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Scientific Annals of Economics and Business
66 (3), 2019, 335-349
DOI: 10.2478/saeb-2019-0025
Determinants of Financial Risk: An Empirical Application on Low-Cost Carriers
Kasım Kiracı*
Abstract
The airline industry has entered a rapid development and transformation process, especially after the
Second World War. In this process, it is seen that the market structure changed and many private
airlines were established. Due to increased competition, airlines have begun to follow various
strategies and business models in order to gain a competitive advantage over each other. One of the
business models successfully applied recently is the low-cost business model. Therefore, this study
focuses on airline companies that applied the low-cost business model. The study aims to reveal the
factors that determine the financial risk in airlines, which implements the low-cost business model. For
this purpose, firstly, airline companies that implement the low-cost business model have been
identified according to the classification in the literature. The study included an analysis of 13 airlines
with the low-cost business model that was fully accessible to financial data for the 2004-2017 period.
Panel data analysis was used in the study and Altman (1968) Z-Score and Springate (1978) S-Score
were used in measuring financial risk. Empirical findings of the study reveal that firm leverage, asset
structure, firm size, firm profitability, and liquidity ratio have an effect on financial risk.
Keywords: financial risk; Altman Z-Score; Springate S-Score; airlines; panel data analysis.
JEL classification: G32, L93, C12.
1. INTRODUCTION
The airline industry has become one of the sectors with the most intensive competitions
as a result of the deregulation movement which started in the US in 1978 before finding its
way to many countries, leading to relatively easy market entry and exit. Airlines have had to
implement a number of competitive strategies to compete against each other in this intense
competitive environment. In the competitive strategies first proposed by Porter (1980; 1985), a
company can achieve a competitive advantage over the others by choosing and implementing
one of the three strategic choices of focus, cost leadership or differentiation (Taşçı and
Yalçınkaya, 2015, p. 180). Airlines have started to implement business models that follow
* Department of Aviation Management, Faculty of Aeronautics and Astronautics, Iskenderun Technical University,
Turkey; e-mail: kasim.kiraci@iste.edu.tr.
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Kiracı, K.
these competitive strategies. These models could be categorized as either charter, low-cost or
traditional. According to Kuyucak Şengür and Şengür (2012), the value created, the customer
profile served and the way of doing business are the main components of the airline business
model. These also include the income and expenditure structure, which is the economic
component of the way of doing business, as well as and the competencies required of the
resources needed to obtain them. As a result, there are some differences between the airlines
that employ different business models in terms of how they operate, the customer profiles they
serve, the income-expense components, the cost structure, and the value created or image.
In the low-cost business model, firms seek to earn above-average returns by operating
at lower costs, as well as reduce the effects of the systematic risks that they may encounter.
In this business model, therefore, airlines adopt many methods that will minimize their costs
(such as not using agents in ticket sales and booking transactions, preferring secondary
airports and including similar aircraft in their fleet structures).
The low-cost airlines are faced with a lot of systematic and non-systematic risks as a
result of the various methods they employ to reduce their costs. Studies also exist in the
literature that has considered such risks (Flouris and Walker, 2005; Malighetti et al., 2009).
However, there are rare studies that focus on the financial risks of the airlines implements
the low-cost business model. The rest of this study, which analyses the factors affecting the
financial risk of airlines applied the low-cost business model, is organized as follows. In the
second section, the studies in the literature are discussed while the third section mentions
research models and established hypotheses. In the fourth section, data and methods of the
study are mentioned and the application of the study and the findings are given in the fifth
section. In the last section, the results of the analysis are evaluated.
2. LITERATURE
In financial literature, many studies have been conducted to determine or measure risk.
The focus of these studies is on whether the financial risks of the companies can be
determined using predetermined financial variables or ratios (such as the Altman Z score or
Springate S score), or whether the financial ratios were below the critical values for
companies that went bankrupt. An example is a study by Kulali (2016) on the period 2000-
2013 on companies listed in Istanbul Stock Exchange (BIST) that went bankrupt. Poyraz
and Ucma (2006) investigated the performance of the basic manufacturing sectors in the
face of crisis and financial risks. Toraman and Karaca (2016) analyzed the financial failures
of firms in the chemical industry listed in Istanbul Stock Exchange for the period from 2010
to 2013. Koç and Ulucan (2016) analyzed the firms in the textile and technology index in
the BIST for the period 2006-2013. In addition to the studies measuring financial failure,
there are also many studies that investigated the credit risk or credit rating of the companies
based on financial failure score (Elliott et al., 2014; Nadirli, 2015; Yıldız, 2014).
