Formation of business portfolios as risk management tooi

Transcription

ISSN 1822-7996
TAIKOMOJi EKONOMIKA:
SISTEMINIAI TYRIMAI: 2007.1
Kristina LEVISAUSKAITE,
Asta PRANCKEVICtUTE
Formation of business portfolios as risk
management tooi
We model the capacities' of o apply a portfolio theory in the risk management of business units. As corporation become large-scale, in order to manage such structures, it is necessary to apply the portfolio theory and
asses diversification level in such structure.
Keywords: diversification, business portfolio, risk
Siame straipsnyje mes modeliuojame portfelio teorijos laikymo galimybes siekiant valdyti verslo rizik^. Verslo stambejimas pletojasi ir valdant tokias strukturas reikia taikyti portfelio teorij^ ir vertinti diversifikacijos
iygi tokiose strukturose siekiant tikslingai valdyti rizik4.
Raktiniai zodiiai: diversifikacija. versly portfelis, rizika.
JEL Classifications: G32/G34/L25/P34
Introduction
Business organizations operating in a fastchanging economy inevitably encounter
risks. A competitive and rapidly changing environment necessitates disposal of
various competencies and opportunities.
Because of this reason companies quite often employ means of mergers and acquisitions, thus accumulating critically important competencies from different business
branches and companies.
Essentially, business combinations are
formed in pursuance of planned positive
economic results. Participants expect to
receive better results than those that they
would receive if they did not enter a business combination. Some of the causes that
are most frequently given for the formation
of business combinations are division of
risks (Nielsen, Mahnke, 2000; Pablo, 1999),
opportunity to use the market (Holm,
Malberg, Solvell, 2003; Zaman, Mavondo,
1999), competition for priority (Zaman,
Mavodo 1999) as well as immediate and
flexible ways of utilizing resources (Pablo,
1999; Zaman, Mavodo 1999 and others).
Before compiling a portfolio of business
combinations it is necessary to appropriately choose partners, to assess the risk and
its management models; in other words, it
is relevant to examine the principles on the
Kristina L E V I S A U S K A I T S - Dr., Prof.. Head of Department of Finance. Faculty of Economics and Management, Vytautas Magnus University. Address: Daukanto st. 28 - 207, Kaunas 44246, Lithuania. Tel: + 370 37 32
78 54. Fax: + 370 37 32 78 57. E-mail: k.levisauskaitei@)evf.vdu.lt
Asta PRANCKEVlClUTE ~ PhD student. Department of Finance, Faculty of Economics and Management,
Kaunas University of Technology. Address: Laisves av. 55 - 407. Kaunas 44246, Lithuania. Tel: + 370 37 30 05
60. Fax: -t- 370 37 30 05 60. E mail: [email protected]
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Kristina LEVISAUSKAITE, Asta PRANCKEVICIUTE
basis of which the portfolio of businesses
should be formed, and how it should be
managed. When constructing a portfolio
of businesses it is necessary to evaluate the
effectiveness and mutual compatibility of
each portfolio component, rejecting those
compotients that only increase probability
of failure of the businesses' combination.
The risk of business combiiiations is
treated in many different ways. In literature, studying business risks and portfolios of business combinations, various
management structures are provided with
difiêrent numbers of structural elements
indicated, functional relations among the
elements differ and not all main features
of risk management are assessed. This
reveals that no uniform concept of the
model for business portfolio risk management has been formulated that would
enable to successfully manage the risk of
the businesses portfolio. The majority of
definitions and concepts from literature
on analysis of financial portfolios can be
parallelly applied in the context of business portfolio management.
Harry M. Markowitz (1952), the originator of the Modern Portfolio Theory
(MPT), formulated the problem of portfolio as the selection of measures and of
a portfolio of different asset types, which
takes advantage of the effect of the reciprocal relationship of return on assets
seeking to diversify portfolio risks (Elton,
Gruber, 1997). William F. Sharpe (1963,
1970), a student of Markowitz, applied the
MPT concept outside the boundaries of
original financial asset management, asserting that the portfolio theory is related
to decisions involving results that cannot
be foreseen with absolute certainty, that
it is necessary to evaluate the uncertainty
and that reciprocal relationships among
the results must be managed accurately. In
the opinion of authors, an analogical reasoning can be used in order to show, how
the MPT can serve the systemic business
portfolio management.
The analogical reasoning requires taking into consideration whether the MPT
aspects are transferred meaningfully to
the context of portfolios of businesses. For
example, management of a combination
of businesses, as a type of asset, differs
from that of shares and bonds. A financial investor rarely can influence the performance results of individual bonds and
shares; however, he/she seeks to optimize
the portfolio's performance results by
combining financial assets and establishing their purchase and sale dates. Similarly, managers of businesses' combinations
can optimize the relation between income
and risk of their portfolio by combining
several businesses and by reasonably selecting the times for the commencement
of a new business or for the termination of
a non-productive business. Nevertheless,
beside the mentioned advantages, companies can actively influence perforrnance
results in pursuance of a synergy between
separate businesses. Finally, in those cases
when monetary return from financial investments is easily assimilated, recording
income from combinations of businesses
is more difficult, for instance, because the
liquidity of such portfolio of businesses is
much smaller.
In spite of these differences there exist strong similarities that allow for proper
application of this reasoning. According
to the MPTs risk diversification logics,
"the reduction, minimizing and sharing
of uncertainty in performance" is distinguished as the main motive for companies
to involve themselves in a combination of
businesses. Eventually, managers of portfolios of both types seek to utilize the re-
FORMATION OF BUSINESS PORTFOLIOS AS RISK MANAGEMENT TOOL
ciprocal dependence between results. Reciprocal dependetice of results in financial
portfolios helps to diminish the return
volatility, while the reciprocal dependence of results between combinations of
businesses helps to reduce the risk and to
create a synergy.
The Modern Portfolio Theory plays a
significant role in directing itivestors' attention to the demand for risk management. Investors need to be capable of
assessing risks by means of systematic
and quantitative methods, and of using
acceptable risk assessment methods; in
Lithuania there is no gradual transition
to performance results coordinated with
risks. Therefore, here remain big differences between theoretical principles on
portfolio investments and practical methodology. One of the main ways how these
differences manifest themselves lies in the
contrast between the market method recommended by the theory and the practitioners' focus on sectors and the individual selection of businesses.
To date, the studies on opportunities
for application of the portfolio theory in
the management of combinations of businesses are rather scarce, but it is evident
that realities of business development
should encourage research in this direction.
Research object - solutions for risk
management of business combinations.
Research objective - to analyze and
evaluate how risks are taken into consideration in business combinations in
Lithuania at a branch level.
Tasks:
- To define the concept of portfolio
and its application in risk management.
- To define the concept of business
combinations and aspects characteristic
to it.
- To characterize the diversification
of business combinations by branches of
industry where Lithuanian companies
participate.
Hypothesis: Principles of investment
portfolio theory can be adapted to the
formation of a portfoho of business combinations.
Diversification principles of
business risk and its management
Risk theory existed long before the creation of the Modern Portfolio Theory
(MPT). It is relevant to discriminate between "risk" and "uncertainty". The concept "risk" is appropriate even when casual
events are studied, for which it is possible
to give a certain probability value for the
occurrence of various consequences under different conditions of environment.
