A hybrid financial analysis model for business failure prediction

Authors:
Shi-Ming Huang;Chih-Fong Tsai;David C. Yen;Yin-Lin Cheng
Affiliations:
Department of Accounting and Information Technology, National Chung Cheng University, Taiwan;Department of Accounting and Information Technology, National Chung Cheng University, Taiwan;Department of Decision Sciences and Management Information Systems, Miami University, USA;Department of Accounting and Information Technology, National Chung Cheng University, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

Accounting frauds have continuously happened all over the world. This leads to the need of predicting business failures. Statistical methods and machine learning techniques have been widely used to deal with this issue. In general, financial ratios are one of the main inputs to develop the prediction models. This paper presents a hybrid financial analysis model including static and trend analysis models to construct and train a back-propagation neural network (BPN) model. Further, the experiments employ four datasets of Taiwan enterprises which support that the proposed model not only provides a high predication rate but also outperforms other models including discriminant analysis, decision trees, and the back-propagation neural network alone.