Capital structure inconclusiveness: evidence from Malaysia, Thailand and Singapore

Capital structure inconclusiveness:evidence from Malaysia,Thailand and Singapore

Razali HaronDepartment of Finance, International Islamic University Malaysia,

Kuala Lumpur, Malaysia

Abstract

Purpose – The purpose of this paper of this study is to examine the possible factors contributing tothe issue of inconclusiveness in capital structure studies. This study also attempts to provide logicalexplanations to the unresolved issue of inconsistencies in the relationship between factors identifiedand leverage in capital structure studies. Comparisons are also made between the emerging marketand the developed market to see whether the findings are consistent with both market landscapes.Design/methodology/approach – This study employs two models in its methodology which arestatic and dynamic models to examine the effects of using different models in the study. The fixedeffect model and partial adjustment model represent the static and dynamic models, respectively.The dynamic model is estimated using generalized method of moments. This study also uses sixdefinitions of leverage to examine the impact of using different leverage definition in capital structurestudies. To test the robustness of the findings comparison were made with past studies done by otherresearchers on developed markets.Findings – This study found that the use of different models (with the same leverage definition)and different leverage definitions (using the same model) give different results including signs.Inconsistencies were more obvious in the different leverage definitions (using the same model)compared to the use of different models (with the same leverage definition). There was also evidencethat the findings were consistent with both the emerging and the developed markets as other studieson developed markets also report inconsistent results when using different models and differentleverage definitions.Research limitations/implications – The sample chosen focussed only on firms in three emergingmarkets (Malaysia, Thailand and Singapore) thus it may not be sufficient for generalization.Originality/value – The issue of inconclusive results and findings in capital structure studies keepsrecurring but no study has been done to further understand the issue. Using data from the selectedcountries, this paper attempts to fill this gap in the literature.

Keywords Capital structure, Southeast Asia, Emerging market, Leverage definitions,Partial adjustment model, Static model

Paper type Research paper

1. IntroductionThe relationship between capital structure and firm value has been widely studied andanalyzed theoretically and empirically by researchers past and present, since a firm’sfinancing behavior can affect the firm value. In tackling the issue of capital structure,two main questions have to be addressed: how do firms choose their capital structure tofinance their operations and how does the choice of capital structure financing affectthe value of the firm?

Despite the extensive research done on the area of capital structure since Modiglianiand Miller (1958) and ever since Myers (1977) published their papers on the determinantsof corporate borrowing, understanding in the area is still inconclusive (Al-Najjar andHussainey, 2011). Empirical work in this area, according to Titman and Wessels (1988),has lagged behind the theoretical research, perhaps because the relevant firm attributes

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/1743-9132.htm

Received 1 March 2012Revised 31 March 2013

30 May 201317 June 2013

3 July 201331 August 2013

Accepted 3 September 2013

International Journal of ManagerialFinance

Vol. 10 No. 1, 2014pp. 23-38

r Emerald Group Publishing Limited1743-9132

DOI 10.1108/IJMF-03-2012-0025

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Capital structureinconclusiveness

are expressed in term of fairly abstract concepts that are not directly observable.Deesomsak et al. (2004) find that empirical evidence on the effect of determinants onleverage is mixed and inconsistent. Beattie et al. (2006) finds that understanding in thearea remains incomplete. They assert that no theory is able, independently, to explainthe complexity encountered in capital structure practice.

The issue of incompleteness and inconclusiveness of the understanding is alsoreported by Al-Najjar and Taylor (2008). They notice that a theoretical explanation is stilllacking and empirical results are not yet sufficiently consistent to resolve the capitalstructure issues on how firms choose between the different methods of financing.Capital structure decision making is even more complicated when it is examined in aninternational context, particularly in developing countries where markets arecharacterized by controls and institutional constraints. Corporate financing decisionsare quite a complex process and existing theories can at best explain only certain facets ofthe diversity and complexity of financing choices. Myers (2001) stresses that there is nouniversal theory of debt-equity choice and no reason to expect one. The reason may bebecause these theories differ in their emphasis.

This issue of inconsistent empirical findings keeps recurring in the literature,with no study is specifically done to tackle the problem. In response, this paper looksinto the issue of inconclusiveness in the context of the use of different definitions ofleverage as well as the use of different models in analyzing the impacts of determinantson leverage. Three Southeast Asian countries are selected for this study: Malaysia,Thailand and Singapore. This study uses data from 2000 until 2009 and employs thestatic model and the dynamic partial adjustment model estimated based on the fixedeffect model and the generalized method of moments (GMM), respectively.

