Culturally Motivated Information Asymmetry:

Implications for Home Bias

Katie Cusack

University of Wyoming

Cusack.katie@gmail.com

Hilla Skiba

University of Wyoming

hskiba@uwyo.edu

This draft: May 2014

Abstract: Portfolio theory suggests that international diversification increases returns and

decreases risk. Consequently, investors should diversify their portfolios across

international markets. In practice, however, investors hold globally under-diversified

portfolios. This study examines a novel cause of US-based institutional investors’ foreign

portfolio deviations from perfect diversification. We attribute the deviation to cultural

similarity between the locales in which the US financial institutions operate and the foreign

countries in which they invest. Specifically, we measure cultural similarity between each

US institutional investor’s local market and every possible investment option outside the

US, directly based on 2010 US Census and the ethnicity of the foreign markets. We show

that portfolio allocation increases with higher cultural similarity in US states and zip codes

and foreign markets. We also show that higher cultural proximity can increase

performance in foreign investments.

Keywords: Culture; Home bias; Institutional investor; Foreign portfolio investment

JEL Classification Codes: G11; G15; G23; Z10

1

I Introduction

Financial theorists predict that, in the absence of barriers to international investment,

investors hold a portfolio that weights assets from all countries in proportion to their share of

world assets (Black 1974); (Cooper and Kaplanis 1986). In practice, however, investors are not

only subject to barriers to investment, but also fail to construct their portfolios according to each

country’s market value (French and Poterba 1991); (Chan, Covrig and Ng 2005). Specifically,

investors exhibit a tendency to bias their portfolios towards assets from their home country; a

phenomenon known as home bias. In addition, the share of investors’ wealth not held in home

market stocks is often not distributed according to foreign market capitalization weights. The

resulting international portfolio under-diversification remains a puzzle in the finance literature.

Whether this bias is an irrational deviation from the efficient portfolio or the product of

investors responding to perceived risk is unclear. The perceived risk of an asset might be

dependent upon the information asymmetry that an investor has with the security’s country of

origin (French and Poterba 1991). Information asymmetry results from investors having less and

lower quality information about the foreign target market. Finance literature focuses largely on

geographic proximity as a cause of information asymmetry (Chan, Covrig and Ng 2005). Less is

known about cultural proximity as a factor in information advantage that results in the under-

and overweighting of assets from a foreign country.

This paper tests the hypothesis that cultural proximity impacts investment allocation in

addition to geographic proximity, and that cultural proximity should in fact matter more than

geographic proximity. With large improvements in technology and investors’ ability to access

information in today’s financial markets, the effect of geographical distance should matter less

and less for asset allocation. However, even with improvements in technology, cultural barriers

2

are more difficult to overcome. It also may be that geographic proximity, which has been shown

to influence portfolio flows merely captures the effect of cultural distance across international

markets.

This paper tests a second hypothesis that higher cultural proximity affects institutional

investor’s performance in foreign markets. As the level of cultural proximity between an

institution’s location and a target country increases, institutional investors should continue to

gain more information and be able to better interpret information about that target country’s

available investments. As this information advantage grows, these institutions should earn

positive abnormal returns.

Few papers in finance have examined whether cultural distance influences investment

allocation (Guiso, Sapienza and Zingales 2009); (Aggarwal, Kearney and Lucey 2012). These

papers document that aggregate portfolio flows between countries are positively related to

cultural proximity. Our paper contributes to the literature by testing the effect of cultural distance

on institutional investors’ portfolio allocation decisions at a more detailed level. Our dataset

consists of institutional investors’ quarterly holdings at the security level. We also construct a

cultural proximity measure between investors and target markets that is more refined than

cultural distance measures used in the past literature.

Most common ways used in the prior literature to measure cultural proximity include

common language between investors, cultural proximity between countries constructed based on

Hofstede’s (or similar) primary dimensions of culture from survey data, and cultural trust

between countries. While the previous cultural proxies have been informative and have provided

great insight into international portfolio allocation, these measures do not necessarily provide a

means to test the impact of cultural ties on information advantage and portfolio allocation. The

3

prior cultural measures also make it difficult to separate the effect of geographic distance from

cultural distance on investment. Our measure of culture is specific to regional level. We use the

US 2010 Census to construct a share of population in each state or zip code in the US that

identifies with certain ethnic background. We then test if institutional investors from a state or

zip code populated by certain ethnicity invest at higher levels to those foreign countries that

share the state or zip code’s ethnic origin.

The findings of the paper can be summarized in the following way. We first show that

US investment to foreign markets is far from diversified. After controlling for home market

investment of the investors, we show that the remaining foreign portfolios of US institutional

investors are not allocated to foreign markets based on their market capitalization weights.

Instead, we observe large portfolio over- and underweights across international markets. Second,

we document that the state and zip code level cultural similarity is an economically significant

determinant of foreign country under- and overweights of US institutions’ portfolios. The result

is robust in many different regression specifications.

Also, we examine the effect of cultural proximity on institutions’ performance in foreign

markets. We document that cultural proximity has an economically and statistically significant

effect upon abnormal returns. In most cases, higher cultural proximity increases abnormal

returns. However, we find that institutions that take large, overweight positions may experience

lower abnormal returns as a result of an increase in cultural proximity.

The paper makes several contributions to the existing literature. To our knowledge, this is

the first paper to show in detail how US investors allocate capital to foreign markets. The paper

also contributes to the new and emerging field of culture and finance. We construct a novel

4

measure of cultural similarity that captures information asymmetry that stems from culture in a

more direct way that previous papers have done.

The rest of the paper is structured in the following way. Section II review related

literature. Section III shows the data and methods used in the paper. Section IV shows our results

and section V concludes.

