Brazilian stock marketperformance and investorsentiment on Twitter (REGE Revista de Gestão, 2024)
Purpose: This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.Design: We analyzed 314,864 tweets between January 1, 2017, and December 31, 2018, collected with the Tweepy library. Companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and VAR models, as variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.Findings: In the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. Quantile analysis showed that the ISI explains the stock market return limited to periods of lower returns. It is possible to state that this effect traces to the informational content of the tweets (sentiment), and not to the volume of tweets.Originality: We present unprecedented evidence that investor sentiment in the Brazilian market can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers, and regulators, as they can be used to obtain abnormal returns.