jason zweig craig israelsen david blanchett and gregory kasten · jason zweig was recently hired as...

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

1July/August 2008www.journalofindexes.com

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Your Money & Your Brainby Jason Zweig . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Why you’re no good at predicting anything.

How Neuroeconomics Can Make You A Better Investoran interview with Jason Zweig . . . . . . . . . . . . . . . 16The journey from neuroscience to brainier investing.

Behavioral Finance Roundtable . . . . . . . . . 20Seven experts on emotion, indexing and investing.

The Frontier From Different Viewsby Craig Israelsen . . . . . . . . . . . . . . . . . . . . . . . . . 26Assessing the frontier by size and style.

ETFs, Spreads and Liquidityby Matt Hougan . . . . . . . . . . . . . . . . . . . . . . . . . . 30A hard look at the data on ETF spreads.

Why ETFs And 401(k)s Will Never Match by David Blanchett and Gregory Kasten . . . . . . . 34ETFs may never gain traction in retirement plans.

Index Strategies And ETF Costsby Richard Ferri . . . . . . . . . . . . . . . . . . . . . . . . . . 42Why complex can be costly in the ETF arena.

Inside The Home Price Indicesby David Blitzer . . . . . . . . . . . . . . . . . . . . . . . . . . 46Behind the scenes of the home price plunge.

The Curmudgeonby Brad Zigler . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64Breaking behavioral finance into its component parts.

f e a t u r e s

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

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Selected Major Indexes . . . . . . . . . . . . . . . . . . . . . . . . . 59Returns Of Largest U.S. Index Mutual Funds . . . . . . . . . 60U.S. Market Overview In Style . . . . . . . . . . . . . . . . . . . . 61 U.S. Economic Sector Review. . . . . . . . . . . . . . . . . . . . . 62Exchange-Traded Funds Corner . . . . . . . . . . . . . . . . . . . 63

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Contributors

David Blanchett is an institutional consultant at Unified Trust Company, where he works primarily with financial advisors on fiduciary, compliance and invest-ment 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 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 was recently hired as personal finance columnist for The Wall Street Journal. Prior to joining the Journal, he had been senior writer and colum-nist 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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July/August 2008

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Editor’s Note

Jim WiandtEditor

Investors Do The Darndest Things

Jim WiandtEditor

omehow 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 round-tables—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.

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By Jason Zweig

Why investors get things wrong

Your Money & Your Brain

July/August 2008 11www.journalofindexes.com

[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 thatclass 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 study-ing 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 con-temporary 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 con-sensus 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 bear-ish 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 great-est 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

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 two-thirds 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 pre-dictions 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 differ-ent? 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 200812

3. Randomness rules. No matter how carefully you research an investment, it can go down for reasons you never antici-pated: 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.

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 deci-sions? That’s what [this excerpt] is all about.

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 pro-cess 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 surpris-ingly 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 descrip-tions of 100 young women, of whom 90 are professional ath-letes 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 descrip-

tions 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?

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 every-one 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 obses-sive 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 oth-ers 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:

It took two psychologists … to deal a death blow to the traditional view that people are always ‘rational.’

July/August 2008www.journalofindexes.com 13

• ��It became a common belief, based on historical num-bers, 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 evi-dence 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 champi-onship, 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/match-vmax.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 pre-cisely 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 psychol-ogy 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 inter-preters 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 practi-cal way to sample the analyst’s entire (and probably medio-cre) 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 recogni-tion in your investing brain:

• It leaps to conclusions. Two in a row of almost any-thing—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

July/August 200814

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 process-ing 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 decision-making as if it were a Platonic problem in reasoning. They don’t have a clue that it’s a biological problem. We’ve got mil-lions 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 aver-age 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 roll-ing 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 infor-mation 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 ear-liest 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 mar-kets 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 high-performance 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 neurosci-entists face as they try to drill down to the biological bedrock of how we make decisions. A German who spent years teach-ing in Switzerland, Schultz seems tailor-made to explore the microstructure of the brain by monitoring the electrochemi-cal 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 neurosci-entist 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


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

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 some-thing, 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 electro-chemical 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-larg-er “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

brains of monkeys earning “income” like sips of juice or mor-sels 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

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 dop-amine 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.

Without the rush of dopamine … “we modern” investors would keep all our money under the mattress.

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July/August 200816

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 partic-ular, 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 profes-sional 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 impor-tant 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 actu-ally exist with pursuing performance right before it disap-pears, 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 ter-ribly 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.

An Interview With Jason Zweig


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 neuroeconom-ics: 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 feed-back. 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 mil-lion 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 profes-sionals who are dealing with other people’s money?

Zweig: Well, there’s very little reason to believe that profession-als 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 observ-ing the financial markets, I certainly don’t see any evidence that professionals are more rational investors than individu-als. 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 plung-ing 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

July/August 200818

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

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 manag-ers 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 intel-ligent I am, how articulate I am, how wealthy I am, how positive I am. All of those judgments will be skewed toward the initial judg-

ment 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 intro-spective 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 tomor-row. 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.

Most people can‘t do what they should, so we need to advise them to do what they can.

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July/August 200820

With Ed McRedmond, William Bernstein, John Prestbo, Ross Miller,

Terrance Odean, Francis Kinniry and David Blitzer

A virtual roundtable

Behavioral Finance And Indexing

www.journalofindexes.com July/August 2008 21

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 self-interest. 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 lead-ing 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 behav-ioral 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 cog-nitive errors—behaviors that contradict the standard assump-tions 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 behav-ioral 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, inves-tors 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 adjust-ments. 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 suc-cessful 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 unconven-tionally. 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 indus-try. 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 differ-ent 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 market-ing budgets convince most investors that they can beat the market (hence the often-heard statement that most invest-ments 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 outper-form the market?

McRedmond: We believe so. The last 10 years have seen a variety of firms whose whole philosophy of outperformance is

July/August 200822

based 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.

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 mar-

ket, 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.


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 redun-dant modified noun.


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 believ-er 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 rebal-ancing 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

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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


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.


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.


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 prog-ress 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.


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.


Prestbo: One would think so, at least in theory, but so far behav-ioral 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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Ross Miller, finance professor, State University of New York (SUNY) at Albany


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 irratio-nality. 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: 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 mani-fest themselves in the markets and they are behavioral in nature.


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 behav-ioral 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.


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.


Miller: While there are advisors who operate in that manner to generate alpha, probably highly quantitative hedge fund man-agers are more efficiently finding the same anomalies. They’re finding those anomalies by studying patterns of returns over dif-ferent 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 behavioral-based 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 psy-choanalyzing and monitoring their own traders.

Terrance Odean, Willis H. Booth Professor of Banking and Finance, University of California at Berkeley


Terrance Odean, University of California at Berkeley (Odean): Due to a number of behavioral biases, many inves-tors 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 inves-tors 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, well-diversified mutual funds.

Being comfortable and following the crowd is rarely

profitable over the long term.

www.journalofindexes.com 25July/August 2008


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.


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 occa-sionally 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.


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.


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 prof-its 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


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 overconfi-dence, 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 prob-ability 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.


Kinniry: By far, the biggest mistake investors make is extrap-olating 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 believ-ing 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.


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 pro-fessionals 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 profes-sional 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 uncer-tain. 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

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 profession-als or nonprofessionals—are capable of making rational and irrational decisions.


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.


Kinniry: Some managers will outperform the market, whether they use behavioral research, technical research, fundamen-tal 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 trad-ing 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 manage-ment techniques and a talented yet humble team of sophisticated investment professionals.

David Blitzer, managing director and chairman of the Index Committee, Standard & Poor’s


David Blitzer, Standard & Poor’s (Blitzer): One of the key fac-tors 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.


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.


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.


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?


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 behav-ior 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 outper-form the market; some do it occasionally, others do it more often; but no one does it all the time.

Behavioral Finance Roundtable continued from page 25

45July/August 2008

July/August 200826

By Craig Israelsen

Size and style matter out on the frontier

The Frontier From Different Views


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 per-cent 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 rea-sonable 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 large-cap value, large-cap growth, mid-cap value, mid-cap growth,

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 index-es, 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 inter-esting. 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-seek-ing MV, LV and SV frontiers. Growth indexes, particularly small-cap growth, generated far worse risk-adjusted perfor-mance 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.

