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Using Hyperheuristics under a GP Framework for Financial Forecasting

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Learning and Intelligent Optimization (LION 2011)

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Abstract

Hyperheuristics have successfully been used in the past for a number of search and optimization problems. To the best of our knowledge, they have not been used for financial forecasting. In this paper we use a simple hyperheuristics framework to investigate whether we can improve the performance of a financial forecasting tool called EDDIE 8. EDDIE 8 allows the GP (Genetic Programming) to search in the search space of indicators for solutions, instead of using pre-specified ones; as a result, its search area is quite big and sometimes solutions can be missed due to ineffective search. We thus use two different heuristics and two different mutators combined under a simple hyperheuristics framework. We run experiments under five datasets from FTSE 100 and discover that on average, the new version can return improved solutions. In addition, the rate of missing opportunities reaches it’s minimum value, under all datasets tested in this paper. This is a very important finding, because it indicates that thanks to the hyperheuristics EDDIE 8 has the potential of missing less forecasting opportunities. Finally, results suggest that thanks to the introduction of hyperheuristics, the search has become more effective and more areas of the space have been explored.

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References

  1. Agapitos, A., O’Neill, M., Brabazon, A.: Evolutionary learning of technical trading rules without data-mining bias. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 294–303. Springer, Heidelberg (2010)

    Google Scholar 

  2. Allen, F., Karjalainen, R.: Using genetic algorithms to find technical trading rules. Journal of Financial Economics 51, 245–271 (1999)

    Article  Google Scholar 

  3. Austin, M., Bates, G., Dempster, M., Leemans, V., Williams, S.: Adaptive systems for foreign exchange trading. Quantitative Finance 4(4), 37–45 (2004)

    Article  Google Scholar 

  4. Backus, J.: The syntax and semantics of the proposed international algebraic language of Zurich. In: International Conference on Information Processing, pp. 125–132. UNESCO (1959)

    Google Scholar 

  5. Baluja, S.: Population-based incremental learning: a method for integrating genetic search based function optimisation and competitive learning, technical Report, Carnegie Mellon University (1994)

    Google Scholar 

  6. Bernal-Urbina, M., Flores-Méndez, A.: Time series forecasting through polynomial artificial neural networks and genetic programming. In: Proceedings of the IEEE Congress on Evolutionary Computation, Hong Kong, pp. 3324–3329 (June 2008)

    Google Scholar 

  7. Binner, J., Kendall, G., Chen, S.H. (eds.): Applications of Artificial Intelligence in Finance and Economics, Advances in Econometrics, vol. 19. Elsevier, Amsterdam (2004)

    Google Scholar 

  8. Burke, E., MacCloumn, B., Meisels, A., Petrovic, S., Qu, R.: A graph-based hyper heuristic for timetabling problems. European Journal of Operational Research 176, 177–192 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  9. Cerny, V.: A thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm. Journal of Optimization Theory and Applications 45, 41–51 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  10. Chen, S.H.: Genetic Algorithms and Genetic Programming in Computational Finance. Springer, New York (2002)

    Book  Google Scholar 

  11. Cowling, P., Chakhlevitch, K.: Hyperheuristics for managing a large collection of low level heuristics to schedule personnel, vol. 2, pp. 1214–1221 (December 2003)

    Google Scholar 

  12. Dempsey, I., O’Neill, M., Brabazon, A.: Live trading with grammatical evolution. In: Proceedings of the Grammatical Evolution Workshop (2004)

    Google Scholar 

  13. Edwards, R., Magee, J.: Technical analysis of stock trends. New York Institute of Finance (1992)

    Google Scholar 

  14. Hart, E., Ross, P., Nelson, J.: Solving a real-world problem using an evolving heuristically driven schedule builder. Evol. Comput. 6(1), 61–80 (1998)

    Article  Google Scholar 

  15. Kablan, A.: Adaptive neuro fuzzy inference systems for high frequency financial trading and forecasting, pp. 105–110 (October 2009)

    Google Scholar 

  16. Kampouridis, M., Tsang, E.: EDDIE for investment opportunities forecasting: Extending the search space of the GP. In: Proceedings of the IEEE Conference on Evolutionary Computation, Barcelona, Spain, pp. 2019–2026 (2010)

    Google Scholar 

  17. Kirkpatrick, S., Gelatt Jr., C., Vecchi, M.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  18. Koza, J.: Genetic Programming: On the programming of computers by means of natural selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  19. Larranaga, P., Lozano, J.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer, Norwell (2001)

    MATH  Google Scholar 

  20. Li, J.: FGP: A Genetic Programming-ased Financial Forecasting Tool. Ph.D. thesis, Department of Computer Science, University of Essex (2001)

    Google Scholar 

  21. Martinez-Jaramillo, S.: Artificial Financial Markets: An agent-based Approach to Reproduce Stylized Facts and to study the Red Queen Effect. Ph.D. thesis, CFFEA, University of Essex (2007)

    Google Scholar 

  22. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of calculations by fast computing machines. Journal of Chemical Physics 21, 1087–1092 (1953)

    Article  Google Scholar 

  23. Özcan, E., Bilgin, B., Korkmaz, E.E.: A comprehensive analysis of hyper-heuristics. Intelligent Data Analysis 12(1), 3–23 (2008)

    Google Scholar 

  24. Poli, R., Langdon, W., McPhee, N.: A Field Guide to Genetic Programming. Lulu.com (2008)

    Google Scholar 

  25. Sapankevych, N., Sankar, R.: Time series prediction using support vector machines: A survey. IEEE Computational Intelligence Magazine 4(2), 24–38 (2009)

    Article  Google Scholar 

  26. Sharma, V., Srinivasan, D.: Evolutionary computation and economic time series forecasting. In: Proceedings of the IEEE Conference on Evolutionary Computation, Singapore, September 25-28, pp. 188–195 (2007)

    Google Scholar 

  27. Tsang, E., Li, J., Markose, S., Er, H., Salhi, A., Iori, G.: EDDIE in financial decision making. Journal of Management and Economics 4(4) (2000)

    Google Scholar 

  28. Tsang, E., Markose, S., Er, H.: Chance discovery in stock index option and future arbitrage. New Mathematics and Natural Computation 1(3), 435–447 (2005)

    Article  MATH  Google Scholar 

  29. Tsang, E., Martinez-Jaramillo, S.: Computational finance. IEEE Computational Intelligence Society Newsletter, 3–8 (2004)

    Google Scholar 

  30. Zhang, Q., Sun, J., Tsang, E.: Evolutionary algorithm with guided mutation for the maximum clique problem. IEEE Transactions on Evolutionary Computation 9(2), 192–200 (2005)

    Article  Google Scholar 

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Kampouridis, M., Tsang, E. (2011). Using Hyperheuristics under a GP Framework for Financial Forecasting. In: Coello, C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_2

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  • DOI: https://doi.org/10.1007/978-3-642-25566-3_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25565-6

  • Online ISBN: 978-3-642-25566-3

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