There are many systematic and non-systemic risks affecting the risks of the enterprises.
In the aviation literature, Lee and Jang (2007) examined the factors that determine the
financial risk of the airlines in the US, while Lee et al. (2011) examined the factors affecting
the systematic risks of the airlines operating in North America, Europe, and Asia. Airlines
may face financial hardships and bankruptcy depending on their levels of market and non-
systematic risks. In addition to the studies on the relationship between the financial risk of
airlines and their capital structure decisions (Chou et al., 2010; Lee et al., 2011; Muigai,
Scientific Annals of Economics and Business, 2019, Volume 66, Issue 3, pp. 335-349
337
2016), there are also studies on the impact of financial and operational hedging policies on
the risk level of airlines (Berghofer and Lucey, 2014).
One of the most important indicators used in the measurement of the risks of airlines is
the share of foreign resources in total resources, i.e. the ratio of foreign resources in the
capital structure. In this context, it is observed that there are also studies that examined the
capital structure and the factors that determine the capital structure of airlines (Capobianco
and Fernandes, 2004; Kiracı and Aydin, 2018b, 2018a). These studies concluded that the
size of the firm, asset structure, and liquidity position have an effect on the debt level.
There are also studies in the literature that examined the relationship between financial
risk and capital structure decisions (Charalambakis et al., 2008; Oliveira et al., 2017;
Turaboglu et al., 2017). For instance, Turaboglu et al. (2017) used Altman Z-Score and
Springate S-Score values as dependent variables in their study. The predictive variables used
in this study including the debt/equity ratio, short term/ total debt ratio, total assets/fixed asset
ratio. The findings of the study revealed that all the variables except the short-term debt ratio
affected the financial failure score. Another study that examined the determining factors of
financial risk uses the firm leverage level as the dependent variable (Charalambakis et al.,
2008). The independent variables include profitability level, tax rate, size of the firm, fixed
asset ratio and industry leverage level. The findings of the study show that the profitability
level variable is negatively affected while the other variables (tax rate, size of the firm, fixed
asset ratio and industry leverage level) are positively affected by the financial risk level.
Oliveira et al. (2017) examined the factors determining the financial risks of firms in the US
that underwent financial distress in the period 1980-2013. In the study, three different models,
which included the sales ratio, short-term debt level and long-term debt level as the dependent
variables, were used. As for independent variables, the study used profitability level, size of
the firm, asset structure, cash ratio, growth opportunities, R & D investment and industry debt
level. Although the findings obtained in the study vary according to the model, it shows that
many variables significantly affect the company risk.
In other studies, the determinants of financial risk have been examined (Halpern et al.,
2009; Kristanti et al., 2016; Miglani et al., 2015). In one of these studies, Halpern et al.
(2009) examined the determinants of financial risks for a sample made of companies that
had encountered financial difficulties and went bankruptcy and a group of companies who
had not had any financial difficulties. The study, which used data of firms from different
sectors, used the size of the firm, debt level, market value/book value ratio and tax rate as
independent variables. The findings of the study revealed a relationship between the cost of
debt and leveraged purchasing activities and emphasized that the debt level increases the
financial risk. Kristanti et al. (2016), empirically examined the relationship between
financial risk and managers' competencies and financial ratios. In the study, it was
concluded that in addition to the managers' competence and independence, the leverage
level and current ratio variables had an impact on the financial risk level. Miglani et al.
(2015) examined the factors affecting the financial risk for a mix of 171 firms undergoing
financial distress and 106 financially well-performing firms. The ratio of independent
directors, ownership structure, the relationship between the CEO and the board of directors
and whether or not there is an audit committee were used as the explanatory variables. In
addition to these variables, firm size and leverage level are also used as independent
variables. The findings of the study show that the leverage level increases the financial risk
and the size of the firms decreases the financial risk.
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Kiracı, K.
