new perspectives March / April 2013 - ETF · PDF filenew perspectives March / April 2013 ... Vol. 16 No. 2 March / April 2013 1 52 46 42 ... Blitzer previously served as chief economist

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SERIOUS IDEAS FOR SERIOUS INVESTORS

Global Value And 10-Year CAPE

Mebane Faber and Prabhat Dalmia

Investor Requirements For Indexes: A Survey

Felix Goltz, Véronique Le Sourd and Masayoshi Mukai

How Smart Is ‘Smart Beta’?

David Blitz

Are Active Mutual Funds Becoming Less Active?

David Blanchett

Plus an interview with Robert Maynard of PERSI, S&P DJI’s Blitzer on ETF closures,

Israelsen on bonds and diversification, Slivka et al. on covered-call ETFs, JOI’s Bell and more!

new perspectives March / April 2013

Mon Feb 4 10:23:52 EST 2013Trim Size = 8.125 X 10.875

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INDEX MAR-APR INDEX_I.pdf

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f e a t u r e s

V o l . 1 6 N o . 2

1March / April 2013

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Global Index Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58Index Funds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Morningstar U.S. Style Overview . . . . . . . . . . . . . . . . . . . . . 60Dow Jones U.S. Industry Review . . . . . . . . . . . . . . . . . . . . . . 61Exchange-Traded Funds Corner . . . . . . . . . . . . . . . . . . . . . 62

SPY Turns 20. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Housing Recovery Continues In November . . . . . . . . . . . 10Nasdaq OMX Rolls Out Global Index Family . . . . . . . . . . 10FINRA Targets ETFs In Letter. . . . . . . . . . . . . . . . . . . . . . . . 10ISE To Acquire NYSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11IndexUniverse Debuts Currency ETFs. . . . . . . . . . . . . . . . 11Indexing Developments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Around The World Of ETFs. . . . . . . . . . . . . . . . . . . . . . . . . . 13Know Your Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Back To the Futures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15On The Move . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Global ValueBy Mebane Faber and Prabhat Dalmia . . . . . . . . . . . . . . . 16Looking at Shiller’s CAPE metric from a global perspective.

PERSI’s Maynard Favors ‘Traditional’ ApproachesBy Journal of Indexes Staff . . . . . . . . . . . . . . . . . . . . . . . . . . 22How PERSI avoids typical pension-plan headaches.

Requirements For Standard And New Forms Of IndexesBy Felix Goltz, Véronique Le Sourd and Masayoshi Mukai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26What do investors really want from their benchmarks?

How Smart Is ‘Smart Beta’?By David Blitz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Pointing out the concerns around a popular trend.

Survival Of The FittestBy David Blitzer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40Are strategy indexes driving the increase in ETF closures?

Are Active Mutual Funds Becoming Less Active?By David Blanchett . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Active funds are beginning to look more like index funds.

Taking A Long View Of Bond PerformanceBy Craig Israelsen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Don’t let recent events disrupt your diversification.

Covered-Call ETFs For BRIC CountriesBy Ronald Slivka, Sharad Bhat and Sridhar Nonabur Srinivasamurthy . . . . . . . . . . . . . . . . . . . 52A suggested blueprint for constructing a product.

How I Learned To Stop Worrying …By Heather Bell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64Things are looking up. Now what?


RIGHT HAND PAGE-------->

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Contributors

2 March / April 2013

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ttDavid Blanchett, CFA, is head of retirement research for Morningstar Investment Management, where he provides research support for the group’s consulting and investment management activities. Blanchett holds a bachelor’s degree in finance and economics from the University of Kentucky, a master’s degree in financial services from the American College, and an MBA from the University of Chicago Booth School of Business.

David Blitz is senior vice president and co-head of Quant Research at Robeco, where he is responsible for coordinating all quantitative equity research efforts. He joined Robeco in 1995 after graduating cum laude in econometrics at Erasmus University in Rotterdam. In 2011, Blitz obtained a Ph.D. in empirical finance from the same university. His research has been published in multiple peer-reviewed academic journals.

David Blitzer is managing director and chairman of S&P Dow Jones Indices’ index committee. He has overall responsibility for security selec-tion 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.

Mebane Faber, CAIA, CMT, is co-founder and chief investment officer of Cambria Investment Management. He is manager of Cambria’s Global Tactical ETF (GTAA), separate accounts and private investment funds for accredited investors. Faber is also author of the World Beta blog, and co-author of “The Ivy Portfolio: How to Invest Like the Top Endowments and Avoid Bear Markets.” He graduated from the University of Virginia with a double major in engineering science and biology.

Felix Goltz is head of applied research at EDHEC-Risk Institute. He does research in empirical finance and asset allocation, with a focus on alter-native investments and indexing strategies. Goltz’s work has appeared in various international academic and practitioner journals and handbooks. He obtained his Ph.D. in finance from the University of Nice Sophia Antipolis after studying economics and business administration at the University of Bayreuth and EDHEC Business School.

Craig Israelsen is an associate professor at Brigham Young University. He writes monthly for Financial Planning magazine. Israelsen is a principal at Target Date Analytics and the designer of the 7Twelve Portfolio. He is also the author of “7Twelve: A Diversified Investment Portfolio with a Plan” (John Wiley & Sons), published in 2010. Israelsen holds a Ph.D. in family resource management from Brigham Young University.

Ronald Slivka is an adjunct professor at the Polytechnic Institute of New York University and a faculty member of the New York Institute of Finance. During his more than 35 years of practical Wall Street experience, Slivka held equity derivative sales and management positions at Salomon Brothers, J.P. Morgan and ABN AMRO. He has written over 35 articles and book chapters on a broad range of derivative topics and holds a Ph.D. in physics from the University of Pennsylvania.

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LET’S FIND OUT.

You can never have too many shoes.

How do you find the right fit when it’s your portfolio?

Copyright © 2012 by S&P Dow Jones Indices LLC, a subsidiary of The McGraw-Hill Companies, Inc., and/or its affiliates. All rights reserved.

S&P Dow Jones Indices LLC is a subsidiary of The McGraw-Hill Companies, Inc. Standard & Poor’s, S&P and S&P 500 are registered trademarks of Standard & Poor’s Financial Services LLC, a subsidiary of The McGraw-Hill Companies, Inc. Dow Jones is a registered trademark and Dow Jones Industrial Average is a 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.

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Editor’s Note

Jim Wiandt

Editor

Sometimes life is just blind luck. At no point in time did we solicit articles for an issue titled “New Perspectives.” We had an entirely different topic planned for March/April, but when we peeked in our hopper, we saw that we had a bevy of

very good but disparate independently submitted articles that offer new perspectives on some established ideas. Not being ones to look a gift horse in the mouth, we took it and ran with it. It has made for some great reading.

Mebane Faber and Prabhat Dalmia of Cambria Investment Management kick off the issue with a discussion of the cyclically adjusted price-to-earnings, or CAPE, ratio that was developed by Robert Shiller. They examine the metric’s applicability across a range of foreign markets and how it can be used in building portfolios.

We then check in with Robert Maynard of the Public Employee Retirement System of Idaho for our regular institutional investor feature to discuss how he manages one of the country’s most successful public pension funds.

Felix Goltz, Véronique Le Sourd and Masayoshi Mukai of the EDHEC-Risk Institute follow up with a discussion of a survey of American investment profes-sionals and their main concerns and requirements with regard to the benchmarks they use, including their views on alternatively weighted indexes. On the same general theme, David Blitz of Robeco then weighs in with a commentary on what he sees as the problems with “smart beta” indexes.

Next up, S&P Dow Jones Indices’ David Blitzer offers a unique angle on the wave of ETF closures that took place in 2012. David Blanchett of Morningstar steps in after that to provide evidence that actively managed mutual funds have become increasingly less active in recent years, raising the question of whether the trend is because of the rise of index funds.

Brigham Young University Professor Craig Israelsen follows with a reality check for investors who might be thinking about jettisoning (or significantly reducing) their fixed-income allocation. And Ronald Slivka, Sharad Bhat and Sridhar Nonabur Srinivasamurthy offer a blueprint for how one might go about constructing a covered-call ETF for an emerging market.

Finally, always-quick-on-the-uptake JOI Managing Editor Heather Bell puts the issue to bed with a meditation on her shocking realization that the sky hasn’t fallen.

We hope you find the issue as useful as we have and that your 2013 is off to a good start.

Taking A Fresh Look

Jim Wiandt

Editor

March / April 20138




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News

March / April 201310

SPY Turns 20“SPY,” the very first U.S.-listed

exchange-traded fund and the big-gest ETF in the world, turned 20 in January—a $125 billion portfolio that’s now in the company of some of the biggest U.S. mutual funds, mak-ing it the perfect symbol for an indus-try that’s on a roll.

Officially known as the SPDR S&P 500 ETF (NYSE Arca: SPY), SPY was seeded on Jan. 22, 1993, and began trading on Jan. 29. It was dreamed up by the late Nate Most as a vehicle for traders that he hoped would help pump up volume at the American Stock Exchange. Most and his entou-rage thought they’d be lucky if SPY hauled in $1 billion.

SPY did indeed end up appealing to traders, and a whole lot more. The biggest ETF in the world now sits atop a universe of more than 1,400 ETFs that’s increasingly poaching market share from actively managed mutual funds. It represents about 9 percent of the record $1.4 trillion now invested in ETFs.

Housing Recovery Continues In November

U.S. home prices were again stron-ger, year-on-year, in November, with the recovery in housing moving for-ward, even as the seasonal downdraft in prices associated with the colder months of the year was also in evidence.

Indeed, the latest S&P/Case-Shiller Home Prices Indices report showed that while home values around the country were 5.5 percent higher in November than they were in the same prior-year period, on a month-to-month basis, several cities saw home prices decline in November.

The 10-City and 20-City compos-ites dropped 0.2 percent and 0.1 per-cent, respectively, in November from October levels, with 10 out of the 20 cities surveyed seeing prices decline

month-on-month. That’s a depar-ture from the market’s performance last summer when all cities surveyed were posting higher and higher home prices on a monthly basis for several consecutive months.

But that slowing momentum isn’t necessarily surprising given that, from a seasonal perspective, the fall and winter months tend to be the housing market’s weakest periods.

Boston, Chicago and New York have been some of the worst-faring cities in recent months. Each has seen more than six months of declining prices in the past 12 months.

In November, Chicago was the worst-performing city, with home prices there declining 1.3 per-cent from month-earlier levels. In New York, home values remained 1.2 percent lower year-on-year in November, while in Boston, homes have appreciated 2.3 percent in the 12 months ended in November.

On the flip side, cities like Phoenix, San Francisco, Detroit and Las Vegas have all managed to tally double-digit gains in home values in the 12-month period.

All in all, an average home in the U.S. in November cost roughly what it did in the fall of 2003, and remains about 30 percent off its highest price level seen when the market peaked in 2006.

Nasdaq OMX Rolls Out Global Index Family

Nasdaq OMX continued its push into the world of indexing in early December with the rollout of the first piece of a comprehensive global fami-ly of equity indexes that will ultimately canvass 98 percent of the investable universe and cover 9,000 securities with a combined float-adjusted mar-ket capitalization of $32 trillion.

The initial rollout covered about 4,000 indexes calculated in dollars.

The remaining indexes were to be organized around different currencies and introduced in different phases at unspecified future dates, Nasdaq said in a press release.

The index family will ultimately result in the development of 24,000 separate indexes, Nasdaq said. The new index family covers 45 countries classified as developed and emerging markets across the following regions: the Americas, Europe, Asia-Pacific and Middle East-Africa.

Like all Nasdaq OMX indexes, the Nasdaq Global Index Family is based on a transparent, rules-based index methodology.

More than 10 years of historical backtested data are available for the entire global family, and price, total return and net total return versions are calculated for each index, the company said.

FINRA Targets ETFs In LetterThe Financial Industry Regulatory

Authority singled out exchange-traded products as one area it will focus on in 2013 as it seeks to manage risks to investors, though sundry concerns related to “significant downside risks” in the bond market easily topped FINRA’s list of 2013 regulatory worries.

Borrowing a page from an inquiry it launched in 2009 concerning leveraged funds, FINRA again singled out such strategies in a letter dated Jan. 11 out-lining its priorities for the coming year. Crucially, a class action suit charging that ProShares didn’t properly warn investors about the risks of its lever-aged and inverse funds was dismissed by a federal court last summer.

More broadly, FINRA said in the let-ter that it worries investors may not grasp crucial differences among the various ETP structures, and highlights leveraged products and new investment areas—such as volatility, emerging mar-




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www.journalofindexes.com March / April 2013 11

kets and currencies—as particular areas of concern. Such concerns from the financial industry’s self-regulator are shared in the industry, which is grow-ing rapidly. ETF and ETN inflows hit a record $188 billion in 2012, according to data compiled by IndexUniverse.

Concern about ETPs notwithstand-ing, FINRA’s chief concerns center on the bond market, which has been ral-lying sharply on and off since the mar-ket crashed in 2008. FINRA is hardly alone in identifying the dangers of the bond market unraveling, but is spe-cifically focused on limiting the pos-sibility of irresponsible marketing of fixed-income products at a potentially crucial time in financial markets.

ICE To Acquire NYSEThe New York Stock Exchange

entered into an agreement to be acquired by the Atlanta-based IntercontinentalExchange in a cash and stock transaction that values the NYSE at $8.2 billion, the companies said in a Dec. 20 press release.

The transaction valued NYSE Euronext at $33 a share—a 37.7 pre-mium over NYSE Euronext’s (NYSE: NYX) closing share price on Dec. 19, 2012. The transaction, which is expected to close in the second half of 2013, will leave NYSE Euronext share-holders with an ownership stake in the new company of about 36 percent.

The companies estimated about $450 million in “run rate expense syn-ergies,” with the bulk of those coming in the second full year following the closing. They also estimated earnings “accretion” of more than 15 percent in the first year after closing.

It’s not the first time ICE has made a bid for NYSE. It tried unsuccessfully to acquire a piece of NYSE Euronext in a joint bid with the Nasdaq exchange in the spring of 2011. But the initiative fell apart a bit more than a month later

after U.S. regulators, citing antitrust concerns, signaled they would reject the proposed transaction.

The companies noted that the 2013 second-half closing is subject to approvals by regulators in Europe and the United States and by shareholders.

IndexUniverse DebutsCurrency ETFs

IndexUniverse LLC and Structured Solutions AG announced the full com-mercial launch of a new family of cur-rency indexes in late January.

Jointly developed by IndexUniverse and Structured Solutions, the index-es are intended to fill a substantial gap in the modern indexing space by measuring the performance of the U.S. dollar against broadly diversified trade-weighted baskets of currencies, according to IndexUniverse.

The benchmarks are available through Bloomberg under the follow-ing symbols and include “investable” indexes:• IndexUniverse–Solactive U.S.

Dollar TW Index (Long USD) (IUSLATL Index)

• IndexUniverse−Solactive U.S.

Dollar TW Index (Short USD) (IUSLATW Index)

• IndexUniverse–Solactive Developed

Markets Currencies TW Index (IUSLADTW Index)

• IndexUniverse–Solactive Emerging

Markets Currencies TW Index (IUSLAETW Index)

• IndexUniverse–Solactive Asia-

Pacific Currencies TW Index (IUSLAATW Index)

• IndexUniverse−Solactive U.S. Dollar

TW Investable Index (Short USD) (IUSLTWI Index)

IndexUniverse LLC and Structured Solutions AG announced the launch of a new family of currency indexes in late January.




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• IndexUniverse–Solactive Developed Markets Currencies TW Investable Index (IUSLDTWI Index)

• IndexUniverse–Solactive Emerging Markets Currencies TW Investable Index (IUSLETWI Index)

• IndexUniverse–Solactive Asia-Pacific Currencies TW Investable Index (IUSLATWI Index)

Index weights are set each year based on Federal Reserve reported data on the level of trade between the U.S. and foreign countries. The index also takes into account the overnight lending rate of each currency, giving investors a full picture of the exposure they get to the currency markets. This process cre-ates a dynamic index series that reflects the purchasing power of the U.S. dol-lar measured against the currencies of trading partners of the United States.

The flagship IU−Solactive U.S. Dollar TW Index holds a trade-weight-ed basket of 26 currencies, led by the Chinese renminbi, which represents 20.37 percent of the index. The U.S. Dollar Index, by comparison, holds six currencies, led by the euro at a 57.60 percent weight. The renminbi is not included in the U.S. Dollar Index.

INDEXING DEVELOPMENTSMSCI Adds To Risk Premia Family

Global index provider MSCI expand-ed its risk premia index family in December with the launch of five new quality indexes. The benchmarks look to capture the performance of equities with quality growth characteristics.

They include the MSCI ACWI Quality Index, the MSCI World Quality Index, the MSCI Emerging Markets Quality Index, the MSCI Europe Quality Index and the MSCI USA Quality Index. The indexes capture two underlying risk premia, growth and low leverage, MSCI said in a press release.

The MSCI Quality Index meth-odology selects stocks based on high return on equity, stable year-over-year earnings growth and low financial leverage. The indexes are designed to underlie investable prod-ucts and to be used as benchmarks, the press release said.

Russell Adds IPOs To Global IndexRussell said in a December press

release that it was adding 48 initial public offerings to the broad Russell Global Index as of Dec. 21. Half of

those IPOs were added from the U.S., and half from outside the U.S.

The U.S. companies included three large-cap stocks, 19 small-cap stocks and two micro-cap stocks, the press release said. Four of the added non-U.S. companies are domiciled in the U.K., with three from Singapore. In all, 10 of the non-U.S. stocks being added non-U.S. to the global index are from Asia, and 10 are from Europe.

CBOE Rolls Out Low-Volatility Index

The Chicago Board Options Ex -change launched the CBOE Low Vol-atility Index (LOVOL) at the end of November, according to a press release.

The new index basically combines equity exposure to the S&P 500 Index with an options overlay strategy that sells calls on S&P 500 options and buys one-month VIX 30-delta calls, the press release said. The exchange described the CBOE LOVOL as a com-bination of the strategies represented by the CBOE S&P BuyWrite Index and the CBOE VIX Tail Hedge Index.

The index strategy’s objective is to reduce the downside volatility of an S&P 500 portfolio without detracting signifi-cantly from the upside performance of the equities, the press release said.

Facebook Joins Nasdaq-100Facebook joined the Nasdaq-100

Index in mid-December, meaning it now will be included in the portfolio of the $30 billion PowerShares QQQ Trust (NasdaqGM: QQQ), as well as the port-folios of other ETFs tied to the index.

Indeed, QQQ, which replicates the Nasdaq benchmark, had already bought more than 11.14 million shares of the social media company, representing a little over 1 percent of the fund’s port-folio, by the time the stock was added to the index, according to PowerShares.

Facebook, which went public in May 2012, replaced Infosys, after the India-based consulting and technol-ogy firm transferred its listing to the New York Stock Exchange.

Facebook also joined the Nasdaq-100 Equal Weighted Index


News

The Chicago Board Options Exchange launched the CBOE Low Volatility Index at the end of November.




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and the Nasdaq-100 Technology Sector Index, Nasdaq said.

Many had hoped that after the Nasdaq OMX Group changed its “seasoning rules” in April, Facebook would be entering Nasdaq’s flagship index by September 2012. Instead, in September, Facebook was first added to the Nasdaq Q-50 Index—the feeder index for the Nasdaq-100.

S&P DJI Teams With Townsend On REIT Index

S&P Dow Jones Indices rolled out the Dow Jones Townsend Core U.S. REIT Index in December, according to a press release. The index was developed with input from The Townsend Group, a real estate investment consulting firm serv-ing institutional investors.

The REIT portfolio of the index is designed to mimic the return/risk profile of a privately held investment in real estate. The components must be focused on longer-term leases and are drawn from specific REIT catego-ries. REITs classified as factory outlets, hotels, manufactured homes, mixed industrial/office and suburban office are excluded from the index, accord-ing to the index’s fact sheet. Individual constituent weights are capped in order to improve diversification.

The new index is part of the Dow Jones Real Estate index family, the press release said.

FTSE Unveils ‘Super Liquid’ Family

In late November, FTSE Group said in a press release that it had launched a family of highly liquid, narrow-based indexes that have similar risk/return characteristics and sector breakdowns to their broader parent indexes.

The FTSE Super Liquid Index Series debuted with 11 initial indexes covering different development lev-els, regions and individual countries. Components are selected from the parent index primarily for liquidity with the intention of creating a highly tradable portfolio that can be easily and cheaply replicated.

The FTSE Developed Large Cap

Super Liquid Index, for example, exhibits a 12-month correlation of 0.995 with its parent index, the FTSE Developed Large Cap Index, accord-ing to a fact sheet. However, as of Dec. 31, 2012, it only had 235 constituents versus 843 in the broader index.

Russell Launches ‘High Efficiency’ Indexes

Russell has rolled out a family of indexes based on its existing “Defensive” index methodology, the index provider said in a late January press release. The Russell High Efficiency Defensive Indexes are designed to target high-quality stocks exhibiting low volatil-ity. They were developed jointly with Westpeak Global Advisors, an invest-ment management and research firm.

The original Russell Defensive Index methodology selects components based on “stability” scores and weights them by market capitalization; how-ever, the methodology for the “High Efficiency” indexes also weights com-ponents by their stability scores. Each index targets a specific level of track-ing error relative to its parent Russell index, the press release said.

When it debuted, the series includ-ed 22 indexes derived from broader U.S. and global indexes.

Stoxx Rolls Out ‘EM Exposure’ Benchmark

Stoxx Limited announced the launch of the Stoxx Global 1800 EM Exposed Index in a December press release. The index targets the compo-nents of the Stoxx Global 1800 Index that derive a significant portion of their revenues from emerging markets.

The index methodology relies on regional revenue breakdowns from the companies, but can also estimate a company’s exposure to emerging markets based on the company’s home country’s GDP, imports and exports. Companies achieving an “exposure score” above 33 percent are included in the index and are assigned a weighting in the index based on their market capitalization and exposure level, the press release said.

‘Real Asset’ Index Targets Inflation

Morningstar Inc. rolled out a multi-asset index in January that it says can be used to help hedge against infla-tion. The Morningstar US Real Asset Index represents the performance of liquid “real assets” such as inflation-protected securities, REITs and com-modities-based investments.

According to Morningstar, inves-tors are concerned about inflation, and a number of new funds launched in the past few years hold portfolios of real assets. The new index is designed to serve as a benchmark for investors in those types of products.

The index has a 40 percent allo-cation toward TIPS and a 30 percent weighting in commodities futures. REITs and commodities-related equi-ties each receive weightings of 15 per-cent, the press release said.

AROUND THE WORLD OF ETFsProShares Debuts Merger Arb ETF

ProShares rolled out a merger-arbitrage ETF in mid-December that tracks the S&P Merger Arbitrage Index; the benchmark seeks to capture the spread between the actual stock price of a company targeted for acquisition at the time of the deal’s announce-ment and the price the acquiring com-pany has said it will pay.

The ProShares Merger ETF (BATS:





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News

MRGR) will go head-to-head with IndexIQ’s IQ ARB Merger Arbitrage ETF (NYSE Arca: MNA), which employs a similar strategy and launched in 2009. However, after waivers and reimburse-ments are taken into account, MRGR is 1 basis point cheaper than MNA, at a net expense ratio of 0.75 percent.

iPath Joins MLP BandwagonAt the start of 2013, Barclays Plc’s

iPath division launched its first ETN to cover master limited partnerships.While the ETN structure is favored for MLP exchange-traded products, and arguably the most successful MLP products are ETNs, iPath had not yet entered the space.

The iPath S&P MLP ETN (NYSE Arca: IMLP) tracks an index that cov-ers both MLPs and publicly traded limited liability companies, which are similar in structure to MLPs. The benchmark includes only U.S.-listed components that fall under the energy sector and gas utilities industry designations in the GICS classification system.

With the addition of IMLP, there are now 12 long-exposure MLP ETFs and ETNs listed in the U.S. IMLP comes with an annual expense ratio of 0.80 percent, undercutting many of its competitors.

PowerShares Closing 13 ETFsPowerShares announced in De-

cember it would be closing 13 of its ETFs in the early months of 2013.

The funds slated for liquidation include a mix of specialty sector, strat-egy and active products, with their assets under management at the time of the announcement mostly coming in under $15 million. PowerShares said the ETFs would stop trading Feb. 26 and would be liquidated on March 7.

The three largest funds in the group include the PowerShares RiverFront Tactical Growth & Income Portfolio (NYSE Arca: PCA), with $15.8 million in assets; the PowerShares RiverFront Tactical Balanced Growth Portfolio (NYSE Arca: PAO), with $15.5 million; and the PowerShares Morningstar StockInvestor Core Portfolio (NYSE Arca: PYH), with $14 million. The small-est fund on the list was the PowerShares Global Steel Portfolio (NasdaqGM: PSTL), with just $1.9 million in assets.

Van Eck’s KWT, MOO Get New Indexes

In early January, Van Eck Global announced that it was changing the indexes on two of its sector-focused equities ETFs to in-house bench-marks in a move the company says will improve liquidity and diversification.

The blockbuster Market Vectors Agribusiness ETF (NYSE Arca: MOO) now tracks the Market Vectors Global Agribusiness Index, replacing its previous benchmark, the DAXglobal Agribusiness Index. Similarly, the Market Vectors Solar Energy ETF (NYSE Arca: KWT) has switched to the Market Vectors Global Solar Energy Index, which replaced the Ardour Solar Energy Index. The new indexes cap the weights of individual constituents in the interests of diver-sification, while KWT’s new index methodology will also broaden its selection universe.

Twenty-five of Van Eck’s 35 equities ETFs now track in-house indexes. The firm’s Market Vectors Index Solutions is a wholly owned Germany-based subsidiary that develops and publish-es all of Market Vectors’ indexes.

First Copper ETF To Launch Soon?After more than two years in registra-

tion, the JP Morgan XF Physical Copper Trust was approved in December by the Securities & Exchange Commission. The approval had been delayed while the SEC investigated the question of whether the product would affect the integrity of the copper market. There were concerns from copper market participants that the fund would arti-


At the start of 2013, Barclays Plc’s iPath division launched its first ETN to cover master limited partnerships.




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ficially drive up the price of copper. It is unclear when the fund may launch.

However, in an interesting twist, the SEC put off ruling on the approv-al of the very similar iShares Copper Trust, which has been in registration for about as long as the JP Morgan fund. The SEC was to have made a decision on Dec. 24, but deferred the decision until February for reasons that weren’t immediately clear.

FlexShares Debuts 3 Dividend ETFsNorthern Trust’s FlexShares unit

launched three dividend-focused ETFs in December that each track in-house indexes. The funds’ names and expense ratios are as follows:• FlexShares Quality Dividend Index

Fund (NYSE Arca: QDF), 0.37 percent after a 0.01 percent fee reimbursement

• FlexShares Quality Dividend Defensive Index Fund (NYSE Arca: QDEF), 0.37 percent

• FlexShares Quality Dividend Dynamic Index Fund (NYSE Arca: QDYN), 0.37 percent

The portfolios each comprise high-quality U.S. securities. Companies included in the various underlying indexes are selected based on expect-ed dividend payments as well as fun-damental factors such as profitability, solid management and reliable cash flow, the company said in the pro-spectus. Each fund targets a different level of volatility, with QDF aiming for volatility on par with the market, while QDEF and QDYN target volatility lev-els that are, respectively, lower and higher than market levels.

ALPS Launches ETFs Tracking GS Indexes

ALPS, mainly known as an ETF dis-tributor, rolled out four in-house ETFs in late December that track indexes provided by Goldman Sachs.

Three of the funds take momentum-based approaches, although all four also are designed to keep volatility in check.

The ETFs that target price momen-tum are all “funds of funds” that invest in other exchange-traded products,

including ETFs covering U.S. fixed-income markets. They and their expense ratios are as follows:• ALPS/GS Momentum Builder

Growth Markets Equities and U.S. Treasuries Index ETF (NYSE Arca: GSGO), 1.29 percent

• ALPS/GS Momentum Builder Multi-Asset Index ETF (NYSE Arca: GSMA), 1.14 percent

• ALPS/GS Momentum Builder Asia ex-Japan Equities and U.S. Treasuries Index ETF (NYSE Arca: GSAX), 1.22 percent

The fourth ETF, the ALPS/GS Risk-Adjusted Return U.S. Large Cap Index ETF (NYSE Arca: GSRA), uses a Goldman-developed methodology to target the U.S. large-cap stocks with the highest risk-adjusted returns. It comes with an expense ratio of 0.55 percent.

KNOW YOUR OPTIONSCBOE Reports 2012 Volumes

The Chicago Board Options Exchange reported a total annual volume of 1.06 billion contracts traded in 2012, for an average daily volume of about 4.2 million con-tracts. The ADV is down about 7 percent from the prior year.

Index options saw their ADV decline about 5 percent to 1.2 mil-lion contracts, while ETF options fell a sharp 15 percent to an ADV of 1.1 million contracts.

