4
Measurement of liquidity, insolvency and market risk levels in the textile sector of
Ecuador
Medición de los niveles de riesgos de liquidez, insolvencia y mercado en el sector
textil del Ecuador
URL: https://revistas.uta.edu.ec/erevista/index.php/bcoyu/article/view/1014
Iván Orellana-Osorio
1
; Marco Reyes-Clavijo
2
; Luis Tonon-Ordóñez
3
; Luis Pinos-Luzuriaga
4
Fecha de recepción: 11 de agosto de 2020 Fecha de aceptación: 21 de diciembre de 2020
Abstract
A company is exposed to different types of financial risk (systematic and non-systematic risks). This research focuses on
analyzing the insolvency, market and liquidity risks of the Textile Sector of Ecuador in the period 2007-2018. Regarding the
methodology, a non-experimental study was carried out with a quantitative approach. The Superintendence of Companies,
Securities and Insurance was used as a source of information; also scientific information on financial risk and the textile sector
in Ecuador was analyzed. In the insolvency risk analysis, through the methodologies of Altman and Ohlson, it was determined
that the riskiest years are 2016 and 2018: Altman score of 5,545 and 5,690 respectively, and a percentage of insolvency risk
of 6,40% and 7,46% in the same years. In the market risk analysis, the Beta coefficient for the textile sector was 1,2. In
addition, microenterprises have a higher level of liquidity risk, with 57,06%. Determining the financial risk of a company is an
important tool for making decisions and helps to have a better vision of the fulfillment of the proposed objectives.
Keywords: Insolvency risk, market risk, Altman, Ohlson, textile sector of Ecuador.
Resumen
Una empresa está expuesta a diferentes tipos de riesgo (riesgos sistemáticos y no sistemáticos). La presente investigación
se enfoca en analizar los riesgos de insolvencia, mercado y liquidez del Sector Textil del Ecuador en el periodo 2007-2018.
En relación a la metodología, se realizó un estudio no experimental con un enfoque cuantitativo; la Superintendencia de
Compañías Valores y Seguros es la fuente de información principal del estudio. En el análisis de riesgo de insolvencia, a
través de las metodologías de Altman y Ohlson, se determinó que los años más riesgosos son el 2016 y 2018: puntaje de
Altman de 5,545 y 5,690 respectivamente, y un porcentaje de riesgo de insolvencia del 6,40% y 7,46 %. El riesgo de mercado
a través del coeficiente Beta para el sector textil fue de 1,2. Además, las microempresas presentan un mayor nivel de riesgo
de liquidez, con un 57,06 %. Determinar el riesgo financiero de una empresa es una herramienta importante para la toma de
decisiones y ayuda a tener una mejor visión del cumplimiento de los objetivos propuestos.
Palabras clave: Riesgo de insolvencia, riesgo de mercado, Altman, Ohlson, sector textil del Ecuador.
.
1
Universidad del Azuay. Facultad de Ciencias de la administración. Observatorio Empresarial. Cuenca-Ecuador. E-mail:ivano@uazuay.edu.ec. ORCID:
https://orcid.org/0000-0001-6279-2734
2
Universidad del Azuay. Facultad de Ciencias de la administración. Observatorio Empresarial. Cuenca-Ecuador. E-mail:mreyes@uazuay.edu.ec. ORCID:
https://orcid.org/0000-0001-5279-4234
3
Universidad del Azuay. Facultad de Ciencias de la administración. Observatorio Empresarial. Cuenca-Ecuador. E-mail:ltonon@uazuay.edu.ec. ORCID:
https://orcid.org/0000-0003-2360-9911
4
Universidad del Azuay. Facultad de Ciencias de la administración. Observatorio Empresarial. Cuenca-Ecuador. E-mail:lpinos@uazuay.edu.ec. ORCID:
https://orcid.org/0000-0002-3894-8652
Boletín de Coyuntura, Nº 27; octubre - diciembre 2020; e-ISSN 2600 - 5727 / p-ISSN 2528 – 7931; UTA-Ecuador; Pág. 4-16
Measurement of liquidity, insolvency and market risk levels in the textile sector of Ecuador
Medición de los niveles de riesgos de liquidez, insolvencia y mercado en el sector textil del Ecuador
5
Introduction
The Oxford Dictionary (2020) defines risk as a situation
involving exposure to danger or the possibility that
something unpleasant or unwelcome will happen. In the
business field, Celaya and López (2004) define risk as the
probability that the company will not be able to face any
situation inherent to its activity. Due to the high degree of
uncertainty, it is necessary to use methodologies that allow
measuring and predicting the level of risk of an activity.
Therefore, it must be borne in mind that, at a higher level of
risk, the investor will demand a higher level of profitability.
According to Circiumaru, Siminica, & Ganea (2009), there is
an indirect relationship between the level of the risk effects
and the level of the efficiency and efficacy: the smaller are
the risk effects, the bigger are the efficiency and the efficacy
and vice-versa.
There are different types of financial risk, according to Lara
(2008), these can be classified into market, credit, liquidity,
legal, operational and reputational risk. In addition, Ross,
Westerfield and Jordan (2010) indicates that risk can be
classified as systematic and unsystematic risk.
Unsystematic risk is caused by the company's own activity
and systematic risk is the one that influences many assets,
and since it has effects on the entire market, it is also called
market risk. With the aim of measuring the different types of
financial risk (systematic and non-systematic risks), this
research focuses on analyzing insolvency risk (credit),
liquidity risk and market risk.