In the literature, many studies have examined the different dimensions of the factors
affecting the financial risks of the enterprises. There are similar studies on the airline
industry regarding financial risk. However, there are very few studies on the determinants of
financial risk considering the business models applied by airlines. As a result, this study is
expected to contribute to the literature in several ways. First, as pointed out, there are few
studies on the determinants of financial risk in air transport literature. This study is expected
to contribute to the literature in this sense. Secondly, there is a limited number of studies on
the financial aspects of airlines that apply low-cost business models. Finally, no study was
found that examines the financial risk by considering the business models applied by
airlines. This study, therefore, seeks to fill these gaps in literature and make a contribution.
3. RESEARCH MODEL
An examination of studies in the literature reveals that many financial indicators are
used as dependent variables in the measurement of financial failure. A review of recent
studies indicated that the scores obtained from the analysis of financial ratios such as
Altman Z-Score, Springate S-Score, Zmijewski J-Score and Ohlson O-Score are used as
indicators of financial failure. This study uses Altman Z-Score and Springate S-Score as
indicators of financial risk as done by similar studies in literature (Ahmad, 2013; Chou et
al., 2010; Lee et al., 2011; Muigai, 2016; Turaboglu et al., 2017). The models created in this
context are as follows:
Model 1:
Z − Sit = β0+ β1STDRit + β2FARit + β3PRit + β4CFRit + β5FSit + β6CRit
+ β7LRit + εit
(1)
Model 2:
S − Sit = β0+ β1STDRit + β2FARit + β3PRit + β4CFRit + β5FSit + β6CRit
+ β7LRit + εit
(2)
In the above models, the Altman Z-Score of the airlines was used as the dependent
variable in the first model (Model 1), while the Springate S-Score was used as the dependent
variable the second model. The dependent, independent variables as well the control
variables used in the study and their related abbreviations are shown in Table no. 1.
Table no. 1 – Variables used in the study
Variables
Abbreviations
Dependent Variables
Altman Z-Score
Z-S
Springate S- Score
S-S
Independent Variables
Short Term Debt Ratio
STDR
Fixed Asset Ratio
FAR
Profitability Ratio
PR
Cash Flow Ratio
CFR
Control Variables
Firm Size
FS
Current Ratio
CR
Leverage Rate
LR
Scientific Annals of Economics and Business, 2019, Volume 66, Issue 3, pp. 335-349
339
Table no. 1 above shows information about the variables used in the study. Altman Z-
Score and Springate S-Score were used as dependent variables. Four different independent
variables were included in the study; short-term debt ratio, fixed asset ratio, firm
profitability ratio and cash flow ratio. Based on the studies in the literature, three control
variables were also included; firm size, current ratio and leverage ratio. The effect of the
independent and control variables used in the study on the financial risk score and the
expectation of a possible sign of variable will also be included in the study.
3.1 Short-Term Debt Ratio
The ratio of liability in the capital structure of the firm and the maturity structure of the
debts are assumed to affect the company risk. Therefore, the short-term debt level can be
said to have an impact on the risk of financial failure. Studies point to the presence of a
significant relationship between short-term debt level and the financial failure score.
Turaboglu et al. (2017) found a negative relationship between short-term debt ratio and the
firm failure score. In addition, when the relationship between debt level and financial risk is
evaluated in the context of capital structure theories, the company risk is expected to
increase with the increase in debt levels according to both the Pecking Order Theory and the
Trade-Off Theory. Consequently, the short-term debt level is expected to have a negative
impact on the financial failure score.
H1. There is a negative relationship between the short-term debt ratio and the financial
failure score.
Measurement method = short-term debt / total assets
3.2 Fixed Asset Rate (Asset Structure)
There is a close relationship between the level of tangible fixed assets and the risk of
the financial failure of firms. According to Kiracı and Aydin (2018a), the fact that fixed
assets can create value even after bankruptcy and that tangible assets can be used as
collateral when borrowing enables firms to obtain debt under more favorable conditions.
Therefore, firms with tangible assets are less likely to experience financial problems. On the
other hand, owing to the fact they can use their asset structure as collateral, there are studies
that emphasize that the consequent borrowing at a lower cost by the firm will lead to an
increased debt level and that the firm may experience financial failure as a result (Muigai,
2016, p. 32).
H2. There is a negative/positive relationship between the short-term debt ratio and the
financial failure score.
