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Financial Distress in Indian Aviation Industry: Investigation Using Bankruptcy Prediction Models

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  • Vivekanand Education Society Institute of Management

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The Indian aviation sector over the recent years has shown a significant growth prospects on various parameters like passenger traffic, freight traffic, aircraft movements and number of airports, among others. But ironically the financial performance of most of the air carrier individually is not at all impressive. Every five years one airline in India is being grounded, latest being Jet Airways which had to suspend its operations in April 2019 due to severe financial crunch. The primary aim of this paper is to assess the current financial health of different Indian airline companies. This study uses four different models i.e. Altman Modified Z” Score Model, Pilarski Model, Fuzzy Logic Model and Kroeze Model to test the existence of financial distress and simultaneously aims to assess the applicability of these models on the Indian aviation sector. The models have been applied on four airline companies. Models indicate the existence of severe financial distress in three out of four chosen airlines and also indicate their suitability to be applied to the sector. The study contributes to the existing literature on Indian aviation by attempting to indicate the suitability of studied models for indicating financial distress which can lead to potential bankruptcy.
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Eurasian Journal of Business and Economics, 13(25), 91-109, (2020).
Financial Distress in Indian Aviation Industry:
Investigation Using Bankruptcy Prediction
Models
Samik SHOME*, Sushma VERMA**
Received: March 26, 2020 Revised: May 12, 2020 Accepted: May 20, 2020.
Abstract
The Indian aviation sector over the recent years has shown a significant growth
prospects on various parameters like passenger traffic, freight traffic, aircraft
movements and number of airports, among others. But ironically the financial
performance of most of the air carrier individually is not at all impressive. Every five
years one airline in India is being grounded, latest being Jet Airways which had to
suspend its operations in April 2019 due to severe financial crunch. The primary aim
of this paper is to assess the current financial health of different Indian airline
companies. This study uses four different models i.e. Altman Modified Z Score
Model, Pilarski Model, Fuzzy Logic Model and Kroeze Model to test the existence of
financial distress and simultaneously aims to assess the applicability of these models
on the Indian aviation sector. The models have been applied on four airline
companies. Models indicate the existence of severe financial distress in three out of
four chosen airlines and also indicate their suitability to be applied to the sector. The
study contributes to the existing literature on Indian aviation by attempting to
indicate the suitability of studied models for indicating financial distress which can
lead to potential bankruptcy.
Keywords: Financial Distress, Bankruptcy, Indian Aviation Sector, Financial Ratios,
Financial Models
JEL Code Classification: G32, G33, G38
UDC: 336.63
DOI: https://doi.org/10.17015/ejbe.2020.025.06
* Corresponding Author, Institute of Management, Nirma University at Ahmedabad, India. Email:
samik@nirmauni.ac.in
** VES Institute of Management Studies and Research at Mumbai, India. Email: sushma.verma@ves.ac.in
Samik SHOME & Sushma VERMA
Page |92 EJBE 2020, 13(25)
1. Introduction
Air transportation has become a significant industry over time and it plays a key role
in global tourism and supply chain functions. This industry generates substantial
employment and contributes majorly to global economic growth (Fung, Law & Ng,
2006). However, it is also susceptible to several intrinsic and extraneous risks. These
risks include economic boom and bust cycles, volatility in oil prices and exchange
rates, infrastructure challenges, protectionism, wars and political upheavals, among
others. This industry is also vulnerable to various other events such as weather
conditions, terrorist attack as well as natural disasters
1
. All these issues lead to
significant fluctuations in the profitability of air carrier or airline, the words used
interchangeably in the paper.
In any free market economy, the competition will increase between the parties
involved. The hallmark of this economic system is that some firms will inevitably fail.
The more efficient firms will succeed, and the poorly managed will fail, allowing
others to take their place. Thus, cost efficiency is of great importance (Lukic, 2014).
According to economic theory, an existing airline will be able to succeed as long as it
is operated and managed efficiently; if it fails, a more efficient airline will replace it.
Hence, it is extremely important to have an early warning signal of financial distress
in such a scenario. Warren Buffett once referred the aviation industry as the death
trap for investors
2
.
The Indian aviation sector which is highly competitive in nature has shown
considerable growth prospects especially in the domestic segment in the past few
years. India is the third largest domestic civil aviation market in the world and is
expected to move to become third largest air passenger market by 2024
3
. As the
airline industry in India operates in a tight competition, the ability of different airline
companies to increase prices is restricted to a great extent. More often prices are
even slashed to attract the customers. But for maximizing profit, revenue
maximization is also as necessary as minimizing cost (Jayaraman & Srinivasan, 2014).
Since profitability is considered to be critical for growth and survival of any industry
(Tyagi & Nauriyal, 2016), the companies belonging to the aviation sector have to
pursue new strategies to maintain profit margins. In such a dynamic scenario, there
is a very high propensity for financial distress for these companies which eventually
lead to financial bankruptcies.
Several instances of financial distress have been observed among airline companies
in India within the last decade. For example, Kingfisher airlines had to stop its
operation in 2012 due to their inability to pay for its liabilities. Spice Jet cancelled
1
Airlines for America (A4A), Airline Handbook, Available at: http://airlines.org/Pages/Airline-
HandbookChapter-1-Brief-History-of-Aviation.aspx, accessed on June 22, 2019.
