re
going to end up. Redemption rights cannot be ignored.
JoI: What about valuations? Because many of those assets
became very difficult to value when the market seized up last
September.
Mangiero: That is another major area of focus. A lot of the
alternative fund managers, hedge fund managers, private
equity managers, venture capital managers and others have
had to come up with mark-to-market or mark-to-model
numbers. We’ve had quite a few institutions ask questions
about how their asset managers are valuing things.
I think there’s still room for improvement. Some of
the feedback we’ve gotten is worrisome. When we talk
to institutions about what they ought to be doing with
respect to setting up a valuation process, the perception
is often that “it’s too much work, and we’re counting on
the fund-of-fund managers and/or the investment consultants
to handle that.” But we tell people all the time to
make sure that, in fact, the fund-of-fund managers and the
investment consultants are vetting those numbers.
We urge institutional investors to really look deep
and hard at service-provider contracts, to the extent they
can and to the extent that they’re made available for the
institutional investors, just to make sure that somebody is
kicking the tires on models, to make sure that somebody
is checking the integrity of the data, to make sure that
somebody is asking a hedge fund or private equity fund
manager if they’re using an independent third party to
value their hard-to-value positions. That’s a big element
of risk management. We are finding, more and more, that
institutional investors think a lot of that work is being
done somewhere along the service provider food chain.
We’re trying to disabuse them of that notion unless they
verify that for themselves.
I’m not 100 percent convinced it is happening, but
hopefully there is also more focus on monitoring of
collateral and counterparty risks. Most of these overthe-counter
derivative instruments involve collateral.
One of the issues that’s arisen is whether anyone is
monitoring the quality of the collateral. More broadly, is
there enough collateral? The collateral issue also arises
with respect to asset-backed securities. Many of these
instruments have underlying collateral that can quickly
change in value.
JoI: We’ve heard a lot of concern about leverage and a general
de-leveraging across the market. Has that had follow-through in
the institutional community?
22
July/August 2009

Mangiero: People are more sensitive about leverage in
lots of different forms: short-selling, margins, derivatives,
portfolio construction.
We hear a lot of pension investment decision makers
say, “Well, our investment policy statement”—assuming
they have one—“prohibits the use of leverage.” But then
you ask them a lot of questions about what instruments
they’re holding, and at the end of the day, usually they’re
holding some kind of security or have monies allocated to
funds that are using derivatives or securities that embed
derivatives. We tell institutional investors that the bottom
line is it’s going to be almost impossible to escape
exposure to leverage in some fashion. But, in the aftermath
of the credit crunch, we think at least that people
are looking at leverage.
Another issue that has come up, with respect to the
credit problem, has been that some investors had put
their money in what they thought were very liquid, relatively
clear-cut “low-risk” investments. And now they find
that some of those clear-cut “low-risk” funds have been
investing in short-term asset-backed securities or things
that were anything but low risk. There are some lawsuits
related to that, alleging that those asset managers and
some of the service providers, like auditors, did not really
vet the true economic risks associated with what was
inside the portfolios.
JoI: How did the liquidity crunch impact interest rates and
how did that impact pension plans as a result?
Mangiero: For a while, as the government kept trying to
depress interest rates overall by adding liquidity to the
market, reported pension liabilities increased. In the aftermath
of the Pension Protection Act of 2006, this was a big
deal for many companies that suddenly found themselves
in a position of being statutorily underfunded.
As a result, more than a few employers found themselves
having to accelerate cash payments to their pension plans
to restore funding normalcy. Now there are quite a few U.S.
corporations seeking relief from the Pension Protection
Act, saying, “Our stock has been hit so hard, and we don’t
want to spend billions of dollars to top off the pension plan
because of short-term issues, and we’re fine in the long
term.” Preceding the central banks’ intervention, crisis conditions
meant a rise in LIBOR. This resulted in higher costs
for pension plan swap floating-rate payors.
JoI: Does the surge in correlations among different asset
classes during times of market crisis have any impact on longterm
planning?
Mangiero: I think the answer would have to be yes. It just
depends, in part, on how pension plans respond. If the
immediate response is to sell positions as correlations
start to converge, the pension plan manager could incur
transaction costs and lock in losses. 401(k) plan participants
encountered the same problem.
Then there is the issue of strategic asset allocation. If
investors do change the short-run mix, how is that likely to
impact long-term performance?
I don’t think most investors had anticipated the flight
to quality that we experienced in the fall of 2008. For
example, if a pension plan had allocated money—and here
I’m talking more about a defined benefit plan—let’s say,
to a hedge fund that was investing a lot in equity, and
the pension plan also had X percent allocated to a longonly
strategy, and the equity market tanked, then all of a
sudden the defined benefit plan is going to see its total
portfolio drop in value from, not only the long equity allocation,
but also the hedge fund allocation. I think many
investment decision makers were just not prepared for
that. It was kind of like a doubling-up, or worse, of the
exposure to some things that were not doing well.
It’s a very tough time for people. The good news is—
and this is what we’ve been encouraging, urging pensions,
endowments and foundations to do—that people can use
this time as an opportunity to focus on risk management,
learn lessons, ask lots of tough questions, make changes
now and hopefully improve the process. There are some
folks who are doing a great job already. But again, the best
defense is a good offense—in terms of risk management,
just getting a very robust process in place. We are seeing
more pension plans starting to consider [using] or just
outright hiring risk managers. And I think that’s a wonderful
step in the right direction.
JoI: Can you talk about how you see benchmark risk, and
what the most critical issues are for pensions to consider in
that area?
Mangiero: That’s a great question. It’s also one that’s difficult
to answer. For example, consider liability-driven
investing, a hot topic in the pension community. I ask a lot
of pension plans the question, “How are you benchmarking
the LDI managers?” And the answer I usually get back is,
“We’re not sure how to do it yet.” Some people are using
a pension surplus-at-risk measure, as opposed to value at
risk. And some people are looking at a deviation around a
cash flow benchmark.
I think for those involved in indexing, there’s a great
opportunity to help the retirement-plan decision makers with
thinking through what the benchmarks should look like.
JoI: Final thoughts?
Mangiero: I think retirement-plan decision makers are
faced with many challenges, whether you’re talking about
defined contribution or defined benefits. There’s almost
no relief. There’s not a one-size-fits-all solution to which
everybody can gravitate and try to make up for losses,
try to protect themselves from losses down the road. The
best defense, we think, is just having a really good process
in place, so that you can identify risk drivers or risk factors,
maybe before things get out of hand.
www.journalofindexes.com
July/August 2009
23

How To Minimize Your
Cost Of Trading ETFs
The case for NAV-based trading
By Gary Gastineau
24
July/August 2009

One of the features that long-term investors like most
about exchange-traded funds is that each shareholder
usually pays only the cost of his or her own
fund share transactions and is protected by the ETF structure
from the cost of other investors’ purchases and sales of
fund shares. In spite of this feature, there is a great deal of
misinformation in circulation about the cost of trading ETFs
and how to trade them efficiently. This article tries to help
you evaluate the quality of ETF markets and keep your ETF
share trading costs low.
Trading ETFs Is Different Than Trading Stocks
A natural reaction to the title of this article is, “Why
bother to write about trading ETFs? They trade “just like a
stock.” In fact, the reason for writing this article is that ETFs
don’t trade like stocks. ETF markets behave differently than
stock markets, and ETF shares trade differently than stocks
in a number of ways.
Figure 1 lists some of the similarities and differences. To
illustrate one difference, ETF investors have learned that
ETF bids and offers usually change much more frequently
than stock bids and offers. These more frequent changes can
make ETF investors uneasy, as they suggest that individuals
do not have as much information as professional traders have
about what the ETF price should be or what the ETF bid or
offer will do next.
Even if your only interest is in day-trading one of the
several dozen major benchmark index ETFs that trade more
than 10 million shares on an average day, the market in ETFs
is different than the market in common stocks in ways that
can affect your trading results. If you trade ETFs that are not
based on major benchmark indexes or that trade fewer than
10 million shares a day, ignoring the significant differences
between ETF and stock trading can be very costly.
This article will help investors find level stretches of the
ETF playing field where professional and amateur traders
have equal footing—and avoid the slippery slopes. Once you
understand how the ETF market works, and how trading
ETFs differs from trading stocks, you will be able to trade ETF
shares confidently and efficiently. In fact, with the introduction
of net-asset-value-based trading in ETFs, trading ETFs
can be much simpler and less stressful than trading stocks. 1
While the focus of these comments is on U.S.-listed ETFs
holding U.S. common stocks, many of the observations apply
to European and Asian ETF markets and to U.S. ETFs with
other portfolio holdings.
The entry in Figure 1 that generates the most questions
from investors is the trading hours for ETFs. Trading after
4:00 p.m. presents both opportunities and hazards to ETF
traders. Trading in ETFs is active until well after 4:00 p.m.,
partly because the major benchmark index ETFs (such as the
S&P 500 SPDRs, the iShares Russell 2000 Index Fund and
ETFs tracking a number of other popular benchmark indexes)
are part of major arbitrage complexes. These arbitrage complexes
typically consist of an index that serves as a template
for index mutual funds and other indexed portfolios and for
a variety of index derivative financial instruments, including
index futures contracts, index options and, of course, index
ETFs. The arbitrage complexes also include derivatives on
these derivatives, such as options on futures, options on
ETFs and securities futures products (single “stock” futures)
on ETFs. There are also exchange-traded and over-thecounter
structured products and risk management contracts
linked to many of these indexes.
Until the NYSE acquired the Amex in 2008, ETFs—like
most of the other tradable components of the index arbitrage
complexes—traded in a regular session that lasted until 4:15
p.m. to provide a structured ETF market that was fully con-
Figure 1
Some Key Differences Between Stock Trading And ETF Trading
Stocks
ETFs
Trading Hours
9:30 am − 4:00 p.m.
9:30 am − 4:00 p.m.
With substantial post-close trading until 4:15 p.m.
Net Asset Value Determined − 4:00 p.m.
Closing Price Determined 4:00 p.m. 4:00 p.m.
Market-on-Close (MOC) Rules to
Offset Order Imbalances
Yes
Yes
Volume Concentrated
First Half Hour, Last Hour
First Half Hour, Last Hour
(More Extreme)
Share Value Determined Stock Market Stock Market
Share Price Determined Stock Market ETF Market
Share Value Published Every Stock Trade Every 15 Seconds
Share Price Published Every Stock Trade Every ETF Trade
Share Bids/Offers Published Every Time Quote Changes Every Time Quote Changes
Frequency of Quote Changes
Loosely Linked to Trading Activity
Derivative Auto Quoting Loosely Linked to
Trading Activity, Volatility and Number of Issues
in the ETF Portfolio
www.journalofindexes.com July/August 2009 25

Figure 2
Offer
NAV Proxy
Bid
Using The Every-15-Second NAV Proxy
To Determine Bids and Offers
A B C D E
15sec 15sec 15sec 15sec 15sec
Time
temporaneous with the futures markets. Since the change to
a formal close at 4:00 p.m., ETF volume between 4:00 p.m.
and 4:15 p.m. has not changed materially. On May 15, 2009,
the day before this article was prepared for submission, ETFs
were four of the five most active issues in after-hours trading.
One of that day’s most active after-hours ETFs is based
on an index that does not have an active futures contract.
ETF Intraday Net Asset Value Proxies
When trading began in the first ETF introduced in the
United States (the S&P 500 SPDR launched in 1993), the
Securities & Exchange Commission (SEC) required that the
sponsors of the SPDR arrange for dissemination of an intraday
share value proxy for the SPDR at 15-second intervals. These
proxies are usually called indicative optimized portfolio values
(IOPVs). The requirement for publishing these values was
extended to every domestic equity ETF launched since 1993
and, with modifications, to ETFs holding foreign equities,
fixed-income instruments and other financial instruments.
In spite of improvements in trading data calculation
technology and the introduction of ETF portfolios that hold
infrequently traded securities, calculations of these intraday
value proxies are still based on the most recent trade of each
portfolio component. Matt Hougan discusses the inadequacy
of last-sale value proxies in an article scheduled to appear
in the July 2009 issue of ETFR. These intraday value proxy
calculations are made and/or disseminated by the National
Securities Clearing Corporation (NSCC) and other service
providers to “support” intraday trading of ETFs.
Professional ETF traders and market makers do not use
the “official” every-15-second proxy value calculations to help
them determine their ETF bids and offers. Professionals develop
their own valuations or subscribe to real-time ETF value calculations
based on contemporary bids and offers rather than
last sales. The fact that they do not use the free IOPV does not
mean that these professionals lack faith in the NSCC’s ability
to calculate correct values. The simple facts are that the last
sale is not a reliable indicator of contemporary values in most
Price
market situations, and the 15-second interval between valuations
is too long for the values to be useful to a trader.
Because an ETF is a derivative security, its current value
changes every time the value of any component of the ETF
portfolio changes. The ETF value proxies used by professional
traders are calculated from the midpoint of the bid and
offer for each position in the ETF portfolio. Sometimes the
value calculations made by and for professionals use the size
of bids and offers and the pattern of “changes” to forecast
short-term trends. Figure 2 illustrates how a naive investor
might attempt to use the “free” intraday proxy information
to develop a bid or offer for ETF shares.
In Figure 2, the latest IOPV is represented by a dot at
the beginning of each of the five 15-second intervals illustrated.
Investors might place limit orders at prices close to
the most recent per-share value proxy. Columns A through
E illustrate how bids and offers entered at equal distances,
respectively, below and above a sequence of these every-15-
second net asset value (NAV) proxy calculations might become
transactions—but not always the transaction that the investor
entering the order hoped to achieve. An offer to sell the ETF’s
shares slightly above the proxy value posted at the beginning
of time interval A would probably be lifted as the fund portfolio
value rose during intervals A and B. That offer was below
the changing per-share proxy value by the time that value was
updated at the beginning of interval C—less than 30 seconds
after the order was entered. The offer was also below the
likely bid in the market at that time. Of course, some of the
last-sale prices used to calculate each 15-second proxy might
be more than a few minutes old at the time the calculation
was made. Using the free 15-second values can be costly, but
even if investors had access to proxy values based on every-
15-second midpoints of bids and offers and could enter orders
as soon as the value was published, a lot can change before or
shortly after the next proxy value is published.
In my experience, many investors are aware of the existence
of the every-15-second last-sale NAV proxy value, but
few know how to find it for a particular ETF and even fewer
think about how, if at all, to use it. It is probably a good thing
that these proxies are not widely used. Any attempt to use
them to manage an order is more likely to lead to disappointment
than to a good execution. The information on share values,
transaction prices, bids and offers summarized in Figure
1 indicates that ETF bid and offer updates are published
more frequently than every 15 seconds (every time portfolio
component bids or offers change materially), making the ETF
share bids and offers a much better indication of the current
market for an actively traded ETF than the every-15-second
last-sale proxy calculation could possibly be.
The posted ETF bids and offers also have the advantage of
being something you can trade with. You cannot trade with
the every-15-second NAV proxy because it does not represent
a bid or offer for the ETF shares. An investor can be confident
that, even if his market data vendor is a bit slower and updates
quotes less promptly than the best data vendors, he will not
be seriously disadvantaged relative to other retail market participants.
Bids and offers for the most actively traded ETFs tend
both to be tighter and to change more frequently than stock
26
July/August 2009

quotes during active trading periods. In active trading periods,
spreads are frequently as narrow as a penny per share between
the bid and offer for some of the most actively traded ETFs.
These periods of active trading are the best time to trade ETFs.
However, if trading is not active, the quotes in the market tend
to reflect a wide spread between the bid and the offer.
The Brave New World
Of High-Frequency Electronic Trading
Few individual investors have the kind of information professional
traders use or the capability to change their bids and
offers as fast as the quotes on an ETF share change in common
market situations. Professional traders and market makers not
only calculate intraday bid/offer values for ETF shares continuously
throughout the trading day, they use automated quote
management systems that can change their ETF bids and offers
in a millisecond or so (a millisecond is 0.001 of a second) every
time the bid or offer changes for any security in the ETF’s portfolio.
The fact that regular-session ETF trading volume usually
exceeds 2 billion shares per day is partly due to the speed of
order entry and execution to capture small changes in value.
The quest for speed of execution has led the Nasdaq market to
boast “peak [round-trip] trading speeds of 250 microseconds”
(a microsecond is 0.001 of a millisecond). In this high-speed
environment, time lags associated with information traveling
even a few hundred miles at approximately the speed of light
confer a premium value on computer centers located within a
few yards of an exchange order-matching system.
If you have access to an institutional equity trading desk,
ask to see an active montage monitor of the best bids and
offers from all the markets trading SPY or QQQQ during regular
trading hours. I have not included a static example of the
montage monitor because a snapshot cannot convey a sense
of the frequent and rapid changes in bid and offer prices and
sizes. I have not seen comparable displays from any other
activity except, perhaps, the last digit in an electronic sign
showing an update of the estimated U.S. population. The
census sign has only one or two high-frequency flashing digits.
The montage monitor looks like a time-lapse video of a
beehive. An individual investor can benefit from high-speed
trading developments to the extent that they compress bid/
offer spreads in actively traded ETFs. Investors using actively
traded benchmark index ETFs trade in a very efficient market
thanks to high-speed trading.
Auto-quote systems eliminate most of the need for handson
attention by a market maker’s or professional trader’s
staff. Consequently, even though the bid/offer spread is usually
wider in a less actively traded ETF, the quote updates in
the less active shares will still be more frequent than quote
updates in a similarly active common stock. The frequency
of quote changes is linked to the number and price volatility
of positions in the ETF portfolio as well as to trading activity
in the ETF shares. While the quality of the quotation services
available to investors varies, it is usually possible to get a current
bid and offer as well as the quantity bid for or offered for
an ETF of interest. Because the best bid and offer are more
useful than the every-15-second NAV proxy (that you cannot
trade with anyway), the size of the best bid and offer and the
spread between them are the best indicators of how many
shares you can trade easily and at what price you might expect
to complete a transaction. This bid and offer information is
key to effective trading in the conventional ETF market.
Figure 3 shows ETF share volume by 15-minute intervals
for a recent week of trading. The relatively heavy trading
volume in ETFs in the first half-hour of trading both
attracts and reflects retail ETF orders. A similar volume
pattern in stock trading is usually attributed to institutional
trading. 2 There is substantial evidence that stock prices
are, on average, very slightly lower in the first half-hour
of trading and very slightly higher in the last half-hour
of trading than over the balance of the trading day. This
daily price pattern is statistically significant and is usually
attributed to an artifact of institutional equity trading
practices. It may not be economically significant for ETF
traders, however. 3 Traders who are concerned about their
trading costs, especially the bid/ask spread and the ability
to trade in size with minimal market impact, will usually
wait until the markets in all the ETF portfolio components
are open and updated quotes are available. Spreads on ETF
shares tend to be relatively wide right after the opening,
when the sizes of market-maker bids and offers are small.
Trading is usually less costly later in the day. As illustrated
in Figure 3, ETF trading volume is highest in the first hour
or so of trading and in the last hour of trading.
A number of organizations and Web sites publish information
on “average” bid/offer spreads for specific ETFs. Most
of this information is based on data provided by NYSE Arca. 4
Depending on what time of day you check the bid and offer
prices and sizes for a specific ETF, you will probably find that the
published “average” spread is narrower than the spread you see
in actual quotes. The spread you observe is likely to be wider
than the “average” spread because the published “average”
spreads are weighted by the size of the bids and offers available
at various times during the day. This weighting scheme means
that a heavier weight is assigned to the spread at times when
bid and offer sizes are larger. Larger bid and offer sizes usually
coincide with times when trading volume is highest and spreads
are tightest—times near the market close.
Trading in many ETFs is active between 4:00 p.m. and 4:15
p.m., but much of this trading is linked to futures markets,
and bids and offers are often erratic. This trading is apparently
not included in the data used to calculate the average
spread. The period between 3:00 p.m. and 4:00 p.m. is generally
the period when the cost of a conventional intraday
trade in an ETF is lowest. This period of high volume and
large bid and offer sizes largely determines the published
“average” spreads. This is when the average spread on the
S&P 500 SPDR (NYSE Arca: SPY) will be less than a penny—
temporarily “locked” markets in the most actively traded ETF
shares are common. Even if you are planning to trade shares
in one of the most actively traded benchmark index funds
that appear regularly on the most active list, the end-of-day
period is almost certainly the best time to enter your ETF
order, assuming that trading cost minimization is a significant
objective of your trading plan and you decide to use the
conventional ETF trading process.
www.journalofindexes.com July/August 2009 27

