Culture and R2:
The Effects of Tightness and Individualism
Cheol S. Euna, Lingling Wangb, Steven Xiaoa
a
Scheller College of Business, Georgia Institute of Technology, Atlanta, GA 30332, USA
b
A. B. Freeman School of Business, Tulane University, New Orleans, LA 70118, USA
October 6, 2012
Abstract
In this paper, we examine the effect of national culture on cross-country differences in
the stock price synchronicity (R2). The literature suggests that people tend to behave in a similar
(different) manner and think more holistically (analytically) in culturally tight (loose) and
collectivistic (individualistic) countries. These cultural differences may influence investor
behaviors and result in a higher stock price co-movement in culturally tighter and more
collectivistic countries. Consistent with this argument, we find that stock prices indeed co-move
more (less) in culturally tight (loose) and collectivistic (individualistic) countries. We also find
that both the market-wide and firm-specific variations are lower in culturally tighter countries.
Individualism, on the other hand, mostly increases the firm-specific variations. In addition, we
document that the influence of culture on the stock price co-movement is weakened in countries
that are more open to international trade and more integrated with the global stock market. This
implies that cultural exchange accompanies economic transactions, weakening investors’
behavioral biases associated with the national culture.
JEL classification: G15; G12; G02.
Keywords: Stock Price Synchronicity, Culture, Tightness, Individualism, Openness
________________________
Contact information: Tel: 404-894-4906, E-mail: cheol.eun@mgt.gatech.edu (Eun); Tel: 504-865-5044, Email:
lwang1@tulane.edu (Wang); Tel: 404-769-5502, E-mail: chong.xiao@mgt.gatech.edu (Xiao). We thank Andrew
Karolyi and seminar participates at the Graduate School of Business at Seoul National University for valuable
comments. We are responsible for all errors.
1
1. Introduction
To what extent stock prices move together is an important question in portfolio analysis and
asset pricing. To shed light on this question, a good number of studies examine cross-country
differences in the stock price synchronicity. The focus of these studies has been on using a
country’s economic fundamentals, such as GDP per capita, institutional development, and the
quality of information environment, to explain stock price co-movement.1 Another important
and neglected factor that differentiates one country from another is the country’s culture. The
effect of culture on people’s behavior is well documented in the management and psychology
literature. 2 The behavioral finance literature suggests that investor behavior can affect stock
price co-movements.3 It stands to reason that the dimensions of culture that introduce systematic
bias in the investor behavior may also affect stock price co-movements. In this paper, we
provide empirical evidence on the effects of cultural tightness and individualism on crosscountry differences in the stock price co-movement.
We also examine how a country’s
economic openness affects the influence of culture on stock price co-movements.
Our focus on culture is motivated in part by some puzzling observations in Morck, Yeung,
and Yu (2000), which are not consistent with the paper’s finding that the stock price
synchronicity is higher in countries with a lower GDP per capita. For example, Japan has the
highest GDP per capita in 1995 among the 40 countries covered in the study, but its stock price
co-movement, as measured by the R2, is among the highest. On the other hand, New Zealand’s
GDP per capita is about the median among the sample countries, but its R2 is among the lowest.
For example, see Morck, Yeung, and Yu (2000), Li, Morck, Yang, and Yeung (2004), Jin and Myers (2006), and
Bris, Goetzmann, and Zhu (2007).
2
For example, see Kroeber and Kluckhohn (1952), Hofstede (1980, 2001, 2010), Gelfand, Nishii, and Raver (2006),
Norenzayan (2011), and Gelfand et al. (2011).
3
See Hirshleifer (2001) and Schiller (2003) for surveys on the behavioral finance literature. Barberis, Shleifer, and
Wurgler (2005) discuss sentiment-based views of the stock return co-movement.
1
2
Taiwan has a much higher GDP per capita than China, but the R2s for these two countries are
very close to each other as the second and the fourth highest among the 40 sample countries.
These observations cannot be easily explained by the economic fundamentals or institutional
development argument suggested by Morck et al. (2000), Li et al. (2004), and Bris et al. (2007),
nor could they be explained by the information opaqueness story offered by Jin and Myers
(2006). Japan has a highly developed financial system and good accounting transparency like
other developed western countries. What differentiates Japan from these countries, however, is
its tight and collectivistic culture. By contrast, New Zealand has a loose and individualistic
culture. While China and Taiwan are quite different in terms of per capita income and
institutional development, they share essentially the same culture. Although these are rather
casual observations, it raises the possibility that national culture may influence the stock price
co-movement within a country.
To examine the effect of culture on the stock price co-movement, we focus on two
dimensions of national culture: tightness-looseness and individualism-collectivism. The first
cultural dimension, tightness vs. looseness, captures external constraints on human behavior and
measures the strength of a country’s social norms and the society’s tolerance for deviant
behavior (Gelfand et al., 2011).
Gelfand, Nishii, and Raver (2006) suggest that cultural
tightness-looseness can explain the degree of similarity/diversity in individual behaviors within a
country. Based on their argument, we expect that the similarity (diversity) in individual behavior
in tight (loose) culture would affect investment behavior and lead to higher (lower) stock price
co-movement.
The second cultural dimension, individualism-collectivism, focuses on internal attributes that
guide one’s behavior (Gelfand et al., 2007; Hofstede, 1980, 2001; Schwartz, 1994). Investors
3
who are more individualistic would be more confident in their ability to acquire and analyze
information and less concerned about having different opinions from others. By contrast,
investors in a collectivist culture rely more on information obtained from their social groups and
are less likely to trade based on differentiated opinions. Therefore, one would expect to observe
more herding behavior in collectivistic countries than in individualistic countries. If investors in
individualistic countries tend to herd less and are inclined to collect and process information to
make their own investment decisions, more firm-specific information may be incorporated into
stock prices and as a result, we expect a lower stock price co-movement in these countries.
Furthermore, people from tight and collectivistic cultures are likely to have holistic thinking
styles, leading them to view stocks more systematically, and are less likely to analyze stocks
individually. They also would rely more on dialectic, as opposed to, analytic reasoning. In
contrast, people from loose and individualistic cultures tend to be more analytic. They detach
objects from the system and tend to focus on the individual attributes of the object. They prefer
to use analysis and logic to explain and predict the object’s behavior (Nisbett, Peng, Choi, and
Norenzayan, 2001; Choi and Nisbett, 2000).
These differences in thinking styles and
information processing are likely to be reflected in stock trading behavior. This provides the
basis for why we expect national cultures to influence the stock price co-movements.
Using the tightness measure from Gelfand et al. (2011) and the individualism measure from
Hoftstede (2001), we examine the influence of culture on the stock price co-movement for a
sample of 46 countries over the period from 1990 to 2010. As expected, we find that countries
that are culturally tighter and less individualistic have higher stock price co-movements, as
measured by both R2 and the fraction of stocks moving in the same direction. These results are
robust to a set of control variables, such as GDP per capita, good government index, and
4
informational opaqueness, which have been shown to affect the cross-country differences in
stock price co-movements.
The influence of culture on stock price co-movement is
economically significant as well. One standard deviation increase in tightness (individualism) is
associated with an increase of 9.5% (decrease of 12.2%) in the stock price co-movement (R2).
The marginal effects of these two culture variables on the stock price synchronicity are
comparable to those of several previously documented determinants of stock price synchronicity,
such as GDP per capita (-10.9%), country size (7.6%), good government index (-14.8%) and the
diversity of analyst forecasts (5.0%). When both of the culture variables are included in the
same regression, we find that each of the variables retains a statistically significant influence on
the stock price co-movement. This confirms the notion that tightness and individualism measure
different dimensions of culture and the effect of one culture variable does not subsume that of the
other.
We next examine the possible channels, firm-specific or market-wide return variations,
through which the two culture variables affect R2. We find that tightness has negative and
significant relations with both the market-wide and firm-specific variations.
The effect of
tightness on the firm-specific variation is much stronger than that on the market-wide variation,
which leads to a higher R2 in culturally tighter countries. Individualism is found to have a
significantly positive relation with the firm-specific variation. We find no significant relation
between individualism and the market-wide variation. These results suggest that individualism
leads to a higher R2 primarily through higher firm-specific variations. Because of the external
constraints in culturally tight countries, individual behaviors tend to be more homogenous and
investors are more likely to conform to the aggregate beliefs in the market. This argument
possibly explains lower market-wide variations in culturally tight countries. Higher firm-specific
5
variations in individualistic countries are consistent with the view that individualistic investors
are more likely to gather and process information actively to assist their own decision making.
To the extent that individualistic investors tend to focus on individual stocks (i.e., detaching
objects from the system) for information gathering and analysis, the trading activities of such
investors may not have a significant effect on the market-wide variation.
Stulz and Williamson (2003) find that the effect of culture on a country’s financial
development is mitigated when a country is more open to international trade. Li, Morck, Yang
and Yeung (2004) provide evidence that a country’s openness, both trade openness and capital
market openness, affects the stock price co-movement in the country. Trade openness exposes
people to different ideas and values and could potentially weaken the effect of the country’s own
culture on people’s behavior (e.g., Cowen, 2002; Jones, 2006). Capital market openness allows
foreign investors to participate in domestic markets, which can mitigate the influence of
domestic culture on the stock price behavior. Based on these arguments, we hypothesize that the
influence of national culture on the stock price co-movement would be weaker in countries that
are economically more open. Consistent with the hypothesis, we find a weaker influence of
tightness and individualism on the stock price co-movement in those countries that are more
open to international trade and more integrated with the global stock market.
We conduct several robustness checks on our results. First, we confirm that our results hold
for alternative stock price co-movement measures. Second, we repeat our analysis in the joint
sample of 30 countries for which both culture measures are available and confirm that our results
are not driven by sample differences. We also verify the robustness of our results with a balanced
panel of countries that have data available for the entire sample period. Third, instead of the
Fama-Macbeth regression approach, we repeat all our regressions on a pooled panel data with
6
year fixed effects and country clustering.
