Análise de Séries Temporais Aplicadas ao Mercado Financeiro com o uso de Algoritmos Genéticos e Lógica Nebulosa
Resumo
Este artigo apresenta uma nova metodologia de parametrização do indicador de análise técnica do mercado financeiro chamado Moving Average Convergence-Divergence (MACD) utilizando algoritmos genéticos e lógica nebulosa. Esse indicador, desenvolvido por Gerald Appel, é utilizado por analistas do mercado financeiro, quando aplicado ao preço de valores mobiliários, para indicar o melhor momento de compra e venda das ações. A metodologia proposta foi validada utilizando as ações da Petrobras PETR4, no período entre fevereiro de 2005 e agosto de 2008, alcançando um lucro superior a 70%.Referências
Abe, M. (2009). Manual de Análise Técnica: Essência e Estratégias Avançadas. Novatec, 1 edition.
Cavalcante, F. (2005). Mercado de capitais: o que é, como funciona. Rio de Janeiro.
Chen, C. W. S., Liu, F. C., and So, M. K. P. (2008a). Heavy-tailed-distributed threshold stochastic volatilit models in financial time series. Australian & New Zealand Journal of Statistics, 50(1):29–51.
Chen, T.-L., Cheng, C.-H., and Teoh, H. J. (2007). Fuzzy time-series based on fibonacci sequence for stock price forecasting. Physica A, 380:377–390.
Chen, Y.-M., Chung, T.-Y., Lai, M.-Y., and Hsu, C.-H. (2008b). Macd-based motion detection approach in heterogeneous networks. EURASIP Journal onWireless Communications and Networking, 2008.
Correia, J. S. (2008). Operando na Bolsa de Valores Utilizando Análise Técnica. Brasil.
Darwin, C. (2004). A Origem Das Espécies. Martin Claret.
Debastiani, C. A. (2008). Análise Técnica de Ações. Brasil.
Elder, A. (1993). Trading for a living: psychology, trading tactics, money management. New York.
Engelbrecht, A. P. (2007). Computational Intelligence: An Introduction. John Wiley and Sons.
Gardner Jr., E. S. and Diaz-Saiz, J. (2008). Exponential smoothing in the telecommunications data. International Journal of Forecasting, 24:170–174.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.
Huang, C.-Y. and Tzeng, G.-H. (2008). Multiple generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method. Technological Forecasting & Social Change, 75:12–31.
Huang, W., Lai, K. K., and Nakamori, Y. (2007). Neural networks in finance and economics forecasting. International Journal of Information Technology & Decision Making, 6:113–140.
Janikow, C. Z. and Michalewicz, Z. (1991). An experimental comparison of binary and floating point representations in genetic algorithms. In Belew, R. and Booker, L., editors, Proceedings of the Fourth International Conference on Genetic Algorithms, pages 31–36, San Mateo, CA. Morgan Kaufman.
Mühlenbein, H. and Schlierkamp-Voosen, D. (1993). Predictive models for the breeder genetic algorithm: I. continuous parameter optimization. Evolutionary Computation, 1(1):25–49.
Rojas, I., Valenzuela, O., Rojas, F., Guillén, A., Herrera, L. J., Pomares, H., Marquez, L., and Pasadas, M. (2008). Soft-computing techniques and arma model for time series prediction. Neurocomputing, 71(4-6):519–537.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, pages 338–353.
Cavalcante, F. (2005). Mercado de capitais: o que é, como funciona. Rio de Janeiro.
Chen, C. W. S., Liu, F. C., and So, M. K. P. (2008a). Heavy-tailed-distributed threshold stochastic volatilit models in financial time series. Australian & New Zealand Journal of Statistics, 50(1):29–51.
Chen, T.-L., Cheng, C.-H., and Teoh, H. J. (2007). Fuzzy time-series based on fibonacci sequence for stock price forecasting. Physica A, 380:377–390.
Chen, Y.-M., Chung, T.-Y., Lai, M.-Y., and Hsu, C.-H. (2008b). Macd-based motion detection approach in heterogeneous networks. EURASIP Journal onWireless Communications and Networking, 2008.
Correia, J. S. (2008). Operando na Bolsa de Valores Utilizando Análise Técnica. Brasil.
Darwin, C. (2004). A Origem Das Espécies. Martin Claret.
Debastiani, C. A. (2008). Análise Técnica de Ações. Brasil.
Elder, A. (1993). Trading for a living: psychology, trading tactics, money management. New York.
Engelbrecht, A. P. (2007). Computational Intelligence: An Introduction. John Wiley and Sons.
Gardner Jr., E. S. and Diaz-Saiz, J. (2008). Exponential smoothing in the telecommunications data. International Journal of Forecasting, 24:170–174.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.
Huang, C.-Y. and Tzeng, G.-H. (2008). Multiple generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method. Technological Forecasting & Social Change, 75:12–31.
Huang, W., Lai, K. K., and Nakamori, Y. (2007). Neural networks in finance and economics forecasting. International Journal of Information Technology & Decision Making, 6:113–140.
Janikow, C. Z. and Michalewicz, Z. (1991). An experimental comparison of binary and floating point representations in genetic algorithms. In Belew, R. and Booker, L., editors, Proceedings of the Fourth International Conference on Genetic Algorithms, pages 31–36, San Mateo, CA. Morgan Kaufman.
Mühlenbein, H. and Schlierkamp-Voosen, D. (1993). Predictive models for the breeder genetic algorithm: I. continuous parameter optimization. Evolutionary Computation, 1(1):25–49.
Rojas, I., Valenzuela, O., Rojas, F., Guillén, A., Herrera, L. J., Pomares, H., Marquez, L., and Pasadas, M. (2008). Soft-computing techniques and arma model for time series prediction. Neurocomputing, 71(4-6):519–537.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, pages 338–353.
Publicado
20/07/2009
Como Citar
MARQUES, Frederico C. R.; GOMES, Rogério Martins.
Análise de Séries Temporais Aplicadas ao Mercado Financeiro com o uso de Algoritmos Genéticos e Lógica Nebulosa. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 7. , 2009, Bento Gonçalves/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2009
.
p. 122-131.
ISSN 2763-9061.