II Literature Review and Hypotheses

A. International Diversification-Gains from Trade

Financial theorists show that investors gain when they diversify their portfolios

internationally. Amongst the first to do this were Grubel (1968), Levy and Sarnat (1970) and

Solnik (1974). These studies all examine international diversification in the context of mean-

variance tradeoff and find that investors achieve higher returns, or lower risk, when they add

assets from foreign markets to their portfolio.

Later studies corroborate this theory by testing the effects of international diversification

outside of the trade-off between risk and return. Grauer and Hakansson (1987) show that there is

a statistical difference in the returns to a portfolio constructed of only United States stocks and a

portfolio that includes domestic and foreign securities. The gains from including foreign

investments persist, despite the greater integration of world financial markets. DeSantis and

Gerard (1997) prove this in their study of contagious market crises and downturns.

B. Home Bias- The Deviation from World Market Portfolio

Despite the documented advantages of including foreign securities in an investment

portfolio, investors rarely hold portfolios that are perfectly diversified. To study this

phenomenon, theorists first describe the state in which we would observe perfectly diversified

5

portfolios. Black (1974) develops an equilibrium model in which a “tax” to international

investment explains the deviation of a portfolio from that of the world market portfolio. Cooper

and Kaplanis (1986) use this equilibrium model to argue that investors in different countries face

different “taxes” which they call barriers to foreign investment. Both studies argue that, in the

absence of barriers to investing in foreign markets, all investment portfolios will be identical to

the world market portfolio.

Other studies document the share of assets that investors allocate to domestic and foreign

securities. French and Poterba (1991) show that investors significantly overweight domestic

securities in their portfolios. This result is known as the home bias. This bias is also found in

more recent studies of portfolio allocation (Kang and Stulz 1997); (Chan, Covrig and Ng 2005).

C. Home Bias- Studies of Causality

Many of these studies not only estimate the magnitude of home bias, but also propose

theories about the causes of this bias. The explicit costs to purchasing a foreign equity are far

easier to quantify than implicit barriers. However, the first general equilibrium models

acknowledge the existence of both as factors in bias, with Black stating:

“The tax is intended to represent various kinds of barriers to international

investment, such as…direct controls on the import and export of capital…It is

even intended to represent barriers created by the unfamiliarity that residents

of one country have with other countries.” (Black 1974)

Cooper and Kaplanis (1986) introduce an equilibrium model in which investors from

different countries can have different barriers to investing internationally. The model resulted in

many empirical studies in which authors construct bilateral factors to explain the portfolio under

6

and over-weights in international portfolio flows. The proposed causes fall into two broad

categories: institutional constraints and investor choices (French and Poterba 1991). Many

studies control for the effects of both direct and indirect barriers to foreign investment. Chan,

Covrig and Ng (2005) find that a mix of these barriers explains much of the underweighting of

foreign assets.

C.1 Explicit barriers to investment

In the equilibrium models that account for the cost, or taxes, on investing internationally,

the costs are typically described as quantitative barriers. Barriers typically cited include capital

controls such as limits to the share of a stock that may be purchased by foreign investors. Studies

show that, despite the statistical significance of these direct barriers, the magnitude of costs is too

low to account for the extent of home bias (Tesar and Werner 1995); (Ahearne, Griever and

Warnock 2004).

C.2 Implicit barriers to investment

To account for the home bias that remains, theorists examine indirect barriers to

investment that result from investors’ choices and access to securities. French and Poterba (1991)

hypothesize that the level of bias is affected by the perceived risk of a foreign investment. When

investors know little about a country, they perceive that country’s assets as riskier. Investors then

make the rational decisions to limit their risk by not purchasing those securities. Kang and Stulz

(1997) find that investors choose foreign assets based on the notoriety and size of firms.

Ahearne, Griever and Warnock (2004) also attribute a portion of home bias to the information

7

available to shareholders by showing that foreign stocks listed on American exchanges are less

underweighted in American portfolios.

D. Information Costs

One indirect barrier to foreign investment that affects investors’ level of home bias is

information asymmetry between investors. A reduction in ambiguity about a firm causes

domestic investors to decrease their underweighting of foreign assets.This can be achieved when

foreign companies increase the amount, or perceived quality, of information that is publically

available (Ahearne, Griever and Warnock 2004). The link between information asymmetry and

home bias persists on an intra-national level. Coval and Moskowitz (2001) show that investors in

the United States not only invest more in firms closest to them, but also have greater abilities to

choose firms with the highest future returns in their locales.

Other studies corroborate the idea that domestic investors have an information advantage

in domestic assets and rely on this in portfolio allocation. This information advantage comes

from the ability of domestic investors to interpret information in the appropriate context (Gehrig

1993). As a result, investors allocate a larger portion of portfolio assets to domestic securities

because they know that this can lead to higher returns.

Not only do investors have an information advantage in domestic assets, but investors

preserve this advantage by concentrating their information gathering in home assets. Financial

theorists suggest that concentration of information gathering efforts is not a result of a lack of

access to foreign information. Rather, this is the result of investors choosing not to learn about

foreign securities (Van Nieuwerburgh and Veldkamp 2009). Van Nieuwerburgh and Veldkamp

8

(2009) find that, when investors take advantage of their ability to learn about foreign firms, the

returns to their prior domestic information advantage dissipate.

E. The Case for Cultural Proximity

This initial information advantage is often attributed to geographic differences between

investors and foreign firms (Chan, Covrig and Ng 2005). However, the reluctance of investors to

learn about foreign firms may come from differences in the cultural background of investors.

Groups that have greater “social distance” interact less than groups with greater social proximity;

this interaction facilitates the transfer of information (Akerlof 1997).The lack of communication

between groups or cultural communities gives rise to a higher level of conformity in each group

(Ellison and Fudenberg 1995). When cultural groups share information and reach a consensus on

the prospects of foreign firms, they likely do not share this with socially and culturally distant

groups. This creates an information advantage that funds in the immediate area benefit from.

