Description
Financial intermediation through asset management firms has many benefits. It helps investors diversify their assets more easily and can provide financing to the real economy as a “spare tire” even when banks are distressed. The industry also has various advantages over banks from a financial stability point of view.
F
inancial intermediation through asset management frms has many benefts. It helps investors diversify
their assets more easily and can provide fnancing to the real economy as a “spare tire” even when banks
are distressed. Te industry also has various advantages over banks from a fnancial stability point of view.
Nonetheless, concerns about potential fnancial stability risks posed by the asset management industry
have increased recently as a result of that sector’s growth and of structural changes in fnancial systems. Bond funds
have grown signifcantly, funds have been investing in less liquid assets, and the volume of investment products
ofered to the general public in advanced economies has expanded substantially. Risks from some segments of the
industry—leveraged hedge funds and money market funds—are already widely recognized.
However, opinions are divided about the nature and magnitude of any associated risks from less leveraged,
“plain-vanilla” investment products such as mutual funds and exchange-traded funds. Tis chapter examines sys-
temic risks related to these products conceptually and empirically.
In principle, even these plain-vanilla funds can pose fnancial stability risks. Te delegation of day-to-day
portfolio management introduces incentive problems between end investors and portfolio managers, which can
encourage destabilizing behavior and amplify shocks. Easy redemption options and the presence of a “frst-mover”
advantage can create risks of a run, and the resulting price dynamics can spread to other parts of the fnancial
system through funding markets and balance sheet and collateral channels.
Te empirical analysis fnds evidence for many of these risk-creating mechanisms, although their importance
varies across asset markets. Mutual fund investments appear to afect asset price dynamics, at least in less liquid
markets. Various factors, such as certain fund share pricing rules, create a frst-mover advantage, particularly for
funds with high liquidity mismatches. Furthermore, incentive problems matter: herding among portfolio managers
is prevalent and increasing.
Te chapter does not aim to provide a fnal verdict on the overall systemic importance of the potential risks or
to answer the question of whether some asset management companies should be designated as systemically impor-
tant. However, the analysis shows that larger funds and funds managed by larger asset management companies do
not necessarily contribute more to systemic risk: the investment focus appears to be relatively more important for
their contribution to systemic risk.
Oversight of the industry should be strengthened, with better microprudential supervision of risks and through
the adoption of a macroprudential orientation. Securities regulators should shift to a more hands-on supervisory
model, supported by global standards on supervision and better data and risk indicators. Te roles and adequacy
of existing risk management tools, including liquidity requirements, fees, and fund share pricing rules, should be
reexamined, taking into account the industry’s role in systemic risk and the diversity of its products.
SUMMARY
International Monetary Fund | April 2015 93
3 C
H
A
P
T
E
R
THE ASSET MANAGEMENT INDUSTRY
AND FINANCIAL STABILITY
Prepared by Hiroko Oura (team leader), Nicolás Arregui, Jonathan Beauchamp, Rina Bhattacharya, Antoine Bouveret, Cristina Cuervo,
Pragyan Deb, Jennifer Elliott, Hibiki Ichiue, Bradley Jones, Yoon Kim, Joe Maloney, Win Monroe, Martin Saldias, and Nico Valckx, with
contributions by Viral Acharya (consultant), and data management assistance from Min-Jer Lee, under the overall guidance of Gaston Gelos
and Dong He.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
94 International Monetary Fund | April 2015
Introduction
In recent years, credit intermediation has been shifting
from the banking to the nonbank sector, including the
asset management industry.
1
Tighter regulations on
banks, rising compliance costs, and continued bank
balance sheet deleveraging following the global fnan-
cial crisis have contributed to this shift. In advanced
economies, the asset management industry has been
playing an increasingly important role in the fnancial
system, especially through increased credit intermedia-
tion by bond funds.
2
For emerging markets, portfolio
fows—many of which are channeled through funds—
have shown steady growth since the crisis. Globally, the
1
In this chapter, the defnition of the asset management indus-
try includes various investment vehicles (such as mutual funds,
exchange-traded funds, money market funds, private equity funds,
and hedge funds) and their management companies (see Annex 3.1).
Pension funds and insurance companies are excluded, as are other
types of nonbank fnancial institutions.
2
See October 2014 Global Financial Stability Report.
industry now intermediates assets amounting to $76
trillion (100 percent of world GDP and 40 percent of
global fnancial assets; Figure 3.1).
Te larger role of the asset management industry
in intermediation has many benefts. It helps inves-
tors diversify their assets more easily and can pro-
vide fnancing to the real economy as a “spare tire”
even when banks are distressed. Te industry also
has advantages over banks from a fnancial stability
point of view. Banks are predominantly fnanced with
short-term debt, exposing them to both solvency and
liquidity risks. In contrast, most investment funds
issue shares, and end investors bear all investment risk
(see Figure 3.2, and see Annex 3.1 for a primer on the
industry). High leverage is mostly limited to hedge
funds and private equity funds, which represent a small
share of the industry.
3
Terefore, solvency risk is low in
3
However, these funds can still be a source of systemic risk, as
shown during the Long-Term Capital Management episode in 1998.
Mutual funds and exchange-traded funds do incur portfolio leverage
0
10
20
30
40
50
60
70
80
90
100
0
5
10
15
20
25
30
2001 02 03 04 05 06 07 08 09 10 11 12
Figure 3.1. Financial Intermediation by the Asset Management Industry Worldwide
2. Size of Investment Funds in Selected Advanced Economies
AUM, trillions of U.S. dollars (right scale)
AUM, percent of sample economies’ GDP
1. World Top 500 Asset Managers’ Assets under Management
1
0
20
40
60
80
100
120
140
0
10
20
30
40
50
60
70
80
90
2000 01 02 03 04 05 06 07 08 09 10 11 12 13
Trillions of U.S. dollars (right scale)
Percent of world GDP
Percent of global ?nancial assets excluding loans
The asset management industry intermediates substantial amounts of
money in the ?nancial system.
The growth of investment funds has been particularly pronounced
among advanced economies during the past decade.
Sources: Organisation for Economic Co-operation and Development; and IMF
World Economic Outlook database.
Note: AUM = assets under management. Economies comprise Canada, Germany,
Ireland, Japan, Luxembourg, United Kingdom, and United States. Investment funds
include mutual funds, money market funds, and exchange-traded funds.
Sources: Bloomberg, L.P.; McKinsey (2013); Pensions and Investments and Towers
Watson (2014); IMF, World Economic Outlook database; and IMF staff estimates.
1
The change of asset under management is determined both by valuation
changes of underlying assets as well as net in?ows to funds.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 95
most cases (see October 2014 Global Financial Stabil-
ity Report). Intermediation through funds also brings
funding cost benefts and fewer restrictions for frms
compared with bank fnancing—it does, however, also
expose frms to more volatile funding conditions, so
the advantages have to be weighed against the risks.
Nevertheless, the growth of the industry has given
rise to concerns about potential risks.
4
By now, the
assets under management of top asset management
companies (AMCs) are as large as those of the largest
banks, and they show similar levels of concentration.
5
For emerging markets, the behavior of fund fows has
for some time been a key fnancial stability concern, as
extensively discussed in the April 2014 Global Finan-
cial Stability Report. Similarly, risks from hedge funds
through derivatives and securities lending, about which only limited
information is disclosed. However, most publicly ofered products
have regulatory leverage caps that are generally much lower than
those for banks (see Table 3.1).
4
A report by the Ofce of Financial Research (2013) summariz-
ing potential systemic risks emanating from the industry spurred an
active discussion among academics, supervisors, and the industry.
A large number of qualitative analyses on this topic (CEPS-ECMI
2012; Elliott 2014; Haldane 2014) are available, but comprehensive,
data-based evidence is still limited.
5
In this chapter, the term AMC does not include asset manage-
ment companies set up to handle distressed assets in the context of
bank restructuring and resolution.
and money market funds are already well recognized.
However, the importance of “plain-vanilla” products
is less well understood (Feroli and others 2014). At
the individual fund level, plain-vanilla funds face
liquidity risk: the shares of open-end mutual funds
and exchange-traded funds are usually redeemable or
tradable daily, whereas assets can be much less liquid.
However, the extent to which such risks at the level
of an individual institution can translate into systemic
risk is subject to ongoing research and debate.
Potential systemic risks from less leveraged segments
of the industry are likely to stem from price externali-
ties in fnancial markets and their macro-fnancial
consequences. Systemically important efects may arise
if features of the industry tend to amplify shocks or
increase the likelihood of destabilizing price dynam-
ics in certain asset markets compared with a situation
in which investors invest directly in securities. Tese
efects can have broader economic implications. For
example, if intermediation through funds raises the
probability of fre sales of bonds that are held by key
players in the fnancial sector or that are used as col-
lateral, then the risk of destabilizing knock-on efects
on other institutions rises, with potentially important
macro-fnancial consequences. Similarly, if funds
exacerbate the volatility of capital fows in and out of
0
5
10
15
20
25
30
Open-end
mutual funds
Money market
funds
Exchange-
traded funds
Private equity Hedge funds
Open-end
mutual funds
41%
Separate
accounts
36%
Other
alternatives
2%
Closed-end
mutual funds
1%
Money market
funds
8%
Exchange-
traded funds
4%
Private
equity
5%
Hedge funds
3%
2007
2008
2009
2010
2011
2012
2013
Figure 3.2. Products Offered by Asset Managers and Their Recent Growth
1. Asset Managers’ Intermediation by Investment Vehicles
(Percent of $79 trillion total assets under management, end-2013)
Plain-vanilla products and privately offered separate account services
dominate the markets as measured by assets under management.
2. Recent Growth of Selected Investment Vehicles
(Assets under management in trillions of U.S. dollars)
Open-end funds, exchange-traded funds, and private equity funds
have shown strong growth since the global ?nancial crisis.
Sources: BarclayHedge; European Fund and Asset Management Association;
Organisation for Economic Co-operation and Development; Preqin; and IMF
staff calculations.
Sources: BarclayHedge; European Fund and Asset Management Association;
ETFGI; Organisation for Economic Co-operation and Development; Pensions and
Investments and Towers Watson (2014); Preqin; and IMF staff estimates.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
96 International Monetary Fund | April 2015
emerging markets or increase the likelihood of conta-
gion, signifcant consequences will be endured by the
recipient economies.
6
Some key features of collective investment vehicles
may give rise to such destabilizing dynamics compared
with a situation without intermediaries. Conceptually,
it is important to distinguish clearly between the types
of risks that result from the presence of intermediaries
and those that are merely a refection of the behavior
of end investors and would occur in the absence of
intermediaries (Elliott 2014). Two main risk channels
that are important in this context, even for unlever-
aged funds, are (1) incentive problems related to the
delegation of portfolio management decisions by end
investors to funds, which, among other things, may
lead to herding, and (2) a frst-mover advantage for
end investors (that is, incentives not to be the last in
the queue if others are redeeming from a fund), which
may result in fre-sale dynamics. Tese issues are dis-
cussed in detail in this chapter.
In recent years, the importance of such risks is
likely to have risen in advanced economies because of
structural changes in their fnancial systems. Not only
has the relative importance of the asset management
industry grown, but banks have also retrenched from
6
Other risks include operational risks and risks related to securi-
ties lending, which are not discussed in detail in this chapter. See
Cetorelli (2014).
many market-making activities, possibly contributing to
a reduction in market liquidity (October 2014 Global
Financial Stability Report). Consequently, large-scale
trading by funds could potentially have a larger efect on
markets than in the past. Moreover, the role of fxed-
income funds has expanded considerably—and price
disruptions in fxed-income markets have potentially
larger consequences than large price swings in equity
markets. Te volume of products ofered to the general
public in advanced economies has grown considerably.
7
Finally, the prolonged period of low interest rates in
advanced economies has resulted in a search for yield,
which has led funds to invest in less liquid assets, and
is likely to have exacerbated the risks described above
(October 2014 Global Financial Stability Report).
Tese considerations have sparked a policy discus-
sion about intensifying oversight across advanced and
emerging economies. In 2014, the Financial Stability
Board (FSB) and International Organization of Securities
Commissions (IOSCO) proposed assessment methodolo-
gies to identify investment funds that might be global
systemically important fnancial institutions (G-SIFIs)
and as such would be regulated diferently from the oth-
ers (FSB and IOSCO 2014). Tis proposal was revised
in March 2015, and includes approaches for identifying
both investment funds and asset managers as G-SIFIs
(FSB and IOSCO 2015). Market regulators in major
jurisdictions (Figure 3.3), such as the U.S. Securities and
Exchange Commission (SEC), are considering revising
their approach to the oversight of asset managers and
the products they ofer, including through stress testing
requirements. Tis is a paradigm shift. Until recently,
securities regulators have mainly focused on investor pro-
tection, with limited attention to fnancial stability risks.
Tis chapter aims to shed more light on the empiri-
cal relevance of these issues, thereby contributing to
the understanding of the systemic risk implications of
the asset management industry. Tis task is challeng-
ing given that the risks of concern have not yet or only
partially materialized in advanced economies; inference,
therefore, often has to be indirect. So far, the literature
has only examined partial aspects of these problems in
individual markets. Tis chapter provides an account of
key risk profles of the largest segments in the industry
and an in-depth, original, data-based analysis of some of
7
Retail investors are often seen to be less sophisticated and
informed than institutional investors, and more prone to chase
returns (Frazzini and Lamont 2008). Tis possibly exacerbates the
incentive problems mentioned earlier.
United States
49%
Japan
3%
Other
developed
9%
Brazil
3%
China
2%
Other emerging
markets 4%
Luxembourg 10%
Ireland 5%
France 5%
United Kingdom 4%
Other developed
Europe 7%
Developed
Europe 31%
Sources: European Fund and Asset Management Association; and IMF staff
calculations.
Figure 3.3. Key Domiciles of Mutual Funds
(Mutual funds by domicile, percent of total assets under management,
end-2014)
The mutual fund industry is dominated by U.S. and European funds.
Among emerging market economies, Brazil has the largest fund sector.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 97
the main issues featured in the public discussion, backed
by interviews with asset managers and supervisors. Te
key questions are the following:
• What are the potential sources of financial stability
risks from the asset management industry, particu-
larly from the less leveraged, plain-vanilla segments?
• What is the empirical evidence on the various spe-
cific risk channels?
• What existing internal risk management and over-
sight tools can be used to mitigate financial stability
risks? What needs to be done to better monitor and
mitigate these risks?
Te detailed empirical analysis fnds evidence for
many mechanisms through which funds can create and
amplify risks, although their importance varies across
asset markets:
• Mutual fund investments appear to affect asset
price dynamics, at least in less liquid markets. The
impact, however, does not seem to have risen over
time. Assets that are held in a concentrated manner
by funds perform worse during periods of stress.
• Various factors create run risk, including certain
fund share pricing rules. To some extent, however,
risks are mitigated by funds’ liquidity management.
• The evidence points to the importance of incen-
tive problems between end investors and portfolio
managers. Herding among U.S. mutual funds has
been rising across asset markets, particularly among
retail-oriented funds (whose end investors are more
fickle and for whom assessing the skills of portfolio
managers is more difficult). The patterns of fund
inflows by end investors also encourage poorly per-
forming portfolio managers to take excessive risks.
• However, larger funds and funds belonging to
larger AMCs do not necessarily contribute more to
systemic risk. The investment focus appears to be
relatively more important than size when gauging
systemic risk.
Overall, the evidence calls for strengthening the
microprudential supervision of risks and adopting
macroprudential oversight of the industry:
• Currently, most securities regulators focus on investor
protection and do not intensively supervise risks of
individual institutions with the help of risk indica-
tors or stress tests. This practice needs to be changed,
supported by global standards on microprudential
supervision and more comprehensive data.
• Moreover, macroprudential oversight frameworks
should be established to address financial stability
risks stemming from the industry. These stability risks
originate in price externalities that can be missed by
microprudential regulators and asset managers.
• The roles and adequacy of existing risk management
tools, including liquidity requirements, fees, and
fund share pricing rules, should be reexamined, tak-
ing into account the industry’s role in systemic risk
and the diversity of its products.
Te chapter frst lays out conceptual issues related
to the nature of potential fnancial stability risks from
the industry. Next, various empirical exercises are
conducted to identify diferent behavioral patterns
of mutual fund investors and their fnancial stability
implications. Te chapter then examines the industry’s
oversight framework and makes recommendations for
reducing fnancial stability risks.
Financial Stability Risks of Plain-Vanilla Funds:
Conceptual Issues
Plain-vanilla mutual funds and ETFs—the largest
segment of the industry—do not sufer much from
the known vulnerabilities of hedge funds and money
market funds. Reforms are already underway to address
risks related to hedge funds (which can incur high
leverage and engage in complex strategies with few dis-
closure requirements) and money market funds (some
of which ofer redemptions at a constant nominal
value per fund share, making their liabilities similar to
deposits and vulnerable to runs). In general, these spe-
cifc risks apply less to typical mutual funds and ETFs
(Table 3.1 and Annex 3.1).
Risk Transmission Channels
Intermediation through plain-vanilla funds is, however,
not risk free (Figure 3.4):
8
8
Apart from Table 3.1 and Annex 3.1, this chapter does not cover
separate accounts in detail because of data limitations. However,
SIFMA (2014) indicates that these accounts mainly invest in simple
securities portfolios with little leverage. For pension fund and insur-
ance company investors, separate accounts are bound by overall
investment restrictions set by their respective regulators. Redemption
risks appear to be limited as well because institutional investors tend
to internalize the cost of their sales, and large redemptions can be
settled in kind.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
98 International Monetary Fund | April 2015
• The delegation of investment decisions introduces
incentive problems between end investors and port-
folio managers that can induce destabilizing behavior
and amplify shocks. Investors delegate day-to-day
portfolio management to portfolio managers. Inves-
tors cannot directly observe managers’ daily actions
or their skills, and therefore provide incentives to
managers to act in investors’ interests (Rajan 2005).
9
A common (and imperfect) way of establishing
9
Legally, asset managers have a duty to act as fduciaries on behalf
of their clients.
incentives is to evaluate funds relative to their peers
and relative to benchmarks. This form of evaluation,
in turn, can lead to a variety of trading dynamics
with potentially systemic implications, such as herd-
ing or excessive risk taking (Box 3.1).
10,11
10
Similarly, the same type of informational issues can make it dif-
fcult for investors to distinguish between problems at the fund level
versus problems at the AMC level, possibly leading to “brand name”
efects, in which operational and reputational concerns about one
fund spill over to others in the same fund family.
11
Separate issues arise from passive, index-linked investing.
Increasing investment of this form has been argued to distort asset
Table 3.1. Summary Characteristics and Risk Pro?les of Major Investment Vehicles
Vehicle 2013 AUM
(trillions of
U.S. dollars)
Publicly
Offered
Collective
Investment
Schemes
Typical
Redemption
and Trading
Practice
Typical
Settlement
Method
Solvency
Risk
Leverage
through
Borrowing
1,2
Portfolio
Leverage
2
(Derivatives)
Main
Investor
Clientele
Disclosure
Gap
3
Open-End
Mutual Fund
25 Yes Yes End of day Cash Low Possible
with cap
Yes with cap Retail,
institutional
Low
Closed-End
Mutual Fund
0.5 Yes Yes N.A.
(primary)
Intraday
(secondary)
Cash Low Some yes
with cap
Yes with cap Retail,
institutional
Low
Money
Market Fund
4.8 Yes Yes End of day Cash Low Possible
with cap
Yes with cap Retail,
institutional
Low
Exchange-
Traded Fund
2.3 Yes Yes Infrequent
(primary)
Intraday
(secondary)
In kind
(primary)
Cash
(secondary)
Low Possible
with cap
Yes with cap Retail,
institutional
Low
Synthetic
ETF
0.1
4
Cash Low Possible
with cap
High
derivative
use
Institutional
Private
Equity Fund
3.5 No Yes N.A.
(closed-end
with long-
term finite
life)
Cash High
5
Some yes,
no cap
No
information
Institutional Medium
Hedge Fund 2.2 No Yes Quarterly
+ lock-up
period +
90 days
advance
notice
Cash High
5
High no cap High no cap Institutional Medium
Separate
Account
6
22
7
No No No
information
Cash or in
kind
Low No
information
8
No
information
8
Institutional High
Sources: BarclayHedge; Deutsche Bank (2014); ETFGI; EFAMA (2014); ICI (2014a, 2014c); McKinsey (2013); Metrick and Yasuda (2011); Morningstar (2012); OFR (2013); Preqin;
PriceWaterhouseCoopers (2013); and IMF staff estimates.
Note: AUM = assets under management; ETF = exchange-traded fund; N.A. = not applicable.
1
Borrowing includes issuing debt or taking bank loans.
2
No cap means no regulatory cap, and with cap means there are regulatory caps on the leverage. For public funds in the United States, leverage is capped at 33 percent of assets
including portfolio leverage. European Undertakings for Collective Investment in Transferable Securities (UCITS) funds can borrow up to 10 percent of assets, but only temporary bor-
rowing is allowed and it should not be used for investment.
3
Disclosure in this column is about securities, borrowing through loans, and cash holdings information. Across all products, there is very little information about derivatives and
securities ?nancing transactions (repurchase agreements and securities lending transactions), their counterparties, and collateral.
4
The ?gure covers European-listed synthetic exchange-traded funds. Synthetic products are mainly seen in Europe and to a lesser extent in Asia. See Annex Table 3.1.1 for a descrip-
tion of synthetic products.
5
In addition to taking leverage, these types of funds risk their own capital and balance sheets when investing given that they comingle client investors’ money with their own money for investment.
6
This is different from “separate account” used among insurance companies. See Annex Table 3.1.1 for description.
7
The ?gure is based on the U.S. data reported in OFR (2013) and the European data reported in EFAMA (2014).
8
Investment strategy should be in line with the mandate set by clients and their regulatory requirements (such as insurance and pension fund regulations).
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 99
• Easy redemption options can create run risks due
to a first-mover advantage.
12
Investors can have an
incentive to exit faster than the others even without
constant net asset value (NAV) or guaranteed returns
if the liquidation value of fund shares declines as
investors wait longer to exit. This decline in value
could happen for various reasons. First, asset man-
agers may use cash buffers and sell relatively more
liquid assets first in the face of large redemptions.
Second, certain funds have fund share pricing rules
that pass the costs of selling assets—possibly at fire-
sale prices—on to the remaining investors (Box 3.2).
Such effects are intensified when funds are investing
in relatively less liquid assets, and thereby create large
mismatches between the market liquidity of assets
and liquidity offered to end investors (October 2014
Global Financial Stability Report).
13
prices and risk-return tradeofs (Wurgler 2010 and Box 3.1). Tis
chapter does not explore these issues.
12
Te incentive to redeem quickly is often referred to as “strategic
complementarity,” and is similar to the mechanism behind bank
runs (as in Diamond and Dybvig [1983]). More generally, problems
related to the delegation of investment decisions or frst-mover
advantage are also present in other forms of fnancial intermediation,
albeit to diferent degrees. For instance, pension funds and insurance
companies face much lower redemption risks.
13
A related issue concerns the pricing of infrequently traded
securities. Te October 2014 Global Financial Stability Report dis-
cusses some of the issues related to the so-called matrix pricing.
A large proportion of funds issue easily redeem-
able shares, and liquidity mismatches have been rising
(Figures 3.5 and 3.6). Open-end funds are exposed
to redemption risk because investors have the ability
to redeem their shares (usually on a daily basis) while
funds have increasingly been investing in relatively
illiquid securities such as high-yield corporate bonds
and emerging market assets.
Large-scale sales by funds may exert signifcant
downward asset price pressures, which could afect
the entire market and trigger adverse feedback loops.
Te efects on asset prices could have broader macro-
fnancial consequences, afecting the balance sheets
of other actors in fnancial markets; reducing collat-
eral values; and reducing credit fnancing for banks,
frms, and sovereigns. Te efects could also be spread
unevenly across jurisdictions. For instance, the main
impact of trades by funds domiciled in advanced
economies could be felt in emerging markets (see
April 2014 Global Financial Stability Report for
details).
Although these potential risks and propagation
channels are recognized as theoretical possibilities,
there is disagreement about their importance in prac-
tice. Advanced economies have experienced few cases
in which asset management activities outside of hedge
funds and money market funds triggered or amplifed
Incentive
problems
of
managers
Run risk
Price
externalities–
?re sales,
contagion,
volatility
Macro?nancial
consequences
Source: IMF staff.
Figure 3.4. Unleveraged Open-End Funds and Systemic Risk
Information gap between managers and investors
• Benchmark-based evaluation
–Excessive risk taking
–Herding
• Brand name effects (spillovers of redemption
within fund family)
First-mover advantage
• Liquidity mismatch
• Managers sell liquid assets ?rst
• Some fund share pricing rules impose cost of
liquidity risk unfairly on second movers
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
100 International Monetary Fund | April 2015
Te delegation of investment decisions introduces
incentive problems between end investors and fund
managers, which can induce destabilizing behavior
and amplify shocks. As discussed in the primer on the
asset management industry (Annex 3.1), end investors
delegate day-to-day control of portfolios to managers.
Investors cannot directly observe managers’ abilities, nor
do they see every single trade and portfolio position.
Investors, therefore, provide incentives to asset managers
to act in investors’ interests (Rajan 2005). A common
way of providing incentives is to evaluate asset managers
relative to their peers and to benchmarks. Tis evalu-
ation can take direct or indirect forms: (1) managers’
compensation can be linked to relative performance
(Ma, Tang, and Gomez 2013) or (2) investors inject
money into funds that perform well relative to their
benchmarks. Te efect of the latter is similar to the
efect of the former if compensation increases with
assets under management (AUM). Tese incentive
problems, in turn, can lead to a variety of dynamics
with potentially systemic implications (Stracca 2006).
More specifcally, they can lead to the following:
• Excessive risk taking—If a fund’s AUM grow more
with good performance than shrink with poor
performance, incentives are created to incur more
risk when the fund is falling behind (Chevalier
and Ellison 1997; Ferreira and others 2012; see the
example in Table 3.1.1). Similar incentives exist in a
“tournament” setting, in which funds are evaluated
based on their interim performance (say, in the
middle of the year) compared with peers (Basak,
Pavlova, and Shapiro 2008).
1
• Contagion—By contrast, if fund managers become
more risk averse in response to past losses, and if
they are evaluated against their peers or bench-
marks, they may be induced to retrench to the
benchmark in response to losses. This behavior, in
turn, can induce the transmission of shocks across
assets and result in momentum trading (Broner,
Gelos, and Reinhart 2006). See Calvo and Mendoza
(2000), Chakravorti and Lall (2003), and Ilyina
(2006) for other types of models linking bench-
mark-based compensation to contagion.
• Herding, return chasing, and incentives to run—Evalu-
ation relative to average performance tends to induce
risk-averse portfolio managers to mimic the behavior
of peers (Scharfstein and Stein 1990; Arora and
Ou-Yang 2001; Maug and Naik 2011). Incentives
to herd are reinforced because end investors can exit
funds quickly, and mutual fund managers cannot
afford to wait until their peers’ private information is
revealed and incorporated fully in asset prices (Froot,
O’Connell, and Seasholes 2001). Vayanos (2004)
shows that when fund managers lose AUM because
of poor performance, ‘‘flights to quality’’ may occur.
Feroli and others (2014) construct a model in which
performance evaluation relative to benchmarks cre-
ates incentives for fund managers to join sell-offs
during downturns and chase yield during upturns.
Buffa, Vayanos, and Woolley (2014) discuss theoreti-
cally how such benchmark-centric assessments can
contribute to the buildup of bubbles.
• Churning and noise trading—Delegated portfolio
management may induce managers to churn (engage
Box 3.1. Possible Incentive Problems Created by Delegated Management
1
Tis is also known as the “risk-shifting” problem. More generally, risk shifting arises when earnings for managers are convex based
on their compensation. Limited liability also contributes to the convexity of manager earnings. See Ross (2004) for a qualifcation of the
payof convexity argument. See also Massa and Patgiri (2009).
Table 3.1.1. An Illustrative Example of Asset Managers’ Incentives for Risk Taking
Because investors reward winners more than they punish poor performers, it pays to take risks.
Options Likelihood (percent)
Outcome: Change in Net
Asset Value
Net Inflows to Fund
(millions of
U.S. dollars)
Additional Fee Income
(1 percent of assets
under management, in
millions of U.S. dollars)
Benchmark Portfolio 100 Same as benchmark 0 0
Gamble
50 10% in excess of
benchmark
100 1
50 10% below benchmark ?20 ?0.2
Expected outcome Same as benchmark 40 0.4
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 101
in noise trading) to signal their talent and superior
knowledge, given that it is difficult to identify talent
and effort (Allen and Gorton 1993; Dow and Gor-
ton 1997; Dasgupta and Prat 2006).
• Market depth and volatility—Performance evalu-
ation relative to a benchmark may lead to higher
price volatility of securities that are included in
the benchmark. Since information acquisition may
be hindered by these relative-performance-based
contracts, the depth of the market may be reduced
(Igan and Pinheiro 2012). Basak and Pavlova
(2014) develop a general-equilibrium asset price
model that incorporates incentives for institutional
investors to do well relative to their index. The
induced investment patterns create excess correla-
tions among stocks belonging to an index. It also
increases the volatility of index stocks and of the
overall market.
Box 3.1 (continued)
Certain forms of fund share pricing can give rise to
a frst-mover advantage for investors to run. Te key
factor is how investment losses and trading costs are
distributed between buy-and-hold and redeeming fund
shareholders. If these are borne by the fund and there-
fore by the buy-and-hold shareholders, investors can
recover more cash by redeeming early.
Infexible net asset value (NAV) pricing can gener-
ate a frst-mover advantage for an open-end mutual
fund (Table 3.2.1). In the United States, funds
issuing redeemable securities are required to sell,
redeem, or repurchase such securities based on the
NAV of the security “next computed” after receipt
of the order. Transaction costs—trading fees, market
Box 3.2. Fund Share Pricing Rules and First-Mover Advantage
Table 3.2.1. Comparison of Fund Pricing Rules
(Millions of U.S. dollars)
Transactions
UCITS
Swing Pricing (Full)
UCITS-AIF
Dual Pricing
U.S. Open-End Mutual Fund
(1940 Act)
Beginning NAV 100 100 100
Net Flows ?15 ?15 ?15
Purchases +5 +5 +5
Redemptions ?20 ?20 ?20
Total Costs of Selling Assets
(0.1 percent, including bid-ask
spread)
0.015 0.015 0.015
Transaction Costs Incurred
by Investors Purchasing
Fund Shares
?0.005
1
0 0
Transaction Costs Incurred
by Investors Redeeming
Fund Shares
0.020 0.015 0
Transaction Costs Incurred by
Fund and Remaining Investors
0 0 0.015
2
Ending NAV 85.000 85.000 84.985
Memo Estimated transaction costs borne by trading investors Actual transaction costs
borne by fund
Source: BlackRock (2014b).
Note: AIF = Alternative Investment Fund (European directive governing products including hedge funds and private equity funds); NAV = net
asset value (mutual fund share price, per share); UCITS= Undertaking of Collective Investment in Transferable Securities (European Union direc-
tive governing publicly offered investment funds). In the United States, investment companies (as de?ned) are regulated primarily under the U.S.
Investment Company Act of 1940.
1
Because fund NAV has swung to the bid price because of net redemptions, purchasing investors bene?t to the extent that they purchase units
that are cheaper than preswung NAV. This bene?t is offset by the costs paid by redeeming clients.
2
In certain circumstances, portfolio managers may choose to use cash buffers or borrow funds (or both) to meet redemptions without incurring
transaction costs.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
102 International Monetary Fund | April 2015
impact, and spread costs—are borne by the funds.
Tis reduces a fund’s NAV, possibly by a substantial
amount if market liquidity dries up. Te European
framework, in contrast, allows for pricing rules such
as swing- or dual-pricing rules, as described in Table
3.2.1, that adequately impose transaction costs on
redeeming shareholders instead of the fund. Tis
helps reduce remaining shareholders’ incentive to
run.
Te share pricing practice of exchange-traded
funds (ETFs) is diferent from that of open-end
mutual funds. As shown in Figure 3.2.1 and Annex
3.1, ETFs do not directly transact with end inves-
tors. “Authorized participants”—typically major
broker-dealers—trade in between. Only autho-
rized participants trade with ETFs in the primary
market, and trades are usually settled in kind.
Intraday liquidity to end investors is ofered in the
secondary market by authorized participants.
1
Te
key diference between ETFs and mutual funds in
the context of frst-mover advantage is that ETFs
are not required to pay cash back to investors at
NAV.
2
Authorized participants trade ETF shares
with clients or on stock exchanges at the ETF share
price determined in the secondary market. Tere-
fore, depending on market conditions, an ETF’s
share price could be higher or lower than the ETF’s
indicative NAV.
Box 3.2 (continued)
1
Although there is a widespread perception that ETFs face higher redemption risks because they ofer intraday liquidity to share-
holders, intraday liquidity (ofered in the secondary market) is not the same as intraday redemption (ofered in the primary market).
Primary market activities, which result in fund fows, are much less frequent than secondary market trading (ICI 2014c; BlackRock
2014a).
2
In the United States, ETFs operate with the Securities and Exchange Commission’s special exemption from the 1940 Act
requirement that open-end funds repay redeeming shareholders at the next NAV calculated after an order is submitted (ICI
2014b).
Figure 3.2.1. Structure of Exchange-Traded Funds
Source: IMF staff.
Note: AP = authorized participant; ETF = exchange-traded fund; NAV = net asset value.
Primary Market
NAV may not be equal to ETF share price, depending on arbitraging capacity of APs
Hold shares, arbitrage trading
ETF
Authorized
participant
Physical
“basket” of
securities
ETF share
price
Liquidity
premium or
discount paid
by investors
Investors,
stock
exchange Securities
NAV represents
market value of
ETF’s assets
Cash
Shares Shares
Secondary Market
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 103
systemic distress.
14
Te realization of brand risk and
redemptions from PIMCO funds in September 2014
did not result in major disruptive market movements
because, overall, bond funds continued to receive net
infows. However, the academic literature has docu-
mented contagion and amplifcation efects for some
14
Tere have been some cases of non–money market mutual
fund distress in emerging markets. For example, in 2001, a fund
managed by Unit Trust of India, which was outside the ambit of
the Securities and Exchange Board’s jurisdiction, became unable to
meet its obligations due to the absence of timely corrective action
to bring the sale/repurchase price of the units in line with the
fund’s net asset value. With a risk of a run on the Unit Trust of
India and possible adverse fnancial market impact, India’s govern-
ment came out with a rescue package. Te total bail-out amounted
to US$76 million.
markets, in particular emerging markets.
15
Moreover,
recent structural shifts in many markets following
the global fnancial crisis require a fresh review of the
evidence.
Against this backdrop, this chapter empirically
explores the precise channels through which mutual
funds and ETFs can afect fnancial stability. Te aim
15
In addition to the literature on emerging markets discussed
in the April 2014 Global Financial Stability Report, various studies
examine the role of funds in transmitting shocks across markets in
advanced economies. Using U.S. data during the global fnancial
crisis, Hau and Lai (2010) fnd that mutual funds helped transmit
shocks from bank equities to nonfnancial frms’ equities, and Man-
coni, Massa, and Yasuda (2012) fnd that mutual funds that incurred
losses from securitized debt sold of corporate bonds, which induced
a price impact on bonds held by these funds.
Redeeming shareholders need to pay for the
cost of market liquidity risk by accepting an ETF
share price below NAV if market liquidity dries up.
Authorized participants are usually arbitrageurs,
and if they see a major gap between NAV and ETF
share prices, they trade in the direction to close the
gap. If investors fnd it easier to sell ETF shares
relative to the underlying assets, this will tend to
result in a discount to NAV. Te discount can be
accentuated when funding conditions reduce autho-
rized participants’ arbitrage capacity (Figure 3.2.2).
Te cost of “fre sales” of ETF shares is borne by the
trading shareholders, not by the ETF or buy-and-
hold shareholders, reducing buy-and-hold share-
holders’ incentive to run.
Economically, these fexible fund share pricing rules
are similar to countercyclical redemption and purchase
fees that refect market liquidity cost and are added
to NAV. If a U.S. 1940 Act fund imposes purchase
and redemption fees that are retained by the fund
3
and refect the bid-ask spreads for transactions (or
ETF NAV and share price gap), the outcome would
be similar to that of funds with fexible share pricing
rules. At the same time, such fees also help ensure
equality between buy-and-hold investors and trading
investors.
Box 3.2 (continued)
3
Current U.S. rules do allow for the introduction of fees that are added to funds’ NAV, which can then be distributed to remaining
shareholders.
–1.5
–1.0
–0.5
0.0
0.5
1.0
J
a
n
.
2
0
0
0
J
u
l
.
0
1
J
a
n
.
0
3
J
u
l
.
0
4
J
a
n
.
0
6
J
u
l
.
0
7
J
a
n
.
0
9
J
u
l
.
1
0
J
a
n
.
1
2
J
u
l
.
1
3
ETF share price > NAV
“Premium” for ETF share
ETF share price < NAV
“Discount” for ETF share
Figure 3.2.2. Difference between NAV and
ETF Share Price
(Percent of NAV, all countries, equity funds)
Sources: Bloomberg, L.P.; and IMF staff calculations.
Note: ETF = exchange-traded fund; NAV = net asset
value.
The ETF share can be traded in the secondary
market at a discount to NAV when markets are
under generalized stress.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
104 International Monetary Fund | April 2015
is not to provide a fnal verdict on the overall systemic
importance of the potential risks, or draw defnite con-
clusions about whether certain AMCs and their funds
should be designated as SIFIs. Rather, the chapter
carries out a quantitative analysis of a number of key
risk transmission and amplifcation channels, test-
ing some of the underlying hypotheses, and updating
and complementing the existing literature. Given the
current absence of a broad-based empirical assessment
of the issues, this chapter flls an important gap. In
particular, whereas most existing studies cover equity
markets, the analysis here also covers bond markets.
Te chapter does not discuss all sources of risk. In
particular, operational risks, risks related to hidden
leverage and securities lending, and issues related to
resolution are only touched upon (FSOC 2014).
16
Financial Stability Risks of the Mutual Fund
Industry: Empirical Analysis
Tis section examines various aspects of potential
fnancial stability risks using a wide range of macro- and
micro-level data. Tree main questions are explored. First,
does fund investment afect asset price dynamics? Second,
what determines fund fows and how do funds manage
liquidity? And third, what is the degree of herding and
interconnectedness, and what is the relationship between
a fund’s contribution to systemic risk and its size?
17
Mutual Fund Investment and Asset Price Dynamics
Aggregate mutual fund fows and asset prices
Do fund fows afect asset price dynamics in the
United States and in emerging markets? For mutual
funds to have a destabilizing efect, fund trades must
frst, at least in the aggregate, have an impact on
prices. Te literature suggests the existence of price
pressures related to mutual fund fows.
18
Te analysis
here updates and complements such fndings, analyz-
ing weekly net infows to U.S. mutual funds invest-
ing in U.S. equities and various types of U.S. bonds,
and their relationship to the respective market index
returns. It also investigates mutual fund investment
fows into bonds and equities in a number of emerging
markets (see Annex 3.2 for details). Te analysis goes
16
Furthermore, the analysis in the chapter does not cover separate
accounts held at funds.
