Alternative Investment Analyst Review

Alternative Investment Analyst Review
Alternative
Investment
Analyst
Review
EDITOR’S LETTER
Why Have Hedge Funds Underperformed?
Hossein Kazemi
WHAT A CAIA MEMBER SHOULD KNOW
An Introduction to Green Bonds
Impact Investing Committee, NEPC
RESEARCH REVIEW
Inflation Hedging Abilities of Indirect Real Estate Investments in Switzerland
Roland Hofmann, Tobias Mathis, ZHAW School of Management and Law, Department of Banking, Finance, Insurance Zurich University of
Applied Sciences
FEATURED INTERVIEW
Studying Financial Disruption: Bubbles and Crashes - An Interview with Didier Sornette
Barbara J Mack, CAIA Association
INVESTMENT STRATEGIES
The Ins and Outs of Investing in Illiquid Assets
Thijs Markwat, Roderick Molenaar, Robeco
Black Ice: Low-Volatility Investing in Theory and Practice
Feifei Li, Engin Kose, Research Affiliates
PERSPECTIVES
Concentrated vs. Diversified Managers: Challenging What You Thought You Knew About “High Conviction”
Keith E. Gustafson, Patricia Halper, Chicago Equity Partners
VC-PE INDEX
Mike Nugent and Mike Roth, Bison
THE MSCI GLOBAL INTEL REPORT
Max Arkey, MSCI Real Estate
Q2 2016, Volume 5, Issue 1
Chartered Alternative Investment Analyst Association®
Call for Articles
Article submissions for future issues of
Alternative Investment Analyst Review
(AIAR) are always welcome. Articles should
cover a topic of interest to CAIA members
and should be single-spaced. Additional
information on submissions can be found
at the end of this issue. Please e-mail your
submission or any questions to:
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Chosen pieces will be featured in future
issues of AIAR, archived on CAIA.org, and
promoted throughout the CAIA community.
Editor’s Letter
Why Have Hedge Funds Underperformed?
It is widely reported that hedge funds have performed poorly in recent months. The typical report focuses on
broad indices of hedge funds such as CISDM or HFR hedge fund indices and uses the S&P 500 index as the
benchmark. The following exhibit displays the performance of the CISDM Equally Weighted Index of all hedge
funds that report to the CISDM/Morningstar database relative to that of S&P500 index.
The CISDM index has underperformed the S&P500 index by an average amount of 0.87% per month since
January 2009, the end of the financial crisis. During the same period, the monthly standard deviation of the
CISDM Index return is about 53% of the monthly standard deviation of S&P500 Index’s return. It is reassuring that
the underperformance of hedge funds has come with lower risk. However, the degree of underperformance
has been too much for some investors, as some institutional investors have recently announced that they are
reducing their allocations to hedge funds. The question is why have hedge funds underperformed so badly since
2009?
First, we need to question whether the S&P500 is the correct benchmark for hedge funds. If the universe of assets
that hedge funds invest in is different from the S&P 500 index, then it should not be surprising that hedge funds
have performed differently than the S&P 500 index. Further, ignoring the fees, it follows that hedge funds have
underperformed the S&P500 index because those parts of the investment universe not covered by the index have
underperformed. Second, we need to find out about the degree to which hedge funds are invested outside
the S&P 500 universe and the implications of these investments for their underperformance. To answer these
questions, we use a 7-factor model. I regressed the underperformance against these seven factors to learn about
the potential sources of underperformance. The following exhibit displays the result.
1
Editor’s Letter
The R-squared of the regression is 78%, indicating that the above factors can explain 78% of the total variation in
the underperformance of hedge funds. We can see that the most important contributing factor is that fact that
hedge funds had low exposure to S&P 500 index, where hedge funds were 80% under-invested. Noting that the
CISDM index covers most hedge fund strategies, with many of them operating in fixed income, real assets and
currency markets, it is not surprising the CISDM index was not fully invested in S&P500 index. With hindsight, not
being fully invested from January 2009 through March 2016 was a bad idea. On the other hand, being exposed
to US Treasuries during this period was a good idea as they performed well (up 0.36% per month). However, next
to under exposure to the S&P 500 index, the most important contributing factor to underperformance was the
large cash positions held by hedge funds. Clearly, hedge funds remained cautious after the financial crisis.
The following exhibit displays the costs and benefits of the long/short positions that are displayed in the above
chart.
For example, the under exposure to S&P 500 index cost almost 1% per month in performance. The short position
in commodities (S&P GSCI) contributed modestly to the performance (0.2% per month) as commodities declined
during this period.
Since the R-squared of the above regression is not 100%, it means there are other sources of returns not captured
by the regression. These sources made a positive contribution of 0.17% per month. If we assume that our seven
factors cover all sources of systematic risks in the economy, then we may call the 0.17% monthly return alpha.
Next, it is useful to see whether hedge funds changed their asset allocations through the period considered in this
note. The following chart displays the dynamics of hedge fund allocations:
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Alternative Investment Analyst Review
We can see that right after the financial crisis and until mid-2012, hedge funds had zero to negative exposures to
S&P 500 index. Then, from 2012 until recently, hedge funds increased their exposures to S&P 500 index. For instance,
they were fully invested by August 2015. Further, since then hedge funds have substantially reduced their exposures
to equities and high yield bonds while increasing their exposures to Treasuries. In other words, hedge funds were too
slow to react to the bull market that started in 2009 and have been cautious since August 2015.
Do these results show that investors are correct in reducing their hedge fund allocations? The answer is, it depends.
First, the S&P 500 is the wrong benchmark for hedge funds, even for equity-oriented strategies. Therefore, if an
investor is determined to use the S&P500 index as a benchmark, it should have little or no allocation to hedge funds
and only to equity-oriented hedge fund strategies. Second, the results reported above are for an index of hedge
funds. Results not reported here is that the top quartile of hedge funds outperformed the S&P 500 index during this
period. Therefore, if an investor has access to managers with a strong track record and the skills to select top quartile
managers going forward, then it will be wise to allocate to hedge funds. Of course, this may mean that very large
allocations to hedge funds will lead to diminishing returns since in some strategies the top quartile consists of a small
group of managers.
Hossein Kazemi
Editor
3
Editor’s Letter
Table of Contents
What a CAIA Member Should Know
An Introduction to Green Bonds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
By Impact Investing Committee, NEPC
ABSTRACT: NEPC, LLC, a leading investment consulting firm takes a look at the
burgeoning green bond landscape and lays out key issues that investors need to be
aware of. Green bonds possess a label signifying that proceeds raised by the bond issue
will be ear-marked or ring-fenced to fund projects intended to benefit the environment,
with issuers agreeing to report on the use of proceeds. These terms are noted within the
bond's issuing documents. This is the key factor differentiating green bonds from the rest
of the fixed-income market; they are otherwise identically structured to their non-green
counterparts. This article examines the unique characteristics of green bonds and their
implications for investors who are interested in Impact Investing.
AIAR STAFF
Hossein Kazemi
Keith Black
Editors
Barbara J. Mack
Content Director
Angel Cruz
Creative and Design
Research Review
Inflation Hedging Abilities of Indirect Real Estate Investments in Switzerland . . . . . . . . . . 11
By Roland Hofmann, Tobias Mathis, ZHAW School of Management and Law Department
of Banking, Finance, Insurance Zurich University of Applied Sciences
ABSTRACT: This article examines the inflation hedging abilities of Swiss indirect real estate
investments. The authors focus on investment solutions that are appropriate for individual
investors and offer insights on how real estate investments might protect private investors
against inflation. The study concludes that indirect real estate investments in Switzerland
do not provide such inflation hedging abilities. However, these findings could be
affected by the special market structure in Switzerland and further study is warranted.
Featured Interview
Studying Financial Disruption: Bubbles and Crashes – An Interview with Didier Sornette 20
By Barbara J. Mack
ABSTRACT: Didier Sornette, Professor and Chair of Entrepreneurial Risks at ETH Zurich
(the Swiss Federal Institute of Technology) has devoted over two decades to studying
bubbles and crashes, producing a book, Why Stock Markets Crash: Critical Events in
Complex Financial Systems and numerous papers on the subject. This interview covers
some of the main themes of his empirical research, the launch of the Financial Crisis
Observatory (FCO) at ETH Zurich, and the development of the FCO Cockpit, a project
that analyzes a vast array of asset classes, searching for evidence of bubbles or crashes
in early stages of their formation.
CONTACT US
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CAIA.org
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Investment Strategies
The Ins and Outs of Investing in Illiquid Assets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
By Thijs Markwat, Roderick Molenaar, Robeco
ABSTRACT: Investments in illiquid asset classes have become more common in recent
decades, with notable growth in activity by pension funds in this space. Among the
most widely known illiquid investments are hedge funds, real estate, private equity, and
infrastructure. There are a number of reasons for their increase in popularity, including
perceptions on expected returns and the benefits of broader diversification. However,
it is not always clear if accepting the illiquidity delivers on these expectations. This
article examines some of the key issues surrounding the pursuit of the illiquidity premium,
including issues with finding trading partners, valuations and pricing, transaction costs,
and legal impediments that may make it difficult to trade efficiently. Investing in illiquid
assets introduces specific risks to any investor and education on the ins and outs of
illiquid markets is strongly recommended.
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Quarter 2 • 2016
Alternative Investment Analyst Review
Table of Contents
Black Ice: Low-Volatility Investing in Theory and Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
By Feifei Li, Engin Kose, Research Affiliates
ABSTRACT: Equity investors have endured two extreme market downturns since the turn of the century. These
devastating experiences reawakened institutional and individual investors to the risks of market volatility and
prompted great interest in low-volatility investing. However, with the emergence of new bull market conditions,
low volatility strategies have languished and investors appear to be less cautious about risk once again. The
authors of this article advise that it may be a good time to harvest some profits and revisit the benefits of lowvolatility approaches, including minimum-variance strategies, taking the impact on portfolios as a whole into
consideration.
Perspectives
Concentrated vs. Diversified Managers: Challenging What You Thought You Knew About “High Conviction” . 41
By Keith E. Gustafson, Patricia Halper, Chicago Equity Partners
ABSTRACT: The general rationale for concentrated portfolios suggests that investment managers cannot
have equal conviction about a large number of stocks. Stock portfolios with many stocks and relatively lower
tracking errors to benchmarks are often considered ”closet indexers,” and not worth active management
fees or the effort relative to a taking a passive approach. This article assesses the properties of Active Share, a
holdings-based calculation that measures the deviation of a portfolio from a benchmark in percentage terms.
Original work on the subject provided evidence on the relationship between a mutual fund’s deviation from
a benchmark and its excess return. While the approach found its way to plan sponsors’ toolboxes, this article
offers a critique of the methodology and debunks some of the notions surrounding the notion that high Active
Share (and/or concentration) necessarily results in higher excess returns.
VC-PE Index
Bison Global Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .48
By Mike Nugent and Mike Roth, Bison
ABSTRACT: Median returns for the industry were mixed during Q3 2015. In spite of this, venture capital’s
momentum continues as median TVPI figures increased median figures for buyouts declined slightly.
The MSCI Global Intel Report
The MSCI Global Intel Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
By Max Arkey, MSCI Real Estate
ABSTRACT: Global property held directly by private investors delivered a total return of 10.7% in 2015,
representing the highest annual return since 2007. The cyclical and structural dynamics of real estate attracted
a wave of capital that has propelled the asset class through a period of strong performance. Further, in recent
years, record-low bond yields and financing costs have kept spreads attractive. This article highlights the
atypical nature of this cycle, noting that while an inflection point may come eventually, for 2015, it remained
illusory.
These articles reflect the views of their respective authors and do not represent the official views of AIAR or CAIA.
5
Table of Contents
What a CAIA Member Should Know
An Introduction to Green Bonds
Impact Investing Committee
NEPC
Introduction
The nascent market for green bonds saw a
growth spurt in 2014 with issuance tripling
from a year earlier, surpassing $38 billion.1 The
growth in green bonds comes amid greater
awareness of climate change and expanding
investor appetite for environmentally-aware
investment products. The prevalence of these
securities is likely to rise as they allow issuers
and investors alike to demonstrate their
commitment to environmentally focused
initiatives.
Bonds labeled ‘green’ signify that proceeds
raised from the issuance will be tagged for
projects intended to benefit the environment—
for instance, the funds could be used
for renewable energy or energy-efficient
endeavors—with the issuer agreeing to report
on the use of proceeds. This is the main factor
distinguishing green bonds from the rest of
the fixed-income market; they are otherwise
identical to their non-green brethren. To be
sure, it is important to note that green bonds
only developed in the last decade and occupy a
tiny sliver—less than 1%—of the global fixedincome market. Additionally, the process for
labelling a bond as green is largely unregulated.
Issuers have full discretion to self-label and
there is no process for formal approval or
standardized reporting. That said, the surge in
issuance in 2014 and increased investor appetite
point to continued growth in this segment.
Green bonds possess a label signifying that
proceeds raised by the bond issue will be
ear-marked or ring-fenced to fund projects
intended to benefit the environment with
issuers agreeing to report on the use of
proceeds. These terms are noted within the
bond's issuing documents. This is the key
factor differentiating green bonds from the
rest of the fixed-income market; they are
otherwise identically structured to their
non-green counterparts.
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Quarter 2 • 2016
Alternative Investment Analyst Review
In line with NEPC’s commitment to keep abreast of developments
and trends in the investment landscape and educate investors, this
paper provides an overview of green bonds and details important
considerations for investors. We believe this area of the market,
like any other, should be analyzed on its merit. To this end,
NEPC’s dedicated Impact Investing Committee, comprising a
cross-discipline team of members from research and consulting,
will continue to monitor the market and vet investment
opportunities for clients as they arise.
The Evolution of Green Bonds
In many ways a green bond is no different than the standard debt
issued by a corporation, government or supranational entity – it
is a coupon-paying instrument bearing a promise by the issuer to
repay interest and principal at maturity. The key difference is that
the proceeds of a green bond are intended to fund initiatives that
benefit the environment. The first green bond was issued by the
European Investment Bank (EIB) in 2007, followed in 2008 by
the World Bank. The goal of these pioneering banks was to create
a high-quality fixed-income security to finance projects aimed at
mitigating climate change. The end product was a standard bond
with a simple label alerting investors to the ‘green’ nature of the
security.
After the bond offerings’ initial success, the EIB and the World
Bank continued to mobilize this source of funding and have
issued several additional green bonds. Other entities followed suit
and the green bond universe gradually grew. The first six years
drew only a few billion dollars of new issuance per annum, but
in 2013 the market reached a tipping point. Since then, there has
been an exponential increase in supply (Exhibit 1).
The growing universe of green bonds has also allowed for
differentiation among issues (Exhibit 2). For example, although
corporate green bonds only entered the market at the end of 2013,
these bonds comprised about a third of total issuance of green
bonds in 2014. Green-labeled asset-backed securities and US
municipal debt also saw an uptick last year. While the majority
of issues are still denominated in US dollars and euros, issuers
from a number of other countries, including China and India,
have begun to enter the market. As such, better diversification
across geography and currency is expected. Projections for 2015
issuance vary widely, ranging from $30 billion to $100 billion, but
actual issuance has been slow so far this year. Approximately $30
billion in new green bonds have been introduced to the market in
2015 through September, according to Bloomberg. Yet, if pacing
follows current trends, we should see an uptick in issuance as the
year progresses.
Borrower Incentives
Given the similarity in structure and terms of green and nongreen bonds, investors often wonder what the incentives are
for issuers to self-label their debt offerings as green. For some
issuers, raising funds through a green bond offering presents an
opportunity to attract new investors, as these securities may be
especially appealing to investors focused on environmental, social
and governance (ESG) factors. Likewise, issuing a green bond
presents a powerful marketing opportunity to demonstrate an
organization’s commitment to sustainability. Tax incentives and
subsidies may also be available for state and local government
issuers within the United States through federal programs, such
as those granting Qualified Energy Conservation Bond (QECB)2
and Clean Renewable Energy Bond (CREB)3 status. Corporations
may also be eligible for federal tax credits and other incentives
by taking steps to make their business operations more energy
efficient — projects that may be funded by issuing green bonds.
Additional incentives may be available based on programs offered
in the country of origin.
Green Bond Issuance By Issuer Type
60
Billions ($)
50
40
30
20
10
0
2007
2008
2009
2010
Supranational, sovereign & agency
US municipal
ABS
Exhibit 1: Issuance of Green Bonds from 2007-2015
Source: Bloomberg New Energy Finance (2015).
7
Green Bonds: An Overview
2011
2012
2013
Labelled corporate
Project
Estimate
2014 YTD Sept
2015
What a CAIA Member Should Know
Corporate: Issued by corporations; repayments are from general corporate funds. Have the same credit rating as other bonds of
similar composition from the same issuer. Bank of America became the first corporate issuer in 2013; other issuers include Iberdrola,
TD, Unilever and Rikshem.
Green ABS: Asset-backed securities with cash flows supplied by a portfolio of underlying receivables (loans, leases and Power
Purchase Agreements (PPA) that are associated with green projects). Issuers include Toyota, SolarCity and Fannie Mae.
Government: Issued by national, regional or local governments/ municipalities to finance green projects. Have the same rating
as other debt issued by the entity. Green municipal bonds may have tax advantages for investors. Issuers include the State of
Massachusetts and the County of Stockholm.
Project Bonds: Backed by the cash flows of an underlying renewable energy project or portfolio of projects. A remote account—
separate from the issuer’s general funds—is created such that the project’s credit rating is distinct from that of the issuing entity.
Repayment is based on cash generated by the venture; these bonds are implicitly more risky as repayment hinges on the success of
the project. Issuers include Berkshire Hathaway Energy (Topaz) and Continental Wind.
Supranational/International: Bonds issued by supranational or international organizations, including multilateral banks,
development banks and export credit agencies. This is the most common type of green bond and typically has high credit ratings.
Issuers include the World Bank and the African Development Bank.
Exhibit 2: Issuance of Green Bonds from 2007-2015
Source: Bloomberg
Labelling, Regulation and Transparency
Currently, the process of labelling a bond as green is largely
unregulated. Issuers have the discretion to self-label and there
is no formal approval or vetting process. Issuers claiming green
bond status must include a brief declaration statement within
their offering documents indicating that the proceeds raised
will be allocated to green projects. There is an expectation that
issuers will also provide reports in the future, detailing the actual
use of proceeds. However, there is no requirement to provide
standardized reporting, so actual reporting may vary greatly
from issuer to issuer. While green bonds are subject to the same
oversight from the Securities and Exchange Commission (SEC)
and the Financial Industry Regulatory Authority (FINRA) as their
non-green counterparts, there is no regulatory body ensuring that
the funds raised through the issuance of green bonds are actually
benefiting green initiatives.
Regulations prohibiting companies with otherwise poor
environmental practices from issuing green bonds are also
non-existent. For these reasons, greenwashing—a term used to
describe the act of a bond issuer self-labelling an issue as green
for marketing purposes without having a true commitment to the
2014-2015 Issues
Other
DNV
Vigeo
None
Cicero
Exhibit 3: Issuance of Green Bonds from 2007-2015
Source: Bloomberg
Quarter 2 • 2016
environment or intention to use the proceeds as indicated—is
a buzzword among investors in the space. To be sure, this is a
potential problem since there are no official requirements for
green labelling. That said, reputational risk may be enough to
prevent pervasive greenwashing.
This lack of regulation has led to the development of a handful of
organizations providing independent opinions on green-labelled
issues. These reviews are funded by the issuer and are not yet
required. The reviews are typically based on an evaluation of the
projects to be financed by a specific green bond; they also may
incorporate a review of the governance, transparency and other
practices of the issuer. A summary of the findings is typically
included in the offering documents for investor reference. The
Center for International Climate and Environmental Research –
Oslo (CICERO), Vigeo Rating and DNV GL are the main firms
offering these services. While not required, there is a preference
among investors for issuers to seek a second opinion prior to
marketing new green issues. However, some issuers opt against
hiring an independent reviewer because second opinions are
costly and the supply of green bonds is still limited. In fact, only
about half of the green bonds issued in 2014 and in the first six
months of 2015 touted this additional verification; however, many
offerings still have been oversubscribed (Exhibit 3). Pressure
from investors is likely necessary for an independent appraisal to
become standard practice.
In an attempt to foster further transparency within the green
bond market, the International Capital Markets Association
(ICMA) collaborated with a group of investors, issuers and
underwriters to form an Executive Committee, which serves as
an unofficial governing body in the space. The group developed
and published the Green Bond Principles (“Principles”) in 2014,
a document providing voluntary process guidelines for green
bond issuers. It includes sections addressing the proper use of
bond proceeds, project evaluation and selection, management
of proceeds, and reporting. While still in its infancy, investors
are beginning to expect issuers adhere to the Principles. In 2015,
a second group of investors, led by Ceres’s Investor Network
8
Alternative Investment Analyst Review
Commonwealth of Massachusetts Series E
2014 General Obligations Green Bonds
Issue Date: 9/24/2014
Issue Amount: $350 million
Coupon: Varies (2.0-5.0)
Credit Quality: Aa1/AA+
Maturity Date: Varies (last bond matures on
9/1/2031
Second-Party Opinion: No
Use of Proceeds: Will benefit a number of
projects, including:
• Improving drinking water quality
• Energy efficiency and conservation in state
buildings
• Land acquisition, open space protection and
environmental remediation
• River revilization, preservation and habitat
restoration
• Marine commerce terminal to support offshore
wind projects
Exhibit 4: Example of a Green Bond
Source: The Commonwealth of Massachusetts Investor Program
Investors interested in green bonds can purchase securities
directly or achieve exposure through a handful of investment
funds dedicated to green bonds. That said, potential investors
should be aware of certain factors when evaluating these
strategies, for instance, the emergence of green bonds is a
relatively recent occurrence. Therefore, dedicated strategies tend
to have short track records and limited assets. Also, the universe
of green bonds is still limited in scope. In addition, less than
50% of issues are denominated in US dollars, further reducing
the opportunity set for many strategies. Some funds navigate
this issue by utilizing broader mandates such as investing in US
Treasuries or by investing in bonds that are not officially labelled
green but benefit green initiatives. For example, many municipal
bonds may qualify as green bonds based on their intended use
of proceeds, for instance, those supporting access to public
transportation or water conservation, but are not labeled as
such. While common among municipals, this is true across the
spectrum of fixed-income securities. In fact, the Climate Bond
Initiative’s 2015 Bond and Climate Change report estimated the
value of the outstanding total climate bond universe at nearly
$600 billion, of which labelled green bonds comprised only
about 11%. Exhibit 4 outlines an example of a green bond from
Massachusetts.
