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Documenting The Collapse Of The Correlation Benefit 

Matt Moran 

The Case For Multiple Weighting Methodologies 

Rob Arnott, Vitali Kalesnik, Paul Moghtader and Craig Scholl 

Actively Passive Or Passively Active? 

Craig Israelsen 

Is Buy-And-Hold Dead? A Roundtable 

John Bogle, Diane Garnick, Jeremy Siegel and others 

Plus Prestbo, Gastineau, Blitzer and more!

www.journalofindexes.com 

Vol. 13 No. 1 

features 

Record-High Correlations Pose Challenges 

For Modern Portfolio Theory 

By Matt Moran ....................... . . 10 

Correlations spiked in 2008; what does it mean? 

Beyond Cap Weight 

By Rob Arnott, Vitali Kalesnik, Paul Moghtader 

and Craig Scholl ........................ 16 

Should you use multiple weighting methodologies? 

What Does ‘Buy-And-Hold’ Really Mean? 

By John Prestbo ........................ 32 

A basic concept is more nuanced than many suspect. 

Is Buy-And-Hold Dead? 

Featuring John Bogle, Diane Garnick, Jeremy 

Siegel, Burton Malkiel and more ........... 34 

Journal of Indexes asks if 2008 changed everything. 

Actively Passive? 

By Craig Israelsen ....................... 40 

“Passive” should not mean “neglect.” 

The Future Of Fund Ratings, Part Two 

By Gary Gastineau . ...................... 46 

Our series on fixing fund rating systems continues. 

The Spirit Of Indexing 

By David Blitzer ........................... 68 

A concerned investor seeks reassurance. 

news 

Dow Jones Sells Stoxx Indexes . . . . . . . . . . . . . . 56 

Schwab ETFs Debut With Competitive Pricing ... 56 

GDP-Weighted Bond Indexes Provide Alternative ...56 

Indexing Developments ................... 56 

Around The World Of ETFs ................ 59 

Back To The Futures . . . . . . . . . . . . . . . . . . . . . 61 

Know Your Options ...................... 61 

From The Exchanges ..................... 61 

On The Move ........................... 61 

10 

40 

data 

Selected Major Indexes ................... 63 

Returns Of Largest U.S. Index Mutual Funds ... 64 

U.S. Market Overview In Style . . . . . . . . . . . . . . 65 

U.S. Industry Review . . . . . . . . . . . . . . . . . . . . . 66 

Exchange-Traded Funds Corner ............. 67 

46 

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January/February 2010 

1

Contributors 

Rob Arnott 

Rob Arnott is chairman and founder of asset management firm Research 

Affiliates, LLC. He is also the former chairman of First Quadrant, LP and has 

served as a global equity strategist at Salomon Brothers (now part of Citigroup) 

and as the president of TSA Capital Management (now part of Analytic). Arnott 

was editor-in-chief at the Financial Analysts Journal from 2002 through 2006, 

and has been widely published in financial journals and magazines. He graduated 

summa cum laude from the University of California, Santa Barbara, in 1977. 

David Blitzer 

David Blitzer is managing director and chairman of the S&P 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 

Standard & Poor’s. Before joining Standard & Poor’s, he was corporate economist 

at The McGraw-Hill Companies, S&P’s parent corporation. A graduate of Cornell 

University, Blitzer received his M.A. in economics from the George Washington 

University and his Ph.D. in economics from Columbia University. 

Gary Gastineau 

Gary Gastineau is a managing member of Managed ETFs LLC and ETF 

Consultants LLC; and a partner in Skyhawk Management, LLC, the adviser to a 

market-neutral hedge fund that buys ETFs and sells them short. A new edition 

of his book, “The Exchange-Traded Funds Manual,” will be published by Wiley 

in 2010. He is also the author of “Someone Will Make Money on Your Funds— 

Why Not You? A Better Way to Pick Mutual and Exchange-Traded Funds” 

(Wiley, 2005). Gastineau is a frequent contributor to the Journal of Indexes. 

Craig Israelsen 

Craig Israelsen is an associate professor at Brigham Young University. He writes 

monthly for Financial Planning magazine. Israelsen is a principal at Target 

Date Analytics (www.TDBench.com) and the designer of the 7Twelve Portfolio 

(www.7TwelvePortfolio.com). He and Phil Fragasso recently published “Your Nest 

Egg Game Plan” (Career Press). Israelsen is also the author of the forthcoming book 

“7Twelve: A Diversified Portfolio with a Plan” (John Wiley & Sons). He holds a Ph.D. 

in family resource management from Brigham Young University. 

Matt Moran 

Matt Moran is vice president, business development, for the Chicago Board 

Options Exchange (CBOE). He also has served as trust counsel at Harris Bank 

and as vice president at Chicago Mercantile Exchange. Moran is an associate 

editor of the Journal of Trading and is on the advisory boards of the Chartered 

Alternative Investments Analyst (CAIA) Association and the Journal of Indexes. 

He is a licensed attorney-at-law who has received M.B.A. and Juris Doctor 

degrees from the University of Illinois. 

John Prestbo 

John Prestbo is editor and executive director of Dow Jones Indexes and chairman 

of the Dow Jones Index Oversight Committee. He joined Dow Jones as a 

reporter for the Wall Street Journal in 1964 and was appointed markets editor 

in 1983. Prestbo became editor of the Dow Jones World Stock Index in 1993, 

which became the Dow Jones Indexes business unit in 1996. He received his 

bachelor’s and master’s degrees from Northwestern University. 

2 

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Editor’s Note 

Was 2008 A Last Gasp? 

Or A Rebirth? 

Jim Wiandt 

Editor 

Sound the bells, weep a thousand tears! Buy-and-hold investing is dead. Here we 

are again, my friends, at another six sigma special, a new paradigm, the never ever 

before a hundred times again. Another Great Depression, ah the drama! 

Ho hum. 

My question, and the question that we are really getting at in this issue is this: Is it 

really all that? I love our opening piece by the always provocative and well-sourced Matt 

Moran, because he sort of indicates that, well, yes, maybe it is. The BIG story that comes 

out of last fall is its double-underlined, highlighted restatement of the dirty little secret of 

modern portfolio theory: The very reason we seek out diversification—to seek shelter in 

times of economic turmoil—is the very time when the correlation benefit of diversification 

tends to fail. 

Or that’s what we heard a million times last fall, even as we watched equity prices, 

bond prices, heck, even gold prices plummet in tandem. The Moran piece takes a real 

good look at the data, and his conclusion is that yes, the magnitude of collapsing correlations 

last fall was exceptional. 

Next up is Rob Arnott, looking at, yes, market weighting with his co-authors. But 

they detail various permutations and combinations of cap, equal and economic—or 

“fundamental”—weighting and their history (and future?) in ways that I’m sure you’ll 

find interesting. 

In a surprise appearance, the former editor of this publication, John Prestbo, comes in 

with his own rather stunning assertion that buy-and-hold just may not be the all-ending 

gospel for a sensible, cautious, thoughtful investor. And this from a man who is indexing. 

Directly addressing our “Is Buy-And-Hold Dead?” theme, we’ve got an exceptional and 

insightful virtual roundtable including John Bogle, Diane Garnick, Jeremy Siegel, Burton 

Malkiel and others weighing in on the subject. 

The always interesting, always thorough Professor Craig Israelsen submits that it is not 

so much if a fund is active or passive that matters, but how that portfolio of funds is managed 

by the end-investor, a topic that seems particularly apt as more and more investors 

use passive products in very active ways, even as some active investors use their funds in 

very passive ways. And of course, the professor has got data. Lots of data. 

Next, Gary Gastineau presents part two of his series on mutual funds and ETFs and 

focuses on how fund ratings systems should really work. 

Bringing us home this issue is David Blitzer, who came up with a column spoofing the 

famous New York Sun letter from “Virginia” asking if Santa Claus was real. Only, Blitzer’s 

writer “Bill” asks instead if buy-and-hold is dead, or lives with us still. 

We’re getting back to the basics this issue, like a broad swath of the investing public, 

and we hope you enjoy it. 

See you in Florida at our Inside ETFs conference! 

Jim Wiandt 

Editor 

8 


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Record-High Correlations 

Pose Challenges For Modern 

Portfolio Theory 

Does recent market behavior call accepted wisdom into question? 

By Matt Moran 

10 


Over the past two years, and particularly during the 

financial crisis of 2008, investors have asked many 

questions about their investments, including: 

(1) Have the correlations for many asset classes recently 

skyrocketed to record-high levels near 1? 

(2) Did volatility reach record-high levels in 2008? 

(3) Do the widely accepted principles of diversification 

and modern portfolio theory (MPT) work anymore? 

(4) How can investors properly diversify and manage 

investment risk in their investment portfolios? 

In this paper, I will provide analyses of various benchmark 

indexes and information that could be helpful in attempts to 

answer these provocative and challenging questions. 

Some Index Correlations Recently Hit 

40-Year Record-High Levels 

For the purposes of this paper, I looked primarily at the 

rolling one-year correlations of weekly returns of the S&P 500 

Index vs. dozens of other indexes. In some of the years prior 

to 2008, there was evidence to suggest that some well-known 

stock and commodity indexes could have provided some good 

diversification benefits for a portfolio. For example, in the year 

2003, the GSCI (commodities) Index and S&P 500 had a negative 

0.38 correlation; in 1978, the MSCI EAFE Index had a negative 

0.08 correlation to the S&P 500, and in 1995, the Russell 

2000 and S&P 500 had a correlation of 0.53 (see Figures 1, 2 

and 4). Professional investors often attempt to add asset classes 

with low or negative correlations to their portfolios with 

the goal of smoothing out the returns for the overall portfolio, 

and minimizing the risk of large losses, in accordance with the 

mandate of ERISA (the Employee Retirement Income Security 

Act of 1974) or other fiduciary standards. 

However, in the 2008-2009 time period, the rolling yearly 

correlations of weekly returns of a number of indexes to the S&P 

500 reached all-time record highs—the S&P GSCI rose to 0.65 

(the highest number since the 1969 inception of the GSCI data), 

Russell 2000 to a very high 0.96, MSCI Emerging Markets Index 

to 0.86, Citigroup High-Yield Market Index to 0.82, S&P Listed 

Private Equity Index to 0.92, S&P Global Infrastructure Index to 

0.87, S&P Global Timber & Forestry Index to 0.91 and the Dow 

Jones U.S. Select REIT Index to 0.88 (see Figures 1 through 4). 

The MSCI EAFE Index had a 0.86 correlation of weekly returns 

to the S&P 500 in 2008, its all-time high for calendar years dating 

back to 1970. A prudent investor certainly is entitled to ask 

whether an asset provides much of a diversification benefit if its 

correlation to a core asset class is higher than 0.85 or 0.90. 

Indexes that generally had a negative correlation to 

the S&P 500 in recent years include the Citigroup 30-year 

Treasury Index, the CBOE Volatility Index (VIX, now based 

on S&P 500 (SPX) options) and the CBOE S&P 100 Volatility 

Index (VXO). I will discuss the diversification potential of 

these indexes later in the paper. 

Volatility Reached High Levels 

In 1929, 1932, 1987 And 2008 

The fourth quarter of 2008 was one of the most volatile 

market periods of the past century. In that quarter, the 

Russell 2000 experienced 21 days of moves of 5 percent or 

more (up or down), the S&P GSCI had 14 days of moves of 5 

percent or more and the S&P 500 moved 5 percent or more 

on 16 trading days. In contrast, in the 58-year time period 

from 1950 through 2007, the S&P 500 moved 5 percent or 

more on only 19 trading days. 

In this paper, “historic volatility” refers to the annualized 

standard deviation of past returns of an index. The 30-day 

historic volatility of the S&P 500 Index rose above 77 in the 

autumns of each of these four years—1929, 1932, 1987 and 

2008 (see Figure 5). As noted in Figure 5, since 1900, the 

30-day historic volatility for key U.S. stock indexes had an 

Figure 1 

Rolling One-Year Correlations Of Weekly Returns Of Equity 

Indexes To The S&P 500 (Jan. 1, 1971-Sept. 11, 2009) 

1.0 

0.5 

0.0 

-0.5 

-1.0 

Jan 

’71 

Jan 

’80 

Jan 

’89 

Sources: Bloomberg and CBOE 

Figure 2 

Jan 

’98 

Jan 

’07 

0.96 

0.89 

0.86 

-0.83 

Russell 

2000 

MSCI 

EAFE 

MSCI 

Emerging 

VXO 

Rolling One-Year Correlations Of Weekly Returns Of 

Alternative Indexes To The S&P 500 (Jan. 1, 1971-Sept. 11, 2009) 

1.0 

0.5 

0.0 

-0.5 

-1.0 

Jan 

’71 

Jan 

’80 

Jan 

’89 


Figure 3 

1.0 

0.5 

0.0 

-0.5 

-1.0 

Jan 

’98 

Jan 

’07 

0.88 

0.78 

0.65 

-0.79 

S&P Private 

Equity 

DJ REIT 

S&P GSCI 

Rolling One-Year Correlations Of Weekly Returns Of 

Fixed-Income Indexes And VIX To The S&P 500 

(Jan. 1, 1971-Sept. 11, 2009) 

Jan 

’71 

Jan 

’80 

Jan 

’89 


Jan 

’98 

Jan 

’07 

0.71 

0.02 

-0.35 

-0.79 

VIX 

Citi High- 

Yield 

Citi BIG 

Index 

30-Yr Treas 

VIX 

www.journalofindexes.com January/February 2010 

11

Figure 4 

Correlations Of Weekly Returns Of Indexes To The S&P 500 In Calendar Years (1970-2008) 

Russell 2000 

(RUT) 

EAFE US 

(EAFE) 

CBOE 

Volatility Index 

(VIX) 

SP GSCI TR 

(GSCI) 

S&P Private Equity 

Index 

(Private) 

Dow Jones U.S. 

Select REIT Index 

(REIT) 

1970 — 0.32 — -0.21 — — 

1971 — 0.19 — 0.16 — — 

1972 — 0.32 — 0.04 — — 

1973 — 0.26 — 0.00 — — 

1974 — 0.48 — -0.20 — — 

1975 — 0.39 — -0.15 — — 

1976 — 0.43 — -0.21 — — 

1977 — 0.32 — 0.18 — — 

1978 — -0.08 — 0.11 — — 

1979 0.92 0.38 — 0.22 — — 

1980 0.85 0.59 — 0.43 — — 

1981 0.87 0.28 — 0.18 — — 

1982 0.87 0.61 — 0.31 — — 

1983 0.81 0.53 — 0.03 — — 

1984 0.88 0.49 — 0.05 — — 

1985 0.86 0.22 — 0.07 — — 

1986 0.86 0.50 — 0.11 — — 

1987 0.88 0.49 — 0.04 — — 

1988 0.62 0.03 — -0.35 — — 

1989 0.81 0.31 — -0.08 — — 

1990 0.84 0.44 — -0.49 — — 

1991 0.77 0.52 -0.45 -0.07 — — 

1992 0.64 0.32 -0.41 -0.01 — — 

1993 0.60 0.12 -0.60 0.15 — — 

1994 0.78 0.33 -0.72 0.02 — — 

1995 0.53 0.33 -0.42 -0.07 — — 

1996 0.69 0.52 -0.61 0.34 — — 

1997 0.73 0.57 -0.57 0.02 — — 

1998 0.83 0.64 -0.81 0.08 — — 

1999 0.66 0.51 -0.79 -0.19 — — 

2000 0.77 0.60 -0.77 0.11 — — 

2001 0.89 0.81 -0.85 0.18 — — 

2002 0.81 0.75 -0.77 0.20 — — 

2003 0.84 0.82 -0.58 -0.38 — — 

2004 0.91 0.63 -0.70 -0.23 0.50 0.38 

2005 0.89 0.66 -0.79 0.05 0.61 0.61 

2006 0.90 0.83 -0.84 -0.02 0.56 0.64 

2007 0.91 0.78 -0.85 -0.03 0.83 0.76 

2008 0.94 0.86 -0.82 0.39 0.91 0.80 

Sources: Bloomberg and CBOE. Note: The highest correlations are shown in bold. Some indexes probably will have new yearly high correlations in 2009. 

average value of 15.8, a median of 13.2 and a maximum value 

of 96.5 on Nov. 29, 1929. Over the past 70 years, the 30-day 

historic volatility of the S&P 500 has surpassed 60 in two different 

time periods—it rose to 87.5 in November 1987 and 

to 80.9 in November 2008. 

In this paper, “implied volatility” refers to the expected 

annualized standard deviation of returns of an index or ETF; 

implied volatility is determined by using index or ETF option 

prices currently existing in the market at the time rather than 

using historical data on the price changes of the index or 

12 


ETF. Among the worldwide indexes measuring implied volatility 

are the CBOE Volatility Index (VIX, now based on S&P 

500 options), the CBOE S&P 100 Volatility Index (VXO, based 

on S&P 100 options), the India VIX Index and the FTSE 100 

Volatility Index. These indexes can be very useful, as they are 

designed to be real-time measures of investor sentiment, and 

they can provide a general indication of how costly it might 

be to insure a portfolio with protective index put options. 

For example, if one takes the VIX Index and divides by 10, 

one can get an approximation of the percentage number of 

the underlying that an investor would need to put up as premium 

to hedge with one-month, at-the-money S&P 500 Index 

options. So for example, if the VIX were at 20 and an investor 

desired to hedge a $1 million portfolio with one-month, 

at-the-money S&P 500 Index options, the investor probably 

would need to post a premium of about $20,000 (which is 2 

percent of the $1 million portfolio). 

With a price history dating from 1990, the VIX Index reached 

its all-time intraday high of 89.53 on Oct. 24, 2008. Prior to 

2008, the highest VIX level was 49.53, on Oct. 8, 1998. The VIX 

Index closed at a record 80.86 on Nov. 20, 2008. The VIX Index 

closed above 50 on 50 trading days in 2008. Peak daily closing 

values reached by some other key implied volatility indexes in 

2008 include the following—CBOE Crude Oil Volatility Index 

(OVX) 100.42 on Dec. 11; CBOE Russell 2000 Volatility Index 

(RVX) 87.62 on Nov. 20; India VIX Index (INVIX) 85.13 on Nov. 

17; CBOE Nasdaq-100 Volatility Index (VXN) 80.64 on Nov. 20; 

FTSE 100 Volatility Index (VFTSE) 78.69 on Oct. 16; CBOE DJIA 

Volatility Index (VXD) 74.60 on Nov. 20; CBOE Gold Volatility 

Index (GVZ) 64.53 on Oct. 10; and CBOE EuroCurrency Volatility 

Index (EVZ) 30.66 on Oct. 31 (see Figure 6). 

Several Key Indexes Suffered Big Losses In The 

17-Month Time Period From Late 2007 To Early 2009 

Investment portfolios can endure huge losses in times 

of rising correlations, higher volatility and declining index 

values. As shown in Figures 7, 8 and 9, numerous indexes 

experienced big double-digit declines in the 17-month 

period from late 2007 through early 2009. Six key stock 

indexes (S&P 500, Russell 1000, Russell 2000, MSCI World, 

MSCI EAFE and MSCI Emerging Markets) all declined by more 

than 55 percent, and the S&P GSCI Index rose in early 2008 

but then fell dramatically in late 2008, as there was concern 

about less worldwide demand for oil and other commodities 

with the worsening of the financial crisis. Many alternatives 

indexes also did not fare well over the 17-month time period 

shown in Figure 9; two major hedge fund indexes were down 

17 percent or more, and the S&P/Case-Shiller index of home 

values in 20 markets declined 26.9 percent. In the year 2008, 

the S&P Listed Private Equity Index (TR U.S. $) was down 

64.1 percent, the Cambridge Associates U.S. Private Equity 

Index was down 24.2 percent and the Cambridge Associates 

Venture Capital Index declined 16.5 percent. 

Questions About Diversification 

And Modern Portfolio Theory 

In light of the issues raised above, concerning higher correlations, 

higher volatility and lower returns, some investors 

have questioned whether the widely accepted principles of 

diversification and modern portfolio theory (MPT) continue 

to be effective. In the 1950s, Harry Markowitz introduced 

MPT with an emphasis on diversification of a portfolio 

and assumptions that investors are rational, markets are 

efficient and financial returns follow a normal distribution. 

Markowitz recently wrote that in 2008, “The S&P 500 fell 

approximately 38.5%; the higher-beta emerging-markets 

asset class fell much farther. Corporate bonds fell in value, 

but much less than equities, and government bonds went 

up. Small stocks went down, but not as much as might have 

been expected, given their historical betas. On average, 

they had a lucky idiosyncratic term during that interval of 

time. Generally, asset classes moved roughly in proportion 

to their historical betas. …the future is always uncertain, 

particularly in times of market volatility. When we make 

our best estimates for the next spin of the wheel, we must 

choose an appropriate point on the efficient frontier, the 

risk-return trade-off curve. Depending on our risk aversion, 

we may select a more cautious portfolio, loaded with lower 

beta securities or asset classes, or we may choose a point 

higher on the frontier, with higher yield but with higher 

beta securities or asset classes. …” 1 

Figure 5 

30-Day Historic Volatility For US Stock Indexes 

(DJIA from 1900 to 1927 and S&P 500 from 1928 to Sept 2009) 

End-of-Week Values 

100 

80 

60 

40 

20 

0 

Jan 

’00 

Jan 

’20 


Figure 6 

Daily Closing Values 

160 

120 

80 

40 

0 

Jan 2 

’86 

96.5 on 

11/29/1929 

Jan 

’40 

Mean 15.8 

Median 13.2 

Jan 

’60 

87.5 on 

11/20/1987 

Jan 

’80 

Indexes Measuring 30-Day Implied Volatility 

(Jan. 2, 1986-Nov. 2, 2009) 

150.19 VXO on 

10/19/1987 

Jan 10 

’91 

Jan 18 

’96 


Jan 30 

’01 

100.42 OVX on 

12/11/2008 

80.86 VIX on 

11/20/2008 

Feb 21 

’06 

80.9 on 

11/14/2008 

Jan 

’00 

OVX CBOE 

Crude Oil 


VIX - CBOE 

Volatility 

Index 

VXO - CBOE 

S&P 100 


(pre-1990) 


13

Diversification And Risk Management In 2008 

While no one can predict the future with 100 percent 

accuracy, we can look at the asset performance in 2008 

to try to gain a better idea of which assets might be good 

diversifiers and risk management tools in the event of 

future severe market crises. 

Markowitz noted that government bonds did well in 2008, 

Figure 7 

5 

4 

3 

2 

1 

0 

Oct 9 

’07 

Three Indexes Over 17-Month Time Period 

(Rescaled to 1 on 10/9/07 through 3/9/09) 

March 31 

’08 


Figure 8 

-34.8% 

-47.7% 

-50.4% 

-55.3% 

-55.4% 

-57.4% 

-58.5% 

-59.5% 

-60.3% 

Sept 16 

’08 

March 6 

’09 

224% 

-48% 

-55% 

S&P 500 

VIX Short-Term 

Futures Index 

S&P GSCI 

S&P 500 

% Change In Total Return Indexes Over 17 Months 

(Oct. 9, 2007-March 3, 2009) 


Figure 9 

-17.0% 

-19.3% 

-25.8% 

-26.9% 

-78.8% 

188.4% 

157.5% 

26.9% 

18.3% 

9.1% 

PUT - CBOE S&P 500 PutWrite Index 

S&P GSCI 

Nasdaq-100 

S&P 500 

Russell 1000 

MSCI World Index Net US$ 

Russell 2000 

MSCI EAFE 

MSCI Emerging Mkts 

% Change In Indexes Over 17 Months 

(Oct. 2007-Feb. 2009) 

S&P 500 VIX Sh-Term Futures Index 

VIX - CBOE Volatility Index 

Citigroup 30-yr Treasury 

Barclays CTA 

Citigroup Br Inv Grade Index 

Credit Suisse Tremont Hedge Fund 

CISDM Hedge Fund Equal-Wtd Mkt 

Citi High-Yield Market 

S&P/Case-Shiller 20 Mkts 

S&P Private Equity 

and indeed the Citigroup 30-Year Treasury Index was up 26.9 

percent in the 17-month time period from October 2007 

through February 2009. In the year 2008, these fixed-income 

funds all experienced gains: iShares Barclays Aggregate Bond 

ETF (NYSE Arca: AGG) up 7.9 percent, Vanguard Total Bond 

Market Index (VBMFX) up 5.1 percent, Pimco Total Return 

Institutional fund (PTTRX) up 4.8 percent and Fidelity US 

Bond Index fund (FBIDX) up 3.8 percent. The iShares Barclays 

TIPS Bond ETF (NYSE Arca: TIP) declined slightly (down 0.5 

percent) (source: Bloomberg). 

In the Oct. 29, 2009, BusinessWeek in “Are Investors 

Ready for Higher Interest Rates?” Ben Steverman noted, 

“… the yield on 10-year U.S. Treasuries rose 0.08 points 

to 3.5 percent. That’s still a historically low rate, reflecting 

the fact that the Federal Reserve is holding the short-term 

federal funds rate near zero in order to stimulate the 

economy. It’s the reason why yields on bank savings and 

money market accounts are so paltry. Such low rates aren’t 

sustainable for long periods of time out of fear, among 

other things, that low rates can overheat the economy, 

spark inflation, or drastically devalue the U.S. dollar.” A big 

rise in interest rates over the next few years could put a 

huge dent in the performance of Treasury bonds. 

While Markowitz focused primarily on traditional investments 

such as stocks and bonds, many investors now are 

looking at alternative investments for diversification and risk 

management purposes. The SPDR Gold Trust (NYSE Arca: 

GLD) was up 4.9 percent in 2008, and up around 29 percent 

in 2009 through mid-November (source: Bloomberg). 

Investors now use options strategies such as protective 

puts, collars, and buy-write and put-write strategies to lessen 

portfolio risk. Over the 23-year time period from mid-1986 

through mid-2009, the CBOE S&P 500 PutWrite Index (PUT) 

had lower volatility and slightly higher returns than the S&P 

500 Index. In the 17-month time period shown in Figure 8, 

the PUT Index did decline by 34.8 percent, but still outperformed 

the S&P 500 by more than 20 percentage points. 

Some investors now are exploring the possibility of using 

assets that perform well in high-volatility markets to lower 

their overall portfolio volatility. While some key hedge 

fund indexes fell in 2008, many managed futures programs 

directed by commodity trading advisers (CTAs) performed 

relatively well in times of high volatility and big price swings, 

as the Barclay CTA Index rose 18.3 percent in the 17-month 

period shown in Figure 9. 

Over the past decade, many investors have expressed an 

interest in tradable products based on the CBOE Volatility 

Index, and in recent years, the following products were 

introduced—VIX futures in 2004, VIX options in 2006 and 

two iPath ETNs on VIX futures in 2009. The iPath ETN (NYSE 

Arca: VXX) is based on the performance of the S&P 500 VIX 

Short-term Futures Index, which was up 224 percent in the 

17-month time period shown in Figure 7. A paper in The 

Journal of Alternative Investments showed that if an investor 

started with a typical portfolio and then added a 10 percent 

allocation to VIX futures over the time period from March 

2006 through December 2008, the portfolio’s annualized 

Sources: Bloomberg and CBOE continued on page 62 

14 


Roundtable continued from page 39 

cast. In economic terms, that means, if inflation is currently 

3 percent, you project 3 percent. In other words, they were 

no better than monkeys throwing darts. 

He found that even the forecasts from the people who impact 

the outcome—the Congressional Budget Office, the Council of 

Economic Advisers and the Federal Reserve—and therefore have 

some degree of controlling it, were not more accurate. 

However, a recently published paper found that taking a 

consensus forecast—while it’s not a good forecast—is better 

than most. The Philly Fed Survey forecasts inflation of roughly 

2.5 percent. And, by the way, there is a swap market for inflation; 

it was also at about 2.5 percent not too long ago. 

JOI: Have you adjusted your investment philosophy during the 

last two years? 

Swedroe: [We have adjusted] nothing. For 15 years we had 

exactly the same investment philosophy because our advice 

is based upon what I would call the science of investing and 

evidence-based investing. The evidence today is—if anything— 

stronger that passive management is the strategy most likely to 

allow you to achieve your financial goals. The only thing that 

we’ve done in the last 15 years is added recommendations that 

investors consider adding a small allocation to commodities. 

With the advent of TIPS, we began to strongly recommend 

that people use TIPS as the dominant portion of their fixedincome 

portfolio in tax-advantaged accounts. Another minor 

thing is that we have adjusted our international allocation 

over time. Fifteen years ago, I think we were recommending 

30 percent U.S. Now we are recommending 40 percent. If we 

were pure market-cap weighters, we would be at 60 percent 

international. But we think there are logical arguments for 

having some home-country bias. 

JOI: What’s the biggest danger/opportunity that you see ahead 

for investors during the next five years? 

Swedroe: I think the biggest danger for investors is what 

they see when they look in the mirror. 

The second [biggest danger] is ignorance, because they 

don’t know the science of investing. They are tempted by 

the wolves of Wall Street and their advice. The third thing I 

would say is, if they can’t resist watching CNBC, they should 

do it with the mute button on. Because what they are likely 

to hear could only cause damage by stirring the emotions 

of fear and envy in bear markets, and greed and envy in bull 

markets. The best strategy is what Warren Buffett advises: 

“Invest in index funds and stay the course.” 

Moran continued from page 14 

return was improved by 3.5 percentage points and the portfolio 

standard deviation was cut by one-third. If there were 

a 3 percent allocation to 25 percent-out-of-the-money long 

VIX calls during that time period, the portfolio’s annualized 

returns were increased by 10.4 percentage points. 2 

VIX and the S&P 500 often have had a negative correlation 

of returns, and investors have been intrigued by the possibility 

that VIX could provide value because there often has 

been high volatility of volatility in difficult investing environments 

(the historic volatility of VIX daily returns in 2008 was 

127 percent). Investors who are bullish on VIX and bearish 

on stocks might consider: (1) long VIX call options, (2) long 

VIX call spreads, (3) short VIX put credit spreads, or (4) long 

VIX futures. Investors who are bearish on VIX and bullish on 

stocks might consider: (1) long VIX put options, (2) long VIX 

put spreads, (3) short VIX call credit spreads, and (4) short 

VIX futures. One cautionary note is that sometimes VIX and 

stock prices move in the same direction. 

Conclusion 

A triple whammy of record-high correlations, very high 

volatility and huge losses for dozens of indexes made the 

year 2008 a very challenging one for most investors. In the 

past two years, investors were disappointed to see that correlations 

for the S&P GSCI and the MSCI EAFE vs. the S&P 

500 reached 40-year highs, and all these indexes experienced 

drawdowns of more than 50 percent. Indexes for gold, managed 

futures, volatility and Treasury bonds actually rose in 

2008, and investors could explore the possibility of whether 

limited allocations to these indexes might be effective for 

diversification and risk management purposes in the event 

of a future financial crisis. 

Options involve risk and are not suitable for all investors. Prior to buying or selling an option, a person must receive a copy of 

Characteristics and Risks of Standardized Options; this publication and supporting documentation for any claims, comparisons, recommendations, 

statistics or other technical data in this paper are available by calling 1-888-OPTIONS, or contacting CBOE at www.cboe. 

com/Contact. Past performance does not guarantee future results. The views expressed in this article are the views of the author and do 

not necessarily represent the views of Chicago Board Options Exchange, Incorporated (CBOE). CBOE ® , Chicago Board Options Exchange ® , 

CBOE Volatility Index ® and VIX ® are registered trademarks and BXM, PUT and SPX are servicemarks of CBOE. All other trademarks and 

servicemarks are the property of their respective owners. 

Endnotes 

1 Harry M. Markowitz, “Crisis Mode: Modern Portfolio Theory under Pressure,” The Investment Professional (spring 2009). 

2 Edward Szado, “VIX Futures and Options: A Case Study of Portfolio Diversification During the 2008 Financial Crisis,” The Journal of Alternative Investments (fall 2009), vol. 12, 

no. 2, pp. 68-85. 