2
https://www.forbes.com/sites/tedreed/2013/05/13/buffett-decries-airline-investing-even-though-at-
worst-he-broke-even/#51554ecb3b5e, accessed on June 27, 2019.
3
https://www.ibef.org/industry/indian-aviation.aspx. Accessed on February 28, 2020.
Financial Distress in Indian Aviation Industry: Investigation Using Bankruptcy Prediction …
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more than 2000 flights at the end of 2014 due to huge accumulated losses. It was
bailed out only by additional funding from promoters. Similarly, in 2016, Air Asia had
to put its expansion plan on hold due to a severe cash crunch. Air Pegasus became
bankrupt in 2017. In the same year, Air Carnival and Air Costa had to close down
their operation. In recent times, in 2019, Jet Airways operations are completely
suspended due to financial crunch. This sequence of financial distress and
bankruptcies of several air carriers in India has led to an interest to study the
probability of others becoming bankrupt in the near future. This prediction of
bankruptcy is considered to be very significant in providing valuable insight to
various stakeholders including customers.
According to Altman (1983), financial distress can be predicted before its occurrence
(maybe even up to three years) using financial ratios with appropriate statistical
analysis. If it can be accurately calculated to predict failures well in time, there is
always a possibility to take corrective measures to reverse the phenomenon. In this
backdrop, the primary aim of this paper is to investigate the current financial health
of different Indian airline companies. An in-depth analysis of the same will be done
using different models available including both generic and aviation industry-
specific. The study also wishes to assess the applicability of these models in the
Indian scenario. The study contributes to the existing literature on Indian aviation by
attempting to indicate the suitability of studied models for indicating financial
distress which can lead to potential bankruptcy.
The organization of the paper is as follows. A snapshot of the Indian aviation industry
has been provided in Section 2. Next, in Section 3, a brief review of the literature is
undertaken. The research methodology used in the paper is discussed in Section 4.
The research findings and discussions are presented in Section 5, followed by certain
concluding remarks in Section 6.
2. Indian Aviation Industry: A Snapshot
The aviation industry in India has observed a rising splurge post 2012. India became
the fastest growing aviation market followed by China. According to the Director
General of Civil Aviation (DGCA)
4
, there was a growth of 18.1 per cent in overall
passenger traffic (domestic plus international) leading to a total of 147.1 million
passengers in 2017-18
5
as compared to the previous year (Table 1). In the same
period, the growth rate in domestic traffic was 18.9 per cent totalling to 123.3 million
travellers and in international traffic, it was 14.4 per cent totalling to 23.8 million
travellers. It can be noted that the number of passengers flown within and outside
the country has increased by more than double in the past seven years. DGCA
predicts that this number of air passengers would increase by almost 3.3 times in the
4
DGCA is the regulatory body for civil aviation in India under the Ministry of Civil Aviation.
5
2017-18 implies financial year. Indian Financial Year (FY) is from 1st April to succeeding 31st March.
Samik SHOME & Sushma VERMA
Page |94 EJBE 2020, 13(25)
next 20 years making it approximately 500 million passenger journeys per annum
6
.
The year 2012-13 showed a negative growth both in terms of domestic as well as
international passengers probably due to grounding of Kingfisher airlines during that
time and relatively higher fare.
Table 1. Domestic and International Passenger Traffic in India
Year
Scheduled
Domestic
passengers
(in million)
Scheduled
International
passengers
(in million)
Yearly growth
in
international
passengers
CAGR
international
Passengers
2010-11
53.84
13.16
13.3
13
2011-12
60.84
14.38
9.3
12.1
2012-13
57.87
13.73
(4.5)
8.5
2013-14
60.67
15.77
14.8
9.6
2014-15
70.08
17.33
9.9
9.6
2015-16
85.20
18.63
7.5
9.4
2016-17
103.75
20.81
11.8
9.6
2017-18
123.32
23.80
14.4
10.1
Source: http://dgca.gov.in/reports/stat-ind.htm
Note: Figures in parentheses implies negative numbers
The growth in air passenger traffic was only 3.7 per cent in 2019, showing a steep
fall from the growth rate of 18-19 per cent of the previous year. This is due to various
reasons, prima facie being grounding of Jet Airways and a subdued economy.
However, there is a very significant growth potential of this sector in India. There are
several factors that may contribute to this growth: (a) decrease in the cost of air
travel; (b) growth in the population of the middle-income group; and (c) inadequate
capacity of other travel alternatives like, railways
7
.
Another important aspect in the airline industry is the significant use of leased
aircraft. Data from Center for Asia Pacific Aviation (CAPA) fleet database exhibits that
out of 652 commercially operated aircraft (including that in storage) in India as on
October 17, 2018, approximately 81 per cent are leased
8
(Figure 1). This is much
higher as compared to 53 per cent of leased aircraft share globally and approximately
52 per cent in Asia. Indigo with 144 leased aircraft and Jet Airways with 106 leased
aircraft together accounts for 47 per cent of the total leased aircraft in India followed
by Air India (87 aircraft) and Spice Jet (58 aircraft).
