Author(s):	Tarek Alexander Hassan, Stephan Hollander, Ahmed Tahoun, Laurence van Lent
Publication Date:	December 2019
Keyword(s):	Brexit, cross-country effects, Machine Learning, sentiment, uncertainty
JEL(s):	D8, E22, E24, E32, E6, F0, G18, G32, G38, H32
Programme Areas:	Financial Economics, International Macroeconomics and Finance, Macroeconomics and Growth
Link to this Page:	cepr.org/active/publications/discussion_papers/dp.php?dpno=14253

Using tools from computational linguistics, we construct new measures of the impact of Brexit on listed firms in the United States and around the world; these measures are based on the proportion of discussions in quarterly earnings conference calls on the costs, benefits, and risks associated with the UK's intention to leave the EU. We identify which firms expect to gain or lose from Brexit and which are most affected by Brexit uncertainty. We then estimate effects of the different types of Brexit exposure on firm-level outcomes. We find that the impact of Brexit-related uncertainty extends far beyond British or even European firms; US and international firms most exposed to Brexit uncertainty lost a substantial fraction of their market value and have also reduced hiring and investment. In addition to Brexit uncertainty (the second moment), we find that international firms overwhelmingly expect negative direct effects from Brexit (the first moment) should it come to pass. Most prominently, firms expect difficulties from regulatory divergence, reduced labor mobility, limited trade access, and the costs of post-Brexit operational adjustments. Consistent with the predictions of canonical theory, this negative sentiment is recognized and priced in stock markets but has not yet significantly affected firm actions.