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Journal of the International Academy of Hospitality Research

Editor: Eliza C. Tse, Ph.D.
Department of Hospitality and Tourism Management
Virginia Polytechnic Institute and State University

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JIAHR publishes refereed papers on all aspects of hospital- ity and tourism research. When judged of sufficient qual- ity, individual papers are sent electronically as a single issue of the Journal to members of the Academy of Hospital- ity Research and to subscribing individuals and libraries. The material is copyrighted. JIAHR is indexed/abstracted in "Lodging and Restaurant Index" and "Leisure, Recreation and Tourism Abstracts."

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Effect of Operating and Financing Leverage on Firm's Risk

Yang H. Huo Francis Kwansa
Virginia Polytechnic Institute and State University

CONTENTS

• Abstract
• Key Words
• Introduction
• Concepts of Risk and Leverage
• Literature Review
• Methodology
• Empirical Findings
• Conclusions and Recommendations
• References

• EDITORIAL BOARD
• ARCHIVAL INFORMATION
• JIAHR IS NOW FREE OF CHARGE
• INSTRUCTIONS TO AUTHORS
• CALL FOR REVIEWERS

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  ABSTRACT

  The recent recession has affected the hospitality industry negatively, especially with regard to raising capital for operations and for capital investment. The purpose of this study is 1) to compare the riskiness of hospitality firms and utility firms during the recessionary period of 1990 to 1991, 2) to identify the effect of hotels' and restaurants' operating and financial leverages on their systematic risk, and 3) to compare this effect with that of utility firms. Findings indicate that although restaurants are riskier relative to the market and hotels are less risky than the market, both are riskier relative to the utility industry during a recession. However, the impact of operating and financing decisions on firm riskiness is relatively less compared utility firms.

  Keywords: Operating leverage, financial leverage, recession, risk, capital structure.

  INTRODUCTION

  In 1991, average occupancy rate for the hotel industry was 60%, down fro 61.8% a year earlier, and this has been attributed to the recessionary economy during this period (Landauer, 1992). The recent recession has affected the hospitality industry negatively, especially with regard to raising capital for operations and for capital investment. Three operational areas will be further taxing management time and investor capital. Well-publicized security lapses at hotels during 1992 are leading to increased emphasis in this area. In addition, the requirements for the Americans with Disabilities Act demand priority attention. And Federal regulations dealing with hotel fire protection add to an owner's many burdens (Landauer, 1992).

  Part of the difficulty hospitality corporations face in obtaining debt financing is the result of the huge number of non- performing real estate loans still being carried by commercial banks. The decline of the real estate market coupled with the sagging conditions of the hotel industry continue to strengthen the perception in the lending community that lending to hotel businesses is highly risky. Risk is an important variable in any investment decision and should be carefully considered by managers, especially those who are contemplating any large capital expenditures.

  Despite its importance to the success of the capital budgeting process it is probably one of the most difficult of the decision variables to measure and quantify. Considering the interest generated in risk analysis largely resulting from the harsh realities of the past recession, and given that hospitality businesses generally exhibit high operating leverage, it is worth examining the extent of the riskiness of hospitality businesses. Particularly for lending purposes, it will be useful to determine how similar or different hospitality businesses are compared to other industries with regard to the impact of operating and financial leverage on their riskiness.

  The purpose of this study is 1) to compare the riskiness of hotels and restaurants and utility firms during the recessionary period of 1990 to 1991, 2) to identify the effect of hotels' and restaurants' operating and financial leverages on their systematic risk, and 3) to compare this effect with that of the utility firms. The utility firms were chosen to provide a contrast to the hotels and restaurants since the utility industry is generally considered to be stable while the hospitality firms are considered volatile. Also the recessionary period was chosen because firms with high operating leverage tend to experience more than average declines in profitability making them more volatile during this period. This analysis will provide information useful in the cost of capital estimation and for financing decisions.

