D B S －１９－０３

Do Global Equity Mutual Funds
Exhibit Home Bias?

Ming Liu
Doshisha University
Takato Hiraki
Tokyo University of Science
Xue Wang
University of International Business and Economics
March 2019

Abstract
We study a sample of U.S. global equity mutual funds that invest in markets around theworld. While the existing literature shows significant home bias among investors, wefind that U.S. global funds on average do not exhibit home bias but with cross-fundvariation. Fund manager teams with more foreign educational background, younger
fund managers, and those with MBA degrees tend to invest more in international
markets. When sorting funds into deciles by the level of home bias, we find a convex
relationship between home bias and performance. Both home- and foreign-biased funds
outperform medium decile funds and hold concentrated portfolios, supporting theinformation hypothesis for home and foreign bias. Further, the well-performing
foreign-biased funds are much larger than other funds, indicating that there are no
decreasing returns to scale among global equity mutual funds.
DBS Discussion Paper Series supported by the OMRON Foundation.DBS

Do Global Equity Mutual Funds Exhibit Home Bias?
Ming Liu
Doshisha University
Takato Hiraki
Tokyo University of Science
Xue Wang
University of International Business and Economics
March 2019

Abstract
We study a sample of U.S. global equity mutual funds that invest in markets around theworld. While the existing literature shows significant home bias among investors, we find that U.S.
global funds on average do not exhibit home bias but with cross-fund variation. Fund manager
teams with more foreign educational background, younger fund managers, and those with MBA
degrees tend to invest more in international markets. When sorting funds into deciles by the levelof home bias, we find a convex relationship between home bias and performance. Both home- andforeign-biased funds outperform medium decile funds and hold concentrated portfolios,
supporting the information hypothesis for home and foreign bias. Further, the well-performing
foreign-biased funds are much larger than other funds, indicating that there are no decreasing
returns to scale among global equity mutual funds.

JEL Classification: G11; G15; G23
Key Words: Home bias; Global equity mutual funds

11. Introduction
Home bias is a puzzling phenomenon in which investors over-invest in domestic financial
assets relative to their weights in the global market portfolio, thus forgoing the benefits ofinternational diversification. Since the early work of French and Poterba (1991), Cooper andKaplanis (1994), and Tesar and Werner (1995), many papers have demonstrated strong home bias
in international investment for investors from different countries. Over the past two decades,
global equity markets have become more integrated, but home bias persists (Karolyi and Stulz,
2003). Researchers have proposed various explanations for home bias, such as barriers tointernational investment, hedging of foreign exchange risk, differing labor market risk,
withholding tax, information asymmetry, and behavioral bias. However, no one factor alone
appears sufficient to justify the magnitude of globally observed home bias. Cooper, Sercu andVanpee (2012) suggest that a combination of the above factors would help explain the puzzle,
with information asymmetry and economic openness the most important.
Most of the empirical evidence for home bias focuses on country-level investment data.
One problem with such studies on home bias at aggregate level is that the dispersion of home bias
among investors remains largely undiscovered. We show new insight on the home bias
phenomena by utilizing fund-level holdings data of U.S.-based global equity mutual funds
investing in global markets, including their home-country market and international markets.1 We
explore the relation between fund allocation in domestic and international markets and manager
characteristics, as well as performance. Compared to country-level studies, one clear advantage
of our study is that, to a large extent, we eliminate the potential effects of macro-level and1

Global equity mutual funds are expected to provide international exposure to fund investors and they normally statethat they will invest certain proportion of portfolio in foreign (or domestic) stocks. However fund managers have thediscretionary power to decide the allocation in domestic and foreign stocks. For example, Vanguard Global Equity
Fund writes in the product summary that “This fund invests in companies of varies sizes from all over the globe, with
the United States representing about 40% of its assets”, however the Fund invests 52% of its asset in U.S. stocks in
May 2017.

2

institutional factors in global investment, such as capital restrictions, withholding tax, and countrylevel uncertainties. All global equity funds domiciled in the U.S. should face institutional
constraints on an equal basis when managers select stocks globally. This allows us to more
rigorously investigate the issue related to home bias explanations: the information vs. behavioral
hypothesis.
Several theoretical papers suggest an information-based explanation for home bias. Gehrig(1993) shows that in a noisy rational expectations model with two countries and two risky assets,
the higher precision of domestic signals than foreign signals results in investor bias toward
domestic shares in equilibrium. Van Nieuwerburgh and Veldkamp (2009) develop a two-country
general equilibrium model in which investors have more (less) information endowment on
domestic (foreign) assets than the average investor of the two countries. In contrast to previous
studies, their model allows investors to choose which information to learn from to maximize theirmean-variance utility. The equilibrium outcome is that home investors choose to learn home risk
factors, which results in a home bias in their portfolios, because home assets bring more profit andappear less risky.
Many empirical studies provide supporting evidence on the information-based explanationof home bias. Kang and Stulz (1997) find that foreign investors investing in Japan tend to holdstocks of large and export-oriented companies that are more well-known to foreigners. Ahearne,
Griever and Warnock (2004) find that the more companies of a foreign country listed on the U.S.
stock market, the more U.S. investors invest in that country. Andrade and Chhaochharia (2010)
find that U.S. foreign direct investment in a destination country in the 1990s is positively related
to U.S. portfolio investment in that country, consistent with the learning hypothesis proposed by
Van Nieuwerburgh and Veldkamp (2009).
Home bias may also be caused by behavioral factors. For example, Huberman (2001),
Hiraki et al. (2003) and Pool et al. (2012) show that investors may perceive familiar assets to have3higher expected return and less return dispersion. Graham, Harvey and Huang (2009) show thatthe more competent investors consider themselves, the more international stocks they invest in.
Morse and Shive (2011) show that country-level patriotism is positively correlated with holdingsof domestic assets by investors.
Home bias at the fund-level is of great importance and interest. First, studying international
investment at the granular level helps us better understand the home bias puzzle documented at
the country-level. Secondly, global fund managers are sophisticated investors who have better
research resources and learning capacity than the average investors modeled in previous work.
Global funds can simply hire managers with an information advantage on foreign countries (e.g.,managers with foreign backgrounds). Alternatively, domestic fund managers may apply what theylearn in domestic markets about specific industries to global markets (Schumacher, 2015). In thelatter case, smart global investment is simply an extension of smart domestic investment. We
address these issues by examining the relations between fund home bias and manager and fundcharacteristics.
Thirdly, it would be interesting to test the implications of information asymmetries and/or
behavioral biases from the viewpoint of professional money managers managing assets entrusted
to them by investors. In fact, some papers (e.g., Van Nieuwerburgh and Veldkamp, 2009, Hau andRey, 2008) call for theoretical work to study home bias in a framework of agency problems as
faced by mutual fund managers. Mutual fund managers may have different objectives than
individual client investors due to principal–agent conflicts (He and Xiong, 2013). Our study on
global fund managers provides several useful empirical findings on fund-level home bias and thus
helps this immediacy called by theoreticians in the literature.
Finally, we address the relationship between competitions and return performance. The
mutual fund industry is very competitive in that fund investors chase funds’ risk-adjusted returns.

