the effect of cultural distance on country etf’s tracking error and annual... · 2018. 3. 15. ·...

The effect of cultural distance on country ETF’s tracking error and

premium

Junmao Chiua, Huimin Chungb, Shih-Chang Hungc

a An Assistant Professor, College of Management, Yuan Ze University, 135 Yuan-Tung

Road, Chung-Li, Taoyuan 32003, Taiwan. b Professor, Department of Information Management and Finance, National Chiao Tung

University, 1001 Ta-Hsueh Road, Hsinchu 30050, Taiwan. c Ph.D. Candidate, Department of Information Management and Finance, National

Chiao Tung University, 1001 Ta-Hsueh Road, Hsinchu 30050, Taiwan.

Abstract:

This study examines whether cultural distance impacts the country ETFs’ tracking

error and premium. We investigate 25 country ETFs that are traded in U.S. market using

data from March 1996 to December 2015. Our results show that cultural distance has

negative effects on ETF tracking error and premium. Country ETFs have larger tracking

error and premium from culturally similar countries. We conduct additional tests by

controllin U.S. market condition and country-level variables and our findings suggest

that cultural distance maintains negatively significant effects on tracking error and

premium.

Keywords: tracking error, premium, cultural distance, similar culture, better

understanding, foreign information

Corresponding author: Tel: +886-3-5712121 ext. 57067; Fax: +886-3-5733260. E-mail addresses: (S.C.

Hung email: [email protected]).

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1. Introduction

The importance of cultural distance1 in finance is highlighted by numerous studies.

Specifically, studies have shown that cultural distance provides important explanations

for corporate decision-making (Kogut and Singh 1988; Dodd et al., 2015), investment

decisions (Beugelsdijk and Frijns, 2010; Anderson et al., 2011; Siegel et al., 2011;

Aggarwal et al., 2012; Karolyi, 2016), cross-border mergers and acquisitions (Morosini

et al., 1998; Ahern et al., 2015; Lim et al., 2016), and success of global venture capitals

(Nahata et al., 2014). Dodd et al. (2015) find that corporate managers are willing to do

cross-listed in culturally similar countries. Aggarwal et al. (2012) suggest that cross-

border cash flow is greater between countries that are culturally more similar.

Beugelsdijk and Frijns (2010) and Siegel et al. (2011) find that cultural distance impacts

corporate international investment. Corporate managers prefer to invest more capital in

countries they are familiar with. Furthermore, Anderson et al. (2011) and Karolyi (2016)

find that institutional investors are not willing to invest in culturally distant countries.

Institutional investors have high culture barriers and serious asymmetric information in

those countries. In cross-border M&A, Ahern et al. (2015) and Lim et al. (2016) show

that acquiring companies prefer to take a premium to merge and acquire target

companies from culturally similar countries. Nahata et al. (2014) find that foreign

venture capitals (VCs) are likely to succeed, especially in culturally distant countries

when foreign VCs invite local VCs to invest in the same case.

In current years, Exchange Traded Funds (ETFs) are a popular financial innovation,

and they have many types such as stock ETFs, bond ETFs, exchange ETFs, country

ETFs2, and other ETFs. In the global market, ETFs assets rapidly grew from 204.3

1 Cultural distance measures the cultural dissimilarities between countries. We measure cultural distance

by following Kogut and Singh (1988) and Beugelsdijk and Frijns (2010). 2 County ETFs are designed to track stock indices in foreign market. ETFs’ share are traded in the local

market, but their underlying assets are traded in foreign markets.

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billion U.S. dollars in 2003 to 3422.2 billion U.S. dollars in 2016. ETFs are based on

their underlying assets. Fund managers have to track underlying assets and rebalance

deviation positions at the end of each trading day. Each ETF has tracking error and

premium in the trading day. The trading behavior of investors are reflected on tracking

error and premium3. An ETF is likely to have larger tracking error and premium when

it is traded by many investors. ETFs decrease more transaction costs, so many investors

adopt ETFs to make their portfolios. For example, country ETFs decrease more

transaction costs and more barriers such as geography and transaction. Investors can

invest foreign stocks by country ETFs in local market, and they also can adopt country

ETFs to make an international portfolio.

Investors are a lack of understanding of foreign information because of culturally

dissimilar (Dodd et al., 2015; Huang, 2015). Huang (2015) also finds that investors

respond more slowly to information from more linguistically and culturally distant

countries. Grinblatt and Keloharju (2001) find that Finland investors are willing to

allocate more money in Finnish companies and companies of CEOs of same culture

background because investors can understand corporate information. Using top 35

Finnish companies, Kalev et al. (2008) suggest that local investors have more

transaction advantages than foreign investors such as language, culture, and geography.

Investors understand foreign information that is important, especially on international

investment. If investors poorly understand foreign information or confront more

uncertainty, they will reduce or reject foreign investment. Country ETFs become a new

tendency on international investment. Investors can readily invest foreign stocks and

foreign assets by country ETFs. Most investors are risk reviser (Beugelsdijk and Frijns,

2010). Investors prefer to invest foreign firms and countries form culturally similar

3 Tracking error and premium are illustrated in section 3. Tracking error is measured by following Tang

and Xu (2013). Premium is measured by following Engle and Sarkar (2006).

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countries, because they have better understanding of foreign information and less

uncertainty risk. Thus, investors are willing to invest in country ETFs from culturally

similar countries. Those ETFs will have large tracking error and premium.

In this paper, we investigate whether cultural distance impacts the trading behavior

of investors on country ETFs. ETFs are popular funds in the global market, and their

assets rapidly grew from 2003 to 2016. The number of ETFs grew from 276 in 2003 to

4779 in 2016. In United States market, the total net assets of ETFs grew form 151

billion U.S. dollars in 2003 to 2524 billion U.S. dollars in 2016, and their assets are

approximately 2470 billion U.S. dollars in 2016. United States market is the main

market for ETFs trading. Country ETFs are a new investment way on international

investment. Investors poorly understand foreign information from culturally distant

countries, so they have less transaction motivation to trade foreign stocks. Country

ETFs remove many investment barriers. When investors understand economic

development of foreign countries, they can trade foreign stocks by country ETFs. Thus,

we adopt country ETFs to investigate the relationship between cultural distance and

investors’ behavior.

This study examines how cultural distance affects investors’ behavior on country

ETFs from March 1996 and December 2015. We adopt 25 country ETFs that are traded

in U.S. market. Using Hofstede (2001) culture dimensions, we first examine how

cultural distance impacts country ETFs’ tracking error and premium. Our results show

that country ETFs have large tracking error and premium from culturally similar

countries. Second, investors are likely to be affected by local market condition (Levy

and Lieberman, 2013), and therefore we control U.S. market condition. The results

show that investors are affected by U.S. market condition. Finally, we control not only

U.S. market condition but also country-level variables. Prior studies suggest that

corporate governance mechanisms can impact the investment motivation of investors

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(Mclean et al., 2012). The results provide that cultural distance still has a significant

effect on tracking error and premium even if we control U.S. market condition and

country-level variables.

Our study contributes to the exiting literature. ETFs’ assets and their numbers had

rapidly grew from 2003 to 2016. Country ETFs remove many investment barriers and

assist investors in investing foreign stocks. Financial institutions can publish many

relative-country ETFs about a country. For example, China ETFs have 44 relative ETFs

in U.S. market, and those ETFs are published by different financial institutions. In the

past, country ETFs have be less investigated. Thus, we investigates the relationship

between cultural distance and investors’ behavior on the trading of country ETFs. Our

results show that trading behavior of investors are impacted by cultural distance.

Investors are willing to allocate more money in country ETFs form culturally similar

countries. Because of similar culture investors have better understanding of foreign

countries (Dodd et al., 2015) and respond more quickly to information (Huang, 2015).

Investors have more transaction motivation to trade country ETFs of similar culture.

Further, we control U.S. market condition and country-level variables. Our results

provide that cultural distance still has a significant effect on investors’ behavior.

Consistent with Levy and Lieberman (2013), the trading behavior of investors are

affected by local market condition. Investors are limited to individual horizon at stake

and are sensitive to events in the local market. Consistent with Mclean et al. (2012),

investors prefer to allocate more money in countries of better legal protection. When

foreign countries have better legal protection, investors’ benefits can be protected.

Firms also have high information transparency. Those results suggest that investors’

behavior also are affected by local market condition and legal protection. Importantly,

cultural distance has important and significant effects on investors’ behaviors,

especially on international investment.

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The paper is organized as follows: Section 2 describes the developed hypotheses.

Section 3 describes ETFs background. Sample selection and the empirical model are

described in Section 4. Section 5 shows empirical and robustness results. Finally, our

conclusion is illustrated in Section 6.

2. Hypothesis

Cultural distance has important explanations for financial decision-making and

financial outcome. Corporate managers are willing to allocate more money in countries

of similar culture (Beugelsdijk and Frijns, 2010; Siegel et al., 2011), and they prefer to

a premium to merge and acquire target companies of similar culture (Ahern et al., 2015;

and Lim et al., 2016). Investors are willing to invest familiar foreign assets because of

similar culture (Anderson et al., 2011; Karolyi, 2016). When both countries are similar

culture, there have similar language and similar national system between countries. The

information transfer have fewer barriers in countries. Managers and investors have

better understanding of foreign information (Dodd et al., 2015; Hung, 2015), and they

respond quickly to information from culturally similar countries (Hung, 2015). Thus,

managers have more motivation to enforce oversea program in familiar countries such

as international investment and cross-listed. Investors also have more motivation to

invest familiar foreign assets.

Cultural distance impacts investors’ understanding of foreign information. Dodd

et al. (2015) find that corporate managers prefer to enforce cross-listed in culturally

similar countries because investors have better understanding of firms’ information and

respond quickly to information. Investors are willing to invest stocks of foreign firms

from culturally similar countries. Furthermore, mangers and shareholders have less

agency conflict that is similar culture between their countries. Shareholders have better

understanding of firms’ operating information, and firms’ value can be reflected on

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stock price. Aggarwal et al. (2012) also find that cultural distance affects investors’

behavior in foreign portfolio investment. Investors prefer to trade foreign portfolio from

culturally similar countries. Because investors have less asymmetric information and

fewer transaction costs, they have more motivation to invest foreign portfolio.

Importantly, investors have better understanding of foreign information. Thus, we

conjecture that country ETFs tend to have large tracking errors when the culture

differences between their home country and US are low.

Hypothesis 1: ETFs with low culture differences in their home countries have large

tracking errors.

Prior studies suggest that investors prefer to allocate more money in domestic and

local firms as the major investment (Coval and Moskowitz, 1999; Huberman, 2001).

Grinblatt and Keloharju (2001) show that Finland investors prefer to invest Finnish

firms. Because investors are similar culture with local firms, they have transaction

advantage and have better understanding of firms’ information. Thus, investors prefer

to invest familiar firms as the major investment. Kalev et al. (2008) also suggest that

investors tend to invest local firms because they can lead to know firm-specific

information. Importantly, investors have information and transaction advantages, and

they can confront less uncertainty risk.

Stocks' return expectations are cared for each investor. Kilka and Weber (2000)

suggest that domestic investors have high expected returns for domestic stocks.

