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Organizacija, Volume 53 Issue 1, February 2020Research Papers
DOI: 10.2478/orga-2020-0006
Dependency Analysis Between Various 
Profit Measures and Corporate Total 
Assets for Visegrad Group’s Business 
Entities
Lucia SVABOVA, Katarina VALASKOVA, Pavol DURANA, 
Tomas KLIESTIK
University of Zilina, Faculty of Operation and Economics of Transport and Communications, Department of Econom-
ics, Univerzitna 1, 010 26 Zilina, Slovakia, lucia.svabova@fpedas.uniza.sk,  katarina.valaskova@fpedas.uniza.sk,  
pavol.durana@fpedas.uniza.sk,  tomas.kliestik@fpedas.uniza.sk
Background and Purpose: Models of identifying and predicting earnings management in companies by using ac-
cruals are in general based on the dependence between total assets of companies and various profit measures. In 
this paper, we focused on an initial dependency analysis between these business indicators in the Visegrad group’s 
business entities. We explore the mentioned relationships, verify, and quantify the strength of the dependencies 
between earnings levels of companies (in terms of economic evaluation of the return on business capital in absolute 
terms) and the value of their total assets (i.e. business capital tied in the assets without its further classification and 
analysis).
Methodology: We use descriptive statistics as well as a correlation analysis based on the real business data on 
almost 300 thousand companies in the V4 countries from the Amadeus database, covering the period from 2013 to 
2017. Finally, we use a comparative analysis to identify disproportion among the results that were found out for each 
of the analysed countries.
Results: The analysis showed that Slovak companies have the average values of profit measures and total assets 
comparable to Hungarian companies. Czech and Polish companies have several times higher average values of 
profit measures and also of total assets than Slovak and Hungarian companies. The analysis of the development of 
the profit measures and the total assets of the companies over the years showed significant differences across the 
four countries during the period covered by this study.
Conclusion: The analysis of relationships between total assets of the companies and their profit measures showed 
that the strength of these dependencies among countries is very similar, and over the years, these results did not 
change. The results of this study can be further used in the creation of the earnings management model in enterpris-
es, both in Slovakia and in other V4 countries.
Keywords: profit measures; total assets; earnings management; correlation
1 
Received: September 19, 2019; revised: January 29, 2020; accepted: February 15, 2020
1 Introduction
Earnings management (EM) is currently discussed but also 
controversial and at the same time very promising topic in 
the field of finance and financial management of compa-
nies (Stolowy and Breton, 2000). The main subject of this 
topic is corporate profits. Earnings management is a kind 
of management that uses accounting techniques to meet 
the executives’ needs for earnings (Chen, 2010). When 
preparing an enterprise’s financial statements, business 
managers have legal opportunities as well as the incentive 
to implement to a certain extent their own judgment and 
subjective estimates in order to satisfy their own needs 
and the needs of the company (Kral and Janoskova, 2015, 
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Organizacija, Volume 53 Issue 1, February 2020Research Papers
Kramarova et al., 2014). This consequently leads to oppor-
tunistic management reporting profits. Information gain 
value, therefore, becomes for users of financial statements 
questionable (Lev, 2018). These reasons are behind the 
fact that the topic of earnings management has become 
more prevalent in many companies and is in recent years 
in the interest of scientists and economists in several coun-
tries (Popescu Ljungholm, 2018).
The most reliable models of identifying and predicting 
earnings management are models of discretionary accruals 
(Beslic et al., 2015) Accordingly, the financial accounting 
literature has made efforts to identify the determinants 
of earnings management behaviour in various industries 
(Mellado-Cid et al., 2019). In Slovakia, no studies have 
been published directly on this topic yet and a similar situ-
ation is in neighbouring Czech Republic and Hungary. Our 
work in the future will lead to the creation of a complex 
model of earnings management for companies in Slovakia 
and also other countries, where we will deal with various 
earnings levels as model output variables and total assets 
as an input variable. This is why we focused in this study 
on companies in Visegrad Group (V4) countries and the 
main aim of this study is to explore relationships and ver-
ify dependencies between various earnings levels of com-
panies and the value of total assets of the companies in 
V4. Finding the dependencies and analyzing the mutual 
relation between profits gained and corporate total assets 
may be helpful in determining the limitations of what can 
be perceived as manipulation with earnings in specific na-
tional environments.  The contribution of the paper lies in 
the use of the real database on almost 300 thousand com-
panies in the V4 countries. The main purpose of the study 
is to quantify the strength of the dependency between the 
earnings of the companies and their total assets and to ana-
lyze the significance of these relationships. This analysis 
is based on the use of statistical methods and procedures. 