4Global funds in particular fiercely compete with each other to provide domestic investors with
global market exposure. Whether home bias at the fund-level, if any, is an equilibrium outcome ofhigh competition in the industry remains an interesting question. We address these issues by
examining the relation between fund-level home bias and performance.
Our paper sets forth several interesting findings. First, we find that U.S. global equity
mutual funds do not exhibit home bias on average. From 1999 to 2014, a median fund over-invests
in domestic assets by insignificant four percentage points to the U.S. market weight in the global
market portfolio. Moreover, the portfolio weight of U.S. stocks has stayed relatively stable overtime. This finding is in sharp contrast with previous studies that document a high level of homebias. While we find no home bias on average, we observe some cross-sectional variation in fundlevel domestic holdings.2
Next, we explain these variations using fund manager characteristics and fund variables.
We find that fund manager teams with more foreign educational backgrounds, younger managers,
and managers with MBA degrees tend to invest more in international stocks, while older managers
and female managers tend to invest more in domestic markets. The finding on managers withforeign educational backgrounds is consistent with both the information and behavioral hypotheses.
In the sample period 1999-2014, the mean proportion of all managers that have foreign education
background is 35%, while the mean proportion of all fund manager teams that include at least one
such manager is 57%.3
To examine fund characteristics at different levels of home bias in detail, we sort funds
into deciles by portfolio weight of U.S. stocks. We find that fund size increases with fund’sinvestment in foreign stocks. Fund size can be a proxy for fund research resources. This result

When we study cross-fund variations on home bias, we simply focus on fund’s portfolio weight on U.S. stocks
because all funds face the same benchmark – U.S. equity market weigh in the global market – at any point of time. In
other words, we use the terms “home bias” and “portfolio weight in U.S. stocks” interchangeably when we examine
fund level variations in home bias. We find this direct measure easier to interpret.
3
We define a manager with foreign education background if she has received either undergraduate or graduate degreefrom a university outside U.S.
2

5

suggests that funds increase international investment significantly along with research capability.
Alternatively, this result may be caused by the fact that funds with more international investments
attract more fund flows, probably due to good performance or provision of international exposure.
Another interesting finding is that both home- and foreign-biased funds hold more concentrated
portfolios.
We differentiate the information and behavioral hypotheses on home (and foreign) bias byexamining performance. Information advantage would lead to a positive relation between the levelof home bias and performance (Van Nieuwerburgh and Veldkamp, 2009), however behavioral bias
would imply a neutral relation at best. We sort funds into deciles based on level of home bias andmeasure the subsequent six-month returns of each decile portfolio. Interestingly, we find a convex
relationship between level of home bias and fund raw returns: the deciles with the highest and thelowest home bias (i.e., highest foreign bias) perform better than the intermediate deciles; however,the performance of the two extreme decile portfolios are indistinguishable. We then risk-adjust
fund portfolio returns using the U.S. four-factor (U.S. Fama–French three factors plus U.S.
momentum factor), global four-factor (global Fama–French three factors plus global momentum
factor), U.S. six-factor (U.S. Fama–French five factors plus U.S. momentum factor), and global
six-factor (global Fama–French five factors plus global momentum factor) models. The convex
relation remains in the risk-adjusted returns. Since funds with more international investment tend
to have larger size, we follow Berk and Binsbergen (2015) to measure the value added by fundmanagers. We use both Vanguard index funds and global six factors as benchmarks. The convex
relationship between home bias and fund value added remains.
In summary, we show that home-biased funds tend to be much smaller and hire lessmanagers with foreign education background. The foreign-biased funds are much larger and hire
more managers with foreign education background. Both home- and foreign-biased funds holdconcentrated portfolios and outperform medium-biased funds. Also, fund size increases with level6

of foreign bias. If fund size is a good proxy to research resource, the results suggest that both thesmallest and largest global funds optimally allocate assets in domestic and international stock
markets based on their research resources and capacities. In addition, it suggests that there is no
diminishing returns to scale in global equity funds, similar to the findings in Ferreira et al. (2013).
The present paper relates to several strands of literature. In addition to extensive studies on
country-level home bias, several papers investigate home bias at the individual portfolio level. Hau
and Rey (2008) show that there is wide dispersion in fund-level home bias across funds andcountries. 4 Coval and Moskowitz (1999) find that U.S. mutual fund managers have strong
preference for local firms, or home bias at home. Other papers study whether households invest
internationally and the associated investor characteristics, using Swedish data (Calvet, Campbell
and Sodini, 2007; Karlsson and Norden, 2007; Norden, 2010), survey data from the U.S. investors(Graham, Harvey and Huang, 2009), and a proprietary data set from a U.S. investment advisor(Bekaert et al., 2016). Very few papers, however, show the performance consequences of homebias, especially in the international investment setting. Our paper relates differing degrees of homebias among U.S.-based global funds to their manager characteristics and performance.
The second strand of the literature that our paper relates to is the relation between mutualfund manager (team) characteristics and investment behavior and performance. Chevalier andEllison (1999) find that managers attending high-SAT undergraduate institutions outperform. Cici
et al. (2015) show that mutual fund managers’ prior industry experience helps their stock picking
from these industries. Patel and Sarkissian (2016) find a manager team with three members
performs better than teams with less or more than three. Jagannathan, Jiao, and Karolyi (2019)show that fund managers receiving undergraduate degree in a foreign country have an information
advantage on stocks of that country. Massa and Schumacher (2015) show that it is optimal for fund

4

Chan, Covrig and Ng (2005) and Choi and Skiba (2014) also use fund level home bias data but their studies focus
on the aggregated country level home bias of the mutual funds.

7

families to outsource when investing in foreign countries due to the information advantage ofoutsourced funds. We relate global fund manager attributes to fund-level home bias in the context
of international investment.
Finally, this paper relates to the extensive literature on mutual fund performance. One
strand of this literature follows Berk and Green (2004), examining this issue from the perspectiveof mutual fund industry dynamics. For example, Pastor, Stambaugh and Taylor (2015) show that
decreasing returns to scale exist in both the active fund industry and at the fund-level. Berk andBinsbergen (2015) develop a new and intuitive measure of fund manager skills that combines fundexcess return and size. To the extent that international markets require higher information cost thandomestic markets and that fund size is related to fund research capability, it is worthwhile toinvestigate the relations among fund home (foreign) bias, fund size, and performance.
This paper proceeds as follows. In Section 2, we introduce the sample and data sources.
Section 3 presents our findings on fund-level home bias and its relation to manager characteristics
and fund variables. Section 4 investigates the relation between fund-level home bias andperformance. Finally, Section 5 describes our conclusions.

2. Data
Our data come from several sources. Mutual fund holdings data are from Morningstar, andfund characteristics data are from the Center for Research in Security Prices (CRSP) Mutual Fund
Database. Both databases are free of survivorship bias. The holdings data set from Morningstar
includes individual holdings’ country and industry information. About 5% of the stock holdings
data of the global funds do not have country or industry information. These records correspond to8

about 9,500 securities. 5 We manually match these names with stock names retrieved from
Datastream to populate the country and industry data.
We merge the Morningstar and CRSP data sets by fund ticker, CUSIP, and fund name. We
document the merge procedures in the Internet Data Appendix. The procedures mainly follow theInternet Data Appendixes of Berk and Binsbergen (2015) and Pastor, Stambaugh, and Taylor(2015). After the merge, we include only global equity funds in our sample. Specifically, we selectglobal equity funds based on fund categories provided by Morningstar and fund investment
objective codes from CRSP. We manually check the fund prospectus to ensure the investment
objective is to invest in global equity markets in order to remove institutional constraints that may
affect fund decisions on international diversification. Finally, we examine funds with largechanges in portfolio weight of U.S. stocks within a short time period. For example, a fund may
have a 0% investment in U.S. stocks at one point, increasing to 50% six months later and remaining
at this level thereafter. In such case, it is likely that the fund has changed from an internationalfund, investing only in foreign stocks, to a global fund, investing both in foreign and U.S. stocks,
by merging with other funds; we remove the fund portfolio records whenever the fund is identified
as an international fund. Our final sample includes 320 global funds from December 1999 to
December 2014. We select the last fund portfolio reporting date for each fund every six months,
yielding 4,161 fund portfolios (82% of fund reporting dates fall in June or December).
We obtain fund manager tenure information from Morningstar Direct and supplement thisdata set with hand-collected data on manager characteristics from Internet resources, such as fundInternet sites, SEC filings, LinkedIn, Bloomberg, and ZoomInfo.6 Fund manager data include:
start and end date at the fund, gender, age, undergraduate institution, MBA school, other graduate

5

The actual number of unique securities is smaller because funds may use different names for the same security. For
example, some funds report in the file “Apple Inc.” while others may report it as “Apple Incorporated.”
6
Patel and Sarkissian (2015) show that Morningstar Direct provides more accurate data on fund managers than othermutual fund databases.

