Effects of financial leverage on performance of listed firms in Ho Chi Minh stock exchange market

993 EFFECTS OF FINANCIAL LEVERAGE ON PERFORMANCE OF LISTED FIRMS IN HO CHI MINH STOCK EXCHANGE MARKET Lecturer Hoang Xuan Que Msc. Hoang Vu Hiep National Economics University Abstract According to Thomas (2013), the success of a business is significantly determined by the way capital is mobilized and utilised. The amount of financial leverage may change across firms and/or time depending on the business culture, administration method, or the industry in which the business operates. In principle, there is no theoretically optimal level for the proportion of debt and equity (Modigliani and Miller, 1958). This research carried out with a panel data of 85 listed firms in HSX market during the period from 2006 to 2017 (financial sector will be excluded) and reveals that short term leverage is significantly positive correlated with business financial performance. Keywords: Capital structure, HSX, financial performance, financial efficiency. 1. Introduction According to Thomas (2013), the success of a business is determined by the way capital is mobilized and utilised. The capital structure is defined as the ratio of long-term debt to equity, both of which are used by a business to pay for its assets (Swanson et al., 2003). This proportion may change across firms and/or time depending on the business culture, administration method, or the industry in which the business operates. In principle, there is no theoretically optimal level for the proportion of debt and equity (Modigliani and Miller, 1958). Modigliani and Miller (1958) could be typically considered as the pioneers in concluding and modelling the relationship between capital structure and firm performance by proposing capital structure irrelevant theory. More recently, Shyam- Sunder and Myers (1999) proposed ―pecking order theory‖ in which they advised that in terms of raising capital, companies should first use internal accruals, followed by debt, and then equity. Kajanathan and Nimalthasan (2013) argued that the impacts of capital structure on business performance and growth must be concluded based on specific characteristics of the firms, industries, or the whole macro economy of countries. It can be seen that the relationship between capital structure and firm performance has been argued widely in the recent years when it comes to financial management issues (Kajanathan and Nimalthasan, 2013). From these points of view, this research aims to examine the impacts of capital structure (represented by the financial leverage) on firm performance of Vietnamese listed firms on HSX. The impacts will be moderated based on industrial characteristics to bring the most precise recommendation for Vietnamese listed firms. 994 2. Variables measurement 2.1. Firm performance measurement In explanations of firm performance, there are many financial indicators which can be used as measures of firm financial performance such as returns on assets, returns on investment, returns on equity, gross and net profit margin, earnings per share, or tobin's q (Soumadi and Hayajneh, 2012; Pratheepkanth, 2011; Kajananthan and Nimalthasan, 2013; and Tan, 2012). In specific, in this study, Returns on Equity (ROE) will be used as dependent variables represents the business financial performance. ROE is one of the most important financial indicator to measure the companies' profitability. It represents the ability of companies to generate profit with the money invested by shareholders. ROE is calculated by dividing the net profit after tax to the book value of shareholders' equity (Soumadi and Hayajneh, 2012; Onaolapo and Kajola, 2010; Krishnan and Moyer, 1997). 2.2. Capital structure measurement This paper will name the independent variables as the market debt ratio as measured by interest-bearing borrowings over the sum of interest-bearing debts and market value of outstanding common shares. In which, short-term and long-term market debt will be used to better characterise the role of each type of debts. The market debt ratio (MDR) is conducted based on the ideas of Flannery and Rangan (2005) to consider the market capital capacity of the firm. In which, SMDRi,t is defined as short-term interest-bearing debt of firm i at time t; LMDRi,t is long-term interest-bearing debt of firm i at time t; Di,t is the total interest-bearing debt, while Si,t indicates the number of common shares outstanding of firm i. Pi,t denotes the