The long- and short-term determinants of the capital structure of Polish companies 3.

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1 Natalia Szomko 12 The long- and short-term determinants of the capital structure of Polish companies 3. Abstract: The aim of this article is to assess the long-term and short-term influence of selected factors on the capital structure of the Polish companies. In the light of the trade-off theory and the pecking order theory, the main factors affecting the capital structure are identified. Afterwards, the set of determinants are analyzed on the basis of previous empirical studies. Taking into consideration the properties of the data describing capital structure, it is argued that the Between and the Within fixed effects estimators can be used to assess the long term and short term impact of selected factors on the debt ratios of Polish companies. In both long and short run, the capital structure of Polish companies is influenced by profitability, tangibility of assets, non-debt tax shield, tax rate, business risk and liquidity. Growth opportunities, dividend payments, capital expenditures and financial deficit have a significant impact on debt ratios only in the long term, while in the short term size and industry median debt ratio play a significant role. The results of the study point out that both the direction and the magnitude of the impact of the factors under review may differ between the long term and short term. Keywords: capital structure, debt ratio, pecking order theory, trade-off theory EFM classification codes: Warsaw School of Economics, Department of Capital Markets, al. Niepodległości 162, Warsaw, Poland, nszomk@sgh.waw.pl 2 Research areas (EFM classification codes): 140, 240, 220, 130; 3 The research project was financed by the Collegium of World Economy, Warsaw School of Economics from the grant-in-aid for research and development of young researchers and doctoral studies participants for year

2 Introduction The aim of this article is to assess the long-term and short-term influence of selected factors on the capital structure of the Polish companies. The answer to this question will contribute to the knowledge concerning the mechanics of capital structure and its changes for the companies listed on Warsaw Stock Exchange. Capital structure of the companies can be described on the basis of two theories: the trade-off theory and the pecking order theory. The first one stipulates that the companies try to reach target capital structure, which is estimated on the basis of the analysis of costs and benefits of additional debt. As far as the pecking order theory is concerned, it suggests that there exists a hierarchy of financing sources, where internal capital is the most favored, while afterwards the companies decide to use debt. The external equity is used only as a last resort. As a result, if the companies do not follow any target capital structure, then the decisions of the companies are not in line with the trade-off theory. According to the trade-off theory, the changes to the capital structure of the companies is driven by the attempt to reach the target capital structure. The pecking order theory stipulates that the current capital structure of the company is the results of the previous decisions concerning the choice of the financing sources. In both cases there should exist a difference in the magnitude (and perhaps direction) of the impact of selected factors on the capital structure of the companies analyzed in the long-run and in the short-run. The financial situation of the company is affected by numerous circumstances which can change in long or short term. Although the companies may not adjust their current capital structure to the target, such an effect may be confused with the differences in the mechanics of long-run and shortrun reaction of capital structure to the changes in its determinants. The number of studies focusing on the capital structure determinants is ample. Most of them focus either on the influence of selected factors on the current capital structure of the companies, or on the identification of the capital structure determinants and their impact on the target debt ratios. The list of factors claimed to shape the values of current debt ratios of the companies is long, though they focus on the short-term impact. Nonetheless, as Lemmon et al. [2008] point out, the debt ratios of the companies are persistent over time, and the historical values of debt ratios have a significant influence on the current capital structure even after traditional capital structure determinants are taken into account. The set of capital structure determinants identified in the previous studies may differently influence the capital structure of the companies in the long-run and short-run. For this reason, the long-term and short-term impact of these factors is estimated with the use of fixed effects estimator and general method of moments estimators. The results show that in the long run, size of the company positively influences debt ratios, while growth opportunities, profitability and non-debt tax shield negatively. In the short run, size, 2

3 tangibility of assets and industry median debt ratio have positive impact on debt ratios, while profitability and non-debt tax shield negative. The rest of the paper is organized as follows. In the first part, the trade-off theory is described and compared with the pecking-order theory. In the second part, the main results of previous empirical research on the target capital structure and the adjustment speeds are presented. Afterwards, the existence of target capital structure is discussed for the companies listed on the Warsaw Stock Exchange. It is followed by a description of the data and methods used in this study. The next part presents the estimation results, i.e. long- and short-term capital structure determinants for the Polish companies. The last section concludes. Theories of capital structure The capital structure of the companies is described on the basis of two main theories: the trade-off theory and the pecking order theory. These theories differ not only in the suggested mechanisms of capital structure decisions, but also in the set of factors affecting the capital structure. According to the trade-off theory, the goal of the financial policy pursued by company s managers is to achieve the optimal capital structure, which maximizes the value of the company [Huang and Ritter, 2009]. The optimal capital structure balances the costs and benefits of additional debt. On one hand, higher value of debt results in higher value of debt tax shield, which diminishes tax liabilities of the company. On the other hand, higher debt increases direct and indirect costs of financial distress, while negatively influencing company s financial elasticity and the ability to pay dividends. It has to be underlined that as the value of these costs and benefits cannot be precisely calculated, the managers focus on the target capital structure. The theoretical models built on the basis of trade-off theory assumptions pay special attention to, among others, such capital structure determinants as tax advantages [Stiglitz, 1973; Strebulaev, 2007], bankruptcy costs [Bradley et al., 1984; Strebulaev, 2007], profitability [Brennan and Schwartz, 1978; Strebulaev, 2007], size [Fischer, Heinkel and Zechner, 1989], non-debt tax shield [Bradley et al., 1984], business risk [Leland, 1994; Miao, 2005], uniqueness of product [Strebulaev, 2007], growth opportunities [Brennan and Schwartz, 1978], and net investment [Brennan and Schwartz, 1978]. In the light of the tradeoff theory, the following determinants have negative influence on target capital structure: growth opportunities, R&D expenditures, non-debt tax shield, business risk, probability of bankruptcy, capital expenditures and uniqueness of product. Factors having positive impact on capital structure are: profitability, size, tangibility of assets, liquidity, asset turnover and business risk. Taking into consideration the target debt ratio, it is important to differentiate between two versions of the trade-off theory. The static trade-off theory stipulates that adjustment to 3

