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Revisit to the determinants of capital structure: A comparison between lodging firms and software firms
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Hospitality Management 26 (2007) 175–187 www.elsevier.com/locate/ijhosman
Revisit to the determinants of capital structure: A comparison between lodging firms and software firms Chun-Hung (Hugo) Tang, SooCheong (Shawn) Jang Department of Hospitality and Tourism Management, Purdue University, W. Lafayette, IN, USA
Abstract This study validates the contradiction between capital structure theories and previous empirical studies, and it further identifies lodging firms’ unique leverage behavior through a comparison to software firms, using a generalized least squares analysis. This study also explores the joint effects of key financial leverage determinants. The findings indicate that fixed assets, growth opportunities, and the joint effect of these two variables are the significant long-term debt determinants of the lodging industry. The analysis of the joint effect also suggests that fixed assets and growth opportunities affect each other’s relationship with long-term debt usage. With the findings on lodging firms’ unique financing rationale, authors hope to provide useful information for corporate financial planners and lending institutions regarding debt-financing behavior. r 2005 Elsevier Ltd. All rights reserved. Keywords: Capital structure; Long-term debt; Debt determinants; Lodging firms
1. Introduction Conventional capital structure theories (Myers, 1977; Jensen, 1986) suggest that firms’ optimal capital structure is related to costs and benefits associated with debt and equity financing. With the optimal debt-to-equity mix, firms could achieve the lowest financing costs and consequently increase the value of shareholders (Sheel, 1994). Although the optimal mix varies from industry to industry (Kim, 1997) and from country to country (Wald, 1999), previous researchers have constantly found capital structure theories applicable when explaining financing decisions. However, Dalbor and Upneja’s (2004) Corresponding author. Tel.: +1 765 496 4929.
E-mail addresses: [email protected] (C.-H. Tang), [email protected] (S. Jang). 0278-4319/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhm.2005.08.002
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study on lodging firms was not consistent with the theories in that it claimed a positive relationship between growth opportunities (GW) and long-term debt. Their finding indicates a possibility that lodging firms may have partly different leverage behavior from what the theories suggest. To fully understand lodging firms’ leverage behavior, it is important to validate the theoretically unexpected result and note why the behavior happens. Thus, this study aims to validate the findings that are not consistent with the theories and to re-examine lodging firms’ leverage behavior in comparison to another industry with contrasting business and financial characteristics: the software industry. Software firms are characterized by their reliance on intangible assets such as patents, copyrights, and human capital, while the lodging business is heavily dependent on fixed assets. Moreover, software firms’ financial characteristics, such as a high sales-to-PP&E (property, plant, & equipment) ratio, high interest coverage, high operating margin, and high liquidity, are distinctly different from lodging firms’ (Myers, 2001). Comparing two industries with different financial characteristics enables us to easily identify the unique leverage determinants of each industry. With the understanding of the unique determinants, this study is expected to clearly explain the lodging-specific leverage behavior. This study is distinct from previous studies in that it investigates the joint effects of the leverage determinants. Despite the fact that traditional theory-based determinants have been examined by many studies, little effort has been made to examine the joint influences of the determinants. As these determinants simultaneously exist in a real-world situation, a model including the interaction terms could be more effective in observing the practical relationships between long-term debt and its determinants. Therefore, the objectives of this study are (1) to revisit the determinants of capital structure of lodging firms to investigate the validity of Dalbor and Upneja’s (2004) finding about growth opportunities and (2) to further identify the uniqueness in leverage behavior through a comparison to software firms. This study also seeks to explore the joint effects of key financial leverage determinants. 2. Capital structure theories Research on capital structure has been conducted to identify the optimal mixes of debt and equity and, resultantly, to maximize the value of shareholders. However, Modigliani and Miller (1958) propose that all mixes of capital structure produce the same financial result in a perfect capital market. In other words, the optimal capital structure is irrelevant to creating shareholders’ wealth, even if it exists. In the real world, however, the optimal capital structure does affect the value of shareholders because of tax, information asymmetry, and agency cost. In order to explain the effects of tax, information asymmetry, and agency cost in relation to capital structure, the tradeoff theory, the pecking order theory, and the agency cost theory have been developed respectively. The tradeoff theory states that a taxable corporation should increase its debt level until the marginal value of tax shield is offset by the present value of possible financial distress costs. This tradeoff theory of capital structure theoretically balances the tax advantages of borrowing against the costs of financial distress. However, this theory is immediately challenged by the fact that many profitable companies, such as Microsoft, with low cost to borrow, still operate at low debt ratios (Myers, 2001). These companies attempt to maximize shareholders’ value by keeping growth opportunities open instead of utilizing
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tax shields. For firms like Microsoft, the cost of missing a growth opportunity is so high that the potential loss can never be made up by a tax shield advantage. In such cases, the agency cost theory makes better sense because it considers the agency cost of debt including the cost of missing growth opportunities. According to the pecking order theory, firms prefer internal finance, such as retained earnings, if available, and choose debt over equity when external financing is required. The pecking order may be a result of information asymmetry (Myers and Majluf, 1984). The use of external financing sources signals the information that the firms are not profitable, which can negatively influence the stock price. When external financing sources are required, issuing new stock, instead of new debts, signals the information that managers think company stocks are overpriced. The managers of a firm usually know more about their firms’ business and financial information than average investors do, and they will not be willing to issue new stock when they think the stock price is low. They tend to issue shares when shares are overpriced or fairly priced in the market. Thus, investors may interpret the announcement of a stock issue as a negative sign about the current stock price. Hence, for financing choices, debt is next on the pecking order after internal funds and before new stock. Therefore, the pecking order theory implies an inverse relationship between profitability and debt usage. The agency cost theory proposes that the higher level of debt increases shareholders’ value by transferring risk to creditors and having managers allocate cash for debt payment rather than for suboptimal or excessive investments (Jensen and Meckling, 1976). Under this theory, there are two kinds of inherited conflicts of interests: manager–shareholder conflict and creditors–shareholder conflict. Jensen and Meckling (1976) maintain that managers always act in their own economic interests. The managers’ interests, however, can agree with shareholders’ interests when managers’ compensation is tied to the company’s performance. The two stakeholders’ interests can usually converge during takeover threats. Therefore, the manager–shareholder conflict can be ultimately minimized. When conflicts arise between debt and equity investors, managers have several financial tactics for transferring value from creditors to shareholders. If managers have common interests with shareholders as discussed previously, managers will make business decisions for shareholders rather than for creditors. The agency cost theory also offers another viewpoint to explain the previously discussed high profitability–low debt ratio correlation. In a profitable firm, it is to the managers’ advantage to keep debt ratio low, since (1) free cash flow is not committed to debt payment and can be used for management’s interests, and (2) managers are free from the debt payment pressure. This causes a loss in a shareholder’s value, and it is called ‘agency cost.’ Thus, the agency cost theory assists the tradeoff theory in explaining why some profitable corporations with abundant cash and low debt financing cost do not maximize their tax shield benefit. 3. Hypotheses The long-term debt ratio (long-term debt/total assets) was used as a dependent variable to measure leverage behavior in this study. Long-term debt was chosen since lodging firms usually possess a high percentage of long-term debt to finance fixed assets such as real estate (Arbel and Woods, 1990). Moreover, the use of long-term debt, instead of shortterm debt, could provide more specific information about the financial leverage behavior of lodging firms because the long-term debt is usually a preferred funding source for