Since the samples from the selected countries are of different stages of capitalmarket development and obviously belong to different institutional environments,the regression outputs may suffer from biasness if the samples are pooled togetherin the analysis approach. Therefore, the samples for this study are analyzed separatelyinstead of pooling them together. This exercise is also practiced by Driffield andPal (2010) in their cross-countries study. Furthermore, the main objective of this studyis to investigate whether the use of different models and the employment of differentleverage definitions are responsible for the inconclusiveness and inconsistenciesof capital structure studies. This study is not intended to examine the nature ofrelationship between the variables and leverage. Hence, the separate analysis approachis sufficient to provide evidence on this issue. In addition, the chosen samples aredifferent in their institutional environments thus offer interesting variations.

1.1 Why use emerging markets?The body of knowledge covering emerging market is very limited. But recently studieson emerging markets are gaining in popularity, because the capital and stock marketsin emerging market, according to Eldomiaty (2007), are relatively less efficient andincomplete than the developed markets causing financing decision to be incompleteand subject to irregularities. This feature makes emerging markets interesting to studyand has driven this study to explore more. In addition, Eldomiaty adds that inemerging markets, information asymmetry is noticeably higher than that of developedmarkets. This problem could lead to a non-optimal financing decision. Hence, there isa need to develop literature on capital structure in emerging markets which havedifferent institutional financing arrangements from those in developed markets.Lack of corporate financial data in the Asia Pacific region limits extensive research to

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be conducted and Deesomsak et al. (2004) express their surprise that so little researchhas been so far conducted despite the economic importance of the Asia Pacific regionand the diversity of the countries in this region. Fan et al. (2011) extend this argumentof developing literature in emerging market when pointing out that, emergingmarkets can offer both challenges and opportunities for researchers. As a result,recently studies covering emerging market have taken up the pace see, for examples,De Jong et al. (2008) and Singh and Kumar (2012).

This paper is organized with the following section briefly laying out the dominantcapital structure theories, followed by Section 3 discusses the issue of inconclusivenessin capital structure studies and includes hypotheses development. Data and methodologyis detailed in Section 4. The findings are discussed in Section 5 and Section 6 concludes.

2. Overview of capital structure theoriesThe most prominent theories of capital structure being studied in the literatureexplaining firms’ financing behavior are the trade-off, pecking order, agency andmarket timing theories. Despites the emergence of different feasible capital structuretheories, there is still no conclusive guidance for the corporate managers in decidingbetween debt and equity in financing their operations (Myers, 1984).

2.1 The trade-off theoryThe trade-off theory of capital structure states that optimal capital structure can beachieved if the net tax advantage of debt financing balances with leverage-relatedcosts. The trade-off of the costs and benefits of borrowing determines the optimal debtratio. Examples of leverages-related costs taken into account in some empiricalcorporate financing investigations can be found in Scott (1977) where he incorporatesbankruptcy costs; agency costs by Jensen and Meckling (1976) and in De Angelo andMasulis (1980) on the loss of non-debt tax shield (NDTS).

2.2 The pecking order theoryThe pecking order theory suggests that investments are first financed by internalfunds, then external debt, and, as a last resort, external equity (Myers and Majluf,1984). The pecking order theory is an alternative to the trade-off theory which hasemerged based on asymmetric information problems. These asymmetric informationproblems occur when one party, for example the manager of a firm, has better qualityinformation than the other parties, such as outside investors and creditors. In suchcases, the financing method can serve as a signal to outside investors. Facinginformation asymmetry between inside and outside investors, firms end up havinga financial hierarchy. Equity is issued only when firms have no more debt capacity(Myers, 1984; Myers and Majluf, 1984).

2.3 The agency theoryThe agency theory is based on another problem due to information asymmetry, that is,the agency problems. Minimizing the costs arising from conflicts between the partiesinvolved can result in an optimal capital structure. Jensen and Meckling (1976) arguethat agency costs play an important role in financing decisions due to the conflict thatmay exist between shareholders and debt holders. The conflict arises when there ismoral hazard inside the firm, which is called the agency costs of equity. It is suggestedthat the use of debt financing can also help in mitigating the agency cost of equity asdebts can discipline managers ( Jensen, 1986). The optimal capital structure can be

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achieved by trading off the agency costs, which include the monitoring expenditureby the principal, the bonding expenditure by the agent and the residual loss, againstthe benefits of debt.

