Volume 41, Issue 3 (May 2013)

    Corporate Performance Forecasting Using Hybrid Rough Set Theory, Neural Networks, and DEA

    (Received 10 February 2012; accepted 17 October 2012)

    Published Online: 2013

    CODEN: JTEOAD

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    Abstract

    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.


    Author Information:

    Lin, Chiun-Sin
    Dept. of Business and Entrepreneurial Management, Kainan Univ., Luzhu Shiang, Taoyuan

    Lin, Tzu-Yu
    Dept. of Management Science, National Chiao Tung Univ., Hsinchu City,

    Chiu, Sheng-Hsiung
    Dept. of Management Science, National Chiao Tung Univ., Hsinchu City,


    Stock #: JTE20120027

    ISSN: 0090-3973

    DOI: 10.1520/JTE20120027

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    Author
    Title Corporate Performance Forecasting Using Hybrid Rough Set Theory, Neural Networks, and DEA
    Symposium , 0000-00-00
    Committee E53