risky business May / June 2014 - ETF · University of Paris VI-Jussieu (1999). ... Bruce Greig,...

www.journalofindexes.com

SERIOUS IDEAS FOR SERIOUS INVESTORS

A Different Risk-Parity Approach

Frank Siu

Tails, You Lose

Stanislas Bourgois and Edward Tom

Risk In Focus: A Round Table

Featuring Ric Thomas, John Feyerer, Ted Lucas, Matt Moran and others

Alternative Index Weighting And Portfolio Risk

Scott Weiner and Nicholas Cherney

Plus S&P DJI’s Blitzer on ‘unknown unknowns,’ an interview with Denison U’s CIO,

Eichen & Longo of Acertus on the AMSI … and much more!

risky business May / June 2014

Thu Apr 3 14:39:40 EDT 2014Trim Size = 8.125 X 10.875

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INDEX MAY-JUN INDEX_II.pdf

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Illuminating

As fi nancial markets evolve, S&P Dow Jones Indices

delivers expertise, ideas and education so you

can imagine new views and capture market energy.

This is Indexology®

Begin your exploration at

spdji.com/indexology

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INDEX MAY-JUN INDEX_1.pdf

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www.journalofndexes.com

f e a t u r e s

V o l . 1 7 N o . 3

52

46

18

d a t a

n e w s

Global Index Data 60Morningstar U S Style Overview 61S&P Dow Jones Indices U S Industry Review 62Exchange-Traded Funds Corner 63

Housing Stumbles Entering 2014 12WisdomTree Taps Europe Market With Purchase 12S&P DJI, Russell Remove BDCs From Indexes 12MSCI Conducts Quarterly Index Review 12 Henderson Debuts Global Dividend Index 13Floating-Rate Treasury ETFs Debut 13Indexing Developments 14Around The World Of ETFs 16Derivatives In Focus 17On The Move 17

Risk-Parity Strategies For Equity Portfolio ManagementBy Frank Siu 18An alternate application for a popular strategy

Denison U CIO Discusses Endowment’s SuccessBy Heather Bell 26How did a midsize endowment beat Harvard and Yale?

Tails, You LoseBy Stanislas Bourgois and Edward Tom 28The anatomy and usage of a tail-hedging index

Risk In Focus: A Round TableFeaturing Ric Thomas, Matt Moran, John Feyerer, Vineer Bhansali and others 34Our panel of experts talks about market risks

It’s What You Don’t Know That MattersBy David Blitzer 44It’s the unknown unknowns that get you!

Alternative Index Weighting And The Impact On Portfolio RiskBy Scott Weiner and Nicholas Cherney 46Which methodology results in the best risk profile?

A New Market Sentiment IndicatorBy Mitchell Eichen and John Longo 52New multifactor benchmark provides market insight

Take A Risk!By Bruce Greig 64What are the chances you can finish the puzzle?

May / June 20142


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

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

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

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

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

DBCPowerShares DB Commodity Index Tracking Fund

DBC




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Contributors

4 May / June 2014

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rDavid Blitzer is managing director and chairman of S&P Dow Jones Indices’ index committee. He has overall responsibility for security selection for the company’s indexes, as well as index analysis and man-agement. 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.

Stanislas Bourgois, CFA, is a director and head of equity derivatives strat-egy EMEA at Credit Suisse. Prior to that, he was a senior algorithmic trader in Credit Suisse Equity Derivatives Trading. Bourgois joined Credit Suisse in 2003 from Societe Generale. He holds a management degree from ESSEC Business School in France (1998) and an M.S. in mathematics from the University of Paris VI-Jussieu (1999).

Mitchell Eichen is the founder and CEO of Acertus Capital management. In addition to his portfolio management responsibilities, he is responsible for overseeing the firm’s strategic and product development initiatives. Eichen is also the founder and CEO of The MDE Group, an independent, nationally rec-ognized wealth management firm. He has an LL.M. in taxation from the New York University School of Law Graduate Division and a J.D. from Georgetown University Law Center. Eichen holds a B.A. in economics from Rutgers College.

Bruce Greig, CFA, CAIA, CMT, joined Altin Holdings LLC in August 2009 as a portfolio manager. His responsibilities include overseeing the firm’s invest-ment process and handling all aspects of portfolio allocation. Greig is also a member of the firm’s Investment Committee. He holds a bachelor’s degree in mathematics and statistics as well as an MBA with a major in finance from the Ross School of Business at the University of Michigan.

Frank Siu specializes in index research at Axioma and is responsible for devel-oping alternative beta ideas that leverage the firm’s expertise in optimization and risk modeling. Prior to joining Axioma in 2006, he worked with risk analyt-ics at MSCI Barra and BlackRock. Siu received his bachelor’s degree in econom-ics from Harvard University and his master’s degree in finance and economics from the London School of Economics and Political Science.

Edward Tom is a managing director and head of equity derivatives strategy at Credit Suisse. His group is responsible for the development of derivatives research and quantitative analytics for hedge funds and major financial institu-tions. Tom joined Credit Suisse in 2001 from Donaldson, Lufkin & Jenrette. He holds a B.S. in management information systems, a B.S. in financial accounting and an M.A. in mathematical economics from New York University.

Scott Weiner, DPhil, is managing director and head of quantitative strategy for VelocityShares. Prior to joining the firm, he was managing director and U.S. head of equity derivatives and quantitative strategy at Deutsche Bank, where he was twice voted to the All-America Research Team in Equity Derivatives Research by Institutional Investor. Weiner holds a finance degree from the Wharton School of the University of Pennsylvania, and masters and doctoral degrees in economics from the University of Oxford.

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orningstar, Inc. All rights reserved. The M

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Editor’s Note

Jim Wiandt

Editor

Ever since the 2008-2009 financial crisis, it seems like investors and financial pro-fessionals have had a heightened awareness of the concept of risk. Of course, risk has always been something people were concerned about, but after the

market’s brush with mortality in that crisis, it has become the constant boogeyman under the bed. Fat tails, black swans and kurtosis have moved from the halls of aca-demia to take up residence in the everyday conversation of investment professionals everywhere, as market participants wait for the other shoe to drop.

We decided to drag the boogeyman out from under the bed, dust him off and take a good long look at risk and its management.

Frank Siu of Axioma opens the issue with a discussion of risk parity and how to apply it to an equity portfolio rather than its traditional usage in an overall asset allocation framework.

We then stop in at Denison University for a chat with the CIO of the school’s endow-ment; Adele Gorrilla explains how her small liberal arts school outperformed Harvard and Yale during a recent five-year period. Two words: hedge funds.

Then Stanislas Bourgois and Edward Tom of Credit Suisse weigh in with an explana-tion of how one can hedge tail risk with indexes, taking an example from their firm’s tool kit of tail-hedging benchmarks. After that, we gather a panel of experts to discuss how they view risk and its management; Matt Moran of the CBOE, John Feyerer of PowerShares, SSgA’s Ric Thomas, Pimco’s Vineer Bhansali and others address a range of topics, from VIX-linked investments to diversification to tail risk.

Next, S&P Dow Jones Indices’ David Blitzer offers his thoughts on the unknowable nature of risk, and then Scott Weiner and Nicholas Cherney of VelocityShares lay out their findings regarding the risk levels of various alternative-weighting methodologies, with special focus given to an equal-risk-weighted approach.

Finally, Mitchell Eichen and John Longo of Acertus Capital Management wrap up the feature articles section with an exploration of the methodology underlying the firm’s market sentiment index. The Acertus Market Sentiment Indicator uses a variety of data inputs, including price/earnings data, momentum, volatility, high-yield bond performance and the TED spread, to determine what stage the market cycle is in.

Bruce Greig closes out the issue with a truly challenging puzzle for all the risk man-agement experts out there. It’s hard to use words like fun in relation to this issue’s topic (terms like “anxiety” and “maximum drawdown” and “cardiac event” are more relevant), but this crossword is a virtual romp through the risk-related nomenclature.

If the risk boogeyman is still making you nervous, you can take comfort in the fact that he’s always been there, and even when he comes out to go on a rampage, the mar-kets always seem to be standing when he retreats. Mainly it boils down to this: Have a solid plan and stick to it. If fear and greed are governing your investment decisions, your chances of performing well diminish.

Happy investing!

Risky Times

May / June 201410

Jim Wiandt

Editor




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VWOBVanguard Emerging Markets Government Bond ETFRound out your clients’ portfolios.

All investing is subject to risk, including the possible loss of the money you invest. The Emerging Markets Government Bond ETF is subject to the risk that an issuer will fail to make payments on time, and that bond prices will decline because of rising interest rates or negative perceptions of an issuer’s ability to make payments. The ETF seeks to track the performance of an index that measures the investment return of dollar-denominated bonds issued by governments of emerging market countries (including government agencies and government-owned corporations). It is subject to risks including country/regional risk, which is the chance that political upheaval, fnancial troubles, or natural disasters will adversely affect the value of securities issued by foreign governments, and emerging market risk, which is the chance that bonds of governments located in emerging markets will be substantially more volatile and substantially less liquid than the bonds of governments located in more developed foreign markets.

To buy or sell Vanguard ETFs, contact your fnancial advisor. Usual commissions apply. Not redeemable. Market price may be more or less than NAV.

For more information about Vanguard ETF Shares, visit advisors.vanguard.com/VWOB, call 800 505-7182, or contact your broker to obtain a prospectus. Investment objectives, risks, charges, expenses, and other important information are contained in the prospectus; read and consider it carefully before investing.

* Source: Morningstar as of 03/01/13. Based on industry average expense ratio of 0.55% for emerging market ETFs and Vanguard Emerging Markets Government Bond Index Fund ETF expense ratio of 0.35%. The next lowest expense ratio is 0.50%.

There may be other material differences between products that must be considered prior to investing.

© 2014 The Vanguard Group, Inc. All rights reserved. U.S. Patent Nos. 6,879,964; 7,337,138; 7,720,749; 7,925,573; 8,090,646; and 8,417,623. Vanguard Marketing Corporation, Distributor.

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Extend your clients’ reach at advisors.vanguard.com/VWOB today.

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Are you Vanguarding® your clients’ portfolios?




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News

May / June 201412

Housing Stumbles Entering 2014One of the housing market’s most

closely watched data series, the S&P/Case-Shiller Home Price Indices, told a tale of uneasiness in its February report. The report, covering December 2013 and all of last year, showed that nation-wide, prices across the country fell 0.1 percent for the second month in a row in December, according to the 20-City Composite. The data track single-family home prices in major metro areas.

On the other hand, the Case-Shiller report showed that year-on-year, the quarterly S&P/Case-Shiller U.S. National Home Price Index rose 11.3 percent in all of 2013, the biggest increase since 2005. Meanwhile, the 10-City and 20-City composite indexes were up 13.6 and 13.4 percent, respectively.

The top-performing cities for the year, according to the press release, were Las Vegas, up 25.5 percent; San Francisco, up 22.6 percent; and Los Angeles, up 20.3 percent. Meanwhile, Cleveland was the worst performer, with a gain of just 4.5 percent, fol-lowed by New York, up 6.3 percent, and Charlotte, up 7.8 percent.

Interestingly, Los Angeles, up 1.4 per-cent; Las Vegas, up 0.6 percent; and San Francisco, up 0.4 percent, were also the best performers in the month of December. Chicago, down 1.2 percent, was the worst performer for the month of December, joined by Charlotte, down 0.8 percent. Four cities saw declines of 0.3 percent for the month: Atlanta, New York, Portland and Washington, D.C.

S&P Dow Jones Indices attributed the decline in home prices in the last months of the year to a number of fac-tors, including higher mortgage rates and the cold weather.

WisdomTree Taps Europe Market With Purchase

WisdomTree has become the lat-est U.S.-based exchange-traded fund

provider to break into the European ETF market, acquiring a majority stake in U.K.-based provider Boost ETP in January.

Boost ETP, which offers leveraged and inverse exchange-traded prod-ucts, launched at the end of 2012 and has seen its assets rise to around $52 (€37.8) million during that time. WisdomTree will invest $20 million in the provider, which will be used as working capital to help build out a local European platform.

The $20 million is worth about 38 percent of the total value of the com-pany, and will give WisdomTree a 75 percent holding, while Boost ETP will retain the remaining 25 percent.

The move is the latest sign that U.S. firms are breaking into the European ETP market, which is worth $400 bil-lion. Vanguard arrived in May 2012 and is now ranked 11th in the region, while First Trust and PowerShares have also launched products in Europe.

Most recently, Warburg Pincus took a majority stake in Source ETP, which will be spearheaded by ex-iShares CEO Lee Kranefuss.

Boost ETP was set up by Hector McNeil and Nik Bienkowski, who both previously worked for ETF Securities, and will continue to run the opera-tions at Boost ETP and take on those for WisdomTree. They are named as co-CEOs of WisdomTree Europe.

A note from Boost said that WisdomTree intends to launch a select range of UCITS ETFs under the WisdomTree brand.

S&P DJI, Russell Remove BDCs From Indexes

In the early months of 2014, S&P Dow Jones Indices and Russell Indexes both decided to jettison business development companies (BDCs) from their broad U.S. indexes to streamline the way mutual funds

and ETFs must account for their presence in a portfolio.

S&P’s decision to nix BDCs affected some of its biggest and most famous indexes, including the MidCap 400 and the SmallCap 600, according to an S&P DJI representative. BDCs are typi-cally closely held entities that exist to invest in relatively small and dynam-ic pockets of the economy. They are favored by investors for the some-times-huge yields they provide.

Behind the index firms’ decisions is the fact that BDCs are considered mutual funds. As far as that goes, U.S. financial regulations strictly limit how much of other mutual funds a given mutual fund can own. More to the point, a BDC that’s included in a fund-of-funds mutual-fund structure—includ-ing in an ETF wrapper—is reflected in so-called acquired fund fees that are expressed in expense ratios.

S&P’s representative stressed that the decision was not a judgment that BDCs are not good investments, but recognition of the fact that their pres-ence in a diversified mutual fund or ETF can amount to more trouble (and expense) than they’re worth.

As an example, acquired fund fees for the Market Vectors BDC Income ETF (BIZD) amount to 793 basis points on top of a 40-basis-point management fee.

The S&P representative noted that while the decision technically affects the S&P 500 Index, BDCs are likely too small to ever find their way into that index.

MSCI Conducts Quarterly Index Review

In February, MSCI announced the results of its most recent quarterly review of its equity index family; the changes became effective as of Feb. 28, according to a press release.

The MSCI ACWI Index saw the addition of six companies and the deletion of four. The U.K.’s Royal




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Mail Group and Asos; Canada’s Inter Pipeline and Japan’s Seiko Epson Corp. were all added to the MSCI World Index. Meanwhile, China’s CSPC Pharmaceutical Group and China Cinda Asset Management were added to the MSCI Emerging Markets Index. One company was deleted from the MSCI World Index, and three from the MSCI Emerging Markets Index.

The firm’s broad MSCI ACWI Small Cap Index saw one addition and 14 deletions, and the “Investable Market Indices,” or IMI, version of the MSCI ACWI saw three additions and 14 deletions.

The broad index in MSCI’s Global All Cap index family, the MSCI World All Cap Index, had two companies added and another seven deleted.

The six companies added to the MSCI ACWI were also included in the MSCI ACWI style index series. Three companies were added to both the MSCI ACWI Value and MSCI ACWI Growth indexes: Inter Pipeline; China Cinda Asset Management and CSPC Pharmaceutical Group. The compa-nies exhibit both growth and value characteristics, and their weights in the indexes are adjusted by “inclu-sion factors” that reflect the degree to which they exhibit growth or value traits. Royal Mail Group, Asos and Seiko Epson were added exclusively to the MSCI ACWI Growth Index.

Additional changes were made to MSCI’s Global Islamic, U.S. and China index families. MSCI also issued a reminder that Qatar and the United Arab Emirates will be reclassified as emerging markets after being promot-ed from a frontier markets designation during the May review.

Henderson Debuts Global Dividend Index

In February, Henderson Global Investors (HGI) launched a global div-

idend index; however, the benchmark does not measure stock performance, just dividend issuance. In conjunction with the publication of the index on a quarterly basis, HGI will issue a report that examines dividend issuance on a global basis, including by region and country, and by sector.

The new index measures the gross dividend payouts from the 1,200 larg-est firms by market capitalization and in U.S. dollars. Outside of the largest firms, HGI estimates the dividend payments for other listed stocks, which account for 11.3 percent of global payments. Data is pro-vided by Exchange Data International. The research covers roughly 45 devel-oped and emerging markets.

Global dividends grew from around $700 billion in 2009 to $1.03 trillion in 2013, an increase of 2.8 percent last year alone, partly thanks to the strength of the U.S. dollar, the inaugural report found.

Of that $1.03 trillion, 37 percent came from the U.S. Continental

Europe followed with 22 percent of global dividends, with Germany and Scandinavia leading the way, but HGI’s report found the euro crisis still weighs on the region.

The U.K. alone contributed 11 per-cent of global dividends for investors, the same percentage for Asia-Pacific as a whole, although Asia-Pacific has risen 79 percent since 2009.

Emerging markets doubled divi-dend payouts between 2009 and 2011, but growth has since stalled. The region contributed 14 percent of global divi-dends in 2013, and now makes up $1 out of every $7 global payout. Emerging markets in particular have low levels of free-floating shares, which can gen-erate large amounts of income, often because governments with big stakes require generous payouts.

Floating Rate Treasury ETFs Debut

iShares rolled out its Treasury Floating Rate Bond ETF (TFLO) on

In February, Henderson Global Investors launched a global dividend index.




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NewsNewsFeb. 3 in an attempt to replicate the success of its $3.7 billion iShares Floating Rate Bond ETF (FLOT | A-99). TFLO tracks the Barclays U.S. Treasury Floating Rate Index.

At the same time, WisdomTree launched its own competing fund, the WisdomTree Bloomberg Floating Rate Treasury Fund (USFR); however, there is a key difference between the two.

BlackRock Fund Advisors, TFLO’s advisor, contractually agreed to waive its advisory fee of 0.15 percent through Feb. 28, 2015, according to a regulatory filing. By comparison, USFR has an annual expense ratio of 0.15 percent.

Right now, investors are scour-ing the ETF landscape for yields in a rising-interest-rate environment. Floating-rate bond ETFs were very popular in 2013, partly because of the yields they deliver, but also for the interest-rate-risk protection they provide. This year, these ETFs should only become more popular as inves-tors brace for the Federal Reserve to begin pushing interest rates higher. And the floating-rate Treasury funds are likely to offer the lowest credit risk in the space.

At least one more floating-rate Treasury ETF is in the works, with State Street Global Advisors filing for the SPDR Floating Rate Treasury ETF at around the same time WisdomTree and iShares made their initial filings.

INDEXING DEVELOPMENTSRussell Announces Reconstitution Schedule

In early March, Russell unveiled the schedule for its 2014 annual index reconstitution.

The index provider will post the changes to the indexes being recon-stituted—the Russell Global Index, the Russell 3000 and the Russell Microcap Index—on its website on Friday, June 13, according to the press release. If there are any updates to the initial changes during the pro-cess, they will be posted on June 20 and June 27, with the final rebalance becoming official on Friday, June 27.

However, the biggest change occur-ring this year will be at the country level, rather than the company level. Egypt will be demoted to frontier-market sta-tus from emerging-market status. The press release noted the change came at the end of a standard three-year review of the country’s classification status that determined that Egypt no longer met Russell’s standards for macro and oper-ational risk in emerging markets.

Solactive Unveils Buyback IndexIndependent index provider

Solactive launched a buyback index in mid-March, allowing investors to tap into an increasingly popular strategy in the U.S. and in Europe.

The Solactive European Buyback Index underlies index-linked products from Societe Generale Corporate & Investment Banking (SG CIB).

A buyback is a way for companies to return cash to their shareholders by increasing earnings per share by purchasing their outstanding shares.

The index has a bias to the U.K. and Nordic countries, with more alpha gen-erated from its weighting in small-cap and value stocks. It tracks companies in 16 Western European countries that have announced a buyback in the last two months. Stocks must have a min-imum market capitalization of €500 million and an average trading value of €2 million over the last three months.

The components are weighted according to their buyback ratio, which is the sum of all shares bought back in the period divided by the number of shares outstanding at the beginning of the period.

ERI Scientific Beta Debuts Indexes

ERI Scientific Beta launched a range of smart-beta indexes in February that provide access to various risk strate-gies in developed countries.

The so-called smart factor indexes are built on the ERI Scientific Beta platform by investors. Investors pick their geogra-phy and one of 24 “risk tilts”; for exam-ple, high momentum, which defines the stock selection. The stocks are then

weighted by taking the average weighting from five strategies: maximum decon-centration, minimum variance, diversi-fied risk weighting, maximum Sharpe ratio and maximum decorrelation.

According to the firm, this results in diversification from a weighting per-spective and a strategy perspective.

In total, 216 new indexes will be launched across nine regions, multiplied by 24 different kinds of stock selection. All developed-world countries will be included in the new index range, with benchmarks targeting the U.S., U.K., eurozone, developed Europe ex-U.K., Japan, developed Asia-Pacific ex-Japan and several other developed countries.

The smart-beta platform, which is the indexing arm of the French think tank and business school Edhec Risk Institute, found that the new indexes provided, on average, 68 percent bet-ter performance than traditional fac-tor indexes in terms of Sharpe ratio.

The index provider charges a flat fee for all indexes.

S&P DJI Debuts South Africa Indexes

In February, S&P Dow Jones Indices rolled out a family of indexes targeting South Africa’s stock market, a press release said. The index provider opened its first office on the African continent in South Africa last September.

The nine indexes include the head-line S&P South Africa Composite Index, which comprises all companies listed on the Johannesburg Stock Exchange (including foreign-domiciled compa-nies) that have more than $100 mil-lion in market capitalization and meet the minimum trading volume require-ment, according to the press release.

The other eight indexes are derived from the composite. Among them are two blue-chip indexes, a completion index, a dividend-focused bench-mark that uses S&P’s “Dividend Aristocrats” methodology, a low-vol-atility index and Shariah-compliant indexes. Beyond those core indexes, the composite also can be broken down into subindexes based on size and sector, the press release said.

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S&P GSCI Family Expands In late February, S&P Dow Jones

Indices rolled out six- and 12-month forward versions of the S&P GSCI, as well as three-, six- and 12-month for-ward versions of its single-commodity indexes using its capped-component methodology, a press release said.

Instead of using near-month con-tracts to track commodity markets, the new indexes use contracts that expire three, six and 12 months out, depending on what their methodol-ogy calls for. The use of forward con-tracts that expire further out on the futures curve is intended to mitigate the effects of contango.

The press release noted that the S&P GSCI Forward Single Capped Component indexes do not exclu-sively include their designated com-modities’ futures contracts—they simply overweight the targeted commodity or commodities at 32 percent, while equal-weighting the other S&P GSCI components in the remaining 68 percent of the index.

Nasdaq Indexes Available To eVestment Customers

The Nasdaq OMX Group said in an early March press release that 19 of its indexes had been made available to customers using the analytic solutions offered by eVestment, a data and ana-lytics provider serving the institutional investor community.

The majority of the offered index-es are broad and size-specific slices and combinations of the U.S., devel-oped non-U.S. and emerging markets. However, the array also includes three of Nasdaq’s “Dividend Achievers” indexes covering the U.S. and inter-national markets. According to the Nasdaq, the data will be included on all of eVestment’s software platforms.

S&P Debuts New Europe IndexesS&P Dow Jones Indices rolled out two

new variations of its market-cap-weight-ed S&P Europe 350 Index in February, one employing an equal-weighting methodology and the other reflecting a low-volatility/high-dividend strategy.

The S&P Europe 350 Low Volatility High Dividend Index tracks the perfor-mance of a subset of 50 constituents of the S&P 350 Index, which covers 17 European markets and represents 70 percent of their market capitalization. All constituents of the new index meet certain diversification, volatility and trading requirements.

The methodology first isolates the top 75 components of the main index in terms of dividend yield. It then selects the 50 stocks from that group that have the lowest realized volatility, the press release said.

Meanwhile, the S&P Europe 350 Equal Weight Index, which launched a little later in the month, simply assigns equal weights to its components.

Euronext Expands French Index Family

NYSE Euronext launched an index in March that tracks small and medi-um-sized businesses (SMEs) in France in order to encourage investment and stimulate the economy.

The CAC PME Index is the first in a planned family of indexes on Euronext Paris, as well as on EnterNext, the exchange’s SME segment.

The new index tracks between 20 and 40 companies that are eligible within PEA PME accounts, an initia-tive by the French government that encourages long-term saving via investment in European equities.

Each index constituent is ranked by trading volume, and the cap on each stock is 7.5 percent. The index is calculat-ed in real time and revised every quarter.

Estimates from the Observatoire des Entrepreneurs indicate that PEA PME accounts could provide €2.5 billion a year worth of new liquidity for SMEs.

PME means “petites et moyennes entreprises”—or SMEs—in French.

Solactive Debuts MLP Bond Index

In late March, Solactive announced the launch of the first bond index to target the MLP market.

The Solactive MLP Bond Index selects only bonds with at least $250

million outstanding and at least a year to maturity, the press release said. The new index’s selection universe encompasses all bonds issued by MLPs, and each issuer’s weight in the index is capped at 20 percent.

The press release noted that the index included 96 component bonds at launch and had a yield to maturity of 3.99 percent. It also said that 95 per-cent of the bonds in the index qualify as investment grade.

Nasdaq Rolls Out IBIS ETF IndexIn February, Nasdaq debuted the

Nasdaq IBIS Focused Growth Index, along with a total return version of the benchmark, a press release said. The index was built using a methodology developed with IBIS Capital and reflects the firm’s quantitatively based strategies.