9

study schools, and CFA designation. We set dummy variables for these characteristics, except for
manager tenure and age measured in years, for each manager. Merging fund manager start and end
dates with portfolio report dates yields the managers making decisions for each portfolio. Whenmanagers form a team, we take the average across all fund managers for each characteristic
variable. For example, AQR Global Equity Fund has five managers in December 2014. All of them
are male and three of them have an MBA degree. So the values for manager team variables
Pcnt_Male and Pcnt_MBA in December 2014 are 1 (5/5) and 0.6 (3/5), respectively. We are able
to collect manager data for 315 (out of the 320) funds and 4,074 (out of the 4,161) fund portfolios.
Finally, to obtain the benchmark for global diversification, we obtain the year-end market
capitalization of stocks for 51 countries from Datastream and calculate each country’s weight as
the total market value of stocks in that country divided by the total market value of all 51
countries.7 For the fund characteristic data from CRSP, such as total net asset (TNA), monthly
fund returns, flows, and expense ratios, we use the fund share class TNA-weighted average values
as the fund variables.

3. Fund-level home bias, manager characteristics, and fund variables
We explain the relation between fund-level home bias, manager characteristics, and fundvariables in this section. We focus on the fund level portfolio weight on U.S. stocks. The key
manager characteristic is manager’s foreign educational backgrounds while we control for othermanager and fund variables.

3.1 Summary statistics on funds and managers
Table 1 reports summary statistics for fund- and manager-related variables. Panel A shows
the statistics for the fund variables. An average fund holds 156.7 stocks from 17.8 countries. The
mean portfolio weight in U.S. stocks is 43.7%. There are some variations in the level of home bias,
7

We list these 51 markets in Appendix.

10

with investment of 26.7% and 60% in the U.S. market for the 10th and 90th percentiles,
respectively. These figures stand in sharp contrast to the previously reported aggregate home bias,
where U.S. investors may hold more than 90% in domestic stocks in their portfolios (e.g., French
and Poterba, 1991). Fund characteristics, such as TNA and returns, include the mean monthly fundvariables for the six-month period leading up to the portfolio reporting date. Mean TNA for global
equity funds is 1.99 billion in 2014 U.S. dollars, and the expense ratio is 1.42%. Average monthly
return and flow are 0.60% and 1.16%, respectively.
Panel B displays fund manager variables. We are able to identify 1,310 managers for 315
funds that report manager names instead of mere “Management Team.” Among the 1,310
managers, 1,159 (88%) are male and 486 (37%) have a foreign educational background. There are
655 managers with an MBA degree and 632 managers with a CFA charter; among these managers,
334 hold both an MBA and CFA. Panel C shows manager characteristics at the portfolio level. For
the period from December 1999 to December 2014, there are 2.96 fund managers per fund
portfolio on average. About 36% of team members have a foreign educational background. Male
managers comprise 90.4% of the management teams. Half the manager teams have MBA degrees
or CFA charters. Only 7.5% of manager team holds doctoral degrees. Out of the 1,310 managers,
we find age information for 1,070 managers by year born or undergraduate graduation year. For
the fund portfolios, the average age of the management team is 46.67 years.
Table 2 reports correlations of the variables at the manager and fund portfolio levels. Panel
A shows correlations of the manager characteristics. It seems that managers with a foreign
educational background are less likely to have an MBA degree or a CFA charter, but are more
likely to have a PhD degree; MBA and CFA are positively correlated. Panel B reports correlations
between home bias and manager team and fund characteristics. Funds managed by manager teams
with higher levels of foreign educational background tend to invest less in home assets. This is11consistent with both the information and behavioral hypotheses. Teams with more managers andyounger managers tend to invest less in domestic market. Larger funds or funds from larger fundfamilies tend to invest more in international markets. Fund size and fund family size can be seen
as proxies for fund research resources. More home-biased funds have higher turnover, consistent
with the notion that these funds trade more in the highly liquid U.S. market.

3.2 Home bias at the fund level
This section provides two snapshots of fund-level home bias. Panel A of Figure 1 comparesfund-level investment in the U.S. market with the percentage weight of the U.S. market in theworld portfolio from 1999 to 2014. We use annual data for this comparison, where both fund andworld portfolio weights are calculated at year-end. Fund-level U.S. holdings are moderate andstable at less than 50% throughout the sample period. We calculate the TNA-weighted average offund-level U.S. holdings for all funds. In contrast to the existing evidence on home bias, U.S.
global funds actually underinvest in the U.S. market compared to the benchmark. That is, during
the sample period, global funds invest 3.68% less than the benchmark weighting. Median fundhome bias is positive by only 4.07%. Combining the results of aggregate and median fund-level
home bias, it seems that larger funds tend to invest more in international markets.
Since new funds may enter and existing funds may exit the sample during the sample period,
we also examine a sample of 25 global funds in existence throughout the 1999–2014 period. The
pattern is similar. These funds actually underinvest in the U.S. market in the earlier period, butthey turn to overinvest somewhat beginning in 2007. In Panel B, we show the frequency histogramof semiannual portfolio weightings of U.S. holdings. There is some dispersion in fund-level homebias, with the highest frequency of portfolio weight ranging from 40% to 50%, and with asomewhat rightward skewness. Both charts in Figure 1 highlight the notable finding that homebias does not exist at the fund-level on average, although there is fund-level dispersion in home12

bias. In addition, we find some evidence for a “flight home” phenomenon among the global funds
following the 2007–2008 financial crisis. We address these issues below.
In Table 3, we illustrate several important characteristics of funds with different levels ofhome bias. We first sort funds into deciles by portfolio weight on U.S. holdings in each semiannual,
then we measure average portfolio weight on U.S. stocks, fund size (TNA), number of countries
and stocks for each decile. Finally, we take the mean and median of these average values over the
sample period. The least home-biased decile (lowest portfolio weight on U.S. stocks) holds 20%
U.S. stocks, while the most home-biased decile holds 75%. Interestingly, funds with the lowest
U.S. home bias (most foreign bias) are much larger than those with the most home bias; the average
TNA for each of these two groups is $3.94 billion and $494 million, respectively. Moreover, fund
TNA increases with a fund’s investment in foreign stocks. Investment in international markets
carries high information cost due to the inherent information barrier (Massa and Schumacher,2015); our results indicate that large funds have greater ability to afford this cost.
The most home-biased fund decile invests in 12 countries on average, while the numbersof invested countries for all other deciles are between 16 and 21. Finally, we show the mean
number of stocks held by funds, a measure of portfolio concentration. Both the most home-biased
and foreign-biased fund deciles hold more concentrated portfolios than the other deciles. These
funds hold 112 and 125 stocks, respectively. This result is particularly interesting for the foreignbiased funds, which have much larger size. This indicates that these funds hold concentrated
portfolios despite their large size and high portfolio weighting in international stocks. Many papers(e.g., Kacperczyk et al., 2005; Sapp and Yan, 2008; Hiraki et al., 2015; Choi et al., 2017) study
the relation between portfolio concentration and performance. We investigate fund performancein Section 4.

