Options for Modelling the Financial Viability of Sofix Companies in the Post-crisis Years
A financial crisis undoubtedly had the enormous negative operating on the real sector in a national and global scale. A grate number of stopping of companies, business restructuring, decrease of production, and staff surplus. Therefore it is vitally important to estimate financial steady development...
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Інститут економіки промисловості НАН України
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irk-123456789-876282015-10-23T03:02:12Z Options for Modelling the Financial Viability of Sofix Companies in the Post-crisis Years Angelov, G. Finance A financial crisis undoubtedly had the enormous negative operating on the real sector in a national and global scale. A grate number of stopping of companies, business restructuring, decrease of production, and staff surplus. Therefore it is vitally important to estimate financial steady development of the Bulgarian companies. Primary objective of such estimation - to identify accessible possibilities for the acceptance of the adequate, self-weighted decisions, to support companies in the process of adaptation to replacement of market requirements. Aim of the article - to foresee main financial pressures, using models for the estimation of authenticity of bankruptcy of companies and to offer the choice of decisions for overcoming these difficulties. An aim was arrived at through the empiric test of existent models in terms of open corporations of index of SO- FIX during four years, from 2011 to 2014. Results from this test then drawn on as a benchmark test in the process of decision-making. Фінансова криза поза сумнівом мала величезну негативну дію на реальний сектор в національному і глобальному масштабі, у вигляді числа припинень компаній, ділової реструктуризації, убування виробництва, і штабної надмірності. Тому життєво важливо оцінити фінансовий стійкий розвиток Болгарських компаній. Головна мета такої оцінки - ідентифікувати доступні можливості для ухвалення адекватних, зважених рішень, щоб підтримувати компанії в процесі пристосування до заміни ринкових вимог. Мета цієї статті - передбачити головні фінансові труднощі, використовуючи моделі для оцінки вірогідності банкрутства компаній і запропонувати вибір рішень для подолання ці труднощі. Мета досягалася через емпіричне випробування існуючих моделей в термінах відкритих акціонерних товариств індексу SOFIX впродовж чотирьох років, з 2011 до 2014. Результати від цього випробуванняпотім використані як еталонний тест в процесі ух- валення рішення. Финансовый кризис несомненно имел огромное негативное воздействие на реальный сектор в национальном и глобальном масштабе, в виде числа прекращений компаний, деловой реструктуризации, убывания производства, и штабной избыточности. Поэтому жизненно важно оценить финансовое устойчивое развитие Болгарских компаний. Главная цель такой оценки - идентифицировать доступные возможности для принятия адекватных, взвешенных решений, чтобы поддерживать компании в процессе приспосабливания к замене рыночных требований. Цель этой статьи - предсказать главные финансовые трудности, используя модели для оценки вероятностей банкротства компаний и предложить выбор решений для преодоления эти трудности. Цель достигалась через эмпирическое испытание существующих моделей в терминах открытых акционерных обществ индекса SOFIX в течение четырех лет, с 2011 до 2014. Результаты от этого испытания затем использованы как эталонный тест в процессе принятия решения. 2014 Article Options for Modelling the Financial Viability of Sofix Companies in the Post-crisis Years / G. Angelov // Економічний вісник Донбасу. — 2014. — № 4(38). — С. 102-108. — Бібліогр.: 22 назв. — англ. 1817-3772 http://dspace.nbuv.gov.ua/handle/123456789/87628 336.76 (497.2) en Економічний вісник Донбасу Інститут економіки промисловості НАН України |
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Finance Finance Angelov, G. Options for Modelling the Financial Viability of Sofix Companies in the Post-crisis Years Економічний вісник Донбасу |
| description |