Some larger, more mainstream investment managers may also
hold green bonds in their portfolios. However, many of these
managers are not investing in green issues because of their
environmental bent. Rather, such investors tend to lump green
bonds with other non-green options and analyze them based on
their assessment of value. Since green bonds represent less than
1% of the total fixed-income market, it is unlikely that a nongreen focused strategy would hold a sizeable allocation to green
bonds.
on Climate Risk (INCR), released A Statement of Investor
Expectations for the Green Bond Market. (Ceres is a nonprofit organization advocating for sustainability leadership.)
This document supports the Principles but provides additional
structure around key elements, including project eligibility, issuer
disclosures, reporting and independent assurance. INCR urges
issuers to observe the Principles and the Statement of Investor
Expectations to facilitate standardization and credibility within
the market.
The recent surge in issuance and increased investor appetite has
led to the launch of several green bond indices, for instance,
Solactive, S&P Dow Jones, Bank of America Merrill Lynch and
Barclays (in partnership with MSCI) released new green bond
indices in 2014. The indices vary in composition and may capture
different segments of the market. It should be noted that while the
indices are meant to provide a snapshot of the green bond space,
some smaller issues may be excluded as they do not meet the
inclusion criteria (minimum issue size for major index inclusion
is typically $250 million). Despite the emergence of these new
indices, few corresponding index funds have been launched.4
Investing in Green Bonds
Alternatives to Labelled Green Bonds
Since green bonds and standard debt issues are nearly identical in
structure, investors should still conduct a fundamental analysis of
the issuer and relative value analysis to evaluate these securities;
investors may also perform further ESG analysis. Green bonds
structured as general obligations will tend to trade at similar levels
and with comparable liquidity to non-green bonds, all else equal.
Typical buyers of green bonds tend to be buy-and-hold investors
due, in part, to the limited availability of these securities. This
investor attribute is attractive to issuers, giving them an additional
incentive to issue green bonds. On an issue-by-issue basis there is
anecdotal evidence of a “green premium” priced into some green
bonds. However, since the investor base is still dwarfed by those
not specifically targeting these bond types, there is little proof of
this premium embedded in the overall market for green bonds.
9
While labelled green bonds expressly support projects that benefit
the environment, climate-conscious investors should be aware
that these instruments are only one of many available options. In
fact, a number of strategies invest assets based on environmental,
social and governance (ESG) considerations. Such managers
invest in equity and debt of companies or other entities highly
rated for their ESG practices. In addition to factors affecting
climate change, these managers may include other criteria, for
instance, an issuer’s hiring practices, working conditions and
board membership. This process may also be helpful in screening
out ‘greenwashed’ investments. Many investors find this approach
attractive as it incorporates a broader subset of issues into the
investable universe.
Green Bonds: An Overview
What a CAIA Member Should Know
Looking Forward
The growth in green bonds comes amid greater awareness
of climate change and expanding investor appetite for
environmentally-aware investment products. The prevalence of
these securities is likely to rise as they allow issuers and investors
alike to demonstrate their commitment to environmentally
responsible initiatives. The growing need for energy efficient
and clean technologies globally, especially in emerging market
countries, also may help drive issuance going forward. These
securities, which form a subset of the fixed-income market,
present issuers with the opportunity to widen their investor base
as they also appeal to ESG investors. As green bonds become
more diversified across credit quality, geography and instrument
type, they will likely integrate more readily with mainstream
investment products.
However, as this segment grows—it currently makes up less than
1% of the global fixed-income market—widespread acceptance
of the Principles and the Statement of Investor Expectations will
be essential to facilitate standardization and credibility within
the market in the absence of an official regulatory body and/ or
independent scrutiny from third-party organizations. We will
continue to monitor this growing market and vet investment
opportunities for clients as they arise. Please contact NEPC if you
have any questions or want to know more about impact investing.
Endnotes
1. Issuance estimates may vary by source. For the purposes of this
paper, data published by Bloomberg New Energy Finance was
utilized.
2. QCEBs are taxable bonds that allow qualified state, tribal and
local government issuers to borrow at lower rates to fund energy
conservation projects. The issuer’s borrowing costs are subsidized by
the US Department of the Treasury.
3. CREBs may be issued by qualifying entities to finance renewable
energy projects. Investors possessing CREBs receive federal tax
credits in lieu of a portion of the traditional bond interest, lowering
the effective interest rate for the borrower.
• All investment programs have unique characteristics and each
investor should consider their own situation to determine if the
strategies discussed in this paper are suitable.
• This report contains summary information regarding the
investment management approaches described herein but is not
a complete description of the investment objectives, portfolio
management and research that supports these approaches.
Author's Bio
NEPC’s Impact Investing Committee
NEPC’s Impact Investing Committee provides strategic oversight
and thought leadership of the firm's Impact Investing efforts.
The Committee includes a cross section of both research and
consulting professionals which works together to help shape
policy, produce ongoing research reports, educate clients and
propose impact oriented solutions for clients.
NEPC has been analyzing impact investing strategies for over
25 years, recognizing its strategic importance to institutional
investors. Today, the firm has over 50 clients pursuing various
impact investing strategies across the entire institutional
spectrum. In addition to authoring numerous white papers,
hosting industry events and speaking at conferences across the
country, NEPC has also partnered with The White House on their
Clean Energy Initiative, joined the United Nations Principles for
Responsible Investment (PRI) and Mission Investors Exchange,
and partnered with MSCI to create customized ESG reporting.
Learn more about NEPC's impact investing insight and resources
at www.nepc.com.
4. The first green bond index fund was launched in 2015 by SSgA.
Bibliography
A Statement of Investor Expectations for the Green Bonds Market.
Investor Network on Climate Risk (2015).
Green Bond Principles. International Capital Market Association (2015).
Labelled Green Bond Data. Climate Bonds Initiative (2015).
Olsen-Rong, T., K. House, B. Sonerud, and S. Kidney. Bonds and Climate
Change: The State of the Market in 2015. The Climate Bonds Initiative
(July 2015).
Q1 2015 Green Bonds Market Outlook. Bloomberg New Energy Finance
(2015).
Disclaimers and Disclosures
• All Investments carry some level of risk. Diversification and other
asset allocation techniques do not ensure profit or protect against
losses.
• The opinions presented herein represent the good faith views of
NEPC as of the date of this report and are subject to change at any
time.
10
Quarter 2 • 2016
Alternative Investment Analyst Review
Research Review
Inflation Hedging Abilities of Indirect
Real Estate Investments in Switzerland
Roland Hofmann
Senior Lecturer
Banking & Finance
Zurich University of Applied
Sciences ZHAW
Winterthur (Switzerland)
Tobias Mathis
Graduate Student
Banking & Finance
Zurich University of Applied
Sciences ZHAW
Winterthur (Switzerland)
Introduction
Real Estate investments have been discussed
for a long time in both academic literature and
practice. Intuitively, many people assume that
real estate returns may have a high correlation
with inflation (Fama and Schwert, 1977: 4;
Anson, 2009: 78). They therefore expect real
estate investments to provide inflation hedging
abilities (Wohlwend and Goller, 2011; Marti,
Meier, and Davidson, 2014: 12). Accordingly,
most of the past research on real estate has
focused on the diversification effects of real
estate in portfolios of stocks and bonds and on
the inflation hedging abilities of real estate.
There are different ways to invest in real estate
(Garay and Ter Horst, 2009: 90): (1) Equity:
One can invest in the equity part of real
estate by buying real estate mutual funds, like
US-American Real Estate Investment Trusts
(REITs)1, or by acquiring real estate physically.
The first is considered as an indirect, public,
securitized, or financial investment. The
11
Indirect Real Estate Investments in Switzerland
latter is said to be a direct, physical, or private
investment. (2) Debt: It is also possible to invest
in the debt part of real estate. This is normally
done in a securitized form, for example, with
mortgage-backed securities (MBS). This paper,
however, only considers indirect investments in
the equity part of real estate.2
There are only a limited number of publications
considering the situation in Switzerland and
this research, which considers Switzerland
particularly, is often not very recent. This is
problematic because inflation hedging abilities
could change over time as it was shown by the
study of Moigne and Viveiros (2008: 282). In
addition, investigations have often focused on
institutional rather than individual investors,
thereby analyzing effects of direct real estate,
which is often not suitable as an investment for
a private person.
This paper examines inflation hedging abilities
of Swiss indirect real estate investments. It only
considers indirect investment solutions3 that are
Research Review
realizable for an individual investor and therefore offers important
insights on how real estate protects private investors against
inflation. As a proxy for indirect investment solutions, we use the
indices of real estate mutual funds.
expected inflation, but that it is only an incomplete hedge against
unexpected inflation when using quarterly data. This study thus
confirms the results of the research conducted by Fama and
Schwert (1977).
In this paper, we provide a new evaluation of the research
question for Switzerland. As shown in the study of Moigne
and Viveiros (2008: 282), the relationship between real estate
returns and inflation can change over time. Their paper shows
that in Canada, this change was due to the decrease in interest
rates. In Switzerland, there was a structural interruption in the
real estate market in the early 1990’s, when a real estate bubble
burst (Alvarez, 2013: 6). Therefore, it makes sense to conduct
new research on the situation in Switzerland using more recent
financial market data.
Hartzell, Hekman, and Miles (1987: 626) also used the Fama and
Schwert approach and found that direct real estate provides a
hedge against expected inflation. However, they also found that
direct real estate is a complete hedge against unexpected inflation.
This result is therefore contradictory to the result of the Fama and
Schwert study. That of Hartzell et al. (1987: 618) used quarterly
holding period returns from over 300 properties.
The paper is organized as follows: After the introduction we
present the previous research on this topic. Afterwards follows a
discussion of the applied research methodology and the data used.
In the subsequent part, the results of the paper are presented. This
is followed by a discussion of the findings and a conclusion.
Literature Review and Previous Research
This section summarizes the results of previous research
regarding the inflation hedging abilities of real estate.
The inflation hedging abilities of real estate have interested
researchers and practitioners since the 1970s. Although a strong
relationship between inflation and real estate returns may sound
intuitive to some people (Fama and Schwert, 1977: 4), there are
some valid reasons to question this relationship. For instance,
if a building were leased at a fixed rent, which does not adjust
to inflation, the value of this building would decline if inflation
increases (Goetzmann and Valaitis, 2006: 2). The two main
reasons for an inflation hedge are that rental and lease payments
are adjusted regularly to inflation and the capital shift from
stock and bonds in times of inflation into real estate, which leads
to price appreciations (Anson, 2009: 79). Graff and Cashden
(1990) have therefore postulated a decomposition of real estate
returns into income returns and capital appreciation returns.
The basic idea is that capital appreciation returns provide a good
inflation hedge, as opposed to income returns, where this hedge is
questionable.
International Evidence
Two of the first researchers who examined the inflation hedging
abilities of real estate were Fama and Schwert (1977). They found
evidence that real estate provides a hedge against unexpected
inflation. However, the coefficient for unexpected inflation
was significantly lower than one, thus rejecting the hypothesis
of a complete hedge against unexpected inflation (Fama and
Schwert, 1977: 130). The estimate for expected inflation was
not significantly different from one, which does not reject the
proposition that all assets should be a hedge against expected
inflation (Fama and Schwert, 1977: 127). The study was conducted
using data ranging from 1953 to 1971. The results changed only
slightly if the researchers used quarterly or semiannual instead of
monthly data.
Miles and Mahoney (1997) used the Fama and Schwert
framework in their research for the United States. They
concluded that direct real estate is a complete hedge against
Quarter 2 • 2016
Moigne and Viveiros (2008: 275) researched Canadian direct
real estate investments and found that real estate acts as a
complete hedge against expected inflation and even “overhedge”
unexpected inflation (γ = 2.04). This is a huge discrepancy to
the results for the US, where real estate seems to provide only an
incomplete hedge against unexpected inflation. However, Moigne
and Viveiros (2008: 282) found that the inflation hedging ability
has disappeared since the mid-1980s when the Canadian inflation
rate decreased significantly.
Research for Singapore has found no significant inflation hedge
for indirect real estate investments but has found an inflation
hedging ability for shop and industrial property (Sing and Low,
2000: 380).
All of the above-mentioned studies applied the approach
developed by Fama and Schwert (1977). However, it should be
noted that there is also some criticism to this approach. Hence,
other authors have used different techniques to research the topic.
Chaudhry, Myer, and Webb (1999) used co-integration techniques
for data of the United States. They concluded that, “… there is
an underlying factor that links the financial-asset [sic] and realassets markets, at least in the long run. When CPI is included
in the three systems, the number of common factors increases
to two, implying that inflation does play an important role in
creating a linkage between these time series.” (Chaudhry et al.,
1999: 347). Furthermore, they found that all of the tested financial
and real estate returns are non-stationary. Therefore, they argue
that conventional statistical methods like the Fama and Schwert
framework should not be applied (Chaudhry et al., 1999: 342).
In Hong Kong, evidence in favor of the inflation hedge was
found with the method of Fama and Schwert, but not with a
co-integration method (Ganesan and Chiang, 1998: 65). This
indicates that there might be a short run relationship between
inflation and real estate returns, but it also indicates that this
relationship may not be stable in the long run. Therefore, the
regression of the Fama and Schwert framework could be spurious
(Ganesan and Chiang, 1998: 65).
Hardin, Jiang and Wu (2010) analyzed the development of
equity REITs dividend yield relative to the expected inflation.
Hardin et al. (2010) came to the conclusion that a certain
inflation protection exists but is undermined due to the inflation
illusion perceived by investors. The results additionally provide
an alternative explanation as to why the yields on REITs often
negatively correlated with expected inflation.
The study of Demary and Voigtlander (2009) focuses on the
inflation protection of direct and indirect real estate investments.
12
Alternative Investment Analyst Review
REITs cannot protect investors from general inflation. As well as
other stocks, they offer no effective protection from inflation, and
analysis of yields and inflation rates show negative correlations. A
rising price level thus adversely affects the actual returns on this
investment. According to Demary and Voigtlander (2009), this
is explained by the fact that investors adjust their expectations
due to inflation and the resulting possible deterioration of the
macroeconomic environment.
Demary and Voigtlander (2009) and Giljohann-Farkas and
Pfleiderer (2008) found that for direct real estate investments a
positive correlation between consumer price index and real estate
index confirms better inflation protection.
The analysis of Simpson, Ramchander and Webb (2007) arrived
at similar conclusions for REITs as inflation protection as did
Demary and Voigtlander (2009). Simpson et al. (2007) concluded
that there is an asymmetric development of yields from REITs
and the inflation rate, while not explicitly postulating a negative
correlation.
The studies of Adrangi, Chatrath and Raffiee (2004), Glascock,
Lu and So (2002), Stevenson (2001) and Chan, Hendershott and
Sanders (1990) conclude from the analyses of the yields of REITs
that no effective protection against inflation can be explained.
The investigation of an unexpected inflation component suggests,
however, that a link between monetary policy and real estate
prices does exist.
From this perspective Hoesli, Lizieri and MacGregor (2008)
also consider the inflation protection properties of direct and
indirect real estate investments, but they cannot explain a causal
link between the development of the inflation rate and yields on
REITs.
Maurer and Sebastian (2002), on the other hand, state that
indirect real estate investments do provide inflation protection
due to the excess returns, whereas the studies of both Maurer
and Sebastian (2002) and Lu and So (2001) come to the result
that the analysis of the development of yields on REITs and
other underlying macroeconomic factors such as monetary
developments are more revealing. Lu and So (2001) concluded
further that the future of inflation could derive from the yields on
REITs. This would confirm the delay effect, where the inflation
expectation in the market prices of REITs is anticipated, and
therefore, if investors are correct, inflation only occurs after a
certain delay.
Although Chatrath and Liang (1998) determined no connection
between REITs yields and inflation in the short term, however, a
certain link could be detected in the long term.
Generally, real estate has its own risk and return profile.
Nevertheless, the public stock and bond markets influence
the performance of the real estate market (Anson, 2012: 59),
especially indirect real estate investments (Garay and Stevenson,
2009: 242; Wohlwend and Goller, 2011; Marti, Meier, and
Davidson, 2014: 17).
In summary, previous results cannot confirm a direct causal
relationship between the inflation rate and the yield of indirect
real estate investments. The studies of Hoesli et al. (1997) and
Hamelink, Hoesli und MacGregor (1997) also join this core
conclusion. In the long term, the total return (price change and
13
Indirect Real Estate Investments in Switzerland
distribution) of indirect real estate can compensate for a loss of
purchasing power, but in the short term no hedge against inflation
exists.
Evidence for Switzerland
There are only few papers that analyze the situation in
Switzerland. Most of the research for Switzerland was conducted
in the 1990s. One of the first papers was written by Anderson and
Hoesli (1991), who found that Swiss stocks, bonds and real estate
mutual funds protected investors from inflation in Switzerland
in the period between 1978 and 1989. The research of Hamelink
and Hoesli (1996: 47) for Switzerland was conducted with direct
and indirect real estate investments using the Fama and Schwert
approach. However, they did not find any inflation hedging
abilities - neither for direct real estate investments nor for indirect
investments.
Hoesli (1994) focused on real estate mutual funds in Switzerland.
The paper analyzes the inflation hedging ability using monthly,
quarterly, annual and five-year data. For all time intervals no
significant inflation hedging ability was found. However, the β
coefficient, in this study being the coefficient for total inflation, is
0.463 for five-year data and the t-statistic is 1.557. This indicates
that real estate funds may provide an inflation hedge in the
long run (Hoesli, 1994: 56).4 All coefficients for expected and
unexpected inflation are as well not significantly different from
zero (Hoesli, 1994: 57).
Liu, Hartzell, and Hoesli (1997) conducted international research
on real estate mutual funds. Although it is known that US Real
Estate Mutual Funds (REITs) do not provide inflation hedging
ability and indeed behave more like stocks than like real estate,
Mengden and Hartzell (1986 in: Liu et al., 1997) argue that this
might not be true for other countries. For example, Swiss real
estate mutual funds are different from US-REITs in that the Swiss
units can be redeemed at the intrinsic value (Hoesli, 1994: 52),
whereas US-REITs have a closed form structure (Liu et al., 1997:
196). One would therefore expect Swiss real estate mutual funds
to behave differently than US-REITs. However, the study does not
find any inflation hedging ability in Switzerland (Liu et al., 1997:
208).
Wohlwend and Goller (2011) conducted a comprehensive study
on the inflation hedging abilities of different asset classes. They
found that, with a high probability, there is no relationship
between real estate and inflation in Switzerland. None of the
studied asset classes offer complete inflation protection in the long
run (Wohlwend and Goller, 2013: 21).
To sum up, it can be stated that direct real estate was found to
provide inflation hedging abilities in most countries around the
world but not in Switzerland. Internationally, direct real estate
seems to provide a good hedge against expected inflation and at
least a partial hedge against unexpected inflation. This is not the
case for Switzerland where real estate does not seem to provide
any hedge against inflation. Indirect real estate investments seem
not to provide protection against inflation, no matter whether the
real estate funds have a closed form or an open form structure.
Exhibit 1 pictures the stylized results of previous research on the
inflation hedging abilities of real estate (see also Anson, 2009:
102).
Research Review
Direct investments
Indirect investments
International
Yes
No
Switzerland
No
Probably not
(focus of the paper)
Exhibit 1: Inflation Hedging Abilities According to Previous Research
Source: Authors' Calculations
Research Methodology
In this section we discuss the Fama and Schwert approach to
determine the inflation hedging abilities of real estate investments.
Previous studies show that this approach has been frequently
used by numerous other authors (e.g. Hamelink and Hoesli,
1996; Miles and Mahoney, 1997) and is still a widely accepted
approach.5
The Fama and Schwert Approach
Fama and Schwert (1977) developed a common approach to
determine inflation hedging abilities. In accordance with Fisher
(1930) they argued that the expected nominal return of an asset is
the sum of the expected real return of the asset and the expected
inflation rate (see also Wohlwend and Goller, 2011). Therefore,
expected inflation is priced in for all assets and a complete
hedge against expected inflation should be provided. Hence,
it is necessary to make a distinction between unexpected and
expected inflation. Fama and Schwert (1977) therefore analyzed
the inflation hedging abilities with a two-factor model. The asset
return is the dependent variable and the expected and unexpected
inflation are the independent variables.
