SHORT SALE RESTRICTIONS, DIFFERENCES OF OPINION, AND SINGLE- COUNTRY, CLOSED-END FUND DISCOUNT
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1 SHORT SALE RESTRICTIONS, DIFFERENCES OF OPINION, AND SINGLE- COUNTRY, CLOSED-END FUND DISCOUNT Lee W. Sanning a, Alexandre Skiba b, Hilla Skiba b,* a Department of Economics, Whitman College, United States b 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: ; Fax: Address: hskiba@uwyo.edu
2 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
3 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
4 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
5 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 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
6 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
7 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 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
8 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 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
9 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 , many countries implemented either temporary or permanent short-sale restrictions. The short-sale restrictions data for the 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 directly 1 and supplemented by the data from Grunewald, Wagner, and Weber (2010) for the 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 Briefings on market practice by Knowledge for Markets 2 Standard and Poor s Sovereign defaults and rating transition data, 2010 update from and Columbia Management s Costs of Sovereign Debt Defaults from 8
10 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 Methodology Computing the discount Consistent with prior literature, we compute the closed-end fund discount as: Discount f, t = MV NAV f, t f, t NAV f, t (1) 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 % (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
11 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
12 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
13 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: Volume ( P * ST ) / SO (2) n, i n, i n, i n, i Where P n,i is the daily closing price of security n, ST n,i is the daily number of shares traded of security n, and SO n,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
14 Abnormal Volume Volume Volume (3) n, i n, i n where the average is computed as a prior 60-day moving average volume of security n: Volume n Volume 1 ni, (4) i 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: Abnormal Volume nt, T i t Abnormal Volume T ni, (5) The monthly volume of security n is the summation of all daily trading volumes in month t: Volume T Volume (6) n, t n, i 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: Abnormal Volume P * SO N n, t n, t N, t * Abnormal Volume N n, t n 1 Pn, t * SOn, t n 1 Country N s monthly disagreement measure based on volume is: Volume P (7) * SO N n, t n, t N, t * Volume N n, t n 1 Pn, t * SOn, t n 1 (8) 13
15 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 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
16 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
17 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. 16
18 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
19 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 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
20 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 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
21 disagreement and short constraint which corresponds to about % 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
22 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
