culture's impact on institutional investors' trading frequency

International Review of Financial Analysis 31 (2014) 34–47

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International Review of Financial Analysis

Culture's impact on institutional investors' trading frequency

Eli Beracha a, Mark Fedenia b, Hilla Skiba a,⁎a Department of Economics and Finance, University of Wyoming, United Statesb Department of Finance, Investment and Banking, Wisconsin School of Business, United States

⁎ Corresponding author at: Department of EconomiWyoming, 1000 E. University Ave., Laramie, WY 82072, U

E-mail address: [email protected] (H. Skiba).

1057-5219/$ – see front matter © 2013 Elsevier Inc. All rihttp://dx.doi.org/10.1016/j.irfa.2013.10.002

a b s t r a c t
a r t i c l e i n f o
Article history:Received 23 January 2013Received in revised form 12 August 2013Accepted 4 October 2013Available online 19 October 2013

JEL classification\:G11G15G23Z10

Keywords:Trading frequencyInstitutional investorCultureHome biasAmbiguity aversionTrust

This paper examines how cross-cultural differences influence institutional investors' trading frequency withintheir own portfolio. We find evidence that as cultural distance between the investors and their stock holdingsincreases, institutions trade with lower frequency. Findings are consistent with our hypothesis that tradingfrequency and cultural distance are negatively related due to increasing difficulty of interpreting investmentenvironments in culturally distant foreign markets. We also show that traders from different culturalbackgrounds behave differently when faced with information asymmetry that cultural differences generate.Specifically, we show that ambiguity aversion and lower trust relate to lower trading frequencies at home andabroad.

© 2013 Elsevier Inc. All rights reserved.

1. Introduction

Culture's impact on economic exchange is a new and emerging fieldin international finance research.Many recent papers have documentedthat cross-cultural psychology is an important determinant and driverof many observed finance phenomena. As cultural differences betweeninvestors' or firms' home markets and foreign target markets increase,access to information, interpretation of information, and understandingof business and market environment becomes increasingly difficult.Cultural differences between countries have been shown to impacttrade flows, foreign direct investment, portfolio flows (Aggarwal,Kearney, & Lucey, 2012; Felbermayr & Toubal, 2010; Guiso, Sapienza,& Zingales, 2009), success of cross-border mergers (Ahern, Daminelli,& Fracassi, in press; Chakrabarti, Gupta-Mukherjee, & Jayaraman,2009), and portfolio performance (Choi, Fedenia, Skiba, & Sokolyk,2013).

In this paper, we contribute to the literature of culture and finance.Specifically, we examine culture's influence on trading frequency ofinstitutional investors. The theory on how often rational investors

cs and Finance, University ofSA. Tel.: +1 307 766 4199.

ghts reserved.

should trade stocks differs from much of the evidence documented inthe literature. Investors trade more frequently than theory suggests,and of all factors, investor competence (Graham, Harvey, & Huang,2009), overconfidence, self-attribution bias (Barber & Odean, 2000,2001), and familiarity (Chan & Covrig, 2012; Coval & Moskowitz,2001) appear to be the most consistent in explaining frequent andoften excessive trading.

The purpose of this paper is to study trading frequencies from a newangle. We study trading behavior of institutional investors ininternational setting. We test how cultural difference between aninvestor's homemarket and themarkets of the investor's stock holdings(target markets) affect the trading frequencies inside each investor'sownportfolio.We argue that amanager froma culturally distantmarkethas a lower ability to access and process information compared to his orher homemarket or a target market that is culturally close to his or herhome market. Therefore, we hypothesize that the institutionalinvestor's trading frequency inside the manager's own portfolio willvary, so that the turnover will be the highest in the investor's homemarket and culturally close markets, where information asymmetry isthe lowest. As cultural distance increases and the informationasymmetry increases, we expect to observe declining trading volumesin the same portfolio. To further study the effect of culturalcharacteristics on trading frequency, we also test whether the level ofambiguity aversion, trust, and overconfidence of the investors' homecountry relate to trading frequency.

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35E. Beracha et al. / International Review of Financial Analysis 31 (2014) 34–47

In addition to the cultural distance, we also examine the impact ofgeographical distance between the investor's home market and targetmarkets on trading frequency. In a recent study by Chan and Covrig(2012), the authors document that an increase in geographical distancebetween an investor's home market and target markets increasestrading volumes because investors are less familiar with distant targetmarkets. Building on Chan and Covrig's (2012) findings, we argue thatgeographical distance is directionally dependent and that acrosslongitudes, as fewer business hours overlap, information barriersbetween home markets and target markets increase. Therefore, wehypothesize that as longitudinal distance increases, the trading volumeswill decrease, so that the effect of longitudinal distance is similar to theeffect of cultural distance on trading volumes.

Consistent with our expectations, the main finding of our analysissuggests that institutional investors trade with higher frequency intheir home market and in markets that are culturally close to theirhome country. The results provide new evidence for observedexcessive trading that takes place in financial markets, and thattrading behavior changes, not only from individual to individualand from institution to institution, but also inside investors' ownholdings. The decline in trading frequency is especially rapid ascultural distance increases between each investor's home marketand the target markets. This suggests that, on average, investorsbelieve that they are unable to benefit from more frequent tradingin target markets where they may be informationally disadvantaged.This finding is consistent with some prior papers that provideevidence on negative relationship between the barriers to obtaininformation and trading and market participation activity (Coval &Moskowitz, 2001; Graham et al., 2009). The difference in tradingfrequencies is large in magnitude. On average, investors' homemarket turnover exceeds culturally close target markets' turnoverby a factor of 7 and the culturally most distant target markets by asmuch as 21 times. In addition, we find evidence that culturalambiguity aversion is related to lower trading frequency and thatcultural trust is related to higher trading frequency. Finally, wedocument that geographical distance matters much more, wheninvestor moves further away from the target market across timezones. Longitudinal distance is negatively related to trading frequencies,and it is statistically and economically significant, whereas latitudinaldistance is not a significant determinant of trading frequency in mostof our analyses.

Our paper makes several contributions to the literature. First, ourfindings reveal that observed excessive trading in financial markets isalso amarket specific phenomenon, so that investors' trading frequencychanges within investors' own portfolio. Second, we document that“home bias” exists not only with respect to asset allocation, as it hasbeen long recognized, but also with respect to trading frequency.Investors' trading frequency appears to be the highest in markets thatare culturally closest to their home market, where informationasymmetry is the lowest. Third, our results add to the new andemerging streamof literature on cross-cultural psychology and investorbehavior. Specifically, we focus on cultural distance betweenmarkets asa determinant of economic exchange, as opposed tomany recent papersthat have linked cultural characteristics of individual countries toexplain firm and investor behavior (for example Beugelsdijk & Frijns,2010; Chui, Titman, & Wei, 2010). Our fourth and final contribution isthat we study culture and trading frequency in a large sample ofinstitutional investors from 37 international markets. Our extensiveholdings' dataset allows us to observe portfolio allocation of institutionsat the security level across the global market place in 46 target markets.The holdings data used in the study are the most detailed internationalportfolio dataset of which we are aware.

The remainder of the paper is organized as follows. Section 2 reviewsthe literature and develops our hypotheses, Section 3 details the dataand methodology used in this study, Section 4 presents and discussesthe results and Section 5 concludes.

2. Literature review and hypotheses development

2.1. Excessive trading

Grossman and Stiglitz (1980) use a rational expectation frameworkand argue that investors only trade if the marginal benefit from tradingexceeds its marginal cost. In practice, however, the finance literatureprovides evidence that overconfident investors trade more often thanthe rational expectation framework suggests. Benos (1998) showsthat overconfident investors who compete with rational investorsusing limit orders may enjoy higher profits due to a “first moveradvantage”. Contrary to Benos (1998), Caballé and Sákovics (2003)develop a theoretical model to illustrate that overconfident investorswill suffer from trading too much.

In their influential study, Barber and Odean (2000) show thatindividual investors who trade stocks most frequently earn returnssignificantly lower in statistical and economic terms than the marketaverage. Moreover, the authors document that these lower returnsare accompanied by investing in stocks that are riskier, on average.The hypothesis that overconfidence can explain, at least in part,excessive trading is supported in their study. Barber and Odean(2001) strengthen their overconfidence argument by showing thatmale traders trade more often and earn lower returns than femaletraders. The authors establish a link from psychology and genderoverconfidence to trading volumes, so that females exhibit lowerlevels of confidence in areas like finance and therefore turn theirportfolios over less frequently.

Graham et al. (2009) study the relationship between investorcompetence and international asset allocation and trading frequency.Their research builds on the seminal work on the “competence effect”by Heath and Trevsky (1991). Heath and Trevsky's (1991) naturalexperiment suggests that when people feel more knowledgeableabout a subject matter, they are more willing to bet on their ownjudgment instead of betting on a lottery that carries the sameprobability of winning. Graham et al. (2009) point out that in financialmarkets, investors are constantly making decisions based on subjectiveprobabilities. The authors document that individual investors withspecific characteristics feel more competent about their ability tounderstand financial information than others, which in turn, translatesinto higher trading volumes.

Evidence on competency and trading frequency is also documentedby Grinblatt, Keloharju, and Linnainmaa (2012) who show thatindividuals' level of competency is related to trading behavior and thatmore competent investors, proxied by their IQ, outperform lesscompetent investors. The better than average effect with relation totrading is also examined by the literature. Dorn and Huberman (2005)observe clients of a German retail broker and show that investors whoperceive themselves as more knowledgeable trade more. Glaser andWeber (2007) use survey data from an online broker's investors, andfind that investors who think that they have better investment skillsand believe to perform above average (but in actuality have averageor below average performance) trade more than other investors.

The literature on trading behavior in non-home markets is youngand only few papers, complementary to ours, have explored the topicrecently. Lai, Ng, Zhang, and Zhang (2013) study trading behavior byglobal mutual funds, but focus on the momentum patterns rather thantrading frequency. The authors find that funds generally follow thesame trading strategies at home and abroad, but their buy and sellintensities vary with the location of trades. That is, the momentuminvesting in local stocks is mostly affected by market momentumanomaly and window-dressing, and that in foreign stocks theinformation environment of the host country plays a leading role.Chan and Covrig (2012) find that global mutual fund investorsrebalance their portfolios more often in less familiar environments,where familiarity is measured by geographical distance, trade flows,and commonality in language.

