better returns · PDF filebetter returns March / April 2014 ... Indexing Developments ......

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SERIOUS IDEAS FOR SERIOUS INVESTORS

Evaluating The EM Equity ETF Landscape

Eric Biegeleisen

Dividend Indexes And Value Indexes: A Comparison

Konrad Sippel

Index Choice Matters In Commodities

John Hyland

A New Era In Country Classification

Mat Lystra

Plus notes on the effective frontier from Israelsen, an interview with OPERS CIO Tillberg, S&P DJI’s Blitzer

on buybacks, Accretive’s Patterson and DeCosta on target-maturity bond indexes and much more!

better returns March / April 2014

Thu Feb 6 15:45:09 EST 2014Trim Size = 8.125 X 10.875

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INDEX MAR-APR INDEX_I.pdf

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To download a copy of the prospectus, visit http://pwr.sh/DBCptp://tp://

The fund is not a mutual fund or any other type of Investment Company within the meaning of the Investment Company Act of 1940 and is not subject to its regulation.

DB Commodity Services LLC, a wholly owned subsidiary of Deutsche Bank AG, is the managing owner of the funds. Certain marketing services may be provided to the funds by Invesco Distributors, Inc. or its affliate, Invesco PowerShares Capital Management LLC (together, “Invesco”). Invesco will be compensated by Deutsche Bank or its affliates. ALPS Distributors, Inc. is the distributor of the funds. Invesco, Deutsche Bank and ALPS Distributors, Inc. are not affliated.

Commodity futures contracts generally are volatile and are not suitable for all investors.

An investor may lose all or substantially all of an investment in the fund.

DBCPowerShares DB Commodity Index Tracking Fund

DBC



INDEX MAR-APR INDEX_II.pdf

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www.journalofindexes.com

www.journalofndexes.com

f e a t u r e s

V o l . 1 7 N o . 2

1March / April 2014

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Global Index Data 54Morningstar US Style Overview 55S&P Dow Jones Indices US Industry Review 56Exchange-Traded Funds Corner 57

Case-Shiller Indexes Continue To Gain 10MSCI GICS Changes For Companies 10WisdomTree’s Busy Day 10S&P 500 Sees Changes 10European Commission Fines 6 Firms Over Rigging 11Solactive Rolls Out Covered-Bond Index 11Indexing Developments 12Around The World Of ETFs 15Derivatives In Focus 17On The Move 33

Evaluating The Emerging Market Equity ETF LandscapeBy Eric Biegeleisen 18VWO and EEM aren’t the only EM ETFs to consider

Dividend Indexes And Value IndexesBy Konrad Sippel 26How similar are their contributions to a portfolio?

The New ‘Effective’ FrontierBy Craig Israelsen 32 Improving on the traditional 60/40 portfolio

OPERS Sticks To Basic PrinciplesBy Heather Bell 34CIO Brad Tillberg talks costs and passive management

The Importance Of Benchmark Choice In CommoditiesBy John Hyland 36It may matter more than it does for stocks or bonds

The Boom In BuybacksBy David Blitzer 38What does this growing trend mean for investors?

What Is The World Coming To?By Mat Lystra 42How individual factors affect country classification

Target Maturity Bond Funds As Retirement Income Tools?By Matthew Patterson and Darrin DeCosta 48A new approach to bond markets

Where Have All The Yields Gone?By Bruce Greig 64Do you have the answers?


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INDEX MAR-APR INDEX_1.pdf

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Contributors

2 March / April 2014

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nEric Biegeleisen, CFA, is the former director of research at Windhaven Investment Management. While at Windhaven, he was responsible for the firm’s investment model, global investment research and product due dili-gence. Previously, Biegeleisen worked in the defense contracting industry with General Dynamics C4 Systems and Textron Defense Systems. He holds a B.S. in electrical engineering from Trinity College, an M.S. in electrical engineering from the University of Southern California and an MBA from Boston University.

David Blitzer is managing director and chairman of S&P Dow Jones Indices’ index committee. He has overall responsibility for security selection for the company’s indexes, as well as index analysis and management. Blitzer previously served as chief economist for Standard & Poor’s and as corporate economist at The McGraw-Hill Companies. A graduate of Cornell University, he received his M.A. in economics from George Washington University and his Ph.D. in economics from Columbia University.

John Hyland, CFA, is the chief investment officer for United States Commodity Funds (USCF), a sponsor and manager of a family of commodity ETFs with more than $3 billion in assets under management. He has been active in the investment industry for the past 25 years. Prior to joining USCF, Hyland found-ed Towerhouse Capital Management LLC, which provided portfolio manage-ment and new fund development expertise to non-U.S. institutional investors. He graduated from the University of California, Berkeley.

Craig Israelsen teaches as an executive-in-residence at Utah Valley University in the Personal Financial Planning program; he previously taught at Brigham Young University and the University of Missouri-Columbia. Israelsen is also the developer of the 7Twelve Portfolio; his most recent book is titled “7Twelve: A Diversified Investment Portfolio with a Plan” (John Wiley & Sons, 2010). Israelsen received a B.S. in agribusiness, an M.S. in agricultural economics from Utah State University, and a Ph.D. in family resource management from Brigham Young University.

Mat Lystra is a senior index research analyst with Russell Investments. As part of the global research and innovation team, he helps develop new global indexes, enhance existing benchmarks and coordinate the Russell Indexes country clas-sification process. From 2006 to 2008, Lystra served as a lead researcher for the U.S. and global index reconstitutions; he was also a member of the research team responsible for the development of the Russell Global Indexes, launched in 2007.

Matthew Patterson is co-founder and head of investment strategy at Accretive Asset Management, a global provider of fixed-income indexing solutions. He focuses his efforts on developing indexing solutions that respond to the needs of financial advisors and their clients. Patterson previously held in-house legal positions at Claymore Group Inc. and Caterpillar Investment Management Ltd. He has a B.A. from the University of Illinois, an MBA from the University of Chicago, and a JD from the University of Illinois College of Law.

Konrad Sippel is global head of business development at Stoxx Ltd., where he is also responsible for the index businesses of Stoxx, Deutsche Boerse and SIX Swiss Exchange. Sippel has spent the last decade in the index busi-ness, primarily as an index developer at Deutsche Boerse. He graduated in mathematics from Queen Mary College in London and received his MBA from Duke University in Durham, N.C.

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S&P Dow Jones Indices LLC is a part of McGraw Hill Financial. Standard & Poor’s, S&P and S&P 500 are registered trademarks of Standard & Poor’s Financial Services LLC, a subsidiary of McGraw Hill Financial. Dow Jones is a registered trademark of Dow Jones Trademark Holdings LLC (“Dow Jones”). All Trademarks have been licensed to S&P Dow Jones Indices LLC. It is not possible to invest directly in an index. S&P Dow Jones Indices LLC, Dow Jones, S&P and their respective affiliates (collectively “S&P Dow Jones Indices”) do not sponsor, endorse, sell, or promote any investment fund or investment vehicle that seeks to provide an investment return based on the performance of an index. This document does not constitute an offer of services in jurisdictions where S&P Dow Jones Indices does not have the necessary licenses. S&P Dow Jones Indices receives compensation in connection with licensing its indices to third parties.

Copyright © 2013 by S&P Dow Jones Indices LLC, a part of McGraw Hill Financial, and/or its affiliates. All rights reserved.

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BNDXVanguard Total International Bond ETFHelp extend the reach of your clients’ portfolios abroad.

All investing is subject to risk, including the possible loss of the money you invest. Foreign investing involves additional risks including currency fuctuations and political uncertainty.

Total International Bond Index Fund is subject to currency hedging risk, which is the chance that currency hedging transactions may not perfectly offset the fund’s foreign currency exposures and may eliminate any chance for a fund to beneft from favorable fuctuations in those currencies. The Fund will incur expenses to hedge its currency exposures. Bond funds are subject to the risk that an issuer will fail to make payments on time, and that bond prices will decline because of rising interest rates or negative perceptions of an issuer’s ability to make payments.

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Editor’s Note

Jim Wiandt

Editor

Jim Wiandt

Editor

Certainly the index investing of today is not your grandmother’s index investing. Indeed, index investing has increasingly drifted into the space of what was previously black-box active management, bringing transparency

to what has always generally been factor investing, albeit nontransparent factor investing, in the active space. Essentially investing boils down to exposures and risks, with a marginal sliver of alpha pursuit where market arbitrage occurs.

In this issue, we explore some of the areas where different market tilts and portfolio mixes are working to lead index portfolios toward better risk-weighted returns.

Eric Biegeleisen, most recently the director of research at Windhaven Investment Management, opens the issue with an exhaustive analysis of the emerging market (EM) ETF space. His results suggest that investors should look into supplementing their EM allocation to the Vanguard FTSE Emerging Markets ETF (VWO | C-89) or the iShares MSCI Emerging Markets ETF (EEM | B-100) with other EM ETFs that demon-strate complementary risk/return characteristics.

Konrad Sippel of Stoxx follows up with his own in-depth analysis comparing the characteristics and sources of return of a dividend-based benchmark against a value index and against a standard market-cap-weighted index. Craig Israelsen, executive-in-residence at Utah Valley University, is next, with a column that demonstrates how adding asset classes to a standard 60/40 portfolio adds diversification and improves returns.

We then chat with Brad Tillberg, CIO of the Oklahoma Public Employees Retirement System, about how his fund keeps things simple by focusing on publicly traded securi-ties and keeping costs under control.

United States Commodity Funds’ John Hyland has contributed an article that explains why index choice matters more for commodities than it does for other asset classes. And David Blitzer of S&P Dow Jones Indices addresses the popularity of stock buybacks, how they compare with dividends and what buybacks can mean for investors.

Mat Lystra of Russell is next, digging into the topic of country classifications. He takes four of the criteria that underlie Russell’s classification decisions and shows how each country would be classified if just one of the factors was used. The piece offers insight into how those factors combine to create a final classification designation for each country, and discusses the increasingly dynamic nature of country classifications.

Matthew Patterson and Darrin DeCosta of Accretive Asset Management, creators of the BulletShares indexes, close out the feature articles section with an explanation of how target-maturity bond investments and strategies based on them can be of use to retail investors, including those who are dealing with retirement issues.

Finally, Bruce Greig offers some fun at the end of the issue with a particularly tricky crossword puzzle built around a relevant topic for investors today: the search for yield.

We hope that our readers find the investment results they are looking for in what is currently a very interesting market environment.

Value Added

March / April 20148




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News

March / April 201410

Case-Shiller Indexes Continue To Gain

A late January press release from S&P Dow Jones Indices indicated that the S&P Case-Shiller Home Price indexes were continuing to rise as of November 2013. The 10-city and 20-city composite indexes were up 13.8 and 13.7 percent, respectively, over the 12-month period ended in November, but both composite index-es fell 0.1 percent during the month.

On a year-over-year basis, Las Vegas had the largest increase, up 27.3 percent, with San Francisco and Los Angeles fol-lowing, with gains of 23.2 and 21.6 per-cent, respectively. Cleveland and New York saw the smallest gains, both up just 6 percent for the 12-month period, with Washington D.C. up 7.8 percent and Charlotte up 8.7 percent.

While all metropolitan areas had positive gains for the 12-month period, the month of November was a mixed bag. Nine cities saw home prices for the month rise, with Miami at the head of the pack, with a 1.4 percent gain, followed by Las Vegas, up 0.6 percent, and San Francisco, up 0.4 percent. The worst per-formers were Chicago, down 1.2 percent, and Charlotte, down 0.6 percent, along with Atlanta, Portland and Washington, D.C., which were all down 0.3 percent.

A representative of S&P DJI said in the press release that even though the indexes fell slightly during the month, it was the best November in terms of performance since 2005.

The press release noted that aver-age U.S. home prices had reached their mid-2004 levels; home prices peaked in the summer of 2006, reaching their post-peak lows in March 2012.

MSCI GICS Changes For Companies

In early January, MSCI announced which companies in its indexes would be affected by the changes to the

Global Industry Classification Standard (GICS) that it rolled out in November 2013. The classification changes were to become effective Feb. 28.

Twelve non-U.S. firms in the MSCI Global Standard Indices were reclas-sified, as were 24 non-U.S. companies in the MSCI Global Small Cap and Micro Cap indices. In the MSCI US Large Cap 300 Index, three companies were reclassified, while four compa-nies were reclassified in the MSCI US Micro Cap Index—those changes are also reflected in the global index series.

Among the most notable shifts in the foreign companies are the moves of Canada’s BlackBerry, Finland’s Nokia Corp. and Japan’s Canon Corp. into the renamed technology hardware, storage & peripherals subindustry. BlackBerry and Nokia have been in the communi-cations equipment subindustry, which has been redefined, while Canon has been in the now-discontinued office electronics subindustry.

In the U.S. indexes, large-cap com-panies Bank of America, Citigroup and JPMorgan Chase are transitioning from the other diversified financial services subindustry to the diversi-fied banks subindustry. Both of those subindustries were redefined slightly.

WisdomTree’s Busy DayWisdomTree Investments closed

out 2013 with a bang, launching six funds in one day in mid-December. Five of the new funds listed on the Nasdaq and are clearly aimed at quell-ing investors’ fears of a rising interest rate related to the Federal Reserve’s announced $10 billion tapering of its economic stimulus.

WisdomTree refers to four of the Nasdaq-listed ETFs as its “Rising Rates ETF Solution Suite”; they are designed to help investors maintain traditional asset allocations, while managing inter-est-rate risk, according to a press release

from WisdomTree. The funds, their tick-ers and expense ratios are as follows:• WisdomTree Barclays U.S. Aggregate

Bond Zero Duration Fund (AGZD), 0.23 percent

• WisdomTree Barclays U.S. Aggregate

Bond Negative Duration Fund (AGND), 0.28 percent

• WisdomTree BofA Merrill Lynch

High Yield Bond Zero Duration Fund (HYZD), 0.43 percent

• WisdomTree BofA Merrill Lynch

High Yield Bond Negative Duration Fund (HYND), 0.48 percent

Meanwhile, WisdomTree also rolled out the WisdomTree Japan Interest Rate Strategy Fund (JGBB) on the Nasdaq; the fund seeks to benefit from rising interest rates in Japan. It invests in U.S. Treasury bills and Japanese government bonds, the press release said. JGBB comes with an expense ratio of 0.50 percent.

The sixth fund listed on the NYSE Arca. The WisdomTree Bloomberg U.S. Dollar Bullish Fund (USDU), which bets on the U.S. dollar appreciation relative to a basket of global currencies, has an expense ratio of 0.50 percent.

S&P 500 Sees ChangesS&P Dow Jones Indices announced

a range of changes that it was making to its S&P 100, S&P 500, MidCap 400 and S&P SmallCap 600 indexes, to be effective as of Dec. 20, 2013.

The most notable of the changes was the inclusion of social networking company Facebook in the S&P 500 and S&P 100 indexes. The new addition displaced The Williams Companies Inc. from the S&P 100, but it remains in the S&P 500. Meanwhile, Teradyne Inc. was demoted from the S&P 500 to the S&P MidCap 400.

Two more companies, Alliance Data Systems Corp. and Mohawk Industries Inc., joined the S&P 500 from the S&P MidCap 400, displacing Abercrombie




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www.journalofindexes.com March / April 2014 11

& Fitch Co. and JDS Uniphase Corp., which both then joined the S&P MidCap 400, the press release said.

Additionally, Teradyne took the place of Scholastic Corp. in the S&P MidCap 400; Scholastic, in turn, moved to the S&P SmallCap 600, bumping Lincoln Education Services Corp from the indexes entirely. Old Dominion Freight Line Inc. and Brunswick Corp., both for-merly constituents of the S&P SmallCap 600, switched places with Arch Coal Inc. and Regis Corp. in the S&P Midcap 400, according to the press release.

European Commission Fines 6 Firms Over Rigging

In December, the European Commission levied record fines on five banks and one U.K. broker for rate rigging following statements in June 2013 that those involved in the market abuse would be subject to penalties.

According to press reports, Deutsche Bank, RBS, J.P. Morgan, Societe Generale, Citigroup and U.K.-based broker RP Martin were hit with fines totaling €1.7 billion for their involvement in the rate-rigging scan-dal that broke last year.

The fines were levied following a two-year investigation into the scan-dal, in which all six financial insti-tutions admitting to being part of a cartel. This involved the manipula-tion of the two global benchmarks, the London interbank offered rate (Libor) and the Europe interbank offered rate (Euribor).

All six agreed to pay fines, with Deutsche Bank receiving the highest charge—€725 million—after admitting to being involved in both the Libor- and Euribor-rigging cartels. The Royal Bank of Scotland also admitted its activity in both, receiving a fine of €391 million.

Societe Generale will pay €466 mil-lion for its involvement in the Euribor benchmark, while J.P. Morgan and

Citigroup will pay €79.8 million and €70 million, respectively, for their abuse of Libor. RP Martin was fined €247,000 for facilitating the Libor-rigging cartel.

However, not all banks allegedly involved in the practice settled with the EU. Credit Agricole and HSBC declined to resolve with the EU and disputed the allegations.

UBS and Barclays also avoided these fines for being the first to alert the authorities to the activity.

Total fines are now almost €4.3 billion ($5.8 billion), with UBS, RBS, Barclays, Rabobank and Icap all previ-ously fined for the practice.

In October, the EC unveiled draft legislation proposing that Libor and other financial benchmarks will be regulated by the European Union.

Under the suggested rules, national regulators and a coordinating European body will be allowed to investigate and monitor any possible rigging of indexes or conflicts of interest. It will also be given the power to issue fines.

Solactive Rolls Out Covered-Bond Index

Independent index provider Solactive launched the Solactive Diversified USD Covered Bond Index in January. The benchmark is made up of covered bonds and is intended to provide exposure to a diversified pool of U.S.-dollar-denominated inter-national bonds that offer high credit quality and attractive yield potential.

The index universe is composed of all AAA-rated and 144A-eligible USD-denominated covered bonds that have a fixed coupon. To be included in the index, bonds should meet several liquidity criteria, including a remain-ing time to maturity of 18 months or more, trading size of $250,000 or below and an amount outstanding of $1 billion or above.

The index had 54 constituents. The country breakdown was as fol-lows: 43 percent Canada, 18 percent Australia, 13 percent Norway, 9 per-cent Sweden, 6 percent U.K., and

The European Commission levied record fines on five banks and one U.K. broker for rate rigging.




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NewsNews11 percent for Switzerland, France, Netherlands and Germany.

The index, which is rebalanced quarterly, is weighted by market value. Caps are applied to ensure that no single issuer exceeds 25 percent of the index and that issuers with a weight of 5 percent or more do not make up more than 50 percent of the index.

The index was adopted by the ProShares USD Covered Bond ETF (COBO | C-26) as its benchmark as of Jan. 15.

INDEXING DEVELOPMENTSPuerto Rico Removed From Muni Indexes

S&P Dow Jones Indices issued a state-ment announcing that all municipal bonds issued by Puerto Rico had been removed from the S&P National AMT-Free Municipal Bond Index as of Jan. 31.

S&P DJI noted in the statement that the index was designed for investabil-ity, and typically excludes the higher-risk and less liquid portions of the municipal bond market. It said Puerto Rico’s munic-ipal bonds had been trading at prices more typical of high-yield corporate debt and exhibiting uneven liquidity.

However, the statement also said that Puerto Rico bonds would continue to be included in S&P DJI’s municipal bond

indexes that are intended for measure-ment and analysis purposes, such as the S&P Municipal Bond Index and the S&P Taxable Municipal Bond Index.

According to the release, Puerto Rico’s bonds currently have the low-est possible ratings a municipality can have and still be considered investment grade by ratings services S&P, Moody’s and Fitch, with all three firms indicat-ing the bonds could be demoted to below investment grade in the future.

S&P DJI Reports Increase In Dividends

In a January press release, S&P Dow Jones Indices said that net dividend issu-ance for U.S. stocks in the fourth quarter of 2013 was up $12.7 billion. Interestingly, although S&P only recorded 885 divi-dend increases in the 2013 fourth quarter versus 1,266 dividend increases in the 2012 fourth quarter, the dollar amount of the dividend increase in the last quarter of 2012 was just $8.4 billion.

At the same time, just 51 compa-nies decreased their dividend rates during the last quarter of 2013; in the same quarter in 2012, 154 compa-nies lowered their dividend rates, the press release said.

During all of 2013, 2,895 issues ratch-eted up their dividend rates, a small

increase from 2,887 in 2012. At the same time, 299 companies decreased their dividend rates, up from 275 in 2012, according to the press release.

S&P DJI tracks the dividend rates and issuance of roughly 10,000 issues listed on U.S. markets.

New Nikkei Index Breaks With Tradition

The Japan Exchange Group and Nikkei Inc. launched a jointly devel-oped index in early January.

The stock exchange group and the newspaper publishing company teamed up to create the JPX-Nikkei Index 400. The aim is to make the Japanese market more attractive to investors compared with other tradi-tional market-cap-weighted bench-marks like the Topix.

The new index tracks 400 compa-nies based on return on equity (ROE), governance, size and liquidity. Index constituents must be listed for more than three years and not have liabili-ties that exceed assets over the past three years. Liquidity is determined by market capitalization and trading value over the same time frame.

According to research from Goldman Sachs, the index will exhibit a higher ROE, lower price-to-earnings ratio and a higher dividend yield than the Topix.

The JPX-Nikkei Index 400 has decreased weightings in financials, construction, power and gas com-pared with the Topix, and has a free-float market cap of 1.5 percent per constituent.

Euronext Adds To AEX LineupEuronext N.V. in December launched

two new indexes that provide exposure to 75 of the largest companies on the Amsterdam stock exchange.

The new indexes are the AEX All-Tradable Index and the AEX All-Tradable Alternative Weighting Index, and both are weighted based on free-float market capitalization.

The former’s constituents have a cap of 15 percent per stock, and this is reviewed annually.


Puerto Rico’s bonds currently have the lowest possible ratings a municipality can have and still be considered investment grade.




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The alternative weighting index has

a cap of 9 percent per stock and this is

reviewed quarterly. In addition, any

stocks weighted at more than 4.5 per

cent can only make up 36 percent of

the total alternative index.

Euronext N.V. is a wholly owned

subsidiary of Intercontinental

Exchange Group. The new indexes

are an expansion of the existing AEX,

AMX and AScX index range.

Both new indexes will be calcu

lated on a price, net and grossreturn

basis every 15 seconds.

Buybacks Reach New HighS&P Dow Jones Indices said in a

late December 2013 press release that

share repurchases for S&P 500 stocks

had reached the highest 12month

level since the fourth quarter of 2007.

S&P 500 companies spent $128.2 bil

lion on stock buybacks in the third quar

ter, an increase of 8.6 percent from the

prior quarter. Over the 12month peri

od, buybacks reached $445.3 billion, an

increase of 15 percent from the 12month

period ending with the second quarter of

2013, the press release said.

The 12month high for buybacks

during the period for which S&P DJI

keeps records was $589.1 billion at the

end of the fourth quarter of 2007.

The information technology sector

has typically been a major driver of

buyback totals, but only represent

ed 25.1 percent of the total due to a

smaller contribution from Apple. The

sector had represented 31.5 percent of

buybacks in the second quarter.

However, Apple paid out the most for

stock buybacks in the third quarter at $4.9

billion. Pfizer, Halliburton, Qualcomm

and Johnson & Johnson rounded out the

top five, each spending more than $3 bil

lion to repurchase shares.

Bloomberg Adds ‘Funding Margin’ Index

Global index provider Bloomberg

Indexes in November 2013 launched

a new index that measures credit risk

and funding rates of highquality

European banks.

The Bloomberg European Banks

Funding Margin Index works by calcu

lating the average spreads of euro cur

rency bonds to ensure transparency

in risk assessment. It aims to provide

an accurate reflection of the borrow

ing costs of major European banks by

measuring a sixmonth moving aver

age of asset swap spreads of the bonds

from 20 highly rated European banks.

According to the firm, the bench

mark is unique in evaluating the credit

risk of banks from bond market prices,

as opposed to the more volatile credit

derivatives market. This enables indi

vidual financial institutions to provide

independent and reliable benchmarks

for lending projects, such as mortgag

es, and gives more precise risk pricing.

The new index’s constituent

bonds are a subset of the Bloomberg

EUR Investment Grade European

Corporate Bond Index.

S&P DJI Adds Palestine, Zimbabwe Indexes

In midDecember, S&P Dow Jones

Indices said it had begun calculating

indexes tracking the frontier markets

Zimbabwe and Palestine.

Neither market is included in the S&P

Frontier BMI or the Dow Jones Global

Total Stock Market Index; instead, they

are calculated as standalone bench

marks and are included on the index

provider’s watch list to be added to the

broader benchmarks if they become eli

gible, according to the press release.

The data for the Palestine indexes

date back to September 2012, while

the data for the Zimbabwe indexes

date back to September 2011.

MSCI Adds To Smart-Beta LineupIn early December, global index

provider MSCI launched a new range

of strategic indexes that offer investors

access to passive indexlinked multi

factor strategies.

MSCI is offering the MSCI Multi

Factor Indexes in standard combina

tions, but investors can also create

customized blends according to their

portfolio needs. Additionally, the

methodology can be applied to core

MSCI indexes such as the MSCI EAFE,

MSCI ACWI, MSCI World and MSCI

Emerging Markets.

Investors wishing to mix their

strategies will also be able to use

IndexMetrics, a customdesigned

analytical tool from MSCI.

MSCI’s factor index family isolates for

such factors as value, size, yield, quality,

volatility and momentum. The newest

additions to the series would allow inves

tors to combine multiple factors and

adjust the exposure to each one.

S&P DJI Launches China Index Family

In December, S&P DJI rolled out its

S&P Total China BMI indexes, featuring

five different indexes. The benchmarks

cover different combinations of China’s

Ashare and Bshare markets, as well as

securities listed in Hong Kong and in

foreign markets, the press release said.

The S&P China A BMI targets the

broad Ashare market in China, cover

ing the small, mid and largecap seg

ments. Similarly, the S&P China A+B

BMI does the same for the Ashare and

Bshare markets combined.

