ABSTRACT
 Top of page
 ABSTRACT
 I. Individualism, Overconfidence, and SelfAttribution Bias
 II. Data Description and Summary Statistics
 III. Individualism, Trading Volume, and Volatility
 IV. Returns on Momentum Portfolios
 V. Individualism and the Profitability of Momentum Strategies: Portfolio Analysis
 VI. Other Determinants of CrossCountry Momentum: Regression Analysis
 VII. Robustness Checks
 VIII. Postholding Period Returns
 IX. Conclusion
 REFERENCES
This paper examines how cultural differences influence the returns of momentum strategies. Crosscountry cultural differences are measured with an individualism index developed by Hofstede (2001), which is related to overconfidence and selfattribution bias. We find that individualism is positively associated with trading volume and volatility, as well as to the magnitude of momentum profits. Momentum profits are also positively related to analyst forecast dispersion, transaction costs, and the familiarity of the market to foreigners, and negatively related to firm size and volatility. However, the addition of these and other variables does not dampen the relation between individualism and momentum profits.
A substantial literature examines what is generally referred to as the momentum effect—the observation that stocks that perform the best in the recent past continue to perform well in the future. For example, Jegadeesh and Titman (1993, 2001) find that stocks in the United States that realize the best (worst) returns over the past 3 to 12 months continue to perform well (poorly) over the subsequent 3 to 12 months. The profitability of momentum strategies is found in equity markets throughout the world (see, for example, Rouwenhorst (1998) for a study of momentum in Europe and Griffin, Ji, and Martin (2003) for a study of momentum around the world). However, there are important exceptions, most notably in Asia (e.g., Chui, Titman, and Wei (2003)).
Given the magnitude of momentum profits, about 12% per year in the United States and Europe, they are unlikely to be explained by riskbased theories. Indeed, most of the focus in the academic literature has been on behavioral explanations for this phenomenon.^{1} For example, Daniel, Hirshleifer, and Subrahmanyam (DHS, 1998) show how the momentum effect can be generated by investors' overconfidence and selfattribution bias and Barberis, Shleifer, and Vishny (BSV, 1998) and Hong and Stein (1999) show how momentum can be generated by investors' initial underreaction to information.
This paper uses international data to examine the extent to which the momentum effect is generated by behavioral biases. In particular, we examine whether momentum profits are greater in those countries where investors are likely to exhibit the psychological biases discussed in the behavioral finance literature. Our focus is on what psychologists refer to as “individualism,” which, according to Hofstede (2001), reflects the degree to which people focus on their internal attributes, such as their own abilities, to differentiate themselves from others. Specifically, we use an individualism index reported by Hofstede (2001) that is based on survey evidence from 50 countries.^{2} Although we are not aware of this index being used in the finance literature, Hofstede's individualism index and other cultural values have become widely accepted since Hofstede published his results in 1980 (Hofstede (1980)) and they have been used by many researchers in other business disciplines.^{3}
Although it does not directly measure the behavioral biases suggested in the momentum literature, we argue that individualism is likely to be correlated with overconfidence and attribution bias. To provide independent support for the idea that investors in more individualistic cultures tend to be more overconfident, we show that the individualism measure is correlated with trading volume and volatility (see Odean (1998), Gervais and Odean (2001), and Scheinkman and Xiong (2003) for models in which overconfident investors trade more and generate excess volatility).
As we show, there are significant crosscountry differences in momentum profits that persist over time. In particular, countries that exhibit the most momentum in the first half of our sample period also tend to exhibit the most momentum in the second half of our sample period. Our analysis indicates that to a large extent, these differences can be explained by crosscountry differences in the Hofstede individualism measure. Specifically, the average monthly returns on a zerocost (long minus short) momentum portfolio are more than 0.6% higher in those countries with individualism indexes in the top 30% than in those countries with individualism indexes in the bottom 30%. This difference in returns is statistically very significant.
In addition to individualism, we consider a number of other variables that can plausibly be related to momentum and that can vary across countries. These include countryspecific variables that proxy for information uncertainty as well as institutional variables that may be related to the development and integrity of the countries' financial markets. Our use of countryspecific proxies for information uncertainty is motivated by Zhang (2006), who suggests that stocks in the United States, for which information uncertainty is higher (e.g., those with more dispersed analyst earnings forecasts), exhibit stronger momentum. Our measures of market development and integrity are motivated by the idea that these measures may be related to trading costs and the flow of information, which may in turn influence the profitability of momentum strategies.
We find that momentum profits are significantly related to some of these countryspecific variables, but their inclusion does not materially affect the significance of the individualism measure. In addition, since we know that both individualism and momentum are weak in East Asian countries (indeed, this was the original motivation of the study), we examine the extent to which our results hold outside of East Asia. We find that the positive relationship between momentum profits and individualism holds even when East Asian countries are excluded from our sample.
To further examine the behavioral momentum theories we examine the longterm returns of momentum portfolios. Both DHS (1998) and Hong and Stein (1999) suggest that momentum is caused by positive feedback trading that leads to a delayed overreaction that is eventually reversed. We find that the reversals observed in the U.S. stock market also occur in most countries around the world. Although the evidence is relatively weak, we find that the magnitude of the reversals tends to be higher in countries with higher individualism, especially in the third year after portfolio formation.
The remainder of this paper is organized as follows. In Section I, we discuss the link between individualism and overconfidence as well as the selfattribution bias. In Section II, we describe the data used in the paper. In Section III, we document a positive link between individualism and trading volume as well as stock volatility. In Section IV, we report the results on momentum profits for each country as well as for portfolios of countries. Section V reports the results on the relationship between individualism and momentum profitability based on portfolio analysis. In Section VI, we test alternative explanations for the momentum effect. Section VII presents the results from robustness checks and Section VIII reports the results from longterm return reversals. Section IX concludes the paper.
