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How Neuroeconomics Can Make You A Better Investor
Jason Zweig
Behavorial Finance Roundtable
The Frontier From Different Views
Craig Israelsen
Why ETFs And 401(k)s Will Never Match
David Blanchett and Gregory Kasten
Plus Blitzer on home prices, Hougan and Ferri on ETFs, and the Curmudgeon, misbehaving

www.journalofindexes.com
Vol. 11 No. 4
features
Your Money & Your Brain
by Jason Zweig .......................... 10
Why you’re no good at predicting anything.
How Neuroeconomics Can
Make You A Better Investor
an interview with Jason Zweig .............. 16
The journey from neuroscience to brainier investing.
Behavioral Finance Roundtable.. . . . . . . . . 20
Seven experts on emotion, indexing and investing.
The Frontier From Different Views
by Craig Israelsen . . . . . . . . . . . . . . . . . . . . . . . . . 26
Assessing the frontier by size and style.
10
ETFs, Spreads and Liquidity
by Matt Hougan ......................... 30
A hard look at the data on ETF spreads.
Why ETFs And 401(k)s Will Never Match
by David Blanchett and Gregory Kasten ...... 34
ETFs may never gain traction in retirement plans.
Index Strategies And ETF Costs
by Richard Ferri ......................... 42
Why complex can be costly in the ETF arena.
Inside The Home Price Indices
by David Blitzer ......................... 46
Behind the scenes of the home price plunge.
The Curmudgeon
by Brad Zigler ........................... 64
Breaking behavioral finance into its component parts.
news
Bear Stearns Rolls Out First U.S. Active ETF ......... 48
Home Prices Tumble ........................... 48
Barclays Launches First All-World Stock ETF . . . . . . . . 49
Vanguard Files For All-World Fund ................ 49
Northern Trust Enters ETF Market . . . . . . . . . . . . . . . . 49
Indexing Developments ........................ 49
Around The World Of ETFs ...................... 51
Into The Futures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
On The Move . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
data
Selected Major Indexes .........................59
Returns Of Largest U.S. Index Mutual Funds .........60
U.S. Market Overview In Style ....................61
U.S. Economic Sector Review. ....................62
Exchange-Traded Funds Corner ...................63
20
34
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July/August 2008
1

Contributors
David Blitzer
Matt Hougan
Craig Israelsen
Richard Ferri
David Blanchett
David Blanchett is an institutional consultant at Unified Trust Company, where
he works primarily with financial advisors on fiduciary, compliance and investment
issues relating to United Trust’s retirement plan services. He completed his
M.S. in Financial Services through The American College. Blanchett is a Certified
Financial Planner, and passed the Level III CFA exam in June of 2006. Blanchett
has published articles in a variety of leading academic and trade journals.
David Blitzer is the chairman of the S&P 500 Index Committee and a member
of Standard & Poor’s Investment Policy Committee and Economic Forecast
Council. He previously served as corporate economist at McGraw-Hill and as
senior economic analyst with National Economic Research Associates. Blitzer
is the author of Outpacing the Pros: Using Indexes to Beat Wall Street’s Savviest
Money Managers, McGraw-Hill, 2001.
Richard Ferri (Rick) is CEO of Portfolio Solutions, LLC, a low-fee investment
management firm. He earned a B.S. in Business Administration from the
University of Rhode Island and an M.S. in Finance from Walsh College, and
holds the designation of Chartered Financial Analyst (CFA). Ferri has written
five books on low-cost investing, including The ETF Book, recently published
by John Wiley.
Matt Hougan is editor of IndexUniverse.com, ETF Watch and the Exchange-
Traded Funds Report, and senior editor of the Journal of Indexes. Expert on ETFs,
Hougan is quoted regularly in The Wall Street Journal, Barron’s, TheStreet.com,
Marketwatch.com and the Associated Press. Prior to joining Index Publications,
he was a freelance speechwriter for clients ranging from Fortune 100 CEOs to
senior government officials. Hougan is a 1998 graduate of Bowdoin College.
Craig Israelsen is an associate professor at Brigham Young University in Provo,
Utah. He holds a Ph.D. in Family Resource Management from Brigham Young
University and an M.S. in Agricultural Economics from Utah State University. He
taught personal and family finance at the University of Missouri for 14 years,
prior to returning to BYU. Primary among his research interests is the analysis of
mutual funds. Israelsen writes monthly for Financial Planning magazine.
Brad Zigler
Brad Zigler formerly served as head of marketing, education and research for the
Pacific Exchange and Barclays Global Investors. He is currently managing editor
for HardAssetsInvestor.com, a commodities-focused Web site. He is a founding
member of the Global Association of Risk Professionals Education Committee,
and has contributed to TheStreet.com, MarketWatch.com, Institutional Investor,
Financial Planning, CRB Trader, Mutual Funds and Registered Rep.
Jason Zweig
Jason Zweig was recently hired as personal finance columnist for The Wall
Street Journal. Prior to joining the Journal, he had been senior writer and columnist
for Money magazine since 1995. His previous positions include editor of
The Intelligent Investor (HarperCollins), guest columnist and reporter-researcher
for Time magazine and reporter for Forbes magazine. Zweig is a graduate of
Columbia College, Columbia University.
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Review Board:
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Editor’s Note
Investors Do The
Darndest Things
Somehow the study of behavioral finance has always seemed a good
fit with index investing. Indexers are focused on taking the emotion
out of investing, carefully calculating a sound asset allocation plan
and then sticking to it. So it seems natural that we would want to
understand how we act and why. If we can understand our investing
id, perhaps we can exist in our economic ego, and thereby graduate
to our financially self-realized superego.
You may think it’s a bunch of Psychology 101 nonsense, but Jason
Zweig certainly thinks it’s important. There’s no one who is more of
an “old school” indexer than Jason, and he has completely immersed
himself in the science of behavioral finance in recent years. We’re delighted to have
both an interview and a book excerpt from Mr. Zweig in this issue. The writing has
sizzle and substance, a rare combination these days.
Following the Zweig contributions, we’ve got one of our always-popular roundtables—this
one focused on behavioral finance with an academic tilt. Sit back and
enjoy the side-by-side responses of William Bernstein, David Blitzer, Francis Kinniry,
Ed McRedmond, Ross Miller, Terrance Odean and John Prestbo as they debate just
how nuts we are as investors.
Is it just me or is Craig Israelsen the ultimate fit for what we’re doing in the Journal
of Indexes? This issue, Professor Israelsen weighs in with another smart, practical and
accessible analysis—this one on asset allocation and the efficient frontier.
From here we really enter the ETF world, first with an outstanding analysis of ETF spreads
by our own Matt Hougan, a provocative and skeptical look at the potential of ETF investing
on 401(k) platforms by David Blanchett and Gregory Kasten, and a look at the range of ETFs
available from beta through alpha and their corresponding costs from Rick Ferri.
Bringing us home in the issue is David Blitzer discussing real estate, the current
poster child of erratic investor behavior, and finally The Curmudgeon with a bit of a
different take on behavioral finance.
Jim Wiandt
Editor
Jim Wiandt
Editor
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July/August 2008

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Your Money & Your Brain
Why investors get things wrong
By Jason Zweig
10
July/August 2008

[The following is an excerpt from Jason Zweig’s recent book, Your
Money & Your Brain: How the New Science of Neuroeconomics
Can Help Make You Rich, Simon & Schuster, August 1, 2007.]
Pecuniary motives either do not act at all—or are of that
class of stimulants which act only as Narcotics.
—Samuel Taylor Coleridge
From Babel To Bubble
In the Mesopotamian galleries of the British Museum in
London sits one of the most startling relics of the ancient
world: a life-size clay model of a sheep’s liver, which served
as a training tool for a specialized Babylonian priest known
as a baru, who made predictions about the future by studying
the guts of a freshly slaughtered sheep. The model is a
catalog of the blemishes, colors, and differences in size or
shape that a real sheep’s liver might display. The baru and
his followers believed that each of these variables could
help foretell what was about to happen, so the clay model is
painstakingly subdivided into sixty-three areas, each marked
with cuneiform writing and other symbols describing its
predictive powers.
What makes this artifact so astounding is that it is as contemporary
as today’s coverage of the financial news. More
than 3,700 years after this clay model was first baked in
Mesopotamia, the liver-reading Babylonian barus are still with
us—except now they are called market strategists, financial
analysts, and investment experts. The latest unemployment
report is “a clear sign” that interest rates will rise. This
month’s news about inflation means it’s “a sure thing” that
the stock market will go down. This new product or that new
boss is “a good omen” for a company’s stock.
Just like an ancient baru massaging the meanings out of a
bloody liver, today’s market forecasters sometimes get the
future right—if only by luck alone. But when the “experts”
are wrong, as they are about as often as a flipped coin comes
up tails, their forecasts read like a roster of folly:
• Every December, BusinessWeek surveys Wall Street’s
leading strategies, asking where stocks are headed
in the year to come. Over the past decade, the consensus
of these “expert” forecasts has been off by an
average of 16 percent.
• On Friday the 13th in August 1982, the Wall Street
Journal and the New York Times quoted one analyst and
trader after another, all spewing gloom and doom: “A
selling climax will be required to end the bear market,”
“investors are on the horns of a dilemma,” the market
is gripped by “outright capitulation and panic selling.”
That very day, the greatest bull market in a generation
began—and most “experts” remained stubbornly bearish
until the rebound was long under way.
• On April 14, 2000, the NASDAQ stock market fell
9.7 percent to close at 3321.29. “This is the greatest
opportunity for individual investors in a long
time,” declared Robert Froelich of Kemper Funds,
while Thomas Galvin of Donaldson, Lufkin & Jenrette
insisted “there’s only 200 or 300 points of downside
for the NASDAQ and 2000 on the upside.” It turned
www.journalofindexes.com
out there were no points on the upside and more
than 2,200 on the downside, as NASDAQ shriveled all
the way to 1114.11 in October 2002.
• In January 1980, with gold at a record $850 per ounce,
U.S. Treasury Secretary G. William Miller declared:
“At the moment, it doesn’t seem an appropriate time
to sell our gold.” The next day, the price of gold fell
17 percent. Over the coming five years gold lost twothirds
of its value.
• Even the Wall Street analysts who carefully study
a handful of stocks might as well be playing “eeny
meeny miny moe.” According to money manager
David Dreman, over the past thirty years, the analysts’
estimate of what companies would earn in the next
quarter has been wrong by an average of 41 percent.
Imagine that the TV weatherman said it would be 60
degrees yesterday, and it turned out to be 35 degrees
instead—also a 41 percent error (on the Fahrenheit
scale). Now imagine that’s about as accurate as he ever
gets. Would you keep listening to his forecasts?
All these predictions fall prey to the same two problems:
First, they assume that whatever has been happening is the only
thing that could have happened. Second, they rely too heavily on
the short-term past to forecast the long-term future, a mistake
that the investment sage Peter Bernstein calls “postcasting.” In
short, the “experts” couldn’t hit the broad side of a barn with a
shotgun—even if they stood inside the barn.
As a matter of fact, whichever economic variable you look
at—interest rates, inflation, economic growth, oil prices,
unemployment, the Federal budget deficit, the value of the
U.S. dollar or other currencies—you can be sure of three
things: First, someone gets paid lots of money to make predictions
about it. Second, he will not tell you, and may not
even know, how accurate his forecasts have been over time.
Third, if you invest on the basis of those forecasts, you are
likely to be sorry, since they are no better a guide to the
future than the mutterings of a Babylonian baru.
The futility of financial prediction is especially frustrating
because it seems so clear that analysis should work. After
all, we all know that studying beforehand is a good way to
improve our (or our children’s) test scores. And the more you
practice your golf or basketball or tennis shot, the better
player you will become. Why should investing be any different?
There are three main reasons why investors who do the
most homework do not necessarily earn the highest grades:
1. The market is usually right. The collective intelligence
of tens of millions of investors has already set a price for
whatever you’re trading. That doesn’t mean that the market
price is always right, but it’s right more often than it’s wrong.
And when the market is massively wrong—as it was about
Internet stocks in the late 1990s—then betting against it can
be like trying to swim into a tidal wave.
2. It takes money to move money. The brokerage costs of
buying and selling a stock can easily exceed 2 percent of the
amount you stake. And the tax man can take up to 35 percent
of your gains if you trade too frequently. Together, those
expenses wear away profitable ideas like sandpaper.
July/August 2008 11

3. Randomness rules. No matter how carefully you research
an investment, it can go down for reasons you never anticipated:
a new product fails, the CEO departs, interest rates
rise, government regulations change, war or terrorism bursts
out of the blue. No one can predict the unpredictable.
What Are The Odds?
It took two psychologists, Daniel Kahneman and Amos
Tversky, to deal a death blow to the traditional view that
people are always “rational.” In economic theory, we process
all the relevant information in a logical way to figure
out which choice offers the best trade-off between risk and
return. In reality, Kahneman and Tversky showed, people
tend to base their predictions of long-term trends on surprisingly
short-term samples of data—or on factors that are not
even relevant. Consider these examples:
1. Two bowls, hidden from view, each contain a mix of
balls, of which two-thirds must be one color and one-third
must be another. One person has taken 5 balls out of Bowl
A; 4 were white, 1 was red. A second person drew twenty
balls out of Bowl B; twelve were red, 8 were white. Now it’s
your turn to be blindfolded, but you can take out only one
ball. If you guess the right color in advance, you will win $5.
Should you bet that you will draw a white ball from Bowl A,
or a red ball from Bowl B?
Many people bet on getting a white ball, since the first
person’s draw from Bowl A was 80 percent white, while the
second person drew only 60 percent red from Bowl B. But
the sample from Bowl B was four times larger. That bigger
drawing means that Bowl B is more likely to be mostly red
than Bowl A is to be mostly white. Most of us know that large
samples of data are more reliable, but we get distracted by
small samples nevertheless. Why?
2. A nationwide survey obtains brief personality descriptions
of 100 young women, of whom 90 are professional athletes
and 10 are librarians. Here are two personality profiles
drawn from this group of 100:
Lisa is outgoing and lively, with long hair and a tan. She
is sometimes undisciplined and messy, but she has an active
social life. She is married but has no children.
Mildred is quiet, with eyeglasses and short hair. She smiles
often but seldom laughs. She is a hard worker, extremely
orderly, and has only a few close friends. She is single.
What are the odds that Lisa is a librarian?
What are the odds that Mildred is a professional athlete?
Most people think Lisa must be an athlete, and Mildred
must be a librarian. While it seems obvious from the descriptions
that Lisa is more likely than Mildred to be a jock,
Mildred is probably a professional athlete, too. After all,
we’ve already been told that 90 percent of these women are.
Often, when we are asked to judge how likely things are, we
instead judge how alike they are. Why?
It took two psychologists … to deal a death blow to the
traditional view that people are always ‘rational.’
So why, despite all the evidence that their efforts are
futile, do today’s financial barus keep on predicting? Why do
investors keep listening to them? Most important of all, if
no one can accurately foresee the financial future, then what
practical rules can you use to make better investing decisions?
That’s what [this excerpt] is all about.
3. Imagine that you and I are flipping a coin. (Let’s flip six
times and track the outcomes by recording heads as an H and
tails as a T.) You go first and flip H T T H T H: a 50/50 result that
looks exactly like what you should get by random chance. Then
I toss and get H H H H H H: a perfect streak of heads that makes
us both gasp and makes me feel like a coin-flipping genius.
But the truth is more mundane: In six coin flips, the odds
of getting H H H H H H are identical to the odds of getting H
T T H T H. Both sequences have a one-in-64, or 1.6 percent,
chance of occurring. Yet we think nothing of it if one of us
flips H T T H T H, while we both are astounded when H H H
H H H comes up. Why?
Pigeons, Rats, And Randomness
The answers to these riddles about randomness lie deep
in our brains and far back in the history of our species.
Humans have a phenomenal ability to detect and interpret
simple patterns. That’s what helped our ancestors survive
the hazardous primeval world, enabling them to evade
predators, find food and shelter, and eventually to plant
crops in the right place at the right time of year. Today,
our skill at seeking and completing patterns helps us
navigate many of the basic challenges of daily life. (“Here
comes the train I have to catch.” “The baby’s hungry.” “My
boss is always a butthead on Mondays.”)
But when it comes to investing, our incorrigible search for
patterns leads us to assume that order exists where it often
doesn’t. It’s not just the barus of Wall Street who think they
know where the stock market is going. Almost everyone has an
opinion about whether the Dow will go up or down from here,
or whether a particular stock will continue to rise. And everyone
wants to believe that the financial future can be foretold.
The pursuit of patterns in random data is a fundamental
function in our brains—so basic to human nature that our
species should not be known only as Homo sapiens, or “man
the wise”; we might better be named Homo formapetens, or
“man the pattern-seeker.” Although most animals have the
ability to identify patterns, humans are uniquely obsessive
about it. Our knack for perceiving order even where
there isn’t any is what the astronomer Carl Sagan called
the “characteristic conceit of our species,” and what others
have called pareidolia, from the Greek for incorrect or
distorted imagery. Some people see an image of the Virgin
Mary in the scorch marks on a ten-year-old grilled-cheese
sandwich—and one was even willing to pay $28,000 for it
on eBay. Others sift through mountains of stock market
data to find “predictable patterns” that might enable them
to beat the market:
12
July/August 2008

• It became a common belief, based on historical numbers,
that U.S. stocks tend to go up on Fridays and
down on Mondays—but, in the 1990s, they did the exact
opposite.
• October (the month of the 1987 market crash) is widely
supposed to be the worst month to own stocks—but,
over the long sweep of history, it has actually averaged
the fifth-best returns of any month.
• Millions of investors believe in technical analysis, which
supposedly predicts future prices on the basis of past
prices; and in market timing, which purports to enable
you to get out of stocks before they go down and back
in before they go up. There is little, if any, objective evidence
that either tactic works in the long run.
• Every year, many Wall Streeters root for National
Football Conference teams to win the Super Bowl, based
on the widely held—and wildly inaccurate—belief that
when teams originating in the old NFL take the championship,
the stock market goes up the next year.
What drives this behavior? For decades, psychologists
have demonstrated that if rats or pigeons knew what a stock
market is, they might be better investors than most humans
are. That’s because rodents and birds seem to stick within the
limits of their abilities to identify patterns, giving them what
amounts to a kind of natural humility in the face of random
events. People, however, are a different story.
In a typical experiment of this kind, researchers flash
two lights, one green and one red, onto a screen. Four out
of five times, it’s the green light that flashes; the other 20
percent of the time, the red light comes on. But the exact
sequence is kept random. (One run of 20 flashes might look
like this: RGRGGGGGRGGGGRGGGGGG. Another might be:
GGGGRGGGGGGGRRGGGGGR. You can view a simplified
version of this task at www.jasonzweig.com/uploads/matchvmax.ppt.)
In guessing which light will flash next, the best
strategy is simply to predict green every time, since you
stand an 80 percent chance of being right. And that’s what
rats or pigeons generally do when the experiments reward
them with a crumb of food for correctly guessing what color
the next flash of light will be.
Humans, however, tend to flunk this kind of experiment.
Instead of just picking green all the time and locking in an
80 percent chance of being right, people will typically pick
green four out of five times, quickly getting caught up in the
game of trying to call when the next red flash will come up.
On average, this misguided confidence leads people to pick
the next flash accurately on only 68 percent of their tries.
Stranger still, humans will persist in this behavior even when
the researchers tell them explicitly—as you cannot do with a
rat or pigeon—that the flashing of the lights is random. And,
while rodents and birds usually learn quite quickly how to
maximize their score, people often perform worse the longer
they try to figure it out. The more time they spend working
at it, the more convinced many people become that they
have finally discovered the trick to predicting the “pattern”
of these purely random flashes.
Unlike other animals, humans believe we’re smart enough to
forecast the future even when we have been explicitly told that
it is unpredictable. In a profound evolutionary paradox, it’s precisely
our higher intelligence that leads us to score lower on this
kind of task than rats and pigeons do. (Remember that the next
time you’re tempted to call somebody a “birdbrain.”)
A team of researchers at Dartmouth College, led by psychology
professor George Wolford, has studied why we think we
can spot patterns where there are none. Wolford’s group ran
light-flashing experiments on “split-brain patients,” people in
whom the nerve connections between the hemispheres of the
brain have been surgically severed as a treatment for severe
epilepsy. When the epileptics viewed a series of flashes that
they could process only with the right side of their brains,
they gradually learned to guess the most frequent option all
the time, just as rats and pigeons do. But when the signals
were flashed to the left side of their brains, the epileptics
kept trying to forecast the exact sequence of flashes—sharply
lowering the overall accuracy of their predictions.
“There appears to be a module in the left hemisphere of
the brain that drives humans to search for patterns and to see
causal relationships,” says Wolford, “even when none exist.”
His research partner, Michael Gazzaniga, has nicknamed this
part of the brain “the interpreter.” Wolford explains: “The
interpreter drives us to believe that ‘I can figure this out.’
That may well be a good thing when there is a pattern to the
data and the pattern isn’t overly complicated.” However, he
warns, “a constant search for explanations and patterns in
random or complex data is not a good thing.”
That’s the investment understatement of the century. The
financial markets are almost—though not quite—as random
as those flashing lights, and they vary in incredibly complex
ways. Although no one has yet identified exactly where in the
brain the interpreter is located, its existence helps explain
why the “experts” keep trying to predict the unpredictable.
Facing a constant, chaotic storm of data, these pundits refuse
to admit that they can’t understand it. Instead, their interpreters
drive them to believe they’ve identified patterns from
which they can project the future.
Meanwhile, the rest of us take these seers more seriously
than their track records warrant, with results that are often
tragic. As Berkeley economist Matthew Rabin points out,
just a couple of accurate predictions on CNBC can make an
analyst seem like a genius, because viewers have no practical
way to sample the analyst’s entire (and probably mediocre)
forecasting record. In the absence of a full sample, a
small streak of random luck looks to us like part of a longer
pattern of reliable foresight. But listening to an “expert”
who made a couple of lucky calls is one of the surest ways
for an investor to get unlucky in a hurry.
It’s vital to recognize the basic realities of pattern recognition
in your investing brain:
• It leaps to conclusions. Two in a row of almost anything—rising
or falling stock prices, high or low mutual
fund returns—will make you expect a third.
• It is unconscious. Even if you think you are fully
engaged in some kind of sophisticated analysis, your
pattern-seeking machinery may well guide you to a
much more instinctive solution.
• It is automatic. Whenever you are confronted with
www.journalofindexes.com July/August 2008
13

anything random, you will search for patterns within it.
It’s how your brain is built.
• It is uncontrollable. You can’t turn this kind of processing
off or make it go away. (Fortunately, as we’ll see, you
can take steps to counteract it.)
How We Got Our Brains
Why are we cursed with this blessing—or blessed with this
curse—of compulsively seeking patterns in random data? “It’s
a really weird thing,” exclaims Paul Glimcher, a neurobiologist
at New York University’s Center for Neural Science. “I hang out
with my economist friends, and they analyze financial decisionmaking
as if it were a Platonic problem in reasoning. They
don’t have a clue that it’s a biological problem. We’ve got millions
of years of primate evolution behind us. We are biological
organisms. Of course there’s something biological going on!
Evolution must drive the decisions we make when we face the
kinds of situations we evolved to encounter.”
For nearly our entire history as a species, humans were
hunter-gatherers, living in small nomadic bands, seeking mates,
finding shelter, pursuing prey and avoiding predators, foraging
for edible fruits, seeds, and roots. For our earliest ancestors,
decisions were fewer and less complex: Avoid the places where
leopards lurk. Learn the hints of coming rainfall, the clues of
antelope just over the horizon, the signs of fresh water nearby.
Understand who is trustworthy, figure out how to collaborate
with them, learn how to outsmart those who are not. Those
are the kinds of tasks our brains evolved to perform.
“The main difference between us and apes,” explains
anthropologist Todd Preuss of Emory University, “seems to
be less a matter of adding new areas [in the brain], and more
a matter of enlarging existing areas and modifying their
internal machinery to do new and different things. The ‘what
if’ questions, the ‘what will happen when’ questions, the
short-term and long-term consequences of doing X or Y—we
have lots more of the brain where that kind of processing
goes on.” Humans are not the only animals that make tools,
show insight, or plan for the future. But no other species can
match our phenomenal ability to forecast and extrapolate, to
observe correlations, to infer cause from effect.
Our own advanced species, Homo sapiens sapiens, is less
than 200,000 years old. And the human brain has barely
grown since then; in 1997, paleoanthropologists discovered
a 154,000-year-old Homo sapiens skull in Ethiopia. The brain it
once held would have been about 1,450 cubic centimeters in
volume. That is at least three times the volume of a gorilla or
chimpanzee brain—but no smaller than the brain of the average
person living today. Our brains are deeply rooted in the
primeval environments in which our earlier ancestors evolved,
long before Homo sapiens arose. Evolution has not stopped,
but most of the “modern” areas of the human brain, like the
prefrontal cortex, developed largely during the Stone Age.
It’s easy to visualize the ancient East African plain: a highly
variable and camouflaged environment, with alternating
dapples of sun and shade, patches of dense foliage, and rolling
open ground broken by sharply banked streambeds. In
that landscape, extrapolation—figuring out the next link that
would complete a simple pattern of repeating visual cues—
became a vital adaptation for survival. Once a sample of information
yielded the correct answer (ample food, safe shelter),
it would never have occurred to the early hominids to look
for more proof that they had made the right decision. So our
ancestors learned to make the most of small samples of data,
and our investing brains today still specialize in this kind of
“I get it” behavior: perceiving patterns everywhere, leaping
to conclusions from fragmentary evidence, overrelying on the
short run when we plan for the long-term future.
We like to imagine that a long history of technological
advancement stands behind us, but domesticated food crops
and the first cities date back only about 11,000 years. The earliest
known financial markets—in which products like barley,
wheat, millet, chickpeas, and silver were sporadically traded—
sprang up in Mesopotamia around 2500 B.C. And formal markets
with regular trading of stocks and bonds date back only
about four centuries. It took our ancestors more than 6 million
years to progress to that point; if you imagine all of hominid
history inscribed on a scroll one mile long, the first stock
exchange would not show up until four inches from the end.
No wonder our ancient brains find the modern challenges
of investing so hard to manage. The human mind is a highperformance
machine—“a Maserati,” says Baylor College of
Medicine neuroscientist P. Read Montague—when it comes
to solving prehistoric problems like recognizing simple
patterns or generating emotional responses with lightning
speed. But it’s not so good at discerning long-term trends,
recognizing when outcomes are truly random, or focusing
on a multitude of factors at once—challenges that our early
ancestors rarely faced but that your investing brain confronts
every time you log on to a financial Web site, watch CNBC,
talk to a financial advisor, or open the Wall Street Journal.
Why Do You Think They Call It Dopamine?
Wolfram Schultz, a neurophysiologist at the University of
Cambridge in England, has closely cropped grey hair and a
neatly trimmed silver mustache. He is so fastidious that he
turns his office teacups upside down on a towel when he’s
not using them, lest they get dusty. The day I visited him,
the only notable decoration in his office was a poster of the
Rosetta Stone, a reminder of how enormous a task neuroscientists
face as they try to drill down to the biological bedrock
of how we make decisions. A German who spent years teaching
in Switzerland, Schultz seems tailor-made to explore the
microstructure of the brain by monitoring the electrochemical
activity of one neuron at a time.
Schultz specializes in studying dopamine, a chemical in the
brain that helps animals, including humans, figure out how to
take actions that will result in rewards at the right time. Dopamine
signals originate deep in the underbelly of the brain, where your
cerebral machinery connects to your spinal cord. Of the brain’s
roughly 100 billion neurons, well under one-thousandth of 1
percent produce dopamine. But this minuscule neural minority
wields enormous power over your investing decisions.
“Dopamine spreads its fingers all over the brain,” as neuroscientist
Antoine Bechara of the University of Southern California
describes it. When the dopamine neurons light up, they don’t
focus their signals as if they were flashlights aiming at isolated
14
July/August 2008

targets; instead, these neural connections shoot forth their bursts
like fireworks, sending vast sprays of energy throughout the parts
of the brain that turn motivations into decisions and decisions
into actions. It can take as little as a twentieth of a second for
these electrochemical pulses to blast their way up from the base
of your brain to your decision centers.
In the popular mind, dopamine is a pleasure drug that
gives you a natural high, an internal Dr. Feelgood flooding
your brain with a soft euphoria whenever you get something
you want. There’s more to it than that. Besides estimating
brains of monkeys earning “income” like sips of juice or morsels
of fruit, Schultz confirmed that when a reward comes as a
surprise, the dopamine neurons fire longer and stronger than
they do in response to a reward that was signaled ahead of
time. In a flash, the neurons go from firing 3 times a second
to as often as 40 times per second. The faster the neurons
fire, the more urgent the signal of reward they send.
“The dopamine system is more interested in novel stimuli
than familiar ones,” explains Schultz. If you earn an unlikely
financial gain—let’s say you make a killing on the stock of
Without the rush of dopamine … “we modern”
investors would keep all our money under the mattress.
the value of an expected reward, you also need to propel
yourself into the actions that will capture it. “If you know that
a reward might happen,” says psychologist Kent Berridge of
the University of Michigan, “then you have knowledge. If you
find that you can’t just sit there, but that you must do something,
then that’s adding power and motivational value to
knowledge. We’ve evolved to be that way, because passively
knowing about the future is not good enough.”
Researchers Schultz and Read Montague, along with Peter
Dayan, now at University College London, have made three
profound discoveries about dopamine and reward:
1. Getting what you expected produces no dopamine kick.
A reward that matches expectations leaves your dopamine
neurons in a kind of steady-state hum, sending out electrochemical
pulses at their resting rate of around three bursts
per second. Even though rewards are meant to motivate you,
getting exactly what you expected is neurally unexciting.
That may help explain why drug addicts crave an ever-larger
“fix” to get the same kick—and why investors have such a
hankering for fast-rising stocks with “positive momentum” or
“accelerating earnings growth.” To sustain the same level of
neural activity, they require a bigger hit each time.
2. An unexpected gain fires up the brain. By studying the
a risky new biotechnology company, or you strike it rich
by “flipping” residential real estate—then your dopamine
neurons will bombard the rest of your brain with a jolt of
motivation. “This kind of positive reinforcement creates a
special kind of attention dedicated to rewards,” says Schultz.
“Rewards are what keep you coming back for more.”
The release of dopamine after an unexpected reward
makes us willing to take risks in the first place. After all,
taking chances is scary; if winning big on long shots didn’t
feel good, we would never be willing to gamble on anything
but the safest (and least rewarding) bets. Without the rush
of dopamine, explains Montague, our early ancestors might
have starved to death cowering in caves, and we modern
investors would keep all our money under the mattress.
3. If a reward you expected fails to materialize, then dopamine
dries up. When you spot the signal that a reward may
be coming, your dopamine neurons will activate—but if you
then miss out on the gain, they will instantly cease firing. And
that will deprive your brain of its expected shot of dopamine.
Instead of a fundamental “I-got-it” response, your brain will
experience a wrenching swing into a motivational vacuum.
It’s as if someone yanked the needle away from an addict just
as he was about to give himself his regular fix.
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www.journalofindexes.com July/August 2008
15

