Increasing the Predictive Power of Financial Distress models—The Case of the New Alert System Proposed by the Italian Nccaae

Federico Beltrame (), Giulio Velliscig (), Gianni Zorzi () and Maurizio Polato ()
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Federico Beltrame: University of Udine
Giulio Velliscig: University of Udine
Gianni Zorzi: Ca’ Foscari University
Maurizio Polato: University of Udine

Chapter Chapter 12 in New Challenges for the Banking Industry, 2023, pp 305-335 from Palgrave Macmillan

Abstract: Abstract This chapter develops and tests an alternative alert system to predict firms’ financial distress 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, the authors compare the predictive accuracy of the mentioned models over the period 2015–2019. Based on the results, they elaborate revised versions of both approaches, aligned to current socio-economic conditions. The authors also provide an alternative combined model. The analysis of the two baseline approaches showed complementary results, with the Z-score overperforming the alert system in predicting non-viable firms, whereas the opposite emerged on viable firms. The revised versions showed enhanced predictive accuracy. The authors’ contribution enriches the post-pandemic debate on financial distress prediction models by pointing out the limits of the NCCAAE alert system and suggesting an alternative and better performing model.

Keywords: Z-score; Alert System; Financial distress prediction; SMEs; NCCAAE (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:pal:pmschp:978-3-031-32931-9_12

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DOI: 10.1007/978-3-031-32931-9_12

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