Various Combinations of LB Agg Bond Index and S&P 500 Index (1980-2006)

22

20

18

16

14

12

10

8

6

27

-Year An

nu

alized R

eturn

(%)

27-Year Standard Deviation of Annual Return (%)

6 8 10 12 14 16

100% Bond

40% Bond, 60% S&P 500

100% S&P 500

18 20 22 24

Figure 1

Source: Morningstar Principia

Combinations of U.S. Equity Indexes & LB Aggregate Bond Index (1980-2006)

22

20

18

16

14

12

10

8

6

27

-Year An

nu

alized R

eturn

(%)


6 8 10 12 14 16

100% Bond

100% S&P 500

SVMV

MGLG SG

LV

18 20 22 24

Figure 2


July/August 200828

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 high-er 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 growth-oriented 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 growth-oriented 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

Source: Morningstar Principia1 Average of Dow Jones Large Growth Index and DJ Wilshire Large Growth Index2 Average of Dow Jones Large Value Index and DJ Wilshire Large Value Index3 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 Index5 Average of Dow Jones Small Growth Index and DJ Wilshire Small Growth Index6 Average of Dow Jones Small Value Index and DJ Wilshire Small Value Index

Figure 3

Annual Index % Performance 1980–2006

Year(Color-Coded

By Decade)

S&P 500 Index

U.S.Mid Growth3

U.S.Mid Value4

U.S. Small Growth5

U.S. Small Value6

U.S. Large Value2

U.S. Large Growth1

LB Aggregate Bond Index

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

13.26 9.10 11.12 14.06 12.16 15.59 10.68 16.16

15.79 7.47 20.68 13.77 21.83 13.78 22.52 15.44

$288,108 $104,923 $172,205 $348,756 $221,465 $500,062 $154,815 $570,743

27-Year Avg

Annualized

Return (%)

27-Yr Std Dev

of Return (%)

Growth of

$10,000


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, large-cap 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 demon-strated a value premium 70 percent of the time, with the

average five-year value premium equaling 505 bps. Large-cap 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 per-formance 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

1980s (1980-1989)

22

20

18

16

14

12

10

8

6

10

-Year An

nu

alized R

eturn

(%)


6 8 10 12 14 16

100% Bond

100% S&P 500

SV

MV

MGLG

SG

LV

18 20 22 24

Figure 4


1990s (1990-1999)

22

20

18

16

14

12

10

8

6

10

-Year An

nu

alized R

eturn

(%)


6 8 10 12 14 16

100% Bond

100% S&P 500

SVMV

MGLG

SG

LV

18 20 22 24

Figure 5


2000s (2000-2006)

20

15

10

5

0

-5

-10

7-Year A

nn

ualized

Retu

rn (%

)


0 5 10 15 20 25 30

100% Bond

100% S&P 500

SV

MV

MG

LG

SG

LV

Figure 6


Figure 7

Value vs. Growth Premium5-Year Rolling Return Premium (basis points)

5-Year Period

U.S. Large-Cap Equity

U.S. Mid-Cap Equity

U.S. Small-Cap Equity


Growth1 Value2 Growth3 Value4 Growth5 Value6

continued on page 57

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

30% 70% 22% 78% 17% 83%

353 505 260 575 257 756

160 385 111 488 94 818

Percent of Years With “Premium”

Mean Premium

(bps)

Median Premium (bps)

1 Average of Dow Jones Large Growth Index and DJ Wilshire Large Growth Index2 Average of Dow Jones Large Value Index and DJ Wilshire Large Value Index3 Average of Dow Jones Mid Growth Index and DJ Wilshire Mid Growth Index4 Average of Dow Jones Mid Value Index and DJ Wilshire Mid Value Index5 Average of Dow Jones Small Growth Index and DJ Wilshire Small Growth Index6 Average of Dow Jones Small Value Index and DJ Wilshire Small Value Index


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 inves-tors) are optimists. They are willing to pay a high price (in the form of volatility) for a relatively rare, but poten-tially large, burst of outperformance relative to small-cap value. They must see a rewarding small-cap growth fron-tier 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.

Israelsen continued from page 29

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 par-ticular 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.

SummaryThere is a clear link between the complexity of index

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.

Figure 5

U.S. Broad Market/Large-Cap Index Strategy Box Pricing Matrix

Source: ETFGuide.com

Quantitative 0.55% 0.60% 0.60%

Screened 0.35% 0.45% 0.45%

Passive 0.20% 0.35% 0.35%

Capitalization Fundamental Fixed Weight

July/August 200830

by Matt Hougan

A close look at the data on ETF spreads.

ETFs, Spreads And Liquidity


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 half-way 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 inves-tors 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 mar-ket 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 pro-duce an average spread for each ETF over that time range.

The ResultsThere 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 mar-ket 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 DIA

iShares Lehman 1-3 yr Treasury SHY

iShares Lehman 20+ yr Treasury TLT

iShares MSCI Australia EWA

iShares MSCI Canada EWC

iShares MSCI EAFE EFA

iShares MSCI Germany EWG

iShares MSCI Hong Kong EWH

iShares MSCI Japan EWJ

iShares MSCI Malaysia EWM

iShares MSCI Singapore EWS

iShares MSCI Taiwan EWT

iShares Russell 1000 Growth IWF

iShares Russell 2000 Index IWM

iShares S&P 100 Index OEF

PowerShares QQQ Trust QQQQ

ProShares UltraShort QQQ QID

ProShares UltraShort S&P 500 SDS

Select Sector SPDR Consumer Discretionary XLY

Select Sector SPDR Consumer Staples XLP

Select Sector SPDR Energy XLE

Select Sector SPDR Financials XLF

Select Sector SPDR Health Care XLV

Select Sector SPDR Industrials XLI

Select Sector SPDR Technology XLK

Select Sector SPDR Utilities XLU

Select Sector SPDR Materials XLB

Semiconductor HOLDRS SMH

SPDR SPY

streetTRACKS Gold Trust GLD

Source: NYSE Acravision. Data for January 1, 2008 through March 21, 2008.

July/August 200832

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.

The Spread On SpreadsThe 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 BasisAnother 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 sharehold-ers that far outweighs any savings on the expense ratio front.

Seventy-four ETFs make the honors list by this measure, post-ing 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), includ-ing a large number of individual country funds.

Larger Funds, Tighter SpreadsOne 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

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 SpreadsLikewise, there is a direct correlation between the amount

of trading in the fund and the average spread. Figure 6 com-pares 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 vol-ume have higher average spreads.

Liquidity Of The Underlying?The old consensus was that the liquidity of the under-

lying stocks determined the tightness of the spreads. But research shows that it is the liquidity of the ETF, and not the

ETF Spreads

500450400350300250200150100

500

Source: NYSE Arcavision. Data for January 1, 2008 through March 31, 2008.

Nu

mb

er of ETFs

Average Spreads

$0.01-$0.10 $0.11-$0.20 $0.21-$0.50 $0.51+

463

134

5514

Figure 2

ETF Spread Percentage - Q1 2008


Nu

mb

er of ETFs

Spread %

74

208

175

85

4229 15 8 5 7

18

0.0% 0.1% 0.2% 0.3% 0.4% 0.5% 0.6% 0.7% 0.8% 0.9% 1%or

more

Figure 3

Figure 4

Fund Company # of ETFs

ETFs With Spreads Less Than 0.1% - Q1 2008


BGI 36

SSgA 17

ProShares 9

HOLDRS 5

Rydex (CurrencyShares) 3

PowerShares 1

Van Eck 1

Vanguard 1

Victoria Bay 1


liquidity of the underlying components that really matters.

S&P 500 ExampleConsider, 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 mar-ket 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 ExampleAnother 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 ori-ented 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 port-folios 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 bas-ket.” 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 rea-son? 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.