36 E. Beracha et al. / International Review of Financial Analysis 31 (2014) 34–47

2.2. Culturally rooted behaviors and financial decision making

Culture is defined as a system of shared values, beliefs, and attitudesthat influence individual perceptions, preferences, and behaviors.National culture has been a topic in many recent studies in the field offinancial economics, but frequently culture has been defined ormeasured in order to help explain variations in institutions or legalpractices rather than individual investor behavior (e.g., Stulz &Williamson, 2003). In contrast, a recent series of papers by Guiso,Sapienza, and Zingales (2004, 2006, 2008) and Guiso et al. (2009)shows that cultural differences in how people trust others help explainstock market participation and other facets of portfolio investment.

In this study, we investigate a link between cultural similarity andtrading frequency. We employ cultural proxies from Hofstede (1980,2001), which is one of the most influential works in cross-culturalpsychology. The study identifies primary dimensions of culture anddifferences in thinking, values, and social behaviors among peoplefrom more than 50 nations. Hofstede's survey-based evidence showsthat countries' cultural attributes can be measured in five primarydimensions: Uncertainty Avoidance, Individualism, Power Distance,Masculinity and Long-Term Orientation. Appendix A defines each ofthese primary dimensions in more detail. Some of Hofstede's culturaldimensions have been used recently in the international economicsand finance literatures to explain economic behavior. Uncertaintyavoidance has been linked to lower level of foreign equity investment(Beugelsdijk & Frijns, 2010), higher levels of under-diversification andhome bias (Anderson, Fedenia, Hirschey, & Skiba, 2011), and todisproportionately slower growth in industries characterized by highinformation asymmetries (Huang, 2008). Individualism has been linkedto self-attribution biases, trading activity, momentum patterns in stockreturns (Chui et al., 2010), and higher levels of foreign equityinvestment (Beugelsdijk & Frijns, 2010). Hofstede's measure formasculinity has been linked to investor overconfidence and, as a result,to lower levels of under-diversification (Anderson et al., 2011).

In this studywe employ Hofstede's primary dimensions of culture inorder to compute the cultural distance between institutional investors'home markets and target markets. We also examine the correlationbetween individual primary dimensions of culture and tradingfrequency. A few prior papers in finance literature have used culturaldistance as an explanatory variable for investor and firm decisionmaking. Aggarwal et al. (2012) and Anderson et al. (2011) show thatcultural similarity relates to higher levels of foreign portfolioinvestment. Chakrabarti et al. (2009) and Ahern et al. (in press) studycultural distance and success of cross-border mergers. Choi et al.(2013) document better performance by institutional investors inculturally similar target markets.

2.3. Hypotheses development

The main contribution of our paper is to explore whether cultureinfluences institutional investors' trading behavior in a cross-countrysetting. We begin with a general hypothesis that bilateral culturaldistance between the investors' home markets and target markets ofthe securities in the investors' portfolio reduces trading frequency. Thehypothesis is motivated by literature on asymmetric information andinvestor competence. Previous literature shows that all financialtransactions involve asymmetric information, and that the largest partof transaction costs stem from the asymmetric information, adverseselection, and agency costs (Hart, 1995). Especially foreign marketfinancial transactions involve high levels of asymmetric informationand because of it, transaction costs can be high enough for investors toshy away from foreign markets completely (Christelis & Georgarakos,2013). Also, market-based transactions involve more asymmetricinformation in general as opposed to institution-based financialintermediation (Aggarwal & Goodell, 2009). In addition to the market-based information asymmetry, differences in the market environment

across countries influence investors' ability to interpret informationeffectively. Findings from Arrow (1974) and Akerlof (1997) supportthe notion that the ability to communicate effectively declines as thecultural and societal distances between the investor and the targetincrease. Therefore, in cross-border transactions, there is moreasymmetric information involved compared to transacting in thehome markets, especially in non-home markets that are culturallydistant. As a result, we expect investors to take on more passive stockpositions when they operate in environments where they suffer fromhigher information asymmetry (Christelis & Georgarakos, 2013). Thisexpectation is also consistent with Van Nieuwerburgh and Veldkamp's(2010) theory on optimal, but under-diversified, portfolios wheninformation acquisition is costly. Consistent with the findings of Dornand Huberman (2005) and Glaser and Weber (2007), we expect thatinvestors will perceive themselves as more knowledgeable andcompetent in culturally close environments and in their home marketsand to transact more often despite the information asymmetry.Formally, we test a hypothesis that relates bilateral cultural distancebetween the investors' home market and target markets to tradingfrequency:

H1. Bilateral cultural distance is negatively related to institutionalinvestors' trading frequency.

We also test the effect of three cultural characteristics of theinvestors' home markets on trading behavior. First, we hypothesizethat agents from ambiguity averse backgrounds will be more sensitiveto information asymmetry that arises from the market-based tradingand/or from bilateral cultural differences. We expect that not onlyinformation asymmetry, but also investors' attitude towards thatinformation asymmetry relate to trading frequency. Aggarwal andGoodell (2009) assert that culture and social values of a country arean important determinant of financial intermediation, so that cultureswith high levels of ambiguity aversion have a preference for financialintermediation that is institution- rather than market-based. Giventhat cultural ambiguity aversion relates to less market-based financialsystems, cultural ambiguity aversion may also influence investors'willingness to transact in information asymmetric equity market andreduce trading frequency. More specifically, if cultural ambiguityaversion is high, investor may be less willing to act in the financialmarkets, which translates into lower trading frequency. The effect ofcultural ambiguity aversion on trading frequency should be magnified,when the cultural distance between the investor and the target marketis large.

In several recent finance papers, scholars have proxied culturalambiguity aversion with uncertainty avoidance from Hofstede (1980,2001). For example, as mentioned in the previous section, uncertaintyavoidance has been linked to lower levels of foreign equity investment(Beugelsdijk & Frijns, 2010), higher levels of under-diversification andhome bias (Anderson et al., 2011), and to disproportionately slowergrowth in industries characterized by high information asymmetries(Huang, 2008). Formally, we test:

H2a. Cultural ambiguity aversion reduces trading frequency.

Second cultural dimension we relate to trading behavior is culturaltrust. This hypothesis postulates that agents from less trustingbackgrounds will be more sensitive to information asymmetry thatmay arise from the market-based trading environment and/or frombilateral cultural differences. Specifically, when cultural trust or trustin certain markets is comparatively low, we should observe lowertrading frequency in the equity markets due to agents' reducedwillingness to act on asymmetric information. According to Franks,Mayer, and Wagner (2006), countries with higher levels of trust havemore market-based financial intermediation. Additionally, trust hasbeen shown to relate to higher rates ofmarket participation and savings


rates (Guiso et al., 2004). Trust in others has been linked to higher tradeflows, foreign direct investment, and portfolio investment (Guiso et al.,2009). Formally, we test:

H2b. Cultural trust increases trading frequency.

The third cultural dimension we relate to trading behavior is culturaloverconfidence. At individual level, overconfidence has been shown tohave a positive effect on trading frequency because overconfidentinvestors are more willing to act on subjective probabilities in themarkets, as documented before by Barber and Odean (2001). In cross-country setting, overconfidence has been proxied by culturalindividualism and by cultural assertiveness (Anderson et al., 2011;Chui et al., 2010) of the country, so that more individualistic and moreassertive countries' investors exhibit higher levels of overconfidence.More formally, we hypothesize:

H2c. Cultural overconfidence increases trading frequency.

In addition to culture and cultural distance, we study the impact ofgeographical distance on trading frequency. Our final hypothesis relatesboth longitudinal and latitudinal distances between investors' homemarket and target markets to trading frequency. Chan and Covrig(2012) and Coval andMoskowitz (2001) have documented a significantrelationship between geographical distance and trading frequency andasset allocation. In this study we examine the relation betweengeographical distance and trading frequency in more detail. We testthe impact of both latitudinal and longitudinal distances on tradingfrequency and portfolio allocation. We believe that both longitudinaland latitudinal distances will make information acquisition moredifficult and result in lower trading frequency, but for different reasons.Longitudinal distance will make information acquisition more difficultbecause of non-overlapping trading and business hours, consistentwith Portes and Rey (2005). Latitudinal distance will make informationacquisition more difficult because of institutional under-developmentin the countries which are located near the equator (and latitudinallyfar from most of the investor countries in our sample). Beck,Demirgüç-Kunt, and Levine (2003) document that colonialization ofthe countries that are located near the equator was merely resourceextraction-based and left these countries with weaker institutions,lower transparency, and weaker rule of law. We also presume thatlongitudinal and latitudinal distances matter with respect to portfolioallocation because of increased information asymmetry. More formally,we test our final hypothesis:

H3. Longitudinal and latitudinal distances are negatively related toinstitutional investors' trading frequency and proportion of asset allocation.

1 The GLOBE cultural dimensions are from a study by House, Hanges, Javidan, Dorfman,and Gupta (2004). GLOBE incorporates data from17,000managers in 951 organizations in62 countries. GLOBE replicates the Hofstede study in some ways, but expands the culturaldimensions to nine: power distance, uncertainty avoidance, institutional collectivism andin-group collectivism, assertiveness and gender egalitarianism, future orientation,humane orientation, and performance orientation.

2 The general results are similar under both measures.3 World Heritage Foundation's trust data: http://www.worldvaluessurvey.org.4 Centre d'Etudes Prospectives et d'Informations Internationales: www.cepii.fr.5 Latitude/Longitude Distance Calculator: www.nhc.gov.6 Legal Origin data is obtained from Andrei Shleifer's website: http://scholar.harvard.

edu/shleifer/.

3. Data and methodology

3.1. Data

We obtain institutional holdings data from Factset Company. Theholdings data comprise all of 13-F filings and similar filings fromeach institutional investor's home country where such informationis reported. The raw sample of this dataset contains security levelholdings information for over 10,000 institutions at the institutions'family level. At the portfolio level, the dataset contains more than35,000 different portfolios, and a total of 36,070,466 institution-security-time observations. This makes this dataset the mostextensive institutional cross-border holdings dataset of which weare aware. The institution-security-time observations span theperiod between the fourth quarter of 1999 and the first quarter of2010. For each holding, we observe how many shares of stock eachinstitutional investor holds in its portfolio and the associated totalmarket value (in USD) of the holding. The data on holdings at the

security level allow us to estimate the institutions' turnover based onthe end-of quarter reported holdings in institutions' domestic markets,as well as turnovers for each individual international market in whichthe institutional investor has invested. Each security is matched withthe company characteristics, which we also obtain from Factset. Thesecharacteristics include the name of the firm, industry, country domicile,stock exchange and the country of the stock exchange, and share typedata.

