Investment barriers and premiums on closed-end country funds

International Review of Economics and Finance 19 (2010) 615–626

Contents lists available at ScienceDirect

International Review of Economics and Finance j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i r e f

Investment barriers and premiums on closed-end country funds Jang-Chul Kim a, Kyojik “Roy” Song b,⁎ a b

Northern Kentucky University, United States Business School, Sungkyunkwan University 53 Myoungnyun-dong, 3-ga, Jongno-gu, Seoul 110-745, Republic of Korea

a r t i c l e

i n f o

Article history: Received 17 June 2009 Received in revised form 9 November 2009 Accepted 19 January 2010 Available online 10 February 2010 JEL classification: G15 Keywords: Closed-end country funds Investment barriers Fund premium

a b s t r a c t We investigate the cross-sectional relation between investment barriers and premiums on closed-end country funds (CECFs) traded in U.S. markets over the period from 1995 to 2004. We find that funds investing in markets with higher indirect investment barriers as measured by market turnover and country risk have higher premiums. We also document that the relation between the country risk and CECF premium is much stronger after the stock market liberalization. Since investors prefer to invest in emerging markets with high indirect barriers through country funds, they increase the premiums of the funds targeting those countries. In addition, we find that direct investment barriers as measured by the investable weight factor do not explain the large variation in the CECF premiums. © 2010 Elsevier Inc. All rights reserved.

1. Introduction A closed-end fund is an investment company in which shares are listed on a stock exchange or are traded in the over-the-counter market. Since closed-end funds do not redeem their shares at net asset value (NAV), investors who wish to buy or sell closed-end fund shares have to do so in the open market. The premium (or discount), which is measured as (share price − NAV) / NAV in this paper, would be equal to zero in a perfectly efficient and internationally integrated market because the NAVs and the prices are two market prices of the same underlying assets. The persistent existence of discounts and the increase in assets under management of closed-end funds have motivated both theoretical and empirical researches. Previous literature has attributed several factors such as investment sentiment, agency costs, arbitrage costs, and illiquidity to the existence of the discounts.1 While closed-end country funds (CECFs hereafter) typically trade on U.S. exchanges, their NAV represents the value of underlying assets that are usually traded in each particular country. The average discount on CECFs has been lower than that on domestic equity funds and some CECFs trade at even premiums. Previous studies document that market segmentation stemming from barriers on foreign investments in emerging markets is the reason of the higher premiums (or lower discounts) on CECFs. If capital controls are the only factor explaining the higher CECF premium, the merit of CECFs should have decreased, since many emerging markets have lifted restrictions on foreign ownership since the late 1980s.2 Yet, the number of CECFs has dramatically increased in the 1990s. The presence of capital controls in emerging markets enables investors to earn diversification benefits from buying CECFs which invest in the countries. The diversification benefit hypothesis posits that the more difficult it is to invest and diversify directly in a foreign market, the greater is the diversification benefit that may be gained by investing indirectly in a closed-end

⁎ Corresponding author. Tel.: +82 2 760 0497; fax: +82 2 760 0440. E-mail address: [email protected] (K.“R.” Song). 1 2

Refer to Dimson and Minio-Kozerski (1999) for a survey of research on closed-end funds. Refer to Edison and Warnock (2003).

1059-0560/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.iref.2010.02.001

616

J. Kim, K.R. Song / International Review of Economics and Finance 19 (2010) 615–626

fund targeted at that country. The hypothesis suggests that there is a positive cross-sectional relation between direct investment barriers (or capital controls) and premiums on CECFs. Consistent with the diversification benefit hypothesis, Bonser-Neal, Brauer, Neal, and Wheatley (1990) and Chowdhury (1994) find that fund premiums decline upon the announcement of pending reductions in restrictions on foreign ownership of local assets, which suggests that capital controls are positively related to CECF premiums. Similarly, Patro (2005) documents that country fund premiums decrease following the announcement of new funds, suggesting that the ability to span a country fund using other new funds from the sample country plays an important role in explaining country fund premiums. Although these studies show that direct barriers explain the higher CECF premiums, they do not test the cross-sectional relation between the direct barriers and the fund premiums. In addition, the effect of the direct barrier on the fund premium should be weaker since developing countries have opened their markets to foreign investors in the late 1980s or early 1990s. Therefore, the relation between the direct barrier and the CECF premium after the market liberalization needs to be re-investigated. Unlike direct barriers, indirect investment barriers arise from differences in information, accounting standards, and investor protection, as well as from emerging market specific risks, which include political risk, macroeconomic instability, and liquidity risk. Bekaert (1995) argues that indirect barriers discourage foreign investment and lead to de facto segmentation. A few studies have investigated the relation between indirect barriers and CECF premiums and find conflicting evidence. Nishiotis (2004) examines the effects of indirect investment barriers on CECF premiums. He finds a positive relation between liquidity and premium and a negative relation between political risk and premium. However, Chan, Jain, and Xia (2008) find that underlying asset illiquidity increases the CECF premium in segmented capital markets. While Nishiotis' (2004) finding shows the negative relation between the indirect barrier and the CECF premium, Chan et al. (2008) suggest the positive relation between the two. Therefore, we need to re-investigate the cross-sectional relation between the indirect barriers and the CECF premiums to solve this controversy. This article focuses on the cross-sectional difference in CECF premiums over the period of the post stock market liberalization (1995–2004). We specifically investigate whether direct or indirect investment barriers explain the variation in the CECF premiums. Indirect barriers might not be binding in the presence of capital controls since strong capital controls of a country keep foreign investors from buying securities in the country regardless of the presence of indirect barriers. However, after many emerging markets removed capital controls, indirect barriers could be more important factors to explain the variation in CECF premiums. We construct cross-sectional time-series data for 437 fund-years representing 55 CECFs traded in the U.S. markets to investigate the relation between investment barriers and CECF premiums. To control for fund characteristics, we collect most of the data from annual reports and proxy statements over the period from 1995 to 2004. To measure indirect barriers, we use the International Country Risk Guide's political risk rating, economic risk rating, financial risk rating, composite risk rating, and exchange rate risk rating. We also use market turnover as another measure of indirect investment barriers and use the “investable weight factor (IWF)” as a measure of direct barrier. Although a few studies have used the IWF, it is our understanding that we are the first to use this measure to investigate the relation between direct investment barriers and CECF premiums.3 We find that funds investing in markets with higher economic, financial, and exchange rate risks have higher premiums (or lower discounts) after controlling for fund characteristics over the period of the post market liberalization. The result is inconsistent with Nishiotis' (2004) finding. Compared to his research that uses the sample funds investing in nine emerging markets, we use a broader sample consisting of the single-country funds investing in 18 emerging markets and 13 developed markets. In addition, Nishiotis (2004) does not control for fund characteristics in the regressions. We also document that funds investing in emerging markets with lower market turnovers (or illiquid markets) have higher premiums, which is consistent with Chan et al.'s (2008) findings. The results suggest that funds investing in the countries with higher indirect investment barriers tend to trade at higher premiums. We then examine whether the relation between country risk and CECF premium has changed after the stock market liberalization. We find that the effect of the indirect barrier on premiums of CECFs investing in emerging markets is much stronger over the period of 1995–2004. We also document that the measure of a direct investment barrier, the investable weight factor, is not related to the large variation in CECF premiums over the period of the post market liberalization. We contribute to the literature by re-investigating the relation between indirect barriers and CECF premiums. We revisit this issue since previous literature provides conflicting evidence. Nishiotis (2004) argues that indirect barriers decrease CECF premiums since they weaken foreign investors' willingness to invest in emerging markets. His argument is inconsistent with the diversification benefits hypothesis. Bekaert and Harvey (1995) argue that indirect barriers can make emerging markets segmented even though foreigners have relatively free access to the markets. When indirect barriers discourage investors from directly investing in the segmented emerging markets, CECFs can be good investment vehicles to invest in the emerging markets for individual investors without sophisticated investment skills. CECFs can incur diversification benefits for the individual investors by allowing them to indirectly invest in the segmented markets. Therefore, indirect barriers have a positive relation with CECF premiums as direct barriers. Our results are consistent with the diversification benefits hypothesis. We also document that direct barriers have an insignificant relation with CECF premiums while indirect barriers increase the fund premiums after the market liberalization. This is in contrast to the findings in the prior studies that the direct barrier (or capital controls) increases the fund premiums prior to the market liberalization. We interpret our result as signifying that

