Vanguard on Active vs. Passive - IndexUniverse.com

<strong>Vanguard</strong> on Active vs. Passive 

Christopher Philips and Francis Kinniry Jr. 

Concentration Drivers of Active Performance 

David Blanchett 

Alpha/Beta Separation 

Robert Whitelaw, Salvatore Bruno and Anthony Davidow 

A Better Correlation Measure 

Gregory Hight 

Plus Active vs. Passive Roundtable, Hougan on Leverage, and Blitzer

www.journalofindexes.com 

Vol. 12 No. 2 

features 

The Active-Passive Debate 

by Christopher Philips and Francis Kinniry Jr. ...... 10 

Market environment is the key to active performance. 

Portfolio Concentration 

And Mutual Fund Performance 

by David Blanchett ........................... 18 

For the best performance, pick sectors, not stocks. 


by Robert Whitelaw, Salvatore Bruno 

and Anthony Davidow ......................... 24 

Uncovering low-cost alternative Beta. 

Active Vs. Passive 

edited by Heather Bell ......................... 30 

A virtual roundtable debate featuring John Bogle, Bruce 

Bond, Seth Ruthen, Chris Ailman and Robert Doll. 

How Long Can You Hold Leveraged ETFs? 

by Matt Hougan .............................. 36 

A historical look at long-term returns. 

Hunting Alpha In The Dark 

by David Blitzer .............................. 42 

Investors must adapt to new market realities. 

A New Way To Look At Correlations 

by Gregory Hight ............................. 44 

A diversification measure that actually works. 

Blogs In Print! 

by Matt Hougan and Jim Wiandt ................. 64 

Matt and Jim bring their rivalry to the Journal of Indexes. 

news 

Pimco Rolls Out New Global Bond Index; Fund Coming 50 

UBS To Buy AIG’s Commodity Index Business ..........50 

Record ETF Inflows In 2008 ......................50 

Grail Files For More-Active Active ETFs .............50 

Indexing Developments ..........................51 

Around The World Of ETFs .......................53 

Back To The Futures ............................55 

Know Your Options .............................55 

From The Exchanges ............................56 

On The Move ..................................56 

data 

Selected Major Indexes ..........................58 

Returns of Largest U.S. Index Mutual Funds ..........59 

U.S. Market Overview In Style .....................60 

U.S. Economic Sector Review .....................61 

Exchange-Traded Funds Corner ...................62 

18 

24 

44 

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March/April 2009 

1

Contributors 

David Blanchett 

David Blanchett is a full-time M.B.A. candidate at the University of Chicago 

Booth School of Business in Chicago, class of 2010. Before returning to school, 

he worked as an internal consultant for Unified Trust Company in Lexington, 

Ky., and as a financial planner for Hilliard Lyons in Louisville, Ky. Blanchett has 

completed an M.S. in financial services through the American College and is a 

Chartered Financial Analyst, a Certified Financial Planner practitioner and an 

Accredited Investment Fiduciary Analyst. 

David Blitzer 

David Blitzer is managing director and chairman of the Standard & Poor’s 

Index Committee. He has overall responsibility for security selection for S&P’s 

indices and index analysis and management. Prior to becoming chairman, 

Blitzer was chief economist for S&P. He previously served as corporate economist 

at The McGraw-Hill Companies, S&P’s parent corporation, and 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. 

Gregory Hight 

Gregory Hight is president of Hight Capital Management, Inc., a Minnesota 

research and educational firm that specializes in developing applications of 

financial mathematics, statistics and algorithms for practical investment problems. 

His articles have appeared in Barron’s, Offshore Financial Review and other 

publications. Hight has a B.A. from Auburn University and an M.B.A. from St. 

Cloud State University. 

Matt Hougan 

Matt Hougan is editor of Index Publications LLC, the leading financial media company 

focused on indexes, index funds and exchange-traded funds. In this capacity, 

he serves as editor of IndexUniverse.com and the Exchange-Traded Funds Report, 

and senior editor of the Journal of Indexes. An expert on ETFs, Hougan’s writings 

have appeared in SmartMoney, MarketWatch, Institutional Investor, Yahoo Finance 

and Financial Advisor magazine. He is widely quoted in The Wall Street Journal, 

Barron’s, TheStreet.com and other publications, and is a regular guest on CNBC. 

Francis Kinniry 

Francis Kinniry Jr., CFA, is a principal of <strong>Vanguard</strong> and a senior member of 

<strong>Vanguard</strong>’s Investment Strategy Group. He and his team are responsible for 

ongoing capital market research, portfolio design, development and implementation 

of customized investment solutions, investment market commentary, 

and research. Before joining <strong>Vanguard</strong> in 1997, Kinniry was a partner 

and senior portfolio manager for Executive Investment Advisors, LLC., and a 

portfolio manager for H. Katz Capital Group, a venture capital and hedge fund 

manager. He received his B.A. and M.B.A. from Drexel University. 

Christopher Philips 

Christopher Philips, CFA, is an analyst in <strong>Vanguard</strong>’s Investment Strategy 

Group. The department is responsible for conducting the research and formulating 

the investment methodology used to support advisory services, 

products and strategies for institutional and high net worth clients. It also 

publishes <strong>Vanguard</strong>’s proprietary research on a variety of investment, economic 

and portfolio management issues. Philips has published and presented 

research on various topics, such as indexing, real estate and benchmark selection. 

He earned his B.A. from Franklin & Marshall College. 

Robert Whitelaw 

Robert Whitelaw, Ph.D., is the chief investment strategist of IndexIQ. He is 

also the Edward C. Johnson 3d Professor of Entrepreneurial Finance and chairman 

of the Finance Department at the Leonard N. Stern School of Business, 

New York University. His papers have been published in multiple academic and 

practitioner journals. Whitelaw has a Ph.D. in finance from Stanford University, 

Graduate School of Business, and a B.S. in mathematics from MIT. 

2 

March/April 2009

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Editor’s Note 

What Is Alpha 

Doing HERE? 

Jim Wiandt 

Editor 

We’ve got a Journal of Indexes full of alpha (or purported alpha) this issue. You might 

ask yourself, “Why?” For an answer, you’ll need to look to the index industry AND 

to active managers, who seem to look more like each other every year. On the 

index side, you not only have more and more thinly sliced segments of the market used 

ever-more actively, but now even “quantitative” indexes that aim to outperform market segments. 

And on the active side? More closet index funds, lower fees and LESS volatility. 

Interesting. 

This issue we try to make some sense of all of this for you. Few investors think of 

<strong>Vanguard</strong> as an active shop, but the majority of its assets are actively managed. And its 

active funds, as Matt Hougan has reported in these pages, do quite well, thank you very 

much. So you’ll read with interest what Christopher Philips and Francis Kinniry Jr. of 

<strong>Vanguard</strong> have to say about “leadership volatility.” 

The always-rock-solid David Blanchett weighs in with an effort of his own on the effect 

of portfolio concentration on active mutual fund performance, while Robert Whitelaw, 

Salvatore Bruno and Anthony Davidow explore the issue of alpha/beta separation and how 

investors can replace high-cost alpha assets (like hedge funds) with low-cost alternative 

beta. Whitelaw is followed by one of our patented roundtables, with industry luminaries 

like John Bogle and Bruce Bond weighing in on the question of where to draw the line 

between active and passive management. 

From there we jump right into the most active—or actively traded anyway—segment 

of the index business, as Matt Hougan takes a look at the leveraged and inverse exchangetraded 

products on the market. These products have been both extraordinarily successful 

and the leading recent source of debate in the exchange-traded fund business. 

And then of course, we’ve got David Blitzer, who, at S&P, has long been at the forefront 

of the active versus passive debate, weighing in with his latest on the subject. Next, 

Gregory Hight submits a meaty exposition on a better way to think about correlation— 

what he calls the incremental diversification effect. 

Bringing home the issue are Matt and me (actively) making absolute fools of ourselves 

on the back page. 

So what’s it going to be? Betting or Beta? 

Jim Wiandt 

Editor 

8 March/April 2009

The Active-Passive Debate 

Market cyclicality and leadership volatility 

By Christopher Philips and Francis Kinniry Jr. 

10 

March/April 2009

Since the advent of indexing as an investment strategy, 

there has been a robust debate over the merits 

of actively and passively managed funds. As a result, 

at various times throughout the year, an investor is likely 

to hear that some percentage of actively managed funds 

have outperformed the stock market over a given period 

of time. 1 If during the period a majority of active managers 

have under-performed the market, the common opinion 

is that indexing is a superior strategy. If a majority of 

active managers have outperformed the market, we hear 

the reverse: that the way to add value going forward is 

through active investment management. But with leadership 

changes common, it’s easy to see how an investor 

may be confused about whether indexing or active management 

is the better choice in seeking higher returns. 

And a natural follow-up question is: Why can leadership 

change so dramatically? 

As is often the case with the financial markets, the 

answer is not a simple one. Of course, over long periods 

we would expect active management strategies to trail 

appropriate benchmarks by the margin of their expenses 

[Philips and Ambrosio, 2008]. However, over shorter and 

intermediate periods there are many factors that can lead 

to significant deviations between a strategy’s returns and 

those of its benchmark. For example, if a manager intends 

to closely track the S&P 500 Index, but strategically holds 

5 percent of her portfolio in cash, she is likely to underperform 

in strong bull markets because of the cash drag, 

but outperform in bear markets. Depending on how long 

a market environment lasts, that manager may experience 

significant periods of relative outperformance or 

under-performance simply because of the cash allocation. 

Similarly, if a manager holds a portfolio of stocks with an 

aggregate market capitalization smaller than that of the 

benchmark, and if all else is equal, he will likely underperform 

during periods in which the larger stocks outperform, 

and outperform during periods in which the smaller 

stocks outperform. 

In this paper, we examine the active-versus-index 

debate from the perspective of market cyclicality, and provide 

context for the changing nature of performance leadership. 

We show that when evaluating the performance 

of active managers versus a given benchmark, investors 

should be acutely aware of the differences in the managers’ 

strategies involving factors such as size (market capitalization), 

style (price/earnings ratio and price/book ratio) and 

relative positioning. 2 Overall, we show that the market 

environment can have a greater impact on relative performance 

than manager skill or even cost differences. Most 

important, we show that during periods of significant performance 

deviation between opposing market segments 

(for example, large- and small-capitalization, or growth and 

value), there will be a wider distribution of returns among 

active managers, and more pronounced differences in how 

managers perform relative to the market. However, during 

a prolonged period of less-severe deviations, we would 

expect fund styles to have less of an impact, and costs to 

have a major influence on relative returns. 

Setting The Stage: Is 10 Years Long Enough? 

Today, a majority of investors would probably consider 

10 years to be a long-term investment horizon. However, 

even over 10 years, historical trends don’t always hold true. 

For example, although stocks have outperformed bonds 

and cash over the very long term, they have lagged bonds in 

11 of 73 rolling 10-year periods since 1926, and even trailed 

cash nine times. The performance of active funds relative 

to a broad market benchmark can be similarly volatile. As 

Figure 1 demonstrates, in the 10 years ended December 31, 

1999, 69 percent of active managers under-performed the 

U.S. stock market, represented by the Dow Jones Wilshire 

5000 Index. But during the 10 years ended December 

31, 2007, we see a dramatic shift in the distribution of 

returns—in this period, 41 percent of active managers 

under-performed the U.S. stock market, a change of 28 percentage 

points over eight years. While this volatility clearly 

implies that 10 years is not long enough to be considered 

“long term,” it also raises the question of what exactly may 

be contributing to this shift in performance leadership. 

Of course, one widely noted change from the 10-year 

period ended 1999 to the 10-year period ended 2007, was 

the nearly simultaneous shift in performance leadership 

from growth stocks to value stocks and from larger stocks 

to smaller stocks. As the large-cap-growth bull market of 

the late 1990s ended, small-cap stocks and value stocks 

began to outperform. Figure 2 demonstrates the magnitude 

of this shift. Focusing on the green line, representing 

the cumulative 10-year spread in performance between 

value stocks and growth stocks, we see from 2000 to 2007 

a reversal in the trend established in the 1990s, when 

growth had dominated value. A similar shift is noticeable 

in the gray line representing the cumulative 10-year spread 

between large-cap stocks and small-cap stocks. By the end 

of 2007, value had outpaced growth over the previous 10 

years by a cumulative 32 percent, and small-cap had outpaced 

large-cap by a cumulative 22 percent. 

Such an extreme spread in performance between largecap 

growth stocks and small-cap value stocks and the 

reversal in dominance between the two segments in 2000 

are important for two main reasons. First, large-cap stocks 

typically account for close to 70 percent of the capitalization 

of the aggregate market, with mid-cap stocks making 

up approximately 20 percent and small-cap stocks 10 percent. 

So it is no surprise that the market will realize a total 

return more similar to that of the large-cap segment than 

to that of any other segment. 

In an environment characterized by such a significant 

return spread between segments, as has been the case 

since the late 1990s, the performance of the large-cap 

segment will play an even greater role in the performance 

of the market, as well as the performance of fund managers. 

For example, in Figure 3, we show a hypothetical 

scenario in which large-cap value stocks outperform 

large-cap growth stocks by 500 basis points. Because of 

the distribution of weights across the six style boxes, the 

market itself outperforms every market segment except 

large-cap value. In such an environment, active managers 

www.journalofindexes.com March/April 2009 

11

Figure 1 

Performance Leadership Can Shift Over Ten-Year Periods 

Under-performed: 

291 funds (69%) 

Less than –8% 

Between –8% and –7% 

Under-performed: 

480 funds (41%) 

Distribution of active manager net excess returns versus market benchmark: Ten years ended December 31, 1999 







Between –1% and 0% 

Annualized excess returns 

U.S. Stock Market Return 


Between 0% and 1% 








Outperformed: 

129 funds (31%) 


Greater than 8% 

Outperformed: 

683 funds (59%) 

80 

60 

40 

20 

0 

200 

160 

120 

80 

40 

Number of Funds Number of Funds 

0 




















Source: Fund data were provided by Morningstar. The U.S. stock market is represented by the Dow Jones Wilshire 5000 Index. 

Notes: We excluded sector funds, specialty funds, and real estate funds. We combined all share classes, using the class with the longest history. 

For this comparison, we evaluated active funds after cost against a market benchmark that incurred no costs. 

in the large-cap value space would probably find it easier 

to outperform the market (since their style outperformed 

the market), while active managers in the remaining style 

boxes would face a headwind beyond their control—an 

environment in which their particular style is decidedly 

out of favor and lags the market. 

When we examine Figure 4, we see that this was the 

scenario in the late 1990s, when the performance of the 

large-cap growth segment far exceeded that of large-cap 

value and most other segments. This resulted in the market 

itself outperforming most of the individual segments, 

including large-cap value. By extension, because the market 

outperformed most of its segments, the market also tended 

to outperform a larger portion of active managers. 

Of course, the opposite can happen as well, as we saw 

during the 10 years through the end of 2007. In this period, 

large-cap stocks in general under-performed smaller stocks. 

And in direct contrast to 1999, large-cap growth dramatically 

under-performed every other style box, including largecap 

value. The result was that the very poor performance of 

large-cap growth stocks depressed the return of the market 

to the point where even large-cap value outperformed the 

market by over 100 basis points. So in contrast to the headwind 

facing these managers in 1999, active managers focusing 

on value and small-cap stocks subsequently benefited 

from a significant tailwind in relative performance because 

the extremely poor performance of the large-cap growth 

segment reduced overall market returns. 

12 

March/April 2009

This brings us to the second reason for the importance 

of the extreme performance spread and the shift in dominance 

between large-cap growth stocks and small-cap 

value stocks: equal weighting across funds. To illustrate 

the importance of the equal-weighting methodology, we 

break the distributions in Figure 1 into the nine style 

box components (large-cap, mid-cap and small-cap, and 

growth, blend and value) for both 10-year periods in 

Figure 5. In line with the performance trends outlined in 

Figure 2, we see a wholesale shift in the style of fund that 

outperformed the market from the 10 years ended 1999 

to the 10 years ended 2007. Indeed, while 87 percent of 

value funds and small-cap funds under-performed the market 

over the 10 years ended 1999, only 29 percent of the 

funds in the same segments under-performed the market 

over the 10 years ended 2007. 3 

But because we are counting each fund to calculate the 

percentage that outperformed the market, the actual number 

of funds in each style box in each time period is just as 

important as the performance of the style box itself. For 

example, as of 1999, 66 percent of all funds were large-cap, 

20 percent were mid-cap and 14 percent were small-cap. 

In 2007, the percentages had shifted to 58 percent largecap, 

20 percent mid-cap and 22 percent small-cap. As a 

result, while 59 percent of active managers outperformed 

the broad market over the 10 years ended Dec. 31, 2007, 

most of that outperformance can be attributed directly to 

the performance of value stocks and small-cap stocks, combined 

with the outsized growth in the number of small-cap 

funds. At the same time, the significant under-performance 

of active managers over the 10 years ended Dec. 31, 1999, 

was largely due to the significant performance of large-cap 

and growth stocks and the significant portion of funds in 

the large-cap blend and large-cap value style boxes. 

The takeaway from this discussion, then, is that during 

periods of significant deviation in performance between 

opposing market segments (such as large and small, or 

growth and value), the distribution of returns among active 

managers will be much more pronounced. This was the case 

in the 1990s and has been the case in the 2000s. During 

a prolonged period of less-severe deviations, we would 

expect fund styles to have much less of an impact, and costs 

to be a major component of relative returns. 

Digging Deeper Into Style Box 

Performance Cyclicality 

In addition to influencing the perception of how active 

managers perform in comparison with the market overall, 

the relative performance of one style box versus another 

may also have implications for how we evaluate active managers 

against their particular style benchmark. For example, 

even within more narrowly defined segments of the market, 

we have seen significant volatility in the distribution of 

returns for active managers around a given benchmark. 

In Figure 6, we evaluate the relative performance of 

active managers versus their style-specific benchmark for 

the 10 years ended 1999, and again over the 10 years ended 

2007. From Figure 6, it is clear that the volatility in the 

percentage of funds outperforming the market observed in 

Figure 5 is also present within each style box. For example, 

we see that over the 10 years ended 1999, 71 percent of 

large-cap growth managers under-performed the large-cap 

growth benchmark, but over the 10 years ended 2007, the 

percentage of managers under-performing the large-cap 

growth benchmark shifted dramatically to only 27 percent. 

While it is possible that large-cap growth managers suddenly 

became more skilled at picking stocks, it is far more 

likely that the volatility is tied to differences in how these 

managers build their portfolios and the dynamics of the 

large-cap growth market over these time periods. 

To generate a return higher than that of a benchmark, an 

active manager’s portfolio must differ from that benchmark 

in some respect. A manager may choose to hold more or 

fewer stocks than the benchmark, to hold stocks at different 

weights from the benchmark, or, more likely, to resort to 

some combination of differences in holdings and weightings. 

For example, a large-cap value manager may hold 50 

stocks in equal proportions (2 percent per stock) and be 

measured against a large-cap value benchmark that has over 

300 names and is market-cap-weighted. The degree to which 

the fund’s holdings and weightings differ from those of the 

benchmark, and the distribution of winners and losers across 

the benchmark, will dictate how the fund performs relative 

to the benchmark. And when we look at Figure 7, we see that 

regardless of the time period, active managers in any market 

segment are a heterogeneous group, characterized by wide 

differences in basic valuation metrics such as size (market 

capitalization) and style (price/book ratio). 

There are several key takeaways from Figure 7. First, and 

most critical to the notion of cyclicality in the performance 

of active managers relative to their style benchmarks, the 

median statistics for funds differ in some respects from the 

median statistics for the benchmark. For example, in 1999, 

the median market capitalization for large-cap value funds 

was $24.23 billion, while the Russell 1000 Value Index had 

a median market capitalization of $31.76 billion. This differ- 

Figure 2 

Size And Style Factors Can Play A Role In Leadership Shifts 

The historic spreads between large- and small-capitalization 

and between growth and value 

80 

Growth outperforms value; 

large outperforms small 

60 

40 

20 

0 

-20 

Value outperforms growth; 

small outperforms large 

-40 

-60 

1979 1983 1987 1991 1995 1999 2003 2007 

Periods ended December . . . 

■ Growth-value differential: 12 months 

■ Large-small differential: 12 months 

■ Growth-value differential: ten years 

■ Large-small differential: ten years 

Notes: The returns of value and growth stocks are represented by the Russell 3000 

Value Index and Russell 3000 Growth Index, respectively. 

Large-cap and small-cap stocks are represented by the Russell 1000 Index and Russell 

2000 Index, respectively. Data are through December 31, 2007. 

Cumulative performance spread 


13

Figure 3 

Large 

Mid 

Small 

Hypothetical Impact Of Style Box 

Dispersion On Market Returns 

35% 

10% 

5% 

Weights 

35% 

10% 

5% 

Large 

ence is significant because over 50 percent of large-cap value 

funds had a smaller market cap than the benchmark median. 

All else being equal, during periods of large-cap outperformance, 

such as 1999, the benchmark will tend to outperform 

a majority of these active managers, simply because of the 

difference in median market capitalization. Examining each 

benchmark across the two time periods, we see that the 

median for each fund style consistently differed from that of 

the benchmark in P/E ratio or market cap, or both. 

Second, within each style box, there is a significant difference 

in the medians between the funds with the largest 

market cap and highest P/E ratio and those with the smallest 

market cap and lowest P/E ratio. For example, in 1999, 

the market-cap spread among large-cap value funds was 

$40 billion. In other words, the top 5 percent of funds were 

exposed to stocks with a median market capitalization over 

Mid 

Small 

22% 

18% 

16% 

Returns 

Value Growth Value Growth 

Note: This hypothetical example does not represent 

the return on any particular investment. 

Source: <strong>Vanguard</strong> 

Figure 4 

Small Mid Large 

17% 

15% 

13% 

Market return 

18.40% 

Relative Ranking Of Style Box Total Returns 

Ten years ended 1999 


Value Blend Growth Value Blend Growth 

5 

15.60% 

6 

13.82% 

9 

12.46% 

3 

18.13% 

4 

15.92% 

8 

13.40% 

1 

20.31% 

2 

18.96% 

7 

13.51% 


7 

7.70% 

1 

10.77% 

3 

9.61% 

8 

6.15% 

2 

10.02% 

4 

9.60% 

Market: 17.58% Market: 6.34% 

9 

4.27% 

6 

7.83% 

5 

8.50% 

Sources: MSCI indexes were created in 2003 and backfilled to 1992, so we used Russell 

indexes for the ten-year period ended December 31, 1999. For large-cap, we used the 

Russell 1000, Russell 1000 Growth, and Russell 1000 Value Indexes; for mid-cap, the 

Russell Midcap, Russell Midcap Growth, and Russell Midcap Value Indexes; for 

small-cap, the Russell 2000, Russell 2000 Growth, and Russell 2000 Value Indexes. For 

the ten-year period ended December 31, 2007, we used MSCI indexes. For large-cap, 

we used the MSCI US Prime Market 750, MSCI US Prime Market Growth, and MSCI US 

Prime Market Value Indexes; for mid-cap, the MSCI US Mid Cap 450, MSCI US Mid 

Cap Growth, and MSCI US Mid Cap Value Indexes; for small-cap, the MSCI US Small Cap 

1750, MSCI US Small Cap Growth, and MSCI US Small Cap Value Indexes. 

four times as large as that for the stocks the bottom 5 percent 

were exposed to. In an environment where the largest 

stocks outperformed (all else being equal), those funds with 

significant exposure to the largest stocks would outperform 

those with exposure to smaller stocks, even within the 

same market segment. 

Third, if we focus again on the large-cap value space in 

1999, we see that the 5 percent of funds with exposure to 

the largest-capitalization stocks also maintained a profile 

that was larger than the index’s. For example, the top 5 

percent of funds had a median market cap of $52.2 billion, 

while the Russell 1000 Value Index had a median market 

cap of $31.76 billion. Again, all else being equal, in an 

environment where the largest stocks outperformed, those 

funds with significant exposure to large stocks would outperform 

the index, while those funds with less exposure to 

large stocks would under-perform the index. 

While the differences between the funds and the benchmark 

are not as significant for the median P/E ratio as for the 

market cap, we do see some differences, particularly in the 

small-cap segments. For example, in both 1999 and 2007, 

the median P/E ratio for small-cap managers was lower than 

for the benchmark, significantly so within small-cap growth. 

Again, during a period when growth dominates value, if the 

median P/E for fund managers is lower than for the benchmark, 

representing a tilt toward value, we would expect a 

majority of the funds to under-perform the benchmark. 

In addition to the median P/E ratios for each style box, 

the spread between the 5 percent of funds with the highest 

median P/E ratio and the 5 percent of funds with the lowest 

median P/E ratio is also significant. In fact, within the largecap 

value space in 1999, we see that the spread in P/E ratio 

between the highest 5 percent and lowest 5 percent is nearly 

double: 24.68 times versus 14.82 times. Naturally, for a 

value investor, this metric would be of interest, as low P/E 

stocks and high P/E stocks would be expected to perform 

quite differently with respect to the market environment. 

Because the highest 5 percent of funds were exposed to 

stocks with higher P/E ratios than the benchmark median, in 

a growth-dominated market, these funds would be expected 

to outperform (all else being equal). On the other hand, 

the lowest 5 percent of funds, with a median P/E lower than 

the benchmark median, would be expected to outperform 

in a value-dominated market. Again, as with market cap, the 

valuation dispersion within a particular style box can lead 

to very different performance across similar funds. 

We have used the large-cap value segment to this point, but 

given the statistics presented in Figure 6, the large-cap growth 

space is of particular interest. If we examine the large-cap 

growth segment as of 1999, we see that not only was there 

very significant dispersion across market caps and P/E ratios, 

but that the benchmark itself was characterized by a market 

cap and P/E that were larger than those for virtually all the 

funds in the sample. Because almost all funds had significantly 

smaller median market caps and P/E ratios than the benchmark, 

and 1999 represented the peak of the bull market dominated 

by large-cap growth stocks, it’s no surprise that the benchmark 

outperformed 71 percent of large-cap growth managers over 

14 

March/April 2009

Figure 5 

The Relative Performance Of All Managers Depends On The Relative Performance Of Market Segments 

Under-performed (#, %) 

Large blend: (74, 73%) 

Large growth: (43, 43%) 

Large value: (73, 97%) 

Mid blend: (21, 81%) 

Mid growth: (30, 59%) 

Mid value: (9, 100%) 

Small blend: (15, 88%) 

Small growth: (13, 52%) 

Small value: (13, 87%) 





Under-performed (#, %) 




Mid blend: (10, 13%) 


Mid value: (5, 16%) 




















Outperformed (#, %) 




Mid blend: (5, 19%) 


Mid value: (0, 0%) 







Outperformed (#, %) 




Mid blend: (68, 87%) 


Mid value: (26, 84%) 




80 

60 

40 

20 

0 

200 

160 

120 

80 

40 

Number of Funds Number of Funds 

0 



















Large blend 

Large growth 

Large value 


Mid blend 

Mid growth 

Mid value 

Small blend 

Small growth 

Small value 

Sources: Fund data were provided by Morningstar. The U.S. stock market is represented by the Dow Jones Wilshire 5000 Index. 

the 10 years ended 1999. On the other hand, because the 

median market cap and P/E for funds in this segment were 

significantly lower than for the benchmark, when small-cap and 

value subsequently dominated, it is easy to see how a majority 

of active managers were able to outperform the large-cap 

growth benchmark over the 10 years ended 2007. 

In Figure 8, we see a constantly shifting relationship 

between active managers and the target index over time, 

with peaks and valleys that seem to coincide with long-term 

relative outperformance of one style or another. In 1998 and 

1999, when large-cap growth represented the dominant style 

in the market, large-cap growth managers found it difficult 

to consistently outperform their benchmark. (Managers who 

outperformed the benchmark are above the x-axis, managers 

who under-performed are below.) 

However, in 2006 and 2007, when large-cap growth lagged 

the other investment styles, large-cap growth managers found 

it easier to outperform their benchmark. As we discussed, 

this cyclicality is probably driven by differences in weighting 

between the active managers’ portfolios and the benchmark. 

And during periods when a larger percentage of stocks outperform 

or under-perform the benchmark, the aggregate 

performance differences between funds and the benchmark 

will be magnified. As we have seen, particularly in the largecap 

growth segment from the late 1990s through 2007, the 

weighting schemes of active managers have appeared to result 


15

Figure 6 

Volatility Of Outperformance Also Occurs Within Style Boxes 


Figure 7 

Percentage of active managers that under-performed style benchmarks 



Value Blend Growth Value Blend Growth 

88% 78% 71% 

100% 65% 

53% 

47% 

63% 

12%* 


70% 58% 27% 

68% 56% 

68% 

64% 

38% 

56% 

Note: Refer to Davis et al. (2007) and Philips and Ambrosio (2008) for additional details 

on why small-cap growth managers appear to perform exceedingly well against 

Russell benchmarks, but in reality are more similar to managers in other style boxes. 

Sources: We used Russell Indexes for the ten-year period ended December 31, 1999. 

For large-cap, we used the Russell 1000, Russell 1000 Growth, and Russell 1000 Value 

Indexes; for mid-cap, the Russell Midcap, Russell Midcap Growth, and Russell Midcap 

Value Indexes; for small-cap, the Russell 2000, Russell 2000 Growth, and Russell 2000 

Value Indexes. For the ten-year period ended December 31, 2007, we used MSCI US 

Prime Market Value Indexes; for mid-cap, the MSCI US Mid Cap 450, MSCI US Mid Cap 

Growth, and MSCI US Mid Cap Value Indexes; for small-cap, the MSCI US Small Cap 

1750, MSCI US Small Cap Growth, and MSCI US Small Cap Value Indexes. Fund data 

were provided by Morningstar. 

in significant volatility in the percentage of managers outperforming 

or under-performing over a given period. The cumulative 

effect was significant under-performance when the index’s 

performance was strongest, and significant outperformance 

when the index’s performance was weakest. 

Conclusion 

In this analysis, we have demonstrated that the volatility 

in the percentage of funds outperforming a given benchmark 

is directly related to underlying trends in the markets. The 

number of active managers in each style box, and the differences 

in the style and size characteristics of those managers’ 

portfolios, will explain a significant portion of outperformance 

or under-performance versus a benchmark. In the end, 

to arrive at a conclusion as to whether active management 

or indexing is a “better” investment strategy requires that 

an investor focus on the rationale for active management. 

Active management offers the opportunity to outperform a 

given benchmark, but at the cost of higher average expenses, 

potentially significant tracking error and the risk of underperforming. 

Indexing does not offer the ability to outperform 

a benchmark, but as a result of low expenses and low tracking 

error relative to a benchmark, an indexed strategy would 

be expected to outperform a majority of similarly positioned 

active managers over the long term. 