Rit = αi + βi(E(πt)) + γi(πt - E(πt)) + εit
(E1)
Where:
is the return of asset i in period t
Rit
E(πt) is the expected inflation for period t
πt - E(πt)is the unexpected inflation for period t
εit is an error term, residual effects that are not explained by
the data
If β = 1, an asset is said to be a complete hedge against expected
inflation. An asset is called a complete hedge against unexpected
inflation if γ = 1. If β = γ = 1, then an asset is said to provide
a complete hedge against inflation (Fama and Schwert, 1977:
117). One would expect all assets to be a complete hedge against
expected inflation (β = 1) but only some assets to provide a
complete, if any, hedge against unexpected inflation (γ = 1) (Fama
and Schwert, 1977: 117).
Further Development of the Fama and Schwert Approach
However, the approach introduced by Fama and Schwert can also
be criticized. The main difficulty of this approach is to distinguish
between expected and unexpected inflation. Fama and Schwert
solved this problem by using treasury bills as a proxy for expected
inflation. The expected inflation equals the T-bill yield minus
the real return (i.e. the real interest rate; Miles and Mahoney,
1997: 32). This made it necessary to assume constant real interest
rates, because one can otherwise not assume that a change in the
T-bill yield was due to a change in inflation expectations (see
also Wohlwend and Goller, 2011). This assumption was true for
Quarter 2 • 2016
the period that Fama and Schwert analyzed, but the assumption
may not hold nowadays (Ganesan and Chiang, 1998: 58). In later
papers, other methodologies have therefore been developed to
find another measure for expected inflation.
For instance, Fama and Gibbons (1982) and Hartzell, Hekman,
and Miles (1987) apply moving-average processes to estimate
expected inflation. Hamelink and Hoesli (1996) researched the
topic for Switzerland. They also used the model of Fama and
Schwert, but they estimated expected inflation using four different
ways.
1.First, they follow Gültekin (1983) by assuming that
expectations are perfect. Hence, expected inflation equals
the actual inflation and there is no unexpected inflation.
This reduces the model of Fama and Schwert to a simple
one-factor model in which actual inflation is the only
independent variable (Hamelink and Hoesli, 1996: 36):
Rit = αi + βi(πt) + εit (E2)
Where:
Rit
is the return of asset i in period t
πt
is the actual inflation for period t
εit
is an error term, residual effects that are not explained by
the data
2.The second approach used to proxy expected inflation by
Hamelink and Hoesli (1996: 36) is a linear regression model,
which specifies the expected inflation rate at time t as a linear
function of the inflation rate at time t-1. This model is:
πt = α + β(πt-1) + εt
(E3)
Where:
πt
is the expected inflation for period t
πt-1
is the actual inflation for period t-1
εt
is an error term, residual effects that are not explained by
the data
3.The third method is a qualitative threshold autoregressive
conditional heteroscedasticity (QTARCH) model, introduced
by Gouriéroux and Monfort (1992). This model leads to
a conditional mean and a conditional variance, which are
endogenous stepwise functions (Hamelink and Hoesli, 1996:
36).
4.The fourth approach is based on an ARCH in mean
(ARCH-M) model. In this model, developed by Engle,
Lilien and Robins (1987), conditional expected inflation is a
function of the conditional variance of the period before. This
method is therefore different from the second and the third
methods because the expected inflation is derived from the
variance of the period before, and not the inflation rate of the
period before (Hamelink and Hoesli, 1996: 37).
14
Alternative Investment Analyst Review
Methodology of the Current Paper
To keep it simple, for our analysis of the inflation hedging abilities
of real estate we have also applied the approach proposed by Fama
and Schwert (1977). We decomposed the actual inflation in an
expected and an unexpected part with two different methods. (1)
We assume for the decomposition that expectations are perfect,
just as Gültekin (1983) did. This reduces the model of Fama
and Schwert to the equation E2, which was presented above.
(2) Furthermore, we apply in this paper the second method
proposed by Hamelink and Hoesli (1996: 36). The decomposition
is conducted by inferring the expected inflation at time t from the
actual inflation at time t-1. This paper uses the formula that was
presented above in equation E3.
Data
Biases in Real Estate Performance Data
Real estate is often considered as an illiquid asset class (Anson,
2012: 45; Marti, Meier, and Davidson, 2014: 12): On the one hand,
the transaction size is high, but on the other hand, real estate
objects are not publicly traded and trading is infrequent. The
“semi strong” notion of market efficiency (all public information
is included in the price) does not exist, because transactions are
regularly private. Without public price information available,
other assessment methods are necessary.
But appraisal based valuation methods tend to lead to an
underestimation (“smoothing”) of the volatility of real estate
investments (Anson, 2009: 84; Marti, Meier, and Davidson, 2014:
12). This could also be a problem not only for the measurement of
the inflation hedge ability, but in the level of index construction
too where different biases can occur (Garay and Stevenson, 2009:
229). We often see a difference between the net asset value of the
mutual fund and its stock market price for indirect real estate
investments products (Garay and Stevenson, 2009: 237).6
Past financial market data are existent as time series and tend to
affect current data. Hence, autocorrelation is a frequent problem
in real estate time series. Autocorrelation leads to problems in
the statistical analysis of the data. As a result, or to counter this,
correction procedures need to be applied (Marti, Meier, and
Davidson, 2014: 16).
We are aware of the difficulties of the performance measurement
for real estate investments. Consequently, we assume for this
study (1) that the investor can realize the performance of the
investment fund and neglect any valuation issues within the fund
(realistic). (2) Further, we assume no transaction costs for the
investor (unrealistic). (3) In addition, we assume that the indices
of the empirical analysis are investable for private investors (not
always true).
on individual investors, the CPI is a better proxy for inflation
than, for example, the GDP-deflator (see also Wohlwend and
Goller, 2011).
During the whole time period of the research interest rates and
inflation rates in Switzerland were extremely low and fluctuated
around zero. Switzerland and the Swiss Franc have acted as safe
havens in recent years, especially since the start of the crisis
in 2007. Therefore, the real estate markets have faced price
appreciations due to huge capital inflows and the “search for
yield”.
In Switzerland, we can distinguish between four groups of real
estate indices (Marti, Meier, and Davidson, 2014: 12): (1) stock
market based real estate indices7, (2) indices by independent real
estate specialists, (3) real estate indices based on selling offers,
and (4) indices based on transaction data. Indices of Group 1 are
suitable for our purposes. These are constructed from the pricing
of real estate stock corporations and real estate mutual funds.
As a proxy for the indirect real estate returns we used three stock
market based real estate indices (group (1), see above): SWIIT:
SXI Real Estate Funds Index, RUEDIF: DB RUEDBLASS IF Index,
and WUPIXF: Wüest & Partner AG Index für Immobilienfonds
(data source: SIX Group, Rüd Blass, Wüest & Partner).
As an example, the SXI Real Estate index (an umbrella structure)
contains real estate funds and real estate companies; the SXI Real
Estate Funds index (a sub structure) contains only real estate
funds (Meier, 2011: 8). It can be assumed that real estate stocks
behave more like stocks than like real estate due to their closed
form structure. Hence, this study uses real estate fund indexes to
track indirect real estate performance.
As performance data, we applied quarterly log-changes of total
return (price changes and distributions) index values.
Results
In this section we discuss the findings of our research regarding
the inflation hedging abilities.
The inflation hedging abilities of Swiss real estate mutual funds
were tested using the approach of Fama and Schwert (1977).
The decomposition of actual inflation in an expected and an
unexpected part was done using two different methods. (1)
Firstly, by assuming that expectations are perfect. (2) Secondly,
by inferring the expected inflation at period t from the actual
inflation at period t-1.
(1) Assumption that expectations are perfect
First, the calculation was carried out under the assumption that
expectations are perfect using equation E2:
Data sources and description
Rit = αi + βi(πt) + εit
The research was conducted with quarterly data using the
inflation rates and real estate fund returns during a 20-year time
span from 1995q1 (SWIIT, RUEDIF) and 1997q1 (WUPIXF) until
2015q2.
This leads to the results on Exhibit 3.
The study uses log-changes in the consumer price index (CPI) of
Switzerland as a proxy for inflation (data source: Swiss Federal
Statistical Office). The CPI represents the price of a typical basket
of goods consumed by a private person. Since the study focuses
15
Indirect Real Estate Investments in Switzerland
(E2)
The negative sign of the beta coefficient of all index returns for
actual inflation would actually suggest that Swiss indirect real
estate acts as a “reverse” hedge against inflation. However, the
standard error of the regression is high in order to state that with
certainty. All coefficients are not significant at standard levels.
The values for R-squared are extremely low. This is an indicator of
poor fit of our model.
Research Review
Variable
Obs
Mean
Std. Dev.
Min
Max
CPI
83
94.81807
4.195792
86.7
100.7
SWIIT_ret
82
0.0146025
0.03309
-0.0783483
0.094881
RUEDIF_ret
82
0.0152531
0.0346421
-0.0846643
0.1014157
WUPIXF_ret
74
0.0139985
0.0323943
-0.0673932
0.0791508
SWIIT: SXI Real Estate Funds Index
universe: all at the SIX Swiss Exchange listed real estate funds, which invest ¾ of the real estate
values in the Switzerland, currently 26 positions
RUEDIF: DB RUEDBLASS IF Index
universe: maximum 10 Swiss real estate funds
WUPIXF: Wüest & Partner AG Index für Immobilienfonds
universe: in Switzerland listed real estate funds, currently 24 positions
Exhibit 2: Summary Statistics Consumer Price index and Real Estate Indices
Source: Authors' Calculations
Variables E2
SWIIT_ret
RUEDIF_ret
WUPIXF_ret
-0.806
(0.643)
-1.001
(0.670)
-0.929
(0.658)
0.0158***
(0.00377)
0.0168***
(0.00394)
0.0152***
(0.00384)
Observations
82
82
74
JB chi2
JB Prob > chi2
0.1791
0.9143
0.7923
0. 6729
1.289
0.5248
BP chi2
BP Prob > chi2
0.02
0.8904
0.23
0.6345
0.37
0.5415
0.019
0.027
0.027
INF
Constant
R-squared
Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
JB: Jarque-Bera test for normality (H0: normality). We cannot reject the hypothesis that our returns
are normally distributed.
BP: Breusch-Pagan / Cook-Weisberg test for heteroscedasticity (H0: constant variance). We cannot
reject the hypothesis that our returns have a constant variance.
Exhibit 3: Regression Results E2
Source: Authors' Calculations
Variables E3
INF
INF_L1
-0.315***
Constant
0.00185***
Observations
(0.102)
(0.000602)
81
R-squared
0.108
Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
Exhibit 4: Regression Results E3
Source: Authors' Calculations
(2) Inferring expected inflation from past actual inflation rates
As a second approach, the expected inflation at time t is inferred
from the actual inflation at time t-1. This is done by a regression
analysis as presented in equation E3 following Hamelink and
Hoesli (1996: 36).
πt = α + β(πt-1) + εt
(E3)
The regression leads to the outcome illustrated in Exhibit 4.
The lagged inflation INF_L1 and the constant are both highly
significant and the R-squared of the regression is 0.108. The
negativity of the beta coefficient for INF_L1 is somewhat
surprising. This indicates that a higher inflation rate in the quarter
t-1 is likely to lead to a lower inflation rate in quarter t. Hamelink
and Hoesli (1996: 40) found a highly significant positive beta,
but for yearly data. In addition, bimonthly data for the US seems
to indicate that inflation normally has a positive autocorrelation
(Bils, Klenow, and Malin, 2012: 2806). The negative coefficient
found in our study may be due to the extraordinary economic
environment after the financial crisis or due to seasonal effects.
16
Quarter 2 • 2016
Alternative Investment Analyst Review
In particular, the monetary policy of the Swiss national bank
was very unusual as the national bank pegged the value of the
Swiss franc at the Euro (from September 2011 to January 2015).
It is also imaginable that autocorrelation of inflation behaves
differently for yearly or bimonthly than for quarterly data.
Using the estimates for πt from the above regression as expected
inflation, it is now possible to compute the values for unexpected
inflation (actual inflation minus expected inflation). After the
values for expected and unexpected inflation were obtained, the
regression as presented in equation E1 was conducted.
Rit = αi + βi(E(πt)) + γi(πt - E(πt)) + εit
(E1)
This regression leads to the following results on Exhibit 5.
According to the theory of Fisher (1930) presented earlier in this
paper, the beta coefficient for expected inflation should be one for
all assets. The present results cannot reject this. In addition, the
results of previous studies, that Swiss indirect real estate is not a
hedge against expected inflation cannot be rejected. However, the
sign is always positive, but the standard error of the coefficient is
very large, which makes it hard to infer anything from the beta
coefficient. We see no significant values as normal levels.
The gamma coefficient for unexpected inflation is always negative
(a “reverse” hedge), but only significant at the p < 0.1 level for
WUPIXF_ret. The hypothesis that Swiss real estate mutual funds
are a hedge against unexpected inflation can be rejected. Most
likely, Swiss indirect real estate does not provide a hedge against
unexpected inflation as suggested by previous studies.
In conclusion, it can be stated that Swiss real estate mutual
funds are not a hedge against inflation. It seems also to be very
reasonable to state that they do not provide any inflation hedge at
all.
Conclusion
Previous research suggested that no inflation hedging ability of
indirect real estate exists in Switzerland. This suggestion could not
be rejected by the research of the current paper, as all coefficients
Variables E1
EX_INF
UNEX_INF
Constant
Observations
JB chi2
JB Prob > chi2
BP chi2
BP Prob > chi2
R-squared
were not significantly different from zero. The relatively small
sample size caused large standard errors of the regression. The
current research could reject the hypotheses that Swiss indirect
real estate is a complete hedge against total inflation and / or a
complete hedge against unexpected inflation. Those results are
also in line with the results of previous research conducted for
Switzerland.
Several interesting questions in this research field remain still
unanswered. Although it is now a widely accepted fact that Swiss
real estate does, in contrast to foreign real estate, not provide
inflation hedging abilities, nobody has yet been able to establish a
theory why this is the case. A possible reason is the rigid tenancy
law for private residential purposes in Switzerland, which leads to
relatively fixed rents. In our study, we analyzed the hedging ability
with quarterly data. We found in the literature some evidence for
inflation hedging in the long run, which could be an indication
for longer lag structures in the data. And finally, the special
situation of Switzerland as a safe haven for investors in turbulent
markets has led in the last few years to extremely low interest and
inflation rates. And the “search for yield” has boosted the real
estate prices in recent years. This could have affected our results.
Endnotes
1.Indirect real estate investments are structured as mutual funds in
Switzerland. There’s no special legal structure like American REITs.
2.Direct real estate is heterogeneous, indivisible, and illiquid (Garay
and Stevenson 2009: 219). Indirect real estate investments are
suitable and appropriate for individual investors due to asset
diversification, divisibility, liquidity, and professional management
of the investment product.
3.An advantage of indirect real estate investments (REITs, mutual
funds) is the access to illiquid and indivisible assets for small
investors. A disadvantage is the listing on a stock exchange (or an
other public market). Real estate prices pick up some systematic risk
of that market. It is a less pure play in real estate (Anson, 2009: 69).
4.Therefore, some studies deal with time-lag structures of the return
and inflation data.
SWIIT_ret
0.617
(2.061)
-0.973
(0.716)
0.0139***
(0.00465)
RUEDIF_ret
0.189
(2.152)
-1.136
(0.747)
0.0152***
(0.00485)
WUPIXF_ret
1.687
(2.081)
-1.238*
(0.695)
0.0115**
(0.00477)
81
0. 3261
0. 8495
0.24
0. 6259
0.024
81
0.9916
0.6091
0. 31
0.5798
0.029
74
2.462
0.2919
0.22
0.6389
0.050
Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
JB: Jarque-Bera test for normality (H0: normality). We cannot reject the hypothesis that our returns are normally distributed.
BP: Breusch-Pagan / Cook-Weisberg test for heteroscedasticity (H0: constant variance). We cannot reject the hypothesis that our
returns have a constant variance.
Exhibit 5: Regression Results E1
Source: Authors' Calculations
17
Indirect Real Estate Investments in Switzerland
Research Review
5.There are other methodologies to test the relationship between real
estate returns and inflation. (1) Ganesan and Chiang (1998: 56)
discuss a simple comparison between these two variables. However,
such approaches are generally considered as oversimplified. (2)
Furthermore, the model of Fama and Schwert is criticized because
it does not reflect possible non-stationarity in the variables
(Goetzmann and Valaitis, 2006: 3). Therefore, researcher might
reject the tested hypotheses too often. To solve these problems
cointegration techniques have been developed. The logic behind
these approaches is that even if the real estate returns and inflation
rates themselves are non-stationary the linear combination of both
might be (Goetzmann and Valaitis, 2006: 3). If this is true the two
variables are cointegrated. The regression of those two variables
would therefore be meaningful (Ganesan and Chiang, 1998: 63). (3)
Wohlwend and Goller (2011) apply a short-term and a long-term
sensitivity measurement.
6.In Switzerland, the exchange price is often above the net asset value.
A positive agio are common for real estate mutual funds.
7.Examples are: Deutsche Bank Rüd Blass Immobilienfonds Indizes
(DBCHREE, DBCHREF); SXI Real Estate Indizes (REAL, REALX,
SWIIT, SWIIP, SREAL, SREALX); Wüest & Partner Indizes
(WUPIX-A, WUPIX-F).
Acknowledgments
We would like to thank the editor of the AIAR and two anonymous
referees for valuable comments and suggestions. It significantly improved
the quality of this article. We are indebted to Larissa Marti, Thomas
Ankenbrand and John Davidson for helpful and constructive comments.
We thank Avni Asani and Mathieu Chaignat for providing us with
literature.
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Authors' Bios
Roland Hofmann
Senior Lecturer
Banking & Finance
Zurich University of Applied Sciences
ZHAW
Winterthur (Switzerland)
Roland Hofmann serves as a senior lecturer
for Banking & Finance at Zurich University
of Applied Sciences ZHAW, Winterthur (Switzerland). He holds
certifications as a Chartered Alternative Investment Analyst
(CAIA) and as a Certified Financial Planner (CFP) and also a
Master of Science in Banking & Finance. He is a PhD candidate
at the University of Lucerne (Switzerland). His research interests
include financial planning, investment planning and public
economics. Before joining ZHAW, he worked in the financial
consulting department of a bank in Switzerland.
19
Indirect Real Estate Investments in Switzerland
Tobias Mathis
Graduate Student
Banking & Finance
Zurich University of Applied Sciences
ZHAW
Winterthur (Switzerland)
Tobias Mathis is a graduate student at the
Zurich University of Applied Sciences ZHAW,
Winterthur (Switzerland) studying towards a Master degree in
Banking & Finance. He holds a BSc in Business Administration
with a Specialization in Banking & Finance. Prior to joining the
Master program he interned and worked at various financial
institutions.
Featured Interview
Studying Financial Disruption: Bubbles and
Crashes - An Interview with Didier Sornette
Barbara J. Mack
CAIA Association
Overview
Interview
The history of the financial markets is
punctuated with extreme events, from the
Dutch Tulip Bubble of the 17th century to
the Global Financial Crisis of 2007-2009.
Didier Sornette, Professor and Chair of
Entrepreneurial Risks at ETH Zurich (the Swiss
Federal Institute of Technology) has devoted
over two decades to studying bubbles and
crashes, producing a book, Why Stock Markets
Crash: Critical Events in Complex Financial
Systems (Princeton University Press, 2003),
and numerous papers and articles. This short
interview covers some of the main themes of his
empirical research, the launch of the Financial
Crisis Observatory (FCO) at ETH Zurich,
and the development of the FCO Cockpit, a
project that analyzes a vast array of asset classes,
searching for evidence of bubbles or crashes in
early stages of their formation.
BJM: Your research on bubbles and crashes
dates back to the mid-1990s; what drew
you to these topics and what are your main
observations on such phenomena?
DS: The fundamental background is my
philosophy that in order to learn about a system
it is good to look at it out of equilibrium,
particularly when it is in an extreme state of
disequilibrium. Many of the systems that we
observe seem to be in balance most of the time,
but underneath their structures are tremendous
conflicting forces that essentially cancel each
other out. At the beginning of my scientific
life, it was just a conjecture that extreme
events could provide a fantastic opportunity to
decipher the hidden forces that are combatting
and counterbalancing each other and therefore
hiding the true nature of the system from the
investigator.
20
Quarter 2 • 2016
Alternative Investment Analyst Review
The work on financial bubbles and crashes also emerged from
an analogy comparing the rupture of the financial system and a
rupture of a material engineering structure. At the starting point
of our research, we saw similar tell signs involving a progressive
maturation towards instability that could be modeled similarly
in both contexts. Specifically, we found that the mathematical
language we developed for predicting the failure of key
engineering structures like the Ariane space rocket turned out to
be very flexible and convenient to apply to financial markets, and
to bubbles in particular. Since that initial observation, the systems
for analysis have become more complicated, because when you
dive into the specifics of the financial markets, you must go
beyond relatively superficial analogies. However, combined with
the scientific and social significance of these phenomena, this was
also part of our motivation and approach.
BJM: There have been numerous dramatic events around the
world over the past few decades, including the Crash of ’87,
the dot-com bubble, and regional crises of various types, so
how has studying these events guided the work up to the most
recent crisis?
DS: One of our group’s most important conceptual breakthroughs
has been to understand how the global financial crisis in 20072008 occurred and examine the way in which it is tied to the
evolution of the previous decades. The financial markets and
national economies are continuously punctuated by phases of
overheating. Some might call it over-enthusiasm, but actually it
is healthy enthusiasm, because this is the kernel of innovation:
taking risks and deploying capital to develop new ideas. This
leads to phases of engineering and advancement, but often the
system overreaches and then there is a correction. The typical
view on these dynamics is based, in part, on a misconception
about economics.