To the contrary, uncertainty is usually applied to conditions where it is impossible
to clearly assess the distribution of such
probability. J. M. Keynes (1935) considered risk as a deviation from average income and believed that risk costs should
be paid to investors who assume the risk.
This resembles the concept of "risk premium" in the Modern Portfolio Theory.
In literature, inconsistency is usually
studied as a risk measure, speaking about
changes, or as an iticonsistency coefficient.
However, although J. Tobin (1958) studied risks by analyzing not more than two
securities, only H. M. Markowitz (1959)
was the first who clearly distinguished between the risk of securities and the risk of
portfolio. H. M. Markowitz demonstrated
that the influence of individual selections
on the total risk of portfolio is important.
In other words, attention must be directed
to the impact of combinations of securi-
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34
Kristina LEVISAUSKAITE, Asta PRANCKEVI6IUTE
ties. This early innovatory work led to the
emergence of "market models" of diverse
form, most of which were created by W.
F. Sharpe (1964), J. Lintner (1965) and E.
Fama (1970). Both J. Lintner and E. Fama
studied "the general essential market factor that affects the return on all assets".
One of the most important motives
for formation of business combinations
is the opportunity for risk diversification.
In acquisitions and mergers of companies, diversification is understood as an
acquisition of an enterprise operating in
another economic field and possessing
uncorrelated cash flows, i.e. larger sales
when the sales of the acquiring company
decrease and vice versa. This is called cash
flow stabilization.
Diversification benefit is conditioned
by three key factors: number of risk positions; concentration of these risk positions
or their respective weights in a portfolio;
correlation between the positions. Generally, diversification benefit increases given
the larger number of positions and decreases given a larger concentration, and
also decreases given a larger correlation.
As the number of positions increases given a certain correlation level, the diversification proportion decreases - i.e. diversification benefit increases. Given a certain
number of positions, the diversification
benefit increases as correlation decreases.
When discussing portfolio diversification, focus is on such formation of a
portfolio that enables to reduce the risk
without diminishing profitability This is
exactly the goal that the investor aims at.
The modern portfolio theory enables to
determine quantitatively the diversification level with the aim to obtain maximum benefit.
Economic logics of diversification
is related to the internal distribution of
capital: if the resources can be transferred
from low-profitability projects to highprofitability projects utilizing internal resources with lower costs than via capital
markets, then diversification can be effective (Engin, Matsusaka, 2005).
It is relevant to understand diversification not only because it represents innovations of an organization, which is a necessity for large companies (Montgomery,
1994), but also because diversification is
frequently evaluated as ineffective, which
conditions management problems more
than value maximization (Jensen, 1986;
Stein 2003).
One of the main subjects of modern
financial research relates to the fact that
there are cases when managers seek their
personal goals by means diversifying
business, thus imposing extra expenses
on shareholders (Jensen, 1986).
Some authors (Lang, Stulz, 1994; Stein,
2003) assert that diversification reduces
value because diversified companies are
trading with discount in comparison with
companies in a separate segment. Other
performed research studies assert that
"Diversification Discount" can be a cause
rather than a consequence (Matsusaka,
2001; Gompa, Kedia, 2002; Burch, Nanda,
Narayanan, 2003; Villalonga, 2004), or a
discount can be a static artifact resulting
from the nature of a segment. Still, other
research studies infer that conglomerates are neither more nor less productive
than specialized enterprises (Maksimovic,
Phillips, 2002; Schoar, 2002). Nevertheless, in the study published in 2005 authors Mechmet Engin Akbulut and John
G. Matsuaka deny the statements by
these authors. Their performed research
analyzed 3667 mergers within the last 55
years. They came to the conclusion that
during the analyzed period the perform-
ance of conglomerates was positive in respect of value creation.
S. X. Li and R. Greenwood (2004)
maintain that diversification inside a
branch gives the following advantages to
enterprises: synergy arising from economy of scales, premium as a result of the
take-over of competence and effectiveness derived from market structuring.
Market structuring is a process according
to which separate market niches are developed from the beginning into related
market niches with the established supporting infrastructure.
Antitrust policy during 1950 - 1970
played a decisive role in the decisiontaking process regarding diversification.
Due to the pressure of antitrust policy it
became complicated for large enterprises
to merge in the same branch of economy.
However, the enduring inclination to
merge encouraged the search for partners
in unrelated businesses, thus determining
diversification (Engin, Matsusaka 2005)
and the development of conglomerate
business structures.
Effectiveness of diversification of conglomerates was especially strong up to
1980, when utilization of capital market
was insufficient. Nevertheless, after the
situation in the market had changed, internal capital distribution, as a measure
of diversification eltectiveness, started to
decrease.
For investigation of diversification effects in business combinations it is logical to apply the diversification method of
the portfolio theory. Portfolio diversification is such formation of a portfolio that
enables to reduce risk without diminishing profitability. It is exactly the goal that
investor aims at. Just as financial portfolio management helps preventing from
market vicissitudes, portfolios of business
combinations help to understand the risks
and opportunities of businesses managed
at the same time. The majority of definitions and concepts from literature on the
analysis of financial portfolios can be applied parallelly in the context of business
portfolio management.
W. E Sharpe (1963, 1970) applied the
MPT concept outside the boundaries of
original financial asset management, asserting that the portfolio theory is related
to decisions involving results that cannot
be foreseen with absolute certainty, that
it is necessary to evaluate the uncertainty
and that reciprocal relations between the
results must be managed accurately.
Managers of the structures of business
combinations can optimize the relation
between income and risk of their portfolio
of businesses by combining several businesses. However, beside the mentioned
advantages, companies can actively influence performance results in pursuance
of a synergy between the relationships of
separate businesses.
Parameters developed for measuring
diversification of enterprises have become
most important to the wide-spectrum
strategy research. Entropy-related component and concentration index are affected
by the properties of the composition of
the enterprises portfolio, which may have
no immediate relation to the cohesion of
the portfolio.
Entropy-related index is usually calculated using NACE (National Activities
Classification of Economic) data. Measurement of coherent diversification is
important to a wide-spectrum strategy
research. Parameters developed in the
analysis of strategy for enterprises' portfolio are used in many research studies on
strategy, economics and finance economics. Studies on strategic management em-
35
36
ployed diversification parameters as independent variables, dependent variables
and control variables, when investigating
such subjects as restructuring, change in
direction, management, merger of companies, sale of assets, strategic changes
(Barker, Duhaime, 1997; Bergh, 1997).
Diversification parameters were especially important in works correlating portfolio strategy with financial effectiveness.
These parameters were important
in earlier studies on diversification additionality (Bettis, 1981; Rumelt, 1974,
1982; Wernerfelt, Mongtomery, 1988) and
they were very important when analyzing "diversification discount", which was
recorded after having determined that
oriented firms operate more effectively
than much diversified companies (Berger,
Ofek, 1995; Lang, Stulz, 1994).
Under the influence of these studies,
certain types of parameters have become
part of the standard instrumentation of
empirical research. Gontinuous parameters of coherent diversification, such as
entropy- and concentration indexes, are
especially widely used in current research
(Barker, Duhaime, 1997; Bergh, 1997).
These indicators are popular because of
various reasons, including also the fact
that they are easily derived from secondary data and they can be measured at the
internal level.
