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Plus Blitzer on the history of options, Citrin on the dark side of index derivatives,<br />

a chat with IMRF’s Shah and a crossword from Greig<br />

considering options November / December 2012<br />

More Options With More Options<br />

Chintan Kotecha, Nitin Saksena, Youssef Brahimi and Benjamin Bowler<br />

Index Options Writing For Enhanced Yields And Alpha<br />

Matt Moran and Mitch Boraz<br />

Creating A Vertical Spread Index<br />

Mark Abssy<br />

The Winner’s Curse<br />

Rob Arnott and Lillian Wu


www.journalofndexes.<strong>com</strong><br />

Vol. 15 No. 6<br />

features<br />

More Options With More Options<br />

By Chintan Kotecha, Nitin Saksena, Youssef Brahimi and<br />

Benjamin Bowler ...................................10<br />

The most liquid ETF options—and what you can do with them.<br />

Index Options Writing For Enhanced Yields And Alpha<br />

By Matt Moran and Mitch Boraz . . . . . . . . . . . . . . . . . . . . . 20<br />

Options indexes for a low-interest-rate environment.<br />

Creating A Vertical Spread Index<br />

By Mark Abssy......................................28<br />

Tracking the performance of options overlay strategies.<br />

Options Before Quants<br />

By David Blitzer ....................................34<br />

Options have been around longer than most folks realize.<br />

20<br />

Human Risk and The Pervasiveness of Index Derivatives<br />

By Jonathan Citrin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36<br />

The popularity of index-based derivatives isn’t without drawbacks.<br />

IMRF’s Shah Stays The Course<br />

By Journal of Indexes Staff...........................38<br />

IMRF’s new CIO talks asset management.<br />

The Winner’s Curse<br />

By Rob Arnott and Lillian Wu . . . . . . . . . . . . . . . . . . . . . . . . 42<br />

The leading <strong>com</strong>panies are also among the most disadvantaged.<br />

Thinking About Options<br />

By Bruce Greig......................................66<br />

How well do you know options? Find out in this <strong>issue</strong>’s puzzle.<br />

news<br />

Home Prices Nearing Recovery? . . . . . . . . . . . . . . . . . . . . . 52<br />

Federal Court Dismisses ProShares Suit . . . . . . . . . . . . . . 52<br />

Russell Debuts ‘GeoExposure’ Index Family . . . . . . . . . . 52<br />

FTSE Signs Deal With Chinese Exchange . . . . . . . . . . . . . 53<br />

FocusShares Closes Up Shop . . . . . . . . . . . . . . . . . . . . . . . . 53<br />

Russell Closing ETFs, Altering Plan . . . . . . . . . . . . . . . . . . 53<br />

Shiller, Barclays Launch ‘CAPE’ Indexes. . . . . . . . . . . . . . 53<br />

data<br />

Global Index Data ..................................58<br />

Index Funds ........................................59<br />

Morningstar U.S. Style Overview . . . . . . . . . . . . . . . . . . . . . 60<br />

Dow Jones U.S. Industry Review . . . . . . . . . . . . . . . . . . . . . . 61<br />

Exchange-Traded Funds Corner . . . . . . . . . . . . . . . . . . . . . 62<br />

28<br />

42<br />

www.journalofindexes.<strong>com</strong><br />

November / December 2012<br />

1


Contributors<br />

Mark Abssy<br />

Mark Abssy is the senior index and ETF manager for the International<br />

Securities Exchange (ISE). In this capacity, he is responsible for all phases<br />

in the development of innovative tradable products, from concept to <strong>com</strong>mercial<br />

launch. Abssy is a member of the CFA Institute and the New York<br />

Society of Security Analysts, and received a BSBA in finance and international<br />

business from Northeastern University.<br />

Robert Arnott<br />

Robert Arnott is chairman and founder of asset management firm Research<br />

Affiliates LLC. He is also the former chairman of First Quadrant LP and<br />

has served as a global equity strategist at Salomon Brothers (now part<br />

of Citigroup) and as president of TSA Capital Management (now part of<br />

Analytic). Arnott was editor-in-chief of Financial Analysts Journal from<br />

2002 through 2006. He graduated summa cum laude from the University of<br />

California, Santa Barbara.<br />

Chintan Kotecha Mitch Boraz<br />

David Blitzer<br />

Jonathan Citrin<br />

David Blitzer is managing director and chairman of S&P Dow Jones Indices’<br />

index <strong>com</strong>mittee. He has overall responsibility for security selection for the<br />

<strong>com</strong>pany’s indexes, and index analysis and management. Blitzer previously<br />

served as chief economist for Standard & Poor’s and as corporate economist<br />

at The McGraw-Hill Companies. A graduate of Cornell University, he<br />

received his M.A. in economics from George Washington University and his<br />

Ph.D. in economics from Columbia University.<br />

Mitch Boraz, CPA, CIMA, is a director at Asset Consulting Group. He<br />

services a wide variety of clients with an emphasis on ACG’s private client<br />

services advisory practice, which includes family offices, high-net-worth<br />

investors, endowments and foundations. Boraz’s responsibilities include<br />

managing and developing client relationships. He is also a member of<br />

ACG’s investment <strong>com</strong>mittee. Boraz received his B.A. in accounting from<br />

the University of Missouri-St. Louis.<br />

Jonathan Citrin is the founder and CEO of CitrinGroup. He is also the<br />

chairman of the firm’s investment advisory board and an adjunct faculty<br />

member in the finance department at Wayne State University’s School<br />

of Business Administration. Prior to forming CitrinGroup, Citrin managed<br />

investment portfolios at Morgan Stanley and Morgan Stanley Dean<br />

Witter. He received his B.A. from Tulane University and his M.A. from<br />

New York University.<br />

Chintan Kotecha joined Bank of America Merrill Lynch Global Research<br />

in 2008 and is a senior analyst in U.S. equity derivatives. He specializes in<br />

innovative uses of VIX, volatility and equity derivatives for hedging and<br />

alpha generation. Kotecha holds an M.S. in <strong>com</strong>putational finance from<br />

Carnegie Mellon University and a B.S. in electrical and <strong>com</strong>puter engineering<br />

from Rutgers University. His research has also been published in the<br />

Journal of Alternative Investments.<br />

Matt Moran<br />

Matt Moran is vice president, business development, for the Chicago Board<br />

Options Exchange. He has also served as trust counsel at Harris Bank and<br />

as vice president at the Chicago Mercantile Exchange. Moran is an associate<br />

editor of The Journal of Trading and The Journal of Index Investing. He is a<br />

licensed attorney who has received MBA and Juris Doctor degrees from the<br />

University of Illinois.<br />

2 November / December 2012


Jim Wiandt<br />

Editor<br />

jwiandt@indexuniverse.<strong>com</strong><br />

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Editorial Board<br />

Rolf Agather: Russell Investments<br />

David Blitzer: S&P Dow Jones Indices<br />

Lisa Dallmer: NYSE Euronext<br />

Henry Fernandez: MSCI<br />

Deborah Fuhr: ETF Global Insight<br />

Gary Gastineau: ETF Consultants<br />

Joanne Hill: ProShare and ProFund Advisors LLC<br />

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Delva, Gary Eisenreich, Richard Evans,<br />

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Jim Novakoff, Rick Redding, Anthony<br />

Scamardella, Larry Swedroe, Jason Toussaint,<br />

Mike Traynor, Jeff Troutner, Peter Vann,<br />

Wayne Wagner, Peter Wall, Brad Zigler<br />

4 November / December 2012<br />

Copyright © 2012 by <strong>IndexUniverse</strong> LLC<br />

and Charter Financial Publishing Network<br />

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People’s futures are in your hands.<br />

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For the support you deserve, visit our Financial Advisors site<br />

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U.S. Pat. No. 6,879,964 B2; 7,337,138; 7,720,749; 7,925,573; 8,090,646.<br />

Follow us @Vanguard_FA for important insights, news and education.


Editor’s Note<br />

Talking About Options<br />

Jim Wiandt<br />

Editor<br />

The end is nigh! For 2012, at least—although if you believe the Mayans, Dec. 21<br />

really is the end. Either way, we here at JoI are closing out the year (or eternity)<br />

with an <strong>issue</strong> focused around options. No, not “options” as in choices—as in<br />

whether you should hide in a bunker or hit the holiday sales—but “options” as in<br />

derivatives. This <strong>issue</strong> we’ll be discussing options on indexes and ETFs, to be specific.<br />

We kick off with a survey of the ETF options space from the derivatives research<br />

crew at BofA Merrill Lynch. Chintan Kotecha and his colleagues discuss the current<br />

state of the ETF options market and how such instruments can be used to implement<br />

different strategies. Following this, the CBOE’s Matt Moran and Mitch Boraz of Asset<br />

Consulting Group tell us more generally how options-based index strategies can help<br />

investors navigate the current economic environment.<br />

Mark Abssy of the International Securities Exchange is up next with an article<br />

about the development of the exchange’s vertical spread indexes that are based on<br />

SPY options, followed by David Blitzer, who provides a historical context for the<br />

entire <strong>issue</strong>. Index and ETF options may be fairly recent innovations, but options as<br />

a concept date back a few hundred years!<br />

Then Jonathan Citrin of CitrinGroup takes a different perspective, pointing out that<br />

there’s a dark side to the growing popularity of index-based derivatives.<br />

Appearing next is our interview with Dhvani Shah, who took over as CIO of the<br />

Illinois Municipal Retirement Fund late last year. IMRF is one of the best-funded pension<br />

systems in the state of Illinois, and index-based investments play a sizable role in<br />

its allocations.<br />

Finally, Rob Arnott and Lillian Wu close out the feature articles lineup with a provocative<br />

and rigorous analysis of the troubles faced by <strong>com</strong>panies that make it to the<br />

tops of their markets and how that translates into lower returns for their investors.<br />

Then Bruce Greig is back, with a crossword puzzle that will test your knowledge of<br />

options market terminology, to end the <strong>issue</strong> on a fun note.<br />

Wishing you and yours a happy holiday season (fingers crossed on the<br />

Mayan Armageddon)!<br />

Jim Wiandt<br />

Editor<br />

8<br />

November / December 2012


©2012 Morningstar, Inc. All rights reserved. The Morningstar name and logo are registered marks of Morningstar. Marks used in conjunction with Morningstar products or services are the property of Morningstar or its subsidiaries.<br />

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More Options<br />

With More Options<br />

Surveying the field of ETF options<br />

By Chintan Kotecha, Nitin Saksena, Youssef Brahimi and Benjamin Bowler<br />

10<br />

November / December 2012


Exchange-traded products span a wide range of offerings,<br />

including broad style, sector, industry, country,<br />

regional and cross-asset benchmarks. A key reason<br />

for their popularity is convenience. Because exchangetraded<br />

products trade and settle like a stock, there is no<br />

additional infrastructure or documentation required. The<br />

most popular exchange-traded product is the exchangetraded<br />

fund. For the remainder of this piece, for readability<br />

and familiarity, we use the term “ETF” to mean exchangetraded<br />

fund and to include other exchange-traded products<br />

such as exchange-traded notes (ETNs).<br />

Since the credit crisis, with cross-asset correlations<br />

rising, there has been increased demand for investments<br />

with broader access. As a result, hedge funds and<br />

traditional institutional investors increasingly are using<br />

ETFs. Common applications include allocating assets,<br />

top-down investing, cash flow management, hedging<br />

either broadly or tactically within sectors and executing<br />

relative value strategies; for example, trading a stock<br />

versus its own sector. ETFs offer flexibility, and given<br />

that the products trade like ordinary equities, some<br />

institutions that are restricted from using traditional<br />

derivatives are able to use ETFs.<br />

Along with growth in the ETF market, there has been<br />

a similar rise in the use of options on exchange-traded<br />

products. The goal of this report is to examine the size,<br />

growth and breadth of the ETF options market and then<br />

provide examples of options strategies this market now<br />

allows. The analysis that follows uses a universe of ETF<br />

options whose underlying ETF country of domicile is the<br />

United States and that which is traded in U.S. dollars. We<br />

exclude ETFs for which the median daily notional option<br />

volume to date (Sept. 7) as of 3Q12 is not at least $10 million.<br />

The ETFs with options that meet these criteria are<br />

in Figure 1, with their respective third-quarter 2012 (todate)<br />

median daily notional volume.<br />

The ETF options market has been growing through the<br />

last decade, with offerings now available across a wide range<br />

of equity, <strong>com</strong>modity, fixed-in<strong>com</strong>e and currency ETFs.<br />

Consequentially, a liquid and broad ETF options market<br />

now allows for options strategies such as cross-asset<br />

hedging, overwriting or relative value—traditionally<br />

placed at the single-stock and index level within equities—to<br />

now be applied at a style, sector, industry, country,<br />

regional or cross-asset level.<br />

The remainder of this piece is in two parts. We survey<br />

the market of options on exchange-traded products,<br />

examining the market size, growth and depth. Then, we<br />

list and explain examples of strategies that the options<br />

market on exchange-traded products now allows.<br />

ETF OPTIONS MARKET SNAPSHOT<br />

Market Size, Growth And Breadth<br />

Market Size And Growth<br />

Over the last year, the total notional traded on ETF options<br />

has ranged from approximately $40 billion to $50 billion per<br />

day (Figure 2). For perspective, listed S&P 500 index notional<br />

option volume ranged from about $80 billion to $95 billion<br />

Figure 1<br />

US-Listed ETFs Traded In USD<br />

With Median Daily Notional Volume Of At Least $10M<br />

Ticker<br />

Description<br />

3M Median Volume<br />

($M)<br />

SPY SPDR S&P 500 $26,295.52<br />

IWM iShares Russell 2000 $3,392.05<br />

GLD SPDR Gold $2,433.35<br />

QQQ PowerShares QQQ $2,234.77<br />

DIA SPDR DJ Industrial Average $691.75<br />

EEM iShares MSCI Emerging Markets $614.11<br />

TLT iShares 20+ Year Treasury Bond $601.52<br />

EFA iShares MSCI EAFE $322.66<br />

XLE Energy Select Sector SPDR $290.71<br />

EWZ iShares MSCI Brazil $278.50<br />

SLV iShares Silver $274.45<br />

FAS Direxion Financial Bull 3x $261.09<br />

USO United States Oil $240.57<br />

FXI iShares FTSE China 25 $237.20<br />

GDX Market Vectors Gold Miners $230.50<br />

VXX iPath VIX Short-Term Futures $226.00<br />

FXE CurrencyShares Euro $170.33<br />

IYR iShares Dow Jones Real Estate $157.15<br />

XLF Financial Select Sector SPDR $138.58<br />

XLI Industrial Select Sector SPDR $114.98<br />

UNG United States Natural Gas $82.16<br />

XRT SPDR S&P Retail $79.74<br />

TNA Direxion Small Cap Bull 3X $78.23<br />

SSO ProShares Ultra S&P 500 $69.78<br />

XOP SPDR S&P Oil & Gas $66.13<br />

SDS ProShares UltraShort S&P 500 $64.63<br />

TBT ProShares UltraShort 20+ Yr Treasury $52.25<br />

TZA Direxion Small Cap Bear 3X $51.07<br />

XLB Materials Select Sector SPDR $47.36<br />

FAZ Direxion Financial Bear 3X $46.92<br />

XME SPDR S&P Metals & Mining $43.99<br />

OIH Market Vectors Oil Service $43.11<br />

XLV Health Care Select Sector SPDR $36.71<br />

XLK Technology Selet Sector SPDR $31.11<br />

MDY SPDR S&P MidCap 400 $29.96<br />

AGQ ProShares Ultra Silver $22.62<br />

XLU Utilities Select Sector SPDR $22.14<br />

XLP Consumer Staples Select Sector SPDR $20.94<br />

FXA CurrencyShares Australian Dollar $19.72<br />

XHB SPDR S&P Homebuilders $17.73<br />

HYG iShares iBoxx High Yield Corporate Bond $17.54<br />

FXY CurrencyShares Japanese Yen $17.48<br />

IBB iShares Nasdaq Biotechnology $17.25<br />

XLY S&P 500 Discretionary Sector $15.24<br />

QID ProShares UltraShort Nasdaq 100 $14.18<br />

GDXJ Market Vectors Junior Gold Miners $12.30<br />

SPXU ProShares UltraPro Short S&P 500 $11.17<br />

EWW iShares MSCI Mexico $11.13<br />

ERX Direxion Energy Bull 3X $11.11<br />

EDC Direxion Emerging Markets Bull 3X $10.99<br />

Source: BofA Merrill Lynch Global Research<br />

Note: Volume data covers June 30 - Sept. 7, 2012.<br />

www.journalofindexes.<strong>com</strong> November / December 2012<br />

11


Figure 2<br />

Median Notional Volume ($B)<br />

70<br />

60<br />

50<br />

40<br />

30<br />

20<br />

10<br />

Source: BofA Merrill Lynch Global Research<br />

Note: Q3-12 data is as of Sept. 7, 2012.<br />

Figure 3<br />

4%<br />

Median Daily Notional Volume<br />

Per Quarter, Q1-99 To Q3-12 ($B)<br />

0<br />

Q1<br />

’99 ’99 Q4<br />

’00 Q3<br />

’01 Q2<br />

’02 Q1<br />

’02 Q4<br />

’03 Q3<br />

’04 Q2<br />

’05 Q1<br />

’05 Q4<br />

’06 Q3<br />

’07 Q2<br />

’08 Q1<br />

’08 Q4<br />

’09 Q3<br />

’10 Q2<br />

’11 Q1<br />

’11 Q4<br />

’12<br />

Q3<br />

■ SPY ■ IWM<br />

Source: BofA Merrill Lynch Global Research<br />

■ QQQ ■ GLD ■ Other<br />

ETF Option Notional Traded Per Day By Asset Class,<br />

Excluding Top Four Traded ETF Options<br />

Outside the top four traded ETF options, there is currently close to $6B options<br />

notional traded per day in the remainder of liquid ETF options with oferings<br />

across four asset classes.<br />

10%<br />

11%<br />

75%<br />

■ Equity $4.416M<br />

■ Debt $671M<br />

■ Commodity $620M<br />

■ Currency $208M<br />

Excluded:<br />

SPY $26,296M<br />

IWM $3,392M<br />

GLD $2,433M<br />

QQQ $2,235M<br />

(the largest of all equity indexes globally) per day for the same<br />

period. 1 Notably, approximately 85 percent of the total ETF<br />

options volume is traded among only four ETFs. These top four<br />

most-liquid ETF options are on SPY (S&P 500), IWM (Russell<br />

2000), QQQ (Nasdaq 100) and GLD (gold). ETF options on<br />

SPY grew considerably from 2007 to 2011, and currently trade<br />

with one-third of the volume of S&P 500 index options. ETF<br />

option volume peaked in the third quarter of 2011 at approximately<br />

$66 billion in notional per day. ETF options volumes,<br />

like those across other asset classes, have been declining over<br />

the last year, as markets appear exhausted from ongoing and<br />

lengthy global macroeconomic concerns.<br />

Notably, volume contracted to $18 billion in the second<br />

quarter of 2009 (credit crisis market bottom) and<br />

then increased sharply (3.5 times more) to peak in the<br />

third quarter of 2011. After the credit crisis, market participants<br />

became increasingly macro focused and topdown<br />

looking, which increased demand for tools that<br />

access macroeconomic views.<br />

Growth in SPY options volume has outpaced the growth of<br />

the total volume traded in listed S&P 500 index options, in part<br />

because ETF options allow for more accessibility than index<br />

options, as they are denominated on a smaller notional.<br />

Market Breadth<br />

A key feature of the ETF market is the accessibility<br />

to trade nonequity assets in an equitylike fashion.<br />

ETFs allow investors to take views on fixed in<strong>com</strong>e,<br />

<strong>com</strong>modities and currencies with the same mechanical<br />

and infrastructural simplicity as when taking views on<br />

stocks. Similarly, within the ETF options market, investors<br />

can now take advantage of the benefits of optionality<br />

across a variety of asset classes.<br />

Even though the majority of ETF options volume is<br />

concentrated within four ETFs (three are equity funds;<br />

the other is gold), there is close to $6 billion of options<br />

notional traded per day in the remainder of liquid<br />

ETF options (Figure 3). Of the remaining ETF options<br />

volume, 75 percent ($4.4 billion) is in equity funds, 11<br />

percent ($671 million) in fixed-in<strong>com</strong>e funds, 10 percent<br />

($620 million) in <strong>com</strong>modity funds and 4 percent<br />

($208 million) in currency funds. In the next section,<br />

we take a closer look at size and growth in ETF options<br />

within each asset class.<br />

SPY, IWM and QQQ the most liquid ETF options<br />

within equities: Options on equity-based ETFs have<br />

approximately $36 billion notional traded per day with<br />

the SPY (S&P 500), IWM (Russell 2000) and QQQ<br />

(Nasdaq 100) making up $32 billion per day (Figure 4).<br />

Since its inception in 2005, options on SPY have grown<br />

tremendously and are now trading at approximately<br />

$26 billion notional per day. This growth can be attributed<br />

to the popularity of S&P 500 optionality (a global<br />

benchmark for equities), <strong>com</strong>bined with SPY’s minimal<br />

tracking error (as its underlying stocks are the largest<br />

U.S. <strong>com</strong>panies) to the S&P 500. Within the equity-asset<br />

class, the top three volume leaders represent a diverse<br />

selection of equities with access to U.S. large- and<br />

small-cap stocks (NYSE Arca: SPY and NYSE Arca: IWM)<br />

and technology stocks (Nasdaq GM: QQQ).<br />

The remaining equity ETF options outside the top three<br />

also represent a diverse array of investments. Liquid ETF<br />

options exist within the following categories:<br />

• International equities: MSCI EEM and EAFE, Brazil,<br />

China and Mexico<br />

Figure 4<br />

Median Notional Volume ($B)<br />

60<br />

50<br />

40<br />

30<br />

20<br />

10<br />

0<br />

Q1<br />

’99<br />

Q1<br />

’00<br />

Q1<br />

’01<br />

Equity-Based ETF Options<br />

Median Notional Volume ($B)<br />

SPY, IWM and QQQ are the<br />

most liquid ETF options<br />

within the equities asset class.<br />

Q1<br />

’02<br />

Source: BofA Merrill Lynch Global Research<br />

Q1<br />

’03<br />

Q1<br />

’04<br />

Q1<br />

’05<br />

Q1<br />

’06<br />

Q1<br />

’07<br />

Q1<br />

’08<br />

Q1<br />

’09<br />

■ SPY ■ IWM ■ QQQ ■ Other<br />

Q1<br />

’10<br />

Q1<br />

’11<br />

Q1<br />

’12<br />

12<br />

November / December 2012


Figure 5<br />

Median notional Volume ($B)<br />

8<br />

7<br />

6<br />

5<br />

4<br />

3<br />

2<br />

1<br />

0<br />

Q2<br />

’07<br />

Q4<br />

’07<br />

<strong>com</strong>modity ETF Options<br />

Median notional Volume ($B)<br />

Dominated by GLD, <strong>com</strong>modity<br />

ETF options trade $3bn<br />

notional/day.<br />

Q2<br />

’08<br />

Q4<br />

’08<br />

Q2<br />

’09<br />

Q4<br />

’09<br />

Q2<br />

’10<br />

■ GLD ■ SLV ■ USO ■ UNG<br />

Source: BofA Merrill Lynch Global Research<br />

Figure 6<br />

Median notional Volume ($B)<br />

0.8<br />

0.7<br />

0.6<br />

0.5<br />

0.4<br />

0.3<br />

0.2<br />

0.1<br />

0.0<br />

Q2<br />

’02<br />

Q2<br />

’03<br />

Q4<br />

’04<br />

Source: BofA Merrill Lynch Global Research<br />

Q4<br />

’10<br />

Q2<br />

’11<br />

■ AGQ<br />

Fixed-in<strong>com</strong>e ETF Options<br />

Median notional Volume ($B)<br />

Options on fixed in<strong>com</strong>e ETFs<br />

showing most consistent<br />

growth.<br />

Q3<br />

’05<br />

Q2<br />

’06<br />

Q1<br />

’07<br />

Q4<br />

’07<br />

Q3<br />

’08<br />

Q2<br />

’09<br />

■ TLT ■ TBT ■ HYG<br />

Q1<br />

’10<br />

Q4<br />

’10<br />

Q4<br />

’11<br />

Q3<br />

’11<br />

Q2<br />

’12<br />

Q2<br />

’12<br />

• U.S. large- and midcap stocks: Dow Jones Industrial<br />

Average and S&P 400 MidCap<br />

• U.S. sectors: S&P 500 stocks categorized by their GICS 2<br />

sectors and broader-sector access to real estate, retail, oil,<br />

gold stocks, biotech and home builders<br />

• U.S. equity volatility: Options on VXX, the short-term<br />

VIX futures ETN<br />

• Levered and inverse products: On a selection of broadbased<br />

U.S. equities, U.S. sectors and international equities.<br />

Notably, options on levered and inverse products have<br />

outsized volume versus open interest relative to other liquid<br />

ETF options. The interpretation is that options on these<br />

funds are likely used more for intraday speculation.<br />

Dominated by GLD, <strong>com</strong>modity ETF options trade $3<br />

billion notional/day: Commodity ETF options are the second-most-liquid<br />

asset class of ETF options traded, and are<br />

dominated by GLD, (gold), which trades approximately $2.4<br />

billion in notional per day (Figure 5). The remaining notional<br />

of <strong>com</strong>modity ETF options is traded primarily through<br />

NYSE Arca: SLV (silver), NYSE Arca: USO (oil), NYSE Arca:<br />

UNG (natural gas) and NYSE Arca: AGQ (two-times levered<br />

silver). Options on <strong>com</strong>modity ETFs are relatively new and<br />

have picked up volume only in the last four years.<br />

Macroeconomic factors help explain the demand for<br />

options on precious metals and energy. Gold and silver<br />

be<strong>com</strong>e more valuable when default risks to global central<br />

banks’ balance sheets increase. And, recurring tensions in<br />

oil-producing countries bring volatility to energy prices. ETF<br />

options on these <strong>com</strong>modities allow investors the opportunity<br />

to hedge and/or speculate on these volatile markets.<br />

Options on fixed-in<strong>com</strong>e ETFs showing most consistent<br />

growth: The options market for fixed-in<strong>com</strong>e ETFs is<br />

dominated by contracts on the 20+ Year Treasury Bonds<br />

(NYSE Arca: TLT) and UltraShort 20+ Year Treasury (NYSE<br />

Arca: TBT) ETFs, which currently account for $654 million<br />

notional per day. Both TLT and TBT are linked to longdated<br />

U.S. Treasury bonds. Another $18 million notional<br />

per day is being traded in options on the iBoxx $ High Yield<br />

Corporate Bond ETF (NYSE Arca: HYG).<br />

Options on these fixed-in<strong>com</strong>e ETFs have been growing<br />

more consistently than ETF options across other assets<br />

(Figure 6). While volumes for equity, <strong>com</strong>modity and currency<br />

ETF options are 50-60 percent off their peaks, volumes<br />

on fixed-in<strong>com</strong>e ETF options are less than 5 percent off theirs.<br />

Euro, Aussie dollar, and yen the most liquid currency<br />

ETF options: Currency ETF options are the least traded<br />

among the four asset classes, with their current volumes<br />

totaling about $208 million in notional per day. The primary<br />

currency ETF options traded are on the FXE (euro), FXA<br />

(Australian dollar) and FXY (Japanese yen), which is consistent<br />

with the most highly traded currency pairs (Figure<br />

7). Among these, the contract on FXE has been a consistent<br />

leader and is currently trading $170 million in notional per<br />

day. The majority of options flows on currencies is over the<br />

counter and on off-listed exchanges, making it harder to<br />

access for individual investors. However, ETFs on currencies<br />

provide a wider audience access to options on these<br />

most <strong>com</strong>monly traded currency pairs. Like the <strong>com</strong>modity<br />

ETF options market, currencies are also relatively new,<br />

with volumes only picking up in the last four years.<br />

ETF Option Applications<br />

Given the breadth of the ETF options market discussed<br />

in the prior section, traditional options strategies primarily<br />

implemented on single stocks or equity indexes can<br />

now be applied simply and efficiently at the equity sec-<br />

Figure 7<br />

Median notional Volume ($B)<br />

0.5<br />

0.4<br />

0.3<br />

0.2<br />

0.1<br />

0.0<br />

Q2<br />

’07<br />

Q4<br />

’07<br />

currency ETF Options<br />

Median notional Volume ($B)<br />

Euro, Aussie dollar and yen are<br />

the most liquid currency<br />

ETF options.<br />

Q2<br />

’08<br />

Q4<br />

’08<br />

Q2<br />

’09<br />

Q4<br />

’09<br />

Q2<br />

’10<br />

■ FXE ■ FXA ■ FXY<br />

Source: BofA Merrill Lynch Global Research<br />

Q4<br />

’10<br />

Q2<br />

’11<br />

Q4<br />

’11<br />

Q2<br />

’12<br />

www.journalofindexes.<strong>com</strong> November / December 2012<br />

13


Figure 8<br />

Trade Proft & Loss ($)<br />

Source: BofA Merrill Lynch Global Research<br />

Figure 9<br />

Trade Proft & Loss ($)<br />

30<br />

20<br />

10<br />

0<br />

-10<br />

-20<br />

Hypothetical Expiry P&L Comparison:<br />

Short TLT Position Vs. Put Option On TLT<br />

-30<br />

100 110 120 130 140 150<br />

Source: BofA Merrill Lynch Global Research<br />

■ TLT Put Option PNL ■ Short TLT PNL<br />

Hypothetical Expiry P&L Of Position<br />

In GLD Hedged With Put Spread<br />

20<br />

15<br />

10<br />

5<br />

0<br />

-5<br />

-10<br />

-15<br />

-20<br />

-25<br />

-30<br />

125 135 145 155 165<br />

■ Long GLD<br />

■ Hedged GLD<br />

tor, country or cross-asset level. In particular, we examine<br />

how ETF options allow for new opportunities within the<br />

following option applications:<br />

• Hedging<br />

• Stock replacement and leverage<br />

• Overwriting and alpha generation<br />

• Relative value trading<br />

Using ETF Options To Hedge<br />

Liquid and well-developed options markets do exist on<br />

nonequity asset classes such as <strong>com</strong>modities, fixed in<strong>com</strong>e<br />

and currencies. However, the accessibility and standardization<br />

of ETF options across assets offers a distinct advantage,<br />

particularly for individual investors. Investors can use ETF<br />

options to efficiently and simultaneously hedge a wide range<br />

of cross-asset views. In the following examples, we show how<br />

ETF options can hedge a rise in U.S. Treasury yields, a decline<br />

in gold prices or express a view on the euro/USD spot rate.<br />

Example 1: Put Options On TLT<br />

To Hedge A Rise In U.S. Treasury Yields<br />

With U.S. Treasury yields hovering at multidecade lows,<br />

there is increased speculation and concern surrounding<br />

a sharp rally in Treasury rates and an end to the 30-year<br />

Treasury bubble. Investors can expect a significant decline in<br />

Treasury bond prices in the event of a sharp rate increase. A<br />

simple way to take advantage of this decline would be to sell<br />

short shares of TLT. This would, however, require putting<br />

up margin and would expose investors to uncapped losses<br />

should Treasury yields continue their historical declines.<br />

Instead of going short TLT, an investor could purchase<br />

put options on TLT at the expectation of a rate increase. A<br />

hypothetical <strong>com</strong>parison of expiry profit and loss of shorting<br />

