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ETF Liquidity Explained
Paul Daley, Phil Dorencz and Dan Bargerstock
The Impact Of Market Models On Liquidity Lisa Dallmer
Fragmentation In The European ETF Market Bart Lijnse and Christiaan Scholtes
Share Lending And ETFs In Europe Leonard Welter
Plus European ETFs on the cusp, alpha in indexes, financial bubbles, target date indexes and more!
www.journalofindexes.com
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Indexes in each case for a specific article. The subscription fee entitles the subscriber to one copy only. Unauthorized copying is considered theft.
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10
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V o l . 1 3 N o . 2
ETF Liquidity ExplainedBy Paul Daley, Phil Dorencz and Dan Bargerstock 10A guide to discerning sufficient liquidity in ETFs.
The Impact Of Market Models On LiquidityBy Lisa Dallmer 18The role of liquidity providers.
The Fragmentation Of The European ETF MarketBy Bart Lijnse and Christiaan Scholtes 22Lack of unified market hurts ETF volumes in Europe.
A Big Bang In European ETF Trading?By Keshava Shastry 26Is Europe’s ETF market reaching a tipping point?
No Shortage Of Share LendingBy Leonard Welter 28Securities lending with ETFs in Europe.
Talking Indexes: Bubble DecisionsBy David Blitzer 30How do you know if the market is in a bubble?
Can Indexes Generate Alpha?By David Blanchett 32Indexes aren’t necessarily just beta.
The Future Of Fund Ratings, Part ThreeBy Gary Gastineau 38A look at freely available investor tools.
Creating A Better Target Date BenchmarkBy Grant Gardner and Mary Fjelstad 42A methodology for comparing families rather than funds.
Fixing The Flaws With Target Date FundsBy Navaid Abidi and Dirk Quayle 46Renovating the target date concept.
The ETFs Of 2010By Dave Nadig 68What’s in store for unsuspecting ETF investors in 2010?
f e a t u r e s
Lead Stories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Indexing Developments . . . . . . . . . . . . . . . . . . . . 56
Around The World Of ETFs . . . . . . . . . . . . . . . . . 58
Back To The Futures . . . . . . . . . . . . . . . . . . . . . . 61
Know Your Options . . . . . . . . . . . . . . . . . . . . . . . 61
From The Exchanges . . . . . . . . . . . . . . . . . . . . . . 61
On The Move . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Selected Major Indexes . . . . . . . . . . . . . . . . . . . . 62
Returns Of Largest U.S. Index Mutual Funds . . . . 63
U.S. Market Overview In Style . . . . . . . . . . . . . . . 64
U.S. Industry Review . . . . . . . . . . . . . . . . . . . . . . 65
Exchange-Traded Funds Corner . . . . . . . . . . . . . . 66
d a t a
n e w s
1March/April 2010www.journalofindexes.com
Contributors
March/April 20102
David Blanchett is a full-time MBA candidate at the University of Chicago Booth
School of Business. Previously, he was employed by Unified Trust Company in
Lexington, Ky., and Hilliard Lyons in Louisville, Ky. Blanchett holds an M.S. in
financial services through The American College and a BBA in finance and eco-
nomics from the University of Kentucky. He is also an Accredited Investment
Fiduciary Analyst and a Chartered Financial Analyst charterholder.
Paul Daley is a senior managing director of sales and trading at Fox River
Execution. He is the product manager for Fox Spotlight, Fox River’s agency block
ETF trading platform. Daley’s primary responsibilities include ensuring best
execution of orders for all of the company’s customers, new product develop-
ment initiatives and business development with new customers. He holds a B.A.
in economics and an MBA in finance from the University of Chicago.
Lisa Dallmer is executive vice president of global index services and exchange-
traded products for NYSE Euronext. Her primary responsibilities are the global
expansion of trading and listing services for exchange-traded products, and
developing multiple marketplaces in which the company is expanding. Dallmer
also oversees the expansion and marketing of NYSE Euronext’s index-related
operations. She holds an MBA from the University of Chicago.
Gary Gastineau is a managing member of Managed ETFs LLC and ETF
Consultants LLC; and a partner in Skyhawk Management, LLC, the adviser to a
market-neutral hedge fund that buys ETFs and sells them short. A new edition
of his book, “The Exchange-Traded Funds Manual,” was released by Wiley in
February. Gastineau is a frequent contributor to the Journal of Indexes.
Bart Lijnse is managing director of arbitrage trading firm Nyenburgh in
Amsterdam. He is part of the firm’s board of directors and holds final respon-
sibility for the firm’s arbitrage trading and IT. Prior to Lijnse’s appointment at
Nyenburgh in 2000, he worked for several years as a derivatives and structured
products trader at MeesPierson. Lijnse holds a master’s degree in mathematics
and computer science.
Keshava Shastry is a director and the head of markets looking after trading of
iShares ETFs within BlackRock. Prior to joining iShares nearly three years ago,
he worked at Citigroup for four years as an FX and interest rate trader. Shastry
has a master’s degree in mathematics and computer science from Imperial
College (London) and is also a Chartered Financial Analyst charterholder.
Leonard Welter is chief technology officer of Data Explorers. He joined Data
Explorers in October 2008 from Morgan Stanley, where he was an executive
director. At Morgan Stanley, Welter was responsible for the global develop-
ment of new securities lending analytic tools and trading systems. He was also
responsible for European securities lending portfolio pricing and trading of
equities and ETFs. Welter holds an MBA from the London Business School.
Pau
l D
ale
y
Lisa
Dall
mer
Gary
Gast
ineau
Bart
Lij
nse
Davi
d B
lan
chett
Kesh
ava
Sh
ast
ry
Leo
nard
Welt
er
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Editorial Board
David Blitzer: Standard & Poor’s
Lisa Dallmer: NYSE Euronext
Deborah Fuhr: BlackRock
Gary Gastineau: ETF Consultants
Joanne Hill: ProShares ETFs and ProFunds
John Jacobs: The NASDAq Stock Market
Lee Kranefuss: BlackRock
Kathleen Moriarty: Katten Muchin Rosenman
Jerry Moskowitz: FTSE
Don Phillips: Morningstar
John Prestbo: Dow Jones Indexes
James Ross: State Street Global Advisors
Gus Sauter: The Vanguard Group
Cliff Weber: NYSE Euronext
Review Board
Jan Altmann, Sanjay Arya, Jay Baker, William
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Jim Novakoff, Rick Redding, Anthony
Scamardella, Larry Swedroe, Jason Toussaint,
Mike Traynor, Jeff Troutner, Peter Vann,
Wayne Wagner, Peter Wall, Brad Zigler
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Editor’s Note
Jim Wiandt
Editor
Jim Wiandt
Editor
March/April 20108
Indexes And Trading: A Modern Marriage
Index investing has long had a bit of an awkward relationship with trading. Good index
fund management has always been to some degree about controlling trading costs, be
they commissions, spreads or market impact costs. After all, the point of good index
fund management is to minimize friction, and trading is the primary friction between an
index investor and obtaining the performance of an index.
ETFs—and now ETNs, ETCs and other ETPs—have complicated that equation even
more. ETFs are, as the acronym suggests, exchange-traded funds, bringing index funds
into tradable units. Add to that the myriad new ways to trade—through electronic trad-
ing networks, exchanges, off the floor and around the back—and you’ve got a complex
mix that any investor who wants to preserve something approaching market returns has
to figure out.
We’re excited about this issue because we’ve got some top-notch articles covering an
array of trading issues—with the balance tilted toward Europe—where we’ll be hosting
our first Inside ETFs Europe event, in Amsterdam on April 12-13. If trading is complicated
in the U.S., it’s a thicket of briars at midnight in Europe, where only a small percentage
of trading happens publicly.
Leading with the U.S. part of the trading issue are Paul Daley, Phil Dorencz and Dan
Bargerstock, with a very strong submission that looks at the rather complicated issue of
understanding how liquid an ETF really is (hint—the daily trading volume in the ETF is
not your best metric).
Next, Bart Lijnse and Christiaan Scholtes weigh in from across the pond with an out-
standing analysis of some of the complications of the fragmented European markets.
They are followed by Keshava Shastry’s column asserting that the European ETF industry
might be just about to turn the corner to find an ocean of trading liquidity. Wrapping
up the trading and liquidity portion of this issue, Leonard Welter offers a window on the
fascinating world of share lending in Europe.
Rounding out the lineup are David Blitzer with a sharply insightful piece on bubbles;
David Blanchett on alpha-generating indexes; Gary Gastineau with his final installment on
fund evaluation methods; two retirement/target date pieces—one from Grant Gardner
and Mary Fjelstad, and the other from Navaid Abidi and Dirk Quayle; and Dave Nadig with
a zany take on where the ETF industry is headed.
Like it or not, trading and indexes are forever married. You can remain in denial, or
find yourself a good counselor. This month the Journal of Indexes is board-certified to help
you work things out.
Jim Wiandt
Editor
Thoroughbreds perform better with blinders on.
Investors don’t.
Precise in a world that isn’t.TM
In today’s economy, many people feel like they’re investing in the dark. So it’s time someone shed a little light on
the matter. With State Street, you can choose from a stable of over 80 SPDR® ETFs. Which means it’s easy to precisely
match your investments to your investment strategy. Interested in Fixed Income? Gold? High-dividend stocks? Whatever
the market segment, you get exactly what’s on the label. Nothing more, nothing less. For a different breed of ETF, visit
spdrs.com. And see why wild horses couldn’t drag our customers away.
Before investing, consider the funds’ investment objectives, risks, charges and expenses. To obtain a prospectus or summary prospectus, which contains this and other information, call 1.866.787.2257 or visit www.spdrs.com. Read it carefully.
ETFs, such as SPDR® S&P 500,® MidCap SPDR,® and Diamonds® trade like stocks, are subject to investment risk and will fluctuate in market value. There is no assurance or guarantee an ETF will meet its objective. SPDR S&P 500, MidCap SPDR, and Diamonds are issued by SPDR Trust, MidCap SPDR Trust, and Diamonds Trust respectively.
The “SPDR®” trademark is used under license from The McGraw-Hill Companies, Inc. (“McGraw-Hill”). No financial product offered by State Street Global Advisors, a division of State Street Bank and Trust Company, or its affiliates is sponsored, endorsed, sold or promoted by McGraw-Hill.
Bond funds contain interest rate risk (as interest rates rise bond prices usually fall); the risk of issuer default; and inflation risk.
Because of their narrow focus, sector funds tend to be more volatile than funds that diversify across many sectors and countries.
Distributor: State Street Global Markets, LLC, member FINRA, SIPC, a wholly owned subsidiary of State Street Corporation. References to State Street may include State Street Corporation and its affiliates. Certain State Street affiliates provide services and receive fees from the SPDR ETFs. ALPS Distributors, Inc., a registered broker-dealer, is distributor for SPDR S&P 500, MidCap SPDR and Dow Diamonds, all unit investment trusts and Select Sector SPDRs.
IBG-0311
Project1 1/25/10 9:33 AM Page 1
March/April 201010
By Paul Daley, Phil Dorencz and Dan Bargerstock
A framework for the ETF trader
ETF Liquidity Explained
March/April 2010www.journalofindexes.com 11
Exchange-traded funds have enjoyed tremendous growth
over the past decade, whether you measure that by
daily trading volume, the number of annual new fund
issuances or assets under management.
Assets under management have grown during the past
decade from less than $100 billion to nearly $800 billion.
Trading volume has soared as well: In 2009, the value of ETFs
traded on U.S. exchanges surpassed $18 trillion, and ETFs
regularly accounted for 30 percent or more of all dollar volume
traded on U.S. exchanges. Meanwhile, over the past four years,
we have seen between 100 and 200 net new funds per year.
Despite all this growth, flaws in the understanding of
how these vehicles trade—and the best manner in which to
trade them—remain. A variety of heuristics exist for deter-
mining which funds can be safely traded and which are too
expensive for practical trading, but nearly all of these rules of
thumb are flawed at best and dead wrong at worst.
One of the most common assertions is that investors should
avoid any funds with fewer than $100 million in assets and
average daily trading volume of fewer than 100,000 shares.
This paper will show that there is virtually no correlation
between those two factors and the true liquidity of an ETF.
For purposes of this paper, Fox River Execution defines the
true liquidity of the ETF to be the combination of the ETF’s
average daily trading volume and the average daily trading
volume of the underlying securities. The combination of these
two factors explains the real-world experience of traders mov-
ing significant sums of money into and out of funds.
Most of the detailed examples reviewed in this paper
involve ETFs with U.S. equity underlying securities because
there are fewer variables to interfere with the precision of
the calculations. However, as shown, this general framework
can be used to both analyze and understand the trading
patterns of ETFs based on currencies, commodities, fixed
income and international equities.
Brief History Of ETF GrowthThe story of the growth of the ETF market has been one of
innovation meeting opportunity repeatedly, but not neces-
sarily immediately. It often takes a high-stress period in the
market for investors to realize that there are new ways to use
the tools at hand.
On Jan. 29, 1993, State Street Global Advisors brought to
market the first ETF—the SPDR S&P 500 (NYSE Arca: SPY)—
to almost no fanfare. On its second anniversary, SPY was
trading 50 percent less volume than it did in its first month
of existence, and even that was not that impressive. It was
not until late 1995 that it began its uninterrupted march to
trading volume leadership.
One theory as to why volume was so light is that the
applications State Street envisioned for the product did not
resonate with investors or traders. Their sales pitch included
asking S&P 500 Index fund managers to replace their 500
stocks with a single security. That was not an appealing pros-
pect to managers, as it represented an effective outsourcing
of their fund management responsibilities (with a resulting
second level of fees charged to the end investor).
It was not until 1999 that the market for ETFs began to
really heat up. 1999 was the year that the Nasdaq-100 QQQs
became known as the proxy for technology stocks. With the
Internet stock bubble at full inflation, investors could not get
enough of the QQQs. For ETF issuers, an entire new market
was discovered: the day trader. Issuers responded to the new
demand with a then-record 57 new fund launches in 2000 (see
Figure 1).
The next wave of growth centered on expanding the asset
types embedded in this unique structure. The first fixed-
income funds were introduced in 2002, commodities followed
in 2004 and currencies in 2006. By creating an entire invest-
ment palette, once again an entire new market of ETF users
was created: The registered investment adviser community
soon recognized that with a full suite of products available,
they could focus their pursuit of alpha at a more strategic
level. Rather than picking individual securities, they could
focus on sectors and asset types. The benefits of doing so
were many, from greater tax efficiencies to less-specific risk
and lower overall trading costs.
2006 also saw the entrance of a new innovation that has
driven significant increases in ETF trading volume: The first
leveraged and inverse ETFs opened the market to more
aggressive risk takers. While not without some controversy,
day trading as well as medium- and long-term investment in
these instruments has driven big volume gains.
Beyond issuance of new products, the second driver of
growth has been volatility. Invariably in times of high uncer-
tainty and stress in the market, investors seek out what they
understand most, and risk managers gravitate toward simple
ways to broadly manage risk. ETFs, with their full holdings
transparency and broad market coverage, fit the bill.
As a result, it is not altogether surprising that trading vol-
ume in SPY is closely correlated with the CBOE Volatility Index,
better known as the VIX. This was demonstrated most recently
in the credit-crisis-driven events of 2007 and 2008 (see Figure
2). Every spike in the VIX Index is almost immediately fol-
lowed by a spike in SPY trading volume. It is also interesting
that while overall market volume spikes, the spread between
SPY volume growth and overall market growth continues to
expand throughout the period. This suggests that those who
find uses for ETFs in times of stress do not abandon them
when the stress is relieved.
Another significant contributor to ETF volume growth that
ETF Market Growth
250
200
150
100
50
0
Source: IndexUniverse.com
‘93 ‘94 ‘95 ‘96 ‘97
Net New ETFs
‘98 ‘99 ‘00 ‘01 ‘02 ‘03 ‘04 ‘05 ‘06 ‘07 ‘08 ‘09
Figure 1
March/April 201012
is not often talked about is the options market. The CBOE lists
options on 229 ETFs, which had a combined trading volume in
2009 of over 1 billion contracts. For every option contract that
trades, some number of ETF shares need to trade for market
makers to hedge their positions. While that volume is down
2 percent from 2008, it is a slightly deceptive statistic given
that volatility in the fourth quarter of 2008 hit 80 percent and
drove option usage temporarily higher. Additionally, with the
recovery in the stock market in 2009, it is likely that the dollar
value of options traded actually increased.
ETFs Do Not Trade Like Stocks: The Difference Between Outright And Arbitrage Markets
Q: What is your house worth?
A: What someone else will pay for it.
Q: What is a share of IBM worth?
A: What someone else will pay for it.
Q: What is an ETF worth?
A: Where someone else can hedge it.
A review of ETF sponsor Web sites yields the following
quotes about the trading characteristics of ETFs: “ETFs …
combine the trading flexibility of individual stocks with the
diversification benefits of mutual funds;” “with the trading
flexibility and continual pricing of individual stocks and
bonds;” “ETFs employ all of the same trading flexibility as
stocks.” Web sites that purport (are designed?) to educate
ETF traders follow a similar theme: “ETFs trade like individual
securities on stock exchanges;” “traded on stock exchanges,
much like stocks or bonds.”
These statements are correct as far as they go, but they
miss a critical nuance. There are important distinctions
between the way stocks trade and the way ETFs trade, derived
from the fact that additional shares of stock cannot be created
by market participants, while additional shares of ETFs can.
Stocks are said to trade in an outright market and ETFs
are said to trade in an arbitrage or derivative market. The
value of a stock is determined by the aggregate opinion of
the outright value of the related company. That opinion gets
expressed by the price discovery process wherein supply and
demand are equated at a market-clearing price. The correct
value of a stock is its current market price.
ETFs derive their value from the value of the securities
that underlie them. While those underlying securities have
values determined in an outright market, the ETF’s value can
only be expressed in relation to them. In this way there is
an arbitrage that exists between the ETF and its underlying
securities. Any deviation from the arbitrage-free price in the
ETF represents an opportunity for guaranteed profit to those
nimble enough to take advantage of the opportunity.
Therefore ETFs do not trade like stocks. They trade like
the sum of the stocks that comprise them. For this reason,
the average daily volume of an ETF is often a meaningless
statistic, as it is the average daily volume of the underlying
securities that determine the real trading characteristics of
the ETF. To truly understand the liquidity of the ETF, then, it
is critically important to understand that arbitrage process.
Understanding The ETF Arbitrage ProcessThe ETF arbitrage process is made possible by the cre-
ation/redemption process that is a basic property of the
open-ended fund structure of an ETF.
For all ETFs, certain market participants called autho-
rized participants, or APs, are able to “create” or “redeem”
(destroy) shares of the fund: literally swelling or shrinking the
number of shares outstanding in the market to accommo-
date rising or falling demand for the fund. This differs from
individual stocks, where the number of shares is typically
fixed, and can only be increased or decreased through direct
corporate actions such as secondary offerings.
APs can create new shares of an ETF by turning in a pre-
defined basket of securities to the ETF sponsor in exchange
for an equivalent value of newly created fund shares.
Alternatively, the AP can turn in ETF shares and receive the
same predefined basket of securities in exchange. This cre-
ates the truest form of arbitrage because all of these transac-
tions occur at the fund’s net asset value (NAV). If at any time
the fund shares are trading at a discount to the NAV, an AP
need only buy the basket (at NAV) and short the fund shares
(above NAV) at the same time. The AP can then exchange the
basket for fund shares at the end of the day to cover its short
10
9
8
7
6
5
4
3
2
1
0April2006
Oct2006
April2007
Oct2007
April2008
Oct2008
April2009
Oct2009
■ SPY Volume ■ US Equity Volume ■ VIX
Gro
wth
Ra
te
Sources: Bloomberg data, Federal Reserve Bank of St. Louis
90
80
70
60
50
40
30
20
10
0
VIX
Ind
ex
A Brief History: SPY Growth
Bear StearnsSuspends Credit HF
Redemption
MoneyMarkets “Dislocate,” Quant
Meltdown I & II
Fed CreatesTAF, TSLF, PDCF; Supports
GSEs and AIG, JPM
Lehman Bankruptcy,Financials Short Sale Ban,
AMLF, TARP, CPFF, TLGP,MMIFF, CPP, RMBSF, CDOF, TALF
Bank FailuresHit 16-Year High, AIG Debt
Restructured, TALF II
Figure 2
March/April 2010www.journalofindexes.com 13
position and lock in a guaranteed profit. The process works
in reverse for redemptions.
Of course, there are costs to executing these trades
(bid/ask spreads, commissions, taxes, clearing fees, market
impact, etc.) that create a band around the NAV within which
arbitrage is not profitable. But outside of these bands there
is free money to be made, and it is made all day, every day by
market participants with the infrastructure to do so.
Individual investors do not need to perform these arbi-
trage trades themselves to secure good executions when
trading ETFs. It is only necessary to know where the arbi-
trage is at, how it functions and how to use this knowledge
to effectively process trades in any particular ETF.
Premium Or Discount: A Critical Factor When Trading ETFs
One of the most important factors in determining what is
a fair price at which to trade an ETF is the extent to which
the ETF deviates from its NAV. While the arbitrage described
above suggests that this should theoretically never happen,
in the real world—and for a variety of reasons—it does.
During the month of December 2009, for instance, our
research identified 50 U.S. equity ETFs that averaged more
than 5 cents of mispricing; an additional 27 ETFs were mis-
priced between 3 and 5 cents per share, on average. While
some of these were lightly traded vehicles, there is often
no correlation between trading volume and mispricing.
A regression of AUM to premium/discount yields an R2 of
only 0.00095. For example, the iShares Russell 2000 Index
Fund (NYSE Arca: IWM) is the third-most-actively traded ETF
in the U.S. based on dollar volume, behind only SPY and
QQQQ. Due in part to the difficulty of trading some of the
small-cap stocks within it, however, it can be a problematic
fund for APs to hedge. As a result, in spite of its enormous
trading volume in December 2009, IWM was mispriced by
at least 5 cents per share for an average of 22.6 percent of
each trading day—or almost one and a half hours of each
six-and-a-half-hour trading session (see Figure 3).
Investors can gain a first-level approximation of whether an
ETF is trading at a premium or discount to NAV by comparing
the ETF’s share price with the indicative NAV (iNAV) as published
by a number of data providers. There are some weaknesses to
this approach though. For one, iNAVs are only updated every 15
seconds and are based on last price rather than bids and offers
in the underlying securities. As bid/ask spreads on the underly-
ing securities become wider, and critically—if those underlying
securities are not trading on a regular basis —iNAV values can
move away from a more real-time view of the fair value of an
ETF. This is particularly a problem as one moves down the
market-cap spectrum. Specialists, APs and dedicated ETF liquid-
ity providers will perform calculations based on the real-time
bid/ask spreads of the underlying components, to create a more
real-time approximation of the true value of the ETF.
Asymmetry And Arbitrage Channels In ETF TradingAsymmetry in the arbitrage process typically occurs when
the bid-offer spread for the underlying constituents is wider
than the bid offer spread for the ETF itself. When this con-
dition exists, the ETF often fluctuates between prices that
are bound by the arbitrage bands: APs are unable to directly
arbitrage the ETF within this band, since the cost of assem-
bling the underlying basket of securities is higher than the
bid/ask spread of the ETF itself. Understanding when and
where this can happen is important for those trading ETFs,
because when there is asymmetry in the arbitrage market
for a given ETF, large buy orders will have a different level
of impact on the ETF market than large sell orders.
For example, in December 2009, IWM had an average
bid/ask spread of $0.010 per share. During the same period,
the underlying basket of securities in IWM had a combined
average bid/ask spread of $0.122 per share. This twelvefold
difference creates a vast amount of space for IWM to trade
within before arbitrage becomes profitable.
Figure 4 is a 10-minute snapshot of the IWM bid/ask spread
and the bid/ask spread of its NAV on December 2009.
Investors should be able to drive most trades in ETFs
within the channel suggested by the NAV bid/ask spread.
But within that channel, large buy and sell orders can
and will drive the price of the ETF toward the top or the
bottom of the channel.
Impact In The Underlying Vs. Impact In The ETF Pre-Trade Model Examples
When determining how difficult a stock is to trade, most
traders rely on pre-trade impact cost estimate models pro-
iShares Russell 2000 (IWM) Mispricing: By Percent Of Day
35%
30%
25%
20%
15%
10%
5%
0%
Source: Fox River Execution
<$0.01 $0.01 - 0.03 $0.03 - 0.05 >$0.05
Figure 3
Asymmetry
57.80
57.70
57.60
57.50
57.40
57.30
57.20
57.10
Source: Fox River Execution
8:41:04 AM 8:41:54 AM
iShares Russell 2000 (IWM)
8:42:44 AM 8:43:34 AM 8:44:29 AM 8:45:20 AM
■ NAV Ask ■ IWM Ask ■ IWM Bid ■ NAV Bid
Asymmetry:Closer to bid
than offer
Figure 4
March/April 201014
vided by their brokers. Upon examination, these models are
typically built on three factors: trade size as a percentage of
average daily volume (ADV), stock intraday volatility and bid/
offer spread. On deeper examination, it is really ADV that
drives the calculation.
This point was driven home at the end of 2007. With the
credit crisis in full bloom and restrictions in place on shorting
financial shares, bid/offer spreads on stocks rose significantly
while intraday volatility was at near-term highs. At the same
time, volume spiked as investors fled the stock market for
more certain investments. Trading stocks was clearly more
difficult and more expensive than prior to the crisis.
Wider bid/ask spreads and higher volatility make securi-
ties more difficult and more costly to trade, while higher
volume typically makes it easier and cheaper. With two of
the three factors that pre-trade models rely on suggesting
trades should cost more to execute, it came as a surprise that
pre-trade cost models suggested trade costs would be lower.
Clearly the models are highly dependent on ADV.
Using pre-trade cost models to either predict the expect-
ed outcome of an ETF trade or to determine which ETFs are
safer to trade than others can be problematic, because the
ADV of the ETF is only one part of the “true liquidity” of the
ETF. Most popular trade impact models today do not take
into consideration the liquidity of the underlying securities
in an ETF, and therefore miss the fund’s true liquidity.
Case Study: JKDJKD is the iShares Morningstar Large Core Index ETF. The
underlying stocks are 78 of the largest, most liquid in the
U.S. market, including names like Johnson & Johnson, Procter
& Gamble and IBM. By any objective measure, JKD’s underly-
ing stocks have a high degree of liquidity with a low degree
of trading costs.
In December 2009, its ADV was 21,826 shares, or approxi-
mately $1.4 million. Using four different pre-trade impact
models, we are led to believe that the cost of quickly trading
50,000 shares (the minimum creation size) will be between
1.47 and 16.1 percent. The absurdity of these numbers
illustrates the point nicely. It is ridiculous to think that the
cost of trading approximately $3 million of the world’s most
liquid stocks should be as high as 16 percent.
To further illustrate the point, one can feed in the basket
of stocks that comprise the 50,000 shares of JKD to one of
the models and find out what the true cost to the arbitrageur
would be (and therefore, the true cost to all market partici-
pants). This yields a more realistic 0.016 percent.
Two conclusions can be drawn. First, mathematical mod-
els are tuned to handle “normal” settings for their inputs.
They tend to break down when extreme values are used.
That explains both the magnitude and the spread in the JKD
impact numbers in Figure 5. When looking at a trade rep-
resenting more than 2x ADV, the models move into an area
which they were not built to explain.
Second, empirical results are more valuable to under-
standing how ETFs trade when true liquidity is factored into
the analysis. Using true liquidity, Fox River Execution calcu-
lates the impact to be 0.0077 percent, less than half of what
even the basket model predicts.
IVV vs. SPY
Another interesting illustration is to examine the results
for IVV and SPY. IVV is the iShares S&P 500 Index Fund. SPY
is the SPDR Trust Series 1. Both ETFs track the performance
of the S&P 500 Index.
It comes as no surprise then that their underlying bas-
kets are virtually identical, with all of the same holdings
and weightings differences that are only discernible at the
hundredth of a percent level (and often at the thousandth of
a percent level). The only obvious difference between these
two is their trading volume: IVV traded an average of 3.4
million shares per day in December 2009, while SPY traded a
whopping 131 million per day over the same period.
A simple comparison of their respective volumes would
suggest that one should be cheaper to trade than the other,
but given that they hold the same underlying securities, why
should this necessarily be? Whether one is trading a basket
of 500 stocks or a certificate representing the 500 stocks,
the costs should be nearly identical. Therefore, from a cost
perspective, it should not matter whether a trader is trading
one certificate or another. Predictably, however, pre-trade
models continued to be fooled by the volume disparity. Using
the most conservative model, we get an estimated impact for
1 million shares of SPY traded with high urgency of 0.057 per-
cent. The same size and urgency in IVV results in an estimated
impact of 2.32 percent. Of course, they both taste like chicken
to the arbitrage community, so the real impact of trading IVV
will be identical to the impact of trading SPY. There were, in
fact, three trades in IVV of greater than 1 million shares in
December 2009. The average size was 2,953,000 shares and
the average impact (defined as distance from the midpoint of
the bid/offer spread) was 0.032 percent.
More Complex ETFs: Imperfect Hedges
So far we have only dealt with ETFs that had relatively
simple-to-calculate underlying baskets. There are a multitude
of ETFs that do not fall into this category. Some U.S. equity
ETFs with less liquid underlyings—including virtually all
fixed-income, commodity, currency and international ETFs—
are more problematic to hedge and also more problematic to
price. Arbitrageurs use a variety of techniques when deter-
mining how to hedge their trades. These imperfect hedges
lead to wider arbitrage bands, but they do not make the ETF
impossible to trade. With a greater understanding of the
Figure 5
Source: Bloomberg
JKD Impact Underlying Impact
Broker A 4.57 %
Broker B 5.86 % 0.016 %
Broker C 16.10 %
Non-Broker 1.47 %
JKD Pre-Trade Impact Models
March/April 2010www.journalofindexes.com 15
techniques and some robust tools for analysis, our research
and market experience suggests that solid price and size dis-
covery in less-trafficked-in ETFs is very achievable.
Optimized BasketsManagers of small-capitalization index funds learned
long ago that it is not always necessary to own every stock
in the index to produce performance with little tracking
error to the index. A variety of optimization techniques are
employed. While the objective function has factors designed
to replicate the characteristics of the broad universe, it
almost always also contains a trade-cost-minimizing objec-
tive that is designed to create a replicating basket with the
lowest cost of implementation and maintenance.
Arbitrageurs employ the exact same techniques when
operating in the ETF market, which creates an interesting
opportunity for traders. These optimized baskets are always
subsets of the entire index. Because one of the optimization
factors is trade cost (often represented by the width of the
bid/offer spread), it is likely that the bid/offer spread of the
optimized basket is narrower than the bid/offer spread of the
entire universe. It is also likely that there will be some asym-
metry in the spreads of the respective basket. The more opti-
mized baskets there are and the more asymmetry there is in
the baskets, the better it is for the market as a whole. This is
because the best bid and the best offer are unlikely to come
from the same optimized basket, making the overall market
tighter than if all participants were using the same basket to
hedge. This is at least part of the reason (though not all) that
IWM trades with a 1 cent bid/offer spread, while the underly-
ing basket trades with a 12 cent bid/offer spread.
Correlated InstrumentsOne special challenge for arbitrageurs is how to hedge
ETF positions when the underlying securities held by the
fund are not trading. This is not an issue for most domestic
equity or fixed-income funds, but for international equity
or fixed-income securities, and certain commodity markets,
the underlying securities may not trade during the U.S.
market day. For instance, the Asian equity and debt markets
are closed during all of the U.S. trading day, and European
markets only overlap U.S. market hours in the morning.
In these situations, arbitrageurs use correlated instru-
ments to hedge their ETF trades. Correlated instruments
can include futures, options, physical commodities, curren-
cies and a vast array of fixed-income securities. [Correlated
instruments can and are used to hedge domestic ETFs as
well, in situations where the correlated instruments are more
liquid or efficient than the underlying in the ETF itself.]
For international markets, for instance, American deposi-
tary receipts (ADRs) or even domestic instruments may be
used to hedge. For commodities, futures or physical assets
can be used as a hedge. It is more likely that futures will
be used because of their easy access, exchange listing and
favorable margin requirements. Physical commodities pres-
ent problems with financing, transportation and storage that
are typically, but not always, avoided. For fixed-income ETFs,
sometimes the most liquid ETFs are used as a correlated
hedge for the least liquid instruments.
In all of the cases listed above, there is a basis risk
involved in the hedge. Basis risk is when the hedge does
not perform exactly the way the instrument being hedged
performs. This leads to wider bid/offer spreads, but does not
necessarily decrease the depth of the market or mean that
large trades cannot be moved through. Often the correlated
instruments are more liquid than the underlying of the ETF
they are hedging, which can lead to situations where arbitra-
geurs are willing to make bigger (albeit wider) markets in the
ETF than in the instrument the ETF tracks.
Event Risk HedgesMany of the correlated hedges described above can
also be put in the category of event risk hedges. This is
particularly the case with using ADRs or domestic equi-
ties to hedge international equity ETFs. As investors have
expanded their investment horizons to include internation-
al investing (whether that is non-U.S. investors investing in
the U.S. or vice versa), correlations among markets around
the world have grown. Most large-cap companies already
have significant businesses overseas, further boosting cor-
relations. For these reasons, domestic events and news
have an impact on foreign markets just as news abroad has
an impact here. In the absence of pre-market news that will
affect the open of trading in the U.S., we often take our lead
from the markets in Asia and Europe. If they trade higher,
we tend to do likewise. When news in the U.S. creates a
big impact on our markets, trading in Asia and Europe is
similarly affected the next day.
Because of higher correlations across markets, the big risk
when trading after markets close is news risk or event risk,
since it can affect all markets. This actually makes it easier
to make markets in ETFs with foreign underlying long after
their markets are closed. Arbitrageurs may not be able to lay
off their specific risk until some time the next day, but they
can lay off their event risk immediately by using an ETF or
related security that is already trading in the U.S. market.
That way, if there is a significant event related to geopolitics,
ETF Trading: The Value Of Knowledge
56.92
56.87
56.82
56.77
56.72
Sources: Fox River Execution, Bloomberg
12:35:00 PM 12:37:09 PM
Begin Sweep
PostFilled 72,600
How do you know where to set the limit? Different techniques. Different results.
12:39:05 PM 12:41:04 PM 12:42:50 PM 12:44:30 PM
NAV Ask ■ IWO Ask ■ IWO Bid NAV Bid
Begin Sweep
Filled 43,800
iShares Russell 2000 (Growth)
Figure 6
March/April 201016
earnings at multinational companies, exchange rates, credit
markets or any of dozens of other potential risks, the arbitra-
geur has at least a broad level of protection.
A secondary effect of this type of hedging activity is that
seemingly unrelated ETFs can begin to exhibit correlation
at the intraday level while not being highly correlated at the
interday level. The MSCI EAFE Index is an index of developed
markets in Europe, Australia and the Far East. In December
2009, the daily returns of this index had a correlation to the
daily returns of the S&P 500 of 0.4267. Those correlations
are measured based on closing prices for the indexes. The
iShares MSCI EAFE Index Fund (NYSE Arca: EFA) tracks the
MSCI EAFE Index. Yet its intraday correlation (as measured
by one-minute returns) to the SPY was 0.8434. One possible
reason for this extraordinarily high intraday correlation is
when APs are hedging trades they do in EFA, they might use
the SPY. This is more likely to occur when the local markets
the EAFE index covers are closed. The Australia and Far East
components are never open during U.S. trading hours, and
Europe is only open for a small portion of the U.S. market
hours (typically 15-30 percent).
The Cost Of Failing To Understand ETF TradingThe cost of failing to understand ETF trading can be large,
both in terms of real and opportunity costs.
As demonstrated, pre-trade price models dramatically
overestimate the impact of pushing large trades through
funds like JKD or IVV. This may dissuade some investors from
considering these and other small-volume ETFs, limiting
their opportunity set without reason.
And for those investors who do trade ETFs, the real-life
costs of failing to understand the ETF arbitrage mechanism
can be large.
Figure 6 illustrates a real-life example using the iShares
Russell 2000 Growth Index Fund (NYSE Arca: IWO). Fund
holds the subset of the Russell 2000 Index that contains
growth stocks. It has very similar trading characteristics to
the broader IWM. The graph covers one 10-minute period
that contained two significant trades in IWO: one for 72,600
shares and one for 43,800 shares. The two trades were
executed differently, and as a result, experienced different
levels of impact on the market. But before getting into the
costs, let’s review some features of the graph.
The dotted lines are the arbitrage bands as represented
by the bid and offer of the NAV. At the time of this snapshot,
they were approximately 10 cents apart, while the ETF itself
traded between 1 and 2 cents wide.
Figure 6 shows many of the features discussed earlier.
There is asymmetry in the pricing, in that more often
than not, the IWO bid is closer to the NAV bid than the
IWO offer is to the NAV offer. For that reason, a large
sell should have less impact than a large buy: IWO does
not have to travel as far to make the arbitrage profitable.
Another way to express this asymmetry is to measure from
the midpoint of each respective spread. Based on that
measurement, IWO can actually be said to be trading at
a discount to NAV during this time period, with the same
logical implication that a large sell should have less impact
than a large buy. Because small trades can be done at the
IWO bid and offer, they will have similar impact of one-
half the bid offer spread.
The first trade is for 72,600 shares and is executed over
a one-minute span beginning just after 12:35:00 p.m. The
trade begins with a sweep of the market, which sold all the
shares on the national best bid and offer from a price of
$56.89 down to $56.84. The remaining shares that needed
to be sold were posted with a limit order of $56.84. The
price where the trade would stop sweeping the book and
post the remaining shares as a limit order was chosen
based on knowledge of two things: where the arbitrage
was given the bid of the NAV and the likelihood that there
was an optimized basket with a better bid than the NAV.
