A Note on Capital Structure Target Adjustment Indonesian Evidence

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A note on capital structuretarget adjustment Indonesian evidence

Ludwig ReinhardUniversity of South Austrailia and Roedl & Partner-Worldwide Dynamics,

Rhineland-Palatinate, Germany

Steven LiUniversity of South Australia, Adelaide, Australia

Abstract

Purpose The purpose of this paper is to investigate whether existing capital structure target

adjustment models are able to identify whether companies adjust their capital structures towards an(unobservable) target.

Design/methodology/approach Existing capital structure target adjustment models are appliedto a specific dataset by using different regression techniques (ordinary least square, fixed effect,Fama-MacBeth, least square dummy variable corrected, SYS-GMM).

Findings Existing capital structure target adjustment models are not able to identify whethercompanies adjust their capital structures towards a target or not. They might indeed indicate targetadjustment behaviour when companies capital structures actually move away from their targets.

Research limitations/implications As target adjustment behaviour is often used as support forthe trade-off and against the pecking order theory, the horse race between both theories seems still tobe open.

Originality/value This paper highlights some of the fallacies of existing capital structure targetadjustment models and demonstrates that the results obtained by those models can be highly

misleading.

Keywords Capital structure, Targets, Indonesia

Paper type Research paper

1. IntroductionAccording to the capital structure trade-off theory, companies have an optimal ortarget[1] capital structure, which is determined by the trade-off between theadvantages and disadvantages of debt financing (Altman, 1984, Scott, 1976). Dynamicversions of the trade-off theory claim that companies would undo the effects thatrandom shocks have on their capital structures by actively re-adjusting them towardstheir target levels. Supported by the results of management surveys (Bancel and

Mittoo, 2004; Brounen et al., 2004, Graham and Harvey, 2001), numerous studiesempirically analyse how long it takes until companies are adjusting their capitalstructures towards their desired capital structure target levels (Antoniou et al., 2008,Fama and French, 2002, Flannery and Rangan, 2006). Depending on the regressionmodel and technique used, those studies typically find that companies adjust theircapital structures with a speed of around 10-30 per cent per year towards their capital

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1743-9132.htm

The authors are grateful to an anonymous referee for some useful suggestions.

Capital structurtarge

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International Journal of Manager

Finan

Vol. 6 No. 3, 20

pp. 245-2

q Emerald Group Publishing Limi

1743-91

DOI 10.1108/174391310110562


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structure targets[2]. Recently, DMello and Farhat (2008) carried target adjustmentstudies to the extremes by claiming that a companys moving average debt ratio wouldbe the best available proxy for a companys optimal capital structure.

Encouraged by the study of DMello and Farhat (2008), we analyse whether or not

capital structure target adjustment studies commonly found in the literature are able toidentify whether companies adjust their capital structures towards an (unobservable)target or not. In other words, we try to identify whether capital structure targetadjustment studies mean what they say.

By replicating the results of prior capital structure target adjustment studies, thispaper finds some evidence that commonly used capital structure target adjustmentmodels are not able to identify whether companies adjust their capital structurestowards a certain target or not. In fact, our results show that obtained capital structuretarget adjustment measures might be highly misleading and indicate a targetadjustment when companies actually move away from their capital structure targets.

The main contribution of this paper is that it highlights some of the weaknesses of

capital structure target adjustment studies and their results, which are commonly usedas support for the trade-off and against the pecking order theory. The identifiedweaknesses of capital structure target adjustment models have important implicationsfor tests of the different capital structure theories, especially for tests of the trade-offand pecking order theory. Based on the identified weaknesses of capital structuretarget adjustment studies and the results obtained in this paper, it appears thatexisting capital structure studies are not able to differentiate between the trade off andthe pecking order theory. The horse race between the two major capital structuretheories (trade-off and pecking order theory) seems thus still to be open.

The remainder of this paper is organised as follows. The next section brieflyreviews important capital structure target adjustment studies. This section is followedby a description of the data and regression techniques used to identify corporate target

adjustment behaviour before the results are analysed.