We obtain similar results from this alternative
estimation method.
Taken together, our paper shows that culture is an important factor that affects investor
behavior and in turn, stock price co-movement. The findings of our paper add to both the
literature on cross-country differences in stock price co-movement (Morck et al., 2000; Jin and
Myers, 2006; Li et al., 2004; Bris et al., 2007; Fernandes and Ferreira, 2009; Froot and Dabora,
1999; Karolyi, Lee, and van Dijk, 2012) and the literature that uses behavioral factors to explain
stock price co-movement (Barberis et al., 2005; Barberis and Shleifer, 2003; Baker and Wurgler,
2006; Kumar and Lee, 2006; Green and Hwang, 2009). Our findings suggest that researchers
should consider the influence of culture when studying cross-country differences in investment
behavior. Shiller and Pound (1989) and Hirshleifer (2001) both point out that it is important to
understand how social norms and interactions among people affect investor decisions and call to
attention a lack of studies in this area. Our findings complement the literature on behavioral
finance by showing that cultural tightness, which is a proxy for the strength of social norms, is an
important factor that affects investor behavior. A significant number of studies discuss the costs
and benefits of trade openness and capital market openness (e.g., Stulz and Williamson, 2003;
Karolyi and Stulz, 2003; Rajan and Zingales,2003; Stulz, 1999; Frankel and Romer, 1999;
among others). Our findings on openness suggest that one potential benefit of country openness
is that it could mitigate the potential behavioral bias associated with the country’s national
culture.
Our paper also adds to the literature that links firm-specific (idiosyncratic) stock price
variations to the information content of stock prices (Roll, 1988; Morck et al., 2000; Durnev,
Morck, and Yeung, 2004, etc.). To explain idiosyncratic stock price variations, most studies in
7
this area examine cross-country institutional differences that may affect the information
environment of a country, such as insider trading prohibitions, disclosure requirements, and
short-sale constraints (e.g., Fernandes and Ferreira, 2009; Bushman et al, 2004; Bris et al, 2002;
Durnev et al. 2003b; Jin and Myers, 2006). Complementing these studies, we examine the
cultural variables that are intrinsic to investors’ behaviors and find higher firm-specific return
variations in those countries that are culturally more loose and individualistic.
A growing body of finance literature examines how national culture affects investor
behaviors and corporate decisions (e.g., Stulz and Williamson, 2003; Grinblatt and Keloharju,
2001; Guiso, Sapienza, and Zingales, 2008; Chui, Titman, and Wei, 2010; Li, Griffin, Yue and
Zhao, 2011 and 2012; Ahern, Daminelli, and Fracassi, 2011). To our knowledge, ours is the first
paper to examine the effect of a newly developed culture dimension, tightness/looseness, on
investor behaviors. We also add to this literature by showing that national cultures introduce
systematic biases to investor behaviors that may affect the stock price co-movement in a country.
The remainder of the paper is arranged as follows. Section 2 discusses in greater detail the
link between culture and stock price co-movements. Section 3 describes the data and variable
construction. Section 4 presents our empirical findings on the effect of culture on the stock price
synchronicity. Section 5 discusses how a country’s openness influences the relation between
culture and the stock price synchronicity. Section 6 discusses our robustness checks, and Section
7 provides conclusion.
2. Tightness, Individualism, and Stock Price Co-movements
The notion that culture could affect stock price movement is supported by several recent
finance studies. Chui et al. (2010) find that cross-country differences in individualism influence
8
the returns of momentum strategies. Grinblatt and Keloharju (2001) show that culture influences
investors’ decisions on stockholding and trading. Guiso et al. (2008) document a higher investor
participation in stock markets in countries with a higher level of trust. Using a survey data from
four countries, Beckmann, Menkhoff, and Suto (2007) find evidence that culture influences asset
managers’ views and behaviors in managing investment portfolios.
Morck et al. (2000) find that the co-movement of fundamentals can only partially explain
stock return co-movement and suggest that limitations on informed arbitrage may explain the
differences in return co-movements. Since then, behavioral factors have also been offered to
explain the stock price co-movement (Barberis and Shleifer, 2003; Barberis, Shleifer, and
Wurgler, 2005; Baker and Wurgler, 2006; Kumar and Lee, 2006, etc.). Based on the premise
that culture affects individual behavior, we expect culture to influence the stock price comovement. We focus on cultural dimensions that (i) can generate similarity or diversity in
investor behaviors and (ii) affect the investor’s propensity to generate their own information for
stock analysis.
The first cultural dimension we consider is tightness vs. looseness, which was first
introduced in Triandis (1989) and recently formalized in Gelfand et al. (2007, 2011).
A
country’s culture is defined as tight (loose) if the country has strong (weak) social norms and a
low (high) tolerance for deviant behavior. Gelfand et al. (2007) argue that individual behaviors
tend to be more homogenous and exhibit a lower degree of variations in culturally tight countries.
We expect that this lower (higher) degree of variation among individual behaviors would be
reflected in investment activities and leads to higher (lower) stock price co-movement for several
reasons.
9
First, as pointed out in Gelfand et al. (2007), culture tightness/looseness “is expected to relate
to preferred ways of gathering, processing, and evaluating information when solving problems”
(page 1230). If investors follow a similar way to process information, they will be more likely to
arrive at similar investment decisions, which may lead them to converge in stock selections and
buy/sell decisions. As a result, stock prices may reflect less firm-specific information and comove more in culturally ‘tight’ countries than in ‘loose’ countries. Second, in countries with
strong norms that define appropriate behavior, individuals are likely to share many common
experiences and perspectives. In contrast, individuals in culturally loose countries would face
fewer constraints on their behaviors and thus, are likely to have varied and idiosyncratic
experiences.
When individuals are more homogenous, it would be easier to identify the
normative expectations and predict their future behaviors. Therefore, investors’ reaction to
information in culturally tight countries would be more similar and exhibits a lower variation,
which again may result in less information incorporated in stock prices and higher stock price
co-movement.
Third, cultural tightness and looseness also affect people’s willingness to
conform to others’ behavior. Investors in a tight culture are more likely to seek conformity in
their investment decisions, while investors in a loose culture would be less concerned about
deviating from the norm. The conformity in decisions would lead to similar trading strategies
and the reluctance to deviate from the aggregate market belief. As a result, we expect to observe
lower variations in the stock market returns and a higher stock price co-movement. Based on the
above arguments, we hypothesize (H1) that the stock price co-movement would be higher in
countries with a tight culture than in countries with a loose culture.
The second cultural dimension we consider is individualism vs. collectivism. Hofstede (1980,
2001) defines individualism as the extent to which people are integrated into groups, which
10
reflects the degree to which people focus on their own internal attributes to differentiate
themselves from others. Experimental and survey studies suggest that people are more likely to
believe that they are above average in individualistic cultures than in collectivistic cultures
(Markus and Kitayama, 1991; Heine, Lehman, Markus, and Kitayama, 1999). Consistent with
this view, Chui, Titman and Wei (2010) argues that individualism relates to investors’ overconfidence and self-attribution bias and find that momentum profits are positively related to
individualism. Because of the confidence in their ability, individualistic investors are more
likely to analyze information on their own and be less concerned about trading on different
opinions than others. As a result, one would expect less herding behavior in individualistic
countries. Supporting this view, Beckmann et al. (2005) find that asset managers from
individualistic cultures are less likely to engage in herding.
If investors in individualistic
countries are more willing to process information to make their own investment decisions, we
expect that stock prices incorporate more firm-specific information, resulting in a lower stock
price co-movement in these countries. Formally, we hypothesize (H2) that the stock price comovement would be higher in countries with a collectivistic culture than in countries with an
individualistic culture.
Globalization of the economy has significantly increased the international transmission of
local cultures to foreign countries. People in an open economy are exposed to the traditions and
norms of other societies, in addition to their own (e.g, Cowen, 2002). Jones (2006) suggests that
trade openness encourages a cultural integration which might be beneficial in reducing the
transaction and information costs of trade. Stulz and Williamson (2003) find that the effect of
culture on a country’s financial development is mitigated when a country is more open to
international trade. Collectively, these arguments suggest that trade openness could weaken the
11
influence of national culture on people’s behaviors and business activities. Similarly, we also
expect capital market openness to mitigate the effect of culture on investor behaviors. Foreign
investors are not influenced by the same culture as domestic investors and do not subject to the
same cultural bias. Thus, the trading activities of foreign investors in domestic markets are
likely to mitigate the influence of national culture on stock price behaviors. Based on these
arguments, we hypothesize (H3) that the influence of national culture on the stock price comovement would be weaker in countries that are more open to trade and integrated with the
global stock market.
3. Data and Variable Construction
3.1. Data
We start with the weekly returns for all the stocks in Datastream from 1990 to 2010. We
calculate stock returns using total return index (RI), which is a stock price index constructed by
Datastream adjusted for dividends and stock splits. We follow Jin and Myers (2006) to filter our
sample. We first exclude stocks that are not traded in their home markets. We then exclude
stocks that have valid return data for less than 30 weeks during a year. For a country to be
included for a particular year, we require that there should be at least 25 stocks with valid data
for the country in that year. To prevent outliners from driving the results, we follow Ince and
Porter (2006) and delete an observation if the stock return is above 300% and reversed in the
following week. The data on cultural tightness are from Gelfand et al. (2011) and available for
33 countries. The data on individualism are from Holfstede (2001) and available for 56 countries.
We exclude countries with data on neither tightness nor individualism. To measure firm and
industry Herfindahl indices and earnings co-movement, we obtain accounting data for our
sample firms from the Worldscope database. Country-level data such as geographical size and
12
GDP per capita are collected from the databank of the World Bank. Our final sample includes
46 countries and 913 country years. Out of the 46 sample countries, 28 countries have data for
the tightness variable and all sample countries have data on individualism.
