Vol. 11 No. 2 (2025)
June

Articles

Foretelling or foresight? Accounting-based bankruptcy prediction models and earnings quality in the case of Polish listed companies

Published 2025-06-30

  • Barbara Grabińska
    • ,
  • Konrad Grabiński

Barbara Grabińska
Krakow University of Economics

Konrad Grabiński
Krakow University of Economics

Abstract

Objective: The article aims to examine the relationship between bankruptcy risk and earnings quality in designing accounting-based bankruptcy prediction models. The models classify companies (firm-year observations) into two groups with high or low (no) bankruptcy risk. We investigated the difference in earnings quality between those two groups.

Research Design & Methods: We used quantitative research methods, such as descriptive statistics, correlation analysis (Pearson’s and Spearman’s rank correlation), and Welch ANOVA. The study sample consisted of firm-year observations of companies listed in the Warsaw Stock Exchange for 17 years (2007-2023) ranging from 5 004 up to 5 688 firm-year observations. We employed five accounting-based bankruptcy prediction models specific to the Polish context and two metrics of earnings quality: accrual and real earnings management. We estimated the proxy of accrual earnings management using the modified Jones model and real earnings management with the Roychowdhury model. We estimated the bankruptcy risk using five prediction models and then analyzed as a continuous (in correlation analysis) and dichotomous variable (Welch ANOVA).

Findings: The research results demonstrate that companies classified by company failure models as high bankruptcy risk are associated with lower earnings quality. The results of the Welch ANOVA analysis are consistent across all combinations of accounting-based prediction models and earnings quality proxies used in the study research. The findings imply that a high bankruptcy risk is associated with managers engaging in more intensive accrual and real earnings management. The results suggest managers are more inclined to influence reporting numbers and operational activities to achieve desired goals.

Implications & Recommendations: Scholars should consider diminishing the quality of earnings associated with higher bankruptcy risk in designing and developing future accounting-based bankruptcy prediction models. Financial statement users like investors, financial analysts, financial auditors, and other stakeholders should also consider earnings quality. The study provides an avenue for future research by calling for research across earnings quality and bankruptcy prediction models.

Contribution & Value Added: The study contributes to a better understanding of the relationship between accounting-based bankruptcy prediction models and how they estimate bankruptcy risk and earnings quality. As far as we know, earnings quality has not been considered a factor in the design of models for bankruptcy prediction.

Keywords

bankruptcy prediction, accounting-based prediction models, earnings quality, accrual earnings management, real earnings management, bankruptcy risk

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Author Biography

Barbara Grabińska

Assistant Professor at the College of Economics and Finance, Krakow University of Economics, Department of Finance and Financial Policy. She received a PhD in economics from the Faculty of Finance, Krakow University of Economics. Her research interests include financing science and higher education, innovation policy, R&D, financial analysis and financial policy.

Konrad Grabiński

Associate Professor at the College of Economics and Finance, Krakow University of Economics, Department of Financial Accounting. He received a PhD in economics from the Faculty of Finance, Krakow University of Economics and completed his habilitation (dr hab.) in economics and finance at the same university. His research interests include earnings quality, earnings management, financial reporting, accounting diversity and corporate finance.