Its output provides insight into the main statistical char-
acteristics of the various profit levels and total assets of 
the companies over the years covered by this study, and 
mainly, the quantification of the strength of relationships 
among these companies’ indicators.
Our study consists of four main parts. The introduction 
highlights the main aspects of the study and a literature 
review of the current state of this topic. The second part 
briefly describes the statistical methods used in this study 
and the data used for this analysis. The third part presents 
the results of the study. The conclusion summarizes the 
results and indicates further possible directions of the re-
search in this field.
2 Literature review
As the first model to identify the presence of Earnings 
Management in companies, the Hepworth (1953) model 
is considered. The author of this study finds smoothing as 
a reasonable and wise action, serving managers to smooth 
their income by using specific means (Saeidi, 2012). 
Moreover, the author has documented various tactics that 
can be used to transfer net profit to subsequent accounting 
periods. But this study does not consider a way to detect 
this transferring of the profits. Among the first studies, 
focusing on the detection of earnings management in 
the company, belong for example the studies of Gordon 
(1964); Dopuch and Drake (1966); Archibald (1967). 
These studies are based on a time series. Gordon et al. 
(1966) were the first who used mathematical modelling 
for the testing of the balancing profit. As a breakthrough 
study on this issue, the study of Jones (1991) is consid-
ered. The author analysed the earnings management using 
a two-step model based on time series data. In the sub-
sequent years, several authors tried to modify the model 
of Jones by supplementing, omitting or modification of 
the variables, for example, Key (1997), Kasznik (1999), 
McNichols (2000), Kothari et al. (2005).
Since then, the interest of scientists in this topic has 
grown significantly. Gim et al. (2019) examined, wheth-
er franchising as a firm characteristic causes any mean-
ingful differences in the earnings management behaviour 
of restaurant firms. The results of their study show that 
franchise restaurants are generally more inclined towards 
earnings management, mainly during their growth phase. 
Mellado-Cid et al. (2019) studied the relationship between 
the stock options’ volatility and real earnings management 
in the company. The authors hypothesised that earnings 
management cause uncertainty in the value of a firm’s 
common stock and really found the association between 
them, especially in put options. The study of Liu et al. 
(2018) aimed at investigating the effects of short selling 
on a firm’s executive compensation and earnings man-
agement. Their analysis showed that the executives of the 
short-selling firms justify their excess compensation by 
improving the pay-performance sensitivity, through the 
real earnings management. The authors found that there is 
a more significant change in real earnings management in 
short-selling firms. In the study of Darrough et al. (2017), 
the authors examine whether managers shift income-de-
creasing special items to discontinued operations. They 
found that managers really tend to classification-shift as-
set write-downs to discontinued operations. Jia and Zhou 
(2019) in their study empirically examined the effect of 
cross-listing on earnings management and its economic 
consequences in companies in China. They considered 
both accrual-based earnings management and real earn-
ings management and the results of the study reveal that 
accrual-based earnings management can maintain debt 
contract efficiency and real earnings management plays a 
role in signalling better performance. Jacoby et al. (2016) 
in their study explored the relationship between corporate 
financial distress and earnings management using a sam-
ple of politically affiliated private firms in China and also 
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Organizacija, Volume 53 Issue 1, February 2020Research Papers
examined the effects of political affiliation and regional 
development on this relation. Their findings suggest that 
financially distressed firms use earnings management 
more than financially healthy firms. They also found that 
political affiliation weakens the association between fi-
nancial distress of the company and small positive earn-
ings management.
In Slovakia, but also in the neighbouring Czech Re-
public and Hungary, this topic is in the scientific com-
munity still new. Some authors from Slovakia have men-
tioned earnings management in their studies, for example, 
Saxunova (2015), Vagner (2015), Paksiova (2017). But 
their studies are not dedicated to this issue and do not deal 
with it deeper. Similarly, in the Czech Republic, there are 
several studies that mention earnings management, for ex-
ample, Prochazka (2017), Jiraskova and Molin (2015), but 
these do not focus deeply on the issue of earnings man-
agement. In Hungary, the situation is very similar. There 
are a few studies that mentioned earnings management, 
such as Markus et al. (1998), Olah et al. (2017a), Olah et 
al. (2017b), but these studies are not focused on earnings 
management directly. In Poland, the situation is quite dif-
ferent. In the past few years, several polish authors have 
been dealing with the topic of earnings management. For 
example, Lizinska and Czapiewski (2018) studied discre-
tionary accruals in Polish companies and their correlation 
with subsequent long-term market value for initial pub-
lic offerings made before the financial crisis. The authors 
consider their conclusions to be robust with respect to the 
latest innovations in proxies for the topic of earnings man-
agement. Di Narzo et al. (2018) in their study used data 
from France, Germany, Italy and the UK to investigate 
how the ability to detect earnings manipulations through 
accruals models are affected by the use of different indus-
try classifications. Their analyses showed that enlarging 
the industry classification reduces the probability of dis-
covering earnings manipulations.