Parameters do not record precisely
the same aspects of portfolio strategy. Although they were frequently considered
as an alternative method helping to solve
the problem of measuring coherent diversification, parameters can produce contradictory results since they react differently
towards the key aspects of portfolio strategyDiversification parameters can be affected by shortcomings related to the use
of the NAGE system (Robins, Wiersema,
1995). Parameters are formed in such a
manner that they are not of one dimension since they reflect two or more different structural parts in one parameter.
The modern concept of coherent diversification has been developing almost
for three decades. Although R. Rumelt's
(1974) innovatory w o r k - Strategy, Structure and Economic Performance - does
not use the term "diversification", it draws
attention, first of all, to the idea that internal relations of a portfolio can be analytically separated from questions regarding
the price and advantages of diversification.
This modern attitude towards coherent diversification can be generalized by
the idea that "the foundation of multibusiness organizations is based on the
division of strategic capacities among
businesses. In absence of divided strategic assets of the firm itself it is likely that
the company will operate worse than the
totality of separate businesses (Robins,
Wiersema, 1995). This idea lays the common foundation for the modern concept
- the idea that a portfolio of businesses
is compiled into one by sharing strategic
resources and capacities. The parameter
that is used as reasonable for the research
of such type must record this general element of "cohesion".
Application and measurement of diversification and cohesion has fallen behind the theory by important aspects. Diversification parameters that were created
in earlier decades for different research
purposes are usually used for analysis of
cohesion in modern studies.
It is important to mention that
Markowitz's diversification strategy gives
focus, first of all, to the co-variation level of the profitability of portfolio assets.
Markowitz's essential merit is that he was
the first who looked at portfolio risk as
the risk of the undivided totality of investments, and not as the risk of separate
units forming the portfolio. This strategy
is oriented to maximally reduce the risk
at the given profitability level, selecting
such assets among which the correlation
between profitability levels would be least
positive.
Markowitz's diversification strategy
stipulates that as the correlation (covariance) of assets profitability increases, variation grows, which means that the standard deviation of the profitability of this
portfolio also grows. Positive effect manifests itself given the negative correlation
of the expected profitability of assets. The
investor can reduce portfolio risk maintaining its expected yield by coordinating
assets with low (expected negative) correlation. The investor's task is to choose
such assets that the portfolio cotnpiled
37
from them would have minimum risk at
the given profitability level, or, vice versa,
would be the highest profitability at the
given risk level.
Research data and methodology
Data
We use firm level data collected by public
institution "Business register". The overall
sample consists of 11 firtns in 2002 and 13
firms in the period 2003-2005. These data
contain income statement and balance
sheet information. We analyzed Financial
key ratios of enterprises: annual financial
indicators for 2002-2005: assets, equity,
net profit to assets (ROA), and net profit
to equity (ROE).
In this study, we also use data from
Department of Statistics. Data are presented on the national level by economic
Table 1
The structure of Business portfolio by economic activity
1
Manufacture of footJ products and beverages (Nacecode: 15)
NACE (2
digit level)
15
2
Manufacture of fooit products and beverages (Nace code: 15)
15
157
1571
3
Manufacture of food products and beverages (Nace code; 15)
15
15
1500
4
Manufacture of food products and beverages (Nace code; 15)
15
155
1552
5
Manufacture of food products and beverages (Nacecode; 15)
Recreational,entertainment,cultural and sporting activities
(Nace code: 92)
15
151
1511
92
922
9220
No.
6
Enterprice present the sector
NACE (3
digit level)
157
NACE (4
digit level)
1571
7
Hotels and reslauraiits (Nace code; 55)
55
553
5530
8
Other business activities (Nace code: 74)
74
746
7460
9
Real estate acliviUes (Nace code: 70)
70
702
7020
50
501
5010
22
220
2200
12 Construction (Nace code: 45)
45
453
4533
13 Other business activities (Nace code; 74)
74
741
7414
Sale of motor vehicles and motorcycles, their maintenance
10
and repair; retail sale of automotive fiiel (Nace code; 50)
Publishing, printing and reproduction of recorded media
11
(Nace code; 22)
38
activities. We analyzed Financial key ratios of sectors: annual financial indicators
for 2002-2005: assets, equity, net profit
to assets (ROA), and net profit to equity
(ROE).
Sectors by economic activity are presented in accordance with the national
version of the Glassification of Economic
Activities (NAGE), at 2 - digit level.
For calculation entropy and concentric
index we use kinds of enterprise grouped
by economic activity in accordance with
the national version of NAGE, at 2 - digit
level, 3 - digit level and 4 - digit level.
Sample of enterprise classification is
shown in Table 1.
Research methodology
In order to identify relationship between
a corporative diversification strategy and
a concentration in the main branch of
company's business, we have applied the
diversification measurement parameters,
which specify a diversification level in relation to the main business branch and a
group.
We performed our empirical research
of businesses' portfolio in Lithuanian
company in the period of 2002/05. A list
of the selected data consists of 11 companies' research in 2002 and 13 companies,
belonging to the group since 2003/05,
when various financial indicators and
their correlation with the same indicators
within the same industry branch were
being researched each sample year. This
group of companies has been selected for
the analysis because they are operating in
different business branches. The data samples are being employed in the analysis of
our diversification level, when a prevailing business concentration influence and
an entropy index are being assessed. The
four most frequently utilized portfolio
variety evaluation measures - general entropy, related entropy and non-related entropy as well as concentration index - are
being employed for the evaluation of the
company's diversification level.
By calculating the whole entropy, related and non-related entropy as well as
concentration indices, businesses' parts
are being determined pursuant to sales
volumes.
The diversification level is being assessed by a connectivity component entropy index, which has been derived by
dividing the whole entropy into the related and non-related ones (Jacquemin,
Berry, 1979). An aggregate entropy (DT)
is being calculated:
(1)
where P. - business portion (sales
volumes) NAGE (Gomparative Economic
Activities Glassificator) code "i", for a corporation with "N" different 4-figure level
NAGE businesses.
The non-related entropy (DU) is being
calculated in the same manner, only employing 2 level figure code NAGE.
(2)
f^l
where P - business portion (sales volumes) NACE code "i", for a corporation
with "N" different 2-figure level NAGE
businesses.
The related entropy (DR) may be calculated in the following manner:
DT-DU=DR
(3)
Further the concentration index is
being assessed, which is being calculated
pursuant to (Mongomery and Hariharan,
1991) recommendations
1=1
A=l
Where:
P^^ ^company's "k" sales percentage in "i" industry
branch
Py= company's "k"sales percentage in "1" industry
branch
d^j = variables' weight factor, when d =0, where I
and 1 belong to the same 3-ligiire NACE calegory, d j
=1, where 1 and 1 belong to the same 2-figure NACE
category, but to (he different 3-figure NACE group,
and dî - 2 , where I and 1 belong to a different 2-figurt- NACE category.
The concentration index is based on
an assembly of all the businesses' pairs,
which has been accumulated from the
selected portfolio. A multiplication member is being established by multiplying an
activity portion in each of the two equal
businesses' pairs. Ponderability has been
granted to the multiplication of pairs and
the total sum of the multiplications with
ponderability is the concentration index.
The two characteristic major portfolio
structure aspects are being analyzed in
this research: a number of businesses in
the portfolio and a relative range of a prevailing business. These aspects, being employed for the purposes of this analysis,
are called "pure diversification" and "prevailing business orientation". Though they
are not the only characters of the portfolio
structure, which may influence the related
diversification indices, they are of utmost
importance, because they serve as the very
major company's portfolio characteristics.
