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A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach

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  • Li, Ming-Yuan Leon
  • Miu, Peter
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    Abstract

    While using the binary quantile regression (BQR) model, we establish a hybrid bankruptcy prediction model with dynamic loadings for both the accounting-ratio-based and market-based information. Using the proposed model, we conduct an empirical study on a dataset comprising of default events during the period from 1996 to 2006. In this study, those firms experienced bankruptcy/liquidation events as defined by the Compustat database are classified as "defaulted" firms, whereas all other firms listed in the Fortune 500 with over a B-rating during the same time period are identified as "survived" firms. The empirical findings of this study are consistent with the following notions. The distance-to-default (DD) variable derived from the market-based model is statistically significant in explaining the observed default events, particularly of those firms with relatively poor credit quality (i.e., high credit risk). Conversely, the z-score obtained with the accounting-ratio-based approach is statistically significant in predicting bankruptcies of firms of relatively good credit quality (i.e., low credit risk). In-sample and out-of-sample bankruptcy prediction tests demonstrated the superior performance of utilizing dynamic loadings rather than constant loadings derived by the conventional logit model.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Empirical Finance.

    Volume (Year): 17 (2010)
    Issue (Month): 4 (September)
    Pages: 818-833

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    Handle: RePEc:eee:empfin:v:17:y:2010:i:4:p:818-833

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    Web page: http://www.elsevier.com/locate/jempfin

    Related research

    Keywords: Binary quantile regression z-score Distance-to-default Bankruptcy;

    References

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    Cited by:
    1. Mensi, Walid & Hammoudeh, Shawkat & Reboredo, Juan Carlos & Nguyen, Duc Khuong, 2014. "Do global factors impact BRICS stock markets? A quantile regression approach," Emerging Markets Review, Elsevier, vol. 19(C), pages 1-17.
    2. Mamatzakis, E & Koutsomanoli-Filippaki, Anastasia & Pasiouras, Fotios, 2012. "A quantile regression approach to bank efficiency measurement," MPRA Paper 51879, University Library of Munich, Germany.
    3. Ruey-Ching Hwang & Huimin Chung & Jiun-Yi Ku, 2013. "Predicting Recurrent Financial Distresses with Autocorrelation Structure: An Empirical Analysis from an Emerging Market," Journal of Financial Services Research, Springer, vol. 43(3), pages 321-341, June.
    4. V. L. Miguéis & D. F. Benoit & D. Van Den Poel, 2012. "Enhanced Decision Support in Credit Scoring Using Bayesian Binary Quantile Regression," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/803, Ghent University, Faculty of Economics and Business Administration.
    5. Lee, Bong Soo & Li, Ming-Yuan Leon, 2012. "Diversification and risk-adjusted performance: A quantile regression approach," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2157-2173.

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