1. No category

Singh, J Tourism Res Hospitality 2014, 4:1
http://dx.doi.org/10.4172/2324-8807.1000143
Journal of Tourism
Research & Hospitality
Research Article
A SCITECHNOL JOURNAL
Impact of Credit Fluctuations on
Risk and Liquidity: An Analysis of
Private U.S. Lodging Firms
Dipendra Singh*
Rosen College of Hospitality Management, University of Central Florida, USA
*Corresponding author: Dr. Dipendra Singh, PhD, Assistant Professor, Rosen
College of Hospitality Management, University of Central Florida, USA; E-mail:
Dipendra.Singh@ucf.edu
Rec date: Feb 03, 2014 Acc date: Oct 16, 2014 Pub date: Oct 20, 2014
Abstract
Capital structure composition of business firms is considered
critical for the overall success of firms. Private lodging firms in
the industry demand an even deeper focus on these decisions
for the nature of this industry and composition of their
businesses. This study empirically investigates the effect of
credit availability on the leverage of large and small private
lodging firms in the United States using multivariate analysis of
variance (MANOVA). Study utilizes Case-Schiller home price
index to identify the two time points of differing credit
availability to businesses in U.S. Leverage and Cash-to-Total
Assets Ratio of large and small U.S. lodging firms were
analyzed at these differing credit availability time points to
assess any significant differences. No significant effects of
credit availability were found on the leverage and Cash-to-Total
Assets Ratio of both; large and small lodging firms.
Keywords: Credit fluctuations; Lodging firms; Private business firms
Introduction
In the last several years the United States together with other world
economies experienced noticeable economic downturns that followed
positive economic peaks in 2004 and 2005. Periodic fluctuations in
economic activity of a country are attributed to the effect of business
cycles. According to Miller and Vanhoose [1] business cycles are
disparities in real gross domestic product of a country around the
product long-run growth path and recession is one of the four stages
of the cycle. Economic performance can be measured through a
variety of means such as gross national product, unemployment rate,
money supply, consumer price index and the like. De Bondt [2] stated
that one of the main triggers of the world economic crisis was caused
by the US real estate market disaster. Sanjeev GM, et al. [3] states that
the hospitality industry was also affected by economic meltdown and
debacle of the banking sector in the beginning of the 21 century.
Multiple studies suggest that the way banks adjust their lending
standards over economic peaks and troughs is one of the major
contributors to the boom and bust nature of business cycles [4-9].
Ferreira [10] found that after a business cycle downturn, businesses
tend to behave very carefully and when environments stabilize firms
begin taking more risks. Again, firms' financial leverage increases;
however some firms may be more exposed to risks than others, and
financial structures may become weak if the growth in debt
commitments of these vulnerable firms becomes greater than the
increase in their desired profits. Consequently, banks may eventually
start to refuse refinancing the loans of these vulnerable firms to face
increasing difficulties and even bankruptcy. The economy moves again
towards a downturn of the business cycle with most firms undertaking
less risky financial behavior.
Generally speaking, bank lending tends to be pro-cyclical, which
means that it contracts during an economic slowdown and rises
during an expansion. Also, the pro-cyclical feature of bank lending to
businesses is also partly driven by demand. Business cycle impacts
banks profitability through decreased demand for credit. During
recessions, the demand for net working capital falls with a decrease in
business investments and employment. During an economic
expansion it happens in the opposite way when more businesses
become eligible for bank loans [11].
Historically, commercial banks have been the largest lenders to the
hospitality industry [12]. Singh [13] suggested that financing in the
lodging industry shows rather clear cyclical patterns when during
certain period’s capital was readily available and during other periods
of time there was a visible shortage of capital. According to Bharwani
and Mathews [14], the lodging industry like other industries is very
sensitive to a variety of economic changes.
Literature Review
The topic of cash holdings has been broadly discussed in academic
literature during the last years. Under the assumptions of perfect
capital market, companies should have access to cash when they need
to. One of the first studies on cash holdings goes back to 1936 when
John Kaynes described the three main benefits for business: holding
cash that were transaction motive, holding cash that were
precautionary motive and holding cash that were speculative motive.