3

For instance, Bae, Chan, and Ng (2004) use this measure to examine the relation between return volatility and investibility.

J. Kim, K.R. Song / International Review of Economics and Finance 19 (2010) 615–626

617

investors do not put emphasis on direct investment barriers when they select single-country funds, because most of the restrictions on foreign investment in emerging markets have been removed since the late 1980s. 2. Data 2.1. Data collection and description on fund characteristics We analyze CECFs traded on U.S. exchanges over the sample period from 1995 to 2004. We select the year 1995 as the starting year because annual reports for most of the funds are electronically available, starting in 1995, from the SEC Edgar Database. Throughout the period, the Wall Street Journal reports net asset values, share prices, and premiums of closed-end funds on Mondays. We include only single-country funds listed under the sections “World Equity Funds” to examine whether investment barriers affect the premiums of CECFs. We do not include world bond funds because the pattern of premiums on bond funds is very different from that on equity funds. We exclude the fund if it has not existed at least three years over the sample period because we construct cross-sectional time-series data. In this way, we find 55 CECFs that have data available at some time during 1995 to 2004 and invest in 31 countries. Table 1 lists the name, ticker, exchange, listing date, and liquidation date of each fund, the country the funds invest in, and whether the country is an emerging or a developed country. The Table shows that 37 CECFs invested in 18 emerging markets, while 18 CECFs invested in 13 developed markets. We construct the panel data for the 55 CECFs to examine which factors explain the differences among premiums. Each year, some funds go public and others liquidate. We omit their IPO year4 and liquidation year5 because closed-end funds usually experience abnormal changes in premiums in those years. This selection criterion produces a different number of sample funds for each year. Our final panel data consist of 437 fund-years for the 55 CECFs. We collect net assets, dividends paid, expense ratio, unrealized capital gains, turnover ratio, the presence of a share repurchase program, and the presence of a minimum dividend policy from the funds' annual reports for each fund-year. We also collect inside ownership and blockholdings from the funds' proxy statements. We obtain monthly net asset values, market prices, and premiums (or discounts) from Lipper Analytic Services. We report descriptive statistics on fund characteristics in Table 2. The Table presents the mean, median, standard deviation, and the minimum and maximum of several measures, representing 437 fund-year observations. Appendix A describes variables used in this research. There is substantial variation across funds on all of the measures. Premiums on CECFs are calculated as (share price − net asset value) / net asset value. The premium is measured at the end of each month and averaged for each fund-year. Premiums widely fluctuate from − 32% to 82% with a mean of −9.93%. The mean of insider holdings is about 0.5%, while the mean of block holdings is about 10.99%. The portfolio turnover ratio ranges from 2.38% to 287%, while the expense ratio ranges from 0.28% to 8.89%. Fund size, measured by net assets, ranges from $5.21 million to $1301 million with a mean of $183.78 million. Unrealized capital gains as a percentage of total assets ranges from −88.95% to 88.43% with a mean of 6.59%. The mean of dividend payout ratios is 4.14%, while the mean of annual performance, calculated as (ending NAV + dividends paid − beginning NAV) / beginning NAV, is 8.82%. To measure the risk of each fund-year, we use the standard deviation of NAV returns for the previous 36 months. The mean standard deviation of NAV returns is 9.11%. The mean age is about 8.89 years. Out of 437 fund-years, 26 (5.95%) have a minimum dividend policy, while 176 (40.27%) have a share repurchase program. 2.2. Description on the measures of investment barriers To measure indirect barriers, we use political, economic, financial, composite and exchange rate risk ratings from the Political Risk Services' International Country Risk Guide (ICRG) for each country. These risk ratings are available for developed markets and emerging markets and are measured annually. Erb, Harvey, and Viskanta (1996) find that risk ratings are correlated with future equity returns even after they controlled for standard measures of risk in equity returns such as beta and standard deviation. The rating for political risk assesses political stability by assigning risk points to a pre-set group of factors, the political risk components. To calculate the rating, points are assigned to the following components: government stability, socioeconomic conditions, investment profile, internal conflict, external conflict, corruption, the role of the military in politics, religious tensions, law and order, ethnic tensions, democratic accountability, and bureaucracy quality. The ratings range from a high of 100 (least risk) to a low of 0 (highest risk). The rating for economic risk assesses economic stability by assigning risk points to a pre-set group of factors, the economic risk components. To calculate this rating, points are assigned to the following components: GDP per head of population, real annual GDP growth, annual inflation rate, budget balance as % of GDP, and current account as % of GDP. The ratings range from a high of 50 (least risk) to a low of 0 (highest risk). The rating for financial risk assesses financial stability by assigning risk points to a pre-determined group of factors, the financial risk components. To calculate the financial risk rating, points are assigned to the following components: total foreign debt as % of GDP, debt service as % of exports of goods and services, current account as % of exports of goods and services, international liquidity as months of import cover, and exchange rate stability as % of change. These ratings also range from a high of 50 (least risk) to a low of 0 (highest risk). 4 5

Weiss (1989) and Peavy (1990) find that closed-end funds tend to go public at a premium, and start to trade at a discount within six months after IPO. Brickley and Challheim (1985) find that prices of closed-end funds approach their NAV at open-ending or liquidation.