TLT versus purchasing an at-the-money put option on TLT<br />

is shown in Figure 8. In this example, TLT is trading at $124<br />

when both positions are initiated. The put option is struck at<br />

$124 for an upfront cost of $3.00. At expiry, both positions pay<br />

off if TLT declines below $124, with the put option position<br />

making $3 less (the upfront premium). If TLT finishes higher<br />

than $124, the short position is subject to uncapped losses.<br />

However, the put option’s losses are capped to the upfront<br />

premium paid. The capped loss in the put option position is<br />

attractive if an investor mistimed the Treasury rate increase.<br />

Example 2: A Put Spread Hedge On GLD<br />

To Reduce Risk In Being Long Gold<br />

With the increased demand for exposure to gold in the last<br />

few years, GLD has been a widely traded ETF, and its options<br />

are some of the most liquid among ETF options (the most liquid<br />

of all <strong>com</strong>modities and also higher than some of the largest<br />

equity ETF options). Perceived as a “real” asset, investors look to<br />

hold gold in times of widespread macroeconomic uncertainty.<br />

An outright position in GLD is subject to the volatility in<br />

gold prices. However, the risk to a long position in GLD can<br />

be reduced by overlaying a put spread on top of a GLD position.<br />

A put spread purchases a near-the-money put and sells<br />

a farther-out-of-the-money put with the same maturity. The<br />

cost of the near-the-money put is partially offset by selling<br />

the farther out-of-the-money put. The near-the-money put<br />

provides full downside protection in the underlying. Often<br />

though, <strong><strong>com</strong>plete</strong> downside protection is too costly, and it<br />

is beneficial to sell a farther-out-of-the-money put that caps<br />

downside protection, but also decreases premium paid.<br />

The profit and loss at expiry of a hypothetical put spread<br />

on GLD is shown in Figure 9. In the example, GLD is at<br />

$150 at trade initiation, and the put spread strikes are $146<br />

for the long put and $139 for the short put. The total cost of<br />

Figure 10<br />

1.60<br />

1.55<br />

1.50<br />

1.45<br />

1.40<br />

1.35<br />

1.30<br />

1.25<br />

1.20<br />

1.15<br />

Dec.<br />

’10<br />

■ Euro/USD Spot Rate<br />

Feb.<br />

’11<br />

Apr.<br />

’11<br />

EUR/USD Spot Rate<br />

Jun.<br />

’11<br />

Aug.<br />

’11<br />

Source: BofA Merrill Lynch Global Research<br />

Oct.<br />

’11<br />

The EUR/USD spot rate has<br />

declined on the back of<br />

increased risks in the eurozone.<br />

Dec.<br />

’11<br />

Feb.<br />

’12<br />

Apr.<br />

’12<br />

Jun.<br />

’12<br />

Aug.<br />

’12<br />

14<br />

November / December 2012


the put spread in this example is $1.00. We assume this put<br />

spread is overlaid on a long GLD position. The put spread<br />

will protect holders of GLD from any losses between $146<br />

and $139, while still participating in GLD upside (less the<br />

upfront amount paid for the put spread).<br />

Example 3: A Contrarian View On<br />

The Euro With Long FXE Call Options<br />

The EUR/USD spot rate has declined as a result of<br />

increased risks in the eurozone over the last year and half<br />

(Figure 10). During this time, policymakers within the region<br />

have been coordinating to ensure the stability of the euro.<br />

However, given the risks still outstanding, a contrarian trade<br />

would be to go long the EUR/USD spot rate with the idea that<br />

the widespread European crisis will be averted.<br />

A contrarian view on the EUR/USD spot rate could be<br />

implemented by purchasing FXE outright. However, a long<br />

FXE position has full downside exposure should the euro<br />

continue to decline. Instead, a call option on FXE would<br />

provide a limited-loss way to take advantage of upside in<br />

the euro. Figure 11 shows the profit and loss of a hypothetical<br />

at-the-money call option on FXE <strong>com</strong>pared with purchasing<br />

FXE outright. In this example, we assume FXE is at<br />

$124, and the call option purchased is struck at-the-money<br />

for a cost of $1.00. If the euro appreciates, the call option<br />

participates in the upside (less the amount paid for the call<br />

option up front). If the euro continues its decline, however,<br />

the losses are limited to the upfront premium.<br />

Stock Replacement And Leverage<br />

Both call and put options offer leverage to their underlying<br />

asset and cost a fraction of the underlying security’s<br />

price. Despite only paying a portion of the upfront price,<br />

the holder of a call (put) option receives the entire upside<br />

(downside) above (below) the strike price less the upfront<br />

premium paid. Therefore, options offer leverage as they<br />

provide the ability to purchase upside (or downside) of a<br />

security without paying for the entire cost of the security.<br />

Investors can benefit from the leverage in options<br />

through “stock replacement” trades. A stock replacement<br />

trade consists of purchasing a call (put) option in place of a<br />

long (short) security position. Since the option costs a fraction<br />

of the cost of the security, an investor can allocate far<br />

less capital to the investment. Investors can then purchase<br />

options with higher notionals as a way to get leverage.<br />

Additionally, stock replacement strategies can be less risky<br />

than outright long/shorts as the position’s maximum loss is<br />

limited to the upfront cost of the options.<br />

Example 4: An Investor Underweight Equities<br />

Misses A Sharp Equity Rally And Catches Up<br />

By Buying Call Options On Equity ETFs<br />

Since the bottom in March 2009, equity markets have had<br />

distinct periods of sharp rallies (Figure 12). However, given<br />

the risks within equities, many investors have been underweight<br />

equities through these rallies. To keep up with benchmarks,<br />

investors that missed rallies may need to catch up. One<br />

approach is to purchase higher-beta equities with the idea that<br />

Figure 11<br />

Trade Proft & Loss ($)<br />

25<br />

20<br />

15<br />

10<br />

5<br />

0<br />

-5<br />

-10<br />

Source: BofA Merrill Lynch Global Research<br />

Figure 12<br />

1500<br />

1400<br />

1300<br />

1200<br />

1100<br />

1000<br />

900<br />

800<br />

700<br />

600<br />

Mar.<br />

’09<br />

Hypothetical Expiry P&L Of Call Option<br />

On FXE Vs. Holding FXE Outright<br />

-15<br />

115 120 125 130 135 140 145<br />

S&P 500 Performance<br />

March 2009 - Sept. 2012<br />

Jun. Sep. Dec. Jun. Sep. Sep. Dec. Jun<br />

’09 ’09 ’09 ’10 ’10 ’10 ’10 ’11 ’11 ’11 ’11 ’12 ’12<br />

■ S&P 500<br />

Source: BofA Merrill Lynch Global Research<br />

Note: Data as of Sept. 7, 2012.<br />

■ ATM Call On FXE ■ Long FXE<br />

Since bottoming in 2009, the<br />

S&P 500 has had distinct<br />

periods of sharp rallies<br />

should the momentum continue, those high-beta names will<br />

outperform their equity benchmarks. However, call options<br />

on equity ETFs (i.e., SPY, IWM, QQQ) offer another solution to<br />

catch up to an equity market rally. With the leverage in options,<br />

investors could purchase options on higher notionals, which<br />

would outperform should the rally continue.<br />

Figure 13 shows the hypothetical profit and loss of a<br />

levered stock replacement trade on SPY <strong>com</strong>pared alongside<br />

an investment in SPY. The stock replacement trade is long<br />

three at-the-money call options. By levering the notional, the<br />

more SPY continues to rally, the greater the stock replacement<br />

trade outperforms (thus allowing investors to catch<br />

up in a rally). On the downside, the loss is limited to the<br />

amount of upfront premium paid. As a reminder, these stock<br />

replacement positions required much less upfront capital<br />

than an outright purchase of SPY, and also provide downside<br />

protection in the case of large declines in SPY.<br />

Overwriting And Alpha Generation<br />

Because options performance is primarily dependent on<br />

the performance of the underlying asset, fundamental investors<br />

with a directional view have a potential edge. Strategies<br />

that sell options also have an advantage, as options generally<br />

trade rich to fair value. Option strategies such as overwriting<br />

and iron condors can help investors efficiently monetize<br />

www.journalofindexes.<strong>com</strong> November / December 2012<br />

15


Figure 13<br />

20%<br />

15%<br />

10%<br />

5%<br />

0%<br />

-5%<br />

-10%<br />

-15%<br />

Hypothetical P&L Of Levered Stock Replacement<br />

Trade On SPY Vs. Holding SPY Outright<br />

Replacing long equity<br />

positions with call options<br />

can provide leverage and<br />

limit losses on the downside.<br />

-10% -5% 0% 5% 10%<br />

Source: BofA Merrill Lynch Global Research<br />

■ Long 3 SPY ATM Calls ■ Long SPY<br />

their directional view and are risk-controlled ways to harvest<br />

the excess premium priced into options.<br />

Overwriting as well as iron condor strategies have been well<br />

documented, analyzed and implemented within the equity<br />

asset class on single-stock and broad-market index options.<br />

Now, however, ETF options allow investors access to sectors<br />

and across asset classes when applying these strategies.<br />

Example 5: Overwriting Specific Sectors<br />

In A Diversified Equity Portfolio<br />

Take, for example, an investor who owns a diversified portfolio<br />

of large-cap stocks and is moderately bullish-to-neutral<br />

on specific sectors within the portfolio. To enhance yield, the<br />

investor decides to sell call options on names in the lowerconviction<br />

sector. With ETF options on equity sectors, instead<br />

of selling call options on each of the individual stocks, the<br />

investor can instead sell a call option on the specific sector ETF.<br />

The current liquidity of options on the Select Sector SPDR<br />

ETFs 3 is shown in Figure 14. Options are most liquid in energy<br />

and financials, at $291 million and $139 million, respectively,<br />

notional traded per day. Open interest on all sectors is in the<br />

billions of dollars of notional. Note that volume in technology<br />

sector ETF options is lower because the majority of interest in<br />

tech optionality is traded through options on QQQ.<br />

ETF options on sectors offer various advantages to selling<br />

individual options on a basket of single stocks, such as:<br />

• Depending on the names, there may be limited liquidity<br />

on the single stocks versus the ETF sector.<br />

• There is less risk in selling upside on a sector versus<br />

a single stock in the event the investor’s view is incorrect.<br />

If there are numerous names on which upside is to be sold,<br />

it be<strong>com</strong>es simpler from a transactional perspective to sell an<br />

option on a handful of ETFs versus a basket of single stocks.<br />

Example 6: Iron Condor On GLD<br />

To Harvest The Richness In Options<br />

Options generally trade rich to fair value due to an embedded<br />

risk premium that accounts for unexpected increases in<br />

the underlying security’s volatility. Therefore, selling options<br />

can be a profitable trade if the underlying does not realize the<br />

amount of volatility priced into its options. However, market<br />

participants are wary of selling uncovered option positions,<br />

particularly in down markets or in times of sharp rallies.<br />

A less risky way to sell options is through a short iron<br />

condor strategy. A hypothetical profit and loss chart for a<br />

short iron condor strategy is shown in Figure 15. In this<br />

example, the position sells 5 percent out-of-the-money call<br />

and put options, and then buys back 10 percent out-of-themoney<br />

call and put, thereby covering the two short options<br />

positions. The trade collects an upfront premium of 3 percent<br />

of notional and has a predetermined maximum loss<br />

of 2 percent. All options positions have the same maturity.<br />

Short iron condor strategies on equity indexes globally<br />

have performed well over the long term. Figure 16 shows the<br />

performance of short iron condor strategies on the S&P 500<br />

(U.S.), Euro Stoxx 50 (Europe) and Nikkei (Japan) indexes.<br />

With ETF options, investors can implement iron condor<br />

strategies to take advantage of the richness in optionality<br />

across various markets, sectors and other asset classes.<br />

For example, the performance of a 1M 88/96 106/114 iron<br />

condor strategy 4 on GLD since June 2008 is shown in Figure<br />

17. The annualized return of the systematic GLD short iron<br />

condor strategy since June 2008 is 3.3 percent, with a volatility<br />

of 7.3 percent giving an information ratio of 0.45.<br />

Figure 14<br />

Median Daily Notional Volume On S&P 500 Sector ETF Options, Q3-To-Date<br />

Ticker<br />

Name<br />

ETF AUM<br />

($M)<br />

Q3 Median Volume<br />

($M)<br />

Q3 Median Open<br />

Interest ($M)<br />

XLE Energy 7,674.9 290.7 6,795.0<br />

XLF Financial 7,204.5 138.6 6,198.9<br />

XLI Industrial 3,395.9 115.0 3,327.3<br />

XLB Materials 2,187.0 47.4 1,904.6<br />

XLV Health Care 4,937.9 36.7 1,421.8<br />

XLK Technology 10,314.3 31.1 1,753.6<br />

XLU Utilities 6,283.9 22.1 1,126.4<br />

XLP Consumer Staples 5,787.9 20.9 1,771.4<br />

XLY Consumer Discretionary 3,296.8 15.2 1,558.8<br />

Source: BofA Merrill Lynch Global Research. Note: Q3-12 data is as of Sept. 7, 2012.<br />

16<br />

November / December 2012


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Relative Value Trading<br />

In lower-correlation environments, there is differentiation<br />

in performance across and within asset classes exists.<br />

Given the breadth of the ETF and ETF options market,<br />

investors can take a view on the spread between two<br />

assets—at the sector, country or cross-asset level.<br />

For example, using sector ETFs, investors can trade a<br />

single stock against a basket of its peers (same sector), thus<br />

allowing one to isolate the fundamentals of a particular<br />

<strong>com</strong>pany in a relative value trade. Or investors can trade<br />

the outperformance between two equity sectors. Likewise,<br />

with country-specific ETFs, investors can play an outperformance<br />

between regions. Even further, ETFs allow for<br />

views across assets to play the relative value between any<br />

Figure 15<br />

Hypothetical Expiry P&L<br />

Of Short Iron Condor Strategy On GLD<br />

10%<br />

8%<br />

6%<br />

4%<br />

2%<br />

0%<br />

-2%<br />

-4%<br />

-6%<br />

-8%<br />

-10%<br />

-20% -15% -10% -5% 0% 5% 10% 15% 20%<br />

Source: BofA Merrill Lynch Global Research<br />

Figure 16<br />

Backtesting Results For Systematic 4-Week Iron Condors On Global Equity Indexes (From Jan. 1, 2002-Sept. 7, 2012)<br />

S&P 500 Iron Condor<br />

ESTX50 Iron Condor<br />

NKY Iron Condor<br />

Bloomberg Ticker<br />

MLBWSPAR Index<br />

MLFPEEAR Index<br />

MLEINKAR Index<br />

Annualized return since ‘02 3.8% 5.8% 6.0%<br />

Volatility since ‘02 8.3% 8.5% 8.0%<br />

Information ratio since ‘02 0.46 0.68 0.75<br />

Source: BofA Merrill Lynch Global Research<br />

Note: Backtesting is hypothetical in nature and reflects application of the trade strategy prior to its introduction.<br />

Figure 17<br />

120<br />

115<br />

110<br />

105<br />

100<br />

95<br />

90<br />

85<br />

80<br />

75<br />

70<br />

Jun.<br />

’08<br />

GLD Iron Condor Strategy<br />

Vs. HFRI Fund-Weighted Composite<br />

Oct. Feb. Jun. Oct. Feb. Jun. Oct. Feb. Jun. Oct. Feb.<br />

’08 ’09 ’09 ’09 ’10 ’10 ’10 ’11 ’11 ’11 ’12<br />

■ GLD Iron Condor<br />

Source: BofA Merrill Lynch Global Research<br />

Note: Data covers June 2008 - Sept. 2012.<br />

A 1-month 88/96 106/114 short iron condor<br />

strategy on GLD since June 2008 provides<br />

a risk-controlled way to capture the<br />

richness in GLD options.<br />

■ HFRI Fund Weighted Composite<br />

pair of equity, <strong>com</strong>modity, fixed-in<strong>com</strong>e or currency assets.<br />

A simple way to implement a long/short relative value<br />

trade is by going long and short the respective assets.<br />

However, the losses of an outright long/short trade can<br />

be uncapped if the assets move in the opposite direction<br />

Jun.<br />

’12<br />

of what was anticipated. Alternatively, a long-call-option/<br />

short-call-spread-option pair also implements a long/short<br />

relative value view, but with capped downside.<br />

Example 7: Long Cyclical Sectors, Short Defensive<br />

In Reflationary Environments<br />

Historically, cyclical stocks strongly outperform defensive<br />

stocks in reflationary environments. To express this in the<br />

U.S., an investor could go long NYSE Arca: XLY (discretionary<br />

sector ETF) and short NYSE Arca: XLI (industrials sector ETF).<br />

However, this trade has uncapped losses in the amount that<br />

XLI potentially outperforms XLY. To help mitigate the risk of<br />

the relative value trade, an investor alternatively could purchase<br />

call options on XLY and sell a call spread on XLI.<br />

This long-call-option/short-call-spread-option pair<br />

trade has limited loss, as the long-call option’s maximum<br />

downside is the upfront premium, while the short call<br />

spread’s losses are capped to the distance between the<br />

two call-option strikes. If indeed XLY outperforms XLI, the<br />

long-call option would be better than the short-call spread,<br />

allowing an investor to capitalize on the relative outperformance<br />

but with limited downside risk.<br />

Endnotes<br />

1 The S&P 500 Index options market is the largest in the world in terms of notional volume.<br />

2 For a definition of the S&P Global Industry Classification Standard (GICS), please see: http://www.standardandpoors.<strong>com</strong>/indices/gics/en/us<br />

3 Select Sector SPDRs are ETFs that divide the S&P 500 into nine sectors. Together, the nine Select Sector SPDRs represent the S&P 500 as a whole.<br />

4 Each month this strategy sells a put that is 4 percent out-of-the-money and a call that is 6 percent out of-the-money and then buys back a put that is 12 percent out-of-themoney<br />

and a call 14 percent out-of-the-money. All options are held to expiry and the trade is reinitiated each month.<br />

Printed by permission. Copyright © 2012 Bank of America Corporation. Further reproduction or distribution is strictly prohibited.<br />

18<br />

November / December 2012


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The S&P 500, SPDRs, and Select Sector SPDRs are trademarks of The McGraw-Hill Companies, Inc. and have been licensed for use. The stocks included in each Select Sector<br />

Index were selected by the <strong>com</strong>pilation agent. Their <strong>com</strong>position and weighting can be expected to differ to that in any similar indexes that are published by S&P. The S&P 500<br />

Index is an unmanaged index of 500 <strong>com</strong>mon stocks that is generally considered representative of the U.S. stock market. The index is heavily weighted toward stocks with large<br />

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ALPS Distributors, Inc. a registered broker-dealer, is distributor for the Select Sector SPDR Trust.


Index Options Writing<br />

For Enhanced Yields And Alpha<br />

Using index options in a low-interest-rate environment<br />

By Matt Moran and Mitch Boraz<br />

20<br />

November / December 2012


Over the past 12 years, U.S. investors have faced<br />

four investment challenges that were much<br />

more severe than they were in the period from<br />

1982 through 1999:<br />

(1) Low interest rates and higher future risk for fixed in<strong>com</strong>e<br />

(2) Sluggish equity returns<br />

(3) Higher correlations among many asset classes<br />

(4) Less liquidity for certain investments in times of stress<br />

In light of these challenges, many investors are exploring<br />

investments beyond the “traditional” investments of<br />

U.S. stocks and bonds. More than $25 billion has been<br />

invested in buy-write funds since the 2002 introduction<br />

of the first major benchmark for options performance,<br />

the CBOE S&P 500 BuyWrite Index (BXM).<br />

Challenge No. 1: Low Interest Rates<br />

And Higher Risk For Fixed In<strong>com</strong>e<br />

Many investors think of fixed-in<strong>com</strong>e investments as<br />

being very safe, and many fixed-in<strong>com</strong>e instruments have<br />

performed relatively well during the past few decades. For<br />

example, as shown later in this article, the Treasury bond<br />

Figure 1<br />

End-Of-Quarter Yield<br />

18%<br />

15%<br />

12%<br />

9%<br />

6%<br />

3%<br />

Figure 2<br />

Yields For US Treasury Notes And S&P 500<br />

(Mar. 1962 - Dec. 2011)<br />

15.84%<br />

6.21%<br />

0%<br />

3/62 3/74 3/86 3/98 3/10<br />

■ 10-Year US Treasury Rate<br />

■ S&P 500 Index - Dividend Yield<br />

2.10%<br />

1.88%<br />

Sources: Bloomberg and S&P Dow Jones Indexes. The S&P 500 yields are based on the<br />

end-of-quarter price and the last 4 quarters of dividend payments. The average yields<br />

in chart above were 6.71% for 10-year Treasurys and 3.07% for the S&P 500 Index.<br />

$1.75<br />

$1.50<br />

$1.25<br />

$1.00<br />

$0.75<br />

$0.50<br />

Indexes Since March 2000<br />

$0.25<br />

3/00 3/02 3/04 3/06 3/08 3/10 3/12<br />

■ BXY - CBOE S&P 500 2% OTM BuyWrite ■ S&P 500 ■ MSCI EAFE (US$)<br />

$1.59<br />

$1.16<br />

$1.11<br />

Sources: Bloomberg and CBOE<br />

Note: Month-end for total return indexes, rescaled to 1 on March 31, 2000. (March<br />

31, 2000-July 31, 2012)<br />

www.journalofindexes.<strong>com</strong><br />

index had slightly higher returns than the S&P 500 Index<br />

over a recent two-decade period.<br />

However, with interest rates so low in 2012, some<br />

<strong>com</strong>mentators are noting that the outlook for U.S. fixedin<strong>com</strong>e<br />

investments might not be very rosy. Professor<br />

Burton Malkiel of Princeton University wrote in a March<br />

2012 op-ed piece:<br />

Bonds are the worst asset class for investors. Usually<br />

thought of as the safest of investments, they are anything<br />

but safe today. At a yield of 2.25%, the 10-year U.S.<br />

Treasury note is a sure loser. Even if the overall inflation<br />

rate is only 2.25% over the next decade, an investor<br />

who holds a 10-year Treasury until maturity will realize<br />

a zero real (after-inflation) return. … Given the likely<br />

trends, U.S. Treasurys and high quality bonds are likely<br />

to be extremely poor investments and are very risky. 1<br />

Further, an August 2012 news article noted:<br />

For many investors, the appeal of investmentgrade<br />

corporate bonds is obvious. Interest rates on<br />

Treasury bonds are even lower … But skeptics say the<br />

ravenous demand for corporate bonds has pushed<br />

yields on the securities down too far to <strong>com</strong>pensate<br />

investors for their risk. … When interest rates eventually<br />

rise, prices of recently <strong>issue</strong>d corporate bonds<br />

will fall. “The guy buying a [new] bond today is a guy<br />

buying a certain loss,” says Anders Maxwell, a managing<br />

director at investment bank Peter J. Solomon<br />

Co. “Rates have to go higher, and when they do these<br />

low-coupon bonds will drop precipitously in value.” 2<br />

The average yields in Figure 1 were 6.71 percent for 10-year<br />

Treasurys, and 3.07 percent dividend yield for the S&P 500.<br />

Key interest rates in the Wall Street Journal on August<br />

20, 2012, included:<br />

• 0.11 percent: 4-week U.S. Treasury bills<br />

• 0.44 percent: 3-month Libor<br />

• 1.41 percent: 5-year CD<br />

• 1.82 percent: 10-year U.S. Treasury notes<br />

• 1.95 percent: Barclays Capital Aggregate<br />

• 5.93 percent: Merrill Lynch High-Yield 100<br />

In light of the low yields and higher risks for many<br />

“traditional” fixed-in<strong>com</strong>e instruments, many investors<br />

now are exploring a variety of strategies, including the<br />

writing of index options, with the goals of enhancing<br />

yields and lowering portfolio volatility. This subject will<br />

be discussed later in this paper.<br />

Challenge No. 2: Sluggish<br />

Equity Returns Since Year 2000<br />

Since the bursting of the tech bubble in the year 2000,<br />

many major stock market indexes for developed countries<br />

have had sluggish returns, as stocks faced crises such as<br />

terrorist attacks and the 2008 financial crisis.<br />

In the period from March 31, 2000 through July 31,<br />

2012, the S&P 500 Index was up 16 percent and the MSCI<br />

EAFE Index was up 11 percent, while the CBOE S&P 500<br />

2 percent OTM BuyWrite Index (BXY) was up 59 percent,<br />

November / December 2012 21


Figure 3<br />

Net New Cash Flow For Mutual Funds<br />

$119 Billion Net New Cash Flow For Bond Mutual Funds In 2011<br />

$500<br />

$400<br />

$300<br />

$200<br />

$100<br />

$0<br />

-$100<br />

-$200<br />

-$300<br />

$109 $96<br />

-$238<br />

2007 2008 2009 2010 2011<br />

Net New Cash Flow For Bond And Equity Mutual Funds In $Billions<br />

Sources: ICI and SIFMA<br />

Figure 4<br />

1<br />

0.5<br />

0<br />

-0.5<br />

-1<br />

1/71<br />

$33<br />

■ Bond Mutual Funds<br />

$375<br />

$245<br />

-$11 -$24<br />

■ Equity Mutual Funds<br />

Rolling One-Year Correlations<br />

Of Weekly Returns To The S&P 500 Index<br />

Sources: Bloomberg and CBOE<br />

■ Russell 2000 ■ MSCI EAFE ■ S&P GSCI ■ VIX<br />

(Jan. 8, 1971 – Aug. 3, 2012)<br />

$119<br />

-$128<br />

1/81 1/91 1/01 1/11<br />

0.96<br />

0.88<br />

0.69<br />

-0.84<br />

as it collected S&P 500 index options premiums every<br />

month (see Figure 2).<br />

The sluggish stock market performance has been a key<br />

factor in the increased disillusionment of many investors<br />

toward equity investing. One clear sign of equity disenchantment<br />

has been the net new cash flows for different types of<br />

mutual funds. As shown in Figure 3, the net new cash flow<br />

for each of the past four years (2008-2011) has been negative<br />

for equity mutual funds and positive for bond mutual funds.<br />

Challenge No. 3: Higher Correlations<br />

Among Many Asset Classes<br />

Another challenge for investors in recent years is the<br />

fact that there have been higher correlations of returns<br />

among many asset classes, and this fact can make it more<br />

difficult to construct a portfolio that is well-diversified. The<br />

U.S. pension law known as ERISA requires fiduciaries to<br />

diversify “the investments of the plan so as to minimize<br />

the risk of large losses, unless under the circumstances it is<br />

clearly prudent not to do so.” 3<br />

Figure 4 shows that the rolling one-year correlations of<br />

weekly returns have risen for some asset classes:<br />

(1) The correlation between the S&P 500 and the S&P<br />

GSCI (<strong>com</strong>modity) Index rose from negative 0.21 on<br />

Jan. 8, 1971, to negative 0.02 on Jan. 8, 1993, and then<br />

to 0.69 on Aug. 3, 2012.<br />

(2) The correlation between the S&P 500 and the MSCI<br />

EAFE Index rose from negative 0.13 on Sept. 14, 1979,<br />

to negative 0.02 on Jan. 8, 1993, to 0.88 on Aug. 3, 2012.<br />

(3) The correlation between the S&P 500 and the<br />

Russell 2000 Index rose from 0.52 on April 7, 2000, to<br />

0.96 on Aug. 3, 2012.<br />

While the correlations among many “traditional”<br />

assets have risen over the past decade, the correlation<br />

between the S&P 500 and the CBOE Volatility Index (VIX)<br />

fell from negative 0.60 on Jan. 11, 1991, to negative 0.84<br />

on Aug. 3, 2012. In recent years, some investors have<br />

explored the possibility of using volatility as an asset<br />

class, and using options and VIX-based products for purposes<br />

of diversifying portfolios.<br />

Challenge No. 4: Less Liquidity<br />

During Stressful Market Periods<br />

During the 2008 financial crisis, many investors became more<br />

concerned about the liquidity of their investment holdings.<br />

A GAO report on the 2008-2009 financial crisis noted:<br />

Some plan representatives described significant<br />

difficulties in hedge fund and private equity investing<br />

Figure 5<br />

CBOE’s Options-Based Benchmarks<br />

Index<br />

Ticker<br />

Hold Stocks<br />

Or Cash<br />

S&P 500 1-Month<br />

Options Sold<br />

S&P 500 3-Month<br />

Options Bought<br />

Price History<br />

Begins<br />

CBOE S&P 500<br />

At-the-money<br />

BuyWrite Index BXM Hold S&P 500 stocks “covered” call options None June 30, 1986<br />

CBOE S&P 500 2% OTM<br />

2% out-of-the-money<br />

BuyWrite Index BXY Hold S&P 500 stocks “covered” call options None June 1, 1988<br />

CBOE S&P 500<br />

At-the-money “cash-secured”<br />

PutWrite Index PUT Hold U.S. Treasury bills put options None June 30, 1986<br />

CBOE S&P 500 95-110 OTM call options at 110% OTM call options at 110% OTM put options<br />

Collar Index CLL of the S&P 500 value of the S&P 500 value at 95% of S&P 500 value June 30, 1986<br />

Source: CBOE<br />

22<br />

November / December 2012


Figure 6<br />

$15<br />

$12<br />

$9<br />

$6<br />

$3<br />

5 Indexes Since Mid-1986<br />

(June 30, 1986 - July 31, 2012)<br />

$-<br />

6/86 6/94 6/02 6/10<br />

■ PUT - CBOE S&P 500 PutWrite ■ S&P 500<br />

■ BXM - CBOE S&P 500 BuyWrite ■ CLL - CBOE S&P 500 95-110 Collar<br />

■ MSCI EAFE (US$)<br />

Sources: Bloomberg and CBOE<br />

Total Return Indexes Re-scaled To $1 On June 30, 1986<br />

$13.40<br />

$10.03<br />

$9.86<br />

$4.90<br />

$4.63<br />

related to limited liquidity and transparency, and<br />

the negative impact of the actions of other investors<br />

in the fund—sometimes referred to as co-investors.<br />

For example, representatives from one plan reported<br />

they were unable to cash out of their hedge fund<br />

investments due to discretionary withdrawal restrictions<br />

imposed by the fund manager, requiring them<br />

to sell some of their stock holdings at a severe loss in<br />

order to pay plan benefits. 4<br />

In addition, Daniel Wallick, principal, Vanguard<br />

Investment Strategy Group, was quoted:<br />

Liquidity was largely ignored before the 2008 crisis.<br />

A lot of endowments had liquidity problems because<br />

their portfolios were heavily weighted to private equity<br />

and capital calls had to be met. The situation back<br />

then has made a lot of endowment CIOs consider the<br />

impact of alternatives on their portfolios now. 5<br />

Index Option Writing And Benchmark Indexes<br />

In light of the four challenges listed above, in the past<br />

decade, many investors have explored the potential of<br />

using relatively liquid index option-writing strategies in<br />

order to add gross in<strong>com</strong>e, and to enhance risk-adjusted<br />

returns for their portfolios.<br />

In the years 2000 and 2001, some options-based<br />

investment managers requested that the Chicago Board<br />

Options Exchange (CBOE) create some options-based<br />

benchmark indexes, and CBOE did so in subsequent<br />

years. The remainder of this paper will analyze the performance<br />

of four benchmark indexes that sell one-month,<br />

cash-settled S&P 500 (SPX) Index options on the third<br />

Friday of every month 6 (see Figure 5).<br />

Certain options-based portfolio managers have said that<br />

a long-term aspirational goal of the buy-write strategy is to<br />

achieve higher “stocklike” returns and lower “bondlike”<br />

volatility. Does past performance of options-based benchmark<br />

indexes suggest this goal has been achieved at times?<br />

In the time period from June 30, 1986 through July<br />

31, 2012, the PUT Index rose 1,240 percent, the S&P<br />

and BXM indexes both rose about 900 percent, and the<br />

CLL and MSCI EAFE (in $US) indexes both rose more<br />

than 360 percent (see Figure 6). The BXY Index is not<br />

included in Figure 6 because its backtested price history<br />

begins in mid-1988.<br />

As shown in Figures 7 and 8, over a recent 20-year<br />

period, (1) the PUT and BXY indexes both had higher<br />

returns and lower volatility than the S&P 500, MSCI<br />

EAFE and S&P GSCI indexes; and (2) three optionsbased<br />

indexes (PUT, BXM and CLL) had less volatility<br />

than the Treasury bond index and the two stock indexes.<br />

Figure 9 shows the risk-adjusted returns and other<br />

statistics for the four options-based indexes highlighted<br />

in this article as <strong>com</strong>pared with other widely<br />

referenced benchmarks.<br />

In Figure 10, the three options-writing indexes (PUT,<br />

BXY and BXM) all are favorably positioned to the northwest,<br />

with higher returns and lower volatility than the<br />

MSCI World and S&P GSCI indexes. Two questions that<br />

have been asked are as follows:<br />

(1) If the PUT and BXM indexes both have had about<br />

70 percent of the volatility of the S&P 500 Index, and if the<br />

indexes have been fairly priced for risk and return, why<br />

haven’t the PUT and BXM indexes also had about 70 percent<br />

of the returns of the S&P 500 Index over longer time periods?<br />

Figure 7<br />

PUT - CBOE S&P 500 PutWrite Index<br />

BXY - CBOE S&P 500 2% OTM BuyWrite<br />

30 Yr T-Bond Index (Citi)<br />

S&P 500<br />

BXM - CBOE S&P 500 BuyWrite Index<br />

CLL - CBOE S&P 500 95-110 Collar<br />

Sources: Bloomberg and CBOE<br />

Figure 8<br />

Annualized Returns Over 20 Years<br />

MSCI EAFE (US$)<br />

S&P GSCI<br />

(July 31, 1992 - July 31, 2012) Total Return Indexes<br />

PUT - CBOE S&P 500 PutWrite Index<br />

CLL - CBOE S&P 500 95-110 Collar<br />

BXM - CBOE S&P 500 BuyWrite Index<br />

30 Yr T-Bond Index (Citi)<br />

BXY - CBOE S&P 500 2% OTM BuyWrite<br />

Sources: Bloomberg and CBOE<br />

3.5%<br />

5.5%<br />

5.5%<br />

Standard Deviations Over 20 Years<br />

S&P 500<br />

MSCI EAFE (US$)<br />

S&P GSCI<br />

10.7%<br />

10.8%<br />

11.0%<br />

12.8%<br />

12.8%<br />

(July 31, 1992 - July 31, 2012) Total Return Indexes<br />

15.1%<br />

8.3%<br />

8.2%<br />

8.2%<br />

17.1%<br />

9.7%<br />

9.4%<br />

21.7%<br />

www.journalofindexes.<strong>com</strong> November / December 2012 23


Figure 9<br />

Metrics for Returns, Risk and Risk-Adjusted Returns (June 30, 1988 - July 31, 2012)<br />

BXM BXY PUT CLL<br />

S&P<br />

500<br />

MSCI<br />

EAFE<br />

30-Year<br />

Treasury<br />

Barclays<br />

US Agg<br />

Return 9.44% 10.56% 10.82% 6.14% 9.35% 4.38% 8.86% 7.29%<br />

Standard Deviation 10.63% 12.45% 10.12% 10.77% 14.97% 17.84% 12.29% 3.81%<br />

Beta vs. Market 0.63 0.78 0.56 0.66 1.00 0.87 -0.07 0.04<br />

Skewness -1.31 -0.92 -2.00 -0.13 -0.57 -0.40 0.24 -0.25<br />

Kurtosis 4.61 2.57 9.07 -0.29 1.08 0.90 3.33 0.58<br />

Sharpe Ratio 0.57 0.58 0.72 0.27 0.43 0.13 0.46 0.93<br />

Semi Standard Deviation 0.09 0.10 0.08 0.08 0.11 0.13 0.09 0.03<br />

Sortino Ratio (MAR = Cash Eq.) 0.72 0.79 0.90 0.36 0.59 0.15 0.69 1.38<br />

Jensen’s Alpha vs. S&P 0.02 0.02 0.04 -0.01 0.00 -0.03 0.06 0.03<br />

Source: Ibbotson & Associates<br />

Note: The BXM, BXY and PUT indexes had risk-adjusted performance that was superior to that of the S&P 500 per metrics such as the Sortino ratio, Sharpe ratio and Jensen’s<br />

alpha. Most of the above indexes had negative skewness, and the measures of risk-adjusted returns are imperfect when measuring non-normal distributions.<br />

(2) Why were the risk-adjusted returns of the CLL (collar)<br />

index so much weaker than the other three optionsbased<br />

indexes (PUT, BXM and BXY)?<br />

Source Of Strong Risk-Adjusted Returns:<br />

S&P 500 Options Have Been Richly Priced<br />

One answer to the two questions posed above is that<br />

one can look at Figure 11 and the <strong>com</strong>parison of implied<br />

500 95-110 Collar Index (CLL) has not had relatively strong<br />

risk-adjusted returns because of the fact that the CLL<br />

Index is both a buyer of (richly priced) put options and a<br />

seller of (richly priced) call options.<br />

As interest rates for U.S. fixed in<strong>com</strong>e have declined,<br />

investors continue to search for instruments with higher<br />

yields. A yield-based strategy that has grown in popularity<br />

over the past decade is the “buy-write” strategy,<br />

A yield-based strategy that has grown in popularity<br />

over the past decade is the “buy-write” strategy,<br />

in which an investor could buy securities and write<br />

(or sell) options on those strategies.<br />

and realized volatility. If the markets were very efficiently<br />

priced, one could expect that there would be little to no<br />

difference between the implied and realized volatility over<br />

the long term. However, the average difference in Figure<br />

11 was about 3.8 volatility points, a substantial number.<br />

Several other studies 7 also have found significant differences<br />

between implied and realized volatility.<br />

Why has there been a difference between implied and<br />

realized volatility? Professor Robert Whaley wrote:<br />

… there is excess buying pressure on S&P 500 index<br />

puts by portfolio insurers. Since there are no natural<br />

counterparties to these trades, market makers must step<br />

in to absorb the imbalance. … implied volatility will<br />

rise relative to actual return volatility. … The implied<br />

volatilities of the corresponding calls also rise from the<br />

reverse conversion arbitrage supporting put-call parity. 8<br />

While the three indexes that incorporate option selling<br />

(PUT, BXM and BXY) into their methodologies appear to<br />

have benefited from the fact that implied volatility usually<br />

has been higher than realized volatility, the CBOE S&P<br />

in which an investor could buy securities and write (or<br />

sell) options on those securities. The most well-known<br />

benchmark for the buy-write strategy—the CBOE S&P<br />

Figure 10<br />

Annualized Returns<br />

12%<br />

10%<br />

8%<br />

6%<br />

4%<br />

Returns And Volatility Over 20 Years<br />

(July 31, 1992 - July 31, 2012)<br />

10-Yr Treasury<br />

1-Yr Treasury<br />

PUT<br />

BXM<br />

CLL<br />

BXY<br />

30-Yr<br />

Treasury<br />

S&P GSCI<br />

2%<br />

0% 5% 10% 15% 20% 25%<br />

Standard Deviation<br />

S&P 500<br />

Sources: CBOE, Bloomberg, Citigroup Fixed In<strong>com</strong>e Indexes<br />

MSCI World<br />

Russell 2000<br />

24<br />

November / December 2012


500 BuyWrite Index (BXM)—was announced in 2002.<br />

The BXM Index assumes that slightly out-of-the-money<br />

S&P 500 options are written on the third Friday of every<br />

month, and the average gross monthly yield for the BXM<br />

Index (using the premium received as a percentage of<br />

the underlying) has been about 1.8 percent. Figure 12<br />

shows the rolling 12-month gross premiums for the BXM<br />

Index, and the rolling 12-month net returns for both the<br />

BXM Index and S&P 500 Index. In Figure 12, note that<br />

the BXM net return has been pretty close to the BXM<br />

gross premiums in times of steadily rising stock markets.<br />

However, in years in which the S&P 500 has experienced<br />

big drops (e.g., 2001 and 2008), the BXM net return was<br />

generally above that of the S&P 500, and far below the<br />

BXM gross premiums as a percentage of the underlying.<br />

Managing Of Left-Tail Risk And ‘Black Swan’ Events<br />

After witnessing two big declines in stock index values<br />

since the year 2000 (see Figure 2), many investors in<br />

recent years have heightened concerns about left-tail risk<br />

and catastrophic “Black Swan” events that could harm<br />

Figure 11<br />

Diference in Volatility Points<br />

Figure 12<br />

80%<br />

60%<br />

40%<br />

20%<br />

0%<br />

-20%<br />

-40%<br />

10<br />

5<br />

0<br />

-5<br />

-10<br />

Implied Volatility (VIX) Minus Subsequent<br />

SPX Realized Volatility<br />

(SPX Options Usually Have Been Richly Priced)<br />

-15<br />

6/90 6/93 6/96 6/99 6/02 6/05 6/08 6/11<br />

(June 8, 1990 - July 20, 2012) Rolling 6-month average of the spread between<br />

end-of-week VIX and the subsequent 30-day SPX realized volatility<br />

Sources: Bloomberg and CBOE<br />

Rolling 12-Month Gross Premiums<br />

And Net Returns<br />

-60%<br />

6/89 6/92 6/95 6/98 6/01 6/04 6/07 6/10<br />

BXM Gross Premiums ■ BXM Net Return ■ S&P 500 Net Return<br />

(June 1989 – July 2012)<br />

Sources: Bloomberg and CBOE<br />

Note: BXM gross premiums are received every month and are expressed as annualized<br />