ETF Trading Volume Is Huge,
Growing And Highly Concentrated
The growth of trading volume in the most actively traded
ETFs has been nothing short of phenomenal, as Figure 4
shows. ETFs now account for more than 20 percent of U.S.
equity trading volume by shares, typically more than 2 billion
shares per day; weighted by dollar volume, the number is
significantly higher, and has been reported as high as 35 to
40 percent on certain days.
Most of the increase in ETF volume has been in the most
actively traded funds. Typically, half of the most active
“stocks” each day are ETFs. The most actively traded funds
are often not the most attractive investments, however.
In any event, you do not necessarily have to pay a wide
spread to trade a less active ETF. Beginning with some useful
“rules of thumb” for intraday ETF trading, let’s look at how
ETF investors can reduce their transaction costs, particularly
on less actively traded ETFs.
How To Trade ETFs Efficiently
If (1) you are trading one of the major benchmark index
ETFs that trades more than 10 million shares a day; (2) the
current price of the shares is consistent with your objectives;
and (3) the quote spread is close to the minimum of $0.01
per share, entering a market order is generally a safe choice.
However, you may want to compare the size of your order
with the quoted size on the other side of the market before
you push the button to execute a market order.
If the thought of entering a market order in a volatile
market environment is unsettling, you can enter a marketable
limit order; that is, an order to buy at the offer price or sell at
the bid price currently posted in the market. This order will
usually be executed in full as long as the quote has not moved
away from the limit on your order by the time your order
reaches the market. Given the rapid changes that are often
characteristic of ETF bids and offers and the heavy volume
characteristic of the last hour of trading, there is always a risk
that your limit order will not be a marketable order when it
reaches the market and, consequently, it will not be executed.
You should compare the opportunity cost of failing to execute
to the possibility of a worse price with a market order.
If an ETF trades less than a million shares per day, take a
close look at the bid/offer spread, at the size of the contemporary
bid and offer, and at recent changes in the bid and
offer; then consider a marketable limit order—or read on for
more analysis and more options.
Most commentary on the cost of trading securities suggests
that the appropriate way to measure the cost of the bid/offer
spread in a purchase or sale is to assume that your cost of
trading will include half of the spread on the purchase and half
of the spread on the sale. That is a reasonable rule of thumb
when you are trading common stocks in small size. However,
it is not safe to assume that an inactively traded ETF’s current
value is between the bid and offer in the intraday market.
Most investor orders to buy or sell shares of an ETF on a given
day will be on the same side of the market. If a fund has just
been introduced, has enjoyed favorable commentary in the
financial press or is being actively purchased by advisers for
their clients’ accounts, most investor orders will probably be
on the buy side for days or weeks at a time. In contrast, if a
particular market segment is out of favor or a fund has underperformed
its peers, the predominance of investor orders for a
fund will probably be on the sell side for long periods.
For very actively traded benchmark index ETFs (where the
spread during the last hour of trading will typically be a penny,
with large quantities available on both sides of the market),
the location of the midpoint of the bid and offer will nearly
always be close to the fair value of the ETF. Arbitrage forces
and heavy trading will ensure their closeness. In the case of
less actively traded ETFs where cross-market arbitrage forces
do not provide much pricing discipline, the midpoint of the
spread will reflect the supply/demand pressures of investor
purchases and sales of the ETF shares much more than the
prices in the underlying portfolio securities markets. If you are
an ETF investor trying to make the same trade as other investors,
you should expect to pay more than half of the posted
spread on most of your trades in less actively traded ETFs.
If an ETF trades less than 100,000 shares a day, investor
supply or demand may move the bid/ask range so that it does
not even encompass the contemporary share value. In other
words, the bid may be above a contemporary NAV calculation,
and the spread to the NAV for a purchaser of the shares may be
greater than the posted bid/offer spread indicates. Arbitrage
forces are undependable when the potential for aggregate
arbitrage profit is small due to lack of volume.
If you are interested in an ETF that trades fewer than
500,000 shares a day, don’t consider anything other than marketable
limit orders when you are trading in the intraday ETF
marketplace. If your order is larger than the number of shares
quoted at your limit, expect to spend some time working the
order—at best. If you are an adviser trading ETFs for a number
of accounts, your broker may give you access to an algorithmic
trading model that manages bids and offers relative to changes
in the bids and offers for the securities in an ETF’s portfolio,
much like a market maker would use. 5 Even better, your broker
may arrange a dialogue with a market maker in the ETF’s shares.
Working with a market maker on a large transaction is usually a
very good idea. The market maker can trade at lower risk if he is
filling an order rather than guessing what an anonymous bid or
offer might mean. The probability of repeat business with you
may also favorably affect the terms of a trade.
After we take a close look at risks and costs of market-onclose
(MOC) orders in ETFs, we will examine a new kind of
trading in which orders can be entered for execution at or
relative to the closing net asset value of the ETF. This new
trading method will be particularly useful to ETF investors
who want a good execution without spending a lot of time
on order management and who have had unfavorable experiences
with the intraday ETF market or with MOC orders for
ETFs. This new trading method is important because ETF
trading volume is increasingly concentrated in the most
actively traded ETFs, while the most attractive investments
are often in the less actively traded ETFs with wider bid/
ask spreads. A trading mechanism that narrows spreads and
reduces total trading costs on less actively traded ETFs can
change the ETF landscape in many important ways.
28
July/August 2009

Figure 3
Consolidated ETF Trading Volume In 15-Minute Increments, Week Of February 23–27, 2009
250,000,000
200,000,000
150,000,000
100,000,000
50,000,000
0
09:00-15
09:15-30
09:30-45
09:45-00
10:00-15
10:15-30
10:30-45
10:45-00
11:00-15
11:15-30
11:30-45
11:45-00
12:00-15
12:15-30
12:30-45
12:45-00
13:00-15
13:15-30
13:30-45
13:45-00
14:00-15
14:15-30
14:30-45
14:45-00
15:00-15
15:15-30
15:30-45
15:45-00
16:00-15
16:15-30
16:30-45
16:45-00
Source: NASDAQ OMX
Figure 4
Average U.S. Daily Regular Session Share Volume
Date ETFs Stocks Total Equities ETF % of Total Stocks % of Total
2009 to Feb 27 2,087,097,954 8,069,538,101 10,156,636,054 20.5% 79.5%
2008 1,531,157,474 7,298,488,173 8,829,645,646 17.3% 82.7%
2007 685,797,955 5,386,666,216 6,072,464,171 11.3% 88.7%
Source: NASDAQ OMX
Market-On-Close Transactions In ETFs
Can Be Surprisingly Costly
There is a great deal of misunderstanding about how MOC
transactions in ETFs work. This section probably contains a
great deal more information on these transactions than most
investors will want, but any investor who considers using ETF
MOC orders will find a careful reading of this material worthwhile.
The principal message of this section is that a MOC
execution in an ETF will not necessarily be priced at or even
very close to the midpoint of the indicative bid and offer
published on the fund’s Web site and in its prospectus.
Reported ETF Premium And Discount Pricing
As a prelude to an examination of MOC orders, it is important
to understand what the information on premiums and discounts
published in fund prospectuses and on fund Web sites
means. ETF issuers collect information on ETF share bids and
offers on the listing exchange market each day at 4:00 p.m.
and compare the midpoint of these quotes with that day’s NAV
calculation for the ETF. Premium and discount tables or graphs
reflecting these comparisons are published in ETF prospectuses
and annual reports. They give investors and the SEC inappropriate
comfort that end-of-day ETF transactions occur very close
to NAV. Market makers in even the most thinly traded ETFs
understand that the midpoint of their daily 4:00 p.m. quote will
be preserved in prospectuses and on ETF Web sites for years.
Market makers have a stake in drawing traders to the ETFs they
trade. Consequently, they monitor their real-time bid/offer NAV
calculations very closely as 4:00 p.m. approaches. Even if they
have to widen their spread for a few seconds, they will work to
get the midpoint of their bid and offer as close to the expected
4:00 p.m. NAV 6 as possible. Their 4:00 p.m. quote is the most
widely scrutinized and least useful bid/offer of the day.
Publication of premium and discount information based
on 4:00 p.m. ETF share quotes has led to overuse of MOC
orders, especially for some ETFs that are thinly traded. Most
investors do not realize that MOC transactions in ETFs are
not reflected in most ETF-reported premiums or discounts
in any way. Nonetheless, MOC orders are often used by individuals
and defined contribution retirement plan investors
who are accustomed to mutual fund trades at net asset value
and to MOC orders on stocks.
MOC Orders For Most ETFs Are Not Likely
To Be Executed At Or Even Near NAV
ETF market-on-close orders often result in executions
at a much greater distance from the fund’s end-of-day NAV
than the reported premium and discount data leads investors
to expect.
Market-on-close orders both in stocks and ETFs are integrated
with the limit order books for these securities. The
hypothetical schedule of bids and offers (limit orders) for an
ETF at the end of the trading day displayed in Figure 5 will
help illustrate how this integration works. In this limit order
book, the best bid is for 2,000 shares at $24.90, and the best
offer is at $25.10 for 2,000 shares. The MOC book will oper-
www.journalofindexes.com July/August 2009 29

Figure 5
Hypothetical ETF Limit Order Book At The
End Of The Trading Day
Bids
Offers
25.30 10,000
25.25
25.20
25.15
25.10 2,000
25.05
25.00
24.95
24.90 2,000
24.85
24.80
24.75
24.70 10,000
ate alongside this limit order book with buyers and sellers
entering market orders of various sizes for execution at the
close. If the balance of the MOC orders is to buy 4,000 shares
of the ETF at the market-on-close and the limit order book
matches the table, all the MOC orders will be filled at $25.30,
unless a market maker or a last-minute customer order
improves on the $25.30 offer. The lowest price at which an
order to buy 4,000 shares can be filled is $25.30, and market
rules require that all 4,000 shares trade at that price.
Until the official trading close for ETFs was changed from
4:15 p.m. to 4:00 p.m., MOC orders for ETFs were not subject to
the same rules as MOC orders on stocks. Now, all MOC orders
are accepted until an exchange-specified cutoff time for such
orders, usually 3:40 p.m. After that time, orders to trade at the
market-on-close will be accepted only on the side of the market
that will reduce any trade imbalance. Specifically, if the balance of
market-on-close orders is to buy 4,000 shares of XYZ and the
limit order book looks like Figure 5, additional MOC buy orders
will not be accepted after 3:40 p.m. Market-on-close sell orders
will be accepted to reduce the imbalance. Regular trades will
interact with the limit order book until 4:00 p.m.
These MOC rules work for very actively traded ETFs
because active trading in index instruments attracts arbitrageurs.
Market-on-close orders for less active ETFs often lead
to trades far from NAV because the order book is sparse and
because market makers tend to widen spreads at 4:00 pm.
Less actively traded ETFs are not subject to continuous monitoring
by arbitrage-motivated traders.
In our numeric example of ETF market-on-close trading,
the net asset value of the fund is $25.00. The midpoint
between the bid and offer at 4:00 p.m. is also $25.00. Yet
barring a last-minute MOC sell order or a new limit order
to sell at least 2,000 shares at less than $25.30, market-onclose
orders will be filled at $25.30, 1.2 percent above the
net asset value. The ETF’s premium/discount calculation for
the day will show the “market price” based on the bid and
offer at 4:00 p.m. matching the net asset value at 4:00 p.m.
Publication of a zero premium or discount based on 4:00
p.m. quotes relative to NAV encourages ETF investors who
do not understand the transaction mechanism to use MOC
orders incautiously.
While the premium/discount information published for ETFs
is calculated as the prospectus says it is, this calculation has
led to unanticipated results for many investors. Even if some
professional investors find market-on-close ETF executions
useful at times, I can think of no reason why a typical investor
should use an MOC order to trade ETFs. For actively traded
ETFs, the intraday market in the last hour of trading operates
well, and spreads are among the tightest of any time during
the day. For less actively traded ETFs and for investors who
want a more easily managed trading option, the NAV-based
market—described at length in the next section—will usually
deliver executions closer to and more consistent with net
asset value than either an intraday or an MOC execution. The
availability of NAV-based secondary market trading will assure
that an investor can lock in a price related to a specific net
asset value calculation. Investors can access trading liquidity
available during the entire period that the NAV-based secondary
market is open—a full-day trading session, at minimum.
If defined contribution plans like 401(k)s and advisers
accustomed to buying and selling mutual funds at net asset
value use MOC orders for ETF transactions, the deviation of
market-on-close executions from NAV may increase as these
plans and advisers make greater use of ETFs, especially less
actively traded ETFs. 7
Introducing NAV-Based Trading In ETFs
As noted in the opening paragraph, one of the compelling
advantages of most ETFs for long-term investors is that each
shareholder pays only the cost of his or her own fund share
transactions and is protected by the ETF structure from the cost
of other investors’ purchases and sales of fund shares. Net asset
value-based trading in ETF shares preserves this protection from
other investors’ fund share trading costs and enables investors
to buy and sell ETF shares at a price related to—and no further
than a predetermined distance from—net asset value. We will
see that, in contrast to trading cost uncertainty in the intraday
trading of ETF shares “just like a stock,” NAV-based trading
makes it possible for investors to know and control their ETF
transaction costs with minimal order monitoring.
Entering an order to buy or sell ETF shares at or relative to
the current day’s NAV is only superficially like entering a limit
order to buy or sell shares in the traditional intraday ETF
market. Buy and sell limit orders are entered and executed
relative to a proxy of 100.00 for the per-share net asset value
that will be calculated based on the value of the ETF’s portfolio
at 4:00 p.m. A transaction at net asset value plus 1 cent
per share would be recorded at 100.01. If the fund’s NAV
for the day turns out to be $20 per share, the 100.01 would
translate to a price of $20.01 because each .01 translates into
$0.01 (1 cent) per share. (We omit $ signs on the proxies to
avoid the implication that the transaction will occur near
$100 per share; 100.00 is merely a reference point.)
Most investors will want to check current bids and offers
30
July/August 2009

in the NAV-based market before entering orders in the conventional
intraday market. The NAV-based quotation might
be stated as “10,000 Bid at 99.99, 20,000 Offered at 100.01”
reflecting buy orders totaling 10,000 shares at one penny
below the end-of-day net asset value, and sell orders totaling
20,000 shares at one penny above the end-of-day net asset
value for the ETF. It may be possible to buy ETF shares below
the NAV or to sell them above the NAV, depending on the
bids and offers available in the market over the course of the
trading day. Even if a standing limit order to sell below NAV
is not available in the marketplace when an investor checks
the quote, an investor can place his own bid below net asset
value at the market close, and that bid may be hit by an order
to sell during the course of daily trading activity. Limit orders
can be cancelled and new limit orders or market orders can
transact with the limit order book until shortly after the close
of traditional ETF trading. Transactions will occur throughout
the day at or relative to NAV and the dollar execution
price will be determined when the NAV is published shortly
after 4:00 p.m. After-hours trading relative to the current
day’s and the next day’s NAV will also be possible.
The ability to create or redeem ETF shares each day
should limit the size of any premium or discount on an ETF
share as long as professional traders acting as market makers
and arbitrageurs are reasonably attuned to the costs and
opportunities of meeting demand for additional shares or
redeeming existing shares of the ETF. Transactions in the
NAV-based and traditional ETF markets are subject to similar
fees and commissions. Information on these costs and trading
choices will be available from your broker or adviser.
Investors can compare differences in the spreads between
contemporary bids and offers in the conventional intraday
market and in the NAV-based market throughout the trading
day. We have seen that locating the midpoint of the bid/
offer range relative to an intraday ETF value is not easy for
the average investor in the traditional ETF market. Even if an
investor has ready access to intraday NAV proxies based on
contemporary bids and offers for an ETF’s portfolio securities,
the portfolio value of many ETFs can change by much
more than the typical bid/offer spread in just a few seconds.
In conventional ETF trading, most investors cannot be confident
that their execution will be as close to the contemporary
NAV as they intend.
The diagrams in Figure 6 compare limit orders in the
NAV-based market (where an unchanged limit order may
be appropriate for most or all of the trading day with little
need for monitoring) with a sequence of limit orders in the
intraday ETF market (where the appropriate level for a bid
or offer can change more quickly than most investors can
react). The right-hand side of Figure 6 illustrates the variability
in the intraday value of the fund and highlights the
limit order management problem that most investors face in
the conventional ETF market as fund share values change. As
noted, an investor’s order can trade only with bids and offers
that are available in the market. Orders must be entered in
the conventional ETF market to buy or sell at the market or
at a specific dollar price, say $20.00 per share. Orders to buy
or sell ETF shares relative to a contemporary or future ETF
(portfolio) value are not accepted in the conventional ETF
market. Orders to trade at or relative to the day’s official NAV
are the essence of this new market that lets buyers and sellers
express their bids and offers relative to net asset value.
The transaction cost to buy or sell an ETF share in the NAVbased
market is the sum of any fees and commissions plus
(or minus) any difference between the execution price and
the net asset value. With limit orders stated relative to NAV,
an investor both knows and controls trading costs by this
measure, whether the ETF is thinly or actively traded.
Professional traders and market makers understand that
continued on page 50
Figure 6
Entering Bids And Offers In NAV-Based And Intraday Markets Relative To End-Of-Day NAV Or A Series Of NAV Proxy Values
NAV-Based ETF Market
Conventional ETF Market
A B C D E
End-of-Day NAV
Offer
Bid
NAV Proxy
Offer
Bid
Price
The Trading Day
15sec 15sec 15sec 15sec 15sec
A bid or offer can be left in the NAV-based market all day without
concern that the order will be picked off by a trader with better
information. You will know and control your transaction cost relative
to the closing net asset value of the shares if the order is executed.
Time
www.journalofindexes.com July/August 2009 31