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growth in the lodging industry, whereas short-term debt is used primarily for current assets and cash shortages (Hovakimian et al., 2001). The hypotheses of this study were based on the assumption that conventional capital structure theories can be applied to both lodging and software firms. Therefore, each variable was expected to have the same sign in both industries and agree with the capital structure theories. However, the degree of influence on debt behavior from each leverage determinant was expected to differ between the two industries because the optimal debt–equity mixes may vary in different industries (Kim, 1997). In order to easily contrast the difference between two industries, this study employed an industry dummy variable as utilized by Sheel (1994). A null hypothesis that determinants influence both industries to the same degree was proposed in this study. Thus, the positive significance of the estimate would support the assumption that lodging and software firms respond to each determinant in the same direction but to a different extent. The fixed assets, measured as a ratio of PP&E to total assets, was expected to be the most important variable in explaining lodging firms’ long-term debt behavior (Dalbor and Upneja, 2004). The PP&E usually serves as inflation-resistant collateral for loans. Consequently, interest rates for lodging firms should be relatively low (Arbel and Woods, 1990), and hotels would increase long-term debt usage as predicted by Jensen and Meckling (1976). Hypothesis 1. PP&E is positively related to long-term debt. The market-to-book ratio is adopted in this study to capture the relative value of the growth opportunities viewed by the market. Market value reflects the market’s expectation on the current net worth of the company and the company’s all-future earnings. Since future earnings serve as the proxy for growth opportunities, the market-to-book ratio presents the current expectation of the company’s future growth opportunities to the book value. Jensen and Meckling (1976) argue that firms with high growth opportunities are more likely to have high agency costs of debt due to the higher debt prices. Creditors usually charge higher prices for debt if managers plan to invest in riskier projects. Also, as the amount of debt increases, the corporate control shifts to creditors, who enforce risk-averse decisions to reject potential profitable investments. Moreover, much of the internal cash flow gets committed to the debt payment and may not be available for good investments. As a result, firms with valuable growth opportunities would maintain a low debt ratio in order to minimize the constraints enforced by creditors and maximize the potential gain. Therefore, a negative relationship was still hypothesized between growth opportunities and the use of long-term debt in both industries. Hypothesis 2. Growth opportunity is negatively related to long-term debt. Dalbor and Upneja (2004) reported a positive relationship between growth opportunities and the debt use of lodging firms, which is contrary to the theories and cases of other industries. They proposed two reasons for their unexpected finding. First, the growth proxy used by the study may not capture the actual growth potential of lodging firms. Second, lodging firms’ growth mostly requires investments in fixed assets, which may be fundamentally different from other industries. Considering Dalbor and Upneja’s (2004) second explanation and the fact that lodging firms have a substantially higher PP&E level than software firms’, the interaction term of PP&E and growth opportunities was explored
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in this study to investigate whether a positive relationship between growth opportunities and long-term debt of hotel firms is caused by the PP&E. The interaction term of PP&E and growth opportunities was of primary interest in this study. Thus, interaction terms between other financial variables in the model were not hypothesized. Since fixed assets, as collateral to the debt, will allow lodging firms to have lower interest rates from the lenders, even when the growth opportunities can increase the debt cost, the interaction variable of a fixed-asset level and growth opportunities was hypothesized to have a positive sign in both industries. Hypothesis 3. The interaction term of PP&E and GW is positively related to long-term debt. A higher earnings volatility (VOL) means greater uncertainty in business, which signifies a higher risk to creditors. The creditors, therefore, would ask for higher compensation for undertaking extra risk. Also, creditors tend to avoid firms with high earnings volatility to reduce their risk level in loans. Thus, a negative relationship between earnings volatility and long-term debt was hypothesized in both industries. The earnings volatility at time t is measured by the standard deviation of earnings before interests and income taxes (EBIT) during the 3-year period (12 quarters) prior to time t. Hypothesis 4. Earnings volatility is negatively related to long-term debt. Barclay and Smith (1995) pointed out that large firms, as compared to small firms, are able to carry a higher level of long-term debt because they can afford the high fixed costs of long-term debt. Moreover, larger firms are less likely to be bankrupt and, consequently, are easier to secure debt at lower cost (Sheel, 1994). Most of the previous studies (Barclay and Smith, 1995; Sheel, 1994; Wald, 1999) also found empirical evidences to support the positive relationship between firm size (SIZE) and financial leverage level. Hypothesis 5. Firm size is positively related to long-term debt. The ratio of free cash flow to total assets (FCF) was employed as a proxy for management agency cost in this study. Jensen (1986) indicates that conflicts of interest between managers and shareholders over payout policies are especially severe when firms generate substantial free cash flow. In such cases, shareholders would be motivated to turn banks or lending institutions into their vehicles for monitoring and curbing management spending by undertaking more debt. Therefore, debt functions as an instrument to reduce the agency cost caused by managers. Thus, free cash flow as a proxy for management agency cost was hypothesized to have a positive sign in both industries. Hypothesis 6. Agency cost is positively related to long-term debt. Return on asset (ROA) was used as a proxy for profitability. According to the pecking order theory, profitable firms would prefer using their internal funds (e.g., retained earnings) to external debt to finance the growth. Subsequently, profitable firms would have a lower debt ratio than unprofitable firms. Hovakimian et al. (2001), Sheel (1994), and Titman and Wessels (1988) all found empirical evidence of supporting the negative relationship between profitability and long-term debt. Hypothesis 7. Profitability is negatively related to long-term debt. (Table 1)
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Table 1 Summary of measurement and hypotheses Variable
Measurement
Hypothesized sign
Fixed asset (PPE) Growth opportunities (GW) Joint effect of PPE and GW (PPE GW) Earnings volatility (VOL) Firm size (SIZE) Agency cost (FCF) Profitability (PROF)
PPE/total assets Market value/book value Multiple of PPE and GW Standard deviation of 3-year EBIT (million $) Total assets (million $) Free cash flow/total assets Return on assets (ROA)
H1: H2: H3: H4: H5: H6: H7:
+ + + +
4. Methodology For both lodging and software firms, the quarterly financial information from 1997 to 2003 was retrieved from the COMPUSTAT database. The selected lodging firms were companies in both GICS 25301020 (hotels, resorts, and cruise lines) and NAICS 721110 (hotels, except casino hotels, and motels). Twenty-seven companies were selected after excluding casinos, cruising operators, travel agencies, and companies without complete data. After deleting outliers, 610 observations for the lodging firms were retained for analysis. In addition, 27 software firms in both GICS 45103020 (systems software) and NAICS 511210 (software publishers) were selected after excluding computer designers, consulting firms, and companies without complete data. After removing extreme cases, 491 observations were analyzed. As explained earlier, the software industry was chosen for comparison purposes because of its contrasting characteristics to the lodging industry. Two models were introduced to examine the relationships between long-term debt and its determinants. The first model was a straightforward ordinary least squares (OLS) regression on the lodging firm’s data as shown in Eq. (1). The long-term debt ratio was the dependent variable, and selected determinants from the literature review were employed as the independent variables. The second regression model incorporates an industry dummy variable to compare the lodging firms to the software firms as presented in Eq. (2): LTD ¼ A þ Bi X i þ E,
(1)
LTD ¼ A þ ðAL AS ÞD þ BSi X i þ ðBL BS Þi DX i þ E,
(2)
where X i is the long-term debt determinant i (including PP&E GW); A is the intercept of the regression line on the Y-axis; Bi is the slope of determinant i; E is the error term; (AL AS ) is the intercept difference between lodging and software firms; D is the industry dummy with a value of 1 for lodging firms and 0 for software firms; BSi the slope of the software for determinant i and (BL BS )i is the slope difference between lodging and software firms for determinant i. The second model used a generalized least squares (GLS) regression designed for heteroskedastic errors between lodging and software firms in the pooled data of these two firm groups. The equality of error variances is required to use the pooled model. However, the error variances of two subsample sets may be different due to the different financial characteristics of the two industries. Thus, the Goldfeld–Quandt (GQ) test was conducted to detect heteroskedasticity (Griffiths et al., 1993) and was calculated as Eq. (3). The result
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indicated that the computed GQ of 1.925 exceeded the corresponding critical F value of 1.153 at the alpha level of 0.05. It was concluded that there was a significant difference in error variances between lodging and software firms. Therefore, instead of OLS regression, the GLS regression procedure was applied to the second model (Eq. (2)): GQ ¼ s2L =s2S ¼ 1:9254F ð6107Þ;ð4917Þ ¼ 1:1534,
(3)
where s2L and s2S are the error variances of lodging (L) and software (S) firms, respectively, from OLS regression In order to apply the GLS regression procedure, the dependent variables were transformed so that the error variances of the two subsamples would be the same. The dependent variable of each subsample set was divided by its own standard deviation of error terms, so that the error variance could be the same and equal to 1 as shown in Eqs. (4) and (5). The transformed dependent variable yielded a GQ of 1.083, which was smaller than the critical value of 1.153. Therefore, the pooled data could be accepted as homoskedastic: qL =sL ¼ aL =sL þ ðbiL =sL Þxi þ eL =sL varðeL =sL Þ ¼ 1=s2L varðeL Þ ¼ s2L =s2L ¼ 1,
ð4Þ
qS =sS ¼ aS =sS þ ðbiS =sS Þxi þ eS =sS varðeS =sS Þ ¼ 1=s2S varðeS Þ ¼ s2S =s2S ¼ 1,
ð5Þ
where qL , qS are the long-term debt ratios; aL , aS are the intercepts of the Eq. (1); biL , biS are the slopes of determinant i; xi is the determinant i; eL , eS are the errors of the prediction of Eq. (1), and sL , sS are the standard deviations of eL and eS for lodging (L) and software (S) firms. The descriptive statistics in Table 2 certainly show that lodging and software firms had contrasting financial characteristics. To test whether the two industries exhibit statistically different behavior in long-term debt, a Chow test was performed (Eq. (6)). The result indicated that the computed F statistic was 52.88, which clearly exceeded the critical F value of 2.01 at a 5% level. Thus, it could be determined that lodging and software firms were statistically different in financial leverage behavior from all the determinants’ perspectives. Therefore, the need for a further detailed comparison of the two industries
Table 2 Descriptive summary of variables Variable
LTD (LTD/asset) PPE (PPE/asset) GW (Market value/book value) VOL (Standard deviation of EBIT) SIZE (Total assets in million $) FCF (Free cash flow in million $) PROF (Return on assets in percentage)
Lodging
Software
Mean
Std.
