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

    Volume 41, Issue 3 (May 2013)

    ISSN: 0090-3973

    CODEN: JTEOAD

    Published Online: 25 March 2013

    Page Count: 7


    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,

    (Received 10 February 2012; accepted 17 October 2012)

    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.


    Paper ID: JTE20120027

    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