Early Warning Systems for financial crises prediction in private companies: Evidence from the Italian context

Titolo Rivista FINANCIAL REPORTING
Autori/Curatori Mario Daniele, Elisa Raoli
Anno di pubblicazione 2024 Fascicolo 2024/2
Lingua Inglese Numero pagine 29 P. 133-161 Dimensione file 222 KB
DOI 10.3280/FR2024-002006
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Purpose: This study compares models for predicting business financial crises, fo-cusing on which are most effective. In light of the new European Directive on business failure, it highlights a trade-off between predictive accuracy and timeli-ness in static models and offers an alternative approach. Design/methodology/approach: This study examines the Italian early warning system (EWS), testing static alert indicators’ predictive ability on a large sample of private companies. It then proposes a dynamic version of the EWS. Findings: The results show a trade-off between predictive ability and timeliness for static models. In contrast, a dynamic system is more accurate in predicting cri-sis events, allowing managers to take corrective actions. Originality: The results highlight the limitations of static prediction models and emphasize the potential of a simple dynamic model that is specifically designed for small- and medium-sized entities (SMEs). Practical implications: This study proposes a dynamic model tailored for SMEs, which are particularly vulnerable to financial crises. This insight can help managers and policymakers balance accurate predictions with timely interventions, especial-ly in European countries implementing crisis prediction models.

