SHORT SALE RESTRICTIONS, DIFFERENCES OF OPINION, AND SINGLECOUNTRY, CLOSED-END FUND DISCOUNT
Lee W. Sanninga, Alexandre Skibab, Hilla Skibab,*
a
b
Department of Economics, Whitman College, United States
College of Business, University of Wyoming, United States
Abstract: The purpose of this paper is to study the closed-end fund discount in
Miller’s (1977) framework. Miller’s theory states that in the simultaneous
presence of (1) short sale restrictions and (2) dispersion of investors’ opinions,
securities become overvalued. We show that discounts of single-country, closedend funds are related to Miller’s two conditions. Consistent with theoretical
predictions, we find that neither dispersion of investor opinion nor short sale
restrictions alone is positively related to the discount. However, when both
conditions exist simultaneously, fund discounts increase.
Keywords: Short sale restrictions; closed-end fund discount; investor disagreement
JEL codes: G14, G19
*
Corresponding author: Address: University of Wyoming, College of Business, Department of Economics
and Finance 3985, 1000 E. University Avenue, Laramie, WY 82071, United States. Tel: 1-307-766-4199;
Fax: +1-307-766-5090. E-mail Address: [email protected]
1. Introduction
Miller’s (1977) theory states that an asset may become overvalued when there are
differences in investor opinions about the security’s risk and expected returns at the same time
when pessimistic investors are kept out of the market because of short-sale restrictions. These
overvaluations are rational in nature, and the asset price adjusts to its true value when either the
short-sale restriction is lifted or when new information reaches the marketplace and investor
differences of opinion narrow.
There is some empirical support for Miller’s (1977) theory. Boehme, Danielsen, and
Sorescu (2006) find robust evidence for overvaluation in a large sample of US stocks when both
of .Miller’s conditions are present. Overvaluation in stocks does not systematically occur if only
one of the conditions holds. Miller’s theory has also been used to explain market anomalies, for
example, the tech bubble and its eventual crash. Ofek and Richardson (2003) suggest that IPO
lockups prevented investors from short selling tech stocks in the late 1990s. Then, when the
lockups expired, pessimistic investors were able to register their opinions and prices adjusted
downward accordingly.
In this paper, we test Miller’s theory in an international setting and relate short-sale
restrictions and investor disagreement to the closed-end fund discount. We believe that Miller’s
theory may provide a partial explanation for the closed-end fund discount. There is a significant
body of evidence documenting that closed-end funds trade at discounts, and very rarely at
premiums, relative to their NAVs. Closed-end fund net asset values (NAVs) and market values
are determined in different marketplaces, and the two valuations are seldom equal. The market
values of US-based, single-country, closed-end funds are determined in the US market, where
short sales are not banned and pessimistic investors are able to trade. Because we focus only on
1
international, closed-end, US funds (funds traded in the US but with holdings in a single foreign
country), the NAVs of the funds are determined by the individual stock prices in the foreign
security market, where short-sale restrictions may be in place. Therefore, an increase in the
investor disagreement in the foreign security market may result in a temporary overvaluation of
the individual stocks, leading to inflated NAVs when the foreign security market has short-sale
restrictions. The market value of the closed-end fund should not change, however, if the price
increase in NAV is not due to fundamentals. Because empirical evidence documents that both of
Miller’s conditions must be present for the overvaluation to occur, we would not expect to
observe widening discounts during times of increased investor disagreement when short selling
is permitted in the foreign security market.
Following Miller’s framework, we compare US funds that invest in foreign markets with
short-sale restrictions against US funds that invest in foreign markets with no short-sale
restrictions. In that framework we test whether greater discounts exist in funds when (1) short
selling is restricted and (2) dispersion of opinions is greater. In our sample of 44 single-country,
closed-end funds from 1996 to 2010, investor disagreement conditioned on a short-sale
restriction in the foreign security market partially explains fund-specific discounts. We find that
as investor disagreement increases, the fund’s discount increases when the foreign security
market has a short-sale restriction. Short-sale restrictions or investor disagreement alone does not
translate to an increase in fund discount. However, when both of Miller’s conditions exist
simultaneously in the foreign security market, the fund’s discount increases. The magnitude of
the increase is both statistically and economically significant.
The contribution of the paper is thus twofold. First, we provide empirical support for
Miller’s hypothesis about rational overvaluation that may occur in the stock markets. Rather than
2
focusing on individual stocks, we provide evidence for market-wide overvaluation when the
entire market restricts short sales. Second, our results provide new insight into the closed-end
fund discount puzzle. Rather than explaining the puzzle by concluding that irrational individual
investor sentiment drives the discount in the US market, we show that the discount arises as a
rational response to different conditions across the two markets. The funds are correctly priced
in the US market, but the underlying securities are temporarily overpriced in the foreign markets
because pessimistic investor opinions are not reflected in the securities’ market values.
The rest of the paper is organized as follows. Section 2 reviews related literature on shortsale restrictions, investor disagreement, and closed-end fund discounts. Section 3 discusses the
data and methods. Section 4 reviews the results, and section 5 concludes.
2. Related literature and testable hypotheses
2.1. Short-sale restrictions and investor disagreement
Miller (1977) asserts that among rational investors under the concept of uncertainty,
investor estimates of the expected return and risk of a given security differ. When short selling is
restricted in the presence of divergent opinions, pessimistic investors are unable to trade. There
may be times when optimistic investors’ opinions are registered in the security prices while the
pessimistic investors choose not to participate in the market. Bubble-like behavior in asset prices
occurs when a short-sale restriction forces the pessimistic investors out of the markets and only
the optimists’ opinions are reflected in the asset prices.
Empirically, there is evidence on the validity of Miller’s theory. In addition to Boehme,
Danielsen, and Sorescu’s (2006) empirical evidence supporting Miller’s theory, Jones and
Lamont (2002) show that US stocks that are expensive to short have high valuations and low
3
subsequent returns. Chen, Hong, and Stein (2002) model “rational bubbles,” where optimistic
investors drive prices up and pessimistic rational investors choose not to participate in the
presence of a short-sale restriction. Chen, Hong, and Stein (2002) proxy short-sale restriction by
breadth of ownership, such that the fewer investors there are holding the stock long, the harder
shorting becomes. Hong and Stein (2003) relate short-sale restrictions and differences in investor
opinions to market crashes. Diether, Malloy, and Scherbina (2002) provide evidence that stocks
with high levels of dispersion of opinion earn low subsequent returns, especially when the stocks
are small caps. Berkman, Dimitrov, Jain, Koch, and Tice (2009) show that stocks that are
characterized by high differences of opinion earn the lowest subsequent returns after earnings
announcements. The negative returns are especially pronounced for stocks that are the most
difficult to short. These stocks that are the most difficult to short experience higher than average
price run-up prior to the earnings announcement when investor disagreement is at its highest,
and then experience a price decrease after the earnings announcement resolves uncertainty.
2.2. Closed-end fund discount
The closed-end fund discount is a widely-studied empirical puzzle in finance literature.
Closed-end funds differ from the more popular open-end funds in the way the funds are
constructed and traded. Open-end fund investors who want to liquidate their holding in the fund
redeem their investment to the fund company for the NAV of the fund. In contrast, a closed-end
fund raises capital though a one-time IPO, after which the fund’s size is fixed. Investors who
want to liquidate their holdings must sell their shares of the closed-end fund to other investors at
the current market price. This market price may differ from the closed-end fund’s NAV, and
4
there is a significance body of evidence documenting that closed-end mutual funds trade at
discounts, and very rarely at premiums, relative to their NAV.