The index’s components are ETFs that are selected via IBIS’s Quantitative Tactical Global Rotation Strategy, according to the press release. The methodology uses a relative-strength approach and can select ETFs focused on large- or small-cap domestic equi-ties and also those targeting devel-oped- or emerging-market equities.

When global equity markets falter, the index transfers its allocations to U.S. government bonds, with the dura-tion determined by current interest rates, the press release said.

Europe’s ICAP Indexes Pushes Into Fixed Income

ICAP Indices, the index arm of data group ICAP Plc, is in discussions with exchange-traded fund houses, struc-tured product providers and invest-ment banks to push into the fixed-income indexing space, according to a report from ETF.com’s European office.

ICAP plans to focus on rates, curren-cies and fixed income, the head of the firm’s index group said. The company is looking at benchmarks such as an interest rate swap index or an interest rate volatility index, and has no plans to bulk out on equities or commodities.

The firm already offers a handful of indexes, most of which were launched last year and mainly cover the com-





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modities and fixed-income spaces. A representative of the firm said that the next index product would come to market by the end of the first quarter.

ICAP Indices plans to generate revenue from selling its intellectual property and index data, and from listing indexes on exchange. Indexes will only be created per client demand and charge according to the amount of assets that the index gathers, as opposed to a flat fee model.

ICAP plans to have three categories of index. One is to provide an index- ref-erencing service for execution business, often together with industry partners. The second is to use interest-rate price discovery to produce references such as an interest-rate swap index. The third is more traditional fixed income and smart beta, which could be used by investors who are worried about the future direc-tion of interest rates and prices.

AROUND THE WORLD OF ETFs

Global X Launches GURU-Type ETFs

Global X unveiled two ETFs that complement its blockbuster hedge-fund-mimicking Global X Guru ETF (GURU | B-50). Launched on NYSE Arca, the Global X Guru Small Cap Index ETF (GURX) and Global X Guru

International Index ETF (GURI) follow methodologies similar to that of the roughly $600 million GURU, which came to market in June 2012.

Like GURU, both new funds use 13F filings used by hedge fund man-agers to drive stock selection in their respective markets—domestic small-cap equities and international equi-ties. Institutional money managers who run more than $100 million in assets are required to submit such 13F filings to regulators quarterly.

According to Global X’s website, the underlying index methodology narrows down the hedge fund pool based on holdings and turnover rates, among other factors, and selects the largest equity holding in the targeted asset class (small-cap or large-cap equities, in this case) from each quali-fying hedge fund. Those component stocks are then equal weighted, with the index rebalanced quarterly.

Both funds have annual expense ratios of 0.75 percent, according to a regulatory filing.

Pimco Closes TRSYOn March 14, Pimco shuttered its

Pimco Broad U.S. Treasury Fund, which tracked a laddered index that included three of the most recently issued two-, three-, five-, seven-, 10- and 30-year U.S. Treasury notes and bonds.

The fund tracked the equal-weight-ed BofA Merrill Lynch Liquid US Treasury Index and came with a net expense ratio of 0.15 percent.

TRSY was launched in October 2010, but managed to gather only $8 million since inception. The fund ceased trading on March 10.

KraneShares Unveils China A-Shares ETF

In early March, KraneShares debuted the KraneShares Bosera MSCI China A Share ETF (KBA), which tracks the MSCI China A Index, a free-float-adjusted market-capitalization-weight-ed index that gives investors exposure to large-cap and midcap Chinese secu-rities, according to a regulatory filing.

KraneShares has teamed up

with a local advisor, Bosera Asset Management, to gain direct access to the mainland market via an initial renminbi qualified foreign institution-al investor (RQFII) quota of 1 billion RMB ($163 million).

Last November, Deutsche Bank rolled out the first-to-market db X-trackers Harvest CSI 300 China A-shares Fund (ASHR), which similarly provides direct exposure to the China A-shares market via a RQFII partnership with a local firm. ASHR now has $157 million in assets under management.

KBA has an annual expense ratio of 1.10 percent, versus an expense ratio of 0.82 percent for ASHR.

SSgA Debuts Int’l Version Of JNKState Street Global Advisors, the

ETF issuer behind the $10 billion SPDR Barclays High Yield Bond ETF (JNK | B-61), in March rolled out an international version of the successful flagship high-yield credit fund.

The SPDR Barclays International High Yield Bond ETF (IJNK) has a primary listing on the New York Stock Exchange’s electronic platform and holds high-yield corporate credits from a variety of non-U.S. issuers.

At launch, IJNK had 718 compo-nents from developed and emerging markets around the world.

According to a regulatory filing, IJNK has an annual expense ratio of 0.40 percent, which is what its prede-cessor fund, JNK, costs.

PowerShares Launches Int’l Buyback ETF

PowerShares brought to market an ETF in late February that targets interna-tional stocks with strong buyback records.

The PowerShares International BuyBack Achievers Portfolio (IPKW) serves up an international angle to the fund-provider’s successful $2.7 bil-lion PowerShares Buyback Achievers Portfolio (PKW | B-92). The fund invests in foreign companies classi-fied as “BuyBack Achievers” that have reduced 5 percent or more of their out-standing shares in the past 12 months.

IPKW’s underlying index is pro-

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www.journalofindexes.com 17May / June 2014

vided by Nasdaq OMX and had 41 components at the end of March. Japan, the U.K. and Canada were the three largest countries in the index in terms of weight, according to the PowerShares website.

The fund has an expense ratio of 0.55 percent, five basis points more than PKW.

AccuShares Files For ‘Spot’ Commodity ETFs

Newcomer AccuShares has put into registration 12 commodity and two volatility ETFs—seven long and short pairs—that hold the promise of helping investors steer clear of contango and other vagaries of the futures market that detract from returns while captur-ing the ever-elusive “spot” prices. Each bull-and-bear pair is organized around a separate underlying index, and tracks changes in the spot prices of the respec-tive commodities or index, according to the preliminary prospectus.

The prices are published as index values by S&P Dow Jones Indices LLC, according to the filing. The funds’ basic premise is that investors who own spot don’t have to worry about contango, but rather just the value of the commodity. Spot prices are what a given commodity costs in real time in actual physical markets.

The “Spot Up” and “Spot Down” ETFs will mainly target the broad S&P GSCI as well as subindexes target-ing agriculture & livestock, industrial metals, crude oil, Brent oil and natural gas. Another pair of ETFs will seek to reflect the spot price of the VIX index.

Each fund will hold only cash, short-dated U.S. Treasurys or collat-eralized U.S. Treasury repurchases and will not invest in commodities, futures, swaps or other assets, accord-ing to the prospectus.

Index Change For ProShares Breakeven ETFs

Credit Suisse informed ProShares Trust that as of March 31, 2014, Dow Jones and Credit Suisse will be end-ing their joint relationship on the production of the Dow Jones Credit

Suisse Breakeven Index series. Going forward, calculation and publication of the indexes will be controlled sole-ly by Credit Suisse.

Therefore, effective from April 1, the Dow Jones Credit Suisse 30-Year Inflation Breakeven Index, which was tracked by the ProShares 30 Year TIPS/TSY Spread (RINF) and the ProShares Short 30 Year TIPS/TSY Spread (FINF), will be called the Credit Suisse 30-Year Inflation Breakeven Index.

Also, the Dow Jones Credit Suisse 10-Year Inflation Breakeven Index, which was tracked by the ProShares UltraPro 10 Year TIPS/TSY Spread (UINF) and the ProShares UltraPro Short 10 Year TIPS/TSY Spread (SINF), will be called the Credit Suisse 10-Year Inflation Breakeven Index.

While the indexes names will change, their methodologies will remain the same in all respects, according to the filing.

DERIVATIVES IN FOCUSCBOE February Options Volumes Up

CBOE Holdings reported a strong year-over-year increase in options vol-ume for February. Total options con-tracts traded stood at 109.38 million for the month, with an average daily volume of 5.76 million contracts. The month’s numbers represented a 37 percent increase from the prior year.

Index options saw average daily volume increase 36 percent from the prior year to 1.92 million con-tracts, while exchange-traded product options similarly saw their volumes increase by 39 percent to 1.38 million.

The press release noted that the five most actively traded index or ETP options contracts for the month were the contracts for SPX, the VIX, the SPDR S&P 500 ETF (SPY), the iShares Russell 2000 Index Fund (IWM) and the iPath S&P 500 VIX Short-Term Futures ETN (VXX).

CBOE Launches Alternate VIX Index

The Chicago Board Options Exchange said in March that it would

launch options on the CBOE Short-Term Volatility Index (VXST) on April 10, though regulatory approval was still pending at the time of the announcement.

According to the press release, the VSXT index, like the VIX, is constructed using options on the S&P 500 to mea-sure market volatility. However, the shorter-term focus of the VXST index means that it uses weekly options to cover nine-day volatility; the VIX uses S&P 500 options that expire monthly to target 30-day volatility.

The CBOE noted that there is a high correlation between the VXST index and the VIX, although the VXST index tends to be the more volatile of the two.

ON THE MOVELegal & General Acquires Global Index Advisors

U.K.-based Legal & General Group Plc said in February that it would acquire Atlanta-based Global Index Advisors Inc., a provider of index-based products in the target-date-fund space.

Founded in 1994, GIA designs index-es for target-date-fund investments and manages assets tied to those indexes. It created the initial indexes that eventu-ally evolved into the Dow Jones Relative Risk and Dow Jones Target Date bench-mark families, and has designed other index families for Dow Jones Indexes, now known as S&P Dow Jones Indices. The firm subadvises the Wells Fargo Advantage Dow Jones Target Date Funds; the funds have $15.6 billion in assets under management.

Legal & General indicated in the press release that the acquisition would provide it with a greater pres-ence in the defined-contribution plan space in the U.S., where target-date funds are increasingly popular, as well as in the U.K. market.

Although Legal & General is paying out initial consideration of $30.75 mil-lion, the final sum for the transaction could be more than $50 million if cer-tain performance benchmarks are met over the next three years, according to the press release.




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May / June 201418

Risk Parity Strategies For Equity Portfolio Management

Can an asset-class strategy translate to equities?

By Frank Siu




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May / June 2014www.journalofindexes.com 19

Risk-based strategies have gained popularity amid market uncertainty, and many are now being touted as “smart beta,” providing a systematic

way to outperform traditional capitalization-weighted benchmarks. Here we examine the notion of risk parity, taking what has almost exclusively been discussed in an asset allocation context and applying its concepts to equity-only portfolio construction.

Risk parity seeks to equalize sources of risk such that the relative marginal contribution to risk (RMCTR) from each source is equal. Historically, research has treated asset classes as the sources of risk, because these are typically quite distinct and relatively uncorrelated (for example, interest rate versus equity market risk). Given an n-vector of loadings on sources of risk W and their covariance matrix Q, a risk parity portfolio satisfies:

In a traditional asset allocation setting where n rep-resents the number of asset classes, n is “small” (<10), and this problem is easy to solve, even using odd formu-lations and mediocre solvers. In equity portfolios, n is usually larger, and the sources of risk may be individual stocks, groupings of stocks (sectors, countries, regions or combinations thereof ) or quantitative risk factors. By modeling risk parity as a set of optimization constraints, we are able to find risk-parity portfolios efficiently for large n. In addition, we will demonstrate how certain formulations of risk parity can in fact be used as an over-lay on top of an existing investment process. The follow-ing discussion is divided into three parts. We begin with the simplest case, where the elements of w represent individual stocks, so each stock constitutes a source of risk (dubbed “asset risk parity” henceforth). Next, the analysis will expand to include risk parity between groupings of stocks (such as country/sector). Finally, we introduce the idea of risk parity between risk factors.

Rather than presenting a series of backtests with the goal of promoting risk-parity strategies and validating their possible superior performance, we discuss meth-ods for constructing risk-parity portfolios and analyze how each variant of risk parity affects the resulting port-folio composition.

Constructing Asset Risk-Parity PortfoliosDespite its straightforward definition and prevalence

in research, there is little discussion—let alone con-sensus—on exactly how risk-parity portfolios are built. Methods discussed include model simplification (e.g., ignoring correlations, per Anderson et al. (2012)), ad-hoc/trial-and-error approaches using rudimentary sta-tistical software (Kazemi, 2012) and iterative numerical algorithms (Chaves et al. 2012). Finding a portfolio that satisfies Equation 1 is sometimes tackled by using a non-linear optimizer to minimize a loss function measuring

the portfolio’s deviation from risk parity, for example, the sum of squared differences between stocks’ RMCTR, as in Maillard et al. (2010):

In the equity world where n is typically large, contrary to this conventional method, it is preferable to model risk parity as optimization constraints. In the case of a long-only portfolio where w represents individual stock weights, Equation 1 can be expressed as a set of convex constraints on the RMCTR of each stock:

Here Li is a diagonal matrix with L

ii =1 and zero else-

where, and n represents the total number of stocks. Equation 3 can be formulated and solved as a convex optimization problem. Additionally, when minimizing risk-parity viola-tions using the objective function, per Equation 2, depend-ing on the quality of the search algorithm, one may end up with portfolios that are in fact not risk parity but have simply exhausted that particular optimizer’s ability to find a better solution. Finally, minimizing departure from risk parity is dangerous because depending on the loss function, it may be difficult to differentiate between large violations involv-ing a few stocks and small violations across the board.

Kaya and Lee (2012) express risk parity as the solution to a utility maximization problem and show that in a long-only and fully invested setting, there exists a unique set of weights w that satisfies Equation 1. This result only applies to asset risk parity in this restricted case; such a claim can-not be made for risk parity between groupings of stocks or risk factors, where the mathematics of finding a solution are far more complex and are discussed later. Moreover, if addi-tional constraints are imposed, the problem may sometimes not have a solution at all. Nevertheless, establishing unique-ness is of paramount importance because it suggests that asset risk parity fully defines a portfolio strategy. As a set of portfolio constraints, asset risk-parity is akin to a weighting scheme: It contains the necessary and sufficient information for uniquely specifying the final portfolio composition.

Asset Risk Parity In ‘Small’ Blue Chip UniversesIf uniqueness is established, the constraints in Equation

3 can be combined with any objective function to yield the same solution. In the following examples, we use a “mini-mize variance” objective. Figure 1 shows the performance of such an implementation of asset-risk parity, applied to vari-ous benchmark universes, with generally positive results. Asset-risk parity seems well suited to blue chip benchmark universes containing relatively small numbers of names, where there is typically concentration risk that can be diver-sified. Of particular interest is the relatively manageable

turnover (figures are annualized one-way) when compared with other risk-based strategies. In most cases, risk par-ity achieves diversification by down-weighting mega-cap names with very large weights, resulting in a slight low-beta tilt; Kaya and Lee (2012) provide an analytical proof of why this is so. Risk reduction compared with the capitalization-weighted benchmark is present but to a lesser extent than, say, minimum-variance strategies, where aggressive low-volatility positioning often results in high tracking error, a form of “risk” some investors find difficult to accept.

Asset Risk Parity In Broad-Market UniversesMoving to broader benchmark universes, the optimi-

zation problem becomes more difficult, because both the number of constraints and the dimensionality of each con-straint increases. Furthermore, the risk profile of the strategy

becomes increasingly dependent on benchmark construc-tion and market structure, due to the implicit requirement that every stock be held. To illustrate, consider applying asset-risk parity to the Euro Stoxx 50 and the Euro Stoxx (about 300 names) universes. Their corresponding ex-ante tracking error decompositions are presented in Figures 2a and 2b.

There are large differences between the risk profiles of the two portfolios. In the full Euro Stoxx universe, roughly two-thirds of names are mid- and small-caps. Maintaining risk parity will systematically overweight many such names, resulting in a large risk contribution from the small size bias (the red area in Figure 2b), whose share of risk in the Euro Stoxx 50 case was almost zero. Similarly, significant underweighting of the largest blue chip names creates large negative-beta and low-volatility tilts. Figure 3 shows the relationship between the number of names in the universe

Figure 1

MIB Italy

IBEX Spain

DAX Germany

CAC France

BEL Belgium

ATX Austria

AEX Netherlands

EUR.STX.50 Eurozone

SMI Switzerland

Performance Of Asset Risk Parity Strategies In Select Markets, Quarterly Backtest, 2005-2013

Total Return 6.88% 8.22% 5.82% 5.76% 6.39% 10.38% 6.52% 2.91% 8.56%

Realized Risk 16.29% 18.23% 20.65% 14.06% 18.32% 18.00% 19.11% 19.29% 13.97%

Sharpe Ratio 0.42 0.45 0.28 0.41 0.35 0.58 0.34 0.15 0.61

Active Return 1.77% 3.30% 3.15% 1.83% 1.94% 1.50% 1.07% 3.00% 1.49%

Specific 0.87% 4.51% 2.87% 2.11% 1.51% 0.87% 2.48% 3.70% 1.17%

Factor 0.91% -1.21% 0.28% -0.28% 0.43% 0.62% -1.41% -0.71% 0.32%

Tracking Error 2.17% 5.48% 7.67% 6.18% 3.39% 4.98% 5.08% 4.94% 5.10%

Information Ratio 0.82 0.60 0.41 0.30 0.57 0.30 0.21 0.61 0.29

Hit Rate 62.96% 63.89% 59.26% 61.11% 57.41% 67.59% 56.48% 58.33% 61.11%

Average Beta 0.95 0.92 0.86 0.80 0.97 0.91 0.91 0.89 0.95

Average Names 50 25 20 20 40 30 35 40 20

Average Turnover 36.43% 35.85% 41.00% 35.40% 28.05% 29.98% 35.37% 33.33% 29.50%

Sources: Axioma, Stoxx, Thomson ReutersNote: Using monthly gross returns in EUR (CHF for SMI). Risk and return figures are annualized.

May / June 201420

Ex-Ante Tracking Error Decomposition For Asset Risk Parity On Blue-Chip Vs. Broad Market Universes, 2005 To 2013

9%

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2005 2011 2012 2013 2005 2006 2007 2008 2009 20010 2011 2012 2013

Euro Stoxx 50 Euro Stoxx (~300 Names)9%

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� Systematic (Market) � Size � Styles ex-Size � Country � Sector � Currency � Speci�c

Ex-Ante Tracking Error Decomposition For Asset Risk Parity On Blue-Chip Vs. Broad Market Universes, 2005-2013

9%

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2006 2007 2008 2009 2010

Euro Stoxx 50a) b) Euro Stoxx (~300 Names)9%

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� Systematic (Market) � Size � Styles ex-Size � Country � Sector � Currency � Speci�c

Figures 2a and 2b

Sources: Axioma, Stoxx

and the resulting risk breakdown as well as portfolio beta. To summarize, by increasing universe breadth, the drivers of risk/return shift disproportionately toward systematic risk factors rather than stock-level differences vis-à-vis the benchmark as a result of “reshuffling” weights to comply with risk parity. When the universe becomes very broad, such as in Figure 2b, the portfolio risk profile approaches that of a low-beta, small-cap strategy. Increasing universe size may not necessarily detract from returns, but changes the investment thesis considerably. Returns become “pol-luted” by factor effects; most notably, low beta and small size dominate the strategy’s exposure profile, dwarfing the effects from risk diversification. Figure 4 shows the risk and performance of asset-risk parity applied to several large-cap benchmark universes from the previous section compared

with their broad-market equivalent. The backtested results are consistent with the trends illustrated in Figure 3—asset-risk parity in large universes is characterized by overweight-ing small-cap stocks and a substantial portion of return arising from low-beta positioning. These two factor bets are largely responsible for increased tracking error, which in turn results in inferior risk-adjusted active returns.

Risk Parity Over Groupings Of StocksIn the previous example of asset-risk parity on the

Euro Stoxx 50, risk was not being diversified across 50 independent sources; in fact, as Figures 5A and 5B show, there is still considerable concentration along sector and country lines. More generally, in any long-only equity-only portfolio, the broad market (beta) accounts for a large portion of risk, and stocks in the same sector or country remain highly correlated.

An alternative to asset-risk parity may be to equal-ize over systematic sources of risk, such as sectors or countries. For global investment universes, the pres-ence of some very small countries renders country risk parity somewhat impractical, and sectors may not be homogeneous across geographies; hence, one could con-sider region-sector groupings, such as Asian technology, European banks, etc. In general, risk parity over group-

ings of stocks can be expressed mathematically by sum-ming up Equation 3 over all stocks in a subset S:

Here, m represents the number of groupings S. Despite the similarity of Equation 4 to the asset-risk parity case of


IGBM IBEX H+SDAX DAX CAC-All CAC EUR.STX. EUR.STX.50 SMI

Performance Of Asset Risk Parity Strategies In Select Markets, Quarterly Backtest, 2005-2013

Total Return 6.88% 6.82% 6.39% 7.52% 10.38% 11.04% 6.52% 1.82% 8.56% 8.79%

Realized Risk 16.29% 16.61% 18.32% 16.83% 18.00% 18.36% 19.11% 17.63% 13.97% 15.47%

Sharpe Ratio 0.42 0.41 0.35 0.45 0.58 0.60 0.34 0.10 0.61 0.57

Active Return 1.77% 1.21% 1.94% 1.86% 1.50% 1.76% 1.07% -2.89% 1.49% 1.61%

Specific 0.87% 2.34% 1.51% 2.93% 0.87% 4.59% 2.48% -0.21% 1.17% 2.83%

Factor 0.91% -1.13% 0.43% -1.06% 0.62% -2.82% -1.41% -2.68% 0.32% -1.22%

Tracking Error 2.17% 3.96% 3.39% 7.25% 4.98% 7.91% 5.08% 9.80% 5.10% 6.89%

Information Ratio 0.82 0.31 0.57 0.26 0.30 0.22 0.21 -0.29 0.29 0.23

Hit Rate 62.96% 62.04% 57.41% 64.81% 67.59% 66.67% 56.48% 53.70% 61.11% 65.74%

Average Beta 0.95 0.87 0.97 0.70 0.91 0.81 0.91 0.74 0.95 0.90

Average Names 50 311 40 260 30 160 35 118 20 50

Average Turnover 36.43% 39.83% 28.05% 52.84% 29.98% 48.26% 35.37% 50.90% 29.50% 35.89%

Eurozone

SMI Expd

France Germany Spain Switzerland

Figure 4

Sources: Axioma, Stoxx, Thomson Reuters

Note: Using monthly gross returns in EUR (CHF for SMI). Risk and return figures are annualized. * The CAC All-Tradable replaced the SBF 250 in early 2011.

Efect Of Universe Breadth On Average Factor

And Specifc Contributions To Tracking Error

0.00%0 50 100 150 200 250 300 350

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rro

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Number Of Names

Factor Specifc Beta (RHS)

Figure 3

Source: Axioma

Note: Quarterly backtest using pan-European universe, 2005 to 2013




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trading or deviation from a benchmark index. This is espe-cially important in risk parity over groupings where solution uniqueness cannot be established; in these cases, minimiz-ing violations of risk parity between groupings alone is not sufficient for producing an attractive investment portfolio. Consider the trivial example where we select one stock at random from each sector and apply asset risk parity to the portfolio—this solution satisfies sector risk parity but is unlikely to have any real-world appeal.

Figure 6 contains several examples of combining risk parity of groupings with other portfolio construction rules, again using the Euro Stoxx universe. By changing the opti-mization objective and/or applying additional constraints, we are able to obtain significantly different performance. In particular, note the differences in risk, beta, name count and turnover. While other risk-based strategies are often accom-panied by large deviations from the capitalization-weighted benchmark, here it appears that sector risk parity can be achieved with a surprisingly small tracking error budget (about 2 percent). We leave it to readers to experiment with additional permutations; for example, constraining risk fac-tor exposures, imposing holdings constraints or even incor-porating an expected returns (alpha) objective.

In Bai et al. (2013), risk parity over sector groupings is solved the conventional way—by minimizing an objective function encapsulating total violations of risk parity—but it is unclear what properties the optimal portfolio satisfies, apart from exhibiting sector risk parity. For example, all three portfolios in Figure 6 are optimal under their formulation, but the user has no control over which variant is returned as the solution.

In short, risk parity over groupings of stocks is a complex problem and requires careful research and calibration of optimization parameters to the particular investment goal and universe. Figure 7 shows two different solutions to sec-tor risk parity; both allocate risk equally across sectors but pursue radically different investment objectives—Figure 7a uses sector risk parity in conjunction with a minimum variance strategy, and Figure 7b applies sector risk parity to a passive indexing strategy. We reiterate and summarize thusly: Asset risk parity has a unique solution and fully

Equation 3, the problem is continuous nonconvex, therefore significantly more difficult to solve. Optimality cannot be guaranteed and neither can the existence of a unique solu-tion. Solution quality and the mere ability to find feasible solutions will depend heavily on the numerical techniques employed by one’s solver. As such, the advantages of enforc-ing parity via optimization constraints rather than the objec-tive function become apparent. (Note also that the number of names is free to vary, no longer dictated by the breadth of the universe.) Given such complexities in implement-ing the optimization, some practitioners may opt to con-struct portfolios by trial and error, perhaps starting with the capitalization-weighted benchmark and manually adjusting allocations to the groupings in an ad hoc fashion until all their risk contributions fall into the same “ballpark.” Chaves et al. (2012) proposes numerical algorithms for computing risk-parity portfolios, but it remains unclear how or whether such methods can be adapted to groupings of stocks where the number of names held can vary.

In the following examples, we solve the problem heuristi-cally and are generally able to find solutions that satisfy, or come very close to, risk parity. Occasionally, the optimizer fails to find a solution, usually in small universes where some groupings are sparsely populated, thereby not providing suffi-cient choices for diversification. Equally problematic are small sectors or countries entering and leaving the universe, induc-ing additional turnover as well as instability in the relative mar-ginal risk contribution share of the remaining groupings pres-ent. In many cases, reducing the right-hand side of Equation 4 by a tiny e is sufficient to yield a solution, though one or more groupings may be marginally out of parity. Having an ample pool of stocks to choose from is thus helpful in ensuring a solu-tion. In contrast to asset risk parity, which is best suited to con-centrated universes with few names, risk parity of groupings of stocks is more appropriate for broad universes.