Literature review and theoretical background
Insolvency Risk
Insolvency risk refers to the uncertainty due to the possibility
that the company cannot meet its financial obligations. This
is understood as the state of financial vulnerability, which
ranges from the impossibility of compliance in the payment
of obligations to the bankruptcy and liquidation of the
company (Terreno et al., 2017). The methodologies to
measure and predict business failure have had a
considerable evolution, and two stages can be highlighted
in the development of business failure measurement
models: descriptive stage and predictive stage.
FitzPatrick (1932) was one of the first researchers to analyze
business failure, who was part of the "descriptive stage of
bankruptcy model development". Smith y Winakor (1935)
analyzed ratios of 183 failed companies from a variety of
industries. Merwin (1942) asserted that bankrupt companies
showed signs of weakness even four or five years before
failure. Chudson (1945) says that the industry to which a
corporation belongs is a significant factor in determining the
structure of the corporation's balance sheet. Jackendoff
(1962) analyzed critical problems regarding the
classification, selection, and use of ratios. Horrigan (1965)
analyzes the statistical nature and susceptibility of financial
ratios. The following group of financial ratios makes up the
basic list of his study:
• Short - term liquidity ratios: current assets to current
debt (current ratio), current assets less inventory to
current debt (quick ratio), cash plus marketable
securities to current debt.
• Long – term solvency ratios: Net operating profit to
interest, net worth to total debt, net worth to long – term
debt, net worth to fixed assets.
• Capital turnover ratios: sales to accounts receivable,
sales to inventory, sales to working capital, sales to
fixed assets, sales to net worth, sales to total assets.
• Profit margin ratios: net operating profit to sales, net
profit to sales.
• Return on investment ratios: Net operating profits to
total assets, net profits to net worth.
Subsequently, the predictive stage begins with Beaver
(1966), who proposes the prediction of business failure and
suggests a methodology for evaluating accounting data for
different purposes and not only to determine solvency. The
author analyzes the following group of ratios:
• Group 1: Cash flow ratios.
• Group 2: Net income ratios.
• Group 3: Debt to total assets ratios.
• Group 4: Liquid – asset to total asset ratios.
• Group 5: Liquid asset to current debt ratios.
• Group 6: Turnover ratios.
At this stage, the studies by Altman (1968) and Ohlson
(1980) emerged, who developed business bankruptcy
forecast models based on univariate, multivariate analysis
techniques and conditional probability econometric models
of logistic regression. Altman initially developed bankruptcy
prediction models aimed at developed countries, later
adaptations have been made to its model, including: Altman,
Baidya y Ribeiro (1979) in Brazil in the period 1973 to 1976.
Pascale (1988) for the Uruguayan manufacturing industry in
the period 1978 -1982. Altman, Hartzell y Peck (1995)
developed the EMS model, the adjusted model incorporates
the credit characteristics of emerging market companies
(model applied in this research).
Similarly, ohlson's model has been applied in multiple
places. For instance, Lieu, Lin and Yu (2008) analyzes
financial distress prediction based on Ohlson's Work; they
used logit regression to establish an early-warning model
using publicly available financial information and includes
emerging stock companies from Taiwan; according to the
author the logit regression model has significant predictive
power and is thus effective in predicting distress. The study
uses financial ratios (financial ratios of five types that are
often used in financial statement analysis) and non-financial
information to establish a financial distress early-warning
model; non-financial variables include ownership structure
and corporate governance indicators. Furthermore,
Krishnasami (2012) uses three regression models in order
to analyse the impact of financial risk on debt – equity mix.
Boritz, Kennedy and Sun (2007) compare Canadian
bankruptcy prediction models developed by Springate
(1978), Altman and Levallee (1980), and Legault and
Véronneau (1986) against the Altman and Ohlson models
using recent data to determine the robustness of all models
over time and the applicability of the Altman and Ohlson
models. The results indicate that the models developed by
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Measurement of liquidity, insolvency and market risk levels in the textile sector of Ecuador
Medición de los niveles de riesgos de liquidez, insolvencia y mercado en el sector textil del Ecuador
6
Springate (1978) and Legault and Véronneau (1986) yield
similar results to the Ohlson (1980) model.
In the study “forecasting business failure in the Valencian
community: Application of the discriminant and logit models,
Ferrando and Blanco (1998) conclude that in general the
success level of the logit model is somewhat higher than that
of the linear discriminant analysis due to its better predictive
capacity . Shehni (2013) says that the Altman model predicts
the probability of bankruptcy in Iranian listed companies
more accurately than the Ohlson model. Efron (1975)
showed that with multivariate normal data, the linear
discriminant function is better than the logistic regression.
The conclusion reached is that none of the methods
consistently outperforms the other, but that the choice of one
or the other method depends on the database used.
While the Z-score and O-score methods are based on a
linear approach, the Neural Network model was introduced
to predict bankruptcy with a non-linear approach. Moreover,
the development of techniques machine learning (support
vector machines, fuzzy systems, neural networks and
evolutionary algorithms), prompted researchers to use these
techniques in business. In this area we can mention: Ahn,
Cho y Kim (2000) who propose an intelligent hybrid system;
Hua, Wang, Xu, Zhang y Liang (2007) who use automatic
support vectors (SVM); Berg (2007) applied generalized
additive models (GAM); Ghazali, Jaafar Hussain, Mohd
Nawi y Mohamad (2009) use higher order neural networks
(HONN); Chaudhuri y De (2011) address bankruptcy
prediction with the Fuzzy Support Vector Machine (FSVM)
artificial neural network model, among others.
