Excessive leverage and firm performance in competitive casino markets

Abstract

As competition and interest rates continue to rise, it becomes more critical to understand how leveraging in a competitive market will impact casino firms whose returns are sensitive to changes in financing costs. However, research on the effects of leverage and competition on firm value is scarce. Therefore, the purpose of this study is to explore the effectiveness of leveraging strategies by examining the moderating effect of competition on the relationship between leverage and firm performance in the U.S. casino industry. Analyzing a panel dataset of U.S. publicly traded casino firms from 1992 to 2014, this study finds an inverted U-shaped relationship between leverage and firm performance in the U.S. casino industry while the negative effect of leverage intensifies for highly leveraged casino firms as levels of competition increase. The findings of this study provide valuable insights into whether or not a leveraged growth strategy can contribute to improving performance in the competitive casino markets.

Keywords

Leverage firm performance competition casino industry

Introduction

Leveraged capital structures have largely supported the U.S. casino industry as the casino business model inherently requires a substantial amount of capital to build properties and facilities. Debt financing, however, can be costly when interest payments outweigh potential profits generated through invested capital. In 2012, with significant debt of approximately $28 billion, Caesars Entertainment Corporation paid over $2.1 billion in interest while adjusted EBITDA (earnings before interest, taxes, depreciation, and amortization) for the same year was approximately $2.0 billion (Caesars, 2013). This indicates that nearly the entire amount of earnings was paid to lenders, leaving no cash available to fund operations and invest in new projects. With excessive leverage, casino firms may ignore otherwise profitable investment opportunities and fail to achieve the intended growth through debt-financed capital. This financial condition is defined as debt overhang, which constrains a firm’s investment due to excessive debt (Fazzari et al., 2000), implying that shareholders are unlikely to see robust long-term returns. Despite these concerns, however, U.S. casino firms have significantly increased the use of debt in the recent low interest rate environment.

Concurrent with struggling with significant burden of debt payments, a dramatic increase in competition among casinos has evolved in recent years. Traditional U.S. casino markets, such as in Las Vegas, NV and Atlantic City, NJ, are facing significant competition in other states which actively seek casinos’ revenues to supplement other taxes. Since 2009, states such as Massachusetts, Ohio, Maryland, and Pennsylvania introduced and expanded gambling venues. In addition, the recent legalization of online gambling has created a more competitive landscape for casinos because, among other factors, competition intensifies as prices drop (Gainsbury et al., 2015; Williams et al., 2012). Recent research shows that legalizing online gambling has particularly increased the competitiveness of the casino industry since online gambling firms have lower operating costs compared to land-based casinos (Gainsbury, 2012; Gainsbury et al., 2013). Four states, Delaware, Nevada, New Jersey, and the U.S. Virgin Islands, have already legalized online gambling and more states including California and Pennsylvania have introduced bills authorizing the operation of internet gaming websites (National Conference of State Legislatures, 2015). The competitive pressures created by online gambling can further cause considerable challenges for highly leveraged casino operators in that they cannot compete aggressively on price (Eadington, 2004; Hirschey, 2016). For example, they are not very flexible responding to price reductions because of lower profit margins and fixed debt-related expenses.

As competition and interest rates continue to rise, it becomes more critical to understand how leveraging in a competitive market will impact casino firms whose returns are sensitive to changes in financing costs. However, research on the effects of leverage and competition on firm value is scarce. Therefore, the main purpose of this study is to explore the effectiveness of leveraging strategies in the casinos’ competitive markets. This study contributes to the literature by focusing on the casino industry, which as of late is facing new competition from online gaming operators and expected increases in interest rates. Analyzing a panel dataset of U.S. publicly traded casino firms from 1992 to 2014, this study finds empirical evidence that: (1) there is an inverted U-shaped relationship between leverage and firm performance in the U.S. casino industry and (2) competition intensifies the negative effect of leverage on firm performance for those highly leveraged. The study findings provide important implications that to secure long-term financial viability and stability, casino operators, shareholders, and lenders should: (1) adopt a more conservative approach to the use of debt and (2) implement a growth strategy to develop new markets and alternative sources of revenue.