Peer-Group Fundamentals Show Wide Dispersion 

Fund Statistics as of December 31, 1999 

Value Funds 

Russell Indexes 

Growth Funds 


Capitalization 


Market 

Percentile P/E Ratio Capitalization 

($B) 

95% 

78% 

24.68 $52.20 

70% 

Median 19.58 

24.23 

5% 

14.82 

12.55 

95% 

Median 

5% 

95% 

Median 

5% 

19.42 

15.62 

12.31 

14.56 

12.66 

10.23 

$11.68 

6.61 

2.21 

$1.31 

0.57 

0.16 

P/E Ratio 

64% 

Market 


($B) 

20.0 $31.76 

N/A 

N/A 

17.4 $0.66 



Percentile 

95% 

Median 

5% 

95% 

Median 

5% 

95% 

Median 

5% 

P/E Ratio 

49.53 

36.87 

26.01 

49.47 

31.30 

20.12 

40.60 

23.14 

16.86 

Market 


($B) 

$110.88 

51.32 

16.29 

$10.17 

4.34 

2.17 

$1.72 

1.13 

0.39 

P/E Ratio 

Market 


($B) 

52.0 $125.60 

N/A 

N/A 

62.4 $1.10 

Fund Statistics as of December 31, 2007 

Value Funds 


Growth Funds 


Percentile 

P/E Ratio 

Market 


($B) 

P/E Ratio 

Market 


($B) 

Percentile 

P/E Ratio 

Market 


($B) 

P/E Ratio 

Market 


($B) 



95% 

Median 

5% 

95% 

Median 

5% 

95% 

Median 

5% 

17.54 

14.74 

12.32 

18.95 

15.14 

11.05 

19.34 

15.78 

12.91 

$77.02 

43.35 

17.29 

$15.98 

6.44 

2.06 

$1.77 

1.12 

0.34 

14.5 $52.34 

16.3 $7.41 

18.4 $1.06 



95% 

Median 

5% 

95% 

Median 

5% 

95% 

Median 

5% 

27.03 

21.28 

17.24 

29.41 

22.99 

16.67 

30.30 

23.81 

17.48 

$53.74 

31.65 

13.38 

$10.45 

5.41 

2.16 

$1.86 

1.16 

0.43 

20.8 $39.77 

22.1 $8.52 

33.8 $1.34 

Sources: Morningstar Direct and <strong>Vanguard</strong>. Large-cap indexes are represented by Russell 1000 Growth and Russell 1000 Value; mid-cap, by Russell Midcap Growth and Russell 

Midcap Value; and small-cap, by Russell 2000 Growth and Russell 2000 Value. 

16 

March/April 2009

Figure 8 

Measuring The Cyclicality Of An Equity Style Box 

Rolling Distribution Of Actively Managed Large-Cap Growth Funds Versus Large-Cap growth Index, Ten-Year Excess Returns 

29% of managers outperformed the benchmark over the ten years ended December 31, 1999 

100% 

80 

Dec 

1996 1997 

June 

Dec 

1997 

June 

1998 

Dec 

1998 

June 

1999 

Dec 

1999 

June 

2000 

Dec 

2000 

27% of managers under-performed the benchmark over the ten years ended December 31, 1999 

June 

2001 

Dec 

2001 

June 

2002 

■ Percentage outperforming a large-cap growth index ■ Percentage under-performing a large-cap growth index 

Sources: We used a Russell benchmark until December 2003, when ten-year returns for MSCI benchmarks became available. Large-cap growth is represented by the 

Russell 1000 Growth Index spliced with the MSCI Prime Market Growth Index. Fund data were provided by Morningstar. 

Dec 

2002 

June 

2003 

Dec 

2003 

June 

2004 

Dec 

2004 

June 

2005 

Dec 

2005 

June 

2006 

Dec 

2006 

June 

2007 

Dec 

2007 

60 

40 

20 

0 

-20 

-40 

-60 

-80 

-100 

Percentage of Funds 

Endnotes 

1 See for example, “Active Vs. Passive Statistical Redux,” by the Journal of Indexes staff. 

2 The price/earnings ratio compares a stock’s current share price with its per-share earnings. The price/book ratio is used to compare a stock’s market value with its book value. 

3 In results not shown here, we also examined markets outside of the United States and found a similar pattern, where 61 percent of active managers outperformed a broad non- 

U.S. benchmark for the 10 years ended December 2007. And of those outperforming funds, over half were focused on value or small-cap stocks, or some combination of them. 

References 

Active Vs. Passive Statistical Redux. Journal of Indexes http://www.indexuniverse.com/index.php?option=com_content&view=article&id=3066&Itemid=285. 

Bliss, Robert R., 1997. “Movements in the Term Structure of Interest Rates.” Federal Reserve Bank of Atlanta Economic Review 82 (4): 16–33. 

Davis, Joseph H., Glenn Sheay, Yesim Tokat and Nelson Wicas, 2007. “Evaluating Small-Cap Active Funds.” Valley Forge, Pa.: Investment Counseling & Research, The <strong>Vanguard</strong> Group, 20 p. 

Golub, Bennet W. and Leo M. Tilman, 1997. “Measuring Yield Curve Risk Using Principal Components Analysis, Value at Risk, and Key Rate Duration.” Journal of Portfolio 

Management, Summer, 72–84. 

Litterman, Robert and Jose Scheinkman, 1991. “Common Factors Affecting Bond Returns.” Journal of Fixed Income, June, 54–61. 

Matzner-Løber, Eric and Christopher Villa, 2004. “Functional Principal Component Analysis of the Yield Curve,” working paper. 

Philips, Christopher B. and Frank J. Ambrosio, 2008. “The Case for Indexing,” Valley Forge, Pa.: Investment Counseling & Research, The <strong>Vanguard</strong> Group, 19 p. 

why advertise in the Journal of Indexes? 

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Index Publications LLC, 419 Lafayette Street, New York, NY 10003 • Advertising and Reprints Inquiries: 626.706.7050 


17

Portfolio Concentration And 

Mutual Fund Performance 

Why active managers are better off looking 

at sectors than individual stocks 

By David Blanchett 

18 

March/April 2009

What impact does portfolio concentration have on 

mutual fund performance? Some of the best-known 

and most successful investors (such as Warren 

Buffett) employ concentrated portfolio strategies. In theory, 

a portfolio manager taking a concentrated investing approach 

is able to exploit his or her best ideas and has lower monitoring 

costs when compared with managing a large portfolio. 

However, just because a portfolio manager holds fewer securities 

does not mean the approach will outperform a more diversified 

one. Moreover, the term “portfolio concentration” can 

mean many things, including (critically) whether that concentration 

is spread across individual stocks or across sectors. 

This paper will explore how “concentrated” portfolios perform 

using the historical performance of mutual funds, and 

will determine the relative impact of different types of concentration 

on an absolute-return, a risk and a risk-adjusted basis. 

Literature Review 

Past research on portfolio concentration has tended to 

note the potential benefits of concentrated portfolios over 

less-concentrated portfolios, although the approaches to 

defining “concentration” have differed considerably. In perhaps 

the most expansive study, Kacperczyk, Sialm and Zheng 

[2005] found that mutual funds with higher levels of industry 

concentrations yield an average abnormal return of 1.58 percent 

per year before deducting expenses and 0.33 percent 

per year after deducting expenses. Kacperczyk et al. attribute 

this outperformance to superior stock selection ability and 

also note that the trades of concentrated portfolios added 

more value than the trades of diversified portfolios. 

Brands, Brown and Gallagher [2005] conducted a study of 

active Australian equity managers and found a positive relationship 

between portfolio concentration and fund performance 

at the stock, industry and sector levels. They defined 

portfolio concentration as “the extent to which the portfolio 

weights held in stocks, industries and sectors deviate from 

the underlying index or market portfolio.” 

Ivkovich, Sialm and Weisbenner [2006] found that stock 

investments made by households that choose to concentrate 

their brokerage accounts in a few stocks outperform 

those made by households with more diversified accounts 

(especially among those with large portfolios). They found 

that when controlling for households’ average investment 

abilities, their trades and holdings perform better when their 

portfolios include fewer stocks. Ivkovich et al. use the term 

“concentrated” to refer to investors who hold only one or two 

stocks in their brokerage accounts, and use the term “diversified” 

to refer to investors who are not as highly focused with 

their portfolio (i.e., hold three or more stocks). 

The Benefits Of Diversification 

The benefits of portfolio diversification have been welldocumented. 

In one of the first studies on portfolio stock 

diversification, Evans and Archer [1968] noted that there 

is little diversification benefit beyond holding eight to 10 

stocks for an equal-weighted portfolio. This confirmed earlier 

advice from Benjamin Graham, who in his 1949 book, “The 

Intelligent Investor,” recommended owning from 10 to 30 

stocks to achieve proper diversification based on instinct and 

experience. More recent research by Statman [1987], though, 

suggested that a well-diversified portfolio of randomly chosen 

stocks must include at least 30 stocks for a borrowing 

investor and 40 stocks for a lending investor. 

While the number of securities necessary to achieve diversification 

has varied, according to past research (and over time, 

according to Campbell, Lettau, Malkiel and Xu [2001]), adequate 

diversification is an important consideration when addressing 

the risk component of a portfolio. De Wit [1998] has noted that 

even a well-diversified portfolio can benefit from additional 

diversification. Holding multiple stocks does not necessarily 

create a diversified portfolio, though, if the correlations among 

the stocks within a portfolio are high (Goetzmann and Kumar 

[2005]). Often, the reason a portfolio manager increases the 

total number of holdings is not out of a desire to own the new 

stocks per se, but rather as a means to accommodate mutual 

fund inflows (Shawky and Smith [2005]). 

Since there is no clear consensus on the number of 

securities necessary for adequate portfolio diversification, 

Figure 1 has been provided as a reference. Figure 1 includes 

the 95 th (worst 1 in 20), 80 th (worst 1 in 5), 50 th (median), 

20 th (best 1 in 5), and 5 th (best 1 in 20) percentile returns for 

randomly created equal-weighted portfolios holding differing 

numbers of securities. The portfolios’ number of holdings 

range from one stock to 400. The securities included in 

the portfolio are the components of the S&P 500 as of June 

10, 2008, with available full-year 2007 performance (which 

reduced the test set to 490 securities). For each security set 

(e.g., five stocks, 100 stocks, 250 stocks, etc.), 1,000 equalweighted 

random combinations were tested (from which 

the percentile bands are determined). The performance of 

the percentiles is compared against the performance for the 

equal-weighted full stock portfolio for the period. 

Figure 1 demonstrates that as the number of securities 

in a portfolio increases, the more likely it will achieve a 

return that is similar to the composite index, and vice versa. 

Portfolio managers who wish to exhibit low levels of tracking 

error are more likely to hold more securities and to 

weight the portfolio similarly to the benchmark index. While 

increasing the number of securities decreases tracking error, 

Figure 1 

The Impact On Return Variability From 

Increasing The Number Of Securities 

20% 

15% 

10% 

5% 

0% 

-5% 

-10% 

-15% 

-20% 

0 50 100 150 200 250 300 350 400 

Number of Equal-Weighted Stocks in Portfolio 

■ 95th Percentile 





Source: Morningstar 

Difference From Equal-Weighted 

Full Stock Performance 


19

Figure 2 

Number Of Funds Per Period With Percentage Of Total Funds Per Year 

Total Number Of Funds 

Asset Category 2001 2002 2003 2004 2005 2006 

Large Growth 232 316 334 317 323 331 

Large Blend 236 268 248 259 229 251 

Large Value 236 206 225 225 217 232 

Mid-Cap Growth 172 179 181 183 184 187 

Mid-Cap Blend 37 62 61 45 60 67 

Mid-Cap Value 62 39 53 50 49 54 

Small Growth 158 193 179 176 168 189 

Small Blend 46 76 68 69 72 99 

Small Value 53 46 47 62 58 69 

Total 1,232 1,385 1,396 1,386 1,360 1,479 

Percentage Of Total Funds Per Year 

Large Growth 18.83% 22.82% 23.93% 22.87% 23.75% 22.38% 

Large Blend 19.16% 19.35% 17.77% 18.69% 16.84% 16.97% 

Large Value 19.16% 14.87% 16.12% 16.23% 15.96% 15.69% 

Mid-Cap Growth 13.96% 12.92% 12.97% 13.20% 13.53% 12.64% 

Mid-Cap Blend 3.00% 4.48% 4.37% 3.25% 4.41% 4.53% 

Mid-Cap Value 5.03% 2.82% 3.80% 3.61% 3.60% 3.65% 

Small Growth 12.82% 13.94% 12.82% 12.70% 12.35% 12.78% 

Small Blend 3.73% 5.49% 4.87% 4.98% 5.29% 6.69% 

Small Value 4.30% 3.32% 3.37% 4.47% 4.26% 4.67% 

Total 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 


it increases monitoring costs and reduces the ability of the 

portfolio manager to exploit his or her “best ideas.” 

Testing Portfolio Concentration 

For this paper, three different definitions of “concentration” 

were tested: 

1. Sector Correlation to Benchmark (Correlation): For this 

test, the correlation between the sector weightings of a 

mutual fund were compared with the sector weightings 

of a respective benchmark. 

2. Total Number of Holdings (# of Holdings): This figure 

is calculated from the most recent available fund 

holdings. It does not include a fund’s short positions 

(according to Morningstar.com). 

3. Percentage of Total Holdings in the Top 10 Holdings 

(Top 10): This figure is calculated in-house by 

Morningstar using the most recent portfolio information 

it has available for the fund. 

The use of both sector and security measures to define 

concentration is an important break with past research, which 

tends to focus primarily on concentration in individual securities. 

The increasing use of sector-based strategies has increased 

interest in the utility of sector-based portfolio concentration. 

This article aims to examine the importance of that issue by 

evaluating it separately from security-based diversification. 

Another important break from past research is that while 

previous research on portfolio concentration has tended to use 

more-precise measures to classify a portfolio’s market exposure 

(e.g., the three-factor model), mutual fund investors tend to rely 

on general definitions of a fund’s market exposure based on 

“style boxes” or “investment categories.” Morningstar’s is one of 

the most common methodologies used by the investing public to 

classify mutual funds (as well as a variety of other investments), 

and is the classification methodology used for this analysis. 

The primary test data set for the analysis was retrieved from 

six separate year-end Morningstar CDs over the six-year time 

period from 2001–2006 (i.e., Morningstar Principia Pro Plus 

for 2001 and Morningstar Principia for the years 2002–2006). 

These year-end periods provided the snapshot periods for test 

purposes, where each data set was independent of the others, 

minimizing any type of look-back or survivorship bias. For the 

analysis, only those funds categorized by Morningstar with the 

same fund category and same style box in the nine domestic 

style boxes (Large Growth, Large Blend, Large Value, Mid-Cap 

Growth, Mid-Cap Blend, Mid-Cap Value, Small Growth, Small 

Blend and Small Value) were considered. Only actively managed 

funds that were not classified as index funds, enhanced 

index funds or ETFs were included in the test set. 

The mutual fund test population was limited to one share 

class per fund to ensure that those funds with multiple share 

classes were not overweighted relative to funds with fewer 

share classes. The share class with the lowest expense ratio 

20 

March/April 2009

and complete information was used as the representative 

proxy for that mutual fund. The lowest-cost share class is used 

since it is assumed to be the most efficient and since it tends 

to have the lowest revenue share, which is an expense for distribution, 

not an expense for money management. 

Mutual funds were only compared against other funds in 

the same investment category for each period. The results 

for each category for each test period were combined on a 

weighted basis, based on the total number of mutual funds 

in the period. For example, if there were 10 Large Growth 

funds, five Large Blend funds and five Large Value funds in the 

Large-Cap Equity category, Large Growth would represent 50 

percent of the Large-Cap total, while Large Blend and Large 

Value would each represent 25 percent of the Large-Cap total. 

This would then be combined with the Small-Cap and Mid-Cap 

results on a relative-weighted basis to get the results for the 

period. The test population of funds for each of the categories 

for each of the year-end periods is included in Figure 2. 

The information on the percentage each category represents 

of the total funds for each of the years is included 

in Figure 2 to give the reader an indication of the relative 

weight of each category for each period. Since certain categories 

(such as Large Growth) tended to have a larger number 

of funds than other categories (such as Mid-Cap Value), 

they will have a greater impact on the results. 

The fundamental assumption of the weighting methodology 

is that there is a greater amount of information involved 

when analyzing a larger number of funds versus a smaller 

number of funds. Taking a simple average among the categories 

would ignore the fact that for some periods there 

are more than five times the number of funds in one category 

versus another. Note, though, that the aggregate results 

from each of the six different year-end periods are averaged 

on an equal-weighted basis. 

The historical performance for each of the funds over the 

previous year is compared against the category average of 

that entire category for the period. Category average was 

used instead of category median to ensure that the net outperformance 

for each quintile and each category would be an 

approximate zero sum. All results are statistically significant 

since the data set represents the entire population of available 

mutual funds (i.e., it is not a sample). 

The benchmarks selected to represent the appropriate 

sector weightings for each annual period were based on the 

Russell iShares ETFs, which are listed in Figure 3. Russell was 

selected as the benchmark proxy because it is one of the 

most common institutional benchmarks. ETFs were selected 

since they represent investible benchmark composites for 

the respective investment categories and because data was 

available on each ETF for the entire test period. 

An additional test was performed to determine what the 

impact on the results would have been had the S&P iShares ETFs 

been used as the benchmark proxy. Using S&P iShares ETFs had 

no material impact on the results of the study versus using the 

Russell iShares ETFs. As a note, the average sector correlation 

among the investment categories of the Russell iShares ETFs to 

the S&P iShares ETFs over the test period was 0.904. 

For each of the three tests, the test population of mutual 

funds was segmented into quintiles (or 20th percentile 

groups), from 1 to 5. The “1s” for each test would be considered 

those mutual funds that are the least concentrated, 

diversified or correlated to the respective benchmark, while 

the “5s” would be those mutual funds that are the most 

concentrated, diversified or correlated to the respective 

benchmark. The average attributes for each of the quintiles 

for each of the categories is included in the Appendix for 

informational and relative comparison purposes. 

The respective performance of each quintile is based on 

the returns over the preceding year versus the average performance 

for that category for that period. Three main tests 

were conducted: 

1. Relative Performance: For this test, the relative performance 

for each quintile for each category for each 

period was compared against the category average performance 

for the period. The average outperformance 

(versus the category average) and the probability of 

each fund outperforming the category average were 

calculated for each quintile. 

2. Relative Risk: For this test, the mean absolute deviation 

and the downside deviation were calculated for 

each fund. The mean absolute deviation was calculated 

by determining the difference in the performance for 

each fund in each quintile versus the respective category 

average. The downside deviation was calculated 

in a similar manner, but only those funds with returns 

below the category average were considered (i.e., 

where the category average return was the MAR, or 

minimum acceptable return). 

3. Relative Risk-Adjusted Return: For this test, the relative 

outperformance for each quintile for each category 

for each period was divided by its relative risk 

for the period, as represented by both mean absolute 

deviation and downside deviation. 

Results 

Since relative outperformance is the most common metric 

investors use when comparing mutual funds, this information 

will be reviewed first and is included in Figure 4. 

The slope of each test set is included to give the reader 

Figure 3 

ETF Sector Benchmark Proxies 

Ticker Investment Name Category 

IWF iShares Russell 1000 Growth Index Large Growth 

IWB iShares Russell 1000 Index Large Blend 

IWD iShares Russell 1000 Value Index Large Value 

IWP iShares Russell Midcap Growth Index Mid-Cap Growth 

IWR iShares Russell Midcap Index Mid-Cap Blend 

IWS iShares Russell Midcap Value Index Mid-Cap Value 

IWO iShares Russell 2000 Growth Index Small Growth 

IWM iShares Russell 2000 Index Small Blend 

IWN iShares Russell 2000 Value Index Small Value 


21

Figure 4 


Figure 5 

Performance Results 

Average Outperformance Vs Category Average By Quintile 

Quintile Correlation # of Holdings Top Ten 

1 0.73% -0.44% -0.08% 

2 0.57% -0.30% -0.15% 

3 -0.38% -0.10% -0.22% 

4 -0.40% 0.19% -0.06% 

5 -0.52% 0.67% 0.54% 

Slope -0.345% 0.270% 0.132% 

% of Funds Outperforming Category Average By Quintile 

1 52.53% 46.53% 48.35% 

2 52.02% 45.07% 47.21% 

3 46.52% 48.21% 46.56% 

4 46.89% 50.04% 48.95% 

5 46.96% 55.15% 53.96% 

Slope -1.626% 2.221% 1.296% 

Risk Results 

Mean Absolute Deviation Vs Category Average By Quintile 


1 8.55% 8.11% 8.97% 

2 6.45% 6.50% 6.11% 

3 5.82% 5.49% 5.55% 

4 5.05% 5.43% 4.98% 

5 4.61% 4.95% 4.69% 

Slope -0.93% -0.74% -0.97% 

Downside Deviation Vs Category Average As MAR By Quintile 

1 5.29% 5.54% 6.02% 

2 4.08% 4.52% 4.02% 

3 4.19% 3.81% 3.96% 

4 3.59% 3.40% 3.45% 

5 3.36% 3.11% 3.02% 

Slope -0.43% -0.60% -0.66% 

Source: Morningstar. MAR is minimum acceptable return. 

an indication of the strength of the relationship among the 

quintiles and the relative “cost” of moving between quintiles. 

For example, the slope for the Correlation group is negative; 

this means that those mutual funds with higher levels of 

sector divergence (i.e., lower correlations to the benchmark 

proxy) tended to under-perform those funds with lower 

levels of divergence. Contrast this with the slopes for the 

# of Holdings and Top Ten tests, which are both positive, 

suggesting that more-diversified portfolios are more likely to 

outperform less-diversified portfolios. 

Based upon the data in Figure 1, the greater the deviation 

from the sector benchmark (i.e., the lower the correlation), 

the higher the performance. This suggests portfolio 

managers were, in the aggregate, able to add value by 

overweighting and underweighting certain sectors based 

on their expectations. However, the portfolio managers did 

not appear to add value through security selection, since 

both portfolio concentration (or diversification) performance 

tests revealed that less-concentrated (or more-diversified) 

portfolios tended to outperform more-concentrated (or lessdiversified) 

portfolios. Taken together, it would appear that 

a portfolio manager’s time would be best spent analyzing 

sector trends, and then—instead of allocating to the stocks 

he or she deemed to be the “best”—diversifying across a 

number of different securities within favored sectors. 

The results of the risk analysis, which focused on mean 

absolute deviation and downside deviation, are included in 

Figure 5. 

Not surprisingly, the more-concentrated portfolios (the 1s) 

had higher mean absolute deviations and downside deviations 

than less-concentrated portfolios. Each of the test sets had 

roughly the same negative slope, implying that those mutual 

funds that were the most diversified (or least concentrated) 

exhibited the lowest risk. Higher levels of portfolio concentration 

are common among those funds exhibiting the highest 

levels of tracking error to a benchmark index. 

The final results that will be discussed are the risk-adjusted 

results, which are included in Figure 6. 

The risk-adjusted results in Figure 6 roughly mirror the performance 

results in Figure 4; however, the strength of the relationship 

of the risk-adjusted relative outperformance (i.e., slope) for 

the Correlation test decreased when viewed from a risk-adjusted 

perspective. This should not surprise the reader, since the top 

Correlation quintile had both high relative outperformance and 

high relative risk. In other words, the benefit of the higher performance 

for the concentrated portfolios (as defined by sector 

divergence) came at the cost of a higher level of risk. 

Figure 6 


Risk-Adjusted Results 

Risk-Adjusted Performance & Mean Absolute Deviation By Quintile 


1 0.051 -0.050 -0.010 

2 -0.032 -0.055 -0.085 

3 -0.019 0.026 -0.018 

4 -0.013 0.062 0.032 

5 -0.028 0.039 0.074 

Slope -0.014 0.030 0.028 

Risk-Adjusted Performance & Downside Deviation By Quintile 

1 0.130 -0.080 -0.025 

2 -0.040 -0.075 -0.124 

3 -0.008 0.038 -0.042 

4 -0.006 0.092 0.046 

5 -0.155 0.082 0.023 

Slope -0.053 0.049 0.027 

22 

March/April 2009


The research conducted for this paper suggests that a 

portfolio manager’s time is best spent determining sector 

weightings and adequately diversifying across that sector 

versus maintaining concentrated positions in indiviual 

stocks within sectors. Mutual funds with higher levels of 

sector concentration outperformed those mutual funds with 

lower levels of sector concentration (by 35 bps per quintile), 

while more-diversified mutual funds (defined as those with 

a higher number of total holdings and a lower percentage 

of total holdings in the top 10 holdings) outperformed lessdiversified 

mutual funds (by 27 bps and 13 bps per quintile, 

respectively). These findings differ slightly with the results 

of previous research, although this can likely be attributed 

to the different classification methodology employed in this 

piece (i.e., Morningstar investment categories), the different 

definitions of concentration and the different combination 

methodology used to aggregate the results. 

Appendix: Average Quintile Attributes 

Average Benchmark Sector Correlations 

Quintile 

Large 

Growth 

Large 

Blend 

Large 

Value 

Mid-Cap 

Growth 

Mid-Cap 

Blend 

Mid-Cap 

Value 

Small 

Growth 

1 0.474 0.588 0.556 0.475 0.153 0.356 0.577 0.456 0.327 

2 0.705 0.826 0.801 0.698 0.582 0.640 0.753 0.705 0.646 

3 0.802 0.906 0.887 0.796 0.737 0.785 0.826 0.825 0.822 

4 0.874 0.950 0.939 0.865 0.847 0.877 0.878 0.892 0.914 

5 0.947 0.981 0.978 0.932 0.926 0.947 0.938 0.952 0.971 

Small 

Blend 

Small 

Value 

Quintile 

Large 

Growth 

Large 

Blend 

Average Total Number Of Holdings 

Large 

Value 

Mid-Cap 

Growth 

Mid-Cap 

Blend 

Mid-Cap 

Value 

Small 

Growth 

1 31 28 39 41 34 36 57 48 46 

2 50 58 58 64 55 59 83 83 82 

3 66 86 76 80 81 78 102 121 125 

4 87 126 95 102 128 98 129 182 213 

5 196 434 209 200 379 179 250 821 588 

Small 

Blend 

Small 

Value 

Quintile 

Large 

Growth 

Large 

Blend 

Average % of Assets In The Top Ten Holdings 

Large 

Value 

Mid-Cap 

Growth 

Mid-Cap 

Blend 

Mid-Cap 

Value 

Small 

Growth 

1 52.31% 63.01% 44.51% 43.88% 48.76% 49.94% 36.77% 40.76% 41.86% 

2 38.54% 34.76% 32.61% 29.74% 34.72% 33.52% 26.43% 26.53% 26.72% 

3 33.23% 28.60% 28.94% 24.52% 26.67% 25.40% 21.74% 21.17% 19.35% 

4 28.90% 24.83% 25.95% 20.46% 19.55% 21.12% 17.97% 15.86% 14.63% 

5 23.07% 19.76% 21.70% 14.98% 12.54% 15.31% 12.88% 9.34% 9.52% 

1 = Lowest Correlation/Least Holdings/Least Diversified 

5 = Highest Correlation/Most Holdings/Most Diversified 


Works Cited 

Brands, Simone, Stephen J. Brown and David R. Gallagher. (2005), “Portfolio Concentration and Investment Manager Performance,” International Review of Finance, vol. 5, no. 3-4 

(September/December): 149–174. 

Campbell, John Y., Martin Lettau, Burton G. Malkiel and Yexiao Xu. (2001), “Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk.” The 

Journal of Finance, vol. 56, no. 1 (February): 1–43. 

De Wit, Dirk P.M. (1998), “Naive diversification.” Financial Analysts Journal, vol. 54, no. 4: 95–100. 

Evans, John L. and Stephen H. Archer. (1968), “Diversification and the Reduction of Dispersion: An Empirical Analysis.” The Journal of Finance, vol. 23, issue 5 (December): 

761–767. 

Graham, Benjamin. (1986), “The Intelligent Investor: A Book of Practical Counsel,” Collins; 4th Rev edition (January). 

Goetzmann, William, N. and Alok Kumar. (2005),“Equity Portfolio Diversification” working paper: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=627321. 

Ivkovich, Zoran, Clemens Sialm and Scott J. Weisbenner. (2006), “Portfolio Concentration and the Performance of Individual Investors.” Available at SSRN: http://ssrn.com 

abstract=568156 (forthcoming in the Journal of Financial and Quantitative Analysis). 

Kacperczyk, Marcin T., Clemens Sialm and Lu Zheng. (2005), “On the Industry Concentration of Actively Managed Equity Mutual Funds,” The Journal of Finance, vol. 60, no. 4: 

1983–2011. 

Shawky, Hany A. and David Smith. (2005), “The Optimal Number of Stock Holdings in Mutual Fund Portfolios Based on Market Performance,” The Financial Review, vol. 40, 

no. 4: 481–495. 

Statman, Meir. (1987), ”How Many Stocks Make a Diversified Portfolio?” Journal of Financial and Quantitative Analysis, vol. 22, no. 3 (September): 353–363. 

Small 

Blend 

Small 

Value 


23


Getting what you pay for 

By Robert Whitelaw, Salvatore Bruno and Anthony Davidow 

24 

March/April 2009

All investment returns can be seen as the culmination 

of the market return (Beta) and excess returns (Alpha). 

The rise of index funds has shown that achieving Beta 

market exposure is inexpensive and easily achievable through 

index mutual funds and exchange-traded funds (ETFs). 

Institutional investors have recognized that in order to 

maximize returns, minimize costs and manage the risks of 

their portfolios, manager performance (Alpha) can be separated 

from Beta using straightforward tools and analytical 

techniques. The academic rigor associated with this process 

has helped uncover an entire new set of asset classes: 

Alternative Beta. 

Alternative Beta and Alpha separation have proven to be tremendous 

tools in the hands of the world’s largest institutions, 

but implementing these strategies on a smaller scale presents 

substantial analytical and implementation challenges. 

The rise of synthetic hedge fund products and low-cost 

ETFs has made the fine-tuning of portfolio exposure through 

these two techniques easier than ever before, and accessible 

to a broad range of investors. 

Understanding Returns: Alpha Vs. Beta 

There are moments in history when the science of investing 

takes a major step forward. The birth of the Capital 

Assets Pricing Model (CAPM) was one of them; the dawn of 

index funds was another. 

Today, another investing revolution is afoot: Alpha/Beta 

separation. No matter what’s in your portfolio, it can be 

described in certain universal ways, like risk and reward. 

Perhaps the most critical of these concepts is that of Alpha 

and Beta. Simply put, Beta is the risk/reward of your portfolio 

that is explained just by being in a particular market. Alpha 

is excess return—that elusive edge that lets you (or your 

investment manager) beat the market. 

For most investors, Alpha and Beta are inseparable. When 

you buy an active mutual fund, for instance, you’re buying a 

lot of Beta and a little bit of Alpha. 

But the most sophisticated investors are now decoupling 

the two, separating their decisions about Alpha from their decisions 

about Beta. This new investing technique allows investors 

to gain increased control over their asset allocation strategies, 

control costs and—most importantly—maximize returns. 

Let’s look at the constituent parts. 

Beta: The Market 

For many investors, the most important investment decision 

they will ever make is simply to invest in the market. Study after 

study shows that our most basic asset allocation decisions determine 

the bulk of our portfolios’ returns. We may spend countless 

hours reading Barron’s, trying to figure out how to beat the 

market. But the most important thing from a returns perspective 

is making sure that we are in the market—getting market-level 

returns for market-level risks, preferably at low cost. 

Fortunately, market returns—aka Beta—are both widely 

available and wonderfully cheap. Mass-market retail products 

such as index mutual funds and exchange-traded funds reliably 

deliver market returns in many traditional asset classes 

at extremely low costs. State Street Global Advisors’ S&P 500 

SPDR ETF (NYSE Arca: SPY), for example, trades millions of 

shares a day, has a net expense ratio just shy of 0.10 percent 

and has exhibited virtually no tracking error to its underlying 

index. Not a bad deal. 

Derivatives offer another efficient tool for accessing 

index-level returns. Both futures and options allow investors 

to gain exposure to most of the world’s markets with minimal 

cost and tremendous flexibility. 

As we’ll explain, new index-based investment products 

are even opening up alternative asset categories, like hedge 

funds, to Beta approaches. 

Regardless of the structure or asset class, however, all 

of these index-based vehicles have one thing in common: 

They provide pure Beta. In other words, their risks and 

returns can be explained nearly entirely by the movement 

of the market they track. 

These index-based strategies provide investors with 

great ways to “buy the market” at low cost and with 

enhanced liquidity. 

Alpha: The Elusive Goal 

What about “beating the market?” 

For most of the history of investing, the role of the advisor, 

investment manager or consultant has been to do better 

than the market. In the simplest terms, that’s Alpha: the portion 

of a portfolio’s return that is the result of a manager’s 

skill, and not the return of the market. 

