draft01192011.dviSignaling, Agency Costs, and the Role of
Institutional Investors∗
Z. Jay Wang∗∗, Vikram Nanda
University of Illinois at Urbana-Champaign, Georgia Institute of
Technology
∗ We are especially grateful to James Curtis at the SEC for
numerous discussions on the institutional
details of the managed distribution policy. We thank Murillo
Campello, Martin Cherkes, Prachi Deuskar,
Fangjian Fu, Michael Weisbach, Lu Zhang, and seminar participants
at the 2008 Financial Intermediation
Research Society Conference, the 2008 China International
Conference in Finance, and the 2009 American
Finance Association Meetings for many helpful comments. We thank
Don Cassidy at Lipper Analytical
Services for providing data. We thank Marek Jochec and Phil Rosen
for excellent research assistance. We
acknowledge the support from National Natural Science Foundation of
China (Project 70803027). The
authors are responsible for all the errors.
∗∗Corresponding author. Fax: +1 217 244 2239
Email addresses:
[email protected] (Z. J. Wang),
[email protected] (V. Nanda)
Payout Policies and Closed-end Fund Discount:
Signaling, Agency Costs, and the Role of Institutional
Investors
Abstract
The adoption of a Managed Distribution Policy or Plan (MDP) by
closed-end funds appears
effective in dramatically reducing, even eliminating, fund
discounts. We investigate two possible
explanations: the signaling explanation proposed in the literature,
that the MDP serves as a posi-
tive signal of future fund performance, and an alternative
explanation based on agency costs. Our
results indicate that signaling is, at best, only part of the
explanation and that the evidence is
generally more consistent with the agency cost hypothesis. For
funds adopting aggressive pay-
out targets of 10% (median target) and above, discounts tend to
disappear, though there is no
discernible improvement in NAV performance. Consistent with the
agency cost hypothesis, it is
often pressure from institutions/large shareholders that leads to
the adoption of aggressive payout
policies. Moreover, aggressive MDPs are associated with a decrease
in fund size and managerial
fees. Suggestive of their activist role in MDP adoptions and/or
informed trading, institutions —
especially ones that are Value oriented — tend to build up their
holdings in a fund prior to the
adoption of an aggressive MDP, and liquidate their positions once
the price rises.
1 Introduction
The closed-end fund (CEF) discount remains one of the more
intriguing anomalies in the finance
literature. The anomaly refers to the fact that shares of
closed-end funds tend to trade at a discount
relative to their net asset value (NAV) — the magnitude of the
discount varying a good deal across
funds and over time.1In recent years, some CEFs, often under
pressure from outside shareholders,
have adopted Managed Distribution Policies or Plans (MDPs) to
reduce fund discounts. Under
MDPs, fund management prescribes a minimum payout target in any
given fiscal year, regardless of
the realized performance of the underlying asset portfolio.2
Typically, the policy involves quarterly
or monthly distribution to common shareholders, either as a fixed
percentage of average NAV or
as a fixed dollar amount. For MDP funds in our sample, the payout
target ranges from a low
of 5% to a high of 20%, with a median around 10% in most years. The
payout target is met
through investment income and realized short-term/long-term capital
gains, with any shortfall
being covered by a distribution of fund capital. In our analysis,
consistent with industry norms3
and with previous research (Johnson et al. 2006), we use the 10%
payout target as the cutoff for
moderate versus aggressive payout policies.
The MDPs appear to have had remarkable success in reducing, if not
eliminating, fund discounts
(see Wang 2004 and Johnson et al. 2006). In our sample, for
instance, MDP funds exhibit an average
(monthly) median discount of only 0.86% over the 1990 to 2006
period, with the more aggressive
MDP funds (payout targets ≥ 10%) trading at an average premium of
2.32%. In striking contrast,
funds without the MDP (hereafter, non-MDP Funds), have an average
(monthly) median discount
of 10.19% over the same period. In this paper we seek to understand
the popularity of MDPs and
their apparent success in reducing fund discounts. We consider two
primary hypotheses — signaling
and agency costs — and explore their empirical implications.
The signaling hypothesis advanced in the literature (Johnson et al.
2006) is that a MDP can
serve as a costly signal of the fund’s future NAV performance. The
claim, based on somewhat
informal arguments, is that MDP funds that perform poorly may be
obliged to return capital to
1As with open-end funds, CEFs typically invest in publicly traded
securities and manage their holdings for income
and capital appreciation. Unlike open-end funds, which offer and
sell their shares continuously, CEFs raise capital by
selling publicly traded shares in an IPO and subsequent equity
offerings. Open-end funds provide liquidity to their
shareholders by redeeming shares at a price based on NAV.
Shareholders of CEFs, on the other hand, can sell their
shares on the secondary market at prices that may vary
significantly from NAV. Lee et al. (1991) provide a detailed
description of the characteristics of CEF discounts. 2While funds
can always terminate the payout policy, they hesitate to do so
given the likely negative shareholder
reaction that would ensue (see, for instance, footnote 5). It is
reasonable to assume, therefore, that investors would
perceive MDPs as a commitment they expect fund management to honor.
3The generally accepted view in the industry is that to be
effective (in terms of reducing discount), MDPs should
involve the distribution of at least 10% of average net asset value
on an annual basis. See The Investor’s Guide to
Closed-End Funds, May 2007 (pg. 27), published by Thomas J.
Herzfeld Advisors Inc. The issue also describes the
history of the MDP and provides a current list of MDP funds.
1
investors. Hence, only funds sufficiently confident of future
performance would commit to such a
target policy. A key implication of the signaling hypothesis is
that MDP adoption signals strong
future NAV performance and will, therefore, tend to boost share
prices and reduce discounts. We
would also expect, as is suggested by most formal signaling models,
that a stronger signal in the
form of a more aggressive MDP will presage an even stronger
performance and induce a larger price
boost. A caveat in our approach to testing the signaling hypothesis
should be pointed out: we take
the arguments made in the literature at their face value and assume
that a signaling equilibrium in
MDPs can exist. However, as we briefly discuss, the theoretical
basis for such a signaling equilibrium
is weak. The reason is that better performing funds face greater
costs from adopting MDPs and
may have little incentive to signal in this fashion. Hence, any
evidence supportive of MDP signaling
needs to be interpreted with some caution.
An alternative approach to understanding MDPs is based on the
agency cost hypothesis. In
developing the hypothesis, we recognize the somewhat different ways
in which agency problems
may be manifested and impact fund discounts and performance. For
instance, agency problems
between managers and investors could lead some funds to become
large relative to managerial
investment abilities/opportunities. In these cases, inducing funds
to shrink their size (or moderate
their growth) benefits investors,4 with the adoption of an
aggressive payout policy leading to an
improvement in both fund performance and discount. However, MDPs
may not necessarily improve
fund performance when agency problems are present. The reason is
that fund discounts may, for
instance, reflect the rent extracted by fund managers in excess of
value-added (Berk and Stanton
2007; Cherkes, Sagi and Stanton 2009). The adoption of an MDP can
induce a wealth transfer from
fund managers to shareholders (Cherkes, Sagi and Wang 2009). In
this case, fund discounts will
be lower due to reduction of the managerial claim on fund assets —
though there may not be any
discernible improvement in the fund’s NAV performance per se. An
essential element of the agency
cost hypothesis is that managers have little incentive to reduce
the assets under their control since
their fees increase with fund size. Hence, unlike the signaling
hypothesis, the adoption of an MDP,
especially one with a high payout level, is expected to be
involuntary, associated with intervention
by shareholders (actual or anticipated).
The two hypotheses give rise to a number of empirical predictions
that we test. We begin by
testing predictions of the signaling hypothesis and examine whether
fund discounts and performance
are consistent with a signaling explanation for MDPs. We then
proceed with tests of the agency
cost hypothesis. Specifically, we examine the impact of MDP
adoption on fund size and managerial
compensation. This is followed by an investigation of the role of
outside (institutional) shareholders
in inducing MDP adoptions — and the shareholding and trading by
institutional shareholders around
4The notion that performance may decline with fund size has been
made by several papers in the context of
open-end funds (e.g., Berk and Green, 2004).
2
these adoptions.
Our tests of the signaling hypothesis are based on the predicted
relation between the level of
MDP and the improvement in discount and performance of MDP funds.
Both the signaling and
the agency hypotheses predict that funds adopting a more aggressive
payout polices will experience
a larger improvement in discount, though the underlying mechanisms
are quite different. The sig-
naling hypothesis predicts that more aggressive MDP funds will also
exhibit a greater improvement
in NAV performance; while the agency cost hypothesis does not make
such a clear prediction.
Our finding is that, consistent with Johnson et al. (2006), MDP
funds exhibit smaller discounts
and stronger NAV performance on average, compared to similar
non-MDP funds. Within the
group of MDP funds, however, the performance is stronger only for
funds with moderate payout
targets (below 10%), contrary to the predictions of signaling
model. Moreover, the improvement
in discount is mainly evident among the more aggressive MDP funds.
