Understanding Bank Failure: A Close Examination of Rules Created by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Garcia-Almanza:2010:CERMA,
  author =       "Alma Lilia Garcia-Almanza and 
                 Biliana Alexandrova-Kabadjova and Serafin Martinez-Jaramillo",
  title =        "Understanding Bank Failure: A Close Examination of
                 Rules Created by Genetic Programming",
  booktitle =    "Electronics, Robotics and Automotive Mechanics
                 Conference (CERMA), 2010",
  year =         "2010",
  month =        "28 " # sep # "-" # oct # " 1",
  pages =        "34--39",
  abstract =     "This paper presents a novel method to predict
                 bankruptcy, using a Genetic Programming (GP) based
                 approach called Evolving Decision Rules (EDR). In order
                 to obtain the optimum parameters of the classifying
                 mechanism, we use a data set, obtained from the US
                 Federal Deposit Insurance Corporation (FDIC). The set
                 consists of limited financial institutions' data,
                 presented as variables widely used to detect bank
                 failure. The outcome is a set of comprehensible
                 decision rules, which allows to identify cases of
                 bankruptcy. Further, the reliability of those rules is
                 measured in terms of the true and false positive rate,
                 calculated over the whole data set and plot over the
                 Receiving Operating Characteristic (ROC) space. In
                 order to test the accuracy performance of the
                 mechanism, we elaborate two experiments: the first,
                 aimed to test the degree of the variables' usefulness,
                 provides a quantitative and a qualitative analysis. The
                 second experiment completed over 1000 different
                 re-sampled cases is used to measure the performance of
                 the approach. To our knowledge this is the first
                 computational technique in this field able to give
                 useful insights of the method's predictive structure.
                 The main contributions of this work are three: first,
                 we want to bring to the arena of bankruptcy prediction
                 a competitive novel method which in pure performance
                 terms is comparable to state of the art methods
                 recently proposed in similar works, second, this method
                 provides the additional advantage of transparency as
                 the generated rules are fully interpretable in terms of
                 simple financial ratios, third and final, the proposed
                 method includes cutting edge techniques to handle
                 highly unbalanced samples, something that is very
                 common in bankruptcy applications.",
  keywords =     "genetic algorithms, genetic programming, bank failure
                 detection, bankruptcy prediction, data set, evolving
                 decision rules, financial ratio, receiving operating
                 characteristic space, banking, sensitivity analysis",
  DOI =          "doi:10.1109/CERMA.2010.14",
  notes =        "Also known as \cite{5692308}",
}

Genetic Programming entries for Alma Lilia Garcia Almanza Biliana Alexandrova-Kabadjova Serafin Martinez Jaramillo

Citations