1

Capital structure and firm value. Empirical evidence

from Romanian listed companies

Author:

Simona Maria DRĂNICEANU

Coordinator:

Prof. Univ. Dr. Anamaria CIOBANU

ABSTRACT

The relationship between capital structure and firm value has been the subject of considerable

debates for both academics and practitioners. However, there is no certainty about the overall effect of debt

on firm value. This paper aims to investigate the impact of capital structure on firm value for Romanian

companies at the same time considering the determinants of leverage. In addition to this, the paper tries to

empirically test the influence of debt structure on firm value given different growth opportunities of

Romanian companies. The sample included 48 companies listed on Bucharest Stock Exchange for the period

2003-2012. Five regression models were used: Pooled regression model, Fixed effects model, Time effects

model, The two way fixed effects model and Simultaneous regressions model. The results show that capital

structure has a positive impact of firm value, for both firms facing low growth opportunities and firms facing

high growth opportunities. Profitability, liquidity and tangibility have been found as negative determinants of

capital structure, while growth opportunities, firm size and firm financial quality have been found as positive

determinants of capital structure.

Key words: capital structure, firm value, growth opportunities, signaling theory, pecking order theory.

JEL code: G30, G32, G33

INTRODUCTION

The relationship between capital structure and firm value is an important question that has been

investigated extensively, both theoretically and empirically.

The theory of capital structure irrelevancy proposed by Modigliani and Miller (1958 and 1963) forms

the basis for modern thinking on capital structure. In their seminal article, Modigliani and Miller (1958)

demonstrate that, in a frictionless world, financial leverage is unrelated to firm value.

The capital structure theories developed later (trade-off theory, pecking order theory, signaling

theory, agency theory, market timing theory) demonstrated that firm value can be affected by capital

structure decisions. For determining the optimal capital structure, these modern theories take into account

taxes and financial distress costs (trade-off theory), agency costs (agency theory), information asymmetry

2

(signaling theory, pecking order theory) and effects of market imperfections (market timing theory).

Starting from these theories, many empirical studies regarding capital structure puzzle have been

developed in three main directions.

A first category is represented by studies which concentrated on the relationship between capital

structure and firm value. Although the empirical relationship between debt financing and firm value has

indeed been examined extensively in prior studies, one cannot formulate a commonly agreed conclusion.

While some studies report a positive relationship [Dalbor et al. (2007), Cheng and Tzeng (2011), Sudivat et

al.(2012), Rathinasamy et al. (2000), Altan and Arkan (2011), Ogbulu and Emeni (2012)], others report a

negative relationship [(Aggarwal and Zhao (2007), Rayan (2008), Aggarwal et al. (2011)] and others report a

negative correlation for high-growth firms and a positive correlation for low–growth firms [McConnel and

Servaes (1995), Chen (2002), Alonso at el. (2005)].

A second category is represented by studies which reviewed factors influencing capital structure

choice or leverage determinants. Empirical studies proved that capital structure is affected by firm

profitability, liquidity, taxes, industry, size, cash-flow or growth opportunities (Fama and French (2002),

Supanvanji (2006), De Jong et al. (2008), Delcoure (2007), Dragotă et al. (2007, 2008), Tong and Green

(2005), Jiraporn and Liu (2008), Frank și Goyal (2009), Gungoraydinoglu și Öztekin (2011)). However, the

way in which each of these factors affects capital structure is still an open question.

A third category includes studies that reviewed leverage-value relationship at the same time with

analyzing the determinants of capital structure. Researchers in this category argued that such approach

allows to reconstruct the capital structure puzzle more complete [Agrawal and Knoeber (1996), De Jong

(2002), Dessí și Robertson (2003), Berger și Bonacorrsi di Patti (2006), Margaritis și Psillaki (2007), Ghosh

(2007)].

Based on previous empirical studies, this paper aims to analyze the value-leverage relationship and

leverage determinants independently, according to the first two categories, and simultaneously, according to

the third one.

LITERATURE REVIEW

Modigliani and Miller theorem (1958)

Modigliani and Miller theorem (1958) forms the basis for modern thinking on capital structure. The

theory, which contains two propositions, states that in the absence of taxes, bankruptcy costs, and

asymmetric information, firm value is unaffected by the way in which assets are financed. According to the

first proposition, the value of a firm is constant regardless of how the firm chooses to finance its assets.

Second proposition of Modigliani and Miller theory states that a firm’s capital structure has no impact on its

weighted average cost of capital (the cost of equity is a linear function of the debt-equity ratio).

3

Trade-off theory

Modigliani and Miller’s propositions were changed according to the trade-off theory as follows

(Myers, 2003):

VL = D + E = VU + VP (tax shields) – VP (bankruptcy costs)

where:

VL - market value of the levered firm; VU - market value of the unlevered firm; VP (tax shields) – present

value of tax shields; VP (bankruptcy costs) – present value of bankruptcy costs.

The trade-off theory states that firms borrow up to the point where the tax savings from an extra

dollar in debt are exactly equal to the costs that come from the increased probability of financial distress.

In accordance with the trade-off theory, a firm will borrow up to the point where the tax savings from

an extra dollar in debt is balanced by the increase in the present value of bankruptcy costs (Myers, 2001).

The first statement regarding the trade-off theory was provided by Kraus and Litzenberger (1973)

and states that the optimal capital structure reflects a trade-off between tax benefits of debt and bankruptcy

costs.