III. Individualism, Trading Volume, and Volatility
 Top of page
 ABSTRACT
 I. Individualism, Overconfidence, and SelfAttribution Bias
 II. Data Description and Summary Statistics
 III. Individualism, Trading Volume, and Volatility
 IV. Returns on Momentum Portfolios
 V. Individualism and the Profitability of Momentum Strategies: Portfolio Analysis
 VI. Other Determinants of CrossCountry Momentum: Regression Analysis
 VII. Robustness Checks
 VIII. Postholding Period Returns
 IX. Conclusion
 REFERENCES
To check the validity of Hofstede's individualism index (Indv) as a measure of overconfidence and selfattribution bias, we examine the extent to which crosscountry differences in trading volume and volatility can be explained by this measure. Our motivation for these validity checks is the theoretical literature that suggests that these behavioral biases generate excess trading volume and volatility in stock markets (e.g., DHS (1998), Odean (1998), Gervais and Odean (2001), and Scheinkman and Xiong (2003)). The relationship between trading volume and overconfidence is quite intuitive. Overconfident investors trade more, because they overestimate the precision of their information. In addition, Odean (1998) argues that trading by overconfident investors leads to excess volatility. Previous theoretical and empirical studies also indicate that overconfidence together with the selfattribution bias generate excess trading volume and volatility (DHS (1998), Statman et al. (2006), and Glaser and Weber (2009)).
As in Griffin, Nardari, and Stulz (2007), we measure a country's trading volume as its turnover (TN), the ratio of the market dollar trading volume to the market capitalization of the Datastream global index for the country.^{17} Similar to Bae, Chan, and Ng (2004) and Bekaert, Harvey, and Lundblad (2007), we use the squared monthly return to measure the monthly volatility of a stock. The average stock volatility in country j in month t(V_{jt}) is the average of the squared monthly returns on the stocks in month t in country j:
 (1)
where R_{ijt} is the return on stock i in country j in month t and N_{jt} is the number of stocks in country j in month t. To calculate the average volatility of country j in month t, we require country j to have at least 30 stocks in month t.^{18}
A. Individualism and Trading Volume
To investigate the relation between individualism and trading volume, we estimate the following regression:
 (2)
where the subscripts j and t represent country j and month t, respectively. Since trading volume is highly persistent, we cluster the residuals by both country and month to compute the tstatistics on the estimated coefficients (see Petersen (2009) for a discussion of the robust tstatistics in this setting). The dependent variable is the natural logarithm of the market trading volume (LnTN). The independent variables include the individualism index (Indv) and other determinants of trading volume.
Although there is no empirical study on the determinants of crosscountry trading volume, existing theoretical and crosssectional research on stocks in the United States suggests that trading volume is likely to be related to the cost of trading, information asymmetries, and uncertainty about the aggregate economy. Based on studies that show that political risk is a determinant of liquidity costs across countries (Bekaert et al. (2007), Eleswarapu and Venkataraman (2006), and Lesmond (2005)), we include the political risk index (Political) from the International Country Risk Guide (ICRG) as a measure of a country's political stability. To capture the effect of asymmetric information, we control for the level of financial development with the ratio of total private credit to GDP (Credit) (as in Stulz and Williamson (2003)) and for the prevalence of insider trading with the insider trading index (Insider) obtained from La Porta, LopezdeSilanes, and Shleifer (2006). To measure the volatility of the overall economy we follow Du and Wei (2004) and use the volatility of the exchange rates (Fxvol), measured as the coefficient of variation of the monthly exchange rate calculated from the previous 5 years, as a proxy for monetary policy uncertainty. Since trading volume is affected by information flows that generate stock return volatility, we also include the natural logarithm of average stock volatility (LnV) as a determinant of crosscountry trading volume.
Panel A in Table II reports the regression results. The estimated coefficients of Political and Credit are positive and significant, which is consistent with the idea that countries with lower liquidity costs tend to have larger trading volume.^{19} The positive and significant coefficients on Fxvol and LnV indicate that trading volume is positively related to the uncertainty of monetary policies and information flow.^{20} However, the estimated coefficient on Insider is significantly negative, indicating that higher insider trading leads to larger trading volume, which is inconsistent with our expectations. After controlling for these variables, we find that the estimated coefficient on Indv is significantly positive (tstatistic = 2.13).