Gastineau continued from page 31
the economics of their business is based on volume and risk
management. Most of them welcome the introduction of
NAV-based trading because it will substantially increase trading
volumes in ETFs that are not actively traded today. These
market professionals will earn far more from small profits on
a lot of trades than from much larger profits per share on
very few shares traded. NAV-based trading also gives market
makers an additional way to reduce their ETF inventory risk
without incurring the costs of frequent creation or redemption
transactions in lightly traded ETFs.
While the conventional ETF market usually works well for
investors who know how to use it to trade the largest and
most actively traded ETFs, it does not serve investors in less
actively traded funds very well. Trading costs are high and
hard to measure in the conventional market for less actively
traded ETFs. When an ETF’s trading volume is low in the conventional
ETF market, its bid/offer spreads are often wide,
and professional traders have little choice but to respond
opportunistically to retail orders rather than to arbitrage
pricing relationships.
NAV-based trading increases the opportunity for ETFs based
on newly developed indexes to compete with ETFs based on
established benchmark indexes. It makes trading easier for
advisers who are used to buying mutual fund shares at net
asset value. In an extension of trading services, we anticipate
the availability of executions based on dollar amounts and fractional
shares provided by a financial intermediary to reduce ETF
trading costs for defined contribution plans and other accounts
that make frequent small transactions. NAV-based trading will
facilitate the development of nontransparent ETFs that offer
active portfolio management and nontransparent indexes that
are not plagued with high composition change costs—in an
ETF structure that protects shareholders from the cost of other
investors’ fund share purchases and sales.
In an earlier paragraph, I noted that there is statistically
significant evidence that the average stock trades at a lower
price near the market opening than near the close on the
same day. A fund net asset value can be calculated from
opening prices as well as from closing prices. If there is
demand for it, NAV-based trading around opening or hourly
portfolio values as well as the traditional end-of-day NAV
calculation is possible.
The author gratefully acknowledges helpful suggestions from
the editor, Matt Hougan; participants at the NASDAQ OMX
Symposium on Exchange Traded Funds at New York University’s
Stern School of Business; and the Nasdaq OMX for the preparation
of Figures 3 and 4.
Endnotes
1 Net-asset-value-based trading is covered by two U.S. patents and a number of pending patent applications.
2 Individual investors probably follow similar trade patterns in stocks. The previous comments reflect conventional wisdom, not a systematic study of the underlying cause.
3 For discussion of these patterns and references to other research that has found similar patterns, see Heston, Steven L., Robert A. Korajczyk, and Ronnie Sadka, “Intraday Patterns in
the Cross-Section of Stock Returns,” March 18, 2009. http://ssrn.com/abstract=1107590. We will return briefly to the issue of intraday price patterns at the end of the article.
4 One of the best articles describing this data is by Hougan, Matt, “ETFs, Spreads and Liquidity,” Journal of Indexes, July/August 2008, pp. 30–33. See also one of Hougan’s blogs
comparing spreads in October 2007 and 2008, http://www.indexuniverse.com/component/content/article/31/4768-etf-spreads-widen-substantially.html?Itemid=3. The Index
Universe website, www.indexuniverse.com, lists monthly ETF average spreads. Click on Section and then on Data.
5 Do not rely on the published IOPV for any purpose.
6 They will use their proprietary value calculations to estimate NAV, not the posted IOPV.
7 The most important paper on ETF trading premiums and discounts is Robert Engle and Depojyoti Sarkar, “Premiums-Discounts in Exchange-Traded Funds,” Journal of Derivatives,
Summer 2006, pp. 27–45. This paper was reprinted in Bruce, Brian R., A Guide to Exchange Traded Funds and Indexing Innovations, Fall 2006, pp. 36–54. Engle and Sarkar found
that ETFs holding domestic stocks had an end-of-day average premium of the closing price over the reported NAV of just +1.1 basis points (a discount would be reported as a
premium with a negative sign). However, this tiny average premium is misleading. The average standard deviation of the last trade premium was 42.1 bps with a range of 17.6 bps
to 142 bps for various funds. A 42-bp standard deviation is more than four-tenths of a percent of the value of the fund share. Using this standard deviation as a rough indicator
of the cost of an MOC execution neglects the effect of “last” transactions that occurred before or after 4:00 p.m. Nonetheless, the average bid-asked spread for these domestic
stock ETFs at the close was 37.7 bps. Neither this spread nor the reported premiums and discounts provide a useful indication of the price an investor should expect on an MOC
transaction today. The study was based on a set of data that ended in September 2000. The procedures for trading ETFs around the market close have changed in a number of
ways since then. The domestic stock ETFs that Engle and Sarkar studied now trade tens of millions of shares daily, but today’s less actively traded ETFs display the same variability
of premiums and discounts found in the earlier data. A similar study of contemporary trading in less active ETFs would make interesting reading.
Callin continued from page 37
Looking forward, what seems almost certain is that investors
will demand transparency both in terms of the risk
exposures in the alpha strategy and the collective portable
alpha approach—and invest only in strategies where they
have a good understanding of the combined risk exposure,
the associated investment rationale and also the downside
risk over their investment horizon. The end result may be
that investors migrate out of some types of portable alpha
strategies and into others.
The fundamental value of portable alpha is still very much
alive and well—and approaches that meet key criteria as highlighted
in Figure 5 are likely to provide powerful results to
long-term investors prospectively, from both return enhancement
and diversification perspectives. In particular, given the
extraordinary level of yield premiums available from very-highquality
fixed-income assets currently, investment-grade fixedincome-based
collateral alpha strategies may provide longterm
investors with attractive returns in the coming years.
This article contains the current opinions of the authors but
not necessarily those of Pimco.
Endnote
1 Source: Pimco. Analysis performed using data from the eVestment Alliance US Large Cap Core Equity universe.
50
July/August 2009

The Future Of Portable Alpha
A new context for a popular type of strategy
By Sabrina Callin and Steve Jones
32
July/August 2009

Portable alpha approaches—which essentially involve
seeking beta from one asset class and alpha from
another—have fallen under a cloud, as have many other
investment strategies, in the wake of the recent market turmoil.
However, they can yield potentially powerful results
for investors both in terms of risk-adjusted excess return and
diversification benefits, which explains, at least in part, the
heightened interest in portable alpha following the equity
market sell-off in 2000–2002. Traditional stock and bond
investment return expectations were relatively low, and
investors were concerned about meeting return targets that
were in many cases in the 7–10 percent range. As a result,
investors as a group were more open to new investment categories
and approaches, including those that employ the use
of derivatives and leverage, like portable alpha.
At the time, portable alpha and related concepts were
touted as a new paradigm in investment management, and
even the holy grail of investing. The variety of different
approaches to porting alpha and the number of providers of
related products ballooned, as did the conferences dedicated
to the topic. Generally speaking, investors seemed happy
with the results. Of course, most of the newer approaches to
portable alpha were implemented in the low- and decliningvolatility
environment that ended in 2007, with risk premiums
initially rising to levels closer to historical averages
before spiking to new highs this past fall.
Unfortunately for some investors, successful portable
alpha implementation has proven to be much more complicated
in practice than it may sound in theory. Portable-alpharelated
losses experienced by some have been highlighted in
the public domain, likely causing some investors to question
the merit of the entire concept.
What does the future hold for portable alpha? The fundamental
concepts that provide the basis for the viability
and benefits of portable alpha for long-term investors are
very much alive and well. There have been important lessons
learned, though. As a result, we are likely to see a bifurcation
of the marketplace where portable alpha approaches that
share certain important characteristics are likely to survive
and even thrive, while others may disappear. In particular,
we believe approaches that capitalize on the attractive risk
premiums available in the high-quality, relatively liquid fixedincome
arena merit serious consideration by investors.
Portable Alpha, Defined
Not surprisingly given the wide variety of approaches
employed, the definitions of the term “portable alpha” vary
depending on who you ask. So far as we are aware, though, all
strategies that fall under the portable alpha umbrella involve
the use of derivatives (or a similar borrowing arrangement)
to obtain the desired asset class or market index exposure,
typically referred to as “beta,” thereby allowing risk-adjusted
excess returns or “alpha” to be sourced from an entirely distinct
asset class or active management strategy. Central to
the idea and also the potential value of portable alpha are the
concepts of borrowing to achieve higher returns, and diversification
as a means to increase the return per unit of risk. Sound
familiar? These are also two of the key concepts that underlie
Figure 1
Alpha
Strategy
Return
over LIBOR
Source: Pimco
Figure 2
$1
$1
$1
Source: Pimco
What Is Portable Alpha?
Alpha
Market
Exposure
“Beta”
Derivatives-Based Market
Exposure “Beta”
Capital Investment
C
“Alpha Strategy”
Alpha: Risk-adjusted
excess return
Desired “beta” market returns
generally obtained using futures
or swaps at LIBOR-based cost
Derivatives Allow Capture Of Additional
Return Source And Risk Diversification
strategy
modern portfolio theory as introduced in the middle of the
20th century and detailed in every investment textbook.
The potential diversification benefit is derived from the
fact that an investor’s capital may be invested in assets that
are independent from—and complementary to—the derivatives-based
beta exposure. Let’s say that an investor has $1.
They can buy $1 of the desired index exposure for just a few
pennies using derivatives, and have the rest of the $1 to place
in an entirely separate and distinct investment. Simplistically
speaking, the combined return will be equal to the total
return of the index (beta) minus the associated financing rate
(as explained below) plus the total return of the collateral or
alpha strategy. The returns from the two components are
additive, while the risk is not entirely additive—as it would
be if the two were perfectly correlated.
It is difficult to predict future correlations: Assets that historically
have not been correlated or have had relatively low
correlations during periods of low volatility may turn out to
have very high correlations during periods of market stress.
Nonetheless, so long as the risk factor exposure in the alpha
strategy is transparent, investors should be able to determine
whether or not a material, positive diversification benefit
should exist over the long term. One example of this may be
the combination of equity derivatives and a carefully risk-controlled,
high-quality, fixed-income alpha strategy portfolio. Of
note is the fact that the Barclays Capital U.S. Aggregate Bond
$2
www.journalofindexes.com July/August 2009
33

Index had a positive return of 5.2 percent in 2008. Historically,
this index has had a negative correlation with equities during
periods of equity market stress, which logically makes sense
due to the flight-to-quality impact.
The borrowing component of portable alpha approaches
is typically accomplished using index futures or swaps that are
designed to provide the total return of the associated market
index, less a financing rate, as noted above. The financing rate
associated with index ownership using derivatives is consistent
with other “buy now, pay later” forms of asset ownership—financing
an appliance, a car or a house, for example.
The important question from the standpoint of prospective
investors in a portable alpha strategy is: Are there readily available,
liquid, cost-effective derivatives available to replicate my
desired index or beta exposure? In the case of certain indexes
or market exposures, the financing cost may be compelling for
long-term investors, while in others, the cost may be significantly
higher if the exposure is even available synthetically.
The Evolution Of Portable Alpha
The vast majority of portable alpha approaches seek
to outperform the equity market, and thus employ equity
index futures or swaps to replicate the returns of the equity
market on an ongoing basis. This is not surprising, as it was
the introduction of S&P 500 index futures by the Chicago
Mercantile Exchange in 1982 that paved the way for broad
market “portable alpha” index enhancement. Specifically, the
advent of the S&P 500 futures contract presented investors
with the opportunity to maintain exposure to the equity market
over the longer term at a short-term money market rate
cost. For equity futures contracts, this cost is embedded in
the price of the futures contracts and is usually very close to
3-month LIBOR, at least for highly liquid futures contracts. In
the event that it drifts higher for some reason, arbitrageurs
will typically step in, thereby pushing the financing rate back
toward LIBOR. In the case of total return index swaps, the
financing cost is specified as part of the contract, and also
most commonly linked to short-term LIBOR rates.
The key to achieving higher returns than the reference
beta or index lies in the performance of the alpha strategy
relative to the money market cost of financing. It follows that
portable alpha strategies may benefit from a “time horizon
arbitrage” of sorts that is unique relative to most traditional
actively managed strategies, due to the mismatch between
the short-term money market cost of the beta exposure and
the longer-term horizon of the investor. Rather than investing
the capital retained solely in money market investments,
investors can capitalize on their longer time horizon by
investing in assets that bear some amount of additional risk
in exchange for a higher expected return. This was the idea
when Pimco launched its StocksPlus strategy shortly after
the introduction of S&P 500 futures contracts more than
two decades ago: Capitalize on long-term equity investor
horizons by owning equity futures contracts collateralized
by a portfolio of liquid, high-quality, short-term fixed-income
assets that provide a modestly higher incremental yield and
expected return above money market rates.
Over the years, investors have gravitated to portable alpha
for multiple reasons. There is a certain appeal to the concept
in that it reconciles the need for higher returns while still
offering many of the same advantages of passive investing. It
is commonly agreed that passive investing offers many significant
advantages for investors across assets classes, including
low costs, liquidity, capacity, diversification and ease of use.
Portable alpha can offer many (in some cases, all) of these
same benefits. It also is particularly compelling in segments
of the market where it is difficult to identify traditional managers
that produce consistently positive excess returns, such
as the U.S. large-cap equity space. Portable alpha strategies
avoid the risk of individual security selection by “owning
the market” through derivatives, but expand the universe of
strategies that can be employed to generate alpha relative
to a given benchmark. In addition, there may be important
improvements in the overall risk profile if there is a low and
relatively stable correlation between the alpha strategy and
the desired market (“beta”) exposure, as noted above, and
also to the extent that the overall strategy provides excess
returns that are uncorrelated with other actively managed
strategies owned within an asset class.
Given all the potential benefits to investors and the
increased interest on the part of investors, it is not surprising
that a large number of variations of the portable alpha concept
have been introduced by investment managers or undertaken
by investors directly, as discussed toward the beginning of
this article. A related reason for all of the interest and flows
into portable alpha strategies is the substantial growth in the
index derivative markets. Relative to even as recently as five
years ago, the number of available futures contracts is much
broader, now including international and emerging markets
equity contracts as examples. At the same time, the overthe-counter
(swap) markets became substantially more liquid
and active. Movement toward standardized over-the-counter
contractual arrangements has also improved the willingness
of investors to establish such exposures.
It’s important to remember, however, that no strategy
is without risks and pitfalls. Over the past few years, we’ve
seen that portable alpha is not immune to the return-hungry,
risk-agnostic attitude that permeated some parts of the marketplace.
The downside of excessive leverage and poor risk
evaluation and measurement ultimately contributed to the
broad-based market dislocations and de-leveraging we’ve seen
recently. Various portable alpha strategies were marketed
without any mention of the downside risk of the alpha component—the
beta component was assumed to have risk, while
alpha strategies that promised returns of 4 percent over LIBOR
or more (after accounting for very high fees) in some cases
were essentially put forth as being risk-less to the end investor,
such that investors may have been led to believe that their
downside was limited to that of the beta component alone.
In addition, often there was very little mention or consideration
of the liquidity that is absolutely critical to the ability to
maintain the derivatives market exposure during volatile and
downward-trending market environments, among other key
aspects of portable alpha that may have been glossed over.
From our perspective, this was quite concerning—and rightly
so, as it turns out—which is why we actually felt compelled
34
July/August 2009

to write the book “Portable Alpha Theory and Practice: What
Investors Really Need to Know,” turning in our first draft to
the publisher well before August 2007. Our colleague Chris
Dialynas put it best in the epilogue:
“Ironically, what began in the early 1980’s as a simple
finance arbitrage Pimco portable alpha strategy has
evolved in some cases to highly leveraged, unregulated
portable alpha hedge fund strategies. Both are referred
to as the alpha source in a portable alpha context,
but they are vastly different in terms of the potential
downside risk.”
Alpha And Beta: It’s the Combination
That Matters, As Does The Execution
This is not to say that all portable alpha strategies that
involve the use of hedge funds as the alpha strategy are bad
or highly risky. There are surely any number of successful,
prudent approaches that involve hedge funds. Regardless of
the approach, though, as a starting point, proper quantification
of investment and operational risk in portable alpha programs
is a necessary ingredient to well-informed investment,
benchmarking, risk budgeting and asset allocation efforts.
Risk management and measurement is a crucial component
of a successful portable alpha strategy.
While portable alpha may seem to be an elegant and
low-risk way to earn excess returns in addition to the return
from the reference market index, there really is no such thing
as a free lunch in the financial markets. The fundamental
laws of investing apply to portable alpha just as they do to
any other type of investment. It is almost always necessary
to take some type of risk in order to generate return over
money market rates. While portable alpha strategies may
seem simple in theory, they cannot outperform the reference
index 100 percent of the time. The primary risks of portable
alpha strategies in this regard can include: (1) the potential
for under-performance in the collateral (alpha) portfolio, (2)
a spike in the financing costs for futures/swaps, (3) margin
calls on the derivatives in a falling market, which force the
liquidation of the most liquid (and highest-quality) parts of
the portfolio, or (4) operational errors.
Investors should carefully evaluate both the risk of
the derivatives-based index exposure and the alpha strategy
when attempting to understand the overall risk of the
strategy. Focusing first on the alpha side of the equation,
investors should understand the liquidity of the portfolio.
Futures margin is typically settled on a next-day basis, and
swap collateral requirements may be settled as frequently
as daily in stressed market environments. Consequently,
understanding portfolio liquidity is critically important.
Similarly, understanding the alpha portfolio’s expected
behavior during periods of market stress may provide
insight into how the portfolio as a whole behaves in down
markets. Well-informed analysis will focus on the sources of
return in the alpha portfolio and the risk factor exposures
that drive those returns. Further analysis should focus on
the likely correlation of those risk factors across different
market environments and to what extent the risks are identifiable,
measurable and can be diversified.
Figure 3
Bonds Plus? Or Something Else Entirely…
Passive
Bonds
(Barclays Agg)
Source: Bloomberg and Hedge Fund Research Inc.;
hypothetical example constructed by Pimco
Portable Alpha
Using Hedge Funds
(Passive Beta =
Barclays Agg)
2008
Return 5.24% -17.19%
Volatility 6.09% 13.04%
10-Year Period
Return 5.63% 9.26%
Volatility 3.84% 8.24%
Beta vs S&P 500 -0.02 0.33
Correlation vs S&P 500 -8.86% 61.25%
Correlation vs BCAG 100.00% 45.84%
For instance, combining a derivatives-based fixed-income
index exposure with a hedge fund alpha strategy may result
in a strategy with a very different risk profile than the passive
fixed-income index. This can be problematic because the
passive fixed-income index presumably serves as the benchmark
for the overall strategy and also serves as the proxy for
the strategy in the investor’s asset allocation. An illustrative
example is shown in Figure 3.
The analysis in Figure 3 compares a hypothetical investment
in a portable alpha strategy where the collateral portfolio
is invested in the HFRI Fund-Weighted Composite Index
(the HFRI composite index provides an equal-weighted average
return of over 2,000 hedge funds). The derivatives-based
beta or index returns are approximated by the return of the
Barclays Aggregate Bond Index minus 3-month LIBOR.
How satisfied would an investor have been with this
investment in 2008? Based on a cursory review of the data in
Figure 3, it is challenging to have much to say on the positive
front in light of:
• A negative excess return of 22.4 percent and negative
absolute return of 17.2 percent (the bond index
returned a positive 5.2 percent)
• Over two times the volatility of the passive bond index
But had you been invested in the strategy for the last 10
years, how satisfied would you be? Even with 2008’s challenges,
the strategy referenced in this example produced a very
healthy excess return of 3.6 percent (excluding any overlay
costs, other costs and fees and “slippage” related to imperfect
alignment between the value of the underlying hedge fund
investment and the bond overlay). Very nice to have the extra
returns for sure, but is this really a bond investment? Look
closely at the risk statistics. The strategy exhibited more than
double the volatility of bonds and was more correlated (61
percent) with the S&P 500 than with the Barclays Aggregate
(46 percent). So not only might it be debatable as to how
to classify this investment, it may be challenging to properly
benchmark the strategy. Based on the historical performance
characteristics of the strategy, can one really assume that it
www.journalofindexes.com July/August 2009
35

Figure 4
will behave in a similar manner as the passive bond index?
Return and especially risk assumptions should be ratcheted up
for any plan-level risk budgeting and asset allocation efforts.
These same challenges are likely to present themselves
when the derivatives-based beta exposure is an equity
index. In addition, the actual and potential volatility and
downside risk of the equity market is very relevant when
considering the appropriate level of liquidity in the collateral
alpha strategy—as many who executed portable alpha
strategies during the relatively benign low-volatility and
positive equity market return period from 2003 to mid-
2007 can certainly now attest.
In Figure 4, note the differences between the passive S&P
500 Index performance in 2008, and over the last 10 years,
relative to a hypothetical portable alpha strategy. As before,
the hypothetical returns assume that the collateral portfolio
is invested in the HFRI Fund-Weighted Composite Index
and the passive index derivative return is approximated by
the return of the S&P 500 minus 3-month LIBOR. Over the
10-year period, the strategy generated very attractive excess
Figure 5
Portable Alpha Example: Equity Overlay
Passive
Equities
(S&P 500)
Source: Bloomberg and Hedge Fund Research Inc.;
hypothetical example constructed by Pimco
Components Required For Successful
Portable Alpha Approaches
Portable Alpha
Using Hedge Funds
(Passive Beta =
S&P 500)
2008
Return -36.99% -51.76%
Volatility 21.02% 29.33%
10-Year Period
Return -1.38% 1.15%
Volatility 15.10% 21.07%
Beta vs S&P 500 1.00 1.36
Correlation vs S&P 500 100.00% 97.19%
Correlation vs BCAG -8.86% -6.64%
“Beta” (Derivatives Exposure)
– Liquidity
– Cost efficiency
“Alpha” Sources (Collateral/Cash Investments)
– Liquidity for margin calls
– Low correlation with beta exposure
– Transparent risk exposures
– Long-term capital preservation characteristics
Implementation
– Derivatives, liquidity and asset management skill and infrastructure
– Consolidated risk management, monitoring and reporting
return. However, most striking from a risk standpoint is that
this combination results in an equity beta coefficient that is
nearly 40 percent higher than the index.
Is this still an equity strategy with a risk profile that is
similar to the S&P 500? The answer to that question for
most is probably a resounding “no.” As an interesting point
of comparison, the 10-year historical beta of this strategy
is higher than that of any traditional active equity manager
universe over this time period. 1
We offer these examples not to disparage the concept
of using hedge fund strategies as the collateral alpha strategy
investment, but rather to illustrate how different portable
alpha approaches can have risk-and-return profiles that differ,
perhaps meaningfully, from the referenced passive market
index. Successful portable alpha implementation over the
long term is contingent on appropriate risk management and
measurement—which, in turn, requires an appropriate level of
transparency. Again, we believe an understanding of the potentially
higher downside risk of such a strategy is important at the
individual investment, asset class and overall plan levels.
Derivatives-Based Beta Management
Should Not Be Free!
Although often overlooked in marketing presentations that
are focused on the alpha side of the equation, derivativesbased
beta management is not nearly as easy or straightforward
as many believe. Derivative instruments are the principal
building block for portable alpha strategies because they allow
investors to finance the desired market exposure at what is
typically a short-term money market rate. However, complexities
exist in liquid markets, such as S&P 500 futures, and even
more so for market exposures that are more complicated to
replicate, like multisector fixed-income indexes. The investment
and operational complexities of establishing and maintaining
such exposures may involve significant costs and result
in additional tracking error and/or counterparty risk.
While a discussion of all of the evolving operational
nuances and requirements associated with the use of derivatives
is beyond the scope of this article, in short, the use of
futures requires Commodity Futures Trading Commission
(CFTC) and other regulatory considerations, daily margin
flow management and usually a quarterly roll of exposure.
The use of swaps introduces legal and contractual considerations.
Other issues to take into account with swaps include
counterparty risk and associated cash flow parameters and
the required typical annual roll of exposure.
The last several months have presented significant tests
for many of the “behind the scenes” efforts related to derivatives
management. Liquidity management was subjected to
an extraordinary stress test at the hands of the severe and
sustained market declines. At the same time, counterparty
risk evaluation became increasingly important in an environment
of uncertainty for many Wall Street broker-dealers.
Disentangling the payables and receivables associated with
derivatives contracts at Lehman Brothers has been an
unpleasant task for many asset managers and their clients.
In circumstances where there were sharp moves in the
net assets of the underlying alpha portfolio, this neces-
36
July/August 2009

