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Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis

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  • ”Dunărea de Jos” University of Galați

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Assessment and estimation of bankruptcy risk is important for managers in decision making for improving a firm’s financial performance, but also important for investors that consider it prior to making investment decision in equity or bonds, creditors and company itself. The aim of this paper is to improve the knowledge of bankruptcy prediction of companies and to analyse the predictive capacity of factor analysis using as basis the discriminant analysis and the following five models for assessing bankruptcy risk: Altman, Conan and Holder, Tafler, Springate and Zmijewski. Stata software was used for studying the effect of performance over risk and bankruptcy scores were obtained by year of analysis and country. Data used for non-financial large companies from European Union were provided by Amadeus database for the period 2006–2015. In order to analyse the effects of risk score over firm performance, we have applied a dynamic panel-data estimation model, with Generalized Method of Moments (GMM) estimators to regress firm performance indicator over risk by year and we have used Tobit models to infer about the influence of company performance measures over general bankruptcy risk scores. The results show that the Principal Component Analysis (PCA) used to build a bankruptcy risk scored based on discriminant analysis indices is effective for determining the influence of corporate performance over risk.
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J. Risk Financial Manag. 2019, 13, 58; doi:10.3390/jrfm13030058 www.mdpi.com/journal/jrfm
Article
Assessment of Bankruptcy Risk of Large Companies:
European Countries Evolution Analysis
Nicoleta Bărbuță-Mișu
1,
* and Mara Madaleno
2
1
Department of Business Administration, “Dunarea de Jos” University of Galati, 800008 Galati, Romania
2
GOVCOPP—Research Unit in Governance, Competitiveness and Public Policy, Department of Economics,
Management, Industrial Engineering and Tourism (DEGEIT), University of Aveiro, 3810-193 Aveiro,
Portugal; maramadaleno@ua.pt
* Correspondence: Nicoleta.Barbuta@ugal.ro; Tel.: +40-724-362-007
Received: 27 January 2020; Accepted: 15 March 2020; Published: 18 March 2020
Abstract: Assessment and estimation of bankruptcy risk is important for managers in decision
making for improving a firm’s financial performance, but also important for investors that consider
it prior to making investment decision in equity or bonds, creditors and company itself. The aim of
this paper is to improve the knowledge of bankruptcy prediction of companies and to analyse the
predictive capacity of factor analysis using as basis the discriminant analysis and the following five
models for assessing bankruptcy risk: Altman, Conan and Holder, Tafler, Springate and Zmijewski.
Stata software was used for studying the effect of performance over risk and bankruptcy scores
were obtained by year of analysis and country. Data used for non-financial large companies from
European Union were provided by Amadeus database for the period 2006–2015. In order to analyse
the effects of risk score over firm performance, we have applied a dynamic panel-data estimation
model, with Generalized Method of Moments (GMM) estimators to regress firm performance
indicator over risk by year and we have used Tobit models to infer about the influence of company
performance measures over general bankruptcy risk scores. The results show that the Principal
Component Analysis (PCA) used to build a bankruptcy risk scored based on discriminant analysis
indices is effective for determining the influence of corporate performance over risk.
Keywords: European large companies; bankruptcy risk; company performance; bankruptcy
prediction; Principal Component Analysis
1. Introduction
Bankruptcy and bankruptcy prediction is a very real issue worldwide both in academic research
and in practice considering the evolution at a global level: the upward trend in business insolvencies
continued in 2018 (increase by 10% in 2018 compared to 2017), mainly due to the surge in China by
60% and, to a lesser extent, an increase in Western Europe by 2% (Euler Hermes 2019).
In Western Europe, although a downside trend in insolvencies was recorded from 2014 to 2017,
the increase mentioned by 2% in 2018 compared to 2017 was determined by different evolution by
other countries: a noticeable upturn of 12% in the UK due to the Brexit-related uncertainties that
added headwinds on businesses; a stabilization of insolvencies can be seen in France, Spain and
Belgium, although in France in 2018, 54,751 companies went bankrupt, corresponding to a fairly high
1.3% of the active business universe (Dun and Bradstreet 2019); an increase in the Nordic countries
of 10% in Sweden, 3% in Norway, 19% in Finland and 25% in Denmark. This trend comes from
economic and fiscal reasons or exceptional factors, especially for Denmark and Finland. At the same
time, other countries of the region registered slower declines in 2018 compared to 2017, notably the
Netherlands (from −23% to −6%), Portugal (−12%), Ireland (−10%) and Germany (−4%). In Italy, 11,207
companies filed for bankruptcy in 2018, down by a significant 5.8%, but the newly-elected populist
J. Risk Financial Manag. 2019, 13, 58 2 of 28
government is likely to embark on a series of populist policies that are at odds with improving the
country’s operating environment (Dun and Bradstreet 2019).
According to Euler Hermes (2019), in Central and Eastern Europe, we can see economies that
forecast to moderate in line with the slowdown in the Eurozone, but remain robust enough to see
another decrease in insolvencies, albeit at more limited time, i.e., Hungary from −18% in 2018 to −11%
in 2019 and the Czech Republic, respectively −17% and −10%. Romania registered a rebound in
insolvencies, −3% in 2018 and +3% in 2019. Other countries continued to rise in insolvencies: 3% for
Bulgaria in 2019 where the changes in the Insolvency law done in 2017 kept on boosting the
bankruptcies of sole proprietorships, Slovakia of 16%, Poland of 5% where businesses have a
structural problem of profitability and will face a noticeable deceleration of the economy.
Over time, researchers have tried to find diverse methods to estimate business failure:
patrimonial method based on net working capital and treasury; financial ratios method especially
based on individual analysis of profitability, liquidity, solvency and financial autonomy; and score
method highlighted in numerous models for which Altman (1968), Ohlson (1980), and Zmijewski
(1984) models are the most cited ones and that are based on accounting variables (Avenhuis 2013).
These bankruptcy prediction models use different explanatory variables and statistical techniques
and may provide valuable information about the financial performance of the companies and their
risks. More than that, we must mention that the predictive power of these bankruptcy prediction
models differ between countries, sectors of activity, time periods, firms’ ages, or firms’ sizes.
There is a constant effort to use the models developed for firms in different economies, even if
decision makers know or at least should know that assumptions used for fitting the original models
are probably not valid anymore. There is a continuous concern and preoccupation for designing
models for prediction risk of bankruptcy. Assessing of the level of advancement of bankruptcy
prediction research in countries of the former Eastern Bloc, in comparison to the latest global research
trends in this area, Prusak (2018) found that the most advanced research in this area is conducted in
the Czech Republic, Poland, Slovakia, Estonia, Russia, and Hungary. In addition, the best world
practices are reflected in the research provided in Poland, the Czech Republic, and Slovakia.
The main problem of the bankruptcy prediction models developed in the literature is that these
models cannot be generalized because these were developed using a specific sample from a specific
sector, specific time period and from a specific region or country. As the above-mentioned statistics
show, there are many other specific factors that increase the bankruptcies in a country: changes in
economic environments, law frameworks, incomparability of populations of interest, etc. (Král’ et al.
2016). That is why it is necessary to adapt these models to the specificity of the sector, country or time
period analyzed and to use combined techniques of estimation in designing these specific models.
In this paper, considering the context presented, the large companies from the European Union
are analysed. The aim of this research is twofold: to improve the knowledge of bankruptcy prediction
for European large companies and to analyse the predictive capacity of factor analysis, such as
Principal Component Analysis (PCA) using as a basis the discriminant analysis (models for assessing
bankruptcy risk, commonly used in the literature). Our paper is distinguishing from other studies by
using a sample of large companies active in the EU-28 countries in the period 2006–2015 and by own
original selection of bankruptcy prediction models (Altman, Conan and Holder, Tafler, Springate and
Zmijewski) used in the PCA analysis.
The rest of the paper is organised as follows: in Section 2, the literature review on risk,
bankruptcy prediction, models and techniques used to assess and forecast the risk of bankruptcy is
presented. The data and methodology are presented in the Section 3. The paper then follows with
analysis of results and discussions in Section 4. Concluding remarks pointing out some policy
implications, future research suggestions and limitations of the study are discussed in the Section 5.
2. Literature Review
Financial risks show the possibility of losses arising from the failure to achieve financial objectives.
The financial risks related to the financial operation of a business may take many different forms:
market risks determined by the changes in commodities, stocks and other financial instruments prices,
J. Risk Financial Manag. 2019, 13, 58 3 of 28
foreign exchange risks, interest rate risks, credit risks, financing risks, liquidity risks, cash flow risk, and
bankruptcy risk. These financial risks are not necessarily independent of each other, the
interdependence being recognized when managers are designing risk management systems (Woods
and Dowd 2008). The importance of these risks will vary from one firm to another, in function of the
sector of activity of the firms, the firm size, development of international transactions, etc.
Bankruptcy refers to the situation in which the debtor company becomes unable to repay its debts
and can be considered to be the consequence of a company’s inability to survive market competition,
reflected in terms of job losses, the destruction of assets, and in a low productivity (Aleksanyan and
Huiban 2016). The risk of bankruptcy or insolvency risk shows the possibility that a company will be
unable to meet its debt obligations, respectively the probability of a company to go bankrupt in the next
few years. Assessing of bankruptcy risk is important especially for investors in making equity or bond
investment decisions, but also for managers in financial decision making of funding, investments and
distribution policy. Failure prediction models are important tools also for bankers, rating agencies, and
even distressed firms themselves (Altman et al. 2017).
The essential information for executive financial decisions, but also for investors decisions are
provided by financial statements. Thus, companies’ financial managers should develop the financial
performance analysis and problem-solving skills (Burns and Balvinsdottir 2005; Scapens 2006), without
limiting their duties in verifying accounting data (Diakomihalis 2012) in order to maintain the firm
attractive for investors. The image of financial performance of companies is affected by the estimation
of its position in front of investors, creditors, and stakeholders (Ryu and Jang 2004). For this estimation
there are used many indicators that reflect the company’s position such as: net working capital, net
treasury, liquidity, solvency, profitability, funding capacity, cash-flow, etc., or a mix between them,
such as Z-scores.
