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International Journal of Economics and Finance; Vol. 7, No. 12; 2015 ISSN X E-ISSN Published by Canadian Center of Science and Education Capital Structure and Firms Performance: Evidence
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International Journal of Economics and Finance; Vol. 7, No. 12; 2015 ISSN X E-ISSN Published by Canadian Center of Science and Education Capital Structure and Firms Performance: Evidence from Vietnam s Stock Exchange 1 Fresenius University, Munich, Germany Tristan Nguyen 1 & Huy-Cuong Nguyen 2 2 Berlin School of Economics and Law, Berlin, Germany Correspondence: Prof. Dr. Tristan Nguyen, Fresenius University, Department for Finance and Accounting, Infanteriestr. 11a, Munich, Germany. Received: September 2, 2015 Accepted: September 21, 2015 Online Published: November 25, 2015 doi: /ijef.v7n12p1 URL: Abstract Our paper examines what impact capital structure has on firms performance in selected firms listed on HCMC Stock Exchange. The data is collected from 147 listed companies during the period from 2006 to The study not only checks the impact the level of leverage has on firms performance, which is found to be negative in this study, but it also uses the short-term and long-term debt ratios to see the effect of debt maturity. However, there is no difference whether it is short-term or long-term. Tangibility is found to be negative with a very high proportion on average. With the suggestion that companies might invest too much in fixed assets and there is a lack of efficiency, this could be the alert for firms to improve their management process. Size and growth are found to be positive, since larger firms have lower costs of bankruptcy and higher growth rates associate with higher performance. Moreover, the study also adds the effects of industry and macroeconomics, and the result shows a correlation between the two factors and firms performance. Keywords: capital structure, firm s performance, Vietnam s stock exchange, macro-economic impact, stocks markets, leverage effect 1. Introduction The mix between debt and equity is varied in the corporate world; therefore, each company has its own proportion of debt and equity in order to finance its business. The capital structure varies by many factors, such as with the ups and downs of the economy; maybe when a crisis is coming, then the proportion of debt will increase rapidly, and even at each stage of their life cycle, there are different mixes between these two; for example, at the start-up stage a company tends to have higher equity over debt, since the cost of interest is a real burden for them at the beginning due to the fluctuation of their income. However, as they grow and become large companies use more and more debt rather than sharing their rights by issuing new equity. Undoubtedly, using debt gives companies many advantages for management s main purpose of increasing the owners wealth, then theoretically financing business through debt means that the return on owners equity would rise, which owners always love to hear. Moreover, debt might be the solution for agency cost, and can also be a benefit as a tax shield. On the other hand, debt also has negative impacts on firms performance. One of these might be the outcome that a company could suffer from high and fixed interest expense that the company cannot afford to pay; for instance, the company does not have stable long-term income any longer, such as in start-up companies or companies which are having difficulty in competing, and when this happens bankruptcy can result. So technically, it is about how much debt and what kind of debt should a company use to have positive impact on their performance. Or put simply, should companies employ leverage, and if they do, what is the best choice? Long term or short term? Therefore, how capital structure affects corporate performance is the question that has been the subject of numerous studies for different stock markets around the world (Chakraborty, 2010 for India; Saeedi & Mahmoodi, 2011 for Iran; Mahfuzah & Raj, 2013 for Malaysia; Ebaid, 2009 for Egypt). Till now, no empirical studies have been done for the growing stock markets in Vietnam. Capital structure refers to the way a company finances its business, whether through debt or equity. There are many empirical studies that have been conducted in order to find out the impact of capital structure on firms performance (Rajan & Zingales, 1995; Arbabiyan & 1 Safari, 2009; Abor, 2005; Chen, 2004; Deesomsak et al., 2004). The majority of the studies have found a negative effect of debt on financial performance, with the same results stated specifically for short-term and long-term debt (Zeitun & Tian, 2007). However, short-term debt could also be positively related to performance (Touseef, 2014). The purpose of this paper is to find out what effect capital structure has on firms performance in Vietnam. Moreover, there is a small amount of empirical research that has been conducted on the impact of capital