Flávia Assis Carneiro de Rezende
Federal University of São Carlos, Brazil
E-mail: flaviaacr.87@gmail.com
Andrei Aparecido de Albuquerque
Federal University of São Carlos, Brazil
E-mail: andrei@dep.ufscar.br
Gustavo Henrique Silva de Souza
Federal University of Alagoas, Brazil
E-mail: souza.g.h.s@hotmail.com.br
Submission: 01/05/2014
Accept: 19/05/2014
ABSTRACT
This Article aimed to identify
whether there is a relationship between good practices of corporate governance
and the real solvency/insolvency ratio of companies from Brazilian electricity
sector, using to this end, four distinct models for the solvency calculation:
Elizabetsky (1976), Kanitz (1978), Matias (1978) and Altman (1979). For this,
it was performed a descriptive and experimental study of discriminant type
using the linear regression analysis between the periods 2007 to 2011. The
results show that there is no a consensus among the models used, because in the
Elizabetsky’s model, the companies analyzed show up insolvent, while in models
of Kanitz and Matias the companies analyzed show up solvents, and in the
Altman’s model there is a balance between solvent and insolvent. Moreover, with
the regression analysis, it was found that there were no standards of
performance or relations between the solvency indexes and the differentiated
levels of corporate governance. That is, good practices of corporate governance
– that allow the insertion of the companies best levels of corporate governance
listed on the stock exchange – do not necessarily imply in better solvency, as one
might assume.
Keywords:
Solvency Index; Corporate Governance; Brazilian Electricity Sector
1.
INTRODUCTION
In
the present, there are many discussions about the consequences of misconduct of
management in large corporations, having, for example, as the most serious the
bankruptcy of companies. Such consequences made that the practices of corporate
governance receive more attention and importance in the recent market scenario
(TALAMO, 2011; KIM; LU, 2013; MIURA, et al., 2013). Specifically, according to
Talamo (2011), after the scandals of Enron, Vivendi, Cirio, Parmalat and Pan Pharmaceuticals,
the debates regarding to the effectiveness of good practices of corporate
governance became strong and constant worldwide.
Corporate
governance emerges as an intangible tool of organizational dirigibility for
companies of any sector, umpiring as a vital role in improving of the
structures of operation and functioning through cohesive and dynamic management
practices, enabling a significant increase in efficiency and decrease of
financial risks (BHAGAT; BOLTON, 2008; SILVA Jr.; JUNQUEIRA; BERTUCCI, 2009; ROSSONI;
MACHADO-DA-SILVA, 2013).
In
this context, several studies conducted about the financial market have found
relationships between the adherence of corporate governance practices and
factors such as efficiency, productivity, volatility of shares, asset
performance, capital cost, sector indexes and market value (e.g., MALACRIDA; YAMAMOTO,
2006; ALMEIDA; SCALZER; COSTA, 2008; IKENAGA; AZEVEDO; PUTVINSKIS, 2009; SILVA Jr.;
JUNQUEIRA; BERTUCCI, 2009; GEOCZE, 2010; SILVA, 2010; LOPES; MARTINS, 2007,
2011; ERKENS; HUNG; MATOS, 2012; GONÇALVES et al., 2012; REYNA; VÁZQUEZ; VALDÉS,
2012; FERREIRA et al., 2013; KIM; LU, 2013; MIURA et al., 2013).
Thus,
this study aims to fill a gap identified in researches on corporate governance,
answering the following question: The higher the corporate governance level of
a company, the better the results for the solvency indexes? This study is
justified by considering that good practices of corporate governance can
influence positively in the management of a company. As was raised the
hypothesis that the bankruptcy cases, mentioned by Talamo (2011), have placed
in greater evidence the discussion regarding the effectiveness of corporate
governance, this study aims to identify whether there is a relationship between
good practices of corporate governance and the real solvency/insolvency ratio
of companies from the Brazilian electricity sector, using to this end, four
distinct models for the solvency calculation: Elizabetsky (1976), Kanitz
(1978), Matias (1978) and Altman (1979).
2. THEORETICAL BACKGROUND
2.1
Corporate Governance
Once
the corporate governance has become a common term among the discussions within
the financial market, various didactic and managerial definitions have situated
governance as a speciality, and not more as a thematic (ANDRADE; ROSSETI,
2009). In the integrative view from Ferreira et al. (2013), corporate
governance should be understood as a series of internal and external mechanisms
to the organization which have the purpose of synchronizing the actions of
managers and the owners’ interests, in order to reduce potential agency
conflicts.
The
purpose of corporate governance, according to La Rocca (2007), is to ensure
that, by reducing the problems generated by agency conflicts, opportunistic
behaviors involving agents and principals do not occur. Moreover, corporate
governance contributes to limit conflicts generated by asymmetric information,
through greater transparency of data available to the market. This results in
lower of capital cost for companies. That is, there is a greater fundraising –
a necessary condition for higher liquidity of assets –, due to investor
recognition of good practices of corporate governance performed by companies.