The GDP of the US, for example, is said to have grown at a
remarkably constant average of 2% per year from 1790 until now.
This is incredible, when you think about the vast technological
advances, shifting demographics, and major wars that have taken
place during this period. Nevertheless, there is an impression of
steady, consistent growth in spite of these dramatic changes in the
environment. However, when we look more closely at the figures,
we find that GDP growth of 2% per year is never happening.
Instead, we see a broad bimodal distribution with growth ranging
between 0-1% (with tails of negative spells associated with
recessions) on one hand, corresponding to an underperforming
economy or recession, and growth of 3, 4, or 5%, on the other
hand, which marks a boom period, hence the long-term average
of 2%, but that itself is not the norm.
In order to understand 2007-2008, we can look back as far as
the post war period; at the end of the Second World War, the
level of technical advancement due to the war effort, largely in
the US, but also in Germany and elsewhere, had spillovers with
extraordinarily good consequences in terms of productivity
growth for the next 30 years, in a period known as “Les Trentes
Glorieuses.” Then a significant change took place and after three
decades of real growth, in capacity and output, the economy
shifted to another regime, starting around 1980, which can be
described as the “Illusion of the Perpetual Money Machine.”
Since that time, two-thirds of the US “productivity” was based
in finance and entailed the rapid growth of credit, debt, and
21
Didier Sornette on Bubbles and Crashes
financialization. Early on, this new paradigm was interrupted
by the global crash of ’87. There was another break in 1991-2
and a larger disruption with the dot-com crash, in 2000-2001.
Finally we have the most recent bubble that formed in response
to the Fed’s interest rate policy and derivatives markets expansion
leading to the crisis of 2007-2008, and we have seen a number of
commodity bubbles as well.
During much of this period before the crisis of 2007-2008, GDP
appeared to be predictable and we generally saw mild volatility,
decreasing unemployment, and low inflation. However, while
people were toasting the “Great Moderation,” they were forgetting
to look at other signatures, i.e. the bubbles acting as the canary
in the financial coal mine, which were telling us that this growth
was not obtained from real productivity growth and would not
be sustainable. So in spite of beliefs to the contrary, the events
of 2007-2008 are not a surprise – in fact, the crisis can be seen
as the culmination of 30 years of relying on indebtedness, credit
creation, and financialization – not real value and productivity
gains.
BJM: When you mention the waves of creation and destruction
– Schumpeter came to mind and this type of cycle seems more
natural than the idea of an endless period of uninterrupted
growth.
DS: Yes, exactly, the point is that during the 25-year story – the
belief was that we could have strong growth and no volatility.
This is a complete misconception. And yet in spite of the crashes,
some bubbles are very beneficial in the longer term. The dot-com
bubble produced a lot of hype and investors lost a great deal of
money, but it also produced a massive amount of human capital,
well educated and experienced young people who were relatively
cheap to employ and ready to develop the next boom that we see
in Google, Facebook, Amazon, and many others. Such social or
tech bubbles create opportunities because they result in creation
of excess capacity, in fiber optics and bandwidth, for example;
once it is installed it will certainly be reused and enables the next
wave of creation. The history of railroads in the UK and the US
in the mid to late 1800s is a similar situation. It is an extreme
version of Schumpeter – bubbles and crashes can have benefits,
but it may take several decades to obtain the return on the
investment, not a few years, which is so often the expectation.
BJM: What is happening with the Financial Crisis Observatory
and the FCO Cockpit reports?
DS: We are interested in developing experiments in finance
just as we are able to do in scientific labs, so we came up with a
methodology for the work of the FCO, started in 2009, which has
integrity and security built in to the observation and reporting
processes. We were watching for the most evident bubbles,
documenting the cases, putting the written work aside for six
months, sealed and encrypted, and publishing the public key
immediately, so that six months later, everyone would be able to
check that the document was legitimate and see how accurate it
was. We used the best encryption technology of the time and this
went very well.
We ran the analytical experiment for two years and then moved
on to actual trading through an Interactive Broker account with
about $100,000 CHF, so now we were testing it in real time and
introducing the operational aspects: risks, transaction costs,
Featured Interview
slippage – all of the practical details. We ran the investment
experiment for one year, (still as an academic project)–and we
did very well. This confirmed to us that there is predictability
in the markets and it is possible to create diagnostics that watch
for turning points successfully. In order to make this feasible
for active investment, it takes a substantial amount of work; our
best performance occurred when we had two dedicated senior
researchers working full time – like real traders. Even so, this
demonstrated that there is something to our analysis in real life.
Since then we have been publishing the FCO cockpit, which is
improving over time.
We have a quadrant to classify the universe of assets in a positive
bubble-negative bubble, high valuation-low valuation framework
and we are running a portfolio on paper to assess the value of
this scheme with back tests. In the future, we will publish it as an
index for investors.
On a daily basis for the public, we offer fresh bubble indictors for
the major markets - indices, commodities, bonds, and so forth,
but right now we are only showing 40-50 assets that people can
watch and experiment on. In our own research, we are watching
25,000 assets every day, so there is much more in the works for
the future.
BJM: Turning to ICBI, you will be speaking about the FCO
there in your talk, “Diagnostic Forecasting of Future Bubbles,
Crashes, and Crises.”
DS: Yes, a part of it will be a diagnostic of the present time, so
we will run the cockpit and present a state of the world – where
are the bubbles and the opportunities. My first paper on bubbles
was published in 1996, so we are celebrating the 20th anniversary
and all that we have developed in my group over the past 20
years. Bubbles and crashes are extremely interesting and complex
phenomena and are deeply connected with policy, regulation,
politics, beliefs, and culture – so they have many facets and we
have developed a number of exciting models that offer new ways
of understanding them – with recent improvements towards more
mathematical rigor and generality while keeping a fundamental
anchor in finance.
Bio
Didier Sornette
Professor
ETH Zurich
Swiss Federal Institute of Technology
Swiss Finance Institute
Didier Sornette is professor of
Entrepreneurial Risks in the department of
Management, Technology, and Economics
at the Swiss Federal Institute of Technology (ETH Zurich),
a professor of finance at the Swiss Finance Institute, and
an associate member of the department of Physics and the
department of Earth Sciences at ETH Zurich.
He uses rigorous data-driven mathematical statistical analysis
combined with nonlinear multi-variable dynamical models,
including positive and negative feedbacks to study the
predictability and control of crises and extreme events in complex
systems. This methodology has applications to financial bubbles
and crashes, earthquake physics and geophysics, the dynamics of
success on social networks, and the complex system approach to
medicine (immune systems, and epilepsy, for example) all leading
towards the diagnostics of systemic instabilities.
Didier has authored numerous papers and articles on the topics of
bubbles, crashes, financial markets, and analytical methodologies.
In 2003, he published Why Stock Markets Crash: Critical Events
in Complex Financial Systems (Princeton University Press: NJ).
In 2008, he launched the Financial Crisis Observatory at ETH
Zurich to test the hypothesis that financial bubbles can be
diagnosed in real-time and their termination can be predicted
probabilistically.
Thinking specifically about the Global Derivatives conference
in May, this field is dominated by financial mathematics and
engineering and yet we do not have many relevant models for
bubbles and crashes. There is enormous work to be done and
I am happy to offer an approach to the challenge in a solid
axiomatic way, rooted in extensive empirical works.
Links
Why Stock Markets Crash: Critical Events in Complex Financial
Systems, by Didier Sornette (Princeton University Press, 2003)
http://press.princeton.edu/titles/7341.html
Didier Sornette TED Talk: https://www.youtube.com/watch?v=C_
eFjLZqXt8
ETH Zurich Chair of Entrepreneurial Risks – Financial Crisis
Observatory
http://www.er.ethz.ch/financial-crisis-observatory.html
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Alternative Investment Analyst Review
Investment Strategies
The Ins and Outs of Investing in
Illiquid Assets
Thijs Markwat
Quant Researcher
Robeco Investment Research
Roderick Molenaar
Director and Portfolio Strategy
Researcher
Robeco Investment Research
23
Introduction
The commonly held view is that investors
should be able to harvest a liquidity premium
from illiquid investments. We look into the
fundamentals on which this view is based.
Investments in illiquid asset classes have
become more common in recent decades.
According to Ang (2014) the share of illiquid
asset classes held by pension funds has risen
from 5% in 1995 to 20% in 2011. There are a
number of reasons for this increasing popularity
including the perception that (expected) returns
are higher as well as that they offer greater
diversification potential. It is however not
always clear what the required extra return or
diversification benefits for the illiquidity should
be. Additionally there are numerous reasons for
illiquidity each with their own challenges.
The most widely known illiquid investments
are probably hedge funds, real estate, private
equity and infrastructure. However, examples
can also be found in more liquid markets. For
The Ins and Outs of Investing in Illiquid Assets
instance, on-the-run (newly issued) bonds
are found to be more liquid than off-the-run
(older) bonds with similar characteristics and
the same remaining maturity. When a certain
asset is illiquid it is usually more difficult to
find counterparties to trade with at a reasonable
price. Therefore, the costs associated with
transactions in illiquid assets can become
large. For some assets, legal impediments make
it sometimes impossible to trade in a timely
manner at all.
Investing in illiquid assets introduces additional
risks. Probably the best known example of a
situation where large positions in illiquid assets
caused significant problems is the Harvard
University endowment case (see Ang 2014).
After a prolonged period of good performance,
during the turmoil in 2008 the endowment’s
illiquid asset investments suffered heavy losses.
The liquid part of the portfolio had become too
small to meet the running expenses. In need
of cash, the Harvard endowment tried to sell
some private equity investments. Although
Investment Strategies
this was possible they faced having to sell at 50% discounts in the
secondary market.1 All in all, the Harvard case showed the world
the dark side of having a large part of a portfolio invested in very
illiquid assets.
In this note we will discuss theoretical and empirical findings on
investing in illiquid assets. We look at why investors in illiquid
assets should be compensated with higher returns (liquidity
premium) than those on comparable liquid assets and whether
this is actually the case. Moreover, we comment on the possible
diversification benefits of investing in illiquid assets and address
some of the associated problems and risks. Do the pros of
investing in illiquid assets outweigh the cons?2
Sources of Illiquidity
Different assets have different liquidity characteristics. There are
many explanations why some assets are more illiquid than others.
There are many effects that determine an asset’s liquidity. Some
assets like public equity can be traded within seconds, while
municipal bonds may trade as little as twice a year and the
average holding period for institutional real estate is a decade.
The academic literature on liquidity related topics is extensive.
Amihud, Mendelson and Pedersen (2005), Khandani and Lo
(2011) and Vayanos and Wang (2012), among others, summarize
and describe various sources of illiquidity.
Liquidity can be defined as the ease of trading a security. The
liquidity of certain assets will be impacted by different factors (see
e.g. Amihud et al. 2005) such as:
i. Exogenous transaction costs
ii. Demand pressure and inventory risk
iii.Private information
iv. Search frictions
Exogenous transaction costs are the most straightforward source
and characteristic of illiquidity. These are the fixed costs that
need to be made to process the trade. For institutional investors
trading in public large cap equities these costs will be small, as
all transactions are processed electronically in a highly regulated
central market. However, for investments in certain alternative
asset classes these costs can become substantial, as sometimes
lawyers and solicitors need to be involved in the process. Higher
costs make trading more expensive and thus the ease of trading
is thereby reduced. In equilibrium, more liquid assets (i.e. assets
with lower transaction costs) are held by investors who trade
more frequently, while those assets that are more expensive to
trade are held by investors with low trading frequency (see e.g. De
Jong and Driessen 2013) .
Demand pressure and inventory risk are another source of
illiquidity. When an investor wants to sell an amount of stock,
there may not necessarily be any buyers. In many markets, a
market maker will then buy the asset from the investor, but
will also require compensation for the risks that he faces due to
warehousing the stock.
Private information is also a potential cause of illiquidity (see for
instance Vayanos and Wang, 2012). If some traders have different
information to others, one party may enter a bad deal. This was
first described by Akerlof (1970) as the market for lemons.3 A
buyer faces the risk that the seller has private information that the
stock is expected to perform badly in the future. To compensate
for the possibility of entering into a bad deal with an informed
seller he therefore gives a bid price that is below the asset’s fair
value. A seller who might be dealing with an informed buyer on
the other hand will quote a higher ask price. In regulated markets
this leads to the well-known bid ask spreads. This phenomenon
makes investors more hesitant to trade, leading to illiquidity.
Search friction is yet another source of illiquidity, as the lack of
a centralized market may result in long waiting times before a
counterparty can be found. In addition to the waiting period, the
transaction price needs to be negotiated and the bid-ask spread
may be very wide if there is little competition in these markets.
This type of transaction may also be hampered by costs such as
due-diligence and lawyer fees etc.
The sources of illiquidity outlined above have some overlap and
might reinforce each other. The inventory risk for instance might
have a larger impact if informed traders are involved. In markets
where search frictions play an important role transaction costs are
usually also higher. In addition, the above sources of illiquidity
often also have a larger impact when the traded volume increases.
While some assets can easily be traded in small quantities, it
might be difficult or impossible to trade them in larger quantities
due for instance to the price impact this will have.
Brunnermeier and Pederson (2009) and Driessen (2014) identify
two types of liquidity: funding and market liquidity. They relate
funding liquidity to the costs of generating cash, for example, to
fulfil the demand for cash flows that can originate from currency
or interest rate hedging positions for institutional investors.
Market liquidity is related to the costs relating to transactions
in both liquid and illiquid assets. The two types of illiquidity are
positively related. In this study we will mainly focus on market
liquidity, and refer readers interested in more information to the
two articles cited above.
In this chapter we have shown that there are different effects
at play that could result in one asset being more illiquid than
another. We have elaborated on four main potential sources
of illiquidity found in the academic literature (see for instance
Amihud et al. 2005). The next chapter explains why investing in
an illiquid asset theoretically should be rewarded
How is Illiquidity Reflected in (Expected) Returns
Investors should demand an extra reward for holding illiquid assets.
This reward should at least compensate for the extra costs that the
investor incurs.
This section uses two examples to look at a possible explanation
of why illiquidity should result in an extra reward. All else being
equal, it would be fair to assume that an investor would always
prefer a liquid investment to an illiquid one. So why do some
institutional investors make investments, sometimes in large
volumes, in illiquid assets? The answer to this question is related
to the fact that they might receive a reward for holding these less
liquid investments. This reward is usually called the “liquidity
premium” and its existence has been a lively subject of debate
between practitioners and academics. Possible diversification
benefits are also an argument for investing in illiquid assets.
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Alternative Investment Analyst Review
However, this chapter focusses on reward in terms of expected
return. In the next section we will discuss several components
of the liquidity premium as we analyze the division between
liquidity level and liquidity risk. Diversification benefits will be
examined in a later chapter. Next we discuss two studies which
explain why a liquidity premium could emerge.
The short horizon investors have no interest in the illiquid
asset, as its transaction costs are too high, so they all hold the
liquid asset. As the assets are risk-free, the gross return should
be the risk-free rate plus a compensation for the trading costs.
This equals 2% (risk-free) + 1% (trading cost) = 3%, which is
summarized in column 2 of Exhibit 1.
Study 1: De Jong and Driessen (2013)
If the long horizon investors decide to hold the liquid asset for 10
years they would also earn the gross return of 3%. However, the
yearly trading cost is now 0.1% instead of 1%, as the long horizon
investors only trade once every ten years. Therefore, they earn a
yearly net return of 2.9% (=3% gross return – 0.1% transaction
costs).
First we analyze a theory on why illiquidity could result in a
liquidity premium. When investors expected trading horizons
differ, for instance short horizon investors versus long horizon
investors, the market is what is known as ‘segmented’. The theory
predicts that this segmentation gives rise to a liquidity premium
over and above the expected transaction costs. It predicts that the
required liquidity premium increases with the expected holding
period.⁴ We use a simplified numerical example from De Jong and
Driessen (2013) to illustrate this theory. The model consists of the
following settings and assumptions:
• short term investors with a 1 year investment horizon
• long term investors with a 10 year investment horizon
• liquid asset with normal transaction costs equal to 1%
• illiquid asset with high transaction costs equal to 5%
• both assets are risk-free
• the risk-free rate is set equal to 2%
The illiquid asset needs to generate at least the same net return of
2.9% if long term investors are going to be encouraged to invest.
The annualized transaction costs for the illiquid asset are equal
to 0.5% (=1/10 * 5%). Thus, the gross return on the illiquid asset
should be at least 3.4% to obtain the aforementioned net return of
2.9%.⁵ Even in a stylized model like this we observe that illiquid
investments must offer long term investors a liquidity premium
(in the gross return) in order to remain on par with liquid
investments.
Study 2: Ang, Papanikolaou and Westerfield (2014)
Another example of why a liquidity premium can exist is found
in Ang, Papanikolaou and Westerfield (2014)⁶. They consider
using a highly stylized model with an investor who consumes a
certain amount of wealth and invests the rest in liquid and illiquid
Short Term
Long term
Liquid
Liquid
Illiquid
Transaction Costs
1%
0.1%
0.5%
Gross Return
3%
3%
3.4%
Net Return
2%
2.9%
2.9%
Exhibit 1: Example of the Liquidity Premium Derived from Segmentation Theory
Source: Authors' Calculations
Expected period over which the
asset cannot be traded
Required liquidity premium
(Yearly)
10 years
6.0%
5 years
4.3%
2 years
2.0%
1 year
0.9%
1/2 year
0.7%
Always tradeable
0.0%
Exhibit 2: Required Annual Liquidity Premium for Various Horizons
Source: Ang (2014)
25
The Ins and Outs of Investing in Illiquid Assets
Investment Strategies
assets. The illiquid asset can only be traded (converted into
liquid wealth) at random times. The more wealth that is invested
in the illiquid asset the greater the probability that at a certain
time the investor will not have enough liquid wealth to consume
(“probability of having nothing to eat”). Therefore, the investor
requires compensation for holding the illiquid asset.
Exhibit 2 shows this compensation derived under the specific
model assumptions, which is denoted as the required liquidity
premium. This is the premium the investor requires as
compensation for not being able to trade for an expected period
of time. The table clearly shows that investors require large
premiums for holding an illiquid asset instead of a liquid asset.
For holding periods of around 5 years, which is also the average
holding period for private equity investments⁷, the net required
compensation is already over 4%.
It is important to note that these numbers result from specific
model assumptions. The example above should only be viewed as
an illustration as to why a liquidity premium should exist. More
refined estimates of liquidity premiums could be quite different
depending on the investment fund in question. This model for
instance assumes that an investor has no intermediate income. For
a very grey pension fund, which receives almost no contributions,
the required premiums could be of the same order of magnitude
as in Exhibit 2. However, for a younger pension fund with regular
contributions the required premiums will be lower than the ones
reported in this example.⁸
Final Remarks on How Investors are Rewarded for Bearing
Illiquidity
Brunnermeier and Pederson (2009), who distinguish between
market and funding liquidity, observe that there is no guarantee
that there will be a substantial liquidity premium. It will depend
on a number of factors including the level of illiquidity, the type
of investor (e.g. long-term vs short-term investors) as well as time.
In times of crisis, for example, both liquidity premiums will be
higher.
Longstaff (2014) analyses the valuation of thinly-traded assets
such as private equity and commercial real estate using an
American option approach. He finds that the value of immediacy
(i.e. the ability to sell immediately) is much higher than that of
future liquidity; the value of the first day of illiquidity is much
higher than that of the second day. Liquidity today is more
valuable than liquidity tomorrow or next week. He confirms the
findings of other studies that the value of illiquid assets can be
heavily discounted in the market; the discount can be as high as
30% for an illiquidity period of 5 years. Finally he finds that the
effects of illiquidity on asset prices are smaller for assets that pay
higher dividends.
Vayanos and Wang (2012) analyze how asymmetric information
and imperfect competition⁹ can affect liquidity and expected
returns. They show that expected returns are higher when
information is not spread evenly between all market participants
compared to those situations where information is widely known
or when the private information is not observed. They identify
two partly overlapping measures of illiquidity. The first one is
related to transaction volume and is based on the idea that trades
in illiquid markets usually have a large price impact. This measure
can be seen as the more permanent component of the price effect.
The other measure is related to the transitory component which
is driven by the fact that trades in illiquid markets can result
in large temporary deviations between the asset’s price and its
fundamental value. Moreover, they show that the relationship
between liquidity or lack of it and expected returns is not always
positive. It depends on several factors including the source of the
illiquidity (asymmetric information or imperfect competition)
and the measure of illiquidity. If the illiquidity is driven by
imperfect competition the relationship can become negative.
In general the liquidity premium is a compensation for not being
able to trade at a fair price at any given time. It is the interplay
between many variables that determine the exact ex-ante reward
required for bearing illiquidity risk. Although it is hard to derive
the exact size of a liquidity premium the academic literature
seems to agree that a liquidity premium should theoretically exist.
Liquidity Level and Liquidity Risk Premium
There are actually two types of liquidity premiums. First,
a compensation for average illiquidity itself and second a
compensation for the risk of illiquidity.
This split is for instance found in Khandani and Lo (2011), who
divide the literature on the impact of liquidity on asset prices into
two groups. The first group (liquidity level) focuses on liquidity
as a deterministic characteristic of securities in the same way
that transaction costs are. As investors prefer liquid assets to
illiquid ones, all other things being equal, they will want to be
compensated for holding an asset with low liquidity. Moreover,
higher costs translate into higher gross expected returns for those
assets. This premium should at least be sufficient compensation
for the illiquid asset’s transaction costs, but may extend beyond
that, as seen in the previous section. The premium resulting from
the liquidity level of an asset is called the liquidity level premium.