6
https://www.iata.org/publications/economics/Reports/India-aviaion-summit-Aug18.pdf accessed on
July 7, 2019.
7
http://www.careratings.com/upload/NewsFiles/Studies/Airlines%20and%20Airports.pdf accessed on
July 7, 2019.
8
https://centreforaviation.com/analysis/reports/aircraft-leasing-in-india-opportunity-knocks-for-an-
indian-lessor -443995, accessed on June 8, 2019.
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Figure 1. Proportion of Leased Aircrafts from Different Air Carriers
Source: CAPA Database (2018)
2.1. Market Share of Different Airlines
In India, Low Cost Carriers
9
(LCC) viz. Indigo, Spice Jet and Go Air, have more than 60
per cent shares in domestic operations compared to Full Service Airlines
10
(FSA) viz.
Air India, Jet Airways and Vistara.
Generally, tickets of LCC are priced lesser as compared to full-service airlines,
however, the increasing competition within the industry has led to a significant
reduction in gap in ticket prices in an endeavour to retain and increase the market
share. The market share of different airlines until January 2019 is depicted in Figure
2. It is evident that Indigo leads the domestic industry with 42.5 per cent share
followed by Spice Jet at 13.3 per cent. In fact, the market share of Indigo is more
than the sum total of the market share of airlines in the next three positions.
It may be noted that Jet Airways has suspended its operations from April 2019 due
to severe financial crunch
11
. Post grounding of Jet Airways, DGCA data of December
2019
12
showed an increase in market shares of different airlines i.e. Indigo (47.1 per
cent), Spice Jet (14.9 per cent), Air India (12.7 per cent), Go Air (10.6 per cent), Air
Asia (6.2 per cent) and Vistara (5.2 per cent).
9
A low-cost carrier is an airline that offers generally low fares in exchange for eliminating many traditional
passenger services.
10
A full-service airline typically offers passengers in-flight entertainment, checked baggage, meals,
beverages and comforts such as blankets and pillows in the ticket price.
11
https://economictimes.indiatimes.com/industry/transportation/airlines-/-aviation/jet-airways-stares-
at-shutdo wn-as-lenders-reject-appeal-for-funds-report/articleshow/68923128.cms, accessed on June 8,
2019.
12
https://data.gov.in › catalog › monthly-air-traffic-statistics on January 27, 2020
Samik SHOME & Sushma VERMA
Page |96 EJBE 2020, 13(25)
Figure 2. Market Share of Different Airline Companies Operating within India
Source: DGCA (accessed on January 31, 2020)
3. Review of Literature
The success of any bankruptcy prediction model lies in its ability to forecast as to
which company would possibly become bankrupt that too few years before they file
for bankruptcy. The initial attempt in this direction was made by Beaver (1966). He
used cash flows for prediction using a univariate model. He identified six financial
ratios as the ratios having discriminating power out of 29 ratios which he examined
between 79 bankrupt and 79 non-bankrupt firms. These six ratios analysed
profitability (net income plus depreciation and amortization/total liabilities, net
income/total assets), long term solvency (total debt/total asset) and short-term
liquidity (net working capital/total assets, current assets/ current liabilities, and cash,
short-term investments, accounts receivable/operating expenses excluding
depreciation and amortization). As per Beaver’s analysis, the profitability ratio, net
income plus depreciation and amortization/total liabilities best predicted the
bankruptcy potential.
Altman (1968) developed the first bankruptcy model by using multiple ratios based
on Multiple Discriminant Analysis (MDA) Model which more popularly is known as Z
score model. It was generic in nature attempting to explain the potential bankruptcy
amongst manufacturing companies that were publicly traded. The Z score model was
later replaced with ZETA model by Altman et al. (1977). Again, in 1993, the five
variable model of Altman Z-Score model was revised to a four variable model
popularly known as Altman Z" score model. The model is believed to be more
effective for non-manufacturing firms. Hanson (2003) used this revised Altman Z"
score model in his doctoral dissertation and this model gave fairly accurate results
by classifying bankrupt service companies to the extent of 92 per cent accuracy
within the first year, 69 per cent in second year and 54 per cent in third year
42.5%
13.3%
12.2%
11.9%
8.7%
5.3% 3.8%
Indigo
Spice jet
Air India
Jet airways
Go air
Air Asia
Vistara
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respectively. Altman and Gritta (1984) used Altman ZETA model for assessing US
aviation sector.
Studies on bankruptcy gained significant attention after the works of Beaver (1966)
and Altman (1968). Various attempts were made from time to time by various
authors to validate, to improve the existing models and to come up with new models.
Ohlson (1980) used logistic regression for predicting bankruptcy. Zavgreen (1985)
developed a logit model that could predict bankruptcy up to five years. Mixed logit
method was used by Jones and Hensher (2004). Gepp and Kumar (2008) combined
discriminant and logit analysis to create a bankruptcy prediction model.
Two discriminant analyses were conducted in 1980s specifically for aviation sector
by Gritta (1982) and Altman and Gritta (1984). Over a while it was felt that industry
specific models can probably give superior or more accurate results as compared to
generic models. AIRSCORE model was developed by Chow et al. (1991) using airline
data. Pilarski and Dinh (1999) developed a model known as P-Score for air
transportation. Davalos, Gritta and Chow (1999) constructed a Neural Network Model
for major US airlines and Gritta et al. (2000) constructed a neural network model for
predicting financial distress in small carriers. Though these models predicted
bankruptcy quite accurately for samples until one year but no major breakthrough
was observed in their prediction capability over MDA or logistic regression
(Gudmundsson, 2002).
