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Theoretical Economics Letters, 2017, 7, 982-1000
http://www.scirp.org/journal/tel
ISSN Online: 2162-2086
ISSN Print: 2162-2078
DOI: 10.4236/tel.2017.74067 June 22, 2017
Impact of Capital Structure on Firm Value:
Evidence from Indian Hospitality Industry
Divya Aggarwal1, Purna Chandra Padhan2
1Fellow Program in Management (Finance), XLRI, Xavier School of Management, Jharkhand, India
2XLRI, Xavier School of Management, Jharkhand, India
Abstract
This study examines the effect of capital structure and firm quality on firm
value of selected BSE listed Indian hospitality firms over a time frame of
2001-15. Variables including firm quality measured through Altman Z score
,
leverage, size, profitability, tangibility, growth, liquidity along with macro v
a-
riables of growth in gross domestic product and inflation are taken into co
n-
sideration for examining their impact on firm value. An empirical study has
been carried out through panel data techniques by applying pooled OLS,
fixed
effects and random effects models. The findings of the study reveal a signif
i-
cant relationship of firm value with firm quality, leverage, liquidity,
size and
economic growth. The study shows that Modigliani miller theorem of capita
l
structure irrelevance does not hold for Indian hospitality sector. It is of pra
c-
tical significance for hotel owners to reassess their capital structure to improve
firm quality and firm’s market performance.
Keywords
Altman Z Score, Firm Value, Leverage, Trade off Theory
1. Introduction
There have been numerous empirical researches on various factors determining
the relation between capital structure and firm value. The debate on the impact
of capital structure variables on firm value is ongoing in the field of corporate
finance. It still hasn’t come to a conclusive result and remains a controversial is-
sue. Few capital structure theories such as the theories of trade off, pecking order
and market timing have been extensively studied and tested empirically in the
literature, but have given mixed results. Therefore, there is no unanimous view
on the relevance of capital structure theories in general and especially on hospi-
tality industries in particular. In lieu of this, the study aims to understand the
How to cite this paper:
Aggarwal,
D. and
Padhan
, P.C. (2017) Impact of Capital Struc-
ture on Firm Value: Evidence from Indian
Hospitality Industry
.
Theoretical Econo
m-
ics Letters
,
7
, 982-1000.
https://doi.org/10.4236/tel.2017.74067
Received:
May 19, 2017
Accepted:
June 19, 2017
Published:
June 22, 2017
Copyright © 201
7 by authors and
Scientific
Research Publishing Inc.
This work is licensed under the Creative
Commons Attribution International
License (CC BY
4.0).
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Open Access
D. Aggarwal, P. C. Padhan
983
role of firm quality and capital structure in determining the firm value in the
hospitality sector in a developing country context. It analyzes selected hotel and
tourism companies listed on Bombay stock exchange (BSE) in India to establish
relationship between firm value and firm quality.
Our motivation to study the hospitality sector is due to many reasons. Firstly,
due to unique characteristics of the hospitality sector, it constantly needs to in-
novate based on need of the hour. It focuses on creating a home away from
home for travelers. Hence, it is always a great challenge for hotels to retain cus-
tomers. Moreover, this industry is highly sensitive to macroeconomic cycles.
Secondly, the slowdown in global economy has hit the Indian hotel industry
which has been facing a slump for over five years now. As per the recent report
in 2014 by the Federation of Hotels and Restaurant association of India
(FHRAI), the occupancy rates in the hotel space have been stagnant since the
past two years. A recent study by Varuni & Sathyanarayanan [1] mentioned how
the global slowdown impacted both business and leisure travel which have led to
a slump in this industry. Thirdly, the hospitality sector is constrained with
funding issues with not so friendly lending policies by Indian banks. A report by
HVS Global hospitality services from Thadani & Mobar [2], analyzed the critical
challenges faced by Indian hotel industry in 2013 highlighting a plethora of is-
sues impacting this sector adversely. Fourthly, most of the empirical work on
understanding determinants of capital structure in Indian context is limited to
the manufacturing sector. The studies in developing countries are less in com-
parison to developed companies and more sparse in services sector.
The importance of the hospitality sector cannot be underestimated in India.
With the first hotel established in India in 1903 (branded as the Taj Hotels by
the founder of Indian conglomerate Tata group), the Indian hotel industry now
is expected to generate almost 13.45 million jobs, accounting for almost 9% of
total employment opportunities. The sector is among the top 10 sectors in India
attracting foreign direct investments (FDI) as per the department of industrial
policy and promotion (DIPP). Approximately USD 9.23 billion as FDI has been
invested in this sector within the year 2000-2016 as mentioned in the report by
IBEF [3].
The results of the study are novel in many ways. Firstly, hardly any studies
have seen done in the services sector to understand the determinants of firm
value with respect to firm attributes, firm quality and its capital structure. The
study has examined the impact of firm leverage, firm quality, firm attributes
(such as size, tangibility, profitability, asset growth) along with macro variables
on firm value. With respect to firm leverage, past studies have given mixed re-
sults. This has been mostly dependent upon the nature of the industry being
examined. Firm quality and firm attributes have shown positive relationship
with firm value. Our results have given interesting insights for disproving the
theory of capital structure irrelevance. While the market equity value of the firm
showed a negative relation with leverage, firm value showed a positive relation.
Firm quality, firm size and liquidity also exhibited a significant positive relation
D. Aggarwal, P. C. Padhan
984
with firm value.