2.4 The market timing theoryBaker and Wurgler (2002) propose the market timing theory of capital structure,arguing that current capital structure is the cumulative outcome of past attempts totime the market. In this theory, there is no optimal capital structure and marketvaluation has a persistent impact on capital structure.

3. Issues of inconclusiveness in capital structure studiesThe issues of inconclusiveness have long been recognized in studies of capitalstructure. Ever since Myers (1984) refers to capital structure as a puzzle, the puzzle stillremains unanswered today (Al-Najjar and Hussainey, 2011). Various issues have beenput forward to explain this phenomenon. Among the issues discussed are the variousdefinitions of leverage used in capital structure studies and the different modelsemployed in the studies.

3.1 Definitions of leverage and hypothesis developmentMany empirical definitions of leverage have been used and opinions on which isa better measure of leverage differ. Referring to past studies, different definitions ofleverage produced different results. This observation is supported by Bevan andDanbolt (2002). They find that results are highly dependent upon the precise definitionof leverage being examined. Rajan and Zingales (1995) add that the definition ofleverage should depend on the objective of the analysis being carried out.

Being the focus of capital structure, it is crucial to have a clear-cut definitionof leverage. Despites hundreds of capital structure studies none has clearly defined whatis meant by leverage in accounting terms. An appropriate leverage measure in a countrymay not be appropriate in another due to institutional and accounting differences betweencountries, some leverage measures may be more appropriate than others for evaluatingparticular capital structure theories. For instance, Rajan and Zingales (1995) argue thatthe debt relative to firm value would be the relevant measure of leverage for studieson agency theory relating to conflicts based on how a firm has been previously financed.Studies related to agency problem would use the debt-to-firm value ratio as the definitionof leverage. Studies on leverage and financial distress would prefer the interest-coverageratio as the definition. Other definitions of leverage include total liabilities-to-totalassets, debt-to-total assets, debt-to-net assets and debt-to-capitalization. Debt could alsobe divided into its various components, and the numerator and denominator could bemeasured in book-value and market-value terms. Debt-to-assets (or debt-to-capital) isfrequently used as a measure of leverage in empirical studies. Titman and Wessels (1988)also use different measures of leverage.

Another question regarding the definition of leverage is whether to use book value ofleverage or market value of leverage. Both book value and market value of leverage havetheir own advocates. Being unaffected by volatility of market prices, book-value leverageoffers a better reflection of management target debt ratio. Market-value leverage, on theother hand, is unable to reflect the underlying alterations initiated by a firm’s decisionmaker because it is dependent on several factors which are not in direct control of thefirm. Book-value leverage is referred to as a “plug number” (Frank and Goyal, 2009)by those who are in favor of market-value leverage because it is used to balance the left

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hand and right hand sides of the balance sheet rather than a managerially relevantnumber (Welch, 2004). Welch also argues that book-value leverage can take negativevalues. It is backward looking and it measures what has already taken place. Market-valueleverage, on the other hand, is forward looking. Realizing the differing nature of these twoconcepts, Frank and Goyal (2009) feel that there is no reason for these two concepts tomatch thus these differences make it more infeasible to solve the puzzle.

The use of different leverage definitions has been found to have an impact on resultseven though the same models are employed across the studies. For example, Bevan andDanbolt (2002) and Al-Najjar and Hussainey (2011) have reported on different resultsderived from the use of different leverage definitions. Arguments put forth above showthe importance of the impact of leverage definitions in determining and examiningboth the level of leverage (Rajan and Zingales, 1995; Bevan and Danbolt, 2002) and thedeterminants of leverage (Bevan and Danbolt, 2002) as different leverage definitionsmay yield different results thereby leading to inconclusive findings in the capitalstructure studies. Therefore, this study hypothesizes that:

H1. The use of different leverage definitions will lead to different results.

3.2 Different models employed and hypothesis developmentEarlier studies on the determinants of capital structure decision have tended toconcentrate on the static model. Only recently have researchers started to look into thechanging aspect of capital structure using a dynamic model. In contrast to the staticmodel, Driffield and Pal (2010) state that there are relatively fewer studies on capitalstructure employing dynamic models. The contrasting nature of these two models is thatthe static model assumes the observed leverage ratio as being optimal. The dynamicmodel, on the other hand, does not assume firms are in equilibrium; rather it relies ona more realistic assumption of partial or incomplete adjustment. Myers and Majluf (1984)suggest that the observed leverage ratio may differ from the optimal level predicted by thestatic trade-off model between the marginal costs and benefits of debt.