Our main variable of interest in this study is the cultural distancebetween different markets and primary dimensions of culture of theinvestors' home markets. To proxy for cultural distance between theinvestor country and the target market, we rely on Hofstede's andGLOBE's primary dimensions of culture, which are commonly used inthe social sciences. We repeat all our analyses using the culturaldistance measure from both, GLOBE's1 and Hofstede's cultural studies,but, for brevity, we only report the main results with Hofstededimensions.2 The reason why we choose to report the results ofHofstede cultural dimension is that it is the most cited cross-culturalstudy in social psychology and widely used in international businessliterature. For selected robustness checks we report the results usingGLOBE dimensions as well. In addition to these two datasets we alsouse bilateral trust in others in a subset of European economies as aproxy for cultural distance. Bilateral trust data are from Guiso et al.(2009) while country specific trust data are from World HeritageFoundation.3

Other data points for the analyses are collected from severaldifferent sources. Geographical distance and countries' coordinates,are from the Centre d'Etudes Prospectives et d'InformationsInternationales (CEPII).4 We base our east–west and north–southdistances, as well as the time zone differential analysis on the country'scapital cities' coordinates and we compute the distances using theLatitude/Longitude Distance Calculator that is available at NationalHurricane Center.5Using the CEPII dataset we are also able to determinewhether the investor and targetmarkets share a common language. Thecommon legal standard indicator is from Djankov, La Porta, Lopez-de-Silanes, and Shleifer (2008).6

We compute countries' market capitalization weights based on FTSEinvestable indexes. For the trading costs associatedwith eachmarket weuse a dataset from Elkins McSherry LLC. Elkins McSherry LLC dataset isbased on more than 24 million institutional investors' transactionsworldwide. The average trading cost for each market includescommissions, fees, market impact of trades, as well as the total cost oftrading. FromCompustat Global's Security Dailywe collect the securities'closing prices,market values, and daily volumes. The volumes are used tocompute a liquidity measure for eachmarket sincemarket liquidity mayaffect investors' turnover. Finally, our macroeconomic control variables,which include GDP and GDP per capita, are obtained from World Bank.

The master dataset we use in the analyses is constructed in thefollowing way. First, we merge the holdings data based on securitylevel identifiers to the security data. Of the 36,070,466 holdingsobservations from 1999:4 to 2010:1, we are able to merge 35,243,928or 97.7%to securities that report their country domicile and countryexchange. Then, we restrict the sample to those securities that haveprice and market value information available. After we compute

http://www.worldvaluessurvey.org

http://www.cepii.fr

http://www.nhc.gov

http://scholar.harvard.edu/shleifer/

http://scholar.harvard.edu/shleifer/

7 We also repeat all the analyses using cultural distances from GLOBE study, whoseprimary dimensions of culture are similar to Hofstede's, except that the study is newerand instead of five dimensions, GLOBE classifies national culture into nine primarydimensions. These results are reported in robustness checks in Tables 4.

8 Consistent with previous research, we omit the fifth cultural dimension, long-termorientation, from the calculation of cultural distance because it is not available for a largenumber of countries.

9 Kandogan (2012) offers an improvement to Kogut and Singh (1988) method. We usethemethod fromKandogan (2012) also as a robustness check and report results in Table 4.We however, report the main results for Kogut and Singh (1988) method, given that it isthe standard method used in the literature.10 We also examine the distance effect on trading for countries that do and do not usedaylight saving time. However, the fact that our data is only available with quarterlyfrequency creates limitation in exploring the daylight saving time phenomenon in moredetail since it is usually applied in the middle on the quarter. Therefore, we recommendthat future research should address this issue using a more appropriate dataset.


turnovers in each security (as shown in Eq. (1a) and (1b) inSection 3.2.1), we aggregate the turnovers based on the security'scountry of exchange, so that the resulting dataset has institutionalinvestor-target country-time observations. Next, we restrict ouranalysis to those investors who are domiciled in countries for whichour macroeconomic and bilateral variables are available. The finaldataset results in 952,822 institutional investor-target country-timeobservations. The turnover data are computed for 10,635 investorfamilies on quarterly basis. The investors included in the final sampleare domiciled in 38 markets and hold securities in 46 target markets.

3.2. Methodology

3.2.1. Institutional investors' turnover in a target marketThemain dependent variable in the study is the portfolio turnover in

market J for each investor i. Turnover is calculated relative to investor's iholdings inmarket J aswell as relative to investor's i holdings in all non-home markets. We measure turnover in these two distinctive ways tohelp clarify whether home bias in trading is compounded by homebias in holdings. We follow Barber and Odean's (2001) method ofcomputing portfolio turnover based on the quarterly holdings' data.The quarterly portfolio turnover consists of one-half of the quarterlypurchase turnover and one-half of the quarterly sales turnover, so thateach target market J's turnover is computed separately. In each quarter,we identify the securities held by each investor in the beginning ofquarter t. The sales, S, are computed as the number of shares sold inquarter t times the beginning of quarter price, p, of security j in USDdivided by the total beginning of the quartermarket value,MV, investedby investor i in market J, or in all non-home markets, J≠ I, in USD. Tocalculate the quarterly purchase turnover, we take the sharespurchased, B, during quarter t, times the beginning of month price pershare, p, divided by the total beginning of the quarter value of theportfolio, MV, invested by investor i in market J, or in all non-homemarkets, J≠ I, in USD. The resulting turnover measure, TO, for investori in each target market J, relative to holdings in that market and relativeto holdings in all non-home markets, is formally presented in Eqs. (1a)and (1b), respectively:

TOi; J;t ¼12�

XSi;t

j¼1

pj;t � Sj;t

� �

MVi; J;tþ 12�

XSi;t

j¼1

pj;t � Bj;t

� �

MVi; J;tð1aÞ

TOi; J;t ¼12�

XSi;t

j¼1

pj;t � Sj;t

� �

XJ≠I

MVi; J;t

þ 12�

XSi;t

j¼1

pj;t � Bj;t

� �

XJ≠I

MVi; J;t

: ð1bÞ

Throughout the analysis, we classify each investor's home marketholdings as those holdings where the country of security's exchange isthe same as the investor's reported domicile. With this classificationwe treat each investor's turnover in cross-listed securities that are listedin the investor's home country as turnover in the home market.Similarly, turnover in securities that are domiciled in the investor'shome market, but listed in foreign exchanges is classified as non-homemarket turnover. We choose this treatment of cross-listed securities(as opposed to treating cross-listed securities abroad that are domiciledin the home country as home market turnover) because we are moreinterested in investor's turnover in differentmarket environments ratherthan in individual securities. Moreover, the number of cross-listedsecurities as a share of investors' portfolio is trivial.

3.2.2. Cultural distanceCultural distance from the investor's i home country I to the target

market J is computed based on primary dimensions of culture from

Hofstede.7 Our cultural distance index is based on four Hofstede'sprimary dimensions of culture: Individualism, Masculinity, PowerDistance, and Uncertainty Avoidance.8Following Kogut and Singh(1988),9we compute the cultural distance, CD, for each investor countryI from each target market J as follows:

CDI; J ¼X4

n¼1

Hn; J−Hn;I

� �2

Vn=4 ð2Þ

Where Hn,I and Hn,J are the nth cultural dimension of an investor fromcountries I and J, respectively, and Vn is the variance of the nth culturaldimension.

3.2.3. Longitudinal distanceIn order to test H3,and the effect of latitudinal and longitudinal

distances on trading frequency and portfolio allocation, we breakdown geographical distance from the investor's i home country I tothe target country J into latitudinal and longitudinal distances. Becauseeach degree of longitude is different, so that the distance betweendegrees is the highest at the equator, we round to the nearest 5° oflatitude and use coordinates of capital cities from CEPII and theLatitude/Longitude Distance Calculator to compute the latitudinaldistance between countries in kilometers.10

3.2.4. Target market liquidityWhen we examine institutional investors' turnover in their home

markets we control for the cultural characteristics of the home market,while also conditioning on homemarket liquidity. To proxy for liquiditywe create a volume variable by aggregating all available home marketsecurities' daily trading volumes from Compustat Global's SecurityDaily into a quarterly trading volume for each country. The tradingvolume is the total value of shares traded each quarter of each stockscaled by the stock's average market value based on its daily closingprice. The quarterly trading volume for all stocks is computed basedon the aggregate quarterly trading volume of all publicly tradedsecurities scaled by the market's total capitalization at the end of eachquarter.

3.2.5. Under/overweighting of target marketsPrevious research has documented a link between cultural similarity

and portfolio allocation (Aggarwal et al., 2012; Anderson et al., 2011).Before we explore this link in more detail, we first confirm previousfindings using our dataset of institutional investors' cross-borderportfolio holdings. Then, we examine whether both geographicaldistances across longitudes and latitudes affect portfolio investment.For this purpose, we generate a bias variable, which is defined as thedifference between each investor's actual investment in a particulartarget market as a share of the investor's non-home market portfolioand the expected allocation to that market. The expected weightallocation of each market is the investable market value of market Jrelative to the total market value of the investable world according to


FTSE. More formally, the under/overweight bias variable is computed asfollows:

biasi; J ¼MVi; JX

J;I≠ J

MVi;I≠ J

−MV JX

J;I≠ J

MV J

ð3Þ

where bias is calculated for each investor i in each available market J,including those markets where investor i has zero investment. MVi,J isthe market value of all securities investor i holds in market J, and MVJis the market capitalization value of the correspondingmarket. A visualpresentation of the expected investment weight for the countriesincluded in our sample is displayed in panels A and B of Fig. 1 for theyears 2000 and 2010, respectively.

JAPAN 10.6%

UNITED KINGDOM 9.8%

FRANCE 4.5%

GERMANY 3.3%

SWITZERLAND 2.8%

NETHERLANDS 2.4%

CANADA 2.3%

ITALY 2.2%

HONG KONG 1.3% OTHER 8.9%

Expected W

UNITED KINGDOM 8.15%

CANADA 4.12%

FRANCE 3.98%

AUSTRALIA 3.40%

GERMANY 3.20%

SWITZERLAND 3.05%

BRAZIL 2.44%

SOUTH KOREA 2.14%

OTHER 19.37%

Expected W

Fig. 1. Expected weights based on float market capitalization weights in 2000 and 2010. Expecdefined as the total market capitalization of each market in U.S. dollars divided by the total ma

3.2.6. Regression specificationsIn our main analysis, to test for the determinants of trading

frequency,we employ different specifications ofmultivariate regressionswith investor country and target country fixed effects, similar to themethodology used by Lane and Milesi-Ferretti (2008). The specificationfor the double fixed effect regression is the following:

TOi; J≠ ¼ ϕI þ ϕ J þ β1ZI; J þ β2Xi≠ þ εi; J≠ ð4Þ

where TOi,J,t is the quarterly turnover by investor i in the target market Jin quarter t.ΦI andΦJ are the indicator variables for each investor's homecountry and target countries I and J. The investor country fixed effectscontrol for country characteristics that may explain generally higher orlower levels of turnover. The target country fixed effects control for

UNITED STATES 51.8%

eights, 2000

UNITED STATES 41.88%

JAPAN 8.26%

eights, 2010

ted weights are computed based on FTSE investable indexes. The weight of eachmarket isrket capitalization of all investable markets in U.S. dollars.

