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Transcript
Smart Beta a referential guide for institutional investors We wish to express our thanks to the public funds such as sovereign wealth funds, pensions, central banks and other asset owners for their invaluable cooperation in providing the information for this new book on Smart Beta. We also wish to thank WisdomTree, Robeco, State Street Global Advisors and Northern Trust for their sponsorship and content assistance. Responsibility for the contents, including remaining errors, lies with the editor. SWFI – Custom Content Solutions Team Copyright © 2014 Sovereign Wealth Fund Institute Inc. All Rights Reserved. Published by Sovereign Wealth Fund Institute Sovereign Wealth Fund Institute 2300 West Sahara Avenue, Suite 800 Las Vegas, NV 89102, United States Email: [email protected] Events: [email protected] Phone: +1 (415) 717-6912 | Fax: +1 (866) 292-3897 Web: www.swfinstitute.org Smart Beta Site: www.betastrategy.co / www.swfi.com/smartbeta CONTRIBUTING ARTIST Christopher Buzelli (Cover) BOOK EDITOR Jess Delaney, [email protected] LEAD DESIGN Den Ward, [email protected] CUSTOM CONTENT SOLUTIONS Vince Berretta, [email protected] EVENTS Autumn Reed, [email protected] COPIES Elizabeth Ochoa, [email protected] CHIEF EDITOR Michael Maduell, [email protected] All Rights Reserved. No part of this publication (text, data or graphic) may be reproduced, stored in a data retrieval system or transmitted, in any form whatsoever or by any means (electronic, mechanical, photocopying, recording or otherwise) without obtaining prior written consent from the Sovereign Wealth Fund Institute. Violators may be subject to legal proceedings and liable for substantial monetary damages per infringement as well as costs and legal fees. Printed in the United States Co-Founders: Michael Maduell & Carl Linaburg 2 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta SWFI ® Sponsor Directory Robeco Coolsingel 120 3011 AG Rotterdam The Netherlands Tel: +31 10 224 1224 Email: [email protected] Site: www.robeco.com Northern Trust 50 South LaSalle Street - M15 Chicago, Illinois 60603 United States Contact: John Kreig, Global Head of Institutional Distribution Tel: +1 312 557 6117 Email: [email protected] Site: www.northerntrust.com WisdomTree 245 Park Avenue, 35th Floor New York, NY 10167 United States Tel: +1 866 909 9473 Site: www.wisdomtree.com State Street Global Advisors Limited 20 Churchill Place London, E14 5HJ United Kingdom Contact: Louis de Montpellier Tel: +44 20 3395 6189 Email: [email protected] Site: www.ssga.com swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 3 THE MORE WE KNOW THE MORE WE CAN DISCOVER Factor Investing Risk is a natural, necessary part of investing. That’s why we aim to know more about it than anyone else. We use proven, quant factor techniques to separate rewarded from unrewarded risks. It’s a proprietary approach that has resulted in performance awards and client mandates across our momentum, value and low-volatility factor strategies. Discover more at robeco.com/factorinvesting Important information This publication is intended for professional investors. Robeco Institutional Asset Management B.V. (trade register number: 24123167) has a license as manager of UCITS and AIFs of the Netherlands Authority for the Financial Markets in Amsterdam. Contents Preface 7 Getting a Smoother Ride: The Power of Quality 50 Smart Beta Rising 9 Smart Beta Timeline: Evolution of Thought 16 The Risk of Unintended Exposure 51 Robeco Five Popular Factors for Institutional Investors The Rise of Factor Investing 52 18 Economic Climate and Market Timing 58 Factor Investing 20 Comparing Costs 58 The Broad Spectrum of Active and Passive 22 The Importance of Track Record 59 Smart Beta Approaches for Fixed Income 26 Faces of Smart Beta: Asset Owner Interviews State Street Global Advisors (SSgA) 60 Advanced Beta Strategies in Fixed Income Default Risk and Credit Momentum 27 WisdomTree Looking Under the Hood of Smart Beta 80 Shifting Models: A New Investing Paradigm 38 An Asset Owner Perception Survey 90 Smart Beta Adoption 39 Challenges of Today 40 Northern Trust Through the Looking Glass: Portfolio Truths. Factor Solutions 108 A New Policy Context 41 Glossary 126 The New Normal 42 References 129 Strategies for Coping with the New Normal 43 Implementation Considerations of Smart Beta 48 swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 5 WisdomTree PIONEERING SMART BETA ETF s since 2006 Learn more at wisdomtree.com/SmartBeta Beta is a measure of volatility. Smart Beta refers to the WisdomTree Fundamentally Weighted Indexing Methodology. Investing involves risk including the loss of principal. Carefully consider the investment objectives, risks, charges and expenses of the Funds before investing. To obtain a prospectus containing this and other important information, visit www.wisdomtree.com. Read the prospectus carefully before you invest. WisdomTree Funds are distributed by ALPS Distributors, Inc. © 2014 WisdomTree Investments, Inc. WisdomTree Funds are distributed by ALPS Distributors, Inc. Preface Over the past decade, “smart beta,” a new suite of indexation strategies touting advantages over both active investment managers and traditional market-capitalizationweighted indexes, has erupted onto the financial scene and increasingly won the favor of institutional investors. However, the rise of smart beta has been enshrouded in controversy. Much, if not all, of the industry dislikes the term itself: smart beta. Practitioners are locked in debate over the effectiveness of these strategies, and with the lack of a unified definition, investors wrestle with the question: What is smart beta? Smart beta has become an umbrella term for a multitude of index strategies with vastly diverse weighting and stock selection methodologies. A plethora of smart beta strategies exist for different equity markets around the world, for various sectors and also for a wide range of asset classes. At its core, smart beta is a departure from traditional marketcapitalization-weighting methods a la S&P 500 or Russell 1000. One of the most common definitions for this catchall term is any index that is not weighted by market capitalization. However, numerous practitioners will refer to certain cap-weighted indexes, particularly those that apply nontraditional screening methods to the stock universe, as smart beta. Under possibly the broadest definition, smart beta is a systematic, rules-based approach to capturing targeted market factors and portfolio tilts. This guide attempts to move beyond the semantic debate and shed light on this growing, and sometimes disorienting, new market of indexes. This book uses the term “smart beta” for two reasons. First, it is perhaps the most widely used and recognized name for these strategies. Second, using a different name could be misconstrued as an endorsement of a specific firm’s brand of alternative index strategies. Sponsored by Northern Trust, Robeco, State Street Global Advisors and WisdomTree, the Sovereign Wealth Fund Institute leveraged its journalistic and research capabilities to develop a comprehensive guide to smart beta strategies using four primary building blocks: Narrative Journalism: The first section of the book consists of a series of articles that provide a general discussion of smart beta strategies: their uses, challenges, implementation and economic motivation. SWFI interviewed top executives at its sponsor firms, drawing from their expertise, as the basis of these articles. Research Papers: Interspersed throughout the book is a collection of research papers produced by SWFI’s sponsors: Northern Trust, Robeco, State Street Global Advisors and WisdomTree. These papers give a more technical look into the subject that is absent in the supplementary articles. Topics address factor investing, fundamental indexation, smart beta fixed income, the risks of unintended factor exposures and more. Investor Interviews: SWFI conducted in-depth interviews with key executives at 8 different public and private institutions from 8 different countries that collectively manage in excess of US$ 250 billion. These interviews, presented in Q&A format in the second section of the book, offer real world examples of the experiences of smart beta investors, many of whom are early adopters and seen as pioneers in this space. Asset Owner Perception Study: SWFI surveyed 72 institutional investors around the globe about their adoption, implementation and perceptions of smart beta. Respondents have combined assets under management of more than US$ 2.9 trillion. SWFI would like to thank the sponsors who willingly gave of their expertise and time for the development of this comprehensive guide to smart beta. Their insight and contributions have been invaluable for bringing clarity, breadth and depth to this subject. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 7 CONNECT INTO THE NETWORK Connect into State Street Global Advisors’ network of expert advice, tailored training and investment excellence. Profit from our Official Institutions Group’s decades-deep experience. Our service is honed to the specific needs of sovereign clients such as central banks, sovereign wealth funds, government agencies and supranational institutions. From Passive to Active, Alternatives to the latest Advanced Beta strategies, the Official Institutions Group can provide the customised client solutions you need. To learn more about how we can help you, please visit our website ssga.com/oig or contact your local representative. Global APAC Hon Cheung Louis de Montpellier e [email protected] e [email protected] t +44 20 3395 6189 t +65 6826 7505 Americas EMEA Carl Riedy e [email protected] t +1 202 429 8427 Kristina Sowah e [email protected] t +44 20 3395 6842 State Street Global Advisors Limited. Authorised and regulated by the Financial Conduct Authority. Registered in England. Registered No. 2509928. VAT No. 5776591 81. Registered office: 20 Churchill Place, Canary Wharf, London E14 5HJ. Telephone: +44 (0)20 3395 6000. Facsimile: +44 (0)20 3395 6350. Web: ssga.com. Investing involves risk including the risk of loss of principal. State Street Global Advisors is the investment management business of State Street Corporation (NYSE: STT), one of the world’s leading providers of financial services to institutional investors. © 2014 State Street Corporation – All rights reserved. EUMKT-3521. Exp. 31/08/2015. Smart Beta Rising The turbulent markets of the last decade have sparked greater interest among institutional investors in transparent strategies along with an increased emphasis on risk management. As investor losses piled up during the dot com bubble and the global financial crisis, skepticism grew over the reliability of both active management and traditional indexing methods for securing returns. In response to this shift in demand, asset managers and index providers have rolled out a new suite of alternative index strategies, commonly referred to as “smart beta.” The line between active and passive management has blurred, costs have come down and investors have more options for meeting their investment objectives and tailoring their exposures. “It feels like smart beta is at an inflection point, in terms of media attention and investor interest,” says Luciano Siracusano, Chief Investment Strategist at WisdomTree. “Investors across various channels are exploring and considering alternatively weighted indexing approaches more seriously now than at any time I can recall over the last ten years. So I think these alternatively weighted index strategies are here to stay, and I expect their usage to grow in the future.” WisdomTree, which prefers the phrase “fundamentally weighted” to describe its approach to indexbased investing, entered the smart beta space in June of 2006 with the launch of its global suite of dividend-weighted strategies. In 2007, the Manhattan-based index creator and ETF sponsor launched its U.S. suite of earnings-weighted strategies. According to Siracusano, “WisdomTree entered the space after conducting extensive research into the history of stock indexes from 2001-2004 and concluded, after the bear market of 2000-2002, that cap-weighted indexes were flawed because they never rebalanced back to any measure of relative value.” WisdomTree has seen smart beta making inroads with a broad cross-section of the institutional community, who are primarily attracted to the potential for higher returns, lower volatility or SWFI conducted an asset owner perception study as part of its research into smart beta. The online survey included a general comments field to give asset owners the opportunity to share their thoughts on smart beta. Verbatim responses appear in the margins throughout this report along with the respondent’s demographic profile (type of institution, region, AUM). swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 9 Engineer equity risk to work in your favor. It’s time you were better compensated for the equity risks you’re taking. At Northern Trust, we use our most active strategy — listening to your unique needs — to help design the smartest equity strategy for your portfolio. Together, we identify the risks worth taking to help you more efficiently achieve your goals. We call it Engineered Equity. You can call it delivering answers beyond the expected. Still skeptical? Find out how Engineered Equity can help you today. Call Steve Potter at +1 866 803 5857 or John Krieg on +44 (0)20 7982 3899 or visit northerntrust.com/equitysolutions. Equity | Fixed | Cash | Multi-Manager DIrECTED TO PrOFESSIONAL CLIENTS ONLY. NOT INTENDED FOr rETAIL CLIENTS. © 2014 Northern Trust Corporation. Head Office: 50 South La Salle Street, Chicago, Illinois 60603 U.S.A. Incorporated with limited liability in the U.S. Products and services provided by subsidiaries of Northern Trust Corporation may vary in different markets and are offered in accordance with local regulation. For legal and regulatory information about individual market offices, visit northerntrust.com/disclosures. Northern Trust Asset Management comprises Northern Trust Investments, Inc., Northern Trust Global Investments Limited, Northern Trust Global Investments Japan, K.K., NT Global Advisors, Inc. and investment personnel of The Northern Trust Company of Hong Kong Limited and The Northern Trust Company. Issued by Northern Trust Global Investments Limited. a combination of the two. Moreover, according to Siracusano, “There has been growing interest across the institutional, RIA and wirehouse segments of the U.S. equity market. Increasingly, financial intermediaries are seeing smart beta approaches as alternatives to both traditional beta exposures and to active managers.” “WisdomTree has seen institutional investor interest in single-country exposures, strategies that hedge out foreign currencies, broad dividendbased strategies and strategies that provide access to hard to reach markets, like emerging market small-caps, or inefficient asset classes, like U.S. and international small-caps,” he notes. Siracusano suggests institutional investors start by working some smart beta strategies into their core passive allocations, especially in less efficient asset classes. “For example, capweighted indexes tend to tilt towards growth over value and towards larger companies over smaller ones,” he says. “Thus, cap-weighted indexes may dilute the ‘value’ or the ‘size’ return premia, relative to a fundamentally weighted index that may capture the premia more efficiently through its weighting mechanism. Institutional investors may also wish to consider some smart beta strategies as replacements for active managers that either underperform or hit investment capacity constraints.” “One needs to draw a distinction between a factor and a risk premium, or more precisely, a ‘return premium,’” he says. “For example, selecting or weighting component securities by book yield, dividend yield or earnings yield may all, to varying degrees, provide access to the ‘value premium’ that has been identified in the academic research over time. The latter is a ‘return premium,’ the former are simply factors that access it. Some smart beta approaches can be as simple as equal weighting stocks in an index. Because this approach is not market- capitalization weighted and weights all components equally, it tilts index weights towards mid-capitalization companies, and thus has been able to generate excess returns relative to comparable cap-weighted indexes over long holding periods. The strategy, in effect, accesses the “size premium” indirectly simply by moving away from cap-weighted indexes.” All of the attention brought to smart beta in recent years, from the marketing buzz to the media spotlight, has prompted many to ask whether this is just a passing fad. Siracusano thinks the research speaks for itself. According to Siracusano, “If you believe the academic research that suggests these return premia have existed in equity markets – and in other asset classes – around the world for decades, then the likelihood is that smart beta is not just a fad. To the contrary, WisdomTree believes that smart beta indexes in general, and fundamentally-weighted indexes in particular, represent the future of indexing. We believe this debate goes to the heart of indexing and will be with us for years to come.” Some critics argue that the success of smart beta strategies is based on market inefficiencies and that excess returns will be arbitraged away as smart beta becomes more widely adopted. Siracusano disagrees. The executive says pricing inefficiencies are likely to persist due to errors caused by human nature as well as the trillions of dollars indexed to traditional cap-weighted indexes. “Put another way, academics wrote about the value premium in the early 1990’s, and hundreds of billions have flowed into value strategies since then. Did that destroy the value premium?” he asks. “To the contrary, the Russell 3000 Value Index has still managed to outperform The Russell 3000 Growth Index by approximately 2 percentage points annually over the last 22 years. If you are calculating the ‘value premium’ using price-to-book ratios, as identified in the academic research since 1992, low price-to- book stocks have continued to beat high price-tobook stocks over the long-term holding period, and they have continued to generate excess returns relative to the overall market over the past 22 years, even after research on how well low price- to-book stocks had performed before 1992 was identified and widely publicized.” swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 11 WisdomTree Research MARKET INSIGHTS [ August 2014 ] WisdomTree and Smart Beta BY LUCIANO SIRACUSANO, CHIEF INVESTMENT STRATEGIST In 2006, WisdomTree launched its family of fundamentally weighted stock indexes, covering major asset classes around the world. WisdomTree did so after conducting extensive research into the history of stock indexes from 2001-2004 and concluded, after the bear market of 2000-2002, that market capitalization-weighted (“cap-weighted”1) indexes were flawed because they never rebalanced2 back to any measure of relative value. Today such alternatively weighted indexes are generally categorized as “smart beta3” indexes -- non-cap-weighted indexes that seek to provide exposures with the potential to outperform the market—or generate better risk-adjusted returns than the market—rather than merely measure the performance of all investable stocks in an equity market. “Smart beta” is a polarizing phrase because it implies that a traditional cap-weighted indexing approach may not be all that smart. That is regrettable, because over time, it has been shown that most active managers, after fees and expenses, failed to deliver returns that exceed those of traditional cap-weighted indexes4. However, the term does suggest that there may be a better way to index, or at least a better way to weight stock indexes. To that extent, having a pithy phrase that can fit in a headline – or on a bumper sticker -- helps to bring awareness to research that spans more than 50 years, documenting how cap-weighting equity markets has been sub-optimal from a risk and return perspective. One study, conducted by Cass Consulting, a research-led consultancy service provided by Cass Business School, concluded that returns of traditional, cap-weighted indexes lagged various fundamentally weighted—or smart-beta—indexes by as much as 2% per year from 1969-20115. A simple way to think about so-called smart beta index strategies is that they represent a bridge between traditional passive “beta6” exposure, and actively managed strategies7 that seek to generate alpha8 through skillful stock selection. If you believe that stock prices are not always efficient and that they are subject to error because of human behavior, then unless we find a way to expunge greed and fear from the human condition, markets will always be vulnerable to periods of mania and periods of depressed prices. 1 2 3 4 5 6 7 8 Cap weighted: Market capitalization = share prices x number of shares outstanding. Firms with the highest values receive the highest weights in approaches designed to weight firms by market cap. Rebalance: An index is created by applying a certain set of selection and weighting rules at a certain frequency. WisdomTree rebalances, or re-applies its rules based selection and weighting process on an annual basis. Smart Beta: A term for rules-based investment strategies that don’t use conventional market-capitalization weightings. Source: Morningstar Direct as of 6/30/2014. Source: Andrew Clare et al. “An Evaluation of Alternative Equity Indices Part 2: Fundamental Weighting Schemes,” Cass Business School, March 2013. Beta: Measure of the volatility of an index or investment relative to a benchmark. A reading of 1.00 indicates that the investment has moved in lockstep with the benchmark; a reading of -1.00 indicates that the investment has moved in the exact opposite direction of the benchmark. Active: Funds that attempt to outperform the market by selecting securities a portfolio manager believe to be the best. Alpha: Measure of risk-adjusted performance that compares how the constituents move relative to a benchmark. WWW.WISDOMTREE.COM 866.909.WISE (9473) WisdomTree Research MARKET INSIGHTS [ August 2014 ] Cap-weighted indexes are remarkably efficient at incorporating such sentiment – and pricing error --into index weights, particularly during bubbles, as was demonstrated by the tech and telecom bubbles in the late 1990’s and the Japanese equity bubble in the late 1980s. Smart beta strategies that contra-trade away from these “market bets” may add value when stock returns revert to their long-term mean. In addition to capitalizing on this inherent flaw in cap-weighted indexes (they never rebalance back to any measure of relative value), smart beta indexes may also be more efficient at capturing “risk premia9” in the market, through their selection, weighting and rebalancing process. While academic research into the existence of the risk premia of size, beta and value has existed for decades, newer research into other risk premia – including momentum, quality, low volatility, even illiquidity is casting new light on potential sources of excess returns within equity markets. As new research becomes available, practitioners may be able to show why capitalization-weighting such identified risk premia may not be the most efficient way of capturing these potential sources of excess return. Fundamentally weighted indexes that weight components based on fundamental factors, such as dividends or earnings, may target specific risk premia more directly than equal weighted indexes and more efficiently capture multiple risk premia, including size, value and quality – relative to a cap-weighted index. When evaluating smart beta indexes, one ought to be mindful of when they were created and when the rule set that governs the selection and weighting process was codified. Some strategies that work well in backtests may not work well in real time if they are too narrow. And if they end up capturing only a small subset of the investable opportunity set, they may have limited investment capacity or introduce more stock selection risk and greater tracking error, relative to an index that reweights broad equity markets based on a fundamental factor. WISDOMTREE’S APPROACH WisdomTree, in its broadest U.S. indexes, seeks to gain representative exposure to an asset class by weighting securities by the earnings they’ve generated in the prior year. Our dividend-based strategies, on the other hand, weight constituents based on their regular cash dividends paid. In addition, all WisdomTree indexes highlighted in Figure 1 rebalance annually back to their respective income streams, in an attempt to identify and capitalize on relative value within the markets. WisdomTree has, in effect, been conducting real time experiments over the past 8 years, measuring what happens when one shifts to weighting markets by income rather than by market value. Those results are presented below in a way that allows the reader to compare the WisdomTree indexes to its relevant cap weighted strategies across the world. 9 2 Risk premia: Equity investments are not risk free, but it is thought that investors buy stocks because the returns they expect are high enough to allow them to take the risk. WWW.WISDOMTREE.COM 866.909.WISE (9473) WisdomTree Research MARKET INSIGHTS [ August 2014 ] FIGURE 1: RISK AND RETURNS OF MAJOR SMART BETA AND CAP-WEIGHTED INDEXES AROUND THE GLOBE Index WT Index Inception Date 1-Year 3-Year 5-Year Since Standard WT Index Deviation Inception WisdomTree Dividend Index 6/1/2006 22.12% 16.73% 20.09% 7.82% Russell 3000 Value Index 23.71% 16.74% 19.28% Russell 3000 Index 25.22% 16.46% 21.54% Russell 1000 Value Index S&P 500 Index Sharpe Ratio Beta Correlation Tracking Error Information Ratio 15.82% 0.42 0.92 0.96 4.68% -0.04 6.81% 17.15% 0.33 1.02 0.98 3.14% -0.39 19.33% 8.02% 16.50% 0.41 1.00 1.00 0.00% 0.00 16.57% 19.26% 7.48% 15.35% 0.41 0.93 0.96 4.24% -0.07 23.81% 16.92% 19.23% 6.81% 16.93% 0.33 1.05 0.99 2.97% -0.33 24.61% 16.58% 18.83% 7.79% 15.93% 0.41 1.00 1.00 0.00% 0.00 28.22% 18.14% 24.80% 9.33% 19.32% 0.42 0.96 0.94 6.64% -0.05 Russell MidCap Value Index 27.76% 17.56% 22.97% 8.89% 19.23% 0.40 1.00 0.98 3.80% -0.20 S&P MidCap 400 Index 25.24% 15.26% 21.67% 9.66% 18.93% 0.45 1.00 1.00 0.00% 0.00 23.08% 17.59% 22.75% 7.99% 21.40% 0.32 0.97 0.93 7.92% 0.01 Russell 2000 Value Index 22.54% 14.65% 19.88% 6.81% 20.77% 0.27 0.99 0.99 3.47% -0.31 Russell 2000 Index 23.64% 14.57% 20.21% 7.90% 20.61% 0.32 1.00 1.00 0.00% 0.00 24.99% 17.18% 19.60% 7.30% 16.74% 0.38 0.97 0.99 1.96% 0.25 25.22% 16.46% 19.33% 6.81% 17.17% 0.35 1.00 1.00 0.00% 0.00 24.43% 17.07% 18.94% 6.88% 16.24% 0.37 0.97 1.00 1.67% 0.20 24.61% 16.58% 18.83% 6.55% 16.58% 0.34 1.00 1.00 0.00% 0.00 30.08% 18.23% 24.83% 10.79% 20.76% 0.48 1.04 0.98 4.30% 0.37 25.24% 15.26% 21.67% 9.21% 19.63% 0.42 1.00 1.00 0.00% 0.00 24.81% 16.77% 22.77% 8.87% 23.52% 0.34 1.06 0.96 6.55% 0.29 23.64% 14.57% 20.21% 7.00% 21.37% 0.29 1.00 1.00 0.00% 0.00 27.22% 8.60% 12.24% 4.99% 19.80% 0.19 1.01 0.99 2.11% 0.53 MSCI EAFE Value Index 26.86% 8.46% 11.24% 3.27% 20.77% 0.10 1.05 0.99 3.02% -0.20 MSCI EAFE Index 23.57% 8.10% 11.77% 3.87% 19.53% 0.14 1.00 1.00 0.00% 0.00 28.50% 11.97% 16.50% 6.81% 20.20% 0.28 0.95 0.99 3.20% 0.47 MSCI EAFE Small Value Index 31.53% 10.61% 15.17% 5.66% 21.43% 0.21 1.02 0.99 2.54% 0.13 MSCI EAFE Small Cap Index 29.08% 9.84% 15.21% 5.32% 20.92% 0.20 1.00 1.00 0.00% 0.00 10.32% -0.36% 10.33% 4.78% 23.63% 0.17 0.91 0.99 3.86% 0.48 14.31% -0.39% 9.24% 2.91% 25.88% 0.09 1.00 1.00 0.00% 0.00 7.91% 1.59% 12.76% 4.72% 24.82% 0.17 0.86 0.98 6.03% 0.51 14.20% 0.58% 11.48% 1.64% 28.34% 0.04 1.00 1.00 0.00% 0.00 WisdomTree LargeCap Dividend Index WisdomTree MidCap Dividend Index WisdomTree SmallCap Dividend Index WisdomTree Earnings Index 6/1/2006 6/1/2006 6/1/2006 2/1/2007 Russell 3000 Index WisdomTree Earnings 500 Index 2/1/2007 S&P 500 Index WisdomTree MidCap Earnings Index 2/1/2007 S&P MidCap 400 Index WisdomTree SmallCap Earnings Index 2/1/2007 Russell 2000 Index WisdomTree DEFA Index WisdomTree International SmallCap Dividend Index WisdomTree Emerging Markets Dividend Index 6/1/2006 6/1/2006 6/1/2007 MSCI Emerging Markets Index WisdomTree Emerging Markets SmallCap Dividend Index MSCI Emerging Markets Small Cap Index 8/1/2007 Source: WisdomTree, Zephyr Style Advisors, as of 06/30/2014. Past performance is not indicative of future results. You cannot invest directly in an index. Definitions: Standard deviation: measures the spread of actual returns around an average return during a specific period. Higher risk indicates greater potential for returns to be farther away from this average; Sharpe ratio: Measure of risk-adjusted return. Higher values indicate greater return per unit of risk, specifically standard deviation, which is viewed as being desirable; Correlation: Statistical measure of how two sets of returns move in relation to each other. Correlation coefficients range from -1 to 1. A correlation of 1 means the two subjects of analysis move in lockstep with each other. A correlation of -1 means the two subjects of analysis have moved in exactly the opposite direction; Tracking error: A divergence between the price behavior of a position or a portfolio and the price behavior of a benchmark; Information ratio: A risk-adjusted return measure calculated by taking the excess return against the benchmark and dividing by the tracking error. 3 WWW.WISDOMTREE.COM 866.909.WISE (9473) WisdomTree Research MARKET INSIGHTS [ August 2014 ] WisdomTree is unique in the industry, in that, we are both an index creator and exchange traded fund (ETF) manager. The original WisdomTree vision was built on a simple but powerful idea: there is a better way to index. We invented dividend weighting equity markets around the world after conducting years of research into the inherent flaws of cap-weighted indexes. Our idea to fundamentally weight equity markets is more than just a smart beta strategy: it is a philosophy on how best to own broad equity markets and it a philosophy that is now accompanied with one of the longest real-time track records in the smart beta space – at both the Index and the ETF level. Today, WisdomTree has $35 billion in assets under management, ranking it as the 5th largest ETF manager in the U.S. and the 8th largest ETP manager in the world10. 10 Source: WisdomTree as of 6/30/2014. Investors should carefully consider the investment objectives, risks, charges and expenses of the Funds before investing. To obtain a prospectus containing this and other important information, call 866.909.WISE (9473) or visit wisdomtree.com. Read the prospectus carefully before you invest. Dividends are not guaranteed, and a company’s future ability to pay dividends may be limited. A company paying dividends may cease paying dividends at any time. There are risks associated with investing, including possible loss of principal. Investments focusing on certain sectors and/or mid- or small cap companies increase their vulnerability to any single economic or regulatory development. This may result in greater share price volatility. WisdomTree Dividend Index: Measures the performance of dividend-paying companies incorporated in the United States that pay regular cash dividends and meet WisdomTree’s eligibility requirements. Weighted by indicated cash dividends. WisdomTree LargeCap Dividend Index: A fundamentally weighted index that measures the performance of the large-capitalization segment of the U.S. dividend-paying market. The Index comprises the 300 largest companies ranked by market capitalization from the WisdomTree Dividend Index. WisdomTree MidCap Dividend Index: A fundamentally weighted index that measures the performance of the mid-capitalization segment of the U.S. dividend-paying market. The Index comprises the companies that constitute the top 75% of the market capitalization of the WisdomTree Dividend Index after the 300 largest companies have been removed. The Index is dividend weighted annually to reflect the proportionate share of the aggregate cash dividends each component company is projected to pay in the coming year, based on the most recently declared dividend per share. WisdomTree SmallCap Dividend Index: A fundamentally weighted index that measures the performance of the small-capitalization segment of the U.S. dividend-paying market. The Index comprises the companies that constitute the bottom 25% of the market capitalization of the WisdomTree Dividend Index after the 300 largest companies have been removed. The Index is dividend weighted annually to reflect the proportionate share of the aggregate cash dividends each component company is projected to pay in the coming year, based on the most recently declared dividend per share. WisdomTree Earnings Index: A fundamentally weighted index that measures the performance of earnings-generating companies within the broad U.S. stock market. WisdomTree Earnings 500 Index: A fundamentally weighted index that measures the performance of earnings-generating companies in the large-capitalization segment of the U.S. stock market. Companies in the Index are incorporated and listed in the U.S. and have generated positive cumulative earnings over their most recent four fiscal quarters prior to the Index measurement date. The Index comprises the 500 largest companies ranked by market capitalization in the WisdomTree Earnings Index. WisdomTree MidCap Earnings Index: A fundamentally weighted index that measures the performance of the top 75% of the market capitalization of the WisdomTree Earnings Index after the 500 largest companies have been removed. WisdomTree SmallCap Earnings Index: A fundamentally weighted index that measures the performance of earnings-generating companies in the small-capitalization segment of the U.S. stock market. The Index comprises the companies in the bottom 25% of the market capitalization of the WisdomTree Earnings Index after the 500 largest companies have been removed. S&P 500 Index: A market capitalization-weighted benchmark of 500 stocks selected by the Standard and Poor’s Index Committee, designed to represent the performance of the leading industries in the United States economy. Russell Midcap Value Index: Measures the performance of the mid-cap value segment of the U.S. equity universe. It includes those Russell Midcap Index companies with lower price-to-book ratios and lower forecasted growth values. S&P MidCap 400 Index: Provides investors with a benchmark for mid-sized companies. The index covers over 7% of the U.S. equity market and seeks to remain an accurate measure of mid-sized companies, reflecting the risk and return characteristics of the broader mid-cap universe on an ongoing basis. Russell 2000 Index: Measures the performance of the small-cap segment of the U.S. equity universe. The Russell 2000 is a subset of the Russell 3000 Index, representing approximately 10% of the total market capitalization of that index. It includes approximately 2,000 of the smallest securities based on a combination of their market cap and current index membership. Russell 2000 Value Index: Measures the performance of the small-cap value segment of the U.S. equity universe. It includes those Russell 2000 Index companies with lower price-to-book ratios and lower forecasted growth values. Russell 3000 Index: Measures the performance of the 3,000 largest U.S. companies based on total market capitalization. Russell 3000 Value Index: Measures the performance of the Russell 3000 Index constituents with value characteristics. WisdomTree DEFA Index: A fundamentally weighted index that measures the performance of dividend-paying companies in the industrialized world, excluding Canada and the United States, that pay regular cash dividends and meet other liquidity and capitalization requirements. It comprises companies incorporated in 16 developed European countries, Japan, Australia, New Zealand, Hong Kong and Singapore. Companies are weighted based on annual cash dividends paid. WisdomTree International SmallCap Dividend Index: A fundamentally weighted index that measures the performance of the small-capitalization segment of the dividend-paying market in the industrialized world outside the U.S. and Canada. The Index comprises the companies that make up the bottom 25% of the market capitalization of the WisdomTree DEFA Index after the 300 largest companies have been removed. Companies are weighted in the Index based on annual cash dividends paid. MSCI EAFE Index: A free float adjusted market cap-weighted index composed of companies representative of the developed market structure o developed countries in Europe, Australasia and Japan. MSCI EAFE Value Index: A free float adjusted market capitalization-weighted subset of stocks within the MSCI EAFE. MSCI EAFE Small Cap Index: A free float-adjusted market capitalization-weighted equity index that captures small-cap representation across developed market countries around the world, excluding the U.S. and Canada. WisdomTree Emerging Markets Dividend Index: The WisdomTree Emerging Markets Dividend Index is a fundamentally weighted index that measures the performance of dividend-paying stocks selected from the following 17 emerging market nations: Brazil, Chile, China, Czech Republic, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Philippines, Poland, Russia, South Africa, Taiwan, Thailand, and Turkey. Companies are weighted in the Index based on annual cash dividends paid. WisdomTree Emerging Markets SmallCap Dividend Index: The WisdomTree Emerging Markets SmallCap Dividend Index is a fundamentally weighted index that measures the performance of primarily small cap stocks selected from the WisdomTree Emerging Markets Dividend Index. Companies included in the Index fall within the bottom 10% of total market capitalization of the WisdomTree Emerging Markets Dividend Index as of the annual index measurement date. Companies are weighted in the Index based on annual cash dividends paid. MSCI Emerging Markets Index: The MSCI Emerging Markets Index captures large and mid cap representation across 23 Emerging Markets (EM) countries. This is a marketcap weighted strategy. MSCI Emerging Markets SmallCap Index: The MSCI Emerging Markets Small Cap Index includes small cap representation across 23 Emerging Markets countries*. With 1,814 constituents, the index covers approximately 14% of the free float-adjusted market capitalization in each country. WisdomTree Funds are distributed by ALPS Distributors, Inc. Luciano Siracusano is a registered representatives of ALPS Distributors, Inc. © 2014 WisdomTree Investments, Inc. “WisdomTree” is a registered mark of WisdomTree Investments, Inc. 4 WWW.WISDOMTREE.COM WIS006878 8/2015 866.909.WISE (9473) Smart Beta: Evolution of Thought Standard & Poor's launches the first market-capitalization -weighted index. 1923 Grinold tests popular benchmarks for five different equity markets and finds that four of the five benchmarks are inefficient. ("Are Benchmark Portfolios Efficient?") Sharpe defines "alpha" and "beta" while rolling out the Capital Asset Pricing Model in "Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk." 1981 1964 1992 1993 Banz documents the size premium in "The Relationship Between Return and Market Value of Common Stocks." 1934 Graham and Dodd acknowledge the quality premium, which they call "sustainable earnings power," in Security Analysis. 1970 Fama defines an efficient market as a market in which prices always fully reflect available information in the seminal paper on the Efficient Market Hypothesis, "Efficient Capital Markets: A Review of Theory and Empirical Work." 1991 Baker and Haugen document the low volatility premium in "The Efficient Market Inefficiency of CapitalizationWeighted Stock Portfolios." 16 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta Jegadeesh and Titman document the momentum premium in "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency." 1992 Fama and French document the value premium and describe equity returns via their three-factor model in "The Cross Section of Expected Stock Returns." 1997 In "On Persistence in Mutual Fund Performance," Carhart extends Fama and French's 3-factor model to include momentum and finds this 4-factor model almost completely explains persistence in equity mutual funds' risk-adjusted returns, implying that active managers add little value. Smart beta, and factor investing more generally, has evolved out of the research of academics and practitioners alike. The timeline below outlines the history of thought that has shaped the development of these new strategies, from the definition of market beta to the discovery of alternative risk premia and ending with the evaluation of alternative weighting methods. While by no means comprehensive, this timeline charts many of the most influential papers in the development of smart beta. In their paper "Fundamental Indexation," Arnott, Hsu and Moore show that fundamentally-weighted indexes provide superior risk-adjusted performace to traditional cap-weighted indexes while retaining many of the benefits. In "On the Properties of Equally-Weighted Risk Contributions Portfolios," Maillard, Roncalli and Teiletche combine minimum variance and equal-weighting techniques to reduce volatility subject to a diversification constraint. 2005 Amenc, Goltz, Martellini and Retkowski develop a maximum Sharpe ratio index construction approach that relates risk premia to downside risk rather than volatility in "Efficient Indexation: An Alternative to Cap-Weighted Indices." 2008 Smart beta indexes enter the market. 2011 2010 2009 2005 2010 Norway's Ministry of Finance commissions professors Andrew Ang, William Goetzmann and Stephen Schaefer to examine Norges Bank Investment Management's use of active managers. Their "Evaluation of Active Management of the Norwegian Pension Fund Global" recommends risk factor investing using passive-like strategies. Chow, Hsu, Kalesnik and Little find that outperformance of smart beta strategies are almost entirely driven by factor exposures in "A Survey of Alternative Equity Index Strategies." Melas and Kang discuss practical uses of smart beta indexes for strategic and tactical asset allocation in "Applications of Systematic Indexes in the Investment Process." In "An Evaluation of Alternative Equity Indices," Clare, Motson and Thomas document the outperformance of numerous popular alternative weighting schemes and find that assigning constituent weights randomly would also produce better risk-adjusted returns than cap-weighting. 