Listed companies' financial distress prediction based on weighted majority voting combination of multiple classifiers

Authors:
Jie Sun;Hui Li
Affiliations:
School of Business Administration, Zhejiang Normal University, Jinhua 321004, Zhejiang Province, PR China;School of Business Administration, Zhejiang Normal University, Jinhua 321004, Zhejiang Province, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

How to effectively predict financial distress is an important problem in corporate financial management. Though much attention has been paid to financial distress prediction methods based on single classifier, its limitation of uncertainty and benefit of multiple classifier combination for financial distress prediction has also been neglected. This paper puts forward a financial distress prediction method based on weighted majority voting combination of multiple classifiers. The framework of multiple classifier combination system, model of weighted majority voting combination, basic classifiers' voting weight model and basic classifiers' selection principles are discussed in detail. Empirical experiment with Chinese listed companies' real world data indicates that this method can greatly improve the average prediction accuracy and stability, and it is more suitable for financial distress prediction than single classifiers.