Pecking-order theory

An alternative to trade-off theory is the pecking order theory developed by Myers and Majluf (1984)

and Myers (1984). These authors propose a ‘‘pecking-order’’ theory of capital structure that is based upon

cost effective choices. The most cost effective and easiest method of financing is the use of retained

earnings. The next choice is debt that has costs associated with it, but can be quite flexible. It does not

always require the firm to go public and offers the tax deductibility of interest payments. The least favorite

choice is new external equity because of the high costs associated with new stock issues. More specifically,

pecking order theory predicts that firms prefer to use internal financing when available and choose debt over

equity when external financing is required.

Agency theory

Jensen and Meckling (1976) found that the interests of managers are not aligned with those of

shareholders, and managers tend to waste free cash flow in perquisites and bad investments. Agency theory

complements trade-off theory: apart from financing issues (tax-related benefits of debt versus its financial

distress costs), this theory underlines the role of debt as controlling mechanism. As such mechanism, debt

has influence on investing behavior of managers, making them investing optimally or not. Due to

consideration of agency costs, an even more complicated trade-off is assumed. Hence, according to this

theory, the value of a levered firm is defined as follows:

VL = VU + VP (tax shields) – VP (bankruptcy costs) - VP(agency costs),

where: VL - market value of the levered firm; VU - market value of the unlevered firm; VP (tax shields) –

present value of tax shields; VP (bankruptcy costs) – present value of bankruptcy costs; ; VP (agency costs) –

present value of agency costs.

4

Market-timing theory

First introduced by Baker and Wurgler (2002), this theory suggests that when there is a chance for

companies to issue equity at higher price, firm is more likely to execute this opportunity. More specifically,

managers are able to identify certain time periods during which equity issuance is less costly due to the high

valuation of company’s stock. When managers issue equity when market value of equity is high, firm’s costs

of equity would be relatively lower. In this case, managers would be increasing the value of the firm at the

expense of new shareholders and the benefits would be transferred to current shareholders.

EMPIRICAL REVIEW

Research on the relationship between capital structure and firm value

Although Modigliani and Miller (1958) suggest that capital structure and firm value are unrelated,

many researchers provide evidence on their correlation, either positive or negative.

Dalbor et al. (2007) tried to determine whether or not the use of long-term debt contributes to the

value of lodging firms. Annual data were used for the U.S. lodging firms over the years 1980-2005.

The regression equation used to determine the relationship between capital structure and firm value

was as follows:

Firm_Valueit = a0 + a1LTDit + a2RISKit + a3CAPEXit + a4NITAit + a5SIZEit + eit1

The regression analysis showed that after controlling for size and risk, a positive relationship is

established between long-term debt and the value of the firm. Return on assets was found to be negatively

related to firm value, and capital expenditures were found unrelated to firm value from a statistical point of

view.

Cheng și Tzeng (2011) investigated whether and to what extent leverage has impact on firm value for

645 companies listed in Taiwan Securities Exchange (TSE) over the period 2000-2009.

The authors used Altman’s Z-score as a proxy of the firm bankruptcy probability and argued that this

score is also a proxy for measuring firm quality. The higher firm‘s quality may improve firm’s credit

rationing by debtholders and equityholders. Better credit rationing will result in a reduced cost of capital and

increased firm value.

By applying the fixed effects model, the results indicated the following:

- the value of leveraged firms is greater than the value of unleveraged firms when not considering

bankruptcy probability;

- taking into account the benefits and costs of debt simultaneously, leverage is positively related to

firm value before reaching firm’s optimal capital structure;

1Firm Value is the natural log of the market value of firm equity, LTD is the ratio of long-term debt to total assets, RISK is

represented by Ohlson’s O Score, CAPEX is the ratio of capital expenditure to total assets, NITA is the ratio of net income to

5

- the positive influence of leverage on firm value tends to be stronger when firm’s financial quality is

better (i e. the greater Z-score).

In a study of international firms from forty-nine countries, Rathinasamy et al. (2000) also reported a

positive correlation between capital structure, measured by total debt ratio and long-term ratio, and market

power measured by Tobin’s Q.

Aggarwal and Zhao (2007) used a sample of 27,237 observations regarding financial data from

COMPUSTAT’s P/S/T and Research annual industrial tapes from 1980 to 2003 and proved that, after

controlling for industry leverage effects in estimating the leverage–value relationship, leverage is negatively

correlated with value for both high and low growth US firms.

Rayan (2008) also investigated whether capital structure positively influences the value of the firm.

The study was conducted on all firms listed on JSE2 excluding the banking industry for the period 1997-

2007. The debt to equity ratio was used as a proxy for capital structure and the following ratios were used for

firm value: Earnings per Share, Price Earnings Ratio, Return on Equity, Return on Assets, Earnings Value

Added, and Operating Profit Margin.

The findings indicated an inverse relationship between financial leverage and firm value. Also, the

results for the various industries proved that capital structure is different for different industries.

According to McConnell and Servaes (1995), the influence of debt on firm value depends on the

presence of growth opportunities. For firms facing low growth opportunities, the debt ratios are positively

related to firm value. For firms facing high growth opportunities, the debt ratios are negatively related to firm

value. There results were also supported by Agrawal and Zhao (1996), Chen (2002) and Alonso at el. (2005)

and were rejected by Harvey et al. (2004) and Aggarwal și Zhao (2007).