The closed-end fund discount puzzle has been explained in theoretical and empirical
research partially by investor sentiment. The idea of fund discount reflecting investor sentiment
was first proposed by Zweig (1973). Lee, Shleifer, and Thaler (1991) find empirical support for
De Long, Shleifer, Summers, and Waldmann’s (1990) model, in which noise trader sentiment
drives closed-end fund discounts over time. Since closed-end funds and small-cap stocks are
typically held by individual investors, positive investor sentiment, measured by positive
performance of small-cap stocks, correlates with smaller closed-end fund discounts. Gemmill
and Thomas (2002) test the impact of noise trading and costly arbitrage on closed-end fund
discount and find that the discount is consistent with the noise trader model and that the discount
is driven by arbitrage costs and managerial expenses. Also, changes in the noise trader sentiment
can explain fluctuations in the discount.
Pontiff (1996) finds that NAVs of closed-end funds are more likely to deviate from their
market values when the funds’ portfolios are difficult to replicate, when funds pay a lower
dividend, in smaller funds, and during times when interest rates are high. Barnhart and
Rosenstein (2010) show that upon introduction of ETFs with underlying assets similar to an
existing closed-end fund, the closed-end fund’s discount widens. Berk and Stanton (2007) show
that the closed-end fund discount is partially driven by the tradeoff between managerial ability
and the fund fees, so that the funds may trade either at discounts or premiums, depending on
whether fees or managerial ability dominate. In UK-based closed-end funds, discounts are larger
for funds that are expensive to arbitrage. Other explanations for the discount also include agency
costs (Boudreaux 1973).
5
In international, closed-end funds, Bonser-Neal, Brauer, Neal, and Wheatley (1990) find
that international investment restrictions are related to the funds’ discounts and that when
restrictions are lifted, the discounts narrow. Bodurtha, Kim, and Lee (1995) document that the
premiums of closed-end, single-country funds move together because the underlying stock prices
and the US market move together and changes in premiums reflect the US market sentiment.
2.3. Testable hypotheses
Based on the work by Miller (1977), there are heterogeneous agents, with varying beliefs
about asset payoffs, in the foreign security markets where the closed-end funds invest. These
agents may or may not be subject to short-sale restrictions, depending on the specific foreign
market’s rules. We demonstrate that in the presence of short-sale restrictions and dispersion of
opinion in the foreign security market, stock prices may deviate from the fundamental values and
become overpriced. If investors in the US funds ignore the increase in the fund’s NAV that is not
due to fundamentals, we would expect the difference between the fund’s market value and NAV
to increase, thus resulting in a wider discount. Our explanation does not claim that an
inefficiency-based discount is driven by individual investor sentiment in the foreign security
market. Rather, the discount is driven by mispricing in the foreign security market that persists
until new information narrows the dispersion in investor opinion.
Extant literature on closed-end fund discounts is based largely on cross-sectional
analyses. We believe that there is a time-varying component to the discount, explained by
changes in investor disagreement. In addition to the cross-sectional approach, we compare the
markets with short-sale restrictions to markets with no short-sale restrictions over time and test
whether greater discounts exist in single-country, closed-end funds when (1) short selling is
6
restricted and (2) dispersion of opinion is greater. We expect to find an increase in the NAVs
followed by periods of high disagreement in markets with restrictions on short sales. When
NAVs inflate due to short selling restrictions in the foreign markets, we expect to find no
corresponding increase in the funds’ market values because short selling is not banned in the US
market where the funds’ market values are determined. Therefore, we expect closed-end fund
discounts to widen during times of disagreement for US funds whose foreign security markets
have short-sale restrictions.
3. Data and methodology
3.1. Data
We obtain NAVs for US-based single-country closed-end funds from Compustat. The
NAVs are available monthly from January 1996 to December 2010. We identify 44 singlecountry closed-end funds based on the fund’s name. We then double check the funds’ portfolios
in their prospectuses to verify they truly are single-country investment funds. We obtain the
market values of the closed-end funds from CRSP and merge them to the monthly NAVs. We
use the last available trading day’s closing price multiplied by fund shares outstanding to
compute the funds’ market values. In addition to the NAVs and market values of the funds, we
collect data on closed-end fund dividends and capital gain distributions from Compustat and
CRSP.
The data on country level short-sale restrictions are collected from several different
sources. For most years in our sample of countries, we are able to find information of shortselling restrictions in various world markets from Charoenrook and Daouk (2009) and/or Bris,
Goetzmann, and Zhu (2007). For the later years in the sample, after the sample period ends in the
7
reference papers, we search for possible changes in the short selling environments from each
country’s exchanges. In addition, we check for rule changes carefully during financial crises
periods. Especially during 2008-2010, many countries implemented either temporary or
permanent short-sale restrictions. The short-sale restrictions data for the 2008-2010 period are
mainly from Grunewald, Wagner, and Weber (2010) and from country exchanges directly. In
some analyses we also focus on more detailed short-selling restrictions such as allowance of
naked short sales and existence of an uptick rule. These data are obtained mainly from each
country’s stock exchanges directly1 and supplemented by the data from Grunewald, Wagner, and
Weber (2010) for the 2008-2010 period specifically.
Other data used in the analysis include closed-end fund expense ratios from Morningstar.
We also control for individual investor sentiment in the US market. The proxy for individual
investor sentiment is the return to the Russell 2000 small cap index. Trading volume data on
foreign security markets to compute investor disagreement are obtained from Compustat
Global’s Daily Security database. GDP per capita data are from World Bank. We also construct a
debt default index for sample countries, and these data are from Standard and Poor’s and from
Columbia Management.2 Data on ETF’s inception dates are from each ETF family website. The
data on existence of put options and index futures of the various target markets are hand
collected from individual countries’ exchanges and other available online documents. Because
1
Most data on naked short sales and uptick rules, in addition to the Grunewald, Wagner, and Weber (2010), are
collected directly from sample countries’ exchanges. We also use the following two online documents to confirm
our findings.
Short-selling restrictions: International developments by Katten Muchin Roseman Cornish LLP
http://www.kattenlaw.com
Briefings on market practice by Knowledge for Markets http://www.ftkmc.com
2
Standard and Poor’s Sovereign defaults and rating transition data, 2010 update from
http://www.standardandpoors.com and Columbia Management’s Costs of Sovereign Debt Defaults from
http://www.columbiafunds.com
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many of the single-country ETFs began their operations in March of 1996, we also begin our
final closed-end fund sample from March 1996, spanning the period until December 2010.
3.2. Methodology
3.2.1 Computing the discount
Consistent with prior literature, we compute the closed-end fund discount as:
Discount f ,t =
MV f ,t  NAV f ,t
(1)
NAV f ,t
where fund f's discount at time t is the difference in the fund’s market value, MV, at time t and
the NAV at time t, scaled by the fund’s NAV at time t. If the discount is a positive number, the
fund is trading at a premium to its market value.
Table 1 lists the 44 funds in the sample that have at least one year of monthly discounts
during the March 1996 to December 2010 period. The third column shows the time-series
average monthly discount (or a premium in few rare instances), which range from a low of 20.1% (India Growth Fund) to a high of 7.0% (Thai Fund).3 Table 1 also shows the funds’
average size, computed based on the NAVs, and the annual average dividend yield during the
sample period. The largest funds in the sample include India Fund, Korea Fund, and Mexico
Fund based on time series average NAVs. The average dividend yields range from 0.0% to
11.4%.
3
The average discount across all countries listed in Table 1 is 9.6%. Fund discounts are significantly impacted
when both short sale constraints and disagreement exist simultaneously. The averages are for the full sample period
thus span time periods when the two conditions may both exist and not exist.
9
3.2.2. Computing the short-sale restriction
The first condition of Miller’s theory is that there are short-sale restrictions. We rely on
the data displayed in table 2 to classify markets into short-sale restriction categories. Based on
the various classifications, we categorize markets into two groups: Those without short-sale
restrictions and those with short-sale restrictions, so that in the regression analyses the short-sale
restriction variable takes on the value of one in markets where short sales are restricted and the
value of zero in markets that allow short sales. Because we focus our analysis on single-country,
closed-end funds, the foreign security market’s short-sale restriction is sufficient to determine the
short-sale restriction for the fund’s entire portfolio.