  CONCEPTS OF RISK AND LEVERAGE

  Total risk is composed of two types of risks, systematic risk and nonsystematic risk. This approach to risk analysis offers advantage of assessing the risk-return relationships in the context of the firm's portfolio of capital assets. Systematic risk is for the most part beyond the control of the hospitality manager since it is a reflection of the risk inherent in the industry and the environment in which that industry exists (Johnson, Olsen, and Van Dyke, 1984). In other words, systematic risk is the market related risk that cannot be diversified away. Market related risk results from factors that systematically affect all firms, such as recession, inflation, and high interest rate. Systematic risk is represented by beta, a measure of a firm's covariance with the market. The regression coefficient, b (the beta coefficient), is a market sensitivity index; it measures the relative volatility of a given stock versus the average stock, or 'the market.' This tendency of an individual stock to move with the market constitutes a risk, because the market does fluctuate, and these fluctuations cannot be diversified away. This component of total risk is the nondiversifiable risk. A firm's total risk can be measured by the standard deviation of its stock return. However, nonsystematic risk is the company-specific risk which can be eliminated through diversification. Nonsystematic risk is caused by factors unique to a particular company, including lawsuits, strikes, and the discovery of a new product. The relationship between a stock's total risk, systematic risk, beta and nonsystematic risk, is presented below:

   
                        Total Risk = Systematic Risk + Unsystematic Risk
                                           OR
                        Total Risk = Nondiversifiable + Diversifiable
  
   
  
                Total Risk = Variance = Market risk + Diversifiable risk
  
   
   
  
                                   2          2       2         2
                              alpha   =   beta   alpha   + alpha
                                   j          j       m         ej
  
   
   
  
                 where:
  
   
  
                      2
                 alpha  is the variance or total risk of stock j,
                      j
  
   
  
                      2
                 beta   is Stock j's beta coefficient,
                      j
  
   
  
                      2
                 alpha  is the variance of the market,
                      m
  
   
  
                          2
                 and alpha e j is the variance of Stock j's regression
  
   
  
                 error term.
  
  
  
  

  Theoretically, systematic risk is priced in the capital market according to the capital asset pricing model (CAPM), whereas the nonsystematic risk is ignored since it is diversifiable. Only the systematic risk affects the cost of capital of the firm. Weston and Brigham (1990) proposed that both internal and external factors affect a firm's systematic risk, beta. The two internal factors that affect beta are asset structure and financial structure. Hospitality firms have the common feature of being fixed assets intensive and highly-leveraged. Therefore, firms in this industry should have similar systematic risks.

  Operating leverage is the effect of fixed costs on the variability of earnings before interest and taxes (EBIT). In other words, it is the responsiveness of the firm's EBIT to fluctuations in sales. The following equation can be employed to find a firm's degree of operating leverage:

  DOL = revenue before fixed costs / EBIT or DOL = S - VC / S - VC - FC

  where S represents sales, VC variable costs and FC fixed cost.

  The greater the firm's degree of operating leverage, the more its profits will vary with a given percentage change in sales. Thus, operating leverage is definitely an attribute of business risk that impacts the company.

  Financial leverage is the additional variability in earnings due to the use of debt. Significant change in asset structure and financial structure can change variable/fixed cost composition and interest expense and result in changes in operating leverage and financial leverage. The degree of financial leverage can be found directly as follows (Keown, Martin, and Petty, 1985):

  DFL = EBIT / EBIT - Interest Expense or EBIT/EBT

  where EBT is earnings before taxes or taxable income.

  The greater the degree of financial leverage, the greater the fluctuations (positive or negative) in earnings per share. The common stockholder is required to endure greater variations in returns when the firm's management chooses to use more financial leverage rather then less (Keown et al., 1985).

  Greater operating leverage and financial leverage may lead to greater variability in earnings and ultimately greater systematic risk for the firm.

  LITERATURE REVIEW

  The relationship of operating leverage and financial leverage with the variability of a firm's profit has been widely discussed in finance literature. As a theory of financial market behavior, the CAPM states only a necessary equilibrium relationship between the prices of securities given their stochastic characteristics over a period of time. It says little about how stock prices are determined by the real variables that financial managers must consider in evaluating strategic, operating, and financial alternatives (Gahlon and Gentry, 1982).

  Lev (1974) used the sample of power companies to find positive relationship between operating leverage and firm riskiness. Myers' study (1977) concluded that financial leverage and both the cyclical nature and volatility of operating earnings can be identified confidently as real determinants of beta. Some researchers have sought to develop a theory of the real determinants of systematic risk (Hite 1977; Myers and Turnbull 1977; and Turnbull 1977). Myers (1977) and Myers and Turnbull (1977) defined cyclicality as the covariance of shifts in investor's earnings expectations with the market return, and identified cyclicality as one of the contributory factors to systematic risk. Brenner and Smidt (1978) developed a model that explored the relationship between a security's beta and the characteristics of its underlying real assets. The model specified unit sales, fixed costs, contribution margin, and covariance of sales as significant influencing factors.