618

J. Kim, K.R. Song / International Review of Economics and Finance 19 (2010) 615–626

Table 1 List of sample funds. Country Developed markets Australia Austria France Germany

Ireland Italy Israel Japan Portugal Singapore Spain

Switzerland United Kingdom Emerging markets Argentina Brazil Chile China

Czech India

Indonesia Malaysia Mexico

Pakistan Philippines Russia South Africa South Korea

Taiwan

Thailand Turkey Vietnam

Fund name

Ticker

Exchange

Listing date

Aberdeen Australia Equity Fund Austria Fund France Growth Fund Germany Fund New Germany Fund Emerging Germany Fund New Ireland Fund Italy Fund First Israel Fund Japan Smaller Capitalization Fund Japan Equity Fund Portugal Fund Singapore Fund Scudder Spain and Portugal Fund*** Growth Fund of Spain Spain Fund Swiss Helvetia Fund United Kingdom Fund

IAF OST FRF GER GF FRG IRL ITA ISL JOF JEQ PGF SGF IBF GSP SNF SWZ UKM

AMEX NYSE NYSE NYSE NYSE NYSE NYSE NYSE AMEX NYSE NYSE AMEX NYSE AMEX NYSE NYSE NYSE NYSE

1985.12.31 1989.09.29 1990.05.31 1986.07.31 1990.01.31 1990.03.30 1990.03.30 1986.02.28 1992.10.30 1990.03.30 1992.08.31 1989.11.30 1990.07.31 1988.04.29 1990.02.28 1988.06.30 1987.08.31 1987.03.31

Argentina Fund Brazil Fund Brazilian Equity Fund Chile Fund The China Fund Greater China Fund Jardine Fleming China Region Fund Templeton China World Fund Templeton Dragon Fund Czech Republic Fund India Growth Fund The India Fund Morgan Stanley India Investment Fund Jardine Fleming India Fund Jakarta Growth Fund Indonesia Fund Malaysia Fund Mexico Fund Mexico Equity and Income Fund Emerging Mexico Fund Pakistan Investment Fund First Philippine Fund Templeton Russia Fund Morgan Stanley Russia and New Europe Fund New South Africa Fund Southern Africa Fund Fidelity Advisor Korea Fund Korean Investment Fund Korea Fund Korea Equity Fund Taiwan Fund Taiwan Greater China/ROC Taiwan Fund Taiwan Equity Fund Thai Fund Thai Capital Fund Turkish Investment Fund Templeton Vietnam Opportunity Fund

AF BZF BZL CH CHN GCH JFC TCH TDF CRF IGF IFN IIF JFI JGF IF MAY MXF MXE MEF PKF FPF TRF RNE NSA SOA FAK KIF KF KEF TWN TFC TYW TTF TF TKF TVF

NYSE NYSE NYSE AMEX NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE AMEX NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE AMEX NYSE NYSE

1991.10.31 1988.03.31 1992.04.30 1989.09.29 1992.07.31 1992.07.30 1992.07.31 1993.09.30 1994.09.30 1994.09.30 1988.08.31 1994.02.28 1994.02.28 1994.03.31 1990.04.30 1990.03.30 19.87.05.29 1981.06.30 1990.08.31 1990.10.31 1993.12.31 1989.11.30 1995.09.29 1996.09.30 1994.03.31 1994.02.28 1994.10.31 1992.02.28 1984.03.31 1993.11.30 1986.12.31 1989.05.31 1994.07.29 1988.02.29 1990.05.31 1989.12.29 1994.09.30

Liquidation date*

2002.04.30 2004.06.30

1999.04.30 2003.02.28

2004.06.30 1999.03.31 1998.12.31

1999.04.30

2001.12.31

2003.08.29 2002.10.31 2003.05.30

2004.05.28 2000.04.28

1999.04.30 2001.06.29 2003.06.30

1999.06.30 2004.11.30 2000.06.30 2001.11.30

2000.05.31

2002.09.30

Net assets** ($ in millions) 178.6 46.9 80.5 140.0 265.8 191.0 97.3 33.1 60.8 183.7 90.0 54.8 93.9 113.0 315.0 87.8 401.5 62.3

131.3 655.0 40.5 159.0 264.9 216.4 64.5 189.5 706.1 94.3 55.2 644.7 465.4 50.3 5.2 42.0 59.0 410.3 33.8 145.9 33.4 26.2 218.6 90.9 41.4 79.1 60.6 48.6 1301.0 48.8 209.2 116.5 67.6 110.4 27.9 63.3 42.4

*If funds were open-ended or liquidated before the end of 2004, the liquidation date is provided. **Net assets are measured at the end of 2004 or at prior-year end of liquidation year if funds are liquidated before 2004. ***About 70% of assets in the Scudder Spain and Portugal fund are invested in Spain.

To produce composite risk ratings, the ICRG system combines the component points for political, financial, and economic risks according to the following formula, which calculates the aggregate political, financial, and economic risk:

Composite risk rating of country X = 0:5 ðpolitical risk rating + financial risk rating + economic risk ratingÞ:

J. Kim, K.R. Song / International Review of Economics and Finance 19 (2010) 615–626

619

Table 2 Descriptive statistics of closed-end country fund characteristics. Variable

Mean

Median

Standard deviation

Minimum

Maximum

Premium Insider holdings Block holdings Expense ratio Turnover ratio Net assets ($ in millions) Unrealized capital gain Dividend yield Annual performance Standard deviation of monthly NAV returns Age Percentage of funds with minimum dividend policy Percentage of funds with share repurchase program

− 0.0993 0.0051 0.1099 0.0200 0.5704 183.78 0.0659 0.0414 0.0882 0.0911 8.89

− 0.1418 0.0050 0.0612 0.0188 0.4100 113.21 0.0911 0.0143 0.0518 0.0807 9.00

0.1501 0.0085 0.1356 0.0083 0.4920 205.00 0.2950 0.0635 0.3963 0.0402 4.23 5.95% 40.27%

− 0.3163 0 0 0.0028 0.0238 5.21 − 0.8895 0 − 0.7569 0.0259 1.00

0.8233 0.1402 0.5550 0.0889 2.8700 1301 0.8843 0.4148 2.1474 0.2471 23.00

Premiums are calculated as (market price−net asset value)/net asset value (NAV). The premiums are measured at the end of each month and averaged for the year. Insider holdings is insider ownership for each fund year. Blockholdings are the sum of beneficial ownership with 5% or more for the year. Friendly blockholdings are calculated as insider ownership plus blockholdings. The expense ratio is based on the expenses incurred for the year divided by the average net assets. The turnover ratio is the portfolio turnover ratio for the year. The fund size is the natural log of net assets. Unrealized capital gains are unrealized capital gains divided by total assets. Dividend yield is calculated by the dividends paid for the year divided by beginning net assets. Annual performance is calculated by (ending NAV − beginning NAV + dividends paid) / beginning NAV. Standard deviation of NAV returns are based on monthly NAV returns and are calculated as for performance, and the standard deviation of NAV returns is calculated for the previous 36 months. Shares repurchased represent the number of shares repurchased for the year divided by the number of shares outstanding at the end of the previous year. The full sample consists of 437 fund-years over the period from 1995 to 2004.