2. Literature reviewIn line with the implications of the trade-off theory, management surveys that analysethe financing decisions of companies in the USA and Europe find empirical evidencefor the notion that companies have capital structure targets that influence theirfinancing decisions (Bancel and Mittoo, 2004; Brounen et al., 2004; Graham and Harvey,2001). Based on those survey results, several studies try to identify how fast companiesare adjusting their capital structures towards their (unobservable) capital structuretargets.

A first and often cited study in this context is from Fama and French (2002), which

analyses the financing decisions of US companies over the years from 1965-1999 andfinds that companies adjust their capital structures at a rate of 7-18 per cent per yeardepending on whether a company pays dividends or not. Even though Leary andRoberts (2005) do not primarily focus on how fast companies adjust their capitalstructures towards their desired target level, they provide some evidence for the notionthat companies actively rebalance their capital structures, which they interpret asbeing consistent with the existence of a target range of leverage (Leary and Roberts,2005, p. 2577). Flannery and Rangan (2006) on the other hand, explicitly analyse the

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adjustment speeds of their US sample companies over the years from 1965-2001 andfind that companies adjust their capital structures rather quickly towards their targetlevels in approximately three years. By using similar regression techniques, Drobetzand Wanzenried (2006) and DeHaas and Peeters (2006) document comparable results

for companies from Central and Eastern Europe. Finally, Antoniou et al. (2008), whoanalyse the financing decisions of companies from the USA, UK, Germany, France and

Japan over the years from 1987 to 2000 find also some support for the considerationthat companies adjust their capital structures towards target levels.

All of the results of the different target adjustment studies analysed so far appear tobe in line with a dynamic capital structure trade-off model, according to whichcompanies would undo the effects of random events that bump their capital structuresaway from their target ratios by actively re-adjusting them towards their target levels.

Yet, in contrast to those capital structure target adjustment studies, Welch (2004)and Lemmon et al. (2008) show that companies are not doing much to offset the effectsof random shocks to their capital structures. This outcome seems to be perplexing, as

the different studies that analyse the financing decisions of companies in the US usealmost identical datasets.

Given these conflicting outcomes, this study aims to answer the question whetherthe different capital structure target adjustment studies are indeed able to identifywhether companies adjust their capital structures towards a target. To this end, wedecided to focus our analysis on a dataset that would easily, i.e. without furtherstatistical analysis, allow us identifying whether companies adjust their capitalstructures towards a certain target or not. We consequently decided to analyse thecorporate financing decisions of companies from Indonesia over the years from1995-2007.

Unfortunately, there is no single indicator available that would provide us with theinformation whether or not companies were able to adjust their capital structurestowards their target levels. We thus decided to use a number of different indicators thatwe believe provide us with an indication whether the Indonesian sample companieshave been able to adjust their capital structures towards their target levels or not.Those indicators are illustrated in the following Table I.

Table I is separated into two parts. The upper part, denominated as externalfinancial sources, provides some information about important external financialsources available in Indonesia in general. The lower part, denominated as internalfinancial sources provides an overview of important internal sources and uses offunds of the Indonesian sample companies (see below) over the years surrounding theAsian crisis.

As can be identified from the upper part of Table I (external financial sources),

there has been a considerable reduction in funds raised by companies from organisedpublic (debt and equity) markets from 1998 onwards. A similar decline (with a delay ofone year) can be identified for working capital loans and investment loans from banks,which both declined dramatically in 1999.

The lower part of Table I shows the mean values of selected financial data from thefinancial statements of the sample companies. As can be identified, there has been aconsiderable reduction in the number of public debt and equity issues of the samplecompanies over the years 1998 and 1999. This reduction seems to have caused the


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reduction in the total sources of funds available for the sample companies over those

years.Starting from 1999 onwards, the Indonesian sample companies retired more

long-term debt than they issued. This financial behaviour might be interpreted to bemotivated by target adjustment behaviour. Yet, given the increase in interest paymentsdue to soaring market interest rates and the overall increase in total liabilities, itappears that these companies tried to survive the Asian crisis period by restructuringtheir debt in a way to use less interest bearing long-term debt and more non-interestbearing short-term debt, such as trade credit.