17
Te main data sources for mutual funds are Lipper (a global
mutual fund database with information at the fund level); the
Center for Research in Security Prices (CRSP) U.S. mutual fund
database (with security-by-security asset holdings information and
details of fee structures); EPFR Global; and Lipper’s eMaxx, which
shows global mutual fund ownership of bonds at the security level.
18
Studies include Warther (1995); Edelen (1999); Edelen and
Warner (2001); Cao, Chang, and Wang (2008); and Ben-Raphael,
Kande, and Wohl (2011). Te main conclusion from these studies
is that aggregate mutual fund fows afect contemporaneous stock
returns. Coval and Staford (2007) show that sudden increases or
decreases in net fows to funds result in price pressure efects even
in the extremely liquid U.S. equity market. Manconi, Massa, and
Yasuda (2012) document a price impact on corporate bonds follow-
ing sell-ofs by funds. Similarly, Jotikasthira, Lundblad, and Ramado-
rai (2012) document that investor fows domiciled in developed
markets induced fre sales in emerging markets, with a signifcant
price impact. Feroli and others (2014) analyze several subsegments
of bond fund fows, and fnd evidence for fow-price feedback loops,
except for U.S. Treasuries.
EM equity MF
Mixed MF
Synthetic equity
ETF
2
Synthetic other
ETF
3
Physical equity
ETF
2
Physical other
ETF
3
Hedge fund
Closed-end MF
4
Private equity
Other AE and
global bond MF
EM bond MF
AE HY bond
More dif?cult
to redeem
Easier
to redeem
Ease of redemption by end investors
1
I
l
l
i
q
u
i
d
i
t
y
o
f
a
s
s
e
t
s
More
“liquidity mismatch”
Illiquid
assets
Liquid
assets
AE and global
equity MF
MMF
Sources: BarclayHedge; Deutsche Bank; ETFGI; European Fund and Asset
Management Association; Lipper; Preqin; and IMF staff estimates.
Note: The liquidity ranking of assets is based on IMF staff’s judgment. AE =
advanced economy; EM = emerging market; ETF = exchange-traded fund; HY =
high yield; MF = mutual fund; MMF = money market fund.
1
For ETFs, the ease-of-redemption measure ranks lower than that for open-end
MFs (all MFs in the ?gure excluding closed-end MFs) because end investors do not
directly redeem shares from funds (see Annex 3.1 and Box 3.2).
2
Generally, equity derivatives markets are less liquid than cash equity markets.
3
For bonds, especially corporate bonds, derivatives markets can offer better
market liquidity than the cash bond market. For some ?rms, the notional principal
for their credit default swaps is larger than their outstanding debt.
4
Closed-end mutual funds tend to invest in relatively less liquid assets than
open-end mutual funds (Chordia 1996; Deli and Varma 2002). Some funds may
repurchase shares.
The mismatch between the redemption risk to funds and market liquidity
of funds’ assets is most notable among bond mutual funds—especially
corporate and emerging market debt funds, though these are relatively
smaller segments.
Figure 3.5. Liquidity Mismatches
(Size of bubbles represents relative global assets under management as
of end-2013)
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 105
beyond most of the literature by examining the price
impact of the “surprise” component of fund fows, fol-
lowing Acharya, Anshuman, and Kumar (2014).
19,20
Te evidence is consistent with mutual fund fows
afecting asset returns in smaller, less liquid markets (Table
3.2). Surprise outfows are associated with lower same-
week asset returns in emerging markets, and to a lesser
extent in U.S. high-yield bond and municipal bond mar-
kets. Te annualized price impact is not negligible: bond
returns rise by about 5 percentage points when aggregate
fund infows are higher than the top 25th percentile, and
fall by a similar magnitude for outfows exceeding the
top 25th percentile across bond categories. In emerging
markets, and also in the U.S. municipal bond market,
the negative price efects from sell-ofs tend to be larger
than the positive price efects from purchases. Te price
impact of surprise fows is signifcantly larger when global
risk aversion (as measured by the Chicago Board Options
Exchange Market Volatility Index, or VIX) is high. More-
19
As will be shown later in this chapter, mutual fund fows partly
respond to past fund returns and are therefore partially predictable.
Surprises are measured by the residuals of a standard vector autore-
gression model for fows and returns; see Annex 3.2.
20
In contrast to much of the literature, this analysis uses weekly,
not monthly, data, which allows for better identifcation of the
efects. Nevertheless, inference remains difcult at this frequency.
Figure 3.6. Growth in Bond Funds by Investment Focus
(Assets under management of bond funds worldwide; billions of U.S.
dollars)
2004 05 06 07 08 09 10 11 12 13 14
Sources: Lipper; and IMF staff calculations.
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
Other Bond Funds
Other advanced economy bonds
Advanced economy high-yield bonds
Emerging market bonds
Table 3.2. Mutual Fund Flows and Asset Returns
Emerging Markets United States
Equity Bond Equity All Bond High-Yield Bond Municipal Bond
Estimation Periods 2004–14 2004–14 2007–14 2007–14 2007–14 2007–14
Single Equation Model with Excess Asset Return as Dependent Variable
Surprise flows have significant
impact on returns
Yes Yes Yes in 2012–14 Yes in 2008–10 Yes* Yes
Asymmetry: Impact of surprise
inflows is different from impact
of surprise outflows
Outflows have
larger impact
than inflows
Outflows have
larger impact
than inflows
Limited** Inflows have
larger impact
than outflows
No Outflows have
larger impact
than inflows
VIX sensitivity: Surprise flows
have higher impact on returns
when the VIX is high
Yes Yes Limited** Limited** Yes Yes
Vector Autoregression with Unadjusted Flows and Returns
Flows help predict returns No Yes No Yes*** No Yes***
Sources: Bank of America Merrill Lynch; Morgan Stanley; Bloomberg, L.P.; EPFR Global; ICI; and IMF staff estimates.
Note: VIX = Chicago Board Options Exchange Market Volatility Index. Surprise ?ows are residuals from a vector autoregression model, VAR, with two endogenous vari-
ables (mutual fund ?ows into each asset class and representative benchmark asset returns for the respective market over the one-month Eurodollar deposit rate) and the
VIX (contemporaneous and lagged) as an exogenous variable. Mutual fund ?ows to emerging markets are investment ?ows into each country from all mutual funds from
various jurisdictions covered by EPFR Global. U.S. fund ?ows data are investors’ ?ows into mutual funds with a stated investment focus, covering funds domiciled in the
United States. U.S. data are from Investment Company Institute, except for U.S. high-yield bond funds, which come from EPFR Global. Explanatory variables in the base
single equation model include contemporaneous and lagged surprise ?ow, lagged excess return, the VIX, and the volatility of excess return (estimated with a generalized
autoregressive conditional heteroskedasticity, GARCH, model). The model is estimated for the whole indicated period as well as rolling three-year periods in between. The
results in the bottom line are based on generalized impulse responses.
*For the entire sample period, the results are not signi?cant. However, three-year subperiod estimates show that the coef?cient on contemporary surprise ?ows is always
statistically signi?cant and positive, but declines steadily over time. Limited ** indicates signi?cance between the 5 percent and 10 percent signi?cance levels. ***Indi-
cates not robust to all speci?cations.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
106 International Monetary Fund | April 2015
over, bond markets show evidence of nonlinearities, with
unusually large surprise infows or outfows associated
with a disproportionate impact on bond returns. Tere is
no evidence, however, for an increase in the price impact
over time—if anything, the evidence across markets indi-
cates a decline in the efect.
21
Te price impact pattern provides support for the
existence of a frst-mover advantage only in less liquid
markets. Flows helping to predict price movements
would be consistent with the presence of incentives to
run.
22
Such predictive power of fows is more likely
to be present in less liquid markets. In line with this
notion, the evidence indicates that fows have an impact
on future returns of emerging market bonds, and to a
lesser extent, in U.S. bond and municipal bond markets.
For the latter two markets, however, the results are not
robust across econometric specifcations. Possibly, the
considered aggregate bond categories may be too broad
and too liquid to unambiguously pick up the efect.
23
Efect of mutual fund holdings and their
concentration on bond yields
Does concentration of holdings among mutual funds
matter during periods of stress? Some mutual funds
have a large footprint in specifc market segments, rais-
ing concerns that decisions by a few portfolio manag-
ers may have a large price impact in those markets.
Since the global fnancial crisis, mutual fund bond
holdings and their concentration have risen some-
what (Figure 3.7, panels 1 and 2).
24
Te evidence in
the literature suggests that concentration matters for
stock price dynamics, in particular during periods of
volatility.
25
Tis section investigates this issue further
21
Te evidence on contemporaneous price efects does not conclu-
sively prove that fund fows drive returns. For example, fund fows
and returns could both be driven by news. Still, this would leave the
question open of why mutual fund fows behave distinctively (since
not everybody can trade in the same direction in response to news).
22
Te argument (as laid out in Stein [2014]) is that if outfows
are frst met with cash and the sale of more liquid assets, while less
liquid assets are sold gradually, predictable downward pressure would
be created on the prices of these less liquid assets. Tis, in turn,
would create an incentive for end investors to pull out quickly if
others are withdrawing.
23
See also Collins and Plantier (2014). Moreover, the efects are
more likely to be present at times of stress, and are therefore not eas-
ily picked up in an estimation spanning a long period.
24
Concentration is measured by identifying, for each individual
bond, the largest fve investors among mutual funds. Alternative
measures (top 10 investor holdings and Herfndahl index) yield
similar results.
25
Greenwood and Tesmar (2011) report that fragility, measured
by the concentration of mutual fund ownership of large U.S. stocks
using security-level bond ownership data, assessing
whether mutual fund holdings and their concentration
were correlated with the degree of bond yield changes
around the global fnancial crisis and the taper shock
in 2013, after controlling for bond-specifc charac-
teristics (see Annex 3.2 for details). Te analysis goes
beyond the literature to date by covering diferent
asset markets, including corporate bonds for advanced
economies, and corporate and public sector bonds for
emerging market economies.
Te fndings suggest that larger mutual fund holdings
and greater ownership concentration adversely afect
bond spreads in periods of stress (Figure 3.7, panels 3
and 4). During the period of sharp price adjustments
around the global fnancial crisis in 2008, bonds with
larger fund ownership and those with a higher con-
centration of ownership experienced higher increases
in credit spreads. Possibly, this is related to incentives
to run created by funds. In the face of price drops of
assets held by their fund, end investors may be induced
to redeem quickly, for fear that they could be disadvan-
taged if they exit late. Te efect was most pronounced
among those securities with the highest initial spreads.
Tis may suggest that funds either try to actively alter
their holdings in a crisis by reducing exposures to riskier
bonds, or are forced to sell riskier securities to meet
investor redemptions. Investor concentration made
bonds from emerging market and developing economies
more vulnerable to the 2013 taper episode, but this was
not the case for bonds from advanced economies.
Behavior of Fund Flows and Fund Liquidity Management
Roles of end investors and asset managers
Mutual fund investments are driven by the decisions
of both end investors (fund fows) and asset managers
(portfolio rebalancing). A fund’s investment in a specifc
asset can increase either because the fund receives money
from end investors that is proportionally allocated to all
assets, or because the portfolio manager invests relatively
more money into the asset (portfolio rebalancing). To
ascertain the relative importance of each factor, the anal-
ysis compares the variances of (1) changes in the return-
adjusted weights of each security in a fund’s portfolio
and (2) fund fows (see Annex 3.2). For U.S.-domiciled
funds, the results indicate that about 70 percent of
and the correlation of trading among investors, strongly predicts
price volatility over 1990–2007. For Spanish stocks, Desender
(2012) fnds that ownership concentration is valued positively (nega-
tively) by the stock market during down (up) market periods.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 107
0
10
20
30
40
50
60
70
80
90
100
0 250 500 750 1,000 0 250 500 750 1,000
Individual bond
0
2
4
6
8
10
12
14
16
18
0
.
1
0
.
4
0
.
8
1
.
4
2
.
0
2
.
6
3
.
2
3
.
9
4
.
6
5
.
4
6
.
3
7
.
3
8
.
5
9
.
7
1
1
.
3
1
3
.
2
1
5
.
7
1
8
.
9
2
3
.
6
3
6
.
1
C
h
a
n
g
e
i
n
c
r
e
d
i
t
s
p
r
e
a
d
s
Top ?ve holdings, 2013:Q1
Top ?ve holdings, 2008:Q2
0
10
20
30
40
50
60
70
80
90
100
Individual bond
Top ?ve holdings, 2013:Q1
Top ?ve holdings, 2008:Q2
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0
.
1
0
.
5
0
.
9
1
.
4
2
.
0
2
.
6
3
.
2
3
.
8
4
.
4
5
.
1
5
.
7
6
.
4
7
.
1
7
.
9
9
.
1
1
0
.
6
1
2
.
1
1
4
.
0
1
7
.
0
3
7
.
4
C
h
a
n
g
e
i
n
c
r
e
d
i
t
s
p
r
e
a
d
s
Mutual fund concentration in bond markets has increased somewhat since the global ?nancial crisis.
(Share of individual bonds held by the ?ve largest mutual funds in 2008 and 2013, percentage points)
Bonds with higher mutual fund holding concentration were more adversely affected during stress periods in 2008 and 2013.
(Increase in credit spreads by share of bonds held by the ?ve largest mutual funds, percentage points)
1. Concentration of Mutual Fund Bond Ownership: U.S. Bonds 2. Concentration of Mutual Fund Bond Ownership: Emerging Market
and Developing Economy Bonds
3. Corporate Bonds Issued by U.S. Issuers, 2008:Q2 and 2008:Q4 4. Bonds Issued by Emerging Market and Developing Economies,
2013:Q1 and 2013:Q2
Sources: eMaxx; and IMF staff calculations.
Note: In all panels, holdings by the ?ve largest mutual funds are identi?ed for each individual bond. Bonds are sorted in different buckets on the horizontal axis
according to the share of the bond held by the ?ve largest mutual funds. The vertical axes in panels 3 and 4 show the average change in credit spreads (bond yields
over benchmark government bond yields of the same currency and similar maturity) for bonds in each bucket, between 2008:Q2 and 2008:Q4, and 2013:Q1 and
2013:Q2, respectively.
Figure 3.7. Bond Ownership Concentration and Its Effects on Credit Spreads
Share of bonds held by the largest ?ve mutual fund investors (percent) Share of bonds held by the largest ?ve mutual fund investors (percent)
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
108 International Monetary Fund | April 2015
the variance of funds’ fows into assets is attributable
to managers’ decisions, with the remaining 30 percent
attributable to end investors. Tis decomposition does
not, however, take into account that, as discussed earlier,
managers’ behavior is to a signifcant extent indirectly
driven by the incentives provided by end investors,
including through the pattern of infows.
Determinants of fund fows
Given the importance of fund infows for mutual
fund investment and induced price efects, this section
investigates the determinants of net fund injections by
end investors. Te analysis uses monthly net infows
for U.S. mutual funds and ETFs at the funds’ share-
class level for open-end bond and equity funds, cover-
ing the period 1998–2014 (Annex 3.2).
26
Explanatory
26
A mutual fund can issue multiple classes of shares that only
difer in the structure of various types of fees (FINRA 2011). Te
sample includes U.S.-domiciled open-end mutual funds and ETFs,
irrespective of their investment focus. For instance, U.S. funds
variables include fund performance (benchmark return
and fund return in excess of the benchmark return),
the VIX, fund characteristics (size, age, clientele) and
structures (purchase and redemption fees, and dum-
mies for index funds and for ETFs), and the liquidity
of the underlying asset class.
End investors’ fows to funds, especially those from
retail investors, are procyclical and display a “fight to
quality” during times of stress (Figure 3.8):
• Fund flows increase after good market performance
of the respective asset class. This indicates that inves-
tors pursue momentum strategies, increasing their
allocation to asset classes that have performed well
in the past, and selling past losers.
• End investors engage in a flight to quality during
episodes of stress. As uncertainty (measured by the
investing in emerging market debt are included. Te focus is on the
United States because of data availability on fees, as a result of more
comprehensive disclosure requirements.
–2
–1
0
1
2
3
4
A
v
e
r
a
g
e
m
o
n
t
h
l
y
f
u
n
d
?
o
w
s
VIX (percent)
Equity funds
Government bond funds
Corporate bond funds
–0.6
–0.5
–0.4
–0.3
–0.2
–0.1
0.0
0.1
0.2
0.3
Increase in the VIX
Decline of benchmark
return
Decline of excess
return over benchmark
Bond funds Equity funds
C
h
a
n
g
e
i
n
m
o
n
t
h
l
y
f
u
n
d
?
o
w
s
Sources: Bloomberg, L.P.; and IMF staff estimates. Additional data: Calculated based on data from the survivor-bias-free U.S. mutual fund database ©2014 Center for
Research in Security Prices (CRSP
®
), The University of Chicago Booth School of Business.
Note: VIX = Chicago Board Options Exchange Market Volatility Index. Estimates in panel 1 are based on a regression of fund ?ows on the VIX, benchmark performance
(lagged), excess performance over benchmark (lagged), age, and size. The model is estimated using share-class-level data covering 1998–2014. For more details on
estimations and data, see Annex 3.2. Panel 2 splits observations into 20 quantiles based on the VIX. For each of these quantiles, the simple average for the VIX and
fund ?ows is reported by type of fund.
Figure 3.8. Drivers of Fund Flows from End Investors
(Monthly fund ?ows, percent of total net assets)
1. Sensitivity of Fund Flows to Fund Performance and Market Conditions
(The effect of a one standard deviation shock to each driver)
Fund ?ows are strongly in?uenced by asset class performance, a fund’s
own performance, and the VIX.
2. Fund Flows and the VIX
Periods with high VIX see a ?ight to quality from equity to bond funds,
especially to government bond funds.
11 13 14 15 17 19 21 24 26 31
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 109
VIX) rises, end investors shift away from equity funds
to bond funds, especially to sovereign bond funds. A
closer look at subgroups of bond funds and emerging
market assets reveals that investors also flee from corpo-
rate and emerging market bonds when the VIX rises.
27
• Relative performance is a main driver of fund
inflows. This behavior by end investors provides
incentives for herding, as discussed earlier.
• Investors disproportionately pour money into funds
with strong recent performance, creating an incentive
for managers of poorly performing funds to increase
risks. Funds with excess returns over their bench-
mark receive disproportionately more inflows (Figure
3.9). In line with the existing evidence based on
U.S. equity mutual fund data (Chevalier and Ellison
1997), investors inject money into winning funds
to a greater extent than they punish poor perform-
ers (implying a convexity in the performance-inflow
relationship). Thus, poorly performing fund managers
have an incentive to take more risky bets (see Box 3.1
27
Based on similar analysis for funds (from all jurisdictions)
investing in emerging market assets using EPFR Global. Tis is in
line with the fndings of the April 2014 Global Financial Stability
Report.
for details).
The convexity is weaker for bond funds.
Similar to the findings in Ferreira and others (2012),
an analysis for non-U.S. funds shows that convex pat-
terns are observed in some but not all economies, with
equity funds generally displaying more convexity.
Client types, fees, and to some extent the market
liquidity of assets and fund characteristics infuence the
sensitivity of fund fows to performance (Figure 3.10):
• Institutional investors appear to be less influenced
by recent past performance. However, this result is
not robust across all subperiods considered. Institu-
tional investors are likely to be more sophisticated
than retail investors, and findings in the April 2014
Global Financial Stability Report show that flows
from institutional investors to emerging market
assets are less sensitive to changes in the VIX.
28
28
However, in the presence of more fundamental fnancial and
macroeconomic problems, institutional investors withdraw more
aggressively than retail investors. For instance, Schmidt, Timmer-
mann, and Wermers (2013) point out that institutional investors
were the frst ones to recognize problems with money market funds
and instigated a run in 2009. Te April 2014 GFSR fnds that insti-
tutional investors sold of more when emerging market sovereigns
were downgraded to below investment grade.
–0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
–2 –1.7–1.4–1.1–0.8–0.5–0.2 0.1 0.4 0.7 1.0 1.3 1.6 1.9 2.2 2.5 –2 –1.7–1.4–1.1–0.8–0.5–0.2 0.1 0.4 0.7 1.0 1.3 1.6 1.9 2.2 2.5
M
o
n
t
h
l
y
f
u
n
d
?
o
w
s
(
p
e
r
c
e
n
t
o
f
t
o
t
a
l
n
e
t
a
s
s
e
t
s
)
Fund’s monthly excess return over benchmark (percent)
Figure 3.9. Convexity of Fund Flow–Performance Relationship
2. Equity Funds 1. Bond Funds
–0.6
–0.4
–0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
M
o
n
t
h
l
y
f
u
n
d
?
o
w
s
(
p
e
r
c
e
n
t
o
f
t
o
t
a
l
n
e
t
a
s
s
e
t
s
)
Fund’s monthly excess return over benchmark (percent)
Sources: Bloomberg, L.P.; and IMF staff estimates. Additional data: Calculated based on data from the survivor-bias-free U.S. mutual fund database ©2014 Center for
Research in Security Prices (CRSP
®
), The University of Chicago Booth School of Business.
Note: Estimates in the two panels are based on a regression of net in?ows on VIX, benchmark performance (lagged), excess performance over benchmark (lagged),
and age. The model allows for different slopes for negative and positive values of excess performance over benchmark. The estimation uses share-class-level data
covering 1998–2014. For more details, see Annex 3.2.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
110 International Monetary Fund | April 2015
Figure 3.10. Liquidity Risk and Fund Structures
1. Relative Sensitivity of Equity Fund Flows to Performance
(Response of ?ows into liquid and illiquid funds to a one standard
deviation decline in benchmark returns, difference with respect to rest
of funds)
Among equity funds, fund ?ows of funds investing in liquid
stocks are less sensitive to performance.
4. Trend of Mutual Fund Fees
(Simple average, percent)
However, mutual fund fees, especially redemption fees, have
declined during the past 15 years because of competitive
pressures in the industry.
Redemption fees are effective in mitigating out?ows.
2. Fund Flows by Redemption Fees
(The effect of a one standard deviation decline of returns)
Redemption fees have helped mitigate redemptions during stress
episodes, especially for emerging market funds.
3. Redemptions during Stress Episodes, by Redemption Fee
Levels
–1.5
–1.0
–0.5
0.0
0.5
1.0
0 2 5 0 2 5
Bond funds Equity funds
M
o
n
t
h
l
y
f
u
n
d
?
o
w
s
(
p
e
r
c
e
n
t
o
f
t
o
t
a
l
n
e
t
a
s
s
e
t
s
)
Redemption fees (percent)
–35
–30
–25
–20
–15
–10
–5
0
5
2008
Equity funds
2013
EM bond funds
2013
EM equity funds
C
h
a
n
g
e
o
f
a
v
e
r
a
g
e
f
u
n
d
?
o
w
s
b
e
f
o
r
e
a
n
d
d
u
r
i
n
g
s
t
r
e
s
s
e
p
i
s
o
d
e
s
(
p
e
r
c
e
n
t
o
f
t
o
t
a
l
n
e
t
a
s
s
e
t
s
)
Funds with low redemption fees
Funds with high redemption fees
Source: IMF staff estimates. Additional data: Calculated based on data from the survivor-bias-free U.S. mutual fund database ©2014 Center for Research in Security
Prices (CRSP
®
), The University of Chicago Booth School of Business.
Note: EM = emerging market; VIX = Chicago Board Options Exchange Market Volatility Index. Fees are maximum reported fees in the prospectus. Redemption fees
include narrowly defined redemption fees and contingent deferred sales charges. Estimates in panels 1 and 2 are based on a regression of net inflows on the VIX,
benchmark performance (lagged), excess performance over benchmark (lagged), age, size, and the reported fund characteristics (added one at a time) interacted with
excess performance over benchmark (lagged). The estimation uses share-class-level data covering 1998–2014. Panel 3 computes the difference between average
flows before the crisis period and average flows during the reported stress episodes (September to December 2008 for the global financial crisis, and May to September
2013 for the tapering episode). Fund flows are standardized by the beginning-of-period total net assets. Funds are classified as having low redemption fees if
redemption fees are equal to zero. Funds are classified as having high redemption fees if redemption fees are greater than or equal to 3 percent in 2008 and 1 percent
in 2013. For more details on estimations and data, see Annex 3.2.
–0.2
–0.1
0.0
0.1
0.2
Liquid subgroup Illiquid subgroup
C
h
a
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g
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o
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m
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n
t
h
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y
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d
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o
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s
(
p
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c
e
n
t
o
f
t
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t
a
l
n
e
t
a
s
s
e
t
s
)
Relatively smaller out?ows
compared with rest of funds
Relatively greater out?ows
compared with rest of funds
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Bonds Equity
Purchase fee
Redemption fee
J
a
n
.
1
4
D
e
c
.
1
2
N
o
v
.
1
1
O
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1
0
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e
p
.
0
9
A
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0
8
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0
7
J
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0
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M
a
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0
5
A
p
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0
4
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0
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F
e
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.
2
0
0
2
N
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1
3
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1
2
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0
9
J
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0
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M
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0
7
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6
M
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F
e
b
.
0
4
J
a
n
.
0
3
D
e
c
.
2
0
0
1
Stress episodes
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 111
• Fees are generally effective in dampening redemp-
tions following short-term poor performance,
though competitive pressures in the industry
challenge their use. In particular, redemption fees
appear to be effective. However, among bond funds,
the effectiveness of fees appears to vary across fund
types: the fees dampen redemptions for emerging
market bond funds, but not for U.S. government
bond or corporate bond funds. Moreover, competi-
tive pressures and transparency requirements in the
industry have driven down fees during the past 15
years (Figure 3.10, panel 4), which would make it
difficult for individual funds to adopt adequate fees
in line with their investment risk without sector-
wide coordination or regulation.
29
• The sensitivity of redemptions to benchmark
performance is larger for equity funds investing
in less liquid stocks. This result is in line with the
findings in Chen, Goldstein, and Jiang (2010) for
U.S. equity funds. As discussed by Stein (2014), a
higher redemption sensitivity of less liquid funds
is consistent with the existence of a first-mover
advantage. Although one would expect the evidence
to be stronger for bond funds (because of their
larger liquidity mismatches; Figure 3.5), that is not
the case. One reason could be that bond funds with
higher liquidity mismatches manage their liquid-
ity risk more carefully, as discussed in the following
section.
Brand name efects are present, albeit weak. Tis
analysis examines 18 events in which a “fagship fund”
of a large AMC experienced large redemptions (see
Annex 3.2 for details). Te test is whether funds in
the fund family hit by the fagship shock experience
larger outfows than similar funds not in the fund
family. Out of the 18 events, 10 cases show statistically
signifcant negative brand name efects, 3 cases show
statistically positive efects, and the other 5 cases show
no signifcant efects (Figure 3.11).
How do funds manage liquidity risks?
Te efects of fund fows on fund investment can
be cushioned by liquidity risk management. For
instance, if a fund holds sufcient cash bufers when
29
Figure 3.10 shows the maximum charge reported in the fund’s
prospectus. In practice, funds often ofer discounts, reducing efective
fees to much lower levels. ICI (2014b) reports that efective purchase
fees declined from nearly 4 percent in 1990 to 1 percent in 2013.
faced with large redemptions, the efect on sales
pressures will be dampened. Moreover, funds’ share
pricing rules and redemption policies can be designed
to reduce redemption risks. Existing research (though
somewhat old and focused on equity funds) shows
that funds investing in illiquid assets tend to take the
form of closed-end funds with no redemption risk,
charge fees for fund share purchases and redemp-
tions, and hold more cash (Chordia 1996; Deli
and Varma 2002). Tis section looks at how fund
managers use these tools to manage liquidity risks by
examining their cash holding patterns in relation to
fow volatility, current fund fows, and various fund
characteristics, including liquidity of assets and client
type (institutional or retail). In contrast to previous
studies, the analysis here also covers bond funds and
uses more recent data.
30
30
Funds can also manage liquidity using derivatives, something
not studied here because of a lack of data.
–30
–25
–20
–15
–10
–5
0
5
–1 0 1 2
Month from event date
Shocked ?agship (all events)
Family (all events)
Shocked ?agship (signi?cant negative events)
Family (signi?cant negative events)
Brand name effect: 18 events with a “?agship fund shock”
(Mean across events; ?ows in percent of total net assets, nonfamily = 0)
Source: IMF staff estimates. Additional data: Calculated based on data from the
survivor-bias-free U.S. mutual fund database ©2014 Center for Research in
Security Prices (CRSP
®
), The University of Chicago Booth School of Business.
Note: “Flagship shocks” for large asset management companies are identi?ed as
periods with large out?ows from ?agship funds (10 percentage points above those
of the median of funds with shared investment objectives). Regression analysis for
each of those events is used to test whether funds in the affected ?agship family
receive lower net in?ows relative to nonfamily funds. See Annex 3.2 for details.
Figure 3.11. Brand Name Effects
(Cumulative fund ?ows from event date in percent of total net assets,
mean difference from median comparator funds)
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
112 International Monetary Fund | April 2015
Generally, asset managers choose cash bufers and
fee policies to limit liquidity risks, though competitive
pressures have been reducing the use of redemption
fees (Figure 3.12):
• Asset managers appear to actively manage their
liquidity risks with precautionary cash buffers
(Figure 3.12). Cash holdings are high for those
funds experiencing very large outflows (in line with
a precautionary motive) and inflows (presumably
because managers take some time to fully invest new
money). Estimation results confirm that funds also
hold higher cash buffers when they face more vola-
tile flows from investors and when these investors
are primarily less stable retail investors. Similarly,
cash holdings are higher for funds investing in rela-
tively less liquid assets.
• Funds with higher liquidity risks tend to charge
higher fees (Figure 3.12, panel 2). Fees are generally
set lower for institutional investors. Funds investing
in more illiquid assets tend to set higher fees than
those investing in liquid assets.
Herding, Interconnectedness, and Contribution to
Systemic Risk
Herding (correlated trading)
How prevalent is herding? Empirical evidence of mutual
fund herding is abundant, although reported mag-
–1.5
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
Institutional Liquid subgroup Illiquid subgroup
Equity funds Bond funds
Relatively less cash
holdings compared
with other funds
Relatively more
cash holdings
compared with
other funds
0
1
2
3
4
5
6
7
8
–14.7 –3.1 –1.8 –1.2 –0.6 –0.1 0.4 1.3 2.9 7.7
C
a
s
h
(
p
e
r
c
e
n
t
o
f
t
o
t
a
l
n
e
t
a
s
s
e
t
s
)
Monthly fund ?ows (percent of total net assets)
Equity funds
Bond funds
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
R
e
t
a
i
l
i
l
l
i
q
u
i
d
R
e
t
a
i
l
a
l
l
R
e
t
a
i
l
l
i
q
u
i
d
I
n
s
t
i
t
u
t
i
o
n
a
l
i
l
l
i
q
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i
d
I
n
s
t
i
t
u
t
i
o
n
a
l
a
l
l
I
n
s
t
i
t
u
t
i
o
n
a
l
l
i
q
u
i
d
R
e
t
a
i
l
i
l
l
i
q
u
i
d
R
e
t
a
i
l
a
l
l
R
e
t
a
i
l
l
i
q
u
i
d
I
n
s
t
i
t
u
t
i
o
n
a
l
i
l
l
i
q
u
i
d
I
n
s
t
i
t
u
t
i
o
n
a
l
a
l
l
I
n
s
t
i
t
u
t
i
o
n
a
l
l
i
q
u
i
d
Equity funds Bond funds
Purchase fee Redemption fee
Figure 3.12. Funds’ Liquidity Risk Management
1. Cash Holding by Fund Flows
(Using monthly share-class-level data for 1998–2014)
3. Differences in Cash Holdings across Funds
(Percent of total net assets)
Cash holdings are high for those funds experiencing large
in?ows or out?ows.
Funds charge higher fees to retail investors and when investing
in illiquid assets…
2. Mutual Fund Fees by Investment Focus and Clientele
(Simple average, percent)
…and hold more cash when investing in relatively illiquid assets,
facing higher fund ?ow volatility. They hold less cash when they have
predominantly institutional clients.
Sources: Calculated based on data from the survivor-bias-free U.S. mutual fund
database ©2014 Center for Research in Security Prices (CRSP
®
), The University
of Chicago Booth School of Business; and IMF staff estimates.
Note: Panel 1 is based on monthly data from 1998 to 2014 for each fund share
class. It splits observations into 20 quantiles based on net fund ?ows (in percent
of total net assets). For each of these quantiles, the panel shows the mean
percentage of cash in funds’ portfolios. In panel 2, fees are maximum reported
fees in the prospectus. Redemption fees include narrowly de?ned redemption
fees and contingent deferred sales charges. Estimates in panel 3 are based on a
regression of cash holdings (in percentage of total portfolio) as a function of net
in?ow volatility, lagged net in?ows, and the reported fund characteristics
dummies.
Flow volatility
sensitivity to a 1
standard
deviation increase
in volatility
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 113
nitudes vary across markets (Grinblatt, Titman, and
Wermers 1995; Wermers 1999; Borensztein and Gelos
2003; Choi and Sias 2009; Brown, Wei, and Wermers
2013). Using data on security-by-security holdings of
U.S. open-end mutual funds, the degree of herding is
measured using the method developed by Lakonishok,
Shleifer, and Vishny (1992).
31
Tis is a measure of cor-
related trading within this investor group. Even though
it does not conclusively allow for an identifcation of
31
See Box 2.5 in the April 2014 Global Financial Stability Report
for details. Te Lakonishok, Shleifer, and Vishny (1992) index
is a highly robust measure for detecting herding (in the sense of
correlated trading patterns). It does, however, have a bias toward
underestimating the magnitude of herding. Correcting for this bias is
difcult and methods for doing so are the subject of ongoing debate.
Te downward bias increases with lower transaction numbers. Given
that over the past fve years, the data show a mild decline in the
number of transactions per security, the results likely underestimate
the true increase in herding shown in Figure 3.1.
“herding” in a strict sense (namely, actions taken only
because investors see other investors taking them), at a
minimum it does provide an informative measure of the
degree to which this class of investors moves together,
regardless of the underlying reasons.
Herding among U.S. mutual funds is on the rise
across fund styles (Figure 3.13). Tis fnding is true for
both U.S. equities and corporate bonds in recent years.
For U.S. equities, mutual funds appear to co-move more
during distress episodes. Retail-oriented funds show con-
sistently higher levels of herding than do institutional-
oriented funds. Tis could be because retail investors
are more prone to quickly reallocate money from funds
with poor recent performance to funds with high recent
returns (Frazzini and Lamont 2008), possibly because it
is more difcult for them than for institutional investors
to assess and monitor portfolio managers. Tis difculty
in assessing and monitoring managers and the result-
0
2
4
6
8
10
12
14
16
18
20
S&P 500 U.S. equity HG bond HY bond EM equity EM debt
End-2009 Mid-2014
More herding
0
2
4
6
8
10
12
14
16
J
u
n
.
2
0
0
6
N
o
v
.
0
6
A
p
r
.
0
7
S
e
p
.
0
7
F
e
b
.
0
8
J
u
l
.
0
8
D
e
c
.
0
8
M
a
y
0
9
O
c
t
.
0
9
M
a
r
.
1
0
A
u
g
.
1
0
J
a
n
.
1
1
J
u
n
.
1
1
N
o
v
.
1
1
A
p
r
.
1
2
S
e
p
.
1
2
F
e
b
.
1
3
J
u
l
.
1
3
D
e
c
.
1
3
M
a
y
1
4
Retail funds
Institutional funds
More herding
Source: IMF staff estimates. Additional data: Calculated based on data from the survivor-bias-free U.S. mutual fund database ©2014 Center for Research in Security
Prices (CRSP
®
), The University of Chicago Booth School of Business.
Note: EM = emerging market; HG = high grade; HY = high yield. The herding measure is that proposed by Lakonishok, Shleifer, and Vishny (1992). It assesses the
strength of correlated trading among mutual funds investing in each security, controlling for their overall trade trends (see Box 2.5 of April 2014 Global Financial
Stability Report). Note that the market as a whole cannot trade in the same direction, since at any given time there must be a buyer for each seller. The measure is 0
when there is no sign of herding among mutual funds. It is calculated every quarter, looking at the fund-level activity in each security, and then averaged across
securities. The measure is computed when there are at least ?ve funds that changed the holdings of a security in each quarter for each security. The CRSP database
contains security-by-security holdings of all U.S.-domiciled open-end mutual funds, covering more than 750,000 securities. To make the analysis computationally
feasible, this chapter works with subsamples of securities that are randomly selected. Except for the S&P 500 sample, the herding measure is calculated with
50,000 randomly selected securities for each of the subgroups. In panel 1, the difference in herding across neighbor categories is statistically signi?cant at the
5 percent con?dence level, except for the case of EM debt versus EM equity, and HY bond versus HG bond. The difference in herding by fund type (panel 2) is
signi?cant at the 1 percent con?dence level.
Figure 3.13. Herding among U.S. Mutual Funds
(Percent)
1. Average Measure of Herding by Security Type
(Mean across securities, four-quarter average)
Recently, U.S. mutual funds have been herding more in U.S.
equity and corporate bond markets.
2. Average Measure of Herding by Fund Type
(Average across all securities, four-quarter average)
Retail funds tend to herd more than institutional funds.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
114 International Monetary Fund | April 2015
ing volatility of infows would exacerbate the role of
incentive problems described earlier in driving herding
behavior. Te rise in herding coincides with the adop-
tion of unconventional monetary policies in the United
States, and could be related to an accentuated search for
yield by mutual funds.
32
Herding levels are higher for
emerging market and high-yield assets and lowest for
the S&P 500 market, consistent with the notion that
herding is more likely to be prevalent in relatively more
opaque and less liquid markets (Bikhchandani, Hirshle-
ifer, and Welch 1992).
Linkages between parent asset management
companies and funds
Mutual funds and most other investment vehicles have
few direct solvency linkages with their AMCs. AMCs’
own balance sheets are legally separated from those of
the mutual funds they manage, as required by regula-
tions.
33
Tis separation does not necessarily apply to
other types of investment vehicles, though. For some
hedge funds and private equity funds, AMCs’ assets
can be comingled with clients’ assets. Another example
of linkage is AMC parents’ support for funds during
crisis episodes. In 2008, because of reputational con-
cerns, some fnancial institutions provided emergency
liquidity support for money market funds and other
fxed-income funds their group AMCs were managing
(Moody’s 2010).
Interconnectedness through ownership
Banks and insurance companies are major own-
ers of AMCs, and the overall stability implications
of these arrangements are unclear (Figure 3.14).
Without proper oversight of related-party exposures
and concentrated exposures, funds could be used
as funding vehicles for their AMC’s parent banks.
34
Moreover, many such banks are G-SIFIs. Tese inter-
relationships increase the concentration of fnancial
services providers across various subsegments of the
fnancial sector, creating potentially very infuential
and complex mega conglomerates. Information shar-
ing between a bank and its group AMC is another
32
For high-grade bonds, econometric estimates of the relationship
between herding and proxies for unconventional monetary policy
show a positive, albeit weak, link.
