The bankruptcy prediction model Z-ScoreM for Italian Manufacturing Listed Companies and Z'-ScoreM for Italian Industrial Company. The work stems from the study of the probability of default started in 2007 and continues today. In particular, this analysis is taken up with the study of the Rating and the credit and liquidity risk carried out during the author's research doctorate. The study is the continuation of other recently published author's e-books. The main objective is to identify a model for Italian companies based on Altman's Z-Score variables. Several researchers have analyzed the probability of failure of large companies, listed or emerging markets, other authors have tried to create a dashboard useful for the analysis of key indicators to be monitored, but this research differs for the creation of a specific indicator for the Italian Industrial Companies based on Altman variables.

This book explores methods and techniques to predict and eventually prevent financial distress in corporations. It analyzes the effects of the global financial crisis on Italian manufacturing companies and, more specifically, whether the crisis has increased the number of firms that are likely to fail. In the first chapter, the authors widely discuss the Corporate Financial Distress as well as the process and costs incurred. The second chapter is based on a review of the most used statistical models, splitting them into accounting-based and market-based models. The following chapter is dedicated to the methodology and the empirical analysis on Italian manufacturing companies from different industries. The last chapter presents practical evidence from Italian manufacturing companies during the recent financial crisis.

Measuring the "health" of Italian SMEs with insolvency prediction models Z '-ScoreM and D-Score. This book comes from the study of probability of Default began in 2007 and that continues to this day. In particular, this analysis is taken up with the study of the Rating and credit and liquidity risk during the PhD. the main objective will be to locate a model based on trusted variables for locating the State of health and level of risk of SMEs. Several studies have analyzed the probability of failure of large companies, listed companies or emerging markets, other studies have attempted to create a useful dashboard to the analysis of core indicators to be kept under observation, but still did not create a quantitative indicator of business health suited to Italian SMEs.

The purpose of this paper is firstly to review the literature on the efficacy and importance of the Altman Z-Score bankruptcy prediction model globally and its applications in finance and related areas. This review is based on an analysis of 33 scientific papers published from the year 2000 in leading financial and accounting journals. Secondly, we use a large international sample of firms to assess the classification performance of the model in bankruptcy and distressed firm prediction. In all, we analyze its performance on firms from 31 European and three non-European countries. This kind of comprehensive international analysis has not been presented thus far. Except for the U.S. and China, the firms in the sample are primarily private and cover non-financial companies across all industrial sectors. Thus, the version of the Z-Score model developed by Altman (1983) for private manufacturing and non-manufacturing firms (Z"-Score Model) is used in our testing. The literature review shows that results for Z-Score Models have been somewhat uneven in that in some studies the model has performed very well, whereas in others it has been outperformed by competing models. None of the reviewed studies is based on a comprehensive international comparison, which makes the results difficult to generalize. The analysis in this study shows that while a general international model works reasonably well, for most countries, with prediction accuracy levels (AUC) of about 75%, and exceptionally well for some (above 90%), the classification accuracy may be considerably improved with country-specific estimation especially with the use of additional variables. In some country models, the information provided by additional variables helps boost the classification accuracy to a higher level.

As the pandemic urged further investigations on the prediction of firms' financial distress, this study develops and tests an alternative measure to the alert system elaborated by the NCCAAE which combines the benefits of the Z-score's multivariate discriminant model with the background employed to develop the NCCAAE' predictors. Using a sample of 43 viable and 43 non-viable Italian SMEs, we first compare the financial distress predictive accuracy of the NCCAAE's alert system to that of the traditional Z-score over the period 2015-2019. On the basis of the results, we elaborate and compare the revised versions of both approaches which align the traditional Z-score to the current socio-economic conditions and provide an alternative measure to the NCCAAE's alert system which embeds a Z-score calculated using the ratios elaborated by the NCCAAE 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. We, therefore, contribute to the literature studying the financial distress prediction developments by elaborating an alternative measure to the alert system developed by the NCCAAE which combines the benefits of the Z-score's multivariate discriminant function with the background employed to develop the NCCAAE' predictors. 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 NCCAAE and suggests an alternative and better performing measure that may be used by third-party bodies to predict financial distress.

It is normal for companies, during their life cycle, to alternate between positive and negative phases, periods of success and failure. When a negative period shifts from temporary to structural and chronic (and thus continues over time), the company is often destined to go bankrupt. The uncertainty regarding the exact moment when this takes place has brought about a plethora of quantitative and qualitative models aimed at predicting bankruptcy. This study applies the most well-known of these models, the Z-Score, through an application to Italian companies subject to Extraordinary Administration (a sort of Italian Chapter 11) between 2000 and 2010. Since Italy is one of the pivotal countries that will likely decide the fate of the Euro, methods to identify firms that may need financial support takes on an even more important effort than in normal times.