62 January/February 2010

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IBG-0311

Beyond Cap Weight 

The empirical evidence for a diversified beta 

Rob Arnott, Vitali Kalesnik, Paul Moghtader and Craig Scholl 

16 


For over 40 years, our industry has relied on the capital 

asset pricing model (CAPM) beta and the capitalizationweighted 

market portfolio for asset allocation, for market 

representation and for our default core equity investments. This 

elegant worldview is now under siege from various directions. 

The “fundamentalists” advocate a portfolio that weights 

companies in accordance with the recent economic scale of 

their businesses, thereby resembling the composition of the 

economy rather than the composition of the stock market. 

The “minimum variance” crowd points to the value of consistency 

between investor objectives and portfolio construction. 

The “egalitarians” advocate equal weighting. Historically, 

these alternative index strategies have delivered higher return 

and lower CAPM beta, which can help an investor to target 

either more return or less risk, or a bit of both. Each of 

these strategies—along with the ever-dominant cap-weighted 

indexes—has strengths and weaknesses, some minor and 

some major. 

The cap-weighted standard is also facing a more subtle 

source of attack. Increasingly, investors are reassessing their 

risk budgets, usually downward. This can create pressure to 

move from active into passive strategies and to lower a fund’s 

exposure to an undiversified single-factor equity risk. But, can 

we lower our risk profile without abandoning our return goals? 

Perhaps it is time to consider a bigger tent, allowing for the 

merits of multiple broad-market indexes and multiple betas. 

We explore the comparative merits of four major categories 

of quasi-passive “index” construction. We do so from a global 

perspective. And we explore the surprising efficacy of combining 

multiple strategies into a diversified beta portfolio. 

Introduction 

Historical concepts regarding market efficiency and singlefactor 

beta are losing favor, as markets have whipsawed even the 

best-diversified portfolios. Just as many investors are increasing 

exposure to passive strategies, they face a new and unsettling 

prospect of “benchmark regret,” to borrow from the terminology 

of behavioral finance, as it’s no longer clear that market-capitalization 

weighting (cap weight) is the only legitimate benchmark 

or core portfolio choice. In fact, institutional investors can choose 

from a wide array of alternative beta strategies, including equal 

weight, minimum variance and economic size (also known as the 

fundamental index approach), to name a few. 

These alternatives have generally offered better returns 

or lower volatility, or both, when compared with cap weight, 

both in historical tests and on live assets, albeit over a 

shorter span than cap weight and on a smaller asset base. 

If the performance advantage of the alternatives persists, a 

decision to commit to cap weight and to ignore the alternatives 

may someday be second-guessed as an overly narrow 

and costly mistake. Although some in the mainstream indexing 

community dismiss these alternative beta strategies as 

cleverly packaged active management strategies, we believe 

that these alternatives provide useful alternatives to the 

single-beta cap-weighted index portfolio. 1 

The historical record for each of these alternative index 

strategies suggests some particular competitive advantage. 

Equal weight has the longest live track record of added value, 

dating back to the early days of the Value Line index; minimum 

variance offers the highest historical Sharpe ratio and lowest 

risk; economic scale portfolios offer the highest information 

ratio; and cap weight offers vast scalability, theoretical purity—in 

an efficient market, the others should not win on a riskadjusted 

basis—and, of course, the lowest tracking error relative 

to the stock market, which is inherently cap-weighted. 

In the rapidly changing world of indexing, any investment 

decision is an active choice, even a switch from active into 

passive exposure. The decision to invest passively provides 

only a starting point for determining which passive or quasipassive 

approach best meets an investor’s needs. Cap weight 

is no longer the only compelling choice, not to mention that 

there are many cap-weighted indexes to choose from. 

Our research focuses on a few of the “index” strategies 

that are gaining traction in the marketplace and explores the 

potential value of a diversified approach in our quest for beta. 

Some call these new ideas beta-prime, some call them enhanced 

indexing, still others dismiss these approaches as active management 

in sheep’s clothing. Whatever we call them, few would 

deny that they are fast changing the investing landscape. 

De-leveraging And Noncap Weighting 

Many investors are reducing their risk budget—some 

term this de-risking—for a host of reasons. Some are doing 

so to reduce the sometimes-frightful gap between assets and 

liabilities. Others are acting out of a fear of an over-leveraged 

global economy and the impact that this leverage can have 

on capital markets’ volatility. Others are doing so to rein 

in the impact that funding ratio volatility can have on their 

earnings. And many are doing so because of a fear that the 

current low yield for stocks, paired with an uncertain inflation 

outlook, casts a cloud of doubt over the future prospects 

for the much-vaunted equity risk premium. 

Keith Ambachtsheer, Marty Leibowitz and Peter Bernstein 

have noted repeatedly over the past quarter-century that 

there’s a peculiar link between inflation and real equity 

returns. When inflation is low and stable, equity valuation 

levels rise and returns are handsome. When deflation strikes, 

government bond prices soar and equity valuation levels 

crater. The correlation between stocks and bonds tumbles. 

And, when reflation leads to sustained high rates of inflation, 

bond yields soar (bond prices fall) and equity valuation levels 

tumble. The correlation between stocks and bonds soars. 

It is far beyond the scope of this paper for us to weigh in on 

the sometimes vitriolic debate about the relative risk of deflation, 

reflation, stagflation or hyperinflation in the years ahead. 

However, it is well worth noting that there are remarkably few 

observers of current markets who harbor hopes for a benign 

move back to the low, stable and well-orchestrated rates of 

inflation of the 1990s and pre-crash 2000s. Because either 

extreme—deflation or stagflation—tends to be rather savage 

to equity valuation levels and real earnings growth prospects, 

these fears fuel additional calls for de-risking. 

De-risking an institutional portfolio can mean many 

things. Typically, de-risking involves investors shifting from 

riskier assets into more defensive assets. This can mean 

lower overall equity allocations, lower beta strategies and/ 

www.journalofindexes.com January/February 2010 17

or allocating more of our risk budget to passive management 

and away from active management. Shifting assets 

from active to passive management is a popular choice for 

many reasons, including lower total costs and the empirical 

evidence that most—but assuredly not all!—active managers 

fail to add value. 

A more subtle reason for de-risking, which can be observed 

but not measured, is public anger at a perception of “Wall 

Street greed.” The low costs of cap weight, and its new quasiindex 

brethren, mean that more of the return of the holdings 

flows to the investors, and less to the managers, brokers, custodians 

and other intermediaries. This pleases many customers! 

For all of these investors, the noncap-weighted “index” 

strategies are important additions to the investment tool kit. 

If these strategies offer higher return and/or lower volatility 

on average over time, as history would suggest, at a fraction 

of the cost of fully active strategies, then investors can 

choose to reach for more return at the same portfolio risk or 

they can choose to reduce equity market exposure without 

necessarily reducing the return. 

Index Alternatives 

Of the alternatives, we have chosen to explore—and 

combine—the four approaches that are garnering the most 

attention as alternative core equity strategies. As we delve 

into their characteristics, let’s also examine the principles and 

tacit core assumptions that lay a foundation for each. 

Some of the key attributes for the four index strategies 

are summarized in Figure 1. For purposes of evaluating 

global results, across all four types of strategies, currencies 

were hedged by using interest-rate differentials to approximate 

the impact of continuous hedging; this did not include 

the impact of actual hedge costs (Lazard, 2009). 

Cap Weight 

Market capitalization remains immensely popular as the 

incumbent and theoretically efficient choice, despite doubts 

about whether its core theoretical underpinnings—the 

efficient market hypothesis (EMH) and CAPM—are precisely 

correct. Cap weight tacitly assumes that share-price-implied 

consensus expectations, regarding the net present value of 

each company’s future growth prospects, are an unbiased 

view of the future. Furthermore, cap weight offers very low 

turnover, trading costs and tax consequences, which the 

newer alternatives can’t quite match. 

As EMH and CAPM gained traction in academia, the theoretical 

result—that a single portfolio could be optimal—was 

revolutionary. The theoretical purity of cap weight, along 

with the difficulties faced by the average active manager, in 

time gave rise to passive investing. The growth in “index 

funds” was fueled by the historical fact that the average 

active manager has had a hard time beating cap-weighted 

indexes, after taking account of fees and transaction costs. 

No student of the capital markets should find this the 

least bit surprising. After all, if we divide the market into the 

passive, cap-weighted indexes and the combined holdings of 

all active, noncap-weighted portfolios—including individual 

investors—the former matches the market in both holdings 

and performance, which means that the latter must also match 

the market, before costs. So, net of costs, the noncap-weighted 

active managers must collectively lag cap weight. None of this 

requires market efficiency, nor is it necessary to believe that we 

cannot “beat the market.” We need only admit that winning 

active managers must have losing managers on the other side of 

their trades! Even as indexing gained traction, a growing body 

of empirical evidence suggested that patient investors could 

achieve above-market performance, with statistical significance, 

most notably with a value tilt. 

If EMH and CAPM are mere approximations of the real 

world, then the assured dominance of cap weight, on a riskadjusted 

basis, evaporates. Suppose we believe that markets 

are inefficient, and that investors are subject to errors that 

result in share prices that deviate from their fair valuations. 

When investors construct portfolios that weight companies 

proportional to capitalization, they inherently overweight the 

overpriced stocks and underweight the underpriced stocks. 

This truism has been acknowledged by many in the indexing 

community, and dismissed because of the equally relevant 

truism that we cannot know which companies are over- or 

undervalued. But, if we can sever the link between over- or 

undervaluation and portfolio weight, perhaps we can improve 

upon cap weight. Or so the “fundamentalists” suggest. 

Figure 1 

Portfolio Construction Comparison 

Portfolio/Index Relative Size Determination Required Forecasts Turnover and Trading Costs 

Cap Weight Market Cap None a Minimal 

Economic Scale 

Fundamental size None Somewhat higher 

(economic footprint) 

than market cap 

Global Equal Weight Equal None b High 

Equal Weight Cap 1000 Equal None b High 

Equal Weight Econ 1000 Equal None b High 

Minimum Variance 

Risk contribution Volatility and correlations Somewhat higher 

(equal marginal change in risk) 

than market cap 

a The weight tacitly reflects a market consensus forecast for future risk-adjusted shareholder distributions. 

b There is a selection bias issue explored in the text: Which stocks do we select for our equal weighting? 

Sources: Research Affiliates, LLC, and Lazard Asset Management 

18 


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MUTUAL FUNDS | ETFs | ETNs | INSURANCE FUNDS | SEPARATE ACCOUNTS | ALTERNATIVE INVESTMENTS

Economic Scale (Or Fundamental Index) 

The economic scale approach uses a company’s fundamental 

economic size—weighting companies according to sales, 

cash flow, book value and dividends, then averaging the four 

measures—both to select the 1,000 largest companies and then 

to assign portfolio weights to each company in an index. 2 

The “fundamentalists” point out that if the market is 

inefficient and prices may stray above or below a company’s 

future true fair value (which Bill Sharpe whimsically terms its 

“clairvoyant value,” because only a clairvoyant could know 

that value), the cap weight of every overvalued company will 

be above its fair value weight, and the cap weight of every 

undervalued company will be below its fair value weight. 

This truism means that a cap weight portfolio will experience 

a performance drag relative to a clairvoyant-value-weighted 

portfolio. 3 This is not controversial. As we cannot know 

future cash flows on our investments, and so cannot construct 

the clairvoyant-value-weighted portfolio, so what? 

Suppose we break the link between over- and underweighting 

relative to clairvoyant value weight (the clairvoyant 

error in weight) and over- and undervaluation relative 

to clairvoyant value (the clairvoyant error in price). In other 

words, suppose that there’s no correlation between the two 

“error gaps”—clairvoyant error in price and clairvoyant error 

in the weight in our portfolio. We still have overvalued and 

undervalued companies; we still have companies that are 

above or below fair value weight. But, these are no longer 

identically the same lists. The errors cancel. 

If we weight companies by their fundamental economic size, 

we enjoy many of the attractive attributes of the cap weight 

portfolio, such as liquidity, low turnover, scalability and objectivity. 

But we no longer assuredly overweight the overvalued 

stocks and underweight the undervalued stocks, relative to the 

unknowable “clairvoyant value weight” of each company. In 

so doing, if the market is inefficient and the price contains a meanreverting 

error, we arguably eliminate the greatest Achilles’ heel 

of cap weight: the performance drag associated with its assured 

overreliance on the overvalued companies. 

Selecting and weighting companies for a stock market 

index, using fundamental economic measures of company 

size, was introduced by Arnott, Hsu and Moore in 2005. 

Some such approaches rely on single measures, such as 

dividends or revenues. Others rely on multiple measures. 

The result is a portfolio where position size is proportional 

to some measure of a company’s “economic footprint.” Just 

as our footprint in the sand at the beach has multiple measures—length, 

width, depth—the footprint of a company in 

the economy has multiple measures. The FTSE RAFI methodology, 

which we use in this research, relies on four measures 

of the size and recent success of a company, including sales, 

cash flow, book value and dividends. This methodology creates 

a representative portfolio, weighted to mirror the look 

and composition of the publicly traded economy, rather than 

the look and composition of the stock market. 

The “fundamentalists” argue that this economic scale 

serves as an anchor for contratrading against the constantly 

shifting expectations of the market, and that this contratrading 

is the primary profit mechanism of economic scale 

portfolios. Some even suggest that economic scale strategies 

do not earn an alpha at all; rather, they suggest that 

cap weight incurs a negative alpha, against its opportunity 

set, which economic scale partly corrects. 

The economic scale portfolios do not have a monopoly on 

this advantage: The same holds true for any index method that 

provides a steady anchor for contratrading against the market’s 

most extreme bets. This same argument may be made for any 

weighting scheme that does not take share price into account 

when setting portfolio weights, which brings us to the other 

two index structures that we wish to explore in this paper. 

Equal Weight 

The equal weight approach assigns an equal weight to each 

company in an index, thereby tacitly assigning zero information 

value to all public and private information about a company 

except for its inclusion in the source index. For instance, 

the S&P 500 Equal Weight Index (S&P EWI) tacitly assigns 

value to a stock’s inclusion or exclusion from the S&P 500 

Index, but no value to any differentiating information, which 

might lead us to prefer any one company over any other. Equal 

weighting was the basis for the first index futures (the Kansas 

City Value Line Index futures from the early 1980s), has the 

longest history of the “index” alternatives, and provides an 

interesting counterpoint to cap weight. 

Suppose we assume that it is impossible for any investor 

to predict a security’s risk or return, or the covariance 

matrix. Then, it follows that holding an equal amount of each 

investable security results in the portfolio with the lowest 

predicted risk, at no sacrifice to our expected return. Put 

another way, if the cap weight portfolio reflects the view that 

the aggregate investor universe fully incorporates risk and 

return forecasts, then equal weight assumes that the aggregate 

investor universe has zero ability to forecast anything. 

For practitioners, the elegant simplicity of an equally weighted 

portfolio is compromised by implementation issues. Because 

equal weight means that we hold small companies on the same 

scale as large ones, the strategy results in higher transaction 

costs and lower capacity than cap weight. Still, absent trading 

costs and any view on forecasting return or risk, equal weighting 

has considerable appeal on a risk-return basis. 

One nuance that has received startlingly little attention in 

the academic and practitioner journals is: Equal weighting of 

what index? If cap weight has a bias toward including overvalued 

companies, then equal weight may exacerbate this bias. For 

instance, a clairvoyant might assert that the future prospects 

of 150 companies in the S&P 500 do not justify inclusion in the 

index. Their “clairvoyant value” market cap is too low. Because 

they will assuredly under-perform eventually, they will pull 

down the S&P 500 return relative to our mythical clairvoyant 

value portfolio. But, where these stocks might comprise 5-10 

percent of the S&P 500, they comprise 30 percent of the S&P 

EWI. We have a possibly severe “selection bias” problem! 

So, what list should we use? 

Suppose, instead, we equal-weight the 1,000 largest companies, 

measured by their economic footprint. We know that 

the 1,000 largest market-cap stocks have considerable overlap 

with the 1,000 largest companies measured in terms of 

20 


economic footprint (a Fundamental Index ® portfolio). Large 

companies are usually large-cap, and vice versa. There would 

typically be 700-800 overlapping companies. So, if we equalweight 

a large-cap 1,000-stock index, there will be 200-300 

companies in the portfolio that are—by definition—small 

companies trading at lofty enough multiples that they become 

large-cap. Empirically, many of these subsequently disappoint. 

Reciprocally, by equal-weighting the 1,000 largest companies 

selected based on fundamental economic scale, we 

include 200-300 companies that are—by definition—large 

companies trading at deep enough multiples to be small-cap. 

While these might comprise 3-5 percent of the economic scale 

portfolio, they comprise 20-30 percent of the equally weighted 

economic scale portfolio. Many of these are of good clairvoyant 

value and many are not; it’s a more random result. The 

empirical result is sharply higher performance than equally 

weighting an index that has been selected by market cap. 

We should note that no one has built a product based 

on equally weighting a Fundamental Index portfolio, but we 

think it’s a very interesting idea. It has similar merits and 

demerits, when compared with the now widely accepted 

equal-weight portfolios based on cap-weighted indexes such 

as the S&P 500. In an efficient market, these two equalweight 

portfolios should have much the same return. In 

practice, they do not. Accordingly, to mitigate the potentially 

serious problem of selection bias, we construct two equally 

weighted 1,000-stock portfolios—cap weight and economic 

scale—and then equal-weight the 1,000 largest, based on 

the combined rankings of size. And we test all three. Applied 

globally, we get our global equal weight portfolio. 

Minimum Variance 

Minimum variance portfolios are designed to reduce 

portfolio risk. In an efficient market, this should not improve 

our risk-adjusted returns. But, if equity returns are not linearly 

related to beta, as CAPM predicts, it may generate high 

risk-adjusted returns. This approach, introduced in the early 

1990s, has been gaining traction recently. It builds portfolios 

without reference to a benchmark, by using historical 

measures of risk with the goal of minimizing the portfolio 

volatility. Its efficacy depends on the market mispricing risk. 

In a world increasingly focused on risk, it is unsurprising that 

this concept is gaining attention. 

Investors have traditionally created equity portfolios that 

manage risk relative to market indices; less attention has 

been paid to the question of which index best meets investors’ 

needs. Minimum variance portfolios are constructed 

to create high risk-adjusted returns by minimizing volatility 

without reference to return expectations. 4 Haugen and 

Baker (1991) were pioneers in this domain; their U.S.-focused 

research principally concluded that, due to investor restrictions 

on short selling, tax situations, and risk and return 

expectations, portfolios could be constructed that dominated 

the market portfolio in terms of risk-adjusted returns. 

Alternatively, this incremental return could potentially 

be explained by the presence of additional unidentified 

risk sources in the low-volatility portfolio. An alternative 

explanation of the incremental return is that these portfolios 

systematically favor risks that the market has mispriced. Risk 

can be mispriced due to differences in measurement as well 

as the relative importance that investors place on different 

measures of risk. The classic definition of risk as the volatility 

of total return is inconsistent with investor experience. 

Falkenstein (2009) suggests a utility function that measures 

risk within the context of relative wealth and that this is 

an outcome of investor preference for status. This perspective 

is consistent with the institutional investor focus on 

information ratio as the preferred measure of risk-adjusted 

returns. Evidence that risk preferences vary among individual 

investors is provided by Dorn and Huberman (2009), who 

examined a large number of broker accounts and found 

that holdings tended to cluster by volatility. Portfolio risk 

considerations are secondary to return expectations and the 

comfort of stocks that are within preferred risk habitats. 

A related opportunity has been identified to invest in 

stocks with low volatility. This portion of the universe has 

been found to have greater-than-market returns, while stocks 

with high volatility empirically tend to deliver lower returns. 

Ang et al. (2006) documented this effect while researching a 

broad universe of U.S. stocks and concluded that the effect 

could not be explained by size, book-to-market, momentum 

and liquidity. Similar effects were found in European and 

Japanese markets by Blitz and van Vliet (2007), who controlled 

for illiquidity and found the results to still be intact: 

Low volatility stocks deliver higher risk-adjusted returns, 

even when controlling for value and size. Investors have used 

this approach within both active and quasi-passive styles. 

Minimum variance offers an interesting challenge for our 

purposes: There are as many ways to construct a minimum 

variance portfolio as there are ways to measure past, present 

or future risk! The strength of the academic evidence in favor 

of minimum variance has prompted benchmark providers, 

such as MSCI Barra, to calculate their own minimum variance 

portfolios. The MSCI World Minimum Volatility Index, 

launched in 2008, experienced approximately 30 percent 

lower volatility than the MSCI World Index over the simulated 

history (1995-2007), with a 50 percent improvement in 

the Sharpe ratio. But, the MSCI Barra methodology is proprietary, 

so we cannot replicate it for our purposes. 

It is not our intent to be exhaustive in exploring the many 

permutations of minimum variance, so we have adopted 

the approach that our authors from Lazard use in one of 

their minimum equity risk strategies. Risk measurement is 

based on a diversified approach that incorporates multiple 

measures, including interest rates, oil prices, region and 

sector as well as size, yield and growth, as calculated by the 

Northfield global risk model. We minimize the absolute risk 

of the portfolio, subject to some constraints to assure broad 

diversification and investability. 5 

Combining The Indexes 

These methods provide discrete choices to the investor, 

with very different and surprisingly complementary 

characteristics. This is by no means an exhaustive list. For 

instance, two organizations in France, TOBAM and EDHEC, 


have developed very interesting “maximum diversification” 6 

and “efficient index” portfolios. The TOBAM team, formerly 

members of the Lehman Brothers quantitative research 

group, constructs a “maximum diversification” portfolio that 

has an equal and lowest-possible correlation with its constituent 

holdings, and for which all excluded assets would boost 

the correlations, if included. The EDHEC “efficient index” 

portfolio is based on presuming that return is linearly linked 

to a general measure of total risk (semi-deviation) and then 

using Markowitz mean-variance optimization to identify the 

tangency portfolio. Both ideas are fascinating variants of the 

broad concept of minimum variance. For another example, 

see Held (2008), for equal-weighted sector rebalancing. 

We think the four strategies that we include in our 

research are the most widely accepted passive and quasi-passive 

alternatives. They can be combined to create a compelling 

investment—and a “diversified beta”—that incorporates 

many of the historical advantages of passive portfolios while 

perhaps earning higher returns or experiencing less risk. This 

paper reviews the comparative characteristics and returns 

for each approach. We then consider the use of a diversified 

set of methods in combination and provide some concluding 

comments and suggestions for further research. 

To test whether investors are better off using combinations 

of passive strategies, we look at two additional strategies. 

The first is an “efficient beta,” which is calculated by equally 

weighting cap weight, economic scale and minimum variance 

strategies. We call this combination “efficient beta,” as it has 

Figure 2 

relatively low transaction costs and substantial investment 

capacity. The second equally weights all four indexes. We tacitly 

assume monthly rebalancing of the three or four strategies 

back to equal weights. Trading costs are also explored, even 

though the resulting turnover for all of these strategies is relatively 

slight, so this layer of costs will be minimal. 

Currencies were hedged by using interest-rate differentials 

to approximate the impact of continuous hedging and 

did not include the impact of actual hedge costs. As is reasonably 

standard in published index returns, our results do 

not reflect transaction costs. However, trading costs matter, 

even with low-turnover index and quasi-index strategies. The 

impact of transaction costs can be inferred based upon the 

annual turnover and average market cap of the respective 

portfolios, as we briefly summarize later. 

Relative Performance 

Our research covers global equity strategies that are fully 

hedged back into U.S. dollars, covering the period January 

1993 through June 2009. This relatively recent span is a 

function of available global data. We required sufficiently 

detailed information across global markets to permit construction 

of all four strategies, and information complete 

enough to include all nonsurviving companies of sufficient 

scale to enter any one of our portfolios. 

Critics may point to the short history in this study, style 

biases in these alternative core portfolios, implementation 

challenges and so forth. Because we wanted to test these 

Annual Comparative Returns, 1/1993-6/2009 

Year 

Cap 

Weight 

Economic 

Scale 

Equal 

Weight 

Equal Econ 

Weight 

Equal Cap 

Weight 

Minimum 

Variance 

Efficient 

Beta 

All Four 

Combined 

1993 18.8% 26.5% 21.3% 26.4% 19.5% 20.3% 21.9% 21.8% 

1994 0.3% 1.6% 0.7% 1.4% -0.7% -2.3% -0.1% 0.1% 

1995 20.2% 22.7% 20.3% 20.4% 20.5% 22.6% 21.8% 21.5% 

1996 16.6% 19.0% 16.0% 17.8% 15.7% 20.7% 18.7% 18.1% 

1997 22.5% 25.0% 19.7% 19.5% 20.3% 28.6% 25.4% 24.0% 

1998 19.9% 17.0% 16.0% 15.2% 16.2% 15.1% 17.4% 17.1% 

1999 32.7% 27.7% 20.7% 20.3% 24.3% 8.8% 22.8% 22.3% 

2000 -8.3% 8.8% 9.6% 13.2% -1.5% 14.0% 4.6% 5.9% 

2001 -14.0% -4.7% -4.7% -0.4% -9.3% -1.2% -6.7% -6.2% 

2002 -22.3% -20.0% -18.7% -16.4% -20.3% -13.5% -18.6% -18.6% 

2003 26.5% 30.5% 31.5% 35.1% 29.3% 17.6% 24.8% 26.4% 

2004 12.6% 13.8% 17.3% 18.4% 16.4% 20.6% 15.6% 16.0% 

2005 18.6% 19.5% 22.3% 23.3% 22.4% 20.3% 19.5% 20.2% 

2006 18.3% 22.2% 20.8% 21.8% 19.7% 22.3% 20.9% 20.9% 

2007 7.4% 6.2% 4.8% 2.9% 6.5% 6.3% 6.6% 6.2% 

2008 -38.6% -38.4% -40.1% -39.9% -41.1% -29.1% -35.5% -36.6% 

2009 5.7% 7.7% 10.3% 14.3% 10.2% 0.5% 4.7% 6.1% 

Hi/Lo 3 vs 8 3 vs 0 — 4 vs 0 0 vs 2 7 vs 5 — — 

Win/Loss — 14 vs 3 11 vs 6 12 vs 5 10 vs 7 11 vs 6 11 vs 6 11 vs 6 


22 


Figure 3 

Portfolio/Index 

Ending 

Value 

of $1 

Geometric 

Return 

* Significant at 95% confidence level. 


Return Characteristics, 1/1993-6/2009 

Volatility 

Sharpe 

Ratio 

Excess Return 

vs. 

Cap-Weighted 

Tracking Error 

vs. 

Cap-Weighted 

Information 

Ratio 

t-Statistic 

for Excess 

Return 

Cap Weight 2.78 6.39% 14.50% 0.18 — — — — 

Economic Scale 4.44 9.46% 14.35% 0.40 3.07% 5.00% 0.62 2.31* 

Global Equal Weight 3.83 8.48% 14.44% 0.33 2.10% 4.65% 0.45 1.71 

Equal Weight Cap 1000 3.12 7.14% 15.10% 0.22 0.75% 3.09% 0.24 1.05 

Equal Weight Econ 1000 4.82 10.00% 14.86% 0.42 3.61% 5.98% 0.60 2.31* 

Minimum Variance 4.31 9.26% 10.70% 0.52 2.87% 7.39% 0.39 1.20 

Efficient Beta 3.80 8.43% 12.81% 0.37 2.04% 3.71% 0.55 1.83 

All-Four-Combined 3.81 8.45% 13.18% 0.36 2.06% 3.75% 0.55 1.89 

ideas on a global scale, and across all four methods, the 

research is necessarily rooted in a relatively short historical 

span, covering just over 16 years of equity market results. 

However, the results mirror the country-by-country results 

of others, testing single methods, and mirror the longer-span 

results observed in less-than-global applications. Although 

this history is not as long as we might prefer, these results do 

span several market cycles, including much of the secular bull 

market of the 1990s and the secular bear market since. 

For a proper apples-to-apples comparison, we created a 

simulated cap weight portfolio. Because the economic scale and 

global equal weight portfolios each span 1,000 companies, we 

wanted to create a cap weight developed markets index that 

is as objective as possible. This portfolio comprises the 1,000 

largest companies domiciled in the 23 developed economies 

contained in the FTSE and MSCI developed world indexes, 

selected and then weighted by market cap. The methodology 

is analogous to a developed world “Russell 1000.” It will come 

as no surprise that it tracks very closely with the published 

currency-hedged FTSE and MSCI developed world indexes. 

The year-by-year results, in Figure 2, show that there are markets 

in which each will shine. Cap weight is best of the bunch in 

1998, 1999 and 2007, years in which growth won handily and 

active managers generally struggled. Economic scale was best in 

three years and the equal-weighted economic scale portfolio was 

best in another four years. And minimum variance was best in 

seven years, surprisingly not just in the weak years for stocks, 

when shunning risk would be expected to win. 

Of course, the composite strategies can never be best or 

worst, because that would require them to beat all of their 

constituent portfolios or lose to them all, over an individual 

year. This affects the combined global equal weight portfolio, 

as well as the efficient beta and all-four-combined strategies. 

They also exhibited differing “batting averages” when 

compared with cap weight, ranging from 14 wins and 3 

losses for economic scale, to 10 wins and 7 losses for equalweighting 

the cap weight portfolio. We should point out 

that, with only 17 years (actually 16½!) of data, a batting 

average of 13-to-4 is required for 95 percent two-tail confidence 

statistical significance. 7 Although many practitioners 

think of 17 years as a long time, statistically it is not. 

As Figure 3 shows, all of the noncap-weighted strategies 

offer superior performance over the simulated cap weight 

strategy over this span. Interestingly, the two subindexes of 

the global equal weight portfolio have very different results. 

When we equal-weight the 1,000 largest companies by market 

capitalization, we outpace the market cap portfolio by 

some 75 bps per annum with notably higher risk. When we 

equal-weight the 1,000 largest companies, based on a blend 

of four measures of the scale of a company’s business, we 

outpace the economic scale portfolio by a smaller margin of 

54 bps, again with higher risk. When we combine the two 

universes (equal-weighting the 1,000 largest, based on the 

sum of the two rankings), we beat cap weight by 210 bps per 

annum while falling less than 100 bps behind the economic 

scale portfolio, with risk very near the average of cap weight 

and economic scale. 

Each strategy has its own strengths and weaknesses. Cap 

weight tautologically has the lowest tracking error and should 

maximize risk-adjusted return—if EMH and CAPM hold fully 

and perfectly true. Minimum variance achieves its objective 

with the lowest volatility of 10.70 percent and highest 

Sharpe ratio of 0.52. The economic scale portfolio, measured 

relative to the cap weight portfolio, has the highest information 

ratio, 0.62, and ties with its own equal-weight variant 

for best statistical significance for alpha, with a t-statistic of 

2.31. Equal-weighting the cap weight portfolio offered the 

lowest tracking error of the noncap-weighted strategies, but 

also delivered the highest volatility. 

The combinations are surprisingly robust. When investors 

are uneasy about a singular reliance on cap weight for their 

core holdings—and, so, choose either beta combination 

strategy—both beta combinations result in higher performance 

and lower volatility when compared with an exclusive 

use of the cap weight strategy. Even relative to the constituent 

noncap-weighted strategies, for both of the combinations 

that we test herein, we wind up with an array of attractive 

attributes: 

• Our return is modestly higher than the average of the 

individual strategy constructs, while our risk is similar, leading 

to a slightly better Sharpe ratio than the average of the 

constituent single portfolios. 


Figure 4 

Figure 5 

Growth of $1, In Different Beta Strategies 

Vs. Cap Weight, 1/1993-6/2009 

Growth of $1, In Combined Beta Strategies 

Vs. Cap Weight, 1/1993-6/2009 

$8 

$7 

$7 

$6 

Growth of $1 

$6 

$5 

$4 

$3 

$2 

Growth of $1 

$5 

$4 

$3 

$2 

$1 

$1 

$0 

Dec 

’92 

Dec 

’94 

Dec 

’96 

Dec 

’98 

Dec 

’00 

Dec 

’02 

Dec 

’04 

Dec 

’06 

Dec 

’08 

$0 

Dec 

’92 

Dec 

’94 

Dec 

’96 

Dec 

’98 

Dec 

’00 

Dec 

’02 

Dec 

’04 

Dec 

’06 

Dec 

’08 

Economic Scale Cap Weight Minimum Variance Equal Weight 

Cap Weight Efficient Beta All Four Combination 



• The tracking error is about 10 percent less than the 

average of the constituents, leading to an information ratio 

that’s quite a bit higher than the average. 