In December 2012, the exchange’s ADV was down 10 percent year-over-year to 3.6 million contracts, with index options registering a decline in ADV of 8 percent to 1.3 million contracts and ETF options’ ADV down 15 percent.

BACK TO THE FUTURESCME December Volume Up Y-O-Y

CME Group said in a press release that its average daily volume for December 2012 rose 1 percent year-over-year to 9.6 million contracts. However, its equity index contracts saw a 5 percent ADV contraction to 2.7 million contracts per day, down from 2.8 million the prior December.

The exchange group’s most actively traded index futures contracts included

the e-mini S&P 500 futures, which saw their total volume for the month fall 18.4 percent from the prior year to 37 million contracts. However, the volume of the e-mini Nasdaq-100 contracts was up 14.1 percent to 5.5 million contracts, while the mini $5 Dow futures contracts saw their volume rise year-over-year by 6.9 percent to 2.6 million contracts.

MSCI Licenses More Indexes To Eurex

In December, MSCI said it had licensed a range of indexes to the derivatives-focused Eurex Exchange.

The agreement authorizes Eurex to launch derivatives based on a range of MSCI indexes, including the MSCI World, MSCI Europe, MSCI Emerging Markets and MSCI Frontier Markets indexes, accord-ing to a press release. The exchange already offered futures and options on MSCI’s Russia index, as well as futures on the MSCI Japan Index.

ON THE MOVEKranefuss Joins Warburg Pincus

Lee Kranefuss, the former chief executive officer of iShares who stepped down from the San Francisco-based company in 2010, has joined global private equity firm Warburg Pincus to help the company’s expan-sion into the ETF market.

Kranefuss, as an executive-in-res-idence, will “work to help Warburg Pincus identify and evaluate invest-ment opportunities in the areas of ETFs, index investing and asset man-agement, particularly in Europe, Asia and Latin America,” the firm said in a December press release.

The New York-based private equity firm, known for its focus on growth investing, manages more than $30 bil-lion in assets, according to informa-tion provided by the company.

Kranefuss oversaw the growth of iShares from its dawn to $600 billion in ETF assets by the time he left office in 2010 after having overseen BlackRock’s acquisition of Barclays Global Investors, iShares’ parent company.


continued on page 63




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By Mebane Faber and Prabhat Dalmia

Building trading models with the 10-year CAPE

Global Value




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March / April 2013www.journalofindexes.com 17

Over 70 years ago, Benjamin Graham and David Dodd proposed valuing securities with earnings smoothed across multiple years. Robert Shiller

popularized this method with his version of the cyclically adjusted price-to-earnings ratio (CAPE) in the late 1990s, and issued a timely warning of poor stock returns to follow in the coming years. We apply this valuation metric across approximately 40 foreign markets and find it both practi-cal and useful. Indeed, we witness even more examples of bubbles and busts abroad than in the United States. We then create a trading system to build global stock portfolios based on valuation, and find significant outperformance by selecting markets based on relative and absolute valuation.

The Futility Of ForecastingInvestors spend an inordinate amount of time and effort

forecasting stock market direction, often with very little suc-cess. The conventional efficient market theory is that mar-kets are not predictable and cannot be forecasted. Value has no place in the efficient market ivory tower, but does it seem reasonable for an investor, or perhaps a retiree, to have allo-cated the same amount of a portfolio to stocks in December 1999 that they did in 1982? Of course not.

However, valuation is best used as a strategic guide rather than as a short-term timing tool. It is most useful on a time scale of years and decades rather than weeks and months (or even days). While we can formulate a hypothesis for where the S&P 500 “should” be trading, the animal spirits contained in the mar-ketplace invariably cause prices to deviate quite substantially from “reasonable” levels, often for years and even decades.

There are numerous models to consider when valuing stock markets, and a great summary can be found in a publication by The Leuthold Group titled “Stock Market Valuation: What Works and What Doesn’t?” The paper covers a number of models, including price-to-earnings (P/E) on trailing 12-month

earnings per share (EPS), P/E on five-year normalized EPS, return on equity (ROE)-based normalized EPS, dividend yield, price-to-book, price-to-cash flow and price-to-sales. In general, they find that many of these metrics are decent at forecasting stock returns. Other models include the Q-ratio, and market capitalization to GNP/GDP (Buffett’s favorite). Another great summary is set forth in the paper “Estimating Future Stock Market Returns” by Adam Butler and Mike Philbrick.

However, we are not going to summarize all of the stock valuation models in existence; rather, we will focus on just one.

A Simple Model: 10-Year Normalized EarningsBenjamin Graham and David Dodd are universally seen

as the fathers of valuation and security analysis. In their 1934 book “Security Analysis,” they were early pioneers in compar-ing stock prices with earnings smoothed across multiple years,

US 10-Year CAPE

1881 – 2011

50

45

40

35

30

25

20

15

10

5

-

1881 1893 1905 1917 1929 1941 1953 1965 1977 1989 2001

Figure 1

Source: Robert Shiller, http://www.econ.yale.edu/~shiller/data.htm

Date10-Year

Real ReturnCAPE

Top

US Stock Real Returns Vs. 10-Year CAPE1881 – 2011


Figure 2

Date10-Year

Real ReturnCAPE

Bottom

#

1 12/31/1948 18.1% 9.88 12/31/1910 -4.7% 12.99

2 12/31/1918 17.7% 5.93 12/31/1964 -4.0% 22.87

3 12/31/1949 17.0% 10.49 12/31/1998 -3.9% 39.87

4 12/31/1988 16.1% 14.68 12/31/1968 -3.6% 21.60

5 12/31/1920 15.8% 4.72 12/31/1999 -3.5% 45.08

6 12/31/1919 15.7% 6.11 12/31/1909 -3.0% 15.16

7 12/31/1946 15.7% 11.43 12/31/1967 -2.7% 21.93

8 12/31/1989 15.3% 17.82 12/31/1965 -2.5% 23.80

9 12/31/1951 15.1% 12.29 12/31/1911 -2.4% 12.82

10 12/31/1990 14.8% 15.86 12/31/1971 -2.3% 17.02

Avg 16.1% 10.92 -3.3% 23.31




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preferably five to 10 years. Using backward-looking earnings allows the analyst to smooth out the business and economic cycle, as well as price fluctuations. This long-term perspective dampens the effects of expansions as well as recessions.

Robert Shiller, the author and Yale professor, popular-ized Graham and Dodd’s methods with his version of this cyclically adjusted price-to-earnings ratio (CAPE).1

One common criticism of CAPE is that the measurement period of 10 years is too long. Critics claim recessions and expansions have an outsized impact long after they have faded from memory.2 Those critics also claim adjustments to CPI and accounting rules render comparisons across decades, or even centuries, meaningless. We agree there may be some variation, but later in the paper we examine CAPE in approximately 40 for-eign markets with supporting results with regard to consistency.

Figure 1 is a chart of CAPE going back to 1881. The long-term series spends about half of the time with values rang-ing between 10 and 20, with an average and median value of about 16. The all-time low reading was 5, reached at the end of 1920, and the high value of 45 was reached at—you guessed it!—the end of 1999.

Asset allocators that believe in efficient markets allocate the same percentage of assets to equities when valuations

% Occurrence

1-Year Fwd Real CAGR


US Stock Average Real Compound Returns Vs. 10-Year CAPE1881-2011


Figure 3




<5 0.8% 25.4% 18.9% 21.6% 22.6% 15.8%

5 to 10 17.1% 14.5% 12.6% 12.6% 11.6% 10.5%

10 to 15 26.4% 10.6% 8.3% 6.7% 6.5% 8.1%

15 to 20 31.0% 6.4% 4.7% 5.2% 5.3% 5.2%

20 to 25 15.5% 1.6% 5.4% 4.9% 4.3% 2.7%

25 to 30 5.4% 1.3% -1.0% -1.3% 1.5% 3.3%

30 to 40 3.1% 1.9% 0.3% -1.1% -0.5% -0.3%

40 to 50 0.8% -12.5% -17.0% -4.8% -1.5% -3.5%

US Stock Average Real Compound ReturnsVs. 10-Year CAPE

1881-2011

20%

15%

10%

5%

0%

-5%

<5 5 to 10 10 to 15 15 to 20 20 to 25

10-Year CAPE

25 to 30 30 to 40 40 to 50


Figure 4

US Stock 10-Year Real Compound Returns Vs. 10-Year CAPE1881-2011

-

5

10

15

20

25

30

20%

15%

10%

5%

0%

-5%

-10%

35

40

45

1886 1896 1906 1916 1926 1936 1946 1956 1966 1976 1986 2001

■ 10-Year CAPE ■ 10-Year Real CAGR

Stocks Expensive

Stocks Cheap

Figure 5


Shiller CAPE Vs. Infation Levels

1880-2011

25

20

15

10

5

0

-16%

to

-4.65%

-4.65%

to

-0.58%

-0.58%

to

1.02%

1.02%

to

1.72%

1.72%

to

2.52%

2.52%

to

3.19%

3.19%

to

4.19%

4.19%

to

5.98%

5.98%

to

9.47%

9.47%

to

23.67%

Figure 6

Sources: Robert Shiller, http://www.econ.yale.edu/~shiller/data.htm; Arnott, “King of

the Mountain,” IndexUniverse.com, Sept. 14, 2011; Trahan, F. and K. Krantz, “The Era of

Uncertainty,” John Wiley & Sons, 2011.

CAPE




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are high as they do when valuations are low. But does that

seem even remotely reasonable looking at Figure 1?

The 10 Best, And Worst, Times In History To InvestTo illustrate this point, we examined all year-end peri-

ods with a holding period for the next 10 years. What

have been the 10 best, and worst, years to invest since

1871? Figure 2 details these years and their corresponding

10-year compounded real returns.

Many of the best starting points seem obvious in retrospect.

1948 and 1949 were great entries, preceding the Nifty Fifty

mania, and of course 1918-1920—right before the Roaring

Twenties—are on the list. 1988 and 1989 certainly would not

be left out, with the Internet bull market ahead, as well.

The same hindsight applies for the bad years, as they

often fell at the end of these massive bull runs. Bear markets

set the stage for future bull markets and vice versa.

One simple takeaway from Figure 2 is the valuations at

Sources: Global Financial Data, MSCI

Figure 7

Median

Global Countries Included In Study And 10-Year CAPE

As Of December 2012

Start Date Latest Min Max

Australia 12/31/1969 14.06 7.65 31.60 16.93

Austria 10/31/1981 8.43 6.04 59.16 26.53

Belgium 12/31/1969 10.27 4.88 29.48 14.84

Brazil 1/31/1988 12.07 11.04 29.73 17.20

Canada 12/31/1969 18.00 5.83 63.34 19.75

Chile 1/31/1988 21.09 9.71 32.95 21.29

China 1/31/1995 15.56 13.52 63.85 23.95

Colombia 12/31/1992 33.81 9.25 48.66 35.00

Egypt 1/31/1996 13.57 10.03 56.43 17.38

France 9/30/1971 11.95 6.20 57.17 19.68

Germany 12/31/1969 14.01 7.83 56.87 17.85

Greece 12/31/1987 2.57 1.95 39.82 15.20

Hong Kong 12/31/1972 16.84 8.55 34.55 18.03

India 12/31/1992 19.50 12.69 47.80 23.86

Indonesia 1/31/1990 24.92 5.05 34.96 16.90

Ireland 5/31/1990 5.00 3.08 23.28 12.21

Israel 6/30/1999 10.55 10.55 21.64 17.29

Italy 4/30/1984 7.41 5.92 52.92 21.61

Japan 12/31/1969 15.41 13.27 94.26 43.79

Malaysia 12/31/1987 20.19 7.77 26.49 18.77

Mexico 12/31/1987 21.31 11.69 35.34 19.68

Netherlands 12/31/1969 11.20 4.62 38.51 11.89

New Zealand 1/31/1988 13.28 9.50 20.34 13.79

Norway 12/31/1969 12.39 6.76 30.55 14.17

Peru 1/31/1993 33.57 16.00 61.17 31.36

Poland 12/31/1992 13.90 7.55 27.47 15.23

Portugal 1/31/1988 8.79 7.02 39.36 15.97

Russia 1/31/1996 7.22 5.13 22.87 8.96

Singapore 12/31/1972 12.48 9.40 37.81 21.74

South Africa 12/31/1992 18.45 10.25 24.30 16.41

South Korea 12/31/1987 15.72 4.74 27.65 17.73

Spain 12/31/1979 8.50 6.50 40.05 17.27

Sweden 12/31/1969 15.04 4.82 74.18 19.42

Switzerland 12/31/1969 16.22 7.12 57.95 18.07

Taiwan 1/31/1988 14.29 9.18 42.28 19.36

Thailand 12/31/1987 16.35 3.00 17.97 12.10

Turkey 12/31/1987 15.72 8.24 42.95 16.77

UK 12/31/1927 12.56 4.43 28.69 11.84

USA 12/31/1881 20.70 4.72 45.08 15.87

Country




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10-Year CAPE Levels And Future Average Real Compound Returns For 39 Countries1980 – 2012


Figure 8

< 10 10.8% 23.3% 15.1% 17.6% 15.8% 12.3%

10 to 15 21.2% 24.8% 16.7% 14.0% 12.1% 9.1%

15 to 20 25.5% 13.1% 11.7% 10.8% 9.3% 8.8%

20 to 25 18.0% 8.6% 7.8% 6.5% 7.5% 6.1%

25 to 30 10.5% 1.0% 2.6% 5.6% 5.4% 4.4%

30 to 40 7.4% 7.1% 1.0% 0.7% 2.6% 2.9%

40 to 50 4.6% -6.0% 0.8% 2.7% 1.5% 1.0%

>50 1.9% -10.7% -11.7% -4.0% -1.5% -2.9%

Avg CAPEBy Bucket

% Occurence

1-Year Real CAGR

3-Year Real CAGR

5-YearReal CAGR

7-Year Real CAGR

10-Year Real CAGR

Portfolios Sorted On CAPE Levels With No Filters, Real Returns1980-2011

Eq Wt Cheapest

Eq Wt Expensive

Eq Wt Spread

Eq Wt All

CAGR 12.0% 8.2% 2.5% 9.8%

Stdev 27% 26% 18% 25%

Maxdd 54% 47% 59% 50%

Yearly

Top 33%


Figure 9

Strategy

Yearly

Top 25%

Yearly

Top 10%

CAGR 12.3% 6.3% 4.5% 9.8%

Stdev 27% 26% 20% 25%

Maxdd 55% 48% 62% 50%

CAGR 16.6% 6.0% 7.0% 9.8%

Stdev 35% 29% 34% 25%

Maxdd 59% 67% 81% 50%

Portfolios Sorted On CAPE Levels With Filters, Real Returns(Max CAPE Of 15 For Long; Min CAPE Of 30 For Short), 1980-2011

Eq Wt Cheapest

Eq Wt Expensive

Eq Wt Spread

Eq Wt All

CAGR 12.6% 0.4% 11.7% 9.8%

Stdev 23% 12% 21% 25%

Maxdd 20% 43% 27% 50%

Yearly

Top 33%


Figure 10

Strategy

Yearly

Top 25%

Yearly

Top 10%

CAGR 13.3% 0.7% 12.0% 9.8%

Stdev 23% 13% 21% 25%

Maxdd 20% 45% 30% 50%

CAGR 17.5% 1.5% 11.1% 9.8%

Stdev 32% 27% 36% 25%

Maxdd 32% 73% 81% 50%

the start of these 10-year periods. The average valuation for the 10 best years was 10.92. The average valuation for the 10 worst years was 23.31, double that of the best starting points.

Buy Low, Sell HighFigure 3 is a table of all of CAPE year-end readings from

1881-2011. We list how often they occur, as well as the real forward returns. The red bar in Figure 4 is where we find ourselves as of the summer of 2012.

What we find is no surprise: It very much matters what price one pays for an investment! Indeed, it is an almost per-fect stair step: Future returns are lower when valuations are high, and future returns are higher when valuations are low.

While more sophisticated models can be built, Figure 5 simply shows the shockingly similar trend lines of an inverse CAPE and future 10-year real stock returns.3

Valuation And InflationBesides general sentiment, what might cause this large

variation in what multiples investors are willing to pay for

stocks? After all, at a current value of around 1374, this means the S&P 500 could trade at either 315 or 2800 based on historical low and high multiples of 5 and 45, respectively. It is difficult for most investors to comprehend the possibility of stocks declining 80 percent or increasing over 100 percent, but both of these multiples have occurred in the past.

One of the determinants of the valuation multiple inves-tors are willing to pay is the inflation rate as seen in Figure 6. The red bar is where we find ourselves as of the summer of 2012. When inflation is in the 1-4 percent “comfort zone,” investors are willing to pay a valuation premium compared with when there is either high inflation or outright deflation.4

Global CAPEThere is very little in the literature regarding global CAPEs

for international equity markets.5 We examined 39 countries with data from MSCI and Global Financial Data, including as much data as we could find, although there is some bias in the study. All the returns are real dollar returns.

Two countries had a century’s worth of data (U.S. and

There is very little in the literature regarding global CAPEs

for international equity markets.




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www.journalofindexes.com 21March / April 2013

Portfolios Sorted On CAPE Levels, Real Returns1980 – 2011

25,600

12,800

6,400

3,200

1,600

800

400

200

100

50

1979 1983 1987 1991 1995 1999 2003 2007

■ Cheapest 33% ■ Expensive 33% ■ Eq Wt ■ Cheapest 33% with flter

Figure 11


the U.K.), but most of the other countries go back to the 1970s and 1980s (see Figure 7).

We examined all the countries on a yearly basis since 1980, CAPE levels and future returns. The sample includes approximately 10 countries in 1980, 20 in 1990 and 30 by 2000. The results are in Figure 8 and largely confirm the U.S. data: Buy low, sell high.

We found most CAPEs averaged around 15-20, bot-tomed out around 7, and maxed out around 45 (a few made the U.S. bubble in the late 1990s look pathetic in compari-son, as when Japan reached a value of nearly 100 in 1989).

A Global Stock Trading SystemBut can we turn this into a trading system? There is evi-

dence that sorting countries on other measures of value works well. A good summary of the dividend literature can be found in the Tweedy, Browne paper titled “The High Dividend Return Advantage.”6 In the paper, they summarize a 1991 study by Michael Keppler titled “The Importance of Dividend Yields in Country Selection“7 that found that rank-ing the universe of countries by dividend yield also resulted in outperformance. He found that the highest-yielding coun-tries outperformed the lowest-yielding from 1969-1989 by more than 12 percentage points per year.

Running a similar study using a different database (Global Financial Data),8 we sorted countries by quar-tiles from 1920-2011, beginning with nine countries and expanding to 18 by study end. We found that countries in the highest-dividend-paying quartile outperformed the countries in the lowest-paying quartile by 11 percentage points per year. (Also see the Appendix for tests on book value, dividends, cash flow and earnings.)

We then set out to test CAPE in a similar manner. Starting in 1980, we sort all countries by CAPE, and invest in the most undervalued x percent, rebalanced yearly. We also show the effects of investing in the most overvalued x percent, as well as a long/short portfolio. These returns are real returns net of inflation, and with yearly data (which will naturally understate drawdown figures). The sample includes approximately 10 countries in 1980, 20 in 1990 and 30 by 2000. Investing in the cheapest countries produces 2 to 7 percentage points of outperformance, while the over-valued countries underperform (see Figure 9). The spread is approximately similar to those appearing in the previ-ously mentioned dividend studies, albeit slightly lower. However, investing in the cheapest countries on a relative basis does not protect the investor when all countries are expensive in a global equity bubble like 1999. We repeated the study, but only invested long if the country was below a CAPE of 15, and only short above a CAPE of 30. If the coun-try does not qualify for the valuation filter, then that part of the portfolio sits in cash (although we do not receive any interest income in this test) (see Figure 10).

For the most part, adding the absolute CAPE-level filter results in better performance with lower drawdowns. This is to be expected, as the portfolio could be sitting in 20, 50 or even 100 percent cash (as with 1999 or 2007). In this case, the returns are higher as well. As many investors look at

this table and salivate over the prospect of 15 percent real returns, recall Figure 7 and note that most of the cheapest countries fall in the troubled eurozone. How many investors have the stomach to invest in these countries with potential for the markets to get even cheaper? How many professional investors would be willing to bear the career risk associated with being potentially wrong in buying these markets?

Figure 11 depicts the equity curves from taking the cheap-est 33 percent of countries (also with filter), the most expen-sive 33 percent of countries and the equal-weight benchmark.

SummaryWarren Buffett famously said, “Price is what you pay. Value

is what you get.” Over periods of years and decades, it is evident that an investor’s real return is heavily dependent on the price paid for the asset. Investors can use CAPE valuation as a guide-


Appendix Data: Cheapest X Percent Of Countries, 1975-2011

12.7% 13.5% 15.6% 13.2% 14.3%

22% 24% 24% 25% 24%

47% 46% 50% 46% 49%

Source: Global Financial Data, MSCI, Fama & French

Figure 12

CAGR

STD

MaxDD

12.7% 15.4% 14.3% 14.0% 14.9%

22% 25% 26% 25% 24%

47% 43% 61% 46% 46%

CAGR

STD

MaxDD

12.7% 18.1% 16.4% 13.5% 12.9%

22% 29% 28% 34% 26%

47% 38% 54% 55% 48%

Buy& Hold

BookYield

EarningsYield

Cash Flow Yield

DividendYield

10%

Buy& Hold

BookYield

EarningsYield

Cash Flow Yield

DividendYield

25%

Buy& Hold

BookYield

EarningsYield

Cash Flow Yield

DividendYield

33%

CAGR

STD

MaxDD




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Profiles In Pensions


Advises picking a strategy

and sticking with it

PERSI’s Maynard Favors

‘Traditional’ Approaches

The $12.6 billion Public Employee Retirement System of

Idaho has been around since 1963, and is one of the most suc-

cessful public pension funds in operation, with a funding per-

centage of more than 87 percent. Robert Maynard has been

the pension fund’s CIO since 1992. The Journal of Indexes

caught up with him recently for a chat about sound pension

fund management policies and the role of passive investment.

Public Employee Retirement System of Idaho

Assets (as of 12/20/2012): $12.6 billion

Funding: 87.3%

Passive/Active Mix: 35%/65%

JOI: Tell us a little bit about PERSI.Maynard: It’s a multiple-employer trust. We have over 100,000 members. About 65 percent of them are active, and about 35 percent are retired. We have teachers and public employees. The mandated ones are the state employees and the teacher systems. Basically, we include all statewide employees and teachers. The discretionary participants, who can choose to go in or out, are cities, hospital districts, water districts, those types of things.

JOI: How much does the fund have in assets?Maynard: As of Dec. 20, the fund has $12.585 billion. As of this morning, we are 87.3 percent funded.

JOI: That’s an amazing number. To what do you attri-bute the fact that PERSI is so well funded?Maynard: Our political system. People will always com-plain about legislatures and whatnot, but our political system has been a hero on this over the years. It has basi-

cally kept the liabilities under control and has always fully funded everything.

Even if you look at the people that are underfunded, it’s been a great 20 or 30 years. All of us have made 9 percent per year, for example. Very few of us—and I can’t think of any, except those who had to be in bonds for a lot of that time—haven’t made well above their hurdle rates. The difference in funding levels is due to either over-promising benefits or under-funding on contributions, or both. For example, New Jersey didn’t pay contributions for years, and that’s the problem. Our legislature has always paid for the benefits, hasn’t over-promised benefits, and has always kept up with what the law requires. They always have to put in the minimum above that 15 percent level, basically what’s called “nor-mal cost.” As a result, we’re in fine shape.

If we were a corporation, we would be probably 110 percent funded. But one thing the public pension funds do is that we assume everybody right now is making their salary at the time of retirement. That basically doubles that liability for active members. If we were cor-porate, we would be in even better shape than that.

If you look at the returns of the New Jersey Division of Investment, for example, the returns have been fine over the decades. I’ve known that system for 30 years. The problem is, for years nobody put in the contributions.

JOI: Would you describe PERSI’s overall investment approach as “conservative,” even in the context of other pension funds?Maynard: I would call it “traditional.” We’re a traditional investment fund. We’re 70 percent at risk. Most people, when they say they are conservative, look more towards bonds and things of that nature.




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www.journalo�ndexes.com March / April 2013 23

JOI: But PERSI’s strategy does seem conservative in the sense that it seems to be strongly opposed to making spur-of-the-moment decisions or chasing returns.Maynard: �at is correct. �e traditional investment approach-es and modern portfolio theory were built and designed to work over �ve- to 15-year periods. �at means: Don’t make quick decisions. Stick to the plan. Expect interim volatility.

If all of a sudden you �nd yourself in a situation where you can’t lose a fair amount on a year-to-year basis, you can’t take the volatility, or you �nd that your return needs are more than the market can bear—like endowments, which have real return needs oftentimes of 7 or 8 or 9 per-cent above in�ation—then modern portfolio theory gives you issues. But we don’t have those problems.

JOI: What are the pitfalls for pension fund investing?What trips up pension fund managers?Maynard: Switching approaches too often, or chasing the most recent good idea. Generally, if you look at the most successful funds over the years, you’ll �nd that the

ones that have been the most successful stick to the same investment approach over decades.

And sometimes they’ll do completely di�erent things. If you look at the Washington State Investment Board, it’s a stunningly good fund. �ey do huge amounts of private equity and always have, much more than we do—something like 25 percent. Over time, we’ll all get about the same return. �e South Dakota Retirement System has one of the best investment records in the world over the last 35 years because they stuck to a certain type of investment. Whatever investment approach you have, you need to stick with it in good times and bad—and par-ticularly the bad times. �e market always takes longer to go through a cycle than you think it will—oftentimes just beyond that three years that’s the average tenure of the CIO, or average of �ve years for a board member. When you have turnover of sta�, they get rid of things that were put in place by a previous group. �en they leave and someone else comes in. �ose are the systems—the ones without those traditions—that can get in trouble.

In large measure, if you look at a period of time over years and years, we all kind of end up around the same place, just at di�erent times. �ere are di�erent routes to investing, and you’ve got to pick one and stick with it. For us, because our liabilities are under control, we stick to basic, market-oriented investments—we don’t have to try for anything bigger.

JOI: How do you use index-based investment strategies in the fund?Maynard: In all of our areas, we have basic, cap-weighted indices, and that tends to be about a third of our fund.

JOI: Why is cap-weighted the way to go?Maynard: You use indexes for a number of di�erent rea-sons. First of all, it’s an easy way to see what the market return would be if you have active management. It is an easy alternative to the active managers or any other approach. It de�nitely represents the money-weighted opinion of value.

It gives you the fewest transaction costs: Cap-weighted automatically rebalances. We’re like big tuna being carved up like sashimi out there when we go out there and try and trade, given the high-frequency traders and everything. It includes pretty much all stocks. You are not leaving some stocks out like you would with a fundamentally weighted index. It includes all stocks and all styles. And it may or may not be the best way to get return for the risk involved, how-ever you measure risk.

When you hear people talking about di�erent sorts of indices, like fundamentally weighted indices, their argument is it makes you more money with less risk. �at may or may not be true—I’m agnostic on that. But clearly other forms of indices beyond cap weighting don’t address all the other rea-

sons you use an index in a broad portfolio—easy transaction, get money in and out, use it to rebalance for other things, good risk control, easy alternative, all of that stu�.

JOI: How do you evaluate an active manager? Maynard: We evaluate whether or not they are being true to their style and with the same sort of resources. If they are, our basic policy is that we don’t �re for near-term performance—and by “near term,” I mean three to �ve years. Poor near-term performance can be an indi-cator of something going wrong, either that something is going wrong within the �rm or that it is a di�erent �rm than you thought you had. But if you look at them and they are the same �rm, following the same style, picking

You use indexes for a number of di�erent reasons. First of all, it’s an easy way to see what the market return

would be if you have active management.

PERSI Asset AllocationDecember 2012

Source: Public Employee Retirement System of Idaho

Figure 1

the same types of stocks, we keep those managers—we don’t fire for performance alone.

As a result, we tend to have managers who have very clear styles, so it is easy to tell if they stray from the man-date. We have deep-value managers, for example, so if they pick Pets.com, you know something is wrong. Or our managers have concentrated portfolios. We tend to prefer those sorts of managers.

JOI: Are there areas where index-based investing is not necessarily the way to go or isn’t appropriate?Maynard: It used to be that I had doubts about emerging markets and REITs because of the newness of the areas. Now, I think the markets are sufficiently mature, and we are starting to do index investing. But, clearly, bank loans, you can’t index. There are some niche markets

where indexing is less appropriate, and I have concerns about index investing in some areas where you may be putting in a bias you don’t want if you index.

For example, high-yield debt, you index according to market weightings. Or in emerging market debt, your assets will tend to go to those countries that are trying to borrow too much money. But other than that, for the big, major markets, U.S. large, mid and small; developed-mar-ket international; TIPS; REITs; emerging markets; general bond; the credit bond market; the government bond mar-ket—for all of those, I think index investing is fine.