Literature review

Leverage and firm performance

Corporate finance literature provides contradictory theoretical predictions on the relationship between leverage and firm performance. On the one hand, some scholars predict positive effects of leverage on financial performance as it increases productivity and a strategic advantage (Brander and Lewis, 1986; Fosu, 2013). The agency theory emphasizes the disciplining effect of debt, arguing that high leverage generally reduces the amount of free cash flow at the disposal of managers, thereby impeding moral hazard (Margaritis and Psillaki, 2010). The trade-off theory also suggests that firms can benefit from balancing the tax benefits of debt against the costs of bankruptcy (Hart and Moore, 1994). On the other hand, others argue negative influences of debt especially when firms are highly leveraged (Baker and Wurgler, 2002; Bhagat and Bolton, 2008; Ghosh, 2008; Kayhan and Titman, 2007). The debt overhang problems suggest that high levels of leverage would discourage investments because of increased costs of financial distress (Diamond and He, 2014; Myers, 1977). Similarly, the agency theory predicts that agency costs resulting from conflicts of interest between shareholders and debt holders can lead to suboptimal investment and reduce market value of the firm (Huang and Ritter, 2009; Jensen, 1986).

Recent research, however, argues that the relationship between leverage and firm performance may not be fully explained using a simple static and linear model, implying the relationship may be non-linear (Fosu, 2013; Strebulaev, 2007). Given the multi-level interactions between leverage and performance, a dynamic trade-off model predicts that net benefits of debt financing might increase for firms with low leverage but decrease for firms with high leverage (Campello, 2006). Analyzing U.S. hotel firms, Jang and Tang (2009) found that while low to moderate levels of leverage led to earnings improvement, excessive levels of leverage diminished a firm’s profitability. Based on theoretical predictions and empirical evidence, the current study predicts an inverted U-shaped relationship between a casino firm’s leverage and performance: leverage will have a positive effect on performance up to a certain level of usage while this effect is expected to decline after this point.

H1 : An inverted U-shaped relationship exists between a firm’s leverage and performance in the U.S. casino industry.

Leverage and market competition

Highly competitive markets allow predatory pricing practices because competition drives prices down (Gainsbury et al., 2015). For example, when a new participant enters a market, incumbent participants can predate on new entrants by reducing prices while eroding profit margins. Several studies found evidence for this negative effect of market competition in the casino industry, such as price reduction in Pennsylvania and New Jersey (Economopoulos, 2015) and loss of gambling revenues in the Missouri–Iowa–Illinois region (Ali and Thalheimer, 2003; Walker and Nesbit, 2014). Examining 100 casinos across the U.S., Hunter (2010) found that adding an additional casino would negatively impact existing casinos’ aggregate gross revenue.

Corporate finance literature further argues that highly leveraged firms are more likely to suffer from the negative effect of market competition (Bolton and Scharfstein, 1990; Dasgupta and Titman, 1998; Opler and Titman, 1994). Campello (2003) maintained that highly leveraged firms are more vulnerable to the risks of liquidation when profits decrease as a result of price/profit reductions, implying that fear of liquidation causes highly leveraged firms to be disadvantaged, competitively, when forced to adjust prices by less highly leveraged competitors with lower pricing structures. This competitive disadvantage relative to pricing increases as levels of competition rise, resulting in negative growth in revenue. Therefore, the current study predicts that competition will intensify the negative effect of leverage on a casino’s performance especially for those highly leveraged. In particular, given the relationship between leverage and firm performance, the expectation is for an inverted curvilinear model, which indicates competition moderates the negative effect of leverage on the right side of the inverted U-shaped curve.

H2 : Competition intensifies the negative effect of leverage on firm performance, especially for highly leveraged firms.

Methodology

Data

The sample for this study is a construct of annual financial and stocks’ pricing information from Compustat and CRSP databases, respectively. The sample included 154 publicly traded U.S. casino firms during the 23-year period, 1992 to 2014. The study period represents the history of gambling legalization, the first wave of which occurred after the recession of 1992, while the second and third waves followed when New York and Maryland entered the casino market in 2004 and 2010. Winsorizing variables at the 1% level on an annual basis minimizes the effect of outliers (Baker and Wurgler, 2002). The final sample consists of 1472 firm-year observations with no missing values.