Secondly, the liquidity risk premium is a compensation for
holding assets that perform poorly when there is a systematic
liquidity shock.10 This premium should be regarded as a
systematic factor premium. Economic theory predicts that assets
that have their lowest returns when the global financial markets
encounter bad times should offer some compensation with
respect to other assets.11 Times of scarce liquidity can also fairly
be categorized as bad times (see e.g. Brunnermeier and Pedersen
2009). Assets that perform badly during these periods should
offer a liquidity premium, otherwise investors have no incentive
to hold them. In this case liquidity is regarded as a risk factor.
Acharya and Pedersen (2005) show that in most cases there will
be a positive relation between both premiums, which makes it
difficult to attribute the premium to either the liquidity level or
the liquidity risk effect. As Lou and Sadka (2011) observe, the
liquidity level can be considered as the mean effect, whereas
the liquidity risk is related to the volatility effect. In addition,
Khandani and Lo (2011) state that even though the two
approaches have an overlap, their effects on empirical analyses can
be quite different. They state that this could explain why there is
little consensus on how to measure liquidity risk.
Particularly for these reasons we will look mostly at the total
liquidity premium, although we believe that the distinction
outlined above is important for understanding why liquidity
premiums exist. However, depending on specific investor
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Alternative Investment Analyst Review
preferences, there may be more focus on one of the individual
premiums rather than on the total liquidity premium.
Time Variation in Illiquidity and the Liquidity Risk Premium
The previous paragraph explained that the exposure to aggregate
liquidity is regarded as a specific risk for which a premium is
demanded: the liquidity risk premium. This premium is thus
closely related to the time variation in illiquidity. Therefore, it is
also important to understand how liquidity varies over time.
In tranquil times liquidity might be abundant, while in times of
crisis it is often very scarce.12 For instance, during the credit crisis
even the usually very liquid money markets became illiquid (see
e.g. Hanson, Scharfstein and Sunderam 2014). In the same period
the liquidity of corporate bonds decreased dramatically which
resulted in much higher transaction costs. Exhibit 3 shows the
Barclays liquidity cost score13, which shows how expensive it was
to trade US high yield bonds and credits during the crisis.
Exhibit 3: Barclays Liquidity Cost Score (LCS)
Source: Barclays POINT
Exhibit 4: Changes in the Monthly Liquidity Factor
Source: https://www2.bc.edu/~sadka/
27
The Ins and Outs of Investing in Illiquid Assets
Assets that have a strong liquidity risk exposure will be vulnerable
to systematic shocks in aggregate liquidity (see Exhibit 4). A
systematic liquidity shock here refers to the situation where the
liquidity in global asset markets suddenly dries up. Thus, when
a systematic liquidity shock happens, prices of these assets will
plunge. Expected returns on these assets should therefore be
higher. Exhibit 4 shows the monthly liquidity factor from Sadka
(2014). He analyses whether the liquidity risk, defined as the
exposure to the shocks in the liquidity factor shown in Exhibit
4, is a priced factor premium. He shows that even within the
universe of liquid indices a higher exposure to the liquidity risk
factor resulted in a higher (although not statistically significant)
return over the period 1994-2010. Jensen and Moorman (2010)
find that aggregate liquidity improves during expansive periods
in monetary policy and deteriorates during restrictive periods.
The prices of illiquid stocks increase relative to those of the more
liquid stocks during periods of monetary expansion.
Investment Strategies
The time variation in liquidity suggests that long horizon
investors might pick up liquidity premiums when these are at
their highest (i.e. after systematic decreases in liquidity). De Jong
and Driessen (2013) review the literature on dynamic trading
strategies based on liquidity. They find that the exact timing of
these liquidity events is difficult to predict and it is therefore
hard to reap liquidity premiums using dynamic strategies. Ang
(2014) states that rebalancing is the easiest way to earn liquidity
premiums as this can be interpreted as providing liquidity
to the markets. Rebalancing supplies liquidity because it is a
countercyclical strategy. More specifically, the investor is actually
selling assets when others want to buy at high prices and buying
when prices are low and others want to sell, which generates
liquidity. He states that an investor is thus rewarded for behaving
in a contrarian way and providing this liquidity. It is important to
also rebalance the illiquid assets when possible. It should be noted
that these strict rebalancing rules are part of the strategic asset
allocation decision.
Asset Allocation
Although it might be possible to earn liquidity premiums, one
should also take into account the risk characteristics of the
investments involved. Investing in illiquid assets can be risky as
illiquidity is usually most prevalent when liquidity is most needed.
Investors can opt to allocate to illiquid assets for various reasons.
In addition to the liquidity premium, investments in illiquid
assets can also be selected because of the possible diversification
opportunities. In the previous sections we have discussed the
theoretical existence of liquidity premiums. In this section we
will discuss some other important elements that need to be taken
into account when investing in illiquid assets. These relate to the
diversification opportunities and the consequences of not being
able to adjust holdings in illiquid assets at times when rebalancing
is required.
Diversification
Another reason for investing in illiquid rather than liquid
assets, apart from the higher return expectations, could be the
diversification offered through exposure to specific underlying
return factors which are yet not available in liquid markets
(infrastructure projects which invest in e.g. inflation generating
projects).
A large part of these diversification opportunities are a direct
result of appraisal based valuations. Exhibit 5 shows the
cumulative returns for listed as well as non-listed real estate.
Although the underlying assets are in theory comparable, the
return patterns differ substantially. Investing in non-listed real
estate would have had a less negative impact on a portfolio’s
performance in 2008-2009 than an investment in listed real estate.
This is because the shocks in non-listed real estate are included in
the prices with a delay. From our point of view this diversification
is therefore mainly artificial, as it can largely be explained as a
consequence of accounting practices. This results in apparently
lower volatilities. In practice it is not possible to trade on the
appraisal based valuation as the Harvard University’s endowment
case has shown. When comparing public listed stocks to private
equity for instance we also expect to see a comparable lag in
returns caused by appraisal based valuation.
Asset Allocation Models
Ang et al. (2014) develop an asset allocation model which takes
illiquidity into account. Their main results are based on a scenario
where an investor consumes a certain amount of their wealth
in each period. The universe consists of three assets: a risk-free
bond, a liquid and an illiquid risky asset. They analyze how
much should be invested in the illiquid risky asset according to
the different levels of illiquidity of this asset. The remaining, liquid
wealth is allocated to the risk-free bond and the liquid risky asset.
The investor consumes out of this liquid wealth. The analysis is
performed for an investor with average risk aversion.
Exhibit 5: Cumulative Returns for US listed (NAREIT) and Non-listed (NCREIF) Real Estate
Source: NCREIF, DataStream
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Expected period over which the asset
cannot be traded
Optimal allocation with consumption
Optimal allocation without
consumption
10 years
4.8%
51.8%
4 years
13.2%
52.0%
2 years
25.1%
52.3%
1 year
37.3%
52.7%
1/2 year
44.2%
53.5%
Always tradeable
59.3%
59.3%
Exhibit 6: Optimal Holdings of Illiquid Assets
Source: Ang et al. (2014)
Exhibit 6 reports the optimal holding of illiquid assets in their
model for different expected periods during which the asset
cannot be traded. Optimal holdings in the illiquid asset are
shown in column 2. The optimal holding in the illiquid risky
asset in case of intermediate consumption sharply decreases as its
illiquidity rises. For instance, the investor’s holding in the risky
asset would decrease from 59% in case of full liquidity to 37% if
the risky asset can only be traded once a year on average. If the
risky asset can only be traded once every four years, which is close
to the average effective duration of private equity and direct real
estate investments, for example, they find an allocation of 13% to
be optimal. Column 3 shows that the results depend largely on
whether or not intermediate consumption is taken into account. If
this is not the case the effect of illiquidity is much smaller. This is
because if there is no need for immediate liquidity in intermediate
periods, there is less risk attached to not being able to access the
funds. The results in Exhibit 6 without intermediate consumption
(i.e. column 3) are in line with the results of Driessen (2014). His
study looks at terminal wealth after 10 years and does not take
into account the intermediate pension payments that need to be
made. In addition, he also assumes that illiquid assets cannot be
traded during the period under consideration.
asset may deviate from the strategic portfolio (see e.g. Driessen
2014). Siegel (2008) shows that in certain economic scenarios, the
share of illiquid assets in the portfolios of institutional investors
can become undesirably high. An institutional investor with large
positions in direct real estate, hedge funds, infrastructure and
private equity could then end up with a very unbalanced portfolio.
In the worst case scenario the fund might not even have enough
liquid assets to be able to pay out their obligations (e.g. pensions).
This is exactly what happened in the Harvard endowment case
we described in the introduction.14 Moreover, the deviation from
the strategic portfolio can be costly as the new portfolio might
have sub-optimal risk-return characteristics. To compensate for
this sub-optimal profile an investor should demand a liquidity
premium. Note that the risks also depend on the demography of
the fund: a young fund with large contributions could take on
more liquidity risk than an old fund with mostly retirees.
Empirical Evidence
The existence and size of liquidity premiums is difficult to determine
due to the subjectivity of illiquidity definitions and data issues.
The results in Exhibit 6 without intermediate consumption and
the findings of Driessen (2014) might underestimate the true
impact of illiquidity as many funds do need immediate liquidity.
On the other hand, the results in Exhibit 6 with consumption
might overestimate the effect of illiquidity because funds in
general receive regular inflows. Therefore, the optimal holdings
in illiquid assets will depend on the balance between inflows and
outflows and will probably somewhere between column 2 and 3 of
Exhibit 6.
Although theory predicts an ex-ante liquidity premium, in this
section we look at whether this is also the case in practice. There
is extensive academic literature that empirically investigates the
existence of liquidity premiums and there seems to be some
empirical evidence that such premiums exist (see Amihud et al.,
2005, Ang 2014 and De Jong and Driessen 2013, for a summary
of the literature). This evidence is however mixed in the sense that
it is only found in certain markets and it depends on the liquidity
measure used. In order to keep this report short, we are not going
to discuss every liquidity premium found, but rather show some
examples of these premiums to clarify the findings of both studies.
Considerations for Allocation to Illiquid Assets
Government Bonds
The above implies the importance of the specific setting in which
the effect of illiquidity is analyzed. Therefore, it is important to
know the liquidity risks the investors face. Pension funds, for
instance, might have an additional source of liquidity risk when
they hedge their interest rate or currency risk mostly using
derivatives. If interest rates will start to rise sharply from the
current low levels, losses on the swap positions might have to be
financed if there are not sufficient assets to serve as collateral. In
this case, liquid assets are needed. As illiquid assets cannot be
rebalanced during a given time period, the position in the illiquid
29
Within fixed income, the yield on government guaranteed agency
bonds can be substantially higher than the yield on otherwise
comparable government bonds, while the (default) risk is the
same because the agency bond is backed by the same government.
As the default risk is the same, the yield difference should be
a result of differences in liquidity only. Government bonds are
generally regarded as more liquid as they are more widely traded,
serve as eligible collateral for many derivative transactions and
offer relatively easy access to cash via the repo market. According
to Longstaff (2004) and Schwarz (2010) yield differences between
The Ins and Outs of Investing in Illiquid Assets
Investment Strategies
these agency bonds and government bonds are usually around
20 to 30 bp. Ejsing, Grothe and Grothe (2012) find that during
crisis periods the agency-treasury bond spreads could widen to 80
bp. Another liquidity premium in this market is found between
on-the-run (newly issued) and off-the-run (older) bonds with the
same remaining maturity and similar characteristics. The newly
issued bonds are usually more liquid and carry a lower yield. The
yield difference is however small and short selling the on-therun bonds and buying the off-the-run bonds is not a profitable
arbitrage strategy due to the shorting costs (see Amihud et al.
2005, and, Krishnamurty, 2002). Finally there is an indication of a
liquidity premium for inflation linked bonds (TIPS) too, although
the premium seems too high to be solely due to liquidity effects
(see e.g. Fleckenstein, Longstaff and Lustig 2014).
Corporate bonds
Within the corporate bond world there is evidence to suggest
that bonds that are less liquid often have a higher return. DickNielsen, Feldhutter and Lando (2012) show that the liquidity level
premium before the financial crisis was 4 bp for investment grade
and 58 bp for high yield. After the crisis these premiums were
found to be 40 to 90 bp for investment grade and 200 basis points
for high yield. Bongaerts, De Jong, and Driessen (2011) take
into account both liquidity level and liquidity risk and find that
substantial liquidity premiums were already present before the
crisis. They report premiums up to 100 bp for investment grade
bonds and up to 200 bp for high yield bonds. The largest part of
total liquidity premium in this market, comes from the liquidity
level premium rather than the liquidity risk premium. This
liquidity premium in corporate bond markets varies considerably
over time, and there may be significant differences in bull and
bear markets. In general it is however not easy to distinguish
between the different premiums (for default and liquidity risks,
for example).
Public Equity
In equity markets, stocks with low liquidity levels appear to
earn higher returns than liquid stocks. It is noteworthy that this
group of more illiquid stocks also comprises microcap stocks.
For instance, Brennan and Subrahmanyam (1996) find that low
liquidity stocks outperform high liquidity stocks by 6.6% per year.
In a more recent study, Acharya and Pedersen (2005) find this
premium in equities to be 3.5%. These premiums are observed
over a longer time span, but have diminished in the recent past
according to Ben-Rephael, Kadan and Wohl (2015). Lou and
Sadka (2011) show that liquidity risk rather than liquidity level
can help explain the cross section of equity returns during the
crisis in 2008; some liquid stocks had larger drawdowns during
this period than the more illiquid stocks with lower exposure to
liquidity risks. Acharya and Pedersen (2005) find the liquidity
risk premium to be 1.1%. This total liquidity premium on equities
according to Acharya and Pedersen (2005) is thus 4.6% (3.5%
level +1.1% risk).
Premiums Within Illiquid Asset Classes
There also seems to be some evidence that illiquidity (for instance
longer lock-up periods) results in higher returns for private equity
(Franzoni, Nowak and Phalippou 2012), hedge funds (Khandani
and Lo 2011) and real estate (Liu and Qian 2012). With respect to
hedge funds, Khandani and Lo (2011) show that the risk adjusted
liquidity premiums for illiquid categories such as convertible
arbitrage were sometimes as high as 10% per year in the period
1986-2006.15 Even more liquid strategies such as managed futures
have premiums of 5%. It is however somewhat puzzling that the
risk adjusted liquidity premium for global macro funds is almost
-6% (although the premium is not statistically significant). There
is not much evidence for a liquidity premium for equity market
neutral funds. Over the period 2002-2006 the premiums have
declined significantly for a number of reasons including lower
volatility for the major asset classes and greater demand for hedge
funds.
Premiums Across Illiquid Asset Classes
It is however much harder to find conclusive research evidence for
the existence of liquidity premiums particularly across alternative
asset classes. This might sound surprising as they are ‘known’
for their high returns. Ilmanen (2011) for instance relates the
average returns of a set of both liquid and illiquid asset classes
to a (subjective) illiquidity measure. As can be seen in Exhibit 7
there seems to be a relation between the average return and the
illiquidity measure. Ilmanen (2011) notes however that the return
differences can also be due to exposures to risk factors which are
not related to liquidity. Also the various biases in the databases of
especially illiquid assets can explain part of the return differences.
Research on returns of illiquid asset classes is hampered by lack
of good quality data. Ang (2014) gives a clear description of
these data issues. This largely explains why it is so difficult to find
conclusive evidence on whether liquidity premiums exist or not
(see also De Jong and Driessen 2013) in these asset classes. For
instance, there is a large ongoing debate on whether private equity
outperforms risk-adjusted public equity or not (see Driessen et al.
2012).
Ang (2014) gives two possible explanations why liquidity
premiums are found within asset classes but not between them.
The first reason could be limited integration of asset classes
where investors tend to look at asset classes individually rather
than together as one group. This might result in imperfections
for the market as a whole, which could lead to mispricing from
the perspective of a completely integrated market. It is difficult
to distinguish between price differences based on illiquidity
and price differences caused by mispricing due to institutional
constraints or slow-moving capital.
Secondly, Ang (2014) poses that investors may simply pay too
much for illiquid assets in their desire to achieve higher returns.
Prices are then bid up high enough to substantially reduce the
liquidity premium that should theoretically exist.
Manager Selection
Finally an important element of investing in illiquid assets is the
manager selection. Exhibit 8 shows that the dispersion between
managers is much higher for investments in hedge funds than
for investments in listed equities (see also e.g. Malkiel and Saha
2005). Due to the high dispersion and the lack of ‘objective’ high
quality benchmarks within illiquid asset classes it is hard to draw
a clear conclusions regarding the existence and level of liquidity
premiums within those asset classes.
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Quarter 2 • 2016
Alternative Investment Analyst Review
Exhibit 7: Average Return (1990-2009) vs. Illiquidity Measure.
Source: Ilmanen (2011)
Exhibit 8: Dispersion Between Managers for Various Asset Classes
Source: Cambridge Associates
As there is no clear consensus on the existence of a liquidity
premium, the decision to invest in an illiquid asset to capture
this premium is mainly supported by investment beliefs. Firstly
the belief, supported by theory, that the premium is out there
and more importantly the belief that the investor is capable of
capturing this premium. In our view, the decision to invest in
illiquid asset classes and how successful this is depends mainly
on the ability to select top-performing managers. This is in line
with Swensen (2009)16, who argues that the reason for investing
in illiquid asset classes should not be higher risk-adjusted returns.
He suggests that alpha opportunities are greater in illiquid
markets than in liquid ones, as information in illiquid markets
is much more difficult to gather and analyze. Skilled investors
in these illiquid markets are able to use this information to
distinguish between good and bad investments.
Conclusion
We have evaluated the common view that investors should be
able to harvest a liquidity premium from illiquid investments.
Unfortunately it is hard to find evidence of such a premium, which
31
The Ins and Outs of Investing in Illiquid Assets
makes the decision to invest in illiquid assets one of the tougher
challenges for investors.
We have looked at several aspects of investing in illiquid assets.
On theoretical grounds we would expect a liquidity premium to
exist. However, the historical evidence for this is mixed. Within
some asset classes more illiquid assets appear to deliver higher
returns than liquid alternatives. In contrast, academics struggle
to find evidence on liquidity premiums between asset classes. For
example, it is hard to find evidence of such premiums for direct
real estate versus listed real estate or private equity versus listed
equity.
Even if liquidity premiums exist, it is questionable whether these
premiums can be exploited in practice and whether they are
large enough to compensate for the extra risks involved. These
risks include the risk of deviating too much from the optimal
strategic portfolio as a result of the inability to rebalance and the
probability of not being able to cover running expenses caused by
too great an allocation to less liquid assets.
Investment Strategies
Allocation to certain illiquid asset classes however may have
a significant effect on a portfolio’s return as it is a top-down
allocation decision. Research on the existence of a liquidity
premium in illiquid asset classes is hampered by the lack of good
quality data. In practice there are many examples of both good
and bad results of investing in illiquid assets. The difference in
performance in these markets depends for a large part on the
managers that are selected. Therefore, in our view investing in
illiquid asset classes could form part of a portfolio strategy, if
it is combined with the capability of selecting top-performing
managers.
If a fund decides to invest in illiquid assets we recommend that
it set a maximum allocation to illiquid assets based on a stress
test. In the worst case scenario there should still be enough liquid
assets to meet obligations. We emphasize that investing in illiquid
assets is a decision that has long-term consequences. The strategy
will need support not just today but also in the future.
Endnotes
1. Harvard endowment finally solved their liquidity problems by
borrowing.
2. We abstract from more detailed analyses of the various illiquid
assets. For these, we refer the reader to specific Robeco white papers
on:
• Real estate (Onroerend goed in portefeuillecontext, 2012)
• Private equity (De rol van Private Equity in een
beleggingsportefeuille, 2013)
• Hedge funds (De toegevoegde waarde van hedgefondsen in een
pensioenportefeuille ,2014)
3. A bad quality second hand car in the US is called a lemon. If the car
that is being sold is of bad quality, the seller is probably aware of it,
while the buyer is not able to determine the quality of the car. This
information asymmetry leads to the probability of “buying a lemon”.
4. If there is no heterogeneity in the expected trading frequency,
investors in illiquid assets will be only compensated for the expected
trading cost according to this theory.
5. In the example the net returns on the liquid and illiquid asset are
equal. In practice however the turnover in the liquid asset might
be higher than once every ten years (i.e. long term investors need
to rebalance their portfolios etc.). In this case the net return of the
illiquid asset will exceed that of the liquid asset.
6. Ang (2014) describes the model and the results from Ang et al.
(2014) in simpler language. Ang defines the illiquidity premium as
the certainty equivalent.
7. Private equity contracts usually span a 10-year period. The effective
average holding period is shorter, because dividends and capital are
returned to the investor before the end date of the investment (see
for instance Driessen, Lin, and Phalippou, 2012).
8. Ang et al. (2014) also consider a case without consumption. In this
case they find that the required liquidity premiums are much lower,
as there is no intermediate risk of not being able to consume.
9. This source of illiquidity is an additional source to the main ones
described in the previous section and in Amihud et al. (2005).
10.We define a systematic liquidity shock as an event during which
liquidity suddenly dries up. Investors and other liquidity suppliers
such as banks are then reluctant to trade. Liquidity shocks can
lead to price volatility, which can increase expectations of future
volatility. This will lead to higher margin requirements as was the
case for S&P 500 futures during the liquidity crises of 1987, 1990,
1998 and 2007 (see e.g. Brunnermeier and Pedersen 2009).