Multiple Solutions And The Choice Of Objective FunctionBy modeling and enforcing risk parity via optimization

constraints, we “free up” the objective function to pursue other investment goals such as minimizing risk, turnover/

May / June 201422

Figures 5a and 5b

Risk Breakdown Of Euro Stoxx 50 Asset Risk Parity Portfolio, September 2013.

� Financials

� Industrials

� Consumer Goods

� Utilities

� Consumer Services

� Oil & Gas

� Telecommunications

� Basic Materials

� Health Care

� Technology

� France

� Germany

� Spain

� Italy

� Netherlands

� Ireland

� Belgium

a) Risk Contribution By Sector

b) Risk Contribution By Country


defines a portfolio strategy; risk parity over groupings of stocks, on the other hand, is a risk control component of a larger overall strategy. Previously, researchers may not have had adequately flexible tools to separate risk-parity require-ments from the optimization objective; hence, this notion of using risk parity as an “overlay” has largely gone unexplored.

Factor Risk ParityLet us begin with a factor risk model that decomposes risk

(the asset-asset covariance matrix Q) into common factor and stock-specific components:

X is a matrix of factor exposures, Ω is the factor covari-ance matrix and Δ is the diagonal matrix of stock-specific variances. Let f = XTw be the factor exposures of a portfolio w. Suppose the exposure matrix X is made up of a set of country, industry and style risk factors:

The factor covariance matrix can be divided into blocks corresponding to factor types/groups:

We can enforce risk parity across the set of country risk factors by:

Like Equation 3, this can be formulated as a set of convex constraints on relative marginal contribution to risk. A similar condition can be used to implement industry-factor risk parity. Other factors, such as those of Fama-French-Carhart (Roncalli and Weisang (2012)) or principal components (Lohre et al. (2012)) are also possible, but researchers have noted difficulties with the optimization arising from the fact that exposures to these factors can take on negative values.

Parity across country factors is different from parity

across country groupings, described previously, and likewise for industries/sectors. Equation 6 should be interpreted as “ensure the contribution to country risk from each country factor is equal.” Measuring country risk (the denominator in Equation 6) requires under-


Sector RP Min Tracking

Error Target 30%

Turnover

Sector RP Min Risk

Target 30% Turnover

Sector RP Min Risk

Benchmark

Performance Of Sector Risk Parity Strategies On The Euro Stoxx Universe, Quarterly Backtest, 2002-2013

Total Return 3.57% 7.77% 8.39% 3.74%

Realized Risk 18.69% 10.83% 10.93% 18.70%

Sharpe Ratio 0.19 0.72 0.77 0.20

Active Return 4.20% 4.82% 0.17%

Specific 1.04% 2.11% -0.22%

Factor 3.17% 2.71% 0.39%

Tracking Error 12.43% 11.13% 2.05%

Information Ratio 0.34 0.43 0.08

Hit Rate 69.44% 68.06% 59.72%

Average Beta 0.51 0.56 1.00

Average Names 41 52 172

Average Turnover 222.08% 42.98% 32.82%

Figure 6


Note: Using monthly gross returns in EUR. Risk and return figures are annualized.

40%

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2001 2013

■ Basic Materials ■ Consumer Goods ■ Consumer Services ■ Financials ■ Health Care ■ Industrials ■ Oil & Gas■ Technology ■ Telecommunications ■ Utilities Euro Stoxx Risk

Time Series Sector Contributions To Total Risk For Two Sector Risk Parity Strategies

2003 2005 2007 2009 2011 2001 20132003 2005 2007 2009 2011

a) Minimize Total Risk b) Minimize Tracking Error

Figures 7a and 7b





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The first factor risk-parity portfolio in Figure 8 may appear attractive because of its similarity to benchmark country allocations, but it is questionable whether risk is being diversified in an intuitive or desired manner. Mathematically, Equation 6 is being satisfied, but only in the context of “pure” factors. The second factor risk-parity strategy provides a more egalitarian risk break-down, but still leaves some puzzling results unresolved. For example, Belgium has one of the highest weights but the lowest risk contribution. Per Figure 9, Belgium has the lowest average levels of stock-specific risk.

standing how the risk model attributes aggregate port-folio risk to individual risk factors.

Importance Of Risk Model Specification

Although a comprehensive discussion of empirical asset pricing and factor risk modeling is beyond the scope of this analysis, we will provide a simple example to illustrate how the portfolio construction implications of Equation 6 depend on nuances pertaining to factor risk model construction. Figure 8 shows country weights and risk contributions from three strategies: The first two use factor risk parity with country factors, and the last one uses risk parity with country groupings.

Furthermore, the first factor risk parity example uses a fac-tor risk model with “pure” country factors, while the second takes the same risk model but embeds broad-market effects into the country factors by means of a linear transformation.

There are large differences in country allocations between the two factor risk-parity strategies even though the risk models used differ only in how they decompose risk by factors. Figure 9 explains this result. France has the lowest “pure” factor volatility; much of its country risk coincides with that of the broad European market. In contrast, Greece’s high risk is largely country-specific, distinct from other core European markets. As a result, if one were to only consider “pure” country factors, Greece would appear almost seven times as risky as France, thus receiving significantly less weight in the first factor risk-parity portfolio. In the second factor risk-parity variant, correlations with the broad market are taken into account, and Greece becomes only twice as risky as France, result-ing in less extreme weight differences. The interpretation of risk model factors, therefore, is of vital importance to structure of the factor risk-parity portfolio.

May / June 201424

Country Weights And Risk Contributions Of Country Factor Risk Parity Strategies On Euro Stoxx Universe

Factor RP (Pure) Factor RP (w/ Mkt) Country RP (groupings)

Figure 8


% of RiskWeight% of RiskWeight

BenchmarkWeight

Weight % of Risk

Avg Specific

Risk

Corr. With Mkt Factorw/MktPure

Characteristics Of Country Factors From The Two Risk Models Used In Figure 8

Figure 9

Source: Axioma

Factor Volatility

Austria 6.39% 15.07% 0.16 21.64%

Belgium 4.73% 13.96% 0.10 15.80%

Finland 6.78% 16.02% 0.29 19.90%

France 3.38% 14.32% 0.38 17.44%

Germany 3.80% 12.63% -0.17 19.04%

Greece 25.91% 27.88% -0.08 30.85%

Ireland 10.01% 14.79% -0.17 23.94%

Italy 8.35% 15.43% 0.03 22.24%

Netherlands 4.44% 13.38% -0.01 17.56%

Portugal 12.35% 17.93% 0.03 21.86%

Spain 9.26% 16.80% 0.15 19.41%

Market 12.69% 12.69% 1.00 19.14%

Austria 0.86% 7.78% 7.30% 8.80% 8.19% 9.89% 9.09%

Belgium 3.64% 10.27% 8.49% 9.31% 7.63% 11.17% 9.09%

Finland 3.24% 11.20% 12.22% 8.47% 9.36% 10.73% 9.09%

France 33.93% 13.98% 15.07% 8.85% 9.10% 8.08% 9.09%

Germany 28.84% 17.38% 17.59% 10.31% 10.71% 9.28% 9.09%

Greece 0.27% 2.82% 2.70% 8.21% 9.97% 9.12% 9.09%

Ireland 1.71% 8.39% 6.48% 10.73% 8.05% 9.60% 9.09%

Italy 7.71% 6.31% 8.88% 8.97% 11.78% 8.55% 9.09%

Netherlands 8.72% 12.43% 11.53% 9.76% 8.96% 9.55% 9.09%

Portugal 0.67% 3.98% 3.54% 8.39% 7.71% 7.60% 9.09%

Spain 10.41% 5.47% 6.20% 8.21% 8.54% 6.42% 9.09%

Tracking Error 1.96% 3.60% 9.25%




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Equation 6 will only equalize contributions to country

factor risk and does not take stock-specific risk into account. Compared with Belgium, other countries claim a larger share of risk owing to their higher average levels of stock-specific risk, despite country factor risk parity having been met. Finally, the third risk-parity portfolio has equal sharing of portfolio risk across countries but involves some very aggressive active country bets.

Additional Interpretations

Recall that Equation 6 seeks to diversify country risk . It is not controlling, contrary to what many

may expect, the contribution of country factors to total port-

folio risk. Such an interpretation of factor risk parity can be accomplished by:

Risk parity at the factor level allows for a very granular level of risk control but may be too flexible for many users. Without a full understanding of the factor risk model con-struction process, factor risk parity may yield portfolios that are ultimately inconsistent with the user’s investment needs.

Positioning Versus Other Risk-Based StrategiesRisk parity is often compared with risk-based strategies

such as minimum variance or maximum diversification. The first-order condition of a minimum-variance problem requires that the marginal contribution to risk (MCTR) of each stock be equal:

Risk parity, on the other hand, equalizes relative MCTR.Maximum diversification and risk parity are both driven by the concept of diversification. Risk parity spreads out risk across its various sources; maximum diversification aims to reduce the share of portfolio risk coming from correlations:

The right-hand size of Equation 9 is defined as the “diversifi-cation ratio,” and its numerator is equivalent to the risk of a port-folio where all stocks have a correlation of 1. Mathematically, Equation 9 is also analogous to maximizing the Sharpe ratio, where each stock’s expected return is equal to its volatility.

Real-world application of risk-based strategies is often accompanied by investment constraints such as trading limits and restrictions on stock weights or risk exposures, because the unconstrained solution may not be imple-mentable. For example, low-volatility themed strategies typically load heavily on components with low risk and/or low correlation with the market, resulting in concentrated weights, high turnover and sometimes poor liquidity. The performance of these strategies will be very sensitive to choice of constraints and their associated parameters.

ConclusionWe have presented an optimization-based approach to

finding risk-parity portfolios that models risk parity as a set of optimization constraints. Compared with the vari-ous numerical or analytical methods sometimes discussed in the literature, this approach is scalable, efficient and allows us to analyze different applications of risk parity.

This discussion began by showcasing asset risk parity because, for a given universe of stocks, there is a unique long-only, fully invested portfolio that exhibits asset risk parity. It can be used as an “off the shelf” strategy with no fine-tuning. Unlike many other risk-based strategies, risk parity, by con-struction, embraces diversification and avoids concentration, offering a high degree of built-in risk and turnover control. Simulated backtests in various markets suggest that asset risk-parity strategies can outperform their corresponding capitalization-weighted benchmarks with reasonable levels of tracking error and trading. The diversification benefits and superior returns make asset risk parity ideal for concentrated benchmark universes comprising relatively few stocks.

Other forms of risk parity can loosely be labeled as prin-ciples of portfolio construction and do not alone define a set of portfolio weights. Their applicability, therefore, depends on the other components of the investment strategy. Rather than serving as a risk-based strategy, risk parity over group-ings of stocks and factor risk parity are best applied as a form of risk control to augment an existing investment process, preferably on a sufficiently broad universe of stocks.


ReferencesAnderson, Robert M., Stephen W. Bianchi and Lisa R. Goldberg, “Will My Risk Parity Strategy Outperform?” Financial Analysts Journal, 68(6):75-93, 2012.

Asness, Clifford, Andrea Frazzini and Lasse Heje Pedersen, “Leverage Aversion and Risk Parity,” Financial Analysts Journal, 68(1):47-59, 2012.

Bai, Xi, Katya Scheinberg and Reha Tutuncu, “Least-squares approach to risk parity in portfolio selection,” http://www.optimization-online.org/DB_FILE/2013/10/4089.pdf, 2013.

Chaves, Denis, Jason Hsu, Feifei Li and Omid Shakernia, “Efficient Algorithms for Computing Risk Parity Portfolio Weights,” Journal of Investing, pages 21(3): 150-163, 2012.

Clarke, Roger, Harindra de Silva and Steven Thorley, “Risk Parity, Maximum Diversification, and Minimum Variance: An Analytic Perspective. Journal of Portfolio

Management, 39(3):39-53, 2013.

Kaya, Hakan and Wai Lee, “Demystifying Risk Parity,” Neuberger Berman white paper, 2012.

Kazemi, Hossein, “An Introduction to Risk Parity,” Alternative Investment Analyst Review, 1, 2012.

Lohre, Harald, Ulrich Neugebauer and Carsten Zimmer, “Diversified Risk Parity Strategies for Equity Portfolio Selection,” Journal of Investing, 21(3): 111-128, 2012.

Maillard, Sébastien, Thierry Roncalli and Jérôme Teiletche, “On the properties of equally-weighted risk contribution portfolios,” Journal of Portfolio Management, 36(4):

60-70, 2010.

Roncalli, Thierry and Guillaume Weisang, “Risk Parity Portfolios with Risk Factors,” working paper, 2012.

Stubbs, Robert A., “Risk Parity: Applying the Concept to Equity Strategies,” http://updatefrom.com/axioma/2012_q1/newsletter.asp, March 2012.

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Institutional Insights

Denison U CIO Discusses

Endowment’s Success

Hedge funds help drive outperformance

By Heather Bell

Denison University is a small private liberal arts college located in Granville, Ohio; it has fewer than 2,500 students. The school, founded in 1831, has a solid reputation, holding a place in the upper levels of US News’ rankings for liberal arts colleges. However, from an investment professional’s point of view, it stands out for reasons other than things like academics or tuition. During the 2007-2012 time period, its endowment (described as “midsize”) achieved an annualized five-year return of 4.1 per-cent, according to Institutional Investor, trouncing the endow-ments of Harvard and Yale, which saw annualized five-year returns of 1.24 and 1.83 percent, respectively.

Adele Gorrilla is the chief investment officer of Denison’s $740 million endowment, joining the university’s invest-ment office in 2008. She spoke recently with the Journal of Indexes about Denison’s financial success.

JOI: Would you talk about Denison’s endowment a little bit and how much it has in assets?Gorrilla: The Denison endowment now stands, as of the

end of January, at right about $740 million, which is siz-

able, considering it’s supporting a small liberal arts school

with 2,100 undergrads. Per student, that’s fairly high. The

mission of it is purely to support the institution. The 5

percent spending rate is used to supplement the operating

budget. That 5 percent spending per annum services about

30 percent of the operating budget.

JOI: Does asset growth mostly come from investment appre-ciation, or is a lot of the growth coming from donations?Gorrilla: It’s a mix of both. In order to keep it growing,

we’re dependent upon having new gifts as well as being

able to generate the investment returns. Historically, we’ve

done very well and have been able to earn above our

spending rate, which has allowed the endowment to grow.

JOI: I noticed you had really good performance over the 2007-2012 time span, and you beat Harvard and Yale in terms of performance. To what do you attribute that performance?Gorrilla: In part, timing. It’s not just pure luck—though

some of it is definitely a function of being in the right place

at the right time, and not purely by insights.

Denison is a heavy user of alternatives. Historically, it

was an early adopter of hedge funds, and that was due to

the composition of the board and the investment commit-

tee—they found comfort in the idea of hedge funds allowing

them to still access equitylike returns but with less volatility.

At its peak, Denison had 50 percent of the investable

assets in hedge funds. That percentage has since come

down, and we’ve used some of that performance and

some of the allocation to help fund less liquid strategies.

But in part, we were able to weather the financial down-

turn a bit better than others, because we were later to the

game in private equity and real assets.

JOI: So you basically invested in those assets when every-thing tanked?Gorrilla: We did a little bit of that. We also had a very

strong stable of hedge fund managers that helped reduce

the impact of the financial blow of the crisis.

JOI: So, the hedge funds outperformed during the finan-cial crisis?Gorrilla: They have outperformed throughout for us.

JOI: How do you select the various hedge funds?Gorrilla: It’s something we spend the majority of our time

on—reviewing and selecting funds. That is the most diffi-

cult thing. The board helps set the asset allocation and the

May / June 201426




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targets for us, and the team here is responsible for select-ing the managers. We have a formal process of going out, meeting them, hearing new ideas. Once we find some we like, it’s a matter of comparing them to others that we’ve heard about, and then getting down into the weeds.

JOI: What percentage do you have in hedge funds right now?Gorrilla: Forty percent.

JOI: Are there other alternative investments, besides hedge funds, in the portfolio?Gorrilla: Yes, there are. We also do private equity investing, and in our case, we include venture capital, growth equity, distressed debt investing and buy-outs in that category. That’s another 20 percent in policy allocation. And beyond that, we also do natural-resource private investing.

JOI: There are more and more products coming out that

are based on indexes that seek to mimic alternative-type investments, such as ETFs that aim to replicate hedge-fund strategies. Do you think that indexes can accurately capture those returns?Gorrilla: Effective hedge-fund replication is limited to a narrow slice of strategies, those with quantified rules. To invest well in more fundamental or subjective hedge fund strategies, I believe it requires an active manager.

JOI: You mentioned you had introduced certain alter-natives like private equity and real assets following the financial meltdown. Were there any other big changes you made within the last five years that you would con-sider significant?Gorrilla: We actually had already made a move to start investing in private equity and real assets before the crisis. And we were not immune to the financial-crisis impact on private equity—along with every other endow-ment, we watched our private equity asset allocation grow beyond the policy target as stock prices tumbled.

That’s something we’ve had to work around and react to, and think about how that would play out in the future. It’s not that we just added the illiquid private investments, post-crisis—we did have them, and we had them look like they were too large a position in the portfolio. As a result, we’ve had to trim what we were budgeting in the way of new commitments.

JOI: So it’s pretty much been fairly static for the past five, 10 years?Gorrilla: We have bands around our policy allocation targets, so there’s a pinpoint number, but we have rea-sonable flexibility around those targets. We haven’t had to change the targets, because the targets are designed to be long-term, and they’re working that way. We can go above and below without having to change the policy.

JOI: What are the challenges you see in the current mar-ket environment, and are there any you see as being specific to endowments in your size range?

Gorrilla: The biggest one—and this is size-agnostic—is trying to achieve your target return, because we all know that we’re going to be spending roughly 5 percent, give or take a little, each year. And you have to make up for what you’re spending as well as inflation.

Hitting that number, which is 8-8.5 percent, is poten-tially tricky. That could be the hardest challenge, and that’s in light of values looking fairly rich. Stocks have had a very nice run over the last five years, and yields are remarkably low, historically low. That’s not leaving a whole lot of room to figure it out—there are no easy places to get your 8-8.5 percent.

JOI: Is Denison’s allocation to alternatives and hedge funds very unusual for an endowment for a university, or is that in the normal range?Gorrilla: It is unusual. It’s higher than normal. If you were to parse out endowments based on their size, we would look similar to the larger universities in terms of asset allocation and more out of line with our peer group.

JOI: Is there a particular reason for that? Gorrilla: Part of it is a categorization problem, and schools try to fit into the NACUBO-CommonFund template, which asks how much you have in five categories. If you only have five options, you tend to force things into the categories.

Hedge funds are tricky. If a school hired a hedge fund manager who has the ability to sometimes go short, but they’re basically in concentrated stock positions, some schools count them as public equity exposure, and some schools count them as hedge funds. We tend to count them all as hedge funds, and that’s why if you were to break apart each individual investment in the asset allo-cation, you might find endowments—even similar-sized endowments—would look more similar.

That’s part of the answer. And the other part is that we just are happy with the performance that we’ve had within hedge funds, and that’s going back to the issue of Denison being an early mover into hedge funds, which created these long-standing relationships with some of the best in the business. It’s really the idea that unless you’re going to be able to go with the best, don’t bother doing it at all.

That works for all alternatives—for hedge funds, for private equity, even for real estate. That’s the other key element to Denison.

JOI: What caused the investment policy committee to head down this different path for an endowment of this size?Gorrilla: This definitely predates me, because I’ve only been here 5½ years. But the committee’s composition helped them do that. The committee includes some hedge fund professionals, and they had access to some of the best hedge funds out there. They said, “This makes sense: We’re going to have decent upside capture, avoid getting caught in the down markets, at least somewhat, and ride through it. Longer term, you’ll make out better even if you don’t get the highest highs.”


continued on page 45




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May / June 201428

Tails, You Lose

The anatomy of a tail-hedging index

By Stanislas Bourgois and Edward Tom




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The three-year period defined by the start of the credit crisis in 2008, the intervening “flash crash,” and the subsidence of the sovereign debt

crisis in 2011 marked one of the most volatile regimes in market history. Of particular note were the successive waves of “tail events,” market dislocations deemed a priori to be statistically improbable. Although differing in both intensity and duration, these events, collectively known as “fat-tail events” or “black swans,” precipitated abrupt and immense drawdowns as stock prices unrav-eled from company and macroeconomic fundamentals.

Why Hedge Tails?As an example of the potential impact of tail events upon a

market portfolio, consider the magnitude of the drawdowns experienced during the heart of the credit crisis in 2008. As seen in Figure 1, under the assumptions of normality embed-ded into modern portfolio theory, it is anticipated that over the course of a trading career, one would observe at most one one-day drawdown in excess of 4 standard deviations (i.e., 5+ percent). Yet as shown in Figure 2, during the four-month period from August to December 2008, the market experienced 10 such declines—negating six years of equity growth in the span of four months. On the surface, therefore, the most obvious and oft-cited reason to hedge against tail events is to mitigate the severity of market drawdowns.

A more subtle and arguably more important benefit of a tail hedge, however, is that it addresses the most disrup-tive feature of a tail shock—specifically, the impact-associ-ated market distortions that often accompany tail events. These market distortions undermine 1) the underlying principles of financial valuation—causing a departure of asset prices from their “fair” values; and 2) the stabilizing assumptions of portfolio construction, including:

• Breakdown in portfolio diversification (via correlation) • Negative feedback loops (via volatility clustering)• Beta instability (via cross-asset contagion) • Discontinuous trading

Volatility Buffer

Often during these events in which in-house volatility-based risk limits are suddenly breached, portfolio managers (“PMs”) and traders are forced to sell out of tactically unat-tractive but strategically desirable positions. Tail hedges can provide a volatility buffer to mitigate the need to exit these positions or to lessen the impact of increased volatility.

Credit Reserve

It is somewhat ironic that downside tail events also provide the best opportunity to outperform. In fact, some of the greatest equity-market outperformances (i.e., upside tail shocks) followed immediately on the heels of the market’s sharpest sell-offs. Take, for example, the crash of 1987, in which the market collapsed 23 percent over the course of one day but recouped the bulk of the losses over the course of the next two days. A good way to recover returns lost due to a tail shock is therefore to invest during times of market duress. However, in

many cases, trader positions are often drastically pared down as the aforementioned risk limits are breached. An important function of tail hedges is therefore to provide a source of funding that accrues as the market is in decline and that can then be used to lever into a long position to allow the portfolio to more quickly recover.

Algorithmic (Signals-Based) Tail Hedging The primary challenge during the current low-vol-

atility environment, however, is that the cost of static, “always on,” tail insurance is often expensive to hold. Accordingly, if a tail event fails to materialize, the buyer of a systematic tail strategy risks significantly underper-forming his unhedged peers. To moderate the cost of carry, hedgers often shift toward dynamic tail-risk strat-egies during times of market stability.

Over the last few years, a vast number of dynamic strate-gies in the form of algorithmic indexes1 have been designed to profit from the realization of tail events and offered as a hedging product to end investors. Algorithmic indexes (aka “algos”) are liquid, transparent and easily investable through delta-one wrappers such as swaps, notes or more advanced products involving the use of derivatives and/or leverage in order to produce a highly asymmetrical payoff.

A Priori Probability Of Actual One-Day Market Declines

A Priori Probability Of Theoretical One-Day Market Declines

SPX Decline

Expected Frequency Outside Range

Pre-Crisis Expected Frequency (In Years)*

Frequency For Daily Trading Event

Date

Sigma

9/29/2008 -8.79% 763,083,992

10/7/2008 -5.74% 584

10/9/2008 -7.62% 2,011,100

10/15/2008 -9.03% 3,180,535,16

10/22/2008 -6.10% 2,501

11/5/2008 -5.27% 96

11/12/2008 -5.19% 96

11/19/2008 -6.12% 2,501

11/20/2008 -6.71% 34,267

12/1/2008 -8.93% 1,550,262,586

1 1 in 3 Twice a week

2 1 in 22 Once a month

3 1 in 370 Once every 1.5 years

4 1 in 15,787 Once every 63 years

5 1 in 1,744,278 Once every 7,000 years

6 1 in 506,797,346 Once every 2 million years

7 1 in 390,682,215,445 Once every 1.5 billion years

Figure 2

Figure 1

*Based on pre-crisis realized volatility

Source: Credit Suisse Equity Derivatives





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hour to finish down 2 percent for the day. For an investor such as a high-frequency trader or an active delta-hedger who was actively trading during that period and therefore realized profit and loss (“P&L”) during those volatile two hours of the day, such an event may in fact qualify as a tail event. However, if one were a “low-frequency,” long-term investor such as a pension fund that did not trade during that day, then a tail event may refer to a protracted deterioration in one’s portfolio caused by a breakdown of the core investment strategy. For the purposes of this case study, we will define a tail risk as a sizable abrupt market

decline that triggers a persistent volatility regime shift from

a low- to high-volatility environment.

Step 2: Benchmark Selection

The second step is to create a “naive” or system-atic hedging benchmark index (the benchmark) using a plain-vanilla options strategy to gauge the relative per-formance of the tail-hedging strategy. In our example, our benchmark is designed as follows:

• Strategy: Every month, on each listed expiry date, we execute a rolling strategy whereupon we purchase new S&P and EuroStoxx 50 90 percent strike put options with a two-month expiry. At any time, we would there-fore have four options in the portfolio with maturities equal to front-month and back-month expiries. All options are let to run until they expire.