2013 2012 Amenc, Goltz, Lodh and Martellini explore how the relative risks induced by deviating from cap-weighting methods can be mitigated in the construction of smart beta portfolios in "Diversifying the Diversifiers and Tracking the Tracking Error: Outperforming Cap-Weighted Indices with Limited Risk of Underperformance." swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 17 Five Popular Factors for Institutional Investors One of the primary sources of controversy regarding smart beta is the term itself, specifically its apparent implication that traditional cap-weighted indexes are “dumb beta.” Numerous smart beta providers have tried to distance themselves from this contentious term, and a dizzying array of new names, such as alternative beta, scientific beta and systematic alpha, have sprung from providers in an attempt to differentiate and stand out in this growing market. Northern Trust uses the term “engineered equity” to describe its suite of smart beta products. These strategies sit in between the gap of active and passive management, capturing the efficiency of passive strategies while also accessing the return drivers offered by active managers, says Matthew Peron, Senior Vice President and Managing Director of Global Equity at Northern Trust. “Engineered Equity is our solution for capturing compensated risks that exist and that we believe will persist in the market,” he says. “It uses our research for portfolio construction to ensure that we capture the compensated risks and deemphasize uncompensated risks. Hence the term ‘engineered’ because we put a lot of intellectual property into the construction of the strategies.” Peron points to value, size, dividend yield and low volatility as some of the compensated risks targeted by smart beta strategies. However, tilting to these risk factors, sometimes referred to as risk premia or return premia, can have unintended consequences, which Northern Trust tries to mitigate through its engineered approach. “Uncompensated risks are typically the negative biases to a compensated risk,” explains Peron. “For example, an investment in low volatility - what we would consider to be a compensated risk – can introduce significant sector, security, and country concentrations, as well as a bias to larger companies if not carefully constructed.” State Street Global Advisors (SSgA) uses the name “advanced beta” for their suite of indices and strategies that target specific factors or risk premia. According to Ana Harris, Vice President, Portfolio Strategist for SSgA’s Global Equity Beta Solutions, State Street Global Advisors chose the name to illustrate the firm’s view that these 18 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta “Smart Beta, like risk-parity two decades ago, is an evolution of portfolio construction, whether it be in the context of a singleasset or a multi-asset portfolio.” - Public Pension, Western Europe, US$ 1-10 Billion AUM strategies are the next step in index investing, more targeted and with more thought devoted to their construction, without disparaging traditional approaches. “For us, it is very much an evolution of indexing,” she says. “We’re coming to this from our passive heritage, our passive approach, and we see these new indexes as transparent, rules based strategies that are trying to capture defined exposures or factors, and these factors have been shown to deliver better risk-adjusted returns than just your conventional indexes.” Although not everyone in the industry agrees on which risk premia factors actually beat the market or even exist, Harris points to five that are widely acknowledged: size, value, low volatility, quality and momentum. Banz (1981) documents the size premium, which is the tendency of small cap stock to outperform large cap stocks over long time horizons. Possibly the most common smart beta approach for capturing the size premium, which may also be the simplest smart beta weighting scheme of all, is equal-weighting. With an equally-weighted index, all constituent stocks are assigned identical weights in the portfolio. According to Harris, “Equally-weighted indexes, compared to a standard cap-weighted benchmark, will give less weight to larger caps and more to the smaller caps, so you’re giving yourself a small cap bias or a larger exposure to this size factor.” Fama and French (1993) document the value premium. Value stocks are those that trade at lower prices than their fundamentals (earnings, dividends, sales, etc.) would seem to suggest and hence would be considered undervalued by a value investor. According to Harris, “The value premium is the target for all the strategies and indexes that are fundamentally weighted, where stocks are not weighted by market capitalization any more but weighted by their fundamentals.” The low volatility premium, documented by Baker and Haugen (1991), is the observed long-term outperformance of lower volatility portfolios over higher volatility portfolios, such as cap-weighted. This anomaly has confounded academics for decades as it seems to violate the intuitive implication of the Capital Asset Pricing Model that higher risk securities must bear higher returns to entice investors. Ana Harris points to two general smart beta methodologies for harvesting this premium. “One way that the indexes are being constructed to capture low volatility over the long term is either looking at some optimized solutions: constructing a portfolio with a risk model that minimizes volatility,” she says. “A different approach, and more transparent sometimes, is weighting the stocks by their volatility or the inverse of their volatility, instead of weighting stocks by their market capitalization. For example, you would give more weight in that index to the lower volatility names and less weight to the higher volatility names.” Jegadeesh and Titman (1993) provide evidence of the momentum premium, which is the idea that there is positive momentum in share prices and that stocks can carry on climbing upward for longer than people might expect. This phenomenon flies in the face of the efficient market hypothesis, which argues that the price of a stock today fully reflects all available information and so any price fluctuations should be independent of past performance history. According to Harris, “A momentum index tends to focus on price momentum. These indexes try to weight names, or overweight names compared to a capweighted index, by price movements over time.” Graham and Dodd (1934) acknowledge the quality premium, the notion that higher quality companies will perform better, referring to it as “sustainable earnings power.” Similar to capturing the value premium, targeting quality also involves examining a company’s fundamentals. “For quality, we have some proprietary strategies using some quality measures. Again, we’re looking at weighting by fundamentals, such as return on assets as a measure of profitability,” says Harris. “We also look at earnings variability and long-term debt over equity. In terms of looking at leverage for a given company, lower leverage is preferred to higher leverage. Other index providers, like MSCI, use very similar metrics. They would use one metric on profitability, one metric around variability of earnings, and then they have another metric around leverage.” “Smart Beta = a new name for factors.” - Public Pension, Western Europe, US$ 26-50 Billion AUM swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 19 Factor Investing Smart Beta indexes capture exposure to market segments and risk factor premia in a transparent, rules-based manner. However, some argue that smart beta may not be the most efficient way to access these return drivers. Factor investing is one of the more popular predecessors of smart beta and has benefited from the recent buzz and new-found interest in risk premia. “Factor investing is a systematic approach to investing strategically in certain parts of the financial market that realize better returns over longer periods than investments in other segments,” says David Blitz, Head of Quantitative Equity Research at Robeco. “Leading academic studies from the seventies on, demonstrate that value, momentum, small cap and low-volatility stocks, for example, systematically generate higher risk-adjusted returns.” Since the 1990s, Robeco has been using factors, such as value and momentum, in its stock-selection model. The Rotterdam-based asset manager began targeting the lowvolatility premium in 2006. Today, the firm offers three factor strategies, which also serve as building blocks for clients looking to achieve a combination of factor exposures. Robeco’s three factor strategies are value equities, designed to capture the value premium; momentum equities, designed to capture the momentum premium and conservative equities, designed to capture the lowvolatility premium. Blitz highlights value, momentum and low-volatility as being the most important factors. Tilting to these proven factors, he says, can increase investors’ likelihood of success, citing the wealth of academic research documenting their existence and persistence in the market. “In general, value seems to be the most widely accepted factor. It is hard to find anyone today who does not believe in the value effect,” explains Blitz. “Factors such as low volatility, small cap and quality are also fairly popular. Momentum seems to be the least popular factor, for various reasons (e.g. high turnover). However, we believe that momentum is key in the mix.” Robeco has been seeing demand for individual factor strategies on the rise as well as growing curiosity about combining factor exposures. 20 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta According to Blitz, the global financial crisis triggered this increase in demand for factor-investing solutions as institutional investors have consequently become more open to new ways of investing and begun searching for new ways to diversify. “Investor interest in factor investing has grown, but it is not a fad,” he says. “First, it is based on decades of empirical research. Take for example the work by Robert Haugen on low volatility and Eugene Fama on value and small caps. And second, the process towards its adoption has been slow. It took a worldwide crisis and several decades to obtain the acceptance it deserved from academia and professional investors.” “The prospect of a more favorable risk/return ratio is very appealing to investors,” claims Blitz. “Other benefits are increased diversification and transparency on how individual factors help to drive returns. Factor investing also strengthens the negotiating position of asset owners because it becomes very clear what kind of exposure they get, and they can easily compare prices with competing propositions.” “Factor investing is derived from classic academic insights, but only recently have some large investors started to implement factor investing in a systematic manner,” says David Blitz. “Smart beta is one way of implementing factor investing, namely by passively following indexes designed to benefit from factor premiums. However, our research shows that this is not the most efficient way to implement a factorinvesting approach.” According to Blitz, smart beta indexes are not specifically designed for capturing risk factor premia in the most efficient manner but primarily for simplicity and appeal instead. Robeco advocates a more sophisticated approach to factor investing, which can be used as a complement or substitute for other active strategies like fundamental investing. The criteria for selecting stocks as well as deciding when to rebalance the portfolio are active decisions. However, the only truly passive strategy, Blitz says, is tracking a market-capitalization-weighted index. “It can be a substitute for marketcapitalization-weighted indexes, but more cautious investors could start by assigning only a portion of the total portfolio to factor investing,” he says. “One of the disadvantages of passive investing is that the market-weighted index also includes less attractive segments of the market, such as highvolatility and growth stocks. Passive investing in a market-weighted index runs contrary to a great deal of scientific evidence on factors.” Although factor investing touts numerous advantages to other forms of investing, it is not without drawbacks. According to Blitz, “Individual factors can have periods of substantial underperformance. We therefore recommend taking a diversified approach by choosing more than one factor. Diversifying across factors reduces relative drawdowns. Others worry that factor premiums might disappear altogether in the future due to excessive investor interest. As factor investing is still only a marginal phenomenon, we are not worried that this might happen in the near future.” “Currently only a small proportion of investors engage in factor investing. This proportion is set to rise, but we expect that it will still be a minority of all investors,” he says. “Furthermore, many of the market inefficiencies are linked to institutional restraints such as the use of benchmarks, or can be explained by behavioral finance. These constraints aren’t likely to disappear in the short term, and of course human nature does not change.” Even though individual factors may experience sustained periods of underperformance, much of the empirical finance literature documents the outperformance of certain factors over the broader market over the long run [e.g. Banz (1981)]. Factor investing, Blitz notes, is especially suited for investors with long-term investment horizons, such as institutional investors like public pension funds, sovereign wealth funds and endowment funds. However, decisions about which factors to include, their weightings and how often to rebalance make implementation more complicated than that for capweighted portfolios. In addition, some institutions may need to adapt in order to properly implement factor strategies. According to Blitz, “Factor investing advocates looking beyond traditional asset classifications and instead allocating to factor premiums whose importance is widely recognized in the academic literature. This shift to factors can also have organizational consequences. Institutional investors are still focused on asset classes in different regions. Implementing factor investing would therefore involve organizational restructuring, which in some cases could be quite challenging.” Another important challenge for implementing factor strategies is the possibility of bringing unwanted exposures into the portfolio. Naïve factor strategies, Blitz says, can involve considerable risks, but these risks are typically not the source of factor premiums and can be eliminated to improve the strategies and more efficiently harvest risk premia, such as value, momentum, and low volatility. When advising clients about factor investing, Robeco measures exposures in the existing portfolio before determining which factor mix is right for the given client. “Watch out for unintended exposure to factors,” cautions Blitz. “Institutional investors often think that exposure to factors does not present a problem. But in reality this is often far from straightforward. A fund can position itself to take advantage of one specific factor, but then unintentionally underweight another. For example, a fund can buy value stocks with a negative momentum. This is why you must pay attention to the portfolio’s exposure to multiple factors.” Blitz emphasizes three key considerations for efficiently capturing factor premia: reducing risk by avoiding risks that are not properly rewarded; enhancing return by avoiding going against other factor premia; and improving after-cost return by avoiding unnecessary turnover. He says, “The optimal factor-investing solution can differ from one investor to another for two reasons: First, the optimal solution depends on investor-specific preferences for risk and return, and the investment goals; and secondly, the optimal Robeco mix also depends on the factor exposures investors might already have in their portfolio.” swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 21 The Broad Spectrum of Active and Passive Smart beta blurs the line between active and passive management. Now, institutional investors can make active decisions with their passive investments and obtain many of the benefits of both investing styles. Armed with these new tools, investors can harvest factor exposures with greater transparency. “Indexing purists would likely call active any indexing strategy that either (a) excludes a meaningful portion of the investable opportunity in its selection process or (b) weights by a measure other than market capitalization,” says WisdomTree’s Chief Investment Strategist, Luciano Siracusano. “So under this definition, smart beta, or the Dow Jones Industrial Average for that matter, would be considered actively managed. WisdomTree believes rather than get bogged down over a semantic sinkhole, investors are better served by realizing indexes exist on a continuum from passive to rules-based active, depending on the frequency of the rebalancing, selectivity of the stock selection and weighting methodology. We believe our fundamentally weighted indexes combine elements of passive and active management.” From State Street Global Advisors’ point of view, smart beta strategies, or advanced beta strategies as they prefer to call them, have passive aspects and active aspects. On the passive side, Ana Harris, Vice President and Portfolio Strategist for the firm’s Global Equity Beta Solutions team, points to their rules-based, transparent implementation and the regular cycle of rebalancing that brings the index back to the targeted exposure. The costs are closer to traditional passive investments than to active manager fees, but she notes there is an important element of these strategies that is active. “How we describe it is that there is a certain level of an active decision compared to before. With just active or passive, you decide either to just own the whole market or you believe active managers can outperform their broad markets,” she says. “When you’re coming into advanced beta, at least you think that the primary decision to influence your returns will be around what factor you want to emphasize. That would be an active decision from an asset owner or from an asset allocator. They have to choose to identify which 22 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta “Smart beta is just fundamental indexing.” - Sovereign Wealth Fund, Middle East, AUM > US$ 200 Billion of these factors they want to bring into their portfolios, but then on the other side you have a passive implementation. It has elements of both camps because there has to be a choice, so there has to be an active decision of what you want to emphasize, but from an implementation perspective, given that these would be indexes, or as close to broad indexes as possible, then it’s a passive implementation.” Matthew Peron, Northern Trust’s Global Head of Equity, claims “Engineered equity strategies can span the spectrum from index to more active implementations. For example, our FlexShares ETFs are implemented as index-based ETFs. For some of the strategies, we will provide an index if the client really wants that. Some clients want a more active design for their specifications. For instance, in separate account form, we will consider more active versions of the same strategies as they will be benchmarked against a passive cap-weighted strategy and treated as active.” According to Peron, the institutional space tends to lean more toward approaches in the more active side of the spectrum. He points to institutional asset owners’ widespread use of benchmarks for tracking investment performance and measuring goals as the driving force behind demand for Northern Trust’s more active implementations of engineered equity. According to Peron, “At the end of the day, the boards of large plans, etc. will benchmark you to the market capweighted indexes. They’ll tend to look at it as more of a way to beat their public indexes - at least that’s how it is now. But, my sense is that will change over time. In this transition phase, I think that’s how a lot of investors are using it. In our ETFs, for example, I think you can argue that they are already ahead of the game in the sense that they use it as core holdings to meet their goals rather than worrying about a benchmark.” “I think the industry is going to force active managers to sort out their value proposition around true alpha versus beta,” he says. “I do think that the good active managers who are delivering alpha will be fine, but the active managers who are delivering excess returns via betas will be more challenged because people recognize that they can get those betas through engineered strategies with greater consistency, without paying active fees.” “The term ‘smart beta’ is hubristic.” - Public Pension, South America, US$ 51-100 Billion AUM swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 23 How Smart is ‘Smart Beta’? Investors increasingly embrace “smart beta” investing, by which we mean passively following an index in which stock weights are not proportional to their market capitalizations, but based on some alternative weighting scheme. Examples include fundamentallyweighted indices and minimum-volatility indices. What are the advantages and disadvantages of smart beta investing? Smart beta, essentially an active strategy We are often asked whether smart beta investing is a form of passive investing. It is important to realize that it is not. Although passive management can be used to replicate smart indices, smart indices themselves are essentially active strategies. The only truly passive investment strategy is the capitalization weighted broad market portfolio, which represents the only buy-and-hold portfolio that could, in principle, be held in equilibrium by every investor. Smart beta indices are fundamentally different, because they require various subjective assumptions and choices. Their active nature is also illustrated by the fact that they require periodic rebalancing to maintain their profile. The advantage is a tilt towards factor premiums The added value of smart beta indices comes from systematic tilts towards classic factor premiums that are induced by these weighting schemes. Smart beta investing is a way to actively tilt a portfolio towards certain factor premiums such as value, momentum and low-volatility. As we are proponents of factor investing, this makes smart beta investing a potentially promising investment approach. The pitfalls arise because smart indices are not specifically designed for harvesting factor premiums in the most efficient manner, but primarily for simplicity and appeal. Fundamental indices tilt towards unrewarded risks In a fundamental index, stocks are weighted in proportion to their fundamentals, such as book value or earnings. Compared to the capitalization weighted index, a fundamental index is tilted towards stocks which are cheap on such ratios, i.e. value stocks. Studies have shown that the added value of fundamental indices is, in fact, entirely attributable to this tilt towards the value premium. Our main concern with straightforward value strategies such as fundamental indexation is that they tilt towards financially distressed firms. Studies have shown, however, that the stocks of financially distressed firms tend to underperform and that the tilt to distressed firms of naïve value strategies increases risk and is harmful to returns. This implies that the value premium can be captured more efficiently by avoiding cheap stocks of financially distressed firms. Low-volatility indices have a onedimensional view of risk Low-volatility indices are designed to benefit from the low-volatility premium: the empirical finding that low-risk stocks have similar or better returns than the market average, with substantially lower risk. Minimum- volatility indices use optimization techniques to create a portfolio with the lowest expected future volatility. The resulting portfolio tends to consist mainly of stocks with low past volatility, although it may also include some higher-volatility stocks if these help to reduce volatility through low correlations. Our first concern with low-volatility indices is SPONSORED COMMENTARY “Investors can do better by avoiding smart beta pitfalls and exploiting rewarded risks.” their one-dimensional view of risk, focusing mainly on past volatility and correlations. We believe that risk cannot be captured by a single number, and our research confirms that a multi-dimensional approach, which also includes forward-looking risk measures, is able to further reduce risk – in particular tail risk. A second concern with low-volatility indices is that they completely ignore expected return considerations. We find that there is, in fact, a large dispersion in the expected returns of stocks with similar volatility characteristics. For example, stocks which are attractive from a volatility perspective, but which go against other factor premiums, e.g. by having unattractive valuation or momentum characteristics, tend to have below-average expected returns. On the other hand , low-volatility stocks which are supported by other factor premiums tend to have above average expected returns. Momentum indices have difficulty in capturing the momentum premium Historically, the momentum premium has been at least as large and consistent as the value and low-volatility factor premiums. Although momentum strategies have shown impressive long-term average returns, they can show a large underperformance over shorter periods of time. Recently introduced momentum indices try to control this risk by limiting the tilt towards momentum stocks. But this approach prevents investors from benefiting from the full potential magnitude of the momentum premium. Our research shows that a more sophisticated momentum strategy by adjusting for systematic risk characteristics such as beta, would result in significantly better risk-adjusted returns. This strategy enables us to create a portfolio which is tilted much more aggressively towards the momentum premium, while staying within the same risk budget. Turnover is also a major concern with momentum strategies, which have relatively high turnover by definition. More sophisticated trading rules may be able to avoid unnecessary turnover. summarized as follows: although smart beta investing may be a good start, we believe that investors can do better. At Robeco we have developed more efficient ways to capture factor premiums with a research-driven investment approach, aimed at avoiding smart beta pitfalls and exploiting rewarded risks. Our research includes extensive empirical testing over longer periods of time and in different markets. Robeco uses a disciplined investment approach that enables us to capture persistent investor behavior biases in a systematic way. Do you want to know more? Equally-weighted indices only partly capture the small-cap effect Several index providers, including MSCI and S&P, have introduced equally-weighted indices. These are typically regarded as a means to harvest the small-cap premium, which is another example of a premium which has been extensively documented in literature. Please visit www.robeco.com/quant Or for further information get in touch via [email protected]. However, we believe that a word of caution is advisable here. The evidence for a small-cap premium in literature mainly concerns the smallest, least liquid stocks in the market. Equally-weighted indices actually do not invest in these stocks, but continue to invest in large and medium-sized firms. Equally-weighted indices are thus able to profit only partly, at best, from the small-cap effect considered in literature. Conclusion: a good start, but investors can do better Our view on smart beta investing can be Important Information This publication is intended for professional investors. Robeco Institutional Asset Management B.V. (trade register number: 24123167) has a license as manager of UCITS and AIF¹s of the Netherlands Authority for the Financial Markets in Amsterdam. Smart Beta Approaches for Fixed Income Numerous providers have begun applying smart beta techniques to other asset classes as well, and smart beta fixed income strategies are gaining traction with institutional investors. “We’ve seen more development on the equity side, more indexes and, in general, just more academic research around factors in equities,” says Ana Harris, Vice President and Portfolio Strategist for Global Equity Beta Solutions at State Street Global Advisors. “Slowly but surely, we’ve seen it come up on the fixed income side, and again it is looking at how the broad benchmarks are constructed and are there better ways of constructing a passive strategy for fixed income that can deliver better risk-adjusted returns over time? One of the strategies that we’re looking at in the corporate space incorporates the idea of quality. So, not just necessarily weighing the names in the benchmark by their outstanding debt but also around the ability of those issuers to pay that debt over the longer term.” As with smart beta equity strategies, much of the impetus behind the push for smart beta fixed income dates back to the global financial crisis. According to Louis de Montpellier, Global Head of Official Institutions at State Street Global Advisors, sovereign investors are open to fixed income strategies that can circumvent the procyclical investment behavior that was necessary for their portfolios in the recent crisis. “When the crisis erupted, a lot of official institutions found themselves clearly too exposed to some sovereign risk in the eurozone. So, they had to rebalance those portfolios quickly, and they realized that these rebalancing implementations were actually procyclical and didn’t help to stabilize the market,” says de Montpellier. “The same happened before when they decided to reduce some exposure of their reserves investment from the banking system in 2008-2009. That was clearly procyclical and didn’t help to stabilize the system.” Smart beta fixed income products can mitigate these issues simply by moving away from traditional fixed income weighting schemes. According to de Montpellier, these approaches could allow public investors more freedom to express their policy objectives, in a more organized and disciplined fashion, than traditional indexes. “Indexes are completely blind to policy challenges. If a central bank invests its reserves blindly following an index of corporate bonds, it will invest 26 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta a sizeable proportion of its corporate exposure in financial institutions,” he says. “This may not be optimal for limiting its exposure to the banking system because this exposure must be reserved for its policy mandates rather than for its reserve management. Similarly, a pure passive index of sovereign bonds will increase exposure to the most indebted countries, because of the market weight of the debt for different countries, and may not be optimal for official institutions which need to maintain their solidity in time of crisis.” In response to the challenges of traditional indexes for sovereign exposure, State Street Global Advisors, as well as other firms, and even some institutional investors themselves, have created sovereign bond indexes that are not purely capital market weighted or based on ratings but are, instead, weighted based on macro-indicators, like GDP, or market indicators, such as pricing of credit default swaps. Smart beta techniques can be useful for tailoring exposure to other fixed income markets as well. “For example, one of the official institutions that we are helping was trying to get exposure to fixed income markets that were not very liquid, not only for return reasons, but also for policy reasons, they wanted to have exposure specifically to certain emerging and frontier markets,” says de Montpellier. “So, we created an assessment process that used a combination of screening and sampling techniques based on investability and quality to replicate an index that we created especially for those objectives.” Advanced Beta Strategies in Fixed Income – Default Risk and Credit Momentum By Ritirupa Samanta, Head of Quant Research and Senior Portfolio Manager and Brian Kinney, Global Head of Fixed Income Beta Team The concept of advanced beta in asset markets has existed for decades. It is more established in equities where numerous studies have shown evidence of systemic return drivers in broad-based indices. Fama and French1 conclude that there are five common risk factors in both equity and bond returns. Three factors: stock size, value and momentum have proven to explain over 90% of diversified stock portfolio performance. Although the bond market has lagged equities in advanced beta strategy development, in a sense the case against the market-cap-weighted paradigm is even stronger in fixed income than in equities. Due to fragmentation and regulation, not all agents in the market are necessarily profit maximizers nor are they all able to access the same opportunity set. Central banks play a key role in these markets and have very specific non-market objectives in issuing debt and particular regulations in what they will hold. Features such as this make the fixed income space a particularly complex but fertile ground for advanced beta strategies that seek to provide exposure to specific risk factors. For example restrictions that many investors have on holding bonds which have fallen below investment grade result in the ‘fallen angels’ phenomenon. Strategies that extract the premium built into these seasoned bonds are an example of a risk premia that exists due to the unique structure of fixed income markets. We believe advanced beta should be viewed as a research-based portfolio construction approach to investing. Traditional, market-weight-based fixed income strategies do not always meet clients specific objectives. Through dedicated research and analysis, it is possible to construct rules-based portfolios that take exposures to specific risk factors or themes which investors should expect to earn a premium. The results of this process can be either risk-reducing or riskseeking relative to a market-value-weighted fund. At SSgA our research on advanced beta in fixed income focuses on recognizable phenomenon tied to familiar sources of risk in More broadly, strategies are being developed that seek to improve these markets. The concept that such premia exist across assets upon the simple concept of market-value-weighted funds. is the basis of well known studies such as Asness et al.’s “Value Methodologies ranging from fundamental reweighting schemes and Momentum Everywhere”.2 In our paper, we look closely at the to duration-hedged funds have already been introduced into the risks that underlie such steady compensation specifically in fixed market. income markets and divide our analysis into two areas of focus, But questions still remain, what does advanced beta mean for fixed income investors? Are advanced beta strategies better than credit quality and momentum. • traditional market-value-weighted funds? What are the costs corporate and sovereign credit markets. We find that ways of associated with these strategies and what is the appropriate tilting toward higher quality issues or countries show reasonable benchmark and holding period for comparison? For a long-term ability to mitigate this risk. investor, “better” can be a relative measure around risk. It can represent a deeper understanding of the performance drivers in First, we consider the issue of credit default risk both in a • Second, we complement this with a corporate credit a portfolio, a clarity that comes from a distillation of a complex momentum strategy that encompasses default and other problem to its core drivers of risk and return. Through this lens, sources of risk premia in fixed income markets. We conclude investors can build more durable, diverse portfolios that meet that credit momentum does exist in fixed income and rules the complex needs of the marketplace. This is the essence of based portfolios can be constructed take advantage of advanced beta. It is not an “improvement on active”, or even an that phenomena. evolutionary step forward for passive. It is a research discipline that seeks to simplify investment assumptions so we can better understand portfolio results. We can use this suite of strategies together to develop a framework through which to consider investible advanced beta approaches within fixed income. 1. Corporate Credit Risk and Quality If we assume that the market price will move toward the fair value, Investors perceive default risk in both the corporate and sovereign those with the widest spreads are expected to have the greatest credit markets. As we learned from our experience in the global financial crisis, depending on rating agencies to measure default profit opportunity in the next period. This process is repeated monthly and portfolios are constructed in line with reasonable risk risk is not always reliable. In our research we focused on defining and return targets accounting for realistic transaction costs. a measure of quality or health in both these markets and then The cumulative return charts shown in this paper represent evaluate their efficacy through strategies that tilt toward higher the growth of 1 unit invested at the start of the period in the “quality” names and avoid those of weaker health. Traditionally simulation described. the job of identifying and measuring such risks in the corporate sector lies with fundamental analysts poring over idiosyncratic data to uncover the extent to which such risks are mispriced. While this results in detailed and accurate individual valuation, without significant investment, accuracy declines as bond markets Figure 1: Cumulated Performance Extending Ratings and Liquidity Screen Cumulative Return 1.9 become broader and more complex. Therefore, we believe a more systematic way of evaluating credit risk or “quality” across a diversified bond fund is necessary. Most models that have been 1.7 1.5 developed in the fixed income markets to systematically evaluate credit have been based on Merton’s research.3 Although we see merit to this approach, our research has concluded that the 1.3 1.1 extension of that approach offered by Moody’s KMV model gives us better insight into corporate credit risk and quality. Kealhofer, 0.9 McQuown and Vasicek (KMV) extend the Black-Scholes Merton — Portfolio framework to produce a model of default probability. In this model the firm’s equity is described as a perpetual call 2007 2008 2009 2011 2012 2014 Time — Equal Weighted Benchmark — Cap Benchmark Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. option with a strike price when the asset value hits a default point. The distance to default is a function of firm volatility, expected net return on firm value and the current market value of the firm. Figure 2: Performance Statistics – Highest Ranked Unconstrained Portfolio The unique insight here is that the distance of default is then mapped to an expected default frequency by being calibrated to a large database of actual US corporate defaults. This then allows one to forecast the expected probability of default of companies with similar characteristics to those that did default in history. The model elegantly ties this fundamental information to a probability measure that is both related to the state of the firm’s health Return Standard Deviation Reward/Risk Max Drawdown Portfolio Equal Weight Benchmark Cap Weight Benchmark 0.074 0.047 1.573 0.083 0.058 0.061 0.958 0.149 0.066 0.059 1.124 0.116 Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. but also to the market pricing of those conditions. The main drawbacks of this signal are that it derives most of its information from dynamics in the equity market and tends to deliver the Figure 3: Performance Statistics – Extended Ratings and Applying Liquidity Cost Filter greatest information content in efficient liquid market conditions. Since the EDF (Expected Default Frequency) changes with asset volatility, it can be particularly sensitive in times of illiquidity and market stress. It is also unable to differentiate among different types of bonds based on their seniority, collateral, covenants or convertibility. We attempt to address some of these concerns in our portfolio design. Return Standard Deviation Reward/Risk Max Drawdown Portfolio Equal Weight Benchmark Cap Weight Benchmark 0.084 0.052 1.629 0.098 0.052 0.044 1.176 0.130 0.058 0.039 1.466 0.094 Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. To define “quality”, we rank bonds based on the spread between the Fair Value Score and the Option-Adjusted Spread (OAS). ID1887-EUMKT-3519_Advanced Beta Strategies in Fixed Income_v10_kh.indd 2 29/08/2014 16:46 ID 4 16:46 1.1 Unconstrained Portfolio We include all the bonds in the investment grade universe A-3 We first evaluate the information from the model output by and above that have a KMV score. This is approximately 90% building a reasonably unconstrained credit portfolio where weights of the coverage of the Barclays IG universe. Since we do not in the top 30% of our quality scores are multiplied by 1.67%, and allow for short positions, negative alpha scores (where Fair Value the bottom 30% is underweight by the same magnitude. Moody’s Spread (FVS) < Option-Adjusted Spread (OAS)) are not held and ratings from Aaa to Baa3 are considered We recognize that there the excess overweight is distributed equally among the neutral may be some issues in being able to source and trade some of portfolio in the benchmark. We construct a benchmark from the bonds. As an initial screen we use Barclays liquidity cost the universe of available bonds and show returns relative to scores and only include bonds in the portfolio that are within the this benchmark. We assume a relatively conservative 125bps of more liquid half of the universe each time the portfolio is formed. round-trip transaction cost. Figure 4: Cumulated Performance Extending Ratings and Liquidity Screen Figure 6: Cumulated Returns with and without Transaction Costs Cumulative Return 4 Percentage 1.9 3 2 1.7 1 1.5 0 1.3 -1 -2 1.1 -3 0.9 2007 2008 2009 2011 2012 Time — Portfolio — Equal Weighted Benchmark 2007 2008 2009 — Without Transaction Costs — Cap Benchmark Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. 2010 2011 2011 2012 2013 Time 2014 — With Transaction Costs Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. As we can see, tilting towards “quality” using this portfolio In order to investigate the source of the return we look at the construction approach leads to significant outperformance relative industry level net over- and underweight position through time. We to both equal weighted and cap weighted benchmarks. find that significant portfolio return comes from the overweighted portfolio through time rather than from avoiding specific 1.2 Investment Grade Constrained Portfolio underweights. We recognize that many investors are constrained to long-only or risk-constrained mandates. We extend this analysis and consider a variation of the strategy that is long only and explicitly accounts for turnover and transaction costs. We develop a long-only application that only overweights the top 50 quality names in the universe. We look at the individual industry weighting in the over- and under-weight portfolios and the subsequent contribution to returns. We note that there is a transfer of weight between financials and industrials in the latter period and an overweight to financials going into the crisis. This is a source of both i) a drawdown during the time of the crisis and ii) a participation in Figure 5: Constrained Long-Only Portfolio Performance Before Transaction Costs Portfolio Benchmark Excess Ann. Mean Ann. Std. Dev. 0.087 0.081 0.005 0.067 0.067 0.011 After Transaction Costs Information Ratio Max. Drawdown 1.297 1.216 0.479 -0.073 -0.063 -0.013 Ann. Mean Ann. Std. Dev. 0.081 0.077 0.004 0.067 0.067 0.011 Information Ratio Max. Drawdown 1.221 1.150 0.400 -0.073 -0.064 -0.013 Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. ID1887-EUMKT-3519_Advanced Beta Strategies in Fixed Income_v10_kh.indd 3 29/08/2014 16:46 Figure 7: Overweight Portfolio Weights Figure 9: Cumulated Returns of the Portfolio and Benchmark % of Market Value Cumulative Return 0.25 2.5 0.20 2.1 0.15 1.7 0.10 1.3 0.05 0.9 0.00 2007 2008 2009 2011 2012 2014 0.5 2007 2008 2009 Time — Financials — Industrials — Portfolio — Utlities 2012 2014 — Benchmark Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. Figure 8: Underweight Portfolio Weights (Inverted Scale) % of Market Value Figure 10: Sector-Neutralized Portfolio Performance 0.06 0.05 Return Standard Deviation Reward/Risk Max Drawdown 0.04 0.03 0.02 0.01 0.00 2011 Time Portfolio Benchmark Excess 0.104 0.103 1.011 0.250 0.079 0.063 1.252 0.142 0.026 0.066 0.390 0.187 Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. 2007 2008 2009 2011 2012 2014 Time — Financials — Industrials — Utlities Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. the rally and outperformance subsequent to the GFC. In conclusion, we see that portfolios tilted toward higher quality names outperform benchmarks within both unconstrained and more constrained settings in the corporate credit universe. Additionally, we also recognize that transaction costs in investment grade credit can be significant when investors look to implement Clearly, in an extreme event like the GFC, we should expect to see concepts such as these. With reasonable conditions relating to a broad fundamental impact across market sectors. The sector turnover, liquidity costs and sector biases, the portfolio shows biases lead us next to develop a version of this portfolio that material outperformance relative to broader benchmarks. We explicitly controls for industry over- and underweights. believe this approach lends itself to a spectrum of applications in 1.3 Sector-Neutralized Portfolio The strategies shown demonstrate value in using default scores both in more heavily constrained and unconstrained settings. line with varying levels of investor risk tolerance and return targets. Key Summary Points: • traditional indices. Below, we marry the unconstrained world we first introduced with the constrained specifications described in section (1.2). We form • a portfolio that is equal long/short within each broad sector and Accounting for liquidity costs and controlling for turnover is critical. then market-cap-weighted to construct the final portfolio. This ensures that there are no sector over- and underweights at the Portfolios built with a quality bias can outperform • This approach can be applied to a variety of portfolio portfolio level. We apply the liquidity cost filter to only consider applications, from enhancements to benchmark oriented funds those bonds that are within the more liquid half of the portfolio. to unconstrained total return strategies. We consider all bonds in the universe within the portfolio. 