Market Risk.
According to Ross, Westerfield and Jaffe ( 2010) the
systematic risk is analyzed in the models of market risk
measurement, which influences many assets and because
it has effects on the entire market, it is also called market
risk. Unsystematic risk can be eliminated through
diversification, but systematic risk cannot be eliminated.
Markowitz (1952) developed a portfolio selection model that
incorporated the principles of diversification, which identifies
a set of efficient portfolios of risky assets, and based on this
set of risky portfolios for any level of risk, only the portfolio
with the highest expected return is the one of interest. Tobin
(1958) analyzes liquidity preference as a behavior towards
risk, and distinguishes two possible sources of liquidity
preference: the lack of elasticity of expectations about future
interest rates and the uncertainty about the future of interest
rates.
Capital Asset Pricing Model (CAPM). - The traditional capital
asset pricing model proposed by Sharpe (1964), Lintner
(1965) and Mossin (1966) asserts that an individual views
the outcome of any investment in probabilistic terms and
considers that the expected return on an asset depends
linearly and positively on its systematic risk, measured by its
Beta. The CAPM model formula is:
( 1)
Where:
• E (Ri) = Minimum profitability expected by
investors.
• Rf = Risk-free rate.
• RM = Average profitability for any period.
• (RM – Rf) = Market risk premium.
• Β = A measure of the volatility–or systematic risk.
The Beta coefficient represents the most critical variable in
the CAPM model, which measures the sensitivity of a
company's performance to changes in market performance.
A Beta greater than 1 indicates that the non-diversifiable risk
of the investment is higher than the market average. The
Beta coefficient can be determined in two ways, according
to Vélez (2011):
(2) ;
(3)
Liquidity risk
The importance of liquidity risk management is related to the
anticipation that companies may have in the face of possible
crises that lead to non-payment of obligations in the short
term. The short and medium term indices that most affect a
company's long-term performance are liquidity,
indebtedness, and portfolio or debtor management (Toro
et al., 2015); these indicators are directly related to liquidity
risk. In the liquidity risk analysis, a logit analysis was used in
order to determine the risk level of the textile companies in
relation to the levels presented by the industry.
Cowan and Hansen (2008) mention that liquidity risk is a
short-term phenomenon linked to the expected cost of debt
refinancing. If interest rates increase, the renewal value of
short-term debt increases automatically and if the company
does not anticipate this rate increase and does not have
enough cash flow to cover this increase, it will begin to sell
the most liquid assets. Leiva (2009) says that liquidity risk
must be analyzed differently from credit and market risk,
since, in this case, a single liquidity event can lead to the
bankruptcy of the company. For this reason, liquidity risk
management must be carried out from its safe side, that is,
its objective is to minimize the probability of its occurrence.
In this context, the aim of this research is to apply the models
mentioned to measure the different types of financial risk in
companies that are part of the manufacture of textile
products in Ecuador, which according to the International
Standard Industrial Classification (ISIC) corresponds to
C13. The sector sheet can be seen inTable 1:
Table 1. Classification of the textile sector
Code
Description
Level
C
Manufacturing
1
C13
Manufacture of textile
2
C131
Spinning, weaving and finishing of textile
3
C1311
Preparation and spinning of textile fibres
4
C1312
Weaving of textiles
4
C1313
Finishing of textile
4
C139
Manufacture of other textile
3
C1391
Manufacture of knitted and crocheted fabric
4
C1392
Manufacture of made-up textile articles, except appare
4
C1393
Manufacture of carpets and rug
4
C1394
Manufacture of cordage, rope, twine and nettin
4
C1399
Manufacture of other textiles n.e.c
4
Source: United Nations Statistical Commission (UNSD) (2008)
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Measurement of liquidity, insolvency and market risk levels in the textile sector of Ecuador
Medición de los niveles de riesgos de liquidez, insolvencia y mercado en el sector textil del Ecuador
7
With the results obtained through the application of
insolvency, market and liquidity risk, the research question
arises: What levels of risk of insolvency, market and liquidity
has the textile sector of Ecuador had in the period 2007 -
2018?
Methodology
Insolvency risk
Altman's Methodology
In order to measure insolvency risk, Altman's insolvency
prediction model developed for emerging markets was first
applied, which classifies companies that are at risk of
business failure: a higher score means that the company is
in the zone safe and not at risk. Altman (1968) chose
multiple discriminant analysis (ADM) as the statistical
technique for his research. The final function proposed by
Altman in his research is:
( 4)
Where:
• X1 = Working capital / total assets.
• X2 = Retained earnings / total assets
• X3 = Profits before interest and taxes / total assets
• X4 = Market value of the equity / book value of the
total debt
• X5 = Sales / total assets
According to Altman (2000) a more convenient specification
of the model is the following:
( 5)
Furthermore, Altman (2000) made new estimates of the
original model, such is the case of the model for closed
capital companies (Z´) and the Altman's model for non-
manufacturing companies with closed capital in general
(Z´´):
( 6)
( 7)
Adjusted model for emerging markets.- Altman, Hartzell y
Peck (1995) developed the EM Score model, which
incorporates the particular credit characteristics of
companies in emerging markets. This is the model that is
applied in the research work.
