Previsão da Direção do Preço de Ação Brasileira Utilizando Redes Neurais e Máquinas de Vetores Suporte
Resumo
A hipótese de mercado eficiente classifica os mercados em eficientes, que não são possíveis de prever o preço futuro, e menos eficientes, que é possível prever o preço futuro. Considerando o possível ganho financeiro ao prever o preço futuro de um ativo, diversos estudos utilizam técnicas de Inteligência Artificial para prever movimentos no mercado de ações. Este artigo se propõe a avaliar a eficácia de modelos por Máquinas de Vetores Suporte e Redes Neurais para prever o comportamento de ações brasileiras, utilizando indicadores técnicos. Os resultados indicam acurácias próximas a 50% para ambos os métodos, sugerindo que o mercado é eficiente e questionando a capacidade de previsão para o mesmo.Referências
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KHOA, B. T.; HUYNH, T. T. Forecasting stock price movement direction by machine learning algorithm. International Journal of Electrical and Computer Engineering (IJECE), v. 12, n. 6, p. 6625, 1 dez. 2022.
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MACIEL, L. Brazilian stock-market efficiency before and after COVID-19: The roles of fractality and predictability. Global Finance Journal, p. 100887–100887, 1 ago. 2023.
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DASH, R.; DASH, P. K. A hybrid stock trading framework integrating technical analysis with machine learning techniques. The Journal of Finance and Data Science, v. 2, n. 1, p. 42–57, mar. 2016.
FAMA, E. F. Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, v. 25, n. 2, p. 383–417, maio 1970.
GÖÇKEN, M. et al. Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction. Expert Systems with Applications, v. 44, p. 320–331, fev. 2016.
HENRIQUE, B. M.; SOBREIRO, V. A.; KIMURA, H. Practical machine learning: Forecasting daily financial markets directions. Expert Systems with Applications, v. 233, p. 120840, 15 dez. 2023.
HUANG, J.-Y.; TUNG, C.-L.; LIN, W.-Z. Using Social Network Sentiment Analysis and Genetic Algorithm to Improve the Stock Prediction Accuracy of the Deep Learning-Based Approach. v. 16, n. 1, 29 maio 2023.
JIANG, J. G.; LIU, J. Predicting Stock Market N-Days Ahead Using SVM Optimized by Selective Thresholds. 15 fev. 2020.
JIANG, M. et al. A novel interval dual convolutional neural network method for interval-valued stock price prediction. Pattern Recognition, p. 109920, 26 ago. 2023.
KARA, Y.; ACAR BOYACIOGLU, M.; 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, v. 38, n. 5, p. 5311–5319, maio 2011.
KHOA, B. T.; HUYNH, T. T. Forecasting stock price movement direction by machine learning algorithm. International Journal of Electrical and Computer Engineering (IJECE), v. 12, n. 6, p. 6625, 1 dez. 2022.
KIM, K. Financial time series forecasting using support vector machines. Neurocomputing, v. 55, n. 1-2, p. 307–319, set. 2003.
MACIEL, L. Brazilian stock-market efficiency before and after COVID-19: The roles of fractality and predictability. Global Finance Journal, p. 100887–100887, 1 ago. 2023.
NAYAK, R. K.; MISHRA, D.; RATH, A. K. A Naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices. Applied Soft Computing, v. 35, p. 670–680, out. 2015.
PATEL, J. et al. Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, v. 42, n. 1, p. 259–268, jan. 2015.
SCIKIT. Scikit Learn: Support Vector Machines. Disponível [link] Acessado em: 02/02/2024.
TAY, F. E. H.; CAO, L. Application of support vector machines in financial time series forecasting. Omega, v. 29, n. 4, p. 309–317, ago. 2001.
ŻBIKOWSKI, K. Using Volume Weighted Support Vector Machines with walk forward testing and feature selection for the purpose of creating stock trading strategy. Expert Systems with Applications, v. 42, n. 4, p. 1797–1805, mar. 2015.
WEINBAUM, D. et al. Option Trading Activity, News Releases, and Stock Return Predictability. Management Science, 9 set. 2022.
Publicado
21/07/2024
Como Citar
MONTEIRO, Carlos A. M.; RIBEIRO, Matheus H. D. M..
Previsão da Direção do Preço de Ação Brasileira Utilizando Redes Neurais e Máquinas de Vetores Suporte. In: ENCONTRO NACIONAL DE COMPUTAÇÃO DOS INSTITUTOS FEDERAIS (ENCOMPIF), 11. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 98-105.
ISSN 2763-8766.
DOI: https://doi.org/10.5753/encompif.2024.1983.