The same holds true for the combination of all four index 

methods (cap weight, economic scale, global equal weight and 

minimum variance). Indeed, adding global equal weight to our 

efficient beta leads to results that are almost indistinguishable 

on most dimensions from our efficient beta portfolio. 

The main conclusion that we draw from these results is 

that all five alternatives to the cap weight portfolio, as well 

as both combined strategies, have historically dominated 

cap weight in returns and/or risk-adjusted returns. A classical 

return attribution would suggest that this is at least 

partly due to the size and value tilts inherent in these various 

strategies. Alternatively, as we’ve suggested in other papers, 

this advantage is perhaps because the noncap and combined 

strategies all contratrade against the market’s constantly 

changing expectations, as reflected in a company’s share 

price and market capitalization. 

Intuitively for an investor, these results are best demonstrated 

with conventional dollar growth charts. The behavior 

of all of these portfolios is very similar. Naturally, bull and 

bear markets and the corresponding peaks and troughs happen 

at roughly the same time. One notable exception is the 

market peak in 2000. The noncap-weighted portfolios all 

peaked over a year later than the cap weight portfolio. By not 

loading up on the Ciscos and Nokias of the world, we are less 

hurt by the collapse of the tech bubble. 

Consistent with the strategy’s design, Figure 4 shows the 

lower risk of the minimum variance portfolio, as is clearly 

evident in the more stable return stream. When compared 

with a market capitalization index, the returns are weaker in 

extreme market rallies and more resilient in bear markets. It is 

a nicer ride—to very nearly the best end-point wealth!—with 

stable returns for investors who are concerned about total 

volatility. We should readily acknowledge, however, that the 

tech bubble of 1999-2000 would have tried the patience of any 

adherent to the noncap-weighted alternatives! 

For the conservative investor who does not like putting all 

eggs into the same “beta basket,” a good alternative may be 

to diversify among different beta strategies. The efficient beta 

portfolio and all-four-combined portfolio preserve some of 

the good characteristics of the economic weighting and minimum 

variance approaches. The cumulative performance of the 

blended strategies is displayed in Figure 5. It is remarkable 

to note that the two combined strategies—despite the allfour-combined 

including a very different global equal weight 

strategy—are near-identical in returns, risk and other characteristics. 

Adding global equal weight does nothing for us. 

When adjusting results for beta, relative to our simulated 

cap weight portfolio, economic scale and minimum variance 

have positive CAPM alphas of 3.53 percent and 5.17 percent, 

respectively, as shown in Figure 6. Betas of the strategies are 

0.93 and 0.94 for economic scale and global equal weight, 

respectively; for minimum variance, the beta is vastly lower, 

at 0.64, exactly as we should expect for a low-volatility strategy 

that is not constructed with reference to a benchmark. 

The striking difference that comes from our risk-adjusted 

results is that the statistical significance soars. If much of 

our tracking error is attributable to a lower beta, then the 

residual risk is actually smaller than the tracking error, 

notably for minimum variance; for much the same reason, 

the added value is larger than it seems. Adjusted for market 

risk, only the global equal weight portfolio and the equally 

weighted portfolio, which is drawn from cap weight, lack 

statistical significance. The economic scale and minimum 

variance portfolios loft to startling statistical significance for 

so short a span. And, in a gratifying surprise, the efficient beta 

portfolio rivals the highest risk-adjusted information ratio of any of 

its constituent strategies, with higher statistical significance than 

any single strategy. 

In Figure 7, we examine the outlier risks of the various 

portfolios. All portfolios have excess kurtosis and negative 

skewness, which are well-known characteristics of most equity 

24 


investing strategies. Drawdown characteristics are similar, 

with minimum variance showing the smallest drawdowns 

and global equal weight showing the largest. Reciprocally, 

minimum variance sharply reduces the largest gains in strong 

months and quarters, though this weakness disappears over 

12-month spans. The minimum variance portfolio also has the 

highest negative skewness. Of course, this asymmetric characteristic 

in the minimum variance return distribution—more 

extreme losers than winners—is mitigated by the significantly 

lower volatility of this strategy as noted in Figures 2 and 3. 

The combined efficient beta portfolio’s outlier characteristics 

are more similar to those of the cap weight portfolio than 

any of the individual noncap strategies. This may serve to reassure 

the risk-averse investor: Moving from a singular reliance 

on cap weighting to a more diversified approach does not subject 

our portfolio to any significant increase in the downside 

risk. In each time span, efficient beta’s greatest win is larger 

than for cap weight, while its greatest loss is smaller. 

Figure 8 reconstructs the Fama-French-Carhart methodology, 

based on the global cap-weighted 1,000 stock portfolio. 8 

In this multivariate context, the beta relative to the cap weight 

soars well above the betas shown in Figure 6. The SMB size 

factor loading is far smaller than most might expect, because 

other risk factors—notably beta—can proxy in a multivariate 

regression. Meanwhile, economic scale and minimum variance 

portfolios both have quite a substantial HML value tilt relative 

to cap weight, while the equally weighted portfolios have only 

modestly more HML value tilt than the strategies from which 

they were sourced. Because global equal weight is partly 

sourced from economic scale, which has a large HML value tilt, 

it also has a reasonably large value tilt. 

Even though much of the alpha is driven by size and value 

effects, both economic scale and minimum variance have 

quite sizable annualized alphas, of 2.07 percent and 1.81 

percent, respectively, net of the Fama-French-Carhart factors. 

The alpha is considerably more significant in all cases than 

the simple CAPM alphas shown in Figure 6, even though the 

alphas are smaller once we net out the impact of size, value 

and momentum effects. Indeed, all of the noncap-weighted 

strategies, without exception, exhibit far more statistical 

significance net of these “style tilts” than they do on either a 

simple value-added or a CAPM alpha basis. 

Figure 6 


Ending 

Value 

of $1 

Geometric 

Return 

CAPM Characteristics, 1/1993-6/2009 

Correlation 

with 

Cap-Weighted 

* Significant at 95% confidence level. ** Significant at 99% confidence level. 


CAPM Beta 

vs. 

Cap-Weighted 

Excess Return 

vs. 

Cap-Weighted 

CAPM Alpha 

vs. 

Cap-Weighted 

Information 

Ratio of 

Alpha 

Cap Weight 2.78 6.39% — — — — — — 

t-Statistic 

for CAPM 

Alpha 

Economic Scale 4.44 9.46% 0.94 0.93 3.07% 3.53% 0.72 2.56* 

Global Equal Weight 3.83 8.48% 0.95 0.94 2.10% 2.45% 0.54 1.91 

Equal Weight Cap 1000 3.12 7.14% 0.98 1.02 0.75% 0.62% 0.20 0.95 

Equal Weight Econ 1000 4.82 10.00% 0.92 0.94 3.61% 3.99% 0.67 2.48* 

Minimum Variance 4.31 9.26% 0.87 0.64 2.87% 5.17% 0.99 2.72** 

Efficient Beta 3.80 8.43% 0.97 0.86 2.04% 2.96% 0.96 2.90** 

All-Four-Combined 3.81 8.45% 0.97 0.88 2.06% 2.84% 0.86 2.68** 

Figure 7 

Outlier Risks, 1/1993-6/2009 


Skewness 

Excess 

Kurtosis 

Maximum 

Monthly 

Return 

Minimum 

Monthly 

Return 

Maximum 

3-Month 

Return 

Minimum 

3-Month 

Return 

Maximum 

Trailing 

12-Month 

Return 

Minimum 

Trailing 

12-Month 

Return 

Cap Weight -0.94 1.39 10.11% -16.05% 23.27% -29.66% 38.96% -41.75% 

Economic Scale -0.76 2.23 16.01% -14.94% 32.90% -27.52% 46.38% -44.08% 

Global Equal Weight -0.98 3.12 15.08% -18.50% 30.26% -32.27% 48.87% -44.17% 

Equal Weight Cap 1000 -0.95 2.46 13.43% -18.86% 28.41% -33.08% 44.56% -44.22% 

Equal Weight Econ 1000 -0.77 3.66 18.63% -18.47% 37.55% -32.42% 54.96% -45.01% 

Minimum Variance -1.20 2.09 6.38% -12.20% 15.61% -20.42% 42.35% -31.40% 

Efficient Beta -1.04 1.97 10.56% -14.39% 22.44% -25.94% 41.39% -39.26% 

All-Four-Combined -1.04 2.25 11.69% -15.42% 24.37% -27.55% 41.48% -40.51% 



The other surprise is the soaring efficacy of the combined 

strategies. Whether we choose efficient beta or all-four-combined, 

the statistical significance of the positive alpha sharply 

exceeds 99.9 percent significance, once we adjust for the value, 

size and momentum effects. Diversifying between different 

indexing strategies does not hurt performance, even—or perhaps 

especially—adjusting for these risk factors. At the same 

time, the HML loading of 0.31 in the case of efficient beta and 

of 0.32 in the case of all-four-combined contributes some of the 

incremental return, documented in the previous tables. 

To model the impact of trade costs and market impact, we 

created a hypothetical $1 billion portfolio for each strategy 

as of Jan. 1, 2009 (see Figure 9). The average two-way turnover 

for the strategy was divided by the number of rebalance 

dates, and this percentage was used to create a proportional 

slice of the portfolio that was then assessed for commissions, 

fees and market impact. Trade costs were estimated using 

the ITG ACE model. 9 Estimated commissions, taxes and fees 

were then included to create a total estimated transaction 

cost. The resulting costs were then multiplied by the number 

of rebalance dates to determine annualized trading costs. 

For the rightmost column, we modeled capacity using another 

simple set of assumptions. We assume that our portfolio will 

become difficult to manage—that tracking the intended “index” 

will be challenging—for any holdings that exceed 10 percent 

of current float or 10 percent of annual share volume. We then 

find the portfolio size at which 5 percent of the portfolio would 

be running up against one or another of these thresholds—i.e., 

would become “difficult.” To our surprise, cap weight shows a 

“capacity” based on these limitations of less than $700 billion in 

size. The other core strategies would run into these same barriers 

at anywhere from $50 billion to a quarter-trillion in assets. These 

are not small sums. But, we can probably infer that these thresholds 

are pretty conservative, based on the simple fact that cap weight is used 

to index vastly more than $700 billion in assets! 

Having the option of higher alpha and somewhat lower 

volatility is quite helpful for the average investor’s portfolio. 

To see this, we plot on Figure 10 the minimum variance 

frontier of a diversified basket of various asset classes, where 

we compare the frontier with equity being represented by 

cap weight and efficient beta indexes. The optimized frontier 

with efficient beta providing equity exposure is clearly 

expanding the set of returns attainable to the investor. 

Over this history, at least, investors could achieve the same 

returns with lower levels of risk, or earn higher returns while 

keeping the same level of risk in the portfolio. 

Intuitively, this is represented in Figure 11, where we plot 

excess annualized returns of the efficient beta over cap weight 

against excess volatility, on rolling three-year spans. Just as in 

standard mean-variance charts, the “northwest quadrant” is 

the preferred position for the investor—higher returns with 

lower volatility. This figure shows that selecting the efficient 

beta moves investors in the desired direction most of the time. 

With the caveats that our history is not terribly long and that 

past is not assuredly prologue, the portfolio never once offers 

higher volatility than cap weight, and delivers less return in 

less than 10 percent of the rolling three-year spans in our 

study, all centered on the peak of the tech bubble. 

Conclusion 

This study focuses on two interrelated pragmatic questions: 

If we want less risk, do we have to lower our equity exposure? 

Alternatively, can we achieve our intended long-term return 

goals with less in equities? Apropos of that simple question, 

our work explores the simple merits of diversifying our core 

portfolio—in effect, our beta risk. In a world in which many 

investors are considering ways to reduce portfolio risk, whether 

because of management pressure or because of fear of the consequences 

of misaligned risks, few would disagree that a bigger 

tool kit—a wider array of interesting alternatives—will be 

broadly welcomed. Some will choose to “de-risk” by lowering 

their equity market exposure, by aligning assets with liabilities, 

by reducing their active management risk or by exploring ways to 

achieve better returns with similar risk in their core equity holdings. 

One can make a very good case that these strategies do 

not offer alpha, but offer “better beta.” After all, none of 

these portfolios uses “stock selection” in any classical sense 

of the term. There are no interviews with management, no 

forecasts of future business prospects and no careful parsing 

of financial statements. In one case, we ask: “How big is the 

Figure 8 

Four-Factor Model In Global Universe, 6/1994-6/2009 

Portfolio/Index Beta SMB HML Momentum 

Monthly 

Intercept 

4-Factor 

Alpha 

4-Factor 

Info Ratio 

Alpha 

t-Statistic 

Cap Weight — — — — — — — — 

Economic Scale 1.06 0.03 0.43 -0.04 0.17% 2.07% 0.78 3.17** 

Global Equal Weight 1.06 0.18 0.36 -0.07 0.15% 1.82% 0.82 3.33** 

Equal Weight Cap 1000 1.04 0.19 0.05 -0.07 0.11% 1.27% 0.59 2.39* 

Equal Weight Econ 1000 1.08 0.24 0.47 -0.10 0.26% 3.08% 1.09 4.42** 

Minimum Variance 0.82 0.02 0.50 -0.07 0.15% 1.81% 0.54 2.18* 

Efficient Beta 0.96 0.02 0.31 0.01 0.11% 1.29% 0.88 3.57** 

All-Four-Combined 0.98 0.06 0.32 -0.01 0.12% 1.42% 0.98 3.97** 

* Significant at 95% confidence level. ** Significant at 99% confidence level. 


26 


Figure 9 

Estimated Transaction Cost 


One-Way 

Turnover % 

Annual 

# Rebalances 

Rebalance Trade 

Cost bps of Trade 

Annualized 

Trade Cost bps 

of Portfolio 

Capacity of 

Current Portfolio 

(in $billions) 

Cap Weight 6.8 1 33 5 690 

Economic Scale 15.2 1 41 12 260 

Global Equal-Weight 29.7 1 60 36 140 

Equal-Wgt Cap 1000 34.4 1 63 43 88 

Equal-Wgt Econ 1000 28.9 1 60 35 80 

Minimum Variance 12.7 4 34 9 51 

Efficient Beta 15.8 12 36 11 139 

All-Four-Combined 21.1 12 43 18 113 


company’s current book of business?” That defines both the 

selection and weight for the economic scale portfolio. In 

another, we ask: “Can we create a portfolio that is designed 

to achieve high risk-adjusted returns without the use of 

return expectations?” That defines the minimum variance 

portfolio. In yet another, we ask: “Why should we favor any 

stock over any other?” That leads to global equal weight, for 

which the only active decision—a nontrivial decision!—is to 

select the universe that we will equal-weight. 

Our research shows that a combination of cap weight, 

economic scale and minimum variance creates a compelling 

risk/return profile. The purists will presumably argue 

that classical finance theory supports only one of these: 

cap weight; they would, of course, be correct. Others—all 

tacitly believers in some form of market inefficiency— 

might argue for one or another alternative to cap weight. 

Advocates of the status quo, with its singular reliance on 

cap weight for core indexed portfolios, will undoubtedly 

point to the fact that past is not prologue: “Past performance 

is no guarantee of future results.” 

Cap-weighted indexes are widely used; they are the generally 

accepted benchmark for gauging investment success. 

This simple reality creates “maverick risk”—a risk of underperforming 

our peers—for those investors who choose 

any of these alternative approaches, including a blended 

approach. Still, the selection of quasi-passive investment 

strategies within equities need not be limited to cap weight, 

nor need we forever rely on a cap-weighted benchmark. 

Perhaps investors can better serve their long-term needs 

by assessing which of these strategies—or combination of 

strategies—best conforms to their appetite for risk. 

Fixed-income investors have a long history of considering 

risk and exposure when choosing the duration and credit 

of active and passive bond portfolios; broad bond-market 

index funds are far less widely used than stock index funds. 

Similarly, currency investors typically do not use “market 

cap” or even GDP as a guide for anything other than liquidity. 

Perhaps it’s time to revisit our automatic reliance on cap 

weight as the sole strategy for measuring stock market suc- 

Figure 10 

Annualized Mean Return 

14% 

12% 

10% 


Figure 11 

Excess Annualized Return Vs. 

Market Capitalization Index 

8% 

6% 

4% 

2% 

10% 

5% 

0% 

-5% 

Efficient Frontier With Efficient Beta 

And With Cap Weight, 1/1993-6/2009 

1-3 Year Debt 

Risk Free 

Long-Term 

Gov’t Bonds 

High-Yield Bonds 

Global Bonds 

0% 

0% 2% 4% 6% 8% 10% 12% 14% 

Annualized Portfolio Volatility 

Efficient Frontier with Efficient Beta 

Efficient Frontier with Cap Weight 

Rolling Three-Year Risk And 

Return Relative To Cap Weight 

-10% 

-5% -3% -1% 1% 3% 5% 

Excess Annualized Standard Deviation Of 

Return Relative To Market Capitalization Index 


Efficient Beta 

Cap Weight 

DJ-AIG 

Commodity 

Index 

16% 18% 20% 

28 


cess, or as the default choice for our core equity holdings. 

It is not our intent in this paper to explore the theoretical 

implications of our work, though we acknowledge that 

they may prove significant. These results—as with so many 

before—are not consonant, in aggregate across time—with 

an efficient market. The empirical results suggest some 

global inefficiencies that may prove to reflect an immense 

gap between expected risk and subsequent observed 

risk, or between expected return and expected utility, or 

between priced risk factors and the risk factors that should 

be priced in an efficient market. It is well beyond the scope 

of this simple empirical study to explore these nuances. 

If an investor does not have a compelling view that favors 

one of these strategies over any other, then a diversified 

approach to beta can perhaps give us access to broad market 

exposure, without undue tilts to any single method, without 

undue reliance on market efficiency and with stronger 

empirical results than any single method. 10 

Co-authors Rob Arnott and Vitali Kalesnik are with Research 

Affiliates, LLC. Co-authors Paul Moghtader and Craig Scholl are 

employees of Lazard Asset Management. 

The authors are grateful to Frank Ashe, Elizabeth Collins, Jaynee 

Dudley, Jason Hsu, Taras Ivanenko, Robert Prugue, Katy Sherrerd, 

to name just a few, for comments that helped to shape this paper. 

The authors are grateful to Li-Lan Kuo and Feifei Li for their help, 

gathering the data and constructing time series for the paper. 

Endnotes 

1. In an effort to finesse the controversy regarding the terms “active,” “passive,” “index” and “strategy,” as relates to noncap-weighted portfolios, we generally refer to all of these 

indexes, including cap weight, as “strategies” or “portfolios.” 

2. The economic scale approach uses the standard Fundamental Index methodology to determine the weights in an index. See Arnott, Hsu and Moore (2005) for details on the 

methodology. Fundamental Index® is patented and related labels and concepts are protected by trademarks, copyrights and patents, owned by Research Affiliates, LLC. 

3. We cannot know “clairvoyant value” for any asset today. We can know the past clairvoyant value of most assets, especially if we go far back in time, so that the long subsequent 

history of distributions can be discounted back to an ancient starting date. For a detailed exploration of the nuances and surprisingly rich implications of clairvoyant value, see 

Arnott, Li, Sherrerd (2009a and 2009b). 

4. Tacitly, this means that a forecast for the covariance matrix drives our portfolio construction. The “forecast” incorporated within the minium variance models relies purely on 

observed and actual historical data. No actual forward-looking forecast is embraced, at least none that are not included in actual long-term figures. 

5. We incorporate a proprietary size factor that controls relative size exposure so the portfolio is not dominated by small, illiquid companies. We also impose constraints that limit 

GICS sectors to a maximum of 20 percent and individual securities to 1.5 percent, and an additional measure that moderates GICS industry group exposures to ensure that the 

portfolio offers a broad distribution of exposure across industries. We also restrict the investable universe by market cap and trading volume, to ensure that the strategy invests 

only in liquid stocks, as measured from the perspective of a large investor in this strategy. The market cap and volume constraints do not materially change the risk and return 

characteristics of the portfolio, but they do have a bearing on scalability and investability. 

6. See Choueifaty and Coignard (2008). 

7. This assumes independence of relative performance between years, which is a reasonable approximation of the observed empirical results. 

8. The Fama-French factors are recalculated using cap weight portfolio, consisting of the 1,000 largest market-cap companies in the 23 countries in the FTSE and MSCI developed 

world indexes. To construct SMB and HML factors, we use MSCI Small Growth, MSCI Small Value, MSCI Medium Growth, MSCI Medium Value, MSCI Large Growth and 

MSCI Large Value, which we use to compute factor returns: 

SMB = 1/2(Small Value + Small Growth) - 1/2(Large Value + Large Growth) 

HML = 1/3(Small Value + Mid Value + Large Value) - 1/3(Small Growth + Mid Growth + Large Growth) 

To define momentum, we use cap weight portfolio, consisting of the 1,500 largest market-cap companies in the 23 countries in the FTSE and MSCI developed world indexes, 

which we sort monthly into three tiers of 500 stocks based on the prior return measured from month -12 to -2. Momentum return is the difference of returns of the top-tier 

equally weighted portfolio minus the bottom-tier equally weighted portfolio. Factor loadings of the portfolios are calculated based on a multivariate regression of portfolio 

returns against these factors. 

9. The model incorporates stock-specific econometric models of volatility and price impact and provides the expected cost of trades as shown above. Its key inputs are stockspecific 

volatility, bid/ask spread, volume, closing price, intraday volume and volatility distribution as well as trade-specific size, side, strategy and expected time to completion. 

The model attempts to balance the competing forces of cost (spread cost and market impact) vs. risk (opportunity cost of uncompleted trades). 

10. For a discussion of whether these non-cap-weighted portfolios are active or passive, or are indexes or strategies, please see the online appendix to this paper at 


References 

Ang, Andrew, Robert J. Hodrick, Yuhang Xing and Xiaoyan Zhang. (2006). “The Cross-Section of Volatility and Expected Returns,” Journal of Finance, vol. LXI, no. 1, February: 259-299. 

Arnott, Robert D. (2004). “Blinded by Theory: How Finance Theory Leads Us Astray,” Journal of Portfolio Management, vol. 30, no. 5, Thirtieth Anniversary Edition: 113-123. 

Arnott, Robert D., Jason C. Hsu and Philip Moore. (2005). “Fundamental Indexation,” Financial Analysts Journal, vol. 61, no. 2, March/April: 83-99. 

Arnott, Robert D., Feifei Li and Katrina Sherrerd. (2009a). “Clairvoyant Value and the Value Effect,” Journal of Portfolio Management, vol. 35, no. 3, spring: 12-26. 

———. (2009b). “Clairvoyant Value II: The Growth/Value Cycle,” Journal of Portfolio Management, vol. 35, no. 4, Summer: 142-157. 

Blitz, David C. and Pim van Vliet. (2007). “The Volatility Effect: Lower Risk Without Lower Return,” Journal of Portfolio Management, fall: 102-113. 

Choueifaty, Yves and Yves Coignard. (2008). “Toward Maximum Diversification,” Journal of Portfolio Management, vol. 35, no. 1, fall. 

Dorn, Daniel and Gur Huberman. (2009) “Preferred Risk Habitat of Individual Investors,” Available at SSRN: http://ssrn.com/abstract=1100687 

Falkenstein, Eric G. (2009) “Risk and Return in General: Theory and Evidence,” Available at SSRN: http://ssrn.com/abstract=1420356 

Haugen, Robert A., and Nardin L. Baker. (1991). “The Efficient Market Inefficiency of Capitalization-Weighted Stock Portfolios,” Journal of Portfolio Management, vol. 17, no. 3, spring: 35-40. 

Held, Jeremy. (2008). “Don’t Get Mad, Get Even,” Institutional Investor Exchange Traded Funds Indexing Innovations, 7th Anniversary Issue. 

Hsu, Jason. (2006). “Cap Weighted Portfolios are Sub-Optimal Portfolios,” Journal of Investment Management, vol. 4, no. 3, Third Quarter: 1-10 

Lazard Quantitative Equity Team. (2009). “Managing Equity Risk–Risk and Reward Redefined,” White Paper. 

Markowitz, Harry M. (2005). “Market Efficiency: A Theoretical Distinction and So What?” Financial Analysts Journal, vol. 61, no. 5, September/October: 17-30. 


B;JÊI8K?B:78;JJ;H?DL;IJC;DJMEHB:$ 

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In Perspective 

What Does ‘Buy-And-Hold’ 

Really Mean? 

Buy-and-hold is dead; long live buy-and-hold. 

By John Prestbo 

Friends, indexers, investors, lend me your tattered 

attention spans. I come to bury buy-and-hold, not to 

praise it. 

The evil that buy-and-hold did from late 2007 through 

early 2009 lives on, and will live on for many years in the 

reduced retirements of millions. The Dow Jones Industrial 

Average fell 34 percent in 2008 alone, and if that was the 

year you planned to stop working, you probably were 

forced to rethink and retrench. 

Authorities ranging from Jim Cramer to Bill Gross say 

the buy-and-hold investment strategy is dead, and they 

are honorable men. Of course, Jack Bogle, founder of the 

Vanguard Group, also is honorable. He doesn’t think buyand-hold 

is dead, though he couches this position in terms 

such as, “We own the stock market, all of us together; we 

buy and hold it.” 

The pundits eager to post death notices seem to portray 

buy-and-hold as a comprehensive investment philosophy that 

deserves to be discredited. After all, they point out, the Dow 

is at the same 10000 level it first attained in 1999. Grievously 

disheartened investors who bought then and held till now 

obviously were terribly abused by this ill-advised dictum. 

But buy-and-hold is not a sacred creed that demands 

obedience. It is a three-word maxim, not nearly robust 

enough to be held accountable for the lost billions of 

retirement dollars. Like any rule of thumb, it needs to be 

applied with common sense. “A watched pot never boils” 

is a hoary bromide that is not literally true but conveys 

meaning nonetheless. 

The subtextual meaning of buy-and-hold is: Don’t trade 

so much. This advice is anathema to ADHD investors and 

to pundits who dispense stock tips for prestige and profit. 

It chills the bones of the many on Wall Street whose sole 

purpose is to seduce you into chasing their latest investment 

ideas. 

Buy-and-hold does not mean “buy indiscriminately and 

hold thoughtlessly.” If that were what it espoused, I would 

grab a shovel and help dig its grave. Properly executed, it 

means being selective about what you buy and holding it 

until it no longer serves your long-range purposes. At that 

point you would (gasp!) sell it and buy an equally well-selected 

something that does support your plan. 

Notice that buy-and-hold says nothing about a plan, 

though I think there is consensus that a plan is the first 

thing anyone should have before becoming an investor. 

College tuition payments start in 10 years? Retirement 

might arrive in 2040? First, figure roughly how much 

money is needed for those life events, then how much 

should be (and can be) saved. Finally, allocate those 

savings among asset classes that in combination can be 

expected to supply a cushion of diversification while delivering 

adequate returns. 

Only then, at about the fourth stage of this process, 

comes “buy”—which entails researching those vehicles 

in each asset class that seem likely to provide reasonable 

returns for that asset—“and hold.” Hold until when? Hold 

until the investment no longer represents the asset class. 

Or until market action has thrown the asset allocation 

out of whack against the original plan and rebalancing is 

needed. Or until the asset allocation itself must be modified 

to suit changed circumstances. 

If those situations arise, some selling may be in order to 

get the portfolio back on track—or onto a whole new track, 

if that is warranted. Until then, though, if the asset vehicles 

are doing their job, hold on to them. Resist the urge to trade 

existing holdings for new ones in hopes of snagging a bit of 

32 


“extra” return here or there. There is a bromide for that, too: 

Churn and burn. And pay commissions for the privilege. 

It is possible to reduce the whole investment process to a 

series of three-word maxims: Make your plan. Keep on saving. 

Allocate your assets. Buy and hold. Review every year. Rebalance 

if needed. In this context, buy-and-hold does not stand out as 

egregious, does it? 

Even so, the buy-and-hold obituaries are being published 

today for a reason. The bear market of 2007-09 devastated 

so many 401(k) and other portfolios partly because it was 

incredibly severe, surprising some “experts.” “Hang on,” 

they said, “it could be worse.” So, many people hung on 

and, sure enough, it got worse. 

Another monstrous aspect was that the bear market 

affected most asset classes simultaneously, which temporarily 

rendered diversification almost useless as a bomb shelter. 

While the U.S. stock market sank 55.4 percent from Oct. 9, 

2007, to March 9, 2009—as measured by the Dow Jones 

Total Stock Market Index on a total return basis—the ex.- 

U.S. markets collectively sagged 59.5 percent. 

The decline came equally from developed and emerging 

markets. Developed ex.-U.S. markets fell 59.1 percent, as 

the countries’ leading banks had been suckered into the 

Only bonds, the Mr. Magoo of the market meltdown, 

sailed through serenely [outside of the riskier corporate 

credits]. As interest rates fell, bond prices rose; interest 

rates could not fall below zero, though Treasury 

bills briefly reached that point. The Barclays Capital U.S. 

Aggregate Bond Index, from Oct. 9, 2007, through March 

9, 2009, rose 7.2 percent. Since then, through Nov. 20, it 

has risen 8.2 percent. 

So, what does buy-and-hold get you in this mess? It does 

not completely protect you from the bear, but neither does 

it leave you hopelessly bereft. To illustrate, take a simple 

asset allocation that can be executed with readily available 

investment vehicles. We put 60 percent into risky assets, 

comprising 35 percent in U.S. stocks, 15 percent in ex-U.S. 

stocks and 5 percent each in commodities and U.S. real 

estate. The remaining 40 percent goes into less risky U.S. 

bonds. For this exercise, we do not rebalance. 

Indexing this portfolio to 100 on Dec. 31, 1998, it reached 

a year-end high of 186.5 on Dec. 31, 2007. It subsequently 

fell 34 percent to 123.1 by March 9, 2009, but from that 

point through Nov. 20, 2009, recovered 34.9 percent to 

166.0. From beginning to end, the annualized return is 4.8 

percent—not what one would have wished, of course, but 

So, what does buy-and-hold get you in this mess? 

It does not completely protect you from the bear, 

but neither does it leave you hopelessly bereft. 

subprime mortgage follies. (See United Kingdom, Ireland, 

Iceland … etc.) Emerging markets dropped 61.9 percent, 

but they recovered the fastest because they were unsophisticated 

enough to not have become mired in the sour 

credits. Emerging markets shot up 110.9 percent from 

March 9 through Nov. 20, 2009, while developed ex-U.S. 

markets jumped 78 percent. The U.S. market placed third, 

gaining 65.2 percent. 

Securities representing the commercial real estate asset 

class were hit even harder, yet they rebounded spectacularly. 

Using the same dates as above, though they do not fit 

exactly, the DJ U.S. REIT Index sank 71.3 percent and since 

March has recoiled upward by 105.7 percent. The DJ Global 

ex-U.S. Select REIT Index fell 70.7 percent but bounced back 

just 103.4 percent. 

Commodities slumped, too, helped along by crude oil’s 

deflation from $145 a barrel. The DJ-UBS Commodity Index 

dropped 56.9 percent from its high point on July 2, 2008, 

through March 2, 2009. Since then, through Nov. 20, the 

index has jumped 32.4 percent. By the way, a literal buyand-hold 

strategy would have worked out okay with commodities. 

In the 10 years ended Nov. 20, 2009, the DJ-UBS 

Commodity Index racked up a cumulative return of 95 percent, 

or 6.9 percent annualized. 

tolerable if there is still time before the kids’ tuition comes 

due or retirement rolls around. 

And if there is not time? Then, indeed, pain is inflicted. 

But buy-and-hold bears only a fractional share of the blame. 

What happened to “review every year” to take into account 

changed circumstances (i.e., the shorter time remaining 

until the planned-for happening)? Was “rebalance if needed” 

observed or neglected? The Hon. Jim Cramer blames 

buy-and-hold for lulling investors into laziness, and he has 

a point. But methinks being lazy predates careless investing 

by more than a few millennia. 