Therefore, the ratings range from a high of 100 (least risk) to a low of 0 (highest risk). The composite risk rating represents the country risk, and it is similar to political risk used in Nishiotis' (2004) research. Nishiotis (2004) uses the Institutional Investor's country credit ratings as a measure of political risk. The rating for exchange rate risk measures the stability of exchange rates. Risk ratings are assigned to the annual percentage change in the exchange rate of the national currency against the US dollar. The ratings range from a high percentage change of either 0.0–9.9% appreciation or 0.1–4.9% depreciation with risk ratings at 10, to a midpoint of either 50% appreciation or more or 30.0–34.9% depreciation with risk ratings at 5 to 100% depreciation or more with 0 ratings. The higher the point (10 is the highest), the lower the risk (0 is the lowest). We present composite risk ratings of each country over the period of 1995–2004 in Appendix B. It shows that, on average, Singapore and Switzerland have the highest composite risk ratings while Turkey and Pakistan have the lowest ratings. We also notice that the risk ratings for the countries most affected by the 1997 East Asian financial crisis, such as Indonesia, South Korea, and Thailand, decrease significantly over the crisis period from 1997 to 1998. We find that, as expected, the composite risk ratings for developed countries are higher, on average, than those of emerging countries. The standard deviation of the composite risk ratings for emerging countries is much higher than those for developed countries. For emerging markets, we add market turnover as another indirect investment barrier measure. We obtain monthly market turnovers for each market from the Standard and Poor's Emerging Markets Database (EMDB) and calculate average market turnovers each year over the sample period from 1995–2004. The greater the market turnover is, the greater the market liquidity is. Since illiquidity may discourage foreign investments, the market liquidity is an important component for measuring indirect barriers of emerging markets. We use the “Investable Weight Factor” (IWF) from EMDB to measure direct barriers. The IWF is the percentage of a stock's total capitalization that is available for foreign investment. It is adjusted for corporate cross-holdings and government-owned shares, and accounts for foreign ownership restrictions, such as limits on foreign investment. Therefore, the IWF can be used to measure direct barriers in each market. The IWF for each market is the average IWF of stocks traded in the market each year over the sample period from 1995 to 2004. An IWF of “unity” indicates that the market does not have any direct barrier on foreign investment. Most of the emerging markets in our sample have an IWF of less than one,6 as reported in Appendix B. 3. Empirical results 3.1. Comparison of developed vs. emerging market funds We first investigate whether the closed-end funds investing in developed markets vs. emerging markets have different characteristics. Panel A of Table 3 shows patterns of annual average premiums on developed market funds and emerging market funds over the sample period, 1995–2004.7 The average premium on emerging market funds tends to be higher throughout the sample period compared to that of developed market funds, which is consistent with Nishiotis (2004). The difference in average 6 Investable weight factors from EMDB are available for only emerging markets. We assume that investable weight factors are one for developed countries since the developed countries tend to have open markets to foreigners. 7 We include regional funds as well as single-country funds to calculate the monthly average premium in Fig. 1.

620

J. Kim, K.R. Song / International Review of Economics and Finance 19 (2010) 615–626

Table 3 Comparison of developed versus emerging market funds. Year

Developed market funds

Emerging market funds

Panel A. Average premiums of developed vs. emerging market funds year by year 1995 − 0.1349 − 0.0489 1996 − 0.1426 − 0.0825 1997 − 0.1367 − 0.1056 1998 − 0.1234 − 0.0872 1999 − 0.1440 − 0.1154 2000 − 0.2016 − 0.1952 2001 − 0.1556 − 0.1515 2002 − 0.1477 − 0.1217 2003 − 0.1408 − 0.0915 2004 − 0.1271 − 0.0659 Variable

Developed market funds (137 fund-years)

Emerging market funds (300 fund-years)

Panel B. Comparison of developed vs. emerging market fund characteristics Premium − 0.1286 − 0.0859 Insider holdings 0.0059 0.0047 Block holdings 0.1199 0.1054 Expense ratio 0.0163 0.0217 Turnover ratio 0.5911 0.5609 Net assets ($ in millions) 150.48 198.98 Unrealized capital gain 0.1062 0.0475 Dividend yield 0.0650 0.0307 Annual performance 0.0993 0.0831 Standard deviation of monthly 0.0694 0.1010 NAV returns Age 10.03 8.36

Difference between emerging and developed market fund premiums 0.0860 0.0601 0.0311 0.0362 0.0287 0.0064 0.0041 0.0260 0.0493 0.0612 Difference test (p-value)

b0.01 0.29 0.32 b0.01 0.55 b0.01 0.03 b0.01 0.46 b0.01 b0.01

Panel A presents average premiums of developed and emerging market funds year by year and the difference between them. Panel B compares the average characteristics of developed vs. emerging market funds. Premiums are calculated as (market price − net asset value) / net asset value (NAV). The premiums are measured at the end of each month and averaged for the year. Insider holdings is insider ownership for each fund-year. Blockholdings are the sum of beneficial ownership with 5% or more for the year. Friendly blockholdings are calculated as insider ownership plus blockholdings. The expense ratio is based on the expenses incurred for the year divided by the average net assets. The turnover ratio is the portfolio turnover ratio for the year. The fund size is the natural log of net assets. Unrealized capital gains are unrealized capital gains divided by total assets. Dividend yield is calculated by the dividends paid for the year divided by beginning net assets. Annual performance is calculated by (ending NAV − beginning NAV + dividends paid) / beginning NAV. Standard deviation of NAV returns are based on monthly NAV returns and are calculated as for performance, and the standard deviation of NAV returns is calculated for the previous 36 months. Shares repurchased represent the number of shares repurchased for the year divided by the number of shares outstanding at the end of the previous year. The full sample consists of 437 fund-years over the period from 1995 to 2004.

premiums between the fund groups becomes narrower after the Asian financial crisis of 1997–1998 and it gets wider again after 2001. Second, we investigate the mean differences of several measures representing 137 developed market fund-years and 300 emerging market fund-years and report the difference test results in Panel B of Table 3. The mean premium on emerging market funds is about 4% higher than that on developed market funds (-8.59% vs. − 12.86%), which suggests that closed-end fund investors pay a higher price when they buy emerging market funds. There are no differences in insider and block holdings between the two sub-samples. Emerging market funds have relatively higher expense ratios and larger fund sizes, pay lower dividends, and are younger, compared to developed market funds. The risk of emerging market funds, measured using the standard deviation of NAV returns, is significantly higher than that of developed market funds (10.10% vs. 6.94%). Other measures on the two subsamples are not statistically different. Overall, emerging market funds have larger size and higher premiums than developed market funds, even though they have higher risk, higher expense ratio, and lower dividend yield. In addition, emerging market funds do not perform better than developed markets funds.

3.2. The effect of investment barriers on CECF premiums We examine the correlation between premiums on CECFs and country risk ratings for the full sample, and report the results in Table 4. The country risk ratings measure indirect barriers. Pearson correlation coefficients between premiums on CECFs and any political, economic, composite, or exchange rate risk ratings are significantly negative, which means that funds investing in countries with higher risk ratings trade at a higher premium (or at a lower discount). This result suggests that investors increase the price of closed-end funds investing in riskier countries, which is not consistent with Nishiotis (2004)'s findings. The correlation coefficient between CECF premium and financial risk ratings is not statistically significant. Also, each risk rating is positively correlated with other risk ratings as expected.

J. Kim, K.R. Song / International Review of Economics and Finance 19 (2010) 615–626

621

Table 4 Pearson correlation between CECF premiums and country risk ratings.

