2022/04/04 by Luca Barbaglia, Sergio Consoli, Sebastiano Manzan · 3 citations
Decision Sciences · Economics, Econometrics and Finance · Social Sciences · #Computational and Text Analysis Methods #Market Dynamics and Volatility #Stock Market Forecasting Methods
paper · pdf · doi:10.1080/07350015.2022.2060988
openalex publication_date 2022/04/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
The goal of this article is to evaluate the informational content of sentiment extracted from news articles about the state of the economy. We propose a fine-grained aspect-based sentiment analysis that has two main characteristics: (a) we consider only the text in the article that is semantically dependent on a term of interest (aspect-based) and, (b) assign a sentiment score to each word based on a dictionary that we develop for applications in economics and finance (fine-grained). Our dataset includes six large U.S. newspapers, for a total of over 6.6 million articles and 4.2 billion words. Our findings suggest that several measures of economic sentiment track closely business cycle fluctuations and that they are relevant predictors for four major macroeconomic variables. We find that there are significant improvements in forecasting when sentiment is considered along with macroeconomic factors. In addition, we also find that sentiment matters to explains the tails of the probability distribution across several macroeconomic variables.