Mean
Std.
.41 .61 1.52 32.64 2063 58.01 .01
.24 .31 5.72 52.54 3429 349.67 12.10
.38 .09 6.10 131.23 2853 596.63 5.96
.10 .07 8.83 341.95 9587 2207.10 37.24
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was justified: F ¼ ½ðRRSS URSSÞ=k=½URSS=ðn 2kÞF k;n2k under H 0 ¼ 55:4157=1:0479 ¼ 52:88264F 7;1087 ¼ 2:01,
ð6Þ
where RRSS (restricted sum of squares) is the sum of the squared residuals of the regression on the pooled data; URSS (unrestricted sum of squares) is RSSL+RSSS; k is the number of parameters estimated and n 2k the degrees of freedom. Furthermore, prior to the regression analysis for both models (Eqs. (1) and (2)), all independent variables were standardized with Z-scores to avoid unequal weights laid on the variables due to different measurement units. 5. Results Table 2 presents the descriptive summary of the variables used in this study. Lodging firms had a higher long-term debt and fixed asset level than software firms, while software firms had much higher growth opportunities and earnings volatility than lodging firms. It is interesting to note that lodging firms emerged profitable but had negative free cash flow, while software firms showed negative ROA but huge free cash flow. This data summary confirms the contrasting nature of lodging and software industries. The two regression models were statistically significant as presented in Tables 3 and 4. Although the models were significant, the explanatory power of the first regression model was not satisfactorily high: an adjusted R2 of 0.208, which signified that the independent variables might be of limited value in predicting the exact long-term debt level of lodging firms. Thus, it is difficult to consider the regression model as a good predictive model. However, since the model was designed to explore long-term debt’s relationships to its determinants, not to predict long-term debt level, the low R2 should not be a critical issue in this study. Moreover, the low explanatory power suggests that the conventional determinants of the model may not be appropriate in explaining the leverage behavior of lodging firms, and more lodging-specific determinants should be identified in future
Table 3 Regression results of model 1 on lodging firms Independent variables
Beta
Standard error
t
Intercept PPE GW PPE GW VOL SIZE FCF PROF
.550 .063 .074 .140 .036 .026 .015 .014
.130 .011 .022 .018 .023 .025 .016 .010
4.233** 5.827** 3.292** 7.684** 1.569 1.035 .944 1.427
Adjusted R2 F
.208 17.641**
Note: *Po.05; **Po.01.
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Table 4 Regression results of model 2 (GLS) on the pooled data Variable
Beta
Intercept PPE GW PPE GW VOL SIZE FCF PROF
.233 .136 .159 .024 .153 .921 1.075 .172
Adjusted R2 F
Standard error .035 .042 .046 .034 .048 .157 .161 .034
t
Variable
Beta
Standard error
t
6.729** 3.221** 3.436** .708 3.194** 5.881** 6.660** 5.047**
DUMMY PPE_D GW_D PPE GW_D VOL_D SIZE_D FCF_D PROF_D
1.759 .387 .235 .458 .396 .728 1.179 .256
.053 .058 .097 .073 .091 .180 .168 .051
33.397** 6.647** 2.433* 6.251** 4.369** 4.035** 7.026** 5.021**
.530 83.569**
Notes: 1. Dummy 1 ¼ lodging firms and 0 ¼ software firms. 2. *Po.05; **Po.01.
studies. Multicollinearity tests were also conducted with variance inflation factors (VIF), and the VIF values were well below the problematic level of 10 (Kennedy, 1998). As presented in Table 3, the positive sign of PP&E in lodging firms was consistent with Jensen and Meckling’s (1976) agency cost theory that fixed assets serve as collateral, which can reduce the debt cost. In Table 4, the PP&E of hotel firms shows a stronger positive relationship to long-term debt than that of software firms, which reinforces Sheel’s (1994) positive but insignificant finding. These two findings, considered together, suggest that the high level of PP&E not only increases the long-term debt use in individual lodging firms but also has a greater influence on lodging firms’ leverage behavior than on software firms’. In software firms, however, fixed assets appeared to have a negative impact on the leverage, which implies that long-term debt cost would increase as software firms with an innate low fixed-asset level try to finance for the increase of their fixed assets. A possible explanation is that the investment on fixed assets, instead of research and development (R&D), could be deemed irrelevant or obstructive to the core business and, thus, might be interpreted as riskier in serving long-term debt obligations. Opposite to the hypothesis, the positive relationship between long-term debt and growth opportunity, which agrees with Dalbor and Upneja’s (2004) result, was exhibited in both lodging and software firms (Tables 3 and 4). Wald (1999) also found a positive correlation between growth opportunities and long-term debt in firms outside the United States. Table 4 indicates that the growth opportunity variable had a stronger positive influence on the long-term debt of lodging firms than on that of software firms. This result might originate from lodging firms’ low-cost debts due to their fixed-asset collaterals and highcost new equities. According to Table 2, lodging firms have to pay a premium when they issue new stock because of their low market-to-book value. Thus, lodging firms’ long-term debt was positively associated with their growth opportunities. The positive relationship was also observed in software firms, even though software firms were able to finance through the lower-cost new stock due to the high market-to-book value (Table 2). This suggests that lenders may have shared the same optimistic viewpoint as the market did
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regarding software firms’ growth opportunities and are still willing to loan to software firms. Nevertheless, lenders would charge higher prices for the noncollateralized loans. This could at least partly explain why software firms’ long-term debt increases at a lower rate than that of lodging firms as their growth opportunities increase. This finding could validate Dalbor and Upneja’s (2004) result about the positive relationship between longterm debt and growth opportunities in lodging firms. This study also suggests that the positive relationship could exist in other industries with large growth opportunities, such as the software industry. Therefore, the application of the agency cost theory needs to be modified in cases of companies with either a high fixed-asset level or large growth opportunities. The interaction term of fixed assets and growth opportunities (PP&E GW) was positive and significant in lodging firms as hypothesized. The meaning of the interaction effect can be explained by the algebraic calculation in Eq. (7). We can see from the result that for every unit of increase/decrease of GW, LTD increases/decreases by 0.074+0.14PP&E. Therefore, the sensitivity of long-term debt to growth opportunities is affected by the level of PP&E. GW would have a greater influence on long-term debt for lodging firms of higher PP&E and a smaller influence for firms with lower PP&E. Likewise, the expected change in long-term debt ratio in relation to PP&E would be lower for firms with low GW and higher for those with high GW. The difference between lodging and software firms regarding the interaction variable (PP&E GW) was positively significant (Table 4), but the interaction term was not found statistically significant in software firms. This finding suggests that it is important to note the joint effects of the two variables while considering the leverage behavior of lodging firms: LTD ¼ 0:063PPE þ 0:074GW þ 0:14PPEGW:
(7)
Suppose that rest of the terms are constant, GW increases by DGW, where GW0 ¼ GW+DGW, LTD0 ¼ 0:063PPE þ 0:074GW0 þ 0:14PPEGW0 ¼ 0:063PPE þ 0:074ðGW þ DGWÞ þ 0:14PPEðGW þ DGWÞ ¼ 0:063PPE þ 0:74GW þ 0:074DGW þ 0:14PPEGW þ 0:14PPE DGW: LTD0 LTD ¼ 0:074DGW þ 0:14PPE DGW ¼ ð0:074 þ 0:14PPEÞDGW: Different from the hypothesis, the earnings volatility (VOL) of lodging firms did not have a significant effect on lodging firms’ long-term leverage behavior as shown in Table 3. The sign of VOL of software firms in the second regression model (Table 4) was positive as opposed to the hypothesis. Kim and Sorensen (1986) also found the same relationship in companies where top managers were the largest shareholders, a common phenomenon in the software industry. They argued that high insider-ownership companies could secure lower-cost debt despite earnings volatility. Therefore, companies of high insider ownership would seek to transfer the financial risk caused by earnings volatility from shareholders to lenders by using more debt, which consequently produces a positive correlation of the earnings volatility and long-term debt. A significant negative difference between lodging and software firms was also found regarding the relationship of earnings volatility to leverage behavior.