Transaction cash motive means having enough cash on hands in order
to maintain everyday operations and the amounts of cash that a
company may choose to hold is determined affected by a nature of
business. Precautionary cash motive means that companies hold cash
as a reserve that would be readily available to meet unexpected
financial demands. The speculative cash motive justifies holding cash
to allow businesses to explore opportunities that they may find
profitable. However, recent studies focus more on capital structure,
agency problems, corporate governance [15-18].
Opler et al. [19] point that lager firms hold less cash because they
have better access to capital. Rajan and Zingales [20] noticed that
larger firms may choose to have smaller cash holdings because they are
usually better diversified and have reduced risks of financial
complications. Financially unconstrained firms have little benefit from
holding cash because they can access and raise funds on the capital
market when needed. Bates, et al. [16] suggest that firms that have
financial constrain have a pattern of holding larger amounts of cash
because it may be more difficult for them to raise a capital. Opler, et al.
[19] suggest that companies that have more growth opportunities have
a tendency to hold more cash as a proportion of a total net assets.
Large businesses that have good credit ratings may choose to have
smaller cash holdings because they think that they can borrow cash
more easily. Sheel [21] investigated the relationship between a firm’s
capital structure, its cost of capital, and its stock value in 33
companies. Information for this study was obtained from
COMPUSTAT annual files covering the years from 1971 to 1988. All
All articles published in Journal of Tourism Research & Hospitality are the property of SciTechnol and is protected by
copyright laws. Copyright © 2015, SciTechnol, All Rights Reserved.
Citation:
Singh D (2014) Impact of Credit Fluctuations on Risk and Liquidity: An Analysis of Private U.S. Lodging Firms. J Tourism Res Hospitality 4:1.
doi:http://dx.doi.org/10.4172/2324-8807.1000143
variables were found to have a significant effect on the total-debt-toasset ratio of both the hotel and manufacturing industries.
UK lodging industry are higher than the debt ratios of the UK retail
industry.
Collateral value of assets, firm size, and earnings volatility were
positively related to the total-debt-to-asset ratio of firms in both
industries but the collateral value of assets in hotels had stronger
negative influence when compared to the manufacturing industries.
Volatility in earnings had a larger negative relation to short-term-debt
for hotel industries as opposed to manufacturing industries. This
reaffirms the importance of cash flows for a firm, as meeting of shortterm obligations primarily depends on it.
Tress [28] looked at 36 projects that included public-private
partnership of hotel developments in the US that were supposed to be
completed between 1992 and 2002. He determined that out of 36
projects, 57% of project costs were privately funded and 43% of them
were funded with public sources [29].
Kim [22], conducted a research study that analyzed corporate
financing decisions of 251 restaurant companies and 81 lodging firms
listed on the U.S. stock exchange, over a period of 1986 to 1992. The
study found that the asset structure of hospitality firms has a strong
positive relation to debt ratio or leverage. The finding was true for
both the restaurant industry and the hospitality industry in general.
On the other hand, profitability was strongly, negatively related to the
leverage of hospitality firms. This study also showed that growing
hospitality firms have less reliance on debt financing.
Upneja and Dalbor [23] analyzed the financial structure of the
restaurant industry in the U.S. They used total debt ratio, long-term
debt ratio, and short-term debt ratio in their empirical model to study
the financial structure decisions of all restaurant firms listed on the
U.S. stock exchange. The authors found that operating cash flow of a
firm has a positive effect on firm’s leverage. A firm’s age in terms of
listing years was also found to be positively related to the debt ratio.
Operating cash flow was found to be negatively related to the shortterm debt.
In another study, Upneja and Dalbor [24] analyzed factors of longterm debt of publicly traded U.S. restaurant firms. Results showed
positive relations between a firm’s size and its long-term debt and one
of the conclusions that the authors made was that it was more difficult
for small firms to pay the substantial fixed cost of long-term debt. The
study also suggested that firms with greater insolvency probability do
not have easy access to the equity market and thus they must seek
long-term debt for their financing needs.
