Computational Economics

, Volume 52, Issue 1, pp 125–144 | Cite as

An Automated Investing Method for Stock Market Based on Multiobjective Genetic Programming

  • Alexandre Pimenta
  • Ciniro A. L. Nametala
  • Frederico G. Guimarães
  • Eduardo G. Carrano


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.


Genetic 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.


  1. Abbass, H. A. (2001). A memetic pareto evolutionary approach to artificial neural networks. Lecture Notes in Artificial Intelligence, 2256, 1–12.Google Scholar
  2. Alfaro-Cid, E., Sharman, K., & Esparcia-Alcázar, A. I. (2014). Genetic programming and serial processing for time series classification. Evolutionary Computation, 22(2), 265–285.CrossRefGoogle Scholar
  3. Allen, F., & Karjalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of financial Economics, 51(2), 245–271.CrossRefGoogle Scholar
  4. 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
  5. Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques—Part ii: Soft computing methods. Expert Systems with Applications, 36(3), 5932–5941. Times Cited: 53 54.CrossRefGoogle Scholar
  6. Barros, R. C., Basgalupp, M. P., De Carvalho, A. C. P. L. F., & Freitas, A. A. (2012). A survey of evolutionary algorithms for decision-tree induction. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(3), 291–312.CrossRefGoogle Scholar
  7. Carrano, E. G., Wanner, E. F., & Takahashi, R. H. C. (2011). A multi-criteria statistical based comparison methodology for evaluating evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 15, 848–870.CrossRefGoogle Scholar
  8. Cleveland, W. S. (1981). Lowess: A program for smoothing scatterplots by robust locally weighted regression. London: American Statistician.Google Scholar
  9. 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
  10. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to Algorithms (3rd ed.). Cambridge: MIT Press.Google Scholar
  11. 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
  12. Dabhi, V. K., & Chaudhary, S. (2015). Financial time series modeling and prediction using postfix-gp. Computational Economics, 1–35.Google Scholar
  13. Elder, A. (1993). Trading for a living: Psychology, trading tactics, money management (Vol. 31). New York: Wiley.Google Scholar
  14. Espejo, P. G., Ventura, S., & Herrera, F. (2010). A survey on the application of genetic programming to classification. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 40(2), 121–144.CrossRefGoogle Scholar
  15. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.CrossRefGoogle Scholar
  16. Hansen, L. K., & Salamon, P. (1990). Neural network ensembles. IEEE Transactions on Pattern Analysis & Machine Intelligence, 12(10), 993–1001.CrossRefGoogle Scholar
  17. Kalyanmoy, D., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.CrossRefGoogle Scholar
  18. Kattan, A., Fatima, S., & Arif, M. (2015). Time-series event-based prediction: An unsupervised learning framework based on genetic programming. Information Sciences, 301, 99–123.CrossRefGoogle Scholar
  19. Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection (Vol. 1). Cambridge: MIT Press.Google Scholar
  20. Luke, S., Panait, L., Balan, G., Paus, S., Skolicki, Z., Popovici, E., et al. (2004). A java-based evolutionary computation research system (online).
  21. Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. Harmondsworth: Penguin.Google Scholar
  22. 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
  23. Opitz, W. D., & Shavlik, J. W. (1996). Actively searching for an effective neural network ensemble. Connection Science, 8(3–4), 337–354.CrossRefGoogle Scholar
  24. Perrone, M. P., & Cooper, L. N. (1992). When networks disagree: Ensemble methods for hybrid neural networks. Technical report, DTIC Document.Google Scholar
  25. 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
  26. Poli, R., Langdon, W. B., McPhee, N. F., & Koza, J. R. (2008). A field guide to genetic programming. Raleigh: Scholar
  27. Potvin, J.-Y., Soriano, P., & Vallée, M. (2004). Generating trading rules on the stock markets with genetic programming. Computers & Operations Research, 31(7), 1033–1047.CrossRefGoogle Scholar
  28. Processo de impeachment de dilma rousseff. (2016).
  29. Sadaei, H. J., Enayatifar, R., Guimaraes, F. G., Mahmud, M., & Alzamil, Z. A. (2016). Combining ARFIMA models and fuzzy time series for the forecast of long memory time series. Neurocomputing, 175, 782–796.CrossRefGoogle Scholar
  30. Talarposhti, F. M., Sadaei, H. J., Enayatifar, R., Guimaraes, F. G., Mahmud, M., & Eslami, T. (2016). Stock market forecasting by using a hybrid model of exponential fuzzy time series. International Journal of Approximate Reasoning, 70, 79–98.CrossRefGoogle Scholar
  31. Vasilakis, G. A., Theofilatos, K. A., Georgopoulos, E. F., Karathanasopoulos, A., & Likothanassis, S. D. (2013). A genetic programming approach for EUR/USD exchange rate forecasting and trading. Computational Economics, 42(4), 415–431.CrossRefGoogle Scholar
  32. Yadolah, D. (2008). The concise encyclopedia of statistics. Berlin: Springer.Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Alexandre Pimenta
    • 1
    • 2
  • Ciniro A. L. Nametala
    • 1
    • 2
  • Frederico G. Guimarães
    • 3
  • Eduardo G. Carrano
    • 3
  1. 1.Department of ComputingInstituto Federal Minas GeraisFormigaBrazil
  2. 2.Graduate Program in Electrical EngineeringUniversidade Federal de Minas Gerais (UFMG)Belo HorizonteBrazil
  3. 3.Department of Electrical EngineeringUniversidade Federal de Minas Gerais (UFMG)Belo HorizonteBrazil

Personalised recommendations