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.
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References
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)
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)
Banzhaf, W., Kuo, P.D.: Network motifs in natural and artificial transcriptional regulatory networks. Biological Physics and Chemistry 4(2), 85–92 (2004)
Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modelling. Springer (2006)
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)
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)
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)
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)
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)
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)
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)
Nicolau, M., Schoenauer, M.: On the evolution of scale-free topologies with a gene regulatory network model. BioSystems 98(3), 137–148 (2009)
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)
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)
O’Neill, M., Ryan, C.: Grammatical Evolution - Evolutionary Automatic Programming in an Arbitrary Language, Genetic Programming, vol. 4. Kluwer Academic (2003)
Pring, M.J.: Technical Analysis Explained: The Successful Investor’s Guide to Spotting Investment Trends and Turning Points. McGraw-Hill (1991)
Rechenberg, I.: Evolutionsstrategie 1994. Frommann-Holzboog, Stuttgart (1994)
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Nicolau, M., O’Neill, M., Brabazon, A. (2012). Applying Genetic Regulatory Networks to Index Trading. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_43
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