There are cases where different models working with the same leverage definitionrecord inconsistencies in the coefficient signs. For example, Serrasqueiro and Nunes (2008)estimated different signs derived from static and dynamic model employed in their studieson the capital structure. They compared the uses of different estimators on determinantsof capital structure using Portuguese companies and recorded different signs ofparameter estimates on NDTS, tangibility and growth between the static and dynamicmodels. Kim et al. (2006) in their Korean studies on capital structure, report that results forgrowth and NDTS on leverage show differing signs and magnitudes between the staticand dynamic models. A more dramatic conclusion made by Reinhard and Li (2010) whenthey study non-financial Indonesian firms, suggest that capital structure models, whetherstatic or dynamic, fail to differentiate between trade-off and pecking order theory, thus thedebate on which one better explains the financing behavior of firms is far from over.

These reported findings highlight the notion that different models can lead toinconsistent results on the impact of factors affecting leverage. Hence, this contributesto the unresolved issue of the inconclusiveness in capital structure studies.Unfortunately, there is no unified model of leverage currently available that candirectly account for the factors affecting capital structure decisions (Frank and Goyal,2009). Therefore, this study hypothesizes that:

H2. The use of different models will lead to different results.

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4. Data and methodology4.1 Data and period of studyThis study employs panel data. Following standard practices, firms from the financialsector such as banks, insurance and finance companies are excluded because of thedifferent accounting categories and rules governing by these firms. The final sample offirms consists of 790 Malaysian firms, 269 Thailand firms and 546 Singaporean firms.This study uses a ten-year period data from 2000 until 2009 where firm-level data aresourced from data stream and country data are from the World Bank database.For observation purposes, only firms with a minimum of three consecutive observationstoward the end of the period are included in the data set (Deesomsak et al., 2004).This means that the firms should at least be listed on the stock exchange from theyear 2007. After removing the outliers, the numbers of observation are 6,531, 2,368 and4,170 for Malaysia, Thailand and Singapore, respectively. Table I presents in detail thestructure of the panel data of sample firms for this study.

4.2 Measures of leverageDespite having a vast literature on various studies of capital structure, there is no clear-cutdefinition of leverage. Being the focus of capital structure studies, it is crucial to havea clear-cut definition of the term. There are two questions facing a researcher in definingleverage: which particular leverage ratio to choose and whether to use book value ofleverage or market value of leverage.

Six measures of leverage are used to meet the objectives of this study. FollowingTitman and Wessels (1988), leverage is defined as the ratio of: total debt to total asset,long-term debt to total asset, short-term debt to total asset (termed as book-valueleverage), total debt to total debt plus total equity, long-term debt to total debt plustotal equity and short-term debt to total debt plus total equity (termed as market-valueleverage). Both market-value and book-value leverage are used to observe anyinconsistent results as argued by past researchers. However, since the market valueof debt is not available, quasi-market leverage will be used, where the book value ofequity will be replaced by the market value of equity but debt, in this case, will be

No. of records on each firm No. of observationsNo. of annualobservationsfor each firm Malaysia Thailand Singapore Malaysia Thailand Singapore

3 34 3 34 102 9 1024 14 2 35 56 8 1405 30 1 16 150 5 806 48 6 52 288 36 3127 63 25 61 441 175 4278 40 22 51 320 176 4089 92 16 50 828 144 450

10 469 194 247 4,690 1,940 2,470Total 790 269 546 6,875 2,493 4,389

Notes: The structure of the panel data shows the number of firms for Malaysia, Thailand andSingapore, respectively. Data covers a period of 2000-2009. For the number of annual observations foreach country the firms should at least be listed on the stock exchange from the year 2007 as only firmswith a minimum of three consecutive observations toward the end of 2009 are included in the data set

Table I.The structureof panel data

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valued at its book value. The six measures are used to check the robustness of theresults obtained in this study. Although the strict notion of capital structure refersexclusively to long-term debt, short-term debt is used in the definition of leveragebecause of the significant proportion of short-term debt in total debt of firms in thesample of firms in this study.

4.3 Explanatory variablesIn total, 13 explanatory variables are incorporated in this study and are dividedaccording to firm and country-specific nature to determine the relationship withleverage. Country-specific variables are incorporated in this study because firmleverage is also influenced by country specific, not merely firm-specific factors(De Jong et al., 2008). Furthermore, a misleading result would be reported if criticalcountry-specific differences are ignored (Fan et al., 2011). The selection of variables andmeasurement used are according to past literature. Table II summarizes theexplanatory variables and measurement used in the study.