A financial crisis undoubtedly had the enormous negative operating on the real sector in a national and global scale. A grate number of stopping of companies, business restructuring, decrease of production, and staff surplus. Therefore it is vitally important to estimate financial steady development of the Bulgarian companies. Primary objective of such estimation - to identify accessible possibilities for the acceptance of the adequate, self-weighted decisions, to support companies in the process of adaptation to replacement of market requirements. Aim of the article - to foresee main financial pressures, using models for the estimation of authenticity of bankruptcy of companies and to offer the choice of decisions for overcoming these difficulties. An aim was arrived at through the empiric test of existent models in terms of open corporations of index of SO- FIX during four years, from 2011 to 2014. Results from this test then drawn on as a benchmark test in the process of decision-making. |
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Article |
| author |
Angelov, G. |
| author_facet |
Angelov, G. |
| author_sort |
Angelov, G. |
| title |
Options for Modelling the Financial Viability of Sofix Companies in the Post-crisis Years |
| title_short |
Options for Modelling the Financial Viability of Sofix Companies in the Post-crisis Years |
| title_full |
Options for Modelling the Financial Viability of Sofix Companies in the Post-crisis Years |
| title_fullStr |
Options for Modelling the Financial Viability of Sofix Companies in the Post-crisis Years |
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Options for Modelling the Financial Viability of Sofix Companies in the Post-crisis Years |
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options for modelling the financial viability of sofix companies in the post-crisis years |
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Інститут економіки промисловості НАН України |
| publishDate |
2014 |
| topic_facet |
Finance |
| url |
http://dspace.nbuv.gov.ua/handle/123456789/87628 |
| citation_txt |
Options for Modelling the Financial Viability of Sofix Companies in the Post-crisis Years / G. Angelov // Економічний вісник Донбасу. — 2014. — № 4(38). — С. 102-108. — Бібліогр.: 22 назв. — англ. |
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| fulltext |
G. Angelov
102
Економічний вісник Донбасу № 4(38), 2014
UDC 336.76 (497.2)
George Angelov1
D. A. Tsenov Academy of Economics, Svishtov, Bulgaria
OPTIONS FOR MODELLING THE FINANCIAL VIABILITY
OF SOFIX COMPANIES IN THE POST-CRISIS YEARS
The1 main objective of any business entity is to
achieve positive financial results which are evidence of
sound managing practices as well as an indicator of
profit and growth.2 During a crisis, Bulgarian compa-
nies face substantial difficulties which lead to shrink in
production, reduced advertising, and in some cases, to
making loss for several years on end. These could be
explained both with the deteriorated economic envi-
ronment in which companies operate during a crisis
and with the possibility that their customers (i.e. other
companies and households)3 might be going through a
difficult period as well. It is therefore necessary to
assess the financial viability of companies by employ-
ing models for predicting bankruptcy probability. Fi-
nancial managers’ awareness about the essence of fail-
ure is vital to the financial-managerial policy of com-
panies. Therefore, financial managers must be familiar
with the nature of failure, what is more, they must be
able to predict a possible failure in advance and have
the knowledge how to deal with the threat of a bank-
ruptcy. Analysis of a potential financial distress which
a company is facing and which may result in liquidity
shortage, insolvency, or even bankruptcy, is a major
component of overall corporate financial management.
As a matter of fact, when undertaking any activity that
is related to starting a production or making some in-
vestment, it is important to take into account different
downturn scenarios. Profit and growth are goals which
all companies pursue, yet their opposites, failure and
liquidation, must be deemed just as likely.4
The ability to predict corporate failure due to in-
solvency far before it has become a fact is important
both to managers and lenders of enterprises.5 Corporate
bankruptcy reflects problems which have occurred in
1 The author is assistant professor in Department
“Finance and credit”, Faculty “Finance” to “D. A. Tsenov
Academy of Economics”, Svishtov, Bulgaria gange-
lov@uni-svishtov.bg.
2 Adamov, V. Finansi na firmata. Biblioteka
Obrazovanie i nauka, # 28, Svishtov. 2012, p. 492.
3 Pavlova, M. Faktorno vyzdeistvie vyrhu
bogatstvoto na domakinstvata v . Bulgaria. Факторное
воздействие на богатство домашних хозяйств в
Республике Болгарии//Економіка України в умовах
глобалізації i регiоналiзацiї: Збірник тез доповідей :
Міжнародної науково - практичної Iнтернет-
конференції студентів та молодих вчених – Тернопіль
4-5 квітня 2014 року., ТНЕУ, 2014, с. 214-217.