Research on relationship between capital structure and firm value after controlling for the

endogeneity of capital structure

De Jong (2002) measured the relationships between leverage, Tobin’s Q and corporate governance

characteristics for Dutch listed non-financial firms over the period 1992-1997. The study used simultaneous

equations model to deal with this simultaneous nature of the relation between leverage and firm value. Thus,

two equations were estimated simultaneously:

{

3

2 Johannesburg Stock Exchange

3 Leverage is the long-term debt ratio; Control1 is a matrix of control variables consisting of dummy variables for the years

1993 to 1997, non-debt tax shields, tangible assets, the standard deviation of operating income and the logarithm of total

assets; Tobinq is Tobin’s q; FCF is free cash flow; GOV is a matrix of thirteen governance variables; and Control2 is a

matrix of control variables consisting of dummy variables for the years 1993 to 1997, the logarithm of total assets, the

logarithm of one plus the growth rate and a dummy variable for a listing in the US or UK.

6

Leverage was found to have a significantly negative impact on firm value. This result rejected the

disciplining and value-enhancing role for leverage.

Dessi and Robertson (2003) also estimated the relationship between capital structure and firm value

using the simultaneous equation method on a sample of 557 UK firms over the period 1967–1989. The

results showed that unobserved firm heterogeneity, as reflected in the fixed effects, is a highly significant

determinant of both leverage and firm value. Within a static framework, leverage had a significant positive

effect on firm value, even when fixed effects were included. However, leverage had no longer a significant

impact on firm value when controlling for the endogeneity of capital structure. The results from the dynamic

model confirmed the findings from the static model: the estimated coefficient for debt in the dynamic

equation is positive and significant when not controlling for the endogeneity of debt, but it becomes

insignificant when controlling for the endogeneity of debt.

Authors argued that these results are consistent with the hypothesis that firms choose their capital

structures optimally, in which case we would not expect to observe a significant relationship between firm

value and debt once the endogeneity of debt is allowed for.

Berger and Bonaccorsi di Patti (2006) argued that prior research did not take into account the

possibility of reverse causality from performance to capital structure, which may result in simultaneous-

equations bias.

They addressed this problem by allowing for reverse causality from performance to capital structure.

A sample of 7320 U.S. commercial banks from 1990 through 1995 was used and a two-equation structural

model was estimated using two-stage least squares (2SLS).4.

The findings were consistent with the agency costs hypothesis – higher leverage was associated with

higher profit efficiency.

With respect to the reverse causality from efficiency to capital structure, the results indicated a

strong, consistent dominance of the efficiency-risk hypothesis over the franchise-value hypothesis5,

suggesting that more efficient companies use more debt than less efficient companies.

Dragotă et al. (2008) addressed the reverse causality between capital structure and firm profitability

of the Romanian listed companies for the period 1997-2005 using Granger Causality Test. The results

indicated that capital structure does not Granger cause financial returns and the hypothesis that financial

returns does not Granger cause capital structure could not be rejected.

4 An equation specifying profit efficiency as a function of the firm’s equity capital ratio and other variables is used to test the

agency costs hypothesis, and an equation specifying the equity capital ratio as a function of the firm’s profit efficiency and

other variables is used to test the net effects of the efficiency-risk and franchise-value hypotheses. 5 According to Berger and Bonaccorsi di Patti (2006), under the efficiency-risk hypothesis, the expected earnings from high

profit efficiency substitute for equity capital in protecting the firm from the expected costs of bankruptcy or financial distress,

whereas under the franchise-value hypothesis, firms try to protect the income from high profit efficiency by holding

additional equity capital.

7

DATA

This paper analyses the relationship between capital structure and firm value for Romanian non-

financial listed companies during the period 2003-2012. Firstly, were excluded from the sample the non -

financial companies because of the specific regulations regarding their activity, hence their leverage being

influenced by several exogenous factors. Secondly, companies that reported negative equity during the study

period were also excluded in order to obtain homogeneous data. Thirdly, the sample contained only the

companies providing sufficient information for performing the study in good conditions. The final sample

consisted of 48 Romanian companies listed on Bucharest Stock Exchange (BSE) over the years 2003-2012.

Accounting and market information were obtained from the following web sites: www.bvb.ro,

www.kmarket.ro, www.ktd.ro and www.tranzactiibursiere.ro. The indicators computed at the end of each

year using the information provided by these web sites and their formulas are presented in Table 1.

The hypotheses tested in this paper are described in Table 2.

METHODOLOGY

In order to analyze the determinants of capital structure and the relationship between capital structure

and firm value, panel data regression models were used, as a result of their advantages6.

Panel data often violates two important assumptions of the classical linear regression model:

homoscedasticity or equal variance of residuals and no autocorrelation between the disturbances.

In order to test the assumption of no autocorrelation between the disturbances, Durbin-Watson Test

was used. According to Gujarati (2004), this is the most celebrated test for detecting serial correlation. The

homoscedasticity assumption was tested using White’s General Heteroscedasticity Test.

Using balanced panel data of 480 observations, the following equations were estimated:

DAT_ACTIVEit = α1,0 + β1,1TOBIN_Qit + β1,2PROFit + β1,3TANGit + β1,4LIQit + β1,5LN_CAit + +

β1,6GROWTH1it + β1,7SCOREit + ε1it

(Eq.1)

TOBIN_Qit = α2,0 + β2,1DAT_ACTIVEit + β2,2LN_CAit + β2,3GROWTH1it + β2,4SCOREit +

β2,5SCOREit*DAT_ACTIVEit_+ β2,6DUMMY_OPORTit + β2,7DUMMY_OPORTit *DAT_ACTIVEit + ε2it

(Eq.2)

.

