Sistema de tomada de decisão no mercado de ações utilizando aprendizado de máquina
Resumo
Este trabalho se concentra no estudo da aplicação do modelo de Random Forest na classificação dos melhores momentos de compra e venda de um ativo no mercado de ações brasileiro, funcionando assim como um sistema de trading. O aprendizado da classificação dos momentos de compra e venda é realizado com uso de indicadores de mercado, calculados a partir da série histórica de preços. Para possibilitar a aplicação de dados não estacionários no modelo, durante a etapa de pré-processamento, a técnica de diferenciação fracionária é aplicada. Além disso, este trabalho inclui o gerenciamento de risco na estratégia de trading.Referências
Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (2017). Classification and regression trees. Routledge.
Chowdhury, U. N., Chakravarty, S. K., Hossain, M. T., and Ahmad, S. (2019). Empirical mode decomposition based ensemble random forest model for financial time series forecasting.
De Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons.
Dickey, D. A. and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a):427–431.
Genuer, R. and Poggi, J.-M. (2020). Random forests. In Random Forests with R, pages 33–55. Springer.
Ho, T. K. (1995). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition, volume 1, pages 278–282. IEEE.
Hosking, J. M.(1981),“. Fractional differencing. Biometrika, 68(1):165–76.
Huang, W., Nakamori, Y., and Wang, S.-Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & operations research, 32(10):2513– 2522.
Jang, G.-S., Lai, F., Jiang, B.-W., and Chien, L.-H. (1991). An intelligent trend prediction and reversal recognition system using dual-module neural networks. In Proceedings First International Conference on Artificial Intelligence Applications on Wall Street, pages 42–43. IEEE Computer Society.
Kumar, M. and Thenmozhi, M. (2006). Forecasting stock index movement: A comparison of support vector machines and random forest. In Indian institute of capital markets 9th capital markets conference paper.
Leung, M. T., Daouk, H., and Chen, A.-S. (2000). Forecasting stock indices: a comparison of classification and level estimation models. International Journal of forecasting, 16(2):173–190.
Nascimento, D., Costa, A., and Bianchi, R. (2020). Stock trading classifier with multichannel convolutional neural network. In Anais do XVII Encontro Nacional de Inteligência Artificial e Computacional, pages 282–293. SBC.
Patel, J., Shah, S., Thakkar, P., and Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1):259–268.
Simon, P. (2013). Too big to ignore: the business case for big data, volume 72. John Wiley & Sons.
White, H. (1988). Economic prediction using neural networks: The case of ibm daily stock returns. In ICNN, volume 2, pages 451–458.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (2017). Classification and regression trees. Routledge.
Chowdhury, U. N., Chakravarty, S. K., Hossain, M. T., and Ahmad, S. (2019). Empirical mode decomposition based ensemble random forest model for financial time series forecasting.
De Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons.
Dickey, D. A. and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a):427–431.
Genuer, R. and Poggi, J.-M. (2020). Random forests. In Random Forests with R, pages 33–55. Springer.
Ho, T. K. (1995). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition, volume 1, pages 278–282. IEEE.
Hosking, J. M.(1981),“. Fractional differencing. Biometrika, 68(1):165–76.
Huang, W., Nakamori, Y., and Wang, S.-Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & operations research, 32(10):2513– 2522.
Jang, G.-S., Lai, F., Jiang, B.-W., and Chien, L.-H. (1991). An intelligent trend prediction and reversal recognition system using dual-module neural networks. In Proceedings First International Conference on Artificial Intelligence Applications on Wall Street, pages 42–43. IEEE Computer Society.
Kumar, M. and Thenmozhi, M. (2006). Forecasting stock index movement: A comparison of support vector machines and random forest. In Indian institute of capital markets 9th capital markets conference paper.
Leung, M. T., Daouk, H., and Chen, A.-S. (2000). Forecasting stock indices: a comparison of classification and level estimation models. International Journal of forecasting, 16(2):173–190.
Nascimento, D., Costa, A., and Bianchi, R. (2020). Stock trading classifier with multichannel convolutional neural network. In Anais do XVII Encontro Nacional de Inteligência Artificial e Computacional, pages 282–293. SBC.
Patel, J., Shah, S., Thakkar, P., and Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1):259–268.
Simon, P. (2013). Too big to ignore: the business case for big data, volume 72. John Wiley & Sons.
White, H. (1988). Economic prediction using neural networks: The case of ibm daily stock returns. In ICNN, volume 2, pages 451–458.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Publicado
06/08/2023
Como Citar
SANTOS, Thiago R. S.; COSTA, Oswaldo L. V..
Sistema de tomada de decisão no mercado de ações utilizando aprendizado de máquina. In: BRAZILIAN WORKSHOP ON ARTIFICIAL INTELLIGENCE IN FINANCE (BWAIF), 2. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 25-36.
DOI: https://doi.org/10.5753/bwaif.2023.229401.