3 Methodology and data
In this study, we focus on the analysis of different levels of 
corporate profits in the V4 countries and their relationship 
with the total assets of the company. In this relationship, 
the models of earnings management identification using 
the accruals are based (Fogarassy, 2018). The value of cor-
porate assets points to the total capital strength of a compa-
ny (absolute indicator); different levels of corporate profit 
used in the study are then the absolute expression of their 
profitability (absolute indicators).
For the analysis, we use data on real companies from 
the V4 from the Amadeus, a database of comparable finan-
cial information for public and private companies across 
Europe. Amadeus contains comprehensive information on 
around 21 million European companies (Bureau Van Dijk, 
2019). Overall, the dataset used in this study contains data 
about 299,355 companies. The number of companies, in-
cluded in the database, from individual countries of V4 is 
shown in the following table.
Country Frequency Percent
Czech Republic 34,970 11.7
Hungary 177,756 59.4
Poland 32,251 10.8
Slovakia 54,378 18.2
Total 299,355 100.0
We have data on companies from 2013 to 2017, which 
was at the moment of writing this study the latest possible 
data from companies’ financial statements. The database 
contains, among other characteristics of the companies, 
values of eight different profit measures (6 absolute in-
dicators, 2 ratio indicators) and the value of total assets 
of companies. Although absolute indicators have limited 
explanatory power in the financial analysis (preference is 
given to ratio indicators) (Kicova and Kramarova, 2013), 
the primary identification of the relationship between as-
sets and profit levels is a logical and de facto “gateway” 
for identifying earnings management. (Kramarova et al., 
2020)
For variables, we use the following designation and 
method of calculation, shown in Table 2. In the table, we 
provide the calculation method as it is provided by the da-
tabase Amadeus.
As a weakness of the Amadeus database, we consider 
the fact that it does not contain all the necessary data, or 
that the data it provides is very limited. Specifically, for 
example, it does not include data from all items in the prof-
it and loss account of enterprises. This fact, for example, 
limited the explanatory power of our findings in case of 
the indirect relationship between the assets and the identi-
fied loss resulting from financial activities in case of some 
countries (see Table 6). We believe that this may be related 
to using of credit forms of financing i.e. with the interest 
payment process (de facto with the price of credit forms 
of financing and the rate of their use in overall process of 
assets financing) or with reported foreign exchange losses. 
However, despite the weaknesses of the Amadeus data-
base, it is very high quality and extensive database. We 
consider the data there to be very valuable, as data from 
several countries allow international comparisons of com-
panies or the creation of international models.
In the first step of processing the database, we focused 
on the existence of outliers that might skew the results of 
Table 2: Frequencies of companies in the database
Source: Own elaboration
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Organizacija, Volume 53 Issue 1, February 2020Research Papers
the analysis. As our database is a multidimensional set, we 
focused on multivariate outliers and to identify them, we 
chose the Mahalanobis distance. This metric measures the 
multidimensional distance of each observation from the 
group centroid. The advantage of Mahalanobis distance is 
its insensitivity to the change in the scale of variables. Ac-
cording to Tabachnic and Fidell (2007), the procedure for 
detecting multidimensional extreme values is as follows. 
We will use a linear regression method, where all variables 
that we have in the database, are used as explanatory varia-
bles. Based on this, we calculate the value of Mahalanobis 
distance for each statistical unit and its realization of indi-
vidual independent variables. To verify this, we create the 
variable P_MD defined using the χ2-distribution and the 
Mahalanobis distance by
(1)
where CDFChisq is the cumulative distribution function of the 
random variable with χ2-distribution, Df is the number of in-
dependent variables in the linear regression model and MD 
is the Mahalanobis distance defined for i-th observation by
(2)
where: xi  is the i-th statistical unit values vector,    is a 
sample centroid, that is, a vector of sample means, s is a 
sample covariance matrix. The PMD  variable is used to 
identify multivariate outliers. If it holds
(3)
this indicates that the unit is a multi-dimensional outlier 
with respect to the other units in the file. A value of 0.001 
is recommended by Tabachnick and Fidell (2013). By se-
quencing the statistical file according to the decreasing Ma-
halanobis distance, we can see whether some cases tend to 
appear as multivariate outliers because its distance values 
are higher than with other companies. Companies that are 
based on this analysis proved to be multivariate outliers, 
were subsequently excluded from our database from fur-
ther analysis. These multivariate outliers were examined in 
each group of companies, defined by the country.
