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Z-SCORE MODELS’ APPLICATION TO ITALIAN COMPANIES SUBJECT TO EXTRAORDINARY

ADMINISTRATION

Edward I. Altman, Alessandro Danovi and Alberto Falini1

1. INTRODUCTION – 2. RELEVANT RESEARCH – 3. RESEARCH: METHODS AND LIMITS – 4. CALCULATING THE Z’’ SCORE

FOR COMPANIES SUBJECT TO EA – 5. RESULTS OF THE CONTROL SAMPLE – 6. CONCLUSIONS – 7. REFERENCES

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.

1.INTRODUCTION

Traumatic events like bankruptcy in large companies have been the subject of numerous Italian studies over the past years. Following the international framework (e.g. Slatter, 1984, 1999, 2003; Hambrick and D’Aveni, 1988, 1992; Gilson, 2010), Italian researchers have investigated the causes (Argenti, 1976; Coda, 1977, 1990; Guatri, 1986, 1995; Confalonieri, 1993; Sciarelli, 1996; Moliterni, 1999; Piciocchi, 2003; Bertoli, 2000), the ways it has been managed using the tools made available by Italian law (Caprio, 1997; Floreani, 1997; Danovi, 2003; Moliterni, Paci and Vallini, 2003; Falini, 2008; Danovi, Montanaro, 2010); and focusing on the process of recovery (eg. Guatri, 1986, 1995; Bertoli, 2000; Danovi, 2003; Danovi and Quagli, 2008). Another international field of study has focused on predicting bankruptcy using statistics and economic-financial indicators. The roots date back to the 1930s (e.g. Smith, 1930; FitzPatrick, 1931, 1932; Ramser and Foster, 1931; Smith and Winakor, 1935; Wall, 1936; when many models were developed to help banks decide whether or not to approve credit requests. At the end of the 1960s and continuing to this very time, the application of univariate and multivariate statistical analysis has been developed. Many authors concentrated on the possibility for prediction using several economic-financial indicators (Tamari, 1966; Beaver, 1966; Altman, 1968,; Deakin, 1972, 1977; Edmister, 1972; Blum, 1974; Elam, 1975; Libby, 1975; Alberici, 1975; Taffler, 1976, 1982; Altman et. al., 1977, 1993; Wilcox, 1976; Argenti, 1976; Ohlson, 1980; Appetiti, 1984; Forestieri, 1986; Lawrence and Bear, 1986; Aziz, Emanuel and Lawson, 1988; Baldwin and Glezen, 1992; Flagg, Giroux and Wiggins, 1991; Bijnen and Wijn, 1994; Kern and Rudolph, 2001); Shumway, 2002; Hillegeist, et. al., 2004; and Altman, and Rijken, et. al., 2010b. Some of these studies were also used by practitioners mainly because of the simplicity of application.

1 Edward Altman is the Max L. Heine Professor of Finance, NYU Stern School of Business and the Director of the

Fixed Income & Credit Markets Research Program at the NYU Salomon Center ealtman@stern.nyu.edu Alessandro Danovi is an Associate Professor of Economics & Management at the Universita degli Studi di Bergamo

(Italy), Alessandro.Danovi@danovi.it. Alberto Falini is Professor of Economics & Management at the Universita degli Studi di Brescia (Italy).

alberto@falini.it.

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2. RELEVANT RESEARCH

One of the most well-known distress prediction models, due to its ability to predict and easy application, is the Altman Z-Score, originally formulated in 1968. That study signaled out four balance sheet and income statement variables, with an additional stock market variable, useful for predicting the likelihood of a company going bankrupt. The chosen variables regarded liquidity, profitability, leverage, solvency and activity and were based on two distinct criteria: their popularity in literature and their potential relevance for the study. Each company was given a score (Z-Score) composed by a discriminant function of the five variables weighted by coefficients. The first application of the model involved a group of 66 American manufacturing companies (33 healthy and 33 bankrupt), listed on the Stock Exchange and showed that companies with a Z Score of less than 1.81 were highly risky and likely to go bankrupt; companies with a score more than 2.99 were healthy and scores between 1.81 and 2.99 were in a grey area with uncertain results). The results are shown in Figure 1. It should be noted that the choice of the various zones, as shown in Figure 1, was specific to the original sample of firms used to construct the model. Since relative scores have changed over time, Altman suggests (see Altman & Hotchkiss, 2006) to utilize bond equivalent ratings instead of these classic, but out-of-date zones.