An Interview With Jason Zweig
How neuroeconomics can make
you a better investor
Jason Zweig, a senior writer for Money magazine, is the
author of Your Money & Your Brain: How the New Science of
Neuroeconomics Can Help Make You Rich, published by Simon
& Schuster. He recently spoke with Journal of Indexes assistant
editor Heather Bell about his new book.
Journal of Indexes (JoI): How did you get the idea for the book?
What got you thinking along the lines of neuroscience?
Jason Zweig (Zweig): About 10 years ago, I read an article
in Scientific American. Most of the way through the article
was this statement that people who have had their brains
surgically snipped in half as a drastic treatment for epilepsy
calculate probability completely differently. I decided I had to
find out more about this.
From that point on I was just hooked. It was a long process
because neuroscience, in general, and neuroeconomics in particular,
are not very accessible fields for the layman. And I certainly
was a layman when I started—and in many ways still am.
JoI: For this book, who do you see as your target audience? It seems like
it has a lot for the retail investor and for the professional investor.
Zweig: I would hope so. I guess I would say it’s really from the
viewpoint of an individual investor, but already many professional
investors have told me that they’ve gotten a lot out of
it, both in terms of understanding general principles and also
some ideas for organizational or procedural improvement in
their analytical process or portfolio construction. The book
is really about emotion, and even though all investors like
to think of themselves as “rational,” I never yet have met
a human being who was not at least partly emotional. For
anyone who does experience emotion when you invest, it’s
important to understand how emotions are generated in the
brain. That’s really what the book is about, and how that
interacts with your investing choices.
JoI: I saw the book as a very strong argument for index funds
simply because that human element is largely removed from an
index fund. Is this a valid conclusion to draw?
Zweig: Well, sure it is. I’m a huge believer in indexing, and
I have been for longer than I can remember. Virtually 100
percent of my own portfolio is in index funds, and I actually
do not own a single individual stock and haven’t for quite
some time. My ultimate conclusion is that there’s an important
distinction that needs to be drawn between what people
should do and what they can do. What people should do is they
should index their entire portfolio and then go on a 30-year
hiatus, and at the end of the 30 years they would have a
substantial amount of wealth built up. In the interim they
would’ve been able to live their lives without all the upset
of paying attention to the daily fluctuations of the market.
That’s what people should do, but it’s not what they can do.
Very few people have the ability to buy a stock, vault
it away in their portfolio and leave it until it makes them
wealthy. I think there’s a reason for that: The brain is
not really very well-suited for that kind of behavior.
Most people will buy more when something goes up
and either sell it or freeze when it goes down. The brain
is really built as a pattern-recognition machine and a
performance-chasing mechanism, and when you combine
automatically perceiving patterns where they don’t actually
exist with pursuing performance right before it disappears,
you have a recipe for disaster.
Most people can’t do what they should, so we need to
advise them to do what they can. Increasingly, my advice for
individuals and for financial advisors who serve them is that
everybody should have two things: a lockbox and a sandbox.
The lockbox has something like 90 percent of your money in
index funds and nothing else. The sandbox, where you have
maybe 5 percent or 10 percent of your money, is where, if
you really want to, you can play a little. There’s nothing terribly
wrong with getting entertainment out of investing, as
long as you understand that’s what you’re doing, and as long
as you don’t do it with all of your money.
JoI: Part of the reason the book seemed like an indirect argument
for index funds was that you give a lot of advice about the right
way to pick stocks in a way that is as free of personal biases as
possible, and it’s really a very labor-intensive process.
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July/August 2008

Zweig: Absolutely, it is a lot of work, and I happen to believe
that there are great investors. I’m not positive we could
identify them in advance, nor do any of them have any of
my investment dollars, but there are any number of active
managers running mutual funds whom I have a lot of respect
for and whom I believe are very, very good at what they do
and may well continue to beat the market in the future. I’m
just not sure enough about it to give them my money, and
in many cases, I’m not sure it’s worth paying the premium
management fee in the first place. But the one thing all have
in common is they really work hard and they think very hard
about what they’re doing. They have a lot of second-guessing
and a lot of checks and balances built into their policies and
procedures. That’s what most individual and professional
investors lack, and it’s why most of them don’t do very well—
other than the fact that they trade too much.
JoI: Is part of the problem that human beings are simply not
evolved to operate in the stock market?
Zweig: Why would we be? Evolution has worked to address
a very specific problem, which is the survival of the species.
Evolution really has only one objective for a species, which is
to maximize its reproductive fitness. Evolution customizes us
to survive long enough to have offspring. That’s what evolution
cares about. It doesn’t care about option-adjusted spreads or
exchange-traded funds or long-term capital management.
The brain has been built to make basic decisions about risk
and reward. We don’t have financial circuitry in the brain. We
haven’t evolved to make decisions specifically about money.
That’s one of the really interesting things about neuroeconomics:
It shows very clearly that when you make a decision about
a profit, it’s processed in the same part of your brain that
processes everything else that feels rewarding, like chocolate
cake, Cheetos and drugs, sex and rock ’n roll. When you make
a decision about risk and losing money, that’s handled by the
same kind of circuitry that responds when you face physical
risk and mortal danger. There’s not much difference in the
brain between having a rattlesnake slither across your living
room carpet and having some stock you own go down 40 or
50 percent. Basically it’s the same response, which is, “I’m in
trouble; how do I get out of here alive?” It’s incredibly rapid.
JoI: Malcolm Gladwell wrote a best-selling book not too long ago
called Blink that was about the importance of our immediate
and instinctive reactions. A lot of your book was about how our
immediate and instinctive reactions can get us in trouble when
we’re investing. Is your book a kind of anti-Blink?
Zweig: The beef I would have with that sort of argument is that
there are circumstances in which intuition or gut feelings are a
very good guide. For example, let’s say you and I meet in a coffee
shop, and we’re deciding whether to go into business together.
I’m a Web designer, and you want to build a Web site for yourself
and you don’t want to get into business with somebody who’s
fishy. Your gut feelings about me would be quite reliable, because
if I don’t seem trustworthy to you, I’m probably not. That’s an
example of an intuition or a gut feeling that’s very useful.
But if you have a gut feeling about whether you should
buy Google stock, that’s not useful at all, because intuitions
are only reliable in the areas of life where you get good feedback.
And you know just from being a human being and from
interacting with people your whole life what the cues are for
trustworthiness. Am I sitting there with my eyes shifting all
over the place? Am I drumming my foot on the floor? Do I not
look at you when I talk to you? Do I immediately ask you for
your credit card number? Those kinds of things just set off fire
alarms in your head, as they should, but there’s no way to do
that in the stock market—it’s just much too complicated an
organism. And every time you think you’ve got some cue that
predicts something, the problem is there are a hundred million
other people combing through the same data looking for
it, and they’ve already been there. By the time you notice it, it
either isn’t really there or other people have already used it. In
either case, it’s not useful to you, but you’ll think it will be.
JoI: You make the point in the book about how making money
produces a similar reaction in the brain to when an addict takes
drugs or a gambler wins. Did you see any studies or experiments
along these lines with fund managers or other financial professionals
who are dealing with other people’s money?
Zweig: Well, there’s very little reason to believe that professionals
and individual investors’ brains are much different. There’s
been a lot of psychological research done on this. There isn’t
much in neuroeconomics yet, but based on 20 years of observing
the financial markets, I certainly don’t see any evidence
that professionals are more rational investors than individuals.
There’s certainly a fair amount of anecdotal evidence that
they’re less rational, but they’re certainly not more. And there’s
no real reason why you would expect them to be.
JoI: When you were doing your research, was there anything in
particular that really shocked or surprised you when you were
talking to these different scientists?
Zweig: The really surprising thing is how little we know about
how we think. J.P. Morgan once said that every man has two
reasons for everything he does: the reason he states and the real
reason. I think he meant something a little different by it, but
what a neuropsychologist or a neuroeconomist would say is that
most of us don’t even know why we do things, and we can often
be in the grip of unconscious emotion or unconscious biases,
feelings and inclinations that are in our mind but we have no
awareness of. You feel it; you just can’t articulate it, and you may
not be aware that it’s there until after it passes. This is one of the
hardest ideas you can ever get someone to admit.
For example, if you’re watching CNBC and the market is plunging
and Jim Cramer is throwing furniture and biting the heads off
live chickens, you may be sort of watching it saying, “Oh wow,
something really bad is happening; the market is crashing.” But
while you’re watching it, your palms are sweating, your breath
is coming fast, your pulse is racing, your muscles are tensing,
your entire body is on red alert. You’re intensely upset by what’s
happening in front of you, but the thinking part of your brain
is so busy trying to make sense of it that it’s not aware of what
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17

the emotional part of your brain is experiencing. And if in that
moment you are suddenly called on to make a choice, “Should I
sell this stock or should I hold it?” ... If you’re making that choice
at that moment while Jim Cramer is screaming in your face, you
will not buy and it’s highly unlikely that you’ll hold ... because all
of that screaming, all the red, all the downward-pointing lines are
so upsetting that you will make a negative decision, even if you’re
not aware at that moment of how upset you are.
The flip side of this is unconscious bias. Just as you can
Most people can‘t do what they should, so we need to
advise them to do what they can.
have a feeling that you’re not aware of having, you also can
have preferences that you don’t realize you have. The simplest
example is what psychologists call “implicit egotism,” which is
a really bad term for liking whatever is closest to you in some
way or another. For example, people are 65 percent more
likely to marry someone whose surname begins with the same
initials as their own. Psychologists have looked at hundreds of
thousands of data points and demonstrated very clearly that
this is true, and that people named Dennis and Denise are
much more likely to become dentists than you would expect
by random chance. People named George are more likely to
become a geoscientist then you would expect by chance alone.
We all come with these strange, unconsciousness preferences.
We don’t think we think that way, but we do.
The best example I can give is in June, I was making a speech
about the book in Edinburgh, Scotland, and I was at one of the
largest global equity managers in the world, and I put up a slide
about these forms of unconscious bias. All the Scots in the room
were chortling: They couldn’t believe how stupid Americans are,
and that anybody would actually do something like this was just
beyond them. Then the chief investment officer of the firm said,
“Well, what about ...?” and he named a stock that this firm is
heavily overweight in. It turned out the ticker for the firm they’re
overweight in matches the firm’s own initials. He said, “I’m very
glad that you pointed this out because I never would’ve realized
it. We probably do have an unconscious bias and now we’re
aware of it. Now maybe if the time ever comes that we need to
sell that stock, we can make a more objective decision.”
So people do stuff like that all of the time, and I’m prepared
to bet that if we did a survey of all equity fund managers within
America and we simply found out the eye color of all the managers
and we then went and looked at their portfolios, we would
find that UPS is over-owned by brown-eyed managers and Jet
Blue is over-owned by blue-eyed managers. I haven’t done this
research yet, but I am very confident that that hypothesis is a
good one. This is something that investors need to be aware
of: Active managers may think they are choosing stuff for one
reason, but actually it’s almost as if the choice has been made for
them by unconscious biases they don’t even realize they have.
JoI: Stock analysts frequently develop relationships with and visit
the companies they cover. Do you think that familiarity makes
them more predisposed to recommend it?
Zweig: Absolutely; no doubt about it. One of the oldest and
best-documented quirks in human psychology is something
called the halo effect, wherein if you rate one quality or aspect
of a person or thing, all your subsequent ratings of all the
other aspects will be colored by the first one.
So if, for example, you were to rate me on a scale of one to
five on how handsome I am, you can then later rate how intelligent
I am, how articulate I am, how wealthy I am, how positive I
am. All of those judgments will be skewed toward the initial judgment
of how handsome I am. And by the way, that’s true whether
your rating was high or low. So if you said, “Well, no, Jason isn’t
handsome at all,” then I wouldn’t be very articulate either and I
wouldn’t be very intelligent. If you said I’m very handsome, then
you would be much more inclined to rate my intelligence higher,
my overall presentation higher, all of those things.
One of the most amusing studies in this field comes from
high school teachers: Psychologists took an answer to an
actual essay question written by a real high school student
and made hundreds of photocopies of it, and distributed
them to real high school teachers with the student’s name
in the upper right-hand corner. In some cases the student
was named David, and in other cases he was named Hubert.
In some cases she was named Lisa, and in other cases she
was named Bertha. David and Lisa, on average, got grades 10
percent higher than Hubert or Bertha, because having a nice
name casts a halo over the quality of the work. And people
are totally unaware of this. They don’t realize that they’re
responding to a halo effect, but they are.
One thing that people who buy index funds can take a lot
of comfort in is that by definition, an index fund should not
be influenced by unconscious bias, and overall, that should be
a good thing over the long run.
JoI: What do you think are the most important advice or findings in
the book that investors should really focus on?
Zweig: If I had to boil it all down to one thing, it’s you need to be
more mindful as an investor. That means you need to keep better
records of your decisions; it means you need to be more introspective
and more retrospective. You have to look back at how
your decisions have worked in the past; you have to think more
carefully about the decisions you’re making in the present.
And I guess if I had to boil it all down to one rule, it would
be if the market is open, your wallet should be closed: If you
get the idea today, you should not actually do it until tomorrow.
Because if you sleep on it, you may wake up the next
morning and your mood may have changed, the data may
have changed, you may just see things in a different light.
It’s quite rare, unless you’re a short-term trader, for anything
significant to change overnight that would leave you worse
off, but you might well make a much better decision if you’d
just wait until the next day.
18
July/August 2008

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10650-0109

Behavioral Finance And Indexing
A virtual roundtable
With Ed McRedmond, William Bernstein, John Prestbo, Ross Miller,
Terrance Odean, Francis Kinniry and David Blitzer
20
July/August 2008

Behavioral finance has been making headlines lately, and with
such attention comes a renewed focus on indexing.
How so?
Because if investors were rational, they’d index. And we know
that the majority of investors don’t index.
As William Bernstein wrote in his classic book, The Four
Pillars of Investing: “The major premise of economics is that
investors are rational and will always behave in their own selfinterest.
There’s only one problem. It isn’t true.”
Murray Coleman, managing editor of IndexUniverse.com and
director of research for Index Publications LLC, spoke with seven leading
academics and practitioners to find out what the latest research
into behavioral finance can tell us about investors and indexing.
Ed McRedmond, executive vice president of
portfolio strategies, Invesco PowerShares
Journal of Indexes (JoI): What does behavioral
finance tell us about investing and indexing?
Ed McRedmond, Invesco PowerShares
(McRedmond): I discussed this topic with my colleagues
here at Invesco PowerShares along with John West at
Research Affiliates, and it’s our collective opinion that behavioral
finance may explain the collective lack of rationality and
consistency with which we reach our investment decisions.
Much of modern finance theory rests upon the assumption
that investors make rational, well-informed decisions based
solely upon a consistent view of risk and reward. However,
inconsistencies and irrational behavior are embedded into
human economic behavior—consider buying a lottery ticket
and an insurance policy with the same paycheck! Behavioral
finance experiments and research have confirmed many cognitive
errors—behaviors that contradict the standard assumptions
of rationality but are part of human nature. These lead
to errors in the pricing of assets.
JoI: What are the biggest mistakes investors make from a behavioral
standpoint?
McRedmond: Some common cognitive errors appear to be:
1. Loss Aversion: Most investors are loss-averse; that is,
the pain they feel from a 10 percent loss is much greater
than the rush and excitement received over a 10 percent
gain. Because of this asymmetrical relationship, investors
tend to change their risk tolerances. As a result,
their asset allocations and portfolio structures move to
more-conservative postures during down periods and
volatile market sell-offs while sustained or dramatic
up markets produce more aggressive portfolio adjustments.
Reversion to the mean typically implies that such
moves produce disappointing results. This may explain
why some of the world’s most successful investors
are contrarians—being comfortable and following the
crowd is rarely profitable over the long term.
2. Herd Mentality: Underperforming managers find it far
easier to review top holdings in exciting and recently successful
growth companies than underperforming stocks
with their host of negative publicity. There’s an old saying
among portfolio managers that “you never get fired for
holding IBM.” Many clients and advisors seem to agree
and find it far more palatable to fail conventionally while
following the crowd than striving to exceed unconventionally.
This dynamic tends to overprice stocks that have
done well recently and are expected to continue doing so
in the future. This often leads popular stocks to become
overvalued and distressed names to be undervalued, thus
explaining the value effect.
3. Law of Small Numbers: A short performance stretch,
such as a quarter or year, by itself, reveals little about a
manager’s skill or the attractiveness of a sector or industry.
However, investors place a large emphasis on the
recent past and tend to extrapolate it well into the future
in forming investment decisions. This often explains
why mutual fund investors dramatically underperform
mutual funds. The recent past causes “returns-chasing”
behavior—investing by looking in the rearview mirror—a
game that can be very costly when the latest investing fad
inevitably reverses.
JoI: Is behavioral finance being used to justify poor investment
decisions and a lack of education?
McRedmond: Behavioral finance helps to explain, not justify,
poor investment decision making. We would like to believe
that humans are all rational and optimize solely on risk and
reward, but this simple assumption gets very cloudy when
you add in fear, greed, overconfidence, career risk and different
measures of investment success. A lack of education may
be a source, but in all likelihood our collective irrational and
poor decision making is more likely the result of evolution,
not education. Our caveman ancestors had to be loss-averse!
A big gain wasn’t worth the potential of a big loss when that
“loss” might mean death.
JoI: If indexing is proven to provide the best odds for long-term
success, why don’t more investors index?
McRedmond: Index funds comprise roughly 20 percent of
the U.S. stock market. Surprisingly, this figure hasn’t really
budged much in recent years despite overwhelming evidence
of their long-term outperformance in many categories. The
relatively small adoption of index funds seems to confirm that
investors don’t make rational decisions.
Overconfidence, greed and large fund company marketing
budgets convince most investors that they can beat the
market (hence the often-heard statement that most investments
are “sold not bought.”) After all, who among us doesn’t
believe that we are above average, either in terms of our
athletic abilities or our investment abilities?
JoI: Can active managers use behavioral insights to outperform
the market?
McRedmond: We believe so. The last 10 years have seen a
variety of firms whose whole philosophy of outperformance is
www.journalofindexes.com July/August 2008 21

ased upon behavioral finance, and these managers have shown some success. The
historical outperformance of value managers versus growth managers would also seem
to support this.
Take a
Closer
Look
William Bernstein, author; co-principal,
Efficient Frontier Advisors
JoI: What does behavioral finance tell us about investing and indexing?
William Bernstein, Efficient Frontier Advisors (Bernstein): It
explains exactly why the average investor underperforms the market,
and why the average mutual fund investor underperforms the funds she owns.
Human behavior was shaped in the struggle for survival in the savannas of Africa,
and the instincts we honed there were of tremendous value in a state of nature.
Unfortunately, they are death in the financial markets.
JoI: What are the biggest mistakes investors make from a behavioral standpoint?
Bernstein: The list is so long, and the mistakes so profound, that it’s almost
impossible to pick just a few. But if I had to, the list would contain these two:
1. Recency: This relates to the belief that the past five years’ return of an asset
class predicts its long-term return.
2. Overconfidence: Most investors don’t realize that the fellow on the other
side of their trade most likely has the name Goldman Sachs or Warren Buffett
on it. It’s like playing tennis against an invisible opponent. Unfortunately,
more times than not, it’s the Williams sisters.
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JoI: Is behavioral finance being used to justify poor investment decisions and a
lack of education?
Bernstein: No, I don’t worry about that. It is being misused as a marketing
gimmick by unscrupulous money managers, if you’ll allow me to use a redundant
modified noun.
JoI: If indexing is proven to provide the best odds for long-term success, why don’t
more investors index?
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Bernstein: See my answers to the second question. The real mystery is just why both
professionals and small investors think that asset management—active or passive—is
so easy. No one in his right mind would walk into the cockpit of an airplane and try to fly
it, or into an operating theater and open a belly. And yet they think nothing of managing
their retirement assets. I’ve done all three, and I’m here to tell you that managing money
is, in its most critical aspects (the quota of emotional discipline and quantitative ability
required) even more demanding than the first two. I think that the reason for this is that
unlike flying or surgery, investing seems easy—tap a few keystrokes, and hey presto,
instant portfolio. It’s almost as easy as turning on a chainsaw, but far more dangerous.
JoI: Can active managers use behavioral insights to outperform the market?
Bernstein: It all depends upon what you call “behavioral.” I’m a strong believer
in the value premium, and I think that most, but not all of it, is behavioral.
So to that extent, it does provide the active manager with tools. (Of course,
it’s even better to value-tilt passively.) The return kicker you get from rebalancing
is also behavioral in origin.
But even if an active manager is able to generate alpha, she still has to deal
with the behavioral flaws of her clients and shareholders. The generation of
alpha by definition involves tilting away from the market portfolio, and that’s
a very noisy process. Even the most skilled active managers underperform for
22
July/August 2008

quite a while, and during those periods, they’re likely to
lose most of their investors. So even if she can overcome
her own behavioral demons, she’ll still get nailed by those
of the folks in the backseat.
John Prestbo, editor and executive
director, Dow Jones Indexes
JoI: What does behavioral finance tell us about
investing and indexing?
John Prestbo, Dow Jones Indexes (Prestbo):
It tells us that irrationality and emotionality stand in the way of
most people being able to manage their investment portfolios
prudently. These people can turn this management over to
professionals, except that those professionals are people too,
and therefore subject to behavioral quirks. Or, they can place
their portfolios in diversified indexed vehicles and reap the
benefits of market returns at lower cost.
JoI: What are the biggest mistakes investors make from a behavioral
standpoint?
Prestbo: First, they follow the crowd—emphasis on follow—
which means they buy high and sell low. Second, they fear loss
more than they desire gain, which causes many investors to
hang on to both winners and losers too long. Third, they weigh
too heavily the implications drawn from small data samples or
the recommendation of a single analyst.
JoI: Is behavioral finance being used to justify poor investment
decisions and a lack of education?
Prestbo: I think it’s more explanation than justification. People
in all walks of life must take responsibility for their investments,
just as they do their tax returns. We’re making considerable progress
in this regard—when I started out with The Wall Street Journal
44 years ago, most “ordinary” folks were totally mystified and
intimidated by investing. Far fewer are bewildered today, though
there’s still plenty of room for improvement.
JoI: If indexing is proven to provide the best odds for long-term
success, why don’t more investors index?
Prestbo: Obviously the superiority of indexing hasn’t been
“proven” to everyone’s satisfaction. Ironically, one of the side
effects of more and more people becoming educated about
investing is that some of them will eschew indexing and take
an active role. And certain people like to try beating the odds.
The day that all investors index will never arrive.
JoI: Can active managers use behavioral insights to outperform
the market?
Prestbo: One would think so, at least in theory, but so far behavioral
finance seems to be an academic phenomenon rather than
a real-world one. Perhaps a way of putting behavioral finance to
work would be an active manager having a robust strategy and
the discipline to stick with it through thick and thin.
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www.journalofindexes.com July/August 2008
23

Ross Miller, finance professor, State
University of New York (SUNY) at Albany
JoI: What does behavioral finance tell us about
investing and indexing?
Ross Miller, SUNY Albany (Miller): I’m not
sure it tells us a lot about indexing. People tend to be
shortsighted about investing. But there are behavioral
anomalies. When people notice these, they tend to go
away. Markets can absorb enormous amounts of irrationality.
We know individuals can be irrational. And we also
know that crowds can actually compensate for individual
behavior. The bottom line is that when dealing with liquid
securities, it’s difficult to beat an index in risk-adjusted
terms. In contrast, the analysis of behavioral influences
might not be the best measure to explain what happens in
illiquid markets. But at least it’s something to consider.
Miller: Probably because the higher profit alternatives are more
aggressively marketed. Even primarily indexing companies such
as Vanguard offer a wide array of actively managed products. So
you can hear John Bogle preaching the value of passive investing,
but at the same time, Vanguard caters to everyone. If they had
that strong a belief in indexing, they’d be purely indexing.
That gets back to one of the behavioral aspects marketers
play on. Even though active management is statistically a bad bet,
marketing plays to individual optimism. In other words, people
overestimate their abilities to pick stocks and money managers.
If you’re in a 401(k) plan that only has active alternatives, then
there’s no way you’re going to be putting money into passive
alternatives. And the reason why those 401(k) plans don’t have
index alternatives is because marketing people have sold the
plan’s advisors on the attributes of active management.
JoI: Can active managers use behavioral insights to outperform
the market?
Being comfortable and following the crowd is rarely
profitable over the long term.
JoI: What are the biggest mistakes investors make from a
behavioral standpoint?
Miller: No. 1 is timing. People try to time markets. A lot of
people who should be investors act like traders. If you have
a 20- to 30-year time horizon, you shouldn’t be trading your
retirement money. There’s evidence that people get sucked
into bubbles. Mutual funds tend to suck in money while
they’re going up. Then people bail out when those same
funds start going down. People get scared and they have
trouble dealing with longer time horizons.
Aside from fear, there’s greed. These characteristics do manifest
themselves in the markets and they are behavioral in nature.
JoI: Is behavioral finance being used to justify poor investment
decisions and a lack of education?
Miller: What’s interesting is investor education. It’s not so
much being inadequate as much as it is a terribly difficult
task. The typical person has a big challenge in becoming
an educated investor. And there are much more important
problems than training individual investors to avoid behavioral
anomalies.
It probably doesn’t help to have computer programs like
Quicken. The front page of Quicken includes a day-by-day
breakdown of what people are worth. I don’t know if most
people really need to know their net worth down to the exact
penny at all times, but that’s the way the world is these days.
You can set it up to update you throughout the day. It’s quite
amusing, but it’s also potentially very dangerous.
JoI: If indexing is proven to provide the best odds for long-term
success, why don’t more investors index?
Miller: While there are advisors who operate in that manner to
generate alpha, probably highly quantitative hedge fund managers
are more efficiently finding the same anomalies. They’re
finding those anomalies by studying patterns of returns over different
time periods. It gives you a broader range of anomalies to
draw on. Then, you can use a behavioral aspect to explain those
gaps in the market. Computers just provide a more valuable tool
to harvest all sorts of data over longer ranges of time.
Perhaps 25 years ago, behavioral approaches were seen
as being more effective. In today’s market, most hedge funds
are populated by quant-based analysts rather than behavioralbased
analysts. Increasingly, behavioral analysis is becoming
a secondary means to explain market anomalies. Where
behavioral science might come more into play with hedge
funds these days is less in studying markets and more in psychoanalyzing
and monitoring their own traders.
Terrance Odean, Willis H. Booth Professor
of Banking and Finance, University
of California at Berkeley
JoI: What does behavioral finance tell us about
investing and indexing?
Terrance Odean, University of California at Berkeley
(Odean): Due to a number of behavioral biases, many investors
make systematic mistakes when buying and selling
stocks. On average, the stocks they sell go on to outperform
those they buy. When it comes to mutual funds, most investors
focus on past performance and pay too little attention to
expenses and other fees. Many investors would be far better
off buying broad-based index funds or other low-cost, welldiversified
mutual funds.
24
July/August 2008