Year 1995 1996 1997 1998 1999 2000

External financial sourcesNumber of IPOs 17 19 34 3 12 25

Volume IPOs 5,682 2,662 3,951 68 805 1,772Bank and finance 16 977 652 0 173 n.a.Others 5,666 1,686 3,299 68 632 n.a.

Number of right issues 10 19 20 10 14 10Volume of right issues 3,182 1,1924 15,887 5,068 130,682 17,548

Bank and finance 380 3,289 3,188 2,561 127,787 n.a.Others 2,802 8,636 12,699 2,506 2,895 n.a.

Number of public debt issues 4 5 15 0 6 15Volume of public debt issues 2,185 2,841 7,205 150 4,284 5,613

Bank and finance 370 91 1,800 0 1,050 518Others 1,815 2,750 5,405 150 3,234 5,095

Working capital loans 175,337 222,478 240,758 314,208 143,356 163,630Investment loans 59,274 70,443 100,735 141,464 57,691 65,276

Internal financial sourcesSale of common and preferred stock 46,876 22,349 71,324 13,773 5,145 25,146LT-debt issuance 132,767 148,750 302,173 155,699 67,947 131,907Disposal of fixed assets 5,057 9,448 11,645 31,666 4,830 6,272Net income 76,929 85,620 25,687 46,162 189,709 712Total sources of funds 319,685 352,308 603,149 495,406 452,257 567,970LT-debt retirement 40,561 69,136 114,755 117,660 188,413 270,871Dividend payments 20,415 22,251 30,086 17,179 25,349 67,635Interest payments 45,236 58,553 117,457 147,270 149,196 172,065Current assets 538,828 548,330 884,921 849,064 1,071,198 1,205,644Current liabilities 321,145 348,925 748,258 824,165 957,244 1,450,950Total common equity 537,276 596,421 683,551 972,881 1,106,816 1,326,415Total liabilities 704,498 797,815 1,635,180 1,681,970 1,817,466 2,412,558Total assets 1,279,732 1,434,854 2,364,643 2,675,875 2,966,856 3,790,929

Notes: This table shows selected external and internal financial indicators of the Indonesian economyin general (external financial sources) and of the sample companies analysed (internal financialsources). All volume data in the upper external financial sources part are in billion IDR while alldata in the lower internal financial sources part are in million IDR. The volume figures in the upperexternal financial sources part are reported in total, for bank and finance companies and othercompanies to show possible effects resulting from external market changes on corporate financingdecisionsSource: Bank Indonesai

Table I.Target adjustmentindicators

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Other indicators that lead us to the conclusion that the Indonesian samplecompanies have not deliberately adjusted their capital structures towards their targetlevels over the years of the Asian crisis are the increase in the disposal of fixed assetsand the decrease in dividend payments, which seem to be elements of a survival

tactic rather than the elements of a deliberate target adjustment strategy.

3. Methodology3.1 DataInitially all listed companies in Indonesia over the years from 1995 to 2007 wereselected. Thereafter, all financial (SIC codes 6000-6999) and utility companies (SICcodes 4900-4999) were excluded as their financing decisions might be influencedby minimum capital requirement regulations or explicit or implicit governmentguarantees. To avoid that outliers influence the results, we excluded alltechnically insolvent companies, i.e. those with a total liabilities to total assetratio of larger than unity. We further required that each company has at least

one year of usable data before (1995-1996), during (1997-2000) and after(2001-2007) the Asian crisis to avoid that the sample is biased towards youngand newly listed companies with a short history of data that have not directlybeen influenced by the Asian crisis. By doing so, we were able to obtain a panelconsisting of 749 firm-year observations.

3.2. Regression model and estimation techniqueTo identify whether or not target adjustment considerations are behind the capitalstructure changes of our Indonesian sample companies, the following capital structuretarget adjustment model is set up, which (in its simplest form) can be expressed asfollows[3].

CSit2

CSit21 b0 b1*

CS*

2CSit21 1it

:

1

The capital structure target adjustment model illustrated in equation (1) is identical tothe one commonly used in the literature (see, e.g. DMello and Farhat, 2008) and statesthat changes in a companys capital structure (CSit CSit21 ) are caused by acompanys desire to adjust its capital structure towards its target level (CS *it).