Variables

The study by Campello (2006) guided construction of variables. This study employs both a market-based and accounting-based measure to estimate a dependent variable, firm performance. A market-based measure is Tobin’s q (Q), measured as the market value of the firm divided by the book value of total assets. An accounting measure of performance is return on assets (ROA), measured as EBITDA divided by total assets.

The two independent variables are leverage and market competition. Leverage (LEV) is the measure of total liabilities divided by total assets. Measuring market competition uses the Boone Indicator (BI). Boone (2008) argued that in a competitive market, inefficiency, such as an increase in marginal costs leading to a decrease in variable profits penalizes firms. This study constructs the BI by estimating the regression

{VROA}_{it} = ν + λ_{t} ln ({MC}_{i}) + μ_{it}

(1)

where VROA is the variable profit, measured as revenue minus cost of goods sold divided by total assets; ln(MC) is the natural logarithm of the marginal cost, measured as cost of goods sold divided by revenue; i and t indicate firm and year, respectively. Measuring the BI is the absolute value of the coefficient, λ_t, where the higher absolute value of the coefficients indicates higher level of competition. The current study also controls for the effect of firm size. Size is an estimate of the log of sales.

Empirical model

To test the proposed hypotheses of this study, the regression formulated is

ROA (Q)_{it} = α + β_{1} {LEV}_{it} + β_{2} {BI}_{t} + β_{3} {LEV 2}_{it} + β_{4} ({LEV 2}_{it} * {BI}_{t}) + β_{5} {SIZE}_{it} + ɛ_{it}

(2)

where ROA is return on assets, Q is Tobin’s q, LEV is leverage, BI is the Boone Indicator, LEV2 is a square term of LEV, LEV2*BI is an interaction term for leverage squared and BI, and SIZE is firm size.

The fixed-effects model estimates equation (2). The current study conducts Hausman (1978) specification test to evaluate the suitability of the fixed-effects model compared to the random-effects model. In addition, this study includes interaction and quadratic terms. A regression equation with interaction and/or quadratic terms can suffer from potential multicollinearity (Aiken and West, 1991). To reduce intercorrelation, this study mean-centered relative variables before creating interaction and quadratic terms (Tabachnick and Fidell, 1996).

Results and discussion

Descriptive statistics

Table 1 summarizes the descriptive information of the variables in this study. The mean of ROA is 0.219, suggesting that casino firms may not have sufficient cash to service debts and continue essential capital spending. Figure 1 presents average total debt, capital investment, and EBITDA for publicly traded U.S. casino firms for the years 1991 through 2014. During this time, total debt increased from under $1 billion to approximately $23 billion while capital investment and EBITDA remained relatively stable, below $4 billion. For example, prior to and during the most recent financial crisis of 2003–2008, earnings improved moderately by less than $1 billion compared to a sharp increase of about $13 billion in total debt. This shows that highly leveraged capital structures of casino firms did not necessarily lead to earnings improvement.

Table 1.

Summary of descriptive statistics.

	Mean	Median	SD	Min	Max
LEV	0.999	0.660	2.157	0.065	19.002
ROA	0.219	0.106	0.475	−3.774	0.462
Q	2.821	1.255	8.923	0.247	81.928
SIZE	1.945	2.076	1.049	0.275	3.932
BI	0.383	0.390	0.091	0.212	0.515

LEV is leverage, measured as the total debt divided by total assets. ROA is a proxy for performance, measured as the ratio of operating income before depreciation to total assets. Q is firm performance, proxied by the market value of the firm divided by the book value of total assets. Firm size, SIZE, is measured as log of revenues. BI is the Boone indicator, indicating the level of market competition. SD: standard deviation.

Figure 1.

Total debt, capital investment, and EBITDA.

Table 2 summarizes the results of analysis using Pearson’s correlation. The relationship between leverage and ROA is negative and statistically significant (p < 0.01). This finding is consistent with the argument that high levels of leverage will have a negative impact on firm performance. In addition, the BI negatively correlates with return on investment (p < 0.01), suggesting firm performance deteriorates as market competition increases. The next section discusses these correlations using the fixed-effects model.

Table 2.

Summary of Pearson correlations.

Variable	N	LEV	ROA	Q	SIZE	BI
LEV	1472	1.00
ROA	1472	−0.67**	1.00
Q	1472	0.78**	0.77**	1.00
SIZE	1472	−0.25**	0.46**	−0.33*	1.00
BI	1472	0.03	−0.08**	0.04	−0.01	1.00

*Significant at 0.05 and **Significant at 0.01.