11.Investors are generally prepared to pay a premium for assets that
pay-off in bad times. This is considered to be insurance. This is one
of the reasons why pension funds still invest in high quality fixed
income instruments. Another reason is their need to comply with
the requirements of the Dutch financial assessment framework
(FTK).
12.Liquidity can be measured in different ways. Measures based on
turnover or autocorrelation in returns are widely used.
13.The Barclays LCS is an indication of the cost of trading a bond,
measured as a percent of the bond’s price.
14.Harvard decided not to liquidate part of its endowment but to issue
bonds and to reduce its payout in 2009.
15.Khandani and Lo (2011) relate the level of illiquidity to the
autocorrelation of the returns; the higher the autocorrelation the
more illiquid the hedge fund strategy is. The liquidity premiums are
lower if they are based on the raw returns. Their approach first ranks
each of the funds in the specific asset class into five quintiles based
on the autocorrelations. Subsequently the average (risk adjusted)
returns of the equal weighted portfolios is calculated. Finally the
spread between the most and the least liquid portfolios is estimated
in order to derive an estimate for the liquidity premium.
16.David Swensen has been chief investment officer of the Yale
Endowment Fund since 1985. His views on asset allocation caused
many endowment funds to start investing in illiquid alternative asset
classes.
References
Acharya, V.V., and, Pedersen, L.H., 2005, “Asset pricing with liquidity
risk”, Journal of Financial Economics 77(2):375–410
Akerlof, G.A., 1970, “The market for ‘lemons’: Quality uncertainty and
the market mechanism”, The Quarterly Journal of Economics, 84(3):
488-500
Amihud, Y., Mendelson, H. and Pedersen, L.H., 2005, “Liquidity and
asset pricing”, Foundations and Trends in Finance, 1:269-364.
Ang, A., 2014, “Asset Management: A Systematic Approach to Factor
Investing”, Oxford University Press
Ang, A., Papanikolaou and D., Westerfield, M.M., 2014, “Portfolio Choice
with Illiquid Assets”, Management Science, 60(11):2737-2761
Ben-Rephael, A., O. Kadan, en A. Wohl, 2015, “The Diminishing
Liquidity Premium”, Journal of Financial and Quantitative Analysis, 50(12):197-229
Bongaerts, D, De Jong, F. and Driessen, J. 2011, “An asset pricing
approach to liquidity effects in corporate bond markets”, working paper
Brunnermeier, M.K. and Pederson, L.H., 2009, “Market Liquidity and
Funding Liquidity”, Review of Financial Studies, 22(6):2201-2238
De Jong, F.C.J.M. and Driessen, J.J.A.G., 2013, “The Norwegian
Government Pension Fund’s Potential for Capturing Illiquidity
Premiums”, Tilburg University research for the Norwegian Ministry of
Finance
Dick-Nielsen, J., Feldhutter, P., and Lando, D., 2012, “Corporate Bond
Liquidity Before and After the Onset of the Subprime Crisis”, Journal of
Financial Economics, 103:471-492
Driessen, J.J.A.G., 2014, “Illiquiditeit voor pensioenfondsen en
verzekeraars”, Netspar Design Paper 26
Driessen, J., Lin, T-C. and Phalippou, L., 2012, “A New Method to
Estimate Risk and Return of Non-traded Assets from Cash Flows: The
Case of Private Equity funds”, Journal of Financial and Quantitative
Analysis, 47(3):511-535
32
Quarter 2 • 2016
Alternative Investment Analyst Review
Ejsing, J., Grothe, M., and Grothe, O., 2012, “Liquidity and credit risk
premiums in government bond yields”, ECB working paper 1440.
or entity in any jurisdiction or country where such distribution or use
would be contrary to local law or regulation.
Fleckenstein, M., Longstaff, F.A. and Lustig, H., 2014, “The TIPSTreasury Bond Puzzle”, Journal of Finance, 69(5):2151-2197
All copyrights, patents and other property in the information contained
in this document are held by Robeco. No rights whatsoever are licensed
or assigned or shall otherwise pass to persons accessing this information.
Franzoni, F., Nowak, E., and Phalippou, L., 2012, “Private equity
performance and liquidity risk”, Journal of Finance. 67 (6):2341-2374
Hanson, S.G., Scharfstein, D.S. and Sunderam, A., 2014, “An Evaluation
of Money Market Fund Reform Proposals’, HBS working paper
Ilmanen, A., 2011, “Expected Returns An Investor’s Guide to Harvesting
Markets Rewards”, Wiley, Chester, U.K.
Jensen, R.J. and Moorman, T., 2010, “Inter-temporal variation in the
illiquidity premium”, Journal of Financial Economics, 98(2): 338-358
Khandani, A., and Lo, A.W., 2011, “Illiquidity Premia in Asset Returns:
An Empirical Analysis of Hedge Funds, Mutual Funds, and US Equity
Portfolios”, Quarterly Journal of Finance, 1:1-59.
Krishnamurty, A., 2002, “The bond/old-bond spread”, Journal of
Financial Economics, 66(2-3): 463-506
Liu, P. and Qian, W., 2012, “Does (and What) Illiquidity Matter for Real
Estate Prices? Measure and Evidence”, Working Paper
Longstaff, F., 2004, “The Flight-to-Liquidity Premium in US Treasury
Bond Prices”, Journal of Business, 77(3):511-526
Longstaff, F., 2014, “Valuing Thinly-Traded Assets”, NBER Working Paper
w20589
Lou, X. and Sadka, R., 2011, “Liquidity level or liquidity risk? Evidence
from the financial crisis”, Financial Analysts Journal, 67(2): 36-44
Malkiel, B.G. and Saha, A., 2006, “Hedge Funds: Risk and Return”,
Financial Analysts Journal, 61(6):80-88
Schwartz, K., 2010, “Mind the Gap: Disentangling Credit and Liquidity in
Risk Spreads”, Wharton school working paper
Sadka, R. 2014, ‘Asset Class Liquidity Risk’, Bankers, Markets & Investors,
128: 20-30
Siegel, L.B., 2008, “Alternatives and Liquidity: Will Spending and
Capital Calls Eat Your “Modern” Portfolio?”, The Journal of Portfolio
Management, 35(1):103-114
Swensen, D.F., 2009, “Pioneering Portfolio Management: An
Unconventional Approach to Institutional Investment”, Free Press, New
York
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Asymmetric Information and Imperfect Competition”, Review of
Financial Studies, 25(5):1339-1365
Important Information
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It is intended to provide the reader with information on Robeco’s specific
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Any investment is always subject to risk. Investment decisions should
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33
The Ins and Outs of Investing in Illiquid Assets
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Authors' Bios
Thijs Markwat
Quant Researcher
Robeco Investment Research
Thijs D. Markwat is a quant researcher at
Robeco Investment Research. He holds a PhD
degree in quantitative finance from Erasmus
University Rotterdam. He has worked in the
financial industry for 10 years. Thijs has two
main fields of interest in research. First, he performs research on
the risk and opportunities of investing in alternative and illiquid
asset classes. These studies include private equity, hedge funds,
and real estate investments. Second, he is interested in designing
defined contribution pension solutions and developing risk
management policies within these solutions.
Roderick Molenaar
Director and Portfolio Strategy Researcher
Robeco Investment Research
Roderick Molenaar MSc is a Director and
portfolio strategy researcher at Robeco
Investment Research, where he advises
Defined Benefit and Defined Contribution
schemes. The advice includes ALM studies
and portfolio construction issues for DB schemes and the
design of the optimal glide paths for DC schemes based on the
human capital theory and its implementation via specialized
funds. Further, he conducts research on the characteristics of
mainly illiquid asset classes. Previously Roderick has worked at
MeesPierson and APG Asset Management. He has worked in the
financial industry for 25 years.
Investment Strategies
Black Ice: Low-Volatility Investing
in Theory and Practice
Feifei Li, Ph.D., FRM
Director
Head of Investment
Management
Engin Kose, Ph.D.
Vice President
Equity Research
Equity investors have endured two extreme
market downturns since the turn of the century.
The broad U.S. market, represented by the S&P
500 Index, fell by 44% in the aftermath of the
dot-com bubble and 51% in the great recession.
These devastating experiences reawakened
institutional and individual investors to the
downside of market volatility and, for a while,
prompted great interest in low-volatility
investing. Over the last six years, however, the
market has been climbing; at the end of July
2015, the price level of the S&P 500 was over
200% higher than its trough in March 2009.1
Low-volatility strategies have languished, and
many investors appear to be sleepwalking
again—possibly toward a cliff.
While human nature conditions us to chase
whatever has been working best—a strategy that
we know will backfire badly for the long-term
investor—we also know that inertia generally
doesn’t pay off. Given the immense gains of
this bull market, it may be timely to take some
profits off the table, and to dampen our overall
portfolio risk through exposure to the welldocumented low-volatility effect.2 But, like most
things that sound inviting, not all low-volatility
portfolio strategies are equally attractive. It
pays to understand the differences. Let’s focus
first on issues surrounding the implementation
of minimum-variance strategies. The same
challenges arise for heuristic low-volatility
portfolio construction; we consider their impact
below.
The Need for Constraints
There are essentially two approaches to
low-volatility investing. One of them, called
minimum-variance investing, is based
on quantitative optimization techniques,3
while the other employs heuristic portfolio
construction rules. Some products use
combinations of the two approaches, but for
this purpose, we will focus on the two primary
approaches.
34
Quarter 2 • 2016
Alternative Investment Analyst Review
• The minimum-variance portfolio approach uses a numerical
optimizer to select a set of non-negative stock weights such
that the resulting predicted portfolio volatility is minimized.
• A heuristic approach to low-volatility investing typically uses
a common risk measure (e.g., beta or volatility) to screen
out volatile companies, and assigns weights to the remaining
securities by their market capitalizations or the inverse of the
company-specific risk measure.
Solidly grounded in finance theory, the minimum-variance
method is clearly a sound approach to constructing a lowvolatility portfolio. Nonetheless, implementing this method may
be more problematic than many investors realize, and the chosen
solutions unavoidably affect investment results.4 The challenges
relate to “implementation shortfall,” including disappointing outof-sample performance due to estimation errors,5 extreme and
unstable portfolio characteristics, and high transaction costs.6
the portfolio weight has a cost; indeed, severing that link is the
main source of alpha for fundamentally weighted and other
non-cap-weighted strategies. As a practical matter, it appears
that optimization-based minimum-variance strategies cannot be
implemented without meaningful slippage.
Empirical Study
To evaluate the impact of typical constraints, we constructed
three hypothetical long-only minimum variance portfolios8 from
the 1,000 stocks with the highest market capitalization in our
universe: a U.S. portfolio, a developed markets portfolio, and an
emerging markets portfolio. The baseline minimum-variance
portfolios, which were rebalanced annually over the simulation
periods, incorporated minimum and maximum weight
constraints on individual stock positions. Then we serially applied
a capacity constraint related to the stocks’ weights in the marketcap-weighted benchmark; sector and regional concentration
constraints; and a ceiling on one-way turnover. (See the Appendix
for details on the constraints and regional makeup.)
In addition to applying advanced statistical techniques,7 asset
managers and index providers often mitigate estimation errors—
and address other minimum-variance implementation issues—by
imposing constraints on the optimization process. They typically
apply minimum and maximum weight constraints to avoid overconcentration in individual stocks; sector and regional weight
constraints to forestall excessive allocations to any one industry
group or geographical area; and turnover constraints to control
trading costs.
In Exhibit 1, we see that the stepwise imposition of constraints
decreases turnover, increases weighted-average market
capitalization (WAMC), increases the effective number of stocks,9
and decreases the aggregate weight of the top 10 names. Just as
intended, the constraints limit trading and give the minimumvariance portfolios greater liquidity, higher capacity, and lower
concentration.
These restrictions are successful in fixing the identified problems,
and as a result, they make minimum-variance portfolios more
investable. But the improvements come at a price. The constraints
progressively nudge the portfolio closer to the market-capweighted index and, more importantly, introduce a link between
the price of a stock and its weight in our portfolio. As we (and
others) have demonstrated, the link between stock price and
In Panel A of Exhibit 2, we see how performance drops, risk rises,
and the Sharpe ratio falters, as we apply more constraints to the
simulated U.S. portfolio. Interestingly, the capacity constraint
helps performance in the hypothetical developed markets (Panel
B) and emerging markets (Panel C) portfolios. In all markets,
tracking error against the cap-weighted benchmark decreases
monotonically with each new constraint. By partially reversing
Exhibit 1: Effect of Constraints on Simulated Portfolio Charcateristics*
Source: Research Affiliates, LLC. using data from Compustat, CRSP, Worldscope, and Datastream
35
Black Ice: Low-Volatility Investing
Investment Strategies
Exhibit 2: Performance of Simulated Minimum Varience Portfolios
Source: Research Affiliates, LLC. using data from Bloomberg, MSCI, Compustat, CRSP, Worldscope, Datastream, and Kenneth French
the optimization, the added constraints move the portfolios away
from the theoretical minimum-variance baseline toward the capweighted benchmark.
The effect of constraints on the ratios of excess return to volatility
and value added to tracking error can be seen in Exhibit 3.
Taken together, the constraints push the U.S. minimum-variance
portfolio in the direction of the cap-weighted benchmark.
We also observe that the U.S. minimum-variance portfolio’s
sector allocation more closely resembles that of the cap-weighted
benchmark when all constraints are in effect. Exhibits 4–6 display
simulated three-month smoothed sector weights using Kenneth
French’s 12-industry classification. In the baseline case, shown
in Exhibit 4, the utilities sector has a very large allocation over
most of the measurement period. The fully constrained portfolio
(Exhibit 5) has a more balanced allocation to economic sectors,
much like the cap-weighted benchmark (Exhibit 6).
So far, we have studied the optimization-based approach to lowvolatility investing. We confirm that the optimization process
must be constrained to assure the minimum-variance portfolio
is implementable. These constraints are also necessary to obtain
reasonable portfolio characteristics such as diversification and
capacity. But they have a cost. The portfolio becomes more like
the market, and the risk increases, with mixed effects on riskadjusted performance over the simulation periods. Let’s now turn
to the heuristic approach to low-volatility investing.
Exhibit 3: Impace of Constraints on U.S. Minimum-Varience Portfolio (Jan. 1967-Sept. 2014)
Source: Research Affiliates, LLC. using data from Compustat, CRSP, Worldscope, Datastream, and Kenneth French
Quarter 2 • 2016
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Alternative Investment Analyst Review
Exhibit 4: U.S. Sector Allocations (Baseline Portfolio, Jan. 1967-Sept. 2014)
Source: Research Affiliates, LLC. using data from Compustat, CRSP, Worldscope, and Datastream
Exhibit 5: U.S. Sector Allocations (Fully Constrained Portfolio, Jan. 1967-Sept. 2014)
Source: Research Affiliates, LLC. using data from Compustat, CRSP, Worldscope, and Datastream
Exhibit 6: U.S. Sector Allocations (Cap-Weighted Benchmark, Jan. 1967-Sept. 2014)
Source: Research Affiliates, LLC. using data from Compustat, CRSP, Worldscope, Datastream, and Kenneth French
37
Black Ice: Low-Volatility Investing
Investment Strategies
Exhibit 7:Performance of Simulated Heuristic Low-Volatility Portfolios
Source: Research Affiliates, LLC. using data from Bloomberg, MSCI, Compustat, CRSP, Worldscope, Datastream, and Kenneth French
Exhibit 8:Performance of Simulated Heuristic Low-Volatility Portfolios
Source: Research Affiliates, LLC. using data from Bloomberg, MSCI, Compustat, CRSP, Worldscope, Datastream, and Kenneth French
38
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Alternative Investment Analyst Review
The Heuristic Approach
We conducted a similar analysis of a heuristic approach to lowvolatility portfolio construction. To construct the simulated
baseline heuristic portfolios, we selected the 200 stocks with the
lowest volatility from fundamentally weighted indices for the
U.S., developed, and emerging markets. To construct region- and
sector-constrained portfolios, we selected from the fundamentally
weighted indices’ constituents the 20% of stocks with the lowest
volatility within each region and sector, thereby conserving the
original allocations. Finally, to incorporate a turnover constraint,
we limited trading to removing stocks whose volatility moves
outside a pre-established band and adding previously ineligible
stocks whose volatility now falls within the band. This approach to
turnover control suits heuristically constructed portfolios better
than the explicit turnover constraints used in minimum-variance
portfolios. Performance statistics for the baseline and constrained
low-volatility portfolios are presented in Exhibit 7. (We showed
the same measures for the simulated minimum-variance
portfolios in Exhibit 2.) In the United States, the minimumvariance and heuristic low-volatility portfolios have roughly
comparable absolute and risk-adjusted returns. In the developed
markets, the heuristic strategy has higher absolute returns and
higher Sharpe ratios; in the emerging markets, the minimumvariance approach has lower absolute returns but higher Sharpe
ratios. Neither approach prevails in all regions.
The heuristic approach is, however, significantly superior in
terms of transaction costs and valuation features. In Exhibit 8,
we see that, across regions, the baseline and constrained heuristic
portfolios have substantially higher weighted-average market
cap, lower price multiples, and higher dividend yields. (Exhibit
1 displayed the same measures for the minimum-variance
portfolios.) In addition, the heuristically constructed portfolios
have lower turnover in the U.S. and developed markets. These
characteristics make the heuristic approach cheaper in terms of
fundamental valuations and, outside the emerging markets, more
efficient in terms of trading activity.
In Closing
As the study summarized here demonstrates, constraints like
those that index providers typically introduce in the optimization
and portfolio construction process succeed in making minimumvariance portfolios more investable by improving liquidity,
avoiding extreme allocations, and controlling transaction costs.
All the same, there are side effects. In general, the constraints tend
to make minimum-variance portfolios look a little more like capweighted indices. In so doing, the constraints increase portfolio
volatility, compromising a key feature (and rendering the term
“minimum variance” technically inaccurate). In comparison,
constraints similarly designed to improve the investability of
heuristically constructed low-volatility portfolios tend to preserve
the intended portfolio characteristics. When evaluating smart
beta alternatives, it clearly pays to understand the trade-offs that
come into play in the transition from theory to practice.
Endnotes
1.The S&P 500 Index closing price level was 676.53 on March 9, 2009,
and 2103.84 on July 31, 2015, a change of 211%.
2.See Chow, Hsu, Kuo, and Li (2014); Soe (2012); Blitz, Pang, and van
Vliet (2012).
39
Black Ice: Low-Volatility Investing
3.The minimum-variance method is offered by several influential
market providers, such as MSCI.
4.See Behr, Guettler, and Miebs (2008).
5.See Jagannathan and Ma (2003); Kempf and Memmel (2003); AGIC
Systematic Investment Team (2012).
6.See Chow, Hsu, Kuo, and Li (2014), and Arnott (2006).
7.Methods available to mitigate the estimation errors inherent in
sample covariance matrices include the Sharpe (1964) factorbased approach, the Elton and Gruber (1973) constant correlation
approach, and the Ledoit and Wolf (2004) statistical shrinkage
approach.
8.In brief, we employed an optimization routine to find a numerical
solution of portfolio weights that minimizes portfolio variance
under constraints. To ensure that the covariance structure inputs
were positive definite, we applied principal component analysis to
the covariance matrix, which was estimated using up to five years of
monthly excess returns.
9.See the Appendix for the mathematical definition of effective N
(here, the effective number of stocks).
References
AGIC Systematic Investment Team. 2012 “Specification of Constraints
in Managed Volatility Strategies.”: Allianz Global Investors Capital.
(September) Available at http://www.allianzgic.com/en/Documents/
Constraints-In-Managed-Volatility_FINAL2.pdf.
Arnott, Robert D. 2006. “Implementation Shortfall.” Editor’s Corner,
Financial Analysts Journal, vol. 62, no. 3 (May/June):6–8.
Behr, Patrick, Andre Guettler, and Felix Miebs. 2008. “Is MinimumVariance Investing Really Worth the While? An Analysis with Robust
Performance Inference.”
Blitz, David, Juan Pang, and Pim van Vliet. 2012. “The Volatility Effect in
Emerging Markets.” Robeco Research Paper (March).
Chow, Tzee-Man, Jason C. Hsu, Li-Lan Kuo, and Feifei Li. 2014. “A Study
of Low Volatility Portfolio Construction Methods.” Journal of Portfolio
Management, vol. 40, no. 4 (Summer):89–105.
Elton, Edwin J., and Martin J. Gruber. 1973. “Estimating the Dependence
Structure of Share Prices—Implications for Portfolio Selection.” Journal
of Finance, vol. 8, no. 5 (December):1203–1232.
Jagannathan, Ravi, and Tongshu Ma. 2003. “Risk Reduction in Large
Portfolios: Why Imposing the Wrong Constraints Helps.” Journal of
Finance, vol. 58, no. 4 (August):1651–1684.
Kempf, Alexander, and Christoph Memmel. 2003. “On the Estimation of
the Global Minimum Variance Portfolio.”
Ledoit, Olivier, and Michael Wolf. 2004. “Honey, I Shrunk the Sample
Covariance Matrix.” Journal of Portfolio Management, vol. 30, no. 4
(Summer):110–119.
Sharpe, William F. 1964. “Capital Asset Prices: A Theory of Market
Equilibrium under Conditions of Risk.” Journal of Finance, vol. 19, no. 3
(September):425–442.