Empirical studies test the effect of cultural proximity between countries through proxies

like common language. All of these proxies establish only an indirect connection between culture

and portfolio investment. A more direct measure of cultural proximity could establish a robust

connection between the effects of information transfer between culturally proximate groups and

the magnitude of home bias. We contribute to the literature by estimating this connection.

F. Hypotheses

F.1 Investment Bias

Given the evidence that institutional investors do not optimally invest in foreign

securities, we form a hypothesis to explain the deviation from a perfectly diversified portfolio.

9

Our hypothesis states that the cultural distance between the location an institution operates and

the target country affects the magnitude of the institution’s under or over-weighting of securities

from the target country. This testable hypothesis is stated formally:

H1: The cultural proximity of an investor to a target country is positively related to the

institution’s allocation to the target country’s securities.

This hypothesis is tested by calculating the difference between the amount a fund invests

in a specific country’s assets and the amount they should invest to have a perfectly diversified

portfolio of foreign securities. This deviation is compared to the proximity of the two cultures.

We expect that the magnitude of the bias, towards or against, a target country will increase as the

culture of the target country and the institution’s location become more similar. The information

asymmetry that results from higher cultural proximity should result in institutions taking large

under or over-weighting positions in a target country to benefit from their information advantage.

F.2 Abnormal Performance

Given the evidence that proximity has the potential to give institutional investors an

information advantage over competitors, we form a second hypothesis to examine the

performance of culturally proximate institutions’ investments in foreign countries. Our

hypothesis states that, given that investors have some cultural proximity to a target country and

act upon that proximity by under or over-weighting that country’s investments, the cultural

proximity of an institution and target country will affect the institution’s performance in that

country. This testable hypothesis is stated formally:

H2: The cultural proximity of an institution to a target country is positively related to the

institution’s performance in the target country’s securities.

10

This hypothesis is tested by calculating the institution’s abnormal return in each target

country relative to the target country’s market return. This performance measure is compared to

cultural proximity between the two cultures and the interaction between that proximity and the

institution’s investment bias. We expect that the abnormal performance will increase as the

culture of the target country and institution’s location in its home market become more similar.

III Data and Methodology

A. Foreign Diversification and Country Bias

The main data in this study include US–based institutional investors’ holdings at security

level for the year end of 2010. The holdings’ data are provided by FactSet Company (former

LionShares). The FactSet holdings data for US institutional investors are the 13-F data from

SEC. We match these holdings to institutional investors’ identifying information that include

information on the investors’ type, style, and address. From these addresses we identify the exact

location of each investor based on the zip code of the institution.

The holdings’ data show each security’s market value and number of shares in

institutions’ portfolios. We also know each security’s country of domicile. We define securities

as foreign securities if the country of their domicile is not the US. From the holdings’ data, we

compute the actual investment to each foreign market as a share of all foreign investment.

The expected investment allocations to foreign countries are computed based on

“investable” shares of international markets. These “investable” market values are computed

from WorldScope data. We then compare the actual portfolio weights to the expected weights of

each target country’s securities as a percentage of the world portfolio.

11

Table I shows summary statistics for each target country included in our sample. The first

column shows the weight that should be given to each country. According to world market value,

foreign portfolios of US institutions should invest the most in the United Kingdom, a country

with 14.23% of world market value when the US is excluded. The United Kingdom is followed

closely by the expected weight of Japan, with 13.76% of world market value.

The second column shows the average actual weight given to each target country’s assets.

The institutional portfolios in the sample invest most of their foreign portfolios in Canadian

securities, with an average actual share of portfolio value of 14.59%. There are also a number of

target countries in which US institutions do not, on average, invest any portion of their foreign

portfolio.

The expected weight of securities of each country in the portfolio is calculated by

comparing the market value of securities from each country to the total market value of world

securities. Market value of world shares outstanding is calculate by multiplying the price in US

dollars of each share by the number of shares outstanding, as shown in equation [1]. This method

overstates the number of shares that are available for purchase. The number of shares available

decreases as more share are held by large shareholders, such as a government or family (La

Porta, Lopez-De-Silanes and Shleifer 1999). Dahlquist et al. (2003) find that the inability of

investors to purchase these shares at a fair market value contributes to home bias. To account for

this, the number of closely held shares is subtracted from the issued shares to calculate the float-

adjusted market value. The float-adjusted market value is calculated based on equation [2].

Using the adjusted market value, equation [3] gives the expected weight of each countries’ assets

in fund portfolios, where jMV is the float-adjusted market value of all securities in country j.

12

The denominatork

k

j

MV represents the total, float-adjusted market value of all foreign

securities.

MV price shares [1]

( )adj out closelyheldMV price shares shares [2]

_j

j k

k

j

MVWeight expected

MV

[3]

The share of assets allocated to a target country in a given portfolio is calculated by

comparing the value of that country’s securities in the portfolio to the value of the institution’s

foreign portfolio. Performing this analysis without the market value of assets held in the United

States gives a better measure of the allocation of assets that are devoted to international assets.

This calculation is depicted in the following equation:

,

,

,

_i j

i j k

i k

j US

MVWeight actual

MV

[4]

where ,i jMV represents the market value of securities headquartered in country j that institution i

holds. The denominator ,

k

i k

j

MV shows the market value of securities headquartered in all

foreign countries held in portfolio i.

The deviation of each portfolio from the expected weight is calculated by subtracting the

expected share of assets from country j from the actual share of assets allocated to country j in

portfolio i. This calculation gives the amount of under or over-weighting of assets from country j

in portfolio i, the target country bias.

13

, i, j ,_ _i j i jBIAS weight actual weight expected [5]

B. Cultural Proximity

Proxies for cultural proximity used by empirical studies include sharing a common

language and the flow of trade between countries. While informative, these measures do not

provide a means to test the impact of cultural ties on information advantage that leads to bias.

