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Evolving Decision Rules to Discover Patterns in Financial Data Sets

In: Computational Methods in Financial Engineering

Author

Listed:
  • Alma Lilia García-Almanza

    (University of Essex)

  • Edward P. K. Tsang

    (University of Essex)

  • Edgar Galván-López

    (University of Essex)

Abstract

A novel approached, called Evolving Comprehensible Rules (ECR), is presented to discover patterns in financial data sets to detect investment opportunities. ECR is designed to classify in extreme imbalanced environments. This is particularly useful in financial forecasting given that very often the number of profitable chances is scarce. The proposed approach offers a range of solutions to suit the investor’s risk guidelines and so, the user could choose the best trade-off between miss-classification and false alarm costs according to the investor’s requirements. The Receiver Operating Characteristics (ROC) curve and the Area Under the ROC (AUC) have been used to measure the performance of ECR. Following from this analysis, the results obtained by our approach have been compared with those one found by standard Genetic Programming (GP), EDDIE-ARB and C.5, which show that our approach can be effectively used in data sets with rare positive instances.

Suggested Citation

  • Alma Lilia García-Almanza & Edward P. K. Tsang & Edgar Galván-López, 2008. "Evolving Decision Rules to Discover Patterns in Financial Data Sets," Springer Books, in: Erricos J. Kontoghiorghes & Berç Rustem & Peter Winker (ed.), Computational Methods in Financial Engineering, pages 239-255, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-77958-2_12
    DOI: 10.1007/978-3-540-77958-2_12
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