Table 1Summary statistics of institutional investors. Table 1 shows turnovers at home and abroadfor institutional investors in the sample based on their self-reported type (in panel A), self-reported style (in panel B), and self-reported turnover (in panel C). The table reports thenumber of investors from each category (#) as well as the average turnovers at home(HTO) and foreign markets (FTO) as a share of investors' home market portfolio andforeign market portfolio, respectively. The reported turnovers are time series averages.The last two columns show the difference (Diff.) between home market and foreignmarket average turnover and the t-statistic of the differential.

# HTO FTO Diff. T-stat

Panel A: Investor typeInvestment Adviser 4229 11.31 5.99 5.32 [22.51]⁎⁎⁎

Hedge Fund Company 1370 21.62 15.75 5.86 [4.18]⁎⁎⁎

Mutual Fund Manager 966 12.05 5.65 6.4 [14.64]⁎⁎⁎

Bank Management Division 813 9.8 5 4.8 [12.61]⁎⁎⁎

Broker 231 14.05 20.18 −6.13 [−2.81]⁎⁎⁎

Insurance Management Division 217 9.98 4.99 4.99 [6.42]⁎⁎⁎

Pension Fund 206 7.08 7.17 −0.09 [−0.12]Insurance Company 153 5.3 4.04 1.26 [1.11]Broker/Inv Bank Asset Mgmt 148 11.67 5.15 6.52 [6.49]⁎⁎⁎

Private Banking Portfolio 111 11.63 10.8 0.83 [0.57]Foundation/Endowment 40 7.97 8.81 −0.85 [0.24]Market Maker 18 25.51 29.33 −3.83 n/aArbitrage 2 48.53 19.4 29.13 n/a

Panel B: Investor styleValue 2484 15.38 5.87 9.5 [22.94]⁎⁎⁎

GARP 2126 11.29 7.24 4.06 [9.07]⁎⁎⁎

Growth 1208 16.91 6.6 10.31 [19.04]⁎⁎⁎

Yield 1153 9.91 6.97 2.94 [6.27]⁎⁎⁎

Deep Value 912 13.86 8.14 5.72 [7.48]⁎⁎⁎

N/A 309 8.09 17.45 −9.36 [−2.75]⁎⁎⁎

Aggressive Growth 274 14.69 6.2 8.49 [9.25]⁎⁎⁎

Index 38 8.5 2.44 6.06 [3.5]⁎⁎⁎

Panel C: Investor turnoverN/A 5060 10.503 5.732249 4.77 [25.52]⁎⁎⁎

Medium 915 16.02 7.18 8.84 [15.11]⁎⁎⁎

Low 887 10.35 5.1 5.25 [11.42]⁎⁎⁎

Very Low 681 6.47 7.34 −0.87 [−0.95]High 639 25.91 15.91 10 [6.13]⁎⁎⁎

Very High 322 35.35 27.24 8.12 [2.31]⁎⁎⁎

⁎ Significant at 10% level.⁎⁎ Significant at 5% level.⁎⁎⁎ Significant at 1% level.


features of the target country that may explain generally higher or lowerturnovers in the target market. ZI,J is a vector of explanatory variables,which includes the main variable of interest for this study— the culturaldistance between the investor country and the target market. ZI,J alsoincludes other bilateral control variables for the investor and targetcountry pairs. These variables include Trust in others, Latitudinal distance,Longitudinal distance, Time zone differential, Common legal standard andCommon language. Xi,t is a vector of explanatory variables that areinvestor i specific, or investor i-target market J specific, and vary overtime. These variables include the size of the investor's total portfolio(investor size), size of the assets in country J (Investor size in target) andthe investor's experience in market J. All the specifications used in theanalysis also include year indicators and the standard errors are two-way clustered errors based on investor home country–target countrypairs. To ensure that multicollinearity is not a concern, we test thevariance inflation factor (VIF) for the bilateral variables and neversimultaneously include two variables in the regression that producehigh VIF score. The VIFs as well as a correlation matrix of the controlvariables included in this paper are reported in Appendix C.

4. Results

4.1. Summary statistics

Table 1 shows the summary statistics of the sample's institutionalinvestors' turnover. Panel A presents the average turnovers by investortype, panel B by style, and panel C by the investors' self-reportedturnover volume. We display the turnovers for each category ofinstitutional investors at home and abroad as well as the differencebetween the two. Additionally, we report the number of investors thatbelong to each category.

Panel A of Table 1 shows that the most common investor type in thesample is Investment Adviser, followed by Hedge Funds and MutualFunds. For all investor types, except for Broker, home market turnover is,on average, larger (or statistically indifferent) compared with the non-home market turnover. The largest differences, that are also statisticallysignificant between home and non-home turnovers, are in InvestmentBank Asset Management, Investment Adviser, and Hedge Funds.

The summary statistics in panel B reveal that the home marketturnover exceeds the non-home market turnover in every investorself-reported style category, but the unreported one. The largestnumber of investors in the sample that report their style belong toValue, GARP, Growth, and Yield investors. The largest differences inhome markets and non-home market turnovers are observed inGrowth and Aggressive Growth investors by a significant margin. Inpanel C, not surprisingly, the most frequent traders are the investorsthat self-report themselves as high turnover investors. Interestingly,for “Very high” turnover investors, the difference between the homeand non-home market turnovers is by far the greatest. The turnoverdifference between home market and non-home market almostmonotonically decreases from “Very high” to “Very low” turnoverinvestors (“Very low” turnover investors actually have lower, butstatistically insignificant, turnover at home). Overall, the resultspresented in Table 1 generally suggest that investors, regardless oftype, style or classification, trade much more frequently in theirhome market than they do abroad.

Table 2 shows the average turnovers of institutional investors basedon their country of domicile. The reported turnovers are the time seriesequally weighted averages for each country. We report turnovers forhome markets as a share of domestic portfolio (HTO) as well as forforeign markets as share of the foreign portfolio (FTO). In addition, wereport cultural characteristics of the countries used in the study. Theseinclude: Individualism (IDV), Uncertainty Avoidance (UAI), TRUST,Assertiveness (ASS), and Institutional Collectivism (COLL). The tablereveals that home market turnovers exceed turnovers abroad in mostinvestor countries. The only exception where the foreign turnover is

higher and statistically significant is in Thai investors' portfolios. Thedifference in home market and foreign market turnover is the greatestin US investors' portfolios followed by Austrian, Taiwanese, Chinese,and Spanish investors.

4.2. Determinants of turnover

In Table 3 we present the results from our first regression analyses.We first test for determinants of institutional investors' turnover ineach of the target markets. As per equation (1b), we measure turnoverfor each of the institutional investor's target markets as a share of theinvestor's total portfolio market value in all non-home markets. Themain variables of interest are cultural distance alongwith other bilateralcontrol variables. In order for the results to be consistent with H1, weexpect Cultural distance to be negatively related to institutions'turnover. Common language and common Legal origin may also proxyfor cultural differences, and we expect their signs to be positive. Inaddition to cultural distance from Hofstede, we also include Trust inothers variable in the analysis for a subset of European investors. Weexpect Trust in others to take on a positive sign, implying that investorstrade with higher frequencies in markets they trust more. Similar toCultural distance, Longitudinal distance, Time zone differential, andLatitudinal distance are also expected to be negatively related toturnover, consistent with H3. We also include simple geographicalDistance in the regression, and expect it to be negative. In all regressions,we also control for institutions' characteristics, which include investors'Experience, measured as quarters of presence in the target market, with

Table 2Turnovers of institutional investors at home and in non-home markets. Table 2 shows the cultural characteristics of the investors' home markets used in the analysis. These includeIndividualism (IDV), Uncertainty avoidance (UAI), Trust, Assertiveness (ASS), and Institutional collectivism (COLL). The table also reports the number of investors from each country (#)as well as the average turnovers at home (HTO) and foreign markets (FTO) as a share of home market portfolio and foreign market portfolio, respectively. The reported turnovers aretime series averages. The last two columns show the difference between home market and foreign market average turnover and the t-statistic of the differential.

COUNTRY IDV UAI TRUST ASS COLL # HTO FTO Diff. T-stat

Argentina 46 86 0.176 4.22 3.66 6 3.73 7.51 −3.78 [−1.29]Australia 90 51 0.461 4.28 4.29 194 8.49 8.56 −0.06 [−0.06]Austria 55 70 4.62 4.3 95 13.39 4.66 8.74 [8.58]⁎⁎,⁎⁎⁎

Belgium 75 94 54 6.95 3.79 3.17 [2.34]⁎⁎⁎

Brazil 38 76 0.094 4.2 3.83 59 7.73 17.22 −9.50 [−1.46]Canada 80 48 0.428 4.05 4.38 392 9.20 10.38 −1.18 [−1.62]China 20 30 0.523 3.76 4.77 100 15.02 6.67 8.35 [6.46]⁎⁎⁎

Czech Rep. 58 74 14 8.79 2.07 6.73 [5.11]⁎⁎⁎

Denmark 74 23 3.8 4.8 71 7.68 3.68 4.00 [3.55]⁎⁎⁎

Finland 63 59 0.589 3.81 4.63 66 7.48 2.91 4.57 [5.20]⁎⁎⁎

France 71 86 0.188 4.13 3.93 317 9.05 5.20 3.85 [4.68]⁎⁎⁎

Germany 67 65 0.368 4.55 3.79 389 11.25 5.87 5.39 [8.57]⁎⁎⁎

Greece 35 112 4.58 3.25 30 11.71 3.94 7.77 [5.90]⁎⁎⁎

Hong Kong 25 29 0.411 4.67 4.13 137 11.17 5.21 5.95 [6.39]⁎⁎⁎

Hungary 80 82 4.79 3.53 12 6.86 2.16 4.70 [3.56]⁎⁎⁎

India 48 40 0.233 3.73 4.38 58 13.93 7.69 6.24 [2.44]⁎⁎⁎

Ireland 70 35 3.92 4.63 46 6.95 7.00 −0.05 [−0.03]Israel 54 81 4.23 4.46 61 8.58 3.38 5.20 [2.78]⁎⁎⁎

