Il rafforzamento della capacità previsiva dei modelli di anticipazione delle crisi di impresa: il caso del sistema di allerta proposto dal CNDCEC



As the pandemic urged further investigations on the prediction of firms’ financial distress, this study develops and tests an alternative alert system which combines the benefits of the Z-score’s multivariate discriminant model and the National Council of Chartered Accountants and Accounting Experts’ predictors. Using a sample of 43 viable and 43 non-viable Italian SMEs, we first compare the financial distress predictive accuracy of the models mentioned over the period 2015-2019. On the basis of the results, we elaborate and compare the revised versions of both approaches which align them to the current socio-economic conditions. Also, we provide an alternative measure which embeds a Z-score calculated using the ratios elaborated by the National Council of Chartered Accountants and Accounting Experts for the alert system. The analysis of the two baseline approaches showed complementary results as the Z-score overperformed the alert system when predicting the status of non-viable firms whereas the opposite emerged as regards viable firms. The revised version of both approaches pointed out an enhanced predictive accuracy with respect to baseline models. In particular, the complementary role of the Z-score has been integrated into the new alert system as major contribute to its enhancement which pointed it out as the best measure employed. Our analysis enriches the post-pandemic debate on refined financial distressed prediction methods by pointing out the limits of the alert system as designed by the National Council of Chartered Accountants and Accounting Experts and suggests an alternative and better performing measure that may be used by third-party bodies to predict financial distress.



  • AGNOLI N., ZAMBONI M., 2021, Intelligenza artificiale e previsione delle crisi aziendali. Il primo standard definisce il framework di riferimento. Amministrazione &Finanza, n.12, 33-40.
  • AL-ALI, M.S., The application of Altman's Z-Score model in determining the financial soundness of healthcare companies listed in Kuwait Stock Exchange, International Journal of Economic Papers 3/2018, pag. 1 e ss.
  • AL-MANASEER, S.R., AL-OSHAIBAT, S.D., Validity of Altman Z-Score model to predict financial failure: Evidence from Jordan, International Journal of Economics and Finance 10/2018, pag. 181 e ss.
  • ALTMAN, E., DANOVI, A., FALINI, A., Z-Score models' application to Italian companies subject to extraordinary administration, Journal of Applied Finance 23/2013, pag. 128 e ss.
  • ALTMAN, E., HARTZELL, J., & PECK, M., A Scoring System for Emerging Market Corporate Bonds, Salomon Brothers High 1995.
  • ALTMAN, EI, IWANICZ‐DROZDOWSKA, M., LAITINEN, EK, SUVAS, A., Financial distress prediction in an international context: A review and empirical analysis of Altman's Z‐Score model, Journal of International Financial Management and Accounting 27/2017, pag.131 e ss.
  • ALTMAN, EI., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance 23/1968, pag. 589 e ss.
  • ALTMAN, EI., Corporate Financial Distress, John Wiley & Sons 1983.
  • BABATUNDE, AA, AKEJU, JB, MALOMO, E., The effectiveness of Altman's Z-Score in predicting bankruptcy of quoted manufacturing companies in Nigeria. European Journal of Business Economics and Accountancy 5/2017, pag. 74 e ss.
  • BEAVER, W., Financial ratios as predictors of failure, Journal of Accounting Research 4/1966, pag. 71 e ss.
  • BORRAJO, M., BARUQUE, B., CORCHADO, E., BAJO, J., CORCHADO, J., Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises. International Journal of Neural Systems 21/2011, pag. 277 e ss.
  • CARMINCHAEL, D. R., The auditor's reporting obligation. The meaning and implementation of the fourth standard of reporting, AICPA 1972.
  • CHIENG, J. R., Verifying the Validity of Altman's Z Score as a Predictor of Bank Failures in the case of the Eurozone, National College of Dublin 2013.
  • DIMITRAS, AI, SLOWINSKI, R., SUSMAGA, R., Business failure prediction using rough sets, European Journal of Operational Research 114/1999, pag. 263 e ss.
