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Category: Project

Description: We introduce a method that identifies from earnings conference calls the attention paid by financial analysts to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. The measures are useful in predicting important real outcomes related to the net-zero transition, notably job creation in disruptive green technologies and green patenting, and they contain information that is priced in options and equity markets. Updates [2023-02-15]: We have updated our data to ensure that the topic measures have zero values when CCExposure=0. Updates [2022-03-11]: We have updated our data to 2021Q4. Updates [2022-02-25]: We have expanded the number of variables provided in the datasets (we re-run the bigram searching algorithm so the original scores change but remain highly correlated with the legacy version.). Updates [2021-05-14]: We have updated our data to 2020Q4. Updates [2021-04-03]: Last update missed 2019 Q3 and Q4. We added the data of these two quarters in the latest version. Updates [2021-01-19]: We have updated our data to 2020Q3.