4.4 MethodologyThis study employs two models, the static model and dynamic model, to determine therelationship between leverage and explanatory variables and to observe anydiscrepancies and inconsistent readings derived from the use of the two models.The fixed effect model and partial adjustment model are employed to represent thestatic and dynamic model, respectively.

4.4.1 Fixed effect model. The model allows for heterogeneity among firms byallowing each entity to have its own intercept value. The differences across firms in therespective countries may be due to the special features of each firm such as managerialstyle, managerial philosophy or the type of market each firm is serving (Gujarati and

Explanatory variable Measurement

Firm specificNon-debt tax shield Annual depreciation expenses over total assetsTangibility Net fixed asset over total assetProfitability EBIT over total assetsBusiness risk Yearly change on firm EBITFirm size Natural logarithm of total assetGrowth opportunities Market value of equity to book value of equityLiquidity Current assets over current liabilitiesShare price performance First difference of the year end share priceCountry specificStock market development Stock market capitalization over GDPBond market development Total bond market capitalization over GDPEconomic growth Annual percentage changes in GDPInterest rates Lending rateCountry governance Aggregate governance indicators, comprising of six

indicators (voice and accountability, political stability andabsence of violence, government effectiveness, regulatoryquality, rule of law and control of corruption)

Notes: The table illustrates the explanatory variables used in this study which are divided intofirm-specific and country-specific variables. The measurements used for each variable are based onwhat have been widely used in the literature

Table II.Explanatory variables

and measurement

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Porter, 2009). This study hypothesizes that leverage is a linear function of a set of kexplanatory variables and the relationship can be expressed as follows:

Yit ¼ ai þ bkXkit þ eit ð4:1Þ

Since the model allows for heterogeneity among firms by allowing each entity to haveits own intercept value, the dummy variables are included as additional regressors toallow for the fixed effect intercept to vary between firms. After adding the dummyvariables to Equation (4.1), this study obtains:

Yit ¼XN�1

i¼1

aidi þ bkXkit þ eit ð4:2Þ

4.4.2 Dynamic model. Using the framework of partial adjustment model, in which issimilar to De Miguel and Pindado (2001), this study assumes that the optimal (target)leverage ratio for a firm is a function of sets of explanatory variables as in thefollowing equation:

Y �it ¼ F Xit;Xi;Xtð Þ ð4:3Þ

where Yit* is the optimal leverage ratio of firm i, at time t, Xit is a vector of firm and timevariant determinants of the optimal leverage, Xi and Xt are unobservable firm-specific andtime-specific effect which is common to all firms and can change through time.

In a perfectly frictionless world with no adjustment cost, the firm would immediatelyrespond with complete adjustment to variations in the independent variables by varyingits existing leverage ratio to equalize its optimal leverage. Thus, at any point in time, theobserved leverage of firm i at time t (Yit) should not be different from the optimal leverage,that is, Yit¼Yit*. This implies that the change in actual leverage from the previous to thecurrent period should be exactly equal to the change required for the firm to be at optimalat time t, that is, Yit�Yit�1¼Yit

*�Yit�1. In practice, however, the presence of significantadjustment costs means that firms will not completely adjust its actual leverage to Y*.Thus, with partial adjustment, the firm’s observed leverage ratio at any point in timewould not equal its optimal leverage ratio. This can be represented by a partialadjustment model as in the following equation:

Yit � Yit�1 ¼ dit Y �it � Yit�1

� �ð4:4Þ

where dit is known as the speed of adjustment, it is representing the magnitude ofdesired adjustment between two subsequent periods or the rate of convergence of Yit,to its optimal value. The effects of adjustment costs are represented by the restrictionthat |dit|o1, which is a condition that Yit-Yit* as t-N. Leverage values that arenot at their optimal level will be referred to as sub-optimal. In other words, Equation(4.4) states that the extent to which the desired adjustment depends on its adjustmentparameter value. First, if dit¼ 1, then the entire adjustment is made within one periodand the firm at time t is at its target leverage. Since dit can vary across firms as well asover time for the same firm, only if dit¼ 1 for all t shall a firm consistently be at itstarget leverage. Second, if dito1, then the adjustment from year t�1 to t falls short ofthe adjustment required to attain the target. Third, if dit41, this means that the firm