Taken this way, determining a portfolio’s returns is simple: 

Alpha 

+ Beta 

– Costs 

Total Real Return 

The problem, of course, is that while Beta has become easier 

and cheaper to acquire than ever before, Alpha remains elusive 

and expensive. The Efficient Market Hypothesis (EMH) tells us 

that the market is, over the long term, efficient: Any one manager’s 

gain is another’s loss, and on average, the market price is 

the “right” price. One can debate whether the EMH is valid over 

shorter intervals, but academic research consistently shows the 

overall efficiency of the market over the long run. 

There are managers who beat the market, of course, but 

they charge for their services. The standard fee for a hedge 

fund, for instance, is 2 percent of assets under management 

and 20 percent of any profits. The most successful hedge 

fund managers charge even more than that. 

Alpha Pollution 

It’s fine to pay a high price for true excess returns; after 

all, Alpha is hard to find. The problem is, investors aren’t 

always sure what they’re paying for. 

Alpha must always be explained relative to some benchmark, 

and defining that benchmark properly is critical. 

Suppose that an investment manager chooses the S&P 500 as 

its benchmark, but holds a portfolio whose default position 

is 50 percent Treasuries and 50 percent stocks. If the stock 

market falls, that manager will outperform, as the steady 


25

Figure 1 

ALPHA 

BETA 

COSTS 

Active 


BETA 

COSTS 

Passive 

fixed-income position will offset the falling stock prices. But 

has the manager really captured Alpha? Not really. Its default 

portfolio simply captured a different market than the index. 

If investors paid Alpha-level fees for this static 50 percent 

bond/50 percent stock portfolio, they were misled. They 

could have achieved the same exposure for less. 

Every investment decision has an implication in Alpha and 

Beta terms. The decision to invest in a passive index is also a 

decision to abandon any attempt at gaining Alpha. Investing 

with an active manager is a decision to pay a premium for a 

blend of Alpha and Beta. An active mutual fund, for example, 

is going to produce returns that are the culmination not only 

of that manager’s skill, but also of the underlying market 

itself. In both cases, total return is degraded by the costs of 

implementing the strategies in question. Worse, you’re paying 

active-management-level fees for the entire portfolio, not 

just the portion of the portfolio actually generating Alpha. 

This blending has a significant impact on overall portfolio 

performance. A study in 2005 [Miller] suggested that the vast 

bulk of returns offered by traditional equity mutual funds 

were Beta returns. Even the best actively managed funds, it 

turned out, could be explained largely by their exposure to 

major market indexes. If you adjusted the funds’ returns to 

isolate just the Alpha, and replaced the Beta exposure with 

hypothetical low-cost index funds, the implied investment 

management fee for the Alpha portion was actually more 

than 7 percent per year. Expensive Alpha indeed. 

The first goal of Alpha/Beta separation is to understand 

exactly what you’re buying and exactly what you’re paying 

for it. That way, you can make sure you aren’t paying Alphalevel 

fees for Beta-level results. 

Can Alpha/Beta Separation Be Applied To Other Fields? 

Perhaps the most problematic issue with Alpha/Beta separation 

is definitional. What precisely is Alpha? How do you 

know that the risk/return pattern from a particular manager 

is in fact unique and idiosyncratic, and not simply part of the 

systematic risk/return of its investment universe? Many in 

academic finance challenge whether true Alpha even exists 

over any meaningful investment horizon—whether essentially 

all investment performance is explained over time as 

short-term Beta, driven by changes in market exposure. 

The problem is fundamentally one of defining what the Beta 

should be for a particular manager’s strategy. For example, if 

a manager claims his benchmark is the S&P 500, it would be 

convenient to simply call the S&P 500 Index return his Beta, 

and consider everything else Alpha. But if in fact that manager 

is consistently selecting from, say, the Russell 3000, then 

extracting the appropriate market benchmark is problematic. 

Fortunately, a better, more consistent way to measure 

Beta has emerged. In academic terms, Beta is the relationship 

between the returns from an investment and the risk 

associated with those returns. More-risky assets should 

have an associated risk premium—the likelihood of higher 

returns. Any given universe of investments, then, can be seen 

to have its own Beta, and true manager skill should only be 

assessed once that Beta is understood. 

The issue of Beta definition becomes more complex the 

more arcane or opaque the investment strategy. Many hedge 

funds are essentially black boxes, where what goes on in the 

day-to-day management is unknown by the public, and holdings 

and performance are reported infrequently. But hedge 

funds have been able to charge substantial fees to deliver 

returns that have been apparently unavailable elsewhere. 

And yet the strategies many hedge funds pursue—marketneutral 

and short-extension strategies—are actually using 

the tools of Alpha/Beta separation themselves. 

Are these funds truly delivering Alpha? 

These are muddy waters, and ones that academic finance 

continues to debate. But one thing is clear: What many have 

considered Alpha in the past may in fact just be another form 

of Beta—Alternative Beta. Alternative Beta is still the result 

of systemic risks; it’s just a different set of systemic risks than 

those commonly experienced in the stock and bond markets. 

This Alternative Beta can be captured using advanced investment 

strategies such as hedge fund replication for a fraction 

of what most investors pay for supposed Alpha. 

Hedge Fund Replication 

This exploration of Alternative Beta underlies a new 

breed of investment strategies: synthetic hedge fund 

products. Hedge funds—a simple name for a range of private 

investment funds that may or may not use hedging or 

any other particular strategy—have been used for decades 

by institutional investors seeking diversification from the 

traditional asset classes like stocks and bonds, or seeking 

strategies typically unavailable in other forms, like leverage 

and shorting. Some of the most successful investors in 

the world—such as the Harvard and Yale endowments— 

make sizable allocations to hedge funds and other alternative 

investments because they are able to deliver steady 

returns that are not correlated to other asset classes. In 

fact, the most sophisticated investors diversify their alternatives 

exposure across multiple strategies and platforms 

to take advantage of the favorable risk/return trade-offs. 

In 2007, the Yale University Endowment allocated over 

23 percent of its assets to absolute return strategies, and 

over 69 percent of its portfolio was allocated to alternative 

investments, 1 as it sought out steadier returns during 

a period of market turmoil. 

26 

March/April 2009

But even the most complicated hedge fund can still be 

understood both in terms of its core market exposure (the real 

Beta of the strategy) and the manager’s skill (the Alpha, positive 

or negative). One way of teasing out the real Beta of the 

hedge fund market is to look into a very shiny rearview mirror. 

In many cases, hedge fund strategies can be successfully replicated 

using easily tradable asset classes. Quantitative analysis 

can identify the factor bets made by a given hedge fund strategy, 

and then produce similar returns and risk profiles using a 

synthetic approach involving options, exchange-traded funds 

or other related instruments. These synthetic hedge fund 

indexes can then be considered the true Beta for a particular 

hedge fund strategy, and the manager’s deviation from that 

Beta will be determined by his skill. 

This continues the primary trend of Alpha/Beta separation. 

Investors are able to access what they were seeking all along 

from the hedge fund market: Alternative Beta, or the low-correlation 

returns that can boost the performance of the portfolio 

overall. And they can do so without paying for something that 

may not really have existed in the first place: Alpha. 

Implementation: Controlling Costs, 

Improving Returns 

How do you implement this understanding of Alpha and 

Beta into a portfolio? 

The most common portfolio strategy for institutions and 

high net worth investors is one that blends both traditional 

active management and the benefits of indexing. 

Let’s take the example of a large pension fund. Suppose 

that the investment manager sets the overall target asset 

allocation for the fund across four different asset classes: U.S. 

equities; international equities; bonds; and alternative assets 

(hedge funds, commodities, private equity, etc.). Inside each 

asset class, it employs a core-and-satellite approach. First, it 

selects the core managers—passive managers that provide 

pure beta exposure at extremely low costs that make up the 

bulk of the pension fund’s returns. 

Then it takes a portion of its assets and applies them to 

specific managers whom it believes have the potential for 

excess returns; perhaps a large-cap manager, or a hedge fund 

with a good track record in long/short strategies. 

In each case, the decision about each manager is made in the 

context of the markets in which it invests, and its appropriate 

benchmark. The large-cap manager isn’t hired simply because 

he’s a good stock-picker; he’s hired because he’s a good largecap 

stock-picker. The benefit of this approach is that the core 

portfolio can be left relatively stable, subject to occasional rebalancing 

and renegotiation. The downside is that the universe of 

potential Alpha managers is limited, and each Alpha manager is 

being paid to produce both Alpha and Beta. 

In an Alpha/Beta Separation Strategy, these decisions 

about asset allocation and manager selection are decoupled. 

Fundamental asset allocation decisions are made using pure, 

core vehicles, but the Alpha managers are selected purely 

for their skill. These Alpha managers are evaluated based on 

the risk and return only of their active management, without 

regard to what they invest in, be it fine wines, small-cap 

stocks or Liberian bonds (see Figure 2). 

The core asset allocation decisions are implemented entirely 

with passive vehicles. Managers that are believed to have the 

potential for pure Alpha are then layered on top of this core 

portfolio, without affecting the core asset allocation strategy. 

In theory, such a methodology has significant appeal: 

Cost Control. In an Alpha/Beta Separation Strategy, a larger 

percentage of overall portfolio assets are in Beta-centric, passive 

investment vehicles. Regardless of whether these vehicles 

are ETFs, separate accounts or derivatives, these passive 

vehicles generally carry low management fees and/or minimal 

transaction costs. If those dollars were with an active manager, 

the investor would be incurring active management fees— 

almost certainly higher. In the Swedish pension system, for 

example, the costs of switching managers were reduced from 

100 basis points on average, to 5 basis points [Engstrom]. 

Reduced Tracking Error: Because Beta generation is now 

entirely segregated, investors can be extremely selective in their 

choice of investment managers. For the largest institutional 

investors, this means having increased buying power when 

negotiating with index managers or swap counterparties. 

Flexibility: Because the core asset allocation is now 

handled entirely with low-cost, highly liquid passive vehicles, 

shifts in asset allocation can be achieved with minimal friction. 

This means that portfolio rebalancing, adjusting for a 

change in risk profile, tax management or even the termination 

of one manager in favor of another can be done quickly 

and easily. 

Better Beta: By segregating the Beta decision from manager 

selection, investors can more cleanly analyze their 

expected portfolio returns. This makes finding uncorrelated 

asset classes more straightforward, as the “noise” of active 

management returns is removed from the analysis. 

Better Alpha: By selecting Alpha managers solely on their 

ability to generate Alpha within certain risk parameters, 

investors have a wider net to cast, looking at any and every 

asset class, including asset classes that are highly illiquid 

(which is where Alpha is most likely to be found). In the ideal 

case, the returns of the Alpha manager are entirely uncorrelated 

with any of the other asset classes in the portfolio—yet 

another boon to the asset allocation process. 

Alternative Beta. The combination of Alpha/Beta separation 

and modern investment techniques yields an entirely 

Figure 2 

Traditional Portfolio Vs. Alpha/Beta Separation 

Traditional Portfolio 

Hedge Funds 

Alpha 

Beta 

Equities 

Alpha 

Beta 

Fixed Income 

Alpha 

Beta 

Alpha 

Hedge Funds 


Beta 

Core 

Alpha 

Equities 

Alpha 

Fixed Income 


27

new asset class: Alternative Beta—the low-correlated Beta 

returns available in alternative asset classes like hedge 

funds. These Alternative Betas can be captured in synthetic 

hedge fund products without the high costs traditionally 

associated with hedge fund strategies. Since the single biggest 

impact on portfolio returns is asset allocation, the 

ability to create an uncorrelated asset class—Alternative 

Beta—is tremendously powerful. 

These factors combine to create a compelling case. The 

combination of lower costs, more-predictable outcomes and 

increased flexibility would seem to be a sure winner. But 

despite these theoretical advantages, the Alpha/Beta separation 

portfolio has unique caveats. 

First and foremost, managing a complete Alpha/Beta separation 

portfolio is not for the casual investor. The combined 

portfolio can be complex, and requires attention and analyses. 

While any asset allocation strategy needs fine-tuning, when 

Alpha is essentially an asset class in itself, paying attention to 

correlation and absolute performance becomes critical. 

But perhaps more importantly, finding Alpha managers 

is nontrivial. Very few managers consistently beat their 

benchmarks. 

Indeed, there are many in academic finance who believe 

that in most markets, Alpha-seeking is a zero-sum game, 

where by definition, every active manager’s win is another’s 

loss. Indeed, even the very definitions of Alpha and Beta 

undergo continuous academic debate. 

Despite the debate, Alpha/Beta separation is far more than 

academic. The world’s largest and most sophisticated institutional 

investors are adopting the approach. In August of 2008, 

the Massachusetts Pension Reserves Investment Management 

Board announced that it was firing its active managers and 

shifting its $50 billion portfolio toward an index/portable 

alpha structure [Appell]. In 2005, the Swedish pension system 

transitioned $14 billion to a strict Alpha/Beta separation system, 

and credits the shift with reducing costs and increasing 

true uncorrelated alpha in its portfolio [Engstrom]. It has since 

shifted an additional $32 billion to the strategy. 

Figure 3 

ALPHA 

U.S. 

Equity 

Active Manager 

Long/Short Alpha Separation 

Short 

U.S. 

Equity 

Passive Hedge 

ALPHA 

Pure Alpha 

Implementation Challenges 

Imagine you are a financial advisor running a modest 

separate account for a high net worth individual—around $1 

million in assets. After a thorough analysis of the investor’s 

other holdings, her risk tolerance and her financial situation, 

you construct a diversified portfolio using ETFs and low-cost 

mutual funds with a blended expense ratio of under 30 basis 

points (0.30 percent). You’ve even used a hedge fund replication 

product to add in additional low-correlated returns to 

the portfolio. If you’ve done your job, your asset allocation 

and selection of Beta vehicles will generate solid results. 

But your client isn’t satisfied. She wants to beat the 

market. She wants Alpha. How do you go about getting it? 

After careful due diligence, you stumble across a mutual 

fund from manager ABC. 

Manager ABC has a consistently outperforming large-cap 

strategy, making effective and profitable tilt-and-timing decisions. 

If you were to add this fund to the client’s portfolio, it would 

increase the asset allocation to large-cap stocks, and create rebalancing 

challenges at the end of the month. What do you do? 

The answer is to separate the Alpha from the Beta. You 

balance your client’s mutual fund investment in ABC with a 

corresponding short position in the S&P 500. Theoretically, 

you’ve now created pure, uncorrelated Alpha from ABC, 

and can manage your equity exposure independent of your 

analysis of the mutual fund’s performance (see Figure 3). 

While simplistic and hypothetical, the example is useful for 

several reasons. Long/short and market-neutral equity strategies 

were the first to offer this kind of pure Alpha to investors, 

and are among the easiest to implement. But there are 

several things working against our hypothetical advisor: 

• Hedging isn’t free. Whether implemented through 

shorting equities, swaps, futures or options, there 

are financing and transaction costs that degrade the 

separated Alpha. In many markets, successful active 

management is measured in basis points. Separation 

only makes sense when the cost of stripping out the 

Beta is substantially less than the expected Alpha of 

the manager. 

• Aligning execution and liquidity is critical. To put on or 

unwind an Alpha separation strategy, multiple transactions 

in different markets must be made. Swaps, equities and 

futures all have different settlement and cash management 

requirements that need to be monitored and managed. 

For these reasons, and others, many investors do not construct 

pure-Alpha exposure on their own, or even through a 

managed separate account. Instead they rely on asset managers 

to either package their own expertise in pure-Alpha form, 

or on fund-of-funds managers who seek to collect high-Alpha 

managers and package their returns in a portable format. 

Packaged approaches are fine, as long as the underlying 

principle remains clear: Pay Alpha fees only for true-Alpha 

returns. And don’t think you have to pay Alpha fees for all 

asset classes, since even alternative asset classes can be captured 

using Alternative Beta. 


In many ways, the history of modern investment managecontinued 

on page 63 

28 

March/April 2009

Whitelaw from page 28 

ment is punctuated by two fundamental revolutions: the creation 

of the first mutual funds in the 1930s and 1940s, and 

the creation of the first index funds in the 1970s. In each case 

there was a tremendous democratization of the investment 

landscape. Broader and broader segments of the population 

gained both the access and the expertise to become effective 

investors, at lower costs than ever before. 

The separation of Alpha and Beta is the next revolution 

in investment science. Investment strategies that effectively 

isolate Beta are tremendously powerful in managing risk and 

containing costs. Those that segregate true Alpha can provide 

investors unique, uncorrelated sources of return. 

But perhaps most important, the academic rigor of analyzing 

the real sources of investment return are uncovering 

hidden Beta—Alternative Beta. Once identified and synthesized, 

these Alternative Betas can themselves be indexed and 

turned into investable products such as hedge fund replication 

funds, giving investors access to entire asset classes that 

were previously inaccessible. 

Endnote 

1 According to the 2007 Yale Annual Report, the Endowment had allocated 23.3 percent of its portfolio to Absolute Return Strategies, 18.7 percent to Private Equity and 27.1 

percent to Real Assets, representing a 69.1 percent allocation to Alternative Strategies. 

References 

Ali, Paul, and Martin Gold. “An Overview of ‘Portable Alpha’ Strategies,” working paper, TC Beirne School of Law, University of Queensland (2007). 

Appell, Douglas. “MassPRIM axes Legg Mason, 4 others.” Pensions & Investments, Aug. 6, 2008. http://www.PIonline.com/apps/pbcs.dll/article?AID=2008660067766 (accessed Oct. 1, 2008). 

Burrill, Scott and Joe Gawronski. “A Brief Primer on 130/30 Strategies,” Rosenblatt Securities, Inc. (2007). 

Chance, Don. “Equity Swaps and Equity Investing,” William H. Wright Endowed Chair for Financial Services, Louisiana State University, Baton Rouge, La. (2003). 

Engstrom, Stefan, Richard Grottheim, Peter Norman and Christian Ragnartz, “Alpha-Beta-Separation: From Theory to Practice,” SSRN (May 26, 2008), http://ssrn.com/abstract-1137673 

(accessed June 27, 2008). 

Gorman, Larry and Robert Weigand. “Measuring Alpha-Based Performance,” academic paper (2007). 

Gupta, Pranay and Jan Straatman. “Alpha and Beta in an Exposure based Framework,” working paper (2005). 

Hubrich, Stefan. “An Alpha Unleashed,” Vers. 4th Draft, SSRN (Jan. 8, 2008), http://ssrn.com/abstract=1015327 (accessed June 27, 2008). 

Johnson, Gordon, Shannon Ericson and Vikram Srimurthy, “An Empirical Analysis of 130/30 Strategies,” The Journal of Alternative Investments 10, no. 12 (fall 2007). 

Kirchner, Thomas. “Negative Alpha Is Built Into 130/30 Funds,” Feb. 4, 2008. http://thedealsleuth.wordpress.com/2008/02/04/negative-alpha-is-built-into-13030-funds/ 

(accessed June 27, 2008). 

Kung, Edward and Larry Pohlman. “Portable Alpha,” The Journal of Portfolio Management (spring 2004). 

Leibowitz, Martin A. “Alpha Hunters and Beta Grazers,” Financial Analysts Journal, pp 32–39 (September/October 2005). 

Miller, Ross M. “Measuring the True Cost of Active Management by Mutual Funds,” working paper (2005). 

Statman, Meir. “The 93.6% Question of Financial Advisors,” Journal of Investing (2000). 

Tofallis, Chris. “Investment Volatility: A Critique of Standard Beta Estimation and a Simple Way Forward,” European Journal of Operational Research 187: pp 1358–1367 (2008). 


63


A roundtable discussion 

Edited by Heather Bell 

30 

March/April 2009

It used to be fairly simple. There was active management and 

then there was passive management, with enhanced index 

schemes floating in the narrow demilitarized zone between 

the two. Now, with the explosion of exchange-traded products 

and the advent of more and more complex indexes, that gray 

area separating the two philosophies has steadily expanded. The 

Journal of Indexes recently presented some questions on the 

subject to a group of experts on both sides of that divide. 

John Bogle, founder, The <strong>Vanguard</strong> Group 

Journal of Indexes (JOI): Where is the line 

between passive and active management, and 

where does it begin to blur? 

John Bogle (Bogle): At one extreme, you 

can take an all-market index fund—all U.S. market or all 

global market as you wish—and do nothing with it; that’s 

the extreme of passive management. And that is the position 

that I endorse. 

I suppose the most extreme active management is characterized 

by high cost, high turnover and great tax inefficiency—let’s 

call it high-cost hyperactive management. That is at 

the other extreme. The blurring comes when you take, for 

example, a passive fund and trade it actively. 

In other words, if you’re trading the S&P 500 Index all 

day long in real time, that’s a passive strategy, but an active 

implementation. And so that’s a big blurring; you lose the 

value of passive management when you do that. 

The second part comes with passively managed investments 

in other countries, in small segments or in industrial 

industry sectors. Even though a Technology fund is run 

passively, for example, the line is blurred [in terms of owning 

the fund] because it’s a passive approach to only that 

portion of your portfolio, and then it’s an active approach 

to implementation again. 

[Investors should] own the market, keep cost out of the 

equation and above all, not allow the true miracle of compounding 

return to be overwhelmed by the true tyranny of 

compounding cost. 

JOI: Is the increasingly active use of index products harmful or 

beneficial to traditional buy-and-hold index investors? 

Bogle: I think if traditional buy-and-hold index investors 

continue to be traditional buy-and-hold index investors, 

the active use of index products will have no impact 

on them one way or the other. The risk of harm would 

come if traditional buy-and-hold investors started getting 

enticed by the romance or the speculative thrill of using 

index products actively. So, as long as they don’t do that, 

it shouldn’t matter. For long-term investors and traditional 

buy-and-hold index investors, the impact is zero. 

JOI: How has the growing acceptance of ETFs by advisors changed 

the way they manage portfolios? And is that good or bad? 

Bogle: Well, none of us really knows how significant that 

change has been. It certainly seems that there is a group 

of advisors who rely much more heavily on buying industry 

sectors through ETFs, or maybe even these highly leveraged 

ETFs that make you go up or down much faster than 

the market. I think that it has changed the way advisors 

manage their portfolios a little bit, but I don’t think it’s 

necessarily bad. 

The question about ETFs really should be: When advisors 

are using them, are they using them to do intelligent things? 

JOI: How granular should the average buy-and-hold investor be? 

Bogle: If by “granular” you mean how much you should 

focus on those small units of the portfolio rather than the 

total portfolio, I’d say, “Granularity: Forget it.” I am an allmarket 

indexer, and I’ve seen too many occasions when 

betting on particular sectors of the market goes awry—in 

part because we all think we’re smarter than we in fact 

are, in part because the market is highly efficient. I want 

to be clear that I did not say, “perfectly efficient,” because 

we have seen many, many inefficiencies in the markets 

from time to time. But in general it’s quite an efficient 

vehicle. So holding the market is the ultimate strategy. 

And betting that you can outguess the market by holding 

more Energy stocks, or more Financial stocks, or more 

Technology stocks, or more Industrial stocks, is, I think, 

on balance a loser’s game for investors. 

JOI: Is qualitative active management dead? 

Bogle: We really have three kinds of management. We 

have passive management—buying the market and holding 

on to it forever. And then we have active management, 

divided into qualitative and quantitative. Indexing is maybe 

15 percent of the total stock market, traditional (qualitative) 

management is about 70 percent of the market and 

quantitative active management is the other 15 percent. 

Quantitative active management has kind of fallen on hard 

times. It turns out that massaging the numbers until hell 

freezes over doesn’t necessarily make you a better investor. 

Qualitative management had a very difficult year last year, 

as a number of well known managers struggled. But that 

doesn’t mean that qualitative active management is dead. 

It’s merely dying, losing a bit of market share year by year 

as investors awaken to the economics of investing. But it’s 

never going to completely die, because there will always be 

someone for whom hope springs eternal. 

H. Bruce Bond, president and CEO, 

Invesco PowerShares 

JOI: Where is the line between passive and active 

management, and where does it begin to blur? 

H. Bruce Bond (Bond): I think our opinion 

of the difference between passive and active management 

depends on what you’re trying to achieve. Passive management 

is simply trying to recreate a benchmark or replicate 


31

a specific group in the marketplace. There is no evaluation 

whatsoever involved within your investment process other 

than adhering to regulatory restrictions. You’re selecting 

securities strictly for their size or their representation within 

a given area. Conversely, for the active portfolio, we’d say 

any attempt to select securities or rotate sectors or something 

like that in order to achieve some sort of alpha over the 

market would be considered active, whether it’s qualitative, 

quantitative or technical. Typically you can assume that the 

active management side is going to have greater turnover 

than the passive side. 



Bond: I believe it’s beneficial. The use of exchange-traded 

fund products by very active investors does not have an 

impact on the ongoing shareholders. 

One of ETFs’ big growth periods was after the markettiming 

scandal for mutual funds [in 2003], when people were 

made aware that the market timing in mutual funds had a 

negative impact on the ongoing shareholders. The beauty of 

ETFs is the people who trade ETFs intraday don’t generally 

affect the long-term holders in a negative way because of the 

structure of the ETF. And so it’s really one structure that can 

meet both needs. 

From a long-term buy-and-hold perspective, by and 

large, ETFs have a creation-redemption approach that 

should not create any taxable burden. Taxes over the 

long term tend to be one of the most overlooked areas 

for wealth creation. An ETF may help protect you from 

cap gains distributions, thereby allowing you to have a 

greater amount of money compounding over time. I think 

tax efficiency is a very important thing for long-term buyand-hold 

investors, and ETFs are one of the more efficient 

products available today for them to use. 

JOI: How has the growing acceptance of ETFs by advisors changed 

the way they manage portfolios? And is that good or bad? 

Bond: I think that the use by advisors of ETFs in a portfolio 

is a very good thing. As there are more alternatives available 

featuring both long and shorting capabilities, many more 

strategies have become available to sophisticated advisors 

to apply to the portfolios of their clients or their own portfolios. 

ETFs have given them additional tools to execute 

portfolios that otherwise would be very difficult for them to 

do either with individual securities or with long-only investment 

products. 


Bond: How granular an investor should be if they’re investing 

on their own depends on their knowledge base or how 

much they understand or know. I think it’s very important to 

note that the S&P 500 is at relatively the same place it was 10 

years ago. I think the long-term buy-and-hold investor said, 

“Well, we just put our money in here, and over any 10- or 

12-year period our assets are going to be higher.” And what 

we’ve just seen over the last 10-to-12-year period is that is 

not always true. 

It is important for investors to understand that there 

may be times that they need a more sophisticated approach 

and need to allocate among things that are going to give 

them an opportunity to grow their assets over time. A buyand-hold 

approach over time with one security or vehicle 

simply doesn’t work. This is also why we think investment 

advice and sophisticated investment advisors are going to 

be sought out during the next several years, because investors 

are going to understand that it’s really key to have good 

advice if they intend to make back what they have lost, or if 

they want to grow their portfolio over time. 


Bond: I would have to say no. I think there’s always going 

to be different qualitative or quantitative active managers 

available in the marketplace, and each one is going to be 

measured by the performance they deliver. It just depends 

on the market period you’re in and the particular segment 

of the market that your investment expertise tends to be in. 

I think if an investor wants exposure to a very low-liquidity 

investment area, or an area that is a little bit obscure and 

not highly traded or highly efficient, an active manager that 

applies a qualitative or fundamental approach is going to be 

important to evaluate—for example, credit risk and things 

like this. However, if you’re dealing with a very efficient 

and highly traded area of the market, I think a quantitative 

approach might be better. 

Seth Ruthen, executive vice president, 

Account Management Group, Pimco 

JOI: Where is the line between active and passive 


Seth Ruthen (Ruthen): I guess just theoretically, 

something that you could achieve with a long-term buyand-hold 

strategy with minimal trading is what I would define 

as a passive strategy. So the more you get away from that, 

the more active you would get, and you can measure the difference 

statistically using tracking error or other statistics. 

If you’re actively trading a lot of different asset classes but 

accessing the asset classes through passive exposure in an 

ETF, it sounds like a very active strategy to me. And there’s 

no way ex ante you could build a static portfolio to replicate 

what the future performance would be of that actively managed, 

tactical asset allocation strategy. So you may be using 

passive vehicles but you’re doing it in a very active sense. 

Within vehicles themselves, you have some very hightracking-error 

active strategies and you have some verylow-tracking-error 

strategies in various asset classes. I 

think you should look at what the tracking error is, and I 

think we could all agree that things that have a tracking 

error greater than zero have the potential to be considered 

active. And certainly, once you start getting into 2 

32 

March/April 2009

percent, 3 percent, 4 percent or higher, it’s hard to call 

them anything but active. The gray area is when you get 

into very low tracking error strategies—things that have 

0.5 percent of tracking error, where they’re very much like 

a passive strategy, although not exactly. 

Just to keep it simple, if you have a legitimate risk of 

under-performance relative to a static buy-and-hold investment, 

you’d have to consider it active. And the compensation 

for that potential under-performance is the fact that you 

might do better. 

It’s really just a question of how much tracking error 

you want and how much excess performance you expect to 

achieve by taking that tracking error. So instead of getting 

into a categorization issue as to whether it’s active or passive, 

I would want to better understand how much tracking 

error people would want to take and how much alpha they 

would hope to get, and make that decision on a continuum 

rather than as if the two were discrete buckets. 

And by the way, I think it’s important to make a case 

that tracking error is not risk. For example, you can be 

underweight a risky part of an index; it shows up as positive 

tracking error, but it’s a risk reduction move. So we shouldn’t 

make the mistake that all tracking error is risk-increasing. 



Ruthen: If anything, it just provides more liquidity to the 

market for buy-and-hold investors. By definition, buy-andhold 

investors probably aren’t looking for a lot of liquidity. 

But to the extent that it’s there when they need it, it has to 

be a good thing. 

JOI: Is active management more effective with fixed income 

than with stocks? 

Ruthen: I think it is. And I guess it depends on what type of 

equity investment you’re talking about. But if you just, for 

example, compared domestic bonds to domestic large-cap 

stock, I think you’d have to say that it is more effective for a 

couple of reasons. The first is that replicating a stock index 

is much easier than replicating all of the positions in a bond 

index. There are thousands of issues in a bond index, some 

of which can be small or hard to access at a retail level. 

Buying the 30 stocks in the Dow or even the 500 stocks in 

the S&P 500 is just not that difficult. 

The evidence is that most large-cap, domestic stock 

managers have not added value over long periods of time. 

Therefore, you could argue, certainly in large-cap domestic, 

that you should either index or implement some kind of 

portable alpha strategy over that index. In fixed income, 

active management is more effective because fixed income 

is by nature a more fragmented market. The evidence does 

show that you can add value by buying undervalued sectors 

or adjusting your interest rate exposure, or positioning for 

changes in the shape of yield curve. 

I think you need to be really careful when you start 

thinking about parts of the fixed-income market which are 

fairly difficult to index effectively. For example, with credit 

indexes, you definitely want to make sure you’re actively 

managed because there’s an asymmetry typically between 

good and bad decisions. And bad decisions—i.e., a default or 

a bankruptcy—can have severe consequences. So you want 

to at least make sure that you’re not passively allocating your 

investments within an index fund to a company that could 

have the most bond issues. 


Ruthen: I think they should think about it more from an economic 

factor approach, the idea being that different classes 

of securities behave differently under different environments. 

For example, in periods of an economic slowdown, typically 

high-quality bonds do well and stocks do poorly. In an inflationary 

time, typically real assets like commodities and TIPS 

should do well. In periods of good economic growth and low 

inflation, stocks do well. So I think they should be more concerned 

about having security types that do well over a wide 

range of environments than the illusion of diversification that 

comes from many different asset classes that are potentially 

highly correlated to each other. 


Ruthen: I would think not, because we live in a highly 

dynamic world where the financial environment is changing 

rapidly. And because it’s changing rapidly, you need to 

understand the paradigm shifts that are going on. That’s a 

fairly qualitative issue. So, for example, it’s very important 

to understand the regulatory response and policy response 

to the financial crisis. And given the dislocations that have 

happened and exist at the moment, I think it’s the proper 

understanding of that macro-context that can create or—if 

you don’t understand it—potentially lose a lot of value over 

the next several years. 

