Dragotă, Ingrid-Mihaela; Dragotă, Victor; Obreja Braşoveanu, Laura; Semenescu, Andreea
CAPITAL STRUCTURE DETERMINANTS: A SECTORIAL ANALYSIS FOR THE ROMANIAN LISTED COMPANIES
Published in: Economic Computation and Economic Cybernetics Studies and Research, Vol. 42, No. 1-2 / 2008, pp. 155-172.
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CAPITAL STRUCTURE DETERMINANTS: A SECTORIAL ANALYSIS FOR THE ROMANIAN LISTED COMPANIES Ingrid Mihaela Dragotă1 Victor Dragotă Laura Obreja Braşoveanu Andreea Semenescu Bucharest University of Economics Abstract This study analyses the differences in financing policies for the Romanian listed companies taking into account the economic sectors. They are financing their assets, in this order, on equity, commercial debt and, finally, on financial debt. However, there are some differences between sectors and between book and market values. Concerning the determinants of capital structure, analysed through OLS regression, the four variables used in the model (tangible assets, size, profitability and market-to-book ratio) are significant. Moreover, the relationship between capital structure and the firm profitability for the Romanian listed shares for the period 1997-2005 was analysed through Granger (1969). Capital structure does not Granger cause financial returns and the hypothesis that financial returns does not Granger cause capital structure can not be rejected. Key words: capital structure, performances, industry analysis, signalling theory, pecking order theory, Romania JEL code: G32 – Financing policy, capital and ownership structure 1. Introduction The analysis of capital structure still remains a main issue in Corporate Finance, even after 50 years after the seminal work of Modigliani and Miller (1958). One concern is related to the relationship between the leverage and the profitability of the companies. As long as “performance” seems to be a vague concept, some conceptual clarifications are needed. Firstly, from an economical point of view, apparently, capital structure has nothing to do with performance. Capital is invested in assets, including human resources, image, etc. As long as companies have performance, these assets are producing added value. In this context, capital structure has no relevance in order to improve the economical efficiency. However, it can be considered the subject of allocation of incomes. The wealth can be distributed for shareholders, to creditors, 1
Corresponding author. E-mail address:
[email protected],. Tel.: +40213191900/ext.264. Mailing address: Piaţa Romană, no.6, Sector 1, Bucharest, Romania, Zip Code 010374, Room 1104.
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or to other stakeholders. From this perspective, the capital structure really matters. This is the reason why there are many studies that are trying to find an optimal capital structure (see Modigliani and Miller (1958); Miller (1977); Diamond (1989); Harris and Raviv (1990), etc.). On the other hand, performance seems to be important from a perspective very similar to Spence (1973), and developed by Leland and Pyle (1977) and Ross (1977). In this context, firms can be structured in different classes, through their capital structure. A levered company can be considered to be a profitable company, as long as banks are interested in financing only companies with attractive investment projects. From this point of view, a higher level for leverage would signal a better performance. The signalling theory, applied on capital structure, is opposed by pecking order theory, generally attributed to Myers (1984). This theory states that the profitable companies will prefer, in this order, financing based on their own resources (retained earnings), debts, and, finally, on equity issues. The study has two main parts. In the beginning the nature of the relationship between leverage and returns for Romanian listed companies in the period 1997-2005 was analysed, taking into consideration the degree of concentration for leverage, both in market and in book values. Relative similar studies were performed by Chang (1987), Lang and Malitz (1985), Kester (1986), Gonedes, Lang, and Chikaonda (1988), Friend and Lang (1988), etc. The second part of the study identifies the determinants of leverage, for each industry, for the Romanian listed companies. The rest of this paper is structured as follows. In Section 2 database and methodology for the causality analysis between leverage and profitability are presented. Section 3 presents the main numerical results. Section 4 synthesises the main international empirical studies for the determinants of capital structure. An industry capital structure analysis for the Romanian listed companies was realised in Section 5 of the study. In Section 6 the determinants of leverage, for each industry, were identified. Section 6 contains the conclusions of the whole study. 