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In Table 3, firm size (SIZE) was not significantly related to leverage behavior in lodging firms. Kim and Sorensen (1986) also failed to find a significant correlation between firm size and long-term debt. This finding indicates that firm size did not function as a significant determinant for long-term financial leverage behavior in lodging firms. The result of the second model shows that the firm size of software firms had a significantly positive relationship with long-term debt as hypothesized. That is, the larger the software firms, the more they would use long-term debt due to the low bankruptcy cost. A significant difference regarding the firm size variable was found between the two industries. Although the agency cost variable (FCF) was found to be not significant in the lodging firm model (Table 3), the positive sign presents a consistency with the agency cost theory as discussed earlier. It is also worthwhile to note that lodging firms’ negative free cash flows (Table 2) imply that there could not be free cash flow-related agency costs. Therefore, it is presumed that no significant relationship between FCF and long-term debt could be found due to the negative free cash flow in lodging firms. On the contrary, Table 4 shows that FCF had a negative sign in the software industry. One reasonable explanation may be that many top managers of software firms are major stockholders, or that the stock option programs have been widely adopted by software firms, which reduces the management agency cost problem. Therefore, higher FCF in software firms with growth opportunities would encourage managers, as owners of the company, to reduce debt leverage in order to avoid the commitment of free cash flow to debt payment. The two industries showed a significant difference in the relationship of FCF to financial leverage as well. As in Table 3, profitability had no significant influence on lodging firms’ long-term debt position. However, Table 4 presents that there was a statistically significant and negative relationship between profitability and long-term debt in the software industry as hypothesized. The lodging and software industries also showed a statistical difference in the relationship of profitability to long-term debt. Overall, only three of the long-term debt determinants tested in this study (PP&E, GW, and PP&E GW) were found statistically significant in lodging firms, whereas all the determinants, except for the interaction term of PP&E and GW, were significant in software firms. As expected, the contrasting differences in financial characteristics between the two groups of firms make the two industries’ financial leverage relationships drastically different, as presented in the second dummy model (Table 4). 6. Conclusions It is worthwhile to note that the first model of this study failed to find significant relationships between long-term debt usage and four of the theory-based variables in the lodging firms. There could be a few reasons for the non-significant relationships between long-term debt and the selected determinants for a specific industry (Titman and Wessels, 1988), but it could also be considered that there may be no such relationships in reality for some industries as opposed to capital structure theories. Our model suggests that the latter might be the case for the lodging industry. The fact that all variables showed significant relationships in software firms may suggest that the variables used in this study were appropriate proxies for the attributes of interest. Hence, earnings volatility, firm size, free cash flow, and profitability neither increase nor decrease debt-financing cost for lodging firms, and therefore, the firms’ leverage behavior would not significantly respond to these variables.
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In sum, fixed assets, growth opportunities, and the interaction term of these two variables had a significant influence on lodging firms’ long-term debt behavior. This finding indicates that, besides the main effects of fixed assets and growth opportunities, the interaction effect of the two variables is important when studying lodging firms. In other words, when the agency cost theory is applied to explain lodging firms’ leverage behavior, the influence of a high PP&E level on financial leverage behavior should be also considered together with growth opportunities. Despite the departure from our hypothesis, the result of this study affirms Dalbor and Upneja’s (2004) finding on the positive effect of the growth opportunities variable on long-term debt level. Some determinants, including growth opportunities and profitability, might seem to behave differently from what the agency cost theory predicts, but these seemingly contradictory results still remain in the scope of the theory if a high PP&E in lodging firms is considered. The discovery of this unique financing rationale of lodging firms bears great implications for the lodging industry as well as the lending institutions. Lodging firms with higher fixed-asset levels should be able to negotiate for more preferable debt arrangements than their lower fixedasset counterparts. Moreover, if investments for growth opportunities are made in the form of fixed assets such as land, buildings, and properties, the lodging firms should be able to borrow at low cost regardless of the potential risks associated with the investment. Based on the findings of this study, it will not be surprising to see firms with a high fixedasset level in other industries follow the same financial leverage behavior. This study is not free from limitations. The major limitation may be the lack of industryspecific variables for measuring the selected capital structure determinants. For example, growth opportunities measured by average daily room rate (ADR), occupancy rate, or revenue per available room (RevPar) could better reflect the growth opportunities related to the hotel business instead of general economic growth measured by market-to-book ratio, revenue growth, or EBIT growth. Unfortunately, those data were not available in the database employed in this study. As Sheel (1994) states, the low explanatory power of the first model for lodging firms and the strong significance of the dummy variable suggest the necessity of using industry-specific variables. This study also assumes that all companies included in this study have similar types of long-term debt contracts. However, some firms might make the long-term debt contracts with covenants that could lower the debt costs. For example, firms may set up sinking funds that reduce creditors’ risks in order to obtain lower-cost debt. Such special arrangements would have an impact on the cost of debts and companies’ long-term debt behavior. Since the lodging business is operated in a global perspective, it would be necessary to investigate lodging companies in different countries before a conclusion about their financing behavior could be made for the hotel industry. In addition, future research that is based on the data of property level instead of company level would also provide useful information to understand the financial characteristics and behavior of individual properties.
References Arbel, A., Woods, R.H., 1990. Debt hitch-hiking: how hotels found low-cost capital. The Cornell Hotel and Restaurant Administration Quarterly 31 (3), 105–110. Barclay, M., Smith Jr., C., 1995. The maturity structure of corporate debt. Journal of Finance 50, 609–631.