I write not to disprove what the (honorable) deathdecree 

promoters say, but I am here to declare what I do 

know: Buy-and-hold was my friend, just and faithful to me. 

And buy-and-hold has brought many others’ portfolios 

home to sufficient fulfillment, without paying tribute to 

brokers and advisers. What cause is there now to turn away 

from this gentle admonition? 

Oh, judgment, thou art fled to brutish beasts, and men 

have lost their reason. Bear with me; my heart is in the 

coffin there with buy-and-hold, and I must pause till it 

come back to me. 

Ross Wiedman contributed research to this article. 



33


Our panel of experts may disagree ... 


The events of 2008 and early 2009 shattered investor confidence 

in how the markets work. Much of what we knew to be “true” was 

revealed as faulty thinking. Assets we thought were uncorrelated 

began trading in lock step; 1-in-100,000 events became commonplace; 

the unthinkable became frighteningly real. 

As we turn toward 2010, the Journal of Indexes editorial staff 

gathered six exemplary thinkers to ask whether, as many claim, 

buy-and-hold investing is “dead,” and how the events of the past 

year should shape how investors approach the future. 

John Bogle, Founder, Vanguard 

Journal of Indexes (JOI): Is buy-and-hold investing 

dead? 

John Bogle (Bogle): I would just say very 

simply, of all the stupid ideas, the idea that 

buy-and-hold investing is dead is [the most] 

ridiculous. I would just ask “Buy and hold what?” 

Buying and holding an individual stock has been dead 

forever, and buying and holding a managed portfolio is probably 

dying. And buying and holding the entire stock market 

is the way to invest in success. Then the question becomes, 

“Buy and hold what kind of portfolio?” I would say buy the 

bond market portfolio and the stock market portfolio and 

hold them both as long as you live, with one caveat. And that 

is, take into account your risk tolerance and your age. So 

adjust that equation—more in bonds when you’re older and 

more in stocks when you’re young. That idea will never die. 

Even worse, when someone says buy-and-hold is dead, they 

have lost sight of one simple elemental fact: As a group, we 

are all buy-and-hold investors—we buy and hold the market 

portfolio, all of us, together. As a group, we are definitely, 

irrevocably, unarguably buy-and-holders; individually we are 

not. So we trade with one another, and who is enriched 

by that? Clearly the people trading aren’t enriched by that, 

because one loses and one wins. The only enrichment is to our 

financial system —the croupiers of America—Wall Street and 

mutual fund managers. To fail to observe the simple fact that 

we all own the buy-and-hold portfolio, but we try and trade 

against it, is consigning ourselves to a shortfall, of some substantial 

portion, to whatever returns the markets are generous 

enough to give to us or mean enough to take away from us. 

JOI: Should investors be concerned about inflation during the 

near term? If so, how can they protect themselves against it? 

Bogle: There is one guarantee of inflation protection—the only 

and closest thing we have to a guarantee against inflation—the 

Treasury inflation indexed bond. Right now it has a very small 

yield because the market is looking for about 2.5 percent inflation. 

If it turns out to be higher than that, the government will 

pay you more money to protect you against it. If it’s lower than 

that, you’ve made a bad bet. It’s the only form of true inflation 

protection on a very short-term basis that I know of. 

Now you have to worry about whether that market projection 

of inflation is right, and I think one could say [there is] 

a high probability that inflation will come back to 2 percent 

or 2.5 percent over the next couple years, but could easily in 

the years beyond that get to 3 or 4 or 5 percent. So while a 

1 percent yield looks very shabby at the moment—and it will 

be shabby this year—you’ve got to be willing to accept that. 

Like everything else in investment, if you want to eliminate 

a risk, you’ve got to give up some return. That’s the way the 

inflation-hedged bond works. 



Bogle: No. I have a very, very conservative investment philosophy. 

I’m in about 70-75 percent bonds—in my retirement 

bond account, largely taxable bond index funds; and in my 

personal account, largely limited-term to intermediate-term 

municipal bonds. I was very well protected when the market 

went down, and obviously I’ve had a much smaller share of 

the gains this year, because I’m only about 20-25 percent in 

our stock funds, largely index funds. But on balance for the 

two years, I’m still well ahead of the game. 

Because we’ve had a stock market that went down 57 

percent and then went up 57 percent, any investor who 

thinks that means they’re even at the end of the period is 

really naive. In fact, they’ve lost 32 percent of their capital. 

With a big plunge and a big recovery, you should never lose 

sight of the mathematics of the marketplace, and that is it 

takes essentially a 100 percent market increase to offset a 50 

percent market decline. We call that, after Justice Brandeis’ 

formulation, the “relentless rules of humble arithmetic.” So 

I’m perfectly comfortable where I am—I missed some of the 

rise, but avoided potentially all of the fall. 

I don’t know how to do market timing, by the way. I’m 

not smart enough to do that. I’m comfortable enough not 

trying to outguess the market—not trading saves me a lot of 

money. I’m also not subject to taxes on those trades, which 

saves me a lot of money, and so on. 



Bogle: I would say the greatest danger is that this financial 

recovery is going to be aborted. I worry about the economy 

and the global system. I would caution investors to be conservative, 

though maybe not as conservative as I am because 

I’m older and I’ve accumulated a certain amount of assets. 

I must confess to being amazed that as each piece of bad 

news comes in, the market goes up. That can’t go on forever. 

In any event, there is plenty to be concerned about, as I say 

in my new book, a fully updated 10th anniversary edition of 

“Common Sense on Mutual Funds.” I’ve updated all the data 

from 10 years ago, and my, how different it looks. But we 

have to deal—as Donald Rumsfeld put it, though it might 

not have been original with him—“not only with the known 

unknowns, but the unknown unknowns,” and there are plenty 

of them out there too. We have an uncertain world; [we 

have] great global competition; we’re a high-priced country; 

we don’t make anything very much anymore; and we have a 

financial system that’s run amok and shows very, very few 

signs of straightening itself out. 

To capture what’s in my most recent article in the Journal 

of Portfolio Management, we need to develop [a solution to] 

this failed agency society we have—because most stocks are 



35

owned by agents of owners and not owners themselves. We 

need to have a federal statute of fiduciary duty for all institutional 

money managers which says they put the interest of 

their investors first, they invest rather than speculate, they do 

due diligence on the securities they hold, they hold for the 

long term and they pay a great deal of attention to corporate 

governance, which is of course immaterial to the speculator and 

means everything in the long run to the long-term investor. 

Diane Garnick, Investment Strategist, 

Invesco Ltd. 

JOI: Is buy-and-hold investing dead? 

Diane Garnick (Garnick): The concept of 

buy-and-hold is really a little bit of a misnomer. 

When buy-and-hold began, one of 

the things we were trying to communicate to people is 

that when the market does poorly, it’s a bad time to take 

all of their money out of the equity market, invest it in 

Treasurys and wait for things to recover. That said, the 

real message behind buy-and-hold was “don’t panic,” and 

that message is still alive. 

When it comes to buy-and-hold investing, there is a subtle, 

but important distinction that we need to help people 

understand. A better message might be “invest widely and 

rebalance frequently.” For example, when the stock market 

does very well, it might be a good time to take money away 

from stocks and invest it in fixed income, focusing more on 

asset allocation rather than gains or losses in a portfolio. 

The natural question that stems from that is, “How often 

should I rebalance my portfolio?” While there is no single answer 

that will work for everyone, generally speaking you probably want 

to rebalance your portfolio more frequently when volatility is very 

high, enabling you to capture some of the more dramatic swings 

in the marketplace. During periods of high volatility, rebalancing 

more frequently makes a lot more sense. 

Now having said that, I don’t think it makes a lot of 

sense for people to invest in the exact same allocation 

between stocks, bonds and cash every month, or even 

every year, for the rest of their lives. People’s willingness 

and ability to absorb risk changes over time and as a 

result, what they really need to do is think about what the 

next set of investment opportunities and risk preferences 

should look like for them. 



Garnick: The inflation issue is one of the most fascinating 

ones being debated right now. Clearly, in the short term, 

what we are looking at is deflationary pressure, primarily 

because so many people are unemployed or underemployed—and 

by “underemployed,” I mean that they’re 

accepting jobs that are not helping them live up to their 

economic potential. In this type of environment, with wage 

growth low and many people out of work, it’s relatively easy 

to see how prices can fall. 

There is a simple test available to everyone that I personally 

do quite regularly: When I go into a store to buy 

an item, before I hand over my hard-earned money, I ask 

them, “What is the recession price?” I’m not necessarily 

interested in saving any money, but I am interested in seeing 

what percentage the merchants will immediately give 

me as a discount. Since the crisis started, I’m finally seeing 

a reversal in the trend. More and more merchants are 

beginning to say there is no recession price; a few are even 

telling me the recession is over. 

However, being concerned about inflation is not unreasonable. 

By most measures the global stimulus packages 

throughout the world now account for more than 3 percent 

of global GDP. This unprecedented injection of liquidity is 

weighing on everyone’s minds. Certainly over the medium to 

long term, inflationary pressures can easily persist. 

Just to give people a framework, I speak with a lot of underprivileged 

children finishing high school who are considering 

whether or not they should go to college. My advice to them is 

very consistent. I tell them they should borrow as much money 

as they can, get into the best college possible and then during 

their junior year of college, begin to hope for inflation. I explain 

to them that in an inflationary environment, it’s much easier to 

pay off today’s debt in tomorrow’s dollars. I suspect there are 

some policymakers who are less concerned about the current 

deficit that the world has been running, because they’re willing 

to let some inflation into the system, hoping to pay back the 

stimulus plans with tomorrow’s dollars. 

The most effective, inexpensive way to protect your portfolio 

from inflation is to go to the U.S. TreasuryDirect Web 

site and buy Treasury Inflation Protected Securities (TIPS). 

If I look right now at the 10-year breakeven inflation rate 

on the TIPS, they’re indicating that we’re only going to have 2 

percent inflation. One of the reasons the breakeven rates are 

so low right now is because people are worried about deflation 

in the short term. This usually means it’s also a good time 

to buy inflation protection. Waiting until inflation is in the 

system to buy protection can quickly become very expensive. 



Garnick: It’s become abundantly clear that the drivers of 

economic growth are going to happen outside of the U.S., 

so we have invested more time and energy focusing on 

understanding the dynamics of the economy outside of the 

U.S. The easiest way to understand why the rest of the world 

might recover faster is what many consider the origin of 

the crisis—the typical American household borrowed more 

money than their assets were ever worth. Conversely, households 

in emerging Asia, Brazil, emerging Europe and even 

frontier countries like those in Africa, had a much lower debt 

burden relative to their assets. 

The net result is, if you could invest $100 in either of those 

types of households as part of a stimulus package, the U.S. 

might not create the best multiplier effect. On one hand, the 

households that don’t have a tremendous amount of debt can 

immediately go out and begin to spend in the economy. On the 

other hand, the households that still have a lot of debt need to 

spend that money paying off bills. They’re going to take their 

$100 and pay it right back to the banks that lent to them. 


Clearly the biggest asset that any country in the world has 

today is their human capital. Another issue plaguing the U.S. 

is that other countries are investing more and more money in 

their human capital relative to the U.S. The U.S. is beginning to 

resemble General Motors in 1980. After a prolonged period of 

growth, perhaps the U.S. has reached its maturation phase. 



Garnick: There are two primary issues keeping me up at 

night. The first is that households will forget how to save 

responsibly. Right now the savings rate in households has 

jumped pretty dramatically, from less than 1 percent to 5-10 

percent. The danger we face is that the moment economic 

stability returns, people stop saving and immediately go on 

a spending spree again. 

The second issue is that people don’t retool themselves 

for the new world. For example, I would be thrilled if young 

women would stop saying they want to grow up and be 

cheerleaders and start saying they want to grow up and 

become biochemical engineers. That’s at the core of one of 

the greatest dangers we all face—50 percent of the “smart” 

people still want to come work in finance. Society needs 

those truly remarkable people to place their efforts into 

other industries if we hope to regain long-term growth. 

Collectively, we have a responsibility to steer them into 

fields where they can make a difference. 

Jeremy Siegel, Senior Investment Strategy 

Adviser, WisdomTree; and Russell E. 

Palmer Professor of Finance, Wharton 

School, University of Pennsylvania 


Jeremy Siegel (Siegel): Absolutely not! Just 

because we have had two major bear markets in the last decade 

does not nullify the buy-and-hold philosophy. Although stocks 

have had poor returns over the past decade, they have had 

annual returns over 8 percent over the past 20 years, and over 

11 percent over the past 30 years, far outdistancing bonds. 



Siegel: I do not think that there will be any significant inflation 

over the next two years, but after that, I expect inflation 

to average 3-4 percent per year after 2012. Stocks offer an 

excellent hedge against such moderate inflation. The government’s 

inflation-indexed bonds could falter if real interest 

rates rise, which I expect. 



Siegel: The last two years have hammered home why stock 

investors earn such a large equity premium over the long 

run—in the short run, stocks are very harrowing investments! 

Many investors settle for meager returns in bonds rather than 

suffer the volatility of stocks. In the long run, these risk-averse 

investors will fall far behind those who stick with equity. 



Siegel: Global growth prospects are excellent, and investors 

must hold globally diversified portfolios. Over half of the 

world’s equity assets are outside the United States, and that 

fraction will grow in the future. The biggest threat to growth 

is sharply escalating energy prices, such as we saw in 2007 

and the first half of 2008. 

Gus Sauter, CIO, Vanguard 


Gus Sauter (Sauter): Nothing that has happened 

over the last couple of years would 

lead us to at all conclude that a buy-and-hold 

strategy isn’t as valid today as it was two years 

or 10 years ago. 

In fact, unless you can see the future—unless you know the 

future—buy-and-hold makes all the sense in the world. Really, 

the advantage of buy-and-hold is that it removes the temptation 

to abandon a long-term plan at just the wrong time. When the 

markets are going down, we all feel some degree of fear and 

some desire to change our strategy and get out of equities. Too 

frequently that happens at or near the bottom of the market. 

At the same time, when markets are moving higher, 

there is natural human greed that kicks in to make us want 

to double up on our bets, or increase our equity exposure. 

What we’ve found is that human behavior leads us to make 

these changes at just the wrong times. So, a buy-and-hold 

strategy helps prevent us from acting in a knee-jerk fashion 

and under-performing because we’re making all the wrong 

moves at the wrong time. 



Sauter: I don’t believe that inflation is a near-term problem, 

for a couple of reasons. First, inflation occurs when the economy 

becomes overheated and resources are in full demand. 

But, in fact, we’re looking at just the opposite right now. 

There is a lot of slack in the economy, capacity utilization is 

in the low 70 percent range and unemployment is pushing 

through 10 percent. Resources are ample and we can really 

increase production without it being at all inflationary. 

Secondly, I think a lot of people are concerned about 

inflation because of the unprecedented actions of the 

Federal Reserve—essentially, the more than doubling in the 

size of the Fed’s balance sheet. I would note, however, that 

while the monetary base, which the Fed controls, has more 

than doubled, the money supply that we spend—the broad 

measure of the money supply—has not at all increased out 

of the ordinary. Yes, the monetary base is the first component 

of money supply, but, really, what we spend is a much 

broader aggregate than that, and that broader aggregate 

has not expanded because the financial system still is not 

functioning properly. As banks get on firm footing, it will be 

necessary that the Fed sterilizes its balance sheet and pulls 

it back in. If the Fed isn’t nimble, and doesn’t restrict its own 

balance sheet while the banks are starting to expand again, 


37

then we will likely see an explosion of the money supply and 

associated inflation, but not in the near term. At this point, 

inflation is not baked in the cake, and we are hopeful the Fed 

will be able to make the right moves. 

In addition, gold has certainly been rising to historic highs, 

and people may think that it’s a signal that inflation is around 

the corner—that the spike in its price is in response to the 

increase in the Fed’s balance sheet. I don’t think that’s what gold 

is signaling to us right now. I think gold has been strong because 

the dollar has been weak. Gold is just more of a stable currency 

than the dollar, and so the dollar is weakening against gold; it’s 

not that gold is strengthening against the dollar for inflationary 

reasons. TIPS also give us a view of the market’s perception of 

future inflation, which is very moderate at this time. 



Sauter: We still believe that a long-term, buy-and-hold, 

broadly diversified approach to investing with low costs is 

the prudent way to go. I might throw in that having liquid 

investments can be a benefit as well, which was proven in 

the latter half of 2008, as liquidity dried up. Yes, the market 

pulled back dramatically, but that’s precisely why an investor 

establishes an asset allocation to bonds as well as stocks. It 

moderates the volatility of their portfolio. When we establish 

our asset allocation, we expect there will be times when 

equities will perform poorly. So, I wouldn’t react to the bad 

equity markets; we should have expected them. Staying the 

course, we think, is the appropriate way to respond. 

While we really haven’t changed our investment philosophy 

in response to the market environment over the last 

couple of years, there certainly are a couple of areas that we 

are researching very diligently. Primarily, we are attempting 

to develop portfolios that, hopefully, preserve reasonably 

high levels of return, while at the same time moderating the 

risk or the volatility of the portfolio. 

When we combine bonds into a portfolio, we’re trying to 

moderate volatility, but, at the same time, we’re not able to 

preserve the same high level of returns that equities have 

historically returned. So, we’ve been trying to look at alternative 

ways to invest in other types of asset classes that have 

their own betas. We think of the stock market beta; there’s 

also a bond market beta and a beta associated with other 

asset classes. We’re looking at some less traditional investments, 

like commodities, for instance, or a subasset class, 

such as REITs or real estate. They would naturally have their 

own inherent betas, as would timber or other more esoteric 

investment alternatives. We’re looking at some of those to 

see if there might be applications in a liquid portfolio for the 

broad market of investors. We’re still in the early stages of our 

research and evaluation, but it’s something we’re pursuing. 

We look for opportunities to create alpha, but I would note 

that beta is much easier to reproduce than alpha, and so we’re 

spending more time searching for new betas right now. 



Sauter: I think there’s still a lot of fear in the marketplace, 

and not participating in a bull market is dangerous to meeting 

your long-term investment goals. We’ve gone for about 

a decade with no equity returns, and I think some people 

have become very concerned that equities aren’t going to 

provide the longer-term historic rates of return in the future. 

So, they may have abandoned equities or lightened up on 

equities. The biggest risk those investors face is that when 

the markets do recover, they won’t be participating and they 

won’t be able to meet their goals. 

One other danger, farther out, is the next inevitable 

bubble. When we go through a very difficult period like 

this, it hopefully teaches us a very valuable lesson: not to be 

greedy, not to participate in bubbles. Unfortunately, we tend 

to repeat the mistakes we have made in the past. So, in the 

future, a danger would be to once again embrace a bubble 

and throw caution to the wind. 

Burton Malkiel, Chemical Bank Chairman’s 

Professor of Economics, Princeton 

University; and CIO, AlphaShares 


Burton Malkiel (Malkiel): I think it is not 

dead. Obviously, after the kind of market 

we had in 2008, it’s very natural for people to say, “Well, 

obviously you should have sold during 2007 and then 

bought back at the bottom in March of 2009.” We all have 

20/20 vision in retrospect. The problem with it is that 

nobody,―and I repeat, nobody―can time the market. 

To time the market, you’ve got to be right twice. You’ve 

got to be right when you sell, you’ve got to be right when 

you buy back. And not only is it my view that no one can do 

it, but all the evidence we have is that when people try to do 

it, they will get it almost perfectly wrong. 

More money came into the stock market in the fourth 

quarter of 1999 and first quarter of 2000, just at the height 

of the Internet bubble, than ever before. And more money 

then left the market in October of 2002, which turned out to 

be the bottom of the market. And again, what was happening 

in 2008, if you look at what individuals were doing, they 

were actually selling; they were liquidating mutual funds by 

the droves at the bottom of the market. 

So what I’m suggesting to you is that all the evidence we 

have is that people are emotional investors; they are invariably 

doing exactly the wrong thing when they try to time 

the market. And it is far better to be a buy-and-hold investor 

than to try to time the market and invariably get it wrong. 

Now, you might say, “Well, maybe individuals are emotional, 

but the institutions know exactly what to do.” And here, 

the evidence is just as clear. Mutual funds have their smallest 

cash positions right at the peak of the market. In other words, 

when you look at institutional investors’ movements of cash, 

you find that they do exactly the wrong thing. 

So, while I understand the argument that if you could 

time the market you’d be much better off not being a buyand-hold 

investor—since nobody can do it and when you try 

to do it you’re going to do the wrong thing—I think it is just 

extraordinarily dangerous to try to time the market. And I 


think no one—neither an individual nor an institution— 

ought to attempt to do it. 



Malkiel: My sense is that individuals should not be concerned 

about inflation over the near term. I think they 

should be far more worried about deflation. The world is 

awash with excess capacity, and I think all of the pressures 

are going to be on the other side; that is, for falling prices 

rather than rising prices. 

Over the longer pull, I think that inflation is a possibility. I 

think it’s a possibility because there is an enormous amount 

of liquidity sloshing around the world. Central banks, such 

as the Federal Reserve, have expanded their balance sheets, 

and there’s no doubt in my mind that eventually that excess 

liquidity is going to have to be mopped up. 

And I think politically, with very high unemployment and 

continuing high unemployment, it’s unlikely that any central 

banks are going to do the mopping up early rather than 

later. So I think there is a longer-run danger of inflation. But 

over the near term, I think absolutely not. 

Now, how do you protect yourself against inflation? 

I think the only thing you can do is have a very broadly 

diversified portfolio. And what that means is that in your 

portfolio you’re going to have equities, which are a longrun 

inflation hedge, and within those equities, you’re 

going to have raw material producers that will certainly 

help if there’s long-run inflation. You will have positions 

in emerging markets such as natural-resource-rich countries 

such as Brazil. And I think that’s the only sensible 

thing for anyone to do. The portfolio ought to be broadly 

diversified. It ought to have some bonds, which will certainly 

be very good in a deflationary environment, which 

is what I expect over at least the next year or so. But it 

ought to have equities, including equities of companies 

that produce raw materials. 

So, diversification is, I think, the answer to investors. 

You ought to have a portfolio that will work well in any 

environment. 



Malkiel: The one change in my thinking on investing and 

asset allocation is that the emerging economies of the world 

are continuing to grow, and grow rapidly. Within the next 

few years, particularly adjusted for purchasing power, China 

will exceed Japan as the world’s second-largest economy. 

And I think before too long, adjusted for purchasing 

power, China will be bigger than the United States. So the 

one adjustment that I have made is, along with the increased 

importance of emerging markets such as China, such as 

Brazil, I have, for myself, changed the equity allocation to 

include a much heavier commitment to these markets, and I 

think all individuals should do so. 

JOI: What do you see as the biggest danger/opportunity for 

investors over the next five years? 

Malkiel: Well, the share of world GDP has been growing rapidly 

for emerging markets. Those are where I see the opportunities. 

Now, where would I see the longer-run dangers? Again, 

while I say I do not expect any kind of inflation over the 

next year, over the next two years, I do see potential inflationary 

dangers over a longer period. I think that, paradoxically, 

probably the safest security you can buy—the 10-year 

Treasury at 3.25 percent —worries me, because I think if we 

have even some moderate inflation of 2 to 3 percent breaking 

out three or four years from now, those will not prove to 

be profitable investments. 

Larry Swedroe, Principal and Director 

of Research, BAM Advisor Services 


Swedroe: I almost have to laugh whenever 

I get a question like that. It’s similar to 

the question, “Is diversification dead?” 

Whenever we get some kind of crisis, all those who believe 

in active management come out of the woodwork with 

this nonsense. All that shows is that they don’t know the 

basics of investing. Any student of history and investing 

would know that during a financial crisis that is systemic 

around the globe, the correlation of all risky assets always 

go towards 1. It has happened many times before. 

But still, they come out of the woodwork … 

There was actually an article in [the Oct. 11, 2009 issue 

of] Investment News, titled, “The days of buy-and-hold have 

gone and went.” So let’s deal with that issue. 

It’s a very simple mathematical fact―which William Sharpe 

demonstrated in his nice little paper called “The Arithmetic of 

Active Management,”―that active management must lose in any 

environment or any asset class. Anyone who says otherwise 

should be required to wear a shirt that says, “I can’t add,” in big, 

bold letters. There are only two types of investors: active or passive. 

So, let’s say you are a passive investor and you just want 

to own Vanguard’s Total Stock Market Fund [NYSE Arca: VTI]. 

If the market goes up 10 percent, before expenses, you must 

get 10 percent, as all passive investors do. And that means, in 

aggregate, the active investors have to get 10 percent, before 

costs. The same math applies in down markets. 

Since active investors have higher expenses, active investors 

must lose after costs. As Sharpe points out in his article, 

anyone who produces a study that finds anything different is 

simply measuring the wrong things. 



Swedroe: Let me say it this way: William Sherden was an 

economist called to testify in front of Congress on nearterm 

inflation. He got the brilliant idea to say, “You know 

what? I ought to check the track records of these other 

geniuses like me who have previously been called to testify 

on the outlook for inflation and see if they got it right.” 

And guess what he found? He found these geniuses had no 

better forecasting record than what is called the naive fore- 

continued on page 62 


39

































































Conclusion 




















Endnotes 



no. 2, pp. 68-85. 


Actively Passive? 

When a little active management can be a good thing 

By Craig Israelsen 

40 


The issue of active vs. passive investing typically centers 

on the nature of the actual investment product: “Is it 

an actively managed fund or a passive index fund?” 

However, this is only one aspect of the active vs. passive 

debate, and presents far too simplistic a view. There is a 

broader, macro issue that applies to the broad portfolio; 

namely, “How is the portfolio of actively managed funds or 

index funds managed over time?” 

For example, if there is a large amount of turnover in a 

portfolio of index funds, it is an actively managed portfolio 

of passive index funds. Very simply, it is an “active-passive” 

portfolio. By contrast, an investor who purchases index funds 

using a buy-and-hold approach has a “passive-passive” portfolio. 

In short, it is possible to be an active manager of passive 

funds, or a passive manager of actively managed funds. 

In sum, the terms “active” and “passive” can apply to 

the nature of the investment instrument level as well as 

how the instruments are managed at the portfolio level. 

Thus, a die-hard index fund advocate may be “passive” at 

the instrument level and “active” at the portfolio management 

level. Conversely, an investor who utilizes actively 

managed funds (i.e., instruments) may be passive in his 

ongoing management of actively managed funds. Viewed 

in this way, it’s difficult to determine which investor is 

passive and which one is active. 

Here, we explore a twist on the classic active/passive 

debate by presenting theoretical parameters for the bestcase 

and worst-case outcomes when actively managing a 

portfolio of passive index-based instruments. 

Background And Data 

The time frame covered in this study was the 39-year 

period from 1970-2008. Investment asset classes included in 

this analysis were seven core indexes: large-cap U.S. equities, 

small-cap U.S. equities, non-U.S. equities, U.S. intermediateterm 

bonds, cash, real estate and commodities (see Figure 1). 

The 39-year historical performance of large-cap U.S. 

equities was represented by the S&P 500 Index, while the 

performance of small-cap U.S. equities was captured by 

using the Ibbotson Small Companies Index from 1970-78, 

and the Russell 2000 Index from 1979-2008. The performance 

of non-U.S. equities was represented by the Morgan 

Stanley Capital International EAFE (Europe, Australasia, 

Far East) Index. U.S. intermediate-term bonds were represented 

by the Ibbotson Intermediate Term Bond Index from 

1970-72 and the Lehman Brothers Intermediate Term Bond 

Index (now called the Barclays Capital U.S. Intermediate 

Credit Index) from 1973-2008. 

The historical performance of cash was represented by 

three-month Treasury bills. The performance of real estate 

was measured by using the annual returns of the NAREIT 

(National Association of Real Estate Investment Trusts) 

Index from 1970-77. Annual returns for 1970 and 1971 were 

regression-based estimates inasmuch as the NAREIT index 

did not provide annual returns until 1972. From 1978-2008, 

the annual returns of the Dow Jones Wilshire REIT Index 

were used. Finally, the historical performance of commodities 

was measured by the Goldman Sachs Commodities Index 

(GSCI). As of Feb. 6, 2007, the GSCI is now known as the S&P 

GSCI Commodity Index. 

Using the annual performance of these seven passive 

indexes, the performance parameters of a passive-passive 

portfolio (buy-and-hold using index funds) were simulated. 

In addition, the performance parameters of an active-passive 

portfolio (active management of index funds) were also simulated. 

The key variable in this analysis is how passive instruments 

are managed: actively or passively. 

Historical Performance 

The 39-year average annualized return on large U.S. 

stocks from 1970-2008 was 9.48 percent. A buy-and-hold 

investor who chose to invest in only the S&P 500 Index 

would have turned a $10,000 investment on Jan. 1, 1970 

into $341,485 by Dec. 31, 2008 (not adjusted for taxes, 

inflation or fund expenses). 

The performance of U.S. large stocks, as previously 

mentioned, is represented in this case by the S&P 500, a 

“passive” index. It represents a typical benchmark against 

which actively managed funds are often compared. There are 

several assumptions behind the performance figure of 9.48 

percent. First, it implies a buy-and-hold investment with no 

additional investments or withdrawals. Second, there are no 

tactical decisions made during the investment period—no 

overweighting or timing-based buys or sells. It is clear that 

these strict assumptions would rarely pertain to even the 

most ardent “passive” investor. 

The performance of several additional “passive” benchmarks 

is presented in Figure 1, such as the EAFE index, the 

Russell 2000 Index and so on. Figure 1 also includes the 

performance of several buy-and-hold multi-index portfolios. 

The first portfolio is a 40/60 allocation, representing a 40 

percent allocation to U.S. large stocks and a 60 percent 

allocation to bonds. This allocation represents a typical 

“income” fund or conservative balanced fund. Here, the 

40/60 portfolio had a 39-year annualized return of 8.74 percent, 

assuming the two allocations were never rebalanced 

back to the original 40/60 allocations. 

The next multi-index portfolio is a 60/40 allocation, 

representing a 60 percent allocation to U.S. large stocks 

and a 40 percent allocation to bonds. This is a common 

allocation in a “balanced” fund. A $10,000 total investment, 

where $6,000 was invested in the S&P 500 and $4,000 was 

invested in the bond index in 1970 (and not rebalanced 

over the intervening 39 years), produced an ending total 

combined balance of $289,144, for a 39-year annualized 

return of 9.01 percent. 

The third multi-index portfolio consists of equal allocations 

to each of the seven core indexes. It will be referred 

to as an equal-weighted seven-index portfolio. Multi-asset 

portfolios are often referred to as “asset allocation” funds. 

This multi-index portfolio had a 9.37 percent average 

annualized return over the 39-year period. It should be 

pointed out that asset allocation funds seldom employ 

equal weighting among the various assets in an asset allocation 

fund. Based on the results in Figure 1, one might 

reasonably ask “Why not?” 


41

Figure 1 

Passive-Passive: Buy-And-Hold Using Index Funds 

Buy-and-Hold 

39-Year Annualized 

Return (%) 

(1970–2008) 

39-Year Standard 

Deviation of 

Annual Returns (%) 

Growth 

of $10,000 

Buy-and-Hold Performance of 7 Core Indexes 

REIT 10.62 20.09 513,069 

U.S. Small Stock 10.25 22.72 448,827 

Commodities 9.92 25.56 399,818 

U.S. Large Stock 9.48 18.20 341,485 

International Stock 8.97 23.08 285,009 

U.S. Bonds (Intermediate) 8.13 5.36 210,633 

Cash (3-Month T-Bills) 6.17 3.13 103,237 

40/60 Portfolio 

(40% U.S. Large Stock, 60% Bonds) 

Buy-and-Hold Performance of Multi-Index Portfolios 

8.74 10.49 262,974 


(60% U.S. Large Stock, 40% Bonds) 

Equal-Weighted 

7-Asset Portfolio 

(14.29% allocation in each of the 7 core assets) 

9.01 13.06 289,144 

9.37 16.88 328,868 

Source: Author calculations using Morningstar 

Each of the three multi-index portfolios produced equitylike 

returns with far less volatility when compared against 

the three top-performing individual indexes (REITs, U.S. 

small stock and commodities). 

