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Dynamic analysis of the forecasting bankruptcy under presence of unobserved heterogeneity

Author

Listed:
  • Ilyes Abid

    (ISC Paris Business School)

  • Farid Mkaouar

    (CNAM)

  • Olfa Kaabia

    (INSEEC Business School)

Abstract

This paper illustrates the importance of referring to a dynamic approach when forecasting firms bankruptcies, paying a particular attention to French SMEs. Based on Shummay’s (J Bus 74:101–124, 2001), we build a duration model and extend it by incorporating unobservable heterogeneity. Moreover, we resort to a dynamic dichotomous specification in which “right side” censored data are taken into account. We emphasize the complexity of the calculations of integrals that must be implemented and show how to overcome this challenge by applying the Geweke, Hajivassiliou and Keane algorithm which involves the technique of the simulated maximum likelihood. The findings prove that our dynamic approach, which integrates macroeconomic variables and takes account of both random effects and exogenous shocks, provides credible results. Besides, our method provides the predictive content of macroeconomic variables and the unobservable heterogeneity, which is helpful in forecasting firms bankruptcies.

Suggested Citation

  • Ilyes Abid & Farid Mkaouar & Olfa Kaabia, 2018. "Dynamic analysis of the forecasting bankruptcy under presence of unobserved heterogeneity," Annals of Operations Research, Springer, vol. 262(2), pages 241-256, March.
  • Handle: RePEc:spr:annopr:v:262:y:2018:i:2:d:10.1007_s10479-016-2143-2
    DOI: 10.1007/s10479-016-2143-2
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    1. Glennon, Dennis & Nigro, Peter, 2005. "Measuring the Default Risk of Small Business Loans: A Survival Analysis Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(5), pages 923-947, October.
    2. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    3. Koopman, Siem Jan & Kräussl, Roman & Lucas, André & Monteiro, André B., 2009. "Credit cycles and macro fundamentals," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 42-54, January.
    4. N. Berger, Allen & F. Udell, Gregory, 1998. "The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle," Journal of Banking & Finance, Elsevier, vol. 22(6-8), pages 613-673, August.
    5. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    6. 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.
    7. Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 45-61, February.
    8. Laroque, Guy & Salanie, B, 1993. "Simulation-Based Estimation of Models with Lagged Latent Variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 119-133, Suppl. De.
    9. 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.
    10. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    11. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    12. 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.
    13. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    14. Tor Jacobson & Jesper Lindé & Kasper Roszbach, 2013. "Firm Default And Aggregate Fluctuations," Journal of the European Economic Association, European Economic Association, vol. 11(4), pages 945-972, August.
    15. Carling, Kenneth & Jacobson, Tor & Linde, Jesper & Roszbach, Kasper, 2007. "Corporate credit risk modeling and the macroeconomy," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 845-868, March.
    16. Fama, Eugene F., 1990. "Term-structure forecasts of interest rates, inflation and real returns," Journal of Monetary Economics, Elsevier, vol. 25(1), pages 59-76, January.
    17. Estrella, Arturo & Hardouvelis, Gikas A, 1991. "The Term Structure as a Predictor of Real Economic Activity," Journal of Finance, American Finance Association, vol. 46(2), pages 555-576, June.
    18. Michael Doumpos & Constantin Zopounidis, 2007. "Model combination for credit risk assessment: A stacked generalization approach," Annals of Operations Research, Springer, vol. 151(1), pages 289-306, April.
    19. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    20. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    21. Chae Woo Nam & Tong Suk Kim & Nam Jung Park & Hoe Kyung Lee, 2008. "Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 493-506.
    22. Chris Charalambous & Andreas Charitou & Froso Kaourou, 2000. "Comparative Analysis of Artificial Neural Network Models: Application in Bankruptcy Prediction," Annals of Operations Research, Springer, vol. 99(1), pages 403-425, December.
    23. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
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    Cited by:

    1. Ayadi, Rim & Abid, Ilyes & Guesmi, Khaled, 2021. "Survival of reorganized firms in France," Finance Research Letters, Elsevier, vol. 38(C).

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