JOI: What sort of role do you see alternative investments playing in the PERSI portfolio? Maynard: We don’t see any role for those—I’m leaving out private real estate, private equity and the Idaho commercial mortgage program from that group. Those, oftentimes, are thrown into the alternative category. But we’re fine with some private equity. We’re fine with some private real estate. We’re fine with our local private commercial mortgage program.

But beyond that, what is called the “hedge fund move-ment,” the letting 100 flowers bloom and diversifying into all these little areas like Sri Lankan distressed debt or whatever you like—that sort of stuff we don’t see a role for. A hedge fund from our standpoint is levered active man-agement and very expensive. It’s doubling down on active management—and it hasn’t done its job.

Hedge funds promise you equitylike returns with bond-like volatility. What they have given you is below-bond returns with equitylike volatility. I think everybody recog-nizes that the average institutional hedge fund or fund of funds isn’t worth it. What they say, though, is that if you

pick the top quartile or top decile, it is worth it. I have no problem with people who go into that area if they are one of those types of funds who can’t handle market returns for one of two reasons—either because market returns are too volatile, or they need much-better-than-market returns.

This traditional approach we use is fine as long as you can meet your liability with around 3 to 5 percent real returns above inflation. If your needs are significantly above that—if you are an endowment that has to make 8 percent real returns or you are so deep in the hole you’ve got to make 8 or 9 percent real returns—then picking the top-quartile active public equity manager doesn’t get you what you need, but picking the top-quartile or -decile hedge fund manager might.

If you have to go into that market, you’d better be aware that the odds are 3-to-1 or 4-to-1 against you. We

don’t have to go there. We’re perfectly fine even with subpar market returns for a decade or two. But if we had to go there, if we had to make 8 percent real returns, then you’re in a different world. Then you’ve got to go into hedge funds and heavily into alternatives or do leveraged strategies, like risk parity.

JOI: Do you use ETFs at all?Maynard: We have our passive index fund that’s cheaper than a Russell 3000 ETF because of our size, so there is no reason to use ETFs. Within our passive index funds, some-times as money goes in or out, we may want an Indian exposure or a U.K. exposure. An ETF may be the cheapest way to do it temporarily, while we rebalance monies in and out, and our passive managers can use them.

We just did a transition where we allowed the transition manager, in order to keep our exposure in the market, to buy ETFs. As a marginal, transactional tool, sure. But as a base exposure, we can get that exposure cheaper directly.

JOI: How much of a concern is the fact that the markets are more volatile? Maynard: Recently, for the last year, markets have been extremely well-behaved; in fact, much less volatile than normal. What we saw in the last decade or two is actually what was predicted for annual volatility. Even 2008-2009, that was maybe a two-standard deviation when looked at on a monthly or a yearly basis.

People were fooled by how calm the ’90s were, lead-ing them to say that’s normal volatility. What we’ve seen in the last 20 years is actually fairly well within the range of what was expected. Remember, our numbers that we


Hedge funds promise you equitylike returns with bondlike volatility.What they have given you is below-bond returns with equitylike volatility.

I think everybody recognizes that the average institutional hedge fund or fund of funds isn’t worth it.




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have been basing modern portfolio theory on go back now to the Civil War. That includes all the stuff that hap-pened in the late 1800s and the Great Depression.

There’s this idea that modern portfolio theory and that the expectations of people when they put these numbers together were wildly off—but no, they were pretty much right on. In 1996—about the time Greenspan spoke about irrational exu-berance—our estimated volatility of the funds back then was 12 percent annual standard deviation. The standard devia-tion of our funds, since 1996—through the supposedly most volatile time since the Great Depression—has been 11.

If you could just not react on a day-to-day basis or a month-to-month basis, the annual volatility has been well within that predicted range, I think for pretty much every-body. But what happened is that people didn’t recognize what those numbers actually meant when they experienced them on a day-to-day basis or a month-to-month basis.

Remember: Modern portfolio theory doesn’t work if you wake up in under five years. If you wake up once every five years, it works perfectly.

JOI: What would induce you to make significant changes

in the portfolio or your overall approach?

Maynard: I’d have to be able to see someone implement an approach that is describable and repeatable. Most of my time is spent looking at new stuff coming in: We would make a change if there was an opening of a new, basic capital mar-ket—like when they opened up REITs and TIPS in the late ’90s—with a clear portfolio benefit.

When TIPS were launched, they went from 3 up to 4.2 percent real yield. None of my bond managers would buy it because they were getting crushed by it, but in a portfolio like ours, it’s a beautiful instrument. And so we had to go in and put 10 percent of our portfolio into TIPS because our active bond managers weren’t doing it. It’s that sort

of opening of a new capital market that has clear benefits and can be easily accessed that would cause us to change.

We’re a fund that does basic equity or fixed-income investing with five or six special things that we think have clear, describable portfolio benefits. If they opened up a new capital market, that’s the type of thing we do—but we haven’t seen that this decade.

The other part of this, too, is if you aren’t willing to put 5 to 10 percent of your total fund into it, it doesn’t make a difference on the risk level. It just doesn’t move the needle. Adding a lot of little things isn’t going to do it.

JOI: Are there any particular asset classes that you see

as driving returns or dragging them down in the future?

Maynard: I’m not going to put my fund at risk because I have particular ideas. The way I manage is if I get a bright idea, I go to a dark room, lay down and wait for the feel-ing to pass. But for cocktail party conversation, I like the equity markets. People assume that this “new normal” means that equities won’t return what they have returned in the past in terms of real returns. I think they do.

I think the equity markets are nicely set up to do so—I’m not talking “spectacular.” But the idea that equities can get you 5 to 7 percent real returns, like they have over the last 200 years, I think is fine. Bonds are likely to be dead money for a while, but you never rely on bonds to get you the returns anyway. They’re the Armageddon asset class. You’ve got to have something to rebalance from if everything else goes down, like in 2008-2009. They did their job just fine there.

I think Europe probably is going to be fine as an equity investment, because we invest in companies; we don’t invest in countries. I think leveraged strategies could run into a period of difficulty, but I thought that last year, and last year they turned out to be the best strategies in the world.


The Economic Role Of The Investment Company

John Bogle

The Bogle Impact: A Roundtable

Featuring Gus Sauter, William Bernstein, Burton Malkiel, Don Phillips, Ted Aronson and more!

Lessons From SPIVA

Srikant Dash

The Case For Indexing

Christopher Philips

Plus an excerpt from Bogle’s forthcoming book and an interview with the man himself,

as well as thoughts on indexes and investing from Agather and Blitzer

the bogle issue March / April 2012

Managed Futures Strategies

Jeremy Schwartz and Chris Jabara

Benchmarking Tail Risk Management

Vineer Bhansali

Indexed Approaches To Long/Short Investing

Peter Little and Greg King

Market-Neutral Factor Investing

Kishore Karunakaran

Plus an interview with Morgan Creek’s Yusko, thoughts on hedge fund indexes

from Bruno & Whitelaw and columns by Vogelzang, Blitzer and Krein

defining alternatives May / June 2012

Commodities Sectors And The Business Cycle

Geetesh Bhardwaj and Adam Dunsby

Commodities In A Portfolio

Sal Gilbertie

Keeping Current With Commodities

Featuring Jim Rogers, Victor Sperandeo, Shonda Warner, Jodie Gunzberg and more

Better Beta In Commodities Indexing

Jonathan Guyer

Plus Mulvey on managed commodities futures, Kaplan on capturing long/short strategies,

and S&P’s Blitzer, DJI’s Krein & Prestbo ... and more!

commodities beta? July / August 2012

Determining Market-Capitalization Breakpoints

Andrew Clark

Index Variation And Portfolio Performance

Craig Israelsen

Sectors And Style

Paul Baiocchi and Paul Britt

Dynamic Correlations

Christopher Philips, David Walker and Francis Kinniry Jr.

Plus an interview with John Prestbo, David Blitzer on the next big thing,

Guido Giese on adding risk control to index methodologies, and more!

big ideas September / October 2012


SERIOUS IDEAS FOR SERIOUS INVESTORS

The Next Emerging Markets

Amy Schioldager and Heather Apperson

The Man Who Invented ‘BRIC’ Weighs In

An Interview with Jim O’Neill

Better Beta

Robert Holderith

Exploring Emerging Market Debt

Rick Harper and Bradley Krom

Plus Blitzer on the ‘great moderation,’ WRS’ Johnson on pension fund management,

S&P DJI’s Orzano and Banerjee on targeted approaches to EM, Russell’s Goodwin, JoI’s Bell ... and more!

emerging emerging January / February 2013

JOURNAL OF INDEXES ADVERTISING INFORMATION AT WWW.JOURNALOFINDEXES.COM/ADVERTISE

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By Felix Goltz, Véronique Le Sourd and Masayoshi Mukai

Insights from a survey of North American investors

Requirements For Standard And New Forms Of Indexes




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Standard cap-weighted market indexes have tradi-tionally been used as a reference for determining how much additional risk one is willing to tolerate, in

terms of deviating from peer group behavior. This presup-poses a basis to consider any index or method as a uni-versally applicable and neutral starting point, but there is ample empirical evidence and theoretical support that this attribution of neutrality is unwarranted and that standard indexes suffer from poor risk/return properties (see e.g., Amenc, Goltz, Martellini and Retkowsky [2011] for a review of the various drawbacks of cap-weighted indexes).1

More recently, index providers have been expanding their offerings to an array of different types of indexes, while the proliferation of ETFs has given a broader range of investors’ direct access to indexing. A clear trend has been emerging in recent years and reflects a move-ment away from standard indexes towards constructing indexes with the purpose of fulfilling a specific set of investment objectives, such as obtaining low risk, high risk-adjusted returns, or calibrated exposure to a given risk factor. While most traditional indexes can be clearly seen as “market indexes” that provide a representation of the average performance of investors in a given market segment, many recent indexes can be seen as “strategy indexes” that aim at achieving a given risk/return objec-tive through a set of systematic rules.

As illustrated in Figure 1, there have been numer-ous index launches in the past few years, many of which involved collaborations of index providers with asset man-agement firms or other third parties.

Due to such heavy index launch activity over recent years, investors are now faced with an increasing variety of index offerings with different construction methods, often from the same provider. In particular, rather than sticking to the default index construction scheme where stocks within a geographic or industry segment are selected by their market cap and then weighted in proportion to their market cap, new index launches often draw on alternative constituent selection schemes and/or alternative weight-ing schemes. Such innovation naturally raises the ques-

tion of what desirable properties an index should have in the first place. Also, in view of this increasing variety of index supply, another relevant question is whether inves-tors who make up the potential demand side accept such new index construction schemes and how they integrate the corresponding products into their overall investment process. In fact, while recent innovation and the continu-ous extension of strategies that are offered from index pro-viders have led to informal debate on where the limits are of what one could reasonably refer to as “an index,” little evidence is available on where index investors actually draw this line. This article focuses on results of the afore-mentioned survey that provide insights into this question.

To better gauge the attitudes of investment profes-sionals with respect to such innovation and to help anticipate the direction indexing is likely to go in the future, EDHEC-Risk Institute has conducted a survey of investment management professionals in North America. A broad and comprehensive set of questions was asked to allow us to present a clear picture of which attributes of indexes users value. The EDHEC survey consisted of a questionnaire given in Q1 and Q2 2011 and answered by 139 North American investment professionals,4 most of whom are institutional investors or asset managers; further, the overwhelming majority of all respondents have used indexes as investments. This survey was done in parallel with similar surveys conducted in Asia and Europe, to help provide a comprehensive global picture of the indexing industry and note any regional disparities (see Amenc, Goltz, Mukai, Narasimhan and Tang [2012] and Amenc, Goltz and Tang [2011]. This article provides a summary of key results of the American survey concern-ing investors’ quality requirements for indexes in general and their use of alternative index strategies. We discuss results for equity indexes as well as fixed-income indexes. An analysis of the complete results of the survey is pre-sented in Amenc, Goltz, Tang and Vaidyanathan [2012].

We first discuss survey results on the current use and satisfaction with indexes in equity and fixed-income investing. We then focus on investors’ views on what the fundamental quality requirements for indexes are before providing an overview of respondents’ views on alterna-tive weighting schemes.

Current Use Of Indexes And Satisfaction RatesBefore discussing the results obtained for each class of

indexes, we first present the general adoption rate and satis-faction rate of indexes for each asset class. Index usage among respondents to our survey is high and broad, as depicted in Figure 2, especially when it comes to equity investments, with about 89 percent of respondents using indexes.

However, as shown in Figure 3, results indicate satis-faction with these indexes was surprisingly low, further emphasizing the importance of investigating the sources of dissatisfaction and issues that index providers need to account for when constructing indexes that would truly address investors’ needs. For example, only about 69 per-cent of equity index users were satisfied with equity indexes,

March / April 2013 27

Main Index Providers And Recent Index Activity

As Of July 1, 20122

Source: Index providers

No. Of New Indexes Launched

01/01/09–01/07/12

No. Of Index Series Launched

In Partnership With 3rd Party

Figure 1

Dow Jones 25 9

FTSE 20 19

MCSI 11 1

Russell 22 10

S&P3 45 27

Stoxx 16 2

Sum across providers 139 68




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which means that although the vast majority of equity investors use equity indexes, equity indexes do not appear good enough for quite a lot of them. The rate of satisfac-tion is even lower with corporate bond indexes, with only 53 percent of respondents answering they were satisfied.

As the academic literature in recent years has produced several formal criticisms5 of indexes, we examine in Figure 4 the extent to which these criticisms are shared by practi-tioners. We first asked respondents whether they thought that significant problems are associated with standard cap-weighted equity indexes. The results in Figure 4 show that about 53 percent of respondents answered that they did, while only about 35 percent answered that they did not see any problems, with the remainder of respondents having indicated that they did not know. For fixed-income indexes, the percentage of respondents who did not see any issues is even lower than for equity indexes, at 33 and 29 percent for government bond indexes and corporate bond indexes, respectively. However, there is a notable gap in familiarity with issues across index types, with corporate and government bond indexes receiving “I don’t know” responses of 27 and 29 percent, respectively, compared with only 12 percent for equity indexes. This suggests that the issues surrounding bond indexes are obscured, in contrast with equity indexes.

Equity Indexes

To elaborate with more precision on the specific con-cerns held by respondents regarding the indexes in differ-ent asset classes, we asked respondents to express their agreement with common concerns about equity indexes and bond indexes. For equity indexes, we examined five prominent issues—including style biases, overinvest-ment in overpriced stocks, sector biases, poor diversifica-tion and lack of representativity of the economy—seek-ing confirmation of their perceived importance (see Figure 5). In particular, we assessed agreement with the criticisms formulated in the existing literature. Several research papers (see Strongin et al. [2000]; Bernstein [2003]; Tabner [2007] among others) have found that cap-weighting leads to concentration in the stocks with the largest market caps (concentration effect, or “size bias”).

March / April 2013

Usage Of Indexes Across Asset Classes

Equity

Indexes

Have you used the following indexes in your investments,

in the respective asset classses?

Corp.

Bond

Indexes

Gov’t

Bond

Indexes

88.9%

73.0%

70.5%

0% 40% 60% 80% 100%20%

Satisfaction Of Those Who Use Indexes

In Diferent Asset Classes

Equity

Indexes

Are you satisfed with the products you have used?

Corp.

Bond

Indexes

Gov’t

Bond

Indexes

68.8%

68.5%

53%

0% 40% 60% 80% 100%20%

Satisfaction Levels Across Asset Classes

Equity Indexes Gov’t Bond Indexes Corp Bond Indexes

Do you think there are signifcant problems with cap-weighted equity indexes, government bond Indexes and corporate bond Indexes?

No

35%

I don’tknow

12%

Yes

53%

Yes

38%

No

33%

I don’tknow

29%

No

29%

Yes

44%I don’tknow

27%

Source: EDHEC

Source: EDHEC

Source: EDHEC

Note: Expanding on the information presented in Figure 3, which displays levels of satisfaction, the above charts depict the percentage of respondents who recognize signifi-

cant problems with indexes across asset classes. Note the significant disparity between the percentage of respondents who stated “I don’t know” for equity indexes and for both

types of bond indexes.

Figure 2

Figure 3

Figure 4




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Moreover, Hsu [2006] and Arnott and Hsu [2008] have shown theoretically that cap-weighting automatically leads to an overinvestment in overpriced stocks; how-ever, that theory has been shown to be based on flawed assumptions (see Perold [2007]; and Graham [2012]).

Literature has also evidenced the poor diversification of cap-weighted indexes, which are often concentrated in a few large firms (Malevergne et al. [2009]). For example, Strongin et al. [2000] conclude that the S&P 500 Index mainly reflects the performance of 86 stocks and the Russell 1000 of 118. Meanwhile, Bernstein [2003] finds that the 10 largest companies accounted for 25 percent of the S&P 500 market value, and the top 25 companies accounted for 40 percent. In addition, Tabner [2007] finds a dramatic increase in the concentration of the top 10 firm/sector holdings between 1984 and 2005 for the FTSE 100 Index. Due to the heavy weightings of the big-gest companies in the index, cap-weighted indexes are not necessarily providing the diversification-related risk reduction most investors expect from a benchmark.

The results of the survey (Figure 5) show that size biases and overinvestment in overpriced stocks were perceived by the respondents as being the biggest problems with index-es, with 92.5 and 89.5 percent of respondents, respectively, finding those issues to be very important or important. In addition to these concerns that standard cap-weighted indexes are being exposed to a performance drag, about 73 percent of survey respondents think that biased expo-sures to sector factors may be problematic with standard equity indexes. This concern corroborates findings that the commonly used equity indexes are exposed to significant shifts in their style or sector exposures (e.g., Amenc et al. [2006]). About two-thirds of respondents find poor diver-sification an important to very important concern. It is worth noting that respondents do not see the potential lack of representativity of cap-weighted indexes as the major

issue, as only about 57 percent of respondents mentioned it as important or very important. In conclusion, the main issues that respondents see with cap-weighted indexes are issues related to the risk and return properties of indexes.

Bond Indexes

We conducted a similar analysis with common criti-cisms of bond indexes. In fact, fixed-income benchmarks are of particular concern because there are several promi-nent problems with bond indexes long noted in academic literature, including the so-called bums problem—the overweighting of heavily indebted issuers—as well as the problem of frequently changing characteristics, which result in fluctuating risk-factor exposures (see e.g., Siegel [2003]). A more recent study documents unstable duration and credit ratings over time in corporate bond indexes (Goltz and Campani [2011]).

Respondents were queried for their opinions on spe-cific issues with government bond indexes, helping illus-trate that a variety of concerns were held by investors ranging from practical implementation matters to risk-factor exposure and risk-factor stability. The results show that difficulty replicating the index is the main issue for respondents, as 81.6 percent of them consider it impor-tant or very important. Overinvestment in more indebted countries—an issue due to the nature of debt-weighting indexes—is also a major concern for 65.8 percent of respondents. This finding parallels those obtained for equity indexes, where a majority of respondents consid-ered overinvestment in overpriced stocks to be a critical issue. Lack of liquidity and proprietary pricing models are also each revealed as important issues for about 63 percent of respondents. Thus, respondents recognize the issues involved in creating an investable product based on government bond indexes (difficulties in tracking and replicability or illiquidity). Other practical concerns

March / April 2013

Size biases

Sector biases

Poor diversifcation

Lacking of representativity

of the economy

Overinvestment in

overpriced stocks

37.3% 55.2%

31.3% 58.2%

40.3% 32.8%

38.8% 29.9%

31.3% 25.4%

Important Very Important

Five Key Issues With Equity Indexes And Their Importance To Investors

0% 40% 60% 80% 100%20%

What do you think are important issues?

Source: EDHEC

Note: The above chart illustrates the percentages of respondents who think five issues that have been discussed in academic literature in relation to equity indexes are impor-

tant or very important. The figures are the result of questions that offered the following possible responses: not important; I don’t know; slightly important; important; and very

important. The percentages exclude nonresponses.

Figure 5




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Difculties to replicate the index

Overinvestment in more

indebted countries

Exposed to security risk

Lack of liquidity

Lack of stable sovereign credit

risk exposure

Inconsistent security selection rules

Proprietary models for pricing

Lack of stable duration in the index


23.7% 42.1%

26.3% 36.8%

36.8% 23.7%

39.5% 18.4%

42.1% 15.8%

31.6% 7.9%

39.5% 23.7%

31.6% 50.0%

0% 40% 60% 80% 100%20%

Issues With Government Bond Indexes That Are Important To Investors


Overinvestment in more

risky companies

Lack of stable credit risk exposure

Lack of stable duration in the index

Exposed to currency risk

Proprietary models for pricing

Lack of liquidity

Inconsistent security selection rules


Difculties in replication due to the

changing index characteristics

42.9% 35.7%

50.0% 21.4%

42.9% 28.6%

26.2% 38.1%

42.9% 21.4%

38.1% 23.8%

42.9% 19.0%

26.2% 7.1%

0% 40% 60% 80% 100%20%

Issues With Corporate Bond Indexes That Are Important To Investors


Source: EDHEC

Note: The above chart depicts the percentages of respondents who think issues related to government bond indexes are important or very important. Similar to the chart in

Figure 5, the percentages are the results of questions that offered the following possible responses: not important; I don’t know; slightly important; important; and very impor-

tant. The percentages exclude nonresponses.

Source: EDHEC

Note: The above chart depicts the percentages of respondents who think issues related to corporate bond indexes are important or very important. Similar to the charts in

Figures 5 and 6, the percentages are the result of questions that offered the following possible responses: not important; I don’t know; slightly important; important; and very

important. The percentages exclude nonresponses.

Figure 6

Figure 7




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regarding liquidity and pricing sources similarly received strong recognition as important issues.

Another implication of the results is that government bond indexes, generally, may lack sufficient coordination with investors’ needs. A substantial percentage of respon-dents recognized risk-factor instability, with stable dura-tion exposure appearing to be more important to respon-dents than stable credit risk: 63.2 percent of respondents found duration instability important or very important, while 57.9 percent found the same for credit risk exposure instability. Thus, a substantial percentage of investors rec-ognize the incongruity between investors’ needs and the unstable risk factor exposures present in government bond indexes—and the accompanying difficulty in integrating unpredictable risk into a portfolio.

Inconsistent security selection rules across index providers received fair attention from investors, with about 58 percent of respondents considering it important or very important. Finally, exposition to currency risk appears to be a major issue for only about 40 percent of respondents. These findings also suggest that the issues associated with government bond indexes are very differ-ent from those with equity indexes (Figure 5).

A similar assessment was conducted with corporate bond indexes, with liquidity and the instability of risk-factor exposure emerging as important issues. As Figure 7 illus-trates, overinvestment in risky bonds is evidently a crucial concern held by investors, and although the distinction

may be less profound in light of the current sovereign credit crisis, corporate bond indexes were perceived as more sus-ceptible to the “bums problem” than their sovereign coun-terparts. Similar views on the importance of credit risk with corporate bond indexes are reflected in the large percentage of respondents (71.4 percent) who view the instability of credit risk exposure as important or very important.

It is evident that there are problems that are recog-nized by investors, and there has been a very recent, rapid trend in indexing development; further changes will like-ly come as indexing innovation matures. Thus, it is also important to analyze perceptions on index properties and construction methodologies, which will both guide and constrain index development in the future.

Basic Quality Requirements:What Indexes Need To Deliver

As a result of the sheer variety of new index offerings and the lack of clarity of market participants on the mean-ing of key terms and concepts such as “active” vs. “pas-sive” management and “beta” vs. “alpha,” the current framework for analyzing indexes is convoluted, leaving some investors clinging to familiar standard indexes as a response. One of the key revelations of our survey was the elucidation of the qualities of indexes deemed most impor-tant by users. Fifteen questions related to various charac-teristics, rules, and general construction and rebalancing methods were asked. Most notable were the responses to

March / April 2013

Avoiding any discretionary choices

through, e.g. committee decisions

The index represents

a buy-and-hold strategy

Full information on the construction

methodology is publicly available

The index has a reasonably

low turnover

Full information on historical

index weights is available

Using objective guidelines

to select constituents

The index has high liquidity

Backed by economic and/or

theoretical concepts


15.1%

30.2% 66.2%

27.3% 62.2%

34.5%

38.1% 44.6%

43.9% 37.4%

35.3% 34.5%

35.3% 34.5%

54.7%

82.0%

0% 40% 60% 80% 100%20%

Do you consider the following characteristics important for selecting and assessing an index?

Important Issues When Selecting/Assessing An Index

Source: EDHEC

Note: The above chart depicts the percentages of respondents who think certain construction and management issues are important or very important when selecting and

assessing an index. The questions apply to indexes generally, and thus reflect the basic qualities and characteristics of an index’s construction and management rules deemed

important by respondents.

Figure 8




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the questions seeking assessment of the perceived level of importance of key index attributes, including the use of objective guidelines for constituent selection, backing by economic concepts and access to full information on the index construction methodology.

In accordance with the related academic literature,6

transparency and liquidity are recognized as the most important qualities of an index. Concerning index trans-parency, 97.1 percent of respondents stated that public disclosure of index construction methodology was impor-tant or very important, while 89.9 percent of respondents said that the availability of constituent weights was impor-tant or very important (Figure 8). Objectivity also emerged as one of the most important characteristics, with over 96.4 percent of respondents finding the objectivity of rules gov-erning constituent selection important or very important.7

The perceived prominence of transparency and objec-tivity are notable in light of the increasingly innovative indexing strategies being provided. Indeed, in light of our results, it is clear that investors will have very little toler-ance for obscurity or discretionary decisions with new index offerings. It should also be noted that some existing standard market indexes may not completely fulfill these basic quality requirements of investors, as the literature on indexes has highlighted issues with discretion (see Arnott, Hsu and West [2008], p. 64) and lack of transparency (see Kamp [2008]) with standard indexes.

Another attribute of indexes cited as prominent in aca-demic literature is the buy-and-hold characteristic. Malkiel [1995] and Bogle [2002] view this as a defining attribute of an index, and one serving a key purpose for investors. The responses we received from index users, however, illustrate an interesting contrast between a strong concern about low turnover (81.3 percent viewing it as important or very important), and a relatively less profound desire for a pure buy-and-hold strategy (only 69.8 percent viewing it as important or very important), indicating that indexes that use some form of dynamic weighting of stocks—and thus deviate from a pure buy-and-hold strategy—are likely to be acceptable, subject to constraints related to transparency and subject to maintaining low levels of turnover.

In addition to identifying the basic qualities valued, an attempt was made to establish which index construction methods are seen as favorable and which are outside of investors’ comfort zone. As depicted in Figure 9, respon-dents were asked which approaches they found accept-able, and were presented with several possible qualities that an index construction methodology could exhibit.

The results indicate substantial acceptance of charac-teristics-based weighting. Characteristics-based weighting refers to index methodologies that attribute weights to stocks in proportion to an attribute such as an account-ing variable like earnings or book value. As the traditional indexing method—weighting by market capitalization—also simply allocates based on a single characteristic, the use of characteristics may not be seen as much of a deviation from precedent methods, and coupled with its lack of complexity, may prove to be a method that finds

widespread popularity with investors. An interesting contrast also emerged from respondents’

views on the acceptability of qualitative versus quantitative rules. In accordance with the strong affirmation of the impor-tance of objectivity, only a little over one-third of respon-dents found qualitative choices acceptable, while one-half of respondents stated that systematic quantitative weighting methods that depart from standard weighting schemes would be acceptable. Thus, after characteristics-based weighting, respondents find systematic quantitative methods as the most acceptable, while a turning point emerges with qualita-tive methods. This reluctance to accept qualitative assess-ments as part of an indexing method is consistent with the strong affirmation of the importance of objectivity described previously (96.4 percent of respondents stated that objective guidelines were important or very important), as it is clear that qualitative assessments necessarily introduce a degree of subjectivity into index construction.

Central to the discourse surrounding indexing innova-tion is whether indexes must be “passive” in nature. Nearly half the respondents in our survey stated that it would be acceptable for indexes to deviate from purely pas-sive strategies. However, only 25 percent of respondents stated it would be acceptable for an index to be based on “alpha.” This contrast between relatively wide acceptance of indexes that are not purely “passive” and relatively wide rejection of indexes that try to create “alpha” implies that for most respondents, an “index” is not required to be “passive” in the sense of a buy-and-hold portfolio without any trading—but most importantly, an “index” is required to be “passive” in a broader sense of refraining from try-ing to generate alpha. Therefore, our results allow us to conclude that the main objective of an index portfolio should be to reflect the risk premia or normal returns avail-able from investing within an asset class as opposed to abnormal returns through the pursuit of alpha strategies. However, the way of implementing this access to risk pre-mia within an asset class does not necessarily constitute a “buy and hold” strategy. Our respondents’ acceptance of

March / April 2013

Acceptability Of Various Index Approaches

Source: EDHEC

Note: This chart expands on the information presented in Figure 8 and depicts the

percentages of respondents who identified various index construction approaches

as acceptable. The percentages shown have been normalized by excluding non-

responses and those who answered “I don’t know.” The response rates are above

90 percent for all questions.