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over-adjusts in the sense that it makes more adjustment than necessary and is still notat the optimal. Since dit represents the speed of adjustment, a higher dit denotes ahigher speed of adjustment. Further, the model assumes that the firm’s long-termtarget is a linear function of all the explanatory variables that this study has identifiedearlier. The firm’s behavior can be represented by the following equation:

Y �it ¼XN

n¼1

bkXkit þ eit ð4:5Þ

Combining Equations (4.4) and (4.5):

Yit ¼ Yit�1 þ ditðY �it � Yit�1Þ ð4:6Þ

Yit ¼ Yit�1 þ ditY�it � ditYit�1 ð4:7Þ

Yit ¼ ð1� ditÞYit�1 þ dit

XN

n¼1

bkXkit þ eit

!ð4:8Þ

Y �it ¼ ð1� ditÞYit�1 þXN

n¼1

ditbkXkit þ diteit ð4:9Þ

To simplify, Equation (4.9) can also be written as:

Y �it ¼ l0Yit�1 þXN

n¼1

lkXkit þ mit ð5:0Þ

where l0¼ 1�dit, lk¼ ditbk and diteit¼ mit (where mit has the same properties as eit).Equation (5.0) above is the dynamic capital structure model of which this study is

intended to estimate using the GMM estimation technique, suggested by Arellano andBond (1991).

5. Findings5.1 Different models (same leverage definitions)The findings reveal that using different models do lead to different results despite theuse of the same leverage definitions[1]. For example, referring to Table III, looking atvariable tangibility for Malaysia, leverage according to definitions Lev2 (long-termdebt at book value), Lev4 (total debt at market value) and Lev5 (long-term debt atmarket value), the use of static model yields negative relationship in contrast to thepositive relationship using the dynamic model. The implication from this is thatdifferent signs would lead to different theoretical argument to support the finding.A positive relationship under the dynamic model supports the trade-off theory.Since tangible assets of a firm represent real guarantees to its creditors, the importanceof those assets among total assets influences the level of debt issued by lenders to

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Book value Market valueFE GMM FE GMM FE GMM FE GMM FE GMM FE GMM

Independent variable Lev1 Lev2 Lev3 Lev4 Lev5 Lev6

MalaysiaNDTS � � � � �Tangibility � � þ � � þ � þ �Profitability � � � � � � �Risk þ � þ þ �Size � þ þ � þ þ þ þ þ þGrowth þ þ � � � � �Liquidity � þ � � � � þ � �SPP � þ � � � � � � �Stock � � þ þBond þ þ � � �Eco growth þ þ � � �Interest þ � � �Governance þ þ � � �ThailandNDTS � � þ �Tangibility � � � � � � �Profitability � � � � �Risk þ þ �Size þ þ þ þ þ þ þGrowthLiquidity � � �SPP þ � þ � � � � �Stock � � � � � � � � � �Bond þ � � � � þEco growth þ þ þ þ � �Interest þ � � � � �Governance þ þ þ � � �SingaporeNDTSTangibility � � � þ � þProfitability þ � � þ �Risk þ þ þSize � þ þ þ þ þ þGrowth �Liquidity � � � þ þ � �SPP � � � � � �Stock � � þ � � � � � �Bond � � þ �Eco growth þ � � � � �Interest � � � � �Governance � � þ þ þ þ þ

Notes: The table shows the coefficient signs of each variable that significantly influenced leverage byusing the two different models with the same leverage definition. The data covers 790, 269 and 546firms with 6,531, 2,368 and 4,170 observations for Malaysia, Thailand and Singapore, respectively, forthe study period of 2000-2009. Model FE refers to fixed effect model (static model) while GMM(dynamic model). Leverage definitions: book value leverage (Lev1¼ total debt/total asset; Lev2¼ long-termdebt/total asset; Lev3¼ short-term debt/total asset); market value leverage (Lev4¼ total debt/(total debtþ total equity); Lev5¼ long-term debt/(total debtþ total equity); Lev6¼ short-termdebt/(total debtþ total equity))

Table III.Different models (sameleverage definition)

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firms. Therefore, the greater the proportion of tangible assets on the balance sheet,lenders should be more willing to supply loans and as a result leverage should behigher. While a negative relationship under the static model supports the agencytheory. According to Titman and Wessels (1988), higher debt level will increasebankruptcy risk thus diminishes the managers’ tendency to squander. This is becausebeing highly levered, debtholder will monitor them very closely. To monitor theinvestment activities of firms with less collateralizable assets is more difficult.This means that the costs associated with this agency relation may be higher relativeto firms with high collateralizable assets. This is why, as argued by Titman andWessels (1988), firms with less collateralizable assets may choose higher debt levels tolimit their managers’ consumption of perquisites. These valid arguments, lookingthrough contrasting theoretical lenses, further enhance what this paper intends toprove which is, still there is no concrete consensus regarding the influence of factors onleverage, especially when different models are put at work.