sitated more routine adjustment in the notional value of
derivatives to align the value of the overlay with the underlying
assets. The level of volatility underscored the challenges
of attempts to separately manage the alpha and beta
components (as opposed to implementation within a single
integrated portfolio). In such “unbundled” approaches,
the investor typically may bear the responsibility for all of
the risk monitoring, reporting selection and oversight of
separate alpha strategy manager(s) and overlay manager(s).
However, the rebalancing may not have been the worst of it
for these investors. One can imagine the liquidity challenges
that may have been faced by investors who collateralized
equity derivatives with hedge fund exposure in the event
that the rapidly declining equity market exhausted their
initial liquidity reserves at the same time their underlying
hedge fund strategies suspended redemptions.
Nonetheless, although most portable alpha approaches
probably under-performed in September–November of 2008
(it is hard to imagine otherwise when virtually all assets underperformed
LIBOR), possibly resulting in under-performance for
the entire calendar year, the long-term value potential of many
different types of approaches, and the concept as a whole, is
still very real—and perhaps even stronger than ever in some
cases. That said, portable alpha is not necessarily universally
applicable and as straightforward as the example referencing
the S&P 500 futures implies, as liquid, cost-efficient derivatives
do not exist for all commonly referenced market indexes. The
broad bond market is perhaps the best example.
Bond Market Beta Replication: It’s Complicated
While a wide variety of liquid equity index derivatives are
available for use in portable alpha programs, there are fewer
liquid and/or low-cost options available for bond market
indexes. Historically, the total return swap market has not
offered reliable, low-cost replication of broad, multisector
bond indexes either. Similarly, at the time of this writing,
there is not a liquid futures contract on a broad multisector
index. So, for investors wishing to be long, for instance, the
Barclays Capital Aggregate Index via total return swaps, there
are very meaningful challenges in attempting to obtain that
index return precisely. The primary challenge is that broad
market bond indexes such as the Barclays Aggregate contain
an enormous quantity of bonds (the Barclays Aggregate
includes approximately 9,000 bonds).
Recognizing the challenges in exact replication of bond
indexes using derivatives, a number of innovative approaches
have been developed to facilitate synthetic (approximate)
replication of broad bond indexes using forward-settling liquid
instruments and liquid derivatives. While a thorough discussion
of the merits of such strategies is beyond the scope
of this article, worthy objectives are to provide meaningful
cost savings relative to expensive total return swap index
replication, not be reliant on any one counterparty for the
derivatives-based exposure, and to potentially deliver modest
performance improvements at the same time.
Fixed-income derivatives are oftentimes more liquid than
the underlying bonds, and in many cases offer opportunities
to generate a higher return. The good news for investors
seeking to replicate fixed-income indexes synthetically (with
minimal-to-moderate cash outlays) is that it is possible to
utilize a derivatives-based replication portfolio designed to
closely track the return of a broad fixed-income index. For
managers such as Pimco, this may be a natural extension of
efforts in core-plus-bond accounts.
Bonds As A Source Of Alpha
Retaining a focus on the fixed-income market, but looking
to the excess return or alpha side of the equation, high-quality
fixed-income strategies may be an excellent alpha source
when paired with higher-risk market exposures (i.e., equities,
commodities, etc.). The generally low correlation of highquality
fixed-income assets to higher-risk assets may result in
risk-reducing diversification. Higher-quality, diversified fixedincome
strategies may also impart important capital preservation
and liquidity characteristics along with structural return
advantages. Finally, compliments of the painful de-leveraging
and extreme market dislocation, high-quality fixed-income
yields outside of the Treasury sector are currently at extraordinary
levels by most measures—relative to history, relative to
LIBOR and relative to the apparent downside risk.
As was seen in 2008 and based on other periods of equity
market decline, high-quality segments of the bond market
have tended to perform relatively well. Looking across asset
classes and across investment strategies during periods of
financial market stress—for example, when the equity market
is experiencing a material price decline—many investments
exhibit a high correlation with equities. The exception
is often high-quality segments of the bond market, which
typically benefit from a flight to quality during such periods.
The end result may be capital preservation, liquidity and
the potential for excess returns when needed most. This
may serve in sharp contrast to portable alpha strategies that
often source alpha from riskier investment strategies that
may exhibit a materially high correlation with equities during
periods of equity market stress.
Importantly, unlike equities and other investments, from
an investor’s standpoint, bonds have the structural benefit of
eventually returning the capital invested (at par value) unless
the issuer defaults. Therefore, yield tends to be a reasonable
indicator of return over longer periods of time for high-quality
fixed-income investments and portfolios. While the shape of
the yield curve and yield spreads relative to money market
instruments may vary over time, the end result is higher
potential returns relative to money market rates across most
market environments. In today’s market environment, of
course, the levels of yield provided by even the highest-quality,
non-Treasury fixed-income securities are compelling.
Portable Alpha Is Alive And Well
2008 represented a noteworthy test of portable alpha
strategies, particularly for those employing lower-quality,
less liquid and/or leveraged investment strategies in the
alpha portfolio. The year also highlighted the importance of
counterparty, investment and operational risk management
of portable alpha programs.
continued on page 50
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37

Gastineau continued from page 31
the economics of their business is based on volume and risk
management. Most of them welcome the introduction of
NAV-based trading because it will substantially increase trading
volumes in ETFs that are not actively traded today. These
market professionals will earn far more from small profits on
a lot of trades than from much larger profits per share on
very few shares traded. NAV-based trading also gives market
makers an additional way to reduce their ETF inventory risk
without incurring the costs of frequent creation or redemption
transactions in lightly traded ETFs.
While the conventional ETF market usually works well for
investors who know how to use it to trade the largest and
most actively traded ETFs, it does not serve investors in less
actively traded funds very well. Trading costs are high and
hard to measure in the conventional market for less actively
traded ETFs. When an ETF’s trading volume is low in the conventional
ETF market, its bid/offer spreads are often wide,
and professional traders have little choice but to respond
opportunistically to retail orders rather than to arbitrage
pricing relationships.
NAV-based trading increases the opportunity for ETFs based
on newly developed indexes to compete with ETFs based on
established benchmark indexes. It makes trading easier for
advisers who are used to buying mutual fund shares at net
asset value. In an extension of trading services, we anticipate
the availability of executions based on dollar amounts and fractional
shares provided by a financial intermediary to reduce ETF
trading costs for defined contribution plans and other accounts
that make frequent small transactions. NAV-based trading will
facilitate the development of nontransparent ETFs that offer
active portfolio management and nontransparent indexes that
are not plagued with high composition change costs—in an
ETF structure that protects shareholders from the cost of other
investors’ fund share purchases and sales.
In an earlier paragraph, I noted that there is statistically
significant evidence that the average stock trades at a lower
price near the market opening than near the close on the
same day. A fund net asset value can be calculated from
opening prices as well as from closing prices. If there is
demand for it, NAV-based trading around opening or hourly
portfolio values as well as the traditional end-of-day NAV
calculation is possible.
The author gratefully acknowledges helpful suggestions from
the editor, Matt Hougan; participants at the NASDAQ OMX
Symposium on Exchange Traded Funds at New York University’s
Stern School of Business; and the Nasdaq OMX for the preparation
of Figures 3 and 4.
Endnotes
1 Net-asset-value-based trading is covered by two U.S. patents and a number of pending patent applications.
2 Individual investors probably follow similar trade patterns in stocks. The previous comments reflect conventional wisdom, not a systematic study of the underlying cause.
3 For discussion of these patterns and references to other research that has found similar patterns, see Heston, Steven L., Robert A. Korajczyk, and Ronnie Sadka, “Intraday Patterns in
the Cross-Section of Stock Returns,” March 18, 2009. http://ssrn.com/abstract=1107590. We will return briefly to the issue of intraday price patterns at the end of the article.
4 One of the best articles describing this data is by Hougan, Matt, “ETFs, Spreads and Liquidity,” Journal of Indexes, July/August 2008, pp. 30–33. See also one of Hougan’s blogs
comparing spreads in October 2007 and 2008, http://www.indexuniverse.com/component/content/article/31/4768-etf-spreads-widen-substantially.html?Itemid=3. The Index
Universe website, www.indexuniverse.com, lists monthly ETF average spreads. Click on Section and then on Data.
5 Do not rely on the published IOPV for any purpose.
6 They will use their proprietary value calculations to estimate NAV, not the posted IOPV.
7 The most important paper on ETF trading premiums and discounts is Robert Engle and Depojyoti Sarkar, “Premiums-Discounts in Exchange-Traded Funds,” Journal of Derivatives,
Summer 2006, pp. 27–45. This paper was reprinted in Bruce, Brian R., A Guide to Exchange Traded Funds and Indexing Innovations, Fall 2006, pp. 36–54. Engle and Sarkar found
that ETFs holding domestic stocks had an end-of-day average premium of the closing price over the reported NAV of just +1.1 basis points (a discount would be reported as a
premium with a negative sign). However, this tiny average premium is misleading. The average standard deviation of the last trade premium was 42.1 bps with a range of 17.6 bps
to 142 bps for various funds. A 42-bp standard deviation is more than four-tenths of a percent of the value of the fund share. Using this standard deviation as a rough indicator
of the cost of an MOC execution neglects the effect of “last” transactions that occurred before or after 4:00 p.m. Nonetheless, the average bid-asked spread for these domestic
stock ETFs at the close was 37.7 bps. Neither this spread nor the reported premiums and discounts provide a useful indication of the price an investor should expect on an MOC
transaction today. The study was based on a set of data that ended in September 2000. The procedures for trading ETFs around the market close have changed in a number of
ways since then. The domestic stock ETFs that Engle and Sarkar studied now trade tens of millions of shares daily, but today’s less actively traded ETFs display the same variability
of premiums and discounts found in the earlier data. A similar study of contemporary trading in less active ETFs would make interesting reading.
Callin continued from page 37
Looking forward, what seems almost certain is that investors
will demand transparency both in terms of the risk
exposures in the alpha strategy and the collective portable
alpha approach—and invest only in strategies where they
have a good understanding of the combined risk exposure,
the associated investment rationale and also the downside
risk over their investment horizon. The end result may be
that investors migrate out of some types of portable alpha
strategies and into others.
The fundamental value of portable alpha is still very much
alive and well—and approaches that meet key criteria as highlighted
in Figure 5 are likely to provide powerful results to
long-term investors prospectively, from both return enhancement
and diversification perspectives. In particular, given the
extraordinary level of yield premiums available from very-highquality
fixed-income assets currently, investment-grade fixedincome-based
collateral alpha strategies may provide longterm
investors with attractive returns in the coming years.
This article contains the current opinions of the authors but
not necessarily those of Pimco.
Endnote
1 Source: Pimco. Analysis performed using data from the eVestment Alliance US Large Cap Core Equity universe.
50
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Bogle’s Corner
The Fiduciary Principle
‘No man can serve two masters’
By John Bogle
[Adapted from a speech given to the Columbia University
School of Business, New York City, NY, April 1, 2009]
We meet at a time of financial and economic crisis
in our nation and around the globe. I venture
to assert that when the history of the financial era
which has just drawn to a close comes to be written, most of its
mistakes and its major faults will be ascribed to the failure to
observe the fiduciary principle, the precept as old as holy writ,
that “a man cannot serve two masters.”
No thinking man can believe that an economy built upon
a business foundation can permanently endure without some
loyalty to that principle. The separation of ownership from management,
the development of the corporate structure so as to
vest in small groups control over the resources of great numbers
of small and uninformed investors, make imperative a fresh and
active devotion to that principle if the modern world of business
is to perform its proper function.
Yet those who serve nominally as trustees, but relieved, by
clever legal devices, from the obligation to protect those whose
interests they purport to represent, corporate officers and directors
who award to themselves huge bonuses from corporate funds
without the assent or even the knowledge of their stockholders ...
financial institutions which, in the infinite variety of their operations,
consider only last, if at all, the interests of those [whose]
funds they command, suggest how far we have ignored the necessary
implications of that principle. The loss and suffering inflicted
on individuals, the harm done to a social order founded upon business
and dependent upon its integrity, are incalculable.
As you may have already figured out, those words
(except for the very first sentence) are not mine. Rather,
they are the words of [Justice] Harlan Fiske Stone, excerpted
from his 1934—yes, 1934—address at the University of
Michigan Law School, reprinted in The Harvard Law Review
later that year. But his words are equally relevant—perhaps
even more relevant—on this very day. For they could
hardly present a more appropriate analysis of the causes of
the present-day collapse of our financial markets and the
economic crisis now facing our nation and our world.
You could easily react to Justice Stone’s words by falling
back on the ancient aphorism, “the more things change,
the more they remain the same,” and move on to a new
subject. But I hope you’ll react differently, and share my
reaction: In the aftermath of the Great Depression and the
stock market crash that accompanied it, we failed to take
advantage of the opportunity to demand that our giant
businesses and financial organizations—the trustees of so
much of our nation’s wealth—measure up to the stern and
unyielding principles of fiduciary duty described by Justice
Stone. Seventy-five years later, for heaven’s sake, let’s not
make the same mistake again.
Fiduciary Duty
The concept of fiduciary duty has a long history, going
back more or less eight centuries under English common
law. Fiduciary duty is essentially a legal relationship of
confidence or trust between two or more parties, most
commonly a fiduciary or trustee and a principal or beneficiary,
who justifiably reposes confidence, good faith and
reliance in his trustee. The fiduciary acts at all times for
the sole benefit and interests of another, with loyalty to
those interests. A fiduciary must not put personal interests
before that duty, and, importantly, must not be placed in a
situation where his fiduciary duty to clients conflicts with
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a fiduciary duty to any other entity.
It has been said, I think accurately, that fiduciary duty is
the highest duty known to the law.
It is less ironic than it is tragic that the concept of fiduciary
duty seems far less embedded in our society today than it
was when Justice Stone expressed his profound conviction.
As ought to be obvious to all educated citizens, over the
past few decades, the balance between ethics and law, on
the one hand, and the markets on the other have heavily
shifted in favor of the markets. As I have often put it: We
have moved from a society in which “there are some things
that one simply does not do,” to one in which “if everyone else
is doing it, I can do it too.”
I’ve described this change as a shift from moral absolutism
to moral relativism. Business ethics, it seems to me,
has been a major casualty of that shift in our traditional
societal values. You will hardly be surprised to learn that I
do not regard that change as progress. My principal objection
to moral relativism is that it obfuscates and mitigates
the obligations that we owe to society, and shifts the focus
to the benefits accruing to the individual.
Self-interest, unchecked, is a powerful force, but a
force that, if it is to protect the interests of the community
of all of our citizens, must ultimately be checked by
society. The recent crisis—which has been called “a crisis
of ethic proportions”—makes it clear how serious that
damage can become.
Causes Of The Recent Crisis
The causes of that crisis are manifold. Metaphorically
speaking, the collapse in our financial system has 1,000
fathers: The cavalier attitude toward risk of our bankers
and investment bankers, holding a toxic mix of low-quality
securities on enormously leveraged balance sheets; the
laissez-faire attitude of our federal regulators, reflected
in their faith that “free competitive markets” would protect
our society against excesses; the Congress, which
rolled back legislative reforms going back to the depression
years; “securitization,” in which the traditional link
between borrower and lender—under which lenders
demanded evidence of the borrowers’ ability to meet their
financial obligations—was severed; and reckless financial
innovation, in which literally tens of trillions of dollars
of derivative financial instruments (such as credit default
swaps) were created, usually carrying stupefying levels of
risk and unfathomable levels of complexity.
The radical increase in the power and position of the
leaders of corporate America and the leaders of investment
America has been a major contributor to these failures.
Today’s dominant institutional ownership position of 70 percent
of the shares of our (largely giant) public corporations
compares with only about 8 percent of all corporate shares a
half-century ago. This remarkable increase in ownership has
placed these managers—largely of mutual funds (holding 25
percent of all shares), pension funds (20 percent), hedge funds
and endowment funds—in a position to exercise great power
and influence over corporate America.
But they have failed to exercise their power. In fact,
the agents of investment America have failed to honor the
responsibilities that they owe to their principals—the lastline
individuals who have much of their capital wealth committed
to stock ownership, including mutual fund share
owners and pension beneficiaries. The record is clear that,
despite their controlling position, most institutions have
failed to play an active role in board structure and governance,
director elections, executive compensation, stock
options, proxy proposals, dividend policy and so on.
Given their forbearance as corporate citizens, these
managers arguably played a major role in allowing the
managers of our public corporations to exploit the advantages
of their own agency, not only in executive compensation,
perquisites, and mergers and acquisitions, but even
in accepting the “financial engineering” that has come to
permeate corporate financial statements, endorsed—at
least tacitly—by their public accountants.
But the failures of our institutional investors go
beyond governance issues to the very practice of their
trade. These agents have also failed to provide the “due
diligence” that our citizen/investors have every reason
to expect of the investment professionals to whom they
have entrusted their money.
How could so many highly skilled, highly paid securities
analysts and researchers have failed to question the
toxic-filled leveraged balance sheets of Citicorp and other
leading banks and investment banks and, lest we forget,
AIG? 1 [How could they miss] the ethics-skirting sales tactics
of Countrywide Financial? Even earlier, what were these
professionals thinking when they ignored the shenanigans
of “special purpose entities” at Enron and “cooking the
books” at WorldCom?
Again, going back to the stock market high reached in 2007,
how many analysts questioned the typical corporate assumption
that their pension plans would earn future returns of 8.5
percent per year, now obviously a deeply flawed assumption
that is sowing the seeds of another crisis in the financing of
our private and public retirement systems.
The Role Of Institutional Managers
The failure of our newly empowered agents to exercise
their responsibilities to ownership is but a part of the
problem we face. The field of institutional investment
management—the field in which I’ve now plied my trade
for almost 58 years—also played a major, if often overlooked,
role.
As a group, we veered off course almost 180 degrees
from stewardship to salesmanship, in which our focus
turned away from prudent management and toward
product marketing. We moved from a focus on long-term
investment to a focus on short-term speculation. The
driving dream of our adviser/agents was to gather everincreasing
assets under management, the better to build
their advisory fees and profits, even as these policies came
www.journalofindexes.com July/August 2009
39

