Download Smart Beta - A referential guide for institutional investors

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
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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,
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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
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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
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t +44 20 3395 6842
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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
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risk to work
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strategy — listening to your unique needs — to help design the smartest equity strategy for your portfolio. Together,
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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
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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
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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).
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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
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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
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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.
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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
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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.
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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.
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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.
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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.
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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
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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.
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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).
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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.
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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
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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.
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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.
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+ 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
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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
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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.
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+ 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.
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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
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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.
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• 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.
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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
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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
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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.
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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.
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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.
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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.
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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.
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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),
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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.
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Portfolios.” Journal of Portfolio
swfi.com/smartbeta : SWFI Presents - SMART BETA - A Referential Guide for Institutional Investors : 129
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