Similar results are found
when we use matched sample analysis to examine the changes in NAV
performance and discounts
around the adoption of MDPs.
As an alternative to the matched sample approach, we use a panel
specification with style
and time fixed-effects and several additional variables such as
fund turnovers and expenses. The
results confirm that the adoption of an aggressive MDP is
particularly effective in reducing a fund’s
discount. However, there is no reliable evidence that performance
improves for the funds adopting
MDPs (whether moderate or aggressive payout). These results cast
doubt on the robustness of
earlier findings on performance changes and suggest that they may
be sensitive to the introduction
of additional explanatory variables. Overall, these results provide
little support for the signaling
hypothesis.
We next investigate whether the adoption of MDPs, especially ones
with aggressive payout,
reduces fund size and managerial compensation (typically based on
NAV). The signaling hypothesis
does not, in general, predict a decrease in fund size. The reason
is that funds that adopt MDPs
should, on average, be those that expect a strong NAV performance.
The agency hypothesis, on
the other hand, is more consistent with a reduction in fund size
and managerial compensation. Our
results indicate that MDPs, especially when accompanied by an
aggressive payout target, can have
a significantly negative impact on fund size and, in turn, on
managerial compensation.
Finally, we study the role of institutional shareholders in
adopting MDPs and the nature of
shareholding around the adoption. The signaling hypothesis is based
on the idea of funds voluntarily
adopting certain payout policies as a way to distinguish themselves
from other funds. Yet, it appears
that in many cases shareholder activists force funds to adopt MDPs,
which is more consistent
3
with the agency hypothesis.5 Our analysis of the 13-D filings with
the SEC at the time of MDP
adoptions supports the notion that outside shareholders play an
active role – especially in the cases
in which a more aggressive payout policy is adopted. We discuss a
specific case of intervention by
an institution to illustrate the potential profitability of such
activism. To investigate aggregate
patterns, we use an event study approach and find that
institutional investors, as a whole, tend to
hold significantly more shares of aggressive-MDP funds, relative to
comparable non-MDP funds,
in the quarters prior to policy implementation. Following policy
implementation, institutional
investors reduce their (aggregate) holdings as the discount is
eliminated or turns into a premium.
Further analysis reveals that institutions actively involved in
trading around the MDP adoptions
are mainly value-style investors.
Our research is related to two strands of the literature. First,
our paper contributes to the exist-
ing research on closed-end discounts.6 The previous literature has
focused on various explanations:
tax liabilities (Malkiel 1977), illiquidity of asset portfolios
(Malkiel 1977, Lee et al. 1990, Cherkes,
Sagi and Stanton 2009), signaling and agency costs (Malkiel 1977,
Thompson 1978, Lee et al. 1990
and 1991, Barclay et al. 1993, Pontiff 1995, Chay and Trzcinka
1999, Dimson and Minio-Kozerski
1999b, Ross 2002, Johnson et al. 2006, Berk and Stanton 2007,
Cherkes, Sagi and Stanton 2009),
and costly arbitrage (De Long et al. 1990, Pontiff 1996 and 1997,
Dimson and Minio-Kozerski
1999b, Spiegel 1999, Gemmill and Thomas 2002). The tax liabilities,
asset illiquidity, and agency
costs provide rational explanations for the existence of CEF
discount, while the costly arbitrage
approach is built on mispricing due to either behavioral factors or
fundamental shocks (see Spiegel
1999 for a model incorporating random shocks on corporate
productivity).
The paper closest to ours is Johnson et al. (2006) that proposes a
signaling explanation for the
impact of MDPs on fund discounts. In addition to signaling, we
propose and investigate an agency
cost explanation for the adoption and impact of MDPs. The argument
that fund discounts might
be driven by agency costs and managerial fees has been made in
papers such as Ross (2002), Berk
and Stanton (2007), and Cherkes, Sagi and Stanton (2009). Our paper
shows that the empirical
evidence is more consistent with the agency cost hypothesis than
with signaling.
Second, our paper is related to the extensive literature on the
role of activist shareholders,
specifically institutional shareholders in affecting corporate
policies. Several authors have argued
that the involvement of large shareholders in monitoring or control
activities has the potential
5Such shareholder pressure was evident, for instance, in the case
of Zweig Total Return Fund (ZTR) where the
board discontinued the 10% MDP. Shareholders disagreed, and the
issue came to a head when a dissident shareholder
group issued its own proxy statement for the ZTR 2004 annual
meeting, proposing insurgent nominees for director
posts and adding a proposal for reinstituting the 10% annual
payout. The dissidents were unsuccessful both in
capturing board seats and in gaining enough votes to pass their
payout policy proposal. However, the ZTR board
subsequently reinstated the 10% MDP. 6See Dimson and Minio-Kozerski
(1999a) for an extensive literature review.
4
to limit agency problems (e.g., Shleifer and Vishny 1986, Huddart
1993, Maug 1998, etc.). The
empirical findings on institutional activism are mixed in terms of
its importance for firm governance
and are well summarized in survey papers such as Gillan and Starks
(1998) and Karpoff (2001). In
the closed-end literature, Bradley et al. (2008) focus on the role
of shareholder activists in open
ending CEFs. Our paper highlights the role of activist
institutional investors in implementing fund
payout policies and in reducing or eliminating fund discounts —
while making sizable profits.
The rest of the paper is organized as follows. In Section 2, we
develop alternative hypotheses
and empirical predictions. Section 3 describes the data sources and
summary statistics. We present
the empirical results in Sections 4 and 5, and conclude in Section
6.
2 Empirical Predictions
In the introduction we outlined the signaling and agency cost
hypotheses that we explore to un-
derstand the adoption and the impact of MDPs. We discuss below the
main empirical predictions
of the hypotheses that can be used to test them.
The signaling hypothesis proposed in the literature (Johnson et al.
2006) relies on the informal
notion that a commitment to pay higher dividends can be a signal of
the fund manager’s ability.
The argument is that higher ability managers are in a better
position to commit to a high divi-
dend payout since they expect to generate greater returns. Hence,
funds adopting MDPs would
be expected to experience a reduction in discount and better NAV
performance. In developing
the testable predictions of the signaling hypothesis, we follow the
hypothesis as proposed in the
literature. However, it needs to be pointed out that the basis for
the signaling argument is actually
quite tenuous since MDPs are expected to impose a greater loss of
fees on higher ability managers.
Managerial fees are usually based on NAV and higher ability fund
managers face a greater loss of
fees because: (i) MDPs cause a greater dollar reduction in fees
since their funds are expected to
grow faster; and (ii) depriving higher ability managers of funds is
more costly in terms of future
NAV.7
In developing the agency cost hypothesis and its empirical
implications, we recognize that, de-
7The authors have sought to develop a signaling model in which MDP
can serve as a signal of managerial ability.
In the model the manager benefits from an improvement in the fund’s
discount. The benefits of signaling may arise
because, for instance, a decrease in discount make it less likely
that the fund is opened or the manager is replaced
and may be similar for higher and lower ability managers. On the
other hand, the signaling cost the manager faces is
a reduction in fees, stemming from a reduction in the fund’s NAV
over time. These costs are larger for higher ability
managers for the reasons mentioned and, in general, a signaling
equilibrium with MDPs will not exist. There are,
however, some extreme assumptions under which a signaling
equilibrium might exist: for instance when the higher
ability managers, in addition to expecting to generate higher
returns, also face greater diseconomies of scale. The
reason the diseconomies of scale come into play is because a
reduction in asset size is then less costly on the margin
for higher ability managers.
5
pending on the nature of the agency problem and relation between
fund size and performance, there
may be somewhat different ways in which MDPs could impact fund
performance and discounts.
Managers, with fees linked to NAV may, in general, have an
incentive to increase the size of their
fund beyond what can be profitably managed and is value maximizing
for shareholders. Hence, it is
possible that the adoption of MDPs, especially when accompanied
with a high payout target, could
result in reducing fund size (or at least moderate its rate of
growth) — leading to an improvement in
both fund performance and discounts. In somewhat different versions
of the agency problem, fund
managers are compensated more than what they deserve, but reducing
fund size may not neces-
sarily lead to a significant improvement in NAV performance. Along
these lines, formal models of
fund discounts, such as in Berk and Stanton (2007) and Cherkes,
Sagi and Stanton (2009), make
the argument that the source of discount is the present value of
rent extracted by fund managers
in excess of value-added. In this case, it is shown by Cherkes,
Sagi and Wang (2009) that the
adoption of MDPs can induce a direct wealth transfer from managers
to shareholders. By reducing
the present value of managerial claim on future fund assets, there
is a reduction in fund discount
— though there may not be any discernible improvement in the fund’s
NAV performance.