Parameters Of Active Portfolio Management 

We will assume that an investor has access to only these 

seven indexes (or portfolio assets). The upper performance 

limit (or parameter) of active portfolio management using passively 

managed indexes is achieved if the investor invests all 

his money on Jan. 1 of each year in the index that will have 

the best performance in the upcoming year. This assumes 

perfect foresight (or luck). 

So, for example, on Jan. 1, 1970, the investor invested 

$10,000 into the bond index (that is, a fund that mimics 

the bond index) because it had the best return of the seven 

indexes in 1970. In fact, it had a return of 16.86 percent. At 

the start of 1971, the investor shifts all of his money ($11,686 

by the end of 1970) into the EAFE index (i.e., EAFE clone 

fund) because it had the best return of the seven indexes 

in 1971 (a return of 29.59 percent). This pattern of perfect 

luck in annual index selection (or best-case active investing) 

continued annually through the end of 2008. 

By the end of 2008, the investor’s original $10,000 at 

the start of 1970 was valued at $958.9 million, equating to 

a 39-year average annualized return of 34.20 percent (see 

Figure 2). This outcome is, of course, based on perfect foresight 

at the beginning of each year and does not represent a 

feasible outcome. It does, however, represent the theoretical 

limit of perfect active portfolio management (assuming 

access to only these seven core “passive” indexes). 

How about an active investor with median luck? This investor 

reallocated all of his investment assets at the start of each 

year into the passive index that ended up being the middle (or 

median) performer among the seven indexes by the end of the 

year. The ending account value of his initial $10,000 investment 

in 1970 grew to a modest $208,834 by the end of 2008. 

This growth represents a 39-year average annualized return of 

8.10 percent and was comparable to the 8.13 percent average 

annualized buy-and-hold return in the bond index. 

The active investor with perfectly bad luck ended up 

reallocating his entire portfolio into the upcoming year’s 

worst-performing index at the start of each year. The ending 

value of his initial $10,000 investment shrank to $166, for 

a 39-year average annualized return of -9.98 percent. This, 

when paired with the first investment scenario, simulates the 

lower and upper performance parameters, respectively, of 

active portfolio management using the seven selected core 

indexes over this specific 39-year period. 

A Random Walk 

What about an investor who simply threw one dart at the 

seven indexes at the start of each year? In other words, an 

investor randomly selected one of the seven indexes at the 

start of each year and reallocated his entire portfolio into 

that index for the coming year. Based on 1,000 simulations, 

a portfolio that invested all assets into a randomly selected 

single index (from among the seven core indexes) at the 

start of each year produced an ending account balance of 

$293,168 (based on a $10,000 initial investment). The average 

annualized return was 9.05 percent over the 39-year 

period from 1970-2008. 

We now have a new baseline performance parameter for 

active portfolio management during this 39-year period— 

42 


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Figure 2 

Active-Passive: Active Portfolio Management Using Passive Index Funds 

Active Portfolio Management 

Using Passive Index Funds 

39-Year Annualized 

Return (%) 

(1970–2008) 

39-Year Standard 

Deviation of 

Annual Returns (%) 

Growth 

of 

$10,000 

Select Best-Performing 

Index at Start of Each Year 

Select Middle-Performing Index 

at Start of Each Year 

Select Worst-Performing Index 


Performance of Actively Managed Single-Index Portfolios 

34.20 14.85 958,982,405 

8.10 12.20 208,834 

(9.98) 13.73 166 

Randomly Selected Single Index 


(1,000 samples) 

9.05 18.69 293,168 

Select Best-Performing 

Index From Prior Year 

(Momentum) 

Select Middle-Performing 

Index From Prior Year 

(Mean Regression) 

Select Worst-Performing 

Index from Prior Year 

(Dogs) 

13.92 24.18 1,611,240 

9.85 20.24 389,628 

8.47 19.25 238,370 

Performance of Actively Managed Multi-Index Portfolios (Annual Rebalancing) 


(40% U.S. Large Stock, 9.07 8.43 295,504 

60% Bonds with Annual Rebalancing) 


(60% U.S. Large Stock, 9.35 11.47 326,598 

40% Bonds with Annual Rebalancing) 

Equal-Weighted 7-Index Portfolio 

(14.29% Allocation in each of the 10.21 10.50 442,671 

7 core Indexes with Annual Rebalancing) 

Source: Author calculations using Morningstar 

an annualized return of 9.05 percent. Said differently, over 

this same 39-year period, an active management protocol 

(whatever it might have been) would have needed to generate 

an annualized return in excess of 9.05 percent to add 

value over a portfolio comprising a single index selected at 

random at the beginning of each year. 

It is worth noting that a randomly selected single-index 

portfolio outperformed the “passive-passive” (i.e., buy-andhold) 

performance of the MSCI EAFE Index, the bond index 

and cash (or T-bills). In fact, randomly selecting one index 

at the start of each year (and reallocating all assets into it) 

produced a 39-year average annual return that was only 43 

bps lower than the return of the S&P 500 Index (9.05 percent 

vs. 9.48 percent). 

The performance of this actively managed random portfolio 

(consisting of passively managed indexes) changes the 

performance “hurdle” that active managers face. Rather 

than being compared against a passive-passive index (such 

as the S&P 500), an actively managed portfolio could/should 

be compared against the performance of an active-passive 

portfolio in which the sole investment asset is a randomly 

selected passive index at the beginning of each year. 

Comparing tactical active management against the performance 

of a passive buy-and-hold index is illogical, but 

tends to dominate the methodology of the “active vs. passive” 

debate. Simply put, determining whether or not tactical 

(active) investing adds value (i.e., generates alpha) should be 

based on its performance in comparison to the performance 

of random active investment protocol, rather than against a 

nontactical single-index benchmark. 

44 


Using Last Year’s Results 

Assuming perfect luck, median luck or worst-case luck 

in the selection of each year’s sole investment is theoretically 

interesting, but not very practical. An active portfolio 

management protocol that uses the performance results 

of each index from the previous year is more feasible. In 

other words, an active investor’s choice of index in which 

to invest for the coming year could be based upon the 

performance of all possible investment assets (in this 

case, seven indexes) during the prior year. Performance of 

indexes (or any investment asset) in the previous year is 

information that is available to all investors at the beginning 

of each new year. 

As shown in Figure 2, an active investing strategy based 

on “momentum” generated a 13.92 percent annualized 

return over the 39-year period. This active strategy consisted 

of reallocating all investment dollars into the previous year’s 

best-performing index. For example, the best-performing 

index in 1970 was bonds. Thus, at the start of the next year 

(1971), all money was shifted into the bond index based on 

its performance in the prior year. By the end of 1971, the 

EAFE Index was the best-performing asset; thus at the start 

of 1972, all money was shifted into the EAFE Index. This pattern 

was continued through 2008. This active investing protocol 

does not rely upon luck. Rather, it utilizes the known 

performance from the prior year to make each new year’s 

investment decision. 

Another active investment approach selected the middleperforming 

asset from the prior year. This is casually referred 

to as a “mean regression” approach, but may or may not 

reflect regression-to-the-mean behavior. This particular active 

investing protocol produced an annualized return of 9.85 

percent, which was considerably lower than the momentum 

strategy over this particular 39-year period. Lastly, an active 

protocol that selected last year’s worst-performing asset produced 

a 39-year annualized return of 8.47 percent. 

Active Vs. Passive Portfolios 

Each of these three “last year results” approaches utilized 

only one of the seven indexes each year as the sole investment. 

This represents very concentrated investing. Far more common 

is the use of several different indexes (i.e., investment assets) in 

a portfolio, whether passively or actively managed. 

In recognition of this, Figure 2 also presents the performance 

of three actively managed multi-index portfolios: a 

40/60 portfolio, a 60/40 portfolio and an equal-weighted 

seven-index portfolio. The buy-and-hold performance of each 

multi-index portfolio was previously presented in Figure 1. 

The “active” portfolio management protocol being 

introduced at this point is very straightforward: systematic 

annual rebalancing. The simple act of rebalancing each 

multi-index portfolio at the start of each year to bring each 

portfolio component back to its original allocation produced 

notable improvements in performance. The 40/60 

portfolio improved from 8.74 percent using buy-and-hold 

to 9.07 percent using active rebalancing—which increased 

the ending account balance by over $32,000. Importantly, 

the actively rebalanced 40/60 portfolio had 20 percent less 

volatility in the annual returns (as measured by standard 

deviation). More return with less risk. 

The actively rebalanced 60/40 portfolio had a 34 bps 

higher annualized return compared to the buy-and-hold 

60/40 portfolio and a standard deviation of annual returns 

that was 12 percent lower. 

Finally, the annually rebalanced equal-weighted sevenindex 

portfolio produced a 39-year annualized return of 

10.21 percent, 84 bps higher than the buy-and-hold sevenindex 

portfolio. The volatility of annual returns was nearly 

40 percent lower in the actively rebalanced seven-index portfolio. 

Perhaps most compelling is an ending account balance 

that was nearly $114,000 higher in the actively rebalanced 

seven-index portfolio. 

Active Benefits 

There is another benefit when “actively” rebalancing “passive” 

indexes. As shown in Figure 3, the ending account balances 

in bonds and cash in the actively rebalanced seven-index 

portfolio were considerably higher than the bond and cash ending 

balances in the buy-and-hold multi-index portfolio (before 

the final rebalance at the end of 2008). Why does this matter? 

Bonds and cash have lower returns than equity and 

equitylike assets over long time frames and, as a result, 

will not be able to keep pace with the other portfolio 

continued on page 55 

Figure 3 

Total Ending Balances In Actively Rebalanced And Buy-And-Hold 7-Asset Portfolio 

(39-Year Period From Jan. 1, 1970 To Dec. 31, 2008) 

$1,428 Starting Balance in 

each of the 7 Indexes 

Large 

U.S. 

Stock 

Small 

U.S. 

Stock 

Non-U.S. 

Stock 

U.S. 

Bonds 

Cash REIT Commodities 

Total Ending 

Portfolio Value 

(Growth of $10,000) 

Actively Rebalanced 

7-Index Portfolio* 

54,461 57,236 48,946 95,462 87,751 52,559 46,257 442,671 

Buy-and-Hold 

7-Index Portfolio 

48,784 64,118 40,716 30,090 14,748 73,296 57,117 328,868 

Source: Author calculations using Morningstar. *Account balances before rebalancing at the end of 2008 


45

Apparent Value Added By Fund Management 

This element is the approximate value that appears to be 

added to the fund by the portfolio management process. 

Analysts eventually will be able to break down the performance 

contribution of active managers in a variety of ways. 

For example, with clean data it is possible to calculate the 

value added by the transactions the fund manager has made 

to change the composition of the portfolio during a quarter 

or a year, or to compare the beginning of the period portfolio’s 

performance with the performance of an index. There 

is scope for ingenuity in analyzing an active manager’s 

ability to add value or highlight aspects of the investment 

process that have worked less well. As the availability of 

fund data from SEC filings improves, analyses of transaction 

costs and value added by a fund’s investment process will 

become more sophisticated. For example, the value added 

by fund management will be broken down into a number 

of distinct components and a noise element. The total 

apparent value added is a residual in the example in Figure 

2. When we subtract the net tracking error (-1.88 percent) 

from the total of the cost elements in the left column (-2.65 

percent), we get a total for the positive elements of (+0.77 

percent). Adding the market timing loss (-0.02 percent), we 

get an apparent value-added residual of (+0.79 percent) for 

the fund manager’s efforts. 

What Is The Significance Of Figure 2? 

Figure 2 is simply an illustration of the kind of fund performance 

breakdown that is theoretically possible today and 

will ultimately be routine. To the extent that tracking error 

is measured relative to a reasonable benchmark, the costs 

incurred by the average fund will cause the net tracking 

error to be negative. The breakdown into cost and performance 

elements and comparisons to other funds and other 

managers will enhance the fund selection process. A fund 

analyst’s ability to perform this kind of analysis—and even 

more detailed breakdowns—depends on the fund industry’s 

adoption of a new data standard: XBRL. 

I will cover XBRL in depth in the next and final installment, to be published in the March/April issue of the Journal of Indexes. 

Endnotes 

1 Gastineau, Gary L., and Kritzman, Mark, “The Dictionary of Financial Risk Management,” Frank J. Fabozzi Associates (now a division of John Wiley & Sons, Hoboken, New Jersey), 

1999. See pp. 283-284. 

2 In addition to the indexes for the Sector SPDRs, the Nasdaq 100 capitalization weightings are modified to make it RIC compliant. The price-weighted Dow Jones Industrial Average 

is usually RIC compliant without modification, but it dropped out of compliance at the end of March 2009. 

3 The constraints will be relatively tight for an index fund, but they should not discourage an index fund manager from trading away from the implementation time of an index 

change. In many large index funds, avoiding the official index implementation trading frenzy is akin to an opportunity to pick up money from the sidewalk. 

4 Israelsen, Craig L. and Gary F. Cogswell, “The Error of Tracking Error,” Journal of Asset Management, March 2007, vol. 7, No. 6, pp. 419-424 appropriately argues that the magnitude 

of tracking error (standard deviation version) is not a suitable performance indicator for active managers. I agree, and I use tracking error (performance difference version) only 

as an organizing framework. 

5 The most popular measure is prior period performance. 

6 See Edelen, Roger M., Richard Evans and Gregory B. Kadlec, “Scale Effects in Mutual Fund Performance: The Role of Trading Costs,” unpublished working paper, March 17, 2007. 

7 A fund that combines small foreign holdings with a largely domestic U.S. portfolio may have an adverse tax effect for some of its shareholders that can be avoided by a fund that 

holds predominantly non-U.S. securities. Global funds with predominantly U.S. holdings are often tax inefficient for U.S. investors. 

Israelsen continued from page 45 

components (in terms of dollar account value). Accordingly, 

the account balances will become disproportional over time 

(as demonstrated in Figure 3). The equity and equitylike 

portfolio components (REITs and commodities) will tend to 

dominate the portfolio. This can be advantageous if, in the 

latter years of a portfolio, the equity-based assets perform 

well. But, if equity and equitylike assets suffer declines, the 

investor can experience heavy losses because of his disproportionately 

large allocations in equity. 

This potential portfolio imbalance can be solved by the 

active strategy of rebalancing. When equity assets have 

strong annual gains, their excesses are diverted (i.e., rebalanced) 

to the fixed-income components of the portfolio 

(bonds and cash). Because bonds and cash seldom have 

negative returns, the gains are preserved in a fixed-income 

“lockbox.” As investors age, the notion of a lockbox becomes 

very appealing. During equity market downturns (i.e., 2008), 

the bond and cash portfolio components—which have been 

systematically replenished through active rebalancing—will 

have sufficient reserves to provide needed liquidity. 

Summary 

The upper limit (or maximum parameter) of active-passive 

investing is an annualized return of 30 to 34 percent. This 

requires perfect luck or perfect foresight, neither of which 

represents a defensible tactical active strategy. By contrast, a 

portfolio with a randomly selected index at the beginning of 

each year generated a 9.05 percent annualized return over the 

39-year period from 1970-2008. This type of random active-passive 

portfolio represents a more logical performance threshold 

against which actively managed funds should be measured. 

Rebalancing a multi-index passive portfolio represents an 

“actively passive” portfolio that outperforms a passive-passive 

multi-index portfolio. Rebalancing represents a potent 

active strategy that seldom receives the attention it deserves. 

Rebalancing is often simply assumed to be a good idea. This 

research has quantitatively demonstrated just how good it is. 

More importantly, rebalancing is an active strategy that passive 

investors can probably live with. 

Most passive investors may recognize that they are, in 

fact, actively passive. 


55

The Future Of 

Fund Ratings 

Bringing mutual fund and ETF evaluations into the 21st century 

Part Two 

By Gary Gastineau 

46 


In Part One of this article, I developed a picture of how traditional 

published expenses and less visible expenses—the 

most important of those being transaction costs—adversely 

affect fund performance. Now, let’s look at a useful framework 

for analysis of these and other elements affecting fund performance: 

Tracking error. 

What Is Tracking Error? 

Tracking error was not widely mentioned in investor 

and adviser discussions of funds until a large number of 

exchange-traded funds tracking multiple indexes were available 

to investors. Index tracking had been discussed in connection 

with index mutual funds, but the number of index 

mutual funds was and is small, and the number of indexes 

used by mutual funds is even smaller. It is useful to discuss 

the growing interest in index tracking in connection with the 

growth of index ETFs and then examine how a tracking error 

framework can be useful in evaluating all types of funds. 

Tracking error is one of a relatively small number of 

financial terms that has been defined differently in different 

situations. In most academic finance papers, tracking error 

is defined as “an unplanned divergence between the price 

behavior of an underlying position or portfolio and the price 

behavior of a hedging position or benchmark.” 1 In these academic 

applications, tracking error is usually expressed as the 

expected or experienced one-standard-deviation annualized 

percentage variation of a portfolio value from a benchmark 

index value. This definition is used largely to measure how 

much an actively managed portfolio strays from the benchmark 

as the manager tries to add value. I will return briefly to 

this definition later, but it is not the definition used in most 

discussions of ETF tracking error and it is not the definition we 

want to use as a framework for fund analysis and evaluation. 

In discussing tracking error in the context of exchangetraded 

index fund returns relative to the fund’s benchmark 

index, the notion of a standard deviation would not be useful 

before data for at least several years had accumulated. 

However, “tracking” comparisons of ETFs are called for and 

made when ETFs have been in operation for even less than 

one year. The purpose of such early comparisons is simply to 

measure how closely the exchange-traded index fund return 

matches the return of the index. Most fund users neither 

know nor care about standard deviations, so the comparison 

fund analysts began publishing was simply the return of the 

fund minus the return of the index. This difference is usually 

expressed in basis points (hundredths of a percent) because 

most index ETFs have tracked their index closely and the difference 

is usually small. 

When a standard deviation is used as the measure of 

tracking error, the tracking error is always expressed as a 

positive number. However, the difference between a fund’s 

performance and the performance of its benchmark can be 

positive or negative, depending on whether the fund performs 

better or worse than the index. Understandably, there 

was some confusion over the sign of a tracking error measure 

until ETF analysts clarified what they were doing. The 

tracking error measure used in connection with ETFs today 

is positive when the fund’s NAV outperforms the index, and 

negative when the fund under-performs the index. 

As a simple example, if a fund return were +8.75 percent 

for the year and the total return on its benchmark index 

were +8.65 percent, the tracking error would be reported 

as +10 basis points. If the returns were reversed, the tracking 

error would be reported as -10 basis points. Calling this 

difference “tracking error” seems reasonable and intuitive to 

ETF analysts and users and it rarely confuses anyone except 

finance Ph.Ds. 

A few people have calculated a “tracking error” as the difference 

between the market value return of the ETF and the return 

of the index, but the most frequent comparison is between the 

net asset value return of the ETF and the return of the index. 

ETF market prices typically will converge on NAV over any long 

period, making the long-term difference between the ETF market 

value and net asset value negligible. While some comparisons 

are done for shorter periods, the most widely discussed 

ETF tracking error comparisons are annual. 

Most ETFs have relatively low expense ratios; consequently, 

there is not complete standardization on how the expense 

ratio is treated in tracking error calculations. Some performance 

tracking calculations add the fund’s expense ratio 

back to the fund return on the theory that the fund manager 

does not control these expenses. However, the expense ratio 

comes out of the investor’s return and it seems appropriate 

to compare funds uniformly after expenses. Consequently, 

most tracking error comparisons do not add the expense 

ratio to the fund’s return, but it is a good idea to check. 

A number of index ETFs deserve extra attention because 

they fail to track their benchmark indexes very well. There 

are five principal reasons why an ETF will exhibit significant 

tracking error by this performance difference measure. These 

reasons are worth discussing because they account for most 

high double-digit or triple-digit (in basis points) index ETF 

tracking errors. 

Non-RIC Or Non-UCITS-Compliant Benchmark Indexes 

The U.S. Internal Revenue Code imposes diversification 

requirements that investment companies in the United 

States must meet to qualify as regulated investment companies 

(RICs) for favorable pass-through tax treatment. RIC tax 

treatment permits the distribution of interest, dividends and 

capital gains to holders of shares in the investment company 

without taxation at the fund level. In Europe, undertakings 

for collective investment in transferrable securities (UCITS) 

rules impose regulatory rather than tax diversification 

requirements on funds. 

The RIC diversification rules require that no more than 

25 percent of a fund’s assets can be in the securities of a 

single issuer except the U.S. government. Furthermore, with 

respect to 50 percent of the assets of the fund, no more than 

5 percent of the securities can be those of a single issuer, 

again except for the U.S. government. 

The UCITS requirements are structure rules, not tax rules, 

and they are a little more complex than the RIC rules. Under 

what is known as the 5/10/40 rule, a nonindex UCITS may 

invest no more than 10 percent of its net assets in transferable 

securities or money market instruments issued by the 


47

Figure 1 

Annual Tracking Error In Two Down Years And Two Up Years 

Down Years 

Up Years 

S&P 500 Annual Performance 

Fund Type 

Symbol 

2002 

-21.98 

2008 

-36.55 

1 Formerly Vanguard Total Market VIPERS. 2 Formerly Nasdaq-100 Index Tracking Stock. 3 Formerly Vanguard Extended Market VIPERS. 4 Formerly iShares Goldman Sachs 

Technology Index Fund. 5 Formerly iShares DJ US Consumer Cyclical. All calculations are Fund Return minus Index Return, so the tracking error includes the expense ratio. 

Sources: Morgan Stanley, Barclays Global Investors, State Street Global Investors, Thomson, Bloomberg 

2003 

28.25 

S&P Depositary Receipts (SPDR) SPY 1 12 -30 -14 

iShares Russell 1000 IWB -7 3 -25 -17 

Vanguard Total Market 1 VTI -10 7 -21 -5 

iShares Russell 3000 IWV -9 1 -29 -20 

iShares DJ US Total Market IYY -9 -10 -33 -25 

PowerShares QQQ 2 QQQQ -12 -10 -34 -26 

Diamonds Trust DIA -11 -1 -30 -23 

S&P MidCap 400 SPDR MDY -5 -43 -46 -30 

iShares S&P MidCap 400 IJH -21 3 -25 -18 

iShares S&P MidCap 400 Growth IJK -24 -1 -19 -6 

iShares Russell MidCap IWR 2 4 -33 -22 

iShares Russell 2000 IWM -4 13 -31 -19 

iShares S&P SmallCap 600 Growth IJT -19 6 -26 -24 

iShares S&P SmallCap 600 IJR -11 -2 -20 -18 

Vanguard Extended Market ETF 3 VXF -24 34 -29 18 

iShares S&P 500 Growth IVW -9 -3 -24 -20 

iShares Russell 1000 Growth IWF -11 -3 -29 -20 

iShares Russell 2000 Growth IWO -3 11 -35 -22 

iShares S&P 500 Value IVE -13 -2 -28 -21 

iShares Russell 1000 Value IWD -16 2 -32 -24 

iShares Russell 2000 Value IWN -9 18 -41 -29 

Technology Sector SPDR XLK -15 40 -46 -22 

iShares S&P North American Techology 4 IGM -31 -18 -61 -53 

Financial Sector SPDR XLF -19 6 -53 -39 

iShares DJ US Financial Sector IYF -46 -6 -86 -62 

Consumer Discretionary Sector SPDR XLY -21 6 -44 -19 

iShares DJ US Consumer Goods 5 IYK -58 -21 -73 -59 

Energy Sector SPDR XLE -19 -9 -49 -28 

Industrial Sector SPDR XLI -22 1 -59 -30 

Column Totals -455 38 -1091 -697 

Average per fund (rounded) -15 1 -36 -23 

2006 

15.49 

same entity. With respect to 40 percent of its investments, 

the UCITS can invest no more than 5 percent of its assets 

in the instruments of a single issuer. Interestingly, there is 

a more flexible UCITS provision for index funds. An indexreplicating 

UCITS fund may invest up to 20 percent of its net 

assets in shares and/or debt securities issued by the same 

entity. The 20 percent limit can be raised to 35 percent under 

“exceptional market conditions.” To qualify for these larger 

positions, the benchmark index must use a recognized methodology 

that generally does not result in the exclusion of a 

major issuer in the market the index represents. 

Benchmark replication inside an ETF does not appear 

to be a significant diversification issue in the EU countries 

where UCITS prevails or elsewhere outside the U.S. However, 

a number of U.S. ETFs are based on non-RIC-compliant indexes. 

A few single-country ETFs and some sector funds issued 

by Barclays Global Investors based on Dow Jones sector 

indexes and by Vanguard based on MSCI sector indexes are 

not based on RIC-compliant indexes. The S&P sector indexes 

used for the Select Sector SPDRs are specially weighted to be 

RIC compliant. Most indexes introduced as ETF benchmarks 

have been RIC and/or UCITS compliant. 2 All or nearly all index 

publishers are willing to provide RIC- and UCITS-compliant 

versions of any of their indexes. The use of noncompliant 

48 


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indexes is an unfortunate decision, usually made by the fund 

issuer. Fortunately, it is easy for an investor to avoid funds 

that are based on noncompliant indexes and that, consequently, 

conceal the quality of their portfolio management 

process. If you already own shares in a non-RIC-compliant 

index fund and prefer not to change, suggest to the fund’s 

manager that they consider changing to a RIC-compliant version 

of the index. 

Optimized Portfolios Designed 

To Reduce Trading Costs 

Many ETFs based on a large number of stocks such as U.S. 

total market funds and multicountry funds often have portfolios 

that are optimized or sampled to reduce custodial costs 

and the administrative costs involved in handling a creation 

or redemption basket that might otherwise include as many 

as several thousand securities. While there is no universal 

pattern for optimized portfolios, they often underweight 

the smaller-capitalization stocks in the index. If small-caps 

are underweighted, the fund will outperform the benchmark 

when the small-cap components under-perform the balance 

of the index. When small-cap stocks outperform the large-cap 

stocks in the benchmark, the optimized fund will under-perform 

the benchmark. Some issuers of funds with a large number 

of issues in their benchmark resort to completion baskets 

or completion swaps covering the small-cap portion of the 

portfolio to improve tracking while simplifying fund management 

and reducing the cost of creation and redemption. 

Small And Illiquid Index Components 

A number of backtested “concept” indexes have been 

designed solely to serve as ETF templates. The resulting ETFs 

are often sold based on a combination of good backtested 

performance and an investment concept that suggests rapid 

future growth. The indexes—and the ETF portfolios—often 

include some small and illiquid issues. If the backtested fund 

succeeds in attracting substantial assets, the portfolio manager 

may—for reasons that include (1) the risk or difficulty 

of trading large positions in illiquid stocks, and (2) position 

reporting requirements—decide not to increase the fund’s 

position in a small company beyond 5 or 10 percent of the 

company’s outstanding shares as the assets of the ETF grow. 

If the portfolio manager does not hold shares in these positions 

at the index weighting, the performance of the fund 

will diverge from the performance of the index. 

The Lazy Portfolio Manager 

Lazy portfolio managers are probably the greatest single 

reason for index fund tracking errors that are consistently 

more favorable (positive) when the market is weak, and 

unfavorable (negative) when the market is strong. One of the 

important differences between investment company ETFs and 

mutual funds is that the ETF portfolio manager does not need 

to hold cash balances to meet cash redemptions. The ETF 

portfolio manager who wants to manage cash aggressively 

can be invested down to the fund’s last few dollars each day 

without any unusual cash flow management problems. In fact, 

however, many ETF portfolio managers do not invest their 

cash aggressively. Many funds show a pattern of returns that 

suggests the manager consistently holds excess cash. During 

years when the market rises, these funds lag the performance 

of their benchmark index because interest on cash balances is 

not keeping up with the return on positions in the index. The 

same index funds will look too good to be true in a year like 

2008 when their cash balances earned modest positive returns 

while portfolio positions performed poorly. In retrospect, a 

cash balance would not have been a bad idea in 2008, but 

the long-term question is what the portfolio manager in an 

index ETF should be doing. Clearly, an important part of the 

mandate for an index fund manager is to deliver a return representative 

of the index. If the portfolio manager’s mandate 

is asset allocation or market timing (in the sense of reducing 

or increasing equity exposure to anticipate the direction of 

the market), then a cash balance is certainly appropriate—at 

times. In most index ETFs, however, holding cash balances all 

the time is not part of the portfolio manager’s job description. 

Figure 1 shows some suspiciously “lazy” returns in two 

strong market years and two weak market years. 

Figure 1 shows tracking error calculations for a group of 

29 U.S. ETFs that have been in existence since at least the 

beginning of 2002. These funds all have domestic U.S. stock 

portfolios. While the S&P 500 annual performance indicated 

at the top of the column for each year studied may not be 

representative of the portfolio in each fund, it is a fair surmise 

that 2002 and 2008 were down years for all of these 

funds, and 2003 and 2006 were up years. As the column 

totals and the average tracking error for each fund in these 

years indicates, the tracking errors were smaller in the down 

years. In 2008, the average ETF even had a small positive 

tracking error. In the up years of 2003 and 2006, every one 

of the ETFs had a negative tracking error, and the size of 

the negative tracking error was usually greater than the size 

of the negative tracking errors in the comparable funds for 

2002 and 2008. The only reasonable interpretation of these 

results is that, on balance, these funds carried significant 

cash balances in all four years. 

Leveraged Long And Leveraged Inverse Funds 

These funds track their leveraged benchmarks pretty well 

on a day-to-day basis, as they are designed to do. Tracking 

is not close when the funds’ performances are measured 

against a simple leveraged multiple of the target benchmark 

for periods longer than one day. This issue is explored 

in depth elsewhere, including in Joanne Hill and George 

Foster’s article in the September/October 2009 issue of the 

Journal of Indexes. 

Apart from showing how intensively an index fund manager 

is keeping your money at work, tracking error can be a 

useful framework for measuring how an active portfolio manager 

is doing. Breaking the total tracking error measure of 

the return difference between an index and a fund into positive 

and negative elements can be a useful way to evaluate 

what is going on in an actively managed fund and how well 

or poorly the fund’s investment process works. In that spirit, 

I turn next to analysis of differences in performance between 

50 


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the net asset value of a fund and one or more benchmark 

indexes as a framework for analysis of an actively managed 

fund’s performance. 

Net Tracking Error As A Framework 

For Fund Performance Evaluation 

This section illustrates one way to organize your examination 

and evaluation of transaction costs and a number of 

other major cost and value-added elements that determine 

fund performance. The example is an actively managed 

mutual fund (to illustrate the breadth of the analytical possibilities), 

but most of the hidden cost and value-added 

elements apply to index and actively managed ETFs just as 

well. After all, even if an index fund is passively managed, 

“passive” should not mean “mindless.” Transaction costs 

offset at least some of any value added by both active and 

passive investment processes. Transaction costs associated 

with index composition changes are a dead-weight drag on 

the performance of any portfolio that replicates an index. In 

most cases, the composition-change transaction costs are 

embedded in the performance of the index, but that does not 

mean we can’t estimate them. 

In my youth, I was a great fan of the late Don Herbert’s 

“Mr. Wizard” science TV show. While childhood memories 

are sometimes inaccurate, I recall having heard from time 

to time, as Mr. Wizard and his youthful apprentices donned 

safety glasses, an admonition something like, “Don’t try 

this at home.” While that may not have been the precise 

warning Mr. Wizard used, it is probably an appropriate 

warning for anyone who might try to assemble the data 

described in this section from the fund databases available 

today. While most of the information described here can be 

developed from SEC filings, the data assembly and calculations 

are beyond what most advisers, let alone individual 

investors, have the resources to undertake. The purpose of 

this discussion is to illustrate what should soon be possible 

and to offer a preview of how better data on mutual funds 

and ETFs that should become available over the next few 

years can raise the level of analytical discourse and improve 

the fund selection process. Improvements in data availability 

should make this kind of fund analysis almost routine 

within a few years. 