In fact, any change in the company's strategy may have an impact upon the pure
diversification and the prevailing business
orientation or upon both of them.
The correlation of the businesses' assets (ROA) and equity (ROE) in terms of
profitability growth as well as the indicators of the same industry branches are
being further assessed in the businesses'
portfolio. By assessing the assembly of
businesses as a portfolio, not single investment, the methodology on the investment
theory portfolio risk assessment is being
applied.
In the first case the businesses' portions are being specified pursuant to the
assets size and in the second one - pursuant to the size of own capital. In the first
case the expected profitability is equalized
to the average of assets profit (ROA) during the analyzed period, in the second
one - own capital profit (ROE) during the
analyzed period.
By accumulating hypothetical portfolios, the businesses' portions in a portfolio
are left as in the case of real portfolios. In
the first case, the expected profitability is
equalized to the average of the appropriate industry branch assets profit (ROA)
during the analyzed period, meanwhile
in the second case - to the average of the
appropriate industry branch own capital
profit (ROE) during the analyzed period.
Correlation is being employed for the
risk assessment. A correlative ratio measures a linear mutual-directional movement of two dimensions. By researching hypothetical portfolios, the industry
branches, classified according to NACE,
are being used in this research, to which
the investigated companies belong, by
employing the two-figure code level assets profit (ROA ) correlation in the first
case, and equity profit (ROE) correlation
in the second one. As well as the correlation among specific portfolio comprising
companies in terms of assets profit (ROA)
in the first case and in terms of the equity
profit (ROE) in the second one.
In order to assess the risk of our analyzed portfolio, leverage separate businesses' risk co-variation - standard deflections
39
40
and leverage relations among businesses
- is being calculated. Our analyzed portfolio risk is being calculated pursuant to
this mathematical equation:
Where: 5p - portfolio standard deflection;
6p^ - portfolio dispersion;
w and w - comparative loads of the respective instrumental i and j values in the total portfolio estimation;
6|ând 6^ - respective instrumental i and j profitability dispersions;
Cov - respective instrumental i and j profitability;
The expected profitability of our available portfolios is being calculated on the
above-given sequence as a leverage average of the expected profitability from
separate investment instruments, assessing a comparative load of each instrument
in the total portfolio value (Sharpe et al.,
1999)
n
(6)
Where: E(R,) - the expected revenues of an investment instrument i during the specified period;
w -a portion of an investment instrument i value in
the total portfolio estimation, when 2^'^' = I
n - a number of investment instruments in a port-
Research outcomes
Establishing business units, Lithuanian
businessmen diversify the profitability growth risk, encompassing companies
from the different industrial branches into
their businesses' portfolio; consequently,
reducing the dependency upon the fluctuation of the profitability growth within
the same business branch. Obviously,
business units, consisting only of Lithuanian industrial companies, are closely
related to Lithuanian economic growth
rates and the risk, associated with it. Tlie
outcomes are being presented in the further chapters of the research that have
been obtained by assessing diversification
level with the help of the entropy indices
and the concentration index.
Diversification assessment
(entropy, concentration index)
We can claim that a component related
to entropy index could be distinguished
by a distinct positive sensitivity to a pure
diversification; in fact, it measures the
diversification related to higher levels, after increasing a number of businesses in
a company's portfolio. However, the pure
diversification, in fact, does not affect the
concentration index in the same manner.
More careful investigation of parameters indicates that a composition of each
index develops possible sensitivities to the
pure diversification. These are only possible sensitivities, because they are based on
a few premises about the companies' portfolios. Though, generally, these premises
are substantiated, it is also quite possible
that a certain group of real companies
may deviate considerably from these typical models.
The pure diversification (a number
of businesses in a company's portfolio)
may affect the whole entropy parameters:
common, related, and non-related. A
component related to the entropy index
is sensitive to three different aspects of
distribution in the company's portfolio:
4-figure NAGE businesses number in the
company's portfolio, 2-figure NAGE businesses number in the company's portfo-
Ho, 4-figure NACE categories distribution
among the 2-figure NACE categories.
Ihe common entropy shall have a positive relation to the pure diversification, if
all the other values remain the same. It is
easy to observe by investigating the case,
when all the businesses in the portfolio
are of equal range.
Thus, the entropy is an increasing logarithmic function of the businesses number
in the portfolio in the case, if all the businesses are of equal range. However, in our
analyzed case, the businesses included in
the portfolio are of different range. Table 2
presents our obtained outcomes by measuring the diversification level.
As it is clearly seen in Table 2, the related entropy was the largest in 2002, the
same as the dominating business size,
meanwhile the number of businesses in a
portfolio was the lowest in the same year
It is also seen that the concentration index
was the largest in 2002.
Possible sensitivities of these parameters to the dominating business orientation are seen in the changes - fluctuation
of the dominating business range. The
changes in two parameters show the essential difference in their sensitivities to
the dominating business orientation. The
concentration index, in fact, is sensitive
to the orientation and the availability of
large-scale dominating business increases
41
the connectivity calculations. On the other hand, the calculations, based on the related entropy index, in fact, reduce, when
a relative dimension of the dominating
business increases. The reduction in the
connectivity entropy calculation has been
more related to the increase of the dominating business range than to the reduction in the pure diversification.
'Ihe concentration index may also
be sensitive to the dominating business
orientation. The index is affected only
by distribution of 3-figure and 2-figure
NACE business activities. Consequently,
the dominating business orientation will
affect on the scale as the 4-figure NACE
business range correlates with the 3-figure or 2-figure NACE business range. As
it has been showed in the description of
the entropy index above, it is reasonable
to maintain a premise about certain sort
of correlation between the relative dimensions of the corresponding 4-figure
or 3-figure NACE codes. In most cases a
weaker relation may be specified between
the 4-figure and 2-figure NACE activities.
In this case, the entropy index related
component would be a direct logarithm of
the 4-figure NACE businesses' number in
each 2-figure NACE division. If a company simply increased 4-figure businesses'
number in its portfolio and did not re-arrange its activity in 2-figure level, the enTable 2
Diversitication level In Business portfolio
2002
2003
2004
2005
Related cnUopy (DR)
1,1660
1,0391
1,0601
l,tO45
Dominant busines.s size
0,0000
0,0000
0,0000
0.0000
11
13
13
13
Total entropy (DT)
1,5952
1,5698
1,5331
1,4826
Unrelated entropy (DU)
0.4292
0,5306
0,4730
0,3780
Concentric index
0,0067
0,0044
0,0052
0,0052
Number of bu.siness in portfolio
42
Table 3
Business portfolio diversification parameters correlation
Unrelated
entropy
(DU)
Concentric index
-0,896
-0,999
0,936
-0,895
-0,998
0,937
1,000
0,833
0,984
-0,973
-0,895
0,833
1,000
0,919
-0,683
-0,999
-0,998
0,984
0,919
1,000
-0,916
0,936
0.937
-0,973
-0,683
•l],916
i,000
Related
entropy
(DR)
Dominant
business
size
Related entropy (DR)
1,000
1,000
Number of
business in
portfolio
-0,992
Dominant business size
1,000
1,000
-0,992
Number of business in portfolio -0,992
-0,992
Total entropy (DT)
-0,896
Unrelated entropy (DU)
Concentric index
tropy related component would increase.