However, they are conservative for the expected return of foreign stocks. Because

domestic investors have information and culture advantages, they have better

understanding of domestic firms’ information and lead to know firm-specific

information. Thus, domestic investors prefer to allocate more money in domestic stocks

and have high expected returns for those stocks. We except that country ETFs have

high premium because investors are willing to invest familiar country ETFs. This

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expectation is tested by the following hypothesis:

Hypothesis 2: Country ETFs have large premium when the culture differences between

their home country and US are low.

Culture dimensions impact financial behavior, especially about individualism and

uncertainty avoidance (Beugelsdijk and Frijns, 2010; Chui et al., 2010; Anderson et al.,

2011; Li et al., 2013). When country culture is high individualism, corporate managers

and investors have more investment motivation. Chui et al. (2010) find that investors

are overconfidence and self-attribution and have strong investment motivations,

especially in countries of high individualism. Thus, stock markets have strong

momentum effect.

Beugelsdijk and Frijns (2010) find that corporate managers prefer to enforce

oversea program because of high individualism. Managers are optimistic and

confidence for investment decisions, and therefore they are willing to develop oversea

businesses. Karolyi (2016) find that cultural distance of individualism impacts financial

behavior of institutional investors. Institutional investors are likely to have excess

investment in familiar countries because they have culture advantage and have better

understanding of foreign information. Prior studies also suggest that investors prefer to

invest familiar firms. Thus, we expect that cultural distance of individualism impacts

the trading behavior of investors. The following hypothesis is developed to test this

expectation:

Hypothesis 3: Country ETFs have large tracking error and high premium when the

individualism component of culture factor between home countries and United States

is small.

Investors are more risk-averse when country culture is high uncertainty avoidance

(Beugelsdijk and Frijns, 2010). Investors are conservative in the decision-making. Li et

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al. (2013) find that corporate managers tend to make conservative decisions, especially

about countries of high uncertainty avoidance. However, managers are willing to try

the decision of risk-taking such as enhancing R&D expenses when country culture is

lower uncertainty avoidance. Anderson et al. (2011) find that institutional investors are

not willing to diversify international investment when home countries are high

uncertainty avoidance. Investors prefer to allocate more money in domestic investment

because they are high risk-averse. Anderson et al. also suggest that institutional

investors prefer to diversify international investment in culturally similar countries.

Although uncertainty avoidance often is investigated in corporate strategies,

cultural distance of uncertainty avoidance seldom is examined in prior studies. Karolyi

(2016) find that the financial behavior of institutional investors are affected by cultural

distance of uncertainty avoidance. Institutional investors are willing to allocate more

money in countries of similar uncertainty avoidance. In prior studies, cultural distance

is confirmed to impact corporate strategies and investors’ behavior. Thus, we expect

that similar culture affects the trading behavior of investors. Country ETFs may have

large tracking error and high premium. This expectation is tested by the following

hypothesis:

Hypothesis 4: When United States is similar uncertainty avoidance culture with foreign

countries, country ETFs have large tracking error and high premium.

3. ETFs Background

Exchange Traded Funds have rapidly grown during the recent decade and have

become an investment tendency. In United States, ETFs are created by large money

management firms. Those financial institutions are called “Authorized Participants”.

Fund managers have to provide a detailed plan to the Securities and Exchange

Composition (SEC). The plan contains ETF composition and related procedures. The

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underlying assets of ETFs often are the basket of shares, and the shares have to send

into custodial banks. Each ETF must be maintained safekeeping in the custodial bank.

Investors can redeem ETFs by two ways. First, ETFs are sold on the secondary market.

Second, ETFs shares are submitted to the fund in exchange for the underlying assets.

Most ETFs are passive management, but a few ETFs are active management. ETFs

are based on the underlying assets, and their outstanding shares are associated with

holding underlying assets. When investors hold (sell) more outstanding shares, fund

managers have to buy (sell) more underlying assets until satisfying safekeeping.

Managers must track underlying assets and rebalance deviation position at the end of

each trading day. There are likely to have tracking errors in the rebalancing process.

The tracking error is defined as the difference of returns of net assets and underlying

assets. Further, ETFs have market price and net asset price. The market price comes

from the trading of market participants, and the net asset price tightly links underlying

assets price. If the market price is higher (lower) than the net asset price, ETFs will

have premium (discount).

ETFs are applied in the investment management such as hedging, diversification,

or international investment. Investors can apply country ETFs to form an international

portfolio in the local market. Country ETFs are one of various ETFs. Those ETFs are

designed to track the index of stock market in foreign countries. ETFs' shares are traded

in U.S. market, but their underlying assets are traded in foreign countries. The trading

of ETFs and underlying assets often have not overlapping trading hours in U.S. and

foreign markets.

4. Data Source and Empirical method

4.1.Data Source

This study investigates the relationship between cultural distance and tracking

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error and premium of country ETFs. We focus on 25 country ETFs4 of U.S. market.

Our data cover the period from March 1996 to December 2015 and consist of ETF

characteristics, cultural distance, U.S. market condition, and country-level variables.

CRSP provides details on ETF data and that includes market price, net per share value,

daily trading volume, shares outstanding, bid price, and ask price. DataStream provides

details on market data and that includes ETFs benchmark index, VIX index, S&P 500

index, 3-month LIBOR rate and 3-month Treasury bill rate.

Culture dimensions are collected from Hofstede (2001). Cultural distance is

measured by individualism, uncertainty avoidance, masculinity, and power distance.

Country-level variables contain corporate governance variables, other culture variables,

and macroeconomics. Corporate governance variables include law system, information

disclosure, and the legal protection of investors. Culture variables contain language and

religion. Corporate governance indices are collected from Chiu and Chung (2017), and

culture variables are collected from Stulz and Williamson (2003). Finally, International

Monetary Fund (IMF) provides GDP growth rate for each country and the market

capitalization of each country are collected from World Bank.

4.2.Cultural Distance

Our study applies Hofstede culture dimensions to measure cultural distance.

Cultural distance is measured by the difference of culture dimensions between United

States and foreign countries, and then the difference takes modulus. The equation is

..,, SUnini CCCD (3.1)

where CDi is cultural distance between United States and ith country. Cn,i is the score

of ith country on the nth culture dimension. Cn, U.S. is the score of United States on the

4 In this study, country ETFs include Australia, Austria, Belgium, Brazil, Canada, China, France,

Germany, Hong Kong, Ireland, Italy, Japan, Malaysia, Mexico, Netherlands, New Zealand, Singapore,

South Korea, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, and United Kingdom.

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nth culture dimension. Further, we apply equation 3.1 to measure cultural distance of

individualism and uncertainty avoidance.

In addition, we measure cultural distance by following Kogut and Singh (1988)

and Beugelsdijk and Frijns (2010). KS2 and KS4 are cultural distance, and they are

measured by following Kogut and Singh (1988). KS2 is measured by individualism and

uncertainty avoidance. KS4 is measured by individualism, uncertainty avoidance,

masculinity, and power distance. The equation is

N

V

CC

CD

N

n n

SUnin

i

1

2

..,,

(3.2)

where CDi is cultural distance between United States and ith country. Cn,i is score of ith

country on the nth culture dimension. Cn, U.S. is the score of United States on the nth

culture dimension. Vn is the variance of culture dimension. N is a number of culture

dimensions.

Further, BF2 and BF4 are cultural distance, and they are measured by following

Beugelsdijk and Frijns (2010). BF2 is measured by individualism and uncertainty

avoidance. BF4 is measured by individualism, uncertainty avoidance, masculinity, and

power distance. The equation is

N

n n

SUnin

iV

CCCD

1

2

..,, (3.3)

where CDi is cultural distance between United States and ith country. Cn,i is score of ith

country on the nth culture dimension. Cn, U.S. is the score of United States on the nth

culture dimension. Vn is the variance of culture dimension.

4.3.Empirical Method

Our study examines whether cultural distance affects tracking error and premium.

The ordinary least squares model is adopted as empirical model. The estimate method

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of tracking error follows Tang and Xu (2013). The tracking error is measured by the

difference of returns of net per share value and benchmark index and has to multiply

by one hundred. The tracking error is defined as

100,,, Benchmark

ti

NAV

titi RRTRAC (3.4)

where TRACit is the tracking error. RitNAV is the return of net per share value. Rit

Benchmark

is the return of benchmark index.

The estimate method of premium follows Engle and Sarkar (2006). The premium

is measured that market price divided by net per share value, and then it has to take

nature log and has to multiply by one hundred. The premium is defined as

100)( ,,, tititi NAVPLnPREM (3.5)

where PREMit is the premium. Pit is the market price. NAVit is the net per share value.

The tracking error and premium are investigated in this study, and therefore we

have two main regression models. The first model examines the effect of cultural

distance on tracking errors. The regression model is

tittt

titititititi

dummyyeardummyregionPS

BASTURNBETABENCHCDTRAC

,6

,5,4,3,2,1,

&

(3.6)

where TRACi,t is the tracking error. CDi,t is the cultural distance. BENCHi,t is the return

of benchmark index. BETAi,t is the ETF beta. TURNi,t is the ETF turnover. BASi,t is

the bid-ask spread. S&Pi,t is the return of S&P 500. Fix effects include region dummy

and year dummy. ε i,t is the residual error. All control variables are defined in

Appendix 1.

The second model examines the effect of cultural distance on premiums. The

regression model is

titttti

titititititi

dummyyeardummyregionPSBAS

TURNBETABENCHLTRACCDPREM

,7,6

,5,4,3,2,1,

&

(3.7)

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where PREMi,t is the premium. CDi,t is the cultural distance. LTRACi,t is the lag

tracking error. BENCHi,t is the return of benchmark index. BETAi,t is the ETF beta.

TURNi,t is the ETF turnover. BASi,t is the bid-ask spread. S&Pi,t is the return of S&P

500. Fix effects include region dummy and year dummy. εi,t is the residual error. All

control variables are defined in Appendix 1.

In this study, cultural distance (CDi,t) is the key variable, and it contains KS4, KS2,

BF4, BF2, IND, and UAI. Control variables mainly refer Tang and Xu (2013). Tang

and Xu suggest that the tracking error (TRACi,t) is affected by the return of benchmark

index (BENCHi,t). Fund managers have to rebalance deviation positions for each

trading day. The return of benchmark index will be referred at the same time when

managers make rebalance-decision. Further, the intimate relationship is existence

between the beta (BETAi,t) and the tracking error. Tang and Xu suggest that the tracking

error is small when the beta closes to 1. This implies that managers can appropriately

rebalance deviation positions.

The turnover rate (TURNi,t) and the bid-ask spread (BASi,t) can affect the tracking

error (Tang and Xu, 2013). ETFs have lower liquidity when they have lower turnover

rate and large bid-ask spread. Managers have to pay more costs to rebalance with lower

liquidity, and that is likely to cause large tracking error. The liquidity can affect not

only tracking error but also premium. Broman (2016) suggests that ETFs have high

premium because of better liquidity, and the demand shock has a positive effect on

premium.