33
See Annex 3.1. AMCs’ own balance sheets are also much smaller
than the clients’ money they manage (2 percent to 12 percent of
assets under management for the top AMCs).
34
For instance, certain types of synthetic ETFs could be used by
their AMCs’ parent banks to obtain cash in exchange for collateral
securities that banks do not want to keep on hand.
potential concern. Massa and Rehman (2008) provide
evidence that such information sharing exists for
banks and AMCs, most likely through informal chan-
nels. However, bank afliation could also have efects
that may be desirable from a fnancial stability point
of view, including access to a central bank’s emer-
gency liquidity facility through AMCs’ parent banks
and more supervisory scrutiny.
Interconnectedness through bank funding
Te roles of mutual funds as funding providers for
banks appear to vary across instruments and countries
(Figure 3.15). Mutual funds are more important pro-
viders of long-term bank fnancing in the United States
than in other economies. However, their role appears
to be less important than that of money market funds’
role in short-term (bank) funding.
Te relationship between size and contribution to
systemic risk
An actively discussed question in global regulatory fora
is whether large asset managers and funds should be
designated as SIFIs and receive more intense oversight.
Tis section does not intend to fully answer this ques-
0
1
2
3
4
5
6
7
8
9
10
Privately held Listed,
independent
Listed, insurer
as parent
Listed, bank as
parent
Sources: Pensions and Investments and Towers Watson (2014); and IMF staff
calculations.
Note: Parent banks include Amundi, Bank of New York Mellon, BNP Paribas,
Deutsche Bank, Goldman Sachs, HSBC, J.P. Morgan Chase, Natixis Global Asset
Management, and UBS. Parent insurance companies include Allianz (for PIMCO),
Axa, Metlife, Generali, Legal and General Group, and Prudential.
Figure 3.14. Ownership Structure of the 25 Largest Global
Asset Management Companies
(Number)
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 115
tion. As discussed earlier, each segment of the industry
has its distinctive risks, many of which are hard to
quantify because of data gaps. However, the analy-
sis attempts to partially address the issue by asking
how funds’ contribution to systemic risk in advanced
economies relates to fund size, investment focus, and
size of their AMCs, using the conditional value-at-risk
(CoVaR) method (see Annex 3.2).
Funds’ contributions to systemic risk depend rela-
tively more on their investment focus than on their
size (Figure 3.16). Estimations based on a sample of
about 1,500 funds (not shown) reveal that investment
orientation, VaR, and fund size, among other character-
istics, are signifcantly related to a fund’s contribution
to systemic risk (Annex 3.2). Te relative importance of
size, however, difers across market segments.
For a given fund size, the systemic risk contribution
bears little relation to the size of a fund’s AMC (Figure
3.16, panel 2). Te average contribution to systemic
risk does not increase with a fund’s AMC’s size (the
picture looks the same when the investment focus of
funds is controlled for), at least not for the top asset
managers considered here. Although this exercise only
examines one segment of the broad asset management
industry and CoVaR is only one of the many possible
systemic risk measures, it highlights the importance
of incorporating product-line and investment-focus
perspectives, in addition to mere size, when discussing
the designation of AMCs and funds as SIFIs.
Revamping the Oversight Framework to
Address Financial Stability Risks
Key Features of Current Regulation
Te industry is regulated, albeit with a focus on inves-
tor protection. Substantial regulatory requirements
are in place for publicly ofered funds.
35
Regulation
focuses on investors being given sufcient information
to understand the investment product, on investors’
35
Regulatory frameworks for funds appear to be generally strong
around the globe—the IMF and World Bank assessments of securities
regulation under the IOSCO Principles show a generally high level
of compliance with principles dealing with disclosure to investors and
other consumer-protection-related standards. Some emerging market
and developing economies, however, have serious gaps in their legal
frameworks that fail to adequately separate the funds’ assets from those
of the asset manager. Tis raises risks to customer assets.
0
2
4
6
8
10
12
United States European
Union
Other
advanced
economies
Offshore Emerging
markets
Issuer domicile
U.S. funds EU funds Other funds
0
10
20
30
40
50
60
70
2005 06 07 08 09 10 11 12 13 14
Euro area, short-term bank funding
United States, repo and CP
Sources: eMaxx; and IMF staff calculations.
Note: EU = European Union.
Sources: European Central Bank; Federal Reserve; and IMF staff estimates.
Note: CP = commercial paper; repo = repurchase agreement.
Figure 3.15. Bank Financing by Mutual Funds and Money Market Funds
1. Share of Long-Term Bank Bonds Held by Mutual Funds
(Percent of total outstanding covered in eMaxx)
Mutual funds invest in long-term bank bonds, but generally
they are not the main holders of bank bonds…
2. Money Market Funds’ Share in Short-Term Funding Markets
(Percent of euro area short-term bank funding and U.S. repo and CP
outstanding)
…whereas money market funds play a more signi?cant
role in short-term funding markets.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
116 International Monetary Fund | April 2015
assets being protected from fraud and other risks, and
on asset managers not taking advantage of investors.
For these purposes, disclosure, investment restrictions
(including concentration limits), caps on leverage,
liquidity risk management, pricing and redemption
policies, and separation of client assets from those of
AMCs play important roles (Table 3.3). Regulatory
requirements for privately ofered products have also
been strengthened since the global fnancial crisis.
AMCs that ofer investment products are subject to
rules that focus on protecting clients from fraud or
negligence and that aim to ensure the business conti-
nuity of the AMC.
Te importance of liquidity risks to the industry is
recognized and is an integral part of current regulation
and industry practices:
• Regulatory requirements to manage liquidity risks exist,
though they are often rather general. Funds are gener-
ally restricted to liquid assets or required to maintain
certain liquid asset ratios; they must have risk manage-
ment frameworks (data collection, profiling of redemp-
tions, and stress testing) in place. Many asset managers
have internal liquidity risk management frameworks for
their funds, with regular monitoring of clients’ liquidity
needs and stress testing. These liquidity management
tools are in line with FSB suggestions (FSB 2013).
• For very large redemptions, funds also have a variety
of tools, subject to local regulatory requirements.
For macroprudential purposes, the FSB (2013) and
the October 2014 Global Financial Stability Report
suggest that regulation and fund contracts should
include tools, such as fees, gates, side-pockets, and
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0.0 1.0 2.0 3.0 4.0 5.0
Assets under management of parent asset management companies
(trillions of U.S. dollars)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
AE sovereign
bonds
AE corporate
bonds
EM bonds AE equity EM equity
Sources: Lipper; Pensions and Investments and Towers Watson; and IMF staff estimates.
Note: AE = advanced economy; AMC = asset management company; CoVaR = conditional value-at-risk; EM = emerging market. The impact of fund A’s distress
on systemic risk is measured by the difference of CoVaR when fund A is in a normal state (median VaR) and in a distressed state (worst 5 percentile VaR). The
?nancial system consists of an equity index for banks and insurers from AEs and about 1,500 mutual funds, taking the largest 100 funds (globally) for each of
the ?ve investment focus categories (AE sovereign, AE corporate bond, EM bond, AE equity, and EM equity) and for three different fund domiciles (the United
States, Europe, and the other advanced economies). Weekly net asset value data are used to compute fund returns and monthly total net asset (TNA) data to
measure the size of each fund from January 2000 to November 2014. The system is measured by a TNA-weighted average of fund returns (the results are
robust when the simple average is used instead). The assets under management of the AMC include assets managed with different investment vehicles such as
separate account and alternative funds. Caution should be taken in comparing the precise ranking of systemic risk contributions across fund categories since
the sample period may not capture the realization of relevant tail risks. Moreover, the measure does not identify whether the contribution is causal or driven by a
common factor.
Figure 3.16. Contribution to Systemic Risk by Mutual Funds
1. Average Contribution to Systemic Risk by Investment Focus
(Percent)
The systemic risk contribution differs across funds’
investment orientations.
2. Contribution to Systemic Risk of Top Fund Families by Size of Asset
Management Company
(Contribution to systemic risk averaged across funds in the same
family, percent)
A fund’s systemic risk contribution is not related to its AMC’s size.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 117
suspension of redemptions, to manage large redemp-
tions.
36
Existing regulation and fund contracts indeed
allow for these tools. In addition, asset managers can
make use of credit lines, delays in cash payout upon
redemption (within regulatory limits), and payment
in kind.
37
The available tools often vary depending
on local requirements.
38
For extreme measures, such
as suspensions, funds are usually required to obtain
permission from regulators, and they are strictly lim-
ited to extraordinary circumstances to prevent abuse.
Consequently, restrictions on redemptions have been
36
Gates constrain redemption amounts to a specifc proportion
on any one redemption day. Suspension is full closure of a fund
to redemption. Side-pockets legally separate impaired or illiquid
securities to prevent them from afecting a fund’s return until market
conditions stabilize.
37
Asset managers argue that payment in kind is particularly useful
for institutional clients. For instance, when institutional clients are
simply changing portfolio managers, they are willing to accept secu-
rities instead of cash and transfer the securities to a new manager to
avoid losses related to large-scale sales. Transfer of securities from one
manager to another is straightforward because the securities are kept
at a custodian bank, segregated from the AMC’s assets.
38
For instance, in some countries, funds are not allowed to take
credit lines or pay in kind to retail investors. Te minimum redemption
frequency for publicly ofered funds is set diferently across jurisdictions,
and funds are not allowed to delay settlement beyond the limit (seven
days in the United States and two weeks in the European Union).
used only rarely in advanced markets, and are gener-
ally associated with the failure or winding down of
a fund—redemptions are suspended to ensure that
pricing of the shares is fair across investors when a
portfolio has become too difficult to price (IOSCO
2011).
Limitations of Current Oversight
Te current oversight framework is not set up to fully
address risks, neither at the institutional nor systemic level:
• Regulation lacking in specificity—Key regulations,
especially regarding liquidity requirements and
liquidity risk management, are broad and lack spe-
cific guidance, allowing for wide-ranging interpreta-
tions and practices across jurisdictions (Table 3.3).
For instance, liquid asset requirements are often stip-
ulated without a precise definition of “liquid assets.”
Requirements for risk management frameworks are
often not detailed in legislation. Regulatory require-
ments themselves also vary substantially across
jurisdictions, reflecting the broad-principle-based
approach of global standards (IOSCO Principles).
• Insufficient supervision of individual and systemic
risks—Supervision of funds and asset managers
Table 3.3. Selected Regulations for Publicly Offered Funds
Issues Requirements
Investment
Restrictions
• Typically, investments in illiquid securities and complex products are restricted and positions cannot be
concentrated in a single issuer.
• Use of leverage and derivatives is capped. Public funds in the United States, for example, can only employ
leverage of up to 33 percent of assets, including portfolio leverage embedded in derivatives. UCITS funds can
only temporarily borrow up to 10 percent of assets. UCITS funds can invest in financial derivatives, subject to
conditions on underlying assets, counterparties, and valuation, and exposure cannot exceed the total net value
of the portfolio.
Liquidity • Publicly offered funds are subject to liquidity requirements.
• Specific fund classes, such as money market funds, have extensive liquidity requirements.
• In the United States, funds can hold only a limited amount of illiquid assets. “Liquid asset” is defined only
broadly by regulation, but more detailed definitions can be included in fund contracts.
• In the European Union, regulators provide a list of assets that are eligible to meet liquidity requirements, but
there is no liquidity ratio requirement. A similar approach is followed by other jurisdictions, such as Brazil.
• In Singapore, liquidity requirements differ by fund type.
• Funds are expected to have risk management frameworks, including liquidity risk management, but few
jurisdictions provide details on how these frameworks should work.
• In 2011, IOSCO established its Principles of Liquidity Risk Management for Collective Investment Schemes.
Pricing of Fund
Assets, Fund Shares,
and Redemption
• Portfolios are generally priced at market value for NAV calculation, although some illiquid assets are valued
following fair value accounting rules. However, during times of distress, some prices may not reflect accurate
market values, especially when there are limited market transactions.
• Rules are in place aiming to ensure that prices for purchases and redemption of shares are set so as to treat
investors fairly, but some rules can result in a first-mover advantage (see Box 3.2 for details).
• Various jurisdictions allow suspension of redemption as an extreme measure.
• Under the European Union’s UCITS scheme, funds can specify redemption restrictions, typically used for funds
investing in less liquid securities.
Source: IMF staff.
Note: IOSCO = International Organization of Securities Commissions; NAV = net asset value; UCITS = Undertaking for Collective Investment in Transferable
Securities (a type of publicly offered fund governed by the European Union UCITS directive).
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
118 International Monetary Fund | April 2015
is generally weak across jurisdictions.
39
In many
jurisdictions, oversight of funds has been focused
on disclosure to protect retail investors. Regular
supervision of risks is generally not the focus of
supervisors.
40
As a result, no financial soundness
indicators have been developed for the industry, and
stress testing of funds and AMCs by regulators has
been rare—a major contrast with bank supervisory
practice. For some regulators, the number of asset
managers and funds impose resource challenges.
Moreover, international coordination and guidance
on supervisory practices is sparse, since the IOSCO
Principles focus on regulations. Good practices by
asset managers provide some comfort, but in the
presence of liquidity and price externalities, each
fund and asset manager is likely to underestimate
liquidity needs and the potential for correlated price
effects in the presence of large shocks (Liang 2015).
Improving Oversight
Securities regulators should enhance the micropruden-
tial oversight of risks (Table 3.4):
• Enhance regulation by providing more specifics for funds’
liquidity requirements—Key regulations should pro-
vide a clearer definition of liquid assets. More specific
guidance should be given to match the liquidity
profile of each fund category to its redemption policy.
• Strengthen the microprudential supervision of risks
related to individual institutions—Regulators should
regularly monitor market conditions and review
whether funds’ risk management frameworks are
sufficient, especially with regard to liquidity risks.
Greater resources should be devoted to supervising
risks, including developing analytical and stress-
testing capacities so that regulators can effectively
challenge asset managers’ practices.
• Ensure that funds do not take excessive leverage—
Caps limit overall leverage of publicly offered
funds. Nevertheless, leverage and its regulatory
39
A consistent fnding in Financial Sector Assessment Programs of
the IMF and the World Bank is that most jurisdictions with substan-
tial asset management industries have sound regulatory frameworks
but show weaknesses in the intensity of supervision of funds and
asset managers.
40
Tere are some exceptions. For instance, supervisors in France
and Brazil have risk-oriented and data-driven fnancial stability risk
management frameworks that foresee collecting the data and using
them to monitor potential risks; the supervisors can conduct stress
testing on their own, and challenge asset managers if risks are found.
compliance should be regularly monitored with
better data on derivatives.
41
• Adopt approaches based on products, activities, or
both—Focusing on activities and products in addi-
tion to size seems appropriate given that the indus-
try is diverse and differences in investment focus
seem to matter significantly for funds’ contribution
to systemic risk.
• Raise the quality of supervisory practices across jurisdictions
by introducing global standards—International standards
and guidelines for better supervision should be sig-
nificantly expanded and enhanced. Supervisors should
share best practices, especially in the area of liquidity
risk. For instance, coordinated efforts should be under-
taken to develop financial soundness indicators as well
as stress-testing frameworks for the industry. The IMF
could play a key role here, based on its experience in
developing common financial soundness indicators and
stress-testing frameworks for banks.
42
A macroprudential perspective should be integrated
into the oversight of the industry, and the adequacy of
existing tools for macroprudential purposes should be
reexamined:
• Bring a macroprudential focus on systemic risk to
oversight of the sector—As illustrated by the empiri-
cal analysis, price externalities are the key channel of
systemic financial stability risk from this industry.
Thus, assessments of individual institutions are not
sufficient for assessing systemic risk. Incorporating
monitoring of linkages to other sectors that rely on
the industry for financing may even be necessary.
43
• Existing risk management tools and rules could be
used with a view to safeguard financial stability—To
41
Adam and Guettler (forthcoming) document that, among U.S.
corporate bond funds, (1) the use of credit default swaps (CDS)
rose from 20 to 60 percent between 2004 and 2008; (2) CDS are
mostly used to enhance credit risk taking, rather than hedging; (3)
funds belonging to a larger fund family are more likely to use CDS;
(4) underperforming funds often increase their CDS exposures to
enhance returns; and (5) CDS users tend to perform worse on aver-
age than non-users.
42
Te Global Financial Stability Report began reporting fnancial
soundness indicators for banks in 2003. At frst, the data were col-
lected from national authorities or commercial databases without
harmonizing methods. Te efort has since developed into a more har-
monized statistical framework (http://www.imf.org/external/np/sta/fsi/
eng/fsi.htm), with a full compilation guide. Te IMF now periodically
publishes details of the indicators. It has also been contributing to the
building of common stress-testing frameworks (IMF 2012).
43
Te October 2014 Global Financial Stability Report discusses
how cooperation between microprudential, macroprudential, and
business conduct regulators could be carried out in practice.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 119
mitigate price externalities, rules on investment
restrictions (such as concentration limits), liquidity
requirements, and redemption policies may need to
be updated in line with funds’ risk profiles (October
2014 Global Financial Stability Report).
• Further efforts should be aimed at reducing the first-
mover advantage—As discussed, and partly confirmed
in the empirical analysis, a first-mover advantage can
arise for various reasons. Some of these are difficult
to address, such as the liquidity pecking order of
sales. Others, however, such as the degree of liquidity
mismatches, can at least partially be addressed with
good supervision. Most important, accounting-based
illiquid asset valuation rules and inflexible fund share
pricing rules that increase investors’ incentives to run
should be revised. In this context, so-called swing- or
dual-pricing rules could play a role (Box 3.2). Charg-
ing redemption fees, which are found to be effective
Table 3.4. Summary of Analysis and Policy Implications for Mutual Funds and ETFs
Results Policy Implications
Does Fund Investment Affect Asset Prices?
Flow-price impact analysis: Fund flows affect aggregate asset
prices, at least in less liquid markets, in both advanced and
emerging market economies.
• Regulators need to monitor financial stability risks from the
industry from a macroprudential perspective, especially in
smaller, less liquid, fixed-income markets.
• Adequacy of concentration limits may need to be reconsidered.
Concentration and price-impact analysis: Mutual funds’
concentration in bond markets has risen. During stress episodes,
bonds with more concentrated mutual fund ownership tend to
experience larger price drops.
What Drives Run Risk? What Can Be Done to Mitigate It?
End investors: End investors, especially retail investors, chase
past returns and display a flight to quality during times of stress,
making fund flows procyclical.
• Properly pricing-in the cost of liquidity is important in reducing
the first-mover advantage, by avoiding passing on to remaining
investors the costs associated with the sales of illiquid assets.
Regulators should examine the benefit of flexible NAV pricing
rules (such as swing and dual pricing), illiquid asset valuation
rules, and ETF structures to adequately reflect liquidity risk costs.
• Consider imposing minimum redemption fees for funds with
large liquidity mismatches. Fees that are added to NAV avoid
harming investors as a whole, while pricing-in the cost of
liquidity.
• More generally, the adequacy of the requirements for liquid
assets and liquidity risk management should be reexamined,
incorporating financial stability risks from the industry.
First-mover advantage: In line with the notion of a first-mover
advantage, among equity funds, redemptions are more sensitive
to returns for less liquid funds. However, the same is not true for
bond funds (which generally have higher liquidity mismatches
than equity funds). In emerging markets, fund flows predict
future price movements, consistent with a first-mover advantage.
Funds’ liquidity risk management: Funds use various liquidity
management tools. They hold higher cash buffers when they
experience large outflows, face higher redemption risks, are retail
focused, and invest in illiquid assets. Fees are generally effective
in reducing redemptions.
Does Asset Managers’ Behavior Amplify Risks?
Managers’ decision vs. end investors’ decision: Portfolio
managers’ trading accounts for about 70 percent of the variance
in funds’ investments.
• Ensure that managers are in compliance with regulatory
requirements and are not taking excess risks (including hidden
leverage).
• Reduce information gaps between managers and investors
(and regulators) by upgrading disclosure requirements to better
reflect the fund’s economic risks, especially regarding the use of
derivatives and securities financing transactions.
• Financial stability risks from mutual funds could stem from
many small funds taking similar positions. Regulators should
pay attention to this possibility, not just focus on the positions of
large funds.
Excessive risk taking: By rewarding winners disproportionately
more than punishing losers, end investors encourage excessive
risk taking by managers in various advanced economies. The
tendency is stronger for equity funds than for bond funds.
Herding: Herding among U.S. mutual funds has been intensifying,
particularly in smaller, less liquid markets. Retail-investor-
oriented funds tend to herd more.
Brand name effects: Evidence suggests that large redemption
shocks to a flagship fund often spill over to other funds in the
family, although the effects have been weak so far.
Contribution to Systemic Risk and Size
Fund size and systemic risk: Generally, larger funds contribute more
to systemic risk, but the investment focus of funds matters more.
• The SIFI discussion for funds and asset managers should take
into account specific risks of products in addition to size.
• Oversight of the industry should not simply focus on large funds
and AMCs.
Parent AMC size and its funds’ systemic risk: There is little
relationship between a fund’s contribution to systemic risk and its
AMC’s size.
Source: IMF staff.
Note: AMC = asset management company; ETF = exchange-traded fund; NAV = net asset value; SIFI = systemically important ?nancial institution.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
120 International Monetary Fund | April 2015
in smoothing redemptions, is another alternative for
pricing-in the cost of liquidity. However, competitive
pressures have probably resulted in fee levels that are
likely too low from a financial stability perspective
(Figure 3.10, panel 4). Therefore, coordinating on
an industry-wide minimum level of fees for funds
investing in illiquid assets could be considered.
44
In
doing so, fee policies should match funds’ specific
characteristics rather than impose one-size-fits-all
requirements.
45
• Caution is needed in the use of gates and suspensions—
They should be part of the toolkit. Nonetheless,
their imposition may also send negative signals to
the market and lead to preemptive runs ahead of the
instruments coming into force (FSB 2013; October
2014 Global Financial Stability Report).
• Be equipped with “better” data—Publicly offered
funds disclose substantial information. However,
the disclosed data—aimed at investor protection—
are often not sufficient for nor suited to systemic
financial stability analysis. For instance, many
jurisdictions do not require standardized quantita-
tive disclosure of derivatives and securities financing
transactions, such as outstanding positions, details
on collateral, and counterparties.
46
Better disclo-
sure and reporting is also important for reducing
information gaps that lead to incentive problems of
delegated portfolio management. Supervisors should
also make further efforts to collect data on privately
offered products, including separate accounts. Even
though investor-protection concerns with regard to
these products are lower, their investment patterns
can affect financial markets.
44
Tese fees would not have to beneft the AMC but could be
added to NAV and be redistributed to investors. For instance, in the
United States, Rule 22c-2 under the 1940 Investment Company Act
as amended provides that the fund board of an open-end fund must
consider whether to impose a redemption fee (up to 2 percent) that
fows back into the fund’s NAV (BlackRock 2014b).
45
Nevertheless, the imposition of such a fee would raise various
practical problems, including those related to cross-border coordina-
tion. An inadequate framework could also drive investors away from
this industry to other, less regulated products.
46
In the United States, mutual funds disclose only qualitative
information on their derivatives positions. In the European Union,
heightened concerns about the use of derivatives by synthetic ETFs
in 2011 (see Annex 3.1) have led the industry to voluntarily disclose
detailed derivatives positions, including derivatives exposures, coun-
terparties, and the type and amount of collateral. Tis practice has
subsequently evolved into requirements for ETFs and more broadly
for UCITS (ESMA 2012). In Brazil, supervisors obtain information
from the central counterparty and from exchanges that clear deriva-
tives transactions.
Various other aspects not covered in the empirical
analysis in this chapter deserve attention by national
authorities. Improving the liquidity and transparency
of secondary markets, specifcally for longer-term
debt markets, would reduce risks related to liquidity
mismatches.
47
For example, expanding trade report-
ing initiatives to all global fxed-income sectors should
help reduce the opacity of secondary markets (October
2014 Global Financial Stability Report). Compensation
structures for portfolio managers may merit scrutiny
(Box 3.1). Te composition of benchmark indices also
deserves attention, with a view to minimizing possible
associated distortions. Te authorities could assess their
ability to provide emergency liquidity to break vicious
feedback loops between funding and market liquid-
ity in times of stress. However, providing emergency
liquidity creates clear moral hazard risk and therefore
requires enhanced supervision (October 2014 Global
Financial Stability Report).
Conclusion
Financial stability risks can emanate from intermedia-
tion through asset managers even in the absence of
leverage and guaranteed returns. Te discussion in
this chapter stresses the importance of separating the
efects that stem from end investors, and would be
present even in the absence of fnancial intermediaries,
from those that are introduced by the presence of asset
managers. Te delegation of day-to-day portfolio man-
agement introduces fundamental incentive problems
between end investors and fund managers, which can
induce destabilizing behavior and amplify shocks. In
addition, easy redemption options can create risks of
runs because of the presence of a frst-mover advan-
tage. Te destabilization of prices in certain asset seg-
ments (particularly bonds) can afect other parts of the
fnancial system through funding markets and balance
sheet and collateral channels.
Te chapter has shed some light on the importance of
various dimensions of these risks. Complementing and
expanding on existing studies, the analysis fnds evidence
consistent with the notion that mutual fund invest-
ments afect asset price dynamics, at least in less liquid
markets. Some factors point to the existence of incen-
tives to run in segments of the industry. Te observed
pattern of fund infows and redemptions by end inves-
tors creates incentives for fund managers to herd and, in
47
Evidence suggests that herding declines with transparency (Gelos
2011).
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 121
some markets, for poorly performing fund managers to
increase risk. Indeed, herding among U.S. mutual funds
has been rising across asset markets. Funds managed
by larger AMCs do not necessarily contribute more to
systemic risk; investment focus appears to be relatively
more important than size when gauging systemic risk.
Although these risks are not fundamentally new,
their relevance has risen with structural changes in the
fnancial sectors of advanced economies. Te relative
importance of the asset management industry has
grown, and banks have also retrenched from many
market-making activities, contributing to a reduction
in market liquidity. Moreover, the role of fxed-income
funds, which entail larger contagion risks than tradi-
tional equity investment, has expanded considerably. A
broader range of products are available to less sophis-
ticated investors. Last, the prolonged period of low
interest rates in advanced economies has resulted in a
search for yield, which has led funds to invest in less
liquid assets.
Te chapter ofers fve main policy messages:
• First, securities regulators should enhance micropru-
dential supervision of risks stemming from individual
institutions building on regulators’ own risk analysis
and stress testing, supported by global standards for
supervision and better data and risk indicators.
• Second, regulatory and supervisory reforms are
needed to incorporate a macroprudential approach.
• Third, liquidity rules, the definition of liquid assets,
investment restrictions, and reporting and disclosure
rules could be enhanced.
• Fourth, consideration should be given to the use of
tools that adequately price-in the cost of liquidity,
including minimum redemption fees, improvements
in illiquid asset valuation, and mutual fund share
pricing rules.
• Fifth, given that the industry is diverse and that differ-
ences in investment focus seem to matter significantly
for funds’ contribution to systemic risk, a product- or
activity-based emphasis seems to be important.
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122 International Monetary Fund | April 2015
Annex 3.1. Primer on the Asset Management
Industry
Investment vehicles are broadly separated into “collec-
tive investment schemes” (referred to as “funds” in this
chapter) that pool money from a number of investors
and invest in fnancial assets, and what are called “sepa-
rate accounts” or “discretionary mandates” that manage
the money of single institutional investors or high
net worth individuals (Annex Table 3.1.1). Collective
investment schemes are further divided into various
products. Most of them are open-end mutual funds
investing in equities (Annex Figure 3.1.1).
Funds are often established as legal entities (corpora-
tions or trusts) that must be separated from an asset
manager, and a fund’s assets are kept at a custodian,
segregated from the assets of AMCs (Annex Figure
3.1.2). Tis segregation of an AMC and the funds it
manages is a key component of the regulatory frame-
work for investor protection.
Annex Table 3.1.1. Features and Risk Pro?les of Key Investment Vehicles
Vehicle Features and Risk Profiles
Separate Account • Providers of separate account services privately manage the money of institutional investors (including pension
funds, insurance companies, and sovereign wealth funds) or high net worth individuals.
• Little is known about this segment because contracts are private and can vary substantially across clients.
• An industry survey (SIFMA 2014) indicates that these accounts entail simple securities portfolios with little
leverage. The accounts are also subject to client investors’ regulatory requirements.
• Redemption risk for this group is moderate because institutional investors tend to internalize the cost of their sales,
and large redemptions can be paid in kind (especially if clients are changing asset managers).
Open-End Mutual
Fund
• These funds issue “redeemable equity securities” and stand ready to buy back their shares at their current net
asset value (NAV)—the price per share of a fund.
• These funds invest in generally liquid publicly traded bonds and equities.
• Many of the funds offer daily liquidity to clients, making liquidity risk the key risk for the fund.
• In particular, some funds invest in relatively illiquid securities (for example, corporate bonds instead of equity).
This is often referred to as “liquidity transformation” that could lead to “liquidity mismatch,” which makes the fund
vulnerable to redemptions.
• These funds have little leverage through borrowing, though they could be taking portfolio leverage using derivatives
(the same applies for money market funds and exchange-traded funds, below). Although regulations impose caps
on the use of leverage, little quantitative information is available.
Closed-End
Mutual Fund
• These funds issue a fixed number of shares in the primary market that trade intraday on the secondary stock
market at market-determined prices. Investors buy or sell shares through a broker, but cannot redeem their shares
directly from the fund, so these funds do not suffer much liquidity risk.
• However, their popularity suffers from the fact that their shares are usually traded in the secondary market at a
lower value than their NAV.
• Many closed-end funds borrow additional money, often using preferred shares, and they also take portfolio
leverage, subject to regulatory limits (ICI 2014a).
Money Market
Fund (MMF)
• These funds invest in short-term cash equivalent instruments such as commercial paper, Treasury bills, and
certificates of deposit, and play a major role in short-term funding markets.
• MMFs experienced major runs and liquidity distress during the global financial crisis. All U.S. MMFs offered
constant NAV (mutual fund price per share) at $1 per share. This structure created a first-mover advantage because
funds continued to honor the $1 per share repayment even though their actual NAV was worth less as the result of
losses from asset-backed commercial paper, which was perceived to be liquid and safe before the crisis.
• Constant NAV MMFs continue to exist in the United States and several other jurisdictions.
Exchange-Traded
Fund (ETF)
• ETF shares are traded in primary and secondary markets (see Box 3.2 for details).
• ETF shares can be created or redeemed in the primary market between the fund and “authorized participants” (APs)
in large units. APs are typically large securities dealers. Only primary market transactions cause fund flows to ETFs.
The settlement between ETFs and APs are usually in kind, meaning that the exchange of ETF shares and the basket
of securities is in line with the ETF’s investment objectives.
• APs then trade the ETF shares in the secondary market with clients and counterparties on stock exchanges. This
intraday trading in secondary markets provides intraday liquidity to end investors.
• Most ETFs are index funds, tracking the performance of a specific index.
Synthetic ETF • Synthetic ETFs are offered mainly in Europe.
• Instead of directly holding underlying assets (called physical ETFs), synthetic ETF returns are generated using
derivatives, especially swaps.
• Synthetic ETFs could be used for various investment strategies, ranging from simple index tracking to leveraged
and short-selling strategies.
• The extensive use of derivatives (asset swaps) has led to strong concerns about portfolio leverage, counterparty
risks, and the quality of collateral for asset swaps. A number of official sectors expressed such concerns in 2011,
including the Financial Stability Board (2011) and the IMF.
• In response, many ETF providers reduced synthetic products and expanded the disclosure of derivatives positions,
including a list of counterparties and the collateral basket for asset swaps (Morningstar 2012).
(continued)
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 123
Annex Table 3.1.1. Features and Risk Pro?les of Key Investment Vehicles (continued)
Vehicle Features and Risk Profiles
Private Equity
Fund
• Private equity is a broad term that refers to any type of equity participation in which the equity is not freely tradable
on a public stock market, such as equities of private companies and public companies that are delisted.
• Private equity funds often monitor and participate in managing the companies whose equity they hold. They aim to
maximize financial returns by a sale or an initial public offering of the companies.
• There are four main subclasses among private equity funds: (1) venture capital that invests in early-stage,
high-potential, growth startup companies; (2) buyout funds that acquire existing business units or business
assets; (3) mezzanine funds that invest in both growth equity and the subordinate debt layer—namely, the
“mezzanine” between senior debt and equity—of buyout transactions; and (4) distressed asset funds, which are a
specialized segment of buyouts that target mature and distressed companies. In addition, there are real estate and
infrastructure funds.
• Some private equity funds could be leveraged, but they are smaller components of the private equity industry
(Metrick and Yasuda 2011).
• Moreover, these alternative investment vehicles offer limited liquidity to end investors, matching the funds’ long-
term investment horizon.
• Contagion risks are also limited because private equity funds invest in companies not traded in markets.
Hedge Fund • These funds cover a large variety of investment strategies, ranging from publicly traded equity (highly liquid
holdings) to distressed debt vehicles and structured credit products (highly illiquid holdings). Use of leverage and
derivatives also varies considerably depending on the strategy. Unlike mutual funds, hedge funds have no cap on
leverage.
• Hedge funds tend to be more nimble than mutual funds regarding their investment strategy, leading to potentially
rapid alterations in their risk characteristics. Depending on their funding and trading strategies, there can be
significant interconnection with other financial institutions.
Sources: ICI (2014a, 2014c); Metrick and Yasuda (2011); Morningstar (2012); TheCityUK (2012); and IMF staff.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
124 International Monetary Fund | April 2015
Annex Figure 3.1.1. Investment Vehicles by Size, Domicile, and Investment Focus
Open-end
mutual funds
63%
Closed-end
mutual funds
2%
Money market
funds 12%
Exchange-traded
funds 6%
Private equity
funds 9%
Hedge funds
5%
Other
alternatives
3%
United States
49%
Japan
3%
Other developed 9%
Brazil
3%
China
2%
Other emerging
markets 4%
Luxembourg 10%
Ireland 5%
France 5%
United Kingdom 4%
Other developed
Europe 7%
Developed
Europe 31%
United States
72%
Europe
18%
Asia
7%
Others
3%
3. Mutual Funds by Investment Focus
(Percent of $30 trillion total assets under management, end-2013)
Most mutual funds invest in equities. (Bond funds, especially high-yield
corporate and emerging market debt funds, are smaller components).
4. Exchange-Traded Funds by Region
(Percent of $2.3 trillion total assets under management, end-2013)
Exchange-traded funds are offered predominantly in the United
States, where the use of exotic structures is restricted.
1. Investment Vehicles
(Percent of $43 trillion total assets under management, end-2013)
Most assets are managed with simple investment vehicles.
2. Mutual Funds by Fund Domicile
(Percent of $32 trillion total assets under management, 2014:Q2)
The mutual fund industry is dominated by U.S. and European funds, but
Brazil and China show a notable presence among emerging markets.
Sources: European Fund and Asset Management Association; and IMF staff
calculations.
Sources: BarclayHedge; European Fund and Asset Management Association;
ETFGI; Organisation for Economic Co-operation and Development; Preqin; and
IMF staff calculations.
Sources: Deutsche Bank; and IMF staff calculations. Sources: European Fund and Asset Management Association; and IMF staff
calculations.
Equity 44%
Bond 24%
Money market
16%
Balanced/mixed
12%
Other 4%
(continued)
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 125
Annex Figure 3.1.1. Investment Vehicles by Size, Domicile, and Investment Focus (continued)
5. Exchange-Traded Funds by Investment Focus
(Percent of $2.3 trillion total assets under management, end-2013)
Exchange-traded funds primarily invest in equities.
6. Private Equity Funds by Type
(Percent of total number of funds participating in Preqin’s survey 2014)
A large number of private equity funds are involved in buyout, venture
capital, and real estate funds.
Private equity funds are primarily located in the United
States and Europe.
A large number of hedge funds are domiciled in off-shore
jurisdictions.
7. Private Equity Funds by Location of Of?ces
(Percent of total number of funds participating in Preqin’s survey,
2014)
8. Hedge Funds by Country
(Percent of $1.4 trillion total assets under management covered in
Hedge Fund Research, 2014)
Source: Preqin.
Note: Some funds are involved in multiple investment strategies.
Sources: Deutsche Bank; and IMF staff calculations.
Sources: Hedge Fund Research; and IMF staff calculations. Source: Preqin.
Note: Some funds have of?ces in multiple countries.
Equity
83%
Fixed income
16%
Commodity
1%
Other
0%
0
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20
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30
35
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GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
126 International Monetary Fund | April 2015
Source: IMF staff.
Note: Examples of asset management companies are BlackRock, Franklin Templeton, and PIMCO; examples of funds are BlackRock iShare Core
S&P 500 ETF and PIMCO total return funds. Custodians are usually large banks such as Bank of New York Mellon, J.P. Morgan, and State Street.
Funds often lend the securities they hold to various counterparties to earn fee income (securities lending). Securities borrowers usually provide
cash collateral. Counterparties are usually investment banks, prime brokers, and other broker-dealers that are engaged in short-selling of the
borrowed securities.
Annex Figure 3.1.2. Operation of a Fund
A fund signs an investment management agreement with an asset management company (AMC), which manages the fund’s
portfolio, risks, trading of securities, and securities ?nancing transactions. End investors are equity shareholders of a fund and
are the owners of the funds’ assets in the sense that each share represents an investor’s proportional ownership of the fund’s
asset holdings and the income those assets generate. However, end investors do not have full control over a fund. They
typically cannot ascertain the exact makeup of a fund’s portfolio at any given time, nor can they directly in?uence which
securities the fund manager buys and sells or the timing of these trades. Fund boards represent and protect shareholder
rights vis-à-vis AMCs.
Investment
management
agreement
End
investors
Asset
management
company
Fund Board
Represents and protects
shareholder rights
Fund
Counterparty
Custodian
Shares
Cash
Cash
F
e
e
s
Securities
Assets Liabilities
Share
units
Open-end mutual fund
Segregates and safeguards
clients’ assets from asset
manager’s assets for a fee
–Manage the assets
of the fund
–Risk management
–Trading of securities
and derivatives
–(Rare) emergency
liquidity support
Other transactions:
–Derivatives
–Securities lending
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 127
Annex 3.2 Empirical Framework
Aggregate fow-price relationship
Te aggregate fow-price relationship analysis exam-
ines whether mutual fund fows have an impact on
asset prices at the macro level. Mutual fund fows to
23 emerging markets
48
are investment fows into each
country from all mutual funds from various jurisdic-
tions covered by EPFR Global. U.S. fund fows data
are investors’ fows into mutual funds with a stated
investment focus, covering funds domiciled in the
United States. U.S. data are from ICI, except for U.S.
high-yield bond funds data, which come from EPFR
Global. Te analyses investigate weekly fows, but
the results are similar using monthly fows. Te price
impact is measured by the total excess return of the
respective index for each asset class in dollar terms over
the one-month Eurodollar deposit rate.