Our first set of predictions concern the relation between the level
of payout promised by MDP
funds and their NAV performance and discounts. Both hypotheses
would predict that, in general,
more aggressive payout policies will be associated with lower
discounts. However, the hypotheses
differ in terms of the relation between the level of payout
promised by MDP funds and their
NAV performance. According to the signaling hypothesis, fund
managers adopting more aggressive
payout polices are sending a stronger and potentially more costly
signal. Hence, a more aggressive
payout should be associated with a stronger performance and a
greater reduction in fund discount
— if the MDPs are, indeed, serving a signaling function. The agency
cost hypothesis, on the other
hand, does not have clear empirical implications with regard to
future NAV performance. We can
therefore state:
Prediction 1 MDP funds’ performance and discount:
• Both the signaling and agency cost hypotheses predict that,
ceteris paribus, more aggressive MDPs will be associated with lower
discounts.
• The signaling hypothesis predicts that more aggressive MDPs will
be associated with stronger NAV performance.
• The agency cost hypothesis does not necessarily predict that more
aggressive MDP funds will be associated with stronger NAV
performance.
We next turn to the issue of whether the adoption of MDPs,
especially aggressive MDPs, will
6
tend to reduce fund size and, in turn, decrease fund manager
compensation. We take the position
that the signaling hypothesis should not predict a decrease in fund
size. The reason is that funds
that adopt MDPs should, in general, be those expected to have a
stronger performance. We are
basing this prediction on the general notion that we would not
expect signaling to impose larger
costs on the funds that choose to signal. The agency hypothesis, on
the other hand, is more
consistent with fund size deceasing, especially with aggressive
MDPs. Hence, we can state:
Prediction 2 MDP adoption and fund size:
The agency hypothesis is consistent with a decline in fund assets,
while the signaling hypothesis is
not.
A third set of predictions concern the role of outside
shareholders, specifically institutions, in
the adoption of MDPs. The signaling hypothesis asserts that the MDP
decision is made by fund
managers to signal their ability and, hence, is inconsistent with
MDPs being adopted under pressure
from outside investors. The signaling hypothesis also has no clear
predictions in terms of changes
in share ownership around the adoption of MDPs. The agency
hypothesis, on the other hand, is
consistent with large shareholders/institutions forcing funds to
adopt MDPs. The opposition from
managers and, hence, the need for pressure from outside investors
is more likely to be observed
when the adopted MDPs are more aggressive.
To influence the adoption of MDPs, especially aggressive MDPs, we
would expect large share-
holders/institutions to build up their holdings in an effort to
exert pressure. Institutions that buy
shares of non-MDP funds at a discount — and then successfully lobby
for the adoption of MDPs — are
likely to make significant profits in the process. Since the
adoption of an MDP tends to increase the
fund’s market value, it is quite possible that some institutional
trades may be information driven.
In our analysis, we also seek to identify and discuss specific
cases of intervention by institutions.
Hence, we can state:
Prediction 3 MDP adoption and shareholder activists:
• The agency hypothesis is consistent with pressure from
shareholder activists to adopt MDPs.
Shareholder activists are more likely to be involved for aggressive
MDPs. The signaling hy-
pothesis is not consistent with such outside pressure.
• The agency hypothesis predicts a build-up of institutional
shareholdings leading up to MDP adoption. Institutional
shareholders are expected to make significant profits by
purchasing
non-MDP fund shares at discount — and then forcing MDP
adoption.
We would like to note that the predictions of the agency cost
hypothesis are not necessarily
7
incompatible with noise trader models, such as the one proposed in
De Long et al. (1990) (see
Wang, 2004).8 In this paper we have confined our hypotheses to
those based on rational investors.
We believe this is appropriate since the notion of sentiment risk
and tests of its empirical validity
are not well settled.
3.1 Data
The data sample used in the analysis consists of 236 closed-end
equity funds that have at least
one year of performance and discount data available over the 1990
to 2006 period. We include
all funds with the following Wall Street Journal classifications:
“General Equity Funds”, “Con-
vertible Funds”, “Preferred Equity Funds”, “Special Equity Funds”
and “World Equity Funds”.
The monthly discount9 data is from Lipper and from ETF Connect.10
The CRSP Stock Database
provides the monthly share prices, distribution amount, and number
of shares outstanding. We
then use discount and share price data to infer NAV for each fund
on a monthly basis.
We carefully review fund annual reports and proxy statements to
identify MDP funds. A total
of 88 funds in our sample had managed distribution policies in
effect for at least part of the time
over the 1990 - 2006 period. For each MDP fund, we identify the
payout target as well as the start
and end dates for the policy. The payout target is usually
expressed as a percentage of average
NAV on an annual basis or else as a flat dollar amount of payout.
For ease of comparison, we
convert flat dollar payouts into percentage terms by normalizing
the annualized dollar commitment
by average NAV over the pertinent period. We identify the start and
end dates for an MDP as the
months in which the Board of Directors publicly adopts or
terminates the policy.
To examine the role of institutional investors in MDP adoptions, we
obtain the quarterly insti-
tutional holding data for all CEFs in our sample from Thomson
Financial Institutional 13(f). We
notice that the reported institutional holdings for some CEFs in
our sample are incomplete. We
8In noise trader models, the discount reflects the fact that
investors have relatively short horizons and are exposed
to the effect of shifts in noise trader sentiment on the value of
CEF shares when they trade. As is shown in Wang
(2004), a commitment to an aggressive payout policy is expected to
reduce the discount in such a model. This is
because an aggressive dividend policy is expected to gradually
reduce the size of the fund’s NAV and, thereby, the
exposure to future sentiment shocks. However, whatever the source
of the discount, the decision to commit to a
MDP is not one that fund managers would be expected to choose
without external pressures, given the significantly
negative impact on their compensation. 9The discount/premium of a
fund’s share price relative to NAV is defined in percentage terms
as (Share Price -
NAV)/(NAV)*100. For ease of exposition, we follow the literature
and sign the discount or premium (negative for
discount / positive for premium) only when there is the possibility
of confusion, as in the tables. 10ETF Connect is a website
(www.etfconnect.com) maintained by Nuveen Investments that provides
comprehensive
information (e.g., share price and NAV history, premium/discount
history, performance history, distribution history,
etc.) for closed-end and index ETF funds.
8
supplement the missing data to the best we can based on the funds’
13D/G filings with the SEC. In-
stitutions are further classified into groups based on their legal
types and investment styles.11 There
are altogether five legal types: Bank Trust (type=1), Insurance
Company (type=2), Investment
Company (type=3), Independent Investment Advisor (type=4), and
Pension Funds, University
and Foundation Endowments, and others (type=5). For empirical
analysis, we lump types 3 and 4
together due to lack of precise distinctions.
Regarding investment styles, we distinguish institutions based on
investment horizons and in-
vestment types. As in Bushee (2001) and Bushee and Noe (2000),
institutions are classified into
three horizon types: Transient (TRA), Dedicated (DED), and
Quasi-indexer (QIX). Transient in-
vestors have a short investment horizon and high portfolio
turnovers, while the other types represent
long term investors. Dedicated investors typically hold a
concentrated portfolio characterized by
large positions on a small set of firms. Quasi-indexers tend to
invest in a highly diversified portfolio
with low turnovers. According to Abarbanell, Bushee, and Raedy
(2003), institutions are classified
into four style types: Large Value (LVA), Large Growth (LGR), Small
Value (SVA), and Small
Growth (SGR).
3.2 Managed Distribution Policies
The history of the MDP dates back to the 1970s. In our sample,
Source Capital first implemented
the policy in 1976. Table 1 documents the substantial increase in
the number and proportion
of CEFs with MDPs over our sample period. By the end of 2006, the
number of MDP funds
increased from 12 in 1990 to 75, almost 40% of all existing equity
funds at the time. The median
payout target, which was 10% from 1990 to 2002, dropped to 9% in
2003, and further decreased
to less than 8% in 2004.12 Table 1 also reports the number of MDP
funds that were “aggressive”
(payout targets ≥ 10%) and less aggressive or “moderate” (payout
targets 10%). The number
of aggressive payout funds increased from 8 in 1990 to 16 in 2006,
while the number of moderate
payout funds expanded from 4 to 59 over the same period.13
The impact of MDPs on fund discount has been well documented in
Wang (2004) and Johnson
et al. (2006). Here we provide further support for these findings
by expanding the sample period to
the end of 2006 and, thereby, including a much larger sample of MDP
funds. Moreover, we present
11All data on type classification is from Brian Bushee’s website:
http://acct3.wharton.upenn.edu/faculty/bushee/. 12A total of 58
closed-end funds in our sample implemented MDPs in the post-2001
period, with a median payout
target at 7.60%. Eight of them committed to a payout target of 10%
or larger. 13As noted earlier, while MDPs have a commitment aspect
(see footnote 5), fund boards have the discretion to
revoke the policy. In our sample we identified only five funds that
stopped the payout policy during the sample period
and continued to operate independently as closed-end funds. There
were another three funds that terminated the
payout policy but reinstated it later due to shareholder
pressure.There are eight cases of the MDP funds leaving the
sample at some point on account of liquidation, change of fund
structure (e.g., open-ending), or merger with other
funds.