As indicated at the beginning of the first article in this 

series, most fund rating systems explicitly recognize the 

limitations of past performance as a predictor of future 

investment results—and then proceed to focus on just such 

performance comparisons. The overwhelming focus of fund 

service ratings is on comparisons of a fund’s historic performance 

to peer group performance. One problem with past 

performance is that it is a “noisy” measure, at best, of the 

value added or subtracted by a fund’s investment process. 

Effective fund evaluation will attempt to break down the 

components of performance to separate the wheat (valueadded 

elements and costs) from the chaff (noise). 

I propose to use the net tracking error difference calculation 

as an organizing framework to incorporate all the favorable 

and unfavorable elements affecting fund performance. 

The fund manager’s objective should be to achieve the best 

possible performance for investors, not the smallest possible 

tracking error relative to a flawed benchmark—or relative to 

any benchmark, for that matter. The objective of the manager 

of any fund—indexed or active—should be to maximize 

positive tracking error relative to an appropriate benchmark, 

subject to risk constraints that are appropriate for the fund. 3 

Calculating tracking error relative to several benchmarks lets 

us use the multiple comparisons to increase our understanding 

of why a fund’s performance has been good or poor. A 

large positive tracking error in fund performance is almost 

certainly more desirable than a negative tracking error (or 

than no tracking error at all), but any useful analysis is much 

more complex than that statement suggests. 4 

To make it most useful, net tracking error should be 

viewed as a summary measure of the positive and negative 

causal elements of value added—or of poor performance. 

Calculating the individual elements of cost and performance 

and displaying them as offsetting components of a net 

tracking error calculation can provide useful insight into the 

interaction of the determinants of performance. Some of 

these calculations require methodology that goes far beyond 

what fund services can offer today, but this kind of analysis 

can enlighten investors in ways that make development and 

application of this methodology inevitable as the available 

data improves. Of course, there will always be random elements 

that limit the value of even the best analysis. Noise 

that is not subject to an unequivocal explanation can be a 

sizable component of any fund evaluation. While noise limits 

the usefulness of the tracking error framework, we are looking 

for the causes of performance and are asking appropriate 

questions. That is a substantial improvement over what is 

being done by most fund evaluators today. 

There is no reason to calculate tracking error only relative 

to the template index of an index fund or only to a benchmark 

for a market segment similar to the fund’s portfolio. Tracking 

error for both indexed and actively managed funds measured 

relative to several indexes can highlight important index and 

fund characteristics. It can reveal elements of index transparency 

costs, the quality of the selections in a fund based on 

a quantitative security selection process or the astuteness 

of the stock picks of a traditional active manager. Multiple 

tracking error calculations can reveal that a poorly performing 

benchmark is being used as an index fund template. Tracking 

error measured relative to competitive funds, particularly 

with the comparative or comprehensive tracking error divided 

into operating costs, trading costs and other elements, can 

highlight features that a skilled fund analyst or a determined 

do-it-yourself investor can use to improve fund selection. 

When the net tracking error is broken down into security 

selection value added, operating costs, transaction costs and 

other measures that incorporate and highlight cost elements 

and the effect of any risks accepted and management decisions 

made, the result is a rich tapestry that reveals important 

characteristics of the fund and its investment process that an 

adviser will want to understand. Harking back to my earlier 

warning, this analysis is not something for the average do-ityourself 

investor or even an adviser to undertake at the present 

time. However, advisers need to prepare their thinking 

52 


for this kind of analysis. Investors increasingly pay advisers for 

advice in fund selection. Advisers who can prepare themselves 

to undertake this kind of analysis over the next few years will 

provide much better service to investors they have “trained” to 

appreciate the analytical nuances that will soon be routine. 

All index ETFs should be evaluated relative to benchmarks 

other than their template index. Whether an index fund is 

based on (1) a popular benchmark, (2) a custom backtested 

index touted to outperform a specific market segment, or (3) 

an index constructed to have less costly composition changes 

or to lack trading transparency, the fund’s performance 

should be evaluated by calculating its tracking error relative 

to one or more appropriate independent benchmarks. ETFs 

using indexes that were created from a backtest can only be 

meaningfully evaluated relative to an independent benchmark. 

In the long run, the most appropriate single measure 

of the value any fund delivers to its shareholders is its tracking 

error relative to an independent benchmark that represents 

a comprehensive, RIC-compliant, float-weighted basket of the 

securities in the universe eligible for inclusion in the fund 

portfolio. The result of such a calculation may be very different 

from what we get from a tracking error calculation relative 

to an index fund’s template index, particularly if tracking 

errors are calculated and evaluated over a period of years. 

The ideal benchmark would be an index that meets the investability 

requirements for a benchmark, yet does not serve as a 

template for any portfolios. The performance of an index that 

serves as a portfolio template will be adversely affected by the 

costs of implementing index composition trades. 

Let’s start to break tracking error down into a few of its 

components to illustrate some of the possibilities. The breakdown 

that follows is certainly not the only way to decompose 

these elements, but it is a useful illustration. Some possible 

values for breakdown components are illustrated in Figure 2, 

but keep in mind that these values are for purposes of illustration. 

They do not represent an actual fund. 

Negative (Cost) Elements Of Net Tracking Error 

Fund Expense Ratio 

Of course the easiest fund cost element to find is the 

fund’s operating expense ratio. The expense ratio—and 

Figure 2 

often a breakdown of its components—is readily available 

for all funds. In some cases, there is a cap on expenses or a 

unitary fee that determines the expense ratio independent 

of actual costs. Expenses are a negative component of the 

net tracking error calculation. The ready availability of the 

expense ratio and publicity given relatively low expense 

ratios by index mutual fund and ETF sponsors has made it 

the second-most-popular measure used for fund evaluation. 5 

’Obscured’ Expenses Charged To Transactions 

Or To Securities Lending Revenue 

Apart from brokerage commissions on portfolio trades 

that are universally excluded from the fund’s expense ratio, 

two other cash expense items are not ordinarily included in 

the fund’s expense ratio. The most common of these excluded 

expenses are transaction-linked ticket charges and line item 

charges collected by the fund’s custodian. These charges are 

usually small and will be reflected in one way or another in 

the cost or proceeds of transactions made for the portfolio. 

These charges are an “obscured,” if not hidden, expense. 

A second expense element that is usually not part of the 

expense ratio is the portion of any securities lending fee 

that is retained by the fund’s securities lending agent. The 

portion of securities lending fees retained by the securities 

lending agent has a negative impact on any tracking error or 

performance calculation. The gross securities lending fees 

received are a positive element. 

Trading Costs 

Trading costs of a fund are not published as an identifiable 

cost item, but they can be calculated with reasonable 

accuracy from data that is reported on a fund’s trades. The 

most easily determined element of trading costs is cash commissions 

paid by the fund to brokers that execute the fund’s 

transactions. Commissions are reported to the Securities and 

Exchange Commission for a fund or for all funds in a series. 

While careful analysis 6 may be necessary to obtain a figure 

for the commission payment per share traded (the most 

common measure used), such calculations are well within the 

capabilities of fund services today. Fund commission rates 

that average more than 4 cents per share will be unusual 

Illustrative Breakdown Of A Mutual Fund’s Net Tracking Error Relative To An Appropriate Benchmark Index 

Negative (Cost) Elements 

Expense Ratio -1.21% 

Expenses Charged to Transactions 

or Securities Lending 

nm 

Trading Costs 

Flow -1.08% 

Discretionary -0.36% 

Withholding Taxes 

nm 

Total Costs -2.65% 

Net Tracking Error -1.88% 

Positive (Value-Added) Elements 

Market Timing -0.02% 

Miscellaneous Fund Income 

nm 

Apparent Value Added 

by Fund Management Decisions +0.79% 

Total Value Added +0.77% 


53

within a few years. 

Commissions are usually the smallest part of a fund’s 

transaction costs, but high commission rates can point to 

other issues. For example, significant “soft dollar” commission 

payments used to pay for services provided to the fund 

manager are equivalent in most respects to an addition to 

the investment management fee. Soft dollar commissions are 

an inefficient way to compensate the investment manager 

indirectly because they encourage the manager to “overtrade.” 

Overtrading increases other trading cost elements by 

a multiple of the additional commissions paid for services. 

The largest components of portfolio transaction costs 

are noncash costs that consist of the effective spread from 

the midpoint of the bid and offer at the time the order is 

entered, plus any price impact caused by the fund’s transactions. 

Edelen, Evans and Kadlec (2007) is particularly helpful 

in describing the character and magnitude of fund transaction 

cost components and how they measured them for a 

large number of funds. 

The Edelen et al. paper illustrates how analysts can 

develop a close estimate of the transaction costs of a fund 

from that fund’s reports of securities transactions to the 

SEC. Edelen et al.’s method will underestimate the noncash 

elements of trading costs in most funds, but for a few 

funds, their calculation might be high. If a fund disagreed 

with a fund analyst’s estimate of its transaction costs, it 

could provide internal transaction cost data reflecting its 

actual trading experience to a calculation agent on a confidential 

basis and the agent could calculate the fund’s actual 

trading costs. The ITG Web site, www.itg.com, has a great 

deal of information on pre-trade transaction cost estimation 

models and post-trade cost analytics. In addition to 

breaking out the difference between the bid-ask spread and 

the market impact of orders executed beyond the spread, it 

is useful to divide mutual fund portfolio trading costs into 

(1) costs associated with accommodating the flow of investors 

into and out of the fund, and (2) costs associated with 

discretionary portfolio composition changes that reflect an 

active manager’s selections. If the fund is an index fund, it 

is useful to evaluate the extent to which transaction costs 

are increased by the transparency of the index. 

In considering the implications of transaction costs 

for fund selection, an investor should view high costs of 

accommodating investor flow into and out of a fund as an 

extremely undesirable feature of a fund with substantial 

flow transactions. While the cost of accommodating flow 

transactions is a significant drag on performance for most 

mutual funds, flow is a particular problem for small-cap 

mutual funds and for any mutual fund with more than 50 

percent annual investor flow. 

I have not seen a comprehensive study of the economic 

impact of the redemption fees imposed by many funds in 

the wake of the market timing and late trading scandals 

of 2003-2004; but small-cap funds, international funds and 

other funds most affected by the cost of flow are more likely 

than large-cap domestic equity funds to require redemption 

fees from investors who enter and leave the fund within a 

short period. Discouraging costly flow in this manner is a 

good idea, but the maximum permissible redemption fee of 

2 percent will not cover the flow transaction costs to ongoing 

shareholders in some of these funds. In most funds that 

impose redemption fees, the fee can be avoided by holding 

the fund’s shares beyond a short period (often just one week) 

during which the redemption fee is imposed. 

Separating discretionary portfolio composition trading costs 

and flow trading costs in an actively managed fund is worthwhile 

because it is discretionary trading that is the source of any value 

an active manager adds with portfolio changes. In contrast, flow 

trading costs should be considered to be the precise equivalent of 

an increase in the fund’s expense ratio. 

Withholding Taxes 

Funds holding equity securities issued in foreign countries 

are often subject to withholding taxes on dividends, 

some of which may be recoverable by application to the foreign 

country’s taxing authorities. The tax withheld would 

be listed on the negative side, and recoveries or tax credits 

would be listed on the positive side of the table. For most 

funds, these taxes and/or recoveries will be insignificant. If 

a fund does not meet the refund requirements of a country 

where a security is issued or traded or U.S. requirements 

for efficient taxation of foreign dividends received by a 

fund, effective tax analysis should reveal the cost of this 

feature of the fund. 7 

Positive (Value-Added) Elements 

In contrast to the negative elements that are always 

costs, some of the “positive elements” listed are not always 

positive numbers that contribute positively to fund returns 

in every case. 

Market Timing 

To the extent that the fund holds a cash balance that is 

earning interest, the market timing contribution to tracking 

error is simply a measurement of the net benefit (or net cost 

if it is negative) of holding whatever cash balance the fund 

held, relative to the effect of having the cash invested in the 

fund’s portfolio securities as discussed in connection with 

Figure 1. This element may be no more than a few basis 

points in some years but it could be significant in a year 

when the fund’s portfolio securities do very well or very 

poorly. Unless the fund manager is expected to add value 

by market timing, a positive return here is not necessarily a 

reason to praise the manager’s performance. 

Miscellaneous Fund Income 

This is the sum of a variety of small income items including 

gross securities lending income and any redemption 

fees levied on mutual fund share traders who redeemed 

fund shares before the fund’s minimum holding period had 

passed. These income items will rarely be significant enough 

to justify a finer breakdown, but securities lending income 

can be surprisingly large for a few small-cap funds and funds 

holding foreign securities. On the other hand, some foreign 

securities lending markets are considerably more efficient— 

and less profitable—than they were just a few years ago. 

54 


Apparent Value Added By Fund Management 

This element is the approximate value that appears to be 

added to the fund by the portfolio management process. 

Analysts eventually will be able to break down the performance 

contribution of active managers in a variety of ways. 

For example, with clean data it is possible to calculate the 

value added by the transactions the fund manager has made 

to change the composition of the portfolio during a quarter 

or a year, or to compare the beginning of the period portfolio’s 

performance with the performance of an index. There 

is scope for ingenuity in analyzing an active manager’s 

ability to add value or highlight aspects of the investment 

process that have worked less well. As the availability of 

fund data from SEC filings improves, analyses of transaction 

costs and value added by a fund’s investment process will 

become more sophisticated. For example, the value added 

by fund management will be broken down into a number 

of distinct components and a noise element. The total 

apparent value added is a residual in the example in Figure 

2. When we subtract the net tracking error (-1.88 percent) 

from the total of the cost elements in the left column (-2.65 

percent), we get a total for the positive elements of (+0.77 

percent). Adding the market timing loss (-0.02 percent), we 

get an apparent value-added residual of (+0.79 percent) for 

the fund manager’s efforts. 

What Is The Significance Of Figure 2? 

Figure 2 is simply an illustration of the kind of fund performance 

breakdown that is theoretically possible today and 

will ultimately be routine. To the extent that tracking error 

is measured relative to a reasonable benchmark, the costs 

incurred by the average fund will cause the net tracking 

error to be negative. The breakdown into cost and performance 

elements and comparisons to other funds and other 

managers will enhance the fund selection process. A fund 

analyst’s ability to perform this kind of analysis—and even 

more detailed breakdowns—depends on the fund industry’s 

adoption of a new data standard: XBRL. 

I will cover XBRL in depth in the next and final installment, to be published in the March/April issue of the Journal of Indexes. 

Endnotes 

1 Gastineau, Gary L., and Kritzman, Mark, “The Dictionary of Financial Risk Management,” Frank J. Fabozzi Associates (now a division of John Wiley & Sons, Hoboken, New Jersey), 

1999. See pp. 283-284. 

2 In addition to the indexes for the Sector SPDRs, the Nasdaq 100 capitalization weightings are modified to make it RIC compliant. The price-weighted Dow Jones Industrial Average 

is usually RIC compliant without modification, but it dropped out of compliance at the end of March 2009. 

3 The constraints will be relatively tight for an index fund, but they should not discourage an index fund manager from trading away from the implementation time of an index 

change. In many large index funds, avoiding the official index implementation trading frenzy is akin to an opportunity to pick up money from the sidewalk. 

4 Israelsen, Craig L. and Gary F. Cogswell, “The Error of Tracking Error,” Journal of Asset Management, March 2007, vol. 7, No. 6, pp. 419-424 appropriately argues that the magnitude 

of tracking error (standard deviation version) is not a suitable performance indicator for active managers. I agree, and I use tracking error (performance difference version) only 

as an organizing framework. 

5 The most popular measure is prior period performance. 

6 See Edelen, Roger M., Richard Evans and Gregory B. Kadlec, “Scale Effects in Mutual Fund Performance: The Role of Trading Costs,” unpublished working paper, March 17, 2007. 

7 A fund that combines small foreign holdings with a largely domestic U.S. portfolio may have an adverse tax effect for some of its shareholders that can be avoided by a fund that 

holds predominantly non-U.S. securities. Global funds with predominantly U.S. holdings are often tax inefficient for U.S. investors. 

Israelsen continued from page 45 

components (in terms of dollar account value). Accordingly, 

the account balances will become disproportional over time 

(as demonstrated in Figure 3). The equity and equitylike 

portfolio components (REITs and commodities) will tend to 

dominate the portfolio. This can be advantageous if, in the 

latter years of a portfolio, the equity-based assets perform 

well. But, if equity and equitylike assets suffer declines, the 

investor can experience heavy losses because of his disproportionately 

large allocations in equity. 

This potential portfolio imbalance can be solved by the 

active strategy of rebalancing. When equity assets have 

strong annual gains, their excesses are diverted (i.e., rebalanced) 

to the fixed-income components of the portfolio 

(bonds and cash). Because bonds and cash seldom have 

negative returns, the gains are preserved in a fixed-income 

“lockbox.” As investors age, the notion of a lockbox becomes 

very appealing. During equity market downturns (i.e., 2008), 

the bond and cash portfolio components—which have been 

systematically replenished through active rebalancing—will 

have sufficient reserves to provide needed liquidity. 

Summary 

The upper limit (or maximum parameter) of active-passive 

investing is an annualized return of 30 to 34 percent. This 

requires perfect luck or perfect foresight, neither of which 

represents a defensible tactical active strategy. By contrast, a 

portfolio with a randomly selected index at the beginning of 

each year generated a 9.05 percent annualized return over the 

39-year period from 1970-2008. This type of random active-passive 

portfolio represents a more logical performance threshold 

against which actively managed funds should be measured. 

Rebalancing a multi-index passive portfolio represents an 

“actively passive” portfolio that outperforms a passive-passive 

multi-index portfolio. Rebalancing represents a potent 

active strategy that seldom receives the attention it deserves. 

Rebalancing is often simply assumed to be a good idea. This 

research has quantitatively demonstrated just how good it is. 

More importantly, rebalancing is an active strategy that passive 

investors can probably live with. 

Most passive investors may recognize that they are, in 

fact, actively passive. 


55

News 

Dow Jones Sells Stoxx Indexes 

Deutsche Boerse and SIX Swiss 

Exchange announced in November that 

they are buying out Dow Jones’ one-third 

stake in Stoxx Ltd. for a consideration of 

206.1 million euros, or $306 million. 

Stoxx was set up as a joint venture 

between Deutsche Boerse, Dow Jones 

and SIX Swiss Exchange in 1998 in 

anticipation of the introduction of the 

euro and the creation of the eurozone. 

Stoxx is Europe’s leading index provider 

in the ETF market and Europe’s 

No. 1 (world’s No. 2) provider in the 

derivatives market, according to the 

company’s Web site. A number of U.S.- 

listed exchange-traded funds are tied to 

the company’s indexes as well. 

Following the transaction’s completion, 

which is due to take place in early 

2010, Deutsche Boerse will have a 

controlling stake in Stoxx of 50 percent 

plus one share and will fully consolidate 

it for accounting purposes. 

In addition, SIX and Deutsche Boerse 

will set up a new entity to perform 

index calculations, in which SIX will 

own 50 percent plus one share. 

According to a Stoxx press release, 

the transaction will allow both Deutsche 

Boerse and SIX to significantly expand 

their positions in the international index 

business, complementing their established 

DAX and SMI index families. 

The sale could be a precursor to the 

long-rumored sale of Dow Jones Indexes. 

The Wall Street Journal reported in 

August that News Corp. was considering 

selling the venerable index provider, 

which has annual revenues of approximately 

$130 million to $170 million. 

Schwab ETFs Debut 

With Competitive Pricing 

That soniclike boom you heard at 

the beginning of November? That was 

the sound made by Charles Schwab’s 

entry into the ETF market. It was probably 

the most anticipated debut of 2009, 

right from the moment that Schwab’s 

first filings with the Securities and 

Exchange Commission were announced. 

Moreover, the fact that a heavy hitter 

like Schwab had deemed the ETF arena 

worthy of its efforts was, for many, a 

sign that the burgeoning industry had 

hit its stride. 

Although it has more in registration, 

Schwab launched just four funds: the 

Schwab U.S. Broad Market ETF (NYSE 

Arca: SCHB), Schwab U.S. Large-Cap ETF 

(NYSE Arca: SCHX), Schwab U.S. Small- 

Cap ETF (NYSE Arca: SCHA) and Schwab 

International Equity ETF (NYSE Arca: 

SCHF). The domestic funds track indexes 

from Dow Jones, while SCHF is tied to 

the FTSE Developed ex-US Index. 

In the days leading up to the launch, 

it became clear that the financial services 

giant was playing for keeps when 

Schwab unveiled the pricing on the 

pending ETFs. SCHB and SCHX charge 

8 basis points, while SCHA and SCHF 

charge 15 basis points. Those fees match 

or beat the costs on all the competing 

ETFs, even the ones from Vanguard. 

But Schwab dropped what might 

have been an even bigger bombshell 

when it announced the day before 

the initial rollout that investors with 

Schwab accounts would not face any 

commission fees when they traded 

the Schwab ETFs. The moves are not 

only calculated to draw assets from 

established ETFs but also to draw new 

investors to the products who might 

not otherwise consider ETFs. Dollarcost 

averaging, for example, would be 

more cost-effective with the Schwab 

funds for account holders. 

GDP-Weighted Bond Indexes 

Provide Alternative 

Barclays Global Investors has 

launched a new family of gross-domestic-product-weighted 

bond indexes, as 

investors look for better ways to benchmark 

the global fixed-income universe. 

BGI isn’t the first to launch GDPweighted 

bond indexes. Pimco debuted 

its own index in January 2009, and 

others are looking at this space for a 

number of reasons. 

For starters, market-cap-weighted 

bond indexes assign larger and larger 

weights to countries that borrow 

more and more money. This is counterintuitive, 

as increased debt may raise 

the likelihood of default. In addition, 

market-cap-weighted bond indexes typically 

underweight emerging markets, 

which have less developed bond markets. 

Barclays notes that 22 emerging 

market countries account for just 15 

percent of global GDP, but form less 

than 0.7 percent of the Barclays Global 

Aggregate Bond Index by market value. 

Barclays’ new GDP-weighted index 

family includes the following flagship 

products: 

• Global Aggregate GDP 

Weighted Index 

• Global Treasury GDP 

Weighted Index 

• Global Treasury Universal Index 

In each variation, the methodology 

leads to a significant underweight in 

Japan and a correspondingly higher 

allocation to countries like the BRICs, 

Mexico, Taiwan and other emerging 

markets. 

INDEXING DEVELOPMENTS 

FTSE Launches Currency Family 

In September, FTSE launched a new 

family of currency indexes, the FTSE 

Currency Forward Rate Bias (FRB) Index 

Series. The first indexes in the series 

to debut were jointly developed by 

FTSE and currency overlay firm Record 

Currency Management. 

The FTSE Currency FRB5 indexes cover 

the 10 currency pairs that can be formed 

from the U.S. dollar, euro, Japanese yen, 

pound sterling and Swiss franc. Each 

index seeks to represent the performance 

of the carry trade (or forward-rate bias 

56 


strategy), tracking the performance of 

the higher-interest currency against the 

lower-interest currency in the pair via 

one-month-forward contracts. The indexes 

are denominated in all five currencies 

covered by the FTSE FRB family. They are 

rebalanced monthly. 

Deutsche Boerse Launches 

Global Sector Indexes 

ETFExpress reported that Deutsche 

Boerse launched four global sector 

indexes. The indexes are extremely 

narrow, each featuring just the largest 

10 components in their respective 

sectors. The new additions include 

the DAXglobal Coal, DAXglobal Gold 

Miners, DAXglobal Shipping and 

DAXglobal Steel indexes. The gold 

miners and steel indexes both require 

that components derive at least 50 

percent of their revenues from their 

target industries. Component weights 

are capped at 15 percent. 

for the DJ-UBS Commodity Index 

(DJ-UBSCI), effective January 2010. The 

top five component commodities are 

fairly similar to what they were in 2009, 

but with different weightings: crude oil, 

14.34 percent; natural gas, 11.55 percent; 

gold, 9.12 percent; soybeans, 7.91 

percent; and copper, 7.64 percent. 

Last year, the target weights for 

the index were as follows: crude oil, 

13.75 percent; natural gas, 11.89 percent; 

gold, 7.86 percent; soybeans, 

7.59 percent; and corn, 5.72 percent. 

The weights are set based on liquidity 

and production data, but also 

consider diversification and minimum 

weighting requirements. Of the top 

five, it’s clear that the largest changes 

have been in the weightings of gold 

and of copper, which displaced corn. 

Although 23 commodities contracts 

are eligible for inclusion in the broad 

commodity index (and each has their 

own calculated subindex), only 19 of 

those contracts are currently assigned 

weightings in the DJ-UBSCI. 

MSCI Completes 

Semiannual Review 

MSCI completed the semiannual 

review of its global equity index family 

in November. The biggest outcome? 

Increased exposure to BRIC economies. 

Surging growth in Brazil and China 

led to significant company additions 

from those markets. 

All in all, 11 Brazilian companies and 

seven Chinese companies were added, 

while only one Chinese company was 

purged from the indexes. Topping the 

list of additions were Brazil’s retailer 

Loja Renner and PDG Realty, as well as 

China-based Nine Dragons Paper. 

Strong overseas growth also helped 

a wide array of small-cap companies 

satisfy the market-cap requirements to 

Dow Jones Adds High-Yield 

Infrastructure Index 

Dow Jones Indexes’ partnership 

with Brookfield Asset Management 

has expanded, with the launch of 

the Dow Jones Brookfield Global 

Infrastructure Composite Yield Index 

in early November. The two companies 

originally fielded a complete 

family of global infrastructure indexes 

in 2008, and the newest addition is 

derived from the broad composite 

index of that family. 

The new high-yield index represents 

the top 70 percent of the components 

of the Dow Jones Brookfield Global 

Infrastructure Composite Index in terms 

of annual yield, with each component 

capped at a 10 percent weighting. 

In September, 

FTSE launched 

a new family of 

currency indexes. 

Pull Quote Pull Quote Pull Quote 



DJ Announces 2010 Weights 

For DJ-UBS Commodity Index 

At the end of October, Dow Jones 

Indexes announced the new weights 


57

News 

be added to MSCI’s global benchmarks. 

The broad-market Global Small Cap 

Indexes added 362 securities during 

this rebalancing and deleted 165. 

Among them, the largest additions 

to the World Small Cap Index 

were U.S.-based FTI Consulting and 

Aqua America, as well as U.K.-based 

Ladbrokes. 

The changes reflect the growing 

strength of emerging markets and recent 

outperformance by small-cap names. 

DJI Launches Islamic 

Index For China 

Investors interested in investing 

according to the dictates of Islam now 

have a benchmark that covers China. 

The Dow Jones Islamic Market Greater 

China Index tracks companies based 

in Hong Kong, Taiwan and mainland 

China, and applies Dow Jones’ methodology 

for screening out stocks that 

are not Shariah compliant. 

Individual component weights in 

the greater China index are capped at 

10 percent of the index, and Taiwan’s 

weight specifically is capped at 30 percent 

of the index. At launch, the index 

comprised 290 components; the top 

five included China Mobile Ltd., China 

Unicom (Hong Kong) Ltd., CNOOC 

Ltd., Hon Hai Precision Industry 

Co. Ltd. and Taiwan Semiconductor 

Manufacturing Co. Ltd. 

Use of the new index as a benchmark 

has already been granted via 

license to Malaysia-based Prudential 

Fund Management Berhad, which 

originally approached Dow Jones 

about designing the index. 

S&P Rolls Out UCITS- 

Compliant S&P GSCI 

In September, Standard & Poor’s 

rolled out three new commodity indexes 

that are designed to comply with the 

requirements of the European Union 

UCITS III rules for diversification. 

The S&P GSCI Capped Commodity 

35/20 Index covers all 24 of the commodities 

in the S&P GSCI, but the first 

component to reach 35 percent of the 

index is capped at that weight. All 

other components are capped at 20 

percent, and sector weights are kept 

in line with the original S&P GSCI. 

The S&P GSCI Capped Component 

35/20 Index covers only 18 of the 24 

component commodities. It caps the 

largest one at 35 percent of the index, 

and the rest at 20 percent, but with 

no regard for sector weights. The S&P 

GSCI Enhanced Capped Component 

35/20 Index uses a similar weighting 

methodology but applies it to the S&P 

GSCI Enhanced Index. 

S&P Launches 

Enhanced Oil Index 

In September, Standard & Poor’s 

launched the S&P GSCI Crude Oil 

Enhanced Index, which covers the WTI 

crude oil futures traded on the CME/ 

NYMEX. The new index is a subindex 

of the broad S&P GSCI Enhanced Index. 

Like its parent index, the oil index uses 

an “enhanced” roll methodology that 

takes “dynamic” factors into account in 

its roll schedule, with the intention of 

outperforming the standard index by 

minimizing the effects of contango. 

According to the methodology for the 

S&P GSCI Enhanced Index, the WTI crude 

oil contract “rolls from the 1st contract 

month to the 6th contract month if the 

contango between 1st and 2nd contract 

month is more than 0.50%.” 

S&P Launches VEQTOR Index 

November saw the launch of the 

S&P 500 Dynamic VEQTOR Index, a 

strategy index that makes daily allocations 

among the S&P 500, the S&P 500 

Short-Term VIX Futures Index and the 

overnight LIBOR rate, which represents 

a cash component. 

The daily adjustments to the weightings 

of the three factors are determined 

by realized and implied volatility trend 

decision variables. A stop-loss provision 

in the index’s methodology moves the 

index’s entire allocation to cash should 

the index drop more than 2 percent 

during the previous five business days. 

The VEQTOR index is designed with 

the intention of providing investors 

with a way to hedge against downside 

risk in the market using volatility, an 

indicator known for its negative correlation 

to stock returns. The new index 

is a member of S&P’s family of indexes 

based on the volatility of the S&P 500. 

Russell Unveils Target Date 

Performance Index 

In September, Russell Investments 

announced the creation of its Russell 

Target Date Metric. 

Essentially a customized index to 

measure the performance of individual 

target date fund families—as 

opposed to individual funds within a 

family—it is targeted at investment 

professionals, such as plan sponsors 

and financial advisers, who need to 

evaluate entire fund families rather 

than just individual funds. 

Technically, the RTDM is an index 

that measures how well a fund family 

performs against any corresponding 

family of target date indexes, but 

Russell uses a benchmark of its own 

as a default. The RTDM also incorporates 

active management, asset allocations, 

glide path design and the 

assumption of cash flows, according 

to Russell. 

At the time of the announcement, 

Russell said it was calculating the 

RTDM for 43 different target date fund 

families, with the launch set for the 

fourth quarter. 

BNY Mellon Launches 

Depositary Receipt Composite 

In November, Bank of New York 

Mellon announced the rollout of the 

BNY Mellon Composite Depositary 

Receipts Index, which essentially covers 

the entire universe of American 

and global depositary receipts listed 

on the NYSE, NASDAQ, NYSE Amex 

and London Stock Exchange, and 

over-the-counter. 

The broad index has 657 component 

depositary receipts. It is weighted by 

market capitalization and is adjusted for 

free float using the float methodology 

developed by Dow Jones Indexes. 

As of the end of September, the largest 

country in the index was the United 

Kingdom, at 19.26 percent, followed by 

Japan and France at 14.09 percent and 

9.87 percent, respectively. 

58 


BarCap Debuts Emerging Asia 

Swap Indexes 

Barclays Capital introduced the 

Barclays Capital Emerging Markets Asia 

Swap Index family in October. The 

index family tracks nominal interest 

rate swaps in Asia’s emerging markets. 

According to Barclays, “Each index 

systematically tracks the return of a 

nominal par coupon interest rate swap 

of a specific tenor, rebalancing to par 

every month end.” 

The family includes core spot indexes 

covering periods within the one- to 

10-year tenor range, as well as one-year 

forward indexes for each of the covered 

markets. Those markets include the 

Singapore dollar, the Hong Kong dollar, 

the Korean won, the Thai baht and the 

Taiwan dollar. 

S&P Debuts Maturity Year 

Muni Indexes 

October saw the launch of the first 

municipal bond indexes to target bonds 

maturing in specific years when S&P 

rolled out its S&P AMT-Free Municipal 

Bond Index Series. 