% of the underlying S&P 500 Index. Net returns are for total return indexes.<br />

Figure 13<br />

160<br />

120<br />

80<br />

40<br />

0<br />

-24% to<br />

-20%<br />

Tail Risk And Frequency Of Monthly Returns<br />

(July 1986 - July 2012)<br />

40<br />

2624<br />

10<br />

11<br />

1 0 1 0 1 0 1<br />

2 1 0<br />

-20% to<br />

-16%<br />

-16% to<br />

-12%<br />

-12% to<br />

-8%<br />

■ S&P 500<br />

-8% to<br />

-4%<br />

105<br />

-4% to<br />

0%<br />

140<br />

127<br />

0% to<br />

4%<br />

■ CLL Index (collar)<br />

4% to<br />

8%<br />

8% to<br />

12%<br />

12% to<br />

16%<br />

During the 26-year time period, the S&P 500 Index had 13 months with returns<br />

worse than -8%, while the CLL Index had only one such month.<br />

Sources: Bloomberg and CBOE<br />

their investment portfolios. An options strategy that could<br />

be explored by risk-averse investors is the “collar” strategy,<br />

in which one purchases stock, buys protective put<br />

options for downside protection and sells covered calls<br />

for in<strong>com</strong>e that can help pay for the put premiums. A key<br />

benchmark for the collar strategy is the CBOE S&P 500<br />

95-110 Collar Index (CLL), which buys S&P 500 puts for<br />

downside protection, and sells S&P 500 calls for in<strong>com</strong>e.<br />

Note that in Figure 13, the CLL Index was able to lessen<br />

much of the left-tail risk. During the 26-year time period,<br />

the S&P 500 Index had 13 months with returns worse than<br />

-8 percent, while the CLL Index had only one month with<br />

a return worse than -8 percent.<br />

Correlations And Diversification<br />

Over the past decade, many investors have be<strong>com</strong>e<br />

more concerned about challenges to diversification, as<br />

there have been increasingly higher correlations among<br />

“traditional” investments. As shown earlier in this paper, in<br />

Figure 4, the correlations of weekly returns versus the S&P<br />

500 for various indexes were:<br />

• 0.96 for the Russell 2000<br />

• 0.88 for the MSCI EAFE<br />

• -0.84 for the CBOE VIX Index<br />

Figure 14 shows correlations among various indexes. The<br />

BXM, BXY, PUT and CLL all hold the S&P 500 stock basket,<br />

and so it is not surprising that all four of those indexes had correlations<br />

of 0.89 or higher versus the S&P 500 Index during the<br />

time period. The BXY Index writes out-of-the money (OTM)<br />

S&P 500 options, and so it can rise and fall with the S&P 500<br />

Index (up to 2 percent up during the monthly time period). Not<br />

surprisingly, the correlation of the BXY and S&P 500 was 0.95.<br />

The CBOE S&P 500 95-110 Collar Index (CLL) buys S&P<br />

500 protective puts for protection. Even though the CLL<br />

Index and S&P 500 Index had a 0.89 correlation in Figure<br />

14, there also is evidence (in Figure 13) to suggest that the<br />

CLL Index can help provide some protection when the<br />

stock markets suffer big drawdowns.<br />

74<br />

60<br />

www.journalofindexes.<strong>com</strong><br />

November / December 2012<br />

25


• -0.70 for the S&P 500 VIX Mid-term Futures Index<br />

(SPVIXMTR)<br />

• -0.63 for the S&P 500 Dynamic VIX Futures Index<br />

(SPDVIXT)<br />

• -0.60 for the S&P 500 VIX Futures Tail Risk Index – Short<br />

Term (SPVXTRST)<br />

Some investors who are exploring the possibility of using index options<br />

have asked the question, Do the index options markets have<br />

the capacity and liquidity to handle large influxes of new institutional<br />

funds that will be dedicated to options-based strategies?<br />

While the negative correlation of the VIX to many traditional<br />

investments can make the VIX appear to be attractive,<br />

the VIX is an index that is considered not investable.<br />

Exhibit K of a paper by Asset Consulting Group, “Key Tools<br />

for Hedging and Tail Risk Management” (February 2012)<br />

showed the correlations of weekly returns of various indexes<br />

in the period from April 7, 2006 through Feb. 3, 2012.<br />

Listed below are the negative correlations versus the S&P<br />

500 Index for three indexes that use VIX futures and are<br />

designed to reflect approximate investable performance<br />

(however, no index is considered 100 percent investable<br />

with no tracking error or transaction costs). 9<br />

Figure 14<br />

Figure 15<br />

Notional value, in $millions<br />

S&P 500 1.00<br />

Sources: Bloomberg and CBOE<br />

Notional Value Of Average Daily S&P 500 (SPX)<br />

Options Volume, With 0.5 Delta Adjustment<br />

$80,000<br />

$60,000<br />

$40,000<br />

$20,000<br />

Correlations Of Weekly Returns<br />

(June 30, 2006 – August 24, 2012)<br />

BXM 0.92 1.00<br />

S&P<br />

500 BXM BXY PUT CLL VIX S&P 500<br />

VIX Mid-Term<br />

Futures Index<br />

BXY 0.95 0.99 1.00<br />

PUT 0.90 0.98 0.97 1.00<br />

CLL 0.89 0.73 0.81 0.71 1.00<br />

VIX -0.72 -0.67 -0.71 -0.66 -0.69 1.00<br />

S&P 500 VIX<br />

Mid-Term -0.70 -0.62 -0.66 -0.63 -0.67 0.74 1.00<br />

Futures Index<br />

$0<br />

1/01 1/03 1/05 1/07 1/09 1/11<br />

Source: CBOE (unofficial estimates)<br />

Note: The notional volume estimate for June 2012 was $51 billion, and was calculated<br />

by multiplying four numbers: 750,652 (avg. daily contract volume); 1362.16 (S&P 500<br />

end-of-month value); $100 (options multiplier); and 0.5<br />

Capacity And Liquidity<br />

Some investors who are exploring the possibility of<br />

using index options have asked the question, Do the<br />

index options markets have the capacity and liquidity<br />

to handle large influxes of new institutional funds<br />

that will be dedicated to options-based strategies? The<br />

listed options markets now offer options on thousands<br />

of securities, and the liquidity and capacity of options<br />

markets on different securities can vary significantly.<br />

The market for options on the S&P 500 Index is the largest<br />

listed options market in the U.S. in terms of notional<br />

size. Figure 15 provides estimates for the notional value<br />

of the average daily SPX options volume, and it uses a 0.5<br />

delta adjustment to make the figure more conservative<br />

and realistic as <strong>com</strong>pared with notional value statistics<br />

that do not use any delta adjustment. The delta-adjusted<br />

notional volume estimate for the average daily volume<br />

in June 2012 was about $51 billion. The liquidity and<br />

flexibility offered by S&P 500 stocks and S&P 500 options<br />

usually have <strong>com</strong>pared favorably <strong>com</strong>pared with investments<br />

such as private equity. Investors should do their<br />

own due diligence and discuss with their brokers <strong>issue</strong>s<br />

regarding capacity and liquidity for any security.<br />

Conclusion<br />

Since 1983, index options have been used by institutional<br />

and individual investors for a variety of purposes,<br />

including managing risk and generating in<strong>com</strong>e<br />

with the goal of boosting risk-adjusted returns. Since<br />

2002, investors have had options-based benchmark<br />

indexes with which to measure the advantages and<br />

disadvantages of key options-based strategies in bullish<br />

and bearish markets.<br />

This paper highlights some of the key features of the<br />

discussed indexes:<br />

Total Growth. Total growth for the following indexes<br />

from June 30, 1986 through July 31, 2012 was 1,240 percent<br />

(10.46 percent annualized) for the PUT Index, 886 percent<br />

(9.2 percent annualized) for the BXM Index, 903 percent<br />

(9.2 percent annualized) for S&P 500 Index, and 390 percent<br />

(6.3 percent annualized) for the CLL Index (Figure 6).<br />

Lower Volatility. The PUT, BXM and CLL indexes all had<br />

26<br />

November / December 2012


volatility that was about 30 percent lower than the volatility<br />

of the S&P 500 Index (Figure 8).<br />

Left-Tail Risk. The worst monthly losses over the<br />

26-year time period for the CLL and S&P 500 indexes<br />

were negative 8.6 percent for the CLL Index versus<br />

negative 21.5 percent for the S&P 500 Index (Figure 13).<br />

Monthly Premium In<strong>com</strong>e. The average for the gross monthly<br />

premiums collected by the BXM Index was 1.8 percent and<br />

the index options usually were richly priced (Figures 11 and 12).<br />

Liquidity. The utilization of S&P 500 stocks and S&P 500<br />

index options can provide liquidity and capacity for those<br />

investors who prefer flexible access to their capital (Figure 14).<br />

References<br />

Asset Consulting Group. “An Analysis of Index Option Writing for Liquid Enhanced Risk-Adjusted Returns,” (January 2012). Available at http://bit.ly/ACG-op-sell<br />

Asset Consulting Group. “Key Tools for Hedging and Tail Risk Management,” (February 2012). Available at http://bit.ly/TailRskACG.<br />

Callan Associates. “An Historical Evaluation of the CBOE S&P 500 BuyWrite Index Strategy,” (October 2006). Available at http://bit.ly/BXM-Call<br />

Cambridge Associates, LLC. “Highlights from the Benefits of Selling Volatility,” (2011). Available at http://bit.ly/CambridgeA-Selling<br />

Chapman, Peter. “Pensions Eye Buy-Writes.” Traders Magazine (December 2011).<br />

Demos, Telis. “In<strong>com</strong>e-generating Funds Find Favour,” Financial Times (January 9, 2012).<br />

EnnisKnupp & Associates. “Evaluating the Performance Characteristics of the CBOE S&P 500 PutWrite Index” (December 2008). Available at http://bit.ly/PUT-Enn.<br />

Feldman, Barry et al. “Passive Options-Based Investment Strategies: The Case of the CBOE S&P 500 BuyWrite Index,” The Journal of Investing (Summer 2005).<br />

Fund Evaluation Group. “Evaluation of BuyWrite and Volatility Indexes: Using the CBOE DJIA BuyWrite Index (BXD) and the CBOE DJIA Volatility Index (VXD) for Asset<br />

Allocation and Diversification Purposes,” (2007). Available at http://bit.ly/BXD-FEG.<br />

Hewitt EnnisKnupp. “The CBOE S&P 500 BuyWrite Index (BXM) – A Review of Performance (2012),” Available at http://bit.ly/HewittEK-BXM<br />

Hill, Joanne et al. “Finding Alpha via Covered Index 2006), Financial Analysts Journal (September.-October2006), pp. 29-46.<br />

Hough, Jack. “Options for Nervous Investors.” Wall Street Journal (Dec. 10, 2011).<br />

Moran, Matthew. “Record-High Correlations Pose Challenges For Modern Portfolio Theory,” The Journal of Indexes (January-February 2010).<br />

———. “Risk-adjusted Performance for Derivatives-based Indexes – Tools to Help Stabilize Returns.” The Journal of Indexes (Fourth Quarter, 2002).<br />

Russell Investments. “Capturing the Volatility Premium through Call Overwriting,” (July 2012). Available at http://bit.ly/Russell-Buy-Write<br />

Schneeweis, Thomas et al. “The Benefits of Index Option-Based Strategies for Institutional Portfolios,” The Journal of Alternative Investments (Spring 2001), pp. 44-52.<br />

Sears, Steven. “Buy-Write Is the Right Buy,” Barron’s (Dec. 31, 2011).<br />

Szado, Edward. “VIX Futures and Options: A Case Study of Portfolio Diversification During the 2008 Financial Crisis.” The Journal of Alternative Investments (Fall 2009), pp. 68-85.<br />

Ungar, Jason et al. “The Cash-secured PutWrite Strategy and Performance of Related Benchmark Indexes.” The Journal of Alternative Investments (Spring 2009).<br />

Whaley, Robert. “Risk and Return of the CBOE BuyWrite Monthly Index” The Journal of Derivatives (Winter 2002), pp. 35-42.<br />

Disclosures<br />

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 (the<br />

“ODD”). The ODD and supporting documentation for claims, <strong>com</strong>parisons, statistics or other technical data in this article are available by calling 1-888-OPTIONS, contacting CBOE at<br />

www.cboe.<strong>com</strong>/Contact, or by visiting www.cboe.<strong>com</strong>. The CBOE S&P 500 BuyWrite Index (BXMSM), BXY Index, CLL Index and PUT Index (the “Indexes”) are designed to represent<br />

proposed hypothetical strategies. Like many passive benchmarks, the Indexes do not take into account significant factors such as transaction costs and taxes. Transaction costs and taxes<br />

for an option-based strategy could be significantly higher than transaction costs for a passive strategy of buying-and-holding stocks. Investors attempting to replicate the Indexes should<br />

discuss with their brokers possible timing and liquidity <strong>issue</strong>s. Past performance does not guarantee future results. Investors should consult their tax advisor as to how taxes affect the<br />

out<strong>com</strong>e of contemplated options transactions. The information in this article is not intended and should not be construed to constitute investment advice or re<strong>com</strong>mendations to purchase<br />

or sell securities. This material has been prepared for informational purposes only, and is not intended to provide, and should not be relied on for accounting, legal or tax advice.<br />

Endnotes<br />

1 Malkiel, Burton. “What Does the Prudent Investor Do Now?” Wall Street Journal (March 22, 2012).<br />

2 Wirz, Matt. “As Corporate-Bond Yields Sink, Risks for Investors Rise.” www.wsj.<strong>com</strong> (Aug. 14, 2012).<br />

3 29 U.S. Code Section 1104.<br />

4 Highlights of GAO-12-324 “Recent Developments Highlight Challenges of Hedge Fund and Private Equity Investing,” (February 2012) (available at http://www.gao.gov<br />

assets/590/588624.pdf).<br />

5 Williamson, Christine. “Endowment Execs Focus on Liquidity, Volatility in Post-crisis Market,” Pensions & Investments Online (March 7, 2011).<br />

6 Please see www.cboe.<strong>com</strong>/benchmarks for more details and studies on the BXM, BXY, CLL and PUT indexes.<br />

7<br />

See, e.g., Asset Consulting Group. “An Analysis of Index Option Writing for Liquid Enhanced Risk-Adjusted Returns,” (January 2012); Callan Associates. “An Historical<br />

Evaluation of the CBOE S&P 500 BuyWrite Index Strategy,” (October 2006); Feldman et al. “Passive Options-Based Investment Strategies: The Case of the CBOE S&P 500<br />

BuyWrite Index,” The Journal of Investing, (Summer 2005); Hewitt EnnisKnupp. “The CBOE S&P 500 BuyWrite Index (BXM) – A Review of Performance,” (2012); Hill, Joanne<br />

et al. “Finding Alpha via Covered Index Writing,” Financial Analysts Journal, (September-October 2006), pp. 29-46; Russell Investments. “Capturing the Volatility Premium<br />

through Call Overwriting,” (July 2012); Schneeweis et al. “The Benefits of Index Option-Based Strategies for Institutional Portfolios,” The Journal of Alternative Investments,<br />

(Spring 2001), pp. 44-52.<br />

8 Whaley, Robert. “Risk and Return of the CBOE BuyWrite Monthly Index,” The Journal of Derivatives (Winter 2002) pp. 35-42.<br />

9 See the February 2012 paper by Asset Consulting Group for more details about the indexes and tail-risk management.<br />

www.journalofindexes.<strong>com</strong><br />

November / December 2012<br />

27


Creating A Vertical<br />

Spread Index<br />

Benchmarking an options overlay strategy<br />

By Mark Abssy<br />

28<br />

November / December 2012


Most investors are familiar with options as powerful<br />

hedging tools suitable for protecting equity<br />

or exchange-traded fund positions. Others have<br />

explored options further by enhancing their positions<br />

through call and put writing. Regardless of the approach,<br />

another attribute of options is that they allow investors<br />

to benefit from the power of leverage, providing notional<br />

exposure to an underlying for a fraction of the actual cost.<br />

Before fully exploring the dynamics of this exposure, let us<br />

review some important elements of options trading. A <strong>com</strong>mon<br />

misconception is that the price change, known as delta,<br />

of a call relative to the underlying stock happens at a 1-1 ratio<br />

(delta = 1) for all underlying prices above the call’s strike.<br />

This is only the case very near to or at expiration or at strike<br />

prices at or further out than expected volatility. The reality<br />

is that with more time to expiration, underlying prices have<br />

a greater chance of deviating from their current price levels.<br />

Accordingly, the delta of the at-the-money (ATM) contract is<br />

0.5 (a 0.5-1 ratio price move as <strong>com</strong>pared with the underlying)<br />

and why any ATM trade is also referred to as the “50 delta<br />

trade.” At a high level, the <strong>com</strong>bination of the option contract<br />

moving deeper in the money and closer to expiration <strong>com</strong>es<br />

together to raise the contract’s delta toward 1. Consider<br />

Figures 1 and 2 (data provided as of authoring of article).<br />

The only major difference between these two tables is<br />

the time to expiration. Figure 1 shows contracts 45 days<br />

from expiration, while Figure 2 shows contracts 10 days<br />

from expiration. What is interesting is that while the delta<br />

of the ATM strike is roughly the same for both expirations,<br />

the 145 strike delta is almost five times as much for the<br />

45-day expiration as it is for the 10-day expiration. Also, it<br />

seems like the market is convinced that underlying prices<br />

equal to or less than 130 are not expected to be realized<br />

regardless of time frame.<br />

Figure 1<br />

SPY Calls, 45 Days To Expiration (SPY@ $140)<br />

Strike Price<br />

Source: Bloomberg<br />

Figure 2<br />

SPY Calls, 10 Days To Expiration (SPY@ $140)<br />

Strike Price<br />

Source: Bloomberg<br />

Call Price<br />

Call Price<br />

Delta<br />

145 $0.82 0.24<br />

140 $3.02 0.54<br />

136 $5.82 0.73<br />

130 $11.00 1.00<br />

Delta<br />

145 $0.06 0.05<br />

140 $1.42 0.57<br />

136 $4.63 0.91<br />

130 $10.48 1.00<br />

In the above example, we see how delta represents<br />

the sensitivity of changes in an options contract’s price<br />

to changes in the price of the underlying. Three other<br />

options Greeks—vega, theta and rho—represent options<br />

price sensitivity to a 1 percent change in volatility,<br />

change in time to expiration and a 1 percent change in<br />

interest rates, respectively. The fifth and final Greek is<br />

gamma, which represents the rate of change of delta<br />

as underlying prices move. Out-of-the-money (OTM)<br />

contracts tend to have low delta and high gamma, while<br />

ATM and in-the-money (ITM) contracts have increasingly<br />

higher delta and lower gamma. Delta moving 0.05<br />

from 0.05 to 0.10 equates to gamma of 1 (“100 gamma”),<br />

whereas delta moving 0.05 from 0.5 to 0.55 equates to<br />

gamma of 0.1 (“10 gamma”).<br />

How can the Greeks assist in explaining the observations<br />

made earlier in the <strong>com</strong>parison of Figures 1 and<br />

2? Without devolving into yet another treatise on the<br />

Black-Scholes options pricing model, suffice it to say<br />

that while the price of the underlying, volatility of price<br />

of the underlying, time-to-expiration and prevailing<br />

interest rate all contribute to options contract price discovery,<br />

the volatility of underlying price changes drives<br />

this equation. Current observed, or historical, 10- and<br />

45-day volatilities of SPY are each approximately 15<br />

percent, which means that over any given 10- or 45-day<br />

period in the past year, investors could reasonably<br />

expect (68 percent of the time given a normal distribution)<br />

a 15 percent movement around any given price of<br />

SPY. In considering just the left tail of this distribution,<br />

this translates to an expected 7.70 percent move, or in<br />

this case, about $10 given the current $140 SPY price.<br />

This explains the 100 delta for contracts at or below<br />

the 130 strike regardless of time-to-expiration. Given<br />

that both observed volatilities in prices of SPY are held<br />

constant when considering any of the contracts in question,<br />

as are interest rates, the only variable left is timeto-expiration.<br />

That there is more time-to-expiration<br />

explains why the delta of the August 145 call is 0.05 while<br />

the delta of the September 145 call is 0.24: There is simply<br />

more time for the unknown to be<strong>com</strong>e known.<br />

Consider a 100-share position of SPY at $140 per share.<br />

This position has a notional value of $14,000. What if<br />

an investor didn’t have or didn’t want to tie up $14,000<br />

but still wanted exposure to the price movement of 100<br />

shares of SPY? To participate in the upside of this trade for<br />

approximately the next month would require getting exposure<br />

to the ATM SPY call expiring as close to one month<br />

from now as possible. The call in question would be the<br />

SPY 140 September call. This contract is priced at $2.93<br />

per share and, with option contracts almost always representing<br />

notional exposure of 100 shares per contract, the<br />

contract would cost $293. As the 140 strike call has a delta<br />

of roughly 0.5, investors would expect to observe roughly<br />

half of the price movement of the underlying reflected in<br />

the price of the option. To fully replicate exposure to 100<br />

shares of SPY, an investor would have to purchase two calls<br />

in order to bring the overall position delta up to 1.<br />

November / December 2012 29


This concept is especially important when using<br />

options to hedge a position. Consider an existing position<br />

of 100 shares of SPY. The inexperienced investor<br />

seeking to fully hedge his position would purchase one<br />

ATM put and rest easy, until the anticipated downward<br />

move occurs and he is left pondering the source of his<br />

newly minted losses. It is only through hedging the<br />

portfolio on a delta basis (delta-hedging) that it will be<br />

fully protected.<br />

These positions are examples of debit strategies, or<br />

strategies that leave investors’ accounts with debits as<br />

they are paying for access to these positions. Credit<br />

strategies are those trades that produce a net credit in<br />

investors’ accounts as they involve the writing, or selling,<br />

of option contracts.<br />

Writing or selling options has been a strategy that is<br />

be<strong>com</strong>ing increasingly popular with investors as a way<br />

to enhance returns on their existing positions. However,<br />

writing options obligates the writer to deliver or take<br />

receipt of underlying shares if the contract is exercised or<br />

assigned. If options are written against existing positions<br />

(covered either by shares or cash), then the investor may<br />

be forced to either deliver his position against the open<br />

written call or receive (purchase from the holder of the<br />

put contract) shares to close out the written put. If options<br />

are written without any collateral, they are said to be<br />

“naked.” Writing naked options can quickly lead to great<br />

financial success, as the writer could end up keeping the<br />

entire collected premium as the contracts written expire<br />

worthless, or to financial ruin as the writer could be on<br />

the hook for the difference between the underlying share<br />

price less the contract strike less collected premium.<br />

Theoretically, this obligation could be infinite.<br />

Consider the ATM September 140 SPY call. If an<br />

investor were convinced that SPY was destined to trade<br />

off sharply, he might consider selling this call. If he is<br />

correct, he realizes a gain of $293 at expiration for each<br />

contract sold. If he is incorrect, he must deliver 100<br />

shares of SPY at $140 per share regardless of the prevailing<br />

price. For example, if SPY is trading at $160 at any<br />

point prior to expiration, he would have to purchase<br />

shares in the open market at $160 and deliver (sell)<br />

them to the buyer of his contract for only $140. Multiply<br />

this scenario by 10 and you can see why the possibility<br />

of earning $2,930 is quickly outweighed by the possibility<br />

of having to source $160,000 and immediately lose<br />

$17,070 ($20,000-$2,930) on the transaction. This is<br />

why most brokerage houses permission their clients’<br />

options activity in tiers, with naked option writing being<br />

one of the highest-level activities allowed.<br />

An easy way to mitigate the risk of this lopsided trade<br />

is through a spread trade. Spread trades can take any<br />

number of forms, such as vertical spreads, horizontal<br />

(calendar) spreads, back spreads or ratio spreads, to<br />

name a few. We are going to consider the vertical spread<br />

trade; specifically, the vertical bear call spread and the<br />

vertical bull put spread.<br />

The vertical bear call spread is a neutral-to-bearish<br />

multileg options strategy (Figure 3). Premium is collected<br />

through the sale of a call (obligation to deliver the underlying<br />

stock). Part of the premium received is used to buy<br />

a call (right to buy the underlying stock) at a higher strike<br />

price. Selling a call theoretically represents unlimited<br />

risk. Buying a call limits the risk to the difference between<br />

the sold and bought strike prices (strike spread) less any<br />

premium collected. For example:<br />

SLD: SPY NOV 140 CALL @ 4.20 (receive $420)<br />

BOT: SPY NOV 143 CALL @ 2.62 (pay $262)<br />

Net Credit: $420 - $262 = $158<br />

$-At-Risk:<br />

Difference in Strikes – Net Credit<br />

((143 – 140) x 100) - $158 = $142<br />

MAX Gain/Loss: Max Gain = $158 (52.67%) /<br />

Max Loss = $142 (-47.33%)<br />

Collateral for this position would generally be determined<br />

by the strike spread. Collateral of $300 would be<br />

Figure 3<br />

$6<br />

$5<br />

$4<br />

$3<br />

$2<br />

$1<br />

$1<br />

$2<br />

$3<br />

$4<br />

$5<br />

$6<br />

Figure 4<br />

$6<br />

$5<br />

$4<br />

$3<br />

$2<br />

$1<br />

$1<br />

$2<br />

$3<br />

$4<br />

$5<br />

$6<br />

Proft<br />

-<br />

-<br />

Loss<br />

Source: : ISE<br />

Proft<br />

-<br />

-<br />

Loss<br />

Source: : ISE<br />

■ Short Call<br />

■ Long Call<br />

■ Payof<br />

Max Loss = $1.93<br />

■ Short Put<br />

■ Long Put<br />

■ Payof<br />

Options – Vertical Bear Call Spread<br />

SPY NOV 140 CALL @ 4.20<br />

Max Gain = $1.58<br />

SPY NOV 143 CALL @ 2.62<br />

Outlook: Neutral to Bearish<br />

The Trade: Sell Call; Buy Call @ Higher Strike<br />

Risk: Strike Spread—Premium Received<br />

Reward: Premium Received<br />

Break Even Point: Premium Paid—Premium Received<br />

Options – Vertical Bull Put Spread<br />

Outlook: Neutral to Bullish<br />

The Trade: Sell Put; Buy Put @ Lower Strike<br />

Risk: Strike Spread—Premium Received<br />

Reward: Premium Received<br />

Break Even Point: Premium Paid—Premium Received<br />

SPY NOV 137 PUT @ 3.45<br />

SPY NOV 140 PUT @ 4.55<br />

Max Gain = $1.10<br />

Max Loss = $1.42<br />

Stock Price<br />

Stock Price<br />

30<br />

November / December 2012


equired to establish this position and is the basis for all<br />

gain/loss calculations.<br />

The vertical bull put spread is a neutral-to-bullish<br />

multileg options strategy (Figure 4). Premium is collected<br />

through the sale of a put (obligation to receive the underlying<br />

stock). Part of the premium received is used to buy<br />

a put (right to sell the underlying stock) at a lower strike<br />

price. Selling a put theoretically represents risk equal to the<br />

contract strike price. Buying a put limits the risk to the difference<br />

between the sold and bought strike prices (strike<br />

spread) less any premium collected. For example:<br />

SLD: SPY NOV 140 PUT @ 4.55 (receive $455)<br />

BOT: SPY SEP 137 PUT @ 3.45 (pay $345)<br />

Net Credit: $455 - $345 = $110<br />

$-At-Risk:<br />

Difference in Strikes – Net Credit<br />

((140 – 137) x 100) - $110 = $190<br />

MAX Gain/Loss: Max Gain = $110 (36.67%) /<br />

Max Loss = $193 (-63.33%)<br />

Collateral for this position would generally be determined<br />

by the strike spread. Collateral of $300 would be required to<br />

establish this position and is the basis for all G/L calculations.<br />

Benchmarking Options Strategies<br />

The spreads described here are typical in that this<br />

trade is usually constructed to sell the ATM and use the<br />

proceeds to purchase insurance a certain percentage<br />

away from ATM. This is a classic, risk-controlled trade.<br />

ISE took the framework of this trade and blended it with<br />

modern portfolio theory to develop a truly diversified<br />

portfolio of vertical spread trades to help transform a<br />

trading vehicle into a potential investment vehicle.<br />

Spread pairs that are very close to or ATM are more<br />

likely to have the underlying stock trade through them<br />

and incur maximum losses (high delta/low gamma).<br />

Pairs that are further away from ATM are less attractive<br />

from a net credit perspective, but require larger moves<br />

of the underlying to put the position at risk (mid delta/<br />

mid gamma). Pairs that are quite far away from ATM<br />

exhibit higher levels of price volatility (low delta/high<br />

gamma) due to the low absolute-price levels at which<br />

they trade. Being positioned too close or too far away<br />

from ATM presents unique risks for each. We identified<br />

optimal spreads from each of these categories and <strong>com</strong>bine<br />

them into a single basket. In doing so, we created<br />

the ISE SPY Bear Options Overlay Index (VCS) and the<br />

ISE SPY Bull Options Overlay Index (VPS).<br />

The ISE Options Overlay indexes provide benchmarks<br />

for investors looking to track the performance of a<br />

diversified portfolio of exchange-listed options utilizing<br />

the vertical spread strategy. As indicated by the name,<br />

each index solely includes option contracts on the SPDR<br />

S&P 500 exchange-traded fund (NYSE Arca: SPY).<br />

Figure 5<br />

Vertical Put Spread Loss-Floor Example<br />

SPDR S&P 500ETF<br />

128<br />

126<br />

124<br />

122<br />

120<br />

118<br />

116<br />

114<br />

112<br />

110<br />

Nov 10<br />

2011<br />

Expiration Date<br />

Nov 17<br />

2011<br />

Nov 24<br />

2011<br />

Dec 1<br />

2011<br />

Dec 8<br />

2011<br />

Expiration Date<br />

Dec 15<br />

2011<br />

Dec 22<br />

2011<br />

Dec 29<br />

2011<br />

260<br />

255<br />

250<br />

245<br />

240<br />

235<br />

230<br />

225<br />

220<br />

215<br />

210<br />

ISE Bull Options Overlay Index<br />

■ SPY<br />

■ VPS High-Low-Close<br />

Source: Bloomberg<br />

Figure 6<br />

Trading Trade Date<br />

Symbol<br />

One Day SPY Price Move<br />

# 3-Month Of Spread Average Pair Components<br />

Daily Volume<br />

# Of Spread Pair Components<br />

Allocated To Cash Proxy<br />

11/18/2011 - Index Rebalance Date -1.90% 50 0<br />

Source: Livevol<br />

Loss-Floor Mechanism<br />

11/22/2011 -0.39% 47 3<br />

11/23/2011 -2.21% 20 27<br />

11/25/2011 -0.19% 14 6<br />

12/16/2011 – Final Day of Period 0.14% 14 0<br />

www.journalofindexes.<strong>com</strong> November / December 31


The following steps are taken to select spread pair <strong>com</strong>ponents<br />

for each index:<br />

i) Establish total population of available SPY option contracts<br />

for the front and second month.<br />

ii) Refine selection universe based on eligibility/liquidity<br />

requirements.<br />

iii) Create all possible ATM vertical spread pairs.<br />

iv) Create and equal-weight overall rank for each pair by<br />

calculating and ranking on various criteria based on factors<br />

such as strike spread and net credit.<br />

v) Based on the sold leg of each spread, determine the<br />

distance away from ATM (money-ness).<br />

vi) Allocate spread pairs along proprietary money-ness<br />

and expiration criteria.<br />

vii) Apply an equal weighting to all spread pairs.<br />

Index <strong>com</strong>ponents are evaluated using bid/ask pricing.<br />

Midpoint pricing is used for the real-time and end-of-day<br />

index level calculation. The index is rebalanced monthly in<br />

conjunction with the regular options expiration calendar.<br />

To manage downside volatility, each index employs<br />

loss floors at the individual spread level. Once a spread<br />

pair has priced through the floor on an end-of-day basis,<br />

the <strong>com</strong>ponent’s weight in the index is allocated away<br />

from the spread pair and moved to a cash proxy until the<br />

next rebalance cycle. While this approach is effective in<br />

managing downside risk over a few days or in between<br />

rebalance cycles, it is ineffective in controlling any oneday<br />

market event. Managing downside risk in this manner<br />

not only limits losses but mitigates the risk of any positions<br />

being assigned and forcing the delivery of shares of<br />

the underlying for any investor following the index.<br />

Figure 5 shows this loss floor in action. This graph<br />

highlights the <strong><strong>com</strong>plete</strong> options cycle for December 2011<br />