Table II. Individualism, Stock Market Trading Volume, and Average Stock Volatility Panel A reports the OLS estimates of the coefficients related to market trading volume. The market trading volume (TN) of country j in month t is measured as the market dollar trading volume of the Datastream Global Index of this country divided by this index's market capitalization in month t. The natural logarithm of monthly market trading volume (LnTN) is regressed on Hofstede's individualism index (Indv), the insider index (Insider), the political risk index (Political), the volatility of the exchange rate (Fxvol), the total private credit expressed as a ratio of GDP (Credit), and the natural logarithm of market volatility (LnV). Panel B reports the OLS estimates of the coefficients related to average stock volatility. The monthly average stock volatility is computed as the average of the monthly squared stock returns. The natural logarithm of monthly average stock volatility (LnV) is regressed on Hofstede's individualism index (Indv), the insider index (Insider), the total private credit expressed as a ratio of GDP (Credit), the volatility of real GDP per capita growth rates (Gwvol88), the volatility of the exchange rate (Fxvol), the ratio between the monthly market value of the S&PIFC market index and the monthly market value of the S&PIFC investable index (Open), the debt ratio (Debt), and the market value expressed as a ratio of GDP (MCap). While Indv, Insider, and Gwvol88 are constant over time, all other variables are updated monthly or annually. The definitions of these variables are described in the Internet Appendix. The sample period is from January 1988 to June 2003. We use the Petersen (2009) procedure to compute the standard errors clustered by country and month. Robust tstatistics are in parentheses. Panel A: Market Trading Volume  Panel B: Average Stock Volatility 

Intercept  −2.588  Intercept  5.378 
(−4.80)   (11.44) 
Indv  0.010  Indv  0.009 
(2.13)   (2.62) 
Insider  −0.351  Insider  −0.325 
(−2.30)   (−3.88) 
Fxvol  0.017  Fxvol  0.015 
(2.17)   (5.18) 
Credit  0.932  Credit  0.149 
(4.28)   (0.89) 
Political  0.027  Gwvol88  0.125 
(3.09)   (2.83) 
LnV  0.300  Open  −0.181 
(4.46)   (−0.56) 
 Debt  1.016 
  (1.68) 
 MCap  0.002 
  (2.84) 
Min. no. of countries  13   12 
Max. no. of countries  38   36 
Median no. of countries  34   31 
To check the robustness of our result to estimation methods, we also use the Fama–MacBeth (1973) procedure to estimate equation (2). Following Chordia, Huh, and Subrahmanyam (2009), who use the Fama–MacBeth procedure to investigate crosssectional variation in turnover in the United States' market, we use Newey–West (1994) heteroskedasticity and autocorrelation consistent estimates of standard errors to compute the tstatistics on the Fama–MacBeth coefficients.^{21} The results from the Fama–MacBeth regressions are similar to those reported in Panel A of Table II except that the significance levels are much higher. We also ran a simple regression using average values of trading volume and independent variables from January 1995 to June 2003 to estimate equation (2) and the result from this OLS regression is again similar to our result from the panel regression. The results on the volume regressions from alternative estimation methods are reported in the Internet Appendix.
In summary, we find evidence of a strong and robust positive relationship between individualism and crosscountry trading volume even after controlling for variables that potentially explain the normal level of trading volume.
B. Individualism and Volatility
In a crosscountry study, Du and Wei (2004) document that market volatility is negatively related to the degree of financial market development, and positively related to the volatility of real GDP growth rates, the volatility of exchange rates (Fxvol), the country's debt ratio (Debt), and the prevalence of insider trading (Insider). In a study of emerging market volatility, Bekaert and Harvey (1997) find that crosscountry volatility is also negatively related to the ratio of market capitalization to GDP (MCap) and the openness of the capital market (Open). Bae et al. (2004) report that volatility in emerging markets is positively related to the investability of the stocks in these markets. We use the ratio of total private credit to GDP (Credit) as a measure of financial market development. The volatility of real GDP growth is computed as the standard deviation of the real GDP growth rates from 1988 to 2003 (Gwvol88) or from 1995 to 2003 (Gwvol95). As in Bekaert et al. (2007), we use the investability index in each country as a measure of stock market openness (Open). The country's debt ratio (Debt) is the average leverage ratio of firms.
Panel B of Table II provides estimates of the following volatility regression with standard errors clustered by country and month:
 (3)
Consistent with Du and Wei (2004), we find that Insider has a negative effect on stock volatility and the volatility of real GDP growth rates (Gwvol88) has a positive effect on stock volatility. Similar to Bekaert and Harvey (1997), we find that the volatility of exchange rates (Fxvol) has a positive effect on stock volatility. In contrast to their finding, we find that the ratio of market capitalization to GDP (MCap) has a strong positive effect on stock volatility. One should note, however, that their finding pertains to emerging markets, while our sample includes both developed and emerging markets. More importantly, we find that the estimated coefficient on Indv is positive and significant (tstatistic = 2.62). The estimated coefficients on other variables are not significant.^{22}
To investigate whether the positive relationship between individualism and volatility is robust to estimation methods, we use the Fama–MacBeth (1973) procedure, with Newey–West (1994) heteroskedasticity and autocorrelation consistent estimates of standard errors, to estimate equation (3). We also run a simple regression using average values of volatility and independent variables from January 1995 to June 2003 to estimate equation (3). The results, in the Internet Appendix, indicate that the estimated coefficient on Indv is positive and significant for all specifications and, with a few exceptions, the coefficient estimates of the other variables are similar to those estimated by the OLS regression.
IV. Returns on Momentum Portfolios
 Top of page
 ABSTRACT
 I. Individualism, Overconfidence, and SelfAttribution Bias
 II. Data Description and Summary Statistics
 III. Individualism, Trading Volume, and Volatility
 IV. Returns on Momentum Portfolios
 V. Individualism and the Profitability of Momentum Strategies: Portfolio Analysis
 VI. Other Determinants of CrossCountry Momentum: Regression Analysis
 VII. Robustness Checks
 VIII. Postholding Period Returns
 IX. Conclusion
 REFERENCES
This section reports, for each country, the profitability of momentum strategies that form portfolios based on stocks' past 6month returns and that hold the stocks for 6 months. For each market, stocks with performance in the bottom onethird during the formation period are assigned to the loser (L) portfolio, while those in the top onethird are assigned to the winner (W) portfolio. These portfolios are equally weighted and are not rebalanced over the 6month holding period.^{23} We use the top and the bottom onethird rather than the 10% cutoffs used by Jegadeesh and Titman (1993) because of the smaller sample sizes in most countries. In addition, to minimize the effect of the bidask bounce and the leadlag effect, we skip 1 month between the ranking period and the holding period.^{24} The returns are all measured in U.S. dollars. However, our findings are virtually identical if we measure returns in local currencies.