Still,
it is worth noting that the corporate governance encompasses four basic
principles which are present in their conceptual framework. These principles
establish essential criteria on ethical conduct which are present in the
practices of the organs responsible by the corporate governance of companies
(ANDRADE; ROSSETTI, 2006; MARQUES, 2007). Thus, the principles that represent
the foundation that supports all approaches to the best formats of administration
are, according to the definitions from Andrade and Rossetti (2006), Malacrida
and Yamamoto (2006) and Marques (2007):
·
Disclosure (transparency) – Obligation to provide all
the information about financial and management performance, and allow access to
any data for individuals interested in some information;
·
Fairness (Equity) – Treat fairly and equitably all
members, managers, employees and any interested party (society at large,
customers or government);
·
Accountability – Accounting for all the acts of market
performance or internally issues, assuming all integrally consequences related
to acts or omissions;
·
Sustainability (corporate responsibility) – Ensure to
the sustainability, honesty, magnanimity and maintenance of the organization,
aiming the longevity of the company by promoting the safety of employees and
actively participating of social and environmental programs.
From
this, it is understandable that the improvement of the corporate Governance
activities relates directly to the establishment of better organizational
structures and to the looking for a more dynamic and cohesive functioning,
enabling a significant increase of productive, operational and managerial
efficiency, which result in the decrease of risks and best assessment of
companies by the market and the investors (ÁLVARES; GIACOMETTI; GUSSO, 2008;
FERREIRA et al., 2013).
2.2
Differentiated Levels of Corporate Governance
Within
the Brazilian model of Corporate Governance has become necessary to generate a
healthy competition that could stimulate organizational improvements and
valorize the companies that eventually adopt these different levels. Thus,
BM&FBovespa (2013) [Stock Exchange of São Paulo, Brazil] created the
differentiated levels of corporate governance – the New Market (highest
governance standard), Level 2 (intermediate governance standard) and Level 1
(low governance standard) –, which have as main objective stipulate specific
and strict standards for guide the activities within the capital market. Thus,
the differentiated levels are intended to enhance the relationship between the
company and investors by raising liquidity and dispersal of assets in the
secondary market (GEOCZE, 2010).
This
format of corporate governance preconizes that the higher the level of
governance, the company will have greater visibility front the market, and
consequently, will have competitive advantages over its competitors (GEOCZE,
2010; BM&FBOVESPA, 2013). So, to join one of the 3 different levels of
corporate governance, companies should achieve to the following membership
requirements stipulated by BM&FBovespa (2013), as can be seen abridged in
Table 1:
Table 1:
Principal Requirements for Accession to the Differentiated Levels of Corporate
Governance
Types of requirements |
New Market |
Level 2 |
Level 1 |
Characteristics of the Outstanding Stocks |
Allows only the existence of common stocks |
Allows the existence of common and preferred stocks (with additional
rights) |
Allows the existence of common and preferred stocks (conform
legislation) |
Minimum of Free Float |
At least 25% of free float |
||
Composition of the Administration Board |
Minimum of 5 members of which at least 20% must be independent with a
unified term of up to 2 years |
Minimum of 3 members (conform legislation) |
|
Financial Statements |
Conform Legislation and bilingual (Portuguese/English) |
Conform Legislation |
|
Granting of Tag Along |
100% for common
stocks |
100% for common and preferred stocks |
80% for common stocks (conform legislation) |
Public offering of stocks acquisition by minimum of the economic value |
Mandatory in case of capital delisting or exit from segment |
Conform Legislation |
|
Accession to the Arbitration Chamber |
Obligatory |
Optional |
Source: Adapted from
BM&FBovespa (2013).
The
difference between the levels is in the modification of the composition
requirements. From New Market for Levels 2 and 1, the most relevant difference
is that the New Market prohibits the issuance of preferred stocks – those that
the holders may have some privilege or preference, such as priority dividend
distribution –, requiring companies have only ordinary shares which give equal
rights to holders, including the right to vote and no restriction or privilege.
Thus, it has as a result by adoption of the New Market the security control
management from all owners (stockholders), ending the problems of power
concentration (GEOCZE, 2010).
Moreover,
the decision of companies to become listed on the New Market brings benefits to
investors due to the greater transparency of information, greater security to
corporate rights, improved monitoring and inspection process, greater accuracy
in the pricing of stocks and risk reduction regarding to the business. Also, it
provides benefits to the company such as improved corporate image, greater
demand for their stocks, lower capital cost and valorization of the stocks,
becoming the companies stronger and competitive, and still boosting the
economy. And in turn, the stock market has benefits because there is an
increased of liquidity and emissions (BM&FBOVESPA, 2009).
This
is occurs because one of the main sources for the analysis of economic and
financial situation of a company is through the disclosure of its financial
data held periodically. Internally, the high management can evaluate how the
economic performance of the company is. And externally, investors find the most
viable alternatives of return for their applications (GEOCZE, 2010).
Several studies have attempted to
prove the existence of beneficial results from good practices of corporate
governance as higher stock returns, lower funding cost, volatility etc. (e.g.,
MALACRIDA; YAMAMOTO, 2006; ROGERS; SECURATO; RIBEIRO, 2006; ALVES; RIBEIRO;
MANTESE, 2007; LOPES; MARTINS, 2007; ALMEIDA; SCALZER; COSTA, 2008; SILVA,
2010; LOPES; MARTINS, 2011; SERAFIM; GOMES, 2011; GONÇALVES et al., 2012).