9
new evidence that highlights the critical role of the MDP payout
level on fund discount. Consistent
with both hypotheses (Prediction 1), we find that higher payout
levels are associated with lower
discounts.
In Figure 1, we plot the month-end median discount level separately
for aggressive-MDP funds,
moderate-MDP funds, and non-MDP funds. For non-MDP funds, the
average month-end median
discount from January 1990 to December 2006 is 10.19%. In contrast,
the average discount for all
MDP funds is only 0.86%. The difference is statistically
significant at the 1% level. The difference is
especially striking for aggressive-MDP funds that traded at an
average premium of 2.32% over the
sample period. The difference is more modest for moderate-MDP funds
that exhibited an average
discount of 6.70% over the same period.14
In Table 2, we analyze the source of annual distribution for a
subset of CEFs in our sample:
General Equity Funds, Convertible Funds, and Preferred Equity
Funds. We collect payout infor-
mation from the annual reports filed with the SEC. For each fiscal
year from 1995 to 2006, we
report the median statistics for each of the three components of
annual distribution: investment
income, realized capital gains (sum of realized short-term and
long-term capital gains), and return
of capital. We also compute and report the median statistics for
two yield measures: Distribution
Yield and True Yield. Distribution Yield is defined as the total
annual distribution (including the
return of capital) per share normalized by the end-of-year share
price. True Yield is defined as
the total annual distribution per share net of the return of
capital, normalized by the end-of-year
share price. Hence, True Yield captures the part of payout that is
funded by the return on assets
generated by fund management.
As shown in Panel A of Table 2, aggressive-MDP funds finance their
annual distribution mainly
through realized capital gains and return of capital. Over the 2001
to 2006 period, return of
capital accounted for a significant portion of total annual
distribution, as the stock market crash
in 2001 and other events caused a steep loss of portfolio values.
In particular, in 2002 and 2003,
virtually all distributions (more than 95%) came from return of
capital. Correspondingly, the
median Distribution Yield was 13.52% (2002) and 8.49% (2003), while
the median True Yield was
only 0.45% in these years. For moderate-MDP and non-MDP funds,
Panels B and C show that
they relied mainly on investment income to fund annual
distributions and that return of capital
14We also investigate the cases in which CEFs adopt MDPs at fund
inception. Among the 214 CEFs that introduced
funds from 1985 to 2005, 43 adopted MDPs at inception. More than
85% (37 out of 43) of the inception-MDP funds
adopted moderate payout targets ( 10%). To investigate the pattern
of discount during the first three years of fund
life, we identify for each inception-MDP fund a matched non-MDP
fund with the same investment objective and
inception year. The median series for the paired discount
difference suggest that the inception-MDP funds typically
trade at a slightly smaller discount when compared to the matched
non-MDP funds for most of the post-inception
period. Given that the inception-MDP group is dominated by the
adoption of moderate payout targets, the evidence
is consistent with the finding that moderate MDPs have some but
limited effects in reducing fund discounts. The
above results are available upon request.
10
was rare.
We also examined the relation between dividend payout policies and
the use of leverage by
CEFs. Under the Investment Company Act of 1940, levered CEFs must
maintain a minimum
2:1 asset to debt ratio. Dropping below the minimum coverage ratio
will automatically trigger a
SEC review and force funds to de-lever, possibly by liquidating
assets and retiring some outstanding
debt. Hence, at least in principle, the combination of an
aggressive payout policy and the minimum
coverage ratio requirement could signal fund management’s
confidence about higher expected cash
flows. Our analysis indicates, however, that the extent of leverage
employed by aggressive-MDP
funds is generally quite small — significantly lower than that by
non-MDP and moderate-MDP
funds.15 Moreover, leverage in CEFs refers primarily to their use
of preferred stock (reported as
leverage by CEFs).
3.3 Institutional Holdings
We investigate the institutional investor base for CEFs in general
and examine whether different
payout groups attract different types of institutional investors.
We first examine whether the
clienteles of CEFs vary with payout policies. In Panel A of Table
3, we report the total institutional
holdings (as a percentage of shares outstanding), the average
holdings per institution, and the
number of institutional investors for non-MDP group, aggressive-MDP
group, and moderate-MDP
group respectively. For each quarter from 1990 to 2006, we first
take the median holdings and then
average over time for each payout group. We report the average of
the median series in the table
and conduct paired t-tests. Figure 2 plots the median series for
total institutional holdings. The
non-MDP group has the largest total institutional holdings (8.86%),
followed by the moderate-
MDP group (5.56%). The aggressive-MDP group, on the other hand, has
the lowest institutional
holdings (1.10%). The paired t-tests for between-group differences
are all statistically significant at
the 1% level. The average holdings per institution appears to be
quite small (less than 1%) for all
three groups, suggesting that institutions typically do not hold
large blocks of shares in CEFs.16
Panel B of Table 3 presents the median difference in institutional
holdings between MDP funds and
funds that were terminated during our sample period. This will be
discussed in details in Section
5.2 where we examine the role of shareholder activism in MDP
adoptions.
15This weakens to an extent the case for signaling as the main
motivation behind the adoption of aggressive MDPs.
We collect the total liabilities and source of financing from the
annual reports and NSAR filings with the SEC. The
detailed statistics on leverage ratios across different payout
groups are available upon request. 16We also separately investigate
the pattern of institutional ownership during the first three years
of fund life for
the inception-MDP funds (see footnote 14). Both the inception-MDP
group and the matched non-MDP group exhibit
low institutional ownership immediately following inception. This
is consistent with the observation that CEFs are
initially “sold” to retail investors. The institutional ownership
increases over time. The trends diverge in the third
year with the non-MDP matched funds attracting a larger presence of
institutional ownership, consistent with the
overall findings. These results are available upon request.
11
The evidence suggests a strong clientele difference between payout
groups. The institutional
investor base for MDP funds is much smaller than that for the
non-MDP funds. Most strikingly,
aggressive-MDP funds are simply dominated by retail investors, with
a minimal presence of insti-
tutions. Why do institutional investors, especially those like
dividend paying stocks, shed away
from aggressive-MDP funds? We believe the answer lies in the
composition of distribution made
by these aggressive-MDP funds. One motivation for holding dividend
paying stocks is to receive a
steady income stream, especially during market downturns. However,
as shown in Panel A of Table
2, the main source of distribution for aggressive-MDP funds during
the 2002-2003 recession was the
return of capital. After taking out the return of capital from the
total distribution, the true yields
were actually minimal — less than 1%. Hence, the aggressive-MDP
funds were simply returning the
capital base back to the investors. This could be unattractive to
institutional investors since they
would have to worry about reinvesting their capital in a very low
interest rate environment.
A second explanation is based on the notion that it is not the
payout policy per se, but rather the
discount that attracts some institutional holding. For instance,
institutional investors, particularly
those that are value-oriented, may regard discounted CEFs as a
worthwhile investment. This may
be because they expect the funds to be forced to open or to adopt
payout policies (a process that
they might actively participate in) that would diminish the
discount. Institutional investors may
also believe that the discounts are, in part, driven by negative
sentiment among retail investors and
which the institutions recognize and hope to benefit from by
holding the discounted stock till the
investor sentiment become more optimistic.
Table 4 presents the statistics for total institutional holdings by
types for non-MDP funds,
aggressive-MDP funds, and moderate-MDP funds. The institutional
holdings by Thomson legal
types are provided in Panel A. For all three payout groups, the
Investment Company and Inde-
pendent Investment Advisor (Type-3 and -4) are the largest
institution type. Consistent with the
pattern observed in Table 3, the non-MDP group has the largest
presence of Type-3 and -4 investors
(4.46%), followed by the moderate-MDP group (3.65%) and the
aggressive-MDP group (0.62%).
For other legal types, the median holdings are typically low across
all payout groups — less than 1%.
Panel B presents the distribution of institutional holdings by
investment horizon. As indicated,
for all three payout groups, Quasi-indexer is the largest type,
followed by the Transient-type. In
contrast, median holdings by the Dedicated-type are minimal.
Panels C and D of Table 4 presents the institutional holdings by
investment style. Based on
Abarbanell, Bushee, and Raedy (2003), an institution is classified
into Large (Small) style if it
has the classification of LVA or LGR (SVA or SGR). Similarly, we
define an institution as Value
(Growth) if it has the classification of LVA or SVA (LGR or SGR).
As shown in Panel C, Large- and
Small-style investors are roughly evenly distributed across all
payout groups. We observe in Panel
12
D that the Value-style institutions have significantly larger
median holdings than the Growth-style
institutions for both the non-MDP group and the aggressive-MDP
groups. The average holdings
across different types of institutions, as discussed above,
obscures differences in terms of the trading
by the different types of institutions. In a later section, we show
that there are substantial differences
in terms of trading by different types of institutions around MDP
adoption.