2. A study of the relationship between leverage and returns for Romanian listed companies. Database and methodology The sample included companies listed on Bucharest Stock Exchange for the period 1997 - 2005. The number of firms considered in this database was different from one year to another, depending by the issues we dealt with in applying the working principles – financial, accounting and statistics – characterizing the testing methods applied. For instance, in 2005, 60 companies were included in the sample. Comparatively, in 1998, this number was 43. Firstly, all the companies included in the category “banks and financial services” were eliminated (according to the classification in the monthly bulletins of the BSE) due to the specific regulation regarding their activity, the leverage of these companies being strongly influenced by exogenous factors, and we focused
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exclusively on the companies considered “non-financial”. Secondly, there were excluded companies without enough information to perform a rigorous study. Information was obtained from the following sources: (i) the internet sites providing stock exchange information, such as www.bvb.ro and www.kmarket.ro, where are available data to determine the market capitalization of the companies listed on BSE (number of shares, moments when issues of equity took place, mergers etc.), but also a part of the financial and accounting necessary information (balance sheets, profit and loss accounts); (ii) the database provided by Reuters Press Agency regarding the market prices of the companies from the sample to determine the market capitalizations; (iii) the financial and accounting information obtained from the site of the Romanian Ministry of Finance. In order to identify the existence of the causality between financial performances and capital structure the following variables were used: EBIT_PER_TA = earnings before interest and tax per total assets, as a proxy for financial returns; LEVERAGE_B = book value of the leverage = book value of financial liabilities per equity, as a proxy for capital structure; LEVERAGE_M = market value of the leverage = market value of financial liabilities per equity, as an alternative proxy for capital structure. There are few companies from the sample with extreme values, which create distortion for the annual results. In order to correct this distortion, we proceeded to make the analysis for average values of leverage and returns for each company from the sample (see Tables no.1.a - 1.b and Tables no. 2.a – 2.b).
EBIT_PER_TA
Table no. 1.a Quintile analysis for average values of earnings before interests and taxes and book value of the leverage
q1 [-0,111; 0,0247] q2 [0,0284; 0,0709] q3 [0,0724; 0,1054] q4 [0,1076; 0,1349] q5 [0,1350; 0,2827]
q1 [-2,6208; 0,0165] ABR, ASA, NVR, TRS
LEVERAGE_B
q2 [0,0186; 0,0606] AMO, ASP, HTR, UCM VNC
EFO, OLT CPR, ECT, ELJ, INX
MJM, PTR
APC, TER
SNO, STZ ARS, BIO, VEL
q3 [0,0614; 0,1246]
q4 [0,1271; 0,2870]
q5 [0,2881; 4,0009]
SOF ENP, OIL, SNP
EPT MPN, RLS
UAM ARM, EXC, SRT MEF, PPL, SCD, TBM
CMF, CMP ATB, CBC, PCL, ZIM ALR, BRM, IMP
COS, PTS ART, IMS, RBR, RRC ARC, CRB, SLC AZO, DOR, PEI
The companies written in italics, from Table no.1.a, are those which were change their places, in quintiles, when the analysis was made in book and, respectively, in market values, presented in Table no.2.a.
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EBIT_PER_TA
Taking into consideration the values calculated in Table no.1.b we can conclude that (excluding the 3rd interval): - 18,3% of the companies have low leverage and low returns; - 15% of the companies have high leverage and low returns; - 16,7% of the companies have high leverage and high returns; - 11,7% of the companies have low leverage and high returns. Conclusively, about 35% from the companies indicated a positive correlation between these two variables and only 26,7% from them a negative one. In Section 6 of this article, in the regression model for the determinants of leverage, this relationship was also analyzed, for each industry, for the Romanian listed companies. Table no. 1.b Quintile analysis for average values of earnings before interests and taxes and book value of the leverage (the table contains the weights) q1 [-2,6208; 0,0165] q1 [-0,111; 0,0247] q2 [0,0284; 0,0709] q3 [0,0724; 0,1054] q4 [0,1076; 0,1349] q5 [0,1350; 0,2827]
LEVERAGE_B q2 [0,0186; 0,0606]
q3 [0,0614; 0,1246]
q4 [0,1271; 0,2870]
q5 [0,2881; 4,0009]
6,7%
6,7%
1,7%
1,7%
3,3%
3,3%
1,7%
5,0%
3,3%
6,7%
6,7%
3,3%
1,7%
3,3%
5,0%
0,0%
3,3%
5,0%
6,7%
5,0%
3,3%
5,0%
6,7%
5,0%
0,0%
A similar analysis is realised for market values of the leverage, and the results is presented in table no.2.a and, respectively, 2.b.