Table 2 shows short-sale restrictions in all the countries where the 44 closed-end funds in
our sample invest. The second column of table 2 shows the short-sale rule we use in the analysis
to define countries as one of the three short-selling environments: 1) countries that allow for
short sales and where short-selling is widely practiced, 2) countries that allow for short sales, but
where short-selling is not widely practiced, and 3) countries that ban short sales. During the
sample period from March 1996 to December 2010, the 44 single-country closed-end funds are
invested in 27 foreign markets. Of these 27 foreign markets, nine can be classified as
environments where short selling is allowed and practiced throughout the entire sample period
(Austria, France, Germany, Japan, Mexico, Singapore, South Africa, Switzerland, and United
Kingdom). Three can be classified as environments where short selling is banned (China, Israel,
and Pakistan) throughout the entire sample period. In the rest of the 15 markets, the short-sale
restriction either changes at least once during the sample period (Argentina, Australia, Chile,
India, Indonesia, Italy, Malaysia, Russia, South Korea, Taiwan, and Thailand) or short selling is
allowed but not widely practiced (Brazil, Philippines, Spain, and Turkey). In our analysis, we use
10
table 2’s short-sale groupings to define the time-series variation in the restrictions, and in the
sensitivity analyses we test our hypothesis by first treating countries with short-sale environment
(item 2) listed above) as a short-sale ban environment and then by excluding all closed-end funds
investing in environment from the analysis.
In robustness checks, we also control for other short-sale restrictions. A country may
allow for short sales and short selling can be widely practiced, but with limitations. For example,
in many of the sample countries naked short sales are not allowed, or stocks can be shorted on an
uptick. In some regressions, we include an indicator variable that equals one if naked short sales
are not allowed and zero if they are, and an indicator variable that equals one if the market has an
uptick rule and zero otherwise.
In addition to investigating the effect of additional short-sale restrictions on the closedend fund discount, we also generate a short index for robustness checks that takes on values from
zero to four, so that the most constrained markets take on the value of four and the least
constraint markets take on the value of zero. More specifically, a market in which short-selling is
allowed and widely practiced, that does not have an uptick rule and that allows for naked shortselling takes on the value of zero. Every additional constraint adds a point to the index (one point
for each: uptick rule, naked short sales are not allowed, and short selling is not widely practiced),
so that the maximum index value in countries that allow for short selling is three. Those
countries that have complete bans on short sales take on the value of four.
The third and fourth columns of table 2 show restrictions and changes in the naked shortselling laws and the existence of the uptick rule during the sample period. The fifth column
shows the inception date of the possible ETF, and the last column shows the country’s GDP per
capita in 2010 in US dollars.
11
3.2.3. Measuring Investor disagreement
The second condition of Miller’s (1977) theory is that there is disagreement among
investors. Standard ways of measuring disagreement in finance literature include securities’ or
markets’ abnormal trading volumes, securities’ or markets’ trading volumes, and analyst forecast
dispersion in securities or markets (see for example Diether, Malloy, and Sherbina 2002;
Boehme, Danielsen, and Sorescu 2006). Consistent with prior literature, we use market-wide
abnormal trading volumes and trading volumes as the main measures of investor disagreement.
When investors disagree more on the fair price of the security, more trading will occur in the
market. If all investors agreed on a security’s price, then the only trades taking place in the
market would be rebalancing in nature and observed volumes and abnormal volumes would be
low. To measure market-wide disagreement, we first compute trading volumes and abnormal
trading volumes for individual securities traded in each of the sample countries. We use the
pricing data from the universe of publicly traded firms from Compustat Global that have nonmissing price information available for the sample countries. We then aggregate the measures
from individual securities to market-wide measures on a value-weighted basis, where the valueweighting is done based on each security’s market value of equity in time t.
Similarly to Chae (2005) and Campbell and Wasley (1996), we first compute security n’s
daily trading volume as:
Volumen,i  ( Pn,i * STn,i ) / SOn,i
(2)
Where Pn,i is the daily closing price of security n, STn,i is the daily number of shares traded of
security n, and SOn,i is security n’s number of shares outstanding on day i. The daily abnormal
trading volume of security n is the differential in day i’s volume and the 60-day average daily
volume:
12
Abnormal Volumen,i  Volumen,i  Volumen
(3)
where the average is computed as a prior 60-day moving average volume of security n:
Volumen 
1
Volumen,i
i 61
60

(4)
To aggregate both Volume from equation (2) and Abnormal Volume from equation (3) to
monthly, market-wide measures, we first compute monthly measures for each security n.
Monthly abnormal volume is the monthly average abnormal trading volume based on the simple
average of daily observations in month t:
T
Abnormal Volumen,t 
 Abnormal Volume
n ,i
i t
(5)
T
The monthly volume of security n is the summation of all daily trading volumes in month t:
T
Volumen,t  Volumen,i
(6)
i t
Finally, we aggregate both of the monthly disagreement measures from equations (5) and (6) to
market-wide proxies by computing the value-weighted averages based on security n’s market
value of equity as a share of country N’s total market value of equity in month t. Country N’s
monthly disagreement measure based on abnormal volume is:
Pn,t * SOn ,t
N
Abnormal VolumeN ,t  
n 1
N
P
n 1
n ,t
* Abnormal Volumen ,t
(7)
* SOn ,t
Country N’s monthly disagreement measure based on volume is:
N
VolumeN ,t  
n 1
Pn,t * SOn,t
*Volumen ,t
N
P
n 1
n ,t
* SOn ,t
13
(8)
Although both measures in equations (7) and (8) have been used as proxies for
disagreement, we believe that abnormal trading volume works better as a cross-market measure
because of comparability and most of our analyses are performed using abnormal trading volume
as the main investor disagreement variable.4
4. Results
4.1. Cross-sectional analyses
To test our main hypothesis of the effect of short-sale restrictions and investor
disagreement on closed-end fund discount, we perform several different tests both in crosssection and in time series. In all the tests, we test the effects of each of Miller’s conditions
separately and when both the conditions are present simultaneously. When the closed-end fund
discount from equation (1) is the dependent variable, we do not expect to observe a significant
relationship between the discount and the short-sale restriction or the discount and the
disagreement when short-sale restriction and disagreement are included in the regressions
separately. We do, however, expect to observe a significant negative relationship with the
interaction of the short-sale restriction variable and the disagreement variable. This maintains
consistency with our empirical prediction that the discount widens when disagreement is high
and pessimistic investors are unable to short the underlying securities in the closed-end funds’
portfolios.
Additional variables that may impact the closed-end funds’ discount or have been shown
to matter in prior literature are also included in the regression analyses as controls. Expense
ratio, is the net fee of the fund. It is expected to take on a negative sign. US investor sentiment is
4
Results using volume from equation (8) are similar, but often omitted in the interest of brevity.
14
expected to take on a positive sign. De Long, Shleifer, Summers, and Waldmann (1990) propose
that, because closed-end funds and small-cap stocks are typically held by individual investors,
positive investor sentiment measured by positive performance of small-cap stocks correlates with
closed-end fund discounts. We therefore control for investor sentiment with the monthly return
on the Russell 2000 small stock index. Dividend yield is expected to take on a negative sign.
Capital gain distribution is expected to take on a positive sign. Fund size is the monthly NAV of
the closed-end fund. The sign of the size variable is unclear because larger funds enjoy
economies of scale but may find it costlier to buy illiquid stocks. ETF indicator equals one if the
closed-end fund’s target market has an ETF and zero otherwise. We expect the ETF indicator to
take on a negative sign so that when a close substitute to a closed-end fund is available, investors
are willing to pay less for the more expensive closed-end fund. Put option indicator and index
future indicators are expected to carry positive signs, because their existence makes
overvaluation in NAVs less likely. We also control for GDP per capita to ensure that the
estimated effect of the short-sale restriction and disagreement proxy is not driven by the crosscountry differences in overall market quality.