  In business terminology, a high degree of operating leverage, other factors held constant, implies that a relatively small change in sales results in a large change in ROE (Brigham and Gapenski, 1991). Gahlon and Gentry (1982) developed a model for calculating beta that included the degree of operating leverage (DOL) and the degree of financial leverage (DFL) as explicit variables. Specifically, the study examined how operating and financial decisions will affect systematic risk and value. They identified the DOL and DFL as real-asset risk measures. Furthermore, they analytically demonstrated that beta is a function of the degrees of operating and financial leverage, the coefficient of variation of the revenues, and the correlation coefficient between the cash flows to the owners and the aggregate dollar return to all capital assets. Mandelker and Rhee (1984) conducted an empirical study on the relationship between the DOL and DFL and beta. They provided empirical evidence that the degrees of operating and financial leverage explain a larger portion (38 to 48 percent) of the cross- sectional variation in beta at the portfolio level.

  Chung (1989), using randomly selected samples from manufacturing and utility industries, found that consumer demand and the two leverages all have impact on the riskiness of a firm. Some (Keown et al., 1991; Weston and Brigham, 1990) have pointed out that reduction of risk can be achieved with a trade-off between the two leverages. Although it has been established that there is a theoretical relationship between cyclicality and beta, the scope of this study is limited to determining the relationship between the DOL and DFL and beta in the hospitality industry during a recessionary period. This relationship is compared to the utility industry during the same period.

  METHODOLOGY

  The empirical investigation was based on publicly traded firms in the restaurant and hotel industries and utility firms listed on the Dow-Jones Utilities Average during the recession period (1990- 1991). The sample firms were selected from Standard and Poor's Annual Compustat database using SIC Codes 5812 and 7011 for restaurant industry and lodging industry respectively. The sample includes twenty restaurants and seven hotels. The 15 companies belonging to Dow-Jones Utility Average were also examined to see if the risks changed significantly. Their financial data were obtained from the Compact Disclosure database. Finally, only the companies that had complete financial data were selected, which included twenty restaurants, seven hotels and motels, and fifteen utilities.

  The beta of the three industries were investigated. In addition, the degree of operating leverage (DOL) and the degree of financial leverage (DFL) were calculated to measure the operating and financial decision. The DOL was calculated based on the following equation:

  DOL = S - VC / S - VC - FC

  The degree of financial leverage was also calculated directly as follows:

  DFL = EBIT / EBIT - Interest Expense or EBIT/EBT

  While the DOL measures the responsiveness of a firm's EBIT to the variation in sales, the DFL measures the responsiveness of a firm's earnings to the variation in EBIT.

  A regression model with DOL and DFL as independent variables and beta as dependent variable was established as follows:

  Beta = a + b1DOL + b2DFL + e;

  where e is residual term.

  Since the purpose of this study was to find the relationship or the effect of the DOL and DFL on systematic risk, other factors such as cyclicality that was associated with the determination of beta were not included in the model. The same regression was run twice for each recessionary period for the three industries. The coefficient of determination was determined.

  The t-test was applied to find the mean difference of beta between restaurants and hotels, hotels and utility firms, and restaurant and utility firms.

  EMPIRICAL FINDINGS

  Table 1 shows the mean betas as well as the results of a t- test of the mean differences in beta among the three industries. Compared to hotels and utility firms the mean of restaurants (beta=1.08) indicates a relatively high beta in both recession years. The t-tests, F values and p-values show that mean betas of restaurants and hotels during the recessionary period were not significantly different from each other. Nevertheless, the betas show that the restaurant firms were generally riskier than the market while hotel firms were less risky than the market. It appears that hospitality firms have similar systematic risks during the recessionary period. The mean beta of utility firms show significant differences compared to that of restaurants and hotels.

            ______________________________________________________________
  
   
  
                                         Table 1
                                      Mean of Beta
  
   
  
                  Year        Restaurants      Hotels        Utilities
  
   
  
                  1,991          1.08           0.83           0.47
                  1,990          1.08           0.76           0.54
  
   
  
            Source: computed from Compustat
  
   
  
                             Mean Difference in Beta
  
   
  
                  Year      Restaurants   vs    Hotels
  
   
  
                  1991      F=1.153             P=0.3676
                  1990      F=1.112             P=0.3866
  
   
  
                  Year      Restaurants   vs    Utilities
  
   
  
                  1991      F=3.6139            P=0.0090
                  1990      F=6.091             P=0.0006
  
   
  
