Evolving Market Index Trading Rules Using Grammatical Evolution

  • Michael O’Neill
  • Anthony Brabazon
  • Conor Ryan
  • J. J. Collins
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)


This study examines the potential of an evolutionary automatic programming methodology to uncover a series of useful technical trading rules for the UK FTSE 100 stock index. Index values for the period 26/4/1984 to 4/12/1997 are used to train and test the model. The preliminary findings indicate that the methodology has much potential, outperforming the benchmark strategy adopted.


Trading System Stock Index Trading Rule Technical Indicator Average Indicator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    O’Neill M., Ryan C. (2001) Grammatical Evolution. IEEE Trans. Evolutionary Computation. 2001.Google Scholar
  2. 2.
    Ryan C., Collins J.J., O’Neill M. (1998). Grammatical Evolution: Evolving Programs for an Arbitrary Language. Lecture Notes in Computer Science 1391, Proceedings of the First European Workshop on Genetic Programming, pages 83–95. Springer-Verlag.Google Scholar
  3. 3.
    Murphy, John J. (1999). Technical Analysis of the Financial Markets, New York: New York Institute of Finance.Google Scholar
  4. 4.
    Brock, W., Lakonishok, J. and LeBaron B. (1992). ‘Simple Technical Trading Rules and the Stochastic Properties of Stock Returns’, Journal of Finance, 47(5):1731–1764.CrossRefGoogle Scholar
  5. 5.
    Hong, H., Lim, T. and Stein, J. (1999). ‘Bad News Travels Slowly: Size, Analyst Coverage and the Profitability of Momentum Strategies’, Research Paper No. 1490, Graduate School of Business, Stanford University.Google Scholar
  6. 6.
    Chan, L.K.C., Jegadeesh, N. and Lakonishok, J. (1996). ‘Momentum strategies’, Journal of Finance, Vol. 51,No. 5, pp. 1681–1714.CrossRefGoogle Scholar
  7. 7.
    Dissanaike, G. (1997). ‘Do stock market investors overreact?’, Journal of Business Finance & Accounting (UK), Vol. 24,No.1, pp. 27–50.CrossRefGoogle Scholar
  8. 8.
    Cross, F. (1973). ‘The Behaviour of Stock prices on Friday and Monday’, Financial Analysts'’Journal, Vol. 29(6), pp.67–74.CrossRefGoogle Scholar
  9. 9.
    DeBondt, W. and Thaler, R. (1987). ‘Further Evidence on Investor Overreaction and Stock Market Seasonality’, Journal of Finance, Vol. 42(3):pp.557–581.CrossRefGoogle Scholar
  10. 10.
    Iba H. and Nikolaev N. (2000). ‘Genetic Programming Polynomial Models of Financial Data Series’, In Proc. of CEC 2000, pp. 1459–1466, IEEE Press.Google Scholar
  11. 11.
    Allen, F., Karjalainen, R. (1999) Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51, pp. 245–271, 1999.CrossRefGoogle Scholar
  12. 12.
    Colin, A. (1994). ‘Genetic Algorithms for Financial Modelling’, in Guido Deboeck (Editor) (1994). Trading on the edge: neural, genetic and fuzzy systems for chaotic and financial markets, New York: John Wiley & Sons Inc.Google Scholar
  13. 13.
    Bauer R. (1994). Genetic Algorithms and Investment Strategies, New York: John Wiley & Sons Inc.Google Scholar
  14. 14.
    Neely, C., Weller P. and Dittmar, R. (1997). ‘Is technical analysis in the foreign exchange market profitable? A genetic programming approach’, Journal of Financial and Quantitative Analysis, Vol. 32,No. 4, pp. 405–428.CrossRefGoogle Scholar
  15. 15.
    Deboeck G. (1994). ‘Using GAs to optimise a trading system’, in Guido Deboeck (Editor) (1994). Trading on the edge: neural, genetic and fuzzy systems for chaotic and financial markets, New York: John Wiley & Sons Inc.Google Scholar
  16. 16.
    Brown, S., Goetzmann W. and Kumar A. (1998). ‘The Dow Theory: William Peter Hamilton’s Track Record Reconsidered’, Journal of Finance, 53(4): 1311–1333.CrossRefGoogle Scholar
  17. 17.
    Pring, M. (1991). Technical analysis explained: the successful investor’s guide to spotting investment trends and turning points, New York: Mc Graw-Hill Inc.Google Scholar
  18. 18.
    Koza, J. (1992). Genetic Programming. MIT Press.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Michael O’Neill
    • 2
  • Anthony Brabazon
    • 1
  • Conor Ryan
    • 2
  • J. J. Collins
    • 2
  1. 1.Dept. Of AccountancyUniversity College DublinIreland
  2. 2.Dept. Of Computer Science And Information SystemsUniversity of LimerickIreland

Personalised recommendations