Neural Networks for Statistical Modeling
Neural Networks for Statistical Modeling
Machine Learning
Computers & Mathematics with Applications
Failure prediction of dotcom companies using hybrid intelligent techniques
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Failure prediction of dotcom companies using neural network-genetic programming hybrids
Information Sciences: an International Journal
International Journal of Bio-Inspired Computation
Fuzzy Support Vector Machine for bankruptcy prediction
Applied Soft Computing
Expert Systems with Applications: An International Journal
A neuro-computational intelligence analysis of the global consumer software piracy rates
Expert Systems with Applications: An International Journal
Evolutional RBFNs prediction systems generation in the applications of financial time series data
Expert Systems with Applications: An International Journal
International Journal of Information Systems and Social Change
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This paper presents a financial distress prediction model that combines the approaches of neural network learning and logit analysis. This combination can retain the advantages and avoid the disadvantages of the two kinds of approaches in solving such a problem. The radial basis function network (RBFN) is adopted to construct the prediction model. The architecture of RBFN allows the grouping of similar firms in the hidden layer of the network and then performs a logit analysis on these groups instead of directly on the firms. Such a manner can remedy the problem of nominal variables in the input space. The performance of the proposed RBFN is compared to the traditional logit analysis and a backpropagation neural network and demonstrates superior results to both the counterparts in predictive accuracy for unseen data.