Rogers,
Securato and Ribeiro (2006) found that companies with high practices of
corporate governance have lower exposure to external risks and can reap more
benefits of economic growth compared to companies with lower practices of
corporate governance. Almeida, Scalzer and Costa (2008) found that between 2000
and 2004, all companies listed in one of Differentiated Levels had assets less
risky than companies in the open market. In turn, Lopes and Martins (2007)
found in a study conducted between 2003 and 2006 (n=96 companies), that the
accession of Differentiated Levels showed in medium and long-term a reduction
in the cost of third-party capital. Also, in a study conducted between 2001 and
2007, Gonçalves et al. (2012) found that the accession of Differentiated Levels
has valorized the stocks of companies and has generated greater flow of roles
within the financial market. And still, Silva (2010), analyzing the years from
2007 to 2009, found that the adoption of good practices of corporate governance
positively influenced the volatility of companies’ returns.
2.3
Analysis of Financial Indexes
Indexes
represent the ratio between accounts or groups of accounts of financial
statements and are used to analyze certain aspects of the financial and
economic situation of a company and its performance (ASSAF NETO, 2002; 2010).
Indexes, according to Matarazzo (2010), can be divided into (1) indexes that
show aspects of the economic situation and (2) indexes that show aspects of the
financial situation of a company.
As
can be visualized in Figure 1, the indexes that show the financial situation of
a company are configured as structure indexes and liquidity indexes. In turn,
indexes that show the economic situation of a company are configured as
profitability indexes.
Figure 1. Economic and Financial Indexes
Source: MATARAZZO (2010, p. 84).
In
its constitutive definitions, Economic and financial indexes are established as
follows:
Structure Indexes
Structure
indexes are linked to the capital composition of a company, i.e., the sources
of capital (own or third-party). Thus, these types of indexes analyze the returns
and risks on the capital structure of the company, generating information about
investment, financing, dividend, profit distribution, and valorization of the
company, which helps in making decision related to the application and
obtaining of resources (SILVA, 2008; RIBEIRO; BOLIGON, 2009; MATARAZZO, 2010).
According to Matarazzo (2010), this type of index, in general, addresses the
participation of third-party capital, the composition of indebtedness, the
immobilization of equity or the immobilization of non-current resources.
Liquidity Indexes
Liquidity
indexes are linked to the company’s ability to pay off short-term financial
obligations, since they analyze the ratio between current assets and debts.
Moreover, it is through these indexes that can be seen the financial situation
of the company, because indicated, for example, the imminence of insolvency
(RIBEIRO; BOLIGON, 2009; GITMAN, 2010; MATARAZZO, 2010). According to Matarazzo
(2010), this type of index, generally, addresses the general liquidity, the
current liquidity or the drought liquidity.
Profitability Indexes
Profitability
indexes are linked to the assessment of profits in relation to factors such as
sales, assets, investments by owners or third-party capital, i.e., evaluate the
company’s ability to obtain profit, established as the economic result of the
company (REIS, 2009; RIBEIRO; BOLIGON, 2009; GITMAN, 2010). According to
Matarazzo (2010), this type of index, in general, addresses the asset turnover,
net margin, the return on assets or return on equity.
2.4
Solvency Indexes
Inserted
into the group of indexes about the financial situation of the company, the
solvency prediction models have aiming to provide, in advance and reasonable
safety, prediction information of default or insolvency, which may reveal
possible bankruptcies (KRAUTER; SOUZA; LUPORINI, 2005; REBELLO, 2010). In
general, when a company appears unable to meet its financial obligations or
when your assets are less than the value of its liabilities, this company is in
insolvent situation (LEV, 1978; GIMENES; URIBE-OPAZO, 2001).
Nascimento,
Pereira and Hoeltgebaum (2010) find that one of the best ways to understand the
companies’ performance is by the insolvency measurement. Through the
application of the models from Elizabetsky, Kanitz, Matias, Altman, Baidya and
Dias, and Silva, the authors analyzed the financial performance of two leading
Brazilian airline industry (2004 to 2008) and explained much of the marketing
and economic consequences that influenced these companies financially. In turn,
Baptista (2011), by the prediction models solvency of Altman, Kanitz and
Matias, analyzed the performance of the largest dealerships of Brazilian
highways with stocks traded on BM&FBovespa (2000-2010). As result, the
author was able to foresee financial difficulties over the period in question
for the companies analyzed.
Rebello
(2010) raises the major issues that a solvency index can usefully explain:
·
The situation of pre-solvency;
·
The hierarchizing of companies in a scale ranging of
solvency to insolvency;
·
Forecasts for the account “debtors doubtful” according
to probability of insolvency conforms the risk level of each client.
However,
as emphasized Mario (2002) and Krauter, Luporini and Souza (2005), the
calculation result of demand forecasting cannot guarantee absolutely that, if
the models point insolvency, the company will actually crash and vice-versa.
Moreover, Brédart (2014) reinforces the need of companies listed in corporate
governance programs appeal to projects for bankruptcy protection, avoiding
complete confidence in solvency indexes. Nevertheless, for the result to be as
consistent as possible with the reality, according to Wang, Dennis and Tu
(2007), it is important that the data of the financial statements are presented
reliably and the calculations are interpreted considering market, economic,
political and social issues which influencing the company investigated.