ConclusionSpreads 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 manage-ment, 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


ETF Spread Percentage By Assets Under Management

Net Assets Number of ETFs

0.1% 0.2% 0.3% 0.4% 0.5%

Average Spread Percentage Decile

>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%

$51-$99 million 75 0% 25% 47% 15% 8% 5%

<$50 million 283 0% 11% 30% 22% 11% 25%


Figure 6

ETF Spread Percentage By Average Daily $ Trading Volume

Average Daily $ Trading Volume

Number of ETFs

0.1% 0.2% 0.3% 0.4% 0.5%

Average Spread Percentage Decile

>0.5%

>$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%

<$10 million 98 0 2 13 24 14 45

July/August 200834

By David Blanchett and Gregory Kasten

Why prudently selected index mutual funds are a better choice than ETFs for most 401(k)s

Why ETFs And 401(k)s Will Never Match


Exchange-traded funds, long known as a low-cost method of investing for individual investors, are receiving increas-ing 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 introduc-tion of the exchange-traded fund. The first ETF introduced was the Toronto Index Participation (TIPS) in Canada, which was fol-lowed 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 tradi-tional index funds. The ability of ETFs to utilize in-kind redemp-tions 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 securi-ties 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)sAlthough 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 avail-able 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 pur-chasing an ETF, since, unlike mutual funds, ETFs are purchased on

July/August 200836

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 repre-sents 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 trad-ing 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 frac-tional 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 partici-pants 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 require-ments) 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 cost-effective 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 PoolingThere 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 miscel-laneous administrative expenses. The implicit costs of pooled accounts relate primarily to the impact of cash drag, which nega-tively 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 dis-cussed 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 can-not 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 asso-ciated 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 repre-sents 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 dif-ferences 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 avail-able 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


Figure 1

Large-Cap Comparison

Ticker Type* Investment Name CategoryMinimum

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


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


Figure 3

Small-Cap Comparison


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

July/August 200838

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 pub-licly 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 impos-sible) 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 impos-sible 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 mutu-al 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 respec-tive 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 respec-tive 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


used as the case study for this paper. Vanguard offers both mutual funds and ETFs that are based on the same underly-ing 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 admin-istrative 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 ShareA common criticism of mutual funds is the payments

(known as revenue sharing) made to retirement plan pro-viders. 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 compensa-tion (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 BacktestingSomething to be aware of with ETFs, which isn’t an issue for

other investments, is that ETFs can use “backtested” or hypo-thetical 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 strate-gies are becoming increasingly esoteric. Therefore, it is important to ensure that the underlying methodology is sound when select-ing an ETF, not just the hypothetical historical performance.

Living On The Wild SideAn additional appeal of ETFs is the ability to gain more special-

ized 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 expo-sures, the low costs of the ETFs coupled with a few vocal par-ticipants 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 buy-ing 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 fidu-

ciary 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 pru-dent investment. A number of studies have shown that par-ticipants 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 vio-lating 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

July/August 200840

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 IndexIVW 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 IndexVV ETF Vanguard Large Cap ETF Large Blend $0.95 0.07% 01/27/04 MSCI US Prime Market 750 IndexIVV 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 IndexVTV ETF Vanguard Value ETF Large Value $2.24 0.11% 01/26/04 MSCI US Prime Market Value IndexIVE 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 IndexVOT ETF Vanguard Mid-Cap Growth ETF Mid-Cap Growth $0.15 0.13% 08/17/06 MSCI US Mid Cap Growth IndexIWP MF iShares Russell Midcap Growth Index Mid-Cap Growth $2.77 0.25% 07/17/01 Russell Midcap Growth IndexIJK MF iShares S&P MidCap 400 Growth Index Mid-Cap Growth $2.05 0.25% 07/24/00 S&P MidCap 400/Citigroup Growth IndexVO ETF Vanguard Mid Cap ETF Mid-Cap Blend $1.19 0.13% 01/26/04 MSCI US Mid Cap 450 IndexIJH MF iShares S&P MidCap 400 Index Mid-Cap Blend $4.90 0.20% 05/22/00 S&P MidCap 400 IndexIWR MF iShares Russell Midcap Index Mid-Cap Blend $3.79 0.20% 07/17/01 Russell Midcap IndexVOE ETF Vanguard Mid-Cap Value ETF Mid-Cap Value $0.20 0.13% 08/17/06 MSCI US Mid Cap Value IndexIWS 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 IndexVBK ETF Vanguard Small Cap Growth ETF Small Growth $0.78 0.12% 01/26/04 MSCI US Small Cap Growth IndexIWO MF iShares Russell 2000 Growth Index Small Growth $2.96 0.25% 07/24/00 Russell 2000 Growth IndexIJT MF iShares S&P SmallCap 600 Growth Small Growth $1.49 0.25% 07/24/00 S&P SmallCap 600/Citigroup Growth IndexVB ETF Vanguard Small Cap ETF Small Blend $0.98 0.10% 01/26/04 MSCI US Small Cap 1750 IndexIWM MF iShares Russell 2000 Index Small Blend $11.31 0.20% 05/22/00 Russell 2000 IndexIJR MF iShares S&P SmallCap 600 Index Small Blend $4.94 0.20% 05/22/00 S&P SmallCap 600 IndexVBR ETF Vanguard Small Cap Value ETF Small Value $0.77 0.12% 01/26/04 MSCI US Small Cap Value IndexIWN MF iShares Russell 2000 Value Index Small Value $4.17 0.25% 07/24/00 Russell 2000 Value IndexIJS MF iShares S&P SmallCap 600 Value Index Small Value $1.81 0.25% 07/24/00 S&P SmallCap 600/Citigroup Value Index


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 partici-pants 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 low-cost investment solutions for participants in an attempt to mini-mize overall plan fees. It’s important to remember that the pur-pose 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 man-agement 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.]

BibliographyCollective 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.

July/August 200842

By Richard Ferri

Alpha, Beta and Cost

The ABCs Of ETFs


Exchange-trade funds are benchmarked to an expand-ing universe of indexes. Those indexes range from traditional passive benchmarks that use capitaliza-

tion 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 bench-marks. 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 inves-tors 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 measure-ment of market value. In a sense, market indexes track mar-ket “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 custom-made 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 bet-ter than ETFs that follow market indexes. WisdomTree pro-motes 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 selec-tion 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 secu-rity weighting strategies form a matrix. Figure 1 shows the nine-box tic-tac-toe design of Index Strategy Boxes.

Analysis Of ETFs And Fees By Index StrategyThe classification of ETFs by Index Strategy Boxes is avail-

able 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 includ-ed 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 infor-mation 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 pas-sive 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.

Index Strategy Boxes

Secu

rity

Sel

ecti

on

Security Weighting

Quantitative

Screened

Passive

Cap

italiz

atio

n

Fund

amen

tal

Fixe

d W

eig

ht

Figure 1



Figure 2

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


July/August 200844

Index Strategies And ETF FeesAfter categorizing all 304 ETFs by their underlying index

security selection and security weighting methods, I calcu-lated 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 secu-rity 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.

ETF Fee-Pricing Model Based On Index StrategyI created an ETF pricing model based on the informa-

tion 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 alpha-seeking 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 compa-

nies 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 strat-egy is similar to indexes by other vendors. For example, ETFs following fundamentally weighted RAFI indexes were consider-ably 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 nonover-lapping 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 valu-able 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 illus-trated in Figure 5.

continued on page 57

Figure 4

Index Strategy Box Pricing Template For ETFs: Added Fees For More Complex Strategies



Screened 0.15% 0.25% 0.25%

Passive — 0.15% 0.15%



Figure 3

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%













Figure 5




Screened 0.35% 0.45% 0.45%

Passive 0.20% 0.35% 0.35%


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 profession-als or nonprofessionals—are capable of making rational and irrational decisions.


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.


Kinniry: Some managers will outperform the market, whether they use behavioral research, technical research, fundamen-tal 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 trad-ing 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 manage-ment techniques and a talented yet humble team of sophisticated investment professionals.

David Blitzer, managing director and chairman of the Index Committee, Standard & Poor’s


David Blitzer, Standard & Poor’s (Blitzer): One of the key fac-tors 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.


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.


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.


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?


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 behav-ior 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 outper-form the market; some do it occasionally, others do it more often; but no one does it all the time.

