AD ALTA
JOURNAL OF INTERDISCIPLINARY RESEARCH
countries around the world. Vochozka and Machová [24] also
used the EVA indicator to determine the value of individual
transport companies and subsequently also to identify their value
generators with a possible prediction of future developments.
In the Czech Republic, quarterly surveys of selected indicators
are carried out, the evaluation of which can be used to determine
the value of a particular company and thus compare its standing
in comprison with the entire economic sector in which it
operates. One of these indicators, which is commonly used, is
the EVA indicator [25].
Based on the knowledge gathered so far about the EVA
indicator, it is clear that the use of this indicator is the right step
to achieve the set goals of the paper.
3 Materials and Methods
The input data for the analysis will be taken from Bisnode's
Magnusweb database. These will be the financial statements of
leasing companies operating in the Czech Republic. According
to the classification of economic activities CZ NACE, this is
section "N" (administrative and support service activities). Data
from subgroup 771100 (rental and leasing of cars and other light
motor vehicles, except motorcycles) will be used. The data will
be from the period 2005-2019. This time frame was be chosen
taking into account the presence of a major global economic
crisis and the subsequent developments after its end. Due to this,
a fluctuation in the number of active leasing companies can be
expected. The data file will therefore contain in individual years
the data of leasing companies which have been in liquidation for
any reason and which have ceased to exist in this year or at the
end thereof. The database also contains different levels of detail
of available information from the financial statements of specific
leasing companies, and therefore the highest levels of detail of
financial statements for each leasing company contained in the
database will always be used for individual calculations.
First, the data will be broken down by year. To refine the
calculation, companies whose return on equity (ROE) will be
outside the range of <-100%; 100%> will be removed.
Companies with indebtedness outside the range of <0 %; 200%>
will also be removed. Furthermore, the data of companies that
have meaningless negative values in their economic indicators
will be deleted. This is data on the amount of bank loans and
advances, total assets, interest expense, inventories and
liabilities. The data will also be adjusted for information that is
not relevant to the calculation of the EVA Equity and EVA
Entity indicators and the data needed to calculate the individual
steps. Therefore, only relevant data will remain. Specifically, the
remaining data will be the year of the financial statements, the
economic result for the accounting period, equity, income tax for
ordinary and extraordinary activities, interest expense and
borrowed capital. Furthermore, companies for which EVA
Equity and EVA Entity indicators would not be calculated due to
missing data in these input datasets will be removed from the
source data.
Furthermore, it will be necessary to supplement the data obtained
from the Magnusweb database with other publicly available data.
This will be risk-free income, which will be taken from the
information portal of the Czech National Bank (CNB). Data on the
yield rate of ten-year government bonds will be worked with
specifically. The yield values of ten-year government bonds for the
years 2005–2019 are shown in Table 1.
Table 1: Yield on ten-year government bonds from 2005–2019
according to the Maastricht criterion in %
Year
Risk-free yield [%]
2005
3.61
2006
3.77
2007
4.68
2008
4.3
2009
3.98
2010
3.89
2011
3.7
2012
1.92
2013
2.2
2014
0.67
2015
0.49
2016
0.53
2017
1.5
2018
2.01
2019
1.51
Source: Czech National Bank [26] (Author’s interpretation).
Furthermore, the data will be supplemented by the values of the
risk premium for the examined years. This data will come from
the website [27]. The values of the risk premium from 2005–
2019 for the Czech Republic in % are given in Table 2.
Table 2: Risk premiums for 2012–2015 in %
Year
Risk premium [%]
2005
1.2
2006
0.9
2007
1.05
2008
1.05
2009
2.1
2010
1.35
2011
1.28
2012
1.28
2013
1.28
2014
1.05
2015
1.05
2016
1.11
2017
1
2018
0.81
2019
0.98
Source: http://pages.stern.nyu.edu/~adamodar/ [27] (Author’s
interpretation).
Last but not least, for the final completion of the data file, data
on the size of the
β unlevered indicator will also be taken from
the same source. Specifically, these will be sets of data from the
financial services sector (excluding banking and insurance) for
the years 2012-2019. The values of the indicator
β unlevered are
given in Table 3.
Table 3:
β unlevered values for the years 2012–2019 in %
Year
β unlevered [%]
2012
0.11
2013
0.14
2014
0.26
2015
0.12
2016
0.13
2017
0.11
2018
0.18
2019
0.16
Source: http://pages.stern.nyu.edu/~adamodar/ [27] (Author’s
interpretation).
The values of the parameter
β unlevered for the years 2005-2011
will subsequently be derived on the basis of the values of the
indicator
β unlevered determined [27] for the USA due to the
absence of this data. The determination of
β unlevered values
will be carried out for each of the years in the period of 2005-
2011 according to the following formula (formula no. 1):
í µí»½ í µí±¢í µí±›í µí±™í µí±’í µí±£í µí±’í µí±Ÿí µí±’í µí±‘ í µí°¸í µí±ˆ
í µí±¦í µí±’í µí±Ží µí±Ÿ í µí±‹
=
í µí»½ í µí±¢í µí±›í µí±™í µí±’í µí±£í µí±’í µí±Ÿí µí±’í µí±‘ í µí°¸í µí±ˆ
í µí±¦í µí±’í µí±Ží µí±Ÿ í µí±‹âˆ’1
∗ í µí»½ í µí±¢í µí±›í µí±™í µí±’í µí±£í µí±’í µí±Ÿí µí±’í µí±‘ í µí±ˆí µí±†í µí°´
í µí±¦í µí±’í µí±Ží µí±Ÿ í µí±‹
í µí»½ í µí±¢í µí±›í µí±™í µí±’í µí±£í µí±’í µí±Ÿí µí±’í µí±‘ í µí±ˆí µí±†í µí°´
í µí±¦í µí±’í µí±Ží µí±Ÿ í µí±‹âˆ’1
(1)
Where:
β unlevered EU
year X
β unlevered EU
is the
value of
β unlevered for the EU in the specific year,
year X-1
is the value of
β
unlevered for the EU in the previous year,
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