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Table 5: Firm characteristics by cash/assets quartiles

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Table 5: Firm characteristics by cash/assets quartiles





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Table 5: Firm characteristics by cash/assets quartiles


CASH1

First Quartile 0 to .0091

Second Quartile .0043 to .0378

Third Quartile .0201 to .1204

Fourth Quartile .0762 to .9675

t-statistic

p-value

Variable

Mean

Median

Mean

Median

Mean

Median

Mean

Median

 

 

CASH1

0.0026

0.0022

0.0154

0.0139

0.0556

0.0513

0.2430

0.1947

161.5763

0.0000

LEV

0.6920

0.7202

0.6671

0.6837

0.6195

0.6242

0.5544

0.5258

-27.3123

0.0000

NWC

-0.1468

-0.1191

-0.1284

-0.0999

-0.1102

-0.0783

-0.1277

-0.0817

4.7631

0.0000

SIZE

8.8044

8.7170

8.4935

8.4338

8.1200

8.0804

7.5518

7.5315

-67.2273

0.0000

GROWOP

0.0494

0.0180

0.0577

0.0238

0.0550

0.0244

0.0509

0.0252

0.3700

0.7114

BANKR

0.5370

0.6259

0.5086

0.5833

0.4654

0.5156

0.3357

0.2552

-43.4846

0.0000

STDEBT

0.4770

0.4190

0.5148

0.4651

0.5448

0.5146

0.6483

0.6824

40.9104

0.0000

CAPEX

0.0741

0.0266

0.0726

0.0337

0.0778

0.0394

0.0644

0.0425

-2.8850

0.0039

TANG

0.7049

0.8240

0.6597

0.7662

0.6180

0.7050

0.4582

0.4835

-66.1969

0.0000

CFLOW

0.0351

0.0330

0.0421

0.0440

0.0578

0.0593

0.0891

0.0857

29.8192

0.0000

VOLCFLOW

0.0367

0.0244

0.0442

0.0292

0.0507

0.0338

0.0662

0.0428

34.0702

0.0000


After carrying out the tests24 which confirm the unsuitability of the Pooled OLS model, the Hausman test concluded that there was evidence of correlation between individual effects and explanatory variables (chi^2= 572.33, p-value=0) therefore rejecting the random effects model. A within-groups estimator was be used to estimate the fixed effects model by applying the Ordinary Least Squares technique on the transformed model after subtracting the individual averages from all the variables. As the fixed effect model using the within estimation excludes the time-invariant variables, alternatively the between estimator will be used to show the explanatory capacity of the VOLCFLOW variable. This estimator runs an OLS regression on the mean values of each firm. In subsequent analyses and tests only the within estimator will be used.

The results for the models are presented in table 6. As observed, both estimations produce homogeneous results, showing similar levels of significance, signs and coefficients. The exception is the SIZE variable, which shows a level of significance of 0.05 in the within estimator and 0.01 with the between estimator. In a general analysis of the models, we observe that firms that are larger, more leveraged, where the greater proportion of debt is short-term and closer relationships are maintained with financing institutions, show lower cash holdings. It is also seen that firms with more liquid assets substituting cash holdings, greater capital expenditure and greater tangibility of assets present lower cash ratios. It also stands out that higher levels of cash-flow and its volatility are associated with higher levels of cash holdings. The models are clear in attributing a negative impact of the financial crisis on cash ratios, showing that the years of financial crisis, which still leave marks in the economies of the countries studied, are reflected in a reduced level of cash in the sample firms. Both models concur in not considering growth opportunities as a determinant of cash ratio.


Table 6: Regression results

Models 1 and 2 estimate “Within” and “Between” regressions respectively; Model 3 adds the quadratic term to the LEV variable; Model 4 includes interactions between independent variables and the CRISIS dummy; Model 5 removes the LEV and CAPEX variables; Model 6 excludes the observations with highest cash ratios (top decile); Model 7 used as the dependent variable CASH2, that is, the ratio of cash plus cash equivalents to total assets minus cash and cash equivalents; Model 8 replaces CFLOW for EBITDA; Model 9 replaces the CRISIS dummy with year dummies. P-values are based on clustered robust standard errors (by firm) to control for heteroskedasticity and autocorrelation, and are reported in parentheses. We report within R2 for all models.