Behavioral Finance Roundtable continued from page 25

45July/August 2008

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 typi-cal. 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 compos-ite index covering all 20 cities saw prices rise 107 percent, peak-ing 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 move-ments for low-, mid- and high-priced homes. The break points

Inside The Home Price Indices

by David Blitzer

Talking Indexes

July/August 200846

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 inno-vations in mortgages drove a large part of the shifts in the

housing markets. In some cases, the range between low- and high-priced homes was large—in San Francisco, low-priced home prices fell 32 percent from their peak, while high-priced homes slid only 6 percent; midpriced units were down 20 percent from May 2006, when prices in San Francisco peaked. However, low-priced homes continued to gain until August 2006, and since then, are down 35 percent. At that peak, low-priced homes were up 276 percent—worth almost three times their January 2000 price. By February of this year, the price was down to only 180 percent of the January 2000 level.

With prices still close to double their level eight years ago, after falling by more than a third, are we near a bottom? Despite careful searching through each month’s release for close to a year, there is no way to tell. There may be a few spots where prices appear to be pausing, but there are no clear signs of when things will turn around. One of the nice parts of maintaining and publishing these indices is that I am not allowed to forecast them—and so can’t get the forecast wrong. All these data, and more, are posted at www.homeprice.stan-dardandpoors.com, so you are welcome to join the search for the turning point in homes.

(Written with data available as of April 29, 2008.)


Figure 1

Ups And Downs Of S&P/Case Shiller® Home Price Indices

Peak DateDecline

From Peak

Decline Rank

Peak Since

Jan-2000Peak Rank

Time From Peak (Months)

Rate Of Decline (Pct Pts/Month)

Rate Of Decline

Rank

Low-, Mid- And High-Priced Homes In San Francisco

300

250

200

150

100

50

0

■ <$513 thousand ■ <$513-$756 thousand ■ >$756 thousand

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

($ T

ho

usan

ds)

Figure 2

Color shading shows cities with the same peak dates. Source: Standard and Poor’s

Source: Standard and Poor’s

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

July/August 200848

Bear Stearns Rolls Out First U.S. Active ETF

Well, it finally happened. The U.S. ETF market got its first actively man-aged 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, Inves-co. 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 Pow-erShares’ parent, Invesco.

The obvious question is what dis-tinguishes 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 fol-lowing day. That one-day window of silence is the key reason PowerShares launched these funds.

Home Prices TumbleThings 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 trail-ing 12-month period. Meanwhile, the 20-city composite was down 2.6 per-cent 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 metro-politan 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 Fran-cisco. 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

News

Things still look unbearably grim as the housing market malaise drags indexes lower and lower.


rapidly 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. repre-sented 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, rebal-ancing 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 per-cent in annual expenses. That expense ratio undercuts the new offering from Barclays Global Investors by 10 basis points (0.10 percent). The new Van-guard 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 North-ern 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 interest-ing 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 sever-al 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 per-cent 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. Trans-parent Value’s methodology, called “Required Business Performance” (hence RBP), uses discounted cash flow analysis to determine how much revenue a company needs to gener-ate 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 per-cent long basket in the new indexes, while stocks with the lowest probabil-ity form the 30 percent short basket.

Currently, the DJ U.S. RBP index family consists of three major index-es: the core Dow Jones RBP U.S. Large-Cap 130/30 Index, plus simi-larly constructed growth and value versions.

Dow Jones expects to launch addi-tional 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

July/August 200850

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 Nige-ria, Cyprus, Kazakhstan and Moroc-co. Vietnam, one of the most spec-tacular frontier market success sto-ries, is also included. Stocks eligible for inclusion must have a minimum market capitalization of $500 million and a minimum three-month aver-age 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.

UBS Index Tracks Food InflationSpurred on by the continu-

ous increases in food prices, UBS AG and Bloomberg Finance have launched the UBS Bloomberg CMCI Food Index. The new commodi-ty index covers 13 different food commodities ranging from orange juice and lean hogs to coffee and soybeans. The index is based on

the same methodology as the UBS Bloomberg Constant Maturity Com-modity Index, which incorporates multiple maturities into its calcula-tion and has a complex, multifac-tored 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 look-ing to buy food-related commodi-ties 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, fol-lowed by soybeans, at 14.94 percent.

According to UBS, the new index is designed to underlie investable

products like ETFs. It is calculated in U.S. dollars, euros and Swiss francs.

KLD Racks Up LicenseesNot 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

index-based funds tied to the bench-marks, 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

Corp. has licensed the KLD Global Sustainability Index, the KLD North America Sustainability Index and the KLD Europe Asia Pacific Sustainabili-ty Index for use in a variety of invest-able products, including ETFs.

Merrill Tracks Emissions Contracts With New CO2 Emissions Index

Merrill Lynch recently launched a carbon emissions benchmark, the MLCX Global CO2 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 com-ponent 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-

News

Merrill Lynch recently launched a carbon emissions benchmark.

Not even a year after it launched its global sustainability indexes, KLD is licensing them near and far.


Newscent of the index, while EUA con-tracts 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 nar-row. Capitalization minimums are the reason. Companies eligible for inclusion must have market capi-talizations 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 deter-mining a company’s float, which should take care of the new restric-tions on capital inflows imposed by the Indian government last year. The index itself was designed to underlie investment products, according to S&P.

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, Nige-ria, South Africa, Tunisia and Zimba-

bwe. 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 com-ponents 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 Strate-gies 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 per-cent 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 IndiaIn March, Direxion Funds expand-

ed 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 track-ing its index.

DXILX charges a net manage-ment fee of 1.50 percent.

AROUND THE WORLD OF ETFSPowerShares Puts A Twist On Things With Three New Funds

In early April, PowerShares launched three new exchange-trad-ed 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 com-bining equal weighting and market-capitalization weighting to track the nuclear energy market. The index is provided by WNA Global Indexes, which was formed last year in part-nership with the World Nuclear

July/August 200852

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 FundsOn the same day it launched

its all-world ETF, BGI launched a trio of single-country iShares: the iShares MSCI Israel Capped Invest-able Market Index Fund (NYSE Arca: EIS), iShares MSCI Turkey Invest-able Market Index Fund (NYSE Arca: TUR) and iShares MSCI Thai-land 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 estab-lished 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 Leh-man 20+ Year Treasury ETF (AMEX: TBT) are designed to deliver twice the inverse of the daily performance

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 her-alded the launch of two new global solar-focused ETFs. Skyrocketing oil prices, along with growing envi-ronmental 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 near-month 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 man-agement 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 ThreatA 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 per-formance of the underlying index, while the “Bear” funds will offer three times the inverse of the perfor-mance 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 man-agement 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 per-cent 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 Secu-rities & Exchange Commission for a frontier markets ETF.

The PowerShares MENA Fron-tier Countries Portfolio will track the Middle East and Africa Fron-tier Countries Index, which covers 50 stocks—five each from Nigeria, Lebanon, Egypt, Morocco, Oman, Jordan, Kuwait, Bahrain, Qatar and the United Arab Emirates. Compo-nents must have market capitaliza-tions of at least $500 million. The index is rebalanced quarterly and takes into account foreign owner-

News


Newsship restrictions at each review. The index provider was not identified.

Investors have begun to turn to frontier markets as they display continued outperformance in com-parison to developed and emerging markets, and as emerging markets begin to correlate more closely with developed markets in terms of per-formance. To date, however, inves-tors have relatively limited choices in ETFs that access these markets.

U.S. Gets Its First Global TIPS ETFThe first U.S.-based global inter-

national Treasury inflation-protect-ed securities ETF launched on March 19, opening exposure for investors to TIPS in 18 different countries and 15 different currencies.

The SPDR DB International Gov-ernment Inflation-Protected Bond ETF (AMEX: WIP) includes TIPS issued in both developed and emerg-

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. Infla-tion Linked Bond Capped Index. In the past 12 months, that benchmark has returned 20.9 percent. About 12 percent of those gains were currency-related, another 5 percent were asso-ciated 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 world-wide flight to quality as credit mar-kets continue to struggle from the U.S.-led mortgage meltdown.

Since the fund deals with govern-

ment debt and buys in foreign curren-cies, 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.