New Forms Of Indexes % Of Yes

Figure 9

Simple Characteristics Weighted 71.3%

Systematic Quantitative Weighted 50.0%

Qualitative Choices 35.7%

Could Deviate From Reflecting Passive Strategies 49.6%

Can Be Based On Alpha 25.2%

Do you accept that indexes can adopt the following approaches in their construction process?




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indexes that do not rely on a buy-and-hold strategy prob-ably reflects an acceptance of new and enhanced index offerings that maintain systematic rules, and thus allow investors to reconcile their concern over reliability and transparency with improvement of risk/reward properties.

After examining the basic quality requirements for indexes and acceptable construction methods, we now turn to an assessment of investors’ views on alternative methods that have been proposed as improvements over traditional indexes.

Views On Alternative Weighting Schemes Although there has been rapid development of index-

ing methods in recent years, cap-weighted indexes have for decades been the standard, and enjoy a place as an established method. This can be explained by the fact that cap-weighting indexes were made popular by the capital asset pricing model (CAPM), formulated by Sharpe [1964], and are thus assumed to represent the average decisions of investors. In addition, extensive track records are available for those indexes. Thus, the place for alternative indexing schemes is not entirely clear, particularly with regard to whether they should be used as substitutes for cap-weight-ed indexes or as complements. When asked about that, the majority of respondents (58.6 percent) stated that the most appropriate approach to employing alternative methods would be as a complement to cap-weighted indexes. Only 23 percent viewed alternatives as a replacement to cap-weighted indexes. However, 27.6 percent of respondents to the survey viewed indexes following alternative weighting schemes as a potential replacement for active managers. This highlights the potential for alternative indexes as pos-sible methods for exercising an investor’s tracking error budget—a task traditionally awarded to active manage-ment. These results are displayed in Figure 10.

It is likely inevitable, however, that alternative weight-ing schemes will always be evaluated relative to their cap-weighted counterparts. The findings displayed in Figure 10 are consistent with the notion that any alternatives to standard cap-weighted indexes are perceived as creating a relative risk with respect to the investor’s peer group, leading investors to become reluctant to completely move away from cap-weighted indexes, which are risk-free relative to the peer group.

In regard to general usage of alternative weighting meth-ods for equity indexes, the responses indicate that further development of alternative methods can be expected, as about 30 percent of respondents stated that they have not yet implemented alternative schemes, but are going to, or are still considering doing so (Figure 11).

To provide further context around the likely areas for development of alternative weighting schemes, respon-dents were asked which types of data or information were integrated into their portfolio construction process. It is clear that one would expect that, if investors accept the use of indexes that deviate from cap weighting, the data used in the construction of those indexes would need to cor-respond to information that investors consider to be rel-

evant for constructing their own equity portfolios. Figure 12 depicts the percentage of respondents who use each respective information type “frequently.”

Figure 12 indicates that most of the respondents are more concerned with country or regional exposure than style and sector exposures, though academic literature did not find clear evidence of dominance of any single type of exposure over the others (Hamelink et al. [2001] Ferreira [2006]). The results demonstrate that risk proper-ties—such as volatility and stock correlation—are viewed as essential, but also that expected returns are perceived to be a very important ingredient in constructing equity portfolios, a result in accordance with concepts of modern portfolio theory (Markowitz [1959]). Given that traditional cap-weighted indexes and even some alternative indexing methods ignore such parameters (notably correlation

March / April 2013

Source: EDHEC

Note: The above chart depicts the percentage of respondents who think each respec-

tive use is the appropriate approach of using alternative-weighted indexes. The chart,

thus, shows how respondents see the relationship between cap-weighted indexes

and alternative weighting schemes.

Source: EDHEC

Note: The above chart depicts respondents’ views on current and future use of

alternative weighted equity indexes. The full range of possible responses (excluding

nonresponses) are given in the chart.

How To Use Alternatively Weighted Indexes

23.0%

27.6%

58.6%

To complement the

cap-weighted indexes

To replace active

managers

To replace the

cap-weighted indexes

What is the most appropriate approach to

use alternative-weighted indexes in practice?

Views On Current And Future UseOf Alternative Weighted Equity Indexes

Do you use alternative weighting schemes of equity indexes?

24%

23%

42%

6%

5%

Yes, we have

No, but we are going to

No, we are still

considering

No, and we are

not going to

We are not familiar with

these approaches

Figure 10

Figure 11




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between stocks, volatility and expected return), it will be interesting to see whether index providers will account for these considerations in their future index innova-tions. Another notable result is that accounting measures are seen as the least relevant of the various information types that are available to build an equity portfolio, while simple characteristics-based indexes that draw precisely on such accounting information are the most widely accepted among practitioners with regard to alternative index strategies methodologies (see Figure 9). All these results should be taken into consideration by index pro-viders for future developments of indexes.

Conclusion Some prominent conclusions can be drawn from the

responses of a broad set of investment professionals. Although index usage is high, there is a lack of satisfaction across asset classes, and the widespread receptiveness to new methods that are apparent from our survey will likely prompt providers to continue to develop innovative index-ing methods; however, respondents make it very clear that they think all indexes should be subject to quality constraints, including the transparency and objectivity of methodol-ogy and construction rules as well as high liquidity and low turnover levels. Two general characteristics that consistently govern investors’ requirements are an index’s objectivity and systematic nature. Thus, further development of any kind in the index space will likely be very systematic, with assur-ances to investors that index characteristics will be evident and predictable (precluding indexes that pursue “alpha” and

eschew buy-and-hold methods). Our survey results clearly show that investors draw a dividing line around transparency and objectivity of rules to differentiate between what they deem acceptable for an index and what they believe brings an approach outside the realm of indexing.

Although alternative methods are widely used and will likely continue to grow in popularity, most investors do not currently see them as replacements to cap-weighted indexes, but rather as serving a supplementary purpose. Standard cap-weighted indexes have remarkably low sat-isfaction rates; however, they remain a de facto refer-ence point, while alternative methods are largely seen as tools for spending relative risk budgets. While alternative index strategies in that sense are seen as substitutes of active management—rather than as substitutes of market indexes—a key difference between active managers and advanced beta indexes is that the decision processes of index strategies are more systematic and rules-based, and hence it is easier in principle to document risks. Ultimately, alternative index strategies hold the promise of allowing investors to obtain more precise information on the risk choices that are made within their portfolios and to make more explicit decisions about their desired risk exposures.

Alternative weighting methodologies may serve investors in two different ways. They could serve as a substitute of cap-weighting indexes, in order to obtain long-term higher performance. Those alternative weighing indexes will have a potentially high tracking error with regard to a cap-weight-ing index, and possibly a rather high drawdown (Figure 13), as reported in Amenc, Goltz, Lodh and Martellini [2012].

March / April 2013

Types Of Information Used When Constructing Equity Portfolio

0% 40% 60% 80%20%

When you construct your equity portfolio, how often do you integrate information on the following perspectives?

Country/regional exposure

Volatility of stocks or

categories of stocks

Sector exposure

Style exposure

Economic risk-factor exposure

Accounting measures

(e.g., earnings, cash fow, etc.)

Correlation between stocks

or categories of stocks

Expected returns of stocks

or categories of stocks54.8%

52.4%

50.8%

38.1%

49.2%

50.8%

42.1%

63.5%

Source: EDHEC

Note: The above chart depicts the percentages of respondents who integrate each respective information type often or very often when constructing equity portfolios. The pos-

sible responses were: never; rarely; sometimes; often; and very often.

Figure 12




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On the other hand, alternative weighting schemes could also provide overperformance relative to cap-weighted indexes while not deviating too far from such standard indexes. In that case, the alternative weight-ing indexes are used as a replacement of benchmarked active management. In fact, generating smooth out-performance, while keeping relative risk in check, is a common objective of traditional active managers. When an alternative weighted index is used as a substitute for traditional active management, the cap-weighted index is still considered the reference index. In this context, the alternative weighted index—rather than replacing the cap-weighted index—serves as a complement to the cap-weighted index. An important question that inves-tors need to address in this context is how to manage the relative risk behind the alternative weighted strategy.

The fact that alternative weighted indexes may be used as a substitute for traditional active managers, however, poses an important question. In fact, it is clear that compared to the widespread procedures on manager selection, the industry has dedicated relatively little attention historically to bench-mark selection. Thus, an important issue for the adoption and future development of alternative index strategies will be the establishment of a framework for analyzing such strategies, and for making selection and investment decisions across the growing range of alternative index strategies.

March / April 2013

Relative Risk of Alternative Weighting Schemes

Risk Measures

S&P 500

Equal

Weight

Index

MSCI USA

Minimum

Volatility

Index

FTSE

EDHEC-

Risk

Efficient

US Index

FTSE RAFI

US 1000

Index

Source: EDHEC

Note: The above table shows historical extremes of underperformance, annualized

excess return over the cap-weighted index (the S&P 500 Index), annualized tracking

error and extreme tracking error of the S&P 500 Equal Weight Index, the MSCI USA

Minimum Volatility Index, the FTSE EDHEC-Risk Efficient US Index and the FTSE RAFI

US 1000 Index with respect to the S&P 500 Index. Maximum relative drawdown for a

strategy is the maximum drawdown of the long/short index whose return is given by

the fractional change in the ratio of strategy index to the benchmark index. Extreme

tracking error corresponds to the 95th percentile of rolling one-year tracking error,

i.e., the annualized standard deviation of a portfolio long in the alternatively weight-

ed index and short in the S&P 500 Index, over the entire horizon. All statistics are

annualized and are based on weekly data from Jan. 3, 2003 to Dec. 30, 2011. Source:

Amenc, Goltz, Lodh and Martellini [2012].

Figure 13

ReferencesAmenc, N., F. Goltz and V. Le Sourd. 2006. Assessing the quality of stock market indices: Requirements for asset allocation and performance measurement. EDHEC-Risk Institute.

Amenc, N., F. Goltz, A. Lodh and L. Martellini. 2012. Diversifying the diversifiers and tracking the tracking error: Outperforming cap-weighted indices with limited risk of

underperformance. The Journal of Portfolio Management 38(3): 72-88.

Amenc, N., F. Goltz, L. Martellini and P. Retkowsky. 2011. Efficient indexation: An alternative to cap-weighted indices. Journal of Investment Management 9(4): 1-23.

Amenc, N., F. Goltz, M. Mukai, P. Narasimhan and L. Tang. 2012. EDHEC-Risk Asian index survey 2011. EDHEC-Risk Institute. (see: http://docs.edhec-risk.com/ERI-Days

Asia-2012/documents/EDHEC-Risk_Asian_Index_Survey_2011.pdf).

Amenc, N., F. Goltz, L. Tang and V. Vaidyanathan. 2012. EDHEC-Risk North American index survey 2011. EDHEC-Risk Institute (see: http://docs.edhec-risk.com/

mrk/000000/Press/EDHEC-Risk_North_American_Index_Survey.pdf).

Amenc, N., F. Goltz and L. Tang. 2011. EDHEC-Risk European index survey 2011. EDHEC-Risk Institute (see: http://docs.edhec-risk.com/mrk/000000/Press/EDHEC

Risk_European_Index_Survey.pdf).

Arnott, R.D. and J.C. Hsu. 2008. Noise, CAPM and the size and value effects. Journal of Investment Management 6(1): 1-11.

Arnott, R., J. Hsu and J. West. 2008. The fundamental index: A better way to invest. New Jersey: John Wiley & Sons, Inc.

Bernstein, P. 2003. Points of inflection: Investment management tomorrow. Financial Analysts Journal 59(4): 18-23.

Bogle, J. 2002. An index fund fundamentalist. Journal of Portfolio Management 28(3): 31-38.

Ferreira, A. 2006. The importance of industry and country effects in the EMU equity markets. European Financial Management 12(3): 341-373.

Fuller, R.J., B. Han and Y. Tung. 2010. Thinking about indices and “passive” versus active management. Journal of Portfolio Management 36(4): 35-47.

Goltz, F. and C.H. Campani. 2011. Review of corporate bond indices: Construction principles, return heterogeneity, and fluctuations in risk exposures.

EDHEC-Risk Institute publication, June.

Goltz, F. and V. Le Sourd. 2010. Does finance theory make the case for capitalization-weighted indexing? EDHEC-Risk Institute.

Graham, J. 2012. Comment on the theoretical and empirical evidence of fundamental indexing. Journal of Investment Management, First Quarter.

Grinold, R. 1992. Are benchmark portfolios efficient? Journal of Portfolio Management 19(1): 34-40.

Hamelink, F., H. Harasty and P. Hillion. 2001. Country, sector or style: What matters most when constructing global equity portfolios? Working paper. FAME.

Haugen, R. and N. Baker. 1991. The efficient market inefficiency of capitalization-weighted stock portfolios. Journal of Portfolio Management 17(3): 35-40.

Hsu, J. 2006. Cap-weighted portfolios are sub-optimal portfolios. Journal of Investment Management 4(3): 1-10.

Kamp, R. 2008. Debunking 130/30 benchmarks. Invesco.

Malevergne, Y., P. Santa-Clara and D. Sornette. 2009. Professor ZIPF goes to wall street. Working paper. National Bureau of Economic Research.


Max Relative Drawdown 13.60% 12.23% 8.43% 12.67%

Start Date 2/23/07 11/21/08 4/21/06 6/29/07

End Date 11/21/08 4/23/10 11/21/08 3/6/09

Annualized Excess ReturnOver Cap-Weighted index

3.24% 1.21% 3.75% 2.18%

Tracking Error (TE) 4.64% 5.69% 3.82% 4.78%

Extreme Tracking Error

(95th Percentile Of 9.57% 7.42% 6.72 % 11.92%

Rolling One Year TE)




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Evaluating Alternatives

March / April 2013

By David Blitz

36

The efficiency of

alternative index approaches

How Smart Is

‘Smart Beta’?

Worldwide, investors are increasingly keen on “smart beta” investing. By this we mean pas-sively following an index in which stock weights

are not proportional to their market capitalizations, but based on some alternative weighting scheme. Well-known examples of smart beta include fundamentally weighted and minimum-volatility indexes.

In this article, we first take a critical look at the pros and cons of smart-beta investing in general. After this, we discuss in turn the most popular types of smart indexes that have been introduced in recent years. The added value of smart-beta indexes has been shown to come from systematic tilts toward classic factor premiums that are induced by their weighting schemes. We will argue that investors should be aware of the potential pitfalls of smart-beta indexes, which arise because they are not specifically designed for harvesting factor premiums in the most efficient manner, but primarily for simplicity and appeal. And although passive management can be used to replicate smart indexes, we believe it is impor-tant for investors to realize that, without exception, smart indexes themselves represent active strategies.

Smart-Beta Investing In GeneralThe argument that is typically used to motivate smart-

beta investing is that the capitalization-weighted index is inefficient, and that a more efficient portfolio can be constructed by applying some alternative stock-weighting scheme. We agree with this view, but think it is important to understand where the added value of such weighting schemes really comes from. Research has shown that the weighting schemes used by alternative indexes tend to result in structural tilts toward stocks that score high (or low) on certain factors, and that the premiums that are known to be associated with these factors are driving performance.1

For example, when compared with capitalization-weighted indexes, fundamentally weighted indexes have a systematic tilt toward value stocks. These exposures enable the strategy to benefit from the well-known value premium, which, in fact, turns out to fully explain its performance. Similarly, a minimum-volatility index captures the low-volatility pre-mium by tilting the portfolio toward low-volatility stocks. Although this may seem obvious to some, many smart-beta index providers are still reluctant to acknowledge that their performance is driven by factor exposures, and that their weighting schemes are merely a novel way of establishing exposures toward classic factor premiums.

We are often asked whether smart-beta investing is a form of passive investing. It is important to realize that it is not. Although passive management can be used to replicate smart indexes, smart indexes themselves are essentially active strategies. The only truly passive investment strat-egy is the capitalization-weighted broad market portfolio, which represents the only buy-and-hold portfolio that could, in principle, be held in equilibrium by every investor. Smart-beta indexes are fundamentally different, because they require various subjective assumptions and choices. Their active nature is also illustrated by the fact that they require periodic rebalancing to maintain their profile. It is true that smart-beta indexes may bear some resemblance to true passive investing (for example, by investing in a large number of stocks with relatively low turnover), but it is important to realize that their deviations from the capi-talization-weighted index, which are the key to their added value, represent active investment decisions.

In sum, so far, smart-beta investing is a way to tilt a portfolio actively toward certain factor premiums. As we are proponents of factor investing, this makes smart-beta investing a potentially promising investment approach.




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For example, in a recent paper, we argued that equity investors should strategically allocate a sizable part of their portfolio to the value, momentum and low-volatility factor premiums.2 Smart-beta investing represents one way in which this could be implemented in practice.

Our view on smart-beta investing can be summarized as follows: Although smart-beta investing may be a good start, we believe investors can do better. The reason is that the main appeal of smart-beta indexes—namely their simplici-ty—is at the same time their biggest weakness. Specifically, we find that the simple tilts toward factor premiums pro-vided by smart-beta indexes often involve significant risks that are undesirable. In addition, smart-beta strategies can be inefficient from a turnover perspective, or can have unattractive exposures to factor premiums other than the one that is primarily targeted.

Another concern with smart-beta indexes is that they are often based on backtests that only go back 10 or 15 years in time. Investors should therefore be careful to avoid chasing recent performance. To properly understand the behavior of a smart index in different environments, we recommend analyzing its performance over long histori-cal periods, covering multiple economic cycles. Investors should also carefully think about whether the factor premi-ums that are driving historical smart-beta index returns are likely to persist in the future.

In the following sections, we will elaborate on these points by discussing the pros and cons of the most popular types of smart-beta indexes that have been introduced in recent years.

Fundamentally Weighted IndexesIn a fundamentally weighted index, stocks are weighted

in proportion to their fundamentals, such as book value or earnings. In other words, instead of letting the market decide on the appropriate weight of a stock, one might say that fundamentally weighted index investors prefer to rely on the assessment of accountants. The differences in weights between a traditional, capitalization-weighted index and a fundamentally weighted index are, by definition, entirely due to differences in valuation ratios of individual stocks, such as differences in book-to-price or earnings-to-price ratios. Compared with the capitalization-weighted index, a fundamentally weighted index is tilted toward stocks that are cheap on such ratios, i.e., value stocks. Studies have shown that the added value of fundamentally weighted indexes is, in fact, entirely attributable to this tilt toward the value premium.3 For a long time, Research Affiliates, the inven-tors of fundamentally weighted indexation, denied that the success of fundamentally weighted indexation is critically dependent on the existence of a value premium. Instead, they argued that, even in the absence of a value premium, random mispricing causes capitalization-weighted indexes to be biased toward overvalued stocks, resulting in a struc-tural drag on performance.4 Nowadays, however, Research Affiliates acknowledges that the value premium does indeed explain most or all of their indexes’ performance.5

Our main concern with straightforward value strategies such as fundamentally weighted indexation is that they

tilt toward financially distressed firms. To understand this, consider a firm that actually gets into financial distress. As a result, its share price drops, and its weight in the cap-weight-ed index drops correspondingly. Initially, the same happens in a fundamentally weighted index. At a certain point, how-ever, a fundamentally weighted index rebalances back to the weight based on past and current fundamentals, which have typically not (or only partly) adapted to the new situation. This exposure to distressed firms might not be a problem if, as some have conjectured, distress risk is the source of the value premium. Studies have shown, however, that the stocks of financially distressed firms tend to underperform, and that the tilt to distressed firms of naive value strategies increases risk and is harmful to returns.6 This implies that the value premium can be captured more efficiently by avoiding cheap stocks of financially distressed firms.

A related concern is that, since rebalancing involves buying stocks that have recently experienced a large price drop, fundamentally weighted indexes tend to go against the momentum premium. As the momentum premium appears to be at least as strong as the value premium, this suggests that the return of a value strategy may be enhanced by avoiding its natural tendency of going against the momentum premium.

Another concern with fundamentally weighted indexes is their sensitivity to settings choices. For example, it has been shown that, in certain calendar years, the arbitrary choice of the annual rebalancing moment of the funda-mentally weighted FTSE RAFI indexes can make the differ-ence between an outperformance of 10 percent or a small underperformance.7 The more recently launched funda-mentally weighted indexes of MSCI, called MSCI Value Weighted indexes, address this concern by rebalancing every six months, while those of Russell rebalance a quar-ter of the portfolio every quarter. In light of these develop-ments, FTSE recently announced that it will also provide such a staggered quarterly rebalanced variant of the FTSE RAFI indexes this year, although these will not replace their current indexes but will coexist with them.

Finally, we note that fundamentally weighted indexes represent a low-conviction approach to capturing the value premium. To understand this, note that a fundamentally weighted index is not exclusively concentrated in stocks with the most attractive valuation characteristics. For example, the FTSE RAFI US and Developed ex-U.S. indexes each invest in 1,000 stocks, and the MSCI Value Weighted indexes invest in all the stocks that are in the regular MSCI indexes. In other words, stocks with the least attractive valuations are still included in these indexes, only with smaller weights.

Low-Volatility IndexesLow-volatility indexes are designed to benefit from the

low-volatility premium: the empirical finding that low-risk stocks have similar or better returns than the market average, with substantially lower risk. Minimum-volatility indexes use optimization techniques to create a portfolio with the low-est expected future volatility. The resulting portfolio tends to consist mainly of stocks with low past volatility, although it may also include some higher-volatility stocks if these help




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to reduce volatility through low correlations. A drawback of optimized low-volatility indexes is their lack of transparency. For example, the most popular minimum-volatility index, the one provided by MSCI, uses the proprietary Barra risk model and optimization algorithm, as a result of which, many inves-tors regard the index to be a “black box.” Another concern is that the raw turnover of minimum-volatility strategies tends to be very high. MSCI addresses this concern by impos-ing turnover constraints,8 but this causes a new drawback; namely, path-dependency. This means that today’s compo-sition of the MSCI Minimum Volatility index depends on its past composition—a feature that is undesirable for investors interested in a fresh minimum-volatility portfolio because they wish to invest in the strategy from scratch.

A more transparent alternative is provided by the S&P 500 Low Volatility Index, which simply invests in the 100 stocks in the S&P 500 index with the lowest volatility over the preceding 12 months.9 Empirical studies have shown that this simple ranking approach results in a very similar risk/return profile to more sophisticated optimization approaches.10 The added value of both approaches comes from their tilt toward low-volatility stocks, which enables them to capture the low-volatility premium.11 We believe, however, that both represent a suboptimal way of benefit-ing from the low-volatility premium.

Our first concern with low-volatility indexes is their one-dimensional view of risk, focusing mainly on past volatility and correlations. We believe that risk cannot be captured by a single number, and our research confirms that a multi-

dimensional approach, which also includes forward-looking risk measures, is able to reduce risk—in particular, tail risk—further.12 A second concern with low-volatility indexes is that they completely ignore expected return considerations. We find that there is, in fact, a large dispersion in the expected returns of stocks with similar volatility characteristics. For example, stocks that are attractive from a volatility perspec-tive, but that go against other factor premiums—for example, by having unattractive valuation or momentum characteris-tics—tend to have below-average expected returns; however, low-volatility stocks that are supported by other factor pre-miums tend to have above-average expected returns. These insights are entirely ignored by generic low-volatility indexes.

We also observe some significant differences in the composition of different low-volatility index portfolios. The S&P 500 Low Volatility Index does not constrain sec-tor weights, resulting in a huge sector concentration. For example, at the time of writing, around 60 percent of this index was invested in only two sectors (utilities and con-sumer staples). The MSCI Minimum Volatility index, on the other hand, does not allow sector weights to deviate

more than 5 percent from their weight in the regular, cap-italization-weighted index. In our view, both approaches are too extreme. The MSCI Minimum Volatility index is overly constrained, while the S&P 500 Low Volatility Index is overly concentrated. Our assessment is that the opti-mum lies somewhere between these two approaches.

Russell recently launched its so-called defensive equity indexes, which can be regarded as a “low-volatility light” alternative. This is because the weight of low-volatility fac-tors in these indexes amounts to only 50 percent. The other 50 percent is based on “quality” factors, such as earnings stability, profitability and leverage. The reason for blending in these other factors is not entirely clear. The backtested index returns indicate that these factors tend to increase, rather than further reduce, volatility. So if volatility does not improve, the benefit should probably come from improved returns. Thus, investors should be convinced that the incre-mental return from tilting toward quality more than offsets the higher volatility induced by these factors.

Maximum Sharpe Ratio IndexesWe next discuss two closely related smart-beta index-

es; namely, the FTSE/TOBAM Maximum Diversification Index and the FTSE/EDHEC Risk Efficient indexes. Both approaches essentially try to maximize the expected Sharpe ratio, i.e., the ratio of expected return to expected risk. Although the way in which expected risk and return are defined is not identical, the differences are relatively small. For example, the Maximum Diversification index

assumes that expected returns are proportional to volatil-ity, while the Risk Efficient index assumes that expected returns are proportional to downside volatility.

The Maximum Diversification and Risk Efficient indexes are often regarded to be alternative low-volatility approach-es. To understand this, note that lowering portfolio volatility helps to maximize the Sharpe ratio, which has volatility in the denominator. However, the indexes actually go against the low-volatility premium by assuming that expected returns are proportional to (downside) volatility, which makes high-risk stocks more attractive in the numerator of the Sharpe ratio. These two opposing forces (i.e., a prefer-ence for low-volatility stocks from a risk perspective versus a preference for high-volatility stocks from a return perspec-tive) can cause the indexes to have either a low-volatility or a high-volatility profile. In the long term, the high-volatility profile actually appears to dominate.13 Compared with the capitalization-weighted index, the indexes also appear to load on the small-cap and value-factor premiums.14

In sum, it seems that, similar to other smart-beta indexes, classic factor premiums fully explain the added

In our view, the main challenge involved with harvesting the momentum

premium is how to control the high risk involved with the strategy.





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value of the Maximum Diversification and Risk Efficient indexes. Unlike fundamentally weighted and minimum-volatility indexes, however, the tilt toward factor premi-ums is less direct and more dynamic in nature.

Momentum IndexesHistorically, the momentum premium has been at least

as large and consistent as the value and low-volatility factor premiums. Momentum indexes are much scarcer though, probably due to the fact that momentum struggled during the most recent decade (while value and low-volatility strategies showed very strong performance over this period) and because the relatively high turnover of momentum strategies fits less well with the idea of a “pas-sive” index strategy. We believe, however, that momentum deserves more attention, if only because it tends to do well when value and low-volatility struggle simultaneously, such as during the tech bubble of the late 1990s.

Although momentum strategies have shown impres-sive long-term average returns, they can show a large underperformance over shorter periods of time. For example, the generic long/short momentum strategy that is typically considered in the academic literature shows a return of -83 percent over the year 2009.14 In our view, the main challenge involved with harvesting the momentum premium is how to control the high risk involved with the strategy. AQR, which recently introduced the first serious momentum indexes, seems to do so by limiting the tilt toward momentum stocks. Specifically, they invest in a relatively broad set of stocks (the top 33 percent, based on a ranking on return over the past 12 months, excluding the most recent month) and they weight these stocks in proportion to their mar-ket capitalization. Although these choices are indeed effective for controlling the risk of a momentum strat-egy, they also prevent investors from benefiting from the full potential magnitude of the momentum premium.

Our research shows that in order to earn the momentum premium, it is not necessary to be exposed to the large risks involved with naive momentum strategies. Specifically, we find that a more sophisticated momentum strategy is highly effective at eliminating precisely those risks that are not properly rewarded, thereby resulting in significantly better risk-adjusted returns.15 The essence of our approach is to adjust the momentum of each stock for the part that is driven by its systematic risk characteristics (for example, high-beta stocks are expected to outperform the market in proportion to their beta). By ranking stocks according to their remaining, idiosyncratic momentum, we obtain a more sophisticated momentum strategy, which is much less sensitive to systematic risk, such as a broad market reversal. This enables us to create a portfolio that is tilted more aggressively toward the momentum premium, while staying within the same risk budget.

Turnover is also a major concern with momentum strategies, which have relatively high turnover by defini-tion. From this perspective, the AQR momentum indexes are clearly not entirely optimal, because they may involve

buying a stock ranked just above the selection threshold and selling it at the next rebalancing, three months later, if its rank has dropped to just below the selection threshold. More sophisticated buy-sell rules may be able to avoid such unnecessary turnover.16

Equally Weighted IndexesSeveral index providers, including MSCI and S&P, have

introduced equally weighted indexes. These are typically regarded as a means to harvest the small-cap premium, which is another example of a premium that has been extensively documented in the literature. However, we believe that a word of caution is appropriate here. The evidence for a small-cap premium in the literature mainly concerns the smallest, least liquid stocks in the market. Equally weighted indexes do not actually invest in these stocks, but continue to invest in large- and medium-sized firms. For example, the S&P 500 Equal Weight Index still invests in 500 of the largest U.S. stocks, while the total number of U.S. stocks is well over 5,000. Thus, equally weighted indexes are better described as strategies that try to exploit a possible difference in return between large stocks and even larger stocks. Equally weighted indexes are thus able to profit only partly, at best, from the small-cap effect considered in the literature.