• The notional of the purchased options is equal to one-fourth of the mark-to-market value of the benchmark on that same day in order to match exposures.

Algorithmic Tail-Risk ConstructionAs of the time of writing, the marketplace currently

has more than 200 active tail-risk algorithmic products spanning five asset classes. However, due to the leverage to downside shocks and the greater liquidity offered by equity volatility products in times of market distress, the majority of algo products invest in equity volatility. Figure 3 provides a cross section of Credit Suisse’s more popular tail-hedging algos (by notional invested), their asset class exposure and a short description of the trading rules.

Algorithmic tail-risk construction generally follows a five-step process:

1. Tail definition2. Benchmark selection3. Trigger design 4. Simulation5. Test of efficacyIn the following pages, we will use the development

of our Equity Dynamic Tail Hedge Index (Ticker: DYTL) as a case study to illustrate the process of constructing a tail-risk algorithm.

Step 1: Tail Definition

The obvious first step to developing a tail-risk algo is to first define what is meant by “tail.” Given the breadth of investment styles, the definition of the term “tail-risk” itself (and therefore the solution) may vary greatly among invest-ment professionals. Take for example, the flash crash, in which the market plummeted 10 percent during the course of one hour and then recovered 8 percent during the next

May / June 201430

Credit Suisse Tail-Hedging Algos

Figure 3

IndexShort Name

UnderlyingDynamic/

StaticSource Of Tail Exposure

Credit Suisse Advanced Defensive Volatility ADVL Equity Static Long VIX Futures

Credit Suisse Equity Dynamic Tail DYTL Equity Dynamic Long SX5E Volatility Skew, iTraxx Credit Index

Credit Suisse Equity Tail Hedge TAIL Equity Static Long SX5E Volatility Skew

Credit Suisse Dynamic Tail S&P DTSP Equity Dynamic Long S&P Volatility Skew, CDX Credit Index

Credit Suisse Equity Tail Hedge S&P TLSP Equity Static Long S&P Volatility Skew

Credit Suisse Cheapest Slide CHPS Equity Static Long SX5E Forward Variance Swaps

Credit Suisse Advanced Volatility Index - AVI FX FX Static FX Volatility

Foreign Exchange Opportunistic Vol

Credit Suisse Tail Risk Overlay Protection Strategy TOPS Fixed Income Static Long Treasury, German Bonds; Long Euro Rate Futures

Benchmark Benchmark Equity Static Long SX5E and S&P Volatility





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hedging strategy would have saved the investor up to $20 by November 2008. However, if a tail event does not materialize, it also shows how the long-term running cost (the carry) of the strategy may gradually eat up the accrued hedging benefits.

This then illustrates the disadvantage of a static tail-hedge strategy: By systematically investing in the same notional, it tends to be under-invested in the period leading up to the shock, causing the investor to be under-hedged, and it tends to be over-invested immediately after the tail event when the price of options is high and the risks have dissipated, resulting in higher performance drag.

Step 3: Choice Of A Tail-Hedging Strategy

Rather than the benchmark and its short-delta/long-volatility bias, we prefer a cleaner tail-hedging strategy that aims to isolate the volatility component typical of tail events.

The underlying fundamental strategy of the Equity Tail Hedge S&P Index can be broken down into five steps:

1. The algorithm completes a monthly sale of vanilla ratio-put spreads on the underlying equity index consisting of:

• short a number of three-month 95 percent puts

• long a number of three-month 80 percent puts

2. The quantity of puts is chosen such that each leg generates a profit/loss of 1% of the strategy notional (i.e. 1 percent vega exposure) per point change in the under-lying index volatility. The position thus naturally adapts to the prevailing level of equity volatility. Specifically, during times of low volatility, when options’ vega is low, the quantity of options needed to generate 1 volatility point increases, resulting in higher exposure to a tail event before it has happened. Likewise, when a tail event has occurred and equity volatility and options’ vega are high, the quantity of options needed to generate 1 vola-tility point is lower, and the strategy naturally delever-ages itself at each reset. The ratio of 95 percent puts to 80

percent puts has a historical average of 1-to-3.15.

3. The position is delta-hedged. (Once the directional component of the position is removed via delta-hedging, what remains is pure exposure to volatility at each strike, thus resulting in a long skew exposure.)

4. The puts are unwound a day before expiration to avoid expiration-day effects and are rolled on a monthly basis.

5. Any cash balance accrues at the relevant rate.

Step 4: Trigger Mechanism

To enhance the performance of the basic “benchmark” tail hedge, we thus introduce the use of a timing indicator or trigger mechanism. The objective in employing a trigger mechanism is to decrease the weighting (and therefore the cost) of the downside hedge in times of quiet markets and to ratchet up exposure in anticipation of a tail event. In our example, we discuss the use of two triggers taken from two asset classes: 1) equity volatility skew; and 2) CDS spreads from the fixed-income markets in the construction of the Credit Suisse Equity Dynamic Tail Hedge Index.

Signal 1 – Skew: Implied equity-market skew is defined as the difference between implied volatility for lower strike options (typically put options purchased for protection)

• Performance calculation: The benchmark is calcu-lated in USD. Payoffs or premiums are paid in and out of a synthetic USD cash account earning the federal funds rate.

The simulated history of the benchmark is shown in Figure 4. We also show the cumulative P&L of the S&P 500

Index and the cumulative P&L of the S&P with a one-to-one overlay of the benchmark as a hedge.

Figure 4 demonstrates the conundrum faced by many systematic plain-vanilla hedging strategies:

When a tail event does materialize, such a strategy can successfully cushion the initial blow of a tail event. In our example, for $100 invested in the portfolio in April 2008, the


Short Description

The index offers efficient long volatility exposure by systematically going long

short- or medium-term VIX futures based on current levels of VIX futures

The index dynamically allocates to the Credit Suisse Equity Tail Hedge

based on the level of the SX5E Skew or the iTraxx credit index

The index offers efficient long SX5E skew exposure by going short delta-

hedged put ratios

The index dynamically allocates to the Credit Suisse Tail Hedge S&P based

on the level of the S&P Skew or the CDX credit index

The index offers efficient long S&P skew exposure by going short delta-

hedged put ratios

The index offers efficient long volatility exposure by systematically going long

the cheapest-to-carry SX5E forward starting variance swap

The index opportunistically goes long/short volatility across 12 major currency

pairs based on a Jump model, with a systematic net long volatility bias

The index offers exposure to tail events by opportunistically going long CBOT

Note Futures, Eurex German Bond Futures, or Euronext and CME Euro rates futures

The index goes systematically long 2-month 90% put options on SX5E and S&P

and carries them to maturity

Benchmark Tail Strategy Versus S&P And SX5E, 2008-2013

160

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100

80

40

60

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120

110

100

90

80

60

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2008 2009 2010 2011 2012 2013

■ Benchmark ■ S&P Benchmark Overlay ■ S&P

Figure 4





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and implied volatility for higher strike options (typically call options purchased for leveraged upside exposure). Historically, during severe market downturns, implied equity-market skew has increased significantly (Figure 5), confirming our choice of equity skew as the primary source of tail protection in Step 3. This may be explained by an increase in demand for downside protection, pushing up implied volatility levels for lower strike levels.

The indicator analyzes the historical distribution of the three-month 80-100 skew on the underlying equity index

over the last three months. If the skew level is significantly above the mean, the signal for a distressed market is acti-vated. This indicator has been historically reactive to mar-ket events signaling the beginning of a tail episode.

Signal 2 – CDS Spreads: The indicator is linked to the five-year CDS spread of companies for the relevant under-lying equity market. If the CDS index is significantly above the mean, the signal for a distressed market is activated. If the CDS index is significantly below the mean, the signal for a distressed market is deactivated. Otherwise, the signal remains unchanged. The indicator captures medium-term risk and is reactive to changes in the macro environment.

The signals just discussed trigger allocations by the Credit Suisse Equity Dynamic Tail Hedge Index to the Credit Suisse Equity Tail Hedge Index, which in turn is long equity-market skew.

To drive the allocation between cash and the index, the two signals are run daily:

• If one of the signals is switched ON, 50 percent of the exposure is allocated to the CS Equity Tail Hedge S&P Index, the index described at Step 3.

• If both signals are ON, 100 percent is allocated to the hedge index. If neither of the signals is ON, 100 percent is invested in cash (U.S. federal funds rate or EONIA).

Historically, at least one of the signals has been ON for 31 percent of the time period. Typically, a distressed macro environment would first activate the CDS signal, indicating that the likelihood of a tail event has increased.

The skew signal would activate when the market crisis gains momentum and equity skew breaks out of range.

This exact methodology is also applied using the Euro Stoxx 50 as an underlying, where the child index (Credit Suisse Equity Dynamic Tail Index) allocates to the par-ent index (Credit Suisse Equity Tail Hedge Index), based on Euro Stoxx 50 skew and European CDS prices.

Step 5: Simulation

In general, our simulation for the CS Equity Dynamic Tail Hedge Index embodied the two traits we felt were desirable in a tail-hedging algo, delivering outsized returns during periods of market crisis, and efficiently reducing the effect of negative carry over stable market periods via the dynamic signals (Figure 6).

An important consideration is that tail-risk strategies that incorporate some element of market timing, regard-less of whether it is actively determined by a PM or signal-

May / June 201432

200

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Apr2008

Dec2008

Aug2009

Apr2010

Dec2010

Aug2011

Apr2012

Dec2012

Aug2013

■ DYTL ■ DTSP ■ Benchmark

CS Dynamic Tail (DYTL) And CS Dynamic Tail S&P (DTSP):

Simulated Performance, 2008-2013

Source: Credit Suisse Equity DerivativesSource: Credit Suisse Equity Derivatives

S&P 3M Normalized Skew (Right) Versus

S&P Index Level (Left)

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■ SPX (LHS) ■ 3M Skew

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Figure 5 Figure 6

In general, our simulation for the CS Equity Dynamic Tail Hedge Index embodied the two traits we felt were desirable in a tail-hedging algo, delivering outsized returns during periods

of market crisis, and efficiently reducing the effect of negative carry over stable market periods via the dynamic signals.




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www.journalofindexes.com 33May / June 2014

Signal Activation During 2011 US Debt Downgrade

■ TLSP ■ DTSP ■ S&P

80

90

100

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7/15 7/22 7/29 8/5

Skew SignalActivates

CDS SignalActivates

8/12 8/19 8/26

Figure 8

DTSP Versus TLSP Performance During Quiet Markets (Carry) And Tail Events (Tail)

4/22/08 9/1/08 Carry -9.3% -28.7%

9/1/08 12/1/08 Tail 36.4% 39.6%

12/1/08 4/1/10 Carry -2.1% -25.2%

4/1/10 5/31/10 Tail 7.9% 5.5%

5/31/10 7/1/11 Carry -5.5% -19.2%

7/1/11 9/30/11 Tail 15.5% 33.4%

9/30/11 3/3/14 Carry -4.1% -17.0%

Average Tail -5.2% -22.5%

Average Carry 19.9% 26.2%

Tail To Carry Ratio 3.8 1.2

Figure 7


From To Tail/Carry DTSP TLSP

based, face the very real risk that a hedge may not be in place when it is needed. One must therefore evaluate the benefit of reducing carry costs in times of stable markets versus the risk of potentially missing the event because the signals have been “switched off”.

The final step to the process of algo construction is there-fore to conduct an additional test of efficacy above and beyond the basic simulation in order to determine 1) whether the inclusion of the proposed signals provide adequate cost reduction to compensate for the risk of the hedge being “deac-tivated” during the days leading up to a tail event; and 2) how the chosen algo stacks up against the nondynamic version.

Step 6: Additional Tests of Efficacy

The primary criteria we use to evaluate the efficacy of tail-risk algos is to compare the tail-to-carry ratio of each strategy with one another. The tail-to-carry ratio is computed by dividing the average performance during tail events by the negative annualized carry. The metric essentially conveys how many years of negative carry can be paid for by one single tail event. The higher the ratio, the more efficient the hedge.

In our first example, we test the efficacy of our signal overlay, by comparing our signal-based Dynamic Tail S&P Strategy index (DTSP) to its unconstrained parent strategy, the Tail Hedge S&P Index (TLSP), which is 100 percent invested at all times.

Figure 7 compares the performance of DTSP versus TLSP from 2008 to 2014. At first glance, one might con-clude the unconstrained “always on” strategy is supe-rior given that DTSP provided comparable returns to TLSP during the Lehman collapse and the emergence of the Greek sovereign crisis in 2008 and 2010, but as shown in Figure 8, because the CDS signal activated late into the tail strategy in summer 2011, DTSP under-performed. Note, however, that during periods of mar-ket stability, DTSP reduced the cost of carry on average by a factor of 4.5, producing a higher and therefore efficient tail-to-carry ratio.

Concluding RemarksPerhaps an ironic aspect of tail events is that it is

not the expected or foreseeable events (aka the known unknowns) that cause the greatest market upheav-als, but rather the events from left field (the unknown unknowns). More often than not, true tail events often 1) have little or no historical precedent; and 2) are dif-ficult to anticipate a priori. Backtesting, by contrast, is by definition a backward-looking process that optimizes “to fight the war.” As a result, hedging strategies that are designed for a specific event or asset class that have

been responsible for tails in the past may be optically attractive from a backtesting perspective but may not necessarily outperform if a future tail event is greatly dissimilar to prior shocks.

Nonetheless, dynamic tail-hedge strategies in the form of algorithmic indexes can provide a liquid, transparent and easily investable solution to mitigate the impact of a “fat tail” or black swan market event. In conclusion, the volatility buffer provided by a tail hedge not only serves to reduce the downswing in overall portfolio performance, but also could allow a credit reserve to put money to work after market shock. A systematic tail hedge that also avoids a heavy cost-of-carry can keep PMs off the side-lines during the very time they should be the most active in navigating periods of market duress.


Endnote1 Algorithmic indexes are rules-based, systematic investment strategies that are created to be transparent, liquid and investable. These indexes can, in turn, be packaged

into structured notes, OTC swaps and options, and even funds. Algorithmic indexes differ from “trading algorithms” which typically focus on the execution of stocks and

baskets of stocks.




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May / June 201434

Risk In Focus

A round table

By Heather Bell




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Risk management received a renewed wave of atten-

tion after the 2008-2009 financial crisis, and as the

markets continue to sail through uncharted waters,

it remains a hot topic. The Journal of Indexes chatted with

various experts on the issue, touching on subjects like volatil-

ity, diversification and tail risk.

JOI: Is a diversified portfolio enough to protect investors,

in terms of risk management?

Ric Thomas, global head of Investment

Strategy and Research, State Street

Global Advisors: It could be, but the problem is that most portfolios that are thought to be diversified aren’t diversi-fied, because most asset classes that we

allocate to are highly correlated to each other. When I just look at correlations of major asset classes to the VIX, which is the level of implied volatility in the equity mar-ket, equities, hedge funds, real estate, private equity, even corporate or high-yield bonds—all of those asset classes are negatively correlated to the VIX, meaning that when volatility rises and the markets are distressed, all of those asset classes fall simultaneously.

They’re really one big factor bet, which is to say they are short volatility. Most asset classes are short volatility. True diversification is buying asset classes that are long volatility, and most investors don’t do that. Now, there are

a handful of investments that are long volatility, but there are not very many. One, for example, is long-duration U.S. Treasury bonds. A lot of corporate DB plans that use liability-driven investing are somewhat diversified nowa-days, because they have a growth pool which has all these short-volatility asset classes, and then they have a long-volatility pool, which are their long-duration Treasury bonds or high-quality corporate bonds. They have factor-ized their portfolio into a liability pool and a growth pool, and those that use that methodology are diversified and can protect themselves in the downmarket.

John Feyerer, vice president, ETF Product

Management, Global ETF Products &

Research, Invesco PowerShares: We think diversifying has meant different things to investors through time. If you look at what diversified might have meant

in years past, it’s certainly different from what diversified means today and might mean going forward. One of the beautiful things about the world of exchange-traded prod-ucts is that there’s an increasing opportunity for investors

to stay diversified via the menu of asset classes that they can implement in a portfolio.

In addition to trying to build a broadly diversified portfolio, investors should look at risk metrics, including downside risk, and how the different constituents in their portfolios will react during down markets. Certainly since 2007 to 2009, we’ve seen investors very concerned with not so much relative risk, but absolute risk, and looking at metrics such as max drawdown and how allocations perform in rougher seas [the markets].

One of the most important things to understand is how well diversified constituents are. Investors should consider inter-asset-class correlations; an increasingly diversified, broad menu of exposures; and then examine how con-stituents may perform in market drawdowns; and more specifically, how they may work together to help protect the portfolio when markets are drawing down.

Ted Lucas, founder & managing partner,

Lattice Strategies: Well, it depends on how you define “diversification,” and I believe that if diversification is simply defined across asset classes, then the term can be very misleading.

In diversifying a portfolio, you need to be very clear on what that represents. We have a very recent example of this, where in the initial tapering discussions of last May-June, you saw very positive downside correlation between

equities and bonds. These asset classes are intended to provide diversification, but in terms of risk management benefits, perhaps the changes that we’re seeing in the correlation structure between equities and bonds may be indicative of some future correlation regime change where the benefits of simple diversification may be muted.

Our belief is that you want to diversify across risk factors, and that means decomposing asset classes into the risk factors that drive them, and then having a dynamic view of not only the past correlation relation-ships between the assets, but a forward-looking view of correlations, and how those may change in different macro and risk regimes.

Matt Moran, vice president of Business

Development, Chicago Board Options

Exchange: Diversification was a big issue in the year 2008. Just to give you an example, let’s look at the 16-month drawdown period from the end of

October 2007 through the end of February 2009, during which the S&P 500 was down 51 percent.

Most portfolios that are thought to be diversified aren’t diversified, because most asset classes that we allocate to are highly

correlated to each other. —Ric Thomas




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You might say, “But I have a diversified portfolio. I have global stocks. I have commodities. I have small-caps. Those should help diversify my portfolio, right?”

During that period, the Russell 2000 was down 52 percent. The S&P GSCI was down 53 percent. And the MSCI Emerging Markets Index was down 62 percent. Diversification and modern portfolio theory, as a result, came under fire.

Government bonds did go up. But people are saying we need to move above and beyond government bonds to diversify because of today’s low interest rates. It doesn’t make sense to rely totally on government bonds to diversify your portfolio.

People are looking above and beyond government bonds to things like managed futures. During the 16-month drawdown period, hedge fund indexes were down, but managed futures indexes were up. Our CBOE Oil ETF Volatility Index was up 83 percent, and the VIX spot index was up 150 percent.

Vineer Bhansali, managing director &

portfolio manager, Pimco: There are two levels to that question. Generally speaking, diversification is a good idea; there’s no question about it. But most people diversify only using underlying asset characteristics,

and that’s very limiting, because assets have a lot of underly-

ing exposures. That’s why, if you’re going to do diversifica-tion, you want to do it using risk factors, not assets. And that’s the way we think about diversification at Pimco.

The second aspect is that there are cases where diversifica-tion fails and fails very badly. May and June of last year were good examples, when stocks and bonds became correlated after being very uncorrelated for a long time, or negatively cor-related. Both stocks and bonds fell during the taper fear. Diversification is definitely a good way to start. One would call it necessary, but it’s not really sufficient for portfolio construction.

I’ve written a book called “Tail Risk Hedging.” Our view is that you need to supplement simple diversification with two other main prongs. The first prong is that you need to have a source of cash or some sort of cash buffer in your portfolio for when diversification fails and you feel you might end up either having liquidity needs or you might actually want to deploy, or take advantage of opportunities. Have some cash in the portfolio—that has to be actively managed and has to be variable in terms of how much you have.

The second element is, we believe that if implied volatil-ities in the market are low, and if opportunities exist, which they almost always do, you should have explicit tail-hedg-

ing in your portfolio. Specific tail-hedging actually controls that severe drawdown or failure of diversification risk.

Kevin Simpson, chief investment officer,

Capital Wealth Planning: For our portfo-lios, we feel diversification can fall short from the standpoint of downside protec-tion, and clearly that has been evident in many occasions historically, not the least of

which was in 2008, where many asset classes pulled down at the same time. And even more recently, at the end of 2011, we saw things that perhaps may have been considered to be somewhat uncorrelated move in the same direction.

We feel there is tremendous reversion to the mean over time when you look at various asset classes, and it’s our opinion that diversification is not enough. What we look for are things that truly are inversely correlated, and that can give an inverse performance versus the markets without leverage. We use nonleveraged inverse ETFs as a way to offer what we consider to be better diversification, because clearly there is going to be some inverse correlation. If the markets are performing really well, like they did in 2013, inverse products are probably not going to fare quite as well, but we usually have them there for risk management. We hold those nonleveraged exchange-traded funds to reduce our volatility, diversify our returns and manage risk.

The second thing we do in all of our portfolios is use covered calls, and covered calls can certainly be an income enhancement, though there is a debate over to what extent they are. Where there is not a debate is that they are a truly inversely correlated asset class. Using covered calls on our ETFs allows us to reduce our standard deviation, diversify our ETFs from themselves and—in modestly rising, flat or declining periods—pro-vide incremental income.

JOI: What are the risks you’re watching in markets

right now?

Thomas: The biggest risk in the market is excessive-ly easy monetary policy and the implications that that causes. Almost all financial bubbles have been preceded by a period of easy monetary policy. That goes for the Great Depression in the 1920s. It goes for the housing bubble in 2007, when the Federal Reserve was excessively easy on monetary policy, to keep the rates low. And that is the chal-lenge now. Is monetary policy too easy? And what bubbles

May / June 201436

Generally speaking, diversification is a good idea; there’s no question about it. But most people diversify only using

underlying asset characteristics, and that’s very limiting, because assets have a lot of underlying exposures. —Vineer Bhansali




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are they potentially creating? What types of asset-class

inflation might they be causing?

You can’t necessarily see the bubble when it’s occurring, or

the asset inflation. You have to be on the lookout for valuation

distortion. When monetary policy is excessively easy, then

rates fall, interest rates fall and everyone is searching for yield.

We’ve seen over the last few years tons of money pouring

into emerging markets, because their currencies have higher

yields than they do in the U.S., and their bonds have higher

interest rates than they do in the U.S. All the money pours into

emerging markets, and then at the very hint of tapering, all the

money piles back out. And so if you were caught on the wrong

side of that, you would lose significant amounts of money.

Long term, we’re OK with emerging markets, but we’re

paying a lot of attention to the currency valuations there.

Even though some of the currency valuations have fallen

significantly, we still find some of the currencies are over-

priced on emerging markets, and so we still think there are

some risks in that market in the short run.

Other markets don’t necessarily seem overpriced right

now. Domestic equities are fairly valued, so we don’t see

the risk there yet, but we’re on the lookout for that.

Feyerer: Certainly that’s always an evolving scenario. One

of our standard answers—and it has been for a while—is

interest-rate risk. We believe bonds are in a different spot

than they have been in past years. The low-yield environ-

ment of today and the prospect for a rise in rates on the

horizon introduces duration risk and a potential hazard that

could take a toll on the fixed-income portion of a portfolio.

We continue to think about solutions for investors that

have an attractive yield to duration profile. We think many

investors attempt to get exposure too far out on the yield

curve, but we believe solutions where we’re trying to maxi-

mize that yield per unit of duration risk are more appropri-

ate at this juncture. Looking at senior loans as an example,

here you’ve got a floating rate associated with an attractive

yield and lower duration. We feel these types of solutions

that aren’t causing investors to have to go out on the curve in

order to get yield are interesting solutions, and might be con-

sidered for use in a portfolio. We firmly believe that effective

risk management and shorter-duration fixed-income alloca-

tions going forward are going to be more tactical in nature.

Additional risks that investors should consider include geo-

political and macroeconomic risks. These risks can obviously

impact portfolios. Specifically, we see these considerations

playing out with how our investors are managing daily risk and

how they manage the portfolio against significant drawdowns.

Lucas: We think about risk a bit more structurally as

opposed to specific short-term events that might be hap-

pening in the market, such as what happened in Ukraine.

We prefer to look at what we identify as the three big struc-

tural risks that investors have to contend with going for-

ward. One risk challenge facing investors is finding a sus-

tained source of portfolio growth, because you generally

see more constrained growth, particularly in the developed

world, due to entitlements, debts accrued, demographics.

A variety of structural impediments would suggest growth,

looking forward, may remain relatively tepid.

The second risk challenge for investors is generating a

source of real income that can keep pace with inflation.

Certainly, everyone is well aware that we’re coming off

a 30-year period where interest rates have declined on

a secular basis—that has been very positive for bond

returns. Going forward, starting from today’s very low

real yield and with the prospect of rising rates, inves-

tors need to be thinking more broadly about where to

find income that will not be completely eroded by rising

interest rates, as would be the case for nominal bonds.

Investors need income that can grow with inflation, so

they’ll be looking at other asset classes that generate

cash flow, such as real estate, master limited partner-

ships and infrastructure. There are other investments

where income can be sourced that is not only positive in

real terms, but also can grow dynamically as the value of

the underlying assets appreciates.

The third area of risk relates to how investors can protect

their portfolios from episodes of turbulence and downside

that we all expect will continue. Investors over the last 30

years have been well-served by having substantial bond

allocations that have performed consistently under periods

of equity stress, effectively serving as beta buffers. Given

what we view as a regime change going forward in terms of

the correlations between stocks and bonds, investors need

to find other sources of beta buffering. Certainly, diversifica-

tion based on a strong understanding of risk factors is one

element of that, but we also believe that in using an expand-

ing array of what one might think of as alternative strate-

gies—in particular, packaged in liquid alternative form—an

investor can really construct a portfolio that has a more

sustained source of downside risk management, as opposed

to just depending on bonds to continue to fulfill that role.