Table 3 presents results from cross-sectional OLS regressions where the dependent
variable is the closed-end fund discount from equation (1). Panel A and B repeat the analyses
with both investor disagreement proxies from equations (7) and (8). Abnormal trading volume
from equation (7) is the measure of disagreement in specifications (1) through (4) and trading
volume from equation (8) is the measure of disagreement measure in specifications (5) through
(8). In panel A, the short-sale restriction is set to one if short sales are banned or allowed but not
widely practiced and zero if short sales are allowed and practiced, so panel A includes all the
15
observations. In panel B, we omit countries where short sales are allowed but not widely
practiced.
Several interesting facts are revealed in the results. First, in both panels A and B, either
short sale restriction or investor disagreement in the foreign security market alone is not enough
to produce a wider discount. In specifications (1) and (5) the coefficient of the short sale
indicator is insignificant. The same is true for both disagreement variables from equations (7)
and (8) in specifications (2) and (6). The sign of disagreement is negative but not statistically
significant. However, when we include both short-sale restriction and disagreement in the
regression simultaneously, the interaction term between short-sale restriction and disagreement is
negative and significant (see specifications (3) and (7)). Contrasting the results with and without
the interaction term reveals important heterogeneity of the disagreement effect. Statistically
significant negative sign of the interaction term means that high disagreement widens the
discount only in the presence of short-sale constraint. In specifications (4) and (8), we include
short-sale restriction and disagreement simultaneously, but with one period lag, and the effect of
disagreement and short-sale restriction is again significant and negative. So, consistent with our
hypothesis, one of the Miller’s conditions alone is not enough to have an effect on the closed-end
fund discount. But when disagreement is high and pessimistic investors are not able to short the
securities in closed-end funds’ portfolios, the difference between the closed-end fund’s NAV and
market value grows and the discount widens. This finding is also consistent with Boehme,
Danielsen, and Sorescu’s (2006) empirical test of the Miller hypothesis where the authors find
that neither of the Miller’s conditions alone is sufficient to produce overvaluation in stocks; but
when both the conditions are present, overvaluation in the US stocks occurs.
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In general, the other control variables take on their expected signs. Fund size is generally
negative and significant so that larger funds have wider discounts. Investor sentiment is
positively and significantly related to closed-end fund discount, consistent with prior literature.
ETF indicator is negative and significant, as predicted, so that the discount widens, or the
investors are willing to pay less for a closed-end fund when a close substitute is available.
Table 4 shows results from OLS regression analyses where the fund discount is
conditioned on the short sale environment. We compare the effect of investor disagreement on
the fund’s discount for funds that are invested in countries with short-sale restrictions in
specifications (1) through (3) to funds that are invested in countries without short-sale
restrictions in specifications (4) through (6). Short sales are treated as restricted if short-selling is
either banned or allowed but not widely practiced. The empirical prediction is that the
disagreement variable should be negatively related to the closed-end fund discount only when
short sales are restricted. We use abnormal trading volume as a proxy for investor disagreement
in all specifications.
The results in table 4 are consistent with our hypothesis. Disagreement is negative and
significant in specifications (1) through (3), when we use either disagreement in time t or in time
t-1 as a control variable. In specification (3) both the current value of the disagreement and the
lagged value are significant and negative. A one standard deviation increase in disagreement
variables in specification (3) corresponds to -0.5% widening in the discount. The effect of
disagreement on the discount is larger in magnitude than the effect of the investor sentiment.
Also consistent with the empirical prediction, disagreement in time t or in time t-1 is not
negatively related to fund discount, when short-selling is allowed. In fact, disagreement is
positive and significant in all of the specifications (4) through (6). The results in table 4 are also
17
consistent with the empirical results of Boehme, Danielsen, and Sorescu. (2006) in that both
Miller’s conditions must be present to create overvaluations.
Results for the other controls are similar to results in table 3. Fund size is negative and
significant, but only in short-sale restriction funds. Investor sentiment is positive and significant
and the ETF indicator is negative, although not significant in any of the specifications.
4.2. Time series analyses
Table 5 shows results from time series regressions with fund fixed effects, where the
dependent variable is the closed-end fund discount. The main variables of interest are again the
short-sale restriction, which equals zero if short sales are allowed and equals one if short sales
are banned, or allowed but not practiced, in specifications (1) through (3). In specification (4),
we omit countries where short sales are allowed but not widely practiced. Disagreement is
proxied with abnormal trading volume from equation (7).
The results from fixed effect regressions are similar to the results in table 3. A short
constraint alone in specification (1) is not enough to widen the discount. In fact, short-sale
restriction is positive and significant. Similarly, an increase in investor disagreement alone in
specification (2) is not significantly related to the closed-end fund discount. However, when
disagreement and short-sale restriction exist simultaneously in specification (3), the effect of the
interaction term on the discount is negative and significant, although the interaction term loses
significance in specification (4), when we omit 10 funds. Overall, the results from the time series
regressions provide further support for our hypothesis.
18
4.3. Additional short sale control variables
In this section we narrow our focus of short-sale restrictions to include more detailed
restrictions that countries may place on short-selling. Specifically, we test for the significance of
bans on naked short sales and an existence of an uptick rule on the closed-end fund discount.
Table 6 shows results from cross-sectional (panel A) and time series (panel B) analyses, similar
to tables 3 and 5. In specifications (1) and (2) of both panels A and B, we proxy short sales with
the index of short-selling that takes on values from zero to four. The construction of the simple
short-selling index is described in section 3.2.2. In specifications (3) and (4), we repeat the
regression analysis with the zero/one short-sale restriction (zero when short sales are allowed and
practiced and one if banned or not widely practiced). In addition to the basic short sale indicator
in specifications (3) and (4), we include indicator variables for absence of an uptick rule (no
uptick) and an allowance of naked short sales (naked allowed), so that each indicator variable
takes on the value of zero if there exist no uptick rule and naked short sales are allowed and one
otherwise.
Results from both cross-sectional and time series analyses are similar to the results in
tables 3 and 5. The interaction term between short-sale restriction (or the short index) and
disagreement is negative and significant in all specifications. The short index alone is not
significant. Also, the uptick rule and the naked short sale indicators are not significantly related
to the closed-end fund discount. However, the economic significance of the interaction of
disagreement and the short-selling index that incorporates uptick rule and naked short sale ban is
larger than the simple short-sale restriction indicator variable. A one standard deviation increase
in the interaction term between disagreement and short index corresponds to a 3.1% widening in
the discount compared with a one standard deviation increase in the interaction term between
19
disagreement and short constraint which corresponds to about 0.5-0.7% widening in the
discount. This demonstrates the contribution of the short sale index to our model.
5. Conclusion
The contribution of our study is twofold: We test Miller’s (1977) hypothesis in an
international setting and we provide new insight to the closed-end fund discount puzzle. Miller
states that in the presence of short-sale restrictions, pessimistic investors do not have an option to
participate in the market and their opinions about a security’s expected return are not registered
in the market price. In this situation, only optimistic investors trade, and security prices become
inflated. The hypothesis does not argue against market efficiency, but explains why asset prices
may deviate from fundamentals even when all investors are rational.
Miller’s theory has been used in finance to explain inflated security prices during the tech
bubble in the 1990s, overvaluations during earnings’ announcements, and market crashes among
other things (Ofek and Richardson 2003; Hong and Stein 2003; Berkman, Dimitrov, Jain, Koch,
and Tice 2009). We test the Miller hypothesis in an international setting during times of varying
investor disagreement by comparing the discounts of closed-end funds that are investing in
single foreign markets that have short-sale restrictions to closed-end funds that are investing in
single foreign markets that do not have short-sale restrictions. Our results indicate that when both
of Miller’s conditions are present in the foreign security market—short sales are restricted and
there is disagreement among investors—the closed-end fund discounts widen. Neither condition
alone is sufficient to produce larger discounts. Additionally, when a country ETF is present, the
closed-end fund discount widens because investors are willing to pay less for closed-end funds
when substitutes are available.
20
Our results provide an alternative explanation for the closed-end fund discount puzzle.