3. METHODOLOGY
3.1
Type of Research
The
aim of this study is to identify whether there is a relationship between good
practices of corporate governance and the real solvency/insolvency ratio of
companies from the Brazilian electricity sector. Therefore, the research
conducted is classified as descriptive regarding to the purpose and
experimental of discriminant type with a quantitative approach regarding to
analysis procedures. Descriptive research seeks to identify relationships
between variables from the delimitation of criteria, assumptions and research
questions. In turn, discriminant analysis, according to Assaf Neto (2002, p.
250):
It identifies the basic characteristics of a universe of variables under
process of analysis, classifying it, as consequence, into categories of similar
performance. Thus, through various economic-financial indicators of enterprises,
the application of discriminant analysis allows one to know the typical
characteristics of each business group, obtaining, thereby, the forecast
factors of solvency and insolvency.
3.2
Procedures
Data
from the companies investigated were collected in two ways. For the calculation
of the solvency solvency/insolvency ratios were consulted financial statements
published by the companies on their own websites. In turn, information relating
to the Balance Sheet and the Statement of Income were extracted from the
terminal of financial information: Bloomberg Professional (Bloomberg, 2013).
3.3
Analysis Model
Simple
linear regression was used to verify the existence of positive or negative
correlation between the levels of corporate governance and the results from
calculations of the solvency/insolvency ratios. According to Samohyl (2009),
the simple regression aims to estimate the relationship between two variables: Yt,
commonly called variable explained, and Xt commonly called variable
explanatory. The equation that represents the simple regression is:
Yt = a + bXt
+ et
In
which, coefficients a and b must be estimated and the criteria
to be used is the minimization of the error et. Once determined the values of a and b
by the method of least squares, the position of the line is determined, as
shown in Figure 2.
Figure
2. Straight Line Estimated of Regression in the Scatter Chart X-Y
Source: Samohyl (2009, p. 201).
For
the determination of the coefficients a and b, the following
equations are used: 3.4 Sample Selection
For
the selection of companies of the Brazilian electricity sector, 2 criteria were
used: (1) companies are inserted into some of the differentiated levels of
corporate governance for at least 7 years. This criterion was established,
since the selection of the period for which financial information was used for
the calculation of solvency indexes are from 2007 to 2011; and (2) the
financial data of these companies were inserted into the Bloomberg terminal for
survey and subsequent analysis.
After
using these selection criteria, we count with a sample of 11 companies of the
Brazilian electricity sector [equivalent to 61% of companies of this sector
listed on BM&FBovespa (N=18)], presenting as the final sample. Table 2
raises the companies selected in their respective levels of corporate
governance and the date of Accession in each segment:
Table 2: Companies Selected
New Market |
Date of Accession |
CPFL
Energia S.A. |
09/29/2004 |
Light
S.A. |
02/22/2006 |
MPX
Energia S.A. |
12/14/2007 |
Tractebel
Energia S.A. |
11/16/2005 |
Level 2 |
Date of Accession |
Celesc - Centrais Elétricas de Santa Catarina S.A. |
06/26/2002 |
Eletropaulo Metropolitana Eletricidade de São Paulo
S.A. |
12/13/2004 |
Transmissora Aliança de Energia Elétrica S.A. |
10/27/2006 |
Level 1 |
Date of Accession |
Eletrobrás - Centrais Elétricas Brasileiras S.A. |
09/29/2006 |
CESP
- Cia Energetica de Sao Paulo |
07/28/2006 |
Cemig - Cia. Energética de Minas Gerais |
10/17/2001 |
CTEEP - Companhia de Transmissão de Energia Elétrica
Paulista |
09/18/2002 |
Source:
BM&FBovespa (2013)
A
sample of 11 companies is justified, since the companies of the survey hold
together 61.13% of the Brazilian energy sector and that related studies have
used sectoral samples to verify financial and accounting impacts on specific
sectors, such as studies by Alves, Ribeiro and Mantese (2007) who used 10
companies from the energy sector, Macedo and Corrar (2009) who used 26
companies from the energy sector, and Serafim and Gomes (2011) who used 14
companies from the energy sector.
3.5 Analysis:
Models of Calculation of Solvency Indexes
Since
the aim of this study is to analyze solvency indexes in a discriminant
perspective, we chose to use the models of calculation from Elizabetsky (1976),
Kanitz (1978), Matias (1978) and Altman (1979), which are not of large
complexity (compared, for example, to the technique of artificial neural
networks) and all use the discriminant analysis through the treatment of
quantitative data to foresee situations of solvency. That is, we calculated the
insolvency indexes of the companies by applying the four models described
below.
Elizabetsky’s Model
Elizabetsky
(1976) used 374 companies, in which 274 were solvents and 100 were insolvents,
to build a predictive model of insolvency, by the identification of
correlations between groups of indexes in a discriminant function, developing a
model calculation. The model developed by Elizabetsky is represented by the
following discriminant function:
Y =
1.93 X1 – 0.21 X2 + 1.02 X3 + 1.33 X4 – 1.13 X5
In which:
Y = Insolvency
Factor
X1 = Net Income /
Sales
X2 = Available /
Fixed Assets
X3 = Receivables
/ Total Assets
X4 = Inventory /
Total Assets
X5 = Current
Liabilities / Total Assets
According
Elizabetsky (1976), the critical point established for their model is 0.5, that
is, values below this point indicate that the company should be classified as
“insolvent” and values above this point indicate that the company should be
classified as “solvent”.