Another concern with equal weighting is that portfolio weights tend to move continuously away from their target levels, so frequent rebalancing is required to maintain equal weights. As this rebalancing involves selling recent winners and buying recent losers, this tends to go against the momentum effect (e.g., in case of annual or semiannual rebalancing). A nice anecdote in this regard is that back in the early 1970s, when the concept of passive investing was conceived, some of the early adopters in fact chose equally weighted portfolios, but soon abandoned this approach because of these practical issues.17 In our view, therefore, a traditional capitalization-weighted (buy-and-hold) index of true small stocks is a more appropriate and also a more efficient way to capture the small-cap premium.

SummaryIn smart-beta indexes—such as fundamentally weighted

and minimum-volatility indexes—stock weights are based not on their market capitalizations, but on some alternative formula. We have argued that the added value of smart-beta indexes comes from systematic tilts toward classic factor premiums that are induced by these alternative weight-ing schemes. We also showed that smart-beta indexes are not specifically designed for harvesting factor premiums in the most efficient manner, but primarily for simplicity and appeal. For a number of popular smart-beta indexes, we have discussed the main pitfalls, and how investors may cap-ture factor premiums more efficiently by addressing these concerns. Finally, it is important to remember that although passive management can be used to replicate smart index-es, investors should realize that, without exception, smart indexes themselves always represent active strategies.






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Talking Indexes

March / April 2013

By David Blitzer

40

Why some ETFs fail

Survival Of The Fittest

In the indexing and ETF world, 2012 may be remem-bered as the year ETF closings reached sufficient num-bers to dominate industry gossip and news about ETFs.

IndexUniverse’s ETF Watch lists about 100 ETF closures in 2012, almost twice the annual pace seen in 2008-2010 dur-ing the financial crisis and recession. There is little chance we will run out of ETFs or stop launching new ones any time soon: The total number of ETFs continues to expand, with nearly 180 added in 2012, and money continues to flow into ETFs, both new and old. By now, some 20 years after the launch of the SPDR S&P 500 ETF (NYSE Arca: SPY) kicked off the rise of the ETF industry, most new ETFs—and recently closed ones—are based on strategy indexes rather than broad-based market indexes. Strategy domi-nates the new issues because there are few, if any, markets left that aren’t already covered by ETFs. Further, strategy indexes—and hence the ETFs linked to those indexes—focus on various investor interests such as dividends or low volatility. Digging into the nature of strategy may explain the 2012 rise in ETF terminations.

While we shouldn’t ignore some of the financial factors cited for ETF closures—rising operating expenses; increases in the minimum size needed to break even; or competition among ETF issuers—understanding the nature of strategies and the indexes that track them is important for understand-ing why some ETFs survive and others fade away.

Financial and economic research going back several decades focuses on why some stocks tend to outperform the market. The results of this research are the raw mate-rial of strategy indexes. Many investors are familiar with ideas that small-cap or value stocks tend to outperform large-cap or growth stocks. The more formal statement of these arguments is the three-factor model of Gene Fama and Ken French,1 who identified size measured by market

capitalization and a value bias measured by the ratio of book value to market value and then added market per-formance as the third factor driving stock performance. Later work by Mark Carhart2 introduced momentum measured by the difference between short-term and intermediate-term performance as a fourth factor. Recent research has expanded some of these ideas with different measures of value or momentum and with new factors such as volatility or liquidity. The three- or four-factor models underlie the first generation of strategy indexes focusing on combinations of growth or value and large-, mid- or small-cap stocks and momentum.

These efforts were only the beginning of strategies. Other factors soon joined, including dividends, specific sectors or industries, mergers, acquisitions, spinoffs and other corporate actions or such company characteristics as family ownership or social policies. Strategy indexes are attempts to exploit times when the market deviates from the theory that all stocks offer the same returns after adjustment for risk and correlation. Some strategies—for example, buying stocks in only one sector—have limited lifetimes, since the market is constantly evolving. Other strategies seek longer lifetimes and more staying power; some claim to do well in various markets.

All strategies face three challenges that could limit their performance. An ETF based on a strategy that underperforms is living on borrowed time. Consider an ETF that holds stocks in only one sector: Markets shift over time, and what works one day may fail miserably the next day. Financial stocks were shunned in 2007-2009 but gained twice as much as the S&P 500 in 2012.

The second challenge is data mining: Given a big database, a fast computer and enough time, an analyst can “discover” some rule guaranteed to pick yesterday’s winning stocks.




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Despite high t-statistics, statistical and economic significance, and an R2 of 99.99 percent, the discovery most often doesn’t work going forward. The cause is simple: Try enough models, equations and ideas, and a few are certain to look good. Even when data mining creates fool’s gold instead of the real thing, some of these do become filings for new ETFs.

The last challenge is “success.” Once word gets around that some new strategy works, everyone rushes in. Suppose you designed an index of technology stocks that pay dividends to combine the stability of dividend payers with growth and it beats the market quite handsomely. Other tech-dividend indexes appear, hedge funds buy up high-dividend tech stocks and CNBC runs a hot idea story, while IndexUniverse lists all the newly registered ETFs targeting the area. Prices of dividend-paying tech-nology stocks would be bid up, and performance going forward would collapse amid falling dividend yields for the group. There was no market rotation and the idea wasn’t data-mined, yet success breeds its own failure.

A recent research paper by McLean and Pontiff3 examines the loss in stock predictability and strategy return caused by research publication. Their study explores 82 investment ideas going back over 20 years, testing losses that might be blamed on data mining as well as crowds. The impact of data mining and statistical analysis is mixed and not statistically significant. The average effect of popularity through publi-cation reduces the expected returns after publication by 35 percent of the returns before publication.

Investment strategies, like many other investment ideas, are often ephemeral. Moreover, the attractiveness of strategy ETFs differs from the attractions of investing in an ETF that tracks a broad-based market index like the S&P 500 or a total market index. An investor owning a strategy ETF hopes it is a good idea that will last long enough; the investors who choose an ETF tracking the S&P 500 or a total market index believe in low costs and participating in the stock market.

Endnotes1 Fama, Eugene F. and French, Kenneth R. (1993). “Common Risk Factors in the Returns on Stocks and Bonds,” Journal of Financial Economics 33 (1): 3–562 Carhart, Mark M. (1997). “On Persistence in Mutual Fund Performance,” Journal of Finance 52 (1): 57–823 McLean, R. David and Pontiff, Jeffrey E. “Does Academic Research Destroy Stock Return Predictability?” (Oct. 3, 2012). AFFI/EUROFIDAI, Paris, December 2012 Finance

Meetings Paper. Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2156623

Faber continued from page 21

post for both opportunities arising from negative geopolitical events as well as a sanity check against bubbling stock markets. Comparing global equity markets on a relative basis allows the portfolio manager to create portfolios of cheap stocks markets, while avoiding or even shorting expensive markets.

Appendix: Other Valuation ModelsSamuel Lee has a great article titled “The Hedgehog’s

Error”9 on Morningstar’s website that sorts glob-

al countries based on value (price/book) using the French/Fama database. Not surprisingly, he finds that sorting on value works well.

We utilize the database to sort the countries (12 in 1975 and rising to 20 by 1991) based on various measure of value. In Figure 12, we demonstrate the results of sorting the countries on a yearly basis and choosing the cheapest x percent of the universe (from 10 to 33 per-cent). Results are U.S. dollar based, nominal.

Endnotes1 Shiller maintains a website with an Excel download that includes historical data with formulas illustrating how to construct his 10-year CAPE: http://www.econ.yale.

edu/~shiller/data.htm. For a step-by-step guide, Wes Gray at Turnkey Analyst has a good post that walks through the steps necessary to construct the metric: http://turn-

keyanalyst.com/2011/10/the-shiller-pe-ratio/2 “Estimating Future Stock Market Returns” by Adam Butler and Mike Philbrick tackles the issue of different measurement periods from one to 30 years (as well as other

valuation models).3 John Hussman has a few good articles on this topic: “Estimating the Long-Term Returns on Stocks” and “The Likely Range of Market Returns in the Coming Decade”;

Joachim Klement also recently published the paper “Does the Shiller-PE Work in Emerging Markets?” that performs a similar analysis.4 Rob Arnott of Research Affiliates touches on this important topic in his white paper “King of the Mountain” (http://www.researchaffiliates.com/Our%20Ideas/Insights/

Fundamentals/Pages/F_2011_Sept_King_of_the_Mountain.aspx). Two other books speak of CAPEs and inflation/deflation levels. The first is “Unexpected Returns:

Understanding Secular Stock Market Cycles” by Ed Easterling, and John Mauldin’s “Bull’s Eye Investing: Targeting Real Returns in a Smoke and Mirrors Market.”5 One such resource is Russell Napier, who authored Anatomy of the Bear: Lessons From Wall Street’s Four Great Bottoms, and who discusses global CAPEs in a video here:

http://video.ft.com/v/946244201001/Long-View-Historian-sees-S-P-fall-to-400 . We also found two great recently published papers: “Does the Shiller-PE Work in Emerging

Markets?” by Joachim Klement (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2088140), and “Value Matters: Predictability of Stock Index Returns” by Angelini,

Bormetti, Marmi and Nardini (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2031406).6 http://www.tweedy.com/research/papers_speeches.php7 http://www.iijournals.com/doi/abs/10.3905/jpm.1991.4093278 http://www.mebanefaber.com/2011/11/17/sorting-countries-by-dividend-yield-2/9 http://etf.morningstar.com/BlogArticle.aspx?postid=3281399




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By David Blanchett

(Hint: The answer is ‘yes’)

Are Active Mutual Funds Becoming Less Active?




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March / April 2013www.journalofindexes.com 43

There is a growing body of research noting that return correlations for individual securities have been increasing. A recent paper by Sullivan and Xiong [2012]

titled “How Index Trading Increases Market Vulnerability” not only documents this occurrence, but also cites a potential culprit: the increasing popularity of index funds. Since index funds tend to be value-weighted—and therefore trade the same securities in the same relative portion—as index funds gain more assets, more and more securities are being traded in the same way at the same time, regardless of the under-lying attributes of the stocks themselves.

The increasing “commonality” across individual securi-ties doesn’t appear to bode well for active managers, who by definition seek to add value through individual security selection. This paper will provide insight as to how the level of “active” management has been changing in actively man-aged mutual funds over the last 21 years by reviewing the historical relationship between gross returns and a bench-mark based on each fund’s respective Morningstar category. As one might expect, given the increase in individual secu-rity correlations, the average mutual fund correlation to its benchmark has increased over the test period, suggesting that active managers are in fact becoming less active.

Here Come The Index FundsIndex investing has exploded over the last two decades,

growing at roughly twice the rate of active investments. Of households that owned mutual funds, 31 percent owned at least one index mutual fund in 2010. The Investment Company Institute estimates that 37 percent of all index

fund assets were invested in S&P 500 index funds, while 32 percent were tracking some other domestic equity index. About 40 percent of the new money that flowed into index funds was invested in funds indexed to bond indexes, while one-third was directed toward funds indexed to global and international stock indexes, and one-quarter went to funds indexed to domestic stock indexes.

While equity index assets are only 14.5 percent of mutual fund assets, Sullivan and Xiong estimate indexes represent roughly one-third of total fund assets today when factoring in ETF assets. The first ETF, the SPDR S&P 500 ETF (NYSE Arca: SPY), was introduced in January 1993. Significant growth in ETF assets really didn’t start until 2000, though, when there were roughly $66 billion in assets and 100 options; those numbers have grown to more than $1 trillion in assets with more than 1,400 ETFs available. ETF trading has grown from virtually nil in 2000 to now accounting for roughly 30 percent of total dollar trade volume and about 20 percent of total share volume.1

Just as indexing has changed the nature of stock own-ership, so too has the rise of institutional investors. The average fraction of a firm’s equity shares held by institu-tions has grown from 24 percent in 1980 to 44 percent in 2000, and reached 70 percent in 2010.2 In a world where all institutional investors are trading according to their own respective beliefs, this may not be a problem; how-ever, with the rise in indexing, more stocks are being held by institutions that seek to replicate the return of a given index and minimize tracking error.

Since the vast majority of indexes are value-weighted, they tend to hold the same stocks in the same relative por-tions. Therefore, when an investor buys (or sells) an index that holds one of these securities, the security is bought (sold) in conjunction with the other securities that make up the index, regardless of the relative attractiveness of the stock itself. This creates an increase in “commonality” among stocks, especially those in the more popular “bas-kets,” such as those in the S&P 500.

Impact On Active Managers

The most obvious impact on actively managed port-folios from increasing individual security correlations would be higher levels of market correlations (i.e., a decrease in the “active” portion of the portfolio). Although this might seem intuitive, it may not neces-sarily be the case, if (for example) the portfolio man-ager was trying to maintain some level of tracking error against his or her respective size and style benchmark. If the portfolio manager were to recognize that correla-

tions among individual securities were increasing, he or she could decide to hold fewer stocks or tilt the portfolio more toward certain sectors.

If a portfolio does not maintain a constant level of tracking error (on average), the portfolio effectively becomes either more active or passive through time. If the portfolio is becoming less “active” and charging the same fee, it becomes increasingly unlikely that the port-folio manager will outperform his or her benchmark. This is because, in the absence of any active management skill (which should cancel out in the aggregate, regardless), the active manager should be expected to underperform the appropriately selected benchmark by the total fees of the portfolio.3 Therefore, in order to outperform the benchmark, the portfolio manager will need to take on active risk, thereby deviating from the benchmark.

If individual securities are, in the aggregate, exhibit-ing less idiosyncratic risk and the portfolio manager

Just as indexing has changed the nature of stock ownership, so too has the rise of institutional investors. The average fraction of

a firm’s equity shares held by institutions has grown from 24 percent in 1980 to 44 percent in 2000, and reached 70 percent in 2010.




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does not change the risk profile through some other means, the probability of having a return more similar to the benchmark increases. This in turn increases the probability the portfolio manager will underperform the benchmark by the total amount of fees.

AnalysisIn order to determine whether or not active manag-

ers are becoming more or less “active,” an analysis was performed. For the analysis, all data were obtained from Morningstar Direct. The mutual funds included in the analysis are those categorized by Morningstar as domes-tic equity mutual funds from January 1991 to December 2011. Funds are included regardless of when they exit or leave the test set, and since the available test population is updated monthly, survivorship bias is not a concern.

In order to be included, the mutual fund must be in one of the nine domestic equity style boxes. Also, to ensure fund “purity,” only those funds with the same Morningstar Category and Style Index are included for a given test period. The oldest share class for each fund

is used, and any fund classified as an enhanced index, index fund, or fund of funds is excluded from the analy-sis. These screens limited the number of funds to 2,059 over the entire test period.

Each fund is compared with its respective Russell Index, across value, blend and growth, using the Russell 1000, Russell Mid Cap and Russell 2000 for large cap, midcap and small cap, respectively. The analysis is conducted on a roll-ing monthly basis and the available test set is determined for that respective historical rolling period. The analysis

uses a 12-month rolling historical period for all calcula-tions. All returns are gross returns; i.e., they do not include the impact of investment management fees. The purpose of the analysis is not to opine on the great active versus passive debate, but rather to determine how the nature of

active management has changed over time. Note, however, that since expense ratios are relatively constant, they would have little effect on the statistics calculated for this paper (primarily correlation and standard deviation).

While the average correlations are determined at the individual Morningstar category level, the category results are aggregated into a single value for each rolling test peri-od based on the weighted average number of funds avail-able for the given test period by category. This approach overweights styles that typically receive more assets (e.g., large cap versus small cap). However, although only the aggregate results are presented, the overall results are vir-tually identical across the individual categories.

ResultsPast research by Sharpe [1992] noted that style and

size explain approximately 80 to 90 percent of mutual fund returns, while stock selection explains only 10 to 20 percent. The results of this analysis confirm these gen-eral findings, with the average correlation of the respec-tive fund to its Russell index based on its category being 0.93 (and the average correlation 0.94). That translates into a coefficient of determination (R²) of 86 percent, which is right in the middle of Sharpe’s original estimate. However, the correlation has not been constant through time, as exhibited in Figure 1, which includes the average

Average Rolling Annual CorrelationTo Respective Category Index

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0.95

0.90

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0.75Dec-91 May-97 Nov-02 May-08

One-Year Period Ending

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y = 1E-0.5x + 0.4614R2 = 0.3697

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Source: Morningstar

Average Rolling Category Correlation Standard Deviation

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0%Dec-91 May-97 Nov-02 May-08

One-Year Period Ending

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y = 6E-0.6x + 0.2932R2 = 0.2517

Figure 2

Source: Morningstar

Past research by Sharpe [1992] noted that style and sizeexplain approximately 80 to 90 percent of mutual fund

returns, while stock selection explains only 10 to 20 percent.The results of this analysis confirm these general findings.




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rolling annual correlation to the respective category index.Since 1991, the average correlation for actively man-

aged mutual funds has been increasing. As of December 2011, it was at its approximate highest level in history, with the average fund having a correlation of 0.982 to its respective category index. Viewed differently, a correla-tion of 0.982 means that 98.2 percent of the return of a given active manager can be described entirely by the underlying benchmark index. This suggests the active manager is only adding roughly one-thirtieth of the total deviation in returns, but in many cases charging 10 times or more than what a comparable passive strategy costs. Note that the t-statistic associated with slope is 11.73, sug-gesting an incredibly high level of statistical significance.

Another way to view the changing “active” exposure of mutual funds through time is the standard deviation of the correlations. This metric captures the dispersion of all active managers through time. It is possible that while the average is increasing, there could also be a greater level of dispersion among portfolio managers. Unfortunately, as demonstrated in Figure 2, the aver-age rolling category correlation standard deviations have also been decreasing. This suggests that, on aver-age, an increasing number of actively managed mutual funds are clustering more and more tightly around their respective category benchmarks. The t-statistic associ-ated with slope is -8.84, suggesting an incredibly high level of statistical significance.

The final test to determine the direction of the “active” portion of actively managed mutual funds is based on the average tracking error of the fund versus its respective cat-egory index (Figure 3). For this test, other than two notice-able spikes, the clear trend has been a decreasing level of tracking error through time. Again, this suggests active managers are in fact less “active” than they used to be. The t-statistic associated with slope is -3.13, suggesting a rela-tively high level of statistical significance.

Conclusion

It is impossible to pinpoint an exact reason why active-ly managed mutual funds are becoming less “active,” but the evidence would certainly suggest this is the case. One theory this author believes could be a driving force behind

this change is increased movement to index investing. As more investors “index” their portfolios, more and more trading volume is based not on some technical analysis about the future earnings of a given company (for exam-ple), but instead whether or not a given security is includ-ed in a particular index (the S&P 500 in particular) and to what extent. Alternatively, it could be that “style purity” is becoming increasingly important for benchmarking pur-poses. Regardless of the reason, active funds have clearly become less active through time.

These findings present an interesting environment for active management. First, as more money flows to index funds, less money must be flowing to active strate-gies. Although active as well as passive investments can be “winners” from a positive flows perspective, the rela-tive gain of either category definitely comes at the cost of the other. Second, if higher levels of flows into passive funds do create an environment that makes it more dif-ficult for active management to outperform, and if an increasing amount of flows go to passive/index strate-gies, the ability of an active manager to outperform is likely to become increasingly hampered. Finally, for an efficient market to exist, there must be some active man-agement. It is beyond the scope of this article to venture a guess as to where this point is, but we don’t appear to be there yet. It will certainly be interesting to see what happens if we do ever get there.

ReferencesInvestment Company Fact Book. 2011. http://www.ici.org/pdf/2011_factbook.pdf

Sharpe, William. 1992. “Asset Allocation: Management Style and Performance Measurement.” Journal of Portfolio Management, vol. 18 No. 2: 7-19

Sias, R.W., L. Starks and S. Titman. 2006. “Changes in Institutional Ownership and Stock Returns: Assessment and Methodology.” Journal of Business, vol. 79 No. 6:2869-2910.

doi:10.1086/508002

Sullivan, R. and Xiong, J. X. 2012. “How Index Trading Increases Market Vulnerability.” Financial Analysts Journal. Forthcoming, available at SSRN: http://ssrn.

com/abstract=1908227

Endnotes1 Sullivan and Xiong [2012]

2 Sias, Starks and Titman [2006]; Sullivan and Xiong [2012]

3 Both explicit management fees and implicit fees that result from trading, such as the bid/ask spread and commissions

Average Rolling Average Category Tracking Error

4.5%

4.0%

3.5%

3.0%

2.5%

2.0%

1.5%

1.0%

0.5%

0.0%Dec-91 May-97 Nov-02 May-08

One Year Period Ending

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y = 6E-0.7x + 0.0394R2 = 0.0409

Figure 3

Source: Morningstar




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By Craig Israelsen

Don’t be distracted by the short term

Taking A Long View Of Bond Performance




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March / April 2013www.journalo�ndexes.com 47

Interest rates go up. And down. And up.Over the past 64 years (1948-2011), that is exactly

what has happened. During the 34-year period from 1948-1981, the Federal discount rate increased—not every year, but as a general trend, as shown in Figure 1. In 1948, the Federal discount rate was 1.34 percent, and by 1981, it was 13.42 percent. During this time frame of rising interest rates, the 34-year average annualized return for U.S. bonds was 3.83 percent. �e year-to-year performance of U.S. bonds is represented in the graph by the vertical bars.

Starting in 1982, the Federal discount rate began its downward trend. At the end of 2011, the rate was 0.75 percent. During the last 30 years (1982-2011), the aver-age annualized return of U.S. intermediate bonds has been 8.98 percent (see Figure 1).

Clearly, the last 30 years have provided a wonderful

environment for bonds to perform well as the Federal discount rate steadily descended. Interestingly, U.S. stocks (represented by the S&P 500 Index) performed essentially the same during both periods. From 1948 to 1981, when interest rates were rising, the S&P 500 Index had an annu-alized return of 11.00 percent. During the recent 30-year period of declining interest rates, the S&P 500 Index generated a 10.98 percent annualized return. Whereas bond returns are markedly impacted by interest rate movement, stocks are largely immune—they march to a variety of drummers. Furthermore, cash (as represented by the three-month T-bill) averaged 4.49 percent during the 34-year period of rising interest rates, and 4.88 percent during the 30-year period of declining interest rates.

With this review of history now in mind, the question of the day is, If I expect interest rates to rise, should I avoid bonds going forward?

First, let’s clarify something. Are we talking about avoiding bonds as our only investment asset, or, are we talking about avoiding bonds as one of the asset classes in our overall asset allocation models? I will assume we are talking about the lat-ter question. To those who invest all their money in one asset class—such as a 100 percent stock portfolio or a 100 percent bond portfolio—this article is not for you.

Let me demonstrate. A one-asset portfolio that held only U.S. bonds (U.S. intermediate government bonds from 1948-1975 and the Barclays Capital Aggregate Bond Index from 1976-2011) was clearly impacted by the period of time. During the 34-year period of rising interest rates, a nondi-versi�ed all-bond portfolio averaged 3.83 percent per year,

whereas during the last 34 years, it would have produced .)2 erugiF ees( tnecrep 89.8 fo nruter dezilaunna egareva na

Realistically, a one-asset portfolio is not a prudent design. How about a two-asset portfolio? Let’s assume the

classic “balanced” design with a 60 percent allocation to stocks (S&P 500) and a 40 percent allocation to bonds (rebalanced annually). As shown in Figure 2, the dif-ferential in performance between the two time periods (1948-1981 and 1982-2011) is much less dramatic, but it clearly favors the more recent 30-year time period, which was more favorable to bond performance—which a�ected 40 percent of the two-asset portfolio.

A four-asset portfolio that allocated 40% to large U.S. stocks, 20 percent to small U.S. stocks, 30 percent to

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Rising Interest Rates: 1948-198134-Year Period

Average Annual Bond Return = 3.83%Average Annual S&P 500 Return = 11.00%

Average Annual T-Bill Return = 4.49%

Declining Interest Rates: 1982-201130-Year Period

Average Annual Bond Return = 8.98%Average Annual S&P 500 Return = 10.98%

Average Annual T-Bill Return = 4.88%

Annual Return of US Bonds (%) Federal Discount Rate (%)

Source: Raw data from Lipper for Investment ManagementNote: Intermediate term U.S. government bond returns from 1948-1975 and the Barclays Capital Aggregate Bond Index returns from 1976-2011

Figure 1

The last 30 years has been a wonderful environment for bondsto perform well as the Federal Discount rate steadily descended.


bonds and 10 percent to cash (with annual rebalancing) generated an annualized return of 9.52 percent during the 34-year period when interest rates were rising, and a 9.96 percent annualized return during the last 30 years in which rates were falling. There was a modest differ-ence of 44 basis points between the two time frames.

Clearly, as a portfolio is more diversified, the impact of the performance of one asset class on the overall portfolio (assuming the allocations are not heavily skewed toward only one asset) is dramatically reduced. This is precisely why portfolios should be diversified—by doing so, we lower the risk of allowing the bad performance of one par-ticular asset class to sink the portfolio’s overall returns.

Let’s now examine how the performance of bonds (actual, worst-case, and best-case) impacted a broadly diversified 12-asset portfolio. I will utilize a portfolio known as the 7Twelve Portfolio.

As shown in Figure 3, the 7Twelve Portfolio includes 12 asset classes that are equally weighted at 8.33 percent of the portfolio. Each asset class is rebalanced annually. During the 10-year period from Jan. 1, 2002 to Dec. 31, 2011, the per-formance of the 7Twelve Portfolio (using the performance of 12 raw indexes) was 8.93 percent, with a standard deviation of annual returns of 15.30 percent. The actual performance of U.S. bonds during this 10-year period (using the Barclays Capital U.S. Aggregate Bond Index) was 5.78 percent.

Asset Allocation Across Time

PortfolioPeriod of Rising Interest Rates

34-Year Period from 1948-1981Period of Declining Interest Rates

30-Year Period from 1982-2011

1-Asset Portfolio100% US Bonds

3.83% Annualized Return4.32% Standard Deviation


2-Asset Portfolio60% Large US Stock

40% Bonds



4-Asset Portfolio40% Large US stock20% Small US Stock

30% Bonds10% Cash



Figure 2

Source: Raw data from Lipper for Investment Management

7Twelve Portfolio Asset Category

(Using Raw Index Performance)

Performance Of A Broadly Diversified Portfolio (2002-2011)

10-Year Annualized % Return

1/1/2002-12/31/2011

10-Year Standard Deviation

of Annual Returns


Figure 3

US Large Cap Equity 2.92 20.50

US Mid Cap Equity 7.04 22.63

US Small Cap Value Equity 6.40 22.52

Developed Non-US Equity 4.67 25.05

Emerging Non-US Equity 13.86 38.00

Real Estate 10.12 25.16

Natural Resources 10.99 26.87

Commodities 14.97 20.34

US Aggregate Bonds 5.78 2.23

Inflation-Protected Bonds 7.57 5.99

International Bonds 8.38 8.34

Cash 1.91 1.86

7Twelve Portfolio Return 8.93 15.30

As a portfolio is more diversified, the impact of the performance

of one asset class on the overall portfolio is dramatically reduced.




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Now, let’s insert the worst 10-year performance for U.S. bonds since 1948, and measure the impact on a broadly diversified 12-asset portfolio. As shown in Figure 4, the worst 10-year period for U.S. bonds between 1948 and 2011 was from 1950-1959. During that 10-year span, U.S. bonds produced an average annualized return of 1.34 percent. The overall return of the 12-asset portfolio dropped from 8.93 to 8.54 percent—a decline of 39 basis points. The standard deviation of the 12-asset portfolio was essentially unchanged.

Next, as shown in Figure 5, I inserted the returns of the best 10-year period for U.S. bonds, which happened to be the period from 1982-1991. During this 10-year period, U.S. bonds generated a 10-year annualized

return of 14.09 percent. The impact of superior bond returns on the portfolio was beneficial, of course. The 10-year return of the 12-asset portfolio was 9.65 percent, with a standard deviation of 15.32 percent.

A summary of the scenarios (based on actual bond performance, worst-case bond performance and best-case bond performance) is provided in Figure 6.

For an investor placing all her investments in one asset, such as bonds or stocks or real estate, timing is everything. As it pertains to bond performance, the dif-ference between the worst-case 10-year time period and best-case 10-year time period for a 100 percent U.S. bond portfolio was nearly 1,300 basis points—resulting in a performance differential of nearly $26,000.