Independent Variable

1 FE

2 BE

3- FE LEV^2

4- FE Interactions

5- FE Reduced-form

6- FE Decil

7- FE CASH2

8- FE EBITDA

9- FE Dummy YEAR

CONSTANT

0.4021

0.4774

0.4379

0.4525

0.2595

0.2187

0.7243

0.3859

0.3975


(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

LEV

-0.1011

-0.1463

-0.1629

-0.1327

-0.0376

-0.2328

-0.1041

-0.1007


(0.001)

(0.000)

(0.000)

(0.000)

(0.000)

(0.001)

(0.001)

(0.001)

NWC

-0.1969

-0.1361

-0.2076

-0.2122

-0.1358

-0.0636

-0.4603

-0.1944

-0.1965


(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

SIZE

-0.0059

-0.0181

-0.0054

-0.0076

-0.0008

-0.0077

0.0001

-0.0040

-0.0056


(0.037)

(0.000)

(0.051)

(0.007)

(0.792)

(0.000)

(0.992)

(0.159)

(0.054)

GROWOP

0.0011

0.0154

0.0011

0.0030

-0.0018

0.0010

-0.0003

0.0004

0.0008


(0.563)

(0.050)

(0.574)

(0.081)

(0.328)

(0.338)

(0.940)

(0.810)

(0.671)

BANKR

-0.0254

-0.0291

-0.0210

-0.0229

-0.0302

-0.0124

-0.0460

-0.0258

-0.0253


(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

STDEBT

-0.1259

-0.0887

-0.1354

-0.1291

-0.0812

-0.0491

-0.2780

-0.1245

-0.1259


(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

CAPEX

-0.0137

-0.0358

-0.0151

-0.0340


-0.0032

-0.0357

-0.0154

-0.0141


(0.004)

(0.000)

(0.001)

(0.000)


(0.079)

(0.002)

(0.003)

(0.004)

TANG

-0.2562

-0.1833

-0.2641

-0.2826

-0.2289

-0.0923

-0.5655

-0.2562

-0.2559


(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

CFLOW

0.1039

0.1081

0.1181

0.0919

0.1557

0.0457

0.2183


0.1035


(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)


(0.000)

VOLCFLOW


0.1761










(0.000)








CRISIS

-0.0093

-0.0254

-0.0107

-0.0629

-0.0069

-0.0094

-0.0144

-0.0094



(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)


LEV_CRISIS




0.0442










(0.000)






CAPEX_CRISIS




0.0316










(0.000)






TANG_CRISIS




0.0422










(0.000)






NWC_CRISIS




0.0219










(0.018)






YEAR_DUMMY05









0.0033










(0.006)

YEAR_DUMMY06









0.0044










(0.003)

YEAR_DUMMY07









-0.0032










(0.069)

YEAR_DUMMY08









-0.0066










(0.003)

YEAR_DUMMY09









-0.0070










(0.002)

YEAR_DUMMY10









-0.0106










(0.000)

YEAR_DUMMY11









-0.0084










(0.001)

LEV^2



0.0107










(0.006)







EBITDA








0.0941










(0.000)


R^2

0.2049

0.1405

0.2195

0.2179

0.1696

0.1068

0.1242

0.2044

0.2063

N

31429

31429

31429

31429

31429

28360

31429

31429

31429

The results obtained in Model 1 show that an increase of, for example, 0.10 in the LEV variable, ceteris paribus, determines a decrease of 1.01 percentage points in cash ratio25. Interpretation of this result has not been consensual. Inasmuch as leverage can serve as a proxy for access to debt, its substitute role is confirmed. An alternative explanation is put forward by Baskin (1987), who explains that the cost of opportunity of investing in liquidity increases for higher levels of leverage. A simpler explanation is supported by Pecking Order Theory which interprets diminishing cash ratios as a sign of internal sources of finance being exhausted, forcing the firm to turn to debt. From an agency cost perspective, it could also be added that this result would be expected inasmuch as more leveraged firms have more monitoring, avoiding the undesirable consequences of management's discretionary power. In general, the literature reports a relationship consistent with the one shown in this study26.