Kanitz’s Model
Kanitz
(1978), following the model of Elizabetsky, used the discriminant analysis to
the construction their own insolvency prediction model, using the financial
indicators whose numerical scale ranges from -7 to +7, also known as “Kanitz’s
Thermometer”. The Kanitz’s model is represented by the following discriminant
function:
Y =
0.05 X1 + 1.65 X2 + 3.55 X3 – 1.06 X4 – 0.33 X5
In which:
Y = Insolvency
Factor
X1 = Net Income /
Net Equity
X2 = (Current
Assets + Long-term Assets) / (Current Liabilities + Long-term Liabilities)
X3 = (Current
Assets – Inventories) / Current Liabilities
X4 = Current
Assets / Current Liabilities
X5 = (Current
Liabilities + Long-term Liabilities) / Net Equity
Thus,
in this model, when a company has a score between 0 and +7, it is considered
solvent. On the other hand, when a company has a score between -4 and -7, it is
considered insolvent. And companies whose results are in the range between -3
and 0, they are considered belonging to a region of the “Kanitz’s Thermometer”
called “penumbra”, which is not able
to discriminate satisfactorily if the company can be considered solvent or
insolvent.
Matias’ Model
Matias
(1978) used a sample of 100 companies, in which 50 of these were solvents and
50 were insolvent, to develop a solvency analysis model. The Matias’ model is
represented by the following discriminant function:
Y =
23.79 X1 – 8.26 X2 – 9.87 X3 – 0.76 X4 – 0.54 X5 + 9.91 X6
In which:
Y = Insolvency
Factor
X1 = Net Equity /
Total Assets
X2 = Bank Loans /
Current Assets
X3 = Suppliers /
Total Assets
X4 = Current
Assets / Current Liabilities
X5 = Operating
Income / Gross Profit
X6 = Available /
Total Assets
According
to Matias (1978), the critical point established for their model is 0 (zero),
that is, values with negative score classified the company as insolvent, while
values with positive score classified the company as solvent.
Altman’s Model
In
turn, Altman (1979) estimated two models, which as others mentioned, sought to
identify and predict companies that have financial situations of solvency. The
models developed by Altman are represented by the following discriminant
function:
Y1 = – 1.44
+ 4.03 X2 + 2.25 X3 + 0.14 X4 + 0.42 X5
Y2 = – 1.84
– 0.51 X1 + 6.32 X3 + 0.71 X4 + 0.53 X5
In which:
Y1 = Insolvency
Factor of Model 1
Y2 = Insolvency
Factor of Model 2
X1 = (Current
Assets – Current Liabilities) / Total Assets
X2 = Reserve and
Earnings Retained / Total Assets
X3 = Total Assets
X4 = Net Equity /
Total Assets
X5 = Sales /
Total Assets
As
the Matias’ model, the critical point of the Altman’s model is equal to 0
(zero) and therefore companies with results higher than zero (positive) are
classified as solvents and less than zero (negative) are classified as
insolvent. In this study, we considered only the equation Y1 to represent the Altman’s model.
4. RESULTS
After
the calculation of the solvency indexes referring to the four models used for
the five years analyzed, we came to the following results.
Elizabestky’s Model
In
Table 3 is possible to view the calculation of Elizabetstky’s model for the
companies analyzed in 3 levels of corporate governance. As pre-specified, the
results below 0.5 indicate companies in insolvent situation and above this
value indicate solvent situation.
Table 3: Results
of Solvency Indexes for the Elizabestky’s Model
|
Level 1 |
Level 2 |
New Market |
||||||||
Eletrobras |
Cesp |
Cemig |
CETEEP |
Celesc |
Eletropaulo |
TAESA |
CPFL |
Light |
MPX |
Tractebel |
|
2007 |
0.07 |
0.06 |
0.15 |
0.77 |
0.10 |
0.07 |
0.75 |
0.15 |
0.35 |
-1.31 |
0.47 |
2008 |
0.46 |
-1.95 |
0.19 |
0.65 |
0.09 |
-0.05 |
0.35 |
0.07 |
0.23 |
48.22 |
0.42 |
2009 |
0.00 |
0.28 |
-0.07 |
-0.03 |
-0.25 |
-0.97 |
-1.78 |
0.11 |
0.02 |
-6.60 |
0.47 |
2010 |
-0.01 |
-0.01 |
0.11 |
-0.48 |
-0.15 |
-4.09 |
-5.78 |
0.03 |
-0.01 |
-5.27 |
0.40 |
2011 |
0.04 |
0.00 |
-0.06 |
-4.42 |
-0.21 |
-3.44 |
0.31 |
0.05 |
-0.07 |
-4.94 |
0.54 |
Source: Research data.
It is
found that all the companies presented themselves insolvent in almost all years
analyzed, except CETEEP (in 2007 and 2008), TAESA (in 2007), MPX (in 2008) and
Tracbetel (in 2011). Indexes that constitute the solvency situation can be
visualized in bold (Table 3).
