Using Genetic Programming Systems as Early Warning to Prevent Bank Failure

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InCollection{Garcia-Almanza:2011:Yap,
  author =       "Alma Lilia {Garcia Almanza} and 
                 Serafin {Martinez Jaramillo} and Biliana Alexandrova-Kabadjova and 
                 Edward Tsang",
  title =        "Using Genetic Programming Systems as Early Warning to
                 Prevent Bank Failure",
  booktitle =    "Information Systems for Global Financial Markets:
                 Emerging Developments and Effects",
  publisher =    "IGI global",
  year =         "2011",
  editor =       "Alexander Y. Yap",
  chapter =      "14",
  pages =        "369--382",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-61350-162-5",
  URL =          "http://www.amazon.com/Information-Systems-Global-Financial-Markets/dp/1613501625",
  DOI =          "doi:10.4018/978-1-61350-162-7.ch014",
  abstract =     "Corporate bankruptcy has been always an active area of
                 financial research. Furthermore, after the Lehman
                 Brothers' default and its consequences on the global
                 financial system, this topic has attracted even more
                 attention from regulators and researchers. This event
                 has brought an imperious urge to change the regulatory
                 framework regardless of whether this is good or bad.
                 Consequently, the need for timely signals for
                 supervisory actions and the development of tools that
                 help to determine which financial information is more
                 relevant to predict distress is very important.

                 During crisis periods the bankruptcy of a bank or a
                 group of banks can make things far worse if contagion
                 effects are transmitted first to other participants of
                 the financial system and then to the real economy. In a
                 previous work, developed by Garcia et al. (2010), an
                 evolutionary technique named Evolving Decision Rules
                 (EDR) was used to identify patterns in data from the
                 Federal Deposit Insurance Corporation (FDIC) for
                 generating a set of comprehensible rules, which were
                 able to predict bank bankruptcy. The major contribution
                 of that work was to show a series of decision rules
                 constituted by simple financial ratios, despite that
                 the method is not restricted to the use of such type of
                 information.

                 The main advantage of creating understandable rules is
                 that users are able to interpret and identify the
                 events that may trigger bankruptcy. By using the method
                 that we propose in this work, it is possible to
                 identify when certain financial indicators are getting
                 close to specific thresholds, something that can turn
                 into an undesirable situation. This is particularly
                 relevant if the companies we are referring to are
                 banks. The contribution of this chapter is to improve
                 the prediction by means of a multi-population approach.
                 The experimental results were evaluated using the
                 Receiver Operating Characteristic (ROC) described in
                 Fawcett and Provost (1997). We show that our approach
                 could improve the Area Under the ROC Curve in 5percent
                 with respect to the same method proposed in Garcia et
                 al. (2010). Additionally, a series of experiments were
                 performed in order to find out the reasons of success
                 of the EDR",
}

Genetic Programming entries for Alma Lilia Garcia Almanza Serafin Martinez Jaramillo Biliana Alexandrova-Kabadjova Edward P K Tsang

Citations