Te analysis here focuses frst on surprise fows fol-
lowing Acharya, Anshuman, and Kumar (2014). As
shown in the fund fows analysis later in this annex,
mutual fund investors chase past returns, making fund
fows predictable to some extent. Markets are likely to
have priced in the efects from predictable fows by the
time the money arrives, which limits the correlation
between fows and returns. One would instead need
to examine the part of fund fows that is not priced
in the market. Surprise fows are estimated as residu-
als µ
Fjt
for each asset class j from the following vector
autoregression (VAR) model with the Chicago Board
Options Exchange Market Volatility Index (VIX) as an
exogenous variable.
R
jt
R
jt–1
R
jt–p
?
?
= A + B
1
?
?
+ ··· + B
p
?
?
F
jt
F
jt–1
F
jt–p
µ
Rjt
+ g
0
VIX
t
+ ··· + g
q
VIX
t–q
+
? ?
(3.1)
µ
Fjt
R
t
and F
t
are excess index return and fund fows,
respectively, and p and q are the lengths of lags. For
U.S. assets, the model is estimated with a standard
48
Economies include current emerging markets as well as “gradu-
ated” emerging markets that were considered to be emerging at some
point during the sample period. For equities, the sample includes
Argentina, Brazil, Chile, China, Colombia, the Czech Republic,
Egypt, Hungary, India, Indonesia, Israel, Jordan, Korea, Malaysia,
Mexico, Pakistan, Peru, the Philippines, Poland, Russia, South Africa,
Taiwan Province of China, and Turkey. For bonds, the sample addi-
tionally includes Bulgaria, Lebanon, Sri Lanka, Ukraine, Uruguay,
and Vietnam, but excludes the Czech Republic, India, Israel, Jordan,
Korea, and Taiwan Province of China.
VAR. For emerging market assets, a panel VAR exclud-
ing the VIX is applied. Te details of the variable
defnitions are given in Annex Table 3.2.1.
Various single-equation models are estimated to
investigate the relationship between surprise fows and
asset returns. More specifcally, the following models are
estimated for each asset class j, using a panel regres-
sion with country fxed efects and robust standard
errors (with clusters to correct for heterogeneity within
countries, in addition to cross-country heterogeneity)
for mutual fund fows into emerging market assets, and
ordinary least squares (with Newey-West standard errors
corrected for autocorrelation and heteroscedasticity) for
end investor asset fows into U.S. mutual funds.
Base model:
R
jt
= ? + ?
P
p=1
?
p
R
jt–p
+ ?
Q
q=0
?
q
µ
?
Fjt–q
+ ?
R
r=0
?
r
VIX
t–r
+ ?
S
s=0
?
s
Asset Volatility
jt–s
(3.2)
Model with asymmetry:
R
jt
= ? + ?
P
p=1
?
p
R
jt–p
+ ?
Q
q=0
{?
1q
µ
?
Fjt–q
+ ?
2q
µ
?
Fjt–q
× Indicator(1 if µ
?
Fjt–q
> 0)} + ?
R
r=0
?
r
VIX
t–r
+ ?
S
s=0
?
s
Asset Volatility
jt–s
(3.3)
Model with nonlinearity by the levels of the VIX:
R
jt
= ? + ?
P
p=1
?
p
R
jt–p
+ ?
Q
q=0
?
1q
µ
?
Fjt–q
+ ?
2
µ
?
Fjt
× Indicator(1 if VIX
t
> Treshold
j
)
+ ?
R
r=0
?
r
VIX
t–r
+ ?
S
s=0
?
s
Asset Volatility
jt–s
(3.4)
in which µ
?
is the estimated residual in equation 3.
In addition, the section examines the dynamic
relationship between unadjusted (that is, nonsurprise)
fows and returns to assess the presence of a frst-mover
advantage. Te analysis is based on generalized impulse
response functions from VARs as in equation (3.1). In
addition, impulse responses based on Cholesky decom-
positions using both possible orderings were computed.
Concentration and its efects on bond yields
Te concentration analysis is based on the Lipper
eMaxx bond ownership data, as used in Manconi,
Massa, and Yasuda (2012). Tis database contains
details of institutional holdings for each fxed-income
security, covering $7 trillion in total fxed-income secu-
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
128 International Monetary Fund | April 2015
Annex Table 3.2.1. List and De?nition of Variables for Empirical Exercises
Variables Description Data Source
Aggregate Flow and Return Analysis
EM equity flows Weekly mutual fund equity investment flows into each economy from all mutual
funds covered by EPFR Global.
EPFR Global
EM bond flows Weekly mutual fund bond investment flows into each economy from all mutual funds
covered by EPFR Global.
EPFR Global
U.S. equity flows Flows from end investors to U.S.-domiciled mutual funds investing in domestic
equities.
ICI
U.S. bond flows, all bonds Flows from end investors to U.S.-domiciled mutual funds investing in domestic
bonds (both government and corporate).
ICI
U.S. HY corp. bond flows Flows from end investors to mutual funds investing in U.S. high-yield corporate
bonds.
EPFR Global
U.S. muni. flows Flows from end investors to U.S.-domiciled mutual funds investing in municipal bonds. ICI
EM equity returns MSCI country equity index. Bloomberg, L.P.
EM bond returns Country index from J.P. Morgan EMBIG Global Index. Bloomberg, L.P.
U.S. equity returns MSCI country equity index. Bloomberg, L.P.
U.S. bond returns, all bonds Bank of America Merrill Lynch total return index for U.S. government and corporate
bonds.
Bloomberg, L.P.
U.S. HY corp. bond returns Bank of America Merrill Lynch total return index for U.S. high-yield corporate bonds. Bloomberg, L.P.
U.S. muni. returns Bank of America Merrill Lynch total return index for U.S. municipal bonds. Bloomberg, L.P.
Benchmark yield One-month Eurodollar deposit rate. Bloomberg, L.P.
VIX Chicago Board Options Exchange Market Volatility Index. Bloomberg, L.P.
Asset volatility Staff estimates based on asset returns data and GARCH in mean model. IMF staff
Price Impact of Concentration in Bond Markets
Spread Bond yield minus the yield of benchmark sovereign bond with the same currency and
similar maturity.
Bloomberg, L.P.
Concentration Share of bonds held by the largest five mutual fund investors for each bond. Quarterly. eMaxx
Bid-ask spread Bid-ask yield spreads for each bond (end of quarter). Bloomberg, L.P.
Modified duration Computed from bond’s yield to maturity, coupon rate, and time to maturity,
assuming semi-annual distributions (end of quarter).
Bloomberg, L.P.
Issue size Log of issuance size. eMaxx
Covenants ratio The number of covenants attached to a bond relative to a maximum of 18. Bloomberg, L.P.
Drivers of Fund Flows and Liquidity Risk Management
Fund flow For each fund (i) and time (t), fund flows (it) = [TNA(it)–TNA(it–1)×{1+return(it)}]/
TNA(it–1). Return(it) is computed by CRSP based on NAV. Monthly.
CRSP
Performance Monthly excess fund return (changes of NAV) over benchmark, averaged over prior
three months.
CRSP
Benchmark performance Monthly return of benchmark index, averaged over prior three months. The same
benchmark is assigned for funds with the same broad investment focus (for
instance, S&P 500 for U.S. domestic equity funds).
DataStream
L.P.
HIGH_VIXD High VIX dummy equals 1 when VIX > 30 percent. DataStream
L.P.
Cash Cash and cash equivalents holdings in percent of total portfolio. Quarterly. CRSP
Flow volatility Standard deviation of flows over the prior 12 months, divided by the mean flows
over the same period.
CRSP
Fund Characteristics
Size (S/M/L) Dummies based on 20th and 80th percentiles. CRSP
Age Years since initial offer. CRSP
Purchase fee Maximum in prospectus. CRSP
Redemption fee Maximum in prospectus (sum of type R [redemption] and C [contingent deferred
sales charge]).
CRSP
Index dummy 1 if index fund. CRSP
ETF dummy 1 if ETF. CRSP
Institutional dummy 1 if institutional but not retail in CRSP. CRSP
Liquid bond fund dummy 1 if a fund’s investment focus is one of the following: short-term U.S. government
funds and Treasury funds or short-term investment-grade debt funds.
CRSP
Illiquid bond fund dummy 1 if a fund’s investment focus is one of the following: corporate debt BBB rated
funds, EM local currency debt funds, EM debt funds, or high current yield funds.
CRSP
Liquid equity fund dummy 1 if a fund investment focus is S&P 500. CRSP
Illiquid equity fund dummy 1 if a fund’s investment focus is one of the following: micro/small cap funds; equity
global small company; equity international small company; emerging markets,
China, India, and Latin America.
CRSP
Note: corp. = corporate; CRSP = Survivor-bias-free U.S. mutual fund database, Center for Research in Security Prices; EM = emerging market; ETF = exchange-
traded fund; HY = high yield; ICI = Investment Company Institute; EMBIG = Emerging Markets Bond Index Global; GARCH = generalized autoregressive
conditional heteroscedasticity; muni. = municipal; S/M/L = small, medium, large; VIX = Chicago Board Options Exchange Market Volatility Index.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 129
rities (based on par value) held by more than 19,000
funds. Institutional investors covered in the database
are U.S. and some European insurance companies;
U.S. mutual funds; top U.S. public pension funds; and
European, Canadian, and Asian mutual funds. Data
are based on disclosure information of security-level
holdings by these institutional investors (especially for
mutual funds and U.S. insurance companies). Tis
analysis focuses on a subcomponent of these data,
specifcally corporate bonds for advanced economies
and both sovereign and corporate bonds for emerging
market economies.
Te casual observation on the efects of ownership
concentration on spreads in Figure 3.7 is confrmed
with formal empirical analysis, reported in Annex Figure
3.2.1. Te dependent variable is the change in indi-
vidual bond yield spreads over a benchmark sovereign
bond yield with the same currency and similar maturity
between 2008:Q2 and 2008:Q4 and between 2013:Q1
and 2013:Q2. Tis change is regressed on various
control factors and measures of mutual fund sector
concentration. Te following cross-section model is esti-
mated using a quantile regression approach (for quantile
j=10th, 25th, 50th, 75th, 90th percentile), because a
preliminary analysis indicates the presence of nonlineari-
ties between the dependent and independent variables
(see Annex Table 3.2.1 for the list of variables):
?Spread
ij
= ?
j
+ ?Spread
ij,t=0
+ ?Bond Characteristics
ij,t=0
+ ?Concentration
ij,t=0
(3.5)
Control factors are Spread, which is the initial level
of the yield spread to control for the credit risk of
the security; and bond-specifc characteristics, includ-
ing liquidity (bid-ask spread), bond price sensitivity
to interest rate changes (duration), issue size, and
covenants, in line with Manconi, Massa, and Yasuda
(2012). Concentration is measured primarily by the
share of bonds held by the largest 5 funds, but key
results are robust to other defnitions, such as the
share held by the largest 10 funds, the share held by
all mutual funds, and the Herfndahl index among
mutual fund investors. All explanatory variables are
measured as of 2008:Q2 or 2013:Q1 to control for
possible endogeneity. Outliers in observed market price
data were reduced by winsorizing the 5 percent tail of
the respective distributions.
Relationship between a fund’s liquidity risk and its
management
Te main mutual fund and ETF data source is the
CRSP survivor-bias-free database covering publicly
ofered open-end mutual funds domiciled in the
United States. Even though CRSP’s data cover only
U.S.-domiciled funds, CRSP provides more details
on funds’ fee structures and assets, including quar-
terly security-level holdings, than other global fund
databases such as EPFR Global or Lipper for Invest-
ment Management. Tese global data are used for
some additional robustness tests or for extending some
analysis to funds domiciled outside the United States.
Data are cleaned for outliers. In line with Coval and
Staford (2007); Jotikasthira, Lundblad, and Ramado-
rai (2012); and Jinjarak and Zheng (2014), the data
are excluded if they meet the following conditions:
(1) monthly returns are higher than 200 percent or
lower than –50 percent; (2) monthly change in total
net assets (TNA) is higher than 200 percent or lower
than –100 percent; or (3) fund TNA is less than US$5
million. In addition, for cash balance analysis, port-
folio allocation weight data by broad asset types are
discarded if the sum of allocation weights is less than
95 percent or greater than 105 percent. Weights may
have a negative value because of derivatives and securi-
ties held in short positions. Outliers are removed by
discarding data when any single weight takes a value of
less than –100 percent.
Te roles of portfolio managers and end investors
Following Raddatz and Schmukler (2012), a fund’s
net investment in a security is divided into fund fows
from end investors and the contribution of the changes
of portfolio weights to the security, determined by
portfolio managers. Te term F
j
is the total investment
in security j (net of valuation efects) from all funds i
in the sample. Tis investment is divided into
Fund i’s holding of asset j
F
j
= ?
i
————————————–—— × ?w
ij
Total asset j held by all funds in sample
Fund i’s holding of asset j
+ ?
i
————————————–——
Total asset j held by all funds in sample
× Fund fows to i (3.6)
In the equation, ?w
ij
is the change in portfolio weight
of fund i to asset j, net of valuation efects. Te frst
term of the equation represents manager’s choice and
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
130 International Monetary Fund | April 2015
the second represents end investor’s choice. Ten, the
variance of F
j
is calculated as the sum of each compo-
nent’s variation. Tis variance is estimated on a quar-
terly basis for all funds covered in the CRSP database
for the period 2005:Q1–2014:Q4, excluding securities
held by fewer than fve funds.
Fund fows analysis
Tis analysis studies the drivers of monthly net fows
for U.S. mutual funds and ETFs at the funds’ share-
class level for open-end bond and equity funds, cover-
ing the period 1998–2014.
49
Explanatory variables
include fund performance and benchmark perfor-
mance, the VIX, and various fund characteristics (size,
age, clientele, purchase and redemption fees, fund
types, and the liquidity of the underlying asset classes).
Te list of variables used in the analysis is explained
in Annex Table 3.2.1. Te following model (for share
49
A fund may issue several classes of shares. Te only diference
across share classes is fees. “Fund’s TNA” means the sum of TNA of
each share class issued by the fund.
class i, month t, and benchmark j ) is estimated with
share-class fxed efects and year fxed efects as in
Chen, Goldstein, and Jiang (2010), and using robust
standard errors. An analogous specifcation was run
including the interaction terms with benchmark per-
formance instead of excess return over benchmark.
Fund fows
it
= ?
0
Benchmark Performance
jt–1
+ ?
1
Performance
it–1
+ ?
2
VIX
t
+ ?
3
HIGH_VIXD
t
+ ?
4
VIX
t
× HIGH_VIXD
t
+ ?Fund Characteristics
i
+ ?Performance
it–1
× Fund Characteristics
i
(3.7)
Te test for convexity in the fow-performance rela-
tionship follows a piecewise-linear specifcation as in Sirri
and Tufano (1998) and Ferreira and others (2012). Tis
approach measures diferent linear slopes for the lowest
Sources: eMaxx; and IMF staff estimates.
–15
–10
–5
0
5
10
15
20
25
30
10th 25th 50th 75th 10th 25th 50th 75th 90th
Percentiles of spread change between 2008:Q2 and 2008:Q4
Initial spread level
Top ?ve funds’ holding
Other
Total
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
90th
Percentiles of spread change between 2013:Q1 and 2013:Q2 among
issuers from emerging market and developing economies
Bonds with lower
increase in credit spreads
Bonds with higher
increase in credit spreads
Annex Figure 3.2.1. Drivers of Changes in Credit Spreads during Stress Episodes
(Changes in credit spreads in percentage points, by the levels of the spread changes)
1. Global Financial Crisis: U.S. Dollar Bonds Issued in the United States
(Changes between 2008:Q2 and 2008:Q4)
During the global ?nancial crisis, bonds that were held in a more
concentrated manner were adversely affected, especially those with
high initial spread levels.
2. Taper Shock: Emerging Market and Developing Economies
(Changes between 2013:Q1 and 2013:Q2)
The same was true for emerging market and developing
economy bonds during the “taper shock” episode.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 131
20th, middle 60th, and top 20th percentiles of perfor-
mance. Each month, funds are ranked according to their
performance, ranging from zero (poorest performance) to
one (best performance). Te following model is estimated,
Fund fows
it
= ?
0
Benchmark Performance
jt–1
+ ?
1
VIX
t
+ ?
2
HIGH_VIXD
t
+ ?
3
VIX
t
× HIGH_VIXD
t
+ ?Fund Characteristics
i
+ ?
1
Low
i,t–1
+ ?
2
Mid
i,t–1
+ ?
3
High
i,t–1
, (3.8)
in which the three levels of relative performance are
defned as follows:
Low
i,t–1
= min{0.2, Rank
i,t–1
}
Mid
i,t–1
= min{0.6, Rank
i,t–1
– Low
i,t–1
}
High
i,t–1
= Rank
i,t–1
? (Low
i,t–1
+ Mid
i,t–1
) Rank ? [0,1]
Analysis of redemption fees in times of stress
Tis analysis examines the role of redemption fees dur-
ing times of stress. It covers two stress events: the 2008
global fnancial crisis and the taper episode in 2013. We
compute the diference between average fows before
the crisis periods (May to August 2008 and December
2012 to April 2013) and average fows during the stress
periods (September to December 2008 and May to
September 2013) for funds with high and low redemp-
tion fees. Funds are classifed as having low redemption
fees if redemption fees are equal to zero. Funds are
classifed as having high redemption fees if redemption
fees are greater than or equal to 0.03 percent in 2008
and 0.01 percent in 2013.
50
Flows are standardized by
the beginning-of-period TNA. For 2008, the focus is on
equity funds because there is evidence of fight to qual-
ity into bond funds. For 2013, the focus is on emerging
market equity and bond funds.
Cash holdings analysis
Drivers of fund cash holdings are investigated by
estimating the model in equation (3.9). For share class
i and quarter t, the model is estimated with a pooled
panel regression at the share-class level, including year
fxed efects and using robust standard errors. Because
50
Te 2013 analysis studies emerging market funds, and therefore
yields very few observations when using the 0.03 threshold.
the cash balance shows a U-shaped pattern with respect
to fund fows (Figure 3.12), the model estimates a
diferent coefcient for funds with large outfows (fund
fows below ? = ?1.5 percent of TNA).
51
Cash
it
= ?
1
Flow volatility
it
+ ?
2
Fund fow
it
+ ?
3
I(Fund fow
it
< ?) + ?
4
Fund fow
it
× I(Fund fow
it
< ?)
+ ?Fund Characteristics
i
(3.9)
Brand name efect analysis
“Flagship shocks for large AMCs” are identifed as follows:
First, a “shock” happens when a fund’s fow-to-TNA ratio
is below the median of its peer group (those with the same
Lipper investment objective code) by 10 percentage points
or more. Second, a fund with a “shock” is identifed as
“fagship” when its TNA is the largest of the funds admin-
istered by the same AMC (a fund family) at the end of the
month before the shock. Tird, the fagship shock corre-
sponds to a large AMC if the fagship fund’s asset manager
was among the top 25 as measured by end-year TNA for
the shock year or any of the previous four years.
Tere are “brand name efects” if, in the three
months including and after the fagship shock (s, s+1,
s+2; where s is the event month), funds in the same
family receive signifcantly lower infows relative to
comparator funds outside the family.
52
For each event
(period s), a separate cross-sectional regression model
is estimated for the diference between the cumulative
net infows to each fund i between dates s and s+2 and
the median cumulative net infows for funds with the
same investment objective j. Explanatory variables are
lagged excess return, age, and a fagship family dummy.
Cumulative Fund fow
ij_{s,s+2}
? Median(CumulativeFund fow
j_{s,s+2}
)
= ?
1
Performance
is–1
+ ?
2
Age
it
+ ?
3
Family Dummy(i ? I
s
)
for all events s and for all funds i with
investment objective j (3.10)
51
Te cash holdings empirical analysis excludes sectoral, hedged,
and short equity funds.
52
Some of the identifed fagship events overlap. Overlapping cases
are treated as a single event and the family dummy is set to 1 if a
share class belongs to either of the afected fagships’ families.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
132 International Monetary Fund | April 2015
institution i is in distress and the CoVaR
i
when institu-
tion i has median return (?CoVaR
i
):
?CoVaR
i
= CoVaR
i
5%
? CoVaR
i
50%
= ??ˆ
i
(VaR
i
5%
? VaR
i
50%
). (3.13)
Te relationship between fund size and its contribu-
tion to systemic risk is examined with the following
cross-section regression model:
?CoVaR
ij
= Constant
j
+ ?VaR
i
+ ?Logsize
i
+ ?Return
i
+ ?
i
. (3.14)
Te model controls for asset class ( j) specifc fxed
efects and fund i’s risk (VaR) and return (average in
the sample period). Fund size is the log of average size
in U.S. dollars over the sample period. Fixed efects are
positive and signifcant for advanced economy equities
and emerging market equities and bonds, negative for
advanced economy sovereign bonds, and not signif-
cant for advanced economy corporate bonds. All the
other coefcients for control variables are signifcant
and positive at the 5 percent level. Te coefcient for
size is positive and signifcant at the 10 percent level.
Alternative regressions that allow the parameters on
VaR, size, and returns to vary by asset class show quali-
tatively similar results.
References
Acharya, Viral, Ravi Anshuman, and Kiran Kumar. 2014. “For-
eign Fund Flows and Stock Returns: Evidence from India.”
Unpublished. New York University.
Adam, Tim, and André Guettler. Forthcoming. “Pitfalls and
Perils of Financial Innovation: Te Use of CDS by Corporate
Bond Funds.” Journal of Banking and Finance.
Adrian, Tobias, and Markus K. Brunnermeier. 2011. “CoVaR.”
Working Paper 17454, National Bureau of Economic
Research, Cambridge, Massachusetts.
Allen, Franklin, and Gary Gorton. 1993. ”Churning Bubbles.”
Review of Economic Studies 60 (4): 813–36.
Arora, Navneet, and Hui Ou-Yang. 2001. “Explicit and Implicit
Incentives in a Delegated Portfolio Management Problem:
Teory and Evidence.” Working Paper, University of North
Carolina, Chapel Hill.
Basak, Suleyman, and Anna Pavlova. 2014. “Asset Prices and
Institutional Investors.” American Economic Review 103 (5):
1728–58.
———, and Alexander Shapiro. 2008. “Ofsetting the Implicit
Incentives: Benefts of Benchmarking in Money Manage-
ment.” Journal of Banking and Finance 32 (9): 1882–993.
I
s
identifes the funds (at share-class level) that
are managed by the same AMC that manages the
“shocked” fagship fund (excluding the fagship itself).
Systemic risk
Systemic risk is measured for the system of mutual
funds, banks, and insurance companies from advanced
economies.
• Mutual funds’ NAV and total net asset data are from
Lipper. For each of the three fund domicile areas
(the United States, Europe, and other advanced
economies) and the five asset classes (advanced
economy equities, advanced economy sovereign
bonds, advanced economy corporate bonds, emerg-
ing market equities, and emerging market bonds),
a sample consisting of the top 100 funds, measured
by total net assets, was selected, resulting in 1,500
funds. Data covering January 2000 to November
2014 were cleaned by dropping funds with fewer
than 10 observations and excluding observations
with weekly NAV returns of less than ?60 percent
or greater than 80 percent.
• For the banking and insurance sectors, weekly
returns are computed using Thomson Reuters equity
indices for European and U.S. banks and insurance
companies.
• The system’s return is computed as the average of
funds, banks, and insurance returns weighted by
their relative asset size. Data on total assets of banks,
insurance companies, and mutual funds are from
quarterly flow-of-funds data for the United States
and the euro area. An alternative measure using a
simple average was also used, yielding similar results.
Systemic risk is estimated following the static
CoVaR approach put forward by Adrian and Brun-
nermeier (2011), using quantile regressions. First, the
returns of the system are regressed on the returns of
each individual institution i when that institution has
the lowest 5th percentile returns:
R
t
System,i
= ?
i
+ ?
i
R
i
t
+ ?
it
. (3.11)
Ten, CoVaR is computed as the VaR of the system
conditional on institution i being in distress (defned as
when its return R
i
is below its 5 percent VaR, –VaR
i
5%
):
CoVaR
i
= ?ˆ
i
+ ?ˆ
i
R
i
= ?ˆ
i
? ?ˆ
i
VaR
i
5%
. (3.12)
Te contribution to systemic risk of an institution i is
computed as the diference between the CoVaR
i
when
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 133
Ben-Raphael, Azi, Shmuel Kande, and Avi Wohl. 2011. “Te
Price Pressure of Aggregate Mutual Fund Flows.” Journal of
Financial and Quantitative Analysis 46 (2): 585–603.
Bikhchandani, Sushil, David Hirshleifer, and Ivo Welch. 1992.
“A Teory of Fads, Fashion, Custom and Cultural Change
as Informational Cascades.” Journal of Political Economy 100:
992–1026.
BlackRock. 2014a. “ETFs Help Improve Market Stability.”
October.
———. 2014b. “Fund Structures and Systemic Risk Mitigants.”
Viewpoint, September.
Borensztein, Eduardo, and Gaston Gelos. 2003. “A Panic Prone
Pack? Te Behavior of Emerging Market Mutual Funds.” IMF
Staf Papers 50 (1): 43–63.
Broner, Fernando A., Gaston Gelos, and Carmen M. Reinhart.
2006. “When in Peril, Retrench: Testing the Portfolio Chan-
nel of Contagion.” Journal of International Economics 69 (1):
203–30.
Brown, Nerissa, Kelsey Wei, and Russ Wermers. 2013. “Analyst
Recommendations, Mutual Fund Herding, and Overreaction
in Stock Prices.” Management Science 60 (1).
Bufa, Andrea, Dimitri Vayanos, and Paul Woolley. 2014. “Asset
Management Contracts and Equilibrium Prices.” Working
Paper 20480, National Bureau of Economic Research, Cam-
bridge, Massachusetts.
Calvo, Guillermo A., and Enrique G. Mendoza, 2000. “Rational
Contagion and the Globalization of Securities Markets.”
Journal of International Economics 51 (1): 79–113.
Cao, Charles, Eric Chang, and Ying Wang. 2008. “An Empirical
Analysis of the Dynamic Relationship between Mutual Fund
Flow and Market Return Volatility.” Journal of Banking and
Finance 32 (10): 2111–23.
Center for European Policy Studies–European Capital Markets
Institute (CEPS-ECMI). 2012. “Rethinking Asset Manage-
ment from Financial Stability to Investor Protection and
Economic Growth.” Report of a CEPS-ECMI Task Force.
Cetorelli, Nicola. 2014. “Hybrid Intermediaries.” Federal Reserve
Bank of New York Staf Reports 705.
Chakravorti, Suji, and Subir Lall. 2003. “Managerial Incentives
and Financial Contagion.” Working Paper 2003–21, Federal
Reserve Bank of Chicago.
Chen, Qi, Itay Goldstein, and Wei Jiang. 2010. “Payof Comple-
mentarities and Financial Fragility: Evidence from Mutual
Fund Outfows.” Journal of Financial Economics 97 (2):
239–62.
Chevalier, Judith, and Glenn Ellison. 1997. “Risk Taking by
Mutual Funds as a Response to Incentives.” Journal of Politi-
cal Economy 106 (6): 1167–200.
Choi, Nicole, and Richard W. Sias. 2009. “Institutional Industry
Herding.” Journal of Financial Economics 94 (3): 469–91.
Chordia, Tarun. 1996. “Te Structure of Mutual Fund Charges.”
Journal of Financial Economics 41 (1): 3–39.
Collins, Sean, and Christopher Plantier. 2014. “Are Bond
Mutual Fund Flows Destabilizing? Examining the Evidence
from the ‘Taper Tantrum’.” Working Paper, Investment Com-
pany Institute, Washington.
Coval, Joshua, and Erik Staford. 2007. “Asset Fire Sales (and
Purchases) in Equity Markets.” Journal of Financial Economics
86 (2): 479–512.
Dasgupta, Amil, and Andrea Prat. 2006. “Financial Equilibrium
with Career Concerns.” Teoretical Economics 1 (1): 67–93.
Deli, Daniel, and Raji Varma. 2002. “Closed-end versus Open-
end: Te Choice of Organizational Form.” Journal of Corpo-
rate Finance 8 (1): 1–27.
Desender, Kurt. 2012. “Ownership Structure and Stock Price
Performance during Turbulent Financial Markets.” Unpub-
lished, Universidad Carlos III de Madrid.
Deutsche Bank. 2014. ETF Annual Review and Outlook. January
16.
Diamond, Douglas W., and Philip H. Dybvig. 1983. “Bank
Runs, Deposit Insurance, and Liquidity.” Journal of Political
Economy 91 (3): 401–19.
Dow, James, and Gary Gorton. 1997. “Noise Trading, Delegated
Portfolio Management, and Economic Welfare.” Journal of
Political Economy 105 (5): 1024–50.
Edelen, Roger. 1999. “Investor Flows and the Assessed Perfor-
mance of Open-end Mutual Funds.” Journal of Financial
Economics 53 (3): 439–66.
———, and Jerold Warner. 2001. “Aggregate Price Efects of
Institutional Trading: A Study of Mutual Fund Flow and
Market Returns.” Journal of Financial Economics 59:
195–220.
Elliott, Douglas. 2014. “Systemic Risk and the Asset Manage-
ment Industry.” Economic Studies at Brookings, Brookings
Institution, Washington.
European Fund and Asset Management Association (EFAMA).
2014. “Asset Management in Europe: Facts and Figures: 7th
Annual Review.” European Fund and Asset Management
Association, Brussels.
European Securities and Markets Authority (ESMA). 2012.
“Guidelines for Competent Authorities and UCITS Manage-
ment Companies.” ESMA/2012/832EN, Paris.
Ferreira, Miguel, Aneel Keswani, Antonio Miguel, and Sofa
Ramos. 2012. “Te Flow-Performance Relationship
around the World.” Journal of Banking and Finance 36 (6):
1759–80.
Feroli, Michael, Anil K. Kashyap, Kermit Schoenholtz, and
Hyun Song Shin. 2014. “Market Tantrums and Monetary
Policy.” Chicago Booth Research Paper 14–09, University of
Chicago Booth School of Business, Chicago.
Financial Industry Regulatory Authority (FINRA). 2011.
“Understanding Mutual Fund Classes.” Investor Alert, Finan-
cial Industry Regulatory Authority, Washington.
Financial Stability Board (FSB). 2011. “Potential Financial Sta-
bility Issues Arising from Recent Trends in Exchange-Traded
Funds.” Financial Stability Board, Basel.
———. 2013. “Strengthening Oversight and Regulation of
Shadow Banking: Policy Framework for Strengthening
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
134 International Monetary Fund | April 2015
Oversight and Regulation of Shadow Banking Entities.” FSB
Policy Document, Financial Stability Board, Basel.
Financial Stability Board (FSB), International Monetary Fund
(IMF), and World Bank. 2011. “Financial Stability Issues in
Emerging Market and Developing Economies.” Report to
the G-20 Finance Ministers and Central Bank Governors,
Financial Stability Board, Basel.
Financial Stability Board (FSB) and International Organization
of Securities Commissions (IOSCO). 2014. “Assessment
Methodologies for Identifying Non-bank Non-insurer Global
Systemically Important Financial Institutions.” Consultation
Document, Financial Stability Board, Basel.
———. 2015. “Assessment Methodologies for Identifying Non-
bank Non-insurer Global Systemically Important Financial
Institutions.” Te Second Consultation Document, Financial
Stability Board, Basel.
Financial Stability Oversight Council (FSOC). 2014. “Notice Seek-
ing Comment on Asset Management Products and Activities.”
U.S. Department of the Treasury, Washington.http://www
.treasury.gov/initiatives/fsoc/rulemaking/Pages/open-notices.aspx.
Frazzini, Andrea, and Owen A. Lamont. 2008. “Dumb Money:
Mutual Fund Flows and the Cross-Section of Stock Returns.”
Journal of Financial Economics 88 (2): 299–322.
Froot, Kenneth A., Paul G. J. O’Connell, and Mark S. Sea-
sholes. 2001. “Te Portfolio Flows of International Investors.”
Journal of Financial Economics 59 (2): 151–93.
Gelos, Gaston. 2011. “International Mutual Funds, Capital Flow
Volatility, and Contagion: A Survey.” IMF Working Paper
11/92, International Monetary Fund, Washington.
Greenwood, Robin, and David Tesmar. 2011. “Stock Price
Fragility.” Journal of Financial Economics 102 (3): 471–90.
Grinblatt, Mark, Sheridan Titman, and Russ Wermers. 1995.
“Momentum Investment Strategies, Portfolio Performance,
and Herding: A Study of Mutual Fund Behavior.” American
Economic Review 85 (5): 1088–105.
Haldane, Andrew. 2014. “Te Age of Asset Management?”
Speech at the London Business School, April 4.
Hau, Harald, and Sandy Lai. 2010. “Te Role of Equity Funds
in the Financial Crisis Propagation.” Research Paper No.
11–35, Swiss Finance Institute, Geneva.
Igan, Deniz, and Marcelo Pinheiro. 2012. “Te Efects of Rela-
tive Performance Objectives on Financial Markets.” MPRA
Paper 43452, University Library of Munich.
Ilyina, Ana. 2006. “Portfolio Constraints and Contagion in
Emerging Markets.” IMF Staf Papers 53 (3): 351–74.
International Monetary Fund (IMF). 2012. “Macrofnancial
Stress Testing—Principles and Practices.” Washington.
———. 2014. “Staf Guidance Note on Macroprudential
Policy.” Washington.
International Organization of Securities Commissions (IOSCO).
2011. “Principles on Suspensions of Redemptions in Collec-
tive Investment Schemes.” Madrid.
Investment Company Institute (ICI). 2014a. “Te Closed-end
Fund Market, 2013.” ICI Research Perspective 20 (1).
———. 2014b. ICI Factbook 2014. Washington: Investment
Company Institute.
———. 2014c. “Understanding Exchange-Traded Funds: How
ETFs Work.” ICI Research Perspective 20 (5).
Jinjarak, Yothin, and Huanhuan Zheng. 2014. “Granular Institu-
tional Investors and Global Market Interdependence.” Journal
of International Money and Finance 46: 61–81.
Jotikasthira, Chotibhak, Christian Lundblad, and Tarun
Ramadorai. 2012. “Asset Fire Sales and Purchases and the
International Transmission of Funding Shocks.” Journal of
Finance 67 (6): 2015–50.
Lakonishok, Josef, Andrei Shleifer, and Robert Vishny. 1992.
“Te Impact of Institutional Trading on Stock Prices.” Journal
of Financial Economics 32 (1): 23–44.
Liang, Nellie. 2015. “Asset Management and Financial Stability.”
Presentation at the Brookings Institution, January 9.
Ma, Linlin, Yuehua Tang, and Juan-Pedro Gomez. 2013.
“Portfolio Manager Compensation in the U.S. Mutual Fund
Industry.”http://ssrn.com/abstract=2024027 orhttp://dx.doi
.org/10.2139/ssrn.2024027.
Manconi, Alberto, Massimo Massa, and Ayako Yasuda. 2012.
“Te Role of Institutional Investors in Propagating the
Crisis of 2007–08.” Journal of Financial Economics 104 (3):
491–518.
Massa, Massimo, and Rajdeep Patgiri. 2009. “Incentives and
Mutual Fund Performance: Higher Performance or Just
Higher Risk Taking?” Review of Financial Studies 22 (5):
1777–815.
Massa, Massimo, and Zahid Rehman. 2008. “Information Flows
within Financial Conglomerates: Evidence from the Banks-
Mutual Funds Relation.” Journal of Financial Economics 89
(2): 288–306.
Maug, Ernst, and Narayan Naik. 2011. “Herding and Delegated
Portfolio Management: Te Impact of Relative Performance
Evaluation on Asset Allocation.” Quarterly Journal of Finance
1 (2): 265–92.
McKinsey. 2013. Financial Globalization: Retreat or Reset?
McKinsey Global Institute.
Metrick, Andrew, and Ayako Yasuda. 2011. “Venture Capital
and Other Private Equity: A Survey.” European Financial
Management 17 (4): 619–54.
Moody’s. 2010. “Sponsor Support Key to Money Market
Funds.” August 9.
Morningstar. 2012. “Synthetic ETFs under the Microscope: A
Global Study.” Morningstar ETF Research.
Ofce of Financial Research (OFR). 2013. “Asset Management
and Financial Stability.” Ofce of Financial Research, U.S.
Department of Treasury, Washington.
Pensions and Investments and Towers Watson. 2014. “Te World’s
500 Largest Asset Managers.” Towers Watson, New York.
PriceWaterhouseCoopers. 2013. Asset Management 2020: A
Brave New World. London: PriceWaterhouseCoopers.
Raddatz, Claudio, and Sergio Schmukler. 2012. “On the
International Transmission of Shocks: Micro-Evidence from
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 135
Mutual Fund Portfolios.” Journal of International Economics
88 (2): 357–74.
Rajan, Raghuram G. 2005. “Has Financial Development Made
the World Riskier?” Paper presented at Te Greenspan Era:
Lessons for the Future. Federal Reserve Bank of Kansas Sym-
posium, Jackson Hole, Wyoming.
Ross, Stephen. 2004. “Compensation, Incentives, and the Dual-
ity of Risk Aversion.” Journal of Finance 59 (1): 207–25.
Scharfstein, David, and Jeremy Stein. 1990. “Herd Behavior and
Investment.” American Economic Review 90 (3): 465–79.
Schmidt, Lawrence, Allan Timmermann, and Russ Wermers.
2013. “Runs on Money Market Mutual Funds.” Working
Paper, January.http://econweb.ucsd.edu/~lschmidt/Schmidt_
Timmermann_Wermers.pdf.
Securities Industry and Financial Markets Association (SIFMA).
2014. “SIFMA Asset Management Group’s Comments to the
FSB and SEC in Response to OFR Study and in Regards to
Separate Accounts.” Securities Industry and Financial Markets
Association, New York and Washington.
Sirri, Erik R., and Peter Tufano. 1998. “Costly Search and
Mutual Fund Flows.” Journal of Finance 53 (5): 1589–622.
Stein, Jeremy C. 2014. “Comments on ‘Market Tantrums and
Monetary Policy,’ a paper by Michael Feroli, Anil K. Kashyap,
Kermit Schoenholtz, and Hyun Song Shin.” Paper presented
at the U.S. Monetary Policy Forum, New York, New York,
February 28.
Stracca, Livio. 2006. “Delegated Portfolio Management: A Sur-
vey of the Teoretical Literature.” Journal of Economic Surveys
20 (5): 823–48.
TeCityUK. 2012. Private Equity 2012.
Vayanos, Dimitri. 2004. “Flight to Quality, Flight to Liquidity,
and the Pricing of Risk.” Working Paper 10327, National
Bureau of Economic Research, Cambridge, Massachusetts.
Warther, Vincent A. 1995. “Aggregate Mutual Fund Flows and
Security Returns.” Journal of Financial Economics 39 (2-3):
209–35.
Wermers, Russ. 1999. “Mutual Fund Herding and the Impact
on Stock Prices.” Journal of Finance 54 (2): 581–622.