Measurement method = fixed assets / total assets
3.3 Firm Profitability Ratio
There are very few studies in the literature examining the relationship between the
level of profitability and the rate of financial failure (Oliveira et al., 2017; Kristanti et al.,
2016). There is a general non-consensus in the existing studies on the impact of profitability
on financial failure score. In the context of the Capital Structure Theory, there is an
emphasis on the fact that that firms with higher profitability have the opportunity to obtain
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Kiracı, K.
debt at lower costs (lower interest rate) resulting to lower financial distress and bankruptcy
costs (Kiracı and Aydin, 2018b, p. 233).
H3. There is a negative / positive relationship between firm profitability and financial
failure score.
Measurement method = net profit / total assets
3.4 Cash Flow Ratio
It has been observed that in companies with higher risks of financial failure or those
that have undergone financial failures, cash flows become irregular, cash flows become
difficult to manage and a series of problems are encountered in meeting short term liabilities
(Kulali, 2014, p. 156; Uzun, 2005, p. 165). As a result, it can be said that there is a
relationship between cash flow ratio risk and financial failure. The cash flow ratio is
expected to have a positive effect on the financial failure score.
H4. There is a positive relationship between the cash flow ratio and the financial
failure score.
Measurement method = (net profit + depreciation) / equity
3.5 Firm Size
In the literature, firm size is often expressed in terms of the company's production or
service capacity, quantity and variety. Therefore, firm size also gives important clues about
the robustness of the financial structure (Muigai, 2016, p. 26). In addition, it is assumed that
large-scale companies generally have stable and diversified cash flows, and therefore these
firms will have a lower probability of bankruptcy (Kiracı and Aydin, 2018b, p. 233).
Therefore, it is expected that there will be a positive relationship between the firm size
variable and financial failure score.
H5. There is a positive relationship between firm size and financial failure score.
Measurement method = log (total assets)
3.6 Current Ratio
There is a close relationship between the level of short-term funding needs of firms and
the current ratio. Accordingly, firms with a high liquidity ratio prefer to use internal funds
instead of debt to fulfill their short-term liabilities. In the literature, there are studies that use
the leverage/debt ratio as an indicator of financial risk in firms (Charalambakis et al., 2008;
Oliveira et al., 2017). In this case, a negative relationship can be said to exist between the
current ratio and the financial risk level because it is assumed that the firms with high
current ratio will use less debt. In this study, since the financial failure score is used as the
indicator for financial risk, the risk of financial failure of firms with a high current ratio is
expected to be low.
H6. There is a positive relationship between the current ratio and financial failure
score.
Measurement method = current assets / short term liabilities
Scientific Annals of Economics and Business, 2019, Volume 66, Issue 3, pp. 335-349
341
3.7 Leverage Ratio
As mentioned in the previous parts of the study, there are studies in literature in which
the leverage levels of firms are used to measure financial risk. However, since the Altman
Z-Score and Springate S-Score were used as indicators of financial failure in this study, the
leverage ratio was included as a control variable. According to Muigai (2016), there is a
close relationship between firm performance and debt level. Accordingly, a higher total debt
ratio or long-term debt ratio in the capital structure negatively affects firm performance.
This may be due to the high cost of debt for firms with a high leverage ratio and an increase
in financial difficulties and bankruptcy probabilities. Therefore, in this study, a negative
relationship is expected between leverage level and financial risk score.
H6. There is a negative relationship between leverage ratio and financial failure score.
Measurement method = total liabilities / total assets
4. DATA AND METHODOLOGY
This study was intended to find out the factors that determine the financial risks of the
airlines that apply the low-cost business model. In this regard, 13 airlines (the list of airlines
included in the analysis is given in Annex 1) with complete financial data for the period from
2004 to 2017 were included in the analysis. The secondary data of the study were taken from
the Thomson Reuters Datastream database. Altman Z-Score and Springate S-Score were used
as dependent variables, and the panel data analysis method was used.
4.1 Altman Z-Score Model
One of the most commonly used methods in the estimation of the financial failure of
firms or in the determination of their financial status is the Z-Score method developed by
Altman in 1968. Altman (1968) examined firms that filed for bankruptcy in the US between
1946 and 1965. The method, which yields very successful results in the estimation of financial
failure, has been applied to different firms and sectors since. The Z-Score developed by
Altman (1968) refers to a positive linear function that consists of four to five financial ratios as
variables which are then multiplied with specific coefficients. The model developed for the
calculation of the Altman Z-Score for services sector firms that are not included in the
manufacturing sector is as follows (Hayes et al., 2010, p. 125; Kulali, 2016, p. 287).
