2019
DOI: 10.2139/ssrn.3501910
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The Global Impact of Brexit Uncertainty

Abstract: 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 * For valuable comments, we thank Tom Ferguson, John van Reenen (discussant), Marcel Olbert, Xiang Zheng,

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Cited by 4 publications
(3 citation statements)
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References 28 publications
(39 reference statements)
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“…To quantify exposure to climate change, we build on Hassan et al (2019Hassan et al ( , 2021Hassan et al ( , 2023a. Extracting climate-related information from text sources is challenging (Webersinke et al ( 2021)).…”
Section: A Discovery Of Climate Change Bigramsmentioning
confidence: 99%
See 1 more Smart Citation
“…To quantify exposure to climate change, we build on Hassan et al (2019Hassan et al ( , 2021Hassan et al ( , 2023a. Extracting climate-related information from text sources is challenging (Webersinke et al ( 2021)).…”
Section: A Discovery Of Climate Change Bigramsmentioning
confidence: 99%
“…A benefit of these measures is that they reflect "soft" information originating from information exchanges between managers and analysts. 1 To construct the climate change exposure measures, we build on recent work using quarterly earnings calls as a source for identifying firms' various risks and opportunities (Hassan et al (2019(Hassan et al ( , 2021(Hassan et al ( , 2023a(Hassan et al ( , 2023b, Jamilov, Rey, and Tahoun (2021)). These studies use the proportion of the conversation during an earnings call that relates to a particular topic to capture the firm's exposure to that topic.…”
mentioning
confidence: 99%
“…As regards to measures of Brexit uncertainty, there are several approaches. For example, Hassan et al (2020) propose a general text-based method using natural language processing to estimate the impact of Brexit related uncertainty, while Graziano et al (2018) identify monthly variation in exports; Graziano et al (2020) use a prediction market-based variable. Bloom et al (2018) use the Decision Maker Panel in August 2016, a representative business survey which collects information on many different aspects of uncertainty, while Steinberg (2017) investigates the impact of higher trade costs following Brexit implementing a dynamic model.…”
Section: Introductionmentioning
confidence: 99%