Analysis of the determinants of capital structure

Analysis of the determinants of capital structure Author: Alupoaie Cristiana Larisa Coordinator: Univ. Dr. Ion Stancu INTRODUCTION This paper tries to highlight important factors that influence a company's capital structure. The aim was to determine these factors for companies operating on the Romanian market especially in the last five years, given the current global economic situation, namely the economic crisis and recent financial years 2007-2010 debt crisis. Theories of debt financing has been growing over the past two decades. In most cases the conclusion was that it wass recorded a higher price on share for a business in debt than a similar business, but with no debt, but also an excessive level of debt would encourage bankruptcy liquidation. THE THEORETICAL FRAMEWORK AND PREVIOUS RESEARCH Hennessy and Whited (2005) argued that a dynamic fiscal policy can also cause a negative relationship between profitability and leverage. Therefore, these firms are more likely to face financing decisions based on capital structure. On the other hand, less profitable firms, the lack of internal funds are likely to face financing decisions based on capital structure, and shows that debt financing is relatively less attractive because of different tax rates. Therefore, a negative relationship between profitability and debt ratio can be induced when firms face financing decisions based on capital structure. Also in this study the authors predicted that there may be a nonlinear relationship between capital structure and profitability. Companies at a lower level of profitability could hire more internal funds, as foreign funds which are expensive. Semenescu and Dragotă in 2008 performed an analysis of capital structure and its determinants. The main conclusion for capital structure analysis was that Romanian listed companies based in financing their assets, in that order, the equity trade and, ultimately, the financial liability. Empirical test was performed and revealed that four variables used in the regression model are significant: physical assets, firm size, profitability and growth opportunity. Badarau and Semenescu (2010) provides a modeling of the cost of debt for a company in the event of income tax and shows that the income tax rate has a complex effect on the cost of debt for a company. The tax shields cause a negative correlation between tax rate and the first external financing for companies. In essence, this second effect is that a higher rate of income tax

leads to lower net income, which is the basis for creating new wealth. In dynamics, this effect tends to become predominant, with a direct reduction of collateral and hence, increased risk of company DATA BASE Aim of this study was to find determinants of capital structure for companies working in the capital market in Romania. To do this, I created a database that includes companies listed on BSE for the period 2009-2011. The number of companies was originally considered 40, and finally after successive eliminations, the database included only 27 companies I eliminated companies for which we have not had enough information to conduct rigorous studies. I considered companies that had incurred losses in all three years of the study, because of distortion of indicators, such as those profitability. All information was obtained from the following sources: Internet sites providing information on companies listed on BSE, such as www.bvb.ro and www.ktdt.ro, the financial statements (profit and loss account and balance sheet) published on the company website In the following I present the averages of these indicators that I considered showing the capital structure for our companies for years considered in the study from 2009 to 2011. They have leverage, long-term debt to total assets, total debt in total assets and interest coverage of operating profit. Tabel 1. Valorile medii şi mediana indicatorii de structură a capitalurilor An Levier DTL/AT Dat toatale/at Grad acoperire Medie Mediana Medie Mediana Medie Mediana Medie Mediana 2009 46.22% 31.13% 20.38% 19.74% 34.61% 33.70% 4.76 2.19 2010 48.87% 30.30% 20.78% 19.15% 35.33% 33.86% 5.43 2.25 2011 50.23% 23.06% 20.43% 15.95% 35.52% 30.40% 6.24 2.99 Medie 48.44% 28.16% 20.53% 18.28% 35.15% 32.65% 5.47 2.48 Very interesting is the big difference between the mean and median, this means that there are companies that have, in our case, very high values of these indicators. Given this, I felt the need to achieve some descriptive statistics about these items. First I wanted to see the minimum and maximum values of these four indicators. These are presented in the table below.