6,56 (X1) + 3,26 (X2) +6,72 (X3) + 1,05 (X4)
+3,25 ( 8)
According to Altman (2000), the companies ‘credit rating is
used according to the Z-score, which is equivalent to the
creditworthiness rating used by Standars & Poor's. Table 2
shows the values that the equation takes:
Table 2. U.S. Bond Rating Equivalent Based on EM Score
U.S equivalent
rating
Average
EM Score
U.S equivalent
rating
Average
EM Score
AAA
8,15
BB+
5,25
AA+
7,6
BB
4,95
AA
7,3
BB-
4,75
AA-
7
B+
4,5
A+
6,85
B
4,15
A
6,65
B-
3,75
A-
6,4
CCC+
3,2
BBB+
6,25
CCC+
2,5
BBB+
5,85
CCC-
1,75
BBB-
5,65
D
0
Source: Own elaboration based on Altman and Hotchkiss (2006).
Ohlson's Methodology
Ohlson (1980) estimated three models composed of an
intersection and nine independent variables. The description
of the logistic model variables is as follows:
• X1 = Size (logarithm of total assets divided by the
price index).
• X2 = Total Liabilities / Total Assets.
• X3 = Working Capital / Total Assets.
• X4 = Current liabilities / Current assets.
• X5 = Dummy. One if total liabilities exceeds total
assets, zero otherwise.
• X6 = Net Income / Total Assets.
• X7 = Funds provided by operations divided by
total liabilities
• X8 = Dummy. One if net income was negative for
the last two years, zero otherwise.
• X9 = Net income t – Net income t-1/| Net income
t|+ | Net income t-1|.
Models 2 and 3 have somewhat weaker goodness-of-fit
statistics. Model 1 predicts bankruptcy within a year and
presents better results, since it correctly classifies 96,12%
of companies. However, in all three models, size appears as
an important prediction.
( 9)
Ohlson's logistic model was applied, through which the
probability that companies have of falling into risk of
bankruptcy or business failure is obtained. In the regression
analysis, for the codification of the dependent variable, the
criterion of lack of equity was used: those companies that
have the total liabilities greater than the total assets; this is
provided by Superintendency of Companies, Securities and
Insurance (2016).
Market risk
In order to calculate the market risk, the Beta (β) coefficient
was used and also the expected return of the sector was
determined through the CAPM. The ROE (return on equity)
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Measurement of liquidity, insolvency and market risk levels in the textile sector of Ecuador
Medición de los niveles de riesgos de liquidez, insolvencia y mercado en el sector textil del Ecuador
8
was used to measure market risk, which, according to
Gitman (2007), is calculated as follows.
( 10)
An adjusted return (adjusted ROE) was used, which was
calculated by dividing the profit for the year by the initial
equity for the period. In order to determine the market risk
the Beta coefficient was calculated: the relationship between
the adjusted ROE of companies in the manufacturing sector
with the adjusted ROE of the textile sector. The risk-free rate
was established through the reference passive rate of the
Central Bank of Ecuador. The final result of the model is
based on the CAPM to obtain the return expected by the
investor.
This research does not use information from the Ecuadorian
stock market because the country's economy is considered
developing; this is why it can be affirmed that its stock
market is poorly developed and that it concentrates its
negotiations on fixed income documents. The
Superintendency of Companies, Securities and Insurance
(2016a) states that "the poor development of the country's
stock market" (p.31) is due to aspects related to the
economic environment, the supply and demand of
securities. Salcedo (2018) says that “the Ecuadorian Stock
Market is dynamic; however, it is incipient” (p.18). Other
authors such as Hablich, Toala and Agila (2018) reinforce
this idea by saying that "the Ecuadorian stock market is
underdeveloped" (p. 791). For this reason, the Ecuadorian
stock market performance measures are not used, and
instead, accounting measures are used to calculate the Beta
coefficient, specifically the adjusted ROE.
For the application of the risk-free interest rate, different
criteria were considered: Campos, Castro, Cuy y Ferrer
(2005), in the case of the Brazilian electric company
Electrobras, considers the country's credit rating, and also
selects a company that is at the same time rating level to
know the value of the bonds. Sánchez (2010) conducts a
study of the food sector in Colombia and used the geometric
average risk-free rate of 4,97% of the United States T-Bonds
over the 10-year term to apply the CAPM. For this research,
the benchmark passive rate of the Central Bank of Ecuador
is considered as a risk-free rate, since in the Ecuadorian
environment investors will demand a rate of return higher
than the rate they obtain for an investment with low risk
exposure.
Liquidity risk
Finally, the liquidity risk was measured through a logistical
analysis. The dependent variable of the logistics model was
established based on companies with lower or higher levels
of relevant financial indicators of the total number of
manufacturing companies analyzed: liquidity index,
indebtedness index and average collection period.
Dichotomous or dummy variables are used in relation to the
manufacturing industry average: Companies without
liquidity risk = 0, and companies with liquidity risk = 1. The
result of the logistic equation indicates the probability of
liquidity risk of the companies in the sector, this, in relation
to the financial indicators of the manufacturing industry.
