Out of Sight, Out of Mind: When Proximities Matter for Mutual Fund Flows

Abstract

We analyze the aggregate investment of 22,900 worldwide mutual funds and question factors that promote accessibility to foreign stock markets for these investors when they allocate their assets outside their domestic market. A gravity model is proposed to test the influence of geographical, institutional and cognitive proximity in explaining asset trading by mutual funds. While mutual funds invest primarily in large stock markets and in countries with similar legal systems and the same language or culture, we find robust evidence of a geographical pattern in the distribution of their assets. Investments are located primarily in countries close to home, attesting that despite the globalization of stock markets and the high mobility of capital, geography is still relevant for understanding transactions of mutual funds. Results depending on which geographical, institutional and cognitive proximity promotes accessibility to foreign markets remain robust when introducing the issue of time horizons of investors.

Key words:

• geography of finance
• accessibility
• proximity
• mutual funds
• gravity model

JEL Classification:

• G10
• G15
• G23
• G28
• K00

Notes

¹ The global mutual funds’ industry had financial assets under management equivalent to $28.87 trillion at the end of 2013, four times the $6 trillion of assets managed at the end of 1996 (Investment Company Institute 2013).

² In line with our available data on worldwide mutual funds.

³ In 1962, Tinbergen used kilometric distance for the first time to analyze economic flows between countries.

⁴ See Anderson (1979) for a theoretical analysis of gravity models.

⁵ Some resistant factors may limit the accessibility to foreign markets.

⁶ In our calculations, we tested for the presence of any heteroscedasticity problem with the Breuch-Pagan test and when we detected heteroscedasticity we estimated a robust OLS equation using White’s correction.

⁷ In our model, we consider investments of mutual funds from country i to country j but not their domestic investments.

⁸ The CEPII database makes available a gravity data set for all pairs of countries in the world for the period 1948 to 2006, from which international flows can be estimated. The Institutional Profiles Database provides indicators for a wide range of institutional characteristics of countries. We refer to Index 6 of the database: Security of Transactions and Contracts. For a complete description of the database, see De Crombrugghe et al. (2009).

⁹ The index ranges from 0 to 4 (4 for good quality institutions and 0 for poor quality institutions).

¹⁰ We refer to the index of La Porta, Lopez-de-Silanes, and Shleifer (1999). The antidirector rights index measures how strongly the legal system favors minority shareholders against managers or dominant shareholders in the corporate decision making process. The antidirector right is an index ranging from 0 to 6 (La Porta et al. 1998).

¹¹ See Murdock (1967) and Kendall et al. (1976).

¹² Appendix 1 ranks countries according to the size of their market capitalization.

¹³ We exclude bond funds from our analyses.

¹⁴ This test is used to detect the colinearity of the regressors with the constant.

¹⁵ The best model is the one whose information criterion has the lowest value.

¹⁶ The standardized coefficient of Origin of Law is .0455905 while the Institutional Profiles coefficient is -.1726482 in Equation 3e.

¹⁷ In Equation 3d standardized coefficients are -.102298 for Distance, .1304618 for Institutional Profiles and .1164946 for Cultural Area.

¹⁸ When we introduce more than 3 dummies, the results of VIF tests are higher than 300.

¹⁹ In Equation 4e, we use values of distance (unstandardized coefficient), values of institutional lag (unstandardized coefficient), the value of the interactive variable (unstandardized coefficient), plus the intercept (unstandardized coefficient).

²⁰ This distinction between patient and impatient mutual funds is the one proposed by TOBO.