Main analysis

Table 3 presents the findings of a series of analyses modeling fixed-effects regressions. Panel A and Panel B exhibit a summary of the analysis for ROA and Q as dependent variables, respectively. Variance inflation factors (VIF) in both Panels are all below acceptable levels, confirming no sign of serious multicollinearity. The Hausman specification test verifies that the fixed-effects model is more suitable than the random-effects model in Panel A (χ²= 823.60; p < 0.000) and Panel B (χ²= 19.41; p < 0.01). Hypothesis 1 predicts an inverted U-shaped relationship between leverage and firm performance in the U.S. casino industry. The coefficient for LEV in Column (1) is positive and significant (t = 11.14; p < 0.000) while the coefficient of LEV2 is negative and significant (t = −2.84; p < 0.01). The inverted U-shaped relationship between leverage and firm performance is also statistically significant when using Q as a dependent variable in Column (3) in Panel B. Therefore, this study finds support for Hypothesis 1. The findings indicate that only low to moderate levels of borrowing create growth sufficient to turn debt into positive leverage, which leads to earnings improvement. However, excessive borrowing seems to create a lack of financial flexibility, leaving little cash available to allow maintenance and new investments, thus hurting a firm’s long-term performance.

Table 3.

Summary of the fixed-effects model regression analysis.

	Dependent variable: ROA
Panel A	(1)	(2)	VIF
LEV	0.098***	0.136***	9.46
	(11.14)	(6.02)
BI	−0.144	−0.131	1.01
	(−1.94)	(−1.84)
LEV2	−0.142**	−0.128**	8.96
	(−2.84)	(−2.69)
LEV2*BI		−0.336***	1.03
		(−10.09)
SIZE	0.162***	0.159***	1.08
	(11.96)	(12.09)
N	1472	1472	χ²= 823.60
R²	0.49	0.54	p < .000
	Dependent variable: Q
Panel B	(3)	(4)	VIF
LEV	2.629***	1.326***	9.47
	(9.74)	(6.75)
BI	−0.969	−1.642	1.01
	(−0.92)	(−1.60)
LEV2	−0.113***	−0.084***	8.96
	(−7.20)	(−6.67)
LEV2*BI		−3.607***	1.03
		(−7.51)
SIZE	−1.646***	−1.366***	1.08
	(−8.50)	(−7.10)
N	1472	1472	χ²= 19.41
R²	0.46	0.49	p < .01

ROA is a proxy for performance, measured as the ratio of operating income before depreciation to total assets. Q is firm performance, proxied by the market value of the firm divided by the book value of total assets. LEV is leverage, measured as the total debt divided by total assets. BI is the Boone indicator, indicating the level of market competition. Firm size, SIZE, is measured as log of revenues. LEV2 is a square term of LEV. LEV*BI is an interaction term for LEV and BI. VIF: variance inflation factors.

*Significant at 0.05, **significant at 0.01, and ***significant at 0.001.

Hypothesis 2 states that for firms with high levels of leverage, the negative effect of leverage on firm performance will intensify as levels of market competition increase in the U.S. casino industry. Using the BI (Boone, 2008) as a proxy for market competition, this study tests the moderating effect of market competition on the relationship between excessive leverage and firm performance. Consistent with prior arguments, a negative relationship between BI and performance appears in Columns (1) and (3), indicating that firm performance decreases as levels of market competition increases. However, the coefficients for BI are not statistically significant. In Columns (2) and (4), this study tests the moderating effect of market competition for highly leveraged firms, finding that the negative effect of leverage increases with higher levels of market competition. The coefficients for the interaction term, LEV2*BI, are negative and statistically significant (t = −10.09; p < 0.000 and t = −7.51; p < 0.000). Hence, Hypothesis 2 gains support. The moderating effect of market competition implies that highly leveraged casino firms are more likely to suffer revenue loss when market competition becomes more intense.

