Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

On the investigation of hyper-heuristics on a financial forecasting problem

  • Published:
Annals of Mathematics and Artificial Intelligence Aims and scope Submit manuscript

Abstract

Financial forecasting is a really important area in computational finance, with numerous works in the literature. This importance can be reflected in the literature by the continuous development of new algorithms. Hyper-heuristics have been successfully used in the past for a number of search and optimization problems, and have shown very promising results. To the best of our knowledge, they have not been used for financial forecasting. In this paper we present pioneer work, where we use different hyper-heuristics frameworks 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 has dramatically increased and sometimes solutions can be missed due to ineffective search. We apply 14 different low-level heuristics to EDDIE 8, to 30 different datasets, and examine their effect to the algorithm’s performance. We then select the most prominent heuristics and combine them into three different hyper-heuristics frameworks. Results show that all three frameworks are competitive, and are able to show significantly improved results, especially in the case of best results. Lastly, analysis on the weights of the heuristics shows that there can be a constant swinging among some of the low-level heuristics, which denotes that the hyper-heuristics frameworks are able to ‘know’ the appropriate time to switch from one heuristic to the other, based on their effectiveness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

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.) Parallel Problem Solving from Nature—PPSN XI, Lecture Notes in Computer Science, vol. 6238, pp. 294–303. Springer (2010)

  2. Allen, F., Karjalainen, R.: Using genetic algorithms to find technical trading rules. J. Financ. Econ. 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)

  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)

  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, pp. 3324–3329. Hong Kong (2008)

  7. Binner, J., Kendall, G., Chen, S.H. (eds.): Applications of artificial intelligence in finance and economics. Adv. Econom. 19. Elsevier (2004)

  8. Burke, E., MacCloumn, B., Meisels, A., Petrovic, S., Qu, R.: A graph-based hyper heuristic for timetabling problems. Eur. J. Oper. Res. 176, 177–192 (2006)

    Article  Google Scholar 

  9. Cao, L., Tay, F.: Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans. Neural Netw. 14, 1506–1518 (2006)

    Article  Google Scholar 

  10. Chen, S.H.: Genetic Algorithms and Genetic Programming in Computational Finance. Springer, New York, LLC (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 (2003). doi:10.1109/CEC.2003.1299807

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

  13. Edwards, R., Magee, J.: Technical Analysis of Stock Trends. New York Institute of Finance (1992)

  14. Gestel, T., Suykens, J., Baestaens, D.E., Lambrechts, A., Lanckriet, G., Vandaele, B., Moor, B., Vandewalle, J.: Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Trans. Neural Netw. 12, 809–821 (2001)

    Article  Google Scholar 

  15. 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 

  16. Huang, W., Nakamori, Y., Wang, S.Y.: Forecasting stock market movement direction with support vector machine. Comput. Oper. Res. 32(10), 2513–2522 (2005)

    Article  MATH  Google Scholar 

  17. Kablan, A.: Adaptive Neuro Fuzzy Inference Systems for High Frequency Financial Trading and Forecasting, pp. 105–110 (2009). doi:10.1109/ADVCOMP.2009.23

  18. 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, pp. 2019–2026. Barcelona, Spain (2010)

  19. Kampouridis, M., Tsang, E.: Using hyperheuristics under a GP framework for financial forecasting. In: Coello Coello, C.A. (ed.) Proc. Fifth International Conference on Learning and Intelligent Optimization (LION5). Lecture Notes in Computer Science 6683, pp.16–30. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  20. Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA, MIT Press (1992)

    MATH  Google Scholar 

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

    Google Scholar 

  22. Li, J.: FGP: a genetic programming-ased financial forecasting tool. Ph.D. thesis, Department of Computer Science, University of Essex (2001)

  23. 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)

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

    Google Scholar 

  25. Poli, R., Langdon, W., McPhee, N.: A field guide to genetic programming. Lulu.com (2008). Accessed 6 March 2012

  26. Sapankevych, N., Sankar, R.: Time series prediction using support vector machines: a survey. IEEE Comput. Intell. Mag. 4(2), 24–38 (2009). doi:10.1109/MCI.2009.932254

    Article  Google Scholar 

  27. Schulenburg, S., Ross, P.: Strength and money: an LCS approach to increasing returns. Lecture Notes in Computer Science, pp. 291–298. Springer Berlin/Heidelberg (2001)

  28. Schulenburg, S., Ross, P.: Explorations in LCS models of stock trading. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) 4th International Workshop Advances in Learning Classifier Systems, IWLCS 2001, vol. 2321, pp. 150–179. Springer Berlin/Heidelberg (2002)

  29. Shachmurove, Y.: Business applications of emulative neural networks. Int. J. Bus. 10. http://www.craig.csufresno.edu/ijb/Volumes.htm#V10 (2005). Accessed 6 March 2012

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

  31. Tino, P., Nikolaev, N., Yao, X.: Volatility forecasting with sparse Bayesian kernel models. In: Proceedings of the 4th International Conference on Computational Intelligence in Economics and Finance (CIEF’05), pp. 1150–1153. Salt Lake City, Utah, USA (2005)

  32. Tsang, E., Li, J., Markose, S., Er, H., Salhi, A., Iori, G.: EDDIE in financial decision making. J. Manag. Econ. 4(4). http://www.econ.uba.ar/servicios/publicaciones/journal4/contents/contents.htm (2000). Accessed 6 March 2012

  33. Tsang, E., Markose, S., Er, H.: Chance discovery in stock index option and future arbitrage, World Scientific. New Mathem. Natural Comp. 1(3), 435–447 (2005)

    Article  MATH  Google Scholar 

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

  35. Wagner, N., Michalewicz, Z.: Adaptive and self-adaptive techniques for evolutionary forecasting applications set in dynamic and uncertain environments. Found. Comp. Intellig. 4, 3–21 (2009)

    Google Scholar 

  36. Wagner, N., Michalewicz, Z., Kouja, M., McGregor, R.: Time series forecasting for dynamic environments: the dyfor genetic program model. IEEE Trans. Evol. Comput. 11(4), 433–452 (2007)

    Article  Google Scholar 

  37. Wong, B., Selvi, Y.: Neural network applications in finace: a review and analysis of literature (1990–1996). Inf. Manag. 34, 129–139 (1998)

    Article  Google Scholar 

  38. Yuan, X., Zou, Y.: Technology program financial forecast model based on caco-svm. In: Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on, pp. 1–4 (2009). doi:10.1109/IWISA.2009.5073158

  39. Zhang, Q., Sun, J., Tsang, E.: Evolutionary algorithm with guided mutation for the maximum clique problem. IEEE Trans. Evol. Comput. 9(2), 192–200 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Electronic Engineering, University of Essex, Essex, UK

    Michael Kampouridis

  2. College of Computer and Information Sciences (CCIS), Imam Muhammad Bin Saud Islamic University, Riyadh, Kingdom of Saudi Arabia

    Abdullah Alsheddy

  3. Centre for Computational Finance and Economic Agents, School of Computer Science and Electronic Engineering, University of Essex, Essex, UK

    Edward Tsang

Authors
  1. Michael Kampouridis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdullah Alsheddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Edward Tsang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Kampouridis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kampouridis, M., Alsheddy, A. & Tsang, E. On the investigation of hyper-heuristics on a financial forecasting problem. Ann Math Artif Intell 68, 225–246 (2013). https://doi.org/10.1007/s10472-012-9283-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10472-012-9283-0

Keywords

Mathematics Subject Classifications (2010)

Search

Navigation