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Dalbor, M., Upneja, A., 2004. The investment opportunity set and the long-term debt decision of US lodging firms. Journal of Hospitality & Tourism Research 28 (3), 346–355. Griffiths, W.E., Hill, R.C., Judge, G.G., 1993. Learning and Practicing Econometrics. Wiley, New York. Hovakimian, A., Opler, T., Titman, S., 2001. The debt–equity choice. Journal of Financial and Quantitative Analysis 36 (1), 1–24. Jensen, M., 1986. Agency costs of free cash flow, corporate finance, and takeovers. American Economic Review 76 (2), 323–329. Jensen, M., Meckling, W., 1976. Theory of the firm: managerial behavior, agency costs and ownership structure. Journal of Financial Economics 3 (4), 305–360. Kennedy, P., 1998. A Guide to Econometrics, fourth ed. MIT Press, Cambridge, MA. Kim, W.G., 1997. The determinants of capital structure choice in the US restaurant industry. Tourism Economics 3 (4), 329–340. Kim, W., Sorensen, E., 1986. Evidence on the impact of the agency costs of debt on corporate debt policy. Journal of Financial and Quantitative Analysis 21 (2), 131–144. Modigliani, F., Miller, M., 1958. The cost of capital, corporate finance, and the theory of investment. American Economic Review 48 (4), 261–297. Myers, S., 1977. Determinants of corporate borrowing. Journal of Financial Economics 5 (2), 147–176. Myers, S., 2001. Capital structure. Journal of Economic Perspectives 15 (2), 81–102. Myers, S., Majluf, N., 1984. Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics 13 (2), 187–221. Sheel, A., 1994. Determinants of capital structure choice and empirics on leverage behavior: a comparative analysis of hotel and manufacturing firms. Hospitality Research Journal 17 (3), 3–16. Titman, S., Wessels, R., 1988. The determinants of capital structure choice. Journal of Finance 43 (1), 1–19. Wald, J., 1999. How firm characteristics affect capital structure: an international comparison. Journal of Financial Research 22 (2), 161–187.
Hospitality Management 26 (2007) 175–187 www.elsevier.com/locate/ijhosman
Revisit to the determinants of capital structure: A comparison between lodging firms and software firms Chun-Hung (Hugo) Tang, SooCheong (Shawn) Jang Department of Hospitality and Tourism Management, Purdue University, W. Lafayette, IN, USA
Abstract This study validates the contradiction between capital structure theories and previous empirical studies, and it further identifies lodging firms’ unique leverage behavior through a comparison to software firms, using a generalized least squares analysis. This study also explores the joint effects of key financial leverage determinants. The findings indicate that fixed assets, growth opportunities, and the joint effect of these two variables are the significant long-term debt determinants of the lodging industry. The analysis of the joint effect also suggests that fixed assets and growth opportunities affect each other’s relationship with long-term debt usage. With the findings on lodging firms’ unique financing rationale, authors hope to provide useful information for corporate financial planners and lending institutions regarding debt-financing behavior. r 2005 Elsevier Ltd. All rights reserved. Keywords: Capital structure; Long-term debt; Debt determinants; Lodging firms
1. Introduction Conventional capital structure theories (Myers, 1977; Jensen, 1986) suggest that firms’ optimal capital structure is related to costs and benefits associated with debt and equity financing. With the optimal debt-to-equity mix, firms could achieve the lowest financing costs and consequently increase the value of shareholders (Sheel, 1994). Although the optimal mix varies from industry to industry (Kim, 1997) and from country to country (Wald, 1999), previous researchers have constantly found capital structure theories applicable when explaining financing decisions. However, Dalbor and Upneja’s (2004) Corresponding author. Tel.: +1 765 496 4929.
E-mail addresses: [email protected] (C.-H. Tang), [email protected] (S. Jang). 0278-4319/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhm.2005.08.002
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study on lodging firms was not consistent with the theories in that it claimed a positive relationship between growth opportunities (GW) and long-term debt. Their finding indicates a possibility that lodging firms may have partly different leverage behavior from what the theories suggest. To fully understand lodging firms’ leverage behavior, it is important to validate the theoretically unexpected result and note why the behavior happens. Thus, this study aims to validate the findings that are not consistent with the theories and to re-examine lodging firms’ leverage behavior in comparison to another industry with contrasting business and financial characteristics: the software industry. Software firms are characterized by their reliance on intangible assets such as patents, copyrights, and human capital, while the lodging business is heavily dependent on fixed assets. Moreover, software firms’ financial characteristics, such as a high sales-to-PP&E (property, plant, & equipment) ratio, high interest coverage, high operating margin, and high liquidity, are distinctly different from lodging firms’ (Myers, 2001). Comparing two industries with different financial characteristics enables us to easily identify the unique leverage determinants of each industry. With the understanding of the unique determinants, this study is expected to clearly explain the lodging-specific leverage behavior. This study is distinct from previous studies in that it investigates the joint effects of the leverage determinants. Despite the fact that traditional theory-based determinants have been examined by many studies, little effort has been made to examine the joint influences of the determinants. As these determinants simultaneously exist in a real-world situation, a model including the interaction terms could be more effective in observing the practical relationships between long-term debt and its determinants. Therefore, the objectives of this study are (1) to revisit the determinants of capital structure of lodging firms to investigate the validity of Dalbor and Upneja’s (2004) finding about growth opportunities and (2) to further identify the uniqueness in leverage behavior through a comparison to software firms. This study also seeks to explore the joint effects of key financial leverage determinants. 2. Capital structure theories Research on capital structure has been conducted to identify the optimal mixes of debt and equity and, resultantly, to maximize the value of shareholders. However, Modigliani and Miller (1958) propose that all mixes of capital structure produce the same financial result in a perfect capital market. In other words, the optimal capital structure is irrelevant to creating shareholders’ wealth, even if it exists. In the real world, however, the optimal capital structure does affect the value of shareholders because of tax, information asymmetry, and agency cost. In order to explain the effects of tax, information asymmetry, and agency cost in relation to capital structure, the tradeoff theory, the pecking order theory, and the agency cost theory have been developed respectively. The tradeoff theory states that a taxable corporation should increase its debt level until the marginal value of tax shield is offset by the present value of possible financial distress costs. This tradeoff theory of capital structure theoretically balances the tax advantages of borrowing against the costs of financial distress. However, this theory is immediately challenged by the fact that many profitable companies, such as Microsoft, with low cost to borrow, still operate at low debt ratios (Myers, 2001). These companies attempt to maximize shareholders’ value by keeping growth opportunities open instead of utilizing
