Predição de tendências em séries financeiras utilizando metaclassificadores

Autores

  • Carlos Alberto Silva de Assis Centro Federal de Educação Tecnológica de Minas Gerais
  • Eduardo Gontijo Carrano Universidade Federal de Minas Gerais
  • Adriano Cesar Machado Pereira Universidade Federal de Minas Gerais

DOI:

https://doi.org/10.11606/1980-5330/ea148159

Palavras-chave:

séries financeiras, inteligência computacional, meta-classificador

Resumo

Neste trabalho foi desenvolvido um metaclassificador baseado em métodos de inteligência computacional para prever tendências em séries temporais financeiras. O kernel do metaclassificador foi baseado na ferramenta (Weka). Sete classificadores foram combinados para realizar a metaclassificação. Testes foram realizados com nove ativos da Bolsa de Valores de São Paulo. Os resultados iniciais foram promissores, com boa acurácia na classificação e ganhos de até 100% do valor de capital inicialmente investido no período de um ano.

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Biografia do Autor

  • Carlos Alberto Silva de Assis, Centro Federal de Educação Tecnológica de Minas Gerais

    Doutorado em Modelagem Matemática e Computacional pelo Centro Federal de Educação Tecnológica de Minas Gerais (CEFET-MG), Programa de Pós-Graduação em Modelagem Matemática e Computacional (PPGMMC), Belo Horizonte (MG), Brasil (2019).

  • Eduardo Gontijo Carrano, Universidade Federal de Minas Gerais

    Universidade Federal de Minas Gerais (UFMG), Programa de Pós-Graduação em Engenharia
    Elétrica (PPGEE), Belo Horizonte (MG), Brasil.

  • Adriano Cesar Machado Pereira, Universidade Federal de Minas Gerais

    Professor Adjunto da Universidade Federal de Minas Gerais (UFMG), Programa de Pós-Graduação em Ciência da Computação (PPGCC), Belo Horizonte (MG), Brasil.

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Publicado

2020-03-01

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Predição de tendências em séries financeiras utilizando metaclassificadores. (2020). Economia Aplicada, 24(1), 29-78. https://doi.org/10.11606/1980-5330/ea148159