Abstract
We use BERT, an AI-based algorithm for language understanding, to decipher regulatory climate-risk disclosures and measure their impact on the credit default swap (CDS) market. Risk disclosures can either increase or decrease credit spreads, depending on whether disclosure reveals new risks or sharpens the signal and decreases the uncertainty. Training BERT to differentiate between transition and physical climate risks, we find that disclosing transition risks increases CDS spreads, especially after the Paris Climate Agreement of 2015, while disclosing physical climate risks leads to a decrease in CDS spreads. These impacts are statistically and economically highly significant.
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