structure on firms performance in developing countries. Most of the topics about capital structure have been about the determinants of capital structure. Therefore, this study is attempting to investigate the relationship between capital structure and firms performance in Vietnam. In addition, it is worth noting that the bond market and mutual funds in Vietnam have not yet been developed completely, hence the source of debt financing mainly comes from banks. Additionally, with the current situation in Vietnam, banks tend not to give too many long-term loans and the interest rate is rather high; therefore, short-term loans are dominating the structure of debt in Vietnamese firms. In this current situation, a majority of companies in Vietnam choose to finance their long-term business plans through short-term debt. Even big Vietnamese companies which are listed in the Ho Chi Minh City (HCMC) stock exchange are confronted with the same problem. In many cases, Vietnamese firms are forced to choose a capital structure which is not optimal, which therefore affects their performance and exposes them to the risk of increased interest rates in the short term. Additionally, it has been found that tangibility has a significant correlation with the capital structure and companies with high growth rates tend to have a high proportion of debt. It was also pointed out that bankruptcy costs, which were presented by firm size, have an important effect on capital structure (Kraus & Litzenberger, 1973; Harris & Raviv, 1991). Therefore, the three aforementioned elements should be used to evaluate firms performance, since they are considered as the determinants of capital structure. The study also contains the effect of industry sector and macroeconomic impact. 2. Literature Review Firms profitability could be influenced by many factors, one of which is the structure of capital. Capital structure relates to the deciding sources to finance companies business. Ordinarily, at the start-up of a firm, equity is used to run the business, since equity charges no fixed cost on the firm; on the other hand, as the firm grows, debt becomes a preferred choice of firms capital, and in the remainder of their life cycle, debt is preferred. In 1958, Modigliani and Miller had conducted a research that pointed out that in an ideal world with no bankruptcy cost, frictionless capital market and no taxes, the value of a firm does not depend on the structure of capital. Various empirical research studies have been conducted to examine Modigliani and Miller s theory, and most of them studied the relevance of capital structure on business firms. As a result, in 1963 Modigliani and Miller included taxes and other market imperfections, and found that firms really can maximize their value by using debt in their operations to take advantage of the tax shield. Other authors (Bradley et al., 1984; Kraus & Litzenberger, 1973; Harris & Raviv, 1991) showed that there is an optimal capital structure of firms financing. Many empirical studies have been conducted to find out the impact of leverage on firms performance. For instance, Simerly and Li (2000) found a negative impact of capital structure on financial performance. Additionally, Zeitun and Tian (2007) found that debt has a negative effect on both market and accounting performance. In contrast, Holz (2002) found a positive relation between capital structure and firms performance; this was because banks would review the projects to guarantee the feasibility before giving loans to firms; therefore, firms could achieve an appropriate return. Margrates and Psillaki (2010) also found a positive relation between leverage and corporate performance. However, not only the level of leverage but also the debt s maturity has a significant impact on firms performance. Appiadjei (2014) found a positive relation between short-term debt and firm performance, and a negative impact of long-term debt was also pointed out. On the other hand, Tian and Zeitun (2007) found a negative impact on financial performance for both short-term debt and long-term debt. The mixed results among the empirical results encourage us to use both short-term debt and long-term debt, with the total debt as the measure for leverage. However, the study would be lacking if it did not include other factors such as growth, tangibility, and size, since these were also proved as determinants of capital structure and to have significant influence on profitability by many empirical studies. 