To characterize groups of companies in every of V4 
countries, statistical descriptive characteristics will be 
used (Popp et al., 2018). For each variable, we list the 
mean, standard deviation, median, minimum and maxi-
mum values. These characteristics can be used to compare 
groups of companies in individual Visegrad countries and 
also to monitor the development of the value of the var-
ious profit measures of companies over the years (Olah 
et al., 2019). To compare the means of profit measures of 
the companies and also means of total assets during the 
years, we conducted Friedman’s nonparametric test (Bin 
et al., 2018). This test is an alternative to the ANOVA test 
Table 2: Variables in the database
Variable Label
PLBT Profit / loss before tax = Operating profit + financial profit
PL
Profit/loss for period = Net income for the Year. Before deduction of Minority interests if 
any (Profit after taxation + Extraordinary and other profit).
PLAT Profit / loss after tax = Profit before taxation - Taxation
OPPL
EBIT (Earnings before interest and taxes, Operating profit/loss) = All operating revenues - 
all operating expenses (Gross profit-Other operating expenses)
EBTA
EBITDA (Earnings before interest, taxes, depreciation and amortization charges) = Opera-
ting profit + Depreciation
FIPL
Finacial profit/loss = (Financial revenue-Financial expenses) = (All financial revenues such 
as interest, incomes from shares, etc.) – (All financial expenses such as interest charges, 
write-off financial assets)
ETMA EBIT margin = (EBIT / Operating revenue) * 100
EBMA EBITDA margin = EBITDA / Operating revenue) * 100
TA Total assets = Total assets (Fixed assets + Current assets)
Source: Own elaboration
x
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Organizacija, Volume 53 Issue 1, February 2020Research Papers
for related samples. Friedman’s test is also an alternative 
to the Wilcoxon’s test. Wilcoxon’s test serves in case of 
two related samples and Friedman’s test is suitable if there 
are at least 3 related samples. This test detects whether the 
median values of profit measures and also total assets of 
companies in individual years (and in the V4 countries) 
differ significantly.
To analyze the dependencies between profit measures 
and total assets of the companies, we use Pearson’s cor-
relation coefficient, which is a measure of the linear rela-
tionship between two quantitative variables. In determin-
ing the degree of dependence, we will proceed from the 
following scale (Ratner, 2009): 
•  weak correlation, if 0<|r|<0.3,
•  moderate correlation, if 0.3≤|r|<0.7,
•  strong correlation, if 0.7≤|r|<1.
In addition to the value of the correlation coefficient, 
we always list the p-value of the test of its statistical sig-
nificance, based on which we identify the correlations 
between total assets and various profit measures that are 
statistically significant and the ones that are not (Valaskova 
et al., 2018).
4 Results
Analysis of the presence of multivariate outliers in files 
has shown that some enterprises are significantly different 
from other enterprises in a given country by their values. 
We defined the variable PMD  according to equation (1), 
while the Mahalanobis distance was calculated according 
to (2). Subsequently, under rule (3), we have identified for 
every company whether it is considered to be a multivar-
iate outlier or not. This analysis showed the numbers of 
multivariate outliers in each country (Table 3). The per-
centage of excluded companies in each country is between 
2.1% and 3.9%.
After this analysis, 291,426 companies remained in 
our database, 7,934 companies were marked as multivari-
ate outliers. Outlying companies account for 2.72% of all 
companies. These companies were excluded from further 
analysis.
In the next step, we focused on the descriptive char-
acteristics of various profit measures as well as their total 
assets. These characteristics were calculated separately for 
companies in each country and separately for each of the 
years 2013 to 2017. For brevity, we present the character-
istics of all the variables of companies in individual coun-
tries in 2017 (Table 4).
Next, we compared individual profit measures, as well 
as total assets of the companies, over the years 2013 to 
2017 using available data. We verified the existence of sig-
nificant differences between the median values of variables 
using the Friedman test with the null hypothesis that there 
were no significant differences between the median val-
ues of variables over the years. Table 5 shows an example 
of the output of Friedman’s test for the total assets of the 
companies in Slovakia.
We performed this test for each of the profit measures 
as well as for total assets. The results of all tests in all 
countries and for all variables are the same: the mentioned 
zero hypothesis was rejected, so that values of profits and 
total assets of companies in V4 countries have changed 
significantly over the years. 