Indicators from Table 3 were used in order to determine the
dependent variable of the model. Dichotomous variables
were used in relation to the average of the manufacturing
industry:
Table 3. Manufacturing sector financial indicators
Year
Average
collection
period
Liquidity
index
Indebtedness index
2007
75,016
2,756
0,684
2008
71,691
2,716
0,700
2009
73,800
2,522
0,685
2010
74,668
2,685
0,689
2011
71,567
2,678
0,686
2012
62,326
2,868
0,669
2013
76,183
2,917
0,655
2014
77,905
2,982
0,643
2015
84,044
3,177
0,628
2016
87,497
3,244
0,638
2017
88,986
3,464
0,638
2018
72,664
3,357
0,637
Source: Own elaboration based on Superintendency of
Companies, Securities and Insurance (2019)
Table 4 shows the explanatory variables used for the logistic
model:
Table 4. Explanatory variables of the model
Independent
variable
Description
X1
Average collection period
X2
Average payment period
X3
Average age of inventories
X4
Cash conversion cycle
X5
Annual cash turnover
X6
Liquidity index
X7
Indebtedness index
X8
Working capital
X9
Need of funds
X10
Need of funds / Sales
Source: Own elaboration based on Superintendency of
Companies, Securities and Insurance (2019)
Data
The quantitative analysis was performed based on
information from the Superintendency of Companies,
Securities and Insurance in the 2007-2018 period. This
information was refined due to inconsistent information. The
database was refined using the following analysis criteria:
companies that present information on assets and
companies that present ordinary income, that is, that have
activity.
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Measurement of liquidity, insolvency and market risk levels in the textile sector of Ecuador
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Once the database was refined, 2,082 companies were
included in the analysis, which are classified as: large = 176,
medium = 539, small = 835 and micro = 532. Moreover, it
was determined that an average of 173 companies have
submitted financial information in the 12 years of analysis.
In addition, in the data analysis the Chauvenet criterion was
used to eliminate outliers.
It is worth mentioning that the data analysis was carried out
through Microsoft Excel. In addition, the SPSS software was
used for the regression analysis in the analysis of insolvency
and liquidity risk.
Results
Insolvency risk
Altman's methodology
Annual insolvency analysis. - Figure 1 shows that, on average, companies are in a safe zone, except for the years 2016 and
2018 where they are in the gray zone.
Source: Own elaboration based on Superintendency of Companies, Securities and Insurance (2019)
Figure 1. Altman's annual analysis.
Insolvency analysis by business size. - Altman's analysis indicates that microenterprises have the lowest score, that is, they
have a greater tendency to fall into an insolvency risk zone. However, the data indicates that companies classified by size are
in a safe area. (see Table 5).
Table 5. Altman analysis by business size
Size
Year
Average
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Micro
6,57
7,64
5,43
6,59
4,19
7,16
6,62
7,38
6,53
5,50
5,46
3,28
6,03
Small
7,21
6,73
7,25
7,31
7,49
7,35
6,65
7,16
6,87
5,05
7,10
5,21
6,78
Medium
7,51
9,78
7,42
7,61
7,02
6,08
6,49
6,58
8,26
6,13
9,12
9,10
7,59
Large
5,09
6,40
5,83
6,83
6,95
6,47
8,58
8,17
6,87
6,44
6,54
8,87
6,92
Source: Own elaboration based on Superintendency of Companies, Securities and Insurance (2019)
Provincial insolvency analysis.- Table 6 shows the provinces with the highest concentration of textile companies, which will be
analyzed.
Table 6. Provinces with the highest concentration of textile companies
Province
Year
Average
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Pichincha
96
99
101
108
109
103
105
99
104
97
96
90
101
Guayas
31
29
32
36
37
44
51
50
56
55
49
41
43
Tungurahua
10
8
12
10
12
10
10
10
10
10
10
8
10
Azuay
3
5
6
5
5
6
5
6
7
6
5
5
5
Imbabura
3
3
3
3
3
4
4
3
2
5
5
4
4
Others
7
8
8
12
12
12
12
10
11
10
10
10
10
Total
150
152
162
174
178
179
187
178
190
183
175
158
173
Source: Own elaboration based on Superintendency of Companies, Securities and Insurance (2019)
6,906
7,595
6,593
7,114
6,262
6,960
6,769
7,202
7,044
5,545
7,110
5,690
4,00
5,00
6,00
7,00
8,00
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Altman score
Year
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Measurement of liquidity, insolvency and market risk levels in the textile sector of Ecuador
Medición de los niveles de riesgos de liquidez, insolvencia y mercado en el sector textil del Ecuador
10
Figure 2 shows that Guayas, Pichincha and Tungurahua
have higher scores, so they are in a safe area.
Source: Own elaboration based on Superintendency of
Companies, Securities and Insurance (2019)
Figure 2. Altman analysis by province in Ecuador.
Ohlson's methodology
The significant variables in the regression analysis are found
in Table 7:
Table 7. Variables that are part of the logistics model
B
Standard error
Wald
Gl
Sig.
Exp(B)
Size
-0,538
0,206
6,803
1
0,009
0,584
Working capital /
Total assets
-3,245
0,421
59,494
1
0,000
0,039
Net Income /
Total Assets
-3,442
0,756
20,754
1
0,000
0,032
Constant variable
-1,058
0,703
2,262
1
0,133
0,347
Source: Own elaboration based on Superintendency of
Companies, Securities and Insurance (2019)
Equation 11 shows the beta coefficients of the logistic
model:
( 11)
Pi = Probability of insolvency
The variables that were statistically significant with a
significance level of5% are: working capital / total assets,
size, and net income / total assets; they all have an inverse
relationship with the probability of business bankruptcy. The
variable that has the most weight when explaining the
probability of bankruptcy is net income / total assets.
Moreover, the Wald test confirms the results mentioned in
the previous paragraph, so it is concluded that the variables
are important to explain the probability of business
bankruptcy.
