Multinational Corporations and Stock Price Crash Risk

A nascent literature in finance and accounting on tail risk in individual stock returns concludes that bad news hoarding by corporate managers engenders sudden, extreme crashes in a firm’s stock price when the bad news is eventually made public. This literature finds that firm-specific crash risk is higher among firms with more severe asymmetric information and agency problems. A hitherto disjointed literature spanning the fields of international business, finance, and accounting suggests that geographic dispersion in a firm’s operations, and especially dispersion across different countries, gives rise to organizational complexities and greater costs of monitoring that can exacerbate asymmetric information and agency problems. Motivated by the confluence of arguments and findings from these two strands of literature, this paper examines whether stock price crash risk is higher among multinational firms than domestic firms. Using a large sample of U.S. headquartered firms during 1987-2011, we find robust evidence that multinational firms are significantly more likely to crash than domestic firms. Moreover, we show that the difference in crash risk between multinational and domestic firms is most acute among firms with weaker corporate governance mechanisms, including weaker shareholder rights, less independent boards, and less stable institutional ownership. Our analysis indicates that stronger monitoring from each of these three governance mechanisms significantly attenuates the positive relation between crash risk and multinationality. Our findings are robust to the use of alternative measures of crash risk and to controlling for known determinants of crash risk identified in prior studies. Our study offers new insights that should hold value for scholars and market participants interested in understanding the implications of heighted agency problems that multinational firms are likely to encounter and scholars and market participants interested in developing models that more accurately predict tail risk in the equity returns of individual firms.