Given where we are in the very long-term growth cycle of 

the U.S., I would say that qualitative active management is 

more important than ever, because it would be very dangerous 

right now to just buy securities you thought were inexpensive, 

based on a comparison of those securities to their 

own history or some objective level, which I think would be a 

more-quantitative style by itself. I think what you really want 

to do is find securities that not only are inexpensive but have 

some sort of a support, or a catalyst, from a macro or qualitative 

environment that might help them do better. 

The dislocations right now are enormous. I mean, if 

you look at the spreads—between the investment-grade 

corporates, for example, and Treasuries, or other types 

between municipals and Treasuries—there’s some really 

large dislocations. But you don’t want to buy things just 

because they’re dislocated, you want to buy things that 

are dislocated and have some mechanism to recover given 

the policy responses we’re seeing by the various governments 

around the world. So, I guess the combination of 

qualitative and quantitative is better than either, which is 

better than neither. 


33

Chris Ailman, chief investment officer, 

CalSTRS 



Chris Ailman (Ailman): I guess I would say 

it’s not a line; it is a blur, it’s gradations. Full replication is 

truly the only form of pure passive indexing, and I think even 

then if you really want to get granular, you realize that how 

you deal with corporate actions is a judgment-based, active 

decision. So the minute you get into sampling, you’re just 

immediately starting to move up in terms of the risk dial. And 

if you’re allowing a certain level of tracking error that widens 

over time, suddenly you can be at active management. 

In terms of active management, from our perspective as 

a very large institution, what I call slightly enhanced—some 

people call index plus—is appealing to us, because it’s a very 

low-cost method to put large pools of money to work where 

you’re trying to at least get some level of alpha. When I got 

to CalSTRS, we were 80 percent passive, so we had the market 

locked in. Why not try and expand a little bit to try and 

add a little bit of tracking error here and there, and try and 

get some alpha if you can? 



Ailman: Some people may disagree, but I still think it’s a bit 

too early to tell. I like the increased liquidity that they can 

and have provided. This is such a volatile market. It is really 

difficult to tell. I’m not a big fan of some of the 2X ETFs for 

the subcategories. I think that adds way too much volatility. 

And there are some cases I think I could make, that, in a way, 

it hurts us, because we’re such a big index investor. But then 

in other cases, I think just more products, more market cap, 

does help with some levels of liquidity. So, I’m going to say 

the jury is still out a bit. 

JOI: Are ETFs an appropriate tool for institutions like CalSTRS? 

Ailman: It depends. We’ve had a circumstance here recently 

where we had a very particular segment of the market we 

needed exposure to. And the ETF is there. It’s kind of a “completeness” 

fund. It’s accessible. It’s fast. 

But the problem with big institutions is the underlying 

cost structure of it is much more than what it would be for us 

to do it ourselves or even hire a company like Barclays Global 

Investors to do it for us. CalSTRS has $130 billion [in assets 

under management]. The economy of scale is different for us, 

and when we want to buy a segment of the market, the size 

really becomes an issue, and so do the cost factors. But even 

then I still think we glance at ETFs periodically. 

For a midsized fund, I think they’re a very useful tool and 

I think they are appropriate. So the answer succinctly is, yes, 

they are a reasonable tool for institutions to use. 


Ailman: I do a lot of speeches to the teachers. We’ve got 

about 800,000 teachers, and obviously they’re a pretty 

homogenous group in terms of their occupation and avocation. 

But I have started asking people about how much 

time they’re willing to spend on investments, because more 

and more, there are people who do this as their full-time 

hobby. They just love it; they’re willing to spend hours at 

night working on it and don’t find it work. There are other 

people where that would just kill them. It’s the last thing in 

the world they want to do. So, I’ve tended to balance the 

advice. If an individual investor loves doing it and thinks it’s 

a hobby, then I think ETFs are frankly a better way to invest 

in the market than to use just mutual funds necessarily, or 

trying to buy individual stocks. 

But if you’re like a lot of the people I know where it’s not 

your hobby, you have other things you like to do, then I don’t 

think it makes sense to try and get too granular, and try and 

find “completeness” funds and the like. I’ve still been a pretty 

strong advocate, even for any individual investor, to just look 

at broad index funds and not try and mess too much with 

active management. 

I have seen individual investors so often burned by trying 

to make individual stock decisions, in which case they’re taking 

out way too much stock-specific risk for their portfolio, 

and also high net worth individuals that end up with a crazy 

stock portfolio based on a registered representative’s picks, 

or worse, what that broker happens to be selling to the 

investment bank that month. And that’s why I think index 

funds should always be a core part of investors’ portfolios. 

And if they want to go to ETFs, that’s the way to do it. 


Ailman: I love that question. No, it’s not. I am very firm in the 

belief that it is not. I think we understand better the environment 

that we’re in and, on both the quantitative side and the 

qualitative side, that the models aren’t perfect—they don’t 

pick up all the circumstances. On the qualitative side, I still 

think there’s a value in good judgment. 

Bob Doll, chief investment officer 

for Equities, BlackRock 



Bob Doll (Doll): I generally think of passive 

management as someone who says, “I just want broad representation 

of the market and I want to mimic the market.” 

Let’s say it’s the U.S. equity market—so I’m going to invest in 

an S&P 500 Index fund, and I’m going to put my money in it 

and the only risk I have is the risk of the market; I don’t have 

risk in terms of individual securities. 

In active management, the participant is saying, “I want 

exposure to the equity market, but I’m going to find an 

investment process or portfolio manager or mutual fund that 

I think, through my analysis and maybe through past performance, 

has the ability to outperform. I don’t want to just 

34 

March/April 2009

have the beta of the S&P 500, as a passive manager would 

have, I also want to have some alpha; in other words, I want 

to find someone who can beat that benchmark.” I think that’s 

the difference between the two. 

I think they begin to blur when passive participants begin 

to say, “Hmm, I think there’s a better way to do it and I’m 

going to try to get some alpha, even though I’m trying to be 

a passive manager.” The active manager can go the other way 

and have so much diversification that they end up with an 

indexed portfolio. And you can do that if you have too many 

products in your mix. 



Doll: I don’t think necessarily either. I think there’s a place 

for both. It depends on what you’re trying to accomplish. The 

buy-and-hold index investor—as opposed to the buy-and-hold 

traditional investor, or active investor—I don’t think is helped 

or hurt by that happening. That there’s more money being 

indexed is generally a function of institutions working on 

what their asset mix should look like, and increasingly they’re 

moving from a lot of active equities to a core that’s indexed or 

enhanced-indexed and then some satellite products that have 

high alpha. So I think that’s the reason, at least from where I 

sit, why there’s more active use of those products. 


Doll: I think at the end of the day you have to pick and 

choose stocks, so you do have to be specific about it if 

you’re not going to use packaged products. But at the 

same time, you have to step back and look at your portfolio 

and be careful about asset allocation. In other words, 

even I—as an active portfolio manager—if I didn’t look at 

the portfolio, and all I did was find good stocks, I could 

say, “Gee, I like this Energy company, I like that Energy 

company and, ooh, I like that Energy company,” and all of 

a sudden have a portfolio that’s half Energy, which would 

not be diversified. While you need to be granular on an 

individual security basis, you still have to step back and 

look at the portfolio. 

From a fund perspective, you’ve got to make sure you 

have some diversity. You don’t want all U.S. products, you 

don’t want all global products, you want a mix. You don’t 

want all big-cap, you want some small-cap. You have to have 

a portfolio that is a “useful blend,” if you will. 


Doll: From the business opportunities that we see, for sure 

not. Is it growing as fast as some other pieces of the investment 

business? No; that’s fair to say. But there are a lot of 

qualitative active management searches; there are a lot of 

retail investors who continue to look for good qualitative 

active managers. I think it is alive and well, but it is not the 

fastest-growing part of the equity business. 

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are “hot” <strong>Vanguard</strong> Dividend VIPERs 

Will be launched shortly 

PowerShares Dividend Achievers (Int'l) PID 6.3 Sep-05 

again after the tax Nasdaq Dividend Achievers 

Has not yet been filed 

UPDATES 

Crude realities 

Even though oil prices more than 

tripled since late 2001, most investors 

could watch but not partake. Gaining 

direct exposure to the commodity itself 

was a challenge for everyday investors 

who could not or did not want to trade 

futures. All that changed when the US 

Oil Fund LP (USO) began trading on 

the American Stock Exchange April 10, 

2006. For the first time, every Jane and 

Joe in the US can make a bet on crude 

www.indexuniverse.com/ETFR 

oil easily and cheaply. 

Like the recently launched Deutsche 

Bank Commodity ETF (DBC), USO is 

not a 1940 act company. Rather it is a 

commodity pool registered with the 

Commodity Futures Trading 

Commission and Securities and 

Exchange Commission. USO, managed 

by California-based Victoria Bay 

Asset Management, buys futures to 

track the price movement of West 

Texas Intermediate light, sweet crude 

oil. WTI is the primary benchmark for 

oil in the US. 

Issue No. 66 

May 2006 

IN THIS ISSUE 

Every Jane and Joe can now take a direct bet 

on oil, easily and relatively cheaply. USO, the 

first ETF to track the price of crude, launched 

in April on the Amex. Like the Deutsche 

Bank Commodity ETF, USO invests in futures. 

Cover 

The QQQQ twist: Nasdaq now offers two new 

takes on QQQQ. QQEW is an equal weighted 

version of the Nasdaq-100, while QTEC is an 

equal weighted version of technology stocks 

in the Nasdaq-100. According to John Jacobs, 

Nasdaq’s executive vice president, the ETFs 

were launched due to investor demand. 

Page 3 

ETFR’s Expert Portfolios have done well this 

quarter. At the end of March, the Tack 80 

was up 4.4%, the PMMA was up 4.3%, INR 

was up 6.1%, and the Dawgs was up 7.2%. 

In comparison, the Diamonds was up 4.6%, 

the Nasdaq 100 was up 6.4%, and the SPDR 

was up 4.7%. Each portfolio manager 

explains how and why his portfolio did well. 

Page 7 

Remember the largecap rally that never happened? 

ETFR looks at the 12 largecap ETFs in 

the market and finds that Rydex’s equal 

weighted version of the S&P 500 outperformed 

the others in the first quarter. 

Page 8 

ETFR’s monthly databank includes ETFs 

from around the globe. 

Page 13 

USO is an unlevered pool, “meaning 

if $100 million comes in, we buy 

$100 million of futures contracts,” said 

John Hyland, USO’s director of portfolio 

research. After the first month, the 

fund rolls the contracts forward, 

month after month, year after year. 

However, because the fund only has a 

small proportion to advance as margin, 

the balance of unused cash sits in 

short-term treasuries. 

“USO is calculated on a total return 

basis, which does not mean the price of 

the ETF equals the price of that 3 

ETFR 


The equalweight 

trend 



weighted S&P 500 ETF (RSP) so attractive. 

It has also carved a niche in an 

It’s not a great surprise that capweighted 

indexes and their respective increasingly crowded market for 

ETFs have lost some allure for investors PowerShares’ ETFs and a slew of equalweighted 

ETFs, like the recently 

during the last five or six years. “If 

you’re looking for long term market launched equal-weighted version of 

exposure, market cap-weighting is the QQQQ (QQEW) and State 10 

way to go,” says Srikant Dash, Standard 

& Poor’s index strategist. But, in this S&P 500 (April 2006) 

market, who wants market exposure? 

Cap Wt Equal Wt 

Just look at the S&P 500, the premier 

tradable index with some $1.1 tril- 

12 mo 15.42 22.42 

YTD 5.61 7.37 

lion tracking it and another $3 trillion 3 yrs 14.68 22.03 

to $4 trillion benchmarked to it. For the 5 yrs 2.70 8.64 

past five years, the cap-weighted version 

of the 500 returned 14.7% annual- 

Source: S&P 

10 yrs 8.94 11.69 

ized while its equal-weighted sibling 

returned 22.0%. 

As mid- and small-caps outperform 

large caps, equal- 

Modifed cap wt (as of April 2006) 

Equal wt vs Cap wt vs 

weighted and modified capweighted 

index-tracking ETFs 

Q1 '06 12 mo 3 yr 

RSP 2.76 22.13 20.90 

have taken center stage. It’s 

SPY 2.69 15.58 14.13 

an index methodology that 

PWC 1.02 23.44 21.39 

has made Rydex’s equal- 

UPDATES 

At long 

Currency ETF (FXE), SLV had been 

mired for months in blocking and tackling 

strategies devised by the Silver 

last, silver 

Almost one year to the day after Users Association. 

Barclays Global Investors filed an application 

with the Securities and Exchange that SLV will corner the market and 

Simply put, SUA’s plaint has been 

Commission for a silver ETF that tracks drive up the price of silver. At the end 

the price of silver, the product finally of 2005 according to the London 

began trading on the American Stock Bullion Market, an ounce of silver was 

Exchange at April’s close. Even though worth about $88.30. At the end of 

the Silver Trust (SLV) is based on a template 

introduced by the gold ETF in about $12.55, and on June 2, it closed 

April, when SLV launched, it was worth 

November 2004 and used by other at $12.15. On June 2, SLV was selling 

products since, like Rydex’s Euro for $121.75 per share. 

IN THIS ISSUE 

Finally, the iShares Silver Trust (SLV) is trading on 

the Amex after months of blocking and tackling 

by the Silver Users Association. SUA objected to 

the ETF because, or so the association heatedly 

claimed, SLV would corner the market and 

inflate the price of silver. It hasn’t so far: After a 

month of trading, the price of silver remains flat. 

Cover 

As investor appetite for dividend ETFs remains 

unabated, another such product began trading. 

There are now eight dividend ETF in the US 

Page 3 

ETFR takes a look at the April returns for its four 

Expert Portfolios. Year to date, XTF 80 was up 

9.8%, PMAA Momentum was up 7.3%, INR 

Moderate was up 6.9%, and Bobo’s Dawgs 

were up 9.9%. Looks like Bobo has done it 

again; but then, Bobo doesn’t have any clients. 

Page 4 

This month we include a market commentary 

on Alpha Lost and Found by James F Peters, CEO 

of Tactical Allocation Group. Over the past six 

years, many investors have been left smarting in 

the wake of the bear market and, writes Peters, 

have been left frustrated and feeling betrayed. 

Yet most advisories go with the general market 

direction. Peters discusses the search for alpha in 

today’s market and explains how ETFs can be 

effective tools in tactical allocation strategies. 

Page 7 

ETFR’s monthly databank looks at ETF performance 

from around the globe. 

Page 13 

Each share of SLV is worth 10 oz of 

silver, and its price reflects the price of 

silver owned by the Trust less expenses 

and liabilities. SLV’s management fee is 

50 basis points. (In comparison, the 

iShares COMEX Gold Trust (IAU) cost 

40 basis points.) Because the shares 

mirror the price of silver held by trust, 

SLV’s market price will be as unpredictable 

as the price of silver has historically 

been. 

This has been the year of commodity 

ETFs. Three other commodity ETFs 

trade on the Amex, DB 

3 

ETFR 


Get the most up-to-date, in-depth news, features and data on the exchange-traded funds (ETF) marketplace 

from the most respected and closely followed publication in the industry. 

Subscribe today to ETFR and see what you’ve been missing. 

Subscribe online at www.indexuniverse.com/subscriptions or e-mail subscriptions@indexuniverse.com. 

Issue No. 67 

June 2006 

ETFRJuly06.qxd 6/26/06 10:32 AM Page 1 

ETFR 


Gold investor’s 

choices widen 


With the gold rush now in its fifth year, 

the first US ETF to hold gold mining 

stocks (Canada has had one for three 

years) began trading on the American 

Stock Exchange May 22. While some 

investors lament that the ETF trading 

under ticker GDX made its appearance 

a year too late, it nonetheless allows 

them to buy a broad basket of 44 gold 

mining stocks (hedged and unhedged) 

for 55 basis points. 

And who better to design and manage 

such a fund than Van Eck Global? 

The New York-based boutique, which 

introduced the first gold-mining-oriented 

mutual fund in the US in 1968, has 

become known as one of the top 

money managers in precious metals 

since the 1970s when it made its name 

in that decade’s gold bull run. In addition, 

the shop also runs money in 

emerging markets, its smallest business, 

and in hard assets (like oil, natural gas, 

and timber), its largest. 

UPDATES 

A flurry of BRICs 

Emerging markets have not been 

immune to recent stock volatility, and 

their double-digit return hegemony of 

the past three years is now being challenged. 

While investors have been 

pouring money into emerging markets 

funds this year, advisors are saying that 

they may have just missed the rally. 

Returns for Brazil, Russia, India, 

and China, known as BRIC countries, 

have descended into negative 

territory during May. Despite 


One week after launch, GDX had 

attracted $54.9 million in assets— 

healthy perhaps for a nascent ETF but a 

mere pittance when compared with 

Van Eck’s gold mutual funds and managed 

accounts that total some $1 billion. 

Why would Van Eck want to launch a 

gold miners ETF? After all, GDX will 

directly compete with a Van Eck mutual 

fund that is also designed for US 

investors and that also invests in international 

gold mining stocks. “That’s the 

way the market is going,” says the 

firm’s gold strategist, Joe Foster, who 

also just happens to run its international 

gold miners mutual fund. “If we hadn’t 

done it, somebody else would 

have.” 

Comparing indexes 

GDX tracks the Amex’s Gold Miners 

index, GDM, which was launched at 

the end of 2004 and designed specifically 

for the ETF. Like so many 10 

declines, investor interest in the 

BRICs is high, and an ETF offering 

exposure to this group would be a 

welcome addition to the relatively 

small number of US mutual funds 

in the space. 

ETF newcomer Claymore Advisors of 

Lisle, Illinois, filed an application with 

the Securities and Exchange 

Commission late May for a BRIC ETF. 

(Claymore, by the way, has plans to 

launch four additional ETFs, all of which 

are in registration; page 3.) The proposed 

BRIC ETF tracks the Bank of New 

Issue No. 68 

July 2006 

IN THIS ISSUE 

Thinking of investing in the BRICs? ETF newcomer, 

Claymore Advisors, filed with the SEC in 

late May for an ETF tracking BONY’s BRIC index, 

which holds 50 ADRs and GDRs. Claymore, by 

the way, has plans to launch four additional 

ETFs, all of which nudge the ETF industry one 

step closer to active management. 

Cover 

Our four ETFR expert portfolios hit their first 

negative month this May. All are down for the 

month, although Bobo, the Dart-Throwing 

Monkey, who is something of an anomaly in 

this august group, is still ahead of his mates. 

XTF 80 is down 3.6%, INR Moderate is down 

3.3%, PMAA Momentum is down 3.2%, and 

Bobo’s Dawgs is down 2.9%. 

Page 4 

After four years of wending their way through 

the SEC, PlusFunds’ eight leveraged and 

inverse ETFs finally began trading on the Amex 

June 21. These ETFs deliver double the daily 

performance—either long or short—of the 

S&P 500, Nasdaq-100, DJIA, and S&P MidCap. 

Page 7 

PowerShares, the first ETF provider to offer 

enhanced indexes for its cadre of ETF, has filed 

for 31 (count ‘em) new ETFs including 10 RAFIbranded 

ETFs, 16 Intellidexes, and five standalones. 

Among the latter group, two are noteworthy: 

the much anticipated Nasdaq-100 dividend 

ETF and an India ETF. 

Page 8 

ETFR’s monthly databank includes ETFs from 

around the globe. 

Page 13 

York’s recently launched BRIC Select 

ADR index, which holds American and 

Global Depositary Receipts trading on 

the New York, American and Nasdaq 

stock exchanges. 

BONY’s BRIC index, which is modified 

cap weighted and a subset of the 

bank’s broader ADR index, holds 50 of 

the largest BRIC companies, including 

15 each from Brazil, India and China, 

and five from Russia. No one stock can 

exceed 23%, and no one country can 

exceed 45% of the index. The index is 

continuously calculated 

3 


35

How Long Can You 

Hold Leveraged ETFs? 

Measuring the error of expectations 

By Matt Hougan 

36 

March/April 2009

Leveraged and inverse exchange-traded funds were 

one of the fastest-growing segments of the exchangetraded 

fund industry in 2008. They were also one of the 

most controversial. 

The growth, at least, comes as no surprise. These “traders’ 

funds” are designed to deliver some multiple of the daily 

return of different benchmark indexes: either 300%, 200%, 

-100%, -200% or -300%, depending on the product. With global 

equity markets crashing and market volatility sky-high, 

any product that helped investors hedge their portfolios—or 

profit on the downside—was sure to be a hit, as these were. 

ProShares, the leading provider of leveraged and inverse 

ETFs, was the fastest-growing ETF company in the world in 

2008, with assets under management rising from $9.7 billion 

to $20.5 billion. 

As 2008 wound to a close, however, concerns arose about 

the performance of these funds. Tom Eidelman summed up 

the problems in his Jan. 12, 2009 article in Barron’s magazine 

(“One-day Wonders”): 

Suppose you had predicted—correctly, as it turned 

out—that the Chinese economy would slow following 

last summer’s Beijing Olympics, causing China’s 

stock markets to tumble. Also suppose that, to profit 

from your insight, you had invested in the ProShares 

UltraShort FTSE/Xinhua China 25, a leveraged exchangetraded 

fund (ticker: FXP) designed to go up by as much 

as twice the percentage that the FTSE/Xinhua China 25 

Index falls on a given day. 

When Chinese stocks crashed by 34% over the following 

four months, shouldn’t you have reaped a gaudy 

return around 68%? Not exactly. In fact, you would have 

lost 56%. 

FXP wasn’t an anomaly. In many of the most volatile corners 

of the market, inverse funds that “should” have been up 

big intuitively ended the year down instead. 

Take real estate. The Dow Jones U.S. Real Estate Index 

had a terrible year in 2008, falling 40.07%. The ProShares 

UltraShort Real Estate ETF (NYSE Arca: SRS) might have 

seemed like a smart way to play it. Its goal is to deliver -200 

percent of the daily return of that index. But instead of rising 

80 percent in 2008, as you might expect, SRS actually closed 

the year down 50 percent. 

Figure 1 highlights the five most surprising examples of 

full-year 2008 returns for leveraged and inverse ETFs. 

For an investor, being caught in one of these situations 

must have been hugely frustrating. Making the right call 

about the direction of the market is difficult. If you predicted 

one of these markets were going to fall, and then bought an 

ETF that promised to deliver -200% of the return of the index 

it tracked, you would have expected to earn a mint. To end 

up losing money … and in some cases, significant amounts 

of money … must have been infuriating. 

It’s important to note, however, that these results were 

not created by a “flaw” in the funds. These funds largely 

delivered on their stated objective, which is to provide -200% 

of the daily return of their benchmark indexes. The problem 

is that -200% of the daily return of an index is very different 

from -200% of the long-term return. 

Compounding 

The difference between daily and long-term returns is welldocumented 

in the literature surrounding leveraged and inverse 

funds. It all stems from the basic math of compounding. 

Suppose you have an index that starts out with a value of 

100. You also have a product that’s designed to provide 200 

percent of the upside exposure of that index. That product 

starts out with a value of $100. 

On Day 1, the index rises 10 percent to 110, and the 

product rises 20 percent to $120. Perfect. But on Day 2, 

the index falls 10 percent to 99, while the product falls 20 

percent to $96. After just two days, the problem is obvious: 

The index is down 1 percent, and the leveraged product is 

down 4 percent. 

Daily Change Index Investment (200%) 

Day 1 100 $100 

10% 10 $20 

Day 2 – Start 110 $120 

-10% -11 ($24) 

Day 2 – Finish 99 $96 

Net Change -1% -4% 

You can play with the numbers to make funny things happen. 

Suppose, for instance, that the index rose 20 percent on 

the first day, fell 25 percent on the second day and rose 15 

percent on the third day. 

Figure 1 

Largest Differentials Between 2008 Actual Returns And Linear Returns For Leveraged And Inverse ETFs 

ETF 

Ticker 

Source: IndexUniverse.com. Data as of December 31, 2008. Includes only ETFs with full-year 2008 returns. 

2008 

Return 

2008 

Index 

Return 

Linear 

(–200%) 

Return 

Differential 

ProShares UltraShort FTSE/Xinhua China 25 FXP –53.61% –49.35% 98.70% 152.31% 

ProShares UltraShort MSCI Emerging Market EEV –24.88% –53.33% 106.66% 131.54% 

ProShares UltraShort Real Estate SRS –50.00% –40.07% 80.14% 130.14% 

ProShares UltraShort Financials SKF 3.61% –50.40% 100.80% 97.19% 

ProShares UltraShort Oil & Gas DUG –9.02% –35.77% 71.44% 80.46% 


37

Daily Change Index Value Investment (200%) 

Day 1 100 $100 

20% 20 $40 

Day 2 – Start 120 $140 

-25% -30 ($70) 

Day 3 – Start 90 $70 

15% 13.5 21 

Day 3 – Finish 103.5 $91 

Net Change 3.5% -9% 

In this case, the index ends Day 3 up 3.5 percent, but the 

leveraged investment closes down 9 percent. 

Flip the numbers around and you can figure out what happened 

to funds like FXP and SRS. Compounding reared its ugly 

head, and what should have been up, ended up being down. 

In other words, the eventual returns on leveraged funds 

like these are “path-dependent.” It matters less where the 

underlying index ends the year, than how it got there. The 

more a market jumps around from day to day, the greater 

the eventual divergence between a leveraged or leveraged 

inverse ETF and the relevant multiple of the index return is 

likely to be. 

To its credit, ProShares and other providers of leveraged 

and inverse ETFs warned investors about this very phenomenon 

on their Web sites. The ProShares Web site, for 

instance, notes: 

Like most leveraged and short funds, ProShares are 

designed to provide a positive or negative multiple 

(e.g., 200 percent, -200 percent) of an index’s performance 

on a daily basis (before fees and expenses). 

Generally, these funds have achieved their daily objective 

with a high degree of accuracy and consistency. 

However, ProShares and other leveraged or short 

funds with daily objectives are unlikely to provide a 

simple multiple (e.g., 2x, -2x) of an index’s performance 

over periods longer than one day. 

It goes on to say that, “[i]n general, periods of high index 

volatility will cause the effect of compounding to be more 

pronounced, while lower index volatility will produce a 

more muted effect.” 

2008 was a period of historic volatility, and that caused 

the performance of leveraged and inverse ETFs to diverge 

significantly from a simple multiple of the index. The more 

volatile the index, the larger that divergence was over time. 

Media Fury 

Just because it’s easy to explain, however, doesn’t make it 

any more palatable for investors, and a media fury erupted in 

2009 surrounding the unusual performance of these funds. 

Paul Justice, CFA, of Morningstar, captured the zeitgeist in a 

Jan. 22, 2009 article titled, “Warning: Leveraged and Inverse 

ETFs Kill Portfolios.” In the piece, Justice tells investors that 

these funds are “appropriate only for less than 1% of the 

investing community.” 

He goes on to say, “If you’re hell-bent on using leverage 

for any period of time longer than a day, you’d be better off 

using a margin account in almost any real-world scenario.” 

The righteousness is justified, but it is also too simple. 

Just as these ETFs do not provide exactly 200 percent or 

-200 percent of the long-term returns of a benchmark index, 

neither do they go wildly aflutter in most situations after a 

single day. 

The leveraged and inverse ETFs have a number of advantages 

over a traditional margin account. For instance, you 

cannot lose more than your original investment in the fund. 

In addition, you never face margin calls, which you do face 

in margin accounts. 

The question, therefore, is simple: How long can you 

really hold these funds and still expect to earn returns that 

stay reasonably close to the linear leveraged or inverse 

results many investors desire? 

Methodology 

To answer that question, this study examines the historical 

performance of three of the first leveraged and inverse 

ETFs to launch in the U.S. These funds, all offered by 

ProShares, are designed to provide 200 percent, -100 percent 

and -200 percent of the daily return of the oldest and 

most established market index in the world: the Dow Jones 

industrial average. 

The funds are: 

• ProShares Ultra Dow30 (NYSE Arca: DDM) 

• ProShares Short Dow30 (NYSE Arca: DOG) 

• ProShares UltraShort Dow30 (NYSE Arca: DXD) 

The study compares the performance of these funds 

versus their benchmark indexes over the following periods: 

one day, one week (five trading days), one month (21 trading 

days), one quarter (63 trading days) and one year (251 

trading days). 

For each period, the study compares the performance 

of the ETF versus the comparable linear performance of its 

benchmark, adjusted for the leverage factor. For instance, 

when looking at the ProShares Ultra Dow30 ETF, the study 

compares the performance of the ETF with the following 

returns: 200 percent of the one-day return, 200 percent of the 

one-week return, 200 percent of the one-month return, etc. 

The study uses the net asset value of the ETF, adjusted 

for distributions, when making performance calculations. The 

choice to use NAV rather than share price was made because, in 

the early days of the study, liquidity was limited in these ETFs, 

causing share prices to occasionally deviate from the NAV. 

The index returns used in this study are price returns, 

not total returns. This creates a small positive skew in the 

results, suggesting that tracking error in the funds are more 

positive than they might otherwise have been. 

The study started at the earliest date that all three funds were 

trading and data was available (July 13, 2006) and ended on Dec. 

15, 2008. The study evaluates the time periods on a rolling basis: 

611 one-day periods, 607 five-day periods, 591 one-month periods, 

549 one-quarter periods and 361 one-year periods. 

One-Day Returns 

On a one-day basis, the funds did a very good job tracking 

their benchmarks, as shown in Figure 2. In the chart, a “posi- 

38 March/April 2009

Figure 2 

One-Day Tracking Error 

0.5% 

0.4% 

0.3% 

0.2% 

0.1% 

0.0% 

–0.1% 

–0.2% 

–0.3% 

–0.4% 

–0.5% 

–0.6% 

Sources: ProShares, Bespoke Investment Group. Data from 7/13/2006 to 12/15/2008. 

This figure compares the total returns of the ETF with the price returns of the index. 

Figure 3 

95% Interval One-Day Tracking Error 

0.5% 

0.4% 

0.3% 

0.2% 

0.06% 

0.09% 0.13% 0.1% 

DDM 

DOG 

DXD 

0.0% 

–0.1% 

–0.13% 

–0.06% –0.13% –0.2% 

–0.3% 

–0.4% 

–0.5% 

–0.6% 



tive” tracking error indicates that the fund outperformed its 

expected return, and a negative tracking error indicates that 

the fund underperformed its benchmark. 

The largest tracking error on both an average and absolute 

basis was in the leveraged-inverse product, DXD. The smallest 

average error was in the simple inverse product, DOG. 

Interestingly, a plurality of tracking errors was positive, 

particularly for the inverse funds. This may be because of the 

structure of the funds. These funds achieve their exposure 

in the market by buying options, futures or swaps. In each 

case, they only have to put up a portion of the value of their 

positions; the remaining cash can be invested in Treasuries, 

earning interest. As a result, they have a small but positive 

daily return. All else being equal, this will give them positive 

tracking error against the benchmarks used in this study. 

The tracking error throughout this study tended to have fat 

tails; i.e., most days produced little or no tracking error, while 

a few anomalous days had high errors. In Figure 3, the colored 

boxes indicate where 95 percent of all tracking error results 

landed. Outlier results are indicated by associated black lines. 

Figure 3 emphasizes that the vast majority of tracking 

error for all three funds was very small: less than 0.13 percent 

on an absolute basis. To put these figures in perspective, 

consider that 95 percent of the one-day returns for 

the Dow Jones industrial average during the period studied 

ranged from 3.1 percent to -3.2 percent. 

In sum, these funds follow through on their promise of 

delivering 200%, -100% and -200% of the daily return of their 

benchmark indexes. 

One-Week Returns 

As expected, tracking errors widen as you move to longer 

time windows. The results of the tracking error study for a 

one-week holding period are shown in Figure 4. Although 

the vast majority of results are still close to their target, the 

tails are fatter, with DXD in particular posting a handful of 

unusual results, including one that was more than 8 percent 

off its benchmark index. As a reminder, this does not mean 

the product was working incorrectly; it’s simply due to the 

nature of leveraged and inverse returns when measured for 

periods longer than one day. 