Figure 1 – Z-Score classification areas

Source: Danovi, Quagli (2008, pp. 164)

The model was extremely accurate since the percentage of correct predictions was about 95% and it received many positive reactions and only a few criticisms2. As indicated by the author (Altman, 1970), the model is not probabilistic but descriptive-comparative. It should be used as a warning device rather than as a definitive prediction tool since the score indicates the proximity of a firm to one group or the other (Teodori, 1989). Its field of action and applicability is now explicitly recognized. The ease of use and the lack of need for the user to have statistical competence (Altman

and La Fleur 1985, pp. 79 said that “30 minutes are enough to evaluate the state of a company’s

2 A few critics: Johnson (1970) and Joy and Tollefson (1975) stigmatized the possible tautology of the model, the

excessive broadness of the so-called grey area and the difficulty of application in predicting bankruptcy ex ante. Regarding this last element, it was stressed how predictions using the Z Score or other analogous methodologies could be a self realizing prophecy since, if adopted by banks, it would be harder for a company with a low score (situated in the grey areas or below) to maintain a credit line, causing it to go bankrupt (Guatri, 1995). Others have questioned the violation of certain statistical assumptions, such as normality.

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insolvency, simply using a pocket calculator and a reclassified balance sheet”) have facilitated the use among practitioners3. The model has been revised several times by its author (Altman, 1983; 2002; Altman, Hartzell, Peck, 1995) who has constantly updated the parameters and adapted the indices for different populations of companies other than American manufacturers quoted on the Stock Market. The Z’-Score (Altman, 1983) is an adaptation for private companies.

The 5 indicators in the two Altman manufacturing firm versions of the studies are listed in Figure

2, with the first four variables of Z’- used in Z”-Score model, introduced in 1995 for non-US, emerging market companies and for non-manufacturers (see below),

Figure 2

The Altman Z and Z’-Score Models

Z Score (1968) Z’ Score (1983) X1: Working Capital/Total Assets

X2: Retained Earnings/Total Assets

X3: EBIT/Total Assets

X4: Market Value Equity/Book Value of Total

Debt

X5: Sales/Total Assets

X1: Working Capital/Total Assets

X2: Retained Earnings/Total Assets

X3: EBIT/Total Assets

X4: Book Value Equity/Total liabilities

X5: Sales/Total Assets

Source: Altman (1968, pp. 594) Source: Altman (1983, pp. 122 )

In both cases, the linear relationship is as follows:

Z = 1.2X1+ 1.4X2 + 3.3X3+0.6X4 + 0.999X5 Z’= 0.717X1+0.847X2+3.107X3 + 0.420X4+ 0.998X5

Source: Altman (1968, pp. 603) Source: Altman (1983, pp. 122)

During the following years, parameters and coefficients were adapted for different situations. The

Z” Score (Altman, Hartzell and Peck, 1995 and Altman & Hotchkiss, 2006, p. 314) was introduced for the non-manufacturing as well as manufacturing sectors or companies operating in developing countries (the 1995 study investigated a sample of Mexican companies). The variables of the Z”-Score were the same as the Z’-Score model with the exclusion of the sales/total assets, activity ratio

3 The popularity among practitioners has induced the author to introduce a new App (Altmanzscoreplus.com)

available for different Z-Score models, where the user can obtain a rapid credit risk analysis, including a probability of default for up to 10 years.

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(X5) in order to filter the function from the possible distortion related to the sector and country. The weighted coefficients thus have different values:

Z’’ = 6.56X1 + 3.26X2 + 6.72X3 + 1.05X4

Source: Altman, Hartzell and Peck (1995, pp. 3)

In calculating the Z” Score for emerging countries, Altman, Hartzell and Peck proposed adding a

constant (+3.25) in order to standardize the results so that scores equal or less than 0 would be equivalent to the default situation. From the application of the Z”-score, Altman and Hotchkiss (2006) mapped a correspondence between the score and the ratings assigned by Standard & Poor’s, as shown in Figure 3. This procedure involves the calculation of the average Z”-Score for the population of firms in each S&P rating class. Figure 3 – Correspondence between Z’’-Score and Standard & Poor Rating