Examining the percent of the population in either state or zip code x that identifies with country j

provides a direct way to estimate the level of information exchange between the two groups. This

relationship is given in the following equation:

x,

x,

x

j

j

PProximity

P [6]

where x, jP denotes the population in state or zip code x that identifies ancestrally or ethnically

with country j and xP gives the total population in state or zip code x.

Surveys conducted by the United States Census Bureau provide both total population and

the population that claims a cultural tie with a specific foreign country by state and zip code.

Ancestry, ethnicity and total population data for 2010 come from the Census Bureau’s American

Community Survey. These data are given for 50 states and the 238 zip code tabulation areas in

which our sample of financial institutions operate. These data are suited to this study because

survey respondents identify themselves. Those who self-identify as members of an ancestral or

ethnic group are more likely to engage in information transfer within the specified group.

The same property that makes Census data ideal for this study- its reflection of

individuals’ opinions about their ancestry-also presents a challenge in calculating cultural

proximity. Namely, respondents may indicate any ancestry or ethnicity without being restricted

14

to any set of prescribed responses. As a result, many individuals identify their ancestry as being

part of a sub-group of another ancestry or an ethnic group without an existing nation. Examples

of this are those who report ancestry such as Scotch Irish, Russian German, or Basque. The

pertinent question becomes: With which country would these individuals identify with most? In

the case of the Scotch Irish ancestral group, a group linked mainly to Northern Ireland, the

strongest country tie is likely the United Kingdom (Montgomery 1995). Other ancestral groups,

such as the Basque community, consider themselves part of a group linked more to culture than a

single, existing country (Davis 1999). To account for the difference between the ancestral groups

represented in the Census data and the existing countries available to invest, we perform two

separate calculations of cultural proximity.

The first consolidates all cultures that identify as a sub-group of a specific, existing

nation. The most notable example of this is the consolidation of the German, Pennsylvanian

German and Russian German populations for each state or zip code. In this instance, the cultural

proximity calculation is modified to reflect the sub-groups, according to equation [7].

,

1,Pr

i

n

x j

ix j

x

P

oximityP

[7]

where 1,x jP is the population of the first sub-group of ancestry j in state or zip code x.

The second method for calculating cultural proximity ignores the existence of sub-groups

of ancestry and considers only those respondents who identify with an existing, specific nation.

In this case, the German cultural proximity would reflect only the population identifying strictly

as German. Here, the populations that identify as Pennsylvanian German or Russian German

would not be included in the cultural proximity calculation. The equation that reflects this

method is the basic cultural proximity calculation found in equation [6].

15

C. Abnormal Performance

The measure of abnormal performance of institution’s holdings of foreign securities is

based on security return data provided by FactSet Company. These data give returns to each

institution’s holdings in each target country in the first quarter of 2010. Returns are calculated

based on security prices from one month prior to one month past the quarterly reporting period.

These return data are combined with data on the return to the index of all available securities in

each target country.

Table II shows summary statistics for each target country in our sample. Ukraine has the

highest average abnormal return in our sample with an average abnormal return of 0.34%.

Institutions in our sample earn the lowest average abnormal performance from their holdings in

Finland. The second column shows the average Sharpe ratio of institution’s investment by target

country. This shows each institution’s country-specific abnormal performance, adjusted for the

value-weighted beta of each institution’s holdings in each target country.

The abnormal return to portfolio holdings in country j is calculated by first finding the

value-weighted return to foreign securities from each target country held by each institution. The

return is weighted by the weight that the institution places on the security as a percentage of their

total portfolio in country j. The return to each target country’s equally-weighted index is

subtracted from the value-weighted, institution-specific returns to create the abnormal returns of

each institution’s holdings in each target country. The procedure described follows equation [8].

,Ji ij j J

jeJ

R w R R [8]

Next, the abnormal returns to each institution’s country-specific holdings are adjusted for

the risk of each holding. A beta measure is computed for each security based on market and firm

16

returns from the three years preceding the reporting period. This beta captures the sensitivity of

each security’s returns to the return of their associated country-specific index. This controls for

the return that may accrue to an investor solely because the investor chooses the riskiest

securities amongst all available securities from a target country. This risk measure is weighted by

the securities’ weight as a value of each institution’s portfolio in country j. The risk-adjusted

abnormal return is calculated according to equation [9].

,i

Adj i

ij j

jeJ

RR

w [9]

D. Other Exogenous Determinants of Diversification Bias

D.1 Financial Centers

This method is based on the assumption that institution managers communicate with the

population where the institution is located, but not necessarily other institutional investors in the

vicinity. This assumption ignores the impact of the relative location of funds. In his 1988 paper,

“On the Mechanics of Economic Development,” Lucas theorizes that the accumulation of human

capital has an effect not only on the individual, but also on society (Lucas 1988).The effect,

which Lucas calls the external accumulation of human capital, is such that an individual

attending school or learning a better production technique increases productivity for society.

However, he admits that external human capital accumulation is effective only insomuch as

those accumulating it personally interact with other. This serves as a theory for the development

of cities, where people locate near high population to benefit from this external effect. (Lucas

1988)

Lucas names the financial industry as a group of institutions located in cities that benefit

from the higher level of external capital accumulation that accompanies higher population.

17

(Lucas 1988) This theory was tested by Christoffersen and Sarkissian (1991), who hypothesize

that higher human capital in cities leads to better performance by mutual fund managers. The

empirical study finds that fund managers do experience improved performance if they work in a

city (Christoffersen 2009). The authors conclude that mutual funds do experience benefits from

operating in cities, where managers are exposed to higher external human capital and knowledge

spillovers from others in the financial industry. (Christoffersen 2009) While many studies omit

observations from commonly recognized financial centers like Chicago and New York City, we

instead construct a dummy variable to account for a state or zip code’s status as a financial

center. First, we find the average number of institution across all states and zip codes. Any state

or zip code with more institutions than two standard deviations above this average is designated

a financial center.

