Skip to main content
Log in

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

  • Published:
Computational Economics Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. The depth of a tree is the length of the longest path between the root and the leaves (Cormen et al. 2009).

  2. In finance, leverage is the general term for any technique that is used to multiply the profitability through debt.

References

  • Abbass, H. A. (2001). A memetic pareto evolutionary approach to artificial neural networks. Lecture Notes in Artificial Intelligence, 2256, 1–12.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Allen, F., & Karjalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of financial Economics, 51(2), 245–271.

    Article  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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  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.

  • Dabhi, V. K., & Chaudhary, S. (2015). Financial time series modeling and prediction using postfix-gp. Computational Economics, 1–35.

  • Elder, A. (1993). Trading for a living: Psychology, trading tactics, money management (Vol. 31). New York: Wiley.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.

    Article  Google Scholar 

  • Hansen, L. K., & Salamon, P. (1990). Neural network ensembles. IEEE Transactions on Pattern Analysis & Machine Intelligence, 12(10), 993–1001.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  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).

  • Opitz, W. D., & Shavlik, J. W. (1996). Actively searching for an effective neural network ensemble. Connection Science, 8(3–4), 337–354.

    Article  Google Scholar 

  • Perrone, M. P., & Cooper, L. N. (1992). When networks disagree: Ensemble methods for hybrid neural networks. Technical report, DTIC Document.

  • 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.

  • Poli, R., Langdon, W. B., McPhee, N. F., & Koza, J. R. (2008). A field guide to genetic programming. Raleigh: Lulu.com.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Processo de impeachment de dilma rousseff. (2016). http://pt.wikipedia.org/wiki/Processo_de_impeachment_de_Dilma_Rousseff/.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Yadolah, D. (2008). The concise encyclopedia of statistics. Berlin: Springer.

    Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Frederico G. Guimarães.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pimenta, A., Nametala, C.A.L., Guimarães, F.G. et al. An Automated Investing Method for Stock Market Based on Multiobjective Genetic Programming. Comput Econ 52, 125–144 (2018). https://doi.org/10.1007/s10614-017-9665-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10614-017-9665-9

Keywords

Navigation