Levy and Lieberman (2013) suggest that the performance of country ETFs are

susceptible to the return of S&P 500, especially for countries of non-synchronized

trading. Investors are limited to individual horizon at stake and are sensitive to events

in the local market. The information of foreign markets are likely to be ignored by

investors. Investors decide investment decision according to holding information. Levy

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and Lieberman also provide that investor refers the conditions of last trading day to

make investment decision. Thus, we adopt the return of S&P 500 (S&Pi,t) and lag

tracking error (LTRACi,t) as control variables.

5. Empirical Results

5.1.Descriptive Statistics

The descriptive statistics is provided in table 1. ETF characteristics include TRAC,

PREM, LTRAC, BENCH, BETA, TURN, and BAS. TRAC’s mean is 0, and its standard

deviation is 0.74. LTRAC is that TRAC lags one period, so TRAC and LTRAC are

consistent descriptive statistics. This implies that fund managers can appropriately

rebalance deviation positions. PREM’s mean and standard deviation are 0.14 and 1.33,

respectively. ETFs have small premium.

BENCH’s mean is 0.02 and BETA’s mean is 0.84. When BETA closes to 1, ETFs

have small tracking error (Tang and Xu, 2013). TURN’s mean is 3.27 and its maximum

is 637.07. BAS’s mean is -1.57 and its minimum is -57.31. This suggest that U.S.

investors may prefer to trade the ETFs of self-preference. Thus, some ETFs have larger

TURN and BAS.

Cultural distance contains IDV, UAI, KS2, KS4, BF2, and BF4. IDV is the cultural

distance of individualism. The mean of IDV is 36.44, and its standard deviation is 24.52.

UAI is the cultural distance of uncertainty avoidance. The mean of UAI is 23.86, and

its standard deviation is 14.12. KS2, KS4, BF2, and BF4 are measured by four (two)

culture dimensions. Their mean range from 1.5 to 2.5, and their standard deviation

range from 1 to 2. IDV and UAI are obviously larger than other culture distance.

S&P, VIX, and LIBOR are U.S. market condition. S&P’s mean is 0.03, and its

standard deviation is 1.25. The mean of VIX is 20.92, and its standard deviation is 8.59.

LIBOR’s mean is 0.35, and its standard deviation is 0.43. U.S. market is steady in the

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long term, and funding constraint is lower in most of time.

LAW, DISC, and PROT are corporate governance variables. Their means range

from 0.3 to 0.7. DISC and PROT not include China data because we cannot collect

relative data. LANG and RELI are culture variables. LANG’s mean is 0.17, and the

mean of RELI is 0.19. Macroeconomics contain GDP and MARKET. The mean of

GDP is 2.64 and MARKET’s mean is 88.69. Finally, all control variables are defined

in Appendix 1.

[Table 1 about here]

Table 2 provides correlation statistics. Cultural distance has a significant effect on

premium. U.S. investors tend to trade country ETFs of similar culture. When U.S. is

similar culture with foreign countries, U.S. investors have culture advantage and have

better understanding of foreign information. Investors will have more motivation to

trade familiar country ETFs.

BENCH, TURN, and BAS have significant effects on tracking error and premium.

The trading behavior of U.S. investors are impacted by the return of underlying assets

and ETFs’ liquidity. U.S. market condition also have significant effects on investors’

behaviors. S&P has a positively significant effect on tracking error and premium, but

VIX, and LIBOR have negatively significant effects on tracking error and premium.

When U.S. market has optimistic sentiment and has lower funding constraint, investors

are optimistic and prefer to allocate more money on country ETFs of similar culture.

U.S. investors’ behavior are affected by corporate governance mechanisms,

macroeconomics, language, and religion. Investors are willing to invest country ETFs

that foreign countries have better governance mechanisms and have higher economic

growth. When foreign countries are same law system, same language, and same religion

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with United States, their country ETFs are favored by U.S. investors.


5.2.Cultural Distance and ETFs’ Tracking Error

Prior studies suggest that cultural distance impacts corporate strategies and the

trading behavior of investors. Corporate managers are willing to take a premium to

merge and acquire target companies of similar culture (Ahern et al., 2015; Lim et al.,

2016). Managers and institutional investors prefer to allocate more money in familiar

countries (Beugelsdijk and Frijns, 2010; Siegel et al., 2011; Anderson et al., 2011;

Karolyi, 2016). When foreign countries are similar culture with United States, U.S.

investors have culture advantage and have better understanding of foreign information

(Dodd et al., 2015; Huang, 2015). Investors respond quickly to information form

culturally similar countries (Huang, 2015), and they have more transaction motivation

to trade country ETFs of similar culture. Thus, our study examines that cultural distance

is associated with the tracking error in this section.

Table 3 shows that the effects of cultural distance on the tracking error. Our results

find that cultural distance impacts the trading behavior of U.S. investors. When foreign

countries are similar culture with United States, both countries have similar language

and similar state system. U.S. investors have more culture and transaction advantages.

Importantly, investors have better understanding of foreign information and respond

quickly to information. Investors will prefer to trade familiar country ETFs. Thus,

country ETFs have large tracking error because of culturally similar. The coefficients

of culture distance (KS2, KS4, BF2, and BF4) are between -0.0138 and -0.0083, and

they are significant at the 1 % level. The results are consistent with hypothesis 1.

Furthermore, ETF characteristics have significant relationships with the tracking

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error. The benchmark’s return (BENCH) impacts the transaction motivation of

investors. If the benchmark index has high return, U.S. investors will be optimistic for

foreign market in the future. Investors are willing to trade country ETFs of those

countries. Beta (BETA) represents whether ETFs timely react the information of

foreign markets. If Beta is large over 1, ETFs may have larger tracking error (Tang and

Xu, 2013). The tracking error is also impacted by ETFs liquidity (TURN, and BAS).

ETFs will have better liquidity if ETFs are popular fund in the market. This implies that

investors prefer to trade those ETFs and are willing to pay high price to buy the ETF.

Finally, the return of S&P 500 (S&P) impacts U.S. investors’ behavior. Investors

are limited individual horizon at stake and may ignore the information of foreign

markets (Levy and Lieberman, 2013). Thus, investors’ behavior are likely to be

impacted by U.S. market condition, and investors not follow the information of foreign

markets to do decision-making.


5.3.Cultural Distance and ETFs’ Premium

In this section, we want to examine the effect of cultural distance on premium.

Relative studies suggest that cultural distance is an important influence on international

investment. Institutional investors prefer to allocate more money in countries of similar

culture, and they also prefer to diversify international investment in culturally similar

countries (Anderson et al., 2011; Karolyi, 2016). Furthermore, investors are willing to

allocate more money in domestic and local firms (Coval and Moskowitz, 1999;

Grinblatt and Keloharju, 2001; Huberman, 2001; Kalev et al., 2008), and they have

high return expectation for domestic stocks (Kilka and Weber, 2000). Because investors

have culture and information advantages from culturally similar countries, they have

18

more motivations to invest familiar foreign firms or assets.

Table 4 shows that cultural distance has highly significant effects on premium. Our

results suggest that U.S. investors prefer to trade country ETFs of similar culture. U.S.

investors have more attention to foreign information from culturally similar countries.

Importantly, investors have better understanding of foreign information, and they are

confidence and optimistic for their decisions. Investors are likely to take a premium to

buy ETFs. Thus, country ETFs have high premium, especially for countries of similar

culture. Cultural distance’s coefficients (KS2, KS4, BF2, and BF4) are between -0.0672

and -0.04 and are significant at the 1 % level. These results are consistent with

hypothesis 2.

ETFs’ net per share value tightly links benchmark indices. If benchmark index

have better performance, net per share value will increase. However, net per share value

is the negative relationship with the premium. This cause that the return of benchmark

index (BENCH) is the negative relationship with premium. Beta (BETA) maintains

positively significant effects on premium. ETFs’ turnover (TURN) also is associated

with the premium. Further, the return of S&P 500 (S&P) still has a highly significant

effect on premium. U.S. investors are affected by U.S. market condition. Investors are

willing to trade country ETFs when S&P 500 has better performance.


5.4.Cultural Distance and United States Market Condition

The trading behavior of U.S. investors are affected by U.S. market condition. U.S.

investors are likely to overreact on country ETFs when S&P 500 has better performance

(Levy and Lieberman, 2013). In addition to S&P 500, we adopt VIX index and LIBOR

rate as U.S. market condition. VIX index represents market volatility and investors’

sentiment. LIBOR rate represents funding constraint or financial costs in the market.

19

Thus, we examine whether cultural distance affects the trading behavior of U.S.

investors after controlling VIX index and LIBOR rate.

Table 5 provides that cultural distance is associated with tracking error and

premium. Panel A of table 5 shows that cultural distance has highly significant effects

on tracking error after controlling VIX and LIBOR. Our results suggest that not only

cultural distance but also U.S. market condition impact investors’ behavior. Cultural

distance’s coefficients range from -0.0134 to -0.0081, and they are significant at the 1

% level. VIX index has a positively effect on tracking error. If VIX index is high, U.S.

investors’ sentiment will be pessimistic and instable. Investors are likely to buy (sell)

in large quantities of holding ETFs. ETFs will have high volatility. Fund managers are

difficult to rebalance positive deviations. Thus, country ETFs have large tracking error,

especially at the period of high market volatility.

Panel B of table 5 shows that cultural distance has a highly significant effect on

premium after controlling VIX and LIBOR. Our results suggest that U.S. investors have

more transaction motivation to trade ETFs when U.S. market has optimistic sentiment

and lower funding constraint. Cultural distance’s coefficients range from -0.0709 to -

0.0415 and are significant at the 1 % level. VIX and LIBOR have negatively significant

effects on premium. Investors are optimistic and confidence in the future when VIX is

lower. If LIBOR is lower, investors have lower capital costs and tend to increase

investment amount. Investors will adjust transactional strategies to trade ETFs

according to investors’ sentiment of market and funding constraint. Thus, investors are

willing to trade familiar country ETFs when U.S. market has optimistic sentiment and

lower funding constraint. Country ETFs will have high premium.


20

5.5.Cultural Distance and Country-level variables.

In this section, we investigate whether cultural distance impacts tracking error and

premium after controlling corporate governance mechanism and macroeconomics.

Prior studies suggest that governance mechanism and macroeconomics impact the

transaction motivation of investors. Investors prefer to invest more money in foreign

countries of better governance mechanism because investors’ benefits can be protected

(Mclean et al., 2012). The disclosure of macroeconomics impacts investors’ behavior.

When the information of macroeconomics are good news, investors have more

transaction motivation (Bertone et al., 2015). Thus, we control governance mechanism

and macroeconomics as control variables.

Table 6 provides that cultural distance has significant relationships with tracking

error and premium after controlling country-level variables. Panel A of table 6 shows

that cultural distance is associated with tracking error. Our results suggest that cultural

distance maintains highly significant effects on tracking error even if we control

governance mechanisms and macroeconomics. The coefficients of cultural distance

range from -0.0139 to -0.0081, and they are significant at the 1% level. Outsiders have

less asymmetric information when foreign countries have better information

transparency. Foreign information can quickly reflect on the market price of country

ETFs. Fund managers also can appropriately rebalance positive deviations, so country

ETFs have small tracking error. Further, the legal protection has an important effect on

tracking error. When a country has better legal protection for investors, investors’

benefits can obtain more protection. Investor are willing to trade country ETFs about

countries of better legal protection. Thus, country ETFs will have large tracking error.