                  Year      Hotels       vs     Utilities
  
   
  
                  1991      F=4.167             P=0.0158
                  1990      F=6.777             P=0.0021
  
   
  
            Note: 20 restaurants: 7 hotels; 15 utilities
  
   
  
            ______________________________________________________________
  
  
  
  

  Table 2 shows the means of degree of operating leverage. The hotels have the highest DOL with 5.12 and 4.48 on 1991 and 1990 respectively. It indicates that in 1990 a one percent change in sales produced a 4.48 percent change in EBIT and in 1991 a one percent change in sales also produced a 5.12 percent change in EBIT. On the other hand, utility firms have relatively stable DOLs for both recessionary period with 2.34 percent change in EBIT and 2.29 percent change in 1990 and 1991 respectively.

   
           _____________________________________________________________
  
   
  
                                      Table 2
                                    Mean of DOL
  
   
  
                Year        Restaurants      Hotels        Utilities
  
   
  
                1,991          3.06           5.12           2.29
                1,990          2.99           4.48           2.34
  
   
  
            ____________________________________________________________
  
   
  
  

  The greater the firm's degree of operating leverage, the more its profits will vary with a given percent change in sales. Thus, operating leverage of hotels is relatively higher than that of utility firms, and as the degree of operating leverage rises this tends to be associated with increasing systematic risk (beta).

  Table 3 shows that the use of debt for all three industries declined considerably. The degree of financial leverage of the utility industry firms in 1990 has the highest DFL with 4.37, which implies that a one percent in EBIT contributes a 4.37 percent change in EPS. In 1990 as EBIT fell by one percent, the hotels suffered a 3.17 percent decline in earnings per share. However, in 1991 the hotels suffered 0.27 percent decline in EPS.

   
            ______________________________________________________________
  
   
  
                                         Table 3
                                       Mean of DFL
  
   
  
                Year       Restaurants      Hotels        Utilities
  
   
  
                1,991         2.03           0.27           1.82
                1,990         4.36           3.17           4.37
  
   
  
           ______________________________________________________________
  
   
  
  

  The greater the degree of financial leverage, the greater the fluctuations in earning per share. In 1991 the financial leverage of restaurants was highest among all three industries.

  In Table 4 the relationship of DOL and DFL with systematic risk during the recessionary period 1991 to 1990 indicates that utility firms' coefficient of determination (R square) has the highest value of R square with 0.46 and 0.71 in 1990 and 1991 respectively. It indicates that DOL and DFL explained 46 percent and 71 percent of the change in the riskiness of utility firms during 1990 and 1991 respectively. On the other hand, DOL and DFL of restaurant firms contributed very little (5 percent in 1990 and 16 percent in 1991) in explaining the riskiness of the firms. The hotels showed modest contribution of DOL and DFL to the riskiness of the firms (13 percent and 45 percent in 1990 and 1991 respectively). Across all three industries, however, the contribution of DOL and DFL in explaining firm riskiness increased from 1990 and 1991.

   
            _____________________________________________________________
  
   
  
                                         Table 4
                                        R Square
  
   
  
               Year        Restaurants      Hotels        Utilities
  
   
  
               1,991          0.16           0.45           0.71
               1,990          0.05           0.13           0.46
  
   
  
            _____________________________________________________________
  
   
  
   

  The following regression equations were estimated:

  For restaurants

  in 1991: Beta = 1.06 + (-0.0268)DOL + (0.0512)DFL, in 1990: Beta = 1.37 + (-.071)DOL + (-0.0833)DFL;

  in 1991: Beta = 0.71 + (0.02)DOL + (0.917)DFL, in 1990: Beta = 0.82 + (-0.022)DOL + (0.0142)DFL;

  For utilities

  in 1991: Beta = 0.13 + (-0.05)DOL + (0.25)DFL, in 1990: Beta = 0.54 + (-0.018)DOL + (0.010)DFL.

  Also, the signs of the regression coefficients suggest that operating leverage can be used to offset the effects of financial leverage on firm riskiness. That is, generally, a tradeoff can be made between these two leverages to yield a combined effect that minimizes overall risk.