EBIT_PER_TA
Table no. 2.a Quintile analysis for average values of earnings before interest and tax and market value of the leverage
q1 [-0,111; 0,0247] q2 [0,0284; 0,0709] q3 [0,0724; 0,1054] q4 [0,1076; 0,1349] q5 [0,1350; 0,2827]
LEVERAGE_M
q1 [0; 0,0763]
q2 [0,0811; 0,2296]
ASA, ASP, TRS
AMO, HTR, NVR
EFO, VNC CPR, ELJ, INX, MJM
OIL, SNP
MPN
EXC, STZ ALR, IMP, SCD, TBM, VEL
PTR ATB, PCL, SNO, SRT, ZIM ARS, BRM, MEF, PPL
APC, BIO, TER
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q3 [0,2436; 0,4940] UCM
q4 [0,5839; 1,0791] COS, EPT, SOF
q5 [1,2340; 6,7115]
ENP, IMS ARC, CMP, ECT, UAM
ABR, PTS ART, OLT, RBR, RLS, RRC CMF, CRB, SLC
ARM, AZO, CBC
DOR, PEI
EBIT_PER_TA
Comparing the figures from Table no.1.a and 2.a, we can conclude that almost half of the companies from the sample change the places in the quintile analysis, when market capitalisation replaced the book value of equity. These changes represent another argument for the fact that the market evaluated companies from a different perspective as for book values. A long period of time, the Romanian market was undervalued, that can explain the changes in quintile distribution. Considering the same type of analysis, but for market values of leverage, the results are the followings (excluding the 3rd interval), as it can be seen in Table no. 2.b: - 16,7% of the companies have low leverage and low returns; - 20% of the companies have high leverage and low returns; - 8,3% of the companies have high leverage and high returns; - 16,7% of the companies have low leverage and high returns. In market values, the results are relatively opposite from the book analysis because for 37% from the companies leverage are negatively correlated with profitability. Table no. 2.b Quintile analysis for average values of earnings before interest and tax and market value of the leverage (the table contains the weights)
q1 [-0,111; 0,0247] q2 [0,0284; 0,0709] q3 [0,0724; 0,1054] q4 [0,1076; 0,1349] q5 [0,1350; 0,2827]
LEVERAGE_B
q1 [-2,6208; 0,0165]
q2 [0,0186; 0,0606]
q3 [0,0614; 0,1246]
q4 [0,1271; 0,2870]
q5 [0,2881; 4,0009]
5,0%
5,0%
1,7%
5,0%
3,3%
3,3%
3,3%
1,7%
3,3%
8,3%
6,7%
0,0%
1,7%
8,3%
5,0%
0,0%
3,3%
8,3%
5,0%
3,3%
5,0%
8,3%
6,7%
0,0%
0,0%
Concluding, the quintile analysis does not reveal a positive or a negative influence and correlation between performances, measured by earnings before interest and tax, and book or market values of the leverage. 3. Results for causality between the returns and leverage The main issue analyzed in the second part of the study is if the returns cause capital structure (Granger causality), respectively how much of the current value of capital structure can be explained by past values of capital structure and then to notice whether adding lagged values of financial profitability can improve the explanation for leverage. Capital structure is said to be Granger-caused by
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profitability if profitability helps in the prediction of capital structure, or, equivalently, if the coefficients on the lagged profitability’s are statistically significant. The statement “profitability Granger causes capital structure" does not imply that capital structure is the effect or the result of financial returns. The Granger causality between the returns and leverage (considering the average values) are presented in the followings two figures and tables. Figure no. 1: The Granger causality between EBIT_PER_TA and LEVERAGE_B (average values) 0.40 0.30 0.20 0.10 0.00 -0.10 -0.20
EBIT_PER_ TA (left)
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LEVERAGE _B (right)
28
29
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31
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6 4 2 0 -2 -4
Table no. 3 The Granger causality between EBIT_PER_TA and LEVERAGE_B (average values) Pair wise Granger Causality Tests Sample: 1 61 Lags: 2 Null Hypothesis: Obs. F-Statistic Probability LEVERAGE_B does not Granger Cause 59 0.11663 0.89014 EBIT_PER_TA EBIT_PER_TA does not Granger Cause 0.01423 0.98588 LEVERAGE_B We cannot reject the hypothesis that LEVERAGE_B does not Granger cause EBIT_PER_TA and the hypothesis that EBIT_PER_TA does not Granger cause LEVERAGE (Dragotă, Braşoveanu and Dragotă, 2007). Figure no. 2: The Granger causality between EBIT_PER_TA and LEVERAGE_B (average values) 0.40
EBIT_PER _TA(left)
0.20 0.00
1
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LEVERAG E_M (right)
27
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-0.20
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3132
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4647
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-1