3.3 Home bias and manager team and fund characteristics

13

We conduct a panel regression analysis on what determines fund-level home bias and report
the result in Table 4. The dependent variable is fund-level portfolio weighting of U.S. stocks,
which we use as a measure of home bias. The key independent variable is the percentage ofmanager team with foreign educational backgrounds, Pcnt_Foredu. We control for other manager
and fund characteristics. We first run the regressions on Pcnt_Foredu without (Model 1) and with
(Model 2) fund fixed effect as a diagnostic test on endogeneity issue. The estimated coefficient are-0.057 and -0.024 respectively. Both coefficients are statistically significant. The change of thecoefficient after including fixed effect is economically significant. Therefore, the inclusion of fixed
effect helps us at least partially solve the omitted variable problem. In Model 3, 4, and 5, we add
fund manager team and fund characteristics as control variables. The coefficient remains largely
the same. Funds with more managers with foreign education background tend to be associatedwith less home bias, or more foreign bias. This finding is consistent with both the information andbehavioral hypothesis. We also find that male managers and managers with an MBA degree tend
to invest less (more) in the U.S. (international) market. Older managers invest more in the U.S.
market. Among the fund variables, recent returns and flows are negatively correlated withinvestment in the U.S. market. This could be so because funds gravitate to quality when funds have
low returns and/or foreign-biased funds perform well and attract more flows (Jagannathan, Jiao
and Karolyi, 2019).
To further address the endogeneity issue, we examine fund home bias surrounding manager
turnover. Out of the 315 funds, 204 funds have hired managers with foreign education background,while the rest 111 funds have never hired such managers in the sample period. We look at thehome bias before and after the 204 funds first time hire foreign-educated managers and we use the111 funds as control group. We find that after hiring the foreign-educated managers the first time,14the funds significantly reduce the investment in U.S. stocks, while the investment of the control
group stays at same level. The difference-in-difference is statistically significant.8
The flight home effect in international financial markets may transmit shocks from one
financial market to another. In the financial crisis of 2007–2008, the flight home effect isdocumented in studies on international banking loans (e.g., Giannetti and Laeven, 2012) and equity
investment (Hau and Lai, 2016). We examine whether there is flight home effect among global
equity funds, focusing on fund holdings in the early stage of the 2007–2008 financial crisis. For
year-end 2006 and 2007, we calculate the difference between fund portfolio weight in U.S. stocks
and that in the world portfolio. A positive difference indicates an overweight in U.S. stocks, orhome bias. We then compare the values for home bias at the end of 2006 and 2007. We show theresults in Table 5. There are 95 funds for both 2006 and 2007. From the end of 2006 to the end of
2007, the level of home bias increases from 7.5% to 10.8%, and the 3.3% flight home rate is
statistically significant, indicating a flight home effect of global equity funds by the end of 2007.
The result is similar when we repeat the tests using the average of two years of home bias measures:
2005–2006 and 2007– 2008.
Do funds with high or low levels of home bias before the 2007 crisis exhibit more flighthome? To answer this question, we classify funds by whether they have home bias before the crisisand examine the flight home for both groups. The results show that flight home is pervasive among
funds, regardless of the level of home bias the funds have before the crisis. Before the crisis, there
are 64 and 31 funds showing positive and negative home bias, respectively; they fly home at rates
of 3.0% and 3.9%, respectively. Next we investigate the question: Do fund managers with foreign
educational backgrounds alleviate the flight home effect? We sort funds by the variable
Pcnt_Foredu before the crisis and compare the flight home effect of the top-half and bottom-half

8

To save the space, we do not report the result in the paper. It is available upon request.

15

groups. We find that both groups show a flight home effect and the difference is statisticallyinsignificant. Overall, we observe a widespread flight home effect among U.S. global equity funds
during the 2007-2008 financial crisis.

4. Fund-level home bias and performance
Previous papers focus on investor’s characteristics when studying home bias at theindividual portfolio level (e.g., Karlsson and Norden, 2007; Bekaert et al., 2016) but do not analyze
the performance consequence of the home bias. In this section, we study the consequence of homebias at the fund level. Specifically, we examine the fund performance implications of different
levels of home bias. We compute the raw returns as well as the risk-adjusted returns (alphas) andvalue-added measure proposed by Berk and Binsbergen (2015, BB hereafter).
4.1 Raw and risk-adjusted returns
Table 6 displays average monthly returns of fund deciles sorted by level of home bias in eachsemiannual period. Fund net returns are obtained from CRSP, and we compute gross returns byadding the expense ratio to net returns. For risk-adjustment, we use U.S./global four-factor (Fama–
French U.S./global three factors plus U.S./global momentum factor) and six-factor (Fama–French
U.S./global five factors plus U.S./global momentum factor) models. All factors are obtained from
Professor Ken French’s website. We observe a convex relationship between home bias and fundreturns. Particularly, the most home-biased decile show significantly higher returns. For example,
the gross return for this decile is 68.1 bps and the risk-adjusted returns are significantly higher thanthe medium decile (Decile 6). For example, when adjusted by U.S. the four-factor model, the return
difference between Decile 10 and Decile 6 generates an alpha of 15.6 bps per month, whichtranslates into 1.87 percent per year. Home-biased fund decile also has higher risk-adjusted returns
than the most foreign-biased decile (Decile 1) when adjusted by global factor models but not U.S.
factor models. The result highlights the importance of benchmark choice when evaluating mutual

16

fund return performance. Chan, Dimmock and Lakonishok (2009) show that holding-based
benchmarks and attributes/factor-based benchmarks may produce different inferences, even on
domestic equity fund performance. This issue can potentially become more severe for investmentsin the global equity market.9 We show that different factor models indeed produce very different
results for performance valuation.
In Table 7, we report the factor loadings of fund decile returns on U.S. and global six factors
in Panels A and B, respectively. The adjusted R2 is higher across deciles in Panel B than in Panel
A. Some of the factor loading patterns across the deciles are very different in Panel A comparedto Panel B. For example, the most foreign-biased funds (Decile 1) do not have statisticallysignificant loading on U.S. size factor. However, Panel B shows that funds in this decile have astatistically significant positive loading on the international size factor, suggesting these funds may
tilt portfolios to small international stocks. Different fund deciles may have different loadings on
the two factors newly developed in Fama and French (2015): profitability and investment. For
example, the most foreign-biased funds have a positive loading on the international RMW (robust
minus weak), while the most home-biased funds have a negative loading on the same factor. It
seems that there is a wide dispersion on the types of stocks held by global mutual funds.

4.2 Berk and Binsbergen (2015) performance measure
Berk and Binsbergen (2015) develop a new measure of fund manager skills. They argue
that because fund investors chase fund returns and there is a decreasing return to fund scale, therisk-adjusted return does not reflect the value that skillful managers add to fund investors.
Therefore, they develop a new performance measure by combining fund returns in excess ofbenchmark and fund size. We find in the previous analysis that the foreign-biased funds are much

9

The Fama-French five factors and global momentum factor are derived from stocks in developed markets.

17

larger than other funds. Thus it is natural for us to examine the BB measure in our sample of global
equity funds.
We analyze the BB measure for funds with different levels of home bias. We calculate theequal- and value-weighted fund portfolio excess gross returns for each decile sorted by fund homebias. Fund size for each decile is the average size of the funds. Therefore, we assume ten
hypothetical funds, each having the same size as the mean of the fund decile and with returns equal
to equal- or value-weighted fund decile returns. Following BB, we calculate the benchmark returns
using 12 Vanguard index funds and the Fama–French global six factors.10
We report the results in Table 8. The convex relationship between fund home bias andperformance still remains. For example, the foreign- and home-biased deciles add 6.17 and 2.21
million dollars more than the medium decile (Decile 5), respectively. Moreover, the most foreignbiased fund decile delivers the highest value added to fund investors. For example, the equalweighted foreign-biased fund decile (Decile 1) add 3.9 million dollar value to fund investors. It is
higher than the medium decile (Decile 5). The result when using global six factors shows a similar
pattern.11 In fact, the foreign-biased funds add more value to investors than home-biased funds
when using global six factors as benchmark. Overall, it seems that the manager teams of theforeign-biased funds add value to fund investors: they invest more in international markets (whichhave high information cost), hold concentrated portfolios, and generate more BB value-added
measure. The large size of these foreign-biased funds suggest that the diminishing return to scaledoes not exist in global equity funds.

5. Conclusion
10

Berk and Binsbergen (2015) use eleven Vanguard index funds as the benchmark funds. We add Vanguard
Developed Market Index Fund to this group. In addition, we differ from BB in that we use global six factors (FamaFrench global five factors plus global momentum factor) as the factor benchmark rather than U.S. four factors. The
twelve Vanguard index funds used in this paper are listed in Appendix.
11
The magnitudes of the value added calculated from the two benchmarks are different. This may be because that the
Fama-French global five factors and global momentum factor do not include emerging market but the Vanguard index
funds include funds with investment objectives in all regions.