2 3. Data and Methodology The data used in this study was taken from the database of HCMC stock exchange (HOSE), containing the information about 147 listed companies. The companies belong to 17 sectors (Rubber, High-Tech, Oil, Energy, Tourism, Pharmacy, Education, Mining, Plastic, Manufacturing, Steel, Food, Commerce, Seafood, Transportation, Construction Material, Construction), and no companies from the financial sector are included in the data, since they are different from all the others and have high leverage by nature. In addition, all the companies in the data were required to have available financial statements from the years 2006 to The interested items were balance sheet and income statement, which provided the information about Fixed Assets, Total Assets, Short-term Debts, Long-term Debts, Total Debts, Owner s Equity, Net Income, Paid Interests, Net Revenues and Total share outstanding. The study uses Return on Asset (ROA) and Return on Shareholders Equity (ROE) to measure financial performance of firms. In our study, we run the following regression: Y = a + b.leverage + c.tangibility + d.size + e.growth + ε (1) Y = a + b.leverage + c.tangibility + d.size + e.growth + ME + Industry + ε (2) Y is corporate performance and alternatively measured by ROA, ROE and Tobin s Q with Net Income Paid Interests ROA Total Assets Net Income ROE Total Shareholders' Equity Leverage measured by using Debt Ratio (DR), Short-term Debt to Total Assets (SDR), Long-term Debt to Total Assets (LDR) and TD/(TE+LTD) with Total Debt DR Total Assets Short term Debt SDR Total Assets Long term Debt LDR Total Assets Total Debt TD /( TE LTD). Total shareholder' s Equity Long term Debt Tangibility is measured by using Fixed Assets to Total Assets (FATA) with Net Fixed Assets FATA Total Assets Growth is measured by the change of net sales. Size is measured by taking the natural logarithm of net sales. Other Macroeconomic Factors (ME) are measured by 9 dummy variables to control time effects from ME 1 to ME 9 represented for 2006 to 2014 respectively. Industry is measured by 17 dummy variables to control the effect of industrial sectors, Industry 1 to Industry 17 represented for Rubber, High-Tech, Oil, Energy, Tourism, Pharmacy, Education, Mining, Plastic, Manufacturing, Steel, Food, Commerce, Seafood, Transportation, Construction Material, Construction respectively. ε is the error term. A strong correlation between leverage and firms performance is expected to be found, whereas short-term debt ratio is expected to have negative impact on firms performance, since it exposes firms to the risk of refinancing. Additionally, long-term debt is also expected to have negative impact on performance, because of the fluctuations of the market during the period. The growth opportunity is measured by using the change in net sales. As a result, a firm with high growth rate is expected to have high performance on its investments. Firm size is measured by using the natural logarithm of net sales. The firm size is expected to have positive relationship with corporate performance, since bankruptcy costs reduce with the size of the firm. Moreover, the industry a firm is operating in would also be a vital point, since different industries have their own optimal capital structure; also the sensitivity to the market varies between different industries. Therefore, by 3 dividing the companies in the sample into 17 distinct sectors (Rubber, High-Tech, Oil, Energy, Tourism, Pharmacy, Education, Mining, Plastic, Manufacturing, Steel, Food Production, Commerce, Seafood, Transportation, Construction Material, Construction), we examine the question of whether there is an industry impact on companies performance or not. The period of the study includes the time when the global financial crisis ( ) happened, and, therefore, there were several macroeconomic factors that affected firms performance at that time, especially before and after the crisis. Therefore, we examine these effects by adding the 9 dummy variables which represent the period of 9 years respectively into our regression for ROA and ROE. A significant correlation between time effect and corporate performance is expected to be found, especially for the period between the time before and after the global financial crisis ( ). 4. Empirical Result Table 1 shows the summary statistics of the sample used in the study. The average ROA of the sample is (10.72%), the lowest ROA (-36%) belongs to Viet Nhat Seafood Corporation in 2010, compared to the highest ROA of 0.73 (73%) in 2010 achieved by Truong Thanh Furniture Corporation. On the other hand, the mean of ROE is (17.23%), with the lowest of (-97%) and the highest of 1.78 (178%); these were the ROAs of LAFOOCO in 2012 and ELCOM in 2006 respectively. Additionally, Tobin s Q has an average of (83.79%); this result is not really impressive since it indicates that on an average term, the market values of selected firms are lower than the book value of their total assets. Lastly, the standard deviations are 0.081, 0.184, and for ROA, ROE and Tobin s Q respectively. Overall, the average ROA and ROE of the companies in the sample can be considered fairly good, since the firms are diversified from 17 sectors. However, considering the average market value of the selected firms, the rate of return is not as much as the investors expected. Table 1. Statistics of the sample Variable Minimum Maximum Mean Std. Deviation N ROA ROE Tobin s Q Growth DR SDR LDR FATA Size TD/(TE+LTD) The mean of Debt ratio (DR) and TD/(TE+LTD) is (47.6%) and (109.43%) respectively, which is most likely acceptable. Noticeably, the