23 References Barnhart, S. W., and S. Rosenstein, Exchange-traded fund introductions and closed-end fund discounts and volume, Financial Review 45, Berk, J., and R. Stanton, Managerial ability, compensation, and the closed-end fund discount, Journal of Finance 62, Berkman, H., V. Dimitrov, P. Jain, P. Koch, and S. Tice, Sell on the news: Differences of opinion, short-sale constraints, and returns around earnings announcements, Journal of Financial Economics 93, Bodurtha J. Jr., D. Kim, and C. Lee, Closed-end country funds and US Market sentiment, Review of Financial Studies 8, Boehme, R., B. Danielsen, and S. Sorescu, Short sale restrictions, differences of opinion, and overvaluation, Journal of Financial and Quantitative Analysis 41, Bonser-Neal, C., G. Brauer, R. Neal, and S. Wheatley, International investment restrictions and closed-end funds prices, Journal of Finance 45, Boudreaux, K., Discounts and premiums on closed-end mutual funds: A study in valuation, Journal of Finance 28, Bris, A., W. Goetzmann, and N. Zhu, Efficiency and the bear: Short sales and markets around the world, Journal of Finance 62, Campbell, C., and C. Wasley, Measuring abnormal trading volume for samples of NYSE/ASE and NASDAQ securities using parametric and nonparametric test statistics, Review of Quantitative Finance and Accounting 6, Chae, J., Trading volume, information asymmetry, and timing information, Journal of Finance 60, Charoenrook, A., and H. Daouk, A study of market-wide short-selling restrictions, Working paper. Chen, J.,.H. Hong, and J. Stein, Breadth of ownership and stock returns, Journal of Financial Economics 66, De Long, J., A. Shleifer, L. Summers, and R. Waldmann, Noise trader risk in financial markets, Journal of Political Economy 98, Diether, K., C. Malloy, and A. Scherbina, Differences of opinion and the cross-section of stock returns, Journal of Finance 57, Gemmill, G., and D. Thomas, Noise trading, costly arbitrage, and asset prices: evidence from closed-end funds, Journal of Finance 57, Grunewald, S. N., A. F. Wagner, and R. H. Weber, Short selling regulation after the financial crisis First principles revisited. International Journal of Disclosure and Governance 7,
24 Hong, H., and J. Stein, Differences of opinion, short sales restrictions, and market crashes, Review of Financial Studies 16, Jones, C., and O. Lamont, Short-sale constraints and stock returns, Journal of Financial Economics 66, Lee, C., A. Shleifer, and R. Thaler, Investor sentiment and the closed-end fund puzzle, Journal of Finance 46, Miller, E. M., Risk, uncertainty, and divergence of opinion, Journal of Finance 32, Ofek, E., and M. Richardson, DotCom mania: The rise and fall of internet stock prices, Journal of Finance 58, Pontiff, J., Costly arbitrage: Evidence from closed-end funds, Quarterly Journal of Economics 111, Zweig, M. E., An investor expectations stock price predictive model using closed-end fund premiums, Journal of Finance 28,
25 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 COUNTRY CEF DISCOUNT SIZE DIV. YIELD ABERDEEN AUSTRALIA EQTY FD AUSTRALIA -7.1% 168, % ABERDEEN CHILE FUND INC CHILE -10.6% 164, % ABERDEEN INDONESIA FUND INC INDONESIA 4.3% 46, % ABERDEEN ISRAEL FUND INC ISRAEL -11.6% 60, % ARGENTINA FUND INC ARGENTINA -16.9% 105, % AUSTRIA FUND INC AUSTRIA -16.0% 94, % BRAZIL FUND BRAZIL -15.2% 377, % BRAZILIAN EQUITY FUND INC BRAZIL -14.1% 38, % CHINA FUND INC CHINA -7.9% 298, % EMERGING MEXICO FUND MEXICO -17.3% 101, % FIDELITY ADVISOR KOREA FD SOUTH KOREA -4.5% 43, % FIRST PHILIPPINE FUND INC PHILIPPINES -14.6% 76, % FRANCE GROWTH FUND INC FRANCE -14.0% 147, % GREATER CHINA FUND INC CHINA -12.0% 205, % GROWTH FUND OF SPAIN INC SPAIN -14.4% 263, % INDIA FUND INC INDIA -7.3% 874, % INDIA GROWTH FUND INC INDIA -20.1% 82, % ITALY FUND INC ITALY -13.0% 97, % JAKARTA GROWTH FUND INC INDONESIA 4.8% 26, % JAPAN