price per share of stock i at time t. 2.3. Control variables Sales growth (SG), measured as rate of change in sales between the observation year and the preceding years, can have a positive effect on performance and growth as companies are able to generate higher profits. This variable has been used in testing the effect of capital structure on financial efficiency by Margaritis and Psillaki (2010); and Zeitun and Tian (2007). Ramaswammy (2001); Frank and Goyal (2003); Jermias (2008), Ebaid (2009) suggest that the firms size may influence its performance; larger firm may have more capacity and capabilities. Therefore, this study controls the differences in firms operating environment by including the size variable in the model. Size is measured by the log of total assets of the firm (TA), as illustrated in the equation below, and included in the model to control for effects of firm size on dependent variable. This research further includes liquidity (LQ), measured in terms of current assets ratio, as another control variable since it helps control for industry-related, firm-specific and business cycle factors. 2.4. Empirical research model The regression analysis focuses on the coefficient for short-term and long-term debt ratio, (𝛽 and 𝛽 ). The control variables for profitability motivated by prior literature, 995 including the firm age, liquidity, and firm size (e.g., Coad et al., 2016; Frank and Goyal, 2003; Jermias, 2008; Ebaid, 2009). Therefore, based on the relevance and reliability of such theories and approaches, the empirical model for this research will be developed and tested through panel regression model. The research‘s empirical model is illustrated below: 𝛽 𝛽 𝛽 𝛽 𝛽 𝑄 The project will attempt to test the hypotheses below: H0: Financial leverage has no impact on firm financial performance H1: Financial leverage has positive impact on firm financial performance In order to confirm the reliability of the quantitative model above, a number of econometric tests will be carried out as mentioned in the next part. Firstly, even though pooled ordinary least square (Pooled OLS) model has been criticised in this study, the author will still use Breusch and Pagan Lagrangian multiplier test to make sure that the Pooled OLS is not appropriate for this research. Secondly, Hausman test will also be used to figure out the appropriateness between fixed-effects and random-effects model. Finally, the autocorrelation and heteroskedasticity will be tested. Details are given below. 3. Research model analysis 3.1. Unit Root Test (Harris-Tzavalis test) The Harris-Tzavalis approach for unit root test has been applied across all variables to ensure the stationarity of the panel data. The results are showed in the tables below. Table 01: Summary of Harris-Tzavalis unit-root test Variables Harris-Tzavalis Statistic p-value ROE 0.1567 0.0000 LnSTD 0.6963 0.0395 LnLTD 0.4779 0.0000 LnSZ 0.6852 0.0216 LQ 0.2012 0.0000 SG -0.1014 0.0000 As can be seen from the table above, all the null hypotheses are rejected at the 5% significance level for all the unit root tests. Therefore, it is evident that the panel data contains no unit root and stationary. 3.2. Breusch and Pagan Lagrangian multiplier Breusch and Pagan Lagrangian multiplier has been proposed by Breusch and Pagan (1980) which is known as a typical test to determine between traditional pooled OLS and random-effect approach. The result is below. 996 Table 07: Breusch and Pagan Lagrangian multiplier test for random effects ROE[firm1,t] = Xb + u[firm1] + e[firm1,t] Estimated results: Var sd = sqrt(Var) --------------------------------------- ROE 314.6469 17.73829 e 235.1386 15.33423 u 48.25641 6.946683 Test: Var(u) = 0 chibar2(01) = 65.10 Prob > chibar2 = 0.0000 Since the result of the table above shows the significance level lower than 5%, so it is suggested to reject the null that OLS residuals do not contain individual specific error components. In other words, Pooled OLS is indicated to be inappropriate as it ignores the difference between units and the time effect. Thus, using this method can lead to bias in estimation of model results. Based on this result, random-effect model is suggested to be used. In the next part, Hausman test will be applied to determine the appropriateness between random-effect and fixed-effect models. 