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The determinants of capital structure choice: evidencefrom Greek listed companiesA. Noulas a & G. Genimakis aa Department of Accounting and Finance , University of Macedonia , 54006 Thessaloniki,GreecePublished online: 17 Jan 2011.

To cite this article: A. Noulas & G. Genimakis (2011) The determinants of capital structure choice: evidence from Greeklisted companies, Applied Financial Economics, 21:6, 379-387, DOI: 10.1080/09603107.2010.532108

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Applied Financial Economics , 2011, 21 , 379387

The determinants of capitalstructure choice: evidence

from Greek listed companiesA. Noulas and G. Genimakis*

Department of Accounting and Finance, University of Macedonia,54006 Thessaloniki, Greece

This article investigates the capital structure determination of firms listedon the Athens Stock Exchange, using both cross-sectional and nonpara-

metric statistics. The data set is mainly composed of balance sheet data for259 firms over a 9-year period from 1998 to 2006, excluding firms from thebanking, finance, real estate and insurance sectors. The first part of thestudy assesses the extent to which leverage depends upon a broader set of capital structure determinants, while the latter provides evidence thatcapital structure varies significantly across a series of firm classifications.The results document empirical regularities with respect to alternativemeasures of debt that are consistent with existing theories and, inparticular, reasonably support the pecking order hypothesis. Overall, thisstudy tries to shed more light on corporate financing behaviour in a way toloosen the capital structure puzzle.

I. Introduction

The problem of optimal capital structure is one of thecentral problems of corporate finance and hasattracted considerable attention by economists inrecent years. Since the 1960s, a number of studieshave revealed contradictory advice on gearing, ini-tialized by the seminal papers of Modigliani andMiller (1958, 1963). Other studies focus on alternativecross-sectional implications deriving from firmsobjective to choose value maximizing mixes of debt

and equity on account of bankruptcy costs and taxdeductibility of interest payments. Static trade-off theory states that a target debtequity ratio isapproached at the point where the tax advantage of debt is offset by the costs of financial distress and thecosts of prevailing market imperfections are mini-mized (Kraus and Litzenberger, 1973). Optimalcapital structure is obtained where the firm value is

maximized and each firm sets a target debtequityratio in an industry class with a gradual attempt toachieve it. However, in the presence of adjustmentcosts, it might be cheaper for firms not to fully adjustto their targets even if they recognize that theirexisting leverage ratios are not optimal (Drobetz andWanzenried, 2006). On the other hand, conflicts of interests and information asymmetry, first describedby Jensen and Meckling (1976) and Ross (1977), havebeen identified as key factors and have resulted in thedevelopment of the pecking order theory (Myers and

Majluf, 1984). The last is based on a financinghierarchy of potential sources but does not indicatean optimal capital structure. Retained earnings arealways preferred to external financing and debt ispreferred to equity if firms issue securities. Besidesthese two prevailing financing theories, a number of empirical studies explore comparable characteristicsand conditions to our study. In any case, managers

*Corresponding author. E-mail: [email protected]

Applied Financial Economics ISSN 09603107 print/ISSN 14664305 online 2011 Taylor & Francis 379http://www.informaworld.com

DOI: 10.1080/09603107.2010.532108


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use capital structure to signal their private informa-tion about the firms prospects.

The purpose of this article is to integrate theinsights of capital structure determination andempirical evidence into a single framework andreveal the way companies actually choose betweenequity and debt. In Sections II and III, we presentthree alternative measures of leverage and the incor-porated determinants of capital structure choice. InSection IV, we provide the data set description andwe apply the regression model to capture the effectsof these determinants. In Section V, we present asystematic description of the empirical results andperform some analysis. Conclusions are reported inSection VI.