Bhansali: There are two ways you can quantify risk. One

is just in big macroeconomic shocks that can happen,

for example, in Asia and Europe and the Middle East.


We think many investors attempt to get exposure too far out on the yield curve, but we believe solutions where we’re

trying to maximize that yield per unit of duration risk are more appropriate at this juncture. —John Feyerer




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There’s a lot of conflict that has been escalating off and

on in the Koreas. In the U.S., it could be Fed-tapering

risk. It could be possibly negative news coming from

company earnings and so on. Those are macro types of

risk, and we’re always watching them. They’re very hard

to quantify in terms of their market impact.

The other way we think about risk, really, in terms of port-

folio construction, is to ask, What are the salient characteris-

tics of the reference portfolio, the relevant portfolio that you’re

trying to hedge or manage? What can happen to various risk

exposures or risk factors within that portfolio? We’re thinking

really in terms of the risk-factor exposures. And I think one of

the biggest risks that the market faces today is a very sharp rise

in rates that is not already built in to the yield curve. People

are not prepared for a sharp rise in interest rates.

One way to prepare or insulate is through diversifi-

cation. You can look at instruments that won’t do very

badly if rates were to rise. Another way to do this would

be to actually look at strategies that benefit from rising

rates, like particular types of foreign bonds that are not

impacted by U.S. rates rising. The third way is actually

specifically hedging inflation tail risk. Today the price

of hedging a shock to interest rates and a shock to infla-

tion is very, very low. So it’s very easy to create synthetic,

primarily derivatives-based portfolios that would hedge

you against a sharp rise in interest rates.

Simpson: We feel there is always risk in equity because of

economic factors, political factors and obviously company-

specific factors to the extent that they affect the indexes or the

ETFs that we own. We use nonleveraged inverse ETFs in a

small portion of the portfolio to hedge that downside, in addi-

tion to the covered calls to also try to offset some of the risk.

We’ve been in a period of declining interest rates for the

past 32 years, although we’ve been told by Chairwoman

Yellen that rates are going to stay down and the Fed is

going to do everything it can to keep rates in check. We

feel there is a better chance that rates would go higher in

the future than rates continuing to go much lower. That

presents a different set of concerns or a different set of risks

that we really haven’t had to contend with for many years.

Looking forward in terms of risk management—in

addition to the normal equity risk that I think a lot of

people have a good handle on—we think there is a fixed-

income risk out there in a period of rising interest rates

where investors or money managers may own fixed-

income products that might be more subject to interest-

rate shock or interest-rate increases than certain other

things. And in many cases, it’s more than just the dura-

tion of the bond portfolio.

The end of a 32-year bull market in bonds is one thing

for which we are comfortable interpreting a market reac-

tion. However, there are significant unknowns in this mar-

ket, which we cannot estimate an outcome for. We can’t

even begin to speculate what the impact of the domestic

and global monetary expansion will have on markets

through the remainder of this decade.

JOI: Volatility is what comes to mind when most people

think about risk. Are there better ways to measure risk?

Thomas: Ultimately, investors care about downside vola-

tility, not upside volatility, so the risk simply is losing

money. But people tend to lose money when volatility

rises. If you look at the VIX—or just any measure of volatil-

ity—it spiked in 2007, people lost tons of money in 2008,

volatility has plummeted since 2008 and investors have

made a lot of money since then. It’s probably a better mea-

sure of risk than anyone else can come up with, but people

are ultimately concerned with the risk of losing money.

Drawdown risk is what people often talk about: What is

the peak-to-trough drawdown? If I invest just at the wrong

time, how much money could I lose?

Feyerer: Many investors are also now asking, What’s the

short-fall risk? Certainly, volatility is a measure of risk,

but it isn’t necessarily the only or most personalized in

the sense that ultimately, many investors are going to ask,

What is the risk that my portfolio is going to fall short of

what I need to achieve whatever my funding needs are?

It’s a measure, but obviously there are numerous ways to

measure risk. We think that—particularly at this point in

time, and for the foreseeable future—investors are going to

be sensitive to downside risk, and focus on how well their

portfolio is positioned should a market drawdown occur.

Where are they going to get that cushion from within their

portfolio? And how are they going to be able to pursue, and

ultimately achieve, their financial objectives?

Lucas: I would suggest volatility is perhaps the worst mea-

sure of risk, because all it does is describe the actual ampli-

tude of price returns for an asset or a portfolio in the past.

To have a more forward-looking view of risk, or what risks

are contained in the portfolio as it stands today, investors

need to understand diversification across risk factors. What

type of valuation does the portfolio have?

If you have a portfolio that has high exposure to assets

that may be overvalued, that suggests there may be more

downside risk in the portfolio than the investor may realize.

May / June 201438

We feel there is always risk in equity because of economic factors, political factors and obviously company-specific factors to the extent that

they affect the indexes or the ETFs that we own. —Kevin Simpson




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As asset prices are mean reverting, having a portfolio that is overallocated to assets that have benefited from inves-tor exuberance—and that is less allocated to “out of favor” investments—can be a significant risk to the investor.

Having a more comprehensive view of forward-looking risk is important. Using volatility is very misleading, and there are more robust ways of statistically describing portfolio risk. We tend to use a variety of downside risk measures that are generated based not on the historical price patterns of the portfolio’s return, but the risk factors that currently exist in the portfolio. That gives us a better sense, looking forward, of what sort of downside risk to expect in a downward shock. In our case, the specific measure we use is expected tail loss—we’re more interested in that than just volatility.

Moran: I think some Ph.D.s would say there are many ways to measure risk. There are dozens or hundreds of risks out there. But when people talk about risk in a portfolio, argu-ably, volatility and standard deviation combine to become the key factors that people look at when they talk about risk.

I will grant you, though, there are many, many other risks out there.

Bhansali: I think volatility is a good starting point because it tells you what the fluctuations in the markets will be, but I think it’s severely limiting in terms of what the true risk of

a portfolio is. The true risk of a portfolio is not the noise in the portfolio, nor how much the portfolio fluctuates. That’s something we sign up for when we decide to manage money. The real risk is a risk of a permanent loss, the real risk is loss of capital, loss from default, loss from a tail event happening where you end up getting forced to liquidate at the wrong time. For those reasons, using volatility as a risk metric is actually very, very limiting. And I think what you need to do is really look at the full forecast distribution of returns—what are all the outcomes, especially when the outcomes are very “fat tail.”

And there are various mathematical metrics to measure it. None of them is perfect, but just drawing your focus to the far-left fat tail, to the risk of permanent losses or drawdown, is probably more important than just looking at volatility.

Simpson: Certainly as an option seller, volatility is impor-tant in our portfolios, in our calculations and in our mea-surement of risk. It’s been muted to a great degree over the past year. A lot of that may have to do with the easy monetary policy and some external factors. I don’t know that you can look at volatility to the same extent as you have in the past and be as reliant on it as to risk metrics,

but I imagine it will always continue to be a very pertinent component in how we measure risk.

We also use standard deviation. We look at standard deviation more closely, because it’s not as general a state-ment on the market as when you’re just talking about the VIX. In our portfolios, we look at our weighted standard deviation, and how our portfolios reflect risk versus the broader markets. You can look back over the past 12 months to get a handle on it, because as with gold and other asset classes that don’t always move in a predicted direction, if you go back too far, sometimes the data might skew your decisions. We use a weighted standard devia-tion metric looking back 12 months as weighted in our portfolios versus the broad market.

JOI: Should investors be concerned about tail risk?

Thomas: They should always have an eye on it, now because tail events almost by definition don’t happen very often. Although they happened twice in the last 12 years, it shouldn’t be investors’ only focus, that’s for sure. But I will say this: Tail-risk hedging can be an offensive strategy, not just defensive. If you can hedge the down-side, if you can hedge during extreme events, what you find is that the best returns found in the marketplace happen just after a tail event. There were great returns in

the equity market in 2003, and that was after a three-year tailspin between 2000 and 2002, when the markets went down significantly. You could have made tons of money in 2003 if you had hedged the downside between 2000 and 2002. You could have made tons of money in the market in 2009 if you hedged the downside in 2008.

If you are able to protect the downside, then you can get more aggressive after the tail event if you have dry powder to reallocate to more offensive asset classes, so it’s really about tactical asset allocation. Tactical trad-ing can be a way to potentially avoid the drawdowns and profit from the upside. Not easy to do, and the average investor probably can’t do it, but there is a value to hedg-ing tail events because opportunities present themselves after the tail event. And so you have to analyze what the cost of the tail-hedging strategy is.

Things like buying put options? Very expensive. Going long the VIX? Very expensive. I think true diversification, and having a position in long bonds, is a decent idea. For institutional investors, they tend to look at CTA managers or managed futures traders, which tend to be good tail-protection strategies. Tail-risk hedging is great in theory, but it’s harder to do in practice.


I would suggest volatility is perhaps the worst measure of risk, because all it does is describe the actual amplitude of price returns

for an asset or a portfolio in the past. —Ted Lucas




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Lucas: Yes, they should absolutely be concerned. Investors lose capital in left-tail events and goals can become further out of reach. Is there a way to hedge this risk? First, you really need to understand what tail-risk exposure, looking forward, might be for a portfolio, to understand how you might hedge it. One of the things we’ve done in our own multi-asset port-folio construction is—even in optimizing portfolios—rather than using volatility as the risk input, we use expected tail-loss. We believe that creating a more robust portfolio that will be more resilient during downside episodes can be accomplished by using more relevant risk inputs.

Similarly, in traditional mean-variance optimization, where you’re using volatility and some historical correla-tion matrix to optimize the portfolio, we know correlations change, based on the market regime. Using a more dynamic set of correlation matrices that take into account both very positive and very negative windows for the market is a way of improving portfolio resilience as well, because you’re taking into account not only the normalized correlation environ-ment, but the stressed correlation environment.

In terms of hedging tail risk, a pretty effective way in the past was simply to have exposure to duration via longer-maturity bonds. I would suggest that may not be a very good source of tail-risk hedging going forward, because we’re starting to see episodes where bonds—rather than providing downside protection during equity sell-offs—

participate in the downside, as well. A less expensive way to hedge downside in a portfolio than using derivatives might be to use the expanding array of liquid alternatives as a way of creating a risk-shaping buffer in a total portfolio context.

Moran: Many investors, even if they don’t know the terms, certainly are very concerned about tail risk. For example, there are investors who say, “I can handle some short-term risks. I can handle 5 or 10 percent drawdowns, but Mr. or Ms. Financial Advisor, you have to help me out, because I have to lock in some gains here, and I can’t afford the catastrophic losses many investors experienced in 2008.”

There certainly has been a lot of interest. Some of the most straightforward ways to manage or hedge tail risk would include buying deep out-of-the-money put options on stocks options or stock indexes. Institutional inves-tors often use S&P 500 options to implement protective puts and collar strategies. However, with the introduction of volatility products, you now can add a little bit of VIX exposure to your portfolio by buying VIX call options or VIX futures to manage catastrophic risk in your portfolio.

CBOE held its 30th annual Risk Management conference recently, and there was at least one attendee there who said, “A lot of this tail-risk stuff is just so expensive.” There is a cost involved there, and you do have to monitor costs.

He implied tail-hedge programs are too costly and not worthwhile, and in the years since 2009, we have not had a major crash when there could be a direct payoff from a tail-hedge program. But another attendee from a pension fund said he believed that if you take a look at the entire port-folio, making a small allocation to some tail-risk products actually could have paid off—even in the past couple years.

How can tail-risk programs be profitable in years in which the stock market has gone straight up, and how could it pay off? A tail-hedge program can pay off in bullish years if you take into account the total portfolio standpoint; if you buy a little bit of tail-risk protection, you may feel more comfortable about increasing your allocation to equities. So if you bought a little bit of tail-risk protection, but you upped your equity allocation from 50 to 80 percent, one could make the case that tail-risk protection actually could have paid off for you from an entire portfolio standpoint.

Simpson: We have spent a lot of the past two years discuss-ing tail risk internally, more so for pension funds, endow-ments and institutions that require a cash-flow component or have enterprise objectives associated with a funding-

status level. It’s not always a question of total return, but of meeting a cash-flow objective, budgeting to a funding-status level or maintaining specific enterprise objectives. You can go through periods like we saw in 2013 where we have wonderful performance, and institutions are able to exceed their actuarial goals, but that excess return is at risk if there is no change in an institution’s risk exposure fol-lowing a run-up. If an institution maintains the same asset allocation after a run-up, they maintain full risk exposure. Even the small pullback we saw in January—sure the mar-kets have recovered, but a 6 percent pullback for a pension plan is a big deal when they’re talking about budgeting or meeting certain enterprise objectives for a fiscal year.

To us there is nothing more important than control-ling tail risk in the institutional space, where people’s lives are dependent upon that income, and institutions have recently begun to focus on funding status as a primary investment objective over returns. And tail-risk manage-ment using option overlay is something we feel very pas-sionately about. Truth be told, we feel it’s a very underused technique, but it’s an add-on that can be used when a con-

May / June 201440

A tail-hedge program can pay off in bullish years if you take into account the total portfolio standpoint; if you buy a little bit

of tail-risk protection, you may feel more comfortable about increasing your allocation to equities. —Matt Moran




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sultant or the board feels it’s appropriate. Using an option overlay as a risk overlay tool is a tremendously attractive solution to manage pension funds, especially after a mar-ket rally. You do not have to go to cash to change risk port-foliowide; you can use the option overlay to manage the equity beta risk of the portfolio.

JOI: What new risks could an investor introduce into

their portfolio by adopting smart-beta strategies like

fundamentally weighted or factor-based indexes?

Thomas: A lot of these smart-beta strategies are pre-mised on the belief that the anomaly that has been observed will continue—such as the fact that value historically has outperformed the market. That is an observation, and the question is, Will it continue or will it not? If everyone piles into the value trade or to the low-vol trade, then you can be sure it will ultimately get arbitraged away. The risk is that they could arbitrage away the anomaly that they seek to exploit.

Now I don’t think they’ve done that yet. I don’t think enough money has poured into those to say that. I still think people have biases against low-volatility stocks and they tend to buy the sexy and exciting stocks, which is why low-volatility stocks work. I could say the same thing for valua-tion. But I think that’s the biggest risk. It’s not really a total-

return risk that they’re going to lose tons of money relative to the cap-weighted market. It just means they might not be able to outperform to the extent they had historically.

Feyerer: We believe it gets back to investor education and understanding what exposure they’re seeking to get, and understanding how that typically works in a portfolio. We’ve invested a lot of time and energy in understanding how factors may perform in different types of market envi-ronments and how they might look in an investor portfolio.

We encourage investors to consider smart-beta solutions, be it single factors such as momentum or low volatility, or alternative-weighting solutions such as fundamental index-ing. By no means does the moniker “smart beta” indicate they will outperform in every market environment. We believe there are a number of strategies that may outperform over full market cycles, but that’s not to say that in every instance and in every portion of that market cycle investors will see a smart-beta strategy earning excess return relative to its benchmark.

When you look at an allocation to a smart-beta strategy and look at it on a short time horizon, it may or may not

outperform depending on a host of other factors in the marketplace. But what we’ve seen in general is that the longer that the allocation is held, the greater the propen-sity for the strategy to deliver excess return relative to its benchmark, and also to perform well relative to many of the active strategies available in the space.

Lucas: I think of them as conditional strategies. So there are periods of time where a single-factor-based strat-egy that focuses on, say, valuation, dividends, quality or momentum—whatever it is, the investor experiences conditional excess returns when they are present. And lit-erature would suggest that, over time, there is a premium associated with those factors, but there can be long peri-ods of time where those are not rewarded.

And in some risk-based strategies, such as low volatility, you can have a lot of concentration in the portfolio—and such concentration tends at times to get pointed at sectors that may be overvalued relative to history. Ironically, you may be taking greater risk by using a one-dimensional risk-based strategy.

Bhansali: I think those are generally very good, because they reflect the graduation of index strategies from just being market-cap-weighted, which is actually very simple and very naive, to something that reflects better isolation of particular biases, and maybe even value. The thing you

have to watch out for is you need to know that all of these types of strategies are based on certain assumptions and certain modeling, and they are not guaranteed to work under every circumstance or every situation. So they’re not necessarily a one-sided better improvement. I think they have another side to them.

And I think to the degree that people adopt some of these strategies, they need to know what those assump-tions are, and not just take them at their face value.

JOI: Are VIX-based products a viable way to address risk

management issues?

Thomas: The challenge there is VIX futures contracts—you can’t trade the VIX, but you can trade futures contracts on the VIX. And the biggest challenge that investors have is the VIX futures curve is extremely steep, on average, and that means it’s very expensive to maintain a long position in the VIX over time. Most of the ETFs that are long the VIX tend to lose significant amounts of money over time, and they don’t track the VIX very well at all.


If everyone piles into the value trade or to the low-vol trade, then you can be sure it will ultimately get arbitraged away. The risk is that they could arbitrage away the anomaly that

they seek to exploit. —Ric Thomas




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May / June 201442

There are times where the VIX is not steep, where the futures curve is flat. In those environments, the VIX may be a decent way to hedge downside risk, but other than that, you just have to be very conscious when trading that contract, or when you’re buying ETFs on it. In fact, there are ETFs that short the VIX to profit from it on the other side, and they have experienced more volume lately, I’ve noticed.

Feyerer: We’ve done a lot of work on this at Invesco PowerShares—taking a look at VIX and seeing how it does as a market hedge. Certainly, when you look at the CBOE VIX index, it has a strong propensity for negative cor-relation to market benchmarks like the S&P 500. And the unfortunate side for investors is that VIX is not investable. Futures are traditionally the method that investors will use to get VIX exposure, but when you look at the futures curve, depending on where VIX levels are, markets can either be in contango or backwardation, which can have a signifi-cant impact on returns as those contracts are rolled.

Typically, what you see is that a static allocation to VIX, when volatility levels are low, will be a significant drag on the portfolio as those contracts roll. We think VIX can be a great hedge against market drawdown, but a static alloca-tion can effectively act like a boat anchor on the portfolio. We’ve done some research taking a look at investors that have tried to be more tactical around flows into and out of the traditional VIX products that are out there. The data supports that investors are terrible at timing allocations to VIX, meaning they are often flowing out money from VIX products precisely right when they need that exposure.

One of the ways we feel is potentially a better way to allocate to VIX is in a more dynamic fashion. In a rules-based construct—where you’re looking at both implied and realized volatility—when VIX levels are low, you’re maintaining an allocation that has very little VIX exposure. However, when you see a combination of the implied and the realized volatility creeping up, the portfolio dynami-cally allocates into a heavier VIX allocation.

We think VIX has the potential to be a very strong alloca-tion in the portfolio, but a static exposure to it can be a drag on returns. We believe a dynamic, more tactical approach similar to the S&P 500 Dynamic Veqtor Index methodology is a better way to implement that in a portfolio.

Moran: Yes, they are viable, although I would definitely say do your homework on the VIX-based products, because not all of them perform in the same fashion, by any means.

CBOE offers VIX futures and options. But if you’re talking about other VIX-based products, there are rough-ly about 40 different exchange-traded products that are offered by different entities worldwide. Entities other than CBOE are the providers of ETFs and ETNs related to VIX.

JOI: Should investors approach their risk management

strategies with a time horizon in mind?

Feyerer: Yes; no question. In the past, there may have been concern about relative risk and how a portfolio may

perform relative to the market. At this point, there is a con-siderable amount of attention paid to absolute risk: How is a portfolio performing relative to the goals and the time horizon identified? These are the key ingredients, and how you might construct a portfolio with a 30-year time horizon is certainly different than a five-year time horizon.

Lucas: There is a natural tension to what investors view as “risk management.” Many investors, when they hear the term “risk management,” simply think “risk mitigation.” For them, holding assets that are less volatile and less susceptible to short-term downside would be a form of risk manage-ment. Of course, the natural asset there is cash or T-bills, and the negative real returns that investor would earn actually creates a very substantial, longer-term risk of not meeting the investor’s goals. With that natural tension, managers and investors are left with this challenge of trying to balance the creation of robust portfolios—meaning that you don’t have catastrophic downside participation during market shocks—while, at the same time, maintaining enough exposure to assets that, over a period of time, generate the capital growth required to meet the needs of individuals with respect to retirement, health care and other vital needs.

Bhansali: Yes, and that really varies depending on what kind of investors you’re speaking to. If you’re thinking of a hedge fund, the horizon usually is much smaller—between a few months to maybe a year. If you’re thinking about an endowment, their horizon is decades, if not centuries, in many cases. And then if you’re looking at most investors in mutual funds or retirees, their horizon shifts as they grow older and their productive working years shorten. Somebody who is 25 years old might have a horizon of 40 or 50 years; somebody who is 60 years might have a horizon of only five years. As you age, as your abil-ity to earn decreases, your risk dollars should change, and this is usually reflected in terms of what we call “glide path” for most retirement accounts and target-date accounts.

In our own glide path and target-date accounts, which I manage, we also have explicit tail-risk hedging, to make sure investors don’t get forced into doing irrational things if the market suddenly has a bad negative outcome. But yes, horizon is very critical. We shouldn’t have the same horizon for all types of investors.

Simpson: I think they should approach all decisions with a time horizon in mind when it’s an investment decision, because so many things change so quickly and more and more every day than in the past. The old adage of having short-, intermediate- and long-term objectives and risk parameters I think is as true today as it always has been, but I think more of an emphasis on shorter-term risk control has become more prevalent and will become more prevalent. A big part of what we do in hedging is sometimes looking more at the shorter-term potential for risk and making that part of our management style, perhaps having more hedging in certain time periods and perhaps having a little less in other time periods. But an emphasis on shorter- to intermediate-risk control is far more important today than it ever has been, and I only expect that trend to continue.




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Talking Indexes

May / June 2014

By David Blitzer

44

The threat of the ‘unknown unknowns’

It’s What You Don’t Know That Matters

Risk—the possibility that things don’t turn out as expected—is inherent in investing. While some people ignore risk and hope for the best, other investors try to

understand or manage it: They ask why the market might go against them and how they can limit any damage or loss.

One approach is to classify perceived risk into various cat-egories: inflation risk, currency risk, interest-rate risk, liquidity risk, credit risk, even the risks of hurricanes or floods. In each risk category, estimates of the likelihood of occurrence, the cost of the likely damage or loss, and the cost of hedging or mitigation are made. Then the investor can decide whether to proceed with the investment and if risk management efforts are desirable. At times, an investment may be abandoned because the potential losses are deemed to be too large or cannot be limited. In other cases, the investment will be made even though expected returns are lower due to either hedg-ing costs or possible losses. In other situations, the risks may appear to be modest and the investment is made without any risk mitigation efforts. All of this analysis and decision-making is premised on correctly identifying and categorizing the risks.

Donald Rumsfeld, a former U.S. defense secretary, once commented, “There are known knowns; there are things we know … there are known unknowns; that is to say, we know there are some things we do not know. But there are also unknown unknowns—the ones we don’t know we don’t know.” Risk management through categorization, analysis and decision-making, as described above, is limited to “known knowns” and “known unknowns.” For example, we know rising interest rates might be a problem, but how much they might rise is unknown. And no list of risks is ever exhaustive.

There are, and always will be “unknown unknowns”—like the “flash crash” on May 6, 2010. No one expected or imagined that the market could plunge fast enough for the S&P 500 to lose 10 percent in a few seconds while VIX doubled in a speck

of time. Another flash crash would be a known unknown, since we’ve experienced the first one. However, there are still many more unknown unknowns waiting in the wings; what they are or how many they might be … well, we don’t know.

Rumsfeld’s remark missed a more important datum: things we absolutely positively know are true; except that they’re not. Ten years ago just about everyone believed that home prices would never fall and that your house was your safest investment. You may recall how that turned out. Until 2008, investors knew the Fed funds rate could never go to zero, that money couldn’t be free. Yet for the last couple of years, the Fed funds rate has been zero, and short-term borrowed money is virtually free.

Dividing risk into neat categories or boxes with prob-abilities and loss estimates provides insight about the perceived risks, the known unknowns. However, that analysis tells us nothing about what we don’t know, and it’s what we don’t know that matters most if we want to understand what could go wrong.

Frank Knight, in his book, “Risk, Uncertainty and Profit,” drew a similar distinction between what could or couldn’t be known and how the difference might affect profits. In Knight’s terminology, risk means events that can be ana-lyzed and predicted with probability and statistics. Hedging a portfolio against movements in foreign exchange rates in the forward market is managing Knightian risk. The clas-sified or categorized risks listed above are risk in Knight’s definition of the word: There is data to analyze the prob-ability that something might happen.

Uncertainty is when there is little if any consistent data, probability distributions aren’t known, and statisti-cal analysis gives way to intuition and guesswork. This is the unknown unknowns and the truths that aren’t. The heroes of Michael Lewis’ “The Big Short,” who under-




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stood that the housing bubble was built on bad mort-gages and saw how to profit from their understanding, prospered from the uncertainty.

Economics distinguishes accounting profit and economic profit. Accounting profit is the return a stock earns based on a risk-free rate of interest, the market’s normal return and the stock’s beta. These risks and expected returns can be calculated. However—and as any experienced investor knows—there are times when an investment earns far more, or far less, than the accounting profit, the expected return.

Knight sees profit as economic profit—accounting profits plus the gains or losses from uncertainty. What matters for earning profits is not the known knowns; it is dealing with uncertainty. In 2013, uncertainty was favorable: The S&P 500 gained 29.6 percent on the year, much more than the expected return. With hindsight, most analysts credit the Fed’s quantitative easing policy

for driving asset prices higher and higher. However, the result was not assured; some predicted rampant inflation and collapsing markets would result from the same poli-cies. Given the limited data and little experience with zero interest rates and quantitative easing, the outcome could not have been predicted statistically. Possibly an investor could have hedged a long position in the S&P 500 against fears of inflation and a market crash, if she could find someone whose intuition was the opposite of hers.

