Desenvolvimento de modelo para predi\c{c}\~ao de cota\c{c}\~oes de a\c{c}\~ao baseada em an\'alise de sentimentos de tweets

TL;DR

This paper develops a sentiment-based machine learning model using Twitter data to predict Petrobras' stock prices, achieving significant gains in simulated trading over random models.

Contribution

Introduces a novel approach combining sentiment analysis from Twitter with XBoot models for stock prediction, outperforming random strategies.

Findings

Achieved a net gain of R$88,82 in 250 days using the model.

Sentiment features from Twitter effectively predict stock movements.

Model outperforms average random models in simulated trading.

Abstract

Training machine learning models for predicting stock market share prices is an active area of research since the automatization of trading such papers was available in real time. While most of the work in this field of research is done by training Neural networks based on past prices of stock shares, in this work, we use iFeel 2.0 platform to extract 19 sentiment features from posts obtained from microblog platform Twitter that mention the company Petrobras. Then, we used those features to train XBoot models to predict future stock prices for the referred company. Later, we simulated the trading of Petrobras' shares based on the model's outputs and determined the gain of R$88,82 (net) in a 250-day period when compared to a 100 random models' average performance.