3.2.The stock price synchronicity measures
Following Morck et al. (2000) and Jin and Myers (2006), we use R2 from an expanded
market model to measure the stock price co-movement in a country. More specifically, we
estimate R2 based on the following model in Jin and Myers (2006):
ri,j,t = αi + β1,i rm,j,t + β2,i [rU.S.,t + EX j,t ] + β3,i rm,j,t−1 + β4,i [rU.S.,t−1 + EXj,t−1 ]
+ 𝛽5,𝑖 𝑟𝑚,𝑗,𝑡−2 + 𝛽6,𝑖 [𝑟𝑈.𝑆.,𝑡−2 + 𝐸𝑋𝑗𝑡−2 ] + 𝛽7,𝑖 𝑟𝑚,𝑗,𝑡+1
+ 𝛽8,𝑖 [𝑟𝑈.𝑆.,𝑡+1 + 𝐸𝑋𝑗𝑡+1 ] + 𝛽9,𝑖 𝑟𝑚,𝑗,𝑡+2 + 𝛽10,𝑖 [𝑟𝑈.𝑆.,𝑡+2 + 𝐸𝑋𝑗𝑡+2 ]
+ 𝑒𝑖𝑡 ,
(1)
where ri,j,t is the weekly return of stock i of country j in week t of a year; rm,j,t is the weekly
market return of country j in week t; rU.S.,t + EXj,t is the US market return adjusted for change in
the exchange rate of country j against the U.S. dollar. The inclusion of lead and lag terms is to
correct for nonsynchronous trading, as in Dimson (1979). To measure the stock price comovement in a country, for each year, we take an equal-weighted average of R2s of individual
stocks in the country. Higher average R2 indicates greater stock price co-movement. We
measure the market-wide (firm-specific) return variation using the average explained (residual)
sum of squares from Equation (1) for each country.
For robustness check, we also use two alternative measures of stock price synchronicity:
(i) the return-variance-weighted average of R2s and (ii) the fraction of stocks moving in the same
direction. We follow Morck et al. (2000) to calculate the latter measure as follows:
13
up
fj,t =
max⁡[nj,t , ndown
]⁡
j,t
up
nj,t + ndown
j,t
(2)
,
up
where nj,t is the number of stocks in country j with a positive return in week t and ndown
is the
j,t
number of stocks with a negative return in the same week. We measure stock price co-movement
of country j in a year by taking an average of fj,t over the year. A higher fraction of stocks
moving in the same direction reflects a higher level of stock price co-movements.
3.3. Culture measures
The country values for our first culture measure, tightness, are from Gelfand et al. (2011).
With support from the National Science Foundation, Gelfand et al. conducted a survey on
cultural tightness and looseness across 33 nations. Appendix A provides the survey questions.
The final tightness score for a nation is based on a six-item Likert scale and is presented in Table
2.4 A higher score indicates a tighter national culture.
Our second culture measure, individualism, is from Hofstede (1980, 2001). Hofstede
constructs this value from a survey of 56 nations. In addition to individualism, Hofstede also
measures four other dimensions of culture: power distance, uncertainty avoidance, masculinity,
and long-term orientation. We focus on individualism instead of other culture dimensions for
several reasons. First, this variable complements the external focus of cultural tightness and
captures internal motivations that guide one’s behavior. Second, the literature (e.g., Chui et al.
2010) provides evidence that the individualism measure relates to stock price movement. Third,
as argued in Ahern et al. (2011), individualism is a popular cultural dimension that is common to
alternative culture definitions as in Schwartz (1994), Trompenaars (1993), and Fiske (1991).
4
A Likert scale is the most widely used approach to scaling responses in survey research. When responding to a
Likert questionnaire, respondents specify their responses based on a symmetric agree-disagree scale for a series of
statements.
14
While correlated, tightness and individualism also differ from each other significantly.
Tightness captures external conditioning on human behavior, whereas individualism relates to
the degree to which people focus on their own internal attributes to differentiate from others.
Countries demonstrate different loadings on these two cultural dimensions as shown in Figure 1.
For example, Brazil is collectivistic and loose, Japan is collectivistic and tight, U.S. is
individualistic and loose, and Germany is individualistic and tight (e.g., Gelfand et al., 2007;
Chan et al., 1996; Triandis, 1989; Carpenter, 2000).
3.4. Other variables
Based on the findings of the previous studies, Morck et al. (2000) and Jin and Myers (2006),
we control for good government index, information opaqueness, GDP per capita, GDP growth
volatility, the number of stocks traded in a country, country geographic size, industry and firm
Herfindahl indexes, and earnings co-movement in our regression analysis.
Good government index is the sum of the percentile rank of two indices from Kaufmann,
Kraay, Mastruzzi (2009): government effectiveness and control of corruption. 5 These two
indices are available from 1996 to 2008 and are updated every two years from 1996 to 2002 and
every year from 2003 to 2008. We use 1996’s values for 1990 to 1996 and 2008’s value for
2008 to 2010. For missing years in between, we assign them the average of the value of the year
before and the value of the year after.
Following Jin and Myers (2006), we construct an
opaqueness measure, diversity of analyst forecasts, as a country’s average dispersion of analysts’
forecasts of the firm’s earnings, divided by mean forecast and then by the square root of the
5
We obtain similar results if we use the good government index in Morck et al. (2000). We choose to use the index
from the world bank because (i) it is available for more countries in our sample; (ii) it is updated every one to two
years and has time-series variation. The good government index in Morck et al. (2000) is based on La Porta et al.
(1998) and is available for 49 countries and is a fix number throughout our entire sample period.
15
number of analysts following the firm.6 Jin and Myers (2006) argue that a country’s information
environment is more opaque if there is more diversity in analyst opinions. We include Ln(GDP
per capita) and Ln(number of stocks) as controls for economic and financial development of the
countries. We control for macroeconomic instability by including GDP growth volatility, which
is measured by the standard deviation of growth in GDP per capita. We include Ln(country size)
to control for the possibility that smaller countries might have more concentrated economic
activities which could lead to greater stock price co-movements. Country size is a country’s
geographical size in square miles. Industry and firm Herfindahl indexes are included so as to
control for stock price co-movement due to economic specialization. Finally, we control for the
synchronicity in firm fundamentals by including a proxy for earnings co-movements as in Morck
et al (2000). For each year, we first estimate R2 and the total sum of squares (SST) for each
individual stock i in country j by running a regression of ROA on the market ROA using a five
year rolling window:
ROAi,j,t = ai,t + bi,t ROAm,j,t + 𝜀𝑖,𝑡
(3)
the market ROAm, j, t is the value-weighted average of the ROAs for all firms in the country. For
each country, we then calculate the earnings co-movement as a SST-weighted average of the R2
for all individual stocks in that country.
3.5. Summary statistics
Table 1 presents the summary statistics of all variables in our study. The top panel of the
table includes descriptive statistics for the three price synchronicity variables.
6
The mean
It is noted that our results are robust to the other four opaqueness measures used in Jin and Myers (2006): a
transparency measure from the global competitiveness report (GCR), the number of auditors (NAuditor), the
accounting standard index in La Porta et al. (1998), and the PricewaterhouseCoopers’s Global Opacity Index (GOI).
We report results on analyst diversity because it is the only time-varying measure and is available for most of the
country years in our sample. All other four measures are time-invariant.
16
(median) equal-weighted R2 for our sample is 0.313 (0.290). The mean (median) of the fraction
of stocks moving in the same direction is 0.655 (0.648). Because our R2 measure and the
fraction measure are bounded within the intervals of [0, 1] and [0.5, 1] respectively, we follow
Morck et al. (2000) and apply logistic transformation to these variables as:
𝑅𝑗2
(4)
Transformed R2 = ln ( 1−⁡𝑅2)
𝑗
Transformed fraction of stocks moving in the same direction = ln (
𝑓𝑗 −0.5
1−⁡𝑓𝑗
)
(5)
The summary statistics on the transformed variables are also presented in Table 1. The means
and medians of these variables are comparable to those reported in Morck et al. (2000) and Jin
and Myers (2006). The average (median) log transformed market-wide variation and firmspecific variation is -1.513 (-2.114) and -0.479 (-1.210). The average for the tightness variable is
6.92 and the median is very close to the mean at 6.80. The nation with the highest tightness
score, 12.3, is Pakistan and the nation with the lowest score, 2.9, is Hungary. The average
individualism score for our sample is about 50 and the median is 51. The country with the
highest value for individualism is the United States (91) and the country with the lowest value is
Venezuela (12). In Table 1, we also report summary statistics on two openness variables and the
control variables. The details on the calculations of the two openness variables are provided in
Section 5.1.
Table 2 lists our sample countries and provides the corresponding values of R2, tightness,
individualism, and openness measures for each of the sample countries. Consistent with our
expectations, countries that are tight (loose) and less (more) individualistic tend to have a higher
(lower) R2. For example, the two countries with the lowest stock price co-movement are the
United States and Australia, which also have the two highest scores for individualism and both
have a relatively low score for tightness. Malaysia, Turkey, and China, which rank among the
17
countries with the highest stock price co-movement, also have high values for tightness and low
values for individualism.
The second column of Table 2 provides the time period for which we have data available for
each country. Out of 46 countries in our sample, 33 countries have data for the entire sample
period, 1990 to 2010. Our main regression results are based on an unbalanced panel of 46
countries. As a robustness check, we repeat all our analysis on a balanced panel of the 33
countries and obtain qualitatively similar results.
We present the correlations among our variables in Table 3. Confirming the pattern we see
in Table 2, the correlation between R2 and tightness is positive and significant and the correlation
between R2 and individualism is negative and significant. These two culture variables are
significantly negatively correlated (-0.557). The correlations between tightness and the two
variations, market-wide and firm-specific, are negative and statistically significant.