4 the target capital structure is made in every period. The target capital structure may be constant for the company, but it is also possible that it changes with the situation of the company [Fischer, Heinkel and Zechner, 1989; Leland, 1994] or that there may exist not a single target capital structure, but rather a range of target capital structures [Fischer, Heinkel and Zechner, 1989; Titman and Tsyplakov, 2007]. Therefore significant changes in the values of the target capital structure determinants can lead to the change in both target capital structure and the size of the adjustment necessary for the capital structure to lie within the target range. Another issue worth considering is the frequency of capital structure adjustments. According to the dynamic trade-off theory, the costs of capital structure adjustment have to be taken into account [Hamada et al., 1984; Fischer, Heinkel and Zechner, 1989; Roberts and Leary, 2004; Strebulaev, 2007; Titman and Tsyplakov, 2007]. The theoretical models listed above conclude that due to the existence of these costs, the companies do not adjust their capital structure in every period. The adjustment will take place only when the discrepancy between the actual and target capital structure is large enough to justify the costs of adjustment. The costs in question are higher when the debt ratio is higher than its target [Hamada et al., 1984]. Nonetheless, due to the similarities in the adjustment criteria, the capital structure of the companies should change in time in the similar manner [Leland, 1994]. The second theory of the capital structure the pecking order theory claims that the managers have strict preferences for the financing sources [Myers, 1984; Myers and Majluf, 1984]. The company s operations are financed firstly with internal sources (retained earnings and cash and its equivalents). Afterwards, the managers increase external financing, while debt is preferred to equity. As a consequence, the capital structure is the result of the previous choices of financing sources, and the optimal capital structure does not exist. The presented order of financing sources is a result of the information asymmetry between the stakeholders and the managers [Roberts and Leary, 2010]. The managers prefer the sources which are safer and the value of which changes less if the managers make public the information that may influence the stakeholders perception of the value of the company. It should also be noted that the static version of pecking order theory stipulates the permanence of the preferences. According to the dynamic version of the theory, the preferences in question may change as a response to the change of the situation on capital markets or the change in the valuation of the company [Lucas and McDonald, 1990]. The theoretical models build on the basis of the pecking order theory do not directly list the factors affecting capital structure of the companies, as they focus rather on the decision concerning the choice between the sources of financing. However, taking into consideration the fact that according to this theory the current capital structure of the company is a result of the previous decisions, it is possible to list the capital structure determinants and identify the direction of their influence. Profitability and liquidity should have negative impact on the debt ratios of the company: the more profit and cash does the company have, 4

5 the less debt it is willing to use. The influence of size on the debt ratios is expected to be positive, as larger companies use more debt financing due to the higher information asymmetry. As far as tangibility of assets is concerned, its negative impact on debt ratios is motivated by the fact that companies with higher share of tangible assets face lower information asymmetry. Financial deficit should have a positive impact on debt ratios: the higher is the deficit, the more external capital is necessary to pursue investment projects. Capital expenditures should have positive impact on debt ratios: the higher is the value of the current investment projects of the company, the more external capital is needed to finance them. Moreover, according to the dynamic version of the pecking order theory growth opportunities should have also positive influence on debt ratios, as the managers may be willing to use more external financing for the current projects when they predict that future investments may require higher value of outlays, thus leaving more internal financing available for the future. The same holds for business risk, as higher variability of results leads the managers to save more internal resources for the future. It should also be noted that if the company pays dividends, then its requirement for financing is higher, thus it positively influences the debt ratios. To sum up, the list of the factors affecting capital structure according to the trade-off theory and the pecking order theory varies significantly. The predicted direction of the influence of these determinants differs for profitability, growth opportunities, tangibility of assets, liquidity and capital expenditures. Results of the empirical research on capital structure determinants Among the previous research concerning the mechanics of capital structure it is possible to distinguish several research questions. Firstly, a significant number of studies focuses on the determinants of current capital structure of the companies. Secondly, the determinants of the target capital structure and adjustment of the actual capital structure to the target is analyzed. There exist also several studies based on the simulated and empirical data, which aim to assess the ability of the currently used statistical methods to describe the mechanics of the capital structure of the companies. The information on the capital structure obtained on the basis of financial data of the companies is complemented by the results of the surveys among the managers, conducted among the companies operating both in United States and Europe. As far as the results of the surveys are concerned, they show that most of the American and European managers claim to have a target capital structure [Graham and Harvey, 2001; Bancel and Mittoo, 2004; Brounen, de Jong and Koedjik, 2006]. Nonetheless, these studies do not precisely state how the target capital structure is defined and do not provide information on the understanding of this term by the managers. Therefore on the basis 5

