Whats in a headline? News impact on the Brazilian economy - Cardoso, Gustavo Romero (2024)
The purpose of this dissertation is to explore the impact of textual data on the Brazilian economic cycle. Utilizing a dataset of articles from \"Valor Econômico\" newspaper spanning July 2011 to December 2022, we employ the topic model Latent Dirichlet Allocation (LDA) to transform this textual data into a series of monthly topic proportions. From this output, we have developed two news indices - NITVP and NLTM - each with distinct methodologies but sharing the objective of assessing the influence of news topics on asset prices. Adopting the identification strategy of Larsen and Thorsrud (2019), we incorporate these indices into a structural VAR model to differentiate between news and noise shocks and to analyze their effects on macroeconomic variables. Our results reveal that news shocks, as captured by the news indices, significantly impact both asset prices and a range of macroeconomic indicators. Both news and noise shocks are found to be crucial in explaining a considerable proportion of the variance in asset prices over short and long-term periods, underscoring the pivotal role of news information in market dynamics. Furthermore, our findings, when contrasted with previous studies like Beaudry and Portier (2006), affirm the effectiveness of employing news indices to identify news shocks, as opposed solely on asset prices. This dissertation contributes to the field of economic literature by showcasing the significance of textual data analysis in understanding economic cycles and highlighting the potential and importance of news as a resource for providing a comprehensive view of the economy.