D.2 Geographical Distance

A zip code specific measure of information cost is included as a predictor of investment

bias because investors exhibit less bias towards countries that are relatively geographically close

(Chan, Covrig and Ng 2005). The geographical distance between each zip code and the capital

city of each target country is calculated using the latitude and longitude coordinates of each

location. The differences between these coordinates alone are not a meaningful measure of

distance without correcting for spherical distance. First, the degree coordinates are converted to

radians using equations [10] and [11]. The radian coordinates are used to calculate spherical

distance according to the Haversine Formula, presented in equations [12a] through [12c], where

R is the radius of the earth in miles. This provides a distance, in miles, between the two points

(Robusto 1957).

18

180

dLatitude [10]

180

dLongitude [11]

2 22 1 2 11 2sin ( ) cos( ) cos( ) sin ( )

2 2a [12a]

2 tan 2( , 1 )c a a a [12b]

d R c [12c]

The log of this distance is included to account for the effect of geographical distance on

investment bias. This zip code distance is also used in regressions where state-specific cultural

proximity is the relevant proximity measure.

D.3 Institution Size

The market value of securities held by each institution may also affect how institutions

diversify. The results of Pollet and Wilson’s 2008 empirical study suggest that, as fund size

increases, the diversification of the fund also increases, albeit at a diminishing rate (Pollet and

Wilson 2008). While this study shows that growth in fund size leads institutions to devote more

to securities they already own, these institutions do face an upward trend in diversification until

they no longer find opportunities to increase returns through diversification (Pollet and Wilson

2008). We include the log of total market value of security holdings for each institution to

control for this small effect on diversification.

D.4 Country Specific Factors

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Chan, Covrig and Ng (2005) find two additional factors that share a statistically

significant relationship with foreign diversification: economic development and taxes on foreign

investment. Economic development is measured by the gross national product per capita in U.S.

dollars for each target country. The perceived risk of investing in an economically developed

country may be lower than that of a developing country. The perceived risk of investing in a

foreign country may also be impacted by the legal institutions of that country. A strong legal

system gives investors assurance that their assets, or proceeds thereof, will not be expropriated

by foreign governments or firm managers (Porta, et al. 2000).

Investors also face a significant cost in the fees levied on foreign investors. The higher

the level of fees levied the fewer securities an investor is willing to purchase because of

increased transaction costs.

If fund managers invest in foreign companies to increase their returns, as the literature

suggests, they may be more attracted to investments if the target country’s equity market has

experienced positive growth in returns. When returns do not grow, or are generally negative,

over an extended period of time, fund managers may hesitate to invest in that target county’s

equities.

These country specific factors likely affect each fund’s investment in the same way.

These variables have a fixed effect on the investment decision, and therefore the bias towards or

against target countries. To control for these factors, we include a dummy variable for each

target country in our sample.

E. Regression

E.1 Investment Bias

20

The bias that each institutional investor has towards each target country is regressed upon

cultural proximity and the vector of exogenous variables to find the impact of shared ethnicity or

ancestry on portfolio diversification. Regressions are performed on the cross-section of bias,

according to the equation [13] where X denotes a vector of zip code specific, exogenous

variables described above and Y denotes a vector of country dummy variables.

, ,| | roximityi j i jBIAS P X Y [13]

E.2 Abnormal Performance

Each institution’s abnormal returns in each target country are regressed upon cultural

proximity, investment bias and the interaction between those variables. Regressions are

performed on the cross-section of returns, according to equation [14]. This regression includes

country clustered errors.

, 1 , 2 , 3 , ,*Adj i i j i j i j i jR Proximity Bias Proximity Bias [14]

IV Results

A. Investment Bias

A.1 State Level Culture

We test the hypothesis that cultural proximity increases the magnitude of positions taken

in target countries using a cross-section of US institutional investors’ portfolios. As stated in

hypothesis one, we expect higher cultural proximity between locations to increase the bias

towards or decrease the bias against a target country’s securities. Table III reports the results of

the OLS regression with robust standard errors on this cross-section of the absolute value of fund

bias.

The first column shows the results when cultural proximity is calculated according to

equation [7]. The positive coefficient of cultural proximity implies that a 1% increase in cultural

21

proximity between a state and foreign country will increase the absolute value of investment bias

by 1.24%. An increase in cultural proximity between a target country and the state in which the

institution is located would increase the magnitude of the bias, regardless of whether the

institution initially over or under-weights that country’s securities. This increase could be caused

by an influx of immigration from the target country to the state where the fund is located.

However, this coefficient is not statistically different from zero.

The positive coefficient on geographical distance implies that a 1% increase in distance

between an institution’s headquarters and a target country would increase the magnitude of

investment bias by an economically insignificant amount. This economic and statistical

insignificance is unexpected, given the literature that shows that geographical distance affects

portfolio allocation.1

The dummy variables representing each foreign country, which are not shown in this

table, are statistically different from zero. This result indicates that US institutions invest in

foreign countries based on country-specific factors, regardless of factors specific to the

institution’s location. If each financial institution in the United States invested identically in

foreign securities, the coefficients on these dummy variables would describe every institution’s

bias for or against assets from each target country.

The value of all foreign securities held by an institution is highly statistically significant.

However, the negative effect of a 1% increase in holdings is small. This shows that, as expected,

an increase in holdings of foreign securities should decrease the magnitude of bias towards or

against specific target countries.

1 It may appear that geographic and cultural proximity should be highly correlated and therefore that cultural

proximity may be taking its significance from its relationship to geographic proximity. However, the correlation

between the two is -0.0291, which suggests that the significance of cultural proximity is not due solely to this

relationship.

