A hybrid financial analysis model for business failure prediction

Shi Ming Huang, Chih Fong Tsai, David C. Yen, Yin Lin Cheng

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

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.

Original languageEnglish
Pages (from-to)1034-1040
Number of pages7
JournalExpert Systems with Applications
Volume35
Issue number3
DOIs
StatePublished - Oct 2008

Keywords

  • Artificial neural networks
  • Business failure prediction
  • Financial analysis

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