JoI: What are the biggest mistakes investors make from a
behavioral standpoint?
Odean: The most costly mistake made by a large number of
investors is under-diversification. Many investors trade too
actively. Investors also pay too little attention to trading costs
and mutual fund fees. They focus too much on the one thing
that they can’t control—market outcomes—and too little on
important factors over which they do have some control—
diversification, costs and taxes.
JoI: Is behavioral finance being used to justify poor investment
decisions and a lack of education?
Odean: I’m not sure I understand the question. The advice I give
investors is to buy low-cost, well-diversified mutual funds such as
index funds. I believe that that is excellent real-life advice. I occasionally
suggest, tongue in cheek, that investors do the opposite
of their instincts (i.e., buy the stocks they are inclined to sell and
vice versa). Of course, this would be an idiotic way to extrapolate
from my own research to real life.
JoI: If indexing is proven to provide the best odds for long-term
success, why don’t more investors index?
Odean: I don’t know if indexing provides the best odds for
long-term success. I do know that it is a very good choice for
most investors. People don’t choose indexing for a variety
of reasons. Some people are overconfident in their ability to
beat the market; others are unaware of the advantages—or
perhaps even the option—of indexing.
JoI: Can active managers use behavioral insights to outperform
the market?
Odean: Yes. Individual investor behavior can affect asset
prices. Active managers who have insights into that behavior
and asset price dynamics could potentially profit from those
insights. I doubt that many active managers currently do profit
from such insights. Even if some active managers are earning
profits from such insights, they may not be passing those profits
on to their clients. For example, my co-authors and I found
that from 1995 through 1999, institutional investors in Taiwan
earned an annual alpha of approximately 1.5 percentage points
after trading costs. If, on average, they charged their clients
less than 1.5 percentage points in fees, then those clients are
benefiting. However, if the fees averaged over 1.5 percentage
points, the managers reaped all of the benefits.
Francis Kinniry, principal and senior
member, Vanguard’s Investment
Strategy Group
JoI: What does behavioral finance tell us about
investing and indexing?
Francis Kinniry, Vanguard (Kinniry): Behavioral finance
has several implications for index investing. At one end of
the spectrum, investors in broad market index funds may be
more patient, cautious, deliberate and cost-conscious in their
decision making, and thus tend to be more immune to the
negative behavioral aspects of investing, such as overconfidence,
which can manifest itself in return chasing, market
timing, wholesale portfolio changes, etc. These investors
don’t “follow the herd”—they own the market, invest for
the long term, adhere to a buy-and-hold strategy and tend
to understand the math and probabilities behind investing.
Specifically, these investors understand that commitment to
a strategic index asset allocation provides the highest probability
for success.
At the other end of the spectrum, investors who follow or
participate in a more tactical or aggressive market rotation
approach or who actively engage in very narrow indexes may
be more risk-tolerant, impatient and overconfident in their
investing skills.
JoI: What are the biggest mistakes investors make from a behavioral
standpoint?
Kinniry: By far, the biggest mistake investors make is extrapolating
recent returns as an indication of future returns. As
a result, they fall into the trap of overbuying the current
outperforming asset class and underowning the current
underperforming asset class. (This statement does not qualify
as an endorsement to underweight the winning strategy or
overweight the losing strategy as others in the investment
community may suggest.)
Another big mistake is investor overconfidence, or believing
that you have unique information about future market
changes or other advantages that no one else has. Since that
is highly unlikely, it could be the reason why professional
active managers on average have tended to not outperform
indexes over time.
JoI: Is behavioral finance being used to justify poor investment
decisions and a lack of education?
Kinniry: It’s easy to use the concept of behavioral finance or
lack of education to explain investors’ decisions. However,
we have seen very sophisticated, educated investment professionals
fall into some of these situations over time. After
all, much, if not most, of the money that trades daily in the
markets is under the control of institutional and professional
money managers. We must remember that investing
is not a science. It is an art that takes on many forms. It is
constantly changing; the future attributes that determine
outcomes are highly eclectic, dynamic and extremely uncertain.
This environment makes predicting or forecasting the
future a great challenge.
For the nonprofessional investor, the investment decision
process runs counter to most other buying decisions we may
make. For example, the concept of “you get what you pay for”
would suggest that like a good meal, quality and costs are
correlated. But, obviously, this is not the case with investing.
Similarly, when shopping, we might utilize services that rate
Continued on page 45
www.journalofindexes.com July/August 2008
25

Behavioral Finance Roundtable continued from page 25
the best-performing and highest-rated products. But again,
this process does not work nearly as well for investment
products. In fact, when comparing funds, index funds are
typically rated as average, while the current winning sectors
are rated high, and out-of-favor funds rated low. So, some
of the concepts of behavioral finance—ill-advised decisions
made on the basis of poor information, lack of understanding
or the impulsiveness of trying to beat the market—also apply
to individual investors.
In the end, behavioral finance is about evaluating the
investing habits of people, and people—whether professionals
or nonprofessionals—are capable of making rational and
irrational decisions.
JoI: If indexing is proven to provide the best odds for long-term
success, why don’t more investors index?
Kinniry: As in many other areas in life, we often overestimate
our capabilities (i.e., we are all better-than-average drivers and
our kids all have higher-than-average IQs). It is no different when
it comes to investing. In many respects, our ego tricks us and
limits our ability to consider that we may be average or even
below average when compared with the competitive and large
playing field of investment professionals. As a result, we tend to
ignore proven strategies such as indexing and think that we can
do a better job following other strategies.
JoI: Can active managers use behavioral insights to outperform
the market?
Kinniry: Some managers will outperform the market, whether
they use behavioral research, technical research, fundamental
research, quantitative research or a combination thereof.
However, the challenge facing active managers is being able to
outperform the market by having information that is superior to
that of all other market participants and by having very low trading
friction. These are not impossible hurdles, but high hurdles.
Perhaps the best chance for active management to be successful
over the long run is to utilize the best of passive management:
low costs, low relative friction along with their active management
techniques and a talented yet humble team of sophisticated
investment professionals.
David Blitzer, managing director and
chairman of the Index Committee,
Standard & Poor’s
JoI: What does behavioral finance tell us about
investing and indexing?
David Blitzer, Standard & Poor’s (Blitzer): One of the key factors
determining whether stock prices rise or fall are investors’
buy/hold/sell decisions. Investors don’t know the future and their
decisions usually depend on a mix of rational analysis, opinions,
fears, greed and wishful thinking. Behavioral finance warns us
that our decisions aren’t always rational and at times will reduce
our profits or increase our losses. One way to reduce the impact
of our irrational or emotional decisions is to invest with a simple
rule: Index. This way, investors can avoid falling in love with
stocks, selling winners too soon or denying the losers’ existence
by refusing to sell them to cut the losses. Indexing is not the only
rules-based emotionless way to invest; however, it is one of the
simplest ways and it does have a proven track record.
JoI: What are the biggest mistakes investors make from a
behavioral standpoint?
Blitzer: Letting any successful investment convince them
that they can beat the market consistently. Someone buys
a stock, it rises 10 percent and they’re a winner—and a
stock market genius. First, they forget that three other
stocks in the same industry rose 15 percent at the same
time. Then they think they can time the market for their
next move. Finally, they read that indices outperform
active managers two out of three times and are absolutely
sure they will consistently be in that top third who always
beat the market. There are some people who escape this—
but they are often the ones who believe that even though
they can’t pick stocks, they have found a money manager
who can pick stocks.
JoI: Is behavioral finance being used to justify poor investment
decisions and a lack of education?
Blitzer: While behavioral finance may explain some poor
investment decisions, it doesn’t justify them. An investor who
says his education is complete and that he fully understands
the markets is an investor who can’t or won’t compare his
results to the markets over the long run.
JoI: If indexing is proven to provide the best odds for long-term
success, why don’t more investors index?
Blitzer: People see indexing as settling for the average result
and no one wants to be “just average.” Further, no one wants
to admit he paid too much, so when they understand that the
key reason indexing outperforms active management is lower
costs, they are even less likely to embrace indexing. Finally,
stock markets are very complex and indexing is simple, so
how could it possibly work?
JoI: Can active managers use behavioral insights to outperform
the market?
Blitzer: Active managers, like any other investors, can use
insights from behavioral finance to improve their results. In
the last 10 years we have seen two massive bubbles; one in
dot-com stocks and the second in housing. Understanding
either requires recognizing the importance of human behavior
and emotions in investing and markets. That said, simply
having read or even understanding much of the behavioral
finance literature would not have guaranteed selling at the
peak of either bubble. Moreover, no managers always outperform
the market; some do it occasionally, others do it more
often; but no one does it all the time.
July/August 2008
45

The Frontier From Different Views
Size and style matter out on the frontier
By Craig Israelsen
26
July/August 2008

The two-asset risk/return frontier is a classic graph.
It conveys information which displays the “price of
return” better than any other graphing technique.
A 27-year two-asset frontier map from 1980 through 2006
is presented in Figure 1. One asset is the Lehman Brothers
Aggregate Bond Index and the other is the Standard & Poor’s
500 Index. A 100 percent investment in the bond index (dark
blue dot) had an average annualized return of 9.1 percent and
a standard deviation of return of 7.5 percent over the 27 years
from 1980–2006. The next dot (pink) represents a 10 percent
allocation to the S&P 500 Index and a 90 percent allocation to
the bond index. Return improves and risk is reduced.
The far right side of the frontier represents a 100 percent
commitment to the S&P 500 Index (red dot). This allocation
produced a 27-year average annualized return of 13.3 percent
with a standard deviation of return of 15.8 percent. An
all-stock portfolio generated a 420 bps return premium over
bonds, but at the price of 830 bps greater volatility in annual
returns. Thus, every basis point of added return came at the
“price” of 2 additional basis points of volatility.
A 60 percent equity/40 percent bond portfolio (magenta
dot) generated a 27-year annualized return of 11.9 percent
with a standard deviation of return of 10.6 percent—
representing a return premium of 280 bps over bonds but
with only 310 bps more volatility than bonds. The risk/
return characteristics of a 60 percent equity/40 percent
bond portfolio is nearly a one-to-one trade-off, meaning
that each additional basis point of return over the return of
an all-bond portfolio produced one additional basis point of
volatility (or “risk”).
While the Lehman Brothers Aggregate Bond Index is a reasonable
approximation of the overall U.S. bond market, the
S&P 500 represents a limited perspective of the U.S. equity
market. As a large-cap blend index, it does not represent
the distinctly different return patterns of large-cap value or
large-cap growth stocks. Moreover, it does not capture the
performance of mid-cap or small-cap stocks. In spite of this,
the S&P 500 is nearly universally chosen to represent “the”
U.S. equity asset class in such a graph.
This paper introduces several new versions (or views) of
a two-asset frontier using six additional U.S. equity asset
classes (beyond the S&P 500 Index). The six include largecap
value, large-cap growth, mid-cap value, mid-cap growth,
Figure 1
Various Combinations of LB Agg Bond Index
and S&P 500 Index (1980-2006)
small-cap value and small-cap growth (see Figure 2).
As shown in Figure 2, value-based indexes—particularly
mid-cap and small-cap—significantly outperformed the S&P
500 Index in both raw return and on a risk-adjusted return
basis over the 27-year period from 1980–2006. Growth indexes,
on the other hand, underperformed the S&P 500 Index on
a risk-adjusted basis and on a raw-return basis.
The performance of small-cap growth is particularly interesting.
Its 27-year annualized return of 10.68 percent was
only 158 bps higher than the return of the LB Aggregate Bond
Index, but small-growth U.S. equity had a standard deviation
of return three times higher than bonds (see Figure 3).
The annual returns of each index (LG, LV, MG, MV, SG,
SV) represent the average performance of two separate
index providers, Dow Jones and Wilshire (Wilshire is
presently known as Dow Jones Wilshire). For example, in
1982, the Dow Jones Large Value Index’s return was 23.26
percent, while the Wilshire Large Value Index had a return
of 17.68 percent. The average of the two LV indexes in
1982 was 20.47 percent, as shown in Figure 3. Each annual
return for each of the six separate style box categories
was calculated accordingly for the 27-year period.
Figure 4 shows the decade of the ’80s. The 10 years from
1980–1989 was a stellar decade for value-based U.S. equity
indexes as demonstrated by the northwest-quadrant-seeking
MV, LV and SV frontiers. Growth indexes, particularly
small-cap growth, generated far worse risk-adjusted performance
compared with the S&P 500 Index. Notice how high
the origin point (i.e., 100 percent bond portfolio) is on the
Y-axis. The 10-year annualized return for the 100 percent
bond portfolio was over 12 percent, while a 100 percent
S&P 500 portfolio averaged nearly 18 percent.
The decade of the ’90s was a very different story (Figure
5). It was a period in which growth-based equity indexes
generally provided superior risk-adjusted return compared
with value-based U.S. equity indexes. However, they were all
outperformed by the S&P 500 Index. No wonder the growth
of index funds (most of which were and are based on the S&P
500) was meteoric during this decade.
As an example, Vanguard Index 500 (VFINX) had net
assets of $2.2 billion at the end of 1990. By the end of
1999, its assets had surged to $105 billion, representing
an increase of over 4,600 percent.
Figure 2
Combinations of U.S. Equity Indexes
& LB Aggregate Bond Index (1980-2006)
22
20
18
16
100% S&P 500
14
12
40% Bond, 60% S&P 500
100% Bond
10
8
6 8 10 12 14 16 18 20 22 24
6
27-Year Standard Deviation of Annual Return (%)
27-Year Annualized Return (%)
MV SV
22
20
18
16
LV
100% S&P 500
14
MG
12
LG
SG
10
100% Bond
8
6 8 10 12 14 16 18 20 22 24
6
27-Year Standard Deviation of Annual Return (%)
27-Year Annualized Return (%)
Source: Morningstar Principia
Source: Morningstar Principia
www.journalofindexes.com July/August 2008
27

Figure 3
Annual Index % Performance 1980–2006
Year
(Color-Coded
By Decade)
S&P
500 Index
LB
Aggregate
Bond Index
U.S. Large
Growth 1
U.S. Large
Value 2
U.S.
Mid Growth 3
U.S.
Mid Value 4
U.S. Small
Growth 5
U.S. Small
Value 6
1980 32.22 2.71 39.70 24.88 47.89 23.56 48.71 22.58
1981 -5.08 6.25 -11.13 1.98 -7.37 9.34 -12.18 13.36
1982 21.46 32.62 16.49 20.47 21.79 26.05 19.91 31.35
1983 22.46 8.36 19.15 24.23 22.22 28.33 21.64 37.86
1984 6.26 15.15 1.59 10.98 -7.60 3.47 -11.33 7.80
1985 31.74 22.10 32.59 31.13 32.69 31.87 27.51 35.71
1986 18.68 15.26 15.51 19.90 10.47 15.60 8.79 14.36
1987 5.26 2.76 6.02 1.46 0.82 3.20 -3.31 -2.01
1988 16.61 7.89 13.45 22.39 12.01 19.31 21.01 26.47
1989 31.68 14.53 32.53 30.18 24.93 24.08 18.04 17.62
1990 -3.12 8.96 -0.73 -6.34 -9.21 -10.83 -14.73 -17.45
1991 30.48 16.00 37.93 24.87 51.88 38.90 49.95 39.36
1992 7.62 7.40 4.25 9.44 11.12 17.82 13.99 22.71
1993 10.06 9.75 0.57 16.39 15.29 15.29 16.23 20.12
1994 1.31 -2.92 3.37 -1.73 -2.94 -2.43 -2.28 -1.81
1995 37.53 18.47 38.02 39.25 35.21 32.26 35.24 26.39
1996 22.94 3.63 23.59 22.53 15.65 22.42 10.71 24.66
1997 33.35 9.65 33.45 34.23 20.56 35.01 14.99 31.35
1998 28.57 8.69 42.15 16.66 9.43 5.04 5.34 -4.18
1999 21.04 -0.82 36.64 3.08 57.46 -1.87 55.87 0.48
2000 -9.10 11.63 -28.11 8.62 -21.75 26.85 -21.32 19.24
2001 -11.88 8.44 -23.02 -5.75 -15.43 6.55 -8.27 12.75
2002 -22.09 10.25 -29.15 -16.06 -28.62 -7.96 -33.72 -5.53
2003 28.67 4.10 28.49 28.21 43.52 35.05 49.76 45.26
2004 10.71 4.34 7.36 13.48 17.14 21.54 17.25 18.99
2005 4.91 2.43 4.84 5.42 15.61 8.17 9.23 5.99
2006 15.79 4.33 8.32 22.11 11.14 16.26 11.43 20.49
27-Year Avg
Annualized
Return (%)
13.26 9.10 11.12 14.06 12.16 15.59 10.68 16.16
27-Yr Std Dev
of Return (%)
15.79 7.47 20.68 13.77 21.83 13.78 22.52 15.44
Growth of
$10,000
$288,108 $104,923 $172,205 $348,756 $221,465 $500,062 $154,815 $570,743
Source: Morningstar Principia
1 Average of Dow Jones Large Growth Index and DJ Wilshire Large Growth Index
2 Average of Dow Jones Large Value Index and DJ Wilshire Large Value Index
3 Average of Dow Jones Mid Growth Index and DJ Wilshire Mid Growth Index
4 Average of Dow Jones Mid Value Index and DJ Wilshire Mid Value Index
5 Average of Dow Jones Small Growth Index and DJ Wilshire Small Growth Index
6 Average of Dow Jones Small Value Index and DJ Wilshire Small Value Index
During the ’90s, the 10-year annualized return of bonds
was markedly lower at just under 8 percent. Compared with
the ’80s, the standard deviation of return was uniformly higher
during the ’90s for all the equity indexes in this study.
Finally, results from the current decade are shown in
Figure 6. With seven years under our belt, the overall
pattern has been less than encouraging for a growthoriented
investor. For the current decade, the Y-axis had
to be modified (in comparison with the Y-axis of Figures
1-4) to accommodate the poor performance of growthoriented
U.S. equity indexes.
Value-based indexes have fared far better this decade,
particularly mid-cap and small-cap value indexes. The S&P
500 Index and the throng of index funds that track it have
not enjoyed the great success of the ’90s. To continue the
example, Vanguard 500 Index had $72 billion in total assets
as of year-end 2006. That represents a decline in assets of 31
percent since its net asset peak in late-1999/early-2000.
The risk/return frontier is far more useful—and
interesting—when considering more than simply the S&P
500 as the representative U.S. equity asset class. When
doing so, the case for a value orientation is compelling
28
July/August 2008

over most time frames. Clearly, there is a value premium
over the long haul, particularly among mid-cap value and
small-cap value indexes.
The premium (growth or value) of each five-year rolling
annualized return from 1980–2006 is shown in Figure 7. For
instance, over the five-year period from 1980–1984, largecap
value U.S. equity demonstrated a 432 bps premium over
large-cap growth U.S. equity. Among mid-cap U.S. equities
during the same period, there was a value premium of 422
bps. Among small caps, the five-year value premium from
1980–1984 was 1,110 basis points.
Over the entire 27-year period, large-cap value demonstrated
a value premium 70 percent of the time, with the
Figure 4
average five-year value premium equaling 505 bps. Largecap
growth outperformed large-cap value 30 percent of
the time by an average of 353 bps.
Among mid-cap equity indexes, value outperformed
growth 78 percent of the time by an average of 575 basis
points. When growth outperformed value (22 percent of
the time), the margin of victory averaged 260 bps. Among
mid caps, a value tilt has historically provided better performance
than a growth tilt.
Among the small-cap equity indexes in this study, value
beat growth 83 percent of the time by an average of 756 basis
points. However, when small-cap growth wins (albeit not very
continued on page 57
Figure 7
1980s (1980-1989)
MV
LV
22
LG
16
MG
14
SV
20
100% S&P 500
18
100% Bond
SG
12
10
6 8 10 12 14 16 18 20 22 24
8
6
10-Year Standard Deviation of Annual Return (%)
Source: Morningstar Principia
Figure 5
1990s (1990-1999)
22
LG
20
100% S&P 500
MG
18
SG
16
LV
MV
14
SV
12
10
8
100% Bond
6
6 8 10 12 14 16 18 20 22 24
10-Year Standard Deviation of Annual Return (%)
Source: Morningstar Principia
Figure 6
2000s (2000-2006)
20
SV
MV
15
100%
10
Bond
LV
5
100% S&P 500
SG
MG
0
LG
-5
-10
0 5 10 15 20 25 30
7-Year Standard Deviation of Annual Return (%)
Source: Morningstar Principia
10-Year Annualized Return (%)
10-Year Annualized Return (%)
7-Year Annualized Return (%)
5-Year
Period
Value vs. Growth Premium
5-Year Rolling Return Premium (basis points)
U.S. Large-Cap
Equity
U.S. Mid-Cap
Equity
U.S. Small-Cap
Equity
Growth 1 Value 2 Growth 3 Value 4 Growth 5 Value 6
1980–1984 432 422 1,110
1981–1985 662 819 1,693
1982–1986 452 556 1,241
1983–1987 260 509 1,007
1984–1988 337 532 818
1985–1989 96 277 388
1986-1990 15 228 152
1987–1991 330 63 118
1988–1992 118 18 18
1989–1993 44 130 9
1990–1994 29 98 9
1991–1995 123 111 71
1992–1996 330 228 377
1993–1997 243 360 519
1994–1998 602 263 214
1995–1999 1,217 897 831
1996-2000 160 314 304
1997–2001 312 684 485
1998–2002 599 912 889
1999–2003 979 919 1,098
2000–2004 1,615 1,988 2,056
2001–2005 859 849 1,121
2002–2006 737 466 864
Percent of Years
With “Premium”
Mean
Premium
(bps)
Median Premium
(bps)
30% 70% 22% 78% 17% 83%
353 505 260 575 257 756
160 385 111 488 94 818
Source: Morningstar Principia
1 Average of Dow Jones Large Growth Index and DJ Wilshire Large Growth Index
2 Average of Dow Jones Large Value Index and DJ Wilshire Large Value Index
3 Average of Dow Jones Mid Growth Index and DJ Wilshire Mid Growth Index
4 Average of Dow Jones Mid Value Index and DJ Wilshire Mid Value Index
5 Average of Dow Jones Small Growth Index and DJ Wilshire Small Growth Index
6 Average of Dow Jones Small Value Index and DJ Wilshire Small Value Index
www.journalofindexes.com July/August 2008
29

Israelsen continued from page 29
often), the margin of victory can be large. For example,
during the five-year period of 1995–1999, small-cap
growth beat small-cap value by 831 basis points. Overall,
when small-cap growth outperformed small-cap value, the
average margin of victory was 257 bps (and the median
margin of victory was 94 bps compared with the median
small-cap value margin of victory of 818 bps).
In light of the historical performance of dominance of
small-cap value over small-cap growth, it is peculiar that
small-cap growth U.S. equity funds outnumber small-cap
value U.S. equity funds more than 2-to-1. Apparently
small-cap growth managers (and small-cap growth investors)
are optimists. They are willing to pay a high price
(in the form of volatility) for a relatively rare, but potentially
large, burst of outperformance relative to small-cap
value. They must see a rewarding small-cap growth frontier
off in the distance. That’s about the only place they
could see it … because such a frontier hasn’t surfaced
very often in the past 27 years.
Ferri continued from page 44
Investors and advisors can refer to the data in Figure
5 to determine fair fees for each ETF that follows a particular
index strategy. For example, assume an advisor is
considering the purchase of a U.S. large-cap growth ETF.
The cost for one ETF under consideration is 0.35 percent,
while the cost for another is 0.60 percent. Which ETF is
more or less overpriced than the other?
The answer is that it depends on the underlying index
strategy of each fund. If the 0.35 percent ETF is a passively
selected and capitalization-weighted “Beta” fund, and
the 0.60% ETF follows an alpha-seeking index that uses a
quantitatively driven index and weights stocks using fixed
weights, then based on index strategy alone, the 0.60
percent fund is a better value than the 0.35 percent fund.
I am NOT suggesting that investors should buy the 0.60
percent quantitative ETF. Rather, I am suggesting that the
0.35 percent beta ETF is overpriced.
Summary
There is a clear link between the complexity of index
Figure 5
U.S. Broad Market/Large-Cap Index
Strategy Box Pricing Matrix
Quantitative 0.55% 0.60% 0.60%
Screened 0.35% 0.45% 0.45%
Passive 0.20% 0.35% 0.35%
Source: ETFGuide.com
Capitalization Fundamental Fixed Weight
strategy and the fees ETF companies charge for products.
It is important for investors and advisors to understand
this relationship when analyzing competing products.
The Index Strategy Box Pricing Template for ETFs is one tool
that can be used to compare the pricing of any category
of funds. The methodology should assist investors with
ETF comparisons and guide product providers to create a
more uniform pricing model.
www.journalofindexes.com July/August 2008
57

ETFs, Spreads And Liquidity
A close look at the data on ETF spreads.
by Matt Hougan
30
July/August 2008

Exchange-traded fund investors love to talk about fees.
After all, the expense ratios charged by ETFs are often
fractions of those for competing mutual funds.
But expense ratios are just one part of the true cost of
investing in ETFs. Brokerage commissions and spreads also
play an important role.
Brokerage commissions are obvious—they are the $9.99
or whatever you pay your broker to execute a stock trade.
Because ETFs are bought and sold like stocks, commissions
apply; in contrast, many mutual funds can be bought and sold
without commissions. A quick calculation will tell you if it’s
worth paying commissions to get the lower expense ratio.
Spreads, however, are a dirty little secret. Until recently, there
was no publicly available data on ETF spreads. Monthly data is
now available on IndexUniverse.com, but still, most investors
ignore spreads when choosing between different investments.
They do so at their peril, as spreads represent a substantial
extra expense for many ETFs.
What Are Spreads?
Like stocks, ETFs are bought and sold on the market by
auction. The bid/ask spread is the difference between the
best price being offered for an ETF (the “bid”) and the best
price at which someone is willing to sell (“the ask”).
Let’s assume that the real market value of an ETF is halfway
between the bid and the ask. If you submit a market
order for an ETF and it gets filled at the “ask,” the difference
between that and the halfway point represents a cost: You are
essentially overpaying for the ETF. The wider the spread, the
more it costs you.
There’s been a major debate in the ETF industry about
how big ETF spreads are, and about what influences the size
of those spreads. ETF promoters (especially promoters of
newer, thinly traded ETFs) claim that spreads are based on
the liquidity of the underlying stocks—that is, the stocks held
by the ETF—and not by the level of trading in the ETF itself.
The reason, proponents say, is that large institutional investors
called “Authorized Participants” (or APs) can create new
shares of an ETF at any time. For instance, APs in the S&P 500
SPDR ETF (SPY) can “create” new shares of SPY by buying up all
500 stocks in the S&P 500 in the right proportions and delivering
them to the product issuer (State Street Global Advisors). The
product issuer will give the AP shares of the ETF in return.
If the bid/ask spread on SPY gets too large, the thinking
goes, APs could simply create new shares, establish a better
price and pocket the difference.
The caveat, of course, is that APs can only create ETF
shares in large lots; typically 50,000 shares or more. If there is
only demand for a few hundred shares, it’s not worth the market
maker’s time to create an entire new group of shares.
So what do spreads really look like for investors?
To find out, I examined data for all available ETFs and ETNs
for the period from January 1, 2008, through March 31, 2008.
That covered 666 funds, ranging from the massive (SPY) to the
tiny (HealthShares Ophthalmology Fund). The data, from NYSE
Arcavision, examined tick-by-tick spreads between the best bid
and best offer, and weighted those spreads by volume to produce
an average spread for each ETF over that time range.
The Results
There are two ways to consider spreads: in absolute dollar
terms and as a percentage of the share price. First, I looked
at the absolute dollar amounts.
For the time period covered, 30 ETFs had the minimum
possible average spread of one penny. These included some
of the largest ETFs on the market (SPY, QQQQ, EFA), all nine
of the highly traded Select Sector SPDR ETFs, a number
of international funds, some fixed-income ETFs and two
ProShares UltraShort ETFs (which are designed to deliver
-200 percent of the daily return of the underlying index). A
complete list is available in Figure 1.
On the flip side, there were a handful of ETFs that reported
outrageous spreads—$1-$3, and even more. These were all
newly launched ETFs with very little liquidity.
A few ETFs had absurd spreads; three had spreads of $10/
share or more. These were clearly anomalies, and not reflective
of true investor experiences. Sometimes, when there is no market
for an ETF (no shares trading), the bids and asks will become
stale, and one can deviate widely from the other. For example,
the iShares MSCI ACWI (ACWI) ETF launched on March 30 and
traded just 600 shares during its first two days on the market
(the period covered by my analysis). The average spread over
that time period was $10.99/share, according to the data. But
Figure 1
ETFs With One Penny Average Spreads — Q1 2008
Diamonds Trust
iShares Lehman 1-3 yr Treasury
iShares Lehman 20+ yr Treasury
iShares MSCI Australia
iShares MSCI Canada
iShares MSCI EAFE
iShares MSCI Germany
iShares MSCI Hong Kong
iShares MSCI Japan
iShares MSCI Malaysia
iShares MSCI Singapore
iShares MSCI Taiwan
iShares Russell 1000 Growth
iShares Russell 2000 Index
iShares S&P 100 Index
PowerShares QQQ Trust
ProShares UltraShort QQQ
ProShares UltraShort S&P 500
Select Sector SPDR Consumer Discretionary
Select Sector SPDR Consumer Staples
Select Sector SPDR Energy
Select Sector SPDR Financials
Select Sector SPDR Health Care
Select Sector SPDR Industrials
Select Sector SPDR Technology
Select Sector SPDR Utilities
Select Sector SPDR Materials
Semiconductor HOLDRS
SPDR
streetTRACKS Gold Trust
Source: NYSE Acravision. Data for January 1, 2008 through March 21, 2008.
DIA
SHY
TLT
EWA
EWC
EFA
EWG
EWH
EWJ
EWM
EWS
EWT
IWF
IWM
OEF
QQQQ
QID
SDS
XLY
XLP
XLE
XLF
XLV
XLI
XLK
XLU
XLB
SMH
SPY
GLD
www.journalofindexes.com July/August 2008
31