Furthermore, we analysed the relationship between 
profit measures and the total assets of the companies. The 
strength of the linear dependence between the individual 
profit measure and the total assets of the companies was 
quantified using the Pearson’s correlation coefficient (Tret-
yak, 2018). Moreover, the value of the correlation coeffi-
cient is always supplemented by the p-value of its signif-
icance test with the null hypothesis that this relationship 
is statistically insignificant. The values of the correlation 
coefficients together with the p-values of their significance 
tests are shown in Table 6.
For brevity, we again present only outputs from this 
analysis from 2017. For the other years included in the 
study, the results of the correlation analysis are very simi-
lar. We used color highlighting of the correlation degrees in 
the table. Using the red data bars we marked the compari-
son of the correlation strengths for each profit measure (i.e. 
the correlation coefficient between total assets and the giv-
en profit measure among the V4 countries is compared in 
each row of the table). In this way, we compared individual 
profit measures among the countries. At the same time, we 
used cell coloring and font colors to indicate the strength 
of the dependence. We used a three-stage scale, where: the 
yellow color indicates a weak linear dependence (i.e., a 
correlation coefficient of (-0.3;0.3)); green indicates mod-
erate dependence (i.e. correlation coefficient (0,3;0,8) or 
(-0.8;-0.3), respectively) and finally, red indicates strong 
dependence (i.e. correlation of (0.8;1), or (- 1;-0.8)). In this 
way, we can immediately visually determine the strength 
of dependence between a given profit measure and the to-
tal assets of companies in a given country.
According to the p-value of the significance tests of 
the correlation coefficient (always the second row of the 
profit indicator in Table 6 above), we can see that the linear 
dependence between EBITDA margin and total assets in 
2017 is insignificant. Also, the linear relationship between 
EBIT margin and total assets is insignificant for Slovak 
companies, but according to other correlation coefficients 
we see that even in other V4 countries, linear relationships 
in this pair of variables are weak. Other correlations are 
statistically significant. Depending on the values and signs 
of the correlation coefficients themselves, we can assess 
the strength of linear relationships between profit measure 
and total assets, as well as the direction of this dependence. 
From 2013 to 2016, which we also analysed in this 
study, the results of the correlation analysis are very simi-
lar. For the sake of brevity, we do not list them in full.
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Organizacija, Volume 53 Issue 1, February 2020Research Papers
Table 3: Frequencies of multivariate outliers
Country
non-outliers outliers Total
Frequency Percent Frequency Percent Frequency Percent
Czech Republic 33,620 96.1 1,350 3.9 34,970 100.0
Hungary 173,982 97.9 3,774 2.1 177,756 100.0
Poland 31,052 96.3 1,199 3.7 32,251 100.0
Slovakia 52,767 97.0 1,611 3.0 54,378 100.0
Source: Own elaboration
Table 4: Characteristics of profit measures (in thousand EUR) of companies in 2017 in V4 group countries
C
ou
nt
ry
 / 
Va
-
ri
ab
le
 2
01
7
P/
L 
be
fo
re
 ta
x
P/
L 
fo
r 
pe
ri
od
P/
L 
af
te
r 
ta
x
E
B
IT
E
B
IT
D
A
Fi
na
nc
ia
l P
/L
E
B
IT
 m
ar
gi
n
E
B
IT
D
A
 
m
ar
gi
n
To
ta
l a
ss
et
s
C
ze
ch
 R
e-
pu
bl
ic
Min -23458.3 -23444.4 -23444.4 -24138.8 -22085.7 -24006.3 -93.1 -99.6 0.0
Max 170884.7 138677.5 138677.5 131839.4 215999.5 39045.3 100.0 97.3 1037890.0
Mean 264.1 214.0 213.7 281.0 444.2 -16.9 15.4 7.9 3898.7
St. Dev 1432.6 1184.5 1184.4 1311.5 1901.2 372.9 21.0 17.1 12901.8
H
un
ga
ry
Min -25561.5 -25562.9 -25562.9 -9058.6 -8446.2 -25546.2 -99.2 -100.0 0.0
Max 431594.5 408991.0 408991.0 444994.2 515849.1 19948.0 100.0 99.6 546026.1
Mean 51.5 47.4 47.4 52.7 75.4 -1.1 14.4 8.5 577.5
St.Dev 1274.3 1221.4 1221.4 1272.9 1543.0 106.6 23.2 22.3 3101.3
Po
la
nd
Min -19446.1 -20380.0 -20380.0 -29470.5 -15492.8 -24746.3 -89.3 -99.1 1.2
Max 106013.2 83675.7 82890.3 109203.5 377481.5 46636.7 99.7 91.4 14449806.6
Mean 388.5 330.3 330.2 410.1 628.4 -21.5 9.1 5.8 6471.1
St. Dev 1593.1 1354.5 1337.6 1590.8 3072.4 452.8 13.5 12.0 86371.2
Sl
ov
ak
ia
Min -13866.9 -13869.8 -13869.8 -13731.0 -4100.9 -5244.4 -96.4 -99.9 0.1
Max 101206.0 79512.0 79512.0 98113.0 156003.0 9462.1 100.0 98.7 1578716.0
Mean 64.0 47.5 47.6 71.9 126.8 -7.8 16.8 7.4 1131.3
St.Dev 638.5 483.8 484.4 636.8 910.9 80.8 22.1 19.7 8397.9
Source: Own elaboration
Hypothesis Test Summary
Null Hypothesis Test Sig. Decision
The distributions of Total assets 2017, Total assets 2016, 
