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Predicting corporate financial distress: Reflections on choice-based sample bias

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  • Harlan Platt
  • Marjorie Platt

Abstract

Financial distress precedes bankruptcy. Most financial distress models actually rely on bankruptcy data, which is easier to obtain. We obtained a dataset of financially distressed but not yet bankrupt companies supplying a major auto manufacturer. An early warning model successfully discriminated between these distressed companies and a second group of similar but healthy companies. Previous researchers argue the matched-sample design, on which some earlier models were built, causes bias. To test for bias, the dataset was partitioned into smaller samples that approach equal groupings. We statistically confirm the presence of a bias and describe its impact on estimated classification rates. Copyright Springer 2002

Suggested Citation

  • Harlan Platt & Marjorie Platt, 2002. "Predicting corporate financial distress: Reflections on choice-based sample bias," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 26(2), pages 184-199, June.
    • Handle: RePEc:spr:jecfin:v:26:y:2002:i:2:p:184-199
    DOI: 10.1007/BF02755985
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    References listed on IDEAS

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    1. 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, September.
    2. McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457, Elsevier.
    3. Guffey, Daryl M & Moore, William T, 1991. "Direct Bankruptcy Costs: Evidence from the Trucking Industry," The Financial Review, Eastern Finance Association, vol. 26(2), pages 223-235, May.
    4. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    5. Gilson, Stuart C., 1989. "Management turnover and financial distress," Journal of Financial Economics, Elsevier, vol. 25(2), pages 241-262, December.
    6. Palepu, Krishna G., 1986. "Predicting takeover targets : A methodological and empirical analysis," Journal of Accounting and Economics, Elsevier, vol. 8(1), pages 3-35, March.
    7. Richard Whitaker, 1999. "The early stages of financial distress," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 23(2), pages 123-132, June.
    8. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    9. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    10. John, Kose & Lang, Larry H P & Netter, Jeffry, 1992. "The Voluntary Restructuring of Large Firms in Response to Performance Decline," Journal of Finance, American Finance Association, vol. 47(3), pages 891-917, July.
    11. Lo, Andrew W., 1986. "Logit versus discriminant analysis : A specification test and application to corporate bankruptcies," Journal of Econometrics, Elsevier, vol. 31(2), pages 151-178, March.
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