𝝰 = Working Capital / Total Assets
𝝱 = Retained profits / Total Assets
𝜸 = EBIT /Total Assets
𝜹 = Market Value / Total Liabilities
Z-Score = 6.5 𝝰 + 3.26 𝝱 + 6.72 𝜸 + 1.05 𝜹
After the calculation of the Z-Score value, the financial failure of the company can be
estimated at certain intervals. Accordingly, if Z <1.1, the company has a financial failure
risk. If 1.1 <Z <2.6, then there is no financial failure risk for the company. However, the
company is not considered successful either. If Z> 2.6, the company does not have any
financial difficulties or risks of financial failure.
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Kiracı, K.
4.2 Springate S-Score Model
The Springate S-Score model was developed in 1978 by LV Gorgon Springate. This
method, which is similar to Z-Score developed by Altman, is agreed within the scope of
multiple discriminant analysis. The S-Score is obtained by calculating the ratios between given
financial variables and the resulting ratios multiplied by certain coefficients. The financial ratios
and calculation method used in the S-Score developed by Springate (1978) are as follows:
𝝰 = Working Capital / Total Assets
𝜸 = Retained profits / Total Assets
𝝋 = EBT / Short Term Debt
𝜽 = Sales/ Total Assets
S-Score = 1.03 𝝰 + 3.07 𝜸 + 0.66 𝝋 + 0.4 𝜽
If the S-score is less than 0.862, the firms are considered to be at risk of financial failure.
4.3 Panel Data Analysis
The panel data equation can be defined in the form of Yit = αit + βitXit + Ɛit which
represents the change of the cross-sectional units, i (i = 1,…,N), with respect to time t (t =
1,…, N) and dependent variable Y, independent variables X. Ɛit here show the error terms.
In the panel data analysis, first, a cross-sectional dependence test is conducted on the
series. In this way, a decision is made whether to do a first or second level unit root tests when
testing for stationarity. After this, the stationary level is determined by conducting unit root
tests to the series. After the stability test of the series has been performed, it is necessary to
make a choice between the classical, fixed effects and random effects models and determine
whether the model to be applied is unidirectional or directional by considering whether the
coefficients in the panel data models change according to the unit and/or time. In the next
stage, variance and autocorrelation tests should be applied to the appropriate models. At the
last stage, certain corrections are applied in order to obtain robust standard errors.
5. APPLICATION AND FINDINGS
The descriptive statistics for the variables used in this study, which examined the
factors that determine the financial risks of the airline applies the low-cost business model,
are shown in Table no. 2.
Table no. 2 – Descriptive Statistics for Variables
Z-S
S-S
STDR
FAR
PR
CFR
FS
CR
LR
Average
0.27822
0.63450
0.07183
0.53404
0.04890
0.01935
6.31991
1.14221
0.36393
Maximum
5.60062
2.27243
0.47861
0.92055
0.27613
3.87426
7.39985
5.59678
0.89239
Minimum
-10.1632
-1.10486
0.00000
0.00430
-0.49742
-15.7983
4.15177
0.16544
0.00000
Std. Deviation
2.94077
0.62518
0.07658
0.21862
0.12068
1.37772
0.64367
0.67740
0.21606
Skewness
-1.37921
-0.42950
2.44508
-0.85190
-1.91415
-8.49486
-0.80163
1.94565
0.05353
Kurtosis
4.93721
3.69371
10.5846
3.07991
8.20494
98.4354
3.26393
11.9903
2.05691
Jarque-Bera
86.1594
9.24488
617.588
22.0624
316.584
71257.3
20.0210
727.756
6.83161
Probability
0.0000
0.0098
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0329
Observation
182
182
182
182
182
182
182
182
182
Scientific Annals of Economics and Business, 2019, Volume 66, Issue 3, pp. 335-349
343
Table no. 2 presents the descriptive statistics of the variables used in the study. The
table shows the average Altman Z-Score of the airlines included in the sample is 0.278 and
the average Springate S-Score is 0.634. When Altman Z-Score critical values are taken into
consideration, it is seen that the sample mean is lower than this critical value. In addition,
the minimum values of PR and CFR variables appear to be negative. This shows that some
of the airlines included in the sample are financially risky and therefore the average
financial risk score of airlines is below the critical value.
