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Article Excerpt
I. INTRODUCTION
A closed-end fund is a publicly traded investment company that holds a portfolio of securities, whose composition is typically determined by the fund manager. Once started, a closed-end fund trades on the secondary market and sells either at a discount or a premium from its underlying value. Typically, it trades initially at a premium, but starts selling at a discount several months later. This fact, and the related observation that large premiums/ discounts are not arbitraged away, lie at the center of the so-called "closed-end fund puzzle" (De Long et al., 1990; Lee et al., 1991; Bodurtha et al., 1995; Pontiff, 1996; Gemmill and Thomas, 2002).
While a great deal of academic work has sought to solve the closed-end fund puzzle, the factors responsible for the time variation of a fund's premium have received little attention. This is the aim of this study and its contribution to the existing literature. The focus is on a relatively new group of closed-end funds--emerging-market (EM) funds, whose underlying portfolios comprise equity and/or bond securities from emerging economies. EM funds have generated increasing interest among investors in the early 1990s (1), but have fallen out of grace by the beginning of 2000s (2). Currently approximately 40 EM funds are listed in the United States with an aggregate market capitalization slightly exceeding 13.95 billion dollars.
Previous studies show that while EM fund premiums depend on the U.S. market returns through fund prices (Hardouvelies et al., 1993; Bodurtha et al., 1995), they are also sensitive to the local market returns and foreign exchange risk due to fund NAVs (Hardouvelies et al., 1993). Domowitz, Glen, and Madhavan (1998) find evidence of a positive and significant relationship between the premium of a U.S.-based Mexican closed-end fund and changes in Mexico's risk premium after the Mexican currency crisis of 1994, which they largely attribute to the market segmentation hypothesis. The importance of U.S. investor sentiment and its relationship with fund premiums have been documented by Hardouvelies et al. (1993), Bodurtha et al. (1995), and Gemmill and Thomas (2002), among others. While both Hardouvelies et al. (1993) and Bodurtha et al. (1995) propose U.S. investor sentiment as a systematic component that explains the variation in EM fund premiums, Gemmill and Thomas (2002) argue that the U.S. investor sentiment fails to account for cross-sectional differences in EM country fund premiums.
Well anchored in this body of work, this paper explores the determinant factors of time variation in emerging markets closed-end fund premiums, prices, and NAVs. In addition to the variables previously proposed in the international closed-end funds literature, such as the U.S. stock market return, local stock market return, U.S. investor sentiment, and the change in the local currency/U.S, dollar exchange rates, I also incorporate the country credit risk, excess volatility, and fund liquidity in the regression models. To examine whether the findings are sensitive to fund type, I group the funds in the sample into three categories: country funds, regional and global equity funds, and global bond funds.
Consistent with the U.S. investor sentiment hypothesis of Bodurtha et al. (1995), premiums and prices of the majority of EM funds in my sample fully capture movements in the U.S. investor sentiment, while fund underlying assets (which determine the NAV) display absolutely no exposure to the U.S. investor sentiment. This finding reflects the time-varying sentiment of U.S. fund investors relative to their foreign counterparts.
Another interesting implication emerging from the difference between the investor base of an EM fund and that of its underlying portfolio lies in the market segmentation hypothesis. If it holds true, U.S. investors may react more slowly than local investors to perceived changes in country credit risk, widening or narrowing a fund's premium. While I find evidence of a strong positive impact of credit risk on bond fund premia, premiums of regional and global equity funds in my sample are in general negatively correlated with the credit spread changes, and country fund premia show no exposure to credit risk.
The rest of the paper proceeds as follows. Section II describes the data. Section III presents the methodology and summarizes the empirical results. Section IV concludes.
II. Data
1. Closed-end funds
The initial sample consists of 57 emerging-market closed-end funds publicly traded on U.S. exchanges between January 1, 1990 and December 31, 2006. For each fund, weekly prices and net asset values (NAVs) were collected from the Wall Street Journal, and weekly volume data from the Center for Research in Security Prices (CRSP) database. Both share prices and NAVs are reported in US dollars. Typically international closed-end fund NAVs are reported as of Friday's close in the foreign country, but few funds are valued as of either Wednesday or Thursday's close (3).
To be eligible for inclusion in this study, a fund has to have a minimum of three years of weekly data. Given its short NAV history, one fund (Emerging Tigers Fund) was excluded from the initial sample, leaving a total of 56 emerging-market closed-end funds in the final sample. Based on their composition, thirty one of the resulting funds are classified as single country funds, 15 are regional and global equity funds, and 10 are global bond funds. Country funds are predominantely equity invested. Descriptive statistics for the closed-end funds in the sample are given in Table 1. The fund premium is computed as follows:
premium = price - NAV/NAV x 100
A negative premium indicates a discount.
Consistent with previous research, most closed-end funds in my sample trade on average at a discount from their NAVs. Table 1 shows that on average regional and global equity funds sell at deeper discounts than either country funds or global bond funds. However, with premiums going as high as 16.37% (Thai Fund) and as low as -17.31% (Pakistan Investment Fund), country funds are by far the most cross-sectionally volatile in my sample. In order to obtain additional insight into the time-series...
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