Uncertainty is not always as rewarding as it was last year. In the fall of 2008, as Lehman Brothers went bankrupt and AIG teetered on the edge, the economy plunged into the abyss of deep recession. As the recently released Federal Reserve meeting transcripts show, it was only luck, guess-work and intuition that prevented a far worse result. The unknown unknowns and nontruths of the financial crisis didn’t fit into any convenient classification of risk.

JOI: How much does the university have in traditional equities and fixed-income investments?Gorrilla: Twenty-five percent is our policy target.

JOI: Do you use mostly active managers for that?Gorrilla: No, we use a mix. We do use index products. We use predominantly futures as a means to access inexpensive beta, and we’re more comfortable with a passive strategy for larger-cap exposures. In some cases, we’re using it for place holders, and we’ll also use it for help with transitioning between managers, and to help to keep us in balance.

JOI: When you say you use futures, does that mean you’re using index futures to get exposure to whole asset classes?Gorrilla: Yes.

JOI: What kind of indexes are you buying futures on? Is liquidity your main consideration?Gorrilla: We’re doing the very basic forms. It’s about liquidity and very low transaction cost. I’m considering that transaction cost as the fee. Futures positions are easy to manage and easy to change if you decide, “We no longer need this position, because we’ve hired someone in place of the index.” We don’t have to call and fire someone. You just sell some of the position.

JOI: Are you managing the futures in-house or do you have an outside manager to do that?Gorrilla: We have the discretion to maintain it in-house, but we use a service provider to buy and sell them.

JOI: Can you give a rough breakdown on what asset classes you’re covering in the equities and fixed-income portfolios? Gorrilla: Sure. In terms of equities, more recently we’ve tried to take a more global view. So we’re benchmarking it to the MSCI ACWI, but we are, within that, breaking it down to U.S., non-U.S., emerging markets.

We look at capitalization as a means to help any strategy, as a means to decide whether or not it’s sensible to use indexes as opposed to hiring active managers. So with larg-er-capped, more liquid things, it’s easier to use an index position. Whereas if it’s micro-cap U.S. stocks, that’s going to be something we’re going to hire an active manager for.

JOI: Are you overweighting any asset classes within equities or are you essentially reflecting the ACWI?Gorrilla: No, we do not match the ACWI breakdown right now. We are a little bit more heavily weighted towards emerg-ing market than the ACWI. The index’s allocation is fairly low.

JOI: What kind of fixed-income investments are you in right now, and has that changed in this low-interest-rate environment?Gorrilla: It has. A few years back, we drastically shortened our duration, which may have been too early. But it’s pre-dominantly U.S. exposure. It’s benchmarked to the Barclays Aggregate, but we have flexibility within there to add in all kinds of fixed-income products.

JOI: Do you find index strategies are effective in fixed income, or is that an area where it’s just more effective to go with active managers?Gorrilla: We aren’t currently using any index products in fixed income. I’m not sure which is easier or better, but that’s where we are.

JOI: If there’s about 40 percent in hedge funds, another 20 percent in other alternatives, and 25 percent in tra-ditional equity and fixed-income investments, what’s the remainder?Gorrilla: So the other 15 percent are real assets, which are for us real estate and natural resources. It’s really 75 percent invested in alternatives. But for the real assets, we’re not buy-ing real estate like Harvard does. We don’t look at properties; we’re hiring third-party managers to do it for us.

Gorrilla continued from page 27




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May / June 201446

Alternative Index Weighting And The Impact On Portfolio Risk

The quest for an efficient portfolio

By Scott Weiner and Nicholas Cherney




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Mean-variance efficiency has been sought since Markowitz’s seminal work on portfolio optimiza-tion in 1952, but generally speaking, attaining such

efficiency critically depends on assumptions involved in modeling.1 With the growing popularity of exchange-traded products that provide packaged index solutions, it is impor-tant to assess the benefits and pitfalls of alternative weighting mechanisms that seek efficiency, many of which purport to be the right solution for the sophisticated investor.

The recent move in the investment community toward other approaches to portfolio management includes some that may sidestep return forecasting altogether and simply focus on volatility. The “mean” in “mean-variance opti-mization” (MVO) is completely ignored. In many cases, investor interest in these approaches is driven by a desire to capture the “low-beta anomaly,” which proposes that low-beta stocks have historically exhibited positive alpha.2 Among others, these approaches include:

1. Minimum variance (MV) portfolios 2. Low-volatility (LV) portfolios 3. Equal weight (EW) portfolios 4. Equal risk weight (EQR) portfolios We will include market-cap weighting for some of our

analysis. In our analysis, we will focus exclusively on the risk charac-

teristics of the different approaches. The approaches we dis-cuss are, by design, independent of return expectations. We propose a simple problem: An investor has a fixed amount of capital to be invested in a fixed number of stocks. The entire amount of capital is invested, and there is no short-selling. In other words, each stock has a weight in the portfolio between 0 percent and 100 percent, and the sum total of the weights in the portfolio equals 100 percent. How the investor invests the capital depends on the method of choice, and each has a different utility optimization function, or none at all.

Our research broadly shows that 1) the assumptions required to make several of these solutions “mean-vari-ance efficient,” such as zero correlation across assets or equal return expectations across assets, are questionable; 2) the range of embedded risk among popular approaches may vary far more widely than investors may be aware of; and 3) investors may not be getting out of some exchange-traded products what they promise, e.g., low-volatility portfolios may not, in fact, be low volatility at all.

In particular, we establish a risk-ordering of five differ-ent index weighting approaches ranging from MV on the low end to EW on the high end:

MV EQR LV MC EW

Low High

Furthermore, we establish that the risk contributions of component stocks (and therefore concentration risk) vary quite dramatically across different approaches, with EQR showing the least variation across component stocks to MV exhibiting the highest:

EQR LV EW MC MV

Low High

To set the stage for what follows, it is constructive to briefly review the mean-variance framework as well as each of the approaches examined in this paper.

Mean-Variance EfficiencyThe thinking behind mean-variance optimization is

straightforward: Investors seek higher returns and lower volatility of those returns. In other words, they try to maximize returns and minimize risk. Consider a scenario where each unit of expected return gave the investor one unit of “utility” and each unit of risk, measured in vari-ance detracted from the investor’s utility. For a given amount of utility, the choices of portfolios available to the investor are infinite, but only one portfolio would provide the investor with the greatest utility for a given level of risk or a given expected return. This set of portfolios, collec-tively, is known as the efficient frontier, and all portfolios on the frontier are considered “mean-variance efficient” (or simply “efficient”). In other words, they each provide the highest possible expected return relative to risk.

Efficient portfolios exist in theory, but investors must attempt to approximate them, and they are the result of some algorithm. Put expected returns and risk charac-teristics of a set of stocks (volatilities, correlations) into a model, run the model and out comes the portfolio weights. For a particular solution to be actually mean-variance efficient in real life, the data that goes into the model and/or the model itself must have assumptions that turn out to be true. If the model requires correlations, the correlation estimate has to be accurate. If it requires returns, you must forecast actual returns.

Note that when we say “efficient,” we are still describing a rather stylized world, even before we consider constraints. In particular, there is no measurement error, no time-vary-ing parameters, etc., and this is not to say that efficient port-folios will outperform other schemes, nor does it guarantee that ex post the portfolio will have been mean-variance efficient. This is an important point, for even when one portfolio has a lower ex ante volatility, for example, there is no guarantee that this portfolio will not have higher volatility than that of a higher expected volatility portfolio.

The models we present all seek to either simplify the optimization (or even eliminate it) and/or reduce or elimi-nate estimation error in the parameters for the model. Expected (excess) returns are very difficult to estimate, as are covariance matrices. Methods that eliminate some of the estimation may trade off some accuracy in the param-eter estimates for accuracy in the optimization.

Below we examine five models and specify the assump-tions that would have to hold true for each model to gener-ate portfolios that were actually efficient.

Market Capitalization Description

The MC weighting scheme, as the name suggests, weights all of the assets in the portfolio according to size, as measured by the outstanding market capitalization of each asset. No optimization is required to construct the




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MC portfolio. If, among other assumptions, all investors were mean-variance motivated, had the same information set and all stock prices fully reflected that information, then each investor would hold the individual portfolio that maximized their utility, and the aggregate market would therefore be mean-variance efficient.

Constraints Required For Efficiency

Several constraints are required, as outlined in Haugen and Baker (1991),3 including that “all investors agree about the risk and expected return for all securities, all investors can short-sell all securities, no investor’s return is exposed to federal or state income tax liability, [and] the invest-ment set for all investors holding any security in the index is restricted to the securities in the capitalization-weighted index.” In addition, all investors must be able to both bor-row and lend at the risk-free rate, must be rational and must have no transaction costs.

Comments

Given the practical reality that many of the assumptions are violated, it is likely that stocks are mispriced, and there-fore MC schemes tend to overweight overvalued stocks that have had outsized historical returns, while underweighting undervalued stocks. In addition, it has been suggested that because many investors do not have access to leverage, they access it through choosing high-beta stocks, therefore causing these stocks to be overvalued, on average.

Minimum VarianceDescription

For the MV portfolio, suppose the investor is extremely risk averse and is only interested in the portfolio that, ex

ante, provides the lowest risk, as measured by portfolio standard deviation or variance. There are several impor-tant features of this portfolio optimization process, which is a constrained version of the MVO to note. The inves-tor can either a) set the risk parameter infinitely high, in which case, the expected excess returns no longer enter the equation; or b) set each of the expected returns equal to one another. By design, the marginal contribution to risk of each asset is the same, since that is effectively the opti-mization condition for the minimum-variance portfolio. This means that incrementally changing the weight of any stock in the portfolio would cause the same increase to the portfolios’ expected risk.


For this approach to actually generate an efficient port-folio, the expected returns of all assets in the portfolio must be the same. In any other case, there is a portfolio with the same expected variance, but a higher expected return.

Comments

MV portfolios tend to be concentrated, not surprisingly, in low-volatility stocks. In the extreme case where no two stocks are correlated, minimum-variance weights will be inversely proportional to variance, meaning that a stock

with half the volatility of another will have a position that is four times greater in the portfolio. This may capture the “low volatility” anomaly (that lower volatility stocks have often outperformed), but the trade-off is often high con-centration and sector-specific risk.4,5

Equal WeightDescription

Equal (dollar) weighting schemes don’t require optimi-zation. If an investor has $X and N stocks under consid-eration, each stock gets a $X/N position in the portfolio. Believe it or not, this is a constrained version of the basic mean-variance optimization.6


In a stylized world where the expected return of each asset is proportional to the sum of its covariances to all assets in the portfolio, the result of a mean-variance opti-mization will yield equal weights on each of the assets. There isn’t much of an intuitive explanation for this assumption; it is simply a mathematical relationship that would equate these two models.

Comments

The benefit of simplicity of this approach is offset by its resulting high volatility relative to other approaches, and it is extremely unlikely to be efficient.

Low VolatilityDescription

The LV model seeks to overweight stocks with low volatility and underweight stocks with high volatility. The model construction is as follows: All of the stocks under consideration are ordered from the highest volatility to the lowest volatility, and then the weights are distributed such that the weights are proportional to the reciprocal of vola-tility (i.e., 1/volatility) for each stock.

For example, a stock with a vol of 15 would have double the weighting of a stock with a vol of 30. The volatility of each individual stock position is therefore equal, but the model makes no statement about portfolio volatility, since correlations—and by extension, covariances—are not inputs into the model.


For the low-volatility solution to be mean-variance effi-cient, the data set requires expected returns that are pro-portional to volatility and zero correlations across all assets in the portfolio. If there is any information about asset correlations—obviously stocks are, in fact, correlated—this model would not be efficient.

Comments

It should be clear from the description of the model that there is actually no optimization of any kind vis-à-vis portfolio volatility. In this sense, LV shares a feature of the EW model: fewer parameters to estimate. Whereas a full covariance matrix requires N*(N-1)/2 parameters, LV

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models require only N parameters. Offsetting this, how-ever, is lack of optimization in the LV framework. There is no formal utility function relating to portfolio volatility or portfolio return, and the LV solution will almost certainly lie off the efficient frontier.

Equal Risk WeightDescription

EQR portfolios match the total contribution that each asset makes to the overall risk of the portfolio. In contexts where the assets in question are broad asset classes (e.g., stocks, bonds, currency, etc.), this approach is sometimes called risk parity. Consider the addition of two stocks to a given portfolio: Stock A is entirely uncorrelated to any asset already in the portfolio, while stock B is highly cor-related to the holdings in the portfolio. All else equal (the size of the position, the volatility of the stock), the addition of stock B will raise the portfolio risk more than the addi-tion of stock A. Each contributes differently to the overall risk of the portfolio. In the EQR model, the investor runs an optimization based on the covariance matrix of all stocks under consideration such that the total contribu-tion to overall risk that each position makes is the same. This is in contrast to an MV portfolio, where the marginal

risk contribution is equal. The solution may yield weights whereby a low-volatility stock that is highly correlated to the portfolio may contribute the same as a high-volatility stock with a low correlation to the portfolio.


Since the EQR model yields the LV model if all correla-tions are constant, the conditions for LV efficiency are suf-ficient for EQR efficiency, but not necessary.

Comments

One way to think about EQR is as an extension of the LV framework. It seeks low volatility in the portfolio by choosing low-volatility stocks but considers correlations as well. If contribution to risk is a function of volatility and correlation, then if the correlation of every stock to every other stock is the same, then the only factor that will matter is the volatility. So in the case where all of the correlations are identical, the solution to the EQR model mimics the solution to the LV model. In other words, the LV solution explicitly ignores correlation, or more specifi-cally, ignores any variation in correlation: The EQR solu-tion will be equivalent to the LV solution if, and only if, all correlations are assumed to be equal. In addition, it has been shown that the EQR portfolio will always lie between the MV portfolio and the EW portfolio.7

AnalysisMuch has been written on each of the models we have

described above. Our interest is in determining the impact on portfolio risk of each of the models in our model set. In partic-ular, we are interested in answering two important questions:

1. What is the risk-ordering, in terms of ex ante portfolio volatility, for the models in question?

2. Which model yields the largest variation in risk contri-butions (idiosyncratic risk) and which yields the smallest?

Methodology

To determine the answers to these questions, we per-form the following analysis:

1. We take all of the stocks that make up the S&P 500 Index (as of 12/31/2013).

2. We randomly draw N stocks from the list for which we have at least one year of daily returns. N is the port-folio size, and we perform the analysis for N=5, 10, 15, 20, 25, 30, 50, 75, 100.

3. We calculate the historical covariance matrix for the selected stocks for the 2013 calendar year.

4. We also capture the market capitalization of each of the selected stocks (for the MC portfolio).

5. We calculate the portfolio weights for the five models under investigation: EQR, EW, LV, MC, MV. Only the EQR and the MV models require optimization.

6. For a given portfolio size (N), we repeat this proce-dure 500 times.

Results: Portfolio Volatility

For each model, we have 500 iterations for each portfolio size. In Figure 1, we plot the average (expected) portfolio volatility for each N for each of the models for a given port-folio size, assuming the covariance matrix forms our expec-tations of forward-looking volatility. This tells, for a given size of portfolio, randomly selected from the S&P 500, which approach will have the lowest portfolio volatility.

We observe, not surprisingly, that with each of the models under review, the average portfolio volatility is decreasing with the size of the portfolio (i.e., the number of stocks in the portfolio). As expected, the two models that don’t incorporate any volatility information, EW and MC, have the highest average portfolio volatility for moderately sized portfolios, but MC actually falls just below low volatility for a portfolio size of 100. The MV portfolios tend to have the sharpest descent relative to portfolio size, while the LV portfolio tends to have the slightest decline, as measured by the difference between average volatility for N=100 versus average volatility for N=5. This observation can serve as a proxy for the impact of diversification through increasing the size (i.e., the number of stocks) in the portfolio. While EQR and LV start out roughly equivalent, the EQR model seems to decline in overall portfolio volatility faster than the LV model, more so at larger portfolio sizes. The relative position of LV versus EQR may surprise some, but to us it makes sense, given EQR’s incorporation of correlations and LV’s assumption that all assets are uncorrelated. Consider the stylized example presented above, in which we had two baskets of stocks, one with 20 percent annualized volatility and perfect correlation, and one with 21 percent annualized volatility and zero correlation. As we continue to increase the size of the portfolio, the basket of 20-vol stocks will continue to maintain a volatility of 20 percent. The volatility of the





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On a related note, it has been shown that under cer-tain expected return conditions, maximum drawdown is directly related to portfolio volatility. Therefore, under those conditions, the risk-ordering solved for in this sec-tion is the same as the maximum drawdown ordering for the same portfolios.8

Results: Risk Contributions

The EQR methodology highlights another way to look at the inclusion of each position in a portfolio; namely, its total contribution to overall portfolio risk. This matters because it indicates how much we may be concentrat-ing our risk to idiosyncratic factors that are not reflected ex ante. We know that the EQR portfolio is designed to equate risk contributions, but how do the other models fare with respect to this metric?

In Figure 5, we plot the ratio of the highest-risk con-tribution stock to the lowest-risk contribution stock for portfolios of 100 stocks randomly selected from the S&P 500. We only plot four of the five strategies under con-sideration, because the MV portfolio would have such an extreme ratio that it is literally off the charts. We also

basket of 21-vol stocks will decline as more uncorrelated stocks are added.

Focusing in on the case where the portfolio contains 100 stocks randomly selected from the S&P 500, we plot the portfolio volatilities from each of the 500 iterations rank-ordered on a per-model basis in Figure 2.

In other words, we rank-order the MV portfolio vola-tilities, the MC portfolio volatilities, etc. What we see is quite illuminating. Needless to say, the MV volatilities are always the lowest volatilities. What we also observe is that the distribution of EQR portfolio volatilities is the next lowest. We can further confirm that the EQR volatility is, in every instance, lower than its LV counterpart, as shown in Figure 3 (where the red line is the 45-degree line).

In other words, for portfolios of size 100, every port-folio had an ex ante volatility that was lower in the EQR solution than in the LV solution. More broadly, every iteration had a confirmed ordering from highest volatil-ity to lowest volatility of EW, LV, EQR and MV, with MC mostly more volatile than LV, EQR and MV, and less volatile than EW. On occasion, the MC model performed outside this range, as seen in Figure 4.

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EQR Portfolio Volatility Versus

LV Portfolio Volatility For N=100

15%

14%

13%

12%

11%

10%

LV Volatility

EQ

R V

ola

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ty

10% 12% 14%12%

Portfolio Volatilities For N=100

14%

12%

10%

8%

6%

4%

2%

0%

■ LV ■ EW ■ EQR ■ MC ■ MV

1 500

Figure 3 Figure 4

Sources: Bloomberg, VelocityShares Sources: Bloomberg, VelocityShares

Average Portfolio Volatility Versus Portfolio Size

18%

16%

14%

12%

10%

8%

6%

4%

2%

0%


5 10 15 20 25 30 50 75 100

Ordered Portfolio Volatility For N=100

14%

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10%

8%

6%

4%

2%

0%


1 500

Figure 1 Figure 2





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see from the figure that the MC portfolio can have risk contributions that are orders of magnitude higher for some stocks than for others. In one iteration, the highest contribution was about 350 times higher than the lowest.

The LV, EW and EQR lie more in the same range, though not without variation: By definition, the EQR model has the lowest ratio in every iteration, a constant of one. EW contribution ratios range from 3 to more than 9, while LV contribution ratios range from about 2.5 to 6.5.

Rather than look at the extremes, we also look at the ratios of the highest-risk contribution to the mean-risk contribu-tion for each portfolio in Figure 6. We see a similar story, where now once again we see large ratios for MC and MV, and smaller ratios for EW, LV and EQR. On average, the EW ratio tends to be a bit more than 2, and the LV ratio tends to be just shy of 1.5. Again, by definition, the EQR ratio is 1.

ConclusionThe efficient portfolio is difficult to achieve in prac-

tice. The standard methodology of market-capitalization weighting has been shown to be suboptimal, and behav-ioral considerations lead to anomalies like the low-vol-atility and low-beta anomalies. With the challenges of expected return estimation and parameter estimation in general, mean-variance optimization can be as difficult in practice as it is elegant in theory.

Investors need a pragmatic methodology that takes optimization considerations into account. We believe that the EQR approach is the best approach, one that seeks to minimize variance but spreads the risk of the portfolio equally across the constituents in the port-folio. EQR importantly takes account of correlation variations, unlike low volatility, market capitalization and equal weighting, and as our results show, strikes a good balance between overall risk of the portfolio and risk concentration among holdings.

We encourage investors to seek transparency in their examination of alternative indexing method-ologies. For example, “low volatility” approaches may only select low-volatility names, but may not address portfolio volatility at all. From a portfolio risk perspec-tive, would an investor prefer a portfolio of 100 stocks, each with 20 percent annualized volatility, with perfect correlation, or would he prefer a portfolio with 100 stocks, each with 21 percent annualized volatility, with zero correlation? The answer is obvious: The “high volatility” portfolio, i.e., the one that contains all stocks with the higher volatility of 21 percent, has a far lower portfolio volatility than the “low volatility” portfolio. Minimum-variance and equal-risk-weight approaches are intriguing, but also may have constraints that investors ought to be aware of.


Ratio Of The Highest-Risk Contribution

To The Lowest-Risk Contribution (Ordered) For N=100

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Ratio Of The Highest-Risk Contribution To

The Mean-Risk Contribution (Ordered) For N=100

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Figure 5 Figure 6


Endnotes1 Markowitz, H., (1952), “Portfolio Selection,” Journal of Finance. vol. 7, No. 1, pp. 77-91.

2 Frazzini and Pedersen (2010) argue that this anomaly stems from investors who cannot use leverage seeking to achieve it by selecting high-beta stocks, causing them to

be overbought.

3 Haugen, R. and Baker, N., (1991), “The Efficient Market Inefficiency of Capitalization-Weighted Stock Portfolios,” Journal of Portfolio Management, vol. 17, No. 1, pp. 35-40.

4 http://www.edhec-risk.com/latest_news/featured_analysis/RISKArticle.2011-11-15.4803?newsletter=yes

5 Note that this is different than the low-volatility model, which uses the inverse of volatility, not variance.

6 The sum of the log weights is constrained to be n•ln(n).

7 Maillard, S., Roncalli, T. and Teiletche, J., (2010), “On the Properties of Equally-Weighted Risk Contributions Portfolios,” Journal of Portfolio Management, vol. 36, No. 4.,

pp. 60-70.

8 See, for example, http://www.cs.rpi.edu/~magdon/talks/mdd_NYU04.pdf




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May / June 201452

A New Market

Sentiment Indicator

Measuring market sentiment via multiple risk factors

By Mitchell Eichen and John Longo




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The cyclical nature of equities trading is one of the eternal truths of the financial markets, with bull and bear markets having been empirically documented

for more than 100 years. Charles Dow,1 co-founder of The Wall Street Journal and a pioneer in technical analysis, compared market movements to ocean waves influenced by tides. Value-investing legend Benjamin Graham2 took a different approach, one emphasizing fundamental anal-ysis. Graham believed that his fundamental approach would be rewarded in the long run, because mispriced securities would ultimately revert back to their fair val-ues. As he so eloquently characterized it, “The market is a pendulum that forever swings between unsustainable optimism and unjustified pessimism.” The emerging field of behavioral finance supports Graham’s point of view, providing a strong theoretical foundation for the overreac-tion bias that is often a driving factor in cyclical markets.

One of the fascinating aspects of market cyclicality is its fundamental relationship to risk and return. Figure 1 shows realized investor returns over the past 20 years compared with the performance of a wide variety of indicators. The astonishing result is that the average investor badly under-performs across the board because he or she is prone to chasing performance near market tops and panicking near market bottoms. For this reason, an effective measure of market sentiment may be of significant value in navigat-ing risk and enhancing returns. The purpose of this paper is to introduce the Acertus Market Sentiment Indicator (AMSI), which is designed to provide an enhanced means of assessing market behavior, and to discuss how it may add value to the investment process.

The Purpose Of The AMSI, A New Multifactor Sentiment Index

While no one can predict the future, it is important for investors to understand where we are today and to be able to view current conditions in a historical context. The purpose of any index or an indicator is to measure the current level of something. There is a formidable list of indicators for a range of market conditions, and, when

appropriately constructed and widely followed, they have the potential to add value when important investment decisions are being made.

We chose to focus on constructing an indicator of “market sentiment,” which, in conjunction with its com-ponent parts, can provide important insight into current investor behavior and place it within a historical context. Understanding investor behavior becomes particularly important during the cyclical phases of markets, especially during market extremes. “Sentiment” refers to a feeling, emotion or attitude about something, and, of course, it can have a range of values. With respect to financial markets, fear represents one extreme, while greed represents the other end of the spectrum. We view sentiment as a con-tinuum, with anxiety and complacency representing less extreme and nuanced forms of fear and greed, respective-ly. In sharply rising markets and near market tops, greed and overreaching clearly come into play, but the authors believe that complacency is the more insidious and poten-tially destructive sentiment because it can lead investors into a failure to manage and monitor risk.

Reference.com defines complacency as “… a feeling of quiet pleasure or security, often while unaware of some potential danger, defect, or the like; self-satisfaction or smug satisfaction with an existing situation, condition.”4 Rather than the excessive desire for wealth that charac-terizes greed, we believe that being unaware of dangers accurately describes the reason for many investor losses. “Anxiety” represents a less extreme form of fear that corre-sponds to distress or a lack of peace of mind. Mild to mod-erate investment losses cause most investors anxiety, but do not necessarily drive them toward abject fear. Figure 2 expresses this range of investor emotions in the context of Graham’s pendulum analogy.

