Previsão das movimentações de ativos financeiros usando ensemble learning para implementação de um portfólio descorrelacionado
Resumo
Este artigo explora a aplicação das técnicas de ensemble learning bagging, boosting, stacking e voting, em algoritmos de negociação voltados para um portfólio de ativos descorrelacionados no mercado de futuros brasileiro. Com base nos princípios da Teoria Moderna do Portfólio (TMP), a pesquisa busca mitigar riscos e melhorar a precisão das previsões, aplicando o método walk-forward para ajustar continuamente os modelos aos dados. A eficácia das previsões é avaliada com métricas tradicionais, enquanto o desempenho das estratégias de negociação é analisado considerando a integração das previsões.Referências
Asad, M. (2015). Optimized stock market prediction using ensemble learning. In 9th International Conference on Application of Information and Communication Technologies (AICT), Rostov on Don, Russia.
Barber, B. and Odean, T. (2000). Trading is hazardous to your wealth: the common stock investment performance of individual investors. SSRN. [link].
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., and Oliveira, A. L. I. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55:194–211.
Chekhlov, A., Uryasev, S., and Zabarankin, M. (2005). Drawdown measure in portfolio optimization. International Journal of Theoretical and Applied Finance, 8(1):13–58.
Golosnoy, V., Gribisch, B., and Seifert, M. (2022). Sample and realized minimum variance portfolios: Estimation, statistical inference, and tests. Wiley Interdisciplinary Reviews: Computational Statistics, 14(5):1–18.
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer.
Haykin, S. S. (1999). Neural networks: a comprehensive foundation. Pearson, 2nd edition.
Kumar, R. (2019). Machine learning quick reference: Quick and essential machine learning hacks for training smart data models. Packt Publishing.
Kyriakides, G. and Margaritis, K. (2019). Hands-On Ensemble Learning with Python: Build highly optimized ensemble machine learning models using scikit-learn and Keras. Packt Publishing.
Liu, Y. (2019). Python Machine Learning By Example. Packt Publishing.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1):77–91.
Nti, I., Adekoya, A., and Weyori, B. (2020). A comprehensive evaluation of ensemble learning for stock-market prediction. Journal of Big Data, 7(20):1–40.
Pinsky, E. (2018). Mathematical foundation for ensemble machine learning and ensemble portfolio analysis. SSRN, pages 1–48.
Santos, A. A. P. and Tessari, C. (2012). Técnicas quantitativas de otimização de carteiras aplicadas ao mercado de ações brasileiro. Revista Brasileira de Finanças, 10(3):369–363.
Sarkar, D. and Natarajan, V. (2019). Ensemble Machine Learning Cookbook: Over 35 practical recipes to explore ensemble machine learning techniques using Python. Packt Publishing.
Shen, W., Wang, B., Pu, J., and Wang, J. (2019). The kelly growth optimal portfolio with ensemble learning. In The Thirty-Third AAAI Conference on Artificial Intelligence, pages 1134–1141.
Barber, B. and Odean, T. (2000). Trading is hazardous to your wealth: the common stock investment performance of individual investors. SSRN. [link].
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., and Oliveira, A. L. I. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55:194–211.
Chekhlov, A., Uryasev, S., and Zabarankin, M. (2005). Drawdown measure in portfolio optimization. International Journal of Theoretical and Applied Finance, 8(1):13–58.
Golosnoy, V., Gribisch, B., and Seifert, M. (2022). Sample and realized minimum variance portfolios: Estimation, statistical inference, and tests. Wiley Interdisciplinary Reviews: Computational Statistics, 14(5):1–18.
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer.
Haykin, S. S. (1999). Neural networks: a comprehensive foundation. Pearson, 2nd edition.
Kumar, R. (2019). Machine learning quick reference: Quick and essential machine learning hacks for training smart data models. Packt Publishing.
Kyriakides, G. and Margaritis, K. (2019). Hands-On Ensemble Learning with Python: Build highly optimized ensemble machine learning models using scikit-learn and Keras. Packt Publishing.
Liu, Y. (2019). Python Machine Learning By Example. Packt Publishing.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1):77–91.
Nti, I., Adekoya, A., and Weyori, B. (2020). A comprehensive evaluation of ensemble learning for stock-market prediction. Journal of Big Data, 7(20):1–40.
Pinsky, E. (2018). Mathematical foundation for ensemble machine learning and ensemble portfolio analysis. SSRN, pages 1–48.
Santos, A. A. P. and Tessari, C. (2012). Técnicas quantitativas de otimização de carteiras aplicadas ao mercado de ações brasileiro. Revista Brasileira de Finanças, 10(3):369–363.
Sarkar, D. and Natarajan, V. (2019). Ensemble Machine Learning Cookbook: Over 35 practical recipes to explore ensemble machine learning techniques using Python. Packt Publishing.
Shen, W., Wang, B., Pu, J., and Wang, J. (2019). The kelly growth optimal portfolio with ensemble learning. In The Thirty-Third AAAI Conference on Artificial Intelligence, pages 1134–1141.
Publicado
05/11/2024
Como Citar
BAPTISTA, Gabriel Costa; RODRIGUES, Carlos Alberto.
Previsão das movimentações de ativos financeiros usando ensemble learning para implementação de um portfólio descorrelacionado. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 24. , 2024, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 149-158.
DOI: https://doi.org/10.5753/erbase.2024.4399.