A first problem, which can be identified from equation (1) is the simultaneitybetween a companys current and target capital structure. That is, according to thetarget adjustment model illustrated in Equation (1) companies would adjust theircapital structures towards a target that they would effectively observe only at the endof the year, i.e. after they already adjusted their capital structures. Most studies simplyignore this problem, which seems to be justifiable, as DeHaas and Peeters (2006)

demonstrate[4].The target adjustment model in equation (1) can be rearranged as follows:

CSit b0 b1*CS

*2 b1

*CSit21 CSit21 1it 2

which can be further simplified to:

CSit b0 b1*CS

* 12 b1

*CSit21 1it: 3


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A problem with the regression model illustrated in equation (3) is that it cannot bedirectly estimated, as a companys target leverage ratio (CS *it) is not directly observable.To overcome this problem, it is usually assumed that a companys target capitalstructure ratio is itself a function of other variables (Xjit)[5] that have been found to

influence a companys capital structure decisions, i.e.:

CS*

Xk

j1

aj*Xjit 4

By inserting equation (4) into equation (3), the following testable model results:

CSit b0 12 b1

*CSit21 b1*Xk

j1

aj*Xjit 1it 5

The main interest in this target adjustment model is the regression estimator (12

b1),which is assumed to provide an indication about a companys capital structure targetadjustment speed.

Several authors estimate regression models similar to the one illustrated in equation(5) either by OLS or Fama-Mac Beth regressions[6] and consequently obtain biasedregression results[7]. To overcome problems resulting from the inclusion of laggeddependent variables, researchers increasingly use corrected least square dummyvariable (LSDVC) or GMM regression models, which are especially designed forregression models with lagged dependent variables (Arellano and Bond, 1991; Arellanoand Bover, 1995; Blundell and Bond, 1998; Bond, 2002; Bruno, 2004, 2005; Bun andKiviet, 2003; Kiviet, 1999; Windmeijer, 2005).

By using Monte Carlo simulations, several studies show that the LSDVC regressionestimators outperform GMM regression estimators especially in panels with a smallnumber of cross-sectional observations (Bruno, 2004; Buddelmeyer et al., 2008; Judsonand Owen, 1999). However, both, the LSDVC and the GMM regression techniques havetheir weaknesses[8]. We consequently report both results below together with the oftenused Fama-MacBeth regression results. In addition to those results, OLS and fixedeffect regression results are reported based on the consideration that OLS regressionestimators are biased upwards while fixed effect regression estimators are biaseddownwards and that the true, i.e. unbiased regression estimators are likely to lie inbetween both estimators (Arellano and Bond, 1991; Bond, 2002).

4. Results

Table II reports the regression results for the capital structure target adjustment modelillustrated in equation (5)[9].

In line with prior capital structure target adjustment studies, we use a companyslong-term debt to total asset ratio as the dependent variable and use the tangibility of acompanys assets (TAN), its size (SIZE), its profitability (PROFIT) and its growth(GROWTH) as the independent variables[10] (DeHaas and Peeters, 2006; Drobetz andWanzenried, 2006; Flannery and Hankins, 2007; Flannery and Rangan, 2006) andcontrol for industry effects.

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OLS

FE

FM

LSDVC

GMM

-SYS

1997

-2000

2001-2005

1997-2000

2001-2005

1997-2000

2001-2005

1997-2000

2001-2005

1997-2000

2001-2005

LT-Debt/TA[t2

1]

0.57***

0.62***

0.02

0.34***

0.57***

0.63***

0.40***

0.68***

0.54***

0.64***

(9.65)

(20.73)

(0.28)

(8.21)

(12.10)

(13.78)

(4.50)

(9.93)

(4.05)

(12.75)

TAN

0.12**

0.10***

0.08

0.09

0.10

0.10**

0.25*

0.01

0.16

0.02

(2.40)

(3.27)

(0.76)

(1.13)

(1.48)

(3.84)

(1.86)

(0.15)

(1.32)

(0.24)

SIZE

0.01**

0.02***

2

0.01

0.02

0.02

0.02**

2

0.01

0.05

2

0.00

0.02

(2.25)

(3.86)

(20.40)

(1.00)

(1.73)

(2.82)

(20.31)