An effective indicator of market sentiment could help investors avoid the pitfalls associated with the investment performance displayed in Figure 1. A number of senti-ment indicators already exist, such as the put-call ratio, the CBOE Volatility Index (VIX), as well as various surveys that attempt to take the “investment pulse” of individual

Average Investor Returns Vs. Asset Class Returns

20-Year Annualized Returns By Asset Class (1993-2012)

12%

10%

8%

6%

4%

2%

0%

Avg.Investor

InfationHomesBondsEAFEOilS&P500

GoldREITs

11.2%

8.4% 8.2% 8.1%

6.5% 6.3%

2.7% 2.5% 2.3%

A Pendulum Of Investor Emotions

Fear

Anxiety

Neutral

Complacency

Greed

Figure 1 Figure 2

Source: J.P. Morgan (2013)3 Source: Acertus Capital Management




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and institutional investors. Each of these indicators has its shortcomings, most often caused by the omission of one or more important factors that may influence market senti-ment. The challenge in designing an effective indicator is to make it relatively simple and understandable, yet robust enough to reasonably explain the levels of and changes in market sentiment. A model that is too simple will provide insufficient information, while a model that is too complex may be difficult to understand and is therefore not useful.

Our approach in selecting a mix of fundamental, tech-nical, short-term and long-term elements for the AMSI was to include variables that make intuitive, logical and empirical sense without becoming unwieldy. The result is an indicator that uses the following five factors:

• Price/earnings (P/E) ratio• Price momentum• Realized volatility• High-yield bond returns• Treasury eurodollar (TED) spreadAs described below, all these factors provide some mea-

sure of market sentiment, but each adds a unique element to the overall index.

Elements Of AMSI

S&P 500 Price to Earnings (P/E) RatioSince stocks represent ownership in a business, it is

clear that valuations are what will ultimately matter in the long run. In the short to intermediate term, however, mar-ket psychology and other factors may cause prices to devi-ate substantially from their fundamental values. Hence, we believe that P/E is one of the important elements for a reasonable assessment of market sentiment.

P/E ratios have a long history of being followed by the analyst community, and for many decades they have been published in the stock tables of leading daily newspapers. The AMSI focuses on trailing P/E, rather than forward P/E, due to the former’s longer time history and the latter’s reliance on ever-changing and usually overly optimistic analyst estimates. In 1960, Francis Nicholson5 was the first among many researchers to find that low-trailing P/E lev-els are generally a predictor of higher stock market returns ahead, while high P/Es usually correspond to lower levels of future returns—findings consistent with the investment maxim, “Buy low and sell high.” Similarly, for the AMSI, low levels of P/E may be indicative of anxiety or fear, while high P/E levels may signal complacency or greed.

Price MomentumSometimes prices move in advance of the fundamentals.

The movement may be due to shifting market psychology, unanticipated events or other factors. For example, inves-tors often legally act on information before it has passed the checks and balances of the published news cycle. Some investors uncover news through original research and make it available to reporters before the news is officially disseminated. Other times, investors may trade in antici-pation of a rumored policy change such as a Fed action or an event such as a military attack that may or may not

come to pass. This dynamic behavior often explains the “buy on the rumor, sell on the news” pattern observed in security price movements. At other times, momentum can be driven simply by investors not wanting to be left behind the crowd. For the AMSI, high levels of price momentum are indicative of complacency or greed, while low lev-els may be indicative of fear or anxiety. We define price momentum as the percentage of S&P 500 Index stocks trading above their 200-day moving averages.

Momentum is important because it may cause stock prices to deviate from the underlying fundamentals, as Graham alluded to in his pendulum analogy. High returns in the recent past may lure investors into performance-chasing out of complacency, greed or the often-mistaken belief that the past is prologue to the future. At the other end of the spectrum, low recent historical returns or mar-ket movements caused by triggered stop-losses may cause investors to sell near market bottoms out of anxiety or fear. In extreme cases, momentum may completely overwhelm the fundamentals, as was the case in the frenzied rise and subsequent fall in the prices of technology stocks during the Internet bubble from the late 1990s to early 2000.

Realized VolatilityIn the short run, volatility begets more volatility.

Complex statistical models are often used to measure this phenomenon. We chose to include the 30-day realized standard deviation of S&P 500 returns as our measure of volatility because of the long-term nature of its data series and the relative simplicity of its calculation and interpreta-tion. By way of contrast, VIX relies on implied or estimated forward volatility, and its data series only goes back to the late 1980s, limiting its usefulness in longer-term analysis.

Volatility may beget volatility due to margin calls, crowd psychology and trend-following strategies like portfolio insur-ance. Volatility, like momentum, may also help explain why prices deviate from fundamentals. High levels of persistent volatility are generally reflective of anxiety or fear, while low levels may signify complacency or greed. But over long peri-ods, volatility tends to revert to the mean. For example, the standard deviation of U.S. stock returns averages about 20 percent per year. Abnormally low or high periods of volatility are unlikely to persist for very long because the market ulti-mately recalibrates the relationship between risk and return.

High-Yield Bond ReturnsInterest rates are an important macro indicator because

they determine the cost of capital for nearly all individuals, businesses and governments. Theoretically, a rise in inter-est rates should result in a declining present value of future cash flows and lower asset prices. The base level of inter-est rates is largely determined by the U.S. Treasury curve. Credit spreads are added on top of U.S. Treasury (or other highly rated sovereign debt) rates in order to determine the appropriate yield to maturity or cost of debt capital for an issuer. When sentiment is low and risk is high, credit spreads widen. Credit spreads generally fall as economic fundamentals and sentiment improve.

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We believe the returns on high-yield bonds represent a good measure of sentiment in the fixed-income markets because they often correspond to changes in the economy and interest rates. U.S. Treasurys can benefit from a “flight to quality” effect during times of market distress and may be artificially depressed when, for example, the Federal Reserve or other central bank is executing a quantitative easing policy. Also, during times of complacency and greed, credit standards often become lax. For these rea-sons, we view the returns on high-yield bonds as a “purer play” on interest rate and credit risk, and have included the Credit Suisse High Yield Bond Index as the fourth compo-nent of the AMSI. The Credit Suisse index was established in late 1985, and, we believe, provides an effective barom-eter of returns for this asset class.

Treasury Eurodollar (TED) SpreadWe included the TED spread as a measure of systemic

financial risk. The TED spread is based on the difference between the three-month Libor and the three-month U.S. Treasury bill interest rates. Banks serve as financial market conduits for nearly all businesses and govern-ments through their capital-raising, market-making and savings activities. As we observed during the credit cri-sis, when banks have a problem, it ultimately becomes everyone’s problem because of their enormous global scale and substantial leverage.

Global financial intuitions today rely not only on cus-tomer deposits to fund their operations, but also on short-term loans from other banks. Therefore, if there is a crisis of confidence among banks, the TED spread is likely to expand dramatically. It historically ranges between 10 and 50 basis points, but it spiked to an astonishing 488 basis points around the time of the Lehman Brothers bankrupt-cy announcement. The TED spread also provides value to the AMSI outside of times of financial crises. For example, a less-than-dramatic rise in the TED spread may portend a credit crunch or slowdown in the economy. Therefore, we believe that the TED spread adds a unique measure of market sentiment to the index.

Architecture Of AMSIFigure 3 provides a summary of the five factors that

comprise the AMSI. The weights of the individual AMSI

factors are proprietary and dynamic, adjusting each time the index is updated. To facilitate the interpretation of the AMSI, we converted the indicator to a percentile value, ranging from 0 (extreme fear) to 100 (extreme greed). The importance of each element with respect to the signifi-cance of their weights in the AMSI, in descending order, are P/E, momentum, realized volatility, high-yield returns and TED spread. AMSI spans the period from January 1986 to the present, i.e., the longest common period over which data for all component elements is available. The absence of data prior to the mid-1980s for high-yield bonds and Libor, which is a component of the TED spread, precludes AMSI from starting at an earlier date.

Figure 4 is a table showing the weights of the different fac-tors at different times, including maximum and minimum weightings. To determine the dynamic weights, we find the percentage ranks of the individual components’ historical data series and run correlations against rolling three-month, six-month, nine-month and 12-month trailing and forward-looking S&P returns. For each rolling data set, we calculate a weight for each component by dividing the absolute value of its correlation by the sum of all of the components’ correla-tions to that data set. We continue this process for each set of rolling returns. The final component weight is a simple aver-age of all of these weights across the four periods.

A graph of AMSI and of the AMSI Six-Month Moving Average from AMSI’s January 1986 inception through November 2013 is shown in Figure 5. The dynamic and cyclical nature evokes the ocean wavelike movement described by Charles Dow and the pendulum analogy


Summary Of Factors In AMSI

WeightDescriptionFactor

Figure 3

Price/Earnings Measure of stock market value High

Price Momentum Measure of market psychology High

Realized Volatility Measure of recent historical risk Medium

High-Yield Returns Measure of credit risk Medium

TED Spread Measure of systemic financial risk Low

Source: Acertus Capital Management

Weights Of Different AMSI Factors Over Time

AverageMaxMin12/31/20139/30/20136/30/2013Factors

Figure 4

Price/Earnings 32.98% 33.16% 33.46% 32.98% 33.46% 33.20%

Price Momentum 26.67% 26.43% 26.35% 26.35% 26.67% 26.48%

30-Day Volatility 19.67% 19.84% 19.91% 19.67% 19.91% 19.81%

CS High-Yield Bond 16.84% 16.91% 16.33% 16.33% 16.91% 16.69%

TED Spread 3.84% 3.66% 3.95% 3.66% 3.95% 3.82%


Note: Min, Max and Average columns cover the period of 6/30/2013-12/31/2013.




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able to forecast future stock market returns with a high degree of accuracy, as we noted earlier, a well-designed indicator of market sentiment can add significant value in the investment process.

In our view, AMSI adds value from several perspec-tives. Tracking AMSI on a regular basis may provide a more robust measure of market sentiment than the VIX, put-call ratio or other indicators. AMSI, along with a concurrent analysis of its component parts and moving average, should help investors gain additional perspec-tive and insight into the current relationship of market levels of risk versus potential return. Lastly, sentiment readings can also play an important role with respect to adherence to investment policy statements or structur-ing portfolios with proper risk management controls. This is the case because the temptation to abandon well-thought-out long-term plans is typically highest at market and sentiment extremes.

Figure 6 provides information on the historical distribu-tion of AMSI and forward-looking S&P 500 returns. In gen-eral, an AMSI level of 0-25 indicates fear, 25-50 indicates anxi-ety, 50-75 indicates complacency and 75-100 indicates greed.

AMSI levels at the extremes are somewhat rare, typical of most bell curve distributions. AMSI levels of 80-100, indicative of high levels of greed (an extreme version of complacency), occurred roughly 1 percent of the time since our measurement period began in January 1986. Similarly, AMSI levels of 0-20, signifying high levels of fear (an extreme version of anxiety), were also uncom-mon, occurring only 6.0 percent of the time. However, market returns for the six- and 12-month periods ahead of these extreme periods are striking and aberration-al. High complacency/greed levels following periods of extreme market performance portended lower-than-average returns, with six- and 12-month gains of 1.7 percent and 8.7 percent, respectively. Conversely, high anxiety/fear levels following significant market sell-offs historically indicated high future returns, delivering on average 8.3 percent and 10.2 percent for the six- and 12-month-periods ahead. A similar return pattern was observed for less extreme AMSI levels in the 20-40 range versus the 60-80 range. An analysis of median returns yielded similar results.

While the findings at the extreme levels are not sta-tistically significant due to the small sample size, they are anecdotally very interesting. The forward returns posted after these extreme AMSI readings are telling, because they point to periods when investors may have been acting either out of anxiety, fear, complacency or greed. This is consistent with behavioral finance studies that have shown that investors tend to follow near-term trends. The value of this information is that it alerts investors to the need to seek protection during high levels of complacency or greed and remain invested or perhaps become increasingly aggressive when anxiety or fear is the reigning sentiment.

As would be expected, during the majority of the peri-ods observed, AMSI provided a reading near the middle

used by Benjamin Graham. Figure 5 also calls to mind the comments made by prominent present-day investor Howard Marks6 of Oaktree Capital Management. Marks said, “I believe strongly that (a) most key phenomena in the investment world are inherently cyclical, (b) these cycles repeat, reflecting consistent patterns of behavior, and (c) the results of that behavior are predictable.”

A moving average is designed to smooth volatile data series, thereby providing a more stable pattern. Moving averages of data series often enable trends to come into clearer focus, while original data series may often “flip flop” in its direction, providing mixed signals. The weakness of moving averages is they are lagged and may be delayed in identifying the turning points of an indicator. We tested a number of moving averages and found the six-month mov-ing average provided the best combination of trend exposi-tion and timeliness. In Figure 5, both line graphs illustrate a similar pattern, but the moving average line is more stable.

The Value Of AMSI In The Investment ProcessIndicators do as their name implies—they indicate. As

such, they need not be predictive to add value. For exam-ple, the Dow Jones industrial average is a barometer of U.S. blue chip stock market performance that is general-ly not used to forecast future stock market returns. While neither AMSI nor any other indicator is a silver bullet

May / June 201456

AMSI Distribution And Forward Returns(January 1986-December 2013)

Avg. SPX 12-Month Forward

Avg. SPX 6-Month Forward

Frequency(%)

Frequency(No.)

AMSI Range

Figure 6

80 - 100 3 0.89% 1.68% 8.73%

60 - 80 108 32.14% 3.57% 8.50%

40 - 60 139 41.37% 3.80% 7.82%

20 - 40 66 19.64% 5.80% 11.18%

0 to 20 20 5.95% 8.31% 10.18%


AMSI And 6-Month Moving Average Of AMSI Readings

From January 1986 Inception Through December 2013

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■ AMSI ■ AMSI 6-Month Moving Average

Figure 5





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of the bell curve distribution. This finding is not surprising given that the markets are reasonably efficient and that sentiment is usually not at extremes. However, readings in the 20-80 range can also prove to be relevant despite less dramatic S&P 500 results on a forward-looking basis. Most notably, changes in the direction of AMSI, and its moving average, may be important because they provide informa-tion on whether the market is trending toward greed or fear. Furthermore, neutral readings (in the area of 50) may not indicate that the values of most components are near their means, but may instead be reflective of mixed inter-nal signals, the analysis of which may provide substantial insight into market sentiment.

Since the AMSI most often finds itself in a neutral range, its effectiveness lies, in part, in offering perspective on both the level and drivers of that sentiment at a specific point in time. For example, high P/E and strong momentum were the principal factors in the high levels of AMSI compla-cency or readings during the height of the Internet bubble. Conversely, low-volatility and high-momentum readings were the main factors behind the complacency/greed peri-od that preceded the credit crisis. The interaction of the dif-

ferent forces that drove AMSI sentiment readings in these examples (i.e., high P/E versus low volatility) demonstrate the value of a multifactor model over a single-factor model such as VIX or the put-call ratio.

Figure 7 provides a summary of the values of AMSI and its components before and after three extreme periods in recent financial market history—the crash of 1987, the Internet bubble and the credit crisis. Looking specifically at the time period around the crash of 1987 (Figure 8), at the end of August near the market’s peak, AMSI had a reading of about 70 (90th percentile), well ensconced in the zone of complacency indicating a heightened level of risk before the ensuing market crash. Net selling continued throughout most of September, with mod-est reprieves, before the catastrophic crash on “Black Monday,” Oct. 19, 1987. After the crash at the end of October, AMSI generated a reading of 12.3 (3rd percen-tile), suggesting that remaining in the market was war-ranted and that the selling was overdone.

During the late stage of the Internet bubble in the sec-ond quarter of 1999, AMSI, bolstered by extremely high levels for the P/E ratio, provided a reading suggestive of


-

AMSI Readings Near Historic Financial Periods

EventAMSI

ReadingTED

SpreadHigh-Yield

ReturnS&P

30-Day VolumeS&P

MomentumS&PP/E

Time Period

Percentile Ranks

Figure 7

Crash of 1987 (Pre) 8/31/1987 79.80% 88.20% 52.90% 51.00% 16.90% 69.5

Crash of 1987 (Post) 10/31/1987 35.40% 0.60% 0.40% 2.40% 1.00% 12.4

Internet Bubble (Pre) 4/30/1999 99.60% 64.80% 21.10% 82.50% 41.50% 69.8

Internet Bubble (Post) 9/30/2002 53.10% 2.70% 8.20% 12.60% 76.30% 24.9

Credit Crisis (Pre) 5/31/2007 46.80% 81.90% 88.60% 43.50% 26.20% 63.1

Credit Crisis (Post) 11/30/2008 2.40% 1.20% 1.00% 0.30% 1.30% 1.4


Figure 8 Figure 9

Note: S&P 500 returns in the graph have been normalized to have the same starting

value as AMSI.


Note: S&P 500 returns in the graph have been normalized to have the same starting

value as AMSI.


AMSI Readings Around The Time Of The Crash Of 1987

AMSI And S&P 500

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■ AMSI ■ S&P 500

AMSI Readings Around The Time Of The Credit Crisis

AMSI And S&P 500

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■ AMSI ■ S&P 500




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Low levels of each indicator signify anxiety or fear, while high levels signify complacency or greed.

For the sake of comparison, all sentiment indica-tors are converted to percentile levels. VIX, designated in red, meandered around its long-term average of 20 (corresponding to its middle percentile levels) from the beginning of 2007 through the middle of 2008, providing little indication of the complacency or risk that had made its way into the market. VIX surged only after the credit crisis moved into full swing in the latter portion of 2008 and remained above its average of roughly 20 (or median percentile) throughout much of 2009. The put-call ratio provided generally high readings throughout the credit crisis. This ratio is often a contrarian indicator, with high levels of the indicator usually considered to be a bullish signal. AMSI, however, provided indications of com-placency in the first half of 2007 and then consistently remained at anxiety and fear levels until the second half of 2009, when the credit crisis began to slowly fade into a bad memory for investors.

too much complacency or greed. As the bubble began to deflate, momentum quickly dissipated and volatility surged, moving the AMSI toward neutral readings and ulti-mately toward anxiety and fear. As the three-year market decline initially triggered by the bursting of the internet bubble began to bottom in late 2002, AMSI flashed read-ings below 25 (10th percentile), suggesting that fear was getting priced into the market and better times lay ahead.

Figure 9 provides a graph of AMSI around the time of the credit crisis. At the end of May 2007, the AMSI read-ing was 63.1 (75th percentile), suggesting the market may have become too complacent. In this time frame, the subprime crisis was in the midst of unraveling. New Century Financial had declared bankruptcy the prior month, and Bear Stearns would follow the next month with a bailout of its mortgage-related investment funds. During the depths of the credit crisis, over the October 2008 to November 2008 time period, AMSI readings were at their lowest ever, indicating that extreme fear had set in and that it might be an appropriate time to increase risk despite rampant market apprehension. AMSI readings continued to be subdued throughout the first half of 2009 as the market searched for a support level near its lows. By the latter part of 2009, fear had largely left the market, and the surge in stock prices pushed AMSI well into the complacency or greed zone.

While, as noted earlier, there are a host of sentiment indicators, for this part of the discussion, we focus on VIX and the put-call ratio, two of the most widely followed indi-cators. Both, along with AMSI, indicated high levels of anxi-ety and fear near the depths of financial crises. However, AMSI signaled high levels of complacency or greed prior to the three financial crises as noted in Figure 10, while VIX and the put-call ratio provided mixed results.

A graph of the three sentiment indicators around spe-cific time periods further illustrates how they differ. For illustration, we have chosen the period around the credit crisis, shown in Figure 11.

To better facilitate comparison to AMSI, we computed 1 minus the percentile values of VIX and the put-call ratio.

May / June 201458

Figure 11

Note: To better facilitate comparison to AMSI, we computed 1 minus the percentile

values of VIX and the put/call ratio. Low levels of each indicator signify anxiety or fear,

while high levels signify complacency or greed.


Sentiment Indicators Around The Credit Crisis

(Percentile Levels: 2007-2009)

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■ AMSI ■ VIX ■ Put/Call

AMSI Vs. VIX And The Put-To-Call Ratio Near Historic Financial Periods

Event AMSIPut-CallVIXPut/CallVIX*AMSITime

Period

Levels Percentiles

Figure 10

Crash of 1987 (Pre) 8/31/1987 69.5 22.33 N/A 67.6% N/A 89.7%

Crash of 1987 (Post) 10/31/1987 12.4 61.41 N/A 99.5% N/A 2.7%

Internet Bubble (Pre) 4/30/1999 69.8 25.07 0.55 79.8% 6.6% 90.0%

Internet Bubble (Post) 9/30/2002 24.9 39.69 1.09 96.8% 89.2% 9.3%

Credit Crisis (Pre) 5/31/2007 63.1 13.05 0.91 13.7% 69.6% 74.7%

Credit Crisis (Post) 11/30/2008 1.4 55.28 1.45 99.2% 100.0% 0.0%

* The methodology for the VIX calculation changed on Sept. 22, 2003. VIX numbers before and after this period may not be fully comparable. Similarly, the data for the put/call

ratio before and after Oct. 17, 2003 may not be fully comparable.





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In our view, AMSI’s use of multiple factors provides it with a more robust indication of market sentiment and, therefore, may give fewer false signals and is capable of adding more value than either VIX or the put-call ratio. During this chal-lenging period, AMSI demonstrated more cyclical behavior than either of the more established indices, consistent with the thoughts of Dow and Graham. The put-call did not pro-vide any indication of the enormous risks that lied ahead. Conversely, VIX stayed in the fear zone throughout much of the credit crisis and its recovery, thereby failing to capture the shift in sentiment that had occurred.

SummaryIn summary, we believe the benefits offered by AMSI are

substantial. First, in our view, tracking7 AMSI on a regular basis will provide a more robust measure of market sentiment

than the VIX, the put-call ratio or other indicators. As such, it should help investors gain a better perspective on and insight into the current relationship between the levels of risk and potential return in the market. Second, given that better per-spective, it can be of help to professional investors, including those advising institutional and ultra high net worth clients, by providing a framework for a more meaningful dialogue about the nature of risk and return with their constituents. Third, during periods of extreme readings, it may offer some insight into the probable direction of the S&P 500 over the six- to 12-month period ahead. Lastly, because the temptation to abandon well-thought-out long-term plans is usually high-est at market and sentiment extremes, tracking AMSI also becomes an important guardrail with respect to adherence to investment policy statements and maintaining proper portfo-lio risk management controls.


References And Endnotes1 Dow, Charles, 1901, “Watching the Tide,” The Wall Street Journal, Jan. 31, p. 1.