Contrary to previous studies that find that the closed-end fund discount is caused by irrational
behavior by the investors in the US funds, our results suggest that short-sale restrictions lead to
inflated security prices in the foreign markets because pessimistic investors cannot participate in
the price formation process, which in turn widens the closed-end fund discount.
Future research should test the relationship between simultaneous disagreement and
short-sale restrictions and the closed-end fund discount in a single market setting. For example,
this could be done where the NAVs and fund markets values are both determined in the US
market but where short-sale restrictions and disagreement vary across securities. This would
provide additional support that the discount widens when disagreement and short-sale
restrictions exist simultaneously.
21
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Berk, J., and R. Stanton, 2007. Managerial ability, compensation, and the closed-end fund
discount, Journal of Finance 62, 529-556.
Berkman, H., V. Dimitrov, P. Jain, P. Koch, and S. Tice, 2009. Sell on the news: Differences of
opinion, short-sale constraints, and returns around earnings announcements, Journal of
Financial Economics 93, 376-399.
Bodurtha J. Jr., D. Kim, and C. Lee, 1995. Closed-end country funds and US Market sentiment,
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Boudreaux, K., 1973. Discounts and premiums on closed-end mutual funds: A study in
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around the world, Journal of Finance 62, 1029-1079.
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markets, Journal of Political Economy 98, 703-738.
Diether, K., C. Malloy, and A. Scherbina, 2002. Differences of opinion and the cross-section of
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from closed-end funds, Journal of Finance 57, 2571-2594.
Grunewald, S. N., A. F. Wagner, and R. H. Weber, 2010. Short selling regulation after the
financial crisis – First principles revisited. International Journal of Disclosure and
Governance 7, 108-135.
22
Hong, H., and J. Stein, 2003. Differences of opinion, short sales restrictions, and market crashes,
Review of Financial Studies 16, 487-525.
Jones, C., and O. Lamont, 2002. Short-sale constraints and stock returns, Journal of Financial
Economics 66, 207-239.
Lee, C., A. Shleifer, and R. Thaler, 1991. Investor sentiment and the closed-end fund puzzle,
Journal of Finance 46, 75-109.
Miller, E. M., 1977. Risk, uncertainty, and divergence of opinion, Journal of Finance 32, 11511168.
Ofek, E., and M. Richardson, 2003. DotCom mania: The rise and fall of internet stock prices,
Journal of Finance 58, 1113-1138.
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23
Table 1. Fund Characteristics
Table reports summary data for single-country, closed-end funds in our sample. The CEF discount is
computed based on equation (1). Size is the net asset value (NAVs in $,000). The CEF discount and size
are monthly averages since funds’ inception. Dividend yield is the annual average yield since inception.
NAME
ABERDEEN AUSTRALIA EQTY FD
ABERDEEN CHILE FUND INC
ABERDEEN INDONESIA FUND INC
ABERDEEN ISRAEL FUND INC
ARGENTINA FUND INC
AUSTRIA FUND INC
BRAZIL FUND
BRAZILIAN EQUITY FUND INC
CHINA FUND INC
EMERGING MEXICO FUND
FIDELITY ADVISOR KOREA FD
FIRST PHILIPPINE FUND INC
FRANCE GROWTH FUND INC
GREATER CHINA FUND INC
GROWTH FUND OF SPAIN INC
INDIA FUND INC
INDIA GROWTH FUND INC
ITALY FUND INC
JAKARTA GROWTH FUND INC
JAPAN EQUITY FUND INC
JAPAN SMALL CAP FUND INC
JF CHINA REGION FUND INC
KOREA EQUITY FUND INC
KOREA FUND
KOREAN INVESTMENT FUND INC
MALAYSIA FUND
MEXICO EQUITY & INCOME FUND
MEXICO FUND INC
MORGAN STANLEY CHINA A SH FD
NEW GERMANY FUND INC
NEW SOUTH AFRICA FUND INC
PAKISTAN INVESTMENT FUND INC
SCUDDER SPAIN&PORTUGAL FUND
SINGAPORE FUND INC
SOUTHERN AFRICA FUND INC
SWISS HELVETIA FUND
TAIWAN EQUITY FUND INC
TAIWAN FUND INC
TEMPLETON CHINA WORLD FD INC
TEMPLETON RUSSIA & E EURO FD
THAI CAPITAL FUND INC
THAI FUND INC
TURKISH INVESTMENT FUND INC
UNITED KINGDOM FUND
COUNTRY
AUSTRALIA
CHILE
INDONESIA
ISRAEL
ARGENTINA
AUSTRIA
BRAZIL
BRAZIL
CHINA
MEXICO
SOUTH KOREA
PHILIPPINES
FRANCE
CHINA
SPAIN
INDIA
INDIA
ITALY
INDONESIA
JAPAN
JAPAN
CHINA
SOUTH KOREA
SOUTH KOREA
SOUTH KOREA
MALAYSIA
MEXICO
MEXICO
CHINA
GERMANY
SOUTH AFRICA
PAKISTAN
SPAIN
SINGAPORE
SOUTH AFRICA
SWITZERLAND
TAIWAN
TAIWAN
CHINA
RUSSIA
THAILAND
THAILAND
TURKEY
UNITED KINGDOM
24
CEF
DISCOUNT
-7.1%
-10.6%
4.3%
-11.6%
-16.9%
-16.0%
-15.2%
-14.1%
-7.9%
-17.3%
-4.5%
-14.6%
-14.0%
-12.0%
-14.4%
-7.3%
-20.1%
-13.0%
4.8%
-0.6%
0.7%
-12.9%
-8.3%
-7.9%
-6.6%
-4.4%
-11.7%
-14.6%
-2.6%
-16.0%
-15.6%
-17.4%
-15.7%
-11.2%
-16.2%
-15.1%
-15.5%
-10.9%
-16.4%
5.3%
3.0%
7.0%
1.2%
-13.9%
SIZE
168,245.8
164,828.1
46,238.4
60,970.5
105,582.5
94,400.0
377,757.4
38,920.8
298,976.8
101,728.0
43,866.4
76,302.5
147,847.4
205,104.5
263,376.0
874,773.3
82,295.6
97,655.8
26,203.4
87,717.3
148,013.1
72,786.7
61,304.7
739,956.3
50,799.7
70,273.5
70,067.1
573,857.2
463,019.1
296,494.4
57,826.9
42,670.2
83,059.8
89,847.9
68,413.1
343,874.8
53,277.2
253,162.6
176,490.2
175,960.0
30,969.5
124,416.1
91,724.9
55,890.3
DIV. YIELD
7.1%
4.8%
0.9%
1.0%
2.9%
5.0%
3.4%
1.6%
0.9%
0.4%
0.0%
0.0%
11.4%
1.1%
1.6%
1.7%
0.0%
1.2%
0.3%
0.3%
0.6%
3.8%
0.1%
0.9%
0.0%
1.6%
2.7%
4.3%
1.3%
3.0%
1.3%
3.2%
1.3%
1.4%
2.0%
0.8%
2.6%
0.4%
2.3%
0.7%
0.5%
1.8%
0.7%
3.3%
Table 2. Target Market Characteristics
COUNTRY
ARGENTINA
AUSTRALIA
AUSTRIA
BRAZIL
CHILE
CHINA
FRANCE
GERMANY
INDIA
INDONESIA
ISRAEL
ITALY
JAPAN
MALAYSIA
MEXICO
PAKISTAN
PHILIPPINES
RUSSIA
SINGAPORE
SOUTH AFRICA
SOUTH KOREA
SPAIN
SWITZERLAND
TAIWAN
THAILAND
TURKEY
UNITED KINGDOM
Short Sale Rule
Banned until 1999. Allowed since 1999, but not widely practiced.
Allowed and practiced. Banned between Sep 2008 and Nov 2008. Restricted
between Dec 2008 and May 2009.
Allowed and practiced.
Allowed, but not widely practiced.
Banned until Dec 2008. Allowed, but not widely practiced since Jan 1999.
