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Otero, Fernando E.B.; Kampouridis, Michael (2014)
Publisher: Springer-Verlag Berlin
Languages: English
Types: Unknown
Subjects: Q335
Financial forecasting is a vital area in computational finance, where several studies have taken place over the years. One way of viewing financial forecasting is as a classification problem, where the goal is to find a model that represents the predictive relationships between predictor attribute values and class attribute values. In this paper we present a comparative study between two bio-inspired classification algorithms, a genetic programming algorithm especially designed for financial forecasting, and an ant colony optimization one, which is designed for classification problems. In addition, we compare the above algorithms with two other state-of-the-art classification algorithms, namely C4.5 and RIPPER. Results show that the ant colony optimization classification algorithm is very successful, significantly outperforming all other algorithms in the given classification problems, which provides insights for improving the design of specific financial forecasting algorithms.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Chen, S.H.: Genetic Algorithms and Genetic Programming in Computational Finance. Springer-Verlag New York, LLC (2002)
    • 2. Cohen, W.: Fast effective rule induction. In: Proceedings of the 12th International Conference on Machine Learning. pp. 115-123. Morgan Kaufmann (1995)
    • 3. Dorigo, M., Stu¨tzle, T.: Ant Colony Optimization. MIT Press (2004), 328 pages
    • 4. Edwards, R., Magee, J.: Technical analysis of stock trends. NYIF (1992)
    • 5. Fayyad, U., Piatetsky-Shapiro, G., Smith, P.: From data mining to knowledge discovery: an overview. In: Advances in Knowledge Discovery & Data Mining. pp. 1-34. MIT Press (1996)
    • 6. Garc´ıa, S., Herrera, F.: An Extension on “Statistical Comparisons of Classifiers over Multiple Data Sets” for all Pairwise Comparisons. Journal of Machine Learning Research 9, 2677-2694 (2008)
    • 7. Kampouridis, M., Tsang, E.: EDDIE for investment opportunities forecasting: Extending the search space of the GP. In: Proceedings of the IEEE World Congress on Computational Intelligence. pp. 2019-2026. Barcelona, Spain (2010)
    • 8. Koza, J.: Genetic Programming: On the programming of computers by means of natural selection. Cambridge, MA: MIT Press (1992)
    • 9. 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)
    • 10. Otero, F., Freitas, A.: Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm. In: Proceedings of the 2013 Genetic and Evolutionary Computation Conference. pp. 73-80. ACM Press (July 2013)
    • 11. Otero, F., Freitas, A., Johnson, C.: A New Sequential Covering Strategy for Inducing Classification Rules With Ant Colony Algorithms. IEEE Transactions on Evolutionary Computation 17(1), 64-76 (2013)
    • 12. Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1993)
    • 13. Tsang, E., Martinez-Jaramillo, S.: Computational finance. IEEE Computational Intelligence Society Newsletter pp. 3-8 (2004)
    • 14. Witten, H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2nd edn. (2005)
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