This study contributes to the literature by examining capital structure and
firm quality determinants to provide empirical support to capital structure theo-
ries in a specific industry, the hospitality sector in Indian context. Moreover, this
is among the first study which analyzes relation between firm value and capital
structure over a large time frame of 15 years for Indian hotel companies. This
study will also help various stakeholders such as; hotel owners, government and
investors to understand the relation between capital structure determinants, firm
quality and firm value for hotel companies. Analyzing the impact of firm quality
on firm value will enable management to reexamine their financial performance
along with assisting in identifying which factors impact their firm value. It will
also enable hotel owners to improve their capital structure decisions. For inves-
tors it will help them in better identification of firms for investments. For regu-
lators it will enable them to identify areas to come up with friendly policies for
hotel industry.
The rest of the study proceeds as follows: Section II discusses the literature re-
view and prior work in empirical tests on firm value and capital structure theo-
ries leading to model specification. Section III presents the methodology fol-
lowed. Section IV explains the results and Section V offers concluding remarks
of the study.
2. Literature Review
Modigliani and Miller [4] stated that market value of a firm does not depends
upon its capital structure and if firms do not provide the required returns, then
individuals can get their desired returns by creating synthetic portfolios. Many
scholars have found the theory of capital structure irrelevance by Modigliani and
Miller [4] to be objectionable and have constantly challenged it. Walter [5] ar-
gued that the famous separation analogy of splitting milk into cream and skim
milk for debt and equity bearing the same cost as whole milk cannot be consi-
dered in a similar fashion when looking at splitting of operating income. It was
shown that an optimal capital structure exists with a certain debt level where the
marginal cost of raising additional debt should be greater than or equal to the
average cost of capital as pointed out by Solomon [6]. Firm failures had always
invited scholarly attention moving from firm quality assessment based on qua-
litative factors to financial ratio analysis. Till 1970s, two kinds of statistical mod-
els were used to predict bankruptcy. These were the univariate model developed
by Beaver [7] and the multivariate discriminant analysis, called the Z score
which became the best known predictor of bankruptcy and is most widely used
till date with continuous score revision, developed by Altman [8].
During 1980s the critique against capital structure irrelevance theory branched
in two areas. One was trade off theory (TOT) and another one incorporated
management behavioral models like signaling and market timing in capital
structure. According to the TOT, an optimal capital structure can be determined
by maintaining a balance between cost of financial distress and tax benefits of
debt.
D. Aggarwal, P. C. Padhan
985
Many scholars challenged the theory of capital structure irrelevance by adding
imperfections of bankruptcy costs. Warner [9] empirically proved that firms
with very volatile earnings had less debt and more diversified firms had more
debt due to different bankruptcy costs. Hence a firm should not borrow “as
much as possible” and act rationally by matching asset maturities and debt. For a
firm there are assets in place representing present value of earnings (which
should be financed by debt) and assets for growth representing present value of
growth opportunities (which should be financed more by equity). Myers [10]
showed that large levels of debt can also lead to under investment., DeAngelo &
Masulis [11] challenged the capital structure irrelevance theory by taking corpo-
rate tax shields as substitute of debt tax shields and showing that each firm has a
unique optimal capital structure dependent on its industry.
A review on theory of capital structure by Raviv [12] showed that apart from
TOT, capital structure theories diverged into two other dimensions of agency
theory and asymmetric information arising due to conflict of interest in asset
ownership. Tracing the developments in finance theory, Weston [13] mentioned
that scholars have identified three root causes of agency problems which were
due to shareholders gaining on expense of debtholders, managers or agents tak-
ing an undue advantage of firm perks with a fractional ownership and informa-
tion asymmetry. The agency theory states that there does exist an optimal capital
structure for a firm by a tradeoff between the agency cost of debt and the benefit
from debt. Jensen & Meckling [14] used this explanation to justify why loan
agreements have covenants to protect lenders from the risk of asset substitution
by shareholders. The information asymmetry led to the development of pecking
order theory by Myers [15] which resides on firm managers using private infor-
mation to time issue of securities and having a preference first for internal funds,
then debt and lastly equity. Hence use of debt, equity or retained earnings also
gives a signal on firm operations and impact firm value.
Various studies have tried to find empirical evidence for these theories by us-
ing firm specific variables to determine their impact on firm value and capital
structure. The literature has evolved in two areas. One analyzes the determinants
of capital structure
i.e.
the factors affecting firm leverage. The other analyzes the
impact of firm capital structure on firm market value. Moreover, studies have
been done on both listed and non-listed firms. This study caters to the impact of
firm capital structure and quality on firm market value by studying listed firms.
Major studies have related firm value with capital structure variables like debt,
equity, size, profitability, risk, tangibility and macro factors like inflation, growth
etc. Some of the pioneering work in this area has been of Rajan and Zingales
[16]; Booth [17]. A detailed explanation of the variables examined in this study
is given in section III.
In Indian context the empirical evidence of capital structure determinants has
been done in a limited way and mostly restricted to manufacturing sector. Major
works include those of Sarma and Rao [18], Dhankar and Boora [19], Bhaduri
[20] and recent works by Mukherjee & Mahakud [21] and Chadha & Sharma
D. Aggarwal, P. C. Padhan
986
[22] have given mixed results. Moreover, majority of these studies have at-
tempted to establish the relation between firm capital structure and firm specific
determinants. Impact of them on firm value is not explored much in Indian
context. This study contributes to the existing literature by analyzing the impact
of capital structure and firm specific variables on firm value of Indian hotel in-
dustry.
A conceptual framework of the linkages of firm attributes, firm quality and
capital structure on firm value is given in Figure 1. The framework indicates the
sign of relationship based on the trade of theory. It has been used to develop
hypothesis for examining the relationships between the variables.
The next section discusses the variables used in depth along with the devel-
opment of model equation for analysis.