But while DXD—and to a lesser extent, DDM and DOG— 

experienced a few “bad” results, in most cases, the funds 

continued to perform well. As is the case for all longer time 

periods, DOG and DXD exhibit higher tracking error than 

DDM, suggesting it is harder to deliver accurate inverse 

returns than it is to deliver accurate leveraged returns. 

Figure 4 

1 

Figure 5 

One-Week Tracking Error 

35 

69 

103 

137 

171 

205 

239 

273 

307 

341 

375 

409 

443 

477 

511 

545 

579 

613 

10.0% 

8.0% 

6.0% 

4.0% 

2.0% 

0.0% 

–2.0% 

–4.0% 

–6.0% 

–8.0% 



95% Interval One-Week Tracking Error 

10.0% 

8.0% 

6.0% 

4.0% 

2.0% 

0.22% 

0.26% 0.54% 

DDM 

DOG 

0.0% 

–0.74% 

–1.00% 

DXD 

-2.0% 

–2.78% -4.0% 

-6.0% 

-8.0% 



The larger tracking errors shown in Figure 5 should be 

measured against the larger average move in the Dow over 

the one-week time frames. Ninety-five percent of the oneweek 

returns for the Dow during the period studied fell 

between 4.39 percent and -5.70 percent. The largest oneweek 

return was 16.91 percent, with the largest decline 

being -18.16 percent. 

Given those results, the tracking errors on both DDM 

and DOG seem quite acceptable. The vast majority of 

returns for these two funds fell within 1 percent of their 

expected result. DXD had more trouble, with returns straying 

as much as 2.78 percent from the linear result. But even 

DXD’s tracking bands were tolerable, given the relatively 

large moves in the Dow itself. 


39

One-Month Returns 

The trend of increased tracking error continued in the 

one-month analysis. The average error grew significantly, 

and the fat tails got fatter, particularly for DXD, which 

missed by as much as 16 percent on both the positive and 

negative side of the equation, as shown in Figure 6. 

The majority of tracking error was still positive, but the 

problems of large negative tracking error became more apparent. 

DXD, for instance, saw 37 cases (nearly 5 percent of results) 

where it trailed its benchmark by more than 5 percent. 

Figure 6 

Figure 7 

One-Month Tracking Error 

20.0% 

15.0% 

10.0% 

5.0% 

0.0% 

–5.0% 

–10.0% 

–15.0% 

–20.0% 



95% Interval One-Month Tracking Error 

0.54% 

DDM 

–2.83% 

DOG 

0.94% 1.94% 

–3.98% 

DXD 

–11.82% 

0.00% 



Figure 7 shows how far the margin of error grew at the 

one-month interval. Even for DDM, the double-leverage ETF, 

the 95 percent interval has expanded to show tracking errors 

as large as -2.83 percent. And for DXD, the tracking error has 

become truly significant: The 95 percent intervals stretched 

as low as -11.82 percent. 

These larger tracking errors must be compared with the larger 

moves in the Dow, of course. The largest absolute one-month 

return for the Dow during the studied period was 7.90 percent, 

with the largest one-month drop hitting -26.63 percent. The 

95 percent interval extends from a 5.86 percent return on the 

upside to a -15.37 percent return on the downside. 

Measured against moves of that size, the returns of DDM 

look quite good. DOG’s performance has wider errors, but 

they are still relatively contained given the broader movements 

in the Dow. DXD’s tracking error grows substantially, 

however. While we might have expected DXD’s tracking 

error to be 2X as large as DOG’s error (after all, it’s tracking 

-200 percent of the returns of the Dow), the downside error 

bands were nearly 3X as large on DXD as they were on DOG. 

Figure 8 

One-Quarter Tracking Error 

15.0% 

10.0% 

5.0% 

0.0% 

–5.0% 

–10.0% 

–15.0% 

–20.0% 

–25.0% 

–30.0% 

–35.0% 



Figure 9 

2.78% 

0.54% DDM 

–1.47% 

95% Interval One-Quarter Returns 

2.52% 

0.94% 

DOG 

–5.17% 

DXD 

4.74% 

–20.80% 

0.00% 



While DXD tracked its linear return well in many, many scenarios, 

there were clearly a significant number of instances 

where the tracking error was wider. 

Quarterly Returns 

Expanding out to look at quarterly returns, the results 

grew more extreme. DXD in particular exhibited a number 

of large errors, particularly on the downside, with negative 

tracking error extending as far as -30 percent. DOG also 

showed a sharp downward tail of negative tracking error, 

while DDM performed much better, as shown in Figure 8. 

Large errors remained when tightening to a 95 percent 

return band, as shown in Figure 9. DDM remained relatively 

tightly confined to its index, staying within 3 percent of the 

index in 95 percent of all cases. DOG did slightly worse, with 

a range that extended to -5.17 percent on the downside. But 

on DXD, the misses were huge, stretching all the way from 

4.74 percent to -20.80 percent. 

Again, these returns must be understood in the context 

of the larger returns of the index itself. Quarterly returns 

on the Dow for the period ranged from 13.49 percent on 

the upside to -35.05 percent on the downside. Ninety-five 

percent intervals stretch from 10.68 percent on the upside 

to -25.51 percent on the downside. 

At those levels, the returns of DDM and DOG are reasonable. 

But DXD remains an outlier, with large potential misses 

threatening returns. 

One-Year Returns 

When the study is extended out to one year, an interesting 

shift occurs. Not only do the outlier tracking errors 

40 March/April 2009

Figure 10 Figure 12 

One-Year Tracking Error 

60.0% 

50.0% 

40.0% 

30.0% 

20.0% 

10.0% 

0.0% 

–10.0% 

–20.0% 

–30.0% 

One-Quarter Tracking Error Vs. Expected Return, DXD 

80.0% 

60.0% 

40.0% 

20.0% 

0.0% 

–20.0% 

–40.0% 





Figure 11 Figure 13 

95% Interval One-Year Returns 

Quarterly Tracking Error Over Time, 

From July 2007–December 2008 

20.0% 

10.02% 

DDM 

–2.83% 

–4.91% 

11.43% 

DOG 

0.14% 

DXD 

23.52% 

–12.60% 

0.0% 

10.0% 

0.0% 

–10.0% 

–20.0% 

–30.0% 



grow significantly, but the skew of results shifts. As Figure 

10 shows, the fat tails in the one-year analysis are primarily 

on the positive side, particularly for DXD, which significantly 

beat its index in a number of different scenarios. 

This is shown again in the 95 percent confidence intervals, 

where the DOG ETF in particular shows no positive 

error at all in the 95 percent view, and both DXD and DDM 

skew sharply higher. In all instances, however, the bounds 

for error are large. 

Of course, the Dow showed great variability of returns 

during the time studied. On a one-year basis, the Dow delivered 

returns as high as 30 percent and as low as -41.82 percent. 

The confidence interval stretches from 22.13 percent 

to -37.12 percent. 

Even measured against the larger returns of the index, 

the tracking error shown by the funds is quite large. DXD in 

particular shows a wide variation in tracking error. 

–40.0% 



Return Patterns 

An important trend emerges for the inverse funds if you 

dig into when and where tracking error appears. For all the 

periods studied except one-year, tracking error grows larger 

… and more negative … as the expected return grows. 

Figure 12 showcases this point. It compares the tracking 

error of DXD to the fund’s expected return. The tracking error 

stayed generally flat-to-positive for all but the most extreme 

positive expected returns. Once those expected returns went 

parabolic—above, say, 20 percent—the tracking error got 

much more volatile and was generally negative. 

This held true for all time periods studied except for oneyear. 

In the one-year returns, tracking errors tended to be 

positive overall, and did not have the negative skew during 

large expected returns. 

Errors Skewed Toward Late-2008 

It is important to note that this study took place over a 

period of historic volatility, particularly in the latter half of 

2008. In fact, the vast majority of the large tracking errors 

recorded during this study took place in the latter half of 

2008. Had the study only extended through mid-2008, tracking 

errors would have been much smaller overall. 

Figure 13 shows quarterly tracking errors for all three 

funds as they occurred in time. As shown, the majority of 

large tracking errors occurred during the tail end of the 

study, when the chaos of 2008 started to impact returns. 

Unfortunately, when those large tracking errors appeared, 

they tended to appear on the negative side of the ledger. It 

is no surprise that this is when public concern about these 

products began to develop. 

How Long Can You Hold ProShares ETFs? 

The results of the study are clear. As you move further 

away from the targeted one-day time period, tracking error 

on these funds grows. The problem is substantially more 

acute for the leveraged-inverse fund (DXD) than it is for the 

straight leverage (DDM) or straight inverse (DOG) funds. 

In most market conditions, the funds stuck close to the 

simple long-term leveraged or inverse return of their index. 

But in the most volatile of markets, significant negative 

tracking error developed in some of the cases studied. 


41

Talking Indexes 

Hunting Alpha In The Dark 

Investors may need to brave the unfamiliar. 

By David Blitzer 

There is a story of a policeman walking his beat and 

coming upon an inebriated gentleman searching for 

something under a street lamp. The cop stops and 

asks the man what he is looking for. He answers he has 

dropped his keys. The cop asks where he dropped them, 

the man points to the other side of the street away from 

the lamppost, saying “over there.” The cop asks why he is 

looking under the streetlight if he dropped his keys on the 

other side. “The light is better here,” is the reply. 

As silly as this tale is, it may say something about finding 

alpha in the markets of 2008 and 2009. We’re still used 

to looking under the streetlight and finding our keys with 

ease, just as we easily found investment performance for 

much of the last three decades. There were a few pauses 

when the light flickered around the 1990–1991 recession 

and a momentary blackout in the 2000–2002 bear market. 

But now, when we need to look where it is pitch dark, why 

do we insist on looking under the streetlight? 

A recent piece in the Financial Times hints at the 

changed times. It commented that in 1932, GM managed 

to show a scant profit of $165,000 and paid out 

$63 million in dividends from its cash reserves even 

though sales were 78 percent below their peak level of a 

few years before. It wasn’t that GM had anticipated the 

Great Depression; far from it. Rather, Alfred Sloan, GM’s 

chairman, remembered that the recession of 1920–1921 

ended the career of GM’s founder and almost bankrupted 

the company. In response, GM had been hoarding cash 

ever since. Moreover, in the four years prior to 1929, 

fixed assets doubled, two-thirds of earnings were paid 

out as dividends and the cash hoard still grew. 

For many companies, times have changed. Not only 

isn’t cash hoarded, cash on the balance sheet is seen as a 

sign of unimaginative or even lazy management. Debt is 

embraced. Even as investment banks were converting to 

commercial banks, accepting more regulation and looking 

forward to getting deposits (more liabilities), a common 

refrain was that their leverage would be forced down, 

reducing their profits and lowering their stock prices— 

although those same stock prices had already collapsed. 

Cash hoards were old-fashioned, unnecessary and irrelevant; 

besides, they were inimical to profits. Think one 

more moment about the American automobile industry: 

Until recently, no one seemed to see why Ford started 

hoarding cash as best it could beginning in late 2006. Two 

years ago it seemed like a silly idea. Last summer’s credit 

crunch changed opinions. 

Leverage may have its place, even in the pitch-dark 

investment world we find ourselves in now. But it is not a 

guaranteed route to higher profits. We need to remember 

that leverage means taking on real risks that include a real 

chance of failure. One thing to look for in the dark might be 

less leverage. While searching, consider the impact of cash 

on a portfolio. While pension funds are often modeled with 

allocations of 60 percent equities, 30 percent bonds and 10 

percent cash, recently, not enough attention was paid to the 

42 March/April 2009

impact of the cash portion. Raising the cash proportion can 

substantially lower the volatility of the overall portfolio: The 

annualized standard deviation of a portfolio that invested 

100 percent in the S&P 500 on Nov. 30, 2007, and sold on 

Dec. 22, 2008, was 40 percent; had that same portfolio been 

50 percent S&P 500 and 50 percent cash, the standard deviation 

would have been 16 percent. 

Note that I am carefully not referring to the standard 

deviation as “risk.” Market volatility is certainly a form 

of risk, and one that, under the street lamp, we were 

confident we understood. But risk is far more than market 

volatility; just ask Bernie Madoff’s investors, who 

discovered that the $50 billion they invested with Madoff 

simply wasn’t there. 

Risk comes in myriad forms, some of which we seem 

to be rediscovering in these markets. When risk is equated 

with volatility, investors tend to think that the only 

risk is whether they get the returns today or tomorrow. 

Waiting a bit longer for the price to rise or the proceeds 

to be available is a risk, and the wait does have a cost, 

but everyone always believed the returns would eventually 

arrive. Market turmoil has made us look at some 

history to see how long “eventually” might be. From its 

peak in October 2007, the S&P 500 is down about 52 percent, 

about the same as the drop in the 2000–2002 and 

1974–1975 bear markets. History includes 1929–1932, 

when the market fell 85 percent. More-telling numbers 

may be when the market reached a new high. Following 

the 2002 low of the S&P 500, the market made a new high 

five years later, in 2007. After the 1932 low, the market 

did not set a new record until the mid-1950s. Risk is more 

than just waiting a little while. 

One characteristic of the boom that just ended was 

a love of statistical and financial models. Investments 

and entire portfolios were built on complex mathematical 

models. Some were so complex there was no way to 

price or trade them without the help of computers. There 

is nothing wrong with models, statistics or computers, 

although their reputations have been damaged somewhat. 

The problem is that we’ve forgotten what we first learned 

about using—or believing in—models. Suppose one builds 

an investment model that assumes that returns are normally 

distributed—with “normally distributed” used in the 

statistical sense that the returns follow a pattern described 

by a particular formula called the Normal, or Gaussian, 

distribution. Assuming that the returns actually follow the 

Normal distribution makes the math easy; Microsoft Excel 

has the Normal distribution built in. What we may have 

forgotten is that the results we get depend on the assumptions 

we made—if the returns don’t follow a Normal distribution, 

the results are wrong. 

Recent market activity presents a strong case that stock 

market returns do not follow the Normal distribution— 

there are far too many large moves or, in the language of 

financial models, fat tails. Investing in the dark should raise 

a larger concern than just needing to assume a Normal or 

some other distribution: With events moving quickly and 

economies and markets changing rapidly, maybe there isn’t 

any distribution available for our analyses. We keep hearing 

that the Fed is trying another new idea every day, that 

economic policy must break new ground and that market 

turmoil like this hasn’t been seen for decades—could we 

be operating in a world where there is no useful historic 

data on which to build models? We just don’t know. 

Some of the turmoil of 2008 extended beyond the 

markets. Scandals seemed to spring up all over the place. 

The biggest was Bernard Madoff’s $50 billion Ponzi 

scheme which, hopefully, set a record that will stand for 

a long time. It dwarfed, or buried, stories about lawyer 

Marc Dreier who was alleged to have defrauded a group 

of hedge funds for $100 million. Competing with both 

of these was the governor of Illinois, Rod Blagojevich, 

who reportedly offered to sell a seat (the very seat of the 

president-elect, no less) in the U.S. senate. 

Whether people are most gullible and free with their 

money during booms and make it easy for con men to 

work their magic, or whether collapsing markets tend to 

reveal the truth, is not clear. It is clear that fraud and deceit 

are not among the biggest investment risks that most of 

us worry about. We may be concerned about naive stock 

recommendations, misguided analysts reports or companies 

that go bankrupt. But we still see stories of outright 

financial fraud such as Enron as rare exceptions. Despite 

the fact that newspapers reported a rise in shoplifting during 

the 2008 Christmas shopping season compared with 

2007, at the same time that retailers saw smaller crowds, 

one should not suggest that bear markets and recessions 

will immediately lower the moral character of business 

and finance. Nevertheless, the possibility that there is no 

company behind the stock someone just bought may be 

another thing to consider when investing in the dark. 

Think back to the street lamp. Maybe the reason we 

all want to search for our missing keys under the light is 

because that is what we know. Once we cross the street 

and face the prospect of investing in the dark, we must 

admit there is a lot we don’t know and can’t anticipate. 

In booms and bull markets, everyone knows everything. 

In these times, that’s not so. So how can we do better, 

despite the dark? Don’t assume leverage is good or that 

hoarding some cash is a sign of weakness; understand as 

best we can what the investment is all about; and, always 

remember that the assumptions matter. Last, and certainly 

not least, remember that just as a bust follows the boom, 

the recovery and the next boom will follow the bust. 

Endnote 

1 Tony Jackson, “Past Performance is no guarantee of future knowledge,” Financial Times, December 22, 2008, page 16. 


43

A New Way To Look 

At Correlations 

The incremental diversification effect measure 

By Gregory Hight 

44 

March/April 2009

Investors’ ability to capitalize on the benefits of the diversification 

effect seems threatened: 

• Economic globalization has reduced international asset 

return independence [Dunis and Shannon, 2005]. 

• Economic shocks spill over international borders at precisely 

the times investors most need the diversification 

benefits of asset allocation [Longin and Solnik, 1995; 

Calvo and Mendoza, 1999; Kodres and Pritsker, 2002]. 

• Correlations between asset classes and subclasses vary 

across time [Krein, 2007]. 

Observations surrounding the equity-market slide that 

accelerated in September 2008 reinforce these findings. 

These phenomena diminish the diversification effect, which 

is the reduction in portfolio risk attributable to imperfect 

correlations between the returns of different portfolio assets 

[Markowitz, 1991; Adair et al., 2006]. Because the diversification 

effect is central to the portfolio management process 

[Israelsen, 2007], these threats to the diversification effect 

underscore the importance of a valid, reliable and direct 

measure of diversification effect that portfolio managers can 

use to aid decision making. 

This article describes a parsimonious diversification effect 

metric and a related metric of incremental diversification effect. 

These metrics require no inferences about the diversification 

effect because they are direct measures. The results, expressed 

as percentages, are easy for investors to understand. As this 

article illustrates, portfolio managers can apply these diversification 

effect metrics to portfolio construction and management. 

Why Asset Allocation Alone Is Not Sufficient 

Investors apply asset allocation mainly to help reduce 

portfolio risk. Part of the asset allocation rationale rests on 

the common sense underlying the familiar idiom, “Don’t put 

all your eggs in one basket.” This process allocates resources 

to at least nominally different asset classes. Unfortunately, 

asset allocation’s idiomatic rationale is overrated: Nominal 

asset allocation alone fails to confer a meaningful effect on 

one important form of risk—portfolio volatility. 

That’s not to say that asset allocation is useless or harmful. 

Asset allocation’s greatest benefit is its capacity to mitigate 

concentration risk. Also, if the investor gets lucky, maybe 

one or more of the selected asset classes will outperform a 

benchmark. This potential return benefit of asset allocation 

is not a risk management strategy at all; it’s a seat-of-thepants 

attempt at increasing the rate of return. 

Concentration risk is easy to manage because asset allocation 

is its simple solution. The greatest risk threat comes 

from portfolio volatility. Volatility is rapid price change. It 

creates havoc with investors whether prices go up or down. 

We can improve the effectiveness of asset allocation as a 

risk-reducing process by measuring the diversification effect 

in the process of planning asset allocation and evaluating 

portfolio performance. 

A Review Of Diversification Effect Metrics And 

Their Shortcomings For Practical Applications 

Diversification effect measurement is not new. Research 

articles (e.g., Conover et al., 2002; Abraham et al., 2001, 

among many others) and some commercial investment analytics 

programs publish correlation coefficient matrices to 

help gauge the diversification effect. These matrices list the 

correlation coefficients of all assets in a given portfolio, and 

they can be onerous. For example, a modest 10-asset portfolio 

yields 45 different correlation coefficients. How are we to 

draw confident conclusions about a portfolio’s diversification 

effect by poring over a table with so many coefficients? 

Correlation coefficient matrices and correlations in general 

present another problem when we use them to gauge 

portfolio risk in an applied setting: Correlation is not a direct 

measure of the diversification effect. It measures covariance. 

Measuring covariance as a proxy for the diversification effect 

is somewhat like measuring absolute price change as a 

proxy for rate of return. Certainly, price change is a critical 

input for rate of return. But a direct rate-of-return measure 

is better. Likewise, a direct measure of diversification effect 

is better than its proxies. 

Researchers often apply more-complicated measures of 

the diversification effect that surely serve their specific 

purposes well. Sharpe [1992], among others, applied factor 

models and the coefficient of determination to quantify 

the diversification effect. Mills [1996] used co-integration 

to measure the tendency for two stationary time series to 

move together in a long-term equilibrium state. From an 

applied perspective, these are indirect metrics because they 

only permit inference about risk reduction as a function of 

imperfect correlations. 

Another approach to diversification measurement quantifies 

the gain in expected returns by allocating from a benchmark 

portfolio to a portfolio located at the same risk level 

on the efficient frontier [Li, et al., 2003; Kandel et al., 1995]. 

This tactic depends on the efficient frontier component of 

Modern Portfolio Theory. Yet, for practical applications, we 

need not appeal to theory if we can directly measure the 

diversification effect. 

The Diversification Effect Metric 

None of the diversification effect metrics above, although 

surely suitable for research purposes, seems as parsimonious 

for practical application as the direct measure of diversification 

effect used by Cheng and Roulac (2007) and De 

Wit (1997). The calculation directly taps the diversification 

effect without the need for inference or theoretical assumptions 

and without onerous matrices. In this calculation, the 

diversification effect lies in the relationship between two 

forms of the portfolio standard deviation equation. The 

first form is the generally accepted form, which weights 

each asset’s standard deviation by its allocation and its 

correlation coefficient with every other asset in the portfolio. 

The second form assigns only allocation weights, 

effectively assuming a perfect positive correlation between 

all portfolio assets. This form is the allocation-weighted 

mean standard deviation of each asset in the portfolio. The 

diversification effect resides in the difference between the 

results of these two equation forms, because the equations 

partition the effect of imperfect correlations. The generally 

accepted form incorporates the effect of correlation coef- 


45

ficients, and the allocation-weighted mean standard deviation 

does not. Cheng and Roulac expressed the relationship 

between the two standard deviation forms in an equally 

weighted portfolio as a ratio of portfolio standard deviation 

to the allocation-weighted mean standard deviation of 

individual assets: 

Where: 

B = Diversification effect 

j 

σ p 

= Portfolio standard deviation weighted by allocations 

and the 

B 

correlations 

= B = 

between each asset in the portfolio 

σ j 

= Allocation-weighted 

σ 

mean p portfolio standard deviation 

B = 

This σratio p 

= σB divides p 

= 

σ 

j 

the generally accepted form of the standard 

deviation, which incorporates both allocation and correlation 

weights, by the allocation-weighted mean standard 

deviation, σ which only weights asset standard deviations with 

allocations. j 

= σ 

σ p 

= 

= 

j B = 

It indicates the percent of risk remaining after 

quantifying the effect of correlation coefficients on portfolio 

risk. For the present σ article, = we prefer: 

σ j 

p 

= 

Where: 

DE = Diversification effect 

⎜ σ 

By subtracting the Cheng 

⎛ 

and Roulac ratio from one, 

σ ⎞ 

DE describes the percent of risk 

p 

DE = 1− 

⎜ ⎟ reduction that occurs as a 

function of DE correlation = coefficients. A larger result indicates 

greater diversification effect. 

⎝ σ 

j ⎠ 

Incremental ⎛ σ diversification ⎞⎛ 

σ ⎞ effect can also be useful, 

p 

p 

particularly = IDE 1− 

⎜in = the 1−⎟process ⎜ 

− DE 

⎟of −evaluating DE expected effects of 

−1 

adding or exchanging j 

j−1 

−1 

portfolio assets. Incremental diversification 

effect is the change ⎛ σ in diversification ⎞ 

⎝σDE j = −1 

⎠⎝ 

σ 

j ⎠ 

p 

effect that occurs 

after assets IDEare = added 1− 

⎜or subtracted 

⎟ − DE 

from j−1 

a portfolio. We can 

express incremental diversification 

σ 

j−1 

as: 

Where: 

IDE 

DE 

= Incremental j−1 

= j−1 

diversification effect 

DE j-1 

= Diversification effect of the original portfolio (the 

IDE = 

portfolio before portfolio assets are changed) 

46 

We can simplify the equation as follows: 

Where: 

σ σ 

p 

p 

B = B = 

σ σ 

j 

⎛σ 

⎞ ⎛σ 

⎞ 

p 

p 

DE = 1DE 

− ⎜ = 1−⎟ 

⎜ ⎟ 

⎝ σ σ 

j ⎠ j ⎝= 

σ 

j ⎠ 

⎛σ 

⎞ 

p 

DE = 1− 

⎜ ⎟ 

⎝ j ⎠ 

DE = DE = 

IDE 

⎝ 

IDE = IDE = 

⎛ σ 

IDE − ⎜ 

IDE = ⎝σ 

DE 

j−1 

= 

⎛σ 

−⎛ 

σ ⎞ 

p−1 

⎞ 

p 

−1 

−σ 

p 

IDE = IDE ⎜ = ⎜ ⎟ ⎟ 

⎝ σ 

j⎝ 

σ 

−1 

j−1 

⎠ ⎠ 

⎛σDE 

j−1 

= ⎞ 

p−1 

−σ 

p 

IDE = ⎜ ⎟ 

⎝ σ 

j−1 

⎠ 

σ p−1 

DE 

j 

σ p 

B = σ 

p 

= 1 − DE 

⎟ 

j−1 

j−1 

= 

⎠ 

⎞ 

⎟ 

⎠ 

σ⎛ 

p−1 

σ 

p= 

−1 

−σ 

IDE = ⎜ 

⎝ σ 

p 

⎞ 

⎟ 

⎠ 


j−1 

σ p−1 

= 

σ p-1 

= The original portfolio standard deviation weighted 

by allocations and the correlations between each asset in 

the portfolio 

This ratio indicates the change in the percent of 

risk reduction that occurs as a function of correlation 

coefficients after adding or exchanging portfolio assets. 

Incremental diversification uses the relationship between 

the allocation-weighted mean standard deviation of the 

original portfolio (σ j-1 ) and the generally accepted form of 

the standard deviation of the new portfolio (σ p ), effectively 

using the first standard deviation as the basis for the second 

and all subsequent comparisons. 

Data 

The following illustrations use daily market data for the 

seven-year period January 2, 2002, through December 31, 2008. 

The source for long-term bond data is daily yields for Moody’s 

Seasoned Baa Corporate Bond Yields reported at stlouisfed.org 

by the Federal Reserve Bank of St. Louis. The SPDR S&P 500 ETF 

(SPY) serves as the U.S. large-capitalization proxy; the iShares 

Russell 2000 Index Fund (IWM) serves as the small-capitalization 

proxy; and the iShares MSCI EAFE Index Fund (EFA) serves as 

the non-U.S. large-capitalization proxy. The commodities data 

source is the Dow Jones-AIG Commodity Total Return index 

reported at djindexes.com by Dow Jones. 

When credit, equity or commodities markets recognized 

different holidays or closed for other reasons, we use linear 

interpolation to fill in missing data. Daily return calculations 

apply dividend and coupon reinvestment, and the 

process assumes assets are held for the entire seven-year 

period without rebalancing. The process of calculating 

returns for the Moody’s Seasoned Baa Corporate Bond 

Yields assumes bonds were purchased at par on January 

2, 2002, and coupons were paid semiannually on the first 

trading day on or after July 1 and January 1, each year. For 

all assets, daily return and standard deviation calculations 

are based on daily value changes that include cumulative 

change in the value of reinvested coupons and dividends. 

We omit the effects of taxes and transaction fees, although 

exchange-traded fund values are net of expense ratios and 

other costs incurred by the fund. Betas are calculated using 

the standard deviation of the asset or portfolio, standard 

deviation of the S&P 500 Index and the corresponding 

correlation coefficient. Sharpe ratios are calculated using 

the daily five-year geometric mean yield of three-month 

Treasury bills, which serves as the riskless rate of return. 

The three illustrations that follow use historical data and 

make changes in portfolio components the way a portfolio 

manager might evaluate prospective portfolio changes in a 

real investment portfolio. However, investors cannot directly 

invest in the indexes used in these illustrations. The reader 

should bear in mind that all data used in portfolio analysis 

and projections are historical. Extrapolations from historical 

data, although practically necessary, must be done with caution, 

particularly when the extrapolations are for relatively 

short periods beyond the historical database and when the 

historical database is a relatively short period. One of the

implications of these facts is that investors should regularly 

reevaluate their data and assumptions. And, of course, past 

performance cannot guarantee future results. 

Diversification Effect Illustration 

The first illustration applies the diversification effect 

metric to a theoretical, equally allocated three-asset portfolio 

consisting of proxies for U.S. large-capitalization 

stocks, long-term bonds and non-U.S large-capitalization 

stocks. Later we add the proxy for the Russell 2000 

Small-Capitalization Index and add the Dow Jones-AIG 

Commodity Total Return Index. Our interest rests primarily 

with diversification effect metrics, though we calculate 

other useful portfolio statistics. 

Figure 1 reports performance data for assets used in the 

present and subsequent illustrations and Figure 2 displays 

correlation coefficients for the present and subsequent illustrations. 

Figure 3 illustrates the effects of equally allocating 

the SPY, 30-year bond and EFA into a three-asset portfolio. 

This allocation yields a diversification effect (DE) of 19.19%, 

which means that correlation coefficients between all three 

portfolio assets decrease portfolio risk by 19.19% compared 

to the allocation-weighted mean standard deviation that 

assumes +1.00 correlations. The portfolio beta falls below 

the arithmetic mean beta (see Figure 1) of the three assets, 

also because of the influence of these correlations. The 

portfolio Sharpe ratio exceeds the arithmetic mean Sharpe 

ratio because the portfolio standard deviation has been 

reduced by imperfect correlations between the assets. 

This illustration explains the effect of correlation coefficients 

on at least three commonly used portfolio metrics, and 

the DE statistic explains the other two. Clearly, the statistics 

are related by the effect correlations have on them. Next we 

change portfolio composition and monitor the results. 

Incremental Diversification Effect Illustration: 

Adding An Asset With Higher Correlations 

In this illustration, we add a fourth asset class with 

historically high returns and higher correlation coefficients 

with other assets in the portfolio. The rationale for 

adding this asset might be to increase expected returns, 

reduce beta, increase the Sharpe ratio or increase DE. We 

add incremental diversification effect (IDE) in the analysis 

to evaluate the diversification benefit of adding a fourth 

Figure 1 

Individual Assets 

Geometric Mean 

Daily Return 

Daily Standard 

Deviation 

Beta 

(vs. S&P 500 Index) 

Sharpe Ratio 

SPDR SPY -0.0074% 0.0136 +0.9864 -0.0128 

30-Year Bond +0.0095% 0.0050 -0.0898 -0.0010 

iShares EFA +0.0126% 0.0151 +0.9702 +0.0017 

iShares IWM +0.0043% 0.0159 +1.0505 -0.0036 

DJ-AIG Commodity +0.0253% 0.0114 +0.1215 +0.0135 

Sources: Bloomberg, Dow Jones Indexes and the Federal Reserve Bank of St. Louis 

Figure 2 

Equally Allocated Three-Asset Portfolio Correlation Matrix 

SPDR SPY 30-Year Bond iShares EFA iShares IWM 

SPDR SPY +1.0000 — — — 

30-Year Bond -0.2213 +1.0000 — — 

iShares EFA +0.8754 -0.1632 +1.0000 — 

iShares IWM +0.8890 -0.2290 +0.7842 +1.0000 

DJ-AIG Commodity +0.1557 -0.0221 +0.2526 +0.1202 


Figure 3 

Equally Allocated Three-Asset Portfolio 

Geometric Mean 

Daily Return 

Allocation- 

Weighted 

Standard Dev. 

Portfolio Standard 


Diversification 

Effect (DE) 

Beta 

(vs. S&P 500 

Index) 

Sharpe Ratio 

+0.0056% 0.0112 0.0091 0.1919 0.6125 -0.0049 



47

asset. We select IWM as a proxy for the Russell 2000 

small-capitalization index. Again, we impose the equal 

allocation constraint. Figure 4 displays results. 