Rating Z”-Score Threshold

Rating Z”-Score Threshold

Safe

Zo

ne

AAA >8.15 BB+ 5.65

Gre

y Z

on

e

AA+ 8.15 BB 5.25

AA 7.60 BB- 4.95

AA- 7.30 B+ 4.75

A+ 7.00 B 4.50

A 6.85 B- 4.15

Dis

tress

Zo

ne

A- 6.65 CCC+ 3.75

BBB+ 6.40 CCC 3.20

BBB 6.25 CCC- 2.50

BBB- 5.83 D <1.75

Source: Altman and Hotchkiss (2006, pp. 314)

Another adaptation was the introduction of the Z-Metrics System (Altman and Rijken, et. al.,

2010b). It refines the original model, includes both market equity levels and volatility, as well as macro-economic variables. The parameters are not made explicit, considering the proprietary nature of this technique. The Z-Metrics approach was used by the authors to assess the sovereign risk, particularly in Europe today, with encouraging results.

3. RESEARCH: METHODS AND LIMITS

This study focuses on applying the most appropriate Z-Score model to Italian manufacturing companies subject to Extraordinary Administration [EA-under Decreto Legislativo 270/1999 and Decreto Legge 347/2003] between 2000 and 2010. This procedure is the most practical way to manage large company crises in Italy. EA was introduced by Law 3rd April 1979, n. 95 with the aim of creating a tool for managing crisis in large companies. It was designed to consider not only the interests of creditors, but also more general interests, like maintaining production levels and employment. 20 years later, with Law 8th July 1999, n. 270 EA was widely reformed, also in response to EU criticisms.

Presently, to file for the EA procedure, companies have to meet the following requirements: a) at

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least 200 employees and b) debts exceeding 2/3 of the total amount of the previous year assets and turnover.4

EA presents some aspects similar to the American Chapter 11, especially the section under Decreto Legge 347/2003, which was profoundly inspired by Chapter 11. As far as the results of the procedure, the most recent empirical studies (Danovi, Montanaro, 2010; Danovi, 2010) highlighted that the economic rebalancing of the companies seems to have been pursued almost exclusively through the sale of the whole company, or at least some divisions to other competitors.5 While EA has proven to be generally efficient, effects on creditor’s rights vary. In some cases there are significant recovery ratios, in others the percentages are not far from the ones creditors could have received if the company had gone bankrupt and had to liquidate.

Between 2000-2010, 93 groups composed of 370 companies underwent the EA procedure. Within each group we singled out the largest company based on the turnover and the number of employees, whose balance sheets were available6. This led to a sample of 89 companies7, 52 of which were manufacturing companies. For these companies, the Z’’ Score indicator was calculated and results for the manufacturing sector were analyzed.

The Z’’-Score was chosen because only 4 companies (less than 5% of those subjected to EA) were public. In addition, the Z’’-Score prediction tool is more suitable for the Italian context than the Z’-Score, in consideration of the long-standing relationship between Italian firms and banks, and the fact that many of the EA firms were not manufacturers. Indeed, Italian firms, even if they look

4 Contrasted from other Italian bankruptcy procedures, Extraordinary Administration (EA) is a sort of hybrid, since

it is under the jurisdiction of both the administrative authority (Ministry of Industry) as well as the Court. Currently there are two distinct phases. During the initial “observation” phase, the company is managed by a Judicial Commissioner who has to verify if there is a real possibility for restructuring. According to the restructuring program, prepared by the Judicial Commissioner, in the second phase the company, following a going concern logic, can either be sold to other investors or guided towards a stand alone recovery. As often happens with bankruptcy laws, there is ample theoretical framework and a very large number of studies have been carried out, focused on the legal aspects (for a general overview in English see Panzani, 2009) while economic issues have not been thoroughly investigated. Among the few we refer to loreani (1997), Leogrande (2003), Danovi (2003), Falini (2008). Some empirical evidence regarding how the procedure was put into effect between 1999 (the year of the reform) and 2008 can be found in Danovi and Montanaro (2010).

5 Among the most significant results we can point out as follows (Danovi, 2010: 68): - The new legislation widened the sphere of application: the regulations, introduced by the Act of 1999, increased

the area of application, extending the intervention of the administrative authority to protect industry and national employment levels;

- The analysis of sectorial distribution illustrates how the phenomenon especially effects different industrial sectors, like engineering and textiles, as well as commerce and wholesale. These were the sectors particularly hard hit by the crisis in Italy;

- Filing for EA often happens too late and this delay has negative effects on the possibilities for recovery. The principle of “merit” introduced by the new legislation, based on the observation phase, is an important filter for identifying companies which are effectively salvageable;

- The aim to protect the workers seems to be reached in the majority of cases through the transfer of a significant number of employees (about 51%) to the company still functioning. EA seems to be useful in safeguarding the work force in most cases;

- The deadlines fixed for the execution of the program have produced tangible benefits on the duration of the procedure, at least as far as regards the recovery phase

- The liquidation phase is the most complex of the entire procedure. In fact, for those still in progress (87%), the total timeframe is more than 6 years, similar to the bankruptcy procedure.