4 Adamov, V. Finansi na firmata. Biblioteka
Obrazovanie i nauka, # 28, Svishtov. 2012, p. 493.
5 Kasarova, V. Modeli i pokazateli za analiz na
finansovata ustoychivost na kompaniyata. Nov balgarski
iniversitet. 2010.
the production, the financial management, or the fund-
ing of a company. There might be a variety of reasons
behind that a deteriorated economic environment, cus-
tomers being in financial distress, delayed payments to
lenders and suppliers, etc. which are the major prereq-
uisites for financial disturbances within a company.
The financial analysis of each company is based on the
assessment of its capital structure and market perfor-
mance; analysis of its profitability and earnings; and
evaluation of its assets and liquidity. A further instru-
ment which might be employed in the analysis might
also be the assessment of bankruptcy probability.
Models for predicting corporate failure are among
the main techniques and instruments for determining
the future status of companies on the basis of applying
a set of financial ratios. The possibility to predict fi-
nancial insolvency is extremely important to private
investors (the shareholders of a company) and from a
social perspective, since this is a signal for public re-
sources mismanagement.
A lot of scientists have proposed different models
for predicting a potential failure of companies. These
models are based on the assessment of the financial
data about companies which are provided in their bal-
ance sheets and their income statements as various
ratios. The first model for predicting bankruptcy by
employing financial ratios was developed by W. H.
Beaver in 1966.6 The underlying objective of his work
was to assess the financial situation of a company ap-
plying for a loan by analyzing its solvency, the terms
on which a loan could be extended7 as well as the ca-
pacity of the company to service its debt in due time.
In order to do so, Beaver determined a ratio which is
calculated as a correlation between the value of the
cash flow and the amount of the liabilities of a compa-
ny.
6 William H. Beaver Empirical Research in
Accounting: Selected Studies 1966 Journal of Accounting
Research Vol. 4, (1966), p. 71-111 / – Mode
http://www.jstor.org/discover/10.2307/2490171?sid=2110
5896258411&uid=2&uid=4/.
7 Marinov, I. Sovremennye aspekty i rolq
na’evropeyskoto zakonodatelstvo, reglamentiruyushtego
usloviya i poryadok zakluycheniya dogovorov
potrebitel’skih kreditov. Ekonomika Ukrainy v umovah
globalizatsii i regionalizatsii: Zbirnik tez dopovidey:
Mizhnarodnoy naukogo-praktichnoy Interent-konferentsii
studentiv ta molodih vchenih – Ternopil’ 4-5 kvitiya 2014
roku., s. 201-204. TNEU, 2014.
G. Angelov
103
Економічний вісник Донбасу № 4(38), 2014
Table 1
Beaver ratio =
Interpretation of the indicator
Companies
performing
normally
5 years
to bankruptcy
A year to
bankruptcy
0.4 – 0.45 0.17 -0.15
E. Altman contributed enormously to corporate
bankruptcy research by designing a number of models
for predicting it. These models are based on the input
of several ratios, each of them acquiring some relative
weight according to how important the author consid-
ers that ratio to be. In a number of research works1
dealing with corporate bankruptcy, Altman developed
and presented his Z-models. The first model2 only
takes into account two factors, corporate liquidity and
indebtedness. The two-factor model does not include
an analysis of profitability (yield, solvency, and effi-
ciency) and is therefore not commonly applied in prac-
tice.
Table 2
Altman’s two-factor model
К1 – Current ratio (Current assets/Current liabilities)
К2 – Financial dependency ratio (Debt/Total assets)
Interpretation of the indicator
Z>0 – Bankruptcy probability exceeds 50%
Z=0 – 50 % bankruptcy probability
Z<0 – Less than 50% bankruptcy probability
The five-factor model3 further elaborated the two-
factor model for predicting corporate bankruptcy. It is
also known as Altman’s Z-Score and is employed to
determine the so-called bankruptcy point. The formula
is based on coefficients used to analyse the liquidity,
yield, indebtedness, solvency, and efficiency of a com-
pany. The objective is to predict bankruptcy probabil-
ity. This model has gained enormous popularity due to
its comprehensive nature and has become a practically
approved criterion for predicting the probability of a
corporate bankruptcy.