53.50
53.45
53.40
53.35
53.30
53.25
53.20
2:4
5:0
3 P
M
2:4
6:0
3 P
M
2:4
7:0
3 P
M
2:4
8:0
3 P
M
2:4
9:0
3 P
M
2:5
0:0
3 P
M
2:5
1:0
3 P
M
2:5
2:0
3 P
M
2:5
3:0
3 P
M
2:5
4:0
3 P
M
2:5
5:0
3 P
M
2:5
6:0
3 P
M
2:5
7:0
3 P
M
2:5
8:0
3 P
M
2:5
8:1
8 P
M
2:5
9:0
3 P
M
Pri
ce
Sources: Fox River Execution, Bloomberg
350,000
300,000290,000
250,000
200,000
150,000
100,000
50,000
0
Vo
lum
e
Trading: How Do I Trade Size At The Close?
■ Volume ■ IWV Bid ■ IWV Ask NAV Bid NAV Ask
(Spread cost 0.005, Impact cost 0.0218)
iShares Russell 3000 (IWV)
Figure 7
March/April 2010www.journalofindexes.com 17
The total impact on the market for this trade was 5 cents
(average impact being less), with only a penny or two of
reversion after the trade.
The second trade is for 43,800 and is executed over a
span of seconds less than 10 minutes after the first trade.
The trade began with a sweep at $56.87 and that sweep
continued until 43,800 shares were sold; the trade is com-
pleted at $56.75. A post was never attempted for this trade.
While Fox River Execution was not party to the specifics
of this trade, one can imagine that the trader or algorithm
executing it did not take into account the arbitrage value
of the NAV when deciding fair pricing for the ETF. The total
impact on the market for this trade was 12 cents, with an
almost immediate reversion of 7 cents, suggesting 5 cents
would have been a fair price impact.
The difference in impact between these trades of approx-
imately 7 cents is the real cost of failing to understand how
ETFs trade based on the arbitrage relationship between the
ETF and its NAV. That does not adjust for the fact that it was
the larger trade that experienced the smaller impact.
How To Use InformationFor More Effective Trading
Since the first markets were formed, it has always been
the more informed trader that has an advantage over the
trader with less information. So it is with ETFs.
An understanding of the structure of ETF arbitrage and how
it translates into prices in the market is the minimum amount
of information any trader or investor should have before
attempting to use ETFs in appreciable size. It has been the
goal of this article to supply that level of understanding.
Beyond an understanding of the structure and market
structure of ETFs, it can be very handy to have a quantita-
tive, real-time assessment of the factors impacting pricing
while trading. It is this piece that can be harder to come by.
Most APs are happy to tell you where they would trade an
ETF, but it is only a few brokers who can first tell you where
they should trade an ETF. Knowing the “should” before find-
ing out the “would” can lead to much more pleasant out-
comes as well as minimizing instances of buyer’s remorse.
A final real-world example can drive this home. Figure 7
shows how an enormous trade can be pushed through an
ETF with minimal price impact.
The chart shows the minute-by-minute trading volume in
the iShares Russell 3000 Index Fund (NYSE Arca: IWV). The
fund trades throughout the day, but the size of the trades
are small. Then, a trade for 290,000 shares was executed 90
seconds before the market close. This volume was equal to
48 percent of all the shares that had traded in the first 6.5
hours of the day. Yet the impact of the trade was only 2.18
cents from the midpoint of the bid/offer spread at the time
the market was entered.
IWV is a relatively liquid ETF to begin with, but the same
rules apply to less liquid products as well. If you know
where to trade, and why, enormous positions can be moved
through the market with relatively little impact.
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March/April 201018
By Lisa Dallmer
The case for lead market makers in ETF markets
The Impact Of Market Models On Liquidity
www.journalofindexes.com 19
Exchange-traded funds have revolutionized investing and
markets in ways we never could have imagined. ETFs
fill the investing pages of the Wall Street Journal and
stream across the ticker line on CNBC all day long. With all of
the attention ETFs have received, however, few investors truly
understand the complexity of what goes on behind the scenes
with ETF trading and market making. There is a diverse array
of players in the market that is truly the key for making ETFs
the efficient windows into market liquidity that they are.
To understand how the market for ETFs works, let’s take
a step back and talk about how an ETF works. ETFs are effec-
tively mutual funds that trade like stocks. But unlike stocks,
where price discovery is a function of supply and demand
throughout the day (i.e., scarcity of shares and opinions), an
ETF is a collection of securities whose underlying valuation
can be calculated as a result of the portfolio’s transparency.
This transparency, coupled with the ETF’s creation redemp-
tion process, creates a constant loop of pricing information
that is used to create an arbitrage opportunity should the
fund’s price get out of line with its expected underlying
net asset value (NAV). As the supply and demand for an ETF
goes up and down, authorized participants exchange the
basket of underlying securities for the shares of the ETF to
increase or decrease the ETF shares available in the market-
place. Thus, generally the price discovery of the ETF is not
affected by long-term supply and demand, because the size
of a fund’s assets under management can grow and shrink
according to demand.
Sources Of LiquidityThere are a variety of exchanges and trading venues that
can be generally characterized as 1) the listed exchange uti-
lizing either the lead market maker (LMM) arranged by the
exchange (U.S. approach) or arranged by the issuer (common
European approach); 2) multilateral trading facilities (MTFs)
with unlisted trading activity; and 3) alternative liquidity
aggregation venues, often called “dark pools,” that do not
expose an order to a public quote. Liquidity providers and
liquidity takers meet on these trading venues in a process
known as price discovery. The basic idea is that through
price discovery, liquidity providers are fundamental to ensur-
ing that ETFs trade at values close to their expected NAVs,
throughout the trading day. But regardless of who is provid-
ing the liquidity, understanding the finer points of exactly
how that liquidity provision is introduced to the exchange is
key to understanding how this market functions.
At the heart of the exchange model, these players are known
as lead market makers. LMMs are akin to designated market
markers (formerly known as specialists) in the floor-based
trading system. Operating in a fully electronic market model,
LMMs support displayed limit order trading because they are
obligated to quote narrow, two-sided markets throughout the
trading day. LMMs are responsible for maintaining share depth,
tight quoted spreads, and using publicly displayed limit orders
to generate the opportunity for price improvement. The LMMs
enhance market quality and supplement natural liquidity from
the collective broker-dealer community.
Academic research supports this point. An article titled
“Paying for Market Quality,” by Anand, Tanggaard and
Weaver published in the Journal of Financial and Quantitative
Analysis (JFQA) in April 2008 reports on the benefits of liquid-
ity providers (referred to as “LPs” in our European markets,
which are akin to our U.S. LMMs): “… price discovery during
the continuous trading period of the trading day increases
significantly following the start of LP services.” Another white
paper, titled “The Value of the Designated Market Makers,”
by Venkataraman and Waisburd published in the JFQA in
September 2007, indicates that: “A dealer enhances market
quality by simply maintaining a regular market presence.”
The research goes on to say that a market maker “reduces
price risk that equilibrium values may shift between order
submission and execution.”
The Lead Market Maker’s RoleOverall, the LMM’s core function is to provide publicly
displayed limit orders, resulting in the formation of price
discovery. LMMs trade using their own proprietary software
to connect to the exchange and establish two-sided markets
for the products they’ve been allocated. To establish competi-
tive quotes, LMMs use real-time data, firm capital and their
knowledge of ETF markets as well as the transparent-known
basket of securities underlying each ETF. These quotes result
in trades that are consistent with arbitrage principles and
in line with the underlying value of the ETF’s portfolio. For
example, the offer on a particular ETF is largely defined as the
collective price at which the LMM could assemble the basket
and access a creation unit in order to sell the ETF into the sec-
ondary marketplace. If trading international products, LMMs
also must have access to non-U.S. markets to get international
data feeds, foreign stock holdings and foreign clearing costs,
and need to develop relationships with brokers who can man-
age off-hours market risk. LMMs have an obligation to make
the bid and ask as relevant as possible to a fund’s underlying
value, net of the cost of assembling the basket to quote, ulti-
mately reducing costs for individual investors who would find
it expensive to assemble such baskets. To do so, they need
technology, knowledge of the underlying markets and access
to those underlying markets.
In addition to maintaining continuous two-sided quotes,
LMMs must meet minimum performance requirements that
include, for each ETF, a percentage of the time that the LMM
quotes are at the national best bid and offer (NBBO), an aver-
age displayed size and an average quoted spread. An exchange
should set the appropriate performance requirements for
each individual security (based on its price and other trading
characteristics). LMMs fulfill these quote and execution obliga-
tions by electronically interacting with the market in displayed
order types and through their willingness to algorithmically
provide price improvement to incoming orders without seeing
the actual orders in advance. The incentive to act as an LMM is
driven by the scalability of trading systems to ETFs and lower
transaction pricing that is reserved for the LMM.
Opening And Closing Auctions The LMM is also involved in two of the most critical
single moments of the trading day: the open and the close.
March/April 2010
To open and close the trading day as a means of creating
the primary market print, exchanges use an auction. In the
U.S. and Europe, a commonly used method is a single-price
Dutch auction, which matches buy and sell orders at a price
to maximize the amount of tradable stock. Using the NYSE
Arca exchange as an example, a requirement of the LMM is
to support these market opening and closing auctions. So at
these 9:30 a.m. and 4 p.m. single-price auctions, the LMM
must be present in the formation of the price discovery, and
to help offset any buy or sell imbalances.
As a part of the auction process, the indicative match price,
indicative match volume and the auction imbalance are con-
tinually calculated and disseminated. This facilitates real-time
price discovery and supports market transparency. Indications
are calculated and broadcast through the Internet and via
market-data feeds to subscribers leading up to the auction.
This process allows any market participant—institutional,
sell-side or retail, as well as the LMMs—to not only gauge the
price, and the size at which to place orders so that they will
get executed, but how to help offset any imbalances.
Any participating trading firm can offset the imbalance, but
the LMM must be there to partially offset the indicative imbal-
ance. All trading firms, including LMMs, have access to the
indicative match price and are able to analyze the valuation of
that underlying portfolio to assess any arbitrage opportunities.
The LMM will use the same arbitrage pricing mechanics during
the core trading session and at the auctions to effectively set
displayed prices. Both the opening and closing auction are the
official opening and closing prices of the primary market.
These types of transparent auctions have been successful
at focusing liquidity for a given point in time. Most exchanges
retain a high percentage of market share during their open
and closing auctions in part because institutional and retail
traders often want the primary listed exchange print for the
first and last trade of the day. Slippage—the percentage
difference of that closing auction price against the volume-
weighted average price (VWAP) of the last two minutes of
the core trading session—with such auctions is minimal, at
approximately 20 basis points; illustrating that the market
prices as the day draws to a close are well represented by the
Figure 1
Figure 2
Quoting Quality For NYSE Arca Listed Domestic ETFs: First Half 2009
Quoting Quality Statistics For NYSE Arca-Listed International And Non-U.S. Treasury/Agency Fixed-Income ETFs: First Half 2009
Source: Arcavision.com
Source: Arcavision.com
Market Share
Market Share
% NBBO
% NBBO
Depth
Depth
Spread
Spread
Quintile
Quintile
CADV Range
CADV Range
NYSEArca
NYSEArca
Nasdaq
Nasdaq
NYSEArca
NYSEArca
Nasdaq
Nasdaq
NYSEArca
NYSEArca
NYSEArca
Nasdaq
Nasdaq Nasdaq
NYSEArca
Nasdaq
1st 641,625-
208,989,995 0.05% 0.05% 21,252 20,543 87.80% 89.18% 26.60% 33.40%
2nd 110,208-
625,584 0.13% 0.18% 1,891 1,984 80.97% 68.41% 28.60% 21.60%
3rd 28,624-
109,562 0.25% 0.50% 2,428 2,101 74.80% 50.92% 35.00% 16.30%
4th 9,671-
27,572 0.58% 0.90% 1,889 1,455 74.54% 49.80% 37.60% 13.30%
5th 400-9,602
ADV 0.42% 1.06% 1,989 1,438 79.11% 51.23% 45.30% 12.80%
1st 211,350-
75,975,042 0.06% 0.08% 12,087 10,781 91.34% 88.44% 34.60% 22.40%
2nd 72,122-
209,190 0.38% 2.16% 2,256 1,519 78.48% 38.22% 44.00% 6.90%
3rd 18,697-
72,050 0.62% 3.77% 2,164 972 81.50% 21.29% 47.50% 4.80%
4th 5,982-
18,423 0.76% 4.95% 2,215 694 86.56% 19.93% 54.40% 5.30%
5th 2-5,825 1.13% 7.05% 1,999 655 89.52% 16.51% 65.70% 4.90%
March/April 201020
auction price and the LMM’s participation.1
In return for fulfilling such obligations, for each order
that adds liquidity to the book, the LMM is paid a “liquid-
ity execution” rebate that is an incremental rebate to what
basic broker-dealer order firms could obtain. Only when a
firm removes liquidity from the book is it charged a transac-
tion fee, and for the LMMs, this transaction fee is lower as
a result of the quoting obligations and price discovery they
perform in the marketplace. For the LMMs on NYSE Arca,
there is no advance knowledge of incoming orders to the
central limit order book.
What Liquidity Really Means In ETFsThe popular press has often misstated the relationship
between ETFs with low trading volumes (volume being an
indicator of activity), and those ETFs with wide quoted
spreads (where spread is an indicator of ease of pricing and
desire to trade). The key driver of an ETF’s spread is both
the liquidity of the ETF at the NBBO at the time of trading,
and the liquidity and volatility of the underlying portfolio,
since the ETF shares themselves can be created and offered
for sale in the secondary market. For example, an ETF may
have a low level of secondary volume (an activity indicator)
but reasonably tight quoted spreads as a result of the ease
of pricing the basket. On the contrary, the LMM’s cost of
quoting is higher for ETFs that hold international equities as
compared with domestic equities. An ETF with a higher cost
of quoting results in a wider quoted spread.
To highlight this example, Figure 1 looks at ETFs based
on indexes holding only listed U.S. equities securities and
places them into quintiles that are sorted by first-half 2009
consolidated average daily volume (CADV). The top quintiles
have tighter median spreads across the board, and although
the spreads for the bottom quintiles widen, they are much
tighter at the LMM-driven NYSE Arca than observed for the
same symbols traded on Nasdaq, where an LMM is not pres-
ent. Even if the product has little secondary trading volume,
the NYSE Arca LMM is required to make a continuous two-
sided quote and maintain a specified spread requirement.
Secondary trading volume is an indicator of the traction and
general demand for trading the ETF, which is in part influ-
enced by the issuer’s brand and overall appeal for the invest-
ment theory set out in the ETF. As products grow in second-
ary trading volume, the pool of general liquidity providers
expands, and the quoted spreads may tighten to levels that
are narrower than the cost of assembling the basket of the
underlying securities. This observation regarding the knock-
on effects of higher trading volume is also consistent with
the depth statistics displayed in Figure 1. Figure 2 groups
all international equity ETFs and non-U.S. Treasury/agency
fixed-income ETFs in quintiles by volume.
Overall, liquidity begets liquidity in the interconnected sys-
tems of trading, but it’s useful to observe the tangible results of
obligated LMM participation, particularly in thinly traded ETFs.
ETFs In EuropeThe major European exchanges also trade on fully elec-
Figure 3
Figure 4
Lyxor ETF Cac40
Lyxor ETF Euro Stoxx 50
Source: Bloomberg
Source: Bloomberg
Volume (€)Depth (euro per side)Spread (bps)
BATS Chi-XNYSE
EuronextBATS Chi-X
NYSE Euronext
BATS Chi-XNYSE
Euronext
Volume (€)Depth (euro per side)Spread (bps)
BATS Chi-XNYSE
EuronextBATS Chi-X
NYSE Euronext
BATS Chi-XNYSE
Euronext
Jun-09 8.69 5.22 4.18 356,153 412,818 599,313 4,930,739 35,681,134 972,285,808
Jul-09 10.02 5.62 4.46 170,342 207,000 613,889 1,905,503 13,101,820 937,853,935
Aug-09 9.17 5.02 3.53 110,389 155,633 372,015 3,030,697 32,192,172 662,337,670
Jun-09 10.79 5.64 5.38 345,147 423,234 506,534 10,568,733 67,609,939 726,355,233
Jul-09 13.92 6.22 5.69 201,812 217,028 315,127 11,863,689 29,844,952 727,930,507
Aug-09 12.58 5.15 5.13 225,206 225,860 402,887 21,045,685 48,840,082 543,124,322
www.journalofindexes.com 21March/April 2010
continued on page 53
March/April 2010www.journalofindexes.com 53
ing an ETF, and in some case, are cheaper than borrowing. If the
memory of Lehman Brothers fades over the course of the year, the
demand that European ETFs saw in 2009 may begin to fade.
An increase in the supply of European ETFs could, in turn,
lead to an increase in demand. As additional supply comes
into the market, the advantages that swaps have over short-
ing ETFs will continue to diminish. The risk of recall and rela-
tively higher cost should diminish if more institutions make
their ETFs available to borrow. The increase in supply could
come about as more ETF owners realize that they can offset
the ETF management fee by putting them into a securities
lending program. This lending activity can help offset the
inherent tracking risk, which may make owning European
ETFs more attractive.
Endnotes1 ETF Landscape Industry Preview Year End 2009, BlackRock Global ETF Research & Implementation Strategy Team
2 Source: Data Explorers
3 Ibid.
tronic order-driven market models with opening and closing
auctions, central limit order books and valuation price feeds.
Similar to the U.S. LMM model, the European ETFs
benefit from a multiliquidity provider model that results
in remarkable market depth and competitive spreads.
For each ETF, there is at least one liquidity provider (LP),
generally appointed by the issuer, that agrees to provide
continuous quotes, minimum market depth and maximum
spreads through the exchange’s trading session. Monitored
by the exchange, so long as they are meeting their pres-
ence, size and spread requirements, LPs may receive incen-
tives from the exchange for providing liquidity in the form
of discounted transaction fees. Auctions are supported by
the LPs through their obligation to provide consistently dis-
played liquidity during the opening to the closing auction.
The number of LPs in Europe has increased significantly in
recent months following the implementation of a new trad-
ing technology and faster data feeds, improving the overall
tool kit for all traders. The increasing diversity of partici-
pants trading ETFs (buy-side, sell-side, retail, etc.) ultimately
leads to more efficient markets, and with the LP activity, we
observe the quoted spreads of several ETFs are now tighter
than those of the underlying indexes, and others reached
their lowest ever.
Although this is a deep dive into only two ETFs (see Figures
3 and 4), a comparison to BATS and Chi-X—nonexchange
multilateral trading facilities (MTFs)—illustrates the market-
quality advantages the exchange-appointed LP model pro-
vides in terms of tighter spreads and deeper markets.
ConclusionOverall, one can easily understand how liquidity provision,
encouraged on a level playing field of fair access, can result
in better expected market quality for a given symbol. In the
popular press, there is a large belief that all ETFs are created
equal—meaning that due to their transparency, the pricing
of any ETF is without costs and will be priced efficiently.
In principle this might be true; however, in practice, just
because an ETF can be efficiently priced doesn’t mean some-
one actually wants to do it at all times. An exchange model
that supports the role of a lead market maker or liquidity
provider with performance obligations is well-positioned in
principle to meet this ideal more often than not.
DISCLAIMER:This article is intended for investment professionals only and
solely for informational and educational purposes. It should not
be relied upon for any investment decisions. The article is based
on data obtained as of Aug. 30, 2009 (unless otherwise noted
herein), which, although believed reliable, may not be accurate
or complete and should not be relied on as such. The author
does not recommend or make any representation as to possible
benefits from any securities, investments, products or services.
Investors should undertake their own due diligence regarding
securities and investment practices.
Dallmer continued from page 21
Endnote1. Source: NYSE Euronext research databases
Endnotes1Amery, P., Inside ETFs Conference 2010: A Focus on Trading, www.indexuniverse.eu/blog/7127.2Fuhr, D. op. cit.3ibid.4ibid.5London Stock Exchange and SIX Swiss Exchange require their members to trade-report their OTC activity in funds that are listed on those venues.6Source: Euronext, LSE, BlackRock.7Amery, P., op. cit.
Shastry continued from page 27
March/April 201022
By Bart Lijnse and Christiaan Scholtes
How does it impact liquidity?
The Fragmentation Of The European ETF Market
www.journalofindexes.com 23
The worldwide exchange-traded fund market has
gained significant momentum since the launch of
the first ETF in the United States, in 1993. The first
ETF in Europe, the iShares DJ Euro Stoxx 50 (EUN2), was
launched only seven years later, in April 2000. Since then,
the European exchange-traded product market has grown
rapidly to include 1,1031 ETFs and exchange-traded com-
modities, with over 3,000 separate listings, at the end of
2009. European ETP assets under management rose to
$223 billion2 by the same date (see Figure 1).
In terms of average daily trading volume, Europe lags
behind the U.S. considerably. European average daily
turnover was $2.3 billion in 2009, compared with $45.8
billion in the U.S. (see Figure 2).
One of the major differences between the European and
the U.S. ETF markets is that, in the U.S., there are only one
or two ETFs tracking each given index, whereas in Europe,
one index is tracked by 78 separate ETF listings, and many
others have multiple listings as well.
In this article, we will examine the reasons for this
fragmentation, and the effect it has on the liquidity of
ETFs in Europe.
Examples Of FragmentationThe fragmentation in Europe is caused by two factors:
1) Competition between issuers; and 2) inefficiencies in the
structure of the European financial markets. Combined,
these have fractured the ETF market with significant
impacts on end investors.
For example, consider the Dow Jones Euro Stoxx 50
Index (“SX5E”). Widely popular, SX5E is tracked by the
most liquid European index futures contract (listed on
Eurex), as well as some of the largest and most liquid ETFs
in Europe.
But as of December 2009, there were 78 different
listings for ETFs tracking the SX5E (see Figure 3). This
excludes numerous structured delta-1 notes listed on plat-
forms like Scoach.
Fragmentation Caused By Competition Between Issuers
Unlike the U.S., the European market is not one single
market (although some politicians are working very hard
to make it that way). Banks still have some weak form of
monopoly in their home markets and are used to charging
high fees to their clients. Given this background, the key
issue for ETF asset growth in Europe is still distribution.
ETFs compete against a variety of products, including mutu-
al funds and structured products from those same banks,
and therefore the accessibility of ETFs and their visibility to
potential clients is very important.
Any ETF issuer will have to reach potential investors in
order to gain AUM. Any U.S. ETF provider seeking brand
awareness can advertise on a national level; for instance,
during the Super Bowl or another high-profile event, or
alternatively, advertise in a national business newspaper like
the Wall Street Journal. By contrast, European ETF provid-
ers need to advertise in a variety of magazines, papers, TV
channels, Web sites and so on, throughout multiple different
European countries, to achieve the same results as their U.S.
counterparts. Furthermore, there are simple information
faults and language confusion, as well as a home-trading bias
among investors in certain countries: Italian retail investors
will have a very hard time finding prices for London-listed
ETFs, and the typical German retail investor will probably
never trade on a foreign exchange. Most issuers therefore
choose to list their ETFs on several exchanges, increasing
the visibility for investors but fracturing the overall liquidity.
In addition, buying ETFs outside an investor’s home market
can prove to be quite costly, as banks typically charge higher
commissions for transactions outside domestic markets.
In response to banks fishing for each other’s customers
(by listing on exchanges outside their home market), we
increasingly see what we call the “defensive” or “me-too”
ETF providers. Defensive ETF providers only start issuing
ETFs when they lose too many fee-paying clients to interna-
tional ETF providers. These defensive ETF providers typically
originate from the asset management division of local banks
and anxiously try to keep competition (like market makers
or high-frequency traders) out of their products and keep
the profits in-house. In our opinion, these ETF providers will
March/April 2010
Figure 1
2002
ETF commodity assets
2003 2004 2005 2006 2007 2008 2009
European ETF And ETC AUM Growth
AU
M (
U.S
. $B
illi
on
s)
Nu
mb
er o
f Liste
d E
TF
s
250 1,000
800
600
400
200
0
200
150
100
50
0
Number of ETFs Number of ETPs
Fixed income ETF equity assets
Sources: Bloomberg; BlackRock ETF Landscape, Year End 2009
Dec’02
Dec’03
Dec’04
Dec’05
Dec’06
Dec’07
Dec’08
Dec’09
Sources: Factset, Bloomberg, Goldman Sachs. All data as of December 2009
U.S. Vs. European ETF Average Daily Trading Volumes
Tra
din
g V
olu
me
(U
.S. $
Bil
lio
ns)
120
100
80
60
40
20
0
U.S. Europe
Figure 2
not profit from economies of scale in the long run, and will
either retreat from the ETF business, or merge with their
larger international competitors.
In response to this, some banks have decided to share
the costs and their distribution networks, and have teamed
up in ETF consortia. There are currently two such consortia
in Europe:
• Source (Morgan Stanley, Goldman Sachs, Merrill Lynch,
Nomura)
• ETF Exchange (ETF Securities, RaboBank, CitiGroup, Merrill
Lynch)
It is very difficult for ETF providers without a strong distri-
bution network to autonomously grow assets under manage-
ment. The one exception in Europe would be ETF Securities
(ETFS), which was founded by ETC pioneer Graham Tuckwell,
and which recently reached $16 billion in AUM. ETFS has
reached this level by focusing only on ETCs and without any
distribution power at the first launch. ETFS’ strategy was to
list their ETCs on multiple exchanges in Europe, and then
use an intensive one-on-one sales approach and marketing
campaign to drive interest.
As ETF providers typically experience the distribution
issues detailed above, assets and trading volume tend to be
concentrated in the country of origin of the ETF provider.
Figure 4 illustrates this.
This fragmentation of trading volume impacts the quality
of the order book. Figure 5 contains an example of a reason-
ably liquid ETF, the db x-trackers DJ Euro Stoxx 50. As can be
seen, the order book is filled with bids and offers, and the
spread is about 7 basis points (2 euro cents vs. a midprice of
29.07, using the best available bid and offer).
ETFs with large trading volume attract market makers and
high-frequency trading firms, creating a full order book. In
contrast to the listing on Xetra, the absence of a customer
base in the U.K. makes for an empty market and a wider
spread of approximately 20 basis points in the LSE listing.
Issues Caused By FragmentationAgain, unlike the U.S., the European clearing and settle-
ment system is not a homogenous entity. Every exchange
Figure 3
Figure 4
SX5E-Related ETFs Across All European Exchanges
Average Daily Exchange Turnover (20 Days), Selected Exchanges And Issuers (U.S. $Millions)
Sources: Bloomberg; BlackRock ETF Landscape, Year End 2009
Sources: Bloomberg; Nyenburgh research
Total Listings
XetraSWXNYSE
EuronextLSE
Exchange
Borsa
Italiana
Domestic
Exchange
Issuers
Issuer
Leveraged InverseLeveragedInverseRegularIssuer
BBVA 1 – – – 1
CASAM 1 1 1 – 3
Comstage 2 2 2 – 6
dbx-trackers 9 5 – – 14
Easy ETF 5 – – 2 7
ETFS – – 4 4 8
ETFlab 1 – – – 1
HSBC 1 – – – 1
iShares 10 – – – 10
Lyxor 8 2 6 3 19
Source 2 – – – 2
UBS 6 – – – 6
Total 78
db x-trackers Xetra 37.4 22.0 9.7 12.7 246.2
ETFS LSE 40.3 85.9 5.1 — 19.0
Lyxor NYSE Euronext 170.4 2.8 271.7 13.2 95.3
Xmatch SWX 2.3 — — 45.8 0.8
March/April 201024
has its own central counterparty (CCP) and central securi-
ties depository (CSD), and therefore every listing settles
in a different place. In order to balance positions between
settlement venues, ETFs need to be transferred from one
location to another, which costs money and can take sev-
eral days. This is only the start of a whole range of issues
arising from different settlement locations, including:
• Different settlement cycles (T+2, T+3)
• Prime brokers and clearinghouses might use an omnibus
account at the CSD. Consequently, ETFs can effectively be
loaned out by the clearinghouse to another trading firm,
without prior knowledge and/or approval from the owner
of the position. Once loaned, the ETFs cannot be trans-
ferred. (It is very difficult to transfer shares that are not
actually in your account.)
• The LSE has a 30-day settlement period, which basically
means that settlements can take up to 30 days. This lee-
way is frequently misused by hedge funds and trading
firms. Again, it is very difficult to transfer an ETF that is
not “settled long.”
• Monte Titoli (Borsa Italiana) does not net-off trades, so a
short sale followed by a cover buy will not be netted off.
You first need to buy somewhere else and then transfer to
Italy for this trade to settle.
• Some CSDs do not connect to each other, so ETFs must be
transferred using a third country, which takes extra time
and costs extra money.
European exchanges have a policy of forcing a buy-in
when ETF trades don’t settle, imposing penalties of up to
100 percent of the notional value of the trade. This practice
further reduces liquidity, as market makers and other liquid-
ity providers are limited in their short-selling capabilities.
Liquidity providers are obligated to put quotes in for every
listing, including the listings that do not trade (they also
have to maintain inventory to prevent buy-ins). For some
ETFs, this can lead to maintaining inventory and bid/offer
prices for more than nine listings.
In the end, all individual quotes are typically smaller in
size than they would have been if there were one single list-
ing. In contrast to other issuers, Source has chosen to list its
ETFs on only one exchange, thereby preventing these effects.
Its goal is to concentrate all liquidity in its products in one
single place. Moreover, unlike Reg NMS, the European MiFID
regulation does not enforce true best execution. Brokers can
choose to route all flow to one exchange, completely ignor-
ing better prices on other exchanges.
Impact On LiquidityIn the end, all client orders are spread over many differ-
ent listings, therefore effectively reducing the liquidity of
the investment product. End-investors almost always have
to cross the bid/offer spread, and working an order against
other investor flow is very difficult. Another consequence is
that a significant proportion of the trading volume in ETFs in
Europe is traded over the counter (OTC); some even estimate
that OTC trading represents more than 50 percent of the total
trading volume. A precise number is hard to determine, as no
obligation exists under the MiFID regulation to report OTC
trades. Brokers who trade off-screen use the appearance of
low screen-based liquidity to promote themselves as alterna-
tive liquidity pools. Although an order might easily have been
executed on-screen, a larger order is usually done OTC. This
is the case for most orders above €10 million, and sometimes
even much smaller orders are traded OTC.
SolutionsWhat can be done to make European ETFs more liquid?
Figure 5
Order Book For db X-trackers DJ Euro Stoxx 50 ETF: Xetra Vs. LSE Listing
Source: Bloomberg
Volume 593,610
Volume 5,000
Total Ask OrderSizeAskBidSizeTotal
Xetra Listing Currency: Euro Last Trade 29.09
LSE Listing Currency: Pence Last Trade 2,553.00
Total Bid
50,000 1 50,000 29.06 29.08 30,000 1 30,000
80,000 1 30,000 29.05 29.09 70,000 3 100,000
100,000 1 20,000 29.04 29.10 2,000 1 102,000
102,000 1 2,000 29.03 29.11 2,000 1 104,000
132,000 1 30,000 29.02 29.12 2,000 1 106,000
162,000 1 30,000 29.00 29.13 3,000 3 109,000
163,000 1 1,000 28.95 29.17 60,000 2 169,000
173,000 1 10,000 28.89 29.30 10,000 1 179,000
173,100 1 100 28.60 29.58 6,528 1 185,528
176,701 1 3,601 28.58 29.61 3,645 1 189,173
5,000 1 5,000 2,540 2,545 5,000 1 5,000
www.journalofindexes.com 25March/April 2010
continued on page 55
March/April 2010www.journalofindexes.com 55
acceptable formal governance ratings highly questionable.
To illustrate the scope for differences of opinion along the
“fee” dimension, Wallison and Litan15 present a strong argu-
ment that requiring fund directors to approve a fund’s invest-
ment management fee discourages price competition among
investment managers. The stickiness of fees in the face of
heavy emphasis on expense ratios in fund comparisons sug-
gests that Wallison and Litan have a point. It would certainly
not harm investors in existing funds to permit managers of
new funds to experiment with a fund’s fee structure. As long
as disclosure of the possible range of fees is adequate from
the first day the fund is offered to investors, changes in fees
by these new funds and adoption of fee structures that are
different from the fulcrum performance fees now required
should also be possible. The fact that a case involving fund
fees has reached the Supreme Court suggests far-from-uni-
versal agreement on fund fee issues.
If a fund service insists on taking a stance on fund gov-
ernance, it should consider any specific governance issue it
deems relevant to a fund and either accept the governance
and ethical standards at a fund company and not discuss
them or reject them entirely with a full explanation of the
reasons behind the rejection. Either a question or problem
is serious enough to encourage investors to avoid the fund
or it is not important enough or definitive enough to affect
an investment decision. Beyond a statement of the facts of
a situation, complexity in fund governance analysis and rela-
tive governance ratings will rarely be either fair or useful.
Endnotes1 The early status of the Investment Company Institute XBRL Initiative is summarized in McMillan, Karrie, “Remarks at XBRL International Conference,” Vancouver, British Columbia,
Dec. 4, 2007. The timing of further XBRL implementation is difficult to forecast but the ICI seems to be the fund industry’s organization of choice for this effort. You can see where
the SEC stands on XBRL by starting at http://www.sec.gov/spotlight/xbrl.shtml. There is even a rudimentary mutual fund viewer that lets you create a simple fund comparison report
for two or three funds. A visit will impress you with both the potential for improved fund data and with how far the process has to go.
2 See Cox, Christopher, “Disclosure from the User’s Perspective,” CFA Institute Conference Proceedings Quarterly, September 2008, pp. 10-15.
3 In fairness to iShares, the cost of licensing a wide range of indexes just for this application would probably be prohibitive.
4 Chua, David B., Mark Kritzman and Sébastien Page, “The Myth of Diversification,” The Journal of Portfolio Management, Fall 2009, vol. 36, No. 1, pp. 26-35 provides a useful look
at the asymmetry of diversification.
5 Cremers, Martijn and Antti Petajisto, “How Active Is Your Fund Manager? A New Measure that Predicts Performance,” Review of Financial Studies, September 2009, vol. 22,
No. 9, pp. 3329-3365.
6 In calculating active share, it is often useful to make the calculation relative to a number of benchmark indexes. While the S&P 500 and the Russell 1000 are highly correlated, a closet
indexer using the Russell 1000 as a fund template might have a greater active share measured against the S&P 500 than measured against the (more relevant for this fund) Russell 1000.
Cremers and Petajisto measured active share against a variety of major indexes and assumed the benchmark was the index that showed the lowest active share, (p. 3340).
7 Cremers and Petajisto, p. 3332.
8 Ibid, pp. 3354-3355.
9 Ibid, pp. 3350-3353.
10 Wright, Christopher, “Cleaning Closets,” CFA Magazine, September/October 2008, vol. 19, No. 5, pp. 20-21.
11 Gastineau, Gary L., Andrew R. Olma and Robert G. Zielinski, “Equity Portfolio Management,” Chapter 7, in Maginn, John L., Donald L. Tuttle, Jerald E. Pinto and Dennis W.
McLeavey, “Managing Investment Portfolios: A Dynamic Process,” pp. 407-476. John Wiley & Sons, Hoboken, New Jersey, 2007.
12 Ertugrul, Mine and Shantaram Hegde, “Corporate Governance Ratings and Firm Performance,” Financial Management, vol. 38, No. 1, Spring 2009, pp. 139-160.
13 Wellman, Jay and Jian Zhou, “Corporate Governance and Mutual Fund Performance: A First Look at the Morningstar Stewardship Grades,” Unpublished Working Paper, March
18, 2008.
14 Haslem, John A., “Mutual Funds,” Wiley, 2010, p. 312.
15 Wallison, Peter J. and Robert E. Litan, “Competitive Equity: A Better Way to Organize Mutual Funds,” The AEI Press, Washington, D.C., 2007.
The distribution problem is something politicians have been
working on for 50 years by trying to form the European
Union. Unfortunately, as long as Europe remains divided,
issuers will have to spend more time, effort and money on
marketing in each individual country. A good start would
be to ease regulations that require ETFs to be listed locally
to be allowed to be sold. The issue of Europe’s fragmented
clearing and settlement system could be solved by having
one central or several linked CSDs, much like the Depository
Trust & Clearing Corporation in the U.S., in combination with
stricter regulations on best execution. Finally, an obligation
to report OTC trades would increase transparency.
Lijnse continued from page 25
Endnotes1 Source: DB Index Research, Weekly ETF reports—Europe, January 21, 2010
2 Source: BlackRock ETF Landscape Year End 2009
ReferencesBlackRock Advisors, ETF Landscape, Industry Preview, Year End 2009
Bloomberg
DB Index Research, Weekly ETF reports—Europe, January 21, 2010
March/April 201026
Europe In Focus
By Keshava Shastry
A Big Bang In European
ETF Trading?
The growth of exchange-traded funds in Europe over the
past 10 years has been remarkable. However, with ETF
assets under management worldwide recently surpass-
ing the $1 trillion milestone, a question comes to mind about
the ETF industry—particularly if we consider the global eco-
nomic upheaval of the past 18 months. What’s next?
What are the arguments indicating further dynamic
growth and, perhaps even more importantly, what conditions
need to be met for this growth to materialize?
It may surprise some to hear a view expressed that,
despite the significant growth of the past two years or so
in the European exchange-traded product market, the real
breakthrough in Europe is only now about to happen.