3.3. Hausman test The result of Hausman test presented below shows the significance level of 17.8% which means that the null hypothesis H0 cannot be rejected. Therefore, the random-effect model will be accepted to be used in this paper. Table 08: Hausman test ---- Coefficients ---- | (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fe re Difference S.E. -------------+---------------------------------------------------------------- STD | -1.09e-06 -1.53e-06 4.44e-07 9.74e-07 LTD | -8.80e-07 -1.04e-06 1.62e-07 1.14e-06 SZ | 6.38e-07 9.54e-07 -3.16e-07 7.67e-07 LQ | -.0785226 -.113974 .0354514 .0191178 SG | 5.568908 5.508314 .0605944 .2242256 ------------------------------------------------------------------------------ b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(2) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 3.45 Prob>chi2 = 0.1780 997 3.4. Random effect model estimation Table 09: Random-effects GLS regression Random-effects GLS regression Number of obs = 1020 Group variable: firm1 Number of groups = 85 R-sq: within = 0.3246 Obs per group: min = 12 between = 0.0057 avg = 12.0 overall = 0.1471 max = 12 Wald chi2(5) = 49.25 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 ROE | Coef. Std. Err. Z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- LnSTD | 1.882123 1.680004 1.12 0.003 -1.410624 5.174869 LnLTD | .2747095 .4306036 0.64 0.523 -.569258 1.118677 LnSZ | -5.675382 2.116236 -2.68 0.007 -9.823129 -1.527635 LQ | -.0525371 .0901778 -0.58 0.560 -.2292823 .1242082 SG | 5.450136 1.021961 5.33 0.000 3.44713 7.453143 _cons | 65.11555 13.21136 4.93 0.000 39.22175 91.00934 -------------+---------------------------------------------------------------- sigma_u | 7.3827718 sigma_e | 14.784374 rho | .19959248 (fraction of variance due to u_i) As can be seen from the table above, the model is significance with the p-value less than 5%. With the R-square of 32.46%, it can be concluded that 32.46% variation of the dependent variable (ROE) is explained by the explanatory variables. The coefficient summary shows that STD, SZ, and SG have correlation with ROE at a statistical significance level of 5%. Meanwhile, there is no statistical evidence for the relationship between ROE and LTD and LQ (with p-value of 52.3% and 56%, respectively). In order to ensure the empirical model is valid and reliable, cross sectional dependence and autocorrelation issues will be tested below. Table 10: Cross-sectional dependence test Pesaran's test of cross sectional independence = 11.042, Pr = 0.3247 Average absolute value of the off-diagonal elements = 0.332 As can be seen from the table 10, the p-value of Pesaran‘s test of cross sectional independence is 32.47% which is much higher than the significance level of 5%. Therefore, null hypothesis will not be rejected, or in other words, there is no cross- sectional dependence. 998 Table 11: Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 84) = 1.026 Prob > F = 0.3190 No auto correlation Ho: Panel Homoscedasticity - Ha: Panel Groupwise Heteroscedasticity - Lagrange Multiplier LM Test = 4.20e+04 P-Value > Chi2(31) 0.0000 - Likelihood Ratio LR Test = 233.5239 P-Value > Chi2(31) 0.0000 - Wald Test = 8.53e+05 P-Value > Chi2(32) 0.0000 ------------------------------------------------------------------------------ As the result of table 11, the Wald test statistic is significant with the p-value of 0.0000, which means the null hypothesis H0 will be rejected. Thus, the empirical model encounters an issue of autocorrelation. This problem can be solved by applying FGLS regression in the table below. Table 12: FGLS regression Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic Correlation: no autocorrelation Estimated covariances = 1 Number of obs = 1020 Estimated autocorrelations = 0 Number of groups = 85 Estimated coefficients = 6 Time periods = 12 Wald chi2(5) = 36.33 Log likelihood = -1631.294 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ ROE | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- LnSTD | .4008238 1.411165 -0.28 0.006 -3.166656 2.365008 LnLTD | .1657248 .3288532 0.50 0.614 -.4788157 .8102653 LnSZ | -1.234379 1.740915 -0.71 0.478 -4.646508 2.177751 LQ | -.2142249 .0902443 -2.37 0.018 -.3911004 -.0373494 SG | 5.1241 1.09138 4.70 0.000 2.985034 7.263166 _cons | 34.33621 9.560688 3.59 0.000 15.5976 53.07481 ------------------------------------------------------------------------------ As can be seen in the table above, FGLS regression result reveals that the data is homoscedastic and there is no autocorrelation. The model is also significant with the p- value of 0.0000. There are minor changes in the coefficient summary part, in which the SZ 999 variable no longer has a significant correlation with ROE, meanwhile, LQ shows a significant relationship with p-value of 1.8%. 