Total assets 2015, Total assets 2014 and Total assets 2013 are 
the same
Related Samples Friedman’s 
Two-Way Analysis of Variance by 
Ranks
0.000
Reject the null 
hypothesis
Table 5: Friedman’s test for homogeneity of medians of total assets of companies in Slovakia
Source: Own elaboration
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Organizacija, Volume 53 Issue 1, February 2020Research Papers
Table 6: Correlations of profit measures and total assets of the V4 enterprises in 2017
Source: Own elaboration
5 Discussion and conclusion
In the study, we focus on the analysis of various profit 
measures of companies in Visegrad group countries in the 
years 2013-2017. We examined the characteristics of indi-
vidual profit measures, their differences within V4 coun-
tries, their evolution over time and their relationship with 
total assets of the company. We based on generally known 
premise that the effective use of a capital tied in compa-
ny assets, in other words, the effective use of assets, is a 
prerequisite for generating corporate profits, while corpo-
rate profits we quantified mainly as absolute values of the 
various profit measures (e.g. EAT, EBT, EBIT, EBITDA 
etc.). The findings point to the fact that the greatest capital 
strength (on average) was characteristic for Polish com-
panies in all analysed years. On the contrary, the smallest 
amount of capital tied in assets was found in the case of 
Hungarian enterprises, again for all periods analysed.
The analysis of average values of profit measures 
among the countries showed that Slovak companies have 
all these values comparable to Hungarian companies 
and at the same time several times lower than Polish and 
Czech companies. Polish companies achieve the highest 
average profit measures. Similar results were for the total 
assets of the companies, where the lowest average value 
is achieved by Hungarian companies, followed by Slovak 
companies. Again, the companies in the Czech Republic 
and Poland have several times higher average values of 
total assets. Based on this we deduce that on average com-
panies in the Czech Republic and Poland are characterized 
by a higher capital strength than companies in Slovakia 
and Hungary (the origin of capital in terms of ownership 
was not examined in the study). We also looked at whether 
the individual profit measures and the total assets of the 
companies were significantly different over the years in-
cluded in this study. Using Friedman’s test, we found that 
all of the companies’ profit measures differed significantly 
across the four countries during the whole period 2013-
2017 included in this study.
Subsequently, the analysis of relationships between to-
tal assets of the companies and individual profit measures 
showed that the strength of dependence among the V4 
countries is very similar, and over the years, these results 
did not change. Total assets of the companies are over-
whelmingly the most correlated with EBIT, EBITDA and 
then with Profit / loss before tax, Profit / loss for period 
and Profit / loss after tax. Negative correlation is only be-
tween Financial profit / loss and total assets of companies 
in Hungary and Slovakia, although according to the value 
of the correlation coefficient, it is a very weak correlation. 
However, this negative correlation is closely related to the 
fact that the mean values of this variable were negative 
for companies in all four countries (Table 4). This may be 
caused by the character of the financial profit/loss, which 
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Organizacija, Volume 53 Issue 1, February 2020Research Papers
is calculated as a difference between the financial revenues 
(e.g. sale of securities, financial operations) and financial 
expenses (e.g. paid interests for loans, exchange losses). 
Due to the underdeveloped capital market of most of the 
V4 countries, it is evident that in most companies the fi-
nancial expenses exceed the financial revenues causing 
the negative mean values of this variable. However, like 
any other profitability ratio, also the financial profit/loss 
provides important information as it quantifies the level of 
financial profit an enterprise may generate considering the 
level of corporate total assets. Thus, the impact of the fi-
nancial profit/loss indicator on the sign of correlation with 
the Total assets of the companies should be more focused 
on further research.
In the case of Slovak companies, except the relation-
ship between Financial profit / loss and EBIT margin with 
Total assets, there are always strong correlations between 
individual profit measure and total assets of the compa-
nies. The weakest correlated are profit measures and Total 
assets of the companies in Poland.