II. Measures of Capital StructureSince the empirical evidence demonstrates differentimplications with regard to different types of debtinstruments, we introduce separate measures of Leverage ( L). Three different ratios are used asproxies of leverage in this study. These are the long-term debt to equity ( L 1 ), the total bank debt to equity(L 2 ) and, finally, the ratio of total liabilities to equity(L 3 ). Data limitations forced us to measure both debtand equity in terms of book values rather thanmarket values. Even though short-term bank debt isconsidered as an inappropriate financing source, the

easy access of firms to this particular source and itsfavourable terms, render its use a frequent phenom-enon in long-term project financing. Moreover, sincetotal liabilities include items like accounts payable,it may overstate the level of leverage. On the otherhand, short-term bank debt and current liabilities areoften used as a buffer for an alteration in a firmscapital structure and it is a common fact to observeinteraction between these sources. All gearing proxiesare calculated as the natural logarithm of the above-mentioned average ratios in a way to capturenormality of residuals. Due to average calculations

of gearing proxies, no firms were found to beunleveraged over the 9-year examined period.Concluding, our study accounts for both financialand capital structure. The first term stands for themix of total debt and equity funds of thefirm, indicating how assets are financed, whereas cap-ital structure is the combination of long-termdebt and equity funds that indicates which long-term sources of capital the firm uses to finance itsassets.

III. Determinants of Capital Structure

In this section, we present a brief discussion of thedeterminants that, according to a variety of differenttheories and empirical work, may affect the capitalstructure choice. These determinants consist of theage of the firm, size, profitability, profit volatility,

tangibility of assets, depreciation, growth rate,credit rating, economic activity classification, sectorclassification, ownership and stock marketcategorization.

Age of the firm (A)

Different financing decisions have been linked withbusiness life cycle issues. Developing firms tend torely on equity finance, whereas mature firms are ableto increase their leverage in order to finance a newinvestment opportunity. Due to restricted ability toacquire debt in the early stages of operation, apositive correlation is anticipated between the age of the firm and its leverage (Kimki, 1997). We use theaverage age of each firm over the examined period of time as a proxy.

Size of the firm (S/W)

A positive correlation is expected between the size of the firm and its leverage, as relatively large firms tendto be more diversified and less prone to bankruptcy.By combining diversified activities, the firm managesto reduce the riskiness of its debt, which, in turn,improves its investment opportunities. Larger firmshave easier access to low cost debt, whereas smallfirms pay more to issue new equity and rely on bankloans (Titman and Wessels, 1988). Consequently,larger firms incur lower transaction costs associatedwith debt and lower information costs because of better quality of financial information. Moreover,smaller firms solve the risk premium problem byissuing short-term borrowing as it involves less riskfor creditors (Bhaduri, 2002). However, a number of empirical studies on large firms find low levels of

leverage due to asymmetric information existence.Gupta (1969) stated that small firms may be moreleveraged as they pay much more than large firms toissue new equity. In this study, company size ismeasured in two different ways; the natural logarithmof (a) the average Sales ( S ) and (b) the number of Workers ( W ) over the examined period of time. Thelogarithmic transformation accounts for the conjec-ture that small firms are particularly affected by a sizeeffect.

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Profitability (P)

Profitability is considered as a fundamental determi-nant of the capital structure choice since low profit-ability may result in financial distress. There areconflicting theoretical predictions on the effects of profitability on equilibrium leverage. According to

Myers (1984) and the pecking order hypothesis, firmsprefer using internal financing as opposed to usingexternal financing; a negative association between theprofitability of a firm and its leverage is expected. Onthe other hand, Jensen (1986) and the static trade-off theory predict a positive relationship that derives fromthe tax advantage of debt through interest paymentsdeductibility. Firms with high profits require greatertax shelter and have more debt-taking capacity(Nunkoo and Boateng, 2010). Profitability is mea-sured by the average ratio of the operating earningsbefore interest and depreciation over total assets.