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Table no. 4 The Granger causality between EBIT_PER_TA and LEVERAGE_M (average values) Pair wise Granger Causality Tests Sample: 1 61 Lags: 2 Null Hypothesis: Obs F-Statistic Probability LEVERAGE_M does not Granger Cause 59 0.45125 0.63921 EBIT_PER_AT EBIT_PER_AT does not Granger Cause 0.21768 0.80509 LEVERAGE_M We cannot reject the hypothesis that LEVERAGE_M does not Granger cause EBIT_PER_TA and the hypothesis that EBIT_PER_TA does not Granger cause LEVERAGE_M. The same analysis was made for annual values of returns and leverage and the results are presented in Table no.5. Table no. 5 The Granger causality between EBIT_PER_TA and LEVERAGE_B / LEVERAGE_M (annual values) Pair wise Granger Causality Tests Sample: 1 404 Lags: 2 Null Hypothesis: Obs FProbability Statistic LEVERAGE_B does not Granger Cause 402 0.02086 0.97936 EBIT_PER_TA EBIT_PER_TA does not Granger Cause LEVERAGE_B 0.01544 0.98468 LEVERAGE_M does not Granger Cause 402 0.12564 0.88197 EBIT_PER_TA EBIT_PER_TA does not Granger Cause LEVERAGE_M 0.11756 0.88912 We cannot reject the hypothesis that capital structure (LEVERAGE_B and LEVERAGE_M) does not Granger causes profitability (EBIT_PER_TA) and the hypothesis that profitability does not Granger cause capital structure. 4. International empirical studies for the determinants of capital structure From the seminal work of Myers (1984) the literature on the determinants of capital structure has grown steadily. Titman and Wessels (1988) took several attributes of firms such as asset structure, non-debt tax shields, growth, uniqueness, industries classification, size, earnings, volatility and profitability to be explanatory variables, but they found only uniqueness as highly significant (Titman, (1988)).
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But Harris and Raviv (1991), in their most important article on the subject, pointed out that the consensus among financial economists was that leverage increased with fixed costs, non-debt tax shields, investment opportunities and firm size. Leverage decreases with volatility, advertising expenditure, and the probability of bankruptcy, profitability and uniqueness of the product. Moh'd, Perry, and Rimbey (1998) employed an extensive time-series and cross-sectional analysis to analyse the impact of agency costs and ownership concentration on the capital structure of the firm. Results indicated that the distribution of equity ownership is important in explaining overall capital structure and that is the reason why managers will reduce the level of debt as their own wealth is increasingly tied-to the firm (Pao, Pikas and Lee, 2003). In more recent articles, it seemed that financial decisions in the developing countries were taken somehow different (Mayer, (1990)). Rajan and Zingales (1995), one of the most important empirical studies for corporate capital structure determinants, which were taken as reference in many other empirical studies after that, has been mainly focused on G7 countries and has found the following variables as being most consistently related to the corporate capital structure: tangibility, size, profitability, and growth opportunities. They found that leverage increased with asset structure and size, but decreased with growth opportunities and profitability. Again firm’s leverage was fairly similar across the G-7 countries. Booth, Aivazian, Demirguc-Kunt, and Maksimovic (2001) took tax rate, business risk, asset tangibility, firm size, profitability, and market-to-book ratio as determinants of capital structure across ten developing countries. They found that long-term debt ratios decreased with higher tax rates, size, and profitability, but increased with tangibility of assets. Again the influence of the market-to-book ratio and the business-risk variables tended to be subsumed within the country dummies. 5. An industry capital structure analysis for the Romanian listed companies Generally, the theories based on asymmetrical information have, as a hypothesis, the fact that managers and other insiders have private information on the expected returns and the quality of future investment opportunities. The signalling theory and the pecking order theory had different perspectives on the signalling instruments: by issuing new debt, or by financing investment opportunities based on their own financial resources. The aim of the second study was to emphasize which is the most appropriate theory for the Romanian listed companies, identifying, in the same time, the particularities for each industry, considered case by case. First of all, the median values for three variables were analysed, for each economic sector for the Romanian listed companies: 1. total debt/total assets (D/AT); 2. financial debt (with interest expenses associated)/total assets (DFIN/AT); 3. commercial debt/total assets (DCOM/AT).
