| Summary: | 碩士 === 嶺東科技大學 === 資訊管理與應用研究所 === 102 === Prior research of corporate failure prediction shows that the large scope with high dimensions characterized by the research sample makes the process of the performance in model prediction time-consuming. This study addresses this issue by proposing a method, which is processed as follow. First, use independent sample T-test to original conditional attributes for dimension reduction. Secondly, the information entropy-based granulation method is used to discretize conditional attributes with continuous numerical value of the research sample to maintain and even improve the prediction accuracy. The classifier of the prediction models includes back-propagation neural networks (BPNN) and support vector machine (SVM). The research sample contains 884 Chinese firms listing in Shanghai Stock Exchange and Shenzhen Stock Exchange during 1996 to 2005, and it includes 268 financial crisis firms and 616 normal firms.
In this study, the conditional attributes are discretized into two (2Rank) and three (3Rank) level for test simulation. The results showed that model with BPNN classifier is able to draw the classification accuracy near to 84% and 87% for 2Rank and 3Rank respectively; while SVM classifier can be drawn nearly equal prediction accuracy of 89% and 90%. Other interesting results showed that the sensitivity of the classifier performance with the discrete granulation conditions to the random selection of the classifier training data set is relatively small, implying that the classifier with discrete granulating conditional attributes proposed in this study is quite robust. Furthermore, the simulation results indicate that BPNN classifier can reach as high as 90% performance which is slightly higher than the classifier without reduction of the attributes. This suggests that conditional attribute reduction prior to constructing the models has its efficacy on the failure prediction of the classifier models.
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