3. Data and Variable Description
3.1. Data
The panel data is used on 22 Indian hotel companies which are listed on the
BSE. The time period of analysis ranges from a period of 2001-15. The data is
sourced from CMIE database. The database contains 37 listed companies under
the hotels and restaurant industry group. A total of 22 firms were selected for the
analysis. The basis of selection of firms was done subject to data availability and
listing prior to 2000. A brief description of the 22 firms is given in the appendix
of the study. The selection of firms is shown in Table 1.
3.2. Variable Descriptions
A variety of capital structure determinants have been used in different studies
depending upon the research objective and industry being analyzed. Empirical
studies have shown the impact of firm specific factors on determination of capi-
Figure 1. Linkages of firm attributes, firm quality and capital structure on firm value.
Firm Value
Leverage
Firm Quality
Size
Tangibility
Profitability
Growth
Liquidity
Macro
Mixed
+ve
+ve
+ve
+ve
+ve
+ve
+ve
D. Aggarwal, P. C. Padhan
987
Table 1. Number of companies selected.
Industry group Total Taken
Hotels & restaurant 37 22
Of which:
Hotels & restaurant services 31 20
Tourism 2 2
Travel agencies 3 0
Restaurants 1 0
tal structure which then impacts the firm value. Since this study examines the
effect of capital structure on firm value, the dependent variables include proxies
of firm value as done in numerous studies. They directly test the Modigliani and
Miller hypothesis on influence of capital structure on firm value as done in the
studies, such as those by Sarma and Rao [18]; Cheng, Liu, & Chien [23]. Three
proxies of firm value are used.
3.2.1. Dependent Variables Include
Dependent variables include in both absolute (EV, MCap) and relative terms
(PB).
1) Enterprise value (EV)—this is the most comprehensive proxy for firm
value as it includes both equity and debt. It has been used in studies by Dhankar
and Boora [19]; Chadha & Sharma [22].
2) Market capitalization (MCap)—this metric is devoid of debt and reflects
how much the equity of the firm is priced in the market in absolute terms. It has
been used in studies by, Dhankar and Boora [19]; Chadha & Sharma [22].
3) Price to Book (P/B)—used as a proxy for relative firm value for companies
having negative earnings. It shows the ratio of market price of equity and its
book value. A higher than 1 ratio means that market has priced its equity more
than its book value. It has been used in various studies including Ozkan [24],
Antoniou [25], and Bevan [26].
A comprehensive summary of the empirical work is done by Harris and Ravi
[12] and Rajan and Zingales [16] which highlight common variables being
shared in multiple studies as determinants of capital structure. This study ana-
lyzes the below variables as explanatory variables.
3.2.2. Independent Variables Include
1) Leverage—various proxies for leverage have been used across multiple stu-
dies depending upon the context of the study. In this study we have used total
outside liabilities by total net worth (TOL_TNW)—one of most comprehensive
ratios as it examines the broadest definition of firm solvency. It indicates how
much the firm relies on equity for repayment of debt and as a rule of thumb is
capped to be around 60%. Prior studies have used market and book value of debt
over equity, such as those by Rajan and Zingales [16]; Bevan [26]; Feidakis &
D. Aggarwal, P. C. Padhan
988
Rovolis [27]; Antoniou [25]; Bhaduri [20]; Charalambakis & Psychoyios [28];
Shah & Jam-e-Kausar [29] etc. and have shown a mixed relation between leve-
rage and firm value. We believe the relation between them to be significant.
Hypothesis 1—firm value and leverage have a significant relation.
2) Firm quality—leverage is also related to financial distress costs as per
agency theory. We have also included the risk of bankruptcy on firm value cal-
culated through Z score as developed by Altman [8]. The higher the score more
is the firm quality and lower is the probability of firm being bankrupt. Rajan and
Zingales [16] mentions probability of bankruptcy as a significant determinant of
capital structure among various studies. We expect a significant relation of Z
score with firm value.
Hypothesis 2—firm value and firm quality are positively related.
3) Size—considerable evidence have been found on a significant relation be-
tween size and firm leverage. We have used natural logarithm of total assets
(Ln_TA) as a proxy for firm size, in this study, based on Naceur & Goaied [30].
Large firms due to diversification benefits and cushion against adverse cash flow
fluctuations tend to be more leveraged. While Titman and Wessels [31]; showed
a negative relation between firm value and short term debt, Rajan and Zingales
[16] showed a positive relation between leverage and size due to reduced infor-
mation asymmetry. It supports the agency theory. Most of the studies have
shown a positive relationship of size and leverage such as those of Ang [32];
Warner [9]; Booth [17]. We expect a positive relation between size and firm val-
ue as studies have referred to size as an inverse proxy of bankruptcy probability
and size also reduces financial distress cost.
Hypothesis 3—firm value and size have a significant positive relation.
4) Tangibility—being a capital intensive sector, the hospitality sector has a
high proportion of fixed assets in the form of land and building. Agency cost
theories and information asymmetry theories stress that the capital structure de-
cisions are impacted by asset structure. High compositions of fixed assets offer
more collateral value, hence providing a safety cushion backup. This study uses
fixed assets to total assets (FA_TA) as a proxy for tangibility. It has been used in
many other studies such as those by Rajan and Zingales [16], Bevan [26], Booth
[17], Voulgaris [33], Shah & Jam-e-Kausar [29]. The tradeoff theory expects a
positive relation between firm leverage and asset tangibility. With more tangible
assets the firm has more collateral to offer and hence, can invest in more projects
due to fund availability. We expect a significant positive relation between firm
value and tangibility due to the nature of the hotel industry.
Hypothesis 4—firm value and tangibility have a significant positive relation.