Besides, the company would achieve larger connectivity with the larger number of
smaller 4-figure businesses, than the one
with the lower number of businesses, if the
2-figure level structure remains constant.
Such outcomes may be expected in many
cases, when 2-figure NACE businesses are
not equal in their ranges and numbers.
Table 3 gives us a correlation matrix,
where the correlation ratios among the
above described parameters are being reflected.
Table 3 shows that the dominating
business and the related entropy correlate
most positively. Significantly positive relations also connect the concentration index and the related entropy as well as the
Total entropy (DT)
concentration index and the dotninating
business size. The most negative correlation relations exist between the non-related entropy and the related entropy as well
as the number of businesses in a portfolio
and the related entropy.
The structures of the analyzed businesses in a portfolio, prepared according
to out selected different criteria, are being
presented in the further portions of the
research.
Portfolios pursuant to companies' own
capital and a hypothetical portfolio
pursuant to own capital
The first portfolio was accumulated pursuant to the size of companies' own capi-
• Co mpanyClN •cccode: ]»
• Compsnv D ^ N I H
• Cnmpiny F (NscFcoie. l^)
• CompsmfilNK
• Company n t N icecode 92)
• Cnmpftnv r (N cecode 55)
• Comparry L f^ ics code- .W)
• CompnrpTiO NV-ecnde T4)
DLrOmpiny J TNir ccnde 74)
• romp.iTyK(N»ceeode 70)
O CompBny MfKicccode 22)
y Company N (N ICC Civf« 4S)
1?)
Q (. [hnpoFiv t (Nfli Ecode 15)
Fig. 1 Portfolio structure pursuant to companies' own capital
• S«bv N <Nii» code « )
D SMnr I-O ( N K * code 74)
• aeanr L ( X B H oode: .tO)
Fig. 2 Hypothetical portfolio structure pursuant to
companies' own capital
tai. A structure of this portfolio is being
presented in Fig. 1., Fig. 2 presents a hypothetical portfolio, accumulated pursuant to load ratios, and comparing them
with the ones, obtained from the companies, representing particular industrial
branches, and connecting the companies
from the same industrial branch in the
portfolio.
43
The largest portion in our analyzed
and hypothetical portfolios makes up the
companies, operating in the food industry.
Table 4 presents the standard deflections
of our analyzed portfolio, load ratios, the
average companies' own capital profitability (ROE) during the analyzed period, as
well as the portfolio average own capital
profitability (ROE), assessing load ratios
and the portfolio risk of each company.
As it is seen from Table 4, the largest load ratios in the portfolio are at the
companies, operating in the food industry
(Nace code: 15). Their portion in the portfolio makes up 87.70%. Company L (Nace
code: 50) respectively has larger portions
- 7.37% representing the sales of engine
motor vehicles and motor-bikes, technical
supervision and repair; motor fuel retail
trade industrial branch and company K
(Nace code: 70) - 4.35% representing the
real estate transactions industrial branch.
After assessing respective companies' porTable 4
Indicators of the porttolio, consisting of companies' own capital
Variable
Std Dev.
Mean
0,026
Company D (Nace code: 15)
Company E (Nace code: 15)
('ompany F (Nace code: 15)
Weight
Company ROB average
W
Eri
0,018
20,02%
5,53%
0,027
0,025
10,47%
2,97%
-0,013
0,056
7.00%
7,98%
0,025
0,067
10,63%
-2,60%
Company G (Nace code: 15)
-0,009
0,018
39,57%
1,20%
Company H (Nace code: 92)
-0,038
0,157
0,17%
-12.36%
Company! (Nace code: 55)
-0,887
1,938
-0,09%
-39,29%
Company ] (Nace code: 74)
0,145
0,304
0.11%
-19,44%
Company K (Nace code: 70)
-0,002
0,014
4,35%
-0,43%
Company L (Nacecode: 50)
-0,021
0,544
7,37%
35.84%
Company M(Nace code: 22)
0,012
0,200
-0,07%
2,32%
Company N (Nace code: 45)
0,068
0,235
0,47%
-3,06%
Companyn O (Nace code: 74)
-0,018
14,910
-0,02%
-366,95%
Total
100,00%
Company C (Nace code; 15)
Portfolio ROE (Erp)
2,20%
Portfolio risk
5,02%
Company C (Nace code:
15)
Company D (Nace code:
15)
Company E (Nace code:
15)
Company F (Nace code:
15)
Company G (Nace code:
15)
Company H (Nace code:
92}
Company I (Nace code:
55)
Company J (Nace code:
74)
Company K (Nace code:
70)
0,768
0,940
0,164
-0,539
0.457
0,999
-0,506
0,492
0,502
0.123
-0,997
0,940
-0,803
-0,557
-0,590
0,730
-0,539
0,768
-0,918
0,438
-0,995
0,958
-0,304
-0,341
0.890
-0,983
0,230
-0,993
0.997
0,476
0,510
-0,789
0,931
-0.669
-0,698
0,623
-0,820
-0,038
-0,078
0,979
VO
Ov
Ov
O
U
o
t
u
n
a.
Company M(Nace code:
-0,038
22}
Company N (Nace code:
-0,926
45}
Company O (Nace code:
0.980
74)
-0.967
-0.953
0.880
-0.236
0,413
0,730
-0,274
0,920
-0,993
0,161
-0,995
-0,590
0,413
0,449
-0.830
0.955
-0,342
0,438
-0.557
-0,997
-0,993
-0,240
-0,047
-0,918
-0.803
0,123
-0,959
0,955
-0,993
0,920
-0,274
-0,047
0,161
-0,342
0.958
-0.993
0,230
-0,983
-0,830
-0.240
-0,959
0,997
0.998
-0,407
0,931
0,449
-0.993
0,162
-0,967
-0.953
0,610
-0,982
-0,236
0,880
0,980
-0,820
-0.926
-0.038
Company O
(Nace
code:
74)
-0,996
Company N
{Nace
code:
45)
Company
M(Nace
code:
22)
Company L
(Nace
code:
50)
VO
O
0,610
0,164
0,457
-0,506
0,680
-0,532
0,318
0,851
0,999
0,492
0,502
0.976
-0.999
0,980
0,881
0,125
0,890
-0,341
-0.304
0,680
0.976
0,125
-0,789
0,476
-0,532
-0,999
-0,983
-0.972
-0,983
0,623
-0,698
-0,669
0,318
0.980
0,912
-0.972
0,912
0,964
-0,897
0,768
0,979
-0,078
-0,038
0,851
0,881
0,964
-0,897
0,768
Company K
(Nace
code:
70)
Company J
(Nace
code:
74)
o
866*0
Company H
(Nace
code:
92)
Company I
{Nace
code:
55)
Company G
{Nace
code:
15)
Company F
(Nace
code:
15)
Company E
(Nace
code:
15)
Company D
(Nace
code:
15)
OlS'O
Company C
(Nace
code:
15)
44
fS
00
Ov
45
Table 6
Hypothetical portfolio, made of the industrial branches pursuant to capital
Variable
Mean
Std Dev.