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DCOM/TA (MV)
DCOM/ TA (BV)
DFIN/TA (MV)
DFIN/TA (BV)
TDEBT/TA (BV)
Sector
TDEBT/TA (MV)
This study has the same data base as the first one, presented above in Sections 2 and 3. In order to explain the “industry effect” on capital structure, the analysis was realized for each economic sector using the information on Bucharest Stock Exchange site, as follows: (1) chemicals; (2) consumer goods; (3) energy; (4) equipment; (5) pharmaceuticals; (6) row materials and intermediary goods; (6) services. The median values for leverage (using different measures, in book and market values) by industry are presented in the table bellow (Dragotă and Semenescu, 2007a): Table no.6 The structure of leverage for the Romanian listed companies with “industry effect” for the period 1997-2005
Chemicals
33.40%
60.59%
4.52%
8.90%
21.91%
42.74%
Consumer goods
28.81%
52.96%
5.06%
6.73%
22.24%
37.20%
Energy
47.00%
56.67%
5.37%
6.99%
32.55%
32.73%
Equipment
39.54%
64.26%
5.84%
8.36%
28.38%
47.48%
Pharmaceuticals
27.27%
23.41%
4.26%
2.52%
19.96%
19.20%
Row materials and intermediary goods
38.40%
69.68%
3.16%
6.73%
25.82%
39.55%
Services
37.84%
41.34%
0.09%
0.25%
26.12%
31.21%
In order to compare the results for each economic sectors with the whole market, in the following table we estimate the median values for total debt, financial debt and commercial debt (Dragotă and Semenescu, 2007b): Table no.7 The structure of leverage for the Romanian listed companies VALUES BOOK VALUES (BV)
DFIN/TA 4,655%
MARKET VALUES (MV)
7,058%
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MEDIAN VALUES DCOM/TA TDEBT/TA 25,269% 35,776% 38,839%
57,488%
The results from table no.6 and 7 revealed some differences in leverage: In book values: 1. only two sectors (energy and equipment) had all values for debt grater than the median values for the whole market; 2. another two sectors (row materials and services) had commercial and total debt with values grater than the median values for the whole market, but not for the financial debt. In market values: 1. only two sectors (chemicals and equipment) had all values for debt grater than the median values for the whole market; Only for the equipment sector the results are similar with those identified in the book values analysis; 2. another sector (row materials and services) had commercial and total debt with values grater than the median values for the whole market, but not for the financial debt. For this industry, the conclusion is analogous with those for the book values. 6. Determinants for capital structure, with “industry effect”, for the Romanian listed companies 6.1. The variables used in the regression model Rajan and Zingales (1995) revealed a certain variability of the results depending on the leverage measurement; there were differences by using short time leverage or long time leverage. This is the reason why three indicators for the leverage were used, especially for the Romanian case, where the commercial debt is prevalent: 1. the total leverage (as percentage of total assets); 2. the medium and long term liabilities (as percentage of total assets); 3. the commercial liabilities (as percentage of total assets); Since the sample contains companies listed on BSE, the study is realized at two levels: based on accounting values of the indicators, determined by using data from the balance sheet and profit and loss account, but also on market values, by using the market capitalization instead of equity (from the balance sheets). The explanatory variables considered in the regression model were: A. Tangible assets The indicator used in this empirical study, has the following formula: FIXED ASSETS (1) X 1 TANGIBLE ASSETS TOTAL ASSETS The studies realized by Titman and Wessels (1988), Rajan and Zingales (1995), Fama and French (2000) argued that the variable fixed assets/total assets should be taken into consideration when analysing the determinants of the financial