at the direct expense of the investor/principals whom,
under traditional standards of trusteeship and fiduciary
duty, they were duty-bound to serve.
Conflicts of interest are pervasive throughout the field
of money management, albeit different in each sector.
Private pension plans face one set of conflicts (i.e., minimizing
plan contributions helps maximize a corporation’s
earnings), public pension plans another (i.e., political pressure
to invest in pet projects of legislators) and labor union
plans yet another (i.e., pressure to employ money managers
who are willing to “pay to play”). But it is in the mutual
fund industry where the conflict between fiduciary duty to
fund shareholder/clients often directly conflicts with the
business interests of the fund manager.
Perhaps we shouldn’t be surprised that our money managers
act first on their own behalf.
As Vice Chancellor Leo E. Strine Jr., of the Delaware
Court of Chancery has observed, “It would be passing
strange if ... professional money managers would, as a class,
be less likely to exploit their agency than the managers of
the corporations that make products and deliver services.”
In the fund industry—by far the largest of all financial
intermediaries—that failure to serve the interests
of fund shareholders has wide ramifications. Ironically,
the failure has occurred despite the clear language of
the Investment Company Act of 1940 that demands that,
“mutual funds should be managed and operated in the
best interests of their shareholders, rather than in the
interests of (their) advisers.” 2
Here, in summary form, are just a few examples of how
far so many fund managers have departed from that basic
fiduciary principle, clearly enunciated in the 1940 Act:
1. The domination of fund boards by chairmen and chief
executives who also serve as senior executives of the
management company that controls the funds.
2. The mutual fund “time zone trading” scandals that
came to light in 2003, in which some 23 companies—including
many of the largest firms in the
field—were implicated.
3. “Pay-to-play” distribution agreements using fund brokerage
commissions (“soft dollars”) to finance share
distribution that benefits the adviser.
4. As fund assets soared during the 1980s and 1990s,
fund fees grew even faster, reflecting higher fee
rates, as well as the failure of managers to adequately
share the enormous economies of scale with fund
shareholders.
5. Rising expense ratios for established funds; the average
ratio of the seven largest funds of 1960 rose from
0.48 percent to 1.02 percent in 2003, an increase of
144 percent. 3
6. Managing assets for giant pension funds for fees that
are dwarfed by those that they charge the mutual
funds that they control. Three of the largest advisers,
for example, charged an average fee rate of 0.08 percent
to their pension clients and 0.61 percent to their
funds, resulting in annual fees averaging $600,000
for the pension funds and $56 million for the mutual
funds (presumably while holding the same stocks in
both portfolios).
7. Spending enormous amounts on advertising—almost
$500 million in the last two years alone—to bring
in new fund investors, using money obtained from
existing fund shareholders.
8. Creating exotic and untested “products” that have far
more ephemeral marketing appeal than investment
integrity.
Given such failures as these, doesn’t Justice Stone’s warning
that I cited at the outset seem even more prescient?
Let me repeat the key phrases: The separation of ownership
from management ... corporate structures that ... vest in small
groups control over the resources of great numbers of small and
uninformed investors ... corporate officers and directors who
award to themselves huge bonuses ... financial institutions which
consider only last, if at all, the interests of those whose funds
they command.
Just as we ignored the fiduciary principle all those years
ago, so we have clearly continued to ignore it in the recent
era. The result in both cases, using Justice Stone’s words:
The loss and suffering inflicted on individuals, the harm done
to a social order founded upon business and dependent upon its
integrity, are incalculable.
[Despite all the negative changes] in the very nature of
corporate ownership, we have failed to change the rules of
the game. Indeed, in the financial sector, we have rolled back
most of the historic rules regulating our securities issuers,
our exchanges and our investment advisers. While we should
have been improving regulatory oversight and administering
existing regulations with increasing toughness, both have
been relaxed, ignoring the new environment and therefore
bearing much of the responsibility for today’s crisis.
Of course American society is in a constant state of flux.
It always has been, and it always will be. I’ve often pointed
out that our nation began as an agricultural economy, then
became largely a manufacturing economy, then largely a service
economy, and most recently an economy in which the
financial services sector had become its dominant element.
Such secular changes are not new, but they are always different,
so enlightened responses are never easy to come by.
To deal with the new and complex economic forces
our failed agency society has created, of course we need a
new paradigm: a fiduciary society in which the interests of
investors come first, and ethical behavior by our business
and financial leaders represents the highest value.
Building A Fiduciary Society
While challenges of today are inevitably different from
those of the past, the principles are age-old. We must
develop a new fiduciary society that guarantees our lastline
owners—those mutual fund shareholders and pension
fund beneficiaries whose savings are at stake—their rights
as investment principals. These rights must include:
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July/August 2009

1. The right to have their money-manager/agents act
solely on their behalf. The client, in short, must
be king.
2. The right to rely on due diligence and high professional
standards on the part of our money managers
and securities analysts who appraise securities for
our portfolios.
3. The right to demand some sort of discipline and
integrity in the mutual funds and financial products
that they offer.
4. The assurance that our agents will act as responsible
corporate citizens, restoring to their principals the
neglected rights of ownership of stocks, and demanding
that corporate directors and managers meet their
fiduciary duty to their own shareholders.
5. The establishment of advisory fee structures that meet
a “reasonableness” standard based not only on rates but
dollar amounts, and their relationship to the fees and
structures available to other clients of the manager.
6. The elimination of all conflicts of interest that could
preclude the achievement of these goals.
More than parenthetically, I should note that this final
provision would seem to preclude the ownership of money
management firms by financial conglomerates, now the
dominant form of organization in the mutual fund industry.
Among today’s 40 largest fund complexes, only six remain
privately held. The remaining 34 include 13 firms whose
shares are held directly by the public, and an astonishing
total of 21 fund managers owned or controlled by U.S. and
international financial conglomerates—including Goldman
Sachs, Bank of America, Deutsche Bank, ING, John Hancock
and Sun Life of Canada. Painful as this separation might be,
it is the single most blatant violation of the principle that
“no man can serve two masters.”
Of course it will take federal government action to
foster the creation of this new fiduciary society that I
envision. Above all else, it must be unmistakable that government
intends, and is capable of enforcing, standards
of trusteeship and fiduciary duty under which money
managers operate with the sole purpose and to the exclusive
benefit of the interests of their beneficiaries—largely
the owners of mutual fund shares and the beneficiaries of
our pension plans.
While the government action is essential, however, the
new system should be developed in concert with the private
investment sector, an Alexander-Hamilton-like sharing
of the responsibilities. The task of returning capitalism to
its ultimate owners will take time. But the new reality—
increasingly visible with each passing day—is that the
concept of fiduciary duty is no longer merely an ideal to be
debated. It is a vital necessity to be practiced.
What I’ve passionately advocated in these remarks is
hardly widely shared among my colleagues and peers. But
soon, perhaps, many others will ultimately see the light.
Only last week the idea of governance reform got encouraging
support from Professor Andrew W. Lo of MIT, one of
today’s most respected financial economists: . . . the single
most important implication of the financial crisis is about the
current state of corporate governance . . . a major wake-up call
that we need to change [the rules]. There’s something fundamentally
wrong with current corporate governance structures, [and]
the kinds of risks that typical corporations face today.
In sum, the change in the rules that I advocate—applying
a federal standard of fiduciary duty to their clients
for institutional money managers—would be designed
in turn to force these managers to use their own ownership
position to demand that the managers and directors
of the corporations in whose shares they invest honor
their own fiduciary duty to the holders of their shares.
Finally, it is these two groups that share the responsibility
for the prudent stewardship over corporate assets and
investment securities alike that have been entrusted to
their care, not only reforming today’s flawed and conflictridden
model, but developing a new model that, at best,
will restore traditional ethical mores.
I owe it to Justice Harlan Fiske Stone’s legacy to conclude
my remarks with yet one more quotation from his
profound and prescient 1934 speech:
In seeking solutions for our social and economic maladjustments,
we are too ready to place our reliance on what (the
policeman’s nightstick of) the state may command, rather
than on what may be given to it as the free offering of good
citizenship . . .
Yet we know that unless the urge to individual advantage has
other curbs, and unless the more influential elements in society
conduct themselves with a disposition to promote the common
good, society cannot function . . . especially a society which has
largely measured its rewards in terms of material gains . . .
We must (square) our own ethical conceptions with the traditional
ethics and ideals of the community at large. (There is)
nothing more vital to our own day than that those who act as
fiduciaries in the strategic positions of our business civilization,
should be held to those standards of scrupulous fidelity which
(our) society has the right to demand.
Note: The views expressed in this speech do not necessarily reflect the views of Vanguard’s present management.
Endnotes
1 I’m speaking here of the “buy-side” analysts employed directly by these managers. The conflicts of interest facing “sell-side” analysts were exposed by the investigations
of New York Attorney General Spitzer in 2002–2003.
2 Securities & Exchange Commission decision, March 15, 1981.
3 2002 data: pg. 199, “The Battle for the Soul of Capitalism,” by John C. Bogle, Yale University Press, 2005.
www.journalofindexes.com July/August 2009
41

Talking Indexes
Managing The Risks In Ourselves
We simply attempt to be fearful when others are
greedy and to be greedy only when others are
fearful.
—Warren Buffett
By David Blitzer
The financial markets turmoil of the last year has
taught, or retaught, all of us a lot about risk and
risk management. In the halcyon days before
2008, risk management was an occasional activity that
we believed we knew something about. Today we are
serious about it. Further, in the short space of about a
year, we may have learned a little—now we know that
the normal distribution is often abnormal, that “impossible”
events happen and that all those uncorrelated
asset classes can sink together—at exactly the moment
you most need the correlation benefit. Our respect for
market turmoil is much greater and our understanding
of markets, maybe, has increased.
However, for investors, the market and its gyrations
are only half the problem—we’re the other half. Or, in the
words of Pogo, 1 “We have met the enemy and he is us.”
As shocking and dismaying as the housing boom-bust
and financial crisis are, they are not unprecedented.
Housing is following the same boom-bust pattern
that markets have experienced for centuries. The oldest
commonly cited cycle is the Dutch tulip bulbs in
1637, as chronicled by Charles MacKay. 2 A more recent
analysis is provided by Charles Kindleberger in “Mania,
Panics, and Crashes.” 3 Kindleberger recounts a number
of infamous booms and busts and describes their typical
pattern as shown in Figure 1. The boom begins with
some event—“displacement” in Kindleberger’s terms—
that disrupts an otherwise normal market. With housing
in the U.S., this was the very-low-interest-rate environment
in 2002–2003, just as investors, shell-shocked by a
bear market, were seeking an alternative to investing in
stocks. Their homes—and second homes—offered the
best alternative. As the boom gathered steam, soaring
prices led people to believe home prices would never
fall, and generated positive feedback to drive the boom.
This psychology was soon joined by subprime mortgages,
which dramatically expanded the market. Everexpanding
securitization of those mortgages sparked
the speculative mania seen at the peak in 2006. By 2008
the boom had turned into a bust, or the “crisis and
revulsion” noted by Kindleberger. This pattern, with
minor changes, can be seen in other booms. Figure 2
tells the story for the technology stock market of 1995–
2000, alongside the housing cycle. One can easily find
Figure 1
2002
Displacement
Positive
Feedback
Euphoria
The Cycle
Speculative Mania
2006
Crisis
And
Revulsion
2008–2009
Source: Based on “Manias, Panics and Crashes,” Charles Kindleberger, 1978, 2005,
John Wiley & Sons, Inc.
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July/August 2009

Figure 2
Stages Of Booms And Busts
Housing 2002–2009? Stocks 1995–2000
Displacement Low interest rates, subprime mortgages Internet and Web
Mortgages drove prices up, higher prices Rising stock prices respond to Internet
Positive Feedback Loops
raised expectations, opened path for moreaggressive
mortgages
Euphoria: Irrational exuberance
Home prices don’t go down
Productivity gains and low inflation
convince people the Internet is real
Speculative Mania: Feeds on
Rising demand for MBS and securities
AOL Time Warner merger
itself and departs from reality
built on MBS
Crisis FNM, FRE, LEH March 24, 2000 … S&P 500 at 1527
many other stories that fit the same pattern.
The markets, however, are only half the game. The
rest is how we reacted to market events and why we
bought into the idea that home prices would never fall.
A common question one hears from investors is why
financial advisers and analysts didn’t realize what was
happening and predict the downturn. A less common
question one hears a few people asking themselves is
why they didn’t realize what was going on and anticipate
the collapse. Very few people think back to how they
felt in 2006 as the housing boom was peaking, or what
they were feeling in 2002 as it was getting under way.
Why Didn’t Anyone Notice?
Let’s begin with 2002. The bear market that started
in March 2000 was bottoming out in the last quarter of
2002, the economy was creeping out of a shallow recession
and many were still nervous about global politics
after the events of Sept. 11, 2001. Investors were worried,
and no one expected the housing boom. Indeed, not
many even expected the market to recover from a 50 percent
loss in only five years. Between fear and greed, fear
ruled the day. Yet both the stock and housing markets
were at a bottom, and were about to surge upward.
Now, consider the summer of 2006, when housing
was peaking. Investors’ attitudes were completely different.
The economy was growing, stocks were gaining
and the S&P 500 had risen from under 800 to over 1200
since the fall of 2002. The big news was in housing,
where home prices were up sharply, with some sunbelt
cities seeing prices over twice the levels of early 2000.
The last thing on most investors’ minds was a collapse.
Between fear and greed, greed ruled the day.
Figure 3 shows the Kindleberger boom-bust cycle
Figure 3
2002
Perceived Risk
The Risk Cycle
Greed
Mid-2006
Markets & the Economy
Perceived Risk
Fear
2009
seen in Figure 1 and adds a second line illustrating the
level of risk that investors perceived. In 2002, when fear
ruled the day, people saw very large risks; in 2006, when
greed dominated, perceived risks were small. The track
of apparent risk is just the opposite of the market—at
market bottoms we see huge risks, while at market tops
we are incredibly bullish.
A large part of risk management is knowing if a particular
risk is justified by the opportunity of reward. In
the markets, as in most things, the size and probability
of the reward varies with time and conditions. Yet the
way most of us think about the markets gets the timing
and probability of success backwards. There is more to
investing than reason and numbers when everyone else
is paralyzed with fear. The trick is to recognize when
the madness of the crowd is prodding you to believe in
the popular delusions.
Endnotes
1. Walt Kelly, cartoonist, in 1971 commenting on Earth Day and pollution
2. Charles MacKay, “Memoirs of Extraordinary Popular Delusions and the Madness of Crowds,” 1841
3. 1978; 5th edition 2005, John Wiley & Sons, Inc.
www.journalofindexes.com
July/August 2009
43

News
CVC To Buy iShares …
Or Maybe Not
Luxembourg-based CVC Capital
Partners agreed to buy Barclays Global
Investors’ iShares exchange-traded fund
unit for $4.4 billion in April. The deal
did not include iShares’ share-lending
business, an extremely lucrative segment
of the iShares operation.
Under the terms of the CVC deal,
Barclays was to provide the bulk of the
financing, providing loans on reasonably
favorable terms for $3.1 billion of
the $4.4 billion deal.
CVC Capital is a private equity firm
with diversified interests; the iShares
purchase was to be its first foray into
the financial sector.
But no sooner had the ink dried on the
deal documents than rumors of alternate
bidders emerged. As part of the deal,
Barclays had a 45-day window to look for
alternative and superior offers. If it found
one, it would have to pay a $175 million
breakup fee to CVC Capital.
As this issue of the Journal of Indexes
goes to press, reports are circulating
that firms such as Black Rock Capital
may be in the running. Black Rock is
said to be entertaining a $10 billion bid
that would encompass not just iShares,
but the broader BGI business. Also mentioned
as potential bidders are private
equity firms Apax Partners, Hellman &
Friedman and BC Partners.
Management at Barclays owns up to
10 percent of iShares, and will be paid a
cash dividend based on its stakes in the
deal. Barclays CEO Robert Diamond Jr. is
expected to earn upward of $6.9 million
from the deal; he did not participate in the
consideration of the iShares transaction.
Dow Jones Rebrands
Two Index Families
This spring saw the rebranding of two
major Dow Jones indexes product lines.
Perhaps most importantly, the index
provider’s agreement with Wilshire
Associates was terminated on March
31. For several years, the two index
providers jointly marketed an index
family that included the rebranded version
of the well-known Wilshire 5000
Index, which represents nearly all the
listed stocks in the U.S.
Both parties have returned to calculating
their own indexes, with the Dow
Jones Wilshire index family rebranded as
the Dow Jones Total Stock Market index
family and Wilshire Associates’ indexes
once again marketed under its own brand
name. The jointly marketed indexes were
identical as of April 1, but will likely
diverge going forward as the two different
providers interpret rules separately
and modify the methodologies over time,
a Wilshire respresentative said.
More recently, the Dow Jones – AIG
Commodity Index changed its name to
the Dow Jones – UBS Commodity Index
as of May 7, following the acquisition of
some of AIG’s assets by UBS.
The index’s methodology remains
exactly the same, but new suggested
ticker symbols reflecting the name
change have been generated for the
main index and its subindexes. Dow
Jones is publishing parallel sets of indexes
under the new and old ticker symbols
for 30 days before phasing out the old
symbols; the index values for both sets
of symbols are exactly the same.
Grim Results For
Active Managers In 2008
More than 70 percent of all actively
managed U.S. equity mutual funds
trailed their benchmarks for the five
years ending 2008, according to the
2008 Standard & Poor’s Index Versus
Active Fund Scorecard (SPIVA).
The report shows that 71.9 percent of
actively managed large-cap funds trailed
the S&P 500; 75.9 percent of actively
managed mid-cap funds trailed the S&P
MidCap 400; and a stunning 85.5 percent
of actively managed small-cap funds
trailed the S&P SmallCap 600.
S&P says the results were consistent
with the previous five-year cycle, from
1999 to 2003.
Actively managed funds also did
poorly over the one-year period: 54
percent of large-cap funds trailed the
S&P 500; 75 percent of mid-cap funds
trailed the S&P MidCap 400; and 84
percent of small-cap funds trailed the
S&P SmallCap 600.
The single worst category for
active managers in 2008 was small-cap
growth, where a dizzying 96 percent
of managers trailed their benchmark.
The only bright spot was large-cap
value funds, which trounced the S&P
500 Value Index in 2008, with 78 percent
of actively managed funds beating
their benchmark.
But the story turns dismal again for
active managers of international funds.
Sixty-three percent of global funds
trailed the S&P Global 1200 on a fiveyear
basis; 84 percent of international
funds trailed the S&P 700; 59 percent of
international small-cap funds trailed the
S&P Developed Ex-US Small-Cap; and
90 percent of emerging market funds
trailed the S&P/IFCI Composite.
Fixed income is no better. Over five
years, the percentage of fixed-income
funds that outperformed their indexes in
all standard domestic categories is less
than 10 percent. The only exceptions
are in high yield, where 48 percent of
funds beat their benchmark; global fixed
income, where 21 percent beat their
benchmark; and emerging markets debt,
where 38 percent beat their benchmark.
All results are adjusted for survivorship
bias.
MacroMarkets Closes Oil Fund,
Cancels Housing IPO
MacroMarkets LLC has been
encountering some problems with its
MacroShares exchange-traded products.
The firm has a patented meth-
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July/August 2009

odology for dueling exchange-traded
trusts that shift assets (in the form of
Treasuries) back and forth between
each other according to the positive or
negative movements of the underlying
commodity price or index.
The flagship products—the Macro-
Shares Oil Up trust and the MacroShares
Oil Down trust—ran into trouble last
year when the price of oil skyrocketed,
sucking all the assets out of the
“Down” fund and forcing the closure
of both products.
MacroMarkets tried again a few
months later with another set of oiltracking
products that had a little more
flexibility—the MacroShares $100 Oil
Up Trust (NYSE Arca: UOY) and the
MacroShares $100 Oil Down Trust
(NYSE Arca: DOY). However, those products
never gained much popularity, and
it was announced in mid-May that the
firm was invoking a rule that allowed it
to terminate the funds if one or both of
them fell below $50 million in assets;
UOY and DOY, which had about $20
million in combined assets at the time
of the announcement, are set to stop
trading on June 25.
Just days later, the firm announced
that its unprecedented initial public
offering for its pending products
tracking the S&P/Case-Shiller Home
Price Composite 10 Index had failed,
and it would be using a traditional,
market-maker-driven process to
launch the Major Metro Housing Up
(NYSE Arca: UMM) and the Major
Metro Housing Down (DMM) trusts.
Apparently, demand for the Up and
Down funds from IPO bidders was
too lopsided; additionally, investors
seemed to be put off by the extra
commission costs that came with purchasing
a traditional IPO.
The home price trusts were expected
to launch with $5 million in seed money
from a market maker shortly after the
failure of the IPO was announced.
INDEXING DEVELOPMENTS
Home Price Indexes
Slow Their Descent
The S&P/Case-Shiller Home Price
indexes, while grim, showed some
mildly positive signs for February.
For the first time in 16 months, the
benchmarks’ fall didn’t set new records
for year-over-year losses, according to the
monthly results released in late April.
In fact, 16 of the 20 metro areas
saw smaller losses in February than in
January, although all 20 metro areas
covered by the indexing series produced
a monthly decline in February. All
in all, average U.S. home prices are at
similar levels to where they were in the
third quarter of 2003. From the peak
in mid-2006, the 10-City Composite
Index was down 31.6 percent and the
20-City Composite was down 30.7 percent
through February 2009.
On an annualized basis, three metro
areas fared the best: Dallas dropped 4.5
percent and turned in the best performance,
while Denver fell some 5.7 percent
and Boston lost 7.2 percent. The
three worst-performing cities continue
to be from the Sun Belt: Phoenix was
down 35.2 percent, with Las Vegas and
San Francisco down 31.7 percent and
31 percent, respectively.
CDR Rolls Out
Government Risk Index
In late March, Credit Derivatives
Research LLC launched a new index
designed to track the creditworthiness
of leading sovereign debtors. The
Government Risk Index tracks credit
default swap spreads for the United
States, the United Kingdom, Germany,
France, Italy, Spain and Japan.
Sovereign CDS essentially insure a
buyer against a credit event, like a default,
related to the issuing country’s debt. With
governments taking actions like buying up
toxic assets in the wake of the credit crisis,
thereby increasing risk levels, CDS costs
have also increased.
Pull Quote Pull Quote Pull Quote
Pull Quote Pull Quote Pull Quote
Pull Quote Pull Quote Pull Quote
Sovereign CDS essentially insure a buyer
against a credit event, like a default,
related to the issuing country’s debt.
www.journalofindexes.com July/August 2009
45