The
correlation between individualism and firm-specific variation is positive and statistically
significant. We find an insignificant correlation between individualism and the market-wide
stock price variation.
4. The Effect of Culture on the Stock Price Synchronicity
4.1. Empirical design
To study the relationship between culture and the stock price co-movement, we estimate a
model similar to Morck et al. (2000) and Jin and Myers (2006):
2
𝑇(𝑅𝑗𝑡
) = 𝛼 + 𝛽1 𝐶𝑢𝑙𝑡𝑢𝑟𝑒 + 𝛽2 𝐺𝑜𝑜𝑑⁡𝐺𝑜𝑣𝑒𝑟𝑛𝑚𝑒𝑛𝑡⁡𝐼𝑛𝑑𝑒𝑥
+ 𝛽3 𝐴𝑛𝑎𝑙𝑦𝑠𝑡⁡𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦⁡𝑅𝑎𝑛𝑘 + 𝛽4 log(𝐺𝐷𝑃)
+ 𝛽5 log(𝑁𝑢𝑚𝑏𝑒𝑟⁡𝑜𝑓⁡𝑆𝑡𝑜𝑐𝑘𝑠) + 𝛽6 𝐺𝐷𝑃⁡𝐺𝑟𝑜𝑤𝑡ℎ⁡𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦
+ 𝛽7 𝐶𝑜𝑢𝑛𝑡𝑟𝑦⁡𝑆𝑖𝑧𝑒 + 𝛽8 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦⁡𝐻𝑒𝑟𝑓𝑖𝑛𝑑𝑎ℎ𝑙
+ 𝛽9 𝐹𝑖𝑟𝑚⁡𝐻𝑒𝑟𝑓𝑖𝑛𝑑𝑎ℎ𝑙 + 𝛽10 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠⁡𝐶𝑜𝑚𝑜𝑣𝑒𝑚𝑒𝑛𝑡 + 𝜀𝑗𝑡
(6)
18
where T(R2jt ) is the logistic transformation of R2jt . We use the Fama-MacBeth (1973) method
with a correction for serial correlation of coefficients in the past 6 years to estimate the model.
As in Jin and Myers (2006), we use the rank of analyst diversity to proxy for the information
opaqueness. Table 4 presents the regression results.
4.2. The effects of culture on the stock price synchronicity
To establish a benchmark for our study, we first replicate the regression analysis in Morck et
al. (2000) and Jin and Myers (2006) and report these results in the first three columns of Table 4.
Consistent with the findings in Morck et al. (2000), we find a negative and statistically
significant coefficient on GDP per capita. The coefficient becomes insignificant once we control
for the good government index as in Morck et al. (2000). As reported in the third column of
Table 4, we find a positive and statistically significant coefficient on the information opaqueness
measure, analyst diversity rank. This is consistent with the finding in Jin and Myers (2006) that
information opaqueness increases stock price co-movement.7
We present results on cultural tightness and individualism in the fourth and fifth columns of
Table 4. The coefficient on the tightness variable is positive and statistically significant at the 5%
level. This is consistent with our hypothesis that stock price co-movement is higher in culturally
tighter countries. As expected, the coefficient on individualism is negative and statistically
significant at the 1% level. This suggests that stock price co-movement is lower when investors
are more individualistic. These results are consistent with our hypotheses that culture affects the
stock price co-movement. The influence of culture on the stock price co-movement is
economically significant as well. One standard deviation increase in tightness (individualism) is
7
We note that our results are robust to the other four opaqueness measures used in Jin and Myers (2006). We report
results on the analyst diversity because it is a time-varying measure and is available for most of the country-years in
our sample.
19
associated with a 9.5% (12.2%) increases (decrease) in the dependent variable, log transformed
stock price co-movement. The marginal impact of culture variables is comparable to those of
other variables. For example, based on the estimates from model (4) of Table 4, one standard
deviation increase in country size is associated with a 7.6% decrease in the stock price comovement. The same marginal effect is -10.9% for GDP per capita in model (1), 5.0% for the
analyst diversity in model (3), and -14.8% for the good government index in model (2).
In the sixth model of Table 4, we include both culture variables in the same regression.
Similar to the results in model (4), the coefficient on tightness is positive and statistically
significant at the 10% level. For individualism, we again find a negative and statistically
significant coefficient at the 1% level as in model (5). The correlation between tightness and
individualism is -0.557. To mitigate the effect of possible multicolinearity between these two
variables, we create an orthogonal variant of individualism by regressing individualism on
tightness and replacing individualism in Model (6) with the residual from this regression.
Column (7) of Table 4 presents the regression results. The estimated coefficients on both
tightness and the residual of individualism have the expected sign and are statistically significant
at the 1% level. These results support the notion that tightness and individualism capture
different dimensions of national culture and the effect of one culture variable does not subsume
that of the other.
4.3.The effects of culture on the market-wide and firm-specific return variations
As in Morck et al. (2000), we decompose our stock price synchronicity measure as the
difference between the log-transformed market-wide variation and firm-specific variation. In this
section, we examine how tightness and individualism affect these two components of R2. A
higher R2 can reflect a low level of firm-specific variation or a high level of market-wide
20
variation. Table 5 presents our analysis of the influence of culture on the market-wide and firmspecific variations. The dependent variable is the market-wide variation in columns (1) and (2)
and the firm-specific variation in columns (3) and (4). The coefficient on tightness is negative
and statistically significant for both the market-wide and firm-specific variations. This result is
consistent with the view that tightness reduces the variation in investor behaviors and the
investor’s likelihood of deviating from the prevailing market prices. We note that the reduction
in the firm-specific variation is greater than the reduction in the market-wide variation. For
example, based on the coefficients in Table 5, one standard deviation increase in tightness
decreases the firm-specific variation by 0.590, a 49% reduction based on the median value of
firm-specific variation (-1.210). In comparison, the same increase in tightness decreases the
market-wide variation by 0.443, a 21% reduction based on the median value of the market-wide
variation (-2.114). As a result of the more significant reduction in the firm-specific variation,
countries with a tighter culture exhibit a higher R2.
The coefficient on individualism is negative and statistically significant for the firm-specific
variation. This finding is consistent with our expectation that more firm-specific information
gets incorporated into stock prices in more individualistic countries because individualistic
investors are more likely to gather and analyze information on their own for investment decisions.
As we observe in the correlation matrix provided in Table 3, we do not find a significant relation
between the market-wide variation and individualism. These results suggest that individualism
lowers R2 primarily through a higher firm-specific variation.
Results in Table 5 suggest that the two culture variables influence R2 through different
channels. Individualistic investors are more willing to obtain information and form their own
beliefs. As a result of this type of behavior, more firm-level information gets incorporated into
21
stock prices in individualistic countries.
Tightness influences how investors process their
information. Investors in a tight society tend to follow a similar process to gather and analyze
information and arrive at similar conclusions, which reduce the variation in their behaviors
(Gelfand et al., 2007). In addition, individuals in a tight culture have a tendency to conform to
the norms in the society. They may be less willing to deviate from the prevailing market prices,
which are likely to represent the aggregate beliefs in the stock market. As a result, we observe
lower time-series variations in stock prices in culturally tight countries.
5. The Effect of Openness on the Relation between Culture and the Stock Price
Synchronicity
5.1. Measures of openness
To empirically examine how openness affects the influence of culture on the stock price comovement in different countries, we estimate the following model:
2
𝑇(𝑅𝑗𝑡
) = 𝛼 + 𝛽1 𝐶𝑢𝑙𝑡𝑢𝑟𝑒 + 𝛽2 𝐶𝑢𝑙𝑡𝑢𝑟𝑒 ∗ 𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠 + 𝛽3 𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠
+ 𝛽4 𝐺𝑜𝑜𝑑⁡𝐺𝑜𝑣𝑒𝑟𝑛𝑚𝑒𝑛𝑡⁡𝐼𝑛𝑑𝑒𝑥 + 𝛽5 𝐴𝑛𝑎𝑙𝑦𝑠𝑡⁡𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦
+ 𝛽6 log(𝐺𝐷𝑃) + 𝛽7 log(𝑁𝑢𝑚𝑏𝑒𝑟⁡𝑜𝑓⁡𝑆𝑡𝑜𝑐𝑘𝑠)
+ 𝛽8 𝐺𝐷𝑃⁡𝐺𝑟𝑜𝑤𝑡ℎ⁡𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦 + 𝛽9 𝐶𝑜𝑢𝑛𝑡𝑟𝑦⁡𝑆𝑖𝑧𝑒
+ 𝛽10 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦⁡𝐻𝑒𝑟𝑓𝑖𝑛𝑑𝑎ℎ𝑙 + 𝛽11 𝐹𝑖𝑟𝑚⁡𝐻𝑒𝑟𝑓𝑖𝑛𝑑𝑎ℎ𝑙
+ 𝛽12 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠⁡𝐶𝑜𝑚𝑜𝑣𝑒𝑚𝑒𝑛𝑡 + 𝜀𝑗𝑡 ,
(7)
where openness is the proxy for either trade openness or capital market openness. We expect the
coefficient, 𝛽2 , to be negative for tightness and positive for individualism to support our
hypothesis that openness mitigates the effects of national culture on the stock price co-movement.
