Investment strategies applied to the Brazilian stock market: A methodology based on Sentiment Analysis with deep learning

Abstract The Efficient Market Hypothesis states that stock market changes reflect the arrival of new information through external events and news. Thus, many recent studies in the literature evaluate the impact of Sentiment Analysis (SA) applied to social media and news in the stock market. However, these studies generally do not present investment strategies that take advantage of sentiments in new publications considering the correlation between news and the stock market, specially when news are written in Portuguese. This paper proposes investment strategies based on Sentiment Analysis of financial news applied to the Brazilian stock market. For such, the following activities were performed: (i) identifying the most suitable Artificial Neural Network (ANN) architecture to perform Sentiment Analysis in financial news in Brazilian Portuguese; (ii) studying the correlation between the predominant sentiment in financial news of three major Brazilian news portals through the Granger causality test; (iii) proposing two categories of investment strategies based on Sentiment Analysis, considering both negative and positive financial news; and (iv) applying the proposed strategies to the Brazilian stock market. Experiments were conducted with financial news from the most popular Brazilian online news sources and the results showed: (i) the most appropriate ANN to perform SA in Portuguese is the Convolutional Neural Network; (ii) there is a significant influence of the predominant daily news sentiment in the stock market; and (iii) investment strategies based on Sentiment Analysis can bring profitability both in short and in long term, surpassing the strategies Random Walk and Buy & Hold.

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