S&P Introduces
Risk-Controlled Indexes
S&P rolled out two new strategy
indexes in mid-May designed for
risk-weary investors that are alternate
versions of two of its bestknown
benchmarks—the S&P 500
and the S&P/ASX 200, which tracks
the Australian market.
The S&P 500 Risk Control 10%
Index and the S&P/ASX 200 Risk
Control 15% Index target different
levels of volatility—10 percent and
15 percent, respectively, as the
indexes’ names would imply. The
indexes adjust their exposure to
match their volatility levels, either
decreasing exposure when volatility
of the original index is too high or
using leverage to increase exposure
when the volatility of the original
index is too low.
The index provider already offered
risk-controlled versions of its benchmark
indexes covering the BRIC countries,
Southeast Asia, Latin America
and the global infrastructure sector.
Barclays Unveils Global
Target ‘Exceed’ Index
May saw the launch of a new
strategy index from Barclays Capital
via its “Exceed” family of indexes.
The Barclays Capital Global Target
Exceed Index, like all the indexes
in the Exceed series, according to
Barclays, “aims to extract alpha from
term premium in the short end of
the yield curves in a fully transparent
and liquid way by trading libor
futures contracts.”
Barclays already has a few indexes
of this nature tracking the yield
curves of the U.S dollar and the
euro, alone and in combination, but
the new index provides exposure to
the yield curves of four currencies:―
the U.S. dollar, the euro, the British
pound and the Japanese yen.
Barclays says the index’s performance
can be accessed via a range of
investable products including swaps,
options and principal-protected
notes, among others.
News
FTSE And Renaissance Capital
Launch IPO Indexes
Index provider FTSE Group has
partnered with research house
Renaissance Capital to launch a
series of indexes tracking the performance
of initial public offerings
in the U.S. The launch of the flagship
index, the FTSE Renaissance IPO
Composite Index, was announced in
mid-April.
IPOs are added to the index on
their first day of trading and remain
for two years, or about 500 trading
days. According to FTSE, IPOs often
must wait three months before joining
most standard benchmarks, causing
investors to miss out on what can
be a very strong performance period.
Renaissance Capital focuses its
research on IPOs, and previously
calculated its own index tracking the
U.S. IPO market.
MSCI Launches ‘Tilt’ Indexes
In late March, MSCI announced
the launch of the first indexes of a
new series. The MSCI Factor indexes
are designed to maximize their
exposure to a single source of risk—
identified as a “factor” in the Barra
risk models—while controlling their
exposure to other sources of risk
and seeking to track the original
benchmark equity index as closely
as possible.
The first two members of the
new index family include the MSCI
Europe Momentum Tilt Index and
the MSCI Europe Value Tilt Index,
which are derived from the benchmark
MSCI Europe Index.
According to MSCI, the new indexes
are designed for use in portfolio
analysis and construction and also to
underlie investable products.
Markit And BlueNext
Create Emissions Indexes
Markit, a firm known for its fixedincome
and CDS indexes, is teaming
up with carbon-trading exchange
BlueNext in a partnership that
includes the creation of a new set of
carbon emission credit indexes.
The first two benchmarks to be
launched under the joint venture are
the Markit BlueNext EUA 1 Spot Index
and the Markit BlueNext CER Spot
Index. The former tracks the performance
of European Union allowances
(EUAs) under the EU Emission Trading
System, while the latter tracks the
performance of certified emission
reductions (CERs), which are defined
by the Kyoto Protocol. Both an EUA
and a CER are equal to one tonne of
carbon dioxide.
The announcement from Markit
and BlueNext implied that more products—indexes
and otherwise—would
be forthcoming, but it did not specify
what might be down the road.
DJI Suspends Distressed
Securities Hedge Index
Dow Jones temporarily suspended
publication of its Dow Jones Hedge
Fund Distressed Securities Strategy
Benchmark in early May. The suspension
is open-ended until further
notice, according to the company.
Dow Jones said the decision was
made jointly with the investment
manager of the managed account
platform that supports the Dow Jones
hedge fund index family and was
made due to market conditions.
Previously, on Jan. 2, the index
provider halted publication of the
Dow Jones Hedge Fund Convertible
Arbitrage Strategy Benchmark. That
index remains suspended. However,
Dow Jones noted that its remaining
strategies—Equity Long/Short, Equity
Market Neutral, Event Driven and
Merger Arbitrage—will continue to
be published on an end-of-day basis.
FTSE And Borsa Italiana
Debut Index Family
It was announced earlier this year
that FTSE would take over the calculation
of the indexes for the Borsa
Italiana from S&P, and the index provider
unveiled a preliminary index
lineup in April.
The indexes are constructed based
46
July/August 2009

on FTSE’s rules and methodologies
for the most part, but the FTSE MIB
will be the same index as the existing
S&P/MIB index, just rebranded.
The new index regime will feature
liquidity screens and minimum float
requirements, among other modifications,
and foreign-listed stocks
will be excluded from all but the
FTSE MIB and the all-share index.
Other notable changes include the
replacement of the mid-cap and allshare
indexes by FTSE-designed indexes
and replacement sector indexes
based on FTSE’s ICB classification system.
FTSE will also launch a small-cap
index and a micro-cap index.
Perhaps most interestingly, the
blue-chip MIB 30 will be discontinued,
along with a few other indexes.
AROUND THE WORLD OF ETFs
PowerShares Closes 19 Funds
Invesco PowerShares closed 19 of
its ETFs, a dozen of which are based
on fundamental indexes created by
Rob Arnott’s Research Affiliates. Their
last day of trading was May 18.
The 12 FTSE RAFI ETFs included
nine U.S. sectors and three international
portfolios. The group also
included five of the firm’s “Dynamic”-
branded ETFs, which are based on
complex quantitative indexes calculated
using fundamental, momentum
and risk factors. The remaining two
ETFs marked for termination cover
high-dividend stocks and international
private equity companies.
PowerShares says it arrived at its
decision based on asset levels, the
funds’ lengths of time on the market
and the potential for future growth,
among other factors.
Grail Enters ETF Arena
With Active Fund
Grail Advisors launched its first
ETF in early May. The new fund
is the first to use a team of active
managers implementing traditional
qualitative stock-picking processes.
The Grail American Beacon Large
Cap Value ETF (NYSE Arca: GVT) is
News
managed by a trio of veteran mutual
fund and institutional subadvisers:
Brandywine Global Investment,
Hotchkis and Wiley Capital
Management, and Metropolitan
West Capital Management.
Combined, the new ETF’s aim is to
outperform the Russell 1000 Index.
However, it won’t track any specific
benchmark when selecting its names.
GVT will publish its full portfolio on a
daily basis, offering complete transparency
into the fund holdings. Of
course, because it uses multiple subadvisers,
no single subadvisers will be
giving away their full strategy; it will
be mixed with the other positions.
The fund charges 0.79 percent in
annual expenses.
Emerging Markets
Currency ETF Debuts
In early May, WisdomTree added
another ETF to its lineup of actively
managed currency funds.
The WisdomTree Dreyfus Emerging
Currency Fund (NYSE Arca: CEW) provides
exposure to a wide range of
emerging market currencies. Those
include: the Brazilian real, Chinese
yuan, Chilean peso, Indian rupee,
Israeli shekel, Mexican peso, Polish
zloty, South African rand, South
Korean won, Taiwanese dollar and
Turkish new lira.
The firm already has eight currency
ETFs, all but one of them
marketed under the “WisdomTree
Dreyfus” brand. This is the first of
its funds to include multiple currencies
bundled together. It charges an
expense ratio of 0.55 percent.
Van Eck Launches
Brazilian Small-Cap ETF
Van Eck Global expanded its suite
of Market Vectors ETFs in mid-May
with the launch of the Brazil Small-Cap
ETF (NYSE Arca: BRF), the first Brazilian
small-cap ETF available in the world.
The fund invests in Brazil-listed
companies with market capitalizations
between $250 million and $3.8
billion. At launch, the BRF fund held
52 positions. On an industry basis,
its heaviest weights were in household
durables (15.6 percent), food
products (9.2 percent), specialty
retail (7.8 percent), and paper and
forestry products (7 percent).
The fund charges 0.73 percent in
expenses.
Direxion Rolls Out
Treasuries Triple Plays
Late April saw the launch of
four leveraged and inverse ETFs
tied to Treasuries. The four new
DirexionShares 3x ETFs are leveraged
bull and bear index-based portfolios
that try to either provide 300 percent
of the daily performance or 300
percent of the inverse of the daily
performance of the NYSE Current 10-
and 30-Year U.S. Treasury indexes.
The new Direxion ETFs include
the following:
• Direxion Daily 10-Year Treasury
Bull 3x Shares (NYSE Arca: TYD)
• Direxion Daily 30-Year Treasury
Bull 3x Shares (NYSE Arca: TMF)
• Direxion Daily 10-Year Treasury Bear
3x Shares (NYSE Arca: TYO)
• Direxion Daily 30-Year Treasury Bear
3x Shares (NYSE Arca: TMV)
Each charges 0.95 percent in
annual expenses.
www.journalofindexes.com
July/August 2009
47

News
New SPDR Offers Twist
On Mortgage Finance
At the end of April, SSgA
launched the SPDR KBW Mortgage
Finance ETF (NYSE Arca: KME),
which holds banks focused on mortgage
loans and related services. It
also includes a healthy dose of title
insurers and claims managers, as
well as homebuilders.
The ETF, which comes with an
expense ratio of 0.35 percent, uses
a benchmark created by investment
banker and asset manager Keefe,
Bruyette & Woods. The new ETF joins
four other SPDRs using KBW indexes
to slice financials into different subsectors.
There are several MBS-focused
ETFs on the market, but they all hold
fixed-income securities rather than the
stocks of mortgage lenders.
First Convertible
Bond ETF Debuts
On April 16, State Street Global
Advisors launched the SPDR
Barclays Capital Convertible Bond
ETF (NYSE Arca: CWB). According
to SSgA, it’s the first ETF to focus
solely on convertible bonds available
to U.S. investors. Convertible
bonds have been gaining attention
lately, in part due to their impressive
year-to-date performance.
Convertible bonds can be ex -
changed—at the option of the holder—for
a specific number of shares
of the issuer’s preferred or common
stock. For investors, that means
convertibles provide the safety of a
bond with the upside potential of
equities; the trade-off is that they
typically pay lower yields than standard
corporate bonds.
Although the ETF’s underlying
index included about 160 issues, the
fund holds only 36 names.
CWB comes with an expense ratio
of 0.40 percent.
Direxion Plans Monthly ETFs
Direxion Funds has filed to offer
40 new inverse and leveraged funds.
In the February-dated request,
Direxion proposed launching the first
ETFs to provide leveraged and inverse
returns on a monthly basis. The company
already has a lineup of successful
ETFs offering 300 percent and -300
percent exposure to the daily movements
of a variety of indexes.
Direxion’s filing covers 10 indexes,
including the likes of the MSCI
EAFE Index and the Russell 2000,
among others. Four different funds
are planned for each index—two
offering 200 percent exposure (one
leveraged, the other inverse), and
two offering 300 percent exposure.
The monthly return focus would
The new ETF joins
four other SPDRs
using KBW indexes
to slice financials into
different subsectors.
change the performance of the funds
dramatically. When funds rebalance
daily, compounding causes the longterm
returns of the funds to deviate
from a simple long-term multiple of
the benchmark performance. Because
they rebalance less often, the monthly
funds would be less exposed to this
problem. However, investors who
bought in the middle of the month
would achieve a different kind of
return from investors who bought on
the first of the month.
Vanguard Launches
Ex-U.S. Int’l Small-Cap ETF
Vanguard launched an international
small-cap ETF on April 6. The
Vanguard FTSE All-World ex-US
Small-Cap ETF (NYSE Arca: VSS) is the
ETF share class of the Vanguard FTSE
All-World ex-US Small-Cap Index
Fund, which launched in March.
The ETF comes with an expense
ratio of 0.38 percent, the cheapest
in its field. It holds a massive 2,100
different names, and is the only
broad-based small-cap ETF to cover
developed as well as emerging markets
in a single fund.
ProShares Looks
To Triple Leverage
ProShares has requested that the
Securities & Exchange Commission
allow it to provide up to 300 percent
leverage and 300 percent inverse exposure
to more than 35 different indexes
covering the domestic stock market,
international stocks and fixed income.
Currently, Direxion is the only firm
offering ETFs providing 300 percent
exposure. ProShares’ offerings currently
provide no more than 200 percent
exposure. Its chief rival, Rydex, is also
awaiting approval from the SEC of its
own proposed triple-exposure ETFs.
Note that the filing does not say
what the exact exposure of the funds
eventually launched based on the filing
will be—just that it will be up to 300
percent; meaning, for example, that
250 percent exposure would be another
possible option for the company.
48
July/August 2009

No portfolio details were provided
by ProShares about the focus
of any future 3 times leveraged or
inverse fund.
ETFS Files For Platinum,
Palladium Bullion ETFs In U.S.
April filings by London-based ETF
Securities (ETFS) indicate that U.S.
investors may soon be able to access
two precious metals via products
structured in a way similar to the SPDR
Gold Shares ETF (NYSE Arca: GLD). The
ETFS Palladium Trust and the ETFS
Platinum Trust each will hold physical
bullion of its respective metal.
ETF Securities already offers platinum
and palladium bullion ETFs in
Europe. The most recent filings bring
the number of ETFS products in registration
in the U.S. to four—all of them
trusts that will hold precious metals
(silver, gold, palladium and platinum).
Although the silver and gold trusts will
compete directly with offerings from
SSgA and BGI, the palladium and platinum
products will likely be the first
ETFs to cover their respective metals.
BACK TO THE FUTURES
CME Contracts Down
In April; E-Minis Up
CME Group’s average daily contract
volume in April 2009 was down
23 percent from April 2008, to 9.2
million. However, the group’s e-mini
equity index contract volume was
up 18 percent from the prior year,
to 2.9 million, representing nearly a
third of the average daily volume.
Although the e-mini contracts are
by no means the sole index-based
contracts trading on the CME, they
do represent the vast majority of the
volume in index-based products.
KNOW YOUR OPTIONS
CBOE Volumes Grow In April
The Chicago Board Options Ex -
change saw April’s average daily volume
jump to over 5 million contracts
this year, a 28 percent increase over
the prior year.
While average daily volume for
News
cash-settled index options was up
just 4 percent, ADV for ETF options
rose a more significant 21 percent.
Combined together, the two categories
are slightly less than 40
percent of the total average daily
volume for the exchange.
FROM THE EXCHANGES
ICE Clears $70 Billion In CDS
The Intercontinental Exchange
said in April that it cleared $70
billion in CDS contracts in its first
four weeks of operation as a CDS
clearinghouse. The ICE US Trust is
the only CDS clearinghouse in the
country that is currently handling
trades, at least for now.
CME Group, partnered with Citadel
Investment Group, has also received
regulatory approval to operate a CDS
clearing platform, but its CMDX has
yet to open for business.
CME Buys Hurricane Index
In April, CME Group announced
the acquisition of the Carvill Hurricane
Index from Carvill America
Inc. The index underlies futures
and options contracts listed with
the CME and has been renamed the
CME Hurricane Index.
The exchange has also enlisted
risk management firm EQECAT as the
index’s calculation agent. EQECAT
uses risk modeling technology based
on a storm’s velocity and radius to
determine the index’s value.
NYSE, SGX Terminate JV
NYSE Euronext and the Singapore
Exchange Ltd. (SGX) announced the
termination of a joint venture signed
by SGX and the American Stock
Exchange, which has since been
acquired by the NYSE. However, the
two exchange groups said that they
would continue to collaborate with
each other. They also said there was
no longer a need for the joint venture
due to good relations between
the two exchanges.
The joint venture dates back to
2001 and essentially created a platform
for cross-listed U.S. ETFs to
trade on the Singapore exchange. The
platform will be terminated and the
six ETFs listed on it will be migrated
to the SGX’s main trading platform.
All but one are U.S.-listed ETFs and
include the SPDR S&P 500 (NYSE
Arca: SPY) and the DIAMONDS (NYSE
Arca: DIA). In all, the SGX says it has a
total of roughly 30 listed ETFs.
ON THE MOVE
ETF Securities Adds Execs
ETF Securities has added two
senior executives to its team.
Scott Thompson, who joins the
firm as head of UK and Irish sales,
was previously head of ETF marketmaking
at JP Morgan, where he
set up the ETF operation in 2006.
Thompson also has a background in
the derivatives team at UBS.
Townsend Lansing is joining the
firm’s product development and
structuring team as a senior manager.
He previously worked at Bank of
America in equity derivatives product
development, and also as a lawyer
in capital markers, mergers and
acquisitions, and private equity.
ETF Securities’ headcount has
increased from 17 to 40 in the last
12 months.
IndexIQ Gains Two VPs
Kevin DiSano and Andrew Cook
have both joined IndexIQ as vice
presidents and regional directors,
the firm said in May.
DiSano will be in charge of the
firm’s sales in the Midwest. He was
previously a senior vice president in the
capital advisory group at Lazard Asset
Management and has 17 years of experience
in the financial services field.
Cook will oversee sales in the
Eastern U.S. He joins IndexIQ from
The Charles Schwab Company, where
he was involved in sales efforts
directed at registered investment
advisers and was a vice president,
regional director and new business
manager. He has 16 years’ experience
in the investment industry.
www.journalofindexes.com
July/August 2009
49