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structure, but the sign of its influence is not clear. Galai and Masulis (1976), Jensen and Meckling (1976) and Myers (1977) argued that the shareholders of a leveraged company tend to invest excessively, generating the classical conflict between shareholders and creditors. However, if there are certain real guarantees, the debtor could be „hold” from investing in risky or inefficient projects. In these conditions, the pecking order theory identified a positive relationship between the percentage of these assets in the total assets and the leverage. B. The size of the company As a measure for the company size in the regression model, it was used the logarithm of commercial sales (X2). The level of the sales is considered positively correlated to the leverage. The company with a big turnover will face with fewer problems regarding the information asymmetry than the small ones. Moreover, it is considered that the big companies have a stronger base for diversifying the investment projects and for limiting the risk of cyclic fluctuations (Warner (1977), Ang, Chua and McConnel (1982), Titman and Wessels (1988)). C. The profitability The indicator to measure the performances of a company, used in this empirical study, has the following formula: EBIT (2) X 3 PROF TOTAL ASSETS This indicator will emphasize the efficiency of using the assets of the company. There are more points of view regarding the type of relationship between profitability and the leverage. According to the pecking order theory, a negative value of the correlation coefficient is expected between them. On the other hand, authors like Ross (1977) or Leland and Pyle (1977) sustained that the capital structure represents an instrument of signalling the performances and the perspectives of the company, and this is the reason why a positive value of the correlation coefficient between the two variables is expected. D. The growth opportunities (Market-to-book-ratio) The indicator to measure the growth opportunities for a company, used in this empirical study, has the following formula: X 4 MBR
TOTAL DEBTBOOK VALUE MARKET CAPITALIZATION TOTAL DEBTBOOK VALUE EQUITYBOOK VALUE
(3)
Rajan and Zingales (1995) suggested that a negative correlation should be identified between „market-to-book-ratio” indicator and debt, according to the agency theory developed by Jensen and Meckling (1976), but also with Myers’ theory (1977). They argue that companies with a high leverage tend to abandon more viable investment projects. Moreover, these companies, if they did not manage to transform the opportunities in real investments, could avoid being leveraged, and if we associate the investment opportunities with more intangible assets, than the explanation for negative relationship between tangible assets and leverage (Bevan and Danbolt, 2000) could be found. The results of the international studies are mixed. According to the pecking order theory, a positive
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correlation between leverage and growing opportunities could be explained. Thus, the debt rose when the internal financing resources were not enough for investment and diminished when these were sufficient. 6.2. The regression model The aim of the study is to identify the significance of the four explanatory variables selected for the analysis of the capital structure determinants based on linear multiple regression model. The dependent variable is the leverage (Yi), measured by those four indicators mentioned above, and the independent variables are: tangible assets (X1), size (X2), profitability (X3) and market-to-book ratio (X4). Using the pool data analysis on the period 1997-2005, the regression model is the following: Yit = αi + 1iX1it+2iX2it +3iX3it +4iX4it + it (4) 6.3. Results For each sector, the empirical results for the Romanian listed companies are presented in the following seven tables.
