A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008.
"In Search of Distress Risk,"
Journal of Finance,
American Finance Association, vol. 63(6), pages 2899-2939, December.
- Campbell, John Y. & Hilscher, Jens & Szilagyi, Jan, 2005. "In search of distress risk," Discussion Paper Series 1: Economic Studies 2005,27, Deutsche Bundesbank, Research Centre.
- John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2005. "In Searach of Distress Risk," Harvard Institute of Economic Research Working Papers 2081, Harvard - Institute of Economic Research.
- Szilagyi, Jan & Hilscher, Jens & Campbell, John, 2008. "In Search of Distress Risk," Scholarly Articles 3199070, Harvard University Department of Economics.
- John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2006. "In Search of Distress Risk," NBER Working Papers 12362, National Bureau of Economic Research, Inc.
- Len Umantsev & Victor Chernozhukov, 2001. "Conditional value-at-risk: Aspects of modeling and estimation," Empirical Economics, Springer, vol. 26(1), pages 271-292.
- Gianluca Oderda & Michel M. Dacorogna & Tobias Jung, 2003. "Credit Risk Models - Do They Deliver Their Promises? A Quantitative Assessment," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 32(2), pages 177-195, 07.
- Michel Dacorogna & Gianluca Oderda & Tobias Jung, 2003. "Credit Risk Models - Do They Deliver Their Promises? A Quantitative Assessment," Risk and Insurance 0306003, EconWPA.
- Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
- Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
- Robert Engle & Simone Manganelli, 2000. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Econometric Society World Congress 2000 Contributed Papers 0841, Econometric Society.
- Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, 09.
- Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
- Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
- Merton, Robert C., 1973. "On the pricing of corporate debt: the risk structure of interest rates," Working papers 684-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
- Gregory Kordas, 2006. "Smoothed binary regression quantiles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 387-407.
- Conley, Timothy G. & Galenson, David W., 1998. "Nativity and Wealth in Mid-Nineteenth-Century Cities," The Journal of Economic History, Cambridge University Press, vol. 58(02), pages 468-493, June.
- Amanda Gosling & Stephen Machin & Costas Meghir, 2000. "The Changing Distribution of Male Wages in the U.K," Review of Economic Studies, Oxford University Press, vol. 67(4), pages 635-666.
- Amanda Gosling & Stephen Machin & Costas Meghir, 1994. "The changing distribution of male wages in the UK," IFS Working Papers W94/13, Institute for Fiscal Studies.
- A Gosling & Stephen Machin, 1995. "The Changing Distribution of Male Wages in the UK," CEP Discussion Papers dp0271, Centre for Economic Performance, LSE.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
- Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
- Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
- Maria Vassalou & Yuhang Xing, 2004. "Default Risk in Equity Returns," Journal of Finance, American Finance Association, vol. 59(2), pages 831-868, 04.
- Stein, Roger M., 2005. "The relationship between default prediction and lending profits: Integrating ROC analysis and loan pricing," Journal of Banking & Finance, Elsevier, vol. 29(5), pages 1213-1236, May.
- Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
- Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August. Full references (including those not matched with items on IDEAS)