The first study on the application of prediction models in the case of businesses in
countries outside the United States was done by Altman and Gritta (1984). This study
covered more than ten countries but included only one type of statistical model.
Various studies have been carried out in the past to find out the best prediction
model. But, most of the studies done were conducted on developed economies.
In the Indian context, most of the researches have focussed on applying Z score and
modified Z" score to select companies belonging to various sectors including aviation
and other industries. Barki and Halageri, (2014) applied Altman Z score model on
select companies in the Indian textile sector to analyse their financial strength.
Panigrahi (2019), tried to assess the existence of financial distress in the Indian
pharmaceutical sector by applying Altman Z Score Model to select companies
belonging to this sector. Kumar and Anand (2013) used both Altman Z score and
modified Z" score to assess the financial health of Kingfisher Airlines using data from
2005-06 to 2011-12. The results confirmed the poor financial health of the airline.
Another study on selected companies in the Indian aviation sector using original and
other variants of Z score model also confirmed that Kingfisher airline was on the
verge of bankruptcy (Vasantha et al., 2013). In another study calculating Altman Z
score for bankruptcy prediction for the airline industry in India, it was concluded that
the overall aviation sector in India is in financial distress excluding only Indigo which
is in the safe zone (Kulkarni, 2018).
Samik SHOME & Sushma VERMA
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While existing researches have used Z score model and modified Z Score Model in
context of Indian Aviation Sector (Pandey & Rathore, 2013; Safiuddin, 2017; Kulkarni,
2018;), none of the previous studies to the best of our understanding have
considered Altman Modified Z Score, P- Score model, Fuzzy Logic and Krueze models
all together for a combined analysis. The present study is an extension of previously
carried researches in the aviation industry. The main purpose of this investigation is
to assess the suitability of major bankruptcy prediction models by applying them to
key airline companies in India. The objectives of this study are: (a) to analyse the
financial situation of Indian airlines companies using various models (generic and
industry specific); (b) to examine the existence of financial distress in the Indian
airline industry; and, (c) to assess the applicability of different models in the Indian
scenario.
4. Research Methodology
This study included four leading airlines of India viz. Indigo, Jet Airways, Air India and
Spice Jet. Purposive sampling is used based on the market share and availability of
data for the air carriers. The audited financial statements provided in the annual
reports of these companies are the main source of financial data. The financial ratios
which serve as input for various bankruptcy models are calculated from these
statements. The data considered for this study is from 2015 to 2018. Only four years
of data are considered as existing literature states that financial ratios can predict
bankruptcy quite accurately within this time period (Kroeze, 2005).
In this paper four different models viz. Altman Z" Score Model, P-Score Model, Fuzzy
Logic Model and Kroeze Model are used to study possibilities of the bankruptcy of
Indian airlines companies using financial data. Of these four models, Z" score is a
generic model and the remaining three models are specifically designed using airline
specific data. The selected models were chosen on the basis of extensive literature
review and their predictive abilities as specified in the literature. Also, another
criterion was that model should be non-proprietary so that intercept terms are
publicly available. A brief about these four models are as follows:
4.1. Altman Model (Z" Score)
The original bankruptcy forecasting model of Altman popularly known as Z score has
been successfully used by Gritta (1982) to predict the bankruptcies of both Braniff
and Continental airlines, several years in advance before their actual filing of
bankruptcies. The model is represented as:
Z = 0.012X1 + 0.014X2 + 0.033X3 + 0.006X4 + 0.999X5 (1)
where, X1 = liquidity ratio calculated as net working capital to total assets; X2 =
profitability ratio measured as retained earnings to total assets; X3 = profitability
ratio measured as operating profit to total assets; X4 = leverage ratio measured as
the market value of equity to book value of debt; X5 = turnover ratio measured as
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operating revenues to total assets. Here, X1 to X5 are different ratios measuring
different aspects of financial strength. This model was developed by Altman (1968)
using multiple discriminant regression.
However, because of the increased use of operating lease in aviation sector, the
reliability of this model has reduced over time (Gritta et al., 1995). There is a
viewpoint that because of the significant use of operating lease, X5 ratio can lead to
distorted results (Gritta et al., 1995). Altman (1983) himself suggested the usage of
a modified model the Z" for service firms.
Z" = 6.56X1 + 3.26X2 + 6.72X3 + 1.05X4 (2)
Where, X1 = net working capital/total assets; X2 = retained earnings/total assets; X3
= operating profit/total assets; and X4 = book value of equity/book value of debt.
Based on the value of Z, the companies are categorized into three groups (Table 2).
Table 2. Categorization of the Companies based on Z Value
Z" value
Indicator
Interpretation
Z 1.10
High degree of Financial
Distress, Bankrupt
Increased Probability of insolvency and
bankruptcy in the near future.
1.1 Z 2.60
Grey Zone
Difficult to predict due to insufficient statistical
significance
Z 2.60
Low degree of Financial
Distress, Stable
Increased Probability that insolvency and
bankruptcy will not happen in the near future.
