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Determinants of flows into retail international equity funds

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Abstract

Diversification benefits appear to be a major reason why investors choose international equity funds. Funds less correlated with the US market tend to receive higher flows from investors. In addition, investors prefer funds that invest in a diversified portfolio of securities from different regions in the world to funds that focus only on a specific region. Risk-adjusted return is shown to exert greater effect on flows into international equity funds than raw return. International equity funds from fund families offering a greater number of investment objectives also receive higher flows. On the other hand, international equity fund investors do not appear to be sensitive to expenses or exchange rates.

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Notes

  1. Several studies (Greene & Hodges, 2002; Goetzmann, Ivković, & Rouwenhorst, 2001) investigate the day trading of international equity funds. The focus of these studies is the fair pricing issue, and the interaction between daily flows and returns for international equity funds, instead of investors' long-term behavior. The rest of the literature on international equity funds focuses on their performances (Cumby & Glen, 1990; Droms & Walker, 1994).

  2. It should be noted that a measure based on regional diversification cannot substitute for a measure based on diversification at the security level – a measure based on the number and weights of different securities in the portfolio. Unfortunately, owing to the lack of portfolio constituent data, such a security-level diversification measure is not studied in this paper.

  3. Even if all data back to 1962, the first year of the CRSP data, are included, observations from 1992 to 2001 will still account for 93.04% of all observations. International equity funds were rare before 1992. The number of international equity funds did not reach 50 until 1987. Given the dominance of the 1992–2001 data, I believe the same qualitative results will still be obtained even if I use data back to 1962.

  4. For the description of each investment objective, please refer to Appendix A to the CRSP Survivor-Bias Free US Mutual Fund Database Guide.

  5. I also conduct tests using data at the portfolio level and obtain the same qualitative results, which are not reported in the paper.

  6. See Chapter 2 of the CRSP Survivor-Bias Free US Mutual Fund Database Guide for details.

  7. Unfortunately, data limitation prevents further analysis of the effect of currency risk. For example, no data can be obtained as to how a fund predicts exchange rate changes, or whether a fund hedges currency risk.

  8. Del Guercio and Tkac (2002) also try to control for any effect to flows due to merger.

  9. As a result, 218 observations are excluded, which account for 0.94% of all observations.

  10. The well-known MSCI EAFE Index covers only developed economies in Europe, Australasia, and Far East.

  11. As an example of indirectly quoted exchange rates, US$1=100 yen. An increase in the exchange rate indicates that the US dollar appreciates.

  12. Data on RM are downloaded from Ken French's website (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).

  13. The high correlation is consistent with findings in the literature, which suggest that foreign stocks respond contemporaneously to common news that affects US stock prices (Eun & Shim, 1989; Goetzmann et al., 2001).

  14. The differences mentioned above have all been shown to be significant based on tests of the equality of medians or means between regionally diversified and focused funds (results not reported).

  15. The panel regression method is used to account for the fact that observations from the same fund are not independent relative to one another in this time-series cross-sectional (panel) data set.

  16. Because SHARPE is calculated with data in the past 12 months (see Eq. (3)), the rolling nature makes SHARPE i , t −1 and SHARPE i , t −2 highly correlated with each other (0.628). Owing to multicollinearity concerns, SHARPE i , t −1 and SHARPE i , t −2 2 cannot be included in the same regression. In addition, SDRET is not included either, because SDRET is used as the denominator to compute SHARPE (see Eq. (3)).

  17. A separate study also reveals that fund age (AGE) and the natural log of fund assets (LASSET) are highly correlated with each other (0.456). As a result, AGE is not included in the model owing to multicollinearity concerns.

  18. Based on coefficient estimates from Table 2 and standard deviations reported in Table 1, for raw return, 5.88=12.68 × 0.299+12.682 × 0.013; for Sharpe ratio, 13.17=0.515 × 17.667+0.5152 × 15.333. Even if I also take the effect of RAW t −2 into consideration, the effect of Sharpe ratio is still more than 40% greater. Similar analysis is also performed in Barber et al. (2005).

  19. As related evidence, international equity fund investors appear to be very sensitive to the risk of a fund, as represented by SDRET.

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Acknowledgements

I would like to thank Lemma W. Senbet (the departmental editor), two anonymous referees, John Boschen, Chun Chang, Li Cui, Bing Liang, Erik Lie, George Oldfield, Chris Taber, Wanda Wallace, Yuan Xiao, and seminar participants at the 2006 China International Conference in Finance and the European Financial Management Association 2006 Annual Conference for very helpful comments. I acknowledge the financial support of China Europe International Business School (CEIBS). Any errors are my responsibility.

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  1. China Europe International Business School, Shanghai, China

    Xinge Zhao

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Correspondence to Xinge Zhao.

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Accepted by Lemma Senbet, Departmental Editor, 29 November 2007. This paper has been with the author for two revisions.

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Zhao, X. Determinants of flows into retail international equity funds. J Int Bus Stud 39, 1169–1177 (2008). https://doi.org/10.1057/palgrave.jibs.8400408

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