5) Profitability—pecking order theory expects a negative relation between
firm profitability and debt issue as shown in studies by Myres [34], Antoniou
[25]; Titman and Wessels [31]; Voulgaris [35], Bevan [26]. With more profita-
bility leading to higher retained earnings firms would use internal funds first,
then issue debt and then issue equity as a last resort. Hence, with higher profita-
bility the firm value should increase. However the tradeoff theory expects a posi-
D. Aggarwal, P. C. Padhan
989
tive relation between firm leverage and profitability since it lowers the cost of fi-
nancial distress. We expect a positive relation between profitability and firm
value. We have used return on assets (ROA) as a measure of firm profitability. It
has been used in various studies including Shah & Jam-e-Kausar [29], Rajan and
Zingales [16], Al-Fayoumi [36].
Hypothesis 5—firm value and profitability have a significant positive relation.
6) Growth—as per pecking theory and trade off theory there exists a positive
and negative relation between firm leverage and growth as shown by Myres [10],
Jensen [37], Harris and Arthur [38]. Pecking order theory predicts a preference
for debt over equity in lieu of growth opportunities. Trade off theory predicts
more agency conflicts from growth opportunities. We expect a significant rela-
tion between firm value and growth since growth opportunities impact capital
structure of the firm. As consistent with many studies we have used growth in
total assets as a proxy for growth (GRWTA), as mentioned in the study by Tit-
man and Wessels [31]. It is more determined due to the nature of the hotel in-
dustry where increase in total assets signifies how the hotel chain has expanded.
Dependent upon the nature of industry, firms with more tangible assets tend to
borrow more than firms with less tangible assets. In Indian context, whether
capital markets have recognized the growth of hotel firms without an adverse ef-
fect on its firm quality, still needs to be explored. However, we expect a signifi-
cant relation between firm growth and firm value.
Hypothesis 6—firm value and growth have a significant relation.
7) Liquidity—with higher liquidity, firms can finance investments with ease
and meet short term financial commitments. Studies like Antoniou [25] have
found a significant relation between liquidity and leverage. Dependent upon the
industry characteristics, various empirical studies have given mixed results on
the impact of liquidity on firm value. We expect a significant relation between
liquidity and firm value. We have used current ratio (CR) as a proxy for liquidity
in the study.
Hypothesis 7—firm value and liquidity have a significant positive relation.
8) Macro factors—we have also included additional variables of inflation and
GDP growth to examine the effect of macro factors on firm value. High inflation
tends to make firms borrow instead of raising equity and high GDP growth
makes firms to raise more equity. The GDP (GDPGR) is taken at current market
prices with the base year 2004-05. Since the period of study is 15 years, we expect
the macro variables to have a significant relation between them. These proxies
have been used by Booth [17].
Hypothesis 8a—firm value and GDP growth are positively related.
Hypothesis 8b—firm value and inflation are positively related.
4. Econometric Methodology
The study has used panel regression equation to examine the impact of capital
structure variables on firm value. The model equation is given below as equation
1 to 3. The model equations used in this study are mentioned below:
D. Aggarwal, P. C. Padhan
990
12 , 3 , 4 .
5 .6 .7 .8 .
9 . 10 . ,
it it it it
it it it it
it it it
EV LEVERAGE FIRM QUALITY LNSIZE
TANG PROF GROWTH LIQUIDITY
GDPGROWTH INFLATION
ββ β β
βββ β
β βε
=++ +
+++ +
+ ++
(1)
12 , 3 , 4 .
5 .6 .7 .8 .
9 . 10 . ,
it it it it
it it it it
it it it
MCap LEVERAGE FIRM QUALITY LNSIZE
TANG PROF GROWTH LIQUIDITY
GDPGROWTH INFLATION
ββ β β
βββ β
β βε
=++ +
+++ +
+ ++
(2)
12 , 3 , 4 .
5 .6 .7 .8 .
9 . 10 . ,
it it it it
it it it it
it it it
PB LEVERAGE FIRM QUALITY LNSIZE
TANG PROF GROWTH LIQUIDITY
GDPGROWTH INFLATION
ββ β β
βββ β
β βε
=++ +
+++ +
+ ++
(3)
Notes:
1
β
is intercept term
,it
ε
is the residual term,
1,2, ,22i=
number
of companies and
1, ,15t=
year. The definition of variables, their nature and
the expected sign are given in Table 2.
We have used panel data as it enables to take into account the heterogeneity of
firm specific characteristics. Combining both time series of all cross section ob-
servations, it is more efficient, informative and gives more degrees of freedom
along with reducing collinearity among variables. The methodology is also re-
ferred from Gujarati [39]. Three types of panel data regression models on the
three dependent variables are used. Those are pooled OLS, fixed and random ef-
fect model. The pooled ordinary least square (OLS) assumes intercept and slope
coefficients to remain constant over time and across firms and assesses the im-
pact of all exogenous variables on the endogenous variable. However, when time
and individual specific effects are present in pooled OLS, it gives biased result as
mentioned by Bevan [26]. To incorporate the effect of firm specific characteris-
tics, fixed and random effect model are used. While the fixed effect model as-
sumes that the firm specific effect is correlated with the independent variable,
the random effect model assumes individual specific effects are uncorrelated
with independent variables. The fixed effect model evaluates differences in in-
tercepts, which may be due to unique features of each firm, such as differences in
management style or managerial talent. By replacing
1i
β
in place of
1
β
in
each of these equations will give rise to fixed effect model, where intercept across
firms are different but not over time with their slopes remaining constant.
Pooled OLS is considered as a restricted model due to the constraints imposed
for a common intercept on all firms. To estimate whether fixed effects model is
better than pooled OLS, a formal test of restricted F test is used. We further use a
random effect panel regression model. In fixed effects panel data, the intercept
for each firm is unique whereas in random effects panel data, the intercept
represents a mean value of all firms with an error component accounting for
deviations of individual firms from the mean value. By placing
11ii
u
ββ
= +
in
place of intercept term
1
β
in each equation from 1 to 3 will be defined as ran-
dom effect model, where
i
u
is error term. To determine whether fixed effect
panel model is better than random effect panel model, we use the formal Haus-
man test. For more details, studies by Bevan [26] and Voutsinas [35] can be re-
ferred.