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tax shields. For firms like Microsoft, the cost of missing a growth opportunity is so high that the potential loss can never be made up by a tax shield advantage. In such cases, the agency cost theory makes better sense because it considers the agency cost of debt including the cost of missing growth opportunities. According to the pecking order theory, firms prefer internal finance, such as retained earnings, if available, and choose debt over equity when external financing is required. The pecking order may be a result of information asymmetry (Myers and Majluf, 1984). The use of external financing sources signals the information that the firms are not profitable, which can negatively influence the stock price. When external financing sources are required, issuing new stock, instead of new debts, signals the information that managers think company stocks are overpriced. The managers of a firm usually know more about their firms’ business and financial information than average investors do, and they will not be willing to issue new stock when they think the stock price is low. They tend to issue shares when shares are overpriced or fairly priced in the market. Thus, investors may interpret the announcement of a stock issue as a negative sign about the current stock price. Hence, for financing choices, debt is next on the pecking order after internal funds and before new stock. Therefore, the pecking order theory implies an inverse relationship between profitability and debt usage. The agency cost theory proposes that the higher level of debt increases shareholders’ value by transferring risk to creditors and having managers allocate cash for debt payment rather than for suboptimal or excessive investments (Jensen and Meckling, 1976). Under this theory, there are two kinds of inherited conflicts of interests: manager–shareholder conflict and creditors–shareholder conflict. Jensen and Meckling (1976) maintain that managers always act in their own economic interests. The managers’ interests, however, can agree with shareholders’ interests when managers’ compensation is tied to the company’s performance. The two stakeholders’ interests can usually converge during takeover threats. Therefore, the manager–shareholder conflict can be ultimately minimized. When conflicts arise between debt and equity investors, managers have several financial tactics for transferring value from creditors to shareholders. If managers have common interests with shareholders as discussed previously, managers will make business decisions for shareholders rather than for creditors. The agency cost theory also offers another viewpoint to explain the previously discussed high profitability–low debt ratio correlation. In a profitable firm, it is to the managers’ advantage to keep debt ratio low, since (1) free cash flow is not committed to debt payment and can be used for management’s interests, and (2) managers are free from the debt payment pressure. This causes a loss in a shareholder’s value, and it is called ‘agency cost.’ Thus, the agency cost theory assists the tradeoff theory in explaining why some profitable corporations with abundant cash and low debt financing cost do not maximize their tax shield benefit. 3. Hypotheses The long-term debt ratio (long-term debt/total assets) was used as a dependent variable to measure leverage behavior in this study. Long-term debt was chosen since lodging firms usually possess a high percentage of long-term debt to finance fixed assets such as real estate (Arbel and Woods, 1990). Moreover, the use of long-term debt, instead of shortterm debt, could provide more specific information about the financial leverage behavior of lodging firms because the long-term debt is usually a preferred funding source for
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growth in the lodging industry, whereas short-term debt is used primarily for current assets and cash shortages (Hovakimian et al., 2001). The hypotheses of this study were based on the assumption that conventional capital structure theories can be applied to both lodging and software firms. Therefore, each variable was expected to have the same sign in both industries and agree with the capital structure theories. However, the degree of influence on debt behavior from each leverage determinant was expected to differ between the two industries because the optimal debt–equity mixes may vary in different industries (Kim, 1997). In order to easily contrast the difference between two industries, this study employed an industry dummy variable as utilized by Sheel (1994). A null hypothesis that determinants influence both industries to the same degree was proposed in this study. Thus, the positive significance of the estimate would support the assumption that lodging and software firms respond to each determinant in the same direction but to a different extent. The fixed assets, measured as a ratio of PP&E to total assets, was expected to be the most important variable in explaining lodging firms’ long-term debt behavior (Dalbor and Upneja, 2004). The PP&E usually serves as inflation-resistant collateral for loans. Consequently, interest rates for lodging firms should be relatively low (Arbel and Woods, 1990), and hotels would increase long-term debt usage as predicted by Jensen and Meckling (1976). Hypothesis 1. PP&E is positively related to long-term debt. The market-to-book ratio is adopted in this study to capture the relative value of the growth opportunities viewed by the market. Market value reflects the market’s expectation on the current net worth of the company and the company’s all-future earnings. Since future earnings serve as the proxy for growth opportunities, the market-to-book ratio presents the current expectation of the company’s future growth opportunities to the book value. Jensen and Meckling (1976) argue that firms with high growth opportunities are more likely to have high agency costs of debt due to the higher debt prices. Creditors usually charge higher prices for debt if managers plan to invest in riskier projects. Also, as the amount of debt increases, the corporate control shifts to creditors, who enforce risk-averse decisions to reject potential profitable investments. Moreover, much of the internal cash flow gets committed to the debt payment and may not be available for good investments. As a result, firms with valuable growth opportunities would maintain a low debt ratio in order to minimize the constraints enforced by creditors and maximize the potential gain. Therefore, a negative relationship was still hypothesized between growth opportunities and the use of long-term debt in both industries. Hypothesis 2. Growth opportunity is negatively related to long-term debt. Dalbor and Upneja (2004) reported a positive relationship between growth opportunities and the debt use of lodging firms, which is contrary to the theories and cases of other industries. They proposed two reasons for their unexpected finding. First, the growth proxy used by the study may not capture the actual growth potential of lodging firms. Second, lodging firms’ growth mostly requires investments in fixed assets, which may be fundamentally different from other industries. Considering Dalbor and Upneja’s (2004) second explanation and the fact that lodging firms have a substantially higher PP&E level than software firms’, the interaction term of PP&E and growth opportunities was explored
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in this study to investigate whether a positive relationship between growth opportunities and long-term debt of hotel firms is caused by the PP&E. The interaction term of PP&E and growth opportunities was of primary interest in this study. Thus, interaction terms between other financial variables in the model were not hypothesized. Since fixed assets, as collateral to the debt, will allow lodging firms to have lower interest rates from the lenders, even when the growth opportunities can increase the debt cost, the interaction variable of a fixed-asset level and growth opportunities was hypothesized to have a positive sign in both industries. Hypothesis 3. The interaction term of PP&E and GW is positively related to long-term debt. A higher earnings volatility (VOL) means greater uncertainty in business, which signifies a higher risk to creditors. The creditors, therefore, would ask for higher compensation for undertaking extra risk. Also, creditors tend to avoid firms with high earnings volatility to reduce their risk level in loans. Thus, a negative relationship between earnings volatility and long-term debt was hypothesized in both industries. The earnings volatility at time t is measured by the standard deviation of earnings before interests and income taxes (EBIT) during the 3-year period (12 quarters) prior to time t. Hypothesis 4. Earnings volatility is negatively related to long-term debt. Barclay and Smith (1995) pointed out that large firms, as compared to small firms, are able to carry a higher level of long-term debt because they can afford the high fixed costs of long-term debt. Moreover, larger firms are less likely to be bankrupt and, consequently, are easier to secure debt at lower cost (Sheel, 1994). Most of the previous studies (Barclay and Smith, 1995; Sheel, 1994; Wald, 1999) also found empirical evidences to support the positive relationship between firm size (SIZE) and financial leverage level. Hypothesis 5. Firm size is positively related to long-term debt. The ratio of free cash flow to total assets (FCF) was employed as a proxy for management agency cost in this study. Jensen (1986) indicates that conflicts of interest between managers and shareholders over payout policies are especially severe when firms generate substantial free cash flow. In such cases, shareholders would be motivated to turn banks or lending institutions into their vehicles for monitoring and curbing management spending by undertaking more debt. Therefore, debt functions as an instrument to reduce the agency cost caused by managers. Thus, free cash flow as a proxy for management agency cost was hypothesized to have a positive sign in both industries. Hypothesis 6. Agency cost is positively related to long-term debt. Return on asset (ROA) was used as a proxy for profitability. According to the pecking order theory, profitable firms would prefer using their internal funds (e.g., retained earnings) to external debt to finance the growth. Subsequently, profitable firms would have a lower debt ratio than unprofitable firms. Hovakimian et al. (2001), Sheel (1994), and Titman and Wessels (1988) all found empirical evidence of supporting the negative relationship between profitability and long-term debt. Hypothesis 7. Profitability is negatively related to long-term debt. (Table 1)