2. Sovereign Credit Risk We estimate the following model for all countries within the We next consider an application in managing default risk in the Barclays global aggregate treasury index each month. sovereign credit space. While some of the issues are conceptually ΔSpread t = α1 + β1 ΔPast Spread t + β2 ΔFB/GDP t + β3 ΔNFA/GDP t + β4 similar, the complexity in a country’s issuance and management ΔConsCPI t + β5 ΔDebt Stab Balance t + Δ6 ΔPrimary Balance t + β7 ΔGross of debt relative to a firm adds different dimensions to the External Debt/GDP t + β8 ΔGovernance Indicator t problem. At an extreme, aside from systemically important firms, a corporate default has contained risks. With the exception of a few cases like Argentina, there are actually very few examples of full-blown sovereign credit default. Instead it is the pricing and perception of sovereign credit risk that is central to identifying and avoiding periods of stress in these markets. While sovereign credit risk is a familiar risk investors face in international debt markets, designing a tractable strategy that mitigates against this is a different challenge. While a country’s level of debt is critical to sovereign risk, its sustainability depends on its composition, duration, servicing costs and a country’s potential to either inflate or generate economic growth. We aim to take all of these components into account in identifying the relevant measures of debt sustainability. Calvo (2003) makes the case for certain macro conditions to serve as multipliers of external shocks so that countries can respond to the same external shock in different ways depending on the domestic state of debt and other stabilizing conditions. Often the motivation for many alternative sovereign indices is to reduce exposure to heavily indebted countries such as Japan or potentially the United States. But a stark underweight to these large countries, that have not defaulted, may materially underperform any global benchmark and more importantly, overstate the probability of crisis to the point of being impractical. As we will see in this study, poor fundamentals are kindling but not the flame in the event of a sovereign crisis. It is the unhappy confluence of poor fundamentals and a loss of market confidence that results in a repricing of sovereign risk. We believe that by considering the interactions between these two factors, one can build a strategy that seeks to mitigate overall sovereign credit risk. Along with a multi-dimensional view of debt we incorporate expectations of inflation. The right any government has to print money and the possibility of inflating away domestic debt is a key element in handling sovereign risk. As we look ahead to a wider divergence in inflation between Europe and elsewhere, this could play an increasingly important role in managing sovereign risk. We also include a component that serves as a governance measure. We use the governance indicators published by the World Bank. This was developed to provide a view across six major areas of governance in over 215 countries globally. The index is reported annually and covers areas ranging from voice and accountability to corruption, government effectiveness and rule of law. In the At SSgA we consider the macroeconomic conditions related Global Financial Stability Report (2007)4 the IMF finds a strong to default risk as separate from the shift in market sentiment. relationship between the level of governance and the investor This is a crucial distinction when considering the information perception that can trigger a speculative run on an economy and to be gleaned from the repricing of credit risk in international trigger a crisis event. In line with this we find that lower levels of debt markets. This trigger is necessary to identify the point at governance are related to higher spreads in yield and associated which countries that may have sustained poor fundamentals levels of sovereign risk. for long periods of time will no longer be able to do so. We first consider the relationship between changes in macroeconomic fundamentals and the change in the yield spread over a reference country. We then choose the US as the reference country in the We estimate a parallel model in eurozone and find that as countries in the same region are pooled together, the relationship between macro fundamentals and sovereign risk is much sharper. global model and Bund yields in the eurozone application. Figure 11: Global Portfolio Estimation Results Regression Statistics – Coefficients Estimate and T-Statistics Global Model Coefficient Estimates T-Statistics (Mean) Adj R-sqrd (%): Intercept 0.18 0.00 9.80 FB2GDP Chg NFA2GDP Chg ConsCPI Chg DebtBal Chg POB Chg Gov Indicator PastSprd Chg 18-day ExDebt Chg -0.01 -1.77 -0.02 -0.69 0.04 0.79 0.00 2.12 0.00 1.21 -0.19 -2.19 0.31 11.31 0.01 0.32 Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. Figure 12: Eurozone Portfolio Estimation Results Eurozone Model Intercept Coefficients T-Statistics (Mean) Adj R-sqrd (%): FB2GDP Chg NFA2GDP Chg ConsCPI Chg DebtBal Chg POB Chg Gov Indicator PastSprd Chg 18-day and 22 data pts ExDebt Chg -0.01 -2.22 -0.03 -0.41 0.02 -0.22 0.00 3.03 0.03 3.50 -0.06 0.33 0.35 9.61 0.01 0.77 0.06 0.02 16.10 Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. The macro data now explains more of the overall variation as If countries continue to enjoy international investor confidence, seen in a higher adjusted R-squared. It is reassuring that the poor fundamentals can be sustained with these risks not same exact specification can be applied to a regional case and being priced in the debt market. The US and Japan are the deliver intuitive results. As we found in the global model, higher best examples of this situation. We have learned from the past levels of fiscal balance, net foreign assets and governance are however, that this stability can erode very quickly. It is here that associated with lower levels of sovereign risk as measured by the the macroeconomic view needs to be combined with an eye on yield spread. On the other hand, increased levels of debt without market sentiment. While a direct measure of such sentiment complementary inflation or growth (debt-stabilizing balance), exists in the form of CDS spreads, this market is relatively less higher levels of primary and external debt are associated with liquid than cash bonds and has a short history. To account for increased risk of sovereign crisis. The same relationships hold in this, we developed a measure that compares a country’s own the eurozone. volatility relative to its peer group and uses this as a barometer of Although the econometric model appears to deliver intuitive market sentiment toward each country. results, countries have been known to carry on with notoriously poor levels of fundamentals for long periods of time. Figure 15: CDS Spread Ireland Figure 13: CDS Spread Greece Basis Points Basis Points 600 800 600 400 400 200 200 0 Apr 2004 2005 2006 2007 2008 2009 Time 0 May 2010 Apr 2004 Volatility Ratio Volatility Ratio 10 10 8 8 6 6 4 4 2 2 Apr 2004 2005 2006 2008 2011 May 2013 2011 May 2013 Time Figure 16: Volatility Ratio Ireland Figure 14: Volatility Ratio Greece 0 2006 2007 Time 2008 2009 May 2010 0 Apr 2004 2006 Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. 2008 Time Effectively countries that the market perceives as having become materially riskier than their peers are downweighted and reallocated to those whose fundamentals are consistent with lower levels of risk. We find that this measure correlates well to the CDS spreads and Figure 17: Eurozone Portfolio Performance – Hedged Returns with Transaction Costs reflects some of the same information as we can see below in the Europe Portfolio Performance after Transaction Cost 5bps Europe Portfolio Annual Return (%) Annual Risk (%) Return/Risk Total Return DrawDown (%) Excess Return DrawDown (%) 1-Year VaR at 95% (Fifth Ptile Quart. Return) (%) Annual Turnover (%) Hit Rate (%) Benchmark Proposed Benchmark: Equally Weighted Portfolio 5.40 3.64 1.48 5.48 5.47 3.61 1.51 5.46 5.25 3.48 1.51 5.38 5.40 3.39 1.59 5.33 — 0.23 0.00 0.75 -4.56 -4.47 -4.35 -4.35 21.20 24.60 7.70 22.90 60.00 — 60.00 — Proposed: Equally Weighted Portfolio cases of Greece and Ireland. We design a simple tilting strategy to see if there is any investable value in using this information. Specifically each month countries that have breached their volatility trigger are underweighted and the full benchmark weight is distributed evenly across the five countries with the lowest forecasted change in yield spread. We apply a transaction cost of 5bps which is significantly higher than those in normal times but could be lower than that prevailing during a crisis — on average, this seems like a reasonable estimate. We find moderately strong performance in the eurozone where there has been material default risk and recent repricing of sovereign credit risk. The model ejects Greece eight months prior to its actual removal from the index. During the eurozone Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. crisis, the process reduces weight to Portugal, Ireland and Greece and reallocates toward United Kingdom, Germany, Austria, Netherlands and Finland. Figure 18: Europe Portfolio Cum Total Return (Hedged) Figure 20: Europe Portfolio Cum Excess Return (Hedged) Basis Points Basis Points 100 1.0 80 0.8 60 0.6 40 0.4 20 0.2 0 0.0 -20 2000 2002 2005 2008 2010 2013 -0.2 Time — Portfolio Return — Benchmark Return Jan 2000 2002 2005 2008 Time — Excess Return 2010 Jun 2013 Figure 19: Europe Portfolio – Under/Overweight Country Cum Excess Return Hedged Basis Points 0.8 0.4 0.0 -0.4 Jan 2000 — Underweight Return 2001 2003 2005 2007 2009 Time — Overweight Return Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. 2011 Jun 2013 We see that in terms of overall portfolio performance, there is Clearly credit quality is only one part of the full range of risks value in the model both from the countries that it underweights as faced in fixed income markets, best seen and measured at the well as those that are now overweighted. individual bond level. But some premia are best observed from an The global version of this model applies similar rules to the full aggregate perspective. universe. We expect this to be a somewhat diluted form of the As seasoned investors, we know that markets can move model applied to a relatively more concentrated region, such as directionally for extended periods of time. the eurozone. The relationships between macro-fundamentals and yield are measured for countries as diverse as Japan and Hungary within the same process. This results in somewhat weaker estimated relationships as we can see in the explanatory power of the model being materially higher for the eurozone relative to the global model. The same portfolio process applied to the global case results in slightly improved performance. More importantly, we note that the model correctly underweights many of the “Animal spirits” and “climbing the wall of worry” are both common expressions used to describe momentum. But can momentum premia be isolated? If identified, can it be exploited by a fixed income investor? As we continue to develop these concepts further, in parallel to our work on quality we have also developed a simple credit momentum strategy. 3. Investment Grade Credit Momentum troubled euro countries, even within the broader universe. Intuitively, we can see that credit spread movements are Our work on corporate and sovereign credit quality manages persistent — there is momentum. When risk appetite changes, exposure toward specific default risk in these spaces. While the it tends to take time for the market to satisfy that demand. event of an actual default is relatively rare in both spaces, we find Fragmentation of the bond market, lack of transparent pricing, opportunities in managing the mispricings in this risk relative to and the relative slowness of OTC bond trading all are contributing its fair value. Just as the KMV model provides us a theoretical factors to the time it takes to achieve a spread equilibrium (fair fair value price for corporate issues, the macroeconomic model value) price for credit. We first see evidence of momentum by generates an expected level of sovereign risk consistent with looking at the persistence in weekly IG-OAS spread changes. macroeconomic conditions. By being rooted in a sense of the The chart below shows the relationship between the current and fair or equilibrium value in each market we are able to generate past 30 weeks of IG spread changes. Clearly the first two weeks straightforward rules-based portfolios that take advantage of these of changes have a significant relationship with current change opportunities. suggesting the basis for the construction of a momentum strategy. Key Summary Points: While there appears to be persistence, given the volatility and • • Sovereign risk depends both on the state of macroeconomic shifts in the actual history of IG spreads, we consider first a model fundamentals and on market confidence. to forecast the spread itself. Particularly we draw on a universe of Portfolio construction rules that combine measures of both can mitigate sovereign risk by downweighting markets in a timely fashion before the loss of market confidence in countries with poor fundamentals results in a significant repricing of sovereign risk. sparse and intuitive economic variables, we include VIX, PE and the past two lags of the spread itself. VIX captures an element of risk aversion and PE proxies for the fact that equities can be a complement to credit in normal market environments. Figure 21: PACF for Changes in IG (Point) OAS Partial Correlation 0.6 0.4 0.2 0.0 -0.2 0 Sample Partial Autocorrelations 5 10 Significance Upper 15 Lag in Weeks 20 Significance Lower Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. 25 30 We found that the variables chosen explained around 30% of Figure 22: Momentum Regime Estimation Results the variation in spread changes. This is a reasonable result given that we are forecasting weekly spread changes. However Low Volatility Regime Results R-Squared 4.73% Parameter Intercept IGspreadt PEt TED t IGspreadt-1 PEt-1 TED t-1 Approx. Std. Err. -0.006 0.116 0.002 0.033 0.085 -0.005 -0.036 0.002 0.060 0.003 0.032 0.052 0.003 0.035 Approx. t-stat Approx. Prob (%) there was a stark difference between that predictability in high -3.666 1.932 0.587 1.041 1.631 -1.590 -1.021 0.05 6.18 33.58 23.20 10.54 11.28 23.69 relative to low volatility periods. In fact during high volatility times, the model explained close to 40% of the variation while during low volatility this would drop to approximately 10%. The signs and significance of the variables remained similar but the overall explanatory power was largely present in environments of high market volatility. This led us to consider the presence of regimes in momentum. High Volatility Regime Results R-Squared 37.1% Parameter Intercept IGspreadt PEt TED t IGspreadt-1 PEt-1 TED t-1 0.003 0.199 -0.053 0.128 0.249 -0.011 -0.004 Approx. Std. Err. Approx. t-stat Approx. Prob (%) 0.006 0.071 0.013 0.034 0.062 0.011 0.034 0.511 2.807 -4.038 3.723 4.001 -0.987 -0.121 35.01 0.78 0.01 0.04 0.01 24.51 39.60 We find that while naïve 1-week momentum is a strongperforming signal in its own right, augmenting this with a forecast of regime information is materially important. We would argue that during high volatility periods, as uncertainty in the market rises, the impact of market fragmentation, pricing frictions etc. become exaggerated, leading to a slower adjustment in prices and hence the presence of higher persistence and potential momentum profits. Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. Figure 25: Smoothed Probabilities (Blue) and IG OAS (Gray) Figure 23: Smoothed Probabilities (Blue) and Change in IG OAS (Gray) Probability Change in OAS Probability Change in OAS 1.00 0.8 1.00 8 0.75 0.4 0.75 6 0.50 0.0 0.50 4 0.25 -0.4 0.25 2 2014 -0.8 0.00 0.00 2000 2003 2006 2008 2011 2000 2003 2006 Time — Smoothed Probabilities — Smoothed Probabilities — Change in OAS Time 2008 2011 2014 — Level OAS Figure 24: Comparison of Forecast and Unsmoothed Regime for IG Model Probability 1.00 0.75 0.50 0.25 0.00 2003 — Unsmoothed Probabilities 2006 — Forecast Probabilities 2008 2011 Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. 2014 0 We can see a clear presence of regimes in spread changes. What Figure 27: Comparison of Excess Return IG Strategies is more interesting, is that the periods of higher volatility coincide Cumulative Return with known times of market stress (2008) but also identify 2.5 periods such as the “taper tantrum” in 2013. More importantly, when we look at a model of predicting regime changes, in 2.0 the move from a low to high volatility regime the model does a good job of capturing this shift in a forward-looking and 1.5 implementable fashion. We use this information to condition a simple momentum strategy. We apply a 10bps transaction cost and consider going to cash when the trading signal is not triggered. In the green line below the strategy is either long or short IG during high volatility periods. We consider a variant of this which is to go long IG only when spreads are already tightening. We allow ourselves a 6-week period to gain confirmation about the regime signal before acting on it. We compare the regime-based momentum strategy against two benchmarks. One is naïve long/short IG-OAS benchmark, effectively earning the IG-OAS spread at all times. The second is a 1-week momentum strategy, going long IG when spreads are tightening 1.0 0.5 — — — — — 2010 2012 2014 Model (Regime Dependent) Long Model (Regime Dependent) Long/Short One-week-momentum (Regime Independent) Long One-week-momentum (Regime Independent) Long Excess IG Return Key Summary Points: • There is persistence in IG-OAS spread returns, and this is disproportionately present during periods of high volatility. • Simple 1-week momentum in IG-OAS spreads is a wellperforming strategy. performance. The information ratio of this simple model is around • Enhancing 1-week momentum with regime information both in a long and long/short application adds value both in forecasting we enhance this with the regime identification either in a long- regimes and harvesting conditional outperformance. only or in a long/short application, performance is clearly further enhanced. It is comforting to note that momentum in IG spreads is 2008 Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. simple random walk model does a reasonably good job in terms of 1, with a drawdown comparable to the full model. However, once 2006 Time and short when they are widening. All results are shown with a 10bps round trip included. It is first interesting to note that the 2004 a robust and durable phenomenon, and a simple naïve application Conclusions of a 1-week rule is able to harvest this intuition. Our analysis shows At SSgA, our initial fixed income advanced beta research initiatives that the persistence and forecastability necessary for momentum to have focused on the question of momentum and credit default hold is disproportionately located in high volatile periods. When we risk both in the corporate and sovereign space. In these areas base our model forecasts to incorporate this regime information the we have developed a suite of research strategies that provide momentum model is further enhanced. ways to mitigate risks while generating outperformance. We have incorporated proxies of transaction and liquidity costs to bring the proof of concept ideas into the more realistic investible arena and Figure 26: IG Credit Momentum Strategy Performance find that the information in the original models can be translated Model Regime Dependant Return Std. Dev. Inf. Ratio Max Drawdown Long Point Ret when spread forecast < 0 Long/Short Point Ret when forecast </> 0 0.039 0.027 1.479 0.036 0.062 0.035 1.777 0.048 Random Walk Regime Independent Long PointRet when spread forecast < 0 Long/Short Point Ret when forecast </> 0 Return Std. Dev. Inf. Ratio Max Drawdown 0.029 0.027 1.043 0.055 0.060 0.038 1.578 0.048 Source: SSgA, as of August 2014. The simulated performance shown is not necessarily indicative of future performance, which could differ substantially. into material returns. As we develop these further, we are making meaningful strides into translating the theoretical presence of risk premia in these markets toward tangible and potentially profitable investment processes. 1 Fama, E. & French, K. (1992, June). The cross-section of expected stock returns. Journal of Finance, 47, 427– 465. Fama, E., & French, K. (1992, June). Common Risk Factors in the returns on stocks and bonds. Journal of Financial Economics, Volume 33, issue 1, , 3 - 56 2 Value and Momentum Everywhere. The Journal Of Finance, Vol. Lxviii, No. 3, June 2013 Clifford S. Asness, Tobias J. Moskowitz, and Lasse Heje Pedersen. 3 Modeling Default Risk, Moodys KMV Working Paper, Dec 18th 2003, Peter Crosbie and Jeff Bohn 4 See http://www.imf.org/External/Pubs/FT/GFSR/2007/01/pdf/text.pdf For more information on our Advanced Beta strategies, please contact: e [email protected] | t 020 3395 6373 | w ssga.com/oig State Street Global Advisors Worldwide Entities Australia: State Street Global Advisors, Australia, Limited (ABN 42 003 914 225) is the holder of an Australian Financial Services Licence (AFSL Number 238276). Registered Office: Level 17, 420 George Street, Sydney, NSW 2000, Australia • Telephone: +612 9240-7600 • Facsimile: +612 9240-7611. 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State Street Global Advisors Limited is authorised and regulated by the Financial Conduct Authority in the United Kingdom. Singapore: State Street Global Advisors Singapore Limited, 168, Robinson Road, #33-01 Capital Tower, Singapore 068912 (Company Registered Number: 200002719D) • Telephone: +65 6826-7500 • Facsimile: +65 6826-7501. Switzerland: State Street Global Advisors AG, Beethovenstr. 19, CH-8027 Zurich • Telephone: +41 (0)44 245 70 00 • Facsimile: +41 (0)44 245 70 16. United Kingdom: State Street Global Advisors Limited. Authorised and regulated by the Financial Conduct Authority. Registered in England. Registered Number: 2509928. VAT Number: 5776591 81. Registered Office: 20 Churchill Place, Canary Wharf, London, E14 5HJ • Telephone: +020 3395 6000 • Facsimile: +020 3395 6350. United States: State Street Global Advisors, One Lincoln Street, Boston, MA 02111-2900 • Telephone: (617) 664-7727. Web: ssga.com The views expressed in this material are the views of Brian Kinney and Ritirupa Samanta through the period ended 27 August 2014 and are subject to change based on market and other conditions. The information provided does not constitute investment advice and it should not be relied on as such. All material has been obtained from sources believed to be reliable, but its accuracy is not guaranteed. This document contains certain statements that may be deemed forward-looking statements. Please note that any such statements are not guarantees of any future performance and actual results or developments may differ materially from those projected. Past performance is not a guarantee of future results. Investing involves risk including the risk of loss of principal. The above targets are estimates based on certain assumptions and analysis made by SSgA. There is no guarantee that the estimates will be achieved. The model portfolio performance shown was created by the SSgA Fixed Income Beta team using backtesting. The model portfolio performance does not reflect actual trading and does not reflect the impact that material economic and market factors may have had on SSgA?s decision-making. The results shown were achieved by means of a mathematical formula. The model performance shown is not indicative of actual future performance, which could differ substantially. Risk associated with equity investing include stock values which may fluctuate in response to the activities of individual companies and general market and economic conditions. Moodys is a registered trademark of Moodys LLC. The information provided does not constitute investment advice as such term is defined under the Markets in Financial Instruments Directive (2004/39/EC) and it should not be relied on as such. It should not be considered a solicitation to buy or an offer to sell any investment. It does not take into account any investor’s or potential investor’s particular investment objectives, strategies, tax status, risk appetite or investment horizon. If you require investment advice you should consult your tax and financial or other professional advisor. All material has been obtained from sources believed to be reliable. There is no representation or warranty as to the accuracy of the information and State Street shall have no liability for decisions based on such information. Projected characteristics are based upon estimates and reflect subjective judgments and assumptions. There can be no assurance that developments will transpire as forecasted and that the estimates are accurate. This document may contain certain statements deemed to be forward-looking statements. All statements, other than historical facts, contained within this document that address activities, events or developments that SSgA expects, believes or anticipates will or may occur in the future are forward-looking statements. These statements are based on certain assumptions and analyses made by SSgA in light of its experience and perception of historical trends, current conditions, expected future developments and other factors it believes are appropriate in the circumstances, many of which are detailed herein. Such statements are subject to a number of assumptions, risks, uncertainties, many of which are beyond SSgA?s control. Please note that any such statements are not guarantees of any future performance and that actual results or developments may differ materially from those projected in the forward-looking statements. All information has been obtained from sources believed to be reliable, but its accuracy is not guaranteed. There is no representation or warranty as to the current accuracy, reliability or completeness of, nor liability for, decisions based on such information and it should not be relied on as such. Risk associated with equity investing include stock values which may fluctuate in response to the activities of individual companies and general market and economic conditions. Bonds generally present less short-term risk and volatility than stocks, but contain interest rate risk (as interest rates rise bond values and yields usually fall); issuer default risk; issuer credit risk; liquidity risk; and inflation risk. These effects are usually pronounced for longer-term securities. Any fixed income security sold or redeemed prior to maturity may be subject to a substantial gain or loss. International Government bonds and corporate bonds generally have more moderate short-term price fluctuations than stocks, but provide lower potential long-term returns. © 2014 State Street Corporation. All Rights Reserved. ID1887-EUMKT-3519 0814 Exp. Date: 31/08/2015 Shifting Models: A New Investing Paradigm “I think there will be a paradigm change from a focus on beating a benchmark to a focus on meeting the goals of the plans,” says Matthew Peron, Northern Trust’s Global Head of Equity. “So, if you’re a pension plan and you’re at X percent funding, you will look more at the probabilities of meeting your objective and that will drive a factor mix. Right now, who’s to say if you beat a passive benchmark by 3% a year that you’re going to meet your goal. I think people are realizing that and there will be a paradigm change to say, ‘beating my benchmark may or may not be related to actually meeting my goal, so why am I focused on the wrong problem?’” According to Peron, the widespread use of benchmarks is tantamount to assuming that there is a one size fits all approach for funds to meet their objectives. This paradigm change surrounding smart beta, he says, is being driven by investors realizing that maybe the way they think about meeting their objectives should be more tailored and more customized to their specific circumstances and needs. The Northern Trust executive highlights volatility reduction and funding status as two prominent goals that may receive more focus from public investors in the future. Giving the example of a target date investor, he points to ending asset amount or pay replacement. There are many different goals, Peron notes, depending on the type of client, where they are in their life cycle and so forth, that investors can use smart beta strategies to meet. “With engineered equity, you can do a better job of aligning your equity exposure to improve the probability of meeting your goals,” he says. “For a very simple example, if you have a 15 year horizon, the math will show that you should probably have a healthy value tilt because value tends to pay off in longer horizons. If you have a 2 or 3 year horizon, you may want a low volatility tilt.” Although he thinks this will be a long lasting paradigm shift, Peron acknowledges that it could take many years or even multiple decades for this paradigm to become the norm for institutional investors. He expects the excitement and publicity surrounding these strategies to die down but sees a parallel with the rise of the core/satellite model. “I think that after the hype blows away there will be a separation of the wheat and the chaff. The solid, well thought out strategies will continue to survive and thrive,” he says. “If you think about the core-satellite model or any previous paradigms, even the style box, they took 20 years to play out and for people to really embrace them. I think that’s the kind of thing we’re looking at here. You have early adopters followed by the second wave and third wave. In the end, people act as if it’s been this way all along.” 38 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta “Ability to achieve direct factor exposures at the broad portfolio level at index-like fees should make the core satellite model obsolete.” - Public Pension, North America, US$ 1-10 Billion AUM Smart Beta Adoption “Europe is already a little bit ahead of the learning curve compared to the US,” says Ana Harris, Vice President and Portfolio Strategist for Global Equity Beta Solutions at State Street Global Advisors. “In general, investors there are a little bit more informed about factors and the idea of investing along the lines of factors. If you look at Europe in more detail, you see that countries in the Nordics are even further ahead of the curve. We expect the adoption of these strategies to continue to grow, but you have to keep in mind that these strategies perform differently from the cap-weighted indexes, and that’s why we like them, but because they perform differently, they can at times underperform. It takes a bit of time for people to understand the different performance patterns and to be comfortable that it is really an investment for the long term, but there will be interim volatility especially if your benchmark of reference remains the cap-weighted index. So, if everything is going well and the markets are all up and running and people are less concerned, for example in the pension fund world, about their deficit or the outlook for the global economy is rosy, the research and investigation of these strategies may slow down. But, if things get a bit more challenging and investors feel like they have to look at their allocations in more detail, then you’ll have a bit more renewed interest. We’ll see demand grow, but it’s not necessarily going to be a smooth journey. It could be fits and starts depending on the overall behavior of markets and how these strategies might be performing in the short term versus a cap-weighted index.” In 2009, Norway’s Ministry of Finance commissioned professors Andrew Ang, William Goetzmann and Stephen Schaefer to examine Norges Bank Investment Management’s use of active managers after being disappointed by the performance of these mandates during the crisis. Their “Evaluation of Active Management of the Norwegian Pension Fund - Global” found many of these managers shared certain factor exposures in common and concluded that the Norwegian sovereign wealth fund should look for a more passive-like approach to capturing those factors. This report marks a turning point for smart beta as it caused numerous institutional investors to rethink the ability of active managers to deliver alpha. “Official institutions are very prudent about risks, but they feel compelled to be imaginative in terms of trying to improve their risk-return tradeoffs,” says State Street Global Advisors’ Global Head of Official Institutions, Louis de Montpellier. “We serve quite a few official institutions who manage very large portfolios, for example in the Middle East and APAC, who are showing a lot of interest in advanced beta techniques. It comes from trying to disentangle what is really alpha from what a pure beta exposure or certain factor exposures can give them.” According to de Montpellier, “some official institutions are very focused on having specific types of exposures while adhering to some very strict matrices in terms of liquidity, avoiding certain tail risk and avoiding particular consequences if they were to go directly into specific types of assets. Sometimes, it goes towards pretty sizable solutions which are very tailor made. For sovereign wealth funds, especially in APAC, it mainly depends on financial objectives in terms of risk-return, type of exposure and diversification in different asset classes.” Matthew Peron, Northern Trust’s Global Head of Equity, outlines the evaluation and adoption process as taking place in three stages. The first stage is a learning stage, marked by investors’ curiosity and inquiry. The next stage is determining which factor tilt is right for the client. The final step involves determining the best implementation approach. Northern Trust works with clients at each stage in the process. “We’re having numerous conversations with clients and prospects, and they’re quite wideranging. Some are just learning about the whole concept of a factorbased strategy because they haven’t contemplated it before; that’s a large percentage of them. The next group is more trying to understand which factor is right for them and why that factor will persist in the market because if they’re going make a tilt that way they want to be sure they will be compensated for it over time. Then, the third stage are clients who really come down swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 39 to implementation; they want to understand the best way to capture a factor and how to implement the strategy. There are some who push back on it as you might expect, but I think we’re seeing less and less of that these days. A lot of people are embracing it more and more, but there are still people who think that certain factors aren’t necessarily compensated or are artifacts of other factors, for example.” In terms of whether smart beta adoption varies based on type of client or demographic characteristics, Peron doesn’t see any pattern or any segment that have embraced more than any other. Geographically, he notes, Europe is probably the farthest along in adoption, with Northern Trust seeing more clients enter the implementation stage. Making a broad generalization, he says, the US isn’t quite as far along, but they have seen it pick up. “In Asia, we have had a lot of inquiry sessions and there’s interest, but we haven’t seen a lot of implementation yet,” he adds. “But, these things have been following a pretty predictable path, so you could have said the same thing about the US a couple of years ago. I think it is part of a paradigm change that will happen in waves in different geographies and different segments.” Peron gives a few scenarios in which Northern Trust has worked with investors to tailor engineered equity strategies to their specific “We have an enhanced index needs. “We had manager that utilizes smart beta. an insurance We are considering switching company that from our active large cap needed income manager to passive/smart beta that they were products.” unable to get via - Public Pension, North America, their fixed income US$ 11-25 Billion AUM portfolio, and they didn’t want to give up beta,” says Northern Trust’s Matt Peron. “So, we engineered a quality dividend portfolio with a beta equal to 1 and kept the concentration risk and other uncompensated risk factors down. We’ve launched it in our ETF form as well, and it’s been very successful.” “We have a lot of record keepers that use our small cap value product, and they like the consistent excess returns,” he says. “They feel safe because they know that it’s a stable product in terms of returns but also stable in terms of exposures. It’s not going to style drift. It’s not going to cheat into different sectors, so they get that consistent exposure. The beauty of the engineered concept is that you can sleep at night knowing you have stable exposures targeting the factors you want.” Challenges of Today The post-crisis world has brought new challenges to institutional investors as well as heightened awareness to risks already present in the market. Louis de Montpellier, Global Head of the Official Institutions Group at State Street Global Advisors, points to two pervasive challenges facing all major institutional investors, which may be even more acute for official institutions as they have to be more conservative given their public fiduciary duty. “On the one hand, official institutions, especially central banks as reserve managers and sovereign wealth funds in their fiduciary duty of managing public assets, are faced with the general challenges of the markets today,” says de Montpellier. “For any conservative, or simply ‘risk-aware’ large institutional investor, whether they are a pension fund, insurance company, intermediary or others, there’s the challenge of extremely low returns, especially if a big part of your assets are fixed income investments or fixed income based.” The second challenge is what he calls the changing nature of risks experienced, highlighting the global financial crisis, followed by the eurozone crisis. According to de Montpellier, “A lot of the correlations on which official institutions, like many other institutional investors, base their investment approach and analysis, 40 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta whether for prudently diversifying or analyzing credit or market risks, proved to be much more unstable than foreseen.” “Other aspects have also proven difficult to assess and mitigate during the crisis, such as the rapid deterioration of sovereign credit value, especially due to the euro crisis, and liquidity evaporating from some markets,” says the executive. “Now, for example, several segments of the fixed income markets are much less liquid as an unintended consequence of the regulatory response to the crisis, which included a higher capital charge for market makers, therefore inducing less appetite for this activity.” A New Policy Context As a result of the global financial crisis, the world has witnessed widespread deviation from traditional economic policy approaches, creating new hurdles for official institutions. What differentiates this group from the rest of the institutional investor space is their involvement in policymaking, whether they are policymakers, like central banks, or part of the policymaking framework, such as sovereign wealth funds. With official institutions’ mandates broadening into more areas and policymaking becoming increasingly complex, sovereign institutions have to account for this new policy context when making investment decisions. “For central banks, monetary policies have become much more active in concept and implementation with quantitative easing techniques as an example,” says SSgA’s Louis de Montpellier. “In addition, their supervisory powers have increased over markets, over commercial banks and sometimes other entities. And then, there is this fundamental concept, which was lacking in the pre-crisis Washington Consensus, of ‘macro-financial stability’: What are the general financial policies that can be implemented to favor macro-financial stability in the major markets and internationally, to continue harvesting the benefits of very open financial markets globally, with free flows of capital, while avoiding major crisis?” According to de Montpellier, when making decisions about investment behavior and techniques, official institutions have to be much more cognizant than before about their policy implementation in order to avoid headline risk or conflicting with their expanding policy objectives. Highlighting the importance of this new policy framework, he points to the effects on central bank reserve management of the credit risk and exposure they take on through accumulating fixed income assets in their policy portfolios for quantitative easing or injecting liquidity into the market. “As an example, before the crisis, a lot of central banks and most sovereign wealth funds were actively diversifying into corporate credit by buying corporate bonds; half of the highly rated corporate universe or investment grade universe is made of financial companies,” says de Montpellier. “During the crisis, quite a lot of central banks had to take much bigger exposure to the banking sector in order to stabilize the system. That meant, in terms of investment management, that they accumulated much more exposure to the banking sector than they had ever expected. So now, when they think about diversification of reserves they must have a more granular view about their investment into corporate credits, for instance: A mechanical diversification of exposure to corporates, whether they would be financial or nonfinancial corporations, should not be viewed as appropriate.” The new risks brought on by much more active monetary policies implies that sovereign institutions need to use a different approach for investment management than they have in the past, de Montpellier explains. “What I see among official institutions, especially central banks but also very much sovereign wealth funds, is that they are much more aware of risk and changing risks, such as liquidity or unstable correlations between different parts of the market, whether it’s credit risks or market risks,” he says. “They are also very aware of tail risks, with a much more granular use of stress tests to analyze those risks. But, there’s also beginning to be a recognition that some classical investment strategies need to be reviewed in view of the broader and much more active policy mandates that the whole central bank and sovereign wealth fund communities are experiencing now.” swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 41 The New Normal The aftermath of the financial crisis, with increased regulation, heightened risk awareness, and the recent departure from traditional monetary policies in favor of more active approaches, has left economists and finance professionals grappling with the question of what will be the new normal. “Investors in general may slowly forget about the crisis, but I think it will take a very long time for official institutions as investors to overlook the lessons of the crisis,” says Louis de Montpellier, Global Head of Official Institutions for State Street Global Advisors. “I think that it has been a game-changing experience, especially in terms of risk awareness and a holistic view about risk. The world is a risky place, and it’s not enough to rely on statistics from the past. You need scenario analysis, comparative history, and institutional awareness.” “I think the new normal will be a more controlled financial sector, hence all the interest in ‘shadow banking’ and other, less regulated, financial market corners , combined with, if not more active, then at least more vigilant monetary policies as one of the instruments to not only guarantee price stability but also help financial stability,” says de Montpellier. “I think we are over the benign neglect that we had in the Washington Consensus, where the monetary authorities were purely in charge of controlling inflation through interest rates. There was a lot of confidence in financial market autostabilization and in the right dispersion of financial risks to the most efficient agents. I think the authorities around the world will continue for a long time to be very much aware and build much more consensus around what is global and national financial stability. They will look at supervisory measures and macro-instruments that can be put in place and used from time to time to stabilize the market when there are pressure points or vulnerabilities.” In addition to the fallout from the crisis and its regulatory and policy consequences, de Montpellier points to another challenge for global investors that is becoming ever more apparent: geopolitical risk. He says, “Numerous regional tensions demonstrate that there are still very different references in the approach to national identities, a stark lesson from history that we have neglected in the Western world with the end of the cold war. These tensions lead to real geopolitical challenges. That, I think, is a new dimension of risk that needs to be analyzed and needs to be 42 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta “We need to study further whether this smart beta really works.” - Sovereign Wealth Fund, East Asia, US$ 51-100 