(1.62)

(20.02)

(1.40)

PROFIT

20.01

2

0.14***

0.04

2

0.17***

0.03

2

0.07

0.21

2

0.09

0.14

2

0.23*

(20.12)

(23.15)

(0.41)

(23.58)

(0.32)

(21.36)

(1.47)

(20.68)

(1.13)

(21.79)

GROWTH

20.02*

2

0.01

2

0.01

2

0.03*

2

0.03*

2

0.02*

2

0.01

0.01

2

0.03

2

0.00

(21.71)

(21.50)

(20.47)

(21.70)

(23.00)

(22.16)

(20.50)

(0.50)

(21.56)

(20.06)

CONST

20.08

2

0.24***

0.31

2

0.22

0.03

2

0.13

n.a.

n.a.

0.03

2

0.20

(20.66)

(23.25)

(0.89)

(20.68)

(0.12)

(21.03)

n.a.

n.a.

(0.12)

(21.27)

R2

0.44

0.68

0.01

0.62

0.51

0.72

n.a.

n.a.

n.a.

n.a.

F

20.46

74.08

0.20

1

8.83

5.67

6.32

n.a.

n.a.

8.23

71.45

p

0.00

0.00

0.96

0.00

0.09

0.04

n.a.

n.a.

0.00

0.00

IAS

2.32

2.67

1.02

1.51

2.33

2.69

1.67

3.08

2.18

2.81

Notes:*denotesignifica

ntlevelsatthe10percentlevel;**denotesignificantlevelsatthe5percent

level;***denotesignificantlevelsatthe1percent

level;thistableshowstheregressionresultsforthecapitalstructuretargetadjustmentmodelillustratedinEquation(5).OLS

ordinary

leastsquare

regressions,FE

fixedeffectregressions,FM

Fama-MacBethregressions,LSDVC

leastsquar

edummyvariablecorrectedregressions,GMM-

SYS

two-stepsystemG

MMregressionresults.Fama-MacBethregressionresultsareobtainedbyusingtheuser-writtenStatacommandxtfmbfrom

DanielHoechle.LSDVCr

esultsareobtainedbyusingtheuser-writtenStatacommandxtlsdvcfromGiovanniS.F.Bruno.TheGMM-SY

Sresultsare

obtainedbyusingtheuse

r-writenStatacommandxtabond2fromDavidRoodman(2006).Theimpliedadjustmentspeed(IAS)inyearsiscalculatedby

theinversedifferenceofunityandtheLT-Debt/TA[t2

1]

estima

tor.T-statisticsareinparentheses

Table IRegression results targadjustment depende

variable: LT-Debt/T


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Even though the regression estimators for the other control variables included in theregression model are not directly comparable with those of other capital structurestudies[11], they appear to be in line with the findings in previous studies. That is,larger companies, those with lower growth opportunities and companies with lower

profits borrow in general more than other companies.In respect to the target adjustment regression estimators, the results reported in

Table II show in general a statistically significant positive regression result for theLT-Debt/TA[t21] variable. Antoniou et al. (2008, p. 75) interpret this result as follows:The statistically significant coefficient of the lagged dependent variable confirms thatfirms have a target capital structure and on average do not fully adjust to the targetevery year . . . .

Furthermore, the smaller implied adjustment speed parameters (IAS) during theyears of the Asian crisis indicate that the Indonesian sample companies adjusted theircapital structures relatively faster towards their target levels than after the Asian crisisperiod[12], which seems to be in contrast with the previous consideration that these

companies have not been able to adjust their capital structures towards their targetlevels during the years of the Asian crisis due to the shortage of internal and externalfunds.

To investigate this issue further, we performed a graphical analysis of the capitalstructure decisions of the Indonesian sample companies (see Figures 1 and 2). We usethis graphical analysis as additional support for our hypothesis that the Indonesiansample companies have not been able to adjust their capital structures towards theirtarget levels during the years of the Asian crisis.