2 Graham, Benjamin. 1949, “The Intelligent Investor,” 1st edition, New York: Harper & Brothers.

3 J.P. Morgan, Guide to the Markets: 2Q:2013, April 1, 2013, p. 63.

4 www.Reference.com (for definition of complacency)

5 Nicholson, Francis, 1960, “Price-Earnings Ratios,” Financial Analysts Journal, 16(4): 43-45.

6 Marks, Howard, 2013, “Howard Marks: We’re Not at Bubble-Type Highs,” Barron’s, Nov. 27. Accessed at http://online.barrons.com/article/SB50001424053111904642604

579223961257360926.html#text.print

7 AMSI and its component values, along with commentary on the index, are published each month at www.acertuscap.com.

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

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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

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Indexed Approaches To Long/Short Investing

Peter Little and Greg King

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

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defining alternatives May / June 2012

Commodities Sectors And The Business Cycle

Geetesh Bhardwaj and Adam Dunsby

Commodities In A Portfolio

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Keeping Current With Commodities

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

Better Beta In Commodities Indexing

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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

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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

www.journalofindexes.com

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

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ETF.com, 201 Mission St., Suite 720, San Francisco, CA 94105 • Advertising and Reprints Inquiries: 415.659.9004

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Global Index Data

May/June 2014Selected Major Equity Indexes Sorted By YTD Returns

Index Name 3-Mo YTD 12-Mo 3-Yr 5-Yr

WisdomTree Equity Income 1.56 1.45 16.06 15.73 26.92

Russell 3000 4.11 1.43 26.74 14.60 23.88

S&P Mid Cap 400 Pure Growth 5.00 1.43 30.56 15.36 32.42

Russell 1000 4.14 1.40 26.34 14.62 23.65

S&P Technology Select Sector 5.06 1.38 24.84 13.11 24.15

MSCI Emerging Markets Small Cap 0.82 1.37 -1.40 -0.67 22.42

MSCI EAFE 2.82 1.31 19.28 6.64 17.62

Russell Top 200 Growth 4.04 1.30 28.07 15.23 -

WisdomTree Earnings 2.82 1.11 25.94 14.99 24.12

WisdomTree Dividend 2.23 1.09 20.26 14.91 24.28

S&P 500 3.51 0.96 25.37 14.37 23.02

Dow Jones US Select Dividend 2.84 0.95 22.19 16.33 24.53

MSCI EAFE Growth 2.41 0.76 17.42 7.02 17.32

MSCI All Country World 2.37 0.64 18.16 8.37 19.59

Russell 1000 Value 3.17 0.62 23.44 14.07 23.20

Russell 3000 Value 3.11 0.61 23.65 13.97 23.36

Russell 2000 Value 2.43 0.53 26.19 12.81 25.16

FTSE RAFI US 1000 2.94 0.45 26.19 14.65 28.16

S&P SmallCap 600 1.88 0.43 32.30 16.86 28.07

Alerian MLP 2.05 0.41 12.74 12.77 27.12

S&P 500 Pure Value 2.94 0.34 37.13 19.82 39.99

Russell Top 200 2.93 0.34 25.11 14.40 -

MSCI ACWI ex USA 1.13 0.25 12.25 3.99 17.27

FTSE All World ex-US 1.04 0.17 12.50 4.45 18.01

S&P Global 100 1.63 0.11 18.63 8.17 17.65

S&P SmallCap 600 Pure Value 1.64 0.02 36.30 16.91 40.32

KBW Bank 2.28 -0.02 30.38 11.13 25.43

FTSE Beyond BRICs 0.17 -0.02 -3.33 3.88 -

S&P Consumer Discr Select Sector 2.29 -0.04 33.80 21.38 32.92

Rogers-Van Eck Hard Assets Producers 2.23 -0.14 5.01 -1.45 14.05

S&P Industrial Select Sector 3.95 -0.25 29.81 14.69 27.81

S&P 100 2.06 -0.25 22.66 13.81 21.44

S&P Mid Cap 400 Pure Value 2.03 -0.26 26.37 14.32 37.93

Russell Top 50 1.36 -0.26 20.94 13.52 20.13

ISE Water 2.91 -0.42 21.45 16.00 23.39

S&P Financial Select Sector 1.56 -0.59 25.65 10.86 25.71

CNX Nifty 2.31 -0.61 -2.16 -3.82 -

Russell Top 200 Value 1.81 -0.62 22.22 13.58 -

S&P Mid-East and Africa BMI 0.04 -0.63 2.28 0.79 16.46

S&P Asia Pacifc Emerging BMI -1.05 -0.64 1.87 1.69 18.69

S&P Small Cap 600 Pure Growth 0.26 -0.76 34.19 17.42 31.41

S&P Energy Select Sector 1.87 -0.89 14.72 5.78 17.06

FTSE EPRA/NAREIT Dev Real Est ex-US -0.97 -0.99 1.59 6.24 21.08

Dow Jones Industrial Average 2.08 -1.07 19.01 13.05 21.49

S&P Consumer Staples Select Sector -0.78 -1.41 14.25 16.06 19.27

Dow Jones Select Microcap -0.67 -1.44 33.92 13.49 26.14

MSCI All Country Asia ex Japan -2.79 -1.72 -0.25 2.50 19.02

Dorsey Wright Technical Leaders 0.77 -1.86 21.23 13.01 24.14

ISE Global Platinum -1.35 -2.03 -16.02 -27.35 6.91

Dorsey Wright EM Technical Leaders -3.83 -2.31 -8.87 3.00 22.11

MSCI Emerging Markets Investable Mkt -4.11 -2.81 -5.45 -1.82 17.56

S&P China BMI -4.84 -2.96 5.95 1.69 16.46

S&P Emerging BMI -3.95 -3.11 -5.28 -2.37 16.95

MSCI Emerging Markets -4.79 -3.40 -6.01 -1.99 16.90

S&P Asia 50 -5.81 -3.47 -0.78 3.15 18.45

Index Name 3-Mo YTD 12-Mo 3-Yr 5-Yr

MAC Global Solar Energy 21.35 32.76 160.64 -15.13 -1.10

NYSE Arca Gold Miners 17.65 22.40 -29.74 -23.35 -3.72

NYSE Arca Biotechnology 22.09 21.15 65.84 30.06 36.50

NASDAQ OMX Glb Gold/Precious Metals 16.10 20.21 -27.39 -21.73 0.13

Solactive Global Uranium 22.02 17.07 -9.39 -34.17 -2.12

WilderHill Clean Energy 15.08 14.38 68.12 -11.18 2.94

MSCI US REIT 9.70 9.40 6.75 9.92 29.62

Wilshire US REIT 9.78 9.31 6.33 9.67 -

FTSE NAREIT Real Estate 50 9.67 8.69 3.60 8.60 27.71

Dow Jones US Real Estate 9.62 8.54 4.99 8.71 28.19

S&P Global Clean Energy 8.14 8.37 57.36 -11.73 -4.51

ISE Global Wind Energy 9.72 7.86 63.50 6.84 6.15

Dow Jones Internet 13.94 7.54 51.97 22.77 36.94

S&P Health Care Select Sector 8.09 7.20 39.45 24.80 23.49

ISE ChIndia 9.95 7.08 44.10 8.91 28.08

Wilshire US Micro Cap 10.27 6.94 45.79 16.69 -

S&P 500 Pure Growth 9.68 6.70 42.54 19.04 31.66

S&P Utilities Select Sector 7.52 6.52 12.50 12.65 14.72

NASDAQ Golden Dragon China 10.10 5.85 71.80 230.15 111.48

Dow Jones Glb Sel Real Estate Sec 5.14 5.37 4.18 7.76 24.99

S&P Global Water 8.75 5.11 24.85 14.31 22.37

IPOX-100 US 10.94 5.05 42.83 26.17 33.27

Dorsey Wright Dev Mkts Technical Ldrs 8.20 4.50 35.05 11.25 23.59

ISE Global Copper 9.35 4.02 -14.03 -15.71 -

Russell Microcap 6.30 4.00 41.92 16.55 28.33

Dow Jones Global Shipping 10.75 3.91 32.48 -3.60 -

MSCI EAFE Small-Cap 6.33 3.90 26.26 9.56 23.39

FTSE EPRA/NAREIT Developed 4.03 3.86 4.35 8.01 24.64

S&P Global Infrastructure 5.21 3.84 18.86 6.78 12.19

S&P Global Nuclear Energy 4.42 3.54 20.08 -3.00 9.92

WisdomTree DEFA 3.54 3.48 19.87 7.47 17.60

S&P Europe 350 5.51 3.29 26.32 8.67 19.35

Cohen & Steers Global Realty Majors 3.25 3.17 2.55 7.70 24.60

NASDAQ-100 6.29 3.16 36.90 17.78 28.39

Russell 2000 Growth 5.13 3.02 37.06 16.00 28.07

MSCI EMU 4.78 2.69 30.69 6.40 17.00

S&P MidCap 400 5.83 2.66 26.58 14.16 26.97

STOXX Europe 50 4.29 2.36 22.36 6.78 16.72

S&P 500 Equal Weight 5.27 2.27 29.17 15.46 29.03

Russell 3000 Growth 5.07 2.22 29.76 15.15 24.35

Russell 1000 Growth 5.07 2.15 29.14 15.08 24.05

Dow Jones EPAC Select Dividend 3.15 2.08 18.23 8.14 24.29

S&P Materials Select Sector 6.96 2.02 25.59 8.73 23.01

FTSE RAFI Developed Markets ex-US 3.34 1.99 24.04 5.80 19.81

Dow Jones Global Select Dividend 3.33 1.89 18.68 9.03 26.04

MSCI EAFE Value 3.24 1.86 21.15 6.21 17.88

MSCI USA ESG Select 4.68 1.84 24.30 12.96 22.36

Russell 2000 3.82 1.81 31.56 14.43 26.65

ISE-REVERE Natural Gas 1.24 1.79 24.34 -3.07 16.31

Euro STOXX 50 3.81 1.75 30.07 4.98 15.47

MSCI KLD 400 Social 4.66 1.74 28.22 15.23 23.30

Zacks Micro Cap 3.82 1.68 33.96 12.33 22.00

S&P Global Timber & Forestry 5.66 1.64 12.97 4.90 22.44

MSCI Kokusai 3.92 1.64 22.40 10.58 21.00

Morningstar Wide Moat Focus 5.10 1.48 28.16 17.47 28.86

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

Data as of February 28, 2014.

May / June 201460

25.94




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

Morningstar U.S. Style Overview Jan. 1-Feb. 28, 2014

Trailing Returns %

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

US Market NA 1.44 26.23 14.73 23.76 7.87

Large Cap NA 0.79 24.75 14.54 22.04 7.02

Mid Cap NA 3.70 30.35 15.42 28.14 10.08

Small Cap NA 1.93 30.08 14.52 28.95 9.75

US Value NA –0.11 22.37 12.68 22.08 7.04

US Core NA 1.50 25.71 16.08 24.67 8.80

US Growth NA 2.83 30.57 15.36 24.50 7.48

Large Value NA –0.85 19.21 11.50 19.31 5.89

Large Core NA 0.67 24.93 16.31 23.22 8.28

Large Growth NA 2.42 30.22 15.75 23.56 6.48

Mid Value NA 1.97 32.54 16.13 29.29 9.89

Mid Core NA 4.38 27.72 16.12 28.58 10.06

Mid Growth NA 4.66 30.65 13.90 26.55 10.05

Small Value NA 1.78 26.57 15.04 30.96 10.27

Small Core NA 2.25 29.31 13.31 28.26 9.67

Small Growth NA 1.75 34.59 15.28 27.78 9.06

Morningstar Market Barometer YTD Return %

US Market1.44

–0.11

Value

1.50

Core

2.83

Growth

0.79Larg

e C

ap

3.70Mid

Cap

1.93Sm

all C

ap

–0.85 0.67 2.42

1.97 4.38 4.66

1.78 2.25 1.75

–8.00 –4.00 0.00 +4.00 +8.00

Sector Index YTD Return %

Real Estate 8.98

Healthcare 7.73

Utilities 6.03

Technology 2.96

Basic Materials 2.22

Consumer Cyclical 0.17

–0.25 Industrials

–0.71 Energy

–1.29 Consumer Defensive

–1.87 Financial Services

–2.02 Communication Services

Industry Leaders & Laggards YTD Return %

Long-Term Care Facilities 23.40

Solar 21.33

Computer Distribution 20.66

Drug Manufacturers - 20.34

Airlines 18.68

Electronic Gaming & 18.19

–8.31 Integrated Shipping & Logistics

–8.76 Savings & Cooperative Banks

–9.39 Specialty Finance

–9.81 Pollution & Treatment Controls

–10.37 Copper

–13.76 Gambling

Biggest Influence on Style Index Performance

YTDReturn %

ConstituentWeight %

Best Performing Index

Mid Growth 4.66

Forest Laboratories, Inc. 62.54 1.02

Illumina, Inc. 55.07 1.00

Akamai Technologies, Inc. 29.57 0.60

O'Reilly Automotive Inc 17.20 0.99

AthenaHealth, Inc. 44.14 0.36

Worst Performing Index

Large Value –0.85

General Electric Co –8.34 5.88

Exxon Mobil Corporation –4.20 9.18

Chevron Corp –6.85 4.98

AT&T Inc –7.97 3.82

Citigroup Inc –6.66 3.31

1-Year

19.21

Value

Larg

e C

ap

24.93

Core

30.22

Growth

32.54

Mid

Cap 27.72 30.65

26.57

Sm

all C

ap

29.31 34.59

–20 –10 0 +10 +20

3-Year

11.50

Value

Larg

e C

ap

16.31

Core

15.75

Growth

16.13

Mid

Cap 16.12 13.90

15.04

Sm

all C

ap

13.31 15.28

–20 –10 0 +10 +20

5-Year

19.31

Value

Larg

e C

ap

23.22

Core

23.56

Growth

29.29

Mid

Cap 28.58 26.55

30.96

Sm

all C

ap

28.26 27.78

–20 –10 0 +10 +20

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

Source: Morningstar. Data as of Feb. 28, 2014





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

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

S&P 500 100.0% 4.57% 3.51% 25.37% 14.35% 23.00% 7.16%

S&P 500 Consumer Discretionary 12.5% 6.24% 2.29% 33.79% 21.35% 32.89% 9.29%

S&P 500 Consumer Staples 9.5% 3.57% -1.17% 13.46% 15.75% 19.02% 9.13%

S&P 500 Energy 10.0% 5.07% 1.53% 13.92% 5.45% 16.77% 12.62%

S&P 500 Financials 15.9% 3.13% 1.56% 25.65% 10.84% 25.69% -0.89%

S&P 500 Health Care 13.7% 6.17% 8.05% 39.26% 24.80% 23.49% 8.71%

S&P 500 Industrials 10.7% 3.92% 3.49% 28.94% 14.44% 27.66% 8.46%

S&P 500 Technology 18.8% 4.63% 6.23% 28.37% 13.18% 24.17% 7.37%

S&P 500 Materials 3.5% 6.92% 6.95% 25.27% 9.02% 23.22% 8.45%

S&P 500 Telecom Services 2.5% -1.04% -4.42% 0.92% 10.49% 14.73% 7.00%

S&P 500 Utilities 3.0% 3.40% 7.48% 12.46% 12.63% 14.71% 9.47%

Risk-Return

Sector Weights

Asset Class Performance

Data as of February 28, 2014. Source: S&P Dow Jones Indices. Past performance of an index is not a guarantee of future results.

Consumer Discretionary

Consumer Staples

Energy

Financials

Health Care

IndustrialsTechnology

MaterialsTelecom Services

Utilities

S&P 500

0%

5%

10%

15%

20%

25%

30%

5% 10% 15% 20% 25%

3-Y

ear

An

nu

alized

Retu

rn

3-Year Annualized Risk

60

70

80

90

100

110

120

130

140

150

160

Feb-11 May-11 Aug-11 Nov-11 Feb-12 May-12 Aug-12 Nov-12 Feb-13 May-13 Aug-13 Nov-13 Feb-14

S&P 500

S&P BMI Global ex-U.S.

Dow Jones Brookfield Global Infrastructure Index

Dow Jones-UBS Commodity Index

Dow Jones U.S. Select REIT Index

12.5%

9.5%

10.0%

15.9%

13.7%

10.7%

18.8%

3.5%

2.5%

3.0%

12.8%

9.6%

7.5%

24.4%

8.5%

13.4%

7.7%

8.6%

4.5%

3.2%

Consumer Discretionary

Consumer Staples

Energy

Financials

Health Care

Industrials

Information Technology

Materials

Telecommunication Services

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

May / June 201462




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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 February 28, 2014.

Total Return % Annualized Return %

Fund Name Ticker ER 1-Mo 3-Mo YTD P/E Launch AUM

Barclays ETN+ FI Enh Global HiYld ETN FIGY 0.80 11.33 5.41 2.31 - 05.22.13 1,398.9

Barclays ETN+ FI Enh Europe 50 ETN FEEU 1.00 12.50 6.79 2.37 - 05.23.13 1,050.3

Vanguard Total International Bond BNDX 0.20 0.48 1.35 1.91 - 06.04.13 834.5

SPDR Blackstone / GSO Senior Loan SRLN 0.90 -0.11 0.73 0.27 - 04.03.13 614.2

Vident International Equity VIDI 0.75 4.63 -1.62 -2.46 14.41 10.29.13 500.5

iShares MSCI USA Quality Factor QUAL 0.15 4.89 2.79 0.50 18.86 07.18.13 284.5

iShares MSCI USA Momentum Factor MTUM 0.15 6.72 7.04 4.25 26.87 04.16.13 226.6

ALPS Barron's 400 BFOR 0.65 5.85 3.83 1.86 16.56 06.04.13 223.1

RiverFront Strategic Income RIGS 0.22 1.24 1.88 1.63 - 10.09.13 218.8

Cambria Shareholder Yield SYLD 0.59 5.81 4.04 2.28 15.42 05.14.13 199.4

Barclays ETN+ Select MLP ETN ATMP 0.95 0.35 2.88 0.17 - 03.13.13 188.9

iSharesBond 2018 Corp ex-Financials IBCC 0.10 0.26 0.69 1.63 - 04.17.13 187.6

db X-trackers CSI 300 China A-Shares ASHR 0.82 -2.36 -13.42 -9.25 11.78 11.06.13 167.2

First Trust Senior Loan FTSL 0.85 0.27 1.26 0.85 - 05.01.13 146.4

Credit Suisse FI Enh Europe 50 ETN FIEU 0.80 12.46 7.05 2.72 - 09.06.13 131.5

iShares MSCI USA Value Factor VLUE 0.15 3.97 2.44 0.26 16.99 04.16.13 130.4

FlexShares Intl Quality Dividend IQDF 0.47 5.72 0.80 -0.17 11.00 04.12.13 125.4

iShares MSCI USA Size Factor SIZE 0.15 4.08 4.21 2.05 21.45 04.16.13 122.9

Fidelity MSCI Health Care FHLC 0.12 6.07 8.71 7.67 25.08 10.21.13 110.9

Vanguard Emerging Mkts Govt Bond VWOB 0.35 2.83 2.68 1.96 - 06.04.13 107.3

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

SPDR S&P 500 SPY 0.09 157,019.8 4.55 3.48 0.87 14.22 22.88 7.08 18.94 - -

iShares MSCI EAFE EFA 0.34 53,543.9 6.13 2.79 0.62 6.41 17.75 6.40 41.86 - -

iShares Core S&P 500 IVV 0.07 49,543.8 4.56 3.49 0.91 14.27 22.87 7.11 18.94 - -

PowerShares QQQ QQQ 0.20 46,361.6 5.15 6.15 3.13 17.52 28.11 10.12 20.93 1.63 1.36

Vanguard Total Stock Market VTI 0.05 40,802.4 4.87 4.32 1.54 14.64 24.02 8.69 20.35 0.89 1.99

Vanguard FTSE Emerging Markets VWO 0.15 40,329.9 3.24 -5.74 -5.47 -3.35 15.58 - 11.84 - -

SPDR Gold GLD 0.40 34,400.5 6.27 5.73 9.90 -2.50 6.62 - - 1.62 1.58

iShares MSCI Emerging Markets EEM 0.67 30,409.9 3.38 -5.95 -5.54 -2.92 15.29 9.17 10.64 - -

iShares Russell 2000 IWM 0.24 27,058.9 4.78 3.93 1.87 14.45 26.47 8.70 86.70 0.96 1.92

iShares Russell 1000 Growth IWF 0.20 22,784.5 5.13 4.98 2.07 14.81 23.82 7.56 23.07 - -

iShares Russell 1000 Value IWD 0.21 20,722.6 4.28 3.05 0.54 13.79 22.88 7.07 17.30 2.58 1.62

iShares Core S&P Mid-Cap IJH 0.15 20,519.0 4.98 5.73 2.68 14.07 26.71 10.03 27.29 1.83 1.35

Vanguard REIT VNQ 0.10 20,437.1 5.07 9.67 9.56 7.21 25.31 - 69.26 1.47 1.99

Vanguard FTSE Developed Markets VEA 0.10 20,061.7 5.95 2.34 0.43 3.35 14.76 - 17.80 - -

Vanguard Total Bond Market BND 0.10 18,743.5 0.47 1.30 2.03 3.69 4.82 - - 1.15 1.83

Vanguard Dividend Appreciation VIG 0.10 18,582.9 4.90 1.59 -0.36 13.23 20.38 - 19.02 1.30 1.91

Financial Select SPDR XLF 0.16 17,040.0 3.04 1.50 -0.73 10.67 25.44 -0.99 16.65 1.73 -

iShares iBoxx $ Inv Gr Corp Bond LQD 0.15 16,903.5 1.14 3.27 3.05 6.62 9.32 5.29 - 3.18 1.35

Vanguard S&P 500 VOO 0.05 15,984.1 4.57 3.54 0.87 14.30 - - 18.94 - -

iShares Core Total US Bond Market AGG 0.08 15,979.7 0.38 1.36 1.92 3.67 4.75 4.32 - 1.32 1.70

SPDR S&P MidCap 400 MDY 0.25 15,857.7 4.86 5.77 2.53 13.89 26.48 9.79 27.29 2.64 1.83

Vanguard FTSE Europe VGK 0.12 15,342.3 7.34 5.44 2.43 8.58 19.82 - 19.95 - -

Vanguard Short-Term Bond BSV 0.10 14,152.4 0.16 0.00 0.68 1.81 2.62 - - - -

iShares Core S&P Small-Cap IJR 0.17 14,048.2 4.41 1.92 0.54 16.81 27.82 10.09 33.78 1.66 1.83

iShares MSCI Japan EWJ 0.50 13,872.1 2.47 -3.02 -4.36 1.99 11.30 3.03 15.25 2.54 0.41

Source: ETF.com. Data as of February 28, 2014. Exp Ratio is expense ratio. 1-Mo is 1-month. 3-Mo is 3-month. YTD is year-to-date. 3-Yr, 5-Yr and 10-Yr are 3-year, 5-year and 10-year annualized returns,

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

AccuShares S&P GSCI Spot Up

ARK Disruptive Innovation

Compass EMP US 500 Volatility Wghtd

db X-trackers Ultra-Short Duration Bond

Direxion Financial Bull 1X

EGShares EM Equal Weight Sector

Global X Latin America Consumer

Guinness Atkinson Dividend

iShares MSCI Europe Min Volatility

iShares MSCI Qatar Capped

iShares Yield Optimized Bond

JPMXF Diversifed Return Emrg Mkts Equity

KraneShares CSI China Government Bond

Market Vectors China A Consumer Demand

PowerShares S&P 500 High Volatility Beta

ProShares UltraPro Oil & Gas

Schwab TargetDuration 2-Month

SPDR MSCI Germany Quality Mix

WBI AbsoluteCore Global SMID Select

WisdomTree Japan Hedged Real Estate

Source: ETF.com’s ETF WatchSource: ETF.com. Data as of February 28, 2014. ER is expense ratio. 1-Mo is 1-month. 3-Mo is 3-month. YTD is year-to-date.

P/E is price-to-earnings ratio.

Exchange-Traded Funds Corner





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Crossword

May / June 201464

Take A Risk!

ACROSS

1. Options strategy to reduce risk (2 words)

7. ETF used as a hedge against inflation risk

9. Remove coupons from bonds

10. Nobel winner focused on risk and correlation

11. One edition of the Wall Street Journal

13. They have nearly 40 percent of the credit card market share

14. Hedge ratio term 15. A builder will often

have one 17. One risk when data

is not normally distributed

21. Morningstar rating component

23. Microsoft’s encyclopedia

26. ___ the mill (2 words)28. End of 3rd qtr. 29. Trading ___ 31. Standard ___

(common risk measure)

32. Commercial prefix with Lodge

34. Sea-___ Airport35. Statistical measure of

co-movement

Nothing ventured,

nothing gained

By Bruce Greig

DOWN

1. Broad category of insurance

2. Risk measure similar to 31-across (abbr.)

3. Financial agents (abbr.) 4. Offloading a stock with no

regard for price 5. Outstanding debt 6. Edward ___ (founder of

London’s oldest insurance marketplace)

7. Short-term govt. securities 8. Products from Domino’s 12. Apple introduction

from 2001

16. Type of virtual “coin” 18. Common Market inits. 19. Risk ___ (guaranteed) 20. Funds with low interest-

rate risk (2 words) 22. Exhibiting more volatility 23. Cost 24. Type of risk from a

borrower’s failure to repay 25. Computer used to predict

the 1952 presidential election

27. Swiss currency 30. Measure of risk

popularized by 10-across 33. ___-Wan Kenobi

Solution

Across: 1. Covered call; 7.TIP; 9. Strip; 10. Markowitz; 11. Asia; 13. Visa; 14. Delta; 15. Tool bag; 17. Skew; 21. Star;

23. Encarta; 26. Run of; 28. Sept; 29. Desk; 31. Deviation; 32. Econo; 34. Tac; 35. Correlation

Down: 1. Casualty; 2. Var; 3. Reps; 4. Dumping; 5. Arrears; 6. Lloyd; 7. T-bills; 8. Pizzas; 12. iPod; 16. Bit; 18. EEC;

19. Free; 20. Bank loan; 22. Riskier; 23. Expense; 24. Credit; 25. Univac; 27. Franc; 30. Beta; 33. Obi

1 2 3 4 5 6 7 8

9 10

11 12 13 14

15 16 17 18

19 20

21 22 23

24 25

26 27 28 29

30

31 32 33

34 35




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INDEX MAY-JUN INDEX_III.pdf

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PowerShares QQQ is based on the Nasdaq-100 Index®. The Fund will, under most circumstances, consist of all stocks in the Index. The Index includes 100 of the largest domestic and international nonfnancial companies listed on the Nasdaq Stock Market based on market capitalization.

Market volatility and volume may delay system access and trade execution.

There are risks involved with investing in Exchange-Traded Funds (ETFs) including possible loss of money. The funds are not actively managed and are subject to risks similar to stocks, including those related to short selling and margin maintenance. Ordinary brokerage commissions apply. Shares are not FDIC insured, may lose value and have no bank guarantee.

Invesco PowerShares does not offer tax advice. Investors should consult their own tax advisors for information regarding their own tax situations.

While it is not Invesco PowerShares’ intention, there is no guarantee that

the PowerShares ETFs will not distribute capital gains to their shareholders.

Shares are not individually redeemable and owners of the shares may acquire those shares from the Funds and tender those shares for redemption to the funds in Creation Unit aggregations only, typically consisting of 50,000 shares.

PowerShares® is a registered trademark of Invesco PowerShares Capital Management LLC. ALPS Distributors, Inc. is the distributor for QQQ. Invesco PowerShares Capital Management LLC is not affliated with ALPS Distributors, Inc.

An investor should consider the Fund’s Investment objective,

risks, charges and expenses carefully before investing. To obtain

a prospectus, which contains this and other information about

the QQQ, a unit investment trust, please contact your broker,

call 800.983.0903 or visit www.invescopowershares.com.

Please read the prospectus carefully before investing.

When it comes to investing, unexpected barriers can hinder your portfolio’s success. However, the transparency of PowerShares QQQ allows for complete visibility of its holdings throughout the day, so you know exactly what you own with no surprises. PowerShares QQQ keeps your investments accessible, cost and tax effcient, and most importantly, gives you the trading fexibility you want.

powershares.com/transparent | @PowerShares



INDEX MAY-JUN INDEX_IV.pdf

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risky business May / June 2014 - ETF · University of Paris VI-Jussieu (1999). ... Bruce Greig,...

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