The design of reliable models to predict bankruptcy is crucial for many decision-making processes
(Ouenniche and Tone 2017). The approach used for bankruptcy prediction has evolved over time
starting to Beaver (1966, 1968) model based on univariate analysis for selected ratios and which had
very good predictive power. Then, Altman (1968) made strides by developing a multiple discriminant
analysis model called the Z-Score model. Bankruptcy prediction models could be divided into two
general categories depending on the type of variable used: static models (Altman 1968, 2000, 2002;
Taffler 1982, 1983, 1984; Ohlson 1980; Zmijewski 1984; Theodossiou 1991) or dynamic models
(Shumway 2001; Hillegeist et al. 2004).
In the literature of bankruptcy prediction, the models of Altman (1968), Ohlson (1980), and
Zmijewski (1984) are the most cited ones that are based on accounting variables. These bankruptcy
prediction models use different explanatory variables and statistical techniques. Therefore, the
predictive power of these bankruptcy prediction models differs. However, when the original statistical
techniques are used, the accuracy rates for the models of Altman (1968), Ohlson (1980), and Zmijewski
(1984) are respectively 80.6%, 93.8%, and 95.3% (Avenhuis 2013). Studying the efficacy of Altman’s z-score
model in predicting bankruptcy of specialty retail firms doing business in contemporary times, Chaitanya
(2005) found that all but two of the bankruptcies (94%) would have been accurately predicted.
Ashraf et al. (2019) found that both models by Altman (1968) and Zmijewski (1984) are still
valuable for predicting the financial distress of emerging markets and can be used by businessmen,
financial specialists, administrators, and other concerned parties who are thinking about investing in
an organization and/or want to enhance their organization performance. Elviani et al. (2020) studied
the accuracy of the Altman (1968), Ohlson (1980), Springate (1978) and Zmijewski (1984) models in
bankruptcy predicting trade sector companies in Indonesia using binary logistic regression. Their
results proved that the most appropriate and accurate models in predicting bankruptcy of trade sector
companies in Indonesia are the Springate and Altman models.
Related to methodologies used in creating bankruptcy risk models we can mention bankruptcy
prediction models based on: statistical methodologies (Models of Altman 1968, 2000, 2002; Altman et
al. 2017; Model of Springate 1978; Model of Conan and Holder 1979; Models of Taffler 1982, 1983, 1984;
Model of Fulmer et al. 1984), stochastic methodologies (Model of Ohlson 1980; Model of Zmijewski
1984; Model of Zavgren 1985; Theodossiou 1991), and artificial intelligence methodologies (Zhang et al.
J. Risk Financial Manag. 2019, 13, 58 4 of 28
1999; Kim and Han 2003; Shin et al. 2005; Li and Sun 2011) and data envelopment analysis (DEA)
methodologies (Koh and Tan 1999; Cielen et al. 2004; Paradi et al. 2004; Shetty et al. 2012; Ouenniche
and Tone 2017). Aziz and Dar (2006) reviewed 89 studies on the prediction of bankruptcy risk in the
period 1968–2003 in order to carry out a critical analysis of the methodologies and empirical findings of
the application of these models across 10 different countries (Finland, Norway, Sweden, Belgium, UK,
Italy, Greece, USA, Korea and Australia). They found that the multi-variable models (Z-Score) and logit
were most popular in the 89 papers studied.
The multitude of models created demonstrate an intense concern for bankruptcy prediction,
considering also the evolution of number of bankruptcies in the world. However, the first bankruptcy
models are still applied and provide important information. For example, Altman’s model was applied
to Jordanian companies, non-financial service and industrial companies, for the years 1990–2006. The
study shows that Altman’s model has an advantage in company bankruptcy prediction, with a 93.8%
average predictive ability of the five years prior to the liquidation incident (Alkhatib and Bzour 2011).
Chung et al. (2008) also examined the insolvency predictive ability of different financial ratios for ten
failed financial companies during 2006–2007 in New Zealand and found that, one year prior to failure,
four of the five Altman (1968) ratios were superior to other financial ratios for predicting corporate
bankruptcy. In other countries, such as Romania aggregate indexes of financial performance assessment
for the building sector companies were created (Bărbuţă-Mişu 2009;rbuţă-Mişu and Codreanu 2014) or
well-known modes, such as the Conan and Holder model were adjusted to the specificity of Romanian
companies (Bărbuţă-Mişu and Stroe 2010). In studies about bankruptcy prediction, in Romania was
preferred Conan and Holder (1979) model to evaluate the financial performance of the companies.
The majority of authors proposed models adapted to the specificity of the economies. Brédart
(2014) developed an econometric forecasting model on United States companies using three simple and
a few correlated and easily available financial ratios as explanatory variables and their results show a
prediction accuracy of more than 80%. Dakovic et al. (2010) developed statistical models for bankruptcy
prediction of Norwegian firms acting in the industry sector. They modelled the unobserved heterogeneity
among different sectors through an industry-specific random factor in the generalized linear mixed
model. The models developed are shown to outperform the model with Altman’s variables.
To solve the problem of bankruptcy prediction some statistical techniques such as regression
analysis and logistic regression are used (De 2014). These techniques usually are used for the company’s
financial data to predict the financial state of company as healthy, distressed, high probability of
bankruptcy. As we know, Altman (1968) used financial ratios and multiple discriminant analysis
(MDA) to predict financially distressed companies. However, further, it was found that the usage of
statistical techniques or MDA depends on the constraint as linear separability, multivariate normality
and independence of predictive variables (Ohlson 1980; Karels and Prakash 1987). Thus, bankruptcy
prediction problem can be solved using various other types of classifiers, such as neural network that
compared to MDA, logistic regression and k-nearest neighbour method proved a higher performance.
For instance, Tam (1991) found that the neural network performs better than other prediction techniques.
Otherwise, Xu and Zhang (2009) have investigated whether the bankruptcy of certain companies
can be predicted using traditional measures, such as Altman’s Z-score, Ohlson’s (1980) O-score, and the
option pricing theory-based distance-to-default, previously developed for the U.S. market, in order to
find if these models are useful for the Japanese market. They have found that the predictive power is
substantially enhanced when these measures are combined.
In addition, Jouzbarkand et al. (2013) compiled two models for the prediction of bankruptcy,
related to the Iranian economic situation. Using the logistic regression method, they studied the Ohlson
(1980) and Shirata (1995) models, examining and comparing the performance of these models. Their
results show that models created are able to predict the bankruptcy. For classifying and ranking the
companies, they used their business law to determine the bankrupt companies and a simple Q-Tobin
to specify the solvent companies.
Discriminant analysis was the prevailing method, and the most important financial ratios came
from the solvency category, with profitability ratios also being important (Altman et al. 2017). The
performance of five bankruptcy prediction models, such as Altman (1968), Ohlson (1980), Zmijewski
J. Risk Financial Manag. 2019, 13, 58 5 of 28
(1984), Shumway (2001) and Hillegeist et al. (2004) was studied by Wu et al. (2010) building their own
integrated model using a dataset for U.S.A. listed firms. Wu et al. (2010) found that Shumway’s (2001)
model performed best, Hillegeist et al.’s (2004) model performed adequately, Ohlson’s (1980) and
Zmijewski’s (1984) models performed adequately, but their performance deteriorated over time, while
Altman’s Zscore performed poorly compared with all other four models analysed. However, the
integrated model outperformed the other models by combining both accounting and market data, and
firms’ characteristics.
The factor analysis is often used together with other methodologies, in order to improve
bankruptcy prediction models (Cultrera et al. 2017). Principal Component Analysis (PCA), the
statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly
correlated variables into a set of values of linearly uncorrelated variables called principal components
started to be used in analysis and prediction of bankruptcy risk. Adalessossi (2015) used discriminant
function named Z-scores model of Altman, financial ratio analysis, and the principal component
analysis on a sample of 34 listed companies from different sectors and sizes in order to find out if the
three methods used in this study converge toward similarity results. The comparison of the three
methods indicates unanimously that, out of the 34 companies, only eight companies have had the best
financial performances and are not likely to go on to bankruptcy.
Onofrei and Lupu (2014) have built a quick warning model for the Romanian companies in
difficulty, using the following methodologies: the Principal Components Analysis, the multivariate
discriminant analysis and the logit analysis in order to determine which are the best predictors of
bankruptcy for the Romanian companies. They found that the best predictor for the Romanian market
is the multiple discriminant analysis method with a predictive power between 68%–95%, while the logit
method registering slightly weaker results with a predictive power between 53%–82%.
De (2014) developed the principal component analysis (PCA) and general regression auto
associative neural network (GRAANN) based hybrid as a one-class classifier in order to test the
effectiveness of PCA-GRAANN on bankruptcy prediction datasets of banks from Spain, Turkey, US
and UK. They concluded that PCA-GRAANN can be used as a viable alternative for any one-class
classifier. Checking related literature, we found that PCA is more used with artificial neural network
methods for prediction bankruptcy risk where the effectiveness was proved. However, in this paper we
proposed to use PCA based on the five discriminant analysis measures, i.e., Z-score determined by the
following models: revised Z-score Altman, Conan and Holder, Tafler, Springate and Zmijewski in
order to test the efficiency in predicting the risk of bankruptcy. Afterwards, we made use of
econometric techniques and the PCA score created by country and year to test its influence over
performance. The principal component analysis to build the bankruptcy risk score of the five models
selected is used, since there is no consensus in the literature so as to which is the best bankruptcy
prediction model. In this way we may capture the components that will exert more impact in
bankruptcy prediction.
3. Data and Methodology
In this section we describe the data and all methodologies used to assess bankruptcy risk, as well
as to create the bankruptcy risk indexes by year and country that are presented in the results section.
It starts by describing the models used to assess bankruptcy risk measures, which are commonly used
in the literature and afterwards describes the Principal Component Analysis (PCA) used to create the
bankruptcy risk index measures by year and country (by country, Greece had to be taken out from
the sample due to missing data able to allow us to create the index for this country).