  CONCLUSIONS AND RECOMMENDATIONS

  The perception of the hospitality industry as being a risky enterprise is partially supported by the results of this study. Although restaurants are riskier relative to the market and hotels are less risky than the market, both are riskier relative to the utility industry during a recession. However, the impact of operating and financial leverages on hotel and restaurant firm riskiness is relatively less compared to utility firms. That is, whereas utility firms can affect their business risk by altering capital structure and capital budgeting decisions, restaurants are least able to do so during a recession. Managers have little control over firm risk during a recession. This suggests that hotel and restaurant operators may make adjustments to their operating and financing leverages prior to the onset of a recession, because such adjustments will have little effect on the riskiness of the firms during a recession. This further suggests that restaurants should develop methods for monitoring changes in the business cycle to enable them to make the appropriate changes in operating and financial leverage in a timely fashion.

  Future research may focus on examining the extent of the impact of other factors that affect systematic risk such as cyclicality. As Turnbull (1977) pointed out, a comprehensive continuous time model of determinants of beta such as cyclicality (i.e., the intrinsic business risk of the firm) should be studied further. Further research may be needed to compare the effect of operating and financial leverages on systematic risk during recessionary and nonrecessionary periods in order to determine whether these decisions are relevant to the hospitality business cycle. Also optimal combinations of operating and financial leverage which minimizes overall risk of the firm needs exploration.

  REFERENCES

  Brenner, M. & Smidt, S. (1978). Asset Characteristics and Systematic Risk. Financial Management, (Winter), 33-39.

  Brigham, E.F., & Gapenski, L.C. (1991). Financial Management: Theory and Practice, 6th Edition, Orlando, Fl.: The Dryden Press, 153-155.

  Chung, Kee H. (1989). The impact of the demand volatility and leverages on the systematic risk of common stocks. Journal of Business Finance and Accounting, 16 (Summer), 343-360.

  Hite, G.L. (1977). Leverage, output effects, and the M-M theorems. Journal of Financial Economics, (March), 177-202.

  Johnson, D.J., M.D. Olsen & T. Van Dyke. (1984). Risk Analysis in the Lodging Industry. In the Practice of Hospitality Management. Ed. Lewis, R., T. Beggs, M. Shaw, and S. Croffet. Westport, CT.: AVI Publishers.

  Keown, D.F., Martin, S., John D., Petty. J.W. (1985). Basic Financial Management. Englewood Cliffs, N.J.: Prentice Hall, Inc.

  Landauer Real Estate Counselors. (1992). 1993 Real estate market forecast. Special Research Supplement To The National Real Estate Investor.

  Lev, Baruch. (1974). On the association between operation and risk. Journal of Financial and Quantitative Analysis, (September). 627-642.

  Mandelker, G. & Rhee, S. (1984). The impact of the degrees of operating and financial leverage on systematic risk of common stock. Journal of Financial and Quantitative Analysis, (March) 45-57.

  Myers, S.(1977). The relationship between real and financial measures of risk and return. Risk and return in Finance, I. Friend and J. Bicksler, Ed., Cambridge: Ballinger Publishing Company.

  ________ & Turnbull, S. (1977). Capital Budgeting and the capital asset pricing model: Good news and bad news. Journal of Finance, (May), 321-333.

  Turnbull, S.(1977). Market value and systematic risk. Journal of Finance, (September), 1125-1142.

  Weston, J.F. & Brigham. E.F.(1990). Essentials of Managerial Finance, 9th Edition. Orlando, Fl.: The Dryden Pres.

  References not Cited in this Text:

  Gahlon, J. & Gentry, J. (1982). On the relationship between systematic risk and the degree of operating and financial leverage. Financial Management, (Summer), 15-23.

  Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics, 47, (February), 13-37.

  Smith Travel Research (1992). Industry Survey.

  Yang H. Huo is a Ph.D. candidate and Francis Kwansa is Assistant Professor in the Department of Hospitality and Tourism, Virginia Polytechnic Institute and State University, Blacksburg, Virginia.

  THE JOURNAL OF THE INTERNATIONAL ACADEMY OF HOSPITALITY RESEARCH

  EDITORIAL BOARD

  Horace A. Divine University of Denver John Bareham Brighton Polytechnic, U.K. Chuck Gee University of Hawaii-Manoa Donald E. Hawkins The George Washington University Mahmood A. Khan Virginia Polytechnic Institute and State University Michael Haywood University of Guelph Ontario, Canada Robert C. Lewis University of Guelph

  William Kent Auburn University Robert C. Mill University of Denver Ken McCleary Virginia Polytechnic Institue and State University Abraham Pizam University of Central Florida Michael Olsen Virginia Polytechnic Institute and State University Brian Wise Queen Victoria University Turgut Var Texas A&M University

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