DEVELOPMENT AND COMPARISON OF MACHINE LEARNING MODELS APPLIED TO THE PREDICTION OF IBOVESPA PERFORMANCE TRENDS
Palavras-chave:
Deep learning, Financial time series, Forecasting, Statistics, Stock market prediction
Resumo
The models presented in this paper were compared across the periods of 2010, 2015, and 2020 until 2023. It was established that for a model to be considered satisfactory, its precision must exceed the percentage of days in which the Ibovespa appreciated during the test period. Four initial experiments were conducted, with multiple technical analysis indicators as input variables. Recurrent Neural Network (RNN) models showed the best average performances. The hybridization of the best-performing models in the initial experiments did not surpass their individual performances in the last experiment. Even with accuracy and precision close to 60%, the best models still performed near the base model, so they can’t be considered good enough to be used as a consistent investment strategy. For future work exploring the prediction of Ibovespa trends over longer periods, such as months or years, rather than just a single day, may provide better results.
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Referências
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NABIPOUR, M.; NAYYERI, P.; JABANI, H.; MOSAVI, A.; SALWANA, E.; SHAHAB, S. Deep learning for stock market prediction. Entropy, Basel, v. 22, n. 8, p. 840. Jul 2020.
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SILVA, J. E.; SÁTIRO, R. M. O poder preditivo dos modelos boosting de machine learning no mercado brasileiro de ações. Brazilian Journal of Quantitative Methods Applied to Accounting, 11, 52-68. Dec 2022.
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WU, X. Top 10 algorithms in data mining. Knowledge and Information Systems, 14, 1-37. Dec 2007.
BLUVOL, L. M. Análise de algoritmos de machine learning e redes neurais para previsão de preços de ações do Ibovespa. 2022. M.S. Thesis. Fundação Getúlio Vargas, 2022.
BOEHMKE, B.; GREENWELL, B. Hands-On Machine Learning with R. Taylor & Francis Group, 2020. Available at: https://bradleyboehmke.github.io/HOML/. Accessed on: Sep. 16, 2025.
BROWNLEE, J. (2020). How to Develop a Naive Bayes Classifier from Scratch in Python. Machine Learning Mastery, Jan 10, 2020. Available: https://machinelearningmastery.com/classification-as-conditional-probability-and-the-naive-bayes-algorithm/. Accessed on: Sep 27, 2023.
GÉRON, A. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems. 2nd ed. O’reilly Media, 20219.
GOODFELLOW, I.; BENGIO, Y.; COURVILLE, A. Deep Learning. The MIT Press, 2016. Cambridge.
HARDESTY, L. Explained: Neural networks. MIT. Apr 14, 2017. Available at: https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414. Accessed on: Nov. 20, 2022.
HYNDMAN, R. J.; ATHANASOPOULOS, G. Forecasting: principles and practice. 3. ed. Melbourne: OTexts, 2021. Disponível em: https://otexts.com/fpp3/. Accessed on: Aug. 21, 2025.
JEONG, Y.; KIM, S.; YOON, B. An Algorithm for Supporting Decision Making in Stock Investment through Opinion Mining and Machine Learning. Proceedings of the Portland International Conference On Management Of Engineering And Technology (PICMET). Honolulu, United States of America. Aug 19-23, 2018.
KARA, Y.; BOYACIOGLU, M. A.; BAYKAN, Ö. K. Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul stock exchange. Expert Systems with Applications, 38, 5311-5319. May 2011.
KUMAR, D.; SARANGI, P. K.; VERMA, R. A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today Proceedings, 49, 3187-3191. Jan 2021.
MOHRI, M. Foundations of Machine Learning. 2nd ed. The Mit Press, 2018. Cambridge.
NABIPOUR, M.; NAYYERI, P.; JABANI, H.; MOSAVI, A.; SALWANA, E.; SHAHAB, S. Deep learning for stock market prediction. Entropy, Basel, v. 22, n. 8, p. 840. Jul 2020.
OBTHONG, M.; TANTISANTIWONG, N.; JEAMWATTHANACHAI, W.; WILLS, G. A Survey on Machine Learning for Stock Price Prediction: algorithms and techniques. Proceedings Of The 2nd International Conference On Finance, Economics, Management And Business. Lisboa, Portugal. Feb 2020.
PATEL, J.; SHAH, S.; THAKKAR, P.; KOTECHA, K. Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, 42, 259-268. Jan 2015.
SILVA, J. E.; SÁTIRO, R. M. O poder preditivo dos modelos boosting de machine learning no mercado brasileiro de ações. Brazilian Journal of Quantitative Methods Applied to Accounting, 11, 52-68. Dec 2022.
TSAI, C. F.; WANG, S. P. Stock Price Forecasting by Hybrid Machine Learning Techniques. Proceedings of the International Multiconference of Engineers and Computer Scientists. Hong Kong. 18-20. Mar 2009.
WU, X. Top 10 algorithms in data mining. Knowledge and Information Systems, 14, 1-37. Dec 2007.
Publicado
2025-10-07
Como Citar
de Paiva Marcotti, J. V. (2025). DEVELOPMENT AND COMPARISON OF MACHINE LEARNING MODELS APPLIED TO THE PREDICTION OF IBOVESPA PERFORMANCE TRENDS. A Economia Em Revista - AERE, 33(1), 18. https://doi.org/10.4025/econrev.v33i1.79396
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