6 of these results it cannot be firmly confirmed that the behavior of the companies is in line with the trade-off theory. For the purpose of this study it is necessary to identify the set of determinants documented to influence the target capital structure. The factors analyzed in the selected studies are presented in Table 1. Table 1. Determinants of the capital structure analyzed in the previous empirical research. Variable growth opportunities profitability size tangibility of assets non-debt tax shield median industry debt ratio R&D expenditures dividends Selected studies Arioglu and Tuan (2014), Brendea (2014), Chang, Chou and Huang (2014), Dang and Garret (2015), Dang, Kim and Shin (2012), Dang, Kim and Shin (2014), Farhat, Cotei and Abugri (2006), Faulkender et al. (2012), Flannery and Rangan (2006), Lemma and Negash (2014), Lemmon, Roberts and Zedner (2008), Lockhart (2014), Matemilola et al. (2015), Oino and Ukaegbu (2015), Ozkan (2001), Oztekin and Flannery (2012) Arioglu and Tuan (2014), Brendea (2014), Chang, Chou and Huang (2014), Dang and Garret (2015), Dang, Kim and Shin (2012), Dang, Kim and Shin (2014), Farhat, Cotei and Abugri (2006), Faulkender et al. (2012), Flannery and Rangan (2006), Lemma and Negash (2014), Lemmon, Roberts and Zedner (2008), Lockhart (2014), Matemilola et al. (2015), Oino and Ukaegbu (2015), Ozkan (2001), Oztekin and Flannery (2012) Arioglu and Tuan (2014), Brendea (2014), Chang, Chou and Huang (2014), Dang and Garret (2015), Dang, Kim and Shin (2012), Dang, Kim and Shin (2014), Farhat, Cotei and Abugri (2006), Faulkender et al. (2012), Flannery and Rangan (2006), Lemma and Negash (2014), Lemmon, Roberts and Zedner (2008), Lockhart (2014), Matemilola et al. (2015), Oino and Ukaegbu (2015), Ozkan (2001), Oztekin and Flannery (2012) Arioglu and Tuan (2014), Brendea (2014), Chang, Chou and Huang (2014), Dang and Garret (2015), Dang, Kim and Shin (2012), Dang, Kim and Shin (2014), Farhat, Cotei and Abugri (2006), Faulkender et al. (2012), Flannery and Rangan (2006), Lemma and Negash (2014), Lemmon, Roberts and Zedner (2008), Lockhart (2014), Matemilola et al. (2015), Oino and Ukaegbu (2015), Oztekin and Flannery (2012) Chang, Chou and Huang (2014), Dang and Garret (2015), Dang, Kim and Shin (2012), Dang, Kim and Shin (2014), Farhat, Cotei and Abugri (2006), Faulkender et al. (2012), Flannery and Rangan (2006), Lockhart (2014), Matemilola et al. (2015), Ozkan (2001), Oztekin and Flannery (2012) Arioglu and Tuan (2014), Chang, Chou and Huang (2014), Dang and Garret (2015), Dang, Kim and Shin (2014), Faulkender et al. (2012), Flannery and Rangan (2006), Lemmon, Roberts and Zedner (2008), Lockhart (2014), Oztekin and Flannery (2012) Chang, Chou and Huang (2014), Dang and Garret (2015), Dang, Kim and Shin (2014), Faulkender et al. (2012), Flannery and Rangan (2006), Lockhart (2014), Oztekin and Flannery (2012) Dang and Garret (2015), Lemma and Negash (2014), Lemmon, Roberts and Zedner (2008) tax rate Dang and Garret (2015), Oino and Ukaegbu (2015), Oztekin and Flannery (2012) business risk Lemma and Negash (2014), Lemmon, Roberts and Zedner (2008) debt rating Chang, Chou and Huang (2014), Flannery and Rangan (2006) liquidity Ozkan (2001), Oztekin and Flannery (2012) Source: own work. Most of the reviewed studies include in the analysis the following determinants: growth opportunities, profitability, size and tangibility of assets. These factors were identified as the most important in the seminal paper of Rajan and Zingales [1995]. The studies 6