Clearly it would be an oversimplification to point toward
a single driver as being capable of changing the market, yet
it cannot pass unnoticed that there is a general consensus
forming about this year being one of trading in ETFs.1
What is fueling this popular belief?
A False View Of LiquidityOne does not need to be a skeptic to observe that there
is an end to all things, and it is no small wonder that there
might be some people who think a 56.8 percent rise in assets
under management in European ETFs in 2009 could be dif-
ficult to replicate in 2010 and coming years.2
One cannot deny that at least part of this impressive
growth came as a result of unique market circumstances.
Investor concerns about counterparty risk, compounded
by plunging financial markets, moved investor interest into
financial products such as ETFs, while the subsequent rally of
late 2009 helped to achieve the strong asset growth figures
witnessed by European ETF providers.
Still, with 215 new ETFs launched in 2009, it is clear there
remains considerable room for growth in the ETF market into
2010 and beyond. Indeed, there are no reasons to assume that
the European market is fundamentally different from the U.S.
one, where ETFs are responsible for roughly one-third of the
total activity in the equity market and where 17 years after the
inception of the first ETF, assets have surpassed $700 billion,
with more than $50 billion changing hands every day.3
This last figure about ETF turnover might actually hold
the key to explaining where growth will come from, and
points clearly to where the main difference lies between
the U.S. and European markets.4 It is the deficit of liquidity,
or to be more precise, the deficit of visible liquidity, that
has impacted the development of ETF markets in Europe.
Even in the eyes of those who have always understood the
primary market creation/redemption mechanism for ETFs,
this apparent deficit of liquidity has led to the misconcep-
tion that ETFs are investment vehicles accessible only to
institutional clients who are able to trade over-the-counter
(OTC) with specialized ETF brokers.
A lot can be said about the origins of this preconception.
The fact that OTC transactions have been excluded from the
scope of the MiFID (Markets in Financial Instruments) direc-
tive has done the market no favors, as it has pushed the bulk
of activity in ETFs into a gray zone of unreported trading.
Similarly, the multitude of exchanges and trading venues
alongside currency cross-listings means that the visible pool
of liquidity is further fragmented and can be confusing. All in
all, the final impression is that of a market with erratic activ-
ity levels and high trading costs.
This is not a fair reflection of the market. On the contrary,
when looking at market structure and trends over the last
two years, it is clear that there are increasing signs indicating
the time is approaching for the big trading bang to happen.
One obvious indicator is the continuing growth of trading
volumes. Although the 0.9 percent increase in on-exchange
trading activity might not seem particularly impressive, when
we include all the OTC flows that are “printed back” onto
Some think 2010 could be a turning point
March/April 2010www.journalofindexes.com 27
trade reporting facilities, a truer picture emerges.
What is striking about this jump of more than 100 per-
cent year-on-year is what it suggests about the real size of
the OTC market in Europe. Given that trade reporting of
ETFs in Europe is mostly voluntary, it is primarily limited
to the entities that have automated infrastructure in place
to report their MiFID-compliant cash market activity.5 As
such, specialized ETF trading houses, which are respon-
sible for the bulk of trading volumes, remain largely in the
shadows, with the broker-to-broker market almost entirely
veiled from external observers. Still, the fact that the trade-
reported segment of the market more than doubled in size
last year shows both the potential for greater transparency
among market participants and the increased understand-
ing of the benefits that this might bring.
Whereas it is clear that liquidity generates liquidity, it is
nevertheless important to remember that there are also other
criteria that need to be met in order to pass the trading “big
bang” threshold point. For market participants to treat the
trading of ETFs as an extra opportunity, at least three other
preconditions must be met: lower trading costs, a deep order
book and ease of comparison between trading venues.
For the first two preconditions, it is possible to show that
2009 was a year that marked a step in the right direction. It
should also be of no particular surprise that a drop in trad-
ing costs usually comes as a result of deeper order books, as
increased competition between market makers forces pro-
viders to accept limited margins and decrease their spreads.
In terms of trading costs associated with investing in
ETFs, there is hardly an exchange in Europe where the
average spread a client needs to pay to trade a fund has
not decreased throughout 2009. Taking Euronext as an
example, the median spread of all listed ETFs was 34.36
bps in December 2009, down from 71.03 bps in December
of 2008. Over the same time period, figures for the
London Stock Exchange (LSE) show a drop from 87.58 bps
to 52.33 bps, while for Deutsche Boerse it is 46.64 bps and
25.46 bps, respectively.6
As powerful as the above-mentioned statistics are, it is
worth remembering that looking at the spread figure as the
only indicator of trading performance might be misleading,
especially when aggressive quotes are not backed by a deep
order book. To ensure easy and cost-effective execution, it
is almost as important to be able to rely on more than one
market maker quoting any given product. Again, the figures
here are encouraging. Whereas in January 2009 iShares ETFs
were on average traded by just under two official exchange
liquidity providers, the equivalent figure jumped to nearly
three in December 2009.
Aside from efforts by the provider, there is also a clear
increase of interest among high-frequency traders—espe-
cially those who run successful franchises in the U.S.—who
are keen to move into Europe early to benefit from a widely
anticipated surge in trading activity.
While tighter spreads and a deeper order book create a
more favorable environment for clients interested in trading
ETFs, it is equally important for them to be able to correctly
assess those parameters and make a conscious and educated
decision about their trading strategies. After all, it is not just
reality but perception that needs to change if 2010 is really
to be remembered as the year of trading in European ETFs.
In this battle of perceptions, progress is being made.
First of all, ETFs are an established phenomenon in the
European market, and the efforts made by issuers into
the education of investors is paying off with the actual
usage and trading of ETFs increasing.7 Equally important,
increased competition between exchanges has forced them
to simplify their requirements and lower trading costs,
making it easier to compare actual execution costs. Finally,
the arrival of multilateral trading facilities (MTFs) and their
appetite for a share of the ETF market translates into future
opportunities for growth.
In this context, one of the most widely discussed concepts
is that of a European consolidated order book—something
that would enable a client to access all venues where a
particular instrument trades through a single connection to
one platform. The trade order routing facility that has been
recently offered by some MTFs, and that now includes some
ETFs, is one of several attempts to provide a remedy to the
problem of the fragmentation of liquidity that has historically
beset the European ETF market.
As with all forecasts, it is not easy to claim with certainty
that 2010 will indeed be a “big bang” year for trading European
ETFs. There is much evidence that would seem to point toward
such a development, and the general sentiment in the market
seems to reinforce it. Nevertheless, it is quite clear that things
are ripe for change and chances are high that once such a shift
happens, it will transform the market quite rapidly.
Still, it is very important to realize that, even now, the
ETF market in Europe is in a state of rapid progress. The
real question is not whether it will develop, but how quickly.
For the rapid growth recently witnessed to continue, there
needs to be an improvement in visible liquidity, which in turn
should work as a magnet to attract the attention of inves-
tors. One crucial variable is the potential legal changes that
might bring ETFs within the scope of the MiFID directive.
Nonetheless, before this happens, it is still possible to see
the continued growth of the European market into spring
and beyond. And who would mind spring, after the winter
we experienced this past year?
European ETF Volume Reported Back To Trade Reporting Facilities
30,000
25,000
20,000
15,000
10,000
5,000
02008 2009
Source: Bloomberg
Trade Reported Volume (€mil)
Figure 1continued on page 53
March/April 2010www.journalofindexes.com 53
ing an ETF, and in some case, are cheaper than borrowing. If the
memory of Lehman Brothers fades over the course of the year, the
demand that European ETFs saw in 2009 may begin to fade.
An increase in the supply of European ETFs could, in turn,
lead to an increase in demand. As additional supply comes
into the market, the advantages that swaps have over short-
ing ETFs will continue to diminish. The risk of recall and rela-
tively higher cost should diminish if more institutions make
their ETFs available to borrow. The increase in supply could
come about as more ETF owners realize that they can offset
the ETF management fee by putting them into a securities
lending program. This lending activity can help offset the
inherent tracking risk, which may make owning European
ETFs more attractive.
Endnotes1 ETF Landscape Industry Preview Year End 2009, BlackRock Global ETF Research & Implementation Strategy Team
2 Source: Data Explorers
3 Ibid.
tronic order-driven market models with opening and closing
auctions, central limit order books and valuation price feeds.
Similar to the U.S. LMM model, the European ETFs
benefit from a multiliquidity provider model that results
in remarkable market depth and competitive spreads.
For each ETF, there is at least one liquidity provider (LP),
generally appointed by the issuer, that agrees to provide
continuous quotes, minimum market depth and maximum
spreads through the exchange’s trading session. Monitored
by the exchange, so long as they are meeting their pres-
ence, size and spread requirements, LPs may receive incen-
tives from the exchange for providing liquidity in the form
of discounted transaction fees. Auctions are supported by
the LPs through their obligation to provide consistently dis-
played liquidity during the opening to the closing auction.
The number of LPs in Europe has increased significantly in
recent months following the implementation of a new trad-
ing technology and faster data feeds, improving the overall
tool kit for all traders. The increasing diversity of partici-
pants trading ETFs (buy-side, sell-side, retail, etc.) ultimately
leads to more efficient markets, and with the LP activity, we
observe the quoted spreads of several ETFs are now tighter
than those of the underlying indexes, and others reached
their lowest ever.
Although this is a deep dive into only two ETFs (see Figures
3 and 4), a comparison to BATS and Chi-X—nonexchange
multilateral trading facilities (MTFs)—illustrates the market-
quality advantages the exchange-appointed LP model pro-
vides in terms of tighter spreads and deeper markets.
ConclusionOverall, one can easily understand how liquidity provision,
encouraged on a level playing field of fair access, can result
in better expected market quality for a given symbol. In the
popular press, there is a large belief that all ETFs are created
equal—meaning that due to their transparency, the pricing
of any ETF is without costs and will be priced efficiently.
In principle this might be true; however, in practice, just
because an ETF can be efficiently priced doesn’t mean some-
one actually wants to do it at all times. An exchange model
that supports the role of a lead market maker or liquidity
provider with performance obligations is well-positioned in
principle to meet this ideal more often than not.
DISCLAIMER:This article is intended for investment professionals only and
solely for informational and educational purposes. It should not
be relied upon for any investment decisions. The article is based
on data obtained as of Aug. 30, 2009 (unless otherwise noted
herein), which, although believed reliable, may not be accurate
or complete and should not be relied on as such. The author
does not recommend or make any representation as to possible
benefits from any securities, investments, products or services.
Investors should undertake their own due diligence regarding
securities and investment practices.
Dallmer continued from page 21
Endnote1. Source: NYSE Euronext research databases
Endnotes1Amery, P., Inside ETFs Conference 2010: A Focus on Trading, www.indexuniverse.eu/blog/7127.2Fuhr, D. op. cit.3ibid.4ibid.5London Stock Exchange and SIX Swiss Exchange require their members to trade-report their OTC activity in funds that are listed on those venues.6Source: Euronext, LSE, BlackRock.7Amery, P., op. cit.
Shastry continued from page 27
March/April 201028
By Leonard Welter
An overview of securities lending and ETFs in Europe
No Shortage Of Share Lending
www.journalofindexes.com 29
Since their inception over nine years ago, the assets of
European-listed exchange-traded funds have grown to
a record $223 billion.1 The rapid recent growth of the
European ETF market indicates that ETFs have obtained accep-
tance as an asset allocation tool by institutional as well as retail
investors. The securities lending market for European-listed
ETFs also saw an increase in 2009, with loan values recovering
from their spring lows. The lending market also saw an increase
in the breadth of trading throughout 2009, and by December
there were more than 300 different ETFs with securities lend-
ing activity in Europe.2 Could this be the year the securities
lending market for European ETFs really takes off?
What Is Securities Lending And Why Is It Important To ETFs?
Securities lending is a global market with more than $1 tril-
lion worth of equity assets out on loan.3 The main purpose of
the market is to facilitate the practice of short selling—a short
seller is required to borrow the security in order to make
onward delivery to the market. The lender of the security
negotiates the price to borrow, with this price generally being
quoted as an annual percentage of the value of the loan, while
retaining the right to recall the loaned security at any time.
The loans are collateralized with either cash or securities and
are governed by standard agreements.
The securities lending market is relatively complex and
the majority of transactions take place over the counter
(OTC). The supply of securities that are made available to
the lending market comes from beneficial owners, such as
pension funds, who make their shares available to lend via
agents such as custodian banks. The demand to borrow is
fueled by hedge funds and proprietary trading desks that use
either internal trading desks or prime brokers to source and
manage their borrowing requirements.
In the United States, the lending and borrowing of ETFs has
been well established for many years. ETFs such as SPY and
QQQQ are very easily borrowed by short sellers who are taking
directional views on the market or are using them as a relatively
cheap and easy way to manage a hedge. It can be argued that
the activity of short sellers in the ETF market helps to provide
market liquidity and trade volume for the largest ETFs in the
U.S. market. There is an additional benefit to the underlying
holder of loaned ETFs, as the lending revenue can offset some,
or all, of the management charge of the fund, which reduces
their tracking error to the benchmark of the ETF.
In Europe, the securities lending market has seen much
slower growth when compared with the U.S. The slow
growth has been due to several factors, all of which come
down to the classic problem of “which came first: the chick-
en or the egg?” or, in the case of lending European ETFs,
“which came first: supply or demand?”
Supply has historically been an issue for ETF securities lend-
ing, as the traditional lenders such as custodian agents either did
not have accounts holding ETFs or they did not understand the
structure and nature of ETFs and therefore did not lend them.
Another disadvantage European ETFs have when compared
with their U.S. counterparts is the structural cost of creation.
In the U.S., ETF supply for lending could be created relatively
easily, by borrowing the underlying constituents of the ETF
and delivering them to the creation agent in return for ETF
units, which could then be lent to the market. This cost of
creation is generally low, which means that the “borrow to
create” (or “create to lend”) ETF transaction is a viable source
of supply. In Europe, on the other hand, stamp taxes and divi-
dend costs make the “borrow to create” transaction prohibi-
tively expensive to the short seller and lending agents.
Demand has historically also been an issue for the European
ETF securities lending market. Hedge funds and proprietary
trading desks can easily substitute a trade that involves the
shorting of an ETF by entering into a swap contract. A swap can
be structured to give the same performance of a short ETF posi-
tion and has been seen to be a less expensive and more stable
alternative to borrowing ETFs. The recall risk when borrowing
ETFs can be significant, as the general lack of supply mean’s
that a hedge or directional short could be forced to close due
to the lender requiring the return of borrowed shares.
Has The Market Changed?The overall dollar value of loans made in European ETFs is
still below the peak that was hit in February 2008, but as shown
in Figure 1, demand has recovered from the March 2009 lows. A
more significant signal of change in demand is the fact that the
overall number of ETFs with lending activity has more than dou-
bled from 113 in March 2009 to 305 in December (Figure 2).
Part of this increase in securities lending for European ETFs
can be attributed to the 2008 collapse of Lehman Brothers.
The collapse highlighted the significant counterparty risk of
swap transactions. With a swap, the buyer is tied to the party
that sold them. Swaps cannot be easily transferred and may
not be able to be closed during a period of market turmoil.
An ETF, on the other hand, trades on an exchange, and short
positions can be transferred to another bank/prime broker or
quickly closed in the market if necessary.
Another explanation for the increase in the demand for
ETFs could also be due to the change in the supply situa-
tion. The value of European ETFs made available to borrow
March/April 2010
Jan’08
Apr’08
Jun’08
Oct’08
Dec’08
Aug’08
Feb’09
Jun’09
Apr’09
Aug’09
Dec’09
Oct’09
European ETFs Value On Loan
US
D B
illi
on
s
$2.5
$2.0
$1.5
$1.0
$0.5
$0
Figure 1
Source: Data Explorers
continued on page 52
March/April 201052
by lenders such as custodian banks saw a marked decrease
in the autumn of 2008 (Figure 3). Part of this decline can be
attributed to the drop in global equity values, and part is due
to the fact that some market participants suspended lending
during the extreme market conditions of 2008 and 2009.
While the value of the supply ended 2009 below the highs
seen in 2008, the strong run-up since June 2009 is an indica-
tion that the value of supply will continue to grow in 2010.
This upward trend in lendable value is also mirrored in
the number of European ETFs that are being made available
to borrow. As illustrated in Figure 4, the number increased
significantly at the tail end of 2009, from roughly 200 in April
to more than 300 by the end of December.
The Top 10 European ETFs Of 2009, By Loan ValueThe ETFs that saw the highest 2009 average U.S. dollar
loan value were predominantly iShares funds. The iShares
FTSE 250 consistently saw the highest level of demand, fol-
lowed by the Lyxor Euro Stoxx 50 ETF.
In terms of lending revenue, the average lending rate for the
top 10 ETFs in most cases exceeded the ETF management fee.
In fact, the lending rate was also in excess of the rates charged
for the underlying basket of securities. Some of this premium
could be put down to the structural cost of creating ETFs to
lend, such as stamp tax for U.K. equities, but the rates do imply
that the market is willing to pay for the convenience of bor-
rowing a single security. Another explanation for the premium
could be that the securities lending market for European ETFs is
still far from mature. While the number and value of European
ETFs in securities lending is increasing, it is still far below the
levels seen in the U.S. market. In the U.S., the lendable value
for the ETF market is in excess of $55.8 billion, with a value
on loan of $23 billion. This depth of supply results in much
lower lending rates for U.S. ETFs. The average rate is close to,
or marginally below, the management fee of the ETF. However,
the higher level of demand means the absolute value of revenue
from lending ETFs is higher than in Europe.
2010: What Does The Future Hold?While the data points to a strong lending market for
European ETFs in 2010, there are still areas of weakness.
On the demand side, while swaps have a significant element of
counterparty risk, they do not have the same recall risk as borrow-
Figure 2
Figure 3
Figure 4
Source: Data Explorers
Source: Data Explorers
Source: Data Explorers
Jan’08
Apr’08
Jun’08
Oct’08
Dec’08
Aug’08
Feb’09
Jun’09
Apr’09
Aug’09
Dec’09
Oct’09
Number Of European ETFs Out On Loan
350
300
250
200
150
50
100
$0
Jan’08
Apr’08
Jun’08
Oct’08
Dec’08
Aug’08
Feb’09
Jun’09
Apr’09
Aug’09
Dec’09
Oct’09
European ETF Lendable Value
US
D B
illi
on
s
$16
$14
$12
$10
$8
$6
$4
$2
$0
Jan’08
Apr’08
Jun’08
Oct’08
Dec’08
Aug’08
Feb’09
Jun’09
Apr’09
Aug’09
Dec’09
Oct’09
Number of European ETFs Made Available to Borrow
350
300
250
200
150
50
100
$0
Figure 5
Securities Lending Top Ten European ETFs
Source: Data Explorers
Avg
Wholesale
Lending Rate
(Fee)
Avg Total
Balance Name
iShares FTSE 250 GBP $79mm 2.70%
Lyxor ETF DJ Euro Stoxx 50 ETF $43mm 1.65%
db x-trackers MSCI USA TR Index ETF $41mm 0.30%
iShares FTSE 100 GBP $40mm 1.00%
iiShares DAX (DE) $34mm 1.50%
iShares DJ Euro Stoxx 50 (DE) $23mm 1.50%
Lyxor ETF CAC 40 $20mm 2.00%
Xact OMXS30 $20mm 0.65%
UBS-ETF DJ Euro Stoxx 50 A $15mm 2.00%
iShares MSCI Japan USD $12mm 1.85%
Welter continued from page 29
March/April 2010www.journalofindexes.com 53
ing an ETF, and in some case, are cheaper than borrowing. If the
memory of Lehman Brothers fades over the course of the year, the
demand that European ETFs saw in 2009 may begin to fade.
An increase in the supply of European ETFs could, in turn,
lead to an increase in demand. As additional supply comes
into the market, the advantages that swaps have over short-
ing ETFs will continue to diminish. The risk of recall and rela-
tively higher cost should diminish if more institutions make
their ETFs available to borrow. The increase in supply could
come about as more ETF owners realize that they can offset
the ETF management fee by putting them into a securities
lending program. This lending activity can help offset the
inherent tracking risk, which may make owning European
ETFs more attractive.
Endnotes1 ETF Landscape Industry Preview Year End 2009, BlackRock Global ETF Research & Implementation Strategy Team
2 Source: Data Explorers
3 Ibid.
tronic order-driven market models with opening and closing
auctions, central limit order books and valuation price feeds.
Similar to the U.S. LMM model, the European ETFs
benefit from a multiliquidity provider model that results
in remarkable market depth and competitive spreads.
For each ETF, there is at least one liquidity provider (LP),
generally appointed by the issuer, that agrees to provide
continuous quotes, minimum market depth and maximum
spreads through the exchange’s trading session. Monitored
by the exchange, so long as they are meeting their pres-
ence, size and spread requirements, LPs may receive incen-
tives from the exchange for providing liquidity in the form
of discounted transaction fees. Auctions are supported by
the LPs through their obligation to provide consistently dis-
played liquidity during the opening to the closing auction.
The number of LPs in Europe has increased significantly in
recent months following the implementation of a new trad-
ing technology and faster data feeds, improving the overall
tool kit for all traders. The increasing diversity of partici-
pants trading ETFs (buy-side, sell-side, retail, etc.) ultimately
leads to more efficient markets, and with the LP activity, we
observe the quoted spreads of several ETFs are now tighter
than those of the underlying indexes, and others reached
their lowest ever.
Although this is a deep dive into only two ETFs (see Figures
3 and 4), a comparison to BATS and Chi-X—nonexchange
multilateral trading facilities (MTFs)—illustrates the market-
quality advantages the exchange-appointed LP model pro-
vides in terms of tighter spreads and deeper markets.
ConclusionOverall, one can easily understand how liquidity provision,
encouraged on a level playing field of fair access, can result
in better expected market quality for a given symbol. In the
popular press, there is a large belief that all ETFs are created
equal—meaning that due to their transparency, the pricing
of any ETF is without costs and will be priced efficiently.
In principle this might be true; however, in practice, just
because an ETF can be efficiently priced doesn’t mean some-
one actually wants to do it at all times. An exchange model
that supports the role of a lead market maker or liquidity
provider with performance obligations is well-positioned in
principle to meet this ideal more often than not.
DISCLAIMER:This article is intended for investment professionals only and
solely for informational and educational purposes. It should not
be relied upon for any investment decisions. The article is based
on data obtained as of Aug. 30, 2009 (unless otherwise noted
herein), which, although believed reliable, may not be accurate
or complete and should not be relied on as such. The author
does not recommend or make any representation as to possible
benefits from any securities, investments, products or services.
Investors should undertake their own due diligence regarding
securities and investment practices.
Dallmer continued from page 21
Endnote1. Source: NYSE Euronext research databases
Endnotes1Amery, P., Inside ETFs Conference 2010: A Focus on Trading, www.indexuniverse.eu/blog/7127.2Fuhr, D. op. cit.3ibid.4ibid.5London Stock Exchange and SIX Swiss Exchange require their members to trade-report their OTC activity in funds that are listed on those venues.6Source: Euronext, LSE, BlackRock.7Amery, P., op. cit.
Shastry continued from page 27
March/April 2010
Talking Indexes
By David Blitzer
30
How do you deal with a pernicious cycle?
Bubble Decisions
The market’s major innovation of the last decade or
two is the return of the bubble. While banks, brokers
and others were hard at work developing new ways
to make money in the investment markets, the market itself
introduced the most powerful, and frustrating, innovation of
all: short, steep boom-and-bust cycles better known as bub-
bles. Some of the bubbles we’ve seen include tech stocks (and
Nasdaq-listed stocks in general) in 2000, home prices in 2005-
2008 (in the U.S., the U.K. and Spain, among other places) and
Chinese A-shares at various times in the last decade.
Few if any of the investment ideas of the last 20 years pro-
vided half as many gains as the bubbles took away. After all,
the S&P 500 opened 2010 lower than where it closed 2000,
or for that matter, 1997. True, some people made money
through the decade of the 2000s, but they were few in num-
ber; the idea that one could participate in overall economic
growth by owning stocks just didn’t work.
Bubbles weren’t new in the 21st century—they’ve been
around since at least the 17th century, when there were tulip
bulbs in Holland. But they have returned with a vengeance.
One of the most unfortunate things about bubbles is that
they don’t usually come with large warning signs reading
“Entering the Bubble Zone.” Rather, they seem to creep up
slowly, giving people time to develop arguments about what
might be happening. Bubbles can be big and explosive or
small and seemingly unchanging; they can switch from mod-
est price increases that slightly stretch valuations to booms
and busts that threaten economies. They can make the tran-
sition in a year or a week.
A defense against bubbles would be welcome. Currently
most regulators—who should be protecting us from such
things—argue that in the middle of a bubble, one can’t tell
that you’re in a bubble, and further efforts to control the
bubble would slow the economy or collapse stock prices.
Neither regulators nor investors are quite so powerless.
Forecasting with 100 percent accuracy may not be possible,
but that hasn’t stopped people from making and acting on
forecasts of bubbles or other market events. The worrisome
thing about trying to control bubbles is that policies to
control a bubble, if applied when there is no bubble, might
indeed—as those regulators suggest—slow the economy
or end a bull market. While many of the myriad books and
articles dissecting the financial crisis offer estimates of the
costs of controlling bubbles or tools to predict bubbles, the
big question is, how do we decide when to attack the bubble?
A little crude analysis may help.
Bubble Damage Decision Analysis
Figure 1
Bubble
True Market Condition
No Bubble
Market Condition Bubble 600 300
Regulators See No Bubble 1,000 0
March/April 2010www.journalofindexes.com 31
What we have, essentially, is a decision problem. If we’re
in a bubble, steps such as restricting trading, increasing
margin requirements, or raising interest rates and trading
costs can rein in the boom before prices reach extremes. But
if these actions are taken and there is no bubble, regulators
will have killed off a bull market for no reason—not a popu-
lar action. On the other hand, if regulators assume there is
no danger and the bubble bursts, the resulting bear market
and recession could be nasty. A diagram with some damage
figures can sort this out (see Figure 1).
The markets are either in a bubble or not. The two col-
umns on the right represent the actual market condition:
Bubble or No Bubble. The two rows represent the regula-
tors’ point of view: They either believe there is, or is not, a
bubble. The numbers in the four boxes represent the dam-
ages caused by each case.
Starting in the upper left number box, if there is a bubble
and the regulators guess correctly, the damage is 600; if the
regulators are wrong—they don’t see the bubble but it is
there—the damages are 1,000. Now suppose that there is
no bubble (extreme right-hand column). If regulators are cor-
rect, the damage is zero (bottom right), while if the regula-
tors invoke policies to control a bubble that isn’t there, the
policy of tight money or trading restrictions still leads to
damages of 300. (All these figures are just for illustration and
are not based on real data or estimates.)
This little diagram, borrowed from statistical decision
analysis, can help people decide what to do. The standard
approach to solving this kind of problem—called minimax
in the literature—seeks to minimize the maximum loss that
could be suffered. Although this sounds a bit pessimistic—
expect the worst and limit its severity—it has been shown to
be successful. For each choice the regulators could make—
bubble or no bubble—we find the maximum damage. Then
we choose the policy—bubble or no bubble—with the small-
est loss. Looking at the numbers here, the maximum loss if
we assume there is no bubble is 1,000 (bubble bursts and
we’re not expecting it), while if we expect the bubble, the
maximum loss is 600. Taking the smaller of 1,000 and 600,
the approach is a policy that assumes a bubble is occurring
and seeks to limit the damage, even though this would be
costly if there is no bubble. Without arguing that the rela-
tive damages make sense, this is not what the Fed and other
regulators did in 2005-2007.
However, this suggests that regulators should see
bubbles everywhere they look and always attack bull mar-
kets. This is not a very attractive policy—we can do better.
While we can’t know for certain if we’re in a bubble until
after the fact, we should be able to estimate the prob-
ability we’re in a bubble. For example, if the probability of
being in a bubble is 30 percent and the probability of no
bubble is 70 percent, we can calculate the expected loss of
each policy choice. The expected loss of the bubble policy
(first row) is 30 percent of 600 plus 70 percent of 300, or
an overall loss of 390; the expected loss of the no-bubble
policy is 300 (30 percent of 1,000 plus 70 percent of zero).
In this case, the regulators should take the “we’re not in a
bubble” approach because the expected loss is less. With a
little experimentation, providing that our loss numbers and
probabilities make some sense, we can figure out (using
our theoretical loss numbers) that if the probability of a
bubble is greater than 43 percent, regulators should attack
the bubble. Different damage costs will lead to a different
number than the 43 percent. The higher the damage from
an unrecognized bubble, the lower the probability thresh-
old for attacking a bubble. Double the 1,000 figure, leave
the other loss estimates unchanged, and the 43 percent
becomes 17 percent.
Finding the necessary probability and loss data isn’t
impossible. Andrew Smithers, an investment analyst based in
London, in a recent book, “Wall Street Revalued,” describes
measures that could be used to estimate the probability of
being in a bubble. Damage figures appear in many of the
analyses of the recent financial crisis. Whether regulators,
or investors, are willing to step into the markets and try to
manage bubbles remains to be seen.
Issue No. 97
December 2008
The Commodity
Futures Trading
Commission’s effort to clamp down on
the role of index investors continues to
impact exchange-tra
ded funds. At th
e end
of September, Deutsch
e Bank announced
that the PowerShares DB Commodity
Index Tracking Fund (NYSE Arca: DBC) and
PowerShares DB Agricultural Commodity
Index Fund (N
YSE Arca: DBA) would mas-
sively diversify
the type and number of
commodity futures they hold.
DBC and DBA are two of th
e most
popular commodity ETFs in th
e world,
with $3.4 billion and $2.2 billio
n in
assets,
respectively. The funds until
now have held concentrated positions
in a select number of commodity
futures: six for D
BC and four for D
BA.
With the CFTC threatening to enact
position lim
its on a broad range of
commodity futures—and with its
recent decision to enforce existi
ng
position lim
its on agricultural futures
for ETFs—Deutsche Bank has decided
the time is
right to sp
read its bets.
The moves w
ill not alter th
e sector-
level distribution in DBC; DBA, however,
will make a significant move into live-
stock, taking on a 25 percent positio
n in
cattle and hogs. O
verall, DBC and DBA
will increase their h
oldings to 14 and 11
different contracts,
respectively.
New Commodity ETF Debuts
A new ETF offering exposure to com-
modity-producing equities launched
IN THIS ISSUE
A look at some key products i
n the ETF
pipeline and what they mean for th
e
industry as a whole.
Page 1
How likely to shut down are the ETFs you
hold? Matt Hougan offers s
ome tips on
how to spot a goner.
Page 5
Harvesting season is n
early upon us—tax
loss harvestin
g, that is
. Some strategies to
consider.
Page 6
ETFs usually avoid big capital gains dis-
tributions, b
ut it’s a diffe
rent story with
leveraged and inverse funds.
Page 11
MSCI EAFE ETFs are not the only products
covering the international developed ex
U.S. area.
Page 14
ETFR’s monthly data bank covers a
ll U.S.-
listed ETFs, in
cluding assets, performance
data and more.
Pages 20-30
News Highlig
hts: DBC And DBA
Diversify, N
ew Commodity ETF Debuts,
Grail Launches More Active Funds, Je
fferies
Plans Wildcatters E
TF … And much more!
Pages 1-4
Issue No. 108
November 2009
www.indexuniverse.com/ETFR
By Heather Bell
We’re in an amazing era in terms of th
e
evolution of ETFs. In just t
he past couple
of years, we’ve seen the launch of th
e first
truly active ETFs, t
he advent of 300 percent
leveraged and -300 percent inverse ETFs
and an explosion in coverage of emerging
and frontier m
arkets. All to
ld, 77 new ETFs
came to market in the firs
t nine months of
2009, with nine of th
ose attractin
g more
than $100 million in assets.
The ETF innovators are constantly
confounding those who say that “all the
good indexes are taken.” That refrain
was first p
ut forward by Lipper in
a white
paper on ETFs in 2002, and the industry
has grown 600 percent by assets and
added 730 new funds since then.
It doesn’t look like things will slow
down in 2010, either.
Our free online publication ETF Watch
monitors all th
e active ETF filings w
ith the
Securities and Exchange Commissio
n on
a daily basis. Each week, w
e find, another
handful of ETF hopefuls hit th
e SEC.
As we sta
rt to look ahead to 2010, w
e
thought it would be useful to
highlight
five of the most intriguing new funds
in registration today; a preview of w
hat
we’ll be covering next year.
1. Old Mutual FTSE Developed
Markets Ex US Fund/Schwab
International Equity
ETF
These two fili
ngs represent one idea, so
we’ll count th
em as a single entry.
The new fund filings from Old
Mutual and Charles Schwab share a lot
in common. They are both part of th
e
first ETF fili
ngs from th
ese mutual fund
giants, and big mutual fu
nd companies
entering the ETF sp
ace promises to be
an overaching theme for ETFs in
2010.
From Schwab and Old Mutual to
UPDATES
DBC And DBA Diversify
Five Funds To
Watch For In 2010
8
3
s Trading
mp down on
s continues to
unds. At th
e end
Bank announced
DB Commodity
NYSE Arca: DBC) and
icultural Commodity
rca: DBA) would mas-
type and number of
s they hold.
BA are two of th
e most
popular commodity ETFs in th
e world,
with $3.4 billion and $2.2 billio
n in
assets,
respectively. The funds until
now have held concentrated positions
in a select number of commodity
futures: six for D
BC and four for D
BA.
With the CFTC threatening to enact
position lim
its on a broad range of
commodity futures—and with its
recent decision to enforce existi
ng
position lim
its on agricultural futures
for ETFs—Deutsche Bank has decided
the time is
right to sp
read its bets.
The moves w
ill not alter th
e sector-
level distribution in DBC; DBA, however,
will make a significant move into live-
stock, taking on a 25 percent positio
n in
cattle and hogs. O
verall, DBC and DBA
will increase their h
oldings to 14 and 11
different contracts,
respectively.
New Commodity ETF Debuts
A new ETF offering exposure to com-
modity-producing equities launched
IN THIS ISSUE
A look at some key products i
n the ETF
pipeline and what they mean for th
e
industry as a whole.
Page 1
How likely to shut down are the ETFs you
hold? Matt Hougan offers s
ome tips on
how to spot a goner.
Page 5
Harvesting season is n
early upon us—tax
loss harvestin
g, that is
. Some strategies to
consider.
Page 6
ETFs usually avoid big capital gains dis-
tributions, b
ut it’s a diffe
rent story with
leveraged and inverse funds.
Page 11
MSCI EAFE ETFs are not the only products
covering the international developed ex
U.S. area.
Page 14
ETFR’s monthly data bank covers a
ll U.S.-
listed ETFs, in
cluding assets, performance
data and more.
Pages 20-30
News Highlig
hts: DBC And DBA
Diversify, N
ew Commodity ETF Debuts,
GraLaunches More Act ve Funds, Je
er es
Grail Launches More Active Funds, Je
fferies
Plans Wildcatters E
TF … And much more!
Pages 1-4
Issue No. 108
November 2009
h
he
ange Commission on
week, we fin
d, another
opefuls hit th
e SEC.
o look ahead to 2010, we
uld be useful to highlight
most intriguing new funds
on today; a preview of what
vering next year.
Mutual FTSE Developed
kets Ex US Fund/Schwab
ernational Equity
ETF
e two fili
ngs represent one idea, so
l count them as a sin
gle entry.
The new fund filings from Old
Mutual and Charles Schwab share a lot
n common. They are both part of th
e
first ETF fili
ngs from th
ese mutual fund
giants, and big mutual fu
nd companies
entering the ETF sp
ace promises to be
an overaching theme for ETFs in
2010.
From Schwab and Old Mutual to
A Diversify
010
8
33
Issue No. 97December 2008
That soniclike boom you heard at the
beginning of November? That was the
sound made by Charles Schwab’s entry
into the exchange-traded funds market.
It was probably the most anticipated
debut of 2009, right from the moment
that Schwab’s first filings with the
Securities and Exchange Commission
were announced. Moreover, the fact
that a heavy hitter like Schwab had
deemed the ETF arena worthy of its
efforts was, for many, a sign that the
burgeoning industry had hit its stride.
Although it has more in registration,
Schwab launched just four funds: the
Schwab U.S. Broad Market ETF (NYSE
Arca: SCHB), Schwab U.S. Large-Cap ETF
(NYSE Arca: SCHX), Schwab U.S. Small-
Cap ETF (NYSE Arca: SCHA) and Schwab
International Equity ETF (NYSE Arca:
SCHF). The domestic funds track indexes
from Dow Jones, while SCHF is tied to
the FTSE Developed ex-US Index.
In the days leading up to the launch,
it became clear that the financial services
giant was playing for keeps when Schwab
unveiled the pricing on the pending ETFs.
SCHB and SCHX charge 8 basis points,
while SCHA and SCHF charge 15 basis
points. Those fees match or beat the
costs on all the competing ETFs, even the
ones from Vanguard.
But Schwab dropped what
IN THIS ISSUEETFR’s Dave Nadig offers some suggestions
on making large trades in low-volume
ETFs.Page 1
Representatives from six different alternate
liquidity providers explain what they do
and how they do it.
Page 6
There’s only a few micro-cap ETFs cur-
rently trading. Which is the best way for
you to access this asset class?
Page 13
ETFR’s monthly data bank covers all U.S.-
listed ETFs, including assets, performance
data and more.
Pages 15-22
News Highlights: Schwab ETFs Debut
With Competitive Pricing, BarCap Halts
Share Creations For PGM, Guggenheim
Completes Claymore Purchase, Claymore
Rolls Out All-Cap China ETF …
And much more!