Impact Of Worst-Case Bond Performance In A Broadly Diversified Portfolio (2002-2011)

10-Year Annualized % Return 1/1/2002-12/31/2011

(US Govt Bonds 1950-1959)


Of Annual Returns


Note: Worst-performing 10-year return for U.S. bonds during 1948-2011 period

Figure 4









US Bonds (1950-1959)* 1.34 2.71



Cash 1.91 1.86




Impact Of Best-Case Bond Performance In A Broadly Diversified Portfolio (2002-2011)

10-Year Annualized % Return 1/1/2002-12/31/2011

(US Agg Bonds 1982-1991)


Of Annual Returns


Note: Best-performing 10-year return for U.S. bonds during 1948-2011 period

Figure 5









US Bonds (1982-1991)* 14.09 8.43



Cash 1.91 1.86





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For an investor who used a diversified approach (in this analysis, a 12-asset portfolio), the performance differential between the worst-case bond period and the best-case bond period was 111 basis points, or $2,430 in ending account value.

Completely avoiding any asset class in a diversified portfolio amounts to a guess that it will underperform and that another asset class will outperform. Building

prudent portfolios is not about guessing and timing; it’s about broad diversification. A broadly diversified portfolio is naturally insulated—not completely, but largely—from the normal swings in performance among its various components. The “underperformance” of one or several of its ingredients will not sink the performance of the overall portfolio.

Figure 6

Summary Of Three Bond Scenarios

Time PeriodDescription Of US

Bond Performance10-Year Annualized Return Of US Bonds

Growth Of $10,000 In US Bonds

10-Year Annualized Return Of

12-Asset Portfolio*

Growth Of $10,000 In A

Diversified Portfolio

2002-2011 Actual Performance 5.78% 17,540 8.93% 23,522

2002-2011(US bond returns from 1950-1959)

Worst-casePerformance

1.34% 11,423 8.54% 22,693

2002-2011(US bond returns from 1982-1991)

Best-case Performance

14.09% 37,365 9.65% 25,123

Difference between Worst-case and Best-case Bond Performance

1,275 bps 25,942 111 bps 2,430


Note: U.S. bonds have an 8.33% allocation (or 1/12th) in the 7Twelve portfolio. All 12 assets were rebalanced annually over the 10-year period.

Endnotes 1 See, for example, Chow, Hsu, Kalesnik & Little (2011), “A Survey of Alternative Equity Index Strategies,” Financial Analysts Journal, vol. 67, No. 5, pp. 37-57.

2 Blitz (2012), “Strategic Allocation to Premiums in the Equity Market,” Journal of Index Investing, vol. 2, No. 4, pp. 42-49.

3 See Asness (2006), “The Value of Fundamental Indexation,” Institutional Investor, (October), pp. 94-99; Blitz & Swinkels (2008), “Fundamental Indexation: an Active Value

Strategy in Disguise,” Journal of Asset Management, vol. 9, No. 4, pp. 264-269.

4 See Arnott, Hsu & Moore (2005), “Fundamental Indexation,” Financial Analysts Journal, vol. 61, No. 2, pp. 83-99.

5 See Chow, Hsu, Kalesnik & Little (2011), “A Survey of Alternative Equity Index Strategies,” Financial Analysts Journal, vol. 67, No. 5, pp. 37-57.

6 See de Groot & Huij (2011), “Is the Value Premium Really a Compensation for Distress Risk?” SSRN working paper no. 1840551.

7 See Blitz, van der Grient & van Vliet (2010), “Fundamental Indexation: Rebalancing Assumptions and Performance,” Journal of Index Investing, vol. 1, No. 2, pp. 82-88.

8 We note that although MSCI aims for a one-way turnover of no more than 20 percent per annum, on several occasions they have relaxed this constraint. For example, a

methodology change implemented at the end of 2009 caused a turnover of 45 percent at that moment.

9 Stock weights in this index are set inversely proportional to their volatility, so the lowest-volatility stocks get the highest weights.

10 See, for example, Soe (2012), “Low-Volatility Portfolio Construction: Ranking versus Optimization,” Journal of Index Investing, vol. 3, No. 3, pp. 63-73.

11 For a discussion of the low-volatility premium, we refer to Blitz & van Vliet (2007), “The Volatility Effect: Lower Risk Without Lower Return,” Journal of Portfolio

Management, vol. 34, No. 1, pp. 102-113.

12 See Huij, van Vliet, Zhou & de Groot (2012), “How Distress Improves Low-Volatility Strategies: Lessons Learned Since 2006,” Robeco research note.

13 See Clarke, de Silva & Thorley (2011), “Minimum Variance, Maximum Diversification, and Risk Parity: An Analytic Perspective,” SSRN working paper no. 1977577. In their

Table 2, they report a volatility of 19.0 percent for a maximum diversification strategy applied to U.S. equities over the 1968-2010 period, which compares with a volatility

of only 15.6 percent for the cap-weighted index over the same period.

14 Returns for this strategy are publicly available on the website of Prof. Kenneth French: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

15 See Blitz, Huij & Martens (2011), “Residual Momentum,” Journal of Empirical Finance, vol. 18, No. 3, pp. 506-521.

16 In all fairness, AQR also acknowledges that mechanically following their momentum indexes would be a suboptimal approach, and recognizes the need for a more efficient

implementation strategy.

17 Quoting Eric Falkenstein: “[…] It should be noted that there were several missteps among the index founding fathers. John McQuown and David Booth at Wells Fargo, and

Rex Sinquefield at American National Bank in Chicago, both established the first passive Index Funds in 1973. These were portfolios targeted at institutions. The Wells

Fargo fund was initially an equal-weighted fund on all the stocks on the NYSE, which, given the large number of small stocks, and the fact that a price decline meant you

should buy more, and at a price increase sell more, proved to be an implementation nightmare. It was replaced with a value-weighted index fund of the S&P500 in 1976,

which eliminates this problem. […]” See http://falkenblog.blogspot.nl/2011_09_01_archive.html.

Blitz continued from page 39




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By Ronald Slivka, Sharad Bhat and Sridhar Nonabur Srinivasamurthy

Can covered-call ETFs benefit global investors in BRIC stock markets?

Covered-Call ETFs

For BRIC Countries




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While the global financial crisis that began in 2007 left emerging markets less affected than G-10 countries, the subsequent price behav-

ior of BRIC stock markets highlighted once again the need for global investors to control risks through portfo-lio diversification. Typically within a single BRIC coun-try, however, the means to diversify are less available or less well developed than found elsewhere. Within the ETF investment universe, the means for portfolio diversification are even further restricted. For ETFs to continue their growth among emerging markets, then, will require wider coverage of assets and employment of strategies beyond traditional passive indexation.

ETF choices among nontraditional, or so-called sec-ond-generation funds, increased notably in 2012. Active management strategies for stocks and bonds now form the basis for rapid growth in developed-market ETFs. This development, which currently lags in BRIC countries, could offer the prospect of increasing the choices for domestic as well as global investors to further diversify their local BRIC holdings and so to increase risk control. BRIC equity markets remain volatile and only mildly trending or even trendless, raising the possibility that covered-call ETFs on recognized indexes could find a foothold in that impend-ing wave of second-generation ETFs. Historical evidence strongly supports the claim that in such an environment, a covered-call strategy delivers superior returns with less than index volatility, thereby making itself an attractive candidate for use in risk control.

Moreover, the continuing strong demand for emerging markets ETFs and investors’ need for enhanced returns in our current low-yield environment suggest that covered-call strategies targeting emerging markets could find a warm welcome in the ETF arena.

In this article, we examine the feasibility of structuring a specialty covered-call ETF to satisfy the rising demand for emerging market ETFs.

Equity Covered-Call ETFsCovered-call ETFs presently can be found linked to

stock markets in the U.S., Canada, Europe and even Korea, a borderline “emerging” market (Figure 1). However, no covered-call ETFs yet exist linked to indexes in the most prominent of the emerging markets: the BRIC countries. Figure 2 shows which BRIC markets have elements from which to construct such a covered-call ETF denominated in local currency. The requirements are simple: There must be both a convenient ability to acquire equity index exposure and an ability to sell index options. The presence of stocks or ETFs that replicate the index can satisfy the exposure requirement. It may also be possible to obtain synthetic equity exposure using index futures and cash [Slivka & Li, 2010] but this method is sometimes incon-venient, unnecessarily complicated and likely to provide more variable returns. The presence of exchange-traded index calls can satisfy the second requirement.

Any instruments used to construct BRIC covered-call ETFs should have sufficient liquidity to support continu-

ous call writing over a multiyear period. Two countries (Brazil and Russia) have options on index futures, but not options on the index itself, making covered-call construc-tion impractical. China presently has no options at all. India, on the other hand, appears to have the necessary requirements for covered-call construction. Index expo-sure can be acquired by direct purchase of stocks replicat-ing the index, while a liquid index options market allows call writing for maturities up to one month and sometimes longer. This makes it possible to construct an ETF using a semi-passive strategy in which one-month covered calls replace written calls as they expire, a topic we next explore.

Covered-Call ReturnsOne generally recognized benefit of selling calls against

long positions in stocks and indexes is that receipt of the time-premium component of the call premium can raise the return on the underlying asset above the return from holding the asset alone. Since time premium for a call is greatest near-the-money, covered-call writers seeking return enhancements often choose strike prices close to the current asset price. A second benefit is that the premium received creates a partial hedge against asset price decline. If the call is written out-of-the-money, the amount of this partial hedge is limited to the pre-mium received. If the call is written in-the-money, the


Equity Covered-Call ETFs/ETNs

BloombergTicker

Country/Region

Equity Covered-Call ETFs/ ETNs

BMO Covered-Call Canadian Banks ZWB:CN Canada

Horizons Enh Inc Equity HEX:CN Canada

Horizons Enh Inc Financials HEF:CN Canada

Can-60 Covered Call LXF:CN Canada

Can-Energy Covered Call OXF:CN Canada

Can-Financials Covered Call FXF:CN Canada

Can-Materials Covered Call MXF:CN Canada

Lyxor ETF EURO STOXX 50 BuyWrite BWE:BQ Europe

MIDAS KOSPI200 Covered Call 137930:KS Korea

PowerShrs S&P 500 BuyWrite PBP:US US

iPath S&P 500 BuyWrite ETN BWV:US US

Figure 1

Source: Bloomberg

Source: Local exchanges

Figure 2

Availability Of Exchange-Traded Instruments In BRIC Countries For Covered-Call Construction

BRIC Country

Stock Index

Stock Index

Futures

Options on Stock

Index

Options on Index Futures

ETF on Index

Brazil Ibovespa Yes No Yes Yes

Russia RTS Yes No Yes Yes

India NIFTY Yes Yes No Yes

China CSI 300 Yes No No Yes




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full amount of premium offers protection against loss. Investors using covered calls to protect against meaning-ful losses often choose options deeper in-the-money.

In our study, we selected pairs of NIFTY index call options listed on the National Stock Exchange of India (NSE) having a one-month time-to-expiration (maturity) and strike prices just above and just below the index price at the time the calls were written. A one-month time-to-option maturity offered excellent call liquidity, while the choice of near-the-money strike prices allowed for high time-premium income.

Two standard returns are customarily calculated to eval-uate the attractiveness of covered-call candidates prior to trade execution. They are the stand-still return (RSS) and the return-if-called (RIC). The RSS is the percentage return-to-option expiry, assuming the underlying index remains unchanged. This return recognizes costs and any dividends payable during the period ending at option expiry. The RIC is the return if the option is in-the-money at maturity and is exercised. The RIC calculation also includes costs and any dividends received by the asset holder between trade date and expiry. A third return, the realized return (RR), is the actual return realized at call expiry, and its calculation must account for any capital gains or losses arising from changes in the underlying index.

Formulas used to calculate these three returns at expiry are as follows:

RSS = [P - MAX[ 0, (I-K) ] + D - G] x t / [ I + G - D - P ] (1)

RIC = [P - MAX[ 0, I-K ] + MAX[ 0, K-I ] + D - G] x t / [ I + G - D - P ] (2)

RR = [ P - MAX[0, Im-K ] + D - G + Im - I ] x t / [I + G - D - P] (3)

Where:I = Index level in points on trade dateIm = Index level in points at call expiry K = Call strike priceP = Call premium in index pointsD = Dividends payable to call expiry in index pointsG = Costs in index points = Index times a = a Ia = 0.65 percentt = 365 / nn = number of days to call expiration

For the RIC calculation, the quantity K-I in the numerator of RIC has a convenient interpretation, as it represents the capital appreciation of the asset if the call is initially written out-of-the-money. Otherwise, this quantity will cancel the intrinsic value of the premium if the call is in-the-money when written. Any such cancellation will correctly leave only the time premium to contribute to the return. When an in-the-money call is written, the RIC and RSS are the same. The RIC equation can also be written simply to reflect these facts and isolate the capital appreciation component as follows:

RIC = RSS + MAX[0, K-I] x t / (I + G - D - P ] (4)

Prices for NIFTY calls and projected dividends pay-able each month were available on Bloomberg and quoted in index points. Typical costs to an institutional covered-call writer appear in Figure 3. For a hypotheti-cal institutional investor, total round-turn covered-call costs amount to an estimated 0.65 percent of traded index value for transactions in which securities are pur-chased and held for one or more days (delivery trans-actions). For retail clients transacting covered calls in smaller size, such costs are easily double this amount.

Covered-Call PerformanceCall-writing strategies employed among the covered-call

ETFs in Figure 1 range from active to semi-passive. The PowerShares S&P 500 BuyWrite Portfolio ETF, which fol-lows the strategy used to create the CBOE S&P 500 BuyWrite Index (BXM), provides an example of a semi-passively man-aged strategy [CBOE, 2010]. The BXM Index is constructed from the results of systematically writing one-month slightly out-of-the-money calls on the S&P 500 Index where the underlying portfolio is an S&P 500 Index fund. At each call expiration, a replacement one-month call is written.

The performance of this covered-call index has been carefully studied by five authors over the period from 1986 through 2012 (“key studies”). Ibbotson Associates studied the BXM behavior over the period 1988 to 2004 [Ibbotson, 2004], while Callan Associates studied the same index from 1988 to 2006 (Callan, 2006), as did Hewitt EnnisKnupp from 1986 to 2012 [EnnisKnupp, 2012] and Asset Consulting Group from 1986 to 2011 [Asset Consulting, 2012]. Separately, Kapadia and Szado studied covered-call writing on another index, the Russell 2000

March / April 2013

Common Institutional Costs For Covered-Call Writing

Charges On Each Leg Of Delivery Transactions As A % Of Traded Value

Brokerage on Turnover (Traded Value) 0.10%

Service Tax on Brokerage 10.30%

Securities Transaction Tax (STT) For Delivery Trades 0.100%

Exch Transaction Charges for NSE and BSE Trades 0.0035%

Stamp Duty 0.010%

SEBI Charges 0.0001%

Subtotal for Stock 0.2239%

Brokerage on Turnover (Traded Value) 0.035%

Service Tax on Brokerage 10.30%

Securities Transaction Tax (STT) 0.0085%

Exch Transaction Charges for NSE and BSE Trades 0.050%

Stamp Duty 0.002%

SEBI Charges 0.0020%

Subtotal for Options 0.1011%

Round Turn for Stock + Options 0.6500%

Figure 3

Source: Interactive Brokers at interactivebrokers.com

Stock Charges

Option Charges




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[Kapadia & Szado, 2007] and reached similar conclusions. All five key studies agreed that:

1. In falling markets, covered-call writing returns out-perform pure index portfolios.

2. In markets trading in a range, covered-call writing also outperforms index portfolios.

3. In rising markets, covered-call-writing returns under-perform index portfolios.

These general findings could have been anticipated by comparing a typical index covered-call gain or loss (P/L) at call expiry, with the gain or loss on the underlying index. A simplified hypothetical P/L profile for this purpose appears in Figure 4, where two breakeven points are easily identified for an at-the-money covered call. The lower breakeven at 95 occurs at a value equal to the strike price of 100 less the ini-tial call premium of 5, while an upper breakeven point at 105 occurs at the value equal to the strike price of 100 plus the initial call premium of 5. These two breakeven points divide the covered-call P/L outcomes into three regions of return:

Region 1: When the index level at call expiry is below the lower breakeven, the investor experiences a realized loss on the covered call but outperforms the index at all outcomes in this region. This result corresponds to the first of the three covered-call findings from the key studies cited above.

Region 2: When the index level at call expiry is between the two breakeven points, the covered call also outperforms the index at all levels, corresponding to the second finding of the key studies.

Region 3: When the index level at call expiry is above the upper breakeven, there is a realized gain on the covered call but this gain is less than that on the index, correspond-ing to the third finding of the key studies.

While each of these findings could have been anticipated from inspection of Figure 4, many useful numerical results from the key studies required careful detailed analysis. Important among the additional findings was that covered-call returns were above that of the index for the period 1986 to 2011 and were achieved with a volatility about one-third lower than that of the index alone. This same period included rising, falling and range-bound markets. In rising markets, covered-call writing underperformed the market. Because returns are capped when calls become in-the-money, correlations between index and strategy returns drop. This latter finding suggests that covered-call writing occupies a position on the capital market line that offers returns in rising markets that are generally below that of the index but above that of cash, much as graphically repre-sented in Figure 5. This position of covered-call writing on the capital market line conveniently suggests that there is also portfolio diversification potential in this strategy.

Covered-Call ETF Benefits And Concerns Financial journalists often make three negative warning

observations about covered-call ETFs. The first warning is that covered-call writing is a strategy that underperforms the market, implying that the strategy should be avoided. The warning is based on the mistaken assumption that this strategy is designed to outperform the market at all

times. As the key studies clearly demonstrate, covered-call returns should be expected to underperform in rapidly rising markets because capital gains are limited above the call strike price. Conveniently overlooked by journalists is that outperformance of covered-call ETFs can be expected both in range-bound and declining markets.

A second mistake made by journalists is to compare price-only returns from covered-call funds against total market returns. Proper return comparisons between strat-egies can be made only by including costs and dividends received. Such inclusions sometimes reveal a favorable comparison, as was the case from 1986 to 2011, when cov-ered-call writing in the U.S. market outperformed indexes.

A third erroneous claim found in the financial press is that covered calls fail to provide a hedge against declining markets. Professional covered-call writers know that sold options pro-vide two sources of partial protection against price declines. For out-of-the-money calls, the premium received provides a partial hedge by lowering the cost basis of the underly-ing asset. For in-the-money calls, the option premium also contains intrinsic value that provides an additional power-ful 1-for-1 offset against asset price declines. Neither in- nor out-of-the-money written calls can provide a complete hedge against a declining market, nor are they designed to do so, as

March / April 2013

Comparison Of Index And Covered-CallOutcomes At Call Expiry

10

8Maturity P/L For At The Money Covered Call Vs. IndexStrike Price At 100; Call Premium 5; No Costs Or Dividends

Lower Breakeven Upper Breakeven

Index At Cal Maturity

Ma

turi

ty P

/L

RealizedLoss

4

-4

6

-6

-8

-10

90 92 94 96 98 100 102 104 106 108 110

2

-2

0

■ Cover-Call P/L Index P/L

Representation Of Covered-Call PerformanceIn Rising Markets

0

5

10

15

20

Re

turn

%

Volatility (%)

0 2 4 6 8 10 12 14 16 18 20

Cash

Covered-Call Writing

Index

Figure 4

Figure 5

Source: Author’s calculations





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any knowledgeable options investor can confirm. Benefits and concerns that are appropriate to covered-

call ETFs include the following:

Benefits

• Lower fees than actively managed ETFs: The costs of acquiring and managing an index fund are meaningfully lower than for actively managed strategies. Thus, while covered-call ETFs on index funds can be expected to have fees slightly higher than for purely passive index ETFs, the fees will be less than those for actively managed ETFs.

• Diversification: The lower risk of covered-call writing on the capital market line (Figure 5) suggests this strategy has potential diversification benefits for investment portfolios.

• Yield: The systematic capture of call premiums and dividends from covered-call writing both raises the yield and lowers the cost basis of the underlying equity. In Figure 4, it can be seen that returns from covered-call writing dominate those from an index investment right up until the “upper breakeven” point.

• Tax treatment: In countries where dividends receive more favorable treatment than capital gains, the after-tax total return on a covered-call ETF will be further enhanced by receipt of dividends.

• Intraday trading: Pricing and trading are available intraday.

Concerns

• Execution costs: Important to understand is that in India, the costs for delivery transactions (Figure 3) are significantly higher than for intraday trades. Covered-call writing does not lend itself to intraday high-frequency trading, so execution costs can create a significant drag on returns. The monthly rolling over of

options as they expire also adds to the total costs of this semi-passive strategy.

• Management fees: The analysis of which options to write and when to write them requires extra care by the ETF manager. The attending costs for this effort must be passed along to the investor.

• Underperformance risk: As has been explained, covered-call ETFs can be expected to underperform the index in rapidly rising markets.

Covered-Call ETF AnalysisWhile the period of our analysis was brief (December

2011 through March 2012), it was sufficient to arrive at useful conclusions regarding the feasibility of ETF cre-

ation, especially knowing that the outcome of any length-ier study of risks and returns would be virtually certain to fit consistently with the findings of the key studies.

The following steps were taken in this covered-call ETF analysis:

Step 1: To identify the best options maturity for cov-ered-call writing, trading volume and open interest were captured for near-the-money NIFTY options from which profiles were constructed. A representative profile appears in Figure 6. Similar open-interest and volume statistics for four consecutive expiry dates suggested that the optimal period for covered-call writing was one to four weeks prior to contract maturity. Writing calls with greater than four weeks to maturity was not sensible due to lower liquidity. Writing calls with less than one week to maturity afforded too little absolute return.

Step 2: In 2011, NIFTY index options were the second-most-actively traded equity options contract in the world [Ackworth, 2012]. Despite this comforting statis-

March / April 2013

600,000

500,000

400,000

300,000

200,000

100,000

0

100 30 40 50 6020

Volume And Open InterestNIFTY January 2012 Expiry Call; Strike Price 4900

Vo

lum

e /

Op

en

In

tere

st

Days To Expiration

■ Open Interest ■ Volume

Figure 6

Source: National Stock Exchange of India (exchange holiday at 20 days to expiration)


Note: Using prices for the NIFTY index and NIFTY calls matched to within one second

Figure 7

Number Of Intraday Covered Calls With RSS Exceeding Mibor*

Trade Date ITM Strike OTM Strike

December 29, 2012 2,981 10,948

January 25, 2012 3,129 6,933

February 23, 2012 9,953 12,891

April 26, 2012 3,299 8,493

In India, the costs for delivery transactions are significantly higher than for intraday trades. Covered-call writing

does not lend itself to intraday high-frequency trading, so excecution costs can create a significant drag on returns.




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tic, we found it prudent to test the regularity with which pro�table covered calls on the NIFTY could be written on any trading day. For this purpose, high-frequency intraday time and options price data was captured and matched to the index price and times within one second. �e number of covered calls having an RSS exceed -ing Mibor was recorded on successive expiration trade dates and appears in Figure 7 for contracts both in-the-money (ITM) and out-of-the-money (OTM).

Step 3: Following a methodology consistent with BXM construction for January through April 2012 expiry con-tracts, nearest in-the-money and the nearest out-of-the-money one-month covered calls were written on each of four consecutive option expiry dates and held to the next

,92 .ceD( yripxe rebmeceD ta ,elpmaxe roF .etad yripxe2011), two covered calls were separately written for com-parison using January 2012 expiry calls, one being slightly in-the-money and one being slightly out-of-the-money, and these positions were held to their Jan. 26 expiry.

Step 4: In addition to expiry dates on which steps 1-3 were taken, intermediate sample dates were chosen on which to conduct the same analysis. Results con�rm the intraday availability of covered-call writing opportunities was substantial on a regular basis using NIFTY options.

As expected, in all three critical regions of the cov-ered-call return pro�le (Figure 1), observed behavior

was consistent with the �ndings of key studies. In-the-money calls provided greater protection against market declines, while out-of-the-money calls provided greater returns in a rising market. Realized returns were also achieved with less volatility than the index, con�rming the diversi�cation bene�ts of covered-call ETFs.

ConclusionsWith more investors turning their attention to emerg-

ing markets, demand is growing for more ways to access them. Pairing covered-call strategies with emerging markets exposure makes sense due to the fact that such strategies tend to dampen volatility and o�er some protection from downside risk. However, a developing market may not possess all the features necessary to execute such a strategy. Our �ndings suggest that cur-rently among BRIC nations, India alone has stock and options markets that make an index covered-call ETF practically achievable. Such an ETF on India’s NIFTY index could provide the valuable bene�ts of yield enhancement over cash with volatility below that of the index, diversi�cation bene�ts due to a lower correlation with the index, and a degree of protection against fall-ing markets. Using spot exchange rates, this ETF could also be listed and quoted on local exchanges in Europe, the U.S. or other major �nancial centers.

ReferencesAckworth, W. (March 1, 2012), Annual Volume Survey. Futures Industry, pp. 24-33.

Asset Consulting, G. (2012), “An Analysis of Index Option Writing for Liquid Enhanced Risk-Adjusted Returns,” Saint Louis: Asset Consulting Group.

CBOE. (2010), BXMDescription-Methodology.pdf, Chicago: Chicago Board Options Exchange.

Callan, A. (2006), “An Historical Evaluation of the CBOE S&P 500 BuyWrite Index Strategy,” San Francisco: Callan Associates Inc.

EnnisKnupp, H. (2012), “�e CBOE S&P 500 BuyWrite Index (BXM),” Lincolnshire: Hewitt EnnisKnupp.

Ibbotson, A. (2004). “Highlights from Case Study on BXM Buy-Write Options Strategy,” Chicago: Ibbotson Associates.

Kapadia, N. & Szado, E. (2007), “Risk and Return Characteristics of the Buy-Write Strategy on the Russell 2000 Index,” Chicago: Options Industry Council.

Slivka, R.T. & Li, X. (September/October 2010). “Hedging and Synthetic Funds Creation in the China Market,” Journal of Indexes, pp. 50-55.

March / April 2013

INDEXING AND EVERYTHING ELSE The latest industry news and data for ETFs and indexing. Three magazines. Two websites. One independent voice.

