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: zhiwang@illinois.edu (Z. J. Wang), vikram.nanda@mgt.gatech.edu (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
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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
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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.
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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
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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