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A semiparametric method for predicting bankruptcy

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  • Ruey-Ching Hwang
  • K. F. Cheng
  • Jack C. Lee

    (Graduate Institute of Finance, National Chiao Tung University, Hsinchu, Taiwan)

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    Abstract

    Bankruptcy prediction methods based on a semiparametric logit model are proposed for simple random (prospective) and case-control (choice-based; retrospective) data. The unknown parameters and prediction probabilities in the model are estimated by the local likelihood approach, and the resulting estimators are analyzed through their asymptotic biases and variances. The semiparametric bankruptcy prediction methods using these two types of data are shown to be essentially equivalent. Thus our proposed prediction model can be directly applied to data sampled from the two important designs. One real data example and simulations confirm that our prediction method is more powerful than alternatives, in the sense of yielding smaller out-of-sample error rates. Copyright © 2007 John Wiley & Sons, Ltd.

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    File URL: http://hdl.handle.net/10.1002/for.1027
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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 26 (2007)
    Issue (Month): 5 ()
    Pages: 317-342

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    Handle: RePEc:jof:jforec:v:26:y:2007:i:5:p:317-342

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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    Cited by:
    1. Harada, Nobuyuki & Kageyama, Noriyuki, 2011. "Bankruptcy dynamics in Japan," Japan and the World Economy, Elsevier, vol. 23(2), pages 119-128, March.
    2. Hwang, Ruey-Ching, 2012. "A varying-coefficient default model," International Journal of Forecasting, Elsevier, vol. 28(3), pages 675-688.
    3. Hwang, Ruey-Ching & Chung, Huimin & Chu, C.K., 2010. "Predicting issuer credit ratings using a semiparametric method," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 120-137, January.

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