Referring to Singapore, for variable profitability, leverage by definition Lev3(short-term debt at book value) and Lev5 (long-term debt at market value), the use ofthe static model leads to positive relationship in contrast to the negative relationshipusing the dynamic model. While for variable tangibility, inconsistencies are reportedfor Lev4 (total debt at market value) and Lev5 (long-term debt at market value)in which the static model reports negative relationship in contrast to a positiverelationship by the dynamic model. The same is detected for Thailand Lev2 (long-termdebt at book value), for variable share price performance. These findings thereforesupport H1 that the use of different models will lead to different results. The differentmethodology of the two models in examining the impact of factors on leverage lead todifferent coefficient signs yielded thus making the results not conclusive.

When the applicability of this analysis is compared to the developed market, thisstudy finds strong evidence of similarities with Banerjee et al. (2004) who detecta significant positive influence of growth on speed of adjustment in their studies on UKfirms using the static model but a significant negative according to the dynamic model.Thus, the findings from this study confirm the notion that using the different models incapital structure studies can lead to inconclusive results regardless of the markets.

5.2 Different leverage definitions (same model)The results from the analysis show that using different leverage definitions leadto different results despite employing the same model. As an example, referring toTable IV, variable liquidity for Malaysia, based on the static model, negativecoefficients for Lev1 (total debt at book value), Lev3 (short-term debt at book value),Lev4 (total debt at market value) and Lev6 (short-term debt at market value) arerecorded in contrast to positive coefficients for Lev2 (long-term debt at book value) andLev5 (long-term debt at market value). Inconsistencies are also detected for bondmarket development and governance. As for Thailand, inconsistencies are detected onbusiness risk under the static model in which leverage defined as Lev1 (total debt atbook value) and Lev4 (total debt at market value) yield positive coefficients in contrastto the negative coefficients under Lev5 (long-term debt at market value). The same isalso detected for country-specific variables with the exception on stock marketdevelopment. Inconsistencies are also depicted in the results for Singapore on stockmarket development and governance. The findings thus conclude that results aresensitive to the various definitions of leverage despite employing the same model.Welch (2010) justifies this phenomenon by claiming that there may not be one best

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FE GMMIndependent variable Lev1 Lev2 Lev3 Lev4 Lev5 Lev6 Lev1 Lev2 Lev3 Lev4 Lev5 Lev6

MalaysiaNDTS � � � � �Tangibility � � � � � � þ þ þProfitability � � � � � � �Risk þ � þ þ �Size � þ � þ þ þ þ þ þ þGrowth þ � � � þ � �Liquidity � þ � � þ � � � �SPP þ � � � � � � � �Stock � þ þ �Bond þ � � � þEco growth þ � � � þInterest � � � þGovernance þ � � � þThailandNDTS � þ � �Tangibility � � � � � � �Profitability � � � � �Risk þ þ �Size þ þ þ þ þ þ þGrowthLiquidity � � �SPP � � � þ þ � � �Stock � � � � � � � � � �Bond þ � � þ � �Eco growth þ þ � � þ þInterest þ � � � � �Governance þ þ � � þ �SingaporeNDTSTangibility � � � � þ þProfitability þ þ � � �Risk þ þ þSize � þ þ þ þ þ þGrowth �Liquidity � � � þ � þ �SPP � � � � � �Stock � � þ � � � � � �Bond � � � þEco growth � � � þ � �Interest � � � � �Governance � � þ þ þ þ þ

Notes: The table shows the coefficient signs of each variable that significantly influenced leverageby using the different leverage definitions with the same model. The data covers 790, 269 and 546firms with 6,531, 2,368 and 4,170 observations for Malaysia, Thailand and Singapore, respectively,for the study period of 2000-2009. Model FE refers to fixed effect model (static model) whileGMM (dynamic model). Leverage definitions: book value leverage (Lev1¼ total debt/total asset;Lev2¼ long-term debt/total asset; Lev3¼ short-term debt/total asset); market value leverage(Lev4¼ total debt/(total debtþ total equity); Lev5¼ long-term debt/(total debtþ total equity);Lev6¼ short-term debt/(total debtþ total equity))

Table IV.Different leveragedefinitions (same model)

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measure (leverage definition) in the capital structure literature as it depends on thequestion being asked. Thus, this study accepts the H2 that the use of different leveragedefinitions will lead to different results.