Annual insolvency analysis. - Shows that there is an
increasing trend in the percentage of insolvency risk. The
year that the highest probability of bankruptcy occurred was
2018.
Source: Own elaboration based on Superintendency of
Companies, Securities and Insurance (2019)
Figure 3. Annual insolvency risk probability - Ohlson's
methodology.
Insolvency analysis by business size.- Table 8 shows the
probability of insolvency risk by business size, where
microenterprises have a higher risk of insolvency. In
contrast, large companies have lower levels of risk, even in
2018 this indicator is reduced by approximately 4
percentage points.
Table 8. Ohlson's analysis by business size
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Average
Micro
1,5%
7,7%
9,4%
5,1%
9,0%
3,7%
6,3%
3,2%
8,7%
9,4%
4,9%
14,6%
6,9%
Small
0,5%
3,9%
4,1%
3,2%
3,2%
4,6%
4,8%
5,1%
5,0%
4,2%
6,1%
5,6%
4,2%
Medium
0,7%
3,5%
3,9%
2,4%
2,6%
5,6%
5,5%
5,2%
5,5%
6,4%
7,4%
1,3%
4,2%
Large
0,6%
2,2%
2,3%
1,9%
3,4%
2,6%
1,8%
1,6%
1,8%
5,4%
4,8%
0,9%
2,4%
Source: Own elaboration based on Superintendency of Companies, Securities and Insurance (2019)
0,000
1,000
2,000
3,000
4,000
5,000
6,000
7,000
Guayas Pichincha Tungurahua Azuay Imbabura
6,982
6,843
6,416
4,248
3,712
Altman score
Province
4,946%
5,608%
3,525%
5,025%
4,317%
5,194%
4,187%
5,951%
6,403%
5,962%
7,461%
3,00%
3,50%
4,00%
4,50%
5,00%
5,50%
6,00%
6,50%
7,00%
7,50%
8,00%
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Probability of risk
Year
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Measurement of liquidity, insolvency and market risk levels in the textile sector of Ecuador
Medición de los niveles de riesgos de liquidez, insolvencia y mercado en el sector textil del Ecuador
11
Insolvency analysis by province.- Figure 4 analyzes the risk
by province. As in Altman's analysis, companies in the
provinces of Guayas, Pichincha and Tungurahua have a low
level of insolvency risk. On the contrary, those of Azuay
have a high probability of risk. Companies in the province of
Imbabura disagree with Altman's analysis, since it has a low
level of risk.
Source: Own elaboration based on Superintendency of
Companies, Securities and Insurance (2019)
Figure 4. Ohlson's analysis by province in Ecuador.
Market risk
The profitability of the market and the textile sector were
obtained through the adjusted ROE. The weighted
percentage was calculated based on the percentage
participation of the manufacturing industry sectors, this, in
relation to the total equity. (See Table 9)
Table 9. Market profitability of the manufacturing and textile
sector of Ecuador
Year
Profitability
Manufacture (C )
Textile sector (C13)
2011
22,37%
18,42%
2012
17,83%
11,79%
2013
16,70%
11,51%
2014
15,55%
9,09%
2015
13,08%
4,38%
2016
9,56%
3,15%
2017
11,32%
6,55%
2018
10,33%
2,60%
Weighted average
14,60%
7,59%
Standard deviation
4,36%
5,38%
Sample variance
0,00189
0,00289
Exchange rate
-0,0167
-0,0198
Source: Own elaboration based on Superintendency of
Companies, Securities and Insurance (2019)
Calculation of the accounting beta
When using equations 2 and 3, with the values
corresponding to the profitability of the manufacturing
market (Rm) and the textile sector (Rs), the Beta coefficient
is obtained, which indicates that due to the 1% variation in
market profitability manufacturing, the profitability of the
textile sector varies by 1,2004%:
= 1,2004
=1,2004
When performing the regression between the average yield
of the textile sector and the average yield of the
manufacturing industry, the following results are obtained
(see Figure 5):
Source: Own elaboration based on Superintendency of
Companies, Securities and Insurance (2019)
Figure 5. Average yield of the textile sector and the average yield
of the manufacturing industry
As can be seen, the variable Rm is statistically significant at
a significance level of 5%, since it has a p value of 0,0000.
Additionally, the independent variable Rm explains 94,31%
of the dependent variable (Rs).
Structuring of the CAPM model. - In the CAPM, the average
of the benchmark passive rate of the Central Bank of
Ecuador for the last 8 years is used as the risk-free rate. (see
Table 10):
Table 10. Ecuadorian benchmark passive rate 2011 - 2018
Year
Passive rate
2011
4,56%
2012
4,53%
2013
4,53%
2014
4,90%
2015
5,34%
2016
5,73%
2017
4,91%
2018
5,13%
Average
4,96%
Source: Central Bank of Ecuador (2018)
0,000%
2,000%
4,000%
6,000%
8,000%
10,000%
12,000%
11,404%
5,802%
4,682%
4,730%
4,126%
Percentage
Province
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Measurement of liquidity, insolvency and market risk levels in the textile sector of Ecuador
Medición de los niveles de riesgos de liquidez, insolvencia y mercado en el sector textil del Ecuador
12
The expected minimum return, after occupying equation 1, is as follows:
The CAPM indicates that the expected return of the Textile sector is 16,53%.
