Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach
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Statistical and artificial intelligence methods have successfully classified organizational solvency, but
are limited in terms of generalization, knowledge on how a conclusion was reached, convergence to
a local optima, or inconsistent results. Issues such as dimensionality reduction and feature selection
can also affect a model’s performance. This research explores the use of the genetic algorithm that
has the advantages of the artificial neural network but without its limitations. The genetic algorithm
model resulted in a set of easy to understand, if-then rules that were used to assess U.S. air carrier
solvency with a 94% accuracy.