The S&P Total China BMI repre

sents the entire investable market of

mainland China, including the com

ponents of the S&P China A BMI and

the older S&P China BMI; the S&P

China BMI covers the portions of

China’s markets that foreign inves

tors can own, such as Bshares,

Hshares, red chips and Pchips and

shares listed on foreign stock mar

kets. Meanwhile, the S&P Total China

+ Hong Kong BMI includes the com

ponents of the Total China Index and

adds in S&P Hong Kong BMI.

Finally, the S&P Greater China BMI

goes a step further still, encompass

ing the S&P Total China BMI, S&P

Hong Kong BMI and S&P Taiwan BMI,

according to the press release.

MSCI Eyes Barclays’ Index Business

MSCI has reportedly approached

Barclays to buy its index busi

ness, according to news reports.

The move, which could see MSCI

build out its fixedincome offering





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and mean a potential windfall for Barclays, comes ahead of pending regulation in the form of Basel III and CRD IV.

These new rules will introduce tougher capital and liquidity require-ments for banks and come in the wake of the Libor and Euribor scan-dal last year. In particular, it means banks will have to set aside more capital on their balance sheets.

Following the Libor-rigging scan-dal, regulators hit Barclays with record fines. The bank offers thousands of benchmarks, many of which are cus-tomized for clients, and has carved out a niche in the fixed-income sector.

At a recent panel, Alain Dubois, managing director and head of new business and product development at MSCI, declined to comment on the matter. Barclays also declined to comment.

ICE’s Benchmark Administration In Charge Of Libor

In mid-January, Intercontinental-Exchange Group said that its ICE Benchmark Administration (IBA) unit would become the new administra-tor for London interbank offered rate (Libor) as of Feb. 1 under the aus-pices of the U.K.’s Financial Conduct Authority, which oversees the bench-mark’s administration.

According to a press release from ICE, IBA was selected via committee to be the Libor administrator in July 2013, and has been consulting with the financial community on how to make the transition without disrupt-ing Libor’s calculation and dissemi-nation. ICE said that there would be no immediate changes to the bench-mark’s methodology.

In its role as Libor administrator, IBA has created a system of checks and balances, such as an independent board and an oversight committee that includes the board and a wide range of market participants. The press release also noted IBA’s new “surveillance methodology,” which is intended to prevent the kind of manipulation that created the recent scandal.

S&P DJI Launches EM Demand Index

In late January, S&P Dow Jones Indices rolled out the S&P Emerging Markets Domestic Demand Index; the benchmark is designed to track emerging market companies oper-ating in sectors that are driven by domestic demand.

Constituents must be domiciled and incorporated in one of 19 emerging market countries, in addition to being listed on the primary stock exchange of their home country, the press release said. Ultimately, 50 companies are cho-sen from the markets of Brazil, Chile, China, Colombia, Czech Republic, Egypt, Hungary, India, Indonesia, Malaysia, Mexico, Morocco, Peru, Philippines, Poland, Russia, South Africa, Thailand and Turkey.

All of the companies must also fall within the consumer staples, consum-er discretionary, telecommunication services, healthcare or utilities sectors of the GICS, the press release said.

Component companies must meet additional size and liquidity require-ments. The index will be reviewed and reconstituted in September on an annual basis.

Stoxx Offers EM Exposure IndexIn December, Europe-based index

provider Stoxx Limited launched a new index offering exposure to emerging markets through European-based companies.

The Stoxx Europe 600 EM Exposed Index will enable investors to access these markets through more liquid securities, according to a note from Stoxx. The index represents those com-panies within the Stoxx Europe 600 Index that derive a substantial part of their revenues from emerging markets countries. All companies’ geographic revenue splits are collected from their annual reports and the publicly avail-able United Nations Commodity Trade Statistics Database, which contains manufactured goods exports data on each country. When data on a specific company are unavailable or not report-ed, ratios are approximated.

To only include companies in the Stoxx Europe 600 EM Exposed Index that have a significant part of their revenues from nondeveloped markets, a final exposure threshold of 33 percent is set.

S&P DJI Debuts BDC IndexS&P Dow Jones Indices kicked off

the month of February with the launch of the S&P Business Development Company Index, a press release said.

The new index consists of private equity firms selected from the com-ponents of the S&P United States BMI; eligible firms must have a BDC structure, as evidenced by SEC fil-ings. The press release also noted that the component weights were adjust-ed for available float and capped at 10 percent of the index. As of the end of 2013, the index had 34 components, with an average market capitalization of $845 million, according to an S&P DJI fact sheet.

The index has been licensed to Japan’s Nikko Asset Management for use as the basis for an index fund, the press release said.

Macquarie Develops Food Inflation Index

Macquarie launched a new index in December that will benchmark and forecast food and agricultural prices, and is intended to help indicate the direction of food inflation.

The Macquarie Agricultural Com-modity Price Index, or MacPI, was designed by Macquarie Commodities Research, and is the first index of its kind whereby it will indicate food infla-tion for the industry and economists.

The index will track the price of futures contracts for 28 agricultural commodities traded on international exchanges and is constructed using a consumption-weighted, rather than a trade-weighted, methodology. This is intended to reflect each commodity in terms of global consumption.

While there are a number of similar commodity indexes already in exis-tence, this is thought to be the first corporate index of its kind for fore-casting prices.

News





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www.journalofindexes.com 15March / April 2014

S&P DJI Rolls Out Shariah Composite Index

In January, S&P Dow Jones Indices launched a new index that will give investors exposure to the highest-yield-ing Shariah-compliant stocks from the Gulf Cooperation Council (GCC).

The new S&P GCC Composite Shariah Dividend Index comprises the 30 highest-yielding stocks from the S&P GCC Composite Shariah Index, which is made up of Shariah-compliant stocks and also acts as a benchmark for the six GCC coun-tries—Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates.

For stocks to be considered Shariah-compliant, and therefore included in either index, they must meet certain criteria regarding liquidity, dividend growth and dividend sustainability.

The component list is subject to a minimum of two stocks per country, and the constituents are weighted by their indicated annual dividend yield.

Russell Debuts UK Midcap IndexRussell Investments launched a new

index in December. The Russell UK Mid 150 Index is the first U.K.-dedicated index from the U.S.-based provider.

It is made up of 150 stocks from the midcap sector listed on the U.K. stock exchanges and denominated in ster-ling. The stocks are then all screened on a monthly basis according to their capacity to be traded in volume, which means that the index as a whole can be easily and quickly traded within an index-based portfolio.

Russell Indexes has also been work-ing with NYSE Liffe on the develop-ment of futures based on the Russell UK Mid 150 Index. They are expected to be made available for trading on Bclear, the exchange’s trade adminis-tration and clearing service.

S&P DJI Launches Mexico Bond Indexes

S&P Dow Jones Indices in mid-December said that at the beginning of 2014, it would roll out a family of five indexes covering the Mexico

bond market via a partnership with Valuacion Operativa y Referencias de Mercado.

The new benchmarks are designed as replacements for the Dow Jones Latixx Mexico Government Bond indexes, which were discontinued on Dec. 31, 2013, the press release said.

The five indexes cover Mexico gov-ernment securities and include the following:• S&P/Valmer Mexico Government

5-10 year Mbonos Index• S&P/Valmer Mexico Government

1-5 year Mbonos Index• S&P/Valmer Mexico Government

CETES Index• S&P/Valmer Mexico Government

Inflation-Linked 1+ year Udibonos Index

• S&P/Valmer Mexico Government

International 1+ year UMS IndexThe indexes each have a base date

starting Jan. 2, 2004, and launch dates from June 2009.

AROUND THE WORLD OF ETFsVident Debuts US ETF

Vident Financial, the new ETF firm that launched the Vident International Equity ETF (VIDI) in October 2013, rolled out the Vident Core U.S. Equity ETF (VUSE) on Jan. 22. The new fund has an annual expense ratio of 0.55 percent.

VUSE has an underlying index that selects stocks based on their above-average corporate governance and accounting practices and the relative strength of their valuations in relation to their overall sector. The index also takes into account earnings quality, growth and market sentiment, VUSE’s prospectus said.

The benchmark selects its com-ponents from all portions of the U.S. stock market. Individual component weightings are based on risk levels, taking into account how each stock responds to volatile markets and its risk contribution to the sector to which it is assigned, according to VUSE’s prospectus. A fact sheet pro-vided by Vident noted the fund had 550 holdings at its launch.

Van Eck Launches ‘Quality’-Focused ETFs

Van Eck Global rolled out a quartet of international and emerging market “quality” and “quality dividend” strate-gies on the NYSE Arca on Jan. 23. The new funds focus on stocks with high returns on equity, stable year-over-year earnings growth and low financial lever-age—as well as those types of stocks that shoot off attractive dividends.

The Market Vectors MSCI Inter-national Quality ETF (QXUS) and the Market Vectors MSCI International Quality Dividend ETF (QDXU) both track alternate versions of the MSCI ACWI ex USA Index, which includes large and midcap stocks across 42 coun-tries, according to a filing. The funds both charge 0.45 percent per year.

The Market Vectors MSCI Emerging Markets Quality ETF (QEM) and the Market Vectors MSCI Emerging Markets Quality Dividend (QDEM) ETF both track alternate versions of the MSCI Emerging Markets Index, which includes large and midcap stocks across 19 emerging market countries, according to a regulatory filing. Both funds come with expense ratios of 0.50 percent.

PowerShares Renovates ETF Lineup

Invesco PowerShares said in December that it was closing four




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ETFs and changing up the underlying

indexes and names of 10 ETFs.

The four funds that were slated for

closure on Feb. 18, 2014 had combined

assets under management of $58.3

million at the time of the announce-

ment, and include:

• PowerShares KBW International Financial Portfolio (KBWX | D-49)

• PowerShares MENA Frontier Countries Portfolio (PMNA | F-61)

• PowerShares Dynamic MagniQuant Portfolio (PIQ | C-57)

• PowerShares Lux Nanotech Portfolio (PXN | D-11)

In addition, the ETFs undergoing name and index changes that were to be

effective Feb. 19, 2014, include 10 of the PowerShares “Dynamic” ETF family; the funds currently track the “Intellidex” benchmarks, which are a family of

smart-beta indexes. The revamped

funds mainly cover sectors and will

switch to Dorsey Wright indexes that incorporate relative-strength principles

and momentum into their methodol-

ogy. The PowerShares Dynamic Basic Materials Sector Portfolio, for example, will become the PowerShares DWA Basic Materials Momentum Portfolio.

New SPDR Looks ‘Beyond BRICs’State Street Global Advisors

debuted the SPDR MSCI EM Beyond BRIC ETF (EMBB) in early December. The fund competes directly with

Emerging Global Advisors’ EGShares Beyond BRICs ETF (BBRC) and Global X’s Next Emerging & Frontier ETF (EMFM).

EMBB—like BBRC and EMFM—avoids the so-called BRIC countries in favor of less popular investment

destinations in the developing world.

The SSgA fund invests in American depositary receipts and global depositary receipts of issu-

ers from Chile, Colombia, Czech

Republic, Egypt, Hungary, Indonesia, Malaysia, Mexico, Morocco, Peru, the Philippines, Poland, South Africa, South Korea, Taiwan, Thailand and Turkey. Its expense ratio is 0.55 per-

cent, undercutting both BBRC and EMFM, which come with expense ratios of 58 basis points.

Interestingly, South Korea and Taiwan have the largest country

weightings in EMBB’s portfolio; the underlying indexes of both BBRC and EMFM exclude those countries.

Recon Capital Debuts Nasdaq Covered-Call ETF

Bronxville, N.Y.-based Recon Capital Advisors unveiled a covered-call ETF dubbed the Recon Capital Nasdaq 100 Covered Call ETF (QYLD) in mid-December. The ETN tracks the CBOE Nasdaq-100 BuyWrite Index. It costs 0.60 percent per year.

A covered call is an options strat-egy whereby an investor holds a

long position in an asset and sells

or “writes” call options on that same asset. The goal is to generate more

income, through the sale of call

options, than the asset would oth-

erwise provide on its own from divi-

dends or other distributions.

Essentially, QYLD will hold the Nasdaq-100’s basket of stocks while sell-ing one-month call options on the index.

Leveraged UBS ETN Targets CEFsIn December, UBS, the bank

behind the Etracs family of exchange-

traded notes, launched a double-

exposure ETN focused on closed-end funds that are expected to shoot off a

dividend of 19.4 percent.The Etracs Monthly Pay 2xLever-

aged Closed-End Fund ETN trades on the NYSE Arca under the symbol “CEFL” and has an “annual tracking rate” of 50 basis points, according to regulatory paperwork.

The ETN is essentially a double-exposure version of the YieldShares High Income ETF (YYY).

DB Adds To Currency- Hedged Lineup

In late January, Deutsche Bank rolled out three additions to its roster

of currency-hedged ETFs. As of the end of the month, the ETF provider

had a family of 11 currency-hedged

ETFs, providing exposure to various

countries and regions.

The new funds and their expense

ratios are:

• db X-trackers MSCI South Korea Hedged Equity Fund (DBKO), 0.58 percent

• db X-trackers MSCI Mexico Hedged Equity Fund (DBMX), 0.50 percent

News


Deutsche Bank rolled out three additions to its roster of currency-hedged ETFs.




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• db X-trackers MSCI All World ex US Hedged Equity Fund (DBAW), 0.40 percent

Currency hedging offers investors some protection against the fluctua-tions of a foreign currency against the U.S. dollar.

Market Vectors PEK Gets Makeover

Van Eck said in January that the Market Vectors China ETF (PEK | F-49), which formerly invested in Chinese securities via derivatives, would be taking a direct route to the Chinese A-share market by owning actual stocks going forward.

Controls imposed by the Chinese government currently limit direct investments in A-shares, so only a limited pool of foreign investors have been approved as qualified foreign institutional investors by the China Securities Regulatory Commission. Market Vectors partnered with China Asset Management (Hong Kong) Limited, which has received its renminbi qualified foreign insti-tutional investor quota of 1 billion RMB ($163.8 million), allowing PEK to have direct exposure to physical China A-shares.

Previously, PEK, which launched in October 2010, was marketed as the first fund of its kind to offer broad exposure to China A-shares via derivative securities using Credit Suisse as a partner. The use of derivatives exposes investors to the inherent risks of any equity invest-ment, plus so-called counterparty risks associated with use of over-the-counter derivatives.

Emerging Global Unveils EM Bond ETFs

Emerging Global Advisors broke new territory when it launched three ETFs in mid-January targeting emerging market bonds. The three funds invest respectively in short-, intermediate- and long-term emerg-ing market investment-grade bonds and are subadvised by asset man-ager The TCW Group.

The new launches include:• EGShares TCW EM Short Term

Investment Grade Bond ETF (SEMF)• EGShares TCW EM Intermediate

Term Investment Grade Bond ETF (IEMF)

• EGShares TCW EM Long Term Investment Grade Bond ETF (LEMF)

All three track indexes from J.P. Morgan that target different dura-tion ranges in the USD-denominated, investment-grade emerging market bonds space, a press release said. The benchmarks include both corporate and sovereign debt. Each fund comes with an expense ratio of 0.65 percent.

iShares To Close 10 ETFsIn late January, iShares said that it

was shuttering 10 All Country World Index (ACWI) ex-U.S. sector funds after the close of business on March 25, 2014, the result of weak asset gath-ering since the funds were launched more than three years ago.

The 10 funds, which altogether had about $54 million in assets at the time of the announcement, were brought to market on July 13, 2010.

The funds getting the ax, and their total assets under management in late January, include:• iShares MSCI ACWI ex U.S.

Consumer Discretionary (AXDI | F-61), $4.25 million

• iShares MSCI ACWI ex U.S. Consumer Staples (AXSL | D-61), $7.71 million

• iShares MSCI ACWI ex U.S. Energy (AXEN | F-78), $5.33 million

• iShares MSCI ACWI ex U.S. Financials (AXFN | F-67), $5.16 million

• iShares MSCI ACWI ex U.S. Healthcare (AXHE | D-65), $12.42 million

• iShares MSCI ACWI ex U.S. Industrials (AXID | F-63), $3.37 million

• iShares MSCI ACWI ex U.S. Information Technology (AXIT | F-62), $3.35 million

• iShares MSCI ACWI ex U.S. Materials (AXMT | F-60), $2.45 million

• iShares MSCI ACWI ex U.S. Telecommunication Services (AXTE | D-64), $3.18 million

• iShares MSCI ACWI ex U.S. Utilities (AXUT | D-74), $6.6 million

In 2013, iShares had its first clo-sure in more than a decade when it shut down the iShares Diversified Alternatives ETF (ALT).

PowerShares Debuts Old-Timers ETF

PowerShares debuted the Power-Shares NYSE Century Portfolio (NYCC) in mid-January. The fund invests in companies that have been incorporated in the U.S. for at least 100 years, have been listed on a major U.S. securities exchange for at least 10 years, and have a market capitaliza-tion of at least $1 billion.

Its nearly 400 components are some of the oldest major public companies in the United States and include such names as Coca-Cola (founded 1886), Ford Motor (1903), US Steel (1901), Campbell Soup (1869) and AT&T (1885).

NYCC comes with an expense ratio of 0.50 percent.

DERIVATIVES IN FOCUSMSCI, NYSE Liffe To Debut Smart-Beta Futures

NYSE London International Financial Futures and Options Exchange (Liffe) is expanding its derivative desk by offering futures contracts linked to smart-beta index-es from MSCI.

Futures linked to minimum vola-tility and equal-weighted indexes were to be available beginning Feb. 3. The new investments were to be available via Bclear, NYSE Liffe’s cleared service for equity deriva-tives. Futures are normally linked to mainstream, capitalization-weight-ed indexes like the FTSE 100.

The new contracts are to global, emerging market, U.S. and Europe indexes.

7IM worked in conjunction with the exchange to launch the new indexes,


continued on page 33




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Evaluating The Emerging Market Equity ETF Landscape

A look beyond VWO and EEM

By Eric Biegeleisen

Fri Feb 7 09:58:22 EST 2014Trim Size = 8.125 X 10.875



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March / April 2014www.journalofindexes.com 19

The exchange-traded fund industry has exploded both in terms of the number of products and asset growth since the introduction of the short-lived

Index Participation Shares tracking the S&P 500 in 1989.1

Besides helping investors gain access to markets previous-ly available only through mutual funds or active managers at a fraction of the price and without the necessary high minimum asset levels, ETFs have also provided the means to gain real-time exposure to asset classes that were previ-ously out of reach. Chief among these is emerging market (EM) equities. Interest in this asset class by retail and insti-tutional investors alike has grown over the past decade, along with the rise in terms such as BRICs, CIVETS and the Next 11. Further, the explosion in ETF product listings has led to an investment universe where 164 of the 974 so-called equity ETFs are considered “emerging markets” (as of July 31, 2013). Of the roughly $130 billion in these 164 funds, $83 billion, or nearly 65 percent, of assets are in just two products: the Vanguard FTSE Emerging Markets fund (VWO | C-89) and the BlackRock iShares MSCI Emerging Markets fund (EEM | B-100).2 This high concentration warrants further analysis of the remaining product set in comparison with these industry giants to shed light on whether there is any value in complementing an existing position in one of these two market leaders with another EM equity ETF, or whether a combination of any of these smaller funds can produce similar or better risk-adjusted performance compared with their market leaders.

It is the intent of this paper to explore the universe of alternative investment options in the EM equity ETF space, from which many products may pair quite well with the Vanguard and BlackRock funds.

Experiment ConfigurationIn performing this analysis, it is important to establish

some guidelines and assumptions that will be paramount in interpreting and/or relying on the results herein. It is necessary to define the universe of candidate EM equity products, the time period and frequency of analysis, an understanding of the underlying holdings, the tracking error between index and product, and the associated fund fees and transaction costs. Next, the experiment will pair two EM funds at a time and naively weight them, i.e., 50/50, with an annual rebalance. Every possible pair com-bination will be examined, including examining a 50/50 weighting of the same ETF, i.e., 100 percent of one ETF.

ConsiderationsBeyond the experimental configuration (as detailed

above), it will also be critical to acknowledge and analyze several considerations rather than outright accepting or rejecting the risk/return performance statistics presented.

Given the short time frame of existence for many of these EM equity ETFs, using the fund’s underlying index to per-form this analysis provides for a longer data set. However, this comes with the trade-off of introducing tracking error between the fund and its index beyond that explained by the fund’s expense ratio. This is due to several pricing fac-

tors; principally, that many of these funds comprise various equities from countries with nonoverlapping trading hours and that may not trade during U.S. hours. This leads to a fund price that may reflect the market’s perception of the underlying securities values, along with currency move-ments, while the corresponding index’s price may have been struck separately, leading to tracking error. Index sampling for exposures versus full index replication is another source of tracking error, albeit more so in EM fixed income rather than equities. Unfortunately, while there are several methods for calculating tracking error, the useful-ness of many of them is questionable or unavailable. The goal would be to perform a tracking error analysis that is consistent across all examined portfolios, regardless of the underlying country exposures. Not only did the tracking error data examined for this experiment from Bloomberg not provide any particular trend, but the method used by Bloomberg does not account for these factors.3

Other considerations to be examined for potential port-folio biases include transaction cost analysis, underlying ETFs’ assets under management (AUM), portfolio expense ratios, underlying market capitalization, underlying coun-try exposures, and underlying sector exposures.

Investment UniverseAlthough there are 164 so-called EM equity funds, many

of these are niche and/or sector-specific, making their inclusion in this analysis unfair, as it would lead to an unde-sirable tilt in one’s overall EM equity exposure. Therefore, using ETF.com’s ETF classification system utility,4 set-ting [Asset Class] to “Equity,” [Economic Development], [Region] and [Geography] to “Emerging Markets” and [Niche] to “Broad Based” while eliminating leveraged and inverse products pruned this down to 48 candidate prod-ucts. This was further pruned down to 34 by eliminating the “Sector” option under [Category], leaving “Size and Style” as well as “Strategy” as potential candidate products. “Size and Style” was then further pruned to only include [Segments] qualified as “Total Market” or “Large-Cap” to be reasonably close in design to the industry betas, which are composed of more than 60 percent large-cap.5 This clas-sification filtering left a final universe size of 29 products.

Next, the time period for analysis was determined as a function of the availability of data for the funds’ underly-ing indexes. In an attempt to have the longest time period possible while accommodating as many candidates as possible, in addition to capturing the full 2008 calendar year, five products were eliminated, leaving 24 for the final analysis, as shown in Figure 1.

ResultsWith 24 candidates available, and given the experi-

ment configuration as described above, this leads to 300 portfolios6 to be examined using monthly data from Dec. 31, 2007 through July 31, 2013 (67 months). From this, four key risk/return statistics can be calculated for each portfolio: compound annual growth rate (CAGR); annual standard deviation; Sharpe ratio; and portfolio maximum




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drawdown. The results of this analysis are shown in

Figure 2, where each portfolio was sorted by statistic and

placed into quintiles, i.e., 60 portfolios to one quintile.

Next, the portfolios within a quintile are averaged to pro-

duce the results shown in Figure 2.

Figure 2 highlights significant differences from top to

bottom quintile performance in each risk/return statistic

being examined. Interestingly, the portfolios appearing in

the top quintile for CAGR were also present in the upper

quintiles for the other risk/return statistics. This suggests

there are properties of many of these products found in the

upper quintiles that benefit not only return, but the other

risk statistics as well. By way of comparison, both VWO

and EEM fall into the fourth quintile for all four statistics.

Ticker BloombergIndex

Start DateAUM

(In MM)ExpenseFund

Investment Universe (Sorted By Index Start Date – Oldest To Newest, As Of 7/31/2013

Figure 1

PXH PowerShares FTSE RAFI Emerging Markets Portfolio TFREMU 12/31/1993 $338.78 0.49%

SCHE Schwab Emerging Markets Equity ETF FTAG01 12/31/1993 $819.22 0.15%

GMM SPDR S&P Emerging Markets ETF STBMEMU 12/30/1994 $208.18 0.59%

IEMG iShares Core MSCI Emerging Markets ETF MIMUEMRN 01/31/1995 $1,856.93 0.18%

EEHB PowerShares S&P Emerging Markets High Beta Portfolio SPEMHBIT 09/30/1997 $1.94 0.29%

EELV PowerShares S&P Emerging Markets Low Volatility Portfolio SPEMLVUT 09/30/1997 $167.90 0.29%

EWEM Guggenheim MSCI Emerging Markets Equal Weight ETF M2EFEWGT 12/31/1998 $9.53 0.61%

EDIV SPDR S&P Emerging Markets Dividend ETF SPGTEDUN 08/31/1999 $487.89 0.61%

EEM iShares MSCI Emerging Markets ETF NDUEEGF 12/29/2000 $35,037.22 0.66%

EEME iShares MSCI Emerging Markets EMEA ETF NDDUEMEA 12/29/2000 $4.88 0.49%

EEMV iShares MSCI Emerging Markets Minimum Volatility ETF M00IEF$O 11/30/2001 $2,454.98 0.25%

DBEM db X-trackers MSCI Emerging Markets Hedged Equity Fund M0EMHUSD 12/31/2001 $12.28 0.72%

TLTE FlexShares Morningstar Emerging Markets Factor Tilt Index Fund MEMMFT 12/31/2001 $120.44 0.65%

EMDR VelocityShares Emerging Markets DR ETF BKDEMT 12/31/2001 $2.34 0.65%

ADRE BLDRS Emerging Markets 50 ADR Index Fund BKTEM 12/31/2001 $282.31 0.30%

VWO Vanguard FTSE Emerging Markets ETF TGPVAN30 12/31/2002 $50,029.56 0.18%

EMCR EGShares Emerging Markets Core ETF SPEMCRT 12/30/2005 $4.00 0.70%

AGEM EGShares GEMS Composite ETF DJEEGT 12/31/2005 $10.68 0.75%

DVYE iShares Emerging Markets Dividend ETF DJEMDIVR 12/31/2005 $161.38 0.49%

DEM WisdomTree Emerging Markets Equity Income Fund WTEMHYTR 05/31/2007 $4,788.97 0.63%

HILO EGShares Low Volatility Emerging Markets Dividend ETF IHILOT 06/29/2007 $97.25 0.85%

BBRC EGShares Beyond BRICs ETF IBBRCT 09/28/2007 $10.19 0.85%

EMDD EGShares Emerging Markets Domestic Demand ETF IEMDDT 09/28/2007 $3.29 0.85%

PIE PowerShares DWA Emerging Markets Technical Leaders Portfolio DWATREM 09/28/2007 $345.25 0.90%

Source: Bloomberg

Experiment Results Of 300 Portfolios Broken Down By Risk/Return Statistic And Averaged By Quintile

(12/31/2007-7/31/2013)

Enhancing Risk-Adjusting Returns By Complementing A VWO Or EEM Allocation

Quintile Average

CAGRSharpe

RatioStandard Deviation

MaximumDrawdown

EEMVWO

Figure 2 Figure 3

Top 4.5% 0.17 22.7% -48.5%

Second 1.8% 0.04 24.9% -53.1%

Third -0.2% -0.03 26.5% -56.2%

Fourth -2.1% -0.10 27.7% -58.6%

Bottom -4.0% -0.17 29.4% -60.8%

CAGR 10 9

Sharpe 11 9

Standard Deviation 10 10

Max Drawdown 10 10

Source: Bloomberg Source: Bloomberg

Number of products available that, when paired with the industry leader product, produce a result for the listed statistic that is in the top three quintiles over the 12/31/2007 to 7/31/2013 time period.