Wurgler, Jefrey. 2010. “On the Economic Consequences of
Index-Linked Investing.” Working Paper 16376, National
Bureau of Economic Research, Cambridge, Massachusetts.
doc_129505392.pdf
Financial intermediation through asset management firms has many benefits. It helps investors diversify their assets more easily and can provide financing to the real economy as a “spare tire” even when banks are distressed. The industry also has various advantages over banks from a financial stability point of view.
F
inancial intermediation through asset management frms has many benefts. It helps investors diversify
their assets more easily and can provide fnancing to the real economy as a “spare tire” even when banks
are distressed. Te industry also has various advantages over banks from a fnancial stability point of view.
Nonetheless, concerns about potential fnancial stability risks posed by the asset management industry
have increased recently as a result of that sector’s growth and of structural changes in fnancial systems. Bond funds
have grown signifcantly, funds have been investing in less liquid assets, and the volume of investment products
ofered to the general public in advanced economies has expanded substantially. Risks from some segments of the
industry—leveraged hedge funds and money market funds—are already widely recognized.
However, opinions are divided about the nature and magnitude of any associated risks from less leveraged,
“plain-vanilla” investment products such as mutual funds and exchange-traded funds. Tis chapter examines sys-
temic risks related to these products conceptually and empirically.
In principle, even these plain-vanilla funds can pose fnancial stability risks. Te delegation of day-to-day
portfolio management introduces incentive problems between end investors and portfolio managers, which can
encourage destabilizing behavior and amplify shocks. Easy redemption options and the presence of a “frst-mover”
advantage can create risks of a run, and the resulting price dynamics can spread to other parts of the fnancial
system through funding markets and balance sheet and collateral channels.
Te empirical analysis fnds evidence for many of these risk-creating mechanisms, although their importance
varies across asset markets. Mutual fund investments appear to afect asset price dynamics, at least in less liquid
markets. Various factors, such as certain fund share pricing rules, create a frst-mover advantage, particularly for
funds with high liquidity mismatches. Furthermore, incentive problems matter: herding among portfolio managers
is prevalent and increasing.
Te chapter does not aim to provide a fnal verdict on the overall systemic importance of the potential risks or
to answer the question of whether some asset management companies should be designated as systemically impor-
tant. However, the analysis shows that larger funds and funds managed by larger asset management companies do
not necessarily contribute more to systemic risk: the investment focus appears to be relatively more important for
their contribution to systemic risk.
Oversight of the industry should be strengthened, with better microprudential supervision of risks and through
the adoption of a macroprudential orientation. Securities regulators should shift to a more hands-on supervisory
model, supported by global standards on supervision and better data and risk indicators. Te roles and adequacy
of existing risk management tools, including liquidity requirements, fees, and fund share pricing rules, should be
reexamined, taking into account the industry’s role in systemic risk and the diversity of its products.
SUMMARY
International Monetary Fund | April 2015 93
3 C
H
A
P
T
E
R
THE ASSET MANAGEMENT INDUSTRY
AND FINANCIAL STABILITY
Prepared by Hiroko Oura (team leader), Nicolás Arregui, Jonathan Beauchamp, Rina Bhattacharya, Antoine Bouveret, Cristina Cuervo,
Pragyan Deb, Jennifer Elliott, Hibiki Ichiue, Bradley Jones, Yoon Kim, Joe Maloney, Win Monroe, Martin Saldias, and Nico Valckx, with
contributions by Viral Acharya (consultant), and data management assistance from Min-Jer Lee, under the overall guidance of Gaston Gelos
and Dong He.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
94 International Monetary Fund | April 2015
Introduction
In recent years, credit intermediation has been shifting
from the banking to the nonbank sector, including the
asset management industry.
1
Tighter regulations on
banks, rising compliance costs, and continued bank
balance sheet deleveraging following the global fnan-
cial crisis have contributed to this shift. In advanced
economies, the asset management industry has been
playing an increasingly important role in the fnancial
system, especially through increased credit intermedia-
tion by bond funds.
2
For emerging markets, portfolio
fows—many of which are channeled through funds—
have shown steady growth since the crisis. Globally, the
1
In this chapter, the defnition of the asset management indus-
try includes various investment vehicles (such as mutual funds,
exchange-traded funds, money market funds, private equity funds,
and hedge funds) and their management companies (see Annex 3.1).
Pension funds and insurance companies are excluded, as are other
types of nonbank fnancial institutions.
2
See October 2014 Global Financial Stability Report.
industry now intermediates assets amounting to $76
trillion (100 percent of world GDP and 40 percent of
global fnancial assets; Figure 3.1).
Te larger role of the asset management industry
in intermediation has many benefts. It helps inves-
tors diversify their assets more easily and can pro-
vide fnancing to the real economy as a “spare tire”
even when banks are distressed. Te industry also
has advantages over banks from a fnancial stability
point of view. Banks are predominantly fnanced with
short-term debt, exposing them to both solvency and
liquidity risks. In contrast, most investment funds
issue shares, and end investors bear all investment risk
(see Figure 3.2, and see Annex 3.1 for a primer on the
industry). High leverage is mostly limited to hedge
funds and private equity funds, which represent a small
share of the industry.
3
Terefore, solvency risk is low in
3
However, these funds can still be a source of systemic risk, as
shown during the Long-Term Capital Management episode in 1998.
Mutual funds and exchange-traded funds do incur portfolio leverage
0
10
20
30
40
50
60
70
80
90
100
0
5
10
15
20
25
30
2001 02 03 04 05 06 07 08 09 10 11 12
Figure 3.1. Financial Intermediation by the Asset Management Industry Worldwide
2. Size of Investment Funds in Selected Advanced Economies
AUM, trillions of U.S. dollars (right scale)
AUM, percent of sample economies’ GDP
1. World Top 500 Asset Managers’ Assets under Management
1
0
20
40
60
80
100
120
140
0
10
20
30
40
50
60
70
80
90
2000 01 02 03 04 05 06 07 08 09 10 11 12 13
Trillions of U.S. dollars (right scale)
Percent of world GDP
Percent of global ?nancial assets excluding loans
The asset management industry intermediates substantial amounts of
money in the ?nancial system.
The growth of investment funds has been particularly pronounced
among advanced economies during the past decade.
Sources: Organisation for Economic Co-operation and Development; and IMF
World Economic Outlook database.
Note: AUM = assets under management. Economies comprise Canada, Germany,
Ireland, Japan, Luxembourg, United Kingdom, and United States. Investment funds
include mutual funds, money market funds, and exchange-traded funds.
Sources: Bloomberg, L.P.; McKinsey (2013); Pensions and Investments and Towers
Watson (2014); IMF, World Economic Outlook database; and IMF staff estimates.
1
The change of asset under management is determined both by valuation
changes of underlying assets as well as net in?ows to funds.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 95
most cases (see October 2014 Global Financial Stabil-
ity Report). Intermediation through funds also brings
funding cost benefts and fewer restrictions for frms
compared with bank fnancing—it does, however, also
expose frms to more volatile funding conditions, so
the advantages have to be weighed against the risks.
Nevertheless, the growth of the industry has given
rise to concerns about potential risks.
4
By now, the
assets under management of top asset management
companies (AMCs) are as large as those of the largest
banks, and they show similar levels of concentration.
5
For emerging markets, the behavior of fund fows has
for some time been a key fnancial stability concern, as
extensively discussed in the April 2014 Global Finan-
cial Stability Report. Similarly, risks from hedge funds
through derivatives and securities lending, about which only limited
information is disclosed. However, most publicly ofered products
have regulatory leverage caps that are generally much lower than
those for banks (see Table 3.1).
4
A report by the Ofce of Financial Research (2013) summariz-
ing potential systemic risks emanating from the industry spurred an
active discussion among academics, supervisors, and the industry.
A large number of qualitative analyses on this topic (CEPS-ECMI
2012; Elliott 2014; Haldane 2014) are available, but comprehensive,
data-based evidence is still limited.
5
In this chapter, the term AMC does not include asset manage-
ment companies set up to handle distressed assets in the context of
bank restructuring and resolution.
and money market funds are already well recognized.
However, the importance of “plain-vanilla” products
is less well understood (Feroli and others 2014). At
the individual fund level, plain-vanilla funds face
liquidity risk: the shares of open-end mutual funds
and exchange-traded funds are usually redeemable or
tradable daily, whereas assets can be much less liquid.
However, the extent to which such risks at the level
of an individual institution can translate into systemic
risk is subject to ongoing research and debate.
Potential systemic risks from less leveraged segments
of the industry are likely to stem from price externali-
ties in fnancial markets and their macro-fnancial
consequences. Systemically important efects may arise
if features of the industry tend to amplify shocks or
increase the likelihood of destabilizing price dynam-
ics in certain asset markets compared with a situation
in which investors invest directly in securities. Tese
efects can have broader economic implications. For
example, if intermediation through funds raises the
probability of fre sales of bonds that are held by key
players in the fnancial sector or that are used as col-
lateral, then the risk of destabilizing knock-on efects
on other institutions rises, with potentially important
macro-fnancial consequences. Similarly, if funds
exacerbate the volatility of capital fows in and out of
0
5
10
15
20
25
30
Open-end
mutual funds
Money market
funds
Exchange-
traded funds
Private equity Hedge funds
Open-end
mutual funds
41%
Separate
accounts
36%
Other
alternatives
2%
Closed-end
mutual funds
1%
Money market
funds
8%
Exchange-
traded funds
4%
Private
equity
5%
Hedge funds
3%
2007
2008
2009
2010
2011
2012
2013
Figure 3.2. Products Offered by Asset Managers and Their Recent Growth
1. Asset Managers’ Intermediation by Investment Vehicles
(Percent of $79 trillion total assets under management, end-2013)
Plain-vanilla products and privately offered separate account services
dominate the markets as measured by assets under management.
2. Recent Growth of Selected Investment Vehicles
(Assets under management in trillions of U.S. dollars)
Open-end funds, exchange-traded funds, and private equity funds
have shown strong growth since the global ?nancial crisis.
Sources: BarclayHedge; European Fund and Asset Management Association;
Organisation for Economic Co-operation and Development; Preqin; and IMF
staff calculations.
Sources: BarclayHedge; European Fund and Asset Management Association;
ETFGI; Organisation for Economic Co-operation and Development; Pensions and
Investments and Towers Watson (2014); Preqin; and IMF staff estimates.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
96 International Monetary Fund | April 2015
emerging markets or increase the likelihood of conta-
gion, signifcant consequences will be endured by the
recipient economies.
6
Some key features of collective investment vehicles
may give rise to such destabilizing dynamics compared
with a situation without intermediaries. Conceptually,
it is important to distinguish clearly between the types
of risks that result from the presence of intermediaries
and those that are merely a refection of the behavior
of end investors and would occur in the absence of
intermediaries (Elliott 2014). Two main risk channels
that are important in this context, even for unlever-
aged funds, are (1) incentive problems related to the
delegation of portfolio management decisions by end
investors to funds, which, among other things, may
lead to herding, and (2) a frst-mover advantage for
end investors (that is, incentives not to be the last in
the queue if others are redeeming from a fund), which
may result in fre-sale dynamics. Tese issues are dis-
cussed in detail in this chapter.
In recent years, the importance of such risks is
likely to have risen in advanced economies because of
structural changes in their fnancial systems. Not only
has the relative importance of the asset management
industry grown, but banks have also retrenched from
6
Other risks include operational risks and risks related to securi-
ties lending, which are not discussed in detail in this chapter. See
Cetorelli (2014).
many market-making activities, possibly contributing to
a reduction in market liquidity (October 2014 Global
Financial Stability Report). Consequently, large-scale
trading by funds could potentially have a larger efect on
markets than in the past. Moreover, the role of fxed-
income funds has expanded considerably—and price
disruptions in fxed-income markets have potentially
larger consequences than large price swings in equity
markets. Te volume of products ofered to the general
public in advanced economies has grown considerably.
7
Finally, the prolonged period of low interest rates in
advanced economies has resulted in a search for yield,
which has led funds to invest in less liquid assets, and
is likely to have exacerbated the risks described above
(October 2014 Global Financial Stability Report).
Tese considerations have sparked a policy discus-
sion about intensifying oversight across advanced and
emerging economies. In 2014, the Financial Stability
Board (FSB) and International Organization of Securities
Commissions (IOSCO) proposed assessment methodolo-
gies to identify investment funds that might be global
systemically important fnancial institutions (G-SIFIs)
and as such would be regulated diferently from the oth-
ers (FSB and IOSCO 2014). Tis proposal was revised
in March 2015, and includes approaches for identifying
both investment funds and asset managers as G-SIFIs
(FSB and IOSCO 2015). Market regulators in major
jurisdictions (Figure 3.3), such as the U.S. Securities and
Exchange Commission (SEC), are considering revising
their approach to the oversight of asset managers and
the products they ofer, including through stress testing
requirements. Tis is a paradigm shift. Until recently,
securities regulators have mainly focused on investor pro-
tection, with limited attention to fnancial stability risks.
Tis chapter aims to shed more light on the empiri-
cal relevance of these issues, thereby contributing to
the understanding of the systemic risk implications of
the asset management industry. Tis task is challeng-
ing given that the risks of concern have not yet or only
partially materialized in advanced economies; inference,
therefore, often has to be indirect. So far, the literature
has only examined partial aspects of these problems in
individual markets. Tis chapter provides an account of
key risk profles of the largest segments in the industry
and an in-depth, original, data-based analysis of some of
7
Retail investors are often seen to be less sophisticated and
informed than institutional investors, and more prone to chase
returns (Frazzini and Lamont 2008). Tis possibly exacerbates the
incentive problems mentioned earlier.
United States
49%
Japan
3%
Other
developed
9%
Brazil
3%
China
2%
Other emerging
markets 4%
Luxembourg 10%
Ireland 5%
France 5%
United Kingdom 4%
Other developed
Europe 7%
Developed
Europe 31%
Sources: European Fund and Asset Management Association; and IMF staff
calculations.
Figure 3.3. Key Domiciles of Mutual Funds
(Mutual funds by domicile, percent of total assets under management,
end-2014)
The mutual fund industry is dominated by U.S. and European funds.
Among emerging market economies, Brazil has the largest fund sector.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 97
the main issues featured in the public discussion, backed
by interviews with asset managers and supervisors. Te
key questions are the following:
• What are the potential sources of financial stability
risks from the asset management industry, particu-
larly from the less leveraged, plain-vanilla segments?
• What is the empirical evidence on the various spe-
cific risk channels?
• What existing internal risk management and over-
sight tools can be used to mitigate financial stability
risks? What needs to be done to better monitor and
mitigate these risks?
Te detailed empirical analysis fnds evidence for
many mechanisms through which funds can create and
amplify risks, although their importance varies across
asset markets:
• Mutual fund investments appear to affect asset
price dynamics, at least in less liquid markets. The
impact, however, does not seem to have risen over
time. Assets that are held in a concentrated manner
by funds perform worse during periods of stress.
• Various factors create run risk, including certain
fund share pricing rules. To some extent, however,
risks are mitigated by funds’ liquidity management.
• The evidence points to the importance of incen-
tive problems between end investors and portfolio
managers. Herding among U.S. mutual funds has
been rising across asset markets, particularly among
retail-oriented funds (whose end investors are more
fickle and for whom assessing the skills of portfolio
managers is more difficult). The patterns of fund
inflows by end investors also encourage poorly per-
forming portfolio managers to take excessive risks.
• However, larger funds and funds belonging to
larger AMCs do not necessarily contribute more to
systemic risk. The investment focus appears to be
relatively more important than size when gauging
systemic risk.
Overall, the evidence calls for strengthening the
microprudential supervision of risks and adopting
macroprudential oversight of the industry:
• Currently, most securities regulators focus on investor
protection and do not intensively supervise risks of
individual institutions with the help of risk indica-
tors or stress tests. This practice needs to be changed,
supported by global standards on microprudential
supervision and more comprehensive data.
• Moreover, macroprudential oversight frameworks
should be established to address financial stability
risks stemming from the industry. These stability risks
originate in price externalities that can be missed by
microprudential regulators and asset managers.
• The roles and adequacy of existing risk management
tools, including liquidity requirements, fees, and
fund share pricing rules, should be reexamined, tak-
ing into account the industry’s role in systemic risk
and the diversity of its products.
Te chapter frst lays out conceptual issues related
to the nature of potential fnancial stability risks from
the industry. Next, various empirical exercises are
conducted to identify diferent behavioral patterns
of mutual fund investors and their fnancial stability
implications. Te chapter then examines the industry’s
oversight framework and makes recommendations for
reducing fnancial stability risks.
Financial Stability Risks of Plain-Vanilla Funds:
Conceptual Issues
Plain-vanilla mutual funds and ETFs—the largest
segment of the industry—do not sufer much from
the known vulnerabilities of hedge funds and money
market funds. Reforms are already underway to address
risks related to hedge funds (which can incur high
leverage and engage in complex strategies with few dis-
closure requirements) and money market funds (some
of which ofer redemptions at a constant nominal
value per fund share, making their liabilities similar to
deposits and vulnerable to runs). In general, these spe-
cifc risks apply less to typical mutual funds and ETFs
(Table 3.1 and Annex 3.1).
Risk Transmission Channels
Intermediation through plain-vanilla funds is, however,
not risk free (Figure 3.4):
8
8
Apart from Table 3.1 and Annex 3.1, this chapter does not cover
separate accounts in detail because of data limitations. However,
SIFMA (2014) indicates that these accounts mainly invest in simple
securities portfolios with little leverage. For pension fund and insur-
ance company investors, separate accounts are bound by overall
investment restrictions set by their respective regulators. Redemption
risks appear to be limited as well because institutional investors tend
to internalize the cost of their sales, and large redemptions can be
settled in kind.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
98 International Monetary Fund | April 2015
• The delegation of investment decisions introduces
incentive problems between end investors and port-
folio managers that can induce destabilizing behavior
and amplify shocks. Investors delegate day-to-day
portfolio management to portfolio managers. Inves-
tors cannot directly observe managers’ daily actions
or their skills, and therefore provide incentives to
managers to act in investors’ interests (Rajan 2005).
9
A common (and imperfect) way of establishing
9
Legally, asset managers have a duty to act as fduciaries on behalf
of their clients.
incentives is to evaluate funds relative to their peers
and relative to benchmarks. This form of evaluation,
in turn, can lead to a variety of trading dynamics
with potentially systemic implications, such as herd-
ing or excessive risk taking (Box 3.1).
10,11
10
Similarly, the same type of informational issues can make it dif-
fcult for investors to distinguish between problems at the fund level
versus problems at the AMC level, possibly leading to “brand name”
efects, in which operational and reputational concerns about one
fund spill over to others in the same fund family.
11
Separate issues arise from passive, index-linked investing.
Increasing investment of this form has been argued to distort asset
Table 3.1. Summary Characteristics and Risk Pro?les of Major Investment Vehicles
Vehicle 2013 AUM
(trillions of
U.S. dollars)
Publicly
Offered
Collective
Investment
Schemes
Typical
Redemption
and Trading
Practice
Typical
Settlement
Method
Solvency
Risk
Leverage
through
Borrowing
1,2
Portfolio
Leverage
2
(Derivatives)
Main
Investor
Clientele
Disclosure
Gap
3
Open-End
Mutual Fund
25 Yes Yes End of day Cash Low Possible
with cap
Yes with cap Retail,
institutional
Low
Closed-End
Mutual Fund
0.5 Yes Yes N.A.
(primary)
Intraday
(secondary)
Cash Low Some yes
with cap
Yes with cap Retail,
institutional
Low
Money
Market Fund
4.8 Yes Yes End of day Cash Low Possible
with cap
Yes with cap Retail,
institutional
Low
Exchange-
Traded Fund
2.3 Yes Yes Infrequent
(primary)
Intraday
(secondary)
In kind
(primary)
Cash
(secondary)
Low Possible
with cap
Yes with cap Retail,
institutional
Low
Synthetic
ETF
0.1
4
Cash Low Possible
with cap
High
derivative
use
Institutional
Private
Equity Fund
3.5 No Yes N.A.
(closed-end
with long-
term finite
life)
Cash High
5
Some yes,
no cap
No
information
Institutional Medium
Hedge Fund 2.2 No Yes Quarterly
+ lock-up
period +
90 days
advance
notice
Cash High
5
High no cap High no cap Institutional Medium
Separate
Account
6
22
7
No No No
information
Cash or in
kind
Low No
information
8
No
information
8
Institutional High
Sources: BarclayHedge; Deutsche Bank (2014); ETFGI; EFAMA (2014); ICI (2014a, 2014c); McKinsey (2013); Metrick and Yasuda (2011); Morningstar (2012); OFR (2013); Preqin;
PriceWaterhouseCoopers (2013); and IMF staff estimates.
Note: AUM = assets under management; ETF = exchange-traded fund; N.A. = not applicable.
1
Borrowing includes issuing debt or taking bank loans.
2
No cap means no regulatory cap, and with cap means there are regulatory caps on the leverage. For public funds in the United States, leverage is capped at 33 percent of assets
including portfolio leverage. European Undertakings for Collective Investment in Transferable Securities (UCITS) funds can borrow up to 10 percent of assets, but only temporary bor-
rowing is allowed and it should not be used for investment.
3
Disclosure in this column is about securities, borrowing through loans, and cash holdings information. Across all products, there is very little information about derivatives and
securities ?nancing transactions (repurchase agreements and securities lending transactions), their counterparties, and collateral.
4
The ?gure covers European-listed synthetic exchange-traded funds. Synthetic products are mainly seen in Europe and to a lesser extent in Asia. See Annex Table 3.1.1 for a descrip-
tion of synthetic products.
5
In addition to taking leverage, these types of funds risk their own capital and balance sheets when investing given that they comingle client investors’ money with their own money for investment.
6
This is different from “separate account” used among insurance companies. See Annex Table 3.1.1 for description.
7
The ?gure is based on the U.S. data reported in OFR (2013) and the European data reported in EFAMA (2014).
8
Investment strategy should be in line with the mandate set by clients and their regulatory requirements (such as insurance and pension fund regulations).
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 99
• Easy redemption options can create run risks due
to a first-mover advantage.
12
Investors can have an
incentive to exit faster than the others even without
constant net asset value (NAV) or guaranteed returns
if the liquidation value of fund shares declines as
investors wait longer to exit. This decline in value
could happen for various reasons. First, asset man-
agers may use cash buffers and sell relatively more
liquid assets first in the face of large redemptions.
Second, certain funds have fund share pricing rules
that pass the costs of selling assets—possibly at fire-
sale prices—on to the remaining investors (Box 3.2).
Such effects are intensified when funds are investing
in relatively less liquid assets, and thereby create large
mismatches between the market liquidity of assets
and liquidity offered to end investors (October 2014
Global Financial Stability Report).
13
prices and risk-return tradeofs (Wurgler 2010 and Box 3.1). Tis
chapter does not explore these issues.
12
Te incentive to redeem quickly is often referred to as “strategic
complementarity,” and is similar to the mechanism behind bank
runs (as in Diamond and Dybvig [1983]). More generally, problems
related to the delegation of investment decisions or frst-mover
advantage are also present in other forms of fnancial intermediation,
albeit to diferent degrees. For instance, pension funds and insurance
companies face much lower redemption risks.
13
A related issue concerns the pricing of infrequently traded
securities. Te October 2014 Global Financial Stability Report dis-
cusses some of the issues related to the so-called matrix pricing.
A large proportion of funds issue easily redeem-
able shares, and liquidity mismatches have been rising
(Figures 3.5 and 3.6). Open-end funds are exposed
to redemption risk because investors have the ability
to redeem their shares (usually on a daily basis) while
funds have increasingly been investing in relatively
illiquid securities such as high-yield corporate bonds
and emerging market assets.
Large-scale sales by funds may exert signifcant
downward asset price pressures, which could afect
the entire market and trigger adverse feedback loops.
Te efects on asset prices could have broader macro-
fnancial consequences, afecting the balance sheets
of other actors in fnancial markets; reducing collat-
eral values; and reducing credit fnancing for banks,
frms, and sovereigns. Te efects could also be spread
unevenly across jurisdictions. For instance, the main
impact of trades by funds domiciled in advanced
economies could be felt in emerging markets (see
April 2014 Global Financial Stability Report for
details).
Although these potential risks and propagation
channels are recognized as theoretical possibilities,
there is disagreement about their importance in prac-
tice. Advanced economies have experienced few cases
in which asset management activities outside of hedge
funds and money market funds triggered or amplifed
Incentive
problems
of
managers
Run risk
Price
externalities–
?re sales,
contagion,
volatility
Macro?nancial
consequences
Source: IMF staff.
Figure 3.4. Unleveraged Open-End Funds and Systemic Risk
Information gap between managers and investors
• Benchmark-based evaluation
–Excessive risk taking
–Herding
• Brand name effects (spillovers of redemption
within fund family)
First-mover advantage
• Liquidity mismatch
• Managers sell liquid assets ?rst
• Some fund share pricing rules impose cost of
liquidity risk unfairly on second movers
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
100 International Monetary Fund | April 2015
Te delegation of investment decisions introduces
incentive problems between end investors and fund
managers, which can induce destabilizing behavior
and amplify shocks. As discussed in the primer on the
asset management industry (Annex 3.1), end investors
delegate day-to-day control of portfolios to managers.
Investors cannot directly observe managers’ abilities, nor
do they see every single trade and portfolio position.
Investors, therefore, provide incentives to asset managers
to act in investors’ interests (Rajan 2005). A common
way of providing incentives is to evaluate asset managers
relative to their peers and to benchmarks. Tis evalu-
ation can take direct or indirect forms: (1) managers’
compensation can be linked to relative performance
(Ma, Tang, and Gomez 2013) or (2) investors inject
money into funds that perform well relative to their
benchmarks. Te efect of the latter is similar to the
efect of the former if compensation increases with
assets under management (AUM). Tese incentive
problems, in turn, can lead to a variety of dynamics
with potentially systemic implications (Stracca 2006).
More specifcally, they can lead to the following:
• Excessive risk taking—If a fund’s AUM grow more
with good performance than shrink with poor
performance, incentives are created to incur more
risk when the fund is falling behind (Chevalier
and Ellison 1997; Ferreira and others 2012; see the
example in Table 3.1.1). Similar incentives exist in a
“tournament” setting, in which funds are evaluated
based on their interim performance (say, in the
middle of the year) compared with peers (Basak,
Pavlova, and Shapiro 2008).
1
• Contagion—By contrast, if fund managers become
more risk averse in response to past losses, and if
they are evaluated against their peers or bench-
marks, they may be induced to retrench to the
benchmark in response to losses. This behavior, in
turn, can induce the transmission of shocks across
assets and result in momentum trading (Broner,
Gelos, and Reinhart 2006). See Calvo and Mendoza
(2000), Chakravorti and Lall (2003), and Ilyina
(2006) for other types of models linking bench-
mark-based compensation to contagion.
• Herding, return chasing, and incentives to run—Evalu-
ation relative to average performance tends to induce
risk-averse portfolio managers to mimic the behavior
of peers (Scharfstein and Stein 1990; Arora and
Ou-Yang 2001; Maug and Naik 2011). Incentives
to herd are reinforced because end investors can exit
funds quickly, and mutual fund managers cannot
afford to wait until their peers’ private information is
revealed and incorporated fully in asset prices (Froot,
O’Connell, and Seasholes 2001). Vayanos (2004)
shows that when fund managers lose AUM because
of poor performance, ‘‘flights to quality’’ may occur.
Feroli and others (2014) construct a model in which
performance evaluation relative to benchmarks cre-
ates incentives for fund managers to join sell-offs
during downturns and chase yield during upturns.
Buffa, Vayanos, and Woolley (2014) discuss theoreti-
cally how such benchmark-centric assessments can
contribute to the buildup of bubbles.
• Churning and noise trading—Delegated portfolio
management may induce managers to churn (engage
Box 3.1. Possible Incentive Problems Created by Delegated Management
1
Tis is also known as the “risk-shifting” problem. More generally, risk shifting arises when earnings for managers are convex based
on their compensation. Limited liability also contributes to the convexity of manager earnings. See Ross (2004) for a qualifcation of the
payof convexity argument. See also Massa and Patgiri (2009).
Table 3.1.1. An Illustrative Example of Asset Managers’ Incentives for Risk Taking
Because investors reward winners more than they punish poor performers, it pays to take risks.
Options Likelihood (percent)
Outcome: Change in Net
Asset Value
Net Inflows to Fund
(millions of
U.S. dollars)
Additional Fee Income
(1 percent of assets
under management, in
millions of U.S. dollars)
Benchmark Portfolio 100 Same as benchmark 0 0
Gamble
50 10% in excess of
benchmark
100 1
50 10% below benchmark ?20 ?0.2
Expected outcome Same as benchmark 40 0.4
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 101
in noise trading) to signal their talent and superior
knowledge, given that it is difficult to identify talent
and effort (Allen and Gorton 1993; Dow and Gor-
ton 1997; Dasgupta and Prat 2006).
• Market depth and volatility—Performance evalu-
ation relative to a benchmark may lead to higher
price volatility of securities that are included in
the benchmark. Since information acquisition may
be hindered by these relative-performance-based
contracts, the depth of the market may be reduced
(Igan and Pinheiro 2012). Basak and Pavlova
(2014) develop a general-equilibrium asset price
model that incorporates incentives for institutional
investors to do well relative to their index. The
induced investment patterns create excess correla-
tions among stocks belonging to an index. It also
increases the volatility of index stocks and of the
overall market.
Box 3.1 (continued)
Certain forms of fund share pricing can give rise to
a frst-mover advantage for investors to run. Te key
factor is how investment losses and trading costs are
distributed between buy-and-hold and redeeming fund
shareholders. If these are borne by the fund and there-
fore by the buy-and-hold shareholders, investors can
recover more cash by redeeming early.
Infexible net asset value (NAV) pricing can gener-
ate a frst-mover advantage for an open-end mutual
fund (Table 3.2.1). In the United States, funds
issuing redeemable securities are required to sell,
redeem, or repurchase such securities based on the
NAV of the security “next computed” after receipt
of the order. Transaction costs—trading fees, market
Box 3.2. Fund Share Pricing Rules and First-Mover Advantage
Table 3.2.1. Comparison of Fund Pricing Rules
(Millions of U.S. dollars)
Transactions
UCITS
Swing Pricing (Full)
UCITS-AIF
Dual Pricing
U.S. Open-End Mutual Fund
(1940 Act)
Beginning NAV 100 100 100
Net Flows ?15 ?15 ?15
Purchases +5 +5 +5
Redemptions ?20 ?20 ?20
Total Costs of Selling Assets
(0.1 percent, including bid-ask
spread)
0.015 0.015 0.015
Transaction Costs Incurred
by Investors Purchasing
Fund Shares
?0.005
1
0 0
Transaction Costs Incurred
by Investors Redeeming
Fund Shares
0.020 0.015 0
Transaction Costs Incurred by
Fund and Remaining Investors
0 0 0.015
2
Ending NAV 85.000 85.000 84.985
Memo Estimated transaction costs borne by trading investors Actual transaction costs
borne by fund
Source: BlackRock (2014b).
Note: AIF = Alternative Investment Fund (European directive governing products including hedge funds and private equity funds); NAV = net
asset value (mutual fund share price, per share); UCITS= Undertaking of Collective Investment in Transferable Securities (European Union direc-
tive governing publicly offered investment funds). In the United States, investment companies (as de?ned) are regulated primarily under the U.S.
Investment Company Act of 1940.
1
Because fund NAV has swung to the bid price because of net redemptions, purchasing investors bene?t to the extent that they purchase units
that are cheaper than preswung NAV. This bene?t is offset by the costs paid by redeeming clients.
2
In certain circumstances, portfolio managers may choose to use cash buffers or borrow funds (or both) to meet redemptions without incurring
transaction costs.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
102 International Monetary Fund | April 2015
impact, and spread costs—are borne by the funds.
Tis reduces a fund’s NAV, possibly by a substantial
amount if market liquidity dries up. Te European
framework, in contrast, allows for pricing rules such
as swing- or dual-pricing rules, as described in Table
3.2.1, that adequately impose transaction costs on
redeeming shareholders instead of the fund. Tis
helps reduce remaining shareholders’ incentive to
run.
Te share pricing practice of exchange-traded
funds (ETFs) is diferent from that of open-end
mutual funds. As shown in Figure 3.2.1 and Annex
3.1, ETFs do not directly transact with end inves-
tors. “Authorized participants”—typically major
broker-dealers—trade in between. Only autho-
rized participants trade with ETFs in the primary
market, and trades are usually settled in kind.
Intraday liquidity to end investors is ofered in the
secondary market by authorized participants.
1
Te
key diference between ETFs and mutual funds in
the context of frst-mover advantage is that ETFs
are not required to pay cash back to investors at
NAV.
2
Authorized participants trade ETF shares
with clients or on stock exchanges at the ETF share
price determined in the secondary market. Tere-
fore, depending on market conditions, an ETF’s
share price could be higher or lower than the ETF’s
indicative NAV.
Box 3.2 (continued)
1
Although there is a widespread perception that ETFs face higher redemption risks because they ofer intraday liquidity to share-
holders, intraday liquidity (ofered in the secondary market) is not the same as intraday redemption (ofered in the primary market).
Primary market activities, which result in fund fows, are much less frequent than secondary market trading (ICI 2014c; BlackRock
2014a).
2
In the United States, ETFs operate with the Securities and Exchange Commission’s special exemption from the 1940 Act
requirement that open-end funds repay redeeming shareholders at the next NAV calculated after an order is submitted (ICI
2014b).
Figure 3.2.1. Structure of Exchange-Traded Funds
Source: IMF staff.
Note: AP = authorized participant; ETF = exchange-traded fund; NAV = net asset value.
Primary Market
NAV may not be equal to ETF share price, depending on arbitraging capacity of APs
Hold shares, arbitrage trading
ETF
Authorized
participant
Physical
“basket” of
securities
ETF share
price
Liquidity
premium or
discount paid
by investors
Investors,
stock
exchange Securities
NAV represents
market value of
ETF’s assets
Cash
Shares Shares
Secondary Market
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 103
systemic distress.
14
Te realization of brand risk and
redemptions from PIMCO funds in September 2014
did not result in major disruptive market movements
because, overall, bond funds continued to receive net
infows. However, the academic literature has docu-
mented contagion and amplifcation efects for some
14
Tere have been some cases of non–money market mutual
fund distress in emerging markets. For example, in 2001, a fund
managed by Unit Trust of India, which was outside the ambit of
the Securities and Exchange Board’s jurisdiction, became unable to
meet its obligations due to the absence of timely corrective action
to bring the sale/repurchase price of the units in line with the
fund’s net asset value. With a risk of a run on the Unit Trust of
India and possible adverse fnancial market impact, India’s govern-
ment came out with a rescue package. Te total bail-out amounted
to US$76 million.
markets, in particular emerging markets.
15
Moreover,
recent structural shifts in many markets following
the global fnancial crisis require a fresh review of the
evidence.
Against this backdrop, this chapter empirically
explores the precise channels through which mutual
funds and ETFs can afect fnancial stability. Te aim
15
In addition to the literature on emerging markets discussed
in the April 2014 Global Financial Stability Report, various studies
examine the role of funds in transmitting shocks across markets in
advanced economies. Using U.S. data during the global fnancial
crisis, Hau and Lai (2010) fnd that mutual funds helped transmit
shocks from bank equities to nonfnancial frms’ equities, and Man-
coni, Massa, and Yasuda (2012) fnd that mutual funds that incurred
losses from securitized debt sold of corporate bonds, which induced
a price impact on bonds held by these funds.
Redeeming shareholders need to pay for the
cost of market liquidity risk by accepting an ETF
share price below NAV if market liquidity dries up.
Authorized participants are usually arbitrageurs,
and if they see a major gap between NAV and ETF
share prices, they trade in the direction to close the
gap. If investors fnd it easier to sell ETF shares
relative to the underlying assets, this will tend to
result in a discount to NAV. Te discount can be
accentuated when funding conditions reduce autho-
rized participants’ arbitrage capacity (Figure 3.2.2).
Te cost of “fre sales” of ETF shares is borne by the
trading shareholders, not by the ETF or buy-and-
hold shareholders, reducing buy-and-hold share-
holders’ incentive to run.
Economically, these fexible fund share pricing rules
are similar to countercyclical redemption and purchase
fees that refect market liquidity cost and are added
to NAV. If a U.S. 1940 Act fund imposes purchase
and redemption fees that are retained by the fund
3
and refect the bid-ask spreads for transactions (or
ETF NAV and share price gap), the outcome would
be similar to that of funds with fexible share pricing
rules. At the same time, such fees also help ensure
equality between buy-and-hold investors and trading
investors.
Box 3.2 (continued)
3
Current U.S. rules do allow for the introduction of fees that are added to funds’ NAV, which can then be distributed to remaining
shareholders.
–1.5
–1.0
–0.5
0.0
0.5
1.0
J
a
n
.
2
0
0
0
J
u
l
.
0
1
J
a
n
.
0
3
J
u
l
.
0
4
J
a
n
.
0
6
J
u
l
.
0
7
J
a
n
.
0
9
J
u
l
.
1
0
J
a
n
.
1
2
J
u
l
.
1
3
ETF share price > NAV
“Premium” for ETF share
ETF share price < NAV
“Discount” for ETF share
Figure 3.2.2. Difference between NAV and
ETF Share Price
(Percent of NAV, all countries, equity funds)
Sources: Bloomberg, L.P.; and IMF staff calculations.
Note: ETF = exchange-traded fund; NAV = net asset
value.
The ETF share can be traded in the secondary
market at a discount to NAV when markets are
under generalized stress.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
104 International Monetary Fund | April 2015
is not to provide a fnal verdict on the overall systemic
importance of the potential risks, or draw defnite con-
clusions about whether certain AMCs and their funds
should be designated as SIFIs. Rather, the chapter
carries out a quantitative analysis of a number of key
risk transmission and amplifcation channels, test-
ing some of the underlying hypotheses, and updating
and complementing the existing literature. Given the
current absence of a broad-based empirical assessment
of the issues, this chapter flls an important gap. In
particular, whereas most existing studies cover equity
markets, the analysis here also covers bond markets.
Te chapter does not discuss all sources of risk. In
particular, operational risks, risks related to hidden
leverage and securities lending, and issues related to
resolution are only touched upon (FSOC 2014).
16
Financial Stability Risks of the Mutual Fund
Industry: Empirical Analysis
Tis section examines various aspects of potential
fnancial stability risks using a wide range of macro- and
micro-level data. Tree main questions are explored. First,
does fund investment afect asset price dynamics? Second,
what determines fund fows and how do funds manage
liquidity? And third, what is the degree of herding and
interconnectedness, and what is the relationship between
a fund’s contribution to systemic risk and its size?
17
Mutual Fund Investment and Asset Price Dynamics
Aggregate mutual fund fows and asset prices
Do fund fows afect asset price dynamics in the
United States and in emerging markets? For mutual
funds to have a destabilizing efect, fund trades must
frst, at least in the aggregate, have an impact on
prices. Te literature suggests the existence of price
pressures related to mutual fund fows.
