THESIS
2022
1 online resource (xii, 131 pages) : illustrations (some color)
Abstract
We construct various measures of firm-level climate risk exposure by utilizing natural
language processing techniques on firms’ quarterly earnings conference call
transcripts. The unsupervised learning method automatically generates five topics
aligned with popular climate change concerns. Investors reward firms’ efforts
to fight against global warming and transition to a low-carbon economy by developing
emission reduction technology and renewable investment. Such firms are
less sensitive to the frontier green technology shock and less likely to be subject
to penalties related to environmental issues. Therefore they have a lower cost of
financing and expected return.
We conduct an empirical analysis on the topic that puts high weight on words
about natural disasters. This disaster expos...[
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We construct various measures of firm-level climate risk exposure by utilizing natural
language processing techniques on firms’ quarterly earnings conference call
transcripts. The unsupervised learning method automatically generates five topics
aligned with popular climate change concerns. Investors reward firms’ efforts
to fight against global warming and transition to a low-carbon economy by developing
emission reduction technology and renewable investment. Such firms are
less sensitive to the frontier green technology shock and less likely to be subject
to penalties related to environmental issues. Therefore they have a lower cost of
financing and expected return.
We conduct an empirical analysis on the topic that puts high weight on words
about natural disasters. This disaster exposure measure has a significant negative
association with firms’ sales growth and profitability. Moreover, firms with
higher disaster exposure tend to earn higher expected stock returns than their
counterparts with lower exposure, suggesting that firms’ disaster risk exposure
significantly affects the cost of equity and market valuations. A long-short portfolio
based on this exposure measure generates a positive return of 5% per annum,
which cannot be explained by common risk factors and other firm characteristics.
Keywords: Climate change, natural language processing, earnings conference
calls, physical risks, transition risks, disaster, cross-section of stock returns.
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