(11/18/2011 to 12/16/2011). In the December period,<br />

SPY trades off early in the cycle and we see the bullish<br />

index trading off as well. What is hidden is the loss-floor<br />

mechanism. Figure 6 provides an analysis of index <strong>com</strong>ponents<br />

during this period.<br />

The loss-floor mechanism allows for those positions<br />

that could cause more drag on performance to be allocated<br />

to cash, and without significant lag. It sometimes<br />

is the case with sharp moves in SPY that the entire index<br />

spends some time during the expiration cycle allocated<br />

<strong><strong>com</strong>plete</strong>ly to the cash proxy, but as mentioned earlier,<br />

the cash proxy weight is reallocated to new spread pairs<br />

in the next rebalance cycle. The example given represents<br />

one period and is unique, meaning it will not<br />

always be the case that a -2.21 percent move in SPY will<br />

result in 27 spread pairs being reallocated to the cash<br />

proxy, or more generically, that an aggregate move of X<br />

percent in the underlying will always result in a particular<br />

action occurring in the index.<br />

Unlike other options strategy indexes, these indexes<br />

solely include options contracts and do not contain any<br />

underlying security as a <strong>com</strong>ponent. In designing these<br />

indexes, we realized that investors looking for optionoverlay<br />

products wanted just that—an option overlay. It<br />

did not make sense to force investors to double-up on<br />

Figure 7<br />

Figure 8<br />

Dates<br />

Source: Bloomberg<br />

Performance Comparison<br />

SPY<br />

(Equity)<br />

VCS<br />

(Index)<br />

VPS<br />

(Index)<br />

01/21/2005 – 12/31/2005 8.50% -30.93% 32.30%<br />

12/31/2005 – 12/31/2006 15.85% -43.93% 55.95%<br />

12/31/2006 – 12/31/2007 5.14% 62.80% -35.15%<br />

12/31/2007 – 12/31/2008 -36.81% 225.41% -12.77%<br />

12/31/2008 – 12/31/2009 26.37% 1.26% 19.62%<br />

12/31/2009 – 12/31/2010 15.06% -60.81% 53.78%<br />

12/31/2010 – 12/31/2011 1.89% -52.49% 16.50%<br />

12/31/2011 – 8/31/2012 13.54% -43.76% 38.57%<br />

Source: Bloomberg<br />

Historical Index Performance, January 2005-August 2012<br />

450<br />

400<br />

350<br />

300<br />

250<br />

200<br />

150<br />

100<br />

50<br />

0<br />

1/21/05 1/21/06 1/21/07 1/21/08 1/21/09 1/21/10 1/21/11 1/21/12<br />

■ VCS Index ■ VPS Index ■ SPY US Equity<br />

the existing underlying stock positions or have to incur<br />

transaction costs or create taxable events in order to get<br />

exposure to these strategies.<br />

Figure 7 shows some periodic returns of each of the<br />

indexes as <strong>com</strong>pared with the underlying (SPY) for<br />

each calendar year since the indexes’ inception. Figure<br />

8 shows the historical trend lines for the indexes versus<br />

SPY. While the indexes’ base dates are both Jan.<br />

21, 2005, the indexes <strong>com</strong>menced operation on Sept.<br />

16, 2011, and all prior index levels are backtested. As<br />

<strong>com</strong>ponent selection for these indexes is a purely quantitative<br />

process, there should be no concerns regarding<br />

survivorship bias or other forms of “cherry-picking”<br />

that could influence any backtested results. The base<br />

date of Jan. 21, 2005 was chosen as it was the date that<br />

ISE and the other U.S. options exchanges first listed<br />

options contracts on SPY.<br />

The development of the VCS and VPS indexes began as<br />

an academic exercise, but they have real-world potential<br />

to help investors gain exposure to options in a sophisticated—yet<br />

straightforward—approach. Composed of option<br />

contracts that are centrally cleared, exchange listed, continuously<br />

quoted and among the most liquid and heavily<br />

traded contracts in the marketplace, the indexes could<br />

provide investors access to a truly unique and useful way<br />

to express their opinions on the market.<br />

32<br />

November / December 2012


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Talking Indexes<br />

Options Before Quants<br />

Looking at a ‘new’ idea that’s<br />

been around a long, long time<br />

By David Blitzer<br />

Today options and investing with options means<br />

quant models, debates over how to measure implied<br />

volatility, or strategies to trade futures on the VIX.<br />

This modern era in options began in 1973 with the publication<br />

of Black-Scholes, 1 the first practical theory about<br />

options prices. But of course, options didn’t begin with<br />

Black-Scholes; some argue that both Greek mythology and<br />

the Bible include discussions or at least allusions to options<br />

trades. Sticking to written historical records and options<br />

similar to current ones, options history can easily reach<br />

back to the early 1600s in Amsterdam.<br />

At that time, Amsterdam was the leading European<br />

financial center, and the center of innovation. In 1602,<br />

the Dutch East India Company was the first stockholderowned<br />

corporation listed with shares that could be traded<br />

and held by people not otherwise linked to the <strong>com</strong>pany.<br />

While trading was active, settlement procedures were<br />

monthly, and recording ownership on the <strong>com</strong>pany’s<br />

books was more <strong>com</strong>plex than today.<br />

Though electronic trading was four centuries in the<br />

future, the market was sophisticated and experienced in<br />

trading equities, <strong>com</strong>modities and fixed-in<strong>com</strong>e instruments.<br />

Options—both puts and calls—on Dutch East<br />

India Company stock were actively traded as well. 2,3 If the<br />

Amsterdam market in this historical context rings a bell for<br />

readers, remember that it is the same market that would<br />

experience a bubble in tulip bulbs in 1634-37—a bubble<br />

built on options and futures, not the cash market.<br />

Options trading without quantitative pricing models<br />

thrived. Research depended on a <strong>com</strong>bination of technical<br />

and fundamental analysis. The latter included information<br />

regarding the local economy where the Dutch<br />

East India Company’s ships would be delivering their<br />

cargos; reports of economic conditions in India and<br />

South Asia where the ships bought and loaded cargos;<br />

and rumors of sailing and weather conditions, shipwrecks<br />

and other hazards. Options trading was supported by two<br />

factors: 1) with limited information, many investors preferred<br />

options to stocks because the downside risks could<br />

be limited; and 2) the share price for the Dutch East India<br />

Company was high, and options were a way to invest<br />

without risking too large a proportion of one’s wealth.<br />

As the old Dutch traders knew, options are analytically—and<br />

historically—similar to insurance. A put on a<br />

stock is one way to purchase insurance to limit downside<br />

risk. Writing a call can be thought of as insurance to the<br />

buyer of a call as well—insuring against the regret suffered<br />

if one doesn’t buy a stock and misses a big move. In<br />

either case, the writer is offering insurance and is paid a<br />

premium for accepting the risks while having no control<br />

of whether the option is exercised.<br />

Options have changed and expanded since the 1600s.<br />

With the support of various quantitative models and<br />

theories, we now think we understand them better. Their<br />

coverage can also be broader today than a single stock or<br />

a single risk. Through options on indexes, one can insure,<br />

or accept risk on, a portfolio of stocks. Further, to the<br />

extent that a particular portfolio is similar to a widely recognized<br />

index, one can insure, or add risk to, one’s own<br />

personal portfolio through the use of options.<br />

Many investors looking back at the last few years of<br />

financial turmoil would like to insure their entire portfolio,<br />

maybe their total net worth, against all risks. Until the S&P<br />

500 lost 50 percent twice in the last decade, we all thought<br />

that was a once-in-a-lifetime event. Until the financial crisis<br />

began in 2007, we were <strong><strong>com</strong>plete</strong>ly convinced the Great<br />

34<br />

November / December 2012


Depression was history, rather than a current worry. One<br />

might add other risks: rioting over debt and taxes as seen in<br />

Greece, or natural disasters that clobber the economy such<br />

as the March 2011 earthquake and tsunami in Japan. If the<br />

premiums were reasonable, many of us would consider<br />

insuring our net worth. The rising interest in tail-risk hedging<br />

suggests a bull market in such insurance.<br />

But the truth is, in one sense, we are all being paid that<br />

premium to accept many of the unknown, and unquantifiable,<br />

risks. There is a long-debated puzzle about the<br />

equity risk premium (ERP)—the extra return one expects<br />

from holding a risky stock instead of a Treasury bill—why<br />

does it seem as large as 3 to 5 percent or more? Research<br />

by Robert Barro 4 argues that the ERP is <strong>com</strong>pensation<br />

for accepting a small risk of a truly catastrophic event.<br />

An example might be a war that would close markets for<br />

several years and devastate an economy. Barro estimates<br />

the probability of such catastrophic events and then links<br />

this to an individual’s utility function to show that the<br />

degree of risk aversion is reasonable. Through the market,<br />

the ERP is <strong>com</strong>pensating investors for risks they are facing<br />

through their investors. The ERP means that each investor<br />

writes a put option on his entire investment holdings and<br />

then sells it to himself. He accepts the risks and receives<br />

the premiums. The market sets the premium. Writing and<br />

buying this put is mandatory if an investor participates<br />

in the market. Those other options—from the Dutch East<br />

India Company to an option on the S&P 500 Index—are<br />

voluntary, not mandatory.<br />

A major benefit of options embracing quant models<br />

is that the range of options and quantitative tools has<br />

expanded greatly since Black-Scholes. Two examples<br />

familiar to index investors are options on indexes led<br />

by the S&P 500 and VIX. A put on the S&P 500 is not the<br />

same as a put option on one’s entire net worth, but it is<br />

a lot more <strong>com</strong>prehensive than a put on a single stock.<br />

VIX <strong>com</strong>es full circle in offering new ways to use options<br />

to protect, or invest, in the market while returning information<br />

to the market from options priced in the market.<br />

Much remains that can be done with options to give<br />

people ways to insure or accept risks. Ten years ago, we all<br />

thought that real estate prices rarely drop. Now the idea of<br />

insuring the economic value of one’s home looks attractive.<br />

One idea is to use the S&P/Case-Shiller Home Price<br />

indexes puts for homeowners or calls for investors on<br />

home values. If one can insure against fire, it only makes<br />

sense to insure against a market collapse.<br />

What does this all have to do with Amsterdam and<br />

Black-Scholes? Only this: Derivatives are thought of by<br />

some as weapons of financial mass destruction, a “new<br />

new thing” that contributed to the financial crisis and that<br />

are dangerous in the hands of unknowing investors.<br />

But from another perspective, options—and optionslinked<br />

products like the VIX—have been with us for<br />

centuries, and used properly, have the opposite effect.<br />

Like the old traders who used options on the Dutch East<br />

India Company because they couldn’t handle the risk of<br />

owning the stock, they are a way of limiting our downside,<br />

insuring against shipwrecks and protecting our<br />

portfolios from harm.<br />

Endnotes<br />

1 Black, Fischer and Myron Scholes, “The Pricing of Options and Corporate Liabilities,” 81(3) Journal of Political Economy, 1973, pp. 637-654.<br />

2 Geoffrey Poitras, “The Early History of Option Contracts,” Simon Fraser University, September 2008.<br />

3 William N. Goetzmann and K. Geert Rouwenhorst, “The Origins of Value,” Oxford University Press, September 2008.<br />

4 Robert Barro, “Rare Events and the Equity Risk Premium,” Harvard University, July 2005.<br />

Why subscribe to the<br />

The Journal of Indexes is the premier source for financial index research, news and<br />

data. Written by and for industry experts and financial practitioners, it is the book of<br />

record for the index industry. Browse content online at www.indexuniverse.<strong>com</strong>/JOI<br />

TO SUBSCRIBE, VISIT:<br />

www.indexuniverse.<strong>com</strong>/JOI/subscriptions<br />

Redefining Credit Risk<br />

William Mast<br />

Credit Derivatives Indexes<br />

Gavan Nolan and Tobias Sproehnle<br />

A Fixed-In<strong>com</strong>e Roundtable<br />

Ken Volpert, Jason Hsu, Waqas Samad, Larry Swedroe and more<br />

The Impact of Bond Fund Flows<br />

David Blanchett<br />

Plus David Blitzer on bubbles, Jeremy Schwartz on dividends and buybacks, Francis Gupta on country<br />

classifications and a biography on Bogle<br />

www.journalofindexes.<strong>com</strong> November / December 2012 35


On Derivatives<br />

Human Risk And The Pervasiveness<br />

Of Index-Based Derivatives<br />

The dark side of innovation<br />

By Jonathan Citrin<br />

Derivative securities are financial instruments<br />

whose earliest beginnings can be traced to 17thcentury<br />

tulip bulbs in Holland and rice in Japan<br />

(Calistru 2011). Derivatives—contracts specifying a transaction<br />

in an underlying asset to be fulfilled at a future<br />

date—found favor in the markets during the last quarter<br />

of the 20th century. They are investment vehicles whose<br />

goal was to mitigate risk in otherwise-volatile <strong>com</strong>modity,<br />

currency, interest-rate and equity markets. Following their<br />

acceptance by mainstream market participants, derivatives<br />

became a <strong>com</strong>mon topic of discussion and research<br />

throughout the field of finance.<br />

Interestingly, from their early days of use to the major<br />

market role they play today, there has been very little agreement<br />

concerning the impact of derivatives on markets and<br />

economies of the world. In fact, the initial raison d’être (for<br />

hedging purposes) is perhaps the only point of universal<br />

consensus amongst academics, professionals and policymakers.<br />

“[They] are mainly used to protect against and manage<br />

risk,” (Deutsche Börse Group 2005). “The key function<br />

of derivatives is to hedge the risk inherent in the underlying<br />

markets, in order to guard against changes in interest and<br />

exchange rates, fluctuations in <strong>com</strong>modity prices and so on,”<br />

(Hawkesby 1999). And, “The need for market <strong><strong>com</strong>plete</strong>ness<br />

has generated the need to create some financial instruments<br />

that will allow investors to hedge and thus, to be secured from<br />

price fluctuations” (Siopis 2007).<br />

The spike in use of derivatives finds root in the changing<br />

global marketplace of the 1970s after policy alterations<br />

motivated market participants to find alternative means for<br />

mitigating volatility’s impact on corporate operations. “The<br />

demand for financial products to manage risk was increased<br />

by soaring international trade and capital flows. Derivatives<br />

seem to meet best the new challenges of financial markets,”<br />

(Calistru 2011). Secondary uses of derivatives include<br />

speculation and arbitrage; however, the main force behind<br />

the development and increasing use remains true to their<br />

original purpose—controlling the downside risk of portfolios<br />

and balance sheets. Figure 1 illustrates the great rise in use of<br />

derivatives over the later 20th and early 21st centuries.<br />

More specifically, index-based derivatives represent contracts<br />

whose settlement is facilitated in cash and determined<br />

by the price at expiration of an underlying index (e.g.,<br />

Standard & Poor’s 500 Index options (SPX): inception July<br />

1, 1983, and Dow Jones Industrial Average options (DJX):<br />

inception Oct. 6, 1997) (CBOE 2012). True to their beginnings,<br />

undisputed attributes of index-based derivatives include:<br />

1) leverage, 2) arbitrage, 3) reduction in bid/ask spread,<br />

4) liquidity, 5) increased participation in underlying markets,<br />

6) low transaction costs, 7) transparency, 8) innovation,<br />

and 9) flexibility in product design. However, despite agreement<br />

on these key characteristics, academic and professional<br />

research yields many varying conclusions and even dissimilarity<br />

in empirical evidence with regard to the impact of<br />

index-based derivatives on underlying market volatility.<br />

The influence of index-based derivatives on underlying<br />

assets has been a source of great discord through recent history<br />

and may never be agreed upon. Significant evidence<br />

exists that both favors and condemns the influence of indexbased<br />

derivatives on the investments they track. Much recent<br />

research, time and money has been spent disputing the<br />

effect of index-based derivatives on the volatility of underlying<br />

indexes, not to mention considerable efforts expended<br />

to determine the role of index-based derivatives in major<br />

market crashes, including the Great Recession of 2008 and<br />

October 1987’s Black Monday. However, <strong>com</strong>mentary on and<br />

36<br />

November / December 2012


academic assessment of the very existence of index-based<br />

derivatives and the implications of the still-growing popularity<br />

of these <strong>com</strong>plex risk mitigation, arbitrage and speculation<br />

instruments are both in short supply.<br />

Figure 4<br />

Options Traded On Global Organized Exchanges<br />

Year-End Notional Principal<br />

Analysis And Interpretation<br />

According to the Bank for International Settlements, the<br />

notional value of global derivatives traded over-the-counter<br />

(OTC) as of December 2011 was nearly $648 trillion, with<br />

gross market values of over $27 trillion. The notional value<br />

of derivative instruments traded on organized exchanges as<br />

of December 2011 reached almost $56.5 trillion, with equity<br />

index-based derivatives reaching roughly $3 trillion (Bank for<br />

International Settlements 2012). See Figures 2-4.<br />

Figure 1<br />

Derivatives Market Volume<br />

Billions Of USD<br />

$60,000<br />

$50,000<br />

$40,000<br />

$30,000<br />

$20,000<br />

$10,000<br />

$0<br />

Source: Bank for International Settlements<br />

Figure 5<br />

1986<br />

1987<br />

1988<br />

1989<br />

1990<br />

1991<br />

1992<br />

1993<br />

1994<br />

1995<br />

1996<br />

1997<br />

1998<br />

1999<br />

2000<br />

2001<br />

2002<br />

2003<br />

2004<br />

■ Interest Rate ■ Currency ■ Equity Index<br />

Global Equity Market Capitalization<br />

2005<br />

2006<br />

2007<br />

2008<br />

2009<br />

2010<br />

2011<br />

Billions Of USD<br />

$700,000<br />

$600,000<br />

$500,000<br />

$400,000<br />

$300,000<br />

$200,000<br />

$100,000<br />

$0<br />

1998<br />

1999<br />

2000<br />

2001<br />

2002<br />

2003<br />

2004<br />

2005<br />

2006<br />

2007<br />

2008<br />

2009<br />

2010<br />

2011<br />

■ OTC ■ Exchanges ■ Global Equity Market<br />

Source: World Federation of Exchanges and Bureau of International Settlements<br />

Figure 2<br />

Billions Of USD<br />

Over-The-Counter (OTC) Derivatives: Total Contracts<br />

Year-End Notional Amounts Oustanding by Category<br />

$700,000<br />

$600,000<br />

$500,000<br />

$400,000<br />

$300,000<br />

$200,000<br />

$100,000<br />

$0<br />

■ Foreign Exchange Contracts ■ Interest Rate Contracts ■ Unallocated<br />

■ Equity-linked Contracts ■ Commodity Contracts ■ Credit Default Swaps<br />

Source: Bank for International Settlements<br />

Figure 3<br />

$30,000<br />

$25,000<br />

$20,000<br />

$15,000<br />

$10,000<br />

$5,000<br />

$0<br />

Billions Of USD<br />

1998<br />

1999<br />

2000<br />

2001<br />

2002<br />

2003<br />

2004<br />

2005<br />

2006<br />

2007<br />

2008<br />

2009<br />

2010<br />

Futures Traded On Global Organized Exchanges<br />

Year-End Notional Principal<br />

1986<br />

1987<br />

1988<br />

1989<br />

1990<br />

Source: Bank for International Settlements<br />

■ Interest Rate ■ Currency ■ Equity Index<br />

2011<br />

1991<br />

1992<br />

1993<br />

1994<br />

1995<br />

1996<br />

1997<br />

1998<br />

1999<br />

2000<br />

2001<br />

2002<br />

2003<br />

2004<br />

2005<br />

2006<br />

2007<br />

2008<br />

2009<br />

2010<br />

2011<br />

Millions Of USD<br />

$70,000,000<br />

$60,000,000<br />

$50,000,000<br />

$40,000,000<br />

$30,000,000<br />

$20,000,000<br />

$10,000,000<br />

$0<br />

Source: World Federation of Exchanges<br />

1990<br />

1991<br />

1992<br />

1993<br />

1994<br />

1995<br />

1996<br />

1997<br />

1998<br />

1999<br />

2000<br />

2001<br />

2002<br />

2003<br />

2004<br />

2005<br />

2006<br />

2007<br />

2008<br />

2009<br />

2010<br />

In <strong>com</strong>parison, the World Federation of Exchanges<br />

calculates the global market capitalization of all equity<br />

markets at just under $47.5 trillion (World Federation<br />

of Exchanges 2012). Despite the upward trend in global<br />

equity market capitalization (see Figure 5), this number is<br />

dwarfed by the dollars traded in derivatives on exchanges,<br />

let alone OTC. Certainly, the attractiveness and widespread<br />

use of index-based derivatives is ubiquitous across markets.<br />

Moreover, as displayed in Figures 2-4, the growth of<br />

both OTC and exchange-traded derivatives, which include<br />

an abundance of index-based vehicles, has been more<br />

than significant in recent years. The towering derivatives<br />

market is a <strong>com</strong>mentary on our mindset as investors and<br />

on our constant need to manipulate the market portfolio<br />

in our favor. Achieving more upside and less downside is<br />

our historic struggle, and the outsized derivatives market is<br />

evidence of the lengths we will go to in our quest for alpha.<br />

The introduction of index-based derivatives is seen by many<br />

as the most important financial market advancement in modern<br />

times. Banks, pension funds, insurance <strong>com</strong>panies, mutual<br />

funds, exchange-traded funds, separately managed accounts,<br />

hedge funds and government holdings all partly consist of<br />

index-based derivatives. Their presence has be<strong>com</strong>e <strong>com</strong>mon<br />

practice to most institutional investors and even many retail<br />

investors. “In fact, index-based contracts have be<strong>com</strong>e such<br />

an indispensable feature of the global financial system that it<br />

would be safe to say that there are many millions in the West<br />

continued on page 41<br />

November / December 2012 37


Profiles In Pensions<br />

IMRF’s Shah Stays The Course<br />

New CIO not anticipating<br />

any big changes<br />

The Journal of Indexes sat down with Dhvani Shah, who<br />

became the CIO of the Illinois Municipal Retirement<br />

Fund last December, to talk about her fund’s allocations<br />

and how it uses index strategies, among other topics.<br />

IMRF is the 51st-largest pension system in the United<br />

States and the second-largest (and one of the best-funded)<br />

public pension system in the state of Illinois.<br />

Illinois Municipal Retirement Fund<br />

Assets: $26.1 billion<br />

Funding: 80.2%<br />

Passive/Active Mix: 25.7% / 74.3%<br />

JOI: How does your team approach the investment process?<br />

Shah: We have an annual review of our investment policy<br />

statement, real estate policy statement and strategic asset<br />

allocation. That’s an opportunity to fine-tune things as needed.<br />

We also conduct an asset liability study about every three<br />

years. Actually, the next one is scheduled for next year, 2013.<br />

Within our public market portfolio, we have active<br />

managers, as well as index investments. For example,<br />

within domestic equity, we have about $3.5 billion in<br />

index investments, which is about 43 percent of the<br />

large-cap portfolio, and 32 percent of the total domestic<br />

equity portfolio, which is $10.9 billion.<br />

We have a mix of active and passive management in<br />

each of the major asset classes. In international equity,<br />

we have $1.7 billion in index investments, which is<br />

about 41 percent of the international large-cap, and 35<br />

percent of the international portfolio. In fixed in<strong>com</strong>e,<br />

we have $1.3 billion in index investments, which is 19<br />

percent of the fixed-in<strong>com</strong>e portfolio.<br />

When you total those three categories, you <strong>com</strong>e to<br />

about $6.7 billion, and our public market portfolio is<br />

about $24.1 billion, so that is about 28 percent in indexed<br />

assets. I believe there’s room for growth in the indexed<br />

portion of the portfolio. The only time you should be in<br />

an active strategy is if you believe that there’s a source of<br />

alpha—that the strategy can outperform. When we conducted<br />

our international equity rebalancing earlier this<br />

year, we were looking at not just the new managers we<br />

were adding, but also at that active/passive mix.<br />

In that rebalancing, the international equity portfolio<br />

stayed at about 67 percent active and 33 percent passive. In<br />

the original scenario, passive management was actually going<br />

to go much lower, but we made that decision to not bring it<br />

down so much. When we evaluate managers, we keep that<br />

passive index option in mind. We are <strong>com</strong>paring their performance<br />

not just to themselves and their peers within the active<br />

space but also to indexed portfolio performance.<br />

JOI: Are you using a core-satellite approach, with<br />

index-based investments at the core?<br />

Shah: Yes, that’s exactly right—that’s exactly how it’s<br />

been built.<br />

JOI: What kind of indexes are you using as the basis<br />

for your passive portion of the portfolio?<br />

Shah: In the domestic space, we have a growth index,<br />

a value index and a market-cap index. In international<br />

equity, we have the MSCI EAFE index. We’re looking<br />

into whether we should be also thinking of ACWI or the<br />

emerging index, and so on. Within fixed in<strong>com</strong>e, we<br />

have the Barclays Aggregate Bond Index.<br />

JOI: Do you believe that a passive strategy can be<br />

effective in emerging markets?<br />

Shah: It’s under consideration. Right now we have only<br />

38<br />

November / December 2012


Alternative Investments<br />

4.0%<br />

Real Estate 2.9%<br />

Fixed In<strong>com</strong>e<br />

31.0%<br />

International Equity<br />

19.4%<br />

Source: IMRF<br />

IMRF Asset Allocation<br />

As Of June 30, 2012<br />

Cash Equivalents<br />

0.9%<br />

Domestic Equity<br />

41.9%<br />

one manager in the emerging markets space. One of the<br />

things we may decide is whether or not we need to add<br />

another manager in this space. When we do that project,<br />

we will definitely consider the passive index option. That’s<br />

when we do a much deeper dive. I don’t discount it right at<br />

the front, so it will definitely be considered.<br />

The reason is there is a huge fee difference, and you<br />

have to keep that in mind. With active management,<br />

the outperformance is there, but is it on a consistent<br />

basis? When we evaluate that segment, we will definitely<br />

be looking at the emerging markets index option.<br />

JOI: Have you ever looked at fundamentally weighted<br />

benchmarks as opposed to the more traditional<br />

market-cap approach to indexing?<br />

Shah: To date, we have not done that. When we do our<br />

JOI: Do you use ETFs at all?<br />

Shah: We don’t currently have them in our portfolio, but I<br />

think we’re going to be talking about them when we do our<br />

asset liability study and discuss the strategies. I don’t know<br />

if I have a strong feeling one way or another.<br />

JOI: Do you believe in the concept of the “new normal,”<br />

that we’re in for a long period of a slow economy<br />

with very low returns?<br />

Shah: I think some of that is going to <strong>com</strong>e through in<br />

the capital market assumptions when we do our annual<br />

strategic review. That’s where our long-term investing<br />

nature <strong>com</strong>es into play. We’re not going to make wholesale<br />

changes, but it’s certainly going to <strong>com</strong>e into play as we do<br />

our strategic plan: What are you really expecting for the<br />

next few years? How do you want to make some changes?<br />

We have a very long investment horizon, and we’re<br />

going to keep that in mind, because you don’t want to be<br />

on the wrong side. You don’t want to <strong><strong>com</strong>plete</strong>ly have a<br />

negative short-term outlook and substantially change your<br />

portfolio when you have a much longer time horizon.<br />

JOI: How are you finding reasonable returns in the<br />

current environment? Has it been more difficult of<br />

late to meet your growth targets?<br />

Shah: With multiple allocations in domestic equity and<br />

international equity —42 percent in domestic equity, 19<br />

percent in international equity—certainly the market movements<br />

affect our returns. And we have 31 percent in fixed<br />

in<strong>com</strong>e. The market environment definitely has an impact<br />

on the shorter-term returns. But over the long term, we also<br />

have other asset classes where we’re hard at work, such as<br />

With multiple allocations in domestic equity and international equity,<br />

certainly the market movements affect our returns.<br />

asset liability study, which is scheduled for early next year,<br />

that is one of the <strong>issue</strong>s I will have on the table. The simple<br />

fact is index investment in general is more cost-efficient,<br />

and so you want to make sure you’re considering all<br />

options when you rebalance your portfolio, and not only<br />

think within the active management space.<br />

I don’t know how accepted that concept is, but it’s going<br />

to be a good conversation with our consultants. But you<br />

know what? The active mandates are not just going to be<br />

a given assumption. Passive index options are going to be<br />

part of the deep dive in the consideration for anything that<br />

we do in the public market.<br />

JOI: Are there any asset classes in particular where you think<br />

that indexing is probably not an appropriate approach?<br />

Shah: I don’t expect to use index investing in alternatives.<br />

With our public markets portfolio, if we are looking at a<br />

segment within the public markets to revise because it’s<br />

out of range or we want to change the manager lineup for<br />

some reason, the index options will be part of that project.<br />

real estate and alternatives. In June, we <strong><strong>com</strong>plete</strong>d a re<strong>com</strong>mendation<br />

regarding our hedge fund review, and we’re<br />

making some changes there. We went down from three fund<br />

managers to one, and it will be a separate account strategy,<br />

so we can keep our entire portfolio in mind as far as the long,<br />

short, credit and macro strategies. It’s going to be much<br />

more customized to our overall portfolio.<br />

JOI: Have you reduced the allocation to alternatives, or<br />

have you just put them all under one manager?<br />

Shah: We have a 6 percent target for alternatives, and we’re<br />

actually under target. The hedge fund allocation increased<br />

slightly during a review earlier this year; it went from roughly<br />

$375 million to $500 million. We also continue to put some<br />

capital into private equity and real estate fund investments.<br />

It all <strong>com</strong>es back to a long-term planning horizon.<br />

Within public markets, you want to make it run as efficiently<br />

as you can, and really think about what strategies<br />

you’re in. Then you also want to make sure within alternatives<br />

that you’re attracting top-quartile managers, because<br />

www.journalofindexes.<strong>com</strong> November / December 2012 39


with below-top-quartile managers, between the fees and<br />

the liquidity <strong>issue</strong>s, you’re better off not doing that. We will<br />

selectively add <strong>com</strong>mitments in the alternative space to the<br />

extent we believe that there are strong managers.<br />

JOI: Can you speak in general about how your asset<br />

allocations may have shifted in the last few years?<br />

Have there been any major changes?<br />

Shah: Within the broader major asset classes, it has not<br />

changed much. It’s been pretty standard as far as the equity,<br />

fixed in<strong>com</strong>e and alternatives mix. We are underallocated in<br />

alternatives and real estate. In the past few years, they did a real<br />

estate surge, and we’re getting a little bit closer to the target.<br />

JOI: Is the eurozone crisis affecting how you approach<br />

your international allocation?<br />

Shah: The international equity rebalancing that we <strong><strong>com</strong>plete</strong>d<br />

did get funded around that time, but net/net, our<br />

allocation to international equity was the same. Some of<br />

the money moved from international growth to international<br />

value, but from a portfolio perspective, we were not<br />

any more or less than we were before in terms of overall<br />

international allocation. We expect to stay at our current<br />

19 percent. During our manager reviews though, we do<br />

JOI: How do you interpret Warren Buffett’s recent<br />

decision to stop insuring several billion dollars’ worth<br />

of municipal debt? Does that seem like a bearish or a<br />

bullish move as far as municipalities are concerned?<br />

Shah: I’m not really changing anything in the portfolio<br />

based on that. I didn’t think in terms of <strong>com</strong>partmentalizing<br />

it as a bullish/bearish move. Our active managers may<br />

use that information and work on the portfolio slightly<br />

differently within their investment guidelines. It’s really<br />

the macro environment you have to keep an eye on.<br />

JOI: IMRF is well-funded, especially <strong>com</strong>pared with<br />

some other Illinois pension funds. Can you talk<br />

about how your strategies or how your structure may<br />

have made that possible?<br />

Shah: Our members and employers have contributed<br />

the required contributions when they were due, and I<br />

think there’s no substitute for that. If you don’t pay your<br />

bills today, it’s just more expensive tomorrow. And if you<br />

look across the country, the public pension plans that are<br />

in stronger positions are the ones that are getting their<br />

contributions on an annual basis. I came from New York<br />

State Teachers before I joined IMRF, and they are also in<br />

a similar position with strong funding for that very reason.<br />

Why is that important? It <strong>com</strong>es back to the investment<br />

program itself. You asked earlier if we had to<br />

change our allocation in a major way in recent years.<br />

No. If you’re in a strong funding position, you can then<br />

be thoughtful about your strategic plan and your asset<br />

allocations, and spend time executing on that plan, the<br />

Our members and employers have contributed the required contributions<br />

when they were due, and I think there’s no substitute for that.<br />

If you don’t pay your bills today, it’s just more expensive tomorrow.<br />

talk in-depth about how they’re handling it, what they are<br />

doing—especially with active managers who are taking a<br />

subset of the opportunities. It does <strong>com</strong>e into play at the<br />

individual manager strategy level, but at the portfolio level,<br />

our allocation’s the same.<br />

JOI: There’s been a lot of talk about different municipalities<br />

filing for bankruptcy, and it looks like it’s happening<br />

more often. Does this affect the way you do your job,<br />

or what your concerns are when you’re thinking about<br />

meeting your goals and how the portfolio is invested?<br />

Shah: We think of it in terms of risk factors: Every investment<br />

has its inherent risk. But we do believe that it’s part of the risk<br />

we expect our fixed-in<strong>com</strong>e managers to take into account.<br />

In our reviews with the managers, nothing major has <strong>com</strong>e<br />

up as a discussion point as far as a poor performance attributed<br />

to some bankruptcy. We believe it’s being properly<br />

managed, and it’s a risk factor we expect to be accounted for.<br />

strategies, all that work that’s involved in managing a<br />

portfolio. You can spend all your energy doing that. I<br />

think that a strong investment program, without the<br />

foundation of strong funding, is hard to execute.<br />

If you’re not getting that contribution, that means you’re<br />

basically borrowing from the fund to pay a payment, and the<br />

fund is short. Now you have less to invest, because the fund<br />

is smaller. You have to have a much higher return than your<br />

actuarial rate of return because you have a smaller base than<br />

what you should have had. It just <strong>com</strong>pounds that problem.<br />

JOI: If by some chance, IMRF be<strong>com</strong>es underfunded,<br />

how would the difference be made up?<br />

Shah: Every employer has a unique contribution rate.<br />

Every employer has their own separate account, so we<br />

know what their assets and liabilities are. Our actuaries<br />

calculate what they need to contribute, and then IMRF collects<br />

the payment from the employers. Our employees also<br />

contribute a fixed percentage of their pay each month to<br />

help cover the cost of their benefit.<br />

The way a defined benefit pension works is when someone<br />

retires, we have every single dollar we need to pay that<br />

person their benefits for the rest of their life. We also have a<br />

separate reserve for retired members, and that’s always 100<br />

percent funded. That’s sort of unique to IMRF.<br />

40<br />

November / December 2012


tion and management. Within it, an important correlation<br />

exists between risk and return (the risk/return continuum).<br />

Risk is directly linked with return, and return directly<br />

linked with risk. The more return one desires, the more risk<br />

one must take. And conversely, the less risk one desires,<br />

the less return one can make. Market participants all too<br />

often, whether consciously or not, try to over<strong>com</strong>e the continuum.<br />

As such, portfolios rid themselves of surface risk—<br />

those risks that are easily identifiable and diversifiable—but<br />

carve a deeper hole insofar as human risk is concerned.<br />

Human risk—the blindness of participants thinking markets<br />

can be outwitted and certain laws of finance evaded—<br />

is omnipresent, and if unchecked, leaves portfolios and the<br />

global financial system open to great loss. Human risk takes<br />

place at the hands of investing ignorance and finds sustenance<br />

in a general lack of humility of participants relative<br />

to the randomness and aptitude of the markets themselves.<br />

The main reason for the rapid rise in popularity of indexbased<br />

derivatives is their seamless fit for mitigating surface<br />

risk—perfectly satisfying participants’ appetite for peace<br />

of mind, while letting far more serious risks go unnoticed.<br />

Unfortunately—and potentially very grave for markets and<br />

the global financial system—the implementation of indexbased<br />

derivatives not only conceals human risk but exacerbates<br />

it. Derivatives—more specifically, index-based derivatives—owe<br />

their popularity to their ability to exaggerate<br />

human irrationality. Index-based derivatives take human<br />

risk—the same risk that makes it so difficult for participants<br />

to buy low and sell high, so difficult for participants<br />

to simply own the market portfolio—and conceal it deeper<br />

between assets of even the best investment platforms.<br />

While index-based derivatives can serve a healthy role in markets,<br />

their extreme and continued popularity is a sign of increasing trouble.<br />

Humans are prone to many innate behaviors that make us<br />

far worse investors than we care to admit.<br />

Citrin continued from page 37<br />

who own, either directly or indirectly (even unknowingly),<br />

index-based derivatives,” (Millo 2007).<br />

Since the invention of the stock index as a concept in the<br />

1890s by Charles Dow, investors have worked to continually<br />

obtain relative outperformance against a <strong>com</strong>parative<br />

benchmark. In doing so, market participants have increasingly<br />

sought to capitalize on any opportunity to increase<br />

yield and/or decrease risk. Anything offering the prospect of<br />

outsmarting markets is afforded a chance. And preference<br />

is given to those securities and investment vehicles that<br />

possess certain fundamental attributes, key among them<br />

liquidity, low transaction costs, low capital requirements,<br />

transparency, and flexibility in design. Index-based derivatives<br />

made for an almost perfect match, offering participants<br />

the occasion to once again endeavor to outmaneuver markets<br />

and defy a fundamental law of finance—the relationship<br />

between risk and return.<br />

Modern portfolio theory and the efficient frontier, as<br />

initially presented in the Journal of Finance by Harry<br />

Markowitz in his seminal 1952 work “Portfolio Selection”<br />

and built upon throughout the second half of the 20th<br />

century, is the foundation for proper portfolio construc-<br />

Conclusion<br />

While index-based derivatives can serve a healthy role in<br />

markets, their extreme and continued popularity is a sign of<br />

increasing trouble. Humans are prone to many innate behaviors<br />

that make us far worse investors than we care to admit. It<br />

is these unfavorable activities that cause us to overuse indexbased<br />

derivatives as hedging mechanisms rather than simply<br />

allocating properly within the market portfolio. More return<br />

means more risk, and less risk means less return. This applies<br />

with or without the presence of index-based derivatives.<br />

Participants cling to investment vehicles with the promise of<br />

superior performance, forgetting the simple concepts within<br />

the laws of finance. Almost no one <strong>com</strong>prehends or respects<br />

the role of human risk in portfolio management. And in the<br />

absence of this lack of understanding, participants quickly<br />

sweep away surface risk but leave in its wake the far graver<br />

element: Participants will do anything possible to interfere<br />

with the market portfolio in an attempt to outsmart the<br />

market itself. This behavior includes running in droves for<br />

multiple decades toward index-based derivatives that simply<br />

promise more than they can deliver.<br />

References<br />

“Annual Market Statistics.” CBOE. .<br />

“Annual Statistics Reports.” World Federation of Exchanges. Sept. 17, 2012. Available at www.world-exchanges.org/statistics/annual.<br />

