Financial distress and insolvency prediction models: A systematic review

Journal title MANAGEMENT CONTROL
Author/s Luca Ianni, Gianluca Marullo, Stefania Migliori, Francesco De Luca
Publishing Year 2021 Issue 2021/2
Language Italian Pages 20 P. 127-146 File size 299 KB
DOI 10.3280/MACO2021-002007
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In January 2019, the Italian government approved the corporate insolvency law reform, henceforth the Code of Crisis (CCI). This law reform has brought back to the center of the scientific debate the financial distress and insolvency prediction models. In recent decades, there has been a large number of studies about predicting models variously developed by scholars. These studies were about ratio-based and non-ratio-based models, and had to do with statistical approach, model types employed, scoring-based models, and so on. Moreover, these studies also related to an increasing evolution of these models, for example, accounting-based vs. market-based value in statistical multivariate estimation models, leading to greater predictive power. Accordingly, the authors conducted a systematic review of studies published between 1960 and 2020. Thereby, this paper aims to map the studies that are concerned with financial distress and related predicting models in order to portray the state of the scientific debate thus far and, at the same time, the different types, approaches and methods employed along with future trends. Furthermore, this study tries to provide a possible framework for further research about this field, thereby improving our understanding of prediction models and their evolution over time in relation to the digital technologies as well.

Keywords: Business crisis, Insolvency, Literature review, Financial distress, Predicting models.

  1. Altman E.I, Marco G., Varetto F. (1994), Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience), Journal of Banking & Finance, 18, 3, pp. 505-529.
  2. Altman E.I. (1968), Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance, 23, 4, pp. 589-609.
  3. Altman E.I. (1983), Corporate financial distress: a complete guide to predicting, avoiding, and dealing with bankruptcy, Wiley Interscience, Hoboken, John Wiley and Sons.
  4. Altman E.I., Danovi A., Falini A. (2013), Z-Score Models’ application to Italian companies subject to extraordinary administration, Journal of Applied Finance, 23, 1, pp. 1-10.
  5. Altman E.I., Hartzell J., Peck M. (1995), Emerging Markets Corporate Bonds: A Scoring System, New York, Salomon Brothers Inc.
  6. Arcari A.M. (2018), Preventing crises and managing turnaround processes in SMEs. The role of economic measurement tools, Management Control, 3, pp. 131-155.
  7. Beaver W.H. (1966), Financial ratios as predictors of failure, Journal of Accounting Research, 4 (Supplement), Empirical Research in Accounting: Selected Studies, pp. 71-111.
  8. Beaver W.H., Correia M., McNichols M.F. (2010), Financial Statement Analysis and the Prediction of Financial Distress, Foundations and Trends in Accounting, 5, 2, pp. 99-173.
  9. Bellovary J.L., Giacomino D.E., Akers M.D., A Review of Bankruptcy Prediction Studies: 1930-Present, Journal of Financial Education, 33, Winter, pp. 1-42.
  10. Bertoni M., De Rosa B., Rossi P. (2021), Early Warnings: An increase in responsiveness or loss of relevance?, Management Control, 1, pp. 175-194. DOI: 10.3280/MACO2021-001009
  11. Cenciarelli V.G., Mattei M.M., Greco G. (2020), Pressione competitiva e previsione dell’insolvenza, Management Control, 3, pp. 35-58. DOI: 10.3280/MACO2020-003003
  12. Charitou A., Neophytou E., Charalambous C. (2004), Predicting corporate failure: empirical evidence for the UK, European Accounting Review, 13, 3, pp. 465-497.
  13. Cybinski P.J. (1995), A discrete-value risk function for modelling financial distress in private Australian companies, Accounting & Finance, 35, 2, pp. 17-32.
  14. De Luca F., Meschieri E. (2017), Financial distress pre-warning indicators: a case study on Italian listed companies, Journal of Credit Risk, 13, 1, pp. 1-22.