References

  1. Altman, E., Iwanicz-Drozdowska, M., Laitinen, E., & Suvas, A. (2016). Financial and Nonfinancial Variables as Long-horizon Predictors of Bankruptcy. Journal of Credit Risk, 12(4), 49-78. https://doi.org/10.21314/JCR.2016.216
  2. Altman, E.I., Sabato, G., & Wilson, N. (2010). The value of non-financial information in small and medium-sized enterprise risk management. Journal of Credit Risk, 6(2), 1-33 https://doi.org/10.21314/JCR.2010.110
  3. Altman, E.I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.2307/2978933
  4. Altman, E.I., Hadleman, R.G., & Narayanan, P. (1977). Zeta Analysis: A New Model to Identify Bankruptcy Risk of Corporations. Banking and Finance, 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6
  5. Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63-93. https://doi.org/10.1016/j.bar.2005.09.001
  6. Ball, R., & Brown, P. (1968). An Empirical Evaluation of Accounting Income Numbers. Journal of Accounting and Economies, 36(1-3), 235-270. https://doi.org/10.2307/2490232
  7. Beaver, W.H. (1966). Financial Ratios As Predictors of Failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171
  8. Beaver, W.H. (1968). The Information Content of Annual Earnings Announcements. Journal of Accounting Research, 6, 67-92. Retrieved from https://www.jstor.org/stable/2490171 on October 30, 2024.
  9. Bellovary, J.L., Giacomino, D.E., & Akers Michael, D. (2007). A Review of Bankruptcy Prediction Studies: 1930 to Present. Journal of Financial Education, 33, 1-42. Retrieved from http://www.jstor.org/stable/41948574 on October 30, 2024.
  10. Bolek, M., & Gniadkowska-Szymańska, A. (2022). Are Bankruptcy Models Adequate for Condition Assessment of Companies Listed on Warsaw Stock Exchange?. Financial Internet Quarterly, 18(2), 1-12. https://doi.org/10.2478/fiqf-2022-0008
  11. Campa, D., & Camacho-Minano, M.M. (2015). The impact of SME’s pre-bankruptcy financial distress on earnings management tools. International Review of Financial Analysis, 42, 222-234. https://doi.org/10.1016/j.irfa.2015.07.004
  12. Dechow, P., Ge, W., & Schrand, C. (2010). Understanding earnings quality: A review of the proxies, their determinants and their consequences. Journal of Accounting and Economics, 50(2-3), 344-401. https://doi.org/10.1016/j.jacceco.2010.09.001
  13. Dechow, P.M., & Dichev, I.D. (2002). The Quality of Accruals and Earnings: The Role of Accrual Estimation Errors. The Accounting Review, 77(s-1), 35-59. https://doi.org/10.2308/accr.2002.77.s-1.35
  14. Dechow, P.M., Sloan, R.G., & Sweeney, A.P. (1995). Detecting Earnings Management. The Accounting Review, 70(2), 193-225. Retrieved from https://www.jstor.org/stable/248303 on July 2, 2024.
  15. Du Jardin, P. (2015). Bankruptcy prediction using terminal failure processes. European Journal of Operational Research, 242(1), 286-303. https://doi.org/10.1016/j.ejor.2014.09.059
  16. Fitzpatrick, P.J. (1932). A Comparison of Ratios of Successful Industrial Enterprises with Those of Failed Firms. Certified Public Accountant, 1(1), 598-605. Retrieved from https://openlibrary.org/books/OL6298050M/A_comparison_of_the_ratios_of_successful_industrial_enterprises_with_those_of_failed_companies on July 4, 2024.
  17. Francis, J.R., & Ke, B. (2006). Disclosure of fees paid to auditors and the market valuation of earnings surprises. Review of Accounting Studies, 11(4), 495-523. https://doi.org/10.1007/s11142-006-9014-z
  18. Garcia Lara, J.M., Osma, B.G., & Neophytou, E. (2009). Earnings quality in ex-post failed firms. Accounting and Business Research, 39(2), 119-138. https://doi.org/10.1080/00014788.2009.9663353
  19. Graham, B., & Dodd, D.L. (1934). Security Analysis. McGraw-Hill.
  20. Graham, J.R., Harvey, C.R., & Rajgopal, S. (2005). The economic implications of corporate financial reporting. Journal of Accounting and Economics, 40(1-3), 3-73. https://doi.org/10.1016/j.jacceco.2005.01.002
  21. Hamrol, M., Czajka, B., & Piechocki, M. (2004). Upadłość przedsiębiorstwa – model analizy dyskryminacyjnej. Przegląd Organizacji, 6, 35-39. https://doi.org/10.33141/po.2004.06.09
  22. Healy, P. (1985). The effect of bonus compensation on accounting decisions. Journal of Accounting and Economics, 7, 85-107. https://doi.org/10.1016/0165-4101(85)90029-1
  23. Healy, P.M., & Wahlen, J.M. (1999). A Review of the Earnings Management Literature and Its Implications for Standard Setting. Accounting Horizons, 13(4), 365-383. https://doi.org/10.2308/acch.1999.13.4.365
  24. Hekanaho, J., Back, B., Sere, K., & Laitinen, T. (1998). Neural Networks and Genetic Algorithms for Bankruptcy Predictions. Expert Systems With Applications.
  25. Jones, J. (1991). Earnings management during import relief investigations. Journal of Accounting Research, 29, 193-228. https://doi.org/10.2307/2491047