6 The search for balance sheets and companies was carried out using the AIDA (Analisi Informatizzata delle

Aziende) database published by Bureau Van Dijk Electronic Publishing. Other balance sheets were obtained using the computerized archive of the Chamber of Commerce Telemaco.

7 The balance sheets of 4 groups were not available either because the company was newly formed (one company) or because the associations or organizations were not obliged to deposit their budgets (three companies).

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distressed from a statistical point of view, were likely to be supported by the banks, and the owners have often preferred to leverage their companies in order to profit from fiscal advantages. This no longer is as pervasive in 2012 as many banks in Italy are struggling due to capital shortages. Finally, it has been shown that the Z”-Score model applied to non-U.S. companies is far more robust than his other models (Altman & Hotchkiss, 2006).

In order to obtain a control sample, we picked manufacturing companies active between 2001 and 20098 presenting full balance sheets9 for the entire period, more than 200 employees and the quantitative requirements listed in letter b) of the abovementioned article 2 D. Lgs. 270/199910. The results of this selection are shown in Figure 4. The Z’’-Score was calculated for all of these companies in order to check its applicability to the Italian manufacturing industry.

Figure 4 – Control sample: Companies meeting requirements for EA 2009 2008 2007 2006 2005 2004 2003 2002 2001 Sample 1,575 1,575 1,526 1,472 1,423 1,397 1,279 1,263 1,205 Number of companies which met EA requirements 413 372 380 358 341 344 325 319 299

Source: authors’ selection from AIDA (fn. 4).

Regarding the grouping of the different companies surveyed, the differences in the economic,

social, operational, temporal and legislative contexts were striking. This is one of the main problems in the application of the model in Italy due to an extremely different context from the one the model was originally developed for. Still, our results are quite convincing as to the model’s applicability, despite cultural and economic differences between the U.S. and Italy.

4. CALCULATING THE Z’’ SCORE FOR COMPANIES SUBJECT TO EA

The Z’’ Score was applied to all the companies subject to EA procedures ex D. Lgs. 270/1999 and D.L. 347/2003 from 2000 to 2010 as can be seen in Figure 511. Examining the Z”-Scores’ Bond Rating Equivalent (BREs) for the five year prior to EA, on average, 72.3% were classified in the distress zone. When nearing bankruptcy, a much higher number of companies was rated in Standard & Poor’s with the letter “D”, meaning default. For example, in year x-4, 4 years before declaring bankruptcy, only 8.7% of the companies had scores in that area, while 67.4% fell in the so-called distress zone with scores lower than 4.50. In year x-1, 65.9% of the companies had a “D” rating and

8 The numbers indicated in “sample” or “population” exclusively regarded companies for whom we possessed the

data necessary to carry out the above-mentioned analysis. Regarding the lengthy period, the databank did not allow surveys for periods longer than 10 years preceding the date that the analysis was elaborated.

9 In order to eliminate possible distortions deriving from bankrupt subjects or those admitted to selection procedures, further filtering based on legal status was applied. Only active companies were chosen. This further sharpening resulted in a population of 1,608 companies from which those recently subjected to EA were eliminated (six companies).

10 The sample presented variables for the constitution of new companies as well as for the undoing of others: in 2001 the population considered was 1,205 companies, in 2008 it was 1,575.

11 The study is based on balance sheets available for the 5-year period preceding the declaration of bankruptcy (referring to the period 1998 – 2009). Considering that the entire five-year period was not found for all of the companies, the sample size is variable. Reasons why the data was not available include the fact that companies do not approve budgets for the years immediately before bankruptcy (in Italy, most balance sheets are approved from late April to June), and the fact that in many cases the budgets drafted were not approved. Some of the companies analyzed were relatively newly formed, thus they had little or no history. In many cases, the companies were objects of extraordinary operations during a 3-year period in an attempt to save the company.