Billion AUM taken into account when you invest globally.” To address these geopolitical risks, de Montpellier recommends that institutional investors, especially those with sizeable assets under management and a need for broad diversification, should draw geopolitical scenarios related to their exposures to gauge the impact of surges in regional tension on their portfolios. “For example, what is the impact of deep tensions in the Middle East on the energy markets over the next two to 10 years? Investors who are exposed to global equity markets, fixed income markets and other markets need to have a way to mitigate the tail risks if they see those tensions rising, given their impact on energy markets,” says de Montpellier. “Another example is renewed East/West tensions, especially thinking about Russia, which can have an impact on energy markets but also on parts of the emerging market universe. I would say the main base scenario that you need to build is around the monetary and fiscal policies of the main drivers of economic growth, which are the US, Europe, Japan and the major emerging markets, especially the BRIC countries.” “What is smart beta?” - Central Bank, Oceania, US$ 1-10 Billion AUM Strategies for Coping with the New Normal In recent years, sovereign institutions have been striving to discover strategies that enhance performance while at the same time adhering to their increased focus on risk. One upcoming challenge, SSgA’s Louis de Montpellier notes, is that the amount of exposure their fixed income portfolios have to the highest credit sovereigns implies that they will underperform because the whole market expects interest rates to increase in each of those markets, not necessarily all at the same time but all at some point. Institutional investors recognize this problem and are looking at ways to avoid it. According to de Montpellier, “Some of them simply look at diversifying fixed income portfolios in classical ways, like diversifying along the yield curve or ‘barbelling’ the portfolio, taking longer-term exposures and short-term exposures while keeping the same type of duration. We see institutions trying to diversify into other sovereign bonds or looking at the largest emerging markets. We also see a lot of interest in commodity currencies, such as Canadian dollars and others that are not generally categorized as reserve currencies but still very highly rated.” Although numerous public investors are still turning to classical strategies in today’s economic climate, according to de Montpellier, one traditional strategy is getting hit with more skepticism: active management. He says that State Street Global Advisors is hearing a lot of sovereign wealth funds and central banks express suspicion over the effectiveness of active management in developed markets. One reason, he notes, is that active managers were unable to protect their institutional clients during the crisis. “Also, one of the responses to the crisis through active monetary policies is trying to make the market as efficient as possible so that they are the best transmission mechanisms into the real economy. We see that with the active involvement of central banks in the US, Eurozone, and Japan,” says de Montpellier. “It is more difficult than before to argue that independent managers in those markets, whose trends and behaviors are at present dominated by central bank policies, are going to be able to create permanent excess return over the medium term.” swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 43 SWFI ® MAKE YOUR RESEARCH SEEN > SWFI Custom Publishing Informative Content Marketing Most content marketing organizations understand marketing, but lack intimate knowledge of the institutional investor space. At SWFI our content marketing team is comprised of researchers that have a deep understanding of public asset owners, including their investment styles and trends. These insights help us to provide better, more relevant data to the groups you are most interested in targeting. For more information contact: SWFI Vince Berretta Phone: +1 702.768.0703 Email: [email protected] Dynamic Timing of Advanced Beta Strategies: Is it Possible? by Ric Thomas, CFA and Rob Shapiro, CFA, CAIA Investing in advanced beta strategies has evolved from a niche concept into an established investment belief among many institutional investors. The widespread acceptance of transparent, rules-based strategies that seek to achieve active-like performance by capturing specific risk premiums in the market is confirmed by various studies and surveys, along with our own experience in working with asset owners. We welcome this trend — it is a topic we have long championed. Initially, investors’ main consideration centered on which of the advanced beta equity premiums — such as size (focused on the added premium provided by small-cap stocks), value (provided by stocks currently out of favor despite strong fundamentals), quality, or low volatility — made the most strategic sense in a diversified investment plan. But more recently, investors have started to ask whether they can dynamically rotate the timing of their exposure to these factors over the long term. The timing question is an important one, because missing from the advanced beta conversation so far has been a discussion of the valuation of these factor portfolios. When investors seek to alter a strategic allocation to equities, high yield bonds, or any other asset class, they naturally look to valuation for guidance. A sharply rising P/E ratio on equities, for example, is often impetus for some investors to reduce their equity allocation in favor of a cheaper growth asset class. Similarly, if we think of advanced beta portfolios as refined asset classes, it makes sense to attempt to measure their valuation over time. Realizing the usefulness of this information for investors, we developed a simple method to track the valuation of advanced beta factor portfolios. We also confirmed that historically advanced beta premiums are positively related to these valuations, meaning the method can help forecast strategy performance. This analysis can, thus, help investors make a more informed prediction of the long-term prospects for their advanced beta portfolios and provide guidance for dynamic rebalancing of portfolio weights. A Valuation Model for Advanced Beta Timing Various studies show that a valuation-based approach provides reasonably accurate long-term forecasts for asset prices. In particular, Campbell and Shiller (1998)1 showed that simple aggregate valuation ratios, such as price-to-earnings, dividend-to-price and book-value-to-price, could accurately predict long-term equity market returns. They argued that P/E multiples are relatively constant in the long-run, always reverting back to a historical norm. Therefore, a high P/E ratio necessitates that either the numerator (P) must fall or the denominator (E) must rise in order to bring the ratio back into equilibrium. In fact, they found it was price, not earnings that adjusted to restore balance, dealing a blow to believers in market efficiency, who would have predicted that higher prices reflect a perfect sharing of information about the prospects for earnings growth (but, as we know, the efficient market hypothesis does have its holes). This finding raises important questions for adherents of factor-based investing. Can valuation ratios also forecast the returns on advanced beta portfolios? Are returns for low volatility equity portfolios (or for quality, small-cap or value, for that matter) poor when valuations for those kinds of stocks get expensive? Can we measure this valuation and is it possible to create a rebalancing rule that favors attractively valued advanced beta portfolios over time? The answer to all these questions, as we suggest above, is yes. Precision and Timing: A Winning Combo for Implementing Advanced Beta The issue of timing has long been part of the discussion surrounding advanced beta strategies. Investors often become interested in advanced beta because of a view they have about a particular risk premium in the market-for example gravitating toward a low-volatility-tilted factor portfolio out of a concern about the exposure of their existing holdings to tail-risk volatility. The challenge with this approach is that a factor that may be compelling during one market regime may be less so in another. So some investors opt for a multi-factor portfolio, which combines multiple factors in one portfolio to take advantage of the potential diversification that a combination of risk premiums provides over time. This method cuts down on the cyclicality of choosing one factor and reduces turnover and transaction costs, offering a kind of set-it-and-forget-it approach to implementing advanced beta across a portfolio. While the multi-factor approach offers much in the way of convenience, it provides for a somewhat limited level of precision and performance attribution. As such, many investors prefer a third approach in which they separate advanced beta factors into various distinct component portfolios, each organized around and tilted toward a single attribute. That way, if investors become more bullish on valuation and less so on low volatility, they can dynamically re-weight the overall portfolio in the same way that they might alter their equity-to-fixed income allocation. The model we develop here for timing advanced beta factors can be a particularly good complement to those investors who favor segmenting their advanced beta allocations. Analysis Measuring the valuation of the various attributes themselves we believe is straightforward. We simply divide the universe of stocks in the MSCI World into four different key attributes — quality, low volatility, size and value. Within each attribute, we determine the top and bottom quintiles (i.e., the 20% highest quality and lowest quality stocks, 20% least and most volatile, etc.) and then calculate the median book-to-price ratios for each pair of quintiles. Finally, we take the difference between these two ratios. A large spread between the ratios for the top and bottom quintiles implies that the attribute is attractively priced, and a low number suggests that the attribute is expensive. Figure 1 plots these valuation ratios for the four distinct advanced beta attributes over time. Consistent with Campbell and Shiller, the chart shows that the valuation ratios are relatively constant. An extreme level of cheapness (such as found in size in 1999) tends to correct itself and reverts back to a more normal ratio (such as found in size in 2004). This observation leads to the question as to whether it is prices, or book values, that adjust to restore this equilibrium. Figure 1: Valuation Spreads of Advanced Beta Attributes Value—Median B/P Spread 2.0 Attractive 1.6 1.2 0.8 0.4 0.0 Expensive Jan 1987 — Spread 1993 — Average Spread 2000 2007 1 Std Dev Above May 2014 1 Std Dev Below Size—Median B/P Spread 1.0 Attractive 0.8 0.6 0.4 0.2 0.0 Expensive Jan 1987 — Spread 1993 — Average Spread 2000 2007 1 Std Dev Above May 2014 1 Std Dev Below Low Volatility—Median B/P Spread A simple plot of the year-end valuation spreads relative to forward subsequent returns indicates that, as with equities as a whole, it is prices that adjust, and valuation spreads can help forecast advanced beta returns. In Figure 2, each point represents the book-to-price spread as of May 30, for each of the various factors, between 1993 and 2010. The Y-axis shows the subsequent three-year excess return over the cap-weighted index of a long-only advanced beta portfolio organized around that factor. The figure suggests that the return premiums to advanced beta portfolios are time-varying but predictable. While the plots confirm a positive relationship between valuation spreads and subsequent returns, two challenges arise from fully implementing a strategy based on this finding. First, the coefficient of determination, or “R-squared,” of these four relationships varies between 0.15 and 0.30. You can see this intuitively by observing the relatively wide dispersion of the points around the lines in Figure 2. Hence, while the advanced beta portfolios may be predicable, there is certainly some margin for error. Second, for each of the factors, the majority of the data points lie above 0% on the Y-Axis. In other words, for many investors, a simple buy and hold methodology with periodic static rebalancing may be enough, since in the long run many of these advanced beta portfolios tend to perform well, even if the starting point of valuation isn’t optimal. 0.4 Attractive 0.2 0.0 -0.2 -0.4 -0.6 -0.8 Expensive Jan 1987 — Spread 1993 — Average Spread 2000 2007 1 Std Dev Above May 2014 1 Std Dev Below Quality—Median B/P Spread 0.0 Attractive -0.2 -0.4 -0.6 - 0.8 -1.0 Expensive Jan 1987 — Spread 1993 — Average Spread 2000 1 Std Dev Above Source: SSgA, MSCI, As of 31 May 2014. 2007 May 2014 1 Std Dev Below Figure 2: Valuation Spreads Versus Future Excess Returns The Results? Value—Subsequent 3-Yr Excess Returns (Percent) Let’s now test one simple rule that illustrates how an investor might apply this analysis to a dynamic rebalancing method. Of course, armed with our analysis, there are many different approaches an investor can take, and the method shown here is but one of many possibilities. 9 6 3 0 -3 0.0 0.5 1.0 1.5 B/P Spreads Size—Subsequent 3-Yr Excess Returns (Percent) 10 5 0 -5 -10 -15 0 0.2 0.4 B/P Spreads 0.6 0.8 Low Volatility—Subsequent 3-Yr Excess Returns (Percent) 20 10 0 -10 -20 -0.6 -0.4 -0.2 B/P Spreads 0.0 0.2 The benchmark is a static equal-weighted portfolio of four advanced beta component portfolios — size, valuation, quality and low volatility. For a possible implementation of a timing strategy, an investor could simply divide a portfolio initially into the same four equal weights. The investor would rebalance the overall portfolio monthly, and continue to allocate to each component equally, unless the book-to-price spread declines by such an amount that it reaches a standard deviation of -1 relative to the average spread (i.e. becoming too expensive). In that case, the timing method completely sells out of the expensive portfolio and reallocates the capital equally to the remaining three. Additionally, in order to mimic a “long-term mindset,” once a component portfolio is sold it cannot be repurchased for three years unless at some point the book-to-price ratio improves to a standard deviation of +1 relative to the average spread (i.e., becoming cheap again). Figure 3 shows the historical performance of this methodology. The chart shows that the timing method would have added value over a purely equal-weighted method fairly consistently over long periods of time. The example methodology we outline here does not have overly high turnover and transaction costs, and we believe that the added value should still be significant when such costs are accounted for. This finding provides some hope to investors wishing to maximize the return premiums of their rules-based equity portfolios. It also highlights the importance, in general, of understanding the valuation characteristics of factor portfolios before making a long-term investment in them. Quality—Subsequent 3-Yr Excess Returns (Percent) Figure 3: A Dynamic Advanced-Beta Methodology 6 12 4 10 2 8 0 6 -2 -4 4 -0.8 -0.6 -0.4 B/P Spreads -0.2 0.0 2 0 Source: SSgA, MSCI, as of 31 May 2014. The calculation method for value added returns may show rounding differences. Past Performance is not a guarantee of future results. Jan 1993 — MSCI World 1998 — Static Weights 2003 2008 Dec 2013 — Dynamic Weights Source: SSgA, MSCI, as of 31 May 2014. The Importance of Timing and Valuation It makes sense to have a good understanding of valuation before making long-term allocations to any asset class, and valuation estimates can be applied to factor portfolios as well as they can to traditional asset classes. For those who because of turnover or other considerations do not wish to implement a dynamic re-balancing process, the valuation methodology we present can be used to at least time their entry into the factor, or factors, of their choosing or help them decide which to access when. Given the long-term better risk-adjusted performance of advanced beta factors generally, we believe the important thing may be merely to be invested in them. However, valuation does matter in the future performance of advanced beta portfolios over time, and implementing a simple rulesbased dynamic rebalancing method based on valuation indeed offers the potential for enhanced returns. 1 Campbell, J. Y., and R. J. Shiller. “Valuation Ratios and the Long-Run Stock Market Outlook.” Journal of Portfolio Management 24.2 (1998): 11-26. For more information on our Advanced Beta strategies, please contact: e [email protected] | t 020 3395 6373 | w ssga.com/oig Implementation Considerations of Smart Beta Institutional investors have a myriad of techniques available for implementing smart beta strategies, and it is important for them to determine which methods and vehicle types are right for their specific needs. Larger and more sophisticated institutions may have the capability to manage smart beta portfolios internally, while others may need to work with external managers and consultants to ensure they achieve their desired factor exposures in the most efficient manner. At State Street Global Advisors, as well as other large passive providers, implementation methodology comes from a discussion with the client. “We have pooled funds, we have ETFs, we have segregated mandates, and it really depend on the client’s size and their individual circumstances,” says Ana Harris, Vice President of Global Equity Beta Solutions for State Street Global Advisors. “They might have restrictions, and it also really depends on their beliefs around these factors. Although we say that there are these five factors, some people may not agree with all of them. Some people might actually say that size or small cap hasn’t worked for a long time and doesn’t exist anymore. So, it would really depend on what the client believes, and then we would work with them either trying to identify a third-party index, like a FTSE index or an MSCI index that we can track for them, or we can build something more customized. Afterward, it is basically deciding what type of vehicle would be more relevant to them. In the sovereign wealth fund space, most of our clients tend to have segregated mandates because they will have a few more restrictions than the normal institution will have, and also because of their size, it is probably more attractive for them to have it in a separate account than to have it in a pooled fund or ETF.” Luciano Siracusano, Chief Investment Strategist at WisdomTree, says, “If you are adding to allocations you already have, think about how the addition of the smart beta indexes gives you additional exposure to distinct return premia. Looking at allocations through traditional lenses may not work with smart beta. For example, there is no ‘growth premium.’ So rather than evaluating exposures based on value or growth tilts, it may make more sense for smart beta investors to evaluate portfolios in terms of value 48 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta and momentum and quality tilts. Over time, it may be the ‘momentum’ and ‘quality’ or ‘profitability’ return premia that helps growth stocks outperform in certain environments, not necessarily the fact that the growth stocks carry higher valuations.” According to Siracusano, WisdomTree, in its broad-based indexes, seeks to gain broad representative exposures to equity asset classes and then weight those markets annually by income. The company’s dividendbased strategies weight based on the regular cash dividends companies pay to shareholders. Its earnings-weighted indexes weight index components based on the earnings generated in the prior year. WisdomTree indexes rebalance annually back to these respective income streams, in an attempt to identify and capitalize on relative value within markets. The firm also has select strategies that aim to identify stocks within equity markets that display either high dividend yields or growth characteristics that could lead to higher dividend growth than the overall market. “Implementation is similar to capweighted portfolios,” says Siracusano. “The WisdomTree indexes rebalance annually. Additions and deletions to the indexes and changes to index component weights are implemented in accordance with the underlying rulesbased methodology, by independent index calculation agents. Portfolio managers running the index funds or segregated accounts then buy and sell securities and rebalance the portfolio so that index funds can track the underlying indexes as closely as possible after fees and expenses. During the rest of the year, there is very little turnover. Stocks are deleted from the dividend indexes if they cancel their dividend payments, delist from major exchanges or file for bankruptcy. But for the most part, turnover is low. During the year, changes in index component weights are largely driven by changes in the stock prices of index components, similar to an index weighted by market value, until the next annual rebalance.” “There is no free lunch in indexing,” he warns. “There are always trade-offs. More precise exposure to a risk premia may mean owning a narrower slice of the market – which can increase tracking error, thereby making it more difficult to generate higher information ratios. The factors one selects to access a return premia matter. Just because something worked in a backtest, doesn’t mean it will also work in real time after transactions costs, market impacts, fees and other expenses enter into the equation—including the liquidity or lack of liquidity in some of the components. Investment capacity is important – both for limiting market impact at rebalances, and for longerterm allocations, so that the investment does not ‘close’ to new investors. No strategy is likely to work in all market environments. WisdomTree smart beta strategies seek to take advantage of ‘reversion to the mean,’ and therefore need to be evaluated over full market cycles.” “We are in the process of evaluation.” - Endowment, Middle East, US$ 11-25 Billion AUM swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 49 Getting a Smoother Ride: The Power of Quality Northern Trust places extra emphasis on improving its engineered strategies through accessing the quality premium. To measure quality, the firm uses a multi-factor approach that employs fundamental factors applied quantitatively, looking at management stewardship of capital, such as management behavior, their use of cash, cash generation, and profitability. According to Matthew Peron, Managing Director of Global Equity at Northern Trust, this quality measure serves as a buffer in times of stress by smoothing some of the drawdowns, helping to improve strategies in an excess return dimension and in a risk/ return dimension. “We have a unique approach in that we include quality, through our proprietary quality measure, in just about all of our strategies,” says Peron. “The pairing is unique because it enhances your risk/return profile and really pushes up your Sharpe ratio. This gives our clients a much smoother ride when factor cycles are going against you - as they will do from time to time. Quality generally has a lower correlation to other factors, so it’s an important way to enhance the risk/return profile of our portfolios. That’s been a unique part of our platform that I think has resonated well with our clients.” Northern Trust has mixed quality with other factors, such as value, size and dividend yield in its engineered equity products. The firm’s longest running strategies are its quality small cap strategies: small cap core and small cap value. Their Quality Dividend Focus strategy, launched almost four years ago, has probably grown the most quickly, turning into a several billiondollar strategy, according to Peron. The quality factor is integral in Northern Trust’s focus on risk-adjusted returns and delivering consistent returns, as defined by high information ratios. The appeal of engineered equity strategies, Peron says, is that clients get a smoother ride due to high information ratios. “With our focus on risk-adjusted returns, we look to maximize information ratios for our Engineered Equity strategies,” he says. “There are two primary ways that we can do this. First, we use a proprietary measure we call the factor efficiency ratio, which is, conceptually speaking, the ratio of compensated risks to uncompensated risks. This focus drives higher information ratios for our strategies. Second, we use a multi-factor approach, combining our proprietary quality measure with another factor. Because of its lack of correlation, the inclusion of our proprietary quality factor tends to drive a higher information ratio by smoothing the factor cycles.” 50 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta “There needs to be further standardization of smart beta products as it encompasses too many products at the moment.” - Strategic State Fund, North Africa, US$ 1-10 Billion AUM The Risk of Unintended Exposure “The key concept that we emphasize to clients is that if you’re going to do a ‘smart beta’ strategy, watch out for uncompensated risks,” says Northern Trust’s Matthew Peron. “You may target low volatility, for example, and then you capture the low volatility but realize you just introduced a huge sector concentration or you’ve introduced an anti-momentum bias or something like that. What you really want to do is make sure you have a high factor efficiency ratio. Part of our optimization is to engineer out these unwanted or uncompensated risks through our optimization process, which we are constantly assessing and improving over time.” The unintended exposures brought into a portfolio by smart beta strategies can offset or even neutralize an investor’s intended factor tilts. Looking at Peron’s example, the anti-momentum bias of a low volatility strategy could nullify the intended exposure of a momentum strategy. To avoid this potential problem, Northern Trust advocates a top down view of portfolio construction when evaluating smart beta strategies. “Some people just want to try it, and they put in 2% and see how it goes. In some sense, that’s not going to get you anywhere. It’s a good way to sort of monitor the strategy, but it’s barely going to move the needle,” says Peron. “The way we recommend that our clients use engineered strategies is to take a holistic, top down look at their plan and look at what exposures they have and what exposures they want, and then migrate their portfolio to that. What that generally means is that they’ll need a healthy allocation, say 10 to 30%, to a factor-based or an engineered strategy to move the dial to get to the factor tilts that they want.” In lieu of this difficulty, clients have approached Northern Trust, expressing that they are not committed to their active managers and would like to reconstruct their equity portfolio from scratch. Peron says they’ve had “one or two multibillion dollar plans come and say they’re ready to go with a white sheet of paper.” He does not think these are isolated incidents and expects this trend to not only continue but start increase in frequency. “I think the most common reason is that some of these plans have 20 active managers, and when they calculate the exposures they find that they all neutralize. They’re paying for all these active managers and getting the index. That’s expensive indexing,” says Peron. “What we counsel is if you have a few active managers that you really believe in, keep them. Then, we can work with the rest of your portfolio and your risk budget to get the factor tilts you want.” Dispatching with active managers altogether is not necessarily required for adopting smart beta. According to Peron, “If they don’t want to start with a white sheet of paper, we can obviously use engineered strategies to offset biases they already have. If they say they’re really wedded to certain managers in some part of the portfolio and we analyze it and find a certain factor exposure they don’t want, we can use our strategies to swing them back to the other way.” “In general, I believe smart beta strategies are important for both equity and non-equity markets including commodities. Their contribution to returns through efficient tactical shifts in the investment portfolio is likely to increase over time especially among institutional investors.” - Sovereign Wealth Fund, Middle East, US$ 1-10 Billion AUM swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 51 WHITEPAPER The rise of factor investing Investors increasingly decide to allocate strategically to factor premiums such as the value, momentum, and low-volatility premiums. We argue that generic factor strategies are suboptimal because they involve significant exposures to unrewarded risks, and that more efficient factor investing approaches are able to generate superior risk-adjusted returns. We also discuss how the optimal combination of factors in an institutional portfolio depends on investor preferences regarding risk and return. The rise of factor investing Is the capitalization-weighted broad market index an efficient portfolio? This is a fundamental question which every investor should think carefully about. If the answer is affirmative, investing is pretty simple. All an investor needs to do in this case is to simply replicate the broad market index, which can be done at minimal costs nowadays. The academic literature, however, indicates that this is probably a suboptimal approach. Numerous studies in the stream of literature on empirical asset pricing have shown that stocks with certain factor characteristics deliver superior risk-adjusted returns. Examples of such factor premiums include the value effect, the momentum effect, and the low-volatility effect. However, this does not mean that it is easy to ‘beat the market’. In fact, since all investors together comprise the market, it is not surprising that mutual funds on aggregate have been found to underperform the market after fees and expenses. Interestingly, however, the literature on mutual fund performance evaluation also documents that certain groups of funds do succeed in systematically generating superior results. Moreover, there appears to be a close link between these studies and the asset pricing literature, as most of the alpha of winner funds can, in fact, be explained by the same factor premiums which have emerged from the asset pricing literature; see, e.g. Carhart (1997). In other words, the best mutual funds appear to benefit from proven factor premiums. Practical implications But what are the practical implications of these findings? The answer was recently provided by three renowned professors: Andrew Ang (Columbia Business School), William Goetzmann (Yale School of Management) and Stephen Schaefer (London Business School). These professors were consulted by Norges Bank 1 2 3 Investment Management (NBIM), one of the largest investment managers in the world, responsible for managing EUR 700 billion of Norway’s oil wealth, to critically evaluate the added value of its active management. In line with Carhart (1997), they found that approximately 70% of all active returns to NBIM since its inception in 1998 could be explained by exposures to various systematic factors. The analysis also highlighted that these factor exposures were actually a byproduct of bottom-up security selection by the managers hired by NBIM. The authors recommended NBIM to begin using a more top-down, intentional approach to strategic and dynamic factor exposures, and to examine how individual factor premiums could be harvested in the most efficient manner. After this research was made public in 2009, strategic allocation to factor premiums was dubbed by some as ‘the Norway model’. Robeco has contributed to this debate by conducting a study on how investors may apply factor investing to their equity portfolio in practice. We found that the value, momentum and low-volatility premiums have been particularly large and robust over time and over different markets. Even using more conservative expected returns for the future, we found that the optimal allocation to these premiums should be sizable. Moreover, the allocation to factor premiums should be diversified (where a simple 1/N approach already seems to be quite efficient) and determined strategically (in order to avoid chasing recently winning styles).1 Interestingly, similar factor premiums appear to be present in other asset classes, such as bonds2, and commodities3. ‘Stocks with certain factor characteristics deliver superior risk-adjusted returns’ Blitz, “Strategic allocation to premiums in the equity market”, Journal of Index Investing, 2012 Houweling, et al, “The low-risk anomaly in credits”, in Low-Volatility Investing, 1st edition, Rotterdam, Robeco collection of articles, 2012 Blitz and de Groot, “Strategic allocation to commodity factor premiums”, forthcoming Journal of Alternative Investments Efficient Factor Investing Strategies Authors David Blitz, PhD Joop Huij, PhD Simon Lansdorp, PhD Pim van Vliet, PhD Pitfalls of factor indices One way to capture factor premiums in practice is by following an index which is either explicitly or implicitly designed to benefit from factor premiums. Examples are value- weighted indices, equalweighted indices and risk-weighted indices. Several index providers (such as MSCI and FTSE) provide such alternatively weighted indexes, and passive managers have introduced index funds and exchange-traded funds which follow such indexes. While factor beta or ‘smart beta’ approaches have proven that they are able to benefit from factor premiums, investors should be aware of their pitfalls.4 Examples of such pitfalls include uncompensated risks, high turnover and going against other factor premiums. A more sophisticated approach may therefore offer significantly better performance. spreads and credit ratings, and used a conventional implementation of a value strategy, based on price-to-book. While we found that conventional value strategies are typically exposed to distress risk, we found no empirical evidence that distress risk explains the value premium.5 We believe our research finding has significant implications for investors in value strategies, as our results show that it is not necessary to take on distress risk in order to profit from the value premium. A more sophisticated value strategy may be designed by explicitly avoiding financially distressed firms. The momentum premium A more sophisticated approach For many years, the Robeco Quantitative Research team has concentrated on analyzing, evaluating, and designing various factor strategies. We found it is of crucial importance to understand the source of a factor premium and then use this information to implement factor strategies efficiently. Key issues with efficient implementation are removing unrewarded risks and limiting unnecessary turnover. Below is a synopsis of what we’ve discovered about value, momentum, and low-volatility investing over the years. The value premium The value effect is the tendency for inexpensive stocks, measured for example by the price- to-book ratio, to have above-market returns. It is well documented in the academic literature, where it has been identified over long time periods and in a variety of regions, including the US, Japan, Europe and emerging markets. One stream of literature proposes that the value premium is a compensation for risk. Professors Eugene Fama and Kenneth French argue that the value premium specifically reflects a reward for relative distress risk, although empirical evidence supporting this assertion has been elusive. We studied the supposed positive relationship between distress risk and the value effect using a simple premise: if the value premium is a compensation for distress risk, the return from value should rise as bankruptcy risk increases. We tested a number of different measures of distress risk, including accounting models, structural models, credit 4 5 6 Momentum is the tendency for stocks that have performed well in the recent past to continue to perform well; and for stocks that have performed poorly to continue to perform poorly. The momentum effect was first documented in the early nineties,6 and has been confirmed in numerous subsequent studies. The momentum effect has also been found to be responsible for most of the persistence in actively managed fund performance. There are two well-documented issues that plague the implementation of a momentum strategy. The first and biggest hurdle to exploiting momentum is the risk associated with momentum investing. Although momentum offers high average returns in the long run, the short-term performance can be very poor, such as in 2009. The second concern with momentum investing is that it involves high turnover and therefore significant trading costs. Our research and experience show that these concerns can be effectively addressed by avoiding unrewarded risks and by not trading too aggressively. While there is a broad consensus that the momentum effect exists, there is no consensus as to why. Just as with other anomalies in the equity market, risk has been proposed as the source of the momentum premium, although, again, this does not convincingly ‘Investors should be aware of the pitfalls of smart beta’ For a more extensive discussion of the pitfalls of smart beta approaches we refer to: Blitz, “How smart is ‘Smart Beta’ investing”, Journal of Indexes, March/April 2013 De Groot and Huij, “Is the value premium really a compensation for distress risk”, SSRN working paper no.1840551. Jegadeesh and Titman. “Returns to buying winners and selling losers: implications for stock market efficiency,” Journal of Finance, 1993 Efficient Factor Investing Strategies explain the premium. Other interpretations attribute the momentum factor to mispricing that arises from a gradual diffusion of information in the market. What we found, in contrast to other academic studies, was that although momentum appears to involve little exposure to risk factors in the long run, these exposures can be huge in the short run. A conventional momentum strategy tends to involve large negative or positive betas, depending on recent market returns. This characteristic is beneficial when markets are trending. But when, for example, they suddenly revert, as occurred in 2009 when many stocks that were hit hard by the credit crisis showed a recovery, a simple momentum strategy may exhibit large negative returns. Our research looked into the risk intrinsic to a momentum strategy.7 We found that half of the risk does not contribute to the strategy’s return. We then developed a proprietary risk management technique to remove these unrewarded risks. The application of this risk management technique for momentum strategies halves the volatility compared with a conventional momentum strategy, while maintaining the strategy’s returns, which results in a doubling of the Sharpe ratio. The low-volatility premium The low-volatility anomaly was first documented by Robert Haugen and others who tested the capital asset pricing model (CAPM) in the early 1970s. In a long-term study of the US market, Haugen demonstrated that contrary to what is expected by CAPM, low-risk stocks have high risk-adjusted returns.8 His research, however, was virtually ignored for decades. We started our work on the subject of low volatility in 2005 and found that the volatility effect is still strongly present in the US market. We also provided strong out-of-sample evidence for the European and Japanese equity markets.9 Moreover, we found that the anomaly seems to have grown stronger over time, and that it is strongly present among the largest, most liquid stocks in the market. Generic low-volatility strategies are typically based on a single backward-looking historical risk measure, such as volatility or beta. 7 8 9 10 11 This construction, however, may expose the strategy to some pitfalls of low-volatility investing, including miscalculated downside risk and underperformance in sharply rising markets. A more sophisticated approach to low- volatility investing can overcome these pitfalls by taking a multi-dimensional view at risk and using a combination of low-risk variables that include both long- and short-term statistical factors. Our research in optimizing low-volatility strategies also finds that limiting distress risk by augmenting backward-looking risk measures with forward-looking measures helps to better estimate and reduce the expected tail risk of a low-volatility strategy.10 We believe that a more sophisticated approach to low-volatility investing is necessary, because not all low-volatility stocks are equal and some are destined to perform better than others. This is especially true when lowvolatility becomes expensive, as is the case in markets now.11 Risk of factor premiums going against each other The examples above illustrate how unrewarded risks that are specific to the value, momentum, and low-risk premiums may be avoided. Another, more general form of unrewarded risk involved with harvesting factor premiums is individual factors having negative exposures to one another. Such a feature is highly undesirable because having negative exposures to factors with positive expected returns lowers the expected return. For example, if a factor index aims to harvest the momentum premium and this index has a negative exposure to the value premium, the expected return on the index is not only a function of the momentum premium, but also of the value premium. And because the expected return of the value premium is positive, the negative exposure of the factor index is expected to hurt its performance. Efficient approaches to obtain factor premium exposure should therefore be designed in such a way that premiums do not go against each other and thereby hurt performance. ‘This risk management technique for momentum strategies halves the volatility’ Blitz, Huij and Martens, “Residual momentum,” Journal of Empirical Finance, 2011 Haugen and Heins, “On the evidence supporting the existence of risk premiums in the capital market,” University of Wisconsin Working Paper, December 1972 Blitz and van Vliet, “The volatility effect: lower risk without lower return”, Journal of Portfolio Management, Fall 2007 Huij, van Vliet, de Groot and Zhou, “How distress risk can improve low-volatility strategies: lessons learned since 2006”, in Low-Volatility Investing, 1st edition, Rotterdam, Robeco collection of articles, 2012 Van Vliet, “Enhancing a low-volatility strategy is particularly helpful when generic low volatility is expensive”, Robeco, June 2012. Available at www.robeco.com/lowvolatility Efficient Factor Investing Strategies Bringing it all together To gauge the economic significance of the insights we discussed in the first part of this note, we performed a series of empirical analyses. First, we analyzed the performance of popular index-based strategies for obtaining value, momentum, and low-volatility factor exposure. For the index-based strategies we used the MSCI World Value Weighted index, the MSCI World Momentum index and the MSCI World Minimum Volatility index. For comparison purposes, the performance characteristics of the conventional capitalizationweighted market index are also included. Intentional and efficient exposure to factor premiums As table 1 below shows, all three index-based strategies deliver a superior risk-adjusted performance relative to the market, with the return/volatility ratio being in the 0.6-0.7 range, versus 0.5 for the market. For value and momentum this improvement mainly comes from a higher return, while for low-volatility it mainly comes from a lower risk. These results empirically confirm the added value of factor investing. Table 1. Performance generic factor strategies MSCI World Return Volatility Return/volatility MSCI Value MSCI Momentum MSCI Low-vol 7.6% 9.4% 10.7% 8.1% 15.3% 15.5% 15.8% 11.4% 0.50 0.61 0.68 0.71 Table 2. Performance Robeco factor strategies MSCI World Value+ Momentum+ Low-vol+ Return 7.6% 13.4% 12.8% 12.2% Volatility 15.3% 15.4% 15.4% 11.6% 0.50 0.87 0.83 1.05 Return/volatility Source: Robeco, MSCI. Average returns are calculated geometrically. Sample period: 1988:05-2013:12. Base currency: USD. Based on simulations. Across the board, the improvements in the Sharpe ratios come from both an increase in return and a decrease in risk. The risk reductions are largely due to avoiding unrewarded risks, as described earlier. The risk budget that is released by avoiding the unrewarded risks also enables the efficient approaches to seek higher exposures to the factor premiums (i.e., through higher concentration and active share) resulting in higher returns. For instance, whereas the MSCI Value Weighted index has an active share of only about 25%, the corresponding figure for the Robeco Value strategy is around 90%. The returns are also higher because of differences in exposures to other factors. For