Figure 1.Capital structure measures

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Figure 1 shows the development of the capital structure ratios (LT-Debt/TA) of the

Indonesian sample companies together with their average (Average LT-Debt/TA) and

moving average (MA LT-Debt/TA) capital structure measures[13] over the years from

1997 to 2005. Figure 2 on the other hand shows the absolute difference (gap) between

the actual capital structure measures of the sample companies (Act) and the two

target measures (Avg and MAvg).As can be identified from Figure 2, the gap between the actual capital structure

measures of the Indonesian sample companies (LT-Debt/TA) and their capital

structure target measures (Average LT-Debt/TA and MA Debt-TA/TA) increased

during the years of the Asian crisis especially from 1999 onwards. This increasing gap

indicates that the sample companies capital structures moved away from their targets

during those years. Starting from 2001 onwards, the gap between the actual and target

capital structure measures declined, indicating that the capital structures of the

Indonesian sample companies approached their target measures.

The graphically identified target adjustment behaviour implies a relatively faster

target adjustment speed during the years after the Asian crisis than during the Asian

crisis years. Yet, the implied adjustment speed measures (IAS) in Table II indicate theopposite. That is, they show a relatively faster target adjustment during the years of

the Asian crisis than thereafter.

Given these contradicting results, it appears that existing capital structure target

adjustment models can be misleading and indicate target adjustment behaviour when

companies actually move away from the targets. In other words, existing capital

structure target adjustment models do not appear to be able to identify whether

companies adjust their capital structures towards a certain target or not. They rather

Figure Gap capital structure

target measur


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simply document a significant relationship between a companys current and pastcapital structure, which can as the results obtained in this study document not beinterpreted by a target adjustment behaviour of the sample companies (see Antoniouet al., 2008, p. 75).

5. RobustnessTo test the robustness of our results we changed the independent variables used in theregression model and used the target variables used by Titman and Wessels (1989)instead[14].The results of those robustness test are reported in Table III.

As can be identified, the results (IAS) of these robustness tests confirm the priorresults that the Indonesian sample companies adjusted their capital structures towardstheir target levels relatively faster during the years of the Asian crisis than thereafter,which is in contrast to the data reported in Table I and the results reported in Figures 1and 2.We performed further robustness tests (not reported) in which we used differently

defined capital structure measures, such as a companys total liabilities to total assetratio. As these results are de facto identical with those reported in Tables II and III wefeel confident that our results are not influenced by the definition of the dependent andindependent variables used.

It should also be noted that a similar study can be carried out to investigate thecapital structure adjustment in other countries in the centre of the Asian financialcrisis. However, due to the constraint of data and space, we focus only on Indonesia inthis paper.

6. Summary and conclusionsAt the beginning of this study, we raised the question whether capital structure target

adjustment studies mean what they say. To answer this question, this study focused ona sample of companies that were going through a period of extremely adverse economicand financial market conditions that would easily allow us identifying whether thesecompanies adjusted their capital structures towards a certain target or not[15].

Based on the results obtained, we cannot be sure whether commonly used capitalstructure tests are really able to identify whether companies adjust their capitalstructures towards a certain target or whether economic fluctuations and financialmarket changes are behind the identified capital structure changes. As the identificationoftheunderlyingreasonsforcapitalstructurechangeshasimportantimplicationsforthevalidity of the different capital structure theories, especially the trade-off and peckingorder theory, the horse race between the last two theories seems still to be open.

Given the problems of existing capital structure target adjustment studies, the

question arises what possible ways forward would be? In our opinion, future tests thataim to provide some evidence for or against the main capital structure theories thetrade-off and pecking order theory have to overcome (at least) the following problems.First, the problem of simultaneity between a companys current and target capitalstructure ratio, which is commonly ignored in most capital structure target adjustmentstudies. Second, the problem that the regression results for the independent variablesincluded in target adjustment regressions models are not directly comparable with thoseof prior (standard) capital structure studies. Finally, future capital structure target

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OLS

FE

FM

LSDVC

GMM

-SYS

1997-2000

2001-2005

1997-2000

200

1-2005

1997-2000

2001-2005

1997-2000

2001-2005

1997-2000

2001-2005

LT-Debt/TA[t-

1]

0.5

9***

0.65***

0.02

0.35***

0.60***

0.65***

0.37***

0.65***

0.44***

0.67***

(10.1

8)

(21.72)

(0.20)

(8.59)

