Evolving Decision Rules to Discover Patterns in Financial Data Sets

Alma Lilia García-Almanza (), Edward P. K. Tsang () and Edgar Galván-López ()
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Alma Lilia García-Almanza: University of Essex
Edward P. K. Tsang: University of Essex
Edgar Galván-López: University of Essex

A chapter in Computational Methods in Financial Engineering, 2008, pp 239-255 from Springer

Abstract: 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.

Keywords: Evolving comprehensible rules; machine learning; evolutionary algorithms (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-77958-2_12

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DOI: 10.1007/978-3-540-77958-2_12

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