7 mentioned above do not agree on the direction of influence of growth possibilities and tangibility of assets on target debt ratios. Profitability has a negative impact on target capital structure, while the impact of size is positive. Among other variables presented in the table above, median industry debt ratio and debt rating have positive influence on the target capital structure, while non-debt tax shield, R&D expenditures, dividends and liquidity negative. Existing studies did not reach an agreement on the direction of impact for tax rate and business risk. It should also be underlined that the list of variables in table 1 is not exhaustive: among other variables included in the previous studies are: probability of bankruptcy [Dang and Garret, 2015], capital expenditures [Dang, Kim and Shin, 2012], financial deficit [Dang, Kim and Shin, 2012], corporate governance standards [Chang, Chou and Huang, 2014], macroeconomic variables [Oztekin and Flannery, 2012] or institutional environment of the country [Nivorozhkin, 2005]. As far as previous research for the companies operating in Poland is concerned, the results are similar to the described above. Debt ratios for Polish companies are influenced by profitability [Gajdka, 2003; Mazur, 2007; Joever, 2012, Białek-Jaworska and Nehrebecka, 2015], growth opportunities [Klapper et al., 2006], size [Klapper et al., 2006; Joever, 2012; Mazur, 2007], tangibility of assets [Klapper et al., 2006; Joever, 2012, Gajdka, 2002], nondebt tax shield [Gajdka, 2002; Mazur, 2007], liquidity [Gajdka, 2002; Mazur, 2007] and dividends [Mazur, 2007]. It should be underlined that according to the previous studies, most of the variability of the debt ratios can be explained on the basis of the factors characteristic for the companies [Balkrishnan and Fox, 1993; Kayo and Kimura, 2011; Joever, 2012]. Nonetheless, the variability of the capital structure determinants used in the empirical research has limited power to explain the variability of the observed debt ratios [Lemmon, Roberts and Zender, 2008; Frank and Goyal, 2005]. The reason for this conclusion, apart from the existence of the omitted variables, may stem from the omission of long-term relations between the capital structure and its determinants in the model specification. Taking into account the high persistence of the debt ratios, in the dynamic specification of the regression models lagged dependent variable is usually used as one of the explanatory variables [Flannery and Hankins, 2013; Roodman, 2006]. It may be argued that the long-run effect of the capital structure determinants is already consumed in the model by the inclusion of this variable. However, there are several reasons for which such handling of the long-term relations may not be sufficient. Firstly, the inclusion of the lagged dependent variable influences the properties of the estimators, resulting in the biased estimates for the parameter for lagged dependent variables when using ordinary least squares or fixed effects estimators. Secondly, it does not enable the researchers to identify the direction and the magnitude of the impact of selected factors on the capital structure in the long term. Thirdly, the lagged debt ratio as one of the explanatory variables describes not only the long-term influence of the identified capital structure determinants, but also a significant part of the 7

8 unobserved variables such as the managerial ability [Ang, Cole and Lawson, 2010; Matemilola et al., 2015]. To sum up, there were numerous capital structure determinants identified in the previous studies. Nonetheless their impact on the debt ratios of the companies was analyzed only in the short-term, mainly by the use of the dynamic specification including lagged dependent variable as one of the explanatory variables. Although such an approach may partially include the long-term effect of selected factors on the debt ratios of the companies, it does not enable the researchers to assess the differences in the long-term and short-term consequences of the changes in these determinants for the capital structure of the companies. Data and methodology The aim of this article is to assess the long-term and short-term impact of selected factors on the capital structure of the Polish companies. The sample consists in companies listed on Warsaw Stock Exchange in Due to the industry-specific financing sources, the sample excludes the companies in the following industries: banking, capital market, conglomerates, insurance and other financial. The sample includes only the companies with PLN as their reporting currency. There are observations in the sample for 426 companies. The dependent variable for the study is the total debt ratio, defined as the ratio of book value of liabilities to the sum of book value of liabilities and market value of equity. The explanatory variables include growth opportunities, profitability, size, tangibility of assets, non-debt tax shield, industry median debt ratio, dividend payments, tax rate, business risk, liquidity, capital expenditures and financial deficit. Growth opportunities are measured as the ratio of market value of equity to book value of equity. Profitability is measured as a ratio of operating income to total assets. Size is measured as the natural logarithm of total assets. Tangibility of assets is measured as the ratio of fixed assets to total assets. Non-debt tax shield is measured as the ratio of depreciation and amortization to total assets. Industry median debt ratio is calculated for every industry in every year. The payment of dividends is included in the study with a dummy variable equal to 1 if the company paid dividend in the year in question and 0 otherwise. Tax rate is calculated as the ratio of tax liability in the year in question to the earnings before taxes. Business risk is defined as the standard deviation of return on assets over 3-year period. Liquidity is measured as the ratio of cash and cash equivalents to total assets. Capital expenditures are included in the study as the ratio of capital expenditures to total assets. Financial deficit is calculated as follows: (- cash flow from operations + net investment + dividends change in cash + change in working capital) / market value of equity. Panel data describing capital structure determinant have small time dimension and large cross-sectional dimension, therefore two-dimensional residuals have to be taken into 8