18

We conduct a comprehensive study on fund-level home bias using a sample of global
equity funds for the period 1999 to 2014. These funds are all domiciled in the U.S. and are bounded
to invest in the global market. Therefore, we eliminate the influence of institutional factors that
may affect home bias. We find that these funds do not exhibit home bias on average. We sort funds
into deciles based on level of home bias and investigate the cross-fund variation in home bias. We
find that management teams with a high percentage of team members with foreign educational
backgrounds, MBA degrees, and younger fund managers tend to invest more in the international
market. Both home- and foreign-biased funds tend to hold concentrated portfolios. We also findthat the foreign-biased funds are much larger.
With respect to the consequences of fund-level home and foreign bias, we find that fundraw returns show a convex relationship between home bias and performance – funds with the mosthome bias and most foreign bias outperform other funds; however there are indistinguishable
returns between these two groups. When fund returns are risk-adjusted by global factors, including
Fama–French global five factors and global momentum factor, funds with the most home-bias
outperform medium-biased funds; however, we do not find the same pattern for the most foreignbiased funds.
The large size of foreign-biased funds motives us to compute the Berk and Binsbergen(2015) performance measure. The convex relation between home bias and performance remains.
More interestingly, the foreign-biased funds generate greater value to fund investors than otherfunds. Combined with the finding that the most foreign-biased funds are much larger and holdconcentrated portfolios, the results are consistent with the notion that there is high information
asymmetry in international markets, but skillful managers are able to take the opportunities
available in the international market, and fund investors reward these managers.

19

Previous literature focuses on home bias at the country-level. Theoretical papers explain
this puzzle by modeling average investors in the market. However, mutual fund managers are notaverage investors. Hence, we provide new insight and evidence about home bias on professional
investors. In addition, compared to the literature on domestic equity mutual funds, few papers
study global equity funds. However, these funds have become increasingly important since they
provide investors the opportunity to invest in global markets. Our findings suggest that the smallest
global equity funds overinvest home assets and deliver positive alphas to investors, while thelargest global funds hire managers with foreign educational backgrounds to overcome theinformation barrier and generate greater value for fund investors.

20Appendix
Table A1.
This table displays the market weights of the 51 markets at the end of 2014.
Country
UNITED STATES
CHINA
JAPAN
UK
FRANCEGERMANYHONG KONG
INDIA
CANADA
SOUTH KOREASWITZERLAND
AUSTRALIABRAZILTAIWANSPAIN
NETHERLANDSSWEDENRUSSIAITALY
MALAYSIA
SOUTH AFRICA
DENMARKTHAILANDMEXICOSINGAPORE
INDONESIA

Weight39.08%
10.46%
6.52%
5.55%
3.59%
3.49%
3.10%
2.81%
2.35%
2.25%
2.24%
1.81%
1.55%
1.47%
1.29%
1.25%
0.93%
0.87%
0.80%
0.73%
0.72%
0.62%
0.60%
0.59%
0.54%
0.50%

CountryCHILE
NORWAYPHILIPPINESFINLANDPOLAND
COLOMBIA
TURKEYISRAELBELGIUMABU DHABIIRELAND
AUSTRIAPERU
EGYPTPAKISTANNEW ZEALAND
ARGENTINA
MOROCCOGREECEPORTUGALBANGLADESHCZECH REPUBLICHUNGARY
CROATIA
ROMANIA21

Weight
0.42%
0.42%
0.39%
0.33%
0.28%
0.28%
0.27%
0.26%
0.19%
0.19%
0.16%
0.13%
0.13%
0.12%
0.11%
0.11%
0.10%
0.09%
0.06%
0.06%
0.06%
0.04%
0.04%
0.03%
0.03%

Table A2.
The table below shows the Vanguard index funds we use to calculate fund performance using themethod from Berk and Binsbergen (2015). All funds start before 1999, the beginning year of oursample.
Fund ticker
VFINX
VTMGX
VEIEX
VBINX
VEURX
VEXMX
VIMSX
VPACX
VISGX
NAESX
VISVX
VVIAX

Fund nameS&P 500 Index Fund
Vanguard Developed Markets Index Fund
Vanguard Emerging Markets Stock Index Fund
Vanguard Balanced Index Fund
Vanguard European Stock Index Fund
Vanguard Extended Market Index Fund
Vanguard Mid Cap Index Fund
Vanguard Pacific Stock Index Fund
Vanguard Small Cap Growth Index Fund
Vanguard Small Cap Index Fund
Vanguard Small Cap Value Index Fund
Vanguard Value Index Fund

22

References
Ahearne, A. G., W. L. Griever, and F. E. Warnock (2004), “Information costs and home bias: An analysis
of U.S. holdings of foreign equities.” Journal of International Economics 62(2), 313–336.
Andrade, S. C., and V. Chhaochharia (2010), “Information immobility and foreign portfolio investment.”
Review of Financial Studies 23(6), 2429–2463.
Bekaert, G., K. Hoyem, W. Y. Hu, and E. Ravina (2016), “Who is internationally diversified? Evidence
from 296 401(k) Plans.” Journal of Financial Economics Forthcoming.
Berk, J., and J. Binsbergen (2015), “Measuring skill in the mutual fund industry.” Journal of Financial
Economics 118, 1–20.
Calvet, L., J. Campbell, and P. Sodini (2007), “Down or out: Assessing the welfare cost of household
investment mistakes.” Journal of Political Economy 115(5), 707-74.
Chan, K., V. Covrig, and L. Ng (2005), “What determines the domestic bias and foreign bias? Evidence
from mutual fund equity allocations worldwide.” Journal of Finance 60(3), 1495-1543.
Chan, L., S. Dimmock, and J. Lakonishok (2009), Benchmarking money manager performance: Issues andevidence.” Review of Financial Studies 22(11), 4553-4559.
Chevalier, J., and G. Ellison (1999), “Are some mutual fund managers better than others? Cross-sectional
patterns in behavior and performance.” Journal of Finance 54(3), 875-899.
Choi, N., M. Fedenia, H. Skiba, and T. Sokolyk (2017), “Portfolio concentration and performance ofinstitutional investors worldwide.” Journal of Financial Economics 123(1), 189-208.
Choi, N., and H. Skiba (2014), “Mutual fund home bias and market uncertainty.” Working Paper.
University of Wyoming.
Cici, G., M. Gehde-Trapp, M. A. Goricke, and A. Kempf (2015), “What they did in their previous lives?
The investment value of mutual fund managers’ experience outside the financial sector.” Working Paper.
William and Mary University.
Cooper, I., and E. Kaplanis (1994), “Home bias in equity portfolios, inflation hedging, and international
capital market equilibrium.” Review of Financial Studies 7(1), 45–60.
Cooper, I., P. Sercu and R. Vanpee (2012), “The equity home bias puzzle: A survey.” Foundations andTrends in Finance 7(4).
Coval, J., and T. Moskowitz (1999), “Home bias at home: Local equity preference in domestic portfolios.”
Journal of Finance 54(6), 2045–2073.
Fama, E. F., and K. R. French (2015), “A five-factor asset pricing model,” Journal of Financial
Economics 116, 1-22.
Ferreira, M. A., A. Keswani, A. F. Miguel, and S. B. Ramos (2013), “The determinants of mutual fundperformance: A cross-country study,” Review of Finance 17, 483-525.

