B3 Stock Price Prediction Using LSTM Neural Networks and Sentiment Analysis

Authors

Keywords:

financial market, stock exchange, recurring neural networks, LSTM, Sentiment Analysis

Abstract

This article presents an approach to predict stock prices which incorporate sentiment analysis from Twitter posts as an input to an Long Short Term Memory (LSTM) Neural Network to help in the decision process. The sentiment analysis measures subjectivity and polarity as well as the number of tweets about the company to capture the market mood, which influences the stock prices, were evaluated. The main company used to evaluate our method is Vale (VALE3). The sentiment analysis helps to reach an Root Mean Squared Error (RMSE) of 0.021. We also validate our method with JHSF (JHSF3) and Usiminas (USIM3), obtaining RMSE of 0.012 and 0.016, respectively.

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Author Biographies

Gabriel Vargas, Universidade Federal do Espírito Santo

Possui bacharelado em Ciência da Computação pela Universidade Federal do Espirito Santo (2021) e técnico em informática pela Escola Técnica Juscelino Kubitschek (2013). Atualmente é cientista de dados na empresa Minds Digital Informática e seus interesses de pesquisa incluem deep learning, machine learning, análise de dados e mercado financeiro.

Leonardo Silvestre, Universidade Federal do Espírito Santo

Possui Doutorado em Engenharia Elétrica pela UFMG (2015), Mestrado em Informática pela UFES (2005) e Graduação em Ciência da Computação pela UFV (2003). Atualmente é professor Adjunto IV do Departamento de Computação e Eletrônica do CEUNES/UFES, e seus interesses de pesquisa incluem redes neurais, deep learning e suas aplicações em saúde, smartgrids e agricultura.

Luís Rigo Júnior, Universidade Federal do Espírito Santo

possui doutorado em Engenharia de Sistemas e Computação pela ufrj (2011). Atualmente é professor associado do Departamento de Computação e Eletrônica (DCEL), UFES. Atua na área de Aprendizado de Máquina, desenvolvendo soluções para problemas em energia, saúde e agricultura, bem como soluções de ensino em inteligência artificial.

Helder Rocha, Universidade Federal do Espírito Santo

Possui Doutorado e Mestrado em Computação Cientifica e Sistemas de Potência - UFF, Bacharelado em Administração - UFRRJ e Graduação em Engenharia Elétrica - UFF. Foi professor no Instituto Federal do Espirito Santo. Ocupa atualmente o cargo de Professor Classe C - Adjunto III do Departamento de Engenharia Elétrica da UFES. É bolsista de Produtividade PQ2.

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Published

2022-05-06

How to Cite

Vargas, G., Silvestre, L., Rigo Júnior, L., & Rocha, H. . (2022). B3 Stock Price Prediction Using LSTM Neural Networks and Sentiment Analysis. IEEE Latin America Transactions, 100(XXX). Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6236