The above two equations were estimated using the following models:

6 Firstly, it allows us to control for unobserved firm heterogeneity that is stable over time, through firm specific fixed effects;

Secondly, by combining time series of cross-section observations, panel data give more informative data, more variability

and more efficiency.

8

1. Pooled Regression Model: The intercept and slope coefficients are constant across time and space

and the error term captures differences over time and individuals. This model may be written as follows:

Yit = α1 + β1X1it + β2X2it + β3X3it +… + εit

(M.1).

2. Fixed Effects Model: The slope coefficients are constant but the intercept varies over individuals.

This model can be written as follows:

Yit = α1i + β1X1it + β2X2it + β3X3it + …+ εit

(M.2)

3. Time Effects Model: The slope coefficients are constant but the intercept varies over time. Its

general form is as follows:

Yit = α 1t + β1X1it + β2X2it+ β3X3it +…+ εit

(M.3)

4. The two way fixed effects model: The slope coefficients are constant but the intercept varies over

individuals and time. This model can be written as follows:

Yit = αit + β1X2it + β2X2it + β3X3it +…+ εit

(M.4.)

5. Simultaneous Equations Model: this model allows the estimation of the two equations

simultaneously, in order to control for the endogeneity of capital structure or the reverse causality between

capital structure and firm value. This model has the following form:

{

6. Granger Causality Test: if variable X (Granger) causes variable Y, then changes in X should

precede changes in Y. Therefore, in a regression of Y on other variables (including its own past values) if we

include past or lagged values of X and it significantly improves the prediction of Y, then we can say that X

(Granger) causes Y. A similar definition applies if Y (Granger) causes X. More specifically, Granger test

implies the estimation of the following VAR models:

+∑ ∑

+∑ ∑

The nule hypoyhesis is that the beta coefficients are statistically zero, which means that X(Y)

does not (Granger) cause Y(X).

9

EMPIRICAL RESULTS

General conclusions regarding the estimated models

Tables 4 and 5 present the results of estimating equations Ec.1 and Ec.2 using the five models

described above. At a first glance, the following conclusions can be drawn:

- Best results were obtained using the two way fixed effects model. In this case, R2 had the highest

values. Also, the high values of adjusted R2 prove that the increase in R2 is not the result of including more

variables in equation (dummy variables). Thus, it can be stated that the relationship between capital structure

and firm value, on the one hand, and the relationship between capital structure and its determinants, on the

other hand, vary over time and companies;

- According to Dessi and Robertson (2003), significant fixed effects suggets that unobserved firm

characteristics are important determinants of both capital structure and firm value;

- The results of the OLS and 2SLS estimations are highly similar. Both the coefficients and the

significance levels of the coefficients are hardly influenced by the estimation method. Similar results

were also obtained in the study of De Jong (2002). From this author point of view, the similarity implies

that the endogeneity problem of including leverage and Tobin’s Q as explanatory variables is of minor

importance for the data set. According to Gujarati (2004, pg. 779), similar results of OLS and 2SLS

doesn’t mean that the 2SLS procedure is worthless. That in the present situation the two results are

practically identical should not be surprising, because the R2 values in the first stage are very high, thus

making the estimated ˆDAT_ACTIVE and ˆTOBIN_Q virtually identical with the actual variables.

- The Durbin-Watson statistic values were around 2 in almost all the estimations and the White test did

not reject the null hypothesis of homoscedasticity of residuals. Thus, the results are not affected by

autocorrelation or heteroscedasticy of residuals.

Results regarding the relationship between capital structure and its determinants (Table 4)

Growth opportunities measured by TOBIN_Q have a positive impact on capital structure. This is in

accordance with the results of De Jong (2002), Dessi and Robertson (2003) or Dragotă (2006) and

Dragotă and Semenescu (2008) for Romanian companies, and in contradiction with the results of

Deesomsak et al. (2004), De Jong et al. (2008), Jiraporn și Liu (2008), Al-Najjar și Hussainey (2011).

The positive correlation between growth opportunities and capital structure is consistent with the

pecking order theory.

Profitability has a negative impact on capital structure. This correlation also supports the

pecking order theory according to which more profitable companies borrow less as they have sufficient

internal funds to finance their investments. This correlation is consistent with the results of Fama and

10

French (2002), Dragotă (2006), Supanvanji (2006), De Jong et al. (2008), Tong și Green (2005),

Delcoure (2007), Jiraporn și Liu (2008) and others.

Fixed asssets are negatively correlated with capital structure. Although different theories

(pecking order theory, trade-off theory) and many previous empirical studies (Chen (2002), Huang and

Song (2006), Delcoure (2007), De Jong et al. (2008), Jiraporn and Liu (2008)) support the positive

correlation between capital strucuture and the proportion of fixed assets, in this case, the negative

correlation identified could be explained by the fact that most of the Romanian companies debt is

represented by short term debt, which is ussually used to finance current assets. Negative correlation

between fixed assets and capital structure was also found by Dragotă (2006) and Dragotă et al. (2008 for

Romanian companies, or Bauer (2004), Mazur (2007), Al-Najjar și Hussainey (2011) for other

countries.

Liquidity has a negative impact on capital structure. This correlation supports once again the

pecking order theory. Deesomsak et al. (2004), Janbaz (2010), Mazur (2007) and others also revealed

the negative correlation between capital structure and liquidity.

Size has a positive impact on capital structure. Thus, one can state that larger companies are less

risky and have lower bankrupty risks and costs. Larger companies benefit from higher levels of leverage

due to the stability of their cash-flows. This correlation is in accordance with the trade-off theory and

with the results of Fama and French (2002), Deesomsak et al. (2004) and others.