Tabel 2. Valorile minime şi maxime ai indicatorii de structură a capitalurilor An Levier DTL/AT Datorii totale/at Grad acoperire Min Max 2009 0.94% 205.95% 0.92% 51.61% 1.84% 76.56% 1.02 22.30 2010 1.28% 228.60% 1.25% 50.36% 2.50% 79.22% 1.06 34.20 2011 1.20% 214.75% 1.17% 50.86% 2.34% 79.77% 1.03 28.30 2009-2011 0.94% 228.60% 0.92% 51.61% 1.84% 79.77% 1.02 34.20 Notice that there are high levels of these indicators in each year of study, which explains the difference between average and median RESEARCH METHODOLOGY The indicators used in this study are primarily indicators quantifying the capital structure of companies such as leverage, interest coverage ratio of operating profit, or simply borrowed capital structure related to total active. Next we make a brief presentation of these indicators to quantify the capital structure. Leverage indicating the relative proportion of equity and debt used to finance the company's assets - which is a risk indicator of the company. This is equal to the reported total debt to equity. If our indicator, liabilities include only long-term debt (DAT). Indebtedness can be explained mainly as well as leverage, but in this case, long-term debt are reported to total company capital (own and borrowed). Indebtedness is a measure of bank financial institutions studied, considering this company as indebted when this indicator is above 0.5%. Coverage of interest in operating profit is an indicator of how can be covered debt costs from operating activities of the company. This indicator is directly linked to profitability, its high value should lead to profitability. A firm that has the value of this indicator is less than a large company with debt management problems which means it will not be able to meet payments to creditors. Datorii pe termen lung/activ total = The proposed model I considered an important measure of capital structure and duties by total weight in total assets. With this indicator we want to see how they influence the

determinants of long-term debt and equity trade payables. It is expected that a large share of commercial debt to have a reduced influence of the rates of return. It is also interesting to see if firm size is likely to influence the commercial debt, virtually the relation between long-term debt and short-term depending on company size. We used the place as the dependent variable indicators outlined above, namely leverage, long-term debt to total assets, total debt to total assets and interest coverage ratio of operating profit. The explanatory variables used are listed below. 1) Company Size 2) The possibility of credit 3) Profitability 4) Opportunities for growth - investors view 5) Capital intensity 6) financial crisis Modeling capital structure In this section we present the model used to highlight the determinants of financial leverage. Therefore, the dependent variable is leverage, the independent variables are those listed above. Ecutaţia is as follows: Leverage = C (1) + C (2) * TANG + C (3) * LN_CA + C (4) * ROA + C (5) * PER + C (6) * INTENSE + C (7) * CRISIS In this equation factors used are as follows Leverage - leverage the company Tang is the share of fixed assets in total assets ln_ca logarithm of turnover is expressed in nominal values ROA is return on economic PER is price earning ratio Intense is the intensity of capital The crisis is a crisis dummy variableanulaţi modificările After running regression I noticed that the coefficient of capital intensity and the crisis are insignificantly different from 0, so we rebuilt regression only with the first four indicators: Leverage = C (1) + C (2) * TANG + C (3) * LN_CA + C (4) * ROA + C (5) * PER As if financial leverage, we modeled long-term debt in total assets of companies in conjunction with microeconomic indicators registered in the company. PONDDTL = C (1) + C (2) * TANG + C (3) * LN_CA + C (4) * ROA + C (5) * PER + C (6) * INTENSE + C (7) * CRISIS In this equation factors used are as follows Ponddtl - long-term debt in total assets