Figure 2
Figure 3
463
134
ETF Spreads
55
14
$0.01-$0.10 $0.11-$0.20 $0.21-$0.50 $0.51+
Average Spreads
Source: NYSE Arcavision. Data for January 1, 2008 through March 31, 2008.
500
450
400
350
300
250
200
150
100
50
0
that’s not really what investors paid. A spot check on April 18
showed the bid/ask spread at $0.80; still high, but well below
the artificial $10.99 figure.
Number of ETFs
74
208
175
ETF Spread Percentage - Q1 2008
85
42
Source: NYSE Arcavision. Data for January 1, 2008 through March 31, 2008.
29
15
8 5 7
0.0% 0.1% 0.2% 0.3% 0.4% 0.5% 0.6% 0.7% 0.8% 0.9% 1%
or
Spread %
more
Figure 4
ETFs With Spreads Less Than 0.1% - Q1 2008
18
Number of ETFs
The Spread On Spreads
The good news is that the vast majority of ETF spreads are
very tight. As Figure 2 shows, 463 of 666 funds had spreads
of less than $0.10/share, and 597 funds had spreads of less
than $0.20/share. It’s not shown on the chart, but 231 funds
had average spreads of $0.05/share or less.
The median ETF had a spread of $0.07/share, and the
mean spread (ignoring obvious outliers) was $0.11/share.
Percentage Basis
Another way to look at spreads is as a percentage of the value
of the ETF itself. Obviously, if an ETF trading for $10/share has a
spread of $0.10, that represents 1 percent of the value of the ETF.
If an ETF trading for $100/share has the same spread, that $0.10/
share represents just 0.1 percent of price … a big difference.
Here again, the data look pretty good: On a percentage basis,
more than half of all ETFs had spreads of less than 0.2 percent of
the portfolio value. The vast majority (615 of the 666) had spreads
of 0.5 percent or less, and just 18 ETFs had spreads of more than
1 percent. Still, that means that for 51 funds, the average spread
was more than 0.5 percent—a significant expense for shareholders
that far outweighs any savings on the expense ratio front.
Seventy-four ETFs make the honors list by this measure, posting
average spreads of less than 0.10 percent for the time period
studied. They included three CurrencyShares fixed-income ETFs
from Rydex, five HOLDRS, nine ProShares ETFs (two leveraged
funds and seven inverse-leveraged funds) and all nine of the
Select Sector SPDR ETFs, among others.
Large funds also did well: all 10 of the top 10 ETFs by total
assets made the list.
BGI had the largest number of ETFs on the list (36), including
a large number of individual country funds.
Larger Funds, Tighter Spreads
One question people ask is whether less-established ETFs
have larger spreads. To analyze this, I broke down available ETFs
by net assets under management. The results are unequivocal.
The very largest ETFs—those with assets of more than $10
billion—all had spreads of 0.05 percent or less. As asset size
falls, the percentage of ETFs meeting this tightest category
Fund Company
# of ETFs
BGI 36
SSgA 17
ProShares 9
HOLDRS 5
Rydex (CurrencyShares) 3
PowerShares 1
Van Eck 1
Vanguard 1
Victoria Bay 1
Source: NYSE Arcavision. Data for January 1, 2008 through March 31, 2008.
falls in lockstep: 56 percent for funds between $1 billion and
$10 billion; 13 percent for funds between $500 million and
$999 million; 4 percent for funds between $100 million and
$499 million; and zero for funds smaller than that.
In fact, as you scroll across the grid in Figure 5, you see
that there is a direct relationship between net assets and
average spread percentage: As assets shrink, spreads widen.
Note, for instance, that zero funds with assets under $100
million had spreads of less than 0.1 percent.
More Trading, Lower Spreads
Likewise, there is a direct correlation between the amount
of trading in the fund and the average spread. Figure 6 compares
the average spread for ETFs with differing levels of
average daily trading volume.
Eighty percent of all funds with greater than $10 billion
in daily trading volume land in the lowest average spread
decile. That shrinks to just 34 percent for funds with between
$1 billion and $9.9 billion in trading; 2 percent for funds
with between $100 million and $999 million; and negligible
amounts for funds with less trading volume.
The reverse is true as well: Funds with lower trading volume
have higher average spreads.
Liquidity Of The Underlying?
The old consensus was that the liquidity of the underlying
stocks determined the tightness of the spreads. But
research shows that it is the liquidity of the ETF, and not the
32
July/August 2008

liquidity of the underlying components that really matters.
S&P 500 Example
Consider, for instance, the S&P 500 SPDR (SPY) and the
RevenueShares Large-Cap ETF (RWL). Both funds hold the
exact same stocks—all 500 components of the S&P 500. The
only difference is that SPY weights those components by market
cap, while RWL weights them by revenues.
That shouldn’t impact the liquidity of the underlying, as all
500 stocks in the S&P 500 are deeply liquid.
When you look at the spreads data, however, there’s no
comparison. For the time period studied, SPY had spreads
of less than 0.01 percent, while RWL’s spread averaged 0.9
percent—90 times wider.
SPY, of course, is the largest ETF in the world, with over
$70 billion in assets; RWL is a newly launched fund, with little
in the way of assets or trading interest so far.
Muni Bond Example
Another example comes from the muni bond space. The muni
bond market is notoriously illiquid, and different ETF providers
take different approaches to handling this illiquidity.
Both BGI and SSgA offer broad-market, nationally oriented
muni bond funds. The two funds—the iShares S&P
National Municipal Bond ETF (MUB) and the SPDR Lehman
Municipal Bond ETF (TFI)—hold roughly equivalent portfolios
with roughly equivalent returns. But they have very
different creation methodologies.
In MUB, APs must go out into the market and buy specific
bonds in order to create new shares. Even though iShares designs
these creation baskets to hold only the most liquid muni bonds,
it is still a hurdle that APs must negotiate to create new shares.
TFI, by contrast, uses what’s called a “cash creation basket.”
In other words, the only thing APs have to do to create
new shares is send cash to SSgA. It couldn’t be easier.
However, for the time period studied, MUB had much tighter
spreads (0.10 percent) compared with TFI (0.30 percent). The reason?
MUB is a larger fund with more inherent trading volume.
It’s interesting to note that the spreads on TFI have shrunk
as the fund has grown. As of early May, TFI had more than
$250 million in assets and higher trading activity, and from
May 1 through May 15, 2008, the average spread on the fund
was down to 0.20 percent. As the fund continues to grow, its
spreads will likely continue to narrow.
Conclusion
Spreads aren’t as simple as ETF proponents make them
out to be. Most ETFs have tight spreads, but not all of
them do, and spreads represent a real cost to investors.
Especially for newer ETFs with low assets under management,
investors would do well to pay attention to spreads
when they trade, and use limit orders to avoid paying too
much above fair value.
Spreads should be incorporated into every ETF trading
decision, much the way that brokerage commissions are.
Their impact will be felt most by short-term traders, but even
long-term investors should consider their planned holding
period and incorporate spreads into their trading decisions.
[This article expands on an article published in the May
2008 issue of the Exchange-Traded Funds Report.]
Figure 5
Net Assets
Number of
ETFs
Source: NYSE Arcavision. Data for January 1, 2008 through March 31, 2008.
ETF Spread Percentage By Assets Under Management
Average Spread Percentage Decile
0.1% 0.2% 0.3% 0.4% 0.5%
$10+ billion 10 100% 0% 0% 0% 0% 0%
$1-$9.9 billion 90 56% 43% 1% 0% 0% 0%
$500-$999 million 53 13% 72% 15% 0% 0% 0%
$100-$499 million 146 4% 53% 31% 8% 2% 1%
Figure 6
$51-$99 million 75 0% 25% 47% 15% 8% 5%
$10 billion 50 80% 20% 0 0 0 0
$1-$9.9 billion 88 38% 63% 3% 0 0 0
$100 - $999 million 194 2% 53% 31% 9% 4% 2%
$10 - $99 million 236 0% 16% 41% 18% 9% 14%
0.5%
>0.5%
www.journalofindexes.com July/August 2008
33

Why ETFs And 401(k)s
Will Never Match
Why prudently selected index mutual funds are
a better choice than ETFs for most 401(k)s
By David Blanchett and Gregory Kasten
34
July/August 2008

Exchange-traded funds, long known as a low-cost method
of investing for individual investors, are receiving increasing
media exposure as a potential solution to reduce
401(k) plan fees. In fact, ETFs have been touted by at least one
firm as the “low-cost solution for 401(k)s.” The reason for the
increased media exposure for ETFs is relatively straightforward:
On average, ETFs cost less (i.e., have lower expense ratios) than
actively managed mutual funds. However, comparing passively
managed ETFs with actively managed mutual funds ignores the
fact that there are already passive index mutual funds that are
being used in retirement plans today.
Similar to ETFs, index mutual funds are less expensive than
actively managed mutual funds. Therefore, the real debate
regarding the potential benefits of ETFs in 401(k)s is not
whether ETFs create cost savings versus actively managed
mutual funds, but whether ETFs create additional cost savings
when compared with traditional index mutual funds.
Unlike traditional mutual funds, though, ETFs are not “401(k)-
ready,” and a variety of costs must be incurred (both explicit and
implicit) in order to make ETFs available in a 401(k) plan. This paper
will explore the benefits and costs associated with using ETFs in
401(k)s and will provide guidance on whether ETFs represent a
better indexing option than traditional index mutual funds.
An Overview Of ETFs
While ETFs were first introduced in the 1990s, the ability to
trade a whole stock basket in a single transaction dates further
back. Major U.S. brokerage firms provided such program trading
facilities as early as the late 1970s, particularly for the S&P 500
Index. With the introduction of index futures contracts, program
trading became more popular. As such, the interest in developing
a suitable instrument that would allow index components to be
negotiated in a single trade increased. This led to the introduction
of the exchange-traded fund. The first ETF introduced was
the Toronto Index Participation (TIPS) in Canada, which was followed
three years later by the Standard & Poor’s 500 Depositary
Receipts (SPDRs) in the U.S. [Deville 2006].
The ETF marketplace experienced its first effective boom in
March 1999, with the launch of the NASDAQ-100 Index Tracking
Stock, popularly known as Cubes or Qubes (in reference to its
initial ticker, QQQ [which has since changed to QQQQ]). In its
second year of trading, QQQ had an average daily volume of 70
million shares, which was approximately 4 percent of the trading
volume of the NASDAQ at the time. Since then, ETF growth in
the U.S. has been considerable: Assets under management rose
27 percent in 2001, 23 percent in 2002, 48 percent in 2003, 50
percent in 2004 and 31 percent in 2005 (source: Investment
Company Institute). Growth in 2006 hit 35.8 percent, according
to Morgan Stanley, and 42.7 percent in 2007.
One reason for the rising popularity of ETFs among individual
investors is the increased tax efficiency of ETFs relative to traditional
index funds. The ability of ETFs to utilize in-kind redemptions
enables an ETF to transfer its underlying holdings with the
biggest unrealized gains, thereby limiting the ETF’s potential for
capital gains distributions. However, tax considerations are not
pertinent to qualified retirement plans (e.g., a 401(k) plan), since
they are tax-deferred savings vehicles [Deville 2006].
Internally, ETFs are more complex entities than mutual
funds. Technically, ETFs are a class of mutual fund since they
fall under the same rules as traditional mutual funds, but they
have a different structure and therefore the SEC has imposed
different requirements on them. Currently, there are three
key legal structures for ETFs (source: http://www.etfguide.
com/exchangetradedfunds.htm):
1. Open-end index fund: This type of ETF structure
reinvests dividends the date of receipt and pays them
out via a quarterly cash distribution. This ETF design
is also permitted to use derivatives, loan securities
and is registered under the Investment Company Act
of 1940. ETFs that utilize this legal structure include
iShares and the Select Sector SPDRs.
2. Unit Investment Trust: This type of ETF structure
does not reinvest dividends in the fund and pays
them out via a quarterly cash distribution. In order
to comply with diversification rules, this ETF design
will sometimes deviate from the exact composition
of a benchmark index. This type of fund is registered
under the Investment Company Act of 1940. The
Diamonds, Cubes and SPDR follow this format.
3. Grantor Trust: This type of ETF structure distributes
dividends directly to shareholders and allows investors
to retain their voting rights on the underlying securities
within the fund. The original fund components of
the index remain fixed and this legal structure is not
registered under the Investment Company Act of 1940.
Merrill Lynch’s HOLDRs follow this format.
Although the SEC states that an ETF is “a type of investment
company whose investment objective is to achieve the same
return as a particular market index,” ETF strategies have been
moving away from traditional indexing strategies. Originally,
ETFs were based on plain-vanilla index methodologies, such as
the S&P 500; however, most of the new ETFs introduced today
comprise more specialized and esoteric investing strategies.
Actively managed ETFs, something the SEC has an outstanding
concept release on (IC-25258), are likely to be a growth area
for the ETF marketplace in the future (source: http://www.sec.
gov/rules/concept/ic-25258.htm#seciii). Indeed, some active
ETFs with transparent portfolios have already launched.
However, there are a number of obstacles, such as arbitrage
and transparency, that will need to be addressed before
actively managed ETFs become widespread.
Getting ETFs In 401(k)s
Although ETFs have been around for over a decade, only
recently have they been considered as potential investments
for the mass 401(k) public. While ETFs have long been available
through 401(k) self-directed brokerage accounts (along
with other investments like individual securities), ETFs have
not been available to plan participants as part of the core
investment lineup. There are a variety of reasons for this, but
transactions costs (the costs incurred buying and selling ETFs
on the open market, such as commissions) and fractional
share issues (since ETFs can only be purchased in whole share
amounts) have been two of the largest obstacles.
There are two primary transaction costs associated with purchasing
an ETF, since, unlike mutual funds, ETFs are purchased on
www.journalofindexes.com July/August 2008
35

the open market. The first cost is the bid/ask spread (or spread)
and the second is commissions. The “bid” price is the price at
which you can sell an ETF, while the “ask” (or offer) price is the
price at which you can purchase an ETF. The bid price is typically
lower than the ask price, which creates the spread. For example,
if we assume the ask (or purchase) price of ETF ABC was $50.10
and the bid price for ETF ABC was $50.00, if an investor were to
instantly purchase and sell ETF ABC, ignoring commissions and
any market movement, he or she would lose $0.10, which represents
the spread. While the actual bid/ask spread is going to vary
by ETF, the average 30-day bid/ask spread for Vanguard’s 33 ETFs
(as of 11/02/07, data obtained from Vanguard’s Web site) was .08%
(or 8 basis points), or 4 bps for each buy or sell transaction. The
spread is an important consideration in ETF investing because it
represents a cost that reduces long-term performance.
The second transaction cost associated with purchasing an
ETF is the commission. A commission must be paid each time
an ETF is bought or sold. Unlike the spread, which is typically a
constant percentage of the underlying ETF (e.g., 4 bps each way),
commissions typically vary based upon the size of the transaction.
Commissions are incurred each time an ETF is bought or sold, so
higher levels of trading activity increase the total commissions
paid. One method that minimizes the per-participant cost of trading
ETFs has been the introduction of pooled accounts, where
buy and sell orders are submitted in blocks. By pooling ETFs into
single orders, it is possible to trade less frequently and therefore
pay less in commissions. While the spread still exists with pooled
accounts, pooling also alleviates the issues associated with fractional
shares, which will be discussed next.
A key problem with ETFs is that they cannot be purchased in
fractional shares. This is especially important for 401(k)s since
participants do not typically defer the exact cost of the ETF
(which is especially difficult given the fact the price of an ETF is
always changing). While mutual funds can be bought and sold in
fractional shares (e.g., 5.673 shares), ETFs can only be purchased
in whole share amounts. By pooling ETFs into a common fund (or
trust), it is possible to overcome this problem by allowing participants
to buy units or shares of an overall pool that purchases the
underlying ETFs. The two primary methods of pooling ETFs for
use in 401(k) plans are at the plan level or in an aggregate account
(such as a collective investment fund, or CIF).
If an ETF is pooled at the plan level, the pooled account is not
required to have the same type of oversight (i.e., audit requirements)
associated with mutual funds or CIFs (which will be
discussed next). Pooling at the plan level is less costly than a CIF
and allows a plan sponsor to introduce ETFs in a relatively costeffective
manner. CIFs are currently the most popular method of
using ETFs in 401(k)s because they allow for greater economies of
scale than pooling at the plan level. A CIF is a bank-administered
trust that holds commingled assets that meet specific criteria
established by 12 CFR 9.18. Unlike a mutual fund, a CIF can only
be used in retirement plans (i.e., not taxable accounts or IRAs).
CIFs are created by banks that act as a fiduciary for the CIF and
hold the legal title to the fund’s assets. Participants in a CIF are
the beneficial owners of the fund’s assets. While each participant
owns an undivided interest in the aggregate assets of a CIF, a
participant does not directly own any specific asset held by a CIF
[Collective Investment Funds: Comptroller’s Handbook].
The Costs Of Pooling
There are a variety of additional expenses associated with
running a pooled account, both explicit and implicit. The explicit
costs of pooled accounts include the costs of unitization, audit
requirements, commissions, the bid/ask spread and other miscellaneous
administrative expenses. The implicit costs of pooled
accounts relate primarily to the impact of cash drag, which negatively
impacts the performance of the pooled account.
The two types of transactions costs incurred by an ETF
investor are the bid/ask spread and commissions. As discussed
earlier, the average bid/ask spread for the Vanguard
ETFs is 8 bps (or 4 bps each buy or sell). This 4 bps “fee” will
be incurred each time an ETF is bought or sold. Commissions,
similar to the bid/ask spread, are a cost paid each time an
ETF is bought or sold, since unlike mutual funds, ETFs cannot
be redeemed at NAV and must be purchased on the open
market. While trade aggregation (through pooling) decreases
commissions, even a commission as low as $.02 per share
will reduce the net performance of an ETF-pooled account
over time. Again, while these transaction costs may appear
to be minor, the bid/ask spread and commissions represent
a definite cost that must be considered when addressing the
relative benefits of ETFs versus mutual funds for 401(k)s.
The costs associated with pooling vary between plan-level
pooling and aggregate pooling (e.g., using a CIF). The costs
associated with pooling ETFs at the plan level vary by provider;
however, a reasonable current estimate would be $500 per
plan ETF (e.g., if a plan wanted an all-ETF investment lineup
consisting of 12 ETFs, the total cost would be $6,000). While
additional expenses, such as an audit, are not necessary for
plan-level pooling, such oversight is likely necessary to ensure
that the unitization is being properly handled, especially for
larger plans. Additional administrative and operational costs
beyond the basic pooling fee may also be incurred.
The costs for pooling an ETF at the aggregate, or CIF level,
are also going to vary by provider. The unitization costs associated
with a CIF are typically not going to be much lower
than 3 bps and can easily exceed 10 bps based on the size
of the unitized account. A CIF must be audited at least once
each 12-month period (in accordance with 12 CFR 9.18(b)
(6)), which will typically cost at least $5,000. However, as
the assets increase, so do the fees associated with the audit,
since the risk of the auditor increases along with the assets.
While an audit fee of $5,000 may seem insignificant, it represents
a cost of 10 bps on a $5 million account, 1 bp on a $50
million account and 0.1 bp on a $500 million account. Every
basis point is important when comparing the relative benefits
of ETFs and indexed mutual funds, since the overall cost differences
between the strategies are already relatively small.
The implicit costs associated with pooled accounts relate
primarily to cash drag. Cash drag relates to the need for any
pooled account, including mutual funds, to have funds available
in order to meet the cash flow (i.e., redemption) needs of
its investors. While cash drag is also a consideration for mutual
funds, it is less so because the impact of cash drag is typically
inversely related to pooled assets. The larger the account, the
lower level of cash that must typically be held, and therefore
the less the impact of cash drag on performance. Since mutual
36
July/August 2008

Figure 1
Large-Cap Comparison
Ticker Type* Investment Name Category
Minimum
Investment
Net
Assets
(Billions)
Expense
Ratio
Bid/Ask
Spread**
Inception
Date
VUG ETF Vanguard Growth ETF Large Growth n/a $2.57 0.11% 0.05% 01/26/04
VIGRX MF Vanguard Growth Index Inv Large Growth n/a $6.92 0.22% n/a 11/02/92
VIGSX MF Vanguard Growth Index Signal Large Growth $1M $0.06 0.11% n/a 06/04/07
VIGIX MF Vanguard Growth Index Instl Large Growth $5M $2.87 0.08% n/a 05/14/98
VV ETF Vanguard Large Cap ETF Large Blend n/a $0.95 0.07% 0.06% 01/27/04
VLACX MF Vanguard Large Cap Index Inv Large Blend n/a $0.32 0.20% n/a 01/30/04
VLCAX MF Vanguard Large Cap Index Adm Large Blend $100,000 $0.23 0.12% n/a 02/02/04
VLISX MF Vanguard Large Cap Index Instl Large Blend $1M $0.10 0.08% n/a 01/30/04
VTV ETF Vanguard Value ETF Large Value n/a $2.24 0.11% 0.06% 01/26/04
VIVAX MF Vanguard Value Index Inv Large Value n/a $4.55 0.21% n/a 11/02/92
VVISX MF Vanguard Value Index Signal Large Value $1M $0.15 0.11% n/a 06/04/07
VIVIX MF Vanguard Value Index Instl Large Value $5M $2.91 0.08% n/a 07/02/98
Source: Vanguard. Data as 11/02/07. *MF = Mutual Fund, ETF = Exchange-Traded Fund. **30-Day Average.
Figure 2
Mid-Cap Comparison
Ticker Type* Investment Name Category
Minimum
Investment
Net
Assets
(Billions)
Expense
Ratio
Bid/Ask
Spread**
Inception
Date
VOT ETF Vanguard Mid Cap Growth ETF Mid-Cap Growth n/a $0.15 0.13% 0.08% 08/17/06
VMGRX MF Vanguard Mid Cap Growth Mid-Cap Growth n/a $1.09 0.47% n/a 12/31/97
VO ETF Vanguard Mid Cap ETF Mid-Cap Blend n/a $1.19 0.13% 0.06% 01/26/04
VIMSX MF Vanguard Mid Cap Index Inv Mid-Cap Blend n/a $8.50 0.22% n/a 05/21/98
VMISX MF Vanguard Mid Cap Index Signal Mid-Cap Blend $1M $0.48 0.13% n/a 03/30/07
VMCIX MF Vanguard Mid Cap Index Instl Mid-Cap Blend $5M $5.83 0.08% n/a 05/21/98
VOE ETF Vanguard Mid Cap Value ETF Mid-Cap Value n/a $0.20 0.13% 0.09% 08/17/06
VMVIX MF Vanguard Mid Cap Value Index Inv Mid-Cap Value n/a $0.20 0.26% n/a 08/24/06
Source: Vanguard. Data as 11/02/07. *MF = Mutual Fund, ETF = Exchange-Traded Fund. **30-Day Average.
Figure 3
Small-Cap Comparison
Ticker Type* Investment Name Category
Minimum
Investment
Net
Assets
Billions
Expense
Ratio
Bid/Ask
Spread**
Inception
Date
VBK ETF Vanguard Small Cap Growth ETF Small Growth n/a $0.78 0.12% 0.09% 01/26/04
VISGX MF Vanguard Small Cap Growth Index Inv Small Growth n/a $2.64 0.23% n/a 05/21/98
VSGIX MF Vanguard Small Cap Growth Index Instl Small Growth $5M $0.67 0.08% n/a 05/24/00
VB ETF Vanguard Small Cap ETF Small Blend n/a $0.98 0.10% 0.08% 01/26/04
NAESX MF Vanguard Small Cap Index Inv Small Blend n/a $0.07 0.23% n/a 10/03/60
VSISX MF Vanguard Small Cap Index Signal Small Blend $1M $0.42 0.13% n/a 12/15/06
VSCIX MF Vanguard Small Cap Index Inst Small Blend $5M $3.55 0.08% n/a 07/07/97
VBR ETF Vanguard Small Cap Value ETF Small Value n/a $0.77 0.12% 0.08% 01/26/04
VISVX MF Vanguard Small Cap Value Index Inv Small Value n/a $4.18 0.23% n/a 05/21/98
VSIIX MF Vanguard Small Cap Value Index Instl Small Value $5M $0.53 0.08% n/a 12/07/99
Source: Vanguard. Data as 11/02/07. *MF = Mutual Fund, ETF = Exchange-Traded Fund. **30-Day Average.
www.journalofindexes.com July/August 2008
37

funds are investments that can be used in a variety of settings
(e.g., foundations, individual accounts, IRAs, etc.), they have a
much larger potential asset base than CIFs, which can only be
used in retirement plans. Also, mutual funds are established
savings vehicles that are relatively easy for participants to
research (should they choose to do so); since CIFs are not publicly
traded, it is more difficult to obtain information on them.
As an example of the impact of cash drag, if you assume
a 4 percent cash return and a 10 percent market return, for
each 1 percent cash position, the return of the CIF would
be decreased by 6 bps. Therefore, a 2 percent cash position
would lead to 12 bps of underperformance. If the market
return increases to 15 percent and the cash return stays at 4
percent, the impact of cash drag increases to 11 bps for each
1 percent of cash in the account.
So what are the total costs of pooling likely to be? Well,
the costs are going to vary based upon a variety of factors,
but based on conservative assumptions, it’s going to cost at
least 4 bps to purchase an ETF (assuming 3 bps for the bid/ask
spread and 1 bp for commissions), and 10 bps for the ongoing
management of an ETF (assuming 5 bps for the overall pooling/
unitization and 5 bps of cash drag). While 4 bps and 10 bps
for trading and ongoing management, respectively, may seem
small, the overall cost differences between index mutual funds
and ETFs for a number of scenarios are actually even smaller.
ETFs vs. Mutual Funds: An Investment Comparison
There are both qualitative (i.e., investment availability) and
quantitative (i.e., cost) issues that need to be addressed when
determining whether to include ETFs in 401(k)s. While the
primary interest in ETFs is cost-related, there are a number of
popular index methodologies that are difficult (if not impossible)
to obtain at a similar cost (or at all) using traditional
mutual funds. As an example, if a plan sponsor wanted to use
the Russell index methodology in a 401(k), it would be impossible
to select mutual funds for each of the nine domestic style
boxes with mutual funds. However, a number of ETFs currently
exist that follow the Russell methodology (see Appendix I).
As another example, it would also be difficult to utilize the
Standard & Poor’s indexing methodology through mutual
funds as well. While there are a large number of S&P 500 (i.e.,
domestic large-blend) mutual funds, there are only a few mutual
funds that cover the other blend categories, and few, if any,
for the remaining value and growth styles (see Appendix I).
While each index methodology has its unique advantages,
the primary concern of most index investors is gaining a specific
market exposure for the lowest total cost. The author likens the
different index methodologies to different ways to cut a pie,
where in the aggregate, each methodology does a more than
adequate job of representing the return of that market exposure.
While there have been noted differences in the performance of
indexes [Israelsen 2006], there is no discernable optimal indexed
methodology. Therefore, when selecting an ETF (or mutual fund)
index tracking investment, the key selection criteria is through
which methodology the market exposure can be obtained at the
lowest cost, or for the lowest expense ratio.
As shown in Appendix I, the Vanguard ETFs (which are
based on MSCI’s index methodology) are less expensive
for each of the nine style boxes compared with the respective
iShares ETFs (both Russell and S&P methodologies).
Therefore, a 401(k) plan sponsor looking to select an ETF
in order to obtain market exposure to each of these nine
domestic asset categories would likely select the Vanguard
ETFs, since they are the low-cost option. Fortunately, unlike
the Russell and S&P methodologies (both offered through
iShares), Vanguard operates mutual funds with the exact
same indexing methodology as the ETFs (MSCI), which allows
for a relatively easy apples-to-apples comparison between
mutual fund and ETF investing strategies. Figures 1, 2 and
3 include a comparison of the Vanguard ETFs for large-cap,
mid-cap and small-cap domestic styles to their respective
index mutual funds.
As shown in Figures 1, 2 and 3, the relative cost benefit
of ETFs depends on the asset size of the investment. The
average Investor-share-class Vanguard mutual fund (i.e., no
minimum required) costs 14 bps more than its respective ETF,
with a median excess cost of 11 bps. [Note: Expense ratios
on Vanguard ETFs and mutual funds have been lowered since
this analysis was conducted, but the spreads remain similar.]
The average excess cost of the Vanguard Signal share-class
mutual funds (which are replacing the Admiral-share classes
in retirement plans) is only 2 bps compared with its respective
ETF, with a median excess cost of 0 bps. The average and
median of the Vanguard Institutional-share class mutual funds,
though, is actually 2 bps less expensive than its respective ETF.
Based on the differences in expense ratios, the benefits of
ETFs clearly depend upon the level of plan assets. A pooled
ETF arrangement would make sense for smaller plans that
would have to use the Investor-share classes if the total costs
of pooling the ETF (both implicit and explicit) are less than
14 bps. For larger plans that could use Signal-share classes, if
the total costs of pooling are less than 2 bps, it could make
sense. If the plan is very large (assets of $200+ million) and
could use the Institutional-share classes, ETFs are never likely
to make sense since the Institutional-share classes were less
expensive than their respective ETFs.
In the aggregate, since the expense ratio differences
between the ETF and mutual fund strategies were so small (at
most, 14 bps for Investor-share classes), it is unlikely that any
material benefits are going to be obtained from unitizing an
ETF, once considering all the costs (both explicit and implicit).
In fact, it appears that once all the costs are considered, in
order to make ETFs 401(k)-ready, it is highly likely that any type
of pooled ETF arrangement would end up costing more than a
mutual fund approach, which can be had for a lot less effort.
Worth noting, though, is that if it were possible to create a
pooled, unitized account that could be offered to the masses
using ETFs that was cheaper than a mutual fund, mutual
fund companies would likely be taking this route. Therefore,
the idea that creating these large pooled ETF accounts that
can somehow be cheaper than a large pooled mutual fund
is somewhat faulty reasoning from the start. Even though a
small price discrepancy between an ETF and mutual fund may
exist, there are likely reasons for this, especially when they
are being offered by the same sponsoring organization. Take
for example Vanguard, the company whose investments were
38
July/August 2008