Allowed and practiced since Dec 2001.
Banned.
Allowed and practiced.
Allowed and practiced.
Allowed, but not widely practiced. Banned since May 2009.
Banned. Allowed but not widely practiced since May 2009.
Banned.
Allowed and practiced. Banned between Oct 2008 and Dec 2008. Restricted
between Jan 2009 and May 2009.
Allowed and practiced.
Allowed, but not widely practiced between Jan1996 and Aug 1996. Allowed
and practiced between Sep 1996 and Jul 1997. Banned between Aug 1997
and Jan 2006. Allowed and practiced since Jan 2006.
Allowed and practiced.
Banned.
Allowed, but not widely practiced.
Allowed and practiced. Banned between Sep 2008 and Jun 2009.
Allowed and practiced.
Allowed and practiced.
Allowed, but not widely practiced. Banned between Oct 2008 and May
2009.
Allowed, but not widely practiced.
Allowed and practiced.
Allowed, but not widely practiced. Banned between Oct 2008 and Nov 2008.
Banned until Oct 1998. Allowed, but not widely practiced between Nov
1998 and Dec 2000. Allowed and practiced from Jan 2001.
Allowed, but not widely practiced.
Allowed and practiced.
Naked Short sales allowed
(Yes/No)
No
Yes, but banned since Jan
2009
Yes
No
No
GDP per
cap
7,815
28,882
Uptick rule (Yes/No)
No, since Jul 1999
Yes, but rule removed in Nov
2008
No
No
No, since Jan 1999
ETF, since
Mar 2011
Mar 1996
Mar 1996
Jul 2000
Nov.2007
25,795
3,868
6,683
N/A
Yes
Yes, but banned in Dec
2008
Yes (until ban)
Yes (since ban lifted)
N/A
Yes, but banned between
Oct 2008-Jul 2009
Yes, but banned between
Oct 2008-Oct 2009
No
N/A
No
No
Mar 1996
Mar 1996
Mar 1996
1,670
24,585
31,441
No (until ban)
No (since ban lifted)
N/A
No
Feb 2008
Jan 2009
Mar 2008
Mar 1996
576
1,000
21,564
20,852
No, but implemented in Jan
2002.
Yes
Mar 1996
35,360
Mar 1996
5,174
Yes
N/A
Yes
Yes, but banned between
Sep 2008-Jun 2009
Yes, but banned in Sep 2008
No
No
Yes
N/A
Yes
No, but implemented in Sep
2008
No
No
Yes
Mar 1996
None
Sep 2010
Apr 2007
5,814
469
1,050
4,424
Mar 1996
Feb 2003
Mar 2000
28,496
3,319
12,036
No
No
Yes, but banned between
Oct 2008-Nov 2008.
No
No
No
No, but implemented between
Sep 2008-Jan 2009)
Yes, but rule removed in Oct
1998
Yes
No
Mar 1996
Mar 1996
Jun 2000
15,083
47,063
13,368
Mar 2008
2,646
Mar 2008
Mar 1996
7,793
23,372
Yes
Yes
Table reports short-sale restrictions during sample period, existence of an ETF in the market, and the country’s average per capita GDP during the sample period, reported in USD.
25
Table 3: Effect of Disagreement and Short Constraint on CEF Discount
Table shows results from cross-sectional OLS regressions where the dependent variable is the closed-end fund discount. The main explanatory variables include
several measures of short-sale restrictions and investor disagreement. In panel A, the short-sale restriction indicator equals one if short sales are banned or allowed
but not widely practiced in time t and zero if short sales are allowed and practiced. In panel B, we exclude markets where short sales are allowed but not widely
practiced. Disagreement variables are from equations (7) and (8). In specifications 1 through4, we use market average abnormal trading volume and in
specifications 5 through 8 we use the market average trading volume as a measure of disagreement. The disagreement variable and the interaction term between
investor disagreement and short-sale restriction are included as current period’s values or with one period lag. Other explanatory variables include: Fund size, fund
fee, dividend, and capital gain distribution. Negative sign of the interaction term coefficient implies that the disagreement widens the discount when short-sale
constraints are present. We also control for investor sentiment (return to Russell 2000), GDP per capita of the target market, and indicator variables that take on
the value of one if the target market has put options, if an ETF of the target market exists, or if target market’s main index has futures. All regressions include year
dummies, and regressions are run with country-clustered standard errors. T-statistics are reported below the coefficients (*10%, **5%, and ***1% level of
significance).
26
Panel A: Short-sale Restriction =1 if Shorting is Banned or Allowed but Not Widely Practiced, =0 if Shorting Is Allowed and Practiced
Disagreement measure:
Abnormal trading volume
Trading volume
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Short-sale restriction
-0.0106
-0.0107
-0.0145
-0.0106
-0.0001
(-0.47)
(-0.47)
(-0.67)
(-0.47)
(-0.01)
Disagreement
-0.0362
0.0838***
-0.0183
0.0352
(-1.04)
(6.32)
(-0.80)
(1.37)
(Short-sale restriction)x(Disagreement)
-0.1525***
-0.0657***
(-7.26)
(-3.86)
Lagged disagreement
0.1564***
(14.29)
(Short-sale restriction)x(Lagged disagreement)
-0.2315***
(-12.61)
Fund fee
-0.0324
-0.0319
-0.0325
-0.0332
-0.0324
-0.0318
-0.0302
(-1.03)
(-1.01)
(-1.03)
(-1.15)
(-1.03)
(-1.00)
(-0.95)
Dividend
-0.0036
-0.0029
-0.0037
-0.0028
-0.0036
-0.0052
-0.0042
(-0.40)
(-0.33)
(-0.41)
(-0.35)
(-0.40)
(-0.56)
(-0.46)
Capital gain distribution
0.0347
0.0330
0.0350
0.0336
0.0347
0.0332
0.0328
(1.47)
(1.38)
(1.41)
(1.45)
(1.47)
(1.37)
(1.35)
Fund Size, log
-0.0190*
-0.0193*
-0.0189*
-0.0166*
-0.0190*
-0.0187*
-0.0178*
(-1.84)
(-1.89)
(-1.84)
(-1.74)
(-1.84)
(-1.81)
(-1.72)
Russell 2000 ret.
0.1293***
0.1335***
0.1312***
0.1397***
0.1293***
0.1349***
0.1327***
(7.61)
(6.99)
(7.21)
(6.16)
(7.61)
(6.44)
(6.69)
ETF =1
-0.0541**
-0.0534**
-0.0549**
-0.0565**
-0.0541**
-0.0542**
-0.0560**
(-2.21)
(-2.11)
(-2.22)
(-2.37)
(-2.21)
(-2.12)
(-2.20)
Puts =1
-0.0006
-0.0012
-0.0009
-0.0056
-0.0006
-0.0018
-0.0002
(-0.02)
(-0.04)
(-0.03)
(-0.18)
(-0.02)
(-0.05)
(-0.01)
Index Future =1
0.0361
0.0334
0.0364
0.0386
0.0361
0.0434
0.0411
(1.35)
(1.27)
(1.35)
(1.52)
(1.35)
(1.25)
(1.20)
GDP per capita
-0.0095
-0.0070
-0.0094
-0.0088
-0.0095
-0.0066
-0.0082
(-0.91)
(-0.68)
(-0.90)
(-0.89)
(-0.91)
(-0.64)
(-0.74)
Constant
0.3363*
0.3098
0.3354*
0.3289*
0.3363*
0.3028
0.3053
(1.73)
(1.69)
(1.73)
(1.73)
(1.73)
(1.64)
(1.56)
Observations
5,109
5,109
5,109
4,561
5,109
5,109
5,109
R2
0.149
0.149
0.150
0.156
0.149
0.151
0.159
27
(8)
-0.0046
(-0.21)
0.0291
(1.15)
-0.0587***
(-3.68)
-0.0308
(-1.05)
-0.0034
(-0.42)
0.0320
(1.37)
-0.0155
(-1.63)
0.1428***
(6.43)
-0.0570**
(-2.33)
-0.0048
(-0.16)
0.0437
(1.32)
-0.0077
(-0.74)
0.2964
(1.56)
4,561
0.163
Panel B: Short-sale Restriction =1 if Shorting is Banned, =0 if Shorting Is Allowed and Practiced
Disagreement measure:
Short-sale restriction
(1)
0.0139
(0.46)
Disagreement
(Short-sale restriction)x(Disagreement)
Abnormal trading volume
(2)
(3)
0.0139
(0.46)
-0.0362
0.0879***
(-1.04)
(7.24)
-0.1222**
(-2.40)
Lagged disagreement
(Short-sale restriction)x(Lagged disagreement)
Fund fee
Dividend
Capital gain distribution
Fund Size, log
Russell 2000 ret.
