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 ] + π½5,π ππ,π,π‘−2 + π½6,π [ππ.π.,π‘−2 + πΈπππ‘−2 ] + π½7,π ππ,π,π‘+1 + π½8,π [ππ.π.,π‘+1 + πΈπππ‘+1 ] + π½9,π ππ,π,π‘+2 + π½10,π [ππ.π.,π‘+2 + πΈπππ‘+2 ] + πππ‘ , (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 = maxβ‘[nj,t , ndown ]β‘ 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 + ππ,π‘ (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: π π2 (4) Transformed R2 = ln ( 1−β‘π 2) π Transformed fraction of stocks moving in the same direction = ln ( ππ −0.5 1−β‘ππ ) (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 π(π ππ‘ ) = πΌ + π½1 πΆπ’ππ‘π’ππ + π½2 πΊπππβ‘πΊππ£πππππππ‘β‘πΌππππ₯ + π½3 π΄ππππ¦π π‘β‘π·ππ£πππ ππ‘π¦β‘π πππ + π½4 log(πΊπ·π) + π½5 log(ππ’ππππβ‘ππβ‘ππ‘ππππ ) + π½6 πΊπ·πβ‘πΊπππ€π‘ββ‘πππππ‘ππππ‘π¦ + π½7 πΆππ’ππ‘ππ¦β‘πππ§π + π½8 πΌπππ’π π‘ππ¦β‘π»πππππππβπ + π½9 πΉπππβ‘π»πππππππβπ + π½10 πΈπππππππ β‘πΆππππ£πππππ‘ + πππ‘ (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 π(π ππ‘ ) = πΌ + π½1 πΆπ’ππ‘π’ππ + π½2 πΆπ’ππ‘π’ππ ∗ πππππππ π + π½3 πππππππ π + π½4 πΊπππβ‘πΊππ£πππππππ‘β‘πΌππππ₯ + π½5 π΄ππππ¦π π‘β‘π·ππ£πππ ππ‘π¦ + π½6 log(πΊπ·π) + π½7 log(ππ’ππππβ‘ππβ‘ππ‘ππππ ) + π½8 πΊπ·πβ‘πΊπππ€π‘ββ‘πππππ‘ππππ‘π¦ + π½9 πΆππ’ππ‘ππ¦β‘πππ§π + π½10 πΌπππ’π π‘ππ¦β‘π»πππππππβπ + π½11 πΉπππβ‘π»πππππππβπ + π½12 πΈπππππππ β‘πΆππππ£πππππ‘ + πππ‘ , (7) where openness is the proxy for either trade openness or capital market openness. We expect the coefficient, π½2 , 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 , andβ‘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. 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Trompenaars, F., 1993, Riding the Wave of Culture, The Economists Books. 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