Soe, Aye M. 2012. “The Low Volatility Effect: A Comprehensive Look.”
(August 1).
Investment Strategies
Appendix
C. EFFECTIVE NUMBER OF STOCKS
A. PORTFOLIO CONSTRAINTS
This is the reciprocal of the Herfindahl ratio, which was developed to
gauge monopoly concentration in industry, repurposed for investment
management. Hypothetically a portfolio of 100% weight in 1 stock
has an Effective N of 1; a portfolio of equal weight to 1,000 stocks has
an Effective N of 1,000. In another words, these minimum variance
portfolios are as diversified as equally weighting only 30–40 stocks.
1. Minimum weight constraint. Weights smaller than 0.05% are
forced to zero.
2. Maximum weight constraint. Individual stock weights are
capped at 5%.
3. Capacity constraint. The weight of a stock is capped at the
lower of 1.5% or 20 times its weight in the corresponding capweighted portfolio. Note that this constraint dominates the
maximum weight constraint.
4. Sector concentration constraint. Sector weights are not allowed
to deviate more than ±5% from the corresponding capweighted sector weights.
5. Region concentration constraint. If the cap-weighted region
weights are less than 2.5%, the minimum-variance region
weights are capped at three times their weight in the capweighted portfolio. Otherwise, they are not allowed to deviate
more than ±5% from the corresponding cap-weighted region
weights.
6. Turnover constraint. The maximum allowable one-way index
turnover is 20%.
B. MARKET AND REGION DEFINITIONS
Developed Markets
Region 1 = DevEME, which includes Austria, Belgium,
Denmark, Finland, Greece, Ireland, Israel, Italy, Luxembourg,
Netherlands, Norway, Portugal, Spain, Sweden, and Switzerland
Region 2 = DevAPAC, which includes Australia, Hong Kong,
New Zealand, and Singapore
Region 3 = France
Region 4 = Germany
Region 5 = United Kingdom
Region 6 = Japan
Region 7 = Canada
Region 8 = United States
Emerging Markets
Region 1 = EMEMEA, which includes Czech Republic, Egypt,
Hungary, Morocco, Poland, and Turkey
Region 2 = EMAPAC, which includes Indonesia, Malaysia,
Philippines, and Thailand
Region 3 = EMAME, which includes Chile, Colombia, Mexico,
and Peru
Authors' Bios
Feifei Li, Ph.D., FRM
Director
Head of Investment Management
Feifei Li leads the Investment Management
group comprising three teams: Product
Research, Portfolio Construction, and
Investment Systems. She works closely with
researchers in designing all the investment
strategies offered by Research Affiliates. She
supervises the execution of the approved methodology for our
strategies, construction and delivery of model portfolios, as well
as risk attributions and analytic support.
Feifei has taught undergraduate and MBA finance classes at the
California Institute of Technology and University of California,
Irvine. She conducts investment related research and has
published numerous articles in both academic and practitioner
journals, as well as chapters in investment related books. In
2015, Feifei and her co-authors won a Bernstein Fabozzi/Jacobs
Levy "Outstanding Article" award for "A Study of Low-Volatility
Portfolio Construction Methods," published in the Journal of
Portfolio Management. She holds the Financial Risk Manager
designation.
Feifei earned a BA from Tsinghua University’s School of
Management and Economics in Beijing. She earned her Ph.D. in
finance at the University of California, Los Angeles, where she
has conducted empirical research on corporate finance and eventdriven investment strategies.
Engin Kose, Ph.D.
Vice President
Equity Research
Engin Kose conducts research on
enhancements of the RAFI™ Fundamental
Index™ methodology. He supports existing
portfolios and develops new strategies. Engin
graduated from McGill University with a
BA degree and Joint Honors in mathematics
and economics. He earned his Ph.D. in finance from Washington
University in St. Louis. He was awarded the Olin Graduate
Fellowship for 2007 to 2012.
Region 4 = South Africa
Region 5 = Russian Federation
Region 6 = India
Region 7 = China
Region 8 = Taiwan
Region 9 = South Korea
Region 10 = Brazil
40
Quarter 2 • 2016
Alternative Investment Analyst Review
Perspectives
Concentrated vs. Diversified Managers:
Challenging What You Thought You Knew About
“High Conviction”
Keith E. Gustafson, CFA
Partner and Managing Director
Chicago Equity Partners, LLC
Patricia A. Halper, CFA
Partner and Managing Director
Chicago Equity Partners, LLC
Introduction
The general rationale for concentrated
portfolios suggests managers can’t possibly
have equal conviction about a large number of
stocks. Under this school of thought, investors
want a portfolio of “best ideas,” rather than a
diversified portfolio that could only represent
diluted alpha information. Stock portfolios
with many stocks and relatively lower tracking
errors to benchmarks are often considered
”closet indexers,” not worth active management
fees or the effort relative to a passive approach.
The work of Cremers and Petajisto (2009)1 gave
credence to these biases with the introduction
of the measure known as Active Share.
In simple terms, Active Share is a holdingsbased calculation that measures the deviation
of a portfolio from a benchmark in percentage
terms. A portfolio with a score of 0% is the
exact same as the benchmark, while a portfolio
with an Active Share of 100% has no overlap
in holdings with the benchmark. The original
41
Concentrated vs. Diversified Managers
paper provided evidence among mutual funds
of a relationship between a fund’s deviation
from a benchmark and its excess return. The
Cremers and Petajisto (2009) paper added
another manager analysis tool to plan sponsors’
toolboxes, but it perpetuated a notion that high
Active Share (and/or concentration) results in
higher excess returns.
Review of Prior Work
Since the publishing of their original paper,
there have been many articles, including work
from AQR,2 Fidelity,3 and Axioma,⁴ challenging
any positive relationship between Active Share
and excess return. Recent work from Andre
Frazzini at AQR uses the same dataset as the
original paper to obtain different results when
mutual funds are grouped by and measured
against more appropriate benchmarks. The
original work organized all managers, from
large-cap to small-cap, into one large data set.
Their results were driven much more by the
variation across capitalizations and benchmarks
Perspectives
than from differences in managers versus their relevant
benchmarks.
Specifically, small-cap managers measured against a broad largecap benchmark, such as the S&P 500 Index, will exhibit much
higher Active Share than large-cap managers. Frazzini’s work
finds the empirical spread between high and low Active Share
managers to have roughly equal numbers of positive and negative
observations, depending on the specific benchmark. In other
words, the results are largely random, and there is no measurable
or statistically significant relationship between Active Share and
excess return during that time period.
A 2014 paper from Fidelity similarly concludes that higher Active
Share leads to higher dispersion and downside risk, attributing
most of the positive relationship between Active Share and excess
return to small-cap size exposure for managers.⁵ In this view,
Active Share merely becomes a proxy for small-cap exposure.
Recently, the markets experienced a small-cap super-cycle that
provided excess returns over large-cap stocks. This super-cycle
was similar in duration and magnitude to 1975-83. This latest
small-cap run largely encompassed the data set covered in the
Active Share papers, which inflated the returns of high Active
Share small-cap strategies measured against the broad market cap
weighted benchmark.
Even the latest paper from Petajisto (2013),⁶ building on the
earlier framework of Cremers and Petajisto (2009), suggests the
problem with most managers with low Active Share and lower
tracking error is simply that the fee structure is too high. With
high fee burdens, higher return potential is required for net of
fee excess returns. In other words, with a lower fee structure,
even lower-risk managers could potentially add value; it’s not
necessarily a function of alpha information related to the number
of names. Moreover, the threshold as relevant for Active Share to
add value is a relatively low 60%. The vast majority of active largecap strategies satisfy this threshold — even those with relatively
low tracking errors. (See the work from Fidelity for distributions
of managers by Active Share). The average large-cap strategy has
an Active Share of 75%, while it is 95% for small-cap strategies,
according to the Fidelity paper.
Our Analysis
As with much of our prior work on the value of active
management, we explored an institutional manager data set,
rather than the retail mutual fund universe. We examined gross
of fee returns for institutional managers, which are of primary
interest to most plan sponsors. We used gross returns because
institutional fees vary across mandates of varying sizes, allowing
the reader to adjust the results based on their own appropriate fee
assumption.
We grouped and categorized our analysis by the number of stocks,
rather than Active Share. We classified large-cap portfolios with
up to 40 stocks as concentrated and those of 100 stocks or more as
highly diversified, with the remainder constituting the third group
that rests in between. The number of stocks in representative
separate account portfolios is accurately and readily available
from Morningstar and other manager databases. This simple
metric is highly intuitive for most investors, while Active Share
percentage is not. Moreover, Active Share is a point in time
measure that requires detailed portfolio holdings and benchmark
designation, which is information not readily available to
investors for most managers. The Active Share metrics that do
show up in databases are often self-reported manager statistics,
rather than metrics that are independently calculated.
There can be a theoretical deviation between Active Share
and the number of stocks for portfolios that hold a few highly
concentrated bets along with a large number of small diversified
stock holdings, but this is not typical. Empirically, we found a
high degree of association between the number of stocks held and
Active Share — an average correlation near -0.5 as of March 31,
2015, across large-cap and small-cap datasets (see appendix for
details).
In the work of Petajisto (2013), the average number of holdings
for the group classified as ‘concentrated’ was nearly 60 stocks, only
slightly fewer than the group labeled as ‘stock pickers.’ Moreover,
the concentrated group contained managers that held 107 stocks
at just one standard deviation above the average of the group. This
broad view of “concentration” strains most common definitions
of the term. Furthermore, the Active Share calculation is highly
dependent upon the benchmark selected. In our analysis, we
avoided this benchmark-relative problem by using concentration
measures based on number of stocks.
We grouped large-cap managers by style into growth, value, and
blend. This style grouping rectified the benchmark-variation
problem identified by Frazzini (2015). We removed composites
that were passive, global, and/or contained bond holdings, short
positions or leverage, sector strategies, and buy-write or covered
call options strategies. We also removed any managers classified
by institutional category with something other than large-cap
domestic equity mandates. The style boxes had to be large cap and
part of the institutional Morningstar category.
As a robustness check, we duplicated the analysis in Evestment
for large-cap and small-cap managers without the style box
consistency criteria or analytic output detail. The number of
stocks for small-cap managers was slightly different to achieve
similar population breakdowns between groups. With this second
dataset, we achieved similar results, which appear in the appendix
of this paper.
The results shown here are for the five-year period ended March
31, 2015, addressing most survivorship bias issues that long-term
time windows of measurement entail. Moreover, this five-year
time period is particularly relevant because it covers the out-ofsample period from the original Cremers and Petajisto (2009)
paper published in the Financial Analyst Journal.
Results
When examining large-cap institutional managers grouped
by style, we did not find an inverse relationship between the
number of stocks in a manager’s portfolio and returns, as would
be implied in the Cremers and Petajisto papers. We did not find
significant underperformance or outperformance of the category
median, but we did find some underperformance of respective
benchmarks in some styles for concentrated strategies. In sharp
contrast, our results showed outperformance for diversified
strategies relative to concentrated peers in all three style groups.
42
Quarter 2 • 2016
Alternative Investment Analyst Review
# Stock Holdings
# of
Managers
3Yr
Total
Return
3 Yr
% Rank
5 Yr
Total
Return
5 Yr
% Rank
3 Yr
5 Yr
Tracking Tracking
Error
Error
3 Yr
Info
Ratio
5 Yr
Info
Ratio
3 Yr
5 Yr
Avg # of
Batting Batting
Stock
Avg
Avg
Holdings
Fewer than 40
123
14.70%
52nd
13.19%
54th
3.72%
4.13%
-0.37
-.031
46.74% 47.04%
30
41 to 99
200
15.72%
47th
13.63%
51st
2.98
3.25
-0.17
-0.22
48.51% 47.75%
60
More than 100
66
16.81%
29th
14.99%
26th
1.81
1.93
0.51
0.38
55.47% 55.05%
249
Exhibit 1: Active Large Cap Blend Managers (For Periods Ended March 31, 2015)
Source: Morningstar. Risk statistics versus Russell 1000 Index. Russell Investment Group is the source and owner of the trademarks, service
marks and copyrights related to the Russell Indexes. Russell® is a trademark of Russell Investment Group.
# Stock Holdings
# of
Managers
3Yr
Total
Return
3 Yr
% Rank
5 Yr
Total
Return
5 Yr
% Rank
3 Yr
5 Yr
Tracking Tracking
Error
Error
3 Yr
Info
Ratio
5 Yr
Info
Ratio
3 Yr
5 Yr
Avg # of
Batting Batting
Stock
Avg
Avg
Holdings
Fewer than 40
92
13.68%
62nd
12.60%
58th
5.04
5.41
-0.40
-0.28
45.65% 46.25%
30
41 to 99
182
15.89%
48th
13.61%
53rd
3.22
3.48
-0.07
-0.24
48.12% 46.97%
62
More than 100
56
17.65%
25th
15.16%
26th
2.71
2.79
0.62
0.29
54.96% 52.50%
168
Exhibit 2: Active Large Cap Value Managers (For Periods Ended March 31, 2015)
Source: Morningstar. Risk statistics versus Russell 1000 Index. Russell Investment Group is the source and owner of the trademarks, service
marks and copyrights related to the Russell Indexes. Russell® is a trademark of Russell Investment Group.
# Stock Holdings
# of
Managers
3Yr
Total
Return
3 Yr
% Rank
5 Yr
Total
Return
5 Yr
% Rank
3 Yr
5 Yr
Tracking Tracking
Error
Error
3 Yr
Info
Ratio
5 Yr
Info
Ratio
3 Yr
5 Yr
Avg # of
Batting Batting
Stock
Avg
Avg
Holdings
Fewer than 40
154
16.04%
50th
15.03%
46th
5.06
5.40
-0.05
0.08
50.39% 51.34%
31
41 to 99
210
15.87%
52nd
14.69%
51st
4.37
4.61
-0.10
0.01
51.19% 51.22%
60
More than 100
41
17.40%
32nd
15.97%
32nd
3.65
3.84
0.37
0.37
56.37% 55.24%
157
Exhibit 3: Active Large Cap Growth Managers (For Periods Ended March 31, 2015)
Source: Morningstar. Risk statistics versus Russell 1000 Index. Russell Investment Group is the source and owner of the trademarks, service
marks and copyrights related to the Russell Indexes. Russell® is a trademark of Russell Investment Group.
Over the five-year period, the information ratios for the managers
holding 100 or more names were all near or above the statistical
t-stat thresholds for 95% significance (t-stat = IR * sq rt (n))
before fees. That threshold t-stat is 2.00 for 60 months and 2.03
for 36 months. Those with a positive information ratio added
value gross of fees versus the benchmark, while negative scores
detracted value. The same general patterns and conclusions also
held for peer group percentile rankings and monthly batting
averages.
The majority of active managers in the Large Cap Blend Category
held between 40 and 100 stocks, but there were nearly twice as
many managers that held fewer than 40 stocks as those that held
more than 100 stocks. The diversified managers were the only
group that added value on average over the latest three and five
year periods, beating the Russell 1000 Index in more than 55% of
the months.
43
Concentrated vs. Diversified Managers
There were slightly fewer active managers in the large-cap value
space, but the group distributions were similar, except there were
slightly fewer concentrated managers on a relative basis. The
peer-relative and benchmark-relative performance stats were
similar. Once again, concentrated managers were below-median
on average (as measured by percentile rank), with negative
information ratios and batting averages less than 50%.
The Large Cap Growth Category demonstrated the highest
absolute and relative number of concentrated strategies and
the fewest diversified strategies. Although the average relative
performance of the concentrated strategies was also best in the
large-cap growth style, the benchmark-relative performance was
essentially flat, and the peer-relative performance was median.
These numbers were largely indistinguishable from the bulk
of managers that hold between 40 and 100 stocks, whereas the
diversified managers holding 100 stocks or more still stood out as
adding more value during this period.
Perspectives
Overall, our results challenge and run counter to the findings
of Cremers and Petajisto (2009) and Petajisto (2013) for the
retail mutual fund universe. The original paper established a
classification framework for managers based on the intersection
of tracking error and Active Share, as shown in Exhibit 4 (below).
Petajisto (2013) maintains the same classification framework,
as does Frazzini (2015), in challenging the methodology of the
original work.
Exhibit 4: Active Share to Tracking Error Quadrant
Source: Cremers and Petajisto (2009)
Our findings challenge even this basic framework, because we
found outperformance in the less concentrated (which we use
as proxy for low Active Share) and low tracking error strategies.
We also challenge this framework’s descriptions of “factor bets,”
“closet indexing,” etc.
Our results clearly showed no outperformance, and even
underperformance in some cases, for highly concentrated
and high tracking error managers before fees. We also found
consistent outperformance for low concentration /low tracking
error managers across large-cap styles. It is possible these
different results can be reconciled to some degree by differences
in time period, relative tracking errors, and use of concentration/
Active Share metrics between our groupings and those shown in
Petajisto (2013).
Group
Label
Exhibit 5 from that paper (below) shows the tracking error of
“stock pickers,” which would be the bulk group, is 8.5%. It also
shows they hold 66 stocks on average. The “concentrated” group
has a tracking error of 15.8%, on average, and 59 stocks. These
large tracking errors can only be explained by the benchmark and
grouping problems noted in Frazzini (2015) and Fidelity (2014).
In sharp contrast, our analysis resulted in tracking errors in
the 3%-4% range for the “bulk” group and 4%-5% for the
“concentrated” group. Despite the fact that the Active Share
papers try to correct for misspecification in the analysis with
4-factor Fama-French-Carhart alphas, such grouping problems
still severely compromise the empirical analysis. As previously
mentioned, the average “concentrated” manager holds 59 stocks,
while just one standard deviation higher in the same group holds
107 stocks. That definition of concentration is most likely based
on benchmark misspecification. In this framework, for instance, a
diversified small-cap core manager with a relatively low tracking
error would show up as a high Active Share/high tracking error
manager relative to a broad market benchmark and would be
labeled as “concentrated.”
Empirical performance statistics based on this type of faulty
grouping scheme lack a legitimate interpretation. Would any
investor truly consider data based on a manager grouping
that has an average tracking error of 15.8%, as shown for the
“concentrated” group? That level of tracking error isn’t possible
without benchmark misspecification. For example, in the
Morningstar separate account composite universe, even the Small
Cap Growth Category constituent returns measured against an
S&P 500 Index only achieved an average tracking error of 9.2%
over the five-year period ended March 31, 2015. Any manager
grouping measured against a relevant benchmark should arrive at
average tracking errors that are only a fraction of those displayed
by Petajisto (2013).
# of Funds
Assets
(Millions)
Active Share
Tracking
Error
Turnover
Expense Ratio
# of Stocks
A. Mean Values
5
Stock Pickers
180
$430
97%
8.5%
83%
1.41%
66
4
Concentrated
45
463
98
15.8
122
1.60
59
3
Factor Bets
179
1,412
79
10.4
104
1.34
107
2
Moderately Active
541
902
83
5.9
84
1.25
100
1
Closest Indexers
180
2,009
59
3.5
69
1.05
161
1,124
$1,067
81%
7.1%
87%
1.27%
104
$858
1.4%
1.9%
78%
0.40%
40
ALL
B. Standard Deviations
5
Stock Pickers
4
Concentrated
1,164
1.5
4.3
132
0.66
48
3
Factor Bets
5,174
12.2
4.2
106
0.49
137
2
Moderately Active
2,575
7.5
1.5
74
0.40
98
1
Closest Indexers
All
6,003
9.3
0.9
54
0.39
177
$3,846
14.0%
3.7%
83%
0.45%
119
Exhibit 5: Samples Statistics Across Various Fund Categories
Source: Petajisto (2013), Sample Statistics for Fund Categories, 1990 - 2009
Notes: This table shows sample statistics for the fund categories defined in (Petajisto 2013 paper), and subsequently used in the performance tables.
The equal-weighted mean and standard deviation of each variable are first computed for each month over the sample period, and the reported
numbers are their time-series averages across all the months.
Quarter 2 • 2016
44
Alternative Investment Analyst Review
Our analysis provided a relevant benchmark specification and
was not driven by comparing small-cap managers or value
managers against a broad market benchmark. As such, our
findings challenge the fundamental groupings shown in Exhibit 5,
which are critical to the original paper’s interpretation of manager
classification. In particular, we believe the Petajisto definition of
“Factor Bets” consistent with high tracking error and low Active
Share is more a function of benchmark misspecification than
anything else. Largely, our interpretation of this is evidence of
diversified portfolios compared with improper benchmarks.
The diversified group in our analysis had similar numbers of
stocks and similar tracking errors to their “Closet Indexing”
group, and yet it was the only group that added significant value
in recent years. Moreover, we believe it most likely that our
diversified group is the primary group employing the systematic
“Factor Bets” identified in the original classification scheme.
Perhaps the problem is the broad definition of “Factor Bets” in
the Petajisto paper. He describes factor bets as follows: “involves
time-varying bets on broader factor portfolios—for example,
overweighting particular sectors of the economy, having a
temporary preference for value stocks, and even choosing to keep
some assets in cash rather than invest in equities.”
This definition is different than how we believe most market
participants would define factor-based investing. Factor-based
investing should build diversified stock portfolios, sampling from
a broad set of stocks to remove stock- specific risk, and focusing
on factor exposures. This approach achieves consistent factor bets
at relatively low tracking errors, consistent with the Fundamental
Law of Active Management.7
Based on the answers to investment process questions in
Morningstar, more than two-thirds of the concentrated and
bulk groups in our analysis are classified as either fundamental
or technical, with less than one-third labeling their processes as
quantitative. The results were inverted for the diversified stock
group, however, with more than two-thirds classifying their
process as quantitative.
