Investigating the Influence of News Sources and Language Models on Climate Beta Estimates

Extending the work by Engle et al. (2020), we seek to measure a news-based climate-change beta. Using five language models of increasing sophistication and five high-quality newspaper sources including the Financial Times, we construct twenty-five unexpected climate news indices (UCNI). We measure the impact of these UCNI, plus UCNI aggregated over all the news sources, on a range of green, brown and green-minus-brown (GMB) equity portfolios constructed by sorting S&P 500 firms based on their carbon intensity. We find that the relationship between the Aggregate UCNI and the brown and GMB portfolio returns is statistically significant over the period from July 2012 to November 2021. This result does not hold for UCNI built from a single newspaper. We find that green firms exhibit only a small, statistically non-significant degree of sensitivity to UCNI variations