Capital structure and firm value. Empirical evidence from Romanian listed companies

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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 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 1

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). 2

3 Trade-off theory Modigliani and Miller s propositions were changed according to the trade-off theory as follows (Myers, 2003): V L = D + E = V U + VP (tax shields) VP (bankruptcy costs) where: V L - market value of the levered firm; V U - 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: V L = V U + VP (tax shields) VP (bankruptcy costs) - VP(agency costs), where: V L - market value of the levered firm; V U - 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. 3

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 The regression equation used to determine the relationship between capital structure and firm value was as follows: 1 Firm_Value it = a 0 + a 1 LTD it + a 2 RISK it + a 3 CAPEX it + a 4 NITA it + a 5 SIZE it + e it 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 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; 1 Firm 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 4

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 JSE 2 excluding the banking industry for the period 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 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. 5

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 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 simultaneousequations 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 hypothesis 5, 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 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. 6

7 DATA This paper analyses the relationship between capital structure and firm value for Romanian nonfinancial listed companies during the period 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 Accounting and market information were obtained from the following web sites: and 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 advantages 6. 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_ACTIVE it = α 1,0 + β 1,1 TOBIN_Q it + β 1,2 PROF it + β 1,3 TANG it + β 1,4 LIQ it + β 1,5 LN_CA it + + β 1,6 GROWTH1 it + β 1,7 SCORE it + ε1 it TOBIN_Q it = α 2,0 + β 2,1 DAT_ACTIVE it + β 2,2 LN_CA it + β 2,3 GROWTH1 it + β 2,4 SCORE it + (Eq.1) β 2,5 SCORE it *DAT_ACTIVE it_ + β 2,6 DUMMY_OPORT it + β 2,7 DUMMY_OPORT it *DAT_ACTIVE it + ε2 it (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. 7

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: Y it = α 1 + β 1 X1 it + β 2 X2 it + β 3 X3 it + + ε it (M.1). 2. Fixed Effects Model: The slope coefficients are constant but the intercept varies over individuals. This model can be written as follows: Y it = α 1i + β 1 X1 it + β 2 X2 it + β 3 X3 it + + ε it (M.2) 3. Time Effects Model: The slope coefficients are constant but the intercept varies over time. Its general form is as follows: Y it = α 1t + β 1 X1 it + β 2 X2 it + β 3 X3 it + + ε it (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: Y it = α it + β 1 X2 it + β 2 X2it + β 3 X3 it + + ε it (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). 8

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, R 2 had the highest values. Also, the high values of adjusted R 2 prove that the increase in R 2 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 R 2 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 9

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 : one unit increase in leverage generates an increase with percentage point in firm value when controlling for the endogeneity of capital structure, and an increase with 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. 10

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 : 1. Tobin_Q i =α 1,0 +β 1,1 DAT_ACTIVE i +β 1,3 MTB i +β 1,4 GROWTH1 i +β 1,5 LN_CA i +ε1 i 2. Tobin_Q i =α 2,0 +β 2,1 DTS_ACTIVE i +β 2,2 DUMMY_DTL i +β 2,3 MTB i +β 2,4 GROWTH1 i +β 2,5 LN_CA i +ε2 i 3. Tobin_Q i =α 3,0 +β 3,1 DTS_ACTIVE i +β 3,2 DUMMY_DTL i +β 3,3 MTB i +β 3,4 GROWTH2 i + +β 3,5 LN_ACTIVE i +ε3 i 7 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. 11

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 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, R 2 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. 12

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17 APPENDIX Variables description Table 1. INDICATOR Market capitalization (CAP) Tobin s Q FORMULA 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(Sales) (LN CA) Size of the company 2 Ln(Assets) (LN_ACTIVE) Growth rate 1 (Growth1) Growth rate 2 (Growth2) ( Bankruptcy risk/ Firm financial quality (SCORE) where: X 1 =Total Assets; X 2 =Sales; X 3 =Total debt; X 4 =Net profit; X 5 =EBIT; X 6 =Market Capitalization. 17

18 Hypotheses regarding determinants of capital structure (DAT_ACTIVE) and firm value (TOBIN_Q) DAT_ACTIVE TOBIN_Q Variables Empirical Expected Empirical Expected evidence relationship evidence relationship DAT_ACTIVE / / +/- +/- TOBIN_Q +/- - / / MTB / / + + PROF +/- - / / LIQ +/- - / / TANG + + / / SIZE + + +/- +/- GROWTH +/ SCORE * SCORE*DAT_ACTIVE / / + + Table 2. * 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 18

19 β 1,3 TANG it + β 1,4 LIQ it + β 1,5 LN_CA it + + β 1,6 GROWTH1 it + β 1,7 SCORE it + ε1 it Table 4. β 2,7 DUMMY_OPORT it *DAT_ACTIVE it + ε2 it Table 5. Classification of correlation coefficients according to Hopkins (2002) Correlation Descriptor Coefficient trivial, very small, insubstantial, tiny, practically zero small, low, minor moderate, medium large, high, major very large, very high, huge nearly, practically, or almost: perfect, distinct, infinite Table 3. Panel regressions results for Eq. 1: DAT_ACTIVE it = α 1,0 + β 1,1 TOBIN_Q it + β 1,2 PROF it + Independent variables Dependent variable: TOBIN_Q PROF TANG LIQ LN_CA SCORE DAT_ACTIVE R 2 (β 1,1 ) (β 1,2 ) (β 1,3 ) (β 1,4 ) (β 1,5 ) (β 1,6 ) M *** *** *** *** *** *** 43.52% M *** *** *** *** * *** 76.82% M *** *** *** *** *** *** 44.38% M *** *** *** *** *** *** 77.57% M *** *** *** *** * *** 46.41% Panel regressions results for Eq 2: TOBIN_Q it = α 2,0 + β 2,1 DAT_ACTIVE it + β 2,2 LN_CA it + β 2,3 GROWTH1 it + β 2,4 SCORE it + β 2,5 SCORE it *DAT_ACTIVE it_ + β 2,6 DUMMY_OPORT it + Dependent variable: TOBIN_Q Independent variables DAT_ACTIVE LN_CA SCORE SCORE*DAT_ DUMMY_OPORT (β 2,1 ) (β 2,2 ) (β 2,3 ) ACTIVE (β2,4) (β 2,5 ) DUMMY_OPORT* DAT_ACTIVE (β 2,6 ) M *** * *** *** *** % M *** *** *** *** % M *** * *** *** *** * 44.35% M *** *** *** *** % M ** * *** *** ** % R 2 19

20 Results regarding the relationship between capital structure and firm value after controlling for growth opportunities Table 6. No. of DAT_ACTIVE DTS_ACTIVE DUMMY_DTL MTB GROWTH SIZE positive/negative correlations Total Significant 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) 20