18
Te analysis
here updates and complements such fndings, analyz-
ing weekly net infows to U.S. mutual funds invest-
ing in U.S. equities and various types of U.S. bonds,
and their relationship to the respective market index
returns. It also investigates mutual fund investment
fows into bonds and equities in a number of emerging
markets (see Annex 3.2 for details). Te analysis goes
16
Furthermore, the analysis in the chapter does not cover separate
accounts held at funds.
17
Te main data sources for mutual funds are Lipper (a global
mutual fund database with information at the fund level); the
Center for Research in Security Prices (CRSP) U.S. mutual fund
database (with security-by-security asset holdings information and
details of fee structures); EPFR Global; and Lipper’s eMaxx, which
shows global mutual fund ownership of bonds at the security level.
18
Studies include Warther (1995); Edelen (1999); Edelen and
Warner (2001); Cao, Chang, and Wang (2008); and Ben-Raphael,
Kande, and Wohl (2011). Te main conclusion from these studies
is that aggregate mutual fund fows afect contemporaneous stock
returns. Coval and Staford (2007) show that sudden increases or
decreases in net fows to funds result in price pressure efects even
in the extremely liquid U.S. equity market. Manconi, Massa, and
Yasuda (2012) document a price impact on corporate bonds follow-
ing sell-ofs by funds. Similarly, Jotikasthira, Lundblad, and Ramado-
rai (2012) document that investor fows domiciled in developed
markets induced fre sales in emerging markets, with a signifcant
price impact. Feroli and others (2014) analyze several subsegments
of bond fund fows, and fnd evidence for fow-price feedback loops,
except for U.S. Treasuries.
EM equity MF
Mixed MF
Synthetic equity
ETF
2
Synthetic other
ETF
3
Physical equity
ETF
2
Physical other
ETF
3
Hedge fund
Closed-end MF
4
Private equity
Other AE and
global bond MF
EM bond MF
AE HY bond
More dif?cult
to redeem
Easier
to redeem
Ease of redemption by end investors
1
I
l
l
i
q
u
i
d
i
t
y
o
f
a
s
s
e
t
s
More
“liquidity mismatch”
Illiquid
assets
Liquid
assets
AE and global
equity MF
MMF
Sources: BarclayHedge; Deutsche Bank; ETFGI; European Fund and Asset
Management Association; Lipper; Preqin; and IMF staff estimates.
Note: The liquidity ranking of assets is based on IMF staff’s judgment. AE =
advanced economy; EM = emerging market; ETF = exchange-traded fund; HY =
high yield; MF = mutual fund; MMF = money market fund.
1
For ETFs, the ease-of-redemption measure ranks lower than that for open-end
MFs (all MFs in the ?gure excluding closed-end MFs) because end investors do not
directly redeem shares from funds (see Annex 3.1 and Box 3.2).
2
Generally, equity derivatives markets are less liquid than cash equity markets.
3
For bonds, especially corporate bonds, derivatives markets can offer better
market liquidity than the cash bond market. For some ?rms, the notional principal
for their credit default swaps is larger than their outstanding debt.
4
Closed-end mutual funds tend to invest in relatively less liquid assets than
open-end mutual funds (Chordia 1996; Deli and Varma 2002). Some funds may
repurchase shares.
The mismatch between the redemption risk to funds and market liquidity
of funds’ assets is most notable among bond mutual funds—especially
corporate and emerging market debt funds, though these are relatively
smaller segments.
Figure 3.5. Liquidity Mismatches
(Size of bubbles represents relative global assets under management as
of end-2013)
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 105
beyond most of the literature by examining the price
impact of the “surprise” component of fund fows, fol-
lowing Acharya, Anshuman, and Kumar (2014).
19,20
Te evidence is consistent with mutual fund fows
afecting asset returns in smaller, less liquid markets (Table
3.2). Surprise outfows are associated with lower same-
week asset returns in emerging markets, and to a lesser
extent in U.S. high-yield bond and municipal bond mar-
kets. Te annualized price impact is not negligible: bond
returns rise by about 5 percentage points when aggregate
fund infows are higher than the top 25th percentile, and
fall by a similar magnitude for outfows exceeding the
top 25th percentile across bond categories. In emerging
markets, and also in the U.S. municipal bond market,
the negative price efects from sell-ofs tend to be larger
than the positive price efects from purchases. Te price
impact of surprise fows is signifcantly larger when global
risk aversion (as measured by the Chicago Board Options
Exchange Market Volatility Index, or VIX) is high. More-
19
As will be shown later in this chapter, mutual fund fows partly
respond to past fund returns and are therefore partially predictable.
Surprises are measured by the residuals of a standard vector autore-
gression model for fows and returns; see Annex 3.2.
20
In contrast to much of the literature, this analysis uses weekly,
not monthly, data, which allows for better identifcation of the
efects. Nevertheless, inference remains difcult at this frequency.
Figure 3.6. Growth in Bond Funds by Investment Focus
(Assets under management of bond funds worldwide; billions of U.S.
dollars)
2004 05 06 07 08 09 10 11 12 13 14
Sources: Lipper; and IMF staff calculations.
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
Other Bond Funds
Other advanced economy bonds
Advanced economy high-yield bonds
Emerging market bonds
Table 3.2. Mutual Fund Flows and Asset Returns
Emerging Markets United States
Equity Bond Equity All Bond High-Yield Bond Municipal Bond
Estimation Periods 2004–14 2004–14 2007–14 2007–14 2007–14 2007–14
Single Equation Model with Excess Asset Return as Dependent Variable
Surprise flows have significant
impact on returns
Yes Yes Yes in 2012–14 Yes in 2008–10 Yes* Yes
Asymmetry: Impact of surprise
inflows is different from impact
of surprise outflows
Outflows have
larger impact
than inflows
Outflows have
larger impact
than inflows
Limited** Inflows have
larger impact
than outflows
No Outflows have
larger impact
than inflows
VIX sensitivity: Surprise flows
have higher impact on returns
when the VIX is high
Yes Yes Limited** Limited** Yes Yes
Vector Autoregression with Unadjusted Flows and Returns
Flows help predict returns No Yes No Yes*** No Yes***
Sources: Bank of America Merrill Lynch; Morgan Stanley; Bloomberg, L.P.; EPFR Global; ICI; and IMF staff estimates.
Note: VIX = Chicago Board Options Exchange Market Volatility Index. Surprise ?ows are residuals from a vector autoregression model, VAR, with two endogenous vari-
ables (mutual fund ?ows into each asset class and representative benchmark asset returns for the respective market over the one-month Eurodollar deposit rate) and the
VIX (contemporaneous and lagged) as an exogenous variable. Mutual fund ?ows to emerging markets are investment ?ows into each country from all mutual funds from
various jurisdictions covered by EPFR Global. U.S. fund ?ows data are investors’ ?ows into mutual funds with a stated investment focus, covering funds domiciled in the
United States. U.S. data are from Investment Company Institute, except for U.S. high-yield bond funds, which come from EPFR Global. Explanatory variables in the base
single equation model include contemporaneous and lagged surprise ?ow, lagged excess return, the VIX, and the volatility of excess return (estimated with a generalized
autoregressive conditional heteroskedasticity, GARCH, model). The model is estimated for the whole indicated period as well as rolling three-year periods in between. The
results in the bottom line are based on generalized impulse responses.
*For the entire sample period, the results are not signi?cant. However, three-year subperiod estimates show that the coef?cient on contemporary surprise ?ows is always
statistically signi?cant and positive, but declines steadily over time. Limited ** indicates signi?cance between the 5 percent and 10 percent signi?cance levels. ***Indi-
cates not robust to all speci?cations.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
106 International Monetary Fund | April 2015
over, bond markets show evidence of nonlinearities, with
unusually large surprise infows or outfows associated
with a disproportionate impact on bond returns. Tere is
no evidence, however, for an increase in the price impact
over time—if anything, the evidence across markets indi-
cates a decline in the efect.
21
Te price impact pattern provides support for the
existence of a frst-mover advantage only in less liquid
markets. Flows helping to predict price movements
would be consistent with the presence of incentives to
run.
22
Such predictive power of fows is more likely
to be present in less liquid markets. In line with this
notion, the evidence indicates that fows have an impact
on future returns of emerging market bonds, and to a
lesser extent, in U.S. bond and municipal bond markets.
For the latter two markets, however, the results are not
robust across econometric specifcations. Possibly, the
considered aggregate bond categories may be too broad
and too liquid to unambiguously pick up the efect.
23
Efect of mutual fund holdings and their
concentration on bond yields
Does concentration of holdings among mutual funds
matter during periods of stress? Some mutual funds
have a large footprint in specifc market segments, rais-
ing concerns that decisions by a few portfolio manag-
ers may have a large price impact in those markets.
Since the global fnancial crisis, mutual fund bond
holdings and their concentration have risen some-
what (Figure 3.7, panels 1 and 2).
24
Te evidence in
the literature suggests that concentration matters for
stock price dynamics, in particular during periods of
volatility.
25
Tis section investigates this issue further
21
Te evidence on contemporaneous price efects does not conclu-
sively prove that fund fows drive returns. For example, fund fows
and returns could both be driven by news. Still, this would leave the
question open of why mutual fund fows behave distinctively (since
not everybody can trade in the same direction in response to news).
22
Te argument (as laid out in Stein [2014]) is that if outfows
are frst met with cash and the sale of more liquid assets, while less
liquid assets are sold gradually, predictable downward pressure would
be created on the prices of these less liquid assets. Tis, in turn,
would create an incentive for end investors to pull out quickly if
others are withdrawing.
23
See also Collins and Plantier (2014). Moreover, the efects are
more likely to be present at times of stress, and are therefore not eas-
ily picked up in an estimation spanning a long period.
24
Concentration is measured by identifying, for each individual
bond, the largest fve investors among mutual funds. Alternative
measures (top 10 investor holdings and Herfndahl index) yield
similar results.
25
Greenwood and Tesmar (2011) report that fragility, measured
by the concentration of mutual fund ownership of large U.S. stocks
using security-level bond ownership data, assessing
whether mutual fund holdings and their concentration
were correlated with the degree of bond yield changes
around the global fnancial crisis and the taper shock
in 2013, after controlling for bond-specifc charac-
teristics (see Annex 3.2 for details). Te analysis goes
beyond the literature to date by covering diferent
asset markets, including corporate bonds for advanced
economies, and corporate and public sector bonds for
emerging market economies.
Te fndings suggest that larger mutual fund holdings
and greater ownership concentration adversely afect
bond spreads in periods of stress (Figure 3.7, panels 3
and 4). During the period of sharp price adjustments
around the global fnancial crisis in 2008, bonds with
larger fund ownership and those with a higher con-
centration of ownership experienced higher increases
in credit spreads. Possibly, this is related to incentives
to run created by funds. In the face of price drops of
assets held by their fund, end investors may be induced
to redeem quickly, for fear that they could be disadvan-
taged if they exit late. Te efect was most pronounced
among those securities with the highest initial spreads.
Tis may suggest that funds either try to actively alter
their holdings in a crisis by reducing exposures to riskier
bonds, or are forced to sell riskier securities to meet
investor redemptions. Investor concentration made
bonds from emerging market and developing economies
more vulnerable to the 2013 taper episode, but this was
not the case for bonds from advanced economies.
Behavior of Fund Flows and Fund Liquidity Management
Roles of end investors and asset managers
Mutual fund investments are driven by the decisions
of both end investors (fund fows) and asset managers
(portfolio rebalancing). A fund’s investment in a specifc
asset can increase either because the fund receives money
from end investors that is proportionally allocated to all
assets, or because the portfolio manager invests relatively
more money into the asset (portfolio rebalancing). To
ascertain the relative importance of each factor, the anal-
ysis compares the variances of (1) changes in the return-
adjusted weights of each security in a fund’s portfolio
and (2) fund fows (see Annex 3.2). For U.S.-domiciled
funds, the results indicate that about 70 percent of
and the correlation of trading among investors, strongly predicts
price volatility over 1990–2007. For Spanish stocks, Desender
(2012) fnds that ownership concentration is valued positively (nega-
tively) by the stock market during down (up) market periods.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 107
0
10
20
30
40
50
60
70
80
90
100
0 250 500 750 1,000 0 250 500 750 1,000
Individual bond
0
2
4
6
8
10
12
14
16
18
0
.
1
0
.
4
0
.
8
1
.
4
2
.
0
2
.
6
3
.
2
3
.
9
4
.
6
5
.
4
6
.
3
7
.
3
8
.
5
9
.
7
1
1
.
3
1
3
.
2
1
5
.
7
1
8
.
9
2
3
.
6
3
6
.
1
C
h
a
n
g
e
i
n
c
r
e
d
i
t
s
p
r
e
a
d
s
Top ?ve holdings, 2013:Q1
Top ?ve holdings, 2008:Q2
0
10
20
30
40
50
60
70
80
90
100
Individual bond
Top ?ve holdings, 2013:Q1
Top ?ve holdings, 2008:Q2
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0
.
1
0
.
5
0
.
9
1
.
4
2
.
0
2
.
6
3
.
2
3
.
8
4
.
4
5
.
1
5
.
7
6
.
4
7
.
1
7
.
9
9
.
1
1
0
.
6
1
2
.
1
1
4
.
0
1
7
.
0
3
7
.
4
C
h
a
n
g
e
i
n
c
r
e
d
i
t
s
p
r
e
a
d
s
Mutual fund concentration in bond markets has increased somewhat since the global ?nancial crisis.
(Share of individual bonds held by the ?ve largest mutual funds in 2008 and 2013, percentage points)
Bonds with higher mutual fund holding concentration were more adversely affected during stress periods in 2008 and 2013.
(Increase in credit spreads by share of bonds held by the ?ve largest mutual funds, percentage points)
1. Concentration of Mutual Fund Bond Ownership: U.S. Bonds 2. Concentration of Mutual Fund Bond Ownership: Emerging Market
and Developing Economy Bonds
3. Corporate Bonds Issued by U.S. Issuers, 2008:Q2 and 2008:Q4 4. Bonds Issued by Emerging Market and Developing Economies,
2013:Q1 and 2013:Q2
Sources: eMaxx; and IMF staff calculations.
Note: In all panels, holdings by the ?ve largest mutual funds are identi?ed for each individual bond. Bonds are sorted in different buckets on the horizontal axis
according to the share of the bond held by the ?ve largest mutual funds. The vertical axes in panels 3 and 4 show the average change in credit spreads (bond yields
over benchmark government bond yields of the same currency and similar maturity) for bonds in each bucket, between 2008:Q2 and 2008:Q4, and 2013:Q1 and
2013:Q2, respectively.
Figure 3.7. Bond Ownership Concentration and Its Effects on Credit Spreads
Share of bonds held by the largest ?ve mutual fund investors (percent) Share of bonds held by the largest ?ve mutual fund investors (percent)
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
108 International Monetary Fund | April 2015
the variance of funds’ fows into assets is attributable
to managers’ decisions, with the remaining 30 percent
attributable to end investors. Tis decomposition does
not, however, take into account that, as discussed earlier,
managers’ behavior is to a signifcant extent indirectly
driven by the incentives provided by end investors,
including through the pattern of infows.
Determinants of fund fows
Given the importance of fund infows for mutual
fund investment and induced price efects, this section
investigates the determinants of net fund injections by
end investors. Te analysis uses monthly net infows
for U.S. mutual funds and ETFs at the funds’ share-
class level for open-end bond and equity funds, cover-
ing the period 1998–2014 (Annex 3.2).
26
Explanatory
26
A mutual fund can issue multiple classes of shares that only
difer in the structure of various types of fees (FINRA 2011). Te
sample includes U.S.-domiciled open-end mutual funds and ETFs,
irrespective of their investment focus. For instance, U.S. funds
variables include fund performance (benchmark return
and fund return in excess of the benchmark return),
the VIX, fund characteristics (size, age, clientele) and
structures (purchase and redemption fees, and dum-
mies for index funds and for ETFs), and the liquidity
of the underlying asset class.
End investors’ fows to funds, especially those from
retail investors, are procyclical and display a “fight to
quality” during times of stress (Figure 3.8):
• Fund flows increase after good market performance
of the respective asset class. This indicates that inves-
tors pursue momentum strategies, increasing their
allocation to asset classes that have performed well
in the past, and selling past losers.
• End investors engage in a flight to quality during
episodes of stress. As uncertainty (measured by the
investing in emerging market debt are included. Te focus is on the
United States because of data availability on fees, as a result of more
comprehensive disclosure requirements.
–2
–1
0
1
2
3
4
A
v
e
r
a
g
e
m
o
n
t
h
l
y
f
u
n
d
?
o
w
s
VIX (percent)
Equity funds
Government bond funds
Corporate bond funds
–0.6
–0.5
–0.4
–0.3
–0.2
–0.1
0.0
0.1
0.2
0.3
Increase in the VIX
Decline of benchmark
return
Decline of excess
return over benchmark
Bond funds Equity funds
C
h
a
n
g
e
i
n
m
o
n
t
h
l
y
f
u
n
d
?
o
w
s
Sources: Bloomberg, L.P.; and IMF staff estimates. Additional data: Calculated based on data from the survivor-bias-free U.S. mutual fund database ©2014 Center for
Research in Security Prices (CRSP
®
), The University of Chicago Booth School of Business.
Note: VIX = Chicago Board Options Exchange Market Volatility Index. Estimates in panel 1 are based on a regression of fund ?ows on the VIX, benchmark performance
(lagged), excess performance over benchmark (lagged), age, and size. The model is estimated using share-class-level data covering 1998–2014. For more details on
estimations and data, see Annex 3.2. Panel 2 splits observations into 20 quantiles based on the VIX. For each of these quantiles, the simple average for the VIX and
fund ?ows is reported by type of fund.
Figure 3.8. Drivers of Fund Flows from End Investors
(Monthly fund ?ows, percent of total net assets)
1. Sensitivity of Fund Flows to Fund Performance and Market Conditions
(The effect of a one standard deviation shock to each driver)
Fund ?ows are strongly in?uenced by asset class performance, a fund’s
own performance, and the VIX.
2. Fund Flows and the VIX
Periods with high VIX see a ?ight to quality from equity to bond funds,
especially to government bond funds.
11 13 14 15 17 19 21 24 26 31
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 109
VIX) rises, end investors shift away from equity funds
to bond funds, especially to sovereign bond funds. A
closer look at subgroups of bond funds and emerging
market assets reveals that investors also flee from corpo-
rate and emerging market bonds when the VIX rises.
27
• Relative performance is a main driver of fund
inflows. This behavior by end investors provides
incentives for herding, as discussed earlier.
• Investors disproportionately pour money into funds
with strong recent performance, creating an incentive
for managers of poorly performing funds to increase
risks. Funds with excess returns over their bench-
mark receive disproportionately more inflows (Figure
3.9). In line with the existing evidence based on
U.S. equity mutual fund data (Chevalier and Ellison
1997), investors inject money into winning funds
to a greater extent than they punish poor perform-
ers (implying a convexity in the performance-inflow
relationship). Thus, poorly performing fund managers
have an incentive to take more risky bets (see Box 3.1
27
Based on similar analysis for funds (from all jurisdictions)
investing in emerging market assets using EPFR Global. Tis is in
line with the fndings of the April 2014 Global Financial Stability
Report.
for details).
The convexity is weaker for bond funds.
Similar to the findings in Ferreira and others (2012),
an analysis for non-U.S. funds shows that convex pat-
terns are observed in some but not all economies, with
equity funds generally displaying more convexity.
Client types, fees, and to some extent the market
liquidity of assets and fund characteristics infuence the
sensitivity of fund fows to performance (Figure 3.10):
• Institutional investors appear to be less influenced
by recent past performance. However, this result is
not robust across all subperiods considered. Institu-
tional investors are likely to be more sophisticated
than retail investors, and findings in the April 2014
Global Financial Stability Report show that flows
from institutional investors to emerging market
assets are less sensitive to changes in the VIX.
28
28
However, in the presence of more fundamental fnancial and
macroeconomic problems, institutional investors withdraw more
aggressively than retail investors. For instance, Schmidt, Timmer-
mann, and Wermers (2013) point out that institutional investors
were the frst ones to recognize problems with money market funds
and instigated a run in 2009. Te April 2014 GFSR fnds that insti-
tutional investors sold of more when emerging market sovereigns
were downgraded to below investment grade.
–0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
–2 –1.7–1.4–1.1–0.8–0.5–0.2 0.1 0.4 0.7 1.0 1.3 1.6 1.9 2.2 2.5 –2 –1.7–1.4–1.1–0.8–0.5–0.2 0.1 0.4 0.7 1.0 1.3 1.6 1.9 2.2 2.5
M
o
n
t
h
l
y
f
u
n
d
?
o
w
s
(
p
e
r
c
e
n
t
o
f
t
o
t
a
l
n
e
t
a
s
s
e
t
s
)
Fund’s monthly excess return over benchmark (percent)
Figure 3.9. Convexity of Fund Flow–Performance Relationship
2. Equity Funds 1. Bond Funds
–0.6
–0.4
–0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
M
o
n
t
h
l
y
f
u
n
d
?
o
w
s
(
p
e
r
c
e
n
t
o
f
t
o
t
a
l
n
e
t
a
s
s
e
t
s
)
Fund’s monthly excess return over benchmark (percent)
Sources: Bloomberg, L.P.; and IMF staff estimates. Additional data: Calculated based on data from the survivor-bias-free U.S. mutual fund database ©2014 Center for
Research in Security Prices (CRSP
®
), The University of Chicago Booth School of Business.
Note: Estimates in the two panels are based on a regression of net in?ows on VIX, benchmark performance (lagged), excess performance over benchmark (lagged),
and age. The model allows for different slopes for negative and positive values of excess performance over benchmark. The estimation uses share-class-level data
covering 1998–2014. For more details, see Annex 3.2.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
110 International Monetary Fund | April 2015
Figure 3.10. Liquidity Risk and Fund Structures
1. Relative Sensitivity of Equity Fund Flows to Performance
(Response of ?ows into liquid and illiquid funds to a one standard
deviation decline in benchmark returns, difference with respect to rest
of funds)
Among equity funds, fund ?ows of funds investing in liquid
stocks are less sensitive to performance.
4. Trend of Mutual Fund Fees
(Simple average, percent)
However, mutual fund fees, especially redemption fees, have
declined during the past 15 years because of competitive
pressures in the industry.
Redemption fees are effective in mitigating out?ows.
2. Fund Flows by Redemption Fees
(The effect of a one standard deviation decline of returns)
Redemption fees have helped mitigate redemptions during stress
episodes, especially for emerging market funds.
3. Redemptions during Stress Episodes, by Redemption Fee
Levels
–1.5
–1.0
–0.5
0.0
0.5
1.0
0 2 5 0 2 5
Bond funds Equity funds
M
o
n
t
h
l
y
f
u
n
d
?
o
w
s
(
p
e
r
c
e
n
t
o
f
t
o
t
a
l
n
e
t
a
s
s
e
t
s
)
Redemption fees (percent)
–35
–30
–25
–20
–15
–10
–5
0
5
2008
Equity funds
2013
EM bond funds
2013
EM equity funds
C
h
a
n
g
e
o
f
a
v
e
r
a
g
e
f
u
n
d
?
o
w
s
b
e
f
o
r
e
a
n
d
d
u
r
i
n
g
s
t
r
e
s
s
e
p
i
s
o
d
e
s
(
p
e
r
c
e
n
t
o
f
t
o
t
a
l
n
e
t
a
s
s
e
t
s
)
Funds with low redemption fees
Funds with high redemption fees
Source: IMF staff estimates. Additional data: Calculated based on data from the survivor-bias-free U.S. mutual fund database ©2014 Center for Research in Security
Prices (CRSP
®
), The University of Chicago Booth School of Business.
Note: EM = emerging market; VIX = Chicago Board Options Exchange Market Volatility Index. Fees are maximum reported fees in the prospectus. Redemption fees
include narrowly defined redemption fees and contingent deferred sales charges. Estimates in panels 1 and 2 are based on a regression of net inflows on the VIX,
benchmark performance (lagged), excess performance over benchmark (lagged), age, size, and the reported fund characteristics (added one at a time) interacted with
excess performance over benchmark (lagged). The estimation uses share-class-level data covering 1998–2014. Panel 3 computes the difference between average
flows before the crisis period and average flows during the reported stress episodes (September to December 2008 for the global financial crisis, and May to September
2013 for the tapering episode). Fund flows are standardized by the beginning-of-period total net assets. Funds are classified as having low redemption fees if
redemption fees are equal to zero. Funds are classified as having high redemption fees if redemption fees are greater than or equal to 3 percent in 2008 and 1 percent
in 2013. For more details on estimations and data, see Annex 3.2.
–0.2
–0.1
0.0
0.1
0.2
Liquid subgroup Illiquid subgroup
C
h
a
n
g
e
o
f
m
o
n
t
h
l
y
f
u
n
d
?
o
w
s
(
p
e
r
c
e
n
t
o
f
t
o
t
a
l
n
e
t
a
s
s
e
t
s
)
Relatively smaller out?ows
compared with rest of funds
Relatively greater out?ows
compared with rest of funds
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Bonds Equity
Purchase fee
Redemption fee
J
a
n
.
1
4
D
e
c
.
1
2
N
o
v
.
1
1
O
c
t
.
1
0
S
e
p
.
0
9
A
u
g
.
0
8
J
u
l
.
0
7
J
u
n
.
0
6
M
a
y
0
5
A
p
r
.
0
4
M
a
r
.
0
3
F
e
b
.
2
0
0
2
N
o
v
.
1
3
O
c
t
.
1
2
S
e
p
.
1
1
A
u
g
.
1
0
J
u
l
.
0
9
J
u
n
.
0
8
M
a
y
0
7
A
p
r
.
0
6
M
a
r
.
0
5
F
e
b
.
0
4
J
a
n
.
0
3
D
e
c
.
2
0
0
1
Stress episodes
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 111
• Fees are generally effective in dampening redemp-
tions following short-term poor performance,
though competitive pressures in the industry
challenge their use. In particular, redemption fees
appear to be effective. However, among bond funds,
the effectiveness of fees appears to vary across fund
types: the fees dampen redemptions for emerging
market bond funds, but not for U.S. government
bond or corporate bond funds. Moreover, competi-
tive pressures and transparency requirements in the
industry have driven down fees during the past 15
years (Figure 3.10, panel 4), which would make it
difficult for individual funds to adopt adequate fees
in line with their investment risk without sector-
wide coordination or regulation.
29
• The sensitivity of redemptions to benchmark
performance is larger for equity funds investing
in less liquid stocks. This result is in line with the
findings in Chen, Goldstein, and Jiang (2010) for
U.S. equity funds. As discussed by Stein (2014), a
higher redemption sensitivity of less liquid funds
is consistent with the existence of a first-mover
advantage. Although one would expect the evidence
to be stronger for bond funds (because of their
larger liquidity mismatches; Figure 3.5), that is not
the case. One reason could be that bond funds with
higher liquidity mismatches manage their liquid-
ity risk more carefully, as discussed in the following
section.
Brand name efects are present, albeit weak. Tis
analysis examines 18 events in which a “fagship fund”
of a large AMC experienced large redemptions (see
Annex 3.2 for details). Te test is whether funds in
the fund family hit by the fagship shock experience
larger outfows than similar funds not in the fund
family. Out of the 18 events, 10 cases show statistically
signifcant negative brand name efects, 3 cases show
statistically positive efects, and the other 5 cases show
no signifcant efects (Figure 3.11).
How do funds manage liquidity risks?
Te efects of fund fows on fund investment can
be cushioned by liquidity risk management. For
instance, if a fund holds sufcient cash bufers when
29
Figure 3.10 shows the maximum charge reported in the fund’s
prospectus. In practice, funds often ofer discounts, reducing efective
fees to much lower levels. ICI (2014b) reports that efective purchase
fees declined from nearly 4 percent in 1990 to 1 percent in 2013.
faced with large redemptions, the efect on sales
pressures will be dampened. Moreover, funds’ share
pricing rules and redemption policies can be designed
to reduce redemption risks. Existing research (though
somewhat old and focused on equity funds) shows
that funds investing in illiquid assets tend to take the
form of closed-end funds with no redemption risk,
charge fees for fund share purchases and redemp-
tions, and hold more cash (Chordia 1996; Deli
and Varma 2002). Tis section looks at how fund
managers use these tools to manage liquidity risks by
examining their cash holding patterns in relation to
fow volatility, current fund fows, and various fund
characteristics, including liquidity of assets and client
type (institutional or retail). In contrast to previous
studies, the analysis here also covers bond funds and
uses more recent data.
30
30
Funds can also manage liquidity using derivatives, something
not studied here because of a lack of data.
–30
–25
–20
–15
–10
–5
0
5
–1 0 1 2
Month from event date
Shocked ?agship (all events)
Family (all events)
Shocked ?agship (signi?cant negative events)
Family (signi?cant negative events)
Brand name effect: 18 events with a “?agship fund shock”
(Mean across events; ?ows in percent of total net assets, nonfamily = 0)
Source: IMF staff estimates. Additional data: Calculated based on data from the
survivor-bias-free U.S. mutual fund database ©2014 Center for Research in
Security Prices (CRSP
®
), The University of Chicago Booth School of Business.
Note: “Flagship shocks” for large asset management companies are identi?ed as
periods with large out?ows from ?agship funds (10 percentage points above those
of the median of funds with shared investment objectives). Regression analysis for
each of those events is used to test whether funds in the affected ?agship family
receive lower net in?ows relative to nonfamily funds. See Annex 3.2 for details.
Figure 3.11. Brand Name Effects
(Cumulative fund ?ows from event date in percent of total net assets,
mean difference from median comparator funds)
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
112 International Monetary Fund | April 2015
Generally, asset managers choose cash bufers and
fee policies to limit liquidity risks, though competitive
pressures have been reducing the use of redemption
fees (Figure 3.12):
• Asset managers appear to actively manage their
liquidity risks with precautionary cash buffers
(Figure 3.12). Cash holdings are high for those
funds experiencing very large outflows (in line with
a precautionary motive) and inflows (presumably
because managers take some time to fully invest new
money). Estimation results confirm that funds also
hold higher cash buffers when they face more vola-
tile flows from investors and when these investors
are primarily less stable retail investors. Similarly,
cash holdings are higher for funds investing in rela-
tively less liquid assets.
• Funds with higher liquidity risks tend to charge
higher fees (Figure 3.12, panel 2). Fees are generally
set lower for institutional investors. Funds investing
in more illiquid assets tend to set higher fees than
those investing in liquid assets.
Herding, Interconnectedness, and Contribution to
Systemic Risk
Herding (correlated trading)
How prevalent is herding? Empirical evidence of mutual
fund herding is abundant, although reported mag-
–1.5
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
Institutional Liquid subgroup Illiquid subgroup
Equity funds Bond funds
Relatively less cash
holdings compared
with other funds
Relatively more
cash holdings
compared with
other funds
0
1
2
3
4
5
6
7
8
–14.7 –3.1 –1.8 –1.2 –0.6 –0.1 0.4 1.3 2.9 7.7
C
a
s
h
(
p
e
r
c
e
n
t
o
f
t
o
t
a
l
n
e
t
a
s
s
e
t
s
)
Monthly fund ?ows (percent of total net assets)
Equity funds
Bond funds
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
R
e
t
a
i
l
i
l
l
i
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d
R
e
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l
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a
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a
l
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t
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I
n
s
t
i
t
u
t
i
o
n
a
l
l
i
q
u
i
d
Equity funds Bond funds
Purchase fee Redemption fee
Figure 3.12. Funds’ Liquidity Risk Management
1. Cash Holding by Fund Flows
(Using monthly share-class-level data for 1998–2014)
3. Differences in Cash Holdings across Funds
(Percent of total net assets)
Cash holdings are high for those funds experiencing large
in?ows or out?ows.
Funds charge higher fees to retail investors and when investing
in illiquid assets…
2. Mutual Fund Fees by Investment Focus and Clientele
(Simple average, percent)
…and hold more cash when investing in relatively illiquid assets,
facing higher fund ?ow volatility. They hold less cash when they have
predominantly institutional clients.
Sources: Calculated based on data from the survivor-bias-free U.S. mutual fund
database ©2014 Center for Research in Security Prices (CRSP
®
), The University
of Chicago Booth School of Business; and IMF staff estimates.
Note: Panel 1 is based on monthly data from 1998 to 2014 for each fund share
class. It splits observations into 20 quantiles based on net fund ?ows (in percent
of total net assets). For each of these quantiles, the panel shows the mean
percentage of cash in funds’ portfolios. In panel 2, fees are maximum reported
fees in the prospectus. Redemption fees include narrowly de?ned redemption
fees and contingent deferred sales charges. Estimates in panel 3 are based on a
regression of cash holdings (in percentage of total portfolio) as a function of net
in?ow volatility, lagged net in?ows, and the reported fund characteristics
dummies.
Flow volatility
sensitivity to a 1
standard
deviation increase
in volatility
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 113
nitudes vary across markets (Grinblatt, Titman, and
Wermers 1995; Wermers 1999; Borensztein and Gelos
2003; Choi and Sias 2009; Brown, Wei, and Wermers
2013). Using data on security-by-security holdings of
U.S. open-end mutual funds, the degree of herding is
measured using the method developed by Lakonishok,
Shleifer, and Vishny (1992).
31
Tis is a measure of cor-
related trading within this investor group. Even though
it does not conclusively allow for an identifcation of
31
See Box 2.5 in the April 2014 Global Financial Stability Report
for details. Te Lakonishok, Shleifer, and Vishny (1992) index
is a highly robust measure for detecting herding (in the sense of
correlated trading patterns). It does, however, have a bias toward
underestimating the magnitude of herding. Correcting for this bias is
difcult and methods for doing so are the subject of ongoing debate.
Te downward bias increases with lower transaction numbers. Given
that over the past fve years, the data show a mild decline in the
number of transactions per security, the results likely underestimate
the true increase in herding shown in Figure 3.1.
“herding” in a strict sense (namely, actions taken only
because investors see other investors taking them), at a
minimum it does provide an informative measure of the
degree to which this class of investors moves together,
regardless of the underlying reasons.
Herding among U.S. mutual funds is on the rise
across fund styles (Figure 3.13). Tis fnding is true for
both U.S. equities and corporate bonds in recent years.
For U.S. equities, mutual funds appear to co-move more
during distress episodes. Retail-oriented funds show con-
sistently higher levels of herding than do institutional-
oriented funds. Tis could be because retail investors
are more prone to quickly reallocate money from funds
with poor recent performance to funds with high recent
returns (Frazzini and Lamont 2008), possibly because it
is more difcult for them than for institutional investors
to assess and monitor portfolio managers. Tis difculty
in assessing and monitoring managers and the result-
0
2
4
6
8
10
12
14
16
18
20
S&P 500 U.S. equity HG bond HY bond EM equity EM debt
End-2009 Mid-2014
More herding
0
2
4
6
8
10
12
14
16
J
u
n
.
2
0
0
6
N
o
v
.
0
6
A
p
r
.
0
7
S
e
p
.
0
7
F
e
b
.
0
8
J
u
l
.
0
8
D
e
c
.
0
8
M
a
y
0
9
O
c
t
.
0
9
M
a
r
.
1
0
A
u
g
.
1
0
J
a
n
.
1
1
J
u
n
.
1
1
N
o
v
.
1
1
A
p
r
.
1
2
S
e
p
.
1
2
F
e
b
.
1
3
J
u
l
.
1
3
D
e
c
.
1
3
M
a
y
1
4
Retail funds
Institutional funds
More herding
Source: IMF staff estimates. Additional data: Calculated based on data from the survivor-bias-free U.S. mutual fund database ©2014 Center for Research in Security
Prices (CRSP
®
), The University of Chicago Booth School of Business.
Note: EM = emerging market; HG = high grade; HY = high yield. The herding measure is that proposed by Lakonishok, Shleifer, and Vishny (1992). It assesses the
strength of correlated trading among mutual funds investing in each security, controlling for their overall trade trends (see Box 2.5 of April 2014 Global Financial
Stability Report). Note that the market as a whole cannot trade in the same direction, since at any given time there must be a buyer for each seller. The measure is 0
when there is no sign of herding among mutual funds. It is calculated every quarter, looking at the fund-level activity in each security, and then averaged across
securities. The measure is computed when there are at least ?ve funds that changed the holdings of a security in each quarter for each security. The CRSP database
contains security-by-security holdings of all U.S.-domiciled open-end mutual funds, covering more than 750,000 securities. To make the analysis computationally
feasible, this chapter works with subsamples of securities that are randomly selected. Except for the S&P 500 sample, the herding measure is calculated with
50,000 randomly selected securities for each of the subgroups. In panel 1, the difference in herding across neighbor categories is statistically signi?cant at the
5 percent con?dence level, except for the case of EM debt versus EM equity, and HY bond versus HG bond. The difference in herding by fund type (panel 2) is
signi?cant at the 1 percent con?dence level.
Figure 3.13. Herding among U.S. Mutual Funds
(Percent)
1. Average Measure of Herding by Security Type
(Mean across securities, four-quarter average)
Recently, U.S. mutual funds have been herding more in U.S.
equity and corporate bond markets.
2. Average Measure of Herding by Fund Type
(Average across all securities, four-quarter average)
Retail funds tend to herd more than institutional funds.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
114 International Monetary Fund | April 2015
ing volatility of infows would exacerbate the role of
incentive problems described earlier in driving herding
behavior. Te rise in herding coincides with the adop-
tion of unconventional monetary policies in the United
States, and could be related to an accentuated search for
yield by mutual funds.
32
Herding levels are higher for
emerging market and high-yield assets and lowest for
the S&P 500 market, consistent with the notion that
herding is more likely to be prevalent in relatively more
opaque and less liquid markets (Bikhchandani, Hirshle-
ifer, and Welch 1992).
Linkages between parent asset management
companies and funds
Mutual funds and most other investment vehicles have
few direct solvency linkages with their AMCs. AMCs’
own balance sheets are legally separated from those of
the mutual funds they manage, as required by regula-
tions.
33
Tis separation does not necessarily apply to
other types of investment vehicles, though. For some
hedge funds and private equity funds, AMCs’ assets
can be comingled with clients’ assets. Another example
of linkage is AMC parents’ support for funds during
crisis episodes. In 2008, because of reputational con-
cerns, some fnancial institutions provided emergency
liquidity support for money market funds and other
fxed-income funds their group AMCs were managing
(Moody’s 2010).
Interconnectedness through ownership
Banks and insurance companies are major own-
ers of AMCs, and the overall stability implications
of these arrangements are unclear (Figure 3.14).
Without proper oversight of related-party exposures
and concentrated exposures, funds could be used
as funding vehicles for their AMC’s parent banks.
34
Moreover, many such banks are G-SIFIs. Tese inter-
relationships increase the concentration of fnancial
services providers across various subsegments of the
fnancial sector, creating potentially very infuential
and complex mega conglomerates. Information shar-
ing between a bank and its group AMC is another
32
For high-grade bonds, econometric estimates of the relationship
between herding and proxies for unconventional monetary policy
show a positive, albeit weak, link.
33
See Annex 3.1. AMCs’ own balance sheets are also much smaller
than the clients’ money they manage (2 percent to 12 percent of
assets under management for the top AMCs).