Liquidity risk
The application of the logistic model is indicated in Table 11:
Table 11. Summary of the logistic model
Variable
Coefficient
Std. Error
Z-statistic
Prob.
Liquidity index
-0,019266
0,00366
-5,263863
0,0000
Indebtedness index
20,09054
0,668856
30,03717
0,0000
Average collection period
0,026233
0,001658
15,82622
0,0000
C
-15,62779
0,520655
-30,01562
0,0000
McFadden R-squared
0,669551
Mean dependent var
0,422251
S.D dependent var
0,493972
S.E of regression
0,240815
Akaike info criterion
0,451812
Sum squared resid
267,1682
Schwarz criterion
0,457395
Log likelihood
-1037,652
Hanna-Quinn criter.
0,453777
Deviance
2075,305
Restr. deviance
6280,258
Restr. log likelihood
-3140,129
LR statistic
4204,953
Avg. Log likelihood
-0,225038
Prob(LR statistic)
0,000000
Obs with dep = 0
2.664
Total obs
4.611
Obs with dep = 1
1.947
Source: Own elaboration based on Superintendency of Companies, Securities and Insurance (2019)
The model specification is as follows
Where:
• Pi= Liquidity risk probability.
When using the maximum likelihood method (large
samples), the standard errors are asymptotic.
Table 11 shows that the probability of illiquidity of companies
is explained by three indicators: liquidity index, debt ratio
and average collection period. The first indicator has an
inverse relationship and the remaining two have a direct
relationship with the probability of business illiquidity. The
most important ratio to explain the probability of illiquidity is
the debt ratio. All variables are statistically significant with a
significance level of 5%.
McFadden's R squared is 0,6695 which, although in this
type of model its interpretation and result is secondary, does
not give a high value as well as the LR statistic with a value
of 4204,953. In general, this indicates that there is a very
good global explanation of the analyzed model.
Annual liquidity analysis.- In Figure 6 it is observed that
there is a decreasing trend in the probability of liquidity risk
in the sector until 2014, subsequently the values increase
until 2018. It should be considered that the values presented
are high, which indicates that on average there is a high risk
of liquidity in textile companies.
Source: Own elaboration based on Superintendency of
Companies, Securities and Insurance (2019)
Figure 6. Annual liquidity risk of the textile sector in the period
2007-2018.
51,41%
46,18%
50,66%
50,18%
50,18%
44,08%
46,50%
40,34%
41,53%
43,10%
44,17%
44,81%
30,00%
35,00%
40,00%
45,00%
50,00%
55,00%
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Percentage
Year
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Measurement of liquidity, insolvency and market risk levels in the textile sector of Ecuador
Medición de los niveles de riesgos de liquidez, insolvencia y mercado en el sector textil del Ecuador
13
Liquidity analysis by company size. - The liquidity risk by
business size is higher in microenterprises, with an average
value in the 2007-2018 period of 57,06%. On the contrary,
large companies have a lower liquidity risk, which is 31,79%.
(See Figure 7)
Source: Own elaboration based on Superintendency of
Companies, Securities and Insurance (2019)
Figure 7. Average liquidity risk by business size of the textile
sector in the period 2007-2018
The liquidity risk levels in the period 2007-2018 have a
decreasing trend, as can be seen in Figure 8:
Source: Own elaboration based on Superintendency of
Companies, Securities and Insurance (2019)
Figure 8. Liquidity risk by business size of the textile sector in the
period 2007-2018.
Provincial liquidity analysis.- Table 12 shows the different
levels of liquidity risk by province, where the provinces with
the highest concentration of companies are analyzed.
Imbabura companies have the highest levels of risk, while
those of Tungurahua the lowest. With respect to Azuay, this
province has a 53% probability of liquidity risk, a result
mainly affected by the risk of 2018 (97%), the year in which
the average collection period increased notably.
Table 12. Average provincial liquidity risk for the period 2007 – 2018
Province
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Avg.
Pichincha
53%
47%
51%
53%
55%
43%
45%
39%
38%
39%
44%
46%
46%
Guayas
44%
44%
45%
51%
44%
41%
52%
46%
48%
50%
46%
46%
46%
Tungurahua
44%
28%
52%
37%
34%
37%
36%
41%
37%
31%
31%
37%
37%
Azuay
44%
37%
38%
44%
72%
36%
40%
52%
52%
72%
55%
97%
53%
Imbabura
49%
91%
84%
66%
74%
68%
47%
48%
76%
57%
65%
66%
66%
Source: Own elaboration based on Superintendency of Companies, Securities and Insurance (2019)
Discussion
Throughout history there have been different ways to
quantify the financial risk of businesses. In this research
work, three types of financial risk are analyzed: insolvency,
market and liquidity.
In the risk of insolvency, FitzPatrick (1932), Smith y Winakor
(1935), Merwin (1942), Jackendoff (1962), Horrigan (1965)
and Beaver (1966), performed descriptive analyzes to
explain the probability of business bankruptcy. The
conclusions they reach are based on analysis of current and
pre-bankruptcy financial ratios.
Later Altman (1968) and Ohlson (1980) introduce new
methodologies: discriminate and logistic analysis
respectively. The methodologies used by the two authors
are different but they seek the same objective: to predict
business bankruptcy and determine which are the variables
that explain this behavior. In our study, we adapted the
models of the two authors to the Ecuadorian reality in the
textile sector.