Bank for International Settlements. “Semiannual OTC derivatives statistics at end-December 2011.” September 2012. Available at www.bis.org/statistics/derstats.htm.<br />

Calistru, Roxana Angela. “The Impact of Derivatives on Market Functioning.” Annals of the University of Craiova Economic Sciences, vol. 4, <strong>issue</strong> 39 (2011):138-141.<br />

Deutsche Börse Group. “The Global Derivatives Market.” (2005):4-42.<br />

Hawkesby, C. “A Primer On Derivatives Markets.” Reserve Bank of New Zealand: Bulletin. vol. 62, No. 2 (1999):24-43.<br />

Markowitz, Harry. “Portfolio Selection.” The Journal of Finance, vol. 7, No. 1 (1952):77-91.<br />

Millo, Yuval. “Making things deliverable: the origins of index-based derivatives.” Sociological Review, vol. 55, <strong>issue</strong> supplement 2 (2007):196-214.<br />

Siopis, Angelos et al. “The Effects of Derivatives Trading on Stock Market Volatility: The Case of the Athens Stock Exchange.” (2007):3-39.<br />

November / December 2012 41


The Winner’s Curse<br />

Too big to succeed?<br />

By Rob Arnott and Lillian Wu<br />

42<br />

November / December 2012


Much ink has been spilled on the perils of allowing<br />

some <strong>com</strong>panies to be<strong>com</strong>e “too big to fail.”<br />

This sentiment assumes that governments,<br />

hence taxpayers, must foot the bill when these top dogs<br />

be<strong>com</strong>e seriously ill, while reinforcing a view that the top<br />

dogs, whose failure might do systemic damage, should<br />

be heavily regulated to mitigate the damage that they<br />

might cause. The flip side of this view receives scant<br />

attention: Companies can be<strong>com</strong>e “too big to succeed.”<br />

Indeed, the “too big to fail” ethos may create head winds<br />

for these self-same <strong>com</strong>panies that can impede their continuing<br />

success. When you are No. 1, you have a bright<br />

bull’s-eye painted on your back. Governments and pundits<br />

are gunning for you, as are <strong>com</strong>petitors and resentful customers.<br />

In a world that generally roots for the underdog,<br />

hardly anyone outside of your own enterprise is cheering for<br />

you to rise from world-beating success to still-loftier success.<br />

For investors, top dog status—the No. 1 <strong>com</strong>pany, by<br />

market capitalization, in each sector or market—is dismayingly<br />

unattractive. We find a statistically significant<br />

tendency for top <strong>com</strong>panies in each sector to underperform<br />

both the overall sector and the stock market as<br />

a whole. In an earlier U.S.-only study, we found that 59<br />

percent of these top dogs underperformed their own sector<br />

in the next year, and two-thirds lagged their sector<br />

over the next decade. We found a daunting magnitude of<br />

average underperformance, averaging between 300 and<br />

400 bps per year, over the next one to 10 years.<br />

In this study, we have broadened the test to examine<br />

whether the “top dog” phenomenon is prevalent elsewhere.<br />

We find the same phenomenon in each and every market,<br />

with no exceptions. Indeed, outside the United States, the<br />

sector top dogs generally underperform their own sector<br />

even more relentlessly than in the United States!<br />

It would appear that our top dogs, the most beloved and<br />

winningest <strong>com</strong>panies in each sector or country, are typically<br />

punished—often severely—in subsequent market action.<br />

Bubble, Bubble, Toil And Trouble<br />

During the global financial crisis, several bellwether<br />

institutions found themselves facing insolvency.<br />

Government agencies, worried that these <strong>com</strong>panies were<br />

“too big to fail,” creating systemic risk for the market at<br />

large, reached for the elixir of public money to bail out<br />

these institutions, while reinforcing a view that stricter<br />

federal oversight is necessary to prevent the negative externalities<br />

created by large <strong>com</strong>panies.<br />

In the meantime, the widespread criticism over the<br />

“too big to fail” policy inevitably invites an exploration<br />

of the other side of the coin: the question of whether or<br />

not <strong>com</strong>panies can be<strong>com</strong>e “too big to succeed.”<br />

Running a large business is not easy. As <strong>com</strong>panies<br />

increase fixed costs, they often sacrifice the flexibility to<br />

respond nimbly to unforeseen challenges; they have more<br />

internal and external distractions; internal rivalries can derail<br />

growth; they can be<strong>com</strong>e the prey of smaller <strong>com</strong>petitors,<br />

who are constantly innovating, in an attempt to slice vulnerable<br />

niche opportunities out of the top dogs’ market share.<br />

www.journalofindexes.<strong>com</strong><br />

The innovations that can loft a smaller <strong>com</strong>petitor to new<br />

heights will barely move the needle for their top dog rivals.<br />

Recent research confirms that larger <strong>com</strong>panies typically<br />

exhibit a lower growth rate and earn a lower return<br />

on capital (e.g., Milano 2011) than smaller <strong>com</strong>panies.<br />

Therefore, in practice, what economists refer to as “diseconomies<br />

of scale” can be a dominating effect as large<br />

<strong>com</strong>panies grow: As a behemoth <strong>com</strong>pany grows to dominate<br />

its sector in terms of production efficiency and scale,<br />

its rosy performance may be<strong>com</strong>e a thing of the past.<br />

Being large puts the <strong>com</strong>pany under the scrutinizing<br />

lights of regulators. Arnott (2005, 2010) points out a<br />

potential connection between sector leaders’ misfortunes<br />

and the increase in government regulation. In a world of<br />

intense regulation, the relentless success of the top <strong>com</strong>panies<br />

makes them ever bigger targets for regulatory scrutiny.<br />

Was Goldman Sachs targeted with civil and criminal<br />

fraud charges in 2009-2010 because it has criminal intent<br />

to defraud its clients, while its <strong>com</strong>petition is pure as<br />

the driven snow? Or has Goldman be<strong>com</strong>e a symbol of<br />

success-to-excess, to an extent that prompts populists<br />

and pundits to want it to suffer?<br />

Is Exxon Mobil regularly pilloried in Washington<br />

because its business practices are monopolistic, its<br />

profit margins obscene and its product viewed as polluting<br />

and distasteful (never mind that we all buy it)? Or<br />

is it because the <strong>com</strong>pany’s relentless business success<br />

makes it a popular target?<br />

Of course, none of this is new.<br />

Initially, Bank of America management thought it would<br />

be lauded by the political elite for buying (and saving!)<br />

Merrill Lynch when Lehman imploded. Instead, it found<br />

itself on the proverbial horns of a dilemma when Merrill disclosed<br />

an extra $20 billion of losses before the deal closed.<br />

Bank of America could have canceled the deal by invoking<br />

the material adverse conditions (MAC) clause, or it could<br />

have proceeded and sought additional sources of capital.<br />

Threats were reportedly made, and Bank of America ultimately<br />

chose to proceed. Instead of being lauded for stepping<br />

up, it was pilloried for needing an infusion of capital,<br />

which it repaid, the CEO was driven out and the <strong>com</strong>pany<br />

was then sued for not canceling the deal.<br />

How much of this controversy was linked to the specific<br />

events surrounding the acquisition of Merrill, and how<br />

much was because Bank of America, by most measures, was<br />

the largest bank in the United States? How many of Citi’s<br />

“moments in the spotlight” have been due to the fact that it<br />

was Bank of America’s predecessor in the No. 1 spot?<br />

We’ll never know the answers to these questions, but the<br />

pattern is familiar. Microsoft’s opportunity in the spotlight<br />

came a decade ago, when it was attacked on the grounds<br />

of “monopolistic” business practices, in a repeat of similar<br />

earlier battles for IBM in the prior decade. In the 1980s, AT&T<br />

was successfully dismantled on the same basis. This script is<br />

now being revived for Google, No. 1 among search engines.<br />

Throughout this article, we’re focusing on market capitalization<br />

as our measure of <strong>com</strong>pany size. 1 The very business<br />

practices that propel an organization to No. 1 in market cap—<br />

November / December 2012 43


aggressiveness, focus, canny outmaneuvering of the <strong>com</strong>petition—be<strong>com</strong>e<br />

unacceptable if you’re wearing the yellow<br />

jersey. 2 Being No. 1 means always having to say you’re sorry!<br />

Being large also pushes the <strong>com</strong>pany into the headlines,<br />

“rewarding” the <strong>com</strong>pany with the highest coverage<br />

rate in mainstream media. Fang and Peress (2009)<br />

find that the coverage rate for NYSE stocks (mainly large<br />

stocks) is three to four times larger than for Nasdaq stocks.<br />

Too much media exposure is not always a blessing: That<br />

same study concludes that stocks with no media coverage<br />

earn higher returns than those with high media coverage.<br />

Moreover, given that their top dog status is partly due to<br />

share price, a high price is often needed to get to the vaunted<br />

No. 1 rank by market cap. The largest market-cap <strong>com</strong>panies<br />

are empirically likely to trade at higher multiples and<br />

higher prices. Existing literature documents various stock<br />

characteristics that empirically presage underperformance.<br />

For example, Basu (1977) studies the returns on the <strong>com</strong>mon<br />

stock of NYSE-listed firms, and suggests that high earningsto-price<br />

(E/P) firms—or low price-to-earnings (P/E) firms—<br />

have earned, on average, higher risk-adjusted returns than<br />

low E/P firms—or high P/E firms. Banz (1981) shows that<br />

stocks of small firms (measured by market cap) earned higher<br />

average returns than large-cap stocks. Further research, Basu<br />

(1983), concludes that small firms tend to have higher returns<br />

even after controlling for E/P. Fama and French (1992) argue<br />

that the superior returns of value strategies <strong>com</strong>pensate for<br />

the higher fundamental risks these strategies are bearing.<br />

An alternative behavioral explanation for the price-toearnings<br />

ratio (P/E) anomaly, documented in Dreman<br />

(1977) and supported more recently in the “clairvoyant<br />

value” work by Arnott, Li and Sherrerd (2009a and<br />

2009b), is that the mispricing of securities can be caused<br />

by a mismatch between market expectations and realized<br />

<strong>com</strong>pany performance. Specifically, market participants<br />

systematically overestimate the future earnings or growth<br />

of the low E/P firms, and systematically underestimate the<br />

future performance of the high E/P firms. This hypothesis<br />

is further supported by Lakonishok, Shleifer, and Vishny<br />

(1994), who show that naïve investors extrapolate firms’<br />

past performance into the future; these investors are often<br />

surprised when some out-of-favor (value) firms recover,<br />

and the stocks of these firms experience high returns.<br />

Companies with high market cap are often “glamour”<br />

stocks, carrying high prices and valuation multiples, reflecting<br />

consensus expectations for lofty growth, low risk or<br />

both. As a <strong>com</strong>pany grows in size, its products be<strong>com</strong>e<br />

more visible and, therefore, subject to a larger pool of investors’<br />

judgments. Investors often tend to project their likes or<br />

dislikes about a <strong>com</strong>pany’s products onto its stock. Apple<br />

successfully creates a near-cult following for its products;<br />

Apple fans are willing to stand in long lines overnight to get<br />

the newest product on the release date. Speculators seem to<br />

approach Apple’s stock with the same zeal—they are eager<br />

to buy Apple stock regardless of how expensive the stock is<br />

relative to its underlying fundamentals.<br />

Furthermore, many investors seem to ignore the fact that<br />

the forces that drove these <strong>com</strong>panies to dominate their<br />

<strong>com</strong>petitive landscape do not guarantee sustained growth<br />

in the future, or a sustained position at the top. Said another<br />

way, these investors do not appear to expect mean reversion<br />

in their growth forecasts; they form biased expectations<br />

based on extrapolating past successes that are often not<br />

predictive of the future. While it is easy (in theory, at least!)<br />

to double market share when a <strong>com</strong>pany holds 1 percent or<br />

2 percent of the market, it is impossible to double market<br />

share once the <strong>com</strong>pany has a 51 percent market share. 3<br />

Investors tend to ignore these simple facts and mistakenly<br />

price glamour stocks as if they were nimble enterprises<br />

whose past growth need never slow. The market<br />

be<strong>com</strong>es aware of such pricing errors only gradually, as the<br />

<strong>com</strong>pany fails to meet the unrealistic growth expectations<br />

imposed upon it. In short, size itself is be<strong>com</strong>ing less of an<br />

advantage and more of a curse.<br />

The organization with the No. 1 rank in market cap<br />

will often be a truly great <strong>com</strong>pany, but empirically is not<br />

necessarily a good investment. Therefore, investors should<br />

anticipate the underperformance of large <strong>com</strong>panies relative<br />

to the overall market.<br />

Too Big To Succeed?<br />

In a short white paper and an earlier FAJ Editor’s Corner,<br />

one of us (Arnott, 2005 and 2010) examined the performance<br />

of top <strong>com</strong>panies (by market cap) in the U.S. market. The<br />

study shows that, on average, the sector leader underperforms<br />

the average stock (equally weighted 4 ) in its own sector<br />

over the subsequent 1-, 3-, 5- and 10-year time horizons.<br />

An updated version of that research is shown in Figure<br />

1. The results are impressive.<br />

Do the added obstacles faced by winners hurt their investors?<br />

Yes. In fact, we find the leader in any sector underperforms<br />

the rest of its sector (equally weighted) by 4 percent<br />

in the next year ... and the next year … and the next year. As<br />

Figure 1 shows, the damage doesn’t really slow down for at<br />

least a decade, as the sector top dog lags its own sector by 3.7<br />

percent per year for the next decade! Put another way, with<br />

<strong>com</strong>pounding, the top stock in each of the 12 U.S. market<br />

sectors declined over 30 percent in value in 10 years, relative<br />

to the <strong>com</strong>petition in its respective sector, over the past 60<br />

years. Adjusting for overlapping samples, we find t-statistics<br />

ranging from 4.7 to 6.2, all highly significant.<br />

These shortfalls are large and statistically significant. But<br />

were these results dominated by a few large outliers? For<br />

example, how consistently did these sector top dogs fall<br />

short relative to the average stock in their own sectors? On a<br />

one-year basis, only 42 percent of the sector top dogs were<br />

able to beat the average for their respective sector <strong>com</strong>petitors.<br />

This anemic win rate keeps tumbling with time. On a<br />

10-year basis, fewer than three of 10 were winners. For the<br />

one-year result, we have 719 samples (60 years of data for<br />

11 sectors, and 59 years for utilities), for which the top dog<br />

won in only 303 cases and lost in 416 cases. That’s a pretty<br />

lopsided coin toss. On a 10-year basis, we have 611 samples;<br />

the sector top dog won in 174 cases, and lost in 437 cases. 5<br />

Our research also shows that sector top dog status changes<br />

frequently. In most sectors, the top dog is replaced several<br />

44<br />

November / December 2012


Figure 1<br />

sectors<br />

No.<br />

of<br />

top<br />

dogs<br />

Relative Performance For The Top Dogs, US Markets By Sector,1952-2011<br />

PaNel a.<br />

RelaTIVe ReTURN VS SeCTOR<br />

1 Yr 3 Yr 5 Yr 10 Yr 1 Yr<br />

3 Yr<br />

5 Yr<br />

10 Yr<br />

Source: Research Affiliates<br />

Note: We use SIC codes to define the 12 sectors. These definitions may vary from the GICS definitions.<br />

PaNel B.<br />

FReQUeNCY OF WINS VS SeCTOR<br />

Average, All Sectors 5.8 -4.2% -4.5% -4.1% -3.7% 42% 36% 33% 28%<br />

Standard Dev 2.9% 3.3% 2.7% 2.1% 6.6% 8.9% 10.9% 14.2%<br />

Adj. t-Statistic -5.02 -4.73 -5.22 -6.16 -4.28 -4.68 -4.28 -4.05<br />

Nondurables 6 -1.1% -1.7% -1.6% -2.7% 42% 45% 50% 33%<br />

Durables 6 -6.8% -7.7% -6.7% -6.1% 42% 31% 23% 14%<br />

Manufacturing 7 -4.7% -4.5% -3.8% -4.4% 45% 36% 38% 27%<br />

energy 1 0.2% 0.1% 0.2% 0.3% 52% 53% 52% 49%<br />

Chemicals 3 -3.2% -2.3% -2.3% -2.7% 53% 41% 38% 33%<br />

Bus equip 4 -4.2% -3.8% -3.9% -3.7% 47% 36% 36% 31%<br />

Tele<strong>com</strong> 4 -7.4% -9.0% -6.9% -6.6% 35% 26% 23% 12%<br />

Utilities (1953-2011) 8 -5.0% -4.5% -3.8% -3.9% 32% 28% 20% 16%<br />

Shops 2 -1.4% -1.5% -2.2% -2.0% 43% 43% 38% 45%<br />

Health Care 6 -3.6% -3.4% -3.1% -1.5% 43% 31% 32% 49%<br />

Finance 10 -3.0% -4.3% -6.0% -5.3% 37% 33% 32% 22%<br />

Other 13 -10.2% -11.2% -9.7% -6.1% 35% 22% 18% 10%<br />

US Nat’l Top Dog 7 -7.5% -6.4% -6.7% -5.4% 40% 33% 23% 14%<br />

times over the 60-year time span. The average sector has<br />

seen six top dogs over that span, while the “other” sector<br />

(stocks that don’t neatly fall into one of the other 11 sectors)<br />

has had a remarkable 13 different top dogs. With 13 different<br />

top dogs claiming and losing the No. 1 spot in the “other”<br />

sector, it’s no wonder that the 1-, 3-, 5- and 10-year shortfall<br />

for these top dogs is nearly always worst on the list.<br />

The title of “big loser” among the sectors has four contenders:<br />

tele<strong>com</strong>, with the demise of the Ma Bell monopoly; “other,”<br />

which we just discussed; durables, with their existential crises<br />

in the early 1980s and during the recent financial crisis; and<br />

finance, with rolling crises toppling one top dog after another.<br />

For these sectors, the top dog lags the average <strong>com</strong>petitor in its<br />

sector, over a subsequent 10-year holding period by an annual<br />

average of 6.6, 6.1, 6.1 and 5.3 percent, respectively!<br />

The “big winner” among sectors? Energy. Exxon Mobil<br />

(and its predecessors, Exxon and Standard Oil of New<br />

Jersey), never lost its top dog status, scoring an average<br />

of 0.3 percent outperformance per annum relative to the<br />

other energy stocks, over the subsequent decade. How did<br />

Exxon Mobil stay on top, when other sectors witnessed a<br />

revolving door of top dog contenders? Perhaps it remained<br />

a winner because it has always stuck to its core <strong>com</strong>petencies,<br />

avoided the <strong>com</strong>bative business practices that got<br />

other top dogs in trouble, was content with solid mainstream<br />

growth and profit margins, has not risen to the bait<br />

when under attack, and kept as low a profile as any top dog<br />

possibly could. The firm’s persistence at the top was clearly<br />

also aided by the 1999 merger of Exxon and Mobil, which<br />

<strong>com</strong>bined the Nos. 1 and 2 <strong>com</strong>panies in that sector.<br />

www.journalofindexes.<strong>com</strong><br />

The national top dog in the United States, beginning<br />

with American Telephone and Telegraph Company<br />

(AT&T) in 1952 and ending with Exxon Mobil in 2011,<br />

shows remarkable rotation at the top, with seven national<br />

top dogs in 60 years. 6 Given the heavy rotation at the top,<br />

it’s unsurprising that the shortfalls are bigger than for the<br />

sector top dogs. The average 10-year shortfall for the U.S.<br />

national top dog, measured against the other 999 stocks in<br />

the U.S. 1000 portfolio, is 5.4 percent, <strong>com</strong>pounded annually.<br />

There were only seven times out of 51—14 percent of<br />

the time, in other words—in which the national top dog<br />

beat the subsequent 10-year performance of our portfolio<br />

of the remaining 999 U.S. stocks.<br />

Other Countries Punish Their Top Dogs, Too<br />

In this paper, we extend the top dog research to include<br />

the G-8 markets: Australia, Canada, France, Germany, Italy,<br />

Japan, the United Kingdom and (of course) the United<br />

States. 7 With eight countries and 12 sectors, we now have<br />

96 sector top dogs—the <strong>com</strong>panies with the largest market<br />

capitalization, in each sector, for each country—every single<br />

year. It would be a painful overkill to scrutinize all of these,<br />

for each of the 30 years in our study. Accordingly, we create<br />

aggregates, first looking at the average spanning all 12 sectors<br />

for each country, then looking at the average across all<br />

eight countries for each sector. Viewed either way, the top<br />

dog performance shortfall in global markets is both larger<br />

and more reliable than it is in the United States.<br />

As shown in Figure 2a, the 10-year average shortfall,<br />

spanning the 12 sector top dogs for each of eight countries<br />

November / December 2012 45


Figure 2a<br />

Relative Performance For The Sector Top Dogs—Selected Global Markets, Average Of 12 Sectors, By Country, 1982-2011<br />

Sectors<br />

Panel A.<br />

RELATIVE RETURN BY COUNTRY, AVG, ALL SECTORS<br />

Panel B.<br />

FREQUENCY OF WIN BY COUNTRY, AVG, ALL SECTORs<br />

1 Yr 3 Yr 5 Yr 10 Yr 1 Yr<br />

3 Yr<br />

5 Yr<br />

10 Yr<br />

Average, 8 Countries -5.3% -5.0% -4.8% -5.1% 44% 42% 39% 34%<br />

Standard Dev 7.5% 6.6% 6.4% 7.2% 8.7% 12.3% 14.3% 22.1%<br />

Adj. t-Statistic -2.44 -2.62 -2.60 -2.45 -2.46 -2.30 -2.66 -2.47<br />

australia -4.8% -4.1% -4.1% -5.8% 48% 47% 48% 37%<br />

Canada -10.6% -11.1% -10.9% -11.5% 40% 36% 28% 19%<br />

France -6.7% -6.2% -5.7% -6.2% 42% 41% 38% 34%<br />

Germany -2.3% -2.2% -2.3% -2.3% 47% 46% 44% 45%<br />

Italy -3.0% -2.7% -3.6% -3.8% 48% 45% 43% 36%<br />

Japan -7.7% -6.3% -4.9% -4.9% 38% 39% 35% 29%<br />

United Kingdom -3.1% -3.2% -3.3% -3.7% 45% 47% 44% 41%<br />

United States -3.5% -4.2% -3.6% -2.8% 43% 34% 33% 33%<br />

Source: Research Affiliates<br />

Note: We use SIC codes to define the 12 sectors. These definitions may vary from the GICS definitions.<br />

Figure 2b<br />

Relative Performance For The Sector Top Dogs—Selected Global Markets, Average Of 8 Countries, By Sector, 1982-2011<br />

Sectors<br />

No.<br />

of<br />

top<br />

dogs<br />

Panel A. Relative Return vs Sector,<br />

average across countries<br />

1 Yr 3 Yr 5 Yr 10 Yr 1 Yr<br />

3 Yr<br />

5 Yr<br />

10 Yr<br />

Source: Research Affiliates. The individual results, country by country, are available upon request.<br />

Note: We use SIC codes to define the 12 sectors. These definitions may vary from the GICS definitions.<br />

Panel B. Frequency of Win vs Sector,<br />

average across countries<br />

Average, All Sectors 3.7 -5.3% -5.0% -4.8% -5.1% 44% 42% 39% 34%<br />

Nondurables 3.9 -3.9% -2.5% -1.5% -1.0% 43% 46% 48% 46%<br />

Durables 3.0 -7.9% -6.8% -6.1% -6.5% 40% 34% 34% 26%<br />

Manufacturing 4.9 -5.8% -7.3% -7.2% -7.3% 39% 36% 28% 21%<br />

Energy 2.1 1.0% -0.4% -0.9% -1.9% 51% 48% 48% 43%<br />

Chemicals 2.5 -2.5% -3.0% -3.6% -4.7% 44% 46% 44% 36%<br />

Bus Equip 5.8 -12.2% -12.4% -12.5% -12.0% 43% 38% 35% 32%<br />

Tele<strong>com</strong> 3.3 -6.1% -6.6% -6.5% -8.5% 48% 42% 34% 19%<br />

Utilities 3.1 -4.1% -3.3% -3.3% -4.7% 43% 43% 37% 31%<br />

Shops 3.1 -4.6% -4.1% -3.8% -4.2% 45% 42% 41% 36%<br />

Health Care 3.5 -5.5% -5.5% -4.8% -4.7% 43% 40% 40% 39%<br />

Finance 4.1 -4.1% -3.1% -3.5% -2.8% 45% 45% 39% 42%<br />

Other 5.1 -7.0% -5.2% -4.2% -3.1% 41% 42% 41% 36%<br />

over the 30-year sample period, ranges from just over 2<br />

percent per year in Germany to an astonishing 11.5 percent<br />

in Canada. On a 10-year basis, sector top dogs underperform<br />

their equal-weighted sectors by a whopping 5.1 percent per<br />

year, on average, across 12 sectors and eight countries. The<br />

odds of sector top dogs outperforming their sector, over a<br />

subsequent 10-year span, are not promising—ranging from<br />

45 percent in Germany to a horrific 19 percent in Canada. In<br />

all G-8 countries, over all four time spans—with no exceptions—the<br />

average sector top dog underperformed the<br />

<strong>com</strong>petition in its sector, from 1982 to 2011. This is not to<br />

say that this performance shortfall occurs in every starting<br />

year, or in every sector for all countries. But on average over<br />

time, these results are rather overwhelming, with all eight<br />

t-statistics <strong>com</strong>fortably significant.<br />

An alternative way to look at this is to average across<br />

all eight countries for each of the 12 sectors. Figure 2b<br />

shows the same average results as Figure 2a, of course.<br />

As we observed in the United States, in most sectors and<br />

in most countries, the top <strong>com</strong>pany changes with some<br />

regularity. The average sector, in the average G-8 country,<br />

has seen anywhere between two and six top dogs,<br />

46<br />

November / December 2012


over the 30-year span. “Business equipment” has seen<br />

an average of nearly six different top dogs in each of the<br />

G-8 countries; this may go a long way toward explaining<br />

why the top dogs in “business equipment” have the most<br />

wretched results, lagging their intracountry <strong>com</strong>petitors<br />

by an average of 12 percent per year over the subsequent<br />

decade. As in the U.S., energy top dogs fare best in the<br />

G-8, but this means they only hurt their investors by a bit<br />

less than 2 percent per year over the subsequent decade.<br />

In Arnott (2010), we found an even stronger relationship<br />

for the overall top dog, the largest <strong>com</strong>pany in the U.S.<br />

stock market by market cap. For purposes of this article,<br />

we term this stock the “national top dog.” In Figure 1, we<br />

saw that the average sector top dog in the United States<br />

underperformed the average stock in its own sector by over<br />

3 percent per year over the next decade; also, we can see<br />

that the U.S. national top dog underperforms the average<br />

Figure 3<br />

Growth Of $1,<br />

1951–2011<br />

10,000<br />

1000<br />

Comparative Performance Of US Top Dogs,<br />

Against Broad Market, CW And EW, 1951–2011<br />

100<br />

10<br />

1<br />

0.1<br />

1951<br />

1956<br />

1961<br />

1966<br />

1971<br />

1976<br />

1981<br />

1986<br />

1991<br />

1996<br />

2001<br />

2006<br />

■ US 1000 EW ■ US 1000 CW ■ Sector Top Dogs ■ National Top Dog<br />

Source: Research Afliates<br />

Figure 4<br />

Growth Of $1,<br />

1951–2011<br />

Relative Performance Of US Top Dogs And CW Market,<br />

Against EW Broad Market, 1951–2011<br />

10.00<br />

1.00<br />

0.10<br />

0.01<br />

1951<br />

1956<br />

1961<br />

1966<br />

Source: Research Afliates<br />

www.journalofindexes.<strong>com</strong><br />

1971<br />

1976<br />

1981<br />

1986<br />

1991<br />

1996<br />

2001<br />

2006<br />

■ US 1000 CW ■ Sector Top Dogs ■ National Top Dog<br />

<strong>com</strong>pany in the U.S. stock market by an average of 5 percent<br />

per year, over the subsequent decade.<br />

At this writing, the U.S. national top dog is Apple Inc.;<br />

however, there were six other <strong>com</strong>panies wearing that<br />

crown over the past 60 years. Whether Apple disappoints is<br />

anyone’s guess. But history is not encouraging; it’s currently<br />

priced to reflect a consensus expectation that it will be<br />

the largest source of profit distributions to its shareholders<br />

of any <strong>com</strong>pany on the planet.<br />

Figures 3 and 4 show the results of a strategy that<br />

concentrates on the top dogs. Here, we first identify<br />

the 1,000 U.S. stocks each year with the largest market<br />

cap, and the largest in each of the 12 sectors. We then<br />

either equal- or cap-weight the largest 1,000 stocks. We<br />

then look at the 12 sector top dogs, cap weighted, and<br />

the national top dog. These “portfolios” are reconstituted<br />

at the start of each year. Equal weighting trumps<br />

cap weighting by a hefty margin, as many others have<br />

already documented over the years. But we find that the<br />

cap-weighted roster of top dogs not only far underperforms<br />

the equal-weighted top 1,000, but even materially<br />

underperforms the cap-weighted market.<br />

While Figure 1 shows the performance of these various<br />

portfolios—ignoring trading costs or implementation<br />

slippage—from 1951 through 2011, Figure 2 shows the<br />

magnitude of wealth that is forfeited by the lesser strategies.<br />

Cap weighting leads to just 35 percent of the final<br />

wealth of the equal-weight portfolio of the top 1,000 <strong>com</strong>panies<br />

(selected by market cap). Holding the 12 sector<br />

top dogs, cap weighted, slices that to less than 20 percent<br />

of the equal-weight final wealth. And holding the national<br />

top dog leaves us with just over a penny of terminal<br />

wealth, relative to the investor with the equal-weighted<br />

1,000. Sobering results, indeed.<br />

The national top dog results in Figure 5 are less consistent<br />

than the results we observed in Figures 2a and<br />

2b. This is unsurprising. The number shown at the top of<br />

each of the columns in Figures 2a and 2b is an average<br />

of 96 samples for up to 30 years (for the one-year results,<br />

this means nearly 3,000 independent samples). Even the<br />

multiyear entries in Figures 2a and 2b represent up to 300<br />

independent samples. But the entries in Figure 5 have only<br />

one national top dog in each country for each year; hence,<br />

far fewer independent samples. This also means that the<br />

results for the individual countries offer up to 30 independent<br />

samples for the one-year results, and as few as three<br />

nonoverlapping independent samples, for the 10-year<br />

results, based on anywhere from just two to eight individual<br />

<strong>com</strong>panies. So, naturally, the results exhibit much<br />

more dispersion and much lower statistical significance.<br />

In Australia and Italy, the national top dog managed to<br />

beat the rest of the country’s market over the full 30 years,<br />

but only by a small margin, and only Italy shows a gain for<br />

the investor who chooses to retain the Italian top dog for 10<br />

years. In Canada, Germany, Japan and the United States,<br />

the national top dog performed far worse than in other<br />

countries, exhibiting a subsequent 10-year shortfall, relative<br />

to the broad market for the country, of 4 to 17 percent<br />

November / December 2012 47


Figure 5<br />

Relative Performance For The National Top Dog, Largest Market-Cap Company In Each Country ,1982-2011<br />

sectors<br />

no.<br />

of<br />

top<br />

dogs<br />

PaNEL a.<br />

RELaTIvE RETuRN BY CouNTRY<br />

1 Yr 5 Yr 5 Yr 10 Yr 1 Yr<br />

5 Yr<br />

5 Yr<br />

10 Yr<br />

Source: Research Affiliates<br />

Note: We use SIC codes to define the 12 sectors. These definitions may vary from the GICS definitions.<br />

PaNEL B.<br />

FREQuENCY oF WIN BY CouNTRY<br />

Average, 8 Countries -5.8% -5.7% -5.9% -4.7% 45% 39% 36% 38%<br />

Standard Dev 5.4 5.8% 4.4% 5.1% 6.0% 9.8% 15.2% 18.1% 18.8%<br />

Adj. t-Statistic -2.85 -3.64 -3.25 -2.22 -1.56 -1.90 -1.94 -1.06<br />

australia 2 0.6% -1.2% -2.1% -1.0% 40% 54% 50% 43%<br />

Canada 6 -8.3% -7.3% -10.5% -17.7% 50% 46% 31% 43%<br />

France 7 -2.0% -11.4% -12.5% -1.0% 50% 14% 4% 67%<br />

Germany 8 -14.6% -10.5% -8.9% -7.2% 40% 32% 23% 14%<br />

Italy 3 2.8% 1.7% 3.1% 1.6% 63% 61% 65% 57%<br />

Japan 6 -8.2% -6.0% -6.3% -4.4% 43% 29% 38% 14%<br />

united Kingdom 6 -9.0% -4.4% -2.6% -1.9% 30% 46% 38% 33%<br />

united States 5 -7.8% -6.2% -7.2% -5.9% 43% 32% 35% 29%<br />

global dev top dog,<br />

vs All dev 1,999 Index<br />

6 -12.5% -11.5% -11.2% -10.5% 33% 18% 15% 5%<br />

per year, <strong>com</strong>pounded. It would seem that the national<br />

top dogs are much more likely to underperform their own<br />

countries’ stock market averages than outperform them.<br />

On the bottom row of Figure 5, we look at the global<br />

developed top dog—the largest market-cap <strong>com</strong>pany<br />

in all developed markets—as <strong>com</strong>pared with the average<br />

stock in the All Developed Index. 8 The global<br />

developed top dog underperforms the other 1,999<br />

stocks in the developed market universe, by 12.5 percent<br />

in one year, fading only slightly to 10.5 percent<br />

per annum over a 10-year span. This global developed<br />

top dog beat its <strong>com</strong>parative universe in just one of 21<br />

ten-year spans. On average, an investor in the global<br />

developed top dog lost two-thirds of his or her wealth,<br />

relative to the investor who simply held the other 1,999<br />

largest market-cap stocks in the developed markets,<br />

and rebalanced once a year. While this out<strong>com</strong>e lacks<br />

statistical significance (we have just 30 independent<br />

one-year results, and only three independent 10-year<br />

results), the numbers are impressive.<br />

Finally, Figures 6 and 7 document the portfolio results<br />

for all 24 of the developed economies’ top dogs. At the<br />

beginning of each year, we first select four portfolios,<br />

selected by market capitalization:<br />

• the largest 2,000 developed stocks, both equal-weighted<br />

and cap-weighted<br />

• the national top <strong>com</strong>pany in each of the 24 countries,<br />

equal-weighted<br />

• the top <strong>com</strong>pany in each of the 12 global sectors, equalweighted<br />