To measure trade openness, we use the natural trade openness proxy constructed by Frankel
and Romer (1999). Frankel and Romer estimate the natural trade openness variable based on a
country’s geographic attributes, such as the distance from potential trade partners. This variable
captures the importance of trade in the countries’ economy. We choose natural trade openness
over actual trade openness because it better captures the geographic closeness of a country to its
22
trade partners. The geographic closeness facilitates cultural integration among these countries by
increasing the probability and frequencies of business visits between the trading parties. For
example, Japan, China, and the United States are Thailand’s top three trading partners. However,
Chinese culture has more impact on Thailand’s culture than Japanese or American culture
because of the geographic closeness between China and Thailand. In addition, a country’s
natural trade openness is likely to be more exogenous than a country’s actual trade openness
which is an outcome of a country’s business activities. We note that the correlation between
natural trade openness and actual trade openness is about 75%, suggesting that the natural trade
openness variable also substantially captures a country’s degree of actual trade openness. Table
1 presents the summary statistics for this variable. The mean (median) value of natural trade
openness is 22.88 (13.97). The difference between the mean and median value suggests
skewness in this variable. To mitigate the impact of skewness, we use the natural log value of
this variable in the regression.
We follow the approach of Pukthuanthong and Roll (2009) to construct a stock market
integration proxy to measure the degree of capital market openness. A market that is better
integrated with the global market is likely to be more accessible to foreign investors. To
construct the proxy, for each of our 45 sample countries, we first collect daily returns of its
market index, in terms of the U.S. dollar, from Datastream. To extract the global factors, we
follow Pukthuanthong and Roll (2009) and choose 17 markets that have the longest history of
return series in Datastream. 8 We extract the first four principal components from the daily
returns of the 17 market indices and use them as the global factors to estimate the following
model:
8
These 17 countries include: Australia, Austria, Belgium, Canada, Denmark, France, Germany, Hong Kong, Ireland,
Italy, Japan, Netherlands, Singapore, South Africa, Switzerland, United Kingdom, and United States.
23
rj,t = α + β1 PC1,t + β2 PC2,t + β3 PC3,t + β4 PC4,t + ej,t
(8)
where rj,t is the daily return for country j on day t and PC1,t , PC2,t , PC3,t , and⁡PC4,t are the first
to fourth principal components. When extracting the principal components of country j, we
include the lagged returns of the U.S. and Canadian markets to account for time zone differences
and exclude returns of country j’s own market index when j is one of the 17 countries. For
example, returns of Austria’s market index are excluded from the estimation of principal
components for Austria. We use the R2 estimated from Equation (8) to measure the degree to
which a market is integrated to the global market. As shown in Table 2, the capital market
openness measure ranges from 0.040 for Venezuela to 0.790 for the Netherlands.
5.2. Regression results
Table 6 presents results on the effect of openness on the relation between culture and the
stock price co-movement. As in Table 4, the coefficient on tightness is positive and statistically
significant in both columns (1) and (3). The coefficient on the interaction term between tightness
and openness is negative and statistically significant at the 1% level for natural trade openness
and at the 5% level for capital market openness. This finding indicates that openness reduces the
positive effect of tightness on the stock price co-movement. In the second and fourth columns of
Table 6, the coefficient on individualism is negative and statistically significant at the 1% level
and the coefficient on the interaction term is positive and statistically significant at the 10% level
for trade openness and at the 1% level for capital market openness. The positive coefficient on
the interaction term between individualism and openness indicates a weaker effect of
individualism on the stock price co-movement when the country is more open.
24
In Table 7, we examine how openness affects the influence of culture on market-wide vs.
firm-specific variations. We find that the interaction between openness and tightness is positive
and significant in all four models for tightness. This positive interaction effect mitigates the
negative impact of tightness on both market-wide and firm-specific variations. In models (4) and
(8), we find a negative coefficient on the interaction term between openness and individualism,
supporting our hypothesis that openness weakens the positive effect of individualism on firmspecific variation. We also find a negative and statistically significant coefficient on the
interaction term for market-wide variation (columns 2 and 6). This suggests that the marketwide variation is lower in countries that are more open and individualistic.
Taken together, results in Tables 6 and 7 are consistent with our hypothesis that openness
mitigates the effect of culture on the stock price co-movement. We also find that openness
weakens the influence of culture on both firm-specific and market-wide variations.
6. Robustness Checks
We conduct several robustness checks on our results. We first repeat our analysis using two
alternative measures of stock price synchronicity: Variance-weighted R2 and the fraction of
stocks moving in the same direction. We use the total variance of each firm as the weight to
calculate a variance-weighted R2 for each country. Table 8 presents the regression results based
on these two alternative measures. The coefficient on tightness is positive and statistically
significant at the 1% level when the dependent variable is the variance-weighted R2 and at the 10%
level when the dependent variable is the fraction of stocks moving in the same direction. The
coefficient on individualism is negative and significant at the 1% level for both dependent
variables. These results support our earlier finding that stock prices co-move less in an
individualistic and culturally loose society.
25
We next verify that our results hold in two subsamples. Some countries in our sample have
values for individualism, but not for tightness. For all our previous analysis, our regressions are
run on two separate samples, one for individualism and one for tightness. We repeat our
regressions on individualism in the smaller sample of tightness and report the results in the
second column of Table 9. Consistent with the findings in previous tables, the coefficient on
individualism is negative and significant. To maximize our sample size, the tests we have
conducted so far are based on an unbalanced panel. As a robustness check, we rerun our
regression in a balanced panel that includes countries with return data for the entire sample
period. Columns 3 and 4 of Table 9 report regression results based on this balanced panel. We
again find that the stock price synchronicity tends to be higher in culturally tighter and more
individualistic countries.
7. Conclusion
Hofstede (2010) argue that culture is “the software of mind”, pointing out the importance of
culture in understanding human behavior. The literature suggests that people tend to behave in a
similar (different) manner and act more holistically (analytically) in culturally tight (loose) and
collectivistic (individualistic) countries. We expect these culture differences to influence investor
behaviors in the stock market and results in a higher stock price co-movement in tighter and
more collectivistic countries.
Consistent with this argument, we find a higher stock price
synchronicity in countries with a tight and collectivist culture. On the other hand, stock prices
tend to co-move less in culturally loose and individualistic countries. We further document that
both the market-wide variation and the firm-specific variation are lower in culturally tight
countries and the higher return co-movement in these countries is primarily driven by
significantly lower firm-specific variations. Consistent with the argument that individualistic
26
investors are more likely to gather and analyze private information, we find that individualism
reduces R2 mainly by increasing firm-specific variations.
We also find that the effect of culture on the stock price co-movement is weaker in countries
that are more open to trade. This result is consistent with the view that international trade
encourages cultural exchange and mitigates the influence of domestic culture on people’s
behavior. Supporting the proposition that the access of foreign investors may dilute the cultural
bias among domestic investors, the effect of culture on the stock price co-movement is also
weaker when the country’s stock market is more integrated with the global market.
Overall, our study suggests that culture is an important omitted variable in the existing
studies that examine cross-country differences in the stock price co-movement. Researchers thus
may want to take culture into consideration when they draw cross-country inferences from stock
markets.
For example, culture may help to explain different stock price behaviors in the
domestic and foreign exchanges for cross-listed stocks.
The findings of our paper have
implications for professional fund managers and investors as well. When constructing portfolios
of international securities, fund managers and investors may want to consider the effect of a
country’s national culture on stock price co-movement because it could affect the efficiency of
diversification in these portfolios.
27
Appendix A: Survey method used to measure cultural tightness-looseness
This appendix presents the six scale items that are asked in the survey conducted by Gelfand et al. (2011).
This survey was offered to a total of 6,960 respondents in 33 nations across five continents. After
removing incomplete surveys with unusable data, the final sample for analyses consisted of 6,823
participants. All data were collected during 2000-2003.
The six scale items asked in the survey are:
1. There are many social norms that people are supposed to abide by in this country.
2. In this country, there are very clear expectations for how people should act in most situations.
3. People agree upon what behaviors are appropriate versus inappropriate in most situations this
country.
4. People in this country have a great deal of freedom in deciding how they want to behave in
most situations. (Reverse coded)
5. In this country, if someone acts in an inappropriate way, others will strongly disapprove.
6. People in this country almost always comply with social norms.
For each statement, the survey respondent chooses from the following symmetric disagree-agree scale:
1
Strongly
Disagree
2
Moderately
Disagree
3
Slightly
Disagree
4
Slightly
Agree
5
Moderately
Disagree
6
Strongly
Agree
Gelfand et al. (2011) calculate the final score for a country in two steps. In the first step, for each survey
response in a country, they compute a within-subject standardized score for that response. To do so, they
first calculate the mean for each person’s responses to all of the items in the survey. Then they
standardize all items in the survey by subtracting each item from that person’s mean response to all items.
In the second step, they calculate the mean of the standardized scores for each country. The final score is
the mean standardized score multiplied by 10. Refer to Gelfand et al. (2011) for details on how the final
score is calculated. The paper also provides detailed discussions on the reliability and validity of the
survey method and the calculation of the tightness score.
28
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Individualism vs. Tightness
100
United States
90
80
Hungary
Individualism
70
Australia
United Kingdom
Netherlands
New Zealand
Belgium Italy
France
Germany
60
Israel
50
Norway
Poland
Austria
Spain
India
Japan
40
Brazil
Greece
Turkey
Mexico
Portugal
Hong Kong
China
30
20
Malaysia
Singapore
South Korea
Pakistan
10
0
0
2
4
6
8
10
12
14
Tightness
Figure 1. The Plot of Sample Countries on the Two Culture Dimensions
This figure presents the plot of the 28 countries with both values for tightness and individualism. Tightness is from
Gelfand et al. (2011). Individualism is from Hofstede (2001). Tightness measures if the country has strong norms
and a low tolerance of deviant behavior. Individualism captures the extent to which people are integrated into groups.
Table 1. Summary Statistics
This table presents summary statistics for the variables in our sample. The sample is from 1990 to 2010 and has 913 country year
observations. The equal-weighted and variance-weighted R2 are averaged R2s of stocks in a country estimated from an expanded
market model (see Equation (1)). Fraction of stocks moving in the same direction is calculated based on Equation (2).
