A Hybrid Bankruptcy Prediction Model with Dynamic Loadings on Accounting-Ratio-Based and Market-Based Information: A Binary Quantile Regression Approach

Posted: 16 Nov 2009 Last revised: 21 Oct 2010

See all articles by Ming-Yuan Li

Ming-Yuan Li

National Cheng Kung University - Graduate Institute of Finance

Peter Miu

McMaster University - DeGroote School of Business

Date Written: April 23, 2009

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.

Keywords: Binary quantile regression, z-score, distance to default, bankruptcy

JEL Classification: G33, C51

Suggested Citation

Li, Ming-Yuan and Miu, Peter, A Hybrid Bankruptcy Prediction Model with Dynamic Loadings on Accounting-Ratio-Based and Market-Based Information: A Binary Quantile Regression Approach (April 23, 2009). Journal of Empirical Finance, Vol. 17, No. 4, 2010, Available at SSRN: https://ssrn.com/abstract=1506656

Ming-Yuan Li

National Cheng Kung University - Graduate Institute of Finance ( email )

1 Univeristy Road
Tainan City, Taiwan 70101
Taiwan

Peter Miu (Contact Author)

McMaster University - DeGroote School of Business ( email )

1280 Main Street West
Hamilton, Ontario L8S 4M4
Canada
905-525-9140 ext 23981 (Phone)

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