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