Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Hybrid neural network models for bankruptcy predictions
Decision Support Systems
Genetic programming for the prediction of insolvency in non-life insurance companies
Computers and Operations Research
Financial distress prediction by a radial basis function network with logit analysis learning
Computers & Mathematics with Applications
On sensitivity of case-based reasoning to optimal feature subsets in business failure prediction
Expert Systems with Applications: An International Journal
A support vector machine-based model for detecting top management fraud
Knowledge-Based Systems
Developing SFNN models to predict financial distress of construction companies
Expert Systems with Applications: An International Journal
International Journal of Intelligent Systems in Accounting and Finance Management
Financial Distress Prediction of Chinese-Listed Companies Based on PCA and WNNs
International Journal of Advanced Pervasive and Ubiquitous Computing
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In 2008, financial tsunami started to impair the economic development of many countries, including Taiwan. The prediction of financial crisis turns to be much more important and doubtlessly holds public attention when the world economy goes to depression. This study examined the predictive ability of the four most commonly used financial distress prediction models and thus constructed reliable failure prediction models for public industrial firms in Taiwan. Multiple discriminate analysis (MDA), logit, probit, and artificial neural networks (ANNs) methodology were employed to a dataset of matched sample of failed and non-failed Taiwan public industrial firms during 1998-2005. The final models are validated using within sample test and out-of-the-sample test, respectively. The results indicated that the probit, logit, and ANN models which used in this study achieve higher prediction accuracy and possess the ability of generalization. The probit model possesses the best and stable performance. However, if the data does not satisfy the assumptions of the statistical approach, then the ANN approach would demonstrate its advantage and achieve higher prediction accuracy. In addition, the models which used in this study achieve higher prediction accuracy and possess the ability of generalization than those of [Altman, Financial ratios-discriminant analysis and the prediction of corporate bankruptcy using capital market data, Journal of Finance 23 (4) (1968) 589-609, Ohlson, Financial ratios and the probability prediction of bankruptcy, Journal of Accounting Research 18 (1) (1980) 109-131, and Zmijewski, Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research 22 (1984) 59-82]. In summary, the models used in this study can be used to assist investors, creditors, managers, auditors, and regulatory agencies in Taiwan to predict the probability of business failure.