As in Jegadeesh and Titman (1993) we construct overlapping momentum portfolios; for example, the winner portfolio formed in January is the equally weighted combination of those stocks with cumulative returns in the top onethird over the previous JunetoNovember period (the W portfolio in November), over the previous MaytoOctober period (the W portfolio in October), and so on up to the previous JanuarytoJune period (the W portfolio in June). If a stock has a missing return during the holding period, we replace it with the corresponding valueweighted market return. If a stock is delisted, we rebalance the portfolio at the end of the delisting month.^{25}
Panel A of Table III presents the average U.S. dollar monthly returns (%) of the winner portfolio, the loser portfolio, and the winnerminusloser portfolio for each of the 41 countries. The results in Table III indicate that all but four countries (Japan, Korea, Taiwan, and Turkey) exhibit positive momentum profits. The profits in 25 of the countries are statistically significant. The highest momentum profits are in Poland (1.764% per month), Bangladesh (1.677% per month), New Zealand (1.582% per month), and Canada (1.345% per month).^{26}
Table III. Momentum Profits by Country At the end of each month, all stocks in each country are ranked in ascending order based on the past 6month cumulative returns. Stocks in the bottom onethird are assigned to the “L” portfolio and those in the top onethird to the “W” portfolio. These equally weighted portfolios are held for 6 months. To increase the power of the tests, overlapping portfolios are constructed. The winner (loser) portfolio is an overlapping portfolio that consists of “W” (“L”) portfolios in the previous 6 ranking months. Returns on these portfolios are measured 1 month after ranking. Returns on these portfolios in month t are computed as (average cumulative returns of the stocks in these portfolios in month t divided by average cumulative returns of these stocks in month (t − 1) − 1). Returns on the winner and loser portfolios are the simple average of the returns on the six “W” and the six “L” portfolios, respectively. If a stock has a missing return during the holding period, it is replaced by the corresponding valueweighted market return. If a stock is delisted, we rebalance the portfolio at the end of the delisting month. The momentum portfolio (W – L) is a zerocost, winnerminusloser portfolio. Panel A reports the average monthly returns (%) on these portfolios in U.S. dollars for each country. The countryaverage portfolio is a portfolio that puts equal weight on each countryspecific momentum portfolio in this portfolio. The formation of the composite portfolio is similar to that of the momentum portfolio in each country. Specifically, at the end of each month, all stocks in each country are ranked in ascending order based on the past 6month cumulative returns. Stocks in the top onethird of past returns in each country are assigned to the “W” portfolio and the bottom onethird stocks are assigned to the “L” portfolio. The minimum number of countries in each portfolio in our sample at any point in time must be at least two. These equally weighted portfolios are held for 6 months. Similar to the countryspecific momentum portfolio, the composite portfolio is an overlapping portfolio. The average monthly returns (%) on these countryaverage and composite portfolios in U.S. dollars are reported in Panel B. Corresponding tstatistics are in parentheses. Panel A: By Country 



Country  Winner (W)  Loser (L)  W Minus L 

Argentina  0.559 (0.66)  0.483 (0.42)  0.076 (0.12) 
Australia  1.639 (3.60)  0.564 (1.18)  1.075 (4.76) 
Austria  1.126 (2.84)  0.501 (1.27)  0.625 (2.70) 
Bangladesh  3.171 (2.81)  1.494 (1.57)  1.677 (2.75) 
Belgium  1.723 (5.90)  0.830 (2.82)  0.893 (5.50) 
Brazil  1.548 (1.43)  1.088 (0.84)  0.459 (0.96) 
Canada  1.823 (5.10)  0.478 (1.18)  1.345 (6.29) 
Chile  2.129 (3.76)  1.136 (2.00)  0.993 (3.60) 
China  1.233 (0.98)  0.976 (0.80)  0.257 (0.92) 
Denmark  1.235 (3.87)  0.273 (0.79)  0.962 (4.29) 
Finland  1.457 (2.58)  0.480 (0.68)  0.977 (2.62) 
France  1.819 (4.91)  0.877 (2.20)  0.942 (4.68) 
Germany  1.218 (3.93)  0.225 (0.59)  0.993 (4.41) 
Greece  2.352 (2.50)  1.767 (1.88)  0.585 (1.49) 
Hong Kong  1.583 (2.65)  0.811 (1.24)  0.772 (3.18) 
India  1.957 (2.18)  0.819 (0.84)  1.138 (2.91) 
Indonesia  0.917 (0.76)  0.781 (0.54)  0.136 (0.30) 
Ireland  1.342 (3.13)  0.458 (0.98)  0.884 (3.06) 
Israel  0.851 (1.03)  0.531 (0.63)  0.320 (1.19) 
Italy  1.309 (3.06)  0.405 (0.89)  0.904 (4.47) 
Japan  0.883 (2.04)  0.922 (1.91)  −0.039 (−0.18) 
Korea  1.257 (1.65)  1.594 (1.83)  −0.337 (−0.81) 
Malaysia  1.427 (1.75)  1.329 (1.37)  0.098 (0.26) 
Mexico  1.181 (1.67)  0.488 (0.62)  0.693 (2.00) 
Netherlands  1.759 (5.56)  0.928 (2.63)  0.831 (4.40) 
New Zealand  2.148 (4.52)  0.566 (1.05)  1.582 (5.01) 
Norway  2.121 (4.90)  1.075 (2.27)  1.046 (3.77) 
Pakistan  1.189 (1.38)  0.729 (0.72)  0.461 (1.05) 
Philippines  0.823 (0.96)  0.450 (0.43)  0.372 (0.68) 
Poland  1.141 (1.16)  −0.623 (−0.60)  1.764 (3.33) 
Portugal  0.806 (1.94)  0.498 (1.00)  0.308 (0.93) 
Singapore  1.064 (1.81)  0.921 (1.25)  0.143 (0.47) 
South Africa  1.540 (3.15)  0.604 (1.15)  0.936 (3.29) 
Spain  1.035 (2.44)  0.410 (0.80)  0.625 (2.24) 
Sweden  1.499 (3.60)  0.787 (1.48)  0.711 (2.27) 
Switzerland  1.285 (4.06)  0.465 (1.45)  0.819 (4.39) 
Taiwan  0.347 (0.39)  0.549 (0.57)  −0.202 (−0.48) 
Thailand  1.739 (2.19)  1.260 (1.36)  0.479 (1.10) 
Turkey  3.044 (2.18)  3.458 (2.43)  −0.414 (−0.96) 
United Kingdom  1.708 (4.92)  0.576 (1.56)  1.132 (7.08) 
United States  1.523 (4.36)  0.735 (1.78)  0.788 (3.44) 
Average  1.476 (16.38)  0.798 (8.58)  0.678 (8.54) 
Panel B: All Countries 



Portfolio Formed Method  Period  Winner (W)  Loser (L)  W Minus L 

Countryaverage  1984:02–2003:06  1.680 (5.84)  0.955 (3.06)  0.725 (7.35) 
Composite  1984:02–2003:06  1.371 (4.72)  0.851 (2.46)  0.519 (3.49) 
To test whether the crosscountry differences in momentum profits are persistent, we divide the whole sample into two subsamples: the first half (February 1984 to June 1993) and the second half (July 1993 to June 2003). For the 36 countries that are in both subperiods, the Spearman rank correlation between their momentum profit ranks in these two subsamples is 0.33 (pvalue = 0.05). For the 22 countries that have at least 60 monthly observations on momentum profits in each subsample the Spearman rank correlation increases to 0.50 (pvalue = 0.02).