4 Signaling
In this section, we test the signaling hypothesis (Prediction 1) in
alternative ways. Section 4.1
analyzes the discount and NAV performance of MDP funds using
non-MDP funds as a control
group. In Section 4.2 we examine the changes in discount and NAV
performance before and after
MDP adoption using a difference-in-difference approach, while
Section 4.3 uses a panel regression
approach and provides additional robustness tests.
4.1 Performance and Discount Relative to Peer Funds
We now investigate whether MDP funds, especially aggressive-MDP
funds, are also associated with
a significantly better performance — as suggested by the signaling
hypothesis (Prediction 1). We
begin our analysis by comparing the four-factor adjusted NAV return
of MDP funds to that of peer
funds. For each MDP fund, we obtain the four-factor alpha by
regressing monthly NAV returns
on the Fama and French (1993) three factors and the Carhart (1997)
momentum factor during the
period in which the MDP is in effect. We identify peer funds as
those that have the same Lipper
investment objective but do not adopt an MDP over the sample
period. The four-factor adjusted
NAV alphas for the peer funds are then estimated over the same time
period as for the MDP
fund. We then compare the risk-adjusted performance of the MDP fund
relative to the median
performance of its peer funds. We require funds to have at least 24
months of NAV return data
available to be included in the analysis. This reduces the number
of MDP funds to 51. The results
are reported in Table 5.17
Panel A of Table 5 compares the four-factor alpha and discount for
all MDP funds to peer funds.
The median MDP fund outperforms its peer by 0.23% per month and the
difference is statistically
significant at the 1% level. In terms of discount, the median
monthly discount for MDP funds is
5.15%, compared to 12.32% for the peer funds. The median difference
in discount is 6.73% and
is statistically significant at the 1% level. These findings are
largely consistent with the evidence
reported in Johnson et al. (2006) and interpreted as supportive of
a signaling explanation. However,
17As a robustness check, we conduct the same tests for the
sub-period from 1993 to 2001 (the data period used in
Johnson et al. 2006) and for the sub-sample of General Equity
Funds. The results obtained are similar.
13
these explanations are less persuasive when the evidence is
analyzed more closely.
In Panels B and C, we examine whether relative performance and
discounts differ across MDP
funds with different payout targets. If signaling is the main
factor driving the results in Panel A,
then the effect of MDP on performance and discount should be more
pronounced for funds with
more aggressive payout targets. However, the results in Panel B
show that for MDP funds with
payout targets of 10% or higher, there is no significant
improvement in performance relative to
peer funds. The median difference in four-factor alpha is 0.04% per
month, and is not statistically
significant. In stark contrast to the relative performance results,
these funds are associated with
significantly lower discount levels. The median aggressive-MDP fund
is priced at a premium of
0.80%, compared to a discount of 12.70% for the median peer fund.
The 12.33% difference in
discount levels is statistically significant at the 1% level.
The results in Panel C show the reverse pattern for MDP funds with
a payout target below
10%. These more moderate payout funds significantly outperform
their peers in terms of four-factor
alpha. The median outperformance is about 0.37% per month and
statistically significant at the
1% level. However, the reduction in discounts experienced by these
funds is far less pronounced
than that experienced by the aggressive payout funds. The median
fund with a moderate payout
target trades at a discount of 8.91%, compared to a discount of
11.49% for the median peer fund.
Overall, the above findings suggest a mismatch between performance
improvement and dis-
count reduction among MDP funds. Funds with aggressive payout
targets show no significant
improvement in risk-adjusted performance (relative to non-MDP
funds) but are associated with
substantially lower discounts. In contrast, funds with moderate
payout targets show significant
improvement in risk-adjusted performance — though this result may
be specification sensitive, as
we show later — but experience a much smaller reduction in the
level of discount.
To address the concern that some funds are dropped from the
previous analysis due to the
24-month return data requirement, we compare the NAV performance
between different payout
groups using a portfolio approach. Since the majority of the
moderate MDPs were adopted in the
later half our sample period, we focus on the period from 1999 to
2006. For each month, we form
four equally weighted portfolios based on the payout policy:
non-MDP portfolio, MDP portfolio,
aggressive-MDP portfolio, and moderate-MDP portfolio. Four-factor
alphas are obtained for the
monthly portfolio returns and the paired differences.
As shown in Table 6, there is no evidence suggesting that the
aggressive-MDP portfolio delivers
better risk-adjusted return relative to the non-MDP and
moderate-MDP portfolios. Overall, the
lack of consistent outperformance of MDP funds (especially
aggressive-MDP funds) over the non-
MDP funds casts doubt on the signaling hypothesis.
14
The results so far do not control for fund heterogeneity in terms
of discounts and performance
prior to the adoption of MDPs. In the discussion that follows we
will control for such heterogeneity
by using a difference-in-difference approach with matched funds as
well as a panel specification
with time and style fixed-effects. The findings on MDP discounts,
as we show below, are robust
under various specifications, while the performance results are
more specification sensitive.
4.2 Change in Performance and Discount: Before vs. After Policy
Adoption
In this section we follow Johnson et al. (2006) and use a matched
algorithm to examine the changes
in performance and discount for MDP funds during the three-year
periods before vs. after policy
implementation. For each MDP fund, we identify a matched fund that
has the same Lipper invest-
ment objective, that experienced a similar level of average
discount during the pre-policy period as
the MDP fund, and that did not adopt a payout policy over the
sample period. We require MDP
funds to have a minimum of two years of return and discount data
available both before and after
the policy implementation to be included in the analysis. This
reduces the sample of MDP funds
to 29. The results are presented in Table 7.
Panel A examines the change in performance and discount when all
MDP funds are included.
During the three-year period prior to implementing the MDP, the
four-factor alpha for the median
MDP fund is indistinguishable from that of the matched fund. In
contrast, during the three-year
period after policy implementation, the median MDP fund
significantly outperforms the matched
fund by 0.21% per month. Regarding the change in discount level
relative to the matched funds, the
median MDP fund experiences a reduction of 1.91% in discount
following policy implementation.
However, the reduction is not statistically significant.
We then investigate whether the change in relative performance and
discount differs between
aggressive and moderate payout funds. As shown in Panel B, the
aggressive payout funds have
four-factor alphas that are indistinguishable from those of matched
funds both before and after
the policy implementation. The difference-in-difference test
indicates that the change in four-factor
alpha is not statistically significant either. Hence, there is no
evidence suggesting that MDP funds
with aggressive payout targets experience any improvement in
performance — as would be predicted
by the signaling hypotheses. Despite the lack of performance
improvement, these aggressive payout
funds experience a meaningful reduction in discount relative to the
matched funds.
In Panel C, we report the results for MDP funds with moderate
payout targets. As before, we
observe a pattern that is quite different from the results for
aggressive-MDP funds. The moder-
ate payout funds on average significantly outperform the matched
funds during both the pre- and
post-MDP periods. The difference-in-difference analysis indicates
that the median improvement in
15
relative performance is 0.33% per month, which is statistically
significant at the 10% level. In con-
trast, the test statistics on discount relative to the matched
funds are all statistically insignificant.
Hence, as with the earlier results, there is no evidence of a
significant discount reduction following
the adoption of a moderate payout target.
The evidence from the matched sample analysis18 confirms the
results reported in Table 5 and is
also largely consistent with the results in Johnson et al. (2006).
As shown in their paper, the MDP
funds significantly outperform the matched funds in terms of
Fama-French three-factor adjusted
returns during the three-year period following the policy
adoption.19 However, when breaking
down the MDP sample into strong (aggressive) and weak (moderate)
payout groups, the median
test result is only statistically significant for funds with lower
payout targets.20
4.3 Panel Regression Specification
In this section we examine the impact of MDPs on fund performance
and discount using an alter-
native panel regression specification with style and time
fixed-effects. The specification controls for
unobserved style and time heterogeneity and also allows us to
explicitly control for factors such as
unrealized capital gains and idiosyncratic risk.
We first examine the change in risk-adjusted performance and
discount before vs. after the
MDP adoption by focusing on funds that experienced a change in
payout policy during our sample
period. The control group consists of funds that did not adopt an
MDP over the sample period.
Specifically, we estimate the following two regressions:
(Four-Factor Alpha) = (MDP_Bef) + (MDP) + (MDP*High) + (Log Fund
TNA)−1 +
(Log Fund Age)−1 + (Expense)−12 + (Turnover)−12 +
(Time Fixed-Effects) + (Style Fixed-Effects)+ (1)
18As a robustness check, we investigate the pre- and post-MDP
performance relative to non-MDP funds using an
equally weighted portfolio approach. There is no evidence
suggesting that the aggressive MDP funds significantly
outperform the non-MDP funds in either the pre- or the post-MDP
period. For the sub-sample of domestic equity
funds, the post-MDP portfolio actually underperforms the non-MDP
portfolio (significant at the 10% level) for
aggressive MDP funds. We do not find significant difference in risk
adjusted performance between the pre- and
post-MDP portfolios either. The results are available upon request.
