Efeito do sentimento do investidor manifesto via Twitter sobre os retornos e o volume negociado no mercado acionário brasileiro

Abstract

The aim of this paper was to identify, through the messages that are posted on Twitter, how the information that is disseminated online is associated with the movements that occur in the Brazilian market, specifically with regard to returns and traded volume. The collection of data related to Twitter took place through the Tweepy library. Financial data were obtained using the Thomson Reuters database. For the development of the sentiment index, a netnographic method was used, in which there was a participant observation in the social network Twitter in order to know the terms that are used to refer to the Brazilian market and the stocks that compose it. The sentiment was attributed through machine learning, by the Google Cloud Natural Language API, which has a sentiment analysis tool. To reach the general objective of the research, seven quantile regression models were estimated in five quantiles, since the data obtained were heterogeneous and did not demonstrate normality, since this type of regression is robust to such problems, so it was possible to have an understanding of the subject and interpretation of data. The analysis and interpretation of the data made it possible to perceive that, in general, an optimistic sentiment in contemporary time will be associated with a greater contemporary return, however this relationship is inverted over the days, so that, an optimistic sentiment in the current period will be associated with a subsequent lower return. It was also found that there is a significant association between the volume of messages that are posted daily on Twitter and traded volume of the Brazilian stock market. In addition, it was seen that the greater the number of messages that have a negative feeling, the greater the traded volume. Thus, it is believed that the results are useful to show that there is a relationship between the information that is released on the social network Twitter and the movements of the Brazilian stock market, bringing contributions to the literature, by expanding the understanding of how emerging markets are associated with activities that occur in the online environment. The study also has practical contributions, since activities that occur online on Twitter can be used as variables in investment strategies, since these are associated with market movements