In
addition it is understood that the relationship of these indices with the
levels of corporate governance not shows apparent relationships. For example,
for those companies classified in the Level 1, all proved be insolvent in all
periods analyzed, except CETEEP in the years 2007 and 2008, presenting indexes
of 0.77 and 0.65 respectively. For those companies classified in the Level 2,
all proved be insolvent in the years 2007 to 2011, with exception of the TAESA
that in 2007 presented a result of 0.75, indicating a solvency situation.
Meanwhile, for those companies classified in the New Market, we can notice the
same behavior of the indexes already displayed in the lower levels, with the
exception of the Tractebel that in 2011 showed an index of 0.54 and the MPX
which presented in 2008 a high index of 48.22, characterizing them in a
solvency situation in those years reported. That is, solvency situation does not
appear often in any level in particular.
Kanitz’s Model
In
Table 4 it is possible to view the calculation of the Kanitz’s model for the
companies analyzed in the 3 levels of corporate governance. As pre-specified,
results between 0.0 and +7.0 show that the company is in a solvency situation,
between -7.0 and -4.0 show that the company is in a insolvency situation, and
between -3.0 and 0.0 show that the company is in the region of “penumbra” – in which cannot accurately
discriminate whether the company is in a solvency or insolvency situation.
Table 4: Results
of Solvency Indexes for the Kanitz’s Model
|
Level 1 |
Level 2 |
New Market |
||||||||
Eletrobras |
Cesp |
Cemig |
CETEEP |
Celesc |
Eletropaulo |
TAESA |
CPFL |
Light |
MPX |
Tractebel |
|
2007 |
8.80 |
4.81 |
5.25 |
10.53 |
5.41 |
4.51 |
7.27 |
4.16 |
5.75 |
17.38 |
4.46 |
2008 |
7.45 |
4.07 |
5.26 |
8.99 |
5.91 |
4.02 |
3.91 |
3.87 |
5.36 |
8.51 |
3.28 |
2009 |
10.25 |
4.64 |
4.13 |
10.88 |
4.84 |
4.50 |
5.91 |
4.60 |
5.87 |
6.75 |
5.52 |
2010 |
7.10 |
4.67 |
5.00 |
9.36 |
4.68 |
4.98 |
12.12 |
4.02 |
4.59 |
4.31 |
4.26 |
2011 |
6.16 |
4.87 |
3.43 |
5.60 |
5.01 |
4.94 |
4.26 |
4.61 |
4.91 |
2.58 |
5.02 |
Source: Research data.
From
the Kanitz’s model, as can be seen in Table 4, the indexes of all companies in
all years analyzed, scored higher than 0.0 indicating that all were in a
solvency situation. Moreover, some indexes presented values above than +7.0,
which exceeds the standard limit of solvency, indicating external or internal
occurrences to the companies (Eletrobras, CETEEP, TAESA and MPX) not explained
by the data collected, but that reflect in the indexes as, for example, an
increase in the purchase of stocks of these companies or a over-valorization of
their assets.
Matias’ Model
In
Table 5 is possible to view the calculation of Matias’ model for the companies
analyzed in the 3 levels of corporate governance. As pre-specified, the
critical point established for this model is 0.0. Thus, results below 0.0
indicate that companies are insolvents and above this value indicate that
companies are solvents.
Table 5: Results
of Solvency Indexes for the Matias’ Model
|
Level 1 |
Level 2 |
New Market |
||||||||
Eletrobras |
Cesp |
Cemig |
CETEEP |
Celesc |
Eletropaulo |
TAESA |
CPFL |
Light |
MPX |
Tractebel |
|
2007 |
13.76 |
7.98 |
5.88 |
13.39 |
6.99 |
5.31 |
7.64 |
4.09 |
4.82 |
19.45 |
7.53 |
2008 |
8.42 |
8.99 |
6.65 |
9.95 |
7.34 |
4.27 |
-3.91 |
3.53 |
4.71 |
508.2 |
1.67 |
2009 |
10.35 |
9.53 |
2.63 |
12.56 |
7.18 |
4.92 |
4.44 |
4.66 |
6.41 |
11.55 |
7.55 |
2010 |
9.90 |
6.99 |
5.07 |
11.90 |
7.25 |
5.26 |
10.33 |
2.77 |
5.03 |
-4.07 |
4.11 |
2011 |
9.11 |
8.79 |
-0.59 |
5.49 |
7.21 |
5.80 |
0.09 |
4.11 |
4.09 |
36.55 |
7.47 |
Source: Research data.
It is
found that all companies showed up solvents in almost all the years analyzed,
with the exception of Cemig (in 2011), the TAESA (in 2008) and MPX (in 2010).
Indexes that make up the insolvency situation can be visualized in bold. Compared
to the previous model of Elizabetstky, the MPX showed in 2008 the same increase
in its index (508.26), indicating an atypical situation for the company in that
year.
Altman’s Model
In
Table 6 is possible to view the calculation of Altman’s model for the companies
analyzed in the 3 levels of corporate governance. As pre-specified, the
Altman’s model follows the same pattern of Matias’ model in which the critical
point is 0.0. Thus, results below 0.0 indicate that the companies are insolvent
and above this value indicate that the companies are solvent. Still, we
considered only the equation Y1 to
represent the Altman’s model.