22

Column two of Table III shows the regression result when cultural proximity is

calculated at the state level according to equation [6]. This regression does provide a statistically

significant proximity variable and the magnitude of its affect upon investment bias is slightly

higher. This method provides a robustness check for the result found in the first regression.

A.2 Zip Code Level Culture

Table IV reports the results of the OLS regression with robust standard errors on the

cross-section of fund bias. The first column shows the results when cultural proximity is

calculated for each zip code according to equation [7].

When the level of cultural proximity becomes more specific to an institution’s location,

the effect of this proximity becomes more statistically significant. The coefficient of cultural

proximity implies that a 1% increase in cultural proximity between a zip code and foreign

country will increase the magnitude of investment bias by 2.26%.

Column two of Table IV shows the regression results when cultural proximity is

calculated at the zip code level according to equation [6]. The coefficient of the cultural

proximity variable implies that a 1% increase in cultural proximity would result in a 2.51%

increase in the absolute value of an institution’s investment bias.

Both regressions result in geographic distance and fund size coefficients that are the same

as those found in the regressions where cultural proximity is calculated at the state level.

B. Abnormal Performance

B.1 State Level Culture

23

We test the hypothesis that higher cultural proximity and an institution’s level of

investment bias given that proximity will increase the abnormal performance of an institution’s

holdings in a target country using the regression shown in equation [14]. Table V reports the

results of the OLS regression with robust standard errors, where cultural proximity is calculated

at the state level. The first column shows the results when cultural proximity is calculated at the

state level according to equation [7].

The marginal effect of an increase in cultural proximity on the abnormal returns to an

institution’s holdings in a target country is ,0.116 0.489* i jBias . If investment bias is held

constant, a 1% increase in proximity would increase abnormal returns, given that the investment

bias is between one and negative one and the average bias over the entire sample is 0.0002.

These coefficients are statistically significant at the 99% level. The coefficient on investment

bias is not statistically significantly different from zero.

The second column shows regression statistics when state level cultural proximity is

calculated according to equation [6]. While the magnitude of the coefficient on cultural

proximity is relatively unchanged, the coefficient on the interaction term is lower than in the

previous regressions. According to these coefficients, the marginal effect of a change in cultural

proximity is ,0.117 0.704* i jBias . This change in the marginal effect does not alter the result

that, for most levels of bias, a change in cultural proximity would increase an institution’s

abnormal returns from that target country’s securities, with all else held constant. These

coefficients are also statistically significant at the 99% level.

B.2 Zip Code Level Culture

The abnormal performance regressions are also performed using cultural proximity

calculated at the zip code level. The results of these regressions are shown in Table VI. Column

24

one shows the results when cultural proximity is calculated at the zip code level according to

equation [7].

The marginal effect of an increase in proximity is ,0.0874 0.461 i jBias . While the

magnitudes of these coefficients suggest that a positive change in cultural proximity would

decrease abnormal performance, investment bias would need to be 18.9% for this to occur. This

level of positive investment bias is seen in a relatively small portion of our sample.

The results shown in column two are found when zip code level cultural proximity is

calculated according to equation [6]. In this case, the marginal effect on abnormal returns from a

change in cultural proximity is ,0.0934 0.523* i jBias . At an investment bias higher than

17.85%, a change in proximity would decrease abnormal returns, all else held constant.

While large positive investment biases are found in a relatively small portion of our

observations, these regression statistics do suggest that, when cultural proximity motivates an

institution to take an extreme position in a target country, the institution may not be acting upon

a rational information advantage. Rather, these extreme positions may be the effect of an

irrational bias towards a target country.

V Conclusion

Finance literature predicts that investors will perfectly diversify their portfolios to take

advantage of the lower risk or higher return that accompany diversification. However,

institutional investors in the United States continue to devote a higher share of their portfolio to

domestic securities than is suggested by perfect diversification. The portion of these portfolios

that is reserved for foreign securities is also not diversified perfectly amongst all foreign

countries’ securities. This study uses 2,737 US institutional investors’ foreign portfolios to find

25

the relationship between this under-diversification and the cultures of the locations where funds

are headquartered.

We examine the deviation of the share of foreign holdings in each portfolio from their

expected share of a perfectly diversified portfolio. Our results suggest that the magnitude of bias

by institutional investors could be increased by an increase in the cultural similarity between the

two locations. Higher cultural proximity contributes to institutional investors taking larger

overweight and underweight positions in a target country. This finding suggests that institutional

investors act upon the information they gather by their proximity to ancestral and ethnic groups

in their location.

We also examine the abnormal performance of institutional investors in foreign countries.

Given that institutional investors act upon the cultural proximity of their location by over or

under-weighting a target country’s securities, our results suggest that higher cultural proximity

can lead to higher abnormal returns. Exceptions to this finding occur only when institutional

investors take large, overweight positions in a target country. These findings suggest that the bias

towards or against a target country may not always be the result of an information advantage.

26

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28

Table I: Summary Statistics

Table I shows summary statistics of each target country included in our sample based on end of year 2010 holdings

data and market values. The first column shows the weight that should be given to each country by the US investors

(expected). The second column shows the average actual investment by US investors to each target market as a share

of their foreign portfolio (actual). Last column shows the average under- or overweight of each target market (bias).