Altman’s model4 has gained recognition in prac-
tice as it makes it possible to assess the condition of
a company by taking into account the combined effect
of multiple factors (financial indicators). The only
shortcoming of the presented model is the fact that it
was designed and tested in the USA and therefore it
1 Altman, E. Haldeman, R. Narayanan. P. ZETA
analysis A new model to identify bankruptcy risk of cor-
porations. Journal of Banking & Finance, Volume 1, Issue
1, June 1977, p 29–54.
2 Altman, I. Corporate Financial Distress and
Bankruptcy: A Complete Guide to Predicting & Avoiding
Distress and Profiting from Bankruptcy. Wiley, 1993.
3 Adamov, V. Finansi na firmata. Biblioteka
Obrazovanie i nauka, # 28, Svishtov. 2012, p. 503.
4 Gabrovski, R. Industrialen risk i menidzhmant.
Akademichno Izdatelstvo Tsenov, Svishtov. 2009.
takes into consideration the characteristics of American
companies and the conditions of the market in which
they operate. Therefore, its application to the Bulgarian
business environment may lead to distortion of results
and to a failure to report the real situation of a compa-
ny.
Table 3
Altman’s five-factor model 5
Х1 = Net working capital/Total assets
Х2 = Earnings/Total assets
Х3 =ЕBIT/Total assets
Х4 =Leverage ratio
Х5 = Sales revenue/Total assets
Interpretation of the indicator
Z>2.99 – The company is not threatened by bankruptcy
Z=1.88-2.99 – Grey zone
Z<1.88 – Bankruptcy is probable
The model designed by Fulmar6, the H-Score
model, is another major contribution to assessing how
probable a corporate failure is. Fulmar presented that
model in his research work, “A Bankruptcy Classifica-
tion Model for Small Firms” which was published in
1984. According to that model, a company is likely to
be declared insolvent if the result of the model is less
than zero. The model includes nine ratios to assess the
financial situation of a company and each of these
ratios is given a certain relative weight.
Table 4
Fulmar’s H-factor model 7
H1 = Earnings/Total assets
H2 = Sales revenue/Total liabilities
H3 = EBIT/Equity
H4 = Sales revenue/Amount of debt
H5 = Debt/Total assets
H6 = Current liabilities/Total assets
H7 = Inventory/Total assets
H8 = Net working capital/Debt
H9 = ЕВІТ/Interests paid on loans
Interpretation of the indicator
H>0 – The company is not threatened by bankruptcy
H<0– Bankruptcy is probable
By developing further the underlying logic of
these models, the English economist R. Lis8 suggested
a four-factor model for assessing the bankruptcy prob-
ability for British companies. The model is based on
combining the importance of the indexes of liquidity,
profitability, and financial independence.
5 Adamov, V. Finansi na firmata. Biblioteka
Obrazovanie i nauka, # 28, Svishtov. 2012, p. 502.
6 Fulmer, J. Moon, J. Gavin, T. and Erwin, J. Н-
score model and its use in foreseeing the risk of а small
enterprises bankrupcy.
7 http://ycharts.com/glossary/terms/fulmer_h_score.
8 Sushko, V., Pavluyk, T. Klasifikatsiya modeley
otsinki imovirnosti bankrutstva pidpriemstv. Ekonomiko-
matematichne modeluyvaniya protsesiv biznesu. 2014.
G. Angelov
104
Економічний вісник Донбасу № 4(38), 2014
Table 5
Lis’ model
Х1 = Net working capital/ Total assets
Х2 = EBIT/ Total assets
Х3 = Earnings/Total assets
Х4 = Equity/Debt
Interpretation of the indicator
Z<0.037 – High probability of a bankruptcy
Z>0.037 – Low probability of a bankruptcy
A reliable model ignoring the influence of the
branch to which companies belong was designed by
G. Springate.1 The author tested his model on 40 com-
panies and the results he obtained proved to predict
company failures within a year with 92.5 per cent accu-
racy. The model was then tested on 50 companies in
1979 and on 24 companies in 1980, the accuracy of
predictions being 88% and 83.3% respectively.