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Table 1 Summary of measurement and hypotheses Variable
Measurement
Hypothesized sign
Fixed asset (PPE) Growth opportunities (GW) Joint effect of PPE and GW (PPE GW) Earnings volatility (VOL) Firm size (SIZE) Agency cost (FCF) Profitability (PROF)
PPE/total assets Market value/book value Multiple of PPE and GW Standard deviation of 3-year EBIT (million $) Total assets (million $) Free cash flow/total assets Return on assets (ROA)
H1: H2: H3: H4: H5: H6: H7:
+ + + +
4. Methodology For both lodging and software firms, the quarterly financial information from 1997 to 2003 was retrieved from the COMPUSTAT database. The selected lodging firms were companies in both GICS 25301020 (hotels, resorts, and cruise lines) and NAICS 721110 (hotels, except casino hotels, and motels). Twenty-seven companies were selected after excluding casinos, cruising operators, travel agencies, and companies without complete data. After deleting outliers, 610 observations for the lodging firms were retained for analysis. In addition, 27 software firms in both GICS 45103020 (systems software) and NAICS 511210 (software publishers) were selected after excluding computer designers, consulting firms, and companies without complete data. After removing extreme cases, 491 observations were analyzed. As explained earlier, the software industry was chosen for comparison purposes because of its contrasting characteristics to the lodging industry. Two models were introduced to examine the relationships between long-term debt and its determinants. The first model was a straightforward ordinary least squares (OLS) regression on the lodging firm’s data as shown in Eq. (1). The long-term debt ratio was the dependent variable, and selected determinants from the literature review were employed as the independent variables. The second regression model incorporates an industry dummy variable to compare the lodging firms to the software firms as presented in Eq. (2): LTD ¼ A þ Bi X i þ E,
(1)
LTD ¼ A þ ðAL AS ÞD þ BSi X i þ ðBL BS Þi DX i þ E,
(2)
where X i is the long-term debt determinant i (including PP&E GW); A is the intercept of the regression line on the Y-axis; Bi is the slope of determinant i; E is the error term; (AL AS ) is the intercept difference between lodging and software firms; D is the industry dummy with a value of 1 for lodging firms and 0 for software firms; BSi the slope of the software for determinant i and (BL BS )i is the slope difference between lodging and software firms for determinant i. The second model used a generalized least squares (GLS) regression designed for heteroskedastic errors between lodging and software firms in the pooled data of these two firm groups. The equality of error variances is required to use the pooled model. However, the error variances of two subsample sets may be different due to the different financial characteristics of the two industries. Thus, the Goldfeld–Quandt (GQ) test was conducted to detect heteroskedasticity (Griffiths et al., 1993) and was calculated as Eq. (3). The result
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indicated that the computed GQ of 1.925 exceeded the corresponding critical F value of 1.153 at the alpha level of 0.05. It was concluded that there was a significant difference in error variances between lodging and software firms. Therefore, instead of OLS regression, the GLS regression procedure was applied to the second model (Eq. (2)): GQ ¼ s2L =s2S ¼ 1:9254F ð6107Þ;ð4917Þ ¼ 1:1534,
(3)
where s2L and s2S are the error variances of lodging (L) and software (S) firms, respectively, from OLS regression In order to apply the GLS regression procedure, the dependent variables were transformed so that the error variances of the two subsamples would be the same. The dependent variable of each subsample set was divided by its own standard deviation of error terms, so that the error variance could be the same and equal to 1 as shown in Eqs. (4) and (5). The transformed dependent variable yielded a GQ of 1.083, which was smaller than the critical value of 1.153. Therefore, the pooled data could be accepted as homoskedastic: qL =sL ¼ aL =sL þ ðbiL =sL Þxi þ eL =sL varðeL =sL Þ ¼ 1=s2L varðeL Þ ¼ s2L =s2L ¼ 1,
ð4Þ
qS =sS ¼ aS =sS þ ðbiS =sS Þxi þ eS =sS varðeS =sS Þ ¼ 1=s2S varðeS Þ ¼ s2S =s2S ¼ 1,
ð5Þ
where qL , qS are the long-term debt ratios; aL , aS are the intercepts of the Eq. (1); biL , biS are the slopes of determinant i; xi is the determinant i; eL , eS are the errors of the prediction of Eq. (1), and sL , sS are the standard deviations of eL and eS for lodging (L) and software (S) firms. The descriptive statistics in Table 2 certainly show that lodging and software firms had contrasting financial characteristics. To test whether the two industries exhibit statistically different behavior in long-term debt, a Chow test was performed (Eq. (6)). The result indicated that the computed F statistic was 52.88, which clearly exceeded the critical F value of 2.01 at a 5% level. Thus, it could be determined that lodging and software firms were statistically different in financial leverage behavior from all the determinants’ perspectives. Therefore, the need for a further detailed comparison of the two industries
Table 2 Descriptive summary of variables Variable
LTD (LTD/asset) PPE (PPE/asset) GW (Market value/book value) VOL (Standard deviation of EBIT) SIZE (Total assets in million $) FCF (Free cash flow in million $) PROF (Return on assets in percentage)
Lodging
Software
Mean
Std.
Mean
Std.
.41 .61 1.52 32.64 2063 58.01 .01
.24 .31 5.72 52.54 3429 349.67 12.10
.38 .09 6.10 131.23 2853 596.63 5.96
.10 .07 8.83 341.95 9587 2207.10 37.24
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was justified: F ¼ ½ðRRSS URSSÞ=k=½URSS=ðn 2kÞF k;n2k under H 0 ¼ 55:4157=1:0479 ¼ 52:88264F 7;1087 ¼ 2:01,
ð6Þ
where RRSS (restricted sum of squares) is the sum of the squared residuals of the regression on the pooled data; URSS (unrestricted sum of squares) is RSSL+RSSS; k is the number of parameters estimated and n 2k the degrees of freedom. Furthermore, prior to the regression analysis for both models (Eqs. (1) and (2)), all independent variables were standardized with Z-scores to avoid unequal weights laid on the variables due to different measurement units. 5. Results Table 2 presents the descriptive summary of the variables used in this study. Lodging firms had a higher long-term debt and fixed asset level than software firms, while software firms had much higher growth opportunities and earnings volatility than lodging firms. It is interesting to note that lodging firms emerged profitable but had negative free cash flow, while software firms showed negative ROA but huge free cash flow. This data summary confirms the contrasting nature of lodging and software industries. The two regression models were statistically significant as presented in Tables 3 and 4. Although the models were significant, the explanatory power of the first regression model was not satisfactorily high: an adjusted R2 of 0.208, which signified that the independent variables might be of limited value in predicting the exact long-term debt level of lodging firms. Thus, it is difficult to consider the regression model as a good predictive model. However, since the model was designed to explore long-term debt’s relationships to its determinants, not to predict long-term debt level, the low R2 should not be a critical issue in this study. Moreover, the low explanatory power suggests that the conventional determinants of the model may not be appropriate in explaining the leverage behavior of lodging firms, and more lodging-specific determinants should be identified in future
Table 3 Regression results of model 1 on lodging firms Independent variables
Beta
Standard error
t
Intercept PPE GW PPE GW VOL SIZE FCF PROF
.550 .063 .074 .140 .036 .026 .015 .014
.130 .011 .022 .018 .023 .025 .016 .010
4.233** 5.827** 3.292** 7.684** 1.569 1.035 .944 1.427
Adjusted R2 F
.208 17.641**
Note: *Po.05; **Po.01.
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Table 4 Regression results of model 2 (GLS) on the pooled data Variable
Beta
Intercept PPE GW PPE GW VOL SIZE FCF PROF
.233 .136 .159 .024 .153 .921 1.075 .172
Adjusted R2 F
Standard error .035 .042 .046 .034 .048 .157 .161 .034
t
Variable
Beta
Standard error
t
6.729** 3.221** 3.436** .708 3.194** 5.881** 6.660** 5.047**
DUMMY PPE_D GW_D PPE GW_D VOL_D SIZE_D FCF_D PROF_D
1.759 .387 .235 .458 .396 .728 1.179 .256
.053 .058 .097 .073 .091 .180 .168 .051
33.397** 6.647** 2.433* 6.251** 4.369** 4.035** 7.026** 5.021**
.530 83.569**
Notes: 1. Dummy 1 ¼ lodging firms and 0 ¼ software firms. 2. *Po.05; **Po.01.