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95.5% were in the Distress Zone. The Z’’ Score seems to adapt itself to companies subjected to EA procedures, which classifies them, with few exceptions, in the distress zone, especially within one year of EA.

Figure 5 –Z’’ Score Results x-1 x--2 x-3 x-4 x-5

Rating Threshold n. % n. % n. % n. % n. % Average values

Safe

Zo

ne

AAA >8.15 0 0.0% 0 0.0% 3 3.4% 3 6.5% 0 0.0% 2.0%

AA+ 8.15 0 0.0% 1 1.2% 0 0.0% 1 2.2% 0 0.0% 0.7%

AA 7.60 0 0.0% 0 0.0% 1 1.1% 0 0.0% 0 0.0% 0.2%

AA- 7.30 0 0.0% 0 0.0% 0 0.0% 1 2.2% 0 0.0% 0.4%

A+ 7.00 1 2.3% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0.5%

A 6.85 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0.0%

A- 6.65 0 0.0% 1 1.2% 1 1.1% 1 2.2% 0 0.0% 0.9%

BBB+ 6.40 0 0.0% 0 0.0% 2 2.2% 1 2.2% 0 0.0% 0.9%

BBB 6.25 1 2.3% 0 0.0% 1 1.1% 2 4.3% 0 0.0% 1.5%

Total 2 4.5% 2 2.4% 8 9.0% 9 19.6% 0 0.0% 7.1%

Gre

y Z

on

e

BBB- 5.85 0 0.0% 1 1.2% 0 0.0% 3 6.5% 0 0.0% 1.5%

BB+ 5.65 0 0.0% 5 5.9% 4 4.5% 2 4.3% 1 16.7% 6.3%

BB 5.25 0 0.0% 2 2.4% 5 5.6% 0 0.0% 1 16.7% 4.9%

BB- 4.95 0 0.0% 2 2.4% 4 4.5% 0 0.0% 0 0.0% 1.4%

B+ 4.75 0 0.0% 7 8.2% 5 5.6% 1 2.2% 1 16.7% 6.5%

Total 0 0.0% 17 20.0% 18 20.2% 6 13.0% 3 50.0% 20.7%

Dis

tress

Zo

ne

B 4.50 2 4.5% 3 3.5% 8 9.0% 5 10.9% 0 0.0% 5.6%

B- 4.15 1 2.3% 5 5.9% 6 6.7% 8 17.4% 1 16.7% 9.8%

CCC+ 3.75 3 6.8% 12 14.1% 12 13.5% 7 15.2% 0 0.0% 9.9%

CCC 3.20 5 11.4% 11 12.9% 13 14.6% 6 13.0% 1 16.7% 13.7%

CCC- 2.50 2 4.5% 14 16.5% 14 15.7% 1 2.2% 0 0.0% 7.8%

D <1.75 29 65.9% 21 24.7% 10 11.2% 4 8.7% 1 16.7% 25.4%

Total 42 95.5% 66 77.6% 63 70.8% 31 67.4% 3 50.0% 72.3%

TOTAL 44 85 89 46 6

Source: authors’ calculations.

The average aggregate value for each area is representative of classifications carried out in the

previous Table, which is why the data shown deserves comment. Given that in year x-1, the year before the declaration of bankruptcy, 95.5% of the companies were classified in the distress zone; the years before show lower percentages which are nonetheless significant and indicate the appropriateness of the classification carried out. Notice that the grey area, where companies’ futures are uncertain as to whether they will fail or recover, is reduced from 50% in x-5 to 20% in x-3 and x-2 and 0.0% in x-1. The broadness of the grey area seems greatly reduced. Figure 6 shows that nearing bankruptcy sharply diminishes the classifications within that area and there is a sharp increase in the distress zone. Note that the data for the year x-5 refers to just 6 companies, really too small to be meaningful.

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Figure 6 –Z’’ Score trend for companies subjected to EA procedures

Source: authors’ calculations.

At first glance, based on the data above, it seems evident that the Z” Score is a trustworthy

indicator for verifying the state of a company’s health. One limit of this analysis is that elaborations are based on ex post applications, on a sample of companies whose destiny was already known, since they went bankrupt and were subjected to EA. On average, over the entire five years of pre-bankruptcy period, in 72.3% of the cases (which rises to 77.8% if we exclude the results from year x-5), the classifications of the indicator are correct. In the most notorious case involving an accounting fraud, Parmalat, the Z’’-Score did not only classify the company as safe, but 2 years before bankruptcy it rated the company as A-. In another important distress case, Giacomelli, the company was classified for 3 out of 4 years in the distress zone.