Springate’s model is based on combining the impact of
four major indicators of company performance.
Table 6
Springate’s model
Х1 = Net working capital/ Total assets
Х2 = EBIT/Total assets
Х3 = Earnings/Current liabilities
Х4 = Sales revenue/ Total assets
Interpretation of the indicator
Z<0.862 – High probability of a bankruptcy
Business development and innovations require
that a model taking into account the impact of new
technologies should be designed and applied. This
means that the models developed so far need to be
further elaborated and oriented to the new prospects in
business development so as to predict corporate fail-
ures more precisely. This is the trend followed by
R. Taffler2 in his model for assessing corporate bank-
ruptcy probability. Similar to Springate’s model, the
branches in which companies operate are irrelevant to
the test.
Table 7
Taffler’s model
Х1 = EBIT/Current liabilities
Х2 = Current assets/Total liabilities
Х3 = Current liabilities/ Total assets
Х4 = Sales revenue/ Total assets
Interpretation of the indicator
Z>0.3 – Low probability of a bankruptcy
Z<0.3 – High probability of a bankruptcy
The analysis of existing models for assessing the
probability of corporate failures is based on employing
1 Springate, Gordon L.V., “Predicting the Possibility
of Failure in a Canadian Firm”. Unpublished M.B.A.
Research Project, Simon Fraser University, January 1978.
2 Taffler R., Finding those companies in danger
using Discriminant analysis and financial ratio data: a
comparative based study city business school, City
University Business School, London, Working paper №3.
publicly accessible data from the financial statements
of Bulgarian companies. The results obtained from
testing the models presented here are assessed by em-
pirically applying them to SOFIX index companies.
The selection of companies was based on their produc-
tion profile, while financial enterprises and special
investment purpose companies (SIPCs) have remained
beyond the scope of our analysis due to the specific
nature of their business. Financial results are assessed
by using publicly accessible information provided by
their financial statements, i.e. their balance sheets and
income statements. The objective of empirically testing
these models is not to undermine the prestige of those
companies or to influence public opinion. The underly-
ing objective of the author is to compare achieved re-
sults and to make a critical analysis of existing models
and then present his views on their practical application
on behalf of financial managers.
Table 8 presents the results about six Bulgarian
SOFIX index companies which were obtained after
applying the models for assessing corporate bankrupt-
cy probability.
The analysis of obtained results is conducted as
follows:
In terms of the Beaver ratio, the companies
included in the analysis are described as unstable, their
bankruptcy impending within five years. The best re-
sults are those of M+S Hydraulic Plc (0.15-0.2), which
are nevertheless much below the interval for a normal-
ly performing company (0.4-0.45). Due to the loss
reported by Neochim Plc over the last three years, the
values of the ratio are zero. This could be approached
as a shortcoming of the presented model since a nega-
tive financial result does not necessarily indicate a
bankruptcy probability for a company;
The employment of Altman’s two-factor mod-
el, due to the reverse interpretation of obtained results,
