Aluno: Johana Pertoldova
Resumo
This thesis explores the relationship between public climate change awareness and financial market behavior by developing a Climate Change Awareness Index (CCAI) using Google Trends data from 2004 to 2024. The index aggregates search interest across 125 climate-related terms and is constructed as a monthly, weighted, first-differenced series to ensure stationarity and comparability over time.
The CCAI is incorporated into an extended Fama-French three-factor model to evaluate its explanatory power on excess returns across industry portfolios. The analysis includes linear regressions, nonlinear specifications (with squared awareness terms), and threshold regressions. Results reveal that the influence of awareness is not uniform: while the index does not significantly explain average market returns, specific sectors—such as Automobiles and Construction—show statistically meaningful responses when public interest surpasses certain thresholds.
To complement the regression analysis, an event study is conducted around key climate-related policy announcements. Cumulative abnormal returns (CARs) are calculated for each event, comparing investor reactions in high- versus low-awareness periods. The findings suggest that heightened climate awareness can amplify market responses to policy signals, particularly in climate-sensitive industries.
Additional quantile regressions and rolling forecasts provide further insight in exploring whether the impact of climate awareness varies under different market conditions. These advanced methods offer a deeper understanding of when and where public attention to climate change has the most influence—revealing that climate awareness tends to have stronger effects during periods of heightened investor optimism (upper quantiles of returns) and in sectors with high regulatory or reputational exposure, such as Automobiles and Construction. While awareness contributes only marginally to short-term forecasting improvements, it offers valuable context for interpreting market dynamics.
Trabalho final de Mestrado