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.
Using natural language processing, we identify and categorize the corporate goals in the shareholder letters of the 150 largest companies in the United...
The use of private capital to finance biodiversity conservation and restoration is a new practice in sustainable finance. This study sheds light on this...
A common argument against divestment is that it jettisons voting power and that it has a small effect on stock prices. We argue that divestment is a form of...