Table 6: Results
of Solvency Indexes for the Altman’s Model
|
Level 1 |
Level 2 |
New Market |
||||||||
Eletrobras |
Cesp |
Cemig |
CETEEP |
Celesc |
Eletropaulo |
TAESA |
CPFL |
Light |
MPX |
Tractebel |
|
2007 |
0.63 |
-0.38 |
0.01 |
1.56 |
-0.41 |
-0.18 |
-0.93 |
-0.73 |
-0.84 |
-1.64 |
-0.45 |
2008 |
-0.68 |
-1.26 |
-0.35 |
1.02 |
-0.27 |
-0.16 |
-0.93 |
-0.81 |
-0.61 |
-1.13 |
-0.43 |
2009 |
-0.03 |
-0.28 |
0.10 |
1.48 |
-0.39 |
0.21 |
-0.11 |
-0.50 |
-0.36 |
-1.67 |
-0.19 |
2010 |
-0.01 |
-0.32 |
-0.06 |
1.13 |
-0.14 |
0.31 |
0.04 |
-0.53 |
-0.43 |
-1.75 |
-0.13 |
2011 |
-0.10 |
-0.35 |
-0.12 |
0.72 |
0.00 |
0.59 |
-0.50 |
-0.67 |
-0.68 |
-1.91 |
0.12 |
Source: Research data.
From
Altman’s model, as can be seen in Table 6, the indexes of all the companies
showed up more balanced and more heterogeneous than results of previous models.
Indexes that constitute the solvency situation can be visualized in bold. In
this model, the New Market showed greater insolvency than the Levels 1 and 2,
since that only the Tracbetel was solvent with an index of 0.12 in 2011, while
all other companies in the New Market showed up insolvent in all years
analyzed.
Furthermore,
with the Altman’s Model, the CETEEP (Level 1) proved to be solvent in all
years, following the previous models, especially of Kanitz and Matias, in which
the CETEEP also was solvent in all years analyzed. In turn, at Level 2, the
best results were of the Eletropaulo which showed up solvent in the years 2009,
2010 and 2011, conform the pattern found in models of Kanitz and Matias.
Linear Regression Equations for Solvency Indexes
With
the data relating to solvency indexes, it was proceeded the simple linear
regression to analyze the relationship between the level of corporate
governance and the indexes of companies. The regression equations allow better
spatial visualization of how the indexes of companies behave, verifying the
correspondence between the results of the solvency indexes for all models
analyzed in the five years calculated and the level of corporate governance
that belong to each company, as can be seen in the graphs shown below.
As
can be seen in Graphs 1, 2, 3 and 4, the vertical axis defines the results of
the solvency indexes and the horizontal axis properly defines the companies
grouped by levels of corporate governance to which each belongs. Thus, the
first group of points in the direction from left to right on the horizontal
axis of the graphs represents the companies belonging to Level 1, the second
group refers to the companies belonging to Level 2 and the third group,
farthest from the axis Y, represents
companies classified in the New Market.
Analyzing
Graph 1, relative to the Elizabetsky’s model, solvency indexes showed a
positive correlation only in 2008. In other years, the better levels of
corporate governance showed, in general, a fall in result of the solvency
indexes calculated.
Graph 1.
Elizabetsky’s model for the years 2007-2011
Analyzing
Graph 2, related to the Kanitz’s model, solvency indexes showed a behavior with
positive correlation only in 2007. During the years 2008 to 2011, however, it
was found that, in general, the better level of governance corporate resulted
in lower values of solvency indexes.
Graph 2. Kanitz’s model for the years 2007-2011
Analyzing
Graph 3, related to the Matias’ model, solvency indexes presented a behavior
with positive correlation between the level of corporate governance and
solvency index only in the years 2008 and 2011. In other years, however, we
have not found any correlation significant.
Graph 3. Matias’ model for the years 2007-2011
Analyzing
the Graph 4, related to the Altman’s model, solvency indexes have been
decreasing in all the years analyzed respecting to the level of corporate
governance. Therefore, it was found for the Altman’s model, a negative
correlation with the level of corporate governance and solvency index.
Graph 4. Altman’s model for the years 2007-2011
Moreover,
as can be seen in Graphs 1, 2, 3 and 4, in the analysis of the calculated
indexes, the lines that describe the trend of the points were added, calculated
from the simple linear regression along with the functions that represent them.
Thus, if the straight line presents an increasing function implies that the
solvency indexes of companies belonging respectively to Levels 1, 2 and New
Market have a tendency to increasing results.
That
is, for the case of an increasing straight line, i.e., with the positive linear
coefficient (number that multiplies the x unknown of the function y = ax + b),
we have a direct correlation between the levels of corporate governance and the
solvency indexes. In turn, for the case of a decreasing straight line, i.e.,
the negative linear coefficient, we have an indirect correlation between the
levels of corporate governance and the solvency index.
Therefore,
in order to identify whether the solvency indexes showed up in increasing or
decreasing, the functions of simple linear regression were specified. Thus, the
solvency indexes were plotted for each of the years analyzed and for all models
used, as can be seen in Table 7.