used as the case study for this paper. Vanguard offers both
mutual funds and ETFs that are based on the same underlying
index methodology (MSCI), and in fact, are share classes
of the same funds. The ETFs are typically less expensive than
their respective Investor-share class mutual funds. These
differences reflect the different record-keeping and administrative
costs associated with the two strategies. While it’s
certainly possible that someone could do it cheaper than the
800-pound gorilla (Vanguard has over $1 trillion of assets
under management), these authors would be highly skeptical
of such a claim after all the costs are considered.
A Word On Revenue Share
A common criticism of mutual funds is the payments
(known as revenue sharing) made to retirement plan providers.
These types of payments can come in a variety of
forms (12(b)-1s, subtransfer agent fees, investment manager
rebates, etc., and exist for a variety of purposes, such as a
method to pay for distribution (12(b)-1s) and record keeping
(subtransfer agent fees). It is important to note, though, that
despite the negative press associated with revenue share,
revenue share dollars are not necessarily a bad thing. From a
practical perspective, if a retirement plan provider is going to
charge 1 percent for its services, the nature of its compensation
(e.g., through revenue share generated from a higher
expense ratio or from an explicit fee billed to clients) is not
going to change the total net cost billed to the plan.
If the revenue share monies from mutual funds are
returned to the plan to offset fees (based upon the Frost
Model, or DOL Advisory Opinion 97-15A), revenue share can
actually decrease the total net cost of the mutual fund. In
some cases, this can make an index mutual fund that has a
higher expense ratio than an ETF actually be less expensive
than the ETF. For example, say a mutual fund has an expense
ratio of 15 bps and the ETF has an expense ratio of 10 bps.
Ignoring the costs associated with pooling, the ETF is clearly
less expensive; however, if the mutual fund offers 10 bps of
revenue share that is returned to the plan to offset expenses,
the net cost of the mutual fund would actually be 5 bps.
Therefore, for this example, the mutual fund is actually net
cheaper than the ETF, even though it has a higher expense
ratio. While a mutual fund with a net expense ratio of 5 bps
may seem too good to be true, we are aware of at least two
different mutual fund organizations that have index funds
available with net costs lower than 5 bps.
Beware of Backtesting
Something to be aware of with ETFs, which isn’t an issue for
other investments, is that ETFs can use “backtested” or hypothetical
returns. This is because ETFs are passive strategies and
the hypothetical performance represents the performance of the
underlying strategy the ETF is following. Therefore, it is possible
for a new ETF to show five- or 10-year performance history in its
marketing materials, despite the fact it’s brand new, although
noting the fact the returns are “hypothetical” somewhere in the
small print. This creates the “what might have happened had you
bought into this strategy 10 years ago” situation, which is also
something known as hindsight bias.
As an example of the potential problems associated with
ETFs using hypothetical performance, let’s assume that stocks
with names that begin with the letters G, W and K dramatically
outperformed all other stocks over the last 10 years. An astute
analyst may contrive some reason for this to have occurred, and
why it is likely to continue to occur, and then create an ETF that
follows such a strategy. The marketing materials would show
strong relative performance against similar indexed or active
strategies despite the fact few people (if any) would have been
likely to invest in this strategy 10 years ago.
While the previous example may seem extreme, ETF strategies
are becoming increasingly esoteric. Therefore, it is important
to ensure that the underlying methodology is sound when selecting
an ETF, not just the hypothetical historical performance.
Living On The Wild Side
An additional appeal of ETFs is the ability to gain more specialized
investment exposures to such sectors as Technology and/or
Energy, or to single countries (e.g., China) and/or more-focused
(e.g., high-dividend funds) investing strategies. While there are
mutual funds available with similar specialized investment exposures,
the low costs of the ETFs coupled with a few vocal participants
may entice a plan fiduciary to include these specialized
ETFs, along with the plain-vanilla ETFs, in 401(k) plan investment
lineups. ETFs are indexes after all, and you can’t go wrong buying
an index, right? Well, not exactly. Just because an investment
follows a passive investing strategy doesn’t mean it’s a prudent
investment for a 401(k) plan. The prudence requirement under
ERISA §404(a)(1)(b) states that a fiduciary:
discharge his duties with respect to a plan solely in the
interest of the participants and beneficiaries with the care,
skill, prudence, and diligence under the circumstances then
prevailing that a prudent man acting in a like capacity and
familiar with such matters would use in the conduct of an
enterprise of a like character and with like aims.
When selecting investments for a 401(k) plan, a plan fiduciary
must consider the nature of the workforce and whether
or not participants have the education, experience and ability
to make intelligent investment decisions [Reish et al. 2001].
Selecting an ETF because it has great recent performance
(e.g., Technology in the 1990s or Emerging Markets today)
doesn’t mean it belongs in a 401(k) and is necessarily a prudent
investment. A number of studies have shown that participants
are poor investors and are ill-suited to make proper
investment decisions (see for example [Hancock 2006],
[Kasten 2005] and [Munnell et al. 2006]).
An example of a “specialized” investment abused by 401(k)
plan participants is investment in their employer’s company
stock. A Hewitt Associates study of 401(k) plan participants
found that more than 27 percent of the nearly 1.5 million
employees surveyed who could invest in company stock had 50
percent or more of their 401(k) plan assets invested in those
shares. A participant who invests more than half of his or her
account balance in his or her employer’s stock is not only violating
some of the basic tenets of investing, but also common
sense as well (i.e., don’t put all your eggs in one basket).
Overall, including specialized investments in a 401(k) is a
lose-lose situation for a plan fiduciary. A participant (and his or
www.journalofindexes.com July/August 2008
39

her attorney) is only likely to sue if the investment returns poorly
and if that participant loses money, yet the plan fiduciary receives
little benefit if things go right. While a plan fiduciary may think
that ERISA §404(c) provides a defense for imprudent investing
at the participant level, §404(c) does not provide protection
with respect to the overall prudence of an investment. For those
readers not familiar with §404(c), it offers a plan sponsor and its
fiduciaries a defense for losses or lack of gains realized by participants
who exercise independent discretionary investment control
over their individual account balances (for additional information
on §404(c), see “ERISA §404(c) Best Practices: Myths versus Facts”
by David J. Witz). A plan can be §404(c)-compliant, yet still have
investments that are deemed imprudent under §404(a).
Conclusion
Given current technology, the cost savings from ETFs in 401(k)
plans appear to be minimal. While the expense ratios for ETFs
may be less than their respective indexed mutual fund peers, this
lower cost is materially eroded by the explicit and implicit costs
associated with making the ETFs “401(k)-ready.” In fact, it is likely
that an ETF 401(k) strategy would end up being more expensive
than a mutual fund strategy after all the costs are considered.
Minimizing plan expenses is an important consideration for a
plan sponsor and plan fiduciaries, but it doesn’t take ETFs for this
to happen. Plan sponsors can select index mutual funds as lowcost
investment solutions for participants in an attempt to minimize
overall plan fees. It’s important to remember that the purpose
of a retirement plan is to help employees and participants
retire, not to necessarily have funds that outperform their peers.
While a discussion of the benefits of active versus passive management
is beyond the scope of this paper, it is always important
to note that index investing is a much easier strategy to defend
(in court) and to monitor than a strategy that involves trying to
find next year’s top active manager (and rarely succeeding).
[A version of this article first appeared in the Journal of Pension
Benefits, Winter, 2007.]
Bibliography
Collective Investment Funds: Comptroller’s Handbook: www.occ.treas.gov/handbook/CIFfinal.pdf.
Deville, Laurent, 2006, “Exchange Traded Funds: History, Trading and Research,” http://halshs.archives-ouvertes.fr/docs/00/16/22/23/PDF/ETF-survey.pdf.
ERISA Section 404(c) Checklist: http://www.reish.com/pa/benefits/404c.cfm.
Israelsen, C. L., 2006, “Things Are Not Always What They Seem,” Journal of Indexes, vol. 8, no. 2 (March/April): 18–24.
Kasten, Gregory K., 2005, “Self-Directed Brokerage Accounts Tend to Reduce Retirement Success and May Not Decrease Plan Sponsor Liability,” Journal of Pension Benefits, vol.
12, no. 2 (Winter): 43–49.
John Hancock Lifestyle Portfolios Produce Better Results for 401(k) Plan Participants 2006: http://www.johnhancock.com/about/news/news_aug1406.jsp
Munnell, Alicia H., Mauricio Soto, Jerilyn Libby, and John Prinzivalli, 2006, “Investment Returns: Defined Benefit vs. 401(K) Plans,” Center for Retirement Research, no. 52: http:/
www.bc.edu/centers/crr/issues/ib_52.pdf.
Reish, Fred, Bruce Ashton and Gail Reich, 2001, “Is It Prudent to Offer Brokerage Accounts to 401(k) Participants?” http://www.reish.com/publications/article_detail.cfm?ARTICLEID=281.
Sammer, Joanne, 2006. “How to Manage the Pitfalls of Company Stock in 401(k) Plans,” Journal of Accountancy Online: http://www.aicpa.org/pubs/jofa/apr2006/sammer.htm.
Appendix I – ETFs For The MSCI, S&P And Russell Indexes
Morningstar Net Expense Inception
Ticker Type* Investment Name Category Assets ($B) Ratio Date Benchmark Index
VUG ETF Vanguard Growth ETF Large Growth $2.57 0.11% 01/26/04 MSCI US Prime Market Growth Index
IVW MF iShares S&P 500 Growth Index Large Growth $5.24 0.18% 05/22/00 S&P 500/Citigroup Growth
IWF MF iShares Russell 1000 Growth Index Large Growth $11.53 0.20% 05/22/00 Russell 1000 Growth Index
VV ETF Vanguard Large Cap ETF Large Blend $0.95 0.07% 01/27/04 MSCI US Prime Market 750 Index
IVV MF iShares S&P 500 Index Large Blend $17.32 0.09% 05/15/00 S&P 500 Index
IWB MF iShares Russell 1000 Index Large Blend $3.62 0.15% 05/15/00 Russell 1000 Index
VTV ETF Vanguard Value ETF Large Value $2.24 0.11% 01/26/04 MSCI US Prime Market Value Index
IVE MF iShares S&P 500 Value Index Large Value $4.39 0.18% 05/22/00 S&P 500/Citigroup Value
IWD MF iShares Russell 1000 Value Index Large Value $9.88 0.20% 05/22/00 Russell 1000 Value Index
VOT ETF Vanguard Mid-Cap Growth ETF Mid-Cap Growth $0.15 0.13% 08/17/06 MSCI US Mid Cap Growth Index
IWP MF iShares Russell Midcap Growth Index Mid-Cap Growth $2.77 0.25% 07/17/01 Russell Midcap Growth Index
IJK MF iShares S&P MidCap 400 Growth Index Mid-Cap Growth $2.05 0.25% 07/24/00 S&P MidCap 400/Citigroup Growth Index
VO ETF Vanguard Mid Cap ETF Mid-Cap Blend $1.19 0.13% 01/26/04 MSCI US Mid Cap 450 Index
IJH MF iShares S&P MidCap 400 Index Mid-Cap Blend $4.90 0.20% 05/22/00 S&P MidCap 400 Index
IWR MF iShares Russell Midcap Index Mid-Cap Blend $3.79 0.20% 07/17/01 Russell Midcap Index
VOE ETF Vanguard Mid-Cap Value ETF Mid-Cap Value $0.20 0.13% 08/17/06 MSCI US Mid Cap Value Index
IWS MF iShares Russell Midcap Value Index Mid-Cap Value $3.65 0.25% 07/17/01 Russell Midcap Value
IJJ MF iShares S&P MidCap 400 Value Index Mid-Cap Value $2.67 0.25% 07/24/00 S&P MidCap 400/BARRA Value Index
VBK ETF Vanguard Small Cap Growth ETF Small Growth $0.78 0.12% 01/26/04 MSCI US Small Cap Growth Index
IWO MF iShares Russell 2000 Growth Index Small Growth $2.96 0.25% 07/24/00 Russell 2000 Growth Index
IJT MF iShares S&P SmallCap 600 Growth Small Growth $1.49 0.25% 07/24/00 S&P SmallCap 600/Citigroup Growth Index
VB ETF Vanguard Small Cap ETF Small Blend $0.98 0.10% 01/26/04 MSCI US Small Cap 1750 Index
IWM MF iShares Russell 2000 Index Small Blend $11.31 0.20% 05/22/00 Russell 2000 Index
IJR MF iShares S&P SmallCap 600 Index Small Blend $4.94 0.20% 05/22/00 S&P SmallCap 600 Index
VBR ETF Vanguard Small Cap Value ETF Small Value $0.77 0.12% 01/26/04 MSCI US Small Cap Value Index
IWN MF iShares Russell 2000 Value Index Small Value $4.17 0.25% 07/24/00 Russell 2000 Value Index
IJS MF iShares S&P SmallCap 600 Value Index Small Value $1.81 0.25% 07/24/00 S&P SmallCap 600/Citigroup Value Index
Source: Vanguard. Data as 11/02/07. *MF = Mutual Fund, ETF = Exchange-Traded Fund. **30-Day Average.
40
July/August 2008

The ABCs Of ETFs
Alpha, Beta and Cost
By Richard Ferri
42
July/August 2008

Exchange-trade funds are benchmarked to an expanding
universe of indexes. Those indexes range from
traditional passive benchmarks that use capitalization
weighting to sophisticated quantitative strategies
and alternative weighting methods. This article links index
strategies to ETFs expenses. There is clear evidence that
ETFs following more sophisticated index strategies charge
higher fees than ETFs following passive market benchmarks.
I use a new database at ETFguide.com to classify
ETFs by index strategy and create a unique pricing model
for ETFs based on index strategy. The model enables investors
to compare the expenses of ETFs with like strategies,
and guide ETF providers toward a sound pricing model.
Index Classification Terminology
Indexes can be classified by basic purpose and specific
strategy. There are two basic types of indexes. A market index
is a traditional “plain vanilla” measure of market value that
uses passive security selection and weights securities based
on market capitalization. To the contrary, a strategy index is a
technique for investing in the markets rather than a measurement
of market value. In a sense, market indexes track market
“Beta,” and strategy indexes attempt to create some type
of “Alpha,” either in financial terms or in expressive terms
such as with socially responsible indexes.
Market indexes are designed to measure the performance
of financial markets. They are characterized by passive security
selection and capitalization weighting. Security selection can
include the entire universe of securities, a sampling of securities
or one item such as the price of gold. Capitalization weighting
can be in the form of full float, free float, liquidity or production
weighting. The primary purpose of market indexes is tied to
measurement, not performance. They provide a measurement of
market risk and return, which can be summed up as beta.
Strategy indexes are investment strategies. They are custommade
to seek “Alpha” in the marketplace in whichever way
their creators define alpha. ETF companies that use strategy
indexes often imply that their products offer something better
than ETFs that follow market indexes. WisdomTree promotes
their fundamental strategy indexes as “Built differently,
with the goal of higher returns with less risk.” PowerShares
claims their ETFs offer “exceptional asset management tools”
through the replication of “enhanced indexes.”
Index strategy classification goes to a different level with the
use of the Index Strategy Box categorization system. ETFs are
separated into different categories based on their security selection
and security weighting techniques. There are three broad
selection strategies: Passive, Screened and Quantitative; and
three broad weighting strategies: Capitalization, Fundamental,
and Fixed. The three security selection methods and three security
weighting strategies form a matrix. Figure 1 shows the ninebox
tic-tac-toe design of Index Strategy Boxes.
Analysis Of ETFs And Fees By Index Strategy
The classification of ETFs by Index Strategy Boxes is available
at ETFguide.com. The database included data on all
ETFs, exchange-traded notes (ETNs), HOLDRS, BLDRS and
other exchange-traded portfolios. For this article, I screened
the database for all U.S. long-only equity ETFs. The list included
funds that follow broad market indexes, market size and
style indexes, industry sectors indexes and thematic indexes.
No inverse or leveraged funds were included. There were 304
funds in the database that matched the criteria.
I sorted the 304 funds by the Index Strategy Box information
and calculated the number of funds across each of
the three broad security selection methods; each of the
three broad weighting methods is shown in Figure 2. I
also showed the distribution of the funds within the nine
Index Strategy Boxes (shaded section).
Figure 2 maps the universe of U.S. equity ETFs by index
strategy. The row labeled “Passive” has 160 total ETFs. Those
funds follow indexes that use a passive security selection
strategy. Of the 160 funds, the 124 in the green block also
follow a “Capitalization” weighting method. These are the
traditional market index funds. The other 36 ETFs in the passive
selection row follow alternative weighting strategies. Of
those, 14 funds weight stocks using a fundamental method
and 22 use a fixed-weight method. These 36 funds follow
strategy indexes. The strategy is alternative weighting.
ETFs that select securities using either basic stock
screens or advanced quantitative methods are highlighted
in different rows. Those rows were also divided into the
three weighting methods. When complete, all 304 ETFs
were in one of the nine boxes.
Figure 1
Security Selection
Source: ETFGuide.com
Figure 2
Source: ETFGuide.com
Index Strategy Boxes
Quantitative
Screened
Passive
Capitalization
Fundamental
Fixed Weight
Security Weighting
The Number Of Long-Only U.S. Equity ETFs
By Index Strategy Box
Quantitative 85 5 2 78
Screened 59 26 18 15
Passive 160 124 14 22
304 155 34 115
Capitalization Fundamental Fixed Weight
www.journalofindexes.com July/August 2008
43

Figure 3
Source: ETFGuide.com
Average Fees By Index Strategy
Quantitative 0.64% 0.60% 0.60% 0.65%
Screened 0.47% 0.47% 0.38% 0.58%
Passive 0.34% 0.30% 0.57% 0.45%
0.34% 0.48% 0.60%
Capitalization Fundamental Fixed Weight
Index Strategies And ETF Fees
After categorizing all 304 ETFs by their underlying index
security selection and security weighting methods, I calculated
the average fee for the ETFs in each security selection
strategy and security weighting strategy. Then I calculated
the average fee for each of the nine Index Strategy Boxes.
The results of that analysis are in Figure 3.
Figure 3 shows that U.S. equity ETFs that follow market
indexes (green shade) charge on average 0.30 percent in
annual fees. Thus, it can be said that basic beta exposure
to various segments of the U.S. equity markets cost 0.30
percent on average. That may seem high at first observation,
but recall that the 124 funds in the box include many types
of market index ETFs. In addition to broad market indexes,
there are many subsets, including industry sector funds;
growth and value funds; and large-, mid- and small-cap
funds. The fees charged for sector and style slices of a broad
market index tend to be higher than the fee for a broad
market index ETF. For example, iShares Dow Jones U.S.
Technology Sector Index Fund (NYSE Arca: IYW) has a fee
of 0.48 percent, while the iShares Dow Jones Total Market
Index ETF (NYSE Arca: IYY) has a fee of only 0.20 percent.
At 0.30 percent, basic “Beta” exposure through ETF
investing is relatively inexpensive, while the quest for
“Alpha” through funds that follow strategy indexes is
more expensive. As security selection methods and security
weighting techniques become more complex, the fees
charged by ETFs to manage portfolios to those indexes go
up. There is a direct correlation between the complexity
of the index and the cost of ETF management.
Figure 4
Source: ETFGuide.com
Index Strategy Box Pricing Template For ETFs:
Added Fees For More Complex Strategies
Quantitative 0.35% 0.40% 0.40%
Screened 0.15% 0.25% 0.25%
Passive — 0.15% 0.15%
Capitalization Fundamental Fixed Weight
ETF Fee-Pricing Model Based On Index Strategy
I created an ETF pricing model based on the information
from the Index Strategy Box fee data. The purpose
of the model is to benchmark ETF fees to the complexity
of each underlying index strategy. Investors have been
conditioned to pay higher fees for ETFs that follow alphaseeking
strategy indexes. However, until now there has
been no model for relating different indexing strategies to
ETF fees. The model is a guide to average strategy pricing.
No assumptions are made as to whether any particular
index strategy is worth the average fee charged by ETF
companies to follow that strategy.
There are two uses for an ETF pricing model based on
index strategy. First, investors can compare the fees of
ETFs using like indexing strategies. Second, ETF companies
can use the data to price new ETFs in line with the
competition, and possibly reprice existing ETFs to align
them with the average.
Some adjustments needed to be made to the raw fee data to
smooth out inconsistencies. Those issues existed mainly from
the pricing of security weighting methods; for instance, an
adjustment for ETFs following fixed weight methods because
of the large number of higher-cost quantitative funds that use
a fixed security weighting method. Also, certain index methods
commanded higher-than-normal fees even though their strategy
is similar to indexes by other vendors. For example, ETFs
following fundamentally weighted RAFI indexes were considerably
more expensive than ETFs following other fundamentally
weighted indexes such as WisdomTree products. Once these
adjustments are made, it was possible to create the Index
Strategy Box pricing template in Figure 4.
Figure 4 represents additional fees added to the average
fee for beta-seeking ETFs in a particular category. Recall
that beta-seeking indexes use passive security selection and
capitalization weighting. As an example, the iShares S&P 100
Index (AMEX: OEF) charges a fee of 0.20 percent. If an ETF
were created that tracked an equal-weighted S&P 100 Index,
a reasonable fee would be 0.35 percent. That is the sum of a
0.20 percent market index strategy plus an extra 0.15 percent
fixed-weight strategy fee.
I checked the pricing against different index styles to test
for consistency of Index Strategy Boxes fees across nonoverlapping
sets of data. The three styles I tested were 1) broad
market and large-cap ETFs, 2) mid-cap and small-cap ETFs,
and 3) industry sector indexes. The fees charged by ETFs in
the three different data sets were remarkably consistent with
the pricing template in Figure 4.
An Example Of Fee Pricing With
Index Strategy Boxes
The Index Strategy Box fee pricing template is a valuable
tool that can be used by investors, advisors and ETF
providers. The following is an example of how this pricing
model can be applied.
I analyzed the fees in the U.S. broad market and large-cap
sectors from the ETFguide.com database. The average ETF
fee for beta exposure in this category is 0.20 percent. Once
the cost of beta was known, I applied the Index Strategy Box
pricing template to the 0.20 percent fee. The results are illustrated
in Figure 5.
continued on page 57
44
July/August 2008

Israelsen continued from page 29
often), the margin of victory can be large. For example,
during the five-year period of 1995–1999, small-cap
growth beat small-cap value by 831 basis points. Overall,
when small-cap growth outperformed small-cap value, the
average margin of victory was 257 bps (and the median
margin of victory was 94 bps compared with the median
small-cap value margin of victory of 818 bps).
In light of the historical performance of dominance of
small-cap value over small-cap growth, it is peculiar that
small-cap growth U.S. equity funds outnumber small-cap
value U.S. equity funds more than 2-to-1. Apparently
small-cap growth managers (and small-cap growth investors)
are optimists. They are willing to pay a high price
(in the form of volatility) for a relatively rare, but potentially
large, burst of outperformance relative to small-cap
value. They must see a rewarding small-cap growth frontier
off in the distance. That’s about the only place they
could see it … because such a frontier hasn’t surfaced
very often in the past 27 years.
Ferri continued from page 44
Investors and advisors can refer to the data in Figure
5 to determine fair fees for each ETF that follows a particular
index strategy. For example, assume an advisor is
considering the purchase of a U.S. large-cap growth ETF.
The cost for one ETF under consideration is 0.35 percent,
while the cost for another is 0.60 percent. Which ETF is
more or less overpriced than the other?
The answer is that it depends on the underlying index
strategy of each fund. If the 0.35 percent ETF is a passively
selected and capitalization-weighted “Beta” fund, and
the 0.60% ETF follows an alpha-seeking index that uses a
quantitatively driven index and weights stocks using fixed
weights, then based on index strategy alone, the 0.60
percent fund is a better value than the 0.35 percent fund.
I am NOT suggesting that investors should buy the 0.60
percent quantitative ETF. Rather, I am suggesting that the
0.35 percent beta ETF is overpriced.
Summary
There is a clear link between the complexity of index
Figure 5
U.S. Broad Market/Large-Cap Index
Strategy Box Pricing Matrix
Quantitative 0.55% 0.60% 0.60%
Screened 0.35% 0.45% 0.45%
Passive 0.20% 0.35% 0.35%
Source: ETFGuide.com
Capitalization Fundamental Fixed Weight
strategy and the fees ETF companies charge for products.
It is important for investors and advisors to understand
this relationship when analyzing competing products.
The Index Strategy Box Pricing Template for ETFs is one tool
that can be used to compare the pricing of any category
of funds. The methodology should assist investors with
ETF comparisons and guide product providers to create a
more uniform pricing model.
www.journalofindexes.com July/August 2008
57

Behavioral Finance Roundtable continued from page 25
the best-performing and highest-rated products. But again,
this process does not work nearly as well for investment
products. In fact, when comparing funds, index funds are
typically rated as average, while the current winning sectors
are rated high, and out-of-favor funds rated low. So, some
of the concepts of behavioral finance—ill-advised decisions
made on the basis of poor information, lack of understanding
or the impulsiveness of trying to beat the market—also apply
to individual investors.
In the end, behavioral finance is about evaluating the
investing habits of people, and people—whether professionals
or nonprofessionals—are capable of making rational and
irrational decisions.
JoI: If indexing is proven to provide the best odds for long-term
success, why don’t more investors index?
Kinniry: As in many other areas in life, we often overestimate
our capabilities (i.e., we are all better-than-average drivers and
our kids all have higher-than-average IQs). It is no different when
it comes to investing. In many respects, our ego tricks us and
limits our ability to consider that we may be average or even
below average when compared with the competitive and large
playing field of investment professionals. As a result, we tend to
ignore proven strategies such as indexing and think that we can
do a better job following other strategies.
JoI: Can active managers use behavioral insights to outperform
the market?
Kinniry: Some managers will outperform the market, whether
they use behavioral research, technical research, fundamental
research, quantitative research or a combination thereof.
However, the challenge facing active managers is being able to
outperform the market by having information that is superior to
that of all other market participants and by having very low trading
friction. These are not impossible hurdles, but high hurdles.
Perhaps the best chance for active management to be successful
over the long run is to utilize the best of passive management:
low costs, low relative friction along with their active management
techniques and a talented yet humble team of sophisticated
investment professionals.
David Blitzer, managing director and
chairman of the Index Committee,
Standard & Poor’s
JoI: What does behavioral finance tell us about
investing and indexing?
David Blitzer, Standard & Poor’s (Blitzer): One of the key factors
determining whether stock prices rise or fall are investors’
buy/hold/sell decisions. Investors don’t know the future and their
decisions usually depend on a mix of rational analysis, opinions,
fears, greed and wishful thinking. Behavioral finance warns us
that our decisions aren’t always rational and at times will reduce
our profits or increase our losses. One way to reduce the impact
of our irrational or emotional decisions is to invest with a simple
rule: Index. This way, investors can avoid falling in love with
stocks, selling winners too soon or denying the losers’ existence
by refusing to sell them to cut the losses. Indexing is not the only
rules-based emotionless way to invest; however, it is one of the
simplest ways and it does have a proven track record.
JoI: What are the biggest mistakes investors make from a
behavioral standpoint?
Blitzer: Letting any successful investment convince them
that they can beat the market consistently. Someone buys
a stock, it rises 10 percent and they’re a winner—and a
stock market genius. First, they forget that three other
stocks in the same industry rose 15 percent at the same
time. Then they think they can time the market for their
next move. Finally, they read that indices outperform
active managers two out of three times and are absolutely
sure they will consistently be in that top third who always
beat the market. There are some people who escape this—
but they are often the ones who believe that even though
they can’t pick stocks, they have found a money manager
who can pick stocks.
JoI: Is behavioral finance being used to justify poor investment
decisions and a lack of education?
Blitzer: While behavioral finance may explain some poor
investment decisions, it doesn’t justify them. An investor who
says his education is complete and that he fully understands
the markets is an investor who can’t or won’t compare his
results to the markets over the long run.
JoI: If indexing is proven to provide the best odds for long-term
success, why don’t more investors index?
Blitzer: People see indexing as settling for the average result
and no one wants to be “just average.” Further, no one wants
to admit he paid too much, so when they understand that the
key reason indexing outperforms active management is lower
costs, they are even less likely to embrace indexing. Finally,
stock markets are very complex and indexing is simple, so
how could it possibly work?
JoI: Can active managers use behavioral insights to outperform
the market?
Blitzer: Active managers, like any other investors, can use
insights from behavioral finance to improve their results. In
the last 10 years we have seen two massive bubbles; one in
dot-com stocks and the second in housing. Understanding
either requires recognizing the importance of human behavior
and emotions in investing and markets. That said, simply
having read or even understanding much of the behavioral
finance literature would not have guaranteed selling at the
peak of either bubble. Moreover, no managers always outperform
the market; some do it occasionally, others do it more
often; but no one does it all the time.
July/August 2008
45

Talking Indexes
Inside The Home Price Indices
by David Blitzer
By now there is probably no one left in America, possibly
no one left on the planet, who doesn’t know that home
prices are falling, and in some cases, falling very fast.
While the headlines on the monthly release of the S&P/Case
Shiller ® Home Price Indices are widely reported, much less
attention or analysis is given to some of the more interesting
details. Digging into the data released at the end of April—
covering prices through February—provides a clearer picture
of what is happening to home prices across the country.
While this is not the first time home prices have declined,
the movements seen since the start of the decade are not typical.
First, the nationwide increase in home prices was far larger
than anything seen recently. The old adage about real estate
and location location location was largely true: Prices might
soar or tumble in one region while doing the reverse or barely
moving in a neighboring state.
This time the home price event is national in scope. Its
breadth reflects that over the last 10 to 20 years the mortgage
market has become a national market. Those who recall the
reversals suffered in the early- to mid-1990s saw the first hint
of a national mortgage market; by a few years ago, it had
arrived. The Fed’s low interest rates, rising willingness by both
lenders and borrowers to take on more risk and the attendant
laxity in lending standards covered the nation. As a result,
home prices across the country rose and then fell. For the last
six months, all 20 of the cities covered by S&P/Case Shiller ®
have seen prices fall month to month; only one city, Charlotte,
N.C., can claim that prices are up over the last 12 months.
National in scope does not mean prices move in lockstep.
Among the 20 cities are Boston, where prices peaked in
October 2005, and Charlotte, N.C., where they didn’t peak
until September 2007, almost two years later. One reason
why Charlotte still is up over the last year is that it hasn’t had
enough time to fall that far—something that could change in
one of the next reports. Not only do different cities experience
their peak prices at different times, the gains and losses vary
across the board. Figure 1 summarizes this data.
Using the beginning of 2000 as the base period—back when
the boom was in dot-com stocks rather than homes—we see that
the rate and level of appreciation varies across the cities. Miami
takes the blue ribbon with prices up 181 percent by September
2007, with Los Angeles, up 174 percent, and Washington, D.C.,
up 151 percent, close behind. However, Los Angeles reached its
peak three months earlier than Miami. At the other end of the
scale is Detroit, where prices rose only 27 percent from January
2000 to January 2006, a compound rate of 4.1 percent; not that
far ahead of inflation. Other than Washington, the big gains were
in the Sun Belt states with Miami, Las Vegas, Phoenix, San Diego
and Los Angeles leading the way. The industrial Midwest and
the midsection of the country trailed with Detroit and Cleveland,
plus Denver and Dallas, seeing only modest gains. The composite
index covering all 20 cities saw prices rise 107 percent, peaking
in August 2006.
There is definitely a pattern of “the bigger they are, the harder
they fall.” Miami scored the largest gain and, with a 22 percent
slide since it peaked, one of the larger declines. Moreover, Miami
was a late bloomer and managed to see home values drop by
more than a fifth in seven months, while Las Vegas home prices
took a year longer to fall only two percentage points more. If one
measures the bust by the speed of descent, Miami “wins.”
There is a second set of data that confirms the pattern of
“the bigger they are, the harder they fall.” For 17 of the 20
cities, there are tiered price indices showing the price movements
for low-, mid- and high-priced homes. The break points
46
July/August 2008