  15. Ferner D.G., Hamilton R.T. (1987), A note on the predictability of financial distress in New Zealand listed companies, Accounting & Finance, 27, 1, pp. 55-63.
  16. Frydman H., Altman E.I., Kao DL (1985), Introducing Recursive Partitioning for financial classification: The case of financial distress, The Journal of Finance, 40, 1, pp. 269-291.
  17. Gilbert L. R., Menon K., Schwartz K. B. (1990), Predicting bankruptcy for firms in financial distress, Journal of Business Finance and Accounting, 17, 1, pp. 161-171.
  18. Gilson S. C., John K., Lang L. H. P. (1990), Troubled debt restructurings: an empirical analysis of private reorganization of firms in default, Journal of Financial Economics, 27, pp. 315-353.
  19. Habib A., Costa M.D., Huang H.J., Bhuiyan MB. U., Sun L. (2020), Determinants and consequences of financial distress: review of the empirical literature, Accounting & Finance, 60, S1, pp. 1023-1075.
  20. Hanlon M. (2005), The persistence and pricing of earnings, accruals, and cash flows when firms have large Book-Tax differences, The Accounting Review, 80, 1, pp. 137-166.
  21. Hill N.T., Perry S.E., Andes S. (1996), Evaluating firms in financial distress: an event history analysis, Journal of Applied Business Research, 12, 3, pp. 60-71.
  22. Hopwood W., McKeown J., Mutchler J. (1988), The sensitivity of financial distress prediction models to departures from normality, Contemporary Accounting Research, 5, 1, pp. 284-298.
  23. Jiang Y., Jones S. (2018), Corporate distress prediction in China: a machine learning approach, Accounting & Finance, 58, 4, pp. 1063-1109.
  24. John K. (1993), Managing financial stress and valuing distressed securities: a survey and a research agenda. Financial Management, 22, 3, 60-78.
  25. Johnsen, T. & Melicher, R. W. (1994). Predicting corporate bankruptcy and financial distress: information value added by multinomial logit models, Journal of Economics and Business, 46, 4, pp. 269-286.
  26. Jones S., Hensher D.A. (2004), Predicting firm financial distress: a mixed Logit Model, The Accounting Review, 79, 4, pp. 1011-1038.
  27. Kallunki J., E Pyykkö E. (2013), Do defaulting CEOs and directors increase the likelihood of financial distress of the firm? Review of Accounting Studies, 18, pp. 228-260.
  28. Lau A.H. (1987), A five-state financial distress prediction model, Journal of Accounting Research, 25, 1, pp. 127-138.
  29. Lee T., Yeh, Y. (2004), Corporate governance and financial distress: Evidence from Taiwan, Corporate governance: An International review, 12, 3, pp. 378-388.
  30. Lieu P., Lin C., Yu H. (2008), Financial early-warning models on cross-holding groups, Industrial Management & Data System, 108, 8, pp. 1060-1080.
  31. Liou F. (2008), Fraudulent financial reporting detection and business failure prediction models: a comparison, Managerial Auditing Journal, 23, 7, pp. 650-662.
  32. Marchi L. (2020), Dalla crisi allo sviluppo sostenibile. Il ruolo dei sistemi di misurazione e controllo, Management Control, 3, pp. 5-16. DOI: 10.3280/MACO2020-003001
  33. Migliori S. (2013), Crisi d’impresa e corporate governance, Collana di Studi Aziendali e Applicati, Vol. XXII, Milano, FrancoAngeli.
  34. Moher D., Liberati A., Telzaff J., Altman D.G., The PRISMA Group (2009), Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement, Plos Medicine, 6, 7, e1000097.
  35. Noga T.J., Schnader A.L. (2013), Book-tax differences as an indicator of financial distress, Accouting Horizons, 27, 3, pp. 469-489.
  36. Ohlson J.A. (1980), Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, 18, 1, pp. 109-131.
  37. Ong SW., Yap V.C., Khong R.W.L. (2011), Corporate failure prediction: a study of public listed companies in Malaysia, Managerial Finance, 37, 6, pp. 553-564.