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This suggests (absent the aforementioned considerations detailed above) that over this time period, an investor would have been considerably better off by combining one of the other ETFs in this universe with one of these leading EM betas. Figure 3 illustrates the number of products avail-able in a naive split that would have produced (by risk/return statistic) a portfolio in one of the top three quintiles when paired with either VWO or EEM.

Finally, Figure 4 lists the 100 percent portfolio of each ticker from best to worst performing over the time period studied for each risk/return statistic, as well as showing its respective place out of the 300 portfolios studied by statistic and color coded by quintile.

AnalysisThis experiment is predicated on a number of assump-

tions and considerations as detailed above. These must be examined for each risk/return statistic to understand these results more completely. One of the most important to be mindful of is the time period being studied. Rather than

reducing the entire time period into a single number (as is shown in Figure 2), examining three-year rolling periods of each statistic for each quintile should help illuminate whether there are any shorter-term biases. More spe-cifically, the quintiles as determined over the whole time period are used for each three-year period, and each point in the graph in Figure 5 represents a three-year window of CAGR. Interestingly, it appears that there is no time period bias; i.e., every data point for each quintile follows the same trend as the aggregated data shown in Figure 2, where the top quintile outperforms the second quintile, and the second quintile outperforms the third, etc.

Although not shown, the chart for Sharpe ratio is nearly identical to the CAGR one in Figure 5 in shape and order of quintile performance. Figure 6, showing three-year rolling periods for standard deviation, also shows the same behavior of persistent lower standard deviation for the higher-ranked quintiles.

Finally, the same behavior is also shown in Figure 7 for portfolio maximum drawdown. While each of these results

CAGRSharpe


MaximumDrawdown

Ticker Ticker Ticker TickerRank Rank Rank Rank

Single Ticker Portfolio Performance By Risk/Return Statistic With Corresponding Overall Rank Out Of The 300 Portfolios Studied

Figure 4

2 DVYE 7.8% 1 EELV 0.36 1 EELV 18.8% 2 HILO -42.4%

3 EELV 7.5% 6 DVYE 0.28 5 DBEM 20.4% 3 EELV -42.4%

6 HILO 6.7% 8 HILO 0.26 12 EEMV 21.7% 14 DEM -46.9%

17 EEMV 5.3% 16 EEMV 0.21 22 HILO 22.7% 28 EEMV -49.1%

22 EMDD 5.0% 22 EMDD 0.18 36 DEM 23.1% 29 DBEM -49.1%

37 DEM 3.8% 33 DEM 0.14 47 EMDD 23.8% 43 DVYE -50.0%

44 BBRC 3.2% 48 BBRC 0.10 101 BBRC 25.1% 45 EMDD -50.2%

82 EMCR 2.0% 87 EMCR 0.05 118 DVYE 25.4% 137 BBRC -55.2%

128 EDIV 0.5% 128 EDIV 0.00 155 ADRE 26.7% 144 ADRE -56.0%

150 EWEM -0.1% 150 EWEM -0.03 160 EDIV 26.9% 149 PXH -56.3%

154 PIE -0.2% 152 PIE -0.03 186 GMM 27.5% 173 EDIV -57.3%

192 PXH -1.7% 192 PXH -0.08 198 SCHE 27.6% 184 EMCR -57.9%

196 DBEM -1.7% 196 GMM -0.09 203 VWO 27.6% 190 GMM -58.0%

197 GMM -1.8% 209 IEMG -0.10 207 EEM 27.6% 202 EWEM -58.4%

215 VWO -2.1% 211 VWO -0.10 216 AGEM 27.8% 212 VWO -58.7%

218 IEMG -2.1% 225 EEM -0.11 223 PXH 27.8% 220 EEM -58.8%

224 EEM -2.4% 230 DBEM -0.12 244 IEMG 28.0% 231 IEMG -59.1%

260 TLTE -3.5% 242 EEHB -0.12 249 PIE 28.1% 253 SCHE -59.8%

272 EEHB -3.8% 260 TLTE -0.14 254 EMDR 28.2% 258 AGEM -59.9%

274 AGEM -3.8% 272 EEME -0.16 257 TLTE 28.3% 260 TLTE -60.0%

278 EEME -4.1% 276 AGEM -0.16 262 EMCR 28.3% 279 EMDR -61.1%

287 SCHE -4.6% 285 SCHE -0.19 280 EWEM 29.1% 282 EEHB -61.3%

293 EMDR -5.2% 293 EMDR -0.21 283 EEME 30.0% 297 EEME -63.1%

300 ADRE -7.5% 300 ADRE -0.30 300 EEHB 35.8% 300 PIE -64.9%

n First Quintile n Second Quintile n Third Quintile n Fourth Quintile n Bottom Quintile

Source: Bloomberg





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cent each. While this helps explain continued investor demand for these securities, these tight spreads are not consistent with the notion that the tighter the spread, the better the performance.

Examining the AUM for each of the underlying portfolio’s ETFs in Figure 9 deceptively indicates that the smaller the AUM, the better the performance for each statistic. While this is true when comparing the top to bottom quintiles, the fourth quintile is an outlier whose AUM numbers dwarf all others. This pattern is explainable due to the increased presence of both VWO and EEM in this quintile, whose combined AUM accounts for more than 87 percent of the investment universe being examined, i.e., the 24 products listed in Figure 1. Similarly, the entire trend of the table may be explainable by the number of appearances of VWO and EEM within each quintile, which is depicted by way of example for the CAGR statistic in the far right column; the figures in the added column embody this trend.

Expense ratios are shown in Figure 10. As may be expected, the top quintile carries the highest expense ratio relative to the bottom for each statistic; however, again, with no discernible trend in between. Interestingly, VWO carries an 18 bp expense ratio, which is lower than anything in Figure 10, while EEM carries an expense ratio of 69 bps, higher than anything in the table. This fact alone suggests that expense ratio is not

suggests that examining the whole time period does not introduce a timeperiod bias, it is not enough to suggest that the time period preceding this analysis and the time period in the future will follow the trends found in this analysis.

Although not a perfectly uniform trend, Figure 8, depicting the average bid/ask spread as a percentage by quintile, shows that the top quintile relative to the bottom quintile demonstrated a tighter bid/ask spread.7 This is only a relative (and not absolute) proxy to examining transaction costs, as many factors must go into performing an indepth transaction cost analysis. This is largely dictated by the overall size of the trade and—more importantly—the exact securities within the published fund creation basket. However, presumably the tighter the spread, the more efficient it is to perform the annual rebalance on the portfolio as is required for this experiment. Notwithstanding, the finding is interesting and suggestive that the securities found in the higher quintiles are either better covered by the market makers or they are more likely just more cost efficient for authorized participants to create and redeem. However, that being stated, VWO and EEM have very tight spreads—0.03 per

3-Year Rolling CAGR By Quintile

40.0%

35.0%

30.0%

25.0%

20.0%

15.0%

10.0%

5.0%

0.0%

-5.0%

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■ Top ■ Second ■ Third ■ Fourth ■ Bottom

3-Year Rolling Maximum Drawdown By Quintile

-15.0%

-25.0%

-35.0%

-45.0%

-55.0%

-65.0%

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013


3-Year Rolling Standard Deviation By Quintile

35.0%

33.0%

31.0%

29.0%

27.0%

25.0%

23.0%

21.0%

19.0%

17.0%

15.0%

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013


Figure 5 Figure 7

Figure 6


Source: Bloomberg

Portfolio Average Bid/Ask Spread

Quintile Average

CAGRSharpe


MaximumDrawdown

Figure 8

Top 0.98% 0.99% 1.11% 0.96%

Second 1.61% 1.60% 1.50% 1.34%

Third 2.16% 2.18% 1.47% 1.67%

Fourth 1.84% 1.82% 2.29% 2.81%

Bottom 3.14% 3.14% 3.36% 2.95%

Source: Bloomberg





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the leading driver of asset flows into EM equity fund products. The top quintile’s outperformance (shown in Figure 2) in CAGR (+4.5 percent) versus VWO (-2.1 percent) is more than made up by the difference in the increased expense ratio, i.e., 57 versus 18 bps.

Differences in the underlying holdings’ average market capitalization may help explain the source of additional return for the top versus the bottom quintiles. Specifically, Figure 11 depicts the portfolio fund’s average underlying market capitalization.8 This consideration shows a strong trend in which the better-performing quintiles have significantly smaller underlying average fund market capitalization than the worst-performing quintiles for CAGR and Sharpe. The trend is not as strong for standard deviation or maximum drawdown, but still present. By way of comparison, VWO and EEM have respective fund average market capitalizations of $39 billion and $43 bil-lion, which generally puts them squarely in the fourth and fifth quintiles across the statistics, respectively. This consideration may suggest that the funds’ underlying average market capitalization is responsible for its out-performance, i.e., the smaller the average underlying capitalization, the better the performance.

Next, the country exposures within the portfolios must be examined, as although EM equities are considered a so-called asset class space, each of the many countries within often exhibit wide variation in terms of culture and socioeconomic status, among many other signifi-cant differences that manifest in variations within their

respective stock and bond markets’ performance. Figure 12 details the portfolio average of the top 15 countries exposure by quintile and by risk/return statistic. There are many uniform trends across the risk/return statistics. First, the top 15 make up more than 90 percent of the total exposure of the portfolios. At the same time, 14 of these countries are found across all risk statistics for the top quintile. This suggests that the weighting of these 14 of the nearly 50 countries found throughout all examined portfolios should largely dictate the differences through-out the top and bottom performers. More specifically, across all statistics, the top quintile relative to the bottom quintile showed larger allocations to Taiwan, Malaysia and Thailand, with smaller relative allocations to China, Russia and South Korea. However, several countries did not show uniformity across risk statistics or across quin-tiles, e.g., greater Turkey exposure led to better CAGR and Sharpe, while it led to worse standard deviation, and was largely neutral with respect to maximum drawdown. On a country-weighted basis, both EEM’s and VWO’s country exposures place them in the fourth quintile (based on the table in Figure 12) on a CAGR and Sharpe measure, but in the third quintile for standard deviation and maximum drawdown with significant variation in terms of having some top-quintile country exposures, e.g., VWO includes Taiwan at more than 13 percent, as well as several bot-tom-quintile exposures.

Given the lack of consistent uniform trends across all countries and statistics, it would appear that certain coun-

Portfolio Average Expense Ratio Portfolio Average Market Capitalization (MM)

Quintile Average

Quintile Average

CAGR CAGRSharpe

RatioSharpe


Standard Deviation

MaximumDrawdown

MaximumDrawdown

Figure 10 Figure 11

Top 0.57% 0.58% 0.55% 0.57%

Second 0.58% 0.57% 0.60% 0.54%

Third 0.59% 0.59% 0.56% 0.57%

Fourth 0.54% 0.52% 0.53% 0.52%

Bottom 0.45% 0.47% 0.51% 0.54%

Top $22,542 $22,620 $30,284 $27,289

Second $30,074 $29,997 $32,652 $32,305

Third $33,006 $32,468 $32,726 $36,889

Fourth $38,470 $38,270 $42,051 $39,423

Bottom $45,920 $46,658 $32,300 $34,107


Portfolio Average AUM (MM)

Quintile Average

Appearances Of VWO And EEM In CAGR

Maximum DrawdownStandard DeviationSharpe RatioCAGR

Figure 9

Top $1,859 $1,843 $2,083 $2,186 3

Second $3,804 $3,820 $3,787 $4,083 9

Third $3,478 $3,460 $3,155 $2,773 8

Fourth $6,233 $6,970 $8,757 $7,748 17

Bottom $4,781 $4,062 $2,374 $3,366 13

Source: Bloomberg





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on a sector basis between the portfolios within each quin-

tile. Recognizing that the sector exposures used in this

analysis are the current exposures and do not account

for the fact that many of these strategies may have

had very different sector exposures historically, and/or

may change on a more frequent basis, it is nonetheless

instructive to examine the trends across quintiles, as well

as relative to VWO and EEM. The common trends across

the statistics by quintile are that the better-performing

portfolios had less exposure to basic materials, energy,

technology and industrials, and more exposure to com-

try exposures during the time period studied had more

impact than others. It should also be noted that many

of these indexes have more radical rebalancings and/

or reconstitutions, indicating this analysis is highly time-

period-dependent and may not be useful over a long time

period. Further, some products may shift their benchmark

index from time to time, as was the case for VWO’s transi-

tion from MSCI to FTSE. In this case, however, even with

the dramatic shift in country exposure, both VWO and EEM

in their current makeups had similar outcomes.

Finally, Figure 13 highlights the differing composition

Portfolio Average Top 15 Countries Exposure By Quintile And Risk/Return Statistic (Sorted By Top-Quintile Country Exposure)

Figure 12

CAGR Sharpe Ratio

Standard DeviationMaximum

Drawdown

Bottom Bottom

Bottom Bottom

4th 4th

4th 4th

3rd 3rd

3rd 3rd

2nd 2nd

2nd 2nd

Top Top

Top Top

Taiwan 12.9% 9.9% 9.1% 10.3% 8.5%

South Africa 11.3% 11.1% 11.2% 9.6% 10.6%

China 10.2% 13.2% 13.0% 15.5% 16.4%

Brazil 9.7% 10.0% 11.1% 12.7% 14.1%

Malaysia 7.9% 5.5% 5.0% 3.7% 2.2%

Mexico 5.6% 6.0% 6.4% 5.3% 5.5%

Thailand 5.4% 4.7% 3.7% 3.0% 2.3%

Russia 5.0% 7.3% 6.9% 7.6% 9.8%

South Korea 4.9% 6.2% 6.3% 8.4% 8.5%

Turkey 4.5% 4.0% 3.5% 3.0% 2.9%

India 4.4% 5.5% 5.4% 5.7% 5.8%

Chile 3.1% 2.3% 2.3% 1.7% 1.4%

Indonesia 3.1% 3.2% 3.0% 2.6% 2.3%

Poland 2.8% 2.5% 2.9% 2.4% 2.0%

Philippines 2.2% 2.0% 1.6% 1.2% 1.0%

Total in Top 15 93% 93% 91% 93% 93%

Taiwan 12.6% 10.2% 9.0% 10.4% 8.5%

South Africa 11.3% 11.1% 11.3% 9.4% 10.7%

China 10.2% 13.2% 13.1% 15.6% 16.2%

Brazil 9.6% 10.1% 11.0% 12.7% 14.2%

Malaysia 7.9% 5.5% 4.9% 3.7% 2.2%

Mexico 5.6% 5.9% 6.3% 5.3% 5.5%

Thailand 5.5% 4.6% 3.8% 3.2% 2.1%

Russia 5.1% 7.2% 6.9% 7.3% 10.1%

South Korea 4.9% 6.2% 6.3% 8.5% 8.4%

Turkey 4.5% 3.9% 3.6% 3.0% 2.8%

India 4.5% 5.4% 5.3% 5.8% 5.8%

Chile 3.1% 2.3% 2.3% 1.7% 1.4%

Indonesia 3.1% 3.2% 3.0% 2.8% 2.2%

Poland 2.9% 2.5% 3.0% 2.2% 2.1%

Philippines 2.3% 1.9% 1.6% 1.3% 0.9%

Total in Top 15 93% 93% 91% 93% 93%

Taiwan 12.0% 9.4% 11.1% 10.6% 7.6%

China 11.8% 13.3% 12.3% 17.0% 14.0%

Brazil 10.9% 10.5% 14.1% 13.4% 8.7%

South Africa 9.5% 11.4% 10.5% 9.4% 13.0%

South Korea 8.2% 5.0% 5.0% 6.4% 9.7%

Malaysia 7.7% 5.1% 4.7% 3.8% 3.0%

Mexico 5.6% 6.3% 6.5% 6.1% 4.4%

Russia 5.5% 8.3% 5.6% 7.7% 9.5%

India 4.4% 5.7% 4.5% 7.0% 5.3%

Thailand 4.2% 5.0% 3.5% 2.8% 3.6%

Chile 3.2% 2.0% 2.1% 1.9% 1.6%

Turkey 3.1% 4.0% 3.8% 2.2% 4.8%

Poland 2.8% 2.6% 3.2% 1.5% 2.7%

Indonesia 2.6% 3.1% 2.8% 2.3% 3.5%

Hong Kong 2.6% 2.5% 3.3% 4.1% 4.4%

Total in Top 15 94% 94% 93% 96% 96%

Taiwan 11.6% 12.0% 8.9% 9.9% 8.3%

China 11.2% 13.3% 13.6% 15.3% 14.9%

South Africa 11.0% 9.5% 10.4% 9.2% 13.7%

Brazil 10.0% 12.2% 12.8% 13.4% 9.1%

Malaysia 7.5% 5.3% 4.5% 3.7% 3.3%

Russia 6.3% 6.7% 6.6% 6.9% 10.0%

South Korea 6.0% 6.5% 6.2% 6.8% 8.8%

Thailand 5.5% 4.0% 3.3% 2.7% 3.5%

Mexico 5.2% 5.3% 7.5% 5.6% 5.1%

India 4.8% 4.9% 5.3% 6.9% 4.8%

Turkey 4.7% 3.2% 3.0% 2.7% 4.3%

Poland 2.9% 2.7% 2.5% 2.3% 2.4%

Chile 2.8% 2.4% 2.0% 2.2% 1.3%

Indonesia 2.7% 3.1% 3.1% 2.8% 2.5%

Philippines 2.3% 1.6% 1.3% 1.3% 1.5%

Total in Top 15 94% 93% 91% 92% 94%

Source: Bloomberg





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munications, consumer cyclicals, consumer non-cyclicals and utilities. No discernible trend from diversifieds or financials was present. Interestingly, these same trends held when comparing the top-quintile average sector exposures to either VWO or EEM. Again, this consider-ation may suggest that the underlying funds’ sector expo-sure is responsible for the top quintile’s outperformance, i.e., the more overweighted and underweighted exposures to the aforementioned sectors relative to VWO and EEM, the better the performance. However, this may also sim-ply suggest a heavy time-period bias; i.e., over the time period studied, sectors that are more consumer-focused outperformed those that are more resource-focused.

ConclusionIn this experiment, 300 naively composed portfolios

were examined to gain a better understanding of the uni-verse of so-called broad-based EM equity ETFs relative to the two market-leading products, i.e., Vanguard’s VWO and BlackRock’s EEM. The results over the time period studied show there are several other products available to complement an existing holding of either of the two industry giant products; however, while acknowledging a variety of considerations, it may be that this increased

performance is due simply to a smaller-capitalization, sector-biased and country-tilted portfolio relative to these industry leaders.

Another commonality between the top performers versus the bottom performers is the weighting scheme as classified by ETF.com’s ETF classification system utility. When examining the top half of the 24 100 percent port-folios ranked by CAGR, there was no consistent weighting scheme type other than the lack of market-cap-weighted ETFs appearing (except in two cases: one had more of a “specific set of sectors” play and the other had more of a “specific exclusion of specific countries” play, yet both had sufficiently broad diversification to be indicated by the utility for use in this study). Alternatively, the bottom half of these portfolios were all listed as market-cap-weighted (except in two cases: one is proprietary and the other is weighted by beta; neither appear anywhere else in the list). It was those portfolios weighted by volatility, dividends, equal-weight, multifactor, fundamental, and in special cases, market-cap, that outperformed those that were simply plain-vanilla market-cap indexes. While this may offer an instant clue to expectations about future performance, it may be that these findings are simply

Portfolio Average Sector Exposure By Quintile And Risk/Return Statistic

Figure 13

CAGR Sharpe Ratio

Standard DeviationMaximum

Drawdown

Bottom Bottom

Bottom Bottom

4th 4th

4th 4th

3rd 3rd

3rd 3rd

2nd 2nd

2nd 2nd

Top Top

Top Top

Basic Materials 7.9% 8.8% 10.1% 10.5% 12.1%

Communications 16.8% 15.9% 15.1% 12.8% 15.0%

Cons., Cyclical 11.6% 10.4% 9.9% 8.5% 5.4%

Cons., Non-Cyclical 13.5% 12.8% 11.5% 10.1% 9.1%

Diversified 2.4% 2.2% 2.7% 2.5% 2.0%

Energy 8.3% 10.3% 10.9% 12.7% 15.0%

Financial 21.8% 21.7% 23.0% 24.0% 23.5%

Industrial 5.4% 6.1% 5.8% 7.3% 6.4%

Technology 4.3% 5.3% 5.2% 6.9% 7.9%

Utilities 7.8% 6.0% 5.3% 4.0% 3.0%

Basic Materials 7.8% 8.9% 10.3% 10.7% 11.7%

Communications 16.8% 15.9% 14.9% 12.7% 15.3%

Cons., Cyclical 11.6% 10.4% 9.9% 8.4% 5.4%


Diversified 2.3% 2.3% 2.7% 2.5% 2.0%

Energy 8.4% 10.2% 10.8% 12.6% 15.1%

Financial 21.8% 21.8% 23.1% 24.0% 23.4%

Industrial 5.5% 6.1% 6.0% 7.5% 6.1%

Technology 4.3% 5.3% 5.1% 6.9% 8.0%

Utilities 7.8% 6.1% 5.4% 3.9% 3.0%

Basic Materials 7.9% 8.0% 11.3% 9.2% 13.0%

Communications 15.6% 17.6% 17.5% 13.6% 11.4%

Cons., Cyclical 9.8% 10.1% 8.7% 8.5% 8.5%


Diversified 2.2% 1.7% 2.3% 2.8% 2.8%

Energy 10.0% 10.9% 10.6% 13.0% 12.7%

Financial 24.0% 20.6% 22.5% 23.3% 23.6%

Industrial 5.2% 5.4% 5.2% 6.4% 9.0%

Technology 5.7% 5.7% 5.4% 7.3% 5.5%

Utilities 6.1% 6.4% 6.7% 3.5% 3.5%

Basic Materials 7.7% 9.6% 10.3% 10.5% 11.3%

Communications 16.3% 16.1% 17.4% 13.4% 12.6%

Cons., Cyclical 10.5% 9.2% 8.7% 8.3% 9.0%


Diversified 1.9% 2.1% 2.3% 2.6% 3.0%

Energy 9.9% 10.7% 11.6% 11.8% 13.2%

Financial 22.4% 22.3% 22.3% 24.0% 23.0%

Industrial 6.0% 5.7% 5.1% 6.9% 7.5%

Technology 5.2% 5.9% 5.8% 6.8% 6.0%

Utilities 7.1% 6.4% 5.0% 4.8% 2.8%

Source: Bloomberg






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Dividend Indexes

And Value Indexes

How similar are they?

By Konrad Sippel




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Dividend-based investment strategies are often

hailed as an easy and simple way of identifying

value investments. The actual mechanics of a

dividend strategy do intuitively seem to point more to an

identification of undervalued stocks, even though they

do not directly contain any elements that are typically

connected with value investments. But how much do div-

idend-based and value-based selection methods really

differ, and how similar are the results? Which method is

more effective in identifying true value investments, and

can one effectively be replaced by the other? How much

of the returns shown by the respective indexes are actu-

ally attributable to the value factor?

In this article, we will examine the difference in the

characteristics of the stock selection made by value and

dividend schemes using the results of the application of

the index rules for a standard dividend index as well as a

typical value index. As a basis, we compare all results to

a standard market-cap-weighted benchmark. To ensure

a consistent data approach, all fundamental data used in

this article have been sourced from Bloomberg with the

same effective date. This ensures that no inconsistency

arises due to the differing data cutoff dates within the index

methodologies when comparing the results of one selec-

tion method with the other based on the respective selec-

tion criteria. Furthermore, we compare the factor break-

down of index returns for a value index versus a dividend

index using a sophisticated multifactor model.

Index MethodologiesWe will take a short look at the selection rules govern-

ing the indexes. While selection of dividend indexes is

primarily focused on selecting stocks with a high divi-

dend yield, the selection mechanics of value indexes are

more complicated, as different fundamental factors need

to be considered. The three indexes mentioned in Figure

1 serve as representatives for the general group of divi-

dend indexes, value indexes and general market indexes,

respectively. Different indexes or investment schemes do

of course differ in various aspects of the methodology;

however, the key selection variables and mechanics are

common among these schemes, allowing a broader com-

parison with similar strategies. Figure 1 outlines the key

selection criteria for the three indexes.

In this article, we do not aim to compare the perfor-

mance characteristics of these indexes and their resulting

portfolios, as multiple differences in the methodologies

dilute the value of such a comparison. The dividend index,

for example, uses a different weighting scheme, and—with

30 components—has significantly fewer components com-

pared with the value index, which is much closer to the

original benchmark that it is modeled on. This can also be

easily observed by looking at Figure 2, as well as some key

data for the three indexes in Figures 3 and 4.

While the dividend index is clearly more volatile and

shows an overall outperformance, this is not an indicator

for any specific bias in the stock selection. Correlation sug-

gests a much closer relationship between the value index

and the base index, but this is primarily due to the alterna-

tive weighting scheme employed by the dividend index and,

hence is not of particular relevance for this analysis.

To draw a conclusion about the results of the selec-

tion methods, we turn our attention to the charac-

teristics of the components that the algorithms have

selected. We will look at each of the portfolios from the

perspective of each of the key selection variables and

fundamentals. It is important to note that here we are

actually looking at the distribution of the results across

the selected components in order to evaluate the stock

selection capabilities of each respective strategy.

