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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

  • Li, Ming-Yuan Leon
  • Miu, Peter
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    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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    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
    Contact details of provider: Web page: http://www.elsevier.com/locate/jempfin

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