| Title: | Financial Distress Prediction by a Radial Basis Function Network with Logit Analysis Learning |
| Authors: | 陳慶隆;Cheng, Chi-bin;Fu, Clay C. -J. |
| Contributors: | 淡江大學會計學系 |
| Keywords: | Financial distress prediction;Radial basis function network;Neural networks;Logit analysis |
| Date: | 2006-02-01 |
| Issue Date: | 2011-10-20 12:45:00 (UTC+8) |
| Abstract: | 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. |
| Relation: | Computers and Mathematics with Applications 51, pp.579-588 |
| DOI: | 10.1016/j.camwa.2005.07.016  全文 |
| Appears in Collections: | [會計學系暨研究所] 期刊論文
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