Artikel
Firm‐Level Climate Change Exposure
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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: The Journal of Finance ; ISSN: 1540-6261 ; Volume: 78 ; Year: 2023 ; Issue: 3 ; Pages: 1449-1498 ; Hoboken, NJ: Wiley
- Klassifikation
-
Wirtschaft
- Ereignis
-
Geistige Schöpfung
- (wer)
-
SAUTNER, ZACHARIAS
VAN LENT, LAURENCE
VILKOV, GRIGORY
ZHANG, RUISHEN
- Ereignis
-
Veröffentlichung
- (wer)
-
Wiley
- (wo)
-
Hoboken, NJ
- (wann)
-
2023
- DOI
-
doi:10.1111/jofi.13219
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- SAUTNER, ZACHARIAS
- VAN LENT, LAURENCE
- VILKOV, GRIGORY
- ZHANG, RUISHEN
- Wiley
Entstanden
- 2023
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