ETF =1
Puts =1
Index Future =1
GDP per capita
Constant
Observations
R2
-0.0474*
(-1.99)
-0.0097
(-0.98)
0.0494
(1.58)
-0.0069
(-0.60)
0.1021***
(5.80)
-0.0914**
(-2.15)
-0.0103
(-0.29)
0.0886***
(3.14)
-0.0110
(-0.69)
0.2598
(1.29)
3,656
0.198
-0.0319
(-1.01)
-0.0029
(-0.33)
0.0330
(1.38)
-0.0193*
(-1.89)
0.1335***
(6.99)
-0.0534**
(-2.11)
-0.0012
(-0.04)
0.0334
(1.27)
-0.0070
(-0.68)
0.3098
(1.69)
5,109
0.149
-0.0474*
(-1.99)
-0.0097
(-0.98)
0.0521
(1.56)
-0.0069
(-0.60)
0.0992***
(5.62)
-0.0914**
(-2.16)
-0.0104
(-0.29)
0.0889***
(3.14)
-0.0110
(-0.69)
0.2602
(1.29)
3,656
0.198
28
(4)
0.0104
(0.35)
0.1417***
(10.41)
-0.1615***
(-3.53)
-0.0428*
(-1.89)
-0.0072
(-0.77)
0.0508
(1.66)
-0.0048
(-0.44)
0.1295***
(4.83)
-0.0932**
(-2.24)
-0.0131
(-0.40)
0.0909***
(3.33)
-0.0103
(-0.67)
0.1429
(0.79)
3,268
0.202
(5)
0.0139
(0.46)
-0.0474*
(-1.99)
-0.0097
(-0.98)
0.0494
(1.58)
-0.0069
(-0.60)
0.1021***
(5.80)
-0.0914**
(-2.15)
-0.0103
(-0.29)
0.0886***
(3.14)
-0.0110
(-0.69)
0.2598
(1.29)
3,656
0.198
Trading volume
(7)
0.0156
(0.51)
-0.0183
0.0126
(-0.80)
(0.47)
-0.0528**
(-2.69)
(6)
-0.0318
(-1.00)
-0.0052
(-0.56)
0.0332
(1.37)
-0.0187*
(-1.81)
0.1349***
(6.44)
-0.0542**
(-2.12)
-0.0018
(-0.05)
0.0434
(1.25)
-0.0066
(-0.64)
0.3028
(1.64)
5,109
0.151
-0.0477*
(-2.00)
-0.0094
(-0.90)
0.0481
(1.51)
-0.0069
(-0.60)
0.0982***
(5.49)
-0.0915**
(-2.15)
-0.0096
(-0.27)
0.0864**
(2.57)
-0.0111
(-0.70)
0.2636
(1.31)
3,656
0.199
(8)
0.0121
(0.41)
0.0059
(0.22)
-0.0505**
(-2.51)
-0.0429*
(-1.89)
-0.0072
(-0.72)
0.0488
(1.57)
-0.0047
(-0.43)
0.1306***
(4.88)
-0.0933**
(-2.24)
-0.0123
(-0.37)
0.0906**
(2.68)
-0.0104
(-0.67)
0.1411
(0.78)
3,268
0.202
Table 4: Closed-End Fund Discount Conditioned on Short-sale restriction
Table shows results from OLS regressions where the dependent variable is the closed-end fund discount. We compare results for funds that are invested in
countries with short-sale restriction (specifications 1-3) to countries without short-sale restrictions (specifications 4-6) while proxying for investor disagreement
with value-weighted country abnormal trading volume (from equation 7). The disagreement variable is included as current period’s value or with one period lag.
Other explanatory variables include: Fund size, fund fee, dividend, and capital gain distribution. We also control for investor sentiment (return to Russell 2000),
GDP per capita of the target market, and indicator variables that take on values of one if the target market has put options, if an ETF of the target market exists, or
if target market’s main index has futures. All regressions include year dummies, and regressions are run with country-clustered standard errors. T-statistics are
reported below the coefficients (*10%, **5%, and ***1% level of significance).
Disagreement
(1)
-0.0526**
(-2.80)
Lagged disagreement
Fund fee
Dividend
Capital gain distribution
Fund size, log
Russell 2000 ret.
ETF =1
Puts =1
Index future =1
GDP per capita
Constant
Observations
R2
-0.0407
(-0.58)
-0.0047
(-0.30)
0.0265**
(2.32)
-0.0277*
(-1.99)
0.1343***
(5.25)
-0.0440
(-1.24)
0.0151
(0.25)
-0.0050
(-0.15)
-0.0135
(-0.73)
0.5527**
(2.18)
2,750
0.141
Short sales restricted
(2)
-0.0658**
(-2.59)
-0.0436
(-0.68)
-0.0038
(-0.30)
0.0256**
(2.54)
-0.0248*
(-1.98)
0.1421***
(4.77)
-0.0486
(-1.47)
0.0047
(0.09)
-0.0025
(-0.08)
-0.0111
(-0.66)
0.4337*
(1.91)
2,436
0.146
(3)
-0.0472***
(-3.08)
-0.0447*
(-1.87)
-0.0436
(-0.68)
-0.0039
(-0.30)
0.0252**
(2.47)
-0.0247*
(-1.98)
0.1476***
(5.09)
-0.0489
(-1.48)
0.0047
(0.09)
-0.0023
(-0.08)
-0.0111
(-0.66)
0.4331*
(1.91)
2,436
0.147
29
(4)
0.0907***
(9.44)
-0.0010
(-0.02)
-0.0072
(-0.57)
0.0538
(0.85)
0.0047
(0.43)
0.1289***
(4.34)
-0.0672
(-1.18)
-0.0002
(-0.01)
0.0961***
(3.27)
-0.0101
(-0.74)
-0.0203
(-0.11)
2,359
0.290
Short sales allowed and practiced
(5)
(6)
0.0441***
(7.32)
0.1464***
0.1335***
(8.94)
(8.61)
-0.0012
-0.0012
(-0.02)
(-0.02)
-0.0063
-0.0063
(-0.50)
(-0.50)
0.0514
0.0542
(0.85)
(0.88)
0.0059
0.0059
(0.52)
(0.52)
0.1377***
0.1357***
(5.32)
(5.11)
-0.0647
-0.0647
(-1.15)
(-1.15)
0.0006
0.0006
(0.03)
(0.03)
0.0972***
0.0973***
(3.43)
(3.43)
-0.0117
-0.0117
(-0.89)
(-0.89)
-0.0224
-0.0223
(-0.12)
(-0.12)
2,125
2,125
0.292
0.292
Table 5: Fixed Effect Regression
Table shows results from OLS regressions with fund fixed effects where the dependent variable is the
closed-end fund discount. The main explanatory variables include short-sale restriction and investor
disagreement. Short-sale restriction indicator equals one if short sales are banned or allowed but not
widely practiced in the current time period and zero if short sales are allowed and practiced in
specifications (1)-(3). In specification (4), short-sale restriction indicator equals one if short sales are
banned and zero if short sales are allowed and practiced. Disagreement is the market average abnormal
trading volume from equation (7). The interaction term between the short-sale restriction and
disagreement measures to what degree the effect of disagreement is contingent on the presence of the
short-sale constraints. Negative sign of the interaction term coefficient implies that the disagreement
widens the discount when short-sale constraints are present. Other explanatory variables include: Fund
size, dividend, and capital gain distribution. We also control for investor sentiment with return to Russell
2000, GDP per capita of the target market, and indicator variables that take on values of one if the target
market has put options, or if an ETF of the target market exists. All regressions include year dummies,
and regressions are run with fund-clustered standard errors. T-statistics are reported below the coefficients
(*10%, **5%, and ***1% level of significance).