• the largest-cap <strong>com</strong>pany among all 24 developed markets<br />

(a one-stock portfolio)<br />

Similar to what we observed in the United States,<br />

various cap-weighted top dog portfolios all underperform<br />

either a cap- or equal-weighted broad market index. As<br />

shown in Figure 6, if we invest $1 into each of these portfolios<br />

at the end of year 1981 and hold through year 2011,<br />

the equal-weighted top 2,000 global developed <strong>com</strong>panies<br />

would yield the highest ending wealth—over $22 for each<br />

$1 invested, after 30 years—while the one-stock global<br />

top dog portfolio, the largest market-cap <strong>com</strong>pany in the<br />

whole developed-markets universe, would only leave us<br />

with a scant 55 cents of terminal wealth. This portfolio<br />

would have lost half of its wealth in 30 years, despite<br />

reinvesting all of the dividends. Net of inflation, this portfolio<br />

is down about 90 percent. And relative to the equalweighted<br />

top 2,000, the global dog portfolio suffered an<br />

opportunity cost of 97.5 percent of its potential wealth.<br />

These results clearly suggest that most top dogs have<br />

a very serious problem. They are usually priced to reflect<br />

a consensus view that they will remain on top, and will<br />

continue to grow handily, but they often don’t. They are<br />

usually high-multiple growth stocks and popular “safe<br />

havens.” If they continue to grow, they can justify current<br />

values and can perform as well as their peers. If they<br />

attract unwel<strong>com</strong>e attention from regulators, or if their<br />

<strong>com</strong>petitors gang up on them, they cannot maintain that<br />

perch indefinitely. Unfortunately, these underperforming<br />

top dogs are indeed big: They <strong>com</strong>prise a substantial<br />

share of the cap-weighted indexes. For this reason, these<br />

<strong>com</strong>panies—and their propensity to disappoint—matter.<br />

We document this top dog concentration in Figure 8.<br />

Consider the column for the United States. On average, the<br />

top dog in durables, energy and chemicals (over most of<br />

this span, these would be GM, Exxon Mobil and DuPont)<br />

has <strong>com</strong>prised over one-fourth of its sector, while the largest<br />

of the utilities and finance <strong>com</strong>prised just 6 percent of<br />

that heavily regulated sector. Across all sectors in the United<br />

48<br />

November / December 2012


States, the average concentration puts 17 percent of our capweighted<br />

dollars into the single largest-cap <strong>com</strong>pany.<br />

National top dogs naturally dominate their country<br />

market less than the sector top dogs dominate their country-specific<br />

sectors. On average, in the United States, the<br />

national top dog <strong>com</strong>prises 3 percent of the entire U.S.<br />

stock market. In other countries, the indexes are more<br />

reliant on their top dogs than that in the United States;<br />

only Japan shows concentration similar to the United<br />

States. For most countries, the concentration is two to<br />

three times as great. In the G-8 developed economies in<br />

this study, the largest single stock <strong>com</strong>prises an average<br />

of 7 percent of the country index, and the sector top dogs<br />

<strong>com</strong>prise an average of 34 percent of their own sector.<br />

Figure 6<br />

Comparative Performance Of Developed-Market<br />

Top Dogs, Against Broad Market, CW And EW, 1981-2011<br />

32.00<br />

The 96 sector top dogs—each of which is the single<br />

largest <strong>com</strong>pany in its sector-country <strong>com</strong>bination—lag<br />

the performance of the average stock in their own sectors<br />

by an average of 5.3 percent in the subsequent year. As<br />

these <strong>com</strong>panies <strong>com</strong>prise an average of 34 percent of<br />

their respective sectors, simple arithmetic suggests that<br />

the sector top dogs pull down investment performance—<br />

for the cap-weighted market portfolio for each of these<br />

countries—by about 1.8 percent per year, globally. Put<br />

another way, we could have historically beat the capweighted<br />

market portfolio in most countries by 1.8 percent<br />

per year, through the simple expedient of excluding<br />

the single largest-cap stock in each sector.<br />

Furthermore, because the performance drag for the sec-<br />

Figure 7<br />

Relative Performance Of Developed-Market Top Dogs<br />

And CW Market, Against EW Market, 1981–2011<br />

10.00<br />

Growth Of $1,<br />

1981–2009<br />

16.00<br />

8.00<br />

4.00<br />

2.00<br />

1.00<br />

.50<br />

.25<br />

1981<br />

1984<br />

1987<br />

1990<br />

1993<br />

1996<br />

1999<br />

2002<br />

2005<br />

2008<br />

Growth Of $1, Relative To Equal Weight<br />

Average 1981–2009<br />

1.00<br />

0.10<br />

0.01<br />

1981<br />

1984<br />

1987<br />

1990<br />

1993<br />

1996<br />

1999<br />

2002<br />

2005<br />

2008<br />

■ Dev 2000 EW ■ Dev 2000 CW ■ Sector Top Dogs<br />

■ National Top Dogs ■ Global Top Dog<br />

■ Developed CW ■ Sector Top Dogs<br />

■ National Top Dogs ■ Global Top Dog<br />

Source: Research Afliates<br />

Source: Research Afliates<br />

Figure 8<br />

Country And Sector Top Dogs Share Of Total Market Cap In Each Country Or Sector, 1982 —2011<br />

Sectors Australia Canada France Italy Germany Japan<br />

Source: Research Affiliates<br />

Note: We use SIC codes to define the 12 sectors. These definitions may vary from the GICS definitions.<br />

United<br />

Kingdom<br />

United<br />

States<br />

Avg. Across<br />

Countries<br />

Avg Across Sectors 42% 32% 40% 50% 43% 20% 32% 17% 34%<br />

Nondurables 33% 41% 27% 25% 22% 12% 19% 16% 24%<br />

Durables 52% 65% 44% 72% 45% 24% 34% 26% 45%<br />

Manufacturing 34% 19% 21% 22% 16% 7% 20% 23% 20%<br />

Energy 31% 21% 57% 97% 91% 34% 48% 28% 51%<br />

Chemicals 62% 51% 59% 58% 52% 11% 46% 27% 46%<br />

Bus Equip 23% 25% 25% 59% 58% 11% 30% 18% 31%<br />

Tele<strong>com</strong> 71% 40% 49% 45% 80% 64% 60% 21% 54%<br />

Utilities 70% 30% 86% 45% 35% 26% 44% 6% 43%<br />

Shops 36% 23% 27% 49% 40% 11% 19% 16% 27%<br />

Health Care 44% 40% 51% 67% 41% 20% 45% 12% 40%<br />

Finance 19% 14% 18% 24% 20% 9% 14% 6% 15%<br />

Other 27% 13% 14% 34% 23% 7% 11% 7% 17%<br />

Nat’l Top Dog Avg. 11% 6% 7% 14% 8% 4% 6% 3% 7%<br />

www.journalofindexes.<strong>com</strong><br />

November / December 2012 49


tor top dogs tends to persist for at least a decade, an investor<br />

might do even better by leaving out all of the <strong>com</strong>panies<br />

that have been sector leaders any time in the past 10 years.<br />

Looking At Emerging Markets<br />

Sector and national top dogs clearly fare badly across the<br />

developed world. But what of the ostensibly less-efficient<br />

emerging markets? The “top dog” effect should arguably be<br />

more powerful in less efficient or less developed markets. On<br />

the other hand, do the (typically) superb political connections<br />

of the emerging market top dogs with their respective nations’<br />

leadership insulate them from the “too big to succeed” syndrome?<br />

As we saw with Yukos in Russia—a particularly vivid<br />

example—political connections can cut both ways.<br />

There are challenges with these tests. First, the emerging<br />

economies are very concentrated. The single largest-cap<br />

stock in each country is pretty dominant in that country’s<br />

economy. Figure 9 shows how very concentrated these markets<br />

are, even today. In even the larger, and more diversified,<br />

emerging market economies, the top 10 stocks by market<br />

capitalization <strong>com</strong>prise an average of over 60 percent of the<br />

entire stock market. The national top dog <strong>com</strong>prises an average<br />

of over 20 percent of the stock market, in these, the largest<br />

and best diversified of the emerging markets. By contrast,<br />

for the G-8 developed economies that <strong>com</strong>prise the core of<br />

our study, the national top dog is typically 9 percent of the<br />

country’s stock market. This concentration in the emerging<br />

markets does not weaken the effect that we’ve documented<br />

in this paper, but it does lead to more erratic results.<br />

Naturally, this kind of concentration at the top does no<br />

harm to these <strong>com</strong>panies’ investors, if this concentration is<br />

a consequence of the <strong>com</strong>panies’ business dominating the<br />

economy, and that the long-term growth prospects of the<br />

national top dogs continue to match or exceed that of the<br />

nation’s economy, and the share price for these top dogs<br />

does not exceed whatever the future growth would justify.<br />

If these conditions do not hold true, then we should see the<br />

Figure 9<br />

Percentage Of Nation’s Stock Market,<br />

Top 10 Companies<br />

90<br />

80<br />

70<br />

60<br />

50<br />

40<br />

30<br />

20<br />

10<br />

0<br />

Israel:<br />

Teva Pharma<br />

Russia:<br />

Gazprom<br />

Mexico:<br />

America Movil<br />

Source: Research Affiliates<br />

Concentration In Emerging Markets,<br />

Top 10 Stocks By % Weight, 2012<br />

Chile:<br />

LATAM Airlines<br />

Indonesia:<br />

Astra International<br />

Thailand:<br />

PTT<br />

Brazil:<br />

Petrobras<br />

China:<br />

China Mobile<br />

South Korea:<br />

Samsung<br />

South Africa:<br />

MTN Group<br />

India:<br />

ITC<br />

Taiwan:<br />

Taiwan Semicond<br />

same “top dog drag” in the emerging markets as we do in<br />

the developed markets. And, indeed, we do.<br />

Secondly, emerging markets data do not extend as far<br />

back as the developed markets: We have good data on 24<br />

emerging market countries, and their constituent stocks,<br />

back to about 1995, so our study must begin in 1996.<br />

Thirdly, because some of the smallest emerging market<br />

economies <strong>com</strong>prise a dozen or fewer <strong>com</strong>panies, the<br />

tests of the 12 sector top dogs in each country will be far<br />

less meaningful. 9 Especially with the smaller markets,<br />

this concentration makes our tests essentially meaningless.<br />

So, we begin by identifying the 12 countries<br />

with the largest average market cap from 1996 through<br />

2011; these countries have <strong>com</strong>prised an average of 88<br />

percent of the emerging markets total, by market cap.<br />

Our universe each year was the 1,000 largest-cap stocks<br />

domiciled in these 12 countries. 10<br />

As we can see in Figure 10, the top dog effect is even<br />

more impressive in emerging economies than in the developed<br />

world. Even though our history for emerging economies<br />

is much shorter than for the developed world, we<br />

see the same rotation among the top dogs as we find in<br />

the developed world. In just 16 years, there are anywhere<br />

from two to eight “national top dogs,” averaging nearly five<br />

per country. This means that the average national top dog<br />

stays on top for only about three years.<br />

For seven of the 12 countries, the national top dog beats<br />

the equally weighted average of all other stocks over the next<br />

year. But the seven winners were small winners (averaging<br />

just 2 percent outperformance), and the five losers were big<br />

losers (averaging 11.5 percent underperformance). For just<br />

two countries—Mexico and Taiwan—the “win” extends an<br />

average of five years. 11 That’s scant <strong>com</strong>fort, even in those<br />

two markets, because past is not prologue; the “normal” pattern<br />

may well be evident in the years ahead. The portfolio<br />

of 12 national top dogs lags the average of all other stocks in<br />

that country by 3.6 percent in its first year, with the shortfall<br />

accelerating over the subsequent four years, so that the average<br />

annual shortfall over a five-year span was 7.2 percent.<br />

The overall emerging markets top dog—the <strong>com</strong>pany with<br />

the largest market cap in the entire emerging markets 1000<br />

universe—turns out to be astoundingly wretched. There were<br />

nine overall emerging markets top dogs in 16 years, which<br />

means that on average, an emerging markets top dog lasted<br />

less than two years. The underperformance over a single<br />

year—measured relative to the equally weighted average of<br />

the other 999 <strong>com</strong>panies in our emerging markets universe—<br />

averaged 20.1 percent, which continued for five years. The<br />

average five-year shortfall was 16.7 percent per year. Over our<br />

brief 16-year history, an investment in the overall emerging<br />

markets top dog would have turned $1 into 9 cents, a 91 percent<br />

loss, while an investment in the equally weighted emerging<br />

market universe would have gained 161 percent.<br />

We saved our most surprising exhibits for last. Figure 11<br />

should be troubling to advocates of efficient markets. For<br />

each of our top dogs, we examine performance relative to<br />

a relevant benchmark, before and after their selection for<br />

one of our top dog portfolios.<br />

50<br />

November / December 2012


Figure 10<br />

sectors<br />

Source: Research Affiliates<br />

Relative Performance For The Country Top Dogs, Largest Stocks In Selected EM Markets, 1996-2011<br />

No.<br />

of<br />

top<br />

dogs<br />

PanEL a.<br />

RELaTIvE RETuRn BY CounTRY<br />

PanEL B.<br />

FREQuEnCY oF WIn BY CounTRY<br />

1 Yr 3 Yr 5 Yr 1 Yr 3 Yr 5 Yr<br />

Avg, All 12 Countries -3.6% -6.7% -7.2% 42% 36% 36%<br />

Standard Deviation 4.7 8.4% 8.6% 8.1% 13.9% 20.8% 17.5%<br />

Adj. t-Statistic -1.49% -2.72 -3.08 -2.07 -2.28 -2.74<br />

Brazil 2 2.6% -12.2% -13.3% 44% 64% 58%<br />

Chile 4 -0.7% 1.3% -2.9% 56% 43% 50%<br />

China 7 3.1% -2.0% -2.2% 56% 50% 42%<br />

India 6 -18.2% -21.6% -18.9% 19% 7% 8%<br />

Korea 3 1.3% -1.5% -6.4% 56% 50% 33%<br />

Malaysia 6 -11.1% -10.1% -9.4% 25% 14% 17%<br />

Mexico 2 3.8% 4.6% 3.9% 56% 43% 50%<br />

Russia 5 0.8% -21.6% -23.9% 38% 14% 17%<br />

South africa 8 -8.5% -7.2% -4.0% 31% 14% 25%<br />

Taiwan 2 2.2% 1.3% 1.4% 50% 64% 58%<br />

Thailand 6 0.4% -3.0% -4.2% 44% 50% 50%<br />

Turkey 5 -19.1% -8.8% -6.2% 25% 21% 25%<br />

EM Top Dog, vs<br />

EM Top 999 Index<br />

9 -20.1% -20.1% -16.7% 25% 14% 0%<br />

In the United States, we <strong>com</strong>pare our 12 sector top<br />

dogs each year with the equal-weighted performance<br />

of their respective sectors, over the previous and subsequent<br />

five years; our national top dog is <strong>com</strong>pared with<br />

the equal-weighted top 1,000 <strong>com</strong>panies in the United<br />

States. In the global developed markets, we <strong>com</strong>pare our<br />

288 sector top dogs for each year (12 sectors, times 24<br />

countries) with the equal-weighted performance of their<br />

respective sectors in their respective countries, over the<br />

previous and subsequent five years. Similarly, our 24<br />

national top dogs are <strong>com</strong>pared with their own equalweighted<br />

national returns (drawn from <strong>com</strong>panies that<br />

are in the top 1,000 market-cap <strong>com</strong>panies in the United<br />

States and the top 1,000 in the world outside of the<br />

United States). Finally, the global top dog is <strong>com</strong>pared<br />

with the remaining 1,999 <strong>com</strong>panies in the Developed<br />

World Index, equally-weighted. Of course, for the earliest<br />

years, we’ll have less than five prior years, and for the<br />

latest years, we’ll have less than five subsequent years.<br />

So, Figure 11 aggregates all of the data that we have.<br />

It should surprise no one to see that our top dogs outperformed<br />

in the five years leading up to their selection for<br />

that title. At some stage, superior performance is necessary<br />

in order for any <strong>com</strong>pany to be<strong>com</strong>e No. 1 in its sector, or<br />

its country or the world. It came as a shock to us to find that<br />

the subsequent underperformance is a mirror image of the<br />

prior outperformance. Country, sector and global top dogs<br />

lose from 17 to 36 percent of an investor’s wealth, over<br />

the subsequent five years, relative to a relevant equally<br />

weighted peer <strong>com</strong>parison.<br />

www.journalofindexes.<strong>com</strong><br />

Figure 11<br />

Relative Performance Of US And Global Top Dogs, Against Relevant<br />

EW Benchmark, Before And After Top Dog Status, 1981-2011<br />

Relative Performance,<br />

Relative To Top 1000 EW<br />

100%<br />

95%<br />

90%<br />

85%<br />

80%<br />

75%<br />

70%<br />

65%<br />

60%<br />

T-5<br />

T-4<br />

T-3<br />

Source: Research Afliates<br />

T-2<br />

T-1<br />

Conclusion<br />

In a world of intense regulation and furious <strong>com</strong>petition,<br />

<strong>com</strong>panies can be<strong>com</strong>e “too big to succeed.” For the<br />

No. 1 <strong>com</strong>pany with the largest market share and largest<br />

market capitalization, the top dog crown not only makes it a<br />

popular target for regulatory scrutiny, but also can prevent<br />

its continuing lofty success. For investors, the most beloved<br />

top dog in any sector or country is often a truly impressive<br />

continued on page 64<br />

T<br />

■ Dev Nat Top Dogs ■ US Nat Top Dog ■ US Sector Top Dog<br />

■ Dev Sector Top Dogs ■ Global Dev Top Dog<br />

T+1<br />

T+2<br />

Years Before And After Top Dog Status<br />

November / December 2012 51<br />

T+3<br />

T+4<br />

T+5


News<br />

Home Prices Nearing Recovery?<br />

U.S. home prices were up again in<br />

June, continuing to stage a recovery<br />

from cycle lows seen earlier this year,<br />

and fueling sentiments that perhaps<br />

the market might be finally finding a<br />

permanent bottom.<br />

Home values across the U.S., on<br />

average, remain roughly a third off<br />

their 2006 peak levels, but the price<br />

trajectory now seems to be pointing<br />

higher, as all 20 cities surveyed for the<br />

monthly S&P/Case-Shiller Home Price<br />

Index saw price improvement again in<br />

June, the second month in a row.<br />

Indeed, the latest report showed<br />

that the 10-City and 20-City <strong>com</strong>posites<br />

saw month-on-month gains of 2.2<br />

and 2.3 percent, respectively, in June.<br />

Both <strong>com</strong>posites have now rebounded<br />

some 6 percent from the most recent<br />

cycle lows forged back in March.<br />

From an annual perspective, home<br />

prices on a national level were up<br />

1.2 percent at the end of the second<br />

quarter year-on-year after the U.S.<br />

National <strong>com</strong>posite—the benchmark<br />

that includes all nine U.S. census<br />

divisions and is released only once<br />

every quarter—rose nearly 7 percent<br />

in the second quarter alone.<br />

Both the 10-City and 20-City <strong>com</strong>posites<br />

were also higher in June from<br />

where they were just a year ago, even<br />

if marginally—the 10-City was up 0.1<br />

percent on the year while the latter<br />

was up 0.5 percent.<br />

Detroit was the best-performing<br />

market in June, with home prices there<br />

rising 6 percent from May levels. But<br />

it’s worth noting that a home in Detroit<br />

today still costs less than it did in<br />

January 2000, something that can also<br />

be said about Atlanta and Las Vegas.<br />

Another market of note is Phoenix,<br />

where home prices have improved<br />

nearly 14 percent in the past year—<br />

the best annual recovery among all<br />

20 markets surveyed. By <strong>com</strong>parison,<br />

home prices in Atlanta in June<br />

remained 12 percent lower than they<br />

were just a year ago.<br />

Federal Court Dismisses<br />

ProShares Suit<br />

In September, a federal court in<br />

New York dismissed a class action suit<br />

against ProShares, the world’s biggest<br />

purveyor of leveraged and inverse<br />

ETFs, saying charges that it didn’t fully<br />

inform investors of risks of investing in<br />

its funds didn’t stand up to scrutiny,<br />

according to court documents.<br />

Furthermore, the U.S. District<br />

Court for the Southern District of<br />

New York found charges by the plaintiffs<br />

that the New York-based fund<br />

<strong>com</strong>pany knew in advance that investors<br />

could lose a lot of money were<br />

“implausible,” the court ruling said.<br />

The lawsuit was filed in August 2009<br />

by investors who were not in full <strong>com</strong>mand<br />

of how the <strong>com</strong>pany’s leveraged<br />

and inverse funds work. The ETFs are<br />

designed to magnify returns of their<br />

underlying indexes by two or three times,<br />

and some also deliver the inverse returns<br />

of their indexes. The ruling appears to<br />

en<strong>com</strong>pass all the remaining legal action<br />

against the <strong>com</strong>pany, putting the entire<br />

episode in the rearview mirror.<br />

The rude awakening for investors<br />

came when they began to grasp that<br />

the funds, because they rebalance daily,<br />

frequently have returns that deviate significantly<br />

from those of their indexes.<br />

At the heart of the matter is the<br />

funds’ performance during the height<br />

of the credit crisis—mid-2008 through<br />

the first half of 2009—when many of<br />

the ProShares funds faced substantial<br />

losses despite “their underlying indexes<br />

having moved in a direction that the<br />

investors expected to be favorable during<br />

the period,” the documents said.<br />

ProShares’ general counsel said<br />

that the firm was “always confident<br />

that the allegations were without<br />

merit” and that it was pleased the<br />

claims were dismissed.<br />

Judge John Koeltl wrote in the Sept.<br />

10 opinion that, contrary to what<br />

52<br />

November / December 2012


investors were charging, ProShares<br />

was explicit in disclosing particular<br />

risks associated with investing in its<br />

leveraged and inverse ETFs.<br />

He said that the registration<br />

statements at <strong>issue</strong> “stated in plain<br />

English” that the ETFs’ objectives<br />

were daily only, and their performance<br />

could “diverge significantly”<br />

from the underlying index when they<br />

were held for longer than one day.<br />

Russell Debuts ‘GeoExposure’<br />

Index Family<br />

Russell Investments rolled out a<br />

group of indexes in mid-September<br />

that serve up emerging markets exposure<br />

through portfolios <strong>com</strong>prising<br />

developed-market <strong>com</strong>panies.<br />

In what the <strong>com</strong>pany calls its<br />

GeoExposure Index series, Russell<br />

designed benchmarks made up of<br />

stocks of developed-country <strong>com</strong>panies<br />

that derive significant portions of<br />

their revenues from emerging economies.<br />

The <strong>com</strong>pany argued that where<br />

a <strong>com</strong>pany is based no longer is crucial<br />

in determining how it makes money.<br />

The methodology relies on financial<br />

statement analysis expertise from<br />

the financial information firm Revere<br />

Data to estimate just how much of a<br />

<strong>com</strong>pany’s revenue stream is <strong>com</strong>ing<br />

from emerging markets, Russell said.<br />

The indexes factor in not only<br />

what percentage of a <strong>com</strong>pany’s revenue<br />

originates in emerging markets<br />

but also the total dollar revenue<br />

<strong>com</strong>ing in from the region. Each<br />

<strong>com</strong>pany’s weighting in the mix is<br />

designed to reflect the significance of<br />

this exposure by taking into account<br />

the percentage of revenue, the dollar<br />

amount and the <strong>com</strong>pany’s market<br />

capitalization. The intention, according<br />

to a Russell representative, is to<br />

eliminate size bias and improve the<br />

tradability of the indexes.<br />

The benchmarks are created from<br />

existing Russell Global indexes and<br />

are rules-based. The series initially<br />

consists of four indexes:<br />

• Russell 1000 Emerging Markets<br />

GeoExposure Index<br />

• Russell Developed Large<br />

Cap Emerging Markets<br />

GeoExposure Index<br />

• Russell Developed Europe<br />

Large Cap Emerging Markets<br />

GeoExposure Index<br />

• Russell Developed ex-North<br />

America Large Cap Emerging<br />

Markets GeoExposure Index<br />

FTSE Signs Deal With<br />

Chinese Exchange<br />

FTSE is teaming up with a subsidiary<br />

of the Shenzhen Stock Exchange<br />

to develop new indexes based on<br />

Chinese A-Shares, with a particular<br />

focus on alternative-weighting<br />

methodologies and socially responsible<br />

investing, the index provider<br />

said in early September.<br />

Under a new agreement, FTSE and<br />

the Shenzhen Securities Information<br />

Co. Ltd will undertake new indexrelated<br />

research and create specialized<br />

A-Share indexes based on so-called<br />

intelligent beta indexing methodologies<br />

and/or on ESG standards that screen<br />

<strong>com</strong>ponents according to environmental,<br />

social and governance parameters.<br />

With an established data license<br />

agreement in place, the agreement<br />

signed in September builds<br />

on an existing relationship with the<br />

Shenzhen Stock Exchange, FTSE said.<br />

Shenzhen Securities Information<br />

Co. Ltd is the organization authorized<br />

by the Shenzhen Stock Exchange to<br />

manage its securities information.<br />

China A-Shares are securities from<br />

mainland China-based <strong>com</strong>panies,<br />

and have been mostly off limits to<br />

foreign investment and tightly regulated<br />

by the government. However,<br />

ongoing financial reforms that aim to<br />

improve foreign investment in mainland<br />

<strong>com</strong>panies are slowly making<br />

A-Shares more accessible.<br />

FocusShares Closes Up Shop<br />

FocusShares, the ETF unit of brokerage<br />

firm Scottrade, announced in<br />

early August it was liquidating its<br />

entire roster of 15 ETFs due to the<br />

funds’ failure to attract enough assets.<br />

The funds had <strong>com</strong>bined net<br />

assets of some $100 million at the<br />

time of the announcement. They last<br />

traded on Aug. 17, and closed to new<br />

investment as of Aug. 20.<br />

Any investor still holding any<br />

of the ETFs by Aug. 30 was promised<br />

a cash distribution equal to the<br />

amount of the net asset value of<br />

those shares as of that date, including<br />

any capital gains and dividends.<br />

There will be no fees tied to that.<br />

The move came just a few short<br />

months after FocusShares launched<br />

an aggressive marketing campaign<br />

designed to bring investor attention<br />

to its low-cost funds. At that time,<br />

the <strong>com</strong>pany hoped to bring to the<br />

limelight the fact that some of its<br />

ETFs were the cheapest on the market<br />

and that Scottrade clients could<br />

trade them <strong>com</strong>mission-free.<br />

Scottrade acquired FocusShares<br />

in 2010, some two years after<br />

FocusShares had shuttered a number<br />

of niche ETFs in the wake of the 2008<br />

market crash. The <strong>com</strong>pany rolled out<br />

its roster of funds back in March 2011.<br />

The list of liquidated ETFs includes<br />

the broad Focus Morningstar US<br />

Market Index ETF (NYSE Arca: FMU),<br />

and three ETFs targeting the large-,<br />

mid- and small-cap segments as well<br />

as 11 sector-focused ETFs.<br />

Russell Closing ETFs,<br />

Altering Plan<br />

Scottrade’s announcement of the<br />

liquidation of its FocusShares ETFs<br />

www.journalofindexes.<strong>com</strong> November / December 2012 53


News<br />

was followed quickly by a similar<br />

announcement from Russell.<br />

The firm, which began launching its<br />

own ETFs in May 2011 with a distilled<br />

focus on indexed smart-beta strategies,<br />

said it was closing all but one of its<br />

funds due to their lack of assets.<br />

However, Russell won’t abandon<br />

its ETF plans entirely. Instead it will<br />

narrow its focus on actively managed<br />

strategies. It made tangible that<br />

strategic shift by leaving open the<br />

actively managed Russell Equity ETF<br />

(NYSE Arca: ONEF), according to a<br />

<strong>com</strong>pany press release.<br />

The <strong>com</strong>pany made a splashy debut<br />

in the spring of last year with the rollouts<br />

of numerous smart-beta ETFs,<br />

such as the Russell 1000 Low Volatility<br />

ETF (NYSE Arca: LVOL), which it said<br />

were the next logical step in the ETF<br />

revolution. Some smart-beta funds<br />

have found success, but Russell’s funds<br />

never really took off. Some industry<br />

sources have said the funds’ strategies<br />

were difficult to grasp.<br />

The press release announcing the<br />

decision noted the 25 funds had total<br />

assets of $310 million as of July 31.<br />

According to Russell, the affected<br />

funds were to close to new investment<br />

on Oct. 9, 2012, with the final<br />

liquidation scheduled for Oct. 24.<br />

Investors who still held shares on<br />

Oct. 16 were to receive cash equal to<br />

the amount of the net asset value of<br />

their shares as of that date.<br />

Shiller, Barclays Launch<br />

‘CAPE’ Indexes<br />

In September, Barclays Plc rolled out<br />

a family of equity indexes with a value<br />

tilt in partnership with Yale economics<br />

professor Robert Shiller; the index<br />

series filters sectors in connection with<br />

changes in price/earnings ratios.<br />

The Shiller Barclays CAPE Index<br />

Family uses the cyclically adjusted<br />

price-to-earnings ratio (CAPE) as a<br />

key driver for the valuation of sectors<br />

and is designed for buy-andhold<br />

investors with a multiyear time<br />

horizon. Although European indexes<br />

are slated to launch in the <strong>com</strong>ing<br />

months, the first three benchmarks<br />

in the Shiller Barclays CAPE Index<br />

Family will initially include three<br />

indexes based on U.S. sectors, each<br />

available in dollar, British sterling<br />

and euro currency versions. Those<br />

three are:<br />

• Shiller Barclays CAPE US Sector<br />

Tilted Index, which is overweight<br />

four favored sectors and underweight<br />

six least-favored sectors<br />

• Shiller Barclays CAPE US Sector<br />

Index, which equal-weights four<br />

favored sectors<br />

• Shiller Barclays CAPE US Sector<br />

Market Hedged Index, which<br />

holds a long position in the Shiller<br />

Barclays CAPE US Sector Index and<br />

a short position according to the<br />

beta of the sectors<br />

The Shiller Barclays CAPE Index<br />

Family is calculated and published<br />

by Barclays Index, Portfolio and Risk<br />

Solutions team, Barclays said. It will be<br />

published on Barclays Live, the Barclays<br />

Index website and on Bloomberg.<br />

INDEXING DEVELOPMENTS<br />

FTSE Debuts Minimum<br />

Variance Series<br />

FTSE kicked off August with the<br />

launch of its FTSE Global Minimum<br />

Variance Index series, according to a<br />

press release from the index provider.<br />

The benchmark family’s methodology<br />

seeks to offer lower volatility than<br />

traditional indexes—thus potentially<br />

improving the risk/return profile—<br />

while still providing broad exposure<br />

to the underlying markets.<br />

The index family is derived from<br />

the FTSE All-World Developed Index<br />

series, the press release said, and the<br />

initial launch included eight indexes:<br />

• FTSE Developed Minimum<br />

Variance Index<br />

• FTSE Developed Europe Minimum<br />

Variance Index<br />

• FTSE Developed Europe ex UK<br />

Minimum Variance Index<br />

• FTSE Eurobloc Minimum<br />

Variance Index<br />

• FTSE Developed Asia Pacific<br />

Minimum Variance Index<br />

• FTSE Developed Asia Pacific ex<br />

Japan Minimum Variance Index<br />

• FTSE USA Minimum Variance Index<br />

• FTSE Japan Minimum Variance Index<br />

FTSE actually launched a minimum-variance<br />

version of its bluechip<br />

U.K. index, the FTSE 100, in late<br />

2011, but it has a separate methodology,<br />

a FTSE fact sheet said.<br />

DJIA Sees Component Change<br />

S&P Dow Jones Indices<br />

announced in mid-September that<br />

the Dow Jones industrial average’s<br />

<strong>com</strong>ponent list would change as of<br />

Sept. 21, according to a press release<br />

from the index provider.<br />

UnitedHealth Group Inc. replaced<br />

Kraft Foods Inc.—the latter <strong>com</strong>pany<br />

is in the process of spinning off its<br />

North American grocery business,<br />

the press release said. Kraft’s breakup<br />

was scheduled to be <strong><strong>com</strong>plete</strong>d on<br />

Oct. 1, with the new <strong>com</strong>pany adopting<br />

the name Kraft Foods Group and<br />

its parent <strong>com</strong>pany going by the<br />

name Mondelez International Inc.<br />

The press release said that the<br />

change was decided upon after the<br />

Dow Jones Averages Index <strong>com</strong>mittee<br />

determined Mondelez’s smaller<br />

size and expected reduction in revenue<br />

from the U.S. would render<br />

it “less representative” of the U.S.<br />

large-cap segment.<br />

UnitedHealth Group is a provider<br />

54<br />

November / December 2012


of health care services, primarily in<br />

the area of insurance. S&P DJI said<br />

that the index <strong>com</strong>mittee believed<br />

the addition “reflects the growing<br />

importance of health care spending<br />

in the U.S. economy.”<br />

The last <strong>com</strong>ponent changes to<br />

the industrial average occurred in<br />

June 2009, when Cisco Systems and<br />

The Travelers Companies replaced<br />

General Motors and Citigroup.<br />

Stoxx Makes Major<br />

Offering Expansions<br />

In separate launches in August and<br />

September, Stoxx Ltd. made some sizable<br />

additions to its lineup of indexes.<br />

The first—and smaller—batch of<br />

indexes debuted in August and included<br />

19 benchmarks, carving out various<br />

subsets of Stoxx’s total market indexes<br />

for the emerging markets, the developed<br />

markets, East Asia and Africa.<br />

The second launch included<br />

more than 1,200 indexes added to<br />

Stoxx’s global index family, including<br />

total market benchmarks and<br />

narrower indexes targeting regions,<br />

supersectors and size segments.<br />

The launch also included four new<br />

total market indexes for China covering<br />

China’s A-Shares, B-Shares,<br />

H-Shares and Red Chips markets,<br />

the press release said.<br />

MSCI Adds 5 Stocks To ACWI<br />

MSCI said in mid-August it would<br />

add five stocks to its All-Country<br />

World Index (ACWI), while pulling<br />

eight from the broad global equities<br />

benchmark as part of its quarterly<br />

review of specific constituents.<br />

The three largest additions to the<br />

MSCI Global World Index measured<br />

by full <strong>com</strong>pany market capitalization<br />

were U.S.-based Camden Property<br />

Trust and Realty In<strong>com</strong>e Corp., and<br />

Israel-based Mellanox Technologies,<br />

the <strong>com</strong>pany said in a press release.<br />

The two additions to the MSCI<br />

Emerging Markets Index measured<br />

by full <strong>com</strong>pany market capitalization<br />

were Korea-based AmoreG and<br />

Brazil-based Marcopolo, MSCI said.<br />

Other MSCI indexes underwent<br />

additions and deletions as<br />

well, including the MSCI Frontier<br />

Markets Index, which had one addition<br />

and no deletions; the MSCI<br />

ACWI Small Cap Index, which saw<br />

seven additions and 26 deletions;<br />

the MSCI ACWI Investable Market<br />

Index and MSCI All Cap Index,<br />

which each had no additions and<br />

22 deletions; and the MSCI ACWI<br />

Islamic Index, which saw 51 additions<br />

and 22 deletions.<br />

All the changes were set to be<br />

implemented on Aug. 31, the <strong>com</strong>pany<br />

said.<br />

S&P DJI Debuts Low-Volatility<br />

Europe Index<br />

In late August, S&P Dow Jones<br />

Indices launched an index that covers<br />

a subset of the <strong>com</strong>ponents of<br />

the S&P Europe 350 Index. The S&P<br />

Europe 350 Low Volatility Index<br />

includes the 100 stocks with the lowest<br />

volatility from the standard index,<br />

and rather than being weighted by<br />

free-float market capitalization, they<br />

are inversely weighted by volatility,<br />

with stocks with the lowest volatility<br />

assigned the highest weightings, the<br />

indexing <strong>com</strong>pany said in a press<br />

release. S&P Dow Jones also noted<br />

there were no restrictions on sector<br />

weights, with volatility the sole determining<br />

factor. So far, the index is only<br />

available denominated in euros.<br />

S&P DJI noted in the press release<br />

it also offers similarly designed<br />

versions of the S&P 500, the S&P/<br />

TSX Composite Index and its broad<br />

emerging markets and developedmarkets<br />

indexes.<br />

Nasdaq Launches Commodity<br />

Equity Indexes<br />

First announced back in March,<br />

Nasdaq in September rolled out a trio<br />

of <strong>com</strong>modity equity indexes that it<br />

developed with Axioma, a provider<br />

of risk management products. The<br />

indexes <strong>com</strong>prise equities with share<br />

prices that track closely the spot prices<br />

of a targeted <strong>com</strong>modity or <strong>com</strong>modity<br />

group, according to the March press<br />

release. The new indexes are rebalanced<br />

on a monthly basis.<br />

The Nasdaq Axioma Equity-<br />

Commodity Oil Index, the Nasdaq<br />

Axioma Equity-Commodity Gold<br />

Index and the Nasdaq Axioma<br />

Equity-Commodity Agriculture Index<br />

are available as price indexes and<br />

total-return indexes. Nasdaq began<br />

disseminating their values over its<br />

data feeds as of Sept. 10.<br />

Stoxx Debuts<br />

Trading-Focused Index<br />

In early September, Stoxx Ltd. rolled<br />

out an index targeting the largest and<br />

most actively traded stocks in the<br />

world, according to a press release.<br />

The Stoxx+ Global Max Traded 200<br />

Index <strong>com</strong>prises 200 <strong>com</strong>ponents<br />

selected from three different sections<br />

of the globe. All <strong>com</strong>ponents of<br />

the Stoxx Global Total Market Index<br />

meeting minimum market capitalization<br />

and trading volume requirements<br />

are eligible for inclusion. The<br />

methodology divides the world into<br />

three time zones, with 50 stocks<br />

selected from each based on threemonth<br />

average daily trading volume.<br />

The final 50 <strong>com</strong>ponents are chosen<br />

from the remaining universe of<br />

stocks based solely on their threemonth<br />

average daily trading volume,<br />

the press release said.<br />

S&P Dow Jones<br />

Adds To GIVI Lineup<br />

S&P Dow Jones Indices in September<br />

expanded its index family focused on<br />

intrinsic value by rolling out the S&P<br />

GIVI Global Growth Markets Tilt Index,<br />

which is a <strong>com</strong>posite of two other<br />

indexes, a press release said.<br />

The original GIVI methodology<br />

screens out high-beta stocks and<br />

weights its <strong>com</strong>ponents by intrinsic<br />

value, as determined by each <strong>com</strong>pany’s<br />

assets and growth opportunities.<br />

S&P DJI also launched a GDPweighted<br />

version of the GIVI index, in<br />

which each country’s weight is determined<br />

by its gross domestic product.<br />

The new “Tilt” index splits each<br />

country’s weight evenly between its<br />

GIVI index and its GDP-weighted<br />

GIVI index, a factsheet said, essenwww.journalofindexes.<strong>com</strong><br />