Premium Exchange rate Political Economic Financial Composite

Premium

Exchange rate

Political

Economic

Financial

Composite

1

− 0.16 (b 0.01) 1

− 0.13 (b0.01) 0.22 (b0.01) 1

− 0.21 (b0.01) 0.40 (b0.01) 0.68 (b0.01) 1

− 0.005 (0.92) 0.55 (b 0.01) 0.42 (b 0.01) 0.64 (b 0.01) 1

− 0.16 (b 0.01) 0.41 (b 0.01) 0.91 (b 0.01) 0.87 (0.01) 0.72 (0.01) 1

Premiums are calculated as (market share − net asset value) / net asset value (NAV). The premiums are measured at the end of each month and averaged for the year. Financial, political, economic, exchange rate, and composite country ratings are taken from the International Country Risk Guide (ICRG). Political risk ratings range from a high of 100 (least risk) to a low of 0 (highest risk). Economic risk ratings range from a high of 50 (least risk) to a low of 0 (highest risk). Financial risk ratings range from a high of 50 (least risk) to a low of 0 (highest risk). Composite risk ratings is (0.5* (political risk rating + financial risk rating + economic risk rating)). Exchange rate risk ratings range from a high of 10 (least risk) to a low of 0 (highest risk).

Next, we examine whether the variation in premiums on CECFs are related to country risk ratings after controlling for fund characteristics. We estimate the following equation using fixed-effects models: PREMi;t = α + βXi;t + ϕRc;t ; where PREMi,t is a premium for fund i and year t, β is a vector of coefficients on a vector of fund characteristics, Xi,t, and ϕ are vectors of coefficients on a vector of investment barriers for country c and year t. We estimate the equation using fixed-effects models since our sample is a time-series cross-sectional data. The fixed effect model assumes that differences across funds can be captured in differences in the constant term. The fund effect is taken to be constant over year t and specific to the individual crosssectional fund i. If we take the constant terms to be the same across funds, ordinary least squares provides consistent and efficient estimates of the estimators. Table 5 presents the regression results for the fixed-effects models. We include the following control variables from prior studies in all the regressions: fund size (natural log of net assets) to proxy for arbitrage costs (see Pontiff (1996)); the ratio of expenses to net assets and friendly blockholdings (inside holdings plus blockholdings) to proxy for agency costs (see Barclay, Holderness, & Pontiff, 1993; Kumar & Noronha, 1992; Malkiel, 1977); the ratio of unrealized capital gains to assets (see Malkiel (1977)); dividend yield (see Pontiff (1996)); annual performance (annual return on NAV, see Malkiel (1977)); average premium on domestic equity funds to proxy for U.S. investors' sentiment (see Bodurtha, Kim, & Lee, 1995; Gutierrez, Martinez, & Tse, 2009; Lee, Schleifer, & Thaler, 1991); and a dummy variable for a minimum dividend policy (Johnson, Lin, & Song, 2006). The coefficients on fund size are significantly negative from Model 1 to Model 5, which supports the arbitrage costs argument by Pontiff (1996) who finds that the larger the fund is, the larger are the discounts to the NAV. The coefficients on friendly blockholdings are significantly negative with p-values of less than one, which is consistent with the manager entrenchment argument by Barclay et al. (1993). Consistent with the findings of Bodurtha et al. (1995), the average premium on domestic equity funds, which proxies for U.S. investors' sentiment, is positively correlated to the premiums of CECFs. We also find that funds with a minimum dividend policy trade at higher premiums, which is consistent with the finding of Johnson et al. (2006). Other control variables are not statistically different from zero. In the tests reported in Table 5, we mainly focus on the relation between fund premiums and the measures of direct and indirect barriers. The coefficients on investable weight factor are not significant from Model 1 to Model 5, which means that direct barriers are not related to CECF premiums. We also find that the correlation coefficient between investable weight factor and the CECF premium is − 0.02 with a p-value of 0.70, which suggests that the direct barrier measure does not explain the variation in CECF premiums. Country risk ratings are used to measure indirect barriers. The coefficient on political risk ratings is insignificant (see Model 2), which is not consistent with Nishiotis's (2004) finding that premiums are higher for funds investing in a country with higher political risk. However, the coefficients on exchange rate, economic, financial, and composite risk ratings in Models 1, 3, 4, and 5 are significantly negative. Because higher ratings indicate lower risk, the results suggest that the funds investing in markets with higher economic, financial, composite, or exchange rate risks tend to have higher premiums. The distance between New York and overseas exchanges can be another proxy for the indirect barrier. In untabulated tests, we include dummy variables indicating funds investing in Asia, Africa, Europe, and Central and South America in the regressions to investigate the relation between the distance and CECF premiums. We find that only the funds investing in Asia have higher premiums. In Table 4, we find that the correlation coefficient between political risk and fund premiums is significantly negative and the correlation coefficient between financial risk and fund premiums is not significant. However, the results in Table 5 show that the effect of political risk on fund premiums is not significant and the effect of financial risk is significant. We find that controlling for fund effects make the results different. Overall, the results in Tables 4 and 5 suggest that the indirect barriers, measured by country risk ratings, increase CECF premiums. The results are consistent with Chan et al. (2008) but inconsistent with Nishiotis (2004). Although individual investors can directly invest in securities in most emerging markets, investors might prefer investing in those markets through closed-end funds due to higher indirect barriers. This can lead to higher premiums for country funds that invest in securities in countries with higher risk ratings.

622

J. Kim, K.R. Song / International Review of Economics and Finance 19 (2010) 615–626

Table 5 Fixed-effects models on the full sample. Financial, political, economic, exchange rate, and composite country ratings are taken from the International Country Risk Guide (ICRG). Variable

Model 1

Model 2

Model 3

Model 4

Model 5

Fund size

− 0.06 (b0.01) 1.49 (0.16) − 0.16 (b0.01) − 0.03 0.39) 0.08 (0.44) 0.03 (0.10) 1.19 (b0.01) 0.08 (0.03) 0.07 (0.15) − 0.14 (b0.01)

− 0.04 (0.02) 1.77 (0.11) − 0.20 (b0.01) − 0.06 (0.06) 0.07 (0.55) 0.03 (0.21) 1.09 (b0.01) 0.07 (0.05) 0.08 (0.12)

− 0.03 (0.09) 1.70 (0.11) − 0.17 (b0.01) − 0.03 (0.40) 0.08 (0.48) 0.01 (0.82) 1.04 (b0.01) 0.08 (0.02) 0.01 (0.89)

− 0.04 (0.05) 1.48 (0.17) − 0.21 (b 0.01) − 0.04 (0.23) 0.06 (0.57) 0.02 (0.39) 1.20 (b 0.01) 0.06 (0.09) 0.05 (0.34)

− 0.03 (0.09) 1.49 (0.17) − 0.19 (b 0.01) − 0.04 (0.21) 0.08 (0.46) 0.01 (0.59) 1.23 (b 0.01) 0.07 (0.06) 0.03 (0.59)

Expense ratio Friendly blockholdings Unrealized capital gains Dividend yield Annual performance Average premium on domestic equity funds Dummy for minimum dividend policy Investable weight factor Exchange rate risk ratings Political risk ratings

0.23 (0.15) − 1.09 (b0.01)

Economic risk ratings

− 0.52 (b 0.01)

Financial risk ratings Composite risk ratings Number of fund-years used R2

430 0.53

430 0.51

430 0.54

430 0.52

− 0.54 (b 0.01) 430 0.53

The regression equations are estimated using fixed-effects models. The dependent variable is a premium on closed-end country funds. Premiums are calculated as (market price − net asset value) / net asset value (NAV). The premiums are measured at the end of each month and averaged for the year. Fund size is the natural log of net assets. The expense ratio is based on the expenses incurred for the year divided by average net assets. Friendly blockholdings are measured by calculating insider ownership plus blockholdings. Unrealized capital gains are unrealized capital gains divided by total assets. Dividend yield equals the dividends paid for the year divided by beginning net assets. Annual performance is (ending NAV − beginning NAV + dividends paid) / beginning NAV. Average premium on domestic equity funds are the average monthly premiums on domestic equity funds each year. The dummy variables for minimum dividend policy is 1 if the fund has the payout policy, otherwise it is 0. Investable weight factor is the percentage of a stock's total capitalization that is available for foreign investment. The numbers in parentheses are p-values.