Applying a quadratic term to the debt ratio (model 3) reveals that the relationship between cash holdings and leverage is not linear. The coefficient of the LEV^2 variable is positive and significant, confirming the results of Guney, Ozkan, & Ozkan (2007) and Drobetz & Grüninger (2007). We conclude that the negative effect on cash ratio becomes less pronounced as the firm becomes increasingly leveraged.

The results of Models 1 and 2 show a negative relationship between substitute liquid assets and cash holdings, considering the negative and highly significant coefficient of the NWC variable, which shows that firms with greater net working capital present lower cash ratios27. Presenting, on average, negative values for net working capital, our results show that the greater the imbalance between current assets net of cash and current liabilities, the greater the cash holdings of lodging firms.

The negative and significant coefficient for the SIZE variable in Model 1 at 0.05 levels of significance and in Model 2 at 0.01 levels indicates some evidence of a negative influence of company size on cash holdings. As is found in the literature as a whole, the result supports the idea that large firms find it easier to obtain external finance (Whited, 1992; Fazzari & Petersen, 1993) or tend to be more diversified (Rajan & Zingales, 1995), which is reflected in less treasury risk28 (Titman & Wessels, 1988). Indeed, if large lodging firms have properties that are geographically dispersed, they will be less likely to experience financing difficulties.

Models 1 and 2 show positive but not significant coefficients for the GROWOP variable. This result, contrary to most of the literature that reveal a positive and significant relationship between the variables29 can be explained by the use of a proxy which, instead of controlling for future growth opportunities, captures current opportunities (D’Mello et al., 2008), these not influencing cash holdings.

The BANKR variable, negative and significant at 0.01 level, suggests that firms with more bank debt show lower cash ratios. The evidence is consistent with the idea that forming close relationships with financing institutions reinforces the firm's capacity to access debt (Ozkan & Ozkan, 2004) through reduced information asymmetry (Ferreira & Vilela, 2004). In addition, this may transmit positive information to the market regarding the firm's solvency (Ozkan & Ozkan, 2004). Free Cash-Flow Theory also supports the evidence obtained in that the greater monitoring carried out by financing institutions reduces management's discretionary power and the tendency to accumulate excessive cash holdings30.

The most unexpected result emerges with the STDEBT variable. The models show a strong negative relationship between short-term debt and cash ratio, through a negative and significant coefficient of the STDEBT variable, at a level of 0.01. The evidence reveals that lodging firms with a greater predominance of short-term debt maintain lower levels of cash. On the contrary, Trade-Off Theory forecasts a positive relationship because the predominance of debt of less than one year's maturity forces the firm, periodically, to renew existing lines of credit. The evidence does not corroborate the results of Bruinshoofd & Kool (2004), who obtained a positive relationship between the variables, nor those of García-Teruel & Martínez-Solano (2008), who found a negative relationship between long-term debt and cash holdings. The result obtained is unusual and reveals the nature of business in lodging firms. Unlike firms in other sectors, these will find it easier to renegotiate the maturity of short-term debt, perhaps because they are able to provide good collateral, avoiding investment in cash holdings as a precautionary motive.

The negative and significant coefficient associated with the CAPEX variable, at a level of 0.01, reflects a negative relationship between capital expenditure and level of cash holdings. This relationship, for which empirically there are differing results31, is supported by Pecking Order Theory, considering that firms prefer to use internal funds for financing.