Pages 1-4
Issue No. 109
December 2009
www.indexuniverse.com/ETFR
By Dave Nadig“How do I know I can get out?”
That’s the question we hear most
commonly from investors and advis-
ers looking to trade low-volume ETFs.
Indeed, a recent Cerulli Associates report
listed liquidity as the No. 1 concern of
advisers considering ETFs.
One way to put your mind at ease
would be to limit your purchases only to
ETFs trading above 500,000 or 1 million
shares per day. This arbitrary cutoff pro-
vides some comfort that you’ll be able to
exit a position whenever you want. But
limiting yourself to only ultra-liquid ETFs
eliminates hundreds of quality funds
from consideration—and what’s more,
it’s completely unnecessary. As savvy
investors know, ETFs offer more liquidity
than meets the eye—provided you know
how to find it.Finding Liquidity
In ETF Trades
Liquidity Or Executability?
The key to finding that liquidity is under-
standing that investors don’t necessar-
ily care about liquidity in and of itself;
what they really care about is executabil-
ity. Investors seeking highly liquid ETFs
essentially want assurance that they can
trade their funds for a fair price. (After all,
short of Armageddon, you’ll always be
able to unload anything at some price.)
Therefore, in an ETF, you want to be sure
your trades are priced fairly vis-à-vis the
true underlying value of the ETF itself.
This is one of the primary differences
between trading an ETFs vs. buying and
selling traditional mutual funds. In a
mutual fund, an end-of-day buy order is
filled at exactly the net asset value of the
fund. All investor orders are filled at the
same price, which is also the price that
appears the next day in the pages of the
Wall Street Journal.With ETFs, though, it’s different. An
investor who puts in a large market order
to buy an ETF will likely get it filled at a
price higher than the fund’s actual fair
value. The reason is simple: To a certain
extent, ETFs trade like stocks.
UPDATESSchwab ETFs Debut With Competitive Pricing
Large Orders, Small ETFs
11
3
Get the most up-to-date, in-depth news, features
and data on the exchange-traded funds (ETF)
marketplace from the most respected and closely
followed publication in the industry.
Subscribe today to and see what
you’ve been missing.
Subscribe online at
www.indexuniverse.com/subscriptions or e-mail
March/April 201032
Comparing index providers
David Blanchett
Can Indexes Generate Alpha?
March/April 2010www.journalofindexes.com 33
There are an increasing number of passive investing
strategies available for investors, each offering a
slightly different market exposure. While we would
expect indexes with the same general market exposure (e.g.,
domestic large value) to have similar returns, past research
by Israelsen [2007], among others, has noted that the returns
of indexes can vary widely, even over prolonged time peri-
ods. Comparing indexes on returns alone, though, does not
account for risk. It could be that a large growth index from
one family outperformed another during a historical period
just because it was less “large” or more “growth.”
Therefore, the best way to compare indexes from different
providers is to do so on a risk-adjusted basis, i.e., determine
their respective alphas. While indexes are beta investments
by definition—not alpha investments—given their varying
returns and construction methodologies, we would expect
some to outperform others on a risk-adjusted basis. This
“alpha” component of seven different index methodologies
(and almost as many providers)—including the Dow Jones,
Dow Jones Wilshire, Morningstar, MSCI, Standard & Poor’s,
Standard & Poor’s Pure and Russell index families—will be
explored in this paper, where alpha is determined using a four-
factor regression model (i.e., Carhart model) consisting of the
three Fama/French factors and momentum.
IndexesInvestors choose to invest in an index, or really an invest-
ment that tracks an index such as a mutual fund or ETF, in order
to capture the return associated with that market exposure
without the variability (and often costs) associated with active
management. While the major index providers have similar
methodologies for their domestic equity indexes (see Appendix
I for a summary of the methodologies for the index providers
included in the study), there are differences among them. These
differences impact the performance and risk attributes for each
index, yet make it difficult for the average investor to compare
the relative strengths and weaknesses of each strategy.
As a shortcut, many investors simply seek out the most
well-known index for investing purposes. For example,
according to the 2009 Investment Company Factbook, 58
percent of all assets invested in domestic equity index mutual
funds were tracking the S&P 500, despite the fact that many
other indexes exist with similar market exposures. A better
approach would be to see which indexes actually outperform
on a risk-adjusted basis, yet little research has been devoted
to this topic. While one may expect that indexes would not
generate alpha using traditional risk-adjusted measures (i.e.,
four-factor alpha), the research conducted for this paper sug-
gests otherwise.
Index Investing TodayAs of June 30, 2008, more than 70 percent of assets in
index mutual funds and ETFs invested within the nine domes-
tic equity styles boxes (defined as Investment Category by
Morningstar) were invested in the large blend category, fol-
lowed by 7 percent in large growth and 5 percent in large
value (making the total large-cap allocation approximately 82
percent). While it is not surprising that the majority of assets
are invested in large cap, given that it is generally defined as
the largest 70 percent of securities based on market capital-
ization, it is somewhat surprising that such a large portion is
invested in a single style: large blend.
Figure 1 includes the rolling three-year annualized perfor-
mance for the large blend indexes from each of the six different
index methodologies (S&P uses the same blend methodology
for both its regular and pure indexes, so the return for the S&P
500 has only been included once) from July 1997 to June 2009.
Note that rolling three-year periods were selected because the
regression analysis in the following section is based on rolling
historical three-year periods (i.e., 36 months).
As shown in Figure 1, while the rolling annualized three-year
returns for the large blend indexes varied across providers, the
returns were relatively similar, although significant differences
did exist at varying points in time. The maximum range in
three-year returns during the entire test period for the six large
blend indexes was the three-year period ending September
2001, where the Morningstar large blend index outperformed
the MSCI large blend index by 7.51 percent (per year, +6.70
percent vs. -0.81 percent, respectively), while the minimum
range was in September 2000, where the Dow Jones large
blend outperformed the S&P large blend index by 1.28 percent
(per year, +17.72 percent vs. +16.44 percent, respectively).
Figure 2 includes the annualized returns of the indexes for
each style from July 1997 to June 2009, or a 12-year period.
Note that these returns were calculated by compounding the
monthly returns obtained from Morningstar Direct, based on
the same values used to create Figure 1.
The annualized performance differences may not appear
large among the indexes in Figure 2, but they are material
given the time period (12 years). For example, the annualized
performance difference between the best-performing large-
cap blend index (Morningstar at 2.78 percent) and the worst-
performing large-cap blend index (MSCI at 0.15 percent) may
be only 2.63 percent, but over 12 years this would result in a
difference of approximately 36 percent (with the investment
in the Morningstar large-cap blend being 36 percent larger,
ignoring contributions). What is less clear, though, is what
the true “alpha” of the strategies is after accounting for their
Rolling Three-Year Performance: Large BlendsJuly 1997–June 2009
■ Dow Jones
■ DJ Wilshire
■ Morningstar
■ MSCI
■ S&P
■ Russell
25%
20%
15%
10%
5%
0%
-5%
-10%
-15%
-20%
-25%Jun‘00
Oct‘01
Mar‘03
Jul‘04
Dec‘05
Apr‘07
Aug‘08
Source: Morningstar
Ro
llin
g A
nn
ua
liz
ed
3
-Ye
ar
Pe
rfo
rma
nce
3-Year Period Ending
Figure 1
March/April 201034
varying market exposures. Using the previous example, it
may be that the outperformance of the Morningstar large
blend index over the MSCI large blend index is entirely due
to the Morningstar index having a higher market weight (i.e.,
higher beta factor), and once this is adjusted for the differ-
ence (or relative alpha), it could become negative. This is
what will be explored in the analysis section of the paper.
AnalysisWhile it is impossible to know which index group (or really
which methodology) will outperform on a risk-adjusted basis
in the future, a review of the historical risk-adjusted attributes
of each methodology should provide insight as to which
methodology does a better job capturing outperformance
relative to its market exposure. To determine the “alpha” or
risk-adjusted outperformance for each index methodology,
a four-factor (i.e., Carhart) regression analysis is performed
using the three Fama/French factors, as well as momentum.
All data for the beta factors, as well as the risk-free rate, was
obtained from Kenneth French’s Web site, and all return data
for the indexes was obtained from Morningstar Direct.
The excess return of the index (which is defined as the return
of the index for the month minus the risk-free rate for the
month) is regressed against a market beta factor (defined as the
return on the market minus the risk-free rate), a value factor (or
HML, defined as the return on value stocks minus the return on
growth stocks), a size factor (or SMB, defined as the return on
small stocks minus the return on big stocks), and a momentum
factor (based on the six value-weight portfolios formed on size
and prior return, the average return on the two high prior-return
portfolios minus the average return on the two low prior-return
portfolios). The four-factor regression equation is:
Rindex
– Rf = α
index + β
index (R
market – R
f) + β
SMB(SMB) +
βHML
(HML) + βMOM
(Momentum) + εasset
Annualized Return: July 1997-June 2009
Figure 2
Category Dow Jones DJ Wilshire Morningstar MSCI S&P S&P Pure Russell
Source: Morningstar
Large Growth -1.25% 1.18% -1.74% -0.22% 1.36% 4.82% 0.73%
Large Blend 1.42% 2.35% 2.78% 0.15% 2.05% 2.05% 2.41%
Large Value 2.74% 3.15% 2.61% 0.28% 2.10% 4.54% 3.30%
Mid-Cap Growth 3.11% 3.14% 1.93% 2.86% 9.45% 9.51% 3.41%
Mid-Cap Blend 4.96% 5.12% 4.82% 3.92% 7.34% 7.34% 5.48%
Mid-Cap Value 5.87% 5.70% 5.54% 6.75% 5.28% 6.08% 5.79%
Small Growth 2.66% 2.75% 0.37% 4.02% 5.01% 6.64% 0.96%
Small Blend 5.02% 4.79% 6.52% 4.19% 5.28% 5.28% 3.41%
Small Value 5.09% 6.11% 6.06% 3.47% 5.35% 6.67% 5.22%
Average 3.29% 3.81% 3.21% 2.82% 4.80% 5.88% 3.41%
Rolling Three-Year Four-Factor Regression Annualized Alphas: July 1997-June 2009
Figure 3
Category Dow Jones DJ Wilshire Morningstar MSCI S&P S&P Pure Russell
Sources: Morningstar, Kenneth French
Large Growth -0.91% 0.40% -0.65% -0.62% 0.68% 3.54% 0.70%
Large Blend -0.55% 0.00% 1.32% -1.84% -0.07% -0.07% 0.17%
Large Value 0.41% -0.21% -0.41% -2.48% -0.73% -1.42% -0.08%
Mid-Cap Growth 2.68% 1.14% 2.21% 0.49% 5.15% 5.66% 2.62%
Mid-Cap Blend 1.81% 0.97% 1.31% -0.10% 2.72% 2.72% 1.56%
Mid-Cap Value 1.50% 0.92% 1.11% 2.18% 0.77% 1.14% 1.48%
Small Growth 0.02% -0.87% -0.18% 0.75% 0.59% 1.67% -3.42%
Small Blend 0.75% -0.67% 2.23% -0.69% -0.45% -0.45% -2.76%
Small Value -0.65% -0.16% -0.33% -2.07% -1.34% -2.31% -1.60%
Average 0.56% 0.17% 0.74% -0.49% 0.81% 1.16% -0.15%
Std Dev 1.16% 0.69% 1.08% 1.42% 1.88% 2.39% 1.94%
t stat 1.45% 0.74% 2.05% -1.03% 1.29% 1.46% -0.23%
Weighted -0.22% 0.06% 1.12% -1.52% 0.18% 0.34% 0.10%
March/April 2010www.journalofindexes.com 35
Where Rindex
is the return on the index, Rf is the risk-free rate,
αindex
is the alpha of the index, βindex
is the index’s beta with
respect to the market, Rmarket
is the return of the market, βSMB
is
the index’s beta with respect to the “large” factor (SMB), βHML
is the index’s beta with respect to the “value” factor (HML),
βMOM
is the index’s beta with respect to the “momentum”
factor (MOM), and εasset
is the error term. All monthly alpha
estimates are annualized for comparative purposes. For those
readers not familiar with four-factor regression approach, see
Fama and French [1993] and Carhart [1997].
Cremers, Petajisto and Zitzewitz [2008] have noted that the
standard Fama-French (three-factor) and Carhart (four-factor)
regression models can produce statistically significant nonzero
alphas for passive indexes primarily from the disproportionate
weight the Fama-French factors place on small value stocks
(which have performed well). While Cremers et al. introduce
regression factors that outperform standard models in their
paper, the traditional four-factor estimates are used for this
research, due to their widespread use and acceptance. While the
reader may contend that an index (i.e., a broad, well-diversified
and passive portfolio) should not have an alpha component by
definition, using a method that is widely employed to determine
alpha for active managers (the four-factor Carhart approach with
the traditional Fama-French factors) can in fact generate one.
For the analysis, regressions are based on rolling three-
year periods, which consist of 36 months of historical data.
Rolling periods are used versus a single period to account
for potential changing market exposures of the indexes over
time, as well as to make the analysis less dependent on the
period studied. For example, if an index methodology did very
well the first and last months of the test look-back period, it
may appear that it generated alpha during the entire study,
despite the fact it dramatically under-performed the months in
between. Also, the average implied holding period for equity
mutual funds is approximately three years based on a cur-
rent redemption rate of 30 percent per year [ICI 2009], which
makes the rolling three-year regression method more relevant
to how investors actually invest in equity mutual funds.
Seven different index methodologies are considered for
the analysis: Dow Jones, Dow Jones Wilshire, Morningstar,
MSCI, Standard & Poor’s, Standard & Poor’s Pure and Russell,
with the actual underlying tested indexes listed in Appendix
II. The time period for the analysis is from July 1997 until
June 2009, which is the longest period for which data was
available for the different indexes (all nine domestic styles for
each of the seven different providers). Using the same period
for all methodologies allows for a more relative comparison
than using the entire period of data available for each index.
The total number of three-year test periods is 109.
Results
The average four-factor regression alphas for each of the
idexes for each style are included in Figure 3, as well as the
average alpha across styles, standard deviation of alphas
across styles and the average alpha across the styles’ t sta-
tistics for each methodology. Information on the weighted
outperformance is also included, where the respective alphas
are weighted by the total net assets invested in all passive
index funds and ETFs as of June 30, 2009. This number
reflects how investors actually invest in index funds at the
total asset level, versus the simple average that is used for
statistical significance purposes for each methodology.
Among the seven methodologies, five had positive average
alphas (Dow Jones, Dow Jones Wilshire, Morningstar, S&P and
S&P Pure), and S&P’s Pure methodology had the highest alpha,
at 1.16 percent, although only Morningstar had an average alpha
that was statistically significant (with an average alpha of 0.74
percent and a t statistic of 2.05). On a weighted basis, five meth-
odologies had positive alphas: Dow Jones Wilshire, Morningstar,
S&P, S&P Pure and Russell, with Morningstar having the highest
weighted alpha, of 1.12 percent, which could largely be attrib-
uted to the alpha of its large blend index (1.32 percent).
The range of outperformance decreases on a risk-adjusted
basis (Figure 3) when compared with the raw outperformance
(Figure 2), to 3.57 percent from 4.28 percent, respectively.
There were also some changes in relative outperformance
when viewed on a risk-adjusted basis. For example, over the
12-year test period the Dow Jones Wilshire Small Growth
Index outperformed the Dow Jones Small Growth Index by
0.09 percent (on an annualized basis, 2.75 percent and 2.66
percent, respectively); however, on a risk-adjusted basis, the
Dow Jones Small Growth Index outperformed the Dow Jones
Wilshire Small Growth Index by .89 percent (on an annual-
ized basis, 0.02 percent and -0.87 percent, respectively).
The respective alpha estimates for the various indexes
were quite consistent during the test period, both on a
relative and absolute basis. Figure 4 provides an example; it
includes the rolling three-year four-factor regression alphas
for the large blend indexes included in the analysis.
As shown in the graph, while the absolute numbers fluctu-
ate over time, the relative rankings change very little during
the test period. In the aggregate, when viewed at the ranked
index level, Dow Jones, Dow Jones Wilshire, Morningstar,
S&P and S&P Pure tended to have relatively consistent rank-
ings that were slightly above average, while MSCI and Russell
had rankings that tended to be significantly below average
(they also were the two methodologies with negative aver-
age alphas). The persistence in alpha should not be that
Rolling Three-Year Four-Factor Regression Annualized Alphas For Large Blend Indexes: July 1997-June 2009
8%
6%
4%
2%
0%
-2%
-4%
-6%Jun00
Oct01
Mar03
Jul04
Dec05
Apr07
Aug08
Sources: Morningstar, Kenneth French
4-F
ac
tor
Alp
ha
3-Year Period Ending
■ Dow Jones
■ DJ Wilshire
■ Morningstar
■ MSCI
■ S&P
■ Russell
Figure 4
March/April 201036
surprising, given the fact the factor estimates for the indexes
were relatively constant over time (they are indexes, after
all). Combined, these findings suggest that it is likely that
some methodologies are likely to persistently generate posi-
tive/negative alphas relative to their peers in the future.
Key TakeawaysThere are a number of important takeaways from the analy-
sis. First, while the S&P methodology had a positive average
alpha for all nine of its indexes, the alpha for the S&P 500 was
negative (-0.07 percent, although not statistically significant).
This has important implications, because the vast majority of
large blend assets that are indexed are invested in a product
that attempts to replicate the S&P 500. The only large blend
index with a statistically significant positive alpha was the
Morningstar Large Core Index (with an average alpha of 1.32
percent and a t statistic of 7.82), and the only other index with
a positive alpha for large blend was the Russell 1000 (with an
alpha of 0.17 percent and t statistic of 1.90). Investors looking
for positive risk-adjusted returns in the large blend space would
appear to be best off investing in these two methodologies.
Second, there can be a tremendous amount of variance
(i.e., a high standard deviation) among the alpha estimates
across the categories within a methodology, with S&P and
Russell having the highest alpha standard deviation and Dow
Jones Wilshire and Morningstar the lowest. This is important
when considering the fact that some investors choose to index
certain styles and not others, although they generally prefer to
utilize a provider’s entire suite of indexes (e.g., use all Russell)
versus combining different methodologies. For example, an
investor would have fared relatively poorly if they had used
large-cap active managers and indexed small cap with Russell-
based index funds; however, they would have done much bet-
ter had they done the reverse. The ideal index methodology
for implementation purposes across all styles would be one
with a positive alpha and a low standard deviation, attributes
in both Dow Jones and Morningstar methodologies.
Third, different investors have different goals, and the goal
can have dramatic impact on the “ideal” index. For example,
while an investor would typically like to invest in an index family
that generates positive risk-adjusted alpha, an active manager
would typically like an index that generates a negative risk-
adjusted alpha, since it should be an easier benchmark to out-
perform. Interestingly, the most popular benchmarking meth-
odology, Russell, had the second-lowest alpha among the meth-
odologies tested (with an average of -15 bps and a t statistic of
-0.23, only MSCI’s was lower, and they specifically build indexes
to “better reflect the investment process of asset managers”).
This suggests, ignoring the potential qualitative benefits/aspects
of Russell’s methodology, that Russell is an easier “hurdle” to
overcome than most of the other indexes studied.
ConclusionThe analysis conducted for this paper introduces a simple
methodology to determine the optimal indexes with which to
invest, both at the individual style level and the overall meth-
odology level, after controlling for risk. Four-factor alphas
varied considerably across providers during the time period
tested. Five methodologies had positive average alphas (Dow
Jones, Dow Jones Wilshire, Morningstar, S&P and Russell),
and while S&P Pure had the highest average alpha at 1.16
percent, only Morningstar’s methodology was statistically
significant (with an average of 0.74 percent with a t statistic
of 2.07). Morningstar also had the highest-weighted alpha, of
1.12 percent, based on how monies were invested in index
mutual funds and ETFs as of June 30, 2009 (although this was
largely a result of the alpha of its large blend index).
The S&P 500 had a negative alpha (-0.07 percent, although
not statistically significant), which is important given the large
amount of assets that track it (58 percent of all indexed assets).
Russell, arguably the most common index for benchmarking
purposes, had the second-lowest average alpha across method-
ology (-15 bps, although not statistically significant), suggesting
that it represents a relatively low hurdle for active managers to
overcome compared with the other methodologies considered
for the analysis. In closing, the results of this study suggest that
some index providers, do, in fact, generate alpha, both on an
absolute basis and relative to their peers.
Appendix I: Index Provider MethodologiesDow Jones: The Dow Jones U.S. Index and its subindexes are constructed and maintained according to a transparent, rules-based methodology. The indexes are weighted based on float-
adjusted market capitalization and are calculated in real time. They are rebalanced quarterly (style indexes semiannually), and in addition are reviewed on an ongoing basis to account for
mergers, acquisitions and other extraordinary events affecting index components. The large-cap and mid-cap indexes measure the top 70 percent and next 20 percent of stocks by market
capitalization. The small-cap index represents the next 5 percent of stocks, excluding the smallest companies based on market capitalization and turnover. The Dow Jones U.S. Style Indexes
measure growth stocks and value stocks. Companies determined to be style-neutral are excluded from the indexes. The style classifications are determined using a multifactor model that
accounts for projected price-to-earnings ratio (P/E), projected earnings growth, price-to-book ratio, dividend yield, trailing P/E and trailing earnings growth. (www.djindexes.com)
Dow Jones Wilshire: Dow Jones Wilshire U.S. Style Indexes are constructed by separating the Dow Jones Wilshire 5000 universe of stocks into four capitalization groups using full
market capitalization and then splitting the capitalization groups into growth and value stocks. The resulting 10 indexes are float-adjusted and market-capitalization weighted.
Instead of 12 subindexes there are 10 style benchmarks because the smallest capitalization group, microcap stocks, is not split into growth and value. Large cap is defined as the 750
largest stocks by market capitalization, small cap is the next 1,750 largest stocks from 751 to 2,500, mid cap is a combination of 500 large and small stocks from the 501st largest
to the 1,000th largest, and micro cap is all stocks in the bottom half of the Dow Jones Wilshire 5000 Index (below the 2,501st largest). The Dow Jones Wilshire style methodology
uses six intuitive fundamentals to define a company as growth or value: next year’s price-to-earnings ratio, forecast long-term earnings growth, price-to-book ratio, dividend yield,
trailing revenue growth for the previous five years, trailing earnings growth for the previous 21 quarters. (www.wilshire.com)
Morningstar: Large cap is defined as the largest 70 percent of investable securities by free-float market capitalization, mid cap is the next 20 percent by market capitalization (70th to 90th
percentile), and small cap is the next 7 percent (90th to 97th percentile). Within each capitalization class, index constituents are assigned to one of three style orientations—value, growth
or core—based on the stock’s overall style score. A stock’s value orientation and growth orientation are measured separately using related but different variables. Value factors: price/
projected earnings (50.0 percent), price/book (12.5 percent) price/sales (12.5 percent), price/cash flow (12.5 percent), dividend yield (12.5 percent). Growth factors: long-term projected
earnings growth (50.0 percent), historical earnings growth (12.5 percent), sales growth (12.5 percent), cash flow growth (12.5 percent), book value growth (12.5 percent). Morningstar
rebalances constituent shares and weights of its indexes quarterly in March, June, September and December (on the Monday following the third Friday). Immediate rebalancing occurs if
two constituents merge or a company’s free-float changes by 10 percent or more. The indexes are reconstituted twice annually, in June and December. (www.morningstar.com)
MSCI: MSCI’s domestic indices are subsets of the MSCI US Investable Market 2500, which are the Large Cap 300, Mid Cap 450 and Small Cap 1750 indexes. Market capitalization is based
on a free-float adjustment. Indexes are reviewed quarterly and rebalanced semiannually. MSCI employs a “buffer zone” approach among size and value/growth dimensions to reduce
March/April 2010www.journalofindexes.com 37
turnover and to better reflect the investment process of asset managers. Eight different variables (three for value and five for growth) are used to better represent value and growth styles.
Value attributes are: book value to price ratio, 12-months forward earnings to price ratio, and dividend yield. Growth attributes are: long-term forward earnings per share (EPS) growth
rate, short-term forward EPS growth rate, current internal growth rate, long-term historical EPS growth trend, long-term historical sales per share growth trend. (www.mscibarra.com)
Standard & Poor’s: Standard & Poor’s U.S. indexes are maintained by the U.S. Index Committee, which meets monthly and comprises eight full-time professional members of
Standard & Poor’s staff. Unadjusted market capitalization of $3 billion or more for the S&P 500 (approximately 75 percent of U.S. equities), $750 million to $3.3 billion for the
S&P Mid Cap 400 (approximately 7 percent of U.S. equities), and $200 million to $1.0 billion for the S&P Small Cap 600 (approximately 3 percent of U.S. equities). The market cap
of a potential addition to an index is looked at in the context of its short- and medium-term historical trends, as well as those of its industry. Adequate liquidity and reasonable
price—the ratio of annual dollar value traded to market capitalization—should be 0.3 or greater. Various domicile requirements; public float of at least 50 percent of the stock;
rules to minimize turnover. Changes to the U.S. indexes are made as needed, with no annual or semiannual reconstitution.
The Style index series divides the complete market capitalization of each parent index approximately equally into growth and value indexes. This series covers all stocks in the parent
index universe, and uses the conventional, cost-efficient market-cap-weighting scheme. The style indexes measure growth and value along two separate dimensions, with three factors
used to measure growth and four factors used to measure value. Growth factors: five-year earnings per share growth, five-year sales per share growth rate and five-year internal growth
rate (IGR). Value factors: book value to price ratio, cash flow to price ratio, sales to price ratio, and dividend yield. A growth score for each company is computed as the average of the
standardized values of the three growth factors. Similarly, a value score for each company is computed as the average of the standardized values of the four value factors.
Style Index Series: This series divides the complete market capitalization of each parent index approximately equally into growth and value indexes while limiting the num-
ber of stocks that overlap between them. This series is exhaustive (i.e., covering all stocks in the parent index universe) and uses the conventional, cost-efficient, market-
capitalization-weighting scheme.
Pure Style Index Series: The pure style index series identifies approximately one-third of the parent index’s market capitalization as pure growth and one-third as pure value.
There are no overlapping stocks, and these indexes do not have the size bias induced by market-capitalization weighting; rather, stocks are weighted in proportion to their
relative style attractiveness. (http://www2.standardandpoors.com/)
Russell: U.S. common stocks are ranked from largest to smallest based on free-float market capitalization at each annual reconstitution date, which is May 31. The largest 1,000 stocks
become the Russell 1000 Index, the largest 800 stocks in the Russell 1000 become the Russell Mid Cap Index and the next largest 2,000 stocks (after the largest 1,000 stocks) become
the Russell 2000 Index. Style is determined by ranking each stock by two variables: the book to price ratio and the I/B/E/S forecast long-term growth mean. The variables are combined
to create a composite value score (CVS) for each stock. The stocks are then ranked by their CVS, and a nonlinear probability algorithm is applied to the distribution to determine style
membership weights. Roughly 70 percent are classified as all value or all growth and 30 percent are weighted proportionately to both value and growth. (www.russell.com)
Appendix II: Benchmark Indices
Dow Jones Dow Jones Wilshire
Large Growth DJ Style US Growth Large Cap DJ US TSM Large Cap Growth
Large Blend DJ US Large Cap DJ US TSM Large Cap
Large Value DJ Style US Value Large Cap DJ US TSM Large Cap Value
Mid-Cap Growth DJ Style US Growth Mid Cap DJ US TSM Mid Cap Growth
Mid-Cap Blend DJ US Mid Cap DJ US TSM Mid Cap
Mid-Cap Value DJ Style US Value Mid Cap DJ US TSM Mid Cap Value
Small Growth DJ Style US Growth Small Cap DJ US TSM Small Cap Growth
Small Blend DJ US Small Cap DJ US TSM Small Cap
Small Value DJ Style US Value Small Cap DJ US TSM Small Cap Value
Morningstar MSCI
Large Growth Morningstar Large Growth MSCI US Large Cap Growth
Large Blend Morningstar Large Core MSCI US Large Cap 300
Large Value Morningstar Large Value MSCI US Large Cap Value
Mid-Cap Growth Morningstar Mid Growth MSCI US Mid Cap Growth
Mid-Cap Blend Morningstar Mid Core MSCI US Mid Cap 450
Mid-Cap Value Morningstar Mid Value MSCI US Mid Cap Value
Small Growth Morningstar Small Growth MSCI US Small Cap Growth
Small Blend Morningstar Small Core MSCI US Small Cap 1750
Small Value Morningstar Small Value MSCI US Small Cap Value
S&P S&P Pure
Large Growth S&P 500/Citi Growth S&P 500/Citi Pure Growth
Large Blend S&P 500 S&P 500
Large Value S&P 500/Citi Value S&P 500/Citi Pure Value
Mid-Cap Growth S&P MidCap 400/Citi Growth S&P MidCap 400/Citi Pure Growth
Mid-Cap Blend S&P MidCap 400 S&P MidCap 400
Mid-Cap Value S&P MidCap 400/Citi Value S&P MidCap 400/Citi Pure Value
Small Growth S&P SmallCap 600/Citi Growth S&P SmallCap 600/Citi Pure Growth
Small Blend S&P SmallCap 600 S&P SmallCap 600
Small Value S&P SmallCap 600/Citi Value S&P SmallCap 600/Citi Pure Value
continued on page 67
March/April 2010www.journalofindexes.com 67
true of any returns-based performance metric. This fact must
be kept in mind in interpreting the Russell TDM: It is a mea-
sure of performance over a given time period, not a predictor
of future performance.
ConclusionDuring June and July 2009, Congress held hearings and
heard testimony regarding the performance of target date
funds. This reflects how important these investment vehicles
have become and how great the need is for credible perfor-
mance measures. The industry needs a measure that:
• Provides a valid estimate of the true value for a given
family of funds, using fund returns over a limited evalu-
ation period.
• Reflects the relative importance of each fund’s posi-
tion on its glide path: Returns of funds near their
target dates have more influence on retirement wealth
than returns of more distant funds. This is because the
primary goal of target date funds is creating wealth at a
certain fixed “cash-out” point in the future. Performance en
route to that final number is important because of how it
influences that end result.
• Takes into account the timing of cash flows as a typical
investor saves for retirement.
• Determines the value over a given performance period
by differences in the returns of the funds in the family
and the benchmark returns. A returns-based measure
will capture the performance differentials that are due
to glide path structure, asset mix and active/passive
implementation, the three key components of target
date fund performance differences.
• Measures performance relative to a passive investable
alternative.
• Can be used to meaningfully compare the performance
of any two families of funds over a common perfor-
mance period.
ReferencesChristopherson, J.A., D.R. Cariño and W.E. Ferson (2009). “Portfolio Performance Measurement and Benchmarking,” McGraw-Hill.
Gardner, G. and A. Sirohi (2009). “The Russell Target Date Performance Metric: Description of Methodology,” Russell Research, August.
Goodwin, T. (1998). “The Information Ratio,” Financial Analysts Journal. July/August, pp. 34-43.
Maxie, D. (2009). “Getting Personal: Target date funds find ways to cut costs.” Wall Street Journal, August 3.
Spaulding, D. and J.A. Tzitzouris, ed. (2009). “Classics in Investment Performance Measurement,” The Spaulding Group.
Endnotes1Maxie (2009).2For calculation specifications, see Gardner and Sirohi (2009).3The total return of global equity as measured by the 67 percent/33 percent mix of the Russell 3000 and Russell Global ex-U.S. Indexes minus the return of the Barclays Capital U.S Aggregate
Bond Index.
Disclosures
Russell Investments is a Washington, USA Corporation, which operates through subsidiaries worldwide and is a subsidiary
of The Northwestern Mutual Life Insurance Company.
Gardner continued from page 45
Russell
Large Growth Russell 1000 Growth
Large Blend Russell 1000
Large Value Russell 1000 Value
Mid-Cap Growth Russell Mid Cap Growth
Mid-Cap Blend Russell Mid Cap
Mid-Cap Value Russell Mid Cap Value
Small Growth Russell 2000 Growth
Small Blend Russell 2000
Small Value Russell 2000 Value
Works Cited“2009 Investment Company Fact Book.” Investment Company Institute. http://www.icifactbook.org/.
Carhart, Mark M. 1997. “On Persistence in Mutual Fund Performance,” Journal of Finance, vol. 52: No. 1, 57-82.
Cremers, Martijn, Antti Petajisto, and Eric Zitzewitz. 2008. “Should Benchmark Indices Have Alpha? Revisiting Performance Evaluation.” Working paper version July 31, 2008.
Fama, Eugene F., and Kenneth R. French. 1993. “Common Risk Factors in the Returns on Bonds and Stocks,” Journal of Financial Economics, vol. 33: 3-53.
French, Kenneth R., http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
Israelsen, Craig. 2007. “Variance Among Indexes.” Journal of Indexes, May/June: 26-29
Blanchett continued from page 37
Appendix II: Benchmark Indices continued
March/April 201038
By Gary Gastineau
Bringing mutual fund and ETF evaluations into the 21st century
Part Three
The Future Of Fund Ratings
March/April 2010www.journalofindexes.com 39
In Parts One and Two of this article series, I examined
how reported fund expenses and less readily measurable
expenses like transaction costs reduce fund returns. I sug-
gested using the definition of index tracking error commonly
used by ETF analysts and advisers to organize the analysis of
the elements that determine how well or how poorly a fund
performs. In this third and final article, I describe XBRL, the
key to the availability of accurate fund data and to the devel-
opment of improved fund evaluation software that investors
and advisers will use to improve fund selection. I also adress
some specialized tools that are useful in examining and
evaluating important fund features.
Extensible Business Reporting Language: The New Data Standard For Corporate And Fund Reporting
So far, fund industry use of XBRL consists of a few over-
publicized SEC filings of risk/return summary information
from a small number of mutual fund prospectuses. The
published information includes a few details of the funds’
investment objectives, costs and historical performance.
The applicability of XBRL to a full range of financial data is
illustrated by the fact that it is now mandatory for many cor-
porate filings with the SEC. With required use of XBRL, the
accuracy of available corporate financial data has improved
dramatically. The Investment Company Institute (ICI) has cre-
ated XBRL categories and templates for mutual fund filings.1
When this project is fully operational, funds will report to the
SEC using the XBRL format, and fund analysts and advisers
will be able to use XBRL to assemble data for a full range of
fund analyses and comparisons.
The significance of full XBRL fund reporting is that ana-
lysts will be able to access specified elements of data, ana-
lyze data from an individual fund or do comparative analyses
of competitive funds. Most of the analyses illustrated in Part
Two will be performed using spreadsheets and macros or
formal programs for periodic reports and comparisons. The
key underlying change will be standardization and tagging of
fund data elements so that the data everyone uses will be the
data the fund files with the SEC.
To understand the potential significance of compre-
hensive XBRL data, one need only read the descriptions of
gathering, “cleaning” and screening mutual fund data in the
academic studies of funds that have been undertaken over
the past 20 years. Mutual fund data extraction has moved
from handwritten ledgers to manual copying of poorly for-
matted hard copy SEC filings to special-purpose text search
methods that extract data from eclectic electronic reports
filed with the SEC. Today changing formats, missing data
items and confusing aggregations of fund family data that
differ in format from one period to the next make data col-
lection the hardest part of any comparative analysis of funds.
Different fund services often publish different numbers for
the same fund. The adoption and widespread use of XBRL for
fund data will not eliminate fund data problems overnight,
but it promises to revolutionize most fund comparisons. The
best description of the advantages XBRL brings to fund data
analysis that I have seen is in a speech former SEC Chairman
Cox gave to a group of financial analysts in late 2008.2 In his
speech, Chairman Cox actually applied the “don’t try this at
home” admonition (which he attributed to reports describ-
ing one of Harry Houdini’s feats) to the difficulty of extract-
ing useful fund data from SEC reports without XBRL. On my
Scouts’ honor, the Mr. Wizard story in Part Two of this article
and the rest of this discussion of XBRL was part of a draft
before the former Chairman made his speech.
XBRL is an open standard. It carries no royalty or licens-
ing fees. The availability of clean data in a standard format
from most funds will permit an adviser or even a committed
individual investor to analyze funds with more reliable data
than the best fund services have today. In addition to data
assembly and analytical macros provided by financial Web
sites, a wide range of analysts and market pundits will be
able to produce custom analyses at low cost. Questions that
are rarely asked because the data to answer them has been
inaccessible will be asked and answered with ease. Everyone
who cares will have free access to a better fund database
than any fund service could assemble today. The fund rating
organizations will be competing with developers of new fund
analysis and evaluation software. Investors and their advisers
will be the beneficiaries of this competition.
The downside to the XBRL story is that a full XBRL report-
ing standard is not yet mandatory for funds. Some funds may
decide not to use XBRL for all data, including important non-
financial data. It is impossible to predict the pace at which
the XBRL standard will be rolled out and the data from it roll
in. If most of the major fund companies submit a full range
of XBRL data, the pressure on other funds to conform will be
powerful. Realistically, however, a critical mass of funds is
unlikely to submit full data without a mandatory standard.
The “financial crisis” of 2008 diverted attention from
the SEC’s normal operations and, unfortunately, diverted
attention from XBRL, of which former Chairman Cox was a
major advocate. Chairman Schapiro is as fully attuned to the
regulatory needs of fund investors as anyone, but she and
her colleagues at the SEC have far too many issues that need
their attention. Fortunately, the case for XBRL fund data is
compelling. The advantages of XBRL data from funds are so
great that the XBRL rollout will provide data necessary to
reduce other elements of the commission’s workload.