Panel B of Table III reports momentum profits from portfolio strategies that exploit the momentum strategy around the world. We refer to the first as the countryaverage momentum portfolio and the second as the composite momentum portfolio.^{27} The countryaverage portfolio equally weights each countryspecific momentum portfolio. The composite momentum portfolio is weighted more toward the countries with more stocks. More specifically, at the end of each month, all stocks in the “W” portfolio in each country are assigned to the “global W” portfolio and all stocks in the “L” portfolio in each country are assigned to the “global L” portfolio. The minimum number of countries in each portfolio in our sample at any point in time must be at least two and the sample period starts in February 1984 and ends in June 2003.^{28}
The result in Panel B of Table III indicates that the average monthly return on the countryaverage portfolio over the period from February 1984 to June 2003 is 0.72% per month (tstatistic = 7.35). The average monthly momentum profit on the composite portfolio is 0.52% per month with a tstatistic of 3.49. The magnitude of these returns is similar to what we observe for momentum portfolios in the United States. However, the tstatistics are much larger because the international momentum portfolio is much more diversified.^{29}
V. Individualism and the Profitability of Momentum Strategies: Portfolio Analysis
 Top of page
 ABSTRACT
 I. Individualism, Overconfidence, and SelfAttribution Bias
 II. Data Description and Summary Statistics
 III. Individualism, Trading Volume, and Volatility
 IV. Returns on Momentum Portfolios
 V. Individualism and the Profitability of Momentum Strategies: Portfolio Analysis
 VI. Other Determinants of CrossCountry Momentum: Regression Analysis
 VII. Robustness Checks
 VIII. Postholding Period Returns
 IX. Conclusion
 REFERENCES
In this section, we investigate the relation between individualism and the profitability of momentum strategies across countries. We classify countries into three groups, from low (bottom 30%) to high (top 30%), based on their scores on the individualism index (Indv). Countryaverage and composite portfolios are formed in each Indvsorted group of countries.
In Table IV we report the average monthly returns on Indvsorted momentum portfolios. These results reveal that momentum profits monotonically increase with the score of the individualism index. The average return on the highIndv countryaverage portfolio is 1.04% per month with a tstatistic of 7.93, and the spread in the average returns between the highIndv and the lowIndv countryaverage portfolios is 0.65% per month, which is highly significant with a tstatistic of 4.30. Similarly, the spread in average returns between the highIndv and the lowIndv composite portfolios is 0.58% per month with a tstatistic of 2.61.
Table IV. Momentum Profits and Individualism This table reports average monthly momentum profits (%) in U.S. dollars for countryaverage portfolios (Panel A) and composite portfolios (Panel B) classified by Hofstede's individualism index. The countryaverage portfolio is a portfolio that puts equal weight on each countryspecific momentum portfolio in this portfolio. The formation of the composite portfolio is similar to that of the momentum portfolio in each country. See Table III for the detailed description of the constructions of the countryaverage and composite portfolios. At the end of each month, all countries in our sample are allocated into three groups, from low (bottom 30%) to high (top 30%), based on their scores on the individualism index. Countryaverage (or composite) portfolios are formed in each individualismsorted group. The test period is from February 1984 to June 2003. The corresponding tstatistics are in parentheses. Portfolio Formed Method  Index on Individualism  Winner (W)  Loser (L)  W Minus L 

Panel A: CountryAverage Portfolios 

Countryaverage  Low  1.628  1.241  0.387 
 (4.30)  (2.96)  (2.80) 
2  1.693  1.004  0.689 
 (5.22)  (2.90)  (5.91) 
High  1.748  0.707  1.041 
 (6.14)  (2.27)  (7.93) 
High minus low  0.120  −0.534  0.654 
 (0.39)  (−1.53)  (4.30) 

Panel B: Composite Portfolios 

Composite  Low  1.465  1.343  0.122 
 (3.60)  (2.89)  (0.74) 
2  1.266  1.028  0.238 
 (3.61)  (2.69)  (1.58) 
High  1.538  0.837  0.701 
 (4.90)  (2.12)  (3.29) 
High minus low  0.073  −0.507  0.579 
 (0.21)  (−1.28)  (2.61) 
We also compute the annual spread in average returns between the highIndv and the lowIndv countryaverage portfolios by calendar year over the period from 1984 to 2002 and find positive annual spreads in 14 out of 19 years.^{30} The average annual spread is 8.57% and it is significantly positive (tstatistic = 3.37). The median, minimum, and maximum annual spreads are 7.02%, –9.75%, and 26.56%, respectively.