Financial quality (bankruptcy risk) has a negative (positive) impact on capital structure. Thus,

companies with a better financial quality (and, implicitly, lower bankruptcy risk) borrow less.

Controlling for fixed and time effects, the above variables proved to explain approximately 77%

of the variance in capital structure of Romanian companies.

Results regarding the relationship between capital structure and firm value (Table 5)

Capital structure was found to have a significant positive impact on the value of Romanian

companies over the period 2003-2012: one unit increase in leverage generates an increase with 0.7591

percentage point in firm value when controlling for the endogeneity of capital structure, and an increase

with 0.8358 percentage point in firm value when controlling for fixed and time effects.

Size has also a positive impact on firm value. This could be due to the fact that larger companies

are most popular on the market and more easily assessed by investors. Additionally, larger firms are

often more established and there is less information asymmetry about their operations in the

marketplace. Therefore, it seems likely that this additional certainty will add to firm value.

11

As expected, financial quality (bankruptcy risk) has a positive (negative) impact on Romanian

companies value. In addition to this, the positive coefficient of the interaction term SCORE*DAT_ACTIVE

indicates that the positive impact of leverage to firm value tends to be stronger when firm financial quality is

better. These results are consistent with the results of Dalbor (2007).

The positive coefficient of the dummy variable DUMMY_OPORT indicates that growth

opportunities lead to an increase in firm value. The insignificant coefficient of the interaction term

DUMMY_OPORT*DAT_ACTIVE reveals that the impact of capital structure on firm value does not

depend on firm’s growth opportunities.

In order to ensure the robustness of this last conclusion, the relationship between capital structure and

firm value was estimated separately for companies with high growth opportunities and companies with low

growth opportunities.

Results regarding the relationship between capital structure and firm value after controlling for

growth opportunities

According to the methodology of McConnell și Servaes (1995), Chen (2002), Alonso et al. (2005),

each year the sample was split into two sub-samples (firms with low or high growth opportunities) by

dividing the whole sample into two groups as a function of the median PER value in that specific year.

The following equations were estimated for each sub-sample in every year of the period 2003-2012:

1. Tobin_Qi=α1,0+β1,1DAT_ACTIVEi+β1,3MTBi+β1,4GROWTH1i+β1,5LN_CAi+ε1i

2. Tobin_Qi=α2,0+β2,1DTS_ACTIVEi+β2,2DUMMY_DTLi+β2,3MTBi+β2,4GROWTH1i+β2,5LN_CAi+ε2i

3. Tobin_Qi=α3,0+β3,1DTS_ACTIVEi+β3,2DUMMY_DTLi+β3,3MTBi+β3,4GROWTH2i+

+β3,5LN_ACTIVEi+ε3i7

The results are centralized in Table 6 and can be formulated as follows: the relationship between

capital structure and firm value is positive for both firms facing low growth opportunities and firms

facing high growth opportunities; long term debt ratio does not have a significant impact on firm value;

MTB has a positive impact on firm value; Growth1 and Growth2 have a significant positive impact on

firm value; the impact of company size on firm value does not depends on company’s growth

opportunities.

Granger Causality Test

The Granger causality between capital structure and firm value is presented in Figure 1. The

hypothesis that TOBIN_Q does not (Granger) cause DAT_ACTIVE and the hypothesis that

DAT_ACTIVE does not (Granger) cause TOBIN_Q cannot be rejected (p-values > 5%).

7 The dummy variable DUMMY_DTL was defined to equal one if the firm’s long-term debt ratio is greater than zero, and

zero otherwise.

12

CONCLUSIONS AND FURTHER RESEARCH

Capital structure decision still remains a main issue in Corporate Finance, even 50 years after the

seminal work of Modigliani and Miller (1958). Two important questions regarding capital structure decision

lay the foundations of numerous debates, both theoretical and empirical: Does capital structure decision

affect firm value? and What are the determinants of capital structure?. In this context, the aim of this paper

was to examine whether or not capital structure influences Romanian companies value and to identify the

determinants of capital structure of these companies.

The sample consisted of 48 non-financial companies listed on Bucharest Stock Exchange during

2003-2012. Multiple regression analysis was used along with five models of estimation.

Best results were obtained using the two way fixed effects model. In this case, R2 had the highest

values, leading to the conclusion that the relationship between capital structure and firm value, on the one

hand, and the relationship between capital structure and its determinants, on the other hand, vary over time

and companies. The results of the OLS and 2SLS estimations were highly similar. Both the coefficients

and the significance levels of the coefficients were hardly influenced by the estimation method. This was

consistent with the fact that the endogeneity problem of including leverage and Tobin’s Q as

explanatory variables is of minor importance for Romanian companies. Granger causality test also

supported this conclusion.

Among the determinants of capital structure, growth opportunities and size proved to have a

positive impact on capital structure, while fixed assets, profitability, liquidity and financial quality

proved to have a negative impact on capital structure. Overall, the capital structure structure of

Romanian companies is consistent with the pecking order theory, as profitable companies, with better

financial quality and higher liquidity borrow less and finance their investments using internal resources.

The relationship between capital structure and firm value was found to be statisticaly significant

and positive, in accordance with the signaling theory. Thus, companies that issue debt send a positive

signal to investors, on the grounds that only companies with high financial performance and future

growth opportunities will use debt to finance their assets. In addition to this, the positive impact was

identified for both firms facing low growth opportunities and firms facing high growth opportunities.

