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Applying Genetic Regulatory Networks to Index Trading

  • Miguel Nicolau
  • Michael O’Neill
  • Anthony Brabazon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7492)

Abstract

This paper explores the computational power of genetic regulatory network models, and the practicalities of applying these to real-world problems. The specific domain of financial trading is tackled; this is a problem where time-dependent decisions are critical, and as such benefits from the differential gene expression that these networks provide. The results obtained are on par with the best found in the literature, and highlight the applicability of these models to this type of problem.

Keywords

Gene Regulatory Network Market Index Genetic Regulatory Network Technical Indicator Grammatical Evolution 
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.

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References

  1. 1.
    Banzhaf, W.: Artificial regulatory networks and genetic programming. In: Riolo, R., Worzel, B. (eds.) Genetic Programming Theory and Practice, ch. 4, pp. 43–62. Kluwer Publishers, Boston (2003)CrossRefGoogle Scholar
  2. 2.
    Banzhaf, W., Beslon, G., Christensen, S., Foster, J.A., Képès, F., Lefort, V., Miller, J.F., Radman, M., Ramsden, J.J.: From artificial evolution to computational evolution: a research agenda. Nature Reviews Genetics 7, 729–735 (2006)CrossRefGoogle Scholar
  3. 3.
    Banzhaf, W., Kuo, P.D.: Network motifs in natural and artificial transcriptional regulatory networks. Biological Physics and Chemistry 4(2), 85–92 (2004)CrossRefGoogle Scholar
  4. 4.
    Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modelling. Springer (2006)Google Scholar
  5. 5.
    Cussat-Blanc, S., Bredeche, N., Luga, H., Duthen, Y., Schoenauer, M.: Artificial gene regulatory networks and spatial computation: A case study. In: Lenaerts, T., Giacobini, M., Bersini, H., Bourgine, P., Dorigo, M., Doursat, R. (eds.) Proceedings of 11th European Conference on Advances in Artificial Life, ECAL 2011, Paris, August 8-12, MIT Press (2011)Google Scholar
  6. 6.
    Hong, H., Lim, T., Stein, J.C.: Bad news travels slowly: Size, analyst coverage, and the profitability of momentum strategies. Journal of Finance 55(1), 265–295 (2000)CrossRefGoogle Scholar
  7. 7.
    Iba, H., Nikolaev, N.: Genetic programming polynomial models of financial data series. In: Proceedings of 2000 Congress on Evolutionary Computation, CEC 2000, San Diego, CA, USA, July 16-19, vol. 2, pp. 1459–1466 (2000)Google Scholar
  8. 8.
    Kuo, P.D., Banzhaf, W.: Small world and scale-free network topologies in an artificial regulatory network model. In: Pollack, J., Bedau, M., Husbands, P., Ikegami, T., Watson, R. (eds.) Artificial Life IX: Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems, pp. 404–409. Bradford Books, USA (2004)Google Scholar
  9. 9.
    LeBaron, B., Lakonishok, J., Brock, W.: Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance 47(5), 1731–1764 (1992)CrossRefGoogle Scholar
  10. 10.
    Leier, A., Kuo, P.D., Banzhaf, W., Burrage, K.: Evolving Noisy Oscillatory Dynamics in Genetic Regulatory Networks. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 290–299. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Lopes, R.L., Costa, E.: ReNCoDe: A Regulatory Network Computational Device. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds.) EuroGP 2011. LNCS, vol. 6621, pp. 142–153. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Nicolau, M., Schoenauer, M.: On the evolution of scale-free topologies with a gene regulatory network model. BioSystems 98(3), 137–148 (2009)CrossRefGoogle Scholar
  13. 13.
    Nicolau, M., Schoenauer, M., Banzhaf, W.: Evolving Genes to Balance a Pole. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 196–207. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    O’Neill, M., Brabazon, A., Ryan, C., Collins, J.J.: Evolving Market Index Trading Rules Using Grammatical Evolution. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, EvoCOP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 343–352. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    O’Neill, M., Ryan, C.: Grammatical Evolution - Evolutionary Automatic Programming in an Arbitrary Language, Genetic Programming, vol. 4. Kluwer Academic (2003)Google Scholar
  16. 16.
    Pring, M.J.: Technical Analysis Explained: The Successful Investor’s Guide to Spotting Investment Trends and Turning Points. McGraw-Hill (1991)Google Scholar
  17. 17.
    Rechenberg, I.: Evolutionsstrategie 1994. Frommann-Holzboog, Stuttgart (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Miguel Nicolau
    • 1
  • Michael O’Neill
    • 1
  • Anthony Brabazon
    • 1
  1. 1.Natural Computing Research & Applications GroupUniversity College DublinDublinIreland

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