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Forecasting Bankruptcy with Incomplete Information

  • Xu, Xin
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    We propose new specifications that explicitly account for information noise in the input data of bankruptcy hazard models. The specifications are motivated by a theory of modeling credit risk with incomplete information (Duffie and Lando [2001]). Based on over 2 million firm-months of data during 1979-2012, we demonstrate that our proposed specifications significantly improve both in-sample model fit and out-of-sample forecasting accuracy. The improvements in forecasting accuracy are persistent throughout the 10-year holdout periods. The improvements are also robust to empirical setup, and are more substantial in cases where information quality is a more serious problem. Our findings provide strong empirical support for using our proposed hazard specifications in credit risk research and industry applications. They also reconcile conflicting empirical results in the literature.

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    File URL: http://mpra.ub.uni-muenchen.de/55024/1/MPRA_paper_55024.pdf
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    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 55024.

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    Date of creation: 28 May 2013
    Date of revision: 31 Mar 2014
    Handle: RePEc:pra:mprapa:55024
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    Web page: http://mpra.ub.uni-muenchen.de

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    1. Darrell Duffie & Leandro Siata & Ke Wang, 2006. "Multi-Period Corporate Default Prediction With Stochastic Covariates," NBER Working Papers 11962, National Bureau of Economic Research, Inc.
    2. Giesecke, Kay & Longstaff, Francis A. & Schaefer, Stephen & Strebulaev, Ilya, 2011. "Corporate bond default risk: A 150-year perspective," Journal of Financial Economics, Elsevier, vol. 102(2), pages 233-250.
    3. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2006. "In Search of Distress Risk," NBER Working Papers 12362, National Bureau of Economic Research, Inc.
    4. Giesecke, Kay, 2006. "Default and information," Journal of Economic Dynamics and Control, Elsevier, vol. 30(11), pages 2281-2303, November.
    5. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
    6. Leland, Hayne E & Toft, Klaus Bjerre, 1996. " Optimal Capital Structure, Endogenous Bankruptcy, and the Term Structure of Credit Spreads," Journal of Finance, American Finance Association, vol. 51(3), pages 987-1019, July.
    7. Hilscher, Jens Dietrich & Campbell, John Y. & Szilagyi, Jan, 2011. "Predicting Financial Distress and the Performance of Distressed Stocks," Scholarly Articles 9887619, Harvard University Department of Economics.
    8. Giesecke, Kay, 2004. "Correlated default with incomplete information," Journal of Banking & Finance, Elsevier, vol. 28(7), pages 1521-1545, July.
    9. Duan, Jin-Chuan & Sun, Jie & Wang, Tao, 2012. "Multiperiod corporate default prediction—A forward intensity approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 191-209.
    10. 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.
    11. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-24, January.
    12. X. Frank Zhang, 2006. "Information Uncertainty and Stock Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 105-137, 02.
    13. Lin, Chen & Ma, Yue & Xuan, Yuhai, 2011. "Ownership structure and financial constraints: Evidence from a structural estimation," Journal of Financial Economics, Elsevier, vol. 102(2), pages 416-431.
    14. Sudheer Chava & Catalina Stefanescu & Stuart Turnbull, 2011. "Modeling the Loss Distribution," Management Science, INFORMS, vol. 57(7), pages 1267-1287, July.
    15. Duffie, Darrell & Lando, David, 2001. "Term Structures of Credit Spreads with Incomplete Accounting Information," Econometrica, Econometric Society, vol. 69(3), pages 633-64, May.
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