19See Table 6 of Johnson et al. (2006). Our results are robust when
the Fama-French three-factor adjusted returns
are used as performance measure. 20The authors acknowledge that
“The positive excess returns for the funds with weak minimum
dividend policies
are puzzling, however, given that the results in Table 3 (of
Johnson et al. 2006) show that the announcement-related
discount reductions do not persist (for the weak payout
group).”
16
and
(Log Fund TNA)−1 + (Log Fund Age)−1 + (Expense)−12 +
(Turnover)−12 + (Past Perf)[−1−36] + (Log Residual Risk)[−1−36]
+
(Unrealized CapGain)−12 + (Time Fixed-Effects) +
(Style Fixed-Effects)+ (2)
Here, and represent fund and month, respectively. The dependent
variables are the four-factor
alpha and discount. For each month, we compute the four-factor
alpha for any given fund using
the factor loadings estimated from the previous 36-month NAV
returns. The independent variables
common to both regressions are: an indicator variable (MDP_Bef)
that equals one if the fund
currently does not have an MDP but adopts one later in the sample
period; an indicator variable
(MDP) that equals one if the fund currently has an MDP in place;
the interaction variable between
(MDP) and an indicator variable (High) that equals one if the fund
commits to an aggressive
payout target (≥ 10%); the logarithm of the previous month-end fund
total net assets (TNA); the
logarithm of the previous month-end fund age measured in months;
the previous year’s expense
ratio; and the previous year’s turnover ratio. In both regressions,
we control for time and style
fixed-effects. The standard errors are clustered by fund.21
The discount regression (2) also includes several additional
controls that may affect the level
of discount: the total distribution (TotDist), the past performance
(Past Perf), the idiosyncratic
portfolio risk (Log Residual Risk), and the accumulated unrealized
capital gains (Unrealized Cap-
Gain). We control for the level of payout by normalizing the total
dollar amount of distribution per
share in the previous year with the year-beginning NAV. We measure
past performance by the four-
factor alphas estimated from the previous 36-month NAV returns. The
idiosyncratic portfolio risk
is defined as the logarithm of the standard deviation of the
residuals from the previous four-factor
regression. This is similar in spirit to the residual risk measure
used in Pontiff (1996) and can be
interpreted as the unhedgeable fundamental risk that may limit the
effectiveness of arbitrage. The
accumulated unrealized capital gains may also contribute to the
level of discount due to investors’
concern about potential future tax liabilities. We normalize the
accumulated unrealized capital
gains in the previous year by the year-end fund TNA.
The data sample used for regressions (1) and (2) consists of all
General Equity Funds, Preferred
Equity Funds, and Convertible Funds. For these funds, we manually
collect information on total
21As a robustness check, we account for potential autocorrelation
in error terms by computing Newey-west standard
errors lagged for up to 5 months. The statistical significance for
key variables in the discount regression remains
virtually unchanged. In the performance regression, the coefficient
estimate for the interaction term (MDP*High) is
statistcally significant at the 10% level.
17
distribution, return of capital, expense ratio, turnover ratio, and
accumulated unrealized capital
gains on an annual basis from the annual reports filed with the
EDGAR database maintained by
the SEC. Since the EDGAR database only keeps electronic filings for
the post-1995 period, we
estimate both regressions using data during the sub-period from
1995 to 2006.22
Table 8 presents the regression results for two cases: when all
three types of funds are included
and when only the General Equity Funds are considered. The results
in the two cases are similar.
The salient finding in the performance regression (1) is that there
is no evidence that MDP funds
outperform non-MDP funds, either before or after the policy
implementation. As shown in regres-
sions (1a) and (1b), the coefficient estimates for (MDP) are
positive but not statistically significant.
Moreover, the coefficient estimates for (MDP*High) are both
negative and statistically significant.
This suggests that aggressive payout funds tend to underperform the
moderate payout funds. The
F-tests of the change in four-factor alpha for aggressive payout
funds before vs. after the policy
implementation are not statistically significant (with
p-values=0.38 and 0.53 for all funds and for
general equity funds respectively). For funds that adopt moderate
MDPs, the change in four-factor
alpha is positive but only marginally significant for general
equity funds (p-value=0.08). The re-
gression results confirm the earlier findings that there is no
discernible improvement in risk-adjusted
performance for aggressive-MDP funds.
In contrast, as shown in the discount regressions (2a) and (2b), an
aggressive-MDP is par-
ticularly effective in reducing the discount level. Specifically,
the discount for MDP funds is not
statistically different from that for non-MDP funds before the
policy adoption. However, after the
adoption of MDPs, the discount for an aggressive payout fund is on
average more than 10% lower
than that for non-MDP funds. The F-statistics testing the change in
discount for aggressive-MDP
funds before vs. after the policy implementation are highly
significant (with p-values 0001). For
moderate payout funds, the effect of MDP on discount is much
smaller in magnitude. When only
general equity funds are included (regression (2b)), there is no
discernible reduction in the discount
for funds that adopt a moderate payout policy.
It is worth pointing out that in both discount regressions we
explicitly control for the level
of total annual distribution. The coefficient estimates for
(TotDist) are positive and statistically
significant at the 1% level. Consistent with Pontiff (1996), the
finding suggests that the average
level of discount is much lower for funds with larger annual
payout. The fact that aggressive-MDP
funds are associated with significantly lower discount levels, even
after controlling for the level of
payout, highlights the important role of commitment in such payout
policies.23
22The regression results are qualitatively similar when full sample
period (1990 to 2006) is considered and we drop
the variables that are only available post-1995. 23The coefficient
estimates for other explanatory variables in the discount
regressions are largely consistent with
the existing literature. Larger and higher turnover funds tend to
experience higher levels of discount. Funds with
18
Hence, the empirical findings thus far suggest that the signaling
hypothesis can, at most, explain
part of the observed impact of MDP on discount. The outperformance
results documented by
Johnson et al. (2006) — that we find to be specification sensitive
— are driven mainly by MDP funds
that commit to a moderate payout target. However, the effect of a
moderate payout target on
reducing the discount level is quite limited. MDP funds with an
aggressive payout target, despite a
lack of performance improvement following policy implementation,
experience a large and significant
reduction in discount level. The disconnect between payout targets,
change in NAV performance
and discounts suggests that other explanations (e.g., agency costs)
may be more consistent with
the evidence.
Finally, we would like to acknowledge that, while a decrease in
discount might be expected
under both the signaling and agency cost hypotheses, it is a
challenge to explain why some of
the aggressive MDP funds would trade at a premium, despite
relatively poor NAV performance.
In terms of rational valuation, the existence of a premium implies
that the fund is expected to
generate a positive alpha (net of management fees). A possibility,
therefore, is that the premiums
reflect investor belief that MDP adoption is likely to be followed
by a gradual improvement in NAV
returns. For instance, fund managers, in order to ward off demands
to open or liquidate the fund,
may be strongly motivated to deliver better performance. Further,
an MDP would be expected to
eventually decrease fund size. If there are diseconomies of scale
in generating fund returns (e.g.,
if it is increasingly harder to find attractive investment
opportunities as fund size increases), fund
performance might be expected to improve over time, as the fund
size decreases.
5 Agency Costs
In this section, we test the agency cost hypothesis (Predictions 2
and 3) by investigating the impact
of MDPs on fund size (and thus managerial compensation) and the
role of institutional investors
(especially the activists) leading up to MDP adoptions.
5.1 Impact of MDPs on Fund Size and Managerial Compensation
We first examine the issue of whether the adoption of MDPs,
especially aggressive MDPs, tends to
reduce a fund’s TNA. As discussed in relation to Prediction 2, the
signaling hypothesis does not,
in general, predict a decrease in fund size. The reason is that
funds that adopt MDPs should be
those that expect to have a strong performance. The agency
hypothesis is more consistent with
higher expenses are associated with lower levels of discount.
Moreover, funds with higher idiosyncratic portfolio risk
have significantly higher levels of discount, consistent with the
findings in Pontiff (1996). The effects of past fund
performance and accumulated unrealized capital gains are not
statistically significant.
19
fund size decreasing, especially with aggressive MDPs.
We use the following panel regression to examine the annual TNA
growth rates of MDP funds
relative to a control group of non-MDP funds:
(TNA Growth) = (MDP) + (MDP*High) + (AvgNAVRet)[−1] +
(Log Fund TNA)−1 + (Log Fund Age)−1 +
(Time Fixed-Effects)+ (Style Fixed-Effects)+ (3)
Here, and refer to fund and calendar year, respectively. The
dependent variable is the annual
TNA growth rate. Most of the right hand side variables have been
defined in earlier regressions.