Explanatory variables
TDEBT/TA (BV)
TDEBT/TA (MV)
DFIN/TA (BV)
DFIN/TA (MV)
DCOM/ TA (BV)
DCOM/TA (MV)
Table no. 8 The determinants of the financial structure for chemical industry
Tangible assets Size MBR Profitability
-*
-*
-*
-
-*
-
+* -* -*
+* -* -*
+* -* -
+* -
+* -* -*
+ -* -*
Tangible assets Size
-* +*
+*
+*
167
-* +
-* +*
DCOM/TA (MV)
DCOM/ TA (BV)
DFIN/TA (MV)
DFIN/TA (BV)
TDEBT/TA (MV)
TDEBT/TA (BV)
Explanatory variables
Table no. 9 The determinants of the financial structure for consumer goods’ sector
+*
MBR Profitability
-
-* +
+*
-* +*
+
-* -
TDEBT/TA (MV)
DFIN/TA (BV)
DFIN/TA (MV)
DCOM/ TA (BV)
DCOM/TA (MV)
Tangible assets Size MBR Profitability
TDEBT/TA (BV)
Explanatory variables
Table no. 10 The determinants of the financial structure for energy sector
-* +* -
-* +* -* -
+* -
+ +* +
-* +* + -
-* -* -* -
TDEBT/TA (MV)
DFIN/TA (BV)
DFIN/TA (MV)
DCOM/ TA (BV)
DCOM/TA (MV)
Tangible assets Size MBR Profitability
TDEBT/TA (BV)
Explanatory variables
Table no. 11 The determinants of the financial structure for equipment sector
-* +* + -*
-* + -* -*
-* +* + -*
-* +* -* -
-* -*
-* -* -* -*
-
168
-
-
DCOM/TA (MV)
DCOM/ TA (BV)
-
DFIN/TA (MV)
-
DFIN/TA (BV)
TDEBT/TA (MV)
Tangible assets
TDEBT/TA (BV)
Explanatory variables
Table no. 12 The determinants of the financial structure for pharmaceutical industry
-
Size MBR Profitability
+ -* +
-* +
+* + -*
+* -* -*
-
-* -* +
TDEBT/TA (MV)
DFIN/TA (BV)
DFIN/TA (MV)
DCOM/ TA (BV)
DCOM/TA (MV)
Tangible assets Size MBR Profitability
TDEBT/TA (BV)
Explanatory variables
Table no. 13 The determinants of the financial structure for row materials and intermediary goods’ sector
-* +* +* -*
+ -*
+* +
+* + +
-* +* +* -*
-* -* +* _-*
+ -
DCOM/TA (MV)
+ -
DCOM/ TA (BV)
-* +* -* -*
DFIN/TA (MV)
-* + -*
DFIN/TA (BV)
TDEBT/TA (MV)
Tangible assets Size MBR Profitability
TDEBT/TA (BV)
Explanatory variables
Table no. 14 The determinants of the financial structure for the services’ sector
-* + -
-* +* -* -
Tangible assets (as a percent of total assets): the financial theory mainly states a positive relationship with leverage. For the Romanian case, when the variable is statistically significant, the relationship is negative. However, the results should be carefully analysed for the Romanian companies having a great proportion of non-banking debt. Hence, the conclusion of a negative relationship with the commercial debt, and, respectively with the total debt (in which, the former are predominant) is logical, because this is how the companies finance the investments in current assets, while the financial debt is used to finance the
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fixed assets. Moreover, the Romanian companies decided to finance their fixed assets from their own resources, because the interest rate was very high, in the period 1997-2003, at least. On the other hand, another possible explanation can be the reticence of banks in financing tangible assets with a liquidation value significantly lower than the market value. One exception is row materials and intermediary goods’ sector with a positive relationship between financial debt and tangible assets, which could reveal a different approach of banking creditors (to finance the acquisition of tangible assets instead of intangible ones) to avoid, in this manner, investments in risky or inefficient projects. Size is positively related to financial debts. The big firms sent a more direct signal to the creditors and could obtain a credit more easily, especially in the context where a bigger turnover is associated to a smaller risk exposure. As it was expected, the signs were different sector by sector when we focused on the commercial debt. For instance, negative and statistically significant relationship was reported for energy, equipment, pharmaceutical and row materials sectors, depending by the industry features. On the other hand, positive relationship was reported for consumer goods’ sector and for services sector. Market-to-book-ratio was negatively (significantly) correlated with the level of debt, so it could be said that an increasing level of leverage is a bad signal for the investors on the market, which will “punish” the levered companies through the market price of their equities. One exception could be noticed for the row materials and intermediary goods’ sector, probably influenced by the industry features. The negative relationship between all types of debt and the profitability could be explained through “pecking order theory”. The profitable companies will not request for debt because they have enough internal resources to sustain their own investment projects. One interesting exception is the consumer goods’ sector, with a positive relationship between profitability and financial debts. 7. Conclusions The first main conclusion is that capital structure does not Granger cause financial performances and the hypothesis that financial performances does not Granger cause capital structure can not be rejected. Taking into consideration the international studies quoted above to identify the determinants for capital structure of the Romanian companies, in the second part of the study, the profitability was considered as explanatory variable, searching the most accurate influence between them. The results for the study about the determinants of capital structure could be considered partially comparable to the ones obtained for developed countries, but also on emerging ones (Rajan and Zingales (1995) for the G-7 countries; Drobetz and Fix (2003) for Switzerland; Chen, Lensink, Sterken (1998), on the German case; Devic and Krstic (2001), on Poland and Hungary cases; Bevan and Danbolt (2000), for the case of the Great Britain).