The modified Z" model has been used in this paper because of significant use of lease
in the Indian aviation sector. One of the limitations of Altman Z" score model is that
it is generic in nature and not specifically designed for the aviation industry.
4.2. The Pilarski or P-Score Model
This is a logit model estimating the probability of bankruptcy developed by Pilarski
and Dinh (1999). This model was primarily used for assessing the financial condition
of major US air carriers and is believed to be giving superior results as compared to
other models (Goodfriend et al., 2005). Popularly called as P-Score Model, this model
gives P value which is the Probability of Bankruptcy. Equation 3 and 4 shows two-
step calculation of Probability of Bankruptcy P.
W = 1.98X1 4.95X2 1.96X3 0.14X4 2.38X5 (3)
where, X1 = operating revenues/total assets; X2 = retained earnings/total assets;
X3 = equity/total debt obligations; X4 = liquid assets/current maturities of total debt
obligations; X5 = earnings before interest and taxes/operating revenues.
P = 1/ [1+e-w] (4)
Here ‘e is a mathematical constant and its value is equal to approximately 2.718.
Samik SHOME & Sushma VERMA
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Higher the P-value more is the probability of bankruptcy and vice-versa. Combining
equation (3) and equation (4), it is clear that the P-Score is a measure of five different
characteristics of the air carriers rather than just profitability. The P-Score model is
considered to be appropriate for this study because it is specific to air carriers with
a prediction rate of approx. 85.1 per cent. This model has also been used by the US
Department of Transportation for tracking the financial strength of their air carriers
(Gritta et al., 2006).
4.3. Fuzzy Logic Model
Another approach used by several researchers for predicting air carrier insolvency is
fuzzy logic. Silva et al. (2005) applied a multivariate technique known as Hybrid
Financial Statement Analysis (HFSAT) and tested the financial situation of a few
American and Brazilian airlines. HFSAT is the combination of a discriminant analysis
multi-variable model and the application of fuzzy logic to the firm’s financial data.
Following is the model used:
Z = 2.637 0.879X1 + 0.466X2 0.268X3 0.28X4 (5)
Where, X1 = Shareholder Funds/Total Assets; X2 = (Current Liabilities + Long Term
Liabilities)/Total Asset; X3 = Net Operating Revenue/Total Assets; X4 = Fixed
Assets/Total Asset.
Based on Z values, five groups have been identified:
Healthy: Z 1.862
Low Risk: 1.862 Z 2.2
Moderate Risk: 2.2 Z 2.515
High Risk: 2.515 Z 2.73
Insolvent: Z 2.73
Chena, H. J. et al. (2009) also used fuzzy logic based models for prediction and have
stated that prediction results based on this model are better as compared to classical
models. Korol (2012) has stated that results of Fuzzy logic based models are superior
as compared to Altman’s Z score.
4.4. Kroeze Model
Kroeze (2005) used the following model for predicting airline bankruptcies:
Ya = 0.268X1 + 0.838X2 + 0.111X3 + έ (6)
Where, Ya = overall index; X1 = working capital/total assets; X2 = retained
earnings/total assets; X3 = book value of equity/total liabilities; έ = error term.
This model uses only three variables and applies Multiple Discriminant Analysis.
Positive value of Ya indicates a non-bankruptcy and the negative value of Ya indicates
a situation of bankruptcy. Variable X2 defined as retained earnings/total assets is
considered to be the most important predictor of bankruptcy as negative retained
earnings are a sign of financial distress. Kroeze used this model to successfully
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predict the situation of Air Canada, US Airways and Hawaiian four years before their
actual bankruptcies and that of Trans World Airlines (TWA) and American Trans Air
(ATA), three and two years respectively before their actual filing of bankruptcies.
5. Research Findings and Discussion
This section is being divided into two parts for bankruptcy prediction and subsequent
discussions. In the first part, all the four models discussed above have been analysed
for four selected air carriers. The second part attempts to relate the results derived
from the application of different models on select air carriers with the prevailing
situation to assess the applicability of these models in Indian context.
5.1. Altman Model (Z" Score)
Table 3 shows the Z" scores for four airlines from 2015 to 2018. It can be observed
that except Indigo, all other airlines are showing a Z" score less than 1.10. As
discussed before, it indicates that these airlines i.e. Jet Airways, Air India and Spice
Jet are under a high degree of financial stress. The X variables are also showing
negative values for most of the airlines which are resulting from negative
profitability, negative net worth and negative equity
13
.