In a study done by Kwansa and Cho [25], the trade-off between
financial distress costs and tax earnings in the U.S. restaurant industry
was investigated. They studied a sample of ten restaurant firms that
went bankrupt between 1980 and 1992. The study reported that a
restaurant firm’s capital structure and its value were significantly
affected by the extent of bankruptcy costs involved. The study also
found that the size of the indirect bankruptcy cost generally outweighs
the size of the tax savings from debt use as a firm nears filing for
bankruptcy.
Upneja and Dalbor [26] analyzed the capital structure of small
restaurant firms in the U.S. with respect to leasing policy and marginal
tax rates. Their study focused on leasing versus borrowing debt for
purchasing assets. They used restaurant firm data downloaded from
COMPUSTAT for the years 1981 to 1992 [27]. A significant positive
relationship between before and after tax rates of U.S. restaurant firms
was identified by their study.
Another study regarding the analysis of prevalent capital structure
in the lodging industry was done by Nuri and Archer [18]. They tested
22 lodging firms operating in United Kingdom and compared them to
134 retail industry firms. The authors found that the debt ratios in the
Volume 4 • Issue 1 • 143
Tang and Jang [30] analyzed 27 lodging firms for a period from
1997 to 2003 to reassess the determinants of capital structure in the
U.S. lodging industry. They reported a significant positive relationship
between the long-term debt level of a lodging firm and the fixed-asset
level, and growth opportunities of a firm.
According to Koh and Jang [30], hotel firms are similar to other
industries in terms of proportion of cash holdings to total assets; but
hotels have more restricted access to cash than other industries.
Methodology
Variable selection
Dependent variables
Leverage: With respect to particular proxies for leverage, the
empirical literature proposes a number of measures in terms of ratios.
These ratios include total liabilities to total assets, total capitalization
(total debt to total equity), and total debt to net assets. This study will
use the ratio of long-term debt to total assets, as a measure of leverage.
Liquid Asset Holdings: Cash-to-Total Assets Ratio is used for
measuring a firm’s strength in terms of liquidity. Cash of a firm is a
liquid investment necessary to support the working capital needs of
the firm, which is closely related to its sales [15].
Independent variables
Years (Time Points): This study uses the Case-Shiller housing
pricing index (HPI) to identify the years when house prices were
highest, and lowest (the recent lowest prices). The Case-Shiller index
was used as a proxy to identify the years with high credit availability –
when house prices were at the peak, and the year with low credit
availability – when house prices were at the lowest. The year 2006 was
identified as the year with relaxed credit regulations and 2009 was
identified as the year of tighter credit availability. Thus, the
independent variable year was utilized with two categories – Low
Credit, and High Credit.
Firm Size: For the purpose of this research analysis, all the lodging
firms in these two different time points with differing credit
availability were divided into large and small firms. This study used
mid-point as the criteria for this grouping so as to get groups with
equal sizes. For ‘Low Credit’ year, total assets worth $12 million dollars
were the criteria for differentiating large and small firms. For ‘High
Credit’ year also, total assets worth $12 million were the criteria for
differentiating large and small firms.
Data analysis
This study utilized 2 X 2 factorial MANOVA (Multivariate Analysis
of Variance) to evaluate the differences in leverage of privately owned
US lodging businesses at two different time points. This analysis was
conducted using data downloaded and gathered through a survey
• Page 2 of 4 •
Citation:
Singh D (2014) Impact of Credit Fluctuations on Risk and Liquidity: An Analysis of Private U.S. Lodging Firms. J Tourism Res Hospitality 4:1.
doi:http://dx.doi.org/10.4172/2324-8807.1000143
questionnaire about relevant financial information from the FRLA
members and other associated individuals with the Statistical Package
for the Social Sciences (SPSS) version 21 statistical software.
Factorial MANOVA uses two or more independent variables, each
with two or more levels. This study will be using the credit availability
and firm size as the independent variables. Credit availability will be
analyzed at two levels, high credit availability, and low credit
availability. Firm’s size will be analyzed at two levels, large firms and
small firms. Using univariate tests in an analysis involving more than
one dependent variable leads to greatly inflated type I error. Also, a
multivariate test will be more powerful when the groups may not be
significantly different on any of the variables individually, but jointly
the set of dependent variables may differentiate the groups [31].
