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A Financial Distress Pre-Warning Study by Fuzzy Regression Model of TSE-Listed Companies

Author

Listed:
  • Wen-Ying Cheng

    (Department of Business Administration, National Ping-tung University of Science and Technology, Shuehfu Rd., Neipu, Pingtung 91201, Taiwan, R.O.C.)

  • Ender Su

    (Department of Insurance and Risk Management, National Kaohsiung University of Science and Technology, Juoyue Rd., Nantz District, Kaohsiung 811, Taiwan, R.O.C.)

  • Sheng-Jung Li

    (Department of Insurance and Risk Management, National Kaohsiung University of Science and Technology, Juoyue Rd., Nantz District, Kaohsiung 811, Taiwan, R.O.C.)

Abstract

The purpose of this paper is to construct a financial distress pre-warning model for investors and risk supervisors. Through the Securities and Futures Institute Network, we collect the financial data of the electronic companies listing on the Taiwan Security Exchange (TSE) from 1998 to 2005. By binary logistic regression test, we found that financial statement ratios show significant difference in different financial stages. On the other hand, using fuzzy regression model, we construct a rating model of financial administration stages for investors and risk supervisors and found that prediction validity for financial distress companies and total companies by fuzzy regression model are better than binary logistic regression model using our research sample (89.77 and 90.98% vs. 85.27 and 90.30%).

Suggested Citation

  • Wen-Ying Cheng & Ender Su & Sheng-Jung Li, 2006. "A Financial Distress Pre-Warning Study by Fuzzy Regression Model of TSE-Listed Companies," Asian Academy of Management Journal of Accounting and Finance (AAMJAF), Penerbit Universiti Sains Malaysia, vol. 2(2), pages 75-93.
    • Handle: RePEc:usm:journl:aamjaf00202_75-93
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    Citations

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    Cited by:

    1. Soliwoda, Michał, 2014. "Bezpieczeństwo Finansowe Gospodarstw Rolniczych W Polsce Z Perspektywy Wspólnej Polityki Rolnej," Village and Agriculture (Wieś i Rolnictwo), Polish Academy of Sciences (IRWiR PAN), Institute of Rural and Agricultural Development, vol. 3(164).
    2. Giovanis, eleftheios, 2008. "A Neuro-Fuzzy Approach in the Prediction of Financial Stability and Distress Periods," MPRA Paper 24659, University Library of Munich, Germany.
    3. Eleftherios Giovanis, 2010. "Application of logit model and self‐organizing maps (SOMs) for the prediction of financial crisis periods in US economy," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 2(2), pages 98-125, June.
    4. Farida Titik Kristanti, 2019. "Integrating Capital Structure, Financial and Non-Financial Performance: Distress Prediction of SMEs," GATR Journals afr175, Global Academy of Training and Research (GATR) Enterprise.
    5. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2019. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?," JRFM, MDPI, vol. 12(2), pages 1-17, April.

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