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Journal of Finance Vol. 78 No. 3 2023

Firm‐Level Climate Change Exposure

Zacharias Sautner1; Laurence van Lent; Grigory Vilkov2,3,4,5,6; Ruishen Zhang2,3,4,5,6

1 e-mail: [email protected] · 2 Dongbei University of Finance and Economics · 3 Chinese University of Hong Kong · 4 Duke Kunshan University · 5 Heinrich Heine University Düsseldorf · 6 Frankfurt School of Finance & Management

open access

Abstract

We develop a method that identifies the attention paid by earnings call participants 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. We show that the measures are useful in predicting important real outcomes related to the net‐zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets.

DOI
10.1111/jofi.13219
Volume
78
Issue
3
Pages
1449-1498
Language
en
Sources
openalex bibtex:phds-export.bib crossref

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