In this work, the model proposed by Altman was adapted to
emerging markets and the results were consistent with the
Ecuadorian reality. From the point of view of business
bankruptcy 2016 was the most complex year. A logistic
regression model was also applied to determine the
probability of business bankruptcy, and there were 3
variables that explain this probability: working capital / total
assets, size and net income / total assets. Unlike the original
Altman and Ohlson models, where the most significant
variable to explain business insolvency is the level of
indebtedness, the most significant variable to explain
insolvency is net income / total assets.
With regard to market risk, there is no developed capital
market and therefore there is not enough information to
calculate a beta through market measures. For this reason,
market risk was calculated through accounting measures
using ROE. The beta was determined by measuring the
relationship between roe of the textile sector with the ROE
of the entire manufacturing sector, obtaining a Beta of 1,20
and a required rate of return of 16,53%.
I. Orellana et al. / Boletín de Coyuntura, Nº 27; octubre – diciembre 2020; e-ISSN 2600-5727 / p-ISSN 2528 – 7931; UTA-Ecuador; Pág.4 - 16
Measurement of liquidity, insolvency and market risk levels in the textile sector of Ecuador
Medición de los niveles de riesgos de liquidez, insolvencia y mercado en el sector textil del Ecuador
14
Regarding liquidity risk, there is no major literature on
models applied to companies in the real sector, but rather it
has been done in companies in the financial sector using
techniques such as liquidity gaps, structural liquidity,
liquidity VAR, among others.
In this work, liquidity risk was quantified using a logistic
regression model. Three variables were found to be
statistically significant: liquidity index, debt ratio, and
average collection period; the debt index is the variable with
the greatest weight to explain the probability of illiquidity of
manufacturing companies in the textile sector.
Conclusions
According to the registered information, 83% of the
companies in the textile sector in Ecuador are located in the
provinces of Pichincha and Guayas, while only 3% are in
Azuay. Furthermore, in this sector there are 35% micro-
enterprises, 34% small, 23% medium, and 8% are large.
Most of the textile companies are only found in two
provinces (Guayas and Pichincha), and a greater number
are micro and small companies. t is important to indicate
that, in this work, it is determined that the Financial Risk of
the Textile Sector is a “moderate risk”, an opinion that is
based on the quantification and behavior of the three risks
that compose it: bankruptcy, market and liquidity.
In the insolvency risk analysis, it was determined that the
highest levels of risk occurred in 2016 and 2018, its trend in
the period analyzed shows a slight decrease in risk. The
companies located in Guayas and Pichincha (both
methodologies) do not present a risk of insolvency; on the
contrary, those in Azuay present a high probability of
bankruptcy risk. In addition, the methodologies used agree
that micro-enterprises are more likely to be at risk of
insolvency. Despite the fact that, on average, the sector is
not in the insolvency risk zone, 22,5% of companies are in
the red zone, indicating that a large number of companies
could become insolvent. Regarding the variables compared,
in Altman's method the most relevant variables to explain
and predict business bankruptcy are: working capital / total
assets and operating profit / total assets, while in Ohlson's
model the variable most important is total liabilities / total
assets. Therefore, in the Altman model corporate
bankruptcy for emerging markets depends on liquidity and
profitability, while in the Ohlson model it depends on the
level of indebtedness.
The models developed to find the probability of bankruptcy
of a company mentioned in the literature have initially been
developed for developed markets. However, adaptations
have been developed for emerging markets, such is the
case of the Altman model used in the research. There is no
standard model to find the probability of bankruptcy of a
company, that is why we found a large number, in fact, there
is no clear consensus regarding which is the optimal one.
Knowing the risk of bankruptcy allows to create an early
warning for risky companies, and based on this make
decisions that improve financial health.
In market risk, the Beta coefficient for the textile sector is
1,2004, which indicates that this sector has a higher risk
than the entire manufacturing industry. The Beta coefficient
also indicates that there is a direct relationship between the
profitability behavior of companies in the textile sector and
companies in the manufacturing sector. In addition, the
CAPM results indicate that the return expected by the
investor is 16,53%, higher than the expected return of the
market (manufacturing sector) which is 14,60%, which
means that the higher the risk, the higher the return. The
research does not use information from the Ecuadorian
stock market because the country's economy is considered
to be developing and. For this reason, it can be said that its
stock market is still incipient and it concentrates its
negotiations on fixed income documents.
The liquidity risk analysis of the sector indicates that there is
a higher level of risk in the years 2007, 2009, 2010 and 2011,
but in general in the period analyzed there is a decreasing
trend in the level of risk. Micro-enterprises have a higher
level of risk, with a 57,06% probability of illiquidity. In the
provincial analysis, it was determined that the Imbabura
companies have a higher risk, followed by the Azuay,
Pichincha, Guayas and Tungurahua companies. In the
research, the liquidity risk was determined through a logistic
analysis, for which financial indicators of the manufacturing
industry were calculated in order to compare them with the
textile sector and be able to determine the dependent
variable.
The importance of measuring risk in the different economic
sectors of the country lies in anticipating financial situations
that harm the financial health of companies. Exposure to
financial markets affects most organizations. When an
organization has financial market exposure, there is a
possibility of loss but also an opportunity for gain or profit. In
addition, the knowledge of the different levels of risk allows
offering greater security to potential investors, supporting
business decision-making, and even obtaining government
support in situations of imminent risk.
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Measurement of liquidity, insolvency and market risk levels in the textile sector of Ecuador
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Medición de los niveles de riesgos de liquidez, insolvencia y mercado en el sector textil del Ecuador