(27.55)

(15.83)

(4.04)

(9.64)

(3.06)

(10.33)

TAN-TW

0.0

5

0.00

0.11*

2

0.06

0.05**

0.01

0.19**

0.03

0.11

0.04

(1.3

8)

(0.06)

(1.71)

(21.17)

(3.29)

(0.61)

(2.08)

(0.56)

(1.07)

(1.33)

SIZE-TW

0.0

1**

0.02***

2

0.04

0.00

0.01

0.02**

2

0.06

0.05

0.03

0.01

(2.0

3)

(4.56)

(21.14)

(0.03)

(1.30)

(2.73)

(21.25)

(1.24)

(1.22)

(1.01)

PROFIT-TW

20.1

4**

2

0.13***

0.09

2

0.45***

2

0.11**

2

0.12***

0.29

2

0.25*

0.01

2

0.05

(22.1

5)

(23.65)

(0.41)

(23.58)

(23.18)

(26.40)

(0.83)

(21.72)

(0.08)

(20.75)

GROWTH-TW

0.2

4

0.05

2

0.09

0.20

2

0.01

2

0.02

2

0.06

0.19

2

0.15

0.13

(1.5

2)

(0.39)

(20.47)

(1.30)

(20.04)

(20.11)

(20.18)

(1.23)

(20.30)

(0.85)

CONST

20.0

6

2

0.18**

0.61

0.25

0.07

2

0.08

n.a.

n.a.

2

0.38

2

0.15

(20.4

8)

(22.35)

(1.40)

(0.69)

(0.36)

(20.69)

n.a.

n.a.

(21.13)

(21.18)

R2

0.4

4

0.67

0.07

0.47

0.49

0.71

n.a.

n.a.

n.a.

n.a.

F

20.2

9

69.65

0.83

17.47

0.09

121.27

n.a.

n.a.

8.56

119.89

p

0.0

0

0.00

0.53

0.00

1.00

0.00

n.a.

n.a.

0.00

0.00

IAS

2.4

4

2.85

1.02

1.55

2.51

2.89

1.59

2.88

1.77

3.01

Notes:*denotesignifica

ntlevelsatthe10percentlevel;**denotesignificantlevelsatthe5percent

level;***denotesignificantlevelsatthe1percent

level;thistableshowstheregressionresultsforthecapitalstructuretargetadjustmentmodelillustratedinEquation(5).OLS

ordinary

leastsquare

regressions,FE

fixedeffectregressions,FM

Fama-MacBethregressions,LSDVC

leastsquar

edummyvariablecorrectedregressions,GMM-

SYS

two-stepsystemG

MMregressionresults.Fama-MacBethregressionresultsareobtainedbyusingtheuser-writtenStatacommandxtfmbfrom

DanielHoechle.LSDVCr

esultsareobtainedbyusingtheuser-writtenStatacommandxtlsdvcfrom

GiovanniS.F.Bruno.TheGMM-SY

Sresultsare

obtainedbyusingtheus

er-writenStatacommandxtabond2

fromDavidRoodman(2006).Thedefinitionsoftheindependentvariables

aregivenin

Section5.Theimpliedadjustmentspeed(IAS)inyearsiscalculatedbytheinversedifferenceofunityandtheLT-Debt/TA[t2

1]estimator.T-statisticsare

inparentheses

Table IIRegression results targadjustment robustne

tests Dependevariable: LT-Debt/T


25


12/16

adjustment studies have to come up with new ways for the identification andmeasurement of a companys capital structure target in order to separate a possiblecapital structure target adjustment behaviour from capital structure changes that arecaused by changes in economic or financial market conditions.

Notes

1. The terms optimal and target capital structure are often used synonymous in theliterature. Following this convention, the term target capital structure is used to describe acompanys optimal capital structure in the following.

2. An adjustment speed of 30 per cent implies that companies need on average around 3.3 years(1/0.30) until they reach their desired capital structure target levels.

3. CSit is a companys current capital structure and CS *itis a companys target capital structure.

4. DeHaas and Peeters (2006) use a one year lagged target capital structure variable in theirtarget adjustment model based on the consideration that companies have to knowtheir capital structure target at the beginning of the year before they can start to adjust theircapital structures towards it. In robustness tests, they show that the use of a companyscurrent or its one year lagged capital structure target variable has no significant influence onthe results obtained.