Figure 1
Peak Date
Ups And Downs Of S&P/Case Shiller® Home Price Indices
Decline
From
Peak
Decline
Rank
Peak
Since
Jan-2000
Peak Rank
Time
From Peak
(Months)
Rate Of
Decline (Pct
Pts/Month)
Rate Of
Decline
Rank
MA-Boston October-05 -12.1% 12 82.5% 12 28 0.433 21
CA-San Diego December-05 -24.0% 3 150.3% 4 26 0.922 11
MI-Detroit January-06 -23.2% 4 27.1% 18 25 0.927 10
CA-San Francisco June-06 -20.1% 8 118.4% 8 20 1.004 8
DC-Washington June-06 -17.5% 9 151.1% 3 20 0.877 14
AZ-Phoenix July-06 -24.1% 2 127.4% 7 19 1.266 5
NY-New York July-06 -8.0% 15 115.8% 9 19 0.424 22
Composite-10 July-06 -15.8% 21 126.3% N/A 19 0.831 15
FL-Tampa August-06 -20.8% 7 138.1% 5 18 1.155 6
OH-Cleveland August-06 -13.5% 11 23.5% 20 18 0.750 17
Composite-20 August-06 -14.8% 21 106.5% N/A 18 0.823 16
CO-Denver September-06 -9.1% 13 40.3% 15 17 0.536 20
NV-Las Vegas September-06 -24.5% 1 134.8% 6 17 1.443 2
CA-Los Angeles October-06 -21.6% 6 173.9% 2 16 1.349 3
IL-Chicago October-06 -9.1% 14 68.6% 14 16 0.566 19
MN-Minneapolis October-06 -14.7% 10 71.1% 13 16 0.920 12
FL-Miami January-07 -22.1% 5 180.9% 1 13 1.702 1
TX-Dallas July-07 -6.9% 17 26.5% 19 7 0.988 9
GA-Atlanta August-07 -7.8% 16 36.5% 16 6 1.299 4
OR-Portland August-07 -5.5% 19 86.5% 11 6 0.918 13
WA-Seattle August-07 -6.5% 18 92.3% 10 6 1.079 7
NC-Charlotte September-07 -3.4% 20 35.9% 17 5 0.686 18
Color shading shows cities with the same peak dates.
for the prices are set city by city and divide the market into
thirds. Only 17 cities are covered because of data availability.
City by city, the least expensive homes saw the largest increase
and the largest decrease in prices. Figure 2 shows the pattern
for San Francisco; it is similar for other cities. These were the
homes where the speculation and aggressive lending and
borrowing was concentrated. It strongly suggests that innovations
in mortgages drove a large part of the shifts in the
Figure 2
Low-, Mid- And High-Priced Homes In San Francisco
0
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
■

News
Bear Stearns Rolls Out
First U.S. Active ETF
Well, it finally happened. The U.S.
ETF market got its first actively managed
exchange-traded fund this spring
when the much anticipated Bear
Stearns Current Yield Fund (AMEX:
YYY) launched on March 25. The fund,
which charges an expense ratio of
0.35 percent, invests in short-duration
U.S. government and corporate debt,
targeting an average duration of 180
days. It functions more or less like an
actively managed money market fund.
YYY’s launch was quickly followed
by the launch of four new actively
managed ETFs from Invesco Power-
Shares. Three of the new funds are
stock funds.
The PowerShares Active AlphaQ
Fund (NYSE Arca: PQY) selects its
holdings from the NASDAQ Stock
Exchange based on a proprietary
quantitative model developed by
AER Advisors; it is mainly a large-cap
growth fund. The PowerShares Active
Alpha Multi-Cap Fund (PQZ), also
advised by AER Advisors, holds stocks
from all size and style segments and
those listed on any U.S. exchange.
Both PQY and PQZ are restricted to
making a limited number of trades on
a weekly basis.
The third equity fund, the Power-
Shares Active MegaCap Fund (PMA),
is a quantitative fund managed by
PowerShares’ parent company, Invesco.
All three of the stock funds charge
expense ratios of 0.75 percent.
The fourth PowerShares fund, the
PowerShares Active Low Duration
Fund (PLK), is a fixed-income fund
that targets a duration of 0–3 years.
It charges an expense ratio of 0.29
percent. PLK is also managed by PowerShares’
parent, Invesco.
The obvious question is what distinguishes
these funds from the other
quantitative funds from PowerShares,
such as its flagship Dynamic Market
Intellidex (AMEX: PWC). The answer is
that these new funds do not track an
index, which should help them avoid the
potential problem of “front-running.”
In regular index-based ETFs, the
index provider publishes the changes
to the index in real time, and the
fund must then trade to match those
changes. Enterprising hedge funds
can and do step in front of funds and
profit from the incipient demand.
The new active funds will be able
to make trades during the trading
day, but those trades won’t be made
public until after settlement the following
day. That one-day window of
silence is the key reason PowerShares
launched these funds.
Home Prices Tumble
Things still look unbearably grim
as the housing market malaise drags
indexes lower and lower. The 10-city
Standard & Poor’s/Case-Shiller Home
Price Indices was down 2.8 percent
for the month of February and a
record 13.6 percent for the trailing
12-month period. Meanwhile, the
20-city composite was down 2.6 percent
for the month and 12.7 percent
for the trailing 12-month period. In
all, the 10-city composite is down
15.8 percent from its June 2006 peak,
while the 20-city composite has fallen
14.8 percent from its July 2006 peak.
Without exception, every metropolitan
statistical area covered by
the indexes showed a decline for the
month of February—ranging from
a 0.4 percent decline for Charlotte
to a 5.0 percent drop for San Francisco.
For the 12-month period, only
Charlotte had a positive return, up
1.2 percent. Las Vegas had the worst
one-year decline, at -22.8 percent,
followed by Miami at -21.7 percent
and Phoenix at -20.8 percent. S&P
noted that Las Vegas and Miami grew
Things still look unbearably grim as the
housing market malaise drags indexes
lower and lower.
48
July/August 2008

apidly in the 2004/2005 periods,
with annual growth rates that were
at times above 50 percent and 30
percent, respectively.
Barclays Launches First
All-World Stock ETF
U.S. consumers looking for a true
all-world fund got one March 28, as
Barclays Global Investors launched
the world’s first all-world ETF.
The iShares MSCI ACWI Index
Fund (NASDAQ: ACWI) tracks an
index of 2,884 different stocks from
developed and emerging markets in
every investable market in the world.
At the end of 2007, the U.S. represented
42 percent of the underlying
index, Europe comprised 31 percent,
emerging markets 11 percent, Japan
8.5 percent and Canada 4 percent.
The new fund carries an expense
ratio of 0.35 percent, and offers
investors their first shot at one-stop
shopping: No more home bias, rebalancing
or anything else.
Vanguard Files For
All-World Fund
Vanguard kicked off April with a
SEC filing to launch the Vanguard
Global Stock Index Fund. The fund
will be available as Investor shares,
Institutional shares and ETF shares.
The ETF shares will charge 0.25 percent
in annual expenses. That expense
ratio undercuts the new offering from
Barclays Global Investors by 10 basis
points (0.10 percent). The new Vanguard
fund will track the FTSE All-
World Index, an index that covers
2,900 stocks from 48 countries.
Northern Trust Enters ETF Market
Northern Trust Global Investments,
the asset management arm of Northern
Trust, has entered the ETF arena
with the launch of its Northern Trust
Exchange-Traded Shares, or “NETS.”
NETS has taken a unique approach
to developing its ETF family. Most
ETF providers use one family of
indexes to cover all global markets;
for instance, iShares has a complete
family of single-country ETFs that
rely on MSCI indexes. In contrast,
NETS has licensed the most popular
(or some of the most popular) local
indexes in each foreign market, such
as the FTSE 100 in the U.K., the CAC
40 in France, etc. It’s an interesting
idea for carving out space in a
crowded ETF market.
The first NETS funds were launched
on April 9 and included the S&P/ASX
200 Index Fund (AMEX: AUS) covering
Australia and the NETS FTSE 100 Index
Fund (AMEX: LDN) in the U.K. They
were followed by seven more funds:
NETS DAX Index Fund (AMEX:
DAX)—Germany
NETS TOPIX Index Fund (NYSE
Arca: TYI)—Japan
NETS CAC 40 Index Fund (NYSE
Arca: FRC)—France
NETS Hang Seng Index Fund (NYSE
Arca: HKG)—Hong Kong
NETS S&P/MIB Index Fund (AMEX:
ITL)—Italy
NETS FTSE Singapore Straits Times
Index Fund (AMEX: SGT)—Singapore
NETS FTSE/JSE Top 40 Index Fund
(AMEX: JNB)—South Africa
JNB charges an expense ratio of
0.65 percent; the rest of the funds
charge 0.47 percent. There are several
more ETFs covering various foreign
markets in registration.
INDEXING DEVELOPMENTS
Dow Jones Embraces RBP
In New 130/30 Indexes
Dow Jones has launched a new
suite of indexes that follow the
“130/30” strategy popular with many
hedge funds.
The 130/30 strategy is designed
to deliver higher-than-market returns
without taking on excessive risk. In
the classic 130/30 strategy, a hedge
fund manager starts with a 100 percent
long position in the market
and then layers a 30 percent long/30
percent short stock-picking portfolio
on top of it. In this “30/30” portfolio,
the manager goes long the stocks she
likes the best and short the stocks
she likes the least. The idea is to
maintain 100 percent net exposure
to the market while adding alpha
with the 30/30 portfolio.
The Dow Jones U.S. RBP indexes
use a unique methodology developed
by Transparent Value LLC to select
stocks for the 30/30 basket. Transparent
Value’s methodology, called
“Required Business Performance”
(hence RBP), uses discounted cash
flow analysis to determine how much
revenue a company needs to generate
each quarter in order to justify its
current stock price. It then examines
historical revenue trends to project
the likelihood that the stock will
meet this RBP requirement.
Stocks with the highest probability
of meeting their RBP form the 30 percent
long basket in the new indexes,
while stocks with the lowest probability
form the 30 percent short basket.
Currently, the DJ U.S. RBP index
family consists of three major indexes:
the core Dow Jones RBP U.S.
Large-Cap 130/30 Index, plus similarly
constructed growth and value
versions.
Dow Jones expects to launch additional
indexes (including non-130/30
indexes) using the RBP methodology.
Merrill Joins The Frontier
In March, Merrill Lynch Global
Research rolled out the Merrill Lynch
Frontier Index, joining the growing
www.journalofindexes.com July/August 2008 49

number of indexers moving into
the frontier market category. The
new index covers 50 of the largest
and most-traded companies from 17
countries in Asia, Africa, Europe and
the Middle East. The list includes
companies from countries like Nigeria,
Cyprus, Kazakhstan and Morocco.
Vietnam, one of the most spectacular
frontier market success stories,
is also included. Stocks eligible
for inclusion must have a minimum
market capitalization of $500 million
and a minimum three-month average
daily turnover of $750,000. They
must also have a foreign ownership
limit of more than 15 percent.
There is not yet an ETF tied to
the index, but many expect one to
launch in the future.
News
the same methodology as the UBS
Bloomberg Constant Maturity Commodity
Index, which incorporates
multiple maturities into its calculation
and has a complex, multifactored
weighting methodology.
“The UBS Bloomberg CMCI Food
Index was developed in response to
a growing demand from investors
looking to hedge against this price
inflation as well as from those looking
to buy food-related commodities
for diversification purposes,”
said UBS head of Commodity Index
Structuring Morgan Metters.
At the index’s launch, corn had the
largest weighting, at 17.33 percent, followed
by soybeans, at 14.94 percent.
According to UBS, the new index
is designed to underlie investable
index-based funds tied to the benchmarks,
which are the company’s first
broad-based, global benchmarks.
The indexes evaluate more than 700
stocks from 24 countries around the
world, and include a flagship index
and five regional subindexes.
Northern Trust rolled out its
Northern Global Sustainability Index
Fund in March. The no-load mutual
fund charges an expense ratio of 65
basis points. Meanwhile, TIAA-CREF
has licensed the index to use in
its previously all-domestic, actively
managed CREF Social Choice variable
annuity account. The portfolio is the
largest socially screened portfolio in
the U.S., with 430,000 investors and
$9.2 billion in assets.
Finally, Pax World Management
Not even a year after it launched its global sustainability indexes,
KLD is licensing them near and far.
UBS Index Tracks Food Inflation
Spurred on by the continuous
increases in food prices, UBS
AG and Bloomberg Finance have
launched the UBS Bloomberg CMCI
Food Index. The new commodity
index covers 13 different food
commodities ranging from orange
juice and lean hogs to coffee and
soybeans. The index is based on
Merrill Lynch recently
launched a carbon
emissions benchmark.
products like ETFs. It is calculated in
U.S. dollars, euros and Swiss francs.
KLD Racks Up Licensees
Not even a year after it launched
its global sustainability indexes,
KLD is licensing them near and far.
As of March 2008, Northern Trust,
TIAA-CREF and Pax Worldwide
Management Corp. now offer KLD
Corp. has licensed the KLD Global
Sustainability Index, the KLD North
America Sustainability Index and the
KLD Europe Asia Pacific Sustainability
Index for use in a variety of investable
products, including ETFs.
Merrill Tracks Emissions
Contracts With New
CO2 Emissions Index
Merrill Lynch recently launched
a carbon emissions benchmark, the
MLCX Global CO 2 Emissions Index,
that tracks two types of emissions
contracts. The contracts are the
European Union Allowance (EUA)
contracts established under the
European Union Emission Trading
Scheme and the Certified Emission
Reduction (CER) contracts that are
part of the Kyoto protocol.
The two contracts have equal
standing in the index, but component
weightings are based on
liquidity. Merrill also calculates a
subindex for both contract types.
According to a Reuters article, CER
contracts represent about 29 per-
50
July/August 2008

cent of the index, while EUA contracts
represent about 71 percent.
It is likely the firm will add other
types of emissions contracts to the
broad emissions index as they begin
trading on the relatively new and
developing global carbon credit
market. Merrill’s intent is for the
MLCX Global CO2 Emissions Index
to be used to underlie investable
products, like ETFs.
S&P Offers A Narrow
Slice Of India
At the end of March, S&P
launched the S&P India 10 Index.
With only 10 components, the new
index seems comparatively narrow.
Capitalization minimums are
the reason. Companies eligible for
inclusion must have market capitalizations
of at least $500 million
and a monthly average daily traded
value of $1 million. Rather than size,
the components are selected and
weighted based on liquidity, with a
20 percent cap placed on individual
components. Stocks are selected
from the S&P/IFCI India Index. All
components trade as ADRs or GDRs
on developed market exchanges.
S&P accounts for the foreign
ownership restrictions when determining
a company’s float, which
should take care of the new restrictions
on capital inflows imposed by
the Indian government last year.
The index itself was designed to
underlie investment products,
according to S&P.
News
S&P Offers Access To Africa
With Three New Indexes
Standard & Poor’s recently
launched its Africa index series,
the first of its kind from a large
index provider.
The S&P Pan Africa Index is
designed to represent 80 percent
of the market capitalization of each
of 12 African markets: Botswana,
Cote D’Ivoire, Egypt, Ghana, Kenya,
Mauritius, Morocco, Namibia, Nigeria,
South Africa, Tunisia and Zimbabwe.
It has a total of 333 companies
and represents about $362 billion
in adjusted market capitalization.
The S&P Africa Frontier Index
excludes Egypt, Morocco, South
Africa and Tunisia and has a market
cap of around $72 billion.
Lastly, there is the narrowly
defined S&P Africa 40 Index, which
includes the largest and most liquid
stocks in the continent. It has components
from eight African countries
and a market cap of $207 billion.
New Alternative Long-Short
Fund From Rydex
In March, Rydex Investments
launched a new long-short index
mutual fund that uses a fund of funds
approach to investing in alternative
benchmarks that include ETFs.
The Rydex Alternative Strategies
Allocation Fund (RYFOX) can
allocate its assets to absolute
strategies in five segments using
ETFs or mutual funds. Around the
time of its launch, the fund was
invested primarily in (by order of
total assets) the Rydex Managed
Futures Strategy Fund (RYMFX),
which tracks the S&P Diversified
Trends Indicator index (47.5 percent
of the portfolio); the Power-
Shares DB G10 Currency Harvest
(AMEX: DBV) ETF (21 percent); and
the Rydex Commodities Strategies
Fund (RYMEX), which follows the
S&P GSCI Commodity Index (14
percent). The remainder of the
portfolio was invested in the Rydex
Real Estate Fund (RYREX).
The RYFOX fund rebalances
monthly but can do so even more
often depending on its quant
screens. The annual expense ratio is
estimated at 1.76 percent. No fund
of funds fees are charged.
Direxion Leverages India
In March, Direxion Funds expanded
its offering of leveraged mutual
funds that track emerging markets
with the launch of its India Bull
2X Fund (DXILX), which aims to
capture 200 percent of the daily
price performance of the MSCI India
Total Return Index.
The new fund invests mainly
in the iPath MSCI India Index
exchange-traded note (NYSE
Arca: INP) and derivatives linked
to it. Direxion says it will use
other exchange-traded products
that track India’s market if INP
continues to have difficulty tracking
its index.
DXILX charges a net management
fee of 1.50 percent.
AROUND THE WORLD OF ETFS
PowerShares Puts A Twist On
Things With Three New Funds
In early April, PowerShares
launched three new exchange-traded
funds that evaluate well-known
markets in innovative ways.
The PowerShares Global Nuclear
Energy Portfolio (NYSE Arca: PKN)
uses an underlying index with a
hybrid weighting methodology combining
equal weighting and marketcapitalization
weighting to track the
nuclear energy market. The index is
provided by WNA Global Indexes,
which was formed last year in partnership
with the World Nuclear
www.journalofindexes.com July/August 2008 51

Association, an industry trade group.
PKN charges 0.75 percent.
The PowerShares FTSE NASDAQ
Small Cap Portfolio (NASDAQ: PQSC)
invests in the smallest 10 percent of
the broader FTSE NASDAQ Index.
The index is capitalization-weighted
and adjusted annually. As of March,
it included some 1,159 companies.
The PowerShares NASDAQ Next-Q
Portfolio (NASDAQ: PNXQ) does just
what its name implies: It buys the
50 securities next in line to replace
stocks in the popular NASDAQ-100
Index (which forms the basis for
the QQQ ETF, hence “Next-Q”). It is
market-cap weighted.
PQSC and PNXQ both charge
0.70 percent.
BGI Launches New Funds
On the same day it launched
its all-world ETF, BGI launched a
trio of single-country iShares: the
iShares MSCI Israel Capped Investable
Market Index Fund (NYSE Arca:
EIS), iShares MSCI Turkey Investable
Market Index Fund (NYSE
Arca: TUR) and iShares MSCI Thailand
Investable Market Index Fund
(NYSE Arca: THD). Each charges
0.74 percent and tracks foreign
markets not previously covered by
U.S.-listed ETFs.
Also included in the Barclays
launch was the iShares MSCI ACWI
ex-US Index Fund (NASDAQ: ACWX),
which charges 0.45 percent and
tracks the same index as the established
SPDR MSCI ACWI Ex-US ETF
(AMEX: CWI) from State Street
Global Advisors.
ProShares Launches
First Inverse Bond ETF
U.S.-based ProShare Advisors
launched the world’s first inverse
fixed-income ETFs on May 1.
The ProShares UltraShort Lehman
7-10 Year Treasury ETF (AMEX: PST)
and the ProShares UltraShort Lehman
20+ Year Treasury ETF (AMEX:
TBT) are designed to deliver twice
the inverse of the daily performance
News
of their underlying index. The two
funds each charge an expense ratio
of 0.95 percent.
Van Eck, Claymore Launch
Solar-Powered ETFs
The rainy month of April heralded
the launch of two new global
solar-focused ETFs. Skyrocketing oil
prices, along with growing environmental
concerns, are creating a
growth area for solar products and
services markets. Currently, solar
power provides less than 1 percent
of the world’s electricity.
The Claymore ETF (TAN) tracks
the MAC Global Solar Energy Index,
while the Market Vectors ETF (KWT)
tracks the Ardour Solar Energy Index.
Both indexes favor pure-play solar
companies, the KWT index more so.
Both indexes also currently contain
about 27 companies—holding 20 of
them in common. First Solar is the
company with the highest weighting
in both indexes. The two indexes’
country weightings are very similar.
Both charge 0.65 percent in
expenses.
New ETF Warms To Heating Oil
Victoria Bay rounded out its
suite of energy ETFs with the April
launch of the United States Heating
Oil Fund (AMEX: UHN). UHN tracks
changes in the price of heating oil
as measured by futures contracts
traded on the New York Mercantile
Exchange. It invests in near-month
contracts, except when the nearmonth
contract is within two weeks
of expiration, in which case it will
invest in the next month’s contract.
The fund’s early switch is designed
to lessen the impact of contango
and related market forces.
Victoria Bay is registered as a
commodity pool operator and had
$1.1 billion in assets under management
as of December 31, 2007.
Investors in UHN will benefit from
interest income as well.
The fund charges an expense
ratio of 0.69 percent.
Direxion’s Triple Threat
A new filing by Direxion Funds
covers 36 proposed ETFs offering
triple-long and triple-short exposure
to some major market indexes. By
construction, the “Bull” funds will
seek to capture three times the performance
of the underlying index,
while the “Bear” funds will offer
three times the inverse of the performance
of the underlying index.
The 18 indexes that will underlie
the funds include the S&P 500, MSCI
Broad Market Index, NASDAQ-100,
Dow Jones Industrial Average, S&P
MidCap 400 Index, Russell 2000
Index, Nikkei 225 Index, MSCI EAFE,
MSCI Emerging Markets Index, S&P
BRIC 40 Index, FTSE/Xinhua China 25
Index, Indus India Index, S&P Latin
America Index, MSCI Commodity-
Related Equity Index, Energy Select
Sector Index, Financial Select Sector
Index, Dow Jones U.S. Real Estate
Index and S&P U.S. Homebuilding
Select Industry Index.
The prospectus lists the management
fees for each ETF at 0.75
percent.
The filing clearly looks to build on
the success of the ProShares family
of ETFs, designed to deliver 200 percent
and -200 percent of the return
of their benchmark indexes. But will
investors really want 3x returns?
PowerShares Files
For Frontier ETF
Invesco PowerShares recently
submitted a prospectus to the Securities
& Exchange Commission for a
frontier markets ETF.
The PowerShares MENA Frontier
Countries Portfolio will track
the Middle East and Africa Frontier
Countries Index, which covers
50 stocks—five each from Nigeria,
Lebanon, Egypt, Morocco, Oman,
Jordan, Kuwait, Bahrain, Qatar and
the United Arab Emirates. Components
must have market capitalizations
of at least $500 million. The
index is rebalanced quarterly and
takes into account foreign owner-
52
July/August 2008

ship restrictions at each review. The
index provider was not identified.
Investors have begun to turn
to frontier markets as they display
continued outperformance in comparison
to developed and emerging
markets, and as emerging markets
begin to correlate more closely with
developed markets in terms of performance.
To date, however, investors
have relatively limited choices
in ETFs that access these markets.
News
ing foreign markets, with 70 percent
developed exposure and 30 percent
emerging markets exposure.
WIP has 47 different holdings,
mostly A-rated and above in credit
quality. The average life of those
bonds is listed at 9.06 years.
The fund follows the Deutsche
Bank Global Government ex-U.S. Inflation
Linked Bond Capped Index. In
the past 12 months, that benchmark
has returned 20.9 percent. About 12
percent of those gains were currencyrelated,
another 5 percent were associated
with inflation adjustments and
2 percent came from coupon interest
payments. Less than 1 percent came
from price appreciation.
The real yield on WIP is around
2.01 percent, reflecting a worldwide
flight to quality as credit markets
continue to struggle from the
U.S.-led mortgage meltdown.
Since the fund deals with government
debt and buys in foreign currencies,
it should be relatively liquid. Some
of the ETF’s currencies include the
euro, yen, pound, real and the krona.
The expense ratio on WIP is
listed at 0.50 percent.
ty Commodity Index (CMCI)—which
UBS says is the first benchmark commodity
index to diversify across both
commodities and maturities—or
one of its subindexes. By spreading
its exposure across multiple maturities,
the fund may mitigate the
impacts of contango and backwardation
and more closely approximate
movements in the spot price of the
targeted commodities.
The UBS ETNs trade under the
following ticker symbols: CMCI
Index (UCI), CMCI Agriculture Index
(UAG), CMCI Livestock Index (UBC),
CMCI Industrial Metals Index (UBM),
CMCI Food Index (FUD), CMCI Energy
Index (UBN), CMCI Gold Index
(UBZ) and CMCI Silver Index (USV).
UBZ charges 0.30 percent, while
USV charges 0.40 percent. The rest
of the ETNs charge 0.65 percent.
In May, the firm followed up with
the launch of the first exchangetraded
products to cover the platinum
market: the E-TRACS UBS Long
Platinum ETN (NYSE Arca: PTM) and
the E-TRACKS UBS Short Platinum
ETN (NYSE Arca: PTD).
Van Eck Enters ETN Market
With Morgan Stanley
Morgan Stanley has teamed up
with Van Eck Global to launch currency
ETNs. The initial products
offer exposure to the Chinese renminbi
and Indian rupee. The Market
Vectors - Chinese Renminbi/
USD ETN (NYSE Arca: CNY) and
Market Vectors - Indian Rupee/USD
ETN (NYSE Arca: INR) are the first
exchange-traded products to offer
exposure to those two currencies.
The notes are designed to go up
in value when the named currency
appreciates against the U.S. dollar,
and down when the dollar strengthens.
Both notes track an index tied
The first U.S.-based global international Treasury inflationprotected
securities ETF launched on March 19.
U.S. Gets Its First Global TIPS ETF
The first U.S.-based global international
Treasury inflation-protected
securities ETF launched on March
19, opening exposure for investors
to TIPS in 18 different countries and
15 different currencies.
The SPDR DB International Government
Inflation-Protected Bond
ETF (AMEX: WIP) includes TIPS
issued in both developed and emerg-
UBS Enters ETN Market
Swiss-based financial services giant
UBS has joined the growing field of
exchange-traded note providers with
the launch of eight commodities and
energy index-based ETNs this April.
The new UBS notes are listed on
the NYSE Arca exchange and are
marketed as E-TRACS. The ETNs aim
to provide a blended approach to
commodities investing by tracking
contracts with different maturities,
i.e., buying not just the July oil contract,
but small positions in the July
contract, the August contract, etc.
Each ETN tracks the performance of
the UBS Bloomberg Constant Maturi-
to currency futures, which allows
them to get around local market
restrictions on spot currency transactions.
The ETNs are underwritten
by Morgan Stanley; Van Eck is the
marketing agent. The notes charge
0.55 percent in annual fees.
In a second venture in May, Van
Eck and Morgan Stanley joined the
double-leveraged and double-short
ETN market with the launch of the
Market Vectors Double Long Euro
ETN (NYSE Arca: URR) and the Market
Vectors Double Short Euro ETN
(NYSE Arca: DRR). URR’s underlying
index doubles the daily performance
of the euro against the dollar, while
DRR’s index does more or less the
opposite. URR and DRR charge 0.65
percent in expenses.
Unlike most currency products,
these four products earn interest
based on the U.S. Federal Funds interest
rate, not local interest rates. Addi-
www.journalofindexes.com July/August 2008 53

tionally, none of these ETNs pays out
interest income; interest is instead
added to the share value of the note.
Interest accrual presents tax complications
for investors, as IRS rules
require investors to pay annual taxes
on this notional interest.
Deutsche Bank Adds
Eight More ETNs
In April, Deutsche Bank added
eight new commodities ETNs offering
short and long exposure—four
covering the agriculture commodities
sector and four that track the
broad-based commodities indexes.
Both product groups include long,
double-long, short and double-short
versions. The long and double-long
funds aim to deliver 100 percent and
200 percent of the monthly return
of the index, while the short and
News
ELEMENT-ary Additions
Within the first two weeks of April,
the ELEMENTS platform saw the
launch of four new ETNs, all issued
by Credit Suisse (rated AA-/Aa1).
Three of the new notes cover sections
of the commodities market,
while the fourth offers exposure to
the industry emerging around the
reduction of global warming.
The three new commodities ETNs
track subindexes of the MLCX (Merrill
Lynch Commodity index eXtra)
that cover livestock, precious metals
and gold. The MLCX Precious Metals
ELEMENTS ETN (NYSE Arca: PMY)
covers gold (52 percent), silver (32
percent), platinum (8 percent) and
palladium (8 percent). The MLCX
Livestock ELEMENTS ETN (AMEX:
LSO) tracks futures contracts in lean
hogs (30 percent) and live cattle (70
The new products trade on the London
Stock Exchange, and all of them
track the Dow Jones-AIG Commodity
Index and its subindexes.
The four short ETFs are linked to the
DJ-AIGCI’s cocoa, lead, platinum and tin
subindexes. The leveraged funds cover
those four commodities plus aluminum,
coffee, copper, corn, cotton, crude oil,
gasoline, gold, heating oil, lean hogs,
live cattle, natural gas, nickel, silver,
soybean oil, soybeans, sugar, wheat,
and zinc. There are also 10 leveraged
funds that track the broad DJ-AIGCI and
nine of its subsectors.
The funds charge an annual fee
of 0.98 percent.
Canada Scoops U.S. With
Grain Commodities ETFs
In March, Canadian firm BetaPro
Management Inc. continued the rollout
In April, Deutsche Bank added eight new commodities ETNs
offering short and long exposure.
double-short funds aim to deliver
-100 percent and -200 percent of the
index’s monthly return.
The ag ETNs were launched first,
on April 15, and include the DB
Agriculture Double Short ETN (NYSE
Arca: AGA), the DB Agriculture Double
Long ETN (NYSE Arca: DAG), the
DB Agriculture Short ETN (NYSE
Arca: ADZ) and the DB Agriculture
Long ETN (NYSE Arca: AGF).
The broad-based commodities
notes were launched April 29 and
are the DB Commodity Double
Short ETN (NYSE Arca: DEE), the
DB Commodity Double Long ETN
(NYSE Arca: DYY), the DB Commodity
Short ETN (NYSE Arca: DDP) and
the DB Commodity Long ETN (NYSE
Arca: DPU). The long ETNs track
the Optimum Yield version of the
DBLCI, while the short funds track
the standard version.
All of the agriculture and broadbased
commodities ETNs carry an
expense ratio of 0.75 percent.
percent). The MLCX Gold ELEMENTS
ETN (AMEX: GOE) invests only in gold
futures contracts. Although PMY and
LSO both charge annual expense
ratios of 0.75 percent, GOE charges
just 0.375 percent.
The Global Warming ELEMENTS
ETN (NYSE Arca: GWO) is a unique
product that tracks the Credit Suisse
Global Warming Index, which covers
50 companies with business activities
focused on reducing global warming,
such as the production of alternative
energy and energy efficiency solutions.
It charges 0.75 percent.
New Commodities ETFs
Launch In London
In mid-March, ETF Securities
(ETFS) rolled out 33 leveraged commodity
ETFs (designed to deliver
200 percent of the daily performance
of the benchmark index) and
four short ETFs (designed to deliver
-100 percent of the daily performance
of the benchmark index).
of its leveraged and short commodities
ETFs with the launch of two funds tied
to the Dow Jones-AIG Grains Sub-Index
on the Toronto Stock Exchange.
The Horizons BetaPro DJ-AIG Agricultural
Grains Bull Plus ETF (HAU)
aims to produce 200 percent of the
daily returns of the underlying index,
while the Horizons BetaPro DJ-AIG
Agricultural Bear Plus ETF (HAD) is
designed to capture 200 percent of
the inverse of the daily returns of the
index. The funds each carry a management
fee of 1.15 percent.
The DJ-AIG Grains sector includes
corn, soybeans and wheat. The U.S.
does not have any exchange-traded
products that offer short or leveraged
exposure to the grains sector
specifically.
INTO THE FUTURES
PHLX Subsidiary Lists
New Futures
The Philadelphia Stock Exchange
launched three futures contracts on its
54
July/August 2008