  • DOUMPOS, M., ZOPOUNIDIS, C., A multinational discrimination method for the prediction of financial distress: the case of Greece, Multinational Finance Journal 3/1999, pag. 71 e ss.
  • FOSTER, G., Financial Statement Analysis, Prentice Hall 1986.
  • FRYDMAN, H., ALTMAN, EI, KAO, D., Introducing recursive partitioning for financial classification: the case of financial distress, The Journal of Finance 40/1985, pag. 269 e ss.
  • GEPP, A., KUMAR, K., BHATTACHARYA, S., Business failure prediction using decision trees, Journal of Forecasting 29/2010, pag. 536 e ss.
  • JANURI, SARI, EN, DIYANTI, A., The analysis of the bankruptcy potential comparative by Altman Z-Score, Springate and Zmijewski methods at cement companies listed in Indonesia Stock Exchange, IOSR Journal of Business and Management 19/2017, pag. 80 e ss.
  • KIM, M.-J., HAN, I., The discovery of experts' decision rules from qualitative bankruptcy data using genetic algorithms, Expert Systems with Applications 25/2003, pag. 637 e ss.
  • LI, H., ADELI, H., SUN, J., HAN, J., Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction, Computers & Operations Research 38/2011, pag. 409 e ss.
  • LI, H., SUN, J., Ranking-order case-based reasoning for financial distress prediction, Knowledge-Based Systems 21/2008, pag. 868 e ss.
  • LI, H., SUN, J., Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II, European Journal of Operational Research 197/2009, pag. 214 e ss.
  • LI, H., SUN, J., Business failure prediction using hybrid2 case-based reasoning, Computers & Operations Research 37/2010, pag. 137 e ss.
  • LI, H., SUN, J., WU, J., Predicting business failure using classification and regression tree: an empirical comparison with popular classical statistical methods and top classification mining methods, Expert Systems with Applications 37/2010, pag. 5895 e ss.
  • MALIK, MS, AWAIS, M., TIMSAL, A., & HAYAT, F., Z-Score Model: Analysis and Implication on Textile Sector of Pakistan, International Journal of Academic Research 4/2016, pag. 140 e ss.
  • MCKEE, T. E., Developing a bankruptcy prediction model via rough sets theory, Intelligent Systems in Accounting Finance & Management 9/2000, pag. 159 e ss.
  • MCKEE, T. E., Greenstein, M., Predicting bankruptcy using recursive partitioning and a realistically proportioned data set, Journal of Forecasting 19/2000, pag. 219 e ss.
  • MERTON R., On the pricing of corporate debt: the risk structure of interest rates, Journal of Finance 29/1974, pag. 449 e ss.
  • POLATO, M., Il debt service coverage ratio nel nuovo contesto regolamentare: un approccio stocastico, Rivista dei dottori commercialisti 3/2019, pag. 393 e ss.
  • SHAHER, TA, SALEM, R., & KHASAWNEH, O., Predicting corporate failure in emerging market: Empirical evidence from Jordan (2001–2008), Archives Des Sciences, 65/2012, pag. 34 e ss.
  • SHIN, K.-S., LEE, Y.-J., A genetic algorithm application in bankruptcy prediction modeling, Expert Systems with Applications 23/2002, pag. 321 e ss.
  • SUN, J., LI, H., HUANG, Q. H., HE, K. Y., Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches, Knowledge-Based Systems 57/2014, pag. 41 e ss.
  • TAM, K., Neural network models and the prediction of bank bankruptcy, Omega 19/1991, pag. 429 e ss.
  • TAM, K., KIANG, M., Managerial applications of neural networks: the case of bank failure prediction, Management Science 38/1992, pag. 926 e ss.
  • VARETTO, F., Genetic algorithms applications in the analysis of insolvency risk, Journal of Banking & Finance 22/1998, pag. 1421 e ss.
  • WANG, Y., WANG, S., LAI, K. K., A new fuzzy support vector machine to evaluate credit risk, IEEE Transactions on Fuzzy Systems 13/2005, pag. 820 e ss.
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