Profit volatility (V)A negative correlation is expected between profitvolatility and leverage, since firms with different cashflows balance at different debt ratios. In more details,firms that exhibit constant levels of profitabilitydepend more upon debt financing. The optimal debtlevel is a decreasing function of the volatility of profits for the reason that as long as this volatilityincreases the probability of financial distress rises, aswell (Bradley et al . 1984). Profit volatility is calcu-lated as the SD of the first difference in operatingearnings before interest and depreciation over total

assets employed that period.

Tangibility of assets (T)

Although Harris and Raviv (1991), under the peckingorder hypothesis, stated that firms with few tangibleassets face greater asymmetric information problems,empirical evidence demonstrates an opposite associ-ation. Tangible assets naturally serve as collateral inbank debt, diminish the risk of the lender and allowfirms to have higher leverage. Undoubtedly, theagency costs of secured debt are lower than those of unsecured debt. Therefore, the greater the proportion

of tangible assets in a firms balance sheet, the higherthe leverage should be. Our proxy for tangibility isthe average of fixed assets of each firm scaled by totalassets.

Depreciation (D)

Tax deductions from depreciation and other nondebttax shields are substitutes for the tax benefits of debtfinancing (DeAngelo and Masulis, 1980). As a result,

a negative association is expected with leverage sincefirms with large nondebt tax shields include less debtin their capital structure (substitution effect). It ismeasured by the average ratio of depreciation overtotal assets.

Growth rate (G)

Growth opportunities are capital assets that addvalue to a firm but cannot be collateralized as thetangible assets. Firms with growth opportunities areassociated with greater bankruptcy risk and, there-fore, have low debt ratios (Stulz, 1990). This inverserelationship due to higher costs of financial distress(Rajan and Zingales, 1995) is consistent with thestatic trade-off theory since firms that expect futuregrowth should be equity financed. Thereby, firmswith ample growth opportunities may face difficultiesin raising debt capital on favourable terms. On thecontrary, a positive sign is more consistent with thepecking order hypothesis (Allen, 1993), which impliesthat firms issue equity when their market perfor-mance is high. One might expect that a firm with asubstantial growth rate could afford to have greaterfinancial leverage since it could generate enoughearnings to support the additional interest expenses.The average annual growth rate of total assets is usedas a growth rate indicator.

Credit rating (R)

Last years assessment of both quantitative andqualitative data describes the current credit worthi-ness of a firm, which is reflected on its credit rating.For the purposes of this study, we use a credit riskmodel developed by ICAP SA and specifically weintroduce the average scoring of the examined periodup to 2005 for each firm of the working sample.ICAP SA is involved in credit risk services and creditrating evaluation of Greek companies. ICAP creditrating expresses an estimation of a companys creditquality with respect to the probability of default andbankruptcy within a 1-year time horizon. Thisestimation is based on an analysis of commercial,financial and trading data derived from public

sources and interviews with the rated companies.Credit ratings appear on a 10-grade scale (Table 1).

Classification of economic activities

Firms are classified into the following fundamentalcategories according to their activity (NACE statisti-cal classification of economic activities in theEuropean Community Nomenclature statistique desActivite s e conomiques des la Communaute Europe enne)

Capital structure determination in Greece 381


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and the Athens Stock Exchange standards (banking,financial services, real estate and insurance activitiesare excluded) in order to capture any industry

effects. Economic activity classification is expectedto be an important determinant of debt level since it isviewed as a proxy of business risk (Table 2).

Sector classification

ICAP SA classifies firms into five main sectors, whichare introduced in our study and described in Table 3.Specifically, enterprises are separated into five basicsectors: industry, trade, services, tourism and other.

Harris and Raviv (1991) noted that firms within thesame sector reveal more similar capital structures thanthose in different sectors. In addition, firms of the

same sector adjust their capital structure towards thesector mean leverage or the leverage of benchmarkfirm, showing that firms are not indifferent to whethertheir gearing varies significantly as to the sector mean.

