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Decision Support Using Machine Learning Indication for Financial Investment

Author

Listed:
  • Ariel Vieira de Oliveira

    (School of Sea, Science, and Technology, University of Vale do Itajaí, R. Uruguai, 458, Itajaí 88302-901, Brazil)

  • Márcia Cristina Schiavi Dazzi

    (School of Sea, Science, and Technology, University of Vale do Itajaí, R. Uruguai, 458, Itajaí 88302-901, Brazil)

  • Anita Maria da Rocha Fernandes

    (School of Sea, Science, and Technology, University of Vale do Itajaí, R. Uruguai, 458, Itajaí 88302-901, Brazil)

  • Rudimar Luis Scaranto Dazzi

    (School of Sea, Science, and Technology, University of Vale do Itajaí, R. Uruguai, 458, Itajaí 88302-901, Brazil)

  • Paulo Ferreira

    (VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
    Department of Economic Sciences and Organizations, Polytechnic Institute of Portalegre, 7300-555 Portalegre, Portugal)

  • Valderi Reis Quietinho Leithardt

    (VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
    COPELABS, Lusófona University of Humanities and Technologies, Campo Grande 376, 1749-024 Lisboa, Portugal)

Abstract

To support the decision-making process of new investors, this paper aims to implement Machine Learning algorithms to generate investment indications, considering the Brazilian scenario. Three artificial intelligence techniques were implemented, namely: Multilayer Perceptron, Logistic Regression and Decision Tree, which performed the classification of investments. The database used was the one provided by the website Oceans14, containing the history of Fundamental Indicators and the history of Quotations, considering BOVESPA (São Paulo State Stock Exchange). The results of the different algorithms were compared to each other using the following metrics: accuracy, precision, recall, and F1-score. The Decision Tree was the algorithm that obtained the best classification metrics and an accuracy of 77%.

Suggested Citation

  • Ariel Vieira de Oliveira & Márcia Cristina Schiavi Dazzi & Anita Maria da Rocha Fernandes & Rudimar Luis Scaranto Dazzi & Paulo Ferreira & Valderi Reis Quietinho Leithardt, 2022. "Decision Support Using Machine Learning Indication for Financial Investment," Future Internet, MDPI, vol. 14(11), pages 1-17, October.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:11:p:304-:d:952775
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    References listed on IDEAS

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