3.1. Data Description
The source of the data is Amadeus database, provided by Bureau van Dijk Electronics. In the
sample we have included only large non-financial companies from the former EU-28 countries, for
the period 2006–2015, that act in all sectors of activity (with the conclusion of the Brexit, the EU is
now with 27 countries, instead of 28. However, UK was used because at the beginning of the analysis
it belonged to the EU-28 and we will keep this representation through the article). The selection
J. Risk Financial Manag. 2019, 13, 58 6 of 28
criteria for large companies included in the sample are in accordance with the classification of the
small and medium enterprises (SMEs) published in Commission Recommendation of 6 May 2003
(European Commission 2003) concerning the definition of micro, small and medium-sized
enterprises. Thus, in order to select the large companies for EU-28 countries, as selection criteria of
these companies we used: number of employees greater than 250, total assets greater than €43 million
and turnover greater than €50 million. These criteria were applied simultaneously for the data
available for the last year included in the sample, i.e., year 2015. We found 22,581 active large
companies. We did not consider small and medium enterprises (SMEs) due to the high fluctuations
over time in foundation and closing of these firms compared to large companies. Our intention was
to study the risk of bankruptcy to large companies that had a more stable activity over time. Our data
period was from 2006 until 2015.
Where it was applicable, because of some data missing, we deleted data for years and companies
with no available information for calculation of variables of risk of bankruptcy models. In addition,
we eliminated from database the inconclusive values and outliers. Thus, remained in the study
154,459 valid year-observations. However, we still worked with an unbalanced panel, due to missing
years of data in the sample. Additionally, we have taken out from our sample all countries which did
not present a number of companies higher than 1000. From the 28 available countries we ended up
working with 20 of these countries.
3.2. Models for Assessing Bankruptcy Risk
As we mentioned in the literature review, there are numerous models for bankruptcy risk
prediction based on Z score method, but in this paper we selected the following five models: Altman’s
Models (1968, 2000), Conan and Holder Model (1979), Springate’s Model (1978), Taffler’s Model
(1982, 1983), Zmijewski’s Model (1984). We used these five models since these are the most referenced
one’s to predict bankruptcy and have a high level of accuracy as we presented in the Section 2. There
are a number of key models that have been developed by various authors and presented in the
bankruptcy prediction literature over the last three decades, but these five appear in most of the
recent studies where bankruptcy models are tested. For these models we determined all variables
necessary and the Z scores for all companies included in the sample for the period 2006–2015.
3.2.1. Altman’s Models
Altman (1968) is the dean of insolvency prediction models and the first researcher that
successfully used the step-wise multiple discriminate analysis to develop a prediction model with a
high degree of accuracy of 95%. The original study included a sample comprising 66 industrial
companies, 33 bankrupts and other 33 non-bankrupts for a period of analysis of 20 years (1946–1965).
The author found a total of 22 potential variables, based on data provided by annual reports of
the companies, and by them, he retains five variables with the highest significance, as a result of using
statistical techniques and discrimination analysis. Generally, these variables include profitability
ratios, coverage ratios, liquidity ratios, capitalization ratios, and earnings variability (Altman 2000).
The final discriminant function of first Altman model (1968) takes the following form:
Z1 Altman = 0.012 X1 + 0.014 X2 + 0.033 X3 + 0.006 X4 + 0.999 X5 (1)
where:
Z1 Altman = Overall Index Altman
X1 = Working Capital/Total Assets
X2 = Retained Earnings/Total Assets
X3 = Earnings Before Interest and Taxes/Total Assets
X4 = Market Value Equity/Book Value of Total Debt
X5 = Sales/Total Assets
J. Risk Financial Manag. 2019, 13, 58 7 of 28
Because this original model cannot be applied to unlisted companies in the Stock Exchange, the
model was completely re-estimated, substituting the Market Value of Equity with Book Values of
Equity in X4 (Altman 2000), resulting the Revised Z-Score Model that is used for our sample.
A Revised Z-Score Model (rza)
This change of the Market Value of Equity determined not only the change of new variable’s
parameter, but determined the change of all coefficients, as well as the classification criterion and
related cut-off scores.
The results of the revised Z-Score model with a new X4 variable is:
Z2 Altman = 0.717 X1 + 0.847 X2 + 3.107 X3 + 0.420 X4 + 0.998 X5 (2)
The description of the variable used is the following:
X1—Working Capital/Total Assets
This ratio is the measure of the net liquid assets of the firm relative to the total capitalization.
Working capital is defined as the difference between current assets and current liabilities. Liquidity
and size characteristics are explicitly considered in this ratio. Ordinarily, a company experiencing
consistent operating losses will have shrinking current assets in relation to total assets.
X2—Retained Earnings/Total Assets
Retained earnings is the account which reports the total amount of reinvested earnings and/or
losses of a firm over its entire life. The account is also referred to as earned surplus. Retained earnings
may be affected by a substantial reorganization or stock dividend and for this reason, in research
studies, some appropriate readjustments should be made to the accounts. In this ratio, the age of the
company is considered implicitly. For example, a relatively young company will probably show a
low ratio because it had not enough time to build up its cumulative profits. Therefore, it may be
argued that a young company is somehow discriminated against in this analysis, and its chance of
being classified as bankrupt is relatively higher than another older company. That’s why we have
included in our sample only large companies that have a higher chance of remaining on the market.
This is precisely the situation manifested in the real world because the incidence of failure is much
higher in a company’s earlier years. Those companies with high retained earnings, relative to total
assets, have financed their assets through retention of profits and have not utilized as much debt.
X3—Earnings before Interest and Taxes/Total Assets
This ratio is a measure of the true productivity of the company’s assets, independent of any tax
or leverage factors. Since a company’s ultimate existence is based on the earning power of its assets,
this ratio appears to be particularly appropriate for studies dealing with corporate failure.
Furthermore, insolvency in a bankruptcy sense occurs when the total liabilities exceed a fair valuation
of the company’s assets with value determined by the earning power of the assets.
X4—Book Value of Equity/Book Value of Total Debt
Equity is measured by the Book Value of Equity divided by Total Debt, debt including both
current and long-term. The measure shows how much the firm’s assets can decline in value
(measured by book value of equity plus debt) before the liabilities exceed the assets and the company
becomes insolvent.
X5—Sales/Total Assets
The capital-turnover ratio is a standard financial ratio illustrating the sales generating ability of
the firm’s assets. This ratio is quite important because it is the least significant ratio on an individual
basis. Because of its unique relationship to other variables in the model, the Sales/Total Assets ratio
ranks second in its contribution to the overall discriminating ability of the model.
The interpretation of the Z2 Altman is:
J. Risk Financial Manag. 2019, 13, 58 8 of 28
Z2 Altman > 2.9 − Safe zone
1.23 < Z2 Altman < 2.9 − Grey zone
Z2 Altman < 1.23 − Distress zone
In order to eliminate industry effects, the next change of the Z-Score model analysed the
characteristics and accuracy of the model without variable X5—Sales/Total Assets (Altman 2002). He
does this in order to minimize the potential industry effect which is more likely to take place when
such an industry-sensitive variable as asset turnover is included. This particular model is also useful
within an industry where the type of financing of assets differs greatly among firms and important
adjustments, like lease capitalization, are not made (Bărbuță-Mișu 2017).
In particular, Altman et al. (1998) have applied this enhanced Z Score model to emerging
markets corporates, specifically Mexican firms that had issued Eurobonds denominated in US
dollars. In the emerging market model, they added a constant term of +3.25 so as to standardize the
scores with a score of zero equated to a default rated bond.
3.2.2. Conan and Holder’s Model (zcc)
The Conan and Holder (1979) model was developed to analyse the degradation of the financial
situation of small and medium enterprises (SMEs). The appraisals for the proposed score function
were based on an initial set of 50 indicators studied by the category: the asset structure, the financial
dependence, the treasury, the working fund, the exploitation, the profitability, etc. Then, the
formulation and model results are based on the analysis of 31 rates (financial variables), applied on
190 small and medium enterprises acting in various fields: industry, trade, services and transport
during 1970–1975. Of the 190 selected companies, 95 companies were bankrupt, and another 95 were
healthy businesses whose activities were appropriate waist and bankrupt companies.
The model developed by Conan and Holder is included in the statistical tested methods, and
has the advantage of simplifying the calculation, so that it continues to be used today.
The Conan and Holder model is:
Z Conan and Holder = 0.24 X1 + 0.22 X2 + 0.16 X3 – 0.87 X4 – 0.10 X5 (3)
where:
Z Conan and Holder = Overall Index Conan and Holder
X1 = Gross Operating Surplus/Total Debts, expresses the profitability by creditors, the profit
achieved by using borrowed capital.
X2 = Permanent Capital/Total Liabilities, expresses the solvency of the company on long term, a
measure of debt guarantees through permanent capital.
X3 = (Current assets − Stocks)/Total Liabilities, expresses the liquidity of the company, the
capacity of paying debts by transforming into cash of receivables, financial short-term investments,
cash, and cash equivalents.
X4 = Financial Expenditures/Net Sales, expresses the rate of financial expenses, the share of
financial expenses in net sales.
X5 = Personnel Expenditures/Added Value, expresses the rate of personnel costs, i.e., the share
of remuneration of the personnel by the added value of the company.
The interpretation of the Z Conan and Holder score function is as follows:
Z Conan and Holder < 0.04 − a probability of a bankruptcy risk of > 65%;
0.04 < Z Conan and Holder < 0.16 − a probability of bankruptcy between 30%–65%;
Z Conan and Holder > 0.16 − a probability of bankruptcy of < 30%.
3.2.3. Springate’s Model (zs)
This Canadian business insolvency prediction model was developed in 1978 at Simon Fraser
University by Gordon L.V. Springate, following procedures developed by Altman in the US data.
Springate (1978) used step-wise multiple discriminate analysis to select four out of 19 popular
J. Risk Financial Manag. 2019, 13, 58 9 of 28
financial ratios that best distinguished between sound business and those that actually failed. This
insolvency prediction model achieved an accuracy rate of 92.5% using the 40 companies tested by
Springate.
