A study of data mining techniques in bankruptcy prediction

A study of data mining techniques in bankruptcy prediction

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Article ID: iaor20042905
Country: South Korea
Volume: 28
Issue: 2
Start Page Number: 105
End Page Number: 128
Publication Date: Jun 2003
Journal: Journal of the Korean ORMS Society
Authors:
Keywords: finance & banking, datamining
Abstract:

In this paper, four different data mining techniques, two neural networks and two statistical modeling techniques, are compared in terms of prediction accuracy in the context of bankruptcy prediction. In business setting, how to accurately detect the condition of a firm has been an important event in the literature. In neural networks, Backpropagation (BP) network and the Kohonen self-organizing feature map, are selected and compared each other while in statistical modeling techniques, discriminant analysis and logistic regression are also performed to provide performance benchmarks for the neural network experiment. The findings suggest that the BP network is a better choice among the data mining tools compared. This paper also identified some distinctive characteristics of Kohonen self-organizing feature map.

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