UBS Enters ETN MarketSwiss-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 con-tract, but small positions in the July contract, the August contract, etc. Each ETN tracks the performance of the UBS Bloomberg Constant Maturi-

ty Commodity Index (CMCI)—which UBS says is the first benchmark com-modity index to diversify across both commodities and maturities—or one of its subindexes. By spreading its exposure across multiple matur-ities, the fund may mitigate the impacts of contango and backwarda-tion 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 Ener-gy 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 exchange-

traded products to cover the plati-num 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 cur-rency ETNs. The initial products offer exposure to the Chinese ren-minbi and Indian rupee. The Mar-ket 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 strength-ens. Both notes track an index tied

to currency futures, which allows them to get around local market restrictions on spot currency trans-actions. 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 Mar-ket 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 inter-est rate, not local interest rates. Addi-

The first U.S.-based global international Treasury inflation- protected securities ETF launched on March 19.

July/August 200854

Newstionally, none of these ETNs pays out interest income; interest is instead added to the share value of the note. Interest accrual presents tax com-plications 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 offer-ing short and long exposure—four covering the agriculture commodi-ties 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

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 Dou-ble 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 Commod-ity 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 broad-based commodities ETNs carry an expense ratio of 0.75 percent.

ELEMENT-ary AdditionsWithin 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 sec-tions 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 (Mer-rill 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

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 solu-tions. It charges 0.75 percent.

New Commodities ETFs Launch In London

In mid-March, ETF Securities (ETFS) rolled out 33 leveraged com-modity ETFs (designed to deliver 200 percent of the daily perfor-mance of the benchmark index) and four short ETFs (designed to deliver -100 percent of the daily perfor-mance of the benchmark index).

The new products trade on the Lon-don Stock Exchange, and all of them track the Dow Jones-AIG Commod-ity 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

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 Agri-cultural 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 man-agement 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 lever-aged exposure to the grains sector specifically.

INTO THE FUTURESPHLX Subsidiary Lists New Futures

The Philadelphia Stock Exchange launched three futures contracts on its

In April, Deutsche Bank added eight new commodities ETNs offering short and long exposure.

July/August 2008 55

NewsPhiladelphia 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 Semi-conductor Sector and PHLX Hous-ing Sector indexes. PHLX options contracts are available for all three indexes and have some of the high-est volumes of PHLX’s 17 different index options contracts.

For the time being, volumes for the new futures remain largely nonexistent.

ON THE MOVEPowers Named President And CEO Of SSgA

Scott Powers has been named president and chief executive offi-cer of State Street Global Advi-sors (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, chair-man and chief executive officer of parent company State Street Corp. He has also been added to State Street’s Operating Group, the top-level 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 trad-ing businesses.

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 infra-

structure indexes fall into this bai-liwick. 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 Hans- ford, 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 GlobalInvestments Gets New CEO

Northern Trust has appointed Wayne Bowers as chief executive officer of Northern Trust Global Investments Limited, the London-based asset management subsidiary of Northern Trust Corporation.

As CEO, Bowers is responsible for the continued growth and develop-ment 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 Lon-don-based fixed-income portfolio management team. He was appoint-ed chief investment officer for NTGI Limited in 2007 and has been acting CEO of NTGI Limited since Decem-ber 2007. Prior to joining Northern Trust, Bowers was employed at ABN Amro Bank and Hambros Bank.

BNP’s Abner Joins WisdomTreeDavid Abner has been hired by Wis-

domTree 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 ChairmanIn April, European index provider

STOXX Limited announced that Wer-ner Bürki, a member of the manage-ment committee of the SWX Swiss Exchange and CEO of EXFEED Ltd., was re-elected for a second consecu-tive 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 com-posed 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 colum-nist 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 Citi-group earlier this year.

Zweig’s weekly column is set to debut July 1.

Zweig is a noted fan of index-ing. He is also currently a guest columnist for Time magazine. He was the mutual funds editor for Forbes before joining Money maga-zine 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.)












Figure 5




Screened 0.35% 0.45% 0.45%

Passive 0.20% 0.35% 0.35%



Global Index Data

Goldman Sachs CommodityDow Jones - AIG CommodityDJ Transportation AverageMSCI Taiwan USD*MSCI Argentina USD*DJ Wilshire REITMSCI Brazil USD*FTSE NAREIT Equity REITsLB Global AggregateGoldman Sachs Nat ResLB US Treasury US TIPSLB Fixed Rate MBSLB US AggregateCSFB Credit Suisse HYCPILB MunicipalS&P Midcap 400DJ Wilshire US Mid Cap ValDJ Wilshire US Small Cap ValDJ UtilitiesRussell 2000 ValueMSCI EM USDS&P Smallcap 600MSCI EAFEDJ Wilshire US Mid Cap GrowthRussell 3000 ValueRussell 1000 ValueDJ Mid Cap GrowthMSCI Europe*Russell Mid Cap GrowthNYSE CompositeS&P 500/Citi GrowthRussell Top 200 ValueDJ Wilshire US Large Cap GrowthAMEX CompositeMorningstar Consumer GoodsDJ Wilshire US Large CapMorningstar US MarketRussell 1000DJ Wilshire US Top 2500MSCI AC World ID*Russell 3000S&P 500DJ Wilshire 5000DJ Wilshire US Large Cap Val Morningstar Large CoreMSCI EM LCL*Morningstar UtilitiesDJ Wilshire 4500DJ Wilshire US Small CapRussell 1000 GrowthRussell 3000 GrowthS&P 100Russell 2000NYSE Arca Tech 100MSCI World LCL*DJ US FinancialRussell 2000 GrowthNASDAQ CompositeDJ US Health Care

18.6813.5113.49

9.729.338.438.287.344.604.202.962.531.960.920.770.55

-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

-15.092.079.81

16.3066.0735.9740.5235.06

6.6416.82

0.415.224.33

11.932.574.84

10.3215.7120.0416.6323.4832.5915.1226.8611.5722.3422.2510.7133.7210.6617.8611.0122.99

9.1516.9017.5515.6315.7015.4615.7918.7815.7215.7915.8821.8715.5425.5724.7716.0716.98

9.079.46

18.4718.37

4.6813.5219.4213.35

9.526.88

32.6716.23

1.435.43

-5.36-17.5675.35

-15.699.48

34.4311.64

6.906.972.664.123.367.98

-1.29-4.1320.11-9.7839.78-0.3011.6311.24-1.01-0.1717.0113.8611.43

6.589.130.25

10.9717.1811.28

6.405.925.775.879.645.145.495.731.848.65

30.4018.16

5.771.90

11.8111.40

6.12-1.577.262.83

-17.667.059.818.36

25.5521.3611.65

3.2859.6913.8249.9612.16-4.4936.48

2.842.612.432.263.393.51

12.565.465.30

25.144.71

34.547.68

14.0216.67

6.857.05

14.549.42

12.106.951.144.607.13

22.642.146.336.526.276.458.836.124.916.325.723.82

31.5414.8010.28

7.375.265.171.174.557.36

13.746.464.151.378.32

14.8015.7115.9813.3937.1412.0859.4211.88

5.5428.41

5.335.434.936.603.253.56

11.208.068.69

14.797.30

34.278.76

16.7413.42

8.278.36

14.6817.5711.81

9.897.297.719.66

16.839.148.919.018.639.02

11.888.648.239.048.068.57

27.6114.4611.14

9.898.868.967.408.627.918.101.149.917.883.25

19.3416.6717.8217.0138.4018.9953.4718.67

6.6330.30

6.354.734.378.623.094.03

15.2012.8114.4521.9514.0835.7614.7320.9217.5112.9212.8516.7120.9315.3012.63

8.3311.2510.0421.8612.0811.3311.7111.2311.7714.3811.4110.6211.8312.5310.6727.6619.1015.8415.40