Weight
W
Sector ROE average
Eri
ScctorC+D+E-HF+G(NBcecode: 15)
0,037
0,028
87,70%
7,68%
Sector H (Nace code: 92)
0,020
0,135
0,17%
10,35%
Sector I (Nace code: 55)
0,055
0,117
-0,09%
0,45%
Sector I-t-O (Nace code: 74)
0,032
0,017
0,09%
10,55%
Sector K (Nace code: 70)
0,024
0,045
4,35%
4,70%
Sector L (Nace code: 50)
0,031
0,018
7,37%
9,45%
Sector M(Nace code: 22)
0,058
0,054
-0,07%
10,55%
Sector N (Nace code: 45)
0,051
0,065
0,47%
17,45%
Total
100,00%
Portfolio ROE (Erp)
7,73%
Portfolio risk
3,59%
tions in the portfolio, the portfolio profitability is 2.20%, meanwhile the portfolio
risk is 5.02%.
Table 5 presents the portfolio, made of
the companies' own capital profitability
correlative matrix.
As it is seen from Table 5, even the
companies, representing the same industry branches, have very different mutual
correlation indices.
Table 6 presents standard deflections
of our hypothetical portfolio, load ratios, compared with the ones from the
companies, representing these industrial
Table 7
Hypothetical porttolio industrial branches' mutual correlation
Sector
Sector
Sector
Sector
Sector
Sector
Sector
Sector
C+D+E+F+G H (Nace 1 (Nace I+O (Nace K (Nace L (Nace M(Nace N (Nace
(Nace code: 15) code: 92) code: 55) code: 74) code: 70) code: 50) code: 22) code: 45)
Sector
C+D+E-^P-^G
(Nace code: 15)
Sector H (Nace
code: 92)
Sector I (Nace
code: 55)
Sector I+O
(Nace code: 74)
Sector K (Nace
code: 70)
Sector L (Nace
code: 50)
Sector M(Nace
code: 22)
Sector N (Nace
code; 45)
1
-0,58028 0,99947
-0,58028
-0.41494
0,81171
0,38351
0,57735
0,59411
1
-0,60637 -0,50022
0,00466
-0,97469
-0.99999
######
0,99947
-0,60637
I
0,79235
0,41324
0,60351
0,61987
-0,41494
-0,50022
-0.38523 1
-0,86822 0,68115
0,50332
0,48535
0,81171
0,00466
0,79235
-0.86822
1
-0,22811
-0,00825
0,01243
0,38351
-0,97469 0,41324
0,68115
-0,22811
1
0,97548
0,97073
0,57735
-0,99999
0,60351
0,50332
-0,00825 0,97548
1
0,99979
0.59411
-0,99985 0.61987
0.48535
0,01243
0,99979
1
-0.38523
0,97073
46
branches, the average industrial branch
own capital profitability (ROE) during the
analyzed period, as well as the portfolio
average own capital profitability (ROE), after assessing the load ratios and the portfolio risk in each industry branch.
The largest load ratios in the portfolio
are in the food industry (Nace code: 15).
Its portion is 87.70% in the whole portfolio. Respectively larger portions are from
the sales of engine motor vehicles and
motor-bikes, technical supervision and
repair; motor fuel retail trade industrial
branch (Nace code: 50) - 7.37% and the
real estate transactions industrial branch
(Nace code: 70) - 4.35%. The portfolio
profitability is being calculated after assessing respective portions of an industry
branch in the portfolio in comparison
with their portions at the representing
companies in the portfolio and this figure is 7.73%, meanwhile the portfolio risk
reaches 3.59%. In this case the hypothetical portfoho risk is lower than the profitability.
Table 7 presents the hypothetical portfolio, made of industrial branches' own
capital profitability correlative matrix.
Table 7 shows a little bit different situation than in the previously analyzed
portfolio. In this case there is an especially positive (+0.9979) correlative relation between the own capital profitability
in the construction industry (Nace code:
45) and pubUshing, printing and recorded files dissemination industrial branch
(Nace code: 22). Also between the food
industry (Nace code: 15) and hotels and
restaurants (Nace code: 55). This figure is
0.99947
A significantly negative correlative (0.99985) relation exists between the construction industry (Nace code: 45) and
the travel organization, cultural and sport
7.73%
Porlfolio risk
Fig. 3. Comparison of the actual and hypothetical
portfolio risks and profit ratios
activities industry (Nace code: 92). Also
between (-0.99999) the travel organization, cultural and sport activities industry
(Nace code: 92) and publishing, printing
and recorded files dissemination (Nace
code: 22) industrial branch own capital
profit ratios.
Fig. 3 presents the comparison between the actual business portfolio and
the hypothetical portfolio, consisting of
own capital, profitability as well as the risk
level.
The businesses' portfolio risk is 1.43%
larger than our hypothetical portfolio;
meanwhile the profitability is lower by
5.53%.
Portfolio pursuant to companies' assets
Our third portfolio is composed on the
basis of the size of the companies' assets.
Fig. 4 presents a structure of this portfolio.
Fig. 5 presents a hypothetical portfolio,
composed on the basis of the load ratios,
compared with companies' load ratios,
representing particular industry branch-
Fig. 4. Portfolio structure pursuant to companies' assets
es and connecting the portions from the
same industrial branches in the portfolio.
of our analyzed portfolio, the load ratios, the average companies' assets profitability (ROA) during the analyzed period,
the portfolio average assets profitability
(ROA) afi:er assessing the load ratios in
each industry branch and the portfolio
risk.
The companies, representing the food
industry (Nace code: 15), have the largest ratios in the portfolio. Their portion is
88.11% in the whole portfolio. Company
L (Nace code: 50) respectively has larger
portions - 6.75% representing sales of
engine motor vehicles and motor-bikes,
technical supervision and repair; motor
fuel retail trade industrial branch and
company K (Nace code: 70) - 4.38% representing real estate transactions industrial branch. After assessing respective
companies' portions in the portfolio, its
profitability is 1.36, meanwhile the portfolio risk is 1.52?/o.
Table 9 presents the portfolio companies' assets profitability correlative matrix.
Even the companies, representing the
same industrial branches, have very dif-
FiQ. 5. Hypothetical portfolio structure pursuant to
companies' assets
ferent mutual correlative ratios.
of our hypothetical portfolio from various
industrial branches, the load ratios compared with the ones from the companies
within the same industrial branches, the
average industrial branches' assets profitability (ROA) during the analyzed period,
the portfolio average assets profitability
(ROA) after assessing the load ratios in
each industry branch and the portfolio
risk.
The food industry (Nace code: 15) has
the largest load ratio in the portfolio. Its
portion is 88.11% in the whole portfolio.
47
Kristina LEVISAUSKAITE, Asta PRANCKEVI6IUTE
48
Table
Portfolio indicators, composed on the basis of the companies' assets
Weight
Company ROA average
Variable
Mean
Std Dev.