At launch, each index included 

between 430 and some 3,040 noncallable 

municipal bonds maturing between 

June 1 and Aug. 31 of a specific year. 

The maturity years currently covered 

by the eight indexes in the series range 

from 2012 to 2020. 

According to S&P, the indexes can 

be used in laddering strategies and in 

matching up investment horizons with 

investment objectives. 

Indexes in the series have already 

been licensed by Barclays Global 

Investors to underlie ETFs that are currently 

in registration. 

Markit Launches 

Convertible Bond Index 

In September, Markit rolled out its 

Markit iBoxx Liquid USD Convertible 

Bond Index, covering the U.S. convertible 

bond market. The components list 

comprises 100 bonds and preferred 

securities included based on their face 

values outstanding at inception. Cashsettled 

forward contracts will be available 

on the index, with new series 

initiating every six months. 

The launch of the CVBX was paired 

with that of the Markit iBoxx Liquid 

USD Delta-Hedging Stock Index, 

which takes long stock positions in 

the companies of each component in 

the CVBX that reflect the initial delta 

of the respective bonds or securities 

at inception. The index can be traded 

via equity swaps. 

AROUND THE WORLD OF ETFs 

Guggenheim Completes 

Claymore Purchase 

In mid-October, privately held institutional 

money manager Guggenheim 

Partners completed its acquisition of 

Claymore Group Inc. The deal covers 

all aspects of Claymore’s operations, 

including Claymore Securities Inc., 

Claymore Investments Inc. in Canada 

and Claymore Advisors, LLC. 

The deal was originally announced 

in July. Claymore is the 13th-largest ETF 

provider in the United States, with 34 

ETFs that have a combined total of $2.2 

billion in assets under management. 

SSgA Launches VRDO ETF 

State Street Global Advisors launched 

the world’s second variable rate demand 

obligation ETF, the SPDR S&P VRDO 

Municipal Bond ETF (NYSE Arca: VRD), 

on Sept. 24. It tracks the performance 

of the S&P National AMT-Free Municipal 

VRDO Index, which holds VRDOs issued 

by U.S. states, local governments and 

agencies. The fund charges 0.20 percent 

in annual expenses. 

VRD follows in the footsteps of the 

PowerShares VRDO Tax-Free Weekly 

Portfolio (NYSE Arca: PVI), an ETF that 

launched in November 2007 providing similar 

exposure to the VRDO market, which 

has accumulated nearly $1 billion in assets. 

VRDOs are municipal bonds that can 

be “put back” to their issuers at full 

value on a weekly basis, and tend to 

hold very little risk. 

At its launch, PVI was paying a taxfree 

30-day SEC yield of 0.84 percent. 

BGI Rolls Out Mega-Cap Funds 

Barclays Global Investors added 

three new mega-cap ETFs to its lineup, 

with the debut of the iShares Russell 

Top 200 Index Fund (NYSE Arca: IWL), 

iShares Russell Top 200 Growth Index 

Fund (NYSE Arca: IWY) and the iShares 

Russell Top 200 Value Index Fund (NYSE 

Arca: IWX). Each charges 0.20 percent 

in annual expenses. 

The Russell Top 200 Index measures 

the performance of the largest 200 U.S.- 

listed companies. 

New iShares Covers 

Eastern Europe 

The parade of new emerging markets 

ETFs continued in early October 

with the launch of the iShares MSCI 

Emerging Markets Eastern Europe 

Index Fund (NYSE Arca: ESR). 

The fund tracks a free-float-adjusted 

market capitalization index measuring 

the equity performance of four 

countries: At launch, Russia dominated 

the lineup at 75 percent of the 

portfolio; followed by Poland, at 13 

percent; Czech Republic at 6 percent; 

and Hungary, at 6 percent. Roughly 

half of the fund is invested in energy 

names, with Gazprom and Lukoil as 

its top two holdings (about one-third 

of the portfolio). 

ESR is the second emerging market 

Europe ETF to hit the market, following 


59

News 

the SPDR S&P Emerging Europe ETF (NYSE 

Arca: GUR). GUR is also heavily concentrated 

in Russia, albeit less so than ESR. 

The new ETF has an annual expense 

ratio of 0.72 percent, compared with 

0.60 percent for GUR. 

BarCap Halts Share 

Creations For PGM 

Barclays announced on Oct. 16 

that it would temporarily stop creating 

more shares of the iPath Dow 

Jones-UBS Platinum Subindex Total 

Return ETN (NYSE Arca: PGM), effective 

immediately. PGM tracks the performance 

of front-month platinum 

futures contracts traded on the New 

York Mercantile Exchange. 

At about $100 million in assets, 

PGM is a relatively small product. 

However, the platinum futures market 

is a very thin market, and the 

New York Mercantile Exchange has 

tight “accountability” limits in the 

platinum space, beyond which the 

exchange can force holders to reduce 

their positions. Currently that limit is 

1,500 net futures positions, almost 

equal to PGM’s portfolio at the time 

of the announcement. 

PGM was the fifth exchange-traded 

product to put a freeze on creations. 

At press time, it was trading at a 22 

percent premium. 

The Commodity Futures Trading 

Commission is expected to announce 

new federal position limits in the commodity 

futures space later this year. 

Claymore Rolls Out 

China All-Cap ETF 

Newly acquired Claymore Securities, 

Inc., launched its third China-focused 

ETF in mid-October. The Claymore/ 

AlphaShares China All-Cap ETF (NYSE 

Arca: YAO) is designed to provide balanced 

exposure to investable small-, 

mid- and large-cap Chinese companies. 

The ETF holds 99 securities, with a 

capitalization breakdown tilted slightly 

toward mid- and small-cap stocks. 

The fund faces competition from 

three established ETFs covering 

China’s market. Its biggest rival is the 

$9.4 billion iShares FTSE/Xinhua China 

25 ETF (NYSE Arca: FXI), which holds 

a focused portfolio of 25 mega-cap 

stocks. Meanwhile, the PowerShares 

Golden Dragon Halter USX China 

Portfolio (NYSE Arca: PGJ) and SPDR 

S&P China ETF (NYSE Arca: GXC) each 

have about $450 million in assets. 

YAO carries an expense ratio of 0.70 

percent, slightly below FXI’s 0.74 percent 

expense ratio, on par with PGJ’s 0.70 percent 

and above GXC’s 0.59 percent. 

USCF Debuts Short Oil ETF 

In September, United States Commodity 

Funds LLC rolled out a new ETF 

designed to provide short exposure to 

the crude oil market. The United States 

Short Oil Fund (NYSE Arca: DNO) aims to 

capture the inverse of the daily return of 

the front-month West Texas Intermediate 

crude oil futures contract, as traded on 

the New York Mercantile Exchange. 

On a one-day basis, the fund should 

mirror the returns of the popular United 

States Crude Oil Fund (NYSE Arca: USO). 

Over longer time frames, the returns 

may diverge due to compounding. 

Unlike most leveraged and inverse 

products, DNO does not use swaps to 

achieve its exposure. Instead, it takes 

short positions in the actual underlying 

futures contracts. 

DNO charges 0.60 percent in annual 

expenses. 

United States Commodity 

Funds LLC rolled out a new 

ETF designed to provide 

short exposure to the 

crude oil market. 

IndexIQ Debuts Two More ETFs 

ETF provider IndexIQ rolled out 

two more ETFs in October. The IQ 

ARB Global Resources ETF (NYSE 

Arca: GRES) and the IQ CPI Inflation 

Hedged ETF (NYSE Arca: CPI) charge 

expense ratios of 0.75 and 0.65 percent, 

respectively. 

GRES invests in commodity-related 

equities using a sector rotation strategy. 

It covers eight distinct commodity 

sectors: energy, industrial metals, precious 

metals, food and fiber, livestock, 

timber, water and coal. Sectors are 

underweighted or overweighted based 

on valuation and price momentum. 

CPI is an ETF of ETFs that seeks to 

outperform the return of the Consumer 

Price Index by 2-3 percent per year. 

Component ETFs are chosen by a rulesbased 

strategy that considers the historical 

performance of various asset classes 

during inflationary environments. 

60 


ALPS Debuts Jefferies Funds 

ALPS is sponsoring three new funds 

from Jefferies Asset Management that 

track the Thomson Reuters/Jefferies 

In-The-Ground CRB Global Commodity 

Equity Index and two of its subindexes. 

The underlying index of the Jefferies 

TR/J CRB Global Commodity Equity 

Index Fund (NYSE Arca: CRBQ) covers 

145 companies that produce energy, 

agricultural products, raw metals and 

precious metals. 

CRBQ, however, was just the first 

fund in the family; it debuted in 

September. October saw the launch 

of the Jefferies TR/J CRB Global 

Agriculture Equity Index Fund (NYSE 

Arca: CRBA) and Jefferies TR/J CRB 

Global Industrial Metals Equity Index 

Fund (NYSE Arca: CRBI). CRBQ and 

CRBA compete directly with existing 

ETFs, but CRBI is the first global, 

broad-based mining ETF to focus 

strictly on industrial and base metals. 

All three funds charge annual 

expense ratios of 0.65 percent. 

UBS Launches DJP Competitor 

Near the end of October, UBS 

launched its first new ETN in more 

than a year. The firm had rolled out its 

E-Tracs family of ETNs in the first half 

of 2008 and then saw them languish— 

no doubt in part because of the credit 

disaster that followed in the second 

half of the year. But the firm’s partial 

ownership of the popular Dow Jones- 

UBS Commodity Index (formerly the 

DJ-AIG Commodity Index) may come 

with some serious upside. 

The E-TRACS Dow Jones-UBS 

Commodity Index Total Return ETN 

(NYSE Arca: DJCI) tracks the popular 

Dow Jones-UBS Commodity Index, a 

broadly diversified index of 19 commodity 

futures. It charges investors 

just 0.50 percent in annual expenses. 

That’s in marked contrast to the 

market-dominating iPath DJ-UBS 

Commodity Index Total Return ETN 

(NYSE Arca: DJP), which charges 75 

basis points. DJP is the largest ETN on 

the market in terms of assets, at $1.9 

billion, but it looks like it could face 

serious competition from DJCI. 

Pimco Launches FIVZ, ZROZ 

Pimco’s newest bond ETFs began trading 

in early November, with the launch of 

the Pimco 3-7 Year U.S. Treasury Index 

Fund (NYSE Arca: FIVZ) and the Pimco 

25+ Year Zero Coupon U.S. Treasury 

Index Fund (NYSE Arca: ZROZ). 

FIVZ tracks the Merrill Lynch 3-7 

Year U.S. Treasury Index, while ZROZ 

reflects the performance of the BofA 

Merrill Lynch Long Treasury Principal 

STRIPS Index. Both come with a price 

tag of 15 basis points. 

BACK TO THE FUTURES 

CME Launches DJ-UBSCI Swaps 

In November, CME Group announced 

the Dec. 7 launch of cash-settled swaps 

on the Dow Jones-UBS Commodity 

Index. The exchange said that it would 

be providing clearing services through 

its CME ClearPort service. 

The new swaps join CME’s existing 

futures contract on the DJ-UBSCI. 

According to the CME, the swaps 

“will be subject to position accountability, 

transaction reporting, and margining 

and risk management standards 

that are comparable to commodity 

index futures contracts.” 

KNOW YOUR OPTIONS 

CBOE Sees Declining Volumes 

Year-Over-Year 

Overall trading average daily volume 

(ADV) at the Chicago Board 

Options Exchange in October 2009 

declined 21 percent from October 

2008. However, the ADV for cashsettled 

index options and ETF options 

was down even further. 

The index options ADV fell 36 

percent year-over-year in October, 

while the ETF options ADV fell 39 percent. 

Year-to-date, the CBOE’s overall 

ADV was down just 6 percent, but 

down 17 percent and 19 percent 

for index options and ETF options, 

respectively. 

During October, the options on the 

S&P 500, the SPDR S&P 500 ETF, the 

PowerShares QQQ Trust, the CBOE 

Volatility Index and the iShares Russell 

2000 Index Fund were the most actively 

traded index and ETF options. 

FROM THE EXCHANGES 

Deutsche Boerse Changes 

Index Licensing Model 

It appears that Deutsche Boerse 

is going to be keeping a tighter grip 

on its index data starting in 2010. 

An October announcement from the 

exchange said that it was moving to 

align its business model with international 

practices. 

Basically, going forward from Jan. 

1, more detailed data on the DAX will 

be made available for professional 

and commercial use only after the 

users sign licensing contracts. Certain 

data will still be publicly available, 

including methodology, constituents 

and some historical information. 

However, the change will not 

necessarily be that abrupt, as the 

exchange has declared a three-month 

transition period that is set to end on 

March 31, 2010. 

ON THE MOVE 

Bond Trims PowerShares Role 

H. Bruce Bond, founder, CEO and 

chairman of Invesco PowerShares, 

announced in November that he will 

be scaling back his role with the ETF 

provider and will no longer hold the 

position of CEO. 

Ben Fulton, currently the executive 

vice president in charge of global 

product development, will be taking 

on the day-to-day management 

responsibilities of the Invesco unit as 

a managing director. 

PowerShares’ leadership team will 

report to Fulton, who will also join 

the Invesco Global Product Committee. 

Invesco executives Andrew Schlossberg 

and David Warren will also take on 

leadership roles with the Invesco 

PowerShares unit. 

Bond said he will remain active and 

involved with PowerShares, but that 

the change will give him more flexibility 

to work on other things. He 

originally founded PowerShares Capital 

Management in 2003. 

Fulton joined the PowerShares team 

in 2005 from Claymore Securities, 

where he was co-founder, president 

and CEO. 


61

































































Conclusion 




















Endnotes 



no. 2, pp. 68-85. 


Global Index Data 

Selected Major Indexes Sorted By YTD Returns January/February 2010 

Index Name YTD 2008 2007 2006 

MSCI Sri Lanka* 

142.56 

Citigroup ESBI Brady 

119.38 

MSCI Indonesia* 

102.75 

MSCI BRIC* 

76.69 

MSCI Pacific Ex Japan 

65.14 

MSCI EM 

64.65 

Barclays Global High Yield 

54.54 

Barclays US Corporate High Yield 51.65 

MSCI EAFE Small Cap 

45.69 

MSCI Nordic Countries 

44.10 

NASDAQ 100* 

37.59 

JSE Gold USD South Africa 

34.77 

S&P 500 Equal Weighted 

32.70 

MSCI EAFE Value 

30.74 

MSCI Europe 

30.03 

S&P MidCap 400/Citi Growth 29.00 

MSCI EAFE 

27.36 

MSCI AC World 

26.68 

JPM EMBI Global 

26.44 

Russell 1000 Growth 

25.39 


25.00 

JP Morgan EMBI 

24.37 

S&P Midcap 400 

24.23 

MSCI EAFE Growth 

24.05 

MSCI Pacific 

22.32 

S&P 500/Citi Growth 

21.81 


20.15 

S&P MidCap 400/Citi Value 

19.65 

Russell 1000 

18.41 

Russell 3000 

18.07 

S&P 1500 

17.42 

S&P 500 

17.05 

Russell Micro Cap 

16.22 

Barclays US Credit 

15.52 

S&P SmallCap 600/Citi Growth 15.15 

S&P 100 

14.27 

Russell 2000 

14.12 

DJ Industrial Average 

13.65 

S&P Smallcap 600 

12.65 

DJ UBS Commodity 

12.64 

FTSE NAREIT All REITs 

12.08 

S&P 500/Citi Value 

12.06 

Barclays Municipal 

11.61 

Russell 1000 Value 

11.33 


11.12 

S&P GSCI 

10.82 

Barclays US Treasury US TIPS 10.82 

S&P SmallCap 600/Citi Value 10.21 


8.63 

Barclays Global Aggregate 

8.35 

Dow Jones Composite Average 8.30 

Barclays US Aggregate Bond 

6.24 

Dow Jones Transportation Average 4.00 

Dow Jones Utilities Average 

1.59 

ML US Treasury Bill 3 Mon 

0.19 

MSCI Morocco* 

-0.62 

Barclays US Government 

-1.20 

Barclays Treasury 

-2.34 

NCREIF Property 

-15.07 

ML US Treasury STRIPS CM 30 Yr -40.93 

-62.09 

-60.81 

-57.57 

-60.27 

-50.50 

-53.33 

-26.89 

-26.16 

-47.01 

-53.52 

-41.89 

-29.97 

-39.72 

-44.09 

-46.42 

-37.61 

-43.38 

-42.20 

-10.91 

-38.44 

-38.44 

-9.70 

-36.23 

-42.70 

-36.42 

-34.92 

-38.54 

-34.87 

-37.60 

-37.31 

-36.72 

-37.00 

-39.78 

-3.08 

-32.94 

-35.31 

-33.79 

-31.93 

-31.07 

-35.65 

-37.34 

-39.22 

-2.47 

-36.85 

-36.25 

-46.49 

-2.35 

-29.51 

-28.92 

4.79 

-27.94 

5.24 

-21.41 

-27.84 

2.06 

-12.97 

12.39 

13.74 

-6.46 

84.22 

-15.15 

-3.32 

50.81 

56.12 

30.73 

39.39 

3.18 

1.87 

1.45 

21.24 

18.67 

-18.00 

1.53 

5.96 

13.86 

13.50 

11.17 

11.66 

6.28 

11.81 

11.40 

6.45 

7.98 

16.45 

5.30 

9.13 

7.05 

2.65 

5.77 

5.14 

5.47 

5.49 

-8.00 

5.11 

5.60 

6.12 

-1.57 

8.88 

-0.30 

16.23 

-17.83 

1.99 

3.36 

-0.17 

-1.01 

32.67 

11.64 

-5.54 

-9.78 

9.48 

8.88 

6.97 

1.43 

20.11 

5.03 

44.02 

8.66 

9.01 

15.84 

12.97 

42.78 

23.86 

69.61 

52.87 

32.02 

32.17 

13.69 

11.85 

19.31 

39.26 

6.79 

5.54 

15.80 

30.38 

33.72 

5.81 

26.34 

20.95 

9.88 

9.07 

9.46 

10.49 

10.32 

22.33 

12.20 

11.01 

13.35 

14.62 

15.46 

15.72 

15.34 

15.79 

16.54 

4.26 

10.54 

18.47 

18.37 

19.05 

15.12 

2.07 

34.35 

20.80 

4.84 

22.25 

22.34 

-15.09 

0.41 

19.57 

23.48 

6.64 

15.71 

4.33 

9.81 

16.63 

4.83 

62.65 

3.48 

3.08 

16.59 

-21.45 

Total Return % Annualized Return % 

2005 2004 2003 2002 3-Yr 5-Yr 10-Yr 15-Yr 

Source: Morningstar. Data as of October 30, 2009. All returns are in dollars, unless noted. YTD is year-to-date. 3-, 5-, 10- and 15-year returns are annualized. Sharpe is 12-month Sharpe ratio. 

Std Dev is 3-year standard deviation. *Indicates price returns. All other indexes are total return. 