The Springate model takes the following form:
Z Springate = 1.03 X1 + 3.07 X2 + 0.66 X3 + 0.4 X4 (4)
Z Springate = Overall Index Springate
X1 = Working Capital/Total Assets measure of the net liquid assets of the firm relative to the
total capitalization.
X2 = Earnings Before Interest and Taxes/Total Assets is a measure of the true productivity of the
firm’s assets, independent of any tax or leverage factors.
X3 = Earnings before Taxes/Current Liabilities is a measure of the true productivity of the firm’s
assets, independent of any leverage factors.
X4 = Sales/Total Assets illustrate the sales generating ability of the firm’s assets. It is one measure
of management’s capability in dealing with competitive condition.
The interpretation of Z Springate model is:
Z Springate > 0.826, the company is performant;
Z Springate <= 0.826, the company is bankrupted.
3.2.4. Taffler’s Model (ztta)
Taffler (1983) proposed a model based on an extensive survey of the vast array of data. The
original model was developed to analyse industrial (manufacturing and construction) companies
only with separate models developed for retail and service companies. Using computer technology,
80 carefully selected financial ratios were calculated using accounts of all listed industrial companies
failing between 1968 and 1976 and 46 randomly selected solvent industrial firms (Agarwal and Taffler
2007).
This information was processed through a series of statistical methods, and the model was built
using multivariate discriminant method. The Z-score model was derived by determining the best set
of ratios which, when taken together and appropriately weighted, distinguished optimally between
the two samples. Leverage, profitability, liquidity, capital adequacy and other parameters were
evaluated for model creation. The model is applicable to companies in the form of joint stock
companies, whose shares were subject to public offering and traded on various stock exchanges
(Belyaeva 2014).
The Z Taffler model is:
Z Taffler = 3.2 + 12.18 X1 + 2.5 X2 – 10.68 X3 + 0.029 X4 (5)
where:
Z Taffler = Overall Index Taffler
X1 = Profit before Tax/Current Liabilities is a measure of the true productivity of the firm’s assets,
independent of any leverage factors.
X2 = Current Assets/Total Liabilities expresses the payment capacity on short-term of the
company, i.e., the ability of current assets to be converted into cash to meet the payment obligations.
This ratio estimates the liquidity of the company by showing the company can pay its creditors with
its current assets if the company’s assets ever had to be liquidated.
X3 = Current Liabilities/Total Assets shows the share of a company’s assets which are financed
through short-term debt. If the ratio is low, most of the company's assets are financed through equity
and long-term debts. If the ratio is high, most of the company's assets are financed through short-
term debt.
X4 = (Quick Assets − Current Liabilities)/Daily Operating Expenses with the denominator
proxied by: (Sales − Profit Before Tax − Depreciation)/365
The interpretations of Z Taffler model is as follows:
J. Risk Financial Manag. 2019, 13, 58 10 of 28
Z Taffler > 0.3 shows that the company has good chances for performance
0.2 < Z Taffler < 0.3 shows the grey zone (undefined area)
Z Taffler < 0.2 shows that the company is almost bankrupt.
Thus, in the case of this model, if the computed Z Taffler score is positive, the firm is solvent and
is very unlikely indeed to fail within the next year. However, if its Z Taffler score is negative, it lies
in the “at risk” region and the firm has a financial profile similar to previously failed businesses. The
high probability of financial distress is depending on how much negative is the Z Taffler score
(Agarwal and Taffler 2007).
3.2.5. Zmijewski’s Score (zzzmij)
The Zmijewski Score (Zmijewski 1984) is a bankruptcy model used to predict a firm’s
bankruptcy in two years. Zmijewski (1984) criticised previous models, considering that other
bankruptcy scoring models oversampled distressed firms and favoured situations with more
complete data.
Thus, in Zmijewski (1984) study, two methodological issues are examined that are related to the
estimation of bankruptcy prediction models. The two biases are choice-based sample biases and
sample selection biases. The choice based bias is the result of over-sampling distressed firms. When
a matched-pair (one-to-one match) design is for a study to predict bankruptcy, the potential of
bankruptcy is overstated. This lead to biased probabilities in the models. The sample selection biases
occur when the probability of distress given complete data are significantly different from the
probability of distress given incomplete data (Avenhuis 2013).
The ratio used in the Zmijewski (1984) score was determined by probit analysis (probit should
be regarded as probability unit) in order to construct the bankruptcy prediction model. Like the logit
function, the probit function maps the value between 0 and 1, and, in this case, scores greater than
0.5 represent a higher probability of default. The accuracy rate of the Zmijewski (1984) model for the
estimation sample was 99%.
The constructed probit function with the variables and estimated coefficients from the study of
Zmijewski (1984) is as follows:
Z Zmijewski = −4.336 − 4.513 X1 + 5.679 X2 + 0.004 X3 (6)
where:
Z Zmijewski = Overall Zmijewski Index
X1 = Net Income/Total Assets is a profitability ratio that measures the net income produced by
total assets during a period by comparing net income to the average total assets.
X2 = Total Liabilities/Total Assets shows the share of a company's assets which are financed
through debt. If the ratio is less than 0.5, most of the company's assets are financed through equity. If
the ratio is greater than 0.5, most of the company's assets are financed through debt.
X3 = Current Assets/Current Liabilities expresses the payment capacity on short-term of the
company.
While Altman used the ratio Earnings before Interest and Taxes (EBIT)/Total Assets for
profitability, where EBIT eliminates the effect of different capital structures and of taxation and make
easier the comparing of the firm profitability, Zmijewski (1984) used the ratio: Net Income/Total
Assets, thus considering the effects of funding sources used and of the firm taxation.
Zmijewski (1984) classified the companies thus:
(i) Firms with probabilities greater than or equal to 0.5 were classified as bankrupt or having
complete data.
(ii) Firms with probabilities less than 0.5 were classified as non-bankrupt or having incomplete data.
J. Risk Financial Manag. 2019, 13, 58 11 of 28
3.3 Principal Component Analysis
There exist many indicators in financial analysis which allow to assess the risk of bankruptcy of
a company (Armeanu et al. 2012; Armeanu and Cioaca 2015; Cultrera et al. 2017; Arroyave 2018;
Prusak 2018).
In order to make an appropriate assessment, we need to reduce the number of indicators. A
solution is indicated by Armeanu et al. (2012): using Principal Component Analysis (PCA), cluster
and discriminant analysis techniques. The authors used these three methods to build a scoring
function and afterwards to identify bankrupt companies. Their sample consisted on listed companies
on Bucharest Stock Exchange. Heffernan (2005) points that bankruptcy risk predicting models,
developed based on discriminant analysis (like Altman and Conan-Holder) can easily mislead. This
is due to the fact that they rely on historical data, but also on the fact that the result is binary (either
the debtor is solvent or not). However, in the present article we consider the following possible
scenarios (Armeanu et al. 2012; Armeanu and Cioaca 2015): delays in monthly repayments, failure to
pay them, failure to pay fees or penalty interest, and so on, and that is why we rely on large
companies’ data. Discriminant analysis models may not include the state of solvency, insolvency and
restructuring at once, and we would like to infer about it using principal component analysis jointly
with discriminant analysis. PCA methods are less recognized in the literature to predict bankruptcy
risk (Cultrera et al. 2017).
We use PCA based on the five discriminant analysis measures identified previously in Section
3.2. Software Stata is used for studying the effect of performance over risk and bankruptcy scores
were obtained by year of analysis and country. Descriptive statistics of this data and Pearson
correlation values considering country scores and year scores are presented in tables presented in
Section 4.
3.4. Econometric Methodologies
In order to analyse the effects of risk scores over firm performance, we applied a dynamic panel-
data estimation model, with GMM estimators to regress earnings before interest and taxes to total
assets over risk by year. By doing so in a Generalized Method of Moments (GMM) context, we may
construct more efficient estimates of the dynamic panel data model (these models contain one or
more lagged dependent variables, allowing for the modelling of a partial adjustment mechanism). In
the context of panel data, we usually must deal with unobserved heterogeneity. Static models are
(almost) always misspecified, because the within-group error terms are serially correlated, thereby
invalidating both point estimates and statistical inference. Conversely, dynamic models tend to be
correctly specified, because the dynamics are in the estimated part of the model rather than displaced
into the error terms, which invalidates static FE/RE estimation. Dynamic models are much richer in
economic content by virtue of being able to distinguish short-run and long-run effects of independent
variables on dependent variables.
Additionally, we used Tobit models to infer about the influence of company performance
measures over general bankruptcy risk scores. The Tobit model, also called a censored regression
model, is designed to estimate linear relationships between variables when there is either left- or
right-censoring in the dependent variable. Our dependent variable is censored from both below and
above provided we have limited the risk variable to be between −3 and 3, inclusively. Tobit models
to predict bankruptcy have also been used by Sigrist and Hirnschall (2019) recently. The assumption
of the Tobit model is that there exists a latent variable Y* which follows, conditional on some
covariates X a Gaussian distribution:
| ~ ((), 
). The mean F(X) is assumed to depend
linearly on the covariates X through ()= 
where β is a set of coefficients. This latent variable
Y* is observed only if it lies in an interval. Mousavi et al. (2019) used instead of PCA, a DEA model
to measure the operational efficiency scores of Japanese companies, in the first step. In the second
step, the efficiency score is used as the dependent variable in a Tobit regression to investigate whether
corporate governance variables influence the operational efficiency of firms.
J. Risk Financial Manag. 2019, 13, 58 12 of 28
4. Results and Discussions
As we presented in the Section 3.1, in this study we used data from European large companies
where insolvencies are more present. Figure 1 plots the frequency of corporate insolvencies in Europe
by country for 2018 (Euler Hermes 2019). We can see that the first place in the frequency of
bankruptcies was occupied by France (with 26.02%) corresponding to 54,965 companies bankrupted,
followed by United Kingdom with 10.26% frequency corresponding to 21,669 companies bankrupted
and 9.16% to Germany with 19,350 companies bankrupted. In our sample we used a great part of
these countries. As we are able to observe, among countries with a high number of corporate
insolvencies were also Italy, Belgium, Romania, Denmark, Sweden, Hungary, Norway, and Austria.