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Global Index Data

March/April 2013Selected Major Indexes Sorted By YTD Returns

Total Return % Annualized Return %

Index Name 2012 2011 2010 2009 2008 2007 2006 2005 3-Yr 5-Yr 10-Yr 15-Yr Sharpe Std Dev

Citigroup Greek GBI 89.73 -61.30 -25.78 6.98 -3.84 13.25 11.88 -8.95 -18.32 -10.93 -0.62 - -0.08 55.10

MSCI Turkey* 60.53 -36.78 18.36 92.00 -63.38 70.04 -9.22 51.60 6.30 -3.32 19.79 5.42 0.34 32.47

Citigroup Portuguese GBI 58.27 -24.91 -14.51 7.72 3.85 13.45 11.78 -9.57 0.53 2.59 6.54 6.18 0.15 27.15

S&P 500/Citi Pure Value 25.59 -0.81 23.06 55.21 -47.87 -3.69 20.04 13.43 15.30 4.40 11.26 8.14 0.82 19.78

S&P SmallCap 600/Citi Pure Value 21.64 -7.50 29.18 63.58 -41.73 -18.61 21.44 11.58 13.28 6.74 10.71 8.40 0.62 24.88

MSCI EMU 21.17 -17.64 -4.25 31.41 -47.57 19.55 36.29 8.80 -1.51 -8.02 7.33 4.19 0.07 26.76

STOXX Europe TMI 20.06 -11.80 5.10 37.50 -46.75 13.07 35.39 10.11 3.63 -4.01 8.96 5.01 0.26 22.72

MSCI EAFE Small Cap 20.00 -15.94 22.04 46.78 -47.01 1.45 19.31 26.19 7.17 -0.86 11.93 - 0.44 20.12

Russell Micro Cap 19.75 -9.27 28.89 27.48 -39.78 -8.00 16.54 2.57 11.87 1.46 8.42 - 0.62 21.50

S&P MidCap 400/Citi Pure Value 19.62 -5.07 23.19 59.18 -42.58 -3.20 19.31 9.37 11.84 5.04 10.84 8.79 0.65 20.32

Barclays Global High Yield 19.60 3.12 14.82 59.40 -26.89 3.18 13.69 3.59 12.30 10.54 11.63 8.50 1.32 9.10

JPM EMBI Global 18.54 8.46 12.04 28.18 -10.91 6.28 9.88 10.73 12.94 10.47 11.56 10.22 1.89 6.53

NASDAQ 100 18.35 3.66 20.14 54.61 -41.57 19.24 7.28 1.89 13.80 5.89 11.12 - 0.81 17.92

MSCI EM 18.22 -18.42 18.88 78.51 -53.33 39.42 32.14 34.00 4.66 -0.92 16.52 - 0.31 21.80

Russell 2000 Value 18.05 -5.50 24.50 20.58 -28.92 -9.78 23.48 4.71 11.57 3.55 9.50 7.19 0.64 20.17

Wilshire 4500 Completion 17.99 -4.10 28.43 36.99 -39.03 5.39 15.28 10.03 13.27 3.95 10.67 6.43 0.75 18.81

Barclays EM 17.95 6.97 12.84 34.23 -14.75 5.15 9.96 12.27 12.50 10.25 11.63 10.09 1.80 6.66

S&P MidCap 400 17.88 -1.73 26.64 37.38 -36.23 7.98 10.32 12.56 13.62 5.15 10.53 9.14 0.79 18.16

MSCI EAFE Value 17.69 -12.17 3.25 34.23 -44.09 5.96 30.38 13.80 2.19 -4.34 8.57 5.41 0.20 20.56

Wilshire US REIT 17.59 9.24 28.60 28.60 -39.20 -17.55 35.97 13.82 18.21 5.25 11.57 9.08 0.99 18.54

Russell 3000 Value 17.55 -0.10 16.23 19.76 -36.25 -1.01 22.34 6.85 10.92 0.83 7.54 5.38 0.72 16.03

MSCI EAFE 17.32 -12.14 7.75 31.78 -43.38 11.17 26.34 13.54 3.56 -3.69 8.21 4.38 0.27 19.65

S&P MidCap 400/Citi Pure Growth 17.29 0.62 35.16 60.34 -35.17 10.30 4.98 12.06 16.84 10.64 14.01 12.41 0.90 19.18

MSCI EAFE Growth 16.86 -12.11 12.25 29.36 -42.70 16.45 22.33 13.28 4.85 -3.09 7.77 3.18 0.34 19.12

MSCI ACWI Ex USA 16.83 -13.71 11.15 41.45 -45.53 16.65 26.65 16.62 3.87 -2.89 9.74 - 0.29 19.53

MSCI AC Asia Paciûc 16.78 -15.11 17.02 37.59 -41.85 14.29 16.49 23.34 5.07 -1.48 9.73 - 0.37 16.88

Wilshire 5000 Growth 16.78 -0.75 16.57 37.93 -37.91 10.65 9.73 7.43 10.55 2.96 8.04 3.95 0.66 17.26

Russell 3000 16.42 1.03 16.93 28.34 -37.31 5.14 15.72 6.12 11.20 2.04 7.68 4.81 0.74 15.95

Russell 2000 16.35 -4.18 26.85 27.17 -33.79 -1.57 18.37 4.55 12.25 3.56 9.72 5.89 0.66 20.48

S&P SmallCap 600 16.33 1.02 26.31 25.57 -31.07 -0.30 15.12 7.68 14.07 5.14 10.45 7.71 0.78 19.22

MSCI ACWI 16.13 -7.35 12.67 34.63 -42.19 11.66 20.95 10.84 6.63 -1.16 8.11 - 0.45 17.37

Wilshire 5000 Total Market 16.06 0.98 17.16 28.30 -37.23 5.62 15.77 6.38 11.15 2.03 7.85 4.86 0.74 15.80

S&P 500 16.00 2.11 15.06 26.46 -37.00 5.49 15.79 4.91 10.87 1.66 7.10 4.47 0.74 15.30

Russell Top 50 Mega Cap 15.66 4.35 9.47 20.46 -33.85 4.92 18.21 0.82 9.73 1.03 5.55 - 0.70 14.58

S&P 500/Citi Pure Growth 15.43 0.75 27.65 50.85 -38.99 6.64 7.43 7.31 14.07 6.44 10.76 7.97 0.80 18.41

Wilshire 5000 Value 15.24 2.76 17.53 18.77 -36.31 1.11 21.63 5.71 11.65 1.03 7.54 5.48 0.82 14.77

Russell 3000 Growth 15.21 2.18 17.64 37.01 -38.44 11.40 9.46 5.17 11.46 3.15 7.69 3.60 0.74 16.21

Russell 2000 Growth 14.59 -2.91 29.09 34.47 -38.54 7.05 13.35 4.15 12.82 3.49 9.80 4.04 0.67 21.01

MSCI BRIC 14.54 -22.85 9.57 93.12 -59.40 58.87 56.36 44.19 -1.07 -5.36 19.78 - 0.07 24.01

S&P Global 100 13.43 -3.18 5.79 26.71 -36.44 11.38 20.42 5.47 5.13 -1.32 6.68 4.08 0.36 17.62

S&P SmallCap 600/Citi Pure Growth 13.03 5.21 28.74 37.70 -33.10 1.49 9.79 7.10 15.25 7.12 11.73 9.51 0.85 18.65

DJ Industrial Average 10.24 8.38 14.06 22.68 -31.93 8.88 19.05 1.72 10.87 2.62 7.32 5.80 0.82 13.62

Barclays US Credit 9.37 8.35 8.47 16.04 -3.08 5.11 4.26 1.96 8.73 7.65 6.23 6.58 2.27 3.69

Dow Jones Transportation Average 7.55 0.01 26.74 18.58 -21.41 1.43 9.81 11.65 10.88 4.90 10.28 4.72 0.61 19.99

Barclays US Treasury US TIPS 6.98 13.56 6.31 11.41 -2.35 11.64 0.41 2.84 8.90 7.04 6.65 7.32 1.94 4.40

Barclays Municipal 6.78 10.70 2.38 12.91 -2.47 3.36 4.84 3.51 6.57 5.91 5.10 5.42 1.69 3.76

Alerian MLP 4.80 13.88 35.85 76.41 -36.91 12.72 26.07 6.32 17.48 12.53 16.48 15.17 1.26 13.56

Barclays Global Aggregate 4.32 5.64 5.54 6.93 4.79 9.48 6.64 -4.49 5.17 5.44 5.98 5.87 1.02 4.97

Barclays US Aggregate Bond 4.21 7.84 6.54 5.93 5.24 6.97 4.33 2.43 6.19 5.95 5.18 5.96 2.46 2.42

Barclays US Government 2.02 9.02 5.52 -2.20 12.39 8.66 3.48 2.65 5.48 5.23 4.66 5.69 1.60 3.32

Barclays Treasury 1.99 9.81 5.87 -3.57 13.74 9.01 3.08 2.79 5.84 5.40 4.75 5.74 1.54 3.68

Barclays Global Treasury 1.83 6.33 5.90 2.63 10.23 10.57 6.44 -6.66 4.67 5.34 6.08 5.89 0.82 5.65

Citigroup WGBI 1.65 6.35 5.17 2.55 10.89 10.95 6.12 -6.88 4.37 5.27 6.04 5.97 0.78 5.59

Dow Jones Utilities Average 1.64 19.71 6.46 12.47 -27.84 20.11 16.63 25.14 9.01 1.01 12.00 7.49 0.89 10.18

S&P GSCI 0.08 -1.18 9.03 13.48 -46.49 32.67 -15.09 25.55 2.54 -8.12 2.75 3.18 0.22 20.45

DJ UBS Commodity -1.06 -13.32 16.83 18.91 -35.65 16.23 2.07 21.36 0.07 -5.17 4.09 4.04 0.09 17.91

Citigroup Japanese GBI -9.43 7.74 17.54 -1.76 27.82 9.47 -0.65 -12.56 4.67 7.57 4.78 4.74 0.50 9.82

S&P Diversiûed Trends Indicator -11.21 -5.58 -2.82 -5.88 8.29 10.66 5.74 7.55 -6.60 -3.65 - - -0.86 7.70

HSBC Global Gold -11.85 -16.68 33.57 33.36 -23.93 20.70 17.28 30.28 -0.64 -0.09 9.32 8.64 0.10 26.64

MSCI Argentina* -38.86 -42.64 70.06 61.12 -55.32 -5.36 66.07 59.68 -15.82 -15.56 10.30 -2.25 -0.28 36.74

Source: Morningstar. (Nasdaq-100 index data provided by Morningstar and Nasdaq OMX.) Data as of December 31, 2012. All returns are in US dollars, unless noted.

3-, 5-, 10- and 15-year returns are annualized. Sharpe is 12-month Sharpe ratio. Std Dev is 3-year standard deviation. *Indicates price returns. All other indexes are total return.





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Morningstar U.S. Style Overview XXXX –XXXX, 2011


Index FundsMarch/April 2013Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions


Fund Name Ticker Assets Exp Ratio 3-Mo 2012 2011 2010 3-Yr 5-Yr 10-Yr 15-Yr P/E Std Dev Yield

Vanguard Total Stock Mkt, Inv Shrs VTSMX 78,935.9 0.18 0.15 16.25 0.96 17.09 11.18 2.18 7.83 4.87 15.5 15.98 2.02

Vanguard Institutional, Instl Shrs VINIX 68,055.1 0.04 -0.39 15.98 2.09 15.05 10.85 1.69 7.11 4.51 15.3 15.30 2.17

Vanguard Total Stock Market, Adm Shrs VTSAX 59,771.5 0.06 0.18 16.38 1.08 17.26 11.32 2.29 7.93 4.95 15.5 16.00 2.13

Vanguard 500, Adm Shrs VFIAX 59,749.3 0.05 -0.39 15.96 2.08 15.05 10.85 1.68 7.09 4.47 15.3 15.31 2.17

Vanguard Institutional, Instl+ Shrs VIIIX 49,286.1 0.02 -0.37 16.00 2.12 15.07 10.88 1.72 7.14 4.54 15.3 15.30 2.19

Vanguard Total Bond Mkt II, Inv Shrs VTBIX 45,757.9 0.12 0.00 3.91 7.59 6.41 5.96 - - - - 2.51 2.30

Vanguard Total Stock Mkt, Instl Shrs VITSX 39,366.6 0.05 0.21 16.42 1.09 17.23 11.32 2.31 7.96 4.99 15.5 15.99 2.14

Vanguard Total Intl Stock, Inv Shrs VGTSX 37,659.4 0.22 6.66 18.14 -14.56 11.12 3.90 -3.03 9.41 5.11 11.8 20.17 2.92

Vanguard Total Bond Mkt, Adm Shrs VBTLX 35,532.6 0.10 0.13 4.15 7.69 6.54 6.11 5.91 5.17 5.81 - 2.54 2.65

Vanguard 500, Sig Shrs VIFSX 27,304.4 0.05 -0.38 15.97 2.08 15.05 10.85 1.68 7.05 4.44 15.3 15.30 2.17

Vanguard 500, Inv Shrs VFINX 24,821.4 0.17 -0.42 15.82 1.97 14.91 10.72 1.57 6.99 4.39 15.3 15.30 2.05

Vanguard Total Bond Mkt, Instl Shrs VBTIX 22,493.5 0.07 0.13 4.18 7.72 6.58 6.15 5.95 5.21 5.87 - 2.54 2.68

Vanguard Instl Total Stock Mkt, Instl+ Shrs VITPX 20,731.2 0.03 0.24 16.53 1.11 17.25 11.37 2.37 8.04 - 15.5 15.99 2.17

Fidelity Spartan 500, Adv Cl FUSVX 20,409.3 0.05 -0.38 15.97 2.06 15.01 10.83 1.65 7.05 4.39 15.1 15.30 2.14

Vanguard Total Bond Mkt II, Instl Shrs VTBNX 18,704.2 0.05 0.02 3.99 7.67 6.47 6.03 - - - - 2.51 2.37

Fidelity Spartan 500, Instl Cl FXSIX 16,513.0 0.04 -0.39 15.96 2.09 14.98 10.82 1.63 7.03 4.38 15.1 15.30 2.16

Vanguard Total Intl Stock, Adm Shrs VTIAX 16,418.7 0.18 6.69 18.21 -14.52 11.04 3.91 -3.03 9.41 5.11 11.8 20.12 2.98

Vanguard Total Bond Mkt, Instl+ Shrs VBMPX 16,294.1 0.05 0.14 4.20 7.74 6.57 6.16 5.89 5.12 5.77 - 2.54 2.70

T. Rowe Price Equity 500 PREIX 15,443.4 0.30 -0.43 15.68 1.87 14.71 10.57 1.45 6.83 4.21 15.3 15.30 2.01

Vanguard Total Intl Stock, Instl+ Shrs VTPSX 13,840.4 0.10 6.69 18.30 -14.49 11.09 3.97 -3.00 9.43 5.12 11.8 20.15 3.03

Schwab S&P 500 SWPPX 12,758.7 0.09 -0.38 15.91 2.07 14.97 10.80 1.67 7.05 4.38 14.7 15.25 2.21

Vanguard Total Bond Mkt, Sig Shrs VBTSX 12,585.5 0.10 0.13 4.15 7.69 6.54 6.11 5.91 5.14 5.79 - 2.54 2.65

Vanguard Total Bond Mkt, Inv Shrs VBMFX 11,794.4 0.22 0.10 4.05 7.56 6.42 6.00 5.80 5.07 5.74 - 2.54 2.55

Fidelity Spartan 500, Inv Cl FUSEX 10,370.3 0.10 -0.39 15.93 2.03 14.98 10.79 1.61 7.03 4.37 15.1 15.30 2.11

Vanguard Total Stock Mkt, Sig Shrs VTSSX 8,025.3 0.06 0.19 16.39 1.09 17.23 11.31 2.29 7.90 4.92 15.5 15.99 2.14

Fidelity Series 100 FOHIX 8,015.0 0.20 -1.92 15.77 2.98 12.39 10.24 1.11 - - 14.7 14.96 8.42

Vanguard Total Intl Stock, Instl Shrs VTSNX 7,882.1 0.13 6.69 18.28 -14.51 11.09 3.95 -3.00 9.43 5.12 11.8 20.15 3.01

Vanguard Balanced, Adm Shrs VBIAX 7,406.9 0.10 0.19 11.49 4.29 13.29 9.62 4.26 7.18 5.73 15.5 9.09 2.15

Fidelity Spartan Total Mkt, Adv Cl FSTVX 7,325.5 0.06 0.19 16.35 1.01 17.44 11.34 2.18 7.87 4.89 15.3 15.91 1.89

Vanguard Emerging Mkts Stock, Adm Shrs VEMAX 7,300.5 0.20 6.84 18.86 -18.67 18.99 4.78 -0.87 16.30 9.50 9.3 22.25 2.20

Vanguard Mid-Cap, Instl Shrs VMCIX 7,057.1 0.08 2.84 16.01 -1.96 25.67 12.64 3.18 10.07 - 16.8 17.71 1.43

Vanguard REIT, Adm Shrs VGSLX 6,916.8 0.10 2.49 17.69 8.62 28.49 17.99 6.07 11.68 8.86 43.3 18.29 3.56

Vanguard Mid-Cap, Adm Shrs VIMAX 6,895.1 0.10 2.83 15.99 -1.97 25.59 12.61 3.15 10.02 - 16.8 17.71 1.41

Vanguard Small-Cap, Adm Shrs VSMAX 6,541.2 0.16 2.79 18.24 -2.69 27.89 13.74 5.12 10.96 6.92 16.9 20.05 1.86

Vanguard Intermed-Tm Bond, Adm Shrs VBILX 6,251.7 0.11 0.46 7.02 10.73 9.49 9.07 7.81 6.34 6.84 - 4.03 3.13

Spartan U.S. Bond, Inv Shrs FBIDX 6,227.7 0.22 0.06 4.06 7.68 6.29 6.00 5.64 4.94 5.76 - 2.48 2.35

Vanguard Growth, Instl Shrs VIGIX 6,188.5 0.08 -1.08 17.04 1.89 17.17 11.80 3.35 7.53 4.61 17.9 16.49 1.53

PIMCO EM Fundamental IndexPLUS, Instl Cl PEFIX 6,097.4 1.25 6.20 28.19 -16.81 25.86 10.31 - - - - 23.38 7.43

Vanguard Extended Mkt, Adm Shrs VEXAX 5,969.6 0.14 3.17 18.48 -3.59 27.57 13.37 4.24 10.73 6.51 16.8 19.27 1.64

Vanguard Small-Cap, Instl Shrs VSCIX 5,954.7 0.14 2.81 18.26 -2.65 27.95 13.78 5.17 11.01 6.99 16.9 20.06 1.87

Vanguard Growth, Adm Shrs VIGAX 5,774.2 0.10 -1.11 17.01 1.87 17.12 11.77 3.31 7.49 4.56 17.9 16.49 1.51

Vanguard Short-Term Bond, Sig Shrs VBSSX 5,570.3 0.11 0.15 2.05 3.08 4.03 3.05 3.80 3.67 4.66 - 1.38 1.54

Vanguard Balanced, Instl Shrs VBAIX 5,554.3 0.08 0.19 11.51 4.31 13.34 9.65 4.30 7.22 5.76 15.5 9.10 2.16

Vanguard Developed Mkts, Instl Shrs VIDMX 5,520.9 0.08 7.51 19.00 -12.44 8.76 4.26 -3.24 8.42 - 11.8 20.06 3.61

Vanguard Extended Mkt, Instl Shrs VIEIX 5,495.8 0.12 3.16 18.50 -3.57 27.59 13.39 4.28 10.78 6.58 16.8 19.28 1.65

Vanguard Extended Mkt, Instl+ Shrs VEMPX 5,476.7 0.10 3.17 18.52 -3.57 27.37 13.33 4.16 10.62 6.43 16.8 19.27 1.68

Vanguard Mid-Cap, Instl+ Shrs VMCPX 5,428.4 0.06 2.83 16.03 -1.91 25.67 12.67 3.20 10.07 - 16.8 17.70 1.45

Fidelity Spartan Extended Mkt, Adv Cl FSEVX 5,240.8 0.07 2.82 18.05 -3.79 28.62 13.46 4.22 10.72 6.39 16.5 19.02 1.74

TIAA-CREF Equity, Instl Cl TIEIX 4,892.5 0.07 0.19 16.33 0.99 16.88 11.15 2.04 7.63 - 15.3 15.93 1.74

Schwab 1000 SNXFX 4,851.3 0.29 0.00 15.77 1.27 15.96 10.78 1.71 7.21 4.59 14.8 15.54 2.02

Vanguard Mid-Cap, Sig Shrs VMISX 4,834.2 0.10 2.84 16.02 -1.99 25.62 12.62 3.15 10.04 - 16.8 17.72 1.42

Fidelity Spartan US Bond, Adv Cl FSITX 4,402.6 0.10 0.07 4.17 7.71 6.29 6.05 5.66 4.95 5.77 - 2.49 2.45

Vanguard Short-Term Bond, Adm Shrs VBIRX 4,401.2 0.11 0.15 2.05 3.08 4.03 3.05 3.80 3.70 4.68 - 1.38 1.54

Vanguard Value, Instl Shrs VIVIX 4,368.8 0.08 0.88 15.20 1.17 14.49 10.09 0.49 7.49 4.64 13.4 15.03 2.75

Fidelity Series Inüation-Protected Bond FSIPX 4,347.3 0.20 0.28 4.77 8.63 5.06 6.14 - - - - 2.85 0.07

Vanguard Small Cap, Sig Shrs VSISX 4,328.6 0.16 2.78 18.25 -2.68 27.85 13.74 5.12 10.92 6.88 16.9 20.07 1.86

Northern Stock NOSIX 4,216.2 0.11 -0.40 15.86 1.89 14.82 10.67 1.46 6.79 4.09 15.3 15.31 2.05

ING US Stock, Cl I INGIX 4,129.6 0.26 -0.38 15.79 1.81 14.74 10.59 1.43 - - 15.2 15.32 1.83

Vanguard Total Intl Stock, Sig Shrs VTSGX 4,009.5 0.18 6.69 18.21 -14.52 11.06 3.92 -3.02 9.42 5.11 11.8 20.15 2.99

TIAA-CREF Bond, Instl Cl TBIIX 3,983.9 0.13 0.18 4.10 7.65 6.32 6.01 - - - - 2.42 1.98

Source: Morningstar. Data as of December 31, 2012. Exp Ratio is expense ratio. 3-Mo is 3-month. YTD is year-to-date. 3-, 5-, 10- and 15-yr returns are annualized.

P/E is price-to-earnings ratio. Std Dev is 3-year standard deviation. Yield is 12-month dividend yield.




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

Morningstar U.S. Style Overview Jan. 1-Dec. 31, 2012

Trailing Returns %

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

US Market 2.61 16.17 16.17 10.91 2.07 7.91

Large Cap 1.69 15.93 15.93 10.15 1.30 6.84

Mid Cap 5.33 16.98 16.98 12.75 3.65 10.46

Small Cap 4.92 16.39 16.39 12.85 4.97 10.92

US Value 4.19 14.11 14.11 9.98 0.26 7.32

US Core 3.62 17.53 17.53 11.68 3.35 8.67

US Growth 0.25 17.17 17.17 11.06 2.40 7.47

Large Value 3.04 12.81 12.81 9.45 –1.31 6.15

Large Core 2.94 17.48 17.48 10.72 2.54 7.81

Large Growth –0.68 17.87 17.87 10.23 2.40 6.18

Mid Value 7.51 17.47 17.47 10.89 3.75 9.83

Mid Core 5.61 17.86 17.86 14.60 5.27 10.74

Mid Growth 3.04 15.72 15.72 12.68 1.85 10.54

Small Value 6.95 18.19 18.19 12.97 6.92 11.68

Small Core 5.27 16.60 16.60 11.88 4.73 10.57

Small Growth 2.58 14.41 14.41 13.69 3.30 10.32

Morningstar Market Barometer YTD Return %

US Market16.17

14.11

Value

17.53

Core

17.17

Growth

15.93Larg

e C

ap

16.98Mid

Cap

16.39Sm

all C

ap

12.81 17.48 17.87

17.47 17.86 15.72

18.19 16.60 14.41

–8.00 –4.00 0.00 +4.00 +8.00

Sector Index YTD Return %

Communication 32.39

Financial Services 29.05

Consumer Cyclical 24.56

Healthcare 19.32

Real Estate 18.61

Basic Materials 16.46

Industrials 15.28

Technology 13.30

Consumer 10.08

Energy 4.32

Utilities 2.19

Industry Leaders & Laggards YTD Return %

Residential Construction 85.84

Oil & Gas Refining & 80.48

Consumer Electronics 77.16

Building Materials 58.57

Real Estate - General 51.75

Broadcasting - Radio 51.68

–9.02 Business Equipment

–14.12 Gold

–17.53 Electronic Gaming & Multimedia

–21.54 Industrial Metals & Minerals

–26.67 Coal

–38.23 Education & Training Services

Biggest Influence on Style Index Performance

YTDReturn %

ConstituentWeight %

Best Performing Index

Small Value 18.19

Terex Corp 108.07 0.49

US Airways Group 166.27 0.27

Tenet Healthcare 58.24 0.73

First American Financial Corp 93.66 0.44

Community Health Systems 77.65 0.52

Worst Performing Index

Large Value 12.81

Bank of America Corp 109.83 1.70

JP Morgan Chase & Co 36.18 3.82

General Electric Co 21.25 5.72

Citigroup Inc 50.55 2.31

Pfizer Inc 20.41 5.03

1-Year

12.81

Value

Larg

e C

ap

17.48

Core

17.87

Growth

17.47

Mid

Cap 17.86 15.72

18.19

Sm

all C

ap

16.60 14.41

–20 –10 0 +10 +20

3-Year

9.45

Value

Larg

e C

ap

10.72

Core

10.23

Growth

10.89

Mid

Cap 14.60 12.68

12.97

Sm

all C

ap

11.88 13.69

–20 –10 0 +10 +20

5-Year

–1.31

Value

Larg

e C

ap

2.54

Core

2.40

Growth

3.75

Mid

Cap 5.27 1.85

6.92

Sm

all C

ap

4.73 3.30

–20 –10 0 +10 +20

Notes and Disclaimer: ©2013 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, 2012





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Exchange-Traded Funds Corner


Dow Jones U.S. Industry Review1-Year Performance Cumulative Performance

Comparison Period Ending December 31, 2012 Comparison Period: December 31, 1991 to December 31, 2012

Short-Term Performance Statistics Long-Term Performance Statistics

Comparison Period Ending December 31, 2012 Comparison Period Ending December 31, 2012

1-Month 3-Month 6-Month 1-Year YTD 3-Year 5-Year 7-Year 10-Year

Avail. Hist.

(12/31/91)

Dow Jones U.S. Basic Materials Index 4.03% 3.14% 8.61% 10.49% 10.49% 7.47% 0.21% 6.74% 9.78% 7.46%

Dow Jones U.S. Consumer Goods Index -0.98% 1.63% 6.14% 12.80% 12.80% 13.62% 6.18% 7.89% 9.33% 8.90%

Dow Jones U.S. Consumer Services Index 0.16% 1.18% 8.15% 24.17% 24.17% 18.07% 8.76% 7.09% 8.77% 8.51%

Dow Jones U.S. Financials Index 3.95% 5.10% 11.45% 26.85% 26.85% 7.61% -6.26% -4.74% 1.29% 8.03%

Dow Jones U.S. Health Care Index -0.27% -0.67% 6.27% 19.26% 19.26% 11.68% 5.53% 6.12% 7.44% 8.85%

Dow Jones U.S. Industrials Index 2.64% 4.58% 9.12% 17.87% 17.87% 13.80% 2.35% 5.48% 9.05% 8.09%

Dow Jones U.S. Oil & Gas Index 0.91% -2.67% 7.39% 4.71% 4.71% 9.27% -0.35% 7.20% 13.77% 11.52%

Dow Jones U.S. Technology Index 0.16% -6.41% -0.25% 12.08% 12.08% 8.12% 3.50% 6.09% 9.16% 10.61%

Dow Jones U.S. Telecommunications Index -0.48% -5.53% 2.22% 18.79% 18.79% 13.29% 1.39% 7.07% 7.03% 5.49%

Dow Jones U.S. Utilities Index 0.05% -2.56% -2.27% 1.76% 1.76% 9.33% 0.52% 5.61% 10.11% 7.26%

Dow Jones U.S. Index 1.12% 0.14% 6.37% 16.32% 16.32% 11.20% 2.16% 4.53% 7.82% 8.29%

Annualized Risk Risk / Return

Comparison Period Ending December 31, 2012 Comparison Period: December 31, 1991 to December 31, 2012

3-Year 5-Year 7-Year 10-Year

Avail. Hist.

(12/31/91)

25.53% 31.29% 27.48% 24.63% 22.22%

Dow Jones U.S. Consumer Goods Index 11.49% 15.45% 13.49% 12.58% 12.98%

Dow Jones U.S. Consumer Services Index 15.59% 19.65% 17.37% 16.04% 17.12%

Dow Jones U.S. Financials Index 19.75% 29.41% 25.57% 22.33% 20.33%

Dow Jones U.S. Health Care Index 12.05% 15.80% 14.13% 12.81% 14.75%

Dow Jones U.S. Industrials Index 19.77% 24.81% 21.50% 19.11% 17.84%

Dow Jones U.S. Oil & Gas Index 22.39% 24.56% 23.00% 21.76% 19.64%

Dow Jones U.S. Technology Index 19.47% 23.10% 20.80% 19.63% 27.62%

Dow Jones U.S. Telecommunications Index 14.15% 18.36% 17.10% 16.30% 19.89%

Dow Jones U.S. Utilities Index 9.54% 14.54% 13.59% 13.15% 14.62%

Dow Jones U.S. Index 15.79% 19.57% 17.06% 15.21% 15.18%

Source: S&P Dow Jones Indices; data as of December 31, 2012.

For more information, please visit the S&P Dow Jones Indices web site at www.spdji.com.

Dow Jones U.S. Index

Dow Jones U.S. Oil & Gas Index

Dow Jones U.S. Technology Index

The index returns shown do not represent the results of actual trading of investor assets. S&P/Dow Jones Indices LLC maintains the indices and calculates the index levels and performance shown or discussed, but does not manage actual assets.

Index returns do not reflect payment of any sales charges or fees an investor would pay to purchase the securities they represent. The imposition of these fees and charges would cause actual and back-tested performance to be lower than the

performance shown. In a simple example, if an index returned 10% on a US $100,000 investment for a 12-month period (or US$ 10,000) and an actual asset-based fee of 1.5% were imposed at the end of the period on the investment plus accrued

interest (or US$ 1,650), the net return would be 8.35% (or US$ 8,350) for the year. Over 3 years, an annual 1.5% fee taken at year end with an assumed 10% return per year would result in a cumulative gross return of 33.10%, a total fee of US$

5,375, and a cumulative net return of 27.2% (or US$ 27,200).

Copyright © 2013 by S&P Dow Jones Indices LLC, a subsidiary of The McGraw-Hill Companies. All rights reserved. “Dow Jones” is a registered trademark of Dow Jones Trademark Holdings LLC (“Dow Jones”). STANDARD & POOR’S and S&P

are registered trademarks of Standard & Poor’s Financial Services LLC.

The Dow Jones U.S. Index and the Dow Jones U.S. Industry Indices were first calculated in February 2000. All information presented prior to this date is back-tested. Back-tested performance is not actual performance, but is hypothetical. The back-

test calculations are based on the same methodology that was in effect when the index was officially launched. Complete index methodology details are available at www.spindices.com. Past performance is not an indication of future results.

Prospective application of the methodology used to construct the Dow Jones U.S. Index may not result in performance commensurate with the back-test returns shown. The back-test period does not necessarily correspond to the entire available

history of the index. Please refer to the methodology paper for the index, available at www.spdji.com or www.spindices.com for more details about the index, including the manner in which it is rebalanced, the timing of such rebalancing, criteria for

additions and deletions, as well as all index calculations. It is not possible to invest directly in an Index.