9 account [Baltagi, 2008]. The dependent variable, i.e. debt ratio, has high persistency. Moreover, the explanatory variables used in the regression model are not strictly endogenous they can be correlated with past and current realizations of the residuals from the regression model. Regression models reflecting all these properties of the data can be estimated with generalized method of moments estimators (GMM). Nonetheless, GMM estimators are not designed to differentiate between the long-term and short-term relationships [Ruiz, 2016]. The second choice would be fixed effects estimators, among which the Between estimator (BE) can be used to obtain long-run estimates, whereas the Within estimator (used as the default fixed effects estimator, therefore called FE) short-run estimates [Baltagi, 2008]. In order to estimate the long-term impact of selected factors on capital structure, a regression model is estimated with the BE estimator. For the short-run impact of the same set of variables, the FE estimator is used. The results are compared with the estimates obtained with GMM-DIFF and GMM-SYS estimators. The differences in the estimation results between the BE and the FE estimators are often considered a result of different misspecification problems [Baltagi, 2008]. However it can be argued that in a dynamic framework, the differences in question may result from the fact that the BE estimator focuses on the long-term effect of the explanatory variables, while the FE estimator on the short-term effect [Baltagi, 2008; Ruiz, 2016]. Previous studies show that the use of the BE estimator gives similar results if instead of the levels of explanatory variables its means for the individuals are used, while the FE estimator if the deviations from means for the individuals are used as an explanatory variable instead of its levels [Ruiz, 2016]. Therefore lagged dependent variable should not be included in the regression specification estimated with the BE estimator. For the FE estimator, it is possible to include the lagged dependent variable as one of the explanatory variables, though its parameter estimate is biased [Baltagi, 2008]. Nonetheless, it should be noted that both fixed effects estimators take into account the two-dimensional residuals [Baltagi, 2008]. With the use of GMM estimators it is possible to fully account for dynamic relation between the capital structure and its determinants, and the lagged debt ratio should be included in the regression model as one of the independent variables. The models estimated with GMM-DIFF and GMM-SYS will be used as a comparison for the results obtained with the BE and the FE estimators. Both GMM estimators are based on the instrumental variables method: there is required an identification of a set of variables, called instruments, which are highly correlated with independent variables and uncorrelated with the residuals. In the GMM-DIFF estimator, the levels of the explanatory variables are used as the instruments for the first-differenced equation. In GMM-SYS, there is also a second equation for the levels of variables estimated, where the first differences of the variables are used as the instruments. Therefore in GMM-SYS time-invariant variables can be included, and the estimator uses more information from the data [Roodman, 2006]. 9

10 The results of the study The table below presents the results of the models describing long-term and short-term determinants of the capital structure of Polish companies. In order to assess the impact of selected factors on the capital structure of the Polish companies in the long-run, Model I was estimated with the BE estimator with robust standard errors. The regression model in question included fixed effects for years. The impact of the capital structure determinants in the short-run was analyzed with the use of Model II, estimated with the FE estimator with robust standard errors. This specification also included fixed effects for years. Both of the regression models, i.e. Model I and II, did not include lagged debt ratio as one of the explanatory variables. This variable was though included in most of the previous studies. For this reason, in order to compare the direction and magnitude of the impact of explanatory variables chosen for this study, additional models were estimated. Model III was obtained with the FE estimator with robust standard errors, with fixed effects for years, while it also included lagged dependent variable as an explanatory variable. Model IV was estimated with GMM-DIFF estimator, while Model V with GMM-SYS estimator. Both of these specifications included fixed effects for years and lagged debt ratio among the explanatory variables. For Models IV and V, forward orthogonal deviation and robust standard errors were used. According to the results of Arellano-Bond test of autocorrelation, both models suffered from the autocorrelation of the first order. To ensure the validity of the instruments, both models were estimated on the basis of instruments lagged two to four periods. Hansen test of overidentifying restrictions confirmed that the chosen specifications of the models are valid. Table 2. The results of parameter estimates (and their standard errors in parentheses) for capital structure models estimated with different estimation methods with basic model characteristics. Model I Model II Model III Model IV Model V estimation method BE FE FE GMM-DIFF GMM-SYS lagged debt ratio *** *** *** (0.0241) (0.0597) (0.0319) growth *** opportunities (0.0003) (0.0012) (0.0005) (0.0019) (0.0013) profitability *** *** *** *** *** (0.0959) (0.0180) (0.0142) (0.0380) (0.0326) size *** *** (0.0062) (0.0110) (0.0088) (0.0186) (0.0036) tangibility of assets ** *** *** * (0.0498) (0.0436) (0.0364) (0.0695) (0.0339) non-debt tax shield ** *** *** * *** 10