Key Selection

Variables

Additional

Criteria

Further

Differences

Index Representative

Aspect Dividend-Based Selection Value-Based SelectionMarket-Capitalization-Based

Selection

Index Selection Criteria

Figure 1

• Dividend yield

• Market capitalization (free float)—

components need to be members

of Stoxx Europe

• Dividend payments need to be

stable and consistent

• Selection of 30 components only

• Stoxx Europe Select Dividend

30 Index

• Price-to-earnings ratio

• Projected long-term earnings

growth

• Price-to-book ratio

• Market capitalization (free float)—

components need to be members

of Stoxx Europe TMI Large Index

• No fixed number of components

due to relative selection process

(around 100 components)

• Stoxx Europe TMI Large

Value Index

• Market capitalization (free float)

• No fixed number of components

due to relative selection process

(around 1,000 components)

• Stoxx Europe TMI Large Index*

*Referred to as the “base index”




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Dividend YieldWe will look at the dividend yield of the selected compo-

nents first. Despite the dividend-yield-based selection, the dividend-yield optimization is, over the long term, not actu-ally the main benefit of a dividend-focused index. Instead, the performance gain from investing in undervalued stock is actually higher than the gain in dividend yield on an annual basis. Comparing Stoxx Europe 50 and Stoxx Europe Select Dividend 30 yields show that on an annual basis, over the period from 1998 to 2012, the average additional dividend yield for the dividend index was 2.8 percent, while the average additional price return performance was 5.2 percent. Figure 5 shows the numbers for each year individually.

Whereas the dividend outperformance is positive in all years, the price return performance is much more volatile;

however, over the long term, more of the outperformance is realized through the portfolio selection than through a yield optimization. For this purpose, true dividend-yield-optimizing concepts have emerged in recent years, such as the Stoxx Europe Maximum Dividend Index, which achieves roughly four times the yield of a regular dividend index but also reshuffles the index portfolio much more frequently to generate the additional dividend yield. For the purpose of this paper, we do not consider this type of yield-optimizing strategy; we focus on the regular dividend-based selection strategy. Naturally, we would expect a higher dividend yield than for the base index, the Stoxx Europe TMI Large Index, and indeed, the aver-age dividend yield per component increases from 4.05 percent for the base index to 5.85 percent for the dividend index as of year-end 2012. Also, the distribution of com-ponents with respect to be dividend yield is highly tilted toward higher-yielding components.

The higher proportion of stocks paying high (>10 per-cent) dividends in the base index compared with the divi-dend index, as shown in Figures 6 and 7, may be explained by the fact that many relatively small stocks with high yields are included in the broader large-cap TMI index, but do not fulfill the market-cap screening criteria employed


Index Performance, 1998–2012

400

350

300

250

200

150

100

50

0’98 ’00 ’01 ’03 ’04 ’06 ’07 ’08 ’10 ’11

■ Stoxx Europe Select Dividend 30 ■ Stoxx Europe TMI Value Large■ Stoxx Europe TMI Large

Breakdown Of Performance Diference By Year,

Stoxx Europe 50 Vs. Stoxx Europe Select Dividend 30

40%

30%

20%

10%

0%

-10%

-20%

-30%

’99 ’00 ’01 ’02 ’03 ’04 ’05 ’06 ’07 ’09’08 ’10 ’11 ’12 Avg.

■ Dividend Yield Gain ■ Return Gain (Price Index)

Dividend Yield Distribution For Broad

Market-Cap-Weighted Index, December 2012

40

35

30

25

20

15

10

5

0

Dividend Yield (%) Range

% O

f C

om

po

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0-2 2-4 4-6 6-8 8-10 >10

Figure 2 Figure 5

Figure 6


Source: Bloomberg

Selected Key Figures (1998-2012)

Correlation Analysis (1998-2012)

Stoxx Europe TMI

Large

STOXX Europe TMI

Large

Stoxx Europe TMI

Value Lg.

STOXX Europe TMI

Value Lg.

Stoxx Eur. Select Div. 30

STOXX Eur. Select

Div. 30

Figure 3

Figure 4

Annualized Volatility 18.90% 22.02% 21.15%

Annualized Performance 2.22% -1.89% -0.72%

Maximum Drawdown -73.58% -64.44% -64.23%

Sharpe Ratio -0.028 -0.210 -0.164

No. of Components 30 92 1,059

STOXX Eur. Select Div. 30 1.000 – –

STOXX Eur. TMI Value Lg. 0.838 1.000 –

STOXX Europe TMI Large 0.818 0.963 1.000

Source: Bloomberg

Source: Bloomberg




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indirectly by a select dividend index. This can be illustrated

by looking at the relationship between market capitaliza-

tion and dividend yield as shown in Figure 8. The majority

of outliers in the dividend yield clearly stem from the very

small companies in the basket.

The dividend methodology hence effectively elimi-

nates the low-dividend-paying stocks from the selection

equation; no components with a dividend yield of less

than 2 percent remain in the basket; and only 16 percent

yield less than a 4 percent dividend yield.

Figures 6 and 7 show the frequency of the respective

range of dividend yields. While the results so far are

hardly surprising, the more interesting analysis is that

of the value portfolio. Despite dividend yield not being

part of the selection process at all, the dividend yield

of the resulting portfolio rises to 5.02 percent, almost

1 full percentage point above that of the base index in

absolute terms.

However, the distribution of the yields is more broadly

spread, as shown in Figure 9: Whereas the select divi-

dend index consequently selects the majority of its com-

ponents with stable but high yields in the 4-6 percent

bucket, the value index has significant portions of lower-

paying stocks in the portfolio, with over one-third of the

portfolio yielding only 4 percent or less, which represents

the average of the base portfolio. Indeed, the high aver-

age is influenced more strongly by the outliers in the

10-percent-or-larger bucket, which stem from very small

companies with distorted yield values that are filtered out

through the tougher market-cap screens in the dividend

basket. From a stock selection perspective, even though

a value approach will, on average, produce a higher

dividend yield, that is by no means a guarantee or assur-

ance of a strong dividend yield, and investors looking for

this characteristic are clearly better off using a selection

theme based on dividend yields directly, which, given the

simplicity of this approach, is quite feasible.

Fundamentals Of Value InvestingBut what about the other way around? Can the relatively

simple and easy-to-follow dividend strategy be considered

a valid proxy for the more complex and less-easy-to-follow

value approach? To determine this while keeping a consis-

tent approach, we need to examine the resulting portfolios

in terms of the value definition employed by the value

selection process. The value selection scheme considers

multiple fundamental factors, which we will examine one

by one, using the same distribution charts as shown before.


Dividend Yield Distribution

For Value Index, December 2012

40

35

30

25

20

15

10

5

0


% O

f C

om

po

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0-2 2-4 4-6 6-8 8-10 >10

Figure 9

Dividend Yield (%) Vs.

Market Cap (MEUR) – Base Index

90

80

70

60

50

40

30

20

10

0

0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000

Market Cap (MEUR)

Div

ide

nd

Yie

ld (

%)

◆◆◆

◆◆◆◆◆

◆

◆

◆

◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆ ◆◆◆◆◆◆◆◆

◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆

◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆◆

◆◆◆

◆

◆◆◆ ◆ ◆

◆◆ ◆

◆◆ ◆ ◆ ◆ ◆

Figure 8

Source: Bloomberg

Source: Bloomberg

Dividend Yield Distribution

For Dividend Index, December 2012

55

50

45

40

35

30

25

20

15

10

5

0


% O

f C

om

po

ne

nts

0-2 2-4 4-6 6-8 8-10 >10

Figure 7

Source: Bloomberg

From a stock selection perspective, even though a value approach will,

on average, produce a higher dividend yield, that is by no means a

guarantee or assurance of a strong dividend yield.




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may well be considered a proxy for earnings strength. While

this is of course true, the results still show that inconsisten-

cies between dividend payments and actual earnings do not

seem to dilute or hurt this selection process.

The next value investment criterion to look at is the price-to-

book ratio of the components. Replacing the earnings compo-

nent with the book values has the potential to result in a larger

difference between the concepts. As expected, the value-based

concept achieves a significant reduction in the average price-

to-book ratio from 2.87 in the base portfolio to 1.84 in the value

portfolio. Distributions again prove that higher ratios are effec-

tively eliminated from the portfolio by the selection process.

Following the previous methodology, we next look at the same

values for the dividend strategy; again, the dividend mecha-

nism does not disappoint. The average price-to-book is even

lower at 1.66, further suggesting that dividend yield provides an

adequate proxy for many value investment criteria. In addition,

analysis of the distribution shows that the quality of the stock

selection is almost identical. However, it is notable that the

value index distribution is more concentrated in the lower end

but does show a small number of outliers in the higher end of

the values; this does not occur in the dividend index distribu-

tion. Figures 13-15 show the respective distributions.

Finally, we stray from current price-based criteria to exam-

ine the final relevant value selection criterion—estimated earn-

ings growth. Again, this criterion features in the selection pro-

cess for the value stocks, so a significant reduction in earnings

growth forecast would be expected. The average value for the

best long-term earnings growth forecast for the value portfolio

is, at 6.45 percent, significantly lower than the 11.04 percent

earnings growth forecast for the base portfolio. But again, with

this criterion, the dividend selection methodology results in an

even lower average long-term earnings growth forecast of only

5.85 percent. The distributions of the value and dividend port-

folio are very similar to each other, and both show a significant

shift to the left compared to the base index. Figures 16-18 show

the distributions of the results and further enhance this picture.

Relevance Of The Number Of ComponentsInvestors comparing components selected by a select

dividend methodology with a value index approach are

The first selection criterion is the price-to-earnings ratio,

which is used to ensure overpriced stocks are not included

in the index. We would therefore naturally expect price-to-

earnings ratios to be lower in the value portfolio than in the

base portfolio. And indeed, the average price-to-earnings

ratio drops from 24.06 to 18.89 in the value portfolio. The

analysis of the distributions shows that the selection mech-

anism successfully eliminates the higher price-to-earnings

ratios from the index portfolio, and shifts the distribution

significantly further to the left, with over 80% of P/E ratios

falling below 20. See Figures 10 and 11.

Turning to the dividend-based approach, the concept actu-

ally further reduces the average price-to-earnings ratio to 12.97,

representing a decrease of almost 50 percent. Also, the resulting

distribution of the results is similar to that of the value index.

The dividend screening produces similar results in terms of

price-to-earnings ratios to the value-based screening.

From Figure 12, we may assume that selection by divi-

dend yield can be considered an equally effective measure

as using price-to-earnings ratios directly to determine com-

ponents with low valuations in this criterion. One may of

course argue that this is hardly surprising given that the price

component in both key figures is the same, and dividends


P/E Ratio Distribution For Base Index, December 2012

30

25

20

15

10

5

0

P/E Ratio Range

% O

f C

om

po

ne

nts

0-5

5-10

10-1515-2020-2525-3030-3535-4040-4545-5050-5555-60

>60

P/E Ratio Distribution For Value Index, December 2012

40

35

30

25

20

15

10

5

0

P/E Ratio Range

% O

f C

om

po

ne

nts

0-5

5-10

10-1515-2020-2525-3030-3535-4040-4545-5050-5555-60

>60

P/E Ratio Distribution For Dividend Index, December 2012

40

35

30

25

20

15

10

5

0

P/E Ratio Range

% O

f C

om

po

ne

nts

0-5

5-10

10-1515-2020-2525-3030-3535-4040-4545-5050-5555-60

>60

Figure 10

Figure 11

Figure 12

Source: Bloomberg

Source: Bloomberg

Source: Bloomberg




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more likely to find stocks fitting the traditional value criteria by following the dividend method. However, this is of course influenced by the fact that the dividend methodology selects a much smaller number of stocks, and hence has more opportunity to avoid less desirable components. We have therefore constructed a simple additional portfolio that uses dividend yield as the soli-

tary selection criterion but that selects a similar number of components (100) as the value portfolio; we analyzed the respective results to ensure a fair comparison.

For the price-to-earnings ratio, the results actually match those for the smaller index. For the top 100 divi-dend-yielding companies, the average price-to-earnings ratio is 14.9, which remains lower than that of the value


Earnings Forecast Distribution


70%

60%

50%

40%

30%

20%

10%

0%

<-10

Distribution Of Earnings Growth Forecasts

-10-0 0-10 10-20 20-30 >30

Earnings Forecast Distribution


70%

60%

50%

40%

30%

20%

10%

0%

<-10


-10-0 0-10 10-20 20-30 >30

Price-To-Book Distribution

For Market-Cap-Weighted Base Index, December 2012

70%

60%

50%

40%

30%

20%

10%

0%

0-2

Distribution Of Price-To-Book Ratios

3-4 5-6 7-8 9-10 >10

Earnings Forecast Distributions

For Market-Cap-Weighted Base Index, December 2012

50%

45%

40%

35%

30%

25%

20%

15%

10%

5%

0%

<-10


-10-0 0-10 10-20 20-30 >30



80%

70%

60%

50%

40%

30%

20%

10%

0%

0-2


3-4 5-6 7-8 9-10 >10



80%

70%

60%

50%

40%

30%

20%

10%

0%

0-2


3-4 5-6 7-8 9-10 >10

Figure 17

Figure 18

Figure 13

Figure 14

Figure 15

Figure 16

Source: Bloomberg

Source: Bloomberg


Source: Bloomberg

Source: Bloomberg




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interested in the exposure to the value factor that the resulting portfolios have actually shown over the time period. Figure 19 shows the results for the style factor breakdown for the value index.

As expected, we see the largest absolute exposure of all style factors to the value factor with an exposure of more than 28 percent, and we see the expected (logical) negative exposure of -23.4 percent to the growth factor, which is specifically screened out in the index method-ology. However, it is clear that other factor exposures also play a significant role in the performance break-down. Additionally, the relatively low performance con-tribution of the relevant style factors of 0.3 percent for value and -0.4 percent for growth—which essentially cancel each other out—seem to suggest relatively little value in this selection process.

Over the time frame covered by this part of the study, the majority of nonspecific or explainable performance contribution actually stems from the market exposure as shown in Figure 20, which is due to the strong perfor-mance of the European market over the time frame, and

is not actually relevant to this analysis. The low specific-return component is typical for the broad index type that is used here for the analysis.

Now we run the same analysis for the dividend index and compare the results. The results of the factor analysis for the dividend index are shown in Figure 21.

The dividend index shows a much lower exposure to the value factor but similar negative exposure to the growth factor, indicating that the actual payment of divi-dends is a stronger counter-indicator for growth than

portfolio, but is in fact slightly higher than the original 12.9 for the dividend portfolio. However, the price-to-book ratio is 3.4, higher than the value portfolio’s 2.9. Long-term growth estimates did not return a sufficient number of results for a comparison, and hence cannot be evaluated at this time.

Factor AnalysisHaving analyzed the selection process in detail, we

turn to the analysis of the index returns. For the pur-pose of this analysis, we have selected a value index (Stoxx Europe TMI Value) and a dividend index (Stoxx Europe Select Dividend 30) and analyzed their monthly returns over the period from Dec. 31, 2010 to Sept. 30, 2013, using the factor model provided by Axioma, a leading provider of quantitative portfolio analysis and factor-based optimization models. We are specifically

Factor Breakdown For Value Index

Factor Exposure

PerformanceContribution

Style Factor

Figure 19

Exchange Rate Sensitivity -0.1% 12.5%

Growth -0.4% -23.4%

Leverage 0.1% 1.7%

Liquidity -0.2% -27.7%

Medium-Term Momentum -0.9% -19.4%

Short-Term Momentum -0.5% -2.6%

Size 0.1% 23.7%

Value 0.3% 28.3%

Volatility 0.5% -1.4%

Performance Contribution Breakdown For Value Index


Figure 20

Specific Return -0.2%

Factor Contribution 5.3%

Style -1.0%

Country -1.8%

Industry -2.0%

Currency 0.5%

Market 9.7%

Source: Axioma

Factor Breakdown For Dividend Index

Factor Exposure


Style Factor

Figure 21

Exchange Rate Sensitivity -0.1% 22.7%

Growth -0.3% -29.3%

Leverage -0.3% 23.2%

Liquidity -0.2% -29.2%

Medium-Term Momentum -1.6% -22.3%

Short-Term Momentum -0.6% -3.7%

Size 0.1% 6.2%

Value 0.0% 10.0%

Volatility 1.9% -20.6%

Source: AxiomaSource: Axioma


A case can be made that simple dividend-based stock selection strategies

with a small number of selected components form an easy and transparent

alternative substitute for more complex value-based schemes.




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www.journalofindexes.com 33March / April 2014

it is an indicator for value. Similarly to the value index,

we see a relatively small actual performance contribu-

tion from the traditional factors. The dividend index, as

expected, shows somewhat of a strong negative expo-

sure to volatility, further supporting the argument for

the stability of high-dividend-paying companies. The

actual return contribution from the volatility factor in

fact easily outweighs the entire value/growth complex.

Comparing the results, it becomes obvious that the

value index selection indeed generates higher exposure to

the value factor and very low exposure to leverage, while

the dividend index has lower exposure to value but shows

significantly lower exposure to volatility and even lower

exposure to the growth factor.

ConclusionGiven the evidence presented above, a case can be

made that simple dividend-based stock selection strate-

gies with a small number of selected components form

an easy and transparent alternative substitute for more

complex value-based schemes, showing even more

effective results in the selection-related categories for

value indexes. However, the same cannot be said with

regard to using a value approach to identify high-divi-

dend-yielding stocks, when the dividend yield of a large

part of the selected stocks lags behind the average of

the base portfolio. Looking at the returns over a longer

period of time suggests that both concepts provide more

conservative approaches to portfolio selection than a

market-cap-weighted index. Investors looking for pure

value exposure fare better with a value-based selection

method; however, if the focus is on lower volatility expo-

sure, dividend-based selection methods may provide a

strong alternative. From the factor breakdown, we can

certainly conclude that while the portfolios may look

similar in terms of basic characteristics, they do differ

quite strongly with regard to their style factor exposures,

justifying the existence of each method.

and plans to adopt the futures in their

investment portfolios, which ETF.com

first reported in October.

CBOE Touts End Of Litigation With ISE

In late January, S&P Dow Jones

Indices and the Chicago Board

Options Exchange announced in a

press release an end to the long-run-

ning litigation with the International

Securities Exchange, a U.S.-based

subsidiary of Deutsche Borse.

In 2006, the ISE had sought to

launch options on the S&P 500

Index and the Dow Jones indus-

trial average, despite the fact that

the CBOE had exclusive licensing

agreements with S&P and Dow

Jones Indexes—which had not yet

merged—that gave it the sole right

to list options based on the bench-

marks. Both sides in the dispute

ended up filing legal actions against

each other, but it appears the situa-

tion has finally been resolved.

On Dec. 18, in U.S. district court,

the ISE lost its case to the defen-

dants when the judge ruled against

the exchange. The deadline for an

appeal passed in late January without

any filings from the ISE, leaving the

CBOE and S&P DJI with their licens-

ing agreement intact.

CBOE Volumes Strong In 2013CBOE Holdings announced its year-

end results in a January press release. Its

options exchanges, the Chicago Board

Options Exchange and the C2 Options

Exchange, saw an average daily volume

in options contracts of 4.71 million in

2013, up 4 percent from the prior year.

While equity options saw their

average daily volume fall by 12

percent, index options achieved

an increase in average daily vol-

ume of 22 percent at 1.48 million

contracts, according to the press

release. Notably, the popular SPX

and VIX options contracts saw their

average daily volumes increase 19

and 29 percent, respectively. The

press release said that both con-

tracts reached record volume levels

during the year.

Meanwhile, options on exchange-

traded products experienced a

decrease in their average daily vol-

ume, falling 2 percent to 1.1 million

contracts.

ON THE MOVEWarburg Pincus Buys Stake In Source

In January, European exchange-

traded product firm Source ETP gave

up a majority stake in its business to

global private equity firm Warburg

Pincus as part of its quest to become

a top-tier ETF provider.

The move, which also sees ETF

guru Lee Kranefuss taking up the role

of executive chairman, is expected to

provide Source with the resources to

develop and launch new products,

and enhance existing products.

Source ETP’s business model

currently includes five existing

shareholders: BofA Merrill Lynch,

Goldman Sachs, J.P. Morgan, Morgan

Stanley and Nomura. However, the

transaction will see Warburg Pincus

hold the majority stake and the

remaining shareholders continue as

minority shareholders.

Kranefuss is currently an execu-

tive-in-residence at Warburg Pincus

and will join Source as executive

chairman. He was formerly global

chief executive officer at iShares and

oversaw the global expansion of that

company from launch in 2000 to

managing more than $600 billion in

assets in 2010.

Source has an open architecture

and has forged partnerships with

global companies such as Pimco, Man

GLG and LGIM since its launch in

April 2009. It ranks sixth in Europe for

ETF assets under management.

The transaction is subject to regu-

latory approval.

News continued from page 17




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Back To Basics


The New ‘Effective’ Frontier

How added diversification improves the efficient frontier

By Craig Israelsen

The basic premise underlying diversification and portfolio design (i.e., asset allocation) can be sum-marized in a simple sentence by Harry Markowitz:

“To reduce risk it is necessary to avoid a portfolio whose securities are all highly correlated with each other.”1

The efficient frontier, popularized by Harry Markowitz, is a graph that demonstrates the risk/return attributes of a portfolio that uses varying allocations of cash (the “risk-free asset”) and stock (the return of “the market”). These two assets have demonstrated low correlation with each other over multiple decades; hence, the combination of these two asset classes have long been used in the depiction of the efficient frontier. In general, the efficient frontier assumes a shape as illustrated in Figure 1 (see blue dotted line).

The left-most blue dot represents a 100 percent cash investment. The return of cash is represented by the per-formance of three-month U.S. Treasury bills. The next blue dot to the right represents an annually rebalanced portfolio consisting of 90 percent cash/10 percent stock (stock is represented by the performance of the Standard & Poor’s 500 Index). The blue dot furthest to the right in the graph represents a 100 percent stock portfolio. Thus, the “efficient” frontier in this example is the various combinations of cash and stock ranging from all cash, 90 percent cash/10 percent stock, 80 percent cash/20 percent stock … to 100 percent stock. The performance of each asset class (cash and bonds) covers the 44-year period from Jan. 1, 1970 to Dec. 31, 2013.

Also shown in Figure 1 (as depicted by red triangles) is what I will refer to as the “effective” frontier—as rep-resented by various combinations of cash and a low-correlation, multiple-asset portfolio. The multi-asset port-folio comprises large U.S. stock, small U.S. stock, non-U.S. stock, REITs, commodities, U.S. bonds and U.S. cash—each equally weighted at 14.3 percent and rebalanced annually.

The average correlation among all seven of these portfolio ingredients over the past 44 years was 0.20.

The actual indexes represented by these seven asset class-es include the S&P 500 Index; the Ibbotson Small Companies Index from 1970-1978 and the Russell 2000 Index from 1979-2013; the Morgan Stanley Capital International EAFE Index (Europe, Australasia, Far East) Index; the Ibbotson Intermediate Term Bond Index from 1970-1975 and the Barclays Capital Aggregate Bond Index from 1976-2013; three-month Treasury bills; the NAREIT Index (National Association of Real Estate Investment Trusts) from 1970-1977 and the Dow Jones US Select REIT Index from 1978-2013; and the Goldman Sachs Commodities Index (GSCI). As of Feb. 6, 2007, the GSCI became known as the S&P GSCI.

As can be seen in Figure 1, the effective frontier (representing various combinations of cash and a multi-asset portfolio) is located above and to the left of the cash/stock efficient frontier. The effective frontier is more diversified and, as a result, offers a superior risk/return trade-off than the efficient frontier. Very simply, more diversification is better than less diversification in achieving superior risk-adjusted returns.

For example, at a standard deviation level of 8 percent, the asset combination on the effective frontier was a 30 percent cash/70 percent multi-asset portfolio, which produced an 8.9 percent annualized return over the 44-year period. By com-parison, at 8 percent standard deviation, the efficient fron-tier was 60 percent cash/40 percent large U.S. stock, which produced a 7.7 percent annualized return over the 44-year period. Thus, the effective frontier produced a return that was 120 basis points higher than the efficient frontier at the same risk level (8 percent annualized standard deviation).

Let’s now consider the development (i.e., risk/return shape) of the effective frontier as assets are combined




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sequentially in order of their individual standard deviation of return (from lowest to highest), as shown in Figure 2.

The first asset, of course, is cash. The all-cash portfolio is represented by the left-most red triangle. A 100 percent cash portfolio had a 44-year annualized return of 5.22 percent and a standard deviation of annual returns of 3.4 percent. The next asset added (in the red graph) was U.S. bonds. Now we have a 50 percent cash/50 percent bond portfolio that was rebalanced annually over the 44-year period from 1970-2013. The two-asset cash/bond portfolio return improved to 6.63 percent and the standard deviation increased slightly to 4.2 percent. The next red triangle represents a three-asset portfolio (33.33 percent cash, 33.33 percent bonds and 33.33

percent large U.S. stock). This three-asset portfolio had a 44-year annualized return of 8.22 percent and a standard deviation of return of 6.9 percent.

As the next three assets are sequentially added (REITs, small U.S. stock, non-U.S. stock), the return of the increas-ingly diversified portfolio increases as does the standard deviation of return. Finally, commodities are added as the seventh asset. The 44-year annualized return increases to 10.22 percent, but interestingly, the standard deviation of return decreases (that is, moves to the left). This is a mani-festation of the low correlation between commodities and all six other asset classes over the past 44 years.

Figure 1

Figure 2

Source: Lipper; calculations by author.

Source: Lipper; calculations by author.