Table 3. Z" Score of Air Carriers
Carrier
Year
X1
X2
X3
X4
Z" Score
Indigo
2015
(0.135)
0.225
0.153
0.117
0.999
2016
(0.003)
0.180
0.225
0.906
3.032
2017
(0.118)
0.183
0.147
1.578
2.464
2018
(0.111)
0.194
0.171
3.158
4.368
Jet Airways
2015
(0.213)
(0.572)
(0.049)
(1.207)
(4.856)
2016
(0.215)
(0.5096)
0.069
(1.062)
(3.720)
2017
(0.569)
(0.945)
0.079
(0.897)
(7.222)
2018
(0.566)
(0.970)
(0.0538)
(1.368)
(8.675)
Air India
2015
(0.588)
(0.151)
(0.068)
(0.556)
(5.391)
2016
(0.433)
(0.105)
0.002
(0.533)
(3.733)
2017
(0.686)
(0.160)
0.008
(0.560)
(5.556)
2018
(0.177)
(0.165)
(0.051)
(0.759)
(9.396)
Spice Jet
2015
(0.598)
(1.231)
(0.232)
(0.891)
(10.433)
2016
(0.584)
(0.923)
0.172
(1.013)
(6.746)
2017
(0.470)
(0.772)
0.149
(0.592)
(5.222)
2018
(0.183)
(0.409)
0.151
(0.0426)
(1.562)
Source: Calculated from Annual Reports of different Airline companies.
Note: Figures in parentheses implies negative numbers
The values of equity/debt ratio (X4) are worth mentioning in this aspect. This ratio
shows the relative proportion of debt and equity in the total capital structure. Higher
is the proportion of debt, riskier is the firm. Except for Indigo, almost all the
companies are showing negative equity/debt ratio and this is because of the
13
Negative equity is a situation where liabilities exceed assets.
Samik SHOME & Sushma VERMA
Page |102 EJBE 2020, 13(25)
negative equity. This negative equity is due to the accumulated losses over the years
and then excessive debt to cover these losses. On the contrary, in the case of Indigo,
the Z" score has been positive in all four years, although with varying degrees. This
indicates that Indigo is clearly in a low degree of financial stress.
5.2. The Pilarski or P-Score Model
As already stated, P is the probability of going bankrupt. Hence, greater the P-value,
the higher is the financial distress and more is the probability bankrupt.
It is clearly evident from Table 4 that except Indigo and Spice Jet, the other two
airlines viz. Jet Airways and Air India are showing more than 50 per cent probability
of going bankrupt. Indigo is evidently out of danger zone with almost nil probability
of going for bankruptcy. The probability of Spice Jet becoming bankrupt has also
gone down significantly from approximately 20 per cent in 2015 to 2 per cent in
2018. The P-Scores are giving a warning signal in case of Jet Airways and Air India. In
case of Jet Airways, the probability of becoming financially bankrupt was significantly
very high since 2015 reaching up to 98 per cent in 2018.
Table 4. P Score of Air Carriers
Carriers
Year
X1
X2
X3
X4
X5
W
P
%
Indigo
2015
1.658
0.225
0.117
0.795
0.141
(4.48)
0.011
1.120
2016
1.391
0.180
0.906
0.995
0.194
(6.02)
0.002
0.2418
2017
1.620
0.183
1.577
0.850
0.133
(7.641)
0.00048
0.048
2018
1.560
0.194
3.157
0.861
0.151
(10.710)
0.000022
0.002
Jet
Airways
2015
0.624
(0.572)
(1.207)
0.702
(0.015)
3.899
0.980
98.01
2016
0.789
(0.510)
(1.063)
0.707
0.141
2.608
0.931
93.14
2017
1.507
(0.945)
(0.897)
0.488
0.145
3.036
0.954
95.42
2018
1.677
(0.970)
(1.368)
0.566
0.004
4.074
0.983
98.32
Air India
2015
0.511
(0.151)
(0.556)
0.189
(0.092)
1.018
0.735
73.47
2016
0.567
(0.105)
(0.533)
0.415
0.017
0.343
0.585
58.48
2017
0.608
(0.160)
(0.560)
0.206
0.159
0.280
0.570
56.95
2018
0.709
(0.165)
(0.759)
0.134
(0.033)
0.960
0.723
72.30
Spice Jet
2015
1.996
(0.123)
(0.891)
0.364
(0.116)
(1.369)
0.203
20.280
2016
1.787
(0.092)
(1.012)
0.419
0.096
(1.385)
0.200
20.027
2017
2.172
(0.077)
(0.592)
0.479
0.069
(2.987)
0.048
4.8031
2018
1.938
(0.041)
(0.043)
0.768
0.078
(3.845)
0.021
2.090
Source: Calculated from Annual Reports of different Airline companies.
Note: Figures in parentheses implies negative numbers
This is a very significant observation as this particular carrier has been grounded
since mid-April 2019 because of financial issues. Government sponsored Air India is
also showing more than 70 per cent probability of becoming bankrupt. This airline is
surviving only due to financial assistance from the government.
Financial Distress in Indian Aviation Industry: Investigation Using Bankruptcy Prediction …
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5.3. Fuzzy Logic Model
From the Fuzzy Logic Z Scores portrayed in Table 5, it is noticeable that none of the
airlines are in a healthy zone with Z < 1.82. Jet Airways and Air India are in the zone
of severe financial distress as good as being insolvent with Z score greater than 2.73
for all the four years since 2015. Very high total debt coupled with negative values
of retained earnings and equity is responsible for this plight of both these air carriers.
Indigo is the only airline that has gradually moved into a low risk category as per the
classification pattern of this model. This is because of the increase in its X1 ratio over
the period of study. Spice Jet is another airline that has moved into medium risk
category shown by consistent improvement in its Z score. However, the negative
value of equity and retained earnings remain a significant cause of concern for Spice
Jet.