Concerning the TANG variable, which shows a negative and highly significant coefficient, we find, for example, that an additional 0.10 in the TANG variable in Model 1, ceteris paribus, determines a reduction in cash ratio of 2.56 percentage points. The evidence, as in Drobetz & Grüninger (2007), supports Trade-Off Theory, since it favours the notion that firms with a great amount of tangibles could convert them into cash holdings when faced with serious financial difficulties. Furthermore, more tangible assets provide collateral which facilitates access to debt (Titman & Wessels, 1988). Unlike firms in other sectors, lodging firms have more collateral for access to external finance, diminishing the incentive to have cash holdings and increasing the incentive for debt, which explains why these firms will be more leveraged.

As expected, according to the Pecking Order Theory, the result for the CFLOW variable is positive and significant32 at a level of 0.01.

The result relating to the VOLCFLOW variable is also in agreement with what is forecasted, being positive and highly significant33. It is confirmed that greater volatility associated with firms' cash-flow leads to higher levels of cash and cash equivalents also in the accommodation sector.

An important and revealing result of the behaviour of the level of cash holdings in lodging firms was obtained through the negative relationship between the CRISIS variable and cash ratio. Both models clearly show this relationship to be negative, with a negative and highly significant coefficient, indicating that the years of financial crisis determined a reduction in cash levels. Theoretically, the opposite relationship between the variables would be foreseeable, since increased macroeconomic risk (Baum et al., 2006) should be an incentive to accumulate cash holdings. The empirical studies of Lian, Sepehri, & Foley (2011) with Chinese firms and Arslan, Florackis, & Ozkan (2006) with Turkish firms revealed that at times of crisis, namely the global financial crisis in the case of the former study, firms' cash level increases due to precautionary motives. We therefore register that both theoretically and empirically, the negative result obtained does not have great support, although it does not surprise us. During the financial crisis, firms face more credit restrictions (Ivashina & Scharfstein, 2010), including the refinancing of existing debt, which puts considerable pressure on the firm's finances.

Further analysis of the impact of crisis was attempted with Model 4 which incorporates interaction variables between the CRISIS variable and those of LEV, CAPEX, TANG and NWC34.

The results show that the coefficient of the LEV_CRISIS variable is positive and highly significant indicating an equal increase in leverage has a more negative impact outside the crisis period than during the crisis.

The coefficient of the TANG_CRISIS variable is positive and significant at a level of 0.01, which demonstrates that the overall effect of the TANG variable on the dependent variable, remaining clearly negative during the crisis, is now a determinant with less impact on cash ratio, but still exerting a strong influence on it. This change may be explained by the credit restrictions imposed even on firms with more tangibles.

The results show a positive and significant coefficient for the CAPEX_CRISIS variable. We can only hypothesize that firms wishing to keep their investment plans and anticipating difficulties in financing, increase cash levels, according to the precautionary motive. These results have strong implications since their initial negative economic impact is almost completely cancelled out by the positive relationship between capital expenditure and cash holdings during the crisis. This being so, the economic effect of capital expenditure on cash and cash equivalents during the crisis is close to zero.

In the case of the NWC_CRISIS variable, we estimated a positive and significant coefficient at a level of 0.05. Overall, the general effect of the NWC variable on the dependent variable remains negative and significant. However, the net working capital loses slightly the economic impact on the cash holdings. As in the accommodation sector firms keep few substitute liquid assets of cash holdings it is short-term debt that plays an important role in determining the value of the net working capital. Therefore, the explanation for the change in the relationship seems to be the greater impact of current debt rather than current assets (other than cash).

The model estimated with all the interaction variables simultaneously maintains the signs and significance of the variables used in Model 1.


5. Robustness tests

According to Opler et al. (1999) the simultaneous determination of decisions related to capital structure, investment and cash holding policy can make the estimation inconsistent. So we will test the robustness of the model omitting the LEV and CAPEX variables of Model 1, as they are proxies for leverage and investment. The results reported in Model 5 show that the signs and significance of the variables are maintained, except for the SIZE variable which is no longer significant. In Model 1 SIZE was seen to be one of the weakest variables in determining cash ratio, and so we conclude that the problem of joint determination of leverage, investment and cash holdings does not affect our results.