Conclusions

Leverage previously contributed to revenue growth for casino firms due to significant industry-wide growth and low financings costs, which outweighed the financial risks involved in debt financing. However, while the growth rate has slowed during the past few years mainly due to the recent, general economic downturn, casino firms are expected to struggle with low profitability driven by increased competition and interest rates. To address these issues, the current study explored the interactive relationship among leverage, competition, and firm performance in the U.S. casino industry. Contrary to a positive or negative linear relationship documented in the extant literature, this study finds an inverted U-shaped relationship between leverage and performance. In addition, using the BI (Boone, 2008), this study finds that especially for highly leveraged firms, the negative effect of leverage on firm performance increases as market competition intensifies. The study results contribute to the extant literature by providing industry-specific evidence of the effect of leverage on performance depending on the degree of competition in the casino market. Although the casino industry is currently facing major challenges from new market players and interest rate hikes, these challenges have not been jointly investigated in the hospitality and tourism literature. The empirical evidence found in this study can help stakeholders in the casino industry respond effectively to the challenges of the new market environment and reinvigorate their business models relative to leveraging strategies.

Industry implications

The findings of this study provide two important implications for the U.S. casino industry. First, the negative effect of excessive debt suggests that casino operators, shareholders, and lenders should employ a more conservative approach to the use of debt. In particular, casino operators and shareholders are best advised to maintain a conservative debt level in capital structure, especially when requiring external financing. For instance, casino firms should avoid aggressive debt financing when core business activities fail to generate strong cash flow to pay debt-related expenses. With excessive debt and interest costs, casino firms will likely jeopardize their entire business when taking risks on new investment projects. On the other hand, lenders need to apply more rigorous policies and analyses to determine appropriate levels of lending to casinos. Lenders must understand that the casino industry is heavily dependent on customers’ disposable incomes, which are susceptible to changes in the overall economy. Conservative lending mechanisms can assure lenders receive repayment and reductions in the probability of default. Finally, casino operators could actively adjust the level of debt by retiring and/or refinancing existing debt when relatively inexpensive credit is available. With proactive and efficient debt management, casino firms can maintain moderate interest payments and thereby, accumulate cash for capital investments, which will lead to a greater long-term financial profitability and stability.

Second, the interaction effect of leverage and market competition on performance suggests that casino operators should plan and implement a growth strategy to develop new markets and alternative sources of revenue. The primary concern for casino firms is that their gaming and gambling businesses are not creating sufficient growth necessary to turn debt into positive leverage. Recently, several casino operators have expanded both domestically and internationally to create additional growth of revenue. For instance, MGM Resorts International is considering entering new markets such as Ontario (Canada), Massachusetts, and Maryland while Caesars Entertainment plans to develop a resort in India. Expansion into new markets can assist in establishing more stable revenue streams by diversifying assets. Furthermore, casino operators can generate additional revenues by offering diverse experiences to customers. For example, non-gaming operations such as hotels, retail, restaurants, spas, and entertainment, integrated into gaming facilities, aids increases of total revenues. Major casino operators, such as Las Vegas Sands and Wynn Resorts, derive approximately 25–28% of revenues from non-gaming operations while generating approximately 65% and 70%, respectively, of their revenues from Macau operations. Since highly leveraged casino firms cannot viably engage in pricing competition (Chevalier, 1995), leveraged casino operators must find alternatives to generate revenues to fund operations and repay debt.

Limitations

Although the findings of this study are informative, this study has some limitations. First, the sample of this study only includes publicly traded U.S. casino firms. Terms and policies for debt financing may vary among countries, such as China and the UK. Similarly, the degree of market competition can also differ. Future research can examine the effect of excessive leverage in various countries. Second, the impact of leverage on performance can benefit from further investigation. Corporate financial literature found a positive relationship between cash flow and investment (Almeida and Campello, 2007; Bushman et al., 2012; Erickson and Whited, 2000), suggesting that future studies can provide a more comprehensive understanding of the effects of leverage by relating the availability of cash flow to investment and performance.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biography

Kwanglim Seo is an assistant professor of the School of Travel Industry Management at the University of Hawaii, Manoa. He received his M.S. in International Hospitality Management from Strathclyde University, and Ph.D. in Hospitality Management from the Pennsylvania State University. His research looks into corporate investment decision-making in the Hospitality and Tourism industries, which describes a process of investing driven by managerial biases. He also takes an interest in areas such as strategic management, capital structure, corporate governance and ownership structure, and franchising.

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