Expert Systems with Applications: An International Journal
Forecasting financial condition of Chinese listed companies based on support vector machine
Expert Systems with Applications: An International Journal
Artificial wavelet neural network and its application in neuro-fuzzy models
Applied Soft Computing
Software development cost estimation using wavelet neural networks
Journal of Systems and Software
Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Ranking-order case-based reasoning for financial distress prediction
Knowledge-Based Systems
An evolution-based approach with modularized evaluations to forecast financial distress
Knowledge-Based Systems
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
The operating status of an enterprise is disclosed periodically in a financial statement. Financial distress prediction is important for business bankruptcy prevention, and various quantitative prediction methods based on financial ratios have been proposed. This paper presents a financial distress prediction model based on wavelet neural networks (WNNs). The transfer functions of the neurons in WNNs are wavelet base functions which are determined by dilation and translation factors. Back propagation algorithm was used to train the WNNs. Principal component analysis (PCA) method was used to reduce the dimension of the inputs of the WNNs. Multiple discriminate analysis (MDA), Logit, Probit, and WNNs were employed to a dataset selected from Chinese-listed companies. The results demonstrate that the proposed WNNs-based model performs well in comparison with the other three models.