studies. Multicollinearity tests were also conducted with variance inflation factors (VIF), and the VIF values were well below the problematic level of 10 (Kennedy, 1998). As presented in Table 3, the positive sign of PP&E in lodging firms was consistent with Jensen and Meckling’s (1976) agency cost theory that fixed assets serve as collateral, which can reduce the debt cost. In Table 4, the PP&E of hotel firms shows a stronger positive relationship to long-term debt than that of software firms, which reinforces Sheel’s (1994) positive but insignificant finding. These two findings, considered together, suggest that the high level of PP&E not only increases the long-term debt use in individual lodging firms but also has a greater influence on lodging firms’ leverage behavior than on software firms’. In software firms, however, fixed assets appeared to have a negative impact on the leverage, which implies that long-term debt cost would increase as software firms with an innate low fixed-asset level try to finance for the increase of their fixed assets. A possible explanation is that the investment on fixed assets, instead of research and development (R&D), could be deemed irrelevant or obstructive to the core business and, thus, might be interpreted as riskier in serving long-term debt obligations. Opposite to the hypothesis, the positive relationship between long-term debt and growth opportunity, which agrees with Dalbor and Upneja’s (2004) result, was exhibited in both lodging and software firms (Tables 3 and 4). Wald (1999) also found a positive correlation between growth opportunities and long-term debt in firms outside the United States. Table 4 indicates that the growth opportunity variable had a stronger positive influence on the long-term debt of lodging firms than on that of software firms. This result might originate from lodging firms’ low-cost debts due to their fixed-asset collaterals and highcost new equities. According to Table 2, lodging firms have to pay a premium when they issue new stock because of their low market-to-book value. Thus, lodging firms’ long-term debt was positively associated with their growth opportunities. The positive relationship was also observed in software firms, even though software firms were able to finance through the lower-cost new stock due to the high market-to-book value (Table 2). This suggests that lenders may have shared the same optimistic viewpoint as the market did
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regarding software firms’ growth opportunities and are still willing to loan to software firms. Nevertheless, lenders would charge higher prices for the noncollateralized loans. This could at least partly explain why software firms’ long-term debt increases at a lower rate than that of lodging firms as their growth opportunities increase. This finding could validate Dalbor and Upneja’s (2004) result about the positive relationship between longterm debt and growth opportunities in lodging firms. This study also suggests that the positive relationship could exist in other industries with large growth opportunities, such as the software industry. Therefore, the application of the agency cost theory needs to be modified in cases of companies with either a high fixed-asset level or large growth opportunities. The interaction term of fixed assets and growth opportunities (PP&E GW) was positive and significant in lodging firms as hypothesized. The meaning of the interaction effect can be explained by the algebraic calculation in Eq. (7). We can see from the result that for every unit of increase/decrease of GW, LTD increases/decreases by 0.074+0.14PP&E. Therefore, the sensitivity of long-term debt to growth opportunities is affected by the level of PP&E. GW would have a greater influence on long-term debt for lodging firms of higher PP&E and a smaller influence for firms with lower PP&E. Likewise, the expected change in long-term debt ratio in relation to PP&E would be lower for firms with low GW and higher for those with high GW. The difference between lodging and software firms regarding the interaction variable (PP&E GW) was positively significant (Table 4), but the interaction term was not found statistically significant in software firms. This finding suggests that it is important to note the joint effects of the two variables while considering the leverage behavior of lodging firms: LTD ¼ 0:063PPE þ 0:074GW þ 0:14PPEGW:
(7)
Suppose that rest of the terms are constant, GW increases by DGW, where GW0 ¼ GW+DGW, LTD0 ¼ 0:063PPE þ 0:074GW0 þ 0:14PPEGW0 ¼ 0:063PPE þ 0:074ðGW þ DGWÞ þ 0:14PPEðGW þ DGWÞ ¼ 0:063PPE þ 0:74GW þ 0:074DGW þ 0:14PPEGW þ 0:14PPE DGW: LTD0 LTD ¼ 0:074DGW þ 0:14PPE DGW ¼ ð0:074 þ 0:14PPEÞDGW: Different from the hypothesis, the earnings volatility (VOL) of lodging firms did not have a significant effect on lodging firms’ long-term leverage behavior as shown in Table 3. The sign of VOL of software firms in the second regression model (Table 4) was positive as opposed to the hypothesis. Kim and Sorensen (1986) also found the same relationship in companies where top managers were the largest shareholders, a common phenomenon in the software industry. They argued that high insider-ownership companies could secure lower-cost debt despite earnings volatility. Therefore, companies of high insider ownership would seek to transfer the financial risk caused by earnings volatility from shareholders to lenders by using more debt, which consequently produces a positive correlation of the earnings volatility and long-term debt. A significant negative difference between lodging and software firms was also found regarding the relationship of earnings volatility to leverage behavior.
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In Table 3, firm size (SIZE) was not significantly related to leverage behavior in lodging firms. Kim and Sorensen (1986) also failed to find a significant correlation between firm size and long-term debt. This finding indicates that firm size did not function as a significant determinant for long-term financial leverage behavior in lodging firms. The result of the second model shows that the firm size of software firms had a significantly positive relationship with long-term debt as hypothesized. That is, the larger the software firms, the more they would use long-term debt due to the low bankruptcy cost. A significant difference regarding the firm size variable was found between the two industries. Although the agency cost variable (FCF) was found to be not significant in the lodging firm model (Table 3), the positive sign presents a consistency with the agency cost theory as discussed earlier. It is also worthwhile to note that lodging firms’ negative free cash flows (Table 2) imply that there could not be free cash flow-related agency costs. Therefore, it is presumed that no significant relationship between FCF and long-term debt could be found due to the negative free cash flow in lodging firms. On the contrary, Table 4 shows that FCF had a negative sign in the software industry. One reasonable explanation may be that many top managers of software firms are major stockholders, or that the stock option programs have been widely adopted by software firms, which reduces the management agency cost problem. Therefore, higher FCF in software firms with growth opportunities would encourage managers, as owners of the company, to reduce debt leverage in order to avoid the commitment of free cash flow to debt payment. The two industries showed a significant difference in the relationship of FCF to financial leverage as well. As in Table 3, profitability had no significant influence on lodging firms’ long-term debt position. However, Table 4 presents that there was a statistically significant and negative relationship between profitability and long-term debt in the software industry as hypothesized. The lodging and software industries also showed a statistical difference in the relationship of profitability to long-term debt. Overall, only three of the long-term debt determinants tested in this study (PP&E, GW, and PP&E GW) were found statistically significant in lodging firms, whereas all the determinants, except for the interaction term of PP&E and GW, were significant in software firms. As expected, the contrasting differences in financial characteristics between the two groups of firms make the two industries’ financial leverage relationships drastically different, as presented in the second dummy model (Table 4). 6. Conclusions It is worthwhile to note that the first model of this study failed to find significant relationships between long-term debt usage and four of the theory-based variables in the lodging firms. There could be a few reasons for the non-significant relationships between long-term debt and the selected determinants for a specific industry (Titman and Wessels, 1988), but it could also be considered that there may be no such relationships in reality for some industries as opposed to capital structure theories. Our model suggests that the latter might be the case for the lodging industry. The fact that all variables showed significant relationships in software firms may suggest that the variables used in this study were appropriate proxies for the attributes of interest. Hence, earnings volatility, firm size, free cash flow, and profitability neither increase nor decrease debt-financing cost for lodging firms, and therefore, the firms’ leverage behavior would not significantly respond to these variables.
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In sum, fixed assets, growth opportunities, and the interaction term of these two variables had a significant influence on lodging firms’ long-term debt behavior. This finding indicates that, besides the main effects of fixed assets and growth opportunities, the interaction effect of the two variables is important when studying lodging firms. In other words, when the agency cost theory is applied to explain lodging firms’ leverage behavior, the influence of a high PP&E level on financial leverage behavior should be also considered together with growth opportunities. Despite the departure from our hypothesis, the result of this study affirms Dalbor and Upneja’s (2004) finding on the positive effect of the growth opportunities variable on long-term debt level. Some determinants, including growth opportunities and profitability, might seem to behave differently from what the agency cost theory predicts, but these seemingly contradictory results still remain in the scope of the theory if a high PP&E in lodging firms is considered. The discovery of this unique financing rationale of lodging firms bears great implications for the lodging industry as well as the lending institutions. Lodging firms with higher fixed-asset levels should be able to negotiate for more preferable debt arrangements than their lower fixedasset counterparts. Moreover, if investments for growth opportunities are made in the form of fixed assets such as land, buildings, and properties, the lodging firms should be able to borrow at low cost regardless of the potential risks associated with the investment. Based on the findings of this study, it will not be surprising to see firms with a high fixedasset level in other industries follow the same financial leverage behavior. This study is not free from limitations. The major limitation may be the lack of industryspecific variables for measuring the selected capital structure determinants. For example, growth opportunities measured by average daily room rate (ADR), occupancy rate, or revenue per available room (RevPar) could better reflect the growth opportunities related to the hotel business instead of general economic growth measured by market-to-book ratio, revenue growth, or EBIT growth. Unfortunately, those data were not available in the database employed in this study. As Sheel (1994) states, the low explanatory power of the first model for lodging firms and the strong significance of the dummy variable suggest the necessity of using industry-specific variables. This study also assumes that all companies included in this study have similar types of long-term debt contracts. However, some firms might make the long-term debt contracts with covenants that could lower the debt costs. For example, firms may set up sinking funds that reduce creditors’ risks in order to obtain lower-cost debt. Such special arrangements would have an impact on the cost of debts and companies’ long-term debt behavior. Since the lodging business is operated in a global perspective, it would be necessary to investigate lodging companies in different countries before a conclusion about their financing behavior could be made for the hotel industry. In addition, future research that is based on the data of property level instead of company level would also provide useful information to understand the financial characteristics and behavior of individual properties.
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