Global Index Data
Selected Major Indexes Sorted By YTD Returns July/August 2009
Total Return % Annualized Return %
Index Name YTD 2008 2007 2006 2005 2004 2003 2002 3-Yr 5-Yr 10-Yr 15-Yr
MSCI Brazil*
MSCI Indonesia*
MSCI EM Latin America
Barcap High Yield Corp Bond
MSCI EM
S&P 500-Information Tech
NASDAQ 100*
Russell Mid Cap Growth
JPM EMBI Global Diversified
S&P MidCap 400/Citi Growth
Barcap Municipal
Russell 1000 Growth
Russell Mid Cap
Russell 3000 Growth
S&P Midcap 400
MSCI EAFE Small Cap
Russell 2000 Growth
Barcap Mortgage-Backed
FTSE AW Ex US
S&P MidCap 400/Citi Value
S&P 500/Citi Growth
CS Tremont Hedge Fund
Barcap Aggregate Bond
FTSE All World
USTREAS Treasury Bill 3 Mon
Russell Mid Cap Value
USTreas Const Mat Treas 3 Yr
S&P SmallCap 600/Citi Growth
MSCI EAFE Value
Russell 1000
Russell 3000
Russell 2000
S&P 1500
Barcap Government
S&P Smallcap 600
Barcap Global Aggregate
S&P 500
MSCI Europe
MSCI EAFE
MSCI Pacific
S&P SmallCap 600/Citi Value
Citi WGBI
MSCI EAFE Growth
S&P 100
JPM GBI Global Ex US
Dow Jones - AIG Commodity
Dow Jones Industrial
MSCI World/Real Estate
S&P 500/Citi Value
Russell 2000 Value
Russell 3000 Value
DJ Composite Average
Russell 1000 Value
DJ Utilities Average
FTSE NAREIT All REITs
DJ Transportation Average
S&P GSCI
S&P 500 Sec/Financials
USTreas Const Mat Treas 20 Yr
DJ US Select Dividend
33.06
31.86
23.20
18.81
17.75
16.91
15.08
10.37
9.60
8.24
6.31
5.09
5.01
5.00
4.93
4.33
3.84
2.49
2.18
1.72
1.63
0.86
0.60
0.39
0.07
-0.43
-0.69
-0.76
-0.94
-1.39
-1.42
-1.81
-1.93
-2.30
-2.33
-2.38
-2.50
-2.64
-2.92
-3.45
-3.83
-4.78
-4.84
-4.96
-5.32
-5.63
-5.86
-6.47
-6.85
-6.89
-7.76
-7.82
-7.85
-8.64
-10.14
-10.45
-11.46
-12.91
-13.11
-14.63
-57.64
-57.57
-51.41
-26.16
-53.33
-43.14
-41.89
-44.33
-12.03
-37.61
-2.47
-38.44
-41.46
-38.44
-36.23
-47.01
-38.54
8.34
-45.26
-34.88
-34.92
-19.07
5.24
-41.76
1.51
-38.45
8.45
-32.95
-44.09
-37.60
-37.31
-33.79
-36.72
12.39
-31.07
4.79
-37.00
-46.42
-43.38
-36.42
-29.51
10.89
-42.70
-35.31
11.40
-35.65
-31.93
-48.09
-39.22
-28.92
-36.25
-27.94
-36.85
-27.84
-37.34
-21.41
-46.49
-55.32
27.08
-30.97
75.35
50.81
50.40
1.88
39.39
16.31
18.67
11.43
6.16
13.50
3.36
11.81
5.60
11.40
7.98
1.45
7.05
6.90
18.17
2.65
9.13
12.56
6.97
12.74
4.77
-1.42
9.27
5.60
5.96
5.77
5.14
-1.57
5.47
8.66
-0.30
9.48
5.49
13.86
11.17
5.30
-5.54
10.95
16.45
6.12
11.30
16.23
8.89
-5.57
1.99
-9.78
-1.01
8.88
-0.17
20.11
-17.83
1.43
32.67
-18.63
10.50
-5.16
40.52
69.61
43.15
11.85
32.17
8.42
6.79
10.66
9.87
5.81
4.84
9.07
15.26
9.46
10.32
19.31
13.35
5.22
28.10
14.62
11.01
13.87
4.33
22.19
5.07
20.22
3.75
10.54
30.38
15.46
15.72
18.37
15.34
3.48
15.12
6.64
15.79
33.72
26.34
12.20
19.57
6.12
22.33
18.47
6.84
2.07
19.05
39.86
20.80
23.48
22.34
15.71
22.25
16.63
34.35
9.81
-15.09
19.19
0.99
19.56
49.96
12.56
50.00
2.74
34.00
0.99
1.49
12.10
10.25
14.42
3.51
5.26
12.65
5.17
12.56
26.19
4.15
2.61
17.27
10.77
1.14
7.61
2.43
11.72
3.34
12.65
0.76
7.07
13.80
6.27
6.12
4.55
5.66
2.65
7.68
-4.49
4.91
9.42
13.54
22.64
8.36
-6.88
13.28
1.17
-9.24
21.36
1.72
14.86
8.71
4.71
6.85
9.49
7.05
25.14
8.29
11.65
25.55
6.48
7.77
3.78
30.49
44.54
39.44
11.13
25.55
2.56
10.44
15.48
11.62
15.78
4.48
6.30
20.22
6.93
16.48
30.78
14.31
4.70
21.73
17.18
6.97
9.64
4.34
16.14
1.43
23.71
0.26
24.29
24.33
11.41
11.95
18.33
11.78
3.48
22.65
9.27
10.88
20.88
20.25
18.98
21.09
10.35
16.12
6.43
12.05
9.15
5.31
35.69
15.03
22.25
16.94
15.58
16.49
30.24
30.41
27.73
17.28
10.91
8.31
18.14
102.85
69.97
73.52
28.97
55.82
47.23
49.12
42.71
22.21
37.32
5.31
29.75
40.06
30.97
35.62
61.35
48.54
3.07
41.60
33.80
27.08
15.44
4.10
34.39
1.05
38.07
0.98
38.50
45.30
29.89
31.06
47.25
29.59
2.36
38.79
12.51
28.69
38.54
38.59
38.48
39.20
14.91
31.99
26.25
18.63
23.93
28.28
36.34
30.36
46.03
31.14
29.40
30.03
29.39
38.47
31.84
20.72
31.13
1.10
30.16
-33.78
38.11
-22.50
-1.41
-6.17
-37.41
-37.58
-27.41
13.65
-19.67
9.61
-27.88
-16.19
-28.04
-14.53
-7.82
-30.26
8.75
-14.33
-9.43
-28.10
3.04
10.26
-18.86
1.68
-9.65
7.83
-16.57
-15.91
-21.65
-21.54
-20.48
-21.31
11.50
-14.63
16.52
-22.10
-18.38
-15.94
-9.29
-12.93
19.50
-16.02
-22.59
22.09
25.91
-15.01
-6.39
-16.59
-11.43
-15.18
-15.94
-15.52
-23.38
5.22
-11.48
32.07
-14.64
17.69
-3.94
2.04
0.00
1.07
-1.16
-5.51
-6.63
-6.41
-11.16
3.41
-8.79
3.88
-8.49
-11.62
-8.81
-9.95
-16.61
-12.10
7.71
-9.88
-11.22
-7.89
-0.27
6.01
-10.09
3.26
-12.56
6.83
-11.26
-12.77
-10.76
-10.94
-12.72
-10.76
7.71
-11.85
5.47
-10.77
-11.97
-12.34
-13.25
-12.54
6.83
-12.00
-9.79
6.81
-11.58
-8.05
-17.17
-13.78
-13.47
-13.25
-7.80
-13.21
-2.31
-17.43
-11.07
-20.16
-29.50
9.83
-14.15
26.73
15.43
22.97
2.34
11.10
-1.35
-0.10
-0.76
7.01
0.91
4.12
-2.39
0.01
-2.32
0.56
0.26
-1.67
6.04
3.52
0.12
-2.78
3.60
4.78
0.70
3.16
0.07
4.42
-0.11
1.09
-2.32
-2.26
-1.45
-2.33
5.61
-0.13
4.87
-2.70
0.98
0.66
-0.16
-0.22
5.55
0.16
-3.36
5.64
-2.76
-2.00
-0.47
-2.78
-1.42
-2.42
1.40
-2.50
7.75
-1.64
3.03
-7.01
-14.93
7.76
-4.04
13.01
10.09
14.02
3.56
8.24
-5.77
-4.18
0.02
9.91
5.84
4.78
-4.40
3.00
-4.15
4.86
-
-1.06
6.15
2.20
3.85
-4.00
7.02
5.71
-0.12
3.21
3.79
4.89
5.04
1.52
-2.03
-1.71
2.53
-1.71
6.04
5.25
5.32
-2.48
0.02
-0.03
-0.22
5.25
5.80
-1.75
-3.16
5.56
6.02
-0.61
1.33
-1.60
5.51
-0.08
0.70
-0.50
4.46
5.27
-0.23
4.43
-6.36
7.43
2.62
10.33
-1.72
-
5.65
-
8.59
9.18
6.28
11.74
-
5.67
5.46
8.22
5.22
9.63
-
3.31
6.83
4.12
-
6.69
9.28
6.48
5.00
3.88
8.71
5.45
-
4.34
6.57
6.45
5.91
-
6.61
-
6.15
6.46
5.89
2.80
-1.22
-
6.19
1.15
6.61
6.13
5.32
7.76
-
-
7.91
7.03
7.61
7.02
7.94
6.62
5.79
3.06
4.70
8.34
8.89
Source: Morningstar. Data as of April 30, 2009. 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.
Sharpe
0.17
0.16
0.12
-0.19
-0.12
-0.30
-0.30
-0.53
0.08
-0.43
0.14
-0.54
-0.55
-0.55
-0.49
-0.74
-0.54
1.30
-0.45
-0.55
-0.56
-0.44
0.67
-0.55
-
-0.58
1.12
-0.53
-0.59
-0.67
-0.67
-0.58
-0.67
0.91
-0.55
0.34
-0.69
-0.56
-0.63
-0.76
-0.56
0.46
-0.65
-0.68
0.41
-0.58
-0.60
-0.60
-0.77
-0.61
-0.76
-0.57
-0.77
-0.26
-0.44
-0.46
-0.71
-0.93
0.49
-0.76
Std Dev
39.12
44.18
33.66
15.97
31.24
23.77
23.48
23.26
12.61
22.83
5.43
19.04
23.15
19.35
22.59
24.54
24.14
3.20
23.90
22.73
17.65
7.81
4.06
21.20
0.57
23.69
3.11
23.26
23.65
19.06
19.35
23.79
19.03
4.74
23.58
6.98
18.66
23.54
22.10
20.12
24.22
8.24
21.09
17.77
9.20
22.48
17.08
29.25
20.67
24.22
20.19
17.38
19.94
16.40
35.84
25.45
29.67
34.05
14.67
21.28
www.journalofindexes.com July/August 2009
51

Global Index Data
Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions July/August 2009
Fund Name Ticker Assets Exp Ratio 3-Mo YTD
Vanguard Tot Stk
Vanguard 500 Index
Vanguard Inst Idx
Vanguard 500 Idx Adm
Vanguard Tot Stk Adm
Vanguard Inst Idx InstPl
Vanguard Total Intl Stk
Vanguard Total Bd Idx
Vanguard Total Bond 2 Inv
Vanguard Total Bd Idx Ad
Fidelity Spar US EqIx
Vanguard 500 Index Signal
Vanguard Total Bd Idx In
Vanguard Tot Stk Inst
Fidelity U.S. Bond Index
Vanguard TotBdMkt Idx Sig
T. Rowe Price Eq Idx 500
Vanguard Tot Stk InstPls
Vanguard Eur Stk Idx
Fidelity Spar 500 Adv
Fidelity Spar 500 Idx
Fidelity 100 Index
Vanguard Mid Cap Idx
Vanguard Em Mkt Idx
Vanguard Gr Idx
Fidelity Spar US Eq Adv
Fidelity Spar Tot Mkt Ix
Vanguard Inst Tot Bd Idx
Vanguard SmCp Idx
Vanguard Mid Cap Idx Ins
Bernstein Tx-Mgd Intl
Vanguard Sh-Tm Bd Idx
Vanguard Inst DevMktsIdx
Vanguard TotStMkt Idx Sig
Vanguard Sh-Tm Bd Sgnl
Fidelity Spar Intl Index
Vanguard Intm Bd Idx
Vanguard Eur Stk Idx Ins
Vanguard ExtMktIdx
Vanguard Pac Stk Idx
Vanguard SmCp Idx Ins
Vanguard Bal Idx
Fidelity Spar Tot Mkt Adv
Vanguard ExtMktIdx Instl
Vanguard Val Idx
Vanguard REIT Index
Schwab Instl Sel S&P 500
Vanguard SmCp Vl Idx
VALIC I Stock
Vanguard Intm Bd Idx Adm
Vanguard Bal Idx Instl
Dimensional USLgCo
Schwab S&P 500 In Sel
Schwab S&P 500 In Inv
Vanguard LongTm Bd Idx
Vanguard Dev Mkts Idx
Vanguard Gr Idx Instl
Vanguard SmCp Gr Idx
Vanguard Tx-Mgd App Adm
Schwab 1000 In Inv
VTSMX
VFINX
VINIX
VFIAX
VTSAX
VIIIX
VGTSX
VBMFX
VTBIX
VBTLX
FUSEX
VIFSX
VBTIX
VITSX
FBIDX
VBTSX
PREIX
VITPX
VEURX
FSMAX
FSMKX
FOHIX
VIMSX
VEIEX
VIGRX
FUSVX
FSTMX
VITBX
NAESX
VMCIX
SNIVX
VBISX
VIDMX
VTSSX
VBSSX
FSIIX
VBIIX
VESIX
VEXMX
VPACX
VSCIX
VBINX
FSTVX
VIEIX
VIVAX
VGSIX
ISLCX
VISVX
VSTIX
VBILX
VBAIX
DFLCX
SWPPX
SWPIX
VBLTX
VDMIX
VIGIX
VISGX
VTCLX
SNXFX
41,529.0
38,045.6
32,431.0
21,950.9
19,374.4
18,972.8
17,680.6
17,043.3
14,441.9
13,875.6
13,614.5
13,351.9
12,534.3
11,061.4
9,469.6
7,626.8
7,408.2
7,029.1
6,026.5
5,957.8
5,298.0
5,066.1
4,826.4
4,497.2
4,450.1
4,395.9
4,238.4
4,226.0
4,222.8
4,201.3
4,085.8
4,037.1
3,725.7
3,581.8
3,483.3
3,366.5
3,265.6
3,234.3
3,183.6
3,048.0
2,793.6
2,757.8
2,755.1
2,709.3
2,651.4
2,502.3
2,475.6
2,400.8
2,377.1
2,371.1
2,360.6
2,271.7
2,251.7
2,208.9
2,165.6
2,165.5
2,154.7
2,029.4
1,995.5
1,983.4
0.19
0.18
0.05
0.09
0.09
0.03
0.32
0.20
-
0.11
0.10
0.09
0.07
0.06
0.32
0.11
0.35
0.03
0.27
0.07
0.10
0.20
0.22
0.45
0.22
0.07
0.10
0.05
0.23
0.08
1.12
0.18
0.12
0.09
0.10
0.10
0.18
0.12
0.25
0.32
0.08
0.20
0.07
0.07
0.21
0.21
0.10
0.23
0.36
0.11
0.08
0.15
0.19
0.37
0.18
0.27
0.08
0.23
0.10
0.50
7.62
6.46
6.48
6.49
7.69
6.49
11.47
1.41
1.31
1.43
6.50
6.50
1.45
7.70
1.71
1.43
6.44
7.70
9.14
6.52
6.51
4.64
12.40
26.54
9.59
6.50
7.66
1.44
13.98
12.47
4.78
0.87
8.15
7.68
0.89
7.77
0.76
9.14
13.50
6.03
14.10
5.57
7.67
13.54
4.12
7.96
6.30
12.71
6.38
0.78
5.61
6.54
6.43
6.37
-1.17
8.08
9.69
15.28
7.62
7.08
-1.27
-2.50
-2.46
-2.47
-1.20
-2.45
-1.85
0.71
-
0.73
-2.45
-2.47
0.75
-1.24
1.26
0.73
-2.52
-1.22
-5.83
-2.45
-2.46
-4.97
4.50
16.17
4.56
-2.44
-1.23
0.71
2.30
4.58
-6.79
1.06
-5.76
-1.22
1.08
-6.02
-0.58
-5.83
3.90
-5.61
2.41
0.10
-1.22
3.94
-7.80
-10.82
-2.67
-1.97
-3.07
-0.55
0.07
-2.37
-2.51
-2.59
-7.34
-5.72
4.61
6.66
-0.76
-1.56
Total Return % Annualized Return %
2008 2007 3-Yr
-37.04
-37.02
-36.95
-36.97
-36.99
-36.94
-44.10
5.05
-
5.15
-37.03
-36.97
5.19
-36.94
3.76
5.15
-37.06
-36.89
-44.73
-37.03
-37.05
-35.44
-41.82
-52.81
-38.32
-37.01
-37.18
5.05
-36.07
-41.76
-49.04
5.43
-41.42
-36.99
5.51
-41.43
4.93
-44.65
-38.73
-34.36
-35.98
-22.21
-37.16
-38.58
-35.97
-37.05
-36.85
-32.06
-37.21
5.01
-22.10
-36.78
-36.73
-36.88
8.64
-41.62
-38.19
-40.00
-37.58
-37.28
5.49
5.39
5.48
5.47
5.57
5.50
15.52
6.92
-
7.02
5.43
5.47
7.05
5.56
5.38
7.02
5.18
5.62
13.82
5.46
5.43
-
6.02
38.90
12.57
5.46
5.57
7.01
1.16
6.22
7.37
7.22
11.04
5.55
7.28
10.72
7.61
13.96
4.33
4.78
1.29
6.16
5.60
4.51
0.09
-16.46
5.52
-7.07
5.13
7.70
6.34
5.44
5.50
5.34
6.59
10.99
12.73
9.63
6.11
5.76
-10.75
-10.84
-10.74
-10.76
-10.66
-10.72
-11.32
5.99
-
6.08
-10.80
-10.78
6.12
-10.64
5.16
6.07
-10.96
-10.58
-12.07
-10.79
-10.82
-
-12.32
-6.14
-8.49
-10.78
-10.75
6.00
-11.99
-12.19
-18.06
5.83
-12.23
-10.68
5.89
-12.44
5.92
-11.96
-11.68
-13.14
-11.85
-3.94
-10.72
-11.52
-12.58
-16.96
-10.74
-12.75
-11.20
6.00
-3.84
-10.65
-10.67
-10.84
5.14
-12.38
-8.34
-11.50
-10.66
-10.72
Source: Morningstar. Data as of April 30, 2009. 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.
5-Yr 10-Yr 15-Yr P/E Std Dev Yield
-2.10
-2.80
-2.69
-2.71
-2.00
-2.66
2.04
4.71
-
4.80
-2.75
-2.76
4.84
-1.98
4.27
4.75
-2.94
-1.92
0.86
-2.74
-2.76
-
0.00
10.56
-2.26
-2.73
-2.05
4.78
-0.60
0.14
-3.23
4.14
0.83
-2.05
4.17
0.64
4.55
1.00
-0.46
0.13
-0.44
0.93
-2.03
-0.28
-2.29
-0.59
-2.70
-0.90
-3.12
4.63
1.04
-2.66
-2.67
-2.85
4.82
0.69
-2.12
-0.54
-2.08
-2.44
-1.59
-2.56
-2.45
-2.49
-1.53
-2.42
0.87
5.44
-
5.51
-2.58
-2.54
5.57
-1.48
5.46
5.47
-2.73
-
0.05
-2.57
-2.58
-
4.43
7.84
-3.50
-2.57
-1.69
-
3.27
4.58
0.17
4.91
-
-1.57
4.92
-0.14
5.85
0.16
1.26
-0.37
3.43
1.59
-1.68
1.44
-1.29
5.43
-2.54
5.18
-2.87
5.91
1.69
-2.54
-2.54
-2.71
6.19
-
-3.37
4.18
-1.75
-2.14
6.35
6.38
6.51
6.43
6.40
6.53
-
6.28
-
6.32
6.32
6.40
6.39
6.45
6.29
6.29
6.18
-
5.97
6.30
6.29
-
-
-
6.72
6.32
-
-
6.67
-
3.25
5.49
-
6.37
5.50
-
6.63
6.05
6.72
-1.28
6.80
6.70
-
6.85
6.16
-
-
-
6.13
6.66
6.77
6.36
-
-
7.39
-
6.82
-
-
6.32
11.0
10.9
10.9
10.9
11.0
10.9
6.7
-
-
-
11.1
10.9
-
11.0
-
-
11.1
10.9
7.4
11.1
11.1
11.3
10.6
8.3
12.6
11.1
11.1
-
11.5
10.6
7.0
-
8.4
11.0
-
8.6
16.5
7.4
11.5
2.9
11.5
10.9
11.1
11.5
9.6
18.0
10.0
10.3
9.4
16.5
10.9
10.9
10.0
10.0
9.0
8.4
12.6
13.1
10.9
10.1
19.26
18.65
18.64
18.65
19.27
18.64
23.89
4.07
-
4.07
18.67
18.65
4.07
19.25
3.71
4.07
18.62
19.28
23.98
18.66
18.67
-
22.66
31.28
18.87
18.66
19.25
4.10
24.60
22.68
22.93
2.48
22.57
19.25
2.48
22.67
6.66
23.98
23.29
20.49
24.60
12.16
19.24
23.30
19.59
38.30
18.60
25.19
18.76
6.66
12.14
18.57
18.58
18.55
11.60
22.60
18.87
25.01
19.18
18.78
2.68
3.02
3.06
3.12
2.77
3.09
3.07
4.62
-
4.70
2.98
3.12
4.75
2.81
4.50
4.70
2.68
2.77
7.79
3.06
3.02
3.17
1.91
4.04
1.29
3.01
2.60
4.64
1.92
2.11
3.95
3.48
5.89
2.77
3.56
3.83
4.77
8.08
1.62
1.81
2.12
3.67
2.63
1.88
4.34
9.19
3.01
2.93
3.32
4.85
3.82
3.10
3.13
2.90
5.65
5.61
1.48
0.78
2.45
2.48
52
July/August 2009