The Efficient And Effective Frontiers 44-Year Risk/Return Analysis, 1970–2013

44

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Standard Deviation Of Annual Returns (1970–2013)

0 2 4 6 8 10 13 14 16 18 20

11

10

9

8

7

6

5

4

100% Cash

50% Cash50% Large U.S. Stock

50% Cash50% Multi-Asset

100% Multi-Asset

100% LargeU.S. Stock

Stepwise Development Of Effective Frontier 44-Year Risk/Return Analysis, 1970–2013

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50% Cash50% Large U.S. Stock

Add Bonds

Add Large U.S. Stock

Add REIT

Add Commodities

Add SmallU.S. Stock

Add Non-U.S. Stock

100% LargeU.S. Stock





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Institutional Insights

OPERS Sticks To

Basic Principles

CIO Tillberg spearheads disciplined

approach to investment

By Heather Bell

Brad Tillberg joined the Oklahoma Public Employees

Retirement System as its chief investment officer in November

2009, taking the helm of the $8.1 billion pension fund’s

investments just as the market was beginning to emerge

from a spectacular meltdown. OPERS has a sizable alloca-

tion to passive strategies and takes a very no-frills approach

to investment management, eschewing alternative assets

such as hedge funds and commodities while emphasizing

the importance of cost control and rebalancing. That meth-

odology is serving the plan pretty well.

Oklahoma Public Employees Retirement System

AUM: $8.1 billion

Passive/Active split: 42%/58%

JOI: Would you talk about how the fund is set up, its assets under management and who it serves?Brad Tillberg: In our system, we administer two defined ben-efit [DB] plans and two defined contribution plans. The largest DB plan is our namesake, OPERS, which is a multi-employer trust serving state employees, elected officials, local govern-ment employees and a certain classification of hazardous-duty employees. It has just over 80,000 members from nearly 300 par-ticipating employers. OPERS has about $8.1 billion in invested assets as of Nov. 30. It’s the second-largest DB plan in the state.

The second DB plan is the Uniform Retirement System for Justices and Judges serving justices of the Supreme Court, judg-es of the Court of Criminal Appeals, Workers’ Compensation Court, Court of Civil Appeals and district courts who serve in the state of Oklahoma. This plan is quite a bit smaller than OPERS—approximately $285 million in invested assets. We also adminis-ter two defined contribution plans—a 401(a) and a 403(b) plan, collectively known as SoonerSave. Those plans had just over $851 million in assets invested primarily in mutual funds.

The system is administered by a 13-member board of trustees. The board, which is the fiduciary for the invest-ments and administration of the system, is appointed by either position or association. Our board has maintained a consistent investment philosophy throughout the years.

JOI: What is the funding level? Wasn’t there a big change that occurred around that in 2011?Tillberg: Like everyone else, we have an actuarial valuation each year, and as of our most recent actuarial valuation, the funded ratio of the large plan was 81.6 percent. That stands in stark contrast to the 66 percent funded level at the end of fiscal year 2011. The change was primarily due to legislation that passed in 2011 that required the cost of delivering future cost of living adjustments [COLAs] to be prefunded and passed by the legislature.

What that effectively did was removed the COLA assumption from our liability stream, and thus dramati-cally improved the funding status of the plan.

JOI: So basically that means because it’s something that’s yet to be decided, they’re not including cost of living increases in the formula for calculating the funding level?Tillberg: The COLAs had always been ad hoc in Oklahoma for our system, but they were included in the calculation because there was, of course, some regularity to them. However, the new legislation says that COLAs cannot be granted unless they’re paid for, which effectively removed them from the liability stream.

JOI: How does that affect the pension members?Tillberg: That remains to be seen. COLAs will be received when the legislature approves and provides funding for them.





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JOI: What role does passive management play in the portfolio? Tillberg: Passive management plays a substantial role in the management of our DB pension plans. I like to say that we’re selective about our active management exposure. Just over 40 percent of our policy portfolio is purely passively managed. We use indexing for domestic equity mandates (Russell 1000, Russell 1000 Growth and Russell 2000), inter-national equity mandates (ACWI ex-US and ACWI ex-US Growth), and fixed-income mandates (TIPS). BlackRock currently provides all of the passive services. Passive man-agement plays a vital role in our overall portfolio.

Another quarter of our portfolio is actively managed, but within a relatively tight tracking error range of each appropriate benchmark. We utilized these enhanced index strategies in the U.S. large-cap space, and have a core fixed-income mandate that is relatively constrained with respect to active risk. The remaining 31 percent of the portfolio comprises various active strategies.

JOI: What percentages of those different asset class port-folios are passively managed?Tillberg: About 55 percent of our U.S. equity exposure is purely indexed. About 63 percent of our international equity is indexed, and about 11 percent of our fixed-income allocation is indexed.

JOI: Do you feel that passive management is more effec-tive in the equity space?Tillberg: From a cost perspective, yes. The index exposure we have in fixed income was largely a function of our want-ing to gain some exposure to inflation protection a couple of years ago. I would say in general that an investor has to be very skeptical and very realistic about not only the potential for value added but also the cost involved with that, and you have to look at it from a net return perspective.

I’m happy with our current active exposures in equity, and on the fixed-income side, the economics are at least as good, or probably a bit better in favor of active management.

JOI: You specifically mentioned growth indexes for both domestic and the international equities. Is there a reason you have a separate allocation for growth equi-ties in both international and domestic?Tillberg: That’s a legacy issue. Those were replacements for active managers. We try to balance the styles, and we retain managers on the value side. So for a sort of structural reason, we retain the growth indexes in both international and U.S. equity.

The last manager change that we had was actually for a growth manager on the international equity side, but we still have an allocation to the growth index. That’s been more of a legacy issue than a philosophical one.

JOI: What role do you see active management playing in the overall portfolio?Tillberg: Credit goes to our board in maintaining a con-sistent investment philosophy. Our portfolio is conven-

tional; we use a traditional core and satellite approach. We use indexes for underlying market exposures, and we do have an expectation of value added where we use active management. Not only are we attempting to derive value from the asset allocation decision, but also from our active management and our rebalancing policy.

JOI: Talk about the breakdown between the U.S. and international equities and fixed income. You had said you have a traditional portfolio, but it sounds like you’ve made some tweaks to the traditional 60/40 breakdown. Tillberg: I’ll be the first to admit that our international allocation—24 percent of our portfolio—is probably well above that of our peers. We’re comfortable having that size of allocation. Before we had the allocation we have now, we were closer to a 60/40 portfolio. But towards the end of last year, our board elected to change the asset allocation ever so slightly. We increased our U.S. equities exposure, and that was at the expense of our fixed income. We effec-tively increased our U.S. equities exposure by 4 percentage points in the policy and reduced our fixed income by the same percentage across the policy.

JOI: Is that a reflection of the lower interest rates in the fixed-income space?Tillberg: Without a doubt. Ninety-nine percent of the time, your rebalancing policy is a very systematic effort that forc-es you to buy low and sell high. That’s a great idea. That’s added quite a bit of value to the system historically, and I think most investors would agree. But given the relative performance of the markets at the time we were looking at this, especially the level of interest rates … I think in a lot of modeling, pricing isn’t a major driver of many asset alloca-tion models, perhaps not as much as it should be.

We elected to not rebalance. Instead of rebalancing the portfolio, we just changed the asset allocation—very modestly mind you, but we did change it. With the equi-ty markets where they were in the September/October time frame that we were looking at this action and the level of yields, the thought was that if your rebalancing



Policy Asset AllocationAs of October 31, 2013

Source: OPERS

Figure 1




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The Importance Of Benchmark Choice In Commodities

Consider the movie ‘Apollo 13’

By John Hyland




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There is a scene in the movie “Apollo 13” where a young engineer jumps up in the middle of an ani-mated group discussion about how to deal with the

crippled space ship and cuts to the heart of the matter. He starts with a simple declarative statement:

“Whoa whoa, guys! Power is everything! Power is everything!”“What do you mean?”“Without it, they don’t talk to us, they don’t correct

their trajectory, they don’t turn the heat shield around. We gotta turn everything off now.”

With a tip of the hat to the screenwriters of that movie, I would offer my observation about investing in a diversi-fied commodity basket. Here it goes: “Your commodity index selection is [almost] everything.”

What?! Isn’t your choice of index equally important when looking at stocks and bonds? Not really. The evi-dence suggests that the index you select as a benchmark for your commmodity investments is vastly more impor-tant than the index you select to benchmark other asset classes, like bonds or stocks.

A practical result from this is that time spent on the tra-ditional tools or metrics you use to compare your imple-mentation choices becomes much less important as the choice of index becomes more important. Spending 80 percent of your time looking at cost, liquidity, tax impact, counterparty risk, the active versus passive debate, or reputational risk between your implementation of two U.S. large-cap stock indexes makes sense when the actual difference between indexes is small. Such an approach does not make sense when the actual difference in index-es might dwarf the cost or liquidity factors.

This can be demonstrated in three simple charts. Figure 1 compares the annual average return and vol-

atility of three major U.S. fixed-income indexes, each marked with a square.1 What is apparent at a glance is that, over time, there appears to be little difference between these three indexes in terms of either average annual return or annual volatility. An investor or an advisor would

have been served equally well, or equally poorly, no matter which index they elected to use. Looking further into the data, you would find that which index was the best per-former and worst performer annually was well distributed among all three, indicating that their relative performance showed a strong tendency toward mean reversion.

A similar pattern can be seen in Figure 2 when look-ing at the results of the three equity indexes,2 marked with triangles. In fact, the equity indexes are more tight-ly grouped together than the bond indexes. Similar to the bond indexes, the relative performance of the stock indexes also appears mean-reverting, with the indexes trading places on an annual basis as the best performer or worst performer. Thus, this year’s winner may easily be next year’s laggard, and vice versa.

Now let’s add six major diversified commodity indexes over the same 10 years, as shown in Figure 3. The commodity indexes are the S&P GSCI Index,

High-Grade U.S. Fixed-Income Indexes, 2003-2013Average Annual Total Return And

Annual Volatility For Selected Indexes

0.18

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

00 0.05 0.1 0.15 0.2 0.25

Re

turn

Risk

U.S. Fixed Income

■ Barclays U.S. Agg. ■ JPM U.S. Agg. ■ FTSE U.S. Gov’t Perf.

U.S. Equity, U.S. Fixed-Income

And Commodity Indexes, 2003–2013Average Annual Total Return And Annual Volatility For Selected Indexes

0.18

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

00 0.05 0.1 0.15 0.2 0.25

Re

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Risk

U.S. Fixed Income

▲ S&P 500 TR ▲ Russell 1000 TR ▲ MSCI U.S. Large TR ■ Barclays U.S. Agg. ■ JPM U.S. Agg. ■ FTSE U.S. Gov’t Perf. ● S&P GSCI TR ● DJ-UBS TR

● SDCI TR ● DBIQ Opt. Yield Com. TR ● ROGR TR ● CCI TR

▲▲▲

Commodities

U.S. Equities

U.S. Large-Cap Equity And High-Grade

U.S. Fixed-Income Indexes, 2003-2013 Average Annual Total Return And Annual Volatility For Selected Indexes

0.18

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

00 0.05 0.1 0.15 0.2 0.25

Re

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U.S. Fixed Income

▲ S&P 500 TR ▲ Russell 1000 TR ▲ MSCI U.S. Large TR■ Barclays U.S. Agg. ■ JPM U.S. Agg. ■ FTSE U.S. Gov’t Perf.

▲▲▲

U.S. Equities

Figure 1 Figure 3

Figure 2

Sources: Bloomberg and USCF Sources: Bloomberg and USCF

Sources: Bloomberg and USCF





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Talking Indexes

March / April 2014

By David Blitzer

40

Exploring a complex subject

The Boom In Buybacks

S&P 500 companies spent more cash to buy back their

stock than to pay dividends in the 2013 third quarter.

Unlike dividends, buybacks are irregular, unpredict-

able and only return cash to those shareholders who sell

their stock and reduce or eliminate their position in the

company. The performance of the S&P 500 Buyback Index

compared with the S&P 500 shows that companies with a

past history of buybacks tended to outperform since 2003.

Some investment websites and blogs tout buybacks as sig-

nals of future performance, while others argue the manage-

ment’s motivation is simply to pump up earnings per share

by reducing the number of shares. However, the reasons

behind buybacks are more complex and do explain the

performance of the buyback index relative to the S&P 500.

Figure 1 compares the performance of the S&P 500

Buyback Index and the equal-weighted version of the S&P

500 from the start of 1995 through the end of 2013. The S&P

500 Buyback Index consists of the 100 stocks in the S&P

500 with the highest buyback yields—the highest ratio of

buybacks in the trailing 12 months to market value at the

start of the period. The index is equally weighted; therefore,

the equally weighted version of the S&P 500 is the appro-

priate benchmark for comparison purposes. The S&P 500

Buyback Index was launched on Nov. 29, 2012, and there-

fore data prior to that date are backtested.

Buybacks are very different from dividends; maybe the only

thing they have in common is the distribution of corporate

cash. Investors prize dividends and dividend-paying compa-

nies for consistency and predictability. Buybacks offer manage-

ment some flexibility: Buyback programs may be announced

in advance but are often modified, reduced or expanded before

completion. Unlike a dividend cut, companies are rarely penal-

ized in the market for revising buyback programs.

The patterns and timing of buybacks and dividends are

also different. Figure 2 shows dividends and buybacks for

the S&P 500 measured as dollars per share on the left scale;

the index value is plotted on the right scale for comparison.

Until about 2000, dividends and buybacks were about the

same size. As the market recovered from the 2000-2002 tech

bust and bear market, buybacks began to climb sharply.

During the financial crisis of 2008-2009, buybacks and divi-

dends dropped, but in the last few years, the dollars spent

on buybacks exceeded the dollars paid out as dividends.

Although buying back stock may appear to signal future

stock performance, it isn’t a typical valuation measure like

dividend yield, earnings per share or price to book. Rather,

buybacks will reduce the number of shares outstanding and,

because cash is consumed in repurchasing stock, may lower

interest earnings or raise interest expense. However, buy-

backs will raise earnings per share and lower the P/E. Stock

repurchase programs do support the stock price by provid-

ing some underlying demand for the stock. When these

programs first became popular in the 1980s, it was suggested

that buyback programs assisted investors who would be bet-

ter off avoiding dividends taxed as ordinary income while

selling portions of their positions to generate income taxed

as capital gains. However, since 2003, when the tax treatment

of dividends was changed, this argument became moot.

Stock repurchase programs would be good investments

if a company could buy its stock at a depressed price

and reissue the shares later at a profit. That happened in

the wake of the 1987 market crash and during the recent

financial crisis. Moreover, in those troubled times, sup-

porting the stock price was welcomed. Today companies

implement buyback programs in bull markets as well as

bear markets. If their timing is right, such programs may be

profitable in a rising market; however, the limited research

on the profitability of buyback programs is inconclusive.




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Left unexamined is the question of whether buying back

stock or using the cash to invest in expanding a company’s

business will provide the best returns.

Researchers who have examined stock repurchase pro-

grams identify factors other than recent market moves as

important in decisions on whether to buy back stock. One

factor is employee stock option and stock grant programs.

Since the 1980s, grants of company stock or options to

purchase company stock are growing parts of salaries and

bonuses. One byproduct of these programs is an increase

in shares outstanding, which dilutes the holdings of

existing shareholders and reduces reported earnings per

share. The response from many corporate managements

is to implement stock repurchase programs to offset the

dilution. The timing of when shares granted to employ-

ees are issued may be partly under management control.

However, the timing of when employee stock options

are exercised depends largely on the performance of the

company’s stock price; a rising stock price is likely to spur

option exercise and increase the pressure to repurchase

stock and reduce dilution.

The result is that when a buyback program seems to be

a buy signal for a stock, we may actually be seeing momen-

tum in the stock: Recent strong price performance of the

company may have sparked option exercise, which led to

stock repurchase and the apparent buy signal.

Figure 1 Figure 2

100

200

300

400

500

600

■ S&P 500 Equal Weight Index ■ S&P 500 Buyback Index

S&P 500 BuyBack Index And S&P 500 Equal Weight Index

19951997

19992001

20032005

20072009

20112013

19981999

20012002

20032004

20052006

20072008

20092010

20112012

2013

Dividends, Buybacks And The S&P 500

$0

$5

$15

$20

$25

$10

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

■ Dividends/Share (Left Scale) ■ Buybacks/Share (Left Scale)■ S&P 500 (Right Scale)

Do

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rs P

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Sh

are In

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ev

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Note: Data are monthly from January 1995 to December 2013. Data for the S&P

Buyback Index from January 1995 to November 2012 are hypothetical data.

Source: S&P Dow Jones Indices

Note: Data are quarterly from the first quarter 1998 to the third quarter 2013.

Source: S&P Dow Jones Indices

/SPY





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What Is The World Coming To?

A new era in country classification

By Mat Lystra




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We are often asked about our views regarding a particular country’s development as it pertains to country classification. Country classification

is the process by which an index provider determines whether a country is to be designated a developed, emerg-ing or frontier market. Ultimately, country classification is the index provider’s assessment of the relative riskiness of a market. Russell’s country classification methodol-ogy evaluates countries using two overarching risk factors: macro (political/economic) risk; and operational (market) risk. Underlying these two broad perspectives are a vari-ety of individual factors that together form the mosaic of relative risk for each country. After a mostly static 10-year period between 2002 and 2012, where there was little movement of countries between classifications, we may be entering a period of global realignment that will leave its mark on global indexes in the coming decade.

Underlying the relative quiet between 2002 through 2012, the stage was being set for a period of increased change. Arguably the biggest catalyst was the growth of emerging markets as barriers to entry fell and assets from global investor poured in. This global integration helped to accelerate market reforms and the implementation of regulatory best practices across many emerging market countries. Meanwhile, the global financial crisis wreaked havoc on many developed countries’ equity markets, caus-ing some to try and shelter their equity markets by curbing accessibility to global investors. Frontier countries, hav-ing witnessed the successes and failures of emerging and developed markets, also adopted global best practices and became more attractive to equity investors seeking diversi-fication and the potential for higher returns.

In this paper, we examine country classification by using several of the factors that underpin Russell’s country classification process. The “Methodology” section below provides the procedural details of our studies. To show that elements for change may exist, but may not be evident in a country’s overall designation, we isolate some of the indi-vidual measures used in our macro risk and operational risk assessments and show what country classifications might look like if we had used just one of four variables: GDP per capita at purchasing power parity (PPP); market cap to GDP; liquidity; and foreign ownership limits (FOL). This exercise is a useful way to challenge views of the world that may have become stagnant after a long period of rela-tively little change. Additionally, this analysis shows just how important it is for an index provider to make holistic assessments of market risk in order to arrive at classifica-tion outcomes that are designed to meet the needs and expectations of global investors. Before diving into the data, we’ll spend the next few paragraphs reviewing the current country classification landscape.

Current Country Classification Landscape There have been many strong suggestions that we may

be entering a period of realignment: Witness the recent shift of Greece from developed to emerging market des-ignation, a move that could be followed by other coun-

tries, such as Portugal and Italy, which were hard-hit by the global financial crisis and the European debt crisis that followed. Iceland, formerly classified as a developed country, saw its outsized banking sector collapse in late 2008, and was ultimately removed (in 2012) from the Russell Global Index (RGI).1 South Korea (hereinafter, “Korea”) and Taiwan have met Russell’s macro risk criteria for developed markets for a number of years, but restric-tions around the convertibility and use of the local curren-cies—which fall into the operational risk category—have held back their move from emerging to developed-market status. However, should these restrictions be lifted or miti-gated, these two markets would have a seemingly clear path to being classified as developed markets.

The development of countries in Africa and the Middle East suggest that these regions will also play featured roles in reshaping the global equity landscape. The Arab Spring, which started in Tunisia in late 2010 and spread in early 2011, has seen its trajectory altered by the recent military-led ouster of Egypt’s democratically elected President Mohammed Morsi. Egypt has failed to regain its economic footing and is now facing a possible move from emerging to frontier status, given that both its macro and its opera-tional risk profiles have deteriorated. Morocco, a member of the monarchy club and a long-shot addition to the Gulf Cooperation Council (GCC) group of countries,2 has also seen its risk signal flicker between emerging and frontier, but Morocco is only in year one of the Russell Indexes reclassification glide path.3 Like Taiwan and Korea, Qatar, a frontier market, has a very stable macro risk profile, and yet it remains one of the most restricted markets we cover, due to its tight limits on foreign ownership. Should these limits be raised and accessibility improved, Qatar would have a plausible path to becoming an emerging market. Finally, African countries like Nigeria, Kenya, Mauritius, Botswana and Ghana are all making strides toward the kind of stable and sustainable growth that could see them become classified as emerging markets.

MethodologyFor our first three studies, we divide the countries in the

RGI and Russell Frontier Index (RFI) into “Tier 1,” “Tier 2” and “Tier 3.” This grouping allows us to generalize the relative ranks among countries. To create the three tiers, we divide countries using the following process: Countries above the 67th percentile are considered Tier 1; those below the 67th and above the 33rd, Tier 2; and those below the 33rd percentile, Tier 3. Our fourth and final study looks at foreign ownership limits and divides countries into “Low,” “Moderate” and “Restrictive” categories based on the percentage of market impacted by FOL. Across all four studies, the “Classification” column indicates the country’s official classification in the RGI.

GDP Per Capita Our first look is at GDP per capita at purchasing power

parity,4 one of the most widely used indicators of eco-nomic development (hereinafter, “GDP per capita”). As




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Country

GDP Per Capita (PPP) Among Developed, Emerging And Frontier Countries As Of Year-End 2012

GDP Per Capita (USD)

Rank

Sources: Russell Indexes, CIA World Factbook

Figure 1

Classification

Tie

r 1

CountryGDP Per

Capita (USD)RankClassification

Tie

r 2

Tie

r 2

Tie

r 3

Qatar Frontier $102,800 1

Luxembourg Developed $80,700 2

Singapore Developed $60,900 3

Norway Developed $55,300 4

Hong Kong Developed $50,700 5

United States Developed $49,800 6

United Arab Emirates Emerging $49,000 7

Switzerland Developed $45,300 8

Kuwait Frontier $43,800 9

Austria Developed $42,500 10

Australia Developed $42,400 11

Netherlands Developed $42,300 12

Ireland Developed $41,700 13

Sweden Developed $41,700 13

Canada Developed $41,500 15

Germany Developed $39,100 16

Taiwan Emerging $38,500 17

Belgium Developed $38,100 18

Denmark Developed $37,700 19

United Kingdom Developed $36,700 20

Finland Developed $36,500 21

Japan Developed $36,200 22

France Developed $35,500 23

Korea Emerging $32,400 24

Israel Developed $32,200 25

Spain Developed $30,400 26

Italy Developed $30,100 27

New Zealand Developed $28,800 28

Slovenia Frontier $28,600 29

Oman Frontier $28,500 30

Bahrain Frontier $28,200 31

Czech Republic Emerging $27,200 32

Cyprus Frontier $26,900 33

Malta Frontier $26,100 34

Greece Emerging $25,100 35

Slovakia Frontier $24,300 36

Portugal Developed $23,000 37

Estonia Frontier $21,200 38

Poland Emerging $21,000 39

Trinidad and Tobago Frontier $20,400 40

Lithuania Frontier $20,100 41

Hungary Emerging $19,800 42

Chile Emerging $18,400 43

Argentina Frontier $18,200 44

Latvia Emerging $18,100 45

Croatia Frontier $18,100 45

Russia Emerging $17,700 47

Gabon Frontier $17,300 48

Malaysia Emerging $16,900 49

Botswana Frontier $16,800 50

Mauritius Frontier $15,600 51

Mexico Emerging $15,300 52

Turkey Emerging $15,000 53

Bulgaria Frontier $14,200 54

Kazakhstan Frontier $13,900 55

Romania Frontier $12,800 56

Brazil Emerging $12,000 57

South Africa Emerging $11,300 58

Colombia Emerging $10,700 59

Peru Emerging $10,700 59

Macedonia Frontier $10,700 59

Serbia Frontier $10,500 62

Thailand Emerging $10,000 63

Tunisia Frontier $9,700 64

China Emerging $9,100 65

Jamaica Frontier $9,100 65

Bosnia and Herzegovina Frontier $8,300 67

Namibia Frontier $7,800 68

Ukraine Frontier $7,600 69

Egypt Emerging $6,600 70

Sri Lanka Frontier $6,100 71

Jordan Frontier $6,000 72

Morocco Emerging $5,300 73

Indonesia Emerging $5,000 74

Philippines Emerging $4,300 75

India Emerging $3,900 76

Vietnam Frontier $3,500 77

Ghana Frontier $3,300 78

Pakistan Frontier $2,900 79

Nigeria Frontier $2,700 80

Papua New Guinea Frontier $2,700 80

Kyrgyzstan Frontier $2,400 82

Bangladesh Frontier $2,000 83

Senegal Frontier $1,900 84

Kenya Frontier $1,800 85

Tanzania Frontier $1,700 86

Zambia Frontier $1,700 86




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seen in Figure 1, we ranked 87 countries in descending order based on their most recent GDP per capita. Viewed in isolation, GDP per capita could lead to some interest-ing country classification results. The obvious standout is Qatar, in the top spot with a GDP per capita of more than USD$102,000. Six more nondeveloped countries—the United Arab Emirates, Kuwait, Taiwan, Korea, Slovenia and Oman—had Tier 1 GDP per capita that exceeded the 67th percentile threshold of USD$28,400. While each of these countries is generally regarded as stable, with lower rela-tive macro risk, each has higher relative operational risk that results in a classification other than developed.

Also notable in Figure 1 is the number of emerging mar-kets that fall into the Tier 3 GDP per capita group, beneath the 33rd percentile. There are 10 emerging markets with GDP per capita falling beneath the USD$11,500 boundary, including three of the five BRICS countries—China, India and South Africa.5 That the bulk of the BRICS countries fall into the bottom third of GDP per capita carries the double message that room for further economic growth remains but these countries have plenty of challenges still to come. As an example, India scores the lowest (33.72) among all countries in the Russell Emerging Markets Index (median = 58.49) with regard to the Russell Development Score (RDS).6 The RDS evaluates countries across six categories, includ-ing adult literacy and child mortality rates—areas where India lags relative to many of its emerging peers. To be fair, China and India are also the two most populous countries on Earth, which puts pressure on any per capita measure, as well as social and economic development. Qatar, the world’s wealthiest country by GDP per capita, is just the opposite, having one of the world’s smallest populations.

