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I modelli predittivi della crisi e dell?insolvenza aziendale. Una systematic review

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  • Luca Ianni
  • Gianluca Marullo
  • Stefania Migliori
  • Francesco De Luca

Abstract

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.

Suggested Citation

  • Luca Ianni & Gianluca Marullo & Stefania Migliori & Francesco De Luca, 2021. "I modelli predittivi della crisi e dell?insolvenza aziendale. Una systematic review," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2021(2), pages 127-146.
  • Handle: RePEc:fan:macoma:v:html10.3280/maco2021-002007
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    References listed on IDEAS

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    1. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    2. Yang, Z. R. & Platt, Marjorie B. & Platt, Harlan D., 1999. "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Elsevier, vol. 44(2), pages 67-74, February.
    3. Mauro Paoloni & Massimiliano Celli, 2018. "Crisi delle PMI e strumenti di warning. Un test di verifica nel settore manifatturiero," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(2), pages 85-106.
    4. 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.
    5. Harlan Platt & Marjorie Platt, 2002. "Predicting corporate financial distress: Reflections on choice-based sample bias," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 26(2), pages 184-199, June.
    6. Wruck, Karen Hopper, 1990. "Financial distress, reorganization, and organizational efficiency," Journal of Financial Economics, Elsevier, vol. 27(2), pages 419-444, October.
    7. Anna Maria Arcari, 2018. "Preventing crises and managing turnaround processes in SMEs The role of economic measurement tools," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(3), pages 131-155.
    8. Varetto, Franco, 1998. "Genetic algorithms applications in the analysis of insolvency risk," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1421-1439, October.
    9. Yi Jiang & Stewart Jones, 2018. "Corporate distress prediction in China: a machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(4), pages 1063-1109, December.
    10. WILLIAM HOPWOOD & JAMES McKEOWN & JANE MUTCHLER, 1988. "The sensitivity of financial distress prediction models to departures from normality," Contemporary Accounting Research, John Wiley & Sons, vol. 5(1), pages 284-298, September.
    11. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    12. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    13. Schipper, K, 1977. "Financial Distress In Private Colleges," Journal of Accounting Research, Wiley Blackwell, vol. 15, pages 1-53.
    14. Johnsen, Thomajean & Melicher, Ronald W., 1994. "Predicting corporate bankruptcy and financial distress: Information value added by multinomial logit models," Journal of Economics and Business, Elsevier, vol. 46(4), pages 269-286, October.
    15. Luciano Marchi, 2020. "Dalla crisi allo sviluppo sostenibile. Il ruolo dei sistemi di misurazione e controllo," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2020(3), pages 5-16.
    16. Edward I. Altman & Alessandro Danovi & Alberto Falini, 2013. "Z-Score Models’ application to Italian companies subject to extraordinary administration," BANCARIA, Bancaria Editrice, vol. 4, pages 24-37, April.
    17. Platt, Harlan D. & Platt, Marjorie B., 1991. "A note on the use of industry-relative ratios in bankruptcy prediction," Journal of Banking & Finance, Elsevier, vol. 15(6), pages 1183-1194, December.
    18. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
    19. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    20. Sunti Tirapat & Aekkachai Nittayagasetwat, 1999. "An Investigation of Thai Listed Firms' Financial Distress Using Macro and Micro Variables," Multinational Finance Journal, Multinational Finance Journal, vol. 3(2), pages 103-125, June.
    21. 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.
    22. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    23. 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.
    24. Turetsky, Howard F & McEwen, Ruth Ann, 2001. "An Empirical Investigation of Firm Longevity: A Model of the Ex Ante Predictors of Financial Distress," Review of Quantitative Finance and Accounting, Springer, vol. 16(4), pages 323-343, June.
    25. Fen-May Liou, 2008. "Fraudulent financial reporting detection and business failure prediction models: a comparison," Managerial Auditing Journal, Emerald Group Publishing, vol. 23(7), pages 650-662, July.
    26. Tsun‐Siou Lee & Yin‐Hua Yeh, 2004. "Corporate Governance and Financial Distress: evidence from Taiwan," Corporate Governance: An International Review, Wiley Blackwell, vol. 12(3), pages 378-388, July.
    27. Kose John, 1993. "Managing Financial Distress and Valuing Distressed Securities: A Survey and a Research Agenda," Financial Management, Financial Management Association, vol. 22(3), Fall.
    28. Platt, Harlan D. & Platt, Marjorie B., 2006. "Understanding Differences Between Financial Distress and Bankruptcy," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 2(2), pages 1-17.
    29. Malcolm Smith & Dah-Kwei Liou, 2007. "Industrial sector and financial distress," Managerial Auditing Journal, Emerald Group Publishing, vol. 22(4), pages 376-391, April.
    30. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
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