From the countries used in our sample, France, United Kingdom, Germany, Turkey, Italy, Belgium,
Romania, Denmark and Sweden were in the top ten of the Frequency of corporate insolvencies in
Europe in 2018 (Figure 1).
Figure 1. Frequency of corporate insolvencies in Europe, by country in 2018. Source: Euler Hermes
Economic Research. 2019. Insolvency Outlook. Euler Hermes, Allianz, Economic Research, January
2019, 1–14. Own elaboration.
Table 1 presents the number of companies from EU-28 countries included in the sample. We can
observe that a high number of firm-year observations from large companies came from United
Kingdom i.e., 28.60% of all observations analysed (also the country with the second number of
bankruptcies), followed by Germany with 16.17%, Italy with 11.49%, France with 9.97% and Spain
with 7.28%. Related to the number of firm-year observations of large companies by years, we can
observe that the highest number of observations was in 2014 (18,513 companies) and 2013 (18,395
companies), respectively 12.02% and 11.94% of the sample analysed.
26.02%
10.26%
9.16%
7.29%
5.47%
4.97%
4.72%
4.17%
3.76%
3.33%
2.56%
2.44%
2.43%
2.37%
1.86%
1.72%
1.47%
1.29%
1.06%
0.91%
0.63%
0.61%
0.47%
0.37%
0.28%
0.22%
0.16%
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00%
France
United Kingdom
Germany
Turkey
Italy
Russia
Belgium
Romania
Denmark
Sweden
Hungary
Norway
Austria
Switzerland
Spain
The Netherlands
Finland
Portugal
Lithuania
Slovakia
Luxembourg
Czech Republic
Poland
Ireland
Latvia
Bulgaria
Estonia
J. Risk Financial Manag. 2019, 13, 58 13 of 28
Table 1. Total number of companies within the sample by country and year.
Acronym Country Number Companies Frequency 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
AT Austria 2175 1.41% 1 56 5 96 340 372 380 401 428 96
BE Belgium 5956 3.87% 579 535 604 610 618 626 633 636 639 476
BG Bulgaria 1119 0.73% 101 92 110 110 106 119 120 120 121 120
CZ
Czech Republic
4270
2.77%
407
393
448
482
461
266
DE Germany 24,917 16.17% 2105 2276 2603 2758 2908 3039 3106 3172 2667 283
ES Spain 11,213 7.28% 1096 993 1179 1194 1228 1261 1285 1304 1298 375
FI Finland 2304 1.50% 193 208 213 231 229 244 250 254 264 218
FR
France
15,356
9.97%
1775
1560
1413
1395
2099
1158
GB Great Britain (UK) 44,060 28.60% 3558 3811 4078 4324 4612 4913 5155 5392 5550 2667
GR Greece 1741 1.13% 167 131 177 188 191 192 194 194 193 114
HR Croatia 1190 0.77% 101 96 116 117 125 126 127 128 128 126
HU
Hungary
1849
1.20%
42
212
223
230
173
124
IE Ireland 1482 0.96% 97 136 140 145 171 175 189 198 194 37
IT Italy 17,697 11.49% 1750 1519 1802 1828 1855 1930 1947 1979 1974 1113
NL The Netherlands 5868 3.81% 345 471 258 597 500 705 785 817 879 511
PL
Poland
1668
1.08%
163
182
198
154
196
95
PT Portugal 2555 1.66% 222 215 248 255 267 281 288 287 271 221
RO Romania 2144 1.39% 234 64 0 0 297 303 310 311 322 303
SE Sweden 5115 3.32% 475 476 548 570 517 506 512 524 537 450
SK
Slovakia
1382
0.90%
136
154
162
141
119
117
Total
154,061
13,547
14,582
15,467
17,194
18,513
8870
Source. Performed by the authors based on data provided by Amadeus database.
J. Risk Financial Manag. 2019, 13, 58 14 of 28
Table 2 presents the data descriptive statistics for the variables used for calculation of Z score
for all five models used. In average, the companies from the sample show a need of exploitation
capital of 14% by the total assets, an operational profitability of 6%, a rotation speed of assets 1.48
times per year, a current liquidity by 2.31 showing the capacity to pay debts by converting of assets
in cash, the share of financial expenditure of 0.11% by sales, the share of personnel expenses of 69%
in value added and a degree of debts of 64% by total assets. In addition, from Table 2 it is visible the
disparity of values of mean and standard deviation of the bankruptcy measures. Moreover, the
different number of observations considered for both the creation of financial ratios as well as
bankruptcy indicators of interest are clearly visible.
Tables A1 and A2 (at the Appendix A) presents the correlation matrix among the variables used
both to produce the bankruptcy risk indicators and the five bankruptcy risk scores. In addition,
Tables A1 and A2 presents the Pearson correlation values and statistical significance. From here it is
seen that there are ratios used to produce the bankruptcy indicators which are highly correlated
among them, significantly, with negative or positive correlation (i.e., strong positive significant
correlation (0.821) between Book Value of Equity/Book Value of Total Debt and Current Assets/Total
Liabilities; strong positive significant correlation (0.778) between Book Value of Equity/Book Value
of Total Debt and (Current assets Stocks)/Total Liabilities, almost perfect positive correlation (0.998)
between EBIT/Current liabilities and Profit Before Tax/Current Liabilities etc.), but mostly have low
to moderate correlation. However, between bankruptcy indicators constructed through discriminant
analysis, correlation values are very low, and very close to zero with statistical significance.
Table 3 indicates that after applying PCA, the number of observations decreased as compared
to Table 2. In fact, by restricting the sample to all those values obtained for the general risk score
greater than 3 or smaller than 3, our sample was reduced to 133,751 firm-year observations. Risk is
the score computed through PCA considering all companies, years and countries.
Overall, countries presented higher mean scores as well as negative mean for some countries,
and also standard deviation is higher for countries scores. A plot of year bankruptcy risk scores will
allow us to see their behaviour along years. Figure 2 presents these data evolution for countries. After
the final data treatment, the total number of companies available to analyse by country and year are
presented in Table 4.
Correlation values (Table 5) seem to be very strong among Austria and Spain, Croatia, Italy, the
Netherlands, Poland and Sweden; strong (higher than 90% and positive; some near perfect linear
positive correlation) between Belgium, Czech Republic, Germany, Finland, France, Great Britain,
Hungary, Portugal, Romania, and Slovakia; Bulgaria and Ireland; Germany, Finland, France, Great
Britain, Hungary, Portugal, Romania, and Slovakia; Spain, Croatia, Italy, the Netherlands, Poland,
and Sweden; Finland, France, Great Britain, Hungary, Portugal, Romania, and Slovakia; between
France, Great Britain, Hungary, Portugal, Romania and Slovakia; among Great Britain and Hungary,
Portugal, Romania, and Slovakia; Croatia, Italy, the Netherlands, Poland, and Sweden; between
Hungary, Portugal, Romania, and Slovakia; Italy, Poland, and Sweden; the Netherlands, Poland and
Sweden; Between Poland and Sweden; Portugal, Romania, and Slovakia; and finally between
Romania and Slovakia. As such, no clear pattern is identified regarding for instant the geographic
distance among the countries, but high correlation values maybe due to commercial transactions
performed among these countries.
Regarding year, whose correlation values are presented in Table 6, the score Pearson correlation
values were very high, near to one and positive. In the next we will be analysing the evolution plots
of scores of bankruptcy risk by country and by year. Figures 2 and 3 present these evolutions
respectively.
J. Risk Financial Manag. 2019, 13, 58 15 of 28
Table 2. Variables, formulas, and descriptive statistics.
Formula
Variable
Obs
Mean
Std. Dev.
Min
Max
Working capital/Total assets
wcta
153,459
0.14
0.76
−198.44
113.86
Retained Earnings/Total Assets
reta
148,986
0.24
1.29
−364.35
274.07
EBIT/Total assets
ebitta
153,459
0.06
0.24
−42.14
61.11
Book Value of Equity/Book Value of Total Debt
bvebvtd
153,278
2.44
176.82
−657.29
50,409.00
Sales/Total assets
sta
153,459
1.48
3.99
0.00
1322.52
Revised Z Altman
rza
148,821
3.02
75.50
−306.70
21,172.06
EBIT/Current liabilities ebitcliabil 151,123 240.93 101,682.60 −4,900,820.00 38,700,000.00
Permanent capital/Total debts ppi 153,278 2.77 176.83 −656.29 50,410.00
(Current assets Stocks)/Total Liabilities
curnt
153,278
2.31
172.72
−38.15
45,178.00
Financial expenditures/Sales
fs
145,515
0.11
8.93
−1.11
2169.55
Personnel Expenditures/Added Value
pexpenditura
140,104
0.69
3.81
−609.22
440.32
Z Connan
zcc
135,073
64.97
25813.04
−1176196.00
9,298,852.00
Working capital/Total assets
wcta_1
153,459
0.14
0.76
−198.44
113.86
Earnings Before Interest and Taxes/Total Assets
ebitta_1
153,459
0.06
0.24
−42.14
61.11
Earnings Before Taxes/Current Liabilities
ebtcl
151,096
229.24
103167.50
−5151934.00
39,400,000.00
Sales/Total Assets
sta_1
153,459
1.48
3.99
0.00
1322.52
Z Springate Model
zs
151,096
152.23
68090.55
−3400276.00
26,000,000.00
Profit Before Tax/Current Liabilities
pbtcl
151,096
229.24
103167.50
−5151934.00
39,400,000.00
Current Assets/Total Liabilities
cat
153,278
2.89
219.41
−39.30
55,223.00
Current Liabilities/Total Assets
clt
153,459
0.43
0.75
−113.76
199.44
(Quick Assets − Current Liabilities)/(Sales − Profit Before Tax − Depreciation)/365
qaclspbtd
144,735
−792,000,000,000
301,000,000,000,000
−115,000,000,000,000,000
10,200,000
Z Taffler
ztta
144,730
−23,000,000,000
8740,000,000,000
−3,320,000,000,000,000
47,900,0000
Net Income/Total Assets
nincomt
153,459
0.04
0.26
−62.33
26.68
Total Liabilities/Total Assets tliat 153,432 0.64 1.12 −71.28 390.32
Current Assets/Current Liabilities
cac
151,123
−653.97
403912.70
−90,700,000.00
84,800,000.00
Z Zmijewski
zzzmij
151,118
−3.44
1615.68
−362744.00
339,315.60
Source. Performed by the authors based on data provided by Amadeus database.