The variable (AvgNAVRet) measures the average monthly NAV return
during the year. We control
for time and style fixed-effects in the regression and cluster the
standard errors by fund.
Table 9 presents the results for regression (3). We report
regressions for all funds as well as for
the sub-sample of General Equity Funds. As indicated, the
coefficient estimates for (MDP) are not
significantly different from zero, suggesting that the moderate
payout MDP funds do not experi-
ence growth rates significantly different from that of non-MDP
funds. In contrast, the coefficient
estimates for (MDP*High) are all negative and statistically
significant at the 5% level, regardless
of whether NAV performance is controlled for. Hence, an aggressive
MDP appears to have a strong
negative impact on asset growth. The magnitude is about 3% to 4%
lower when compared to
non-MDP funds.
To investigate the impact of MDPs on managerial compensation we
examine the five-year period
from 2001 to 2006, following the analysis in Cherkes, Sagi and Wang
(2009). During the 2001-2006
period, due in part to the recession in 2001, the average TNA
dropped by 53 million dollars
for MDP funds. In contrast, for the matched non-MDP funds, there
was no significant change
in average TNA over this period. We then analyze the consequences
for management fees over
this period. Compared to the average compensation in 2000, managers
of MDP funds received
47 thousand dollars less per year in the next five years, while
there was no significant change
in average annual compensation for managers of the matched non-MDP
funds. The difference-in-
difference test suggests that, compared to the matched non-MDP fund
managers, the drop in annual
compensation for the MDP fund managers is about 70 thousand dollars
— statistically significant
at the 1% level.
Hence, consistent with Prediction 2, the evidence suggests that the
adoption of MDPs has
a strong negative impact on future TNA growth, as well as on
managerial compensation. This
evidence is supportive of agency costs, rather than signaling, as
an explanation for MDPs.
20
5.2 Shareholder Activism and Implementation of MDPs
Another way to distinguish between signaling and agency hypotheses
is to investigate the cir-
cumstances leading up to the MDP adoption. The signaling hypothesis
implies voluntary MDP
adoptions by skilled managers in an effort to signal their type. On
the other hand, the agency hy-
pothesis suggests that intervention by outside investors are likely
since adopting MDPs is associated
with a reduction in managerial rent.
In general, the role of investor pressure in affecting fund
policies has received scant attention.
Our investigations suggest, however, that fund managers face a
market for corporate control that
is not unlike the one faced by other corporate managers. There
exists an active takeover market in
the CEF industry — one in which funds with large discounts are
frequently targeted by professional
activist shareholders (directly or through hedge funds, trusts, or
investment advisory firms under
their control).24 In a typical hostile control contest, activist
shareholders launch or threaten to
launch a proxy fight to pressure the incumbent Board of Directors
into adopting meaningful policies
to reduce the discount.25
Shareholder activism in the context of CEFs has generally been
regarded in terms of efforts
to open the funds — which, at first glance, seems to be an obvious
and permanent solution to the
discount problem. In this context, Bradley et al. (2008) show that
shareholder activism designed
to open CEFs has become more frequent since the SEC’s reform of the
proxy rules in 1992 that
lifted restrictions on communication among shareholders. However,
our investigations suggest that
an alternative to open-ending the fund, i.e., pressing fund
management to institute an MDP, has
been growing. Pushing management to adopt an MDP may be easier in
some cases — it may also
be more profitable since aggressive MDPs sometimes result in the
fund trading at a premium.
We examined the 13-D filings of the 49 funds that adopted MDPs in
our sample to determine
if activist shareholders were present.26 We found 22 funds that
implemented MDPs under pressure
from activist shareholders. Of the 26 funds that adopted aggressive
payout targets, 13 were under
the pressure from activist shareholders. In all the forced MDP
adoptions, the mean (median)
level of shares accumulated by activist shareholders prior to the
change in payout policy was
17% (11%). Two observations can be made here. First, the cases in
which outside intervention
is observed tend to be ones in which aggressive MDPs are adopted —
which is consistent with
24Among investors that appear to specialize in targeting closed-end
funds are Phillip Goldstein, Stewart Horejsi,
Arthur Lipson, Ron Olin, and his brother-in-law, Ralph Bradshaw.
25Sometimes the contest for seats on the board includes a proposal
to terminate the advisory contract between the
fund and its investment advisor — a tactic unique to contests for
control of closed-end funds. Section 15 of the ICA
gives fund shareholders the right to terminate the contract if a
percentage of shares, specified in Section 2(a)(42) of
the ICA, vote to do so. 26The SEC requires shareholders with more
than 5% of beneficial ownership to file 13-Ds and to disclose
their
trading intention in the “Purpose of Transaction” section.
21
fund managers being more reluctant to adopt aggressive payout
policies on their own. Second, of
the funds that “voluntarily” adopted MDPs, some may have done so as
a defensive measure to
avoid confrontation with activist shareholders. Overall, the role
played by activist shareholders in
affecting MDP adoption — especially in the case of aggressive MDPs
— appears to be inconsistent
with the notion of fund manager signaling, though consistent with
the agency hypothesis.
We further investigate the impact of MDPs on fund attrition rates.
The notion is that, if
the adoption of MDPs indeed alleviates the agency problems, the
likelihood of hostile terminations
should be significantly lower during the post-MDP periods. Our
analysis suggests that the attrition
rates for MDP funds, especially for aggressive-MDP funds, are much
lower than the non-MDP
funds. Specifically, almost 20% of non-MDP funds were either
liquidated or open ended under the
pressure from activist attacks, compared to 6% for moderate-MDP
funds and none for aggressive-
MDP funds. This is consistent with the conjecture that, through
committing to an aggressive
payout policy, some fund managers chose to reduce their rents in
exchange for a longer tenure.
As we have noted above, in some cases pushing management to adopt
an MDP may be easier
for institutional shareholders than open-ending or liquidating the
fund. In particular, MDPs may
be more likely to emerge as a ‘compromise’ solution when
institutional shareholdings are more
dispersed. The notion is that dispersed ownership may make it
harder for the institutions to act
in a unified way and open-end the fund. To address this
possibility, we examine the year-end
institutional holdings three years prior to either MDP adoption or
fund termination. We report
the median holdings for both groups of funds and conduct the median
tests. As shown in Panel
B of Table 3, the MDP group has significantly lower institutional
ownership and lower ownership
concentration when compared to the terminated funds. In particular,
the median total (average)
institutional holdings for MDP funds are 7.37% (1.01%) lower than
that for terminated funds. On
the other hand, the MDP funds on average have 5 more institutional
investors than the terminated
funds. All differences in medians are statistically significant at
the 5% level or better. These
findings are consistent with the notion that dispersion in
institutional ownership is more likely to
result in MDP adoptions.
5.2.1 Bankgesellschaft Berlin AG vs. Aberdeen Australia Equity
Fund: A Case Study
We discuss a specific case that illustrates how institutional
investors can sometimes profit by ac-
cumulating shares in a heavily discounted fund and forcing the
adoption of an aggressive payout
policy. The activist institutional shareholder involved in the case
is Bankgesellschaft Berlin AG
(hereafter BBAG), a German bank. The CEF targeted by BBAG is
Aberdeen Australia Equity
Fund (hereafter AAE), a country fund primarily investing in
Australian equities. Table 10 shows
22
the sequence of events from October 2002 to May 2007. The event
dates are either the last trading
date by BBAG as reported in each 13-D filing or the date on which
significant events occurred.
In the second and third columns, we report the number of AAE shares
bought or sold by BBAG
between two consecutive 13-D filings and the percentage of
beneficial ownership on the last trading
date as reported in each 13-D filing. The fourth and fifth columns
report the average trading price
for all transactions reported in each 13-D filing and the
end-of-month discount/premium. The last
column describes the actions taken by either party on each event
date.
The sequence of events can be summarized as follows. On October 22,
2002, BBAG purchased
5,348,149 common shares of AAE from Mira, L. P. in a private
transaction at a price of $5.14 per
share. After the transaction, BBAG controlled more than 30% of the
outstanding AAE common
shares. In the “Purpose of Transaction” part of the 13-D filing,
BBAG expressed concerns about
the persistently high discount level and vowed to take necessary
actions to pressure AAE’s board.
At that time, AAE shares were trading at a discount of more than
16%. On November 20, BBAG
further increased its holdings to 31.40% and proposed to nominate
its own candidates for board
directors.
To fend off BBAG’s challenge, AAE made an in-kind tender offer on
February 19, 2003 for 40%
of outstanding shares at a price equal to 90% of the NAV. BBAG
applauded the green mail and
withdrew its board nominations. Due to the restriction of 1940
Investment Company Act, BBAG
needed to obtain an exemptive order from the SEC in order to
participate in the tender offer.
However, on August 28, 2003, the SEC denied BBAG’s request for such
an exemptive order.