Company C (Nace code: 15)
0,020
0,014
14,54%
4,02%
Company D (Nace code: 15)
0,023
0,021
6,87%
2,48%
CômpanyE (Nace code: 15)
-0,013
0,026
13,60%
3,54%
Company F (Nace code: 15)
0,019
0,046
7,64%
-1,81%
Company G (Nace code: 15)
-0,006
0,011
45,45%
0,69%
-11,89%
Eri
W
Company H (Nace code: 92)
-0,038
0,152
0,10%
Company I (Nace code: 55)
0,322
0,997
0,13%
3,65%
Company J (Nace code: 74)
-0,097
0,221
0,08%
-14,64%
Company K (Nace code: 70)
-0,001
0,008
4,38%
-0,25%
Company L (Nace code: 50)
•0,028
0.069
6.75%
14,61%
Company M(Nace code: 22)
-0,004
0,091
0.08%
-1,53%
Company N (Nace code: 45)
0,056
0,206
0,27%
-2,37%
Companyn O (Nace code: 74)
-0,014
0,563
0,11%
-12,93%
Total
100,00%
Portfolio ROA (Erp)
1,36%
Porlfolio risk
1.52%
Respectively larger portions comes from
the sales of engine motor vehicles and
motor-bikes, technical supervision and
repair; motor fuel retail trade industrial
branch (Nace code: 50) - 6.75% and the
real estate transactions industrial branch
(Nace code: 70) - 4.38%. The portfolio
profitability is being calculated after assessing respective industrial branches'
portions in the portfolio in comparison
Table 10
Hypothetical portfolio made of the industry branches pursuant to assets
Weight
Std Dev.
Variable
Std Err
Mean
Sector ROA
average
W
Eri
Sector C+D+E+F+G (Nace code: 15)
0,014
0.005
0,003
88,11%
4,83%
Sector II (Nace code: 92)
0,004
0,057
0,033
0,10%
4,88%
0,023
0,046
0,026
0,13%
0,43%
Sector I+O (Nace code: 74)
0,021
0,010
0,006
0,19%
7,40%
0,008
0,018
0,010
4,38%
2,63%
Sector L (Nace code: 50)
0,011
0,006
0,004
6,75%
4,40%
Sector M(Nacc code: 22)
0,029
0,024
0,014
0,08%
5,93%
Sector N (Nace code: 45)
0,019
0,026
0,015
0,27%
9.55%
Total
Portfolio ROA (Erp)
4,71%
Portfolio risk
0,64%
#######
OS
m
o
—
O
o
o
o
d
o
o
d'
-
o
o
-
d
o
0,009
•N
-0,130
o
-0.943
d
(N
o
0.0
o
d
m
• ^
0,0
d
(-*
0,6
a S.S 3 ^
C7S
0,9
IN
00
49
d
OS
OS
r-l
Ov
Ov
OS
o
d
o
d
0,488
0,225
0,045
0.315
-0.714
0,996
-0,930
-0,402
0,8
0,7
o
o
d
o
o
T
m
OO
m
so
(N
OO
O
d
d
o
d
rr.
m
o
av
d
t^
vO
d
r-i
.—•
0.0
r~.
o
o
d
OS
0,8
CO
00
0,99
-0,3
0,92
0.40
so
Ov
O
m
in
o
d
_
av
r-l
00
0,3
r-l
«
0,7
0,3
-f
o
d
o
O
VO
so
d
d
m
a.
b
D,
H
H
U
a.
1;
H
H
U IS
a?:
a
H
as as
00
o
o
0,8
in
0.9
OS
code:
0.4
0.6
0,5
0,2
1^
o
code:
Fâce
c
a
Q.
•—'
[^
o
Nace code:
u.
ij
-o
o
0.0
u
d
Nace code:
Q
d
o
as
OC
00
•o
0,8
CJ
o
n-,
O
Nace code:
U
•a
d
Mace code;
d
o
[^
''Jacec
-
VO
• ^
Nace code;
0,9
CO
Nace code:
o
Nace code;
r^
Nace code:
. " « ..
IN
IJ'
Nace code;
-
•a*
o
0.8
o
° « Z o "« d
a
-0.6
0.39
d
o
o
• ^
o
a.
-0,6
0,13
-
. Q « ..
B >-•>.' u
a s.;= 312
d
00
d
d
OS
0,6
in
—
o
( ^
a S.5 3 2
OS
0.9
d
d
OV
• ^
a ^5 3 ^
. « « ..
o
0,66
-0.8
d
o
OO
a S.S 8 12
00
d
0.26
Company 1
(Nace
code:
55)
d
d
c
z'o-
Company H
(Nace
code:
92)
0,56
u
,9 2 Z o •v
tn
f^
0,9
d
o
0.0
0,8
>^ u
o
CO
o
0,65
c
r-
m
0.9
. « « ..
r9
p
U 2
a. Z
-^ 0
ij t^
0.9
d
0,09
o
0.69
d
0.9
o
-0.2
o
0.0
tn
d
0.9
o
0,43
• 1
o
0,066
Company L
(Nace
code:
50)
ace
°
™ Z om o
Z
Z
?.
Z
o
c
F
U Ol
P
50
Table 11
Hypothetical portfolio industrial branches' mutual correlation
Sector
C+D+E+F+G
(Nace code:
15)
Sector
H (Nace
code:
92)
Sector
I (Nace
code:
55)
Sector
HO
(Nace
code: 74)
Sector
K (Nace
code:
70)
SecSector
tor M
L (Nace
(Nace
code:
code:
50)
22)
Sector
N (Nace
code:
45)
Sector C+D+E+F+C
(Nace code: 15)
1,000
-0,996
0.654
-0,323
-0,277
0,907
1,000
0,881
Sector H (Nace code: 92)
-0,996
1,000
-0,716
0,402
0,195
-0,868
-0,997
-0,838
0,654
-0,716
1,000
-0,927
0,546
0,274
0,658
0.218
Sector J+O (Nace code:
74)
-0,323
0,402
-0.927
1,000
-0.820
0,106
*0,327
0,163
-0,277
0,195
0,546
-0.820
1,000
-0,656
-0,273
-0,699
0.905
0.998
Sector L (Nace code: 50) 0,907
-0,868
0.274
0,106
-0,656
1.000
Sector M(Nace code: 22)
1,000
-0,997
0,658
-0,327
-0,273
0,905
1,000
0,879
Sector N (Nace code: 45) 0,881
-0,838
0,218
0,163
-0,699
0,998
0,879
1,000
with their representing companies' portions in the portfolio pursuant to their assets and this figure is 4.71%, meanwhile
the portfolio risk is 0.639%. In this case
the hypothetical portfolio risk is lower
than the profitability.
Table 11 presents the hypothetical
portfolio industrial branches' assets profitability correlative matrix.
Table 11 presents a different situation
than the one in the previously analyzed
portfolio. In this case there is significantly
positive (-1-0.998) correlative relation between the assets profitability in the construction industry (Nace code: 45) and the
sales of engine motor vehicles and motorbikes, technical supervision and repair;
motor fuel retail trade industrial branch
(Nace code: 50). Also between the food
industry (Nace code: 15) and publishing,
printing and recorded files dissemination
industry (Nace code: 22). This relation is
0.907.
Significantly negative correlative (0.996) relation exists between the assets
profit ratios in the food industry (Nace
code: 15) and in the travel organization,
cultural and sport activities industry (Nace
code: 92). The negative relation (-0.997)
exist also between the assets profit ratios
within the travel organization, cultural
and sport activities industry (Nace code:
92) and publishing, printing and recorded
files dissemination (Nace code: 22).
Fig. 6 presents the comparison of the
actual businesses' portfolio and the hypo-
• 471%
136%
mess poflfolio
1 52'
Portfolio risk
Fig. 6. Comparison ot the actual and hypothetical
portfolios' risks and profit ratios
thetical portfolio, made of on the basis of
the assets profit ratios, and the comparison of the risk level.
The businesses' portfolio risk is larger
by 0.88% than our hypothetical portfolio;
meanwhile the profitability is lower by
3.35%.