34
For instance, certain types of synthetic ETFs could be used by
their AMCs’ parent banks to obtain cash in exchange for collateral
securities that banks do not want to keep on hand.
potential concern. Massa and Rehman (2008) provide
evidence that such information sharing exists for
banks and AMCs, most likely through informal chan-
nels. However, bank afliation could also have efects
that may be desirable from a fnancial stability point
of view, including access to a central bank’s emer-
gency liquidity facility through AMCs’ parent banks
and more supervisory scrutiny.
Interconnectedness through bank funding
Te roles of mutual funds as funding providers for
banks appear to vary across instruments and countries
(Figure 3.15). Mutual funds are more important pro-
viders of long-term bank fnancing in the United States
than in other economies. However, their role appears
to be less important than that of money market funds’
role in short-term (bank) funding.
Te relationship between size and contribution to
systemic risk
An actively discussed question in global regulatory fora
is whether large asset managers and funds should be
designated as SIFIs and receive more intense oversight.
Tis section does not intend to fully answer this ques-
0
1
2
3
4
5
6
7
8
9
10
Privately held Listed,
independent
Listed, insurer
as parent
Listed, bank as
parent
Sources: Pensions and Investments and Towers Watson (2014); and IMF staff
calculations.
Note: Parent banks include Amundi, Bank of New York Mellon, BNP Paribas,
Deutsche Bank, Goldman Sachs, HSBC, J.P. Morgan Chase, Natixis Global Asset
Management, and UBS. Parent insurance companies include Allianz (for PIMCO),
Axa, Metlife, Generali, Legal and General Group, and Prudential.
Figure 3.14. Ownership Structure of the 25 Largest Global
Asset Management Companies
(Number)
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 115
tion. As discussed earlier, each segment of the industry
has its distinctive risks, many of which are hard to
quantify because of data gaps. However, the analy-
sis attempts to partially address the issue by asking
how funds’ contribution to systemic risk in advanced
economies relates to fund size, investment focus, and
size of their AMCs, using the conditional value-at-risk
(CoVaR) method (see Annex 3.2).
Funds’ contributions to systemic risk depend rela-
tively more on their investment focus than on their
size (Figure 3.16). Estimations based on a sample of
about 1,500 funds (not shown) reveal that investment
orientation, VaR, and fund size, among other character-
istics, are signifcantly related to a fund’s contribution
to systemic risk (Annex 3.2). Te relative importance of
size, however, difers across market segments.
For a given fund size, the systemic risk contribution
bears little relation to the size of a fund’s AMC (Figure
3.16, panel 2). Te average contribution to systemic
risk does not increase with a fund’s AMC’s size (the
picture looks the same when the investment focus of
funds is controlled for), at least not for the top asset
managers considered here. Although this exercise only
examines one segment of the broad asset management
industry and CoVaR is only one of the many possible
systemic risk measures, it highlights the importance
of incorporating product-line and investment-focus
perspectives, in addition to mere size, when discussing
the designation of AMCs and funds as SIFIs.
Revamping the Oversight Framework to
Address Financial Stability Risks
Key Features of Current Regulation
Te industry is regulated, albeit with a focus on inves-
tor protection. Substantial regulatory requirements
are in place for publicly ofered funds.
35
Regulation
focuses on investors being given sufcient information
to understand the investment product, on investors’
35
Regulatory frameworks for funds appear to be generally strong
around the globe—the IMF and World Bank assessments of securities
regulation under the IOSCO Principles show a generally high level
of compliance with principles dealing with disclosure to investors and
other consumer-protection-related standards. Some emerging market
and developing economies, however, have serious gaps in their legal
frameworks that fail to adequately separate the funds’ assets from those
of the asset manager. Tis raises risks to customer assets.
0
2
4
6
8
10
12
United States European
Union
Other
advanced
economies
Offshore Emerging
markets
Issuer domicile
U.S. funds EU funds Other funds
0
10
20
30
40
50
60
70
2005 06 07 08 09 10 11 12 13 14
Euro area, short-term bank funding
United States, repo and CP
Sources: eMaxx; and IMF staff calculations.
Note: EU = European Union.
Sources: European Central Bank; Federal Reserve; and IMF staff estimates.
Note: CP = commercial paper; repo = repurchase agreement.
Figure 3.15. Bank Financing by Mutual Funds and Money Market Funds
1. Share of Long-Term Bank Bonds Held by Mutual Funds
(Percent of total outstanding covered in eMaxx)
Mutual funds invest in long-term bank bonds, but generally
they are not the main holders of bank bonds…
2. Money Market Funds’ Share in Short-Term Funding Markets
(Percent of euro area short-term bank funding and U.S. repo and CP
outstanding)
…whereas money market funds play a more signi?cant
role in short-term funding markets.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
116 International Monetary Fund | April 2015
assets being protected from fraud and other risks, and
on asset managers not taking advantage of investors.
For these purposes, disclosure, investment restrictions
(including concentration limits), caps on leverage,
liquidity risk management, pricing and redemption
policies, and separation of client assets from those of
AMCs play important roles (Table 3.3). Regulatory
requirements for privately ofered products have also
been strengthened since the global fnancial crisis.
AMCs that ofer investment products are subject to
rules that focus on protecting clients from fraud or
negligence and that aim to ensure the business conti-
nuity of the AMC.
Te importance of liquidity risks to the industry is
recognized and is an integral part of current regulation
and industry practices:
• Regulatory requirements to manage liquidity risks exist,
though they are often rather general. Funds are gener-
ally restricted to liquid assets or required to maintain
certain liquid asset ratios; they must have risk manage-
ment frameworks (data collection, profiling of redemp-
tions, and stress testing) in place. Many asset managers
have internal liquidity risk management frameworks for
their funds, with regular monitoring of clients’ liquidity
needs and stress testing. These liquidity management
tools are in line with FSB suggestions (FSB 2013).
• For very large redemptions, funds also have a variety
of tools, subject to local regulatory requirements.
For macroprudential purposes, the FSB (2013) and
the October 2014 Global Financial Stability Report
suggest that regulation and fund contracts should
include tools, such as fees, gates, side-pockets, and
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0.0 1.0 2.0 3.0 4.0 5.0
Assets under management of parent asset management companies
(trillions of U.S. dollars)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
AE sovereign
bonds
AE corporate
bonds
EM bonds AE equity EM equity
Sources: Lipper; Pensions and Investments and Towers Watson; and IMF staff estimates.
Note: AE = advanced economy; AMC = asset management company; CoVaR = conditional value-at-risk; EM = emerging market. The impact of fund A’s distress
on systemic risk is measured by the difference of CoVaR when fund A is in a normal state (median VaR) and in a distressed state (worst 5 percentile VaR). The
?nancial system consists of an equity index for banks and insurers from AEs and about 1,500 mutual funds, taking the largest 100 funds (globally) for each of
the ?ve investment focus categories (AE sovereign, AE corporate bond, EM bond, AE equity, and EM equity) and for three different fund domiciles (the United
States, Europe, and the other advanced economies). Weekly net asset value data are used to compute fund returns and monthly total net asset (TNA) data to
measure the size of each fund from January 2000 to November 2014. The system is measured by a TNA-weighted average of fund returns (the results are
robust when the simple average is used instead). The assets under management of the AMC include assets managed with different investment vehicles such as
separate account and alternative funds. Caution should be taken in comparing the precise ranking of systemic risk contributions across fund categories since
the sample period may not capture the realization of relevant tail risks. Moreover, the measure does not identify whether the contribution is causal or driven by a
common factor.
Figure 3.16. Contribution to Systemic Risk by Mutual Funds
1. Average Contribution to Systemic Risk by Investment Focus
(Percent)
The systemic risk contribution differs across funds’
investment orientations.
2. Contribution to Systemic Risk of Top Fund Families by Size of Asset
Management Company
(Contribution to systemic risk averaged across funds in the same
family, percent)
A fund’s systemic risk contribution is not related to its AMC’s size.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 117
suspension of redemptions, to manage large redemp-
tions.
36
Existing regulation and fund contracts indeed
allow for these tools. In addition, asset managers can
make use of credit lines, delays in cash payout upon
redemption (within regulatory limits), and payment
in kind.
37
The available tools often vary depending
on local requirements.
38
For extreme measures, such
as suspensions, funds are usually required to obtain
permission from regulators, and they are strictly lim-
ited to extraordinary circumstances to prevent abuse.
Consequently, restrictions on redemptions have been
36
Gates constrain redemption amounts to a specifc proportion
on any one redemption day. Suspension is full closure of a fund
to redemption. Side-pockets legally separate impaired or illiquid
securities to prevent them from afecting a fund’s return until market
conditions stabilize.
37
Asset managers argue that payment in kind is particularly useful
for institutional clients. For instance, when institutional clients are
simply changing portfolio managers, they are willing to accept secu-
rities instead of cash and transfer the securities to a new manager to
avoid losses related to large-scale sales. Transfer of securities from one
manager to another is straightforward because the securities are kept
at a custodian bank, segregated from the AMC’s assets.
38
For instance, in some countries, funds are not allowed to take
credit lines or pay in kind to retail investors. Te minimum redemption
frequency for publicly ofered funds is set diferently across jurisdictions,
and funds are not allowed to delay settlement beyond the limit (seven
days in the United States and two weeks in the European Union).
used only rarely in advanced markets, and are gener-
ally associated with the failure or winding down of
a fund—redemptions are suspended to ensure that
pricing of the shares is fair across investors when a
portfolio has become too difficult to price (IOSCO
2011).
Limitations of Current Oversight
Te current oversight framework is not set up to fully
address risks, neither at the institutional nor systemic level:
• Regulation lacking in specificity—Key regulations,
especially regarding liquidity requirements and
liquidity risk management, are broad and lack spe-
cific guidance, allowing for wide-ranging interpreta-
tions and practices across jurisdictions (Table 3.3).
For instance, liquid asset requirements are often stip-
ulated without a precise definition of “liquid assets.”
Requirements for risk management frameworks are
often not detailed in legislation. Regulatory require-
ments themselves also vary substantially across
jurisdictions, reflecting the broad-principle-based
approach of global standards (IOSCO Principles).
• Insufficient supervision of individual and systemic
risks—Supervision of funds and asset managers
Table 3.3. Selected Regulations for Publicly Offered Funds
Issues Requirements
Investment
Restrictions
• Typically, investments in illiquid securities and complex products are restricted and positions cannot be
concentrated in a single issuer.
• Use of leverage and derivatives is capped. Public funds in the United States, for example, can only employ
leverage of up to 33 percent of assets, including portfolio leverage embedded in derivatives. UCITS funds can
only temporarily borrow up to 10 percent of assets. UCITS funds can invest in financial derivatives, subject to
conditions on underlying assets, counterparties, and valuation, and exposure cannot exceed the total net value
of the portfolio.
Liquidity • Publicly offered funds are subject to liquidity requirements.
• Specific fund classes, such as money market funds, have extensive liquidity requirements.
• In the United States, funds can hold only a limited amount of illiquid assets. “Liquid asset” is defined only
broadly by regulation, but more detailed definitions can be included in fund contracts.
• In the European Union, regulators provide a list of assets that are eligible to meet liquidity requirements, but
there is no liquidity ratio requirement. A similar approach is followed by other jurisdictions, such as Brazil.
• In Singapore, liquidity requirements differ by fund type.
• Funds are expected to have risk management frameworks, including liquidity risk management, but few
jurisdictions provide details on how these frameworks should work.
• In 2011, IOSCO established its Principles of Liquidity Risk Management for Collective Investment Schemes.
Pricing of Fund
Assets, Fund Shares,
and Redemption
• Portfolios are generally priced at market value for NAV calculation, although some illiquid assets are valued
following fair value accounting rules. However, during times of distress, some prices may not reflect accurate
market values, especially when there are limited market transactions.
• Rules are in place aiming to ensure that prices for purchases and redemption of shares are set so as to treat
investors fairly, but some rules can result in a first-mover advantage (see Box 3.2 for details).
• Various jurisdictions allow suspension of redemption as an extreme measure.
• Under the European Union’s UCITS scheme, funds can specify redemption restrictions, typically used for funds
investing in less liquid securities.
Source: IMF staff.
Note: IOSCO = International Organization of Securities Commissions; NAV = net asset value; UCITS = Undertaking for Collective Investment in Transferable
Securities (a type of publicly offered fund governed by the European Union UCITS directive).
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118 International Monetary Fund | April 2015
is generally weak across jurisdictions.
39
In many
jurisdictions, oversight of funds has been focused
on disclosure to protect retail investors. Regular
supervision of risks is generally not the focus of
supervisors.
40
As a result, no financial soundness
indicators have been developed for the industry, and
stress testing of funds and AMCs by regulators has
been rare—a major contrast with bank supervisory
practice. For some regulators, the number of asset
managers and funds impose resource challenges.
Moreover, international coordination and guidance
on supervisory practices is sparse, since the IOSCO
Principles focus on regulations. Good practices by
asset managers provide some comfort, but in the
presence of liquidity and price externalities, each
fund and asset manager is likely to underestimate
liquidity needs and the potential for correlated price
effects in the presence of large shocks (Liang 2015).
Improving Oversight
Securities regulators should enhance the micropruden-
tial oversight of risks (Table 3.4):
• Enhance regulation by providing more specifics for funds’
liquidity requirements—Key regulations should pro-
vide a clearer definition of liquid assets. More specific
guidance should be given to match the liquidity
profile of each fund category to its redemption policy.
• Strengthen the microprudential supervision of risks
related to individual institutions—Regulators should
regularly monitor market conditions and review
whether funds’ risk management frameworks are
sufficient, especially with regard to liquidity risks.
Greater resources should be devoted to supervising
risks, including developing analytical and stress-
testing capacities so that regulators can effectively
challenge asset managers’ practices.
• Ensure that funds do not take excessive leverage—
Caps limit overall leverage of publicly offered
funds. Nevertheless, leverage and its regulatory
39
A consistent fnding in Financial Sector Assessment Programs of
the IMF and the World Bank is that most jurisdictions with substan-
tial asset management industries have sound regulatory frameworks
but show weaknesses in the intensity of supervision of funds and
asset managers.
40
Tere are some exceptions. For instance, supervisors in France
and Brazil have risk-oriented and data-driven fnancial stability risk
management frameworks that foresee collecting the data and using
them to monitor potential risks; the supervisors can conduct stress
testing on their own, and challenge asset managers if risks are found.
compliance should be regularly monitored with
better data on derivatives.
41
• Adopt approaches based on products, activities, or
both—Focusing on activities and products in addi-
tion to size seems appropriate given that the indus-
try is diverse and differences in investment focus
seem to matter significantly for funds’ contribution
to systemic risk.
• Raise the quality of supervisory practices across jurisdictions
by introducing global standards—International standards
and guidelines for better supervision should be sig-
nificantly expanded and enhanced. Supervisors should
share best practices, especially in the area of liquidity
risk. For instance, coordinated efforts should be under-
taken to develop financial soundness indicators as well
as stress-testing frameworks for the industry. The IMF
could play a key role here, based on its experience in
developing common financial soundness indicators and
stress-testing frameworks for banks.
42
A macroprudential perspective should be integrated
into the oversight of the industry, and the adequacy of
existing tools for macroprudential purposes should be
reexamined:
• Bring a macroprudential focus on systemic risk to
oversight of the sector—As illustrated by the empiri-
cal analysis, price externalities are the key channel of
systemic financial stability risk from this industry.
Thus, assessments of individual institutions are not
sufficient for assessing systemic risk. Incorporating
monitoring of linkages to other sectors that rely on
the industry for financing may even be necessary.
43
• Existing risk management tools and rules could be
used with a view to safeguard financial stability—To
41
Adam and Guettler (forthcoming) document that, among U.S.
corporate bond funds, (1) the use of credit default swaps (CDS)
rose from 20 to 60 percent between 2004 and 2008; (2) CDS are
mostly used to enhance credit risk taking, rather than hedging; (3)
funds belonging to a larger fund family are more likely to use CDS;
(4) underperforming funds often increase their CDS exposures to
enhance returns; and (5) CDS users tend to perform worse on aver-
age than non-users.
42
Te Global Financial Stability Report began reporting fnancial
soundness indicators for banks in 2003. At frst, the data were col-
lected from national authorities or commercial databases without
harmonizing methods. Te efort has since developed into a more har-
monized statistical framework (http://www.imf.org/external/np/sta/fsi/
eng/fsi.htm), with a full compilation guide. Te IMF now periodically
publishes details of the indicators. It has also been contributing to the
building of common stress-testing frameworks (IMF 2012).
43
Te October 2014 Global Financial Stability Report discusses
how cooperation between microprudential, macroprudential, and
business conduct regulators could be carried out in practice.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 119
mitigate price externalities, rules on investment
restrictions (such as concentration limits), liquidity
requirements, and redemption policies may need to
be updated in line with funds’ risk profiles (October
2014 Global Financial Stability Report).
• Further efforts should be aimed at reducing the first-
mover advantage—As discussed, and partly confirmed
in the empirical analysis, a first-mover advantage can
arise for various reasons. Some of these are difficult
to address, such as the liquidity pecking order of
sales. Others, however, such as the degree of liquidity
mismatches, can at least partially be addressed with
good supervision. Most important, accounting-based
illiquid asset valuation rules and inflexible fund share
pricing rules that increase investors’ incentives to run
should be revised. In this context, so-called swing- or
dual-pricing rules could play a role (Box 3.2). Charg-
ing redemption fees, which are found to be effective
Table 3.4. Summary of Analysis and Policy Implications for Mutual Funds and ETFs
Results Policy Implications
Does Fund Investment Affect Asset Prices?
Flow-price impact analysis: Fund flows affect aggregate asset
prices, at least in less liquid markets, in both advanced and
emerging market economies.
• Regulators need to monitor financial stability risks from the
industry from a macroprudential perspective, especially in
smaller, less liquid, fixed-income markets.
• Adequacy of concentration limits may need to be reconsidered.
Concentration and price-impact analysis: Mutual funds’
concentration in bond markets has risen. During stress episodes,
bonds with more concentrated mutual fund ownership tend to
experience larger price drops.
What Drives Run Risk? What Can Be Done to Mitigate It?
End investors: End investors, especially retail investors, chase
past returns and display a flight to quality during times of stress,
making fund flows procyclical.
• Properly pricing-in the cost of liquidity is important in reducing
the first-mover advantage, by avoiding passing on to remaining
investors the costs associated with the sales of illiquid assets.
Regulators should examine the benefit of flexible NAV pricing
rules (such as swing and dual pricing), illiquid asset valuation
rules, and ETF structures to adequately reflect liquidity risk costs.
• Consider imposing minimum redemption fees for funds with
large liquidity mismatches. Fees that are added to NAV avoid
harming investors as a whole, while pricing-in the cost of
liquidity.
• More generally, the adequacy of the requirements for liquid
assets and liquidity risk management should be reexamined,
incorporating financial stability risks from the industry.
First-mover advantage: In line with the notion of a first-mover
advantage, among equity funds, redemptions are more sensitive
to returns for less liquid funds. However, the same is not true for
bond funds (which generally have higher liquidity mismatches
than equity funds). In emerging markets, fund flows predict
future price movements, consistent with a first-mover advantage.
Funds’ liquidity risk management: Funds use various liquidity
management tools. They hold higher cash buffers when they
experience large outflows, face higher redemption risks, are retail
focused, and invest in illiquid assets. Fees are generally effective
in reducing redemptions.
Does Asset Managers’ Behavior Amplify Risks?
Managers’ decision vs. end investors’ decision: Portfolio
managers’ trading accounts for about 70 percent of the variance
in funds’ investments.
• Ensure that managers are in compliance with regulatory
requirements and are not taking excess risks (including hidden
leverage).
• Reduce information gaps between managers and investors
(and regulators) by upgrading disclosure requirements to better
reflect the fund’s economic risks, especially regarding the use of
derivatives and securities financing transactions.
• Financial stability risks from mutual funds could stem from
many small funds taking similar positions. Regulators should
pay attention to this possibility, not just focus on the positions of
large funds.
Excessive risk taking: By rewarding winners disproportionately
more than punishing losers, end investors encourage excessive
risk taking by managers in various advanced economies. The
tendency is stronger for equity funds than for bond funds.
Herding: Herding among U.S. mutual funds has been intensifying,
particularly in smaller, less liquid markets. Retail-investor-
oriented funds tend to herd more.
Brand name effects: Evidence suggests that large redemption
shocks to a flagship fund often spill over to other funds in the
family, although the effects have been weak so far.
Contribution to Systemic Risk and Size
Fund size and systemic risk: Generally, larger funds contribute more
to systemic risk, but the investment focus of funds matters more.
• The SIFI discussion for funds and asset managers should take
into account specific risks of products in addition to size.
• Oversight of the industry should not simply focus on large funds
and AMCs.
Parent AMC size and its funds’ systemic risk: There is little
relationship between a fund’s contribution to systemic risk and its
AMC’s size.
Source: IMF staff.
Note: AMC = asset management company; ETF = exchange-traded fund; NAV = net asset value; SIFI = systemically important ?nancial institution.
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120 International Monetary Fund | April 2015
in smoothing redemptions, is another alternative for
pricing-in the cost of liquidity. However, competitive
pressures have probably resulted in fee levels that are
likely too low from a financial stability perspective
(Figure 3.10, panel 4). Therefore, coordinating on
an industry-wide minimum level of fees for funds
investing in illiquid assets could be considered.
44
In
doing so, fee policies should match funds’ specific
characteristics rather than impose one-size-fits-all
requirements.
45
• Caution is needed in the use of gates and suspensions—
They should be part of the toolkit. Nonetheless,
their imposition may also send negative signals to
the market and lead to preemptive runs ahead of the
instruments coming into force (FSB 2013; October
2014 Global Financial Stability Report).
• Be equipped with “better” data—Publicly offered
funds disclose substantial information. However,
the disclosed data—aimed at investor protection—
are often not sufficient for nor suited to systemic
financial stability analysis. For instance, many
jurisdictions do not require standardized quantita-
tive disclosure of derivatives and securities financing
transactions, such as outstanding positions, details
on collateral, and counterparties.
46
Better disclo-
sure and reporting is also important for reducing
information gaps that lead to incentive problems of
delegated portfolio management. Supervisors should
also make further efforts to collect data on privately
offered products, including separate accounts. Even
though investor-protection concerns with regard to
these products are lower, their investment patterns
can affect financial markets.
44
Tese fees would not have to beneft the AMC but could be
added to NAV and be redistributed to investors. For instance, in the
United States, Rule 22c-2 under the 1940 Investment Company Act
as amended provides that the fund board of an open-end fund must
consider whether to impose a redemption fee (up to 2 percent) that
fows back into the fund’s NAV (BlackRock 2014b).
45
Nevertheless, the imposition of such a fee would raise various
practical problems, including those related to cross-border coordina-
tion. An inadequate framework could also drive investors away from
this industry to other, less regulated products.
46
In the United States, mutual funds disclose only qualitative
information on their derivatives positions. In the European Union,
heightened concerns about the use of derivatives by synthetic ETFs
in 2011 (see Annex 3.1) have led the industry to voluntarily disclose
detailed derivatives positions, including derivatives exposures, coun-
terparties, and the type and amount of collateral. Tis practice has
subsequently evolved into requirements for ETFs and more broadly
for UCITS (ESMA 2012). In Brazil, supervisors obtain information
from the central counterparty and from exchanges that clear deriva-
tives transactions.
Various other aspects not covered in the empirical
analysis in this chapter deserve attention by national
authorities. Improving the liquidity and transparency
of secondary markets, specifcally for longer-term
debt markets, would reduce risks related to liquidity
mismatches.
47
For example, expanding trade report-
ing initiatives to all global fxed-income sectors should
help reduce the opacity of secondary markets (October
2014 Global Financial Stability Report). Compensation
structures for portfolio managers may merit scrutiny
(Box 3.1). Te composition of benchmark indices also
deserves attention, with a view to minimizing possible
associated distortions. Te authorities could assess their
ability to provide emergency liquidity to break vicious
feedback loops between funding and market liquid-
ity in times of stress. However, providing emergency
liquidity creates clear moral hazard risk and therefore
requires enhanced supervision (October 2014 Global
Financial Stability Report).
Conclusion
Financial stability risks can emanate from intermedia-
tion through asset managers even in the absence of
leverage and guaranteed returns. Te discussion in
this chapter stresses the importance of separating the
efects that stem from end investors, and would be
present even in the absence of fnancial intermediaries,
from those that are introduced by the presence of asset
managers. Te delegation of day-to-day portfolio man-
agement introduces fundamental incentive problems
between end investors and fund managers, which can
induce destabilizing behavior and amplify shocks. In
addition, easy redemption options can create risks of
runs because of the presence of a frst-mover advan-
tage. Te destabilization of prices in certain asset seg-
ments (particularly bonds) can afect other parts of the
fnancial system through funding markets and balance
sheet and collateral channels.
Te chapter has shed some light on the importance of
various dimensions of these risks. Complementing and
expanding on existing studies, the analysis fnds evidence
consistent with the notion that mutual fund invest-
ments afect asset price dynamics, at least in less liquid
markets. Some factors point to the existence of incen-
tives to run in segments of the industry. Te observed
pattern of fund infows and redemptions by end inves-
tors creates incentives for fund managers to herd and, in
47
Evidence suggests that herding declines with transparency (Gelos
2011).
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 121
some markets, for poorly performing fund managers to
increase risk. Indeed, herding among U.S. mutual funds
has been rising across asset markets. Funds managed
by larger AMCs do not necessarily contribute more to
systemic risk; investment focus appears to be relatively
more important than size when gauging systemic risk.
Although these risks are not fundamentally new,
their relevance has risen with structural changes in the
fnancial sectors of advanced economies. Te relative
importance of the asset management industry has
grown, and banks have also retrenched from many
market-making activities, contributing to a reduction
in market liquidity. Moreover, the role of fxed-income
funds, which entail larger contagion risks than tradi-
tional equity investment, has expanded considerably. A
broader range of products are available to less sophis-
ticated investors. Last, the prolonged period of low
interest rates in advanced economies has resulted in a
search for yield, which has led funds to invest in less
liquid assets.
Te chapter ofers fve main policy messages:
• First, securities regulators should enhance micropru-
dential supervision of risks stemming from individual
institutions building on regulators’ own risk analysis
and stress testing, supported by global standards for
supervision and better data and risk indicators.
• Second, regulatory and supervisory reforms are
needed to incorporate a macroprudential approach.
• Third, liquidity rules, the definition of liquid assets,
investment restrictions, and reporting and disclosure
rules could be enhanced.
• Fourth, consideration should be given to the use of
tools that adequately price-in the cost of liquidity,
including minimum redemption fees, improvements
in illiquid asset valuation, and mutual fund share
pricing rules.
• Fifth, given that the industry is diverse and that differ-
ences in investment focus seem to matter significantly
for funds’ contribution to systemic risk, a product- or
activity-based emphasis seems to be important.
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122 International Monetary Fund | April 2015
Annex 3.1. Primer on the Asset Management
Industry
Investment vehicles are broadly separated into “collec-
tive investment schemes” (referred to as “funds” in this
chapter) that pool money from a number of investors
and invest in fnancial assets, and what are called “sepa-
rate accounts” or “discretionary mandates” that manage
the money of single institutional investors or high
net worth individuals (Annex Table 3.1.1). Collective
investment schemes are further divided into various
products. Most of them are open-end mutual funds
investing in equities (Annex Figure 3.1.1).
Funds are often established as legal entities (corpora-
tions or trusts) that must be separated from an asset
manager, and a fund’s assets are kept at a custodian,
segregated from the assets of AMCs (Annex Figure
3.1.2). Tis segregation of an AMC and the funds it
manages is a key component of the regulatory frame-
work for investor protection.
Annex Table 3.1.1. Features and Risk Pro?les of Key Investment Vehicles
Vehicle Features and Risk Profiles
Separate Account • Providers of separate account services privately manage the money of institutional investors (including pension
funds, insurance companies, and sovereign wealth funds) or high net worth individuals.
• Little is known about this segment because contracts are private and can vary substantially across clients.
• An industry survey (SIFMA 2014) indicates that these accounts entail simple securities portfolios with little
leverage. The accounts are also subject to client investors’ regulatory requirements.
• Redemption risk for this group is moderate because institutional investors tend to internalize the cost of their sales,
and large redemptions can be paid in kind (especially if clients are changing asset managers).
Open-End Mutual
Fund
• These funds issue “redeemable equity securities” and stand ready to buy back their shares at their current net
asset value (NAV)—the price per share of a fund.
• These funds invest in generally liquid publicly traded bonds and equities.
• Many of the funds offer daily liquidity to clients, making liquidity risk the key risk for the fund.
• In particular, some funds invest in relatively illiquid securities (for example, corporate bonds instead of equity).
This is often referred to as “liquidity transformation” that could lead to “liquidity mismatch,” which makes the fund
vulnerable to redemptions.
• These funds have little leverage through borrowing, though they could be taking portfolio leverage using derivatives
(the same applies for money market funds and exchange-traded funds, below). Although regulations impose caps
on the use of leverage, little quantitative information is available.
Closed-End
Mutual Fund
• These funds issue a fixed number of shares in the primary market that trade intraday on the secondary stock
market at market-determined prices. Investors buy or sell shares through a broker, but cannot redeem their shares
directly from the fund, so these funds do not suffer much liquidity risk.
• However, their popularity suffers from the fact that their shares are usually traded in the secondary market at a
lower value than their NAV.
• Many closed-end funds borrow additional money, often using preferred shares, and they also take portfolio
leverage, subject to regulatory limits (ICI 2014a).
Money Market
Fund (MMF)
• These funds invest in short-term cash equivalent instruments such as commercial paper, Treasury bills, and
certificates of deposit, and play a major role in short-term funding markets.
• MMFs experienced major runs and liquidity distress during the global financial crisis. All U.S. MMFs offered
constant NAV (mutual fund price per share) at $1 per share. This structure created a first-mover advantage because
funds continued to honor the $1 per share repayment even though their actual NAV was worth less as the result of
losses from asset-backed commercial paper, which was perceived to be liquid and safe before the crisis.
• Constant NAV MMFs continue to exist in the United States and several other jurisdictions.
Exchange-Traded
Fund (ETF)
• ETF shares are traded in primary and secondary markets (see Box 3.2 for details).
• ETF shares can be created or redeemed in the primary market between the fund and “authorized participants” (APs)
in large units. APs are typically large securities dealers. Only primary market transactions cause fund flows to ETFs.
The settlement between ETFs and APs are usually in kind, meaning that the exchange of ETF shares and the basket
of securities is in line with the ETF’s investment objectives.
• APs then trade the ETF shares in the secondary market with clients and counterparties on stock exchanges. This
intraday trading in secondary markets provides intraday liquidity to end investors.
• Most ETFs are index funds, tracking the performance of a specific index.
Synthetic ETF • Synthetic ETFs are offered mainly in Europe.
• Instead of directly holding underlying assets (called physical ETFs), synthetic ETF returns are generated using
derivatives, especially swaps.
• Synthetic ETFs could be used for various investment strategies, ranging from simple index tracking to leveraged
and short-selling strategies.
• The extensive use of derivatives (asset swaps) has led to strong concerns about portfolio leverage, counterparty
risks, and the quality of collateral for asset swaps. A number of official sectors expressed such concerns in 2011,
including the Financial Stability Board (2011) and the IMF.
• In response, many ETF providers reduced synthetic products and expanded the disclosure of derivatives positions,
including a list of counterparties and the collateral basket for asset swaps (Morningstar 2012).
(continued)
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 123
Annex Table 3.1.1. Features and Risk Pro?les of Key Investment Vehicles (continued)
Vehicle Features and Risk Profiles
Private Equity
Fund
• Private equity is a broad term that refers to any type of equity participation in which the equity is not freely tradable
on a public stock market, such as equities of private companies and public companies that are delisted.
• Private equity funds often monitor and participate in managing the companies whose equity they hold. They aim to
maximize financial returns by a sale or an initial public offering of the companies.
• There are four main subclasses among private equity funds: (1) venture capital that invests in early-stage,
high-potential, growth startup companies; (2) buyout funds that acquire existing business units or business
assets; (3) mezzanine funds that invest in both growth equity and the subordinate debt layer—namely, the
“mezzanine” between senior debt and equity—of buyout transactions; and (4) distressed asset funds, which are a
specialized segment of buyouts that target mature and distressed companies. In addition, there are real estate and
infrastructure funds.
• Some private equity funds could be leveraged, but they are smaller components of the private equity industry
(Metrick and Yasuda 2011).
• Moreover, these alternative investment vehicles offer limited liquidity to end investors, matching the funds’ long-
term investment horizon.
• Contagion risks are also limited because private equity funds invest in companies not traded in markets.
Hedge Fund • These funds cover a large variety of investment strategies, ranging from publicly traded equity (highly liquid
holdings) to distressed debt vehicles and structured credit products (highly illiquid holdings). Use of leverage and
derivatives also varies considerably depending on the strategy. Unlike mutual funds, hedge funds have no cap on
leverage.
• Hedge funds tend to be more nimble than mutual funds regarding their investment strategy, leading to potentially
rapid alterations in their risk characteristics. Depending on their funding and trading strategies, there can be
significant interconnection with other financial institutions.
Sources: ICI (2014a, 2014c); Metrick and Yasuda (2011); Morningstar (2012); TheCityUK (2012); and IMF staff.
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124 International Monetary Fund | April 2015
Annex Figure 3.1.1. Investment Vehicles by Size, Domicile, and Investment Focus
Open-end
mutual funds
63%
Closed-end
mutual funds
2%
Money market
funds 12%
Exchange-traded
funds 6%
Private equity
funds 9%
Hedge funds
5%
Other
alternatives
3%
United States
49%
Japan
3%
Other developed 9%
Brazil
3%
China
2%
Other emerging
markets 4%
Luxembourg 10%
Ireland 5%
France 5%
United Kingdom 4%
Other developed
Europe 7%
Developed
Europe 31%
United States
72%
Europe
18%
Asia
7%
Others
3%
3. Mutual Funds by Investment Focus
(Percent of $30 trillion total assets under management, end-2013)
Most mutual funds invest in equities. (Bond funds, especially high-yield
corporate and emerging market debt funds, are smaller components).
4. Exchange-Traded Funds by Region
(Percent of $2.3 trillion total assets under management, end-2013)
Exchange-traded funds are offered predominantly in the United
States, where the use of exotic structures is restricted.
1. Investment Vehicles
(Percent of $43 trillion total assets under management, end-2013)
Most assets are managed with simple investment vehicles.
2. Mutual Funds by Fund Domicile
(Percent of $32 trillion total assets under management, 2014:Q2)
The mutual fund industry is dominated by U.S. and European funds, but
Brazil and China show a notable presence among emerging markets.
Sources: European Fund and Asset Management Association; and IMF staff
calculations.
Sources: BarclayHedge; European Fund and Asset Management Association;
ETFGI; Organisation for Economic Co-operation and Development; Preqin; and
IMF staff calculations.
Sources: Deutsche Bank; and IMF staff calculations. Sources: European Fund and Asset Management Association; and IMF staff
calculations.
Equity 44%
Bond 24%
Money market
16%
Balanced/mixed
12%
Other 4%
(continued)
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 125
Annex Figure 3.1.1. Investment Vehicles by Size, Domicile, and Investment Focus (continued)
5. Exchange-Traded Funds by Investment Focus
(Percent of $2.3 trillion total assets under management, end-2013)
Exchange-traded funds primarily invest in equities.
6. Private Equity Funds by Type
(Percent of total number of funds participating in Preqin’s survey 2014)
A large number of private equity funds are involved in buyout, venture
capital, and real estate funds.
Private equity funds are primarily located in the United
States and Europe.
A large number of hedge funds are domiciled in off-shore
jurisdictions.
7. Private Equity Funds by Location of Of?ces
(Percent of total number of funds participating in Preqin’s survey,
2014)
8. Hedge Funds by Country
(Percent of $1.4 trillion total assets under management covered in
Hedge Fund Research, 2014)
Source: Preqin.
Note: Some funds are involved in multiple investment strategies.
Sources: Deutsche Bank; and IMF staff calculations.
Sources: Hedge Fund Research; and IMF staff calculations. Source: Preqin.
Note: Some funds have of?ces in multiple countries.
Equity
83%
Fixed income
16%
Commodity
1%
Other
0%
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Country of domicile Country of operation
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
126 International Monetary Fund | April 2015
Source: IMF staff.
Note: Examples of asset management companies are BlackRock, Franklin Templeton, and PIMCO; examples of funds are BlackRock iShare Core
S&P 500 ETF and PIMCO total return funds. Custodians are usually large banks such as Bank of New York Mellon, J.P. Morgan, and State Street.
Funds often lend the securities they hold to various counterparties to earn fee income (securities lending). Securities borrowers usually provide
cash collateral. Counterparties are usually investment banks, prime brokers, and other broker-dealers that are engaged in short-selling of the
borrowed securities.
Annex Figure 3.1.2. Operation of a Fund
A fund signs an investment management agreement with an asset management company (AMC), which manages the fund’s
portfolio, risks, trading of securities, and securities ?nancing transactions. End investors are equity shareholders of a fund and
are the owners of the funds’ assets in the sense that each share represents an investor’s proportional ownership of the fund’s
asset holdings and the income those assets generate. However, end investors do not have full control over a fund. They
typically cannot ascertain the exact makeup of a fund’s portfolio at any given time, nor can they directly in?uence which
securities the fund manager buys and sells or the timing of these trades. Fund boards represent and protect shareholder
rights vis-à-vis AMCs.
Investment
management
agreement
End
investors
Asset
management
company
Fund Board
Represents and protects
shareholder rights
Fund
Counterparty
Custodian
Shares
Cash
Cash
F
e
e
s
Securities
Assets Liabilities
Share
units
Open-end mutual fund
Segregates and safeguards
clients’ assets from asset
manager’s assets for a fee
–Manage the assets
of the fund
–Risk management
–Trading of securities
and derivatives
–(Rare) emergency
liquidity support
Other transactions:
–Derivatives
–Securities lending
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 127
Annex 3.2 Empirical Framework
Aggregate fow-price relationship
Te aggregate fow-price relationship analysis exam-
ines whether mutual fund fows have an impact on
asset prices at the macro level. Mutual fund fows to
23 emerging markets
48
are investment fows into each
country from all mutual funds from various jurisdic-
tions covered by EPFR Global. U.S. fund fows data
are investors’ fows into mutual funds with a stated
investment focus, covering funds domiciled in the
United States. U.S. data are from ICI, except for U.S.
high-yield bond funds data, which come from EPFR
Global. Te analyses investigate weekly fows, but
the results are similar using monthly fows. Te price
impact is measured by the total excess return of the
respective index for each asset class in dollar terms over
the one-month Eurodollar deposit rate.
Te analysis here focuses frst on surprise fows fol-
lowing Acharya, Anshuman, and Kumar (2014). As
shown in the fund fows analysis later in this annex,
mutual fund investors chase past returns, making fund
fows predictable to some extent. Markets are likely to
have priced in the efects from predictable fows by the
time the money arrives, which limits the correlation
between fows and returns. One would instead need
to examine the part of fund fows that is not priced
in the market. Surprise fows are estimated as residu-
als µ
Fjt
for each asset class j from the following vector
autoregression (VAR) model with the Chicago Board
Options Exchange Market Volatility Index (VIX) as an
exogenous variable.
