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

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, ShyamSunder 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.

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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,

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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.

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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)

=
Prob>chi2 =

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3.45
0.1780


3.4. Random effect model estimation
Table 09: Random-effects GLS regression
Random-effects GLS regression

Number of obs

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)
corr(u_i, X) = 0 (assumed)
ROE |

Coef. Std. Err.

Prob > chi2
Z

P>|z|

=

=

1020

49.25

= 0.0000

[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 crosssectional dependence.

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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

0

Number of groups =

Estimated autocorrelations =
Estimated coefficients

=

6

Time periods

=
=

Wald chi2(5)
Log likelihood

= -1631.294

Prob > chi2

1020
85
12

=

36.33

= 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
LQ | -.2142249 .0902443
SG |

5.1241

1.09138

_cons | 34.33621 9.560688

-2.37 0.018
4.70 0.000
3.59 0.000

-4.646508

2.177751

-.3911004 -.0373494
2.985034
15.5976

7.263166
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 pvalue of 0.0000. There are minor changes in the coefficient summary part, in which the SZ

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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 shortterm 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

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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).
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