J. Risk Financial Manag. 2019, 13, 58 16 of 28
Table 3. Descriptive Statistics of scores computed based over Principal Component Analysis (PCA).
Variable Obs Mean Std. Dev. Variable Obs Mean Std. Dev.
risk 133,751 −0.00331 0.004657 riskAT 133,751 0.23914 3.094626
risk2015 133,751 0.004167 0.006804 riskBE 133,751 0.433947 50.73316
risk2014
133,751
−0.01011
0.001642
riskBG
133,751
0.485776
14.48578
risk2013 133,751 0.264434 26.89755 riskCZ 133,751 0.555987 60.98777
risk2012 133,751 0.006104 1.469264 riskDE 133,751 −0.01741 0.468755
risk2011 133,751 0.085679 9.797604 riskES 133,751 0.188694 3.364935
risk2010
133,751
0.001579
1.400829
riskFI
133,751
1.197073
115.5954
risk2009 133,751 0.012124 2.556249 riskFR 133,751 −0.00996 0.00155
risk2008 133,751 0.029394 3.814389 riskGB 133,751 0.731819 71.07321
risk2007 133,751 −0.00539 0.608735 riskHR 133,751 0.226101 3.129303
risk2006
133,751
−0.01938
0.606729
riskHU
133,751
0.158191
19.79164
riskIE 133,751 0.061467 10.7725
riskIT 133,751 0.297428 2.719817
riskNL 133,751 −0.29214 3.178018
riskPL
133,751
−0.07491
3.281825
riskPT 133,751 3.345667 299.3375
riskRO 133,751 1.151802 109.7751
riskSE 133,751 −0.30931 3.435378
riskSK
133,751
0.317604
36.03616
Source. Performed by the authors based on data provided by Amadeus database.
Table 4. Number of firms after limiting the risk values by country and year.
Country
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Total
AT
1
0
4
90
315
347
352
363
396
80
1948
BE
576
463
600
606
613
620
628
634
635
471
5846
BG
95
65
107
104
102
119
120
120
121
120
1073
CZ
403
273
388
445
455
480
488
490
459
265
4146
DE
2018
1693
2501
2664
2840
2973
3042
3113
2613
273
23,730
ES
1086
760
1167
1172
1213
1241
1269
1286
1280
367
10,841
FI
155
138
174
186
194
204
207
211
223
170
1862
FR
1738
1265
1542
1389
1572
1381
1095
1633
2064
1132
14,811
GB
3217
2772
3702
3884
4078
4363
4553
4773
4890
2347
38,579
HR
100
64
116
117
124
124
126
127
128
126
1152
HU
36
99
202
212
220
217
222
224
162
115
1709
IE
89
93
125
127
149
153
170
166
160
30
1262
IT
1746
1221
1798
1825
1855
1930
1946
1976
1973
1113
17,383
NL
239
199
55
16
13
17
25
29
26
0
619
PL
72
52
76
85
75
51
59
61
83
17
631
PT
221
141
245
253
259
270
274
264
257
205
2389
RO
152
0
0
0
297
303
310
311
322
303
1998
SE
218
205
269
282
244
245
249
261
273
240
2486
SK
130
94
149
153
128
139
139
121
117
116
1286
Total 12,292 9597 13,220 13,610 14,746 15,177 15,274 16,163 16,182 7490 133,751
Source. Performed by the authors based on data provided by Amadeus database.
J. Risk Financial Manag. 2019, 13, 58 17 of 28
Table 5. Pearson correlation values among scoring PCA bankruptcy risk variables obtained by country.
Score
riskAT
riskBE
riskBG
riskCZ
riskDE
riskES
riskFI
riskFR
riskGB
riskHR
riskHU
riskIE
riskIT
riskNL
riskPL
riskPT
riskRO
riskSE
riskSK
riskAT 1
riskBE
0.093 ***
1
riskBG 0.037 *** 0.337 *** 1
riskCZ 0.093 *** 0.998 *** 0.363 *** 1
riskDE
0.093 ***
0.998 ***
0.363 ***
1.000 ***
1
riskES 0.997 *** 0.016 *** 0.008 *** 0.0 16 *** 0.016 *** 1
riskFI
0.093 ***
1.000 ***
0.337 ***
0.998 ***
0.998 ***
0.016 ***
1
riskFR 0.093 *** 0.998 *** 0.363 *** 1.000 *** 1.000 *** 0.016 *** 0.998 *** 1
riskGB 0.093 *** 1.000 *** 0.337 *** 0.998 *** 0.998 *** 0.016 *** 1.000 *** 0.998 *** 1
riskHR
1.000 ***
0.098 ***
0.039 ***
0.098 ***
0.098 ***
0.997 ***
0.098 ***
0.098 ***
0.098 ***
1
riskHU 0.093 ** * 0.998 *** 0.363 *** 1.000 *** 1.000 *** 0.016 *** 0.998 *** 1.000 *** 0.998 *** 0.098 *** 1
riskIE
0.037 ***
0.337 ***
1.000 ***
0.363 ***
0.363 ***
0.008 ***
0.337 ***
0.363 ***
0.337 ***
0.039 ***
0.363 ***
1
riskIT 0.982 *** -0.098 *** -0.03 2 *** -0.098 *** -0.098 *** 0 .994 *** -0.098 *** -0.098 *** -0.098 *** 0.981 *** -0.098 *** -0.0 32 *** 1
riskNL 0.997 *** 0 .174 *** 0.065 *** 0.174 *** 0.174 *** 0.987 *** 0.174 *** 0.174 *** 0.174 *** 0.9 97 *** 0.174 *** 0.065 *** 0.963 *** 1
riskPL
1.000 ***
0.085 ***
0.033 ***
0.085 ***
0.085 ***
0.998 ***
0.085 ***
0.085 ***
0.085 ***
0.999 ***
0.085 ***
0.033 ***
0.983 ***
0.996 ***
1
riskPT 0 .093 *** 0.998 *** 0.363 *** 1.000 *** 1.000 *** 0.016 *** 0.998 *** 1.000 *** 0.9 98 *** 0.098 *** 1.000 *** 0.363 *** -0.098 *** 0.174 *** 0.085 *** 1
riskRO 0.093 *** 0 .998 *** 0.363 *** 1.000 *** 1 .000 *** 0.016 *** 0.998 *** 1.000 *** 0.9 98 *** 0.098 *** 1.000 *** 0.363 *** -0.098 *** 0.174 *** 0.085 *** 1.000 *** 1
riskSE
0.999 ***
0.071 ***
0.029 ***
0.071 ***
0.071 ***
0.999 ***
0.071 ***
0.071 ***
0.071 ***
0.999 ***
0.071 ***
0.029 ***
0.986 ***
0.995 ***
0.999 ***
0.071 ***
0.071 ***
1
riskSK 0.09 3 *** 0.998 ** * 0. 363 *** 1.000 *** 1.000 *** 0.016 *** 0.998 *** 1.000 *** 0.998 *** 0.098 *** 1.000 *** 0.363 *** -0.098 *** 0.174 *** 0.085 *** 1.000 *** 1.000 *** 0.071 *** 1
Source. Performed by the authors based on data provided by Amadeus database. Note: *, **, ***, represent statistically significant at 10%, 5% and 1%, respectively.
Table 6. Pearson correlation variables among scoring PCA bankruptcy risk variables obtained by year.
Scores risk risk2015 risk2014 risk2013 risk2012 risk2011 risk2010 risk2009 risk2008 risk2007 risk2006
risk 1
risk2015 1.000 *** 1
risk2014 1.000 *** 1.000 *** 1
risk2013
1.000 ***
1.000 ***
1.000 ***
1
risk2012 1.000 *** 1.000 *** 1.000 *** 1.000 *** 1
risk2011 0.998 *** 0.998 *** 0.998 *** 0.998 *** 0.998 *** 1
risk2010 1.000 *** 1.000 *** 1.000 *** 1.000 *** 1.000 *** 0.998 *** 1
risk2009
1.000 ***
1.000 ***
1.000 ***
1.000 ***
1.000 ***
0.998 ***
1.000 ***
1
risk2008 0.998 *** 0.998 *** 0.998 *** 0.998 *** 0.998 *** 1.000 *** 0.998 *** 0.998 *** 1
risk2007 1.000 *** 1.000 *** 1.000 *** 1.000 *** 1.000 *** 0.998 *** 1.000 *** 1.000 *** 0.998 *** 1
risk2006 1.000 *** 1.000 *** 1.000 *** 1.000 *** 1.000 *** 0.998 *** 1.000 *** 1.000 *** 0.998 *** 1.000 *** 1
Source. Performed by the authors based on data provided by Amadeus database. Note: *, **, ***, represent statistically significant at 10%, 5% and 1%, respectively.
J. Risk Financial Manag. 2019, 13, 58 18 of 28
Figure 2 plots the evolution of the score values obtained through PCA from the discriminant
indices calculous by country. There are some countries which evidence a very similar behaviour like
Belgium, Czech Republic, Finland, France, Great Britain, Hungary, Portugal, Romania, Slovakia and
Germany. Another group of similar behaviour in terms of scores is that of Austria, Spain, Italy,
Croatia, the Netherlands, Poland and Sweden. The two other similar countries in terms of scores are
Ireland and Bulgaria.