Summary and conclusions
The diversification assessment measures
are based on hierarchic NACE classification. The entropy and concentration indices are very sensitive to the number of
industry branches, where a group of companies is operating.
The concentration index is directly
related to the dominating business range
in the company's portfolio and it may
be negatively related to the number of
businesses in the portfolio. Oppositely, a
component related to the entropy index
increases together with the number of
businesses in the company's portfolio and
reduces with the increase of the dominating business range.
This research has aimed at assessing
whether it is possible to apply a portfolio
theory in the risk management of business units. On the basis of the obtained
outcomes, we may conclude that it is possible to apply the portfolio theory in the
risk management of business units. By
applying a portfolio analysis it is possible
to assess the potential of the portfolio and
its previous scale. If a physical and hypothetical portfolio is not optimal, a gap is
caused not by industry branch, but the indicators of the very company. As various
businesses become large-scale, in order to
manage such structures, it is necessary to
apply the portfolio theory.
By testing the businesses' unit and
hypothetical portfolio, we have been attempting to assess whether this unit is
near to the industry branch indicators.
By modeling the actual and hypothetical portfolios, it has been obtained that
the portfolio risks and profit ratios do
not correspond. Next step should be the
research, which factors have influenced
these discrepancies on a micro level, as
well as analyzing why the actual portfolio
risk is larger than the hypothetical one;
How to re-arrange the portfolio, because
we deviate from the industrial hypothetical one; How to transfer capital from one
business into another inside the unit. An
analysis of the reasons would be a further
investigation, which needs to be analyzed
and assessed not only on an industrial level, but at the very company, because the
problems arise at the very company, but
not in the branch.
Larger deviations between the risks
and the profit ratios have been obtained
by analyzing the portfolios, made of on
the basis of capital and its profitability.
However, it is more correct to assess invested capital and an option for capital internal distribution within portfolios.
According to the performed research
we may conclude that the business structure diversification according to sectors
attains lower profitability than the portfolio according to the branch profit ratios.
If we make a portfolio from respective industry branches, in this case, the
portfolio profitability must be equal to
an industry branch profit, after assessing
a portion of each branch in the portfolio.
If there is a discrepancy, it is caused not
by the branch, but the company. Consequently a structure of the company should
be modified.
An establishment or optimization of
such a portfolio allows the managers to
51
Kristina LEVISAUSKAITE, Asta PRANCKEVICiUTE
52
adopt decisions related to the re-structuring of the businesses' portfolios. Certain assessments should be done in order
to find out what should be changed so
that the managed portfolios equal to the
branch risk and profitability. Thus, an application of the portfolio theory in managerial decisions may serve to modeling the
branches and their loads in the portfolio
pursuant to their present characteristics.
correlative ratios and mutual conciliation
until optimal portfolio were made of in
each concrete case.
A basis of the further research should
be to encompass a larger number of business units as well as to find the way how
to assess businesses' portfolios, correct
and change a portfolio structure, efficiently diversify among the industrial
branches.
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The paper submitted: June 11, 2007
Prepared for publication: September 01. 2007
Kristina LEVISAUSKAIT^, Asta PRANCKEVlClOTt
VERSLy PORTFELIO FORMAVIMAS KAIP RIZIKOS VALDYMO PRIEMONE
Santra uk a
Diversifikacijos [vertinimopriemones yra hierarcbines ERVK sistemos klasifikacijos pagrindu. Entropijos ir koncentracijos indeksai yra labai jaulrus
verslo Saki|, kuriose dirba firmij grupe, skaicîui.
Koncentracijos indeksas tiesiogiai siejasi su
vyraujancio verslo apimtimi bendroves portfelyje
ir jis gali negatyviai ,sietis su verslq skaiciumi portfelyje. Priesingai, entropijos indekso susietas komponentas dideja kartu su verslif skaiciumi firmos
portfelyje ir maicja didejant vyraujan6o verslo
apimciai.
Siame tyrime bandyta jvertinti ar galima taikyti porlfelio teorij^ verslo junginiij rizikos valdymui. Pagal gautus rezultalus galima daryti iSvad^,
kad porlfeiio teorija galima taikyti verslij junginiij
rizikos valdymui. Naudojant portfelio analiz? gahma jvertinti koks potencialiai galetq buti portfelis ir
koks buvo. Jei fizinis ir hipotetinis portfelis ncra optimalus, tai t^ atotrukj lemia ne sakos, o pacios jmones rodikliai, Verslo stambejimas pletojasi ir valdant
tokias struktijras reikia taikyti portfelio teorij^,
Sio vcrslq junginio testavimu ir hipotetiniij
portfeliLj sukurimu bandeme jvertinti ar sis verslij
junginys yra arciau Sakos rodiklii).
Modeliuojant faklinius ir bipotetinius portfelius
gauta. kad portfeliq rizikos ir pelningumai neatitinka, Sekantis zingsnis tureti] buti tyrimas kas jtakojo
neatitikimus mikro lygyje analizuojant, kodel faktinii) portfeliq rizika yra didesne nei hipotetiniq, Kaip
pertvarkyti portfelj nes nukrypstame nuo pramones
53
54
hipotetinio portfelio. Junginio viduje perkelti kapital4 ii vieno verslo j kit^. Priezasciij analize, tai
tolimesnis tyrimas, reikia nagrineti ir vertinti ne tik
pramones lygyje, o pacios jmones nes problemos
atsiranda pacioje jmoneje, o ne sakoje kuri jkomponuota j portfcjj.
Didesni nukrypimai tarp rizikos ir pelningumt} gauti analizuojant portfelius sudarytus pagal
kapital^ ir kapitalo pelningum^. Ta<îau vertinant
investuot^ kapital^ ir galimyh^ kapitalo vidinio paskirstymo pagal kapital^ vertinti portfelius yra korekti^kiau.
Pagal atlikl^ tyrim^ darytina isvada. kad versto
strukturos diversifikacija pagal sektorius pasiekia
iemesnj pelningum^ nei portfelio pagal Sakos pelningumus.
Jei portfelj formuojame is atitinkamq 5akq tai
portfelio pelningumas turi buti lygus Sakq pelnin-
gumui (vertinus kiekvienos sakos dalj portfelyje, jei
egzistuoja neatitikimas tai lemia ne saka o jmone,
tai reikia keisti jmones lygmenyje struktura.
Tokio portfelio formavimas - optimizavimas
leidzia vadovams daryti sprendimus del verslq portfelio restrukturizacijos, Jvertinti k^ reiketi^ keisti,
kad savo valdomus portfelius prilyginti sakos rizikai ir pelningumui, Taip portfelio teorijos taikymas vadyhiniuose sprendiniuose gali pasitarnauti
modeliuojant sakas ir ji( svorius portfelyje pagal jtj
esamas charakteristikas, koreliacijos koeficientus ir
tarpusavio suderinamum^, ko! butij pasiektas optimalus konkreciu atveju portfelis.
Tolimesnio tyrimo bazi^ reikia isplesti didesniu
skaiciumi verslo junginiq ir kry^nis buti) surasti
bud^ kaip galima jvertinti verslij portfelj, koreguoti
ir keisti portfelio struktura, efektyviai diversifikuoti
tarp pramones Sakij.

Formation of business portfolios as risk management tooi

Transcription

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