determines the companies which are subject to analysis
as stable entities with very little bankruptcy probabil-
ity. The values registered for Albena Plc, Neochim Plc,
Sopharma Plc, and Chimimport Plc range in the inter-
val from -1 to -2. Therefore, according to the as-
sessment model, they are stable; the bankruptcy proba-
bility for them is small; and their viability increases
with an increase in these negative values. M+S Hy-
draulic Plc is the most viable entity again, its values
ranging between -4 and -5 throughout the whole peri-
od. According to Altman’s two-factor model, Monbat
Plc is stable, too, the value of the ratio growing from -2
to -5.7 in the period between 2012 and 2014;
The results obtained after applying Altman’s
five-factor model are relatively constant for each com-
pany, yet there are substantial differences when com-
paring them to other SOFIX index companies. Never-
theless, all companies are described as relatively stable
with no short-term bankruptcy probability, except for
Albena Plc (1) which is threatened by failure. Due to
the specific nature of the calculations made for the
index which gives the greatest importance to corporate
profitability, quite logically (due to the high values of
G. Angelov
105
Економічний вісник Донбасу № 4(38), 2014
Table 8
Results of applying the models for predicting corporate bankruptcy
Models for predicting corporate bankruptcy 2014 2013 2012 2011
NEOCHIM PLC
Beaver’s model 0.000 0.000 0.000 0.105
Altman’s two-factor model -1.078 -1.240 -1.513 -1.701
Altman’s five-factor model 2.181 1.984 1.998 2.743
Fulmar’s H-factor model -1.293 -0.610 -0.306 4.654
Lis’ model -0.008 -0.002 0.003 0.023
Springate’s model 0.480 0.563 0.633 1.414
Taffler’s model 0.965 1.026 0.862 1.143
MONBAT PLC
Beaver’s model 0.600 0.109 0.049 0.043
Altman’s two-factor model -5.696 -2.968 -1.687 -1.926
Altman’s five-factor model 2.158 2.296 1.639 1.670
Fulmar’s H-factor model 3.055 4.457 -0.517 -0.020
Lis’ model 0.037 0.037 0.014 0.016
Springate’s model 1.483 1.526 0.753 0.802
Taffler’s model 0.840 0.872 0.685 0.704
ALBENA PLC
Beaver’s model 0.018 0.029 0.034 0.018
Altman’s two-factor model -0.999 -0.947 -1.053 -0.837
Altman’s five-factor model 0.462 0.490 0.538 0.480
Fulmar’s H-factor model -4.543 -4.000 -3.995 -4.692
Lis’ model 0.004 0.006 0.007 0.003
Springate’s model 0.289 0.414 0.545 0.280
Taffler’s model 0.998 1.160 1.239 1.341
SOPHARMA PLC
Beaver’s model 0.019 0.037 0.047 0.055
Altman’s two-factor model -1.778 -1.792 -1.832 -1.845
Altman’s five-factor model 1.964 1.967 1.972 2.024
Fulmar’s H-factor model -1.284 -1.284 -1.248 -0.938
Lis’ model 0.013 0.016 0.018 0.020
Springate’s model 0.682 0.739 0.778 0.837
Taffler’s model 0.504 0.511 0.501 0.504
M+S HYDRAULIC PLC
Beaver’s model 0.148 0.144 0.156 0.208
Altman’s two-factor model -4.636 -4.668 -5.265 -3.922
Altman’s five-factor model 2.501 2.499 2.533 3.002
Fulmar’s H-factor model 37.459 28.754 21.115 16.191
Lis’ model 0.055 0.055 0.057 0.062
Springate’s model 2.101 2.063 2.293 2.448
Taffler’s model 1.075 1.032 1.208 1.194
CHIMIMPORT PUBLIC HOLDING COMPANY
Beaver’s model 0.010 0.013 0.017 0.021
Altman’s two-factor model -1.675 -1.719 -1.780 -1.626
Altman’s five-factor model 2.898 2.656 2.573 2.302
Fulmar’s H-factor model -4.407 -4.378 -4.346 -4.308
Lis’ model 0.010 0.011 0.013 0.010
Springate’s model 0.208 0.243 0.287 0.243
Taffler’s model 0.201 0.205 0.210 0.218
Source: The financial statements of the companies, infostock.bg, investor.bg, calculations by the author.