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For the Romanian (listed) companies, the empirical research revealed the conclusions that we have an “industry effect”. Certain correlations between the explanatory variables it were identified, but always some of industries had different evolutions, which will request a special treatment from the part of the investors and a different approach from the management (with implications on the Corporate Governance Code applied “case-by-case”). Acknowledgements This research was supported by Grant No. 86 / 2007, AT, financed by National University Research Council (CNCSIS). References: [1] Ang, J. S., Chua, J.H., McConnell, J.J. (1982). “The Administrative Costs of Corporate Bankruptcy: A Note”, Journal of Finance, vol. XXXVII, 1, 219-226. [2] Bevan, A., Danbolt, J. (2000). “Capital Structure and its Determinants in the United Kingdom. A Decompositional Analysis”, Working paper. [3] Booth, L., Aivazian, V., Demirguc-Kunt, A., Maksimovic, V. (2001). “Capital structure in developing countries”, Journal of Finance, 56, 87–130. [4] Chang, C. (1987). “Capital Structure as Optimal Contracts”, Carlson School of Management, University of Minnesota, Working Paper. [5] Chen, L., Lensink, R., Sterken, E. (1998). “The Determinants of Capital Structure: Evidence from Dutch Panel Data”, Working paper. [6] Devic, A., Krstic, B. (2001). “Comparatible Analysys of the Capital Structure Determinants in Polish and Hungarian Enterprises”, Facta Universitatis, series Economics and Organisation, vol.I, no.9. [7] Diamond, D. (1989). “Reputation acquisition in debt markets”, Journal of Political Economy, vol. 97, 4, 828-862. [8] Dragotă, M., Braşoveanu, L., Dragotă, V. (2007). „Capital structure and performance: an analysis for the Romanian listed companies”, International Conference „Economy, Society, Civilization”, Bucharest, 6-7 July. [9] Dragotă, M, Semenescu, A. (2007a). “An industry analysis of capital structure determinants. Empirical results for Romanian listed companies”, International Conference „Economy, Society, Civilization”, Bucharest, 6-7 July. [10] Dragotă, M., Semenescu, A. (2007b). “Capital structure analysis under asymmetric information. Evidence on Romanian capital market”, 4ème Conférence Internationale de Finance, IFC4, Hammamet, Tunisia. [11] Drobetz, W., Fix, R. (2003). “What are the Determinants of the Capital Structure? Some Evidence for Switzerland”, Working paper. [12] Friend, I., Lang, L (1988). “An empirical test of the impact of managerial self-interest on corporate capital structure”, Journal of Finance, 43, 271-281. [13] Galai, D., Masulis, R. W. (1976). “The option pricing model and the risk factor of stock”, Journal of Financial Economics, 3, 53–81. [14] Gonedes, N., Lang, L., Chikaonda, M. (1988). “Empirical results on managerial incentives and capital structure”, The Wharton School, University of Pennsylvania, Working Paper.
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Appendix no. 1: Descriptive statistics Sample: 1 61
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability Sum Sum Sq. Dev.
EBIT_PER_TA
LEVERAGE_B
LEVERAGE_M
0.082055 0.088341 0.282690 -0.110983 0.079759 -0.159543 3.590650 1.145489 0.563975 5.005335 0.381694
0.234014 0.085830 4.000909 -2.620761 0.701178 1.781608 19.37606 713.8828 0.000000 14.27488 29.49905
0.846057 0.317288 6.711468 0.000000 1.336598 2.676139 10.49453 215.5710 0.000000 51.60945 107.1896
Correlation Matrix EBIT_PER_TA LEVERAGE_B LEVERAGE_M
EBIT_PER_TA 1.000000 -0.040276 -0.156522
Covariance Matrix EBIT_PER_TA LEVERAGE_B LEVERAGE_M
EBIT_PER_TA 0.006257 -0.002216 -0.016413
LEVERAGE_B -0.040276 1.000000 0.516470
LEVERAGE_M -0.156522 0.516470 1.000000
LEERAGE_B -0.002216 0.483591 0.476097
LEVERAGE_M -0.016413 0.476097 1.757206
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