There Is A Wide Range Of Quality In Fund Touts, Tools And Techniques
After an adviser and investor have worked together for a
reasonable period of time, the investor might understand the
adviser’s thinking process well enough that a simple recom-
mendation to buy or sell a specific ETF might be accepted
at once in the context of a specific investment application.
Obviously, that level of acceptance will only be possible after
the investor is thoroughly familiar with the adviser’s deci-
sion-making processes and has no specific questions about
the proposed transaction. The investor will know that if he
has a question, the adviser will have the answer and, from
experience, that the answer will be fully satisfactory.
An investor might develop a high degree of confidence
in the analysis and recommendations of a published fund
evaluation or rating service; but most published recommen-
March/April 201040
dations either (1) are based on one or a very small number
of fund characteristics; (2) proceed from a complex weight-
ing of factors that is not thoroughly revealed and that may
vary considerably from one recommendation to another in a
way that makes it difficult for the investor to understand the
recommender’s “thinking” process; or (3) appear without any
context or substantiation. Common sense suggests that an
investor should try to understand what is behind any recom-
mendation. Be skeptical and ask questions. If you can’t get
your questions about a recommendation fully answered, you
should look for another source of investment advice.
Some of the data available from the fund rating services
can be very useful, particularly data that is organized to
answer the kinds of questions an investor or adviser might
ask. However, much fund data aggregation and commentary
does not explain the data assembly or analytical process,
and many recommendations are just hanging in space with
no visible means of support.
One of the most frustrating and least useful practices is
publication of recommended lists of mutual funds and/or
ETFs from a person without obvious credentials, an anony-
mous webmaster, or one or more “staff” members. It is not
unusual to find an unrevealed bias in the recommendations
from one of these sources. I came across a list of recom-
mended ETFs on a popular Web site that included no funds
from one of the largest ETF providers. When I examined the
funds on the recommended list, they were uncomfortably
similar to the funds that advertised on the Web site.
We buy one fund rather than another because we expect
the chosen fund to deliver a better return. Even if we believe
securities returns proceed from a random process, the fund
that holds the securities does not have random costs and
random quirks. There is certainly scope for fund analysis and
evaluation even if you believe that security selection is use-
less. Accepting and acting on what may be, at best, a random
fund recommendation is not a sound investment policy.
Looking For Useful ToolsFree tools abound on the Internet. Free is a great price,
but we have to understand the reason something is free and
consider how that reason affects how we can safely use the
tool. In short, we need some criteria to screen tools before
we use the tools to screen funds.
I happen to like a relatively new fund comparison tool
that is available on the iShares Web site. It is simple, clear
and it offers relevant (if not comprehensive) comparisons of
up to five mutual funds and ETFs. The tool is designed to
permit advisers (or investors) to construct a comparison of an
iShares ETF with mutual funds and other iShares ETFs. The
graphics are attractive, and most of the comparisons made
are relevant to the selection of one fund over another. The
usefulness of the tool is limited by the fact that the only ETFs
that can be used for cross-fund comparisons are iShares ETFs.
I can certainly understand the thinking behind restricting the
usable ETFs to your own funds, but comparison software that
permitted use of a wide range of competitive ETFs would
certainly be more useful. I like this tool, but I would suggest
using it as part of your fund selection process in a different
way than the iShares folks have in mind. Accept the limita-
tion to iShares ETFs and compare one or more iShares ETFs
to appropriate mutual funds. If you determine that an iShares
ETF looks better than any of the mutual funds, use another
method to compare the iShares ETF(s) to other ETFs.
A number of stand-alone fund evaluation tools and tech-
niques are worthy of investor and adviser attention. I do
not suggest these tools as comprehensive methods of fund
selection, but they can offer useful insights, particularly
when used in combination or in conjunction with a more
comprehensive analysis along the lines of the tracking error
breakdown illustrated in Part Two. Unfortunately, one of the
greatest problems in using tools provided on fund Web sites
is that many of them are thoroughly “lawyered,” reducing
their usefulness and, in many cases, reducing the access of
investors to the tools. A number of ETF and other fund Web
sites have two levels of access: one for “investors” and anoth-
er for “professionals.” The professional level often has the
best and most robust information. Standards as to what kind
of information can be made available to most investors and
what can be provided only to advisers are not consistently
applied over all Web sites, but there do not appear to be any
requirements that you prove you are an adviser.
Portfolio Return Correlation ToolsThe most important reason to hold a diversified portfolio
is that financial instrument returns are not perfectly corre-
lated. Diversification is usually the easiest and lowest-cost
way to improve the risk-adjusted return of a portfolio. Risk,
defined as the variability of returns, is almost automatically
reduced by sensible diversification. If all securities are fairly
priced relative to their risk and return contribution to the
market portfolio, diversification toward the market portfo-
lio’s composition should improve an investor’s risk-adjusted
results. In the context of diversification, most asset allocation
discussions focus at one point or another on the correlations
between and among baskets of assets or asset classes.
The arguments for diversification emphasize the risk-
offsetting effect of imperfect correlation among the posi-
tions that are combined to create a portfolio. Useful data for
any fund being evaluated includes correlations to standard
indexes and frequently used combinations of indexes.
Some of the better ETF Web sites provide correlation
information and even correlation tools. The best of the tools
I have seen are on the Sector SPDR Web site, www.spdrsin-
dex.com, and the iShares Web site at www.ishares.com. The
latter provides index correlations only with indexes used by
iShares products, but (in the spirit of using tools in the way
they are most useful to us) it is possible to calculate correla-
tions between an iShares index or fund and a competitor’s
ETF that is based on the index of interest.3 Limited rolling
historical correlation calculations are possible with both
the iShares and the Sector SPDRs correlation tools, but they
are not always easy to set up, and the period covered by
historic data for some of these correlations is pretty short.
Another excellent correlation tool available on the Internet
is Asset Correlations (www.assetcorrelations.com), which
allows comparison of funds from multiple providers.
March/April 2010www.journalofindexes.com 41
The reason for measuring correlation over a sequence
of periods is that correlations that may be low in normal
markets are often high in bear markets. This is most often
observed in cross-border equity markets where low correla-
tion in bull markets is replaced by near-perfect correlation
in bear markets—the precise time when lack of correlation
or, even better, negative correlation is most valuable.4 The
absence of a simple, comprehensive and highly flexible cor-
relation tool illustrates the greatest problem with the largely
free but limited—function tools available to fund investors
on the Internet: A good correlation tool is inherently costly
to maintain because it requires (1) a database of historical
returns for a large number of financial instruments, including
proprietary indexes; and (2) the capability to combine finan-
cial instruments and portfolio products in a variety of combi-
nations and weightings over a number of time intervals.
Active ShareActive security selection is undertaken to create a portfolio
that is different from a fund’s benchmark index in ways that
are expected to improve investor returns. A useful measure
of portfolio differentiation relative to a benchmark index is a
calculation called active share. This calculation is described
and developed extensively by Cremers and Petajisto.5 Active
share measures the extent to which a portfolio differs from a
benchmark index. To calculate a fund’s active share relative
to a particular index, the easiest procedure is to calculate the
percentage composition of the index, security by security,
and perform a similar percentage composition calculation for
the portfolio being evaluated to measure the correspondence
between the portfolio and the index. To the extent that the
same security appears on both lists, the smaller percentage
for that security in either the index or the fund portfolio is
listed in the Correspondence column and the percentages
in that column are summed as illustrated in the calculation
in Figure 1. If the correspondence percentage or overlap
between the index and the portfolio sum to 65 percent, the
active share—that is the difference in portfolio composition
as a result of the fund’s active investment process—is 100
percent minus 65 percent, or a 35 percent active share.
The greater the active share, the greater the divergence
of the fund portfolio from the benchmark index and, pre-
sumably, the more “active” the investment process. A fund
with a high active share shows indications of being a truly
actively managed fund—at least relative to the index used as
a benchmark.6 The active share can be a useful measure of
the intensity of the fund manager’s effort to deliver an active
management return. A fund with a low active share suggests
the manager’s lack of confidence in an active investment pro-
cess or, simply, inability to deliver active management.
Some “enhanced” index funds attempt to provide only
modest deviations from the benchmark and keep tracking
error (standard deviation definition) low relative to the
benchmark index. This is a reasonable investment strat-
egy, and failure to achieve a substantial active share is not
necessarily an indictment of an enhanced index fund man-
ager whose mandate includes a standard deviation tracking
constraint. However, a small active share does suggest a
relatively modest effort at return enhancement and should
command a relatively modest active management fee.
The greatest significance of active share is that Cremers
and Petajisto found that funds with higher active shares have
tended to deliver significantly better performance. The best
single explanation for that result is that the managers of
funds with low active share measures were closet indexers
with active management fees. In that context, it is certainly
worth looking at an active share calculation as an indication
of the nature of a fund’s investment process.
The magnitude of a fund’s tracking error (standard devia-
tion definition) has no apparent effect on performance, sug-
gesting that individual stock selection is more likely to be a
successful fund management strategy than factor bets.7 An
above-average prior-year return combined with a large active
share tends to presage further above-average performance.8
Average tracking error (standard deviation definition) has
little correlation with performance.9 An active share over 80
combined with a modest tracking error (standard deviation)
suggests careful risk management and a serious attempt to
deliver value for investors.
There is an important caveat to bear in mind when con-
sidering the implications of active share. Like many other
variables that are measured, the act of measuring active
share may cause its significance to change. That the act of
measurement can change the characteristics of the item
measured is a maxim in such diverse disciplines as quan-
tum physics and monetary policy. If active share becomes
a popular calculation, a closet indexer might create an
artificially high active share by systematically increasing
a portfolio’s composition differences relative to an index
without even attempting to improve the fund’s return. The
possibility of gaming a solitary active share measure is a
strong argument for the proposition that no single fund
evaluation measure should stand alone.
Statistical Measures Of Active Manager PerformanceIn addition to correlation and active share calculations,
a number of other tools and calculations are available to
permit investors to measure the nature of the management
process and the effectiveness of the manager. Beta can pro-
vide a measure of the extent to which the portfolio manager
Figure 1
Percentage Holdings
Stock Fund Index Correspondence
A 35% 20% 20%
B 40% 20% 20%
C 20% 35% 20%
D 5% 25% 5%
Total 100% 100% 65%
Active Share = = 100% minus Correspondence
= 100% - 65% = 35%continued on page 54
March/April 201054
is increasing or reducing the risk in the portfolio, usually to
reflect increased bullishness or bearishness on the overall
market and on the portfolio’s specific components. Analysis
of Sharpe ratios, information ratios and return-based perfor-
mance analysis are additional tools that fund performance
analysts can bring to bear on the analysis of active manage-
ment efforts. For information on some of these tools, see
Wright10 and Gastineau, Olma and Zielinski.11
Tax EfficiencySome of the tax efficiency comparisons provided by exist-
ing fund services are acceptable—as far as they go—but
some attempts to rank funds by tax efficiency are seriously
misleading. Two measures of different aspects of expected
and actual tax efficiency are appropriate for most funds, be
they conventional mutual funds or ETFs. The first and most
important of these measures is capital gains overhang. Capital
gains overhang is a fund portfolio’s net unrealized gains less
any accumulated realized losses carried forward. It is usually
measured as a percentage of the fund’s assets. Capital gains
overhang can be calculated from fund shareholder reports
as of the end of any fund reporting period for which balance
sheet and gain and loss information is reported.
Another calculation that is useful in assessing a fund’s tax
efficiency (and the portfolio manager’s attention to detail)
is the percentage of any eligible dividend distribution that
is qualified for the reduced qualified dividend tax rate—
percentage of eligible dividends qualified. While some fund
“dividend” distributions—e.g., short-term capital gains and
distributions from real estate investment trusts (REITs) and
bonds—are not eligible for treatment as qualified dividends,
fund shareholders should be able to count on most eligible
dividends being delivered to them as qualified dividends.
Simple percentages for capital gains overhang and percent-
age of eligible dividends qualified for several recent years
will provide an investor with all the information needed to
estimate the probable future tax efficiency of most funds. The
temptation to translate these simple and useful percentage
numbers into proprietary relative ratings should be firmly
resisted. These numbers are most useful in a simple percent-
age format. Giving them different names and calculating differ-
ent relationships simply confuses investors and advisers who
use more than one source of fund information.
Short InterestSome exchange-traded funds regularly have short inter-
est percentages in excess of 100 percent. A 100 percent
short interest percentage means that a fund with 1 million
shares issued by the fund has 2 million shares carried long in
accounts held by various investors. A short interest over 100
percent indicates that some financial intermediaries have
loaned and reloaned securities to other firms to facilitate
short sales in the ETF shares. To the extent that the securi-
ties trading and lending process turns into a round robin, it
is not at all difficult to have an ETF with a short interest of
several hundred percent; that is, where the shares held long
in accounts are a multiple of the actual shares issued by the
fund. Sometimes this occurs because a specific group of
investors finds that selling an ETF short is easier, less costly
or better meets their objectives than the purchase of an
inverse fund (e.g., some of the “short” funds or exchange-
traded notes offered by Direxion, ProShares, Rydex and
Barclays Capital) or using an index derivative like a futures
contract to take a short equivalent position. A large short
interest can sometimes suggest an inefficient index or an
ineffective investment manager. These latter possibilities are
among the reasons to consider the possible negative implica-
tions of an unusually high short-interest percentage.
Fund GovernanceThe mutual fund scandals of 2003-2004 and various efforts
to mandate fund governance changes have led some fund
services to offer evaluations of fund governance. The ethics,
reputation and business practices of the manager of a fund are
certainly appropriate concerns for an investor and an adviser
who are considering ownership of shares in the fund. It is
also appropriate for a fund service to provide information and
even basic analysis of various aspects of governance includ-
ing the relative independence of the board, the nature and
timing of any regulatory investigations or settlements with
the SEC or state attorneys general, etc. On the other hand,
complex relative evaluations of governance practices at funds
are of doubtful value as long as the fund’s practices comply
with relevant laws and regulations. Ertugrul and Hegde12
found that corporate governance ratings (which have been
around far longer than fund governance ratings) have been
of little value in predicting company operating and stock
market performance. In a very short-term study of fund gov-
ernance ratings, Wellman and Zhou13 found that the initial
Morningstar governance ratings were more closely correlated
with performance before the ratings were published than with
subsequent performance. Nearly all of the “predictive” value
for Morningstar’s post-ratings performance was in two (board
quality and fees) of the five components of the overall ratings.
For some reason, Morningstar has doubled the weighting of
“Corporate Culture,”14 which Wellman and Zhou found to have
no significant performance predictive value.
Codifying regulatory actions by the SEC, state securities
commissioners, or other regulators or law enforcement orga-
nizations can be a useful service, but fund rating services have
no obvious qualifications that make them more appropriate
commentators on fund governance issues than anyone else.
The notion of turning largely nonquantitative information into
a governance rating is a stretch. The publication of a formal
adverse governance rating tends to discourage investors and
advisers from examining the facts and making their own con-
sidered decisions based on their personal circumstances and
values. Furthermore, a numerical rating lets a fund governance
analyst act as judge and jury, perhaps without adequate disclo-
sure of the full story behind the rating.
Differences in investor values are behind the fact that
both sin funds and SRI (socially responsible investing) funds
find investor constituencies. That there is less-than-universal
agreement on a number of governance issues suggests that
differences in personal values make the notion of universally
Gastineau continued from page 41
March/April 2010www.journalofindexes.com 55
acceptable formal governance ratings highly questionable.
To illustrate the scope for differences of opinion along the
“fee” dimension, Wallison and Litan15 present a strong argu-
ment that requiring fund directors to approve a fund’s invest-
ment management fee discourages price competition among
investment managers. The stickiness of fees in the face of
heavy emphasis on expense ratios in fund comparisons sug-
gests that Wallison and Litan have a point. It would certainly
not harm investors in existing funds to permit managers of
new funds to experiment with a fund’s fee structure. As long
as disclosure of the possible range of fees is adequate from
the first day the fund is offered to investors, changes in fees
by these new funds and adoption of fee structures that are
different from the fulcrum performance fees now required
should also be possible. The fact that a case involving fund
fees has reached the Supreme Court suggests far-from-uni-
versal agreement on fund fee issues.
If a fund service insists on taking a stance on fund gov-
ernance, it should consider any specific governance issue it
deems relevant to a fund and either accept the governance
and ethical standards at a fund company and not discuss
them or reject them entirely with a full explanation of the
reasons behind the rejection. Either a question or problem
is serious enough to encourage investors to avoid the fund
or it is not important enough or definitive enough to affect
an investment decision. Beyond a statement of the facts of
a situation, complexity in fund governance analysis and rela-
tive governance ratings will rarely be either fair or useful.
Endnotes1 The early status of the Investment Company Institute XBRL Initiative is summarized in McMillan, Karrie, “Remarks at XBRL International Conference,” Vancouver, British Columbia,
Dec. 4, 2007. The timing of further XBRL implementation is difficult to forecast but the ICI seems to be the fund industry’s organization of choice for this effort. You can see where
the SEC stands on XBRL by starting at http://www.sec.gov/spotlight/xbrl.shtml. There is even a rudimentary mutual fund viewer that lets you create a simple fund comparison report
for two or three funds. A visit will impress you with both the potential for improved fund data and with how far the process has to go.
2 See Cox, Christopher, “Disclosure from the User’s Perspective,” CFA Institute Conference Proceedings Quarterly, September 2008, pp. 10-15.
3 In fairness to iShares, the cost of licensing a wide range of indexes just for this application would probably be prohibitive.
4 Chua, David B., Mark Kritzman and Sébastien Page, “The Myth of Diversification,” The Journal of Portfolio Management, Fall 2009, vol. 36, No. 1, pp. 26-35 provides a useful look
at the asymmetry of diversification.
5 Cremers, Martijn and Antti Petajisto, “How Active Is Your Fund Manager? A New Measure that Predicts Performance,” Review of Financial Studies, September 2009, vol. 22,
No. 9, pp. 3329-3365.
6 In calculating active share, it is often useful to make the calculation relative to a number of benchmark indexes. While the S&P 500 and the Russell 1000 are highly correlated, a closet
indexer using the Russell 1000 as a fund template might have a greater active share measured against the S&P 500 than measured against the (more relevant for this fund) Russell 1000.
Cremers and Petajisto measured active share against a variety of major indexes and assumed the benchmark was the index that showed the lowest active share, (p. 3340).
7 Cremers and Petajisto, p. 3332.
8 Ibid, pp. 3354-3355.
9 Ibid, pp. 3350-3353.
10 Wright, Christopher, “Cleaning Closets,” CFA Magazine, September/October 2008, vol. 19, No. 5, pp. 20-21.
11 Gastineau, Gary L., Andrew R. Olma and Robert G. Zielinski, “Equity Portfolio Management,” Chapter 7, in Maginn, John L., Donald L. Tuttle, Jerald E. Pinto and Dennis W.
McLeavey, “Managing Investment Portfolios: A Dynamic Process,” pp. 407-476. John Wiley & Sons, Hoboken, New Jersey, 2007.
12 Ertugrul, Mine and Shantaram Hegde, “Corporate Governance Ratings and Firm Performance,” Financial Management, vol. 38, No. 1, Spring 2009, pp. 139-160.
13 Wellman, Jay and Jian Zhou, “Corporate Governance and Mutual Fund Performance: A First Look at the Morningstar Stewardship Grades,” Unpublished Working Paper, March
18, 2008.
14 Haslem, John A., “Mutual Funds,” Wiley, 2010, p. 312.
15 Wallison, Peter J. and Robert E. Litan, “Competitive Equity: A Better Way to Organize Mutual Funds,” The AEI Press, Washington, D.C., 2007.
The distribution problem is something politicians have been
working on for 50 years by trying to form the European
Union. Unfortunately, as long as Europe remains divided,
issuers will have to spend more time, effort and money on
marketing in each individual country. A good start would
be to ease regulations that require ETFs to be listed locally
to be allowed to be sold. The issue of Europe’s fragmented
clearing and settlement system could be solved by having
one central or several linked CSDs, much like the Depository
Trust & Clearing Corporation in the U.S., in combination with
stricter regulations on best execution. Finally, an obligation
to report OTC trades would increase transparency.
Lijnse continued from page 25
Endnotes1 Source: DB Index Research, Weekly ETF reports—Europe, January 21, 2010
2 Source: BlackRock ETF Landscape Year End 2009
ReferencesBlackRock Advisors, ETF Landscape, Industry Preview, Year End 2009
Bloomberg
DB Index Research, Weekly ETF reports—Europe, January 21, 2010
March/April 201042
By Grant Gardner and Mary Fjelstad
Looking for an innovative performance measure
Creating A Better Target Date Benchmark
www.journalofindexes.com 43
Target date funds are becoming increasingly important
as investment solutions for retirement savings plans. In
2007, the U.S. Department of Labor recognized target
date funds as a possible suitable choice as the default investment
option for defined contribution plans, and subsequently there
has been a surge of assets into these funds. As of April 2009,
assets under management in target date funds are estimated to
be close to $314 billion.1 Investment managers have responded
with new products and redesigns of existing products.
For the individual investor, investment adviser or plan spon-
sor, selecting from among the variety of target date products is
a formidable task. One of the fundamental problems is the lack
of an objective, returns-based measure of performance that is
appropriate for evaluating target date funds. While investment
decisions should never be based solely on past performance,
any investor choosing among families of target date funds
(whether an individual investor, personal investment adviser,
plan sponsor or plan participant) is going to ask: “How have
they performed? Have they done better than some simple
but reasonable benchmark? How has the family of funds I am
considering done relative to peers?” Over time, the investor
will also need to know: “How will I be able to tell if my fund is
doing what the investment manager said it would do?”
In this article, we provide an introduction to target date
funds and identify the key determinants of differences in
performance across target date fund families. We elucidate
why the traditional approach to benchmarking and perfor-
mance analysis, which has long been tested for single-asset-
class and static-mix investment products, fails to meet the
needs of target date fund performance measurement. We
identify the desirable properties such a measure would have
and introduce a measure that meets those requirements.
How Do Target Date Funds Work?Although target date funds are offered by many invest-
ment managers with varying investment philosophies, they
nonetheless share common features. The investor chooses a
fund with a target date close to his or her retirement—for
example, Target Date Fund 2040—and makes regular contri-
butions. The fund manager selects appropriate asset classes,
specifies an allocation among them that evolves over the life
of the fund, and devises the best investment strategy within
each asset class. Thus, there are three major components to
target date fund performance: 1) the glide path (the evolu-
tion of the mix between equity and fixed income; 2) the
allocation among the sectors of the broad equity and fixed-
income asset classes; and 3) implementation through active
and/or passive vehicles within each asset class. While all of
these components determine performance, the glide path
is the most important determinant of the risk and return
characteristics of a target date fund.
The glide paths of target date funds have a common
feature: The allocation to equity declines as the fund
approaches the target date. Younger investors in funds with
distant target dates therefore will have a higher allocation
to equity than older investors in funds with nearby target
dates. Despite this common framework, there is no com-
monly accepted glide path. Figure 1 demonstrates how dif-
ferent the glide paths—the dynamic allocation to equity and
bonds—can be from one fund family to another.
Conventional Performance Measures Do Not Work For Target Date Funds
Traditional fund performance measures use time-weight-
ed portfolio returns over various periods—one month, one
year, three years, etc. They group similar funds into a per-
formance universe, comparing them against each other and
against a passive market index benchmark.
These measures work well for typical single-asset-class
funds and can be adapted to evaluate multi-asset-class funds
with static asset allocations. However, they have serious
shortcomings when applied to target date funds.
For one, the choice of a benchmark portfolio for a given
target date fund is problematic. Over any evaluation period,
performance will differ among the target date funds in a
fund family, because each fund has a different asset alloca-
tion. It seems sensible that each target date fund should
have its own benchmark. For example, the return of a 2035
fund could be compared with the return of a weighted com-
posite of stock and fixed-income indexes that is appropriate
for 2035 funds. This date-specific return would be based
upon the performance of a “benchmark” target date fund
that evolves along a benchmark glide path. Calculation of
this benchmark return, however, necessitates assumptions
about the glide path (the structure of the changing alloca-
tions to stocks and bonds over the life of the target date
fund) and the asset mix within the stock and bond asset
classes. Existing target date fund index providers employ
differing complex glide path and asset mix assumptions and
different methodologies regarding glide path construction.
There is also no metric for a fund family’s aggregate
performance. Even if benchmark portfolios for individual
target date funds are available to produce performance
numbers on a fund-by-fund basis, using such benchmarks
can lead to poor choices. Comparing funds across differ-
ent target dates is problematic. Consider this example:
March/April 2010
Glide Paths
100%
90%
80%
70%
60%
50%
40%
30%
� Fund A � Fund B � Fund C � Fund D
45 40 35 30 25 20 15 10 5 0
Years
Retirement at “0”
% Equity
Figure 1
This hypothetical example is for illustration only and is not intended to reflect
any actual investment.
Suppose that Fundco’s 2020 fund has a higher one-year
return than SaveMore’s 2020 fund and that the funds’ rank-
ings are reversed for their 2040 funds. Current approaches
in performance evaluation would say that Fundco’s 2020
performed better than SaveMore’s, and that its 2040 per-
formed worse. But this is unhelpful, since both of these
Fundco funds are, after all, simply different aspects of
the same target date strategy. Even if it were feasible to
choose specific target date funds from among different
providers—say, the 2020 from Fundco and the 2040 from
SaveMore—over time (20 years, in this example), the 2040
SaveMore would evolve into the 2020 SaveMore. In this
sense, when you buy one target date fund from a family,
you are buying all of that family’s funds, since they all move
along the same glide path. Furthermore, since you cannot
feasibly mix target date fund selections between providers,
no actionable information for participants or plan sponsors
is contained in this comparison.
Finally, traditional approaches do not meaningfully mea-
sure a target date fund in terms of meeting its invest-
ment goal. Traditional time-weighted returns are purposely
designed to remove the effects of the timing of cash flows.
This is appropriate for measuring the performance of an
equity or bond manager who faces cash inflows and out-
flows that are beyond his or her control. Yet the essential
purpose of a target date fund is to take a stream of cash
flows over time and create wealth. To measure the success
of target date funds in a manner consistent with the primary
investment purpose, it is necessary to incorporate both the
size and the timing of cash flows. Time-weighted returns
assume away a critical aspect of target date performance. In
particular, time-weighted returns ignore the fact that returns
in the final few years before the target date have much more
impact on the retirement wealth of a typical investor than do
returns in the early years. Thus, Russell believes an appropri-
ate performance metric for target date funds should give
greater importance to returns nearer the target date.
Essential Characteristics Of A Target Date Performance Metric
Regardless of what type of fund is being evaluated, we
believe that a performance metric should have the following
characteristics:
• It should allow comparison with a benchmark portfolio
that is an investable alternative strategy.
• It should allow the construction of a performance universe
of similar funds that provides a fair, objective comparison.
• It should be based on actual fund returns.
• It should measure the fund’s success in performing an
investment “task” over a specified period.
Traditional time-weighted returns satisfy these standards
when applied to conventional equity and fixed-income funds
and to balanced funds with static allocations. In developing
a performance metric for target date funds, these same stan-
dards should be met.
For target date funds, additional requirements need
to be met:
• The benchmark portfolio must be based on a transpar-
ent and investable glide path structure and asset mix.
• It must measure the performance of a family of target
funds.
• The measurement must be made relative to the primary
investment goal of building retirement wealth.
• It must capture the impact of the timing of cash flows
and returns.
An Innovative ApproachTo meet these new needs, a new metric has been devel-
oped that will combine the monthly returns of a fund fam-
ily’s suite of target date funds to generate a performance
measure over a specified period. The Russell Target Date
Metric (TDM) is the ratio of retirement wealth generated by
a fund family to the wealth generated by investing in a set
benchmark over the same period.
The intuition behind the TDM is simple. The longest-dat-
ed target date funds typically have about 45 years until the
target date. If we had 45 years (540 months) of return data
for a given target date fund and for the benchmark fund, and
a path of 540 monthly contributions, it would be possible to
calculate the “true value” of the TDM and measure success.2
That true value would be the ratio of ending wealth gener-
ated by the target date fund to the ending wealth generated
by the benchmark fund. By construction, it would take into
account the entire glide path and the timing of cash flows.
Unfortunately, 45 years is a long time to wait to measure
performance. We need something that gives us useful infor-
mation about the target date fund over shorter periods, such
as three months, year-to-date, and one, three and five years,
that are typical of performance measures.
Constructing this performance measure based on limited
periodic returns means that certain assumptions about target
date funds must be made. These assumptions should reflect
empirical realities of the actual products in the marketplace
and the behavior of investors. Unfortunately, there is limited
evidence on many of the needed assumptions. When there
is limited evidence and divergence of opinion, it’s often best
to start with the simplest assumptions. As this marketplace
matures, these assumptions may change, and the methodol-
ogy of any new target date metric can evolve as well.
The current assumptions employed in the TDM are:
• The glide paths for target date funds begin 45 years before
the target date. This is based on the observation that few
fund families currently have target dates beyond 2050.
• For each fund family, target date funds exist at five-year
intervals. If there are gaps in the fund lineup, the returns
of the missing funds are generated either by taking the
average return of the funds with next highest and next
lowest target dates, or, if no fund with a higher target
date exists, by making a linear projection based on the
two funds with the closest target dates.
• $1 is deposited at the beginning of each month for
45 years (540 periods). This assumption is made for
simplicity. While conventional wisdom and empirical
evidence suggest that defined contribution plan par-
March/April 201044
ticipants’ contributions increase as the target date gets
closer, estimates of the growth in contributions vary.
• A simple and feasible (if primitive) benchmark is a
constant allocation of 40 percent Russell 3000®, 20
percent Russell Global ex-U.S. Index and 40 percent
Barclays Capital U.S. Aggregate Bond Index. This
benchmark reflects the returns to a balanced fund
with a constant allocation mix to stocks and bonds,
and as such, is a transparent, investable alternative
to target date funds.
Using these assumptions, we calculate a periodic bench-
mark over a specific evaluation horizon that is a valid esti-
mate of the true value of the target date metric.
Interpreting The TDMThe TDM is the ratio of the wealth generated by a family
of target date funds to the wealth generated by the bench-
mark fund over a specific time period. If a fund family’s TDM
over a three-month period is 105, that indicates that the
fund family generated 5 percent more in target date wealth
over those three months than did the benchmark portfolio.
Each evaluation horizon—three months, one year, three
years, etc.—will have its own value of the TDM.
The TDMs of different families over the same evaluation
period can be compared directly with each other, meaning that
conventional performance universes can be constructed at the
family-of-funds level. For example, suppose that for this three-
month period, the TDM for the Fundco target date funds was
110, while the competitor SaveMore target date funds had a
TDM of 121. These values mean that over these three months:
• Fundco’s target date funds added 10 percent more to
retirement wealth than the benchmark portfolio, while
SaveMore’s target date funds added 21 percent more.
• SaveMore’s funds outperformed Fundco’s funds—
SaveMore’s funds added 10 percent more to retire-
ment wealth than did Fundco’s (121 is 10 percent
larger than 110).
Performance Universe ExampleFigure 2 shows TDM calculations for nine randomly
selected actual fund families over various performance
intervals ending in June 2009. This table illustrates a basic
performance universe.
The essential requirement of a performance universe is to
provide an unambiguous rank ordering of the universe mem-
bers over a specified return history. This ordering creates a
single number that represents the overall performance of all
target date funds in a family. Moreover, as just discussed,
the TDM quantifies the magnitude by which each fund fam-
ily outperforms the benchmark, as well as the magnitude by
which one family outperforms another universe member.
From Figure 2 we observe:
• Family 1 is the only one that outperforms the bench-
mark over the one- and two-year periods.
• Over the most recent quarter, all fund families have
outperformed the benchmark; Family 1 comes in sev-
enth out of nine over the quarter.
While every aspect of a fund family’s investment pro-
cess—the glide path, allocations among sectors of the
fixed-income and equity asset classes, the use and success of
active management, etc.—influences the returns and hence
the TDM, it is possible to determine some general character-
istics of the investment policy and the return environment
that drive universe ranking.
The primary drivers are the overall equity/fixed-
income allocation along the glide path and the relative
returns to equity and fixed income over an evaluation
interval. Generally, fund families with higher overall
equity allocations will rank higher than those with lower
equity allocations in periods when stocks outperform
bonds. This characteristic seems obvious, and would be
trivial if the families’ glide paths never “crossed.” That is
to say, if for any possible pair of glide paths in the uni-
verse, one family had a higher allocation to equity than
the other family at every point on the glide path, then
the family with the consistently higher equity allocation
would likely have a higher TDM than the other over
evaluation periods when stocks outperformed bonds.
However, glide paths of families do indeed cross, and it
is important to consider an overall measure of relative
performance for this very reason.
The sample universe in Figure 2 gives a sense of the
range of values in a TDM performance universe. Note
that for every evaluation period except the most recent
quarter, bonds outperformed stocks by a significant mar-
gin. The distinct difference between the performance of
Family 1 and the other members of the universe suggests
that Family 1 may have a generally higher bond exposure
than the other families. Family 9’s performance over the
different periods suggests a high equity allocation. This
sample universe demonstrates that the TDM captures
the impact on performance of notable differences among
target date fund products.
Performance ranking is sensitive to time period. This is
www.journalofindexes.com 45March/April 2010
Sources: Russell, Barclays Capital, Morningstar
Figure 2
TDM For Periods Ending
June 30, 2009
Family3
Months
1Year
2Year
3Year
Family 1 114.1 100.3 105.0 92.1
Family 2 125.2 86.3 76.4 74.6
Family 3 110.4 80.0 75.9 74.3
Family 4 142.2 73.5 64.8 66.5
Family 5 112.8 73.0 68.4 63.2
Family 6 123.2 69.0 70.8 63.0
Family 7 131.0 65.0 63.6 60.0
Family 8 125.6 74.6 62.7 53.9
Family 9 133.6 50.2 45.2 42.9
TDM Equity – Bond Return3 19.2% –33.8% –49.1% –40.8%
continued on page 67
March/April 2010www.journalofindexes.com 67
true of any returns-based performance metric. This fact must
be kept in mind in interpreting the Russell TDM: It is a mea-
sure of performance over a given time period, not a predictor
of future performance.
ConclusionDuring June and July 2009, Congress held hearings and
heard testimony regarding the performance of target date
funds. This reflects how important these investment vehicles
have become and how great the need is for credible perfor-
mance measures. The industry needs a measure that:
• Provides a valid estimate of the true value for a given
family of funds, using fund returns over a limited evalu-
ation period.
• Reflects the relative importance of each fund’s posi-
tion on its glide path: Returns of funds near their
target dates have more influence on retirement wealth
than returns of more distant funds. This is because the
primary goal of target date funds is creating wealth at a
certain fixed “cash-out” point in the future. Performance en
route to that final number is important because of how it
influences that end result.
• Takes into account the timing of cash flows as a typical
investor saves for retirement.
• Determines the value over a given performance period
by differences in the returns of the funds in the family
and the benchmark returns. A returns-based measure
will capture the performance differentials that are due
to glide path structure, asset mix and active/passive
implementation, the three key components of target
date fund performance differences.
• Measures performance relative to a passive investable
alternative.
• Can be used to meaningfully compare the performance
of any two families of funds over a common perfor-
mance period.
ReferencesChristopherson, J.A., D.R. Cariño and W.E. Ferson (2009). “Portfolio Performance Measurement and Benchmarking,” McGraw-Hill.
Gardner, G. and A. Sirohi (2009). “The Russell Target Date Performance Metric: Description of Methodology,” Russell Research, August.
Goodwin, T. (1998). “The Information Ratio,” Financial Analysts Journal. July/August, pp. 34-43.
Maxie, D. (2009). “Getting Personal: Target date funds find ways to cut costs.” Wall Street Journal, August 3.
Spaulding, D. and J.A. Tzitzouris, ed. (2009). “Classics in Investment Performance Measurement,” The Spaulding Group.
Endnotes1Maxie (2009).2For calculation specifications, see Gardner and Sirohi (2009).3The total return of global equity as measured by the 67 percent/33 percent mix of the Russell 3000 and Russell Global ex-U.S. Indexes minus the return of the Barclays Capital U.S Aggregate
Bond Index.
Disclosures
Russell Investments is a Washington, USA Corporation, which operates through subsidiaries worldwide and is a subsidiary
of The Northwestern Mutual Life Insurance Company.
Gardner continued from page 45
Russell
Large Growth Russell 1000 Growth
Large Blend Russell 1000
Large Value Russell 1000 Value
Mid-Cap Growth Russell Mid Cap Growth
Mid-Cap Blend Russell Mid Cap
Mid-Cap Value Russell Mid Cap Value
Small Growth Russell 2000 Growth
Small Blend Russell 2000
Small Value Russell 2000 Value
Works Cited“2009 Investment Company Fact Book.” Investment Company Institute. http://www.icifactbook.org/.
Carhart, Mark M. 1997. “On Persistence in Mutual Fund Performance,” Journal of Finance, vol. 52: No. 1, 57-82.
Cremers, Martijn, Antti Petajisto, and Eric Zitzewitz. 2008. “Should Benchmark Indices Have Alpha? Revisiting Performance Evaluation.” Working paper version July 31, 2008.
Fama, Eugene F., and Kenneth R. French. 1993. “Common Risk Factors in the Returns on Bonds and Stocks,” Journal of Financial Economics, vol. 33: 3-53.