TARGET COUNTRY EXPECTED ACTUAL BIAS

ARGENTINA 0.0010 0.0029 0.0019

AUSTRALIA 0.0589 0.0162 -0.0427

AUSTRIA 0.0033 0.0012 -0.0021

BAHAMAS 0.0000 0.0000 0.0000

BANGLADESH 0.0004 0.0000 -0.0004

BELGIUM AND LUXEMBOURG 0.0095 0.0109 0.0014

BERMUDA 0.0052 0.1211 0.1159

BRAZIL 0.0159 0.0244 0.0085

BULGARIA 0.0000 0.0000 0.0000

CANADA 0.0282 0.1459 0.1178

CHILE 0.0057 0.0012 -0.0045

CHINA 0.1184 0.0420 -0.0764

COLOMBIA 0.0003 0.0005 0.0002

CROATIA 0.0001 0.0000 -0.0001

CYPRUS 0.0006 0.0000 -0.0005

CZECH REPUBLIC 0.0007 0.0003 -0.0005

DENMARK 0.0079 0.0056 -0.0023

ECUADOR 0.0000 0.0000 0.0000

EGYPT 0.0002 0.0004 0.0002

ESTONIA 0.0000 0.0000 0.0000

FIJI 0.0000 0.0000 0.0000

FINLAND 0.0093 0.0037 -0.0056

FRANCE 0.0482 0.0310 -0.0171

GERMANY 0.0379 0.0233 -0.0146

GHANA 0.0000 0.0000 0.0000

GREECE 0.0018 0.0017 -0.0001

HUNGARY 0.0006 0.0001 -0.0005

ICELAND 0.0000 0.0000 0.0000

INDIA 0.0336 0.0096 -0.0240

INDONESIA 0.0060 0.0012 -0.0048

IRELAND 0.0106 0.0731 0.0625

ISRAEL 0.0052 0.0389 0.0337

ITALY 0.0071 0.0066 -0.0005

JAMAICA 0.0000 0.0000 0.0000

JAPAN 0.1376 0.0427 -0.0949

JORDAN 0.0002 0.0000 -0.0002

29

KENYA 0.0003 0.0000 -0.0003

KOREA 0.0230 0.0087 -0.0144

LATVIA 0.0000 0.0000 0.0000

LEBANON 0.0000 0.0000 0.0000

LITHUANIA 0.0001 0.0000 -0.0001

MALAYSIA 0.0100 0.0012 -0.0088

MALTA 0.0002 0.0000 -0.0002

MEXICO 0.0059 0.0077 0.0017

MONGOLIA 0.0002 0.0000 -0.0002

MOROCCO 0.0004 0.0000 -0.0004

NETHERLANDS 0.0192 0.0571 0.0379

NEW ZEALAND 0.0009 0.0003 -0.0006

NIGERIA 0.0011 0.0000 -0.0010

NORWAY 0.0067 0.0028 -0.0039

PAKISTAN 0.0006 0.0000 -0.0005

PALESTINE 0.0001 0.0000 -0.0001

PANAMA 0.0001 0.0017 0.0016

PERU 0.0011 0.0031 0.0020

PHILIPPINES 0.0010 0.0004 -0.0006

POLAND 0.0031 0.0004 -0.0027

PORTUGAL 0.0017 0.0010 -0.0007

PUERTO RICO 0.0002 0.0114 0.0112

ROMANIA 0.0001 0.0000 -0.0001

RUSSIAN FEDERATION 0.0052 0.0034 -0.0018

SERBIA AND MONTENEGRO 0.0000 0.0000 0.0000

SIERRA LEONE 0.0000 0.0000 0.0000

SINGAPORE 0.0119 0.0121 0.0002

SLOVAKIA 0.0000 0.0000 0.0000

SLOVENIA 0.0003 0.0000 -0.0003

SOUTH AFRICA 0.0201 0.0064 -0.0137

SPAIN 0.0150 0.0057 -0.0093

SRI LANKA 0.0004 0.0000 -0.0003

SWEDEN 0.0235 0.0122 -0.0113

SWITZERLAND 0.0573 0.1099 0.0526

TAIWAN 0.0372 0.0079 -0.0292

THAILAND 0.0057 0.0012 -0.0045

TRINIDAD AND TOBAGO 0.0001 0.0000 -0.0001

TURKEY 0.0042 0.0011 -0.0031

UKRAINE 0.0001 0.0000 -0.0001

UNITED KINGDOM 0.1423 0.1128 -0.0295

VENEZUELA 0.0000 0.0000 0.0000

VIET NAM 0.0002 0.0000 -0.0002

ZIMBABWE 0.0000 0.0000 0.0000

30

TOTAL 0.0120 0.0123 0.0002

Table II: Performance Summary Statistics

Table II shows summary statistics of each target country included in our sample based on first quarter 2010 return

data. The first column shows the average abnormal return to institutions in our sample from each target country

(abnormal returns). The second column shows the average Sharpe ratio of institutions’ investments in each target

country (Sharpe ratio).