Short-sale restriction
(1)
0.1435*
(1.71)
(2)
Disagreement
-0.0140
(-0.43)
(Short-sale restriction)x(Disagreement)
Dividend
Capital gain distribution
Fund size, log
Russell 2000 ret.
ETF =1
Puts =1
GDP per capita
Constant
Observations
Number of funds
R2
0.0119
(1.44)
0.0219
(1.23)
0.0150
(1.04)
0.1211***
(5.69)
-0.0241
(-0.93)
-0.1009*
(-1.84)
-0.0098
(-0.29)
-0.2137
(-0.68)
5109
44
0.156
0.0173**
(2.08)
0.0232
(1.20)
0.0160
(1.10)
0.1133***
(5.70)
-0.0221
(-0.83)
-0.0941
(-1.63)
0.0013
(0.04)
-0.2405
(-0.77)
5109
44
0.115
30
(3)
0.1438*
(1.71)
0.1162***
(5.38)
-0.1699***
(-4.48)
0.0118
(1.43)
0.0230
(1.19)
0.0153
(1.07)
0.1212***
(5.55)
-0.0256
(-0.97)
-0.1006*
(-1.83)
-0.0107
(-0.32)
-0.2098
(-0.67)
5109
44
0.157
(4)
0.1342**
(2.55)
0.1077***
(4.62)
-0.0953
(-1.22)
0.0000
(0.00)
0.0247
(0.74)
0.0243*
(1.75)
0.0989***
(3.97)
-0.0111
(-0.25)
-0.2340***
(-6.19)
0.0305
(0.77)
-0.5411
(-1.56)
3656
34
0.209
Table 6: Additional Short Restrictions
Table shows results from repeated analyses from table 3 and table 5 with additional measures for short-sale restrictions.
Panel A shows selected results from cross-sectional analyses similar to table 3 and panel B shows selected results from
time series analyses similar to table 5. In both panels the dependent variable is the closed-end fund discount. The main
explanatory variables include short-sale restrictions and investor disagreement. In both panels the short-sale restriction
indicator equals one if short sales are banned or allowed but not widely practiced in current time period, and zero if short
sales are allowed and practiced. In addition to this (0,1) indicator in specifications (3) and (4), we include indicator
variables for absence of an uptick rule (no uptick) and an allowance of naked short sales (naked allowed). In specifications
(1) and (2) of each panel, we include a short-sale restriction index that takes on values between zero and four (see section
3.2.2 for complete description of the short index variable). Disagreement variable in the regressions is either the market
average abnormal trading volume or market average trading volume. The interaction term between the short-sale
restriction and disagreement measures to what degree the effect of disagreement is contingent on the presence of the
short-sale constraints. Negative sign of the interaction term coefficient implies that the disagreement widens the discount
when short-sale constraints are present. Other explanatory variables include: Fund size, fund fee, dividend, and capital
gain distribution. We also control for investor sentiment with return to Russell 2000, GDP per capita of the target market,
and indicator variables that take on values of one if the target market has put options, if an ETF of the target market exists,
or if target market’s main index has futures. All regressions include year dummies, and regressions are run with countryclustered errors. T-statistics are reported below the coefficients (*10%, **5%, and ***1% level of significance).
Panel A: Summary of Cross-sectional Analyses with Additional Short Restriction Measures
0-4
0-4
1,0
Short-sale restriction
Abnormal trading
Abnormal trading
Disagreement measure:
volume
Trading volume
volume
(1)
(2)
(3)
Short-sale restriction
0.0030
0.0069
-0.0172
(0.33)
(0.72)
(-0.80)
Disagreement
0.0690***
0.0399
0.0886***
(4.12)
(1.60)
(6.65)
(Short-sale restriction)*(Disagreement) -0.0441***
-0.0248***
-0.1583***
(-7.28)
(-4.48)
(-8.88)
No uptick
-0.0273
(-1.04)
Naked allowed
0.0063
(0.32)
Fund fee
-0.0323
-0.0313
-0.0273
(-1.03)
(-1.00)
(-0.83)
Dividend
-0.0023
-0.0037
-0.0037
(-0.24)
(-0.38)
(-0.38)
Capital gain distribution
0.0330
0.0312
0.0327
(1.35)
(1.26)
(1.36)
Fund size, log
-0.0189*
-0.0169*
-0.0165
(-1.91)
(-1.74)
(-1.60)
Russell 2000 ret.
0.1335***
0.1327***
0.1295***
(6.99)
(6.45)
(7.25)
ETF =1
-0.0538**
-0.0566**
-0.0537**
(-2.08)
(-2.12)
(-2.08)
Puts =1
0.0008
0.0041
0.0203
(0.02)
(0.12)
(0.43)
Index future =1
0.0315
0.0405
0.0288
(1.07)
(1.14)
(0.99)
GDP per capita
-0.0053
-0.0044
-0.0120
(-0.49)
(-0.38)
(-1.07)
Constant
0.2824
0.2467
0.3190
(1.46)
(1.26)
(1.63)
Observations
5,109
5,109
5,109
R2
0.150
0.161
0.155
31
1,0
Trading volume
(4)
-0.0094
(-0.43)
0.0319
(1.35)
-0.0692***
(-3.03)
-0.0326
(-1.22)
-0.0001
(0.00)
-0.0261
(-0.80)
-0.0043
(-0.43)
0.0301
(1.26)
-0.0145
(-1.43)
0.1324***
(6.45)
-0.0555**
(-2.07)
0.0238
(0.52)
0.0344
(1.04)
-0.0104
(-0.86)
0.2809
(1.42)
5,109
0.166
Panel B: Summary of Fixed Effect Analyses with Additional Short Restriction Measures
0-4
0-4
1,0
Short-sale restriction
(1)
(2)
(3)
Short-sale restriction
0.0334*
0.0334*
0.1463*
(1.94)
(1.94)
(1.72)
Disagreement
0.0945***
(4.97)
(Short-sale restriction)*(Disagreement)
-0.0445***
(-3.56)
No uptick
0.017
(0.71)
Naked allowed
-0.028
(-1.21)
Dividend
0.015
0.015
0.013
(1.57)
(1.57)
(1.51)
Capital gain distribution
0.022
0.022
0.023
(1.25)
(1.21)
(1.22)
Fund size, log
0.015
0.015
0.016
(1.05)
(1.07)
(1.10)
Russell 2000 ret.
0.1224***
0.1226***
0.1222***
(5.60)
(5.49)
(5.81)
ETF =1
-0.021
-0.022
-0.021
(-0.79)
(-0.82)
(-0.81)
Puts =1
-0.0943*
-0.0940*
-0.0998*
(-1.70)
(-1.70)
(-1.82)
GDP per capita
0.002
0.002
-0.010
(0.07)
(0.05)
(-0.32)
Constant
-0.306
-0.305
-0.222
(-1.02)
(-1.02)
(-0.73)
Observations
5,109
5,109
5,109
Number of funds
44
44
44
R2
0.137
0.138
0.158
32
1,0
(4)
0.1465*
(1.72)
0.1157***
(5.31)
-0.1687***
(-4.50)
0.017
(0.69)
-0.029
(-1.21)
0.013
(1.51)
0.024
(1.18)
0.016
(1.13)
0.1223***
(5.65)
-0.023
(-0.86)
-0.0995*
(-1.82)
-0.010
(-0.35)
-0.218
(-0.72)
5,109
44
0.159