Italy 76 75 0.292 4.07 3.68 129 11.33 4.75 6.58 [7.43]⁎⁎⁎

Japan 46 92 0.391 3.59 5.19 162 11.10 7.91 3.19 [3.06]⁎⁎⁎

Malaysia 26 36 0.088 3.87 4.61 65 9.90 5.30 4.59 [4.31]⁎⁎⁎

Mexico 30 82 0.156 4.45 4.06 7 11.31 5.54 5.77 [2.44]⁎⁎⁎

Netherlands 80 53 0.45 4.32 4.46 63 7.92 8.41 −0.49 [−0.32]New Zealand 79 49 0.512 3.42 4.81 18 5.71 8.25 −2.54 [−0.83]Norway 69 50 0.742 54 8.37 5.74 2.63 [1.26]Poland 60 93 0.19 4.06 4.53 53 7.56 4.76 2.80 [1.86]⁎

Portugal 27 104 3.65 3.92 59 9.50 5.90 3.60 [1.60]Singapore 20 8 4.17 4.9 112 10.31 4.45 5.86 [5.76]⁎⁎⁎

South Africa 65 49 0.188 4.6 4.62 108 10.69 6.63 4.06 [2.37]⁎⁎⁎

South Korea 18 85 0.282 4.4 5.2 65 8.34 11.52 −3.18 [−1.32]Spain 51 86 0.2 4.42 3.85 234 12.73 4.58 8.15 [11.2]⁎⁎⁎

Sweden 71 29 0.68 3.38 5.22 137 9.23 4.44 4.79 [5.17]⁎⁎⁎

Switzerland 68 58 0.539 4.51 4.06 400 9.51 6.47 3.04 [4.54]⁎⁎⁎

Taiwan 17 69 0.242 3.92 4.59 68 16.61 8.22 8.39 [3.77]⁎⁎⁎

Thailand 20 64 0.415 3.64 4.03 52 6.79 8.10 −1.31 [−2.11]⁎⁎⁎

UK 89 35 0.305 4.15 4.27 884 13.15 6.89 6.26 [7.37]⁎⁎⁎

USA 91 46 0.393 4.55 4.2 3728 15.36 3.73 11.64 [20.13]⁎⁎⁎



a negative expected sign, investors' total value of portfolio (Investorsize), and investors' total market value in each target market (Investorsize in target).We expect this lattermarket value to be positively relatedto turnover, as larger holdings are naturally tradedmore in dollar terms.Investor country, target country and year indicators are included in all ofthe regressions.

Consistent with our hypothesis, both panels suggest a negative andstatistically significant relation between the cultural distance betweenthe investor and the targetmarket. Cultural distance is negatively relatedto turnover with statistical significance. Of the other cultural variables,common Legal origin is positive and significantwhile Common Languageand Trust in others are not significant. Interestingly, of the geographicbilateral controls, only the Longitudinal distance, Time zone differential,and simple Distance are negative and significant. Latitudinal distance,however, is insignificant in both specifications. These results suggestthat the distance effect on investors' turnover is directionally depended.The turnover declines substantially as investorsmove farther from theirhome market, but more so when they move away from their homemarket across time zones. Overall, the results from the bilateral controlsprovide support to H1 and H3.11

11 As a robustness test we also independently examine the relation between longitudinaland latitudinal distanceswith trading frequency in four different regions of theworld (Asia,Europe, North America and Pacific). While the result of the sign of longitudinal distancecoefficient is mostly stable, the sign of the latitudinal distance coefficient is less robust.

As for the investor control variables, Table 3 reveals that experienceis negatively related to trading frequency. A possible explanation is thatafter the initial accumulation of a position, investors decrease thefrequency of their trading activity. While investors' total portfolio sizeis weakly positively related to turnover, the coefficient of investors'portfolio size in the target market is positive and significant, consistentwith expectation.

Table 4 repeats the analyses from Table 3, but with several differentcultural proxies. The dependent variable is investors' turnover in targetmarket I as a share of the investors' total portfolio market value in non-homemarkets. The variables of interest are the investor-target countrypair variables that vary across country pairs and include severalmeasures of cultural differences between the investor and the targetmarket. These independent variables include the Cultural distancebetween the investor and the target market, measured based onHofstede's primary dimensions of culture (Eq. (2)) from Table 3. Thealternative cultural variables include: Globe, which is the culturaldistance between investor and target market from GLOBE study's nineprimary dimensions of culture. CD, SD, and MD are the CorrelationDistance, Standardized Euclidean Distance, and Mahalanobis Distancerespectively , all computed using Hofstede's four dimensions of culturebased on Kandogan (2012). Kandogan suggests these measures tocontrol for some cross-correlations in Hofstede's primary dimensionsof culture as an improvement to the Kogut and Singh's (1988) method.Investor level controls are the same fromTable 3. The results reported in

Table 3Determinants of investors' turnover relative to all holdings abroad. Table 3 shows results fromOLS regressions,where thedependent variable is the investors' turnover in targetmarket J asa share of the investors' total portfoliomarket value in non-homemarkets (Eq. (1b)). All the specifications include investor and target countryfixed effects. The variables of interest are theinvestor-target country pair variables. These bilateral variables include the Cultural distance between the investor and the targetmarket,measured basedonHofstede's primary dimensionsof culture (Eq. (2)), Trust in others for the European subset of the sample, geographicalDistance in the log of kilometers, the log of distance in latitude and longitude in kilometers (Distance,latitude andDistance, longitude), Time zone differential in hours, common Legal origin indicator, and a Common language indicator between the investor and the targetmarket. Investor levelcontrols include investors' Experience in each target market J, measured in quarters present. Investor size and Investor size in target in logs, which are the quarterly total market value of theinvestor and the quarterlymarket value invested in each target market J, respectively. All regressions also include year indicators. The standard errors are two-way clustered errors basedon home country–target country pairs. The t-statistics values are reported under the coefficients.1515 We repeat the analysis of this table only with countries that use daylight savings time with respect to time zone differential and longitudinal distance. The results are similar in

magnitude and significance to the results reported above and available upon request.

(1) (2) (3) (4) (5) (6) (7) (8)

Cultural Distance −0.4067⁎⁎⁎ −0.2907⁎⁎⁎

[6.01] [4.64]Trust in others 0.4172

[1.07]Distance −0.7783⁎⁎⁎

[5.75]Distance, latitude −0.1395 0.0453

[1.32] [0.70]Distance, longitude −0.5709⁎⁎⁎ −0.5318⁎⁎⁎

[5.38] [5.17]Time zone differential −0.2502⁎⁎⁎

[5.68]Legal origin 0.7115⁎⁎⁎ 0.2566⁎

[3.78] [1.67]Common language −0.1207

[0.70]Experience −8.0165⁎⁎⁎ −5.7281⁎⁎⁎ −8.0028⁎⁎⁎ −8.1050⁎⁎⁎ −7.9902⁎⁎⁎ −8.0567⁎⁎⁎ −8.0257⁎⁎⁎ −7.9151⁎⁎⁎

[5.69] [6.23] [5.52] [5.61] [5.54] [5.69] [5.68] [5.62]Investor size in target 0.1737⁎⁎⁎ 0.0504 0.1430⁎⁎⁎ 0.1803⁎⁎⁎ 0.1458⁎⁎⁎ 0.1493⁎⁎⁎ 0.1748⁎⁎⁎ 0.1402⁎⁎⁎

[4.93] [1.27] [3.55] [4.91] [3.65] [3.85] [4.88] [3.57]Investor size 1.0697⁎ 3.7371⁎⁎ 1.1065⁎ 0.9703 1.1363⁎ 1.0364⁎ 1.1039⁎ 1.2008⁎⁎

[1.84] [2.31] [1.84] [1.61] [1.91] [1.73] [1.87] [2.08]Observations 231,620 58,718 231,620 231,620 231,620 231,620 231,620 231,620Adjusted R2 0.1853 0.1829 0.1910 0.1811 0.1902 0.1914 0.1836 0.1936



Table 4 mainly confirm the results from Table 3 that support H1. Allcultural distance measures are negative and statistically significantwith the exception of the Globe cultural distance. The result is stillnegative, but statistically insignificant.

In Table 5, we report results from regressions that allow for investors'home country characteristics to vary while fixing the characteristics ofthe target country. This type of regression allows us to test whetherinvestors' home country specific cultural characteristics influencetrading behavior in addition to the bilateral cultural and geographicvariables. The investor country specific cultural characteristics includeUncertainty avoidance, Individualism and Trust of the investor country.We also include GDP per capita and GDP in logarithm of the investorcountry to ensure that omitted macroeconomic characteristics are notdriving the result of the cultural characteristics.

The results presented in Table 5 continue to confirmH1 andH3. Bothcultural distance and distance across longitudes are negative andsignificant across specifications. Distance across latitudes is notsignificant. Common language and legal origin are positive as expected,but statistically insignificant. The country specific cultural dimensionsprovide support for H2a. As the Uncertainty avoidance of the investorcountry increases, the trading frequency in foreign markets decreases.In other words, more ambiguity averse investors are less willing totransact in equity markets when information asymmetry is higher.While Trust has the expected positive sign, it is not statisticallysignificant. As for Individualism, it carries the expected positive sign, asper H2c, when it is included without GDP per capita control variable.However, the sign turns negative and significant when the GDP percapita is included in the regression. It should benoted, that Individualismis highly correlated with the wealth of the nation (GDP per capita), sothat richer countries also tend to be more individualistic. Previous

literature documents a positive relationship between trading activityand individualism, but only without the wealth control, which mayexplain the difference in our results to the results in Chui et al. (2010).

4.3. Under-diversification and turnover

It is possible that the relationship between trading frequency andcultural distance and longitudinal distance is driven partially byinvestors' asset allocation to the target markets. In this section, wefirst closely examine the relationship between asset allocation andcultural distance. We then repeat the analysis presented in Table 3,while defining target market turnover as a share of target marketinvestment, so that the turnover measure captures the possible bias inasset allocation.