Transformed R2s and fraction of stocks moving in the same direction are the logistic transformation of these variables using
Equation (4) and (5), respectively. Ln(Market-wide Variation) (Ln(Firm-specific Variation)) is the natural logarithm of the
average explained (residual) sum of squares from Equation (1). Tightness is from Gelfand et al. (2011). Individualism is from
Hofstede (2001). Tightness measures if the country has strong norms and a low tolerance of deviant behavior. Individualism
captures the extent to which people are integrated into groups. Trade openness is from Frankel and Romer (1999) and is a
constructed traded openness variable based on the firm’s geographic location and population. Capital market openness measures
the degree of market integration following Pukthuanthong and Roll (2009). Ln(number of stocks) is the natural logarithm of the
number of stocks in a national stock market. GDP Growth Volatility is the standard deviation of the growth rate of GDP per
capita. Ln(Country Size) is the natural logarithm of the geographical size of a country in squared miles. Industry herfindahl
index is the sum of squared market share of all the industries in a country. Firm herfindahl index is the sum of squared market
share of all the firms in a country. Earnings co-movement is the SST-weighted average of R2s of stocks in a country estimated
from Equation (3). Good government index is the sum the quantile of two indices from Kaufmann et al. (2009): government
effectiveness and control of corruption. Diversity of analyst forecasts is the average dispersion of analyst forecasts of stocks in a
country, as calculated in Jin and Myers (2006). We winsorize price co-movement measures and control variables at the 1st and
99th percentile levels.
Obs.
Mean
Median
Std. Dev.
Min
Max
913
0.313
0.290
0.088
0.123
0.744
913
0.274
0.254
0.104
0.044
0.783
913
0.655
0.648
0.053
0.552
0.870
913
-0.811
-0.897
0.401
-1.968
1.066
Transformed Variance-weighted R
Transformed Fraction of Stocks Moving in
the Same Direction
Ln(Market-wide Variation)
913
-1.034
-1.075
0.543
-3.081
1.283
913
-0.847
-0.869
0.512
-2.160
1.046
913
-1.513
-2.114
2.416
-4.354
16.456
Ln(Firm-specific Variation)
913
-0.479
-1.210
2.621
-3.339
18.055
Price Co-movement Measures:
Equal-weighted R2
2
Variance-weighted R
Fraction of Stocks Moving in the Same
Direction
Transformed Equal-weighted R
2
2
Culture Variables:
Tightness
571
6.922
6.800
2.634
2.900
12.300
Individualism
913
50.135
51.000
24.388
12.000
91.000
899
22.876
13.970
39.379
2.300
281.290
892
0.396
0.381
0.253
0.002
0.961
Ln(Number of Stocks)
913
5.735
5.513
1.202
3.219
9.342
Ln(GDP per capita)
913
9.393
9.825
1.258
5.943
11.314
GDP Growth Volatility
913
0.022
0.017
0.017
0.002
0.118
Ln(Country Size)
913
11.660
11.780
2.234
5.598
15.703
Industry Herfindahl Index
910
0.111
0.091
0.079
0.017
0.566
Firm Herfindahl Index
910
0.041
0.024
0.053
0.001
0.540
Earnings Co-movement
882
0.392
0.370
0.202
0.003
1.000
Good Government Index
913
1.023
1.022
0.571
0.043
2.000
Diversity of Analyst Forecasts
879
0.045
0.037
0.042
-0.412
0.362
Openness Variables:
Trade Openness
Capital Market Openness
Control Variables
Table 2. Stock Price Synchronicity (R2), Culture Measures, and Openness by Country
This table presents the available time period, R2, and culture measures for 46 countries in our sample. The equal-weighted R2 are
averaged R2s of stocks in a country estimated from an expanded market model (see Equation (1)). Tightness is from Gelfand et al.
(2011). Individualism is from Hofstede (2001). Tightness measures if the country has strong norms and a low tolerance of
deviant behavior. Individualism captures the extent to which people are integrated into groups. Trade openness is from Frankel
and Romer (1999) and is a constructed traded openness variable based on the firm’s geographic location and population. Capital
market openness is constructed as in Pukthuanthong and Roll (2009) and captures the extent to which the domestic stock market
is integrated with the global stock market.
Country
Sample periods Equal-weighted R2 Tightness Individualism
United States
1990-2010
0.170
5.1
91
Australia
1990-2010
0.244
4.4
90
Canada
1990-2010
0.249
80
Germany
1990-2010
0.254
7.5
67
Luxembourg
1992-2009
0.254
60
Brazil
1992-2010
0.255
3.5
38
Denmark
1990-2010
0.255
74
Ireland
1990-2002
0.255
70
Czech Republic
1994-2007
0.256
58
South Africa
1990-2010
0.258
65
New Zealand
1990-2010
0.260
3.9
79
United Kingdom
1990-2010
0.261
6.9
89
Peru
1991-2010
0.262
16
Russian Federation
1997-2010
0.263
39
France
1990-2010
0.264
6.3
71
Chile
1990-2010
0.275
23
Belgium
1990-2010
0.287
5.6
75
Portugal
1990-2010
0.288
7.8
27
Philippines
1990-2010
0.288
32
Pakistan
1993-2010
0.296
12.3
14
Indonesia
1990-2010
0.297
14
Austria
1990-2010
0.297
6.8
55
India
1990-2010
0.300
11.0
48
Switzerland
1990-2010
0.300
68
Norway
1990-2010
0.305
9.5
69
Mexico
1990-2010
0.314
7.2
30
Israel
1990-2010
0.315
3.1
54
Finland
1991-2010
0.315
63
Netherlands
1990-2010
0.316
3.3
80
Hong Kong
1990-2010
0.319
6.3
25
Sweden
1990-2010
0.320
71
Venezuela
1994-2009
0.324
12
Thailand
1990-2010
0.333
20
Hungary
1994-2010
0.334
2.9
80
Poland
1995-2010
0.350
6.0
60
Japan
1990-2010
0.361
8.6
46
South Korea
1990-2010
0.365
10.0
18
Spain
1990-2010
0.366
5.4
51
Singapore
1990-2010
0.370
10.4
20
Argentina
1993-2010
0.380
46
Italy
1990-2010
0.381
6.8
76
Greece
1990-2010
0.387
3.9
35
Malaysia
1990-2010
0.391
11.8
26
Taiwan
1990-2010
0.450
17
Turkey
1990-2010
0.452
9.2
37
China
1993-2010
0.549
7.9
20
Trade
Openness
2.56
4.07
4.97
18.47
281.29
3.03
30.89
33.85
21.07
8.90
8.19
13.47
7.03
3.68
15.26
7.25
52.46
18.78
8.84
8.04
4.47
36.64
3.29
32.57
23.54
4.52
54.17
21.64
35.84
35.88
18.22
8.94
9.45
26.92
13.84
5.47
14.36
12.38
48.90
5.60
13.97
27.01
16.82
17.92
11.26
2.30
Capital Market
Openness
0.307
0.456
0.426
0.721
0.283
0.329
0.405
0.426
0.255
0.360
0.356
0.642
0.200
0.320
0.750
0.261
0.667
0.520
0.212
0.050
0.283
0.536
0.165
0.684
0.494
0.379
0.210
0.526
0.790
0.413
0.607
0.040
0.235
0.349
0.370
0.290
0.377
0.659
0.423
0.263
0.592
0.319
0.343
0.248
0.200
0.056
Table 3. Variable Correlations
This table presents correlation matrix for the variables in our sample. The sample is from 1990 to 2010 and has 892 country year observations. The equalweighted R2 are averaged R2s of stocks in a country estimated from an expanded market model (see Equation (1)). Tightness is from Gelfand et al. (2011).
Individualism is from Hofstede (2001). Tightness measures if the country has strong norms and a low tolerance of deviant behavior. Individualism captures the
extent to which people are integrated into groups. Refer to Table 1 for other variable definitions.
(a)
2
Transformed Equal-weighted R (a)
(b)
(c)
(d)
(f)
(g)
(h)
(i)
(j)
(k)
0.751a
Transformed Fraction of Co-moving Stocks (c)
0.753a 0.557a
Ln(Market-wide Variation) (d)
-0.193a -0.282a -0.006
Ln(Firm-specific Variation) (e)
-0.334a -0.467a -0.121a 0.980a
Tightness (f)
0.239a 0.185a
Individualism (g)
-0.361a -0.273a -0.549a 0.045
Analyst Diversity Rank (i)
0.213a 0.177a
0.365a 0.007
Ln(Trade Openness) (j)
0.020
-0.043 -0.321a -0.308a -0.117b 0.154a
Capital Market Openness (k)
-0.059 -0.067c -0.301a -0.130a -0.106b -0.222a 0.433a
a
a
0.057
a
a
c
a
-0.030 0.213a
a
b
-0.199 -0.083 -0.404 -0.122 -0.095 -0.444
Variance in GDP growth (m)
0.105b 0.083c
Ln(Number of Stocks) (n)
-0.128a -0.159a -0.254a 0.232a 0.247a 0.231a
Ln(Country Size) (o)
-0.108b -0.125a -0.044 0.338a 0.337a 0.001
a
0.134
a
-0.531a
(p)
(q)
a
0.244a 0.095b 0.071c 0.236a
-0.016 -0.043 -0.098
0.499a -0.051
0.506a -0.346a 0.273a
a
0.853a -0.367a 0.535a
-0.312a 0.254a -0.074c -0.285a -0.251a
0.161a
0.119a -0.232a -0.389a 0.091b 0.078c -0.220a
0.0574
c
-0.440a 0.048
a
0.323
a
0.261a -0.026 -0.058 -0.166a -0.035
-0.091b 0.110b 0.246a
Earnings Co-movement (r)
0.008
0.050
-0.094b 0.010
-0.050
-0.114 0.187
-0.880a -0.175a -0.413a 0.062
a
0.173a 0.161a
-0.007 -0.028
-0.126
a
Firm Herfindahl Index (q)
0.003
0.565a
-0.311a
0.141
0.047
0.253
a
0.632
-0.478a
Industry Herfindahl Index (p)
p<0.01, p<0.05, p<0.10
(o)
a
Ln(GDP per capita) (l)
c
(n)
0.098b -0.557a
-0.232 -0.121 -0.441 -0.163 -0.125a -0.268a 0.629a
b
(m)
0.283a -0.111b -0.140a
Good Government Index (h)
a
(l)
1
Transformed Variance-weighted R (b)
2
(e)
a
b
-0.227 -0.088 0.292
0.242a
a
-0.602a -0.163a
-0.238a -0.115a 0.191a -0.554a -0.162a 0.775a
-0.067c -0.072c -0.102b 0.011
-0.061 0.062
0.034
-0.002
Table 4. The Influence of Culture on the Stock Price Synchronicity (R2)
This table presents the Fama-MacBeth regression results of R2 on tightness and individualism. The dependent
variable is the transformed equal-weighted R2. The equal-weighted R2 are averaged R2s of stocks in a country
estimated from an expanded market model (see Equation (1)). Transformed R2 is the logistic transformation of these
variables using Equation (4). Tightness is from Gelfand et al. (2011). Individualism is from Hofstede (2001).
