An Automated Investing Method for Stock Market Based on Multiobjective Genetic Programming
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Stock market automated investing is an area of strong interest for the academia, casual, and professional investors. In addition to conventional market methods, various sophisticated techniques have been employed to deal with such a problem, such as ARCH/GARCH predictors, artificial neural networks, fuzzy logic, etc. A computational system that combines a conventional market method (technical analysis), genetic programming, and multiobjective optimization is proposed in this work. This system was tested in six historical time series of representative assets from Brazil stock exchange market (BOVESPA). The proposed method led to profits considerably higher than the variation of the assets in the period. The financial return was positive even in situations in which the share lost market value.
KeywordsGenetic programming Multiobjective optimization Technical analysis Stock exchange market Feature selection BOVESPA
The authors would like to thank the Brazilian agencies CAPES, CNPq, and FAPEMIG for the financial support. Funding was provided by Conselho Nacional de Desenvolvimento Científico e Tecnológico and Fundação de Amparo à Pesquisa do Estado de Minas Gerais.
- Abbass, H. A. (2001). A memetic pareto evolutionary approach to artificial neural networks. Lecture Notes in Artificial Intelligence, 2256, 1–12.Google Scholar
- Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying Stock Market Forecasting Techniques - Part I: Conventional Methods. Journal of Computational Optimization in Economics and Finance, 2(1), 45–92.Google Scholar
- Cleveland, W. S. (1981). Lowess: A program for smoothing scatterplots by robust locally weighted regression. London: American Statistician.Google Scholar
- Cook, R. D., & Hawkins, D. M. (1990). Unmasking multivariate outliers and leverage points: Comment. Journal of the American Statistical Association, 85(411), 640–644.Google Scholar
- Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to Algorithms (3rd ed.). Cambridge: MIT Press.Google Scholar
- Cortez, P. A. R. (2002). Modelos Inspirados na Natureza para a Previsão de Séries Temporais. 2002. 188 f. PhD thesis, Tese (Doutorado em Informática)–Departamento de Informática, Universidade do Minho, Braga.Google Scholar
- Dabhi, V. K., & Chaudhary, S. (2015). Financial time series modeling and prediction using postfix-gp. Computational Economics, 1–35.Google Scholar
- Elder, A. (1993). Trading for a living: Psychology, trading tactics, money management (Vol. 31). New York: Wiley.Google Scholar
- Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection (Vol. 1). Cambridge: MIT Press.Google Scholar
- Luke, S., Panait, L., Balan, G., Paus, S., Skolicki, Z., Popovici, E., et al. (2004). A java-based evolutionary computation research system (online).http://cs.gmu.edu/~eclab/projects/ecj.
- Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. Harmondsworth: Penguin.Google Scholar
- Myszkowski, P. B., & Rachwalski, Ł. (2009). Trading rule discovery on warsaw stock exchange using coevolutionary algorithms. In Proceedings of the international multiconference on computer science and information technology (vol. 3, pp. 81–88).Google Scholar
- Perrone, M. P., & Cooper, L. N. (1992). When networks disagree: Ensemble methods for hybrid neural networks. Technical report, DTIC Document.Google Scholar
- Pimenta, A., Carrano, E. G., Guimaraes, F. G., Nametala, L., Aparecido, C., & Takahashi, R. H. C. (2014). Goldminer: A genetic programming based algorithm applied to brazilian stock market. In 2014 IEEE symposium on computational intelligence and data mining (CIDM) (pp. 397–402). IEEE.Google Scholar
- Poli, R., Langdon, W. B., McPhee, N. F., & Koza, J. R. (2008). A field guide to genetic programming. Raleigh: Lulu.com.Google Scholar
- Processo de impeachment de dilma rousseff. (2016). http://pt.wikipedia.org/wiki/Processo_de_impeachment_de_Dilma_Rousseff/.
- Yadolah, D. (2008). The concise encyclopedia of statistics. Berlin: Springer.Google Scholar