Table no. 3 – Correlation Matrix for Independent Variables
STDR
FAR
PR
CFR
FS
CR
LR
STDR
1
FAR
0.20655
1
PR
-0.39373
-0.05399
1
CFR
-0.07440
0.04394
0.49748
1
FS
-0.24737
-0.17264
0.31277
0.14614
1
CR
-0.44819
-0.18124
0.26881
0.05814
-0.02300
1
LR
0.58881
0.09185
-0.21468
-0.13939
0.02528
-0.40682
1
Table no. 3 shows the correlation matrix among the independent variables. The
existence of a high correlation between the independent variables included in the regression
model (over 0.80) causes the problem of multicollinearity. When the correlation matrix for
the independent variables is examined, the correlation coefficients between the variables are
found to be well below the critical value.
Table no. 4 – Descriptive Statistics for Variables
CDLM adj. (PUY, 2013)
Variables
Stat
Prob.
Z-S
-0.656
0.7440
S-S
-0.261
0.6030
STDR
-0.065
0.5260
FAR
3.927
0.0000
PR
-0.935
0.8250
CFR
-0.772
0.780
FS
0.012
0.4950
CR
-1.798
0.9640
LR
1.212
0.1130
Table no. 4 shows the results of the cross-sectional dependency test. Accordingly,
hypothesis H0 which states that there is 'no cross-sectional dependency' in all variables
except FAR is rejected. This indicates that when conducting the stationarity tests, the
second-generation unit should be applied to FAR variable while the first-generation unit
tests are applied to the other variables.
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Kiracı, K.
Table no. 5 – Panel Unit Root Test Results
LLC -t-test
IPS -W test
Stat
Prob.
Stat
Prob.
Z-S
-11.4194
0.0000
-10.8106
0.0000
S-S
-19.9635
0.0000
-17.7168
0.0000
STDR
-11.8188
0.0000
-9.44519
0.0000
PR
-4.20881
0.0000
-2.16435
0.0000
FS
-15.0049
0.0000
-13.3396
0.0000
CR
-16.6766
0.0000
-12.7267
0.0000
LR
-9.44242
0.0000
-5.51677
0.0000
CADF Unit Root Test
Level
1st difference
FAR
-2.524
-3.029**
Note: The critical values of CADF test of Pesaran (2007) statistics were obtained from Table II (c).
The table critical values were -3.09 -2.83 -2.69 at 1%, 5% and 10% significance levels.
Table no. 5 shows the unit root test results for the variables. According to this, all
variables except FAR is stationary at level. In this case, variables, other than FAR, can be
used in the analysis with level values. FAR variable is included in the model after
performing the first difference.
Table no. 6 – Model Determination Results
F test
LM Test
Hausman
Stat.
Prob.
Stat.
Prob.
Stat.
Prob.
Model 1
19.290
0.0000
167.234
0.0000
85.6400
0.0000
Model 2
3.4001
0.0002
3.1943
-0.0739
16.3000
0.0225
After the stationarity tests for the series have been determined, a decision should be
reached on the model to be used between the classical, the fixed effects and the random effects
models. To achieve this, the F-test is used to check the validity of the classical model, i.e.
whether it has the unit and/or time effects; the Breusch-Pagan LM test to test the suitability of
the classical model over the random effects model, and the Hausman test to choose between
fixed effects and random effects models. The results in Table no. 6 show that the fixed effects
model is suitable for the first model (Model 1) and the second model (Model 2).
Table no. 7 – Variance and Autocorrelation Test Results
Modified Wald
Durbin Watson
Baltagi–Wu
Stat.
Prob.
Stat.
Stat.
Model 1
16099.00
0.0000
0.81087
1.03606
Model 2
2416.26
0.0000
1.61385
1.79401
Table no. 7 shows the variance (heteroscedasticity) and autocorrelation test results for
Model 1 and Model 2 as predicted by the fixed effects model. In the fixed effects model
(Model 1 and Model 2), the Modified Wald test is used to test the heteroskedasticity. The
Modified Wald test results show that the H0 hypothesis was rejected for both models. This
shows that the variance is not constant in Model 1 and Model 2 and that there is a
Scientific Annals of Economics and Business, 2019, Volume 66, Issue 3, pp. 335-349
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heteroskedasticity problem. In order to test the presence of autocorrelation in the fixed
effects models, the DW autocorrelation test of Bhargava, Franzini and Narendranathan, and
Baltagi, and Wu's LBI autocorrelation tests are used. Although there is no specified critical
value for DW and LBI autocorrelation tests in the literature, a statistical value of less than 2
indicates the presence of autocorrelation. And given that the statistical values obtained for
both models are less than 2, both models can be said to have autocorrelation.