Table 7: Summary
of the Functions from the Solvency Indexes for the Models Used
Model |
Year |
Function |
Description |
Elizabetsky |
2007 |
y = -0.003x + 0.325 |
Decreasing |
2008 |
y = 0.117x – 2.123 |
Increasing |
|
2009 |
y = -0.014x + 0.019 |
Decreasing |
|
2010 |
y = -0.010x – 0.784 |
Decreasing |
|
2011 |
y = -0.000x – 1.093 |
Decreasing |
|
Kanitz |
2007 |
y = 0.006x + 6.783 |
Increasing |
2008 |
y = -0.011x + 6.127 |
Decreasing |
|
2009 |
y = -0.016x + 7.099 |
Decreasing |
|
2010 |
y = -0.020x + 7.060 |
Decreasing |
|
2011 |
y = -0.007x + 5.063 |
Decreasing |
|
Matias |
2007 |
y = -0.011x + 9.448 |
Decreasing |
2008 |
y = 1.141x – 12.99 |
Increasing |
|
2009 |
y = -0.011x + 8.066 |
Decreasing |
|
2010 |
y = -0.060x + 9.266 |
Decreasing |
|
2011 |
y = 0.068x + 4.155 |
Increasing |
|
Altman |
2007 |
y = -0.012x + 0.410 |
Decreasing |
2008 |
y = -0.003x – 0.291 |
Decreasing |
|
2009 |
y = -0.009x + 0.356 |
Decreasing |
|
2010 |
y = -0.008x + 0.290 |
Decreasing |
|
2011 |
y = -0.007x + 0.166 |
Decreasing |
Source: Research data.
As
can be seen in Table 7, only 20% of the results showed a positive correlation
(increasing) between the levels of corporate governance and the solvency
indexes, while 80% of the results showed a negative correlation (decreasing)
for the same correspondents.
Following,
the results obtained are discussed in detail to light of the main issues that
surround the theoretical and empirical implications from the findings.
5. DISCUSSION
Initially,
it is understood that there was no a consensus among the four approaches used
(Elizabetsky, Kanitz, Matias and Altman) regarding to the results of the
indexes calculated. In the Elizabetsky’s model, the companies analyzed proved
to be insolvent. In the Kanitz’s model, the companies analyzed proved to be
solvents. In the Matias’ model also, the companies analyzed proved to be
solvents with some years, but not in all. On the other hand, the Altman’s model
there was a balance between the companies that were solvent and insolvent.
Moreover, it has not been possible to find performance patterns or
relationships among solvency indexes and levels of corporate governance only by
observing the indexes descriptively.
Thus,
it was performed a further analysis that could reveal related behaviors
correlated between solvency indexes and the levels of corporate governance –
the simple linear regression. With the regression analysis, we observed the
relationships required, however, they did not show a pattern of results,
indicating both positive and negative relationships, the latter being the most
frequent.
Still,
the fact of the indexes show that companies in a moment were in a solvency
situation, and in another moment were in a insolvency situation, did not result
in positive (increasing) or negative (decreasing) relationship between solvency
index and level of corporate governance. That is, for both situations (solvency
or insolvency) were possible to find positive and negative correlations (Table
7). It is understood is that not only models do not come into a consensus of
results, but there is no relationship between solvency and level of corporate
governance.
6. FINAL
CONSIDERATIONS
This
study aimed to identify whether there is a relationship between good practices
of corporate governance and the solvency of companies from the Brazilian
electricity sector, using to this end, four distinct models for the solvency
calculation: Elizabetsky (1976), Kanitz (1978), Matias (1978) and Altman
(1979). These models used for calculating the solvency index aim to identify
and predict unfavorable financial situations to companies through the analysis
of its financial results. Thus, the practical implication of this study is in
the finding that correlations between these two factors (solvency and governance)
could represent additional information to investors in choosing of a more
viable and safe option of investment and that represents a lower risk to
capital invested.
However,
results show that it is not possible to affirm the existence of correlation
between the solvency indexes and higher levels of corporate governance. That
is, good practices of corporate governance, allowing the insertion of the
companies listed on the stock exchange in a best position on the market, does
not necessarily imply better solvency.
Moreover,
it is noteworthy that the solvency indexes calculated should not be analyzed
alone without taking into account external and internal environmental factors
to the organization. As could be evidenced, this occurs because the solvency
indexes did not show a consensus of results that indicates usefully which
purports to predict: the solvency or insolvency. Nevertheless, solvency indexes
represent additional tools and not solely sufficient to aid in decision making
on the financial and economic ambit.
From
this, there are some recommendations to be made for future studies related to
the topic. Basically, it would be worthwhile for future studies that they reply
which has been proposed here in other sectors of the economy, so that it could
assess more accurately the lack of correlation between the differentiated
levels of corporate governance and the solvency, or even refute the
considerations raised here. Also, for future studies, it could be developed
researches that use other models for calculating the solvency, such as logistic
regression or neural networks.
Finally,
we consider that the limitation of this study is the sample size. Regarding to
the sample, it can be noticed that 11 companies are a limited number of
companies to perform inferences. However, it is noteworthy that this is a
sectoral study which focused strictly on a sector fairly sensitive to
environmental factors, reflecting this sensibility in their indexes.
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