Ownership

The literature suggests that ownership structureaffects the manager shareholder conflict. We classifyfirms into three groups as shown in Table 4: privately

Table 2. Economic activity classification of firms

Economic activity FrequencyPercentagefrequency

Cumulativepercentage frequency

Oil and gas 3 1.2 1.2Chemical 10 3.9 5.0Basic resources 17 6.6 11.6Construction and materials 33 12.7 24.3Telecommunications 3 1.2 25.5Food and beverage 31 12.0 37.5

Industrial goods and services 28 10.8 48.3Healthcare 8 3.1 51.4Retail 15 5.8 57.1Personal and household goods 47 18.1 75.3Media 14 5.4 80.7Travel and leisure 17 6.6 87.3Utilities 5 1.9 89.2Technology 28 10.8 100.0

Total 259 100.0

Table 1. I CAP credit rating 10-grade scale

Low credit risk Medium credit risk High credit risk Without rating

AA A BB B C D E F G H NR/NT

AA The AA rating indicates the lowest credit risk and is assigned to companies of exceptional credit qualityA The A rating indicates specifically low credit risk and is assigned to companies of particularly

good credit qualityBB The BB rating indicates very low credit risk and is assigned to companies of very good credit qualityB The B rating indicates low credit risk and is assigned to companies of good credit quality

C The C rating indicates average credit risk and is assigned to companies of moderate credit qualityD The D rating indicates relatively increased credit risk and is assigned to companies of associated

low credit qualityE The E rating indicates increased credit risk and is assigned to companies of low credit quality

F The F rating indicates significantly increased credit risk and is assigned to companies of considerablylow credit quality

G The G rating indicates very high credit risk and is assigned to companies of very low credit qualityH The H rating indicates the highest credit risk and is assigned to companies of extremely low credit quality

NR Not Rated. The NR class does not constitute a rating grade and includes companies that cannot be ratedNT Not Trading. The NT class does not constitute a rating grade and includes companies that

have ceased to operate

Source : ICAP SA.



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owned, state owned and multinational enterprises.Both state owned and multinational corporations areconsidered of low credit risk and they constitutepreferable choices for bank financing. State ownedfirms are often large firms with substantial ability tocompete internationally. They may have easier accessto credit but private firms are the ones that take onmore debt (Colombo, 2001). Even though multina-tional firms represent the best possible investmentopportunity from banks perspective, they are oftenfinanced through the parent company or foreignbank channels.

Stock market categorization (Table 5)

According to the Athens Stock Exchange, firms areclassified into the following five categories at the timeof our study: big capitalization, medium and smallcapitalization, low dispersion and specific features,under supervision and under suspension. Undeniably,this classification influences the psychology and theattitude of investors towards new equity or bondissues.

IV. Data Description and the ModelSpecification

The Greek sample is made of companies listed on theAthens Stock Exchange after excluding firms from

the banking, finance, real estate and insurance sectorsbecause their leverage is strongly influenced byexplicit investor insurance schemes such as depositinsurance. The sources of the data are the informa-tion databases of ICAP SA, Hellastat SA and AthensStock Exchange and the annual reports of theexamined firms. Using these criteria, we gatheredaccounting and discriminant data for 259 firms over a9-year period from 1998 to 2006. Our sample consistsof a significant proportion of the listed firms on theAthens Stock Exchange during the examined period.A selection bias arises from the fact that only listedcompanies are reported that do not represent theaverage Greek firm.

To measure the relationships between these vari-ables and evaluate the impact of the above-mentioneddeterminants on capital structure, we use the

Ordinary Least Squares (OLS) estimation based onthe following regression model:

L jit 1Ait 2W it 3S it 4P it 5V it 6T it 7D it 8Git 9R it eit 1

where L j is the leverage proxy ( L 1 , L 2 or L3 ) of eachfirm, i denotes each individual Greek listed firm, t theexamined time period from 1998 to 2006, theconstant term, the regression coefficients on capitalstructure determinants and eit the random error term.