23

French, K. R., and J. M. Poterba (1991), “Investor diversification and international equity markets.”
American Economic Review 81(2), 222–226.
Gehrig, T. (1993), “An information based explanation of the domestic bias in international equity
investment.” Scandinavian Journal of Economics 95(1), 97–109.
Giannetti, M., and L. Laeven. 2012. “The flight home effect: Evidence from the syndicated loan market
during financial crises.” Journal of Financial Economics 104(1), 23–43.
Graham, J. R., C. R. Harvey, and H. Huang (2009), “Investor competence, trading frequency, and homebias.” Management Science 55(7), 1094–1106.
Hau, H., and S. Lai (2016), “The role of equity funds in the financial crisis propagation.” Review of
Finance, Forthcoming.
Hau, H., and H. Rey (2008), “Home bias at the fund level.” American Economic Review 98, 333-338.
He, Z., and W. Xiong (2013), “Delegated asset management, investment mandates, and capital
immobility,” Journal of Financial Economics, 107, 239–258.
Hiraki, T., A. Ito, and F. Kuroki (2003), “Investor familiarity and home bias.” Asia-Pacific Financial
Markets 10, 281–300.
Hiraki, T., M. Liu, and X. Wang (2015), “Country and industry concentration and the performance ofinternational mutual funds.” Journal of Banking and Finance 59, 297–310.
Huberman, G. (2001), “Familiarity breeds investment.” Review of Financial Studies 14(3), 659–680.
Jagannathan, M., W. Jiao, and A. Karolyi (2019), “Is there a home field advantage in global markets?”
Working Paper. Binghamton University.
Kacperczyk, M., C. Sialm, and L. Zheng (2005), “On the industry concentration of actively managed
equity mutual funds.” Journal of Finance 60(4), 1983-2011.
Kang, J.-K., and R. M. Stulz (1997), “Why is there a home bias? An analysis of foreign portfolio equity
ownership in Japan.” Journal of Financial Economics 46(1), 3–28.
Karlsson, A., and L. Norden (2007), “Home sweet home: Home bias and international diversification
among individual investors.” Journal of Banking and Finance 31(2), 317–333.
Karolyi, G. A., and R. M. Stulz (2003), “Are financial assets priced locally or globally?” In: G. M.
Constantinides, M. Harris, and R. M. Stulz (eds.): Handbook of the Economics of Finance. Elsevier, pp.
975–1020.
Massa, M., and D. Schumacher (2015), “Information barriers in global markets: Evidence frominternational subcontracting relationships.” Working Paper. Insead University.
Morse, A., and S. Shive (2011), “Patriotism in your portfolio.” Journal of Financial Markets 14(2), 411–
440.

24

Norden, L. (2010), “Individual home bias, portfolio churning and performance.” European Journalof Finance 16(4), 329-351.
Pastor, L., R. Stambaugh, and L. Taylor (2015), “Scale and skill in active management.” Journal of
Financial Economics 116, 23–45.
Patel, S., and S. Sarkissian (2015), “To group or mot to group? Evidence from mutual fund databases.”
Journal of Financial and Quantitative Analysis, forthcoming.
Pool, V. K., Stoffman, N., and Yonker, S. E. (2012), “No place like home: Familiarity in mutual fundmanager portfolio choice.” Review of Financial Studies 25(8), 2563–2599.
Sapp, T., and S. Yan (2008), “Security concentration and active fund management: Do focused funds
offer superior performance?” The Financial Review 43(1), 27–49.
Schumacher, D. (2015), “Home bias abroad: Domestic industries and foreign portfolio choice.” Working
Paper. McGill University.
Tesar, L. L., and I. M. Werner (1995), “Home bias and high turnover.” Journal of International Moneyand Finance 14(4), 467–492.
Van Nieuwerburgh, S., and L. Veldkamp (2009), “Information immobility and the home bias puzzle.”
Journal of Finance 64(3), 1187–1216.

25

Figure 1. Fund-level home bias 1999–2014

Panel A. Portfolio Weight on U.S. Holdings
60%
50%
40%
30%
20%
10%
0%
1999 2000

2001

2002

2003 2004

2005

2006

2007 200820092010 2011

US in World Portfolio
Fund Aggregate US Holdings
Median Fund US Holdings
Median Fund US Holdings (25 Funds Exist Through 1999-2014)

26

2012

2013

2014

Panel B: Portfolio Weight on U.S. Stocks by Global Funds
800
700
600

Frequency

500
400
300
200
100
0
0%

10%

20%

30%

40%

50%

60%

Portfolio Weight on US Stocks

27

70%

80%

90%

100%

Table 1. Summary statistics
This table shows fund and manager summary statistics. Panel A displays all fund variables. Tot_Numfund shows the total
number of global funds in our sample. Num_stock shows the number of stocks held by each fund portfolio report date.
Portwt_US is the portfolio weight of U.S. stocks. Num_cnty is the number of countries held by the funds. TNA is total net
assets of the funds at 2014 value. MRet is the monthly net of fee return. MExp is expense ratio and Mflow is monthly fundflows (in %). Panel B displays fund manager information. Tot_NumMgr is total number of fund managers. Foredu isnumber of managers with foreign educational background. Male is the number of male managers. MBA, CFA, and PhD
are number of managers with MBA degree, CFA charter, and PhD degree, respectively. Panel C shows fund manager
characteristics at the portfolio level. We first define our dummy variables, such as CFA and MBA degree, at the manager
level and find managers working in the fund for each half year by matching the fund manager start/end date with portfolio
dates. Then, we calculate the average characteristics of all fund managers if the fund is managed by a team. Num_Fundand Num_Portfolio are the number of funds and fund portfolios for which we find manager information, respectively.
Num_Mgr is the average number of managers for each fund portfolio date. Pcnt_Foredu is the percentage of managers(or, the average of the dummy variables for each manager in the team) that have foreign educational experience.
Pcnt_Male, Pcnt_CFA, Pcnt_MBA, and Pcnt_PhD are the percentage of managers who are male, with a CFA charter, an
MBA degree, or a PhD degree, respectively. Avg_Age is the average age of the managers.

Panel A: Fund variables
Tot_Numfund
Num_Portfolio
Num_Stock
Num_Cnty
Portwt_US
Mret (%)
TNA (Mil, in 2014 $)Mexp (%)
Mflow (%)
Turnover (%)
FamTNA (Mil, in 2014 $)MeanMedianPctl_10

Pctl_90320
4161
156.700
17.8550.437
0.595
1991.698
1.413
1.160
74.07482475.156

89
17
0.434
1.062
216.7521.373-0.1655426640.097

36
9
0.267
-5.1898.351
0.895-2.54415377.965

275
27
0.600
6.014
2726.436
1.970
5.773
158
209592.875

28

Panel B: Fund manager characteristics
Tot_NumMgr
1310
Foredu
486
Male
1159
MBA
655
CFA
632
MBA&CFA
334
PhD
99
Panel C: Fund manager (team) variables at portfolio level
Num_Fund315
Num_Portfolio
4074
Num_Mgr
2.960
2Pcnt_Foredu
0.358
0.333
Pcnt_Male
0.904
1
Pcnt_MBA
0.509
0.500
Pcnt_CFA
0.489
0.500
Pcnt_PhD
0.075
0
Avg_Age46.677
46

1
0
0.600
0
0
0
38

29

6
1
1
1
1
0.333
56

Table 2. Variable correlations
This table shows the correlations between manager and fund level variables. Panel A reports the fund manager characteristics. There are 1310 managers. Panel B shows the correlation between fundhome bias (portfolio weight in U.S. market) and fund and manager team characteristics. The fund manager team characteristics is the mean of manager characteristics of the team at the decision timeof the portfolio (we require the manager start working at the fund at least two months before the portfolio report date). The fund variables are the average monthly values in the six-month periodleading up to the portfolio report date. We report the Pearson correlations. ***, **, * represents the 1%, 5%, and 10% significance level, respectively.
Panel A: Correlations at fund manager level
Foredu