Unable to participate in the tender offer, BBAG became hostile
again and notified AAE on
January 16, 2004 that it intended to nominate three nominees of the
bank to the board at the 2004
annual meeting. In response, AAE sought to narrow the discount and
declared, on February 1,
2004, that it was going to implement a managed distribution policy
with a 10% payout target. Soon
after, with the discount narrowing and then turning into a premium,
BBAG dropped its hostile
actions against AAE, withdrew its own board nominations, and
gradually unloaded its shares to
realize trading profits. By December 2005, BBAG’s beneficial
ownership of AAE dropped to about
27%. By April 2007, BBAG controlled only 13.60% of AAE’s common
stock. On May 8, 2007,
BBAG registered with the SEC to offer and sell all remaining
2,592,641 shares held by the bank at
a maximum price of $15.91 per share.
Over the five year period from 2002 to 2007, BBAG’s engagement with
AAE generated a
handsome trading profit. BBAG established its position in AAE
shares at an average cost of $5-$6
per share. After successfully pressing AAE to adopt an aggressive
MDP, the bank was able to
offload its shares at an average price of $11-$16 per share. The
total trading profit over the five
23
year period came to about $46.60 million or a 168.61% return on
investment.
5.2.2 Pattern of Institutional Holdings around MDP Adoption
The fact that institutional investors can actively impact a fund’s
payout policy is illustrated by
the case study above. We now investigate the evidence at the
aggregate level. We rely on an
event study approach to examine the change in institutional
holdings for MDP funds around the
policy implementation quarter. For each MDP fund, we first identify
the quarter during which the
Board of Directors announced the MDP adoption. We then track excess
institutional holdings and
excess discount for the 8 quarters immediately before and the 8
quarters immediately following
the announcement quarter. For each MDP fund, we calculate the
excess quarter-end institutional
holdings and discount by subtracting, respectively, the median
institutional holdings and discount
for a control group of funds. The control group consists of funds
that have the same Lipper
investment objective as the MDP fund but did not adopt an MDP
during the event window.
Table 11 presents the event study results. When all MDP funds are
considered, institutional
investors on average hold significantly more shares of MDP funds
than the control group from
the 5th quarter before policy adoption to the 3rd quarter after the
adoption. Starting from the
4th quarter after policy adoption, excess holdings drop and become
in general not statistically
different from zero. In contrast, the change in excess discount
exhibits the opposite pattern. Over
the 8 quarters prior to policy adoption, MDP funds tend to have a
comparable level of discount
relative to the control group. Note that a negative (positive)
excess discount represents a larger
(smaller) discount relative to the control group. Starting from the
1st quarter after MDP adoption,
excess discounts become increasingly positive and statistically
significant. By the end of the 8th
quarter, the median discount for MDP funds is about 7% lower than
the non-MDP control group.
Observe that the quarter in which MDP funds exhibit a significant
reduction in discount relative
to the control group coincides with the quarter in which
institutional investors start to reduce
their holdings. The above findings are consistent with the notion
that some institutional investors
accumulate fund shares, influence the adoption of MDPs and then
unwind their positions once the
discount is eliminated or turns into a premium.
Table 11 also reports the event study results separately for
aggressive and moderate payout
funds. It is evident that the observed institutional trading and
discount patterns are mainly present
for funds that adopt aggressive MDPs. For these aggressive payout
funds, institutional holdings
are about 8% to 13% higher than the control group during the window
from the 5th quarter
before the MDP adoption to the 3rd quarter after the adoption.
Starting from the 5th quarter
following the MDP adoption, the excess institutional holdings
decline significantly and become
24
statistically insignificant from zero. In terms of discount change,
these funds exhibit comparable
levels of discount relative to the control group prior to the
policy adoption. Starting from the 2nd
quarter following MDP adoption, discount levels are significantly
lower than for the control group.
In contrast, for MDP funds with a moderate payout target, the
excess holdings and discounts are
generally not significantly different from zero. In Figure 3, we
plot the changes in excess institutional
holdings and excess discount around the policy announcement quarter
for aggressive-MDP funds
and moderate-MDP funds, respectively.27
Although institutional investors tend to have only a small presence
in aggressive-MDP funds
(Table 3), an interesting finding in Table 11 is that some
institutions are actively involved in trading
fund shares around the MDP adoptions. Consistent with agency
explanation, some (likely activist)
institutions accumulate shares and exert pressure on fund
management. These institutions benefit
from their intervention by unwinding their shares when fund
discount disappears or changes into a
premium.
Finally, we investigate how the holdings of various types of
institutional investors change around
the MDP adoption. We replicate the event study in Table 11 for each
type of institution based on
horizon, size, and value factors.
Table 12A shows that the pattern of institutional holdings around
aggressive-MDP adoptions
(as observed in Table 11) is almost exclusively driven by the
Value-style investors. Compared to the
control group, the median holdings by Value investors increase
steadily from 4% higher in the 8th
quarter before the MDP adoption to more than 10% higher in the 1st
quarter just before the MDP
adoption. Following the MDP adoption, the median holdings by Value
investors decrease steadily
to 3% higher than the control group by the 5th quarter and then
become statistically insignificant
after that. There is some evidence suggesting that the Small-style
investors also accumulate shares
around the adoption of aggressive MDPs, though the magnitude is
much smaller than for Value
investors. There is no evidence that investors with different
horizons trade differently around the
MDP adoption. Table 12B presents the change in institutional
holdings around moderate-MDP
adoptions. As with aggressive MDPs, the Value- and Small-style
investors accumulate shares around
the MDP adoption and unwind their investments later on. However,
the economic magnitude is
much smaller than for the aggressive MDP case. In Figure 4, we plot
the changes in median
holdings by Value- vs. Growth-style investors during the event
window for aggressive-MDP funds
and moderate-MDP funds, respectively.
Give the apparently active role played by Value-style investors
around MDP adoptions, we
27As a robustness check, we examine the change in institutional
holdings and discount around MDP adoption using
a panel regression approach. The results are very similar to the
findings in event studies and are available upon
request.
25
examine them more closely, focusing on investors that hold at least
1% of the fund’s outstanding
shares during the quarter prior to the MDP adoptions. The
Value-style investors in our sample
are of three types (based on Thomson Legal Types): Banks/Trusts,
Investment Companies, and
Endowments. We also distinguish between activists and non-activists
based on whether they filed
13D with the SEC indicating any hostile intention against fund
management. The information on
these investors is presented in Table 13. The table shows, for each
type category, the mean and
median statistics for average holdings per institution, total
institutional holdings, and the excess
holdings relative to the median total holdings by Value-style
investors for the group of same-style
non-MDP funds during the same quarter.
There are 31 Value-style investors that held more than 1% of
outstanding shares during the
quarter prior to MDP adoption. The dominant group is that of 26
investment companies (including
hedge funds and investment banks). There are also 3 banks and 2
university endowments. As shown
in Panel A of the table, investment companies and endowments hold
large number of shares when
involved. The total holdings are on average 10-12 by these two
types of institutions, more than
7 percentage points higher than the median level holdings by
Value-style investors for the control
group. In Panel B, we compare the holding difference between
activists and non-activists. The
activists group has a much bigger presence than the non-activists
group when involved in MDP
adoptions. The total holdings by the activists are on average 13%,
almost 10 percentage points
higher than the median holdings by Value-style investors for the
control group. In contrast, the
total holdings by non-activists are on average 5% and not
significantly different from the control
group.
The 10 activists we identified include well known names actively
involved in open-ending and
MDP adoption by closed-end funds. For example, Bulldog Investors,
Deep Discount Advisors,
Millennium Management LLC, and Western Investment are all repeated
players in the industry.
The two endowments belong to two prestige universities: Harvard and
Yale.
Hence, consistent with Prediction 3, there is strong evidence
overall that institutional investors
(especially Value-style investors) actively engage in pressuring
fund managers into actions that are
effective in unlocking shareholder value. They benefit from such
intervention by winding down
their holdings after fund discounts disappear.
6 Conclusion
CEFs have been under increasing pressure from activist investors to
adopt meaningful policies to
reduce the discount. One increasingly “popular” strategy among
activist shareholders is to pressure
fund management into adopting a managed distribution policy. Under
MDP, fund management
26
commits to a fixed payout target, either as a percentage of average
net assets or as a flat dollar
amount. Empirical evidence, confirmed in our paper, suggests that
the adoption of an MDP,
especially when coupled with an aggressive payout target, is quite
effective in reducing or even
eliminating fund discounts.
We investigate two explanations: the signaling explanation offered
in the literature — that
the MDP serves as a positive signal of managerial ability — and an
alternative based on agency
costs. Our results indicate that signaling is, at best, only part
of the explanation and that the
evidence is generally more consistent with the agency cost
hypothesis. For funds adopting aggressive
payout targets of 10% (median target) and above, discounts tend to
disappear, though there is no
discernible improvement in NAV performance.
We document that the adoption of aggressive payout poli