All regressions, including the separate gearingmeasures, were estimated by the OLS and the results

are analysed in the following section. Due to severedeviations from normality in the dependent variables,the nonparametric KruskalWallis test and theMonte Carlo simulation were performed to determinewhether economic activity classification, sector clas-sification, ownership and stock market categorizationhave a significant impact on the capital structuredetermination. Monte Carlo simulation is a class of computational algorithms that relies on repeatedrandom sampling (10 000 samples).

Table 5. Stock market categorization of firms

Stock marketcategorization Frequency

Percentagefrequency

Cumulativepercentagefrequency

Big capitalization 66 25.5 25.5Medium and small

capitalization

144 55.6 81.1

Low dispersion andspecific features

23 8.9 90.0

Under supervision 17 6.6 96.5Under suspension 9 3.5 100.0

Total 259 100.0

Table 3. Sector classification of firms

Sector FrequencyPercentagefrequency


Industry 118 45.6 45.6Trade 54 20.8 66.4

Services 80 30.9 97.3Tourism 7 2.7 100.0

Total 259 100.0

Table 4. Categorization of firms based on ownership

Ownership FrequencyPercentagefrequency


Privatelyowned firms

239 92.3 92.3

State owned firms 10 3.9 96.1

Multinationalcorporations 10 3.9 100.0

Total 259 100.0



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V. Results

Regression results

Due to multicollinearity problems among the explan-atory variables, the magnitude of the coefficient of each determinant cannot be compared within the

same standards. Condition indices, computed as thesquare roots of the ratios of the largest eigenvalue toeach successive eigenvalue, are much less than 10.The above regression was estimated using bothEconometric Views (EViews) and StatisticalPackage for the Social Sciences (SPSS) and thesignificance of each variable was examined withthe stepwise regression model. All variables passedthe tolerance criterion to be entered in the equationsand all coefficients are significant at the 5% level of significance. The equation system displayed a com-paratively high degree of explanatory power forcross-sectional regressions, as the coefficients of determination lie between 0.1993 and 0.6012.Contrary to what we expected, the explanatorypower increases if we use the ratio of total liabilitiesto equity as a leverage proxy. The F -statistics(Analysis of Variance, ANOVA) are significant atthe 1% level, demonstrating that all models are asignificant fit of the data overall. The Breusch PaganGodfrey test showed that the null hypothesisfor no heteroscedasticity in all equations could not

be rejected. It is a Langrance Multiplier (LM) test of regression misspecification that involves an alterna-tive hypothesis in which the regression errors vari-ance is proportional to a linear combination of theregressors. The test is performed by completing anauxiliary regression of the squared residuals from theoriginal equations and it determined that the error

terms are not heteroscedastic as all probabilities forthe F -statistics are greater than 5%. Finally, theHausman specification test was implemented in orderto check whether endogeneity is present and the OLSestimates are biased. The suspect variables wereinitially regressed on all exogenous variables andthen, the leverage functions were re-estimated includ-ing the residuals from the first regressions as addi-tional regressors. The null hypothesis of consistentOLS estimates at conventional levels could not berejected and the coefficients on the first stage resid-uals were not significantly different from zero. Hence,the OLS is an appropriate estimation technique.

Table 6 contains the regression results and illus-trates the association of the capital structure deter-minants with each leverage measure for the wholesample and per firm sector over the examined period1998 to 2006. However, a regression model was notbuilt for the tourist sector since it consists of thelimited number of seven firms.

As shown in Table 6, the three measures of leverageare significantly negatively related to the age of the

Table 6. Association of capital structure determinants with the gearing measures, as extracted from the multipleregressions (OLS)

A W S P V T D G R R 2 Sig. F CISig.F BPG

Total sampleL 1 Negative None Positive None None Positive None Positive Positive 0.3509 0.0000 1.9647 0.0852L 2 Negative None Positive Negative None None None None Positive 0.1993 0.0000 1.8618 0.0647L 3 Negative None Positive None None Negative None None Positive 0.3093 0.0000 1.9507 0.2009