9.529.798.86

13.7712.1210.31

6.4413.3210.50

6.10

12.5310.92

5.151.106.20

12.4615.3711.69

6.3012.36

7.705.945.965.802.785.169.648.348.539.947.74

13.547.417.054.936.055.975.866.845.754.302.214.781.96

11.895.154.154.244.244.373.964.283.894.365.994.32

11.518.416.006.701.661.673.565.349.382.063.932.202.594.25

8.889.799.603.348.23

12.5619.1612.42

6.54--

6.276.287.522.665.61

13.1712.1912.62

9.7911.6911.31

-8.159.79

11.1411.15

9.7911.7110.22

8.94-

10.599.12

11.9710.28

9.999.95

10.1110.02

7.0010.00

9.9810.0110.49

-16.80

9.0710.4010.96

8.438.23

10.029.54

16.196.14

11.236.799.01

11.93

13.4336.3015.9219.9517.4021.5610.4515.0410.2510.4012.8410.7711.9913.7214.13

9.7910.5411.2811.18

3.5612.2812.86

5.1812.4322.92

9.8015.5912.87

3.309.499.362.532.56

13.460.43

13.8613.70

7.808.597.89

12.529.727.741.267.30

13.176.997.676.927.097.087.68

18.8714.63

8.4617.70

6.8729.5019.6135.77

Index Name YTD 2007 2006 200517.28

9.1527.73

6.5424.5733.1630.4931.58

9.2724.57

8.464.704.34

11.963.344.48

16.4817.8819.6130.2422.2525.9522.6520.7018.9416.9416.4915.3520.8815.4812.16

6.9713.34

9.5322.2211.9411.6512.3511.4112.5313.3011.9510.8812.6213.5513.9913.2123.4018.5119.46

6.306.936.43

18.3311.73

9.4913.3914.31

8.594.55

20.7223.9331.8440.0198.5336.18

102.8537.1312.5134.01

8.403.074.10

27.932.045.31

35.6234.9446.8629.3946.0356.2838.7939.1743.4031.1430.0343.6538.5442.7129.2827.0826.7527.4642.3622.1828.9030.7329.8930.9631.6231.0628.6931.6430.5524.7142.3424.9743.9549.0329.7530.9726.2547.2552.1422.7632.2348.5450.0119.43

2004 200350.0824.70

3.9319.77

3.07-13.7970.36

-12.5111.7828.0811.33

7.396.87

-0.843.672.79

-2.76-11.76-11.89

1.32-15.1325.71-9.04-1.31-2.28-9.49-8.972.10

-1.64-1.93-3.41-0.42-7.970.204.560.03

-3.91-4.53-4.62-4.58-2.01-5.16-4.68-4.74-8.08-1.1017.78-1.28-5.71-9.38-0.23-0.79-3.64

-10.96-5.60-9.16

-24.55-6.71-4.45-9.20

12-Mo 3-Yr 5-Yr 10-Yr 15-Yr Std Dev Total Return % Annualized Return %

Selected Major Indexes Sorted By YTD Returns July/August 2008

*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.

July/August 200860

Vanguard 500 IndexVanguard Tot StkVanguard Inst IdxVanguard 500 Idx AdmVanguard Total Bd IdxVanguard Total Intl StkVanguard Tot Stk AdmVanguard Inst Idx InstPlVanguard Eur Stk IdxFidelity Spar US EqIxVanguard 500 Index SignalVanguard Tot Stk InstVanguard Em Mkt IdxVanguard Total Bd Idx AdVanguard Pac Stk IdxVanguard Total Bd Idx InT. Rowe Price Eq Idx 500Fidelity Spar 500 AdvFidelity U.S. Bond IndexVanguard Tot Stk InstPlsDimensional Intl SmCpValVanguard Inst Tot Bd IdxDimensional US LgCpValVanguard Mid Cap IdxFidelity Spar 500 IdxFidelity 100 IndexDimensional EmergMrktsValFidelity Spar US Eq AdvVanguard Gr IdxVanguard TotBdMkt Idx SigVanguard Mid Cap Idx InsVanguard SmCp IdxDimensional Intl ValDimensional Intl Small CoDimensional US Micro CpVanguard ExtMktIdxFidelity Spar Intl IndexFidelity Spar Tot Mkt IxVanguard Inst DevMktsIdxVanguard Eur Stk Idx InsVanguard REIT IndexVanguard TotStMkt Idx SigVALIC I StockVanguard Val IdxVanguard Dev Mkts IdxFidelity Spar Tot Mkt AdvSchwab S&P 500 In SelGatewayVanguard Bal IdxVanguard SmCp Idx InsVanguard SmCp Vl IdxVanguard EmgMkts Idx AdmrDimensional USLgCoSchwab S&P 500 In InvSchwab 1000 In InvDimensional US Sm CpDimensional TaxMgUSSmCpVlVanguard Intm Bd IdxVanguard Tx-Mgd App Adm

VFINXVTSMXVINIXVFIAX

VBMFXVGTSXVTSAXVIIIX

VEURXFUSEXVIFSXVITSXVEIEXVBTLXVPACXVBTIXPREIX

FSMAXFBIDXVITPXDISVXVITBXDFLVXVIMSXFSMKXFOHIXDFEVXFUSVXVIGRXVBTSXVMCIXNAESXDFIVXDFISXDFSCXVEXMX

FSIIXFSTMXVIDMXVESIXVGSIXVTSSXVSTIXVIVAXVDMIXFSTVXSWPPXGATEXVBINXVSCIXVISVX

VEMAXDFLCXSWPIXSNXFXDFSTXDTMVX

VBIIXVTCLX

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

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

-5.08-4.95-5.04-5.041.88

-3.57-4.93-5.04-4.50-5.06-5.05-4.90-3.231.91

-1.491.92

-5.14-5.041.62

-4.8913.67

1.922.14

-4.52-5.05-5.9434.41-5.05-4.751.91

-4.50-4.2010.0813.87-0.81-4.53-3.32-4.99-3.57-4.498.14

-4.95-5.14-5.25-3.61-4.98-5.160.28

-2.12-4.16-2.39-3.19-5.05-5.21-5.050.24

-0.672.34

-4.80

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

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

16.516.716.516.5

-15.116.716.513.815.616.516.718.3

-16.3

-15.615.6

-16.614.1

-13.617.015.615.411.415.621.0

-17.017.813.417.219.217.411.815.814.513.825.616.715.613.614.515.815.516.016.717.815.118.316.615.515.519.215.9

-17.0

8.909.298.918.912.88

11.829.298.91

11.158.918.909.29

19.672.88

12.252.888.908.912.649.29

10.012.849.64

11.248.91

-16.77

8.9110.01

2.8911.2512.60

9.759.89

13.7611.8210.88

9.2910.7211.1716.59

9.298.909.01

10.769.308.863.815.37

12.6011.4819.67

8.888.878.96

13.5413.62

3.849.29

1.991.812.052.084.882.721.902.083.202.022.061.921.834.972.424.991.751.964.921.902.164.851.361.351.931.101.632.050.854.961.501.343.071.971.981.212.431.643.053.314.651.891.692.822.961.671.941.823.041.512.321.941.991.771.511.820.804.791.67

Fund Name Ticker Assets Exp Ratio YTD 8.108.798.228.194.86

18.558.898.24

17.228.168.148.91

32.604.95

13.714.997.908.194.318.98

30.204.95

16.0710.91

8.16-

48.638.199.284.87

11.079.45

27.6827.5613.4810.3816.26

8.9216.2517.3611.67

8.837.868.09

16.088.948.117.677.379.617.28

32.718.167.958.48

13.7115.08

5.059.04

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

2007 2006 3-Yr 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

5-Yr 10-Yr 15-Yr Mkt Cap P/E Std Dev Yield Total Return % $US Millions Annualized Return %

Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions July/August 2008

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.