R
jt
R
jt–1
R
jt–p
?
?
= A + B
1
?
?
+ ··· + B
p
?
?
F
jt
F
jt–1
F
jt–p
µ
Rjt
+ g
0
VIX
t
+ ··· + g
q
VIX
t–q
+
? ?
(3.1)
µ
Fjt
R
t
and F
t
are excess index return and fund fows,
respectively, and p and q are the lengths of lags. For
U.S. assets, the model is estimated with a standard
48
Economies include current emerging markets as well as “gradu-
ated” emerging markets that were considered to be emerging at some
point during the sample period. For equities, the sample includes
Argentina, Brazil, Chile, China, Colombia, the Czech Republic,
Egypt, Hungary, India, Indonesia, Israel, Jordan, Korea, Malaysia,
Mexico, Pakistan, Peru, the Philippines, Poland, Russia, South Africa,
Taiwan Province of China, and Turkey. For bonds, the sample addi-
tionally includes Bulgaria, Lebanon, Sri Lanka, Ukraine, Uruguay,
and Vietnam, but excludes the Czech Republic, India, Israel, Jordan,
Korea, and Taiwan Province of China.
VAR. For emerging market assets, a panel VAR exclud-
ing the VIX is applied. Te details of the variable
defnitions are given in Annex Table 3.2.1.
Various single-equation models are estimated to
investigate the relationship between surprise fows and
asset returns. More specifcally, the following models are
estimated for each asset class j, using a panel regres-
sion with country fxed efects and robust standard
errors (with clusters to correct for heterogeneity within
countries, in addition to cross-country heterogeneity)
for mutual fund fows into emerging market assets, and
ordinary least squares (with Newey-West standard errors
corrected for autocorrelation and heteroscedasticity) for
end investor asset fows into U.S. mutual funds.
Base model:
R
jt
= ? + ?
P
p=1
?
p
R
jt–p
+ ?
Q
q=0
?
q
µ
?
Fjt–q
+ ?
R
r=0
?
r
VIX
t–r
+ ?
S
s=0
?
s
Asset Volatility
jt–s
(3.2)
Model with asymmetry:
R
jt
= ? + ?
P
p=1
?
p
R
jt–p
+ ?
Q
q=0
{?
1q
µ
?
Fjt–q
+ ?
2q
µ
?
Fjt–q
× Indicator(1 if µ
?
Fjt–q
> 0)} + ?
R
r=0
?
r
VIX
t–r
+ ?
S
s=0
?
s
Asset Volatility
jt–s
(3.3)
Model with nonlinearity by the levels of the VIX:
R
jt
= ? + ?
P
p=1
?
p
R
jt–p
+ ?
Q
q=0
?
1q
µ
?
Fjt–q
+ ?
2
µ
?
Fjt
× Indicator(1 if VIX
t
> Treshold
j
)
+ ?
R
r=0
?
r
VIX
t–r
+ ?
S
s=0
?
s
Asset Volatility
jt–s
(3.4)
in which µ
?
is the estimated residual in equation 3.
In addition, the section examines the dynamic
relationship between unadjusted (that is, nonsurprise)
fows and returns to assess the presence of a frst-mover
advantage. Te analysis is based on generalized impulse
response functions from VARs as in equation (3.1). In
addition, impulse responses based on Cholesky decom-
positions using both possible orderings were computed.
Concentration and its efects on bond yields
Te concentration analysis is based on the Lipper
eMaxx bond ownership data, as used in Manconi,
Massa, and Yasuda (2012). Tis database contains
details of institutional holdings for each fxed-income
security, covering $7 trillion in total fxed-income secu-
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
128 International Monetary Fund | April 2015
Annex Table 3.2.1. List and De?nition of Variables for Empirical Exercises
Variables Description Data Source
Aggregate Flow and Return Analysis
EM equity flows Weekly mutual fund equity investment flows into each economy from all mutual
funds covered by EPFR Global.
EPFR Global
EM bond flows Weekly mutual fund bond investment flows into each economy from all mutual funds
covered by EPFR Global.
EPFR Global
U.S. equity flows Flows from end investors to U.S.-domiciled mutual funds investing in domestic
equities.
ICI
U.S. bond flows, all bonds Flows from end investors to U.S.-domiciled mutual funds investing in domestic
bonds (both government and corporate).
ICI
U.S. HY corp. bond flows Flows from end investors to mutual funds investing in U.S. high-yield corporate
bonds.
EPFR Global
U.S. muni. flows Flows from end investors to U.S.-domiciled mutual funds investing in municipal bonds. ICI
EM equity returns MSCI country equity index. Bloomberg, L.P.
EM bond returns Country index from J.P. Morgan EMBIG Global Index. Bloomberg, L.P.
U.S. equity returns MSCI country equity index. Bloomberg, L.P.
U.S. bond returns, all bonds Bank of America Merrill Lynch total return index for U.S. government and corporate
bonds.
Bloomberg, L.P.
U.S. HY corp. bond returns Bank of America Merrill Lynch total return index for U.S. high-yield corporate bonds. Bloomberg, L.P.
U.S. muni. returns Bank of America Merrill Lynch total return index for U.S. municipal bonds. Bloomberg, L.P.
Benchmark yield One-month Eurodollar deposit rate. Bloomberg, L.P.
VIX Chicago Board Options Exchange Market Volatility Index. Bloomberg, L.P.
Asset volatility Staff estimates based on asset returns data and GARCH in mean model. IMF staff
Price Impact of Concentration in Bond Markets
Spread Bond yield minus the yield of benchmark sovereign bond with the same currency and
similar maturity.
Bloomberg, L.P.
Concentration Share of bonds held by the largest five mutual fund investors for each bond. Quarterly. eMaxx
Bid-ask spread Bid-ask yield spreads for each bond (end of quarter). Bloomberg, L.P.
Modified duration Computed from bond’s yield to maturity, coupon rate, and time to maturity,
assuming semi-annual distributions (end of quarter).
Bloomberg, L.P.
Issue size Log of issuance size. eMaxx
Covenants ratio The number of covenants attached to a bond relative to a maximum of 18. Bloomberg, L.P.
Drivers of Fund Flows and Liquidity Risk Management
Fund flow For each fund (i) and time (t), fund flows (it) = [TNA(it)–TNA(it–1)×{1+return(it)}]/
TNA(it–1). Return(it) is computed by CRSP based on NAV. Monthly.
CRSP
Performance Monthly excess fund return (changes of NAV) over benchmark, averaged over prior
three months.
CRSP
Benchmark performance Monthly return of benchmark index, averaged over prior three months. The same
benchmark is assigned for funds with the same broad investment focus (for
instance, S&P 500 for U.S. domestic equity funds).
DataStream
L.P.
HIGH_VIXD High VIX dummy equals 1 when VIX > 30 percent. DataStream
L.P.
Cash Cash and cash equivalents holdings in percent of total portfolio. Quarterly. CRSP
Flow volatility Standard deviation of flows over the prior 12 months, divided by the mean flows
over the same period.
CRSP
Fund Characteristics
Size (S/M/L) Dummies based on 20th and 80th percentiles. CRSP
Age Years since initial offer. CRSP
Purchase fee Maximum in prospectus. CRSP
Redemption fee Maximum in prospectus (sum of type R [redemption] and C [contingent deferred
sales charge]).
CRSP
Index dummy 1 if index fund. CRSP
ETF dummy 1 if ETF. CRSP
Institutional dummy 1 if institutional but not retail in CRSP. CRSP
Liquid bond fund dummy 1 if a fund’s investment focus is one of the following: short-term U.S. government
funds and Treasury funds or short-term investment-grade debt funds.
CRSP
Illiquid bond fund dummy 1 if a fund’s investment focus is one of the following: corporate debt BBB rated
funds, EM local currency debt funds, EM debt funds, or high current yield funds.
CRSP
Liquid equity fund dummy 1 if a fund investment focus is S&P 500. CRSP
Illiquid equity fund dummy 1 if a fund’s investment focus is one of the following: micro/small cap funds; equity
global small company; equity international small company; emerging markets,
China, India, and Latin America.
CRSP
Note: corp. = corporate; CRSP = Survivor-bias-free U.S. mutual fund database, Center for Research in Security Prices; EM = emerging market; ETF = exchange-
traded fund; HY = high yield; ICI = Investment Company Institute; EMBIG = Emerging Markets Bond Index Global; GARCH = generalized autoregressive
conditional heteroscedasticity; muni. = municipal; S/M/L = small, medium, large; VIX = Chicago Board Options Exchange Market Volatility Index.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 129
rities (based on par value) held by more than 19,000
funds. Institutional investors covered in the database
are U.S. and some European insurance companies;
U.S. mutual funds; top U.S. public pension funds; and
European, Canadian, and Asian mutual funds. Data
are based on disclosure information of security-level
holdings by these institutional investors (especially for
mutual funds and U.S. insurance companies). Tis
analysis focuses on a subcomponent of these data,
specifcally corporate bonds for advanced economies
and both sovereign and corporate bonds for emerging
market economies.
Te casual observation on the efects of ownership
concentration on spreads in Figure 3.7 is confrmed
with formal empirical analysis, reported in Annex Figure
3.2.1. Te dependent variable is the change in indi-
vidual bond yield spreads over a benchmark sovereign
bond yield with the same currency and similar maturity
between 2008:Q2 and 2008:Q4 and between 2013:Q1
and 2013:Q2. Tis change is regressed on various
control factors and measures of mutual fund sector
concentration. Te following cross-section model is esti-
mated using a quantile regression approach (for quantile
j=10th, 25th, 50th, 75th, 90th percentile), because a
preliminary analysis indicates the presence of nonlineari-
ties between the dependent and independent variables
(see Annex Table 3.2.1 for the list of variables):
?Spread
ij
= ?
j
+ ?Spread
ij,t=0
+ ?Bond Characteristics
ij,t=0
+ ?Concentration
ij,t=0
(3.5)
Control factors are Spread, which is the initial level
of the yield spread to control for the credit risk of
the security; and bond-specifc characteristics, includ-
ing liquidity (bid-ask spread), bond price sensitivity
to interest rate changes (duration), issue size, and
covenants, in line with Manconi, Massa, and Yasuda
(2012). Concentration is measured primarily by the
share of bonds held by the largest 5 funds, but key
results are robust to other defnitions, such as the
share held by the largest 10 funds, the share held by
all mutual funds, and the Herfndahl index among
mutual fund investors. All explanatory variables are
measured as of 2008:Q2 or 2013:Q1 to control for
possible endogeneity. Outliers in observed market price
data were reduced by winsorizing the 5 percent tail of
the respective distributions.
Relationship between a fund’s liquidity risk and its
management
Te main mutual fund and ETF data source is the
CRSP survivor-bias-free database covering publicly
ofered open-end mutual funds domiciled in the
United States. Even though CRSP’s data cover only
U.S.-domiciled funds, CRSP provides more details
on funds’ fee structures and assets, including quar-
terly security-level holdings, than other global fund
databases such as EPFR Global or Lipper for Invest-
ment Management. Tese global data are used for
some additional robustness tests or for extending some
analysis to funds domiciled outside the United States.
Data are cleaned for outliers. In line with Coval and
Staford (2007); Jotikasthira, Lundblad, and Ramado-
rai (2012); and Jinjarak and Zheng (2014), the data
are excluded if they meet the following conditions:
(1) monthly returns are higher than 200 percent or
lower than –50 percent; (2) monthly change in total
net assets (TNA) is higher than 200 percent or lower
than –100 percent; or (3) fund TNA is less than US$5
million. In addition, for cash balance analysis, port-
folio allocation weight data by broad asset types are
discarded if the sum of allocation weights is less than
95 percent or greater than 105 percent. Weights may
have a negative value because of derivatives and securi-
ties held in short positions. Outliers are removed by
discarding data when any single weight takes a value of
less than –100 percent.
Te roles of portfolio managers and end investors
Following Raddatz and Schmukler (2012), a fund’s
net investment in a security is divided into fund fows
from end investors and the contribution of the changes
of portfolio weights to the security, determined by
portfolio managers. Te term F
j
is the total investment
in security j (net of valuation efects) from all funds i
in the sample. Tis investment is divided into
Fund i’s holding of asset j
F
j
= ?
i
————————————–—— × ?w
ij
Total asset j held by all funds in sample
Fund i’s holding of asset j
+ ?
i
————————————–——
Total asset j held by all funds in sample
× Fund fows to i (3.6)
In the equation, ?w
ij
is the change in portfolio weight
of fund i to asset j, net of valuation efects. Te frst
term of the equation represents manager’s choice and
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
130 International Monetary Fund | April 2015
the second represents end investor’s choice. Ten, the
variance of F
j
is calculated as the sum of each compo-
nent’s variation. Tis variance is estimated on a quar-
terly basis for all funds covered in the CRSP database
for the period 2005:Q1–2014:Q4, excluding securities
held by fewer than fve funds.
Fund fows analysis
Tis analysis studies the drivers of monthly net fows
for U.S. mutual funds and ETFs at the funds’ share-
class level for open-end bond and equity funds, cover-
ing the period 1998–2014.
49
Explanatory variables
include fund performance and benchmark perfor-
mance, the VIX, and various fund characteristics (size,
age, clientele, purchase and redemption fees, fund
types, and the liquidity of the underlying asset classes).
Te list of variables used in the analysis is explained
in Annex Table 3.2.1. Te following model (for share
49
A fund may issue several classes of shares. Te only diference
across share classes is fees. “Fund’s TNA” means the sum of TNA of
each share class issued by the fund.
class i, month t, and benchmark j ) is estimated with
share-class fxed efects and year fxed efects as in
Chen, Goldstein, and Jiang (2010), and using robust
standard errors. An analogous specifcation was run
including the interaction terms with benchmark per-
formance instead of excess return over benchmark.
Fund fows
it
= ?
0
Benchmark Performance
jt–1
+ ?
1
Performance
it–1
+ ?
2
VIX
t
+ ?
3
HIGH_VIXD
t
+ ?
4
VIX
t
× HIGH_VIXD
t
+ ?Fund Characteristics
i
+ ?Performance
it–1
× Fund Characteristics
i
(3.7)
Te test for convexity in the fow-performance rela-
tionship follows a piecewise-linear specifcation as in Sirri
and Tufano (1998) and Ferreira and others (2012). Tis
approach measures diferent linear slopes for the lowest
Sources: eMaxx; and IMF staff estimates.
–15
–10
–5
0
5
10
15
20
25
30
10th 25th 50th 75th 10th 25th 50th 75th 90th
Percentiles of spread change between 2008:Q2 and 2008:Q4
Initial spread level
Top ?ve funds’ holding
Other
Total
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
90th
Percentiles of spread change between 2013:Q1 and 2013:Q2 among
issuers from emerging market and developing economies
Bonds with lower
increase in credit spreads
Bonds with higher
increase in credit spreads
Annex Figure 3.2.1. Drivers of Changes in Credit Spreads during Stress Episodes
(Changes in credit spreads in percentage points, by the levels of the spread changes)
1. Global Financial Crisis: U.S. Dollar Bonds Issued in the United States
(Changes between 2008:Q2 and 2008:Q4)
During the global ?nancial crisis, bonds that were held in a more
concentrated manner were adversely affected, especially those with
high initial spread levels.
2. Taper Shock: Emerging Market and Developing Economies
(Changes between 2013:Q1 and 2013:Q2)
The same was true for emerging market and developing
economy bonds during the “taper shock” episode.
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 131
20th, middle 60th, and top 20th percentiles of perfor-
mance. Each month, funds are ranked according to their
performance, ranging from zero (poorest performance) to
one (best performance). Te following model is estimated,
Fund fows
it
= ?
0
Benchmark Performance
jt–1
+ ?
1
VIX
t
+ ?
2
HIGH_VIXD
t
+ ?
3
VIX
t
× HIGH_VIXD
t
+ ?Fund Characteristics
i
+ ?
1
Low
i,t–1
+ ?
2
Mid
i,t–1
+ ?
3
High
i,t–1
, (3.8)
in which the three levels of relative performance are
defned as follows:
Low
i,t–1
= min{0.2, Rank
i,t–1
}
Mid
i,t–1
= min{0.6, Rank
i,t–1
– Low
i,t–1
}
High
i,t–1
= Rank
i,t–1
? (Low
i,t–1
+ Mid
i,t–1
) Rank ? [0,1]
Analysis of redemption fees in times of stress
Tis analysis examines the role of redemption fees dur-
ing times of stress. It covers two stress events: the 2008
global fnancial crisis and the taper episode in 2013. We
compute the diference between average fows before
the crisis periods (May to August 2008 and December
2012 to April 2013) and average fows during the stress
periods (September to December 2008 and May to
September 2013) for funds with high and low redemp-
tion fees. Funds are classifed as having low redemption
fees if redemption fees are equal to zero. Funds are
classifed as having high redemption fees if redemption
fees are greater than or equal to 0.03 percent in 2008
and 0.01 percent in 2013.
50
Flows are standardized by
the beginning-of-period TNA. For 2008, the focus is on
equity funds because there is evidence of fight to qual-
ity into bond funds. For 2013, the focus is on emerging
market equity and bond funds.
Cash holdings analysis
Drivers of fund cash holdings are investigated by
estimating the model in equation (3.9). For share class
i and quarter t, the model is estimated with a pooled
panel regression at the share-class level, including year
fxed efects and using robust standard errors. Because
50
Te 2013 analysis studies emerging market funds, and therefore
yields very few observations when using the 0.03 threshold.
the cash balance shows a U-shaped pattern with respect
to fund fows (Figure 3.12), the model estimates a
diferent coefcient for funds with large outfows (fund
fows below ? = ?1.5 percent of TNA).
51
Cash
it
= ?
1
Flow volatility
it
+ ?
2
Fund fow
it
+ ?
3
I(Fund fow
it
< ?) + ?
4
Fund fow
it
× I(Fund fow
it
< ?)
+ ?Fund Characteristics
i
(3.9)
Brand name efect analysis
“Flagship shocks for large AMCs” are identifed as follows:
First, a “shock” happens when a fund’s fow-to-TNA ratio
is below the median of its peer group (those with the same
Lipper investment objective code) by 10 percentage points
or more. Second, a fund with a “shock” is identifed as
“fagship” when its TNA is the largest of the funds admin-
istered by the same AMC (a fund family) at the end of the
month before the shock. Tird, the fagship shock corre-
sponds to a large AMC if the fagship fund’s asset manager
was among the top 25 as measured by end-year TNA for
the shock year or any of the previous four years.
Tere are “brand name efects” if, in the three
months including and after the fagship shock (s, s+1,
s+2; where s is the event month), funds in the same
family receive signifcantly lower infows relative to
comparator funds outside the family.
52
For each event
(period s), a separate cross-sectional regression model
is estimated for the diference between the cumulative
net infows to each fund i between dates s and s+2 and
the median cumulative net infows for funds with the
same investment objective j. Explanatory variables are
lagged excess return, age, and a fagship family dummy.
Cumulative Fund fow
ij_{s,s+2}
? Median(CumulativeFund fow
j_{s,s+2}
)
= ?
1
Performance
is–1
+ ?
2
Age
it
+ ?
3
Family Dummy(i ? I
s
)
for all events s and for all funds i with
investment objective j (3.10)
51
Te cash holdings empirical analysis excludes sectoral, hedged,
and short equity funds.
52
Some of the identifed fagship events overlap. Overlapping cases
are treated as a single event and the family dummy is set to 1 if a
share class belongs to either of the afected fagships’ families.
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
132 International Monetary Fund | April 2015
institution i is in distress and the CoVaR
i
when institu-
tion i has median return (?CoVaR
i
):
?CoVaR
i
= CoVaR
i
5%
? CoVaR
i
50%
= ??ˆ
i
(VaR
i
5%
? VaR
i
50%
). (3.13)
Te relationship between fund size and its contribu-
tion to systemic risk is examined with the following
cross-section regression model:
?CoVaR
ij
= Constant
j
+ ?VaR
i
+ ?Logsize
i
+ ?Return
i
+ ?
i
. (3.14)
Te model controls for asset class ( j) specifc fxed
efects and fund i’s risk (VaR) and return (average in
the sample period). Fund size is the log of average size
in U.S. dollars over the sample period. Fixed efects are
positive and signifcant for advanced economy equities
and emerging market equities and bonds, negative for
advanced economy sovereign bonds, and not signif-
cant for advanced economy corporate bonds. All the
other coefcients for control variables are signifcant
and positive at the 5 percent level. Te coefcient for
size is positive and signifcant at the 10 percent level.
Alternative regressions that allow the parameters on
VaR, size, and returns to vary by asset class show quali-
tatively similar results.
References
Acharya, Viral, Ravi Anshuman, and Kiran Kumar. 2014. “For-
eign Fund Flows and Stock Returns: Evidence from India.”
Unpublished. New York University.
Adam, Tim, and André Guettler. Forthcoming. “Pitfalls and
Perils of Financial Innovation: Te Use of CDS by Corporate
Bond Funds.” Journal of Banking and Finance.
Adrian, Tobias, and Markus K. Brunnermeier. 2011. “CoVaR.”
Working Paper 17454, National Bureau of Economic
Research, Cambridge, Massachusetts.
Allen, Franklin, and Gary Gorton. 1993. ”Churning Bubbles.”
Review of Economic Studies 60 (4): 813–36.
Arora, Navneet, and Hui Ou-Yang. 2001. “Explicit and Implicit
Incentives in a Delegated Portfolio Management Problem:
Teory and Evidence.” Working Paper, University of North
Carolina, Chapel Hill.
Basak, Suleyman, and Anna Pavlova. 2014. “Asset Prices and
Institutional Investors.” American Economic Review 103 (5):
1728–58.
———, and Alexander Shapiro. 2008. “Ofsetting the Implicit
Incentives: Benefts of Benchmarking in Money Manage-
ment.” Journal of Banking and Finance 32 (9): 1882–993.
I
s
identifes the funds (at share-class level) that
are managed by the same AMC that manages the
“shocked” fagship fund (excluding the fagship itself).
Systemic risk
Systemic risk is measured for the system of mutual
funds, banks, and insurance companies from advanced
economies.
• Mutual funds’ NAV and total net asset data are from
Lipper. For each of the three fund domicile areas
(the United States, Europe, and other advanced
economies) and the five asset classes (advanced
economy equities, advanced economy sovereign
bonds, advanced economy corporate bonds, emerg-
ing market equities, and emerging market bonds),
a sample consisting of the top 100 funds, measured
by total net assets, was selected, resulting in 1,500
funds. Data covering January 2000 to November
2014 were cleaned by dropping funds with fewer
than 10 observations and excluding observations
with weekly NAV returns of less than ?60 percent
or greater than 80 percent.
• For the banking and insurance sectors, weekly
returns are computed using Thomson Reuters equity
indices for European and U.S. banks and insurance
companies.
• The system’s return is computed as the average of
funds, banks, and insurance returns weighted by
their relative asset size. Data on total assets of banks,
insurance companies, and mutual funds are from
quarterly flow-of-funds data for the United States
and the euro area. An alternative measure using a
simple average was also used, yielding similar results.
Systemic risk is estimated following the static
CoVaR approach put forward by Adrian and Brun-
nermeier (2011), using quantile regressions. First, the
returns of the system are regressed on the returns of
each individual institution i when that institution has
the lowest 5th percentile returns:
R
t
System,i
= ?
i
+ ?
i
R
i
t
+ ?
it
. (3.11)
Ten, CoVaR is computed as the VaR of the system
conditional on institution i being in distress (defned as
when its return R
i
is below its 5 percent VaR, –VaR
i
5%
):
CoVaR
i
= ?ˆ
i
+ ?ˆ
i
R
i
= ?ˆ
i
? ?ˆ
i
VaR
i
5%
. (3.12)
Te contribution to systemic risk of an institution i is
computed as the diference between the CoVaR
i
when
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 133
Ben-Raphael, Azi, Shmuel Kande, and Avi Wohl. 2011. “Te
Price Pressure of Aggregate Mutual Fund Flows.” Journal of
Financial and Quantitative Analysis 46 (2): 585–603.
Bikhchandani, Sushil, David Hirshleifer, and Ivo Welch. 1992.
“A Teory of Fads, Fashion, Custom and Cultural Change
as Informational Cascades.” Journal of Political Economy 100:
992–1026.
BlackRock. 2014a. “ETFs Help Improve Market Stability.”
October.
———. 2014b. “Fund Structures and Systemic Risk Mitigants.”
Viewpoint, September.
Borensztein, Eduardo, and Gaston Gelos. 2003. “A Panic Prone
Pack? Te Behavior of Emerging Market Mutual Funds.” IMF
Staf Papers 50 (1): 43–63.
Broner, Fernando A., Gaston Gelos, and Carmen M. Reinhart.
2006. “When in Peril, Retrench: Testing the Portfolio Chan-
nel of Contagion.” Journal of International Economics 69 (1):
203–30.
Brown, Nerissa, Kelsey Wei, and Russ Wermers. 2013. “Analyst
Recommendations, Mutual Fund Herding, and Overreaction
in Stock Prices.” Management Science 60 (1).
Bufa, Andrea, Dimitri Vayanos, and Paul Woolley. 2014. “Asset
Management Contracts and Equilibrium Prices.” Working
Paper 20480, National Bureau of Economic Research, Cam-
bridge, Massachusetts.
Calvo, Guillermo A., and Enrique G. Mendoza, 2000. “Rational
Contagion and the Globalization of Securities Markets.”
Journal of International Economics 51 (1): 79–113.
Cao, Charles, Eric Chang, and Ying Wang. 2008. “An Empirical
Analysis of the Dynamic Relationship between Mutual Fund
Flow and Market Return Volatility.” Journal of Banking and
Finance 32 (10): 2111–23.
Center for European Policy Studies–European Capital Markets
Institute (CEPS-ECMI). 2012. “Rethinking Asset Manage-
ment from Financial Stability to Investor Protection and
Economic Growth.” Report of a CEPS-ECMI Task Force.
Cetorelli, Nicola. 2014. “Hybrid Intermediaries.” Federal Reserve
Bank of New York Staf Reports 705.
Chakravorti, Suji, and Subir Lall. 2003. “Managerial Incentives
and Financial Contagion.” Working Paper 2003–21, Federal
Reserve Bank of Chicago.
Chen, Qi, Itay Goldstein, and Wei Jiang. 2010. “Payof Comple-
mentarities and Financial Fragility: Evidence from Mutual
Fund Outfows.” Journal of Financial Economics 97 (2):
239–62.
Chevalier, Judith, and Glenn Ellison. 1997. “Risk Taking by
Mutual Funds as a Response to Incentives.” Journal of Politi-
cal Economy 106 (6): 1167–200.
Choi, Nicole, and Richard W. Sias. 2009. “Institutional Industry
Herding.” Journal of Financial Economics 94 (3): 469–91.
Chordia, Tarun. 1996. “Te Structure of Mutual Fund Charges.”
Journal of Financial Economics 41 (1): 3–39.
Collins, Sean, and Christopher Plantier. 2014. “Are Bond
Mutual Fund Flows Destabilizing? Examining the Evidence
from the ‘Taper Tantrum’.” Working Paper, Investment Com-
pany Institute, Washington.
Coval, Joshua, and Erik Staford. 2007. “Asset Fire Sales (and
Purchases) in Equity Markets.” Journal of Financial Economics
86 (2): 479–512.
Dasgupta, Amil, and Andrea Prat. 2006. “Financial Equilibrium
with Career Concerns.” Teoretical Economics 1 (1): 67–93.
Deli, Daniel, and Raji Varma. 2002. “Closed-end versus Open-
end: Te Choice of Organizational Form.” Journal of Corpo-
rate Finance 8 (1): 1–27.
Desender, Kurt. 2012. “Ownership Structure and Stock Price
Performance during Turbulent Financial Markets.” Unpub-
lished, Universidad Carlos III de Madrid.
Deutsche Bank. 2014. ETF Annual Review and Outlook. January
16.
Diamond, Douglas W., and Philip H. Dybvig. 1983. “Bank
Runs, Deposit Insurance, and Liquidity.” Journal of Political
Economy 91 (3): 401–19.
Dow, James, and Gary Gorton. 1997. “Noise Trading, Delegated
Portfolio Management, and Economic Welfare.” Journal of
Political Economy 105 (5): 1024–50.
Edelen, Roger. 1999. “Investor Flows and the Assessed Perfor-
mance of Open-end Mutual Funds.” Journal of Financial
Economics 53 (3): 439–66.
———, and Jerold Warner. 2001. “Aggregate Price Efects of
Institutional Trading: A Study of Mutual Fund Flow and
Market Returns.” Journal of Financial Economics 59:
195–220.
Elliott, Douglas. 2014. “Systemic Risk and the Asset Manage-
ment Industry.” Economic Studies at Brookings, Brookings
Institution, Washington.
European Fund and Asset Management Association (EFAMA).
2014. “Asset Management in Europe: Facts and Figures: 7th
Annual Review.” European Fund and Asset Management
Association, Brussels.
European Securities and Markets Authority (ESMA). 2012.
“Guidelines for Competent Authorities and UCITS Manage-
ment Companies.” ESMA/2012/832EN, Paris.
Ferreira, Miguel, Aneel Keswani, Antonio Miguel, and Sofa
Ramos. 2012. “Te Flow-Performance Relationship
around the World.” Journal of Banking and Finance 36 (6):
1759–80.
Feroli, Michael, Anil K. Kashyap, Kermit Schoenholtz, and
Hyun Song Shin. 2014. “Market Tantrums and Monetary
Policy.” Chicago Booth Research Paper 14–09, University of
Chicago Booth School of Business, Chicago.
Financial Industry Regulatory Authority (FINRA). 2011.
“Understanding Mutual Fund Classes.” Investor Alert, Finan-
cial Industry Regulatory Authority, Washington.
Financial Stability Board (FSB). 2011. “Potential Financial Sta-
bility Issues Arising from Recent Trends in Exchange-Traded
Funds.” Financial Stability Board, Basel.
———. 2013. “Strengthening Oversight and Regulation of
Shadow Banking: Policy Framework for Strengthening
GLOBAL FI NANCI AL STABI LI TY REPORT: NAVI GATI NG MONETARY POLI CY CHALLENGES AND MANAGI NG RI SKS
134 International Monetary Fund | April 2015
Oversight and Regulation of Shadow Banking Entities.” FSB
Policy Document, Financial Stability Board, Basel.
Financial Stability Board (FSB), International Monetary Fund
(IMF), and World Bank. 2011. “Financial Stability Issues in
Emerging Market and Developing Economies.” Report to
the G-20 Finance Ministers and Central Bank Governors,
Financial Stability Board, Basel.
Financial Stability Board (FSB) and International Organization
of Securities Commissions (IOSCO). 2014. “Assessment
Methodologies for Identifying Non-bank Non-insurer Global
Systemically Important Financial Institutions.” Consultation
Document, Financial Stability Board, Basel.
———. 2015. “Assessment Methodologies for Identifying Non-
bank Non-insurer Global Systemically Important Financial
Institutions.” Te Second Consultation Document, Financial
Stability Board, Basel.
Financial Stability Oversight Council (FSOC). 2014. “Notice Seek-
ing Comment on Asset Management Products and Activities.”
U.S. Department of the Treasury, Washington.http://www
.treasury.gov/initiatives/fsoc/rulemaking/Pages/open-notices.aspx.
Frazzini, Andrea, and Owen A. Lamont. 2008. “Dumb Money:
Mutual Fund Flows and the Cross-Section of Stock Returns.”
Journal of Financial Economics 88 (2): 299–322.
Froot, Kenneth A., Paul G. J. O’Connell, and Mark S. Sea-
sholes. 2001. “Te Portfolio Flows of International Investors.”
Journal of Financial Economics 59 (2): 151–93.
Gelos, Gaston. 2011. “International Mutual Funds, Capital Flow
Volatility, and Contagion: A Survey.” IMF Working Paper
11/92, International Monetary Fund, Washington.
Greenwood, Robin, and David Tesmar. 2011. “Stock Price
Fragility.” Journal of Financial Economics 102 (3): 471–90.
Grinblatt, Mark, Sheridan Titman, and Russ Wermers. 1995.
“Momentum Investment Strategies, Portfolio Performance,
and Herding: A Study of Mutual Fund Behavior.” American
Economic Review 85 (5): 1088–105.
Haldane, Andrew. 2014. “Te Age of Asset Management?”
Speech at the London Business School, April 4.
Hau, Harald, and Sandy Lai. 2010. “Te Role of Equity Funds
in the Financial Crisis Propagation.” Research Paper No.
11–35, Swiss Finance Institute, Geneva.
Igan, Deniz, and Marcelo Pinheiro. 2012. “Te Efects of Rela-
tive Performance Objectives on Financial Markets.” MPRA
Paper 43452, University Library of Munich.
Ilyina, Ana. 2006. “Portfolio Constraints and Contagion in
Emerging Markets.” IMF Staf Papers 53 (3): 351–74.
International Monetary Fund (IMF). 2012. “Macrofnancial
Stress Testing—Principles and Practices.” Washington.
———. 2014. “Staf Guidance Note on Macroprudential
Policy.” Washington.
International Organization of Securities Commissions (IOSCO).
2011. “Principles on Suspensions of Redemptions in Collec-
tive Investment Schemes.” Madrid.
Investment Company Institute (ICI). 2014a. “Te Closed-end
Fund Market, 2013.” ICI Research Perspective 20 (1).
———. 2014b. ICI Factbook 2014. Washington: Investment
Company Institute.
———. 2014c. “Understanding Exchange-Traded Funds: How
ETFs Work.” ICI Research Perspective 20 (5).
Jinjarak, Yothin, and Huanhuan Zheng. 2014. “Granular Institu-
tional Investors and Global Market Interdependence.” Journal
of International Money and Finance 46: 61–81.
Jotikasthira, Chotibhak, Christian Lundblad, and Tarun
Ramadorai. 2012. “Asset Fire Sales and Purchases and the
International Transmission of Funding Shocks.” Journal of
Finance 67 (6): 2015–50.
Lakonishok, Josef, Andrei Shleifer, and Robert Vishny. 1992.
“Te Impact of Institutional Trading on Stock Prices.” Journal
of Financial Economics 32 (1): 23–44.
Liang, Nellie. 2015. “Asset Management and Financial Stability.”
Presentation at the Brookings Institution, January 9.
Ma, Linlin, Yuehua Tang, and Juan-Pedro Gomez. 2013.
“Portfolio Manager Compensation in the U.S. Mutual Fund
Industry.”http://ssrn.com/abstract=2024027 orhttp://dx.doi
.org/10.2139/ssrn.2024027.
Manconi, Alberto, Massimo Massa, and Ayako Yasuda. 2012.
“Te Role of Institutional Investors in Propagating the
Crisis of 2007–08.” Journal of Financial Economics 104 (3):
491–518.
Massa, Massimo, and Rajdeep Patgiri. 2009. “Incentives and
Mutual Fund Performance: Higher Performance or Just
Higher Risk Taking?” Review of Financial Studies 22 (5):
1777–815.
Massa, Massimo, and Zahid Rehman. 2008. “Information Flows
within Financial Conglomerates: Evidence from the Banks-
Mutual Funds Relation.” Journal of Financial Economics 89
(2): 288–306.
Maug, Ernst, and Narayan Naik. 2011. “Herding and Delegated
Portfolio Management: Te Impact of Relative Performance
Evaluation on Asset Allocation.” Quarterly Journal of Finance
1 (2): 265–92.
McKinsey. 2013. Financial Globalization: Retreat or Reset?
McKinsey Global Institute.
Metrick, Andrew, and Ayako Yasuda. 2011. “Venture Capital
and Other Private Equity: A Survey.” European Financial
Management 17 (4): 619–54.
Moody’s. 2010. “Sponsor Support Key to Money Market
Funds.” August 9.
Morningstar. 2012. “Synthetic ETFs under the Microscope: A
Global Study.” Morningstar ETF Research.
Ofce of Financial Research (OFR). 2013. “Asset Management
and Financial Stability.” Ofce of Financial Research, U.S.
Department of Treasury, Washington.
Pensions and Investments and Towers Watson. 2014. “Te World’s
500 Largest Asset Managers.” Towers Watson, New York.
PriceWaterhouseCoopers. 2013. Asset Management 2020: A
Brave New World. London: PriceWaterhouseCoopers.
Raddatz, Claudio, and Sergio Schmukler. 2012. “On the
International Transmission of Shocks: Micro-Evidence from
CHAPTER 3 THE ASSET MANAGEMENT I NDUSTRY AND FI NANCI AL STABI LI TY
International Monetary Fund | April 2015 135
Mutual Fund Portfolios.” Journal of International Economics
88 (2): 357–74.
Rajan, Raghuram G. 2005. “Has Financial Development Made
the World Riskier?” Paper presented at Te Greenspan Era:
Lessons for the Future. Federal Reserve Bank of Kansas Sym-
posium, Jackson Hole, Wyoming.
Ross, Stephen. 2004. “Compensation, Incentives, and the Dual-
ity of Risk Aversion.” Journal of Finance 59 (1): 207–25.
Scharfstein, David, and Jeremy Stein. 1990. “Herd Behavior and
Investment.” American Economic Review 90 (3): 465–79.
Schmidt, Lawrence, Allan Timmermann, and Russ Wermers.
2013. “Runs on Money Market Mutual Funds.” Working
Paper, January.http://econweb.ucsd.edu/~lschmidt/Schmidt_
Timmermann_Wermers.pdf.
Securities Industry and Financial Markets Association (SIFMA).
2014. “SIFMA Asset Management Group’s Comments to the
FSB and SEC in Response to OFR Study and in Regards to
Separate Accounts.” Securities Industry and Financial Markets
Association, New York and Washington.
Sirri, Erik R., and Peter Tufano. 1998. “Costly Search and
Mutual Fund Flows.” Journal of Finance 53 (5): 1589–622.
Stein, Jeremy C. 2014. “Comments on ‘Market Tantrums and
Monetary Policy,’ a paper by Michael Feroli, Anil K. Kashyap,
Kermit Schoenholtz, and Hyun Song Shin.” Paper presented
at the U.S. Monetary Policy Forum, New York, New York,
February 28.
Stracca, Livio. 2006. “Delegated Portfolio Management: A Sur-
vey of the Teoretical Literature.” Journal of Economic Surveys
20 (5): 823–48.
TeCityUK. 2012. Private Equity 2012.
Vayanos, Dimitri. 2004. “Flight to Quality, Flight to Liquidity,
and the Pricing of Risk.” Working Paper 10327, National
Bureau of Economic Research, Cambridge, Massachusetts.
Warther, Vincent A. 1995. “Aggregate Mutual Fund Flows and
Security Returns.” Journal of Financial Economics 39 (2-3):
209–35.
Wermers, Russ. 1999. “Mutual Fund Herding and the Impact
on Stock Prices.” Journal of Finance 54 (2): 581–622.
Wurgler, Jefrey. 2010. “On the Economic Consequences of
Index-Linked Investing.” Working Paper 16376, National
Bureau of Economic Research, Cambridge, Massachusetts.
doc_129505392.pdf