Table 5. Fuzzy Logic Z Scores
Carrier
Year
X1
X2
X3
X4
Z" Score
Indigo
2015
0.041
1.009
1.358
0.476
2.574
2016
0.235
0.853
1.391
0.409
2.341
2017
0.330
0.996
1.620
0.331
2.285
2018
0.480
0.952
1.560
0.310
2.154
Jet Airways
2015
(0.230)
1.287
1.099
0.500
3.004
2016
(0.171)
1.257
1.200
0.481
2.917
2017
(0.605)
1.786
2.013
0.458
3.333
2018
(0.653)
0.608
2.101
0.263
2.858
Air India
2015
(0.522)
1.663
0.511
0.863
3.492
2016
(0.480)
1.641
0.567
0.692
3.478
2017
(0.558
1.831
0.608
0.822
3.587
2018
(0.767)
2.370
0.709
0.817
3.997
Spice Jet
2015
(0.485)
1.485
1.996
0.657
3.037
2016
(0.365)
1.365
1.787
0.572
2.955
2017
(0.214)
1.263
2.172
0.568
2.672
2018
(0.0107)
1.037
1.939
0.397
2.498
Source: Calculated from Annual Reports of different Airline companies.
Note: Figures in parentheses implies negative numbers
5.4. Kroeze Model
Table 6 portrays the Ya score of various air carriers. It shows that except for Indigo,
Ya scores of all other airlines is negative which implies a situation of probable
bankruptcy. The retained earnings/total assets (variable X2) is negative for all the
airlines except Indigo. As X2 considers retained earnings, it is quite obvious that no
company can survive for long with a negative value of retained earnings. However,
Spice Jet is showing an improvement in performance over a period of time with its
Ya score improving despite being in distress zone. For Air India and Jet Airways, the
Ya score is continuously deteriorating.
Samik SHOME & Sushma VERMA
Page |104 EJBE 2020, 13(25)
Table 6. Kroeze Model Ya Scores
carrier
x1
x2
x3
ya score
Indigo
(0.135)
0.225
0.117
0.166
(0.003)
0.180
0.906
0.251
(0.118)
0.183
1.577
0.297
(0.111)
0.194
3.158
0.483
Jet Airways
(0.212)
(0.572)
(1.207)
(0.670)
(0.215)
(0.510)
(1.063)
(0.603)
(0.569)
(0.945)
(0.897)
(1.044)
(0.566)
(0.970)
(1.368)
(1.117)
Air India
(0.588)
(0.151)
(0.556)
(0.346)
(0.433)
(0.105)
(0.533)
(0.263)
(0.686)
(0.160)
(0.560)
(0.380)
(1.177)
(0.165)
(0.759)
(0.538)
Spice Jet
(0.598)
(1.231)
(0.891)
(1.291)
(0.583)
(0.923)
(1.013)
(1.042)
(0.470)
(0.772)
(0.592)
(0.839)
(0.182)
(0.409)
(0.043)
(0.396)
Source: Calculated from Annual Reports of different Airline companies.
Note: Figures in parentheses implies negative numbers
5.5. Comparison of All Four Models
Table 7 shows the consolidated result of all the four models used in this paper. Indigo
is found to be the only stable and consistent air carrier in the Indian aviation industry
among the four major airlines. The data presented in the balance sheet also confirms
the same. The Operating Revenue of Indigo has improved consistently from INR
13,925.3 crore
14
in March 2015 to INR 23,020.9 crore in March 2018. The company
remained profitable continuously on year to year basis with a profit of INR 1,304.2
crore in March 2015 to INR 2,242.4 crore in March 2018
15
.
Spice Jet has shown continuous improvement in its financial performance over the
period of the study. Operating Revenue of Spice Jet has improved from INR 5,201.53
crore in March 2015 to INR 7,795.09 crore in March 2018. The company became
profitable with a profit of INR 566.65 crore in March 2018 from a loss of INR 687.05
crore in March 2015
16
. It has revived splendidly over the last few years from almost
a near-death like situation in 2014, when the airline was almost about to shut down
its operations due to severe cash crunch
17
. The probable reasons for this positive
development may be tightening of cost and expansion to new routes which
14
INR is the Indian Rupee and the currency of India. As on May 11, 2020, 1 $US = 75.53 INR. Similarly,
crore is in the Indian numbering system. One crore denotes ten million.
15
Annual Report of Indigo FY 2014-15, 2017-18 on June 7, 2019.
16
Annual Report of Spice Jet FY 2014-15, 2017-18 on June 7, 2019.
17
https://www.livemint.com/Companies/T2BOBSwziSYSnEDPMJ2xEM/The-SpiceJet-turnaround-story-
and-how-it-became-worlds-best.html, accessed on June 9, 2019.
Financial Distress in Indian Aviation Industry: Investigation Using Bankruptcy Prediction …
EJBE 2020, 13(25) Page |105
generated better revenues. However, Spice Jet is yet to be considered out of danger
as per the scores of various models in Table 7.