example, the MSCI World Value Weighted and the MSCI World Minimum Volatility indexes both exhibit a negative exposure to the momentum premium, whereas the efficient factor premium strategies are designed to avoid negative exposures to other factor premiums. Based on the above results, we can conclude that the added value of our research insights is sizable. Source: Robeco, MSCI. Average returns are calculated geometrically. Sample period: 1988:05-2013:12. Base currency: USD. Largely based on simulations and partly on real-life data. Next, we computed the same performance metrics incorporating the insights described in the first part of this paper. The results are displayed in table 2. Compared with table 1, we observe a significant further improvement in performance, with return/volatility ratios in the 0.8-1 range. This implies that the added value of the Robeco factor solutions is over double that of the index-based solutions. In fact, this still understates the difference in added value, because for the Robeco factor solutions the impact of trading costs is conservatively taken into account, while the index returns conveniently ignore such costs. ‘All three index-based strategies deliver a superior risk-adjusted performance’ Efficient Factor Investing Strategies Combining factors Confirmed by live track-records Our live track-records confirm the added value of Robeco factor strategies. As table 3 shows, our factor funds have not only handsomely outperformed the regular capitalization-weighted index, but also their corresponding factor indices. We note that these results would be even better on a risk-adjusted basis, in particular for our low- volatility (Conservative Equities) strategies, which have lived up to their promise of delivering a much lower volatility than the capitalization-weighted index. Table 3. Live performance Robeco factor strategies versus MSCI factor indices Start month Value all- Momentum Low-vol Low-vol country all-country developed emerging Jan 2014 Sep 2012 Oct 2006 Mar 2011 Finally, we consider various approaches to constructing factorpremium portfolios. Table 4 below shows the performance of different combinations of the value, momentum and low- volatility factors. We consider an equally-weighted (1/N) portfolio, a maximum-return portfolio, a minimum-volatility portfolio and a risk-weighted portfolio. For the maximum- return portfolio we take a fifty-fifty combination of value and momentum, assuming that these factors have the highest future expected return. The minimum-volatility portfolio is fully invested in the low-volatility factor. The risk-weighted portfolio weighs the individual factor strategies by the inverse of their long-term volatility, thereby establishing equal risk contributions. In unreported tests, we constructed several other portfolios, including portfolios optimized in-sample for maximum Sharpe ratio or maximum information ratio. Versus regular index Robeco 10.58% 18.96% 7.21% 10.30% MSCI 6.86% 15.18% 4.30% 1.29% Excess return 3.72% 3.78% 2.91% 9.01% Table 4. Portfolio performance of different combinations of efficient factor strategies Versus factor index Robeco Equally Maximum Minimum Risk weighted return volatility weighted Absolute 10.58% 18.96% 7.21% 10.30% MSCI 6.66% 13.55% 4.63% 7.68% Return 12.9% 13.2% 12.2% 12.8% Excess return 3.92% 5.40% 2.57% 2.62% Volatility 13.5% 14.8% 11.6% 13.3% 0.95 0.89 1.05 0.96 Outperformance 5.3% 5.6% 4.5% 5.2% Tracking error 4.9% 4.7% 7.1% 5.1% Information ratio 1.06 1.19 0.64 1.02 Value+ 33.3% 50% - 30.1% Momentum+ 33.3% 50% - 30.0% Low-volatility+ 33.3% - 100% 39.9% Source: Robeco, MSCI. Returns are gross of fees and annualized for periods longer than 12 months. Base currency: EUR. Data through 30 June 2014. Strategies are: Robeco Quantitative Value, Robeco Momentum and Robeco Conservative Equities. Indices are MSCI Value-Weighted, MSCI Momentum and MSCI Minimum Volatility (net return). The value of your investments may fluctuate. Results obtained in the past are no guarantee for the future. Return/volatility Relative Factor allocation Source: Robeco, MSCI. Average returns are calculated geometrically. Sample period: 1988:05-2013:12. Base currency: USD. Based on simulations. ‘Factor investing is beneficial in the long run in all of the cases’ Efficient Factor Investing Strategies Contact Basically, for all of the portfolios that we considered, we observe an improvement of the return/volatility ratio from 0.5 for the market portfolio to roughly 0.9 to 1.1 for the various factor-premium portfolios. While the performance improvement is significant in all cases, we observe substantial differences in returns, volatilities, and tracking errors across the alternative factor premium portfolios. For example, the minimum-volatility portfolio has the lowest absolute volatility, but also the highest tracking error. The maximum-return portfolio, on the other hand, has the highest absolute volatility, but also the lowest tracking error. We conclude that factor investing is beneficial in the long run in all of the cases that we examined. There is, however, no single optimal factor-investing portfolio. Optimal mix depends on investor preferences The optimal factor-investing portfolio depends on investor-specific preferences for risk and return. For example, a pension fund where funding-ratio stability is the primary concern would probably be best off with a low-volatility implementation. On the other hand, an investor focused on maximizing expected return given a fixed allocation to equities, would probably be best off with a maximumreturn implementation. We would generally advise, however, to diversify across multiple factor premiums, since individual factors may exhibit long drawdown periods and the drawdown period of a diversified factor portfolio is substantially shorter. Robeco (Headquarters) P.O. Box 973 3000 AZ Rotterdam The Netherlands E [email protected] David Blitz, PhD Head Quantitative Equity Research Joop Huij, PhD Senior Quantitative Researcher Final words Institutional investors are increasingly allocating strategically to factor premiums. We recommend these investors to avoid risks that are not rewarded and that are not necessary for capturing factor premiums. We also recommend avoiding going against other factor premiums; limiting turnover and creating portfolios with a large active share. Simon Lansdorp, PhD Quantitative Researcher Pim van Vliet, PhD Portfolio Manager Conservative Equities Important information This publication is intended for professional investors. Robeco Institutional Asset Management B.V. (trade register number: 24123167) has a license as manager of UCITS and AIF’s of the Netherlands Authority for the Financial Markets in Amsterdam. Efficient Factor Investing Strategies Economic Climate and Market Timing Matthew Peron, Managing Director of Global Equity at Northern Trust, notes that it is possible that a more positive economic outlook could draw investors’ attention away from these strategies. However, he notes that there is growing support for smart beta and emphasizes that institutional investors shouldn’t lose sight of their investment objectives. “These are long term goals, and I think there’s enough momentum that it’s going to carry through any change in the market environment,” he says. “Clients really should be looking at their plans through this lens and regardless of the market environment. Although, investors are human beings at the end of the day, and there’s a strong behavioral tendency here. The market environment typically shifts focus from one factor to another so that a factor becomes the flavor of the month. A year and a half ago, it was dividends, and then it became low volatility and so on. Interest levels in factors tend to change, unfortunately, depending on their most recent performance. I think we’ll see more of that rather than abandoning the paradigm altogether. The paradigm just makes so much sense in any market environment.” With regards to market timing of factor exposures, Northern Trust typically warns clients against it. Peron acknowledges that there are some institutional investors that are sophisticated enough to have the scope for market timing, especially those with a strong track record in a smart beta framework, but, he says, it’s a lot of extra work for dubious returns. “It’s hard to do, and you’re typically very early if you’re right. Also, it can end up confusing things, and you end up abandoning the strategy,” he says. “We would only advise market timing to the most sophisticated of clients, and we generally only advise it when a factor gets to the extreme. We actually have a factor pricing dashboard that looks for extreme values of factors, which indicate that this is either a great time or a horrible time.” Comparing Costs The ability to access factor exposures at lower costs compared to active manager fees has been one of the more desirable traits of smart beta strategies, notes SSgA’s Ana Harris. According to her, “There’s an element of recognizing the existence of these factors and recognizing that you can actually try to tap into these factors through a passive-like strategy. Also, because it is more passive than active, in terms of cost, an advanced beta strategy should also be more attractive. And, what we’ve seen a little bit in the UK and in Europe is this pressure on institutional investors to keep costs low. And, that is forcing them to sometimes rethink their allocations between active, passive and everything else in between.” Costs for smart beta index strategies typically lie in between those for cap-weighted portfolios and active managers. According to Harris, “It could be just a fraction of what you would’ve paid an active manager to manage a portfolio in a similar space. 58 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta So, it would be on the basis point scale rather than almost on the percentage point scale that you would get on the active side. Then, if you think of an advanced beta strategy in the same kind of region or same universe, then it would be a lot closer to what you would be paying for a passive-like strategy or standard cap-weighted than it would be to the active manager.” Compared to cap-weighted index funds, the extra cost of smart beta investments is partially due to the added intellectual property behind index design and construction. Also, smart beta strategies carry the potential for larger transaction costs than those associated with cap-weighted indexes, in spite of their similarities to passive implementation. “For example, if my portfolio management team is looking at one advanced beta index that we’re using from an index provider like FTSE, MSCI, S&P, or Russell, we will be doing the same things that we would normally do for a cap-weighted strategy,” says Harris. “However, these strategies have higher turnover, and because there is higher turnover, there are higher transaction costs and that has an impact on overall performance. These strategies have a higher turnover because, while before with a cap-weighted benchmark you’re capturing a whole market, with these strategies we’re trying to capture a subset. When the market moves and the companies move, we have to rebalance back to where we want to be. So, we have to move around more to stay with the exposure we want in the first place resulting in a higher turnover.” Harris highlights a few additional sources of difficulty that may drive costs up as well, saying “These advanced beta strategies tend to be a little bit more concentrated sometimes than standard cap-weighted and they also tend to sometimes have a little bit more of smaller and maybe less liquid names. So, from an initial implementation perspective, for the first time you would put an advanced beta strategy in place, either replicating an index from somebody else or trying to do something proprietary, those initial costs might be higher than a similar size fund or mandate for a standard cap-weighted index.” The Importance of Track Record Luciano Siracusano, Chief Investment Strategist at WisdomTree, foresees demand for smart beta strategies increasing over time and highlights the relatively short duration of real-time performance data available as one of the most common hang ups with investors. Siracusano says that, although it is difficult to generalize, most institutional investors are somewhat suspicious of backtested or simulated return data. Skeptics can argue that backtested performance benefited from hindsight, especially as more factors are accounted for in the backtests. “We believe that as investors have more access to longer realtime performance data that shows consistent excess returns generated by smart beta approaches, demand will increase for these strategies,” says Siracusano. “To the extent that public pensions are underfunded, and need to generate higher returns in a low return environment for equities, marketbeating, passive strategies may come into greater vogue.” Investors’ concerns over track record may have important implications for those firms hoping to jump on the smart beta bandwagon. Siracusano hints at a first mover advantage for managers in the alternative index market, and prospective market entrants might want to reconsider. According to him, “The field is already crowded, so they may wish to stay out. Pioneers in the space with longer real-time track records and a consistent philosophy on how equity markets work probably have an advantage over recent converts trying to capitalize on recent media attention or investor demand.” swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 59 Faces of Smart Beta To give a more intimate look into smart beta user experiences, SWFI conducted in-depth interviews with public and private investors around the world. The institutions represented vary with respect to the indexes they use, the size of their allocations and their methods of implementation. Some manage their smart beta investments internally while others use external managers. Some sought the expertise of consultants while several indicated that consultants played no role in their decisions whatsoever. In spite of all of these differences in their learning and adoption processes, some remarks and experiences echo throughout the group. • Smart beta strategies have to be viewed with respect to a long-term investment horizon. • The ability to capture rebalancing and factor premiums without paying alpha fees is advantageous. • Diversifying across multiple smart beta strategies can be beneficial. • Smart beta investments could complement or replace either active management or traditional cap-weighted indexes depending on the individual’s circumstances. • Investors need to address liquidity and capacity concerns and be aware of the risks of higher tracking error and unintended exposures. SWFI interviewed key executives at 8 different public and private institutions from 8 different countries. These 8 institutions collectively manage more than US$ 250 billion. • Sweden’s AP2, an active investor with regard to ethical and environmental issues, is one of northern Europe’s largest pension funds. The fund has been investing in smart beta strategies since 2002. • Fonditel was created in 1992 to manage the pension fund assets for Telefonica of Spain. In 2003, they began managing pension plans for other companies as well. Fonditel perceives smart beta as a cheap way to harvest risk premia but is wary that the growing popularity of these strategies may lead to a diminution in returns. • France’s Fonds de Réserve pour les Retraites was founded in 2001, becoming fully functional in 2003. Pulling from passive, capitalization-weighted strategies, FRR has 20% of its developed equity investments allocated to smart beta strategies. • Media Pensioen Diensten provides pension services to pension funds in the media and healthcare sectors in the Netherlands. Although skeptical of the term, MPD believes smart beta strategies will outperform over a longer time horizon. • National Employment Savings Trust (NEST) is an auto-enrollment DC pension scheme in the UK. It was established under the Pensions Act 2008 which required all workers to opt out of (as opposed to opt in to) an employer sponsored pension plan. NEST made its first allocation to a smart beta strategy – an emerging market equity fund - in the second quarter of 2014. • QIC (Queensland Investment Corporation) was formed by the Queensland government in 1991 and serves over 90 institutional investors both in Australia and internationally. QIC finds smart beta strategies beat active strategies in transparency and cost; however, it recognizes that these benefits are tempered by the strategies’ possibility to expose a portfolio to unintended risk. • UPS Investment Group manages pensions on behalf of the United Parcel Service. It has US$ 28 billion in assets under management, and it has allocated 40% percent of its equity portfolio to smart beta strategies. • Varma Mutual Pension Insurance Company, with US$ 51.3 billion in assets under management, is the largest private investor in Finland. Varma manages their smart beta strategies internally. It views smart beta strategies as appropriate for all asset classes. Edgar Eijking Head of Investments, Media Pensioen Diensten (MPD) Edgar Eijking is Head of Investments at Media Pensioen Diensten (MPD). MPD is the fiduciary manager for Dutch Pension Fund PNO Media, an industry wide pension scheme for media related companies. MPD manages €4.3 billion in assets. Edgar Eijking has a Masters in Economics from the University of Amsterdam and has over 13 years of experience in asset management. 62 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta HOW WOULD YOU DEFINE SMART BETA? Eijking: We never use the term smart beta since it implies there is such a thing as dumb beta. We also do not consider these strategies as being beta strategies. Our way of looking at things is that beta is the total investable universe. In other words, you do not make any selection or allocation decisions. What you call smart beta we see as a rule-based active strategy. You apply a certain set of selection criteria and use a weighting scheme to construct the portfolio. IN WHICH KINDS OF SMART BETA STRATEGIES DOES YOUR FUND INVEST, AND FOR HOW LONG HAVE YOU HAD THESE MANDATES? Eijking: So far we have started with the simplest of rule-based strategies, an equal weight portfolio. We started this strategy about three years ago. Even though we acknowledge that there are some disadvantages to this strategy, like a higher volatility, we like the simplicity and the way it captures the rebalancing premium. There is an abundance of “smart beta” strategies that all claim to outperform the market cap benchmark in one way or another. Even, as some studies show, the reverse of these strategies will give you an outperformance. What they all have in common is that they periodically rebalance the portfolio. We believe that a lot of the outperformance in all the rule-based strategies comes from this rebalancing premium. WHAT ADVANTAGES AND DISADVANTAGES DO YOU SEE WITH SMART BETA? Eijking: There is a lot of marketing going on with regard to smart beta. When every asset manager starts selling smart beta products, not just in equities but also in fixed income, you have to be cautious. Many of these strategies have been around for quite a while. Factor premiums, such as the value premium, have been known for decades. James Montier of GMO called smart beta “old snake oil in new bottles.” That is a bit strong in our view. Rule-based strategies do have merit but, in our opinion, should be viewed as active strategies and not as an alternative for passive benchmark investing. Typically the costs of these strategies are a bit lower than traditional active strategies. They also appeal to investors that are wary of the human element in managing portfolios. Those investors may feel more comfortable with a “fixed” set of investment rules. To other investors, this latter reason may be a disadvantage to smart beta. WHAT KIND OF CHALLENGES DID YOU FACE IMPLEMENTING THESE STRATEGIES? Eijking: Implementation of rulebased strategies can be done easily or can be difficult. There is a risk of creating strategies that are too complex, both at the level of portfolio construction and at the implementation level. There are also asset managers that rely too much on mathematics. This is supposed to give it an allure of science, which it isn’t. They also run the risk of data mining. When selecting a strategy and an asset manager to run the strategy, this should be a major consideration. If you have to be a mathematician to understand what your manager is doing, you probably should stay clear of them. You need to feel comfortable with the investment process. Fortunately, there are also a lot of managers that have avoided these issues. HOW DO YOU ENVISION THE FUTURE OF SMART BETA? WHAT IS THE NEXT STEP FOR YOUR FUND’S SMART BETA PROGRAM? Eijking: We believe that the popularity of rule-based strategies will grow but not as strongly as many predict. Compared to the total AUM word wide, smart beta strategies still make up for just a fraction of that. It has the potential of producing alpha compared to the market cap benchmark but with a lower fee structure compared to traditional active strategies. In the Dutch pension fund industry there is a lot of focus on reducing costs. Introducing rule-based strategies could be one answer to this. On the other hand, investors must realize that although these strategies may produce outperformance over the long run, they can also underperform significantly over the short and medium term. Risk in terms of tracking error are quite high. Once you have decided to allocate some of your assets to these strategies, you have to stick with them for the long run, say a minimum of 10 years. Not many investors have such a long-term perspective, unfortunately. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 63 Mark Fawcett Chief Investment Officer, National Employment Savings Trust Mark has been an investment manager for the last 26 years and has led the investment team at NEST (PADA) since 2008. He has managed money at a variety of institutions. At Gartmore, Mark was head of Japanese equities while at American Express Asset Management International, he was Chief Investment Officer. Before joining PADA, Mark was a Partner at the boutique investment manager Thames River Capital LLP. He has a number of external appointments including sitting on the Board of the National Association of Pension Funds (NAPF). Mark has an MA from Oxford University and an MSc from London Business School. HOW WOULD YOU DEFINE SMART BETA? Fawcett: Smart beta, or alternative indexing as we prefer to call it at NEST, is about trying to access market betas in ways other than the traditional market capitalization weighted approach. The principle behind adopting an alternative index strategy is that market cap weighted might not be the most efficient way of earning the return of that asset class. There are many different alternative weighting methodologies so in that respect, smart beta is a fairly generic term. WHICH SMART BETA FUNDS DOES NEST HOST ON ITS INVESTMENT PLATFORM AND WHY WERE THEY CHOSEN? Fawcett: NEST brought its first alternative index fund onboard in Q2 2014. The HSBC GIF Economic Scale Index Global Emerging Market Equity Fund weights companies by measures of their economic footprint. GDP growth is high in emerging markets and we believe companies which share in, and contribute to, that growth are those which are likely to perform best over the medium to long term. 64 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta ARE THERE ANY OTHER SMART BETA STRATEGIES THAT NEST IS CONSIDERING FOR THE FUTURE? WHY OR WHY NOT? Fawcett: When we conducted the search and selection process for our emerging market equity mandates, we evaluated each index fund on its merits; we knew we wanted the alternative index to have a value bias, but other than that we were fairly open minded. This will most likely be our approach to how we research mandates in future. WHAT MADE YOU CHOOSE SMART BETA FUNDS AS A MEANS OF INCREASING EXPOSURE TO EMERGING MARKETS? DOES SMART BETA OFFER ANY SPECIFIC ADVANTAGES OVER OTHER STRATEGIES WITH REGARDS TO THIS ASSET CLASS? Fawcett: We believe alternative indexing has a role to play in developed markets as well as emerging ones. However, given the large exposure pension schemes often take to global developed equity, we are very cost sensitive within that asset class; alternative index funds can be a little more expensive than market cap weighted due, in part, to the intellectual property incorporated into the index design as well greater portfolio turnover. However there are certain features of emerging markets which lead us to focus on alternative strategies; one aspect is that many large companies in these economies are closely held, somewhat skewing the representativeness of market capitalization. We are also concerned about the presence of environmental, social and governance (ESG) risks in emerging markets; this led us to award a mandate to Northern Trust for their Emerging Market Custom ESG Fund. This is market cap weighted but excludes companies if, for example, they have poor governance structures. DO YOU HAVE ANY SPECIFIC FEARS OR CONCERNS ABOUT SMART BETA FUNDS? HOW HAVE THEY BEEN ADDRESSED? Fawcett: Smart beta as a term covers a diverse range of strategies – there is no particular reason to be concerned but of course any approach needs to be thoroughly researched and understood before we exposure our members’ money to it. The most important thing in any strategy is that it is based on sound economic principles – and of course the costs of running the strategy are no more than the expected value added by the alternative weighting methodology. One of the key issues with alternative indexing is the regret risk. Behavioral biases will often lead to an alternative index strategy being chosen after a period of outperformance over market cap weighted. As outperformance may be cyclical, there is a risk that there is then a period of underperformance, which may lead to trustees questioning why the strategy was adopted. We believe the way to mitigate this risk is firstly, to ensure there is a clear and agreed long horizon investment approach and, secondly, to be very disciplined about timing of entry and sizing of positions. HOW DID YOU LEARN ABOUT SMART BETA? WHAT ROLE DID CONSULTANTS AND EXTERNAL MANAGERS PLAY IN YOUR EDUCATION OF SMART BETA AND IN NEST’S CHOICE OF SMART BETA FUNDS? Fawcett: NEST has an experienced in-house investment team with strong groundings in both quant and fundamental investing disciplines – we leverage this resource wherever possible, including in our research into smart beta methodologies. DO YOU SEE SMART BETA AS A REPLACEMENT FOR EITHER ACTIVE MANAGEMENT OR M A R K E T - C A P I TA L I Z AT I O N WEIGHTED INDEXES? Fawcett: NEST’s Statement of Investment Principles (SIP) carries an investment belief that “Indexed management, where available, is generally more efficient than active management”. This means we believe that, in certain markets, it is really difficult for active managers to consistently add value (generating alpha) so instead our focus should be using index funds to capture the market beta; and using alternative indexing along with market cap may well be sensible. There are of course markets where active management may prove superior – especially where indexing isn’t a viable option, such as direct real estate. WHAT IS THE NEXT STEP FOR NEST’S SMART BETA PROGRAM? Fawcett: We don’t have a smart beta program per se; as we add new asset classes and expand the number of mandates for asset classes we already have exposure to, we’ll research the whole spectrum of investment styles and we are always interested in innovative approaches. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 65 Tomas Franzén Chief Investment Strategist, AP2 Tomas Franzén is the Chief Investment Strategist at the Second Swedish National Pension Fund (AP2) in Gothenburg. He is responsible for issues related to Investment Policy and Strategic Asset Allocation. AP2 has some US$ 40 billion in assets under management invested in a broad-based global portfolio of public and private assets. Tomas holds a B.A. in Economics from the University of Stockholm, Sweden. He is also a holder of CEFA (Certified EFFAS Financial Analyst). Tomas is also the Chairman of the International Advisory Board of EDHEC Risk Institute and a member of the Investment Committee at The Foundation of Chalmers University of Technology, Gothenburg. He is furthermore the Editor of the Anthology; A Decade of Challenges, A Collection of Essays on Pension and Investment and a frequent speaker at investor meetings around the world. 66 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta HOW WOULD YOU DEFINE SMART BETA? Franzén: I define smart beta as non-market-cap-weighted indices or portfolios. I don’t really like the label as it draws too much attention to the alternative weighting schemes, when market-cap-weighting really is the flaw. IN WHICH KINDS OF SMART BETA STRATEGIES DOES YOUR FUND INVEST AND FOR HOW LONG HAVE YOU HAD THESE MANDATES? Franzén: We invest in Equalweight, Value-weight, GDP-weight, Risk-weight and Minimum Volatility. We started in 2002 and added the most recent component last year. DO YOU HAVE A FAVORITE INDEX? Franzén: Not really, they are supposed to diversify each other over the long term. WHAT ADVANTAGES AND DISADVANTAGES DO YOU SEE WITH SMART BETA Franzén: You can avoid market-cap exposure and tilt to rewarded factors and anomalies. However, capacity and liquidity issues must be addressed properly. WHAT ROLE DO SMART BETA STRATEGIES PLAY IN YOUR PORTFOLIO? Franzén: They are benchmarks in the policy portfolio. At AP2, we replicate the indices with a certain quant-based enhancement. HOW DID YOU LEARN ABOUT SMART BETA? WHAT ROLE DID CONSULTANTS AND EXTERNAL MANAGERS PLAY IN YOUR EDUCATION OF SMART BETA AND IN YOUR FUND’S DECISION TO ALLOCATE TO SMART BETA? Franzén: We almost stumbled over them (equal weighting) ten years ago when we wanted our active managers to stop hugging the highly concentrated Swedish equity benchmark at the time. We were therefore quite open to Fundamental Indexation when that was presented in 2005. Over the past few years, academic research has been the main catalyst for further refinement. WHAT KIND OF CHALLENGES DID YOU FACE IMPLEMENTING THESE STRATEGIES? Franzén: The challenges we faced when implementing these strategies were liquidity and a realization that the value-add can be quite episodic and long-term in nature. WERE YOUR SMART BETA ALLOCATIONS FUNDED BY ACTIVE OR PASSIVE FUNDS? Franzén: Both DO YOU SEE SMART BETA AS A REPLACEMENT FOR EITHER ACTIVE MANAGEMENT OR M A R K E T - C A P I TA L I Z AT I O N WEIGHTED INDEXES? Franzén: At the core, we see it as a replacement for cap-weighted indices but also for traditional active management. WHAT IS THE NEXT STEP FOR AP2’S SMART BETA PROGRAM? Franzén: We are planning to introduce value-weighting as a benchmark for part of our Emerging Market equity allocation. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 67 Syed Haque Portfolio Manager – Global Equities, United Parcel Service Syed Haque serves as the Portfolio Manager – Global Equities for United Parcel Service, where he maintains investment oversight for US$ 10 billion to US$ 12 billion of equity portfolio of defined benefit assets. His responsibilities include portfolio construction, investment selection, risk management, options overlay, performance monitoring and presentation for the equities portfolio. Prior to joining UPS Investments, he was Senior Investment Risk Analyst at Emory Investment Management, where he was responsible for design and implementation of risk analytics and systems, across asset classes for the overall Endowment portfolio. Before Emory, he worked for leading Technology and Telecom companies. He is a MBA graduate of Duke University Fuqua School of Business and received his MS in Computer Science from George Mason University and BS in Civil Engineering from Indian Institute of Technology (BHU), Varanasi. HOW WOULD YOU DEFINE SMART BETA? Haque: I would define smart beta as an investment style/strategy that moves away from the traditional capweighted paradigm in asset allocation. Instead, the strategy employs alternative weighting schemes, such as volatility or dividend, that have been proven to deliver superior risk/return profiles compared to purely cap-weighted portfolios. IN WHICH KINDS OF SMART BETA STRATEGIES DOES YOUR FUND INVEST, AND FOR HOW LONG HAVE YOU HAD THESE MANDATES? Haque: We’ve invested in Quality, Value, Mid-Cap, Volatility, and thematic based strategies. We’ve been investing in these strategies since summer of 2011. UPS HAS ALLOCATED 40% OF ITS EQUITY PORTFOLIO TO SMART BETA INVESTMENTS. WHAT DO YOU FIND SO ATTRACTIVE ABOUT SMART BETA? ARE YOU REBELLING AGAINST EITHER 68 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta ACTIVE MANAGEMENT OR MARKET CAP-WEIGHTED INDEXES? Haque: I think of our investments in alternative indices as efficient strategic allocation that enables exposure to systematic factors in a cost-effective way. These indices provide a way to mitigate the problem of concentration in large stocks that tends to occur in a cap-weighted portfolio while taking into account the correlation structure between the portfolio components. I see smart beta as coexisting with cap-weighted indices and active management in a way that produces the best possible combination of risk and return. My objective is to capture the different risk premia in the most costeffective and flexible way. I strongly believe that there’s a time and place for all three investment styles. WHAT ROLE DO SMART BETA STRATEGIES PLAY IN YOUR OVERALL PORTFOLIO? WHERE DO YOU USE ACTIVE MANAGERS? HOW ARE THEY BENCHMARKED? Haque: Smart beta has been the vehicle for getting the desired factor exposure and mitigating the cap-weighted indices challenges in certain geographies. We have active managers for both US and Non-US equity exposures. So far, we’ve used the comparable cap-weighted index as the broader portfolio benchmark. In some cases, we’ve used Sharpe Ratio or volatility adjusted exposure to benchmark performance. HOW DO YOUR SMART BETA INVESTMENTS STACK UP IN TERMS OF PERFORMANCE? HOW DO THEY COMPARE WITH ACTIVE MANAGERS IN TERMS OF PERFORMANCE AND FEES? Haque: So far, the performance of our smart beta investments has exceeded expectations. Our volatilitybased strategies did not do as well relative to cap-weighted indices last year, but this was expected to some extent. On the other hand, our Sharpe ratio based indices have done quite well since their implementation. The fees for most of these strategies have been 2-10 basis points higher than similar cap-weighted indices. WHAT RISKS DO YOU SEE IN MOVING AWAY FROM MARKETCAP WEIGHTED INDEXES, AND HOW ARE YOU ADDRESSING THESE CONCERNS? Haque: Moving away from capweighted indices can introduce some unintended risks to the portfolio such as sector concentration, small cap bias, increased illiquidity or even idiosyncratic risks. Investors should also be aware of the possibility of increased tracking error as a result of deviating from the cap-weighted benchmark. We’ve tried to address that concern in some cases by limiting the type of stocks which can be considered for inclusion into the portfolio. We’ve also attempted to diversify among several different smart beta strategies. HOW DID YOU LEARN ABOUT SMART BETA? WHAT ROLE DID CONSULTANTS AND EXTERNAL MANAGERS PLAY IN YOUR EDUCATION OF SMART BETA AND IN YOUR FUND’S DECISION TO ALLOCATE TO SMART BETA? Haque: My interest in this topic was awakened through relevant articles published in leading journals in the area of asset allocation and by attending investor forums organized by a major index provider. Additionally, the research produced by EDHECRisk Institute has been of great help in enhancing my knowledge and understanding of this topic. We don’t have any consultant for the UPS Group Trust. WHAT KIND OF CHALLENGES DID YOU FACE IMPLEMENTING THESE STRATEGIES? Haque: One of the challenges faced by us was a lack of sufficient data going back in time. Most of the return data produced has been a result of backtesting. I should mention that one has to be extremely careful in relying on backtested data. In fact, in certain market geographies, even the backtested data does not go back far enough, which makes implementing smart beta in such locations extremely challenging. WHAT ADVICE DO YOU HAVE FOR OTHER INSTITUTIONAL INVESTORS THAT ARE CONSIDERING STARTING SMART BETA PORTFOLIOS? Haque: Investors should be aware that moving away from cap-weighted indices tilts the portfolio towards certain risk factors, and if they’re unsure about the additional risks involved, they should adjust their exposure accordingly. It’s easy to see how a relatively simple strategy can induce small cap or illiquidity bias in the portfolio. Furthermore, optimization based strategies can bring in estimation and model error. Investors need to carefully evaluate and manage the risk accordingly. HOW DO YOU ENVISION THE FUTURE OF SMART BETA? WHAT IS THE NEXT STEP FOR UPS’ SMART BETA PROGRAM? Haque: I see smart beta as another useful tool in the toolbox available to investors. The next step in the UPS program is to optimally combine the different risk premium generating strategies and incorporate them in a more efficient way in the overall asset allocation decision. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 69 Carlos Hernández Director of Multi Asset Investing, Fonditel With over 14 years of professional experience managing portfolios with different institutions, Carlos Hernández focuses on a primarily top down process driven approach to investing. The post 2008 crisis environment forced him to rethink all given assumptions about money management, starting with the suitability of the risk management tools he was using. Carlos’ focus during the last 6 years and now as Director of Multi Asset Investing in Fonditel (Telefonica Corporate Pension Plan Manager) is on improving the investment process. He continually evolves their systematic investment processes and develops and implements systematic and dynamic strategies (tactical, strategic, asset/risk allocation), active and passive risk budgeting, alternative beta and risk premia harvesting, and tail risk hedging within a diversified multi asset framework. 70 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta HOW WOULD YOU DEFINE SMART BETA? Hernández: An alternative way of systematically and efficiently harvesting the traditional and nontraditional risk premia. IN WHICH KINDS OF SMART BETA STRATEGIES DOES YOUR FUND INVEST AND FOR HOW LONG HAVE YOU HAD THESE MANDATES? Hernández: We invest in systematic strategies in Momentum, Value, Carry, Size and Volatility in different assets. Recently, we made our first steps in smart indexing starting with tilts in value and momentum and equal/low volatility/risk parity weightings. We started implementing these strategies many years ago, but now we can allocate to them in what we call “smart beta strategies” in a more explicit way. ARE YOU SATISFIED WITH YOUR SMART BETA INVESTMENTS? DO YOU HAVE A FAVORITE INDEX? Hernández: We should not be satisfied. We are still evolving, and obviously there is a long way to go. The first step is to understand the value that this new use of risk factors adds to the portfolios. The second step is to implement them statically, and the third step will be to dynamically allocate risk to them. We develop and implement some of the strategies in-house while selecting providers for others if we consider it more efficient in terms of cost and operational issues. So, we don’t have favorites. We select which will better suit our portfolios, and that changes over time. WHAT ADVANTAGES AND DISADVANTAGES DO YOU SEE WITH SMART BETA? Hernández: Smart beta gives us the opportunity to capture a wider range of risk premia (making our portfolios more efficient). We get access to risk premiums that used to cost 2/20 in a cheaper way, and it also helps to harvest the inefficiencies of market capitalization indexes. Growing popularity and allocations into these strategies could be an issue giving rise to diminishing returns in the foreseeable future. WHAT ROLE DO SMART BETA STRATEGIES PLAY IN YOUR PORTFOLIO? Hernández: For the time being smart beta strategies play a small role in our portfolios, and the idea is to improve what we have before making new steps. HOW DID YOU LEARN ABOUT SMART BETA? WHAT ROLE DID CONSULTANTS AND EXTERNAL MANAGERS PLAY IN YOUR EDUCATION OF SMART BETA AND IN YOUR FUND’S DECISION TO ALLOCATE TO SMART BETA? Hernández: We continuously question our processes and search for better ways of doing things while trying to think outside the box. We have proactively gotten closer to a strand that blends ideas from the big Nordic pension funds and US Endowments, working with concepts such as Risk On/ Risk Off, dynamic risk management, and alternative betas just after the 2008 crisis. An important part of the process has been in-house while realizing during the last years that many other investors and consultants were working in the same field using more exotic names. WHAT KIND OF CHALLENGES DID YOU FACE IMPLEMENTING THESE STRATEGIES? Hernández: The same that explain why more investors don’t escape concentrated equity risk: unconventionality could be risky; aversion in finance to leverage, shorting, derivatives; uncertainty about sustainability; capacity and cost concerns. DO YOU SEE SMART BETA AS A REPLACEMENT FOR EITHER ACTIVE MANAGEMENT OR M A R K E T - C A P I TA L I Z AT I O N WEIGHTED INDEXES? Hernández: Although passive management can be used to replicate smart indices, we think that smart indices/strategies themselves represent active strategies. So it is not a replacement for active management but another way of being active and not paying “alpha fees” for what is beta. Market capitalization weighted indexes will continue to rule the industry for the coming years, but smart indexing will take its own place. We should not forget that the simple tilts towards factor premiums provided by smart beta indices involve significant risks that are undesirable and therefore no free lunch. HOW DO YOU ENVISION THE FUTURE OF SMART BETA? WHAT IS THE NEXT STEP FOR FONDITEL’S SMART BETA PROGRAM? Hernández: Smart beta is another step in this changing world, and we are still at the pioneer stage of a multi-year evolution. As I have said we take it as a non-stop process, so we will continue improving what we’ve done, include new smart beta strategies, and work in a dynamic allocation between them. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 71 Olivier Rousseau Executive Director, French Pension Reserve Fund (FRR) Olivier Rousseau was appointed as a member of the management Board of the French Pension Reserve Fund (FRR) in November 2011. FRR is a pension buffer fund with 35 B€ in assets. After graduating from the French National School of Administration (ENA) in 1986, he joined the French Treasury in Paris. He worked 11 years for BNP Paribas in international banking and finance in Paris, Tokyo, London, Singapore, Hong Kong and Sydney. His postings spanned asset swap portfolio management in Tokyo, head of fixed income origination for French issuers, head of corporate and institutional banking at BNP London, chairman of the management committee of BNP Prime Peregrine, group managing director of BNP Paribas equities Australia. He also served on the resident Board of directors of the European Bank for Reconstruction and Development in London (2004-2006) and as regional economic counsellor at the French embassy in Stockholm (2006-2011). 72 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta HOW WOULD YOU DEFINE SMART BETA? Rousseau: I would define smart beta as any indexation not based on market capitalization. IN WHICH KINDS OF SMART BETA STRATEGIES DOES YOUR FUND INVEST, AND FOR HOW LONG HAVE YOU HAD THESE MANDATES? Rousseau: Our current smart beta strategies include the following: RAFI (2009 and 2013), Minimum Volatility (since 2011), EDHEC (since 2012), ERC (since 2013), Minimum Variance (2014). DO YOU HAVE A FAVORITE INDEX? Rousseau: Yes. We run a composite index. WHAT ADVANTAGES DO YOU SEE WITH SMART BETA? Rousseau: The advantages that I see with smart beta are better diversification, small cap and value biases and extraction of a rebalancing premium. WHAT ROLE DO SMART BETA STRATEGIES PLAY IN YOUR OVERALL PORTFOLIO? WHERE DO YOU USE ACTIVE MANAGERS? Rousseau: I would say that smart beta strategies are “smarter” than passive capitalization weighted indexes. We use passive mandates at this stage HOW DID YOU LEARN ABOUT SMART BETA? WHAT ROLE DID CONSULTANTS AND EXTERNAL MANAGERS PLAY IN YOUR EDUCATION OF SMART BETA AND IN YOUR FUND’S DECISION TO ALLOCATE TO SMART BETA? Rousseau: We learned about smart beta particularly through the available literature and exchanges with peers and asset managers. There is no role for consultants. WHAT KIND OF CHALLENGES DID YOU FACE IMPLEMENTING THESE STRATEGIES? Rousseau: One of the biggest challenges that is faced when implementing these types of strategies is overcoming conservatism. WERE YOUR SMART BETA ALLOCATIONS FUNDED BY ACTIVE OR PASSIVE FUNDS? Rousseau: Our smart beta allocations were funded from passive, capitalization-weighted strategies. DO YOU SEE SMART BETA AS A REPLACEMENT FOR EITHER ACTIVE MANAGEMENT OR M A R K ET- C A PI TA L I Z AT ION WEIGHTED INDEXES? Rousseau: Smart beta can be a replacement for both. But at this stage we do not give our managers much tracking error. Thus, we see it as more of a replacement for capitalization-weighted, passive management. HOW DO YOU ENVISION THE FUTURE OF SMART BETA? WHAT IS THE NEXT STEP FOR FRR’S SMART BETA PROGRAM? Rousseau: We envision smart beta as growing consistently for the market, and, to some extent, us too. Our smart beta investments, today, represent 20% of our developed equity investments. