We use BERT, an AI-based algorithm for language understanding, to quantify regulatory climate risk disclosures and analyze their impact on the term structure in the credit default swap (CDS) market. Risk disclosures can either increase or decrease CDS spreads, depending on whether the disclosure reveals new risks or reduces uncertainty. Training BERT to differentiate between transition and physical climate risks, we find that disclosing transition risks increases CDS spreads after the Paris Climate Agreement of 2015, while disclosing physical risks decreases the spreads. In addition, we also find that the election of Trump had a negative impact on CDS spreads for firms exposed to transition risk. These impacts are consistent with theoretical predictions and economically and statistically significant.
Climate change may have a detrimental effect on a firm's financial performance. Using a forward-looking measure of climate risk exposure based on textual analysis of firms' 10-K reports, we assess whether climate risks-as disclosed to the regulator-are priced in the credit default swap (CDS) market. We construct this novel climate risk measure based on BERT, an advanced language understanding algorithm, and adapt it for our purpose. We differentiate between physical and transition risks and find that transition risk increases CDS spreads, especially after the Paris Climate Agreement of 2015. However, we do not find such an effect for physical risk.
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