Therefore a MANOVA will be performed on the independent variable
to determine whether statistically significant differences exist on the
set of dependent variables based on credit availability and size of the
firm. MANOVA and ANOVA test results were analyzed at the
significance level of 0.05, which is a widely accepted norm in social
sciences [32].
Test
Value
F
Hypothesis
df
Error df
Sig.
Pillai's Trace
0.015
0.664
2
87
0.517
Wilks'
Lambda
0.985
Hotelling's
Trace
0.015
Roy's Largest
Root
0.015
Results
This analysis used two independent variables: credit availability
with two levels (low credit, high credit), and firm size (large firms,
small firms). This study used mid-point of total assets as the criteria
for this grouping so as to get groups with equal sizes for large and
small firms. Total value of assets worth $12 million dollars was the
criteria for differentiating large and small firms. Firms having total
value of assets less than 12 million were categorized as small and firms
having total assets more than $12 million were categorized as large
firms.
Using an alpha level of 0.001 to evaluate homogeneity of variancecovariance matrices assumption, Box’s M test of homogeneity of
covariance was significant (p<0.001). The Box’s M test is highly
sensitive, more than often it results in a significant value. Since in this
case the homogeneity of variance-covariance matrices assumption is
violated, instead of Wilk’s lambda criterion Pillai’s Trace criterion was
utilized for assessing the multivariate significance. Multivariate test
results are presented in Table 1.
Using Pillai’s Trace as the omnibus test statistic, the combined
dependent variables resulted in non-significant main effects for ‘Credit
Level’, F (2,87)=0.664, p>0.05; and ‘Firm Size’, F (2,87)=0.08, p>0.05.
Credit Level
Firm Size
Pillai's Trace
0.056
Wilks'
Lambda
0.944
Hotelling's
Trace
0.06
Roy's Largest
Root
0.06
4.57
2
87
0.08
Discussion and Future Research
The study used the total value of assets for categorizing private
lodging firms into large and small firms. As is evident from the results,
there were no significant effects of either credit availability or firm size
on the overall riskiness of the business, as measured by the leverage or
debt ratio; as well as the liquidity of these private lodging firms. These
results are contrary to what Singh, Raab, Mayer, and Singh [33] found
in the case of publicly owned U.S. lodging firms. There were
significant effects of both the credit levels and firms size on the
leverage of publicly owned firms. This non-significant phenomenon
needs to be confirmed a with a larger sample size, since this study
relied on a very small sample of only 46 private lodging firms. Also,
the sample was geographically restricted, thus did not provide a more
generalizable holistic findings. The authors will attempt in future to
analyze a more geographically diverse and large sample to find even
clearer and deeper insights into the impacts of credit availability and
firm size on the leverage and liquidity of private lodging businesses.
References
Credit Level X
Firm Size
1.
Pillai's Trace
0.012
Wilks'
Lambda
0.988
Hotelling's
Trace
0.012
Roy's Largest
Root
0.012
3.14
2
87
0.601
2.
3.
Table 1: Multivariate F-test of Significance for Credit Level, Firm Size
and Credit Level X Firm Size.
Volume 4 • Issue 1 • 143
4.
5.
6.
Miller RL, VanHoose DD (2007) Money, banking, and financial
markets. South-Western Cengage Learning, Mason, Ohio.
De Bondt W (2010) The crisis of 2008 and financial reform.
Qualitative Research in Financial Markets 2: 137-156.
Sanjeev GM, Gupta K, Bandyopadhyay R (2012) Financial
challenges in the Indian hospitality industry. Worldwide
Hospitality and Tourism Themes 4: 163-173.
Farmer RE (1985) Implicit contracts with asymmetric
information and bankruptcy: The effect of interest rates on
layoffs. Review of Economic Studies 52: 427-442.
Farmer RE (1988) Money and contracts. Review of Economic
Studies 55: 431-446.
Greenwald BC, Stiglitz JE (1993) Financial market imperfections
and business cycles. Quarterly Journal of Economics 108: 77-114.
• Page 3 of 4 •
Citation:
Singh D (2014) Impact of Credit Fluctuations on Risk and Liquidity: An Analysis of Private U.S. Lodging Firms. J Tourism Res Hospitality 4:1.
doi:http://dx.doi.org/10.4172/2324-8807.1000143
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
Smith BD (1995) Sectoral employment and cyclical fluctuations
in an adverse selection model. International Economic Review
36: 261.