Following Chan et al. (2008), we test whether the relation between indirect barriers and fund premiums is more significant in emerging markets funds, and report the results in Table 6. We add the interaction term between a dummy variable indicating emerging markets and composite risk ratings in Model 1. The coefficient on the composite risk ratings is now insignificant, but the coefficient on the interaction term is significantly negative. This indicates that the effect of country risk on fund premium is more prominent in emerging market funds. We then limit our sample to emerging markets funds (300 fund-years) in Models 2, 3, and 4 for further tests. The coefficient on composite risk ratings in Model 2 is more negative with −0.61, compared to the coefficient of −0.54 in Model 5 of Table 5. This also suggests that the effects of indirect barriers on fund premiums are more significant in emerging market funds. We find that the effect of current fund performance on fund premium is not significant in Table 5. The past performance of funds can predict the premiums if investors buy the funds based on the past performance. We use the performance of a previous year in Model 3 of Table 6 to test this possibility. The coefficient on the past performance is 0.06 with p-value of 0.01, which indicates that the better performance in the previous year increases the fund premium in the current year. This is consistent with the finding of Chay and Trzcinka (1998). We include market turnover in Model 4 as another measure of indirect barriers. We find that the correlation coefficient between market turnover and CECF premiums is −0.16 with a p-value of 0.005, which suggests that investors pay higher premiums for funds investing in emerging markets with lower liquidity (market turnover). We also find in Model 4 that the market turnover is negatively related to CECF premiums, which means that the funds investing in emerging markets with lower liquidity tend to have higher premiums. This result is consistent with the finding of Chan et al. (2008). 3.3. Indirect barriers and CECF premiums after stock market liberalization To investigate whether the effect of indirect barrier on CECF premiums has changed after the stock market liberalization in developing countries, we extend the data on premiums and composite risk ratings back to 1988. We then divide the full sample

J. Kim, K.R. Song / International Review of Economics and Finance 19 (2010) 615–626

623

Table 6 The effects of indirect barriers on premiums of emerging markets funds. Composite country ratings are taken from the International Country Risk Guide (ICRG). Variable

Fund size Expense ratio Friendly blockholdings Unrealized capital gains Dividend yield Annual performance

Full sample

Emerging markets funds only

Model 1

Model 2

Model 3

Model 4

− 0.02 (0.12) 1.42 (0.19) − 0.19 (b 0.01) − 0.04 (0.22) 0.07 (0.49) 0.01 (0.61)

− 0.01 (0.66) 1.05 (0.40) − 0.29 (b 0.01) − 0.09 (0.04) − 0.11 (0.49) 0.02 (0.34)

− 0.01 (0.61) 0.93 (0.48) − 0.20 (b0.01) − 0.07 (0.05) − 0.19 (0.35)

− 0.04 (0.12) 1.13 (0.37) − 0.26 (b 0.01) − 0.11 (b 0.01) − 0.02 (0.93) 0.05 (0.04)

Past performance Average premium on domestic equity funds Dummy for minimum dividend policy Investible weight factor Composite risk ratings Dummy for emerging markets*composite risk ratings

1.23 (b 0.01) 0.08 (0.03) 0.02 (0.73) 0.36 (0.49) − 1.02 (0.06)

1.47 (b 0.01)

0.06 (b0.01) 1.26 (b0.01)

0.02 (0.71) − 0.67 (b 0.01)

0.04 (0.61) − 0.61 (0.01)

Market turnover Number of fund-years used R2

430 0.53

293 0.52

265 0.56

1.30 (b 0.01)

0.02 (0.72)

− 0.29 (b 0.01) 293 0.52

The regression equations are estimated using fixed-effects models. The dependent variable is a premium on closed-end country funds. Premiums are calculated as (market price − net asset value) / net asset value (NAV). The premiums are measured at the end of each month and averaged for the year. Fund size is the natural log of net assets. The expense ratio is based on the expenses incurred for the year divided by average net assets. Friendly blockholdings are measured by calculating insider ownership plus blockholdings. Unrealized capital gains are unrealized capital gains divided by total assets. Dividend yield equals the dividends paid for the year divided by beginning net assets. Annual performance is (ending NAV − beginning NAV + dividends paid) / beginning NAV, and past performance is measured during the previous year. Average premiums on domestic equity funds are the average monthly premiums on domestic equity funds each year. The dummy variable for minimum dividend policy is 1 if the fund has the minimum dividend policy, otherwise it is 0. Investable weight factor is the percentage of a stock's total capitalization that is available for foreign investment. Dummy for emerging markets composite risk ratings is the interaction term between a dummy variable indicating emerging markets and composite risk ratings. Market turnover is the average of monthly market turnovers each year for each country. The numbers in parentheses are p-values.

into sub-samples in the stock market liberalization period (1988–1994) and in the post liberalization period (1995–2004). To control for fund characteristics, we obtain the data on market capitalization and dividend yield from Center for Research in Security Prices (CRSP). We use composite risk ratings to measure indirect barrier since the risk ratings represent the country risk and are similar to political risk used in Nishiotis' (2004) research. We first examine Pearson correlation coefficients between the composite risk ratings and CECF premiums. The correlation coefficient is 0.03 with a p-value of 0.71 over the period from 1988–1994 while it is −0.16 with a p-value of less than 0.01 over the period from 1995–2004. The result indicates that the effect of the country risk on the fund premium becomes significant after the market liberalization. We then run the regressions on the CECF premiums using the extended sample, and report the results in Table 7. In Models 1 and 2, we use the full sample over the period of 1988–2004. The coefficients on control variables, log of market capitalization, dividend yield, and average premium on domestic equity funds have expected signs. The coefficient on composite risk ratings in Model 1 is −0.44 with a p-value of less than 0.01, which means that CECFs investing in countries with higher country risk have higher premiums. We add the interaction term between composite risk ratings and the dummy variable indicating the period after the market liberalization in Model 2. The coefficient on the interaction term is negative and significant, which means that the relation between CECF premiums and composite risk ratings is much stronger in the period after the market liberalization (1995– 2004). We then use the data in the market liberalization period (1988–1994) in Model 3. The coefficient on composite risk ratings is not significant, which means that there is no relation between country risk and CECF premiums over the period from 1988–1994. The results are consistent with our argument that the effect of indirect barriers on CECF premiums is minimal before the market liberalization. After emerging markets removed the capital controls, the indirect barriers make an impact on investors' decisions when they invest in the markets.