French, Kenneth R., http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
Israelsen, Craig. 2007. “Variance Among Indexes.” Journal of Indexes, May/June: 26-29
Blanchett continued from page 37
Appendix II: Benchmark Indices continued
March/April 201046
By Navaid Abidi and Dirk Quayle
Mitigating risk and adding value through a new index framework
Fixing The Flaws With Target Date Funds
March/April 2010www.journalofindexes.com 47
Selecting the appropriate target date fund (TDF) is a
challenge for even the most sophisticated profession-
als. As evidenced over the past two years, there can
be a wide disparity in the performance of TDFs, making the
risk of picking a single provider significant. This paper pres-
ents a new TDF index based on a dynamic market average,
thereby avoiding risks associated with a single provider’s
methodology. This paper also presents a methodology
framework that can be used to build custom target date
solutions based on different market assumptions. The TDF
index mitigates asset allocation and retirement methodol-
ogy risks while allowing an institution/adviser to add value
based on investment selection. The index’s methodology
framework allows an institution to create a customized ver-
sion of a target date index based on its specific needs.
TDFs are designed to automatically manage inves-
tor assets with an age-appropriate investment strategy
that becomes more conservative as the target date is
approached. Target date funds can provide a good option
for plan sponsors and advisers looking for an automated
way to maintain appropriate diversification over time, but
selecting the appropriate target date funds has been a
challenge for even the most sophisticated professionals.
This is because TDFs bundle investment management with
retirement advice to combine four methodologies that
result in a single package that is easy to use, but difficult
to decipher.
Four methodologies bundled by TDF managers:
1. an asset allocation methodology that specifies a set of
efficient portfolios
2. a retirement methodology that determines a glide path
with a changing portfolio allocation
3. a fund selection methodology that tracks asset class
benchmarks
4. a rebalancing methodology of funds that tracks the
target asset allocation
As was apparent in 2008 and 2009, making active decisions
about four contributing methodologies that apply to TDFs
provides many opportunities for performance divergence.
Target Date Funds: Risks ExposedTDFs experienced their first real test during the market
turmoil of the past two years, and to some degree, they
failed. Consider three 2010 funds from Oppenheimer,
Wells Fargo and Fidelity. Figure 1 shows what an investor
would have seen had they looked at the 2007 performance
of each fund before making a TDF investment decision at
the beginning of 2008.
The returns look similar enough. Yet Figure 2 shows how
the investor would have done in 2008 and 2009.
That was an incredible performance range for funds tar-
geting an imminent retirement date in two years. With that
short time horizon, it was logical to assume a consistent,
conservative risk/return profile across the industry, but that
clearly wasn’t the case. This performance variance highlights
the difficulty for investing professionals expected to effec-
tively analyze and select a fund/methodology initially, and
then monitor with rigor in the future.
Risk Of Bundled Services The TDF manager’s bundling of four methodologies into a
single product can mask the embedded risks associated with
poor choices at any of the four steps. Major differences in
asset allocation exist but are not always obvious. For example,
some TDFs have high-yield bonds, TIPS and emerging market
investments in their asset class models; others don’t. A poorly
designed retirement methodology may not provide young
investors with enough market risk to grow their capital, or
may expose late-stage workers to excessive market risk. A poor
investment manager selection in any one of the tracker funds
for asset classes could result in overly concentrated portfolios,
excess turnover with high tracking error and high manage-
ment fees. Some TDFs in 2008 were caught with illiquid fixed-
income securities that had been characterized as low-risk cash
equivalents. Lastly, a poorly designed rebalancing algorithm
can result in high tracking error to the target asset allocation,
or might not reinvest the dividends on a timely basis.
Existing Target Date Industry IndexesStandard indexes are created in order to measure market
performance by providing a reference point for peer invest-
ment funds managers. In addition, standard indexes provide
an alternative for investors to implement using low-cost
mutual funds and ETFs. The TDF indexing market is more
complex since there are multiple types of risk embedded
in the target date market. Defining the market is the first
step. Firms such as Dow Jones and Morningstar have created
target date benchmark index series based on proprietary
methodologies (see Figure 3). These firms derive and pres-
ent an index series for advisers and investment managers to
track and benchmark against. But since most asset managers
believe they are equally or more qualified to derive asset allo-
cations and glide paths, Dow Jones and Morningstar indexes
can be classified in the same category as any asset manager:
They are third-party methodologies encapsulating the index
creators’ own unique proprietary views and risks. Investable
Figure 1
2007 Performance Of TDF Managers
Source: Morningstar
2007 Return
Oppenheimer 2010 Fund +7.16%
Fidelity Freedom 2010 Fund +7.43%
Wells Fargo Advantage 2010 Fund +7.10%
Figure 2
2008-09 Performance Of TDF Managers
Source: Morningstar
2008 Return 2009 Return
Oppenheimer 2010 Fund -41.20% 23.80%
Fidelity Freedom 2010 Fund -25.30% 24.82%
Wells Fargo Advantage 2010 Fund -10.75% 12.76%
March/April 201048
TDF solutions based on these TDF indexes pose the same
risks of a single provider as discussed above.
The only major benchmark that is market based is the
S&P Target Date Index Series, which is constructed through
an annual survey of the holdings of target date funds in the
industry. The index series provides a fixed perspective of the
TDF industry’s asset allocation based on S&P’s chosen asset
class index benchmarks.
Another approach to creating a market index is to analyze
the behavior of the actual investments. One of the fundamen-
tal assumptions of modern financial asset pricing theory is
that the expected returns of investment funds are driven by
how the investments behave relative to systematic risk factors
and not by what they hold. The new MarketGlide Target Date
Index Series is the first behavior-based target date indexing
methodology, and tracks the average performance of target
date managers more closely than any other index.
MarketGlide Target Date Index SeriesThe MarketGlide Target Date Index (MGI) Series is a set of
portfolios that track the consensus asset allocation of the tar-
get date industry in five-year increments for investors retiring
in 2010 through 2050 (see Figure 4).
The MGI is based on an equally weighted statistical esti-
mate of the asset allocation glide path of each TDF manager
with at least $100 million in assets under management (AUM)
and at least 24 months of returns history. MGI is constructed
using a set of asset class indexes that provide coverage of the
general investable risk exposure of the TDF industry.
The asset allocation glide path developed by any TDF man-
ager is based on two primary sets of assumptions: (1) a set of
capital market assumptions; and (2) the profile of a representa-
tive investor’s savings and retirement requirements. The specific
choices within these two sets of assumptions provide significant
opportunity for TDF investment performance disparities. As a
result, TDFs can have significantly different asset class portfolios
with respect to target date. A particular TDF manager will have
superior returns to peers only to the extent that the random
realization of financial markets returns most closely align with
the manager’s particular allocation strategy and security selec-
tion. MarketGlide estimates the custom asset allocation glide
path of each TDF family using a new statistical technique that
calibrates the systematic risk factors exposure by target date.
The MarketGlide index series reflects the collective methodol-
ogy assumptions of the major public TDF families, and results
in asset allocations that have robust performance in the face of
extreme financial market co-movements, in part because they
avoid the idiosyncrasies or timing errors that are associated
with individual managers.
Figure 5 shows how the out-of-sample monthly performance
of MGI Series portfolios closely tracks the average returns of
Figure 3
Existing TDF Benchmark Indexes
Morningstar
Lifetime IndexMethodology S&P TD Index
DJ Real
Return TD Index
Theory Descriptive: Holdings style
analysis for industry consensus.
Prescriptive: MPT, constant lifetime
market portfolio targeting, through
estimation of human capital.
Prescriptive: Risk targeting
real returns.
Glide Path Methodology
Peer average of holdings of manag-
ers that meet AUM criteria. Index
calculation excludes 10% extreme
outliers of asset allocation.
MPT combined with human capital
theory-based glide path with LDI
targeting.
Methodology Unclear.
Target level of total equity alloca-
tion gliding down from 90% to
about 30% at retirement.
Maturity TreatmentTerminal portfolio 3 years after
retirement year.
Through date target. Investor
spends down capital through
retirement based on income fund.
To date target. Investor liquidates
funds at retirement. Capital accu-
mulation during early years, real
inflation hedge near retirement.
Equity Allocation2045 - 90% 2045 - 90% 2045 - 90%
2010 - 40% 2010 - 47% 2010 - 30%
Number of Asset Classes 9 9 (+8) 5 (+4)
Reconstitution Annually Annually Semiannually
Rebalancing Annually Quarterly Semiannually
Source: Index providers
Figure 4
Years To Maturity
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Sources: Business Logic; Morningstar
-50510152025303540
■ Cash ■ TIPS ■ US Bonds ■ High Yield ■ REIT ■ Emerg Eq ■ Int Dev Eq ■ US Small Cap ■ US Large Value ■ US Large Growth
MarketGlide Asset Allocation Glide Path
March/April 2010www.journalofindexes.com 49
the TDF managers. The tracking error is asymmetrical in up and
down markets. MGI results include a 25-basis-point manage-
ment fee to put it on similar footing as a low-cost index fund.
Figure 6 shows the annual and cumulative performance of
the MarketGlide 2010 and 2040 indexes compared with the
performance mean and range of the industry from 2005 through
2009. MGI achieves its stated goal of closely tracking the indus-
try’s average annual returns performance. MGI outperforms the
average manager’s cumulative return over the five-year period
due to the asymmetrical tracking error in down markets.
Methodology Behind The MGI
Glide Path Style AnalysisMGI relies on a new technique—glide path style analysis
(GPSA)—that optimizes an asset allocation glide path and
thereby recovers the systematic risk behavior of a TDF family.
The technique assumes TDFs are long-term investments with
a stable asset allocation glide path policy that is consistent
across different dated funds of a given family. Generally, TDFs
are fully invested, do not employ leverage and are likely to hold
diversified investments reflective of broad asset class exposure
of equities and fixed income, and the asset allocation changes
slowly over time as the TDF gets closer to the target date.
GPSA extends returns-based style analysis (RBSA) to cover
TDFs. RBSA assumes that the style of a manager is constant
through time and cannot be used for analysis of investments
such as TDFs, which change their asset allocation over time.
It is a common industry practice to use a “rolling window”
RBSA to check for shift in investment style over time. For
example, Figure 7 depicts 18-month rolling windows used
to analyze the historical behavior of the TDF 2010 and 2040
fund members for a family. As expected, the funds changed
their asset allocation over time, but there is also a significant
Figure 5
Average Monthly Tracking Error Of MarketGlide
Index Vs. TDF Managers
Sources: Business Logic; Morningstar
2010 2020 2030 2040MGI
TDF Manager
2005-09 Target Date Funds
MGI Avg 0.08% 0.06% 0.04% 0.03%
Tracking Error
Up Market -0.13% -0.10% -0.11% -0.14%
Months Error
Down Market 0.50% 0.40% 0.38% 0.39%
Months Error
MarketGlide 2040 Index versus TDF Industry (c) MarketGlide 2040 Index versus TDF Industry (d)
MarketGlide 2010 Index versus TDF Industry (a) MarketGlide 2010 Index versus TDF Industry (b)
Sources: Business Logic; Morningstar
Performance Of MarketGlide Target Date Index Series
Cu
mu
lati
ve
Re
turn
An
nu
al
Re
turn
Cu
mu
lati
ve
Re
turn
An
nu
al
Re
turn
■ Max ■ Min ● Mean ■ MarketGlide
MaxMinMeanMarketGlide
MaxMinMeanMarketGlide
MaxMinMeanMarketGlide
MaxMinMeanMarketGlide
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
Year
31.5% -10.8% 9.5% 14.4% 6.3%12.8% -41.2% 2.9% 3.3% 1.4%22.7% -23.8% 6.2% 9.5% 4.5%21.5% -19.0% 6.5% 10.2% 4.8%
2009 2008 2007 2006 2005-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
Year
Year Year
31.5% 5.6% 13.5% 23.3% 25.0%12.8% -27.2% -22.0% 1.1% 6.7%22.7% -6.7% -1.0% 9.6% 14.9%21.5% -1.6% 4.8% 15.5% 21.1%
2009 2008-2009 2007-2009 2006-2009 2005-2009
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
40.6% -31.2% 11.0% 18.3% 9.1%26.1% -41.3% 1.8% 4.7% 2.9%31.8% -37.4% 6.9% 14.4% 7.1%32.1% -35.4% 7.4% 15.3% 7.0%
2009 2008 2007 2006 2005-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
40.6% -11.2% -5.4% 6.2% 14.2%26.1% -22.4% -17.8% -6.3% 1.8%31.8% -17.7% -11.9% 0.2% 7.3%32.1% -14.6% -8.3% 5.7% 13.1%
2009 2008-2009 2007-2009 2006-2009 2005-2009
Figure 6
March/April 201050
amount of noise. As can be seen from the rolling style analy-
sis results of TDF Family A 2040 fund, the fund had above a
90 percent allocation to stocks in 2002 and that allocation
seemed to shift slightly toward bonds after 2003, while the
TDF 2010 fund shows a much more significant and growing
exposure to bonds.
Each window RBSA provides an estimate of the asset alloca-
tion around the midpoint time of the window used. This insight
is crucial to the GPSA method. GPSA transforms these rolling
window results into a common time-based measure by subtract-
ing the midpoint time period of the RBSA window from the tar-
get date of the fund. The center of each time window provides
an estimate of the fund manager allocation based on the length
of time from target date. This common time unit allows GPSA to
consolidate all the rolling windows style analysis (RWSA) results
for all the funds from a particular target date family. The aggre-
gate results for TDF Family A in Figure 8 provides new clarity to
the funds’ behavior. From this, it is apparent that a TDF family’s
asset allocation style becomes more conservative as each fund
gets closer to its target date.
To address the RWSA measurement noise, the GPSA meth-
od performs a nonlinear least-squares fit of a multivariate func-
tion to filter the noise in portfolio transitions over time. The
“S-curve” function family is chosen because its shape naturally
fits the shape of an asset allocation glide path. This function
has five parameters that dictate its shape: a starting value
asymptotic allocation, ending asymptotic allocation, time of
maximum rate of allocation change, the maximum growth rate
of allocation/year, the last parameter that dictates asymmetry
of the curve. When GPSA performs the function fit, it assumes
the TDF manager followed a consistent asset allocation glide
path across all funds within the same TDF family. As can be
seen in Figure 9, different values of the S-curve parameters can
generate shapes of equity glide paths that span a wide range
of plausible investment policy choices of TDF managers.
The curve-fitting problem for the entire asset allocation
glide path is solved using a hierarchical nonlinear least-
squares fit algorithm that first fits the equity/fixed split of
the manager, and then traverses down to fit more granular
asset classes. The last aggregate cumulative portfolio is
assumed to sum to a 100 percent allocation at each point in
the glide path. The algorithm requires specification of the
hierarchical tree structure of the grouping of the asset class
benchmarks. Specifying this asset class hierarchy ensures
that the GPSA algorithm partitions the analysis so that it
first focuses on the macro view, and iteratively zooms in to
fit the micro view. The resulting glide paths quickly become
stable at the equity/fixed level and are further refined by
tuning the finer details with arrival of new returns data. The
entire process is embedded within a model simplification
algorithm to build the most statistically powerful model
with the fewest asset classes that explain the largest per-
centage of returns variance of the TDF manager.
GPSA validation can be performed using results based on
leading TDF families that resemble actual industry reference
points. The estimated glide path is then used to create a
custom TDF benchmark for each target date fund of the fam-
ily. The performance of each fund can be measured against
this benchmark. The GPSA-based custom TDF benchmarks
account for more than 99.9 percent of the variance of the
idealized TDFs and fit the asset allocation portfolios to with-
in 0.1 percent. These adjusted R-squared statistics reflect
the average for each of the funds of a TDF family. The results
validate that the GPSA algorithm can reverse-engineer glide
paths of known TDF families and can be applied to the real
world with confidence.
The MGI selection criteria for TDF managers is that they
Figure 7
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
■ Cash ■ TIPS ■ US Bonds ■ High Yield ■ REITS ■ Emerg Eq ■ Int Dev Eq ■ US Small Growth ■ US Large Value ■ US Large Growth
1999 2001
2010 fund 2040 fund
2003 2005 2007 2009 2001 2002 2003 2004 2005 2006
Sources: Business Logic; Morningstar
Rolling Window Style Analysis Of TDF Family A 2010 And 2040 Funds
Figure 8
Family A Asset Allocation Glide Path
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Sources: Business Logic; Morningstar
-6.5-1.53.58.513.518.5
Years to Maturity
23.528.533.540.5
■ Cash ■ TIPS ■ US Bonds ■ High Yield ■ REITS ■ Emerg Eq
■ Int Dev Eq ■ US Small Cap ■ US Large Value ■ US Large Growth
March/April 2010www.journalofindexes.com 51
are publicly traded, have over $100 million in AUM and have
24 months of publicly available returns history for institutional
share class investments. The GPSA process was first performed
with a simple set of five broad asset classes including U.S.
large and small cap, international equities, U.S. bonds and
short-term T-bills. Of the selected TDF managers, four manag-
ers had average family adjusted R-squared statistics over 99
percent, half of the managers had values above 98 percent and
all had values above 92 percent. The model fit was validated
and revealed valuable information about the TDF industry.
About half of all the TDF families are nearly perfectly repli-
cated by our simple GPSA five-asset-class model; these are
managers who generally invest in well-diversified broad asset
classes and have a stable asset allocation glide path policy. The
results also indicate that TDF managers with lower adjusted-
R-squared statistics violate some assumptions of our simple
five-asset-class GPSA analysis validation test. One major reason
is that the five-asset-class benchmarks set used for this basic
GPSA validation test did not span all the risk factors invested
in by the other half of the selected TDF managers.
In an effort to achieve higher adjusted R-squared statistic
results, the GPSA analysis was performed again with a set of
10 asset class benchmarks (Appendix A) that better reflect
the general investment risk taken within the TDF industry.
The worst adjusted R-squared result for the previously unex-
plained TDF manager shows significant improvements, from
92 percent to above 96 percent. The improved TDF manag-
ers require a more granular asset allocation glide path for
GPSA to explain these managers’ behavior. The improvement
of fit is especially important for managers with significant
allocations into nontraditional asset classes such as emerging
markets, REITs, TIPS and high yield.
SummaryThe target date fund market has the potential to simplify
investor behavior for a large segment of the retirement mar-
ket, but it has experienced significant growing pains with
wide performance disparities that have been difficult to pre-
dict due to the complexity of underlying building blocks. New
index approaches are required to offer better insight into the
target date market and thereby reduce target date methodol-
ogy risk for investment professionals and investors.
ReferencesBlack, F., Jensen, M.C., and Scholes, M. (1972). “The Capital Asset Pricing Model: Some Empirical Tests,” in “Studies in the Theory of Capital Markets.”
Bodson, L., Hübner, G., and Coën, A. (2008). “Dynamic Hedge Fund Style Analysis with Errors-in-Variables.”
Fama, E.F. and French, K.R. (1992). “The Cross-section of Expected Stock Returns.”
Jukic, D. and Scitovski, R. (2003). “Solution of the Least-Squares Problem for Logistic Function,” Journal of Computational and Applied Mathematics.
Lucas, L. and Riepe, M.W. (1996). “The Role of Returns-Based Style Analysis: Understanding, Implementing, and Interpreting the Technique,” Working Paper, Ibbotson Associates.
Pizzinga, A., Vereda, L., Atherino, R. and Fernandes, C. (2008). “Semi-strong dynamic style analysis with time-varying selectivity measurement,” Applied Stochastic Models in
Business and Industry.
Roll, R. and Ross, S.A. (1984). “The Arbitrage Pricing Theory Approach to Strategic Portfolio Planning,” Financial Analysts Journal.
Sharpe, W.F. (1992). “Asset Allocation: Management Style and Performance Measurement,” Journal of Portfolio Management.
Swinkels, L.A.P. and van der Sluis, P.J. (2002). “Return-Based Style Analysis with Time-Varying Exposures.”
Appendix A: Asset Class Benchmark Indexes
Benchmark Index
U.S. Large Cap Growth Russell 1000 Growth
U.S. Large Cap Value Russell 1000 Value Index
U.S. Small Cap Russell 2000 Index
International Developed Equities MSCI EAFE Index
Emerging Market Equities MSCI Emerging Markets Index
U.S. REIT MSCI US REIT Index
High-Yield Bonds Barclays High Yield Index
U.S. Bonds Barclays U.S. Aggregate Gov/Credit Index
Cash Citigroup 3-Month T-Bills Index
U.S. TIPS Barclays US TIPS Index
Figure 9
S-Curve-Generated Sample Equity Allocation Glide Paths
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
040 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0 -2 -4 -6
Years To Maturity
Source: Business Logic
Defensive
Conservative
Moderate
Aggressive
March/April 201052
by lenders such as custodian banks saw a marked decrease
in the autumn of 2008 (Figure 3). Part of this decline can be
attributed to the drop in global equity values, and part is due
to the fact that some market participants suspended lending
during the extreme market conditions of 2008 and 2009.
While the value of the supply ended 2009 below the highs
seen in 2008, the strong run-up since June 2009 is an indica-
tion that the value of supply will continue to grow in 2010.
This upward trend in lendable value is also mirrored in
the number of European ETFs that are being made available
to borrow. As illustrated in Figure 4, the number increased
significantly at the tail end of 2009, from roughly 200 in April
to more than 300 by the end of December.
The Top 10 European ETFs Of 2009, By Loan ValueThe ETFs that saw the highest 2009 average U.S. dollar
loan value were predominantly iShares funds. The iShares
FTSE 250 consistently saw the highest level of demand, fol-
lowed by the Lyxor Euro Stoxx 50 ETF.
In terms of lending revenue, the average lending rate for the
top 10 ETFs in most cases exceeded the ETF management fee.
In fact, the lending rate was also in excess of the rates charged
for the underlying basket of securities. Some of this premium
could be put down to the structural cost of creating ETFs to
lend, such as stamp tax for U.K. equities, but the rates do imply
that the market is willing to pay for the convenience of bor-
rowing a single security. Another explanation for the premium
could be that the securities lending market for European ETFs is
still far from mature. While the number and value of European
ETFs in securities lending is increasing, it is still far below the
levels seen in the U.S. market. In the U.S., the lendable value
for the ETF market is in excess of $55.8 billion, with a value
on loan of $23 billion. This depth of supply results in much
lower lending rates for U.S. ETFs. The average rate is close to,
or marginally below, the management fee of the ETF. However,
the higher level of demand means the absolute value of revenue
from lending ETFs is higher than in Europe.
2010: What Does The Future Hold?While the data points to a strong lending market for
European ETFs in 2010, there are still areas of weakness.
On the demand side, while swaps have a significant element of
counterparty risk, they do not have the same recall risk as borrow-
Figure 2
Figure 3
Figure 4
Source: Data Explorers
Source: Data Explorers
Source: Data Explorers
Jan’08
Apr’08
Jun’08
Oct’08
Dec’08
Aug’08
Feb’09
Jun’09
Apr’09
Aug’09
Dec’09
Oct’09
Number Of European ETFs Out On Loan
350
300
250
200
150
50
100
$0
Jan’08
Apr’08
Jun’08
Oct’08
Dec’08
Aug’08
Feb’09
Jun’09
Apr’09
Aug’09
Dec’09
Oct’09
European ETF Lendable Value
US
D B
illi
on
s
$16
$14
$12
$10
$8
$6
$4
$2
$0
Jan’08
Apr’08
Jun’08
Oct’08
Dec’08
Aug’08
Feb’09
Jun’09
Apr’09
Aug’09
Dec’09
Oct’09
Number of European ETFs Made Available to Borrow
350
300
250
200
150
50
100
$0
Figure 5
Securities Lending Top Ten European ETFs
Source: Data Explorers
Avg
Wholesale
Lending Rate
(Fee)
Avg Total
Balance Name
iShares FTSE 250 GBP $79mm 2.70%
Lyxor ETF DJ Euro Stoxx 50 ETF $43mm 1.65%
db x-trackers MSCI USA TR Index ETF $41mm 0.30%
iShares FTSE 100 GBP $40mm 1.00%
iiShares DAX (DE) $34mm 1.50%
iShares DJ Euro Stoxx 50 (DE) $23mm 1.50%
Lyxor ETF CAC 40 $20mm 2.00%
Xact OMXS30 $20mm 0.65%
UBS-ETF DJ Euro Stoxx 50 A $15mm 2.00%
iShares MSCI Japan USD $12mm 1.85%
Welter continued from page 29
March/April 2010www.journalofindexes.com 53
ing an ETF, and in some case, are cheaper than borrowing. If the
memory of Lehman Brothers fades over the course of the year, the
demand that European ETFs saw in 2009 may begin to fade.
An increase in the supply of European ETFs could, in turn,
lead to an increase in demand. As additional supply comes
into the market, the advantages that swaps have over short-
ing ETFs will continue to diminish. The risk of recall and rela-
tively higher cost should diminish if more institutions make
their ETFs available to borrow. The increase in supply could
come about as more ETF owners realize that they can offset
the ETF management fee by putting them into a securities
lending program. This lending activity can help offset the
inherent tracking risk, which may make owning European
ETFs more attractive.
Endnotes1 ETF Landscape Industry Preview Year End 2009, BlackRock Global ETF Research & Implementation Strategy Team
2 Source: Data Explorers
3 Ibid.
tronic order-driven market models with opening and closing
auctions, central limit order books and valuation price feeds.
Similar to the U.S. LMM model, the European ETFs
benefit from a multiliquidity provider model that results
in remarkable market depth and competitive spreads.
For each ETF, there is at least one liquidity provider (LP),
generally appointed by the issuer, that agrees to provide
continuous quotes, minimum market depth and maximum
spreads through the exchange’s trading session. Monitored
by the exchange, so long as they are meeting their pres-
ence, size and spread requirements, LPs may receive incen-
tives from the exchange for providing liquidity in the form
of discounted transaction fees. Auctions are supported by
the LPs through their obligation to provide consistently dis-
played liquidity during the opening to the closing auction.
The number of LPs in Europe has increased significantly in
recent months following the implementation of a new trad-
ing technology and faster data feeds, improving the overall
tool kit for all traders. The increasing diversity of partici-
pants trading ETFs (buy-side, sell-side, retail, etc.) ultimately
leads to more efficient markets, and with the LP activity, we
observe the quoted spreads of several ETFs are now tighter
than those of the underlying indexes, and others reached
their lowest ever.
Although this is a deep dive into only two ETFs (see Figures
3 and 4), a comparison to BATS and Chi-X—nonexchange
multilateral trading facilities (MTFs)—illustrates the market-
quality advantages the exchange-appointed LP model pro-
vides in terms of tighter spreads and deeper markets.
ConclusionOverall, one can easily understand how liquidity provision,
encouraged on a level playing field of fair access, can result
in better expected market quality for a given symbol. In the
popular press, there is a large belief that all ETFs are created
equal—meaning that due to their transparency, the pricing
of any ETF is without costs and will be priced efficiently.
In principle this might be true; however, in practice, just
because an ETF can be efficiently priced doesn’t mean some-
one actually wants to do it at all times. An exchange model
that supports the role of a lead market maker or liquidity
provider with performance obligations is well-positioned in
principle to meet this ideal more often than not.
DISCLAIMER:This article is intended for investment professionals only and
solely for informational and educational purposes. It should not
be relied upon for any investment decisions. The article is based
on data obtained as of Aug. 30, 2009 (unless otherwise noted
herein), which, although believed reliable, may not be accurate
or complete and should not be relied on as such. The author
does not recommend or make any representation as to possible
benefits from any securities, investments, products or services.
Investors should undertake their own due diligence regarding
securities and investment practices.
Dallmer continued from page 21
Endnote1. Source: NYSE Euronext research databases
Endnotes1Amery, P., Inside ETFs Conference 2010: A Focus on Trading, www.indexuniverse.eu/blog/7127.2Fuhr, D. op. cit.3ibid.4ibid.5London Stock Exchange and SIX Swiss Exchange require their members to trade-report their OTC activity in funds that are listed on those venues.6Source: Euronext, LSE, BlackRock.7Amery, P., op. cit.
Shastry continued from page 27
March/April 201054
is increasing or reducing the risk in the portfolio, usually to
reflect increased bullishness or bearishness on the overall
market and on the portfolio’s specific components. Analysis
of Sharpe ratios, information ratios and return-based perfor-
mance analysis are additional tools that fund performance
analysts can bring to bear on the analysis of active manage-
ment efforts. For information on some of these tools, see
Wright10 and Gastineau, Olma and Zielinski.11
Tax EfficiencySome of the tax efficiency comparisons provided by exist-
ing fund services are acceptable—as far as they go—but
some attempts to rank funds by tax efficiency are seriously
misleading. Two measures of different aspects of expected
and actual tax efficiency are appropriate for most funds, be
they conventional mutual funds or ETFs. The first and most
important of these measures is capital gains overhang. Capital
gains overhang is a fund portfolio’s net unrealized gains less
any accumulated realized losses carried forward. It is usually
measured as a percentage of the fund’s assets. Capital gains
overhang can be calculated from fund shareholder reports
as of the end of any fund reporting period for which balance
sheet and gain and loss information is reported.
Another calculation that is useful in assessing a fund’s tax
efficiency (and the portfolio manager’s attention to detail)
is the percentage of any eligible dividend distribution that
is qualified for the reduced qualified dividend tax rate—
percentage of eligible dividends qualified. While some fund
“dividend” distributions—e.g., short-term capital gains and
distributions from real estate investment trusts (REITs) and
bonds—are not eligible for treatment as qualified dividends,
fund shareholders should be able to count on most eligible
dividends being delivered to them as qualified dividends.
Simple percentages for capital gains overhang and percent-
age of eligible dividends qualified for several recent years
will provide an investor with all the information needed to
estimate the probable future tax efficiency of most funds. The
temptation to translate these simple and useful percentage
numbers into proprietary relative ratings should be firmly
resisted. These numbers are most useful in a simple percent-
age format. Giving them different names and calculating differ-
ent relationships simply confuses investors and advisers who
use more than one source of fund information.
Short InterestSome exchange-traded funds regularly have short inter-
est percentages in excess of 100 percent. A 100 percent
short interest percentage means that a fund with 1 million
shares issued by the fund has 2 million shares carried long in
accounts held by various investors. A short interest over 100
percent indicates that some financial intermediaries have
loaned and reloaned securities to other firms to facilitate
short sales in the ETF shares. To the extent that the securi-
ties trading and lending process turns into a round robin, it
is not at all difficult to have an ETF with a short interest of
several hundred percent; that is, where the shares held long
in accounts are a multiple of the actual shares issued by the
fund. Sometimes this occurs because a specific group of
investors finds that selling an ETF short is easier, less costly
or better meets their objectives than the purchase of an
inverse fund (e.g., some of the “short” funds or exchange-
traded notes offered by Direxion, ProShares, Rydex and
Barclays Capital) or using an index derivative like a futures
contract to take a short equivalent position. A large short
interest can sometimes suggest an inefficient index or an
ineffective investment manager. These latter possibilities are
among the reasons to consider the possible negative implica-
tions of an unusually high short-interest percentage.
Fund GovernanceThe mutual fund scandals of 2003-2004 and various efforts
to mandate fund governance changes have led some fund
services to offer evaluations of fund governance. The ethics,
reputation and business practices of the manager of a fund are
certainly appropriate concerns for an investor and an adviser
who are considering ownership of shares in the fund. It is
also appropriate for a fund service to provide information and
even basic analysis of various aspects of governance includ-
ing the relative independence of the board, the nature and
timing of any regulatory investigations or settlements with
the SEC or state attorneys general, etc. On the other hand,
complex relative evaluations of governance practices at funds
are of doubtful value as long as the fund’s practices comply
with relevant laws and regulations. Ertugrul and Hegde12
found that corporate governance ratings (which have been
around far longer than fund governance ratings) have been
of little value in predicting company operating and stock
market performance. In a very short-term study of fund gov-
ernance ratings, Wellman and Zhou13 found that the initial
Morningstar governance ratings were more closely correlated
with performance before the ratings were published than with
subsequent performance. Nearly all of the “predictive” value
for Morningstar’s post-ratings performance was in two (board
quality and fees) of the five components of the overall ratings.
For some reason, Morningstar has doubled the weighting of
“Corporate Culture,”14 which Wellman and Zhou found to have
no significant performance predictive value.
Codifying regulatory actions by the SEC, state securities
commissioners, or other regulators or law enforcement orga-
nizations can be a useful service, but fund rating services have
no obvious qualifications that make them more appropriate
commentators on fund governance issues than anyone else.
The notion of turning largely nonquantitative information into
a governance rating is a stretch. The publication of a formal
adverse governance rating tends to discourage investors and
advisers from examining the facts and making their own con-
sidered decisions based on their personal circumstances and
values. Furthermore, a numerical rating lets a fund governance
analyst act as judge and jury, perhaps without adequate disclo-
sure of the full story behind the rating.
Differences in investor values are behind the fact that
both sin funds and SRI (socially responsible investing) funds
find investor constituencies. That there is less-than-universal
agreement on a number of governance issues suggests that
differences in personal values make the notion of universally
Gastineau continued from page 41
March/April 2010www.journalofindexes.com 55
acceptable formal governance ratings highly questionable.
To illustrate the scope for differences of opinion along the
“fee” dimension, Wallison and Litan15 present a strong argu-
ment that requiring fund directors to approve a fund’s invest-
ment management fee discourages price competition among
investment managers. The stickiness of fees in the face of
heavy emphasis on expense ratios in fund comparisons sug-
gests that Wallison and Litan have a point. It would certainly
not harm investors in existing funds to permit managers of
new funds to experiment with a fund’s fee structure. As long
as disclosure of the possible range of fees is adequate from
the first day the fund is offered to investors, changes in fees
by these new funds and adoption of fee structures that are
different from the fulcrum performance fees now required
should also be possible. The fact that a case involving fund
fees has reached the Supreme Court suggests far-from-uni-
versal agreement on fund fee issues.
If a fund service insists on taking a stance on fund gov-
ernance, it should consider any specific governance issue it
deems relevant to a fund and either accept the governance
and ethical standards at a fund company and not discuss
them or reject them entirely with a full explanation of the
reasons behind the rejection. Either a question or problem
is serious enough to encourage investors to avoid the fund
or it is not important enough or definitive enough to affect
an investment decision. Beyond a statement of the facts of
a situation, complexity in fund governance analysis and rela-
tive governance ratings will rarely be either fair or useful.
Endnotes1 The early status of the Investment Company Institute XBRL Initiative is summarized in McMillan, Karrie, “Remarks at XBRL International Conference,” Vancouver, British Columbia,
Dec. 4, 2007. The timing of further XBRL implementation is difficult to forecast but the ICI seems to be the fund industry’s organization of choice for this effort. You can see where
the SEC stands on XBRL by starting at http://www.sec.gov/spotlight/xbrl.shtml. There is even a rudimentary mutual fund viewer that lets you create a simple fund comparison report
for two or three funds. A visit will impress you with both the potential for improved fund data and with how far the process has to go.
2 See Cox, Christopher, “Disclosure from the User’s Perspective,” CFA Institute Conference Proceedings Quarterly, September 2008, pp. 10-15.
3 In fairness to iShares, the cost of licensing a wide range of indexes just for this application would probably be prohibitive.
4 Chua, David B., Mark Kritzman and Sébastien Page, “The Myth of Diversification,” The Journal of Portfolio Management, Fall 2009, vol. 36, No. 1, pp. 26-35 provides a useful look
at the asymmetry of diversification.
5 Cremers, Martijn and Antti Petajisto, “How Active Is Your Fund Manager? A New Measure that Predicts Performance,” Review of Financial Studies, September 2009, vol. 22,
No. 9, pp. 3329-3365.
6 In calculating active share, it is often useful to make the calculation relative to a number of benchmark indexes. While the S&P 500 and the Russell 1000 are highly correlated, a closet
indexer using the Russell 1000 as a fund template might have a greater active share measured against the S&P 500 than measured against the (more relevant for this fund) Russell 1000.
Cremers and Petajisto measured active share against a variety of major indexes and assumed the benchmark was the index that showed the lowest active share, (p. 3340).
7 Cremers and Petajisto, p. 3332.
8 Ibid, pp. 3354-3355.
9 Ibid, pp. 3350-3353.
10 Wright, Christopher, “Cleaning Closets,” CFA Magazine, September/October 2008, vol. 19, No. 5, pp. 20-21.
11 Gastineau, Gary L., Andrew R. Olma and Robert G. Zielinski, “Equity Portfolio Management,” Chapter 7, in Maginn, John L., Donald L. Tuttle, Jerald E. Pinto and Dennis W.
McLeavey, “Managing Investment Portfolios: A Dynamic Process,” pp. 407-476. John Wiley & Sons, Hoboken, New Jersey, 2007.
12 Ertugrul, Mine and Shantaram Hegde, “Corporate Governance Ratings and Firm Performance,” Financial Management, vol. 38, No. 1, Spring 2009, pp. 139-160.
13 Wellman, Jay and Jian Zhou, “Corporate Governance and Mutual Fund Performance: A First Look at the Morningstar Stewardship Grades,” Unpublished Working Paper, March
18, 2008.
14 Haslem, John A., “Mutual Funds,” Wiley, 2010, p. 312.
15 Wallison, Peter J. and Robert E. Litan, “Competitive Equity: A Better Way to Organize Mutual Funds,” The AEI Press, Washington, D.C., 2007.
The distribution problem is something politicians have been
working on for 50 years by trying to form the European
Union. Unfortunately, as long as Europe remains divided,
issuers will have to spend more time, effort and money on
marketing in each individual country. A good start would
be to ease regulations that require ETFs to be listed locally
to be allowed to be sold. The issue of Europe’s fragmented
clearing and settlement system could be solved by having
one central or several linked CSDs, much like the Depository
Trust & Clearing Corporation in the U.S., in combination with
stricter regulations on best execution. Finally, an obligation
to report OTC trades would increase transparency.