Sector classification: industryL 1 Negative None None None None Positive None Positive None 0.2504 0.0000 8.2185 0.3344L 2 Negative None Positive None None None None None Positive 0.2526 0.0000 2.2392 0.3588L 3 Negative None Positive None None Negative None None Positive 0.3074 0.0000 2.2928 0.1702

Sector classification: tradeL 1 None None Positive Negative None None Positive None None 0.2796 0.0035 1.5335 0.4400L 2 None None N one Negative N one None Positive N one None 0.3069 0 .0004 5 .5624 0 .2712L 3 Negative None Positive None None None None Positive Positive 0.6012 0.0000 1.5705 0.4531

Sector classification: servicesL 1 None None Positive N one Positive Positive None None None 0.4629 0 .0000 2 .1207 0 .7627L 2 Positive None Positive None None None None Positive Positive 0.3958 0.0000 1.9806 0.8756L 3 None None Positive None Positive None None Positive Positive 0.4241 0.0000 1.7449 0.5730

Note : L1 long-term debt to equity; L2 total bank debt to equity; L3 total liabilities to equity; A age of the firm;S sales; W number of workers; P profitability; V profit volatility; T tangibility of assets; D depreciation;G growth rate; R credit rating; R2 coefficient of determination; Sig. F significance of the F -statistic (ANOVA);CI condition index; Sig. F BPG significance of the F -statistic for BreuschPaganGodfrey test.



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firm and significantly positively related to sales andcredit rating for the entire sample. These two positivecorrelations provide evidence that smaller firms paymore for debt financing than larger firms and lowcredit quality increases both probability of defaultand creditors unwillingness to finance a firmsproject. The unexpected negative association of thefirms age with leverage is consistent with Myers(1984) pecking order theory and the existence of asymmetrically distributed information to themarket, especially in the case of Greece because of the lack of financial disclosure. Most importantly, thenegative relationship between the total bank debt toequity and profitability implies once again the dom-inance of the pecking order hypothesis as profitablefirms seem to use less debt. The positive sign ongrowth rate implies that firms issue equity when theirmarket performance is high, whereas the negativecorrelation among tangibility and debtequity ratio is

justified by potential close relationships with credi-tors (Berger and Udell, 1994). This finding is in linewith the study of Ferri and Jones (1979) who arguedthat the use of fixed assets can magnify the variabilityin the firms future income. Number of workers,volatility of profits and depreciation are not signif-icant determinants of capital structure choice for thewhole sample.

A positive correlation between growth rate anddebtequity ratio occurs for the trade sector, whichverifies once more the pecking order hypothesis(Baskin, 1989; Allen, 1993). Moreover, the antici-pated positive sign on the age of the firm is noticedonly in the services sector and for the total bank debt

to equity. The analysis provides conflicting evidenceon the relationship between leverage and tangibility,depending on the measure of gearing applied. Anegative association with leverage takes place forthe industrial firms only when debtequity ratio is thedependant variable and this is consistent with thepecking order theory. Otherwise, a positive correla-

tion exists between tangibility and leverage, indicat-ing that assets serve as collateral in debt. Theestimated coefficients on profit volatility and depre-ciation have abnormal positive signs for the trade andservices sector. A possible explanation for this directrelation with depreciation is that nondebt tax shieldsare an instrumental variable for the assets secur-ability, with more securable assets leading to higherdebt ratios (Trezevant, 1992). Additionally, the pos-itive relationship between profit volatility and gearingis not inconsistent with theory, which suggests thatthe relative strengths of agency and bankruptcy costsdetermine the sign of this association (Bennett andDonnelly, 1993). Finally, the number of workers as aproxy of firm size does not affect the capital structuredetermination in all three examined sectors.