Comparing results in Figures 3 and 4, the new four-asset 

portfolio return practically adds no additional return or 

improvements in the Sharpe ratio, and beta rises. The new 

asset’s correlations with the other two equity assets (Figure 

2) are high. Because of the higher correlation coefficients, 

the new DE is smaller than the three-asset DE, which means 

that the new four-asset portfolio delivers less diversification 

effect than the original three-asset portfolio. Because the 

new asset adds so much risk without a corresponding decline 

in the correlation coefficients, the four-asset portfolio standard 

deviation is higher than the standard deviation of the 

three-asset portfolio. Consequently, IDE is negative. We conclude 

that adding this new asset compromises DE. 

Incremental Diversification Effect Illustration: 

Adding An Asset With Lower Correlations 

If the investor decides the risk of adding IWM exceeds its 

benefits, the decision might be to replace IWM with an asset 

that co-varies less with existing portfolio assets. The Dow 

Jones – AIG Commodity TR Index (DJAIGTR) correlation coefficients 

range from +0.2526 to -0.0221. This near-zero range 

could yield a favorable IDE if we use it to replace IWM. Figure 

5 illustrates results of replacing IWM with DJAIGTR. 

In this scenario, compared to the original three-asset 

portfolio, the portfolio standard deviation falls, and 

because of the larger DE attributable to DJAIGTR’s nearzero 

correlations, IDE is positive. Beta drops and the 

Sharpe ratio increases. This portfolio is clearly superior to 

the three-asset portfolio and the IWM four-asset portfolio 

according to these measures. 

The purposes of this illustration and the one before it 

are to show how DE and IDE can help evaluate portfolio 

risk in particular and, in general, how DE and IDE can be 

used with other portfolio statistics to make better-informed 

investment decisions. 

These illustrations are not intended to advocate use of 

DJAIGTR-linked investments. In scenarios with different 

details, we would not necessarily realize favorable changes 

in returns, betas and the Sharpe ratios when we also realize 

an increase in DE or a positive IDE. Results for different time 

periods using these assets could be very different than the 

results obtained in the present illustrations. 

Figure 4 

Geometric 

Mean Daily 

Return 

Equally Allocated Four-Asset Portfolio: High Returns And Higher Correlations 

Conclusions 

Diversification effect is a potential result of asset allocation 

and a function of imperfect correlation coefficients between 

the returns of portfolio assets. Meaningful diversification effect 

may not result from asset allocation alone, so it follows that 

active risk management requires measurement. This article 

illustrates how a direct diversification effect metric provides 

useful information about important portfolio properties. 

The first illustration started with a three-asset portfolio. 

When we added the fourth asset, less imperfect correlation 

coefficients yielded a negative incremental diversification 

effect. The fourth asset’s relatively high correlations with existing 

assets diminished any risk-attenuating benefit that might 

have been gained by adding the asset. A secondary lesson in 

this second illustration reminds us that adding nominally different 

assets does not necessarily increase diversification effect. 

Replacing the original fourth asset with a low-correlated 

asset enabled a positive incremental diversification effect. 

In this example, the values of other important portfolio 

properties improved. 

DE metrics presented here directly measure diversification 

effect and do not require any inference or dependence 

upon theoretical assumptions. They also do not duplicate the 

information value of other metrics. 

Diversification effect measurement should not be confused 

with total risk measurement. If an investor wants to 

change a portfolio’s total risk level, the standard deviation is 

the correct metric. However, if an investor wants to capital- 


Weighted 

Mean Standard 


Portfolio 

Standard 



Effect (DE) 

Incremental 


Effect (IDE) 

Beta (vs. S&P 

500 Index) 

Sharpe Ratio 

+0.0052% 0.0124 0.0104 0.1642 -.1149 0.7229 -0.0046 


Figure 5 

Geometric 

Mean Daily 

Return 

Equally Allocated Four-Asset Portfolio: Low Returns And Lower Correlations 


Weighted 

Mean Standard 


Portfolio 

Standard 


+0.0112% 0.0113 0.0079 0.2974 +0.1029 0.4503 +0.0015 



Effect (DE) 

Incremental 


Effect (IDE) 

Beta (vs. S&P 

500 Index) 

Sharpe Ratio 

48 

March/April 2009

ize on diversification effect or evaluate the extent to which a 

certain allocation of certain assets reduces risk as a function 

of imperfect correlation coefficients, DE provides necessary 

and sufficient information because it directly measures diversification 

effect. Likewise, if an investor wants to evaluate the 

impact of changing portfolio assets on diversification effect, 

IDE provides necessary and sufficient information. 

One perspective might hold that the variability of correlation 

coefficients diminishes the information value of DE and 

IDE. Research on the relationship between real estate investment 

trusts (REITs) and the S&P 500 Index suggests that 

correlations increase after REIT down months and decrease 

after REIT up months [Chandrashekaran, 1999]. Correlations 

among stocks increase during market downturns [Campbell, 

Koedijk and Kofman, 2002]. Also during stock market 

downturns, stock-bond correlations turn negative, but they 

approach unity during stock market upturns [Gulko, 2002]. 

Speidell and Sappenfield (1992) report rising correlations 

among developed markets due to economic convergence 

and interdependence. We would expect similar variability in 

other modern portfolio statistics such as returns, standard 

deviation of returns, beta and Sharpe ratios. 

Yet all of these caveat examples provide evidence that 

favors actively measuring and managing diversification 

effect. None of these facts concerning variability of markets 

compromises the value of measuring diversification effect 

any more than uncertainty about the future compromises 

the value of planning. These facts are all the more reason to 

directly measure diversification effect. 

References 

Abraham, A, Fazal, J, and Seyyed, A A (2001) “Analysis of diversification benefits of investing in the emerging gulf equity markets,” Managerial Finance, 27, (10/11), 47-57. 

Adair, A, McGreal, S and Webb, J R (2006) “Diversification effects of direct versus indirect real estate investments in the U.K.” Journal of Real Estate Portfolio Management, 12 (2), 85-90. 

Calvo, G A and Mendoza, E G (1999) “Regional contagion and the globalization of securities markets,” NBER Working Papers 7153, National Bureau of Economic Research, Inc. 

Campbell, R A C G, Koedijk, K C G and Kofman, P II (2002) “Covariance and correlation in international equity returns,” Financial Analysts Journal, 58 (1), 87-94. 

Chandrashekaran, V (1999) “Time-series properties and diversification benefits of REIT returns,” The Journal of Real Estate Research, 17 (1), 91-112. 

Cheng, P and Roulac, S E (2007) “Measuring the effectiveness of geographical diversification,” Journal of Real Estate Management, 13 (1), 29-44. 

Conover, C M, Friday, H S and Sirmans, G S (2002) “Diversification benefits from foreign real estate investments,” Journal of Real Estate Portfolio Management, 8 (1), 17-25. 

De Wit, D P M (1997) “Real estate diversification benefits,” The Journal of Real Estate Research, 14 (2), 117-136. 

Dunis, C L and Shannon, G (2005) “Emerging markets of south-east and central Asia: Do they still offer a diversification benefit?” Journal of Asset Management, 6 (3), 168-190. 

Gulko, L (2002) “Decoupling,” Journal of Portfolio Management, Spring, 59-66. 

Israelsen, Craig (2007) “The benefits of low correlation,” Journal of Indexes, 10 (6), 18-26. 

Kandel, S, McCulloch, R and Stambaugh, R (1995) “Bayesian inference and portfolio efficiency,” Review of Financial Studies, 8, 1-53. 

Kodres, L E and Pritsker, M (2002) “A rational expectations model of financial contagion,” The Journal of Finance, 57 (2), 769-799. 

Krein, D (2007) “Mapping a market for correlation,” Journal of Indexes, 10 (6), 33-35. 

Li, K, Sarkar, A. and Wang, Z. (2003) “Diversification benefits of emerging markets subject to portfolio constraints,” Journal of Empirical Finance, 10 (1), 57-80. 

Longin, F and Solnik, B (1995) “Is the correlation in international equity returns constant: 1960–1990?” Journal of International Money and Finance, 14 (1), 3-26. 

Markowitz, H. (1991) “Portfolio Selection,” Blackwell Publishing. 

Mills, T C (1996) “The Econometrics of the ‘Market Model’: Cointegration, Error Correction and Exogeneity,” International Journal of Finance & Economics, 1(4), 275-286 

Sharpe, W F (1992) “Asset allocation: Management style and performance measurement,” Journal of Portfolio Management, Winter, 7-19. 

Speidell, L S and Sappenfield, R (1992) “Global diversification in a shrinking world,” The Journal of Portfolio Management, 19 (1), 57-67. 


49

News 

Pimco Rolls Out New Global 

Bond Index; Fund Coming 

Pacific Investment Management Co. 

launched a new global benchmark in 

January that rethinks the way global 

bond markets are indexed. A mutual 

fund tracking the index is forthcoming. 

The Pimco Global Advantage Bond 

Index (GLADI) weights bonds by gross 

national product rather than traditional 

market-cap sizing. Pimco says that a 

GDP methodology will help investors 

avoid pricing bubbles that are prone 

to develop for a benchmark trying to 

monitor markets worldwide. 

The GLADI consists of five regional 

benchmarks, as well as the global 

umbrella index. The regions, and their 

weights in the global index, are as follows: 

U.S. (27.4 percent of the broad 

index), Eurozone (22.6 percent), Japan 

(9.8 percent), Emerging Markets (27.9 

percent) and other industrialized economies 

(12.3 percent). 

The Pimco GLADI consists of a mix of 

derivatives and fixed-income instruments, 

including corporate bonds and Treasury 

Inflation-Protected Securities, U.S. agency 

mortgage-backed securities, and interest 

rate swaps, among other investment 

vehicles. Each of those regional categories 

will be rebalanced every year. The GLADI’s 

subindexes that break down the different 

bond constituents by type will be rebalanced 

and reconstituted quarterly. 

This marks Pimco’s first foray into 

bond indexing. It is the largest manager 

of fixed-income assets in the world. 

ETFs experienced record inflows in 2008 

despite the most challenging market 

environment in recent memory. 

UBS To Buy AIG’s 

Commodity Index Business 

UBS Investment Bank announced in 

January it has entered into a binding 

agreement to purchase the commodity 

index business of AIG Financial Products 

Corp., including AIG’s rights to 

the DJ-AIG Commodity Index, which is 

used as the benchmark for a number of 

broad commodity ETFs. 

The purchase price for the transaction 

is $15 million, payable upon 

closing, and additional payments of up 

to $135 million over the following 18 

months based upon future earnings of 

the purchased business. The closing is 

subject to a number of regulatory and 

other conditions. No assurances can be 

given that any such conditions will be 

satisfied. 

The transaction is expected to close 

by May 2009. 

Record ETF Inflows In 2008 

Exchange-traded funds experienced 

record inflows in 2008, despite the 

most challenging market environment 

in recent memory. 

Investors poured more than $178 

billion in new money into ETFs for 

the year, according to data from the 

National Stock Exchange. For comparison, 

Emerging Portfolio Funds Research 

says that investors pulled $320 billion 

out of conventional mutual funds over 

the same time period. 

Despite the inflows, total assets 

under management for the U.S. ETF 

industry fell by 13 percent over yearago 

levels, to close at $539 billion. 

Trading volume soared, however, 

rising 70 percent over 2007 levels, 

which was itself up 114 percent over 

2006. ETFs now account for 30–40 percent 

of the total daily traded volume in 

the U.S. equity market. 

Grail Files For More- 

Active Active ETFs 

Grail Advisors has requested approval 

from the Securities and Exchange 

Commission to launch two new ETFs, 

both of which are designed to press a 

more active mandate than is currently 

available in the ETF market. 

In a filing dated Jan. 14, the San 

Francisco-based asset manager proposed 

the creation of the Grail American 

Beacon Large Cap Value ETF and 

50 

March/April 2009

the Grail American Beacon International 

Equity ETF. Neither would follow an 

index, and both portfolios would be 

subadvised by longtime mutual fund 

manager American Beacon. 

Importantly, the new funds won’t 

carry any restrictions on trading. That 

would put it in the same ballpark as 

the PowerShares Active Mega Cap Fund 

(NYSE Arca: PMA). PMA theoretically 

can trade at any time. But PowerShares 

has said that it anticipates the fund 

executing trades only on a monthly 

basis over the longer term. The new 

Grail-sponsored ETFs will follow a more 

flexible mandate, although whether 

they will wind up trading as much as 

their mandates allow is still to be determined. 

The company says target ranges 

for turnover rates for each fund will 

be addressed in the future as specific 

managers are selected and investment 

strategies are formalized. 

According to Grail executives, the 

funds will be entirely transparent, and 

their expense ratios will be competitive. 

While the large-cap value fund 

will be benchmarked to the Russell 

1000 Value Index, the actively managed 

foreign stock ETF will seek to beat the 

MSCI EAFE Index. 

INDEXING DEVELOPMENTS 

S&P Launches Indexes 

Tracking U.S. CDS Markets 

Standard & Poor’s launched a set 

of indexes designed to deliver greater 

transparency into the private credit 

derivative swap (CDS) market in mid- 

January. The Standard & Poor’s CDS 

U.S. Indices will track the price of CDS 

contracts on top U.S. companies as well 

as investment-grade corporate debt and 

high-yield bond issues. 

Credit default swaps are private contracts 

between parties that act like 

insurance in case of default by a company 

on its bonds. It has become a 

popular way for institutional investors 

to hedge their bets—or to speculate 

on a firm’s failure—most notably of late 

in financial services. The agreements 

guarantee a certain payout if a company 

defaults on its debt. In return, participants 

in such swaps make regular payments 

for a specified period. Indexes 

tracking activity in CDS markets essentially 

provide real-time gauges of how 

markets are pricing the possibility that 

a big bank or asset manager might face 

Pull Quote Pull Quote Pull Quote 



serious financial problems. 

The benchmarks include the following: 

• The S&P 100 CDS Index. It includes 

the most liquid CDS market participants 

in the S&P 100 Stock Index. It’s 

expected that it will include about 

80–90 different names, each weighted 

based on its corresponding weight 

in the S&P 100. 

• The S&P CDS U.S. Investment Grade 

Index. It is an equal-weighted benchmark 

of 100 highly liquid investmentgrade 

U.S. corporate credits. 

S&P launched a set of indexes 

designed to deliver greater 

transparency into the private 

CDS market. 

• The S&P CDS U.S. High Yield Index. It is 

also equal-weighted and holds about 

80 liquid so-called “junk”-rated corporate 

credits. 

S&P is using CMA DataVision, the 

credit information specialist, as its primary 

source of pricing for the indexes. 

The trio of new indexes is designed 

to underlie investment products such 

as index funds, index portfolios, and 

derivatives. 

NASDAQ Launches TARP Index 

The NASDAQ OMX Group rolled out 

its Government Relief Index in early 

January. The new index will follow not 

only companies getting financial backing 

through the Troubled Asset Relief 

Program but also any firm receiving 

direct government investments from 

other programs. Presumably, that could 

include President Obama’s proposed 

$775 billion in additional bailout support 

to boost jobs and the economy. 

Potential benefactors in business 

www.journalofindexes.com March/April 2009 51

from such moves could include 

industries focused on infrastructure 

improvements and alternative energy 

sources, among others. 

The new index includes firms that 

have received a direct investment 

from the U.S. government greater 

than $1 billion. NASDAQ says it plans 

to launch a series of related indexes 

in the future. 

Markit Revamps 

Bond Index Pricing 

Markit has announced that it will 

implement changes to the rules governing 

the consolidation of prices 

for the Markit iBoxx fixed-income 

indices. The indices are calculated 

daily, using price contributions from 

10 banks that are consolidated by 

Markit using stringent filters and 

consolidation algorithms. 

Recent volatility and a reduction 

in liquidity in the fixed-income markets 

have resulted in diminished 

News 

price contributions and greater variance 

among prices. This, in turn, 

has led to the rejection of a greater 

number of prices due to the Markit 

iBoxx consolidation rules. 

To solve the problem and improve 

pricing in illiquid and volatile markets, 

Markit has updated its rules. 

Starting Dec. 29, an additional step 

was introduced in the calculation 

of the daily index price whereby a 

new “Control Price,” based upon a 

forward matrix model, will serve as 

a proxy price for bonds affected by 

low liquidity or disparate pricing. 

FTSE Takes Over 

Italian MIB Indices 

FTSE Group announced in January 

it has agreed to terms to take 

over the existing equity indices for 

the Italian market on behalf of Borsa 

Italiana as of March 30, 2009. 

The calculation of the main 

benchmark, the S&P/MIB index, will 

be unchanged in the transition, 

although it will be re-branded as 

the FTSE MIB. FTSE will also create 

a new suite of investable equity 

indices covering market segments 

and sectors. 

S&P partnered with Borsa Italiana 

back in 2003 to create the S&P/MIB 

index, which replaced the exchange’s 

MIB 30 index. The S&P/MIB is the 

basis for a handful of ETFs in Europe 

as well as the NETS S&P/MIB Index 

Fund (NYSE Arca: ITL), which was 

launched in the U.S. in 2008. 

Pakistan Removed 

From MSCI Indexes 

MSCI Barra has announced it is 

removing Pakistan from its MSCI 

Emerging Markets Index. The index 

provider still calculates a stand-alone 

benchmark for the country, but it is no 

longer included in broader indexes. 

The reason, according to an MSCI 

press release, is the market “floor” 

that was instituted on Aug. 27 that 

prevented Pakistan-listed securities 

from trading below their levels 

on that day. According to MSCI, it 

“resulted in the practical shutdown 

of the Pakistani equity market,” 

skewing prices and wreaking havoc 

on the exchange’s transparency. 

Although the floor rule was lifted 

on Dec. 15, the removal from the 

MSCI indexes was effective Dec. 31. 

MSCI has said it would revisit the 

issue if Pakistan’s markets should 

return to normalcy, but implied it 

may be reclassified as a frontier market 

instead of an emerging market. 

The calculation of the main benchmark, 

the S&P/MIB index, will be unchanged 

in the transition, although it will be 

re-branded as the FTSE MIB. 

FTSE Expands 

EPRA/NAREIT Indexes 

In mid-January, FTSE expanded its 

FTSE EPRA/NAREIT Global Real Estate 

Index Series to include 12 emerging 

markets indexes. The indexes 

were created in partnership with the 

European Public Real Estate Association 

(EPRA) and the National Association 

of Real Estate Investment Trusts 

(NAREIT). They track publicly traded 

equity REITs and listed property 

52 

March/April 2009

companies. 

The new emerging market indexes 

cover a total of 70 securities from 

13 emerging markets: Brazil, China, 

Egypt, India, Indonesia, Malaysia, 

Mexico, the Philippines, Poland, 

South Africa, Taiwan, Thailand and 

Turkey. 

The following are among the 

newly launched indexes: 

• FTSE EPRA/NAREIT 

Emerging Index 

• FTSE EPRA/NAREIT Emerging 

EMEA Index 


Europe Index 


Middle East/Africa Index 


Americas Index 


Asia Pacific Index 

AROUND THE WORLD OF ETFS 

ProShares Jumps Into 

Currencies & Commodities 

ProShares added 12 inverse and 

leveraged commodities and currency 

ETFs to its lineup in late 2008. The 

new products are ProShares’ first 

forays into commodities and currency 

ETFs. 

Six of the funds are “Ultra” funds, 

aimed at delivering 200 percent of 

the daily return of the underlying 

benchmarks. The other six funds 

are “UltraShort” and offer -200 percent 

of the daily return of those 

same benchmarks. 

Each of the new funds charges an 

expense ratio of 0.95 percent. 

The first batch of funds was 

launched Nov. 25 and included the 

following ETFs: 

• ProShares Ultra DJ-AIG Commodity 

(NYSE Arca: UCD) 

• ProShares UltraShort DJ-AIG 

Commodity (NYSE Arca: CMD) 

• ProShares Ultra DJ-AIG Crude 

Oil (NYSE Arca: UCO) 

• ProShares UltraShort DJ-AIG 

Crude Oil (NYSE Arca: SCO) 

• ProShares Ultra Euro (NYSE 

Arca: ULE) 

News 

• ProShares UltraShort Euro 

(NYSE Arca: EUO) 

• ProShares Ultra Yen 

(NYSE Arca: YCL) 

• ProShares UltraShort 

Yen (NYSE Arca: YCS) 

The second batch was launched 

Dec. 4: 

• ProShares Ultra Gold (NYSE 

Arca: UGL) 

• ProShares UltraShort Gold 

(NYSE Arca: GLL) 

• ProShares Ultra Silver (NYSE 

Arca: AGQ) 

• ProShares UltraShort Silver 

(NYSE Arca: ZSL) 

VIX-Tracking ETNs On The Way 

At long last, it looks like there 

will soon be exchange-traded products 

tied to the much-followed 

CBOE VIX Volatility Index, or VIX. 

Barclays Capital, the unit of Londonbased 

Barclays PLC and a sibling of 

Barclays Global Investors, has filed 

to launch a pair of exchange-traded 

notes that will track the futures 

based on the VIX. 

There’s no word yet when the 

iPath S&P 500 VIX Short-Term 

Futures and the iPath S&P 500 VIX 

Mid-Term Futures ETNs will debut, 

but it could be within weeks rather 

than months, from the looks of the 

most recent filing. 

According to the filings, the 

new VIX ETNs will charge net fees 

to investors of 0.89 percent per 

year and trade on the NYSE Arca 

exchange. Both ETNs will continuously 

roll over contracts. 

The short-term iPath will trade 

one- and two-month VIX futures. 

The benchmark’s goal is to maintain 

a weighted average of one month, 

according to the prospectus. The 

midterm iPath will rotate among 

four-, five-, six- and seven-month 

contracts as they come due, shooting 

for a weighted average maturity 

of five months. 

The VIX Index is calculated based 

on the prices of put and call options 

on the S&P 500. The filing noted 

that futures on the VIX Index provide 

investors the ability to invest 

in forward volatility based on their 

view of the future direction or 

movement of the VIX Index. 

The ETNs’ underlying benchmarks 

were created by Standard & Poor’s 

and have limited backtested performance. 

Also, it notes that VIX 

futures have only traded freely since 

March 2004, “and not all futures of 

all relevant maturities have traded at 

all times since that data.” 

During a two-year period ended 

in late December 2007, backtested 

performance graphs included in the 

documents show the short-term VIX 

benchmark would have doubled in 

value. In the same time frame, the 

S&P 500 Total Return Index actually 

lost value. 

The question for investors— 

and it is a big one—is how well 

these rolling futures contracts will 

do in tracking the VIX itself. The 

spot VIX is hugely volatile, spiking 

on days of market crisis and 

then quickly receding back to quieter 

levels. Many believe that VIX 

futures do not adequately capture 

the near-term volatility revealed in 

the VIX itself. Still, these will be 


March/April 2009 53

etter than nothing. 

<strong>Vanguard</strong> To Add International 

Small-Cap Fund 

In December, <strong>Vanguard</strong> Group 

announced it had filed papers with the 

SEC to launch the <strong>Vanguard</strong> FTSE All- 

World ex-US Small-Cap Index Fund. 

The fund will come in Institutional, 

Investor and ETF share classes. 

The absence of a small-cap international 

option among its funds has 

been an issue for investors in <strong>Vanguard</strong>’s 

funds for years. <strong>Vanguard</strong> 

executives reportedly have said they 

were focusing on picking the right 

index and making sure it provided 

access to the most investable areas 

of what can be a highly illiquid overseas 

marketplace. 

The FTSE All-World ex-US Small- 

Cap ETF will come with an expense 

ratio of 0.38 percent. 

Market Vectors Family 

Adds Indonesia ETF 

The largest economy in Southeast 

Asia now has its first ETF, with the 

January launch of the Market Vectors 

Indonesia Index ETF (NYSE Arca: IDX). 

The new fund’s expense ratio is 

expected to wind up at 0.71 percent. 

According to Van Eck, Indonesia 

has one of the lowest correlations 

to developed markets among 

emerging markets. 

IDX’s underlying index had 25 

stocks, entering 2009, each of which 

was based in Indonesia or had at 

least 50 percent of its revenues generated 

within that country’s borders. 

Banks made up 31 percent of its 

constituents; Energy 15.7 percent; 

Telecom 12.7 percent and Materials 

11.4 percent. 

Direxion Launches Continue 

A little more than a month after 

launching the most highly leveraged 

ETFs yet, Direxion Shares expanded 

its lineup in December with six 

funds covering the international 

and U.S. technology markets. It followed 

up in January with another 

pair of funds covering the domestic 

News 

mid-cap segment. 

So far, the Direxion ETFs have 

been launched in pairs, with the 

“Bull” fund of each pair seeking to 

capture 300 percent of the daily 

returns of the underlying index and 

the “Bear” fund offering the inverse. 

The funds launched in December 

are tied to the MSCI EAFE Index, the 

MSCI Emerging Markets Index and the 

Russell 1000 Technology Index, while 

the pair of funds launched in January is 

tied to the Russell MidCap Index. 

The launches bring the total number 

of DirexionShares on the market to 18. 

The new funds include the following: 

• Direxion Developed Markets 

Bull 3x Shares (NYSE Arca: DZK) 

• Direxion Developed Markets 

Bear 3x Shares (NYSE Arca: DPK) 

• Direxion Emerging Markets 

Bull 3x Shares (NYSE Arca: EDC) 

• Direxion Emerging Markets 

Bear 3x Shares (NYSE Arca: EDZ) 

• Direxion Technology Bull 3x 

Shares (NYSE Arca: TYH) 

• Direxion Technology Bear 3x 

Shares (NYSE Arca: TYP) 

• Direxion MidCap Bull 3x 

Shares (NYSE Arca: MWJ) 

• Direxion MidCap Bear 3x 

Shares (NYSE Arca: MWN) 

Each carries a net expense ratio 

of 0.95 percent. 

WisdomTree Turns 

To Large-Cap Growth 

WisdomTree Investments expanded 

its offerings into a new area by 

launching an ETF focused squarely on 

large-cap growth stocks on Dec. 4. 

The WisdomTree LargeCap 

Growth ETF (NYSE Arca: ROI) differs 

from the large-cap growth pack 

mainly through its use of earnings to 

weight stocks in its portfolio. ROI’s 

index selects and weights around 

300 stocks based on four growth 

factors: earnings per share, sales 

per share, book value per share and 

stock price per share. By contrast, 

the Russell 1000 Growth Index uses a 

combination of price-to-book values 

and projected earnings estimates to 

select stocks for its index. 

ROI charges an expense ratio of 

0.38 percent. 

RevenueShares Rolls 

Out ADR Fund 

RevenueShares launched the RevenueShares 

ADR Fund ETF (NYSE 

Arca: RTR) on Nov. 21. 

RTR tracks the RevenueShares 

ADR Index, a custom benchmark 

designed by the ETF company to 

take a revenues-based slant on market-cap-weighted 

methodologies. 

The rules-driven process of RTR 

reweights the constituent securities 

of the S&P ADR Index according to 

the revenue earned by the companies 

in that index, subject to certain 

tax diversification requirements. 

The resulting RevenueShares ADR 

Index is designed to contain the 

same securities as the S&P ADR 

Index, but in different proportions. 

In addition to the EAFE countries, 

it also covers Canada, Mexico and 

South America. 

RTR comes with an expense ratio 

of 0.60 percent. 

AirShares ETF Launches 

The second exchange-traded 

product listed in the U.S. providing 

access to the global carbon emissions 

markets launched Dec. 15. 

The AirShares EU Carbon Allowances 

Fund (NYSE Arca: ASO) is actually a 

commodity pool that tracks a basket of 

exchange-traded futures contracts for 

European Union Allowances (EUAs). 

Each contract provides for delivery of 

1,000 EUAs at a specified price. Each 

EUA permits the holder to emit 1 

tonne of carbon dioxide. 

The new “ETF-like” product holds 

contracts that expire each December, 

beginning in 2009 and extending 

through 2012. As contracts approach 

their December expiration, the fund 

sells expiring contracts and replaces 

them with contracts of later expirations, 

according to XShares Advisors, 

which is sponsoring the shares. 

54 

March/April 2009

News 

ASO started with $5 million in 

seed money and charges an expense 

ratio of 0.85 percent annually. 

ETF Securities To 

Enter U.S. Market 

ETF Securities, a leading provider 

of commodity-based exchange-traded 

products in Europe, is entering 

the U.S. market. 

The London-based company has 

more than $6.5 billion in assets under 

management and more than 100 different 

products trading in Europe. 

On Dec. 19, the company filed papers 

with the SEC for the right to launch a 

silver bullion ETF in the U.S. 

The fund itself appears to be similar 

to existing precious metals ETFs. 

It will hold silver bullion as its sole 

asset, in much the same way as the 

iShares Silver Trust (NYSE Arca: SLV). 

There is no word yet on how much 

the fund will charge in expenses or 

when the fund will list. 

CME Group announced 

that hardwood pulp 

index futures and 

options on futures would 

begin trading 

on the CME 

Globex platform. 

BACK TO THE FUTURES 

CME Group Achieves 

Record Volume In 2008 

CME Group, arguably the world’s 

largest derivatives exchange, traded 

a record 3.3 billion contracts in 

2008, representing $1.2 quadrillion 

in notional values. The average daily 

volume for the year was nearly $13 

million contracts, up 4 percent from 

2007. It should be noted, however, 

that the CME Group’s ADV for the 

fourth quarter of 2008 was down 14 

percent from the prior-year quarter 

and down 22 percent year-over-year 

for December, indicating that the 

year did not close on a high note. 

The exchange’s E-mini family of 

index-based futures saw their ADV 

increase 37 percent during the year 

to 3.5 million contracts. The E-minis 

represented nearly 27 percent of the 

exchange’s total ADV in 2008, up 

from just 20 percent in 2007. 

Although the E-minis are not the 

only index-based futures trading at the 

CME Group, they do have the highest 

volumes of its index-based products. 

The E-mini S&P 500 contracts saw 

their total volumes for the year jump 

nearly 53 percent over 2007 to 634 

million contracts in 2008. In addition 

to the S&P 500, there are E-mini 

contracts tied to the NASDAQ-100, 

the MSCI EAFE and the Russell 2000, 

among other major indexes. 

CME Group Lists 

Hardwood Pulp Contracts 

CME Group announced in December 

that hardwood pulp index futures 

and options on futures would begin 

trading on the CME Globex platform 

as of Jan. 12, 2009. 

The exchange already lists softwood 

pulp index futures contracts 

that launched in September 2007. 

The cash-settled hardwood pulp 

contracts are tied to the PIX BHKP 

Europe Index from FOEX Indexes 

Ltd., a provider of pulp and paper 

price indexes based in Finland. The 

index tracks the prices of bleached 

hardwood kraft pulp. 

KNOW YOUR OPTIONS 

CBOE Hit 1.2 Billion In 2008 

The Chicago Board Options 

Exchange saw its volume jump 26 

percent over 2007 to 1.2 billion 

contracts in 2008 for an ADV of 4.7 

million contracts. That’s the highest 

annual volume in the CBOE’s 

35-year history. 

The volumes for cash-settled index 

options were up 13 percent over 

2007 to about 259 million contracts 

in 2008, an ADV of a little more than 

1 million contracts. ETF options saw 

their volume jump far more dramatically, 

up 55 percent over 2007 

to a total for the year of roughly 330 

million contracts (an ADV of 1.3 million). 

The exchange’s market share on 

index options is 89 percent, but just 

30 percent on ETF options. 

Among the index and ETF 

options, there were some stand- 



55

outs in terms of volume. The volume 

of options on the Financial 

Select SPDR (NYSE Arca: XLF) was 

up 283 percent over 2007 to nearly 

26 million contracts for the year. 

Options on the S&P 500 Index saw 

their volumes increase more than 

13 percent over 2007 to 179 million 

contracts. 

CBOE Lists Options 

On Gold, Silver ETFs 

The CBOE listed options on the 

iShares COMEX Gold Trust (NYSE 

Arca: IAU) and the iShares Silver 

Trust (NYSE Arca: SLV) on Dec. 8. 