Lijnse continued from page 25
Endnotes1 Source: DB Index Research, Weekly ETF reports—Europe, January 21, 2010
2 Source: BlackRock ETF Landscape Year End 2009
ReferencesBlackRock Advisors, ETF Landscape, Industry Preview, Year End 2009
Bloomberg
DB Index Research, Weekly ETF reports—Europe, January 21, 2010
News
March/April 201056
Global ETF Assets At All-Time High
Global exchange-traded product
assets hit $1.14 trillion, a new all-time
high, according to a new report from
BlackRock. That figure was up 48 per-
cent from the close of 2008.
Total ETP listings rose as well, albeit
more slowly, from 2,103 to 2,508.
The bulk of the ETP assets at the end
of November 2009 were in U.S.-listed
ETFs, which held $754 billion, up 39
percent from the end of 2008. At the
end of November 2009, there were 896
U.S.-listed ETPs, up from 834 at the
start of the year.
In Europe, the story was more dra-
matic, with assets surging 58 percent,
from $150 billion to $237 billion. The
number of listings jumped from 756
to 969, an increase of 28 percent. The
region is clearly still in a period of
heavy growth in ETFs and other ETPs.
Asia Pacific (ex-Japan) is still in the ear-
liest stages of its growth, with total assets
in the region of just $39 billion. That
belies overall ETF use, however, as many
institutional investors in the region have
significant money in U.S.-listed ETFs.
In Japan, ETF assets were actually
down 11 percent from the end of 2008,
to roughly $24 billion, even though their
numbers increased slightly, from 61 to
67. Other ETP assets were up just about 8
percent, to $370 million in just five sepa-
rate products, one of which was added
during the first 11 months of 2009.
‘Father’ Of Commodity Investing Launches New Index
Yale University professor K. Geert
Rouwenhorst, one of the “fathers” of
broad-based commodity investing, has
co-founded a new firm and is launching a
new generation of commodity indexes.
Rouwenhorst is best known for his
2004 paper with Gary Gorton, “Facts and
Fantasies about Commodity Futures,”
which kicked off the surge of commodity
investing seen in the past five years.
His new firm, SummerHaven Index
Management, in December launched
the Summerhaven Dynamic Commodity
Index. The index is designed to avert the
more disastrous effects of contango and
other pitfalls of passive commodities
investment strategies. SummerHaven
described it as “the first long-only active
benchmark for commodity investors.”
Each month, SDCI picks 14 commodi-
ties from a pool of 27 based on funda-
mental indicators, weighting the selected
commodities equally in the portfolio.
Eligible commodities must have active
and liquid futures markets on exchanges
in developed markets, with the contracts
denominated in U.S. dollars.
The index has been licensed by U.S.
Commodity Funds to underlie the firm’s
first nonenergy-related ETF.
T. Rowe, Goldman Sachs To Enter ETF Industry
During the last quarter of 2009, two
major players in the financial services
industry tossed their hats into the ETF
ring, as both T. Rowe Price and Goldman
Sachs filed 40-APP forms with the
Securities and Exchange Commission.
40-APP filings clear the regulatory way
for a firm to launch an ETF, and are the
first steps in launching an ETF family.
In early December, T. Rowe Price
asked the SEC for approval to launch a
family of actively managed ETFs, includ-
ing U.S. equity, global equity and fixed-
income funds. That bombshell was fol-
lowed by a Christmas Eve filing from
Goldman Sachs that requested broad
relief from the SEC to launch a variety
of funds, including equity, fixed-income
and blended portfolios.
Although both firms are known for
their actively managed strategies, only
T. Rowe indicates that it will be focusing
on actively managed funds—Goldman’s
filing only mentions index-based ETFs.
However, unlike T. Rowe, Goldman is
not a complete stranger to the ETP
world: It is the issuer of the GS Connect
S&P GSCI Enhanced Commodity ETN
(NYSE Arca: GSC), which has more than
$60 million in assets, a reasonably siz-
able amount for an ETN.
Both firms are looking to follow other
big financial services names into the now-
established ETF industry. The leap has
already been made by such luminaries as
Charles Schwab, Old Mutual and Pimco.
With assets under management of $366
billion and $871 billion, respectively, T.
Rowe and Goldman are clearly equipped
to play with the big dogs—the real ques-
tion is whether any of these firms will be
able to mount a serious threat to estab-
lished ETF issuers like iShares, Vanguard
and State Street Global Advisors.
BlackRock Completes BGI Acquisition
It’s official: The merger between
BlackRock Inc. and Barclays Global
Investors was completed as of Dec. 1.
The new company is operating under
the BlackRock name, but the iShares
brand—BGI’s exchange-traded funds
business—will be retained.
BlackRock has some $3.2 trillion in
assets under management and it holds
the title of the world’s largest institutional
money management firm. The mega-merg-
er was announced back in June and it came
with a price tag of some $13.5 billion.
BlackRock’s board of directors is absorb-
ing Barclays PLC’s Chief Executive John
Varley and President Robert Diamond Jr.
Following the acquisition, BlackRock
will continue to serve as the marketing
agent for the iPath family of exchange-
traded notes, which are offered by
Barclays Capital.
INDEXING DEVELOPMENTS
Russell Launches Global SMID Index
Russell Investments recently rolled
out a benchmark covering what the
Pull Quote Pull Quote Pull Quote Pull Quote Pull Quote Pull Quote Pull Quote Pull Quote Pull Quote
March/April 2010www.journalofindexes.com 57
firm calls the global “SMID” segment.
The new index covers the middle
chunk of the market and then a little
more—from the upper ranks of the
small-cap segment to the bottom ech-
elons of the large-cap segment. The
premise of the index seems to be
that investment managers recognize
that the largest companies don’t have
a lot of significant growth ahead of
them, while the very smallest compa-
nies often have significant risk factors,
especially in emerging markets; SMID
might be the happy medium.
The SMID index covers nearly 4,000
stocks ranging in size from $370 mil-
lion to $5.3 billion, and can be broken
down into multiple subindexes cover-
ing different styles or regions, such as
emerging markets.
S&P 500 Buybacks Jump In 3QStandard & Poor’s said in December that
third-quarter stock buybacks for the S&P
500 were up 44 percent from Q2, when
they hit the lowest level ever recorded
since S&P started keeping record in 1998.
Third-quarter buybacks reached
$34.8 billion, up from $24.2 billion in
Q2. By comparison, third-quarter totals
remained more than 61 percent below
the results seen in the third quarter of
2008, and nearly 80 percent lower than
levels seen in the period two years ago.
Howard Silverblatt, senior index ana-
lyst at S&P Indices, noted in a press release
that companies cautiously increased buy-
backs as the market recovery continued,
while keeping an eye on expenditures.
Silverblatt projected that stock buybacks
could jump another 10 percent in the
next quarter, but going into 2010, overall
buybacks should remain well below peak
2007 levels, as these kinds of expendi-
tures are “highly correlated to the recov-
ering economy,” he said.
From a sector standpoint, financial
sector buybacks jumped 80 percent in
the third quarter from second-quarter lev-
els, but remained more than 92 percent
below 2007 levels. Similarly, information
technology saw a boost of 121 percent in
the quarter, snagging a 30 percent slice
of all buybacks in the period.
By contrast, energy sector buybacks
dropped 15 percent quarter-over-quarter.
DJI Adds New Index To Target Date Family
In January, Dow Jones Indexes updated
its Dow Jones Target Date and Real Return
index series with the addition of the Dow
Jones U.S. Target Date 2050, Dow Jones
Global Target Date 2050 and Dow Jones
Real Return 2050 indexes. At the same
time, DJI phased out the Dow Jones Target
2005, Dow Jones U.S. Target 2005 and
Dow Jones Real Return 2005 indexes.
DJI’s target date indexes cover target
years set at five-year intervals within a
40-year span, with indexes retired five
years after they have reached their tar-
get date. The multi-asset indexes gener-
ally shift more weight into fixed income
and cash, thereby reducing risk levels, as
they move along their respective glide
paths. The U.S. and Global index families
track combinations of stocks, bonds and
cash, while the global family includes
an international component. Meanwhile,
the Real Return series adds in TIPS, com-
modities and real estate securities.
S&P And BGCantor Announce Treasury Indexes
In December, S&P said it had entered
into an agreement with market data
provider BGCantor Market Data LP to
collaborate with each other in the cre-
ation of a new family of U.S. Treasury
indexes. BGCantor is a subsidiary of
BGC Partners and specializes in fixed-
income and derivatives data.
The indexes will be calculated by S&P
based on data provided by BGCantor;
S&P’s announcement said that the index-
es would launch in the first quarter of
2010. According to S&P, the benchmarks
were created with custom requirements,
liability-driven investing and portfolio-
building strategies in mind.
The initial launch will include 11
indexes:
• S&P/BGCantor 0-3 Month U.S.
Treasury Bill Index
• S&P/BGCantor 3-6 Month U.S.
Treasury Bill Index
• S&P/BGCantor 6-9 Month U.S.
Treasury Bill Index
• S&P/BGCantor 9-12 Month U.S.
Treasury Bill Index
In January, Dow Jones Indexes updated its Dow Jones Target Date and Real Return Index series.
March/April 2010
News
58
• S&P/BGCantor 0-1 Year U.S.
Treasury Bond Index
• S&P/BGCantor 1-3 Year U.S.
Treasury Bond Index
• S&P/BGCantor 3-5 Year U.S.
Treasury Bond Index
• S&P/BGCantor 7-10 Year U.S.
Treasury Bond Index
• S&P/BGCantor 10-20 Year U.S.
Treasury Bond Index
• S&P/BGCantor 20+ Year U.S.
Treasury Bond Index
• S&P/BGCantor U.S. TIPs Index
Russell Creating Factor-Based Indexes
Russell Investments has teamed up
with Axioma Inc., a provider of portfo-
lio optimization and risk analysis tools,
to create a family of indexes built
around risk factors used in Axioma’s
proprietary risk models.
The indexes will be based on Russell’s
existing market-cap-weighted indexes,
but will incorporate Axioma’s risk mod-
els; the new indexes will be designed
to minimize turnover and factor track-
ing error, maximize exposure to the
specified risk factor and provide neutral
exposure to nontarget risk factors.
The first group of indexes will target
momentum and include the Russell-
Axioma U.S. Large Cap Momentum
Index, the Russell-Axioma U.S. Small
Cap Momentum Index and the Russell-
Axioma U.S. Momentum Index. The
new indexes will be derived from the
Russell 1000, Russell 2000 and Russell
3000 indexes, respectively.
S&P Adds To Commodity Index Lineup
In December, S&P rolled out a vari-
ety of commodities indexes.
The main index to debut was the S&P
GSCI All Metals Index, which combines
the precious metals and industrial met-
als subindexes of the S&P GSCI. In con-
junction with that index’s rollout, S&P
also introduced the S&P GSCI All Metals
Capped Commodity Index as well as the
S&P GSCI Agriculture Capped Component
Index. The “Capped” indexes are designed
to underlie investments complying with
the European Union UCITS III rules, which
set standards for weightings and diversifi-
cation within a portfolio.
At the same time, S&P provided a
list of commodity indexes that would
be calculated going forward in real
time in euros: the S&P GSCI All Metals
Capped Commodity Index, the S&P
GSCI Agriculture Capped Component
Index, the S&P GSCI Light Energy Index
and the S&P GSCI Non-Energy Index.
Nasdaq Re-Ranks Nasdaq-100 Components
The Nasdaq OMX Group’s annu-
al re-ranking of the securities in its
Nasdaq-100 Index and Nasdaq Q-50
Index took effect Dec. 21.
The seven new names entering the
Nasdaq-100 Index included Vodafone
Group, Mattel Inc., BMC Software
Inc., Mylan Inc., Qiagen N.V., SanDisk
Corporation and Virgin Media Inc. The
companies removed from the index
included Akamai Technologies Inc.,
Hansen Natural Corp., IAC/InterActive
Corp., Liberty Global (Class A),
Pharmaceutical Product Development
Inc., Ryanair Holdings plc and Steel
Dynamics Inc.
The Nasdaq-100 Index tracks the 100
largest U.S. and international nonfinancial
companies listed on the Nasdaq stock
market based on market capitalization.
More than $20 billion in U.S.-listed
ETF assets, including geared funds, is tied
to the performance of the Nasdaq-100.
New Eurozone Bond Index Debuts From S&P
Index provider Standard & Poor’s
tossed its hat into the eurozone bond
arena recently with the launch of the S&P
Eurozone Government Bond Index. The
market-value weighted index measures
the performance of the government
debt of 11 of 16 eurozone countries,
with Italy, Germany, France and Spain
receiving the largest weightings in the
index. A number of smaller and newer
members of the eurozone are excluded
from the index, which targets the more
developed countries in Europe.
The index excludes inflation-linked,
floating-rate and zero-coupon bonds,
and has liquidity screens in place to
ensure that the index is investable.
S&P has also launched a series of
subindexes that break the main index
down into various maturities: 1-3 year,
3-5 year, 5-7 year, and 10+ years.
According to ETFWorld, the euro-
zone is the world’s second largest gov-
ernment bond market, and is about half
the size of the U.S. market.
FTSE, EDHEC Collaborate On Index Launch
Index provider FTSE Group and the
EDHEC-Risk Institute jointly created a
new series of indexes that launched in
mid-January.
The FTSE EDHEC-Risk Efficient index
series draws its components from FTSE All
World equity indexes. However, the new
indexes constituents are weighted by their
Sharpe ratio rather than by the traditional
method of market capitalization.
The Sharpe ratio is a measure of
excess return per unit of risk, with
risk defined as the standard deviation
(volatility) of returns.
Professor Noël Amenc, director of
the EDHEC-Risk Institute, noted, “The
methodology minimizes excessive con-
centration of risk and affords investors
the ability to benefit from the maxi-
mum Sharpe ratio portfolio.”
The indexes are designed to exploit
the fact that higher returns are generally
linked to higher risk levels, while lower-
risk stocks generally see lower returns.
AROUND THE WORlD Of ETfs
Palladium, Platinum ETFs LaunchETF Securities launched new ETFs in
early January, providing U.S. investors with
easy access to physical platinum and palla-
dium for the first time. The ETFS Platinum
Trust (NYSE Arca: PPLT) and ETFS Palladium
Trust (NYSE Arca: PALL) both hold physical
bullion in a vault as their sole asset.
The funds quickly attracted attention
from investors, gathering more than
$100 million in new creations on their
first day and trading in large volumes.
In fact, the funds may be attracting
too much attention: Both the platinum
and palladium markets are notoriously
tight, and there is some concern that
the two ETFs will create a shortage in
March/April 2010www.journalofindexes.com 59
those markets. To mitigate those con-
cerns, ETF Securities placed limits on
how large the funds could grow: PPLT,
for example, is limited to just 7 percent
of net platinum demand each year.
Shortly after its launch, that trans-
lated into $750 million in assets, while
PALL (which has similar limitations) had
an asset cap of around $500 million.
Should the two funds hit their limits,
they would likely trade to a premium
above net asset value while regula-
tors consider whether to allow them
to expand. The two funds charge 0.60
percent in annual expenses.
Old Mutual Debuts Fee-Free ETF Teaser
Old Mutual followed Charles Schwab
into the ETF market in early December
with the launch of the GlobalShares FTSE
Emerging Markets Fund (NYSE Arca: GSR).
The fund debuted with an expense
ratio of zero. However, as of Jan. 31,
2010, GSR’s price tag was set to rise to 39
basis points. The firm also said the price
tag could go up earlier if the assets hit $1
billion before that date. (They didn’t.)
GSR tracks the FTSE Emerging Index,
which covers mid- and large-cap stocks
in 22 emerging markets.
Old Mutual, an established player in
the mutual fund market, has another
four ETFs—all with an international
flavor—currently in registration.
Vanguard Expands Fixed-Income Offerings
In late November, Vanguard launched
seven new bond ETFs, nearly doubling
its offerings in the ETF bond space,
an area that saw significant inflows
throughout 2009.
The funds include the following:
• Vanguard Short-Term Government
Bond Index Fund (Nasdaq: VGSH)
• Vanguard Intermediate-Term
Government Bond Index Fund
(Nasdaq: VGIT)
• Vanguard Long-Term Government
Bond Index Fund (Nasdaq: VGLT)
• Vanguard Short-Term Corporate
Bond Index Fund (Nasdaq: VCSH)
• Vanguard Intermediate-Term
Corporate Bond Index Fund
(Nasdaq: VCIT)
• Vanguard Long-Term Corporate
Bond Index Fund (Nasdaq: VCLT)
• Vanguard Mortgage-Backed Securities
Index Fund (Nasdaq: VMBS)
The ETFs each represent a separate
share class of a traditional index mutual
fund. Each ETF charges an expense
ratio of 15 basis points.
BGI Launches Its First Active ETF
In mid-November, iShares launched
the iShares Diversified Alternatives Trust
(NYSE Arca: ALT), its first actively man-
aged ETF and one of the first managed
futures products to hit the market.
ALT’s portfolio comprises exchange-
traded futures contracts on everything
from commodities, currencies and inter-
est rates to stock and bond indexes, as
well as foreign currency forward con-
tracts. The fund’s overall investment
strategy looks at relative value; it capital-
izes on the spread between assets and
asset categories that deviate from the
norm. To achieve this—and in an effort
to minimize volatility—it takes both long
and short positions in correlated assets.
Ultimately, ALT will use a combina-
tion of strategies to capitalize on various
spread opportunities, including techni-
cal and fundamental strategies as well as
yield and futures curve arbitrage.
Its portfolio targets an annualized
return volatility of 6 to 8 percent.
ALT charges an annual expense ratio
of 0.95 percent.
New Van Eck ETF Tracks Junior Gold Miners
The Market Vectors Junior Gold
Miners ETF (NYSE Arca: GDXJ) debuted
in November, offering targeted access
to a range of small- to mid-cap gold-
mining and -producing companies for
the first time in an ETF.
Van Eck Global, the issuer, is also the
creator of the large-cap-focused Market
Vectors Gold Miners ETF (NYSE Arca:
GDX), which launched in 2006 and cur-
rently has some $5.6 billion in assets.
GDXJ tracks the Market Vectors Junior
Gold Miners Index, a rules-based, modi-
fied market-cap-weighted, float-adjusted
index comprising securities and deposi-
tary receipts of companies with market
caps of at least $150 million. Additionally,
each company needs to generate at least
50 percent of its revenues from gold
and/or silver mining to be included in
the index. The index is heavily weighted
toward small-cap companies.
GDXJ comes with a net expense ratio
of 60 basis points.
Claymore Liquidates Four ETFsClaymore Securities liquidated four
“lightly followed” ETFs at the end of 2009:
• Claymore/Morning-star Manufac-
turing Super Sector Index ETF
(NYSE Arca: MZG)
• Claymore/Morningstar
Information Super Sector Index
ETF (NYSE Arca: MZN)
• Claymore/Morningstar Services
Super Sector Index ETF (NYSE
Arca: MZO)
• Claymore U.S.-1-The Capital Market
Index ETF (NYSE Arca: UEM)
All together, they represented less
than 0.7 percent of the company’s
roughly $2.5 billion in ETF assets at the
time of the announcement.
While the company has never been
hesitant about closing under-perform-
ing funds, these are the first to go
following Guggenheim Partners’ acqui-
sition of Claymore Group mid-October.
March/April 2010
News
60
The move could reflect the beginning of
a product rationalization by the firm.
Trading of these funds was halted
Dec. 11 and liquidation of shares con-
cluded between Dec. 14 and Dec 18.
Investors were paid the full net asset
value of each fund in cash.
12-Month Natural Gas ETF Debuts
United States Commodity Funds’ lat-
est ETF, the U.S. 12-Month Natural Gas
Fund (NYSE Arca: UNL), began trading
on Nov. 18. The fund is designed to
lessen the impact of contango, which has
dragged down another USCF fund, the
U.S. Natural Gas Fund (NYSE Arca: UNG).
Instead of only buying the front-month
natural gas futures contracts as UNG
does, UNL purchases an equally weighted
basket of futures contracts with delivery
dates in each of the next 12 months. Two
weeks from rollover time, the fund sells
the front-month contract and buys the
one 12 months out, essentially pushing
the basket forward in time.
This methodology should protect the
latter somewhat from contango’s vicious
sting, although it won’t insulate it entirely.
UNL charges an expense ratio of
0.75 percent.
MacroShares Terminated—AgainMacroMarkets LLC announced in late
December that the MacroShares Major
Metro Housing Up (NYSE Arca: UMM)
and MacroShares Major Metro Housing
Down (NYSE Arca: DMM) would cease
trading on Dec. 28, 2009. Shareholders
of record on that date received cash
payments equal to the full net asset
value of the trusts, minus expenses.
MacroMarkets said that the trusts
were being liquidated because they
failed to accrue at least $50 million in
assets under management. Launched in
June, the trusts had a combined $20.8
million in assets as of Dec. 22, 2009.
This is not the first time that
MacroMarkets has shut down its unique
exchange-traded fundlike products: Two
previous sets of products were terminat-
ed on previous occasions. With its third
set of products failing to thrive and no
additional products trading, it’s unclear
what lies in store for MacroMarkets.
Source Unveils U.S. Sector ETFsIn January, Source announced the launch
of nine new U.S. equity sector ETFs for trad-
ing on the London Stock Exchange.
The ETFs track new versions of S&P’s
Select Sector indexes, with individual
constituent weights capped at 20 per-
cent in order to ensure compliance
with Europe’s UCITS regulations. The
covered sectors include consumer dis-
cretionary, consumer staples, energy,
financials, health care, industrials,
materials, technology and utilities.
The new funds carry a total expense
ratio of 0.30 percent per annum. They
use swap-based replication to track their
underlying benchmarks. Source operates
an “open architecture” platform, under
which BofA Merrill Lynch, Goldman Sachs,
Morgan Stanley and Nomura are the swap
providers, and Nyenburgh, All Options,
Banca IMI, IMC Group, Flow Traders and
UniCredit are the key market makers.
According to the Dec. 24 Deutsche Bank
ETF Liquidity Trends report, Source had
€2.13 billion in exchange-traded product
assets under management, ranking the issu-
er twelfth amongst European providers.
db x-trackers Hedge Fund ETF Hits $1 Billion
European ETF issuer db x-trackers
reported in mid-December that its
hedge fund ETF had reached $1 billion
in assets under management.
The db x-trackers db Hedge Fund
Index ETF replicates hedge fund per-
formance by investing in a group of
funds operating on Deutsche Bank’s
managed account platform. The ETF
charges 0.90 percent per annum in
management fees and is listed in the
U.K., Germany and Switzerland.
The index tracked by the ETF con-
sists of five subindexes, each tracking
a different sector of the hedge fund
universe. The percentage weightings
allocated to these subindexes reflect
the predominance of each hedge fund
strategy group in worldwide hedge
fund assets under management, as
measured by Hedge Funds Research
(HFR). This results in nearly 80 percent
of the ETF’s assets being devoted to the
“equity hedge” and “event-driven” cat-
egories. The underlying subindexes are
proprietary indexes owned by Deutsche
Bank, the issuer of the db x-trackers.
Xetra Launches ETNsXetra, the Deutsche Boerse trading
platform, expanded its product range in
December with the launch of the iPath
S&P 500 VIX Short-Term Futures Index
exchange-traded note and the iPath
S&P 500 VIX Mid-Term Futures Index
ETN in Europe. The ETNs track the S&P
500 short-term and mid-term futures
indexes, that respectively replicate a
rolling position in short- and mid-term
implied volatility futures on the S&P
500 equity index.
In the U.S., iPath launched a pair of
similar ETNs in January 2009, and those
With its third set of products failing to thrive and no additional products trading, it’s unclear what lies in store for MacroMarkets.
March/April 2010www.journalofindexes.com 61
products quickly attracted more than
$1 billion in assets.
The two Xetra-listed ETNs carry an
annual management fee of 0.89 percent.
Watson Wyatt Questions ETF Attractiveness
European consultancy firm Watson
Wyatt has cast doubt on the appeal
of exchange-traded funds, calling them
an “unattractive long-term investment
option for most institutional investors.”
According to the consultant, while
ETFs have opened up a world of poten-
tially interesting market exposures,
they “generally have higher fees than
many institutional index products; may
have tax implications that require spe-
cialist advice; and often contain coun-
terparty risks which investors may not
be compensated for.”
The criticism of unrewarded counter-
party exposures within ETFs is not new.
A year ago at a conference at the London
Stock Exchange, Chris Sutton, senior
consultant at Watson Wyatt, described
securities lending as “picking up pennies
in front of a steamroller.” Sutton was
chief executive of iShares Europe and a
director of parent company BGI before
joining the consultancy firm in 2007.
However, Watson Wyatt’s assertion
that “most investment strategies can also
be implemented more cheaply and effi-
ciently using index funds, index futures
or swaps,” calls ETFs’ attractiveness into
question at a more fundamental level.
Furthermore, the consultant argues
that most (non-ETF) passive funds have
been structured with clearly defined
tax positions for institutional investors,
whereas the treatment of ETFs is much
more variable, which typically neces-
sitates tax advice.
“Where the ETF industry has
engaged in product proliferation, we
would rather press for genuine inno-
vation in the investment content of
index products. If investors are look-
ing for more efficient market expo-
sures, their first step should be to
review the indices underlying their
existing investments with a view to
seeing if there are better alterna-
tives,” said Sutton.
KNOW YOUR OPTIONS
CBOE 2009 Volumes Over 1 Billion
The Chicago Board Options
Exchange’s total 2009 volume was down
5 percent from 2008 to 1.13 billion con-
tracts, but last year was nevertheless
the second consecutive year for which
the exchange’s total contracts traded
exceeded 1 billion. Average daily vol-
ume was 4.5 million contracts, down
from 4.7 million in 2008.
Equity options had a great year, with
volumes up 5 percent; however, index
and ETF options were the chief drags
on the exchange’s total volume. Index
options saw their ADV and total vol-
umes decline 14 percent to 884,000 and
223 million, respectively; meanwhile,
ETF options volumes fell 16 percent to
an ADV of just over 1 million and a total
2009 volume of 277 million.
The most actively traded of the ETF
and index options were those on the
S&P 500 Index (SPX), Standard & Poor’s
Depositary Receipts (SPY), PowerShares
QQQ Trust (QQQQ), CBOE Volatility
Index (VIX) and iShares Russell 2000
Index Fund (IWM).
CBOE Plans S&P 500 Dividend Index Options
The CBOE announced in December
that the SEC had cleared it to launch
options on the S&P 500 Dividend Index
(DVS), though no listing date was given
at the time.
The underlying index tracks the accu-
mulated ex-dividend amounts of the
S&P 500’s component securities during
a quarterly accrual period. The options
contracts will be useful for investors
who want to hedge the differences
between expected and actual ex-divi-
dend amounts, the CBOE said. Also, the
dividend index’s calculation methodol-
ogy is very similar to that of the S&P 500
(same components, divisor, shares out-
standing, and weighting methodology),
so it can easily be used in trading strate-
gies involving other S&P 500 options.
The European-style contract is the
first of its kind to be listed in the
U.S., according to the CBOE, which has
exclusive listing rights on the index.
BACK TO THE FUTURES
CME Group ADV Down In 2009CME Group, the world’s largest deriva-
tives exchange, saw volumes rise 13 per-
cent year-over-year in December 2009,
but the ADV for 2009 overall fell to 10.3
million, down 20 percent from 2008.
In particular, the ADV for equity
index contracts as a group was down
20 percent to 2.9 million. Interestingly,
in 2008, equity index contracts rep-
resented roughly 28 percent of the
exchange’s total ADV, and that percent-
age held steady in 2009.
FROM THE EXCHANGES
Nasdaq Rolls Out Leveraged Nasdaq-100
In November, Nasdaq OMX Group, Inc.
introduced the Nasdaq-100 Leveraged
Index. The new index magnifies the
daily returns of the widely followed
Nasdaq-100 index by 200 percent, but
also incorporates the financing costs
associated with achieving such exposure
in a portfolio into its returns.
The index could prove particularly
useful to investors in the ProShares
Ultra QQQ (NYSE Arca: QLD), which
offers 200 percent leveraged exposure
to the Nasdaq-100.
ON THE MOVE
Management Shake-Up At StoxxEuropean index provider Stoxx has
announced changes in its senior man-
agement.
Dr. Hartmut Graf, previously head
of the index business at the German
stock exchange, Deutsche Boerse, joins
Stoxx as chief executive officer, replac-
ing Ricardo Manrique, who is leaving
the company. Patrick Valovic, who was
previously director of business opera-
tions at Stoxx, becomes CFO.
In November 2009, Deutsche Boerse
and SIX Swiss Exchange announced the
acquisition of Dow Jones’ one-third share
in the index provider, with Deutsche
Boerse acquiring a controlling stake.
A new board of directors has also
been appointed, with two members
joining from the SIX group, one from
Deutsche Boerse and one from Swiss
law firm Lenz & Staehelin.
Global Index Data
MSCI Sri Lanka*
Citigroup ESBI Brady
MSCI Brazil*
MSCI BRIC*
MSCI EM
ML Global High Yield
Barclays Global High Yield
NASDAQ 100
Credit Suisse HY
MSCI EAFE Small Cap
S&P 500 Equal Weighted
S&P MidCap 400/Citi Growth
S&P Midcap 400
Russell 1000 Growth
Russell 3000 Growth
Wilshire 4500 Completion
Russell 2000 Growth
MSCI EAFE Value
Barclays EM
Russell Top 200 Growth
S&P MidCap 400/Citi Value
MSCI EAFE
S&P 500/Citi Growth
MSCI EAFE Growth
Russell 1000
S&P SmallCap 600/Citi Growth
Russell 3000
JPM EMBI Global
Russell Micro Cap
FTSE NAREIT All REITs
S&P 1500
Russell 2000
S&P 500
JP Morgan EMBI
S&P Smallcap 600
Russell Top 200
S&P SmallCap 600/Citi Value
DJ Industrial Average
S&P 100
S&P 500/Citi Value
Russell 2000 Value
Russell 3000 Value
Russell 1000 Value
DJ UBS Commodity
Dow Jones Transportation Average
Barclays US Credit
Russell Top 200 Value
S&P GSCI
Barclays Municipal
Dow Jones Utilities Average
Barclays Global Aggregate
Barclays US Aggregate Bond
ML Global Govt
ML US Treasury Bill 3 Mon
Barclays US Government
Barclays Treasury
NCREIF Property
Barclays US Treasury 20+ Yr
Citigroup STRIPS 20-25 Year
Citigroup STRIPS 25+ Year
-15.15
-3.32
75.35
56.12
39.39
3.06
3.18
19.24
2.65
1.45
1.53
13.50
7.98
11.81
11.40
5.39
7.05
5.96
5.15
12.15
2.65
11.17
9.13
16.45
5.77
5.60
5.14
6.28
-8.00
-17.83
5.47
-1.57
5.49
6.45
-0.30
5.89
-5.54
8.88
6.12
1.99
-9.78
-1.01
-0.17
16.23
1.43
5.11
0.25
32.67
3.36
20.11
9.48
6.97
10.41
5.03
8.66
9.01
15.84
10.15
11.02
12.71
-62.09
-60.81
-57.64
-60.27
-53.33
-27.86
-26.89
-41.57
-26.17
-47.01
-39.72
-37.61
-36.23
-38.44
-38.44
-39.03
-38.54
-44.09
-14.75
-36.06
-34.87
-43.38
-34.92
-42.70
-37.60
-32.94
-37.31
-10.91
-39.78
-37.34
-36.72
-33.79
-37.00
-9.70
-31.07
-36.07
-29.51
-31.93
-35.31
-39.22
-28.92
-36.25
-36.85
-35.65
-21.41
-3.08
-36.09
-46.49
-2.47
-27.84
4.79
5.24
14.35
2.06
12.39
13.74
-6.46
33.72
43.67
77.10
42.78
23.86
40.52
52.87
32.17
13.47
13.69
-
11.92
19.31
15.80
5.81
10.32
9.07
9.46
15.28
13.35
30.38
9.96
8.56
14.62
26.34
11.01
22.33
15.46
10.54
15.72
9.88
16.54
34.35
15.34
18.37
15.79
10.49
15.12
15.53
19.57
19.05
18.47
20.80
23.48
22.34
22.25
2.07
9.81
4.26
22.99
-15.09
4.84
16.63
6.64
4.33
4.59
4.83
3.48
3.08
16.59
0.93
-1.09
4.09
-2.95
-3.23
18.01
5.41
5.11
6.39
6.34
2.51
5.33
-7.59
-3.62
-0.03
-1.83
-1.89
-2.06
-4.16
-4.00
-7.35
6.36
-1.32
-3.67
-6.04
-2.24
-4.78
-5.36
-3.14
-5.42
6.66
-10.95
-13.10
-5.30
-6.07
-5.63
6.58
-4.79
-5.61
-6.48
-3.12
-5.66
-9.10
-8.22
-8.91
-8.96
-3.83
-1.86
5.74
-9.79
-6.95
4.41
-0.84
7.05
6.04
8.24
2.41
6.10
6.14
-1.29
5.00
4.45
4.47
11.27
3.53
28.20
20.14
15.51
6.75
7.21
-
5.99
3.50
2.30
3.87
3.27
1.63
1.58
2.23
0.87
3.36
8.24
1.42
2.57
3.54
0.96
3.65
0.79
1.46
0.76
8.10
-3.33
-0.92
0.69
0.51
0.42
8.39
1.36
0.16
1.16
1.95
0.12
-0.27
-0.01
-0.24
-0.25
1.96
2.99
4.67
-1.14
-3.00
4.32
7.31
4.56
4.97
4.51
3.02
4.87
4.84
6.16
4.87
5.51
6.94
10.09
7.88
15.08
11.04
9.78
6.94
8.61
-
7.07
-
5.14
5.70
6.36
-3.99
-3.79
1.76
-1.37
3.53
10.57
-4.83
6.92
1.17
-3.57
-1.31
-0.49
5.52
-0.20
10.52
-
10.18
-0.20
3.51
-0.95
10.94
6.35
-2.26
7.02
1.30
-2.34
1.208.27
2.88
2.47
7.13
4.63
6.64
0.37
5.05
5.75
7.40
6.49
6.33
6.08
2.99
6.17
6.15
7.83
7.57
9.34
10.05
0.47
-
11.23
-
-
-
9.40
-
7.74
-
10.05
-
11.66
6.87
6.66
8.60
4.99
6.41
11.78
6.77
-
4.92
8.03
3.32
8.23
-
8.13
12.32
-
9.35
8.31
7.73
8.04
12.79
-
7.64
-
9.24
8.04
-
9.87
8.94
8.93
6.23
8.63
7.15
8.15
4.91
6.14
9.76
6.67
6.80
6.22
3.77
6.59
6.56
9.08
8.14
10.46
11.08
48.02
43.06
40.29
37.34
32.80
17.89
17.54
23.81
15.76
26.63
24.91
23.74
23.84
20.01
20.31
23.78
25.20
25.54
14.94
18.58
24.32
23.91
18.60
22.86
20.33
24.71
20.61
12.44
25.97
37.35
20.27
25.19
19.91
12.12
25.07
19.03
25.76
18.48
18.88
22.25
25.98
21.65
21.40
23.23
25.88
8.06
20.34
31.06
6.00
16.83
7.64
4.17
8.30
0.64
5.02
5.65
-
16.55
24.20
30.58
Index Name 2008
184.15139.18121.25
88.7978.5161.9859.4054.6154.2246.7846.3141.0837.3837.2137.0136.9934.4734.2334.2334.0133.7331.7831.5729.3628.4328.3528.3428.1827.4827.4527.2527.1726.4625.9525.5724.2122.8522.6822.2921.1820.5819.7619.6918.9118.5816.0414.5913.4912.9112.47
6.935.930.440.21
-2.20-3.57
-15.07-21.40-28.56-42.88
2009 2007 2006
30.70
5.95
49.96
39.81
34.00
1.48
3.59
-
2.26
26.19
8.06
14.39
12.56
5.26
5.17
10.03
4.15
13.80
12.27
2.88
10.80
13.54
1.14
13.28
6.27
7.02
6.12
10.73
2.57
8.29
5.66
4.55
4.91
11.86
7.68
3.77
8.33
1.72
1.17
8.71
4.71
6.85
7.05
21.36
11.65
1.96
4.60
25.55
3.51
25.14
-4.49
2.43
-6.01
3.06
2.65
2.79
20.06
8.57
16.04
17.82
7.81
11.50
30.49
13.63
25.55
12.42
13.17
-
11.95
30.78
16.95
15.79
16.48
6.30
6.93
18.10
14.31
24.33
11.89
3.74
17.19
20.25
6.97
16.12
11.40
24.27
11.95
11.73
14.14
30.41
11.78
18.33
10.88
11.77
22.65
8.31
21.06
5.31
6.43
15.03
22.25
16.94
16.49
9.15
27.73
5.24
13.34
17.28
4.48
30.24
9.27
4.34
8.43
1.33
3.48
3.54
14.48
8.99
15.99
16.33
2005 2004 3-Yr 5-Yr 10-Yr 15-Yr
0.10
0.10
0.57
0.28
0.26
0.32
0.32
0.14
0.27
-0.24
-0.11
0.03
-0.04
-0.10
-0.10
-0.15
-0.12
-0.25
0.35
-0.09
-0.12
-0.23
-0.14
-0.19
-0.27
-0.09
-0.27
0.41
-0.39
-0.24
-0.27
-0.20
-0.30
0.41
-0.15
-0.32
-0.21
-0.19
-0.32
-0.41
-0.28
-0.42
-0.43
-0.14
-0.02
0.46
-0.50
-0.14
0.39
-0.09
0.65
0.91
0.74
1.14
0.79
0.72
-
0.25
0.21
0.22
Sharpe Std Dev
Total Return % Annualized Return %
Selected Major Indexes Sorted By 2009 Returns March/April 2010
Source: Morningstar. Data as of December 31, 2009. All returns are in dollars, unless noted. 3-, 5-, 10- and 15-year returns are annualized. Sharpe is 12-month Sharpe ratio.