624

J. Kim, K.R. Song / International Review of Economics and Finance 19 (2010) 615–626

Table 7 The effects of indirect barriers on premiums before and after the market opening. Composite country ratings are taken from the International Country Risk Guide (ICRG). Variable

Log of market capitalization Dividend yield Average premium on domestic equity funds Composite risk ratings

The whole period (1988–2004)

For the data before the market opening (1988–1994)

Model 1

Model 2

Model 3

− 0.02 (0.09) 0.25 (b0.01) 0.72 (b0.01) − 0.44 (b0.01)

− 0.02 (0.09) 0.26 (b 0.01) 0.73 (b 0.01) − 0.22 (0.09) − 0.12 (b 0.01) 656 0.44

− 0.02 (0.18) 0.58 (b 0.01) 0.44 (0.12) − 0.04 (0.86)

Composite risk ratings * Dummy for the period after the market opening Number of fund-years used R2

656 0.38

210 0.36

The regression equations are estimated using fixed-effects models. The dependent variable is premiums on closed-end country funds. Premiums are calculated as (market price − net asset value) / net asset value (NAV). The premiums are measured at the end of each month and averaged for the year. The log of market capitalization is measured at the end of each year. Dividend yield equals the dividends paid for the year divided by beginning net assets. Average premiums on domestic equity funds are the average monthly premiums on domestic equity funds each year. The dummy for the period after the market opening takes 1 if the data belongs to the period of 1995–2004 and 0 otherwise.

Overall, our findings show that the direct investment barrier does not explain the cross-sectional variation in the premiums of CECFs investing in emerging markets funds. Since the late 1980s, most emerging markets have lifted direct barriers on foreign investments, and investors can directly invest in those markets. However, the indirect barrier discourages individual investors who lack sophisticated investment skills from directly investing in emerging markets with higher risk ratings or lower market liquidity. These investors can invest in emerging markets through closed-end country funds and increase the premium of the funds. This might explain our findings that funds investing in markets with higher indirect barriers have higher premiums. 4. Conclusion We investigate whether direct and indirect investment barriers explain the cross-sectional variation in the premiums of closed-end country funds after controlling for fund characteristics in the period of the post stock market liberalization. We find no relation between the direct barrier, measured by the investable weight factor, and CECF premiums for emerging markets. We also find that funds investing in markets with higher indirect barriers (or lower market turnover and higher country risk) have higher premiums. We argue that for closed-end investors, most of whom are individual investors, the direct investment barrier does not seem an important factor because most countries have lifted controls on foreign investments since the late 1980s. However, the investors seem to be concerned about indirect barriers when they invest in foreign markets. Higher indirect barriers discourage individual investors from directly investing in emerging markets. Closed-end country funds seem to be good investment vehicles when they invest in emerging markets with high indirect barriers. These indirect barriers explain some cross-sectional variation in premiums of closed-end country funds.

Acknowledgement This paper was supported by the Faculty Research Fund, Sungkyunkwan University, 2006.

Appendix A. Descriptions on variables

A.1. Variables on fund characteristics Premiums: premiums are calculated as (market share − net asset value) / net asset value (NAV). The premiums are measured at the end of each month and averaged for the year. Insider holdings: insider ownership for the year. Blockholdings: sum of beneficial ownership with 5% or more for the year. Friendly blockholdings: insider ownership + blockholdings.

J. Kim, K.R. Song / International Review of Economics and Finance 19 (2010) 615–626

625

Expense ratio: expenses incurred for the year divided by average net assets. Turnover ratio: portfolio turnover ratio for the year. Fund size: a natural log of net assets. Unrealized capital gains: unrealized capital gains divided by total assets. Dividend yield: dividends paid in the year divided by beginning net assets. Annual performance: (ending NAV − beginning NAV + dividends paid) / beginning NAV. Standard deviation of NAV returns: monthly NAV returns are calculated as for performance, and the standard deviation of NAV returns is calculated for the previous 36 months.

A.2. Variables for investment barriers Political risk ratings: these ratings range from a high of 100 (least risk) to a low of 0 (highest risk). Economic risk ratings: these ratings range from a high of 50 (least risk) to a low of 0 (highest risk). Financial risk ratings: these ratings range from a high of 50 (least risk) to a low of 0 (highest risk). Composite risk ratings: 0.5* (political risk rating + financial risk rating + economic risk rating). Exchange rate risk ratings: these ratings range from a high of 10 (least risk) to a low of 0 (highest risk). Investable weight factor (IWF): the percentage of a stock's total capitalization that is available for foreign investment. It is adjusted for corporate cross-holdings and government-owned shares, and accounts for foreign ownership restrictions, such as limits on foreign investment. The IWF for each market is the average IWF of stocks traded in the market each year over the sample period from 1995 to 2004. An IWF of “unity” indicates that the market does not have any direct barrier on foreign investment, while an IWF of less than one means that the markets have some direct barrier. Market turnover: the market turnover of each country for the sample period 1995–2004. The monthly market turnovers are averaged to obtain the annual market turnover.

Appendix B. Country risk ratings and investable weight factors B.1. Composite risk ratings

Country

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Developed markets Australia Austria France Germany Ireland Italy Israel Japan Portugal Singapore Spain Switzerland United Kingdom

82 82.5 78.5 85.5 84 76.5 72.5 86 82 86 76 88.5 80.5

85.5 89.5 82.5 85 88.5 82.5 68.5 89.5 85.5 91 80.5 88.5 83

81.5 84.25 82 83 86 82.25 69.5 86.25 83.25 91.25 81.25 87.25 83.5

78.25 85.25 82 83.75 88.25 81 65.7 83.25 84.5 89.25 79.75 87 83.5

81.8 83 78.3 81.3 86.8 75.5 69.2 83.3 80.8 89.3 76 87 84.3

80.5 81.5 78.5 84.3 84.3 78.8 68 83.8 78.8 90.5 79 89.5 83.5

81.5 86.5 81 83.8 88.8 81.8 67.7 84.3 78.3 89.3 80.5 92.5 83.5

82.5 86.8 81.3 82.8 88.8 79.3 65.2 85.3 78.3 90 80.8 91.5 82

81.8 86 78 81.3 87.3 78.3 73 87 78.3 87.5 79.8 89.5 84

83 85.5 75.5 78.8 85.5 79.3 69.2 85 76.5 89.8 79.3 91.3 78

Emerging markets Argentina Brazil Chile China Czech India Indonesia Malaysia Mexico Pakistan Philippines Russia South Africa South Korea Taiwan Thailand Turkey Vietnam

71.5 63.5 79.5 72.5 83 68.5 69.5 80.5 68.5 59 67.5 62.5 76.5 81.5 84.5 77 60 60.5

73.5 67 82 74.5 83.5 69 70 81.5 70 62 71.5 62.5 72.5 85 87 81 57 70.5

75.25 67.5 80.5 75 74.5 66.75 60.25 72.5 70.75 59.75 70.75 64.7 74.5 75.75 83.25 65.25 53 65.7

