A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis

Authors:
Sangjae Lee;Wu Sung Choi
Affiliations:
College of Business Administration, Sejong University, 98 Kunja-dong, Kwangjin-gu, Seoul 143-747, Republic of Korea;Seoul Credit Rating & Information Inc., 281-1/2 Sangsu-dong, Mapo-gu, Seoul 121-828, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

Citing 11
Cited 1

Managerial applications of neural networks: the case of bank failure predictions

Management Science
Integrating artificial neural networks with rule-based expert systems

Decision Support Systems - Special issue on neural networks for decision support
Neural network ensemble strategies for financial decision applications

Computers and Operations Research
A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem

Computers and Operations Research
An integrative model with subject weight based on neural network learning for bankruptcy prediction

Expert Systems with Applications: An International Journal
Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks

Expert Systems with Applications: An International Journal
Alternative diagnosis of corporate bankruptcy: A neuro fuzzy approach

Expert Systems with Applications: An International Journal
Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks

Expert Systems with Applications: An International Journal
A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance: For bankruptcy prediction

Expert Systems with Applications: An International Journal
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters

Expert Systems with Applications: An International Journal
Using partial least squares and support vector machines for bankruptcy prediction

Expert Systems with Applications: An International Journal

Hybrid classification and regression models via particle swarm optimization auto associative neural network based nonlinear PCA

International Journal of Hybrid Intelligent Systems

Quantified Score

Hi-index	12.05

Visualization

Abstract

The accurate prediction of corporate bankruptcy for the firms in different industries is of a great concern to investors and creditors, as the reduction of creditors' risk and a considerable amount of saving for an industry economy can be possible. This paper presents a multi-industry investigation of the bankruptcy of Korean companies using back-propagation neural network (BNN). The industries include construction, retail, and manufacturing. The study intends to suggest the industry specific model to predict bankruptcy by selecting appropriate independent variables. The prediction accuracy of BNN is compared to that of multivariate discriminant analysis. The results indicate that prediction using industry sample outperforms the prediction using the entire sample which is not classified according to industry by 6-12%. The prediction accuracy of bankruptcy using BNN is greater than that of MDA. The study suggests insights for the practical industry model for bankruptcy prediction.