30.70 

5.95 

12.56 

39.81 

13.81 

34.00 

3.59 

2.74 

26.19 

15.65 

1.49 

43.34 

8.06 

13.80 

9.42 

14.39 

13.54 

10.84 

10.73 

5.26 

5.17 

11.86 

12.56 

13.28 

22.64 

1.14 

4.15 

10.80 

6.27 

6.12 

5.66 

4.91 

2.57 

1.96 

7.02 

1.17 

4.55 

1.72 

7.68 

21.36 

8.29 

8.71 

3.51 

7.05 

6.85 

25.55 

2.84 

8.33 

4.71 

-4.49 

9.49 

2.43 

11.65 

25.14 

3.06 

8.69 

2.65 

2.79 

20.06 

18.82 

7.81 

11.50 

44.54 

13.63 

28.46 

25.55 

13.17 

11.13 

30.78 

27.45 

10.44 

-27.75 

16.95 

24.33 

20.88 

15.79 

20.25 

15.23 

11.73 

6.30 

6.93 

11.77 

16.48 

16.12 

18.98 

6.97 

14.31 

17.19 

11.40 

11.95 

11.78 

10.88 

14.14 

5.24 

24.27 

6.43 

18.33 

5.31 

22.65 

9.15 

30.41 

15.03 

4.48 

16.49 

16.94 

17.28 

8.46 

21.06 

22.25 

9.27 

15.58 

4.34 

27.73 

30.24 

1.33 

17.62 

3.48 

3.54 

14.48 

16.23 

42.09 

31.68 

69.97 

84.18 

45.77 

55.82 

32.42 

28.97 

61.35 

43.37 

49.12 

11.47 

40.97 

45.30 

38.54 

37.32 

38.59 

33.99 

25.66 

29.75 

30.97 

28.82 

35.62 

31.99 

38.48 

27.08 

48.54 

33.80 

29.89 

31.06 

29.59 

28.68 

66.36 

7.70 

38.50 

26.25 

47.25 

28.28 

38.79 

23.93 

38.47 

30.36 

5.31 

30.03 

31.14 

20.72 

8.40 

39.20 

46.03 

12.51 

29.40 

4.10 

31.84 

29.39 

1.15 

43.02 

2.36 

2.24 

8.99 

0.57 

29.76 

4.98 

38.10 

-15.18 

-6.42 

-6.17 

4.13 

-1.41 

-7.82 

-25.84 

-37.58 

130.33 

-18.18 

-15.91 

-18.38 

-19.71 

-15.94 

-19.32 

13.11 

-27.88 

-28.03 

14.24 

-14.53 

-16.02 

-9.29 

-28.10 

-30.26 

-9.51 

-21.65 

-21.54 

-21.31 

-22.10 

-16.10 

10.52 

-16.57 

-22.59 

-20.48 

-15.01 

-14.63 

25.91 

5.22 

-16.59 

9.60 

-15.52 

-15.18 

32.07 

16.57 

-12.93 

-11.43 

16.52 

-15.94 

10.25 

-11.48 

-23.38 

1.78 

-12.71 

11.50 

11.79 

6.74 

21.37 

-2.02 

-2.82 

14.37 

8.52 

5.28 

6.34 

6.31 

5.46 

-5.50 

-3.29 

-1.27 

-3.44 

-5.70 

-6.17 

-5.35 

-2.27 

-5.22 

-4.92 

6.86 

-4.05 

-4.28 

6.87 

-4.22 

-4.32 

-4.98 

-3.96 

-6.88 

-6.19 

-6.84 

-6.98 

-6.81 

-7.02 

-12.50 

5.77 

-5.74 

-6.69 

-8.51 

-4.41 

-7.34 

-5.36 

-15.87 

-10.20 

4.17 

-9.79 

-9.84 

-8.30 

6.10 

-8.96 

-10.26 

7.91 

-4.87 

6.35 

-7.09 

-3.30 

2.69 

9.41 

6.57 

6.66 

-1.29 

6.63 

7.03 

2.52 

23.52 

21.21 

12.46 

16.78 

7.37 

6.13 

5.86 

7.96 

2.32 

-0.12 

2.20 

5.11 

5.36 

4.12 

5.10 

3.73 

8.50 

1.27 

1.26 

8.84 

3.24 

5.03 

4.52 

0.76 

0.95 

2.27 

0.71 

0.71 

0.61 

0.33 

-2.57 

4.64 

1.28 

0.08 

0.59 

1.95 

1.26 

-0.39 

-1.63 

-0.25 

4.15 

-0.05 

-0.05 

-6.40 

4.83 

1.15 

0.08 

5.70 

3.10 

5.05 

2.05 

6.73 

3.09 

22.67 

5.02 

5.03 

6.16 

2.99 

9.82 

8.14 

11.29 

13.90 

9.64 

11.16 

8.72 

6.51 

- 

3.87 

-4.48 

4.54 

4.55 

3.90 

2.80 

6.66 

2.04 

0.96 

11.16 

-3.39 

-3.14 

11.70 

6.45 

0.02 

0.42 

-2.97 

0.12 

6.14 

-0.46 

-0.14 

-0.19 

-0.95 

- 

6.54 

6.11 

-2.00 

4.11 

1.25 

6.48 

7.10 

8.87 

0.47 

5.66 

1.70 

2.11 

5.66 

7.62 

6.60 

7.53 

6.48 

2.69 

6.31 

3.02 

5.56 

3.07 

6.80 

6.19 

6.20 

7.83 

6.56 

-1.56 

- 

0.33 

- 

6.08 

- 

8.95 

7.23 

- 

10.58 

9.75 

-4.97 

9.14 

5.85 

7.74 

- 

4.38 

- 

11.77 

6.12 

5.90 

12.19 

10.64 

2.80 

0.01 

7.36 

4.08 

- 

7.49 

7.37 

- 

7.33 

- 

7.16 

- 

7.45 

6.85 

8.59 

- 

6.03 

8.61 

- 

6.08 

8.19 

8.19 

4.74 

- 

- 

9.02 

6.73 

8.70 

6.85 

7.35 

9.14 

3.83 

- 

6.69 

6.68 

9.08 

9.01 

Sharpe 

0.11 

0.12 

0.48 

0.35 

0.24 

0.28 

0.30 

0.26 

-0.16 

-0.02 

-0.04 

0.03 

-0.21 

-0.21 

-0.18 

-0.08 

-0.20 

-0.21 

0.40 

-0.23 

-0.24 

0.41 

-0.17 

-0.18 

-0.24 

-0.26 

-0.26 

-0.25 

-0.37 

-0.37 

-0.37 

-0.40 

-0.48 

0.43 

-0.22 

-0.41 

-0.33 

-0.29 

-0.28 

-0.22 

-0.34 

-0.49 

0.31 

-0.49 

-0.49 

-0.19 

0.45 

-0.34 

-0.39 

0.75 

-0.32 

0.93 

-0.26 

-0.27 

1.11 

0.40 

0.86 

0.78 

- 

0.28 

Std Dev 

48.50 

43.03 

45.75 

37.69 

29.80 

32.98 

17.54 

17.11 

26.84 

31.80 

23.50 

39.18 

24.60 

25.67 

25.68 

23.53 

24.01 

22.59 

12.44 

19.63 

19.93 

12.13 

23.51 

22.91 

21.59 

18.27 

24.64 

23.88 

20.01 

20.29 

19.96 

19.58 

25.52 

8.05 

24.14 

18.56 

24.68 

18.07 

24.53 

23.48 

36.98 

21.95 

6.01 

21.18 

21.42 

31.48 

8.78 

25.25 

25.54 

7.27 

18.08 

4.03 

25.23 

16.32 

0.64 

23.66 

4.79 

5.39 

- 

31.33 


63

Index Funds 

Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions January/February 2010 

Fund Name Ticker Assets Exp Ratio 3-Mo YTD 

Vanguard Tot Stk 

Vanguard 500 Index 

Vanguard Inst Idx 

Vanguard 500 Idx Adm 

Vanguard Tot Stk Adm 

Vanguard Total Intl Stk 

Vanguard Inst Idx InstPl 

Vanguard Total Bd Idx 

Vanguard TtlBdMkt2IdxInv 

Vanguard Total Bd Idx Ad 

Fidelity Spar US EqIx 

Vanguard 500 Index Signal 

Vanguard Total Bd Idx In 

Vanguard Tot Stk Inst 

Fidelity U.S. Bond Index 

T. Rowe Price Eq Idx 500 

Vanguard Tot Stk InstPls 

Schwab S&P 500 In Sel 

Vanguard Em Mkt Idx 

Fidelity Spar 500 Adv 

Fidelity Spar 500 Idx 

Vanguard Mid Cap Idx 

Vanguard Eur Stk Idx 

Fidelity 100 Index 

Vanguard Mid Cap Idx Ins 

Vanguard SmCp Idx 

Vanguard Gr Idx 

Fidelity Spar US Eq Adv 

Fidelity Spar Tot Mkt Ix 

Vanguard Inst DevMktsIdx 

Vanguard Sh-Tm Bd Idx 

Fidelity Spar Intl Index 

Vanguard Sh-Tm Bd Sgnl 

Schwab 1000 In Inv 

Vanguard Inst Tot Bd Idx 

Vanguard ExtMktIdx 

Vanguard SmCp Idx Ins 

Vanguard Intm Bd Idx 

ING LB US Aggt Bd Idx I 

Fidelity Spar Tot Mkt Adv 

Vanguard REIT Index 

ING Stock Indx I 

Vanguard Bal Idx 

Vanguard Val Idx 

Vanguard ExtMktIdx Instl 

Vanguard SmCp Vl Idx 

Vanguard Dev Mkts Idx 

VALIC I Stock 

Vanguard Pac Stk Idx 

Vanguard Bal Idx Instl 

Dimensional USLgCo 

Vanguard Gr Idx Instl 

Vanguard SmCp Gr Idx 

Vanguard Value Index Is 

Vanguard Intm Bd Idx Adm 

Fidelity Spar Ext Mkt Ix 

Dreyfus S&P 500 Index 

Nations Lg-Cp Idx Pr A 

Vanguard LongTm Bd Idx 

Vanguard Mid Cap Idx Sgnl 

VTSMX 

VFINX 

VINIX 

VFIAX 

VTSAX 

VGTSX 

VIIIX 

VBMFX 

VTBIX 

VBTLX 

FUSEX 

VIFSX 

VBTIX 

VITSX 

FBIDX 

PREIX 

VITPX 

SWPPX 

VEIEX 

FSMAX 

FSMKX 

VIMSX 

VEURX 

FOHIX 

VMCIX 

NAESX 

VIGRX 

FUSVX 

FSTMX 

VIDMX 

VBISX 

FSIIX 

VBSSX 

SNXFX 

VITBX 

VEXMX 

VSCIX 

VBIIX 

ILBAX 

FSTVX 

VGSIX 

INGIX 

VBINX 

VIVAX 

VIEIX 

VISVX 

VDMIX 

VSTIX 

VPACX 

VBAIX 

DFLCX 

VIGIX 

VISGX 

VIVIX 

VBILX 

FSEMX 

PEOPX 

NINDX 

VBLTX 

VMISX 

52,578.6 

45,505.8 

40,396.6 

26,340.4 

25,120.3 

24,329.5 

23,676.1 

19,469.3 

19,069.9 

17,193.2 

16,230.3 

15,421.2 

14,861.1 

14,563.9 

10,577.5 

10,475.4 

9,353.2 

8,718.0 

7,024.2 

6,454.0 

6,422.8 

6,057.1 

5,789.1 

5,633.4 

5,350.3 

5,343.4 

5,318.8 

5,271.4 

5,142.0 

5,123.1 

4,985.3 

4,785.9 

4,724.3 

4,279.2 

3,896.4 

3,895.5 

3,759.8 

3,542.3 

3,422.8 

3,370.9 

3,321.5 

3,311.4 

3,268.7 

3,182.8 

3,111.8 

2,975.8 

2,935.7 

2,904.0 

2,845.7 

2,781.1 

2,719.8 

2,703.1 

2,664.6 

2,616.4 

2,491.1 

2,349.0 

2,240.1 

2,156.8 

2,143.9 

2,103.0 

0.19 

0.18 

0.05 

0.09 

0.09 

0.32 

0.03 

0.20 

0.19 

0.11 

0.10 

0.09 

0.07 

0.06 

0.32 

0.35 

0.03 

0.19 

0.45 

0.07 

0.10 

0.22 

0.27 

0.20 

0.08 

0.23 

0.22 

0.07 

0.10 

0.12 

0.18 

0.10 

0.10 

0.50 

0.05 

0.25 

0.08 

0.18 

0.45 

0.07 

0.21 

0.28 

0.20 

0.21 

0.07 

0.23 

0.27 

0.36 

0.32 

0.08 

0.15 

0.08 

0.23 

0.08 

0.11 

0.10 

0.50 

0.14 

0.18 

0.10 

5.23 

5.44 

5.47 

5.47 

5.22 

6.54 

5.49 

2.64 

2.62 

2.67 

5.44 

5.49 

2.68 

5.26 

2.45 

5.40 

5.28 

5.37 

7.13 

5.47 

5.47 

6.61 

8.46 

5.28 

6.66 

3.71 

5.12 

5.48 

5.02 

6.49 

1.63 

6.32 

1.66 

5.37 

2.66 

3.57 

3.74 

3.29 

2.56 

5.02 

15.96 

5.39 

4.27 

5.75 

3.64 

4.90 

6.44 

5.54 

2.34 

4.32 

5.45 

5.17 

2.59 

5.80 

3.32 

3.74 

5.37 

5.43 

3.81 

6.62 

18.46 

17.07 

17.18 

17.17 

18.54 

29.75 

17.21 

6.29 

- 

6.38 

17.09 

17.17 

6.43 

18.55 

6.79 

16.96 

18.64 

16.87 

60.40 

17.12 

17.09 

25.87 

26.26 

14.16 

26.08 

22.11 

24.59 

17.15 

18.19 

23.19 

4.44 

23.01 

4.52 

17.92 

6.42 

23.15 

22.33 

7.41 

6.30 

18.20 

13.30 

16.84 

14.39 

11.47 

23.36 

17.64 

23.14 

16.84 

17.20 

14.47 

17.21 

24.77 

26.58 

11.61 

7.49 

23.11 

16.73 

17.00 

3.86 

25.98 


2008 2007 3-Yr 

-37.04 

-37.02 

-36.95 

-36.97 

-36.99 

-44.10 

-36.94 

5.05 

- 

5.15 

-37.03 

-36.97 

5.19 

-36.94 

3.76 

-37.06 

-36.89 

-36.72 

-52.81 

-37.03 

-37.05 

-41.82 

-44.73 

-35.44 

-41.76 

-36.07 

-38.32 

-37.01 

-37.18 

-41.42 

5.43 

-41.43 

5.51 

-37.28 

5.05 

-38.73 

-35.98 

4.93 

- 

-37.16 

-37.05 

-37.12 

-22.21 

-35.97 

-38.58 

-32.05 

-41.62 

-37.21 

-34.36 

-22.10 

-36.78 

-38.19 

-40.00 

-35.88 

5.01 

-38.45 

-37.28 

-37.08 

8.64 

-41.78 

5.49 

5.39 

5.47 

5.47 

5.57 

15.52 

5.50 

6.92 

- 

7.02 

5.43 

5.47 

7.05 

5.56 

5.37 

5.18 

5.62 

5.50 

38.90 

5.46 

5.43 

6.02 

13.82 

- 

6.22 

1.16 

12.56 

5.46 

5.57 

11.04 

7.22 

10.72 

7.28 

5.76 

7.01 

4.33 

1.29 

7.61 

- 

5.60 

-16.46 

5.28 

6.16 

0.09 

4.51 

-7.07 

10.99 

5.13 

4.78 

6.34 

5.44 

12.73 

9.63 

0.21 

7.70 

5.38 

5.03 

5.37 

6.59 

6.17 

-6.65 

-7.06 

-6.97 

-6.98 

-6.58 

-3.60 

-6.95 

6.31 

- 

6.41 

-7.05 

-6.98 

6.45 

-6.54 

5.49 

-7.19 

-6.48 

-6.95 

5.57 

-7.03 

-7.06 

-7.02 

-5.39 

- 

-6.86 

-6.62 

-3.98 

-7.02 

-6.75 

-5.27 

5.89 

-5.37 

5.96 

-6.86 

6.36 

-6.56 

-6.47 

6.83 

- 

-6.72 

-15.07 

-7.21 

-1.14 

-9.29 

-6.37 

-8.38 

-5.42 

-7.30 

-5.50 

-1.00 

-6.89 

-3.82 

-5.09 

-9.16 

6.92 

-6.06 

-7.41 

-7.12 

6.38 

-6.91 

5-Yr 10-Yr 15-Yr P/E Std Dev Yield 

0.88 

0.25 

0.36 

0.34 

0.97 

6.55 

0.39 

5.00 

- 

5.09 

0.29 

0.31 

5.13 

1.00 

4.53 

0.11 

1.07 

0.34 

15.82 

0.31 

0.28 

2.30 

5.31 

- 

2.46 

1.79 

1.31 

0.32 

0.91 

5.10 

4.41 

4.89 

4.45 

0.59 

5.06 

2.04 

1.96 

5.10 

- 

0.93 

-0.34 

0.11 

2.91 

0.23 

2.24 

0.90 

4.92 

0.02 

4.12 

3.03 

0.39 

1.47 

2.48 

0.36 

5.18 

2.47 

-0.11 

0.23 

5.53 

2.41 

-0.03 

-1.02 

-0.91 

-0.95 

0.03 

3.08 

-0.88 

6.04 

- 

6.11 

-1.04 

-1.00 

6.17 

0.09 

6.08 

-1.19 

- 

-1.01 

11.14 

-1.02 

-1.03 

6.15 

2.83 

- 

6.32 

4.96 

-2.49 

-1.02 

-0.11 

- 

5.12 

1.84 

5.15 

-0.56 

- 

2.70 

5.13 

6.81 

- 

-0.10 

9.10 

- 

2.79 

0.83 

2.88 

7.14 

- 

-1.29 

0.08 

2.90 

-1.00 

-2.35 

5.47 

0.96 

6.87 

2.64 

-1.42 

-1.15 

7.74 

6.25 

7.28 

7.25 

7.38 

7.30 

7.33 

- 

7.40 

6.64 

- 

6.69 

7.19 

7.27 

6.76 

7.38 

6.64 

7.05 

- 

- 

6.53 

7.18 

7.17 

- 

7.88 

- 

- 

7.78 

7.35 

7.20 

- 

- 

5.64 

- 

5.65 

7.22 

- 

7.69 

7.91 

7.19 

- 

- 

- 

- 

7.43 

7.26 

7.84 

- 

- 

7.01 

-0.18 

7.51 

7.23 

7.46 

- 

7.36 

7.23 

- 

6.80 

7.07 

8.38 

- 

13.6 

14.6 

13.5 

14.6 

13.6 

10.4 

13.5 

- 

- 

- 

16.7 

14.6 

- 

13.6 

- 

16.7 

13.6 

14.6 

8.4 

16.7 

16.7 

13.4 

11.4 

16.6 

13.4 

14.8 

17.1 

16.7 

16.6 

11.4 

- 

13.5 

- 

14.7 

- 

15.7 

14.8 

- 

- 

16.6 

20.6 

13.5 

14.7 

12.7 

15.7 

11.7 

12.3 

16.1 

15.4 

14.7 

14.6 

17.1 

15.9 

12.7 

- 

17.0 

16.1 

16.1 

- 

13.4 

20.22 

19.58 

19.57 

19.58 

20.23 

26.13 

19.58 

4.04 

- 

4.04 

19.60 

19.58 

4.04 

20.21 

3.68 

19.55 

20.25 

19.50 

33.32 

19.59 

19.59 

23.88 

26.46 

- 

23.90 

25.72 

19.43 

19.58 

20.19 

24.76 

2.46 

24.88 

2.46 

19.71 

4.08 

24.34 

25.73 

6.64 

- 

20.18 

39.62 

19.63 

12.76 

20.81 

24.34 

26.55 

24.81 

19.73 

22.17 

12.75 

19.50 

19.43 

25.82 

20.81 

6.64 

23.43 

19.57 

19.58 

11.80 

23.90 

1.99 

2.24 

2.27 

2.33 

2.07 

2.32 

2.30 

4.15 

- 

4.24 

2.24 

2.33 

4.30 

2.10 

3.91 

2.01 

2.06 

2.61 

2.93 

2.51 

2.48 

1.58 

5.81 

2.64 

1.75 

1.61 

1.11 

2.26 

2.17 

4.50 

2.95 

2.93 

3.04 

2.07 

4.24 

1.37 

1.78 

4.40 

2.29 

2.19 

4.73 

2.26 

2.80 

2.95 

1.58 

2.44 

4.30 

2.83 

1.46 

2.95 

2.36 

1.28 

0.66 

3.11 

4.48 

1.28 

2.28 

2.53 

5.12 

1.72 

Source: Morningstar. Data as of October 30, 2009. Exp Ratio is expense ratio. YTD is year-to-date. 3-, 5-, 10- and 15-yr returns are annualized. P/E is price-to-earnings ratio. 

Std Dev is 3-year standard deviation. Yield is 12-month. 

64 


Morningstar U.S. Style Overview Jan. 1 – Oct. 30, 2009 

Trailing Returns % 

3-Month YTD 1-Yr 3-Yr 5-Yr 10-Yr 

Morningstar Indexes 

US Market 5.28 18.26 11.08 –6.56 1.04 –0.12 

Large Cap 5.15 15.96 8.91 –6.56 0.58 –1.87 

Mid Cap 6.25 25.10 17.98 –6.64 2.26 4.44 

Small Cap 3.87 23.15 14.42 –7.03 1.56 5.94 

US Value 5.54 10.31 4.68 –10.01 0.10 2.66 

US Core 4.83 16.11 10.93 –5.25 1.94 1.64 

US Growth 5.51 29.31 17.68 –4.73 0.63 –5.31 

Morningstar Market Barometer YTD Return % 

Large Cap 

US Market 

18.26 

15.96 

Value 

10.31 

Core 

16.11 

Growth 

29.31 

5.87 13.07 30.88 

Large Value 4.80 5.87 0.47 –10.92 –0.55 1.04 

Large Core 4.58 13.07 8.27 –4.74 1.85 –0.01 

Large Growth 6.20 30.88 18.56 –4.34 –0.22 –7.40 

Mid Cap 

25.10 

22.85 24.81 27.60 

Mid Value 7.57 22.85 17.78 –7.93 1.58 6.59 

Mid Core 5.69 24.81 19.30 –6.86 1.89 5.81 

Mid Growth 5.53 27.60 16.56 –5.52 2.99 0.31 

Small Cap 

23.15 

23.44 26.40 19.50 

Small Value 7.69 23.44 14.05 –6.93 1.78 8.75 

Small Core 5.00 26.40 16.66 –7.73 1.78 9.03 

Small Growth –0.87 19.50 12.36 –6.89 0.76 0.25 

–8.00 –4.00 0.00 +4.00 +8.00 

Sector Index YTD Return % 

Hardware 47.95 

Software 37.38 

Industry Leaders & Laggards YTD Return % 

Broadcasting - Radio 388.33 

Auto Manufacturers - Major 205.68 

Biggest Influence on Style Index Performance 

Best Performing Index 

YTD 

Return % 

Large Growth 30.88 

Constituent 

Weight % 

Media 25.77 

Consumer Services 22.37 

Industrial 19.73 

Business Services 19.24 

Copper 177.69 

Online Retail 124.35 

Marketing Services 115.86 

Personal Computers 106.58 

Apple Inc. 120.86 4.09 

Microsoft Corp. 45.39 8.28 

Google Inc. Cl A 74.26 3.95 

Cisco Systems Inc. 39.94 5.17 

Schlumberger Ltd. 48.76 2.72 

Consumer Goods 16.28 

Energy 16.01 

Financial Services 11.74 

Healthcare 8.77 

Utilities 0.90 

0.08 

–15.00 Water Utilities 

Worst Performing Index 

Large Value 5.87 

–21.13 Regional - Mid -Atlantic Banks 

JPMorgan Chase & Co. 34.68 4.95 

–24.13 Regional - Pacific Banks 

Morgan Stanley 103.93 0.65 

–25.96 Regional - Southeast Banks Ford Motor Co. 205.68 0.22 

–40.32 Major Airlines 

Anadarko Petroleum Corp. 59.04 0.74 

Chevron Corp. 6.54 6.32 

–43.01 Photographic Equipment & Supplies 

1-Year 

3-Year 

5-Year 

Value 

Core 

Growth 

Value 

Core 

Growth 

Value 

Core 

Growth 

Large Cap 

0.47 

8.27 

18.56 

Large Cap 

–10.92 

–4.74 

–4.34 

Large Cap 

–0.55 

1.85 

–0.22 

Mid Cap 

17.78 

19.30 16.56 

Mid Cap 

–7.93 

–6.86 –5.52 

Mid Cap 

1.58 

1.89 2.99 

Small Cap 

14.05 

16.66 12.36 

Small Cap 

–6.93 

–7.73 –6.89 

Small Cap 

1.78 

1.78 0.76 

–20 –10 0 +10 +20 

–20 –10 0 +10 +20 

–20 –10 0 +10 +20 

Source: Morningstar. Data as of 10/30/09 

Source: Morningstar. Data as of 2/29/08 

Notes and Disclaimer: ©2009 Morningstar, Inc. All Rights Reserved. Unless otherwise noted, all data is as of most recent month end. Multi-year returns are annualized. NA: Not Available. Biggest Influence on Index Performance lists 

are calculated by multiplying stock returns for the period by their respective weights in the index as of the start of the period. Sector and Industry Indexes are based on Morningstar's proprietary sector classifications. The information ? 

contained herein is not warranted to be accurate, complete or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information. 



65

Dow Jones U.S. Economic Industry Review Sector Review 

Dow Jones U.S. Industry Review 

Performance 

Index Name Weight 1-Month 3-Month YTD 1-Year 3-Year 5-Year 10-Year 

Dow Jones U.S. Index 100.00% -2.36% 5.40% 18.58% 11.29% -6.52% 1.00% -0.27% 

Dow Jones U.S. Basic Materials Index 3.19% -3.52% 8.01% 45.27% 27.58% -0.29% 5.09% 4.65% 

Dow Jones U.S. Consumer Goods Index 10.21% -0.74% 3.52% 15.91% 10.90% -0.92% 4.25% 4.26% 

Dow Jones U.S. Consumer Services Index 11.49% -2.20% 7.48% 22.38% 17.50% -6.83% -0.77% -1.60% 

Dow Jones U.S. Financials Index 15.72% -5.46% 8.08% 12.17% -4.89% -21.80% -8.89% -1.58% 

Dow Jones U.S. Health Care Index 12.03% -2.68% 1.03% 9.01% 8.02% -2.74% 2.69% 1.82% 

Dow Jones U.S. Industrials Index 12.31% -4.28% 6.03% 13.84% 8.06% -6.81% 0.33% -0.02% 

Dow Jones U.S. Oil & Gas Index 11.62% 2.10% 8.36% 14.42% 9.51% 1.72% 11.25% 10.39% 

Dow Jones U.S. Technology Index 16.82% -0.96% 6.48% 47.12% 32.50% -0.14% 3.69% -4.36% 

Dow Jones U.S. Telecommunications Index 2.70% -4.20% -2.93% -1.68% 5.03% -8.92% 0.32% -8.29% 

Dow Jones U.S. Utilities Index 3.90% -3.28% -1.00% 1.92% 1.17% -4.63% 4.67% 4.43% 

Risk-Return 

5% 

Oil & Gas 

Basic Materials 

3-Year Annualized Return 

0% 

Consumer Goods 

-5% 

-10% 

-15% 

-20% 

Health Care 

Utilities 

Composite 

Technology 

Consumer Services 

Telecommunications 

Industrials 

Financials 

-25% 

14% 16% 18% 20% 22% 24% 26% 28% 30% 32% 34% 

3-Year Annualized Risk 

Industry Weights Relative to Global ex-U.S. 

Asset Class Performance 

Basic Materials 

-7.62% 

U.S. [81.69] Global ex-U.S. [91.01] Commodities [84.77] 

REITs [57.65] Infrastructure [95.24] 

Consumer Goods 

-2.17% 

160 

Consumer Services 

4.55% 

140 

Financials 

-10.16% 

Health Care 

6.13% 

120 

Industrials 

-0.03% 

100 

Oil & Gas 

1.10% 

80 

Technology 

11.91% 

Telecommunications 

Utilities 

-2.86% 

-0.85% 

60 

40 

-15% -10% -5% 0% 5% 10% 15% 

Underweight Overweight 

20 

10/06 1/07 4/07 7/07 10/07 1/08 4/08 7/08 10/08 1/09 4/09 7/09 10/09 

Chart compares industry weights within the Dow Jones U.S. Index to industry weights within the Dow Jones U.S. = Dow Jones U.S. Index | Global ex-U.S. = Dow Jones Global ex-U.S. Index 

Global ex-U.S. Index 

Commodities = Dow Jones-UBS Commodity Index | REITs = Dow Jones U.S. Select REIT Index 

Infrastructure = Dow Jones Brookfield Global Infrastructure Index 

© Dow Jones & Company, Inc. 2009. All rights reserved."Dow Jones", "Dow Jones Indexes", "Dow Jones U.S. Index", "Dow Jones Global ex-U.S. Index" and "Dow Jones U.S. Industry Indexes" are service marks of Dow Jones & Company, Inc. "UBS" is a registered trademark of UBS AG. "Dow Jones-UBS Commodity Index" is a service 

mark of Dow Jones & Company, Inc. and UBS. "Brookfield" is a service mark of Brookfield Asset Management Inc. or its affiliates. The "Dow Jones Brookfield Infrastructure Indexes" are published pursuant to an agreement between Dow Jones & Company, Inc. and Brookfield Asset Management. Investment products that may be based 

on the indexes referencedare not sponsored,endorsed, sold or promoted by Dow Jones, and Dow Jones makes no representationregarding the advisability of investing in them. Inclusion of a company in these indexes does not in any way reflect an opinion of Dow Jones on the investment merits of such company. Index performance is for 

illustrative purposes only and does not represent the performance of an investment product that may be based on the index. Index performance does not reflect management fees, transaction costs or expenses. Indexes are unmanaged and one cannot invest directly in an index. 

The Dow Jones U.S. Index, the Dow Jones Global ex-U.S. Index and the Dow Jones U.S. Industry Indexes were first published in February 2000. The Dow Jones Brookfield Infrastructure Index was first published in July 2008. To the extent this document includes information for the index for the period prior to its initial publication date, 

such information is back-tested (i.e., calculations of how the index might have performed during that time period if the index had existed). Any comparisons, assertionsand conclusions regarding the performance of the Index during the time period prior to launch will be based on back-testing. Back-tested information is purely hypothetical 

and is provided solely for informational purposes. Back-tested performance does not represent actual performance and should not be interpreted as an indication of actual performance. Past performance is also not indicative of future results. 

Data as of October 30, 2009 

Source: Dow Jones Indexes Analytics & Research 

For more information, please visit the Dow Jones Indexes Web site at www.djindexes.com. 

66 


Exchange-Traded Funds Corner 

Largest New ETFs Sorted By Total Net Assets In $US Millions 

Covers ETFs and ETNs launched during the 12 months ended October 30, 2009 

Fund Name 

Direxion Financl Bear 3x 

Direxion Financl Bull 3x 

iPath S&P500 VIX Mid ETN 

Market Vectors Brazil Small-Cap 

Direxion Small Cp Bear 3x 

JPMorgan Alerian MLP Index ETN 

Direxion Large Cp Bear 3x 

Vanguard FTSE AW xUS SmCp 

Direxion Small Cp Bull 3x 

iShares Barclays Agncy Bond 

Direxion Emerging Mkts Bull 3x 

iShares S&P Shrt Nat Muni 

ETFS Physical Swiss Gold 

Direxion Large Cp Bull 3x 

Market Vectors Indonesia 

ProShares Ultra DJ-UBS Cr Oil 

ProShares UltraPro Short S&P 500 

ProShares Ultra Gold 

SPDR BarCap Convertible Bond 

ETFS Silver Trust 

Ticker 

FAZ 

FAS 

VXX 

BRF 

TZA 

AMJ 

BGZ 

VSS 

TNA 

AGZ 

EDC 

SUB 

SGOL 

BGU 

IDX 

UCO 

SPXU 

UGL 

CWB 

SIVR 

Source: Morningstar. Data as of October 30, 2009. ER is expense ratio. YTD is year-to-date. 1-Mo is 1-month. 3-Mo is 3-month. 

ER 

0.94 

0.94 

0.89 

0.73 

0.95 

0.85 

0.95 

0.38 

0.95 

0.20 

0.94 

0.25 

0.39 

0.95 

0.71 

0.95 

0.95 

0.95 

0.40 

0.30 

YTD 

-93.57 

-46.60 

- 

- 

-70.18 

- 

-61.85 

- 

6.29 

2.07 

130.84 

4.00 

- 

30.60 

- 

-3.58 

- 

30.54 

- 

- 

1-Mo 

10.34 

-17.70 

-3.54 

3.06 

17.63 

2.80 

4.06 

-2.71 

-19.99 

0.10 

-12.21 

-0.73 

3.75 

-7.76 

-6.92 

17.75 

2.99 

7.06 

-1.38 

-1.92 

3-Mo 

-33.18 

17.64 

-22.19 

22.09 

-13.61 

4.54 

-19.39 

9.24 

1.09 

1.20 

11.42 

-1.16 

- 

13.56 

2.85 

8.27 

-18.96 

18.88 

6.03 

17.20 

Launch Date 

11/6/2008 

11/6/2008 

1/29/2009 

5/12/2009 

11/5/2008 

4/2/2009 

11/5/2008 

4/2/2009 

11/5/2008 

11/5/2008 

12/17/2008 

11/5/2008 

9/9/2009 

11/5/2008 

1/15/2009 

11/24/2008 

6/23/2009 

12/1/2008 

4/14/2009 

7/24/2009 

Assets 

1,238.0 

1,186.2 

664.6 

497.9 

487.9 

448.4 

361.0 

283.3 

281.8 

238.4 

229.3 

198.2 

197.5 

191.5 

187.7 

173.9 

161.2 

151.2 

145.0 

141.9 

Selected ETFs In Registration 

Claymore Corporate Bond 2016 

Direxion Homebuilders Bull 3X 

EGS INDXX India Infrastructure 

ETFS Platinum Trust 

FaithShares Catholic Values 

Global X China Consumer 

Grail McDonnell Core Taxable Bnd 

IQ Global Cr Oil Small Cap Equity 

IQ International Australia SmCap 

iShares 2013 S&P AMT-Free Muni 

Jefferies Energy Wildcatters Equity 

Market Vectors Metals 

Old Mutual FTSE Emerging Markets 

Pimco Prime Ltd Maturity Strategy 

PowerShares CEF Inc Composite 

ProShrs Ultra BarCap 20+ U.S. Treas 

SPDR S&P Russia 

Vanguard Mortgage-Bkd Securities 

WCM/BNY Mel Focused Growth ADR 

WisdomTree Real Return 

Source: IndexUniverse.com's ETF Watch 

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


Fund Name Ticker Assets Exp Ratio 3-Mo YTD 

2008 2007 

3-Yr 5-Yr Mkt Cap P/E 

Sharpe Std Dev Yield 

SPDRs (S&P 500) 

SPDR Gold Trust 

iShares MSCI Emerg Mkts 

iShares MSCI EAFE 

iShares S&P 500 

iShares BarCap TIPS Bond 

PowerShares QQQQ 

Vanguard Emerging Markets 

iShares iBoxx $ Inv Gr Corp Bond 

Vanguard Total Stock Market 

iShares Russell 2000 

iShares BarCap Aggregate 

iShares Brazil 

iShares R1000 Growth 

iShares FTSE/Xinhua China 

iShares R1000 Value 

iShares BarCap 1-3 Treas 

DIAMONDS Trust 

MidCap SPDR (S&P 400) 

Financial SPDR 

iShares S&P 400 MidCap 

Vanguard Total Bond Mkt 

iShares S&P 500 Growth 

Energy SPDR 

SPY 

GLD 

EEM 

EFA 

IVV 

TIP 

QQQQ 

VWO 

LQD 

VTI 

IWM 

AGG 

EWZ 

IWF 

FXI 

IWD 

SHY 

DIA 

MDY 

XLF 

IJH 

BND 

IVW 

XLE 

67,356.5 

36,885.8 

35,027.3 

33,928.5 

20,055.2 

17,172.0 

16,222.5 

15,536.6 

12,945.4 

12,130.1 

11,178.7 

10,868.5 

10,309.3 

10,106.9 

9,324.0 

8,147.9 

7,552.3 

7,500.7 

7,462.6 

6,549.8 

5,822.3 

5,782.0 

5,447.9 

5,433.9 

0.09 

0.40 

0.74 

0.34 

0.09 

0.20 

0.20 

0.27 

0.15 

0.09 

0.20 

0.20 

0.69 

0.20 

0.74 

0.20 

0.15 

0.17 

0.25 

0.23 

0.20 

0.14 

0.18 

0.23 

5.32 

9.83 

5.00 

5.73 

5.36 

4.06 

3.93 

6.55 

2.60 

5.16 

1.61 

2.66 

19.50 

4.71 

-0.38 

5.91 

0.78 

6.43 

5.31 

8.33 

5.40 

2.31 

5.95 

9.65 

16.76 

18.50 

51.61 

21.25 

17.10 

8.25 

38.24 

59.52 

8.73 

18.56 

15.49 

3.65 

98.15 

24.91 

44.57 

10.90 

0.61 

13.54 

24.41 

14.22 

24.69 

3.83 

21.27 

17.42 

-36.70 

4.92 

-48.87 

-41.00 

-37.00 

-0.53 

-41.72 

-52.54 

2.44 

-36.68 

-34.15 

7.90 

-54.37 

-38.21 

-47.73 

-36.45 

6.61 

-32.10 

-36.40 

-54.90 

-36.18 

6.88 

-34.78 

-38.97 

5.12 

31.10 

33.11 

9.94 

4.92 

11.93 

19.13 

37.32 

3.76 

5.36 

-1.76 

6.61 

74.82 

11.48 

54.81 

-0.73 

7.35 

8.78 

7.20 

-19.19 

7.30 

- 

8.84 

36.86 

-7.05 

19.40 

4.92 

-5.78 

-7.05 

5.97 

-0.90 

5.08 

5.27 

-6.61 

-8.42 

6.35 

21.63 

-4.22 

15.51 

-9.85 

5.13 

-4.58 

-4.53 

-24.23 

-4.41 

- 

-4.19 

1.16 

0.30 

- 

15.67 

4.50 

0.33 

4.68 

2.66 

- 

4.07 

0.96 

0.63 

4.86 

33.09 

1.15 

21.19 

-0.15 

3.89 

1.74 

2.93 

-10.82 

3.14 

- 

1.11 

10.97 

41,507 

- 

22,448 

29,039 

41,517 

- 

35,576 

16,831 

- 

20,957 

736 

- 

41,642 

33,191 

70,700 

29,831 

- 

92,055 

2,567 

38,914 

2,562 

- 

50,476 

44,218 

16.7 

- 

- 

12.0 

16.7 

- 

25.3 

8.4 

- 

13.6 

15.6 

- 

11.4 

18.5 

18.2 

15.0 

- 

15.1 

18.1 

24.2 

18.1 

- 

16.8 

11.7 

-0.40 

0.85 

0.24 

-0.21 

-0.40 

0.43 

-0.02 

0.24 

0.28 

-0.36 

-0.34 

0.72 

0.64 

-0.24 

0.50 

-0.50 

1.40 

-0.31 

-0.18 

-0.64 

-0.18 

- 

-0.27 

0.09 

19.45 

20.58 

32.36 

24.99 

19.49 

8.82 

23.56 

32.44 

12.07 

20.17 

24.11 

5.40 

40.45 

19.64 

41.46 

20.99 

1.97 

17.85 

23.19 

36.33 

23.13 

- 

18.35 

26.88 

2.22 

- 

1.54 

2.75 

2.14 

3.13 

0.43 

3.11 

5.38 

2.08 

1.47 

4.04 

2.83 

1.44 

1.24 

2.62 

2.48 

2.76 

1.40 

2.68 

1.47 

4.16 

1.43 

1.75 

Source: Morningstar. Data as of October 30, 2009. Exp Ratio is expense ratio. 3-Mo is 3-month. YTD is year-to-date. 3-Yr and 5-Yr are 3-year and 5-year annualized returns, respectively. 

Mkt Cap is geometric average market capitalization. P/E is price-to-earnings ratio. 


67

The Spirit Of Indexing 

HUMOR 

Dear Editor: 


By David Blitzer 

Dear Editor: 

I am 88 years old and my younger friends tell me that buy-and-hold investing doesn’t 

work anymore and is dying. Please tell me the truth: Is there any hope under the sun for 

passive investing? 

Bill 

With apologies to 

Virginia O’Hanlon, 

Francis Pharcellus 

Church and The New 

York Sun … and 

Santa Claus* 

Dear Bill: 

Your new and younger friends are wrong. They have been affected by the market 

turmoil of the new era. They do not believe what they should have learned from long 

experience. Everyone overreacts to what has happened in the immediate past and forgets 

what they’ve seen over longer time periods. Each investor is but a small part of the 

total market, wishfully thinking he will know the time the tide will turn. In this world of 

markets of ours, it is the long run that counts, when the market weighs results, instead 

of the short run, when it votes by popularity. 

Yes, Bill, buy-and-hold investing lives and works. It exists as certainly as mean reversion 

exists and the importance of low fees and diversification exist, and you know that 

they are here and continue to drive the market. Alas, how boring the world would 

be if everyone could truly predict the markets. There would be no challenges and no 

winners in the investing game. No systems, no recommendations, no technicians and 

no market timers. We would have no one to question or complain about. The eternal 

challenge of the market that fills the world of investing would be extinguished. 

Not believe in buy-and-hold investing? You might as well not believe in IPO runups! 

You might ask your discount e-broker to watch the markets and catch those who 

never trade because they have only bought and held. But even if he never found them, 

what would that prove? They are quietly watching their portfolios grow and tending 

to their 401(k) plans. The most real things in investing are those that neither investors 

nor academics see in the mathematics: that predicting the market is incredibly difficult 

and that being right twice in a row is even more difficult. 

You may tear apart an active mutual fund to see what makes it miss the market, but 

there is a veil of cost over it that eclipses the returns of even the strongest portfolio 

manager. Only experience in watching the markets over years, nay decades, can hint at 

the true picture of its linkage to earnings and the economy through herds of bulls and 

mobs of bears and herds of bulls again as tides of investing style and fashion flow in 

and out. Ah, Bill, there is little else of abiding results but buying and holding indices. 

No buy-and-hold!? Thank Markowitz, Bogle, Fama and French that it lives on, gathering 

ever-larger indexed assets! A thousand corrections, nay Bill, 10 times a thousand 

corrections from now, buy-and-hold will still be justified by low costs, diversification 

and mean reversion. 

Happy investing! 

The Editor 

*as well as to any and all who feel they were, or were not, mentioned 

68 


VGSH 

VGIT 

VANGUARD SHORT-TERM 

GOVERNMENT BOND ETF 

VANGUARD INTERMEDIATE-TERM 


VGLT 

VANGUARD LONG-TERM 


New Bond ETFs from Vanguard I Offering targeted exposure to the government 

sector of the domestic fixed income market, these ETFs provide options for 

tailoring your portfolio within the government sector. Choose short-, intermediate-, 

or long-term coverage. Lower costs,* tight tracking, and diversification may reduce 

manager risk and provide returns more in line with the index. Expertise, lower 

costs, and trusted name. Just what you’d expect from Vanguard. Connect 

with Vanguard ® at 800-523-1178 or visit our dedicated advisors’ website at 

advisors.vanguard.com/etf. 

To buy or sell Vanguard ETFs, contact your financial advisor. Usual commissions apply. Not redeemable. Market price 

may be more or less than NAV. Visit advisors.vanguard.com/etf to obtain a Vanguard prospectus which contains 

investment objectives, risks, expenses, and other information; read and consider carefully before investing. All ETFs are subject to 

risk, including possible loss of principal. Investments in bond funds are subject to interest rate, credit, and inflation risk.*Source: Lipper 

Inc. as of December 31, 2008. Based on 2008 industry average expense ratio of 1.19% and Vanguard average expense ratio of 0.20%. 

©2010 The Vanguard Group, Inc. All rights reserved. U.S. Pat. No. 6,879,964 B2; 7,337,138. Vanguard Marketing Corporation, Distributor.

ETF Trading 

Knight’s exchange-traded fund 

global trading capabilities include 

agency and risk executions, ETF 

trading and selection assistance, 

as well as ETF market insights. 

Our highly experienced ETF trading 

group supports competitive risk 

markets and capital facilitation in 

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To learn more about Knight’s ETF Trading, 

please contact: 

Phone: 212.336.8760 

Email: ETF@knight.com 

www.knight.com 

To learn about Knight Capital Group, Inc. (Nasdaq: NITE) go to knight.com 

©December 2009 Knight Capital Group, Inc. All rights reserved. 

Knight Equity Markets, L.P. and Knight Capital Markets LLC are members of FINRA and SIPC.

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