74.5 65.25 74.75 75.5 76.7 64.75 41 67.75 67.5 53.75 69 49 68 70 85 69.25 52.25 58.5

67.5 59.5 69 74 74.5 64.3 51.8 74.5 68.8 53.3 71 49.7 69.5 79.3 83.5 74 52.8 64

68.8 64.5 74.8 73.8 73.2 61.8 54.8 75.8 73 53.8 65 66.2 68 78 82.5 75.3 55.5 70

64.8 62.5 76.3 74.3 75.7 65.3 56.3 76 70.8 56 70 69.5 68.8 79.3 81.5 73.8 48.5 69.5

48 62.3 76.8 75 76.2 66.3 58.3 77.5 70.8 58.5 71 70 68.8 79.8 82 76.3 59.8 70.2

64.3 67.3 77 77.3 77.7 69.5 60.8 77 70.5 64 69 75.2 70.8 81.3 83 76 62.5 69.2

67.5 69.8 80 75.5 75.7 71.8 63.8 80 75.5 63.5 69.5 77.5 73.8 82.5 83.8 73.8 67.3 69.7

626

J. Kim, K.R. Song / International Review of Economics and Finance 19 (2010) 615–626

B.2. Investable weight factors for emerging markets Country

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Argentina Brazil Chile China Czech India Indonesia Malaysia Mexico Pakistan Philippines Russia South Africa South Korea Taiwan Thailand Turkey Vietnam

0.88 0.70 0.79 0.13 0.08 0.19 0.42 0.97 0.80 0.45 0.43 NA 0.68 0.13 0.11 0.28 0.85 NA

0.85 0.66 0.83 0.14 0.07 0.14 0.48 0.86 0.75 0.37 0.42 NA 0.66 0.18 0.20 0.31 0.85

0.85 0.63 0.84 0.16 0.08 0.14 0.55 0.87 0.81 0.40 0.40 0.44 0.66 0.23 0.30 0.30 0.82

0.89 0.65 0.83 0.22 0.12 0.14 0.82 0.85 0.79 0.36 0.37 0.55 0.70 0.67 0.34 0.28 0.78

0.92 0.72 0.72 0.24 0.23 0.15 0.63 0.74 0.77 0.27 0.29 0.31 0.80 0.75 0.45 0.26 0.78

0.87 0.68 0.69 0.24 0.19 0.16 0.59 0.77 0.84 0.28 0.28 0.35 0.89 0.87 0.48 0.31 0.71

0.65 0.64 0.58 0.21 0.16 0.15 0.34 0.59 0.72 0.22 0.26 0.35 0.80 0.79 0.41 0.26 0.60

0.62 0.58 0.52 0.24 0.16 0.16 0.22 0.40 0.54 0.25 0.22 0.37 0.76 0.73 0.44 0.20 0.51

0.50 0.57 0.52 0.24 0.23 0.18 0.22 0.35 0.47 0.25 0.30 0.39 0.72 0.72 0.48 0.23 0.55

0.52 0.53 0.54 0.23 0.35 0.20 0.26 0.33 0.44 0.29 0.32 0.39 0.72 0.69 0.53 0.23 0.49

Composite risk ratings are calculated as 0.5* (political risk rating + financial risk rating + economic risk rating). These ratings range from a high of 100 (least risk) to a low of 0 (highest risk). Investable Weight Factor (IWF) is the percentage of a stock's total capitalization available for foreign investment. It is adjusted for corporate cross-holdings and government-owned shares, and accounts for foreign ownership restrictions, such as limits on foreign investment. The IWF for each market is the average IWF of stocks traded in the market each year over the sample period from 1995 to 2004. An IWF of “unity” indicates that the market does not have any direct barrier on foreign investment, while an IWF of less than one means that the markets have some direct barrier.

References Bae, K., Chan, K., & Ng, A. (2004). Investibility and return volatility. Journal of Financial Economics, 71, 239−263. Barclay, M. J., Holderness, C. G., & Pontiff, J. (1993). Private benefits from block ownership and discounts on closed-end funds. Journal of Financial Economics, 33, 263−291. Bekaert, G. (1995). Market integration and investment barriers in emerging equity markets. World Bank Economic Review, 9, 75−107. Bekaert, G., & Harvey, C. R. (1995). Time-varying world market integration. Journal of Finance, 50, 403−444. Bonser-Neal, C., Brauer, G., Neal, R., & Wheatley, S. (1990). International investment restrictions and closed-end country fund prices. Journal of Finance, 45, 523−547. Bodurtha, J. N., Kim, D., & Lee, C. M. C. (1995). Closed-end country funds and U.S. market sentiment. Review of Financial Studies, 8, 879−918. Brickley, J. A., & Challheim, J. S. S. (1985). Lifting the lid on closed-end investment companies: A case of abnormal returns. Journal of Financial and Quantitative Analysis, 20, 107−117. Chan, J. S. P., Jain, R., & Xia, Y. (2008). Market segmentation, liquidity spillover, and closed-end country fund discounts. Journal of Financial Markets, 11, 377−399. Chay, J., & Trzcinka, C. A. (1998). Managerial performance and the cross-sectional pricing of closed-end funds. Journal of Financial Economics, 52, 379−408. Chowdhury, A. R. (1994). The behavior of closed-end country fund prices in the Asian NIEs. Applied Economics Letters, 1, 219−222. Dimson, E., & Minio-Kozerski, C. (1999). Closed-end funds: A survey. Financial Markets, Institutions, and Instruments, 8,: New York University Salomon Center. Edison, H., & Warnock, F. (2003). A simple measure of the intensity of capital controls. Journal of Empirical Finance, 10, 81−103. Erb, C. B., Harvey, C. R., & Viskanta (1996). Political risk, economic risk, and financial risk. Financial Analysts Journal, 52, 29−46. Gutierrez, J. A., Martinez, V., & Tse, Y. (2009). Where does return and volatility come from? The case of Asian ETFs. International Review of Economics and Finance, 18, 671−679. Johnson, S. A., Lin, J., & Song, K. R. (2006). Dividend policy, signaling, and discounts on closed-end funds. Journal of Financial Economics, 81, 539−562. Kumar, R., & Noronha, G. (1992). A re-examination of the relationship between closed-end fund discounts and expenses. Journal of Financial Research, 15, 139−147. Lee, C. M. C., Schleifer, A., & Thaler, R. H. (1991). Investor sentiment and closed-end puzzle. Journal of Finance, 44, 76−110. Malkiel, B. G. (1977). The valuation of closed-end investment company shares. Journal of Finance, 32, 847−859. Nishiotis, G. P. (2004). Do indirect investment barriers contribute to capital market segmentation? Journal of Financial and Quantitative Analysis, 39, 613−630. Patro, D. K. (2005). Stock market liberalization and emerging market country fund premiums. Journal of Business, 78, 135−168. Peavy, J. W. (1990). Returns on initial public offerings of closed-end funds. Review of Financial Studies, 3, 695−708. Pontiff, J. (1996). Costly arbitrage: Evidence from closed-end funds. Quarterly Journal of Economics, 111, 1135−1151. Weiss, K. (1989). The post-offering performance of closed-end fund shares. Financial Management, 18, 57−67.