November / December 2012 55


News<br />

tially providing both a “value and<br />

growth tilt through intrinsic value<br />

and economic weighting.”<br />

Facebook Joins<br />

Nasdaq’s Q-50 Index<br />

Facebook officially became one<br />

of the 50 securities included in the<br />

Nasdaq Q-50 Index on Sept. 24.<br />

Many had hoped that after the<br />

Nasdaq OMX Group changed its<br />

“seasoning rules” in April for three<br />

of its most popular indexes—including<br />

the Nasdaq 100 Index—Facebook<br />

would be entering Nasdaq’s flagship<br />

index by September. Instead, after<br />

bleeding more than half of its value in<br />

its first four months as a public <strong>com</strong>pany,<br />

Facebook has been added to<br />

the Nasdaq Q-50 Index—the feeder<br />

index for the Nasdaq 100, according<br />

to a press release from Nasdaq.<br />

The benchmark, which is designed<br />

to track the performance of the 50<br />

stocks that would be next in line for<br />

inclusion in the Nasdaq 100 Index,<br />

appears to have no ETFs attached to it.<br />

Aside from Facebook, other<br />

names being added to the Nasdaq<br />

Q-50 on Sept. 24 include Groupon,<br />

Concur Technologies, Mellanox<br />

Technologies, NXP Semiconductors,<br />

ONYX Pharmaceuticals, Royal Gold,<br />

TW tele<strong>com</strong> and Western Digital Corp.<br />

Nasdaq noted that Facebook<br />

can also be found in the Nasdaq<br />

Composite Index, which underlies<br />

the Fidelity Nasdaq Composite Index<br />

Tracking ETF (Nasdaq GM: ONEQ),<br />

as well as in the Nasdaq Computer<br />

Index, the Nasdaq Global Select<br />

Market Composite and the Nasdaq<br />

Global Select Computer Index.<br />

AROUND THE WORLD OF ETFs<br />

Schwab Cuts Costs<br />

On All 15 ETFs<br />

Charles Schwab took the battle<br />

in fees to its arch rival Vanguard by<br />

cutting prices on all 15 of its ETFs by<br />

25 to 60 percent, resulting in each of<br />

the ETFs be<strong>com</strong>ing cheapest in their<br />

respective Lipper categories.<br />

As an example, the Schwab U.S.<br />

Broad Market (NYSE Arca: SCHB) will<br />

now cost 0.04 percent, <strong>com</strong>pared with its<br />

previous expense ratio of 0.06 percent.<br />

Company officials said the<br />

moves, which became effective<br />

Sept. 20, brought the weighted average<br />

overall expense ratio of its ETFs<br />

down to 0.077 percent.<br />

Some analysts speculate that<br />

Schwab’s bigger plan is to attract<br />

more clients and financial advisors<br />

to its overall platform, and once they<br />

have arrived, hope they make use of<br />

Schwab products and services that<br />

are more expensive than its low-cost<br />

ETFs, which can also be traded <strong>com</strong>mission-free<br />

by Schwab clients.<br />

The move definitely raises the<br />

bar on Vanguard, whose reputation<br />

rests largely on its low-cost<br />

funds. What Vanguard chooses to<br />

do remains to be seen, but it’s clear<br />

that Schwab’s low-cost strategy is<br />

working every bit as well as it is for<br />

Vanguard. Schwab, which launched<br />

its first ETFs in November 2009, had<br />

$6.33 billion in 15 separate ETFs as<br />

of Sept. 20, 2012, according to data<br />

<strong>com</strong>piled by <strong>IndexUniverse</strong>.<br />

iShares Debuts Frontier<br />

Markets ETF<br />

In mid-September, iShares rolled<br />

out the iShares MSCI Frontier 100<br />

Index Fund (NYSE Arca: FM), a<br />

fund that serves up focused exposure<br />

to the least mature and least<br />

liquid economies globally.<br />

FM tracks an MSCI benchmark that<br />

taps into equities from 20 frontier markets,<br />

including Argentina, Bangladesh,<br />

Croatia, Estonia, Jordan, Kazakhstan,<br />

Kenya, Kuwait, Lebanon, Mauritius,<br />

Nigeria, Oman, Pakistan, Qatar,<br />

Romania, Serbia, Sri Lanka, Ukraine,<br />

the United Arab Emirates and Vietnam.<br />

The portfolio is heavily allocated<br />

to energy, financial and tele<strong>com</strong>munication<br />

names, and <strong>com</strong>es with an<br />

annual expense ratio of 0.79 percent.<br />

iShares is the first ETF sponsor<br />

to create a broad frontier markets<br />

fund since Guggenheim predecessor<br />

Claymore launched the first-to-market<br />

Frontier Markets ETF (NYSE Arca:<br />

FRN) in 2008. But the new iShares also<br />

looks to be the first pure-play frontier<br />

fund, as the $142 million Guggenheim<br />

fund has heavy allocations to countries<br />

that some index providers consider<br />

emerging rather than frontier.<br />

Vanguard Plots Rival<br />

To STPZ, STIP<br />

Vanguard filed regulatory paperwork<br />

in late July to market a short-term<br />

inflation-protected securities index<br />

fund that would include an ETF share<br />

class that would <strong>com</strong>pete with similar<br />

products from Pimco and iShares.<br />

The prospectus detailed plans for<br />

the Vanguard Short-Term Inflation-<br />

Protected Securities Index Fund that<br />

would track the Barclays U.S. Treasury<br />

Inflation-Protected Securities (TIPS)<br />

0-5 Year Index, and invest in inflation-protected<br />

U.S. Treasury securities<br />

that have a remaining maturity of<br />

less than five years.<br />

The fund would serve up four share<br />

classes, including an ETF share class<br />

that would cost 0.10 percent in fees, or<br />

half the price tag of <strong>com</strong>peting funds.<br />

The iShares Barclays 0-5 Year<br />

TIPS Bond Fund (NYSE Arca: STIP)<br />

and Pimco 1-5 Year U.S. TIPS Index<br />

Fund (NYSE Arca: STPZ) both cost<br />

0.20 percent a year, and have assets<br />

of $358 million and nearly $1 billion,<br />

respectively.<br />

UBS Closes All VIX ETNs<br />

Except XVIX<br />

UBS redeemed all but one of its<br />

VIX-related ETNs, as the securities<br />

were not getting the traction the firm<br />

had hoped—in part because of the<br />

presence of a number of VIX-related<br />

ETFs that are now on the market. The<br />

largest of the funds at the time of the<br />

announcement was just under $20<br />

million in assets, with most of the rest<br />

<strong>com</strong>ing in under $15 million.<br />

The 12 closed ETNs included six long<br />

and short pairs that offered exposure<br />

to different portions of the VIX futures<br />

curve. The call settlement date was Sept.<br />

12, with the call settlement amount set<br />

at the current principal of amount of the<br />

securities as of Sept. 7.<br />

Significantly, UBS decided to leave<br />

56 November / December 2012


open the Etracs Long-Short S&P 500<br />

VIX Futures ETN (NYSE Arca: XVIX),<br />

a security designed to take advantage<br />

of the steepness of the short end<br />

of the VIX curve. XVIX had roughly<br />

$18.6 million in assets, as of Sept. 7.<br />

EGA Adds To<br />

EM-Focused Lineup<br />

Emerging Global Advisors rolled<br />

out two ETFs targeting some of the<br />

more obscure reaches of the developing<br />

markets space.<br />

The EGShares Beyond BRICs ETF<br />

(NYSE Arca: BBRC) tracks the Indxx<br />

Beyond BRICs Index and invests<br />

in equities from countries that are<br />

often overlooked by many emerging<br />

markets strategies already in the<br />

space such as Chile, Colombia, Czech<br />

Republic, Egypt, Hungary, Indonesia,<br />

Malaysia, Mexico, Morocco, Peru,<br />

Philippines, Poland, South Africa,<br />

Thailand and Turkey.<br />

The EGShares Emerging Markets<br />

Domestic Demand ETF (NYSE<br />

Arca: EMDD) tracks the Indxx EM<br />

Domestic Demand Index and invests<br />

in the five economic sectors EGA<br />

sees as those that are directly linked<br />

to domestic demand: consumer discretionary,<br />

staples, tele<strong>com</strong>munications,<br />

utilities and health care. The<br />

ETF serves up exposure to 11 countries<br />

and currencies.<br />

Both funds carry an expense ratio<br />

of 0.85.<br />

WisdomTree Rolls Out<br />

China Payout ETF<br />

WisdomTree debuted an ETF in<br />

September that offers unique exposure<br />

to China’s market. The WisdomTree<br />

China Dividend ex-Financials Fund<br />

(Nasdaq GM: CHXF) tracks a proprietary<br />

index that measures the performance<br />

of the 10 largest stocks by floatadjusted<br />

market capitalization in nine<br />

sectors, excluding financial names.<br />

Components are weighted by annual<br />

cash dividends paid. CHXF charges<br />

0.63 percent in annual fees.<br />

The fact that WisdomTree is planning<br />

an ETF that steers clear of financial<br />

<strong>com</strong>panies appears to reflect<br />

underlying worry among analysts<br />

that some big Chinese banks may<br />

be on the hook for billions of dollars<br />

in bad loans. The move is also<br />

an attempt to increase diversification<br />

away from the traditional financialheavy<br />

strategies, the <strong>com</strong>pany said.<br />

Companies are eligible to be included<br />

in the index if they have at least<br />

$1 billion in float-adjusted market<br />

capitalization, are domiciled in China<br />

and are listed on the Hong Kong Stock<br />

Exchange, the <strong>com</strong>pany said in a filing.<br />

KNOW YOUR OPTIONS<br />

CBOE Sees August Volumes Fall<br />

CBOE Holdings said in a press<br />

release that its exchanges had a total<br />

contract volume (futures and options<br />

<strong>com</strong>bined) for August of 91 million, a<br />

40 percent decline over the prior-year<br />

total. On an average daily volume<br />

basis, that translated into a 41 percent<br />

decline for options and a decline<br />

of just 5 percent for futures contracts.<br />

Interestingly, index and ETF<br />

options were hit the hardest. While<br />

equity options saw their average daily<br />

volume fall just 16 percent, index and<br />

ETF options volumes declined by 49<br />

and 56 percent, respectively.<br />

The five most actively traded<br />

options contracts based on indexes or<br />

ETFs for the month of August were the<br />

options on the S&P 500 Index, SPDR<br />

S&P 500 ETF, CBOE VIX, PowerShares<br />

QQQ Trust and iShares Russell 2000<br />

Index Fund, the press release said.<br />

BACK TO THE FUTURES<br />

CME Group Lists Brazil<br />

Index Futures<br />

In September, CME Group said<br />

in a press release that U.S.-dollardenominated<br />

Ibovespa futures would be<br />

cross-listed on the CME as of Oct. 22.<br />

The Ibovespa is the blue-chip<br />

index of the BM&FBOVESPA and<br />

covers 69 stocks, representing roughly<br />

80 percent of the exchange’s volume<br />

and 78 percent of its total market<br />

capitalization, the press release said.<br />

The press release said that the CME<br />

would clear the contracts, which <strong>com</strong>ply<br />

with the rules and regulations of the CME.<br />

CME August Volumes Decline<br />

CME Group said in a press<br />

release that its August 2012 average<br />

daily volume came in at 10.3 million<br />

contracts, a 40 percent decline<br />

from the prior year. However, the<br />

exchange also noted that the prior-year<br />

month saw record volume<br />

due to an exceptionally tumultuous<br />

market environment.<br />

Equity index contracts saw the<br />

steepest declines of any category<br />

by far, with average daily volumes<br />

declining a whopping 58 percent.<br />

Total volume for the most actively<br />

traded index futures contract—the<br />

e-mini S&P 500—fell nearly 60 percent<br />

from August 2011. The e-Mini<br />

Nasdaq 100 and Mini $5 Dow contracts<br />

each saw their total volumes for<br />

the month fall by almost 50 percent.<br />

ON THE MOVE<br />

Stoxx Hires Da Costa, Rodino<br />

Stoxx Ltd. said in late August it had<br />

made two significant hires.<br />

Anthony Da Costa is the index<br />

provider’s new chief operating officer<br />

and is based in its Zurich offices.<br />

As such, he oversees the firm’s index<br />

operations and production, and also<br />

holds a chair on Stoxx’s management<br />

board, the press release said.<br />

Prior to joining Stoxx, Da Costa was<br />

employed by FTSE as director of service<br />

delivery.<br />

Mark Rodino, previously the head<br />

of ETF Sales at HSBC, is based in<br />

London and will spearhead Stoxx’s<br />

sales efforts as its global head of sales,<br />

the press release said.<br />

Russell Replaces Unger<br />

With Horowitz<br />

Pensions & Investments reported<br />

in early August that Dorsey<br />

Horowitz had been named to the<br />

position vacated when Shelton<br />

Unger left Russell earlier in 2012.<br />

Horowitz replaces Unger as the<br />

director of global asset owner and<br />

consultant sales, and Julie Williams<br />

has taken on Horowitz’s previous<br />

role as director of global client relations,<br />

P&I said.<br />

www.journalofindexes.<strong>com</strong> November / December 2012<br />

57


Global Index Data<br />

Selected Major Indexes Sorted By YTD Returns<br />

November/December 2012<br />

Total Return % Annualized Return %<br />

Index Name YTD 2011 2010 2009 2008 2007 2006 2005 3-Yr 5-Yr 10-Yr 15-Yr Sharpe Std Dev<br />

MSCI Egypt* 48.44 -48.78 9.47 32.77 -53.92 54.85 14.84 154.49 -7.56 -6.53 24.96 7.41 -0.09 32.67<br />

Citigroup Portuguese GBI 34.71 -24.91 -14.51 7.72 3.85 13.45 11.78 -9.57 -4.72 1.01 6.00 5.29 -0.05 27.02<br />

MSCI Belgium 27.44 -10.62 -0.42 57.49 -66.48 -2.73 36.66 9.05 7.35 -10.42 5.04 3.43 0.43 21.79<br />

MSCI Denmark 24.32 -16.02 30.73 36.57 -47.56 25.59 38.77 24.50 10.63 0.65 14.43 10.35 0.55 23.16<br />

MSCI Singapore 23.25 -17.92 22.14 73.96 -47.35 28.35 46.71 14.37 12.79 4.09 13.93 7.55 0.66 21.88<br />

NASDAQ 100 22.67 3.66 20.14 54.61 -41.57 19.24 7.28 1.89 20.53 7.69 12.01 - 1.10 18.08<br />

FTSE NAREIT Composite 18.31 7.30 27.56 27.80 -37.84 -17.83 - - 23.16 3.36 - - 1.26 17.90<br />

Wilshire Internet 16.59 -2.22 10.21 68.37 -47.17 11.71 13.87 3.57 15.60 2.56 11.33 5.69 0.78 21.22<br />

Russell 1000 Growth 14.55 2.64 16.71 37.21 -38.44 11.81 9.07 5.26 15.59 3.69 7.02 3.99 0.97 16.16<br />

Russell 3000 Growth 14.26 2.18 17.64 37.01 -38.44 11.40 9.46 5.17 15.58 3.63 7.19 3.93 0.96 16.51<br />

Russell Micro Cap 14.06 -9.27 28.89 27.48 -39.78 -8.00 16.54 2.57 12.14 -0.74 7.86 - 0.62 22.62<br />

S&P 500 13.51 2.11 15.06 26.46 -37.00 5.49 15.79 4.91 13.62 1.28 6.51 4.89 0.89 15.63<br />

Russell 1000 13.37 1.50 16.10 28.43 -37.60 5.77 15.46 6.27 13.82 1.47 6.86 5.14 0.88 16.00<br />

Russell 3000 13.15 1.03 16.93 28.34 -37.31 5.14 15.72 6.12 13.83 1.50 7.01 5.16 0.87 16.34<br />

Wilshire 5000 Total Market 13.02 0.98 17.16 28.30 -37.23 5.62 15.77 6.38 13.76 1.54 7.24 5.20 0.87 16.16<br />

Barclays High Yield Municipal 12.98 9.25 7.79 32.74 -27.01 -2.28 10.75 8.58 13.38 4.82 - - 1.92 6.65<br />

S&P 500/Citi Pure Value 12.77 -0.81 23.06 55.21 -47.87 -3.69 20.04 13.43 15.06 0.98 9.11 7.94 0.78 20.75<br />

Barclays EM 12.40 6.97 12.84 34.23 -14.75 5.15 9.96 12.27 13.25 10.14 12.14 9.68 1.81 7.00<br />

Russell 1000 Value 12.19 0.39 15.51 19.69 -36.85 -0.17 22.25 7.05 12.08 -0.85 6.57 5.66 0.78 16.14<br />

S&P MidCap 400/Citi Pure Growth 12.14 0.62 35.16 60.34 -35.17 10.30 4.98 12.06 20.55 10.07 13.22 11.76 1.04 19.94<br />

Russell 3000 Value 12.05 -0.10 16.23 19.76 -36.25 -1.01 22.34 6.85 12.10 -0.72 6.71 5.75 0.77 16.44<br />

Barclays Global High Yield 11.94 3.12 14.82 59.40 -26.89 3.18 13.69 3.59 13.95 9.69 11.75 8.14 1.41 9.57<br />

S&P 500 Equal Weighted 11.64 -0.11 21.91 46.31 -39.72 1.53 15.80 8.06 15.10 3.17 9.56 7.55 0.88 17.79<br />

Wilshire 4500 Completion 11.62 -4.10 28.43 36.99 -39.03 5.39 15.28 10.03 15.05 2.73 9.90 6.41 0.81 19.67<br />

S&P MidCap 400 11.61 -1.73 26.64 37.38 -36.23 7.98 10.32 12.56 15.73 3.97 9.63 9.21 0.86 18.89<br />

S&P 500/Citi Pure Growth 11.42 0.75 27.65 50.85 -38.99 6.64 7.43 7.31 18.94 6.21 10.86 7.54 1.01 18.94<br />

S&P SmallCap 600 11.21 1.02 26.31 25.57 -31.07 -0.30 15.12 7.68 16.18 3.12 9.79 7.63 0.84 20.12<br />

Russell 2000 Growth 10.75 -2.91 29.09 34.47 -38.54 7.05 13.35 4.15 15.49 2.94 9.40 3.74 0.76 22.02<br />

Russell 2000 10.60 -4.18 26.85 27.17 -33.79 -1.57 18.37 4.55 13.89 1.90 9.00 5.79 0.71 21.41<br />

Barclays US Corporate High Yield 10.59 4.98 15.12 58.21 -26.16 1.87 11.85 2.74 14.47 9.60 10.68 7.04 1.77 7.78<br />

S&P SmallCap 600/Citi Pure Value 10.55 -7.50 29.18 63.58 -41.73 -18.61 21.44 11.58 8.28 1.74 9.64 8.23 0.42 27.24<br />

Russell 2000 Value 10.43 -5.50 24.50 20.58 -28.92 -9.78 23.48 4.71 12.24 0.73 8.49 7.29 0.65 21.00<br />

S&P SmallCap 600/Citi Pure Growth 10.07 5.21 28.74 37.70 -33.10 1.49 9.79 7.10 17.67 5.70 11.65 8.97 0.91 20.06<br />

MSCI EM Small 9.51 -27.18 27.17 113.79 -58.23 42.26 32.35 29.17 7.18 -0.84 15.63 5.82 0.41 22.72<br />

MSCI ACWI 9.43 -7.35 12.67 34.63 -42.19 11.66 20.95 10.84 7.72 -1.66 7.02 - 0.50 17.60<br />

DJ Industrial Average 9.19 8.38 14.06 22.68 -31.93 8.88 19.05 1.72 14.34 2.44 6.90 6.03 1.03 13.88<br />

STOXX Europe TMI 8.63 -11.80 5.10 37.50 -46.75 13.07 35.39 10.11 2.84 -5.19 7.35 4.93 0.23 22.80<br />

S&P MidCap 400/Citi Pure Value 8.57 -5.07 23.19 59.18 -42.58 -3.20 19.31 9.37 12.06 1.91 9.12 8.76 0.62 21.89<br />

MSCI EAFE Small Cap 8.13 -15.94 22.04 46.78 -47.01 1.45 19.31 26.19 4.85 -3.66 9.83 - 0.33 20.09<br />

Barclays US Corporate Inv Grade 7.91 8.15 9.00 18.68 -4.94 4.56 4.30 1.68 9.47 8.08 6.69 6.80 2.23 4.06<br />

MSCI EAFE Growth 7.69 -12.11 12.25 29.36 -42.70 16.45 22.33 13.28 4.85 -3.56 6.57 2.45 0.34 19.16<br />

MSCI EMU 6.94 -17.64 -4.25 31.41 -47.57 19.55 36.29 8.80 -3.35 -8.92 5.48 3.92 0.00 26.91<br />

MSCI EAFE 6.92 -12.14 7.75 31.78 -43.38 11.17 26.34 13.54 2.40 -4.81 6.67 3.55 0.21 19.64<br />

MSCI EAFE Value 6.07 -12.17 3.25 34.23 -44.09 5.96 30.38 13.80 -0.05 -6.12 6.68 4.48 0.09 20.50<br />

Barclays US Treasury US TIPS 5.71 13.56 6.31 11.41 -2.35 11.64 0.41 2.84 9.86 8.11 6.85 7.33 1.94 4.87<br />

MSCI EM 5.61 -18.42 18.88 78.51 -53.33 39.42 32.14 34.00 6.64 -0.37 15.00 - 0.40 22.16<br />

MSCI AC Asia Paciûc 5.45 -15.11 17.02 37.59 -41.85 14.29 16.49 23.34 3.83 -2.66 7.71 - 0.30 16.78<br />

Barclays Municipal 5.43 10.70 2.38 12.91 -2.47 3.36 4.84 3.51 7.02 6.24 5.20 5.60 1.59 4.26<br />

S&P GSCI 4.96 -1.18 9.03 13.48 -46.49 32.67 -15.09 25.55 7.09 -3.31 4.02 2.67 0.43 20.46<br />

DJ UBS Commodity 3.86 -13.32 16.83 18.91 -35.65 16.23 2.07 21.36 5.22 -1.86 5.41 3.91 0.37 17.86<br />

Barclays US Aggregate Bond 3.85 7.84 6.54 5.93 5.24 6.97 4.33 2.43 6.51 6.66 5.48 6.25 2.31 2.71<br />

Dow Jones Utilities Average 3.72 19.71 6.46 12.47 -27.84 20.11 16.63 25.14 12.62 3.53 11.07 8.92 1.17 10.62<br />

Barclays Global Aggregate 3.56 5.64 5.54 6.93 4.79 9.48 6.64 -4.49 5.35 6.44 6.43 6.06 0.93 5.72<br />

Barclays US Government 2.35 9.02 5.52 -2.20 12.39 8.66 3.48 2.65 5.50 6.20 5.01 6.05 1.46 3.66<br />

DJ Transportation Average 0.87 0.01 26.74 18.58 -21.41 1.43 9.81 11.65 12.88 2.32 9.84 5.32 0.68 20.79<br />

MSCI BRIC 0.86 -22.85 9.57 93.12 -59.40 58.87 56.36 44.19 1.31 -3.09 18.34 - 0.17 24.55<br />

MSCI Greece -11.40 -62.77 -44.87 25.05 -66.01 32.91 35.05 16.10 -46.01 -38.35 -10.27 - -1.05 46.85<br />

MSCI Portugal -11.58 -23.05 -11.31 40.41 -52.15 24.00 47.37 -1.87 -13.08 -15.52 2.79 - -0.43 25.38<br />

Citigroup Greek GBI -22.67 -61.30 -25.78 6.98 -3.84 13.25 11.88 -8.95 -40.73 -24.33 -8.14 - -0.90 44.68<br />

MSCI Argentina* -47.97 -42.64 70.06 61.12 -55.32 -5.36 66.07 59.68 -16.66 -19.00 11.09 -3.62 -0.34 35.13<br />

Source: Morningstar. (Nasdaq-100 index data provided by Morningstar and Nasdaq OMX.) Data as of August 31, 2012. All returns are in US dollars, unless noted.<br />

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.<br />

58<br />

November / December 2012


Morningstar Index Funds U.S. Style Overview XXXX –XXXX, 2011<br />

Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions<br />

November/December 2012<br />

Total Return % Annualized Return %<br />

Fund Name Ticker Assets Exp Ratio 3-Mo YTD 2011 2010 3-Yr 5-Yr 10-Yr 15-Yr P/E Std Dev Yield<br />

Vanguard Total Stock Mkt, Inv Shrs VTSMX 73,450.2 0.18 7.59 13.16 0.96 17.09 13.86 1.66 7.21 5.18 14.8 16.36 1.72<br />

Vanguard Institutional, Instl Shrs VINIX 66,940.1 0.04 7.93 13.49 2.09 15.05 13.61 1.31 6.51 4.93 14.5 15.63 1.91<br />

Vanguard 500, Adm Shrs VFIAX 58,281.0 0.05 7.94 13.49 2.08 15.05 13.61 1.30 6.49 4.88 14.5 15.64 1.92<br />

Vanguard Total Stock Mkt, Adm Shrs VTSAX 57,367.6 0.06 7.65 13.26 1.08 17.26 13.99 1.77 7.31 5.26 14.8 16.39 1.83<br />

Vanguard Institutional, Instl+ Shrs VIIIX 47,245.0 0.02 7.94 13.50 2.12 15.07 13.64 1.33 6.54 4.96 14.5 15.63 1.93<br />

Vanguard Total Bond Mkt II, Inv Shrs VTBIX 42,497.7 0.12 1.49 3.82 7.59 6.41 6.40 - - - - 2.80 2.48<br />

Vanguard Total Stock Mkt, Instl Shrs VITSX 36,484.7 0.05 7.62 13.26 1.09 17.23 13.98 1.78 7.34 5.30 14.8 16.37 1.83<br />

Vanguard Total Bond Mkt, Adm Shrs VBTLX 34,522.8 0.10 1.48 3.90 7.69 6.54 6.49 6.66 5.46 6.09 - 2.86 2.83<br />

Vanguard Total Intl Stock, Inv Shrs VGTSX 34,326.0 0.22 9.40 6.89 -14.56 11.12 3.25 -4.01 7.80 4.10 10.6 20.30 2.87<br />

Vanguard 500, Inv Shrs VFINX 26,520.1 0.17 7.90 13.39 1.97 14.91 13.47 1.19 6.39 4.81 14.5 15.63 1.80<br />

Vanguard 500, Sig Shrs VIFSX 25,642.7 0.05 7.92 13.48 2.08 15.05 13.61 1.30 6.45 4.85 14.5 15.63 1.92<br />

Vanguard Total Bond Mkt, Instl Shrs VBTIX 23,024.1 0.07 1.49 3.92 7.72 6.58 6.52 6.70 5.50 6.16 - 2.86 2.85<br />

Vanguard Instl Total Stock Mkt, Instl+ Shrs VITPX 19,764.1 0.03 7.66 13.35 1.11 17.25 14.04 1.84 7.42 - 14.8 16.38 1.86<br />

Fidelity Spartan 500, Adv Cl FUSVX 19,644.7 0.06 7.93 13.48 2.06 15.01 13.57 1.26 6.45 4.80 14.7 15.63 1.87<br />

Vanguard Total Bond Mkt II, Instl Shrs VTBNX 17,342.9 0.05 1.51 3.87 7.67 6.47 6.45 - - - - 2.80 2.56<br />

Fidelity Spartan 500, Instl Cl FXSIX 16,160.6 0.04 7.95 13.49 2.09 14.98 13.57 1.25 6.44 4.79 14.7 15.63 1.88<br />

T. Rowe Price Equity 500 PREIX 14,663.8 0.30 7.88 13.29 1.87 14.71 13.32 1.07 6.23 4.63 14.6 15.63 1.76<br />

Vanguard Total Bond Mkt, Instl+ Shrs VBMPX 14,592.1 0.05 1.49 3.94 7.74 6.57 6.51 6.63 5.40 6.05 - 2.86 2.87<br />

Vanguard Total Intl Stock, Adm Shrs VTIAX 14,297.5 0.18 9.37 6.91 -14.52 11.04 3.25 -4.01 7.80 4.10 10.6 20.26 2.89<br />

Vanguard Total Intl Stock, Instl+ Shrs VTPSX 12,390.8 0.10 9.39 6.99 -14.49 11.09 3.30 -3.98 7.81 4.11 10.6 20.28 2.98<br />

Schwab S&P 500 SWPPX 12,306.1 0.09 7.97 13.49 2.07 14.97 13.54 1.29 6.45 4.80 15.0 15.58 1.79<br />

Vanguard Total Bond Mkt, Inv Shrs VBMFX 12,302.7 0.22 1.45 3.83 7.56 6.42 6.37 6.55 5.36 6.03 - 2.86 2.71<br />

Vanguard Total Bond Mkt, Sig Shrs VBTSX 12,043.8 0.10 1.48 3.90 7.69 6.54 6.49 6.66 5.42 6.07 - 2.86 2.83<br />

Fidelity Spartan 500, Inv Cl FUSEX 10,547.1 0.10 7.92 13.46 2.03 14.98 13.54 1.23 6.43 4.78 14.7 15.63 1.84<br />

Fidelity Series 100 FOHIX 7,906.8 0.20 8.81 14.85 2.98 12.39 13.16 1.04 - - 14.3 15.19 1.77<br />

Vanguard Total Stock Mkt, Sig Shrs VTSSX 7,524.5 0.06 7.62 13.23 1.09 17.23 13.98 1.77 7.28 5.22 14.8 16.38 1.83<br />

Fidelity Spartan Total Mkt, Adv Cl FSTVX 6,938.4 0.07 7.60 13.22 1.01 17.44 13.95 1.67 7.24 - 14.9 16.29 1.66<br />

Vanguard Balanced, Adm Shrs VBIAX 6,883.3 0.10 5.16 9.54 4.29 13.29 11.36 4.25 6.94 6.04 14.8 9.35 2.10<br />

Vanguard Mid-Cap, Instl Shrs VMCIX 6,638.2 0.08 5.63 10.52 -1.96 25.67 15.54 2.07 9.24 - 16.2 18.54 1.25<br />

Vanguard REIT, Adm Shrs VGSLX 6,636.0 0.12 7.58 16.97 8.62 28.49 23.87 3.93 11.22 9.55 38.0 19.13 3.24<br />

Vanguard Mid-Cap, Adm Shrs VIMAX 6,587.8 0.10 5.65 10.51 -1.97 25.59 15.51 2.03 9.19 - 16.2 18.54 1.23<br />

Vanguard Emerging Mkts Stock, Adm Shrs VEMAX 6,504.9 0.20 5.96 5.59 -18.67 18.99 6.53 -0.60 14.77 7.56 9.6 22.73 2.25<br />

Vanguard Total Intl Stock, Instl Shrs VTSNX 6,428.8 0.13 9.39 6.97 -14.51 11.09 3.29 -3.99 7.81 4.11 10.6 20.28 2.97<br />

Vanguard Growth, Instl Shrs VIGIX 6,192.9 0.08 7.76 15.95 1.89 17.17 16.03 4.02 6.99 5.13 17.0 16.72 1.15<br />

Vanguard Small-Cap, Adm Shrs VSMAX 6,162.6 0.16 7.26 11.94 -2.69 27.89 15.55 3.36 10.19 6.79 16.6 21.08 1.23<br />

Vanguard Intermediate Bond, Adm Shrs VBILX 5,999.9 0.11 2.13 6.08 10.73 9.49 9.25 8.52 6.74 7.09 - 4.48 3.26<br />

Spartan US Bond, Inv Cl FBIDX 5,955.6 0.22 1.59 3.90 7.68 6.29 6.39 6.23 5.28 6.07 - 2.76 2.52<br />