* * *

We now analize the Z’’ Score for companies included in the control sample. As can be seen in

Figures 7a and 7b, during the considered period, more than 50% of the control sample’s firms are classified inside the safe zone. This percentage remains quite steady; even so it is very interesting to note that the average of “healthy” firms dropped from 51% in 2008 to almost 49% in 2009, highlighting the first effects of the credit crunch that started hitting and weakening the Italian private sector in 2008.

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

x-1 x-2 x-3 x-4 x-5

Safe

Grey

Distress

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Figure 7a –Z’’-Score Results (2005-2009)

2009 2008 2007 2006 2005

Rating Threshold n. % n. % n. % n. % n. %

Safe

Zo

ne

AAA >8.15 329 20.9% 320 20.3% 322 21.1% 302 20.5% 277 19.5%

AA+ 8.15 60 3.8% 69 4.4% 70 4.6% 81 5.5% 68 4.8%

AA 7.60 49 3.1% 53 3.4% 59 3.9% 56 3.8% 57 4.0%

AA- 7.30 61 3.9% 49 3.1% 64 4.2% 60 4.1% 50 3.5%

A+ 7.00 30 1.9% 34 2.2% 28 1.8% 32 2.2% 35 2.5%

A 6.85 45 2.9% 63 4.0% 50 3.3% 48 3.3% 42 3.0%

A- 6.65 58 3.7% 65 4.1% 64 4.2% 52 3.5% 58 4.1%

BBB+ 6.40 36 2.3% 51 3.2% 35 2.3% 39 2.6% 35 2.5%

BBB 6.25 100 6.3% 100 6.3% 103 6.7% 105 7.1% 117 8.2%

Total 768 48.8% 804 51.0% 795 52.1% 775 52.6% 739 51.9%

Gre

y Z

on

e

BBB- 5.85 51 3.2% 60 3.8% 57 3.7% 58 3.9% 52 3.7%

BB+ 5.65 109 6.9% 111 7.0% 114 7.5% 117 7.9% 120 8.4%

BB 5.25 81 5.1% 85 5.4% 92 6.0% 93 6.3% 87 6.1%

BB- 4.95 69 4.4% 45 2.9% 51 3.3% 51 3.5% 58 4.1%

B+ 4.75 76 4.8% 65 4.1% 58 3.8% 69 4.7% 62 4.4%

Total 386 24.5% 366 23.2% 372 24.4% 388 26.4% 379 26.6%

Dis

tress

Zo

ne

B 4.50 78 5.0% 86 5.5% 87 5.7% 72 4.9% 75 5.3%

B- 4.15 87 5.5% 86 5.5% 83 5.4% 74 5.0% 64 4.5%

CCC+ 3.75 93 5.9% 84 5.3% 88 5.8% 68 4.6% 67 4.7%

CCC 3.20 62 3.9% 65 4.1% 45 2.9% 41 2.8% 39 2.7%

CCC- 2.50 41 2.6% 40 2.5% 21 1.4% 26 1.8% 22 1.5%

D <1.75 60 3.8% 44 2.8% 35 2.3% 28 1.9% 38 2.7%

Total 421 26.7% 405 25.7% 359 23.5% 309 21.0% 305 21.4%

TOTAL 1,575 1,575 1,526 1,472 1,423

Source: authors’ calculations. Figure 7b –Z’’-Score Results (2004-2001) 2004 2003 2002 2001

Rating Threshold n. % n. % n. % n. % Average values

(2001-2009)