The operations of Jet Airways are being suspended since the middle of April 2019
due to mounting financial burdens and this poor situation of Jet airways is illustrated
appropriately by all the models used in this paper. This company is struggling with a
debt of approx. INR 11,261 crore
18
. The airline has not been able to pay to any of the
stakeholders including, banks, creditors, vendors, lessors and most regrettably,
salary to its employees. Its aggressive debt-based expansion plans have mounted its
debt level significantly from less than INR 3,000 crore in 2005 to approximately INR
16,600 crore in March 2009. Despite levelling to a great extent, the overall debt level
continued to be on a higher side at around INR 7,600 crore in March 2018. Similarly,
its net worth turned red first in March 2012, with a negative value of INR 10 crore
and it has consistently worsened since then. Hence, with a negative net worth and
significantly higher debt levels, the airline leverage ratio has been very unfavourable
over the years. The financial statements of Jet Airways also show a continuous loss
from 2008 onwards with an exception of marginal profits in 2016 and 2017
19
.
Table 7. Consolidated Scores of Various Models for Selected Airlines
Carrier
Year
Z” Score
P Score
Fuzzy Logic
Ya Score
Indigo
2015
0.999
0.01119
2.574
0.166
2016
3.032
0.00241
2.341
0.251
2017
2.464
0.00048
2.285
0.297
2018
4.368
0.00002
2.154
0.483
Jet Airways
2015
(4.856)
0.980
3.004
(0.670)
2016
(3.720)
0.931
2.917
(0.603)
2017
(7.222)
0.954
3.333
(1.043)
2018
(8.675)
0.983
2.858
(1.117)
Air India
2015
(5.391)
0.735
3.492
(0.347)
2016
(3.733)
0.585
3.478
(0.263)
2017
(5.555)
0.570
3.587
(0.380)
2018
(9.396)
0.723
3.997
(0.538)
Spice Jet
2015
(10.571)
0.203
3.037
(1.291)
2016
(6.631)
0.200
2.955
(1.042)
2017
4.943)
0.048
2.672
(0.839)
2018
(1.703)
0.021
2.498
(0.396)
Note: Figures in parentheses implies negative numbers
All four models also portray the adverse financial situation of Air India. With a laid-
back attitude and poor operational efficiency, this particular airline is surviving only
because of government packages. It is virtually in a bankrupt position for all practical
purposes. After pumping in INR 28,000 crore by the Indian government for its
18
https://www.thehindubusinessline.com/economy/logistics/jets-gross-debt-likely-to-add-up-to-11261-
cr/article 26905023.ece, accessed on May 10, 2019.
19
Various Annual Reports of Jet Airways on June 7, 2019.
Samik SHOME & Sushma VERMA
Page |106 EJBE 2020, 13(25)
turnaround in 2014, this airline could barely generate an operating profit of INR
403.03 crore in two financial years (2015-16 and 2016-17)
20
.
To sum up, the result of all the models depicts a relatively healthy low risk situation
for Indigo and gradually improving condition for Spice Jet. In case of Jet Airways and
Air India, all the models captured their severely bad financial position over the period
of study.
6. Concluding Remarks
The primary objective of this paper was to analyse the financial situation of four
major Indian airline companies. Except for Indigo, all the other chosen airlines (viz.
Spice Jet, Jet Airways and Air India) have been under financial distress as specified
by all the four chosen models i.e. Altman Z" Score, P-Score, Fuzzy Logic and Kroeze
Model. For Indigo, Altman Z" Score, P-Score and Kroeze Model have shown healthy
stable financial position. Fuzzy logic has put Indigo also in medium to low risk
category based on its financial situation.
The models have highlighted the existence of severe financial distress in the Indian
aviation sector. The results are in line with the previous studies conducted (Tyagi and
Dutta, 2017; Kulkarni, 2018) which have also indicated the existence of financial
distress in the overall aviation sector in India. The study also aimed to assess the
suitability of different models in the Indian context. These models despite drawing
data from financial statements for calculating various ratios give different weightage
to various ratios and employ different statistical techniques.
The scores from various models have been good indicators and successfully captured
the prevailing financial conditions of different air carriers in India. All the four models
have depicted the existence of extreme financial stress for Jet Airways leading to a
very high probability of bankruptcy well in advance right from the year 2015. Jet
Airways operations are suspended since April 2019. From the study it can be safely
concluded that all of these models can be applied in Indian Aviation Sector for
predicting the possibility of bankruptcy.
However, it should be noted that the existence of financial distress does not always
lead to a situation of bankruptcy. It only indicates a likelihood of future failure which
can also be reversed by taking appropriate corrective measures and strategies either
by the company itself or by the government. This particular analysis of the Indian
airline companies is also limited to the extent of availability of accurate financial
data. The result of this study shows the potential for further research in this sector.
Further studies can focus on identifying various variables leading to the financial
distress and in identifying variables critical in improving the financial performance of
airlines on a case to case basis. This kind of analysis for any industry can be useful for
different stakeholders like stockholders, bankers, customers, lessors, and other
20
https://www.businesstoday.in/current/economy-politics/a-loss-making-airline-for-almost-a-decade-
air-india-has-no-reason-to-exist/story/281813.html, accessed on June 5, 2019.
Financial Distress in Indian Aviation Industry: Investigation Using Bankruptcy Prediction …
EJBE 2020, 13(25) Page |107
creditors, among others. The management of different companies and the
government can also take appropriate policy measures well in advance to improve
the financial performance on a case to case basis.
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