This conclusion suggests that the signaling function of debt exceeds the influence of growth

opportunities on agency cost of debt. Size and MTB also have a positive impact on firm value.

Further research on this topic should have in view: other determinants of capital structure (i.e.

cash-flows, industry, business risk, taxes), a larger sample, corporate governance variables (corporate

governance variables are important determinants of both capital structure and firm value), a comparative

analysis before and after the financial crisis from 2008 or a sectorial analysis.

13

REFERENCES

Aggarwal, R., Kyaw, N.A. & Zhao, X. (2011), "Financial environment and the value-leverage

relation", Journal of International Business and Economy, Vol.12, Nr.2, pag. 1- 26.

Aggarwal, R. & Zhao, X. (2007), "The leverage–value relationship puzzle: An industry effects

resolution", Journal of Economics and Business, Vol.59, Nr.4, pag. 286-297.

Agrawal, A. & Knoeber, C. R., (1996). "Firm performance and mechanisms to control agency

problems between managers and shareholders", Journal of Financial and Quantitative Analysis, Vol.31,

pag. 377–397.

Al-Najjar, N. & Hussainey, K. (2011), "Revisiting the capital-structure puzzle: UK evidence",

The Journal of Risk Finance, Vol. 12, Nr. 4, pag. 329 - 338

Alonso, P.D.A., Iturriaga, F.J.L. & Sanz, J.A.R. (2005) "Financial decisions and growth

opportunities: A Spanish firm’s panel data analysis", Applied Financial Economics, Vol.15, pag. 391–

407.

Altan, M. & Arkan, F. (2011), ”Relationship between firm value and financial structure: A study

on firms in ISE industrial index”, Journal of Business & Economics Research, Vol.9, Nr.9, pag. 61–65.

Antwi, S., Mills, F.E & Zhao, X. (2012), “Capital structure and firm value: Empirical evidence

from Ghana”, International Journal of Business and Social Science, Vol. 3 Nr. 22, pag, 103-111.

Baker, M. & Wurgler, J. (2002), “Market timing and capital structure”, Journal of Finance, Vol.

57, Nr. 1, pag. 1-32.

Bauer, P. (2004), “Determinants of capital structure: Empirical evidence from the Czech

Republic”, Czech Journal of Economics and Finance, Vol. 54, Nr.1-2, pag. 2-21.

Berger, A.N., & Bonaccorsi di Patti, E. (2006), ”Capital structure and firm performance: A new

approach to testing agency theory and an application to the banking industry”, Journal of Banking &

Finance, Vol.30, pag. 1065-1102.

Chen, J.J. (2002), “Determinants of capital structure of Chinese-listed companies”, Journal of

Business Research, Vol. 57, Nr. 12, pag. 1341-1351.

Chen, K. (2002), "The influence of capital structure on company value with different growth

opportunities”, working paper, EFMA 2002 London Meetings.

Cheng, M-C. & Tzeng, Z-C. (2011), ”The effect of leverage on firm value and how the firm

financial quality influence on this effect”, World Journal of Management, Vol. 10, Nr. 3, pag. 30-53.

14

Dalbor, M.C, Lee, S. & Upneja, A. (2007), "An investigation of long-term debt and firm value in

the lodging industry", Emerald Group Publishing Limited, Vol 3. pag. 195 – 204.

Deesomsak, R., Paudyal, K. and Pescetto, G. (2004), “The determinants of capital structure:

Evidence from the Asia Pacific region”, Journal of Multinational Financial Management, Vol. 14, Nr.

4-5, pag. 387-405.

De Jong, A. (2002), “The disciplining role of leverage in Dutch firms”, European Finance

Review, Vol. 6, Nr. 1, pag. 31-62.

De Jong, A., Kabir, R. & Nguyen, T.T. (2008), "Capital structure around the world: The roles of

firm- and country-specific determinants", Journal of Banking & Finance, Vol. 32, Nr. 9, pag. 1954-

1969.

Delcoure, N. (2007), “The determinants of capital-structure in transitional economies”, Journal

of International Review of Economics and Finance, Vol. 16, Nr. 3, pag. 400-415.

Dessí, R. & Robertson, D. (2003), "Debt, incentives and performance: Evidence from UK panel

data" The Economic Journal, Vol. 113, Nr. 490, pag. 903-919.

Dragotă, I.M. (2006), Decizia de investire pe piața de capital, Ed. ASE, București.

Dragotă, I.M., Dragotă, V., Oreja-Braşoveanu, L. & Semenescu, A. (2008), “Capital structure

determinants: A sectorial analysis for the Romanian listed companies”, Economic Computation and

Economic Cybernetics Studies and Research, Vol. 42, Nr. 1-2, pag. 155-172.

Dragotă, I.M. & Semenscu, A. (2008), ”A dynamic analysis of capital structure determinants.

Empirical results for Romanian capital market”, Theoretical and Applied Economics, Nr. 4, pag. 65-80.

Dragotă I.M., Semenescu, A., Pele, T. & Lipară, C. (2007), ”Capital structure and financial

performance – related or independent variables? Empirical study of Romanian companies listed on

capital market”, Theoretical and Applied Economics. Supplement, pag. 143-150.

Fama, E.F. & French, K. R. (2002), "Testing trade‐off and pecking order predictions about

dividends and debt", Review of Financial Studies, Vol. 15, Nr. 1, pag. 1-33.

Frank, M.Z. & Goyal V.K. (2009), "Capital structure decisions: Which factors are reliably

important?", Financial Management, Vol. 38, Nr. 1, pag. 1-37.