The designated primary market 

maker for these ETF options is Barclays 

Capital. 

IAU and SLV hold physical gold 

bullion and silver bullion rather than 

futures contracts. The contracts, however, 

are not the first options on a 

precious metals ETF—that distinction 

goes to the SPDR Gold Trust ETF 

(NYSE Arca: GLD), which saw options 

tied to it launch in June 2008. 

FROM THE EXCHANGES 

Clearing Corp. Moves To 

Improve ETF Trading Efficiency 

The Depository Trust & Clearing 

Corporation (DTCC), the central 

clearing agency for the U.S. funds 

industry, began accepting cash-only 

creations and redemptions for ETFs. 

Previously, creations and redemptions 

for ETFs using commodities, 

foreign equities, credit default 

swaps and exchange-traded notes 

were not eligible for processing at 

DTCC subsidiary National Securities 

Clearing Corporation (NSCC), which 

serves as a central counterparty 

guarantee for investors. 

NSCC also trimmed the settlement 

cycle for ETF participants, 

offering an optional shortened 

settlement cycle of one day (trade 

day + 1) instead of three (trade 

day + 3) for ETF transactions. 

That change better mirrors markets 

where underlying securities 

of ETFs already have a shorter 

News 

settlement cycle, such as the commodities 

market, as well as some 

fixed-income categories. 

LSE Group Reports 89% ETF/ETC 

Trading Increase In 2008 

ETF and ETC trading figures for 

2008 at the London Stock Exchange 

and Borsa Italiana showed a strong 

increase. The value traded on the 

London Stock Exchange reached 

£65.8 billion (€82.2 billion), up 89 

percent over the 2007 total. The 

total number of ETF and ETC trades 

was 1.8 million, a 20 percent yearover-year 

increase. By contrast, the 

total value of all trades at the LSE 

group (including all instruments 

and asset classes) fell by 11 percent, 

when compared with 2007. 

During December 2008, the value 

traded in ETFs and ETCs was up 110 

percent on the previous December, 

reaching £6.1 billion (€6.4 billion), 

while the number of trades grew 47 

percent to 150,273. 

ON THE MOVE 

ProFunds Hires Chief 

Investment Officer 

Asset manager ProFunds Group 

has plucked Todd Johnson from 

index and quantitative institutional 

money manager World Asset Management 

to be chief investment 

officer. 

The CIO position has been somewhat 

of a revolving door at Pro- 

Funds and its ETF group ProShares. 

Johnson is the fourth official to hold 

the CIO title in the past three years. 

George Foster, who had been director 

of portfolios, assumed the CIO 

role in early 2008, and heads back to 

spearhead product development. 

World Asset Management has a 

large footprint in fixed income, and 

Johnson could help ProFunds extend 

its lineup of bond ETFs. World Asset 

also manages more than $13 billion 

for institutions. As institutions 

increase their use of ETFs, ProFunds 

and ProShares could be looking to 

gain a greater share of that market. 

Mazzilli Joins IndexIQ As Advisor 

Paul Mazzilli, one of the first 

dedicated ETF analysts, who helped 

build Morgan Stanley’s team tracking 

the industry in the U.S., has 

landed at alternative investments 

shop IndexIQ. 

The Rye Brook, N.Y.-based indexing 

developer appointed Mazzilli, 

whose departure from Morgan Stanley 

became public in November, as 

a senior advisor and member of the 

company’s advisory board. 

Mazzilli, who joined Morgan 

Stanley in 1975, oversaw analysis 

of actively managed closed-end 

funds as well as passively managed 

index-linked exchange-traded 

products at the struggling financial 

services firm. He also guided the 

management of Morgan Stanley’s 

Strategic Equity Portfolios (STEP) 

products and asset allocation models 

using ETFs. 

At the time of his stepping down 

from full-time duties at Morgan 

Stanley, Mazzilli was believed to be 

looking for part-time assignments as 

a board-level advisor to ETF-related 

firms he found interesting. 

DeAngelo Exits WisdomTree 

Ray DeAngelo, who served as 

director of ETF distribution at WisdomTree 

since July 2005, has left 

the independent ETF provider. His 

responsibilities have been assumed 

by Luciano Siracusano, who has 

been promoted from director of 

research to director of sales and 

chief investment strategist at the 

firm, in a move designed to streamline 

the business and better align 

the research and sales efforts. 

DeAngelo joined the start-up ETF 

provider in the summer of 2005, 

before it had launched its first products. 

He previously spent three years 

as head of institutional sales at Barclays 

Global Investors’ iShares unit. 

Jeremy Schwartz, formerly the 

deputy director of research at WisdomTree, 

has replaced Siracusano as 

director of research at the firm. 

56 

March/April 2009

Global Index Data 

Selected Major Indexes Sorted By 2008 Returns March/April 2009 

Index Name 2008 2007 2006 

USTREAS Constant Mat Treasury 20 

Barclays US Treasury Long 

Barclays US Government Long 

Barclays US Government 

Citi WGBI 

Barclays Fixed Rate MBS 

Barclays US Govt/Credit 

Barclays US Aggregate 

Barclays Global Aggregate 

ML US Treasury Bill 3 Mon 

BLS CPI US 

Barclays US Treasury US TIPS 

Barclays Municipal 

Barclays US Credit 

DJ Transportation Average 

Barclays US Corporate HiYield 

DJ Utilities Average TR 

DJ Composite Average 

Russell 2000 Value 

S&P SmallCap 600/Citi Value 

JSE Gold South Africa 

S&P Smallcap 600 

Dow Jones Industrial 

S&P SmallCap 600/Citi Growth 

Russell 2000 

S&P MidCap 400/Citi Value 

S&P 500/Citi Growth 

S&P 100 

Dow Jones - AIG Commodity 

S&P Midcap 400 


MSCI Pacific 


S&P 500 

Russell 3000 

DJ Wilshire 5000 

Russell 1000 

S&P MidCap 400/Citi Growth 

FTSE NAREIT Equity REITs 

Russell 1000 Growth 



DJ Wilshire REIT 

S&P 500/Citi Value 

MSCI World 

Goldman Sachs Natural Resources 

MSCI EAFE Growth 

MSCI EAFE 

MSCI World Ex US 

MSCI EAFE Value 

MSCI Europe 

Goldman Sachs Commodity 

MSCI EAFE Small Cap 

MSCI World/Real Estate 

MSCI Pacific Ex Japan 

MSCI EM 

Citi ESBI Capped Brady 

MSCI Austria 

MSCI Ireland* 

27.08 

24.03 

22.69 

12.39 

10.89 

8.34 

5.70 

5.24 

4.79 

2.06 

0.65 

-2.35 

-2.47 

-3.08 

-21.41 

-26.16 

-27.84 

-27.94 

-28.92 

-29.51 

-29.97 

-31.07 

-31.93 

-32.95 

-33.79 

-34.88 

-34.92 

-35.31 

-35.65 

-36.23 

-36.25 

-36.42 

-36.85 

-37.00 

-37.31 

-37.34 

-37.60 

-37.61 

-37.73 

-38.44 

-38.44 

-38.54 

-39.20 

-39.22 

-40.71 

-42.55 

-42.70 

-43.38 

-43.56 

-44.09 

-46.42 

-46.49 

-47.01 

-48.09 

-50.50 

-53.18 

-60.81 

-68.41 

-72.74 

10.50 

9.81 

9.65 

8.66 

10.95 

6.90 

7.23 

6.97 

9.48 

5.03 

4.12 

11.64 

3.36 

5.11 

1.43 

1.88 

20.11 

8.88 

-9.78 

-5.54 

-18.00 

-0.30 

8.89 

5.60 

-1.57 

2.65 

9.13 

6.12 

16.23 

7.98 

-1.01 

5.30 

-0.17 

5.49 

5.14 

5.73 

5.77 

13.50 

-15.69 

11.81 

11.40 

7.05 

-17.56 

1.99 

9.04 

34.44 

16.45 

11.17 

12.44 

5.96 

13.86 

32.67 

1.45 

-5.57 

30.73 

39.78 

-3.32 

2.17 

-21.87 

0.99 

1.85 

2.06 

3.48 

6.12 

5.22 

3.78 

4.33 

6.64 

4.83 

2.57 

0.41 

4.84 

4.26 

9.81 

11.85 

16.63 

15.71 

23.48 

19.57 

5.54 

15.12 

19.05 

10.54 

18.37 

14.62 

11.01 

18.47 

2.07 

10.32 

22.34 

12.20 

22.25 

15.79 

15.72 

15.88 

15.46 

5.81 

35.06 

9.07 

9.46 

13.35 

35.97 

20.80 

20.07 

16.85 

22.33 

26.34 

25.71 

30.38 

33.72 

-15.09 

19.31 

39.86 

32.02 

32.59 

24.65 

36.54 

43.86 

Total Return % Annualized Return % 

2005 2004 2003 2002 3-Yr 5-Yr 10-Yr 15-Yr 

7.77 

6.50 

6.61 

2.65 

-6.88 

2.61 

2.37 

2.43 

-4.49 

3.06 

3.39 

2.84 

3.51 

1.96 

11.65 

2.74 

25.14 

9.49 

4.71 

8.36 

43.34 

7.68 

1.72 

7.07 

4.55 

10.77 

1.14 

1.17 

21.36 

12.56 

6.85 

22.64 

7.05 

4.91 

6.12 

6.32 

6.27 

14.42 

12.16 

5.26 

5.17 

4.15 

13.82 

8.71 

9.49 

36.61 

13.28 

13.54 

14.47 

13.80 

9.42 

25.55 

26.19 

14.86 

13.82 

34.54 

5.76 

24.64 

-4.74 

*Indicates price returns. All other indexes are total return. Source: Morningstar. Data as of December 31, 2008. All returns are in dollars, unless noted. 3-, 5-, 10- and 15-year returns are annualized. 

Sharpe is 12-month Sharpe ratio. Std Dev is 3-year standard deviation. 

8.31 

7.71 

7.94 

3.48 

10.35 

4.70 

4.19 

4.34 

9.27 

1.33 

3.34 

8.46 

4.48 

5.24 

27.73 

11.13 

30.24 

15.58 

22.25 

21.09 

-27.75 

22.65 

5.31 

24.29 

18.33 

17.18 

6.97 

6.43 

9.15 

16.48 

16.94 

18.98 

16.49 

10.88 

11.95 

12.62 

11.41 

15.78 

31.58 

6.30 

6.93 

14.31 

33.16 

15.03 

14.72 

24.59 

16.12 

20.25 

20.38 

24.33 

20.88 

17.28 

30.78 

35.69 

28.46 

25.95 

11.31 

71.52 

39.16 

1.10 

2.48 

2.61 

2.36 

14.91 

3.07 

4.67 

4.10 

12.51 

1.15 

2.04 

8.40 

5.31 

7.70 

31.84 

28.97 

29.39 

29.40 

46.03 

39.20 

11.47 

38.79 

28.28 

38.50 

47.25 

33.80 

27.08 

26.25 

23.93 

35.62 

31.14 

38.48 

30.03 

28.69 

31.06 

31.64 

29.89 

37.32 

37.13 

29.75 

30.97 

48.54 

36.18 

30.36 

33.11 

34.40 

31.99 

38.59 

39.42 

45.30 

38.54 

20.72 

61.35 

36.34 

45.77 

56.28 

24.32 

56.96 

39.43 

17.69 

16.79 

16.99 

11.50 

19.50 

8.75 

11.04 

10.26 

16.52 

1.78 

2.48 

16.57 

9.61 

10.53 

-11.48 

-1.41 

-23.38 

-15.94 

-11.43 

-12.93 

130.33 

-14.63 

-15.01 

-16.57 

-20.48 

-9.43 

-28.10 

-22.59 

25.91 

-14.53 

-15.18 

-9.29 

-15.52 

-22.10 

-21.54 

-20.86 

-21.65 

-19.67 

3.82 

-27.88 

-28.04 

-30.26 

3.58 

-16.59 

-19.89 

-12.99 

-16.02 

-15.94 

-15.80 

-15.91 

-18.38 

32.07 

-7.82 

-6.39 

-6.42 

-6.00 

8.79 

16.55 

-28.07 

12.35 

11.53 

11.15 

8.11 

9.30 

6.82 

5.56 

5.51 

6.95 

3.97 

2.42 

3.06 

1.86 

2.03 

-4.34 

-5.59 

0.36 

-3.17 

-7.49 

-7.32 

-15.37 

-7.51 

-4.09 

-7.84 

-8.29 

-8.49 

-7.62 

-6.66 

-8.60 

-8.76 

-8.26 

-9.10 

-8.32 

-8.36 

-8.63 

-8.43 

-8.66 

-9.18 

-10.83 

-9.11 

-9.12 

-9.32 

-12.00 

-9.19 

-8.10 

-3.37 

-6.54 

-7.35 

-7.25 

-8.25 

-6.56 

-15.53 

-13.76 

-11.82 

-5.11 

-4.62 

-22.13 

-23.90 

-32.58 

10.60 

9.73 

9.58 

6.06 

6.05 

5.54 

4.64 

4.65 

5.01 

3.25 

2.87 

4.07 

2.71 

2.65 

4.54 

-0.80 

10.50 

2.82 

0.27 

0.88 

-8.89 

0.88 

-1.12 

0.82 

-0.93 

-0.11 

-3.13 

-2.62 

0.23 

-0.08 

-0.72 

1.85 

-0.79 

-2.19 

-1.95 

-1.67 

-2.04 

-0.15 

0.91 

-3.42 

-3.33 

-2.35 

0.65 

-1.31 

-0.51 

8.96 

1.43 

1.66 

1.91 

1.79 

1.53 

-2.36 

1.14 

1.33 

4.55 

8.02 

-11.08 

-1.19 

-16.49 

8.44 

8.10 

8.07 

6.16 

5.90 

6.04 

5.64 

5.63 

5.22 

3.45 

2.65 

6.79 

4.26 

4.85 

2.39 

2.17 

5.49 

2.91 

6.11 

5.35 

4.99 

5.18 

1.67 

4.79 

3.02 

3.59 

-3.15 

-1.54 

7.61 

4.46 

1.69 

1.86 

1.36 

-1.38 

-0.80 

-0.63 

-1.09 

5.33 

7.42 

-4.27 

-4.01 

-0.76 

7.65 

-0.25 

-0.64 

8.37 

-1.30 

0.80 

1.19 

2.72 

0.37 

7.35 

- 

3.46 

6.36 

9.31 

0.64 

2.67 

-11.70 

8.77 

8.51 

8.50 

6.50 

6.55 

6.44 

6.19 

6.18 

6.21 

4.04 

2.55 

- 

4.91 

5.81 

6.17 

4.26 

7.70 

7.90 

8.39 

- 

-6.23 

- 

8.11 

- 

5.89 

- 

- 

6.79 

6.10 

9.05 

7.50 

-0.19 

7.49 

6.46 

6.36 

6.34 

6.47 

- 

8.21 

4.82 

4.60 

2.77 

8.24 

- 

4.53 

- 

- 

3.52 

3.78 

- 

6.26 

4.39 

- 

- 

2.22 

2.73 

- 

1.43 

-1.92 

Sharpe 

0.69 

0.72 

0.70 

0.94 

0.72 

0.87 

0.36 

0.43 

0.50 

0.74 

-0.84 

-0.05 

-0.35 

-0.20 

-0.32 

-0.63 

-0.14 

-0.45 

-0.49 

-0.49 

-0.36 

-0.51 

-0.51 

-0.52 

-0.51 

-0.57 

-0.69 

-0.67 

-0.44 

-0.57 

-0.71 

-0.65 

-0.72 

-0.74 

-0.72 

-0.71 

-0.74 

-0.56 

-0.34 

-0.72 

-0.70 

-0.52 

-0.36 

-0.76 

-0.62 

-0.11 

-0.43 

-0.48 

-0.47 

-0.53 

-0.40 

-0.52 

-0.71 

-0.55 

-0.22 

-0.14 

-0.76 

-0.68 

-1.30 

Std Dev 

12.43 

10.62 

10.42 

4.30 

7.49 

3.28 

4.98 

4.03 

6.47 

0.49 

1.74 

8.00 

4.98 

7.35 

19.33 

13.60 

15.29 

13.63 

19.41 

19.16 

37.97 

19.01 

13.74 

19.23 

20.13 

18.96 

15.36 

14.44 

22.82 

19.28 

15.75 

18.02 

15.58 

15.29 

16.02 

16.03 

15.78 

19.98 

30.06 

16.63 

16.91 

21.56 

31.21 

16.05 

17.26 

27.85 

19.65 

19.51 

19.73 

19.61 

20.71 

30.19 

22.38 

23.97 

25.59 

29.08 

30.36 

34.96 

29.31 

58 

March/April 2009

Morningstar U.S. Style Overview Global Index Jan. 1 – Feb. Data 29, 2008 

Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions March/April 2009 

$US Millions Total Return % 

Annualized Return % 

Fund Name Ticker Assets Exp Ratio 3-Mo 2008 

<strong>Vanguard</strong> Tot Stk 

<strong>Vanguard</strong> 500 Index 

<strong>Vanguard</strong> Inst Idx 

<strong>Vanguard</strong> Total Bd Idx 

<strong>Vanguard</strong> 500 Idx Adm 

<strong>Vanguard</strong> Tot Stk Adm 

<strong>Vanguard</strong> Total Intl Stk 

<strong>Vanguard</strong> Inst Idx InstPl 

Fidelity Spar US EqIx 

<strong>Vanguard</strong> 500 Index Signal 

<strong>Vanguard</strong> Total Bd Idx Ad 

<strong>Vanguard</strong> Total Bd Idx In 

<strong>Vanguard</strong> Tot Stk Inst 

<strong>Vanguard</strong> Eur Stk Idx 

Fidelity U.S. Bond Index 

T. Rowe Price Eq Idx 500 

<strong>Vanguard</strong> Inst Tot Bd Idx 

<strong>Vanguard</strong> TotBdMkt Idx Sig 

<strong>Vanguard</strong> Tot Stk InstPls 

Fidelity Spar 500 Adv 

<strong>Vanguard</strong> Pac Stk Idx 

Fidelity Spar 500 Idx 

Fidelity 100 Index 

<strong>Vanguard</strong> Em Mkt Idx 

<strong>Vanguard</strong> Mid Cap Idx 

Fidelity Spar US Eq Adv 

<strong>Vanguard</strong> Gr Idx 

<strong>Vanguard</strong> SmCp Idx 

Fidelity Spar Tot Mkt Ix 

<strong>Vanguard</strong> Mid Cap Idx Ins 

<strong>Vanguard</strong> Inst DevMktsIdx 

<strong>Vanguard</strong> TotStMkt Idx Sig 

<strong>Vanguard</strong> Sh-Tm Bd Idx 

Fidelity Spar Intl Index 

<strong>Vanguard</strong> Intm Bd Idx 

<strong>Vanguard</strong> Eur Stk Idx Ins 

<strong>Vanguard</strong> Sh-Tm Bd Sgnl 

<strong>Vanguard</strong> ExtMktIdx 

<strong>Vanguard</strong> REIT Index 

<strong>Vanguard</strong> Bal Idx 

Fidelity Spar Tot Mkt Adv 

<strong>Vanguard</strong> Val Idx 

<strong>Vanguard</strong> SmCp Idx Ins 

VALIC I Stock 

<strong>Vanguard</strong> LongTm Bd Idx 

<strong>Vanguard</strong> Intm Bd Idx Adm 

<strong>Vanguard</strong> ExtMktIdx Instl 

<strong>Vanguard</strong> SmCp Vl Idx 

Schwab S&P 500 In Sel 

<strong>Vanguard</strong> Dev Mkts Idx 

Dimensional USLgCo 

<strong>Vanguard</strong> Bal Idx Instl 

Schwab Instl Sel S&P 500 

Schwab S&P 500 In Inv 

Schwab 1000 In Inv 

<strong>Vanguard</strong> Tx-Mgd App Adm 

<strong>Vanguard</strong> Gr Idx Instl 

<strong>Vanguard</strong> Value Index Is 

Dreyfus S&P 500 Index 

VTSMX 

VFINX 

VINIX 

VBMFX 

VFIAX 

VTSAX 

VGTSX 

VIIIX 

FUSEX 

VIFSX 

VBTLX 

VBTIX 

VITSX 

VEURX 

FBIDX 

PREIX 

VITBX 

VBTSX 

VITPX 

FSMAX 

VPACX 

FSMKX 

FOHIX 

VEIEX 

VIMSX 

FUSVX 

VIGRX 

NAESX 

FSTMX 

VMCIX 

VIDMX 

VTSSX 

VBISX 

FSIIX 

VBIIX 

VESIX 

VBSSX 

VEXMX 

VGSIX 

VBINX 

FSTVX 

VIVAX 

VSCIX 

VSTIX 

VBLTX 

VBILX 

VIEIX 

VISVX 

SWPPX 

VDMIX 

DFLCX 

VBAIX 

ISLCX 

SWPIX 

SNXFX 

VTCLX 

VIGIX 

VIVIX 

PEOPX 

39,440.4 

38,778.1 

31,543.3 

29,687.1 

23,009.4 

18,780.6 

17,746.2 

17,643.4 

13,814.8 

13,098.6 

12,978.4 

12,431.2 

10,781.7 

10,342.2 

9,172.0 

7,453.4 

7,437.2 

7,372.0 

6,630.6 

5,941.6 

5,340.3 

5,283.9 

5,216.5 

5,142.6 

4,652.0 

4,441.4 

4,278.5 

4,050.2 

3,932.7 

3,819.6 

3,801.6 

3,717.4 

3,679.7 

3,331.6 

3,318.2 

3,300.8 

3,211.9 

3,079.9 

2,731.7 

2,730.7 

2,678.0 

2,617.8 

2,545.2 

2,544.4 

2,518.1 

2,457.5 

2,442.4 

2,434.6 

2,331.8 

2,328.2 

2,300.7 

2,299.4 

2,254.9 

2,226.6 

2,077.0 

2,045.3 

2,016.0 

1,991.9 

1,968.1 

0.19 

0.18 

0.05 

0.20 

0.09 

0.09 

0.32 

0.03 

0.10 

0.09 

0.11 

0.07 

0.06 

0.27 

0.32 

0.35 

0.05 

0.11 

0.03 

0.07 

0.32 

0.10 

0.20 

0.45 

0.22 

0.07 

0.22 

0.23 

0.10 

0.08 

0.12 

0.09 

0.18 

0.10 

0.18 

0.12 

0.10 

0.25 

0.21 

0.20 

0.07 

0.21 

0.08 

0.36 

0.18 

0.11 

0.07 

0.23 

0.19 

0.27 

0.15 

0.08 

0.10 

0.37 

0.50 

0.10 

0.08 

0.08 

0.50 

-22.73 

-21.94 

-21.91 

4.37 

-21.92 

-22.72 

-21.03 

-21.90 

-21.95 

-21.92 

4.40 

4.41 

-22.69 

-21.78 

3.53 

-21.94 

4.26 

4.40 

-22.67 

-21.96 

-14.19 

-21.96 

-20.15 

-27.82 

-25.63 

-21.96 

-23.88 

-26.66 

-22.88 

-25.62 

-19.19 

-22.72 

3.71 

-19.07 

6.83 

-21.70 

3.73 

-26.60 

-38.16 

-12.54 

-22.88 

-20.44 

-26.64 

-22.03 

13.03 

6.85 

-26.55 

-25.57 

-21.80 

-19.41 

-21.77 

-12.49 

-21.88 

-21.85 

-22.34 

-22.26 

-23.81 

-20.41 

-22.04 

-37.04 

-37.02 

-36.95 

5.05 

-36.97 

-36.99 

-44.10 

-36.94 

-37.03 

-36.97 

5.15 

5.19 

-36.94 

-44.73 

3.76 

-37.06 

5.05 

5.15 

-36.89 

-37.03 

-34.36 

-37.05 

-35.44 

-52.81 

-41.82 

-37.01 

-38.32 

-36.07 

-37.18 

-41.76 

-41.42 

-36.99 

5.43 

-41.43 

4.93 

-44.65 

5.51 

-38.73 

-37.05 

-22.21 

-37.16 

-35.97 

-35.98 

-37.21 

8.64 

5.01 

-38.58 

-32.06 

-36.73 

-41.62 

-36.78 

-22.10 

-36.85 

-36.88 

-37.28 

-37.58 

-38.19 

-35.88 

-37.28 

2007 2006 3-Yr 

5.49 

5.39 

5.48 

6.92 

5.47 

5.57 

15.52 

5.50 

5.43 

5.47 

7.02 

7.05 

5.56 

13.82 

5.40 

5.18 

7.01 

7.02 

5.62 

5.46 

4.78 

5.43 

- 

38.90 

6.02 

5.46 

12.57 

1.16 

5.57 

6.22 

11.04 

5.55 

7.22 

10.72 

7.61 

13.96 

7.28 

4.33 

-16.46 

6.16 

5.60 

0.09 

1.29 

5.13 

6.59 

7.70 

4.51 

-7.07 

5.50 

10.99 

5.44 

6.34 

5.52 

5.34 

5.76 

6.11 

12.73 

0.21 

5.03 

15.51 

15.64 

15.79 

4.27 

15.75 

15.63 

26.64 

15.81 

15.72 

15.66 

4.36 

4.40 

15.69 

33.42 

4.33 

15.41 

4.30 

4.29 

15.76 

15.75 

11.99 

15.71 

- 

29.39 

13.60 

15.75 

9.01 

15.66 

15.73 

13.78 

26.34 

15.57 

4.09 

26.15 

3.91 

33.64 

4.09 

14.27 

35.07 

11.02 

15.77 

22.15 

15.82 

15.41 

2.67 

3.98 

14.46 

19.24 

15.67 

26.18 

15.71 

11.10 

15.80 

15.48 

15.20 

14.44 

9.16 

22.31 

15.24 

-8.45 

-8.44 

-8.35 

5.41 

-8.36 

-8.38 

-6.49 

-8.32 

-8.42 

-8.39 

5.50 

5.54 

-8.34 

-5.67 

4.50 

-8.58 

5.45 

5.48 

-8.28 

-8.40 

-8.34 

-8.42 

- 

-5.35 

-11.18 

-8.39 

-8.87 

-9.23 

-8.44 

-11.05 

-6.33 

-8.40 

5.57 

-6.48 

5.47 

-5.53 

5.62 

-9.94 

-10.77 

-2.85 

-8.42 

-7.84 

-9.10 

-8.67 

5.94 

5.55 

-9.77 

-9.03 

-8.26 

-6.49 

-8.29 

-2.73 

-8.28 

-8.43 

-8.58 

-8.82 

-8.72 

-7.72 

-8.78 

5-Yr 10-Yr 15-Yr P/E Std Dev Yield 

-1.76 

-2.29 

-2.18 

4.57 

-2.21 

-1.68 

2.69 

-2.16 

-2.26 

-2.26 

4.66 

4.70 

-1.65 

2.08 

4.02 

-2.45 

4.63 

4.61 

-1.59 

-2.25 

2.33 

-2.27 

- 

7.15 

-0.80 

-2.25 

-3.14 

-0.76 

-1.75 

-0.66 

2.35 

-1.73 

3.93 

2.20 

4.67 

2.23 

3.95 

-0.89 

0.77 

0.96 

-1.73 

-0.68 

-0.61 

-2.52 

6.30 

4.75 

-0.70 

-0.27 

-2.18 

2.19 

-2.19 

1.09 

-2.17 

-2.34 

-2.13 

-1.84 

-2.99 

-0.55 

-2.64 

-0.66 

-1.46 

-1.35 

5.37 

-1.40 

-0.60 

1.77 

-1.32 

-1.51 

-1.44 

5.43 

5.49 

-0.54 

0.74 

5.32 

-1.65 

- 

5.39 

- 

-1.49 

1.93 

-1.50 

- 

8.89 

4.08 

-1.50 

-3.30 

3.32 

-0.73 

4.24 

- 

-0.64 

4.87 

1.07 

5.81 

0.86 

4.88 

1.62 

7.14 

2.08 

-0.72 

0.62 

3.47 

-1.73 

6.65 

5.86 

1.80 

5.22 

-1.45 

- 

-1.47 

2.18 

- 

-1.61 

-1.18 

-0.67 

-3.17 

0.74 

-1.88 

6.24 

6.37 

6.50 

5.98 

6.42 

6.28 

- 

6.52 

6.30 

6.39 

6.02 

6.08 

6.33 

6.59 

5.95 

6.17 

- 

5.99 

- 

6.29 

-0.09 

6.28 

- 

- 

- 

6.31 

6.11 

6.42 

- 

- 

- 

6.25 

- 

- 

- 

6.67 

- 

6.28 

- 

6.45 

- 

6.64 

6.54 

6.12 

- 

- 

6.41 

- 

- 

- 

6.34 

6.52 

- 

- 

6.22 

- 

6.22 

6.73 

5.91 

14.0 

13.9 

13.9 

- 

13.9 

14.0 

10.8 

13.9 

10.7 

13.9 

- 

- 

14.0 

9.2 

- 

13.9 

- 

- 

14.0 

10.7 

11.4 

10.7 

11.0 

10.7 

14.2 

10.7 

15.8 

15.4 

10.8 

14.2 

11.5 

14.0 

- 

7.7 

- 

9.2 

- 

15.4 

30.0 

14.0 

10.8 

12.4 

15.4 

11.6 

- 

- 

15.4 

13.9 

15.0 

11.5 

15.1 

14.0 

15.1 

15.0 

15.3 

13.8 

15.8 

12.4 

11.6 

15.95 

15.28 

15.28 

4.07 

15.28 

15.95 

20.94 

15.28 

15.29 

15.28 

4.07 

4.08 

15.94 

20.69 

3.72 

15.25 

4.09 

4.07 

15.95 

15.29 

17.81 

15.29 

- 

28.80 

19.62 

15.29 

16.72 

20.14 

15.94 

19.64 

19.42 

15.94 

2.47 

19.58 

6.51 

20.69 

2.47 

19.80 

30.23 

9.98 

15.95 

15.09 

20.12 

15.30 

11.13 

6.51 

19.80 

19.27 

15.20 

19.48 

15.22 

9.97 

15.28 

15.20 

15.55 

16.09 

16.72 

15.08 

15.28 

2.69 

3.01 

3.06 

4.68 

3.12 

2.79 

3.01 

3.09 

3.01 

3.12 

4.77 

4.81 

2.83 

7.33 

4.60 

2.68 

4.60 

4.77 

2.83 

2.98 

1.71 

2.95 

3.01 

4.70 

1.95 

3.05 

1.29 

1.98 

2.55 

2.17 

5.55 

2.80 

3.78 

3.52 

4.74 

7.61 

3.86 

1.71 

8.06 

3.63 

2.59 

4.09 

2.18 

3.24 

5.16 

4.82 

1.94 

2.90 

3.05 

5.29 

3.02 

3.78 

2.93 

2.83 

2.44 

2.43 

1.50 

4.27 

2.66 

Source: Morningstar. Data as of December 31, 2008. Exp Ratio is expense ratio. YTD is year-to-date. 3-, 5-, 10- and 15-yr returns are annualized. P/E is price-to-earnings ratio. 

Std Source: Dev is Morningstar. 3-year standard Data deviation. as of 2/29/08 Yield is 12-month. 