If there is a heteroskedasticity, autocorrelation, and/or cross-sectional dependence in
the model, then the standard errors must be corrected (to obtain robust standard errors)
before the estimation of the parameters, or more appropriate methods which could eliminate
the errors should be used in the estimation. Here, we will discuss the estimators used to
obtain robust standard errors in the fixed effects (Model 1 and Model 2) models.
In the case of heteroskedasticity and autocorrelation problems in the fixed effects
models, Driscoll and Kraay corrections can be applied. The Driscoll and Kraay (1998)
method make a Newey-West type correction for the cross-sectional mean series. It was
created as an alternative to the Parks-Kmenta or PCSE approaches that produce consistent
covariance matrix estimators, especially encountered in microeconomic panels, only in
cases where the size dimensions of the cross-sectional are weak and the time dimension, T is
large. This method has been shown to achieve consistency even in cases where N is infinite.
In addition, it shows that the standard errors obtained from the estimated covariance matrix
are also robust to very general forms of spatial and periodic correlation. Driscoll and Kraay
(1998) corrections are used to obtain robust standard errors in the classical model and fixed
effects model (Tatoglu, 2016, pp. 256-278; Driscoll and Kraay, 1998, p. 1).
This study, which examined the financial risks of airline companies which apply the
low-cost business model, the parameters were estimated using two different models. The
Altman Z-Score was used in the first model (Model 1) and the Springate S-Score used in the
second model (Model 2) as dependent variables. The results obtained in the study are shown
in the tables below.
Table no. 8 – Model 1 Panel Regression Results
Dependent Variables: Z-Score
Variable
Estimation of
Coefficient
Driscoll-Kraay
Standard Error
t
Prob.
[%95 Confidence
Interval]
STDR
-6.08147
1.98963
-3.06
0.0100
-10.4165
-1.74643
D_FAR
1.91963
0.97171
1.98
0.0720
-0.19754
4.03679
PR
8.73842
1.3351
6.55
0.0000
5.82949
11.6474
CFR
-0.15742
0.01828
-8.61
0.0000
-0.19725
-0.11759
FS
1.12581
0.56384
2.0
0.0690
-0.10269
2.3543
CR
1.73305
0.38566
4.49
0.0010
0.89276
2.57333
LR
-0.74128
0.78041
-0.95
0.3610
-2.44165
0.95908
C
-8.58267
3.98383
-2.15
0.0520
-17.2627
0.09736
R-Square: 0.7640
Sample Period: 2004-2017
F-Statistics: 215.55
Cross-Section No: 13
Prob (F- Statistics): 0.0000
Observations: 169
Table no. 8 shows the results of the robust estimator of Model 1 in which the Altman
Z-Score is used as a dependent variable. According to the results of the fixed effects model,
346
Kiracı, K.
short-term debt levels and cash flow ratio variables of the airlines have a negative effect on
the financial failure score. The results also show that the ratio of fixed assets, firm
profitability, firm size and current ratio affect the financial failure score positively.
Table no. 9 – Model 2 Panel Regression Results
Dependent Variable: S-Score
Variables
Estimation of
Coefficient
Driscoll-Kraay
Standard Error
t
Prob.
[%95 Confidence
Interval]
STDR
-0.72385
0.37666
-1.92
0.0790
-1.54452
0.09682
D_FAR
-0.00027
0.07801
0.00
0.9970
-0.17024
0.16970
PR
4.33535
0.16866
25.70
0.0000
3.96787
4.70283
CFR
-0.02714
0.00726
-3.74
0.0030
-0.04295
-0.01133
FS
-0.28518
0.03899
-7.31
0.0000
-0.37013
-0.20023
CR
0.11443
0.04044
2.83
0.0150
0.02632
0.20254
LR
-0.66490
0.14178
-4.69
0.0010
-0.97380
-0.35599
C
2.39025
0.25357
9.43
0.0000
1.83776
2.94273
R-Square: 0.9228
Sample Period: 2004-2017
F-Statistics: 1114.52
Cross-Section No: 13
Prob (F- Statistics): 0.0000
Observations: 169
Table no. 9 shows the results of a robust estimator of Model 2, where the Springate S-
Score is used as the dependent variable. The findings show that short-term debt level, cash
flow ratio, firm size and leverage ratio of airlines have a negative effect on their financial
failure score. The results also show that the firm profitability ratio and current ratio variables
affect the financial failure score positively.