Global Index Data

www.journalofindexes.com

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 3.12Mid Cap 4.43 –3.70 –6.63 10.93 15.60 7.11Small 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 6.22US Core 3.36 –4.30 –2.99 8.98 11.85 5.36US Growth 5.72 –5.87 0.41 9.56 9.63 0.28

Large Value 0.41 –4.42 –9.05 8.79 12.92 5.53Large Core 2.38 –5.13 –1.10 8.55 10.66 4.32Large Growth 5.70 –5.80 0.12 7.68 7.06 –1.48

Mid Value –0.71 –5.23 –16.56 6.36 14.37 7.79Mid Core 6.56 –1.46 –7.20 10.35 15.03 7.71Mid Growth 7.07 –4.54 3.85 15.77 16.96 5.08

Small Value 2.48 –2.18 –12.92 7.55 14.68 8.46Small Core 5.80 –2.63 –12.13 9.21 14.85 9.26Small Growth 1.71 –10.64 –6.89 9.37 13.16 1.72

Morningstar Market Barometer YTD Return %

US Market–4.87

–4.45

Value

–4.30

Core

–5.87

Growth

–5.14Larg

e Ca

p

–3.70Mid

Cap

–5.36Smal

l Cap

–4.42 –5.13 –5.80

–5.23 –1.46 –4.54

–2.18 –2.63 –10.64

–8.00 –4.00 0.00 +4.00 +8.00

Sector Index YTD Return %

Energy 4.18

Consumer Services 0.25

–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

Industry Leaders & Laggards YTD Return %

Land Transport 23.88

Discount Stores 15.21

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

Biggest Influence on Style Index Performance YTD

Return %Constituent

Weight %Best Performing Index

Mid Core –1.46

Tesoro Corp. –47.16 0.64UAL Corp. –55.14 0.40Coventry Health Care Inc. –24.51 0.90R.H. Donnelley Corp. –86.87 0.25Health Net Inc. –39.36 0.52

Worst Performing Index

Small Growth –10.64

Onyx Pharmaceuticals Inc. –36.79 0.88SiRF Technology Holdings Inc. –76.48 0.40Sigma Designs Inc. –67.61 0.45Cheniere Energy Inc. –70.16 0.42Tessera Technologies Inc. –51.35 0.56

1-Year

–9.05

Value

Larg

e Ca

p

–1.10

Core

0.12

Growth

–16.56

Mid

Cap –7.20 3.85

–12.92

Smal

l Cap –12.13 –6.89

–20 –10 0 +10 +20

3-Year

8.79

Value

Larg

e Ca

p

8.55

Core

7.68

Growth

6.36

Mid

Cap 10.35 15.77

7.55

Smal

l Cap 9.21 9.37

–20 –10 0 +10 +20

5-Year

12.92

Value

Larg

e Ca

p

10.66

Core

7.06

Growth

14.37

Mid

Cap 15.03 16.96

14.68

Smal

l Cap 14.85 13.16

–20 –10 0 +10 +20

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 listsare calculated by multiplying stock returns for the period by their respective weights in the index as of the start of the period. Sector and Industry Indexes are based on Morningstar's proprietary sector classifications. The informationcontained herein is not warranted to be accurate, complete or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information.

?

Morningstar U.S. Style Overview Jan. 1 – Apr. 30, 2008

Source: Morningstar. Data as of 4/30/08

61July/August 2008

July/August 200862

Source: Dow Jones Indexes. Data is based on total return index values as of 12/31/07.

DOW JONES INDEXES | QUARTERLY U.S. SUMMARY | QUARTER 1, 2008

Page 9

DOW JONES U.S. INDUSTRY INDEXES

Basic Materials 538.78 3.97%

Consumer Goods 1,313.94 9.68%

Consumer Services 1,462.13 10.77%

Financials 2,284.38 16.82%

Health Care 1,548.56 11.41%

Industrials 1,906.20 14.04%

Oil & Gas 1,638.51 12.07%

Technology 1,907.78 14.05%

Telecommunications 415.26 3.06%

Utilities 562.19 4.14%

DOW JONES U.S. INDUSTRY REPRESENTATION

Market Cap Weight inIndustry (USD Billions) DJ U.S. Index

Financials16.82%

Health Care11.41%

Industrials14.04%

Oil & Gas12.07%

Consumer Services10.77%

Basic Materials3.97%

Consumer Goods9.68%Technology

14.05%

Utilities4.14%Telecom.

3.06%

HISTORICAL DOW JONES U.S. INDUSTRY REPRESENTATIONS (%)

Basic Materials 357.45 3.47% 144.63 5.89% 36.70 4.43% 538.78

Consumer Goods 1,028.64 9.99% 216.26 8.80% 69.04 8.33% 1,313.94

Consumer Services 1,090.50 10.60% 265.62 10.81% 106.01 12.79% 1,462.13

Financials 1,697.90 16.50% 431.38 17.56% 155.10 18.71% 2,284.38

Health Care 1,278.25 12.42% 185.17 7.54% 85.13 10.27% 1,548.56

Industrials 1,235.00 12.00% 490.21 19.95% 180.99 21.83% 1,906.20

Oil & Gas 1,339.22 13.01% 240.78 9.80% 58.51 7.06% 1,638.51

Technology 1,535.30 14.92% 272.29 11.08% 100.20 12.09% 1,907.78

Telecommunications 361.95 3.52% 51.66 2.10% 1.64 0.20% 415.26

Utilities 367.95 3.58% 158.60 6.46% 35.64 4.30% 562.19

Total 10,292.17 75.80% 2,456.60 18.09% 828.97 6.11% 13,577.73

DJ U.S. DJ U.S. DJ U.S. DJ U.S. DJ U.S. DJ U.S. DJ U.S.Large-Cap Large-Cap Mid-Cap Mid-Cap Small-Cap Small-Cap Index

Industry Market Cap Weight Market Cap Weight Market Cap Weight Market Cap

DOW JONES U.S. INDUSTRY REPRESENTATION BY SIZE (IN BILLIONS USD)

Industry 2008 Q1 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996

Basic Materials 3.97 3.70 2.95 2.74 2.67 2.28 1.98 2.47 2.34 3.53 4.31 5.02 6.28

Consumer Goods 9.68 9.15 9.17 9.40 10.22 8.44 8.14 8.34 11.63 13.79 14.72 15.09 15.62

Consumer Services 10.77 10.64 13.99 13.43 12.97 12.84 10.45 14.49 12.57 10.51 9.59 10.09 11.45

Financials 16.82 17.33 21.25 21.01 21.08 19.04 18.02 14.11 16.92 19.31 16.89 15.11 13.51

Health Care 11.41 11.62 12.17 13.33 14.56 14.28 14.23 9.21 12.44 11.35 11.08 11.11 9.90

Industrials 14.04 13.46 12.64 11.87 11.68 12.07 12.54 11.98 11.89 13.47 14.35 14.39 14.50

Oil & Gas 12.07 11.64 7.10 6.05 6.32 5.88 6.00 4.51 5.46 7.17 7.88 7.45 7.91

Technology 14.05 15.06 14.50 16.10 13.53 17.12 19.89 25.59 16.43 11.78 12.25 9.99 8.76

Telecommunications 3.06 3.26 3.00 3.09 3.81 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

Data based on market-cap information as of March 31, 2008.


Page 8

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

Total Return (%) Annualized Total Return (%)

Industry 1-Month 3-Month YTD 2007 1-Year 3-Year 5-Year 10-Year Since Inception

HISTORICAL PERFORMANCE RETURNS


CORRELATION COEFFICIENTS

CORRELATION COEFFICIENTS: U.S. INDUSTRY INDEXES VS. U.S. INDEX AND SIZE INDEXES

Data based on total-return index values as of March 31, 2008. Inception date December 31, 1991. Correlation data based on monthly total-return index values fromMarch 31, 2005 to March 31, 2008.

Industry 1000 3000 5000 8000 4000 2000 0001 9000 6000 7000

Basic Materials 0.5403

Consumer Goods 0.5170 0.6451

Consumer Services 0.2809 0.5255 0.6924

Financials 0.2006 0.6809 0.3773 0.4925

Health Care 0.7037 0.6606 0.7611 0.5178 0.2971

Industrials 0.6500 0.4013 0.1184 0.0473 0.2116 0.3425

Oil & Gas 0.6382 0.7493 0.6808 0.4628 0.4532 0.7985 0.4569

Technology 0.3368 0.5404 0.5676 0.6234 0.4044 0.6276 0.2387 0.5630

Telecommunications 0.2451 0.5761 0.1899 0.2952 0.5559 0.1861 0.4270 0.3489 0.3734

Utilities 0.7017 0.8486 0.7897 0.7177 0.6213 0.8318 0.5357 0.8818 0.7108 0.5040

Industry DJ U.S. Index DJ U.S. Large-Cap Index DJ U.S. Mid-Cap Index DJ U.S. Small-Cap Index

Basic Materials 0.7017 0.6484 0.7622 0.7459

Consumer Goods 0.8486 0.8694 0.7321 0.6934

Consumer Services 0.7897 0.7542 0.7970 0.7912

Financials 0.7177 0.7343 0.6174 0.6088

Health Care 0.6213 0.6546 0.5144 0.4269

Industrials 0.8318 0.8089 0.8184 0.7817

Oil & Gas 0.5357 0.4923 0.5857 0.5641

Technology 0.8818 0.8673 0.8327 0.8302

Telecommunications 0.7108 0.7283 0.6002 0.6141

Utilities 0.5040 0.5073 0.4613 0.4216


Page 9


Basic Materials 538.78 3.97%

Consumer Goods 1,313.94 9.68%

Consumer Services 1,462.13 10.77%

Financials 2,284.38 16.82%

Health Care 1,548.56 11.41%

Industrials 1,906.20 14.04%

Oil & Gas 1,638.51 12.07%

Technology 1,907.78 14.05%

Telecommunications 415.26 3.06%

Utilities 562.19 4.14%

DOW JONES U.S. INDUSTRY REPRESENTATION

Market Cap Weight inIndustry (USD Billions) DJ U.S. Index

Financials16.82%

Health Care11.41%

Industrials14.04%

Oil & Gas12.07%

Consumer Services10.77%

Basic Materials3.97%

Consumer Goods9.68%Technology

14.05%

Utilities4.14%Telecom.

