The Use of a Genetic Algorithm in Forecasting Air Carrier Financial Stress and Insolvency

Sergio Davalos, Richard D. Gritta, Bahram Adrangi and Jason Goodfriend

No 208166, 46th Annual Transportation Research Forum, Washington, D.C., March 6-8, 2005 from Transportation Research Forum

Abstract: While statistical and artificial intelligence methods such as Artificial Neural Networks (ANN) have been used successfully to classify organizations in terms of solvency or insolvency, they are limited in degree of generalization either by requiring linearly separable variables, lack of knowledge of how a conclusion is reached, or lack of a consistent approach for dealing with local optimal solution whether maximum or minimum. This research explores the use of a method that has the ability of the ANN method to deal with linearly inseparable variables and incomplete, noisy data; and resolves the problem of falling into a local optimum in searching the problems space. The paper applies a genetic algorithm to a sample of U.S. airlines and utilizes financial data from carrier income statements and balance sheets and ratios calculated from this data to assess air carrier solvency.

Keywords: Research and Development/Tech Change/Emerging Technologies; Research Methods/Statistical Methods (search for similar items in EconPapers)
Pages: 8
Date: 2005-03
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/208166/files/2005_GeneticAlgorithm_paper.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:ndtr05:208166

DOI: 10.22004/ag.econ.208166

Access Statistics for this paper

More papers in 46th Annual Transportation Research Forum, Washington, D.C., March 6-8, 2005 from Transportation Research Forum
Bibliographic data for series maintained by AgEcon Search ().