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Predicting Corporate Financial Distress: A Time-Series CUSUM Methodology

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  • Kahya, Emel
  • Theodossiou, Panayiotis

Abstract

The ability to predict corporate financial distress can be strengthened using models that account for serial correlation in the data, incorporate information from more than one period and include stationary explanatory variables. This paper develops a stationary financial distress model for AMEX and NYSE manufacturing and retailing firms based on the statistical methodology of time-series Cumulative Sums (CUSUM). The model has the ability to distinguish between changes in the financial variables of a firm that are the result of serial correlation and changes that are the result of permanent shifts in the mean structure of the variables due to financial distress. Tests performed show that the model is robust over time and outperforms similar models based on the popular statistical methods of Linear Discriminant Analysis and Logit. Copyright 1999 by Kluwer Academic Publishers

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Bibliographic Info

Article provided by Springer in its journal Review of Quantitative Finance and Accounting.

Volume (Year): 13 (1999)
Issue (Month): 4 (December)
Pages: 323-45

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Handle: RePEc:kap:rqfnac:v:13:y:1999:i:4:p:323-45

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Web page: http://springerlink.metapress.com/link.asp?id=102990

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Cited by:
  1. Premachandra, I.M. & Chen, Yao & Watson, John, 2011. "DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment," Omega, Elsevier, vol. 39(6), pages 620-626, December.
  2. Foreman, R. Dean, 2003. "A logistic analysis of bankruptcy within the US local telecommunications industry," Journal of Economics and Business, Elsevier, vol. 55(2), pages 135-166.
  3. Aaro Hazak & Kadri Männasoo, 2007. "Indicators of corporate default : an EU based empirical study," Bank of Estonia Working Papers 2007-10, Bank of Estonia, revised 04 Sep 2007.
  4. S. Balcaen & H. Ooghe, 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classical statistical methods?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/249, Ghent University, Faculty of Economics and Business Administration.
  5. Rida Prihatni SE. Ak. MSi. Author_Email: hatney_yes@yahoo.com & Adam Zakaria SE. Ak. MSi., 2011. "The Financial Performance Analysis Using Altman Z-Score And Its Effect To Stock Price Banking Sector In Indonesian Stock Exchange," 2nd International Conference on Business and Economic Research (2nd ICBER 2011) Proceeding 2011-187, Conference Master Resources.
  6. Elisa Ughetto & Andrea Vezzulli, 2008. "Guarantee-backed loans and R&D investments. Do mutual guarantee consortiums value R&D?," KITeS Working Papers 227, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Dec 2008.
  7. Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.
  8. Ch. Spathis & M. Doumpos & C. Zopounidis, 2002. "Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques," European Accounting Review, Taylor & Francis Journals, vol. 11(3), pages 509-535.
  9. G. Yi & S. Coleman & Q. Ren, 2006. "CUSUM method in predicting regime shifts and its performance in different stock markets allowing for transaction fees," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(7), pages 647-661.
  10. Kosmidou K. & Doumpos M. & Zopounidis C., 2002. "A Multicriteria Hierarchical Discrimination Approach for Credit Risk Problems," European Research Studies Journal, European Research Studies Journal, vol. 0(1-2), pages 53-68, January -.

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