Tightness measures if the country has strong norms and a low tolerance of deviant behavior. Individualism captures
the extent to which people are integrated into groups. Refer to Table 1 for other variable definitions. We present in
parentheses the t-statistics based on standard errors with the Newey-West correction for serial correlation of
coefficients in the past 6 years.
Base Regressions
(1)
(2)
(3)
Tightness
Tightness
Individualism
(4)
(5)
0.036**
(2.26)
Individualism
Both Culture Both Culture
Variables
Variables
(6)
0.030*
(1.73)
-0.005***
(-4.03)
-0.059***
(-3.37)
Analyst Diversity Rank
0.004*** 0.003
(3.14)
(0.72)
Good Government Index
0.002*
(1.74)
0.002
(0.44)
0.002
(0.44)
-0.318*
(-1.91)
-0.153*
(-1.88)
-0.271
(-1.57)
-0.271
(-1.57)
0.027
(0.76)
0.074
(0.91)
0.071
(1.49)
0.088
(1.17)
0.088
(1.17)
-1.376
(-0.56)
-1.042
(-0.84)
-1.555
(-0.66)
-1.555
(-0.66)
0.011
(1.21)
-0.034*
(-1.83)
-0.034*
(-1.83)
-0.021
(-1.14)
-0.021
(-1.14)
-0.259*** -0.218**
(-3.51)
(-2.16)
-0.087*** 0.014
(-5.74)
(0.74)
GDP Growth Volatility
0.023
(0.01)
-0.278
(-0.18)
-0.064
(-0.05)
Ln(Number of Stocks)
-0.012
(-1.38)
-0.001
(-0.09)
0.009
(1.36)
Ln(Country Size)
-0.045*** -0.052*** -0.049*** -0.034**
(-3.45)
(-3.78)
(-3.22)
(-2.29)
Industry Herfindahl Index
0.117***
(3.38)
-0.003***
(-3.37)
Individualism (Residual)
Ln(GDP per capita)
(7)
-0.048***
(-4.12)
-0.025***
(-2.92)
0.495
(0.89)
0.363
(0.60)
0.628
(1.16)
-0.115
(-0.28)
0.444
(0.66)
0.288
(0.44)
0.288
(0.44)
Firm Herfindahl Index
-0.743
(-0.81)
-0.103
(-0.08)
0.577
(0.42)
1.872
(1.26)
1.145
(0.85)
2.034
(1.44)
2.034
(1.44)
Earnings Co-movement
0.044
(0.61)
0.051
(0.66)
0.095
(0.90)
0.190*
(1.96)
0.092
(1.18)
0.198**
(2.48)
0.198**
(2.48)
Constant
0.554*
(1.73)
-0.122
(-1.35)
Observations
882
R-square
0.090
*** p<0.01, ** p<0.05, * p<0.10
-0.534*** -0.923*
(-5.57)
(-1.90)
-0.962***
(-5.93)
-1.139*** -1.090***
(-3.10)
(-4.62)
882
859
552
859
552
552
0.119
0.171
0.260
0.198
0.242
0.242
Table 5. The Influence of Culture on the Market-wide and Firm-specific Variations
This table presents the Fama-MacBeth regression results of market-wide variation and firm-specific variation on
tightness and individualism. Tightness is from Gelfand et al. (2011). Individualism is from Hofstede (2001).
Tightness measures if the country has strong norms and a low tolerance of deviant behavior. Individualism captures
the extent to which people are integrated into groups. Refer to Table 1 for other variable definitions. We present in
parentheses the t-statistics based on standard errors with the Newey-West correction for serial correlation of
coefficients in the past 6 years.
Tightness
Market-wide Variation
(1)
(2)
-0.168***
(-3.93)
Individualism
Firm-specific Variation
(3)
(4)
-0.223***
(-3.99)
0.004
(0.62)
Analyst Diversity Rank
-0.009
(-0.58)
-0.003
(-0.34)
Good Government Index
-0.001
(-0.00)
-0.555***
(-2.96)
Ln(GDP per capita)
-0.014
(-0.06)
GDP Growth Volatility
0.012**
(2.09)
-0.014
(-0.70)
-0.007
(-0.76)
0.368
(0.93)
-0.454
(-1.71)
0.146*
(1.81)
-0.165
(-0.52)
0.011
(0.09)
49.360***
(2.89)
16.227**
(2.13)
55.915***
(3.04)
21.686**
(2.41)
Ln(Number of Stocks)
0.987*
(1.95)
0.746**
(2.39)
1.123**
(2.20)
0.781**
(2.52)
Ln(Country Size)
0.307***
(5.78)
0.297***
(4.94)
0.347***
(6.54)
0.309***
(4.95)
Industry Herfindahl Index
20.093**
(2.29)
7.164**
(2.78)
21.916**
(2.46)
7.471***
(3.20)
Firm Herfindahl Index
-2.563
(-0.52)
4.984
(1.48)
-5.180
(-0.86)
3.316
(0.77)
Earnings Co-movement
0.291
(0.43)
0.229
(1.15)
-0.171
(-0.26)
-0.010
(-0.04)
-10.718*
(-1.78)
-10.000***
(-4.59)
Constant
Observations
R-square
*** p<0.01, ** p<0.05, * p<0.10
-12.018**
(-2.18)
552
0.247
-11.565***
(-5.80)
859
0.266
552
0.270
859
0.248
Table 6. Openness, Culture, and the Stock Price Synchronicity (R2)
This table presents the Fama-MacBeth regression results of R2 on tightness and individualism. The dependent
variable is the transformed equal-weighted R2. The equal-weighted R2 are averaged R2s of stocks in a country
estimated from an expanded market model (see Equation (1)). Transformed R2 is the logistic transformation of these
variables using Equation (4). Tightness is from Gelfand et al. (2011). Individualism is from Hofstede (2001).
Tightness measures if the country has strong norms and a low tolerance of deviant behavior. Individualism captures
the extent to which people are integrated into groups. Refer to Table 1 for other variable definitions. Trade openness
is from Frankel and Romer (1999) and is a constructed traded openness variable based on the firm’s geographic
location and population. Capital market openness is constructed as in Pukthuanthong and Roll (2009) and captures
the extent to which the domestic stock market is integrated with the global stock market. We present in parentheses
the t-statistics based on standard errors with the Newey-West correction for serial correlation of coefficients in the
past 6 years.
Tightness
Tightness × Openness
Ln (Trade Openness)
(1)
(2)
0.160***
(5.63)
-0.043***
(-3.82)
Individualism
Capital Market Openness
(3)
(4)
0.054**
(2.81)
-0.055**
(-2.41)
-0.013***
(-4.72)
Individualism × Openness
-0.011***
(-8.56)
0.003*
(1.89)
Openness
0.164**
(2.59)
Analyst Diversity Rank
0.004
(0.83)
Good Government Index
-0.209
(-1.14)
-0.182**
(-2.13)
Ln(GDP per capita)
0.120
(1.60)
0.101**
(2.34)
GDP Growth Volatility
0.655
(0.29)
0.011***
(4.00)
-0.070
(-0.49)
0.179
(0.89)
0.001
(0.98)
0.002
(0.38)
-0.330**
(-2.22)
-0.495***
(-4.24)
0.002
(1.42)
-0.151*
(-1.78)
0.087
(1.17)
0.078
(1.52)
0.384
(0.37)
-2.097
(-0.92)
-0.641
(-0.51)
Ln(Number of Stocks)
-0.100***
(-5.84)
0.061***
(4.48)
-0.061***
(-3.77)
0.014
(1.03)
Ln(Country Size)
-0.045
(-1.64)
0.001
(0.09)
-0.033**
(-2.31)
-0.018*
(-1.78)
Industry Herfindahl Index
-0.102
(-0.18)
0.444
(0.50)
-0.288
(-0.74)
0.239
(0.44)
Firm Herfindahl Index
0.915
(0.76)
2.128
(1.16)
1.832
(1.45)
1.831
(1.68)
Earnings Co-movement
0.165
(1.62)
0.138
(1.50)
0.208*
(1.91)
0.081
(1.25)
Constant
Observations
R-square
*** p<0.01, ** p<0.05, * p<0.10
-1.589***
(-5.56)
552
0.291
-1.659**
(-2.62)
-0.974**
(-2.36)
848
0.241
552
0.227
-0.904***
(-4.08)
841
0.208
Table 7. Openness, Culture, and the Market-wide and Firm-specific variations
This table presents the Fama-MacBeth regression results of R2 on tightness and individualism. The dependent
variable is the transformed equal-weighted R2. The equal-weighted R2 are averaged R2s of stocks in a country
estimated from an expanded market model (see Equation (1)). Transformed R 2 is the logistic transformation of these
variables using Equation (4). Tightness is from Gelfand et al. (2011). Individualism is from Hofstede (2001).
