@article{Branco Neto_Salvi_Souza_2020, title={Hybrid Neural Networks Applied to Brazilian Stock Market}, volume={27}, url={https://seer.ufrgs.br/index.php/rita/article/view/RITA_VOL27_NR2_42}, DOI={10.22456/2175-2745.88911}, abstractNote={<div class="page" title="Page 1"><div class="section"><div class="layoutArea"><div class="column"><p><span>The stock market is a stochastic, dynamic environment and is in constant evolution, and its prediction represents a big challenge. Many studies presented in the state of the art are facing this challenge, by making use of Artificial Neural Networks (ANN) as a tool to make such prediction. In this paper a comparative study is made with different methods in order to predict the Brazilian stock market through the Bovespa Index. An ANN was developed and its performance was compared against a hybrid model, in which a Genetic Algorithm (GA) is proposed as an alternative to improve the performance of this ANN. The results obtained were an average accuracy of 55.04% and 55.73% respectively, demonstrating that algorithms such as a GA have the capability of improving the performance of ANN for the stock market prediciton.</span></p></div></div></div></div>}, number={2}, journal={Revista de Informática Teórica e Aplicada}, author={Branco Neto, Wilson Castello and Salvi, Andrey de Aguiar and Souza, William Passig de}, year={2020}, month={Apr.}, pages={42–65} }