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 73 Adriaan Ryder, CIO, QIC Adriaan is responsible for formulating and implementing strategic asset allocation and liability driven policies for major clients. This encompasses: risk management, asset allocation and portfolio construction, and capital market and asset class research across a widely diversified range of asset classes. Adriaan has over 35 years’ experience in developing and executing investment strategies and solutions for institutional clients, across a variety of funds and regions. He is an Actuary and a member of several Advisory Boards, Investment Committees and Professional Associations. 74 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta HOW WOULD YOU DEFINE SMART BETA? Ryder: QIC has historically used what we call a “managed beta” approach, which is essentially what’s now referred to as smart beta. At the heart of this approach is the fundamental belief in inefficiencies in commonly used benchmarks for passive beta. Market capitalization weighted equity benchmarks and asset weighted fixed income benchmarks are the two most cited examples. Smart beta is a departure from these traditional passive beta exposures to improve risk-adjusted return by accessing higher return for a similar level of absolute risk (e.g. factor returns), capturing the same beta at a lower level of risk (e.g. different methods of portfolio construction), or a combination of the two. The distinguishing feature of smart beta is the systematic approach used to capture these effects. WHAT ADVANTAGES AND DISADVANTAGES DO YOU SEE WITH SMART BETA? Ryder: Smart beta offers a framework for understanding the various sources of risk and returns. Smart beta also offers the ability to directly isolate and manage the risk premia and factor exposures, attributes and portfolio construction methodologies in portfolios, and dynamically manage them according to the expected future returns from each of these sources. The choice can then be made to select and size any active (alpha) strategies (if any) independently of the smart beta as part of the portfolio construction process. Smart beta also offers a high level of transparency as to the source of riskadjusted return relative to a benchmark often not offered by active (alpha) strategies. This approach also provides a very cost efficient way of capturing those elements. However, smart beta doesn’t provide all the answers. In order to be efficient and economical, smart beta portfolios must to some degree be approximations for underlying effects. Whilst the idea of isolating a given factor is appealing, in practice it can be very difficult to get a pure factor exposure in a cost efficient and effective manner. Smart betas will therefore often be accompanied by some level of unintended risk. We’re also finding that the labeling of strategies as “smart” beta doesn’t necessarily make them so. It still requires a significant amount of resources to identify and implement truly value-adding approaches. Poor implementation of this approach can significantly erode any potential benefits. There will be a great need for clarity in the communication and explanation of the various smart beta strategies so that the subtle differences between approaches and strategies are fully understood and the strength of the concept can be realized. The risk of mismatch between investor requirements and expectations against the strategy in which they’re invested will increase over time. WHAT KINDS OF CHALLENGES DO YOU FACE IMPLEMENTING THESE STRATEGIES? Ryder: We implement these strategies both internally and to specialist managers external to QIC. Different approaches are adopted for different risk premia and factors. We focus our efforts on identification of smart beta strategies that add particular positive attributes to our broader portfolios. We also spend a great deal of effort on portfolio design and construction which incorporates the specific risks of each of the respective smart beta approaches and the “states of the world” in which they will perform well or poorly. When we engage an external manager to implement smart beta for our clients, we go to great lengths to ensure a minimum of leakage from such things as turnover and market impact. Whilst smart beta is a lower cost approach than active management, there can often be material hidden costs in its implementation which need to be managed in mandate design. WHAT FACTORS DO YOU SEE DRIVING INSTITUTIONAL INVESTORS’ DEMAND FOR SMART BETA STRATEGIES? HOW DO YOU FORESEE DEMAND CHANGING OVER TIME? Ryder: We expect demand for smart beta to continue to grow. It is still early days for this approach, and whilst some of the factors and concepts have been around for decades or more, isolating and capturing them is a relatively new thing. Knowledge and acceptance will grow as specialist smart beta strategies are expanded in provider offerings. This demand accelerated after the financial crisis, which had caused many investors to question the effectiveness of active management as an investment strategy, particularly in listed markets. Research supported that, after fees and turnover costs, persistence of alpha is difficult to find and that “styles” may be a better approach. Cost is a major issue for many institutional investors. Whilst smart beta differs in many aspects from traditional active and quantitative management, it can deliver many of the benefits of the risk premia/ factor exposure at a lower cost. Many institutional investors are now looking at the total level of fees paid to external investment managers, particularly as assets continue to grow significantly. This is driving demand for smart beta and the development of internal investment management capability. WHAT ROLE DO CONSULTANTS PLAY IN YOUR CLIENTS’ DECISIONS TO ALLOCATE TO SMART BETA? Ryder: There is general acceptance by consultants that smart beta can play a material role in client portfolios, and they’re promoting the strategic concept swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 75 at present by educating them on the strengths and weaknesses of the approach. Of course there isn’t a one size fits all answer, and smart beta is likely to be just one piece in a consultant’s client solution package. The consulting industry in Australia is very sophisticated with institutional clients receiving and acting upon their advice, so it’s likely to be a key determinant. DO YOU HAVE ANY ADVICE FOR INSTITUTIONAL INVESTORS THAT ARE CONSIDERING ALLOCATING TO SMART BETA? Ryder: We feel it is a sound approach to portfolio allocation and design. Smart beta however is a term used to categorize an enormous number of different strategies. The important thing is to look beyond the catch phrase at each individual strategy and understand what it’s attempting to capture and why the risk premium or inefficiency exists. We also find that even if something makes sense, it can be quite inefficiently implemented. It’s important to make sure that the benefits of the strategy aren’t eroded through poor implementation. HOW DO YOU ENVISION THE FUTURE OF SMART BETA? WHAT IS THE NEXT STEP FOR QIC’S SMART BETA PROGRAM? Ryder: Like passive index management, smart beta is here to stay due to its strategic effectiveness, simplicity and cost effectiveness, particularly when managing large multi-asset portfolios targeting specific investment objectives. Our experience over the last 8 years of using managed/smart beta has highlighted many issues not immediately apparent and helped us develop processes to model and utilize more unique and specific smart betas across client portfolios. We will continue to develop our requirements for these types of 76 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta strategies with less reliance on offthe-shelf style solutions toward strategies more designed to our client portfolio’s specific needs. Our activity in this area has not only been focused on the equity risk premia but also on other risk premia in assets such as currencies, commodities and fixed income to name a few. We expect to continue to expand our research, development and investment in this arena. Sovereign Wealth Fund Institute Membership Join a Global Membership of Organizations Dedicated to Participating in the Public Investor Community For more information: Vince Berretta [email protected] Sovereign Wealth Funds / Public Pensions / Central Banks / Finance Ministries Government / Superannuation Funds / Other Public Investors SWFI ® Sovereign Wealth Fund Institute ® and SWFI ® are registered trademarks of the Sovereign Wealth Fund Institute. Kari Vatanen Senior Portfolio Manager, Varma Mutual Pension Insurance Company Kari Vatanen works as a Quantitative Strategist and Portfolio Manager for cross-asset derivative overlay strategies at Varma Mutual Pension Insurance Company. He has experience developing systematic investment strategies and market risk models for balanced portfolios. During recent years, he has developed and managed alternative risk premium strategies for equities, bonds, FX and commodities. Previously, Kari has been responsible for investment risk management functions and has worked in the investment management industry since 2000. He has received MSc in Engineering Mathematics from Helsinki University of Technology and MA in Philosophy from University of Helsinki. He is a CFA charterholder and also holds the Financial Risk Manager (FRM) certification. 78 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta HOW WOULD YOU DEFINE SMART BETA? Vatanen: We define smart beta as a systematic, rule-based portfolio construction method that differs from the traditional market-capweighted beta and utilizes different return drivers to generate a positive risk premium. IN WHICH KINDS OF SMART BETA STRATEGIES DOES VARMA INVEST, AND FOR HOW LONG HAVE YOU HAD THESE MANDATES? Vatanen: Smart beta strategies are managed mainly internally in the derivative overlays. We have constructed market neutral strategies that have tilts to different alternative risk premia in different asset classes. Risk premium strategies have been studied, developed and implemented internally since 2011. ARE YOU SATISFIED WITH YOUR SMART BETA INVESTMENTS? DO YOU HAVE A FAVORITE INDEX? Vatanen: We have received mixed results depending on the strategy tilts. Some risk premia have performed poorly from time to time, but others have been very profitable at the same time. Wide diversification between strategies and asset classes is the key to high risk-adjusted performance over time. WHAT ADVANTAGES AND DISADVANTAGES DO YOU SEE WITH SMART BETA? Vatanen: The major advantage is the huge risk diversification potential that is embedded in the different smart beta strategies across asset classes. Tilted strategies and risk-based portfolio construction methods enable a more efficient use of dimensionality in the portfolio construction process compared to the traditional indices. My main concern is the liquidity of some strategies. Higher demand of smart beta strategies might destroy the positive risk premia of the strategies in the longer run. HOW DID YOU LEARN ABOUT SMART BETA? WHAT ROLE DID CONSULTANTS AND EXTERNAL MANAGERS PLAY IN YOUR EDUCATION OF SMART BETA AND IN YOUR FUND’S DECISION TO ALLOCATE TO SMART BETA? Vatanen: Alternative risk premium strategies have been studied and developed within the company for some years based on academic research and on the research studies of various investment banks. The label “smart beta” has been introduced only recently to refer to systematic strategies that deviate from traditional beta. WHAT KIND OF CHALLENGES DID YOU FACE IMPLEMENTING THESE STRATEGIES? Vatanen: We have the luxury of having an internal cross-asset derivative overlay team which can develop and implement different kinds of alternative strategies. Although, even we have been relatively skeptical about the continuity of positive returns of historically simulated smart beta portfolios. It is definitely more challenging to ensure the upper management of these kinds of strategies if they don’t have a long enough live performance history. DOES VARMA HAVE ANY INTEREST IN SMART BETA STRATEGIES FOR FIXED INCOME OR OTHER ASSET CLASSES BESIDES EQUITY? Vatanen: We invest in different alternative risk premium strategies for fixed income, FX, and commodities and are fully open to using them for all asset classes. WHAT ADVICE WOULD YOU GIVE TO OTHER INSTITUTIONAL INVESTORS THAT ARE CONSIDERING ALLOCATING TO SMART BETA? Vatanen: Be sure that you understand the construction methodology of the smart beta strategy and the economic reasoning behind the return drivers. Do not believe in one strategy, but take advantage of the high diversification potential of different kinds of strategies. Be patient - you will never get positive risk premium without risk. HOW DO YOU ENVISION THE FUTURE OF SMART BETA? WHAT IS THE NEXT STEP FOR VARMA’S SMART BETA PROGRAM? Vatanen: Smart beta is here to stay. It will offer tools for institutional investors to diversify their portfolio further and give an alternative to active investment mandates. It will definitely enrich the debate about passive versus active investment strategies. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 79 WisdomTree Research MARKET INSIGHTS [ August 2014 ] Looking Under the Hood of Smart Beta BY JEREMY SCHWARTZ, CFA®, DIRECTOR OF RESEARCH The recent months have seen a wide proliferation of the term “smart beta1,” which in its simplest terms indicates an index construction that does not weight constituents by market capitalization2 but incorporates some type of rules-based rebalancing process. A wide array of strategies are starting to have live performance histories greater than five years, making them eligible for consideration across a broader clientele. What are these various smart beta index strategies really doing when one looks under the hood? Some have called smart beta just “small-cap tilted.” Some have called smart beta just repackaged value3 strategies, and others have even referred to it as making an active4 bet on the market. We utilize a regression analysis5 to help explain factor loadings6 of various indexing strategies to quantify how big a “bet” these strategies are making on various factors, which can help explain their return patterns and factors driving their relative performance. SMART BETA STRATEGIES UNDER ANALYSIS Smart beta encapsulates a wide range of strategies. In this paper, we focus on explaining the various factor exposures and performance for WisdomTree’s broadly-focused U.S. and developed international equity strategies with at least five years of performance history: + Dividend-Focused Strategies: WisdomTree has a family of U.S. dividend-weighted7 Indexes, which we compare here to three other widely followed indexes that focus on dividend payers: 1) S&P High Yield Dividend Aristocrats Index, 2) NASDAQ US Dividend Achievers Select Index, and 3) Dow Jones U.S. Select Dividend Index. + Earnings-Focused Strategies: WisdomTree also has a family of earnings-weighted8 Indexes, which look to measure the performance of profitable U.S. firms weighted by earnings. 1 2 3 4 5 6 7 8 Smart Beta: A term for rules-based investment strategies that don’t use conventional market cap weightings. Market capitalization: Market cap = share price x number of shares outstanding. Value: Characterized by lower price levels relative to fundamentals, such as earnings or dividends. Prices are lower because investors are less certain of the performance of these fundamentals in the future. Active: Selecting securities different from those held by a benchmark portfolio in hopes for better performance. Regression analysis: Statistical process for estimating the relationships among variables. It helps one understand how the typical value of the dependent variable (Y-variable) changes when any one of the independent variables is varied while the other independent variables are held fixed. Factor loadings: For the purposes of this piece, factor loadings are synonymous with coefficients determined by a regression analysis. They provide estimates of the sensitivity of a series of returns to different external variables. A coefficient is meant to be multiplied by a value for a particular input that can be varied and is meant to indicate the degree of sensitivity of a potential outcome to changes in that input. Dividend-weighted: A type of weighting where each stock eligible for inclusion in an index is weighted by its share of the Dividend Stream® (which is the sum of regular cash dividends paid by all the companies in the index). Earnings-weighted: Earnings for all constituents within an index are added together, and individual constituents are subsequently weighted by their proportional contribution to that total. WWW.WISDOMTREE.COM 866.909.WISE (9473) WisdomTree Research MARKET INSIGHTS [ August 2014 ] + S&P 500 Equal Weight Index: We see “factor-based” indexes as a popular subcomponent of smart beta and therefore wanted to include this index, which indicates how the factor sensitivities look if each of the 500 constituents is weighted equally. It’s one of the simplest takes on smart beta, applied to one of the most widely followed equity indexes in the world. + Internationally: WisdomTree has one of the broadest sets of indexes focused on international equities that are not weighted by market capitalization. The WisdomTree DEFA index for instance has 2,366 securities across 21 countries, while the MSCI EAFE Index has approximately 900 constituents across 21 countries9. In this analysis, we also include the MSCI EAFE IMI (Investable Market Index), the core MSCI EAFE, and representative slices of the size (large, mid, and small caps) and style indexes (Growth and Value). WHAT IS SMART BETA? We think an important component of these index strategies regards the type of factor exposures being generated and how those exposures explain returns. One way to do this is through the use of a statistical factor model. Professors Eugene Fama and Kenneth French have developed a factor-based approach to analyzing the performance of a particular investment strategy or index. In essence, there are four factors, each meant to have some degree of explanatory power over returns. It’s important to note that this analysis is wholly dependent upon the period of study: + Market: This factor is meant to denote exposure to the market’s “risk premium10”—a figure that is calculated by looking at the equity market’s return minus the risk-free rate11. Higher values here indicate an increased sensitivity to potentially amplify the impact of market movements. + Size: This factor is meant to denote exposure to different market capitalization size segments. More negative values indicate exposure to the larger capitalization size segments, whereas more positive values indicate exposure to smaller capitalization size segments. + Value: One of the most widely referenced strategy style distinctions is the differentiation between “value” or “growth12” exposure, as each can have a very unique risk13/return profile. In these results, a more positive figure indicates a greater sensitivity to the value style, whereas a more negative figure indicates a greater sensitivity to the growth style. + Momentum: One factor that has received attention more recently is momentum, which measures the propensity of an investment strategy to capture different trends exhibited by the market. A more negative value here indicates essentially a lack of momentum, whereas a more positive value indicates a greater potential sensitivity to this factor. In theory, each one of these factors has the potential to become more or less favored over time, so if smart beta approaches give more weight to some and less weight to others, this could be of particular interest as people look to set up exposures congruent with their broader economic thinking. Sources: Bloomberg, Standard & Poor’s, with data as of 6/30/2014. Risk premium: Equity investments are not risk free, but it is thought that investors buy stocks because the returns they expect are high enough to allow them to take the risk. 11 Risk-free rate: Typically an interest rate on a bond issued by a government entity, where the risk of default is so small as to be deemed nonexistent. 12 Growth: Characterized by higher price levels relative to fundamentals, such as dividends or earnings. Price levels are higher because investors are willing to pay more due to their expectations of future improvements in these fundamentals. 13 Risk: Also “standard deviation,” which measures the spread of actual returns around an average return over a specific period. Higher risk indicates greater potential for returns to be farther away from this average. 9 10 2 WWW.WISDOMTREE.COM 866.909.WISE (9473) WisdomTree Research MARKET INSIGHTS [ August 2014 ] FIGURE 1: FACTOR EXPOSURES OF U.S. EQUITY INDEXES Category Dividend-Focused Earnings-Focused Equal -Weight Market Cap-Weighted (Large Caps) Market Cap-Weighted (Mid-Caps) Market Cap-Weighted (Small Caps) Market Factor Size Factor Value Factor Momentum Factor Model R-Squared Average Annual Returns WisdomTree Dividend Index 0.89 -0.24 0.32 -0.04 97.8% 6.42% WisdomTree LargeCap Dividend Index 0.89 -0.34 0.30 -0.03 97.6% 6.01% WisdomTree MidCap Dividend Index 0.94 0.20 0.43 -0.11 97.0% 7.95% WisdomTree SmallCap Dividend Index 0.81 0.71 0.65 -0.18 96.4% 6.57% WisdomTree Equity Income Index 0.82 -0.37 0.66 -0.16 91.2% 4.61% S&P High Yield Dividend Aristocrats Index 0.70 -0.02 0.48 -0.10 90.0% 7.16% NASDAQ U.S. Dividend Acheivers Select Index 0.84 -0.17 0.07 0.03 97.2% 7.31% Dow Jones U.S. Select Dividend Index 0.72 -0.12 0.62 -0.03 89.7% 5.67% WisdomTree Earnings Index 0.97 -0.11 0.04 -0.06 99.7% 7.30% WisdomTree Earnings 500 Index 0.97 -0.22 0.04 -0.04 99.6% 6.88% WisdomTree MidCap Earnings Index 1.01 0.51 0.01 -0.17 98.6% 10.79% WisdomTree SmallCap Earnings Index 0.93 0.89 0.26 -0.27 98.8% 8.87% S&P 500 Equal Weight Index 1.04 0.15 0.05 -0.15 99.4% 8.68% Russell 1000 Index 1.01 -0.09 0.00 0.00 99.5% 6.80% Russell 1000 Value Index 0.98 -0.15 0.30 -0.01 99.4% 5.20% Russell 1000 Growth Index 1.04 -0.04 -0.29 0.00 99.3% 8.28% S&P 500 Index 1.00 -0.15 0.03 0.00 99.9% 6.55% S&P 500 Value Index 0.99 -0.17 0.31 -0.05 99.3% 4.69% S&P 500 Growth Index 1.01 -0.14 -0.23 0.04 99.2% 8.36% Russell MidCap Index 1.08 0.27 -0.06 -0.05 98.5% 8.10% Russell MidCap Value Index 1.02 0.24 0.24 -0.07 98.6% 7.49% Russell MidCap Growth Index 1.13 0.30 -0.35 -0.05 97.8% 8.43% S&P MidCap 400 Index 1.05 0.42 -0.04 -0.02 98.2% 9.21% S&P MidCap 400 Value Index 1.02 0.42 0.14 -0.03 98.3% 8.37% S&P MidCap 400 Growth Index 1.08 0.41 -0.22 0.00 97.1% 10.05% Russell 2000 Index 1.02 0.85 0.12 0.01 99.7% 7.00% Russell 2000 Value Index 0.95 0.77 0.46 0.00 99.1% 5.46% Russelll 2000 Growth Index 1.09 0.92 -0.23 0.01 99.3% 8.47% S&P SmallCap 600 Index 0.96 0.81 0.18 -0.02 99.2% 8.49% S&P SmallCap 600 Value Index 0.95 0.83 0.34 -0.03 98.9% 7.45% S&P SmallCap 600 Growth Index 0.98 0.79 0.02 0.00 98.6% 9.56% Regression Period (Feb 2007 – Jun 2014) Sources: WisdomTree, Bloomberg, Zephyr StyleADVISOR, Kenneth French Data Library. Period 2/1/2007–6/30/2014 due to full history of live performance for WisdomTree Earnings Indexes. Past performance is not indicative of future results. You cannot invest directly in an index. 3 WWW.WISDOMTREE.COM 866.909.WISE (9473) WisdomTree Research MARKET INSIGHTS [ August 2014 ] Over this particular period in time, a few interesting themes emerged: + Growth Outperformed Value: The S&P MidCap 400 Growth, S&P 500 Growth, S&P SmallCap 600 Growth and Russell 2000 Growth indexes performed strongly—much more so than their “value” counterparts. So, in essence, much of the research from Professors Fama and French, which discussed the exposures of the “value premium” and hence the model to estimate exposure to value, did not hold over this period. There was some headwind for the WisdomTree Indexes, which have a high loading to the value sensitivity factor—which was especially true for the Dividend Indexes. + The fact that the WisdomTree MidCap Earnings Index had the highest return of all indexes shown and the WisdomTree SmallCap Earnings Index outperformed the Russell 2000 Index shows that earnings indexes in particular should be thought of as more than just value-tilted strategies. + Moreover, the WisdomTree LargeCap Dividend Index, MidCap Dividend Index and SmallCap Dividend Index had valueloading factors either similar to or higher than the Russell 1000 Value, Russell Midcap Value or Russell 2000 Value, yet in each of those size-based comparisons, the WisdomTree Indexes had better returns over the period studied, despite the deeper value-loading factors. This illustrates to us that the performance of the WisdomTree Dividend Indexes was more than just the value factor exposures, especially since value underperformed and the WisdomTree Indexes had higher value-loading factors. + Mid- & Small Caps Outperform Large Caps (index families exhibiting this ranking): • WisdomTree MidCap, SmallCap and Earnings 500 Indexes • WisdomTree MidCap Dividend, SmallCap Dividend and LargeCap Dividend Indexes • S&P MidCap 400, SmallCap 600 and 500 indexes • Russell Midcap, 2000 and 1000 indexes + WT Earnings Indexes Outperform WT Dividend Indexes: For each respective market capitalization size segment, the WisdomTree Earnings Index outperformed its WisdomTree Dividend Index counterpart. Another way to think about this is that since we already saw “growth” outperform “value” over this period and that dividend-focused strategies—especially those that weight by cash dividends—tilt further toward the value end of the spectrum than those that weight by earnings, it doesn’t surprise us that over this particular period our earnings indexes outperformed our dividend indexes. + Market Factor Takeaways: When viewing the market factor, it becomes clear that dividend-focused strategies exhibit lower market factor sensitivity than either earnings-weighted, equal-weighted or market capitalization-weighted approaches. This is another way of quantifying the lower volatility14 (lower market beta15) of traditional dividend-based strategies. • Mid-Caps Have Higher Market Beta: With the WisdomTree MidCap Dividend Index as the lone exception, every index focused on exposure to the mid-capitalization size segment had a market factor above 1, possibly explaining some of the strong mid-cap performance seen in figure 1 for this period. 14 15 4 Volatility: A measure of the dispersion of actual returns around a particular average level. Beta: Measure of the volatility of an index or investment relative to a benchmark. A reading of 1.00 indicates that the investment has moved in lockstep with the benchmark; a reading of -1.00 indicates that the investment has moved in the exact opposite direction of the benchmark. WWW.WISDOMTREE.COM 866.909.WISE (9473) WisdomTree Research MARKET INSIGHTS [ August 2014 ] + Size Factor Takeaways: The WisdomTree LargeCap Dividend and Equity Income Indexes indicated size factors more than twice that of the S&P 500 Index. When critics characterize smart beta as being a tilt to small caps, they are clearly not talking about WisdomTree’s large-cap dividend or earnings approaches, which are more large cap than the S&P 500. Part of this distinction is caused by the fact that WisdomTree selects stocks by market capitalization for its size-based segments, while weighting securities by the dividends or Earnings Streams. In our opinion, that selection rule results in more pure size segmentations across the spectrum. • Interestingly, of all the dividend payers, the WisdomTree Dividend Index, which includes more than twice the securities of the S&P 500 over this entire period, still has a great large-cap bias and more loading to large caps than the S&P 500 (more negative size factor than the S&P 500 Index). • Equal Weighting: Looking at the equal-weighting the S&P 500 Index illustrates how different weighting methodologies can influence the size factor of a particular index. We see that the S&P 500 Index registers a negative value (meaning that it’s tilting toward exposure to larger companies) whereas the S&P 500 Equal Weight Index registers a positive value (meaning that its size exposure is tilting toward smaller companies). It’s interesting that the S&P 500 Equal Weight Index has a very similar size factor to the WisdomTree MidCap Dividend Index. • Earnings Weight vs. Dividend Weight: One thing we can say is that the respective size cuts of the dividend indexes (broad, large, mid, small) tended to be larger than the respective size cuts of the earnings indexes. The WisdomTree SmallCap Earnings Index actually had the largest factor sensitivity to small cap of all the indexes included, with the sole exception of the Russell 2000 Growth Index. + Value Factor Takeaways: With the exception of the NASDAQ US Dividend Achievers Select Index (the only dividend-focused index shown here to be weighted by market capitalization), the dividend-focused strategies all tilt significantly toward the value end of the spectrum. On the other hand, the earnings-weighted strategies in each respective size segment tilt much more toward core16 exposures. The WisdomTree MidCap Earnings Index, the best-performing Index over this period, is the closest fundamentally weighted Index to having a slight growth tilt. • The WisdomTree LargeCap Dividend Index had a value loading of .30, which was equal to the Russell 1000 Value Index’s of .30, but below that of the S&P 500 Value Index, which was .31. Its value orientation was clear over this period. + Momentum Factor Takeaways: Most of the indexes shown exhibit either a negative sensitivity to the momentum factor or a tiny positive sensitivity to the momentum factor. The largest positive sensitivity is shown by the S&P 500 Growth Index. + The rules-based rebalancing of smart beta indexes typically involves a process to sell the stocks that outperform their fundamentals—which, to a large extent, is an anti-momentum process. This negative momentum factor exposure was strongest in the WisdomTree SmallCap Earnings, WisdomTree SmallCap Dividend and WisdomTree MidCap Earnings Indexes. The negative momentum of these three WisdomTree Indexes was even stronger than the negative factor exposure to momentum in the S&P 500 Equal Weight Index, which by definition—in order to maintain its equal-weight strategy—adds weight to all losing positions and subtracts weight from all winning positions with no regard to fundamental changes in those companies. 16 5 Core: Characterized by exposure across stocks exhibiting both value and growth characteristics. WWW.WISDOMTREE.COM 866.909.WISE (9473) WisdomTree Research MARKET INSIGHTS [ August 2014 ] USING FACTOR LOADINGS TO TARGET EXPOSURES Now that we’ve calculated the factor exposures and highlighted some of the results, the logical next question is what to do with this information on a strategic basis building portfolios looking for exposures to certain factors. One of the most widely followed equity indexes is the S&P 500, viewed as a measure of the performance of large-cap U.S. equities. Of course, we believe many are more interested in the concept of utilizing different strategies with the potential to outperform the S&P 500 Index consistently on a risk-adjusted basis. Looking at the results in figure 1 is one way to come up with a framework from which to build such strategies. Now, the S&P 500 Index over this particular period had: A market factor of 1.00, a very slight value factor of 0.03, a momentum factor of 0.00, a size factor of -0.15. Therefore, the only factor to really consider based on these figures is size. Now, while many think of the S&P 500 Index as a largecap option, with a size factor of -0.15 it clearly isn’t the largest option shown in figure 1—the WisdomTree LargeCap Dividend Index (WTLDI), with a size factor of -0.34 is significantly larger. The WisdomTree Earnings 500 Index (WTEPS) is also larger than the S&P 500 Index, with a size factor of -0.22. We think it’s important to consider what percentage blends of mid caps with WTLDI and WTEPS would bring the weighted average size exposure into alignment with that of the S&P 500 Index, and the two mid-caps we use are the WisdomTree MidCap Earnings Index (WTMEI) and the WisdomTree MidCap Dividend Index (WTMDI). FIGURE 2: WISDOMTREE LARGE-CAP BLENDS TO MATCH SIZE EXPOSURE OF THE S&P 500 INDEX Market Factor Size Factor Value Factor Momentum Factor Average Annual Return 77% WTLDI / 23% WTMEI 0.91 -0.15 0.23 -0.06 7.17% 64% WTLDI / 36% WTMDI 0.91 -0.15 0.35 -0.06 6.76% 91% WTEPS / 9% WTMEI 0.97 -0.15 0.03 -0.06 7.25% 85% WTEPS / 15% WTMDI 0.96 -0.15 0.10 -0.05 7.07% S&P 500 Index 1.00 -0.15 0.03 0.00 6.55% Index/Blends to Match S&P 500 Size Factor Sources: WisdomTree, Bloomberg, Zephyr StyleADVISOR, Kenneth French Data Library. Period 2/1/2007–6/30/2013 due to full history of live performance for WisdomTree Earnings Indexes. Past performance is not indicative of future results. You cannot invest directly in an index. + Raising the Value Factor: Each of the blends applies an annual rebalance with a focus on relative value. On the other hand, the 500 stocks selected by Standard & Poor’s for the S&P 500 Index are weighted by their market capitalizations, and it is not an approach that is sensitive to relative valuation. It’s also interesting that the all earnings blend (91% WTEPS / 9%WTMEI) actually had the same loading to the value factor as that of the S&P 500 Index. This is another case in point that these strategies are more “core” oriented than their dividend-focused counterparts. + Potential for “Anti”-Momentum: Another attribute of a relative value rebalance is the disciplined way in which it tends to remove weight from stocks whose price increase exceeded the growth of their fundamentals. In essence, this disciplined removal of weight from stocks that have tended to perform strongly and redeployment into the potential performers of tomorrow could account for the minus sign in front of the momentum factor. 6 WWW.WISDOMTREE.COM 866.909.WISE (9473) WisdomTree Research MARKET INSIGHTS [ August 2014 ] As people consider ways in which to potentially outperform the S&P 500 on a risk-adjusted basis, we believe this is an example of one approach that could be of interest. It also shows that as one looks to add in the WisdomTree large-cap options, there is a large-cap bias to their construction, at least relative to the size factor of the S&P 500 that could impact desired allocations in other market capitalization size segments. FIGURE 3: FACTOR EXPOSURES OF DEVELOPED INTERNATIONAL EQUITY INDEXES Market Factor Size Factor Value Factor Momentum Factor Model R-Squared Average Annual Returns WT DEFA 0.99 -0.23 0.06 0.01 99.2% 4.99% WT DEFA Equity Income 1.01 -0.34 0.18 -0.02 98.1% 4.69% WT Int Large 0.98 -0.36 0.02 0.03 99.0% 4.53% WT Int Mid 1.01 0.10 0.20 -0.05 98.8% 5.85% WT Int Small 1.00 0.55 0.24 -0.06 98.4% 6.81% MSCI EAFE 0.99 -0.16 0.05 0.01 99.7% 3.87% MSCI EAFE Value 0.98 -0.19 0.29 -0.10 99.2% 3.27% MSCI EAFE Growth 0.99 -0.12 -0.19 0.11 99.5% 4.39% MSCI EAFE Mid 1.02 0.16 0.07 -0.02 99.0% 4.09% MSCI EAFE Mid Value 0.95 0.14 0.37 -0.12 97.8% 4.24% MSCI EAFE Mid Growth 1.07 0.18 -0.14 0.05 99.0% 3.80% MSCI EAFE Small 1.06 0.68 0.12 -0.05 98.9% 5.32% MSCI EAFE Small Value 1.03 0.60 0.35 -0.16 98.6% 5.66% MSCI EAFE Small Growth 1.09 0.64 -0.12 0.05 98.9% 4.98% MSCI EAFE IMI 0.99 -0.07 0.05 0.00 99.7% 4.05% Developed International Indexes Sources: WisdomTree, Bloomberg, Zephyr StyleADVISOR, Kenneth French Data Library. Period 6/1/2006–6/30/2014 due to inception dates of the WisdomTree Indexes shown. Past performance is not indicative of future results. You cannot invest directly in an index. Here are some key takeaways on the developed international indexes and their factor loadings: + Market Factors Takeaways: In the U.S. analysis presented above, the dividend-weighted indexes had showed lower market sensitivity or factor loadings. On the international front, the market sensitivity factors much more closely resemble the traditional market cap weighted indexes. One reasons for this is the U.S. is an outlier country in terms of dividend payout ratios17 and internationally the indexes provide greater representation and coverage of the market. + Value Factor Takeaways: All WisdomTree related strategies listed above, weights securities by their dividend streams. As we have shown in our U.S. centric piece, these strategies tend to tilt more strongly toward the value segment of the markets. The developed international region is no exception. The value loadings on the international segments are not as strong as the value loadings in the U.S. strategies. 17 7 Dividend payout ratio: Proportion of a firm’s earnings paid out as dividends, with higher numbers indicating greater amounts of dividend payments relative to the firm’s earnings. WWW.WISDOMTREE.COM 866.909.WISE (9473) WisdomTree Research MARKET INSIGHTS [ August 2014 ] • The value factor is particularly strong in dividend strategies focused on small and mid caps segments. Although the small cap segment has a much stronger value tilt than the mid caps, both have a much larger tilt to the value spectrum than the broad market strategies. Note the market cap weighted value strategies were more value tilted than the dividend weighted approach. The dividend weighting therefore was in between the traditional market cap weighted indexes and the value segments of those indexes when measured on sensitivity to this value factor. + Size Factor Takeaways: In terms of size tilts, WT DEFA, WT DEFA Equity Income, and WT International Large Cap have the biggest large cap tilts relative to all other strategies listed above. In other words, the three strategies have the “purest” large cap return attribution in the developed international space. One cannot describe these indexes as just having exposure to the small cap premium – rather more purely a value/high dividend premium. + Momentum Factor Takeaways: While dividend strategies in the U.S. tend to display strong anti-momentum tendencies, it is less clear in the developed international space. Mid and Small Caps were the most notable on this front. FOCUSING ON THE MSCI EAFE SIZE EXPOSURE Earlier, we showed analysis on how to use the factor loadings to construct a portfolio that targeted certain market capitalization characteristics. Here we extend this analysis internationally to target the size loading of the MSCI EAFE Index. FIGURE 4: MATCHING THE SIZE EXPOSURE OF THE MSCI EAFE INDEX Market Factor Size Factor Value Factor Momentum Factor Average Annual Return 55% WT International LargeCap / 45% WT International MidCap 0.99 -0.16 0.10 0.00 5.15% 77% WT International LargeCap / 23% WT International SmallCap 0.98 -0.16 0.07 0.01 5.09% 77% WT DEFA / 23% WT International MidCap 0.99 -0.16 0.09 0.00 5.20% 91% WT DEFA / 9% WT International SmallCap 0.99 -0.16 0.08 0.00 5.17% MSCI EAFE 0.99 -0.16 0.05 0.01 3.87% Matching the MSCI EAFE Size Factor Sources: WisdomTree, Bloomberg, Zephyr StyleADVISOR, Kenneth French Data Library. Period 6/1/2006–6/30/2014 due to inception dates of the WisdomTree Indexes shown. Past performance is not indicative of future results. You cannot invest directly in an index. The only criterion was that each of our blends must match the size factor of the MSCI EAFE Index: + It is interesting to see that it takes a 55% allocation to the WT International Large Cap Dividend Index and 45% allocation to the WT International MidCap Dividend Index to target the same exposure as the MSCI EAFE Index. + Another blend can incorporate small caps only with large caps and the percentages here are 77% large cap with 23% small caps would also target the same size loading the MSCI EAFE Index. Each of the WT blends that, as can be seen, matched the size factor loading of the MSCI EAFE Index, but beat the MSCI EAFE Index in terms of average annual returns by more than 1.2% per year. This is noteworthy in that these strategies aren’t using “smart beta” methodologies to overload to small caps—they generated their higher returns by other means. 