  26. Jones, S., Johnstone, D., & Wilson, R. (2017). Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks. Journal of Business Finance & Accounting, 44(1-2), 3-34. https://doi.org/10.1111/jbfa.12218
  27. Korol, T. (2010). Systemy ostrzegania przedsiębiorstw przed ryzykiem upadłości. Warsaw: Wolters Kluwer.
  28. Lev, B. (1989). On the Usefulness of Earnings and Earnings Research: Lessons and Directions from Two Decades of Empirical Research. Journal of Accounting Research, 27, 153-192. Retrieved from https://www.jstor.org/stable/2491070 on July 2, 2024.
  29. Li, L., & Faff, R. (2019). Predicting corporate bankruptcy: What matters?. International Review of Economics & Finance, 62, 1-19. https://doi.org/10.1016/j.iref.2019.02.016
  30. Mączyńska, E., & Zawadzki, M. (2006). Dyskryminacyjne modele predykcji upadłości przedsiębiorstw. Ekonomista, 2(2), 205-235. Retrieved from https://bazekon.uek.krakow.pl/rekord/110891493 on July 2, 2024.
  31. Merwin, C.L. (1942). Financing Small Corporations in Five Manufacturing Industries, 1926-36. National Bureau of Economic Research, Inc, merw42-1. Retrieved from https://www.nber.org/books-and-chapters/financing-small-corporations-five-manufacturing-industries-1926-36 on July 2, 2024.
  32. Misankova, M., Zvarikova, K., & Kliestikova, J. (2017). Bankruptcy Practice in Countries of Visegrad Four. Economics and Culture, 14(1), 108-118. https://doi.org/10.1515/jec-2017-0010
  33. Nelson, M.W., & Skinner, D.J. (2013). How should we think about earnings quality? A discussion of “Earnings quality: Evidence from the field”. Journal of Accounting and Economics, 56(2-3), 34-41. https://doi.org/10.1016/j.jacceco.2013.10.003
  34. Ohlson, J.A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395
  35. Olszewska, K., & Turek, T. (2018). Analiza dyskryminacyjna jako narzędzie informacyjne w zakresie kondycji finansowej przedsiębiorstwa. Zeszyty Naukowe Politechniki Częstochowskiej Zarządzanie, 31(1), 175-186. https://doi.org/10.17512/znpcz.2018.3.15
  36. Ooghe, H., & Prijcker, S., de (2008). Failure processes and causes of company bankruptcy: a typology. Management Decision, 46(2), 223-242. https://doi.org/10.1108/00251740810854131
  37. Pawełek, B., Baryła, M., & Pociecha, J. (2020). Study of the classification accuracy measures for predicting corporate bankruptcy taking into account changes in the economic environment. Argumenta Oeconomica, 2019(1), 5-17. https://doi.org/10.15611/aoe.2020.1.01
  38. Pilch, B. (2021). An Analysis of the Effectiveness of Bankruptcy Prediction Models – an Industry Approach. Folia Oeconomica Stetinensia, 21(2), 76-96. https://doi.org/10.2478/foli-2021-0017
  39. Prusak, B. (2018). Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries. International Journal of Financial Studies, 6(3), 60. https://doi.org/10.3390/ijfs6030060
  40. Prusak, B. (2019). Corporate Bankruptcy Prediction in Poland Against the Background of Foreign Experience. E-Finanse, 15(1), 10-19. https://doi.org/10.2478/fiqf-2019-0002
  41. Reisz, A.S., & Perlich, C. (2007). A market-based framework for bankruptcy prediction. Journal of Financial Stability, 3(2), 85-131. https://doi.org/10.1016/j.jfs.2007.02.001
  42. Roychowdhury, S. (2006). Earnings management through real activities manipulation. Journal of Accounting and Economics, 32, 373-386. https://doi.org/10.1016/j.jacce
  43. Schipper, K. (1989). Commentary on earnings management. Accounting Horizons, 3(4), 91-102. Retrieved from https://www.scirp.org/reference/ReferencesPapers?ReferenceID=2440010 on July 1, 2024.
  44. Séverin, E., & Veganzones, D. (2021). Can earnings management information improve bankruptcy prediction models?. Annals of Operations Research, 306, 247-272. https://doi.org/10.1007/s10479-021-04183-0
  45. Tamari, M. (1966). Financial Ratios as a Means of Forecasting Bankruptcy. Management International Review, 6(4), 15-21. Retrieved from http://www.jstor.org/stable/40226072 on October 10, 2024.
  46. Tucker, J.W., & Zarowin, P.A. (2006). Does Income Smoothing Improve Earnings Informativeness?. The Accounting Review, 81(1), 251-270. https://doi.org/10.2308/accr.2006.81.1.251
  47. Wierzba, D. (2000). Wczesne wykrywanie przedsiębiorstw zagrożonych upadłością na podstawie analizy wskaźników finansowych – teoria i badania empiryczne. Zeszyty Naukowe Wyższej Szkoły Ekonomiczno-Informatycznej w Warszawie, 9, 79-105.
  48. Wilson, R.L., & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11(5), 545-557. https://doi.org/10.1016/0167-9236(94)90024-8
  49. Zięba, M., Tomczak, S.K., & Tomczak, J.M. (2016). Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert Systems with Applications, 58, 93–101. https://doi.org/10.1016/j.eswa.2016.04.001

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