EQUITY FUND INC JAPAN -0.6% 87, % JAPAN SMALL CAP FUND INC JAPAN 0.7% 148, % JF CHINA REGION FUND INC CHINA -12.9% 72, % KOREA EQUITY FUND INC SOUTH KOREA -8.3% 61, % KOREA FUND SOUTH KOREA -7.9% 739, % KOREAN INVESTMENT FUND INC SOUTH KOREA -6.6% 50, % MALAYSIA FUND MALAYSIA -4.4% 70, % MEXICO EQUITY & INCOME FUND MEXICO -11.7% 70, % MEXICO FUND INC MEXICO -14.6% 573, % MORGAN STANLEY CHINA A SH FD CHINA -2.6% 463, % NEW GERMANY FUND INC GERMANY -16.0% 296, % NEW SOUTH AFRICA FUND INC SOUTH AFRICA -15.6% 57, % PAKISTAN INVESTMENT FUND INC PAKISTAN -17.4% 42, % SCUDDER SPAIN&PORTUGAL FUND SPAIN -15.7% 83, % SINGAPORE FUND INC SINGAPORE -11.2% 89, % SOUTHERN AFRICA FUND INC SOUTH AFRICA -16.2% 68, % SWISS HELVETIA FUND SWITZERLAND -15.1% 343, % TAIWAN EQUITY FUND INC TAIWAN -15.5% 53, % TAIWAN FUND INC TAIWAN -10.9% 253, % TEMPLETON CHINA WORLD FD INC CHINA -16.4% 176, % TEMPLETON RUSSIA & E EURO FD RUSSIA 5.3% 175, % THAI CAPITAL FUND INC THAILAND 3.0% 30, % THAI FUND INC THAILAND 7.0% 124, % TURKISH INVESTMENT FUND INC TURKEY 1.2% 91, % UNITED KINGDOM FUND UNITED KINGDOM -13.9% 55, % 24
26 Table 2. Target Market Characteristics COUNTRY Short Sale Rule Naked Short sales allowed (Yes/No) Uptick rule (Yes/No) ETF, since GDP per cap ARGENTINA Banned until Allowed since 1999, but not widely practiced. No No, since Jul 1999 Mar ,815 AUSTRALIA Allowed and practiced. Banned between Sep 2008 and Nov Restricted Yes, but banned since Jan Yes, but rule removed in Nov Mar ,882 between Dec 2008 and May AUSTRIA Allowed and practiced. Yes No Mar ,795 BRAZIL Allowed, but not widely practiced. No No Jul ,868 CHILE Banned until Dec Allowed, but not widely practiced since Jan No No, since Jan 1999 Nov ,683 Allowed and practiced since Dec CHINA Banned. N/A N/A Mar ,670 FRANCE Allowed and practiced. Yes No Mar ,585 GERMANY Allowed and practiced. Yes, but banned in Dec No Mar , INDIA Allowed, but not widely practiced. Banned since May Yes (until ban) No (until ban) Feb INDONESIA Banned. Allowed but not widely practiced since May Yes (since ban lifted) No (since ban lifted) Jan ,000 ISRAEL Banned. N/A N/A Mar ,564 ITALY Allowed and practiced. Banned between Oct 2008 and Dec Restricted Yes, but banned between No Mar ,852 between Jan 2009 and May Oct 2008-Jul 2009 JAPAN Allowed and practiced. Yes, but banned between No, but implemented in Jan Mar ,360 Oct 2008-Oct MALAYSIA Allowed, but not widely practiced between Jan1996 and Aug Allowed No Yes Mar ,174 and practiced between Sep 1996 and Jul Banned between Aug 1997 and Jan Allowed and practiced since Jan MEXICO Allowed and practiced. Yes Yes Mar ,814 PAKISTAN Banned. N/A N/A None 469 PHILIPPINES Allowed, but not widely practiced. Yes Yes Sep ,050 RUSSIA Allowed and practiced. Banned between Sep 2008 and Jun Yes, but banned between No, but implemented in Sep Apr ,424 Sep 2008-Jun SINGAPORE Allowed and practiced. Yes, but banned in Sep 2008 No Mar ,496 SOUTH AFRICA Allowed and practiced. No No Feb ,319 SOUTH KOREA Allowed, but not widely practiced. Banned between Oct 2008 and May No Yes Mar , SPAIN Allowed, but not widely practiced. No No Mar ,083 SWITZERLAND Allowed and practiced. No No Mar ,063 TAIWAN Allowed, but not widely practiced. Banned between Oct 2008 and Nov Yes, but banned between No, but implemented between Jun ,368 Oct 2008-Nov Sep 2008-Jan 2009) THAILAND Banned until Oct Allowed, but not widely practiced between Nov No Yes, but rule removed in Oct Mar , and Dec Allowed and practiced from Jan TURKEY Allowed, but not widely practiced. Yes Yes Mar ,793 UNITED KINGDOM Allowed and practiced. Yes No Mar ,372 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
27 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
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