Panel B of table 6 shows that cultural distance is associated with premium. Our

results suggest that cultural distance maintains highly significant effects on premium.

Cultural distance’s coefficients are between -0.0552 and -0.0355 and are significant at

21

the 1% level. Country ETFs have high premium, especially about countries of common

law system. When foreign countries also are common law system, foreign countries

have similar state system with United States. U.S. investors have transaction advantage,

and they have better understanding for relative law. Consistent with Panel A, investors

prefer to trade country ETFs, especially for countries of better legal protection. Thus,

country ETFs have high premium when foreign countries are common law system and

have better legal protection.

Finally, the trading behavior of U.S. investors are affected by macroeconomics.

Investors are willing to invest country ETFs about countries of high economic growth,

and therefore ETFs’ premium is the positive significant relationship with the economic

growth.


5.6. Robustness Results

5.6.1 Cultural Distance of individualism and uncertainty avoidance

In the past, studies find that individualism and uncertainty avoidance can affect

financial decision-making and financial outcome (Kogut and Singh, 1988; Chui et al.,

2010; Beugelsdijk and Frijns, 2010; Anderson, et al., 2011; Li et al., 2013). Using over

5000 institutional investors data, Karolyi (2016) suggest that institutional investors are

willing to allocate more money in countries of similar individualism and uncertainty

avoidance. Thus, we examine how the trading behavior of U.S. investors is affected by

the cultural distance of individualism and uncertainty avoidance (IDV and UAI).

Table 7 provides that IDV and UAI are associated with tracking error and premium.

Panel A of table 7 shows that tracking error has the significant relationship with IDV

and UAI. Our results suggest that U.S. investors are willing to trade country ETFs of

22

similar individualism and uncertainty avoidance because they have culture and

transaction advantages. Importantly, investors have better understanding of foreign

information and respond quickly to information. Thus, country ETFs have large

tracking error, especially for culturally similar countries. IDV’s and UAI’s coefficient

are -0.001 and -0.0004 and are significant at the 1% and 5% level. The results are

consistent with Hypothesis 3.

Panel B of table 7 shows that premium has the significant relationship with IDV

and UAI. Our results suggest that U.S. investors have more transaction motivation to

trade familiar country ETFs because they have better understanding of foreign

information. Investors can confront less uncertainty risk, and they are optimistic and

confidence for self-decision. Thus, U.S. investors prefer to trade country ETFs from

culturally similar countries. Those ETFs will have high premium. The coefficients of

IDV and UAI are -0.002 and -0.004, and they are significant at the 1% level. The results

are consistent with Hypothesis 4.


5.6.2 Cultural Distance, Language, and Religion

Language and religion can affect the trading behavior of investors (Grinblatt and

Keloharju, 2001) and financial system (Stulza and Williamson, 2003). Investors are

willing to trade familiar firms from cultural similar countries because they have similar

culture and similar language. The legal protection of shareholders and creditors also are

affected by language and religion. Thus, we investigates whether cultural distance

maintains significant effects on tracking error and premium after controlling language

23

and religion (LANG and RELI5).

Table 8 provides that cultural distance is associated with tracking error and

premium. Panel A of table 8 shows that cultural distance still has highly significant

effects on tracking error even if we control LANG and RELI as control variables.

Cultural distance’s coefficients range from -0.0155 to -0.0089 and are significant at the

1% level. In addition, LANG has weak effects on tracking error, and RELI is not

associated with tracking error.

Panel B of table 8 shows that cultural distance still has significant effects on

premium. U.S. investors are willing to trade country ETFs from English-speaking

countries because both countries are similar language and similar culture. United States

and foreign countries are similar state system when foreign countries also are Protestant

countries. Investors have lower culture barriers and have less asymmetric information.

Foreign information can be quickly responded on ETFs’ market price, so ETFs have

small premium, especially for Protestant countries. Thus, country ETFs have large

premium form culturally similar countries and English-speaking countries.


6. Conclusion

This study investigates how cultural distance impact the trading behavior of U.S.

investors. Empirical data contain 25 country ETFs in U.S. market and cover the interval

between March 1996 and December 2015. Our findings suggest that cultural distance

has important explanations for U.S. investors’ behavior. Investors are willing to trade

country ETFs of similar culture. Investors have better understanding of foreign

5 LANG is a dummy variable, and that the primary language is English equals 1. RELI also is a

dummy variable, and that the primary religion is Protestant equals 1.

24

information and respond quickly to information from culturally similar countries.

Foreign information can quickly reflect on the market price of country ETFs. Thus,

investors have more transaction motivation to trade country ETFs, especially for

culturally similar countries. Country ETFs will have large tracking error and high

premium.

U.S. investors are affected by U.S. market condition (Levy and Lieberman, 2013).

In our study, U.S. market condition include the return of S&P 500, VIX index, and

LIBOR rate. Our findings provide that cultural distance maintains highly significant

effects on investors’ behavior after controlling U.S. market condition. Consistent with

Levy and Lieberman (2013), U.S. market condition impact investors’ behavior.

Investors are limited to individual horizon at stake, and they are sensitive to events in

local market. Investors are likely to ignore foreign market’s information. Thus, the

trading behavior of investors are affected by U.S. market condition, especially for the

return of S&P 500 and VIX index.

Foreign countries have better governance mechanism and better economic growth

that are favored by investors (Mclean et al., 2012; Bertone et al., 2015), so we control

governance mechanisms and macroeconomics as control variables. Governance

mechanisms contain law system, information transparency, and the legal protection of

investors. Macroeconomics contain GDP growth and stock market capitalization. Our

findings suggest that cultural distance still has highly significant effects on investors’

behavior even if we control governance mechanisms and macroeconomics. Consistent

with Mclean et al. (2012) and Bertone et al. (2015), investors’ behavior are impacted

by governance mechanisms and macroeconomics. Foreign countries are common law

system, and they have better legal protection of investors. Investors have better

understanding for relative law, and they can protect self-benefits by relative law. Further,

foreign countries are high economic growth. This implies that those countries have

25

many business opportunities. Firms are likely to have more income in the future. Thus,

investors are willing to trade country ETFs that foreign countries are common law

system, better legal protection of investors, and high economic growth.

26

REFERENCE:

Aggarwal, R., Kearney, C., Lucey, B., 2012. Gravity and culture in foreign portfolio

investment. Journal of Banking & Finance 36, 525-538.

Ahern, K.R., Daminelli, D., Fracassi, C., 2015. Lost in translation? The effect of

cultural values on mergers around the world. Journal of Financial Economics 117, 165-

189.

Anderson, C.W., Fedenia, M., Hirschey, M., Skiba, H., 2011. Cultural influences on

home bias and international diversification by institutional investors. Journal of

Banking & Finance 35, 916-934.

Bertone, S., Paeglis, I., Ravi, R., 2015. (How) has the market become more efficient?

Journal of Banking & Finance 54, 72-86.

Beugelsdijk, S., Frijns, B., 2010. A culture explanation of the foreign bias in

international asset allocation. Journal of Banking & Finance 34, 2121-2131.

Broman, M.S., 2016. Liquidity, style investing and excess comovement of exchange-

traded fund returns. Journal of Financial Markets 30, 27-53.

Chiu, J., Chung, H., 2017. National governance and fragility in liquidity: the role of

information environment. Working Paper.

Chui, A.C.W, Titman, S., Wei K.C.J., 2010. Individualism and Momentum around the

World. Journal of Finance 1, 361-392.

Coval, J.D., Moskowitz, T.J., 1999. Home Bias at Home: Local Equity Preference in

Domestic Portfolios. The Journal of Finance 54, 2045-2073.

Dodd, O., Frijns, B., Gilbert, A., 2015. On the Role of Cultural Distance in the Decision

to Cross List. European Financial Management 21, 706-741.

Engle, R., Sarkar, D., 2006. Premiums-discounts and exchange traded funds. The

Journal of Derivatives Summer, 27-45.

Grinblatt, M., Keloharju, M., 2001. How Distance, Language, and Culture Influence

27

Stockholding and Trades. Journal of Finance 3, 1053-1073.

Hofstede, G., 2001. Culture’s consequences: comparing values, behaviors, institutions,

and organizations across nations. 2d edition. Thousand Oaks, CA: SAGE.

Huang, X., 2015. Thinking Outside the Borders Investors’ Underreaction to Foreign

Operations Information. The Review of Financial Studies 28, 3109-3152.

Huberman, G., 2001. Familiarity breeds investment. The Review of Financial Studies

14, 659-680.

Kalev, P.S., Nguyen, A.H., Oh, N.Y., 2008. Foreign versus local investors：Who knows

more？Who makes more？ Journal of Banking & Finance 32, 2376-2389.

Karolyi, G.A., 2016. The gravity of culture for finance. Journal of Corporate Finance

41, 610-625.

Kilka M., Martin Weber, M., 2000. Home Bias in International Stock Return

Expectations. Journal of Psychology and Financial Markets 1, 176-192.

Kogut, B., Singh, H., 1988. The effect of National Culture on the Choice of Entry Mode.

Journal of International Business Studies 19, 411-432.

Levy, A., Lieberman. O., 2013. Overreaction of country ETFs to US market returns：

Intraday vs. daily horizons and the role of synchronized trading. Journal of Banking &

Finance 37, 1412-1421.

Li, K., Griffin, D., Yue, H., Zhao, L., 2013. How does culture influence corporate risk-

tasking? Journal of Corporate Finance 23, 1-22.

Lim, J., Makhija, A.K., Shenkar, O., 2016. The asymmetric relationship between

national cultural distance and target premiums in cross-border M&A. Journal of

Corporate Finance 41, 542-571.

McLean, R.D., Zhang, T., Zhao, M., 2012. Why Does the Law Matter? Investor

Protection and Its Effects on Investment, Finance, and Growth. Journal of Finance 1,

313-350.

28

Morosini, P., Shane, S., Singh, H., 1998. National Cultural Distance and Cross-Border

Acquisition Performance. Journal of International Business Studies 29, 137-158.

Nahata, R., Hazarika, S., Tandon, K., 2014. Success in Global Venture Capital Investing：

Do Institutional and Cultural Differences Matter? Journal of Financial and Quantitative

Analysis 49, 309-342.

Siegel, J.I., Licht, A.N., Schwartz, S.H., 2011. Egalitarianism and international

investment. Journal of Financial Economics 102, 621-642.

Stulza, R.M., Williamson, R., 2003. Culture, openness, and finance. Journal of

Financial Economics 70, 313-349.

Tang, H., Xu, X.E., 2013. Solving the Return Deviation Conundrum of Leveraged

Exchange-Traded Funds. Journal of Financial and Quantitative Analysis 48, 309-342.

29

Appendix 1: Definitions of variables

Variables Definition / Source

Cultural Distance:

Individualism-Distance (IDV)

The IDV is the cultural distance of individualism. The distance is the difference between the dimensions of foreign countries and United States, and then it takes the modulus. Source: Hofstede (2001).