Std Dev is 3-year standard deviation. *Indicates price returns. All other indexes are total return.
42.09
31.68
102.85
84.18
55.82
30.71
32.42
-
27.94
61.35
40.97
37.32
35.62
29.75
30.97
43.84
48.54
45.30
26.93
26.63
33.81
38.59
27.08
31.99
29.89
38.43
31.06
25.66
66.36
38.47
29.59
47.25
28.68
28.83
38.79
26.68
39.09
28.28
26.25
30.36
46.03
31.14
30.03
23.93
31.84
7.70
26.75
20.72
5.31
29.39
12.51
4.10
11.68
1.15
2.36
2.24
8.99
1.80
0.42
-0.95
2003
29.76
4.98
-33.78
-15.18
-6.17
-1.14
4.13
-
3.10
-7.82
-18.18
-19.71
-14.53
-27.88
-28.03
-17.80
-30.26
-15.91
12.16
-27.98
-9.51
-15.94
-28.10
-16.02
-21.65
-16.57
-21.54
13.11
-16.10
5.22
-21.31
-20.48
-22.10
14.24
-14.63
-23.36
-12.93
-15.01
-22.59
-16.59
-11.43
-15.18
-15.52
25.91
-11.48
10.52
-18.02
32.07
9.60
-23.38
16.52
10.25
17.05
1.78
11.50
11.79
6.74
17.00
21.22
21.53
2002
March/April 201062
Source: Morningstar. Data as of 2/29/08
Morningstar U.S. Style Overview XXXX –XXXX, 2010
Source: Morningstar. Data as of XXXXXXXX
www.journalofindexes.com March/April 2010 63
Vanguard Tot StkVanguard 500 IndexVanguard Inst IdxVanguard 500 Idx AdmVanguard Tot Stk AdmVanguard Total Intl StkVanguard Inst Idx InstPlVanguard TtlBdMkt2IdxInvVanguard Total Bd IdxVanguard Total Bd Idx AdFidelity Spar US EqIxVanguard 500 Index SignalVanguard Tot Stk InstVanguard Total Bd Idx InT. Rowe Price Eq Idx 500Vanguard Tot Stk InstPlsFidelity U.S. Bond IndexSchwab S&P 500 In SelVanguard TotBdMkt Idx SigVanguard Em Mkt IdxVanguard Total Bond Mkt II Inst ClFidelity Spar 500 AdvVanguard Mid Cap IdxFidelity Spar 500 IdxVanguard Eur Stk IdxVanguard Mid Cap Idx InsFidelity 100 IndexVanguard SmCp IdxVanguard Gr IdxVanguard Inst DevMktsIdxFidelity Spar US Eq AdvFidelity Spar Intl IndexFidelity Spar Tot Mkt IxVanguard Sh-Tm Bd IdxVanguard Sh-Tm Bd SgnlVanguard TotStMkt Idx SigSchwab 1000 In InvVanguard ExtMktIdxVanguard SmCp Idx InsVanguard Inst Tot Bd IdxVanguard REIT IndexFidelity Spar Tot Mkt AdvING LB US Aggt Bd Idx IVanguard ExtMktIdx InstlVanguard Intm Bd IdxING Stock Indx IVanguard Bal IdxVanguard Val IdxVanguard SmCp Vl IdxVanguard Dev Mkts IdxVALIC I StockVanguard Pac Stk IdxVanguard SmCp Gr IdxVanguard EmgMkts Idx AdmrVanguard Gr Idx InstlVanguard Bal Idx InstlVanguard Value Index IsFidelity Spar Ext Mkt IxVanguard Intm Bd Idx AdmDreyfus S&P 500 Index
VTSMXVFINXVINIXVFIAXVTSAXVGTSXVIIIX
VTBIXVBMFXVBTLXFUSEXVIFSXVITSXVBTIXPREIXVITPXFBIDX
SWPPXVBTSXVEIEX
VTBNXFSMAXVIMSXFSMKXVEURXVMCIXFOHIXNAESXVIGRXVIDMXFUSVXFSIIX
FSTMXVBISXVBSSXVTSSXSNXFXVEXMXVSCIXVITBXVGSIXFSTVXILBAXVIEIXVBIIXINGIXVBINXVIVAXVISVXVDMIXVSTIXVPACX
VISGXVEMAXVIGIXVBAIXVIVIX
FSEMXVBILXPEOPX
0.180.180.050.090.090.340.030.190.220.140.100.090.060.080.350.030.320.090.140.390.070.070.270.100.290.090.200.280.280.130.070.100.100.220.140.090.290.300.090.050.260.070.450.090.220.260.250.260.280.290.390.290.280.270.090.090.090.100.140.50
58,004.0 48,312.8 44,401.0 28,379.9 27,762.3 26,043.7 24,767.0 20,431.8 19,554.9 17,932.2 17,406.7 16,590.2 16,047.0 15,692.5 11,082.3 10,520.3 10,383.0
9,382.5 8,450.1 7,765.4 7,076.0 6,904.0 6,788.8 6,697.0 6,423.9 5,960.1 5,926.0 5,913.3 5,770.5 5,767.8 5,574.4 5,555.1 5,388.6 5,282.9 5,080.0 4,757.4 4,542.3 4,309.3 4,161.7 4,148.7 3,763.2 3,697.2 3,501.0 3,494.1 3,478.8 3,476.4 3,430.8 3,356.9 3,279.1 3,123.9 3,111.8 3,079.2 3,017.6 2,984.8 2,913.1 2,869.1 2,812.3 2,583.6 2,508.4 2,381.3
28.7026.4926.6326.6228.8336.7326.66
-5.936.04
26.5126.6128.83
6.0926.3328.92
6.4526.25
6.0475.98
-26.5440.2226.5031.9140.5122.1436.1236.2928.1726.5528.4828.39
4.284.38
28.8527.6837.4336.40
6.0629.5828.43
5.8837.69
6.7926.2220.0519.5830.3428.1726.1621.1841.8576.1836.5020.1819.7936.65
6.8926.04
-5.10-5.66-5.57-5.58-5.02-4.07-5.55
-5.976.07
-5.65-5.58-4.996.11
-5.79-4.935.19
-5.546.074.87
--5.63-4.72-5.66-6.03-4.56
--4.16-1.82-5.88-5.62-5.90-5.225.645.72
-5.02-5.39-4.23-4.016.04
-12.00-5.19
--4.046.44
-5.81-0.29-8.49-6.29-6.00-5.92-5.89-2.285.00
-1.66-0.15
-8.35-3.946.53
-6.01
-0.27-1.03-0.91-0.96-0.202.29
-0.89-
6.066.13
-1.04-1.00-0.156.19
-1.19-
6.08-1.016.099.82
--1.026.13
-1.031.996.30
-4.36
-2.75-
-1.031.03
-0.325.095.12
-0.24-0.651.714.52
-10.40-0.31
-1.896.83
-2.641.237.69
--1.28-0.684.849.87
-2.622.761.361.756.89
-1.41
8.057.978.108.028.10
-8.12
-6.576.627.907.998.156.697.76
-6.58
-6.608.22
-7.89
-7.898.44
--
8.678.12
-7.91
--
5.655.678.087.978.698.81
----
8.837.12
-7.877.97
--
7.690.37
-8.258.247.958.07
-7.177.51
16.716.716.716.716.713.816.7
---
17.616.716.7
-16.716.7
-14.6
-15.4
-17.616.817.612.416.817.216.918.613.417.611.417.5
--
16.714.717.416.9
-29.517.5
-17.4
-16.716.715.015.113.417.616.618.915.418.616.715.017.0
-17.6
20.5319.9019.9019.9120.5526.0919.90
-4.224.22
19.9219.9120.52
4.2219.8820.56
3.8219.83
4.2233.21
-19.9224.2419.9226.4124.27
-26.1619.7924.7019.9124.8220.50
2.622.62
20.5320.0324.7526.16
4.2439.9820.49
-24.76
6.8719.9612.9321.0626.9124.7620.0522.1226.3333.2319.7912.9221.0723.80
6.8719.90
1.882.052.172.151.962.392.19
-4.074.171.982.151.974.221.711.963.731.414.171.21
-2.031.112.003.791.252.271.001.031.082.002.201.792.812.911.961.751.001.174.234.311.822.261.194.430.602.662.731.881.142.152.640.291.311.202.812.891.154.531.64
Fund Name Ticker Assets Exp Ratio 2009
5.856.036.066.065.843.176.060.240.070.106.016.065.880.116.005.900.176.010.118.220.236.036.526.032.256.585.744.077.881.616.032.145.800.240.285.876.065.054.110.149.055.820.225.12
-0.036.023.554.413.791.676.030.474.378.267.943.604.464.950.005.96
3-Mo
-37.04-37.02-36.95-36.97-36.99-44.10-36.94
-5.055.15
-37.03-36.97-36.94
5.19-37.06-36.89
3.76-36.72
5.15-52.81
--37.03-41.82-37.05-44.73-41.76-35.44-36.07-38.32-41.42-37.01-41.43-37.18
5.435.51
-36.99-37.28-38.73-35.98
5.05-37.05-37.16
--38.58
4.93-37.12-22.21-35.97-32.05-41.62-37.21-34.36-40.00-52.76-38.19-22.10-35.88-38.45
5.01-37.28
5.495.395.475.475.57
15.525.50
-6.927.025.435.475.567.055.185.625.375.507.02
38.90-
5.466.025.43
13.826.22
-1.16
12.5611.04
5.4610.72
5.577.227.285.555.764.331.297.01
-16.465.60
-4.517.615.286.160.09
-7.0710.99
5.134.789.63
39.0912.73
6.340.215.387.705.03
2008 2007 3-Yr
0.910.340.450.431.005.260.48
-4.905.000.380.401.045.040.191.104.430.434.97
14.53-
0.402.280.373.882.45
-1.801.633.650.403.630.954.454.500.970.682.061.964.970.590.98
-2.254.980.192.870.050.803.500.102.732.56
14.631.792.990.192.385.06
-0.02
5-Yr 10-Yr 15-Yr P/E Std Dev Yield
Total Return % Annualized Return %
Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions March/April 2010
Source: Morningstar. Data as of December 31, 2009. Exp Ratio is expense ratio. 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.
Index Funds
Trailing Returns %
3-Month YTD 1-Yr 3-Yr 5-Yr 10-YrMorningstar Indexes
US Market 10.40 28.45 28.45 –5.01 1.09 –0.11
Large Cap 9.77 24.76 24.76 –5.30 0.63 –1.88
Mid Cap 12.34 39.03 39.03 –4.48 2.33 4.50
Small Cap 11.20 37.75 37.75 –4.35 1.61 5.68
US Value 6.66 17.95 17.95 –9.41 –0.12 3.58
US Core 10.32 25.96 25.96 –3.54 1.79 2.21
US Growth 14.61 43.00 43.00 –2.25 1.20 –6.26
Large Value 5.12 11.38 11.38 –10.82 –0.94 1.77
Large Core 9.59 21.52 21.52 –3.27 1.65 0.48
Large Growth 15.19 44.37 44.37 –1.94 0.60 –8.19
Mid Value 10.27 36.05 36.05 –6.26 1.76 8.06
Mid Core 12.36 38.94 38.94 –4.60 1.84 6.78
Mid Growth 14.58 42.05 42.05 –2.96 3.09 –1.21
Small Value 12.26 40.28 40.28 –4.14 2.13 10.22
Small Core 11.80 39.86 39.86 –5.48 1.69 8.79
Small Growth 9.44 32.98 32.98 –3.89 0.65 –1.44
Morningstar Market Barometer YTD Return %
US Market28.45
17.95
Value
25.96
Core
43.00
Growth
24.76Larg
e C
ap
39.03Mid
Cap
37.75Sm
all C
ap
11.38 21.52 44.37
36.05 38.94 42.05
40.28 39.86 32.98
–8.00 –4.00 0.00 +4.00 +8.00
Sector Index YTD Return %
Hardware 65.01
Software 52.65
Media 43.27
Business Services 34.08
Industrial 33.79
Consumer Services 30.24
Consumer Goods 23.52
Healthcare 21.29
Energy 19.46
Financial Services 16.58
12.67
Utilities 11.74
Industry Leaders & Laggards YTD Return %
Broadcasting - Radio 400.00
Long Distance Carriers 348.55
Auto Manufacturers - Major 336.68
Copper 204.82
Semiconductor - Memory 191.54
Online Retail 155.22
–6.91 Grocery Stores
–8.93 Regional - Pacific Banks
–12.09 Regional - Midwest Banks
–15.56 Regional - Southeast Banks
–16.14 Regional - Mid -Atlantic Banks
–35.87 Photographic Equipment & Supplies
Biggest Influence on Style Index Performance
YTDReturn %
ConstituentWeight %
Best Performing Index
Large Growth 44.37
Apple Inc. 146.90 4.14
Microsoft Corp. 60.50 8.39
Google Inc. Cl A 101.52 4.00
Cisco Systems Inc. 46.87 5.23
Goldman Sachs Group Inc. 102.54 1.84
Worst Performing Index
Large Value 11.38
JPMorgan Chase & Co. 34.36 4.71
Ford Motor Co. 336.68 0.21
Merck & Co. Inc. 26.73 2.57
Morgan Stanley 87.93 0.62
Dow Chemical Co. 89.96 0.56
1-Year
11.38
Value
Larg
e C
ap
21.52
Core
44.37
Growth
36.05
Mid
Cap 38.94 42.05
40.28
Sm
all C
ap
39.86 32.98
–20 –10 0 +10 +20
3-Year
–10.82
Value
Larg
e C
ap
–3.27
Core
–1.94
Growth
–6.26
Mid
Cap –4.60 –2.96
–4.14
Sm
all C
ap
–5.48 –3.89
–20 –10 0 +10 +20
5-Year
–0.94
Value
Larg
e C
ap
1.65
Core
0.60
Growth
1.76
Mid
Cap 1.84 3.09
2.13
Sm
all C
ap
1.69 0.65
–20 –10 0 +10 +20
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 listsare calculated by multiplying stock returns for the period by their respective weights in the index as of the start of the period. Sector and Industry Indexes are based on Morningstar's proprietary sector classifications. The informationcontained herein is not warranted to be accurate, complete or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information.
?
Morningstar U.S. Style Overview Jan. 1 – Dec. 31, 2009
Source: Morningstar. Data as of Dec. 31, 2009.
March/April 201064
Exchange-Traded Funds CornerDow Jones U.S. Industry Review
PerformanceIndex Name Weight 1-Month 3-Month 1-Year 3-Year 5-Year 10-Year
Dow Jones U.S. Index 100.00% 2.65% 6.05% 28.79% -4.98% 1.06% -0.39%
Dow Jones U.S. Basic Materials Index 3.33% 2.42% 9.92% 65.51% 2.64% 5.95% 4.87%
Dow Jones U.S. Consumer Goods Index 10.07% 1.30% 6.07% 23.86% 0.32% 3.43% 5.05%
Dow Jones U.S. Consumer Services Index 11.51% 3.55% 6.83% 33.68% -4.96% -0.76% -2.26%
Dow Jones U.S. Financials Index 15.52% -0.05% -1.30% 17.11% -21.79% -9.47% -0.51%
Dow Jones U.S. Health Care Index 12.13% 2.61% 8.66% 21.71% 0.60% 3.34% 3.42%
Dow Jones U.S. Industrials Index 12.53% 2.41% 6.01% 26.07% -4.70% 0.65% -0.19%
Dow Jones U.S. Oil & Gas Index 10.88% -0.22% 4.63% 17.26% 0.52% 10.83% 10.41%
Dow Jones U.S. Technology Index 17.31% 6.47% 10.73% 64.48% 2.83% 4.34% -6.21%
Dow Jones U.S. Telecommunications Index 2.78% 5.10% 7.04% 9.85% -6.76% 1.27% -7.42%
Dow Jones U.S. Utilities Index 3.95% 5.69% 6.84% 12.58% -2.58% 5.28% 6.17%
Risk-Return
Industry Weights Relative to Global ex-U.S. Asset Class Performance
Data as of December 31, 2009
Source: Dow Jones Indexes Analytics & Research
For more information, please visit the Dow Jones Indexes Web site at www.djindexes.com.
The Dow Jones U.S. Index, the Dow Jones Global ex-U.S. Index and the Dow Jones U.S. Industry Indexes were first published in February 2000. The Dow Jones Brookfield Infrastructure Index was first published in July 2008. To the extent this document includes information for the index for the period prior to its initial publication date,
such information is back-tested (i.e., calculations of how the index might have performed during that time period if the index had existed). Any comparisons, assertionsand conclusions regarding the performance of the Index during the time period prior to launch will be based on back-testing. Back-tested information is purely hypothetical
and is provided solely for informational purposes. Back-tested performance does not represent actual performance and should not be interpreted as an indication of actual performance. Past performance is also not indicative of future results.
© Dow Jones & Company, Inc. 2009. All rights reserved. "Dow Jones", "Dow Jones Indexes", "Dow Jones U.S. Index", "Dow Jones Global ex-U.S. Index" and "Dow Jones U.S. Industry Indexes" are service marks of Dow Jones & Company, Inc. "UBS" is a registered trademark of UBS AG. "Dow Jones-UBS Commodity Index" is a service
mark of Dow Jones & Company, Inc. and UBS. "Brookfield" is a service mark of Brookfield Asset Management Inc. or its affiliates. The "Dow Jones Brookfield Infrastructure Indexes" are published pursuant to an agreement between Dow Jones & Company, Inc. and Brookfield Asset Management. Investment products that may be based
on the indexes referencedare not sponsored,endorsed,sold or promoted by Dow Jones, and Dow Jones makes no representationregarding the advisability of investing in them. Inclusion of a company in these indexesdoes not in any way reflect an opinion of Dow Jones on the investment merits of such company. Index performance is for
illustrative purposes only and does not represent the performance of an investment product that may be based on the index. Index performance does not reflect management fees, transaction costs or expenses. Indexes are unmanaged and one cannot invest directly in an index.
Chart compares industry weights within the Dow Jones U.S. Index to industry weights within the Dow Jones
Global ex-U.S. Index
U.S. = Dow Jones U.S. Index | Global ex-U.S. = Dow Jones Global ex-U.S. Index
Commodities = Dow Jones-UBS Commodity Index | REITs = Dow Jones U.S. Select REIT Index
Infrastructure = Dow Jones Brookfield Global Infrastructure Index
Composite
Basic Materials
Consumer Goods
Consumer Services
Financials
Health Care
Industrials
Oil & Gas
Technology
Telecommunications
Utilities
-25%
-20%
-15%
-10%
-5%
0%
5%
14% 16% 18% 20% 22% 24% 26% 28% 30% 32% 34%
3-Year Annualized Risk
3-Y
ear
An
nu
alized
Retu
rn
-0.83%
-2.57%
12.27%
0.41%
0.03%
6.22%
-9.58%
4.58%
-2.23%
-8.30%
-15% -10% -5% 0% 5% 10% 15%
Utilities
Telecommunications
Technology
Oil & Gas
Industrials
Health Care
Financials
Consumer Services
Consumer Goods
Basic Materials
Underweight <= U.S. vs. Global ex-U.S. => Overweight
20
40
60
80
100
120
140
160
12/06 3/07 6/07 9/07 12/07 3/08 6/08 9/08 12/08 3/09 6/09 9/09 12/09
U.S. [85.79] Global ex-U.S. [89.68] Commodities [88.94]
REITs [64.39] Infrastructure [99.32]
Dow Jones U.S. Industry Review
www.journalofindexes.com March/April 2010 65
March/April 201066
SPDRs (S&P 500)
SPDR Gold Trust
iShares MSCI Emerg Mkts
iShares MSCI EAFE
iShares S&P 500
Vanguard Emerging Markets
PowerShares QQQQ
iShares Barclays Capital TIPS Bond
Vanguard Total Stock Market
iShares Russell 2000
iShares iBoxx $ Inv Grade Corp Bond
iShares Russell 1000 Growth
iShares Barclays Capital Aggregate
iShares Brazil
iShares FTSE/Xinhua China
DIAMONDS Trust
iShares R1000 Value
MidCap SPDR (S&P 400)
iShares BarCap 1-3 Yr Treasury
Financial SPDR
iShares S&P 400 MidCap
Vanguard Total Bond Market
iShares S&P 500 Growth
Energy SPDR
SPY
GLD
EEM
EFA
IVV
VWO
QQQQ
TIP
VTI
IWM
LQD
IWF
AGG
EWZ
FXI
DIA
IWD
MDY
SHY
XLF
IJH
BND
IVW
XLE
0.09
0.40
0.72
0.35
0.09
0.27
0.20
0.20
0.09
0.24
0.15
0.20
0.24
0.65
0.73
0.17
0.20
0.25
0.15
0.21
0.21
0.14
0.18
0.21
84,908.0
40,223.0
39,209.2
35,207.1
21,729.2
19,445.3
18,560.4
18,489.7
13,442.6
13,081.2
12,840.6
11,350.9
11,234.0
11,093.8
10,180.1
9,045.5
8,795.2
8,485.0
7,667.1
6,848.4
6,519.3
6,241.5
5,796.3
5,616.0
26.31
24.03
68.77
26.88
26.61
76.29
54.67
8.95
28.89
28.53
8.58
36.73
3.01
121.18
47.28
22.72
19.23
37.52
0.36
17.50
37.81
3.67
31.13
21.81
0.41
19.63
14.89
3.25
0.42
-
3.16
4.52
1.03
0.54
4.01
1.45
4.72
31.00
19.84
1.83
-0.35
3.01
3.91
-11.58
3.21
-
1.43
10.97
44,056
-
22,806
28,840
44,074
17,639
38,949
-
23,218
739
-
36,198
-
32,195
72,013
98,013
31,309
2,566
-
37,728
2,566
-
54,406
48,610
17.6
-
5.9
11.4
17.6
15.4
25.8
-
16.7
16.0
-
19.3
-
13.8
18.6
16.4
15.8
18.4
-
21.6
18.4
-
18.6
14.8
19.79
21.87
32.38
24.96
19.81
32.40
23.89
8.89
20.49
24.62
12.16
20.03
5.57
40.62
39.99
18.29
21.22
23.52
2.10
36.33
23.46
-
18.69
26.42
1.95
-
1.40
2.60
1.93
1.33
0.46
3.90
1.96
1.15
5.50
1.39
3.89
3.69
1.28
2.70
2.36
1.22
2.41
1.71
1.28
4.04
1.38
1.82
Fund Name Ticker Assets Exp Ratio 2009
6.11
8.56
7.49
2.00
6.04
7.84
8.47
1.80
5.85
4.07
-0.64
8.07
-0.39
13.58
3.82
8.04
4.07
5.57
-0.11
-3.26
5.51
0.11
7.83
6.30
3-Mo
-36.70
4.92
-48.87
-41.00
-37.00
-52.54
-41.72
-0.53
-36.97
-34.15
2.44
-38.21
7.90
-54.37
-47.73
-32.10
-36.45
-36.40
6.61
-54.90
-36.18
6.88
-34.78
-38.97
5.12
31.10
33.11
9.94
4.92
37.32
19.13
11.93
5.36
-1.76
3.76
11.48
6.61
74.82
54.81
8.78
-0.73
7.20
7.35
-19.19
7.30
-
8.84
36.86
2008 2007
-5.63
19.49
4.73
-6.29
-5.77
4.73
2.40
6.65
-5.05
-5.96
4.89
-1.98
5.82
20.83
6.02
-3.22
-9.06
-2.12
4.73
-24.62
-1.91
-
-2.36
0.58
3-Yr 5-Yr Mkt Cap P/E Std Dev Yield
Total Return % Annualized Return %
Largest U.S.-listed ETFs Sorted By Total Net Assets In $US Millions
Source: Morningstar. Data as of December 31, 2009. ER is expense ratio. 1-Mo is 1-month. 3-Mo is 3-month. Source: IndexUniverse.com's ETF Watch
iPath S&P 500 VIX Mid ETN
JPMorgan Alerian MLP Index ETN
Market Vectors Brazil Small-Cap
Market Vectors Junior Gold Miners
Vanguard FTSE All-Wld ex-US SmCp
ETFS Physical Swiss Gold
ProShares Short 20+ Year Treasury
SPDR Barclays Capital Convertible Bond
WisdomTree Dreyfus Emerging Currency
Market Vectors Indonesia
PIMCO 1-3 Year U.S. Treasury
ProShares UltraPro Short S&P 500
ETFS Silver
Direxion Daily Real Estate Bull 3x
PIMCO 1-5 Year U.S. TIPS
SPDR BarCap Short-Term International Treasury
iShares S&P/Citi International Treasury
iShares S&P/Citi 1-3 Yr International Treasury
Market Vectors High-Yield Municipal
iShares MSCI All Peru Capped
Claymore Corporate Bond 2020
Claymore Wilshire 5000 Eq-Wtd
Direxion Nasdaq-100 Bull 3x
Emerging Global Shares INDXX Brazil Mid Cap
Global X Finland
Global X United Arab Emirates
HTE Global Relative Value
IQ International Canada Small Cap
iShares MSCI ACWI ex US Utilities
JETS Contrarian Opportunities
Market Vectors Latin America Small-Cap
McDonnell Core Taxable Bond
Old Mutual FTSE Developed Markets ex US
PowerShares Industrial Corporate Bond
PowerShares S&P SmallCap Healthcare
ProShares Ultra Swiss Franc
Rydex 2x Energy Investing
SPDR S&P India
WCM / BNY Mellon Focused Growth ADR
WisdomTree Commodity Currency
WisdomTree Israel Total Dividend
VXX
AMJ
BRF
GDXJ
VSS
SGOL
TBF
CWB
CEW
IDX
TUZ
SPXU
SIVR
DRN
STPZ
BWZ
IGOV
ISHG
HYD
EPU
0.89
0.85
0.73
0.60
0.38
0.39
0.95
0.40
0.55
0.71
0.09
0.95
0.30
0.95
0.20
0.35
0.35
0.35
0.35
0.63
Fund Name Ticker
1/29/2009
4/2/2009
5/12/2009
11/10/2009
4/2/2009
9/9/2009
8/20/2009
4/14/2009
5/6/2009
1/15/2009
6/1/2009
6/23/2009
7/24/2009
7/16/2009
8/20/2009
1/15/2009
1/21/2009
1/21/2009
2/4/2009
6/19/2009
Launch Date
795.5
713.3
699.2
660.8
343.6
331.3
262.4
232.0
223.4
201.6
175.3
158.0
155.5
149.3
139.4
137.4
134.1
124.9
119.7
110.5
AssetsER
-31.85
16.06
24.18
-
3.57
8.57
6.88
5.34
1.98
5.25
0.08
-20.14
1.20
19.79
2.18
-1.84
-3.14
-2.45
-1.81
1.65
3-Mo
-15.42
6.75
5.01
-5.45
1.95
-7.21
6.29
2.39
0.13
3.61
-0.63
-6.28
-8.66
21.87
-0.58
-4.49
-5.31
-4.73
1.11
-0.81
1-Mo
Largest New ETFs Sorted By Total Net Assets In $US Millions Selected ETFs In Registration
Covers ETFs and ETNs launched in 2009.
Source: Morningstar. Data as of December 31, 2009. Exp Ratio is expense ratio. 3-Mo is 3-month. 3-Yr and 5-Yr are 3-year and 5-year annualized returns, respectively.
Mkt Cap is geometric average market capitalization. P/E is price-to-earnings ratio. Std Dev is 3-year standard deviation. Yield is 12-month.
Exchange-Traded Funds Corner
March/April 2010www.journalofindexes.com 67
true of any returns-based performance metric. This fact must
be kept in mind in interpreting the Russell TDM: It is a mea-
sure of performance over a given time period, not a predictor
of future performance.
ConclusionDuring June and July 2009, Congress held hearings and
heard testimony regarding the performance of target date
funds. This reflects how important these investment vehicles
have become and how great the need is for credible perfor-
mance measures. The industry needs a measure that:
• Provides a valid estimate of the true value for a given
family of funds, using fund returns over a limited evalu-
ation period.
• Reflects the relative importance of each fund’s posi-
tion on its glide path: Returns of funds near their
target dates have more influence on retirement wealth
than returns of more distant funds. This is because the
primary goal of target date funds is creating wealth at a
certain fixed “cash-out” point in the future. Performance en
route to that final number is important because of how it
influences that end result.
• Takes into account the timing of cash flows as a typical
investor saves for retirement.
• Determines the value over a given performance period
by differences in the returns of the funds in the family
and the benchmark returns. A returns-based measure
will capture the performance differentials that are due
to glide path structure, asset mix and active/passive
implementation, the three key components of target
date fund performance differences.
• Measures performance relative to a passive investable
alternative.
• Can be used to meaningfully compare the performance
of any two families of funds over a common perfor-
mance period.
ReferencesChristopherson, J.A., D.R. Cariño and W.E. Ferson (2009). “Portfolio Performance Measurement and Benchmarking,” McGraw-Hill.
Gardner, G. and A. Sirohi (2009). “The Russell Target Date Performance Metric: Description of Methodology,” Russell Research, August.
Goodwin, T. (1998). “The Information Ratio,” Financial Analysts Journal. July/August, pp. 34-43.
Maxie, D. (2009). “Getting Personal: Target date funds find ways to cut costs.” Wall Street Journal, August 3.
Spaulding, D. and J.A. Tzitzouris, ed. (2009). “Classics in Investment Performance Measurement,” The Spaulding Group.
Endnotes1Maxie (2009).2For calculation specifications, see Gardner and Sirohi (2009).3The total return of global equity as measured by the 67 percent/33 percent mix of the Russell 3000 and Russell Global ex-U.S. Indexes minus the return of the Barclays Capital U.S Aggregate
Bond Index.
Disclosures
Russell Investments is a Washington, USA Corporation, which operates through subsidiaries worldwide and is a subsidiary
of The Northwestern Mutual Life Insurance Company.
Gardner continued from page 45
Russell
Large Growth Russell 1000 Growth
Large Blend Russell 1000
Large Value Russell 1000 Value
Mid-Cap Growth Russell Mid Cap Growth
Mid-Cap Blend Russell Mid Cap
Mid-Cap Value Russell Mid Cap Value
Small Growth Russell 2000 Growth
Small Blend Russell 2000
Small Value Russell 2000 Value
Works Cited“2009 Investment Company Fact Book.” Investment Company Institute. http://www.icifactbook.org/.
Carhart, Mark M. 1997. “On Persistence in Mutual Fund Performance,” Journal of Finance, vol. 52: No. 1, 57-82.
Cremers, Martijn, Antti Petajisto, and Eric Zitzewitz. 2008. “Should Benchmark Indices Have Alpha? Revisiting Performance Evaluation.” Working paper version July 31, 2008.
Fama, Eugene F., and Kenneth R. French. 1993. “Common Risk Factors in the Returns on Bonds and Stocks,” Journal of Financial Economics, vol. 33: 3-53.
French, Kenneth R., http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
Israelsen, Craig. 2007. “Variance Among Indexes.” Journal of Indexes, May/June: 26-29
Blanchett continued from page 37
Appendix II: Benchmark Indices continued
H U M O R
68
Fabulous investment
opportunities await
ETF investors
this year.
March/April 2010
The Future Of ETFs
By Dave Nadig
The ETFs Of 2010
One of the things we pride ourselves
on at Index Publications is our crystal
ball. By poring over obscure SEC filings,
combing through patent and trademark
cases and good old-fashioned shoe-leath-
er reporting, we think we’ve got a pretty
good handle on where the indexing and
ETF industry is headed. Here for the first
time is a selection of ETFs we’re particu-
larly looking forward to seeing.
As always, market conditions and SEC
approval can delay the launches.
WEED: With medical marijuana now
legal in 14 states, it was only a matter of
time before the ETF industry cashed in
on the trend. Based on the “High Times
Righteous Buzz Index,” WEED will invest
in agribusiness companies, the hemp
futures market and fungible stockpiles.
CHTO: WEED’s physically backed sister
fund will consist entirely of high-calorie
snack foods. The fund’s assets, which will
be placed in a vault in Amsterdam, will be
audited biannually, or whenever WEED’s
custodians get the munchies.
SCUM: This “special situations” fund
focuses on the assets of some of the
country’s most creative investors and
accountants, old and new. Not just focus-
ing on cleanup operations in the after-
math of Madoff or the credit crisis, SCUM
will track down the current “hot picks”
from such luminaries as Bernie Ebbers
and Charles Keating.
FLOP: A true value play, FLOP will invest
solely in companies whose stocks have
been unnecessarily punished. Key hold-
ings include Crocs, Vonage, Transmeta,
and the recovered assets of Internet
retailer Webvan.
SCAM: This actively managed stock-
picking fund from a major active mutual
fund manager will be the first to take
advantage of a special SEC “blinding”
exemption. While the NAV of the fund
will be published, creation baskets will
be made entirely of precious metals.
Redemptions will be made in cheese.
FAT: Cashing in on the continued high
levels of unemployment, demographic
trends and the American obsession with
fast food, this ETF will invest solely in
fast-food companies and manufacturers
of insulin pumps.
SPAM: Following up on the success of
OOK, this ETF invests solely in compa-
nies based in the Hawaiian Islands, and
Hormel Foods Corp (with some inexpli-
cable crossover into Russian Internet
start-up firms)
LOL: A new active fund managed by an
expert group of Blackberry-armed 14-year-old
girls, who seek to replicate trends hidden in
the catalog of Lady Gaga.
BOO: Tracks the Scooby Doo Phantom
Index of amusement parks owners and
hotel operators.
LENO: Invests solely in seven-month
call options on companies with second-
place positions in their markets.
Ever wonder exactly what you’re investing in? Every day, we providea complete list of holdings for each of our tax-effi cient ETFs -including the PowerShares QQQ. Visit www.InvescoPowerShares.comto see what you may be missing.
There are risks involved with investing in ETFs including possible loss of money. The funds are not actively managed and are subject to riskssimilar to stocks, including those related to short selling and margin maintenance. Ordinary brokerage commissions apply. Shares are notFDIC insured, may lose value and have no bank guarantee. Invesco PowerShares does not offer tax advice. Investors should consult theirown tax advisor for information regarding their own tax situations. PowerShares is a registered trademark of Invesco PowerShares CapitalManagement LLC. ALPS Distributors, Inc. is the distributor for QQQ. Invesco PowerShares Capital Management LLC is not affi liated with ALPSDistributors, Inc.
An investor should consider the Fund’s investment objective, risks, charges and expenses carefully before investing. To obtain aprospectus, which contains this and other information about the QQQ, a unit investment trust, please contact your broker, call800.983.0903 or visit www.invescopowershares.com. Please read the prospectus carefully before investing.
Shares are not individually redeemable and owners of the shares may acquire those shares from the Funds and tender those shares for redemption to the funds in Creation Unit aggregations only, typically consisting of 50,000 shares.
www.invescopowershares.com | 800.983.0903
Project2 1/22/10 9:30 AM Page 1
To buy or sell Vanguard ETFs,™ contact your financial advisor. Usual commissions apply. Not redeemable. Market pricemay be more or less than NAV. Visit advisors.vanguard.com/etf to obtain a Vanguard prospectus which contains
investment objectives, risks, expenses, and other information; read and consider carefully before investing. All ETFs are subject torisk, including possible loss of principal. Investments in bond funds are subject to interest rate, credit, and inflation risk.*Source: LipperInc. as of December 31, 2008. Based on 2008 industry average expense ratio of 1.19% and Vanguard average expense ratio of 0.20%.©2010 The Vanguard Group, Inc. All rights reserved. U.S. Pat. No. 6,879,964 B2; 7,337,138. Vanguard Marketing Corporation, Distributor.
New Bond ETFs from Vanguard I Offering targeted exposure to the government
sector of the domestic fixed income market, these ETFs provide options for
tailoring your portfolio within the government sector. Choose short-, intermediate-,
or long-term coverage. Lower costs,* tight tracking, and diversification may reduce
manager risk and provide returns more in line with the index. Expertise, lower
costs, and trusted name. Just what you’d expect from Vanguard.™ Connect
with Vanguard® at 800-523-1178 or visit our dedicated advisors’ website at
advisors.vanguard.com/etf.
VANGUARD INTERMEDIATE-TERM
GOVERNMENT BOND ETF
VANGUARD SHORT-TERM
GOVERNMENT BOND ETF
VANGUARD LONG-TERM
GOVERNMENT BOND ETF
VGSH
VGLT
VGIT
Project1 12/2/09 11:13 AM Page 1
WallachBeth Capital LLC is an ‘inter-market-broker’ specializing in exchange-listed equityoptions, index products, ETF’s, and equities.
Operating on a full-disclosed agency basis, our role is to demystify the challenges of executingtrades within highly fragmented markets. By doing so, we are able to provide a vital link for
those seeking liquidity and best price execution with complete transparency.
Our team is comprised of product experts that understand market dynamics and the nuances oftransacting in all trading environments. We are relied upon for leveraging trading system
technologies and industry-wide relationships to efficiently and cost-effectively execute complex andpotentially market-impacting orders.
www.wallachbeth.com
WallachBeth Capital, LLC100 Wall Street Suite 6600 New York N.Y. 10005
Tel. 646.237.8585 Fax 212.495.0270
Sourcing Liquidity...At The Right Price
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SM
WB_joi310.qxd 2/4/10 4:44 PM Page 1
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