If we focus on the strength of dependence between in-
dividual profit measures and Total assets, we can conclude 
that total assets have the following linear relationship with 
profit measures:
• in the Czech Republic: 
• a weak linear relationship with Financial profit 
/ loss, EBIT margin and EBITDA margin;
• a moderate linear relationship with Profit / loss 
before tax, Profit / loss for period and Profit / 
loss after tax;
• a strong linear relationship with EBIT and 
EBITDA. 
• in Hungary: 
• a weak linear relationship with Financial profit 
/ loss, EBIT margin and EBITDA margin;
• a moderate linear relationship with the other 
profit measures.
• in Poland: 
• a moderate linear relationship with Financial 
profit / loss;
• a strong linear relationship with EBITDA;
• a weak linear relationship with the others.
• in Slovakia: 
• a weak linear relationship with Financial profit 
/ loss, EBIT margin and EBITDA margin;
• a strong linear relationship with the other profit 
measures. 
If we compare between the countries, we find that in 
the case of significantly correlated profit measures, the 
strongest dependencies are found in Slovakia, followed 
by the second Czech Republic. We would call these rela-
tionships very similar and indicate stronger to moderate 
dependencies between total assets and other profit mea-
sures (with the exception of the last two/three profit mea-
sures in Table 5, for which a linear relationship is very 
weak). Slightly weaker relationships were quantified in 
Hungarian enterprises and weakest are the correlations in 
Polish enterprises. In this comparison, Poland is the only 
country that differs from the other three V4 countries, and 
this applies to the Total assets of enterprises with all profit 
measures except EBITDA. This result follows the conclu-
sions in Svabova et al. (2019), where it was found that the 
average EBITDA of enterprises, except from the couple 
Czech Republic - Poland, differs significantly in each pair 
of V4 countries, which also could have an impact on our 
differences in correlations with the Total assets of corpo-
rations. The mentioned study also found that in the Finan-
cial profit/loss, except for the couple Poland - Slovakia, 
the average values are not significantly different across 
countries. Again, this may affect the strength of the rela-
tionship of this profit measure with the total assets of en-
terprises identified in this study. In the case of the Finan-
cial profit/loss, this correlation is similar across countries, 
with the exception of Polish companies. Concretely, in the 
case of the Financial profit/loss measure, Polish compa-
nies show the strongest (namely, moderate and the only 
one positive) correlation with total assets. This is followed 
by Hungarian companies where this relationship is weak 
and indirect. In this case, the Czech and Slovak companies 
achieve also similar results, where the relationship is very 
weak and indirect. From this mathematical measure of de-
pendence between financial profit/loss and total assets, it 
can be concluded that a change in one indicator has only a 
small (negative) effect on the change in the other. Howev-
er, it should be borne in mind, that the Pearson correlation 
coefficient is only a measure of the linear dependence be-
tween variables. In this case, the dependence may be of a 
different, more complex type, which also follows from the 
economic interpretation of the profit measures.
The source for these similarities and also differences 
among the values of earning levels of the companies in 
V4 countries arises not only from the economic situation 
in these companies but also from the economic situation 
in the countries. Recalling the main indicators of the V4 
countries, for example, the annual rates of change in GDP 
in 2017 were similar in all V4 countries, varying from 3.4 
% in Slovakia to 4.6 % in Poland. Moreover, the distribu-
tion of gross value added by the section is very similar in 
all four of these countries. The annual rate of change in 
the volume of industrial production and volume of retail 
trade turnover was also similar in 2017. The volume of ex-
port and import was very similar in Slovakia and Hungary 
and also in the Czech Republic and Poland, the volume of 
import was very similar (Main Indicators of the Visegrad 
Group Countries, 2018) The difference of Polish enter-
prises from other countries can also be partly explained by 
the fact that in Poland the publication of accounting data 
is voluntary for enterprises. Thus, only those enterprises 
that voluntarily provided this data are included in the sam-
ple, not all enterprises, as in the other three countries. The 
88
Organizacija, Volume 53 Issue 1, February 2020Research Papers
calculated average values, and related other values, may, 
therefore, be biased by the effect of this self-selection.
All the results described were interpreted from the val-
ues calculated from the 2017 indicators. Similarly as in 
the year 2017, in the years 2013 to 2016, which we also 
analyzed in this study, the correlation analysis showed that 
the Total assets of the company are the weakest correlat-
ed with Financial profit / loss, EBIT margin and EBITDA 
margin. At the same time, the weakest correlation of profit 
measures with Total assets are mainly in Polish compa-
nies.
To summarize, all the results obtained from the char-
acteristics of individual profit measures as well as total 
assets, the comparison of their values over the years and 
the correlation analysis between profit measures and to-
tal assets will be further used for the development of an 
econometric model of dependence, showing the connec-
tion between variables and for the creation of the earnings 
management model in enterprises, both in Slovakia and in 
other V4 countries.