VI. Other Determinants of CrossCountry Momentum: Regression Analysis
 Top of page
 ABSTRACT
 I. Individualism, Overconfidence, and SelfAttribution Bias
 II. Data Description and Summary Statistics
 III. Individualism, Trading Volume, and Volatility
 IV. Returns on Momentum Portfolios
 V. Individualism and the Profitability of Momentum Strategies: Portfolio Analysis
 VI. Other Determinants of CrossCountry Momentum: Regression Analysis
 VII. Robustness Checks
 VIII. Postholding Period Returns
 IX. Conclusion
 REFERENCES
In this section, we examine other possible crosscountry determinants of momentum. To do so, we regress momentum profits on the individualism index and other potential determinants:^{31}
 (4)
where Mom_{jt} is the return on the momentum portfolio in country j in month t and Indv_{j} is the individualism index of country j. While F_{j} is a vector of explanatory variables that are constant over time, A_{jy} and M_{jt} are vectors of explanatory variables that are updated annually and monthly, respectively. The Internet Appendix provides more information about these explanatory variables. The ɛ_{jt} is an error term. We use the Fama–MacBeth (1973) procedure to estimate equation (4). The tstatistics of the averages of the timeseries estimates from these monthbymonth, crosssectional regressions are adjusted for heteroskedasticity and autocorrelation using the Newey and West (1994) method.
A. Firm Characteristics Suggested by Behavioral Research
A number of studies examine the extent to which stocks in the United States with different firm characteristics suggested by behavioral research exhibit more or less momentum. In this section we describe some of these crosssectional determinants and examine the extent to which crosscountry differences in the average values of these characteristics explain differences in momentum profits across countries.
The variables that we consider have previously been used to proxy for the speed of information flow and information uncertainty. These variables include turnover (examined in Lee and Swaminathan (2000) and Verardo (2009)), firm size (examined in Jegadeesh and Titman (1993), Daniel and Titman (1999), Hong, Lim, and Stein (2000) and Zhang (2006)), analyst coverage (examined in Hong, et al. (2000), Zhang (2006), and Verardo (2009)), cash flow volatility (examined in Zhang (2006)), dispersion in analyst forecasts (examined in Zhang (2006) and Verardo (2009)), and return volatility (examined in Zhang (2006) and Verardo (2009)). Following this earlier work we include the following variables: market trading volume (TN), average dispersion in analyst forecasts in a country (Disp), the average volatility of the individual stocks in a market (V), the volatility of the growth of cash flows (Cfvol) computed from each country's cash flow component of the Datastream global index, the median firm size in a country (SZ), and the average number of analysts following each stock in a country (Ana).^{32}
The results from the Fama–MacBeth (1973) regressions reported in Panel A of Table V reveal that the coefficient on Indv is positive and quite significant after controlling for these other potential determinants of momentum profits. Among the other explanatory variables, only the estimated coefficients on LnSZ, LnDisp, and LnV are significant. Consistent with Zhang (2006) and Hong et al. (2000), crosscountry differences in momentum are positively related to dispersion in analyst forecasts (LnDisp), but are negatively related to firm size (LnSZ). In contrast to Zhang (2006) and Verardo (2009), the estimated coefficient on stock market volatility (LnV) is negative. Based on joint Ftests, we conclude that the coefficients on this group of variables are reliably different from zero both with and without the individualism variable.^{33}
Table V. Determinants of Momentum Profits across Countries: Results from the Fama–MacBeth Regressions Monthly returns on countryspecific momentum portfolios are regressed on Hofstede's individualism index (Indv) and different sets of explanatory variables. Panel A reports the results related to a set of variables that are suggested by behavioral momentum models. These variables include the natural logarithm of market trading volume (LnTN), the natural logarithm of analyst coverage (LnAna), the natural logarithm of the dispersion of analyst forecasts (LnDisp), the logarithm of stock market volatility (LnV), the cash flows growth rate volatility (Cfvol), and the logarithm of median firm size (LnSZ). Panel B shows the results related to a set of proxies for the financial market development. These proxies are the total private credit expressed as a ratio of GDP (Credit), the average common language dummy variable (Lang), the ratio between the monthly market value of the S&PIFC market index and the monthly market value of the S&PIFC investable index (Open), and an index on control of capital flows (Control). Panel C reports the results related to a set of variables related to institutional quality. This set of variables includes the insider index (Insider, a higher score indicates that insider trading is less prevalent), the ICRG corruption index (Crp, a higher value indicates a lower corruption level), the ICRG political risk index (Political), the natural logarithm of the transaction cost index (LnTran), and the investor protection index (Protection). Panel D reports the results from the comprehensive model. The descriptions of all the variables are listed in the Internet Appendix. The row “Starting date” shows the starting month for the test in each panel and all the tests end in June 2003. This table reports the timeseries averages of crosssectional OLS estimates of the coefficients. The tstatistics are in parentheses. The Newey–West (1994) heteroskedasticity and autocorrelation consistent estimates of standard errors are used to compute these tstatistics. F_{1} (an Fstatistic) is used to test the hypothesis that all the estimated slope coefficients except the coefficient on Indv are jointly equal to zero. F_{2} (an Fstatistic) is used to test the hypothesis that all the estimated slope coefficients are jointly equal to zero. The pvalues are in parentheses.  Panel A: Behavioral Models  Panel B: Market Development  Panel C: Institutional Quality  Panel D: Comprehensive 

Intercept  5.974 (4.95)  0.385 (1.41)  −1.891 (−1.96)  3.501 (2.90) 
Indv  0.015 (4.29)  0.014 (5.24)  0.019 (6.24)  0.015 (4.44) 
LnTN  −0.158 (−1.01)    
LnDisp  0.181 (1.64)    0.205 (1.94) 
LnV  −0.839 (−4.60)    −0.651 (−3.97) 
Cfvol  −0.005 (−0.52)    
LnSZ  −0.305 (−2.69)    −0.260 (−3.97) 
LnAna  0.163 (1.22)    
Credit   −0.162 (−0.93)   
Lang   1.330 (1.92)   1.944 (2.65) 
Open   −0.412 (−1.05)   
Control   −0.008 (−0.30)   
Insider    −0.099 (−0.54)  
Crp    0.066 (0.65)  
Political    0.003 (0.18)  
LnTran    0.405 (3.16)  0.295 (2.33) 
Protection    −0.070 (−0.28)  
F_{1}  6.10 (0.00)  2.03 (0.09)  2.09 (0.07)  8.40 (0.00) 
F_{2}  8.55 (0.00)  5.97 (0.00)  8.24 (0.00)  12.64 (0.00) 
Min. no. of countries  28  15  17  17 
Max. no. of countries  39  37  33  36 
Med. no. of countries  36  34  32  35 
Starting date  January 1992  January 1987  January 1987  January 1987 
B. Financial Market Development and Institutional Quality
The informational efficiency of a country's financial markets may be related to the development and integrity of the financial markets. The idea is that better developed stock markets with greater integrity facilitate the flow of information and reduce trading costs.
