B3 Stock Price Prediction Using LSTM Neural Networks and Sentiment Analysis

被引：1
|
作者：
Vargas, Gabriel M. [1 ]
Silvestre, Leonardo J. [1 ]
Rigo Jr, Luis O. [1 ]
Rocha, Helder R. O. [1 ]
机构：
[1] Univ Fed Espirito Santo UFES, Sao Mateus, ES, Brazil
来源：
IEEE LATIN AMERICA TRANSACTIONS | 2021年 / 20卷 / 07期
关键词：
Sentiment analysis; Long short term memory; Social networking (online); RNA; Blogs; Recurrent neural networks; IEEE transactions; financial market; stock exchange; recurring neural networks; LSTM; Sentiment Analysis;
D O I：
10.1109/TLA.2021.9827469
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：1067 / 1074
页数：8
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