59

Morningstar U.S. Style Overview: U.S. Style January 1 Overview - December 31, 2008 Jan. 1 – Dec. 31, 2008 

Trailing Returns % 

3-Month YTD 1-Yr 3-Yr 5-Yr 10-Yr 

Morningstar Indexes 

US Market –29.83 –36.95 –36.95 –8.26 –1.58 –0.81 

Large Cap –28.05 –36.13 –36.13 –7.59 –1.95 –2.18 

Mid Cap –35.12 –40.37 –40.37 –10.50 –0.69 2.58 

Small Cap –33.28 –35.99 –35.99 –9.40 –1.08 4.04 

US Value –24.65 –35.72 –35.72 –7.87 –0.31 1.76 

US Core –28.20 –33.01 –33.01 –6.22 0.06 1.25 

US Growth –36.32 –42.54 –42.54 –11.29 –4.97 –6.17 

Morningstar Market Barometer YTD Return % 

Large Cap 

US Market 

–36.95 

–36.13 

Value 

–35.72 

Core 

–33.01 

Growth 

–42.54 

–36.03 –31.40 –41.79 

Large Value –22.76 –36.03 –36.03 –7.15 –0.47 0.73 

Large Core –25.80 –31.40 –31.40 –4.89 0.36 0.17 

Large Growth –35.41 –41.79 –41.79 –11.61 –6.48 –8.30 

Mid Cap 

–40.37 

–35.87 –38.65 –46.19 

Mid Value –29.61 –35.87 –35.87 –10.39 –0.06 4.05 

Mid Core –35.31 –38.65 –38.65 –10.50 –1.25 3.52 

Mid Growth –39.99 –46.19 –46.19 –10.98 –1.10 –0.51 

Small Cap 

–35.99 

–31.59 –36.11 –39.84 

Small Value –28.68 –31.59 –31.59 –8.99 –0.36 5.99 

Small Core –35.60 –36.11 –36.11 –9.89 –0.79 6.84 

Small Growth –35.19 –39.84 –39.84 –9.76 –2.49 –0.47 

–8.00 –4.00 0.00 +4.00 +8.00 

Sector Index YTD Return % 

–22.02 Consumer Goods 

–23.18 Healthcare 

Industry Leaders & Laggards YTD Return % 

Insurance (Title) 9.43 

Education 3.83 

Biggest Influence on Style Index Performance 

Best Performing Index 

YTD 

Return % 

Large Core –31.40 

Constituent 

Weight % 

–26.55 Consumer Services 

–28.11 Utilities 

–33.17 Telecommunications 

–34.03 Business Services 

–37.30 Energy 

–37.95 Software 

–40.73 Media 

–43.52 Hardware 

–46.13 Industrial Materials 

–50.12 Financial Services 

Biotechnology 3.66 

Water Utilities 1.88 

Radio 0.00 

–0.63 Toys/Hobbies 

–68.18 Aluminum 

–68.68 Photography & Imaging 

–69.16 Rubber Products 

–70.58 Plastics 

–70.88 Gambling/Hotel Casinos 

–75.43 Auto Makers 

General Electric Co. –53.98 9.98 

Intel Corp. –43.47 4.13 

Goldman Sachs Group Inc. –60.41 2.06 

UnitedHealth Group Inc. –54.26 2.00 

Hewlett-Packard Co. –27.57 3.47 

Worst Performing Index 

Mid Growth –46.19 

MEMC Electronic Materials Inc. –83.86 2.10 

NVIDIA Corp. –76.28 1.95 

Electronic Arts Inc. –72.54 1.90 

Smith International Inc. –68.68 1.53 

Las Vegas Sands Corp. –93.94 1.12 

1-Year 

3-Year 

5-Year 

Value 

Core 

Growth 

Value 

Core 

Growth 

Value 

Core 

Growth 

Large Cap 

–36.03 

–31.40 

–41.79 

Large Cap 

–7.15 

–4.89 

–11.61 

Large Cap 

–0.47 

0.36 

–6.48 

Mid Cap 

–35.87 

–38.65 –46.19 

Mid Cap 

–10.39 

–10.50 –10.98 

Mid Cap 

–0.06 

–1.25 –1.10 

Small Cap 

–31.59 

–36.11 –39.84 

Small Cap 

–8.99 

–9.89 –9.76 

Small Cap 

–0.36 

–0.79 –2.49 

–20 –10 0 +10 +20 

–20 –10 0 +10 +20 

–20 –10 0 +10 +20 

Source: Morningstar. Data as of 12/31/08 

Notes and Disclaimer: ©2006 Morningstar, Inc. All Rights Reserved. Unless otherwise noted, all data is as of most recent month end. Multi-year returns are annualized. NA: Not Available. Biggest Influence on Index Performance lists 

are calculated by multiplying stock returns for the period by their respective weights in the index as of the start of the period. Sector and Industry Indexes are based on Morningstar's proprietary sector classifications. The information ? 

contained herein is not warranted to be accurate, complete or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information. 

60 

March/April 2009

DOW JONES INDEXES | QUARTERLY SUMMARY | QUARTER 4, 2008 

Exchange-Traded Dow DOW JONES Jones U.S. INDUSTRY U.S. INDEXESEconomic Funds Corner Sector Review 

HISTORICAL PERFORMANCE RETURNS 

Total Return (%) Annualized Total Return (%) 

Industry 1-Month 3-Month YTD 2008 1-Year 3-Year 5-Year 10-Year Since Inception 

Basic Materials 2.95 -34.94 -50.82 32.86 -50.82 -8.40 -1.82 2.14 4.76 

Consumer Goods -0.64 -18.56 -25.69 9.69 -25.69 -2.16 1.56 1.73 7.14 

Consumer Services 5.47 -20.08 -30.82 -7.18 -30.82 -9.78 -4.50 -2.52 5.91 

Financials 1.55 -33.45 -50.40 -17.66 -50.40 -21.28 -10.05 -1.93 7.59 

Health Care 7.04 -13.35 -22.80 8.36 -22.80 -3.66 0.25 1.00 7.65 

Industrials 2.12 -24.71 -39.55 13.57 -39.55 -7.88 -0.80 -0.14 6.14 

Oil & Gas -4.17 -22.39 -35.77 34.84 -35.77 2.07 13.56 10.68 11.59 

Technology DOW JONES INDEXES | QUARTERLY 2.36 U.S. SUMMARY -25.35 -42.87 | QUARTER 15.70 4, 2008 -42.87 -10.05 -5.21 -5.17 8.50 

DOW JONES INDEXES | QUARTERLY U.S. SUMMARY | QUARTER 4, 2008 

Telecommunications 0.68 -4.56 -32.93 10.04 -32.93 0.32 2.85 -6.72 3.92 

DOW JONES U.S. INDUSTRY INDEXES 

Utilities DOW JONES U.S. INDUSTRY -2.02 INDEXES -12.08 -30.25 17.76 -30.25 -0.13 7.34 3.45 6.59 

DOW JONES U.S. INDUSTRY REPRESENTATION 

CORRELATION COEFFICIENTS 

HISTORICAL PERFORMANCE RETURNS 

Utilities 

Industry 1000 Market 3000Cap 

5000 Weight 8000 in 4000 2000 0001 Basic 9000Materials6000 7000 

Industry (USD Billions) Total Return (%) DJ U.S. Index 

Annualized 4.60% Total Return (%) 

Telecom. 

2.56% 

Industry Basic Materials 0.6554 1-Month 3-Month YTD 2008 1-Year 3.40% 3-Year 5-Year 10-Year Consumer Since Inception 

Consumer Basic Goods Materials 0.4354 1.0000 234.04 2.56% 

Goods 

Basic Materials 2.95 -34.94 -50.82 32.86 -50.82 

Consumer Consumer Services Goods 0.3797 0.6930 975.80 1.0000 10.67% 

Technology 

-8.40 -1.82 2.14 10.67% 4.76 

Consumer Goods -0.64 -18.56 -25.69 9.69 -25.6913.57% 

-2.16 1.56 1.73 7.14 

Financials Consumer Services 0.2101 1,029.17 0.6765 0.7484 11.25% 1.0000 

Consumer Services 5.47 -20.08 -30.82 -7.18 -30.82 -9.78 -4.50 -2.52 Consumer 5.91 

Health Financials Care 0.4824 1,337.79 0.7688 0.5839 14.63% 0.5635 1.0000 

Financials 1.55 -33.45 -50.40 -17.66 -50.40 -21.28 -10.05 -1.93 Services 7.59 

Industrials DOW Health JONES Care INDEXES | QUARTERLY 0.4615 U.S. 1,291.02 0.6876 SUMMARY 0.8312 | QUARTER 14.12% 0.6211 4, 2008 0.5378 1.0000 

Oil & Gas 

11.25% 

Health Care 7.04 -13.35 -22.80 8.36 -22.80 -3.66 0.25 1.00 7.65 

Oil & Gas Industrials 0.6353 1,186.63 0.1634 0.1808 12.97% -0.0221 0.1825 0.3721 1.0000 

Industrials 

12.24% 

DOW JONES U.S. INDUSTRY 2.12 INDEXES -24.71 -39.55 13.57 -39.55 -7.88 -0.80 -0.14 

Technology Oil & Gas 0.5693 1,119.76 0.6625 0.7423 12.24% 

Financials 6.14 

0.4989 0.5783 0.8422 0.5175 1.0000 

Oil & Gas -4.17 -22.39 -35.77 34.84 -35.77 2.07 13.56 10.68 14.63% 11.59 

Telecommunications Technology 0.5614 1,240.86 0.5805 0.7376 13.57% 0.5931 0.4788 0.7755 0.4049 0.7317 1.0000 

Technology 2.36 -25.35 -42.87 15.70 -42.87 -10.05 -5.21 -5.17 8.50 

UtilitiesTelecommunications 1.0000 0.4354 DOW 310.73JONES 0.3797 U.S. 3.40% INDUSTRY 0.2101 REPRESENTATION 

0.4824 0.4615 Industrials 0.6353 0.5693 0.5614 1.0000 

Telecommunications 0.68 -4.56 -32.93 10.04 -32.93 0.32 2.85 -6.72 3.92 

Utilities 420.56 4.60% 

12.97% 

Health Care 

Utilities -2.02 -12.08 -30.25 17.76 -30.25 -0.13 14.12% 

Market Cap Weight in 

Utilities 7.34 3.45 6.59 

CORRELATION COEFFICIENTS: U.S. INDUSTRY INDEXES VS. U.S. TMI AND 

4.60% 

Basic Materials 

Industry (USD Billions) DJ U.S. Index Telecom. 

SIZE INDEXES 2.56% 

Industry DJ U.S. DJ U.S. Large-Cap Index 3.40% DJ U.S. Mid-Cap Index DJ U.S. Small-Cap Consumer 

Basic Materials 234.04 CORRELATION 2.56% COEFFICIENTS 

Index 

Goods 

Basic Industry Consumer Goods DOW 

Materials 1000JONES U.S. 975.80 

0.7410 3000INDUSTRY 5000 REPRESENTATION 10.67% 

BY SIZE (IN BILLIONS USD) 

0.6915 8000 4000Technology 

2000 0.83730001 9000 0.7343 6000 10.67% 7000 

Consumer Services DJ U.S. 1,029.17 DJ U.S. DJ U.S. 11.25% DJ U.S. 13.57% DJ U.S. DJ U.S. DJ U.S. 

Consumer Goods 0.7578 0.7870 0.6310 0.6655 

Basic Materials Financials Large-Cap 0.6554 Large-Cap 1,337.79 Mid-Cap 14.63% Mid-Cap Small-Cap Small-Cap TMI 

Consumer Industry Services Market Cap 0.8518 Weight Market Cap0.8386 Weight Market Cap 0.8270 Weight Market 0.8354 

Consumer 

Consumer Health Goods Care 0.4354 1,291.02 1.0000 

14.12% 

Cap Services 

Financials 

Basic Materials 158.45 

0.7020 

2.25% 52.80 

0.7327 

3.52% 22.79 

0.5756 

3.75% 234.04 

0.6133 

Consumer Industrials Services 0.3797 1,186.63 0.6930 1.0000 12.97% 

Oil & Gas 

11.25% 

Health 


Care 

Goods 777.68 

0.6927 

11.05% 152.21 

0.7087 

10.16% 12.24% 45.91 

0.6130 

7.55% 975.80 

0.6013 

Financials Oil & Gas 0.2101 1,119.76 0.6765 0.7484 12.24% 1.0000 

Industrials 

Consumer Services 783.51 

0.8987 

11.13% 175.17 

0.8829 

11.69% 70.49 

0.8894 

11.58% 1,029.17 

0.8503 Financials 

Health Technology Care 0.4824 1,240.86 0.7688 0.5839 13.57% 0.5635 1.0000 

14.63% 

Oil 

Financials 

& Gas 

920.55 

0.5461 

13.08% 285.51 

0.5107 

19.06% 131.74 

0.6136 

21.65% 1,337.79 

0.5290 

Industrials Telecommunications 0.4615 0.6876 310.73 0.8312 3.40% 0.6211 0.5378 1.0000 

Technology 

Health Care 1,098.05 

0.9109 

15.60% 116.86 

0.8962 

7.80% Industrials 76.10 

0.8879 

12.51% 1,291.02 

0.8833 

Oil & Gas Utilities 0.6353 0.1634 420.56 0.1808 4.60% -0.0221 0.1825 0.3721 1.0000 

Telecommunications 

Industrials 763.43 

0.8330 

10.84% 299.61 

0.8353 12.97% 

20.00% 123.60 

0.7791 Health Care 

Technology 0.5693 0.6625 0.7423 0.4989 0.5783 0.8422 0.5175 20.31% 1.0000 1,186.63 

0.7573 

14.12% 

Utilities 

Oil & Gas 1,005.05 

0.6542 

14.28% 91.22 

0.6435 

6.09% 23.49 

0.6642 

3.86% 1,119.76 

0.5629 

Telecommunications 0.5614 0.5805 0.7376 0.5931 0.4788 0.7755 0.4049 0.7317 1.0000 

Utilities Technology 993.96 1.0000 14.12% 0.4354 0.3797 169.65 0.2101 11.32% 0.4824 0.4615 77.25 0.6353 12.70% 0.5693 1,240.86 0.5614 1.0000 

Telecommunications 271.36 3.85% 37.46 2.50% 1.91 0.31% 310.73 

Utilities DOW 267.77JONES U.S. 3.80% INDUSTRY 117.63 REPRESENTATION 7.85% BY SIZE 35.17 (IN BILLIONS 5.78% USD) 420.56 

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

Total 7,039.80 DJ U.S. 76.97% DJ U.S. 1,498.12 DJ U.S. DJ 16.38% U.S. DJ 608.44 U.S. DJ 6.65% U.S. DJ 9,146.36 U.S. 

Large-Cap Large-Cap Mid-Cap Mid-Cap Small-Cap Small-Cap TMI 

Industry 

Industry Market Cap 

DJ U.S. 

Weight Market 

DJ U.S. 

Cap 

Large-Cap 

Weight 

Index 

Market 

DJ U.S. 

Cap 

Mid-Cap 

Weight 

Index DJ U.S. 

Market 

Small-Cap 

Cap 

Index 

Basic 

Data Materials 

based on total-return index values 158.45 0.7410 

as HISTORICAL of December 2.25% 31, DOW 2008. JONES Inception 52.80 0.6915 

U.S. date December INDUSTRY 3.52% 31, 1991. REPRESENTATIONS Correlation 22.79 0.8373 

data based 3.75% (%) on monthly total-return 234.04 0.7343 

index values from 

December Consumer 30, Goods 2005 to December 31, 777.68 2008. 0.7578 11.05% 152.21 0.7870 10.16% 45.91 0.6310 7.55% 975.80 0.6655 

Industry 2008 Q4 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 

Consumer Services 783.51 0.8518 11.13% 175.17 0.8386 11.69% 70.49 0.8270 11.58% 1,029.17 0.8354 

Financials Basic Materials 2.56 3.70 2.95 2.74 2.67 Page 2.28 7 1.98 2.47 2.34 3.53 4.31 5.02 6.28 

920.55 0.7020 13.08% 285.51 0.7327 19.06% 131.74 0.5756 21.65% 1,337.79 0.6133 

Health Consumer Goods 10.67 9.15 9.17 9.40 10.22 8.44 8.14 8.34 11.63 13.79 14.72 15.09 15.62 

Care 1,098.05 0.6927 15.60% 116.86 0.7087 7.80% 76.10 0.6130 12.51% 1,291.02 0.6013 

Industrials Consumer Services 11.25 10.64 13.99 13.43 12.97 12.84 10.45 14.49 12.57 10.51 9.59 10.09 11.45 

763.43 0.8987 10.84% 299.61 0.8829 20.00% 123.60 0.8894 20.31% 1,186.63 0.8503 

Oil Financials 14.63 17.33 21.25 21.01 21.08 19.04 18.02 14.11 16.92 19.31 16.89 15.11 13.51 

& Gas 1,005.05 0.5461 14.28% 91.22 0.5107 6.09% 23.49 0.6136 3.86% 1,119.76 0.5290 

Technology Health Care 14.12 11.62 12.17 13.33 14.56 14.28 14.23 9.21 12.44 11.35 11.08 11.11 9.90 

993.96 0.9109 14.12% 169.65 0.8962 11.32% 77.25 0.8879 12.70% 1,240.86 0.8833 

Telecommunications Industrials 12.97 13.46 12.64 11.87 11.68 12.07 12.54 11.98 11.89 13.47 14.35 14.39 14.50 

271.36 0.8330 3.85% 37.46 0.8353 2.50% 1.91 0.7791 0.31% 310.73 0.7573 

Utilities Oil & Gas 12.24 11.64 7.10 6.05 6.32 5.88 6.00 4.51 5.46 7.17 7.88 7.45 7.91 

267.77 0.6542 3.80% 117.63 0.6435 7.85% 35.17 0.6642 5.78% 420.56 0.5629 

Technology 13.57 15.06 14.50 16.10 13.53 17.12 19.89 25.59 16.43 11.78 12.25 9.99 8.76 

Total 7,039.80 76.97% 1,498.12 16.38% 608.44 6.65% 9,146.36 

Telecommunications 3.40 3.26 3.00 3.09 3.81 5.03 5.19 6.96 7.20 5.67 5.36 7.29 7.24 

Source: Dow Jones Indexes. Data through 12/31/2008. The Correlation Coefficients table can be interpreted as a standard correlations crossing table, with the 

order Utilitiesof the sectors on the X axis 4.60corresponding 4.13 to 3.24 the numbers 2.98 on the 3.15 Y axis (those 3.02 numbers 3.56 refer to 2.34 the ICB 3.12 industry codes 3.43 for the 3.57 relevant sectors). 4.45 Correlations 

4.82 

are monthly correlations measured over the trailing three years. 

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

Industry 2008 Q4 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 

www.journalofindexes.com Basic Materials 2.56 3.70 2.95 2.74 2.67 2.28 1.98 2.47 2.34 3.53 

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

March/April 4.312009 

5.02 6.28 

61 

Data 


based on 

Goods 

total-return index 

10.67 

values as 

9.15 

of December 

9.17 

31, 2008. 

9.40 

Inception 

10.22 

date December 

8.44 8.14 

31, 1991. Correlation 

8.34 11.63 

data based 

13.79 

on monthly 

14.72 

total-return 

15.09 

index 

15.62 

values from 

December Consumer 30, Services 2005 to December 11.25 31, 2008. 10.64 13.99 13.43 12.97 12.84 10.45 14.49 12.57 10.51 9.59 10.09 11.45

Exchange-Traded Funds Corner 

Largest New ETFs Sorted By Total Net Assets In $US Millions 

Covers ETFs and ETNs launched in 2008. 

Fund Name 

Ticker 

ProShares UltrShrt Barclays 20+Trsy 

TBT 

PowerShares DB Cr Oil Dbl Long 

DXO 

iShares MSCI AC Asia xJapan 

AAXJ 

DB Gold Double Long ETN 

DGP 

iShares MSCI ACWI 

ACWI 

iShares MSCI ACWI ex US 

ACWX 

PowerShares Preferred 

PGX 

Direxion Large Cp Bull 3X 

BGU 

<strong>Vanguard</strong> Total World 

VT 

SPDR DB Intl GovInfl Bond 

WIP 

Direxion Financl Bull 3X 

FAS 

Market Vectors Coal 

KOL 

Direxion Small Cp Bull 3X 

TNA 

WisdomTree India Earnings 

EPI 

ProShares UltrShrt Barclays 7-10Trsy 

PST 

Market Vectors 2xShrt Euro ETN 

DRR 

Direxion Large Cp Bear 3X 

BGZ 

ProShares Ultra DJ-AIG Cr Oil 

UCO 

Claymore/MAC Glbl Solar Energy 

TAN 

iShares MSCI Israel 

EIS 

Source: Morningstar. Data as of December 31, 2008. ER is expense ratio. 

SPDRs (S&P 500) 

iShares MSCI EAFE 

SPDR Gold Trust 

iShares MSCI Emerg Mkts 

iShares S&P 500 

PowerShares QQQQ 

iShares Russell 2000 

iShares R1000 Growth 

iShares Barclays Aggregate 

iShares R1000 Value 

<strong>Vanguard</strong> Total Stock Market 

DIAMONDS Trust 

iShares Barclays TIPS Bond 

Financial SPDR 

iShares Barclays 1-3 Treas 

iShares GS$ InvesTop 

MidCap SPDR (S&P 400) 

iShares FTSE/Xinhua China 

iShares Russell 1000 

iShares Japan 

<strong>Vanguard</strong> Emerging Markets 

iShares S&P 500 Growth 

Energy SPDR 

iShares DJ Sel Dividend 

SPY 

EFA 

GLD 

EEM 

IVV 

QQQQ 

IWM 

IWF 

AGG 

IWD 

VTI 

DIA 

TIP 

XLF 

SHY 

LQD 

MDY 

FXI 

IWB 

EWJ 

VWO 

IVW 

XLE 

DVY 

Data 4a 

Data 4b 

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

$US Millions Total Return % 

Annualized Return % 

Fund Name Ticker Assets Exp Ratio 3-Mo 2008 2007 2006 3-Yr 5-Yr Mkt Cap P/E 

93,922.2 

31,910.7 

21,691.1 

19,210.4 

15,652.6 

12,539.0 

10,694.5 

10,688.0 

9,522.0 

9,235.1 

9,199.1 

8,966.0 

8,676.3 

7,791.8 

7,691.7 

6,957.4 

6,782.7 

5,940.4 

5,724.5 

5,640.3 

5,035.8 

4,957.3 

4,453.4 

3,966.5 

0.09 

0.34 

0.40 

0.74 

0.09 

0.20 

0.20 

0.20 

0.20 

0.20 

0.07 

0.17 

0.20 

0.23 

0.15 

0.15 

0.25 

0.74 

0.15 

0.52 

0.25 

0.18 

0.23 

0.40 

ER 

0.95 

0.75 

0.74 

0.75 

0.35 

0.35 

0.50 

0.95 

0.25 

0.50 

0.95 

0.65 

0.95 

0.88 

0.95 

0.65 

0.95 

0.95 

0.79 

0.68 

-22.20 

-19.33 

1.70 

-25.91 

-22.13 

-24.12 

-27.05 

-23.32 

7.38 

-21.85 

-22.74 

-18.76 

-1.26 

-36.14 

2.79 

15.35 

-26.28 

-14.99 

-22.69 

-9.41 

-28.48 

-20.52 

-24.16 

-21.38 

1-Mo 

-22.05 

-29.56 

10.03 

13.06 

6.13 

8.01 

9.49 

-2.56 

5.41 

10.37 

-16.91 

8.25 

-1.51 

12.22 

-9.97 

-18.32 

-15.73 

-38.88 

9.10 

0.19 

-37.22 

-41.05 

4.92 

-48.89 

-37.01 

-41.72 

-34.15 

-38.22 

7.91 

-36.46 

-36.68 

-32.31 

-0.53 

-54.91 

6.62 

2.46 

-36.77 

-47.76 

-37.40 

-26.99 

-52.47 

-34.78 

-38.97 

-32.80 

3-Mo 

-39.49 

-81.37 

-18.86 

-6.41 

-20.99 

-22.67 

5.15 

- 

-22.82 

-6.32 

- 

-49.11 

- 

-28.42 

-21.15 

-1.8 

- 

- 

-51.05 

-28.26 

5.17 

10.01 

31.10 

33.17 

4.93 

19.13 

-1.75 

11.49 

6.61 

-0.72 

5.37 

8.79 

11.93 

-19.17 

7.35 

3.76 

7.23 

54.89 

5.32 

-5.47 

37.32 

8.84 

36.86 

-5.66 

Data 4c 

15.81 

25.79 

21.95 

31.40 

16.34 

7.05 

18.26 

8.94 

3.91 

22.48 

15.68 

18.91 

0.29 

18.82 

3.89 

4.24 

9.96 

82.98 

15.42 

5.84 

29.33 

10.88 

18.01 

19.44 

Data 4d 

Launch Date 

5/1/2008 

6/16/2008 

8/13/2008 

2/27/2008 

3/26/2008 

3/26/2008 

1/31/2008 

11/5/2008 

6/24/2008 

3/13/2008 

11/6/2008 

1/11/2008 

11/5/2008 

2/22/2008 

5/1/2008 

5/6/2008 

11/5/2008 

11/25/2008 

4/15/2008 

3/26/2008 

-8.56 

-6.56 

18.82 

-3.65 

-8.39 

-9.42 

-8.54 

-9.13 

6.13 

-8.24 

-8.27 

-4.33 

3.74 

-24.34 

5.94 

3.48 

-9.32 

13.98 

-8.70 

-9.94 

-5.49 

-7.67 

-0.48 

-8.85 

Assets 

1,548.90 

554.70 

468.00 

388.10 

323.70 

268.40 

263.10 

237.30 

228.60 

189.30 

174.60 

168.00 

165.70 

160.10 

153.40 

116.20 

107.70 

107.20 

97.40 

92.00 

-2.36 

1.92 

- 

8.13 

-2.23 

-3.54 

-1.16 

-3.56 

4.88 

-0.88 

-1.54 

-1.39 

4.39 

-12.60 

3.98 

3.46 

-0.57 

- 

-2.13 

0.65 

- 

-2.86 

13.09 

-1.70 

Selected ETFs In Registration 

iPath S&P 500 VIX Short-Term Futures ETN 

Claymore NYSE Arca Airlines ETF 

Direxion 30-Year Treasury Bear 3X Shares 

Emerging Global Shrs DJ EM Titans Comp 

ETFS Silver Trust 

Global X Egypt ETF 

Grail American Beacon Intl Equity 

JETS DJ Islamic Market International 

sShares KLD Europe AsiaPac Sustainability 

PIMCO 1-3 Year U.S. Treasury Index Fund 

PowerShrs Alt-A Non-Agcy RMBS Opprtnty 

SPDR S&P Food & Beverage ETF 

WisdomTree Dreyfus Oil Exporters Currcy 

XShares/CRB-Res Glbl Energy Efficiency 

Market Vectors - Vietnam 

SPDR S&P Commercial Paper ETF 

ProShares Short CDX N America Inv Grade 

NETS OMXS30 Index Fund (Sweden) 

ETSpreads High Yield CDS Tighten Fund 

MacroShares Major Metro Housing Up Shrs 

Source: IndexUniverse.com's ETF Watch 

38,575 

22,142 

- 

12,423 

38,169 

24,910 

686 

26,616 

- 

32,786 

25,387 

90,112 

- 

25,208 

- 

- 

2,060 

41,812 

29,572 

11,891 

14,140 

47,952 

58,006 

5,789 

Source: Morningstar. Data as of December 31, 2008. Exp Ratio is expense ratio. 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. 

10.7 

5.0 

- 

8.1 

10.9 

15.0 

12.3 

11.8 

- 

10.0 

14.0 

10.7 

- 

11.8 

- 

- 

10.3 

9.9 

10.9 

9.1 

10.7 

10.8 

6.7 

10.9 

Sharpe Std Dev Yield 

-0.77 

-0.44 

0.75 

-0.11 

-0.75 

-0.53 

-0.53 

-0.72 

0.45 

-0.73 

-0.70 

-0.54 

0.04 

-1.21 

1.07 

0.03 

-0.61 

0.44 

-0.74 

-0.81 

-0.18 

-0.69 

-0.02 

-0.80 

15.16 

19.50 

21.26 

28.54 

15.25 

21.31 

19.92 

16.62 

5.24 

15.36 

15.88 

13.63 

8.10 

23.56 

1.86 

11.16 

19.11 

38.75 

15.71 

15.93 

28.48 

15.42 

26.36 

14.95 

3.02 

4.25 

0.00 

3.48 

2.94 

0.46 

1.76 

1.67 

4.59 

3.71 

3.50 

3.35 

6.46 

6.15 

3.41 

5.55 

1.82 

2.70 

2.49 

1.44 

5.00 

1.67 

1.85 

5.85 

Source: 

62 

March/April 2009

Back-Page Blog 

Jim Wiandt 

Matt Hougan 

Blogs In Print! 

Matt Hougan and Jim Wiandt write daily 

blogs on IndexUniverse.com. These blogs often 

feature biting disagreements that get to the 

heart of key investing issues and, according to 

Matt and Jim, are must-reading for anyone who 

wants to stay current on indexing and ETFs. 

Jim Wiandt, editor, Journal of Indexes 

(Wiandt): Matt, I know I’ve called you 

many things in the blogs—Chicken Little, a 

market-timer and a little girl, among other 

things. I want to put all of that behind us 

and give you a chance to truly demonstrate 

your mettle and your wisdom. Here’s my 

question to you: As an active investor, how 

do you feel about ETFs making up 30%-40% 

of equity trading volume, “quantitative” 

and “fundamental” indices, stylized new 

“asset classes” and the rest? 

Matt Hougan, editor, IndexUniverse. 

com (Hougan): Let’s get things straight, 

Jim. You can call me whatever you want, 

but what you should call me is “right.” Your 

Chicken Little epithet came in August 2007, 

when I predicted that national real estate 

prices would fall as much as 30%. You 

called it “hard to imagine.” As of Q3 2008, 

national home prices were down 26% from 

their peak, and heading lower. Chicken 

Little? How about Mr. Right? 

As for the blurring of the lines between 

active and passive, you know how I feel. 

My investing style is as boring as dirt, but 

I love writing about this stuff, so the more 

product innovation the better. I’ll add that I 

think the new long-short commodity funds 

are very interesting. I know you’re going 

to jump up and down on that one, but we 

can’t let bluster get in the way of truth. 

Wiandt: How about instead of calling 

you “Mr. Right,” we go with “Mr. Light,” 

as in light on the facts, long on the wind. 

You’ve been dancing on the grave of the 

real estate market for six months now. So 

jump for joy; I know you’ll still be hollering 

about it in five years—every dog does 

have its day. Ease off, though, on the party 

honkers while grandma gets evicted from 

her house and the global economy heads 

straight for the apocalypse. 

Oh wise one, can you bring out your 

crystal ball and tell everyone what we’re 

in for in 2009? Since you are the master 

marker of markets, surely you’ll know. Real 

estate creeps up, oil continues to trickle 

down, unemployment is up, and the dollar 

is down? I know you’ve already called the 

market floor. Tell us, Mr. Light. 

Hougan: How about this? Indexing will 

beat active. Costs will matter. And ETFs 

will take market share from the mutual 

fund industry. 

Oh, and one more thing: The Celtics will 

crush your unidimensional Cavaliers to win 

the Championship. 

Wiandt: You mean the same Boston 

Celtics that the Cavs just thrashed this 

month? Those Boston Celtics? Enjoy last 

year’s Victory of the Old Men, Mr. Hougan, 

and brace yourself for dynasty. We are all 

witnesses. On the rest, I’m glad to see an 

active investor (long/short commodities 

indeed!) make the only sense he has in 

this whole thread. Mr. Bogle, I’m sure, is 

proud right now of this one child who’d 

gone astray. 

Hougan: As the saying goes, Jim, “Judge 

not lest ye be judged.” And as the other 

saying goes, “Put your money where your 

mouth is.” 

I’ve noticed a big gap open up between 

what you say and what you do in the 

market. You say you love indexing, but 

you’ve owned General Motors common 

stock since 1972. You say you love asset 

allocation, but you’ve never owned a bond 

in your life. You say you’re not a markettimer, 

but you snapped up shares of XLF 

(the Financials ETF) in October 2008. By my 

calculation, you’re down about 30% on the 

trade. Oops! 

At least you talk a good game, though. 

Kind of like the Cavaliers. 

[For more Matt and Jim, check out 

www.indexuniverse.com.] 

64 

March/April 2009

Vanguard on Active vs. Passive - IndexUniverse.com

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