As suggested by Stulz and Williamson (2003), we use the ratio of total private credit to GDP (Credit) as a measure of financial market development. To measure the extent to which foreign institutions can invest in the market, we use an index on capital flow restrictions (Control, where a higher value indicates more restrictions), the average common language dummy variable (Lang, where a higher score indicates more common languages) used by Chan, Covrig, and Ng (2005), and, following Bekaert et al. (2007), the ratio of the market capitalization of the stocks comprising the S&PIFC investable index to the market capitalization of the stocks comprising the S&PIFC global index in each country as a measure of stock market openness (Open).
Our regression results from Panel B of Table V indicate that individualism is still significantly positive when these variables are included in the regression.^{34} However, of the financial development variables included, only language (Lang) is statistically significant, and the Ftest measuring the significance of the variables other than individualism fails to reject the null hypothesis that the coefficients of these variables are all equal to zero.
To measure market integrity we include the prevalence of insider trading (Insider, where a higher score indicates that insider trading is less common), and investor protection (Protection, where a higher score indicates a higher level of protection). These variables were considered previously by La Porta et al. (2006). We also include the corruption index (Crp, where a higher score indicates a lower level of corruption) and the political risk index (Political, where a higher scores indicates a lower risk level). These indexes, from the ICRG, have been shown to be related to liquidity across countries (Lesmond (2005) and Eleswarapu and Venkataraman (2006)). In addition, we include the estimate of transaction costs (Tran, where a higher value indicates a higher trading cost) used by Chan et al. (2005) as the measure for the cost of trading stocks in each country.
Panel C of Table V reports the results from the Fama–MacBeth (1973) regressions that contain these variables.^{35} Again, we find that the estimated coefficient of Indv remains significantly positive after the inclusion of these variables. In addition, the coefficient of the natural logarithm of the transaction cost index (LnTran) is positive and significant, but the estimated coefficients of the other integrity variables are insignificant and the Ftest fails to reject the null hypothesis that the coefficients of the variables other than individualism are all equal to zero.
C. Rational Momentum Models
This section examines the extent to which the crosscountry differences in momentum profits can be explained by variables suggested in the rational momentum literature. First, as discussed in Jegadeesh and Titman (1993) and Conrad and Kaul (1998), momentum profits can be partially attributed to the crosssectional variation in expected stock returns. In addition, Johnson (2002) and Sagi and Seasholes (2007) suggest that momentum profits are higher for firms with better growth options.
To measure the variation in expected stock returns in each country, we use the standard deviation of beta estimates (StdBeta). To measure the availability of growth options, we use the Bekaert et al. (2007) measure of the average local growth opportunities (LGO) of a country. We also investigate whether variation in momentum profits across countries is related to earnings growth volatility (Eavol) and dividend growth volatility (Divvol). We compute these volatilities from the earnings and dividends of the Datastream global index of each country.
The results from the Fama–MacBeth regressions reported in the Internet Appendix suggest that when these variables are included in our regressions, the estimated coefficient on Indv remains significantly positive. However, the estimated coefficients on the rational momentum variables are insignificant. Indeed, the Ftest fails to reject the null hypothesis that the coefficients of the variables other than individualism are all equal to zero at conventional significance levels.
D. A Comprehensive Model
It would be of interest to include all the variables in equation (4) instead of estimating the coefficients in groups. However, since our crosscountry sample has a relatively limited number of countries (41 at a maximum), we have limited degrees of freedom.
In this subsection we present regressions that include only those variables that are significant at the 10% level or better in Table V. These regressions include the common language dummy variable (Lang), the natural logarithm of the transaction cost index (LnTran), the natural logarithm of median firm size (LnSZ), the natural logarithm of dispersion in analyst forecasts (LnDisp), and the natural logarithm of stock market volatility (LnV). The coefficient estimates from this Fama–MacBeth regression, reported in Panel D of Table V, are all significant and have the same signs as those in previous regressions.^{36}
VIII. Postholding Period Returns
 Top of page
 ABSTRACT
 I. Individualism, Overconfidence, and SelfAttribution Bias
 II. Data Description and Summary Statistics
 III. Individualism, Trading Volume, and Volatility
 IV. Returns on Momentum Portfolios
 V. Individualism and the Profitability of Momentum Strategies: Portfolio Analysis
 VI. Other Determinants of CrossCountry Momentum: Regression Analysis
 VII. Robustness Checks
 VIII. Postholding Period Returns
 IX. Conclusion
 REFERENCES
Behavioral momentum models suggest that, over longer horizons, the momentum effect is subsequently reversed. In this section we examine the postholding period returns of the momentum portfolios and test whether these returns are indeed more negative in countries with higher scores on the individualism index.