4. Discussion and Conclusion As the result of the FGLS regression table, it can be concluded a significant positive correlation between short term debt and financial performance of 85 listed firms in Vietnam with the coefficient of 0.4, which means if the short-term debt increases by 1 unit, ROE will increase accordingly by 0.4 unit. The results of this model show that the short- term financial leverage has the positive effect on financial performance. According to the capital structure theory, the debt ratio increases the profit of the enterprise by benefiting from the tax shield, debt is the leverage for businesses to increase revenue, thereby increasing profits. The results show that Vietnamese listed businesses made good use of short-term debt efficiently and the benefits from debt financing can offset the costs incurred from in debt. During the process of collecting data and information of 85 Vietnamese listed firms in particular and the whole economy in general, there are several problems found out related to their capital structure and financial performance. In details, in Vietnamese economy, the capital retained from annual income after tax was less concerned. The State Own businesses, which have finance sponsored by the Government, has no pressure for raising capital (Phan, 2016). Therefore, most of them are operating inefficiently and have no retained earnings for capital. This also applied to small and medium enterprises in Vietnam which are currently unable to generate profit which leads to capital deficit. Furthermore, in Vietnamese economy there are a lot of insolvencies between huge number of companies. This leads to the situation of the amount of bad debts is rising gradually and affects badly to the whole economy. Besides, capital gained from issuing shares on stock market also brings about advantages and disadvantages for Vietnamese businesses (Bui and Nguyen, 2017). In facts, stock market has been developing strongly in the last 12 years and become the most crucially important financing method for every listed companies. Although the market is still new and somehow unstable, the ability to attract capital from issuing shares is completely necessary and realistic. By the way, businesses can build up their brands' images and reputation through this activity. However, there is a problem existed in this too-fast development of stock market. In specific, it is now too easy for firms to go public and issue shares to raise capital, which can result in the imbalance in their capital structure and unexpected risks. The reason might be due to the unprofessional of Vietnamese investors while they do not pay attention on the profits and risks of their investment but mainly buying and selling shares to get income from short-term changes in share prices. By this way, it brings a lot of risks to businesses when they need capital for a project investment which can generate profit after few years; investors, however, still require companies to pay annual dividends and expect for rises in share prices. In case that company cannot guarantee a stable amount of dividends, share prices will absolutely fall down incessantly. More crucially, some companies issued shares and used the amount of capital gained from that to pay their debts rather than to run the business (Le, 2017). On 1000 the other hand, the source of capital from borrowings also has problems. In Vietnam, bank credit usually used for State Own companies, the private sector is less concerned although they contributed more than 42% of GDP (Le, 2017). Within 3 companies, only one of them is able to approach borrowing capital from banks, the rest of them are difficult to approach or unable to approach. One of the most crucial reason is that they do not have enough assets for mortgage, and even in case that they have some mortgage, they can only borrow 70% of the value of their assets (Nguyen and Dang, 2017). 5. References 1. Breitung, J. (2000) The local power of some unit root tests for panel data. Advances in Econometrics, Volume 15: Nonstationary Panels, Panel Cointegration, and Dynamic Panels, ed. B. H. Baltagi, 161–178. Amsterdam: JAY Press. 2. Breitung, J., and S. Das. (2005) Panel unit root tests under cross-sectional dependence. Statistica Neerlandica 59: 414–433. 3. Breusch, T. and Pagan, A. 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