Tang is the share of fixed assets in total assets ln_ca logarithm of turnover is expressed in nominal values ROA is return on economic PER is price earning ratio Intense is the intensity of capital The crisis is a crisis dummy variable After running regression I noticed that the intensity of capital coefficient, variable crisis and the share of fixed assets are not significantly different from 0. In these conditions we recovered regression in this form: PONDDTL = C (1) + C (2) * LN_CA + C (3) * ROA + C (4) * PER As the cases above, we modeled the share of total debt in total assets of companies in conjunction with microeconomic indicators registered in the company. This time I wanted to see whether determinants have influenced payable. We expect this because during the period studied many companies have turned to external financing, but given the large values of leverage and therefore an impossibility of long-term debt, allegedly resorted to short term funding. In these conditions the ecuation looks like this: PONDDT = C (1) + C (2) * TANG + C (3) * LN_CA + C (4) * ROA + C (5) * PER + C (6) * INTENSE + C (7) * CRISIS In this equation factors used are as follows Ponddt - share of total debt to total assets Tang is the share of fixed assets in total assets ln_ca logarithm of turnover is expressed in nominal values ROA is return on economic PER is price earning ratio Intense is the intensity of capital The crisis is a crisis dummy variable After running regression I noticed that the coefficient of capital intensity and the variable of the crisis are insignificantly different from 0, so we rebuilt regression only as the first four indicators: PONDDT = C (1) + C (2) * TANG + C (3) * LN_CA + C (4) * ROA + C (5) * PER Finally we tried to shape the coverage of interest in operating profit. We considered this indicator and a measure of capital structure because this relationship is presented as a debt interest incurred by the company. GRADE = C (1) + C (2) * TANG + C (3) * ROA + C (4) * INTENSE + C (5) * CRISIS In this equation factors used are as follows Grade - interest coverage ratio of operating profit Tang is the share of fixed assets in total assets ROA is return on economic

Intense is the intensity of capital The crisis is a crisis dummy variable After running regresieri I noticed that in this crisis variable coefficient is insignificantly different from 0, so we rebuilt regression only significant indicators: GRADE = C (1) + C (2) * TANG + C (3) * ROA + C (4) * INTENSE RESULTS First I made a table used for modeling the determinants of capital structure and expected results to see later if we get the same answers. These results are obtained in most studies. Waited results following the regresion Efect asupra Factor determinant Formula strucurii capitalurilor Posibilitatea de creditare IMO/AT (-) Marimea firmei CA (+) Profitabilitatea EBIT(1-t)/AE (-) Oportunitatea de creştere PER (+)/(-) Intensitatea capitalului AT/CA (+) Criza financiară dummy (+) FIRST REGRESION Dependent Variable: LEVIER Variable Coefficient Std. Error t-statistic Prob. C 0.345497 0.400976 0.861640 0.3921 TANG -0.666847 0.285572-2.335130 0.0226 LN_CA 0.049380 0.034410 1.435061 0.0989 ROA -2.210643 0.808293-2.734953 0.0080 PER 0.003957 0.001945 2.034419 0.0460 R-squared 0.324833 Mean dependent var 0.514144 Adjusted R-squared 0.283284 S.D. dependent var 0.581092 S.E. of regression 0.491947 Akaike info criterion 1.487858 Sum squared resid 15.73078 Schwarz criterion 1.648465 Log likelihood -47.07504 F-statistic 7.818119 Durbin-Watson stat 0.412556 Prob(F-statistic) 0.000033

Regression results show that leverage is positively influenced by PER and LN_CA. This is conclusive with the Committee mentioned in the literature. Remember that the financial crisis variable and the capital intensity does not influence the decision to finance the company. SECOND REGRESION Dependent Variable: PONDDTL Variable Coefficient Std. Error t-statistic Prob. C -0.102288 0.086438-1.183366 0.2409 LN_CA 0.030162 0.007145 4.221586 0.0001 ROA -0.726611 0.169073-4.297615 0.0001 PER 0.000695 0.000415 1.673705 0.0989 R-squared 0.436959 Mean dependent var 0.213936 Adjusted R-squared 0.411366 S.D. dependent var 0.138259 S.E. of regression 0.106076 Akaike info criterion -1.593886 Sum squared resid 0.742633 Schwarz criterion -1.465401 Log likelihood 59.78601 F-statistic 17.07354 Durbin-Watson stat 0.582559 Prob(F-statistic) 0.000000 Regression results show that long-term debt to total assets is positively influenced by the turnover and profitability and low PER. This is conclusive as mentioned in the literature. Remember that the variable of financial crisis, credit squeeze and capital intensity does not influence the financing decision of the company. THIRD REGRESION Dependent Variable: PONDDT Variable Coefficient Std. Error t-statistic Prob. C -0.024007 0.128504-0.186819 0.8524 TANG -0.299019 0.091519-3.267280 0.0017 LN_CA 0.049904 0.011028 4.525427 0.0000 ROA -1.020507 0.259040-3.939576 0.0002 PER 0.001232 0.000623 1.976446 0.0524