G. Angelov
106
Економічний вісник Донбасу № 4(38), 2014
Beaver’s ratio Altman’s five-factor model
Fulmar’s H-factor model Lis’ model
Springate’s model Taffler’s model
Fig. 1. Financial sustainability of Bulgarian public companies
0,00
0,05
0,10
0,15
0,20
0,25
2014201320122011
NEOCHIM PLC MONBAT PLC
ALBENA PLC SOPHARMA PLC
CHIMIMPORT PLC M+S HYDRAULIC PLC
0,00
0,50
1,00
1,50
2,00
2,50
3,00
3,50
2014201320122011
NEOCHIM PLC MONBAT PLC
ALBENA PLC SOPHARMA PLC
M+S HYDRAULIC PLC CHIMIMPORT PLC
-10
-5
0
5
10
15
20
25
30
35
40
2014201320122011
NEOCHIM PLC MONBAT PLC
ALBENA PLC SOPHARMA PLC
M+S HYDRAULIC PLC CHIMIMPORT PLC
-0,02
-0,01
0,00
0,01
0,02
0,03
0,04
0,05
0,06
0,07
2014201320122011
0,00
0,50
1,00
1,50
2,00
2,50
3,00
2014201320122011
NEOCHIM PLC MONBAT PLC
ALBENA PLC SOPHARMA PLC
M+S HYDRAULIC PLC CHIMIMPORT PLC
0,00
0,20
0,40
0,60
0,80
1,00
1,20
1,40
1,60
2014201320122011
NEOCHIM PLC MONBAT PLC
ALBENA PLC SOPHARMA PLC
M+S HYDRAULIC PLC CHIMIMPORT PLC
G. Angelov
107
Економічний вісник Донбасу № 4(38), 2014
the profits made), the companies which score best are
Chimimport Plc and M+S Hydraulic Plc (2.3-2.9).
They are followed by Sopharma Plc, Neochim Plc, and
Monbat Plc with similar results (2-3). Therefore, ac-
cording to the model most frequently employed to
assess bankruptcy probability, Bulgarian public com-
panies are not endangered by bankruptcy;
Fulmar’s H-factor model states that when the
value of H is below zero, corporate bankruptcy is in-
evitable. Over the analysed period, the highest values
of the ratio were reported by M+S Hydraulic Plc (max-
imum 37.46), the trend being towards a continuous
growth. Monbat Plc also recorded positive values in
the interval between 0 and 3. The rest of the compa-
nies, however, are in an unfavourable situation, the
lowest values throughout the whole period being rec-
orded for Chimimport Plc and Albena Plc (-5);
According to Lis’ model, the most stable com-
pany not endangered by bankruptcy is M+S Hydraulic
Plc. Provided that values of Z>0.037 indicate little
bankruptcy probability, this is the only company which
had values between 0.055 and 0.062. Over the last two
years, Monbat Plc also recorded near-border values of
0.037. All the other companies had values indicating
their potential failure. What is more, due to the loss
which Neochim Plc recorded over the last three years,
the values for the company are negative;
The criteria underlying Springate’s model,
which assumes that for
Z<0.862 a company is in poor financial health
and is undergoing substantial financial distress, indi-
cate imminent financial failure for four of the compa-
nies included in the analysis. The lowest results are
those of Chimimport Plc with its relatively constant
values of 0.25. The company is followed by Albena Plc
with values between 0.28 and 0.54, and Neochim Plc
with its low values between 0.48 and 0.54 over the last
three years. The top ranking company is M+S Hydrau-
lic Plc with its constant maximum values between 2.06
and 2.45. Over the last two years included in the analy-
sis, Monbat Plc also recorded high values of about 1.5;
Bulgarian public companies scored best in
terms of Taffler’s model. Provided that the minimum
value required for guaranteeing financial stability was
above 0.3, the values for all companies were about 1,
except for Sopharma Plc with its value of 0.5. Accord-
ing to Taffler’s model, the only company endangered
by a recent bankruptcy is Chimimport Plc with its con-
stant value of 0.2 throughout the period from 2011 till
2014.
The different models for assessing bankruptcy
probability we presented in this paper give different
results when empirically applied to one and the same
Bulgarian company. What is more, in some cases, the
results obtained are quite contradictory. The company
which scored best in the assessment of corporate finan-
cial sustainability according to all presented models is
definitely M+S Hydraulic Plc. In terms of the models
analysed here, all the other companies are relatively
unstable, which poses a risk to their normal perfor-
mance. On the one hand, the main reasons behind this
trend might be due to the fact that each model has been
developed and tested in a specific economy (those of
the USA, Great Britain, etc.), which leads to substan-
tial deviations when they are applied to Bulgarian
business environment. On the other hand, the ratios
presented in this paper are financial methods which
have proved their reliability for assessing the condition
of a company, yet the importance of each ratio (i.e. its
relative weight) is determined on the basis of financial
reporting and the significance which the information
provided by these ratios has in the country where each
model was developed and applied. A major factor for
obtaining such contradictory results might be that the
branches in which analysed companies operate was
ignored and therefore the same ratios have different
values depending on the specific nature of the business
of each company. It is therefore appropriate to employ
these models as a further analytical tool for assessing
corporate financial viability, provided that the neces-
sary adjustment to the specific environmental and eco-
nomic conditions is made in advance.