  38. Oz, I.O., Yelkenci, T. (2017), A theoretical approach to financial distress prediction modeling, Managerial Finance, 43, 2, pp. 212-230.
  39. Paolone G. (2008), Gli istituti di cessazione aziendale. Cause originatrici e forme di manifestazione, Collana di Studi Aziendali e Applicati, Vol. VIII, Milano, FrancoAngeli.
  40. Paoloni M., Celli M. (2018), Crisi delle PMI e strumenti di warning. Un test di verifica nel settore manifatturiero, Management Control, 2, pp. 85-106. DOI: 10.3280/MACO2018-002005
  41. Petticrew M., Roberts H. (2006), Systematic reviews in the Social Sciences. A practical guide, Oxford, UK, Blackwell Publishing.
  42. Platt H.D., Platt M.B. (1991), A note on the use of industry-relative ratios in bankruptcy prediction, Journal of Banking and Finance, 15, 6, pp. 1183-1194.
  43. Platt H.D., Platt M.B. (2002), Predicting corporate financial distress: reflections on choice-based sample bias, Journal of Economics and Finance, 26, 2, pp. 184-199.
  44. Platt H.D., Platt M.B. (2006), Understanding differences between financial distress and bankruptcy, Review of Applied Economics, 2, 2, pp. 141-157.
  45. Pozzoli S., Paolone F. (2017), Corporate financial distress. A study of the Italian manufacturing industry, Switzerland, Springer.
  46. Pranowo K., Achsani N.A., Manurung A.H., Nuryartono N. (2011), Determinant of Corporate Financial Distress in an Emerging Market Economy: Empirical Evidence from the Indonesian Stock Exchange 2004-2008, International Research Journal of Finance and Economics, 52, pp. 81-90.
  47. Schipper K. (1977), Financial distress in private colleges, Journal of Accounting Research, 15, Supplement, pp. 1-40.
  48. Smith M., Graves C. (2005), Corporate turnaround and financial distress, Managerial Auditing Journal, 20, 3, pp. 304-320.
  49. Smith M., Liou D. (2007), Industrial sector and financial distress, Managerial Auditing Journal, 22, 4, pp. 376-391.
  50. Taffler R., Tisshaw H. (1977), Going, going, gone – four factors with predict, Accountancy, 88, 1003, pp. 50-54.
  51. Tan C.N.W., Dihardjo H. (2001), A study of using artificial neural networks to develop an early warning predictor for credit union financial distress with comparison to the probit model, Managerial Finance, 27, 4, pp. 56-77.
  52. Tirapat S., Nittayagasetwat A. (1999), An Investigation of Thai Listed Firms’ Financial Distress Using Macro and Micro Variables, Multinational Financial Journal, 3, 2, pp. 103-125.
  53. Turetsky H. F., McEwen R. A. (2001), An empirical investigation of firm longevity: a model of the ex-ante predictors of financial distress, Review of Quantitative Finance and Accounting, 16, 323-343.
  54. Varetto F. (1998), Genetic algorithms applications in the analysis of insolvency risk, Journal of Banking & Finance, 22, 10-11, pp. 1421-1439.
  55. Wruck K. H. (1990), Financial distress, reorganization, and organizational efficiency, Journal of Financial Economics, 27, 2, pp. 419-444.
  56. Yang A.R., Platt M.B., Platt H.D. (1999), Probabilistic neural networks in bankruptcy prediction, Journal of Business Research, 44, 2, pp. 67-74.
  57. Zmijewski M.E. (1984), Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research, 22 (Supplement), pp. 59-82.

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Luca Ianni, Gianluca Marullo, Stefania Migliori, Francesco De Luca, I modelli predittivi della crisi e dell’insolvenza aziendale. Una systematic review in "MANAGEMENT CONTROL" 2/2021, pp 127-146, DOI: 10.3280/MACO2021-002007