Male

MBA

CFA

Foredu
Male

0.01

MBA

-0.20***

0CFA-0.12***-0.02

0.05**

PhD

0.12***

0.02-0.11***-0.09***

Panel B: Correlations at fund portfolio level
Portwt_US

Num_Stock

Num_Cnty

Num_Mgr

Pcnt_Foredu

Pcnt_MalePcnt_MBAPcnt_CFA

Pcnt_PhD

Avg_Age

TNA

Mret

Mexp

Mflow

TurnoverPortwt_US
Num_Stock

0.01

Num_Cnty-0.27***

0.50***

Num_Mgr-0.05***

0.25***

0.29***

Pcnt_Foredu-0.15***-0.04**

0.04***

0

Pcnt_Male

0.01-0.10***-0.06***-0.07***

0.05***

Pcnt_MBA

0.05***

0.08***

0.10***

0.08***-0.18***-0.12***

Pcnt_CFA

0.02-0.05***

00-0.19***-0.04***

0.03*

Pcnt_PhD-0.01

0.06***

0.09***

0.02

0.12***

0.05***-0.18***

0.01

Avg_Age

0.09***

0-0.11***-0.03*-0.17***

0.09***

0.11***-0.02

0.04***

TNA-0.15***

0.06***

0.21***

0.25***

0.02-0.08***

0.05***

00.04***

0.04***

Mret-0.06***

0.01

0.05***

0.01

0.02

0

0.02

0.02

-0.01

0.02

0

Mexp

-0.01

-0.09***-0.08***-0.13***-0.02

0.04**-0.03*-0.12***-0.08***-0.10***-0.20***

0.01

Mflow

0.04**

-0.01

-0.03

-0.01

0.02

0.01

-0.02

0.02

0.07***

-0.02

0

-0.02

-0.01

Turnover0.11***-0.03

0.09***-0.05***

0.04***

0.04**

0.06***-0.06***-0.02-0.14***-0.14***

00.17***

0.06***

FamTNA-0.14***

0.08***

0.34***

0.24***-0.02-0.08***

0.13***-0.06***

0.01

-0.03

0.51***

0-0.24***

-0.02

30

-0.05***

Table 3. Fund portfolio weights in U.S. stocks
This table shows the average of the mean and median values of home bias, fund TNA, number of countries andstocks held by funds. In each semiannual period from December 1999 to December 2014, we sort funds by
portfolio weight in U.S. stocks (our measure of home bias) into deciles, then calculate the mean and median values
for the four variables in each decile. We then calculate the average values of these mean and median values for thefull sample period.
Portwt_US
TNA ($ mil)Num_Cnty
Num_Stock
Home bias decile
mean median mean
mean
medianmean
medianmedian
Low
0.22
0.22
20.11
20.29
125.2686.74
3937.912923.932
0.30
0.30
4347.34
3862.98
20.71
20.84
129.87106.1930.35
0.35
3214.72
3342.99
21.08
20.76
153.59109.7740.38
0.38
2228.52
1666.35
21.13
20.32
159.33107.4250.42
0.42
1370.35
1116.71
20.90
19.94
192.89107.8660.44
0.44
841.11472.3921.20
20.26
221.25117.867
0.47
0.47
599.88506.3319.74
18.57
213.91104.508
0.51
0.51
601.18469.4219.04
18.77
183.77111.980.57
0.57
521.97406.9415.70
14.39
164.53
86.10
9
High
0.75
0.73
493.59445.95
11.57
9.55
111.81
74.21

31

Table 4. Home bias and manager and fund characteristics
This table reports the panel regression result of the determinants of fund-level home bias. The dependent variable is fund portfolio
weight of U.S. stocks. We take the fund portfolio for each semiannual. The independent variables include fund manager
characteristics and fund-level variables, which are taken in the six-month window before the portfolio report date. Lg_Avg_Age
and Lg_TNA are the natural logs of average age of the manager team and fund TNA, respectively. All other variables are definedin Table 1. Fund-level variables are the monthly average values of fund characteristics in the six-month window. ***, **, *represent 1%, 5%, and 10% significance levels, respectively.
(2)
(1)
(3)
(4)
(5)
-0.024***
Pcnt_Foredu-0.057***-0.023***-0.025***-0.022***(-3.51)(-9.64)(-3.01)(-3.56)(-2.83)
Num_Mgr-0.0010.000(-0.82)(0.04)
Pcnt_Male-0.037***-0.038***(-3.35)(-3.40)
Pcnt_CFA
0.019***
0.023***(2.65)(3.21)
Pcnt_MBA-0.031***-0.027***(-4.41)(-3.76)
Pcnt_PhD-0.046***-0.050***(-3.72)(-3.91)
Lg_Avg_Age
0.074***
0.095***(4.67)(5.66)
Lg_TNA
0.000-0.002(-0.21)(-1.00)Mret-0.305***-0.313***(-4.96)(-5.01)Mexp
0.309
1.502*(0.38)(1.78)
Mflow-0.038***-0.040***(-3.27)(-3.40)Turnover0.004
0.007**(1.18)(2.01)

32

Fund fixed effectNum. of Obs.

No
4074

Yes
4074

Yes
3871

Yes
3768

Yes
3587

R2

0.022

0.706

0.710

0.706

0.710

33

Table 5. Flight home in financial crisis
This table shows the flight home of U.S. global funds during the 2007–2008 financial crisis. We
compare fund-level home bias before the financial crisis (2005–2006) and during the financial crisis(2007–2008). Fund-level home bias is defined as the difference between a fund's portfolio weight on
U.S. stocks and U.S. market weight in the world market at the end of the year. The change of this fundlevel home bias from before the financial crisis to during the financial crisis is a measure of flighthome. U.S. market weight in the world market is calculated from Datastream for 51 global markets.
For each fund, average value of this home bias measure is calculated for 2005 and 2006, and 2007 and2008. There are 95 funds that exist in both of these periods. ***, **, * represent the 1%, 5%, and 10%
significance levels, respectively.
Flight home from 2006 to 2007
# of funds
Mean
HB_200695
0.075
HB_2007
0.108
Difference
0.033***
p-value< 0.0001
Flight home from 2005-2006 to 2007-2008
HB_2005-2006
95
0.063
HB_2007-2008
0.108
Difference
0.045***
p-value< 0.0001
Flight home by funds with positive / negative home bias before crisis(2006)
HB_Positive64
0.030
HB_Negative31
0.039
Difference
0.009
p-value
0.45
Flight home by fund management teams' foreign educational level
before crisis (2006)
Foredu_low
47
0.037
Foredu_high48
0.053
Difference
0.017
p-value
0.23

34

Table 6. Home bias and performanceThis table displays fund portfolio performance when funds are sorted into groups based on portfolio weight of U.S. stocks.
In each half year, we sort funds into deciles based on portfolio weight of U.S. stocks. Decile 1 (10) represents the decilewith low (high) investment in U.S. stocks. We then measure fund performance for the following 6 months. Fund net returns
are obtained from CRSP. Average net and gross returns of the fund decile portfolios are reported, where gross returns are
expense ratio added back to net returns. We also report risk-adjusted returns of the fund portfolios using Fama–French U.S.
and global three- and five-factor models plus U.S. and global momentum factor, resulting in an adjustment with U.S./global
four- and six-factor models. We report t-value in parentheses, and ***, ** and * represent 1%, 5%, and 10% significancelevels, respectively.
Home bias
Net return Gross return US 4-factor
Global 4-factor
US 6-factor
Global 6-factor
1—low
0.503
0.642
-0.023-0.0370.000-0.067(1.27)(1.66)(-0.17)(-0.52)(0.00)(-0.90)
2
0.500
0.619
0.026
0.042
-0.013-0.032(1.34)(1.66)(0.21)(0.62)(-0.10)(-0.48)
3
0.470
0.585
-0.080-0.053-0.068-0.065(1.17)(1.46)(-0.70)(-0.95)(-0.56)(-1.10)
4
0.464
0.587
-0.090-0.046-0.025-0.003(1.11)(1.41)(-0.76)(-0.78)(-0.20)(-0.06)
5
0.428
0.551
-0.138-0.062-0.0700.008(0.99)(1.27)(-1.19)(-0.88)(-0.58)( 0.11)
6
0.425
0.547
-0.112-0.052-0.086-0.027(1.03)(1.32)(-1.04)(-0.88)(-0.76)(-0.44)
7
0.446
0.572
-0.0880.016
0.036
0.135*(1.07)(1.37)(-0.79)(0.20)(0.31)(1.83)
8
0.396
0.520
-0.139-0.063-0.0600.031(0.93)(1.23)(-1.40)(-1.03)(-0.58)( 0.55)
9
0.404
0.520
-0.083-0.016-0.1300.000(1.02)(1.34)(-0.87)(-0.20)(-1.31)(-0.00)10—high
0.570
0.681*
0.044
0.202**
0.064
0.330***(1.45)(1.73)(0.56)(2.07)(0.79)(3.61)
Difference (10-1)
Difference (10-6)