Keywords:corporate failure; early warning systems; crisis; crisis prediction

Jel codes:G01, M4, M400, M410, M480

  1. Agarwal, V., & Taffler, R.J. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking & Finance, 32(8), 1541-1551.
  2. Alonzo, T. A. (2002). Distribution-free ROC analysis using binary regression techniques. Biostatistics, 3(3), 421-432.
  3. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
  4. Altman, E. I. (1993). Corporate financial distress and bankruptcy (2nd ed.). John Wiley & Sons.
  5. Atiya, A. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks, 12(4), 929-935. DOI: 10.1109/72.935101
  6. 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.
  7. Barniv, R., Mehrez, A., & Kline, D. M. (2000). Confidence intervals for controlling the probability of bankruptcy. Omega, 28(5), 555-565. DOI: 10.1016/S0305-0483(99)00073-0
  8. Bauweraerts, J. (2016). Predicting Bankruptcy in Private Firms: Towards a Stepwise Regression Procedure. International Journal of Financial Research, 7(2), 147-153.
  9. Bava, F., Cane, M., & di Trana, M. G. (2020). Can a quantitative approach be mitigated? Proposals for the application of the “early warnings” required by the new Italian Insolvency Code. Financial Reporting, 2, 33-61.
  10. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111.
  11. Burgstahler, D., & Dichev, I. (1997). Earnings management to avoid earnings decreases and losses. Journal of Accounting and Economics, 24(1), 99-126. DOI: 10.1016/S0165-4101(97)00017-7
  12. Bussiere, M., & Fratzscher, M. (2006). Towards a new early warning system of financial crises. Journal of International Money and Finance, 25(6), 953-973.
  13. Caggiano, G., Calice, P., & Leonida, L. (2014). Early warning systems and systemic banking crises in low income countries: A multinomial logit approach. Journal of Banking & Finance, 47, 258-269.
  14. Caggiano, G., Calice, P., Leonida, L., & Kapetanios, G. (2016). Comparing logit-based early warning systems: Does the duration of systemic banking crises matter?. Journal of Empirical Finance, 37, 104-116.
  15. Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63(6), 2899-2939.
  16. Canbas, S., Cabuk, A., & Kilic, S. B. (2005). Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case. European Journal of Operational Research, 166(2), 528-546.
  17. Candelon, B., Dumitrescu, E. I., & Hurlin, C. (2014). Currency crisis early warning systems: Why they should be dynamic. International Journal of Forecasting, 30(4), 1016-1029.
  18. Charalambakis, E. C., & Garrett, I. (2019). On corporate financial distress prediction: What can we learn from private firms in a developing economy? Evidence from Greece. Review of Quantitative Finance and Accounting, 52, 467-491.
  19. Charitou, A., Neophytou, E., & Charalambous, C. (2004). Predicting Corporate Failure: Empirical Evidence for the UK. European Accounting Review, 13(3), 465-497. DOI: 10.1080/0963818042000216811
  20. Charitou, A., Lambertides, N., & Trigeorgis, L. (2011). Distress risk, growth and earnings quality. Abacus, 47(2), 158-181.
  21. Chuang, C. L. (2013). Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction. Information Sciences, 236, 174-185.
  22. Ciampi, F., Giannozzi, A., Marzi, G. & Altman, E.I. (2021). Rethinking SME default prediction: a systematic literature review and future perspectives. Scientometrics, 126, 2141-2188.
  23. De Meyst, K. J., Lowe, D. J., Peecher, M. E., Pickerd, J., & Reffett, A. (2020). Factors affecting the outcomes of legal claims against auditors. Current Issues in Auditing, 15(2), 10-16.
  24. European Commission. (2023). 2022 SBA Fact Sheet. -- https://single-market-economy. ec.europa.eu/document/download/2bef0eda-2f75-497d-982e-c0d1cea57c0e_en?file name=Annual%20Report%20on%20European%20SMEs%202024.pdf.
  25. European Union. (2019). Directive (EU) 2019/1023 of the European Parliament and of the Council of 20 June 2019 on preventive restructuring frameworks, on discharge of debt and disqualifications, and on measures to increase the efficiency of procedures concerning restructuring, insolvency and discharge of debt, and amending Directive (EU) 2017/1132 (Directive on restructuring and insolvency). Official Journal of the European Union, L 172, 18-55. -- https:// eur-lex.europa.eu/eli/dir/2019/1023/oj.
  26. Foreman, R. D. (2003). A logistic analysis of bankruptcy within the US local telecommunications industry. Journal of Economics and Business, 55(2), 135-166. DOI: 10.1016/S0148-6195(02)00133-9
  27. Ge, W., & Whitmore, G. A. (2009). Binary response and logistic regression in recent accounting research publications: A methodological note. Review of Quantitative Finance and Accounting, 34(1), 81-93.
  28. Habib, A., Costa, M.D., Huang, H.J., Bhuiyan, M.B.U. & Sun, L. (2020). Determinants and consequences of financial distress: review of the empirical literature. Accounting & Finance, 60, 1023-1075.
  29. Hamdaoui, M. (2016). Are systemic banking crises in developed and developing countries predictable? Journal of Multinational Financial Management, 37-38, 114-138.