Figure 2. Plot of scoring bankruptcy risk by country. Source. Performed by the authors based on data
provided by Amadeus database.
Figure 3. Plot of scoring bankruptcy risk by year. Source. Performed by the authors based on data
provided by Amadeus database.
Regarding years, the years 2006 until 2012 were very similar years in terms of score behaviour.
As such, unstable values are more observed in these years with peaks and downs, which included all
J. Risk Financial Manag. 2019, 13, 58 19 of 28
countries. In the following we decided to apply first a dynamic panel-data model by regressing the
ratio EBIT over Total Assets in the bankruptcy scoring variables by year and a probit estimation
considering as dependent variable risk and as independent variables firm performance measures.
Table 7 presents the estimation results of the panel-data model.
Table 7. Dynamic panel data results.
Dynamic Panel-Data Estimation
Wald chi2(4) 8.04
Prob > chi2 0.0901
ebitta Coef. z P > |z|
risk2014
310280
2.04
0.041
risk2013 −9.35136 −2 0.045
risk2011 −0.03367 −0.33 0.743
risk2009 −101.797 −2.08 0.038
GMM-type:
L(2/.).wcta
Source. Performed by the authors based on data provided by Amadeus database.
The dynamic panel data results indicate that the only score risk variables which have not been
omitted due to collinearity issues were the risk measures for years 2014, 2013, 2011 and 2009. The
years 2009 until 2011 are characterized by the financial crisis which has spread out through Europe,
having a negative influence over firm performance as measured by the ratio of Earnings Before
Interest and Taxes and Total Assets, but with significance only for the year 2009 at 5%.
Aleksanyan and Huiban (2016) study confirm also the dramatic increase in bankruptcy risk in
the French food industry observed over the period 2010–2012, highlighting that among food industry
sub-sectors, the meat industry was primarily responsible for the evolution of bankruptcy risk in the
period mentioned.
The years of 2013 and 2014 were years of starting recovery, and we might infer from the results
that despite the negative influence of 2013 risk score over performance, in 2014 we already have a
positive contribution of bankruptcy risk score over performance, both years with statistical
significance at 5%.
Table 8 reports the Tobit estimation results for general risk among countries, while Table 9
presents the same Tobit estimation results but this turn by country. This turn we are testing the
influence of performance measures over risk scores since we are analysing the dependent censored
variable risk.
Table 8. Tobit estimation results.
Tobit Regression: Dependent = Risk
Coef t p > t Coef t p > t
ebitta 0.00012 ** 2.05 0.041 0.00019 * 1.90 0.057
sta
0.000006
0.96
0.339
wcta 0.0001 *** 3.68 0.000
const −0.00332 −250.91 0.000 −0.00334 *** −213.66 0.000
LR chi2 4.19 LR chi2 18.9
prob chi2
0.0406
prob chi2
0.0003
Source. Performed by the authors based on data provided by Amadeus database. Note: *, **, ***
statistically significant at 10%, 5% and 1%, respectively. Ebitta = earnings before interest and taxes
(ebit)/total assets; sta = sales/total assets; wcta = working capital/total assets.
Model significance was confirmed at 5% and results seem to indicate that performance measures
positively influence risk scores. Thus the higher the performance is the higher will be the risk score
and as such bankruptcy risk decreases with performance, a result which was expected. Bankruptcy
is one of the most discussed topics in the literature, owing to its importance to the economy of any
country. Bankruptcy costs are high and authors have tried to develop bankruptcy prediction models
J. Risk Financial Manag. 2019, 13, 58 20 of 28
through years. Our scoring methodology through PCA applied to discriminant analysis of
bankruptcy risk therefore indicates that performance is the solution to decrease this risk.
Discriminant analysis of bankruptcy risk argues that positive high values of bankruptcy risk
positions companies in the safe zone, meaning a low risk of bankruptcy or a probability of bankruptcy
lower than 30% (zcc index). Lower values positions firms between the grey zones or in the distress
zone (see Section 3.2). Therefore, we may argue that for our sample of firms, these large companies
had good chances for performance provided their higher results, thus being non-bankrupt or with
lower chances to become so. However, these results depended on the year of analysis provided that
Table 7 demonstrates that 2009, 2011 and 2013 were years of negative influence of bankruptcy risk
scores over companies’ results.
Company performance variables were all statistically significant and with a positive impact over
the bankruptcy risk score in Austria, Bulgaria, Spain, Finland, Great Britain, Croatia, Ireland, Italy,
The Netherlands, Portugal, Romania, and Sweden. The ratio sales to total assets had a negative and
non-significant impact over the risk score in Belgium, Czech Republic, Hungary and Slovakia. It is
positive and non-significant in Poland and France. The only countries where performance
(independently of its measure) did not seem to exert an influence over the bankruptcy risk score were
Germany and Poland.
Table 9. Tobit estimation results by country.
Tobit Regression: Dependent = Risk
AT = Austria BE = Belgium BG = Bulgaria
Indep.
Coef
t
p > t
Coef
t
p > t
Coef
t
p > t
ebitta 0.00060 *** 39.82 0.0000 −0.00017 0.53 0.598 0.00007 *** 19.19 0.0000
sta 0.000002 *** 8.01 0.0000 −0.00002 −0.89 0.375 0.000002 *** 4.30 0.0000
wcta 0.000032 *** 36.01 0.0000 0.00018 * 1.83 0.067 0.00004 *** 15.75 0.0000
const −0.00337 *** −6822.46 0.0000 −0.00332 *** −74.97 0 −0.00337 *** −3544.58 0.0000
LR chi2 2114.45 4.50 646.96
prob chi2 0.0000 0.2126 0.0000
CZ = Czech Republic
DE = Germany
ES = Spain
Indep. Coef t p > t Coef t p > t Coef t p > t
ebitta 0.00020 *** 26.54 0.0000 0.00056 0.90 0.3710 0.00007 *** 5.04 0.0000
sta −0.00000 −0.77 0.4440 −0.00006 −1.09 0.2760 0.000003 * 1.93 0.0530
wcta 0.00004 *** 12.80 0.0000 0.00011 1.38 0.1660 0.00002 *** 3.12 0.0020
const −0.00337 *** −2204.68 0.0000 −0.00310 *** −27.57 0.0000 −0.00337 *** −1570.65 0.0000
LR chi2 3370.88 3.62 53.54
prob chi2 0.0000 0.3060 0.0000
FI = Finland FR = France GB = Great Britain (UK)
Indep. Coef t p > t Coef t p > t Coef t p > t
ebitta 0.00011 *** 19.32 0.0000 0.00102 ** 2.37 0.0180 0.00005 *** 15.28 0.0000
sta 0.000004 *** 8.10 0.0000 0.00006 1.37 0.1720 0.000003 *** 6.45 0.0000
wcta 0.00003 *** 8.97 0.0000 0.00005 1.09 0.2770 0.00003 *** 15.21 0.0000
const −0.00338 *** −2800.20 0.0000 −0.00345 *** −39.57 0.0000 −0.00337 *** −4094.65 0.0000
LR chi2 527.87 9.77 787.56
prob chi2 0.0000 0.0206 0.0000
HR = Croatia HU = Hungary IE = Ireland
Indep. Coef t p > t Coef t p > t Coef t p > t
ebitta 0.00006 *** 10.42 0.0000 0.00014 ** 2.21 0.0270 0.00008 *** 26.52 0.0000
sta 0.000004 *** 5.64 0.0000 −0.00000 −0.07 0.9450 0.000003 *** 7.28 0.0000
wcta 0.00003 *** 13.65 0.0000 0.00011 *** 4.97 0.0000 0.00003 *** 27.08 0.0000
const −0.00337 *** −3219.78 0.0000 −0.00337 *** −293.47 0.0000 0.00337 *** −5373.66 0.0000
LR chi2 476.75 34.35 1265.69
IT = Italy NL = The Netherlands PL = Poland
Indep. Coef t p > t Coef t p > t Coef t p > t
J. Risk Financial Manag. 2019, 13, 58 21 of 28
ebitta 0.00004 *** 5.25 0.0000 0.00006 *** 14.94 0.0000 0.00009 0.90 0.3710
sta 0.000004 *** 4.41 0.0000 0.000003 *** 9.42 0.0000 0.000012 1.28 0.2010
wcta 0.00004 *** 11.45 0.0000 0.00003 *** 12.46 0.0000 0.00005 1.26 0.2100
const −0.00338 *** −2531.81 0.0000 −0.00337 *** −4554.27 0.0000 −0.00339 *** 214.04 0.0000
LR chi2 247.05 428.15 6.47
prob chi2 0.0000 0.0000 0.0909
PT = Portugal RO = Romania SE = Sweden
Indep.
Coef
t
p > t
Coef
t
p > t
Coef
t
p > t
ebitta 0.00006 *** 31.90 0.0000 0.00004 *** 13.50 0.0000 0.00007 *** 30.34 0.0000
sta 0.000005 *** 20.11 0.0000 0.000002 *** 5.15 0.0000 0.000002 *** 5.92 0.0000
wcta 0.00002 *** 36.12 0.0000 0.00002 *** 10.94 0.0000 0.00003 *** 19.77 0.0000
const −0.00337 *** −0.0001 0.0000 −0.00337 *** −4030.59 0.0000 0.00337 *** −4809.04 0.0000
LR chi2 2477.79 815.74 1272.57
prob chi2 0.0000 0.0000 0.0000
SK = Slovakia
Indep. Coef t p > t
ebitta 0.00010 *** 3.40 0.0010
sta −0.000002 −0.53 0.5970
wcta 0.00006 *** 4.65 0.0000
const −0.00336 *** −539.59 0.0000
LR
chi2 52.95
prob
chi2 0.0000
Source. Performed by the authors based on data provided by Amadeus database. Note: *, **, ***
statistically significant at 10%, 5% and 1%, respectively. Ebitta = earnings before interest and taxes
(ebit)/total assets; sta = sales/total assets; wcta = working capital/total assets.