D. Aggarwal, P. C. Padhan
991
Table 2. Summary of determinants of capital structure and their predicted signs.
Dependent
variables Measurement
Predicted
relationship
& sign
Enterprise
value (EV)
Enterprise value of the firm on
Bombay stock exchange (BSE)
Market
capital (Mcap)
Market capitalization of the firm on
Bombay stock exchange (BSE)
Price to
book ratio (PB)
Price to book ratio of the firms on
Bombay stock exchange (BSE)
Independent
variables
Leverage
(TOL_TNW)
Measures total outside liabilities of a company
against the value of the company net of outside
liabilities
i.e.
tangible net worth. Total outside
liabilities include all borrowings and current liabilities
Significant & −ve
Firm Quality
(Z score)
Calculated using Altman z score by below formula:
Z score = 1.2* (Working capital by total assets) + 1.4*
(Retained earnings by total assets) + 3.3*
(Earnings before interest and taxes over total assets) +
0.6* (Market capitalization over total liabilities) +
1* (Total income by total assets)
Significant & +ve
Size (LnTA) Logarithm of total assets of the firm which
refers to the sum of all current and non-current assets Significant & +ve
Tangibility
(FA_TA)
Ratio of net fixed assets over total assets.
Net fixed assets defined as sum of net intangible assets,
land & building, plant & machinery etc. after
adjusting for arrears of depreciation and impairments
Significant & +ve
Profitability
(ROA)
Returns generated by an enterprise on the total funds
deployed in business. Calculated as a ratio of profit after
tax net of prior period and extra ordinary transactions
over average of beginning and year-end total assets
Significant & +ve
Growth
(GrwTA) Year on year change in average total assets Significant & +ve
Liquidity (CR)
Measures the ability to pay short term obligations;
Short term liabilities include those payable
within a year over short term assets
Significant & +ve
GDP growth Year on year growth of GDP at current prices Significant & +ve
Inflation Annual inflation rate of wholesale price index Significant & +ve
5. Empirical Results Analysis
5.1. Descriptive Statistics
The preliminary analysis consisting of descriptive statistics is given in Table 3.
The descriptive statistics have given interesting results. Firstly, the coefficient of
variation (CoV) is very high for Mcap in comparison to EV and PB, showing
high variability in the data sets. Also with respect to independent variables, the
CoV is high for leverage and liquidity variables. The Jarque-Bera test statistic for
D. Aggarwal, P. C. Padhan
992
Table 3. Descriptive statistics.
MCAP EV PB TOL_TNW Z LNTA FA_TA ROA GRWTA CR GDPGR INFL
Mean 6010.29 8556.81 2.23 1.93 1.50 7.32 0.50 5.04 12.51 1.72 11.59 7.11
Median 942.00 1307.70 1.45 0.90 1.30 7.25 0.50 4.30 8.50 0.80 12.20 6.30
Max. 94,353.2 117,223.7 20.7 152.10 5.40 11.20 0.90 28.70 160.60 75.00 16.80 15.00
Min. 5.10 −6.20 0.00 −32.50 −0.6 3.70 0.00 −32.2 −31.70 0.10 7.20 3.20
Std. Dev. 14,239.91 17,854.31 2.60 8.85 1.11 1.84 0.25 6.90 19.58 4.85 2.52 3.11
CoV 2.37 2.09 1.16 4.60 0.74 0.25 0.51 1.37 1.57 2.83 0.22 0.44
Skewness 3.72 3.18 3.25 14.76 0.87 0.12 −0.24 −0.03 3.50 11.46 −0.10 0.99
Kurtosis 17.69 13.68 18.1 254.09 3.59 2.15 2.09 6.01 23.63 163.05 2.64 3.41
Jarque-Bera 3733.86 2125.84 3685 87,8861.4 46.1 10.67 14.62 124.61 6506.85 35,9445.9 2.33 55.85
P value 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.31 0.00
Observations 330 330 328 330 330 330 330 330 329 330 330 330
Note: Here and henceforth following the abbreviations used in the text frequently stands for MCAP = market capitalization, EV = Enterprise value, PB =
Price to book, TOL_TNW = ratio of total outside liabilities to total net worth, Z = Altman Z score, LNTA = log of total assets, FA_TA = Fixed assets by total
assets, ROA = return on total assets, GRWTA = growth in total assets, CR = current ratio, GDPGR = growth in GDP and INFL = inflation, VIF = Variance
Inflation Factor).
normality leads to rejection of null hypothesis of a normal distribution for all se-
ries except growth in GDP, suggesting non-normal distribution, as expected in
panel data. The growth in total assets on an average is 12.5% which reflects that
the firms have grown at a high rate, indicating a growing industry. The tangible
assets measured as a ratio of fixed assets to total assets is almost 50% which is
high considering the nature of the industry. The profitability ratio is low, almost
5% of total assets along with low current liquidity of 1.7 on average. However, a
key concern is the low Z score which is 1.4 on an average. A Z score below 1.8
indicates low firm quality. The ratio of total outside liabilities by total net worth
is also high at an average of 1.9. It measures the firm’s total outside liabilities
over its tangible net worth, a higher ratio demonstrates excessive reliance on ex-
ternal funds, limiting the future borrowing ability. Though the industry has been
growing but it is subjected to low firm quality and low returns. The inflation rate
and growth in GDP are 7% and 11.5% on average over the period of 2001-15 in-
dicating a rising trend.