In Table 6 we test for determinants of investors' asset allocation totarget market J. The purpose of this table is not only to mainly confirmprevious literature's findings, but also to examine the effect of latitudinaland longitudinal geographical distances on portfolio allocation.Investors' allocation Bias to target market J is the dependent variableand the bilateral control variables, which include Cultural distance,Longitudinal distance, and Latitudinal distance are the main variables ofinterest. These three control variables are expected to take on negativesigns. We also include the Trust in others variable for the small subsetof European investors, which is expected to be positive (consistentwith Guiso et al., 2004). Additionally, we include bilateral controls forTime zone differential, simple geographic Distance, common Languageand Legal origin. The Bias in investors' allocation to target market J iscalculated as per Eq. (3) and effectively measures the under-andoverweighting of each target market J in the investor's portfolio as sshare of all the investor's foreign portfolio allocation.

Table 4Alternative cultural variables and investors' turnover relative to all holdings abroad. Table 4 repeats the analyses from Table 3, but with several different cultural proxies. The dependentvariable is the investors' turnover in targetmarket J as a share of the investors' total portfoliomarket value in non-homemarkets. All the specifications include investor and target countryfixed effects. The variables of interest are the investor-target country pair bilateral variables. They include several measures of cultural differences between the investor and the targetmarket. These independent variables include the Cultural distance between the investor and the target market, measured based on Hofstede's primary dimensions of culture (Eq. (2)).Globe is the cultural distance between investor and target from GLOBE study's nine primary dimensions of culture. CD, SD, and MD are the Correlation Distance, Standardized EuclideanDistance, andMahalanobis Distance, all computed using Hofstede's 4 dimensions of culture based on Kandogan (2012). Investor-level controls include investors' Experience in each targetmarket J, measured in quarters of presence. Investor size and Investor size in target are in logs. All regressions also include year indicators. The standard errors are two-way clustered errorsbased on home country–target country pairs. The t-statistics values are reported under the coefficients.

(1) (2) (3) (4) (5)

Cultural distance −0.4067⁎⁎⁎

[6.01]Globe −0.0705

[1.37]CD −0.8575⁎⁎⁎

[5.08]SD −0.5939⁎⁎⁎

[6.66]MD −0.5906⁎⁎⁎

[6.16]Experience −8.0165⁎⁎⁎ −5.8729⁎⁎⁎ −8.0220⁎⁎⁎ −7.9272⁎⁎⁎ −7.9510⁎⁎⁎

[5.69] [6.99] [5.72] [5.71] [5.70]Investor size in target 0.1737⁎⁎⁎ 0.1631⁎⁎⁎ 0.1720⁎⁎⁎ 0.1693⁎⁎⁎ 0.1704⁎⁎⁎

[4.93] [6.04] [4.86] [4.81] [4.83]Investor size 1.0697⁎ −0.4370 1.1302⁎⁎ 1.1032⁎ 1.0837⁎

[1.84] [0.67] [1.97] [1.91] [1.86]Observations 231,620 167,466 231,620 231,620 231,620Adjusted R2 0.1853 0.1600 0.1859 0.1875 0.1865



The results reported in Table 6 support the idea that thebilateral variables that are related to investors' trading frequencyare also related to portfolio weights of different target markets.Cultural distance and all of the geographic distance variables arenegatively related to the portfolio weights of the target markets.This implies that culturally and geographically distant targetmarkets are relatively underweighted in investment portfolios whilegeographically and culturally close target markets are relativelyoverweighed.12 Although the results show that both Latitudinal distanceand Longitudinal distance are related to Bias in portfolio allocation,Longitudinal distance is more significant and larger in magnitude. Thisresult provides new evidence to what determines portfolio allocationsacross the globe. While geographical distance is a known determinantof portfolio allocation, we document that the geographical distancemattersmuchmore, when investormoves further away from the targetmarket across time zones.13 Of the other bilateral controls, commonLegal origin is positive and significant as expected and Common languageis not statistically significant.

Homebias and international under-diversification in asset allocationhave been documented before (Beugelsdijk & Frijns, 2010; Chan, Covrig,& Ng, 2005; Coval & Moskowitz, 1999, 2001; French & Poterba, 1991;Grinblatt & Keloharju, 2001 among others). The portfolio allocationanalysis presented in Table 6 examines the international under-diversification behavior differently. Most previous studies focus onportfolio level under-diversification by computing a home bias andforeign bias measures for each investor. Our analysis contributes tothe literature by focusing on the determinants of portfolio allocation

12 For robustness, we repeat the analysis reported in Table 6 but with several differentcultural proxies. These independent variables include the cultural distance betweeninvestor and target from GLOBE study's nine primary dimensions of culture, CorrelationDistance, Standardized Euclidean Distance, and Mahalanobis Distance. The results areconsistent with the results presented in Table 6 and omitted from this version of the paperfor brevity.13 As a robustness testwe also independently examine the relation between longitudinaland latitudinal distances with Bias in four different regions of the world (Asia, Europe,North America and Pacific).While the result of the sign of longitudinal distance coefficientis mostly stable, the sign of the latitudinal distance coefficient is less robust.

at the country level. We use country-pair specific bilateral controlvariables in order to explain portfolio allocation to target countries,similar to Aggarwal et al. (2012). Our dataset also allows us toinvestigate determinants of portfolio allocation at the institutionallevel, unlike previous studies that focus on aggregate country levelportfolio flows.

As Table 6 indicates, Cultural distance and Longitudinal distance relatenegatively to portfolio allocation. These findings suggest that we aremore likely to observe higher turnovers in the markets where highershares of the portfolio are allocated (as documented in Table 3), whenwe compute turnover as a share of all foreign portfolio values for theinvestors (as per Eq. (1b)).

Although in Table 3 we control for total investment in each targetmarket, as a robustness check, in Table 7 we examine whether Culturaldistance is related to trading volumes, when turnover is scaled by eachtarget market J's assets in investors' portfolio (Eq. (1a)).

Overall, the results presented in Table 7 are similar to the resultspresented in Table 3, but the coefficients of the Cultural distance aresomewhat smaller in magnitude. Also, when Geographical Distanceis separated into Longitudinal distance and Latitudinal distance, theLongitudinal distance is more significant and larger in magnitude.This highlights the robustness of our initial results subject to astricter turnover definition. It also appears that, unlike in Table 3,total Investor size is negatively related to turnover. This suggeststhat large investors trade less, possibly due to the price impact thatthey are likely to impose.

4.4. Culture and home market turnover

To further investigate the relationship between cultural characteristicsof investors' home markets and the observed trading frequencies,we also test whether the country-specific cultural characteristicsrelate to turnover in home market securities. Table 8 shows theresults from OLS regressions, where the dependent variable isinvestors' turnover in home market as a share of the investors'portfolio in the home market. The measures of culture to test

Table 5Investor country's culture and investors' turnover relative to all holdings abroad. Table 5 shows the results from analyses that repeat Table 3's analyses, but in addition to investor country–target country specific variables, we also include investor country specific cultural variables. The dependent variable is the turnover from Eq. (1b). All the specifications include targetcountry fixed effects and the main variables of interest are the bilateral variables and the investor country cultural variables. These bilateral variables include the Cultural distancebetween the investor and the target (Eq. (2)), distance in latitude and longitude in logs of kilometers (Distance, latitude and Distance, longitude), common Legal origin indicator, and aCommon language indicator between the investor and the target market. Investor country controls include cultural Individualism, Uncertainty avoidance, and Trust. Additionally, weinclude the log of GDP and GDP per capita of the investor. Investor level controls include investors' Experience in each target market J measured in quarters of presence. Investor size andInvestor size in target are in logarithm. All regressions also include year indicators. The standard errors are two-way clustered errors based on home country–target country pairs. Thet-statistics values are reported under the coefficients.1616 The results of this tablewere also replicated using the alternative turnovermeasure of Eq. (1a) as the dependent variable. Results are similar inmagnitude and significance.We discuss

the result in the text, but omit the table for brevity.

(1) (2) (3) (4) (5)

Cultural distance −0.3101⁎⁎⁎ −0.2876⁎⁎⁎ −0.2941⁎⁎⁎ −0.3227⁎⁎⁎ −0.3129⁎⁎⁎

[5.66] [5.50] [5.50] [5.15] [4.99]Distance, latitude −0.0162 0.0515 0.0300 0.0509 0.0535

[0.20] [0.70] [0.43] [0.63] [0.68]Distance, longitude −0.2639⁎⁎⁎ −0.4304⁎⁎⁎ −0.4255⁎⁎⁎ −0.4483⁎⁎⁎ −0.4401⁎⁎⁎

[3.04] [4.51] [4.65] [4.48] [4.26]Legal origin 0.2280 0.2352 0.2565 0.2255 0.2659

[1.37] [1.47] [1.58] [1.25] [1.47]Common language 0.3237⁎ 0.2785 0.2101 0.2344 0.1689

[1.66] [1.52] [1.20] [1.13] [0.83]Experience −7.7635⁎⁎⁎ −8.0647⁎⁎⁎ −8.2158⁎⁎⁎ −8.7487⁎⁎⁎ −8.7398⁎⁎⁎

[5.95] [6.21] [6.46] [6.12] [5.93]Investor size in target 0.1612⁎⁎⁎ 0.1475⁎⁎⁎ 0.1306⁎⁎⁎ 0.1405⁎⁎⁎ 0.1385⁎⁎⁎

[4.33] [4.03] [3.58] [3.33] [3.38]Investor size 1.2522⁎⁎ 1.2674⁎⁎ 1.2658⁎⁎ 1.5660⁎⁎ 1.5734⁎⁎

[2.18] [2.21] [2.20] [2.47] [2.49]Individualism 0.0283 −1.2625⁎⁎⁎ −1.3847⁎⁎

[0.06] [2.78] [2.19]Uncertainty avoidance −0.3058 −0.9311⁎⁎

[1.04] [2.06]Trust 0.2242 0.0362

[0.28] [0.05]GDP per capita 0.1573 0.0109 0.1852 0.1436

[0.82] [0.06] [0.65] [0.56]GDP 0.3143⁎⁎⁎ 0.2367⁎⁎⁎ 0.2581⁎⁎⁎ 0.3484⁎⁎⁎

[5.94] [4.30] [2.85] [4.22]Observations 231,620 231,620 231,620 201,931 201,931Adjusted R2 0.1841 0.1883 0.1873 0.1970 0.1980

⁎ Significant at 10% level.⁎⁎ Significant at 5% level.⁎⁎⁎ Significant at 1% level).


hypotheses H2a, H2b, and H2c include Uncertainty avoidance, Trust,and Individualism. As a robustness check for Individualismwealso includetwo possible overconfidence measures from the GLOBE study:Institutional Collectivism (opposite interpretation to individualism) andAssertiveness. We also control for financial market and macroeconomiccharacteristics. The financial market controls are market Volume, marketCapitalization and total Trading cost. The total Trading cost control is alsobroken into Commissions, Fees, and Market impact in specification (1).The macroeconomic controls comprise of the logarithms of GDP andGDP per capita. Finally, we include the following investor level controls:Investors' Experience in home market measured in quarters and theInvestor size, which is the logarithm of total market value of theinstitution.