6. CONCLUSION
Within the scope of the study, the financial data for the period of 2004-2017 of the 13
airlines applied the low-cost business model were empirically analyzed. In the study, two
different models were formed, and Altman Z-Score and Springate S-Score were used as
indicators of financial risk. The independent variables and the control variables used in the
study included short-term debt ratio, fixed asset ratio, firm profitability ratio, cash flow
ratio, firm size, current ratio and leverage ratio.
An examination of the results of Model 1, which used the Altman Z-Score as the
dependent variable, reveals that short-term debt levels of the airlines have a negative effect
on the Altman Z-Score. This shows that by increasing their short-term debt levels, firms also
increase their financial risk. These findings are consistent with both established hypotheses
and studies in the literature. As a matter of fact, Turaboglu et al. (2017) found that short
term debt level increased the financial risk. The results of the study show that the fixed asset
ratio, firm profitability ratio, firm size and current ratio variables have a positive effect on
financial failure score for Model 1. In other words, the fixed assets ratio, firm profitability
ratio, firm size and current ratio decrease the financial risk in the airlines that implement a
low-cost business model. The findings are in line with both the theoretical expectations
mentioned in the previous sections of the study as well as the studies in the literature
Scientific Annals of Economics and Business, 2019, Volume 66, Issue 3, pp. 335-349
347
(Halpern et al., 2009; Oliveira et al., 2017). Cash flow ratio variable, on the other hand,
increased the financial risk.
In the second model of the study (Model 2), the Springate S-Score was used as a
dependent variable. The findings for Model 2 show that the variables of short-term debt
ratio, cash flow ratio, firm size and leverage ratio significantly affect the Springate S-Score.
Accordingly, the findings suggest that the increase in the short-term debt ratio and leverage
level have a negative impact on the financial failure score. It is, therefore, possible to say
that an increase in the short-term or total debt ratio of airlines negatively affects the financial
failure score and increases the financial risk. Studies in the literature also indicate that total
debt ratio (Muigai, 2016) and short-term debt level (Turaboglu et al., 2017) increase the risk
of financial failure. The results of the second model also show that firm size and cash flow
ratio variables negatively affect the financial failure score. These results are not consistent
with theoretical expectations. The study findings also show that the current ratio variable
positively affects the financial failure score. Therefore, these findings are consistent with the
studies in the literature and the hypotheses established.
When the findings of this study, which examines the factors affecting the financial risk
score of the airlines applying the low-cost business model, are evaluated, a significant part
of the results is found to be consistent with the theoretical expectations and the hypotheses
established. Given that most airlines have only recently begun to implement the low-cost
business model, the number of companies whose financial data can be reached, and the
period of analysis that is quantitatively limited. Nevertheless, the findings can be seen to
provide important clues about the determinants of financial risk for airlines applied the low-
cost business model.
Future studies could examine the risk of financial failure for airlines that adopt Porter's
other competitive strategies (differentiation or focus strategy). In this way, the factors
affecting the financial risks of traditional airlines can also be revealed. Thus, it may be
possible to identify differences in the determinants of financial risk among the groups of
airlines with different competitive strategies or business models.
ORCID
Kasım Kiracı http://orcid.org/0000-0002-2061-171X
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ANNEX 1 Airline Companies Included in the Analysis
ID
Airline
ID
Airline
1
SOUTHWEST AIRLINES
7
AIRASIA GROUP
2
WESTJET AIRLINES
8
EASYJET PLC
3
ALLEGIANT TRAVEL
9
GOL LINHAS AEREAS
4
CEBU AIR INC
10
JET AIRWAYS
5
SPICEJET LIMITED
11
JETBLUE AIRWAYS CORP
6
VUELING AIRLINES, SA
12
NORWEGIAN AIR
13
RYANAIR HOLDINGS PLC