Another limitation of back-tested hypothetical information is that generally the back-tested calculation is prepared with the benefit of hindsight. Back-tested data reflect the application of the index methodology and selection of index constituents in

hindsight. No hypothetical record can completely account for the impact of financial risk in actual trading. For example, there are numerous factors related to the equities (or fixed income, or commodities) markets in general which cannot be, and

have not been accounted for in the preparation of the index information set forth, all of which can affect actual performance.

Dow Jones U.S. Telecommunications Index

Dow Jones U.S. Utilities Index

Dow Jones U.S. Health Care Index

Dow Jones U.S. Industrials Index

Dow Jones U.S. Basic Materials Index

Dow Jones U.S. Consumer Goods Index

Dow Jones U.S. Consumer Services Index

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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, 2012.


Fund Name Ticker ER 1-Mo 3-Mo Mkt Cap P/E Inception Assets

PIMCO Total Return BOND 0.55 -0.02 1.60 - - 2/29/2012 3,870.5

SPDR Barclays Short-Term HiYld Bond SJNK 0.40 1.32 2.56 - - 3/14/2012 621.6

iShares Aaa-A Rated Corporate Bond QLTA 0.15 -0.04 0.48 - - 2/14/2012 316.5

iShares Core MSCI EAFE IEFA 0.14 4.19 - 22,472 12.4 10/18/2012 279.4

iShares Core MSCI Emerging Markets IEMG 0.18 6.78 - 13,804 9.8 10/18/2012 261.0

iShares MSCI Glb Metals/Mining Prod PICK 0.39 9.15 10.96 23,810 11.5 1/31/2012 253.6

Market Vectors Intl HiYld Bond IHY 0.40 2.80 5.80 - - 4/2/2012 209.9

iShares Emerging Markets HiYld Bond EMHY 0.65 2.09 5.65 - - 4/3/2012 198.3

iShares Barclays US Treasury Bond GOVT 0.15 -0.56 -0.24 - - 2/14/2012 146.2

UBS FI Enh Big-Cap Growth ETN FBG 1.20 -0.57 -3.07 - - 6/8/2012 140.2

First Trust North Amer Energy Infrastr EMLP 0.95 0.23 -1.65 8,303 20.1 6/20/2012 123.7

Market Vectors Mstar Wide Moat MOAT 0.49 2.08 3.55 13,221 16.3 4/24/2012 115.4

PIMCO Glb Adv Inü-Linked Bond Strat ILB 0.60 2.19 2.71 - - 4/30/2012 110.4

Vanguard Short-Tm Inüation-Prot Sec VTIP 0.10 0.06 - - - 10/12/2012 108.3

Market Vectors Pref Sec ex Financials PFXF 0.40 1.52 2.00 4,397 7.9 7/16/2012 100.1

Yorkville High Income MLP YMLP 0.82 -2.47 -6.32 1,683 12.0 3/13/2012 98.1

WisdomTree Emrg Mkts Corp Bond EMCB 0.60 1.19 3.56 - - 3/8/2012 96.4

PowerShares S&P Emrg Mkts Low Vol EELV 0.29 5.54 6.47 5,478 11.0 1/13/2012 88.3

iShares Morningstar Multi-Asset Inc IYLD 0.60 -0.08 0.37 10,295 11.1 4/3/2012 86.3

ALPS Sector Dividend Dogs SDOG 0.40 0.25 0.25 15,430 16.1 6/29/2012 70.1

Fund Name Ticker Exp Ratio Assets 3-Mo 2012 2011 2010 3-Yr 5-Yr Mkt Cap P/E Std Dev Yield

SPDR S&P 500 SPY 0.09 123,001.0 -0.38 15.99 1.89 15.06 10.79 1.66 54,844 15.0 15.28 2.18

SPDR Gold GLD 0.40 72,239.3 -5.74 6.60 9.57 29.27 14.72 14.46 - - 18.68 -

Vanguard MSCI Emerging Markets VWO 0.20 59,875.2 7.83 19.20 -18.75 19.46 4.98 -0.63 19,047 9.3 23.69 2.20

iShares MSCI Emerging Markets EEM 0.69 48,189.6 8.00 19.10 -18.82 16.51 4.05 -0.55 20,339 9.5 24.08 1.71

iShares MSCI EAFE EFA 0.34 38,814.7 8.44 18.82 -12.25 8.15 4.08 -3.33 32,256 12.4 20.18 3.14

iShares Core S&P 500 IVV 0.07 34,911.5 -0.23 16.06 1.86 15.09 10.81 1.64 54,822 15.0 15.25 2.10

PowerShares QQQ QQQ 0.20 30,416.9 -4.48 18.11 3.38 19.91 13.55 5.71 77,323 16.4 17.96 1.26

iShares iBoxx $ Inv Gr Corp Bond LQD 0.15 25,350.4 0.59 10.58 9.73 9.33 9.88 8.06 - - 5.34 3.83

Vanguard Total Stock Market VTI 0.06 24,270.5 0.24 16.46 0.97 17.42 11.35 2.32 32,918 15.5 15.98 2.14

iShares Barclays TIPS Bond TIP 0.20 22,284.7 0.35 6.39 13.28 6.14 8.55 6.75 - - 4.42 2.21

Vanguard Total Bond Market BND 0.10 17,968.3 -0.01 3.89 7.92 6.20 5.99 5.78 - - 2.48 2.72

iShares Russell 1000 Growth IWF 0.20 16,907.0 -1.20 15.22 2.33 16.52 11.17 3.02 45,094 18.0 15.88 1.66

iShares Russell 2000 IWM 0.23 15,997.1 1.92 16.69 -4.44 26.93 12.28 3.68 1,044 16.4 20.53 2.00

iShares iBoxx $ HiYld Corp Bond HYG 0.50 15,972.3 3.33 11.66 6.77 11.89 10.08 7.20 - - 9.37 6.63

Vanguard REIT VNQ 0.10 15,406.7 2.51 17.63 8.62 28.37 17.93 6.11 8,100 43.3 18.28 3.56

iShares Core Total US Bond Market AGG 0.08 15,335.8 -0.02 3.76 7.69 6.37 5.93 5.72 - - 2.58 2.54

iShares Russell 1000 Value IWD 0.20 14,536.2 1.60 17.46 0.12 15.49 10.74 0.56 37,841 13.5 15.74 2.30

iShares Core S&P Mid-Cap IJH 0.15 13,558.4 3.65 17.79 -2.18 26.72 13.45 5.13 3,636 18.0 18.09 1.43

SPDR Barclays High Yield Bond JNK 0.40 12,502.3 3.48 13.46 5.12 14.20 10.85 7.11 - - 9.62 6.78

Vanguard Dividend Appreciation VIG 0.13 12,042.9 0.64 11.65 6.16 14.74 10.79 3.59 42,697 15.5 12.84 2.37

iShares Gold Trust IAU 0.25 11,645.3 -5.74 6.89 9.57 29.46 14.88 14.57 - - 18.72 -

Vanguard MSCI EAFE VEA 0.12 10,981.8 8.21 18.57 -12.30 8.35 4.05 -3.14 29,401 11.8 20.46 3.00

SPDR DJ Industrial Average Trust DIA 0.17 10,923.4 -1.89 9.94 8.06 14.01 10.64 2.44 114,113 13.6 13.65 2.53

iShares S&P US Preferred Stock PFF 0.48 10,747.3 1.59 18.20 -2.00 13.81 9.65 6.90 - - 8.75 6.02

SPDR S&P MidCap 400 MDY 0.25 10,616.7 3.67 17.82 -2.13 26.28 13.35 4.96 3,585 17.8 18.06 1.14

Source: Morningstar. Data as of December 31, 2012. Exp Ratio is expense ratio. 3-Mo is 3-month. Mkt Cap is market cap. 3-Yr and 5-Yr are 3-year and 5-year annualized returns, respectively.

Mkt Cap is geometric average market capitalization. P/E is price-to-earnings ratio. Std Dev is 3-year standard deviation. Yield is 12-month.

Direxion Daily European Equity Bull 3X

EGShares Beyond BRICs Emrg Asia Infrastr

ETFS Physical Zinc

First Trust Mstar Diversiûed Futures

Forensic Accounting ETF

Global X Risk Parity

Guggenheim Intl High Dividend

IQ Bear Industry Leaders US Equity

iShares MSCI USA Risk Weighted

LocalShares Nashville

Market Vectors Non-Agency RMBS

Pimco EM Agg US$-Denominated Bond

PowerShares Fundamental EM Local Debt

ProShares Global Direct Infrastructure

SPDR Russell 1000 Low Volatility

United States Nat Gas Double Inverse

Vanguard Total International Bond

VelocityShares Russia Select DR

Yorkville High Inc Infrastructure MLP

Zacks MLP

Source: IndexUniverse.com’s ETF WatchSource: Morningstar. Data as of December 31, 2012. ER is expense ratio. 1-Mo is 1-month. 3-Mo is 3-month.

Mkt Cap is market cap. P/E is price-to- earnings ratio.

Exchange-Traded Funds Corner





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Malkiel, B.G. 1995. Returns from investing in equity mutual funds 1971-1991. Journal of Finance 50(2): 547-572.

Markowitz, H. 1959. Portfolio selection: efficient diversification of investments. John Wiley & Sons, Inc., New York and Chapman & Hall, Limited, London.

Perold, A.F. 2007. Fundamentally flawed indexing. Financial Analysts Journal 63(6): 31-37.

Ranaldo, A. and R. Häberle. 2007. Wolf in sheep’s clothing: the active investment strategies behind index performance. European Financial Management 14(1): 55-81.

Sharpe, W.F. 1964. Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance. 19(3): 425-441.

Siegel, L.B. 2003. Benchmarks and investment management. The Research Foundation of the Association for Investment Management and Research, Charlottesville, Virginia.

Strongin, S., M. Petsch and G. Sharenow. 2000. Beating benchmarks. Journal of Portfolio Management 26(4): 11-27.

Tabner, I. 2007. Benchmark concentration: Capitalization weights versus equal weights in the FTSE 100 index. Working paper. University of Stirling.

Endnotes1 Further, investors have different profiles and investment contexts (liability constraints, income risk, etc.), which makes a unique reference somewhat questionable.

2 Data taken from press releases on each provider’s website. Note that, more often than not, a press release announcing the launch of an index actually refers to a new index

series being launched, which may contain several indexes such as different indexes for geographic segments. For the purpose of our analysis, we count the number of

announcements by the index provider and hence tend to capture the launch of new index series rather than of individual indexes.

3 Information on index launches was only available on the S&P website going back to Jan. 1, 2012.

4 The largest category was institutional investment management professionals, including asset owners and third-party asset managers with a focus on institutional clients,

making up 80 percent of respondents. Eighty-one percent of respondents were from the United States and the remainder from Canada.

5 For equity indexes, see Haugen and Baker [1991]; Grinold [1992]; Amenc, Goltz and Le Sourd [2006]; Hsu [2006]; Ranaldo and Häberle [2007]; Tabner [2007]; Malevergne et

al. [2009]; Fuller et al. [2010]; Goltz and Le Sourd [2010] among others.

6 See, e.g., Kamp (2008).

7 While they are related concepts, objectivity and transparency do not necessarily go together. For example, an index can be completely transparent about data, history and

the fact that it has delegated all decision-making responsibilities, not to an objective set of rules, but to a committee; similarly, an index can have very objective, systematic

rules, but impose barriers to the access of key attributes such as data and application of methodology (e.g., an index with the goal of minimizing risk does not provide precise

descriptions of statistical methods employed).

Goltz continued from page 35

News continued from page 15

63March / April 2013

Malkiel Named CIO Of Wealthfront

Burton Malkiel, the investment-world legend and author of the sem-inal work on indexing “A Random Walk Down Wall Street,” was named chief investment officer of investment advisor firm Wealthfront.

Wealthfront has a minimum account size of $5,000 and manages clients’ first $25,000 free. The firm provides its ser-vices online using the tenets of modern portfolio theory as the backbone of its asset allocation plans.

As CIO, Malkiel will help Wealthfront improve its services, including the choice of asset classes, the way it allo-cates among different classes, the choice of securities and the methods by which it evaluates risk and applies those evaluations to client portfolios, the company said in a blog it published on its website in November 2012.

Wealthfront’s advisory fee, which is separate from fund expense ratios, is considerably lower than what many advisors charge. The firm puts together its low-cost portfolios using only ETFs.

Malkiel, who is also a Princeton University professor emeritus of eco-

nomics, will additionally meet with select groups of Wealthfront clients, and offer investing insights to clients and the public, the registered invest-ment advisor said.

Schapiro Leaves SEC, Succeeded By Walter

The Securities and Exchange Commission is under new leader-ship after the year-end departure of Chairwoman Mary Schapiro, one of the longest-serving heads of the agency. Her successor for a year is Elisse Walter, who has been an SEC commissioner since 2008 and whose term as a commissioner comes to a close at the end of 2013.

Walter previously served as an exec-utive charged with regulatory policy and programs at the Financial Industry Regulatory Authority, and also led an in-depth review of the municipal secu-rities markets at the SEC, according to her bio on Wikipedia.

Appointed by President Barack Obama and unanimously confirmed by the U.S. Senate, Schapiro first took the helm of the SEC in January 2009 in the wake of the credit crisis that sent the U.S. economy into its worst downturn

since the Great Depression. As head of the SEC, Schapiro has also served on the Financial Stability Oversight Council, the FHFA Oversight Board, the Financial Stability Oversight Board and the IFRS Foundation Monitoring Board.

The White House statement didn’t mention a successor for Walter in the late November statement it issued on the transition.

Changes To Morningstar’s Passive Fund Research Team

Morningstar said in early De- cember that it had appointed Ben Johnson global director of passive funds research. Johnson was already overseeing the firm’s passive fund research teams for Europe and Asia, and the new role means he now also oversees the North American team.

Johnson earned a bachelor’s degree in economics at the Uni-versity of Wisconsin. He was hired by Morningstar in 2006.

Paul Justice previously had respon-sibility for Morningstar’s passive fund research team in North America; currently, he is focused on research aimed at institutional investors.




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H U M O R

64

. . . and love gardening.

March / April 2013

ME Musings

By Heather Bell

How I Learned To Stop Worrying . . .

SHHHH! Don’t tell anyone, but we put the March/April issue of Journal of Indexes together in January. It’s just

how the publication cycle works. And put-ting the publication together at the start of 2013 really brings home the topic of “New Perspectives.” I mean, what is the start of a brand-new year if not a chance to take stock of what has come before and to tweak your approach for what lies ahead?

I don’t know about you, but since late 2008—what with the financial collapse and the growing political polarization in this country—I’ve had it in the back of my mind that I should invest in a bunker instead of my 401(k). I mostly gravitate that way anyway—when there’s a snowstorm com-ing, I’m right in there with the rest of the nutsos, desperately buying up bread and milk, even though I don’t consume much of either item. But that 24 hours when I might be snowbound COULD be the 24-hour period in which I am struck by an unconquerable desire for a double-decker sandwich and a big frosty glass of milk.

(Note: Head out to the grocery store early if you don’t want to be stuck with the raisin bread—it does NOT go well with peanut butter and jelly.)

This kind of neurotic response to unpleasant global events runs in my fam-ily. Not long after the market tanked, I had to talk my 60-something mother out of putting her entire retirement sav-ings into physical silver—and explain that her jewelry collection didn’t qualify. However, when things took a downward turn, I elected not to take up political extremism, load up on ammo or move to Canada. Instead, I bought a Costco membership and started a garden.

The former has led to an impressive stockpile of toilet paper, paper towels, plas-tic wrap and frozen broccoli florets (if you

have a good recipe involving them, please email me as soon as possible). My friends started talking about a Hoarders-style inter-vention this past autumn when they real-ized I had enough rolls of toilet tissue to last me to the next Olympics. They don’t hesitate to stop by for a few rolls when they run out, however, so who’s the crazy one?

The garden I view as an investment. OK, right now I probably spend about $20 for every luscious heirloom tomato I har-vest, but one day—hopefully before I’m ready for retirement—I will have a cost-effective supply of organic vegetables for my post-crisis Cobb salad.

So after all that warehouse shopping and a ton of potting, I awoke on this past New Year’s Day to the realization that, contrary to my radical fears and the vehement beliefs of market pundits, society might just be safe and sound for a while. And here I sit with enough Saran Wrap for the world’s leftovers. There’s a lesson here somewhere.

No, I’m not giving up my Costco mem-bership, even if I’ll probably never have to buy TP again. Nor am I abandoning my gardening efforts—it appeals to my crunchy granola side. But I’m not going to let irratio-nal fears of the U.S. turning into Greece—or Somalia, God forbid—affect how I manage my port folio. I’m not going to allocate an out-sized portion of my investments to gold. I’m not going to put all my money into emerg-ing markets because the U.S. and its fellow developed markets have a bit too much debt. And I’m certainly not burying an Airstream trailer in the backyard as an ad hoc bomb shelter, even if Costco has them on sale.

I am, however, going to continue to invest responsibly and not spend my duc-ats on frivolous things. Repeat after me: The odds are good that it’s gonna be OK—maybe not great or even pretty cool, but definitely, at a minimum, “OK.”




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There are over 1,000 eTFs on The markeT. own The righT ones.

} Complete eTF due

diligence process

} robust scoring system:

efficiency, Tradability, Fit

} Unbiased institutional analysis

} Plain-english explanations

} Best data in the eTF industry

Analyze, compare and select the

right ETF for every investment strategy

with IndexUniverse ETF Analytics.

ETF Analytics

©2012 IndexUniverse LLC, IndexUniverse ETF Analytics

Learn more at analytics.indexuniverse.com/joi or by contacting our

ETF Analyst team at 415-501-0939 or [email protected]

Equity: U.S. EnergyEquity Segment Report

15 June 2012

| Equity: U.S. Energy | 15 June 2012 | Source data provided by

OVERVIEW

IndexUniverse Insight

Eight ETFs offer very different approaches to the energy space, providinginvestors with access to everything from the broad market—dominated

Eight ETFs offervery differentapproaches to theenergy space.

by names like Exxon Mobil or

ConocoPhillips—to quant-basedstrategies that attempt to pickwinners from the many companies

and sub industries in U.S. energy.

Four funds—VDE, IYE, XLE and

FEG—deliver broad, market-likesector exposure that ranges from very good to great, though they divergesharply on costs and risks. IYE and XLE offer the most representative

portfolio of stocks, but IYE's 0.47% expense ratio is far and away thehighest of these four “plain vanilla” funds. In contrast, XLE delivers thebest combination of broad exposure at the segment-lowest fee: 0.18%.

The fund brings something else to the party--massive liquidity. As one ofthe most liquid ETFs in the world, XLE trades about $1 billion a day,

making its all-in costs from trading and fees tough to beat. VDE alsodelivers a market-like basket at a low fee (0.19%). Then there’s FEG, thepolar opposite from XLE on trading volume. Despite an excellent

portfolio offered at the second lowest price in the segment (tied with VDEat 0.19%), FEG suffers from poor on-screen liquidity and carries highfund-closure risk from its low asset base.

Four other funds offer clear alternatives to traditional sector exposuredue to their strategy or selection universe. PSCE differs the most from the

sector by focusing exclusively on small-cap energy companies. FXN andPXI use quant strategies to pick winners from the sector instead of merelyowning the market like the vanilla funds. RYE offers an equal-weighted

version of XLE. As they stray from vanilla exposure, all four fundscome—in varying degrees—with greater risks, higher price tags andless-than-perfect tracking, but contribute to a well-rounded segment:

There’s something for everyone here.

Related ETFs

Snapshot

Ticker Fund Name

Overall

Rating Efficiency Tradability Fit Notes

IYE iShares Dow Jones U.S. Energy A 97 88 98 97

VDE Vanguard Energy A 93 91 98 93

XLE Energy Select SPDR A 93 94 99 93

FEG Focus Morningstar Energy B 94 85 78 94

PXI PowerShares Dynamic Energy Portfolio B 65 81 86 65

RYE Guggenheim S&P Equal Weight Energy B 52 82 79 52

FXN First Trust Energy AlphaDEX B 41 80 83 41

PSCE PowerShares S&P SmallCap Energy B 26 89 74 26

Segment Average | Ranked by Overall Score

1-Year Total Return

-20%

-10%

0%

10%

Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun

2012

1-Year

PXI -7.97%

Bench -8.44%

IYE -9.08%

FEG -9.41%

XLE -10.11%

VDE -10.54%

RYE -16.60%

PSCE -19.54%

FXN -24.28%

As of 06/14/12

Page 1 of 6


TRADABILITY

IndexUniverse Tradability Insight

U.S. energy funds vary dramatically in Tradability, with XLE reigning over

all. For on-screen liquidity, XLE is not only the most liquid ETF in the

U.S. energy fundsvary dramatically inTradability.

segment, it’s one of the most liquid

ETFs in the world, trading an

average of about $1 billion daily.

VDE and IYE are distant seconds by

volume, with ADV averaging $13

million and $9 million,

respectively. Still, VDE and IYE deliver excellent liquidity for most

investors. All of the top three funds trade multiples of their creation unit

sizes (50,000 shares), with average spreads between 0.01% and 0.04%.

PXI occupies a second tier of Tradability within the U.S. Energy segment.

With its ADV of $2 million, it trades well above our minimum threshold

of $1 million. Spreads range a good deal wider for PXI than for the top

tier, averaging around 0.14% ($0.06). Still, like the top tier, PXI scores

high with regard to block liquidity—indicating it’s still easy to trade in

size.

Funds that trade around and below $1 million a day in volume land at

the bottom tier of Tradability—PSCE, RYE, FXN and FEG. Of these, FEG

may be the least lucky, considering its portfolio of securities is among the

most liquid. FEG was victim to a pricing error on its first day of trading

that led to wacky trades and bad press—likely scaring away investors at a

critical early stage for the new fund. While all four funds experience

wider spreads—with averages as high as 0.24% in the case of PSCE—their

holdings are incredibly liquid, as indicated by their high block-liquidity

scores. The irony of this is that large investors will find these funds easy

to trade in size (with the help of a liquidity provider), but small investors

only have limit orders to use as an aid when placing trades.

Ticker Tradability Rating

Average Daily

Volume ($)

Average

Spread

Median

Premium/

Discount

Maximum

Premium

Maximum

Discount

Creation

Basket Size

XLE 99 1.07 B 0.02% 0.00% 0.38% -0.17% 50,000

IYE 98 9.55 M 0.04% 0.00% 0.16% -0.20% 50,000

VDE 98 13.08 M 0.04% 0.00% 0.17% -0.15% 100,000

PXI 86 2.32 M 0.14% -0.03% 0.60% -1.03% 50,000

FXN 83 963.16 K 0.13% -0.04% 0.38% -0.50% 50,000

RYE 79 309.18 K 0.12% -0.02% 1.97% -1.80% 50,000

FEG 78 309.04 K 0.14% 0.00% 1.93% -1.35% 50,000

PSCE 74 621.02 K 0.24% -0.05% 0.68% -0.52% 50,000

Segment Average | Ranked by Tradability Score

Spread Dispersion

0.2%

0.4%

0.6%

0.8%

JunJul Aug Sep Oct Nov Dec Jan Feb Mar Apr May

2012

PSCE

FXN

PXI

FEG

RYE

VDE

IYE

XLE

Premium/Discount Dispersion

-1%

0%

1%

Premium

Discount

JunJul Aug Sep Oct Nov Dec Jan Feb Mar Apr May

2012

PSCE

VDE

FXN

XLE

IYE

FEG

PXI

RYE

IndexUniverse / Knight Block Liquidity

IYE

5

4

3

2

1

VDE

5

4

3

2

1

XLE

5

4

3

2

1

FEG

5

4

3

2

1

PXI

5

4

3

2

1

RYE

5

4

3

2

1

FXN

5

4

3

2

1

PSCE

5

4

3

2

1

This measure shows how easy it is to trade25,000 shares of any given ETF. It reflectsthe liquidity and hedgeability of a fund’sunderlying securities. A score of 5 means afund is extremely liquid.

|

Page 3 of 6


FIT

IndexUniverse Fit Insight

Segment funds deliver clear choices regarding their portfolios, roughly

split along the lines of those that try to match the sector and those that

Segment fundsdeliver clearchoices regardingtheir portfolios.

don’t. VDE, IYE, XLE and FEG all

land in the first camp. They aim to

deliver the broad market and

generally do a great job at it, with

IYE and XLE edging out the rest.

These four funds all follow a

market-cap weighting scheme that

gives them top-heavy

concentration of major players such as Exxon Mobil and Chevron, and

generally heavy exposure to the integrated oil & gas industry. However,

there are differences to note. For one, XLE selects from a mid-to-large

universe within the S&P 500, but due to the capping restrictions of the

S&P indexes, the fund ends up with a portfolio slightly skewed toward

midcaps. VDE, the second-broadest fund in the segment, slightly

overweights exploration & production firms, making it a tad riskier than

the market with a beta of 1.03. That said, it’s FEG that tilts the most

toward smaller companies (among the four vanilla funds), with a

weighted average market cap of $130 billion vs. the sector’s $150 billion.

The other 4 funds diverge from the market, and from each other, by

nature of their unique strategies. PSCE scores lowest in Fit, but with good

reason—the fund doesn’t attempt to represent the broad market—it

focuses solely on small-caps. PSCE’s firm size-universe affects industry

exposure: It overweights service and equipment firms and completely

ignores the integrated oil & gas industry. (PSCE passes on names like

Chevron and Exxon Mobil in favor of SEACOR Holdings and Lufkin

Industries.) Though PSCE has outperformed its competitors over the

past year, its beta of 1.42 indicates that it’s risky relative to the broad

market, though this shouldn’t surprise (or necessarily deter) investors

looking for small-cap energy companies.

Investors looking for an equal-weight strategy will want to examine RYE.

The fund strays from the broad market—instead it overweights midcap

companies with zero exposure to small-caps. As a result, RYE loads up on

the exploration & production industry—a space that tends to be

dominated by midcap firms. RYE’s beta of 1.18 indicates the fund is

relatively risky compared to the broad market.

PXI and FXN follow similar strategies: They use quant strategies to select

winners within the energy space. While their goals are similar, their

selection screens differ. As a result, PXI heavily overweights mid- and

small-cap stocks. FXN also dips into the small-cap space, but loads up

massively on midcaps. By industry, both funds skimp on the integrated

oil & gas space, while loading up on exploration & production firms. In

the case of PXI, its current portfolio is particularly exposed to refining

and marketing firms. Both funds are relatively risky in comparison to the

broad market. FXN and PXI have yet to show any statistically significant

alpha relative to the market.

Ticker Fit Rating Weighting Selection P/E P/BAverage

Market Cap# of

HoldingsGoodnessof Fit (R�) Note

Index Methodology

IYE 97 Market Cap Market Cap 9.7 1.6 $144.96 B 91 99.94% Broad exposure, high

cost

FEG 94 Market Cap Market Cap 10.0 1.6 $130.36 B 100 99.86% Youngest fund

VDE 93 Market Cap Market Cap 9.8 1.6 $131.08 B 172 99.90% Broad exposure,

second for lowest cost

XLE 93 Market Cap Proprietary 9.8 1.6 $126.55 B 44 99.80% AUM giant, cheapest

fund

PXI 65 Tiered Multi-Factor 9.9 1.5 $26.45 B 60 95.75% Quant-based strategy

RYE 52 Equal Proprietary 10.7 1.3 $31.77 B 44 96.91% Equal-weighted

exposure

FXN 41 Tiered Multi-Factor 9.2 1.2 $27.27 B 54 95.42% Quant-based strategy

PSCE 26 Tiered Proprietary 15.0 1.1 $1.02 B 24 90.04% Small-cap play

Segment Average | Ranked by Fit Score

Geographic Exposure

United States Switzerland Bermuda Netherlands Brazil Canada

Benchmark 96.84% 2.00% 0.65% 0.32% 0.10% 0.09%

IYE 97.15% 2.19% 0.22% 0.44% 0.00% 0.00%

VDE 97.74% 1.26% 0.43% 0.42% 0.00% 0.15%

XLE 98.87% 0.60% 0.52% 0.00% 0.00% 0.00%

PXI 96.15% 0.00% 1.30% 1.24% 0.00% 1.30%

RYE 96.04% 2.20% 1.76% 0.00% 0.00% 0.00%

FXN 96.51% 0.00% 2.16% 1.34% 0.00% 0.00%

PSCE 100.00% 0.00% 0.00% 0.00% 0.00% 0.00%

FEG 97.81% 1.88% 0.32% 0.00% 0.00% 0.00%

Balanced Underweight 5%+ 3% to 5% 1% to 3% Overweight 1% to 3% 3% to 5% 5%+

Page 4 of 6



INDEX MAR-APR INDEX_III.pdf

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INDEX MAR-APR INDEX_IV.pdf

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new perspectives March / April 2013 - ETF · PDF filenew perspectives March / April 2013 ... Vol. 16 No. 2 March / April 2013 1 52 46 42 ... Blitzer previously served as chief economist

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