The results reported in Table 8 continue to support H2a. Itappears that higher ambiguity aversion of the investors' homemarket is related to lower trading frequency in the home marketsecurities. The sign of Individualism is negative, which is inconsistentwith our overconfidence hypothesis, but statistically insignificant.On the other hand, cultural Assertiveness is positively related totrading frequency. Cultural Trust is also positive, but without anystatistical significance.

The other control variables mostly carry their expected signs.Overall, trading volume in the home market and capitalization of thehome market are both positively related to trading frequency. Tradingcosts are negatively related to trading frequency, but only withoutcontrols for market capitalization.

5. Conclusion

In this study we examine the effect of culture in investors' homemarket and cultural distance between investors' home markets andtarget markets on institutional investors' trading frequency. We makeseveral contributions to the existing literature. First, we find strongevidence that institutions trade at much higher frequency in theirhome market and in markets that are culturally closer to their homemarket compared with more distant markets. Our findings supportthe idea that as cultural distance increases, information about targetmarkets' securities becomes more difficult to access and interpret, andas a result, investors choose more passive positions and are less willingto bet on their knowledge. Previous literature documents that aninvestor's competence influences trading frequency. Here we showthat the same investor's competence can vary from market to market,and as a result we observe different magnitudes of turnover withinthe investor's own portfolio. Second, we show that investors frommore ambiguity averse counties trade less frequently at home andabroad.

Third, we find that geographical distance has an impact on tradingfrequency, so that investors' turnover is lower in geographically distantmarkets, but only when the distance is measured in longitude.Latitudinal distance is not a significant determinant of tradingfrequency. These results provide new evidence on possible reasons forobserved turnovers across the international markets and merit a newavenue for future research.

Table 6Determinants of investor portfolio allocation to target. Table 6 shows the results from OLS regressions, where the dependent variable is the investors' portfolio allocation bias in targetmarket J. Investors' bias is calculated as per Eq. (3). All the specifications include investor and target country fixed effects. The variables of interest are the investor-target country bilateralvariables. These independent variables include the Cultural distance between the investor and the target market, measured based on Hofstede's primary dimensions of culture (Eq. (2)),Trust in others for the European subset of the sample, geographical Distance in the log of kilometers, distance in latitude and longitude in logs of kilometers (Distance, latitude and Distance,longitude), Time zone differential in hours, common Legal origin indicator, and a Common language indicator between the investor and the target market. Investor level controls includeinvestors' total market value in logarithm (Investor size). All regressions also include year indicators. The standard errors are two-way clustered errors based on home country–targetcountry pairs. The t-statistics values are reported under the coefficients.1717 We repeat the analysis of this table onlywith countries that use daylight savings timewith respect to time zone differential and longitudinal distance. Results are similar inmagnitude

and significance to this table and available upon request.

(1) (2) (3) (4) (5) (6) (7) (8)

Culturaldistance

−0.1642⁎⁎⁎ −0.1366⁎⁎⁎

[2.89] [2.72]Trust in others 4.0463⁎⁎⁎

[4.34]Distance −0.7255⁎⁎⁎

[3.85]Distance,latitude

−0.2113⁎⁎⁎ −0.1376⁎⁎⁎

[3.59] [2.96]Distance,longitude

−0.3651⁎⁎⁎ −0.3528⁎⁎⁎

[3.99] [3.53]Time zonedifferential

−0.1244⁎⁎⁎

[3.57]Legal origin 0.4029⁎⁎ 0.2752⁎

[2.36] [1.65]Commonlanguage

−0.0072[0.04]

Investor size −0.0407 −0.0003 −0.0354 −0.0447 −0.0331 −0.0366 −0.0399 −0.0370[1.38] [0.00] [1.24] [1.53] [1.17] [1.26] [1.35] [1.29]

Observations 1,976,908 182,338 1,976,908 1,976,908 1,976,908 1,976,908 1,976,908 1,976,908Adjusted R2 0.3296 0.0202 0.3346 0.3300 0.3329 0.3320 0.3297 0.3339


Table 7Determinants of investor turnover relative to target holdings. Table 7 shows results from OLS regressions, where the dependent variable is the investors' turnover in target market J as ashare of the investors' portfolio in market J (Eq. (1a)). All the specifications include investor's home country and target country fixed effects. The variables of interest are the investor'shome country–target country pair variables. These bilateral variables include the Cultural distance between the investor and the target market, measured based on Hofstede's primarydimensions of culture (Eq. (2)), Trust in others for the European subset of the sample, geographical Distance in the log of kilometers, distance in latitude and longitude in logs of kilometers(Distance, latitude and Distance, longitude), Time zone differential in hours, common Legal origin indicator, and a Common language indicator between the investor and the target market.Investor level controls include investors' Experience in each target market J, measured in quarters of presence. Investor size and Investor size in target are in logarithm. All regressionsalso include year indicators. The standard errors are two-way clustered errors based on home country–target country pairs. The t-statistics values are reported under the coefficients.18,1918 We repeat the analysis of this table onlywith countries that use daylight savings timewith respect to time zone differential and longitudinal distance. Results are similar inmagnitude

and significance to this table and available upon request.19 We repeat the analysis with several cultural variables, similar to Table 4. Results are similar to this. Cultural distance is negative, but smaller inmagnitude comparedwith the result in

Table 4.

(1) (2) (3) (4) (5) (6) (7) (8)

Cultural distance −0.1101⁎⁎ −0.1127⁎

[1.99] [1.89]Trust in others −0.0779

[0.14]Distance −0.3213⁎⁎⁎

[3.84]Distance, latitude −0.0970⁎ −0.0088

[1.65] [0.17]Distance, longitude −0.2271⁎⁎⁎ −0.2223⁎⁎⁎

[3.70] [3.49]Time zone differential −0.0792⁎⁎⁎

[3.46]Legal origin 0.0267 −0.1917

[0.21] [1.38]Common language 0.0416

[0.24]Experience 1.9748⁎ 9.3510⁎⁎⁎ 1.8581⁎ 1.9509⁎ 1.8796⁎ 1.8894⁎ 1.9771⁎ 1.8780⁎

[1.79] [4.17] [1.68] [1.76] [1.70] [1.71] [1.78] [1.70]Investor size −3.0418⁎⁎⁎ −9.3786⁎⁎⁎ −3.0328⁎⁎⁎ −3.0852⁎⁎⁎ −3.0209⁎⁎⁎ −3.0600⁎⁎⁎ −3.0515⁎⁎⁎ −3.0273⁎⁎⁎

[2.67] [3.80] [2.67] [2.70] [2.67] [2.70] [2.69] [2.68]Observations 223,674 56,528 223,674 223,674 223,674 223,674 223,674 223,674Adjusted R2 0.0421 0.0563 0.0423 0.0421 0.0423 0.0422 0.0420 0.0423



Table 8Determinants of homemarket turnoverwith cultural proxies. Table 8 shows the results fromOLS regressions, where the dependent variable is the investors' turnover in homemarket as ashare of the investors' portfolio in thehomemarket. Thedependent variable is computedbased onquarterly buys and sells for homemarket, similar to Eq. (1a). The variables of interest arethe investor country variables that measure the culture of the investors' homemarket. They include the Individualism, Uncertainty avoidance, Trust in others, and twomeasures of culturefrom GLOBE:Institutional Collectivism (opposite interpretation to individualism) and Assertiveness. We also control for financial market and macroeconomic characteristics. We includemarket Volume, market Capitalization, total Trading cost that is also broken into Commissions, Fees, and Market impact. The log of GDP and GDP per capita is included as macroeconomiccontrols. In addition, we include investor level controls: investors' Experience in home market measured in quarters and the Investor size which is the log of total market value of theinstitution. All regressions also include year indicators. The standard errors are clustered errors based on investor country. The t-statistic values are reported under the coefficients.

(1) (2) (3) (4) (5) (6) (7)

Volume 0.1284 −0.0075 0.0176 0.0156 4.3598⁎⁎⁎ 4.3005⁎⁎⁎ 0.0113[1.38] [0.17] [0.42] [0.43] [6.38] [7.06] [0.28]

Capitalization 2.2880⁎⁎⁎ 1.2833⁎ −0.1681 0.1618 −0.3084 1.3625⁎⁎

[8.45] [1.88] [0.22] [0.24] [0.41] [2.69]Trading cost 0.0985⁎⁎⁎ 0.0852⁎ 0.0770⁎⁎ 0.1002⁎⁎ 0.0757⁎ 0.1017⁎⁎

[3.41] [1.94] [2.43] [2.19] [1.83] [2.13]Commissions −0.2350⁎⁎⁎

[2.96]Fees −0.0003

[0.00]Market impact −0.2248⁎⁎

[2.13]Individualism −0.0290

[1.26]Uncertainty −0.0437⁎

[1.80]Collectivism −0.8676

[0.87]Assertiveness 2.7739⁎⁎⁎

[3.13]Trust 1.1878

[0.28]Investor size 16.3402⁎ −6.7996 −5.3583 −3.2151 1.0784 1.6499 −2.8879

[1.93] [1.60] [0.84] [0.77] [0.11] [0.18] [0.58]Experience −0.1461⁎⁎ −0.1701⁎⁎⁎ −0.1735⁎⁎⁎ −0.1752⁎⁎⁎ −0.1777⁎⁎⁎ −0.1779⁎⁎⁎ −0.1785⁎⁎⁎

[2.58] [3.84] [4.13] [4.29] [4.39] [4.42] [4.53]GDP per capita 0.3660 0.4860 0.5901 0.3445 −0.0565

[0.58] [0.96] [0.83] [0.52] [0.08]GDP 1.0916⁎⁎ 1.9565⁎⁎⁎ 1.0087⁎ 1.1399⁎⁎ 1.0228⁎⁎

[2.28] [3.35] [1.77] [2.64] [2.67]Observations 234,440 234,440 234,440 234,440 219,888 219,888 227,626Adjusted R2 0.0235 0.0318 0.0329 0.0335 0.0369 0.0378 0.0321



Appendix A. Hofstede's primary dimensions of culture14

1. Uncertainty avoidance index (UAI) deals with a society's tolerancefor uncertainty and ambiguity. It indicates to what extent a cultureprograms its members to feel either uncomfortable or comfortablein unstructured situations. Unstructured situations are novel,unknown, surprising, or different from usual. Uncertainty avoidingcultures try to minimize the possibility of such situations by strictlaws and rules, safety and security measures. Uncertainty avoidingcountries are also more emotional and are motivated by innernervous energy.