Other determinants of capital structure choice

Table 7 indicates that firms within an economicactivity classification, facing similar conditions andrisk characteristics, have similar leverage ratios. Thenonparametric KruskalWallis test is performed,which is based on a one-way ANOVA using onlyranks of the data. Both KruskalWallis test andMonte Carlo simulation give p-values less than 5%for all gearing measures, revealing that economic

Table 7. Mean leverage and p -values according to KruskalWallis test based on the economic activity classificationof firms

Economic activity classification Leverage L 1 Leverage L 2 Leverage L 3

Means Total 0.3162 0.8037 1.7353Oil and gas 0.3437 1.4300 2.3673Chemical 0.2841 0.9099 1.4729Basic resources 0.4264 0.8312 1.2281Construction and materials 0.2039 0.4762 0.9432Telecommunications 0.5563 0.7037 1.2873

Food and beverage 0.2930 0.7481 1.2122Industrial goods and services 0.1845 0.4401 0.8913Health care 0.2143 0.6510 1.1125Retail 0.2032 0.9784 5.4353Personal and household goods 0.3006 0.9281 1.8541Media 0.1826 0.7221 1.6898Travel and leisure 1.1115 1.5692 3.0048Utilities 0.8696 1.0016 1.4940Technology 0.1218 0.7853 1.7655

p-values 0.0003* 0.0361* 0.0101*

Note : * Denotes significance at the 5% level.



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activity classification determines capital structure.The p-values of the tests represent the probability of incorrectly inferring difference in capital structureaccording to the firm classification.

On the other hand, as illustrated in Table 8,sector classification has an impact on the capitalstructure determination only for the deptequity

ratio. For the other two gearing measures, wecannot reject the null hypothesis that capitalstructure is not affected by sector classification.Furthermore, the tables in this section present theaverage leverage by each firm category for all threegearing measures.

Finally, it can be argued that firms ownership(Al-Fayoumi and Abuzayed, 2009) and stock marketcategorization do not determine capital structure.The KruskalWallis statistic is applied to the workingsample and rejects the hypothesis that leverage isdifferent across these two firm classifications.Tables 9 and 10 demonstrate that the attempt to

link firms ownership and stock market categoriza-tion to capital structure determination is withoutmerit, which is also verified by the high p-values of the Monte Carlo simulation.

VI. Conclusions

In this article, we analyse the determinants of capitalstructure decision of Greek listed companies with aview to fill the existing gap between influentialtheories of corporate leverage and empirical evidence.To summarize this articles results, the first partextends empirical work on corporate financingbehaviour and focuses on the relationship of ninequantitative factors with three different proxies of leverage, measured in terms of book values ratherthan market values. The regression results varysubstantially with each leverage measure applied

Table 10. Mean leverage and p -values according to KruskalWallis test based on the stock market categorization of

firms

Stock market categorization Leverage L 1 Leverage L 2 Leverage L 3

Means Total 0.4341 1.2941 2.2073Big capitalization 0.5164 0.7444 1.2489Medium and small capitalization 0.3642 0.8720 1.6050Low dispersion and specific features 0.5435 0.9443 1.5830Under supervision 0.5982 7.7959 11.8906Under suspension 0.3600 0.6911 2.1756

p-values 0.8532 0.4731 0.1312

Table 8. Mean leverage and p -values according to KruskalWallis test based on the sector classification of firms

Means

Leverage Total Industry Trade Services Tourism p-values

Leverage L1 0.3162 0.2776 0.1942 0.4368 0.5313 0.1577Leverage L2 0.8037 0.7382 0.9920 0.7833 0.6861 0.0574Leverage L3 1.7353 1.3564 2.8671 1.5990 0.9493 0.0002*

Note : * Denotes significance at the 5% level.

Table 9. Mean leverage and p -values according to KruskalWallis test based on firms ownership

Means

Leverage TotalPrivatelyowned firms

Stateowned firms

Multinationalcorporations p-values

Leverage L1 0.3162 0.3033 0.6585 0.2827 0.5378Leverage L2 0.8037 0.8169 0.8108 0.4793 0.5295Leverage L3 1.7353 1.7701 1.5293 1.1102 0.5533



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2011 a.noulas&g.genimakis

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