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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.


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