Table VI reports the average monthly returns for the Indvsorted countryaverage and composite portfolios for the 3 years subsequent to the formation date. Consistent with our previous results, during the first year after formation, the average monthly profits on these momentum portfolios are positive and increase with the degree of individualism. However, in the 24 subsequent months, the momentum portfolios exhibit negative returns, which is consistent with the findings of Jegadeesh and Titman (2001) for firms in the United States.
Table VI. Individualism and Postholding Period Returns on Momentum Portfolios This table presents average monthly momentum profits (%) in U.S. dollars for countryaverage portfolios (Panel A) and composite portfolios (Panel B) classified by Hofstede's individualism index (a lower score indicates a lower degree of individualism). The construction of these portfolios is discussed in Table III. The average monthly momentum profits are calculated over different postholding periods. There is a 1month gap between the portfolio formation period and the holding period. The test period is from February 1984 to June 2003. All tstatistics are in parentheses. The Newey–West (1994) heteroskedasticity and autocorrelation consistent estimates of standard errors are used to compute these tstatistics. Individualism Rank  Months 1–12  Months 13–24  Months 25–36  Months 13–36 

Panel A: CountryAverage Portfolios 

Indvlow  0.065  −0.274  0.010  −0.145 
(0.42)  (−2.16)  (0.07)  (−1.29) 
Indv2  0.562  −0.021  −0.123  −0.090 
(4.59)  (−0.17)  (−1.18)  (−0.95) 
Indvhigh  0.740  −0.146  −0.296  −0.222 
(4.90)  (−1.35)  (−2.77)  (−2.60) 
High minus low  0.675  0.128  −0.306  −0.078 
(3.82)  (0.88)  (−1.82)  (−0.61) 

Panel B: Composite Portfolios 

Indvlow  −0.122  −0.326  −0.232  −0.305 
(−0.78)  (−3.65)  (−2.60)  (−5.25) 
Indv2  0.119  −0.302  −0.269  −0.299 
(1.23)  (−3.05)  (−3.22)  (−4.46) 
Indvhigh  0.367  −0.646  −0.543  −0.604 
(2.80)  (−4.47)  (−3.78)  (−5.16) 
High minus low  0.489  −0.320  −0.311  −0.299 
(3.68)  (−2.06)  (−1.79)  (−2.25) 
During the second year after portfolio formation, the magnitude of the return reversals is significantly higher in high individualism countries than in low individualism countries (difference =−0.32% per month with a tstatistic of −2.06) only for the composite momentum portfolio.^{43} In contrast, during the third year after portfolio formation, the difference in the return reversals between high and lowindividualism countries is statistically significant at the 10% level for both countryaverage and composite momentum portfolios.
In the Internet Appendix we show that the extent of the return reversals is stronger when we limit our analysis to a sample of smaller stocks with a market capitalization below the median of all the stocks within a given country in any month in our sample. Within this sample of smaller stocks we do find that, indeed, the extent of the longterm return reversals is significantly stronger in high individualism countries than in low individualism countries for both countryaverage and composite momentum portfolios.
IX. Conclusion
 Top of page
 ABSTRACT
 I. Individualism, Overconfidence, and SelfAttribution Bias
 II. Data Description and Summary Statistics
 III. Individualism, Trading Volume, and Volatility
 IV. Returns on Momentum Portfolios
 V. Individualism and the Profitability of Momentum Strategies: Portfolio Analysis
 VI. Other Determinants of CrossCountry Momentum: Regression Analysis
 VII. Robustness Checks
 VIII. Postholding Period Returns
 IX. Conclusion
 REFERENCES
It is always interesting to compare the profitability of investment strategies across international markets. In addition to providing a robustness check on results generated from excessively mined U.S. data, a crosscountry study can potentially provide evidence on how cultural differences as well as institutional differences affect the efficiency of financial markets.
The Jegadeesh and Titman (1993) momentum effect provides a major challenge to the efficient market hypothesis. Looking just at U.S. data, one might conclude that the momentum effect is both too persistent (i.e., it generates positive returns in all postwar decades) and too strong (i.e., it generates implausibly high Sharpe ratios) to be explained by risk. As our analysis demonstrates, the momentum strategies generated with global data provide even higher Sharpe ratios and thus provide an even greater challenge to traditional finance theories.
The crosscountry differences in momentum profits described in this paper provide a challenge to behavioral as well as traditional riskbased theories. Although the riskbased theorists must explain why momentum returns are risky in the United States and Europe but not in Japan and in most East Asian countries, the behavioral theorists must explain why individuals in some, but not all countries, are subject to the psychological biases that cause momentum.
The evidence in this paper indicates that culture can have an important effect on stock return patterns, which is consistent with the idea that investors in different cultures interpret information in different ways and are subject to different biases. One interpretation of our results on the relation between momentum profits and cultural differences is that in less individualistic cultures investors put less weight on information that they come up with on their own and more weight on the consensus of their peers. In other words, individuals in less individualistic cultures act less like the overconfident/selfattribution biased investors described by Daniel et al. (1998), and thus tend not to make investment choices that generate momentum profits.
Of course, there are a number of competing theories of momentum and our evidence in support of a behavioral theory should be viewed more as circumstantial than definitive. By identifying a crosscountry relationship between momentum profits and individualism we hope that this study can help motivate future research on how cultural differences influence stock returns. For example, since Hong et al. (2003) find that earnings momentum is stronger in Western countries than in East Asian countries, it might make sense to examine the crosscountry relation between earnings momentum and individualism. Another possibility worth considering is that investors in less individualistic cultures place too much credence on consensus opinions, and may thus exhibit herdlike overreaction to the conventional wisdom.^{44}