Another problem that can make estimation inconsistent is raised by the univariate analysis. As can be observed, firms in the 4th quartile of cash ratio have different characteristics from those in the 1st quartile and some variables do not have a linear behaviour between quartiles. If the results were being influenced by firms with high cash ratios, a new test of robustness can be carried out estimating the regression of Model 1 after excluding the observations that in each year were in the highest decile of cash ratio (Opler et al., 1999). The results of Model 6 show no significant changes. The SIZE variable becomes significant at a level of 0.01 and the CAPEX variable loses significance slightly, no longer being significant at a level of 0.05. The results, overall, appear to be robust.

Additional robustness tests were carried out by using alternative proxies for both the dependent variable and some independent variables, such as CFLOW and CRISIS. Model 7 uses the CASH2 proxy, Model 8 the EBITDA proxy and Model 9, to control for the temporal effects, uses year dummies rather than the CRISIS dummy. The results obtained for the new proxy used as dependent variable are consistent with our initial findings. Only the significance of the SIZE variable changes and, as in the other additional tests, it is no longer significant. The signs and significance related to the other variables are maintained, which allows us to conclude that using an alternative proxy for the dependent variable does not alter the main conclusions. In the same way, use of the EBITDA variable or the year dummies does little to change the initial conclusions, indicating the model's good level of consistency. Once again, the SIZE variable ceases to be significant and the other variables keep their significance and signs. The year dummies inserted corroborate the effect, already highlighted, of the financial crisis on cash ratio. From 2008, macroeconomic effects are seen to have a negative and significant impact (0.01) on cash level. The years of 2005 and 2006 had a positive and significant (0.01) effect on cash ratio.

In general, the robustness tests support the conclusions drawn from the initial models, despite emphasizing some weakness in the SIZE variable, which sometimes loses significance.

6. Conclusions

This study analyzed the determinants of cash holdings for the accommodation industry in Southern European countries (Spain, Greece, Italy and Portugal) using a sample of 5964 firms during the period 2003-2011.

We documented a significant fall in cash holdings in 2007 and 2008 when the lowest cash ratios were recorded in our sample period. In the following years, cash ratios remained close to these minimum levels, which would anticipate a negative effect of the financial crisis on cash levels in accommodation firms.

The results of a fixed effects panel data model and subsequent robustness tests suggest that larger, more leveraged companies, where most debt is short-term and better relationships are formed with financial institutions, present lower cash to assets ratios. Liquid asset substitutes, capital expenditure and asset tangibility (the most statistically significant variable) also have a negative effect on cash levels.

As expected, cash holdings are positively influenced by cash-flow and cash-flow volatility. These results are mostly in support of the transaction motive for holding cash and are in accordance with Pecking Order Theory.

We show a negative impact of the financial crisis on cash holdings and therefore do not identify a precautionary motive. Obviously, this evidence is somewhat expected as a consequence of the significant impact of the 2008 crisis on industry cash-flows, which decreased from 7.3% of total assets in 2003 to 3.45% in 2009. A distinctive feature of the accommodation industry seems to be the little importance of the precautionary motive as an incentive to accumulate cash. This is also visible in the negative relationship we find between leverage and short-term debt and cash and cash equivalents. The non-significant relationship between growth opportunities and cash holdings points to the same conclusion.

The model estimated with interaction variables shows a diminishing impact of some variables (leverage, tangibility, capital expenditure and net working capital) after 2008.

A motive of concern that our study revealed is the increased fragility of lodging firms in these countries, a joint effect of the economic and financial crisis and the traditional high leverage and low cash levels of the industry. Precautionary reasons seem to advice for higher cash holdings in this industry but as the impact of cash on performance is not consensual this would be a matter for future research.

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