Philadelphia Board of Trade subsidiary
in March. Previously, the exchange
traded only currency futures on six
different foreign currencies. The new
contracts are linked to three of the
PHLX’s homegrown indexes.
Futures are now trading on the
PHLX Oil Service Sector, PHLX Semiconductor
Sector and PHLX Housing
Sector indexes. PHLX options
contracts are available for all three
indexes and have some of the highest
volumes of PHLX’s 17 different
index options contracts.
For the time being, volumes
for the new futures remain largely
nonexistent.
ON THE MOVE
Powers Named President
And CEO Of SSgA
Scott Powers has been named
president and chief executive officer
of State Street Global Advisors
(SSgA). Powers was previously
CEO of Old Mutual U.S., the U.S.
operating unit of London-based Old
Mutual plc.
In his current position, Powers
reports to Ronald E. Logue, chairman
and chief executive officer of
parent company State Street Corp.
He has also been added to State
Street’s Operating Group, the toplevel
executive team responsible for
the company’s direction.
Powers succeeds SSgA’s interim
president and CEO James S. Phalen,
who is returning to his position as
head of international operations for
State Street’s investment servicing
and investment research and trading
businesses.
News
Ades Leaves Dow Jones,
Joins FTSE
Ronnee Ades has left her position
as senior director of institutional
markets at Dow Jones Indexes and
joined FTSE Group as the head of its
“Alternatives” business unit.
Currently, the index provider’s
REIT, commercial property, hedge
fund, private banking and infrastructure
indexes fall into this bailiwick.
Ades will be responsible for
growing those areas and also for
identifying new areas in alternative
assets and investment that present
opportunities for FTSE.
She will be based in FTSE’s New
York office and report to Paul Hansford,
FTSE’s director of product
management.
While with Dow Jones Indexes,
Ades was also the executive director
of the Dow Jones Wilshire Indexes
Technical Advisory Committee.
Northern Trust Global
Investments Gets New CEO
Northern Trust has appointed
Wayne Bowers as chief executive
officer of Northern Trust Global
Investments Limited, the Londonbased
asset management subsidiary
of Northern Trust Corporation.
As CEO, Bowers is responsible for
the continued growth and development
of Northern Trust’s investment
management business in Europe, the
Middle East and Africa, and Asia-
Pacific. He will report to Steve Potter,
who was recently named president of
Northern Trust Global Investments.
Bowers joined Northern Trust
in 1999 as director of Global Fixed
Income, responsible for the London-based
fixed-income portfolio
management team. He was appointed
chief investment officer for NTGI
Limited in 2007 and has been acting
CEO of NTGI Limited since December
2007. Prior to joining Northern
Trust, Bowers was employed at ABN
Amro Bank and Hambros Bank.
BNP’s Abner Joins WisdomTree
David Abner has been hired by WisdomTree
Investments Inc. as director
of Institutional ETF Sales. Mr. Abner
joins WisdomTree from BNP Paribas,
where he was the managing director
heading up its ETF trading operations.
Abner came to BNP Paribas in
2006 from Bear Stearns, where he
was the head of the company’s ETF
trading business.
STOXX Re-Elects Chairman
In April, European index provider
STOXX Limited announced that Werner
Bürki, a member of the management
committee of the SWX Swiss
Exchange and CEO of EXFEED Ltd.,
was re-elected for a second consecutive
term as chairman of the STOXX
supervisory board. The position of
chairman is up for election annually.
Mr. Bürki has been a member of
the management committee of the
SWX Swiss Exchange since July 2002
and the CEO of EXFEED Ltd. since
October 2001. He joined the STOXX
supervisory board in December 2002.
The supervisory board is composed
of one representative from
each of STOXX Limited’s three joint
venture partners: Deutsche Börse
AG, Dow Jones & Company, and
SWX Swiss Exchange AG.
Zweig Named To Wall Street
Journal Column
The Wall Street Journal hired Jason
Zweig, a senior writer and columnist
for Money magazine, to be
its new personal finance columnist.
Zweig’s column will more or less
fill the space vacated by Jonathan
Clements’ “Getting Going” column;
Clements left the Journal for Citigroup
earlier this year.
Zweig’s weekly column is set to
debut July 1.
Zweig is a noted fan of indexing.
He is also currently a guest
columnist for Time magazine. He
was the mutual funds editor for
Forbes before joining Money magazine
in 1995.
Zweig is the author of the
recently published Your Money &
Your Brain: How The New Science
Of Neuroeconomics Can Help Make
You Rich (Simon & Schuster, 2007),
which looks at how neuroscience
can be applied to investing. (An
excerpt from Zweig’s new book,
as well as an interview with the
author, appears on page 10 in this
issue of the Journal of Indexes.)
July/August 2008 55

Israelsen continued from page 29
often), the margin of victory can be large. For example,
during the five-year period of 1995–1999, small-cap
growth beat small-cap value by 831 basis points. Overall,
when small-cap growth outperformed small-cap value, the
average margin of victory was 257 bps (and the median
margin of victory was 94 bps compared with the median
small-cap value margin of victory of 818 bps).
In light of the historical performance of dominance of
small-cap value over small-cap growth, it is peculiar that
small-cap growth U.S. equity funds outnumber small-cap
value U.S. equity funds more than 2-to-1. Apparently
small-cap growth managers (and small-cap growth investors)
are optimists. They are willing to pay a high price
(in the form of volatility) for a relatively rare, but potentially
large, burst of outperformance relative to small-cap
value. They must see a rewarding small-cap growth frontier
off in the distance. That’s about the only place they
could see it … because such a frontier hasn’t surfaced
very often in the past 27 years.
Ferri continued from page 44
Investors and advisors can refer to the data in Figure
5 to determine fair fees for each ETF that follows a particular
index strategy. For example, assume an advisor is
considering the purchase of a U.S. large-cap growth ETF.
The cost for one ETF under consideration is 0.35 percent,
while the cost for another is 0.60 percent. Which ETF is
more or less overpriced than the other?
The answer is that it depends on the underlying index
strategy of each fund. If the 0.35 percent ETF is a passively
selected and capitalization-weighted “Beta” fund, and
the 0.60% ETF follows an alpha-seeking index that uses a
quantitatively driven index and weights stocks using fixed
weights, then based on index strategy alone, the 0.60
percent fund is a better value than the 0.35 percent fund.
I am NOT suggesting that investors should buy the 0.60
percent quantitative ETF. Rather, I am suggesting that the
0.35 percent beta ETF is overpriced.
Summary
There is a clear link between the complexity of index
Figure 5
U.S. Broad Market/Large-Cap Index
Strategy Box Pricing Matrix
Quantitative 0.55% 0.60% 0.60%
Screened 0.35% 0.45% 0.45%
Passive 0.20% 0.35% 0.35%
Source: ETFGuide.com
Capitalization Fundamental Fixed Weight
strategy and the fees ETF companies charge for products.
It is important for investors and advisors to understand
this relationship when analyzing competing products.
The Index Strategy Box Pricing Template for ETFs is one tool
that can be used to compare the pricing of any category
of funds. The methodology should assist investors with
ETF comparisons and guide product providers to create a
more uniform pricing model.
www.journalofindexes.com July/August 2008
57

Global Index Data
Selected Major Indexes Sorted By YTD Returns July/August 2008
Index Name YTD 2007 2006 2005
Goldman Sachs Commodity
Dow Jones - AIG Commodity
DJ Transportation Average
MSCI Taiwan USD*
MSCI Argentina USD*
DJ Wilshire REIT
MSCI Brazil USD*
FTSE NAREIT Equity REITs
LB Global Aggregate
Goldman Sachs Nat Res
LB US Treasury US TIPS
LB Fixed Rate MBS
LB US Aggregate
CSFB Credit Suisse HY
CPI
LB Municipal
S&P Midcap 400
DJ Wilshire US Mid Cap Val
DJ Wilshire US Small Cap Val
DJ Utilities
Russell 2000 Value
MSCI EM USD
S&P Smallcap 600
MSCI EAFE
DJ Wilshire US Mid Cap Growth
Russell 3000 Value
Russell 1000 Value
DJ Mid Cap Growth
MSCI Europe*
Russell Mid Cap Growth
NYSE Composite
S&P 500/Citi Growth
Russell Top 200 Value
DJ Wilshire US Large Cap Growth
AMEX Composite
Morningstar Consumer Goods
DJ Wilshire US Large Cap
Morningstar US Market
Russell 1000
DJ Wilshire US Top 2500
MSCI AC World ID*
Russell 3000
S&P 500
DJ Wilshire 5000
DJ Wilshire US Large Cap Val
Morningstar Large Core
MSCI EM LCL*
Morningstar Utilities
DJ Wilshire 4500
DJ Wilshire US Small Cap
Russell 1000 Growth
Russell 3000 Growth
S&P 100
Russell 2000
NYSE Arca Tech 100
MSCI World LCL*
DJ US Financial
Russell 2000 Growth
NASDAQ Composite
DJ US Health Care
18.68
13.51
13.49
9.72
9.33
8.43
8.28
7.34
4.60
4.20
2.96
2.53
1.96
0.92
0.77
0.55
-1.83
-2.95
-3.22
-3.32
-3.57
-3.68
-3.75
-3.76
-3.91
-4.22
-4.27
-4.48
-4.48
-4.49
-4.53
-4.56
-4.67
-4.72
-4.74
-4.80
-4.87
-4.87
-4.90
-4.92
-4.95
-4.99
-5.03
-5.04
-5.04
-5.13
-5.13
-5.15
-5.22
-5.23
-5.46
-5.70
-5.81
-6.13
-6.18
-7.22
-7.45
-8.35
-9.03
-9.66
50.08
24.70
3.93
19.77
3.07
-13.79
70.36
-12.51
11.78
28.08
11.33
7.39
6.87
-0.84
3.67
2.79
-2.76
-11.76
-11.89
1.32
-15.13
25.71
-9.04
-1.31
-2.28
-9.49
-8.97
2.10
-1.64
-1.93
-3.41
-0.42
-7.97
0.20
4.56
0.03
-3.91
-4.53
-4.62
-4.58
-2.01
-5.16
-4.68
-4.74
-8.08
-1.10
17.78
-1.28
-5.71
-9.38
-0.23
-0.79
-3.64
-10.96
-5.60
-9.16
-24.55
-6.71
-4.45
-9.20
32.67
16.23
1.43
5.43
-5.36
-17.56
75.35
-15.69
9.48
34.43
11.64
6.90
6.97
2.66
4.12
3.36
7.98
-1.29
-4.13
20.11
-9.78
39.78
-0.30
11.63
11.24
-1.01
-0.17
17.01
13.86
11.43
6.58
9.13
0.25
10.97
17.18
11.28
6.40
5.92
5.77
5.87
9.64
5.14
5.49
5.73
1.84
8.65
30.40
18.16
5.77
1.90
11.81
11.40
6.12
-1.57
7.26
2.83
-17.66
7.05
9.81
8.36
-15.09
2.07
9.81
16.30
66.07
35.97
40.52
35.06
6.64
16.82
0.41
5.22
4.33
11.93
2.57
4.84
10.32
15.71
20.04
16.63
23.48
32.59
15.12
26.86
11.57
22.34
22.25
10.71
33.72
10.66
17.86
11.01
22.99
9.15
16.90
17.55
15.63
15.70
15.46
15.79
18.78
15.72
15.79
15.88
21.87
15.54
25.57
24.77
16.07
16.98
9.07
9.46
18.47
18.37
4.68
13.52
19.42
13.35
9.52
6.88
25.55
21.36
11.65
3.28
59.69
13.82
49.96
12.16
-4.49
36.48
2.84
2.61
2.43
2.26
3.39
3.51
12.56
5.46
5.30
25.14
4.71
34.54
7.68
14.02
16.67
6.85
7.05
14.54
9.42
12.10
6.95
1.14
4.60
7.13
22.64
2.14
6.33
6.52
6.27
6.45
8.83
6.12
4.91
6.32
5.72
3.82
31.54
14.80
10.28
7.37
5.26
5.17
1.17
4.55
7.36
13.74
6.46
4.15
1.37
8.32
Total Return % Annualized Return %
12-Mo 2004 2003 3-Yr 5-Yr 10-Yr 15-Yr Std Dev
17.28
9.15
27.73
6.54
24.57
33.16
30.49
31.58
9.27
24.57
8.46
4.70
4.34
11.96
3.34
4.48
16.48
17.88
19.61
30.24
22.25
25.95
22.65
20.70
18.94
16.94
16.49
15.35
20.88
15.48
12.16
6.97
13.34
9.53
22.22
11.94
11.65
12.35
11.41
12.53
13.30
11.95
10.88
12.62
13.55
13.99
13.21
23.40
18.51
19.46
6.30
6.93
6.43
18.33
11.73
9.49
13.39
14.31
8.59
4.55
20.72
23.93
31.84
40.01
98.53
36.18
102.85
37.13
12.51
34.01
8.40
3.07
4.10
27.93
2.04
5.31
35.62
34.94
46.86
29.39
46.03
56.28
38.79
39.17
43.40
31.14
30.03
43.65
38.54
42.71
29.28
27.08
26.75
27.46
42.36
22.18
28.90
30.73
29.89
30.96
31.62
31.06
28.69
31.64
30.55
24.71
42.34
24.97
43.95
49.03
29.75
30.97
26.25
47.25
52.14
22.76
32.23
48.54
50.01
19.43
*Indicates price returns. All other indexes are total return. Source: Morningstar. Data as of 4/30/2008. All returns are in dollars, unless noted. 3-, 5-, 10- and 15-year returns are annualized.
14.80
15.71
15.98
13.39
37.14
12.08
59.42
11.88
5.54
28.41
5.33
5.43
4.93
6.60
3.25
3.56
11.20
8.06
8.69
14.79
7.30
34.27
8.76
16.74
13.42
8.27
8.36
14.68
17.57
11.81
9.89
7.29
7.71
9.66
16.83
9.14
8.91
9.01
8.63
9.02
11.88
8.64
8.23
9.04
8.06
8.57
27.61
14.46
11.14
9.89
8.86
8.96
7.40
8.62
7.91
8.10
1.14
9.91
7.88
3.25
19.34
16.67
17.82
17.01
38.40
18.99
53.47
18.67
6.63
30.30
6.35
4.73
4.37
8.62
3.09
4.03
15.20
12.81
14.45
21.95
14.08
35.76
14.73
20.92
17.51
12.92
12.85
16.71
20.93
15.30
12.63
8.33
11.25
10.04
21.86
12.08
11.33
11.71
11.23
11.77
14.38
11.41
10.62
11.83
12.53
10.67
27.66
19.10
15.84
15.40
9.52
9.79
8.86
13.77
12.12
10.31
6.44
13.32
10.50
6.10
12.53
10.92
5.15
1.10
6.20
12.46
15.37
11.69
6.30
12.36
7.70
5.94
5.96
5.80
2.78
5.16
9.64
8.34
8.53
9.94
7.74
13.54
7.41
7.05
4.93
6.05
5.97
5.86
6.84
5.75
4.30
2.21
4.78
1.96
11.89
5.15
4.15
4.24
4.24
4.37
3.96
4.28
3.89
4.36
5.99
4.32
11.51
8.41
6.00
6.70
1.66
1.67
3.56
5.34
9.38
2.06
3.93
2.20
2.59
4.25
8.88
9.79
9.60
3.34
8.23
12.56
19.16
12.42
6.54
-
-
6.27
6.28
7.52
2.66
5.61
13.17
12.19
12.62
9.79
11.69
11.31
-
8.15
9.79
11.14
11.15
9.79
11.71
10.22
8.94
-
10.59
9.12
11.97
10.28
9.99
9.95
10.11
10.02
7.00
10.00
9.98
10.01
10.49
-
16.80
9.07
10.40
10.96
8.43
8.23
10.02
9.54
16.19
6.14
11.23
6.79
9.01
11.93
13.43
36.30
15.92
19.95
17.40
21.56
10.45
15.04
10.25
10.40
12.84
10.77
11.99
13.72
14.13
9.79
10.54
11.28
11.18
3.56
12.28
12.86
5.18
12.43
22.92
9.80
15.59
12.87
3.30
9.49
9.36
2.53
2.56
13.46
0.43
13.86
13.70
7.80
8.59
7.89
12.52
9.72
7.74
1.26
7.30
13.17
6.99
7.67
6.92
7.09
7.08
7.68
18.87
14.63
8.46
17.70
6.87
29.50
19.61
35.77
www.journalofindexes.com
July/August 2008
59

Global Index Data
Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions July/August 2008
Fund Name Ticker Assets Exp Ratio YTD
Vanguard 500 Index
Vanguard Tot Stk
Vanguard Inst Idx
Vanguard 500 Idx Adm
Vanguard Total Bd Idx
Vanguard Total Intl Stk
Vanguard Tot Stk Adm
Vanguard Inst Idx InstPl
Vanguard Eur Stk Idx
Fidelity Spar US EqIx
Vanguard 500 Index Signal
Vanguard Tot Stk Inst
Vanguard Em Mkt Idx
Vanguard Total Bd Idx Ad
Vanguard Pac Stk Idx
Vanguard Total Bd Idx In
T. Rowe Price Eq Idx 500
Fidelity Spar 500 Adv
Fidelity U.S. Bond Index
Vanguard Tot Stk InstPls
Dimensional Intl SmCpVal
Vanguard Inst Tot Bd Idx
Dimensional US LgCpVal
Vanguard Mid Cap Idx
Fidelity Spar 500 Idx
Fidelity 100 Index
Dimensional EmergMrktsVal
Fidelity Spar US Eq Adv
Vanguard Gr Idx
Vanguard TotBdMkt Idx Sig
Vanguard Mid Cap Idx Ins
Vanguard SmCp Idx
Dimensional Intl Val
Dimensional Intl Small Co
Dimensional US Micro Cp
Vanguard ExtMktIdx
Fidelity Spar Intl Index
Fidelity Spar Tot Mkt Ix
Vanguard Inst DevMktsIdx
Vanguard Eur Stk Idx Ins
Vanguard REIT Index
Vanguard TotStMkt Idx Sig
VALIC I Stock
Vanguard Val Idx
Vanguard Dev Mkts Idx
Fidelity Spar Tot Mkt Adv
Schwab S&P 500 In Sel
Gateway
Vanguard Bal Idx
Vanguard SmCp Idx Ins
Vanguard SmCp Vl Idx
Vanguard EmgMkts Idx Admr
Dimensional USLgCo
Schwab S&P 500 In Inv
Schwab 1000 In Inv
Dimensional US Sm Cp
Dimensional TaxMgUSSmCpVl
Vanguard Intm Bd Idx
Vanguard Tx-Mgd App Adm
VFINX
VTSMX
VINIX
VFIAX
VBMFX
VGTSX
VTSAX
VIIIX
VEURX
FUSEX
VIFSX
VITSX
VEIEX
VBTLX
VPACX
VBTIX
PREIX
FSMAX
FBIDX
VITPX
DISVX
VITBX
DFLVX
VIMSX
FSMKX
FOHIX
DFEVX
FUSVX
VIGRX
VBTSX
VMCIX
NAESX
DFIVX
DFISX
DFSCX
VEXMX
FSIIX
FSTMX
VIDMX
VESIX
VGSIX
VTSSX
VSTIX
VIVAX
VDMIX
FSTVX
SWPPX
GATEX
VBINX
VSCIX
VISVX
VEMAX
DFLCX
SWPIX
SNXFX
DFSTX
DTMVX
VBIIX
VTCLX
59,443.9
51,596.9
45,047.7
35,065.1
31,991.9
28,814.6
27,466.1
25,929.3
25,167.9
20,880.4
20,756.5
13,840.4
13,505.3
11,649.1
10,958.1
10,870.5
10,601.6
9,331.6
9,187.1
8,707.9
8,642.4
7,722.7
7,704.5
7,626.7
7,509.5
7,131.1
6,800.0
6,683.1
6,626.1
6,244.6
6,043.5
5,927.3
5,805.9
5,663.0
4,938.2
4,916.4
4,905.6
4,895.1
4,735.1
4,531.9
4,531.3
4,526.6
4,290.0
4,044.1
3,922.3
3,838.4
3,804.4
3,743.6
3,617.9
3,595.6
3,486.4
3,447.4
3,428.2
3,423.3
3,396.7
3,395.4
3,290.6
3,242.9
3,179.7
0.18
0.19
0.05
0.09
0.20
0.32
0.09
0.03
0.27
0.10
0.09
0.06
0.45
0.11
0.32
0.07
0.35
0.07
0.32
0.03
0.75
0.05
0.30
0.22
0.10
0.20
0.70
0.07
0.22
0.11
0.08
0.23
0.48
0.64
0.55
0.25
0.10
0.10
0.12
0.12
0.21
0.09
0.36
0.21
0.27
0.07
0.19
0.95
0.20
0.08
0.23
0.30
0.15
0.37
0.50
0.40
0.55
0.18
0.10
-5.08
-4.95
-5.04
-5.04
1.88
-3.57
-4.93
-5.04
-4.50
-5.06
-5.05
-4.90
-3.23
1.91
-1.49
1.92
-5.14
-5.04
1.62
-4.89
13.67
1.92
2.14
-4.52
-5.05
-5.94
34.41
-5.05
-4.75
1.91
-4.50
-4.20
10.08
13.87
-0.81
-4.53
-3.32
-4.99
-3.57
-4.49
8.14
-4.95
-5.14
-5.25
-3.61
-4.98
-5.16
0.28
-2.12
-4.16
-2.39
-3.19
-5.05
-5.21
-5.05
0.24
-0.67
2.34
-4.80
$US Millions Total Return %
Annualized Return %
2007 2006 3-Yr
8.10
8.79
8.22
8.19
4.86
18.55
8.89
8.24
17.22
8.16
8.14
8.91
32.60
4.95
13.71
4.99
7.90
8.19
4.31
8.98
30.20
4.95
16.07
10.91
8.16
-
48.63
8.19
9.28
4.87
11.07
9.45
27.68
27.56
13.48
10.38
16.26
8.92
16.25
17.36
11.67
8.83
7.86
8.09
16.08
8.94
8.11
7.67
7.37
9.61
7.28
32.71
8.16
7.95
8.48
13.71
15.08
5.05
9.04
15.64
15.51
15.79
15.75
4.27
26.64
15.63
15.81
33.42
15.72
15.66
15.69
29.39
4.36
11.99
4.40
15.41
15.75
4.33
15.76
28.39
4.30
20.18
13.60
15.71
-
37.93
15.75
9.01
4.29
13.78
15.66
34.15
24.88
16.16
14.27
26.15
15.73
26.34
33.64
35.07
15.57
15.41
22.15
26.18
15.77
15.67
10.14
11.02
15.82
19.24
29.48
15.71
15.48
15.20
16.61
18.85
3.91
14.44
8.10
8.79
8.22
8.19
4.86
18.55
8.89
8.24
17.22
8.16
8.14
8.91
32.60
4.95
13.71
4.99
7.90
8.19
4.31
8.98
30.20
4.95
16.07
10.91
8.16
-
48.63
8.19
9.28
4.87
11.07
9.45
27.68
27.56
13.48
10.38
16.26
8.92
16.25
17.36
11.67
8.83
7.86
8.09
16.08
8.94
8.11
7.67
7.37
9.61
7.28
32.71
8.16
7.95
8.48
13.71
15.08
5.05
9.04
5-Yr 10-Yr 15-Yr Mkt Cap P/E Std Dev Yield
10.48
11.60
10.60
10.57
4.29
22.30
11.69
10.63
20.86
10.52
10.50
11.73
34.79
4.38
19.60
4.42
10.28
10.54
4.04
11.82
30.96
4.37
15.58
15.61
10.52
-
41.82
10.54
9.41
4.30
15.78
15.01
25.17
27.63
17.98
15.33
20.29
11.65
20.54
21.02
18.17
11.62
10.24
12.75
20.38
11.66
10.48
7.41
8.78
15.17
14.15
34.86
10.52
10.29
10.82
16.95
18.53
4.64
11.68
3.81
4.29
3.94
3.87
5.69
7.59
4.34
3.96
6.93
3.77
3.83
4.40
14.07
5.74
6.31
5.82
3.61
3.79
5.79
-
14.45
-
9.49
-
3.78
-
-
3.78
2.97
5.70
-
6.21
11.51
12.15
11.73
5.79
6.73
4.28
-
7.04
11.52
4.30
3.55
4.81
-
4.29
3.75
5.80
5.22
6.37
-
14.10
3.77
3.58
4.06
9.93
-
6.34
4.56
9.88
9.85
10.01
9.92
6.07
-
9.89
10.04
11.85
9.81
9.89
9.94
-
6.10
2.64
6.17
9.66
9.79
6.13
-
-
-
-
-
9.78
-
-
9.81
9.73
6.07
-
10.44
-
-
14.87
10.42
-
-
-
11.93
-
9.86
9.61
10.09
-
-
-
7.21
8.61
10.56
-
-
9.82
-
9.79
12.80
-
-
-
56,041
31,021
56,030
56,041
-
33,396
31,021
56,030
50,382
49,529
56,041
31,021
20,062
-
19,638
-
49,638
49,595
-
30,884
944
-
18,911
6,702
49,595
104,880
3,269
49,529
39,587
-
6,702
1,546
26,887
914
431
2,344
39,456
27,612
38,066
50,382
4,860
31,021
49,906
57,302
38,068
27,612
53,864
51,045
31,080
1,546
1,452
20,062
56,022
53,864
41,281
833
861
-
36,487
16.5
16.7
16.5
16.5
-
15.1
16.7
16.5
13.8
15.6
16.5
16.7
18.3
-
16.3
-
15.6
15.6
-
16.6
14.1
-
13.6
17.0
15.6
15.4
11.4
15.6
21.0
-
17.0
17.8
13.4
17.2
19.2
17.4
11.8
15.8
14.5
13.8
25.6
16.7
15.6
13.6
14.5
15.8
15.5
16.0
16.7
17.8
15.1
18.3
16.6
15.5
15.5
19.2
15.9
-
17.0
8.90
9.29
8.91
8.91
2.88
11.82
9.29
8.91
11.15
8.91
8.90
9.29
19.67
2.88
12.25
2.88
8.90
8.91
2.64
9.29
10.01
2.84
9.64
11.24
8.91
-
16.77
8.91
10.01
2.89
11.25
12.60
9.75
9.89
13.76
11.82
10.88
9.29
10.72
11.17
16.59
9.29
8.90
9.01
10.76
9.30
8.86
3.81
5.37
12.60
11.48
19.67
8.88
8.87
8.96
13.54
13.62
3.84
9.29
1.99
1.81
2.05
2.08
4.88
2.72
1.90
2.08
3.20
2.02
2.06
1.92
1.83
4.97
2.42
4.99
1.75
1.96
4.92
1.90
2.16
4.85
1.36
1.35
1.93
1.10
1.63
2.05
0.85
4.96
1.50
1.34
3.07
1.97
1.98
1.21
2.43
1.64
3.05
3.31
4.65
1.89
1.69
2.82
2.96
1.67
1.94
1.82
3.04
1.51
2.32
1.94
1.99
1.77
1.51
1.82
0.80
4.79
1.67
Source: Morningstar. Data as of April 30, 2008. P/E is price-to-earnings ratio. Exp Ratio is expense ratio. Assets are total net assets in $US millions. YTD is year-to-date. 3-, 5-, 10- and 15-yr returns are annualized.
Mkt Cap is geometric average market capitalization in $US millions. Std Dev is 3-year standard deviation. Yield is 12-month.
60
July/August 2008

