Interpretable Bayesian machine learning for assessing the effects of climate news shocks on firm-level returns. WP

Abstract. We propose a Bayesian Asset Pricing framework that uses machine learning to capture nonlinear interactions between firm exposures to climate change risks and a number of possible effect modifiers. Climate change risks are measured by two indicators that are obtained through textual analysis. Considering a portfolio of 430 US stocks, we find that news about climate change impact stock returns nonlinearly and asymmetrically. Our findings indicate that sectors with high pollution levels are most affected, with the highest polluting firms within these sectors bearing the brunt of the impact. In addition, we find that the insurance and transportation sectors are most affected by natural disaster news.

SSRN.