Again, comparing the results with past studies on developed markets, similarities onthe impact of using different leverage definitions are observed. Al-Najjar and Hussainey(2011) in their revisiting study on the UK, also find that the coefficient signs for somevariables like market-to-book ratio and non-executive directors change according tothe definition of leverage. They thus conclude that “changing the definition ofcapital-structure may result in changing the sign and the number of determinantsthat may affect the capital-structure decision” (p 336). Therefore, this study confirms thenotion that the use of different leverage definitions will yield different results regardless ofthe market landscapes.

5.3 Summary of inconsistenciesTable V shows the summary of the inconsistencies found in the coefficient signsaccording to the use of first, different models with the use of same leverage definitionand second, different leverage definitions with the use of same model. From thesummary, it can be observed that inconsistencies are more obvious in the use ofdifferent leverage definitions with the same model employed as compared to thedifferent models with the same leverage definition. To date, no study has reallyhighlighted this interesting evidence and there are dire needs to further investigate thisfinding. Since no one universal leverage definition is there in the literature so far,this scene is expected to be repeated in the studies of capital structure. This scenariowould consequently lead to the unresolved issue of inconclusive findings on the capitalstructure studies. The findings of this study enhance the emphasis on why this area isstill inconclusive despites countless studies being done.

6. ConclusionDespites years of effort and many published studies, there is still no single answer tothe question of what is the optimal leverage ratio for a firm that would maximize firmvalue. The question of “how do firms choose their capital structures?” posed by Myers(1984, p. 575) remains unanswered.

Model Definition CountryInconsistencies of coefficient signs in

relationship

Different Same Malaysia TangThailand SPPSingapore Tang Profit

Same Different Malaysia Risk Size Growth Liquid SPP Stock Bond Econ GovernThailand NDTS Risk SPP Bond Econ Interest GovernSingapore Tang Size Liquid Stock Econ Govern

Notes: This table summarizes the inconsistent results yielded for the variables when using differentmodels with the same leverage definition and also when the same model is used with different leveragedefinitions. The use of different leverage definitions with the same model yielded more inconsistenciescompared to using different models with the same definition across the three countries. Dependentvariables: Tang, tangibility; SPP, share price performance; Profit, profitability; Size, firm size; NDTS,non-debt tax shield; Growth, growth opportunities; Liquid, liquidity; Stock, stock market development;Bond, bond market development; Econ, economic growth; Interest, interest rates; Govern, governance

Table V.Summary of

inconsistencies ofcoefficient signs

in relationship

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This study uses data of three Southeast Asian countries, namely Malaysia, Thailand andSingapore and concludes that capital structure puzzles remain because there is no clearempirical explanation within each country how firm chooses between different methodsof financing. This study finds that the different leverage definitions being usedin capital structure studies lead to the inconsistent results. Bevan and Danbolt (2002)argue that different definitions of leverage give different results and this study confirmsthis notion using new data from emerging markets.

Employing different models is also identified as another contributing factor ofinconclusiveness in capital structure studies. This study reveals that the contrastingnature of the static and the dynamic models influence the results of findings whereeach model yields different coefficient signs. Evidence is more obvious when differentleverage definitions are put to work with the same model. This study proves this notionthat when the same model is working with different leverage definitions different signsare yielded hence inconsistent results are recorded.

Although being debated and studied for decades studies on capital structure stillrepresent one of the main unsolved issues in the corporate finance literature. Countlesstheoretical studies as well as empirical research have tried to attend to these issues,still no one theory stands out to explain accurately the corporate financing behavior offirms past and present. Indeed, what makes the capital structure debate so exciting isthat only a few of the developed theories have been tested by empirical studies and thetheories themselves lead to different, not mutually exclusive and sometimes opposed,results and conclusions (Bevan and Danbolt, 2002). This study contributes to theliterature by enriching the body of knowledge on emerging markets and bridgingthe gap between emerging and developed markets by illustrating similar results in thedifferent markets using different models and different leverage definitions. This studyconfirms that variable definitions influence findings in capital structure studies,regardless of the markets.

Note

1. The full regression outputs of fixed effect model and generalized method of moments(first difference) for each country will be available upon request.

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Corresponding authorAssistant Professor Razali Haron can be contacted at: [email protected]

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Capital structure inconclusiveness: evidence from Malaysia, Thailand and Singapore