Morningstar U.S. Style Overview Jan. 1 – Apr. 30, 2008
Morningstar U.S. Style Overview: January 1 - April 30, 2008
Trailing Returns %
3-Month YTD 1-Yr 3-Yr 5-Yr 10-Yr
Morningstar Indexes
US Market 3.13 –4.87 –4.52 8.99 11.69 4.24
Large Cap 2.76 –5.14 –3.43 8.41 10.29 3.12
Mid Cap 4.43 –3.70 –6.63 10.93 15.60 7.11
Small Cap 3.33 –5.36 –10.35 8.85 14.34 6.39
US Value 0.31 –4.45 –10.79 8.29 13.44 6.22
US Core 3.36 –4.30 –2.99 8.98 11.85 5.36
US Growth 5.72 –5.87 0.41 9.56 9.63 0.28
Morningstar Market Barometer YTD Return %
Large Cap
US Market
–4.87
–5.14
Value
–4.45
Core
–4.30
Growth
–5.87
–4.42 –5.13 –5.80
Large Value 0.41 –4.42 –9.05 8.79 12.92 5.53
Large Core 2.38 –5.13 –1.10 8.55 10.66 4.32
Large Growth 5.70 –5.80 0.12 7.68 7.06 –1.48
Mid Cap
–3.70
–5.23 –1.46 –4.54
Mid Value –0.71 –5.23 –16.56 6.36 14.37 7.79
Mid Core 6.56 –1.46 –7.20 10.35 15.03 7.71
Mid Growth 7.07 –4.54 3.85 15.77 16.96 5.08
Small Cap
–5.36
–2.18 –2.63 –10.64
Small Value 2.48 –2.18 –12.92 7.55 14.68 8.46
Small Core 5.80 –2.63 –12.13 9.21 14.85 9.26
Small Growth 1.71 –10.64 –6.89 9.37 13.16 1.72
–8.00 –4.00 0.00 +4.00 +8.00
Sector Index YTD Return %
Energy 4.18
Consumer Services 0.25
Industry Leaders & Laggards YTD Return %
Land Transport 23.88
Discount Stores 15.21
Biggest Influence on Style Index Performance
Best Performing Index
YTD
Return %
Mid Core –1.46
Constituent
Weight %
–2.59 Business Services
–3.01 Industrial Materials
–4.36 Media
–4.80 Consumer Goods
–5.15 Utilities
–7.57 Financial Services
–8.29 Hardware
–9.83 Healthcare
–9.89 Telecommunications
–12.54 Software
Steel/Iron 14.97
Coal 14.63
Transport Equipment 13.71
Hospitals 12.16
–21.39 Wireless Service
–23.51 Physicians
–23.79 Air Transport
–29.76 Managed Care
–32.54 Audio/Video Equipment
–33.45 Oil/Gas Products
Tesoro Corp. –47.16 0.64
UAL Corp. –55.14 0.40
Coventry Health Care Inc. –24.51 0.90
R.H. Donnelley Corp. –86.87 0.25
Health Net Inc. –39.36 0.52
Worst Performing Index
Small Growth –10.64
Onyx Pharmaceuticals Inc. –36.79 0.88
SiRF Technology Holdings Inc. –76.48 0.40
Sigma Designs Inc. –67.61 0.45
Cheniere Energy Inc. –70.16 0.42
Tessera Technologies Inc. –51.35 0.56
1-Year
3-Year
5-Year
Value
Core
Growth
Value
Core
Growth
Value
Core
Growth
Large Cap
–9.05
–1.10
0.12
Large Cap
8.79
8.55
7.68
Large Cap
12.92
10.66
7.06
Mid Cap
–16.56
–7.20 3.85
Mid Cap
6.36
10.35 15.77
Mid Cap
14.37
15.03 16.96
Small Cap
–12.92
–12.13 –6.89
Small Cap
7.55
9.21 9.37
Small Cap
14.68
14.85 13.16
–20 –10 0 +10 +20
–20 –10 0 +10 +20
–20 –10 0 +10 +20
Source: Morningstar. Data as of 4/30/08
Notes and Disclaimer: ©2006 Morningstar, Inc. All Rights Reserved. Unless otherwise noted, all data is as of most recent month end. Multi-year returns are annualized. NA: Not Available. Biggest Influence on Index Performance lists
are calculated by multiplying stock returns for the period by their respective weights in the index as of the start of the period. Sector and Industry Indexes are based on Morningstar's proprietary sector classifications. The information ?
contained herein is not warranted to be accurate, complete or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information.
www.journalofindexes.com
July/August 2008
61

DOW JONES INDEXES | QUARTERLY SUMMARY | QUARTER 1, 2008
Dow DOW JONES Jones U.S. INDUSTRY U.S. INDEXESEconomic Sector Review
HISTORICAL PERFORMANCE RETURNS
Total Return (%) Annualized Total Return (%)
Industry 1-Month 3-Month YTD 2007 1-Year 3-Year 5-Year 10-Year Since Inception
62
Basic Materials -2.58 -3.13 -3.13 32.86 17.75 15.91 21.40 7.35 9.46
Consumer Goods 2.20 -4.57 -4.57 9.69 2.83 7.49 12.94 4.31 9.15
Consumer Services -0.76 -6.66 -6.66 -7.18 -14.89 0.25 7.51 2.69 8.16
Financials -2.16 -12.80 -12.80 -17.66 -26.37 -0.81 7.48 3.47 11.77
Health Care -4.14 -10.96 -10.96 8.36 -4.53 3.93 6.57 4.28 8.97
Industrials 2.32 -5.58 -5.58 13.57 4.22 9.27 16.19 4.22 9.40
Oil & Gas -2.26 -6.23 -6.23 34.84 22.57 20.97 28.09 13.82 14.80
Technology 0.43 -15.70 -15.70 15.70 -1.86 6.22 11.30 2.12 11.55
Telecommunications 4.10 -14.63 -14.63 10.04 -12.36 9.90 13.00 -2.01 5.66
Utilities 0.62 -9.76 -9.76 17.76 -2.45 12.37 19.01 6.92 8.61
DOW JONES INDEXES | QUARTERLY U.S. SUMMARY | QUARTER 1, 2008
DOW JONES INDEXES | QUARTERLY U.S. SUMMARY | QUARTER 1, 2008
DOW JONES U.S. INDUSTRY INDEXES
DOW JONES U.S. INDUSTRY INDEXES
DOW JONES U.S. INDUSTRY REPRESENTATION
CORRELATION COEFFICIENTS
HISTORICAL PERFORMANCE RETURNS
Market Cap Weight in
Utilities
Industry 1000 3000 5000 8000 4000 2000 0001 9000 6000 7000
Total Return (%) Annualized 4.14% Total Return Basic (%)
Industry (USD Billions) DJ U.S. Index
Telecom.
Materials
Industry Basic Materials 0.5403 1-Month 3-Month YTD 2007 1-Year 3.06% 3-Year 5-Year 10-Year 3.97% Since Inception
Basic Materials 538.78 3.97%
Consumer
Consumer Goods 0.5170 0.6451
Basic Materials Goods
Consumer Goods -2.58 1,313.94 -3.13 -3.13 9.68% 32.86 17.75 Technology 15.91 21.40 7.35 9.46
Consumer Services 0.2809 0.5255 0.6924
9.68%
Consumer Consumer Goods Services 2.20 1,462.13 -4.57 -4.57 10.77% 9.69 2.83 14.05% 7.49 12.94 4.31 9.15
Financials 0.2006 0.6809 0.3773 0.4925
Consumer Financials Services -0.76 2,284.38 -6.66 -6.66 16.82% -7.18 -14.89 0.25 7.51 2.69 8.16
Health Care 0.7037 0.6606 0.7611 0.5178 0.2971
Consumer
Financials DOW Health JONES Care INDEXES | QUARTERLY -2.16 U.S. 1,548.56 -12.80 SUMMARY -12.80 | QUARTER 11.41% -17.66 1, 2008-26.37 -0.81 7.48 3.47 Services 11.77
Industrials 0.6500 0.4013 0.1184 0.0473 0.2116 0.3425
Health Industrials Care -4.14 1,906.20 -10.96 -10.96 14.04% 8.36 -4.53 Oil & Gas 3.93 6.57 4.28 10.77% 8.97
Oil
Industrials DOW & Gas 12.07%
Oil & JONES Gas
U.S. INDUSTRY 0.6382
2.32 INDEXES 0.7493 0.6808 0.4628 0.4532 0.7985 0.4569
1,638.51 -5.58 -5.58 12.07% 13.57 4.22 9.27 16.19 4.22 9.40
Technology 0.3368 0.5404 0.5676 0.6234 0.4044 0.6276 0.2387 0.5630
Oil & Gas Financials
Technology -2.26 1,907.78 -6.23 -6.23 14.05% 34.84 22.57 20.97 28.09 13.82 14.80
Telecommunications 0.2451 0.5761 0.1899 0.2952 0.5559 0.1861 0.4270 0.3489 16.82% 0.3734
Technology Telecommunications 0.43 415.26 -15.70 -15.70 3.06% 15.70 -1.86 6.22 11.30 2.12 11.55
Utilities 0.7017 0.8486 DOW JONES 0.7897 U.S. INDUSTRY 0.7177 REPRESENTATION
0.6213 Industrials 0.8318 0.5357 0.8818 0.7108 0.5040
Telecommunications Utilities 4.10 562.19 -14.63 -14.63 4.14% 10.04 -12.36 9.90 13.00 -2.01 5.66
14.04%
Health Care
Utilities 0.62
Market
-9.76
Cap
-9.76
Weight
17.76
in
-2.45 12.37 Utilities 19.01 6.92 8.61
11.41%
4.14% Basic Materials
Industry CORRELATION COEFFICIENTS: (USD Billions) U.S. INDUSTRY DJ U.S. Index INDEXES VS. Telecom. U.S. INDEX AND SIZE INDEXES
3.06%
3.97%
Industry Basic Materials DJ U.S. 538.78 Index CORRELATION DJ U.S. 3.97% Large-Cap COEFFICIENTS
Index DJ U.S. Mid-Cap Index DJ U.S. Small-Cap Consumer Index
Goods
Consumer Goods
Industry DOW 1000 JONES 1,313.94 U.S. 3000 INDUSTRY 5000 REPRESENTATION 9.68%
8000 4000 BY Technology
Basic Materials 0.7017 0.6484 SIZE 2000 (IN BILLIONS 0001 USD) 9000 6000 9.68% 7000
Consumer Services
DJ U.S.
1,462.13
DJ U.S. DJ U.S.
10.77%
14.05% 0.7622 0.7459
Consumer Goods 0.8486 0.8694 DJ U.S. DJ U.S. 0.7321 DJ U.S. DJ 0.6934 U.S.
Basic Materials Financials Large-Cap
0.5403
Large-Cap 2,284.38 Mid-Cap 16.82% Mid-Cap Small-Cap Small-Cap IndexConsumer
Consumer Services 0.7897 0.7542 0.7970 0.7912
Consumer Industry Health Goods Care Market 0.5170 Cap 1,548.56 Weight 0.6451 Market 11.41% Cap Weight Market Cap Weight Market Cap Services
Financials 0.7177 0.7343
Consumer Basic Materials Industrials Services 357.45 0.2809 1,906.20 0.5255 3.47% 0.6924 144.63 14.04%
Oil & Gas 0.6174 0.6088 10.77%
5.89% 36.70 4.43% 538.78
Health Care 0.6213 0.6546 12.07% 0.5144 0.4269
Financials Consumer Oil Goods & Gas 1,028.64 0.2006 1,638.51 0.6809 9.99% 0.3773 216.26 12.07% 0.4925 8.80% 69.04 8.33% 1,313.94
Industrials 0.8318 0.8089 0.8184 Financials 0.7817
Health Consumer Care Technology Services 1,090.50 0.7037 1,907.78 10.60% 0.6606 0.7611 265.62 14.05% 0.5178 10.81% 0.2971 106.01 12.79% 1,462.13
Oil & Gas 0.5357 0.4923 0.5857 0.5641 16.82%
Industrials Financials Telecommunications 1,697.90 0.6500 16.50% 0.4013 415.26 0.1184 431.38 3.06% 0.0473 17.56% 0.2116 0.3425 155.10 18.71% 2,284.38
Technology 0.8818 0.8673 Industrials 0.8327 0.8302
Oil Health & Gas Care Utilities 1,278.25 0.6382 12.42% 0.7493 562.19 0.6808 185.17 4.14% 0.4628 7.54% 0.4532 0.7985 85.13 0.4569
14.04% 10.27% Health Care 1,548.56
Telecommunications 0.7108 0.7283 0.6002 0.6141
Technology Industrials 1,235.00 0.3368 12.00% 0.5404 0.5676 490.21 0.6234 19.95% 0.4044 0.6276 180.99 0.2387 21.83% 11.41% 0.5630 1,906.20
Utilities 0.5040 0.5073 0.4613 0.4216
Telecommunications Oil & Gas 1,339.22 0.2451 13.01% 0.5761 0.1899 240.78 0.2952 9.80% 0.5559 0.1861 58.51 0.4270 7.06% 0.3489 1,638.51 0.3734
Utilities Technology 1,535.30 0.7017 14.92% 0.8486 0.7897 272.29 0.7177 11.08% 0.6213 0.8318 100.20 0.5357 12.09% 0.8818 1,907.78 0.7108 0.5040
Telecommunications 361.95 3.52% 51.66 2.10% 1.64 0.20% 415.26
DOW JONES U.S. INDUSTRY REPRESENTATION BY SIZE (IN BILLIONS USD)
Utilities 367.95 3.58% 158.60 6.46% 35.64 4.30% 562.19
CORRELATION DJ U.S. COEFFICIENTS: DJ U.S. U.S. DJ INDUSTRY U.S. INDEXES DJ U.S. VS. U.S. DJ INDEX U.S. AND DJ SIZE U.S. INDEXESDJ U.S.
Total 10,292.17 75.80% 2,456.60 18.09% 828.97 6.11% 13,577.73
Industry
Large-Cap
DJ U.S.
Large-Cap
Index
Mid-Cap
DJ U.S. Large-Cap
Mid-Cap
Index DJ
Small-Cap
U.S. Mid-Cap
Small-Cap
Index DJ U.S.
Index
Small-Cap Index
Industry Market Cap Weight Market Cap Weight Market Cap Weight Market Cap
0.7017 0.6484 0.7622 0.7459
Data
Basic
based
Materials
on total-return index values
357.45
as
HISTORICAL
of March 31,
3.47%
2008.
DOW
Inception
144.63
JONES
date
U.S.
December
5.89%
INDUSTRY
31, 1991.
REPRESENTATIONS
Correlation
36.70
data based on
4.43%
(%)
monthly total-return
538.78
index values from
March Consumer 31, 2005 Goods to March 31, 2008. 1,028.64 0.8486 9.99% 216.26 0.8694 8.80% 69.04 0.7321 8.33% 1,313.94 0.6934
Industry Consumer Services 2008 Q1 1,090.50 2007 0.7897 2006 10.60% 2005 2004 265.62 0.7542 2003 10.81% 2002 2001 106.01 2000 0.7970 12.79% 1999 1998 1,462.13 0.7912 1997 1996
Page 8
Basic Financials Materials 3.97 1,697.90 3.70 0.7177 2.95 16.50% 2.74 431.38 2.67 0.7343 2.28 17.56% 1.98 2.47 155.10 0.6174 2.34 18.71% 3.53 4.31 2,284.38 0.6088 5.02 6.28
Consumer Health Care Goods 9.68 1,278.25 9.15 0.6213 9.17 12.42% 9.40 10.22 185.17 0.6546 8.44 7.54% 8.14 8.34 85.13 11.63 0.5144 10.27% 13.79 14.72 1,548.56 0.4269 15.09 15.62
Consumer Industrials Services 10.77 1,235.00 10.64 0.8318 13.99 12.00% 13.43 12.97 490.21 0.8089 12.84 19.95% 10.45 14.49 180.99 12.57 0.8184 21.83% 10.51 9.59 1,906.20 0.7817 10.09 11.45
Financials Oil & Gas 16.82 1,339.22 17.33 0.5357 21.25 13.01% 21.01 21.08 240.78 0.4923 19.04 9.80% 18.02 14.11 58.51 16.92 0.5857 19.31 7.06% 16.89 1,638.51 0.5641 15.11 13.51
Health Technology Care 11.41 1,535.30 11.62 0.8818 12.17 14.92% 13.33 14.56 272.29 0.8673 14.28 11.08% 14.23 9.21 100.20 12.44 0.8327 12.09% 11.35 11.08 1,907.78 0.8302 11.11 9.90
Industrials Telecommunications 14.04 361.95 13.46 0.7108 12.64 3.52% 11.87 11.68 51.66 0.7283 12.07 2.10% 12.54 11.98 1.64 11.89 0.6002 13.47 0.20% 14.35 415.26 0.6141 14.39 14.50
Oil Utilities & Gas 12.07 367.95 11.64 0.5040 7.10 3.58% 6.05 158.60 6.32 0.5073 5.88 6.46% 6.00 4.51 35.64 0.4613 5.46 4.30% 7.17 7.88 562.19 0.4216 7.45 7.91
Technology Total 14.05 10,292.17 15.06 14.50 75.80% 16.10 2,456.60 13.53 17.12 18.09% 19.89 25.59 828.97 16.43 11.78 6.11% 12.25 13,577.73 9.99 8.76
Telecommunications Source: Dow Jones Indexes. Data 3.06 is based 3.26 on total 3.00 return index 3.09 values 3.81 as of 12/31/07. 5.03 5.19 6.96 7.20 5.67 5.36 7.29 7.24
Utilities 4.14 4.13 3.24 2.98 3.15 3.02 3.56 2.34 3.12 3.43 3.57 4.45 4.82
HISTORICAL DOW JONES U.S. INDUSTRY REPRESENTATIONS (%)
Industry
July/August 2008
2008 Q1 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996
Data Basic based Materials on market-cap information 3.97 as 3.70 of March 2.95 31, 2008. 2.74 2.67 2.28 1.98 2.47 2.34 3.53 4.31 5.02 6.28

Exchange-Traded Funds Corner
Largest New ETFs Sorted By Total Net Assets In $US Millions
Covers ETFs launched in the year ending April 30, 2008.
Fund Name
Market Vectors Agribusiness ETF
Vanguard Europe Pacific
SPDR Lehman International Treasury Bond ETF
UltraShort FTSE/Xinhua China 25 ProShares
iShares S&P National Municipal Bond Fund
Claymore S&P Global Water
SPDR Lehman High Yield
UltraShort MSCI Emerging Markets ProShares
PowerShares Global Water
ELEMENTS Rogers Agric ETN
Market Vectors GlbAlt Ene
SPDR S&P BRIC 40
WisdomTree India Earnings Fund
SPDR Lehman Municipal Bond ETF
SPDR Lehman 1-3 Mo
Market Vectors Glbl Nclr
Market Vectors Coal ETF
WisdomTree Emerg MktYiHld
PowerShares GlbCleanEnrgy
Ticker
MOO
VEA
BWX
FXP
MUB
CGW
JNK
EEV
PIO
RJA
GEX
BIK
EPI
TFI
BIL
NLR
KOL
DEM
PBD
Assets
1,701.10
1,143.40
809.30
672.00
564.20
371.60
349.00
340.50
335.10
308.30
302.10
291.80
253.30
224.60
220.20
197.90
195.10
187.30
182.60
ER
0.65
0.12
0.50
0.95
0.25
0.72
0.40
0.95
0.75
0.75
0.65
0.40
0.88
0.20
0.14
0.65
0.65
0.63
0.75
3-Mo
11.21
4.61
3.17
-35.02
-0.41
5.09
2.64
-20.45
2.90
-1.06
12.98
15.30
-
-1.07
0.44
0.27
13.06
11.77
7.43
YTD
4.48
-3.51
6.52
-16.46
0.94
-4.38
-0.31
-6.98
-8.44
3.32
-13.23
-2.83
-
0.23
0.81
-11.79
-
2.97
-13.25
Launch Date
8/31/2007
7/20/2007
10/2/2007
11/8/2007
9/7/2007
5/14/2007
11/28/2007
11/1/2007
6/13/2007
10/17/2007
5/3/2007
6/19/2007
2/22/2008
9/11/2007
5/25/2007
8/13/2007
1/10/2008
7/13/2007
6/13/2007
Selected ETFs In Registration
Direxion Russell 2000 Bull 3X Shares
Direxion Emerging Markets Bear 3X Shares
SPA MarketGrader Healthcare Sector
CurrencyShares Russian Ruble Trust
Market Vectors - Africa
Market Vectors - Global Frontier
Vanguard Global Stock Index Fund
WT Int'l LargeCap Growth
WisdomTree Middle East Dividend Fund
PowerShares MENA Frontier
iShares EAFE Small-Cap Index Fund
iShares S&P EM Infrastructure
NETS ISEQ 20 Index Fund
ProShares UltraShort Swiss Franc
SPDR Leisuretime
SPDR Outsourcing & IT Consulting
Wilder Worldwide Emerging Markets
Wilder Healthy Lifestyle
AirShares EU Carbon Allowances
Market Vectors - Gulf States
Source: Morningstar. Data as of April 30, 2008. Assets are total net assets in $US millions. ER is expense ratio. 3-Mo is three-month. YTD is year-to-date.
Source: IndexUniverse.com's ETF Watch.
Largest U.S.-listed ETFs Sorted By Total Net Assets In $US Millions
$US Millions Total Return %
Annualized Return %
Fund Name Ticker Assets Exp Ratio YTD
2007 2006 2005
3-Yr 5-Yr Mkt Cap P/E
Sharpe Std Dev Yield
SPDRs (S&P 500)
iShares MSCI EAFE
iShares MSCI Emerg Mkts
iShares S&P 500
PowerShares QQQQ
streetTRACKS Gold Shares
iShares R1000 Growth
Vanguard Total Stock Market
iShares Lehman 1-3 Treas
DIAMONDS Trust
iShares Russell 2000
iShares R1000 Value
iShares Lehman Aggregate
MidCap SPDR (S&P 400)
iShares Brazil
iShares Japan
Financial SPDR
iShares FTSE/Xinhua China
Vanguard Emerging Markets
iShares Lehman TIPS Bond
iShares S&P 500 Growth
iShares DJ Sel Dividend
Energy SPDR
iShares S&P 400 MidCap
SPY
EFA
EEM
IVV
QQQQ
GLD
IWF
VTI
SHY
DIA
IWM
IWD
AGG
MDY
EWZ
EWJ
XLF
FXI
VWO
TIP
IVW
DVY
XLE
IJH
75,056.3
47,362.9
26,329.3
19,029.1
17,729.3
16,247.4
13,447.0
10,449.7
9,295.9
9,205.6
9,105.8
8,842.5
8,782.5
8,307.1
8,263.8
7,985.0
7,935.2
7,164.8
7,059.1
6,673.9
6,038.2
5,901.9
5,196.5
4,856.0
0.08
0.34
0.74
0.09
0.20
0.40
0.20
0.07
0.15
0.17
0.20
0.20
0.20
0.25
0.69
0.52
0.23
0.74
0.25
0.20
0.18
0.40
0.23
0.20
-5.04
-3.91
-3.42
-5.04
-7.89
3.98
-5.51
-4.92
2.21
-2.67
-6.13
-4.31
2.08
-3.78
11.30
-1.42
-8.34
-9.64
-3.17
2.96
-4.58
-7.19
3.72
-1.85
5.41
10.97
34.55
5.44
19.07
31.07
11.63
5.56
7.30
8.72
-1.47
-0.29
6.57
7.64
76.60
-4.33
-18.79
58.66
39.05
11.46
8.93
-5.37
36.34
7.80
15.69
26.00
30.71
15.70
7.03
23.44
8.86
15.66
3.83
18.81
18.17
22.00
4.13
10.05
44.27
5.49
18.90
83.19
29.53
0.29
10.81
19.41
18.34
10.14
4.79
13.39
33.78
4.83
1.64
16.65
5.08
6.10
1.48
2.36
4.46
6.92
2.16
12.18
52.46
24.65
6.20
14.15
-
2.65
3.81
2.98
40.17
12.48
8.13
16.04
31.85
8.15
10.81
25.48
8.66
8.90
4.86
10.29
8.57
8.19
4.74
10.16
63.03
9.32
0.12
44.47
32.75
5.20
7.45
3.32
27.94
11.06
10.50
20.18
34.56
10.53
11.84
-
9.31
11.69
3.31
11.04
13.66
12.66
-
14.36
57.59
16.07
4.99
-
-
-
8.47
-
31.71
15.03
49,549
36,401
23,324
51,893
31,696
-
34,270
31,021
-
110,970
1,071
45,900
-
3,290
36,514
16,861
38,289
89,337
20,062
-
59,039
8,754
64,786
3,520
15.6
12.5
15.5
16.3
24.2
-
18.6
16.7
-
15.2
17.4
14.5
-
16.4
15.2
13.3
12.4
17.9
18.3
-
17.5
13.3
12.5
17.6
0.45
1.04
1.29
0.45
0.46
1.20
0.46
0.51
0.31
0.70
0.36
0.46
0.16
0.55
1.66
0.43
-0.24
1.21
1.34
0.19
0.36
-0.06
1.12
0.60
8.89
10.94
19.84
8.89
15.78
16.75
9.97
9.29
1.75
8.58
13.45
9.00
2.79
11.12
29.96
12.59
13.30
31.41
19.68
5.15
9.67
9.07
20.11
11.47
2.02
2.64
1.33
2.05
0.32
0.00
0.99
1.90
3.84
2.25
1.02
2.57
4.86
1.08
0.85
1.05
3.47
1.33
1.94
5.20
1.18
4.18
0.98
1.03
Source: Morningstar. Data as of 4/30/08. Assets are total net assets in $US millions. Exp Ratio is expense ratio. YTD is year-to-date. Mkt Cap is geometric average market capitalization. P/E is price-to-earnings ratio.
Sharpe is Sharpe ratio. Std Dev is 3-year standard deviation. Yield is 12-month.
www.journalofindexes.com
July/August 2008
63

The Curmudgeon
HUMOR
Behavioral Finance’s
Fractured Future
Investing in behavior
and finance
By Brad Zigler
People over-think behavioral finance.
They use complex scientific studies, MRI
exams, brain-imaging technology and lots
of other scientific gibber-jabber to try to
show that you can use behavioral finance to
improve your investment activity.
But it’s really very simple, if you just look
at the words. There’s behavior and there’s
finance. Words we know. What’s complicating
things is how much attention these two
words, joined together, are getting.
There’s always money to be made monitoring
and modifying others’ behavior—
financial or otherwise. Let’s look at the
intersection of behavior and finance for
clues to profiting from behavioral finance,
whether you believe this stuff or not.
There are companies in the business of
behavior modification on an inpatient and
an outpatient basis.
The inpatient stuff goes into the behavior
bucket—the Behavioral Index, if you
please. The outpatient stuff goes in the
financial bucket.
The Behavioral Index is led by Tennessee-based
Psychiatric Solutions, Inc.
(NASDAQ: PSYS). Don’t ask me how or
why the Volunteer State has become the
nexus for psychiatric treatment. All I know
is that PSYS’ hometown of Franklin, a
Nashville suburb, is one of the wealthiest
cities in one of the wealthiest counties in
the United States. I leave it to you to make
whatever connection between craziness
and money you’d like.
Like the other companies in the Index,
Magellan Health Services, Inc. (NASDAQ:
MGLN) and Universal Health Services, Inc.
(NYSE: UHS), PSYS owns and operates behavioral
health centers—lots and lots of ‘em. At
last count, PSYS owned or leased 90 inpatient
facilities in 31 states, the commonwealth of
Puerto Rico and the U.S. Virgin Islands.
Over the past four years, PSYS’ share price
has grown at a 12.1 percent compound annual
rate, far exceeding the 5 percent appreciation
of the S&P 500. Blended with MGLN
and UHS, the Behavioral Index’s 6.8 percent
annual growth rate offers a (ahem) healthy
alternative to dull and boring blue chips.
Now, let’s look at the outpatient side—
the finance bucket. There are two public
companies that specialize in modifying
people’s financial behavior. These
denizens of the Financial Training Index
include Investools, Inc. (NASDAQ: SWIM)
and Whitney Information Network, Inc.
(Pink Sheets: RUSS).
Florida-based RUSS offers real estate
and stock market training courses in the
United States, Canada, the United Kingdom
and Costa Rica, while SWIM claims to have
337,000 graduates of its core financial
market training course and 103,000 paid
subscribers to its Web sites.
The past four years have been a roller
coaster for financial training. While SWIM’s
share price trajectory has been upward,
gaining an average 61.6 percent per year,
RUSS has lurched along losing an average
of 17.6 percent per year. Collectively,
though, these two issues offered a 13.1
percent annual appreciation potential,
more than double that of the S&P 500.
Now, consider these companies as subindexes
of a broader Behavioral Finance
Index. Equally weighted and rebalanced
monthly, these five issues would have rendered
a market-beating 6.8% annual growth
rate. Volatility? Yes, more than the S&P, but
hey, what do you expect from folk crazed
by the get-rich syndrome?
If the connection between behavior and
finance still seems murky, think of future
synergies. Sooner or later, one of these outfits
will figure out how to market inpatient
financial training. And when that happens,
a lot of trust fund babies will start sweating
about involuntary commitments.
64
July/August 2008