Palabras clave
lexical approach
financial news
Cómo citar
Resumen
Purpose – The proliferation of news disseminated through electronic media influences investor decision-making and stock prices. This study investigates the statistical association between investor sentiment, as measured using the Loughran and McDonald, VADER, and Harvard-IV lexical dictionaries, and the performance of the Brazilian stock market (Ibovespa).
Theoretical framework – The study is grounded in behavioral finance, emphasizing investor sentiment and cognitive biases as determinants of market behavior. This concept is supported by prior research linking financial news sentiment to stock market performance.
Design/methodology/approach – A dataset of 9,515 exclusive financial news articles from Valor +News (November 2021 to August 2024) was retrieved from Valor Econômico and analyzed using Python. The empirical strategy combines multiple linear regression with lagged specifications, vector autoregression (VAR), Granger causality tests, monetary policy subperiod analyses, and first-difference robustness models. Gold, Bitcoin, the exchange rate, country risk, and news volume were included as control variables.
Findings – VADER and Loughran and McDonald exhibit consistent positive associations with Ibovespa returns across the main specifications, whereas Harvard-IV is more sensitive to model specification. Additional analyses indicate that the evidence is predominantly contemporaneous and should be interpreted as statistical associations rather than causal effects. The exchange rate, Bitcoin, and country risk are also statistically significant, while news volume is not.
Practical and social implications of the research – The findings highlight the usefulness of lexical sentiment measures for analyzing financial markets and emphasize the importance of interpreting sentiment-return relationships as contemporaneous associations.
Originality/value – This study provides new evidence for the Brazilian market by combining an exclusive financial news dataset with multiple lexical dictionaries and complementary robustness analyses.
Citas
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