Vanguard Short-Term Bond, Sig Shrs VBSSX 5,829.1 0.11 0.67 1.68 3.08 4.03 3.23 4.40 3.89 4.82 - 1.65 1.65<br />

Vanguard Growth, Adm Shrs VIGAX 5,637.7 0.10 7.78 15.97 1.87 17.12 16.00 3.98 6.95 5.08 17.0 16.73 1.13<br />

Vanguard Extended Mkt, Adm Shrs VEXAX 5,615.8 0.14 6.27 12.10 -3.59 27.57 15.41 2.85 9.97 6.46 16.2 20.20 1.02<br />

Vanguard Small-Cap, Instl Shrs VSCIX 5,530.8 0.14 7.26 11.95 -2.65 27.95 15.59 3.40 10.24 6.86 16.6 21.08 1.25<br />

PIMCO EM Fundamentl IndexPLUS, Instl Cl PEFIX 5,441.6 1.25 10.02 13.30 -16.81 25.86 12.57 - - - - 23.71 1.92<br />

Vanguard Extended Mkt, Instl Shrs VIEIX 5,431.1 0.12 6.27 12.12 -3.57 27.59 15.43 2.88 10.02 6.54 16.2 20.22 1.03<br />

Vanguard Balanced, Instl Shrs VBAIX 5,398.8 0.08 5.17 9.55 4.31 13.34 11.39 4.29 6.97 6.07 14.8 9.37 2.11<br />

Vanguard Developed Mkts, Instl Shrs VIDMX 5,187.6 0.08 11.03 7.60 -12.44 8.76 2.73 -4.60 6.77 - 10.7 20.12 3.50<br />

Vanguard Extended Mkt, Instl+ Shrs VEMPX 5,096.7 0.10 6.27 12.12 -3.53 27.37 15.36 2.76 9.86 6.39 16.2 20.21 1.06<br />

Vanguard Mid-Cap, Instl+ Shrs VMCPX 4,932.6 0.06 5.66 10.54 -1.91 25.67 15.56 2.08 9.24 - 16.2 18.52 1.26<br />

Fidelity Spartan Ext Mkt, Adv Cl FSEVX 4,788.9 0.07 6.05 12.05 -3.79 28.62 15.48 3.06 9.98 - 15.9 19.85 1.16<br />

Schwab 1000 SNXFX 4,788.5 0.29 7.59 13.03 1.27 15.96 13.53 1.31 6.59 4.98 15.3 15.90 1.73<br />

Vanguard Mid-Cap, Sig Shrs VMISX 4,554.0 0.10 5.68 10.54 -1.99 25.62 15.50 2.03 9.21 - 16.2 18.54 1.23<br />

Fidelity Inüation-Protected Bond FSIPX 4,453.8 0.20 0.81 4.02 8.63 5.06 - - - - - - 0.15<br />

Vanguard Value, Instl Shrs VIVIX 4,400.0 0.08 7.53 10.83 1.17 14.49 11.50 -0.88 6.79 4.92 12.8 15.42 2.62<br />

Vanguard Short-Term Bond, Adm Shrs VBIRX 4,293.6 0.11 0.67 1.68 3.08 4.03 3.23 4.40 3.92 4.85 - 1.65 1.65<br />

ING US Stock, Cl I INGIX 4,267.2 0.26 7.87 13.36 1.81 14.74 13.35 1.05 - - 14.5 15.65 1.61<br />

Fidelity Spartan US Bond, Adv Cl FSITX 4,262.7 0.11 1.53 3.89 7.71 6.29 6.40 6.23 5.28 6.07 - 2.78 2.60<br />

Vanguard Mid-Cap, Inv Shrs VIMSX 3,964.9 0.24 5.60 10.39 -2.11 25.46 15.33 1.89 9.06 - 16.2 18.52 1.07<br />

Vanguard Small Cap, Sig Shrs VSISX 3,890.8 0.16 7.26 11.96 -2.68 27.85 15.56 3.36 10.14 6.75 16.6 21.09 1.24<br />

Vanguard Small Cap, Inv Shrs NAESX 3,884.1 0.30 7.24 11.83 -2.80 27.72 15.39 3.22 10.06 6.70 16.6 21.07 1.08<br />

Vanguard FTSE All-World ex-US, Instl Shrs VFWSX 3,642.7 0.13 9.64 6.99 -14.21 11.93 3.76 -3.28 - - 10.5 20.38 3.29<br />

ING US Bond, Cl I ILBAX 3,619.3 0.41 1.51 3.77 7.20 6.14 6.14 - - - - 2.63 2.28<br />

Source: Morningstar. Data as of August 31, 2012. Exp Ratio is expense ratio. 3-Mo is 3-month. YTD is year-to-date. 3-, 5-, 10- and 15-yr returns are annualized.<br />

P/E is price-to-earnings ratio. Std Dev is 3-year standard deviation. Yield is 12-month dividend yield.<br />

www.journalofindexes.<strong>com</strong><br />

November / December 2012<br />

59


Morningstar U.S. Style Overview Jan. 1 - Aug. 31, 2012<br />

Trailing Returns %<br />

3-Month YTD 1-Yr 3-Yr 5-Yr 10-Yr<br />

Morningstar Indexes<br />

US Market 7.63 13.31 17.72 13.68 1.99 7.26<br />

Large Cap 8.13 14.09 19.28 13.06 1.54 6.25<br />

Mid Cap 6.12 11.16 13.22 15.12 2.69 9.63<br />

Small Cap 6.60 11.02 14.09 15.00 3.71 10.21<br />

US Value 7.58 9.60 14.97 10.81 –1.11 6.42<br />

US Core 7.61 13.53 19.51 14.20 3.23 7.86<br />

US Growth 7.61 16.98 18.60 16.05 3.60 7.22<br />

Morningstar Market Barometer YTD Return %<br />

Large Cap<br />

US Market<br />

13.31<br />

14.09<br />

Value<br />

9.60<br />

Core<br />

13.53<br />

Growth<br />

16.98<br />

9.56 14.23 18.78<br />

Large Value 8.18 9.56 15.36 10.14 –2.35 5.42<br />

Large Core 8.27 14.23 20.83 13.31 2.93 7.00<br />

Large Growth 7.82 18.78 21.60 15.77 3.77 5.94<br />

Mid Cap<br />

11.16<br />

9.36 11.70 12.40<br />

Mid Value 5.96 9.36 12.85 11.82 1.38 8.42<br />

Mid Core 5.51 11.70 17.12 16.93 3.73 9.85<br />

Mid Growth 6.83 12.40 9.58 16.57 2.65 10.26<br />

Small Cap<br />

11.02<br />

10.61 10.85 11.62<br />

Small Value 6.13 10.61 17.01 14.73 4.80 10.39<br />

Small Core 6.13 10.85 11.49 14.06 2.46 9.86<br />

Small Growth 7.63 11.62 14.01 16.17 3.69 10.13<br />

–8.00 –4.00 0.00 +4.00 +8.00<br />

Sector Index YTD Return %<br />

Communication 26.06<br />

Technology 18.03<br />

Industry Leaders & Laggards YTD Return %<br />

Residential Construction 61.52<br />

Oil & Gas Refining & 54.13<br />

Biggest Influence on Style Index Performance<br />

Best Performing Index<br />

YTD<br />

Return %<br />

Large Growth 18.78<br />

Constituent<br />

Weight %<br />

Real Estate 17.90<br />

Consumer Cyclical 17.37<br />

Financial Services 17.09<br />

Healthcare 15.01<br />

Consumer 10.49<br />

Basic Materials 8.69<br />

Industrials 8.51<br />

Energy 3.62<br />

Utilities 3.18<br />

Consumer Electronics 45.15<br />

Lumber & Wood Production 40.06<br />

Broadcasting - Radio 37.35<br />

Airports & Air Services 35.96<br />

–13.11 Pollution & Treatment Controls<br />

–13.50 Steel<br />

–21.79 Electronic Gaming & Multimedia<br />

–37.72 Coal<br />

–40.79 Solar<br />

–41.59 Education & Training Services<br />

Apple Inc. 64.96 11.41<br />

Amazon.<strong>com</strong> Inc. 43.43 1.85<br />

Oracle Corp. 24.18 3.03<br />

Philip Morris International Inc. 15.84 4.19<br />

eBay Inc. 56.51 1.02<br />

Worst Performing Index<br />

Mid Value 9.36<br />

Sprint Nextel Corp. 107.26 0.77<br />

HollyFrontier Corp. 81.30 0.50<br />

Western Digital Corp. 35.12 0.79<br />

CA Inc. 32.56 0.82<br />

Tesoro Corp. 70.64 0.37<br />

1-Year<br />

3-Year<br />

5-Year<br />

Value<br />

Core<br />

Growth<br />

Value<br />

Core<br />

Growth<br />

Value<br />

Core<br />

Growth<br />

Large Cap<br />

15.36<br />

20.83<br />

21.60<br />

Large Cap<br />

10.14<br />

13.31<br />

15.77<br />

Large Cap<br />

–2.35<br />

2.93<br />

3.77<br />

Mid Cap<br />

12.85<br />

17.12 9.58<br />

Mid Cap<br />

11.82<br />

16.93 16.57<br />

Mid Cap<br />

1.38<br />

3.73 2.65<br />

Small Cap<br />

17.01<br />

11.49 14.01<br />

Small Cap<br />

14.73<br />

14.06 16.17<br />

Small Cap<br />

4.80<br />

2.46 3.69<br />

–20 –10 0 +10 +20<br />

–20 –10 0 +10 +20<br />

–20 –10 0 +10 +20<br />

Source: Morningstar. Data as of Aug. 31, 2012<br />

Source: Morningstar. Data as of Feb. 29, 2012.<br />

Notes and Disclaimer: ©2012 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<br />

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 ?<br />

contained herein is not warranted to be accurate, <strong><strong>com</strong>plete</strong> or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information.<br />

60<br />

November / December 2012


Exchange-Traded Dow Jones U.S. Industry Funds Review Corner<br />

Performance<br />

Index Name Weight 1-Month 3-Month YTD 1-Year 3-Year 5-Year 10-Year<br />

Dow Jones U.S. Index 100.00% 2.47% 7.66% 13.34% 17.22% 13.96% 1.71% 7.18%<br />

Dow Jones U.S. Basic Materials Index 3.00% 3.35% 5.77% 3.57% -4.20% 11.64% 1.49% 8.64%<br />

Dow Jones U.S. Consumer Goods Index 10.62% 1.07% 4.38% 9.53% 13.77% 16.37% 6.58% 8.28%<br />

Dow Jones U.S. Consumer Services Index 12.99% 3.38% 7.46% 19.07% 25.21% 20.93% 6.15% 7.62%<br />

Dow Jones U.S. Financials Index 15.92% 2.78% 8.05% 17.21% 16.81% 5.10% -9.88% -0.04%<br />

Dow Jones U.S. Health Care Index 11.01% 1.69% 8.80% 15.27% 21.17% 14.09% 5.54% 6.90%<br />

Dow Jones U.S. Industrials Index 12.18% 2.06% 5.81% 10.53% 16.50% 15.94% 1.13% 7.73%<br />

Dow Jones U.S. Oil & Gas Index 10.46% 2.48% 12.54% 4.11% 6.83% 12.74% 1.96% 13.55%<br />

Dow Jones U.S. Technology Index 17.08% 5.41% 8.42% 18.73% 24.13% 15.88% 5.42% 9.87%<br />

Dow Jones U.S. Tele<strong>com</strong>munications Index 2.99% -2.21% 9.66% 20.66% 26.14% 17.75% 1.17% 8.93%<br />

Dow Jones U.S. Utilities Index 3.75% -3.83% 2.76% 2.95% 11.47% 12.77% 2.79% 9.41%<br />

Risk-Return<br />

25%<br />

3-Year Annualized Return<br />

Consumer Services<br />

20%<br />

Tele<strong>com</strong>munications<br />

Consumer Goods<br />

Composite<br />

Industrials<br />

15%<br />

Health Care<br />

Technology<br />

Utilities<br />

10%<br />

Financials<br />

5%<br />

Oil & Gas<br />

0%<br />

5% 10% 15% 20% 25%<br />

3-Year Annualized Risk<br />

Industry Weights Relative to Global ex-U.S.<br />

Basic Materials -7.47%<br />

Consumer Goods<br />

-3.52%<br />

Asset Class Performance<br />

U.S. [148.01] Global ex-U.S. [111.92] Commodities [116.48]<br />

REITs [191.09] Infrastructure [166.76]<br />

200<br />

Consumer Services<br />

Financials<br />

-7.69%<br />

4.83%<br />

180<br />

Health Care<br />

4.21%<br />

160<br />

Industrials<br />

-0.65%<br />

Oil & Gas<br />

0.13%<br />

140<br />

Technology<br />

12.25%<br />

120<br />

Tele<strong>com</strong>munications<br />

-2.06%<br />

Utilities<br />

-0.03%<br />

100<br />

-15% -10% -5% 0% 5% 10% 15%<br />

Underweight Overweight<br />

80<br />

8/09 11/09 2/10 5/10 8/10 11/10 2/11 5/11 8/11 11/11 2/12 5/12 8/12<br />

Chart <strong>com</strong>pares 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<br />

Global ex-U.S. Index<br />

Commodities = Dow Jones-UBS Commodity Index | REITs = Dow Jones U.S. Select REIT Index<br />

Infrastructure = Dow Jones Brookfield Global Infrastructure Index<br />

Copyright © 2012 by S&P/Dow Jones Indices LLC, a subsidiary of The McGraw-Hill Companies. All rights reserved. “Dow Jones” is a registered trademark of Dow Jones Trademark Holdings LLC (“Dow Jones”). STANDARD & POOR’S and S&P are registered trademarks of Standard &<br />

Poor’s Financial Services LLC.<br />

The Dow Jones U.S. Index and the Dow Jones U.S. Industry Indexes were first calculated in February 2000. All information presented prior to this date is back-tested. Back-tested performance is not actual performance, but is hypothetical. The back-test calculations are based on the same<br />

methodology that was in effect when the index was officially launched. Complete index methodology details are available at www.spindices.<strong>com</strong>.<br />

Past performance is not an indication of future results. Prospective application of the methodology used to construct the Dow Jones U.S. Index may not result in performance <strong>com</strong>mensurate with the back-test returns shown. The back-test period does not necessarily correspond to the entire<br />

available history of the index. Please refer to the methodology paper for the index, available at www.spdji.<strong>com</strong> or www.spindices.<strong>com</strong> for more details about the index, including the manner in which it is rebalanced, the timing of such rebalancing, criteria for additions and deletions, as well as<br />

all index calculations. It is not possible to invest directly in an Index.<br />

Another limitation of back-tested hypothetical information is that generally the back-tested calculation is prepared with the benefit of hindsight. Back-tested data reflect the application of the index methodology and selection of index constituents in hindsight. No hypothetical record can<br />

<strong><strong>com</strong>plete</strong>ly account for the impact of financial risk in actual trading. For example, there are numerous factors related to the equities (or fixed in<strong>com</strong>e, or <strong>com</strong>modities) markets in general which cannot be, and have not been accounted for in the preparation of the index information set forth, all<br />

of which can affect actual performance.<br />

The index returns shown do not represent the results of actual trading of investor assets. S&P/Dow Jones Indices LLC maintains the indices and calculates the index levels and performance shown or discussed, but does not manage actual assets. Index returns do not reflect payment of any<br />

sales charges or fees an investor would pay to purchase the securities they represent. The imposition of these fees and charges would cause actual and back-tested performance to be lower than the performance shown. In a simple example, if an index returned 10% on a US $100,000<br />

investment for a 12-month period (or US$ 10,000) and an actual asset-based fee of 1.5% were imposed at the end of the period on the investment plus accrued interest (or US$ 1,650), the net return would be 8.35% (or US$ 8,350) for the year. Over 3 years, an annual 1.5% fee taken at<br />

year end with an assumed 10% return per year would result in a cumulative gross return of 33.10%, a total fee of US$ 5,375, and a cumulative net return of 27.2% (or US$ 27,200).<br />

Source: S&P Dow Jones Indices; Data as of August 31, 2012.<br />

For more information, please visit the S&P Dow Jones Indices web site at www.spdji.<strong>com</strong>.<br />

www.journalofindexes.<strong>com</strong><br />

November / December 2012 61


Exchange-Traded Funds Corner<br />

Largest New ETFs Sorted By Total Net Assets In $US Millions<br />

Covers ETFs and ETNs launched during the 12-month period ended August 31, 2012.<br />

Fund Name Ticker ER 3-Mo YTD P/E Inception Assets<br />

PIMCO Total Return BOND 0.55 3.14 - - 2/29/2012 2,531.1<br />

Market Vectors Oil Services OIH 0.35 14.29 5.24 15.0 12/20/2011 957.9<br />

iShares MSCI AC World Min Volatility ACWV 0.35 6.37 9.75 16.5 10/18/2011 578.8<br />

Schwab US Dividend Equity SCHD 0.17 7.56 10.65 14.2 10/20/2011 507.3<br />

iShares Barclays US Treasury Bond GOVT 0.15 0.35 - - 2/14/2012 485.7<br />

FlexShares Mstar Glb Upstream Nat Res GUNR 0.48 8.63 2.64 10.6 9/16/2011 459.8<br />

FlexShares iBoxx 3-Yr Target Dur TIPS TDTT 0.20 0.70 2.07 - 9/19/2011 432.8<br />

iShares MSCI Emrg Mkts Min Volatility EEMV 0.25 8.95 12.23 9.9 10/18/2011 387.2<br />

iShares MSCI USA Minimum Volatility USMV 0.15 5.48 10.90 18.5 10/18/2011 356.3<br />

Market Vectors Semiconductor SMH 0.35 6.12 8.35 15.4 12/20/2011 348.4<br />

ProShares Ultra VIX Sh-Tm Futures UVXY 1.56 -73.53 -92.74 - 10/3/2011 291.2<br />

FlexShares iBoxx 5-Yr Target Dur TIPS TDTF 0.20 1.04 4.23 - 9/19/2011 248.0<br />

SPDR Barclays Sh-Tm HiYld Bond SJNK 0.40 3.89 - - 3/14/2012 234.4<br />

PowerShares KBW Bank KBWB 0.35 7.65 21.47 11.4 11/1/2011 201.9<br />

Market Vectors Pharmaceutical PPH 0.35 7.73 9.10 16.8 12/20/2011 176.0<br />

Market Vectors Biotech BBH 0.35 12.32 39.86 24.1 12/20/2011 127.2<br />

FlexShares Mstar US Mkt Factor Tilt TILT 0.27 6.82 11.59 14.9 9/16/2011 116.2<br />

iShares S&P Intl Preferred Stock IPFF 0.55 6.76 7.20 3.9 11/15/2011 109.2<br />

UBS Fisher Enh Big Cap Growth ETN FBG 1.20 - - - 6/8/2012 101.0<br />

VelocityShares 3X Long Silver ETN USLV 1.65 38.94 18.24 - 10/14/2011 88.9<br />

Source: Morningstar. Data as of August 31, 2012. ER is expense ratio. 3-Mo is 3-month. YTD is year-to-date. P/E is price-to- earnings ratio.<br />

Selected ETFs In Registration<br />

Columbia Dividend In<strong>com</strong>e<br />

Direxion Daily Dow 30 Bull 2X<br />

EGShares Emrg Mkts Core Balanced<br />

First Trust Glb Agriculture AlphaDex<br />

FlexShares Quality Div Defensive<br />

Global X Africa Consumer<br />

Guggenheim BulletSh 2021 Corp Bond<br />

IQ Bear Emerging Markets Equity<br />

iShares ACWI Investable Market<br />

iShares Australian Dollar Cash Rate<br />

Market Vectors Short HiYld Muni<br />

PowerShrs Fundamental EM Lcl Debt<br />

ProShares Merger Arbitrage<br />

PureFunds ISE Diamond/Gemstone<br />

Sage Low Volatility Dividend<br />

SPDR Barclays Breakeven Inüation<br />

United States Asian Commodity<br />

Vanguard Total International Bond<br />

WisdomTree China Div Ex-Financials<br />

Yorkville High Inc Infrastructure MLP<br />

Source: <strong>IndexUniverse</strong>.<strong>com</strong>’s ETF Watch<br />

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

Total Return % Annualized Return %<br />

Fund Name Ticker Exp Ratio Assets 3-Mo YTD 2011 2010 3-Yr 5-Yr Mkt Cap P/E Std Dev Yield<br />

SPDR S&P 500 SPY 0.09 106,000.0 7.92 13.55 1.89 15.06 13.51 1.24 57,186.9 14.9 15.63 1.91<br />

SPDR Gold GLD 0.40 68,320.2 8.31 8.05 9.57 29.27 20.70 19.81 - - 20.24 -<br />

Vanguard MSCI Emerging Markets VWO 0.20 53,532.2 5.49 4.97 -18.75 19.46 6.50 -0.83 18,200.5 9.6 24.26 2.25<br />

iShares MSCI EAFE EFA 0.34 35,353.0 10.63 6.67 -12.25 8.15 2.34 -4.90 29,451.7 10.9 20.23 3.33<br />

PowerShares QQQ QQQ 0.20 35,025.2 10.08 22.57 3.38 19.91 20.31 7.53 79,360.3 16.7 18.22 0.76<br />

iShares MSCI Emerging Markets EEM 0.67 34,362.2 5.49 4.82 -18.82 16.51 5.52 -0.55 17,230.9 9.8 24.82 2.08<br />

iShares S&P 500 IVV 0.09 31,309.8 8.11 13.67 1.86 15.09 13.52 1.30 57,115.3 14.9 15.58 1.88<br />

iShares iBoxx $ Inv Gr Corp Bond LQD 0.15 24,033.2 4.33 8.74 9.73 9.33 9.66 8.11 - - 5.62 3.97<br />

iShares Barclays TIPS Bond TIP 0.20 23,307.6 0.82 5.48 13.28 6.14 9.62 7.96 - - 4.85 2.22<br />

Vanguard Total Stock Market VTI 0.06 22,488.7 7.80 13.36 0.97 17.42 14.00 1.82 30,506.4 14.8 16.37 1.83<br />

Vanguard Total Bond Market BND 0.10 17,605.0 1.46 3.71 7.92 6.20 6.32 6.56 - - 2.73 2.90<br />

iShares iBoxx $ HiYld Corp Bond HYG 0.50 16,160.1 7.04 7.50 6.77 11.89 12.53 6.81 - - 9.96 6.96<br />

iShares Russell 1000 Growth IWF 0.20 16,089.3 7.27 14.97 2.33 16.52 15.51 3.65 48,469.1 18.4 16.15 1.25<br />

iShares Barclays Aggregate Bond AGG 0.20 15,528.8 1.34 3.50 7.69 6.37 6.12 6.39 - - 2.94 2.41<br />

iShares Russell 2000 IWM 0.23 15,248.5 7.11 10.87 -4.44 26.93 13.93 2.00 997.9 16.5 21.40 1.53<br />

Vanguard REIT VNQ 0.10 14,154.1 7.62 16.93 8.62 28.37 23.86 3.95 8,155.0 38.0 19.10 3.24<br />

iShares Russell 1000 Value IWD 0.20 12,504.1 8.28 12.09 0.12 15.49 11.86 -0.96 37,594.9 12.8 16.16 2.16<br />

SPDR Barclays High Yield Bond JNK 0.40 11,939.9 7.24 8.84 5.12 14.20 13.30 - - - 10.24 7.06<br />

Vanguard Dividend Appreciation VIG 0.13 11,658.1 6.17 8.90 6.16 14.74 13.23 3.10 40,531.6 15.0 13.14 2.05<br />

SPDR DJ Industrial Average DIA 0.17 11,324.4 6.44 9.19 8.06 14.01 14.14 2.33 117,350.6 13.9 13.93 2.40<br />

iShares DJ Select Dividend DVY 0.40 10,966.2 5.13 8.31 11.81 17.79 16.81 0.23 11,780.2 15.5 12.04 3.41<br />

iShares S&P 400 MidCap IJH 0.21 10,796.3 5.42 11.63 -2.18 26.72 15.58 3.84 3,495.5 17.4 18.83 1.24<br />

Technology Select SPDR XLK 0.18 10,119.6 9.58 20.65 2.61 11.39 16.74 4.69 113,254.6 17.4 17.39 1.34<br />

iShares Gold Trust IAU 0.25 10,008.3 8.35 8.21 9.57 29.46 20.82 19.85 - - 20.26 -<br />

iShares S&P US Preferred Stock PFF 0.48 9,945.2 5.50 15.50 -2.00 13.81 12.08 4.24 - - 9.91 5.74<br />

Source: Morningstar. Data as of August 31, 2012. 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.<br />

Mkt Cap is geometric average market capitalization. P/E is price-to-earnings ratio. Std Dev is 3-year standard deviation. Yield is 12-month.<br />

62 November / December 2012


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Arnott continued from page 51<br />

<strong>com</strong>pany, with a remarkable history, but it’s often not a profitable<br />

prospective investment. The diminishing agility and<br />

flexibility as a <strong>com</strong>pany grows, the natural human tendency<br />

to punish the winners, the media coverage—arguably with<br />

a bias against the biggest winners—along with often-lofty<br />

starting valuation, all may contribute to our empirical evidence<br />

for subsequent disappointing results.<br />

Our previous studies suggested that top dog status is<br />

of no advantage in the United States; indeed, it’s often<br />

something of a curse. As we globalize this study, the<br />

results confirm the global relevance of “too big to succeed.”<br />

In the G-8 test of developed economies, we find<br />

the same phenomenon in each and every market: a statistically<br />

significant performance shortfall for top <strong>com</strong>panies,<br />

relative both to the <strong>com</strong>panies’ sectors and the stock<br />

market as a whole, with no countries immune to the effect.<br />

Less extensive tests for stocks from all 24 developed countries<br />

and the 12 largest-cap emerging markets confirms the<br />

effect. These tests also suggest that top dogs typically suffer<br />

a larger performance shortfall in the smaller developed and<br />

emerging economies outside the United States than in the<br />

United States. We see no indication that <strong>com</strong>panies that rise to<br />

the top of their sector can long continue to dominate their sector<br />

to an extent that would justify their often-lofty stock price.<br />

One sobering observation, for those who merely dismiss<br />

these top dog results as a direct consequence of the welldocumented<br />

size and value effects, is that our average top<br />

dog, whether a sector top dog or a national top dog, or a<br />

global top dog or an emerging markets top dog, historically<br />

delivers a return that is considerably lower than domestic<br />

cash yields. We know of no argument in neoclassical modern<br />

finance theory that supports a persistent negative equity risk<br />

premium for any category of stocks, including the top dogs.<br />

One simplistic solution would be to index, using whatever<br />

weighting scheme one chooses, but to omit either the<br />

largest-cap <strong>com</strong>pany in the country or the largest in each<br />

sector. While either rule would assuredly not work 100<br />

percent of the time, the results are pretty jarring. Such a<br />

portfolio would win, over long periods of time, with statistical<br />

significance, in most markets around the world.<br />

Based on chance alone, we would expect to find many<br />

sector or national top dogs that can reliably outperform over<br />

long spans. We do not; they are barely more <strong>com</strong>mon than<br />

unicorns. Said another way, the very business practices that<br />

drive an enterprise to the top might not necessarily make<br />

the <strong>com</strong>pany a good investment. Bigger is not always better.<br />

A lightly edited version of this paper is available in the<br />

December/January <strong>issue</strong> of the European Financial Review.<br />

The two journals have agreed to joint publication.<br />

References<br />

Arnott, Robert D. 2005. “What Cost ‘Noise’?” Financial Analysts Journal, vol. 61, no. 2 (March/April):10-14.<br />

———. 2010. “Too Big To Succeed.” Fundamentals (June).<br />

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.<br />

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

Banz, Rolf W. 1981. “The Relationship Between Return and Market Value of Common Stocks.” Journal of Financial Economics, vol. 9, no. 1 (March):3-18.<br />

Basu, Sanjoy. 1977. “Investment Performance of Common Stocks in Relation to Their Price-Earnings Ratios: A Test of the Efficient Market Hypothesis.” Journal of Finance,<br />

vol. 32, no. 3 (June):663-682.<br />

———. 1983. “The Relationship between Earnings Yield, Market Value and Return for NYSE Common Stocks: Further Evidence.” Journal of Finance, vol. 12, no. 1<br />

(March):126-156.<br />

Chow, Tzee-Man, Jason Hsu, Vitali Kalesnik and Bryce Little. 2011. “A Survey of Alternative Equity Index Strategies.” Financial Analysts Journal, vol. 67, no. 5 (September/<br />

October):37-57.<br />

Dreman, D. 1977. “Psychology and the Stock Market: Why the Pros Go Wrong and How to Profit.” New York: Random House.<br />

Fama, Eugene F., and Kenneth R. French. 1992. “The Cross-Section of Expected Stock Returns.” Journal of Finance, vol. 47, no. 2 (June):427–465.<br />

Fang, Lily, and Joel Peress. 2009. “Media Coverage and The Cross-Section of Stock Returns.” Journal of Finance, vol. 64, no. 5 (October):2023-2052.<br />

Lakonishok, Josef, Andrei Shleifer and Robert W. Vishny. 1994. “Contrarian Investment, Extrapolation, and Risk.” Journal of Finance, vol. 49, no. 5 (December):1541-1578.<br />

Milano, Gregory V. 2011. “Too Big to Succeed?” CFO (April 29): http://www.cfo.<strong>com</strong>/article.cfm/14572986/c_14573206<br />

Endnotes<br />

1 We could have used measures of economic footprint, popularized by the growing interest in the Fundamental Index® concept, such as sales, book value, profits, dividends,<br />

buybacks, number of employees, and so forth. Market capitalization is, of course, a product of economic footprint and valuation multiples. For instance, <strong>com</strong>pany sales<br />

times the price/sales ratio gives us market cap. So one might argue that we’re looking at a blend of <strong>com</strong>pany size and <strong>com</strong>pany popularity. Indeed, we are. For purposes<br />

of this paper, our “top dogs” are the <strong>com</strong>panies that—with very few exceptions—are both a dominant player within their business and popular enough to carry a premium<br />

multiple. If a <strong>com</strong>pany has the largest market cap in its sector (or country), this tacitly implies a consensus expectation that it will deliver larger profit distributions to its<br />

shareholders in future decades than any other <strong>com</strong>pany in its sector (or country). These <strong>com</strong>panies are also expected to continue to increase their dominance.<br />

2 In the Tour de France bicycle race, the leader after each day’s race wears a yellow jersey the next day so that <strong>com</strong>petitors can recognize the leader from a distance.<br />

3 Observers may sensibly suggest that a <strong>com</strong>pany with 51 percent market share can still double if its market doubles. Of course, growing the market helps <strong>com</strong>petitors in like proportion.<br />

4 In countless empirical studies (e.g., see Chow et al. 2011), equal weighting tends to beat cap weighting by 1-2 percent per year. Equal- or cap-weighting will not change<br />

the basic findings in our research. It bears mention that we do not exclude the top dog from its own sector return or country return. So while some might argue that equalweighting<br />

our benchmarks will lead to a larger shortfall, we would counter that including our top dogs in the benchmarks will have the opposite effect. In any event, the top<br />

dog effects that we explore in this article are much more powerful than the effects of benchmark construction.<br />

5 The 719 one-year samples were statistically independent, both cross sectionally and intertemporally. The 611 10-year samples were based on rolling 10-year results, so they<br />

are roughly equivalent to 60 independent samples.<br />

6 Now that Apple has taken over at the top, we now have eight U.S. national top dogs in 61 years!<br />

7 We also carry out additional tests on the sector top dogs for 24 developed economies; these are handled separately, because most of the 24 countries are much less diversified,<br />

with much stronger dominance by their top dogs than the G-8 primary countries that we tested. The data is “noisier,” with big outliers, so we <strong>com</strong>pile averages across<br />

these markets. Still, we are interested to test whether the “too big to succeed” story applies globally.<br />

8 This index spans the 1,000 largest-cap stocks in the US market and the 1,000 largest-cap stocks in the Developed ex-US markets, hence <strong>com</strong>prising 2,000 stocks. We refer to<br />

this list as the “top 2,000,” although it’s actually the <strong>com</strong>bination of two top-1,000 lists.<br />

9 We don’t show the sector results here. But they are impressive, albeit with considerable variability.<br />

10 Even relying on the 12 countries with the largest average market cap, this leads to remarkable concentration in some years. As one example, for Russia in 1996, only four<br />

<strong>com</strong>panies ranked in the top 1,000 in the emerging markets, by market cap. In 1997 and 1999, only six made the cut.<br />

11 For purposes of this paper, we ignore the 10-year results, as there are not even two independent samples.<br />

64<br />

November / December 2012


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©2012, THE NASDAQ OMX GROUP, INC. ALL RIGHTS RESERVED.<br />

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©2012, THE NASDAQ OMX GROUP, INC. ALL RIGHTS RESERVED.<br />

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The NASDAQ Global Index Family SM covers 98% of the investable universe. Our rules-based<br />

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Crossword<br />

Thinking About Options<br />

Test your<br />

knowledge!<br />

By Bruce Greig<br />

ACROSS<br />

1. High-default-risk<br />

instrument (2 words)<br />

5. “Soft” <strong>com</strong>modity<br />

10. Dependent<br />

11. Finished (2 words)<br />

12. Arkansas’ ___<br />

Mountains<br />

13. Option strategies<br />

that benefit from<br />

significant price<br />

movements in<br />

either direction<br />

14. Co-founder of<br />

Long-Term Capital<br />

Management and<br />

options pioneer<br />

(2 words)<br />

19. Options pricing<br />

model (2 words)<br />

24. What “theta”<br />

measures (2 words)<br />

26. Insured’s<br />

contribution<br />

27. Palmer and<br />

Schwarzenegger<br />

28. Alternative to<br />

“active” investing<br />

29. Magnetic<br />

induction units<br />

30. Real estate<br />

transactions<br />

1 2 3 4 5 6 7 8<br />

10 11<br />

12 13<br />

17 18<br />

DOWN<br />

14 15 16<br />

19 20 21<br />

24 25 26<br />

27 28<br />

29 30<br />

1. Lingo<br />

2. Type of mutual fund<br />

(hyphenated)<br />

3. iShares parent <strong>com</strong>pany<br />

4. After expenses<br />

6. Like many movies<br />

nowadays (2 words)<br />

7. Investment industry giant<br />

8. Shedding light on<br />

9. ___ Stock Exchange<br />

(Lebanon)<br />

13. Atlanta-to-Miami dir.<br />

15. “Go team!”<br />

9<br />

22 23<br />

16. What one does to a<br />

22-Down to redeem<br />

17. Brief summary of a<br />

research article<br />

18. Bondholders’<br />

expectations<br />

20. It replaced the album<br />

sleeve (2 words)<br />

21. International oil<br />

<strong>com</strong>pany, informally<br />

22. Financial derivative<br />

23. Some rental trucks<br />

25. Also known as the<br />

“hedge ratio”<br />

28. Pay___<br />

Solution<br />

Across: 1. Junk bond; 5. Coffee; 10. Reliant; 11. Ended up; 12. Ozark; 13. Straddles; 14. Robert Merton;<br />

19. Black-Scholes; 24. Time decay; 26. Copay; 27. Arnolds; 28. Passive; 29. Teslas; 30. Closings<br />

Down: 1. Jargon; 2. No-load; 3. BlackRock; 4. Net; 6. On DVD; 7. Fidelity; 8. Exposing; 9. Beirut; 13. SSE;<br />

15. Rah; 16. Exercises; 17. Abstract; 18. Payments; 20. CD case; 21. OXY; 22. Option; 23. Ryders; 25. Delta; 28. Pal<br />

66<br />

November / December 2012

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