8 WWW.WISDOMTREE.COM 866.909.WISE (9473) WisdomTree Research MARKET INSIGHTS [ August 2014 ] Also critically important is the fact that these strategies loaded to the value factor and out-performed the core MSCI EAFE Index, even though value strategies themselves under performed growth strategies over the period in question. This also suggests that there is something more at work with WisdomTree’s dividend weight approach in this international set of indexes than just value- T tilts and size tilts. CONCLUSION This paper quantified the exposures within the WisdomTree Index family using a rigorous framework across the spectrum of factors academics often study: market, size, value and momentum. We addressed some of the common critiques of “smart beta” indexes. A common perception is that smart beta indexes always involve a small-cap bias in their index construction. This is just not true for WisdomTree’s broad-based and large-cap Indexes, which often have a larger-cap bias than traditional market cap-weighted indexes, and we quantified in this piece how much more large cap they are. We also addressed the comment that smart beta indexes inherently are value-tilted strategies. We showed there was a headwind for value-based exposure over this period, yet WisdomTree Earnings Indexes especially, but also the Dividend Indexes, performed well given those headwinds and the value-loading factors of the WisdomTree Dividend Indexes—especially on the international side. As is common with index analyses, we believe it makes sense to look under the hood of strategies to see the desired exposure. We hope this paper helps frame why and how WisdomTree’s U.S. Index family achieved the results it did over the time period of live performance analyzed. T 9 WWW.WISDOMTREE.COM 866.909.WISE (9473) WisdomTree Research MARKET INSIGHTS [ August 2014 ] Investors should carefully consider the investment objectives, risks, charges and expenses of the Funds before investing. To obtain a prospectus containing this and other important information, call 866.909.WISE (9473) or visit wisdomtree.com. Read the prospectus carefully before you invest. Dividends are not guaranteed, and a company’s future ability to pay dividends may be limited. A company paying dividends may cease paying dividends at any time. There are risks associated with investing, including possible loss of principal. Investments focusing on certain sectors and/or mid- or small cap companies increase their vulnerability to any single economic or regulatory development. This may result in greater share price volatility. Foreign investing involves special risks, such as risk of loss from currency fluctuation or political or economic uncertainty. Investments in emerging, offshore or frontier markets are generally less liquid and less efficient than investments in developed markets and are subject to additional risks, such as risks of adverse governmental regulation and intervention or political developments. WisdomTree Dividend Index: Measures the performance of dividend-paying companies incorporated in the United States that pay regular cash dividends and meet WisdomTree’s eligibility requirements. Weighted by indicated cash dividends. WisdomTree LargeCap Dividend Index: A fundamentally weighted index that measures the performance of the large-capitalization segment of the U.S. dividend-paying market. The Index comprises the 300 largest companies ranked by market capitalization from the WisdomTree Dividend Index. WisdomTree MidCap Dividend Index: A fundamentally weighted index that measures the performance of the mid-capitalization segment of the U.S. dividend-paying market. The Index comprises the companies that constitute the top 75% of the market capitalization of the WisdomTree Dividend Index after the 300 largest companies have been removed. The Index is dividend weighted annually to reflect the proportionate share of the aggregate cash dividends each component company is projected to pay in the coming year, based on the most recently declared dividend per share. WisdomTree SmallCap Dividend Index: A fundamentally weighted index that measures the performance of the small-capitalization segment of the U.S. dividend-paying market. The Index comprises the companies that constitute the bottom 25% of the market capitalization of the WisdomTree Dividend Index after the 300 largest companies have been removed. The Index is dividend weighted annually to reflect the proportionate share of the aggregate cash dividends each component company is projected to pay in the coming year, based on the most recently declared dividend per share. WisdomTree Equity Income Index: Measures the performance of the 30% highest-yielding dividend-paying equities in the WisdomTree Dividend Index; weighted by indicated cash dividends. S&P High Yield Dividend Aristocrats Index: Designed to track the performance of dividend-paying companies in the U.S. that have increased their annual dividend payments for the last 20 or more consecutive years. NASDAQ US Dividend Achievers Select Index: Designed to track the performance of dividend-paying companies in the U.S. that have increased their annual dividend payments for the last 10 or more consecutive years. Dow Jones U.S. Select Dividend Index: A modified market capitalization approach and weights by dividend yield. Stocks are selected for fundamental strength relative to their peers, subject to various screens such as dividend quality and liquidity. WisdomTree Earnings Index: A fundamentally weighted index that measures the performance of earnings-generating companies within the broad U.S. stock market. WisdomTree Earnings 500 Index: A fundamentally weighted index that measures the performance of earnings-generating companies in the large-capitalization segment of the U.S. stock market. Companies in the Index are incorporated and listed in the U.S. and have generated positive cumulative earnings over their most recent four fiscal quarters prior to the Index measurement date. The Index comprises the 500 largest companies ranked by market capitalization in the WisdomTree Earnings Index. WisdomTree MidCap Earnings Index: A fundamentally weighted index that measures the performance of the top 75% of the market capitalization of the WisdomTree Earnings Index after the 500 largest companies have been removed. WisdomTree SmallCap Earnings Index: A fundamentally weighted index that measures the performance of earnings-generating companies in the small-capitalization segment of the U.S. stock market. The Index comprises the companies in the bottom 25% of the market capitalization of the WisdomTree Earnings Index after the 500 largest companies have been removed. S&P 500 Equal Weight Index: Designed to track the equally weighted performance of the 500 constituents in the S&P 500 Index. Russell 1000 Index: A measure of the performance of the 1,000 largest companies by market capitalization in the Russell 3000 Index. Russell 1000 Value Index: A measure of the large-cap value segment of the U.S. equity universe, selecting from the Russell 1000 Index. Russell 1000 Growth Index: A measure of the large-cap growth segment of the U.S. equity universe, selecting from the Russell 1000 Index. S&P 500 Index: A market capitalization-weighted benchmark of 500 stocks selected by the Standard and Poor’s Index Committee, designed to represent the performance of the leading industries in the United States economy. S&P 500 Value Index: A market capitalization-weighted benchmark designed to measure the value segment of the S&P 500 Index. S&P 500 Growth Index: A market capitalization-weighted benchmark designed to measure the growth segment of the S&P 500 Index. Russell Midcap Index: Measures the performance of the mid-cap segment of the U.S. equity universe. The Russell Midcap is a subset of the Russell 1000 Index. It includes approximately 800 of the smallest securities based on a combination of their market cap and current index membership. Russell Midcap Value Index: Measures the performance of the mid-cap value segment of the U.S. equity universe. It includes those Russell Midcap Index companies with lower price-to-book ratios and lower forecasted growth values. Russell Midcap Growth Index: Measures the performance of the mid-cap growth segment of the U.S. equity universe. It includes those Russell Midcap Index companies with higher price-to-book ratios and higher forecasted growth values. S&P MidCap 400 Index: Provides investors with a benchmark for mid-sized companies. The index covers over 7% of the U.S. equity market and seeks to remain an accurate measure of mid-sized companies, reflecting the risk and return characteristics of the broader mid-cap universe on an ongoing basis. S&P MidCap 400 Value Index: Provides investors with a measure of the performance of the value segment of the S&P MidCap 400 Index. S&P MidCap 400 Growth Index: Provides investors with a measure of the performance of the growth segment of the S&P MidCap 400 Index. Russell 2000 Index: Measures the performance of the small-cap segment of the U.S. equity universe. The Russell 2000 is a subset of the Russell 3000 Index, representing approximately 10% of the total market capitalization of that index. It includes approximately 2,000 of the smallest securities based on a combination of their market cap and current index membership. Russell 2000 Value Index: Measures the performance of the small-cap value segment of the U.S. equity universe. It includes those Russell 2000 Index companies with lower price-to-book ratios and lower forecasted growth values. Russell 2000 Growth Index: Measures the performance of the small-cap growth segment of the U.S. equity universe. It includes those Russell 2000 Index companies with higher price-to-book ratios and higher forecasted growth values. S&P SmallCap 600 Index: A market capitalization-weighted measure of the performance of small-cap equities within the United States, with constituents required to demonstrate profitability prior to gaining initial inclusion. S&P SmallCap 600 Value Index: A market capitalization-weighted measure of the performance of small-cap value equities within the United States, with constituents required to demonstrate profitability prior to gaining initial inclusion. S&P SmallCap 600 Growth Index: A market capitalization-weighted measure of the performance of small-cap growth equities within the United States, with constituents required to demonstrate profitability prior to gaining initial inclusion. Russell 3000 Index: Measures the performance of the 3,000 largest U.S. companies based on total market capitalization. Russell 3000 Value Index: Measures the performance of the Russell 3000 Index constituents with value characteristics. Russell 3000 Growth Index: Measures the performance of the Russell 3000 Index constituents with growth characteristics. WisdomTree DEFA Index: A fundamentally weighted index that measures the performance of dividend-paying companies in the industrialized world, excluding Canada and the United States, that pay regular cash dividends and meet other liquidity and capitalization requirements. It comprises companies incorporated in 16 developed European countries, Japan, Australia, New Zealand, Hong Kong and Singapore. Companies are weighted based on annual cash dividends paid. WisdomTree DEFA Equity Income Index: A fundamentally weighted index that measures the performance of dividend-paying companies in the industrialized world, excluding Canada and the United States, that pay regular cash dividends and are among the 30% highest-yielding equities within the WisdomTree DEFA Index as of the annual Index screening date. WisdomTree International LargeCap Dividend Index: A fundamentally weighted index that measures the performance of the large-capitalization segment of the dividend-paying market in the industrialized world outside the U.S. and Canada. The Index comprises the 300 largest companies ranked by market capitalization from the WisdomTree DEFA Index. Companies are weighted in the Index based on annual cash dividends paid. WisdomTree International MidCap Dividend Index: A fundamentally weighted index that measures the performance of the mid-capitalization segment of the dividend-paying market in the industrialized world outside the U.S. and Canada. The Index comprises the companies that make up the top 75% of the market capitalization of the WisdomTree DEFA Index after the 300 largest companies have been removed. Companies are weighted in the Index based on annual cash dividends paid. WisdomTree International SmallCap Dividend Index: A fundamentally weighted index that measures the performance of the small-capitalization segment of the dividend-paying market in the industrialized world outside the U.S. and Canada. The Index comprises the companies that make up the bottom 25% of the market capitalization of the WisdomTree DEFA Index after the 300 largest companies have been removed. Companies are weighted in the Index based on annual cash dividends paid. MSCI EAFE Index: A free float adjusted market cap-weighted index composed of companies representative of the developed market structure o developed countries in Europe, Australasia and Japan. MSCI EAFE IMI: A free float adjusted market cap-weighted index composed of companies representative of the developed market structure of developed countries in Europe, Australasia and Japan, covering the large-cap, mid-cap and small-cap segments of the capitalization spectrum. MSCI EAFE Mid Cap Index: A free float-adjusted market capitalization-weighted equity index that captures mid-cap representation across developed market countries around the world, excluding the U.S. and Canada. MSCI EAFE Mid Cap Value Index: A free float-adjusted market capitalization-weighted equity index that captures mid-cap representation across developed markets around the world, excluding the U.S. and Canada, focusing on those with higher book value-to-market value ratios. MSCI EAFE Mid Cap Growth Index: A free float-adjusted market capitalization-weighted equity index that captures mid-cap representation across developed markets around the world, excluding the U.S. and Canada, focusing on those with higher earnings growth characteristics. MSCI EAFE Value Index: A free float adjusted market capitalization-weighted subset of stocks within the MSCI EAFE Index that have lower share prices relative to their earnings or dividends per share. MSCI EAFE Growth Index: A free float adjusted market capitalization-weighted subset of stocks within the MSCI EAFE Index that have higher share prices relative to their earnings or dividends per share. MSCI EAFE Small Cap Index: A free float-adjusted market capitalization-weighted equity index that captures small-cap representation across developed market countries around the world, excluding the U.S. and Canada. MSCI EAFE Small Cap Value Index: A free float-adjusted market capitalization-weighted equity index that captures small-cap representation across developed market countries around the world, excluding the U.S. and Canada, focusing on those with higher book value-to-market value ratios. MSCI EAFE Small Cap Growth Index: A free float-adjusted market capitalization-weighted equity index that captures small-cap representation across developed market countries around the world, excluding the U.S. and Canada, focusing on those with higher earnings growth characteristics. The Dow Jones U.S. Select Dividend Index is calculated, distributed and marketed by Dow Jones Indexes, a licensed trademark of CME Group Index Services LLC, and has been licensed for use. WisdomTree Funds are distributed by ALPS Distributors, Inc. Jeremy Schwartz is a registered representatives of ALPS Distributors, Inc. © 2014 WisdomTree Investments, Inc. “WisdomTree” is a registered mark of WisdomTree Investments, Inc. 10 WWW.WISDOMTREE.COM WIS006874 8/2015 866.909.WISE (9473) An Asset Owner Perception Survey During the summer of 2014, the Sovereign Wealth Fund Institute conducted a survey of public institutional investors regarding their perceptions and adoption of smart beta strategies. ¬The survey targeted CEOs, CIOs and other key decision makers at public funds across the globe. Among the 72 respondents, the majority represented sovereign wealth funds and public pensions. All together, the institutions surveyed manage in excess of US$ 2.9 trillion in public assets. Key Takeaways: • Among the asset owners surveyed, 67% have smart beta allocations or are currently evaluating smart beta strategies. • The majority of those with smart beta investments are only testing the waters with small allocations of between 0-5% of their equity portfolios. However, 15% of respondents allocated more than 20% of their equity portfolios to smart beta strategies. • Those with smart beta investments are unlikely to decrease their allocation or terminate a smart beta mandate in the next year. In contrast, the majority are likely to increase their percent allocation to smart beta. • Fundamental and Low Volatility strategies are the most widely used. • Responses suggest smart beta can be both a complement and a substitute for cap-weighted indexes. • The majority of respondents agreed that smart beta is a complement to active management, but there is no consensus whether the strategy itself should be considered active management or whether it is a substitute. Public Investor Sample Demographics From June to August of 2014, the SWFI conducted a detailed survey of public institutional investors regarding their perceptions and adoption of smart beta strategies. The survey targeted CEOs, CIOs and other key decision makers at public funds across the globe. The online survey received responses from 72 public institutional asset owners representing a variety of different investor classes and geographies with combined assets under management in excess of USD 2.9 trillion. Fund size ranged from less than USD 1 billion AUM to greater than USD 200 billion. Exhibits 1-3 provide a demographic breakdown of the respondents. Public pensions, representing 46% of the sample, accounted for the largest share of responses. Sovereign wealth funds were the second largest demographic (22% of the sample), followed by central banks (15%), superannuation funds (8%), other (6%) and endowments (3%). “Other” includes two public foundations, a strategic state fund and a development fund. Exhibit 1: Participation by Type of Institution Respondents Other Funds 6% Endowment 3% Central Bank 15% Public Pension 46% Superannuation Fund Sovereign Wealth Fund 92 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta 8% 22% The survey drew responses from 12 different geographic regions. Respondents from North America and Europe accounted for 53% of the sample, with the remaining respondents distributed throughout the Middle East (11%), Asia (11%), Oceania (11%), Latin America (8%) and Africa (6%). Exhibit 2: Participants by Region 32% North America 18% Western Europe Oceania 11% Middle East 11% Latin America 8% 6% Africa 6% Southeast Asia 4% East Asia 3% Eastern Europe 1% Southern and Central Asia 0 5 10 15 20 25 30 35 Exhibit 3: Participants by Total Assets Under Management The 72 respondents have combined assets under management of more than US$ 2.9 trillion. By AUM, 17% of respondents manage more than US$ 100 billion while the five largest respondents collectively manage over US$ 1 trillion in assets. The five largest institutions in the sample included 3 sovereign wealth funds and 2 central banks. Only 7% of respondents had less than US$ 1 billion in AUM, and 67% had more than US$ 10 billion. 7% > 200 4% 151 - 200 6% 101 - 150 15% 51 - 100 26 - 50 17% 18% 11 - 25 1 - 10 26% <1 7% 0 5 10 15 20 25 30 As an aside, the actual value of combined AUM is likely to be much higher than US$ 2.9 trillion given the presence of three large Middle Eastern sovereign wealth funds, each with over US$ 200 billion in assets. Based on SWFI estimates, the combined AUM of these three respondents alone is likely in excess of US$ 1 trillion. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 93 Adoption and Allocation Exhibit 4: ALL RESPONDENTS - What best describes your institution’s involvement with smart beta? Respondents Has smart beta allocation 43% Currently evaluating smart beta but does not have smart beta allocation 24% Does not have smart beta allocation and not evaluating smart beta 33% The majority of respondents had smart beta allocations or were currently evaluating smart beta strategies. The combined groups comprised 67% of the sample, with only 33% of respondents indicating that they did not have a smart beta allocation and were not evaluating smart beta strategies. Those respondents with smart beta investments collectively manage more than US$ 1.4 trillion. Those asset owners that are currently evaluating smart beta strategies have combined AUM in excess of US$ 540 billion. Exhibit 5 presents a regional breakdown for smart beta adoption. Given the small sample size induced by this segmentation, we refrain from comparing and contrasting the prevalence of smart beta adoption among regions. This breakdown is intended to give the reader a more intimate look at our sample of public investors, and the reader should not generalize these results to the much broader space of institutional investors in each region. NORTH AMERICA All together, the 23 funds represented by respondents in North America manage more than US$ 1 trillion in assets. Of those funds, 78% have allocations to smart beta strategies or are currently evaluating smart beta strategies, while 22% do not allocate to and are not evaluating smart beta. LATIN AMERICA Latin America includes South America, Central America and the Caribbean. All together, the 6 funds represented by 94 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta respondents in Latin America manage more than US$ 50 billion in assets. Of those funds, 33% have allocations to smart beta strategies while 67% do not and are not evaluating smart beta. None of the respondents from Latin America indicated that their fund was currently evaluating smart beta strategies. EUROPE All together, the 15 funds represented by respondents in Europe (Western and Eastern) manage more than US$ 350 billion in assets. The majority of European respondents indicated that their institutions have smart beta allocations. Of those institutions, 73% have allocations to smart beta strategies or are currently evaluating smart beta strategies, while 27% do not allocate to and are not evaluating smart beta. Exhibit 5: ALL RESPONDENTS - By Region1 North America Latin America Europe Has smart beta (SB) allocation 48% 33% 53% Currently evaluating SB but does not have SB allocation 30% 0% 20% Does not have SB allocation and not evaluating SB 22% 67% 27% Respondents 23 15 Middle East North Africa Asia Oceania Has smart beta (SB) allocation 40% 62% 13% Currently evaluating SB but does not have SB allocation 30% 13% 37% Does not have SB allocation and not evaluating SB 30% 25% 50% Respondents 10 8 8 MENA Sovereign wealth funds comprised 60% of the Middle East and North Africa sample. All together, the 10 funds represented by respondents in the Middle East and North Africa manage more than US$ 700 billion in assets. The majority of the total assets for respondents in this region are accounted for by 3 large sovereign wealth funds with AUM of more than US$ 200 billion each. Based on SWFI estimates, the combined AUM of these three respondents alone is likely in excess of US$ 1 trillion. Of the funds in the Middle East and North Africa, 70% have allocations 1 6 to smart beta strategies or are currently evaluating smart beta strategies, while 30% do not allocate to and are not evaluating smart beta. ASIA Asia includes Central and Southern Asia, East Asia, and Southeast Asia. Approximately 60% of Asian respondents represented sovereign wealth funds with the remainder being divided between public pensions and central banks. All together, the 8 funds represented by respondents in the Asia manage more than US$ 500 billion in assets. The prevalence of smart beta adoption is surprisingly high among Asian respondents. The majority of Asian respondents indicated that their funds have allocated to smart beta strategies. Of the institutions, 75% have allocations to smart beta strategies or are currently evaluating smart beta strategies, while 25% do not allocate to and are not evaluating smart beta. The speed of smart beta adoption in Asia is widely regarded as being slower than the United States or Europe. The results of this survey, however, suggest that smart beta strategies may be gaining a stronger foothold in the Asian asset owner space than is typically described in mainstream media outlets. Still, the size of this segment is too small We omit the breakdown for the region of Sub-Saharan Africa. There were two respondents from Sub-Saharan Africa with combined assets of over US$ 102 billion. Neither had smart beta allocations nor were they evaluating smart beta strategies. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 95 Exhibit 6: Sovereign Wealth Fund Adoption Respondents 37.5% Has smart beta allocation Currently evaluating smart beta but does not have smart beta allocation Does not have smart beta allocation and not evaluating smart beta to generalize about the much broader Asian institutional investor space, and these results should, at most, be taken as a call for further research into the subject. OCEANIA All together, the 8 funds represented by respondents in the Oceania region manage more than US$ 90 billion in assets. Half of the respondents from Oceania indicated that their fund does not have a smart beta allocation and is not evaluating smart beta strategies. Of the remaining respondents, only 12% indicated that their institution had a smart beta allocation while the rest are currently evaluating smart beta strategies. SOVEREIGN WEALTH FUND ADOPTION All together, the 16 sovereign wealth funds in the sample manage more than US$ 900 billion in assets. But to reiterate once more, the actual value of combined AUM is likely to be much higher given the presence of three large Middle Eastern sovereign wealth funds, each with over US$ 200 billion in assets and combined AUM likely in excess of US$ 1 trillion. 96 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta 25% 37.5% By geography, 38% of the sovereign wealth funds in the sample are based in the Middle East. Asia accounted for an additional 31% of the SWF segment. The remaining 31% of the SWF respondents hailed from North America, Latin America, and Europe. Of the sovereign wealth funds, 62% have allocations to smart beta strategies or are currently evaluating smart beta strategies, while 38% do not allocate to and are not evaluating smart beta. Exhibit 7: HAS ALLOCATION What percentage of your equity portfolio is allocated to smart beta? 52% 0% - 5% 16% 6% - 10% 10% 11% - 15% 16% - 20% 6% 21% - 25% 6% 3% 26% - 30% 31% - 35% 36% - 40% 3% > 40% 3% 0 10 20 30 40 50 60 Of those respondents that had smart beta allocations, the majority had allocated 0-5% of their equity portfolios to smart beta strategies. However, almost half of those with smart beta investments had allocated more than 5% of their equity portfolios to smart beta. In total, 26% of those with smart beta investments allocated 6-15% of their equity portfolios to smart beta while 21% allocated more than 16% of their equity portfolios to smart beta. Two large public pensions from North America and Western Europe indicated that their fund allocates in excess of 36% of its equity portfolio to smart beta. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 97 Evaluation and Investments The survey asked respondents to rate the likelihood of their institution making various investment decisions in the next 12 months. The questions range from evaluating a new strategy to terminating an existing one. Responses suggest asset owners have a positive outlook for smart beta in general. The overall sample showed considerable interest in smart beta strategies. The majority expect to begin or continue evaluating a smart beta strategy in the next 12 months while only one quarter say this is not likely. Respondents that currently invest in smart beta tend to be loyal to these investments. The overwhelming majority of this segment are not likely to terminate a smart beta investment in the next year or decrease their allocation to smart beta funds. Moreover, most of them expect to increase their percent allocation to this asset class in the next year. Of all respondents, 55% indicated that their fund was either likely or very likely to begin or continue evaluating a smart beta strategy in the next 12 months. In contrast, 25% responded unlikely or very unlikely. Of the asset owners that did not have allocations to smart beta and were not evaluating, 21% responded that their institution was likely to begin evaluating smart beta in the next 12 months while 15% were Exhibit 8: ALL RESPONDENTS - Likelihood of Evaluating Smart Beta Strategy in Next 12 Months 50 40 30 20 10 0 Begin or continue evaluating a smart beta strategy. Very Likely 13% Likely 42% Neutral 19% Unlikely 6% Very Unlikely neutral, and the rest replied unlikely or very unlikely. Of the respondents that have invested in smart beta, 63% indicated that their fund was either likely or very likely to begin or continue evaluating a smart beta strategy in the next 12 months while only 12% responded unlikely or very unlikely. Among those respondents that have or are evaluating smart beta investments, 41% said their fund was likely or very likely to invest in a new smart beta strategy over the next year. In contrast, 23% of this segment responded that they were unlikely or very unlikely to invest in a new smart beta strategy in the next 12 months. This suggests that numerous funds are looking to 19% 98 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta Exhibit 9: HAS ALLOCATION AND EVALUATING Likelihood of Investing in New Smart Beta Strategy in Next 12 Months 40 35 30 25 20 15 10 5 0 Invest in a new smart beta strategy. Very Likely 14% Likely 27% Neutral 36% Unlikely 18% Very Unlikely diversify their smart beta portfolios, and there may be space for smart beta providers even amongst funds that have already invested in a competitor’s strategy. Of those asset owners that have allocated to smart beta, twice as many expect to increase allocation to a currently used smart beta strategy as those that do not. Specifically, 44% said their fund was likely or very likely to increase its allocation to a currently used smart beta strategy in the next year. In contrast, 22% of this segment responded that they were unlikely or very unlikely to increase its allocation to a currently used smart beta strategy in the next 12 months. Among those respondents that have allocated to smart beta, 61% 5% said their fund was unlikely or very unlikely to decrease its allocation to a currently used smart beta strategy in the next year. In contrast, only 4% of this segment responded that they were likely to decrease their allocation to a currently used smart beta strategy in the next 12 months. Of the public investors that have allocated to smart beta, 55% said their fund was likely or very likely to increase its percentage allocation to smart beta in the next year. In contrast, 15% of this segment responded that they were unlikely or very unlikely to increase their percentage allocation to smart beta in the next 12 months. Of those asset owners that have allocated to smart beta, 71% said their fund was unlikely or very unlikely to decrease its percentage allocation to smart beta in the next year. In contrast, only 4% of this segment responded that they were very likely to decrease their percentage allocation to smart beta in the next 12 months. Among those asset owners that have allocated to smart beta, 72% said their fund was unlikely or very unlikely to terminate a currently used smart beta strategy in the next year. In contrast, only 4% of this segment responded that they were likely to terminate a currently used smart beta strategy in the next 12 months. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 99 Exhibit 10: HAS ALLOCATION - Likelihood of Making Investment Decisions in Next 12 Months 50 40 30 20 10 0 Increase % allocation to smart beta. Decrease % allocation to smart beta. Terminate a currently used smart beta strategy. Very Likely 11% 4% 0% Likely 44% 0% 4% Neutral 30% 25% 24% Unlikely 11% 50% 36% 4% 21% 36% Very Unlikely 100 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta Exhibit 10: HAS ALLOCATION - Likelihood of Making Investment Decisions in Next 12 Months 50 40 30 20 10 0 Decrease allocation to a currently used smart beta strategy. Increase allocation to a currently used smart beta strategy. Very Likely 0% 11% Likely 4% 33% Neutral 36% 33% Unlikely 43% 15% Very Unlikely 18% 7% swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 101 Evaluation of Individual Strategies The survey asked those asset owners with smart beta investments to indicate which smart beta strategies they were using. Respondents were allowed to choose multiple strategies. Fundamental and low volatility strategies were the most widely used, each being reported by over half of the segment. Next in line were quality, dividend and multi-factor strategies. The three least popular options were equally-weighted, momentum and maximum Sharpe ratio strategies. A small but non-negligible portion of respondents (11%) indicated that they invested in “other” strategies besides those mentioned. IN WHICH STRATEGIES ARE INVESTORS MOST INTERESTED? Those respondents who have smart beta allocations or are currently evaluating were asked to rate their level of interest in the strategies in which they have not invested. Exhibit (12) display the results for each strategy. The three strategies garnering the most investor interest were low volatility, fundamental and multi-factor. For each of these strategies, over 30% of respondents replied that they were interested in making an allocation while 15% or less said they were not interested. There were three strategies for which more respondents chose not interested than interested: equallyweighted, maximum Sharpe ratio and “other.” For equally weighted strategies, almost twice as many respondents indicated that they were not interested in making an allocation than those that said they were interested. The disparity was less pronounced with the other two strategies. However, 30% of respondents indicated that they were not interest in making an allocation to maximum Sharpe ratio strategies compared to 23% which were interested. Out of all of the strategies, the “other” category received the highest percent of respondents indicating that they had not evaluated the strategy and the highest percent of respondents declaring neutrality. This would suggest that fewer respondents may have been aware of other smart beta strategies beyond those mentioned. Exhibit 11: HAS ALLOCATION - Percent Investing in Individual Strategies 57% Fundamental 52% Low Volatility 33% High Quality 27% High Dividend 24% Multi-Factor Equally-Weighted 21% Momentum 21% 18% Maximum Sharpe Ratio Other 11% 0 10 20 30 40 50 102 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta 60 Exhibit 12: HAS ALLOCATION - Level of Interest in Individual Strategies 50 40 30 20 10 0 Fundamental Low Volatility High Quality High Dividend Interested in making an allocation 36% 38% 25% 19% Neutral about making an allocation 36% 31% 50% 45% Not interested in making an allocation 12% 15% 7% 16% Have not evaluated the strategy 16% 15% 18% 19% 50 40 30 20 10 0 Multi-Factor Momentum Equally-Wtd Maximum Sharpe Ratio Interested in making an allocation 35% 19% 16% 23% Neutral about making an allocation 42% 48% 41% 40% 6% 16% 31% 30% 16% 16% 13% 7% Not interested in making an allocation Have not evaluated the strategy swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 103 Implementation The survey asked public investors to choose their preferred vehicle for smart beta strategies for both strategic and tactical asset allocation. In a strategic asset allocation framework, institutions set target allocation percentages and periodically rebalance their portfolios back to those targets as investment returns skew their actual allocation percentages. For tactical asset allocation, an asset owner chooses a range of percentages for allocation to each asset class. Separate account dominated the vehicle types for strategic asset allocation, garnering 46% of responses. The second most popular choice was exchange traded funds (ETFs), chosen by 15% of respondents. The clear preference of the segment for separate accounts reflects the high concentration of larger funds in the sample. As noted previously in the book, larger funds with tighter restrictions and sizeable mandates typically prefer to open separate accounts for their smart beta investments. The results for tactical asset allocation do not show as strong a preference for any one vehicle type. Again, separate accounts and ETFs were the most popular choices, now accounting for 25% and 23% of the segment, respectively. Derivatives showed a large increase in favor, taking the number 3 spot with 20% of the vote. Exhibit 13: HAS ALLOCATION AND EVALUATING - Strategic Asset Allocation Exhibit 14: HAS ALLOCATION AND EVALUATING - Tactical Asset Allocation Respondents Respondents Separate Account ETF 46% Separate Account 25% 15% ETF 23% Bespoke 6% Bespoke 7% Mutual Fund 9% Mutual Fund 5% Commingled Fund 6% Commingled Fund 5% Derivatives Other 6% 11% Derivatives 20% Other 16% 104 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta INTERNAL VS. EXTERNAL MANAGEMENT The survey asked participants whether their fund would prefer its smart beta portfolios to be managed internally, externally or some combination of the two. For those respondents that have or are evaluating smart beta strategies, the most common response, accounting for 46% of the segment, indicated a preference for external management. Over half of the segment indicated a preference for internal management or some combination of internal and external management. Internal management of smart beta portfolios is typically only an option for the large, sophisticated institutional investors. Given that two-thirds of the asset owners surveyed have AUM in excess of US$ 10 billion, it is unsurprising to see a majority preference for at least some internal management. Exhibit 15: HAS ALLOCATION AND EVALUATING How would your fund prefer smart beta portfolios to be managed? Respondents Both 36% Externally managed 46% Internally managed 18% Applications and Demand In the survey, all respondents were asked to evaluate a series of statements regarding their perceptions of smart beta strategies and their potential uses. The questions aimed to gauge how smart beta stacks up against capweighting and active management as well as perceptions of the long-run demand for smart beta. The results strongly suggest that smart beta indexes can be both complements and substitutes to their cap-weighted counterparts. There was less consensus regarding smart beta’s relationship to active management. A large majority responded that smart beta is a complement to active strategies. However, there was little agreement as to whether smart beta is a substitute to active strategies or whether they fall into the category of active management. Similarly, no clear consensus emerged regarding whether demand for smart beta would persist over time. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 105 Exhibit 16: ALL RESPONDENTS - Smart Beta vs. Cap-Weighting SMART BETA VS. CAPWEIGHTING Of all respondents, 45% agree or strongly agree that smart beta indexes are substitutes for cap-weighted indexes. At the same time, 59% agree or strongly agree that smart beta indexes are complements to cap-weighted indexes. Of the respondents, 20% expressed some level of agreement with both statements. In contrast, 21% disagree or strongly disagree that smart beta indexes are substitutes while 11% disagree or strongly disagree that they are complements. 50 40 30 20 10 0 Smart beta is a complement to capitalizationweighted indexes. Smart beta is a substitute for capitalizationweighted indexes. 3% 11% Agree 42% 48% Neutral 33% 29% Disagree 18% 11% 3% 2% Strongly Agree Strongly Disagree Exhibit 17: ALL RESPONDENTS - Demand for Smart Beta 40 35 30 25 20 15 10 5 0 Strongly Agree LONG-TERM DEMAND FOR SMART BETA No clear consensus arose regarding whether demand for smart beta will decrease in the long run. Responses were nearly symmetrically distributed with the sample being almost evenly divided in thirds in terms of agreement, neutrality and disagreement. Demand for smart beta will decrease in the long run. 8% Agree 24% Neutral 36% Disagree 26% Strongly Disagree 6% 106 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta Exhibit 18: ALL RESPONDENTS - Smart Beta vs. Active Management 80 70 60 50 40 30 20 10 0 Smart beta is active management. Smart beta is a complement to active management. Smart beta is a substitute for active management. 6% 3% 8% Agree 26% 61% 27% Neutral 36% 22% 32% Disagree 24% 12% 29% 8% 1% 5% Strongly Agree Strongly Disagree SMART BETA VS. ACTIVE MANAGEMENT Of all respondents, 35% agree or strongly agree that smart beta indexes are substitutes for active management. At the same time, 64% agree or strongly agree that smart beta indexes are complements to active management. Of the respondents, 20% expressed some level of agreement with both statements. In contrast, 34% disagree or strongly disagree that smart beta indexes are substitutes while 13% disagree or strongly disagree that they are complements. Responses regarding whether smart beta is a form of active management are almost symmetrically distributed. The sample was nearly evenly divided in thirds for agreement, neutrality and disagreement. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 107 line of SIGHT The Equity Imperative THROUGH THE LOOKING GLASS: PORTFOLIO TRUTHS. FACTOR SOLUTIONS. Investors are faced with an increasingly complex array of decisions … from reconciling long-term objectives with the current market environment to selecting the most efficient implementation strategy from a plethora of investment options. The heightened regulatory environment and need for ever-more transparency to boards, trustees and constituencies adds to the challenge. These changing dynamics in equity investing have led to an evolution in investors’ perspectives. We have studied these evolving business dynamics and the changes they are bringing about, and we saw a growing use of passive strategies and a blurring of the line between passive and active management. A key reason for this shift: many investors today are less concerned about beating a benchmark and more interested in meeting their overall objective. BLURRING LINES Cap-Weighted Index Engineered Equity Traditional Active 108 | Line of Sight: Through the Looking Glass | northerntrust.com Although it’s clear there is an emerging category of strategies and products in the space between passive and active management, the terminology used to describe these strategies differs across the industry. At Northern Trust, we call these strategies “Engineered Equity.” They aim to capture exposure to specific factors, either individually or in combination, to meet investors’ specific goals. Engineering exposure to certain factors, while engineering out unintended exposures are both equally critical to achieving objectives. While there has been much discussion about these emerging strategies at a theoretical level, we wanted to explore how institutional investors are implementing them into their portfolios and analytic systems. We wanted to help investors interested in moving beyond a traditional active vs. passive strategy to do so – to help them learn from the experience of early adopters and benefit from robust research. ■ ■ ■ In 2014, we engaged in a multi-step research project that included three main components: Quantitative Survey: An online survey1 of 139 global institutional investors to gain insights into how they’re addressing strategic risk; Portfolio Analysis: Analysis of a sampling of complex institutional portfolios to understand how to better execute Engineered Equity strategies to achieve successful outcomes; and Qualitative Insights: Conversations with existing users of Engineered Equity strategies to provide a roadmap for effective implementation. What we discovered helped further refine the asset allocation process we developed for implementing a factor-based Engineered Equity strategy (see Appendix). SURVEY INSIGHTS: ADDRESSING STRATEGIC RISK We asked investors what risks they were most concerned about in their equity portfolios. Their top four – overexposure to certain factors or regions; absolute volatility; unexpected factor bias; and tracking error versus the benchmark – all could be addressed by employing the right Engineered Equity strategy. This question also led us to look at how well investors understood the current factor exposure in their portfolios. If their key concern is overexposure to a certain factor or region, being able to look across the portfolio to understand how that exposure looks is imperative. We found the knowledge gap was widespread: only 18% of the 139 investors surveyed globally felt they were certain of their overall equity portfolio’s actual factor exposures (see Exhibit 1). northerntrust.com | Line of Sight: Through the Looking Glass | 109 EXHIBIT 1: INVESTOR KNOWLEDGE OF ACTUAL FACTOR EXPOSURE EXHIBIT 2: ASSESMENT OF OVERALL FACTOR EXPOSURE Fairly uncertain 27% Moderately certain 51% My consultant 17% Unaware 4% Very certain Other 6% Internal team 57% 18% Don’t assess/ not a priority 20% If you look at your listed equity portfolio as a whole across all your investment managers, how certain are you of your actual risk factor exposures? How do you assess and monitor the risk/style factor exposure in your overall listed equity allocation? Source: Northern Trust Equity Investor Survey 2014 Source: Northern Trust Equity Investor Survey 2014 We then explored how the respondents monitored their risk factor exposure across their entire portfolio; the majority did so internally (see Exhibit 2). Surprisingly, one fifth of respondents indicated they do not undertake any form of monitoring. We also asked survey respondents to rank their overall concerns about their portfolio. Overexposure to unintended factors ranked highest, followed closely by absolute volatility (see Exhibit 3). EXHIBIT 3: TOP INVESTOR CONCERNS Within your total listed equity portfolio, across all of your investment managers, please rank the following issues in order of what concerns you most; 1 = most concerned, 5 = least concerned. Overall rank Item TOTAL SCORE* 1 Overexposure to certain factors/regions 336 2 Absolute volatility 332 3 Unexpected factor bias within the overall combined exposure 317 4 Tracking error (ex-post) versus benchmark 279 5 Managers’ style combinations and unexpected results *Score is a weighted calculation. Items ranked first are valued higher than the subsequent ranks. The score is the sum of all weighted rank counts. Source: Northern Trust Equity Investor Survey. Total respondents: 134 110 | Line of Sight: Through the Looking Glass | northerntrust.com 0 KNOWING YOUR PORTFOLIO: REAL-LIFE ANALYSIS OF FACTOR EXPOSURE To better understand how investors analyze the factor risk in their portfolios, we decided to examine how well real-life equity portfolios actually met their investors’ objectives. We explored whether investors really had the exposures they thought they had, and if not, what their actual factor exposures were. We worked with three substantial and experienced pension funds2 from the United Kingdom, Europe and the United States to analyze the factor exposure of their overall equity portfolios. Our analysis of data, taken as a snapshot in time, focused on the entire listed equity component in each pension fund. We analyzed both the portfolios’ equity holdings and the benchmark index holdings using a third-party factor model3 designed for forecasting global equity risk. Our analysis determined exposure to common factors, including: ■ ■ ■ Volatility Value Size ■ ■ ■ Momentum Yield Leverage What we found was that, regardless of the approach used to define the asset allocation – asset liability management, core satellite, tactical or strategic – the portfolios didn’t always reflect the investors’ goals, objectives and intended exposures. Why? Irrespective of an investor’s sophistication, a portfolio’s performance is a function of a multitude of conditions, both intended and unintended, such as valuation, idiosyncratic events, leverage, tilts, hedging, bias, style drift, external managers and cash levels. When these factors influence a portfolio in conjunction with one another, they can cause shifts in exposures that may not be easy to identify. We learned that in many cases, investors incorporating a wide range of active and passive equity strategies in their overall portfolio end up with a neutral factor exposure – despite intended tilts to one or more factors (See Exhibit 4). In addition to analyzing the current factor exposure of these portfolios, we also wanted to explore what types of allocation changes would be necessary to bring the portfolios in EXHIBIT 4: OVERALL PORTFOLIO FACTOR TILT VS. WHAT INVESTORS EXPECTED # of Equity Portfolios # of Equity Benchmarks Investor Objectives Actual Factor Tilt Pension Fund 1 – base currency, sterling. Uses a mix of strategies ranging from active to passive, and spanning developed markets, emerging markets and small capitalization stocks. 