標題: | Corporate Performance Forecasting Using Hybrid Rough Set Theory, Neural Networks, and DEA |
作者: | Lin, Chiun-Sin Lin, Tzu-Yu Chiu, Sheng-Hsiung 管理科學系 Department of Management Science |
關鍵字: | corporate governance;rough set theory;neural network;data envelopment analysis;backpropagation network |
公開日期: | 1-五月-2013 |
摘要: | This paper proposed the hybrid model using rough set theory (RST), neural networks (NN), and data envelopment analysis (DEA) to predict the corporate performance directly. First, to evaluate corporate performance, the DEA was employed. Second, integrated RST with BPN techniques, which is one of the popular used models of NN, named RST+BPN, was used to build the corporate performance-prediction model and the corporate governance variables are used as predictive variables. This hybrid method enabled us to evaluate an individual firm and provided performance information without comparing it with other companies. The experimental result showed that the proposed model outperforms the NN model with nonextracted predictive variables and provides a promising alternative in corporate performance prediction. |
URI: | http://dx.doi.org/10.1520/JTE20120027 http://hdl.handle.net/11536/21911 |
ISSN: | 0090-3973 |
DOI: | 10.1520/JTE20120027 |
期刊: | JOURNAL OF TESTING AND EVALUATION |
Volume: | 41 |
Issue: | 3 |
起始頁: | 359 |
結束頁: | 365 |
顯示於類別: | 期刊論文 |