Uncertainty Avoidance-Distance (UAI)

The UAI is the cultural distance of uncertainty avoidance. The distance is the difference between the dimensions of foreign countries and United States, and then it takes the modulus. Source: Hofstede (2001).

Kogut and Singh (2) (KS2)

The KS2 is a comprehensive cultural distance and follows Kogut and Singh (1988). This distance is composed of individualism and uncertainty avoidance. These culture indices come from Hofstede culture dimensions. Source: Hofstede (2001).

Kogut and Singh (4) (KS4)

The KS4 is a comprehensive cultural distance and follows Kogut and Singh (1988). This distance is composed of individualism, uncertainty avoidance, masculinity, and power distance. These culture indices come from Hofstede culture dimensions. Source: Hofstede (2001).

Beugelsdijk and Frijns (2) (BF2)

The BF2 is a comprehensive cultural distance and follows Beugelsdijk and Frijns (2010). The distance is composed of individualism and uncertainty avoidance. These culture indices come from Hofstede culture dimensions. Source: Hofstede (2001).

Beugelsdijk and Frijns (4) (BF4)

The BF4 is a comprehensive cultural distance and follows Beugelsdijk and Frijns (2010). The distance is composed of individualism, uncertainty avoidance, masculinity, and power distance. These culture indices come from Hofstede culture dimensions. Source: Hofstede (2001).

ETF Characteristic:

ETF Tracking Error (TRAC)

The tracking error is the difference between the return of net per share value and benchmark return. This variable is measured according to Tang and Xu (2013). Source: CRSP and DataStream.

ETF Premium (PREM)

The premium is measured that ETF price divided by net per share value, and then the value take the natural log. This variable follows Engle and Sarkar (2006). Source: CRSP.

Lag ETF Tracking Error (LTRAC)

The lag tracking error is that the tracking error lags one period. Source: CRSP and DataStream.

Benchmark Return (BENCH)

The benchmark return is the daily return of ETF benchmark index. Source: DataStream.

ETF Beta (BETA)

The ETF beta is measured by the slope coefficient of the CAPM. Source: CRSP and DataStream.

ETF Turnover (TURN)

The ETF turnover is measured that daily trading volume divided by shares outstanding. Source: CRSP.

Bid-Ask Spread (BAS)

The bid-ask spread is measured that the spread of bid and ask divided by midquote. Source: CRSP.

30

United States Market Condition:

S&P 500 Return (S&P)

The S&P 500 return is the daily return of S&P 500 index. Source: DataStream.

VIX Index (VIX)

The VIX index is an index of the implied volatility of 30-day options on the S&P 500 index calculated from a wide range of calls and puts. Source: DataStream.

LIBOR Rate (LIBOR)

The LIBOR rate is that 3-month LIBOR rate minus 3-month Treasury bill rate. Source: DataStream.

Country-level variables:

Law System (LAW)

The law system equals one if a country is common law system, and otherwise zero. Source: Chiu and Chung (2017).

Information Disclosure (DISC)

The index is an arithmetic mean of (1) prospectus; (2) compensation; (3) shareholders; (4) inside ownership; (5) contracts irregular; and (6) transactions. Source: Chiu and Chung (2017).

Investor Protection (PROT)

The index is composed of the indices of disclosure requirements, liability standards, and anti-director rights. Source: Chiu and Chung (2017).

Language (LANG)

LANG equals one if the primary language of country is English, and otherwise zero. Source: Stulz and Williamson (2003).

Religion (RELI)

RELI equals one if the primary religion of country is Protestant, and otherwise zero. Source: Stulz and Williamson (2003).

GDP Growth Rate (GDP)

The GDP growth rate is the annual growth rate for each home country. Source: International Monetary Fund.

Market Capitalization (MARKET)

The market capitalization is the stock market capitalization for each home country. Source: World Bank.

31

Appendix 2: The mean of descriptive statistics for each country

Panel A Nation Region Market IDV UAI KS2 KS4 BF2 BF4 GDP MARKET

Australia Asia Pacific Developed 1 5 0.02 0.02 0.22 0.29 3.22 73.34 Austria Europe Developed 36 24 1.57 1.47 1.77 2.42 1.74 75.77 Belgium Europe Developed 16 48 2.31 1.55 2.15 2.49 1.77 97.67 Brazil Latin America Emerging 53 30 3.08 2.14 2.48 2.92 2.85 52.51 Canada United States Developed 11 2 0.10 0.12 0.45 0.70 2.48 78.44 China Asia Pacific Emerging 71 16 4.29 3.07 2.93 3.50 8.48 87.82 France Europe Developed 20 40 1.78 1.59 1.89 2.52 1.55 87.08 Germany Europe Developed 24 19 0.79 0.42 1.26 1.30 1.33 87.27 Hong Kong Asia Pacific Developed 66 17 3.77 2.35 2.75 3.07 3.47 78.87 Ireland Europe Developed 21 11 0.47 0.34 0.96 1.17 3.61 127.35 Italy Europe Developed 15 29 0.95 0.58 1.38 1.52 0.37 101.29 Japan Asia Pacific Developed 45 46 3.56 2.66 2.67 3.26 0.74 107.58 Malaysia Asia Pacific Emerging 65 10 3.49 3.90 2.64 3.95 4.81 86.81 Mexico Latin America Emerging 61 36 4.17 3.08 2.89 3.51 2.82 68.97 Netherlands Europe Developed 11 7 0.14 1.70 0.53 2.60 1.85 102.89 New Zealand Asia Pacific Developed 12 3 0.12 0.26 0.50 1.02 2.52 124.25 Singapore Asia Pacific Developed 71 38 5.37 3.48 3.28 3.73 5.35 80.24 South Korea Asia Pacific Emerging 73 39 5.67 3.44 3.37 3.71 3.69 108.00 Spain Europe Developed 40 40 2.75 1.82 2.34 2.70 2.09 68.31 Sweden Europe Developed 20 17 0.59 2.63 1.08 3.24 2.49 91.63 Switzerland Europe Developed 23 12 0.56 0.34 1.06 1.17 1.91 91.39 Taiwan Asia Pacific Emerging 74 23 4.89 2.83 3.13 3.37 3.74 87.47 Thailand Asia Pacific Emerging 71 18 4.35 3.06 2.95 3.50 2.90 156.58 Turkey Europe Emerging 54 39 3.73 2.46 2.73 3.13 3.47 91.29 United Kingdom

Europe Developed 2 11 0.11 0.08 0.48 0.57 2.12 102.79

Panel B Nation PREM TRAC LTRAC BENCH BETA TURN BAS LAW DISC PROT LANG RELI N

Australia 0.24 0.0039 0.0044 0.02 0.75 2.25 -1.48 1 0.75 0.78 1 1 4943 Austria 0.09 0.0008 0.0005 0.02 0.90 1.93 -1.34 0 0.25 0.1 0 0 4721 Belgium 0.06 -0.0080 -0.0106 0.02 0.82 2.54 -1.26 0 0.42 0.07 0 0 4718 Brazil 0.11 -0.0137 -0.0140 0.04 1.18 10.42 -2.53 0 0.25 0.44 0 0 3842

32

Canada 0.14 0.0002 -0.0007 0.03 0.95 1.67 -1.53 1 0.92 0.96 1 0 4864 China 0.02 -0.0006 -0.0006 0.00 0.69 12.88 -1.72 0 - - 0 0 1874 France 0.08 -0.0010 -0.0006 0.03 0.83 2.43 -1.32 0 0.75 0.47 0 0 4938 Germany 0.12 0.0020 0.0016 0.03 0.81 2.26 -1.51 0 0.42 0 0 1 4953 Hong Kong 0.08 -0.0001 -0.0002 0.02 0.71 3.18 -1.74 1 0.92 0.85 0 0 4954 Ireland 0.80 -0.0167 -0.0163 0.09 0.86 1.83 -1.28 1 0.67 0.48 1 0 1079 Italy 0.06 -0.0006 -0.0006 0.01 0.85 2.69 -1.41 0 0.67 0.2 0 0 4532 Japan 0.16 -0.0007 -0.0010 0.01 0.61 2.65 -1.30 0 0.75 0.42 0 0 4960 Malaysia 0.39 -0.0089 -0.0097 0.01 0.82 3.19 -1.77 1 0.92 0.73 0 0 4950 Mexico -0.07 -0.0027 -0.0030 0.05 1.21 8.01 -2.13 0 0.58 0.1 0 0 4900 Netherlands 0.12 -0.0022 -0.0033 0.02 0.83 1.67 -1.23 0 0.5 0.54 0 0 4853 New Zealand 0.07 -0.0074 -0.0068 0.04 0.68 1.10 -1.02 1 0.67 0.46 1 1 1321 Singapore 0.11 -0.0045 -0.0046 0.01 0.82 1.85 -1.91 1 1 0.77 0 0 4959 South Korea -0.03 0.0084 0.0085 0.03 0.79 4.48 -1.60 0 0.75 0.36 0 1 3021 Spain 0.08 0.0002 0.0000 0.02 0.84 3.21 -1.35 0 0.5 0.55 0 0 4854 Sweden 0.14 -0.0017 -0.0004 0.04 0.90 1.85 -1.48 0 0.58 0.39 0 0 4847 Switzerland 0.25 0.0052 0.0045 0.02 0.72 1.30 -1.35 0 0.67 0.3 0 0 4906 Taiwan 0.16 -0.0003 0.00001 0.00 0.76 5.68 -1.79 0 0.75 0.55 0 0 3869 Thailand 0.05 -0.0037 -0.0038 0.02 0.85 3.71 -1.55 1 0.92 0.37 0 0 1935 Turkey 0.04 -0.0331 -0.0335 0.04 1.03 3.46 -2.16 0 0.5 0.34 0 0 1935 United Kingdom

0.41 0.0009 0.0016 0.01 0.82 1.62 -1.44 1 0.83 0.78 1 1 4909

Note: The table provides the means of empirical variables for each country. Our data cover the period from March 1996 to December 2015. The panel A has cultural distance and macroeconomics. Cultural distances include IDV, UAI, KS2, KS4, BF2, and BF4 from Hofstede (2001). IDV is the cultural distance of individualism. UAI is the cultural distance of uncertainty avoidance. KS2 and KS4 are comprehensive distances and follow Kogut and Singh (1988). BF2, and BF4 are comprehensive distances and are measured according to Beugelsdijk and Frijns (2010). Macroeconomics include GDP and MARKET. GDP is the GDP growth rate. MARKET is the stock market capitalization. The panel B has ETF characteristics and country-level variables. ETF characteristics include PREM, TRAC, LTRAC, BENCH, BETA, TURN, and BAS. PREM is the premium and follows Engle and Sarkar (2006). TRAC is the tracking error and is measured according to Tang and Xu (2013). LTRAC is the lag tracking error. BENCH is the return of benchmark index. BETA is the ETF beta. TURN is the ETF turnover. BAS is the bid-ask spread. U.S. market conditions include S&P, VIX, and LIBOR. S&P is the S&P 500 return. VIX is the VIX index. LIBOR is the LIBOR rate. Country-level variables include LAW, DISC, PROT, LANG, RELI, GDP, and MARKET. LAW is the law system. DISC is the information disclosure. PROT is the investor protection. LAW, DISC, and PROT are collected from Chiu and Chung (2017). LANG is the dummy variable of primary language. RELI is the dummy variable of primary religion. LANG and RELI are collected from Stulz and Williamson (2003). All empirical variables are defined in Appendix 1. DISC and PROT do not include China because corporate governance indices of China cannot be obtained from Chiu and Chung (2017)