Since Germany is on the top ten of the number of corporate insolvencies, this might mean that
other corporate variables despite the ones considered here to represent performance in our analysis,
might be influencing bankruptcy risk scores under the years in analysis. The Principal Component
Analysis here employed to build a bankruptcy risk scored based on discriminant analysis indices was
found to be effective for determining the influence of corporate performance over risk. It was useful
to understand that different countries evidence different results regarding this influence, as well as
different risk scores with respect to years reveal to be different. It could be useful to understand this
impact in the future by using other scoring techniques, like data envelopment analysis, or even by
detailing years and countries analysis.
5. Conclusions
The purpose of this paper was to improve the knowledge of bankruptcy prediction of companies
and to analyse the predictive capacity of factor analysis based over discriminant analysis using five
models for assessing bankruptcy risk well-known in the literature: Altman, Conan and Holder,
Tafler, Springate and Zmijewski. We used data for non-financial large companies from Europe for
the period 2006–2015. In order to analyse the effects of risk scores over firm performance, we applied
a dynamic panel-data estimation model, with GMM estimators to regress firm performance indicator
over risk by year and we used Tobit models to infer about the influence of company performance
measures over general bankruptcy risk scores by country. In summary, results evidence that PCA
used to build a bankruptcy risk scored based on discriminant analysis indices is effective for
determining the influence of corporate performance over risk.
Results reveal a negative influence of risk scores over firm performance in the financial crisis
years of 2009–2011. However, bankruptcy risk scores increase performance (as measured through the
ratio Earnings before Interest and Taxes over Total Assets) in the upcoming years of recovery,
especially from 2014 onwards. These results were obtained by applying dynamic panel data
J. Risk Financial Manag. 2019, 13, 58 22 of 28
estimations. Afterwards, using Tobit estimations we analyze the influence of performance measures
over risk score (the variable risk was censored between three, negative and positive, inclusively). The
higher the performance the higher the risk score, meaning the lower the bankruptcy risk probability.
The scoring methodology through PCA applied to discriminant analysis of bankruptcy risk
indicators used to obtain the bankruptcy risk scores by year and country highlight that higher
performance is the solution to decrease bankruptcy risk.
Therefore, and provided that bankruptcy can be caused by poor management, improper sales
forecasting, inexperienced management, rapid technological advances, preference changes, and
inability of the firm to follow as a leader in these changes, our sample of large companies in Europe
and results obtained lead us to conclude that firms’ strategy is vital in terms of market survival. The
literature already points that better corporate governance simultaneously improve firm performance
and reduce firm risk, especially during crisis (Wang et al. 2019). Our results seem to highlight the
importance of good corporate governance as a key indicator for firm performance and lower
bankruptcy risk, with clear differences among European countries. In future works we intend to use
other scoring techniques to predict bankruptcy risk like data envelopment analysis in order to be able
to understand differences among countries and years, and to test the performance of bankruptcy
models using different risk build scores.
Author Contributions: Conceptualization, N.B.-M. and M.M.; methodology, N.B.-M. and M.M.; results and
discussions, N.B.-M. and M.M.; formal analysis, N.B.-M.; resources, N.B.-M. and M.M.; data curation, M.M..;
writing— N.B.-M. and M.M.; writing—review and editing, N.B.-M. and M.M. All authors have read and agreed
to the published version of the manuscript.
Funding: This work was funded by the project “Excellence, performance and competitiveness in the Research,
Development and Innovation activities at “Dunarea de Jos” University of Galati”, acronym “EXPERT”, financed
by the Romanian Ministry of Research and Innovation in the framework of Programme 1—Development of the
national research and development system, Sub-programme 1.2—Institutional Performance—Projects for
financing excellence in Research, Development and Innovation, Contract no. 14PFE/17.10.2018.
Acknowledgments: This work was supported by the SOP IEC, under Grant SMIS-CNSR 815-48745, no. 622/2014.
This work was in part financially supported by the research unit on Governance, Competitiveness and Public
Policy (UID/CPO/04058/2019), funded by national funds through FCT—Fundação para a Ciência e a Tecnologia.
Conflicts of Interest: The authors declare no conflict of interest.
J. Risk Financial Manag. 2019, 13, 58 23 of 28
Appendix A
Table A1. Pearson Correlation values.
Variable wcta reta ebitta bvebvtd sta rza ebitcliabil ppi curnt fs pexpenditura zcc wcta_1
wcta 1
reta −0.256 *** 1
ebitta −0.3086 *** 0.4387 *** 1
bvebvtd
0.006 **
0.002
−0.002
1
sta −0.658 *** 0.394 *** 0.404 *** −0.004 1
rza −0.029 *** 0.040 *** 0.034 *** 0.998 *** 0.055 *** 1
ebitcliabil −0.000 0.001 0.000 0.002 −0.001 0.001 1
ppi 0.008 *** 0.002 −0.002 1.000 *** −0.004 0.998 *** 0.002 1
curnt
0.008 ***
0.001
−0.002
0.778 ***
−0.003
0.777 ***
−0.001
0.778 ***
1
fs −0.002 −0.003 −0.005* 0.019 *** −0.004 0.007 *** 0.000 0.019 *** 0.013 *** 1
pexpenditura
0.002
−0.004
−0.011 ***
−0.004
0.003
−0.001
−0.000
−0.004
−0.002
−0.002
1
zcc −0.000 0.001 0.000 0.005* −0.001 0.001 1.000 *** 0.005 * −0.001 −0.000 −0.000 1
wcta_1 1.000 *** −0.256 *** −0.309 *** 0.006** −0.658 *** −0.029 *** −0.000 0.008 *** 0.008 *** −0.002 0.002 −0.000 1
ebitta_1
−0.309 ***
0.439 ***
1.000 ***
−0.002
0.404 ***
0.034 ***
0.000
−0.002
−0.002
−0.005*
−0.011 ***
0.000
−0.309 ***
ebtcl −0.000 0.001 0.000 0.002 −0.001 0.001 0.998 *** 0.002 −0.001 0.000 −0.000 0.998 *** −0.000
sta_1 −0.658 *** 0.394 *** 0.404 *** −0.004 1.000 *** 0.055 *** −0.001 −0.004 −0.003 −0.004 0.003 −0.001 −0.658 ***
zs −0.000 0.001 0.000 0.002 −0.001 0.001 0.998 *** 0.002 −0.001 0.000 −0.000 0.998 *** −0.000
pbtcl −0.000 0.001 0.000 0.002 −0.001 0.001 0.998 *** 0.002 −0.001 0.000 −0.000 0.998 *** −0.000
cat
0.008 ***
0.001
−0.002
0.821 ***
−0.003
0.819 ***
−0.000
0.821 ***
0.996 ***
0.008 ***
−0.002
−0.000
0.008 ***
clt −0.928 *** 0.246 *** 0.350 *** −0.006 ** 0.715 *** 0.033 *** −0.001 −0.009 *** 0.005 ** −0.003 0.002 −0.001 −0.928 ***
qaclspbtd 0.000 −0.002 −0.012 *** −0.008 *** 0.000 0.005* 0.000 −0.009 *** 0.000 0.000 0.000 0.000 *** 0.000
ztta 0.000 −0.002 −0.012 *** −0.008 *** 0.000 −0.005* 0.000 −0.009 *** 0.000 0.000 0.000 0.998 *** 0.000
nincomt 0.012 *** 0.354 *** 0.658 *** −0.000 0.030 *** 0.010 *** 0.001 −0.000 −0.001 −0.011 *** −0.009 *** 0.002 0.012 ***
tliat
−0.741 ***
0.341 ***
0.495 ***
−0.006**
0.824 ***
0.042 ***
−0.001
−0.006**
−0.005 **
0.000
0.002
−0.001
−0.741 ***
cac −0.002 0.001 0.001 0.001 −0.000 0.000 0.337 *** 0.001 0.000 0.000 −0.000 0.337 *** −0.002
zzzmij −0.005* 0.002 0.002 0.001 0.003 0.003 0.337 *** 0.001 0.000 0.000 −0.000 0.337 *** −0.005*
Source. Performed by the authors based on data provided by Amadeus database. Note: *, **, *** represent statistically significant at 10%, 5% and 1% respectively.
J. Risk Financial Manag. 2019, 13, 58 24 of 28
Table A2. Pearson Correlation values.
Variable
ebitta_1
ebtcl
sta_1
zs
pbtcl
cat
clt
qaclspbtd
ztta
nincomt
tliat
cac
zzzmij
wcta
reta
ebitta
bvebvtd
sta
rza
ebitcliabil
ppi
curnt
fs
pexpenditura
zcc
wcta_1
ebitta_1
1
ebtcl
0.000
1
sta_1
0.404 ***
−0.001
1
zs
0.000
1.000 ***
−0.001
1
pbtcl
0.000
1.000 ***
−0.001
1.000 ***
1
cat
−0.002
−0.000
−0.003
−0.000
−0.000
1
clt 0.350 *** 0.001 0.715 *** −0.001 0.001 −0.005 * 1
qaclspbtd
−0.012 ***
0.000
0.000
0.000
0.000
0.001
0.002
1
ztta
−0.012 ***
0.000
0.000
0.000
0.000
0.001
0.002
1.000 ***
1
nincomt
0.658 ***
0.001
−0.030 ***
0.001
0.001
−0.001
0.012 ***
−0.016 ***
−0.016 ***
1
tliat
0.495 ***
−0.001
0.824 ***
−0.001
−0.001
−0.005 **
0.727 ***
0.001
0.001
0.019 ***
1
cac
0.001
0.363 ***
−0.000
0.363 ***
0.363 ***
0.000
0.001
0.000 ***
0.000 ***
0.001
−0.001
1
zzzmij
0.002
0.363 ***
0.003
0.363 ***
0.363 ***
0.000
0.004
0.000
0.000
0.001
0.003
1.000 ***
1
Source. Performed by the authors based on data provided by Amadeus database. Note: *,**,*** represent statistically significant at 10%, 5% and 1% respectively.
J. Risk Financial Manag. 2019, 13, 58 25 of 28
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