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

Author

Listed:
  • Alexandre Pimenta

    (Instituto Federal Minas Gerais
    Universidade Federal de Minas Gerais (UFMG))

  • Ciniro A. L. Nametala

    (Instituto Federal Minas Gerais
    Universidade Federal de Minas Gerais (UFMG))

  • Frederico G. Guimarães

    (Universidade Federal de Minas Gerais (UFMG))

  • Eduardo G. Carrano

    (Universidade Federal de Minas Gerais (UFMG))

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.

Suggested Citation

  • Alexandre Pimenta & Ciniro A. L. Nametala & Frederico G. Guimarães & Eduardo G. Carrano, 2018. "An Automated Investing Method for Stock Market Based on Multiobjective Genetic Programming," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 125-144, June.
    • Handle: RePEc:kap:compec:v:52:y:2018:i:1:d:10.1007_s10614-017-9665-9
    DOI: 10.1007/s10614-017-9665-9
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    References listed on IDEAS

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    1. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    2. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
    3. Georgios Vasilakis & Konstantinos Theofilatos & Efstratios Georgopoulos & Andreas Karathanasopoulos & Spiros Likothanassis, 2013. "A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading," Computational Economics, Springer;Society for Computational Economics, vol. 42(4), pages 415-431, December.
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