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Ангелов Г. Вибір для моделювання еконо-
мічної доцільності компаній индекса SOFIX в
посткризові роки
Фінансова криза поза сумнівом мала величез-
ну негативну дію на реальний сектор в національ-
ному і глобальному масштабі, у вигляді числа при-
пинень компаній, ділової реструктуризації, убуван-
ня виробництва, і штабної надмірності. Тому жит-
тєво важливо оцінити фінансовий стійкий розвиток
Болгарських компаній. Головна мета такої оцінки -
ідентифікувати доступні можливості для ухвалення
адекватних, зважених рішень, щоб підтримувати
компанії в процесі пристосування до заміни ринко-
вих вимог.
Мета цієї статті – передбачити головні фінан-
сові труднощі, використовуючи моделі для оцінки
вірогідності банкрутства компаній і запропонувати
вибір рішень для подолання ці труднощі. Мета
досягалася через емпіричне випробування існую-
чих моделей в термінах відкритих акціонерних
товариств індексу SOFIX впродовж чотирьох років,
з 2011 до 2014. Результати від цього випробування
потім використані як еталонний тест в процесі ух-
валення рішення.
Ключові слова: корпоративне банкрутство, ак-
тиви, ліквідність, прибутковість, виручка.
Ангелов Г. Выбор для моделирования эко-
номической целесообразности компаний индек-
са SOFIX в посткризисные годы
Финансовый кризис несомненно имел огром-
ное негативное воздействие на реальный сектор в
национальном и глобальном масштабе, в виде чис-
ла прекращений компаний, деловой реструктуриза-
ции, убывания производства, и штабной избыточ-
ности. Поэтому жизненно важно оценить финансо-
вое устойчивое развитие Болгарских компаний.
Главная цель такой оценки - идентифицировать
доступные возможности для принятия адекватных,
взвешенных решений, чтобы поддерживать компа-
нии в процессе приспосабливания к замене рыноч-
ных требований.
Цель этой статьи – предсказать главные фи-
нансовые трудности, используя модели для оценки
вероятностей банкротства компаний и предложить
выбор решений для преодоления эти трудности.
Цель достигалась через эмпирическое испытание
существующих моделей в терминах открытых ак-
ционерных обществ индекса SOFIX в течение че-
тырех лет, с 2011 до 2014. Результаты от этого ис-
пытания затем использованы как эталонный тест в
процессе принятия решения.
Ключевые слова: корпоративное банкротство,
активы, ликвидность, прибыльность, выручка.
Аngelov G. A choice for the design of financial
viability of companies of SOFIX-index in post-crisis
years
A financial crisis undoubtedly had the enormous
negative operating on the real sector in a national and
global scale. A grate number of stopping of companies,
business restructuring, decrease of production, and
staff surplus. Therefore it is vitally important to esti-
mate financial steady development of the Bulgarian
companies. Primary objective of such estimation - to
identify accessible possibilities for the acceptance of
the adequate, self-weighted decisions, to support com-
panies in the process of adaptation to replacement of
market requirements.
Aim of the article – to foresee main financial
pressures, using models for the estimation of authentic-
ity of bankruptcy of companies and to offer the choice
of decisions for overcoming these difficulties. An aim
was arrived at through the empiric test of existent
models in terms of open corporations of index of SO-
FIX during four years, from 2011 to 2014. Results
from this test then drawn on as a benchmark test in the
process of decision-making.
Keywords: corporate bankruptcy, assets, liquidity,
profitability, profit yield.
Received by the editors: 14.11.2014
and final form 23.12.2014
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