Ghosh, S. (2007), ”Leverage, managerial monitoring and firm valuation: A simultaneous

equation approach”, Research in Economics, Vol. 61, pag. 84–98..

Gujarati, D. (2004), Basic Econometrics - Fourth Edition, McGraw-Hill, New York.

Gungoraydinoglu, A. & Öztekin, Ö. (2011), "Firm- and country-level determinants of corporate

leverage: Some new international evidence", Journal of Corporate Finance, Vol. 17, Nr. 5, pag. 1457-

1474.

15

Harvey, C.R., Linsc, K.V. & Roperd, A.H. (2004), ”The effect of capital structure when

expected agency costs are extreme”, Journal of Financial Economics, Vol. 74, Nr.1, pag 3–30.

Hopkison, W.G. (2002), “A new view of statistics: Effect magnitudes”,

<http://www.sportsci.org/resource/stats/effectmag.html>.

Huang, G. & Song, F.M. (2006), “The determinants of capital structure: Evidence from China”,

China Economic Review, Vol. 17, Nr. 1, pp. 14-36.

Janbaz, M. (2010), “Capital structure decisions in the Iranian corporate sector”, International

Research Journal of Finance and Economics, Vol. 58, Nr. Nr.1, pag. 24-31.

Jiraporn, P. & Liu, Y. (2008), ”Capital structure, staggered board and firm value”, Financial

Analysts Journal, Vol. 64, Nr. 1, pag. 49-60..

Kraus A. & Litzenberger, R.H. (1973), “A state-preference model of optimal financial leverage”,

Journal of Finance, Vol. 28, Nr. 4, pag. 911-922.

Margaritis, D. & Psillaki, M. (2007), ”Capital structure and firm efficiency”, Journal of Business

Finance & Accounting, Vol. 34, Nr. 10, pag. 1447–1469.

Mazur, K. (2007), “The determinants of capital structure choice: Evidence from Polish

companies”, International Advances in Economic Research, Vol. 13, Nr. 4, pag. 495-514.

McConnel, J.J. & Servaes, H. (1995), “Equity ownership and the two faces of debt”, Journal of

Financial Economics, Vol. 39, pp. 131 – 157.

Modigliani, F. & Miller, H.M. (1958), "The cost of capital, corporation finance and the theory of

investment", The American Economic Review, Vol. 48, Nr. 3, pag. 261-297.

Modigliani, F. & Miller, H.M., (1963), "Corporate income taxes and the cost of capital: A

correction." The American Economic Review, Vol. 53, Nr. 3, pag. 433-443.

Myers S. (1984), “The capital structure puzzle”, Journal of Finance, Vol. 39, Nr. 3, pag. 575-

592.

Ogbulu, O.M. & Emeni, F.K. (2012), “Capital structure and firm value: Empirical evidence from

Nigeria”, International Journal of Business and Social Science, Vol. 3 Nr. 19, pag. 252-261.

Phillips, G.M. & McKay, P. (2005), ”How does industry affect firm financial structure?”, Review

of Financial Studies, Vol. 18, Nr. 4, pag. 1433-1466.

Rathinasamy, R.S., Krishnaswamy, C.R. & Mantripragada, K.G. (2000), ”Capital structure and

product market interaction: An international perspective”, Global Business and Finance Review, Vol. 5,

Nr.2, pag. 51-63.

Rayan, K. (2008), “Financial leverage and firm value”, Gordon Institute of Business Science,

University of Pretoria.

16

Ross, S.A. (1977), “The determination of financial structure - The Incentive Signaling

Approach”, Bell Journal of Economics and Management Science, Vol 8, No 1, pp. 1-23.

Sudiyat, B., Puspitasari, E. & Kartika, A. (2012), “The company's policy, firm performance and

firm value: an empirical research on Indonesia Stock Exchange”, American International Journal of

Contemporary Research, Vol. 2, Nr.12, pag. 30-40.

Supanvanij, J. (2006), ”Capital structure: Asian firms vs. multinational firms in Asia”, Journal of

American Academy of Business,Vol.10, pag. 324–330.

Tian, G.G & Zeitun, R. (2007),”Capital structure and corporate performance: Evidence from

Jordan”, The Australasian Accounting Business & Finance Journal, Vol. 1, Nr. 4, pag. 16-37.

Tong, G. & Green, C.J. (2005), “Pecking-order or trade-off hypothesis? Evidence on the capital

structure of Chinese companies”, Applied Economics, Vol. 37, Nr. 19, pag. 2179-2189.

[www.bvb.ro]

[www.kmarket.ro]

[www.ktd.ro]

[www.tranzactiibursiere.ro]

17

APPENDIX

Variables description

Table 1.

INDICATOR FORMULA

Market capitalization (CAP)

Tobin’s Q

Price to Earnings Ratio (PER)

Market to Book Ratio (MTB)

Debt Ratio

(DAT_ACTIVE)

Long term debt Ratio

(DTL_ACTIVE)

Short term debt ratio

(DTS_ACTIVE)

Debt to Equity Ratio

(DAT_CPR)

Times Interest Earned

(TIE)

Profitability (PROF)

Liquidity (LIQ)

Tangibility (TANG) Fixed assets/Total assets

Size of the company 1

(LN__CA)

Ln(Sales)

Size of the company 2

(LN_ACTIVE)

Ln(Assets)

Growth rate 1 (Growth1)

Growth rate 2 (Growth2) (

Bankruptcy risk/

Firm financial quality (SCORE)

where: X1=Total Assets; X2=Sales; X3=Total debt; X4=Net profit; X5=EBIT;

X6=Market Capitalization .