Morningstar U.S. Style Overview: U.S. Style January 1 - Overview April 30, 2009 Jan. 1 – Apr. 30, 2009
Trailing Returns %
3-Month YTD 1-Yr 3-Yr 5-Yr 10-Yr
Morningstar Indexes
US Market 7.51 –1.33 –34.70 –10.55 –1.88 –1.72
Large Cap 5.51 –3.07 –34.82 –10.06 –2.52 –3.32
Mid Cap 12.27 3.46 –36.02 –11.97 –0.22 2.27
Small Cap 14.89 2.92 –30.48 –12.28 –0.82 4.17
US Value 3.42 –8.48 –38.51 –13.07 –2.28 0.11
US Core 7.19 –2.67 –31.94 –8.87 –0.72 0.09
US Growth 11.91 8.00 –34.17 –10.20 –3.14 –6.15
Morningstar Market Barometer YTD Return %
Large Cap
US Market
–1.33
–3.07
Value
–8.48
Core
–2.67
Growth
8.00
–11.25 –5.05 8.51
Large Value –0.16 –11.25 –40.68 –13.29 –2.99 –1.33
Large Core 5.24 –5.05 –31.42 –7.95 –0.88 –1.25
Large Growth 11.52 8.51 –33.04 –9.55 –4.45 –8.29
Mid Cap
3.46
–1.18 4.19 7.48
Mid Value 12.52 –1.18 –33.21 –13.04 –0.76 3.51
Mid Core 11.80 4.19 –35.22 –11.43 –0.71 2.98
Mid Growth 12.55 7.48 –39.51 –11.79 0.45 –0.41
Small Cap
2.92
–0.92 5.25 4.38
Small Value 14.81 –0.92 –30.79 –12.26 –0.66 5.73
Small Core 16.35 5.25 –31.02 –12.63 –0.24 7.32
Small Growth 13.57 4.38 –29.82 –12.38 –1.98 –0.34
–8.00 –4.00 0.00 +4.00 +8.00
Sector Index YTD Return %
Hardware 20.29
Software 9.27
Industry Leaders & Laggards YTD Return %
Auto Makers 118.88
Plastics 65.40
Biggest Influence on Style Index Performance
Best Performing Index
YTD
Return %
Large Growth 8.51
Constituent
Weight %
Consumer Services 8.80
Media 2.55
Business Services 1.53
Industrial 0.79
–2.38 Telecommunications
–4.35 Energy
–5.74 Consumer Goods
–7.37 Healthcare
–10.60 Utilities
–12.17 Financial Services
Department Stores 49.21
Rubber Products 48.54
Hospitals 42.36
Online Retail 39.40
–22.05 Insurance (Life)
–22.52 Personal Services
–26.36 Regional Banks
–28.66 Super Regional Banks
–38.02 Air Transport
–53.65 Photography & Imaging
Apple Inc. 47.43 3.91
Google Inc. Cl A 28.71 3.78
Cisco Systems Inc. 18.53 4.95
Qualcomm Inc. 18.67 3.03
Schering-Plough Corp. 36.02 1.42
Worst Performing Index
Large Value –11.25
Exxon Mobil Corp. –16.07 18.69
Pfizer Inc. –22.91 5.50
Bank of America Corp. –36.40 3.25
Citigroup Inc. –54.43 1.68
Chevron Corp. –9.81 6.92
1-Year
3-Year
5-Year
Value
Core
Growth
Value
Core
Growth
Value
Core
Growth
Large Cap
–40.68
–31.42
–33.04
Large Cap
–13.29
–7.95
–9.55
Large Cap
–2.99
–0.88
–4.45
Mid Cap
–33.21
–35.22 –39.51
Mid Cap
–13.04
–11.43 –11.79
Mid Cap
–0.76
–0.71 0.45
Small Cap
–30.79
–31.02 –29.82
Small Cap
–12.26
–12.63 –12.38
Small Cap
–0.66
–0.24 –1.98
–20 –10 0 +10 +20
–20 –10 0 +10 +20
–20 –10 0 +10 +20
Source: Morningstar. Data as of 4/30/09
Source: Morningstar. Data as of 2/29/08
Notes and Disclaimer: ©2009 Morningstar, Inc. All Rights Reserved. Unless otherwise noted, all data is as of most recent month end. Multi-year returns are annualized. NA: Not Available. Biggest Influence on Index Performance lists
are calculated by multiplying stock returns for the period by their respective weights in the index as of the start of the period. Sector and Industry Indexes are based on Morningstar's proprietary sector classifications. The information ?
contained herein is not warranted to be accurate, complete or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information.
www.journalofindexes.com
July/August 2009
53

Dow Jones U.S. Economic Sector Review
Dow Jones U.S. Industry Indexes
Performance
Index Name Weight 1-Month 3-Month YTD 1-Year 3-Year 5-Year 10-Year
Dow Jones U.S. Index 100.00% 10.35% 7.56% -1.24% -34.77% -10.56% -1.99% -1.81%
Dow Jones U.S. Basic Materials Index 2.91% 17.95% 22.48% 12.87% -46.76% -8.88% 1.99% 0.77%
Dow Jones U.S. Consumer Goods Index 10.27% 7.38% 3.60% -4.07% -24.94% -4.35% -0.48% 1.45%
Dow Jones U.S. Consumer Services Index 12.21% 12.78% 16.24% 6.09% -24.63% -9.07% -3.56% -3.17%
Dow Jones U.S. Financials Index 13.47% 19.98% 16.15% -11.65% -52.65% -26.37% -12.20% -4.28%
Dow Jones U.S. Health Care Index 12.81% -0.47% -6.02% -7.23% -20.74% -5.55% -1.96% 0.49%
Dow Jones U.S. Industrials Index 12.66% 18.00% 8.29% -3.86% -40.20% -12.14% -1.42% -1.43%
Dow Jones U.S. Oil & Gas Index 11.86% 6.88% -1.84% -4.38% -40.95% -3.87% 10.93% 7.93%
Dow Jones U.S. Technology Index 16.38% 12.26% 21.20% 18.00% -25.45% -6.33% -0.29% -4.60%
Dow Jones U.S. Telecommunications Index 3.28% 4.28% 7.46% -3.45% -28.13% -4.75% 1.29% -7.83%
Dow Jones U.S. Utilities Index 4.15% 1.13% -9.44% -10.42% -34.66% -4.12% 4.76% 3.13%
Risk-Return
0%
3-Year Annualized Return
-5%
-10%
-15%
-20%
-25%
Consumer Goods
Utilities
Health Care
Composite
Telecommunications
Consumer Services
Oil & Gas
Technology
Industrials
Basic Materials
Financials
-30%
14% 16% 18% 20% 22% 24% 26% 28% 30% 32%
3-Year Annualized Risk
Industry Weights Relative to World ex-U.S.
Basic Materials
-7.30%
Asset Class Performance
U.S. [71.54] World ex-U.S. [70.38] Commodities [69.14]
REITs [53.69] Infrastructure [88.05]
Consumer Goods
-2.87%
160
Consumer Services
4.34%
140
Financials
-9.78%
Health Care
6.63%
120
Industrials
-0.15%
100
Oil & Gas
2.28%
80
Technology
11.02%
Telecommunications
-2.85%
60
Utilities
-1.34%
40
-15% -10% -5% 0% 5% 10% 15%
Underweight Overweight
20
4/06 7/06 10/06 1/07 4/07 7/07 10/07 1/08 4/08 7/08 10/08 1/09 4/09
Chart compares industry weights within the Dow Jones U.S. Index to industry weights within the Dow Jones U.S. = Dow Jones U.S. Index | World ex-U.S. = Dow Jones World ex-U.S. Index
World 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
© Dow Jones & Company, Inc. 2009. All rights reserved."Dow Jones", "Dow Jones Indexes", "Dow Jones U.S. Index", "Dow Jones World ex-U.S. Index" and "Dow Jones U.S. Industry Indexes" are service marks of Dow Jones & Company, Inc. "UBS" is a registered trademark of UBS AG. "Dow Jones-UBS Commodity Index" is a service
mark of Dow Jones & Company, Inc. and UBS. "Brookfield" is a service mark of Brookfield Asset Management Inc. or its affiliates. The "Dow Jones Brookfield Infrastructure Indexes" are published pursuant to an agreement between Dow Jones & Company, Inc. and Brookfield Asset Management. Investment products that may be based
on the indexes referencedare not sponsored,endorsed, sold or promoted by Dow Jones, and Dow Jones makes no representationregarding the advisability of investing in them. Inclusion of a company in these indexes does 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.
The Dow Jones U.S. Index, the Dow Jones World 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.
Data as of April 30, 2009
Source: Dow Jones Indexes Analytics & Research
For more information, please visit the Dow Jones Indexes Web site at www.djindexes.com.
Source: Dow Jones Indexes. Data through 4/30/2009.
Source:
54
July/August 2009

Exchange-Traded Funds Corner
Largest New ETFs Sorted By Total Net Assets In $US Millions
Covers ETFs and ETNs launched during the 12 months ended April 30, 2009
Fund Name
Direxion Financl Bear 3X
Direxion Financl Bull 3X
PowerShares DB Cr Oil Dbl Long
iShares MSCI AC Asia ex-Japan
ProShares Ultra DJ-AIG Cr Oil
Direxion Large Cp Bear 3X
Direxion Large Cp Bull 3X
Vanguard Total World
Direxion Small Cp Bull 3X
Direxion Small Cp Bear 3X
ProShares Short Financials
Direxion Energy Bull 3X
iShares Barclays Agncy Bd
ProShares Ultra Gold
WisdomTree Dryfs ChneseYn
PowerShares DB Cr Oil Dbl Short
JPMorgan Alerian MLP Index ETN
Market Vectors HiYld Muni
WisdomTree Dryfs Brazil Real
ProShares UltShort Yen
Ticker
FAZ
FAS
DXO
AAXJ
UCO
BGZ
BGU
VT
TNA
TZA
SEF
ERX
AGZ
UGL
CYB
DTO
AMJ
HYD
BZF
YCS
ER
0.95
0.95
0.75
0.74
0.95
0.95
0.95
0.25
0.95
0.95
0.95
0.95
0.20
0.95
0.45
0.75
0.85
0.35
0.45
0.95
YTD
-76.72
-67.99
7.06
15.84
-44.56
-23.63
-18.14
-1.64
-25.08
-37.94
-14.31
-31.44
0.18
-2.50
2.49
19.11
-
-
1.20
13.87
1-Mo
-59.84
47.82
-1.44
17.34
-9.86
-29.48
31.21
12.06
46.83
-42.21
-21.37
16.62
0.08
-7.51
-0.27
1.12
-
1.37
6.02
-1.23
3-Mo
-83.51
-11.09
1.87
26.15
-26.74
-35.81
8.25
10.34
7.90
-49.48
-30.04
-22.29
0.79
-11.47
1.77
-3.32
-
-
9.55
17.30
Launch Date
11/6/2008
11/6/2008
6/16/2008
8/13/2008
11/24/2008
11/5/2008
11/5/2008
6/24/2008
11/5/2008
11/5/2008
6/10/2008
11/6/2008
11/5/2008
12/1/2008
5/14/2008
6/16/2008
4/1/2009
2/4/2009
5/14/2008
11/24/2008
Source: Morningstar. Data as of April 30, 2009. ER is expense ratio. YTD is year-to-date. 1-Mo is 1-month. 3-Mo is 3-month.
Assets
1,307.6
1,136.3
633.8
426.3
369.2
340.5
284.6
251.5
219.1
180.3
155.8
141.1
139.8
126.5
96.8
86.8
83.6
78.7
69.8
68.6
Selected ETFs In Registration
ALPS Equal Sector Weight ETF
iShares Diversified Alternatives Trust
iShares MSCI ACWI ex US Industrials
Direxion Homebuilders Bull 3X Shares
Emrg Global Shares DJ Em Mkt Titans Tech
ETFS Palladium Trust
FocusShrs Progr Princpl Protn 2035 Tgt Date
Global X Argentina ETF
Grail American Beacon Intl Eqty
IQ ARB Merger Arbitrage ETF
sShares KLD N America Sustainability
PowrShrs Alt-A Non-Agency RMBS Opporty
ProShares Ultra CDX N America HiYield
SPDR Wells Fargo Preferred Stock ETF
SPDR S&P Municipal VRDO ETF
Market Vectors China A Share ETF
WisdomTree DEFA Hedged Fund
CurrencyShares Singapore Dollar Trust
PowerShares Ireland Portfolio
JETS DJ Islamic Market International
Source: IndexUniverse.com's ETF Watch
Largest U.S.-listed ETFs Sorted By Total Net Assets In $US Millions
Total Return % Annualized Return %
Fund Name Ticker Assets Exp Ratio 3-Mo YTD
2008 2007
3-Yr 5-Yr Mkt Cap P/E
Sharpe Std Dev Yield
SPDRs (S&P 500)
SPDR Gold Trust
iShares MSCI EAFE
iShares MSCI Emerg Mkts
iShares S&P 500
PowerShares QQQQ
iShares Lehman TIPS Bond
iShrs iBoxx $ Inv Grd Bnd
iShares Lehman Aggregate
Vanguard Total Stock Market
iShares R1000 Growth
iShares Russell 2000
iShares Lehman 1-3 Treas
iShares FTSE/Xinhua China
DIAMONDS Trust
MidCap SPDR (S&P 400)
Vanguard Emerging Markets
iShares R1000 Value
iShares Brazil
Financial SPDR
iShares S&P 500 Growth
iShares Japan
Energy SPDR
iShares Russell 1000
SPY
GLD
EFA
EEM
IVV
QQQQ
TIP
LQD
AGG
VTI
IWF
IWM
SHY
FXI
DIA
MDY
VWO
IWD
EWZ
XLF
IVW
EWJ
XLE
IWB
60,678.3
31,353.0
27,281.4
25,767.8
16,379.5
13,388.2
12,074.5
10,645.1
9,579.3
9,440.3
9,152.6
8,474.6
7,391.0
7,073.8
6,980.7
6,933.6
6,770.4
6,739.6
6,057.2
5,097.1
4,915.7
4,535.7
4,277.3
4,195.6
0.09
0.40
0.34
0.74
0.09
0.20
0.20
0.15
0.20
0.07
0.20
0.20
0.15
0.74
0.17
0.25
0.25
0.20
0.69
0.23
0.18
0.52
0.23
0.15
6.22
-4.42
8.32
26.58
6.43
18.15
1.73
-2.07
0.56
7.52
10.39
9.45
0.19
27.36
2.91
12.49
28.88
3.95
27.04
17.23
6.60
0.95
-1.91
7.10
-2.43
0.87
-6.55
14.82
-2.22
15.45
1.69
-3.82
-1.43
-1.14
4.73
-1.13
-0.25
9.94
-5.67
5.04
17.40
-8.08
28.86
-13.48
1.41
-11.17
-3.66
-1.40
-36.70
4.92
-41.01
-48.86
-37.01
-41.72
-0.53
2.44
7.90
-36.97
-38.21
-34.15
6.61
-47.73
-32.10
-36.40
-52.47
-36.45
-54.36
-54.90
-34.78
-26.97
-38.97
-37.39
5.12
31.10
9.94
33.11
4.92
19.13
11.93
3.76
6.61
5.37
11.49
-1.76
7.35
54.81
8.78
7.20
37.32
-0.73
74.84
-19.19
8.84
-5.50
36.86
5.31
-10.82
10.27
-12.67
-4.92
-10.81
-6.10
5.15
2.70
5.94
-10.68
-8.72
-12.89
5.64
8.47
-8.21
-10.32
-5.95
-13.28
3.56
-29.69
-8.10
-16.00
-5.88
-10.85
-2.70
-
0.55
11.94
-2.72
0.22
4.73
2.71
4.70
-1.99
-2.60
-1.47
3.96
-
-2.16
0.28
-
-2.58
28.99
-15.17
-2.46
-2.70
10.38
-2.41
33,667
-
21,933
16,486
34,710
25,618
-
-
-
22,073
26,222
678
-
59,593
81,810
1,919
10,635
27,475
14,939
18,983
44,444
10,215
42,330
26,805
11.1
-
9.7
10.1
12.1
17.6
-
-
-
11.0
13.2
12.9
-
9.9
10.0
11.4
8.3
11.1
8.1
12.5
12.2
8.3
7.0
12.2
-0.70
0.43
-0.62
-0.11
-0.70
-0.28
0.25
0.01
0.50
-0.66
-0.55
-0.61
1.19
0.32
-0.62
-0.52
-0.15
-0.79
0.21
-0.96
-0.57
-0.98
-0.23
-0.68
18.51
20.05
22.77
30.78
18.55
23.58
8.81
11.70
5.45
19.21
19.05
23.26
1.95
40.37
16.86
22.28
30.46
19.68
39.18
33.66
17.75
19.18
25.63
18.99
3.02
0.00
4.38
3.00
2.89
0.45
5.03
5.86
4.47
2.78
1.61
1.86
3.26
2.41
3.58
1.69
4.30
3.86
4.77
6.08
1.64
1.58
1.99
2.57
Source: Morningstar. Data as of April 30, 2009. Exp Ratio is expense ratio. 3-Mo is 3-month. YTD is year-to-date. Mkt Cap is geometric average market capitalization. P/E is price-to-earnings ratio.
Sharpe is Sharpe ratio. Std Dev is 3-year standard deviation. Yield is 12-month.
www.journalofindexes.com July/August 2009
55

Fly On The Wall
HUMOR
Inside The Dow Jones
Indexing Conclave
A glimpse into how
new Dow components
are really selected.
By Dave Nadig
It’s the dirty little secret of the index community—none
of us understands the Dow.
Sure, we can tell you what’s in it, but its allure
as a touchstone for the entire global stock
market seems inexplicable. We spend ages
running sophisticated quant models, and then
we turn to a price-weighted, overconcentrated
benchmark handpicked by a gaggle of journalists
when we refer to “the market.”
Enough is enough. The editors of the
Journal of Indexes decided to get to the bottom
of the Dow’s Svengali-like grasp of the
stock market world the only way we knew
how: subterfuge. Thus, we present this
transcript of a recent meeting of the Dow
Jones indexing cabal, obtained through
secret wiretaps in the DJIA Containment
Bunker. Unfortunately, the identities of this
“star chamber” remain unknown.
(Door opening)
Mr. Black: Mr. Pink, you’re late.
Mr. Pink: If you had to be Mr. Pink, you’d
be late too.
Black: You got Pink fair and square.
After all, you picked Bank of America to
enter the index in 2008.
We saw how that turned out.
Ms. White: Can we get on with things?
We’re all here now.
Black: I hereby call this meeting of the
Dow Jones Index Oversight Committee to
order. I remind my esteemed colleagues
that this meeting is to be held in strictest
confidence. We’ve maintained the illusion
that this is an “editorial” decision for far
too long to blow it now.
Ms. Brown: So GM’s out. Next?
Black: It’s not that simple and you know
it. General Motors has been part of our plan
for over 80 years. Should we not mourn its
passing with a moment of silence?
(Sighs, a brief pause.)
Brown: Right. Well then, all those in
favor of UPS?
White: You’re kidding, right? We’re
already under fire for being behind the
times. I’m thinking health care.
Brown: You aren’t talking Amgen again,
are you?
White: What’s wrong with Amgen? Are
we or are we not trying to represent the
real American economy?
Pink: Oh that’s a good one. The American
economy. Have you seen the American
economy lately? Personally, I think it’s time
we diversify. All these companies are international
anyway.
Black: Mr. Pink, we’ve been through this
before. You are NOT getting Toyota into
this index on my watch.
Pink: Come on, do we like cars or do we
not like cars? Who are you going to put in
then, Ford?
Black: Actually, I was going to suggest that.
Pink: Have you driven a Ford lately? I’m
guessing no, Mr. Lexus.
Brown: Well, OK, then how about Google?
I mean, Google’s just awesome. I never get
spam anymore since I switched to Gmail.
Pink: You should be off this committee.
How can you suggest Google after you convinced
us to boot Chevron for Intel in ’99?
White: He’s right, Brown. We’ll never
live down Chevron. Out in 1999, back in
2008, 140% higher. Nice work.
Black: Allow me to make a less controversial
suggestion. B of A for Wells Fargo.
Brown: Can I get a ride in the Stagecoach?
Black: Of course. So that’s done then.
Better than the last one we did.
Pink: Hey, Kraft was a good call.
Black: Oh right. I remember that argument
vividly: “Google’s fun to play around with,
but the country really runs on cheese.”
White: So then, for GM’s slot, we have
Amgen?
Black: Actually, I was thinking Goldman.
White: Are you serious? Goldman?
Half the country doesn’t even know what
Goldman does!
Black: That’s the beauty of it. Nobody
can argue effectively against what they
don’t understand.
(Mutterings. Silence.)
White: Can you promise me we’ll look at
Amgen next time?
Black: Let’s not be hasty. This whole
“biotech” thing might just be a fad.
Brown: Just like Gates said about the
Internet.
56
July/August 2009

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