5. The previously mentioned capital structure target adjustment studies include variousvariables (Xjit) that they use as a proxy for a companys optimal or target leverage ratio. Noconsensus has, however, been reached on what variables to include in the vector Xjit that isused to determine a companys capital structure target.

6. Fama-MacBeth regressions models run t cross sectional regressions and to take the averageof the tregression estimators as an approximation of the true regression estimator (Famaand MacBeth, 1973). The averaging of the regression estimators, assumes that thet regression estimators are independent of each other. In the presence of unobserved timeinvariant effects, this assumption does however not hold resulting in biased regression

outcomes. To overcome this problem, some authors suggest adjusting the Fama-MacBethregression estimators and standard errors for the autocorrelation that exists between theregression estimators. Yet, Petersen (2007) shows that those adjustments do not change thebias of the Fama-MacBeth regression estimators. The reason for this (remaining) bias is thatthe serial correlation that exists between the tregression estimators and which is used tocorrect for the autocorrelation among them is not the same as the one that causes the biasin the Fama-MacBeth regression estimators.

7. In a panel data regression model with unobserved effects, OLS regressions are biased, asboth, the dependent and the lagged dependent variable, are a function of the unobservedeffect (see, e.g. Baltagi, 2005). A fixed effect panel data regression model, which eliminatesthe unobserved effects, would still be biased, as the dependent and lagged dependentvariable are both correlated with the time-demeaned error terms. Nickel (1981) shows thatfixed effect regression estimators are biased even if the number of cross-sectional units goes

to infinity. If, on the other hand, the number of time-periods increases, the bias of the fixedeffect regression estimator decreases. Monte Carlo simulations of Judson and Owen (1999)demonstrate however that the bias of the fixed effect regression estimator in laggeddependent variable regression models remains significant, even if 30 time-periods areavailable.

8. For example, the LSDVC regression model assumes that all regression estimators are strictlyexogenous. GMM regression models on the other hand do not allow cross-sectionalcorrelations of the error terms and might suffer from weak instrument bias (Bun andWindmeijer, 2007).

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9. As can be identified, the regressions are run for the periods 1997-2000 (crisis period) and theperiod 2001-2005 (post-crisis period). Four years of data are lost (1995-1996 and 2006-2007)due to the variable calculation procedure used for the robustness tests (see below). To allow adirect comparison of the results reported in Table II and the robustness tests reported in

Table III, we decided to run both regressions for the same periods.10. The tangibility variable is defined as a companys net property plant and equipment scaled

by its total assets. A companys size is proxied by the natural logarithm of its total assets.The profitability variable is defined as a companys return on total assets and the growthvariable is a companys market to book ratio.

11. That is because they are the product of the adjustment speed and their own regressionestimator.

12. Not reported significance tests indicate that the adjustment speed measures for the periodafter the Asian crisis period are statistically significant different from the adjustment speedmeasures during the years of the Asian crisis.

13. According to DMello and Farhat (2008) the average respectively moving average capitalstructure measures are the best available proxies for a companys target capital structure

measure.14. That is, in this robustness test a companys tangibility (TAN-TW) is calculated by the sum

of a companys inventory and gross property, plant and equipment scaled by its total assets.The alternative size (SIZE-TW) figure is calculated as a three-year moving average figure ofa companys logarithm of total assets. Similarly, a companys profitability (PROFIT-TW) iscalculated by its three-year moving average operating profit and the alternative growthmeasure (GROWTH-TW) is calculated by the quotient of a companys capital expendituresscaled by a companys total assets.

15. As the results of the target adjustment regressions for the sample of Indonesian companiesused in this study are de facto identical to previous capital structure target adjustmentstudies, we believe that similar shortcomings of capital structure target adjustment modelscan be identified for other samples of companies (e.g. US, European, etc.) and investigationperiods, which future research will have to prove.

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Further reading

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Corresponding authorLudwig Reinhard can be contacted at: [email protected]


259

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A Note on Capital Structure Target Adjustment Indonesian Evidence

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