range of DR is from 0.01 (1%) to 0.98 (98%), meaning that there are companies which use only debt as the source for financing, and there are companies which consider only equity as the dominant source for financing. Specifically, Vimedimex, a pharmaceutical company, has been maintaining a Debt ratio around 95% over the years. On the other hand, the companies which choose equity as the main source of financing belong to the tourism and energy sectors; Vinagolf Corporation and Thac Mo HPC both had a Debt ratio of 0.01 (1%) in 2006 and they have been maintaining relatively low Debt ratio over the years. Overall, the sample has an acceptable Debt ratio; however, most of the debt is short-term debt which has the average ratio of (37.4%) and range from 0.01 (1%) to (96%). Additionally, long-term debt only takes the proportion of (10.2%) on average and range from 0.00 (0%) to (74%). The standard deviations are 0.217, 0.205, 0.143, for DR, SDR, LDR, TD/(TE+LTD) respectively. The mean of growth is (22.39%) and range from (-97%) to 6.67 (667%). This indicates that on average, firms in the sample have relatively high growth. The FATA has a mean of (31.36%) and range from 0.00 (0%) to 0.94 (94%). The 0.00 of FATA was reported as the fixed asset with the value of 0 in Hamico group s balance sheet, which was explained in their financial statement by the fact that they made all of their fixed assets into investments to another company. Lastly, the sample has an average Size of and range from 7.81 to 17.37, and the standard deviations are 0.516, 0.208, for Growth, FATA, and Size respectively. 4 For the first model, we run the regressions respectively for each measure of leverage: DR, SDR and LDR, TDTC. Furthermore, we examine the effect of Industry sector and macroeconomic factors respectively. Table 2 shows the results using DR for the regression of model 1. Table 2. Regression for model 1 using DR (Note 1) ROA ROE Tobin s Q Constant *** DR *** *** *** FATA ** *** ***0.385 Size ***0.013 ***0.022 ***0.207 Growth ***0.003 *** Adjusted R F-Statistics *** ***22.71 *** Observations The regression leads to the following equations for ROA, ROE, and Tobin s Q: ROA = DR FATA Size Growth (3) ROE = DR FATA Size Growth (4) Tobin s Q = DR FATA Size Growth (5) The regression coefficients of DR for Tobin s Q, ROA and ROE are , and respectively. This means that if DR changes by 0.1, it would decrease Tobin s Q, ROA and ROE by , and respectively, assuming the other factors remain unchanged. And they are all significant at 1% level, as expected DR has negative effects on the three measurements in the study. As a result, a high proportion of liabilities in the capital structure would lead to lower market value, Return on Asset and Return on Shareholders Equity. In addition, the coefficients of FATA are , and for ROA, ROE and Tobin s Q respectively. This indicates that if FATA changes by 0.1, ROA, ROE and Tobin s Q will move by , and respectively. The coefficients are significant at 1% level for ROE and Tobin s Q, and at 5% level for ROA. Overall, FATA has negative impact on accounting measures (ROA and ROE in this study) and positive relation to market performance (Tobin s Q). Lastly, Size and Growth both have positive relation to the three measures, and the coefficients are significant at 1% level except for the coefficient of Growth to Tobin s Q; therefore, the results need to be interpreted carefully. The R 2 s show that overall the model for ROA can explain 14.8% of all the variability, only 6.2% is accounted for by ROE and 15.9% is accounted for by Tobin s Q. And the F-statistics indicate that overall the significant level of the model is at 1% level. Table 3 shows the result of the regression for model 1 using short-term debt to total assets instead of total debt to total assets. Table 3. Regression for model 1 using SDR (Note 2) ROA ROE Tobin s Q Constant * *** SDR *** *** *** FATA ** *** Size ***0.010 ***0.020 ***0.194 Growth ***0.028 *** Adjusted R F-Statistics *** *** *** Observations The regression leads to the following equations for ROA, ROE and Tobin s Q: ROA = SDR FATA Size Growth (6) ROE = SDR FATA Size Growth (7) 5 Tobin s Q = SDR FATA Size Growth (8) The results show that short-term debt has negative impact on all the three measurements. Specifically, the coefficients of SDR for Tobin s Q, ROA and ROE are , and respectively, with the significant level of 1%. This indicates that if SDR increases by 0.1, it will decrease Tobin s Q, ROA and ROE by , and respectively under the condition that the other factors are holding the same. The results lead to a conclusion that using short-term overall would somehow lead to lower Tobin s Qs, ROAs and ROEs for firms. The other variables for ROA and ROE are quite similar to what the first regressions have shown. However, there is one difference in the regression for Tobin s Q, which is the coefficient of FATA. It is insignificant. The models are accounted for 13.3%, 8% and 14.6% of all the variability of R
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