Dissecting the Sentiment-Driven Green Sector Premium in China with a Large Language Model

Abstract

The general financial theory predicts a carbon premium, as brown stocks bear greater uncertainty under climate transition. However, a contrary green premium has been identified in China, as evidenced by the return spread between green and brown sectors. The aggregated climate transition sentiment, measured from news data using a large language model, explains 12%-33% of the variability in the anomalous return. This factor intensified after China announced its national commitments. The sentiment-driven green premium is attributed to speculative trading by retail investors targeting green “concept stocks.” Additionally, the discussion highlights the advantages of large language models over lexicon-based sentiment analysis.

Keywords: Climate transition risk, Market sentiment, Textual analysis, Green premium