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Bankruptcy Prediction with a Doubly Stochastic Poisson Forward Intensity Model and Low-Quality Data

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  • Tomasz Berent

    (Capital Markets Department, Warsaw School of Economics, 02-554 Warszawa, Poland)

  • Radosław Rejman

    (Capital Markets Department, Warsaw School of Economics, 02-554 Warszawa, Poland)

Abstract

With the record high leverage across all segments of the (global) economy, default prediction has never been more important. The excess cash illusion created in the context of COVID-19 may disappear just as quickly as the pandemic entered our world in 2020. In this paper, instead of using any scoring device to discriminate between healthy companies and potential defaulters, we model default probability using a doubly stochastic Poisson process. Our paper is unique in that it uses a large dataset of non-public companies with low-quality reporting standards and very patchy data. We believe this is the first attempt to apply the Duffie–Duan formulation to emerging markets at such a scale. Our results are comparable, if not more robust, than those obtained for public companies in developed countries. The out-of-sample accuracy ratios range from 85% to 76%, one and three years prior to default, respectively. What we lose in (data) quality, we regain in (data) quantity; the power of our tests benefits from the size of the sample: 15,122 non-financial companies from 2007 to 2017, unique in this research area. Our results are also robust to model specification (with different macro and company-specific covariates used) and statistically significant at the 1% level.

Suggested Citation

  • Tomasz Berent & Radosław Rejman, 2021. "Bankruptcy Prediction with a Doubly Stochastic Poisson Forward Intensity Model and Low-Quality Data," Risks, MDPI, vol. 9(12), pages 1-24, December.
  • Handle: RePEc:gam:jrisks:v:9:y:2021:i:12:p:217-:d:693252
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    References listed on IDEAS

    as
    1. 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.
    2. Caporale, Guglielmo Maria & Cerrato, Mario & Zhang, Xuan, 2017. "Analysing the determinants of insolvency risk for general insurance firms in the UK," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 107-122.
    3. 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.
    4. Maria Aluchna & Tomasz Berent & Bogumił Kamiński, 2019. "Dividend Payouts and Shareholder Structure: Evidence from the Warsaw Stock Exchange," Eastern European Economics, Taylor & Francis Journals, vol. 57(3), pages 227-250, May.
    5. Leland, Hayne E, 1994. "Corporate Debt Value, Bond Covenants, and Optimal Capital Structure," Journal of Finance, American Finance Association, vol. 49(4), pages 1213-1252, September.
    6. 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.
    7. 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.
    8. Viral V. Acharya & Matteo Crosignani & Tim Eisert & Christian Eufinger, 2024. "Zombie Credit and (Dis‐)Inflation: Evidence from Europe," Journal of Finance, American Finance Association, vol. 79(3), pages 1883-1929, June.
    9. Ke Wang & Darrell Duffie, 2004. "Multi-Period Corporate Failure Prediction With Stochastic Covariates," Econometric Society 2004 Far Eastern Meetings 747, Econometric Society.
    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. Ryan Niladri Banerjee & Boris Hofmann, 2018. "The rise of zombie firms: causes and consequences," BIS Quarterly Review, Bank for International Settlements, September.
    14. Duan, Jin-Chuan & Kim, Baeho & Kim, Woojin & Shin, Donghwa, 2018. "Default probabilities of privately held firms," Journal of Banking & Finance, Elsevier, vol. 94(C), pages 235-250.
    15. Robert A. Jarrow & Fan Yu, 2008. "Counterparty Risk and the Pricing of Defaultable Securities," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 20, pages 481-515, World Scientific Publishing Co. Pte. Ltd..
    16. Pierre Collin‐Dufresne & Robert S. Goldstein, 2001. "Do Credit Spreads Reflect Stationary Leverage Ratios?," Journal of Finance, American Finance Association, vol. 56(5), pages 1929-1957, October.
    17. repec:bla:jfinan:v:44:y:1989:i:1:p:19-40 is not listed on IDEAS
    18. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    19. 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..
    20. Beaver, Wh, 1968. "Market Prices, Financial Ratios, And Prediction Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 6(2), pages 179-192.
    21. 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.
    22. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
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