Market Cap To GDPThe second macro-level indicator we isolated was mar-

ket cap to GDP—which is a widely used gauge of capi-tal-market development.7 In general, a higher percent-age indicates greater capital-market development and an advanced economy. However, several of the top countries found in Figure 2, like Switzerland, Luxembourg and Hong Kong, are either tax havens and/or “super regional exchanges” that draw equity listings from multiple coun-tries—explaining their extraordinarily high market cap to GDP ratios. We find an interesting—and perhaps unex-pected—mix of countries in two of our three ranking cate-gories. Of the 30 countries having Tier 1 market cap to GDP ratios, nearly half are not classified by Russell as developed markets. Malaysia and South Africa are the top among emerging markets, as is Qatar among frontier markets. Moving below Brazil at the cutoff of the 67th percentile, the picture becomes murkier, as the Tier 2 group contains an almost-even distribution of developed, emerging and frontier countries. Portugal has the distinction of being the developed market with the lowest market cap to GDP, after Russell’s reclassification of Greece as an emerging market (formerly developed) in June 2013.

The Tier 3 grouping of countries falling below Greece at the 33rd percentile predominantly comprises frontier

markets—fewer surprises here. Egypt and Hungary are two notable emerging markets in this group, Egypt having only a 7 percent market cap to GDP ratio. The lower levels of capital-market development among this group of coun-tries highlight not only their relative frailty but also their attractiveness to some global investors. Frontier market asset managers specifically target these underdeveloped capital markets in an attempt to exploit greater informa-tional and behavioral inefficiencies.8

LiquidityOur rankings of GDP per capita PPP and market cap to

GDP produced results that might challenge some of the conventional notions about country classification—spe-cifically, that emerging and frontier countries are always

poorer, with brittle capital markets. We now turn our attention to two measures associated

with operational risk—liquidity; and foreign ownership limits—both of which relate to the general accessibility of a market. Russell also examines other elements of operation-al risk—ease of local currency use, trading infrastructure and settlement processes. Using the same ranking method, we determine whether the operational risk profiles across our universe of countries produce results similar to those we found by use of the macro risk measures.

Liquidity is a key element within any operational risk framework. Professional investors look to liquidity as a gauge of market efficiency, as well as to cost. The most liq-uid markets offer asset managers the least implementation shortfall, which is a collection of costs that includes broker commissions, custodian fees, trading costs and market impact cost. Figure 3 shows the 2012 total dollar value trad-ed for all 87 countries in our study. Korea emerges as the most liquid developing market, followed closely by China. Contrasting our findings by use of GDP per capita, where none of the BRICS countries were represented in the Tier 1 category, all five countries are above the 67th percentile for liquidity—underscoring the very different outcomes that could potentially result when using only a single measure. In total, 11 emerging markets crack the Tier 1 liquidity threshold, and another 12 are in the Tier 2 liquidity group. Of emerging markets, only Latvia falls into the Tier 3 liquid-ity group, which otherwise comprises frontier countries.9

Kuwait and Qatar were the most liquid frontier markets, both falling squarely into the “standard” group, ahead of emerging markets such as Egypt, Peru and Morocco.

Foreign Ownership LimitsFinally, we turn our attention to FOL. These limits are

sometimes set by companies themselves, but it is also com-mon for their regulators or governments to specify what percentage of a public company may be owned by foreign-ers. Generally, these limits apply to industries/sectors a government views as sensitive and/or strategic (such as defense, commercial air carrier, natural resources), and the media can often be subject to FOLs as well. Some countries have aggressive limits in place, which may restrict the ability of global investors to hold their preferred numbers of shares.




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Country

Market Cap To GDP Among Developed, Emerging And Frontier Countries As Of Year-End 2012

Market Cap To GDP

Rank

Sources: Russell Indexes, CIA World Factbook

Figure 2

Classification

Tie

r 1

CountryMarket Cap

To GDP RankClassification

Tie

r 2

Tie

r 2

Tie

r 3

Switzerland Developed 379% 1

Luxembourg Developed 271% 2

Hong Kong Developed 219% 3

Singapore Developed 137% 4

Sweden Developed 136% 5

Australia Developed 131% 6

Canada Developed 122% 7

United Kingdom Developed 119% 8

Denmark Developed 111% 9

United States Developed 107% 10

Netherlands Developed 97% 11

Norway Developed 92% 12

Malaysia Emerging 87% 13

South Africa Emerging 86% 14

Finland Developed 80% 15

Taiwan Emerging 79% 16

Chile Emerging 78% 17

Japan Developed 76% 18

France Developed 70% 19

Ireland Developed 67% 20

Qatar Frontier 67% 21

Korea Emerging 66% 22

Belgium Developed 65% 23

Cyprus Frontier 65% 24

Papua New Guinea Frontier 60% 25

Israel Developed 56% 26

Kuwait Frontier 55% 27

Thailand Emerging 54% 28

Colombia Emerging 49% 29

Brazil Emerging 49% 30

Germany Developed 45% 31

Jordan Frontier 45% 32

Spain Developed 40% 33

Philippines Emerging 37% 34

New Zealand Developed 32% 35

Mexico Emerging 31% 36

Russia Emerging 30% 37

Mauritius Frontier 29% 38

Indonesia Emerging 28% 39

Austria Developed 28% 40

Italy Developed 27% 41

China Emerging 27% 42

Bahrain Frontier 26% 43

Portugal Developed 25% 44

Turkey Emerging 24% 45

India Emerging 23% 46

Morocco Emerging 21% 47

United Arab Emirates Emerging 21% 48

Trinidad and Tobago Frontier 21% 49

Peru Emerging 20% 50

Oman Frontier 18% 51

Poland Emerging 18% 52

Malta Frontier 17% 53

Kenya Frontier 15% 54

Croatia Frontier 15% 55

Czech Republic Emerging 13% 56

Greece Emerging 13% 57

Nigeria Frontier 11% 58

Hungary Emerging 10% 59

Vietnam Frontier 9% 60

Slovenia Frontier 8% 61

Jamaica Frontier 8% 62

Gabon Frontier 8% 63

Sri Lanka Frontier 7% 64

Botswana Frontier 7% 65

Egypt Emerging 7% 66

Estonia Frontier 7% 67

Tunisia Frontier 6% 68

Bangladesh Frontier 6% 69

Pakistan Frontier 6% 70

Zambia Frontier 5% 71

Kazakhstan Frontier 4% 72

Romania Frontier 4% 73

Argentina Frontier 4% 74

Serbia Frontier 2% 75

Namibia Frontier 2% 76

Ukraine Frontier 2% 77

Lithuania Frontier 2% 78

Latvia Emerging 1% 79

Bulgaria Frontier 1% 80

Slovakia Frontier 0.7% 81

Macedonia Frontier 0.5% 82

Ghana Frontier 0.3% 83

Tanzania Frontier 0.1% 84

Bosnia and Herzegovina Frontier 0.0% 85

Kyrgyzstan Frontier 0.0% 85

Senegal Frontier 0.0% 85




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Country

Dollar Value Traded (Equities In Millions USD) Of Developed, Emerging And Frontier Countries For 2012

2012 USD Value Traded

Rank

Sources: Russell Indexes, CIA World Factbook, Bloomberg, and exchange data

Figure 3

Classification

Tie

r 1

Country2012 USD

Value TradedRankClassification

Tie

r 2

Tie

r 2

Tie

r 3

United States Developed $8,116,077 1

Japan Developed $3,453,117 2

United Kingdom Developed $1,382,787 3

Germany Developed $1,167,490 4

France Developed $1,038,481 5

Korea Emerging $1,037,596 6

Australia Developed $1,004,989 7

Canada Developed $1,002,963 8

China Emerging $959,063 9

Spain Developed $819,762 10

Brazil Emerging $733,236 11

Taiwan Emerging $672,056 12

Switzerland Developed $665,012 13

Italy Developed $619,863 14

India Emerging $465,781 15

Netherlands Developed $406,706 16

Sweden Developed $351,297 17

Russia Emerging $317,873 18

Hong Kong Developed $289,795 19

Turkey Emerging $278,555 20

South Africa Emerging $276,864 21

Singapore Developed $190,336 22

Thailand Emerging $189,038 23

Norway Developed $135,302 24

Finland Developed $122,434 25

Mexico Emerging $118,373 26

Malaysia Emerging $98,487 27

Belgium Developed $97,591 28

Ireland Developed $94,283 29

Denmark Developed $94,054 30

Indonesia Emerging $86,847 31

Luxembourg Developed $67,364 32

Poland Emerging $50,321 33

Israel Developed $48,692 34

Chile Emerging $41,849 35

Philippines Emerging $25,971 36

Portugal Developed $24,872 37

Austria Developed $24,275 38

Kuwait Frontier $21,112 39

United Arab Emirates Emerging $18,573 40

Colombia Emerging $18,388 41

New Zealand Developed $17,937 42

Greece Emerging $15,234 43

Qatar Frontier $15,153 44

Egypt Emerging $12,481 45

Hungary Emerging $10,581 46

Pakistan Frontier $9,652 47

Peru Emerging $7,390 48

Papua New Guinea Frontier $6,959 49

Cyprus Frontier $6,715 50

Bangladesh Frontier $6,646 51

Argentina Frontier $6,054 52

Vietnam Frontier $4,693 53

Nigeria Frontier $3,595 54

Czech Republic Emerging $2,714 55

Morocco Emerging $2,648 56

Romania Frontier $2,207 57

Bahrain Frontier $1,904 58

Mauritius Frontier $1,751 59

Oman Frontier $1,729 60

Ukraine Frontier $1,339 61

Kenya Frontier $892 62

Sri Lanka Frontier $881 63

Jordan Frontier $755 64

Malta Frontier $586 65

Tunisia Frontier $501 66

Croatia Frontier $297 67

Kazakhstan Frontier $260 68

Slovenia Frontier $254 69

Bosnia and Herzegovina Frontier $252 70

Estonia Frontier $159 71

Jamaica Frontier $157 72

Lithuania Frontier $153 73

Bulgaria Frontier $146 74

Senegal Frontier $99 75

Ghana Frontier $96 76

Botswana Frontier $79 77

Slovakia Frontier $69 78

Serbia Frontier $68 79

Gabon Frontier $67 80

Namibia Frontier $54 81

Trinidad and Tobago Frontier $46 82

Zambia Frontier $22 83

Latvia Emerging $17 84

Tanzania Frontier $10 85

Kyrgyzstan Frontier $8 86

Macedonia Frontier $8 87




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Country

Level Of Foreign Ownership Limits And Total Market Cap Of Developed, Emerging And Frontier Countries For 2012

Market Cap FOL

Source: Russell Indexes

Figure 4

Classification Country Market Cap FOLClassification

Macedonia Frontier 115.9 Low

Tanzania Frontier 103.2 Low

Senegal Frontier N/A Low

Kyrgyzstan Frontier N/A Low

Spain Developed 559,610.1 Low

Serbia Frontier 1,920.5 Low

Canada Developed 1,766,936.1 Moderate

Australia Developed 1,256,350.6 Moderate

Korea Emerging 1,072,711.9 Moderate

Russia Emerging 753,618.6 Moderate

Taiwan Emerging 710,425.9 Moderate

Mexico Emerging 540,165.3 Moderate

Malaysia Emerging 428,982.1 Moderate

Indonesia Emerging 339,999.5 Moderate

Turkey Emerging 272,926.0 Moderate

Colombia Emerging 245,381.4 Moderate

Israel Developed 138,764.5 Moderate

Kuwait Frontier 90,425.9 Moderate

Pakistan Frontier 29,220.5 Moderate

Jordan Frontier 17,411.9 Moderate

Cyprus Frontier 15,258.6 Moderate

Kenya Frontier 11,761.6 Moderate

Romania Frontier 10,674.2 Moderate

Kazakhstan Frontier 9,446.4 Moderate

Sri Lanka Frontier 9,004.6 Moderate

Bahrain Frontier 8,586.7 Moderate

Ukraine Frontier 7,322.9 Moderate

Mauritius Frontier 5,946.8 Moderate

Estonia Frontier 1,939.9 Moderate

Malta Frontier 1,875.4 Moderate

Slovakia Frontier 943.5 Moderate

Bosnia and Herzegovina Frontier 0.0 Moderate

China Emerging 3,311,041.6 Restrictive

India Emerging 1,109,941.2 Restrictive

Thailand Emerging 346,392.2 Restrictive

Philippines Emerging 155,696.4 Restrictive

Qatar Frontier 126,129.2 Restrictive

United Arab Emirates Emerging 57,449.1 Restrictive

Vietnam Frontier 28,359.8 Restrictive

Argentina Frontier 28,288.7 Restrictive

Oman Frontier 16,180.7 Restrictive

Tunisia Frontier 6,444.1 Restrictive

Trinidad and Tobago Frontier 5,653.4 Restrictive

United States Developed 16,708,431.2 Low

Japan Developed 3,505,067.8 Low

United Kingdom Developed 2,765,807.5 Low

France Developed 1,585,500.6 Low

Germany Developed 1,442,710.5 Low

Switzerland Developed 1,374,911.6 Low

Brazil Emerging 1,145,597.1 Low

Hong Kong Developed 798,068.9 Low

Netherlands Developed 687,745.1 Low

Sweden Developed 539,027.4 Low

South Africa Emerging 499,797.8 Low

Italy Developed 492,834.5 Low

Singapore Developed 446,193.0 Low

Belgium Developed 273,654.1 Low

Norway Developed 256,651.5 Low

Chile Emerging 247,757.6 Low

Denmark Developed 232,333.9 Low

Finland Developed 157,798.0 Low

Poland Emerging 142,892.4 Low

Ireland Developed 129,141.8 Low

Luxembourg Developed 114,523.6 Low

Austria Developed 99,248.9 Low

Peru Emerging 63,863.0 Low

Portugal Developed 60,676.4 Low

Nigeria Frontier 51,347.3 Low

New Zealand Developed 41,450.7 Low

Egypt Emerging 37,922.7 Low

Czech Republic Emerging 37,268.3 Low

Morocco Emerging 36,387.6 Low

Greece Emerging 35,578.9 Low

Hungary Emerging 19,107.3 Low

Bangladesh Frontier 18,262.4 Low

Croatia Frontier 12,171.7 Low

Papua New Guinea Frontier 11,043.9 Low

Slovenia Frontier 4,869.3 Low

Botswana Frontier 2,245.6 Low

Jamaica Frontier 2,052.5 Low

Gabon Frontier 2,043.8 Low

Lithuania Frontier 1,246.4 Low

Bulgaria Frontier 1,215.2 Low

Zambia Frontier 1,160.5 Low

Latvia Emerging 494.9 Low

Namibia Frontier 397.9 Low

Ghana Frontier 267.3 Low




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see A-shares included in global equity benchmarks, which, depending on the level of openness, could shift China into the “moderate” foreign ownership limit category.

Conclusion

Each of the four factors we have reviewed—GDP per capita; market cap to GDP; liquidity; and foreign owner-ship limits—has given us four distinct perspectives of country classification. Each, in isolation, could result in developed, emerging and frontier market indexes that look very different from those used today. Russell’s multifactor approach reflects our belief that examining both macro risks and operational risks is necessary to the accurate designation of a country’s market as developed, emerging or frontier. Within Russell’s country classifica-tion methodology, per capita income is one of several equally weighted factors that together form a composite macro risk score for a country. Liquidity and foreign own-ership limits feed into a country’s composite operational risk score, along with elements of the settlement process, ease of local currency use, etc. These scores are compared on a relative basis and validated by market participants to determine the appropriate country classification.12

As we have observed, countries from the Middle East, Asia and Africa already rank higher by GDP per capita or market cap to GDP than their current country classifica-tion might suggest. As these markets continue to upgrade their technology infrastructure and enact capital market regulations that bring them more in line with global standards, the likelihood of being promoted to emerging or developed-market status increases. And as we have already seen in the case of Greece, other countries may falter in an increasingly competitive global market and backslide into the higher-risk emerging or frontier cat-egories. Exactly which countries, and when, are not clear, but what does seem clear is that the next 10-year period will not be nearly as quiet as the last 10.

For index providers, FOL is an important consideration: Ignoring these limits could lead to a benchmark holding more shares of a company than can actually be bought. Figure 4 shows each country’s classification as having either a “low,” “moderate” or “restrictive” FOL regime. Because the FOL classifications are not a continuous variable set, we have used market cap as a secondary sorting factor to differentiate between countries in the same category.

For the most part, FOL meets expectations, with almost all developed markets having “low” levels of restrictiveness. What is notable in the “low” category, however, is the num-ber of frontier markets represented. There may be several reasons for this, including a desire to attract foreign capital, a large state- or family-owned business not being publicly listed, or open-ended laws regarding foreign ownership. It is this last possibility that carries the most potential danger for foreign investors, if a country determines the value of a com-pany or industry to be so great that it should be controlled by the government or the “people.”10 Argentina is an example of a country that has adopted an increasingly aggressive stance toward foreign ownership, thereby earning a “restrictive” designation. Other countries, such as Qatar and the UAE, are so independently wealthy that they have historically had less incentive to open their markets. Although those two mar-kets are still classified as “restrictive,” they have taken some incremental steps toward greater accessibility by relaxing the FOLs for select companies and industries.

China is the largest country by weight in the Russell Emerging Markets Index, despite the inaccessibility of approximately two-thirds of its equity market, the China A-share market, which is not represented in standard global benchmarks. Based on the long list of restrictions on foreign ownership of China A-shares, Russell Indexes currently con-siders this portion of the Chinese market ineligible. However, authorities in China have expressed an interest in increasing foreign investors’ access to the domestic share class.11 If the barriers to foreign ownership fall, the next decade could


Endnotes

1 A single Icelandic stock listed on the Copenhagen Stock Exchange remained in the RGI until 2012.

2 Salisbury, P. (2013). “Plans stall for an expanded GCC,” Middle East Economic Digest, 57(2), 24.

3 The “glide path” term used by Russell describes the normal three-year period during which a market must sustain a change to its risk profile in order to see its classification

change (e.g., from emerging to developed).

4 GDP at purchasing power parity is the sum of all goods and services produced in a country and then valued at prices in the U.S. for 2012.

5 The BRICS group of nations includes Brazil, Russia, India, China and South Africa.

6 The Russell Development Score is an experimental variable with a value between 0 and 100. It ranks countries on six of the World Bank’s development indicators: urban

population; mortality rate (children 5 and under); literacy rate (adult population); Internet usage (per 100 people); mobile phone usage (subscriptions per 100 people); and

number of domestic patent applications per country.

7 The market-cap figures reflect gross market cap; GDP figures are based on 2012 purchasing power parity.

8 Speidell, L. (2011). “Frontier Market Equity Investing: Finding the Winners of the Future,” CFA Institute.

9 Latvia has no active membership in the Russell Emerging Markets Index; it is in the Russell Indexes glide path toward a reclassification as a frontier market.

10 In 2012, Argentina nationalized oil producer YPF, then a subsidiary of Spanish company Repsol S.A. Zimbabwe has also pressed an “indigenization” program focused on

natural resources, requiring at least 51 percent domestic ownership.

11 See “To Develop a More Open and Inclusive Capital Market in China,” Asian Financial Forum, Hong Kong: http://www.csrc.gov.cn/pub/csrc_en/newsfacts/release/201301/

t20130114_220400.htm.

12 For a more detailed description of Russell Indexes’ country classification process, please see the Russell Global Indexes Construction and Methodology document, available

at: http://www.russell.com/indexes/documents/Global_Indexes_Methodology.pdf.




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Target Maturity Bond Funds As Retirement Income Tools

An alternative to buying individual bonds or broad bond funds

By Matthew Patterson and Darrin DeCosta




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For most of its recent history, the U.S. investment management industry has focused its efforts on helping baby boomers accumulate assets, primarily

through mutual funds, but increasingly through alterna-tive vehicles such as exchange-traded funds. The results have been impressive, with assets in U.S. investment companies (which include mutual funds, closed-end funds, ETFs and unit investment trusts) growing from $3 trillion in 1995 to nearly $15 trillion as of the end of 2012, according to the Investment Company Institute.

With the oldest baby boomers beginning to retire, how-ever, the investment needs of this massive generation are changing. Increasingly, baby boomers are shifting from the accumulation phase of their investing lives to the distribu-tion phase. During the distribution phase, investors begin to draw down their savings to help meet their consump-tion needs. To a large extent, the investment management industry has failed to develop products that help retirees generate income while managing the liquidation of their retirement savings in an orderly fashion.

A new type of fixed-income vehicle, the target maturity bond fund, seeks to address both the savings and dis-tribution needs of retirees by combining the benefits of bond funds and individual bonds. Target maturity bond funds invest in bonds that mature in a particular desig-nated year of maturity. Each target maturity bond fund generally seeks to hold the bonds in its portfolio until they mature or are called. At the end of its designated year of maturity, each target maturity bond fund terminates and distributes its net assets to shareholders.

The Benefits Of Individual Bonds For Retail Investors

Financial advisors have long known that individual bonds offer unique features that render them particu-larly useful for retired investors. Unlike traditional bond funds, individual bonds pay fixed coupons and have maturity dates that provide for the return of principal to investors at a predetermined date, thereby allowing investors to estimate in advance what they can expect to earn from an investment in an individual bond.

More importantly, the sensitivity of a bond’s price to changes in interest rates, referred to as its duration by investment practitioners, declines as the maturity date of the bond approaches. In contrast, traditional bond funds are designed to operate in perpetuity and generally seek to maintain a relatively constant duration over time by rolling maturing or near-term bonds into long-term bonds on a regular basis. This means an investor has no way of estimating in advance what he can expect to earn from an investment in a traditional bond fund and may face an inappropriate level of interest-rate risk as the planned redemption of his investment draws nearer.

To illustrate how interest-rate risk can harm an inves-tor seeking to withdraw assets from her retirement sav-ings, consider an investor who expects to need to with-draw $50,000 five years into the future and is choosing between an investment in an individual bond with five years to maturity and a traditional intermediate-term

bond fund. For simplicity’s sake, we will assume that the duration at the time of the investment for each instru-ment is five, which means that the value of the instru-ment can be expected to decrease 5 percent for each 1 percent increase in prevailing interest rates.1

At the time of the investment, both the bond and the bond fund have the same sensitivity to changes in interest rates. The problem arises as the investor’s need to access her money draws nearer. Four years after making the investment, the duration of the individual bond will have declined to one, while the duration of the intermediate-term bond fund will have remained around five. Accordingly, an increase in interest rates at that time would have a 500 percent greater negative impact on the net asset value of the bond fund than on the market value of the individual bond.

In theory, by rolling its maturing and short-term bonds into long-term bonds, the bond fund would ulti-mately make up for this greater mark-to-market loss by generating a higher income stream down the road, described in Leibowitz and Bova.2 This assumes that interest rates don’t continue rising, further damaging the fund’s NAV. It also ignores the investor’s need to access her money in one year to finance her retirement.

In addition to generating less interest-rate risk as its maturity date approaches, the individual bond provides the investor with alternatives unavailable with a bond fund in a rising-rate environment. One option would be to hold the bond until maturity. While she would receive less interest over the remainder of its term than the prevailing interest rate on bonds of similar duration and credit risk would otherwise permit, she would ultimately receive her principal back at maturity. Alternatively, if the investor has capital gains from other investments, she may wish to sell the bond and reinvest the proceeds in another bond of similar duration and credit quality, thereby realizing a tax loss on her investment in the first bond and earning the higher prevailing interest rate on the second bond.

The Drawbacks Of Individual

Bonds For Retail Investors

Individual bonds are not without their flaws, however. Two factors in particular combine to make it difficult for individual investors to build diversified portfolios of indi-vidual bonds. The first of these, a limited selection of indi-vidual bonds available at the retail level, has been exacer-bated in recent years by reforms under the Dodd-Frank Wall Street Reform and Consumer Protection Act that have caused many large banks to reduce the inventory of bonds they make available to retail investors.

Even when retail investors can locate suitable indi-vidual bonds, such bonds often trade in minimum lot sizes of $100,000, making it difficult for investors of rela-tively modest means to acquire a broad range of issuers and maturities at reasonable prices.

Consider an investor with $1 million choosing between building a portfolio of individual corporate bonds from 10 different issuers and investing in a corpo-rate bond fund that holds bonds from 100 different issu-




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ers. While the individual bond portfolio offers the inves-tor an opportunity to invest across a customized range of maturities that will permit him to distribute his assets in an orderly fashion, it also leaves him more vulnerable to the impact of a bond default. If we assume that one issuer in each portfolio defaults, and that the recovery rate on the defaulted bond is 40 percent, the relative lack of diversification in the individual bond portfolio will disproportionately harm the investor (Figure 1).

While the example in Figure 1 employs simplifying assumptions that likely don’t hold true in reality (for example, the bond fund is more likely than the individual bond portfolio to have a bond default), diversification nevertheless remains a critical component in mitigating downside risk and maximizing risk-adjusted returns. Investment strategies that result in concentrated port-folios render investors vulnerable to unsystematic risk.

Even if retail investors are able to build a well-diver-sified portfolio of individual bonds, they typically pay a premium for such bonds relative to the prices that insti-tutional investors pay.3 This is because, unlike stocks, individual bonds typically trade “over the counter” rather than on regulated exchanges. While institutional investors have the resources and brokerage networks to source individual bonds at competitive prices, retail investors typically depend upon a limited number of brokerage relationships to acquire bonds. This problem becomes particularly acute if a retail investor’s plans change, necessitating the sale of an individual bond before it reaches maturity. In some cases, retail inves-tors have trouble even securing bids for the sale of an individual bond. At best, research suggests they are likely to take a significant haircut on the sale of a bond.

Bond Funds That Act Like BondsTarget maturity bond funds seek to bridge the divide

between traditional bond funds and individual bond funds by combining the benefits of both approaches (Figure 2). Like individual bonds, target maturity bond funds have predetermined maturity dates. Like traditional bond funds, they provide a high level of liquidity while offering access to a diversified portfolio of bonds. The result is a set of tools that investors can use to generate income while managing the orderly liquidation of their portfolios.