33

Table 1：Descriptive Statistics

Variables Mean Std Dev Min. Median Max. Q 1 Q 2 N ETF Characteristics:

TRAC 0.00 0.74 -26.84 0.00 33.01 -0.30 0.31 101637 PREM 0.14 1.33 -29.73 0.13 29.87 -0.36 0.63 101637 LTRAC 0.00 0.74 -26.84 0.00 33.01 -0.30 0.31 101637 BENCH 0.02 1.38 -21.46 0.01 26.19 -0.61 0.69 101637 BETA 0.84 0.34 -1.13 0.84 5.97 0.63 1.06 101637 TURN 3.27 6.85 0.00 1.60 637.07 0.66 3.56 101637 BAS -1.57 1.30 -57.31 -1.26 0.00 -1.90 -0.84 101637

Cultural Distance: IDV 36.44 24.52 1 24 74 15 65 101637 UAI 23.86 14.12 2 23 48 11 38 101637 KS2 2.25 1.80 0.02 1.78 5.67 0.56 3.77 101637 KS4 1.80 1.21 0.02 1.82 3.90 0.42 2.83 101637 BF2 1.88 0.99 0.22 1.89 3.37 1.06 2.75 101637 BF4 2.44 1.11 0.29 2.70 3.95 1.30 3.37 101637

United States Market Condition: S&P 0.03 1.25 -9.03 0.06 11.58 -0.53 0.61 101637 VIX 20.92 8.59 9.89 19.19 80.86 14.85 24.33 101637 LIBOR 0.35 0.43 -0.18 0.18 4.15 0.13 0.41 101637

Country-level variables: LAW 0.33 0.47 0 0 1 0 1 101637 DISC 0.66 0.21 0.25 0.67 1 0.5 0.83 99763 PROT 0.47 0.27 0 0.46 0.96 0.3 0.73 99763 LANG 0.17 0.37 0 0 1 0 0 101637 RELI 0.19 0.39 0 0 1 0 0 101637 GDP 2.64 2.83 -7.36 2.51 15.24 1.24 4.09 101637 MARKET 88.69 35.16 6.28 89.24 245.63 63.03 111.50 101637

Note: This table provides the descriptive statistics for all empirical variables. Our data cover the period from March 1996 to December 2015. Cultural distance includes IDV, UAI, KS2, KS4, BF2, and BF4 from Hofstede (2001). IDV is the cultural distance of individualism. UAI is the cultural distance of uncertainty avoidance. KS2 and KS4 are comprehensive distances and follow Kogut and Singh (1988). BF2 and BF4 are comprehensive distances and are measured according to Beugelsdijk and Frijns (2010). ETF characteristics include PREM, TRAC, LTRAC, BENCH, BETA, TURN, and BAS. PREM is the premium and follows Engle and Sarkar (2006). TRAC is the tracking

34

error and is measured according to Tang and Xu (2013). LTRAC is the lag tracking error. BENCH is the return of benchmark index. BETA is the ETF beta. TURN is the ETF turnover. BAS is the bid-ask spread. U.S. market conditions include S&P, VIX, and LIBOR. S&P is the S&P 500 return. VIX is the VIX index. LIBOR is the LIBOR rate. Country-level variables include LAW, DISC, PROT, LANG, RELI, GDP, and MARKET. LAW is the law system. DISC is the information disclosure. PROT is the investor protection. LAW, DISC, and PROT are collected from Chiu and Chung (2017). LANG is the dummy variable of primary language. RELI is the dummy variable of primary religion. LANG and RELI are collected from Stulz and Williamson (2003). GDP is the GDP growth rate. MARKET is the stock market capitalization. All empirical variables are defined in Appendix 1.

35

Table 2: Correlation Statistics

TRA

C PREM

IDV UAI KS2 KS4 BF2 BF4 LTRAC

BENCH

BETA

TURN

BAS S&P VIX LIBO

R LAW DISC

PROT

LANG

RELI GDP MARKET

TRAC 1 PREM -0.01 1 IDV-Dist.

0.00 -0.03 1

UAI-Dist.

0.00 -0.05 0.35 1

KS2 0.00 -0.04 0.94 0.58 1 KS4 0.00 -0.03 0.85 0.42 0.85 1 BF2 0.00 -0.04 0.93 0.66 0.98 0.85 1 BF4 0.00 -0.04 0.82 0.51 0.83 0.98 0.86 1

LTRAC

0.01 -0.01 0.00 0.00 0.00 0.00 0.00 0.00 1

BENCH

0.07 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.01 1

BETA 0.00 0.00 0.03 0.04 0.02 0.04 0.03 0.05 0.00 0.00 1 TURN -0.01 0.02 0.19 0.08 0.18 0.15 0.19 0.15 -0.01 -0.01 0.08 1 BAS 0.04 0.02 -0.15 -0.03 -0.13 -0.12 -0.13 -0.10 0.04 0.10 -0.15 -0.22 1 S&P 0.10 0.41 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 0.39 -0.01 0.00 0.06 1 VIX -0.03 -0.10 0.00 0.01 0.00 0.00 0.00 0.01 -0.02 -0.10 0.06 0.07 -0.54 -0.12 1

LIBOR

-0.01 -0.04 -0.02 0.01 -0.02 -0.01 -0.01 -0.01 -0.02 -0.04 -0.05 0.05 -0.35 -0.03 0.43 1

LAW 0.00 0.05 0.01 -0.51 -0.04 -0.09 -0.16 -0.23 0.00 0.00 -0.07 -0.10 -0.02 0.00 -0.01 -0.01 1 DISC 0.00 0.03 0.22 -0.24 0.23 0.18 0.12 0.04 0.00 0.00 -0.16 -0.09 0.00 0.00 0.00 -0.01 0.72 1 PROT 0.00 0.04 0.03 -0.45 0.00 0.01 -0.13 -0.11 0.00 0.00 -0.09 -0.06 -0.02 0.00 0.00 0.00 0.77 0.72 1 LANG 0.00 0.05 -0.55 -0.57 -0.54 -0.63 -0.66 -0.74 0.00 0.00 -0.02 -0.10 0.04 0.00 -0.02 -0.01 0.64 0.32 0.53 1 RELI 0.00 0.02 -0.34 -0.29 -0.30 -0.44 -0.39 -0.53 0.00 0.00 -0.08 -0.06 0.04 0.00 -0.02 0.00 0.26 0.03 0.04 0.53 1 GDP 0.00 0.03 0.31 -0.09 0.27 0.27 0.22 0.21 0.00 0.00 -0.04 0.00 0.08 0.00 -0.18 0.00 0.21 0.21 0.23 0.00 -0.03 1 MARKET

0.00 -0.02 -0.06 -0.03 -0.04 -0.01 -0.05 -0.01 0.00 0.00 -0.10 -0.01 0.21 0.00 -0.30 0.03 0.04 0.13 -0.06 0.02 0.07 0.05 1

Note: The correlation statistics are provided for the empirical variables. Our data cover the period from March 1996 to December 2015. The correlation coefficient matrix is composed of IDV-Dist., UAI-Dist., KS2, KS4, BF2, BF4, PREM, TRAC, LTRAC, BENCH, BETA, TURN, BAS, S&P, VIX, LIBOR, LAW, DISC, PROT, GDP, and MARKET. Cultural distances include IDV, UAI, KS2, KS4, BF2, and BF4 from Hofstede (2001). IDV is the cultural distance of individualism. UAI is the cultural distance of uncertainty avoidance. KS2 and KS4 are comprehensive distances and follow Kogut and Singh (1988). BF2 and BF4 are comprehensive distances and are measured according to Beugelsdijk and Frijns (2010). ETF characteristics include PREM, TRAC, LTRAC, BENCH, BETA, TURN, and BAS. PREM is the premium and follows Engle and Sarkar (2006). TRAC is the tracking error and is measured according to Tang and Xu (2013). LTRAC is the lag tracking error. BENCH is the return of benchmark index. BETA is the ETF beta. TURN is the ETF turnover. BAS is the bid-ask spread. U.S. market conditions include S&P, VIX, and LIBOR. S&P is the S&P 500 return. VIX is the VIX index. LIBOR is the LIBOR rate. Country-level variables include

36

LAW, DISC, PROT, LANG, RELI, GDP, and MARKET. LAW is the law system. DISC is the information disclosure. PROT is the investor protection. LAW, DISC, and PROT are collected from Chiu and Chung (2017). LANG is the dummy variable of primary language. RELI is the dummy variable of primary religion. LANG and RELI are collected from Stulz and Williamson (2003). GDP is the GDP growth rate. MARKET is the stock market capitalization. All empirical variables are defined in Appendix 1. The correlation coefficient in bold face is statistically significant at the 5% level.

37

Table 3: Cultural Distance and Tracking Error

Dependent Variable

TRAC

Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic KS4 -0.0092 -4.69*** KS2 -0.0083 -5.80*** BF4 -0.0096 -4.81*** BF2 -0.0138 -5.78*** BENCH 0.0151 10.42*** 0.0151 10.43*** 0.0151 10.42*** 0.0151 10.42*** BETA 0.0135 2.24** 0.0131 2.18** 0.0131 2.18** 0.0127 2.10** TURN 0.0008 2.96*** 0.0009 3.14*** 0.0009 3.01*** 0.0009 3.17*** BAS -0.0036 -2.58*** -0.0038 -2.68*** -0.0036 -2.56** -0.0038 -2.66*** S&P 0.0253 15.56*** 0.0252 15.55*** 0.0253 15.55*** 0.0252 15.54*** Intercept 0.0018 0.14 0.0073 0.58 0.0068 0.52 0.0120 0.92 Region Dummy Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Adj. R2 0.01 0.01 0.01 0.01 Note: The table provides the detail of cultural distance for the tracking error. The ordinary least squares model is used as the empirical model. The regression model is

tittttitititititi dummyyeardummyregionPSBASTURNBETABENCHCDTRAC ,6,5,4,3,2,1, &

The dependent variable is TRACi,t. TRACi,t is the tracking error and is measured according to Tang and Xu (2013). The independent variables include cultural distance, ETF characteristics, and United States market condition. CDi,t is the cultural distance. Cultural distance includes KS2, KS4, BF2, and BF4 from Hofstede (2001). The comprehensive distances are KS2, KS4, BF2, and BF4. KS2 and KS4 follow Kogut and Singh (1988). BF2 and BF4 are measured according to Beugelsdijk and Frijns (2010). ETF characteristics include BENCHi,t, BETAi,t, TURNi,t, and BASi,t. BENCHi,t is the return of benchmark index. BETAi,t is the ETF beta. TURNi,t is the ETF turnover. BASi,t is the bid-ask spread. S&Pt is the S&P 500 return and is a variable of U.S. market condition. All empirical variables are defined in Appendix 1. The t-tests examine whether the regression coefficient is significantly different from zero or not. ***, **, and * indicate significance at the 1％, 5％, and 10％ levels.