TARGET COUNTRY ABNORMAL RETURNS SHARPE RATIO

ARGENTINA 0.0617 0.0340

AUSTRALIA -0.0069 -0.1062

AUSTRIA 0.0095 -0.0113

BERMUDA -0.0436 -0.1755

BRAZIL 0.0290 0.0802

BULGARIA -0.0013 -0.0014

CANADA 0.0467 0.1393

CHILE -0.0291 -0.0223

CHINA 0.0775 0.1714

COLOMBIA -0.0335 -0.0189

CROATIA 0.0370 0.0278

CZECH REPUBLIC 0.0130 0.0012

DENMARK 0.0775 0.1764

EGYPT 0.0933 0.0863

ESTONIA 0.0982 0.0945

FINLAND -0.1021 -0.1148

FRANCE -0.0181 -0.0367

GERMANY 0.0487 0.0282

GREECE 0.0444 0.0556

HUNGARY 0.1426 0.0918

ICELAND 0.2607 0.6245

INDIA -0.0470 -0.0771

INDONESIA -0.0883 -0.1104

IRELAND -0.0011 -0.0121

ISRAEL -0.0190 -0.1700

ITALY 0.0133 0.0049

JAMAICA -0.0340 -0.0549

JAPAN -0.0621 -0.1016

JORDAN 0.0331 0.0222

KENYA -0.0349 -0.0343

LEBANON -0.0046 -0.0047

LITHUANIA -0.0820 -0.0575

MALAYSIA 0.0105 -0.0401

31

MEXICO 0.0210 0.0111

MOROCCO -0.0084 -0.0093

NETHERLANDS 0.0244 0.0200

NEW ZEALAND -0.0447 -0.0674

NIGERIA -0.0542 0.1257

NORWAY 0.0044 0.0023

PAKISTAN 0.0939 0.0904

PANAMA 0.0026 0.0020

PERU -0.0502 -0.0428

PHILIPPINES -0.0141 -0.0680

POLAND -0.0071 -0.0989

PORTUGAL 0.0295 0.0284

PUERTO RICO -0.0136 -0.0896

RUSSIAN FEDERATION -0.0863 -0.0899

SINGAPORE 0.0007 -0.0072

SLOVENIA 0.0864 0.1016

SOUTH AFRICA 0.0065 0.0789

SPAIN 0.0037 -0.0019

SRI LANKA -0.0098 -0.0068

SWEDEN 0.0810 0.0769

SWITZERLAND -0.0122 -0.0296

TAIWAN -0.0524 -0.0875

THAILAND 0.0206 0.0123

TURKEY -0.0193 -0.0515

UKRAINE 0.3417 0.2652

UNITED KINGDOM 0.0061 0.0043

TOTAL 0.0044 -0.0059

32

Table III: Determinants of investment allocation

Table III shows results from cross-sectional OLS regressions that test for the determinants of portfolio allocation by

each institution to each target market relative to the country’s market capitalization weight. The dependent variable

is BIAS from equation [5]. The main variable of interest is cultural proximity from equation [6] in specification (1)

and from equation [7] in specification (2), calculated at the state level. The other independent variables include

geographical distance, number of institutions in the same state, and the market value of each institution’s total

holdings. All regressions are run with target country fixed effects and include country clustered errors. These robust

t-statistics are reported under the coefficients (*** p<0.01, ** p<0.05, * p<0.1).

State Level Culture Regressions

(1) (2)

Consolidated Culture Unconsolidated Culture

Cultural Proximity 0.0124 0.0153*

[0.00824] [0.00787]

Log of Distance 0.00141 0.00139

[0.000890] [0.000891]

Log of Total Holdings -0.00120*** -0.00120***

[5.69e-05] [5.70e-05]

Constant 0.00858 0.00879

[0.00835] [0.00836]

Observations 195,088 195,088

R-squared 0.306 0.306

33

Table IV: Determinants of investment allocation

Table IV shows results from cross-sectional OLS regressions that test for the determinants of portfolio allocation by

each institution to each target market relative to the country’s market capitalization weight. The dependent variable

is BIAS from equation [5]. The main variable of interest is cultural proximity from equation [6] in specification (1)

and from equation [7] in specification (2), calculated at the zip code level. The other independent variables include

geographical distance, number of institutions in the same zip code, and the market value of each institution’s total

holdings. All regressions are run with target country fixed effects and include country clustered errors. These robust

t-statistics are reported under the coefficients (*** p<0.01, ** p<0.05, * p<0.1).

Zip Code Level Culture Regressions

(1) (2)

Consolidated Culture Unconsolidated Culture

Cultural Proximity 0.0226** 0.0251**

[0.0101] [0.0102]

Log of Distance 0.00109 0.00109

[0.000898] [0.000898]

Log of Total Holdings -0.00117*** -0.00117***

[6.15e-05] [6.15e-05]

Constant 0.0111 0.0111

[0.00844] [0.00844]

Observations 165,020 165,020

R-squared 0.304 0.304

34

Table V: Cultural proximity effect on abnormal returns

Table V shows results from cross-sectional OLS regressions that test for the impact of cultural bias on institutional

investor abnormal performance in each foreign country. The dependent variable is ,Adj iR from equation [9]. The

main variables of interest are cultural proximity and the interaction term, where cultural proximity is calculated from

equation [6] in specification (1) and from equation [7] if specification (2) and both are calculated at the state level.

The other independent variable is investment bias of each institution to each target country. Robust t-statistics are

reported under the coefficients (*** p<0.01, ** p<0.05, * p<0.1).

State Level Culture Regressions

(1) (2)

Consolidated Culture Unconsolidated Culture

Cultural Proximity 0.116*** 0.117***

[0.0130] [0.0138]

Investment Bias 0.00924 0.0125

[0.00774] [0.00778]

Cultural Proximity* Investment

Bias -0.489*** -0.704***

[0.0940] [0.0994]

Constant 0.00103 0.00124

[0.000934] [0.000932]

Observations 17,169 17,169

R-squared 0.004 0.005

35

Table VI: Cultural proximity effect on abnormal returns

Table VI shows results from cross-sectional OLS regressions that test for the impact of cultural bias on institutional

investor abnormal performance in each foreign country. The dependent variable is ,Adj iR from equation [9]. The

main variables of interest are cultural proximity and the interaction term, where cultural proximity is calculated from

equation [6] in specification (1) and from equation [7] if specification (2) and both are calculated at the zip code

level. The other independent variable is investment bias of each institution to each target country. Robust t-statistics

are reported under the coefficients (*** p<0.01, ** p<0.05, * p<0.1).

Zip Code Level Culture Regressions

(1) (2)

Consolidated Culture Unconsolidated Culture

Cultural Proximity 0.0874*** 0.0934***

[0.0132] [0.0140]

Investment Bias 0.0112 0.012

[0.00865] [0.00854]

Cultural Proximity* Investment

Bias -0.461*** -0.523***

[0.0869] [0.0926]

Constant 0.00223** 0.00218**

[0.00107] [0.00106]

Observations 14,087 14,087

R-squared 0.004 0.004