A key feature of target maturity bond funds is that they permit investors to invest in broadly diversified port folios of bonds that mature in the same calendar year. This allows investors to greatly reduce the impact of defaults


The Importance Of Diversification

Individual Bond Portfolio

Bond Fund

Figure 1

Starting Value $1,000,000 $1,000,000

Number Of Bonds 10 100

Starting Value Per Bond $100,000 $10,000

Loss From Default $60,000 $6,000

% Loss From Single Default 6.00% 0.60%

Source: Accretive Asset Management LLC



Individual Bonds

2014 2017 20202015 2018 20212016 2019 2022

Traditional Bond Index Funds Target Maturity Bond Funds

Combining The Best Attributes Of Bonds And Bond Funds

Issuers By Maturity Year In Nasdaq BulletShares USD Corporate Bond Indexes (As Of 11/8/2013)

Figure 2

Figure 3

Diversification Hard to diversify Highly diversified Highly diversified

Precision High Low High

Liquidity Illiquid Liquid Liquid

Income Semiannual Monthly Monthly

Strategy Hold to maturity Maintain constant duration Hold to maturity

Customization High Low High

# Of Issues 211 278 276 252 259 178 173 192 189

# Of Unique Issuers 169 190 204 204 217 167 153 161 179




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while maintaining precise exposure to a particular year of maturity and locking in a predetermined maturity date.

It should be apparent that the creation of diversified target maturity bond funds depends upon the existence of broad credit markets with a large number of issuers and outstanding bonds. Fortunately, the U.S. credit markets are the largest in the world and offer plenty of issuers and outstanding bonds to create target maturity bond funds across a range of fixed-income market segments.

For example, the Nasdaq BulletShares USD Corporate Bond indexes, which provide target maturity exposure to the U.S.-dollar-denominated investment-grade corporate bond market, include no fewer than 153 unique issuers in any single maturity year from 2014 through 2022 (Figure 3).

The average financial advisor doesn’t have the time or resources to buy, monitor and sell the thousands of securities available in the corporate bond market. The advent of target maturity bond funds permits these advi-sors to obtain targeted, yet broadly diversified, exposure to the bond market with one trade.

Moreover, because target maturity bond funds are typically structured as ETFs or mutual funds, retail inves-tors can trade them more cheaply and with greater price transparency than individual bonds. For example, round-trip bid/ask spreads on established target maturity bond ETFs rarely exceed 0.50 percent (50 bps) and are gener-ally less than 0.15 percent (15 bps). In contrast, data from FINRA’s TRACE bond reporting database suggest that retail investors typically pay round-trip bid/ask spreads on individual bonds ranging from 0.90 percent (90 bps) to 1.60 percent (160 bps).

Target maturity bond funds further simplify the task of managing a fixed-income portfolio by paying a monthly distribution consisting of net income earned by the portfolio. Individual bonds, on the other hand, pay semiannual coupons that may include a return of capital (in the case of bonds purchased at a premium) or may not reflect the full income earned on the bond (in the case of bonds purchased at a discount). Since many retirees have recurring monthly expenses, regu-lar monthly income distributions can better help them manage their day-to-day cash flow needs.

Like target maturity bond funds, traditional bond funds pay monthly income distributions and provide liquid access to diversified bond portfolios. Unlike tar-get maturity bond funds, however, traditional bond funds have no predetermined maturity date and gener-ally seek to maintain a relatively constant duration over their perpetual lifetimes. While the strategy employed by traditional bond funds provides investors with expo-sure to fixed-income markets, it does not provide a fixed-income experience characterized by a predictable return and a predetermined maturity date.

Target maturity bond funds seek to hold bonds until maturity and return investment proceeds to investors at the end of a fund’s designated year of maturity. This permits investors to make reasonably accurate assumptions about the returns they can expect to

receive from an investment in a target maturity bond fund. Moreover, it allows retirees to arrange for the orderly liquidation of their fixed-income investment assets as they draw down their savings.

Consider the historical duration profile of the Nasdaq BulletShares USD Corporate Bond 2012 Index, which tracked the performance of U.S.-dollar-denominated investment-grade corporate bonds with maturities in the calendar year ending Dec. 31, 2012. Seven years prior to reaching maturity, the index had a duration of 5.49. With one year to maturity, the index’s duration had declined to 0.57 (Figure 4).

As noted earlier, a declining duration results in less interest rate sensitivity over time, which makes target maturity bond funds useful tools for investors seeking to plan for the orderly liquidation of their retirement savings. Instead of hoping an interest-rate shock does not occur before they redeem their bond fund investments, investors can instead automatically reduce their interest-rate risk as their need to access their assets draws nearer. This means investors experience less volatility (Figure 5) and less risk of loss as their investment time horizon shortens.


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Nasdaq BulletShares USD Corporate Bond 2012 IndexHistorical Modifed Duration

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Nasdaq BulletShares USD Corporate Bond 2013 IndexHistorical Volatility

Ro

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Figure 4

Figure 5






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12/31/2013

12/31/2013

Bond Maturity Date

Bond Maturity Date

12/31/2016

12/31/2016

12/31/2014

12/31/2014

12/31/2017

12/31/2017

12/31/2015

12/31/2015

Total Par

Total Par

Pre-Distribution Portfolio Of Traditional Laddered Bond Fund

Post-Distribution Portfolio Of Traditional Laddered Bond Fund

Figure 6

Figure 7

Bond 1 $100,000 $100,000

Bond 2 $100,000 $100,000

Bond 3 $100,000 $100,000

Bond 4 $100,000 $100,000

Bond 5 $100,000 $100,000

Portfolio $500,000

Bond 1 $80,000 $80,000

Bond 2 $80,000 $80,000

Bond 3 $80,000 $80,000

Bond 4 $80,000 $80,000

Bond 5 $80,000 $80,000

Portfolio $400,000

Drawbacks Of Target Maturity Bond FundsWhile target maturity bond funds provide valuable

benefits to investors, they are not without their draw-backs. Unlike individual bonds, for example, target maturity bond funds do not pay fixed coupons; rather, they pay monthly distributions that may vary over time. A variety of factors may cause a target maturity bond fund’s monthly distribution to vary over time, the most obvious being changes in prevailing interest rates as the fund grows and takes in new bonds.

When a target maturity bond fund grows and takes in new bonds, prevailing interest rates may be higher or lower than they were at the time the fund acquired its existing bond portfolio. Higher prevailing interest rates will gener-ally push up a target maturity bond fund’s distribution rate as it grows and acquires new bonds with higher yields. The opposite occurs when prevailing bond yields are lower and the fund acquires new bonds with lower yields.

While changes in prevailing interest rates can change the distribution rate of a target maturity bond fund, they generally do not alter the total return an inves-tor receives from an investment in a target maturity

bond fund (assuming the characteristics of the bond portfolio remain unchanged). Rather, changes in the distribution rate an investor receives due to interest-rate movements are generally offset by changes in the amount the investor can expect to receive upon termi-nation of a target maturity bond fund. Nevertheless, this variability of cash flows does make target matu-rity bond funds somewhat less useful than individual bonds at creating customized portfolios that deliver predetermined amounts and types (whether interest or return of capital) of cash flows.

The success of target maturity bond funds to date suggests that many investors are comfortable accept-ing some variability in income distributions in order to gain the benefits that target maturity bond funds offer over individual bonds. For the time being, how-ever, investors can only access target maturity bond funds in a few fixed-income market segments. Until target maturity bond funds are available across a broad range of fixed-income market segments, investors will be unable to take full advantage of this innovative approach to fixed-income investing.




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Building Retirement Solutions Using Target Maturity Bond Funds

Individual investors today have thousands of packaged financial products to choose from to generate income for their clients in retirement. Despite the availability of a broad range of fixed-income mutual funds, ETFs, closed-end funds and UITs, flow-of-funds data from the U.S. Federal Reserve indicate that individual bonds remain significantly more popular with indi-vidual investors than fixed-income packaged products.

Unlike pension funds, insurance companies and other institutional investors, individual investors can’t rely on average life expectancies and average investment returns to prepare for and manage their retirement. They alone must manage the risk of living too long without suffi-cient assets to cover their entire post-retirement life span. While insurance products like annuities can provide some protection against outliving retirement assets, investors have historically relied upon individual bonds to provide income and preserve capital during retirement.

The sad state of retirement fund balances in America means that most retirees cannot reasonably expect to

retire solely on the income their investment portfolios generate. To bridge the gap between the portfolio income they earn and their consumption needs, many if not most retirees draw down their savings over the course of their retirement. Target maturity bond funds can be used like individual bonds to liquidate savings in an orderly fashion.

In contrast, the perpetual nature of traditional bond funds renders them ill suited for investors requiring distributions in excess of the income-generating potential of their port-folio. Consider a 75-year-old retiree with a $500,000 Treasury bond fund investment needing to generate a minimum of 100,000 in nominal cash flows on Dec. 31 each year. Assume that the fund’s portfolio consists of a zero- to five-year ladder of zero coupon Treasurys (Figure 6).

If the investor held the underlying individual bonds, she would automatically receive $100,000 in principal upon the maturity of the first bond in the portfolio. Since she owns a fund, however, she must instead submit a redemption order to the fund for one-fifth of her shares. Figure 7 illustrates the impact on the investor’s fixed-income portfolio of such a redemption.

The investor’s redemption results in the effective sale by the investor of a pro-rata interest in each and every bond in the fund’s portfolio, not just the bond that matured on the redemption date. Effectively, investors in traditional bond funds are forced to sell a slice of their entire bond portfolio every time they sell fund shares, crystallizing capital gains and losses across the entire portfolio rather than simply receiving the proceeds of the first maturing underlying bond.

Moreover, because this traditional zero- to five-year laddered bond fund operates in perpetuity, its portfolio manager will reinvest the maturing proceeds of the first bond into other bonds further out on the yield curve. For an investor who plans to access all of her funds over the original five-year period, this will result in a mismatch between the fund’s duration and the investor’s remaining cash flow needs.

The same investor could use target maturity bond funds to create a zero- to five-year bond ladder. Because a target matu-rity bond fund in the ladder will mature each year, she will not need to redeem a pro-rata slice of her entire bond portfolio to

meet her cash flow needs, and will retain a match between her interest-rate exposure and future cash flow needs.

Most people have much more complicated financial lives than the investor in the example above. They may have large obligations in the future, such as balloon mortgage payments or college tuition payments, that are expected and yet irregu-lar. Target maturity bond funds allow for the creation of highly customized fixed-income portfolios that address the specific future cash flow needs of each investor. While plain-vanilla bond ladders offer a tried-and-true approach to fixed-income investing, many investors will find that they require more customized solutions as they acquire more assets.

Target maturity bond funds can also be valuable tools during the accumulation phase of investors’ lives. Such funds can give investors more control over their interest-rate risk and a better liability match than target maturity bond funds. The benefits of a fixed-income experience, as opposed to fixed-income exposure, are likely to become readily apparent to investors in an environment character-ized by rising interest rates.


Endnotes1 Experienced practitioners will recognize that we are ignoring the impact of convexity and assuming the bond is a zero-coupon instrument whose duration is equal to its

term to maturity.

2 Martin Leibowitz and Anthony Bova, “Portfolio Strategy: Duration Targeting, A New Look at Bond Portfolios,” Morgan Stanley Research, 12/18/2012.

3 DeCosta, Del Vicario and Patterson. Research Note: “The Bond Market: Where the Customers Still Have No Yachts,” Fall 2011.

The success of target maturity bond funds to date suggests that

many investors are comfortable accepting some variability in

income distributions in order to gain the benefits that target

maturity bond funds offer over individual bonds.




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Global Index Data

March/April 2014Selected Major Equity Indexes Sorted By YTD Returns

Index Name 1-Mo 3-Mo 2013 3-Yr 5-Yr

MSCI Kokusai 2.25 7.81 26.64 12.11 15.88

MSCI France 1.64 4.82 26.33 8.40 10.00

Dow Jones US Oil & Gas 2.92 7.09 26.14 11.20 14.06

S&P Homebuilders Select Industry 4.34 8.14 25.90 25.51 24.21

S&P Europe 350 2.14 7.14 25.76 10.20 13.15

ISE-REVERE Natural Gas -0.54 2.43 25.73 0.89 11.84

FTSE RAFI Developed Markets ex-US 1.32 5.82 25.41 7.58 14.04

MSCI USA Minimum Volatility 1.26 6.77 25.33 19.29 17.82

S&P 500 Low Volatility 1.13 6.56 23.59 16.09 16.16

MSCI ACWI IMI 1.77 6.51 23.55 9.82 15.62

MSCI All Country World 1.73 6.60 22.80 9.73 14.92

MSCI EAFE 1.50 5.25 22.78 8.17 12.44

S&P Developed Ex-US BMI 1.46 4.95 20.88 7.25 13.43

MSCI United Kingdom 2.74 7.41 20.67 10.66 16.13

Dow Jones US Basic Materials 4.80 10.38 20.38 4.29 19.85

S&P Global Timber & Forestry 3.92 5.20 19.79 6.67 -

Dow Jones Global Select Dividend 1.42 5.64 18.91 10.00 20.14

DAXGlobal Nuclear Energy -0.47 6.25 17.90 -9.96 -

S&P Global Nuclear Energy 0.85 5.17 16.75 -2.16 -

FTSE Global All Cap ex US 0.97 4.77 15.59 5.24 13.75

MSCI All Country World ex USA 0.88 4.23 15.29 5.14 12.82

Dow Jones US Utilities 1.26 2.59 15.20 11.78 11.13

S&P Global Infrastructure 1.32 3.55 14.00 7.65 10.15

CBOE S&P 500 BuyWrite 2.05 6.36 13.26 8.00 10.92

MSCI Hong Kong 0.14 3.33 11.09 6.17 18.75

S&P Global ex-US Property 0.23 -0.28 7.71 7.87 15.88

MSCI Pacifc ex-Japan -1.25 0.11 5.49 4.65 18.29

S&P Emerging Markets Infrastructure -0.57 2.84 4.89 3.15 -

MSCI Australia -1.78 -1.03 4.16 4.23 18.00

MSCI China -3.43 3.79 3.64 1.25 12.00

S&P Emerging Markets Under USD 2B 0.20 2.02 3.21 -2.31 19.56

S&P United States REIT 0.24 -2.16 2.40 9.43 16.71

Wilshire US REIT 0.61 -2.28 1.86 9.37 16.69

S&P Russia Capped BMI 2.40 1.11 1.66 -4.18 20.31

MSCI Emerging Markets Small Cap -0.51 0.97 1.07 -3.47 19.59

MSCI EM 50 -1.55 2.33 0.96 -0.52 14.08

S&P Global Natural Resources 2.52 4.55 0.96 -3.00 -

MSCI EM Eastern Europe 0.02 -0.22 0.72 -2.37 14.63

FTSE China 25 -4.39 2.83 0.38 -0.65 9.31

MSCI Emerging Markets Growth -1.37 2.02 -0.18 -0.84 15.67

MSCI Emerging Markets -1.45 0.92 -2.60 -2.07 14.79

MSCI India Total Return 3.26 9.29 -3.88 -8.71 13.29

S&P Mid-East and Africa BMI 0.79 2.45 -4.04 -1.64 13.47

BNY Mellon Emerging Markets DR -1.32 0.38 -4.76 -5.44 9.25

MSCI Emerging Markets Value -1.53 -0.19 -5.12 -3.34 13.88

MSCI Emerging Markets EMEA -0.98 -0.80 -5.16 -2.73 13.77

FTSE RAFI Emerging Markets -2.01 -1.00 -6.79 -4.00 13.73

DJ EM Select Dividend -3.22 -3.10 -10.01 1.34 22.10

ISE Global Platinum 0.62 -0.54 -11.99 -27.54 4.99

S&P Latin America BMI -1.91 -4.19 -13.03 -8.16 13.59

ISE Global Copper 5.13 1.57 -23.82 -16.75 26.11

MSCI Indonesia Investable Market -1.46 -7.90 -24.49 -6.37 20.33

MSCI Turkey Investable Market -15.68 -16.57 -26.11 -8.16 14.94

Nasdaq OMX Glb Gold/Prec Metal -3.41 -12.54 -48.95 -27.21 -

NYSE Arca Gold Miners -3.88 -12.59 -53.61 -28.93 -7.77

Index Name 1-Mo 3-Mo 2013 3-Yr 5-Yr

MAC Global Solar Energy -8.60 0.07 126.59 -19.82 -14.63

NASDAQ Internet 5.70 8.83 65.68 26.10 38.91

WilderHill Clean Energy 0.62 -0.36 57.89 -14.11 -5.00

Dow Jones US Sel Aerospace/Defense 2.99 14.29 57.62 23.89 22.90

Dow Jones Internet 5.95 9.72 54.36 21.23 34.54

Wilshire US Micro Cap 3.54 9.31 48.90 16.52 24.53

S&P 500 Pure Value 2.59 12.72 48.11 22.65 28.65

S&P 500 Pure Growth 2.80 9.79 43.85 18.71 26.36

Russell 2000 Growth 2.05 6.62 43.30 16.82 22.58

WisdomTree Japan Hedged Equity 2.24 7.78 42.83 12.41 -

S&P Small Cap 600 Growth 1.30 8.60 42.69 19.20 22.71

Dow Jones US Consumer Services 2.24 10.12 42.17 23.67 25.62

Dow Jones US Health Care 0.80 8.03 41.98 23.68 19.20

Russell 2000 Equal Weight 2.46 7.63 41.32 14.82 21.26

S&P SmallCap 600 1.45 8.48 41.31 18.42 21.37

NASDAQ-100 Equal Weighted 3.80 7.25 40.99 16.81 25.59

Dow Jones US Industrials 4.30 11.54 40.61 18.03 21.17

NASDAQ Composite 2.94 9.73 40.12 17.74 22.86

S&P Small Cap 600 Value 1.59 8.36 39.98 17.73 20.12

NASDAQ-100 Technology 5.50 8.61 39.01 12.74 26.16

Russell 2000 1.97 7.35 38.82 15.67 20.08

S&P Completion 3.03 7.09 38.24 16.39 22.57

WisdomTree Middle East Dividend 4.68 8.77 37.20 9.78 13.57

NASDAQ-100 3.03 10.80 36.92 18.87 25.56

MSCI KLD 400 Social 2.86 9.30 36.20 16.15 18.22

S&P 500 Equal Weight 2.93 8.93 36.16 16.97 23.34

Russell 2000 Value 1.88 8.14 34.52 14.49 17.64

S&P Mid Cap 400 Value 2.88 6.79 34.25 15.80 20.58

Dow Jones US Financials 2.45 9.08 34.22 14.06 14.39

S&P MidCap 400 3.09 6.74 33.50 15.64 21.89

Russell 1000 Growth 2.86 9.37 33.48 16.45 20.39

S&P 1500 2.54 9.34 32.80 16.22 18.38

S&P MidCap 400 Growth 3.29 6.68 32.77 15.54 23.22

S&P 500 Growth 2.71 10.13 32.75 16.77 19.24

Russell 1000 Value 2.53 9.14 32.53 16.06 16.67

Russell Top 200 2.58 10.19 32.41 16.47 17.16

S&P 500 2.53 9.63 32.39 16.18 17.94

S&P 500 Value 2.34 9.08 31.99 15.62 16.61

ISE Water 3.34 7.20 31.90 17.01 18.50

MSCI USA Momentum 2.65 9.57 31.72 18.23 17.80

MSCI USA ESG Select 2.79 8.29 31.53 14.15 17.45

Dorsey Wright Technical Leaders 2.69 7.08 31.42 16.12 20.26

MSCI Germany 2.79 12.18 31.37 12.10 13.84

Russell 3000 2.47 8.57 30.95 13.93 16.31

Dow Jones US Consumer Goods 1.17 7.33 30.55 17.01 18.85

Russell 1000 2.53 8.72 30.44 13.92 16.13

S&P 100 2.32 9.92 30.39 16.01 16.52

Dow Jones Industrial Average 3.19 9.76 29.65 15.71 16.74

MSCI EAFE Small-Cap 2.35 5.45 29.32 9.26 18.50

Russell Top 50 2.40 10.63 29.16 15.95 15.50

WisdomTree Dividend 2.08 8.50 28.36 16.71 17.57

WisdomTree Large Cap Dividend 2.09 8.92 27.85 16.67 16.88

MSCI Japan 0.80 2.32 27.16 5.63 7.65

Dow Jones US Technology 4.18 11.52 26.96 12.54 21.42

MSCI World 2.12 7.31 26.68 11.49 15.02

Source: IndexUniverse. All returns are in US dollars. 3- and 5-year returns are annualized.

Data as of December 31, 2013.


Source: ETF.com. All returns are in US dollars. 3- and 5-year returns are annualized.

Data as of December 31, 2013




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Source: Morningstar. Data as of Feb. 29, 2012.

Morningstar U.S. Style Overview Jan. 1-Dec. 31, 2013

Trailing Returns %

3-Month YTD 1-Yr 3-Yr 5-Yr 10-YrMorningstar Indexes

US Market 14.05 33.13 35.06 16.22 19.08 8.09

Large Cap 13.93 31.81 33.69 16.18 17.58 7.26

Mid Cap 14.09 36.29 38.27 16.39 22.95 10.23

Small Cap 15.18 37.91 40.23 15.80 23.35 9.95

US Value 13.00 32.04 33.92 14.98 16.32 7.50

US Core 12.94 34.21 35.83 17.44 19.33 9.10

US Growth 16.14 33.34 35.62 16.22 21.72 7.40

Large Value 12.18 28.92 30.74 14.14 14.02 6.40

Large Core 12.24 34.49 35.94 17.84 17.84 8.64

Large Growth 17.25 32.46 34.78 16.56 21.15 6.35

Mid Value 14.91 42.18 44.16 17.57 22.29 10.28

Mid Core 14.58 32.79 34.72 16.85 23.50 10.16

Mid Growth 12.82 34.07 36.08 14.68 22.95 10.00

Small Value 15.54 35.71 37.85 16.12 23.59 10.44

Small Core 15.41 36.31 38.53 14.55 22.86 9.85

Small Growth 14.68 41.86 44.49 16.80 23.56 9.33

Morningstar Market Barometer YTD Return %

US Market33.13

32.04

Value

34.21

Core

33.34

Growth

31.81Larg

e C

ap

36.29Mid

Cap

37.91Sm

all C

ap

28.92 34.49 32.46

42.18 32.79 34.07

35.71 36.31 41.86

–8.00 –4.00 0.00 +4.00 +8.00

Sector Index YTD Return %

Consumer Cyclical 42.85

Financial Services 42.82

Healthcare 42.68

Industrials 42.03

Technology 28.45

Consumer 27.95

Energy 27.34

Communication 26.28

Basic Materials 20.38

Utilities 14.44

Real Estate 1.75

Industry Leaders & Laggards YTD Return %

Semiconductor Memory 243.06

Solar 148.31

Computer Systems 98.64

Airlines 92.01

Broadcasting - TV 79.15

Biotechnology 78.78

–3.21 Industrial Metals & Minerals

–6.49 REIT - Healthcare Facilities

–8.05 REIT - Residential

–11.24 Coal

–47.33 Gold

–51.88 Silver

Biggest Influence on Style Index Performance

YTDReturn %

ConstituentWeight %

Best Performing Index

Mid Value 42.18

Best Buy Co Inc 244.20 0.34

Lincoln National Corp (Radnor, PA) 102.16 0.71

Xerox Corporation 82.65 0.82

Mylan Inc 58.11 1.12

Principal Financial Group 77.19 0.81

Worst Performing Index

Large Value 28.92

General Electric Co 37.89 5.58

Exxon Mobil Corporation 20.14 9.98

JPMorgan Chase & Co 36.71 4.36

Wells Fargo & Co 36.71 4.32

Pfizer Inc 26.22 4.27

1-Year

30.74

Value

Larg

e C

ap

35.94

Core

34.78

Growth

44.16

Mid

Cap 34.72 36.08

37.85

Sm

all C

ap

38.53 44.49

–20 –10 0 +10 +20

3-Year

14.14

Value

Larg

e C

ap

17.84

Core

16.56

Growth

17.57

Mid

Cap 16.85 14.68

16.12

Sm

all C

ap

14.55 16.80

–20 –10 0 +10 +20

5-Year

14.02

Value

Larg

e C

ap

17.84

Core

21.15

Growth

22.29

Mid

Cap 23.50 22.95

23.59

Sm

all C

ap

22.86 23.56

–20 –10 0 +10 +20

Notes and Disclaimer: ©2014 Morningstar, Inc. All Rights Reserved. Unless otherwise noted, all data is as of most recent month end. Multi-year returns are annualized. NA: Not Available. Biggest Influence on Index Performance listsare calculated by multiplying stock returns for the period by their respective weights in the index as of the start of the period. Sector and Industry Indexes are based on Morningstar's proprietary sector classifications. The informationcontained herein is not warranted to be accurate, complete or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information.

Source: Morningstar. Data as of Dec. 31, 2013





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S&P Dow Jones Indices U.S. Industry Review Performance

Weight 1-Month 3-Month 1-Year 3-Year 5-Year 10-Year

S&P 500 100.0% 2.53% 10.51% 32.39% 16.18% 17.94% 7.41%

S&P 500 Consumer Discretionary 12.5% 2.34% 10.81% 43.08% 23.46% 27.69% 9.44%

S&P 500 Consumer Staples 9.8% 0.59% 8.66% 26.14% 16.78% 15.86% 9.95%

S&P 500 Energy 10.3% 3.12% 8.35% 25.07% 11.07% 13.44% 13.44%

S&P 500 Financials 16.2% 2.17% 10.33% 35.63% 13.16% 13.75% -0.26%

S&P 500 Health Care 13.0% 0.83% 10.13% 41.46% 23.42% 18.29% 8.35%

S&P 500 Industrials 10.9% 4.25% 13.53% 40.68% 17.28% 19.84% 8.58%

S&P 500 Technology 18.6% 4.15% 13.26% 28.43% 14.73% 21.90% 7.17%

S&P 500 Materials 3.5% 4.82% 10.66% 25.60% 9.23% 18.80% 8.25%

S&P 500 Telecom Services 2.3% -0.28% 5.47% 11.47% 11.91% 12.67% 8.14%

S&P 500 Utilities 2.9% 0.92% 2.79% 13.21% 11.20% 10.17% 9.23%

Risk-Return

Sector Weights

Asset Class Performance

Data as of December 31, 2013. Source: S&P Dow Jones Indices. Past performance of an index is not a guarantee of future results.