  30. Hastie, T., Tibshirani, R., Friedman, J. (2001). The Elements of Statistical Learning. New York: Springer.
  31. Hernández-Orallo, J. (2013). ROC curves for regression. Pattern Recognition, 46(12), 3395-3411.
  32. Hillegeist, S.A., Keating, E.K., Cram, D.P., & Lundstedt, K.G. (2004). Assessing the Probability of Bankruptcy. Review of Accounting Studies, 9(1), 5-34.
  33. Italian Republic. (2019). Decreto legislativo 12 gennaio 2019, n. 14. Gazzetta Ufficiale della Repubblica Italiana -- https://www.gazzettaufficiale.it/eli/id/2019/ 02/14/19G00007/sg.
  34. Jemović, M., & Marinković, S. (2019). Determinants of financial crises – An early warning system based on panel logit regression. International Journal of Finance and Economics, 26(1), 103-117.
  35. Jones, S. (2023). A literature survey of corporate failure prediction models. Journal of Accounting Literature, 45(2), 364-405.
  36. Kim, S. Y., & Upneja, A. (2014). Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models. Economic Modelling, 36, 354-362.
  37. Laitinen, E. K. (1991). Financial ratios and different failure processes. Journal of Business Finance and Accounting, 18(5), 649-673.
  38. Laitinen, T., & Kankaanpaa, M. (1999). Comparative analysis of failure prediction methods: The Finnish case. European Accounting Review, 8(1), 67-92. DOI: 10.1080/096381899336159
  39. Li, H., & Sun, J. (2008). Ranking-order case-based reasoning for financial distress prediction. Knowledge-Based Systems, 21(8), 868-878.
  40. Liang, D., Tsai, C. F., & Wu, H. T. (2015). The effect of feature selection on financial distress prediction. Knowledge-Based Systems, 73, 289-297.
  41. Lin, F., Liang, D., & Chen, E. (2011). Financial ratio selection for business crisis prediction. Expert Systems with Applications, 38(12), 15094-15102.
  42. Lin, S.-M., Ansell, J., & Andreeva, G. (2012). Predicting default of a small business using different definitions of financial distress. Journal of the Operational Research Society, 63(4), 539-548.
  43. Liou, F. M., & Yang, C. H. (2008). Predicting business failure under the existence of fraudulent financial reporting. International Journal of Accounting and Information Management, 16(1), 74-86. DOI: 10.1108/18347640810887771
  44. Min, J. H., & Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603-614.
  45. Muñoz‐Izquierdo, N., Laitinen, E. K., Camacho‐Miñano, M., & Pascual‐Ezama, D. (2019). Does audit report information improve financial distress prediction over Altman’s traditional Z‐Score model? Journal of International Financial Management & Accounting, 31(1), 65-97.
  46. Nadarajah, S. (2009). The skew logistic distribution. AStA Advances in Statistical Analysis, 93(2), 187-203.
  47. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. DOI: 10.2307/2490395
  48. Oster, E. (2017). Unobservable selection and coefficient stability: Theory and evidence. Journal of Business & Economic Statistics, 37(2), 187-204. DOI: 10.1080/07350015.2016.1227711
  49. Platt, H.D. & Platt, M.B. (2006). Understanding differences between financial distress and bankruptcy. Review of Applied Economics, 2(2), 141-157.
  50. Putnam, R. D., Leonardi, R., & Nonetti, R. Y. (1993). Making democracy work: Civic traditions in modern Italy. Princeton, NJ: Princeton University Press.
  51. Schonlau, M. (2020). CROSSVALIDATE: Stata module to perform k-fold crossvalidation (Version S458860). Boston College Department of Economics. -- https://ideas.repec.org/c/boc/bocode/s458860.html.
  52. Serrano-Cinca, C., & Gutiérrez-Nieto, B. (2013). Partial least square discriminant analysis for bankruptcy prediction. Decision Support Systems, 54(3), 1245-1255.
  53. Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74(1), 101-124. DOI: 10.1086/209665
  54. Sun, J., Jia, M. Y., & Li, H. (2011). AdaBoost ensemble for financial distress prediction: An empirical comparison with data from Chinese listed companies. Expert Systems with Applications, 38(8), 9305-9312.
  55. Sun, J., Li, H., Huang, Q. H., & He, K. Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41-56.
  56. Tinoco, M.H., & Wilson, N. (2013). Financial Distress and Bankruptcy Prediction among Listed Companies Using Accounting, Market and Macroeconomic Variables. International Review of Financial Analysis, 30, 394-419.
  57. Tseng, F. M., & Lin, L. (2005). A quadratic interval logit model for forecasting bankruptcy. Omega, 33(1), 85-91.
  58. Veganzones, D., & Severin, E. (2020). Corporate failure prediction models in the twenty-first century: A review. European Business Review, 33(2), 204-226. DOI: 10.1108/EBR-12-2018-0209
  59. Wang, D., Song, X., Yin, W., & Yuan, J. (2015). Forecasting core business transformation risk using the optimal rough set and the neural network. Journal of Forecasting, 34(6), 478-491.

Mario Daniele, Elisa Raoli, Early Warning Systems for financial crises prediction in private companies: Evidence from the Italian context in "FINANCIAL REPORTING" 2/2024, pp 133-161, DOI: 10.3280/FR2024-002006