R-squared 0.499162 Mean dependent var 0.366346 Adjusted R-squared 0.468341 S.D. dependent var 0.216222 S.E. of regression 0.157658 Akaike info criterion -0.788026 Sum squared resid 1.615645 Schwarz criterion -0.627420 Log likelihood 32.58093 F-statistic 16.19564 Durbin-Watson stat 0.595345 Prob(F-statistic) 0.000000 Regression results show that the share of total debt to total assets is positively influenced by the turnover and profitability and negative PER and growth opportunities. This is conclusive with the Committee mentioned in the literature. Mention here that the financial crisis variable and the capital intensity does not influence the decision to grant the company seen by the investors. FOURTH REGRESION Dependent Variable: GRAD Variable Coefficient Std. Error t-statistic Prob. C 2.113271 1.729702 1.221754 0.2261 TANG -6.785827 3.502367-1.937498 0.0570 ROA 54.38030 8.270099 6.575532 0.0000 INTENS 0.967508 0.341489 2.833205 0.0061 R-squared 0.459334 Mean dependent var 5.755608 Adjusted R-squared 0.434759 S.D. dependent var 7.580299 S.E. of regression 5.699061 Akaike info criterion 6.373925 Sum squared resid 2143.634 Schwarz criterion 6.502411 Log likelihood -219.0874 F-statistic 18.69058 Durbin-Watson stat 2.004703 Prob(F-statistic) 0.000000 Regression results show that leverage is positively influenced by the profitability and capital intensity and negative by credit opportunity. Variable financial crisis, turnover and growth opportunities affect the financing decision not seen the company through this indicator.

CONCLUSIONS Levier DAT/AT TD/AT Grad acoperire Posibilitatea de creditare (-) 0 (-) (-) Marimea firmei (+) (+) (+) 0 Profitabilitatea (-) (-) (-) (+) Oportunitatea de creştere (+) (+) (+) (+) Intensitatea capitalului 0 0 0 (+) Criza financiară 0 0 0 0 These results are in some cases different from those presented in the literature. This may be because the data used is based on accounting standards in Romania compared to most studies that have the U.S. market benchmark. Data are also used for low orizond analysis was performed only for a period of three years. Another thing to note is that some results are either ambiguous or statistically insignificant. We can assume that the sample would be lost if more than one, the conclusions would have been more clear and accurate. A solution to this problem would be collecting data for a longer period and use of other system can the panel data regression. Unfortunately this approach has a downside: the leverage is the result of evolution of equity and long-term debt. BIBLIOGRAPHY [1] Ang, J. S. 1991. Small Business Uniqueness and the Theory of Financial Management. Journal of Small Business Finance 1 (1): 1 13. [2] Badarau C. & Semenescu A. (2010), Fiscal policy and the cost of external finance to firms: Microeconomic and macroeconomic implications, Emerging Markets, Finance and Trade, vol. 46s: pp. 36-50 [3] Brealey, Richard A. Myers, Stewart C. Fundamentals of corporate finance, ediţia a patra, McGraw-Hill, 2004 [4] Damodaran, Aswath Investment valuation, 2nd Edition, John Wiley, 2002 [5] Dragotă, M., Dragotă, V., Braşoveanu, L.& Semenescu, A. (2008), Capital Structure Determinants: A Sectorial Analysis for the Romanian Listed Companies, Economic

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