11 Model I Model II Model III Model IV Model V estimation method BE FE FE GMM-DIFF GMM-SYS (0.3148) (0.1188) (0.0637) (0.2069) (0.1379) industry median *** debt ratio (0.2265) (0.0443) (0.0382) (0.0793) (0.0472) dividends *** ** (0.0332) (0.0098) (0.0079) (0.0167) (0.0135) tax rate ** *** *** (0.0005) (0.0001) (0.0001) (0.0020) (0.0016) business risk *** *** *** * (0.0956) (0.0243) (0.0149) (0.0313) (0.0197) liquidity *** *** * (0.0806) (0.0470) (0.0365) (0.0696) (0.0527) capital ** expenditures (0.1770) (0.0540) (0.0498) (0.1787) (0.1275) financial deficit * (0.0076) (0.0012) (0.0007) (0.0045) (0.0039) constant ** ** (0.1651) (0.1524) (0.1195) number of observations number of instruments R Arellano-Bond test for AR(1) Arellano-Bond test for AR(2) Arellano-Bond test for AR(3) Arellano-Bond test for AR(4) Sargan test Hansen test Source: own work (0.000) (0.000) (0.638) (0.563) (0.179) (0.128) -0, (0.335) (0.296) (0.000) (0.000) (0.804) (0.857) *** significant at 0.01 level; ** significant at 0.05 level; * significant at 0.1 level. The parameter for lagged debt ratio was included in Models III, IV and V. The models in question were presented in the table for the purpose of comparison. It is nonetheless worth noting that the differences in the parameter in question are high, ranging from for the Model III estimated with the Within estimator to for the Model V estimated with GMM-SYS estimator. It confirms the conclusion from the previous studies that the lagged dependent variable is the most important factor affecting the capital structure of the companies, though the magnitude of its influence depends on the estimation method used. 11

12 Growth opportunities significantly influence the debt ratio only in the long-run model (Model I). The larger the growth opportunities of the company, the lower its debt ratio in the long run. The results are not in line with the trade-off theory, but support the pecking order theory. Moreover, the direction of influence of this factor is consistent with the results of the previous studies. As far as profitability is concerned, it has a significantly negative impact on the total debt ratio in all the models. However the magnitude of its influence in the long-run model (Model I) is almost seven times higher than the short-run effect in Model II. The parameter estimate for the variable in question obtained with the models III to V, including lagged debt ratio as an explanatory variable, are close to the short-run relation observed with the model II. The negative impact of this variable is often claimed to be in line with the pecking order theory. However it is also possible to explain such a direction of impact with the dynamic trade-off theory [Strebulaev, 2007]. The negative influence of profitability was also confirmed by the previous studies. Size significantly influences the total debt ratio according to models II and III, i.e. models estimated with the FE estimator. In the short term, the higher the value of the company's total assets, the higher is its total debt ratio. The positive relation between size and capital structure supports both the trade-off theory and the pecking order theory, and it is also confirmed by the previous studies. The effect of tangibility of assets on the total debt ratio is positive in the short term, but negative in the long-run specification. The higher the tangibility of assets, the higher is the total debt ratio in the short term. At the same time in the long term, higher tangibility of assets leads to lower debt ratios. It should be also noticed that the magnitude of the influence in question in the short term is higher for Model II than for the models including lagged debt ratio as an explanatory variable (Models III to IV). It proves that the estimates obtained with the GMM estimators are close to the short-term dynamics obtained with model II. Positive impact of this factor is not only predicted by the trade-off theory, but also it was reported in the previous studies. Taking into consideration the non-debt tax shield, it has a significant negative impact on the total debt ratio for all the models. The magnitude of the impact is the highest in the long term, while the lowest estimate was obtained for Model III, i.e. the model including the lagged dependent variable estimated with the FE estimator. The direction of the influence of non-debt tax shield is in line with the trade-off theory, while the previous studies do not agree on the direction of influence of this factor. Significant positive influence of the industry median debt ratio on the total debt ratio was observed only for Model II, describing the short-run relationship. The higher is the median debt ratio of other companies in the same industry, the higher is the debt ratio of the company in question in the short run. The parameter for this variable is not significantly 12

13 different from zero for the models including lagged debt ratio. The positive impact of the factor in question supports the results of the previous studies. The payment of dividends influences capital structure only in the long term, and the direction of this impact is negative. The companies paying dividends have lower debt ratios in the long run. Such a relation is consistent with the results of previous studies. It should also be noticed that in Model IV, i.e. estimated with the GMM-DIFF estimator, the impact of this variable is positive and significant, which would be in line with the predictions of the pecking order theory. The variable describing tax rate of the company has a significantly negative impact on the capital structure both in long-term and short term. The higher is the company s tax rate, the lower is its debt ratio. Tax rate does not play a significant role in the capital structure decisions according to the regression models estimated with GMM estimators. It is worth underlining that the magnitude of the impact of tax rate is more pronounced in the long term. The observed direction of the impact does not support the trade-off theory, while the results of empirical studies were mixed for this factor. The negative impact of business risk on debt ratio in the long run, while positive in the short run was observed. The positive influence of this factor was also recorded in models IV and V. The difference in the impact of business risk on capital structure of Polish companies is visible not only in the direction of the influence, but also in the magnitude, which is 5 times higher for the long run relationship. Both the trade-off theory and the pecking order theory suggest that higher business risk should lead to decrease in debt ratios, which is in line with the results of this study for the long term. The previous research do not agree on the direction of impact of this factor. As far as liquidity is concerned, it has a significantly negative influence on the debt ratios of Polish companies both in the short and long run, while the magnitude of the latter is 3 times higher than the former. Companies with higher liquidity have lower debt ratios. The parameter for this variable is insignificantly different from zero in the models estimated with the GMM estimators. The direction of the impact is in line with the predictions of the pecking order theory, but contrary to the trade-off theory. The negative impact of liquidity was also observed in the previous research. The impact of the capital expenditures on the capital structure is significantly negative in the long run. Higher capital expenditures result in lower debt ratios in the long term. It is in accordance with the predictions of the trade-off theory, but contrary to the pecking order theory. Moreover, the results of the previous research do not agree on the direction of influence of this factor. The positive influence of the financial deficit was observed only in the long run. Companies with higher financial deficits can be expected to have higher debt ratios in the long term. It is in line with the predictions of the pecking order theory, as well as with the results of the previous studies. 13