One of the limitations of the study is given by the re-
cording and recalculating policy of the financial indicators 
in the Amadeus database - among the V4 countries, only 
Slovakia uses Euros as the official currency. Other coun-
tries, Poland, Hungary, and the Czech Republic still use 
their national currencies (zloty, forint, and Czech crowns). 
The values are converted into Euros using the exchange 
rate for the conversion. However, this exchange rate has 
changed over the years. The year-on-year comparison of 
absolute rates is therefore also affected by this exchange 
rate and its changes over the years, which was not tak-
en into account when determining the significance of the 
differences in this study. At the same time, we consider 
the weakness of this study to be the fact that in the case 
of correlation analysis, we did not take into account the 
existence of relationships between individual profit mea-
sures. In our next study, we would like to focus on the 
relationships between individual variables and then the re-
lationships between total assets and profit measures of the 
companies quantified using partial coefficients of multiple 
correlation. Also, the negative (indirect) result of the lin-
ear relationship between total assets and Financial profit / 
loss should be further examined.
Acknowledgement
This research was financially supported by the Slovak Re-
search and Development Agency – Grant NO. APVV-17-
0546: Variant complex model of Earnings management in 
conditions of Slovak republic as an essential tool of the 
market uncertainty.
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University of Zilina (Slovak Republic). Research 
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statistical methods, counterfactual impact evaluation, 
financial derivatives.
Katarina Valaskova, (Ing., PhD) is an Assistant 
Professor at the Department of Economics, 
Faculty of Operation and Economics of Transport 
and Communications, University of Zilina (Slovak 
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management, consumer policy and artificial intelligence 
such as fuzzy logic, neural networks.
Pavol Durana, (Ing., PhD) is an Assistant Professor 
at the Department of Economics, Faculty of Operation 
and Economics of Transport and Communications, 
University of Zilina (Slovak Republic). Research 
interests: quality, management, economic applications 
of statistics.
Tomas Kliestik, (prof., Ing., PhD) head of the 
Department of Economics, Faculty of Operation 
and Economics of Transport and Communications, 
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econometric modelling.
91
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Analiza odvisnosti med različnimi merami dobička in skupnimi sredstvi podjetij za poslovne subjekte Više-
grajske skupine 
Ozadnje in namen: Modeli določanja in napovedovanja upravljanja dobička v podjetjih z uporabo nastanka poslov-
nega dogodka na splošno temeljijo na odvisnosti med celotnim premoženjem podjetij in različnimi profitnimi mera-
mi. V tem prispevku smo se osredotočili na začetno analizo odvisnosti med temi kazalniki poslovanja v poslovnih 
subjektih držav Višegrajske skupine. Preučujemo omenjena razmerja, preverjamo in količinsko opredelimo moč 
odvisnosti med stopnjami dobička podjetij (v smislu absolutnega ekonomskega vrednotenja donosa na poslovni 
kapital) in vrednostjo njihovega skupnega premoženja (tj. poslovnega kapitala, povezanega v sredstva brez njegove 
nadaljnje razvrstitve in analize).
Metodologija: Uporabljamo opisno statistiko in korelacijsko analizo, ki temelji na dejanskih poslovnih podatkih o 
skoraj 300 tisoč podjetjih v državah V4 iz baze Amadeus, ki zajemajo obdobje od leta 2013 do 2017. Primerjalno 
analizo smo uporabili za ugotavljanje nesorazmerje med rezultati, ki so bili ugotovljeni za vsako analizirano državo.
Rezultati: Analiza je pokazala, da imajo slovaška podjetja povprečne vrednosti mer za dobiček in skupna sredstva, 
primerljiva z madžarskimi podjetji. Češka in poljska podjetja imajo nekajkrat višje povprečne vrednosti mer za do-
biček in tudi celotno premoženje kot slovaška in madžarska podjetja. Analiza razvoja profitnih ukrepov in celotnega 
premoženja podjetij v letih je pokazala pomembne razlike v štirih državah v obdobju, ki ga zajema ta študija.
Zaključek: Analiza razmerij med celotnim premoženjem podjetij in njihovimi profitnimi ukrepi je pokazala, da je moč 
teh odvisnosti med državami zelo podobna, z leti pa se ti rezultati niso spreminjali. Rezultate te študije je mogoče 
nadalje uporabiti pri oblikovanju modela upravljanja zaslužka v podjetjih, tako na Slovaškem kot v drugih državah V4.
Ključne besede: ukrepi za dobiček, bilančna vsota, upravljanje dohodka, korelacija