The authors observe that behavioral biases can affect stock price co-movements. Given that culture affects the behavior of individuals, they argue that it is a relevant factor in explaining the cross-country differences in stock price synchronicity.

In examining the impact of culture on stock price co-movement, they focus on two key variables: tightness versus looseness and individualism versus collectivism. Using analysis based on the tightness measure of Gelfand, Raver, Nishii, Leslie, and Lun (Science 2011) and the individualism measure of Hofstede (Culture’s Consequences 2001), they find an economically significant impact of culture on share price synchronicity. An increase of one standard deviation in cultural tightness (individualism) leads to a 12.9% increase (18.2% decrease) in stock price co-movement from the mean. These magnitudes are similar to the effects of other variables on stock price co-movement: GDP per capita is –12.4%, country size is –9.3%, good government index is –15%, and the diversity of analyst forecasts is 6.1%.

The authors identify differences in stock trading correlations and the transparency of the information environment as the key mechanisms through which the identified cultural variables affect stock price synchronicity. They also find that trade openness and capital market openness mitigate the effect of domestic culture on stock price co-movement. Trade openness exposes domestic investors to other ideas, and open capital markets allow foreign investors with different cultures to participate in domestic markets.

The authors suggest that culture is an important variable in share price synchronicity and provide quantitative support for this claim. The approach they take has a number of relevant practical uses for a range of practitioners. The authors’ findings that national cultures affect firm-specific return variations contribute to the existing literature on the determinants of firm-specific stock variations. This finding creates the potential to explore and refine areas of finance that draw on marketwide and firm-specific return variations, such as the capital asset pricing model. Investment management professionals will also find the authors’ approach a useful additional tool to explain variations in share price returns.

The authors use weekly returns of all the stocks in Datastream from 1990 to 2010. They exclude stocks that are not traded in their home market and stocks with fewer than 30 weeks of valid data.

In any particular year, a country with fewer than 25 stocks with valid data is excluded for that particular year. All observations for which the stock return is greater than 300% and reverses the following week are deleted to avoid data errors. The final sample consists of 47 countries and 932 country-year observations. Data on cultural tightness are from Gelfand et al. and are available for 28 countries in the sample. Data on individualism are from Hofstede and cover all countries in the sample. Trade openness is estimated by using the natural trade openness measure constructed by Frankel and Romer (American Economic Review 1999). Capital market openness for countries is measured following the approach of Pukthuanthong and Roll (Journal of Financial Economics 2009).

Finally, stock price synchronicity is estimated by using the R² from the multifactor model specified in Jin and Myers (Journal of Financial Economics 2006). For each country in the sample, the authors regress the weekly returns of each stock against the weekly market return of the country. The model includes lead and lag terms to correct for nonsynchronous trading. The co-movement of stocks within a country for each year is then estimated as the equal-weighted average of the R² of the individual stocks in the country. A higher average R² indicates greater stock co-movement.

Much of the benefit of this article derives from the fact that culture is pervasive, and the authors have shown empirically the effect of specific cultural variables on financial metrics. The approach makes the conclusions as well as the modelling techniques useful to a wide range of participants—academics across the spectrum (economists, finance professionals, sociologists, anthropologists), investment professionals, and policymakers.