14 For the models estimated with the BE and FE estimators, it is possible to assess the quality of the models with the coefficient of determination (R 2 ). The model describing the long-term impact of the selected variables on the debt ratio has an R 2 of , which means that 23,71% of the variance of the dependent variable can be explained by the model. Such a value of this measure is considered as low. Model II explains 10.77% of the variance of total debt ratio, while Model III 38.31%. Therefore it should be said that the model including lagged dependent variable as one of the explanatory has a significantly better explanatory power. The reason for this observation is that the set of factors chosen for this study is too narrow to explain the changes of the total debt ratio, both in the long-term and in the short term. In the long run, the capital structure of the Polish companies is dependent on the growth opportunities, profitability, tangibility of assets, non-debt tax shield, dividend payment, tax rate, business risk, liquidity, capital expenditures and financial deficit. The companies facing higher growth opportunities may have lower debt ratios due to the higher information asymmetry (according to the pecking order theory) or higher costs of bankruptcy (according to the trade-off theory). More profitable companies use less debt, which may stem from their higher value of internal sources of financing (according to the pecking order theory) or less frequent capital structure adjustment (according to the dynamic trade-off theory). The negative impact of tangibility of assets on the debt ratios may be explained as the result of lower sensitivity to the information asymmetry (according the pecking order theory). Companies with higher non-debt tax shield have lower debt ratios due to the lower need for tax shield resulting from higher debt (according to the trade-off theory). Companies paying dividends have lower debt ratios, which is not in line with the capital structure theories. Higher tax rate results in lower debt ratios, which also does not find support in the existing theories. The negative impact of business risk on debt ratios of companies may be a consequence of higher asymmetry of information faced by these companies (according to the pecking order theory) or increased costs of financial distress (according to the trade-off theory). Companies with higher liquidity have lower debt ratios due to the higher financing needs (according to the pecking order theory). Higher capital expenditures also result in lower debt ratios, which may stem from higher financing needs (according to the pecking order theory) or from the expected rise in the costs of financial distress (according to the trade-off theory). The positive impact of the financial deficit on debt ratios supports the view that these companies have higher financing needs (according to the pecking order theory). In the short term, the factors influencing the capital structure of the Polish companies are profitability, size, tangibility of assets, non-debt tax shield, industry median debt ratios, tax rate, business risk and liquidity. It should be noted that in the short term, the capital structure of Polish companies does not respond to changes in growth opportunities, dividend payments, capital expenditures and financial deficit, which were proved to impact the debt ratios in the long run. Moreover, the influence of size and industry median debt ratio is 14

15 observed only in the short term. Companies with higher value of assets have higher debt ratios, which may be explained on the basis of higher information asymmetry (according to the pecking order theory) or lower costs of financial distress (according to the trade-off theory). The positive impact of industry median debt ratio proves that there are similar factors that drive the changes in the capital structure of the companies operating in the same industries. To sum up, the capital structure determinants identified in the previous studies significantly influence the capital structure of Polish companies both in the long and short run. In both cases, debt ratios are dependent on the profitability, tangibility of assets, non-debt tax shield, tax rate, business risk and liquidity. Growth opportunities, capital expenditures and financial deficit significantly influence debt ratios only in the long term, while size and industry median debt ratio only in the short term. It should also be underlined that the impact of tangibility of assets and business risk is negative in the long run, while positive in the short run. Moreover, in the long term, such factors as profitability, non-debt tax shield, dividend payment, tax rate, business risk and liquidity have more pronounced impact on capital structure of Polish companies than in the short term. Conclusion The aim of this article was to assess the long-term and short-term influence of selected factors on the capital structure of the Polish companies. The set of factors assumed to influence the capital structure of the companies differ according to the trade-off theory and the pecking order theory. Moreover, the theories in question do not always agree on the direction of this impact. After a thorough review of the capital structure theories, there were presented the results of the previous studies. Special attention was paid to the identified capital structure determinants and their direction of impact on the debt ratios of the companies. To analyze the differences in the influence of the capital structure determinants observed in the long term and short term, there were estimated models explaining total debt ratios of the Polish companies. The BE estimator was used to describe the long-run impact, while the FE estimator the short-run impact. Moreover, regression models including lagged debt ratio as one of the explanatory variables were estimated for the purpose of comparison. The results of this study confirm that not only the set of factors affecting the capital structure of the Polish companies differ in the long term and short term, but also the magnitude and the direction of the impact may be different. In the long term, total debt ratios are positively influenced by financial deficit, while negatively by growth opportunities, profitability, tangibility of assets, non-debt tax shield, dividend payment, tax rate, business risk, liquidity and capital expenditures. In the short term, total debt ratios are positively influenced by size, tangibility of assets, industry median debt ratio and business risk, while negatively by 15