Safe

Zo

ne

AAA >8.15 291 20.8% 247 19.3% 240 19.0% 236 19.6% 20.1%

AA+ 8.15 66 4.7% 69 5.4% 64 5.1% 61 5.1% 4.8%

AA 7.60 45 3.2% 37 2.9% 42 3.3% 37 3.1% 3.4%

AA- 7.30 55 3.9% 54 4.2% 45 3.6% 36 3.0% 3.7%

A+ 7.00 24 1.7% 24 1.9% 31 2.5% 24 2.0% 2.1%

A 6.85 44 3.1% 38 3.0% 35 2.8% 30 2.5% 3.1%

A- 6.65 51 3.7% 49 3.8% 45 3.6% 51 4.2% 3.9%

BBB+ 6.40 48 3.4% 27 2.1% 33 2.6% 27 2.2% 2.6%

BBB 6.25 99 7.1% 79 6.2% 76 6.0% 99 8.2% 6.9%

Total 723 51.8% 624 48.8% 611 48.4% 601 49.9% 50.6%

Gre

y Z

on

e

BBB- 5.85 47 3.4% 52 4.1% 46 3.6% 38 3.2% 3.6%

BB+ 5.65 105 7.5% 86 6.7% 93 7.4% 84 7.0% 7.4%

BB 5.25 73 5.2% 67 5.2% 61 4.8% 60 5.0% 5.5%

BB- 4.95 48 3.4% 48 3.8% 41 3.2% 42 3.5% 3.6%

B+ 4.75 60 4.3% 59 4.6% 59 4.7% 49 4.1% 4.4%

Total 333 23.8% 312 24.4% 300 23.8% 273 22.7% 24.4%

Dis

tress

Zo

ne

B 4.50 83 5.9% 83 6.5% 74 5.9% 80 6.6% 5.7%

B- 4.15 72 5.2% 75 5.9% 73 5.8% 55 4.6% 5.3%

CCC+ 3.75 81 5.8% 73 5.7% 83 6.6% 87 7.2% 5.7%

CCC 3.20 53 3.8% 51 4.0% 56 4.4% 55 4.6% 3.7%

CCC- 2.50 25 1.8% 33 2.6% 35 2.8% 18 1.5% 2.1%

D <1.75 27 1.9% 28 2.2% 31 2.5% 36 3.0% 2.6%

Total 341 24.4% 343 26.8% 352 27.9% 331 27.5% 25.0%

TOTAL 1,397 1,279 1,263 1,205

Source: authors’ calculations.

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The average percentage of firms classified in each of the three zones has remained stable: each year one firm out of four has been classified in the distress zone and two in the safe zone. This means that the average classifications inside the grey zone amount to 25%, i.e., just one firm out of four could resemble either distressed companies or healthy ones in the following year. This result is totally consistent with the Z-Score models’ experience in the United States in the last decade. Hence, the distress zone size appears to also be suitable within the Italian context. Figure 8 graphically shows the average rating distribution by rating equivalent from 2001-2009. Figure 8 – Z’’ Score average distribution in Italy, by rating equivalent

Source: authors’ calculations.

From Figure 8, we can observe that close to half of the Italian private sector in 2008 and 2009 had a non-investment grade bond rating equivalent, although about a quarter have an extremely good rating equivalent of AAA or AA+. In our opinion, this shows that the Z”-Score model is well suited for the Italian economy, showing the recent deterioration in 2008 and 2009, reminiscent of the problems of 2001-2003. In addition, Altman has commented in a recent OpEd (June 2012) that the deterioration of the Italian private sector in 2011 has been significant and indicative of the nation’s sovereign default risk situation.

6. CONCLUSIONS

From the analysis it seems evident that Altman’s Z”-Score model is applicable to the Italian manufacturing context, with a few cautions. Applying the indicator to the sample in EA highlighted the high percentage of companies classified in the distress zone. The so-called grey zone is relatively narrow compared to the Z’-Score model, at least in terms of the average classifications. The bond rating equivalents allow analysts to understand the nuances regarding the state of health of a company. Within the grey zone, 5 classes of rating equivalents help to give greater clarity on what will be the short-term future for a company: insolvency or recovery. These tools cannot, however, identify distress situations if the balance sheet figures are manipulated.

The analysis carried out showed the potential to reformulate the parameters based on the characteristics of Italian companies, namely: low capitalization, heavy use of bank credit and budget policies which at times are not transparent. This study only considered companies of a certain size, those with at least 200 employees with full balance sheets. The dimension of the Italian

0.0%

5.0%

10.0%

15.0%

20.0%

25.0% Average distribution (2001-2009)

11

manufacturing industry is very small if we consider that out of a total population of 174,010 companies, we used a sample of 1,602 companies, less than 10% of the total. To hypothesize applying the model on a larger scale, we need to define parameters which can be adapted to large, small and medium-sized companies. With few exceptions, large Italian manufacturing companies maintain some typical traits of the small and medium sized businesses. A few qualitative and strategic aspects remain unchanged, like the choice of financing and governance.

For these reasons, the application of the Z”-Scores in the Italian context is extremely informative, but not without its complications. We are convinced that such models can be extremely helpful to investors, regulators and even political decision makers.

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