38

Table 4: Cultural Distance and Premium

Dependent Variable

PREM

Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic KS4 -0.0400 -10.09*** KS2 -0.0389 -13.45*** BF4 -0.0518 -12.84*** BF2 -0.0672 -13.92*** LTRAC -0.0084 -1.66* -0.0086 -1.72* -0.0086 -1.71* -0.0087 -1.73* BENCH -0.1217 -41.93*** -0.1216 -41.92*** -0.1216 -41.95*** -0.1216 -41.93*** BETA 0.0698 5.66*** 0.0685 5.57*** 0.0703 5.71*** 0.0666 5.42*** TURN 0.0099 16.94*** 0.0101 17.37*** 0.0100 17.17*** 0.0102 17.48*** BAS -0.0031 -0.95 -0.0039 -1.21 -0.0030 -0.93 -0.0038 -1.16 S&P 0.4850 150.33*** 0.4850 150.38*** 0.4850 150.37*** 0.4850 150.39*** Intercept 0.4861 17.82*** 0.5191 19.00*** 0.5355 19.16*** 0.5472 19.58*** Region Dummy Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Adj. R2 0.21 0.21 0.21 0.21 Note: The table provides the detail of cultural distance for the premium. The ordinary least squares model is used as empirical model. The regression model is

tittttititititititi dummyyeardummyregionPSBASTURNBETABENCHLTRACCDPREM ,7,6,5,4,3,2,1, &

The dependent variable is PREM. PREM is the premium and follows Engle and Sarkar (2006). The independent variables include cultural distance, ETF characteristics, and U.S. market conditions. CDi,t is the cultural distance. Cultural distance includes KS2, KS4, BF2, and BF4 from Hofstede (2001). The comprehensive distances are KS2, KS4, BF2, and BF4. KS2 and KS4 follow Kogut and Singh (1988). BF2 and BF4 are measured according to Beugelsdijk and Frijns (2010). ETF characteristics include LTRACi,t, BENCHi,t, BETAi,t, TURNi,t, and BASi,t. LTRACi,t is the lag tracking error. BENCHi,t is the return of benchmark index. BETAi,t is the ETF beta. TURNi,t is the ETF turnover. BASi,t is the bid-ask spread. S&Pt is the S&P 500 return and is a variable of United States market condition. All empirical variables are defined in Appendix 1. The t-tests examine whether the regression coefficient is significantly different from zero or not. ***, **, and * indicate significance at the 1％, 5％, and 10％ levels.

39

Table 5: Cultural Distance, and U.S. Market Condition for Tracking Error and Premium

Panel A Dependent Variable

TRAC

Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic KS4 -0.0090 -4.57*** KS2 -0.0081 -5.63*** BF4 -0.0094 -4.72*** BF2 -0.0134 -5.63*** BENCH 0.0156 10.78*** 0.0156 10.78*** 0.0156 10.77*** 0.0156 10.77*** BETA 0.0148 2.46** 0.0144 2.40** 0.0144 2.40** 0.0140 2.32** TURN 0.0009 3.13*** 0.0009 3.31*** 0.0009 3.18*** 0.0010 3.33*** BAS 0.0032 2.06** 0.0030 1.96* 0.0032 2.08** 0.0030 1.98** S&P 0.0274 16.76*** 0.0274 16.75*** 0.0274 16.76*** 0.0274 16.75*** VIX 0.0040 11.37*** 0.0040 11.34*** 0.0040 11.38*** 0.0040 11.35*** LIBOR -0.0043 -0.54 -0.0044 -0.56 -0.0043 -0.54 -0.0044 -0.55 Intercept -0.0596 -4.34*** -0.0541 -3.93*** -0.0546 -3.88*** -0.0495 -3.52*** Region Dummy Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Adj. R2 0.01 0.01 0.01 0.01 Panel B

Dependent Variable

PREM

Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic KS4 -0.0424 -10.78*** KS2 -0.0415 -14.42*** BF4 -0.0538 -13.42*** BF2 -0.0709 -14.78*** LTRAC -0.0100 -2.00** -0.0103 -2.06** -0.0102 -2.05** -0.0103 -2.07** BENCH -0.1239 -42.97*** -0.1238 -42.95*** -0.1239 -42.98*** -0.1238 -42.97*** BETA 0.0562 4.59*** 0.0548 4.49*** 0.0565 4.61*** 0.0527 4.32***

40

TURN 0.0091 15.77*** 0.0094 16.23*** 0.0092 16.00*** 0.0094 16.33*** BAS -0.0655 -17.93*** -0.0667 -18.26*** -0.0654 -17.91*** -0.0665 -18.19*** S&P 0.4710 145.72*** 0.4709 145.76*** 0.4710 145.77*** 0.4709 145.77*** VIX -0.0235 -33.02*** -0.0236 -33.17*** -0.0235 -33.02*** -0.0236 -33.14*** LIBOR -0.1121 -7.08*** -0.1129 -7.13*** -0.1123 -7.09*** -0.1128 -7.13*** Intercept 0.8815 30.21*** 0.9187 31.42*** 0.9307 31.22*** 0.9467 31.75*** Region Dummy Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Adj. R2 0.22 0.22 0.22 0.22 Note: The table provides the detail of cultural distance for the tracking error and premium. The ordinary least squares model is used as the empirical model. The panel A provides the detail of the tracking error. The regression model of panel A is

titt

ttttitititititi

dummyyeardummyregion

LIBORVIXPSBASTURNBETABENCHCDTRAC

,

876,5,4,3,2,1,

&

The premium are provided in the panel B. The regression model of panel B is

titt

ttttititititititi

dummyyeardummyregion

LIBORVIXPSBASTURNBETABENCHLTRACCDPREM

,

987,6,5,4,3,2,1,

&

The dependent variables are TRACi,t and PREMi,t. TRACi,t is the tracking error and is measured according to Tang and Xu (2013). PREMi,t is the premium and follows Engle and Sarkar (2006). The independent variables include cultural distance, ETF characteristics, and United States market conditions. CDi,t is the cultural distance. Cultural distance includes KS2, KS4, BF2, and BF4 from Hofstede (2001). The comprehensive distances are KS2, KS4, BF2, and BF4. KS2 and KS4 follow Kogut and Singh (1988). BF2 and BF4 are measured according to Beugelsdijk and Frijns (2010). ETF characteristics include LTRAC, BENCH, BETA, TURN, and BAS. LTRACi,t is the lag tracking error. BENCHi,t is the return of benchmark index. BETAi,t is the ETF beta. TURNi,t is the ETF turnover. BASi,t is the bid-ask spread. U.S. market conditions include S&Pt, VIXt, and LIBORt. S&Pt is the S&P 500 return. VIXt is the VIX index. LIBORt is the LIBOR rate. All empirical variables are defined in Appendix 1. The t-tests examine whether the regression coefficient is significantly different from zero or not. ***, **, and * indicate significance at the 1％, 5％, and 10％ levels.

41

Table 6: Cultural Distance, U.S. Market Condition, and Country-level variables for Tracking Error and Premium


TRAC

Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic KS4 -0.0083 -3.61*** KS2 -0.0081 -4.60*** BF4 -0.0100 -4.21*** BF2 -0.0139 -4.75*** BENCH 0.0166 10.76*** 0.0166 10.75*** 0.0166 10.76*** 0.0166 10.75*** BETA 0.0138 2.21** 0.0136 2.19** 0.0139 2.23** 0.0133 2.14** TURN 0.0010 3.34*** 0.0011 3.51*** 0.0010 3.35*** 0.0011 3.52*** BAS 0.0031 1.97** 0.0029 1.85* 0.0031 1.94* 0.0030 1.87* S&P 0.0281 16.53*** 0.0281 16.50*** 0.0281 16.53*** 0.0281 16.50*** VIX 0.0039 10.32*** 0.0039 10.11*** 0.0039 10.32*** 0.0038 10.10*** LIBOR 0.0089 1.15 0.0091 1.18* 0.0088 1.14 0.0093 1.20 LAW -0.0096 -1.25 -0.0123 -1.62 -0.0134 -1.70* -0.0121 -1.60 DISC -0.0580 -3.37*** -0.0454 -2.55** -0.0593 -3.49*** -0.0468 -2.66*** PROT 0.0345 2.73*** 0.0231 1.81** 0.0356 2.82*** 0.0199 1.54 GDP -0.0005 -0.52 -0.0002 -0.20 -0.0005 -0.47 -0.0004 -0.34 MARKET -0.00004 -0.51 -0.0001 -1.14 -0.00004 -0.49 -0.0001 -1.21 Intercept -0.0254 -1.35 -0.0172 -0.90 -0.0146 -0.75 -0.0074 -0.38 Region Dummy Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Adj. R2 0.01 0.01 0.01 0.01 Panel B

Dependent Variable

PREM

Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic KS4 -0.0355 -7.80*** KS2 -0.0370 -10.58*** BF4 -0.0430 -9.13***

42

BF2 -0.0552 -9.53*** LTRAC -0.0119 -2.38** -0.0119 -2.38** -0.0120 -2.38** -0.0119 -2.38** BENCH -0.1285 -43.03*** -0.1284 -43.04*** -0.1284 -43.03*** -0.1285 -43.05*** BETA 0.0485 3.90*** 0.0480 3.87*** 0.0490 3.95*** 0.0453 3.66*** TURN 0.0101 16.76*** 0.0103 17.13*** 0.0101 16.79*** 0.0103 17.10*** BAS -0.0644 -17.20*** -0.0656 -17.53*** -0.0646 -17.27*** -0.0650 -17.37*** S&P 0.4597 139.17*** 0.4595 139.15*** 0.4597 139.19*** 0.4595 139.14*** VIX -0.0239 -31.54*** -0.0243 -31.98*** -0.0239 -31.55*** -0.0242 -31.90*** LIBOR -0.0598 -3.99*** -0.0592 -3.95*** -0.0601 -4.01*** -0.0582 -3.89*** LAW 0.0985 6.44*** 0.0821 5.40*** 0.0820 5.23*** 0.0916 6.07*** DISC -0.0296 -0.86 0.0329 0.93 -0.0345 -1.02 0.0099 0.28 PROT 0.1106 4.41*** 0.0599 2.36** 0.1157 4.61*** 0.0532 2.08** GDP 0.0138 6.72*** 0.0158 7.63*** 0.0141 6.90*** 0.0143 7.00*** MARKET 0.0001 0.89 -0.0001 -0.59 0.0002 0.95 -0.0001 -0.49 Intercept 0.6997 17.89*** 0.7396 18.73*** 0.7458 18.69*** 0.7683 19.02*** Region Dummy Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Adj. R2 0.21 0.21 0.21 0.21 Note: The table provides the detail of cultural distance for the tracking error and premium. Country-level variables are added into empirical model. The ordinary least squares model is used as the empirical model. The panel A provides the detail of the tracking error. The regression model of panel A is

titttiti

iiittttitititititi

dummyyeardummyregionMARKETGDP

PROTDISCLAWLIBORVIXPSBASTURNBETABENCHCDTRAC

,,13,12

11109876,5,4,3,2,1,

&


titttitii

iittttititititititi

dummyyeardummyregionMARKETGDPPROT

DISCLAWLIBORVIXPSBASTURNBETABENCHLTRACCDPREM

,,14,1312

1110987,6,5,4,3,2,1,

&

The dependent variables are TRACi,t and PREMi,t. TRACi,t is the tracking error and is measured according to Tang and Xu (2013). PREMi,t is the premium and follows Engle and Sarkar (2006). The independent variables include cultural distance, ETF characteristics, and United States market conditions. CDi,t is the cultural distance. Cultural distance includes KS2, KS4, BF2, and BF4 from Hofstede (2001). The comprehensive distances