Tightness measures if the country has strong norms and a low tolerance of deviant behavior. Individualism captures
the extent to which people are integrated into groups. Refer to Table 1 for other variable definitions. Trade openness
is from Frankel and Romer (1999) and is a constructed traded openness variable based on the firm’s geographic
location and population. Capital market openness is constructed as in Pukthuanthong and Roll (2009) and captures
the extent to which the domestic stock market is integrated with the global stock market. We present in parentheses
the t-statistics based on standard errors with the Newey-West correction for serial correlation of coefficients in the
past 6 years.
Ln(Trade Openness)
Market-wide
Variation
(1)
(2)
Tightness
Tightness×Openness
Capital Market Openness
Firm-specific
Variation
(3)
(4)
Market-wide
Variation
(5)
(6)
-1.366***
(-4.57)
-1.554***
(-6.12)
-0.356**
(-2.70)
0.414***
(4.92)
0.461***
(7.02)
0.560*
(1.93)
Individualism
Ind. × Openness
-0.393***
(-2.98)
0.516*
(2.07)
0.072***
(4.71)
0.093***
(5.82)
0.032
(1.43)
0.046*
(1.93)
-0.024***
(-4.14)
-0.029***
(-4.33)
-0.051*
(-1.96)
-0.064**
(-2.32)
Openness
-2.403*** 0.845
(-4.54)
(1.15)
-2.536*** 1.053
(-6.01)
(1.27)
-6.543*** 0.588
(-3.12)
(0.38)
Analyst Diversity Rank
-0.008
(-0.27)
-0.016
(-0.47)
0.000
(0.04)
-0.028
(-1.36)
-0.455**
(-2.71)
-0.332
(-0.54)
-0.310
(-1.25)
-0.528
Good Government Index (-1.20)
Firm-specific
Variation
(7)
(8)
0.004
(0.36)
-6.039*** 1.297
(-3.22)
(0.83)
-0.017*** -0.031
(-3.39)
(-1.22)
-0.022***
(-3.26)
0.031
(0.06)
-0.551*
(-2.08)
0.403
(0.63)
-0.440
(-1.26)
Ln(GDP per capita)
-0.462
(-1.40)
-0.038
(-0.47)
-0.660*
(-1.76)
-0.226**
(-2.62)
0.025
(0.05)
0.148
(1.16)
-0.100
(-0.18)
-0.006
(-0.03)
GDP Growth Volatility
21.470
(1.63)
7.270
(1.19)
26.192
(1.69)
10.378
(1.52)
44.332**
(2.59)
6.164
(0.96)
51.218**
(2.74)
10.354
(1.36)
Ln(Number of stocks)
1.303**
(2.71)
0.383
(1.11)
1.502*** 0.348
(3.08)
(1.00)
0.884*
(1.77)
0.585*
(1.86)
1.032*
(2.06)
0.614*
(1.98)
Ln(Country Size)
0.189*** 0.154
(8.09)
(1.71)
0.254*** 0.150
(9.84)
(1.51)
0.237*** 0.281*** 0.285*** 0.289***
(3.95)
(3.50)
(5.85)
(3.32)
Industry Herfindahl
Index
20.314*
(1.78)
5.262*** 22.079*
(2.89)
(1.91)
5.141*** 23.315*
(4.07)
(1.89)
Firm Herfindahl Index
-0.559
(-0.13)
3.708
(0.97)
-1.688
(-0.33)
0.968
(0.18)
0.060
(0.13)
0.058
(0.21)
-0.405
(-1.01)
-0.233
(-0.73)
0.266
(0.37)
0.012
(0.00)
-8.225*
(-1.92)
1.772
(0.25)
-6.068
(-1.25)
-7.870
(-0.95)
848
0.291
552
0.338
848
0.281
552
0.288
Earnings Co-movement
Constant
Observations
552
R-square
0.323
*** p<0.01, ** p<0.05, * p<0.10
7.872**
(2.77)
-11.498*** -1.450
(-3.11)
(-0.44)
0.123
(0.55)
25.621*
(2.07)
-14.153*** -3.553
(-3.27)
(-0.90)
-0.177
(-0.25)
-10.312*** -7.307
(-4.49)
(-0.86)
841
0.294
8.243***
(3.17)
552
0.281
-0.064
(-0.25)
-8.749***
(-3.43)
841
0.267
Table 8. Robustness Checks: Alternative Measures of the Stock Price Synchronicity
This table presents the Fama-MacBeth regression results of R2 on tightness and individualism. The dependent
variable is the transformed variance-weighted R2 and transformed fraction of stocks moving in the same direction.
The variance-weighted R2 are averaged R2s of stocks in a country estimated from an expanded market model (see
Equation (1)). Fraction of stocks moving in the same direction is calculated based on Equation (2). Transformed R 2s
and fraction of stocks moving in the same direction are the logistic transformation of these variables using Equation
(4) and (5), respectively. Tightness is from Gelfand et al. (2011). Individualism is from Hofstede (2001). Tightness
measures if the country has strong norms and a low tolerance of deviant behavior. Individualism captures the extent
to which people are integrated into groups. Refer to Table 1 for other variable definitions. We present in parentheses
the t-statistics based on standard errors with the Newey-West correction for serial correlation of coefficients in the
past 6 years.
Variance-weighted R2
(1)
Tightness
0.055***
(3.38)
Individualism
(3)
0.005
(1.10)
Good Government Index
-0.369*
(-2.04)
0.151
(1.69)
(4)
0.030*
(1.99)
-0.008***
(-5.12)
Analyst Diversity Rank
Ln(GDP per capita)
(2)
Fraction of Stocks Moving in the
Same Direction
0.005**
(2.75)
-0.101
(-1.06)
0.135**
(2.23)
-0.008***
(-7.40)
0.007**
(2.57)
0.005**
(2.83)
-0.291**
(-2.28)
-0.188***
(-2.86)
-0.001
(-0.01)
5.860**
(2.81)
0.040
(0.93)
GDP growth Volatility
-6.555**
(-2.23)
-5.459*
(-2.01)
0.871
(0.64)
Ln(Number of stocks)
-0.136***
(-6.60)
-0.035***
(-3.53)
-0.057
(-1.58)
-0.015
(-0.48)
Ln(Country Size)
-0.040**
(-2.09)
-0.012
(-1.16)
-0.035**
(-2.66)
-0.023*
(-1.92)
Industry Herfindahl Index
-1.823***
(-3.60)
-0.307
(-0.66)
-0.784
(-0.87)
-0.253
(-0.23)
Firm Herfindahl Index
2.618
(1.40)
1.668
(1.41)
2.231
(1.42)
2.606
(1.72)
Earnings Comovement
0.461***
(4.88)
0.239***
(3.55)
0.234
(1.32)
0.161*
(1.92)
-1.565***
(-3.65)
-0.384
(-1.07)
Constant
-1.300**
(-2.14)
-0.520***
(-4.68)
Observations
552
838
552
838
R-square
0.186
0.162
0.318
0.386
*** p<0.01, ** p<0.05, * p<0.10
Table 9. Robustness Checks: Joint Sample and a Balanced Panel
This table presents the Fama-MacBeth regression results of R2 on tightness and individualism. The dependent
variable is the transformed equal-weighted R2. The joint sample includes countries that have values for both
tightness and individualism. The balanced panel sample includes countries with data for the entire sample period:
1990 to 2010. The equal-weighted R2 are averaged R2s of stocks in a country estimated from an expanded market
model (see Equation (1)). Transformed R2 is the logistic transformation of these variables using Equation (4).
Tightness is from Gelfand et al. (2011). Individualism is from Hofstede (2001). Tightness measures if the country
has strong norms and a low tolerance of deviant behavior. Individualism captures the extent to which people are
integrated into groups. Refer to Table 1 for other variable definitions. We present in parentheses the t-statistics based
on standard errors with the Newey-West correction for serial correlation of coefficients in the past 6 years.
Joint Sample
(1)
Tightness
Balanced Panel
(2)
0.036**
(2.26)
Individualism
(3)
(4)
0.055**
(2.46)
-0.005***
(-9.25)
-0.006***
(-3.45)
Analyst Diversity Rank
0.003
(0.72)
0.003
(1.04)
Good Government Index
-0.318*
(-1.91)
-0.186
(-1.53)
0.074
(0.91)
0.058
(0.99)
0.080
(1.02)
0.135*
(2.04)
GDP growth Volatility
-1.376
(-0.56)
-1.812
(-0.78)
1.251
(0.87)
2.315
(1.11)
Ln(Number of stocks)
-0.048***
(-4.12)
-0.023
(-1.56)
-0.142***
(-9.72)
-0.134***
(-6.90)
Ln(Country Size)
-0.034**
(-2.29)
-0.020
(-1.13)
-0.042***
(-3.68)
-0.001
(-0.22)
Industry Herfindahl Index
-0.115
(-0.28)
0.031
(0.04)
-3.739***
(-6.30)
-1.169***
(-3.51)
Firm Herfindahl Index
1.872
(1.26)
1.766
(1.42)
5.095**
(2.13)
Earnings Comovement
0.190*
(1.96)
0.161
(1.65)
0.179
(0.82)
0.116
(0.84)
-0.054
(-0.09)
-0.681
(-1.35)
Ln(GDP per capita)
Constant
-0.923*
(-1.90)
-0.660***
(-3.40)
-0.000
(-0.08)
-0.446**
(-2.80)
0.001
(0.24)
-0.229**
(-2.83)
-1.243
(-0.59)
Observations
552
552
378
483
R-square
0.260
0.221
0.439
0.307
*** p<0.01, ** p<0.05, * p<0.10