5.2. Correlation Matrix
The correlation coefficients in Table 4 among all independent variables show
that none of the pairwise correlations are high enough to lead to multicollinear-
ity problem. Even after conducting the variance inflation factor (VIF) test for the
variables, all the values came out to be below 3. We have only reported the VIF
of tangibility variable with all the other variables in Table 4. It shows that the
model to be estimated does not suffer from multicollinearity problem.
D. Aggarwal, P. C. Padhan
993
Table 4. Correlation coefficients.
VIF FA_TA LNTA CR ROA Z GRWTA TOL_TNW GDPGR INFL
FA_TA 1.02 1.00 −0.15 −0.29 −0.21 −0.12 −0.20 0.11 −0.03 −0.05
LNTA 1.05 −0.15 1.00 −0.21 −0.32 −0.36 0.04 0.13 0.05 0.15
CR 1.09 −0.29 −0.21 1.00 0.02 0.07 −0.09 −0.05 −0.03 −0.08
ROA 1.05 −0.21 −0.32 0.02 1.00 0.76 0.13 −0.20 0.26 0.15
Z 1.01 −0.12 −0.36 0.07 0.76 1.00 −0.01 −0.17 0.19 0.12
GRWTA 1.04 −0.20 0.04 −0.09 0.13 −0.01 1.00 −0.02 0.18 0.14
TOL_TNW 1.01 0.11 0.13 −0.05 −0.20 −0.17 −0.02 1.00 −0.03 −0.03
GDPGR 1.00 −0.03 0.05 −0.03 0.26 0.19 0.18 −0.03 1.00 0.42
INFL 1.00 −0.05 0.15 −0.08 0.15 0.12 0.14 −0.03 0.42 1.00
Note: Here and henceforth following the abbreviations used in the text frequently stands for MCAP = market capitalization, EV = Enterprise value, PB =
Price to book, TOL_TNW = ratio of total outside liabilities to total net worth, Z = Altman Z score, LNTA = log of total assets, FA_TA = Fixed assets by total
assets, ROA = return on total assets, GRWTA = growth in total assets, CR = current ratio, GDPGR = growth in GDP and INFL = inflation, VIF=Variance
Inflation Factor.
5.3. Panel Regression Model
The results of pooled OLS, fixed effects and random effects model for each of the
three equations are given in Tables 5-7 respectively. Since each firm has the
same number of observations, the study has performed balanced panel using
Eviews 9.5 software.
As pooled OLS assumes no significant time and individual effects, the inter-
cept and slope coefficients of the 22 hotel firms are assumed to be constant over
the entire period of 15 years. However, the fixed effect regression assumes con-
stant slope coefficients with varying intercepts for all firms. The key challenge is
to determine whether to use pooled OLS model or fixed effect model or random
effect model. For determining which model is better between fixed effect and
pooled OLS, the F statistic calculated for all the three equations is given in
Tables 5-7. Since the F statistic value is highly statistically significant, the re-
stricted model of pooled OLS is invalid. Therefore, the unrestricted fixed effect
model is better than the restricted pooled OLS.
Again, a critical factor in determining which model to use between fixed and
random effect is the correlation between the individual firm error components
and the explanatory variables. If they are correlated then fixed effect model is
used whereas if they are uncorrelated, random effect model is used. We use the
formal test developed by Hausman [40] which is used in various studies to select
between fixed and random effect model. The null hypothesis is random effect
model is consistent and efficient and alternative of random effect inconsistent.
In otherworld’s, the null hypothesis assumes that individual firm error compo-
nents and explanatory variables are not correlated. Rejection of null hypothesis
implies selection of fixed effect model over random effect model and vice versa.
The test follows χ2 distribution.
When performing the Hausman test for Equations (1)-(3); Equations (1) and
D. Aggarwal, P. C. Padhan
994
Table 5. Panel data Equation (1) with dependent variable-EV.
Independent
variable
Pooled OLS Fixed effect Random effect
Coefficient t-statistic
P
value Coefficient t-statistic
P
value Coefficient t-statistic
P
value
Constant −53277.59 −47.31 0.00* −38646.72 −26.07 0.00* −38805.38 −13.64 0.00*
Leverage (TOL_TNW) 124.56 7.21 0.00* 106.84 8.90 0.00* 106.75 8.90 0.00*
Firm quality (Z score) 4321.41 20.46 0.00* 2267.24 12.48 0.00* 2266.91 12.50 0.00*
Size (Ln TA) 7288.81 75.79 0.00* 5480.17 28.15 0.00* 5506.70 28.54 0.00*
Tangibility (FA_TA) 2965.68 4.32 0.00* 1258.79 1.54 0.12 1255.61 1.54 0.12
Profitability (ROA) −298.58 −8.45 0.00* −219.19 −7.67 0.00* −218.48 −7.65 0.00*
Growth (GRWTA) −10.25 −1.26 0.20 −11.42 −1.96 0.05** −11.50 −1.97 0.04**
Liquidity (CR) 174.80 5.13 0.00* 109.72 4.51 0.00* 110.25 4.53 0.00*
GDP growth (GDPGR) 260.38 3.85 0.00* 351.24 7.66 0.00* 351.38 7.66 0.00*
Inflation (Infl) −198.62 −3.68 0.00* −23.29 −0.57 0.56 −25.86 −0.64 0.51
R2 0.500 0.776 0.193
Adjusted R2 0.499 0.775 0.192
F-statistic 803.082 835.803 193.139
Prob (F-statistic) 0.000 0.000 0.000
D-W statistics 0.541 1.206 1.202
F test F statistic = 412.903,
P
= 0.00000
Hausman test Chi square statistic = 4.36604,
P
value = 0.8857
(*Significance at 1%, **Significance at 5% and ***Significance at 10%).