2. Individualism (IDV) as opposed to collectivism, is the degree towhich individuals are integrated into groups. On the individualistside we find societies in which the ties between individuals areloose: everyone is expected to look after herself and her immediatefamily. In collectivist societies people from birth onwards areintegrated into strong, cohesive groups.

3. Power distance index (PDI) measures the extent to which the lesspowerful members of organizations and institutions accept andexpect that power is distributed unequally. It suggests that a society'slevel of inequality is endorsed by the followers as much as by the

14 From Geert Hofstede's website: http://www.Geert-Hofstede.com and from CultureConsequences, 2001, 2nd edition, pages xix–xx).

leaders. Power and inequality are extremely fundamental facts ofany society and while all societies are unequal, some are moreunequal than others.

4. Masculinity (MAS) versus femininity refers to the distribution ofroles between the genders. The survey studies reveal that (a)women's values differ less among societies than men's values; (b)men's values from one country to another contain a dimension fromvery assertive and competitive and maximally different fromwomen's values on the one side, to modest and caring and similar towomen's values on the other. The assertive pole has been called‘masculine’ and the modest, caring pole ‘feminine’. The women infeminine countries have the same modest, caring values as the men;in the masculine countries they are somewhat more assertive andcompetitive, but not as much as the men, so that these countriesshow a gap between men's values and women's values.

5. Long-Term Orientation (LTO) versus short-term orientation: thisfifth dimension was found in a study among students in 23 countriesaround the world. Values associatedwith Long-Term Orientation arethrift and perseverance.

Appendix B. Correlation matrix, Variance Inflation Scores (VIFs).

The table below shows the correlation matrix of the bilateralindependent variables used in the analyses. Time zone differential,

http://www.Geert-Hofstede.com


longitudinal distance and simple distance are highly correlated and arenot included in our regressions simultaneously (correlationsN0.85).

Culturaldistance

Commonlanguage

Commonlegal

Distance Timezonediff.

Distance,latitude

Distance,longitude

Culturaldistance

1

Commonlanguage

−0.2397 1

Commonlegal

−0.3423 0.5441 1

Distance 0.2567 0.0828 −0.0026 1Time zonediff.

0.2865 0.1005 0.0208 0.8798 1

Distance,latitude

0.2016 0.055 0.1693 0.5895 0.3703 1

Distance,longitude

0.2257 0.0772 −0.0141 0.9374 0.9049 0.4069 1

Appendix C. Variance Inflation Factors (VIFs).

The table below reports the Variance Inflation Factors (VIF)associated with a regression that includes all the bilateral controls,trading frequency and the other selected controls as independentvariables and turnover as the dependent variable. Time zonedifferential, longitudinal distance and simple distance are highlycorrelated and never included simultaneously in any of theregressions (VIF scores N10).

Variable VIF 1/VIF

Panel A: All bilateral variablesCultural distance 1.37 0.731Common language 1.49 0.672Common legal 1.72 0.580Distance 16.30 0.061Time zone diff. 6.55 0.153Distance, latitude 2.44 0.410Distance, longitude 13.11 0.076

Panel B: Bilateral variablesincluded simultaneously

Cultural distance 1.28 0.783Common language 1.47 0.680Common legal 1.63 0.612Distance, latitude 1.31 0.762Distance, longitude 1.39 0.718

References

Aggarwal, R., & Goodell, J. W. (2009). Markets and institutions in financialintermediation: National characteristics as determinants. Journal of Banking andFinance, 33, 1770–1780.

Aggarwal, R., Kearney, C., & Lucey, B. (2012). Gravity and culture in foreign portfolioinvestment. Journal of Banking and Finance, 36, 525–538.

Ahern, K. R., Daminelli, D., & Fracassi, C. (in press). Lost in translation? The effect of culturalvalues on mergers around the world. Journal of Financial Economics (in press).

Akerlof, G. A. (1997). Social distance and social decisions. Econometrica, 65, 1005–1027.Anderson, C. W., Fedenia, M., Hirschey, M., & Skiba, H. (2011). Cultural influences on

home bias and international diversification by institutional investors. Journal ofBanking and Finance, 35, 916–934.

Arrow, K. J. (1974). The limits of organization. New York: W. W. Norton & CO, Inc.Barber, B.M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock

investment performance of individual investors. Journal of Finance, 55, 773–806.

Barber, B.M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and commonstock investment. Quarterly Journal of Economics, 116, 261–292.

Beck, T., Demirgüç-Kunt, A., & Levine, R. (2003). Law, endowments, and finance. Journal ofFinancial Economics, 70, 137–181.

Benos, A. V. (1998). Overconfident speculators in call markets: Trade patterns andsurvival. Journal of Financial Markets, 1, 353–383.

Beugelsdijk, S., & Frijns, B. (2010). A cultural explanation of the foreign bias ininternational asset allocation. Journal of Banking and Finance, 34, 2121–2131.

Caballé, J., & Sákovics, J. (2003). Speculating against an overconfident market. Journal ofFinancial Markets, 6, 199–225.

Chakrabarti, R., Gupta-Mukherjee, S., & Jayaraman, N. (2009). Mars–Venusmarriages:Culture and cross-border M&A. Journal of International Business Studies, 40,216–236.

Chan, K., & Covrig, V. (2012). What determines mutual fund trading in foreign stocks?Journal of International Money and Finance, 31, 793–817.

Chan, K., Covrig, V., & Ng, L. (2005). What determines the domestic bias and foreign bias?Evidence from mutual fund equity allocations worldwide. Journal of Finance, 60,1495–1534.

Choi, N., Fedenia, M., Skiba, H., & Sokolyk, T. (2013). Informed trading or familiarity bias?Evidence from the performance of international institutional investors.Working paper.

Christelis, D., & Georgarakos, D. (2013). Investing at home and abroad: Different costs,different people? Journal of Banking and Finance, 37, 2069–2086.

Chui, A.C. W., Titman, S., & Wei, K. C. J. (2010). Individualism and momentum around theworld. Journal of Finance, 65, 361–392.

Coval, J.D., & Moskowitz, T. J. (1999). Home bias at home: Local equity preference indomestic portfolios. Journal of Finance, 54, 2045–2073.

Coval, J.D., & Moskowitz, T. J. (2001). The geography of investment: Informed trading andasset prices. Journal of Political Economy, 109, 811–841.

Djankov, S., La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (2008). The law and economicsof self-dealing. Journal of Financial Economics, 88, 430–465.

Dorn, D., & Huberman, G. (2005). Talk and action:What individual investors say andwhatthey do. Review of Finance, 9, 437–481.

Felbermayr, G. J., & Toubal, F. (2010). Cultural proximity and trade. European EconomicReview, 54, 279–293.

Franks, J., Mayer, C., &Wagner, H. (2006). The origins of the German corporation—finance,ownership and control. Review of Finance, 10, 537–585.

French, K. R., & Poterba, J. M. (1991). Investor diversification and international equitymarkets. American Economic Review, 81, 222–226.

Glaser, M., & Weber, M. (2007). Overconfidence and trading volume. Geneva RiskInsurance Review, 32, 1–36.

Graham, J. R., Harvey, C. R., & Huang, H. (2009). Investor competence, trading frequency,and home bias. Management Science, 55, 1094–1106.

Grinblatt, M., & Keloharju, M. (2001). How distance, language, and culture influencestockholdings and trades. Journal of Finance, 56, 1053–1073.

Grinblatt, M., Keloharju, M., & Linnainmaa, J. (2012). IQ, trading behavior, andperformance. Journal of Financial Economics, 104, 339–362.

Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficientmarkets. American Economic Review, 70, 393–408.

Guiso, L., Sapienza, P., & Zingales, L. (2004). The role of social capital in financialdevelopment. American Economic Review, 94, 526–556.

Guiso, L., Sapienza, P., & Zingales, L. (2006). Does culture affect economic outcome?Journal of Economic Perspectives, 20, 23–48.

Guiso, L., Sapienza, P., & Zingales, L. (2008). Social capital as good culture. Journal of theEuropean Economic Association, 6, 295–320.

Guiso, L., Sapienza, P., & Zingales, L. (2009). Cultural biases in economic exchange?Quarterly Journal of Economics, 124, 1095–1131.

Hart, O. (1995). Firms, contracts, and financial structure. New York: Oxford University Press.Heath, C., & Trevsky, A. (1991). Preferences and beliefs: Ambiguity and the competence in

choice under uncertainty. Journal of Risk Uncertainty, 4, 5–28.Hofstede, G. (1980). Culture's consequences: International differences in work-related values.

Beverly Hills, CA: Sage.Hofstede, G. (2001). Culture's consequences: Comparing values, behaviors, institutions and

organizations across nations (2nd ed.)Thousand Oaks, CA: Sage.House, R. J., Hanges, P. J., Javidan, M., Dorfman, P. W., & Gupta, V. (2004). Culture, leadership,

and organizations: The GLOBE study of 62 societies. Thousand Oaks, CA: Sage.Huang, R. R. (2008). Tolerance for uncertainty and the growth of ambiguous industries.

Journal of Development Economics, 87, 333–353.Kandogan, Y. (2012). An Improvement to Kogut and Singh measure of cultural distance

considering the relationship among different dimensions of culture. Research inInternational Business and Finance, 26(2), 196–203.

Kogut, B., & Singh, H. (1988). The effect of national culture on the choice of entry mode.Journal of International Business Studies, 19, 411–432.

Lai, S., Ng, L., Zhang, B., & Zhang, Z. (2013). Domutual funds trade differently at home andabroad? Working paper.

Lane, P. R., & Milesi-Ferretti, G. M. (2008). International investment patterns. Review ofEconomics and Statistics, 90, 538–549.

Portes, R., & Rey, H. (2005). The determinants of cross-border equity flows. Journal ofInternational Economics, 65, 269–296.

Stulz, R. M., & Williamson, R. (2003). Culture, openness, and finance. Journal of FinancialEconomics, 70, 313–349.

Van Nieuwerburgh, S., & Veldkamp, L. (2010). Information acquisition and under-diversification. Review of Economic Studies, 77, 779–805.

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