0.066(0.48)
0.145(1.52)

0.039(0.29)
0.134(1.42)

0.068(0.57)
0.156*(1.92)

0.239*(1.92)
0.253***(3.00)

35

0.064(0.52)
0.150*(1.78)

0.398***(3.35)
0.358***(4.42)

Difference (1-6)

0.079(0.86)

0.095(1.03)

0.088(1.12)

0.014(0.17)

36

0.086(1.03)-0.040(-0.48)

Table 7. Factor loadings on U.S. and global six-factor models
This table reports the factor loadings of fund portfolio returns on US and Global Fama-French five-factor and momentum factor. The dependent variable
is the time-series returns of fund portfolios where the portfolios are grouped by the holdings of US stocks. The independent variables are the U.S. andglobal Fama-French five factors plus momentum factor downloaded from Professor Ken French’s website. We report t-value in parenthesis and ***, **and * represents 1%, 5% and 10% significance level, respectively.

Panel A: US factors
1 - low
2
3
4
5
6
7
8
9
10 - highDifference (10-1)
Difference (10-6)
Difference (1-6)

Mkt_RF

SMB

HML

RMW

CMA

WML

0.874***(23.01)0.875***(25.04)0.937***(29.07)
0.953***(28.74)
0.977***(30.37)0.983***(32.67)0.963***(31.64)0.985***(35.71)0.999***(37.67)
0.973***(44.72)0.074(1.43)
0.037(0.79)
0.091**(2.07)
0.093**(2.06)
0.138***(3.14)
0.059(1.45)
0.122***(2.94)
0.078**(2.07)
0.001(0.03)
0.102***(3.43)

0.147**(2.30)
0.100*(1.70)
0.111**(2.04)
0.103*(1.85)
0.033(0.60)
0.070(1.39)
0.000(0.00)
0.042(0.90)
0.065(1.46)-0.033(-0.91)-0.028(-0.39)0.092(1.37)-0.018(-0.29)-0.110*(-1.73)-0.142**(-2.32)-0.043(-0.75)-0.198***(-3.42)-0.126**(-2.39)0.100**(1.98)-0.089**(-2.14)-0.059(-0.69)-0.049(-0.63)-0.029(-0.40)-0.061(-0.83)0.006(0.08)-0.028(-0.43)-0.105(-1.55)-0.082(-1.34)-0.035(-0.60)0.090*(1.85)

0.011(0.40)-0.048*(-1.97)0.025(1.10)
0.036(1.55)
0.017(0.73)
0.021(1.00)
0.062***(2.90)
0.059***(3.03)-0.046**(-2.50)-0.029*(-1.91)

0.099***(3.04)-0.010(-0.46)-0.109***0.028(0.62)
0.042(1.39)
0.015-0.180***(-3.28)-0.104***(-2.75)0.077**-0.061(-0.97)-0.045(-1.06)0.015

0.148**(2.04)
0.118**(2.37)-0.030-0.040*(-1.73)-0.050***(-3.21)-0.011(-4.93)(0.49)(2.06)(0.36)(-0.61)(-0.69)37

Adj-R2
0.837
0.855
0.890
0.894
0.909
0. 912
0. 919
0.929
0.929
0.955

0.232
0.138
0.205

Panel B: Global factors
1 - low
2
3
4
5
6
7
8
9
10 - highDifference (10-1)
Difference (10-6)
Difference (1-6)

0.947***(47.48)0.932***(52.46)
0.986***(62.83)0.999***(62.06)1.022***(53.88)1.002***(61.47)0.982***(49.74)0.987***(64.27)0.961***(44.88)0.920***(37.75)0.206***(5.44)
0.061*(1.79)
0.126***(4.23)
0.136***(4.46)
0.164***(4.55)
0.058*(1.88)
0.164***(4.37)
0.072**(2.47)-0.125***(-3.07)-0.049(-1.06)0.075(1.58)
0.018(0.43)
0.022(0.59)
0.025( 0.64)-0.043(-0.96)-0.031(-0.79)-0.115**(-2.46)-0.049(-1.36)-0.007(-0.13)-0.108*(-1.87)0.084*(1.67)
0.213***(4.75)
0.018(0.45)-0.100**(-2.45)-0.205***(-4.28)-0.082**(-1.99)-0.315***(-6.31)-0.243***(-6.27)-0.059(-1.08)-0.401***(-6.50)0.014(0.23)
0.026(0.48)
0.030(0.62)-0.057(-1.14)-0.019(-0.32)0.011(0.22)-0.097(-1.59)-0.085*(-1.78)0.022(0.33)
0.009(0.12)

0.007(0.36)-0.066***(-4.00)0.030**(2.06)
0.049***(3.32)
0.045**(2.55)
0.021(1.38)
0.091***(5.02)
0.089***(6.31)-0.059***(-3.00)-0.015(-0.67)0.956-0.026(-0.84)-0.081***(-3.76)-0.055**-0.255***(-4.24)-0.107***(-2.61)0.148***-0.183**(-2.44)-0.078(-1.52)0.106**-0.485***(-6.07)-0.319***(-5.85)0.166***-0.005(-0.06)-0.002(-0.03)0.003-0.022(-0.75)-0.036*(-1.80)-0.0140.299(-2.44)( 3.47)( 1.98)( 2.93)( 0.05)(-0.68)

38

0.964
0.975
0.976
0.969
0.975
0.967
0.979
0.955
0.945

0.221
0.209

Table 8. Home bias and performance: value added
This table displays the Berk and Binsbergen (2015) fund skill measures (value added, in $mil). Funds are first sorted into deciles, following the same procedure
as previous tables. The monthly skill measure is calculated as TNA-weighted excess gross returns. Excess gross returns are calculated as mean fund decile
returns minus benchmark return. We calculate both equal-weighted and value-weighted mean fund decile returns. The benchmark returns use two sets offactor returns: 12 Vanguard index funds and Fama–French global five factors (Mkt_FR, SMB, HML, RMR, and CMA) plus global momentum factor (WML).
We report p-value for the value added.
Vanguard Index Funds

FF Global 6-factor

Home bias
1—low
2
3
4
5
6
7
8
9
10—high

Value added
3.906-1.1301.355-4.876**-2.262**-0.966-0.938***-1.144-0.676-0.048p-value
0.237
0.686
0.385
0.011
0.033
0.125
0.005
0.010
0.105
0.887

Value added
0.058
0.465
-1.365-2.097-2.242-1.396-1.096*-1.035-0.514-0.118p-value
0.984
0.895
0.602
0.284
0.147
0.210
0.052
0.108
0.331
0.794

EW
Value added
10.546***
4.71
4.475*
2.288
1.332
0.579
0.952**
0.608
0.984*
2.531***

Difference (10-1)
Difference (10-6)
Difference (1-6)
Difference (10-5)
Difference (1-5)-3.9550.917
4.872
2.214**
6.169*

0.237
0.148
0.132
0.033
0.084

-0.176
1.278
1.454
2.124
2.300

0.951
0.286
0.611
0.183
0.390

-8.015**
1.952**
9.968***
1.199
9.214**

EW

VW

39

VW

p-value
0.003
0.161
0.054
0.262
0.150
0.418
0.016
0.179
0.062
<.0001

Value added
8.251**
6.485
5.59*
6.127
0.833
1.078
1.057*
1.066
1.221
2.121***

p-value
0.037
0.150
0.091
0.112
0.540
0.354
0.093
0.102
0.150
0.006

0.023
0.012
0.004
0.192
0.010

-6.13
1.043
7.172*
1.288
7.418*

0.104
0.429
0.071
0.353
0.056