Table 6. Panel data Equation (2) with dependent variable-Mcap.
Independent
variable
Pooled OLS Fixed effect Random effect
Coefficient t-statistic
P
value Coefficient t-statistic
P
value Coefficient t-statistic
P
value
Constant −39497.58 −40.72 0.00* −25164.13 −19.20 0.00* −25407.74 −10.64 0.00*
Leverage (TOL_TNW) −101.78 −6.85 0.00* −40.77 −3.84 0.00* −41.05 −3.87 0.00*
Firm quality (Z score) 4626.95 25.44 0.00* 2610.21 16.26 0.00* 2608.95 16.28 0.00*
Size (Ln TA) 5398.29 65.17 0.00* 3348.93 19.46 0.00* 3387.77 19.89 0.00*
Tangibility (FA_TA) 208.14 0.35 0.72 1822.10 2.52 0.01* 1797.22 2.50 0.01*
Profitability (ROA) −247.84 −8.15 0.00* −117.81 −4.67 0.00* −116.88 −4.63 0.00*
Growth (GRWTA) −5.06 −0.73 0.47 6.87 1.34 0.18 6.72 1.31 0.19
Liquidity (CR) 91.00 3.10 0.00* 90.15 4.19 0.00* 90.77 4.22 0.00*
GDP growth (GDPGR) 219.30 3.77 0.00* 258.11 6.37 0.00* 258.47 6.38 0.00*
Inflation (Infl) −316.38 −6.82 0.00* −100.44 −2.83 0.00* −104.29 −2.94 0.00*
R2 0.417 0.725 0.130
Adjusted R2 0.416 0.724 0.129
F-statistic 574.456 635.566 120.213
Prob (F-statistic) 0.000 0.000 0.00
D-W statistics 0.598 1.260 0.535
F test F statistic = 374.850,
P
= 0.00000
Hausman test Chi square statistic = 6.738177,
P
value = 0.6644
(*Significance at 1%, **Significance at 5% and ***Significance at 10%).
D. Aggarwal, P. C. Padhan
995
Table 7. Panel data equation 3 with dependent variable-PB.
Independent
variable
Pooled OLS Fixed effect Random effect
Coefficient t-statistic
P
value Coefficient t-statistic
P
value Coefficient t-statistic
P
value
Constant −4.126 −26.962 0.000* −1.928 −7.710 0.000* −2.096 −7.265 0.000*
Leverage (TOL_TNW) 0.084 34.648 0.000* 0.088 41.332 0.000* 0.088 41.277 0.000*
Firm quality (Z score) 2.369 82.701 0.000* 2.225 72.752 0.000* 2.218 73.106 0.000*
Size (Ln TA) 0.224 17.091 0.000* 0.038 1.151 0.249 0.059 1.890 0.058**
Tangibility (FA_TA) 1.210 12.973 0.000* −0.267 −1.938 0.052** −0.196 −1.446 0.1481
Profitability (ROA) −0.175 −36.452 0.000* −0.117 −24.392 0.000* −0.117 −24.516 0.000*
Growth GRWTA 0.014 13.523 0.000* 0.015 16.013 0.000* 0.015 16.010 0.000*
Liquidity (CR) −0.028 −6.261 0.000* −0.011 −2.797 0.005* −0.011 −2.755 0.005*
GDP growth (GDPGR) 0.093 10.194 0.000* 0.068 8.856 0.000 0.069 8.972 0.000*
Inflation (Infl) 0.006 0.897 0.369 0.014 2.125 0.033** 0.012 1.905 0.056**
R2 0.5652 0.7005 0.5586
Adjusted R2 0.5647 0.6993 0.5580
F-statistic 1037.9470 558.6899 1010.3240
Prob (F-statistic) 0.0000 0.0000 0.0000
D-W statistics 0.9290 1.2552 1.2459
F test F statistic = 147.7796,
P
= 0.00000
Hausman test Chi square statistic = 38.538,
P
value = 0.000
*Significance at (*Significance at 1%, **Significance at 5% and ***Significance at 10%).
(2) indicate use of random effect model by failing to reject the null hypothesis;
whereas Equation (3) rejects the null hypothesis and proposes use of fixed effect
model. As for Equations (1)-(3), Chi square statistic with P value is 4.36604 (
P
=
0.8857), 6.738177 (
P
= 0.6644), and 38.538 (
P
= 0.000) respectively. We tested for
the correlation coefficient between the dependent variables to find explanation be-
hind these results. The correlation coefficient between EV and Mcap came out to
be extremely high as 0.95. Whereas, the correlation coefficient of PB with Mcap
and EV was low, being 0.17 and 0.16 respectively. Since EV and Mcap are highly
correlated, both Equations (1) and (2) when subjected to Hausman test gave simi-
lar results of preference for random effect model. Whereas with PB having low
correlation with EV and Mcap, the model preferred through Hausman test was
different from the model of random effect. The results are reported in Tables 5-7.
Hence, the study interprets the results of random effect model for Equations
(1) and (2) along with the fixed effect model for Equation (3). The results inter-
preted are from the perspective of effect of variables under study on firm abso-
lute and relative measures. While EV and MCap are absolute firm value meas-
ures, PB is a relative firm value measure. We estimate the random effect (with
Swamy and Arora estimator of component variances) model. The model will
become more parsimonious and robust.
The results show that firm value and leverage have a significant relation.
D. Aggarwal, P. C. Padhan
996
However the relation is positive for Equations (1) and (3) but is negative for Eq-
uation (2). The negative relation is consistent with the study of Feidakis & Rovo-
lis [27] and Dang [41] which showed a negative relation between share price
performance and firm leverage. With market capitalization as a dependent vari-
able, higher leverage leads to an adverse effect on it, whereas firm enterprise
value shows a positive relation with leverage. It supports the pecking order
theory that use of debt is preferred over equity especially in a sector like hospi-
tality with a high gestation period. Hence, Modigliani Miller theorem of capital
structure irrelevance does not hold for Indian hotel firms.