3.06%

HISTORICAL DOW JONES U.S. INDUSTRY REPRESENTATIONS (%)

Basic Materials 357.45 3.47% 144.63 5.89% 36.70 4.43% 538.78

Consumer Goods 1,028.64 9.99% 216.26 8.80% 69.04 8.33% 1,313.94

Consumer Services 1,090.50 10.60% 265.62 10.81% 106.01 12.79% 1,462.13

Financials 1,697.90 16.50% 431.38 17.56% 155.10 18.71% 2,284.38

Health Care 1,278.25 12.42% 185.17 7.54% 85.13 10.27% 1,548.56

Industrials 1,235.00 12.00% 490.21 19.95% 180.99 21.83% 1,906.20

Oil & Gas 1,339.22 13.01% 240.78 9.80% 58.51 7.06% 1,638.51

Technology 1,535.30 14.92% 272.29 11.08% 100.20 12.09% 1,907.78

Telecommunications 361.95 3.52% 51.66 2.10% 1.64 0.20% 415.26

Utilities 367.95 3.58% 158.60 6.46% 35.64 4.30% 562.19

Total 10,292.17 75.80% 2,456.60 18.09% 828.97 6.11% 13,577.73

DJ U.S. DJ U.S. DJ U.S. DJ U.S. DJ U.S. DJ U.S. DJ U.S.Large-Cap Large-Cap Mid-Cap Mid-Cap Small-Cap Small-Cap Index

Industry Market Cap Weight Market Cap Weight Market Cap Weight Market Cap

DOW JONES U.S. INDUSTRY REPRESENTATION BY SIZE (IN BILLIONS USD)

Industry 2008 Q1 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996

Basic Materials 3.97 3.70 2.95 2.74 2.67 2.28 1.98 2.47 2.34 3.53 4.31 5.02 6.28

Consumer Goods 9.68 9.15 9.17 9.40 10.22 8.44 8.14 8.34 11.63 13.79 14.72 15.09 15.62

Consumer Services 10.77 10.64 13.99 13.43 12.97 12.84 10.45 14.49 12.57 10.51 9.59 10.09 11.45

Financials 16.82 17.33 21.25 21.01 21.08 19.04 18.02 14.11 16.92 19.31 16.89 15.11 13.51

Health Care 11.41 11.62 12.17 13.33 14.56 14.28 14.23 9.21 12.44 11.35 11.08 11.11 9.90

Industrials 14.04 13.46 12.64 11.87 11.68 12.07 12.54 11.98 11.89 13.47 14.35 14.39 14.50

Oil & Gas 12.07 11.64 7.10 6.05 6.32 5.88 6.00 4.51 5.46 7.17 7.88 7.45 7.91

Technology 14.05 15.06 14.50 16.10 13.53 17.12 19.89 25.59 16.43 11.78 12.25 9.99 8.76

Telecommunications 3.06 3.26 3.00 3.09 3.81 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

Data based on market-cap information as of March 31, 2008.


Page 8

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

Total Return (%) Annualized Total Return (%)

Industry 1-Month 3-Month YTD 2007 1-Year 3-Year 5-Year 10-Year Since Inception

HISTORICAL PERFORMANCE RETURNS


CORRELATION COEFFICIENTS

CORRELATION COEFFICIENTS: U.S. INDUSTRY INDEXES VS. U.S. INDEX AND SIZE INDEXES

Data based on total-return index values as of March 31, 2008. Inception date December 31, 1991. Correlation data based on monthly total-return index values fromMarch 31, 2005 to March 31, 2008.

Industry 1000 3000 5000 8000 4000 2000 0001 9000 6000 7000

Basic Materials 0.5403

Consumer Goods 0.5170 0.6451

Consumer Services 0.2809 0.5255 0.6924

Financials 0.2006 0.6809 0.3773 0.4925

Health Care 0.7037 0.6606 0.7611 0.5178 0.2971

Industrials 0.6500 0.4013 0.1184 0.0473 0.2116 0.3425

Oil & Gas 0.6382 0.7493 0.6808 0.4628 0.4532 0.7985 0.4569

Technology 0.3368 0.5404 0.5676 0.6234 0.4044 0.6276 0.2387 0.5630

Telecommunications 0.2451 0.5761 0.1899 0.2952 0.5559 0.1861 0.4270 0.3489 0.3734

Utilities 0.7017 0.8486 0.7897 0.7177 0.6213 0.8318 0.5357 0.8818 0.7108 0.5040

Industry DJ U.S. Index DJ U.S. Large-Cap Index DJ U.S. Mid-Cap Index DJ U.S. Small-Cap Index

Basic Materials 0.7017 0.6484 0.7622 0.7459

Consumer Goods 0.8486 0.8694 0.7321 0.6934

Consumer Services 0.7897 0.7542 0.7970 0.7912

Financials 0.7177 0.7343 0.6174 0.6088

Health Care 0.6213 0.6546 0.5144 0.4269

Industrials 0.8318 0.8089 0.8184 0.7817

Oil & Gas 0.5357 0.4923 0.5857 0.5641

Technology 0.8818 0.8673 0.8327 0.8302

Telecommunications 0.7108 0.7283 0.6002 0.6141

Utilities 0.5040 0.5073 0.4613 0.4216

Dow Jones U.S. Economic Sector Review


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

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

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

-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

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

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

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

Fund Name Ticker Assets Exp Ratio YTD

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

2007 2006 2005

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

3-Yr 5-Yr Mkt Cap P/E

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

Sharpe Std Dev Yield

Total Return %$US Millions Annualized Return %

Largest U.S.-listed ETFs Sorted By Total Net Assets In $US Millions

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.

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

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

MOO

VEA

BWX

FXP

MUB

CGW

JNK

EEV

PIO

RJA

GEX

BIK

EPI

TFI

BIL

NLR

KOL

DEM

PBD

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

Fund Name Ticker8/31/2007

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

Launch Date 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

Assets ER11.21

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

3-Mo4.48

-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

YTD

Largest New ETFs Sorted By Total Net Assets In $US Millions Selected ETFs In RegistrationCovers ETFs launched in the year ending April 30, 2008.

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.

Exchange-Traded Funds Corner

July/August 200864

Behavioral Finance’s Fractured Future

H U M O R

The Curmudgeon

By Brad Zigler

Investing in behavior and finance

Puerto Rico and the U.S. Virgin Islands.Over the past four years, PSYS’ share price

has grown at a 12.1 percent compound annu-al rate, far exceeding the 5 percent appre-ciation 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 pub-lic companies that specialize in modi-fying 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 aver-age 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 sub-indexes of a broader Behavioral Finance Index. Equally weighted and rebalanced monthly, these five issues would have ren-dered 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 out-fits 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.

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 complicat-ing things is how much attention these two words, joined together, are getting.

There’s always money to be made moni-toring 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 behav-ior bucket—the Behavioral Index, if you please. The outpatient stuff goes in the financial bucket.

The Behavioral Index is led by Ten-nessee-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 behav-ioral health centers—lots and lots of ‘em. At last count, PSYS owned or leased 90 inpatient facilities in 31 states, the commonwealth of

jason zweig craig israelsen david blanchett and gregory kasten · jason zweig was recently hired as...

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