Most market observers would likely agree that quantitative
investing is generally associated with systematic factor bets,
diversified stock portfolios, and lower tracking errors, which is
inconsistent with the Petajiisto Active Share classification of the
world.
Conclusion
The Active Share measure and the empirical evidence it is based
on have had a strong influence on generational thinking about
manager value-added and potential value-added. Most likely, this
is because it gave empirical credence to biases that were already in
place regarding high-conviction managers. Particularly, after 2007
and the relative short-term underperformance of quantitative
approaches thereafter, it also gave a basis for criticism of such
strategies in a formal framework. Recently, this entire framework
has come under scrutiny from many different venues. The latest
work challenges numerous fundamental points of the original
paper, as well as its empirical findings and conclusions.
We have shown the empirical evidence for the Active Share papers
is based on groupings with benchmark misspecification that do
not stand up to logical scrutiny. Recent work, using the original
45
Concentrated vs. Diversified Managers
Active Share dataset but with proper benchmark specification,
shows no consistent long-term relationship between Active Share
and outperformance.⁸ Moreover, the Active Share measure will be
clustered above 95% for most small-cap managers, which allows
for little delineation in many manager data sets. Yet, these same
managers still can have large differences in numbers of stocks
held, tracking error, and other meaningful measures.
Most institutional investors are interested in separate account
composite returns of institutional managers and not retail
mutual funds. There is also some question as to whether the
groupings arrived at in prior Active Share papers and classified
as concentrated, high conviction approaches are accurate
descriptions. We use an institutional manager dataset and a
number of stocks to ascertain any outperformance of clearly
concentrated, high conviction strategies. Our results indicate
clearly there is no associated outperformance for concentrated
strategies in recent years. Our time period of analysis represents
a time frame that is out of sample from the original Cremers and
Petajisto (2009).
Interestingly, our results do show statistically significant
outperformance of diversified strategies. Moreover, the grouping
tracking errors and number of stocks challenge the classification
scheme of Cremers and Petajisto (2009) and Petajisto (2013).
The classification of “factor bets” as high tracking error with low
Active Share seems unfounded. There is no reason to assume
that a portfolio cannot deploy systematic factor bets that have
the potential to add value, while achieving such with a diversified
portfolio of stocks at a relatively low tracking error. In fact, the
recent appetite for Smart Beta products, whether active or passive,
is predicated on just that supposition.
Active Share measures active deviation from a benchmark. As
with any benchmark-relative measure, the choice of benchmark
matters a great deal. The measure does not take into account
where the active bets come from —whether industry deviation
or factor bets — so it reflects little qualitative information. Active
Share is one measure among many in an analytical toolbox for
evaluating managers, but we find little to no information on
implications for potential alpha.
Bibliography and Endnotes
1.Cremers, Martijn and Antti Petajisto, 2009, “How Active Is Your
Fund Manager? A New Measure that Predicts Performance,” Review
of Financial Studies, 22 (9), pp. 3329-3365
2.Frazzini, Friedman and Pomorski, AQR White Paper, April 2015,
“Deactivating Active Share”
3.Cohen, Leite, Nielsen and Browder, Fidelity Investments Investment
Insights, February 2014, “Active Share: A Misunderstood Measure in
Manager Selection”
4.Dieter Vandenbussche, Vishv Jeet, Sebastian Ceria, and Melissa
Brown, Axioma Research Paper, August 2015, “Active Is as Active
Does: Wading Into the Active-Share Debate”
5.Shagrin, Michael. “Father of Active Share Fires Back at
Detractors.” Fund Fire. 30 April 2015. Web. http://fundfire.com/
pc/1110413/117883
6.Petajisto, Antti, 2013, “Active Share and Mutual Fund Performance,”
Financial Analyst Journal, 69 (4), pp. 73-93
Perspectives
7.Grinold, Richard C. 1989. “The Fundamental Law of Active
Management.” Journal of Portfolio Management, vol. 15, no. 3
(Spring): 30-38.
8.Cremers, Ferreira, Matos and Starks, SSRN Working Paper, 2013,
“The Mutual Fund Industry Worldwide: Explicit and Closet
Indexing, Fees and Performance”
Appendix
Data shown here represent correlations between number of stocks and
Active Share (which is self-reported by managers relative to their own
preferred benchmark) in each Evestment dataset. Correlations would
be negative if as the number of portfolio holdings increased the Active
Share reported decreased, indicating a positive relationship between
concentration and Active Share.
3/31/15
Correlations
ALL LCC
-0.3965
ALL LCG
-0.5473
ALL LCV
-0.4930
ALL SCC
-0.4565
ALL SCG
-0.4455
ALL SCV
-0.5583
Data shown here represent correlations between number of stocks
and excess return to their appropriately specified benchmarks in the
Evestment dataset. Correlations would be negative if concentrated
strategies were associated with excess returns and positive if diversified
strategies were associated with excess returns.
3/31/15 Correlations
# of Managers
3 Yr
5 Yr
All LCC
304
0.0819
0.0804
ALL LCG
311
0.1337
0.0710
ALL LCV
350
0.0841
0.0529
ALL SCC
168
0.2021
0.1283
ALL SCG
180
0.0680
-0.0077
ALL SCV
230
0.0885
0.0387
This material has been distributed for informational purposes only
and does not constitute investment advice or a recommendation of any
security or investment service offered by Chicago Equity Partners, LLC.
The material presented reflects the opinions of the author and is subject
to change without notice. The opinions and themes discussed herein may
not be suitable for all investors. Past performance is not indicative of
future results.
No part of this material may be reproduced in any form without the
express written permission of Chicago Equity Partners, LLC.
Data shown here represent averages for groups drawn from the Evestment universe. Returns are average total returns for three- and five-year trailing
returns and % rank represents percentile ranks, with 1 being best and 100 being worst. The results are consistent to those found in the Morningstar
universe as described above.
3/31/15 Averages
# of Managers
3 Yr Total Return
3 Yr % Rank
5 Yr Total Return
5Yr % Rank
LCC: <=40
54
15.58
55
14.27
54
LCC: 41-99
144
16.18
53
14.26
53
LCC: >=100
106
16.73
38
14.87
39
LCG: <=40
102
15.83
51
15.28
47
LCG: 41-99
163
16.00
52
15.04
52
LCG: >=100
46
17.28
33
14.78
38
LCV: <=40
109
15.62
53
13.59
51
LCV: 41-99
182
16.07
48
13.69
50
LCV: >=100
59
17.03
36
14.35
39
SCC: <=60
42
15.11
65
14.77
63
SCC: 61-139
73
16.76
53
15.97
51
SCC: >=100
53
18.44
35
17.07
39
SCG: <=60
45
16.62
58
17.38
51
SCG: 61-139
107
17.39
50
17.18
52
SCG: >-140
28
18.51
39
17.92
42
SCV: <=60
74
15.46
55
14.73
48
SCV: 61-139
109
15.99
50
14.35
53
SCV: >=140
47
17.09
41
15.36
41
46
Quarter 2 • 2016
Alternative Investment Analyst Review
Authors' Bios
Keith E. Gustafson, CFA
Partner and Managing Director
Chicago Equity Partners, LLC
Mr. Gustafson is a member of Chicago
Equity Partners’ quantitative analysis
group, which is responsible for the firm’s
proprietary quantitative model and its
ongoing developmental efforts. Prior to
joining our firm, he held positions at Ibbotson Associates and
SEI Corporation. Mr. Gustafson earned bachelor’s degrees in
history and economics from University of Pennsylvania, an
MBA from Loyola University Chicago, and a master’s degree in
financial economics from the University of London. He holds the
Chartered Financial Analyst (CFA) designation, and is a member
of the CFA Institute, the CFA Society of Chicago, the Chicago
Quantitative Alliance, the American Finance Association, and the
American Economics Association.
47
Concentrated vs. Diversified Managers
Patricia A. Halper, CFA
Partner and Managing Director
Chicago Equity Partners, LLC
Ms. Halper is a member of Chicago Equity
Partners’ quantitative analysis group, which
is responsible for the firm’s proprietary
quantitative model and its ongoing
developmental efforts. Prior to joining our
firm, she worked at the institutional futures sales desk at Paine
Webber. Ms. Halper holds a bachelor’s degree in mathematics
from Loyola University Chicago and a master’s degree in financial
mathematics from the University of Chicago. She holds the
Chartered Financial Analyst (CFA) designation, and is a member
of the CFA Institute, the Chicago Quantitative Alliance, and the
Economic Club of Chicago. Additionally, she is on the Board
of Trustees for La Rabida Children’s Hospital, and is a board
member of the CFA Society of Chicago.
VC-PE Index
VC-PE Index
A Look at North American
Private Equity as of Q3 2015
Mike Nugent
CEO/Co-Founder
Bison
Mike Roth
Research Manager
Bison
North American median returns were mixed
in Q3 2015. Focusing on the seven vintage
years from 2007 through 2013, we found that
median TVPI figures for North America All PE
averaged a 0.52% increase. This was driven by
the venture capital industry.
Median TVPI figures for venture capital saw
positive changes in five of the seven vintage
years in our analysis. The average increase in
median TVPI for venture capital was 5.9%. This
is in comparison to North American buyouts,
which only saw positive changes in three of
the seven vintage years. The average change in
median TVPI for buyouts was -1%.
Median DPI figures for the venture capital
industry jumped noticeably in several vintage
years. For the 2008 through 2010 vintage years,
median DPI figures in venture capital jumped at
least 25%. On an absolute basis, venture capital
distributions are still lagging behind the buyout
industry for most vintage years. This is not
unexpected given venture's longer maturation
period but it also means venture capital funds
are more exposed to fluctuating valuations.
48
Quarter 2 • 2016
Alternative Investment Analyst Review
Exhibit 1: North American TVPI
Source: Bison
Exhibit 2: North American Median DPI
Source: Bison
49
VC-PE Index
VC-PE Index
Authors’ Bios
Mike Nugent
CEO/Co-founder, Bison
Mike Roth
Research Manager, Bison
Prior to founding Bison, Mike Nugent held
senior roles at SVG Advisers, LP Capital
Advisors and HarbourVest Partners, and
has more than $3B in private market
commitments to his credit. Mike started
his career in the public markets with the
NASDAQ Stock Market, and also gained significant operating
experience while running operations for a textiles manufacturer.
He received his MBA from Boston College, and his BA from
St. Bonaventure University. Mike lives on the North Shore of
Massachusetts with his wife and two sons.
Mike Roth is the Research Manager at
Bison and oversees the data collection and
content production. Before Bison, Mike spent
six years on the investment team at SVG
Advisers. There, he conducted research and
due diligence on buyout and venture capital
funds in the Americas. Mike received his BA in Economics from
Boston College and is a CFA Charterholder.
50
Quarter 2 • 2016
Alternative Investment Analyst Review
MSCI Global Intel Report
Global Property Performance
Max Arkey
Vice President
Product Management
MSCI Real Estate
Summary
Global property held directly by private
investors delivered a total return of 10.7% in
2015, marking the sixth consecutive year of
positive performance since the global financial
crisis (GFC) and the strongest annual return
since 2007. Global performance edged modestly
upward from 10.0% in 2014, to reach its highest
level since 2007. Ireland continued to lead
global markets, though returns moderated from
near 40% in 2015 to 25.0% in 2015. Ireland’s
performance was followed by Spain, at 15.3%,
and Sweden, at 14.1%. The UK (13.1%) and
USA (12.1%) also provided double-digit returns
above their long-term averages and above the
global index in 2015.
The cyclical and structural dynamics of real
estate attracted a wave of capital in this cycle
that has propelled the asset class through a
period of strong performance. The appeal was
initially cyclical, as depressed prices attracted
capital in the immediate aftermath of the GFC.
51
IPD Intel
In a typical cycle, tightening real estate yields
would slow the flow of capital, but in recent
years, record-low bond yields and financing
costs have kept spreads attractive. The atypical
nature of this cycle continues to keep investors
on alert for the inevitable inflection point that,
at least in 2015, remained illusory.
Six Consecutive Years of Strong Global
Performance
The IPD Global Annual Property Index
registered a total return of 10.7% in 2015, the
sixth consecutive year of strong returns since
the GFC, and the best performance since 2007.
Global performance has remained remarkably
steady through the post-recession years, with
fewer than 350 basis points of variation in the
headline number since 2010.
Capital Growth Returns to Pre-Recession
Levels
Over the long term, real estate generates most
of its performance through income, with over
MSCI Global Intel Report
Exhibit 1: Total Returns to 2015 Across National Markets
Source: MSCI; KTI
All property annual returns in local currency
Exhibit 2: Global All Property Total Return History
Source: MSCI; KTI
Including contributing components of total return
80% of total return sourced through the income stream over the
past 15 years. In 2015, the global income return narrowed to
just 5.1%, with value growth representing more than half of total
return for the first time since 2006. This recent trend has been
driven by the weight of capital moving into real estate and with
it, yield compression. Although income return has fallen over the
last five years it held above 5%, still significantly higher than for
equities and bonds.
Volatile, Opportunistic Markets Lag Pre-Recession Value Peaks
As investors weigh important tactical considerations for new
acquisitions and for existing portfolios, they are likely to reflect
on the cyclical position of individual markets. Through the
most recent cycle, a few countries have fully recovered value lost
during the downturn, including Canada, Sweden, and Australia.
Quarter 2 • 2016
Others such as Switzerland and South Korea showed resilience
during the worst years of the GFC and had little if any significant
losses to be recovered. Large markets like the USA and UK had
recovered nearly all of their lost value by 2015 while the year’s best
performers—the volatile markets of Ireland and Spain—intrigued
opportunistic investors, in part, because they remained, even in
2015, well below the capital value levels experienced in 2015.
In the Long View, Real Estate Remains an Income Play
The squeezing of the income yield across so many global markets
is notable but it is nonetheless cyclical, not structural, and it
obscures the fact that, on average, roughly 80% of the total return
in real estate investments is derived from rents, not from value
growth. Looking backward and annualizing the components of
total return incrementally through the GFC and into prior years,
52
Alternative Investment Analyst Review
Exhibit 3: Capital Value Growth Across Markets, 2007-2015
Source: MSCI; KTI
2007 indexed to 100
Exhibit 4: Cumulative Contributions to Global Total Return Over Time
Source: MSCI; KTI
Composition of global total return over annualized periods of 1 to 15 years as of 2015
*Note: Approximate shares exclude residual effects. Income return shown as 100% where capital growth is negative.
the components eventually begin to level out, with income return
roughly 80% of total performance.
Real Estate Has Performed well in the Post-Recession Period
The attractiveness of wide spreads can be seen more clearly
when placed in the broader perspective of the global investment
environment. The post-GFC period of capital flows to real
estate is part of a long-term trend of investors moving toward
alternative investments. Cumulative annual reviews of pension
asset allocations in seven key global markets by Willis Towers
Watson shows that investors in 2015 allocated 24% to alternatives,
a percentage that has moved up incrementally from a level of 5%7% in the 1990s (Willis Towers Watson, 2016 (and prior years)).
53
MSCI Global Intel Report
Unlisted direct real estate outpaced both equities and bonds
during 2015 by wide margins, though over the longer periods of
three, five, and ten years, this degree of outperformance was less
visible. A close examination of multi-asset class returns below also
shows that unlisted fund level real estate outperformed unlisted
direct or asset level real estate over the one, three, and five year
periods where the series is available. The strong performance at
the fund level has much to do with the timing of the real estate
cycle as funds benefited strongly from the use of leverage at low
interest rates. By contrast, the unlisted total returns of directly
owned assets are calculated on an unlevered basis.
MSCI Global Intel Report
Exhibit 5: Comparative Global Performance across Asset Classes
Source: MSCI World Index (EQUITIES); J.P. Morgan, GBI Global (BONDS); MSCI World Real Estate index (LISTED PROPERTY); IPD Annual
Global Property Index (UNLISTED PROPERTY - ASSET LEVEL); IPD Quarterly Global Property Fund Index (UNLISTED PROPERTY - NET
FUND LEVEL)
Annualized results at 1, 3, 5, and 10 years
Exhibit 6: Total All Property Returns by Domestic Market
Source: MSCI; KTI
Note: Scale of chart excludes Ireland.
Improving Performance in 2015 Extended into Core Europe
Even Within Countries, Cities Varied in Performance in 2015
A more explicit way of demonstrating the movements of
markets through their cycles is to compare the most recent year’s
performance against the average over the past five years. This
cross-plot, with the axes representing the global index at one and
five years belies the recovering markets in continental Europe.
Investors in Germany, for example, enjoyed an all property total
return of 8.1% in 2015, the highest level achieved in that market
in the last 15 years. In a global context, Germany’s performance
may appear sluggish as the exhibit implies, but some of this may
be due to the process of German property valuations which can
distort the shape of cycles more than appraisals in other countries
(Crosby, 2007). In fact, the majority of European markets
performed better in 2015 than they did on average over the past
five years.
City-specific variations in performance can be significant, even
within national markets. In 2015, more than 1000 bps separated
the best and worst performing cities in the USA, Canada, and
Australia. Even in the smaller, more densely populated European
markets, spreads exceeding 500 bps between the top and bottom
performing cities in 2015 could be found in the UK, Germany,
and Belgium.
For a property investor, the implication is a two-level approach
to geographic allocations. The macroeconomic issues of interest
rates, currency rates, market transparency, etc., represent the
first level of consideration. These are variables that impact
national markets, and in many ways, they represent relatively
straightforward concepts, with associated risks that can be
generally understood and effectively monitored and measured.
54
Quarter 2 • 2016
Alternative Investment Analyst Review
Exhibit 7: Performance of Cities within Countries, 2015
Source: MSCI
All property annual total returns
Exhibit 8: Range of Asset Level Total Returns across London Submarkets
Source: MSCI
Annual total return (%), 2015
But from inside a national market, city-level economic structures,
strategic location, demographic trends, land use policies and
constraints, and supply fundamentals can all lead to differences
in cyclical performance and investment opportunities from
one metropolitan area to the next. At this subnational level of
allocation, the nuances can become more difficult to grasp as
well as to measure. The underlying drivers and property type
compositions of Las Vegas and Washington, DC, for example, are
not necessarily comparable, nor are Tokyo and Sapporo, Munich
and Dusseldorf, or Vancouver and Montreal.
And Asset Selection Mattered Too
So if an investor’s allocation decisions had led incrementally, first
to real estate, then to the UK, then to London, and from there,
55
MSCI Global Intel Report
specifically to Camden, the next step would be the selection of the
asset. A review of 2015 total returns of individual assets in each
submarket shows a wide range of performance, so wide in fact
that the asset performing at the 95th percentile in London’s worst
performing submarket (Belgravia Knightsbridge) provided a
return of more than five times the asset in the 5th percentile in the
best performing submarket (Camden).
The drilldown into results in 2015 from the global index all the
way to an individual asset in London provides anecdotal evidence
to corroborate earlier findings. Previous research suggests that
around 50% of the variation in real estate performance relates to
property specific factors rather than strategic choices of markets
and property types.
MSCI Global Intel Report
Conclusion
In 2015, global real estate experienced its sixth consecutive year of
steady, positive returns since the GFC. The headline global return
of 10.7% was supported by significant variations in performance
and cyclical movements across countries, property types, and
cities. These variations represent opportunities for investors and
managers but, as markets move through their performance cycles,
the challenge of maintaining consistent and strong real estate
performance rises. As the results of 2015 show, income returns are
being squeezed to record lows across most markets. Meanwhile,
strong global performance has recently been pulled up by the two
largest countries in the global index, the UK and USA, both of
which have a history of volatility in real estate performance. These
two markets together contributed 6.4% of the total 10.7% global
return in 2015. The UK and USA cannot continue to generate
such strong performance indefinitely, and our overview of income
security issues in these two markets (along with Sweden and
Ireland) illustrates how vulnerable seemingly strong markets can
be in their income security.
Against this backdrop, the global appetite for real estate continues
to be strong, driven by the wide spreads between real estate and
bond yields, even in the UK and USA where spreads, though a
bit narrower than a year earlier, still exceeded 250 bps at yearend 2015. The difficulty of gauging the current pricing and
prospects for real estate markets represents a major challenge for
investors and managers of existing portfolios in their deployment
of new capital to real estate. It also relates to more asset-specific
considerations such as levels of development and the approach to
vacant space, credit quality, and lease length. These challenges are
not new for real estate investors, but they become more complex
during periods of macroeconomic uncertainty.
Author’s Bio
Max Arkey
Vice President
Product Management
MSCI Real Estate
Max Arkey works in product management
at MSCI Real Estate where he heads up
indexes and market information products.
These analytics are mission critical to the
investment process for 19 of the top 20 largest global asset
managers, all the way through to specialized domestic investors.
For further details contact: max.arkey@msci.com\
About MSCI
For more than 40 years, MSCI’s research-based indexes and
analytics have helped the world’s leading investors build and
manage better portfolios. Clients rely on our offerings for
deeper insights into the drivers of performance and risk in their
portfolios, broad asset class coverage and innovative research. Our
line of products and services includes indexes, analytical models,
data, real estate benchmarks and ESG research. MSCI serves 98 of
the top 100 largest money managers, according to the most recent
P&I ranking.
©2016 MSCI Inc. All rights reserved
56
Quarter 2 • 2016
Alternative Investment Analyst Review
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Alternative Investment Analyst Review
Q2 2016, Volume 5, Issue 1
Chartered Alternative Investment Analyst Association®
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