The firm quality score has a significant positive relation with firm value for all
equations as expected. The coefficient value for Equations (1)-(3) is 2266.91,
2608.95 and 2.225 respectively with all being significant at 1% significance level.
A high Z score demonstrates high firm quality by reducing the probability of
bankruptcy. It leads to a positive impact on firm value. With a high gestation pe-
riod, variables used to calculate firm quality reflect the firm’s capability of gene-
rating operating returns and operations.
Size also has a positive relation with firm value for all equations. However, the
Ln_size coefficient is statistically significant at 1% significance level for Equa-
tions (1) and (2) only. In case of Equation (3), the coefficient is not statistically
significant with a large p value of 0.249. The positive significant relation with
absolute firm value measures is consistent with the studies of Feidakis & Rovolis
[27], Antoniou [25] and Dang [41]. Theoretically, trade off view and size being
inversely related to bankruptcy probability support the above result.
For tangibility and growth in total assets, the results are inconclusive for its
effects on firm value. With respect to Equation (3), relative measure of firm val-
ue PB, tangibility is significant at 5% significance level with growth in total assets
significant at 1% significance level. With respect to absolute firm value measures,
tangibility is significant at 1% significance level for Mcap but is insignificant for
EV. On the contrary, growth in total assets is significant at 5% significance level
for EV but is insignificant for Mcap. Prior studies including works of Rajan and
Zingales [16], Feidakis & Rovolis [27], Antoniou [25] and Dang [41], have
shown a positive significant relationship between firm leverage and tangibility
on account of trade off view and agency costs. It is yet to be explored further
whether the ownership of hotel land and building resides with the firm or are
leased. In such cases a more detailed study of the nature of ownership is re-
quired. Currently, it is beyond the scope of this study to examine it in depth.
Tangibility and growth in fixed assets will also determine the level of secured
debt the firms are targeting based on collateralized assets. It also needs to be ex-
plored with respect of how secured debt impact firm value. In our study, debt
has been taken as total outside liabilities only and not demarcated between se-
cured or unsecured.
Profitability when measured as return on total assets seems to have a signifi-
cant negative relationship with firm value in all equations. Due to heavy capital
intensive nature of the industry with a long gestation period, more than the
D. Aggarwal, P. C. Padhan
997
profitability, the firm value is determined by the quality of the operations. In this
case firm liquidity has a positive significant relation with absolute firm value
measures. With sufficient funds to support short term operating liabilities en-
sures smooth functioning of the hotel industry’s operations.
While GDP has a positive significant relation for all equations, the effect of in-
flation is mixed. It indicates that economic growth has a significant positive im-
pact on firm quality in hospitality industry.
From the results it is evident that firm quality, size, leverage and current li-
quidity have a significant influence on firm value. Thus Modigliani Miller theo-
rem does not hold in Indian hospitality context. Firm size and cost of financial
distress measured through a firm quality score having a significant influence on
firm value; reflect the theoretical support of trade off theory along with agency
cost theory.
6. Conclusions and Discussion
This study has aimed to contribute to the existing literature in various ways.
Firstly, it is one of the few studies which enhance the understanding of factors
affecting firm value of hospitality sector in India over a large time frame of 15
years. Large gestation period and funding structure being key concerns of hos-
pitality sectors, make it imperative to understand unique factors which influence
firm value. Majority of the studies are done in manufacturing sector. Secondly, it
is one of the first empirical tests to use firm quality score of Z value as it has not
been tested in hospitality sector in Indian context.
Based on the findings of the study, we suggest few policy measures for Indian
hospitality firms. With firm quality and liquidity having a significant influence
on firm value, financial institutions should focus on offering funds which can
ensure smooth functioning of operations. With a long gestation period, a better
access to funds with friendly lending policy can enable improvement in opera-
tions. With negative relation with profitability, it could be that markets prefer
hospitality firms to leverage debt for expansion, which could be due to unique
attribute of the industry itself. However, at the same time, boost tourism as an
external force can help improve the income in hospitality sector. Along with ex-
pansion in asset properties, a parallel expansion in revenue generation is also
needed. With leverage having a significant influence on firm value, the study
highlights the importance of capital structure on firm value. It is of use for hotel
owners to relook at their debt equity mix and fulfill multiple objectives of im-
proving firm quality and firm liquidity by making better financial decisions. For
listed firms, market performance is vital and improvement in firm quality by op-
timal capital mix which can ascertain value creation. The results also show
growth in GDP having a significant positive impact on firm value. The results
from the study are supported by the existing TOT and agency cost theories.
With size having a significant positive relationship with firm value, it urges to
ask “Does big means better?”
The study suffers from certain limitations. Firstly it only analyzes listed hos-
D. Aggarwal, P. C. Padhan
998
pitality companies. With few number of hospitality companies only listed, the
scope of the research could be expanded to understand the determinants of cap-
ital structure. Secondly, the explanatory power of the model can be improved by
taking more relevant variables. However, our study was limited to the use of re-
levant variables based on the availability of data.
The study has laid the foundation to explore the role of asset structure in
depth in determining firm value. Future work is needed to understand the role
of asset ownership and firm expansion by developing new hypothesis for their
influence on firm value. Moreover, a more detailed account of financials over a
large time frame is needed to understand the dynamics of the hospitality sector.
Acknowledgements
The authors of the study thank Dr. Joy Deng for their editorial assistance and the
anonymous referee for their constructive feedback and suggestions.
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