11 9 Value Tilt Neutral Pension Fund 2 – multi-billion U.S.-dollar AUM European pension fund. Uses a mix of active and passive strategies spanning 11 equity asset classes. 25 9 Low Volatility Tilt Neutral 4 Structuring portfolio to match liabilities Neutral Pension Fund 3 – large U.S. pension fund with a liability-driven structure allocated approximately 60% to long-duration fixed income, 30% to equities and the remaining 10% to real assets and private equity. 4 Note: Based on portfolio holdings at a snapshot in time, rather than over a longer performance period. Source: Northern Trust northerntrust.com | Line of Sight: Through the Looking Glass | 111 line with the investors’ expectations. We discovered that in general, dabbling in factorbased investing did not produce the desired results; investments in factor-based strategies needed to be a significant size to have the desired effect. However, we were able to bring the portfolio’s risk and performance more in line with expectations by thoughtfully moving an equivalent, albeit substantial holding, from one strategy to another better-suited strategy. Portfolio Case Study Analysis: European Pension Fund We undertook several research case studies as part of the analysis for this paper. One of the case studies was a European Pension Fund which holds 11 equity asset classes in 25 portfolios. The base currency of its investments is the U.S. dollar. This pension fund uses a combination of active and passive strategies to implement its asset class exposure, and uses market-capitalization-weighted indices for its policy benchmark. This investor’s goal is to achieve a long-term return over the risk-free rate. It has incorporated exposure to low volatility within the equity portfolio with a goal of reducing the total equity portfolio’s absolute volatility by approximately 10%. Additionally, the fund is exploring the possibility of adding other factors such as quality, momentum and value to the mix. Exhibit 5 provides a summary of the investor’s equity portfolios, benchmarks and allocations. Portfolio Truths: Findings From Original Portfolio Analysis The portfolio’s total risk is 15.3%, which is consistent with a diversified equity basket. The total risk of individual strategies within the portfolio ranged from 12.7% to 19.2%. The overall portfolio tracking error of 0.68% is rather tight considering that 19 of the 25 equity portfolios are active strategies. This demonstrates the tracking error reduction benefit of manager diversification and correlation between managers. However, given the cost associated with active strategies, this is an expensive way to achieve a market portfolio. With a goal of reducing absolute equity portfolio volatility by 10%, the investor allocated one-fifth of the total portfolio to a global equity low volatility strategy comprising four distinct managers – this is shown as Portfolio 11 in Exhibit 5. This move mirrors a trend we have observed, of investors seeking to reduce volatility by including low volatility strategies in an equity mix. Our ongoing research also has highlighted that risk reduction is the primary objective for investors using alternative weighted indices or Engineered Equity. While Portfolio 11 has a much lower volatility (active total risk) than Portfolios 1 through 10 (Exhibit 5), did its inclusion in the overall portfolio help reach the investor’s goal of reducing the absolute volatility? To answer, we needed to undertake a more thorough review. Factor analysis often requires peeling back the layers of a total portfolio to uncover the sources of equity return that are common across securities. Portfolio 11 is composed of four managers: Low Vol A through Low Vol D in Exhibit 6. We isolated the return associated with specific style factor exposures. The global volatility factor exposures for the managers Low Vol A, Low Vol B and Low Vol C are all meaningful, however Low Vol D 112 | Line of Sight: Through the Looking Glass | northerntrust.com EXHIBIT 5: EQUITY PORTFOLIO SUMMARY FOR EUROPEAN PENSION FUND Equity Porfolio Benchmark Index Equity Portfolio Weight (%) Total Risk (Annualized Standard Deviation) Active Total Contribution to Risk (Tracking Total Risk (%) Error) 100.0% 15.3 100.0% 0.68 Europe ex UK Large & Mid Cap Passive MSCI Europe ex UK 20.1% 19.2 22.2% 7.32 Europe Including UK Small Cap Active MSCI Europe Small Capitalization 3.9% 17.5 4.3% 5.96 3 UK Large Cap Passive MSCI United Kingdom 4.9% 18.0 5.4% 7.05 4 UK Large Cap Active MSCI United Kingdom 5.7% 17.2 5.2% 6.79 US Small Cap Active & Passive MSCI USA Small Capitalization 3.2% 17.3 3.1% 7.22 US Large & Mid Cap Active & Passive MSCI USA 18.3% 14.8 15.9% 6.25 Canada Large & Mid Cap Passive S&P Toronto Stock Exchange (TSX) 1.6% 17.4 2.0% 8.67 8 Pacific Including Japan Passive MSCI Pacific incl. Japan 6.0% 16.2 5.0% 10.52 9 Global Emerging Markets Passive MSCI Global Emerging Markets 2.3% 18.4 2.2% 6.97 10 Global Emerging Markets Active MSCI Global Emerging Markets 13.4% 18.6 18.3% 6.76 11 Global Equity Low Volatility MSCI World Developed 20.5% 12.7 16.4% 3.97 1 2 5 6 7 Notes: Porfolio and index holdings are as of 12/31/2013. Actual portfolio manager names remain confidential. General strategy names are used to indicate style. Total risk is measured in standard deviation at the individual strategy level and overall equity portfolio. Equity Portfolio Weight does not add up to 100% due to rounding. Source: Northern Trust contains style exposure to growth, high volatility, leverage and small cap securities, which are not attributes typically associated with dedicated low volatility products. To get a sense of the total equity portfolio’s volatility prior to the introduction of the low volatility allocation, we excluded the Global Equity Low Volatility portfolio (Portfolio 11) and proportionally distributed its 20% allocation to the other existing equity portfolios (see Exhibit 6). This results in an overall portfolio volatility of 16.00%. Introducing Portfolio 11 brings the absolute volatility down slightly to 15.30%, While the investor believed that including a 20% allocation to the low volatility mandates in Portfolio 11 would help reduce the overall portfolio’s volatility, the actual reduction in volatility was only 4% – well short of the 10% goal. This likely is a result of both the size of the allocation and the underlying exposure taken by the four individual managers in Portfolio 11. northerntrust.com | Line of Sight: Through the Looking Glass | 113 Exhibit 6 highlights each individual strategy in Portfolio 11. The individual volatilities by strategy ranged from 11.83% to 14.42%. It is important to note the slightly higher overall tracking error when the low volatility strategies were included (0.68 vs. 0.52). The tracking error is measured versus the policy benchmark; however, this investor’s primary objective was to lower absolute volatility even at the expense of tracking error. Investors measure risk in a variety of ways. When structuring a portfolio, it’s important to consider how boards of directors, trustees and stakeholders interpret risk and tracking error. Our survey found that tracking error versus the benchmark ranked fourth in the priority of concerns (see Exhibit 3), while overexposure to certain factors or regions ranked first. Absolute volatility was the second highest concern and unexpected factor bias was the third highest concern for investors. While the investor believed a 20% allocation to the low volatility mandates would help reduce the overall portfolio’s volatility, the actual reduction was only 4% – short of the 10% goal. EXHIBIT 6: GLOBAL FACTOR EXPOSURE OF LOW VOLATILITY MANAGERS COMPRISING PORTFOLIO 11 Factor Low Vol A Portfolio 11 Northern Trust QLV 0.56 –0.02 –0.22 0.10 0.18 0.00 –0.20 – 0.33 –0.38 0.32 –0.24 –0.63 – 0.04 –0.06 0.06 0.04 –0.03 0.39 0.41 –0.38 0.18 0.27 0.35 0.00 –0.15 0.02 –0.11 – 0.09 –0.05 0.22 0.02 –0.17 –0.52 –0.97 –0.28 –0.01 Low Vol B Low Vol C – 0.05 – 0.24 –0.30 0.04 – 0.17 Global Volatility – 0.28 Global Liquidity – 0.05 Global Yield 0.14 Global Value – 0.10 0.13 – 0.47 – 0.37 Global Growth Global Momentum Global Leverage Global Size Low Vol D (Consolidated) Note: This analysis shows factor exposure in units of standard deviation. Any exposure within +/– 0.20 is not considered significant. Source: Northern Trust Factor Solutions: Hypothetical Portfolio Optimization and Analysis To see what allocation changes might better align the portfolio with the investor’s objectives of reducing the portfolio’s absolute volatility by 10%, we substituted our own Quality Low Volatility (QLV) strategy for the entire 20% allocation to Portfolio 11. Northern Trust’s QLV strategy has low-volatility factor exposure of nearly three times that of the consolidated Portfolio 11, as shown in Exhibit 6 (–0.63 versus –0.24). The resulting absolute volatility using the QLV strategy (as shown in Exhibit 7) is 14.81%, a risk reduction of 7.5%, providing the investor a more efficient way of meeting the original objective. However, the tradeoff is that tracking error nearly doubles from 0.52 to 1.02. Since tracking error was not raised as a priority for the investor, this approach might suit its needs. Exhibits 8 and 9 compare the factor exposures of the original portfolio and the portfolio using the Northern Trust QLV strategy. In the charts, the blue bar represents the investor’s policy benchmark, the red bar shows the portfolio’s absolute factor exposure and the gold bar shows the style exposure relative to the benchmark. Evidence of lower volatility factor exposure in the original portfolio was missing, a surprise given the 20% allocation to low 114 | Line of Sight: Through the Looking Glass | northerntrust.com volatility portfolios. Using the Northern Trust QLV, which has significant low volatility exposure, does change the results, but not dramatically. Incorporating the Northern Trust QLV would move the overall equity portfolio closer to achieving the investor’s objective of low volatility exposure (taking it from 0.02 to –0.06), and would also neutralize unintended exposure, such as to size (from –0.24 to –0.18). When we shifted the low volatility allocation to the Northern Trust QLV strategy, we saw some reduction in overall volatility. This helped move towards achieving the investor’s primary goal, but it wasn’t significant enough to fully achieve the 10% objective. If investors want to meet a factor-based goal of this sort, they will need to make substantial allocations to achieve meaningful adjustments to the portfolio’s outcomes. This move also brought a higher tracking error to the market-capitalization weighted policy benchmark. That being said, if tracking error is a concern, the investor could amend the policy benchmark to incorporate a low volatility index. EXHIBIT 7: VOLATILITY SUMMARY FOR PORTFOLIO 11 Total Risk (Annualized Active Total Standard Risk (Tracking Deviation) Error) Equity Portfolio: Excluding Low Volatility Strategies 16.00 0.52 Equity Portfolio: Including Low Volatility Strategies 15.30 0.68 Low Volatility Strategies Consolidated 12.73 2.85 Low Volatility Strategy 1 12.80 4.14 Low Volatility Strategy 2 12.48 3.28 Low Volatility Strategy 3 11.83 3.86 Low Volatility Strategy 4 14.42 3.90 Northern Trust Quality Low Volatility Strategy 10.57 5.17 Total Portfolio Using Northern’s QLV 14.81 1.02 Notes: Factor exposure estimates are provided in units of standard deviation. Active risk (tracking error) is calculated versus the policy benchmark. Source: Northern Trust THE SIGNIFICANCE THRESHOLD Factor exposures produced by the risk model are used to provide a comprehensive assessment of risk. The numerical values for each factor exposure range between –4 and 4, and represent the number of standard deviations from the mean exposure of all assets within the local market. The materiality threshold of +/–0.20 has been determined by practical considerations. To sufficiently tilt toward a single factor or set of factors, small deviations in unintended factor exposures must be taken. While subtle, correlations among risk factors make it difficult to isolate a single factor exposure while keeping all other factors neutral. We have found through our backtests that +/–0.20 works reasonably well as a range for neutrality. northerntrust.com | Line of Sight: Through the Looking Glass | 115 EXHIBIT 8: STYLE FACTOR EXPOSURE OF ORIGINAL PORTFOLIO 0.20 0.16 0.15 0.10 0.10 0.10 0.06 0.03 0.05 0.00 –0.05 0 0 0 –0.01 0.02 0.04 0.02 0 –0.01 –0.03 0.04 0.05 0.04 0.04 0.01 0 –0.02 –0.08 –0.10 –0.15 Significance Threshold –0.20 –0.25 –0.30 –0.24 Global Global Global Growth Momentum Volatility Global Liquidity Exposure Benchmark Global Yield Global Value Exposure Portfolio Global Leverage Global Size Exposure Active Notes: Factor exposure estimates are provided in units of standard deviation. Active risk (tracking error) is calculated versus the policy benchmark. Source: Northern Trust EXHIBIT 9: STYLE FACTOR EXPOSURE OF PORTFOLIO WITH QLV 0.20 0.16 0.15 0.10 0.09 0.10 0.05 0.04 0.04 0.02 0.00 –0.01 –0.05 –0.04 –0.04 –0.01 –0.01 –0.03 –0.10 0.04 0 –0.01 0.02 0 0 0 –0.02 –0.03 –0.06 –0.08 –0.15 –0.20 –0.18 Global Global Global Growth Momentum Volatility Exposure Benchmark Global Liquidity Global Yield Exposure Portfolio Global Value Global Leverage Exposure Active Notes: Factor exposure estimates are provided in units of standard deviation. Active risk (tracking error) is calculated versus the policy benchmark, which was redefined as a blend of the MSCI World Developed Index and the Dow Jones U.S. Total Stock Market Index. Source: Northern Trust 116 | Line of Sight: Through the Looking Glass | northerntrust.com Global Size Lessons Learned: What the Case Study Analyses Mean to You It is intriguing to see that across all three investor portfolios, the starting equity exposure was almost entirely factor neutral – their actual exposures were all well under the level generally considered significant. We also saw, with our hypothetical optimization and analysis that achieving an investor’s goals using factor-based strategies requires a substantial commitment. A small allocation may may provide some improvement, but is not enough to have a significant impact on the portfolio’s results. With this in mind investors may want to consider adopting more meaningful factor tilts using an active risk budgeting approach4. Our in-depth analysis of the portfolios reinforced what the survey data told us: many investors don’t truly understand their factor exposure. This is true for large investors with internal portfolio management resources as well as for smaller investors. Determining the factor exposure is made more challenging by the myriad factors that can affect a portfolio’s performance. Investors may want to consider adopting more meaningful factor tilts using an active risk budgeting approach. IMPLEMENTATION ROADMAP: LEARNING FROM EXPERIENCE Typically, an investor is not provided with the opportunity to develop a portfolio from scratch. Instead, investors must follow a course of tweaking and adjusting existing allocations. Based on the experience of our early adopters, we wanted to explore real-world strategies that investors could use to implement an Engineered Equity strategy. So how do investors who have successfully implemented Engineered Equity strategies overcome the challenges inherent in incorporating these strategies into their portfolios? To answer this, we talked with some early adopters who have already incorporated factorbased strategies into their portfolios. We interviewed four large, sophisticated institutional investors in Europe and Asia with a combined total assets under management in excess of $375 billion. Using their experience, we can draw a roadmap for others considering implementation themselves. northerntrust.com | Line of Sight: Through the Looking Glass | 117 Determining Objectives While they all used factor-based strategies, the investors we interviewed had adopted these strategies in different ways – from just starting out with a small allocation to alternative strategies to managing highly complex portfolios that are primarily factorbased. Despite this, we found they shared similar goals: better risk-adjusted returns or an improved information ratio. ■ AP3 is one of the Swedish national pension buffer funds. One of the boldest characteristics of AP3’s policy has been to disband traditional active mandates, preferring instead to organize its allocation to public securities by a handful of categories of risk. AP3 has a stated total-fund objective of delivering an annualized return of 4% over inflation; it does not use traditional active management mandates to meet this goal, instead preferring to allocate according to risk exposure. ■ Bureau of Labor Funds (BLF) looks after retirement savings totalling more than $80 billion for millions of Taiwanese workers. It began using nontraditional equity strategies three years ago. BLF is a major index investor with many members to satisfy. As the last investor of our quartet of investors to adopt factor based strategies, its move has been tentative. It has defined its main objective for the step as delivering better returns for lower risk than the market-cap-weighted index – a popular objective according to our research. ■ PGGM is a Dutch fiduciary manager that is responsible for the pensions investing of 2.5 million people in the Netherlands. PGGM began using an alternative approach to equity investing almost 10 years ago in response to a realization that its traditional active portfolio was not performing as hoped, falling short of its stated benchmark. The majority of equity assets are passively managed to market-cap indices, but a large minority is run with fixed exposures to three alternative indices. ■ PKA is a €22 billion pension fund for health workers based in Denmark. PKA has received many plaudits since it decided to end traditional mandates in equities and manage its equity exposure according to risk premia instead. PKA uses factor-based strategies because it believes that while alpha does exist, it is not persistent. PKA seeks long-term outperformance but has often found that it can be explained by tilts to certain factors. Unlike PGGM, PKA is far more dynamic in its exposure to different risk premia and to some extent employs a risk parity approach. 118 | Line of Sight: Through the Looking Glass | northerntrust.com EXHIBIT 10: EARLY ADOPTERS AT A GLANCE Fund Objective Implementation Approach AP3 Annualized return of 4% over inflation Market Timing With Factors Performance: better risk-adjusted returns than cap-weighted indices Alternative Indices BLF Monitoring Performance Outsources to asset manager E.g., value tilt at right time In-house exposure analysis Absolute return/Sharpe ratios MSCI Minimum Volatility Index RAFI Global Equities Index PGGM Performance: cost efficient Equally Weighted Factors Gauge relative performance vs. cap-weighted indices External systems: Value Style Research Minimum Variance FactSet Quality PKA Performance: long term, persistent Risk Parity/Factor Bets Discounts “newer” risk premia Value Still relies on market beta Momentum Quality Three key takeaways emerged from their experience: ■ Analyzing what is currently in your portfolio before making any future investment decisions is crucial to success. ■ Failing to base future investment decisions on a strong understanding of your current portfolio can lead to unintended bias or cancel out intended bias. ■ Using Engineered Equity strategies in your portfolios can provide more risk-efficient and cost-effective outcomes. To see how the investors we interviewed created a roadmap for successful implementation, we focused on how they applied the last two steps of our four-step Engineered Equity asset allocation process: determining their implementation approach and monitoring their progress. FOUR-STEP ENGINEERED EQUITY ASSET ALLOCATION PROCESS 1 2 3 4 Objectives Analysis Implementation Monitoring Define equity goals Understand portfolio exposure Plan approach and assemble factors Evaluate success against chosen criteria “Engineer Equity” Quantitative Survey Portfolio Analysis Qualitative Insights northerntrust.com | Line of Sight: Through the Looking Glass | 119 Determining Implementation Approach The complexity of the approach of the investors we interviewed varied from a relatively straightforward to the highly complex. The BLF’s approach was the mildest – an initial testing of the waters. As the fund’s first foray into alternative equity investing, the Taiwanese investor has chosen to put, in the near term, approximately 15% of its overall portfolio in mandates tracking the MSCI Minimum Volatility index as well as a fundamental RAFI global equities index. These alternative index exposures have been sourced from the BLF’s considerable allocation to traditional passive market-capitalization-weighted index tracking. Consequently, the BLF considers these allocations close to passive, with complementary diversifying portfolios, but nevertheless not active management. At the same time, the fund’s goals for the alternative equity mandates resemble those for its numerous active managers: to outperform the cap-weighted market indices over the long run. By allocating to two alternative indices, the BLF hopes to reduce the influence of the herd; the BLF believes that because alternative indexing is still new, the crowding effect has not yet set in with these new indices. Even if it does, the distinction between the two indices chosen is expected to provide diversification from each other. While they have begun to test the waters of alternative equity investing, the lion’s share of the fund’s equity investments continue to follow the market-cap weighted index. As the majority of the new flows into the fund are from defined contribution scheme participants, the BLF’s guiding principle is to not surprise its millions of contributors. As such, BLF tends to take one year at a time. This is not just a cliché: asset allocation between equities and bonds is reviewed every April. In time there may be greater allocation to alternative equity strategies, but they need to prove themselves first. PGGM’s implementation appears superficially to be very simple, but the simplicity is deceptive. The fund has an equal weighting to value, minimum variance and quality, and claims no attempt to time them. PGGM insists that it wants a robust combination over the full economic cycle, equally weighting its factor exposures to avoid over-engineering the solution. However, much is going on beneath the surface of this seemingly calm policy. First, while the allocations are fixed, PGGM is monitoring its exposure to a large number of factors all the time. External systems such as Style Research, a holdings-based style and risk analysis tool, and FactSet, a manager, composition and asset allocation monitoring tool, help it evaluate its exposure to growth and momentum, for example. Although PGGM makes no intervention at this level, the fund does acknowledge that the strategies themselves can be changed from time to time to improve their effectiveness. In other words, the fund doesn’t attempt to time factor exposure, on either risk or return grounds, but the volume of observable data can be used to implement fundamental changes to the strategies for the long term. 120 | Line of Sight: Through the Looking Glass | northerntrust.com While PKA would, in an ideal world, allocate across all its strategies on an equal risk basis, this is impractical as some of the rarer strategies it employs lack the depth and liquidity to allow this. PKA is not a huge fund, but it is aware of moving markets adversely when dealing in esoteric strategies, particularly given that some of these also involve shorting, which makes them even more vulnerable to illiquidity. Instead, PKA applies a framework for actual risk and return across all risk premia that guides its allocations – a form of risk parity. If a strategy begins to appear costly (i.e., expected returns exceed the risk budget), PKA lowers or withdraws exposure. This is an acknowledgement of the time-varying nature of risk premia; important factors such as the size or value premium can underperform for many years. An in-house team at PKA oversees the factor bets on value, momentum and quality (implementation is by a raft of external managers). This means any crossover of bets, notably with the long-only market betas, can be seen and marshaled clearly. AP3’s implementation of factor-based investing stems from a belief that markets are inefficient. As such, profits can be reaped by tilting toward particular factors such as value at the right time. At the same time, the Stockholm-based fund recognizes that the strength of factors themselves waxes and wanes, so this approach to investing is not particularly easy. Evaluating factors is difficult because so much of their analysis is purely historical. Banks and fund managers analyze industrial sectors, but there isn’t a comparable depth of analysis on factors. AP3 is confident that its feeds from the stock markets are sufficient to guide it on adapting to the change in a factor’s strength in its “home” market of Europe. This analysis is conducted in-house. But for the world’s biggest equity market, the United States, AP3 does not attempt factor-based investing and instead passively tracks the market-cap index. Monitoring Your Progress The final step in the Northern Trust allocation process is monitoring your allocations so you can feel confident they are delivering on your objectives. This step brings us back full circle to the prerequisite step of understanding what your portfolio is doing for you. Our early adopters all take practical steps to ensure they stay on track. The BLF, before outsourcing, prefers to use absolute return and Sharpe ratios to gauge relative performance of alternative equity indices against market-cap weighted indices. After selecting a specific alternative equity index to use as the benchmark of passive mandate, it will look at the tracking error as set forth in the investment guidelines. All of the BLF’s equity mandates are long-only, so its portfolio is very dependent on market beta across the whole asset class. It is not clear the extent to which this has been modified by the alternative equity mandates (or active management). Currently the BLF is not in a position to look at aggregate exposures to factors, even well established ones such as industrial sectors or regions. The BLF says it works to identify any suitable future additional components or factor combinations for its portfolio. northerntrust.com | Line of Sight: Through the Looking Glass | 121 We have previously mentioned how the team at PGGM uses external systems such as Style Research and FactSet to help them monitor their various factor exposures across the portfolio. This level of monitoring and the ability to change their holdings improve the portfolio’s effectiveness. However, PGGM’s large internal team undertook extensive research prior to building its strategies, and created them to be durable. The exposures by stock, sector and risk weighting to the trio of strategies were devised to be practical. Without a large internal resource, other investors would need help from their asset managers to do this at the development stage. At AP3, the team highlighted the need for a partnership with a trusted asset manager. According to AP3 “it is easy to analyze the data and get a 30-year back test, but it is far harder to act upon the analysis. You need either a manager you can trust or an in-house team to understand what you exposures you have. Much of the problem rests on the fact that while commonly understood metrics for valuing companies exist, most of the arguments for factor-based investing rely on historical data. There are utilities analysts and pharmaceutical analysts at work in brokerage houses but they have no equivalents covering momentum or value.” PKA remains open-minded about the sustainability of some strategies. Pragmatically, the in-house team also accepts that for all its innovation, PKA still relies more on market beta than other premia. But this is no cause for complacency. The Danish fund notes that “newer” risk premia are heavily discounted. This is one incentive for a well-organized fund to exploit them – the discount can be excessive because the market overcompensates for a lack of certainty. Nevertheless, there is also a much larger, holistic reason: the long-term nature of pension funds makes them natural suppliers of liquidity on capital markets. DRAWING CONCLUSIONS: PUTTING THE RESEARCH TO WORK IN YOUR PORTFOLIO Investors are faced with increasingly complex decisions when attempting to select the most efficient implementation strategy from a plethora of investment options. Consolidating the experience of the early Engineered Equity adopters we spoke to with our research and experience, we have three key takeaways: ■ Taking stock of what is currently in your portfolio before making any future investment decisions is crucial to success. ■ Failing to base future investment decisions on a strong understanding of your current portfolio can lead to unintended bias or cancel out intended bias. ■ Using Engineered Equity strategies in your portfolios can provide more risk-efficient and cost-effective outcomes. Our analysis showed that to realize noticeable results, you need to make a deliberate and substantial commitment to Engineered Equity strategies. Dipping a toe in the Engineered Equity waters will typically not have a significant impact on your results. You also need to be prepared for the possibility of increased tracking error versus a standard benchmark. This is not a simple decision, since board members and trustees continue to monitor investment performance against standard benchmarks. 122 | Line of Sight: Through the Looking Glass | northerntrust.com Our analysis showed that to realize noticeable results, you need to make a deliberate and substantial commitment to Engineered Equity strategies. APPENDIX: FOUR-STEP ENGINEERED EQUITY ASSET ALLOCATION PROCESS In our 2013 paper, “The New Active Decision in Beta Management,” we defined a framework for implementing an alternative beta strategy. We did this because the expanding range of investment solutions falling between traditional passive strategies and active management meant that the decision-making process was changing. The proliferation and increasing acceptance of Engineered Equity strategies meant asset owners were no longer simply choosing between active management and indexing. Instead, they were assessing their objectives, identifying appropriate corresponding factors (such as value, low volatility or quality) or strategies, choosing indices, defining a weighting strategy among the indices, and determining metrics to measure success. Our subsequent research has led us to further refine the process and emphasize the importance of analyzing the portfolio to understand existing factor exposure. Omitting this step can result in your existing factor exposure neutralizing the intended tilts you make. FOUR-STEP ENGINEERED EQUITY ASSET ALLOCATION PROCESS 1 2 3 4 Objectives Analysis Implementation Monitoring Define equity goals Understand portfolio exposure Plan approach and assemble factors Evaluate success against chosen criteria “Engineer Equity” Quantitative Survey Portfolio Analysis Qualitative Insights WANT TO LEARN MORE? As investors learn more about the benefits of Engineered Equity strategies, we expect to see growing numbers embracing the idea of using factor-based investing to help better achieve their investment objectives. If you would like to learn more about how you can benefit from using factor-based investing or Engineered Equity solutions in your portfolio, contact your relationship manager or visit northerntrust.com/equityimperative. northerntrust.com | Line of Sight: Through the Looking Glass | 123 ENDNOTES 1 Our global Equity Investor Survey incorporated views of 139 institutional investors, approximately 45% of whom had more than $1 billion in assets under management. Of the respondents, 49.3% were from the United States, 24.3% from Europe, 13.2% from the United Kingdom, 11.1% from Asia, 1.5% from the Middle East and 0.7% from Africa. 2 The names of the specific investors in these case studies remain confidential, as do the actual portfolio manager names. We have described the types of investor and used general strategy names to indicate style. 3 The BARRA Global Equity Model 2 (GEM2). This model features a broad estimation universe based on the MSCI All Country World Investable Market Index. A broad estimation universe is necessary to accurately represent the investment opportunity set for institutional investors and to generate robust risk forecasting results. 4 For more information about improving your active risk budgeting approach please read: Active Risk Budgeting, Northern Trust, May 2014. LEGAL, INVESTMENT AND TAX NOTICE. Information is not intended to be and should not be construed as an offer, solicitation or recommendation with respect to any transaction and should not be treated as legal advice, investment advice or tax advice. Clients should under no circumstances rely upon this information as a substitute for obtaining specific legal or tax advice from their own professional legal or tax advisors. All material has been obtained from sources believed to be reliable, but the accuracy, completeness and interpretation cannot be guaranteed. The views expressed are those of the authors as of the date noted and not necessarily of the Corporation and are subject to change based on market or other conditions without notice. There is no guarantee that the investment objectives of any fund or strategy will be met. Risk controls and models do not promise any level of performance or guarantee against loss of principal. Past performance is no guarantee of future results. Returns of the indices also do not typically reflect the deduction of investment management fees, trading costs or other expenses. It is not possible to invest directly in an index. Indices are the property of their respective owners, all rights reserved. © 2014 Northern Trust Corporation. Head Office: 50 South La Salle Street, Chicago, Illinois 60603 U.S.A. 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CONTRIBUTORS TO THIS PAPER INCLUDE: Christopher Jenks, Equity Strategist; Gaurav Baid, Senior Investment Risk Analyst; Greg Behar, Manager, Equity Strategy; John Krieg, Global Head of Institutional Sales and Consultant Relations; Mamadou-Abou Sarr, Global Head of ESG Investing; Meggan Friedman, Equity Strategist; Ravi Gautham, Head of Northern Trust Asset Management, India; Suraj Nichani, Senior Investment Risk Analyst. 124 | Line of Sight: Through the Looking Glass | northerntrust.com Park Alpha = Problem Solvers Consulting & Advisory Services Park Alpha is a global full-service consulting firm providing a broad range of services to: institutional investors, governmental funds, investment managers, and corporations. We are a trusted advisor to our clients and find solutions to complex, sophisticated issues. CONTACT [email protected] Park Alpha is the consulting arm of the Sovereign Wealth Fund Institute, Inc., a privately held company. Park Alpha is not a brokerage or 3rd party marketer. northerntrust.com | Line of Sight: Through the Looking Glass | 125 Glossary ACTIVE MANAGEMENT A portfolio management style where a money manager makes choices about the purchase, sale and holding of securities in an attempt to outperform a given benchmark. ALPHA The excess return, relative to a given benchmark, of an asset or portfolio on a risk-adjusted basis. BENCHMARK A standard against which the performance of a security, fund or investment strategy can be measured, such as a market index or return target. BETA A measure of the sensitivity of the return on a security or portfolio to fluctuations in the entire market. A beta of one implies that the security or portfolio moves in line with the market. A beta greater than one indicates a portfolio that is more volatile than the market, while a beta less than one indicates lower volatility. CAPITAL ASSET PRICING MODEL A popular financial model that theoretically explains variations in a security’s rate of return as a function of the rate of return on the broad market, the return to a risk-free asset (generally proxied by government bonds) and the security’s beta. COMMINGLED FUND (POOLED FUND) A fund that holds assets from multiple accounts. The assets are pooled together in order to reduce costs and risk. CONCENTRATION RISK The risk associated with having large exposure to a single asset, security or market segment. EARNINGS-PER-SHARE (EPS) Company net income divided by outstanding shares of common stock. EQUALLY-WEIGHTED INDEX An index that is constructed by allocating capital across each security or sector in equal proportion. 126 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta EXCHANGE-TRADED FUND A security that tracks an index and trades on an exchange like a stock. FUNDAMENTALLY-WEIGHTED INDEX An index that is constructed by allocating across securities based on fundamental factors such as financial metrics. Some of these factors include: sales, dividends, cash flow, book value or earnings per share. Fundamentally weighted indexes sever the link with price. Methodologies vary. INFORMATION RATIO The ratio of a portfolio’s excess returns (compared to a benchmark) to its tracking error. LIQUIDITY RISK The risk of being unable to prevent or mitigate losses arising from an asset that is difficult to buy or sell in haste. LOW VOLATILITY INDEX A smart beta index in which constituent stocks are assigned weights based on their relative volatility. In general, there are two common approaches: selecting stocks with low volatility or choosing stocks in a way that minimizes the overall volatility of the index. Methodologies vary. MARKET CAPITALIZATION Total value of a publicly traded company’s outstanding shares, which is calculated by the price of one share multiplied by the total number of outstanding shares. MARKET CAP-WEIGHTED INDEX Currently, this is the most frequently utilized type of market index whose individual components are weighted according to their market capitalization, so that larger components carry a larger percentage weighting within the Index. MEAN REVERSION A financial theory that prices and returns will eventually migrate back towards the mean. This mean can be a historical average of the price, another type of average such as economic growth or average return of an industry. MODEL RISK The risk of losses arising from an investor’s choice of financial models, such as a stock selection model. MOMENTUM INDEX A smart beta index in which constituent stocks are weighted based on movements in price. Methodologies vary. MUTUAL FUND A fund of pooled capital from multiple individual investors or investor groups. Investors that buy into a mutual fund buy mutual fund shares or units. Each shareholder gains or loses in proportion to the number of mutual fund shares they own. PARADIGM SHIFT A fundamental change in the approaches or assumptions used in the investment process by the broad financial community. PASSIVE MANAGEMENT An investment strategy in which the portfolio replicates the asset weighting and returns of a given benchmark, often achieved through investing in an index fund. REBALANCING The act of bringing a portfolio back in line with its target asset allocation through the purchase and sale of securities. RISK PREMIUM The expected return needed to entice investors into taking on extra risk. swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 127 SHARPE RATIO It was derived in 1966 by William Sharpe. A risk-adjusted metric of return which is calculated as a portfolio’s annualized excess return over a cash equivalent divided by the portfolio’s annualized standard deviation. SMART BETA A systematic, rules-based approach to capturing specific market factors and portfolio tilts. SOVEREIGN WEALTH FUND A state-owned investment fund that is commonly established from balance of payments surpluses, official foreign currency operations, the proceeds of privatizations, governmental transfer payments, fiscal surpluses, and/ or receipts resulting from resource exports. The definition of a sovereign wealth fund excludes, among other things, foreign currency reserve assets held by monetary authorities for traditional balance of payments or monetary policy purposes, stateowned enterprises in the traditional sense, government-employee pension funds (funded by employee/employer contributions), or assets managed for the benefit of individuals. TAIL RISK The risk that the probability distribution for changes in an asset’s price has “fatter tails” than the normal distribution. The tails of the distribution are considered fatter when an asset’s price has a greater probability of moving more than 3 standard deviations from its current price than it would if price changes were normally distributed. Intuitively, tail risk can be thought of as the probability of rare events (sizeable price changes). TRACKING ERROR A measure of how closely a portfolio follows its benchmark index. Specifically, it is the standard deviation of difference between returns of the portfolio and the returns of the benchmark. Higher tracking error means more deviation from the benchmark. TURNOVER The rate at which securities in a portfolio are purchased or sold over a given period. 128 : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : swfi.com/smartbeta TRANSACTION COSTS Expenditures arising from the purchase or sale of securities. VOLATILITY A measure for the variation in price of a security or portfolio over time, calculated as its standard deviation. Intuitively, a higher volatility means more uncertainty about the size of price changes for the investment, and hence indicates a riskier investment. References Amenc, N., F. Goltz, A. Lodh, and L. Martellini. “Diversifying the Diversifiers and Tracking the Tracking Error: Outperforming CapWeighted Indices with Limited Risk of Underperformance.” Journal of Portfolio Management, Vol. 38, No. 3 (2012), pp. 72-88. Amenc, N., F. Goltz, A. Lodh, L. Martellini, and P. Retkowsky. 2010. “Efficient Indexation: An Alternative to Cap-Weighted Indices.” EDHEC-Risk Institution Publication, January 2010. Ang, A., W.N. Goetzman, and S.M. Schaefer. “Evaluation of Active Management of the Norwegian Government Pension Fund - Global.” Available at http://www.regjeringen.no/ upload/fin/statens%20pensjonsfond/ rapporter/ags%20report.pdf Arnott, R.D., J. Hsu, and P. Moore. “Fundamental Indexation.” Financial Analysts’ Journal, Vol. 61, No. 2 (2005), pp. 83-99. Banz, R.W. “The Relationship Between Return and Market Value of Common Stocks.” Journal of Financial Economics, Vol. 9, No. 1 (1981), pp. 3-18. Carhart, M.M. “On Persistence in Mutual Fund Performance.” Journal of Finance, Vol. 52, No. 1 (1997), pp. 5782. Chow, T., J. Hsu, V. Kalesnik, and B. Little. “A Survey of Alternative Equity Index Strategies.” Financial Analysts’ Journal, Vol. 67, No. 5 (2011), pp. 37-57. Clare, A., N. Motson, and S. Thomas. “An Evaluation of Alternative Equity Indices - Part 1: Heuristic and Optimised Weighting Schemes.” Cass Consulting, March 2013. Fama, E.F. “Efficient Capital Markets: A Review of Theory and Empirical Work.” Journal of Finance, Vol. 25, No. 2 (1970), pp. 383-417. Graham, B., and D. Dodd. Security Analysis. McGraw-Hill. 1934. Grinold, R. “Are Benchmark Portfolios Efficient?” Journal of Portfolio Management. Vol. 19, No. 1 (1992), pp. 34-40. Management, Vol. 17, No. 3 (1991), pp. 35-40. Jegadeesh, N., and S. Titman. “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” Journal of Finance, Vol. 48, No. 1 (1993), pp. 65-91. Maillard, S., T. Roncalli, and J. Teiletche. “The Properties of Equally-Weighted Risk Contribution Portfolios.” Journal of Portfolio Management, Vol. 36, No. 4 (2010), pp. 60-70. Melas, D., and X. Kang. “Applications of Systematic Indexes in the Investment Process.” Journal of Indexes, Sept/Oct Issue (2010), pp. 10-18. Sharpe, W.F. “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk.” Journal of Finance, Vol. 19, No. 3 (1964), pp. 425-442. Haugen, R.A., and N.L. Baker. “The Efficient Market Inefficiency of Capitalization-Weighted Stock Portfolios.” Journal of Portfolio swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 129 The SWFI is a global organization designed to study sovereign wealth funds, public pensions, superannuation funds, central banks and other long-term public investors in the areas of investing, asset allocation, risk, governance, economics, policy, trade and other relevant issues. 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