IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v16y2000i3p207-236.html
   My bibliography  Save this article

Genetic Programming Prediction of Stock Prices

Author

Listed:
  • M. A. Kaboudan

Abstract

Based on predictions of stock-prices using genetic programming (or GP), a possibly profitable trading strategy is proposed. A metric quantifying the probability that a specific time series is GP-predictable is presented first. It is used to show that stock prices are predictable. GP then evolves regression models that produce reasonable one-day-ahead forecasts only. This limited ability led to the development of a single day-trading strategy (SDTS) in which trading decisions are based on GP-forecasts of daily highest and lowest stock prices. SDTS executed for fifty consecutive trading days of six stocks yielded relatively high returns on investment.

Suggested Citation

  • M. A. Kaboudan, 2000. "Genetic Programming Prediction of Stock Prices," Computational Economics, Springer;Society for Computational Economics, vol. 16(3), pages 207-236, December.
    • Handle: RePEc:kap:compec:v:16:y:2000:i:3:p:207-236
    as

    Download full text from publisher

    File URL: http://journals.kluweronline.com/issn/0927-7099/contents
    Download Restriction: Access to the full text of the articles in this series is restricted.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Massimiliano Kaucic, 2009. "Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 34(2), pages 173-193, September.
    2. Phillips, Peter C.B., 2005. "Automated Discovery In Econometrics," Econometric Theory, Cambridge University Press, vol. 21(1), pages 3-20, February.
    3. Marcos Álvarez-Díaz & Lucy Amigo Dobaño, 2003. "Métodos No-Lineales De Predicción En El Mercado De Valores Tecnológicos En España. Una Verificación De La Hipótesis Débil De Eficiencia," Working Papers 0303, Universidade de Vigo, Departamento de Economía Aplicada.
    4. Marco Corazza & A. Malliaris & Elisa Scalco, 2010. "Nonlinear Bivariate Comovements of Asset Prices: Methodology, Tests and Applications," Computational Economics, Springer;Society for Computational Economics, vol. 35(1), pages 1-23, January.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
    6. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming," IREA Working Papers 201711, University of Barcelona, Research Institute of Applied Economics, revised May 2017.
    7. Marcos Alvarez Díaz & Manuel González Gómez, 2003. "Modelización semiparamétrica y validación teórica del método de valoración contingente. Aplicación de un algoritmo genético," Hacienda Pública Española / Review of Public Economics, IEF, vol. 164(1), pages 29-47, march.
    8. de Menezes, Lilian M. & Nikolaev, Nikolay Y., 2006. "Forecasting with genetically programmed polynomial neural networks," International Journal of Forecasting, Elsevier, vol. 22(2), pages 249-265.
    9. Forouzanfar, Mehdi & Doustmohammadi, A. & Hasanzadeh, Samira & Shakouri G, H., 2012. "Transport energy demand forecast using multi-level genetic programming," Applied Energy, Elsevier, vol. 91(1), pages 496-503.
    10. Manahov, Viktor & Hudson, Robert & Hoque, Hafiz, 2015. "Return predictability and the ‘wisdom of crowds’: Genetic Programming trading algorithms, the Marginal Trader Hypothesis and the Hayek Hypothesis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 85-98.
    11. Chen, Yan & Wang, Xuancheng, 2015. "A hybrid stock trading system using genetic network programming and mean conditional value-at-risk," European Journal of Operational Research, Elsevier, vol. 240(3), pages 861-871.
    12. Alvarez-Diaz, Marcos & Caballero Miguez, Gonzalo, 2008. "The quality of institutions: A genetic programming approach," Economic Modelling, Elsevier, vol. 25(1), pages 161-169, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:16:y:2000:i:3:p:207-236. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.