Bassett WF, Zakrajsek E (2003) Recent developments in business
lending by commercial banks. Fed Res Bull 89: 477-492.
Gorton G, Kahn JA (1993) The design of bank loan contracts,
collateral, and renegotiations. NBER Working paper 4273.
da Silva GF (2002) The impact of financial system development
on business cycles volatility: Cross-country evidence. Journal of
Macroeconomics 24: 233-253.
Bernanke B, Gertler M (1989) Agency costs, net worth, and
business fluctuations. American Economic Review 79: 14-31.
Schmidgall RS (2011) Hospitality industry managerial
accounting. (7th edn), Lansing, MI: American Hotel and Lodging
Educational Institute.
Singh AJ (2004) Securitization of Lodging Real Estate Finance.
Hospitality Review 22: 7.
Bharwani S, Mathews D (2012) Risk identification and analysis
in the hospitality industry: practitioners' perspectives from India.
Worldwide Hospitality and Tourism Themes 4: 410-427.
Dittmar A, Mahrt-Smith J, Servaes H (2003) International
corporate governance and corporate cash holdings. Journal of
Financial and Quantitative analysis 38: 111-134
Bates TW, Kahle KM, Stulz RM (2009) Why do US firms hold so
much more cash than they used to? The Journal of Finance 64:
1985-2021.
Florackis C, Ozkan A (2004) Agency costs and corporate
governance mechanisms: Evidence for UK Firms. International
Journal of Managerial Finance 4: 37-59.
Sibilkov V (2009) Asset liquidity and capital structure. Journal of
Financial and Quantitative Analysis 44: 1173-1196.
Opler T, Pinkowitz L, Stulz R, Williamson R (1999) The
determinants and implications of corporate cash holdings.
Journal of Financial Economics 52: 3-46.
Rajan RG, Zingales L (1995) What do we know about capital
structure? Some evidence from international data. The Journal of
Finance 50: 1421-1460.
Sheel A (1994) Determinants of capital structure choice and
empirics on leverage behavior: A comparative analysis of hotel
Volume 4 • Issue 1 • 143
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
and manufacturing firms. Journal of Hospitality and Tourism
Research 17: 1-16.
Kim W (1995) The determinants of financial structure in the
hospitality industry. Ann Arbor, MI: A Bell and Howell.
Upneja A, Dalbor MC (2001) An examination of capital
structure in the restaurant industry. International Journal of
Contemporary Hospitality Management 13: 54-59.
Upneja A, Dalbor M (2001) The choice of long-term debt in the
US lodging industry. UNLV Journal of Hospitality, Tourism and
Leisure Science.
Kwansa F, Cho M (1995) Bankruptcy cost and capital structure:
The significance of indirect cost. International Journal of
Hospitality Management 14: 339-350.
Upneja A, Dalbor M (1999) An examination of leasing policy, tax
rates and financial stability in the restaurant industry. Journal of
Hospitality and Tourism Research 23: 85-99.
Nuri J, Archer S (2001) Target adjustment model against pecking
order model of capital structure. European Financial
Management Association Annual Meeting, Lugano, Switzerland.
Tress BH (2003) Using public-private partnerships to fund hotel
development projects. Journal of Retail and Leisure Property 3:
21-31.
Tang C, Jang S (2007) Revisit to the determinants of capital
structure: A comparison between lodging firms and software
firms. International Journal of Hospitality Management 26:
175-187.
Koh JH, Jang SS (2011) Cash Holdings of Hotel Firms: The
Financial Constraints Perspective. Gradconf Hospitality.
Stevens JP (2002) Applied multivariate statistics for the social
sciences. Lawrence Erlbaum Associates, New Jersey.
Tabachnick BG, Fidell LS (2007) Using multivariate statistics.
(5th edn), MA: Allyn and Bacon, Boston.
Singh D, Raab C, Mayer K, Singh A (2014) Credit Fluctuations
and Lodging Firms: An Investigation of the Differing Capital
Structures in the U.S. Lodging Industry. International Journal of
Hospitality and Tourism Administration.
• Page 4 of 4 •