Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 13, 2022 erstellt
Beschreibung:
We measure sentiment towards physical and regulatory aspects of climate change by constructing several Climate-Related Investor Sentiment Excerpted by Search (CRISES) indexes using machine learning and text mining techniques on a wide range of social media platforms and news outlets. We quantify the impacts of our indexes on price reversal and excess volatility of different types of asset classes at the aggregate market level. Using daily observations between 2010 and 2022, we find that our physical CRISES index predicts short-term return reversal in equity ETFs and long-term reversal in clean energy stocks. It also predicts temporary excess volatility in both asset classes. Despite the notable high volume of climate policy mentions on social media, our regulatory CRISES index only predicts daily flows out of money market funds and into bond funds. Surprisingly, mutual equity fund flows do not react to any of our CRISES indexes