43

are KS2, KS4, BF2, and BF4. KS2 and KS4 follow Kogut and Singh (1988). BF2 and BF4 are measured according to Beugelsdijk and Frijns (2010). ETF characteristics include LTRACi,t, BENCHi,t, BETAi,t, TURNi,t, and BASi,t. LTRACi,t is the lag tracking error. BENCHi,t is the return of benchmark index. BETAi,t is the ETF beta. TURNi,t is the ETF turnover. BASi,t is the bid-ask spread. U.S. market conditions include S&Pt, VIXt, and LIBORt. S&Pt is the S&P 500 return. VIXt is the VIX index. LIBORt is the LIBOR rate. Country-level variables include LAWi,t, DISCi,t, and PROTi,t from Chiu and Chung (2017). LAW is the law system. DISC is the information disclosure. PROTi,t is the investor protection. GDPi,t is the GDP growth rate. MARKETi,t is the stock market capitalization. All empirical variables are defined in Appendix 1. These results do not include China because corporate governance indices of China cannot be obtained from Chiu and Chung (2017). The t-tests examine whether the regression coefficient is significantly different from zero or not. ***, **, and * indicate significance at the 1％, 5％, and 10％ levels.

44

Table 7: Individualism, Uncertainty avoidance, Tracking Error, and Premium


TRAC

Coefficient t-statistic Coefficient t-statistic IDV -0.001 -5.64***

UAI -0.0004 -2.20** BENCH 0.017 10.76*** 0.013 8.63*** BETA 0.014 2.26** 0.013 2.23** TURN 0.001 3.56*** 0.002 5.20*** BAS 0.003 1.68* -0.008 -3.29*** S&P 0.028 16.48*** 0.023 13.64*** VIX 0.004 10.01*** 0.006 15.17*** LIBOR 0.009 1.16 0.015 1.87* LAW -0.011 -1.52 -0.002 -0.34 DISC -0.049 -2.87*** -0.160 -9.81*** PROT 0.022 1.73* 0.044 3.63*** GDP 0.0003 0.30 -0.006 -5.89*** MARKET -0.0001 -1.40 0.0001 0.70 Intercept -0.006 -0.29 0.011 0.62 Year Dummy YES YES Region Dummy YES YES Adj. R2 0.01 0.02 Panel B

Dependent Variable

PREM

Coefficient t-statistic Coefficient t-statistic IDV -0.002 -6.70***

UAI -0.004 -10.17*** LTRAC -0.012 -2.36** -0.012 -2.39** BENCH -0.128 -43.03*** -0.129 -43.09*** BETA 0.044 3.53*** 0.040 3.22*** TURN 0.010 16.91*** 0.010 16.76*** BAS -0.065 -17.30*** -0.062 -16.74*** S&P 0.460 139.12*** 0.460 139.19*** VIX -0.024 -31.74*** -0.024 -31.57*** LIBOR -0.059 -3.93*** -0.056 -3.74*** LAW 0.118 8.06*** 0.082 5.33*** DISC -0.040 -1.17 -0.007 -0.21 PROT 0.078 3.10*** 0.062 2.43** GDP 0.014 6.60*** 0.010 5.01*** MARKET 0.000 -0.03 0.000 0.14 Intercept 0.729 18.17*** 0.747 18.81*** Year Dummy YES YES Region Dummy YES YES Adj. R2 0.21 0.21 Note: The table provides the detail of cultural distance for the tracking error and premium. The ordinary least squares model is used as the empirical model. The panel A provides the detail of the tracking error. The regression model of panel A is

tittti

tiiiitt

ttitititititi

dummyyeardummyregionMARKET

GDPPROTDISCLAWLIBORVIX

PSBASTURNBETABENCHCDTRAC

,,13

,121110987

6,5,4,3,2,1,

&

45


tit

ttitii

iitttti

titititititi

dummyyear

dummyregionMARKETGDPPROT

DISCLAWLIBORVIXPSBAS

TURNBETABENCHLTRACCDPREM

,

,14,1312

1110987,6

,5,4,3,2,1,

&

The dependent variable is TRACi,t and PREMi,t. TRACi,t is the tracking error and is measured according to Tang and Xu (2013). PREMi,t is the premium and follows Engle and Sarkar (2006). The independent variables contain cultural distance, ETF characteristics, United States market condition, and country-level variables. CDi,t is the cultural distance. Cultural distance is IDV and UAI from Hofstede (2001). IDV is the cultural distance of individualism. UAI is the cultural distance of uncertainty avoidance. ETF characteristics include LTRACi,t, BENCHi,t, BETAi,t, TURNi,ti,t, and BASi,t. LTRACi,t is the lag tracking error. BENCHi,t is the return of benchmark index. BETAi,t is the ETF beta. TURNi,t is the ETF turnover. BASi,t is the bid-ask spread. U.S. market conditions include S&Pt, VIXt, and LIBORt. S&Pt is the S&P 500 return. VIXt is the VIX index. LIBORt is the LIBOR rate. Country-level variables include LAWi,t, DISCi,t, and PROTi,t from Chiu and Chung (2017). LAWi,t is the law system. DISCi,t is the information disclosure. PROT is the investor protection. GDPi,t is the GDP growth rate. MARKETi,t is the stock market capitalization. All empirical variables are defined in Appendix 1. These results do not include China because corporate governance indices of China cannot be obtained from Chiu and Chung (2017). The t-tests examine whether the regression coefficient is significantly different from zero or not. ***, **, and * indicate significance at the 1％, 5％, and 10％ levels.

46

Table 8: Cultural Distance, Language, and Religion for Tracking Error and Premium


TRAC

Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic KS4 -0.0099 -4.46*** KS2 -0.0089 -5.72*** BF4 -0.0112 -4.72*** BF2 -0.0155 -5.75*** BENCH 0.0156 10.78*** 0.0156 10.78*** 0.0156 10.78*** 0.0156 10.78*** BETA 0.0150 2.48** 0.0145 2.41** 0.0147 2.43** 0.0140 2.33** TURN 0.0009 3.09*** 0.0009 3.27*** 0.0009 3.13*** 0.0009 3.30*** BAS 0.0031 2.04** 0.0030 1.93* 0.0031 2.05** 0.0030 1.94* S&P 0.0274 16.76*** 0.0274 16.75*** 0.0274 16.76*** 0.0274 16.75*** VIX 0.0040 11.37*** 0.0040 11.35*** 0.0040 11.38*** 0.0040 11.35*** LIBOR -0.0043 -0.54 -0.0044 -0.56 -0.0043 -0.54 -0.0044 -0.56 LANG -0.0100 -1.17 -0.0150 -1.74* -0.0131 -1.50 -0.0169 -1.94** RELI 0.0027 0.44 0.0083 1.37 0.0014 0.23 0.0063 1.05 Intercept -0.0571 -4.06*** -0.0515 -3.69*** -0.0490 -3.34*** -0.0447 -3.09*** Region Dummy Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Adj. R2 0.01 0.01 0.01 0.01 Panel B

Dependent Variable

PREM

Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic KS4 -0.0101 -2.26** KS2 -0.0202 -6.45*** BF4 -0.0199 -4.20*** BF2 -0.0322 -5.97*** LTRAC -0.0096 -1.92* -0.0099 -1.99** -0.0098 -1.96* -0.0099 -1.99**

47

BENCH -0.1241 -43.13*** -0.1241 -43.11*** -0.1241 -43.12*** -0.1241 -43.11*** BETA 0.0518 4.23*** 0.0537 4.40*** 0.0531 4.35*** 0.0523 4.29*** TURN 0.0095 16.48*** 0.0097 16.72*** 0.0096 16.55*** 0.0097 16.72*** BAS -0.0642 -17.60*** -0.0652 -17.84*** -0.0644 -17.65*** -0.0650 -17.79*** S&P 0.4710 145.98*** 0.4710 145.99*** 0.4710 145.99*** 0.4710 145.99*** VIX -0.0235 -33.01*** -0.0236 -33.11*** -0.0235 -33.04*** -0.0236 -33.09*** LIBOR -0.1116 -7.06*** -0.1122 -7.10*** -0.1118 -7.08*** -0.1122 -7.10*** LANG 0.3188 18.54*** 0.2908 16.75*** 0.3031 17.26*** 0.2901 16.47*** RELI -0.0745 -6.10*** -0.0662 -5.45*** -0.0794 -6.47*** -0.0706 -5.83*** Intercept 0.7986 26.80*** 0.8420 28.42*** 0.8317 26.88*** 0.8508 27.86*** Region Dummy Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Adj. R2 0.22 0.22 0.22 0.22 Note: The table provides the detail of cultural distance for the tracking error and premium. The ordinary least squares model is used as the empirical model. The panel A provides the detail of the tracking error. The regression model of panel A is

titttiti

ttttitititititi

dummyyeardummyregionRELILANG

LIBORVIXPSBASTURNBETABENCHCDTRAC

,,10,9

876,5,4,3,2,1,

&


titttt

ttttititititititi

dummyyeardummyregionRELILANG

LIBORVIXPSBASTURNBETABENCHLTRACCDPREM

,1110

987,6,5,4,3,2,1,

&

The dependent variables are TRACi,t and PREMi,t. TRACi,t is the tracking error and is measured according to Tang and Xu (2013). PREMi,t is the premium and follows Engle and Sarkar (2006). The independent variables include cultural distance, ETF characteristics, and United States market condition. CDi,t is the cultural distance. Cultural distance includes KS2, KS4, BF2, and BF4 from Hofstede (2001). The comprehensive distances are KS2, KS4, BF2, and BF4. KS2 and KS4 follow Kogut and Singh (1988). BF2 and BF4 are measured according to Beugelsdijk and Frijns (2010). ETF characteristics include LTRAC, BENCH, BETA, TURN, and BAS. LTRACi,t is the lag tracking error. BENCHi,t is the return of benchmark index. BETAi,t is the ETF beta. TURNi,t is the ETF turnover. BASi,t is the bid-ask spread. UNITED STATES market conditions include S&Pt, VIXt, and LIBORt. S&Pt is the S&P 500 return. VIXt is the VIX index. LIBORt is the LIBOR rate. Country-level variables include LANGi,t and RELIi,t. LANGi,t is the dummy variable of primary language. RELIi,t is the dummy variable of primary religion. LANGi,t and RELIi,t are

48

collected from Stulz and Williamson (2003). All empirical variables are defined in Appendix 1. The t-tests examine whether the regression coefficient is significantly different from zero or not. ***, **, and * indicate significance at the 1％, 5％, and 10％ levels.

the effect of cultural distance on country etf’s tracking error and annual... · 2018. 3. 15. ·...

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