An integrative model with subject weight based on neural network learning for bankruptcy prediction

Authors:
Sungbin Cho;Jinhwa Kim;Jae Kwon Bae
Affiliations:
School of Business, Sogang University, #1 Sinsu-dong, Mapo-gu, Seoul 121-742, Republic of Korea;School of Business, Sogang University, #1 Sinsu-dong, Mapo-gu, Seoul 121-742, Republic of Korea;School of Business, Sogang University, #1 Sinsu-dong, Mapo-gu, Seoul 121-742, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

This study proposes an integration strategy regarding how to efficiently combine the currently-in-use statistical and artificial intelligence techniques. In particular, by combining multiple discriminant analysis, logistic regression, neural networks, and decision trees induction, we introduce an integrative model with subject weight based on neural network learning for bankruptcy prediction. The strength of the proposed model stems from differentiating the weights of the source methods for each subject in the testing data set. That is, the relative weights consist of N by I matrix, where N denotes the number of subjects and I denotes the number of the source methods. The experiments using a real world financial data indicate that the proposed model can marginally increase the prediction accuracy compared to the source methods. The integration strategy can be useful for a dichotomous classification problem like bankruptcy prediction since prediction can be improved by taking advantage of existing and newly emerging techniques in the future.