16 profitability, non-debt tax shield, tax rate and liquidity. It should also be underlined that the magnitude of the impact and its direction may differ between the long and short term. The most important limitations of this study are the limited set of factors included in the analysis and the focus on the Polish companies. Therefore future research could include a larger set of factors that can possibly influence total debt ratios, including institutional factors and macroeconomic variables. Moreover, it would be advisable to repeat the study on the basis of a sample of companies from different countries and for companies not listed on the stock exchange. Taking into consideration the scarcity of the studies comparing long-term and short-term impact of selected variables on the capital structure, this study can be viewed as a prelude for further studies. References 1. J.S. Ang, R.A. Cole, and D. Lawson. The role of owner in capital structure decisions: an analysis of single-owner corporations, E. Arioglu and K. Tuan, Speed of adjustment: evidence from borsa Istanbul, Borsa Istanbul Review, vol. 14, no. 2, s , S. Balakrishnan and I. Fox, Asset specificity, firm heterogeneity and capital structure. Strategic Management Journal, 14(1), 3-16, B. Baltagi, Econometric analysis of panel data. John Wiley & Sons, F. Bancel and U. R. Mittoo, Cross-country determinants of capital structure choice: a survey of European firms, Financial Management, s , A. Białek-Jaworska and N. Nehrebecka, Determinants of Polish Companies Debt Financing Preferences, Social Sciences, vol. 87, no. 1, s , M. Bradley, G. A. Jarrell, and E. Kim, On the existence of an optimal capital structure: Theory and evidence, The Journal of Finance, vol. 39, no. 3, s , G. Brendea, Financing behavior of Romanian listed firms in adjusting to the target capital structure, Finance a Uver, vol. 64, no. 4, p. 312, M. J. Brennan and E. S. Schwartz, Corporate income taxes, valuation, and the problem of optimal capital structure, Journal of Business, s , D. Brounen, A. De Jong, and K. Koedijk, Capital structure policies in Europe: Survey evidence, Journal of Banking & Finance, vol. 30, no. 5, s , Y.-K. Chang, R. K. Chou, and T.-H. Huang, Corporate governance and the dynamics of capital structure: New evidence, Journal of Banking & Finance, vol. 48, s , V. A. Dang and I. Garrett, On corporate capital structure adjustments, Finance Research Letters, vol. 14, s ,

17 13. V. A. Dang, M. Kim, and Y. Shin, Asymmetric adjustment toward optimal capital structure: Evidence from a crisis, International Review of Financial Analysis, vol. 33, s , V. A. Dang, M. Kim, and Y. Shin, Asymmetric capital structure adjustments: New evidence from dynamic panel threshold models, Journal of Empirical Finance, vol. 19, no. 4, s , J. Farhat, C. Cotei, and B. Abugri, The pecking order hypothesis vs. the static trade-off theory under different institutional environments. preliminary draft, M. Faulkender, M. J. Flannery, K. W. Hankins, and J. M. Smith, Cash flows and leverage adjustments, Journal of Financial Economics, vol. 103, no. 3, s , O. Fischer, R. Heinkel, and J. Zechner, Dynamic capital structure choice: Theory and tests, The Journal of Finance, vol. 44, no. 1, s , M. J. Flannery and K. W. Hankins, Estimating dynamic panel models in corporate finance, Journal of Corporate Finance, vol. 19, s. 1 19, M. J. Flannery and K. P. Rangan, Partial adjustment toward target capital structures, Journal of Financial Economics, vol. 79, no. 3, s , J. Gajdka, Teorie struktury kapitału i ich aplikcja w warunkach polskich. Wydaw. Uniwersytetu Łódzkiego, J. R. Graham and C. R. Harvey, The theory and practice of corporate finance: Evidence from the field, Journal of Financial Economics, vol. 60, no. 2, s , R. Hamada, A. Kane, A. J. Marcus, and R. L. McDonald, How big is the tax advantage to debt?, The Journal of Finance, vol. 39, no. 3, s , R. Huang and J. R. Ritter, Testing theories of capital structure and estimating the speed of adjustment, Journal of Financial and Quantitative analysis, vol. 44, no. 2, s , K. Jõeveer, Firm, country and macroeconomic determinants of capital structure: Evidence from transition economies, Journal of Comparative Economics, vol. 41, no. 1, s , E. K. Kayo and H. Kimura, Hierarchical determinants of capital structure, Journal of Banking & Finance, vol. 35, no. 2, s , L. F. Klapper, V. Sarria-Allende, and R. Zaidi, A firm-level analysis of small and medium size enterprise financing in Poland, 3984, H. E. Leland, Corporate debt value, bond covenants, and optimal capital structure, The Journal of Finance, vol. 49, no. 4, s , T. T. Lemma and M. Negash, Determinants of the adjustment speed of capital structure: Evidence from developing economies, Journal of Applied Accounting Research, vol. 15, no. 1, s ,

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