Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks

  • Gabriel Lopes Silva Universidade Federal do Pampa
  • Sandro da Silva Camargo Universidade Federal do Pampa

Resumen

This work aims to evaluate the accuracy of Long Short-Term Memory Neural Networks to recommend Buy/Sell signals of some Brazilian Stock Market Blue Chips. The population of this study was composed by top 5 volume stocks, which represented nearly 40% of the total volume of Brazilian Stock Market in 2019. It was analyzed the following features: volume traded, closing and opening price, maximum and minimum price, and last five-day closing prices. Models created can forecast the next day's opening or closing price. Obtained results show that forecasting
and real values have a coefficient of determination (R2) from 0.91 to 0.99, depending on the stock.

Publicado
2022-12-14
Cómo citar
Lopes Silva, G., & da Silva Camargo, S. (2022). Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks. Memorias De Las JAIIO, 8(2), 75-87. Recuperado a partir de https://ojs.sadio.org.ar/index.php/JAIIO/article/view/266
Sección
ASAI - Simposio Argentino de Inteligencia Artificial