18

Hypotheses regarding determinants of capital structure (DAT_ACTIVE)

and firm value (TOBIN_Q)

Table 2.

DAT_ACTIVE TOBIN_Q

Variables Empirical

evidence

Expected

relationship

Empirical

evidence

Expected

relationship

DAT_ACTIVE / / +/- +/-

TOBIN_Q +/- - / /

MTB / / + +

PROF +/- - / /

LIQ +/- - / /

TANG + + / /

SIZE + + +/- +/-

GROWTH +/- + + +

SCORE - - + +

*SCORE*DAT_ACTIVE / / + +

*DUMMY_OPORT / / + +

*DUMMY_OPORT*

DAT_ACTIVE / / + +

Legend: +: positive relationship between variables; : negative relationship between variables;

*DUMMY_OPORT is a dummy variable with value of one if the company has growth opportunities,

and zero otherwise. For a firm-year to qualify as having growth opportunities, its PER has to be above the

median PER for all firms in that specific year. The interaction term DUMMY_OPORT*DAT_ACTIVE was

introduced in order to test if the influence of leverage on firm value depends on firm’s growth opportunities.

Similary, the interaction term SCORE*DAT_ACTIVE was introduced in order to test if the influence of

leverage to firm value tends to be stronger (if the coefficient is positive) when firm financial quality is better

19

Classification of correlation coefficients according to Hopkins (2002)

Table 3.

Correlation

Coefficient

Descriptor

0.0-0.1 trivial, very small, insubstantial, tiny, practically zero

0.1-0.3 small, low, minor

0.3-0.5 moderate, medium

0.5-0.7 large, high, major

0.7-0.9 very large, very high, huge

0.9-1 nearly, practically, or almost: perfect, distinct, infinite

Panel regressions results for Eq. 1: DAT_ACTIVEit = α1,0 + β1,1TOBIN_Qit + β1,2PROFit +

β1,3TANGit + β1,4LIQit + β1,5LN_CAit + + β1,6GROWTH1it + β1,7SCOREit + ε1it

Table 4.

Panel regressions results for Eq 2: TOBIN_Qit = α2,0 + β2,1DAT_ACTIVEit + β2,2LN_CAit +

β2,3GROWTH1it + β2,4SCOREit + β2,5SCOREit*DAT_ACTIVEit_+ β2,6DUMMY_OPORTit +

β2,7DUMMY_OPORTit *DAT_ACTIVEit + ε2it

Table 5.

TOBIN_Q PROF TANG LIQ LN_CA SCORE

(β1,1) (β1,2) (β1,3) (β1,4) (β1,5) (β1,6)

M1 +0.0868*** -0.5975*** -0.3438*** -0.0148*** +0.0160*** -0.0261*** 43.52%

M2 +0.0678*** -0.3556*** -0.2359*** -0.0063*** +0.0193* -0.0179*** 76.82%

M3 +0.0917*** -0.6372*** -0.3399*** -0.0144*** +0.0176*** -0.0264*** 44.38%

M4 +0.0731*** -0.4209*** -0.2284*** -0.0058*** +0.0301*** -0.0186*** 77.57%

M5 +0.2568*** -0.6175*** -0.2183*** -0.0087*** +0.0046* -0.0382*** 46.41%

Independent variables

R2

Dependent variable:

DAT_ACTIVE

DAT_ACTIVE LN_CA SCORE DUMMY_OPORT

(β2,1) (β2,2) (β2,3) (β2,5)

M1 +0.6605*** +0.0314* +0.0437*** +0.3622*** +0.3261*** -0.2736 38.02%

M2 +0.8447*** +0.0071 +0.0441*** +0.44026*** +0.2330*** -0.1834 57.73%

M3 +0.6615*** +0.0277* +0.0386*** +0.3537*** +0.3479*** -0.3500* 44.35%

M4 +0.8358*** -0.0384 +0.0369*** +0.4456*** +0.2636*** -0.2671 63.94%

M5 +0.7591** +0.0309* +0.0467*** +0.3521*** +0.3562** -0.3427 36.07%

DUMMY_OPORT*

DAT_ACTIVE (β2,6)R

2

Independent variables

SCORE*DAT_

ACTIVE (β2,4)

Dependent variable:

TOBIN_Q

20

Results regarding the relationship between capital structure and firm value after

controlling for growth opportunities

Table 6.

Legend: ↗: companies with high growth opportunities; ↘: companies with low growth opportunities;

+: positive impact on firm value; : negative impact on firm value;

Total: number of equations in which the relationship between firm value and its determinants was positive/negative;

Significant: number of equations in which the positive/negative relationship between capital structure and firm value was

significant at p<10%.

Figure 1.

The Granger Causality Test between capital structure (DAT_ACTIVE)

and firm value (TOBIN_Q)

- + - + - + - + - + - + - + - + - + - + - + - +

Total 1 9 2 8 4 16 7 13 8 12 6 14 0 30 0 30 10 20 14 16 12 18 6 26

Significant 0 8 0 7 0 15 0 10 1 0 0 1 0 25 0 30 5 1 2 3 0 3 1 7

SIZE

↘ ↗↘ ↗ ↘ ↗ ↘ ↗

No. of

positive/negative

correlations

↘ ↗ ↘ ↗

DAT_ACTIVE DTS_ACTIVE DUMMY_DTL MTB GROWTH