Early warning of enterprise decline in a life cycle using neural networks and rough set theory

Authors:
Yu Cao;Xiaohong Chen;Desheng Dash Wu;Miao Mo
Affiliations:
School of Business, Central South University, Changsha 410083, PR China;School of Business, Central South University, Changsha 410083, PR China;Reykjavik University, Kringlunni 1, IS-103 Reykjavık, Iceland and RiskLab, University of Toronto, 1 Spadina Crescent, Toronto, ON, Canada M5S 3G3;Xiangya School of Medicine, Central South University, Changsha 410083, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Early warning of whether an enterprise will fall into decline stage in a near future is a new problem aroused by the enterprise life cycle theory and financial risk management. This paper presents an approach by use of back propagation neural networks and rough set theory in order to give an early warning whether enterprises will fall into a decline stage. Through attribute reduction by rough set, the influence of noise data and redundant data are eliminated when training the networks. Our models obtained favorable accuracy, especially in predicting whether enterprises will fall into decline or not.