Consumer Discretionary

Consumer Staples

Energy

Financials

Health Care

Industrials

Technology

Materials

Telecom ServicesUtilities

S&P 500

0%

5%

10%

15%

20%

25%

5% 10% 15% 20% 25%

3-Y

ea

r A

nn

ua

lize

d R

etu

rn

3-Year Annualized Risk

60

80

100

120

140

160

180

Dec-10 Mar-11 Jun-11 Sep-11 Dec-11 Mar-12 Jun-12 Sep-12 Dec-12 Mar-13 Jun-13 Sep-13 Dec-13

S&P 500

S&P BMI Global ex-U.S.

Dow Jones Brookfield Global Infrastructure Index

Dow Jones-UBS Commodity Index

Dow Jones U.S. Select REIT Index

12.5%

9.8%

10.3%

16.2%

13.0%

10.9%

18.6%

3.5%

2.3%

2.9%

12.0%

9.3%

8.3%

25.5%

7.6%

12.6%

7.6%

8.8%

5.1%

3.2%

Consumer Discretionary

Consumer Staples

Energy

Financials

Health Care

Industrials

Information Technology

Materials

Telecommunication Services

Utilities S&P 500 S&P BMI Global Ex-U.S.





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Largest New ETFs Sorted By Total Net Assets In $US Millions Selected ETFs In Registration

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

Covers ETFs and ETNs launched during the 12-month period ended December 31, 2013.

Total Return % Annualized Return %

Fund Name Ticker ER 1-Mo 3-Mo Launch AUM

Barclays ETN+ FI Enh Global HiYld ETN FIGY 0.80 3.00 11.49 05.22.13 1,393.7

Barclays ETN+ FI Enh Europe 50 ETN FEEU 1.00 3.37 13.28 05.23.13 1,052.4

Vanguard Total International Bond BNDX 0.20 -0.60 0.34 06.04.13 807.9

SPDR Blackstone / GSO Senior Loan SRLN 0.90 0.36 1.33 04.03.13 586.4

Vident International Equity VIDI 0.75 -1.31 - 10.29.13 513.2

iShares MSCI USA Quality Factor QUAL 0.15 2.42 10.83 07.18.13 275.7

db X-trackers CSI 300 China A-Shares ASHR 1.08 -3.95 - 11.06.13 216.3

ALPS Barron's 400 BFOR 0.65 2.09 8.45 06.04.13 202.2

iShares MSCI USA Momentum Factor MTUM 0.15 2.98 10.64 04.16.13 194.4

Barclays ETN+ Select MLP ETN ATMP 0.95 2.45 5.00 03.13.13 180.3

Cambria Shareholder Yield SYLD 0.59 2.17 7.80 05.14.13 179.5

iSharesBond 2018 Corp ex-Financials IBCC 0.10 -0.57 0.57 04.17.13 170.6

RiverFront Strategic Income RIGS 0.22 0.48 - 10.09.13 150.6

First Trust Senior Loan FTSL 0.85 0.40 1.65 05.01.13 138.5

iShares MSCI USA Value Factor VLUE 0.15 2.58 9.86 04.16.13 127.7

iShares MSCI USA Size Factor SIZE 0.15 2.36 7.41 04.16.13 120.5

Vanguard EM Government Bond VWOB 0.35 0.32 0.83 06.04.13 106.1

AdvisorShares Multi-Sector Income MINC 0.75 -0.01 1.12 03.19.13 98.8

First Trust Tactical High Yield HYLS 1.19 0.94 4.07 02.27.13 92.5

Credit Suisse FI Enh Europe 50 ETN FIEU 0.80 0.04 0.14 09.06.13 88.6

Fund Name Ticker Exp Ratio AUM 1-Mo 3-Mo 2013 3-Yr 5-Yr 10-Yr P/E P/B Yield

SPDR S&P 500 SPY 0.09 174,445.3 2.52 9.59 32.21 16.05 17.83 7.31 18.94 2.62 -

iShares Core S&P 500 IVV 0.07 55,340.7 2.53 9.61 32.31 16.10 17.86 7.34 18.94 2.62 1.92

iShares MSCI EAFE EFA 0.34 52,616.5 1.48 5.16 22.62 8.07 12.27 6.77 41.86 1.67 1.80

Vanguard FTSE Emerging Markets VWO 0.18 46,212.7 -0.97 0.69 -5.00 -2.80 14.00 - 11.84 1.58 1.23

PowerShares QQQ QQQ 0.20 44,958.1 3.01 10.72 36.61 18.61 25.31 9.94 20.93 4.10 -

iShares MSCI Emerging Markets EEM 0.69 39,921.5 -1.37 0.90 -3.14 -2.67 12.92 10.49 10.64 1.53 2.15

Vanguard Total Stock Market VTI 0.05 38,812.5 2.64 9.09 33.51 16.24 18.86 8.11 20.35 2.62 1.78

SPDR Gold GLD 0.40 31,274.2 -4.15 -7.01 -28.09 -5.58 6.37 - - - -

iShares Russell 2000 IWM 0.24 27,670.3 1.97 7.37 38.85 15.69 20.07 9.06 86.70 2.29 2.66

iShares Russell 1000 Growth IWF 0.20 22,550.9 2.83 9.30 33.19 16.22 20.15 7.64 23.07 5.02 1.10

iShares Core S&P Mid-Cap IJH 0.15 22,540.0 3.08 6.72 33.40 15.51 21.73 10.23 27.29 2.44 2.18

iShares Russell 1000 Value IWD 0.21 20,661.9 2.50 9.05 32.18 15.81 16.46 7.43 17.30 1.77 0.92

Vanguard Dividend Appreciation VIG 0.10 19,370.3 1.96 8.09 28.99 15.20 15.91 - 19.02 3.35 1.23

Vanguard FTSE Developed Markets VEA 0.10 18,903.5 1.65 5.05 22.12 8.19 12.01 - 17.80 1.61 3.76

Vanguard Total Bond Market BND 0.10 17,845.7 -0.64 -0.15 -2.14 3.12 4.37 - - - -

Vanguard REIT VNQ 0.10 17,530.6 0.27 -2.20 2.42 9.39 16.89 - 69.26 2.05 -

Financial Select SPDR XLF 0.18 17,017.3 2.15 9.47 35.37 12.96 13.68 -0.38 16.65 1.32 -

SPDR S&P MidCap 400 MDY 0.25 15,413.7 3.06 6.65 33.08 15.32 21.51 10.03 27.29 2.44 11.61

iShares iBoxx $ Inv Grade Corp Bond LQD 0.15 15,396.0 -0.07 1.47 -2.49 5.85 7.73 5.25 - - 4.73

iShares Core Total US Bond Market AGG 0.08 15,196.6 -0.58 -0.06 -2.15 3.08 4.13 - - - -

iShares iBoxx $ HiYld Corporate Bond HYG 0.50 15,113.7 0.45 3.23 5.90 8.47 15.01 - - - 1.85

Vanguard S&P 500 VOO 0.05 14,785.7 2.52 9.62 32.33 16.15 - - 18.94 2.62 2.83

iShares Core S&P Small-Cap IJR 0.17 14,230.6 1.45 8.49 41.36 18.37 21.29 10.55 33.78 2.31 1.40

Vanguard Short-Term Bond BSV 0.10 13,920.6 -0.41 0.12 0.17 1.74 2.72 - - - -

iShares MSCI Japan EWJ 0.53 13,856.5 0.83 2.27 26.48 5.03 6.99 3.64 15.25 1.30 -

Source: IndexUniverse. Data as of December 31, 2013. Exp Ratio is expense ratio. 1-Mo is 1-month. 3-Mo is 3-month. 3-Yr, 5-Yr and 10-Yr are 3-year, 5-year and 10-year annualized returns, respectively.

P/E is price-to-earnings ratio. P/B is price-to-book ratio. Yield is 12-month.

Ark Genomics Revolution

CSOP Source FTSE China A50

db X-trackers China A-Shrs SmCap

Direxion Daily Chile Bull 3X

EGShares Blue Chip EM Achievers

Global X MSCI Saudi Arabia

Horizons KOSPI 200

iShares US Bond Mkt Yield Optimized

JPMorgan Global Equity

KraneShares BAML Ultra Short Bond

Market Vectors China A Quality

Market Vectors Redeemable Gold

PowerShares S&P 500 High Momentum

ProShares Global Direct Infrastructure

Reality Shares Isolated US Div Growth

RevenueShares Emerging Market

SPDR Barclays 1-10 Year TIPS

Stock Split

WisdomTree Japan Hdgd Financials

Workplace Equality

Source: ETF.com’s ETF WatchSource: IndexUniverse. Data as of December 31, 2013. ER is expense ratio. 1-Mo is 1-month. 3-Mo is 3-month.

Exchange-Traded Funds Corner


Source: ETF.com. Data as of December 31, 2013. Exp Ratio is expense ratio. 1-Mo is 1-month. 3-Mo is 3-month. 3-Yr, 5-Yr and 10-Yr are 3-year, 5-year and 10-year annualized returns, respectively.

P/E is price-to-earnings ratio. P/B is price-to-book ratio. Yield is 12-month.

Source: ETF.com. Data as of December 31, 2013. ER is expense ratio. 1-Mo is 1-month. 3-Mo is 3-month.




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Moving from the efficient frontier to the effective frontier is achieved by genuine diversification. By “gen-uine,” I mean to imply that if one is willing to diversify, one needs to do so in a material way. Trivial alloca-tion (under 2 percent) to real estate or commodities, for example, does not represent genuine diversifica-tion. Rather, such minute allocations might be better described as “dabbling” in diversification. Also impor-tant in building diversified portfolios is implementing a protocol of systematic rebalancing. Rebalancing a multi-asset portfolio once per year typically produces better results than rebalancing monthly.

Building an investment portfolio that resides on the effective frontier is now easier than ever, thanks to a rich array of investable asset classes at our disposal in the form of mutual funds, index-based funds and exchange-traded funds.

As seen before in Figure 1, the effective frontier in Figure 2 is above and to the left of the efficient frontier.

If the investment objective was to produce a portfolio that produced (more or less) a 10 percent standard deviation of annual returns, the required asset mix on the efficient frontier would have been approximately 60 percent stock/40 percent cash. The annualized return of a 60 percent large U.S. stock/40 percent cash mix over this 44-year period was 8.72 percent with a standard deviation of 10.7 percent.

By comparison, the 44-year standard deviation of the fully deployed seven-asset model (highest red triangle) was 10.2 percent, but also produced an annualized return of 10.29 percent. Thus, at a standard deviation level of approxi-mately 10 percent, the “effective frontier” multi-asset port-folio produced a performance premium of more than 157 bps compared with an “efficient frontier” two-asset model.

Israelsen continued from page 35

Endnote1 Markowitz, H., 1991, “Portfolio Selection,” Blackwell Publishing

has led product issuers to launch them into the mar-ketplace, while avoiding those index-based approaches with subpar performance, thereby pushing VWO and EEM down a quintile. Therefore, it is possible that mean reversion will play a role at some point; in which case, those that recently outperformed will underperform; which may have the opposite effect of pushing VWO and EEM back into the third or even second quintile. Alternatively, while this study did not examine the underlying index construction methodologies of the various indexes, many do adapt over time; thus, it is pos-sible that continued outperformance may persist as size, sector and country rotations in these strategies allow these portfolios to remain on top. Notwithstanding the potential for time-period bias, the analysis in this paper demonstrates that any holder of EEM or VWO would have been considerably better off from a risk and return standpoint at the very least by simply combining their allocation naively with a number of available products over the time period studied, as shown in Figure 4.

time-period-biased, and that which occurred in the past may not necessarily be indicative of future performance.

Regarding time-period bias, it is worth considering that this study was entirely in-sample, and the outper-formance that persisted during this time period may not necessarily be indicative of persistence into the future, e.g., it may be the case that the resource-driven companies outperform the consumer-based ones going forward. This idea may help explain some of the find-ings. Given that VWO and EEM are market-cap industry benchmarks, it would be a reasonable hypothesis a prio-ri to assume that these two products would have fallen in the third quintile with an even distribution of risk/return metrics of other products around these benchmarks, as certain sector-, country- and size-biased factors deter-mine which side of the distribution they fall in. However, VWO and EEM fell squarely in the fourth quintile, sug-gesting perhaps that it is only recent outperformance of these other index-based products’ approaches that

Biegeleisen continued from page 25

References1 Gastineau, Gary (2002). “The Exchange-Traded Funds Manual.” John Wiley and Sons. p. 32. ISBN 978-0-471-21894-4

2 http://www.etf.com/etfanalytics/etf-finder as of 7/31/2013

3 As determined by Bloomberg field RK764: NAV_TRACKING_ERROR with parameter RK500: RISK_MEASURES_TIME_FRAME=2, meaning three months.

4 http://www.etf.com/etfanalytics/etf-finder as of 7/31/2013. Developed by IndexUniverse, it is a hierarchical, rules-based, nonoverlapping system for classifying ETFs.

Overview and Description document for this classification system is available on its website.

5 Source: Bloomberg

6 Using the all combinations formula: n!/(k!(n-k)!) + n, this is the number of combinations of n things taken k at a time, while allowing for the same two to also be picked,

where n = 24, and k = 2.

7 As determined by Bloomberg field PY001: AVERAGE_BID_ASK_SPREAD_% with parameters PX393: CALC_INTERVAL=30d and PX392: END_DATE_OVERRIDE=20130731.

8 As determined by Bloomberg field FD102: FUND_AVG_MKT_CAP




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policy is helping you to buy low and sell high, look at what you’re buying and selling.

There are many arguments one could make affirma-tively or negatively on whether the stock market was overvalued at that time. I think very few would argue against the idea that with rates as low as they were, and continuing to be at historical lows, that rebalancing into this could effectively be likened to selling high and buy-ing even higher. So the board did make that change, and we’re pretty happy with it.

We went from 36 percent fixed-income allocation to a 32 percent fixed-income allocation in October 2013. It’s not dramatic in any sense, but it did save the cost of rebalancing.

I don’t have a magic number as to what rates need to be before fixed income is attractive; I don’t have a number on yield levels. But I think the yield on the 10-year was around 2.5 percent when we made the move. It’s hard to make your actual rate of return when a large part of your portfolio is really not going to be a major contributor to returns, in a sense. We still have a high allocation to fixed income for the diversification benefits, though.

JOI: You’d mentioned your last change was to add an active manager. Do you see the index allocation in the portfolio increasing or decreasing? Tillberg: I just want to make sure the portfolio is struc-tured properly, so I don’t have a view on whether we need to increase index exposure here or decrease it there. I want to be sure I can answer, What are our risk exposures currently in the portfolio? Look at those. Are we bal-anced? Or do we have some sort of an exposure that we need to either increase or decrease or balance?

I don’t look at it like passive versus active decision. I look at it in terms of exposures. Once you recognize where your exposures are, then you can start to answer the ques-tion, What is going to be most effective and efficient in this space? I think that makes sense.

JOI: You don’t have any exposure to alternative assets or hedge funds or things like that. Is there a reason OPERS avoids those asset classes? They’ve become more popu-lar with pension funds.Tillberg: Yes, much more popular. I think our board is rightly focused on the correct drivers of return, and fee levels are a big part of that. We have had those sorts of exposures in the distant past. We no longer have those. I think our board is very comfortable with that; our board is very sensitive to the fee levels we pay.

We don’t manage any of the assets internally, so we have advisors for every dollar here at the system. Our total investment management fees last year that we paid for fiscal 2013 were 11.8 basis points of average assets. That is one of the lowest fee levels in the nation, and we’re proud of that. I also don’t believe our performance has been disadvantaged by it, either.

Our performance lands solidly in the second quartile in relation to peers. And when I say “solid second quartile,”

I think we’re closer to first quartile than we are to third. That’s not just in the last couple of years; that includes our long-term numbers. We would put those up against anyone’s. The primary difference is that we probably cost a fraction of what many of our peers cost to run.

JOI: The fund came through the 2008 and 2009 market meltdown fairly well. Can you elaborate on that? What kept the fund in a good spot?Tillberg: I joined the system in late 2009, so everything that I’m telling you is secondhand. But I know that we rebal-anced during that period. And, of course, that was an exciting time—maybe that’s a slight understatement. The fact that we were able to rebalance our portfolio—liquidity is something I think sometimes is mis-valued. Having a portfolio with which you can quickly take action is necessary for rebalancing—it’s a benefit I think is sometimes also undervalued. For us, it cer-tainly did add value because we weren’t dramatically under-weight in equities just as the market started to rally.

JOI: There were a lot of liquidity issues during that time. How did OPERS stay liquid?Tillberg: Everything that we own is either publicly traded or is a fund where the underlying is publicly traded. It was a function of our structure, of our asset allocation. We weren’t suffering capital calls. All the things that were chal-lenges for other plans or other styles didn’t have an impact on us. Of course, we were subject to the same market vola-tility, but we were able to avoid the other things.

JOI: What are the challenges in the current investment environment? Tillberg: The biggest challenge for me—and I think for most of my peers—is generating and maintaining the actuarial assumed rate of return given the current level of yields. Fixed income still is a pretty big chunk of quite a few portfolios. Ours is probably more than a lot of our peers. We have a higher allocation to fixed income than a lot of our peers, which may be more problematic for us. But it’s generating that actuarial assumed return, because if you maintain a diversified portfolio, you’re going to be exposed to asset classes that are not going to contribute on a forward-looking basis the way they have in the past.

JOI: Can you use your active managers in the equity space to achieve more yield? A lot of retail investors have been looking to things like MLPs, REITs and high-dividend-yielding stocks. Is that a place your active managers might go?Tillberg: No. It’s not something we’ve targeted. It’s still about appreciating that your fixed-income allocation is there to dampen the volatility of your stock allocation, which is the primary driver of your total nominal returns. Now I know there are a lot of different mousetraps that are sort of coming through from a product or model perspective, but we’re comfortable where we are. While fixed income is not going to be the contributor that it has been in the past from a return perspective, it’s still a volatility dampener.

Tillberg continued from page 37





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the Dow Jones-UBS Commodity Index, the Deutsch Bank Liquid Optimal Yield Index, the Rogers Commodity Index, the Reuters Continuous Commodity Index and the SummerHaven Dynamic Commodity Index, which are collectively the best-known and most widely followed commodity indexes. The results of the six diversified com-modity indexes are marked as circles. The range of results looking at the risk measure are notably wider than for stocks or bonds. However, the real difference is in the range in average annual total returns, which are extremely wide.

Of course, Figure 3’s use of 10-year averages may not give a complete view of the dispersion of results over time. Figure 4 shows, for each year and each asset class, the dif-ference between the best-performing stock, bond or com-modity index that year, and the worst-performing stock, bond and commodity index—the spread between the top and the bottom annually, as it were.

The difference from the top to the bottom for the stock indexes is actually quite small; only 1.6 percent each year on average. Even the biggest year’s difference, in 2009, is not very large. The same is true for bonds, where the aver-age annual difference is only 3.3 percent.

However, the average annual difference among commod-ity indexes is actually several times greater than the biggest difference among either stocks or bonds. Even the smallest spread in annual commodity performance—2013, with a spread of 8.9 percent—was greater than the largest annual spread for the stock indexes and for the bond indexes.

As mentioned, we also found in the data that with the stock and bond indexes, over time, an index that is the top performer some years will also be the bottom per-former other years. Winners and losers move around. However, that does not appear to be true with commod-ity indexes. Two of the listed commodity indexes, the S&P GSCI and the Dow Jones-UBS Commodity Index, accounted for eight of the 10 annual worst performanc-

es. Three other indexes—the SummerHaven Dynamic Commodity Index, the Rogers Commodity Index and the Deutsch Bank Optimal Yield Index—accounted for seven of the 10 best performances and none of the worst annual performances. Winners and losers do not appear to rotate as much with commodity indexes as they do with stocks and bonds.

A discussion of why commodity indexes have such disparate results is a lengthy topic of its own. Commodity index history and development has been covered in other places as well, notably in “A Brief History Of Commodities Indexes” (Journal of Indexes, May/June 2010). As a gen-eral rule, however, the biggest dividing line in long-term performance appears to be between those indexes that always include in their holdings only the front-month or second-month futures contract for each commodity ver-sus those indexes that select the contract months based on the then-current degree of backwardation or contango present in each commodity’s futures market. An addition-al point of differentiation is between those indexes that have concentrated positions in a subset of the commodity universe, typically energy commodities, versus those that are more broadly diversified.

What is the practical outcome of these results for an investment professional? I do not think it means an inves-tor or advisor must now add hours of additional research to his already-busy schedule. Rather, I would suggest it means the advisor will need to flip around how he spends his time evaluating commodity investments compared with how he views stocks and bonds.

An advisor currently might spend 20 percent of her time looking under the hood at the various stock and bond indexes she uses in her practice, and 80 percent of her time looking at the various costs and risks of imple-menting her investment decisions. Such issues include deciding between passive or active portfolios and between ETFs, ETNs or mutual funds; investigating counterparty risks; reviewing bid/ask spreads; and considering high-cost choices versus low-cost choices.

Fair enough. The evidence suggests that is a reasonable allocation of effort for stocks and bonds given how little differentiation there is in index results. What the evidence above also suggests is that in the particular case of com-modity investing, advisors need to flip their priorities. They should really be spending 80 percent of their time deciding on the right commodity index and the remaining time deciding between ways to implement their choices. If commodity ETF “A” is tracking index “X,” while ETF “B” is tracking index “Y,” historical results seem to suggest that an advisor needs to focus on the difference between index X and index Y much more than trying to figure out which ETF costs 10 basis points more or less a year in management fees, or which trades 2 cents wide versus which trades 4 cents wide.

Even if you elect to go for an active approach instead of a passive approach, it is crucial that you measure your active manager’s result versus the appropriate bench-mark. Paying “1 percent and 10 percent” (or “2 percent

Range Between Best-Performing IndexAnd Worst-Performing Index

Year U.S. Bonds U.S. Stocks Commodities

Figure 4

2004 1.1% 2.4% 29.1%

2005 0.7% 1.9% 19.4%

2006 1.6% 0.6% 57.8%

2007 2.0% 0.9% 19.9%

2008 7.9% 1.6% 23.9%

2009 8.7% 3.2% 16.9%

2010 0.2% 2.6% 14.7%

2011 1.7% 1.1% 12.1%

2012 2.2% 0.4% 9.2%

2013 6.4% 1.0% 8.9%

Average Difference

3.3% 1.6% 21.2%

Sources: Bloomberg and USCF


Hyland continued from page 39

percent and 20 percent”) for a manager to beat some benchmark takes on a whole new meaning when the difference between two passive indexes can be 500 or 1,000 basis points in a year. If an active equity manager tells you he has beaten the S&P 500 by 300 basis points a year for 10 years, you can assume his results versus other U.S. large-cap indexes will be similarly impres-sive. However, an active commodity manager who has beaten her benchmark by 300 basis points a year may or may not actually have achieved a noteworthy outcome for her investors, depending on which benchmark she is comparing herself to over the 10 years.

The actual impact may even go much deeper than what has already been suggested. When deciding if a new asset class should be added to a traditional opti-mized portfolio, many investment professionals will plug in historical returns, volatility and correlations with other asset classes to determine what weighting should be allocated to the new asset. The historical range in average annual total returns demonstrated in the data above is so extreme that it would be possible for one investment professional to conclude that a zero weight-

ing was desirable, if he happened to measure the asset class results by the index with the worst average total-return results or highest volatility, while another invest-ment professional could run the exact same analysis using a different commodity index and come to a com-pletely opposite conclusion. Such an outcome would be very difficult to imagine when using different stock or bond index results. This simply reinforces the need for an investor or investment professional to take the time to understand the differences in commodity indexes.

There is no magic bullet when it comes to investors or advisors looking at their commodity investment choices. The fact that some indexes have done better over the last year, three years or even 10 years should not be taken as assurance that they will always be a top performer on a relative basis. Rather, this suggests that the investor or advisor needs to learn about why some indexes have done better in the past, and why the historical record suggests that reversion to the mean is not as apparent in the commodity area as with stocks and bonds. However, the historical record also suggests that such additional research will be time well spent.


Endnotes

1 The three bond indexes are the Barclays US Aggregate Bond Index, the JP Morgan Aggregate Bond Index and the FTSE US Government Bond Index.

2 The three equity indexes are the S&P 500, the Russell 1000 and the MSCI US Large Cap.

/VWO





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Crossword


Where Have All The Yields Gone?

ACROSS

1. Income-producing investments (2 words)

9. Rob, of fundamental-based index investing

10. Investment method or approach

11. Raw metals12. Lab maze critter14. Increase one’s debt

levels (2 words)18. The allure of many

income-based ETFs (2 words)

22. What yield seekers often seek (2 words)

25. The “opposite” of passive

27. To be in debt to29. Risk of ___ (what VaR

measures)30. Like some risky

investments31. Strategy using

intermediate-duration bonds

32. Investments for yield seekers (hyphenated)

Good luck in

your search!

By Bruce Greig

DOWN

2. Baseball mistakes 3. Birds found on

Canadian coins 4. George Harrison

instrument, at times 5. Money in the bank, say 6. Fuming mad 7. Like some leveraged ETFs 8. Symbol for the largest

(for now) ETF13. Date married folks

celebrate (abbr.)15. Great Barrier, and others16. Academic types often

hired by quant shops17. Neighbor of Libya

18. 30-down plus one19. Provide funding for

a foundation20. Co-founder of Yahoo,

Jerry21. 1973 World Series

stadium23. Detail, as on a tax return24. Employ (2 words)26. Setting for “Napoleon

Dynamite”27. __ the other (either)

(2 words)28. Include within, as some

bonds29. Archaeological find30. III + IV

Solution

Across: 1. Real estate funds; 9. Arnott; 10. Strategy; 11. Ores; 12. Rat; 14. Gear up; 18. Enhanced yield;

22. High dividends; 25. Active; 27. Owe; 29. Ruin; 30. Volatile; 31. Bullet; 32. Income producing

Down: 2. Errors; 3. Loons; 4. Sitar; 5. Asset; 6. Enraged; 7. Ultra; 8. SPY; 13. Anniv; 15. Reefs; 16. PhDs; 17. Chad;

18. Eight; 19. Endow; 20. Yang; 21. Shea; 24. Hire on; 26. Idaho; 27. One or; 28. Embed; 29. Relic; 30. VII

1 2 3 4 5 6 7 8

9 10

11 12 13 14 15 16

17

18 19 20

21

22 23

24

25 26 27 28 29

30 31

32




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